CpG islands (CGIs) are short GC rich sequences with a high frequency of CpGs that are associated with the active chromatin mark H3K4me3. Most occur at gene promoters and are often free of cytosine methylation. Recent work has begun to clarify the functional significance of CGIs with respect to chromatin structure and transcription. In particular, proteins associated with histone-modifying activities, such as Cfp1 and Kdm2a, bind specifically to non-methylated CGIs via their CxxC domains. For example, artificial promoterless CpG-rich sequences integrated at the 3’ UTR of genes recruit Cfp1 and generate novel peaks of H3K4me3 in mouse ES cells without apparent RNA polymerase recruitment. There is also evidence that G+C-rich DNA recruits H3K27me3, a gene silencing mark. In this thesis I am exploring the constraints on DNA sequence and genomic location that are required to impose both H3K4me3 and H3K27me3 at CGI sequences. Showing that the generation of novel peaks of H3K4me3 and H3K27me3 over a promoter-less CpG rich sequence in a gene desert region is independent of it’s location in the genome extends earlier findings. These findings suggest that shared features of the primary DNA sequence at CGIs directly influence chromatin modification. Thus CGIs are not passive footprints of other cellular mechanisms, but play an active role in setting up local chromatin structure. However, the relative contribution of CpG frequency versus G+C content remains unclear. Therefore a sequence was generated that contains low levels of CpGs, comparable to the bulk genome, but has a G+C content similar to that of CGIs (Low CpG / High G+C). When this sequence was inserted into a gene desert neither marks of H3K4me3 or H3K27me3 were formed, indicating the importance of CpGs. Surprisingly, the reverse sequence with a high CpG frequency similar to that of CGIs and a low G+C content similar to that of the bulk genome (High CpG / Low G+C) did not establish H3K4me3 or H3K27me3 either. However, it was found that this sequence becomes heavily methylated in contrast to CGI-like sequences that remained unmethylated when introduced into a gene desert. This finding suggests that a high G+C content is important for keeping CGI-like sequences methylation free. Upon insertion of this High CpG / Low G+C sequence into mouse ES cells that were devoid of the de-novo DNA methyltransferases 3a and 3b (Dnmt3a/3b -/-) both H3K4me3 and H3K27me3 marks were established at the inserted sequence. This discovery confirms the importance of CpGs for setting up local chromatin structure.

The phenomenon of cell type-specific DNA methylation has received much attention in recent years and a number of DNA methylation differences have been described between cells of the immune system. Of particular interest when studying DNA methylation are CpG islands (CGIs) which are distinct from the rest of the genome due to their elevated CpG content, generally unmethylated state and promoter association. In the instances when they become methylated this is associated with gene repression although it is unclear the extent to which differential methylation corresponds to differential gene expression. I have used an immune system model to assess the role of CGI methylation and the role of the methylation reader MBD2 in regulation of gene expression. A relatively small number of DNA methylation differences were seen between immune cell types with the most developmentally related cells showing the fewest methylation differences. Interestingly, the vast majority of CGI-associated cellspecific methylation occurred at intragenic CGIs located, not at transcription start sites, but in the gene body. Increased intragenic CGI methylation tended to associate with gene repression, although the precise reason for this remains unclear. Most differentially methylated CGIs were depleted for the active chromatin mark H3K4me3 regardless of their methylation state but some of these were associated with the silencing mark H3K27me3 when unmethylated. These findings suggest that intragenic CGIs are a distinct class of genomic element particularly susceptible to cell type-specific methylation. I also looked at the effect of removing the methyl- CpG binding domain protein MBD2 from immune system cells. Immune cells from Mbd2-/- mice showed a number of previously uncharacterised phenotypes as well as a number of differences in gene expression compared to wild-type animals. Most of these genes increased their expression in the absence of MBD2 consistent with MBD2’s role as a transcriptional repressor and Mbd2-/- Th1 cells showed increases in histone H3 acetylation compared to wild-type Th1 cells. This work provides an insight into the role played by cell-specific CGI methylation and MBD2 in regulating gene expression.

Thesis (Ph.D.)--Medical University of Ohio, 2006. / "In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Medical Sciences." Major advisor: Michael A. Pereira. Includes abstract. Document formatted into pages: v, 152 p. Title from title page of PDF document. Title at ETD Web site: Involvement of DNA methylation and CpG endonuclease activity in environmental carcinogenesis and cancer chemoprevention. Bibliography: pages 65-66, 90-92, 123-125, 137-150.

Cheung Kin Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 87-105). / Abstracts in English and Chinese. / Abstract (English version) --- p.i / Abstract (Chinese version) --- p.iii / Acknowledgments --- p.vi / Table of Contents --- p.v / List of Tables --- p.viii / List of Figures --- p.iv / List of Abbreviations --- p.xi / Chapter CHAPTER 1 --- GENERAL INTRODUCTION --- p.1 / Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.3 / Chapter 2.1 --- Multiple myeloma --- p.3 / Chapter 2.2 --- Epidemiology of MM --- p.3 / Chapter 2.3 --- Risk factors --- p.4 / Chapter 2.4 --- Pathophysiology of MM --- p.5 / Chapter 2.5 --- Clinical presentations and diagnosis --- p.6 / Chapter 2.5.1 --- Diagnosis --- p.6 / Chapter 2.5.1.1 --- Laboratory testing of blood and urine --- p.6 / Chapter 2.5.1.2 --- Radiographic evaluations --- p.1 / Chapter 2.5.1.3 --- Bone marrow biopsy --- p.7 / Chapter 2.6 --- Staging and classification --- p.9 / Chapter 2.6.1 --- Staging --- p.9 / Chapter 2.6.2 --- Classification --- p.11 / Chapter 2.6.2.1 --- Monoclonal gammopathy of undetermined significance --- p.11 / Chapter 2.6.2.2 --- Asymptomatic MM --- p.12 / Chapter 2.6.2.3 --- Smouldering MM --- p.12 / Chapter 2.6.2.4 --- Indolent MM --- p.12 / Chapter 2.6.2.5 --- Symptomatic MM --- p.12 / Chapter 2.7 --- Treatment --- p.14 / Chapter 2.8 --- Epigenetics: DNA methylation --- p.15 / Chapter 2.9 --- Fundamental aspects of DNA methylation --- p.16 / Chapter 2.9.1 --- CpG islands --- p.16 / Chapter 2.9.2 --- Roles of DNA methylation --- p.16 / Chapter 2.9.3 --- Proposed mechanisms of transcriptional repression mediated by methylation --- p.18 / Chapter 2.10 --- Possible mechanisms to initiate aberrant DNA methylation --- p.21 / Chapter 2.11 --- DNA methylation in tumorigenesis --- p.22 / Chapter 2.11.1 --- Oncogenic point C → T mutation --- p.22 / Chapter 2.11.2 --- Global DNA hypomethylation --- p.23 / Chapter 2.11.3 --- Regional DNA hypermethylation --- p.23 / Chapter 2.12 --- Aberrant DNA methylation in MM --- p.25 / Chapter 2.12.1 --- Self-sufficiency in growth signals --- p.25 / Chapter 2.12.2 --- Evading apoptosis --- p.26 / Chapter 2.12.3 --- Insensitivity to antigrowth signals --- p.26 / Chapter 2.12.4 --- Tissue invasion and metastasis --- p.27 / Chapter 2.12.5 --- Infinite replicative potential --- p.28 / Chapter 2.12.6 --- Genome instability --- p.30 / Chapter 2.13 --- Methodologies of DNA methylation analysis --- p.32 / Chapter 2.13.1 --- Genome wide screening method: MS.AP-PCR --- p.32 / Chapter 2.13.2 --- Combined bisulfite restriction analysis --- p.34 / Chapter 2.13.3 --- Cloned bisulfite genomic sequencing --- p.36 / Chapter 2.13.4 --- Treatment with demethylating agent --- p.36 / Chapter CHAPTER 3 --- MATERIALS AND METHODS --- p.38 / Chapter 3.1 --- MM specimens --- p.38 / Chapter 3.1.1 --- MM samples --- p.38 / Chapter 3.1.2 --- MM cell lines --- p.38 / Chapter 3.2 --- Magnetic cell sorting of CD138-positive plasma cells --- p.39 / Chapter 3.3 --- Isolation of nuclear pellet from PB --- p.41 / Chapter 3.4 --- "DNA extraction from MM cell lines, MM plasma cells and PB" --- p.41 / Chapter 3.5 --- MS.AP-PCR --- p.42 / Chapter 3.5.1 --- Restriction enzyme digestion of genomic DNA --- p.42 / Chapter 3.5.2 --- Arbitrarily primed polymerase chain reaction --- p.42 / Chapter 3.5.3 --- Isolation of differentially methylated DNA fragments --- p.43 / Chapter 3.6 --- Cloning of differentially methylated DNA fragments --- p.46 / Chapter 3.6.1 --- TA cloning --- p.46 / Chapter 3.6.2 --- Heat shock transformation --- p.46 / Chapter 3.6.3 --- Screening of positive clones by PCR --- p.46 / Chapter 3.6.4 --- Alkaline lysis for plasmid DNA preparation --- p.47 / Chapter 3.7 --- MS.AP-PCR sequence analysis --- p.47 / Chapter 3.7.1 --- Nucleotide sequencing --- p.47 / Chapter 3.7.2 --- CpG islands analysis of differentially methylated sequences --- p.48 / Chapter 3.8 --- DNA methylation analysis --- p.48 / Chapter 3.8.1 --- Sodium bisulfite modification --- p.48 / Chapter 3.8.2 --- Combined bisulfite restriction analysis --- p.49 / Chapter 3.8.3 --- Cloned bisulfite genomic sequencing --- p.49 / Chapter 3.9 --- Gene expression analysis --- p.50 / Chapter 3.9.1 --- RNA extraction --- p.50 / Chapter 3.9.2 --- Reverse transcription PCR --- p.50 / Chapter 3.9.3 --- 5'-aza-2'-deoxycytidine treatment --- p.51 / Chapter CHAPTER 4 --- RESULTS --- p.53 / Chapter 4.1 --- Generation of DNA methylation patterns by MS.AP-PCR --- p.53 / Chapter 4.1.1. --- Global methylation content in MM samples and normal PB lymphocytes --- p.56 / Chapter 4.1.2. --- Differential methylation in MM --- p.56 / Chapter 4.2 --- UCSC BLAT analysis of differentially methylated DNA fragments --- p.60 / Chapter 4.3 --- Identification of two candidate genes with downregulated expression --- p.60 / Chapter 4.4 --- Zinc fingers and homeoboxes 2 (ZHX2) --- p.62 / Chapter 4.4.1 --- ZHX2 CpG islands BLAT search analysis --- p.62 / Chapter 4.4.2 --- Hypermethylation of ZHX2 in MM cell lines --- p.63 / Chapter 4.4.3 --- Downregulated expression of ZHX2 in methylated MM cell lines --- p.66 / Chapter 4.4.4 --- Restoration of ZHX2 expression by 5-Aza-dC treatment --- p.67 / Chapter 4.4.5 --- Unmethylation of ZHX2 in primary MM tumors --- p.68 / Chapter 4.5 --- Ring finger protein 180 (RNF180) --- p.69 / Chapter 4.5.1 --- RNF180 CpG islands BLAT search analysis --- p.69 / Chapter 4.5.2 --- Hypermethylation of RNF180 in MM cell lines --- p.70 / Chapter 4.5.3 --- Downregulated expression of RNF180 in methylated MM cell lines --- p.73 / Chapter 4.5.4 --- Restoration of RNF180 expression by 5-Aza-dC treatment --- p.74 / Chapter 4.5.5 --- Methylation of RNF180 in primary MM tumors --- p.75 / Chapter CHAPTER 5 --- DISCUSSION --- p.76 / Chapter 5.1 --- Importance of methylation in MM --- p.76 / Chapter 5.2 --- Genome-wide screening approach by MS.AP-PCR --- p.76 / Chapter 5.3 --- Sample selection in MS.AP-PCR --- p.78 / Chapter 5.4 --- Methylation patterns in MM --- p.79 / Chapter 5.5 --- Candidate genes selection strategies --- p.81 / Chapter 5.6 --- Zinc fingers and homeoboxes 2 --- p.81 / Chapter 5.7 --- Ring finger protein 180 --- p.83 / Chapter 5.8 --- Limitations --- p.84 / Chapter CHAPTER 6 --- CONCLUSION --- p.86 / REFERENCES --- p.87

Dong Shumin. / "February 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 187-220). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Master of Engineering / This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combined frequency matrices, and principal components (PCs) of each matrix are generated to construct the new feature space. ANN built classifiers are used to test the discriminability of each feature space. Finally, the 3 and 5-mer feature matrix is selected as the context feature in this model. Two proposed schemes of HPR-PCA model are discussed and the implementations of sub-modules in each scheme are introduced. The context features selected by PCA are III used to build three promoter and non-promoter classifiers. CpG-island modules are embedded into models in different ways. In the comparison, Scheme I obtains better prediction results on two test sets so it is adopted as the model for HPR-PCA for further evaluation. Three existing promoter prediction systems are used to compare to HPR-PCA on three test sets including the chromosome 22 sequence. The performance of HPR-PCA is outstanding compared to the other four systems.

DNA can be represented abstrzctly as a language with only four nucleotides represented by the letters A, C, G, and T, yet the arrangement of those four letters plays a major role in determining the development of an organism. Understanding the signi cance of certain arrangements of nucleotides can unlock the secrets of how the genome achieves its essential functionality. Regions of DNA particularly enriched with cytosine (C nucleotides) and guanine (G nucleotides), especially the CpG di-nucleotide, are frequently associated with biological function related to gene expression, and concentrations of CpGs referred to as \CpG islands" are known to collocate with regions upstream from gene coding sequences within the promoter region. The pattern of occurrence of these nucleotides, relative to adenine (A nucleotides) and thymine (T nucleotides), lends itself to analysis by machine-learning techniques such as Hidden Markov Models (HMMs) to predict the areas of greater enrichment. HMMs have been applied to CpG island prediction before, but often without an awareness of how the outcomes are a ected by the manner in which the HMM is applied. Two main ndings of this study are: 1. The outcome of a HMM is highly sensitive to the setting of the initial probability estimates. 2. Without the appropriate software techniques, HMMs cannot be applied e ectively to large data such as whole eukaryotic chromosomes. Both of these factors are rarely considered by users of HMMs, but are critical to a successful application of HMMs to large DNA sequences. In fact, these shortcomings were discovered through a close examination of published results of CpG island prediction using HMMs, and without being addressed, can lead to an incorrect implementation and application of HMM theory. A rst-order HMM is developed and its performance compared to two other historical methods, the Takai and Jones method and the UCSC method from the University of California Santa Cruz. The HMM is then extended to a second-order to acknowledge that pairs of nucleotides de ne CpG islands rather than single nucleotides alone, and the second-order HMM is evaluated in comparison to the other methods. The UCSC method is found to be based on properties that are not related to CpG islands, and thus is not a fair comparison to the other methods. Of the other methods, the rst-order HMM method and the Takai and Jones method are comparable in the tests conducted, but the second-order HMM method demonstrates superior predictive capabilities. However, these results are valid only when taking into consideration the highly sensitive outcomes based on initial estimates, and nding a suitable set of estimates that provide the most appropriate results. The rst-order HMM is applied to the problem of producing synthetic data that simulates the characteristics of a DNA sequence, including the speci ed presence of CpG islands, based on the model parameters of a trained HMM. HMM analysis is applied to the synthetic data to explore its delity in generating data with similar characteristics, as well as to validate the predictive ability of an HMM. Although this test fails to i meet expectations, a second test using a second-order HMM to produce simulated DNA data using frequency distributions of CpG island pro les exhibits highly accurate predictions of the pre-speci ed CpG islands, con- rming that when the synthetic data are appropriately structured, an HMM can be an accurate predictive tool. One outcome of this thesis is a set of software components (CpGID 2.0 and TrackMap) capable of ef- cient and accurate application of an HMM to genomic sequences, together with visualization that allows quantitative CpG island results to be viewed in conjunction with other genomic data. CpGID 2.0 is an adaptation of a previously published software component that has been extensively revised, and TrackMap is a companion product that works with the results produced by the CpGID 2.0 program. Executing these components allows one to monitor output aspects of the computational model such as number and size of the predicted CpG islands, including their CG content percentage and level of CpG frequency. These outcomes can then be related to the input values used to parameterize the HMM.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 126-144).

In eukaryotic genomes ranging from plants to mammals, DNA methylation is a major epigenetic modification of DNA by adding a methyl group exclusively to cytosine residuals. In mammalian genomes such as humans, these cytosine bases are usually followed by guanine. Although it does not change the primary DNA sequence, this covalent modification plays critical roles in several regulatory processes and can impact gene activity in a heritable fashion. What is more important, DNA methylation is essential for mammalian embryonic development and aberrant DNA methylation is implicated in several human diseases, in particular in neuro-developmental syndromes (such as the fragile X and Rett syndromes) and cancer. These biological significances disclose the importance of understanding genomic patterns and function role of DNA methylation in human, as a initial step to get to know the epigenotype and its manner in connecting the phenotype and genotype. Two key papers back in 1975 independently suggested that methylation of CpG dinucleotides in vertebrates could be established de novo and inherited through somatic cell divisions by protein machineries of DNA methyltransferases that recognizes hemi-methylated CpG palindromes. They also indicated that the methyl group could be recognized by DNA-binding proteins and that DNA methylation directly silences gene expression. After almost four decades, several key points in these foundation papers are proved to be true. Take the mammalian genome for example, there are several findings indicating the epigenetic repression of gene expression by DNA methylation. These include X-chromosome inactivation, gene imprinting and suppressing the proliferation of transposable elements and repeat elements of viral or retroviral origin. In addition to these, many novel roles of DNA methylation have also been revealed. For example, DNA methylation can regulate alternative splicing by preventing CTCF, an evolutionarily conserved zinc-finger protein, binding to DNA. By using the technique of fluorescence resonance energy transfer (FRET) and fluorescence polarization, DNA methylation has also been shown to increase nucleosome compaction through DNA-histone contacts. What is more important, DNA methylation is essential for mammalian embryonic development and aberrant change of DNA methylation has been related to disease such as cancer. However, it is also notable there are several lines of evidence contradicting the relationship between DNA methylation and gene silencing. For example, comparison of DNA methylation levels in human genome on the active and inactive X chromosomes showed reduced methylation specifically over gene bodies on inactive X chromosomes. Not only in human, DNA methylation is found to be usually targeted to the transcription units of actively transcribed genes in invertebrate species. These results prove that the function of DNA methylation is challenging to be unravel. Besides, due to the development of sequencing technique, whole genome DNA methylation profiles have been detected in diverse species. Comparing genomic patterns of DNA methylation shows considerable variation among taxa, especially between vertebrates and invertebrates. However, even though extensive studies reveal the patterns and functions of DNA methylation in different species, in the mean time, they also highlight the limits to our understanding of this complex epigenetic system. During my Ph.D., in order to perform in-depth studies of DNA methylation in diverse animals as a way to understand the complexity of DNA methylation and its functions, I dedicated my efforts in investigating and analyzing the DNA methylation profiles in diverse species, ranging from insects to primates, including both model and non-model organisms. This dissertation, which constitutes an important part of my research, mainly focuses on the DNA methylation profile in primates including human and chimpanzee. In general, I will use three chapters to elucidate my work in generating and interpreting the whole genome DNA methylation data. Firstly, we generated nucleotide-resolution whole-genome methylation maps of the prefrontal cortex of multiple humans and chimpanzees, then comprehensive comparative studies for these DNA methylation maps have been performed, by integrating data on gene expression as well. This work demonstrates that differential DNA methylation might be an important molecular mechanism driving gene-expression divergence between human and chimpanzee brains and also potentially contribute to the human-specific traits, such as evolution of disease vulnerabilities. Secondly , we performed global analyses of CpG islands (CGIs) methylation across multiple methylomes of distinctive cellular origins in human. The results from this work show that the human CpG islands can be distinctly classified into different clusters solely based upon the DNA methylation profiles, and these CpG islands clusters reflect their distinctive nature at many biological levels, including both genomic characteristics and evolutionary features. Moreover, these CpG islands clusters are non-randomly associated with several important biological phenomena and processes such as diseases, aging, and gene imprinting. These new findings shed lights in deciphering the regulatory mechanisms of CpG islands in human health and diseases. At last, by utilizing the DNA methylome from human sperm and genetic map generated from the International HapMap Consortium project, we investigated the hypothesis suggesting a potential role of germ line DNA methylation in affecting meiotic recombination, which is essential for successful meiosis and various evolutionary processes. Even thought the results imply that DNA methylation is a important factor affecting regional recombination rate, the strength of correlation between these two is not as strong as the previous report. Besides, high-throughput analyses indicate that other epigenetic modifications, tri-methylation of histone 3 lysine 4 and histone 3 lysine 27 are also global features at the recombination hotspots, and may interact with methylation to affect the recombination pattern simultaneously. This work suggests epigenetic mechanisms as additional factors affecting recombination, which cannot be fully explained by the DNA sequence itself. In summary, I hope the results from these work can expand our knowledge regarding the common and variable patterns of DNA methylation in different taxa, and shed light about the function role and its major change during animal evolution.

Master of Engineering / This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combined frequency matrices, and principal components (PCs) of each matrix are generated to construct the new feature space. ANN built classifiers are used to test the discriminability of each feature space. Finally, the 3 and 5-mer feature matrix is selected as the context feature in this model. Two proposed schemes of HPR-PCA model are discussed and the implementations of sub-modules in each scheme are introduced. The context features selected by PCA are III used to build three promoter and non-promoter classifiers. CpG-island modules are embedded into models in different ways. In the comparison, Scheme I obtains better prediction results on two test sets so it is adopted as the model for HPR-PCA for further evaluation. Three existing promoter prediction systems are used to compare to HPR-PCA on three test sets including the chromosome 22 sequence. The performance of HPR-PCA is outstanding compared to the other four systems.