Conclusion. Taken together, paternal factors carried in ASG secretion affect genomic imprinting of developing embryos. The outcome of research work described here deepens our understanding of the role of ASG in maximizing reproductive performance mediated by regulating the epigenetic marks of the genome and in particular the imprinted genes. / Introduction. Our previous in vivo studies in golden hamster have shown the accessory sex glands (ASG) secretion facilitate the development of embryos to term but the underlying mechanism is still not clear. Since the deleterious effect caused by the lack of sperm exposure to ASG secretion is heritable to developing fetus and even after birth, we hypothesized that the paternal factor carried in ASG secretion may change the epigenetic regulation and in particular the imprinted genes of embryonic genome. / Materials and methods. Golden hamster and ICR mouse were used in this study. Hamster is a well-established animal model to study the effect of individual ASG but the genetic background of hamster is poorly known. To verify the specificity of our molecular probe and antibodies used in hamster, a mouse model was also established. Five groups of male hamsters and two groups of male mice were established by surgical treatment. In hamster, (SH) sham-operated, (VPX) ventral prostate-removed, (TX) all ASG-removed, (VPVX) castrated with ASG-removed except ventral prostate and (VX) castrated with intact ASG were established. In mouse, SH and VPX were established. In single-mating of hamster, male was copulated with female at estrus for 15 min. In double-mating of hamsters, female mated with each male for 10 min each. In single-mating of mouse, male was caged with female for 1 h. Epididymal sperm, uterine sperm, fertilized oocytes, pre-implantation embryos and fetuses at 13 days gestation (E13) were collected. Global DNA methylation of sperm, fertilized oocytes, early embryos and E13 fetuses were investigated by indirect immunofluorescence and DNA dot-blot using antibody against methylated DNA. Using the same technique, histone acetylation at lysine 5 residue was detected in male pronuclei of fertilized oocytes, protamine 1 and 2 content were detected in sperm, DNA methyltransferase 1, 3a and 3b activities were detected in early embryos. The crown-rump length and weight of fetuses were measured. Morphology was also examined under scanning electron microscope. Two sets of co-ordinately regulated but oppositely expressed imprinted genes Igf2/H19 and Dlk1/Gtl2 were investigated. H19 differentially methylated region (DMR) and Gtl2 promoter were examined by bisulfite sequencing in sperm and E13 fetuses. Expression of Igf2 and Dlk1 were examined by in situ hybridization and real-time PCR in pre-implantation embryos and E13 fetuses. / Results. Uterine sperm in VPX and TX groups showed no change of DNA methylation level and protamine 1 and 2 content. Fertilized oocytes in VPX and TX groups showed similar DNA methylation level as SH group in both hamster and mouse. Histone hypoacetylation was observed in male pronuclei of hamster but not in mouse. Early embryos in VPX and TX groups showed abnormal level of DNA methylation and Dnmt3b during embryo development in hamster. Replenishment of ASG secretion to sperm from VPX and TX group by double-mating restored the DNA methylation level to normal in early embryos. E13 fetuses of VPX and TX groups in hamster and VPX group in mouse showed DNA hypomethylation. E13 fetuses of VPX group in hamster showed increase in average crown-rump length and body weight with larger variations between individuals. One E13 fetus of VPX group in mouse showed polydactyly and malformation in the head. Real-time PCR showed abnormal expression of Igf2 and Dlk1 in both pre-implantation embryos and E13 fetuses of VPX and TX groups. Bisulfite sequencing showed hypermethylation of H19 DMR in VPX and TX groups of hamster and hypomethylation of Gtl2 promoter in VPX group of mouse. / "August 2007." / Adviser: Pak Ham Chow. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4739. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 194-224). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

Fung, Man See Joyce. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 111-120). / Abstracts in English and Chinese. / Table of Contents / Acknowledgements --- p.I / Abstract --- p.II / 論文摘要 --- p.VII / Table of Contents --- p.X / List of Figures --- p.XIV / List of Tables --- p.XVI / Abbreviations --- p.XVII / Chapter Chapter 1 - --- Introduction --- p.l / Chapter 1.1 --- Biology of HPV --- p.2 / Chapter 1.1.1 --- History --- p.2 / Chapter 1.1.2 --- Classification --- p.2 / Chapter 1.1.3 --- Genome structure --- p.3 / Chapter 1.2 --- HPV and cervical cancer --- p.8 / Chapter 1.2.1 --- Classification of cervical lesions --- p.8 / Chapter 1.2.2 --- Natural history of development of cervical cancer --- p.9 / Chapter 1.2.3 --- Risk factors --- p.11 / Chapter 1.3 --- Prevention of cervical cancer --- p.12 / Chapter 1.3.1 --- Vaccination --- p.12 / Chapter 1.3.2 --- Screening --- p.12 / Chapter 1.3.2.1 --- Pap test --- p.12 / Chapter 1.3.2.2 --- HPV DNA test --- p.13 / Chapter 1.3.2.3 --- Methylation pattern as a novel marker --- p.13 / Chapter 1.4 --- Biology of Methylation --- p.14 / Chapter 1.4.1 --- Definition --- p.14 / Chapter 1.4.2 --- Silencing effect --- p.18 / Chapter 1.4.3 --- Roles in normal development --- p.20 / Chapter 1.5 --- Methylation and human diseases --- p.20 / Chapter 1.5.1 --- Genetic diseases --- p.20 / Chapter 1.5.2 --- Cancers --- p.21 / Chapter 1.5.3 --- Methylation and oncogenic viruses --- p.23 / Chapter 1.5.4 --- Potential of methylation pattern as a novel biomarker of cancer --- p.24 / Chapter 1.5.5 --- Epigenetic therapy --- p.25 / Chapter 1.6 --- Methylation and HPV --- p.25 / Chapter 1.6.1 --- History --- p.25 / Chapter 1.6.2 --- Potential roles in transcription regulation of HPV --- p.26 / Chapter 1.6.3 --- Viral gene methylation --- p.27 / Chapter Chapter 2 - --- "Hypotheses, Objectives and Study Design" --- p.28 / Chapter 2.1 --- Hypotheses --- p.29 / Chapter 2.2 --- Objectives --- p.30 / Chapter 2.3 --- Study Design --- p.30 / Chapter Chapter 3 - --- Materials and Methods --- p.34 / Chapter 3.1 --- Work flow --- p.35 / Chapter 3.2 --- Study subjects --- p.37 / Chapter 3.2.1 --- Invasive cervical cancer group --- p.37 / Chapter 3.2.2 --- Low-grade group --- p.37 / Chapter 3.2.3 --- Cell lines --- p.38 / Chapter 3.3 --- DNA extraction --- p.38 / Chapter 3.4 --- HPV genotyping --- p.39 / Chapter 3.5 --- PCR of HPV16 LCR --- p.39 / Chapter 3.6 --- Sequencing of HPV 16 LCR --- p.42 / Chapter 3.6.1 --- Purification of PCR products --- p.42 / Chapter 3.6.2 --- Cycle sequencing reaction --- p.42 / Chapter 3.6.3 --- Purification of cycle sequencing products --- p.43 / Chapter 3.6.4 --- Sequencer and data analysis --- p.43 / Chapter 3.7 --- Bisulfite modification --- p.43 / Chapter 3.8 --- PCR of bisulfite modified LCR --- p.45 / Chapter 3.9 --- Cloning --- p.48 / Chapter 3.9.1 --- Ligation --- p.48 / Chapter 3.9.2 --- Transformation --- p.48 / Chapter 3.9.3 --- Colony PCR --- p.49 / Chapter 3.10 --- Sequencing of clones --- p.51 / Chapter 3.10.1 --- Purification of PCR products --- p.51 / Chapter 3.10.2 --- Cycle sequencing reaction --- p.51 / Chapter 3.10.3 --- Purification of cycle sequencing products --- p.52 / Chapter 3.10.4 --- Sequencer and data analysis --- p.52 / Chapter 3.11 --- Statistical methods --- p.52 / Chapter Chapter 4 - --- Results --- p.54 / Chapter 4.1 --- Sample selection --- p.55 / Chapter 4.2 --- HPV16 LCR PCR and sequencing --- p.57 / Chapter 4.3 --- Methylation patterns --- p.61 / Chapter 4.3.1 --- Cell lines --- p.61 / Chapter 4.3.2 --- Cancer group --- p.63 / Chapter 4.3.2.1 --- Overview --- p.63 / Chapter 4.3.2.2 --- Methylation pattern of the cancer samples --- p.66 / Chapter 4.3.2.3 --- Methylation pattern of the promoter region --- p.74 / Chapter 4.3.3 --- Low-grade group --- p.76 / Chapter 4.3.3.1 --- Overview --- p.76 / Chapter 4.3.3.2 --- Methylation pattern of the low-grade samples --- p.79 / Chapter 4.3.4 --- Comparison of the methylation patterns of the cancer samples and the low-grade samples --- p.84 / Chapter Chapter 5 - --- Discussion --- p.95 / Chapter 5.1 --- Sequence variations of HPV 16 LCR --- p.96 / Chapter 5.2 --- Methylation patterns of CaSki and SiHa cell lines --- p.98 / Chapter 5.3 --- Methylation pattern of the cancer samples --- p.99 / Chapter 5.4 --- Methylation pattern of the low-grade samples --- p.100 / Chapter 5.5 --- Comparison of methylation patterns of the cancer samples and the low-grade samples --- p.101 / Chapter 5.5.1 --- Promoter region in 3' LCR --- p.102 / Chapter 5.5.1.1 --- SP1 binding site --- p.102 / Chapter 5.5.1.2 --- E2BS3 and E2BS4 --- p.103 / Chapter 5.5.2 --- Silencer region --- p.104 / Chapter 5.5.3 --- Enhancer region in central LCR --- p.105 / Chapter 5.5.4 --- CpG sites within 5' LCR --- p.106 / Chapter 5.6 --- Role of methylation in HPV 16 --- p.107 / Chapter 5.7 --- Potential as novel biomarker --- p.108 / Chapter 5.8 --- Conclusions --- p.109 / References --- p.111 / Appendix A

Kan, Hoi Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 103-124). / Abstracts in English and Chinese. / ABSTRACT --- p.i / 摘要 --- p.iv / ACKNOWLEDGEMENTS --- p.vi / TABLE OF CONTENTS --- p.viii / LIST OF TABLES --- p.xii / LIST OF FIGURES --- p.xiii / LIST OF ABBREVIATIONS --- p.xiv / PUBLICATION --- p.xvi / Chapter SECTION I: --- BACKGROUND --- p.1 / Chapter Chapter 1: --- Hepatocellular Carcinoma (HCC) --- p.2 / Chapter 1.1. --- Epidemiology of HCC --- p.3 / Chapter 1.2. --- Etiology of HCC --- p.3 / Chapter 1.2.1. --- Cirrhosis --- p.4 / Chapter 1.2.2. --- Hepatitis virus --- p.4 / Chapter 1.2.3. --- Plant carcinogens --- p.5 / Chapter 1.2.4. --- Miscellaneous factors --- p.6 / Chapter 1.3. --- Clinical presentation of HCC --- p.6 / Chapter 1.4. --- Existing diagnostic tests for HCC --- p.6 / Chapter 1.4.1. --- Alpha-fetoprotein (AFP) --- p.7 / Chapter 1.4.2. --- Imaging --- p.7 / Chapter 1.5. --- Treatment of HCC --- p.8 / Chapter 1.5.1. --- Surgical Resection and Transplantation --- p.8 / Chapter 1.5.2. --- Tumor Ablation or Embolization --- p.8 / Chapter 1.5.3. --- Chemotherapy and Radiotherapy --- p.9 / Chapter 1.6. --- Tumor marker development for HCC detection --- p.10 / Chapter 1.6.1. --- Oncofetal antigens and glycoprotein antigens --- p.11 / Chapter 1.6.2. --- Enzymes and isoenzymes --- p.12 / Chapter 1.6.3. --- Growth factors --- p.12 / Chapter 1.6.4. --- Genetics and epigenetics - mRNA and methylation --- p.13 / Chapter Chapter 2: --- Hypermethylation of tumor suppressor genes in cancer --- p.14 / Chapter 2.1. --- Cancer epigenetics --- p.14 / Chapter 2.2. --- DNA methylation in normal cells --- p.15 / Chapter 2.3. --- Physiological role of DNA methylation in normal cells --- p.18 / Chapter 2.4. --- Aberrant DNA methylation in cancer --- p.19 / Chapter 2.4.1. --- DNA hypomethylation in cancer --- p.20 / Chapter 2.4.2. --- DNA hypermethylation in cancer --- p.20 / Chapter 2.5. --- Development of methylation markers in tumor diagnosis --- p.21 / Chapter 2.5.1. --- Methods for the analysis of DNA methylation markers --- p.22 / Chapter 2.5.2. --- Detection of tumor-associated methylated DNA in the circulation of cancer patients / Chapter 2.6. --- Aim of thesis --- p.27 / Chapter SECTION II: --- MATERIALS AND METHODS --- p.28 / Chapter Chapter 3: --- Methods for detecting DNA methylation --- p.29 / Chapter 3.1. --- Subject recruitment --- p.29 / Chapter 3.2. --- Sample collection and processing --- p.29 / Chapter 3.2.1. --- Tumor tissue samples --- p.29 / Chapter 3.2.2. --- Peripheral blood samples --- p.29 / Chapter 3.3. --- DNA extraction --- p.30 / Chapter 3.3.1. --- Plasma samples --- p.30 / Chapter 3.3.2. --- Blood cells --- p.33 / Chapter 3.3.3. --- Tumor tissue --- p.33 / Chapter 3.4. --- Quantitative analysis of methylated DNA using methylation-sensitive restriction enzyme-mediated real-time quantitative PCR (MSRE-qPCR) --- p.34 / Chapter 3.4.1. --- Methylation-sensitive restriction enzyme-mediated real-time quantitative PCR --- p.34 / Chapter 3.4.3. --- Real-time PCR primer design --- p.36 / Chapter 3.4.4. --- Duplex real-time PCR --- p.40 / Chapter 3.4.5. --- "Real-time detection of GSTP1, SOCS1, A PC, pl6 and ACTB sequences" --- p.41 / Chapter 3.4.6. --- Statistical analysis of real-time PCR results --- p.41 / Chapter 3.5. --- "Methylation study of GSTP1, SOCS1, APC, pl6 and ACTB in tumor tissues and blood cells using bisulfite sequencing" --- p.46 / Chapter 3.5.1. --- Principle of bisulfite modification --- p.46 / Chapter 3.5.2. --- Bisulfite conversion --- p.47 / Chapter 3.5.3. --- Sequencing primer design --- p.47 / Chapter 3.5.4. --- Conventional PCR after bisulfite treatment --- p.49 / Chapter 3.5.5. --- Cloning and bisulfite genomic sequencing --- p.53 / Chapter 3.5.6. --- Data acquisition and interpretation --- p.54 / Chapter SECTION III: --- DEVELOPMENT OF METHYLATION MARKERS IN HCC DETECTION / Chapter Chapter 4: --- Evaluation of the real-time PCR assay for quantification of methylated tumor suppressor genes --- p.57 / Chapter 4.1. --- Development of real-time PCR assays --- p.57 / Chapter 4.2. --- Methylation analyses by bisulfite sequencing were concordant with the real-time quantification results --- p.61 / Chapter Chapter 5: --- Clinical application of methylated markers in the detection of hepatocellular carcinoma --- p.69 / Chapter 5.1. --- Demographics of HCC patients and HB V carriers --- p.69 / Chapter 5.2. --- Quantitative analysis of hypermethylated tumor suppressor genes in tumor and plasma samples --- p.71 / Chapter 5.3. --- Effect of cirrhosis on the plasma methylated tumor suppressor gene concentrations --- p.77 / Chapter 5.4. --- Changes in the concentration of the tumor suppressor genes one month after surgical resection of the cancer --- p.81 / Chapter 5.5. --- Concurrent use of serum AFP level and plasma methylated markers for HCC diagnosis --- p.84 / Chapter 5.6. --- Prognostic value of plasma methylated TSGs --- p.86 / Chapter SECTION IV: --- DISCUSSION --- p.90 / Chapter Chapter 6: --- Discussion --- p.91 / Chapter 6.1. --- Tumor and plasma detection of hypermethylated tumor suppressor genes --- p.92 / Chapter 6.2. --- No effect of cirrhosis on plasma methylated DNA level --- p.94 / Chapter 6.3. --- Clearance of methylated TSG sequences after tumor resection --- p.95 / Chapter 6.4. --- Concurrent use of serum AFP level and the presence of methylated markers in the plasma in HCC diagnosis --- p.95 / Chapter 6.5. --- Prognostic significance of circulating methylated tumor markers --- p.96 / Chapter SECTION V: --- CONCLUDING REMARKS --- p.98 / Chapter Chapter 7: --- Conclusions and future perspectives --- p.99 / REFERENCES --- p.103

DNA methylation is an epigenetic mechanism critical for tissue development, cell specification and cellular function. Mammalian brains consist of millions to billions of neurons and glial cells that can be subdivided into many distinct types of cells. We hypothesize that brain methylomes are heterogeneously methylated across different types of cells and the transcription factors play key roles in brain methylome programming. To dissect brain methylome heterogeneity, in Chapter 2, we first focused on the identification of cell-subset specific methylated (CSM) loci which demonstrate bipolar DNA methylation pattern, i.e., hypermethylated in one cell subset but hypomethylated in others. With the genome-scale hairpin bisulfite sequencing approach, we demonstrated that the majority of CSM loci predicted likely resulted from the methylation differences among brain cells rather than from asymmetric DNA methylation between DNA double strands. Importantly, we found that putative CSM loci increased dramatically during early stages of brain development and were enriched for GWAS variants associated with neurological disorder-related diseases/traits. It suggests the important role of putative CSM loci during brain development, implying that dramatic changes in functions and complexities of the brain may be companied by a rapid change in epigenetic heterogeneity. To explore epigenetic regulatory mechanisms during brain development, as described in Chapter 3, we adopted unbiased data-driven approaches to re-analyze methylomes for human and mouse frontal cortices at different developmental stages. We predicted Egr1, a transcriptional factor with important roles in neuron maturation, synaptic plasticity, long-term memory formation and learning, plays an essential role in brain epigenetic programming. We performed EGR1 ChIP-seq and validated that thousands of EGR1 binding sites are with cell-type specific methylation patterns established during postnatal frontal cortex development. More specifically, the CpG dinucleotides within these EGR1 binding sites become hypomethylated in mature neurons but remain heavily methylated in glia. We further demonstrated that EGR1 recruits a DNA demethylase TET1 to remove the methylation marks at EGR1 binding sites and activate downstream genes. Also, we found that the frontal cortices from the knockout mice lacking Egr1 or Tet1 share strikingly similar profiles in both gene expression and DNA methylation. Collectively, the study in this dissertation reveals EGR1 programs the brain methylome together with TET1 during postnatal development. This study also provides new insights into how life experience and neuronal activity may shape the brain methylome. / Ph. D. / DNA methylation is a widespread epigenetic mark on DNA, serving as a “switch” to turn on or off gene expression. It plays essential roles in cellular functions, tissue development. Mammalian brains contain millions to billions of neurons and glial cells, which can be further divided into many different types of cells. We hypothesize that brain cells have different methylation profiles across the genome, and transcriptional factors play important roles in programming methylation in the mammalian brain genome. To study the diversity of methylation profiles across the genomes of different brain cells, in Chapter 2, we first focused on the identification of cell-subset specific methylated (CSM) genomic regions which show bipolar DNA methylation pattern, i.e., hypermethylated in one type of cell but hypomethylated in others. By applying a technique called the genome-scale hairpin bisulfite sequencing to mouse frontal cortices, we demonstrated that the majority of CSM genomic regions predicted likely resulted from the methylation differences among brain cells, rather than from methylation differences between DNA double strands. Surprisingly, we found that these predicted CSM genomic regions increased dramatically during early stages of brain development and were enriched for GWAS variants associated with neurological disorder-related diseases/traits. It suggests the importance of predicted CSM genomic regions, implying that dramatic changes in brain function and structure may be companied by a rapid change in DNA methylation diversity during brain development. To explore underlying epigenetic mechanisms during brain development, as described in Chapter 3, we re-analyzed methylomes for human and mouse frontal cortices at different developmental stages, and predicted Egr1, a transcriptional factor with important roles in neuron maturation, synaptic plasticity, long-term memory formation and learning, plays an essential role in brain methylome programming. We found thousands of EGR1 binding sites showed cell-type specific methylation patterns, and were established during postnatal frontal cortex development. More specifically, the methylation level of these EGR1 binding sites was low in mature neurons but pretty high in glial cells. We further demonstrated that EGR1 recruits a DNA demethylase TET1 to remove the methylation marks at EGR1 binding sites and activate downstream genes. Also, we found that the frontal cortices from the Egr1 knockout or Tet1 knockout mice show strikingly similar profiles in both gene expression and DNA methylation. Collectively, the study in this dissertation reveals EGR1 works together with TET1 to program the brain methylome during postnatal development. This study also provides new insights into how life experience and neuronal activity may shape the brain methylome.

No description available.

DNA methylation is involved in various biological phenomena, and its dysregulation has been demonstrated as being correlated with a number of human disease processes, including cancers, autism, and autoimmune, mental health and neuro-degenerative ones. It has become important and useful in characterising and modelling these biological phenomena in or-der to understand the mechanism of such occurrences, in relation to both health and disease. An attempt has previously been made to map DNA methylation across human tissues, however, the means of distinguishing between methylated, unmethylated and differentially-methylated groups using DNA sequence features remains unclear. The aim of this study is therefore to: firstly, investigate DNA methylation classes and predict these based on DNA sequence features; secondly, to further identify methylation-associated DNA sequence features, and distinguish methylation differences between males and females in relation to both healthy and diseased, sta-tuses. This research is conducted in relation to three samples within nine biological feature sub-sets extracted from DNA sequence patterns (Human genome database). Two samples contain classes (methylated, unmethy-lated and differentially-methylated) within a total of 642 samples with 3,809 attributes driven from four human chromosomes, i.e. chromosomes 6, 20, 21 and 22, and the third sample contains all human chromosomes, which encompasses 1628 individuals, and then 1,505 CpG loci (features) were extracted by using Hierarchical clustering (a process Heatmap), along with pair correlation distance and then applied feature selection methods. From this analysis, author extract 47 features associated with gender and age, with 17 revealing significant methylation differences between males and females. Methylation classes prediction were applied a K-nearest Neighbour classifier, combined with a ten-fold cross- validation, since to some data were severely imbalanced (i.e., existed in sub-classes), and it has been established that direct analysis in machine-learning is biased towards the majority class. Hence, author propose a Modified- Leave-One-Out (MLOO) cross-validation and AdaBoost methods to tackle these issues, with the aim of compositing a balanced outcome and limiting the bias in-terference from inter-differences of the classes involved, which has provided potential predictive accuracies between 75% and 100%, based on the DNA sequence context.

published_or_final_version / Obstetrics and Gynaecology / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / toc / Surgery / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Medicine / Doctoral / Doctor of Philosophy

published_or_final_version / Paediatrics and Adolescent Medicine / Master / Master of Philosophy