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UBC Theses and Dissertations

Epigenetic regulation of enhancer regions in breast cancer cells in response to pterostilbene Harandi-Zadeh, Sadaf 2020

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 Epigenetic Regulation of Enhancer Regions in Breast Cancer Cells in Response to Pterostilbene  by Sadaf Harandi-Zadeh BSc (Honours), The University of British Columbia, 2017   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Human Nutrition)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)    April 2020 © Sadaf Harandi-Zadeh, 2020   ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:  Epigenetic Regulation of Enhancer Regions in Breast Cancer Cells in Response to  Pterostilbene  submitted by Sadaf Harandi-Zadeh in partial fulfillment of the requirements for the degree of Master of Science  in Human Nutrition  Examining Committee: Dr. Barbara Stefanska, Assistant Professor, Human Nutrition, UBC Supervisor  Dr. Vivien Measday, Associate Professor, Food Science, UBC Supervisory Committee Member  Dr. Crystal Karakochuk, Assistant Professor, Human Nutrition, UBC Supervisory Committee Member Dr. Bradford Hoffman, Associate Professor, Surgery, UBC Additional Examiner       iii Abstract  Epigenetic alterations are estimated to be linked to 30% of sporadic breast cancer cases. Interestingly, certain dietary polyphenols, such as pterostilbene found abundantly in blueberries, have been shown to regulate gene expression and reverse tumour development by altering epigenetic patterns. Our group has proposed the involvement of DNA methyltransferase 3B (DNMT3B) and oncogenic transcription factor OCT1 as vital players in polyphenol-mediated targeting of oncogenes. We have also identified enhancers as important regulatory regions with altered DNA methylation in response to polyphenols. However, the genome wide effects of pterostilbene-mediated alterations in the occupancy of DNMT3B and OCT1 in enhancer regions of oncogenes remains to be elucidated.  In this study, following chromatin immunoprecipitation (ChIP) sequencing analyses of highly invasive MCF10CA1a breast cancer cells treated with 7μM pterostilbene for 9 days, we discovered that pterostilbene treatment leads to altered occupancy of DNMT3B and OCT1 at enhancer regions of genes with oncogenic functions. In addition, trimethylation at lysine 36 of histone 3 (H3K36me3) enrichment was measured to indicate decrease in gene transcriptional activity. QPCR and pyrosequencing were performed to assess gene expression and DNA methylation of the selected oncogenes, respectively.   We identified 20 candidate genes whose enhancers showed increased binding of DNMT3B, decreased occupancy of OCT1 and reduced enrichment of H3K36me3 in MCF10CA1a upon pterostilbene exposure compared with control untreated cells (p<0.05). Of those 20 candidates, we selected 4 genes for further analyses (i.e., PITPNC1, TNNT2, DANT2 and LINC00910).  Using pyrosequencing, we found that PITPNC1 and TNNT2 enhancer regions, encompassing 5 and 3 CpG sites respectively, showed 8-16% and 6-19% increase in DNA methylation throughout the region upon pterostilbene treatment. DANT2, a long noncoding RNA, was hypermethylated by 3-7% across 7 CpG sties and LINC00910 was hypermethylated by 2-28% across 8 CpG sites.  iv These changes coincided with 84%, 87%, 41% and 92% down-regulation of PITPNC1, TNNT2, DANT2 and LINC00910, respectively, upon pterostilbene treatment.  This work provides novel insights into the mechanisms of dietary polyphenols in driving epigenetic silencing of enhancer regions within genes with oncogenic functions in breast cancer cells.                   v Lay Summary  Epigenetics is the study of changes in gene expression (phenotype) that do not involve changes in the underlying DNA sequence (genome). DNA methylation is a reversible epigenetic modification that can be altered in response to environmental factors including our diets. Alterations in the patterns of DNA methylation have been observed in many human diseases, particularly in cancer. The goal of the research presented in this thesis is to understand how a dietary compound, called pterostilbene, that is abundantly found in blueberries, can reverse abnormal DNA methylation and silence cancer-causing genes in breast cancer. Through understanding the potential biological effects of pterostilbene on important players involved in cancer development, we will open new avenues to implement dietary compounds into cancer prevention and support of anti‐cancer therapies.             vi Preface This thesis work is the result of the analyses of genome-wide chromatin immunoprecipitation sequencing (ChIP-seq) data in highly invasive MCF10CA1a breast cancer cells treated with 7μM pterostilbene (PTS) for 9 days. ChIP-seq was carried out by Dr. Katarzyna Lubecka, a postdoctoral fellow in Dr. Stefanska’s laboratory. Megan Beetch, a PhD student in Dr. Stefanska’s laboratory, completed ChIP-seq analyses including calling and assigning peaks with gene names and chromatin states, in collaboration with Dr. LeAnn Howe from the Department of Biochemistry at the University of British Columbia, and Dr. Benjamin Martin, a PhD candidate in Dr. Howe’s Lab at that time. I analyzed the lists of peaks with differential binding of DNMT3B and OCT1, and differential enrichment of H3K36me3. This work contains my unpublished and original work to partially fulfill the requirement for a Master of Science in Human Nutrition at the University of British Columbia.  Contributions to various parts of the research:  Dr. Barbara Stefanska and I envisioned the data analysis plan and experimental design.  Contributions to analyses of data: Following generation of the ChIP-seq raw data files, I conducted analyses of lists of peaks to identify enhancers and corresponding genes with increased enrichment of DNMT3B and decreased occupancy of the oncogenic transcription factor OCT1 in response to pterostilbene (PTS) treatment. Enrichment of an active transcription mark, H3K36me3 was also measured to indicate gene transcriptional activity. Selected genes corresponding to these enhancer regions were analyzed in terms of their biological functions and signaling pathways. Contributions to experiments:  Once I established candidate oncogenes with enhancer regions characterized by differential binding of DNMT3B, OCT1 and H3K36me3, I performed QPCR and pyrosequencing to assess the expression levels and DNA methylation of the candidate genes, respectively.     vii Table of Contents   Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ ix List of Figures ................................................................................................................................ x List of Abbreviations ................................................................................................................... xi Acknowledgments ...................................................................................................................... xiii Dedication ................................................................................................................................... xiv Chapter 1:  Introduction .............................................................................................................. 1 1.1 Breast Cancer ...................................................................................................................... 1 1.1.1  Definition and epidemiology .......................................................................................... 1 1.1.2 Molecular subtypes of breast cancer ............................................................................... 1 1.1.3 Risk factors ..................................................................................................................... 3 1.2 Epigenetics .......................................................................................................................... 3 1.2.1 Definition ........................................................................................................................ 3 1.2.2 Factors influencing the epigenome ................................................................................. 4 1.3 Components of the Epigenome ........................................................................................... 5 1.3.1 DNA methylation machinery .......................................................................................... 5 1.3.2 DNA methyltransferases ................................................................................................. 6 1.3.3 TET-mediated DNA demethylation ................................................................................ 7 1.3.4 DNA methylation and the affinity of transcription factors to DNA ............................... 8 1.3.5 Post-translational modifications of histones ................................................................... 9 1.3.6 Interactions between DNMTs and histone modifications ............................................. 10 1.3.7 Histone H3 trimethylation at lysine 36 (H3K36me3) ................................................... 11 1.4 Epigenetic Aberrations in Cancer ..................................................................................... 12 1.4.1 Alterations in DNA methylation patterns during carcinogenesis ................................. 12 1.4.2 Loci-specific changes in DNA methylation in cancer .................................................. 13 1.4.3 Global DNA hypomethylation in cancer ...................................................................... 14 1.4.4 Alterations in the DNA methylation machinery during carcinogenesis ....................... 14  viii 1.4.5 Alterations in histone modifications during carcinogenesis ......................................... 16 1.4.6 Epigenetic anti-cancer strategies................................................................................... 16 1.4.7 Barriers to effective epigenetic anti-cancer therapies ................................................... 17 1.5 Dietary Bioactive Compounds as Regulators of Cancer Methylome ............................... 18 1.5.1 Effects of dietary compounds in shaping the epigenome ............................................. 18 1.5.2 Dietary polyphenols modulate DNA methylation landscapes in cancer ...................... 19 1.5.3 Stilbenoids..................................................................................................................... 20 1.5.4 Anti-cancer effects of stilbenoids ................................................................................. 21 1.5.5 Stilbenoids remodel the DNA methylation patterns in cancer ...................................... 21 1.6 Enhancer Elements............................................................................................................ 23 Research Objectives and Hypotheses ........................................................................................ 25 Chapter 2: Research Design & Methods .................................................................................. 26 2.1 Cell culture and treatment with pterostilbene (PTS)......................................................... 26 2.2 Chromatin immunoprecipitation (ChIP) ........................................................................... 26 2.3 Analysis of ChIP-seq data ................................................................................................. 27 2.4 DNA isolation and pyrosequencing .................................................................................. 28 2.5 RNA isolation, cDNA synthesis and QPCR ..................................................................... 28 2.6 Statistical analysis ............................................................................................................. 31 Chapter 3: Results....................................................................................................................... 32 3.1 Overview of DNMT3B binding in highly invasive MCF10CA1a breast cancer cells in response to pterostilbene (PTS) .................................................................................................... 32 3.2 DNMT3B recruitment coincides with decrease in OCT1 binding in response to PTS .... 36 3.3 H3K36me3 marks the genes targeted by DNMT3B for methylation at OCT1-occupied loci in response to PTS ................................................................................................................. 37 3.4 Candidate genes with enhancers, that are characterized by increased DNMT3B and reduced OCT1 and H3K36me3 occupancy, are hypermethylated and downregulated upon exposure to PTS ............................................................................................................................ 39 Chapter 4: Discussion ................................................................................................................. 45 Overall Conclusions .................................................................................................................... 49 References .................................................................................................................................... 54  ix List of Tables Table 1.  Classification of Molecular Subtypes of Breast Cancer. . . . . . . . . . . . . . . . . . . . . . . . . 2 Table 2.  Primer sequences used in DNA methylation analysis by pyrosequencing (A) and in gene expression analysis by qPCR (B). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29  Table 3.  Detailed characteristics of DNMT3B-enriched, OCT1- and H3K36me3-reduced peaks associated with enhancers of the selected candidate oncogenes in highly invasive MCF10CA1a breast cancer cells upon treatment with pterostilbene (PTS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42     x List of Figures  Figure 1.1.  Schematic representation of DNA methylation reaction. . . . . . . . . . . . . . . . . . . . . . . 6 Figure 1.2.  Methylation status of tumor suppressor genes and oncogenes in cancer. . . . . . . . . . 13  Figure 1.3.  Chemical structure of pterostilbene (PTS) and resveratrol (RSV). . . . . . . . . . . . . . . 20  Figure 3.1.  Overview of ChIP-sequencing analysis of DNMT3B binding in highly invasive MCF10CA1a breast cancer cells in response pterostilbene (PTS). . . . . . . . . . . . . . . . . . . . . . . . . 33  Figure 3.2.  Peaks and genes associated with increased binding of DNMT3B, decreased occupancy of OCT1 and decreased enrichment of H3K36me3 in response of MCF10CA1a breast cancer cells to pterostilbene (PTS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38  Figure 3.3.  Changes in gene expression in response to pterostilbene (PTS). . . . . . . . . . . . . . . . 40  Figure 3.4. Quantitative analysis of methylation status of PITPNC1 and TNNT2 oncogenes as determined by pyrosequencing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43  Figure 3.5.  Quantitative analysis of methylation status of DANT2 and LINC00910 long non- coding RNAs as determined by pyrosequencing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44            xi List of Abbreviations  AML  Acute myeloid leukemia BER  Base excision repair ChIP-seq Chromatin immunoprecipitation sequencing DNMT DNA methyltransferase DNMTi DNA methyltransferase inhibitor EGCG  Epigallocatechin gallate ER  Estrogen receptor eRNA  Enhancer RNA ESC  Embryonic stem cell FE  Fold enrichment GO  Gene ontology  HAT  Histone acetyltransferase HCC  Hepatocellular carcinoma HDAC  Histone deacetylase HER2  Human epidermal growth factor receptor 2 HKMT Histone lysine methyltransferase HMEC  Human mammary epithelial cells HMT  Histone methyltransferases IGV  Integrative genomic viewer 5-mC  5-methylcytosine PR  Progesterone receptor PRMT  Protein arginine methyltransferase  xii PTS  Pterostilbene  PWWP Proline-tryptophan-tryptophan-proline RSV  Resveratrol SAH  S-adenosyl-L-homocysteine SAM  S-adenosyl-L-methionine SETD2  Set domain containing protein 2  TDG  Thymine-DNA-glycosylase TET  Ten-eleven translocation methylcytosine dioxygenase TF  Transcription factor TNBC  Triple negative breast cancer TSS  Transcription start site  UHRF1 Ubiquitin like with PHD and ring finger domains              xiii Acknowledgments First and foremost, my deepest gratitude goes to Allah, the Most Compassionate, the Most Merciful. The one who grants grace in response to our good actions and thoughts.   I would like to express my sincere gratitude and profound respect to my supervisor, Dr. Barbara Stefanska. The completion of this thesis work would not have been possible without her continuous guidance, enthusiastic encouragement, immense knowledge and insightful comments. I am very grateful to her for giving me the opportunity to explore research and teaching me to be fearless throughout my graduate studies.   I would also like to thank my supervisory committee, Dr. Vivien Measday and Dr. Crystal Karakochuk. I am very appreciative for the time they have committed and the support they have extended to me on this thesis work.   I am thankful to my amazing colleague, Megan. I was very lucky to work with you every day. I will miss you and our “Farsi Friday” hours, but the memories we shared will always remain in my heart.   To my best friend, Dina.  I am forever thankful for your immeasurable support and encouragement. You never failed to boost my morale.  Last, but not least, a huge shout-out thanks to my family. My beautiful sisters, Milad, Golbou and Niloufar, I am immensely thankful for you every day. I owe a depth of gratitude to two very special people, my amazing parents. Mom and dad, thank you for your unconditional love, countless sacrifices and prayers to help me get to where I am today. You are the reason for who I am. I hope I can make you both proud one day.     xiv Dedication     To My Parents.                  1 Chapter 1:  Introduction     1.1 Breast Cancer   1.1.1  Definition and epidemiology  Breast cancer is a multi-stage and multifactorial disease characterized by aberrant cell cycle activity that results in uncontrolled cellular proliferation, tumor formation and metastasis. Breast cancer can affect both men and women, but its occurrence is far more frequent in women. In fact, breast cancer is the most commonly diagnosed malignancy and the second leading cause of cancer-related deaths among women (Wagner et al., 2012; Siegel et al., 2019). On average 74 Canadian women are diagnosed with breast cancer every day (Canadian Cancer Society, 2019). Furthermore, in 2018, approximately 627,000 women died from breast cancer worldwide, embodying 15% of all cancer mortality among women (World Health Organization, 2018). Due to the increasing rates of this disease, there is a desperate need of novel approaches for prevention and early detection, as well as effective treatments to significantly reduce breast cancer incidence and mortality.   1.1.2 Molecular subtypes of breast cancer  Based on the expression profile of the receptors, namely, the estrogen receptor (ER), the progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER2), breast tumors are categorized into four major subtypes, as shown in Table 1 (Bouchal et al., 2019). Luminal A breast cancers are slow growing tumors and are HER2 negative, ER and/or PR receptor positive (Fragomeni et al., 2018). Due to the presence of hormone receptors, luminal A tumors  2 have the best favorable outcomes with use of hormonal therapy in comparison to other molecular subtypes of breast cancer (Inic et al., 2014; Gao et al., 2018). Luminal B breast cancers are either HER2 negative or positive and hormone-receptor positive (ER and/or PR) (Fragomeni et al., 2018). Luminal B tumors grow faster and have a slightly worse prognosis than the luminal A subtype (Feng et al., 2018). Hormonal therapy in combination with an anti-HER2 antibody, trastuzumab (herceptin), are often used for treatment of HER2-positive luminal B breast cancers. Lastly, triple negative breast cancer (TNBC), the most aggressive subtype of breast cancer, which accounts for 15–20% of all breast cancer cases (Anders et al., 2008), clinically tests negative for the expression of all three receptors (HER2, ER and PR). Interestingly, this molecular subtype is more frequent in women with BRCA gene mutations and demonstrates significant aggressive phenotype, such as high nuclear grade and larger tumor burden (Chen et al., 2018).  The standard systemic chemotherapy (anthracyclines and taxanes) remains the most promising and mainstay treatment for this molecular subtype (Collignon et al., 2016), indicating a need for other therapeutic options for TNBC (Table 1).   Table 1. Classification of Molecular Subtypes of Breast Cancer  Molecular subtype ER/PR HER2 Therapy Luminal A Positive Negative Hormonal Therapy Luminal B Positive Positive/negative Hormonal Therapy Trastuzumab (Herceptin) HER2 + Negative Positive Trastuzumab (Herceptin) Basal-like/Triple negative Negative Negative Systemic chemotherapy ER: Estrogen receptor ; PR: Progesterone receptor ; HER2: Human epidermal growth factor receptor 2  3 1.1.3 Risk factors  Tremendous research has been conducted to identify a complex interplay between genetics, epigenetics, and environmental factors which contribute to the onset and progression of breast cancer. Genetic predisposition has been shown to play a critical role in the development of breast cancer cases. For instance, a woman with a first-degree relative diagnosed with breast cancer has nearly double the risk of a woman without a family history (Colditz et al., 1993; Brewer et al., 2017). Genetic factors such as inherited mutations in BRCA1/2 tumor suppressor genes, that function in regulation of DNA repair, gene transcription and cell cycle checkpoint, have been consistently attributed to an increased familial risk of breast cancer (Mehrgou et al., 2016; Godet et al., 2017; Wendt et al., 2019). In addition to genetic factors, a woman's risk of breast cancer is also affected by her hormonal status and reproductive history. For instance, a prolonged exposure to estrogen caused by early menarche and late onset of menopause is greatly associated with an increased  risk of breast cancer in women (Dossus et al., 2015; De Silva et al., 2019). Furthermore, environmental exposures can have profound effect on breast cancer risk. It is estimated that at least 30% of sporadic breast cancer cases are not related to genetic mutations but caused by epigenetic aberrations upon environmental influences and lifestyle factors (Colditz et al., 2014; Howell et al., 2014; Kleibl et al., 2016; Lubecka et al., 2016).   1.2 Epigenetics    1.2.1 Definition  Epigenetics refers to alterations in gene expression (phenotype) that do not involve changes in the underlying DNA sequence (genotype). Epigenetic modifications, which include DNA  4 methylation, covalent histone modifications, regulation by non-coding RNAs, and chromatin remodeling complexes play important roles in DNA accessibility, transcript stability, the activity of transcriptional machinery, and chromosomal integrity (Kanherkar et al., 2014; Jones et al., 2016; Liu et al., 2016b). Due to the dynamic and reversible nature of epigenetic modifications, epigenetic aberrations have attracted significant attention in terms of disease prediction, diagnosis, prevention and treatment (Handy et al., 2011). Indeed, DNA methylation is one of the most extensively studied epigenetic modifications during embryonic development and disease states, particularly, in cancer.   1.2.2 Factors influencing the epigenome  Intrinsic and extrinsic factors play an integral part in regulation of the epigenetic machinery and shaping the complexity of the epigenome. From a genetic standpoint, the phenotypic and epigenetic similarities of monozygotic twins have been shown to be substantially greater than those of dizygotic twins, a phenomenon that was termed “epigenetic supersimilarity” by Van Baak and his colleagues (Van Baak et al., 2018). However, in monozygotic twins, the DNA methylation profile has been shown to become variable within the first year of life (Martino et al., 2011; Martino et al., 2013), indicating the importance of postnatal environment in driving epigenetic divergence in twins. In fact, the interplay between genes and environment has been a focus of research for many years. Environment-mediated changes, including toxic chemicals, psychological states, exercise, diet, smoking, and alcohol consumptions are great examples of extrinsic factors, all of which can cause changes to DNA methylation landscapes and other epigenetic components (Alegria-Torres et al., 2011; Kanherkar et al., 2014; Martin et al., 2018).    5 1.3 Components of the Epigenome    1.3.1 DNA methylation machinery DNA methylation refers to a covalent transfer of a methyl group onto the C5 position of the cytosine ring in cytosine residues located mostly in CpG dinucleotides in mammalian DNA, leading to formation of 5-methylcytosine (5-mC) (Jin et al., 2011; Kanherkar et al., 2014). DNA methylation reactions are catalyzed by enzymes called DNA methyltransferases (DNMTs) that transfer a methyl group from S-adenosyl-L-methionine (SAM), the ubiquitous methyl donor, to cytosine (Gruenbaum et al., 1981; Mahmoud et al., 2019) (Figure 1.1). Methylation of gene regulatory regions, including enhancers and promoters, are associated with transcriptional repression (Mohn et al., 2008; Varley et al., 2013; Ambrosi et al., 2017), whereas gene body methylation is positively correlated with gene expression (Wagner et al., 2014; Yang et al., 2014). In mammals, 5-mC poses a fundamental role during early stages of embryonic development, X-chromosome inactivation, and genome stability, all of which can determine cell fates and gene expression (Jones et al., 2001; Meng et al., 2015; Andersen et al., 2018). DNA methylation contributes to genome stability through silencing of transposons and repetitive DNA sequences. DNA methylation can also occur at sites other than non-CpG sequences (i.e. CpT, CpC, or CpA (Woodcock et al., 1987; Jang et al., 2017). Intriguingly, these non-CpG methylation is limited to specific cell types, for instance oocytes (Tomizawa et al., 2011; Guo et al., 2014), neurons, and glial cells (Lister et al., 2013), however its biological function remains to be elucidated.   6  Figure 1.1. Schematic representation of DNA methylation reaction. 5-methylcytosine is produced upon transfer of a methyl group from SAM (S-adenosyl-L-methionine) to SAH (S-adenosyl-L-homocysteine) by the action of DNA methyltransferases (DNMTs).    1.3.2 DNA methyltransferases  In mammals, there are three DNMT enzymes with catalytic activities: DNMT1, DNMT3A and DNMT3B (Jin et al., 2013). The major function of DNMT1, the most abundant DNMT, is to maintain already existing methylation patterns during DNA replication. Thus, DNMT1 is responsible for propagating the DNA methylation pattern from the parental strand onto the daughter strand in a cell type-specific manner (Kar et al., 2012; Li et al., 2014). Coordinated dialogue between DNMT1 and ubiquitin-like with PHD and ring finger domains 1 (UHRF1) is required to facilitate this process (Liu et al., 2013a).  UHRF1 recognizes and binds to hemi-methylated DNA at CpG sites resulting in recruitment of DNMT1 to these sites (Liu et al., 2013a). Furthermore, the maintenance of DNA methylation by DNMT1 at the replication fork is determined by its interaction with proliferating-cell nuclear antigen (PCNA) which enhances the processivity of leading strand formation during DNA replication (Mortusewicz et al., 2005).   7 DNMT3A and DNMT3B are predominantly associated with de novo methylation during early stages of gametogenesis and embryogenesis (You et al., 2012; Hervouet et al., 2018). DNMT3L, a catalytically inactive member of DNMT3 family, regulates de novo DNA methylation through co-localizing with DNMT3A/3B and stimulating their enzymatic activities (Suetake et al., 2004). The proline-tryptophan-tryptophan-proline (PWWP) domain of DNMT3A and DNMT3B, a conserved region with ~150 amino acid residue, reads the epigenetic marks to guide DNA methylation (Chen et al., 2004). The PWWP domain targets DNMT3B to various genomic loci and has recently been shown to mediate DNMT3B binding to trimethylated lysine 36 of histone H3 (H3K36me3), an active histone mark, to control gene transcription (Rinaldi et al., 2016; Gagliardi et al., 2018). Furthermore, the ADD (ATRX-DNMT3-DNMT3L) domain found in DNMT3A, DNMT3B, and DNMT3L recognizes the N-terminal tail of unmethylated histone H3 lysine 4 (H3K4me0), a repressive mark, thereby facilitating long-term repression through DNA methylation (Ooi et al., 2007; Otani et al., 2009; Zhang et al., 2010).  1.3.3 TET-mediated DNA demethylation  DNA methylation is a dynamic modification that can be reversed through DNA demethylation, which is considered a critical process in epigenetic reprogramming during embryonic development. DNA demethylation refers to a removal of a methyl group from cytosine nucleotides and it can be either passive or active (Kohli et al., 2013; Wu et al., 2014). Passive DNA demethylation occurs in the absence of DNA methylation machinery during DNA replication, leading to inability to copy the DNA methylation pattern from the parental strand onto the daughter strand. By contrast, active demethylation is an enzymatic process catalyzed by ten-eleven translocation methylcytosine dioxygenase (TET) enzymes. TET proteins promote DNA  8 demethylation through oxidizing 5-mC to 5-hydroxymethylcytosine (5-hmC), 5-formylcytosine (5-fC), and 5-carboxylcytosine (5-caC), all of which are DNA demethylation intermediates (Rasmussen et al., 2016). Then, through the base excision repair (BER) pathway, thymine-DNA-glycosylase (TDG) recognizes these oxidized cytosine derivatives and subsequently replaces them with unmodified cytosine (Rasmussen et al., 2016).   1.3.4 DNA methylation and the affinity of transcription factors to DNA DNA methylation is known to impact the interaction of DNA with transcription factors (TFs) and chromatin-remodelling complexes at gene regulatory regions such as promoters and enhancers. TFs are proteins that bind to DNA and regulate gene transcription through interacting with the transcriptional machinery (Frietze et al., 2011). Investigations regarding sensitivity of TFs to DNA methylation of sequences they recognize or neighbouring sequences began in the late 80s, however mechanistic interactions between TF and 5-mC remain largely unknown. For instance, methylation of a central CpG dinucleotide within the E-box DNA sequences (CACGTG) of CASP8 and EGFR genes were shown to block the access of n-Myc to its binding site in a cell-type specific manner (Perini et al., 2005). OCT1, a transcription factor overexpressed in many types of cancer and suggested to have a role in tumor initiation and progression (Vazquez-Arreguin et al., 2016), is another example of methylation-sensitive TFs. The sensitivity of OCT1 to DNA methylation within its binding region has been shown within IL2, DAPK and HSPA2 promoters (Murayama et al., 2006; Han et al., 2013; Kisliouk et al., 2017). A more recent study determined that overexpression of CDX2 in leukemia is associated with OCT1 binding at non-methylated CDX2 promoter (Jafek et al., 2019). Healthy individuals showed substantial methylation in the same region and CDX2 downregulation (Jafek et al., 2019).  9  Although the presence of 5-mC often reduces the accessibility of DNA, a subset of TFs have been shown to function through binding to methylated DNA sequences, indicating a wide range of sensitivity of TFs to methylated cytosine residues. For instance, in HeLa cells, Kasio, a transcription factor containing N-coR complex, was shown to specifically bind to MTA2 promoter leading to DNA methylation-dependent repression (Yoon et al., 2003). Similarly, Zinc finger protein 57 homolog (ZFP57) and its cofactor KAP1 were shown to have specific affinity to DNA-methylated alleles and H3K9me3-enriched sites in embryonic stem cells (ESC), resulting in the maintenance of DNA methylation at specific loci required for early embryogenesis (Quenneville et al., 2011). In addition, Kruppel-Like Factor 4 (KLF4), a conserved transcription factor that regulates diverse cellular processes, exhibited preferential binding to distal methylated enhancer regions (Ghaleb et al., 2017; Wan et al., 2017). Altogether, interactions between DNA methylation and transcription factors appear to have critical implications in regulation of gene expression.   1.3.5 Post-translational modifications of histones Alterations in DNA methylation are often accompanied by changes to covalent modifications of core histones, another mechanism by which epigenetic modifications impact chromatin dynamics and gene expression (Ordog et al., 2012). The DNA double helix is packed inside the nucleus on proteins called histones. The complex of  DNA and protein is called chromatin, and nucleosomes are the functional units of this complex (McGinty et al., 2015). Each nucleosome is composed of around 146-147 base pairs of DNA wrapped around eight histone proteins that forms an octamer (Luger et al., 1997; Campos et al., 2009). Histone proteins are subject to dynamic post-translational modifications including methylation, acetylation, ubiquitination, and phosphorylation of amino acid residues (Sadakierska-Chudy et al., 2015). These modifications mostly occur on the  10 protruding N-terminal histone tails (Campos et al., 2009; Lee et al., 2009). Histone lysine methyltransferases (HKMTs) and protein arginine methyltransferases (PRMTs) enzymes are responsible for catalyzing histone methylation (Li et al., 2012). On the other hand, acetylation and deacetylation are catalyzed by enzymes known as histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively (Legube et al., 2003). Depending on the type and location of histone modifications in the genome, they greatly affect the chromatin accessibility, transcriptional activity of genes and affinity of proteins to DNA. For example, tri-methylation of lysine-27 on histone-3 (H3K27me3), an indicator of a repressive mark, causes chromatin condensation and transcriptional inhibition (Wiles et al., 2017). In contrast, tri-methylated histone H3 at lysine 36 (H3K36me3) is a mark indicating active transcription of gene regulatory regions including enhancers (Chantalat et al., 2011). Moreover, Messer et al. showed that acetylation of histone H3 at lysine 4 (H3K4ac) in the promoter region of active genes was positively correlated with cancer-related phenotypic traits, such as activation of epithelial-to-mesenchymal transition pathways in breast cancer cells (Messier et al., 2016). On the other hand, methylation of histone H3 at lysine 4 (H3K4me3) is detected in 75% of all ESC, where it is associated with actively transcribed genes during early development (Pan et al., 2007). In ESC, H3K4me3 can also be co-localized with H3K27me3 repressive mark in the promoters of key differentiation genes such as Hox clusters, leading to their transcriptional silencing (Zhao et al., 2007; Vastenhouw et al., 2010). Overall, histone modifications can lead to establishing transcriptionally active or repressed regions, which largely depends on specific developmental stage and time point of disease.   1.3.6 Interactions between DNMTs and histone modifications Mounting evidence supports the bidirectional crosstalk between DNA methylation and histone  11 modifications (Cheng et al., 2010; Law et al., 2010; Badeaux et al., 2013). It has been suggested that DNMTs and histone methyltransferases (HMTs) work closely together to dictate chromatin structure and transcriptional activity of genes. For instance, in the presence of unmethylated H3K4, DNMT3A shifts from an autoinhibitory form to an active form which then targets specific loci and increases DNA methylation (Guo et al., 2015). In embryonic stem cells, promoters with  increased deposition of H3K27 histone methylation by enhancer of zeste homolog 2 (EZH2), the catalytic subunit of the polycomb repressive complex 2 (PRC2), have been shown to gain DNA methylation during differentiation and carcinogenesis, leading to long-term inactivation of target genes (Schlesinger et al., 2007; Shen et al., 2008). Lastly, Rinaldi et al. showed that the PWWP domain of DNMT3B associates with distal enhancer regions of actively transcribed genes in an H3K36me3 dependent-manner to further regulate expression of corresponding genes (Rinaldi et al., 2016). Altogether, coordinated changes in DNA methylation and histone modifications facilitate epigenetic events that regulate chromatin structure and gene expression.   1.3.7 Histone H3 trimethylation at lysine 36 (H3K36me3)  Methylation of histone 3 at lysine 36 (H3K36me3) appears to be a signature of chromatin accessibility, gene activation and transcriptional elongation. SETD2 (set domain containing protein 2) is a histone methyltransferase that is specific to H3K36 and interacts with RNA polymerase II, driving gene transcription in human cells (Wagner et al., 2012). Genome-wide analyses of H3K36me3 have demonstrated alterations in this epigenetic mark in different malignant tumors (Fontebasso et al., 2013; Lien et al., 2018). H3K36me3 was also suggested as an independent predictor of high tumor grade and poor prognosis (Lien et al., 2018). In addition, Dominguez et al. showed that H3K36me3 was highly enriched in genes associated with cancer cell proliferation (Dominguez et al., 2016). Most importantly, H3K36me3 has been shown to  12 mediate DNMT3B selective binding to the bodies of transcribed genes which led to their preferential methylation in mouse embryonic stem cells (Baubec et al., 2015). Recruitment of DNMT3B to cell-type specific actively transcribed enhancers, followed by their hypermethylation, was also demonstrated to be mediated by recognition of H3K36me3 in human epidermal stem cells (Rinaldi et al., 2016). Depletion of SETD2 particularly affected DNA methylation at H3K36me3 sites implying that SETD2-mediated H3K36me3 is key to guide loci-specific recruitment of DNMT3B (Baubec et al., 2015).  1.4 Epigenetic Aberrations in Cancer   1.4.1 Alterations in DNA methylation patterns during carcinogenesis  Early studies have shown that cancer initiation, promotion and progression are almost always accompanied by profound alterations in DNA methylation (Feinberg et al., 1983), leading to altered expression of key genes involved in cell cycle, apoptosis, differentiation, proliferation and signaling pathways. Alterations in DNA methylation profiles, extensively studied in multiple cancer types, mostly include loci-specific DNA hypermethylation, loci-specific DNA hypomethylation and global DNA demethylation (Cheung et al., 2009; Melnikov et al., 2009; Kobayashi et al., 2011; Kirby et al., 2017; Shi et al., 2017; Le et al., 2018; Losi et al., 2018; de Almeida et al., 2019; Gu et al., 2019).   13 1.4.2 Loci-specific changes in DNA methylation in cancer Loci-specific hypermethylation of CpG islands in the promoter regions of tumor suppressor genes is an important epigenetic hallmark of human cancers (Figure 1.2A) (Esteller et al., 2002; Baylin et al., 2016). Tumor suppressor genes, for example CDH13, BRCA1, CHFR and APC, are often methylated and transcriptionally silenced during carcinogenesis, leading to uncontrolled tumor growth (Stefansson et al., 2012; Murria et al., 2015; Liu et al., 2016a). On the other hand, oncogenes and pro-metastatic genes, such as  MAML2, MYCN, CCND1 and CTNNB1, lose DNA methylation within their regulatory regions, including promoters and enhancers, and become actively transcribed (Figure 1.2B) (Seeger et al., 1985; Shigemitsu et al., 2001; Mukherjee et al., 2009; Lubecka et al., 2016). Together, loci-specific aberrant DNA methylation patterns are greatly associated with cancer-promoting and metastatic properties. Figure 1.2. Methylation status of tumor suppressor genes and oncogenes in cancer. (A) DNA hypermethylation resulting in transcriptional silencing of tumor suppressor genes. (B) DNA hypomethylation resulting in activation of oncogenes.    14 1.4.3 Global DNA hypomethylation in cancer Global loss of DNA methylation has been proposed as a contributing factor to genome instability and chromosomal rearrangements through activation of repetitive sequences and transposable elements, which compromise almost half of the genome (Ehrlich, 2002; Hoffmann et al., 2005; Ehrlich et al., 2006; Sheaffer et al., 2016; Pfeifer, 2018). Long interspersed elements (LINE)-1 and juxtacentromeric (centromere-adjacent) satellite 2 are one of the most commonly studied examples of repetitive regions in cancer (Narayan et al., 1998; Kitkumthorn et al., 2011). Demethylation of repetitive sequences and transposable elements influences the three-dimensional cancer genome and spatial organization of chromatin leading to genomic instability. In addition, activated transposable elements can further interact with microRNAs and non-coding RNAs to modulate target gene expression (Anwar et al., 2017).  1.4.4 Alterations in the DNA methylation machinery during carcinogenesis De-regulation of the activity and expression of maintenance and de novo DNMTs have been shown to substantially contribute to malignant transformation. For instance, overexpression of DNMT1 is involved in lymph node metastasis (Peng et al., 2006; Zhao et al., 2011), hepatocellular carcinogenesis (Park et al., 2006) and hypermethylation-mediated tumor suppressor gene silencing in breast cancer (Pathania et al., 2015; Liu et al., 2019). Aberrations in DNMT1-UHRF1 complex, an important regulator of DNA methylation, has been reported to initiate DNA hypomethylation and tumorigenesis (Ashraf et al., 2017). In hepatocellular carcinoma, overexpression of UHRF1 resulted in abnormal localization of DNMT1 in the nucleus, which led to global loss of DNA methylation and accelerated tumor onset (Mudbhary et al., 2014). In acute myeloid leukemia (AML), mutation in DNMT3A results in hypomethylation and  15 activation of HOX genes, leading to enhanced tumor cell migration and cell autophagy (Brunetti et al., 2017; Li et al., 2019).  Overexpression of DNMT3B has been observed in many human cancers, including breast cancer, where 30% of cases show upregulation of this gene (Bishop et al., 2015). In addition, in glioblastoma and AML, DNMT3B overexpression represents a marker of poor prognosis (Hayette et al., 2012; Purkait et al., 2016). However, in other contexts, reduced expression of DNMT3B was linked to accelerated carcinogenesis, suggesting tumor suppressor rather than oncogenic role of DNMT3B. The tumor suppressor function of DNMT3B may be linked to the role of DNMT3B in mediating DNA methylation and epigenetic silencing of genes with oncogenic functions. For example, in a mouse model of MYC-induced T-cell lymphomagenesis, ablation of Dnmt3b function using a conditional knockout in T cells led to gradual promoter demethylation and re-expression of proto-oncogene Ment, which coincided with increased cell proliferation and accelerated lymphomagenesis (Hlady et al., 2012). Zheng and colleagues similarly reported that Dnmt3b deletion in a mouse model of acute myeloid leukemia results in upregulation of genes with oncogenic functions (Zheng et al., 2016). Increase in expression of Pdk1, responsible for protecting cells against apoptosis, and transcription factor c-Jun, activating Kras-driven transcription, could explain the increase in the cancer stem cell population and leukemia progression (Zheng et al., 2016). Interestingly, in normal human cells, namely primary epidermal keratinocytes, overexpression of DNMT3B was shown to lead to methylation and downregulation of VAV3 (Peralta-Arrieta et al., 2017), an oncogene involved in the regulation of Rho GTPases that activate pathways leading to actin cytoskeletal rearrangements and transcriptional alterations (Chen et al., 2015b). Suppressing VAV3 upon DNMT3B upregulation further implies a tumor suppressor role for DNMT3B.  16 1.4.5 Alterations in histone modifications during carcinogenesis Apart from aberrant DNA methylation patterns, alterations in histone modifications have also been linked to changes in expression of genes with important functions in cancer development and progression. There is increasing evidence that cancer tissues show both gene-gene differences and cell-cell differences in the occupancy of various histone modifications (Kurdistani, 2007). A study by Elsheikh et al. suggested that deregulation of histone modifications may serve as an early marker of breast cancer (Elsheikh et al., 2009).  H4K16ac, an indicator of chromatin condensation, was detected at low levels in the majority of breast cancer cases (78.9%), whereas high levels of global histone methylation and acetylation were associated with favorable outcome in patients with luminal breast cancer (Elsheikh et al., 2009). In basal carcinomas and HER2 positive breast tumors, moderate levels of lysine acetylation (e.g., H3K9ac and H3K18ac) and lysine methylation (H3K4me2 and H4K20me3) were detected. In another study, Suzuki et al. assessed the protein levels of HDACs as well as the presence of acetylated histone-4 and H4K12 in breast cancer tissue (Suzuki et al., 2009). Reduction in histone acetylation levels was associated with progression from   normal epithelium to ductal carcinoma in situ and to invasive carcinoma. However, protein levels of HDAC1, HDAC2, and HDAC6 were reduced, suggesting that imbalance of HDAC and HAT enzymatic activity may play a prominent role in breast cancer. 1.4.6 Epigenetic anti-cancer strategies  The reversible nature of epigenetic modifications and their role in cancer initiation and progression makes them excellent targets in cancer prevention and therapy.  DNA methyltransferase inhibitors (DNMTi), such as azacitidine (5-azacytidine) and decitabine (5-aza-2′-deoxycytidine), are demethylating agents approved by the Food and Drug Administration (FDA) for the treatment of  17 acute myeloid leukemia and myelodysplastic syndromes and under investigation in solid tumors (Christman, 2002; Diesch et al., 2016). These drugs demonstrate the ability to hinder hypermethylation of tumor suppressor genes through covalent binding to DNMTs and inhibiting their enzymatic activities (Gnyszka et al., 2013; Morris et al., 2015). Interestingly, Yu et al. showed that low-concentration and prolonged exposure of triple negative breast cancer cell lines to decitabine degraded DNMT1, DNMT3A and DNMT3B protein levels through lysosome-dependent manner, which resulted in decreased tumor growth and may represent an additional mechanism of decitabine action  (Yu et al., 2018). Furthermore, many HDAC inhibitors have been developed as anti-cancer agents with varying efficacy and pharmacokinetic properties (Dong et al., 2018). These inhibitors exert their inhibitory function by chelating the zinc co-factor in the enzyme active site, suppressing HDAC catalytic activity. To date, four HDAC inhibitors, romidepsin, panobinostat, vorinostat, and belinostat, have received approval by FDA to be used clinically for cancer treatment (Yoon et al., 2016).  1.4.7 Barriers to effective epigenetic anti-cancer therapies Despite the promise of epigenetic therapies for cancer treatment, lack of specificity of these drugs is limiting their clinical efficacy (el Bahhaj et al., 2014; Kronfol et al., 2017; Patnaik et al., 2019). It has been shown that DNMTi are not selective for cancer-specific epigenetic marks and enhance global demethylation which may result in genomic instability (Howell et al., 2010). Moreover, many cancer patients are often exposed to high toxic doses of DNMTi and report adverse effects while obtaining few therapeutic benefits (Gravina et al., 2010; Marques-Magalhaes et al., 2018). Furthermore, enzymatic activities of HDACs in regulating gene expressions are not restricted to histones and chromatin regulatory complexes. HDACs target different biological processes  18 including macromolecular metabolism, cell cycle progression, cell migration, splicing and nuclear transport (Haggarty et al., 2011; Chen et al., 2015a). Choudhary et al. showed that the use of HDAC inhibitors lead to hyperacetylation of 1750 proteins in human cancer cell lines, indicating that non-histone proteins constitute important HDAC substrates (Choudhary et al., 2009). Thus, the narrow specificity and the occurrence of unintended consequences associated with the use of epigenetic drugs have resulted in an increased effort to search for alternative cancer preventive and therapeutic approaches.     1.5 Dietary Bioactive Compounds as Regulators of Cancer Methylome   1.5.1 Effects of dietary compounds in shaping the epigenome  Among the environment-mediated epigenetic modifiers, the diet  has been shown to greatly impact epigenetic patterns that subsequently define physiological outcomes throughout the lifespan (McGowan et al., 2008; Sapienza et al., 2016). For instance, the intake of folate and other methyl donors have been shown to significantly alter DNA methylation in loci-specific and global manners (Anderson et al., 2012; Zeisel, 2017; Mahmoud et al., 2019). Interestingly, maternal nutrition and the availability of methyl donors during pregnancy determine DNA methylation status in the offspring (Wolff et al., 1998). Extensive research has shown that dietary bioactive compounds can largely remodel the epigenome through interacting with enzymes involved in DNA methylation and histone modifications, by changing the availability of substrates necessary for those reactions, or by modifying the interactions between proteins and DNA (Choi et al., 2010; Tiffon, 2018).  19 1.5.2 Dietary polyphenols modulate DNA methylation landscapes in cancer  Polyphenols embody a wide variety of bioactive compounds found in fruits, vegetables, green tea, peanuts, chocolate and other foods. The compounds have a common phenolic ring structure, but they differ in the nature and position of substituents on the ring. Thus, based on their chemical skeletons, polyphenols are divided into several categories including flavonoids, stilbenes, lignans, phenolic acids and others (Beetch et al., 2020). Polyphenol-rich diets have been shown to provide numerous health benefits and protection against the development and progression of many chronic diseases, including cancer (Arts et al., 2005; Scalbert et al., 2005; Garcia-Lafuente et al., 2009; Beetch et al., 2020). There is evidence showing  that polyphenols exert their anti-cancer properties through modulating various components of the epigenetic machinery (Link et al., 2010; Stefanska et al., 2012a; Beetch et al., 2020). For instance, curcumin from spice turmeric led to DNA hypomethylation of  Liver Cancer 1 (DLC1) promoter, a commonly repressed tumor suppressor gene, which coincided with DLC1 transcriptional activation (Teiten et al., 2013; Liu et al., 2017; Hassan et al., 2019). Epigallocatechin gallate (EGCG) from green tea activated numerous methylation-silenced tumor suppressor genes through decrease in promoter methylation (Fang et al., 2003; Lee et al., 2005), similarly to genistein from soybean (Fang et al., 2005; Majid et al., 2010; Liu et al., 2013b; Xie et al., 2014).  Interestingly, a genome-wide DNA methylation study in colorectal cancer cells exposed to curcumin shows that curcumin caused loci-specific both hyper- and hypomethylation, predominantly in partially methylated CpG sites (Link et al., 2013). In a later study on a candidate gene, curcumin induced upregulation of DNMT3A and DNMT3B activity in multiple myeloma cells, contributing to hypermethylation of mTOR promoter region and consequently inhibition of the oncogenic signaling pathway (Chen et al., 2019). Thus, curcumin effects on the DNA  20 methylation patterns are differential and likely depend on CpG location and gene function as suggested for another group of polyphenols, namely stilbenoids (Lubecka et al., 2016; Beetch et al., 2018).  1.5.3 Stilbenoids Stilbenoids have been extensively studied for their anti-cancer, anti-diabetic, cardioprotective, anti-inflammatory, and anti-oxidant effects (Akinwumi et al., 2018). Resveratrol (RSV, trans-3,5,40-trihydroxystilbene) and pterostilbene (PTS, trans-3,5-dimethoxy-40-hydroxystilbene) are the monomeric stilbenes that are most abundantly found in grapes and blueberries, respectively. Two additional methoxy groups in the structure of PTS compared to RSV make PTS more lipophilic and bioavailable, more metabolically stable, and more biologically active (Figure 1.3) (Kapetanovic et al., 2011; Wang et al., 2018).                    Figure 1.3. Chemical structure of pterostilbene (PTS) and resveratrol (RSV).                   21  1.5.4 Anti-cancer effects of stilbenoids Potent anti-carcinogenic properties of stilbenoids have been well-established in a number of cancer types (Kondratyuk et al., 2011; Grosso et al., 2013; Yang et al., 2013; Rauf et al., 2018; Ma et al., 2019; Zhou et al., 2019). Molecular changes exerted by RSV and PTS bring about tumor-inhibitory effects through targeting processes and pathways that are involved in cancer development. For example, Kuhajda et al. showed that treatment of HER2+ breast cancer cells with RSV inhibited HER2 expression and enhanced apoptosis, which was linked to downregulation of fatty acid synthase (FASN), an activator of PI3K/AKT/mTOR and MAPK oncogenic signaling pathways (Kuhajda et al., 2000). Hagiwara et al. demonstrated that treatment of breast cancer cells with PTS lead to expression and increased activity of argonaute2 (Ago2), a central RNA inference component, which consequently inhibited breast cancer stem-like cell phenotype (Hagiwara et al., 2012). In addition, RSV and PTS inhibit angiogenic pathways (Garvin et al., 2006; Lubecka et al., 2016) and activate NRF2 anti-oxidant pathway (Singh et al., 2014). Although these are just examples of numerous anti-cancer effects of stilbenoids, molecular mechanisms behind those effects are largely unknown. Mounting evidence suggests that stilbenoids exhibit anti-cancer effects through epigenetic regulation of gene transcription, specifically modulating DNA methylation patterns (Stefanska et al., 2010; Papoutsis et al., 2012; Stefanska et al., 2012b; Gracia et al., 2014; Lou et al., 2014; Lubecka et al., 2016; Medina-Aguilar et al., 2016).  1.5.5 Stilbenoids remodel the DNA methylation patterns in cancer  It is well established that stilbenoids reverse promoter hypermethylation and re-activate tumor suppressor genes leading to anti-cancer effects (Stefanska et al., 2010; Papoutsis et al., 2012;  22 Stefanska et al., 2012b; Beetch et al., 2019a; Farhan et al., 2019). RSV has also been reported to regulate one of the major anti-oxidant pathways, namely the Nrf2 pathway, through DNA hypomethylation of the Nrf2 promoter and consequent upregulation of Nrf2 (Singh et al., 2014). These reports were strengthened by a genome-wide study performed by our group to demonstrate remodeling of the DNA methylation patterns in breast cancer cells exposed to stilbenoids (Lubecka et al., 2016). We observed increases and decreases in DNA methylation levels at thousands of CpG loci. This was further confirmed in breast cancer cells exposed to RSV by an independent group (Medina-Aguilar et al., 2016). However, the underlying mechanisms by which these compounds impact epigenetic modifications is yet poorly understood and remain to be elucidated. In addition, the epigenetic effects of stilbenoids on oncogenes and pro-metastatic genes are noticeably understudied. Our group showed for the first time that RSV and PTS may exert bidirectional effects on DNA methylation of breast cancer cells. In our study, we observed a profound effect of RSV on genome-wide DNA methylation, i.e., hypomethylation of tumor suppressor genes and hypermethylation of oncogenes. For the first time, we demonstrated that DNMT3B may be the key enzyme catalyzing hypermethylation that occurs in regulatory regions of genes with oncogenic functions, e.g., MAML2 enhancer, in response to stilbenoids in cancer cells, which consequently downregulated MAML2, inhibited oncogenic NOTCH signal transduction, and reduced cell proliferation and cell ability to migrate through extracellular matrix (Lubecka et al., 2016). Additionally, DNMT3B binding was accompanied by reduced occupancy of oncogenic transcription factor OCT1, indicating a potential crosstalk between DNA methylation and transcriptional machinery. Our later investigation showed that SEMA3A tumor suppressor gene was demethylated and activated by stilbenoids. Interestingly, a distinct mechanism, involving DNMT3A rather than DNMT3B, plays  23 a role in epigenetic activation of tumor suppressor genes in response to stilbenoids (Beetch et al., 2019b).  1.6 Enhancer Elements   Enhancers are distal cis-regulatory and the most dynamic elements in the genome that play a critical role in controlling tissue-specific gene expression (Pennacchio et al., 2013). Based on their activities, enhancers can broadly be classified as active, poised, silent and primed. Active enhancers are characterized by high levels of H3K27ac and H3K4me1, and low levels of H3K4me3 (Sharifi-Zarchi et al., 2017). Human genomes contain hundreds of thousands of enhancers which are predominately located thousands of base pairs from transcription start sites (TSS) of their associated gene, and often within shelves of CpG islands (Pennacchio et al., 2013). These regulatory regions are usually 30-200 base pairs long and may be located upstream or downstream of their target genes (Bondarenko et al., 2002; Maston et al., 2006). In fact, it has been shown that enhancers do not necessary interact with the nearest promoter, but they regulate genes located more distantly on a chromosome. Other cis-regulatory elements such as silencers and insulators can also attenuate and block the activity of enhancers, respectively (Chetverina et al., 2014). Furthermore, the activity of enhancers is highly dependent on a cell type, physiological stimuli and a specific time point in development. These distant regulatory elements increase the likelihood of gene transcription by interacting with their target gene promoters via looping of DNA in association with transcription factors, RNA polymerase and other cofactors (Pennacchio et al., 2013). Another way through which enhancers can impact the expression of target genes is through their own activation. Interestingly, transcription of enhancer RNA (eRNA) has been shown to be positively correlated with mRNA synthesis of their target genes (Ding et al., 2018). It becomes  24 apparent that enhancer regions play a crucial role in regulating gene transcription. However, to date, the mechanisms of regulation of the activity of enhancers remain to be elucidated. It is unknown how dietary polyphenols, including stilbenoids, impact the activity of enhancers.                  25 Research Objectives and Hypotheses  My main hypothesis is that the dietary polyphenol, pterostilbene (PTS), modulates DNA methylation patterns at enhancer regions of genes with oncogenic functions in highly invasive MCF10CA1a breast cancer cells. We propose the involvement of DNMT3B and oncogenic transcription factor OCT1 as vital players in polyphenol-mediated targeting of oncogenes. An increase in DNA methylation within these regulatory regions is accompanied by reduced enrichment of H3K36me3, which could indicate transcriptional silencing of the epigenetically activated oncogenes.   Objective 1: Identify candidate oncogenes with changes in the occupancy of DNMT3B, OCT1, and H3K36me3 at enhancer regions upon treatment of highly invasive MCF10CA1a breast cancer cells with PTS. Hypothesis: PTS treatment leads to increased binding of DNMT3B, decreased occupancy of OCT1 and decreased enrichment of H3K36me3 active histone mark at enhancer regions of oncogenes in breast cancer cells. Objective 2: Examine the transcriptional activity of candidate oncogenes with enhancers that are characterized by changes in DNMT3B, OCT1 and H3K36me3 occupancy upon treatment with PTS. Hypothesis: Candidate oncogenes with enhancers, that are characterized by increased DNMT3B and reduced OCT1 and H3K36me3 occupancy, are downregulated upon exposure to PTS in highly invasive MCF10CA1a breast cancer cells. Objective 3: Determine changes in DNA methylation at enhancer regions of the candidate oncogenes, which are accompanied by increased DNMT3B and reduced OCT1 and H3K36me3 occupancy in response to PTS. Hypothesis: Enhancer regions with increased DNMT3B and reduced OCT1 and H3K36me3 occupancy are hypermethylated in response to PTS in MCF10CA1a breast cancer cells.  26 Chapter 2: Research Design & Methods  2.1 Cell culture and treatment with pterostilbene (PTS)  Human breast cancer cell line, MCF10CA1a, used in this study was cultured in DMEM/F12 (1:1) medium (Gibco) supplemented with 5% horse serum (Gibco), 1U/ml penicillin and 1µg/ml streptomycin (Gibco), and grown in a humidified atmosphere of 5% carbon dioxide at 37°C. The cell line was derived from tumor xenografts of MCF10A cells transformed with constitutively active Harvey-ras oncogene and represents lowly differentiated malignant tumor with high invasive potential. Pterostilbene (PTS, Cayman Chem., Ann Arbor, MI, USA) was resuspended in ethanol and 10mM stock solution was stored at -20°C. Next, cells were plated at a density of 2-3 x 105 per 10-cm tissue culture dish 24 hours prior to treatment with PTS. Freshly diluted PTS was prepared prior to adding to the culture medium. Cells were exposed to 7μM PTS for 4 days. Cells were then split at 1:50 ratio and exposed to the compound for additional 4 days (9-day exposure).   2.2 Chromatin immunoprecipitation (ChIP)  Chromatin immunoprecipitation was performed as previously described (Brown et al., 2008; Lubecka et al., 2016). Briefly, cells were fixed with 1% formaldehyde and incubated at 37°C for 15 minutes in the presence of protease inhibitors. Fixed cells were lysed and subjected to 20 cycles of sonication, with each cycle consisting of 5 seconds of sonication and 15 seconds break at an amplitude of 40. Samples were pre-cleared with protein G agarose followed by centrifugation. Supernatants were divided into three sub-samples. One sub-sample was maintained as input. The second sub-sample served as negative control and was incubated overnight at 4°C with IgG non- 27 specific antibody (negative control, Santa-Cruz Biotechnology). The third sub-sample was incubated overnight at 4°C with anti-DNMT3B rat antibody (Millipore, MABE305), anti-OCT1 mouse antibody (Millipore, MAB5434) or anti-trimethyl-Histone H3 Lys36 rabbit antibody (H3K36me3, Millipore, ABE305). The next day, the unbound fraction was removed, and the fraction of DNA bound to antibodies was washed and DNA was eluted. The antibodies were degraded using proteinase K treatment. Input and bound DNA was processed for ChIP sequencing (ChIP–seq). Library preparation was carried out using NEBNext Ultra DNA Library Prep Kit for Illumina reagents according to the manufacturer’s protocol. ChIP libraries were sequenced using PE150bp reads in HiSeq2500, as described previously (Lopez et al., 2017; Laufer et al., 2019; Vogel Ciernia et al., 2019).  2.3 Analysis of ChIP-seq data   ChIP-seq data were analyzed using Bioconductor tool in R, as described previously (Lopez et al., 2017; Laufer et al., 2019; Vogel Ciernia et al., 2019). Adapter sequences were trimmed from sequencing reads using cutadapt (MARTIN, 2011), and reads were aligned to the GRCh37/hg19 human reference genome using the Burrows-Wheeler Aligner (Li et al., 2009). Duplicate and low-quality reads were filtered out. MACS2 peak calling software was used to identify distinct patterns of enrichment in response to polyphenol treatment. MACS2 (Zhang et al., 2008) was used for peak calling and to generate fold enrichment tracks. Briefly, the callpeak function was used to peaks in the control or treated samples, using the pooled ChIP-seq inputs as the background control and using 200bp as the estimated fragment size and an effective genome size of 2,700,000,000. The broad option was used for calling H3K36me3 peaks. Differential peaks were called using the bdgdiff function. Fold enrichment over input tracks were generated from pileup tracks using the  28 bdgcmp function, using the -m FE option. To visualize accurate representation of our results, read-extended bigwig files of our obtained genomic data were implemented in the genome browser. Next, ChIPSeeker Bioconductor package was used to associate the identified peaks to their target genes. The ChIP quality control (CHIPQC) Bioconductor package calculated ChIP-seq specific quality metric for each sample and input in our experiment. CHIPQC further identifies both fragment length peak and a read length peak based on cross-coverage around the centers of binding sites. CHIPQC was used to both measure inequality of coverage across the genome via standardized Standard Deviation (SSD) and assess distribution of ChIP-seq signal over genomic regions.  2.4 DNA isolation and pyrosequencing   DNA was isolated using standard phenol:chloroform extraction protocol. DNA bisulfite conversion was performed as previously described (Colella et al., 2003; Lubecka et al., 2016). HotStar Taq DNA polymerase (Qiagen) and biotinylated primers were used to amplify bisulfite converted DNA with primers specific for studied gene regulatory regions (please see Table 2A for primer sequences). Pyrosequencing of the biotinylated DNA strands was performed in the PyroMark Q48 Autoprep instrument (Qiagen), as previously described (Tost et al., 2007). Percentage of methylation at a single CpG site resolution was calculated using PyroMark Q48 software.  2.5 RNA isolation, cDNA synthesis and QPCR  TRIzol reagent (Invitrogen, USA) was used to isolate total RNA from MCF10CA1a cells treated with 7μM PTS for 9 days. cDNA was synthesized using 1 μg of total RNA as a template and 20 U  29 of AMV reverse transcriptase (Roche Diagnostics). The QPCR reaction was carried out in CFX96 Touch Real-Time PCR Detection System (Bio-Rad) using 2 µl of cDNA, 10 µl of SsoFast EvaGreen Supermix (Bio-Rad) and 400 nM forward and reverse primers (please see Table 2B for sequences), in a final volume of 20 µl. The amplification reactions were performed in biological triplicate under the following condition: denaturation at 95 °C for 10 min, amplification for 40 cycles at 95 °C for 10s, annealing temperature for 10s, 72 °C for 10s, and final extension at 72 °C for 10 min. Genes transcript levels (quantification of the gene expression level) were quantified using the CFX Maestro Software (Bio-Rad) with a standard curve-based analysis. Relative gene expression levels of target genes are presented as gene of interest/REF, where REF refers to the geometric mean of expression for two reference genes, GAPDH and 18S.  Table 2. Primer sequences used in DNA methylation analysis by pyrosequencing (A) and in gene expression analysis by QPCR (B).   Table 2A. Primer sequences used in DNA methylation analysis Gene  Primer sequences  Annealing temperature [°C] Amplicon length            [bp] Pyrosequencing PITPNC1 (5 CpG) FW 5'- AGGAATAGTTTGAATTTGGGAGG -3'                                                                                              RVBio 5'- AACCTCTACAACCTACTTATTAACTACAT-3'                                                                                               Seq 5'- AGGAGAAGGTTGTAGTGA -3'                                                                            58.5 181 PITPNC1 (5 CpG) FW 5'- GGATTTAGTTAGTTTGTATTTAGGTGAAA -3'                                                                                              RVBio 5'- TCCCATACCATAATAATTCTTCTTTCT -3'                                                                                               Seq 5'- TTGTATTTAGGTGAAATAAATAGT -3'                                                                            58.5 181 TNNT2 (2 CpG) FW 5'- TGGAAATTTTGGGTTAAATGAGTGAA -3'                                                                                              RVBio 5'- AACCTCTACAACCTACTTATTAACTACAT -3'                                                                                               Seq 5'- GGGTTAAATGAGTGAATTAG -3'                                                                            55 114 TNNT2 (1 CpG) FW 5'- GGAGAAATTAAGGTTGGTAAGAATAG -3'                                                                                              RVBio 5'- ACTTCTCTTCCAAATCCTTTCTA -3'                                                                                               Seq 5'- TGGTAAGAATAGTTTTTTTATAGAT -3'                                                                            49 102  30  Table 2A. Primer sequences used in DNA methylation analysis   Gene  Primer sequences  Annealing temperature [°C] Amplicon length            [bp] Pyrosequencing DANT2 (4 CpG) FW 5'- AGGTGGGTATTTTTGGAGTAATAT -3'                                                                                              RVBio 5'- ACCTACCCTAATCCTACCTAAT -3'                                                                                               Seq 5'- GGAGTAATATTGTTATTAAGAGG -3'                                                                            55 132DANT2 (9 CpG) FW 5'- TGTGTTAGTTTGGGGAGGAGT -3'                                                                                              RVBio 5'- AACTAATCCTAACCCCTCTCTCTAAAACTT -3'                                                                                               Seq 5'- ATTAGGTAGGATTAGGGTA -3'                                                                            55 162 LINC00910 (8 CpG)  FW 5'- TTGAGGGTTGGGATTTTTATTAGTAT -3'                                                                                              RVBio 5'- ACCTCCTAACTACCTCCTCTCTAATTAC -3'                                                                                               Seq 5'- AGTGTTTGGTAAGTTGA -3'                                                                            58.5 165 LINC00910 (7 CpG) FW 5'- TTAGAGAGGAAGTAGTTAGGAGGTTATTGG -3'                                                                                              RVBio 5'- CTCAAAAAAAAAATTTATCCCAACCTTAC -3'                                                                                               Seq 5'- GTTAGGAGGTTATTGGTT -3'                                                                            55 165  Table 2B. Primer sequences used in gene expression analysis by QPCR   Gene Primer sequences Annealing temperature [°C] Amplicon length            [bp] PITPNC1 FW 5'- GGACAACAAAGGAAGCAATGAC-3'   RV 5'-  TTGTAGTAGCGCTCTGGAATTT-3'                                                                                                                                                                                                                                                          59 111 TNNT2 FW 5'- AGTCCAGACAGAGCGGAAA -3'                                                                          RV 5'- TCTTCATTCAGGTGGTCAATGG -3'                                                                                                                                                                                    59 108 DANT2 FW 5' ATCAGAGGTAGTCGGACCTTTC -3'                                    RV 5'- CCGGGATCGCAGAGGTAT -3' 59 91 LINC00910 FW 5'- ACGTTCACAGGTACACAAAGG -3'                                    RV 5'- TCCCAGTATCCGACTAGCTTC -3' 59 92 GAPDH FW 5'-TGCACCACCAACTGCTTA-3'                                    RV 5'-AGAGGCAGGGATGATGTTC-3' 59 177 18S FW 5'-TCGGAACTGAGGCCATGATT-3'                                    RV 5'-CTTTCGCTCTGGTCCGTCTT-3' 59 101      31 2.6 Statistical analysis  Unpaired t-test with two-tailed distribution was used for statistical analysis of QPCR and pyrosequencing data. Each value represents the mean ± S.D. of three independent experiments. The results were considered statistically significant when P < 0.05.                     32 Chapter 3: Results  3.1 Overview of DNMT3B binding in highly invasive MCF10CA1a breast cancer cells in response to pterostilbene (PTS)  The proper distribution of DNA methylation that contributes to the overall epigenetic regulation of cell homeostasis results from a set equilibrium between the activity of DNA methylating (DNMTs) and demethylating enzymes (TETs), mediated by appropriate functions of transcriptional machinery and histone modifying enzymes. During carcinogenesis this equilibrium is disrupted, which brings about a cellular dysfunction due to perturbations in the DNA methylation machinery and altered DNA methylation patterns. Among DNMTs, understanding the role of DNMT3B in cancer development has been a challenge. Functions of DNMT3B in establishing de novo DNA methylation patterns during early embryogenesis and in mediating transcriptional repression at repetitive sequences have been explored (Gagliardi et al., 2018). However, studies of DNMT3B role in cancer deliver contradictory evidence, suggesting that DNMT3B plays different roles in transcriptional regulation that are context-dependent and determine its oncogenic or tumor suppressor properties (Gagliardi et al., 2018). A study from our group suggests that DNMT3B plays a mechanistic role in stilbenoid-mediated epigenetic inactivation of oncogenes via hypermethylation of gene regulatory regions, including enhancers (Lubecka et al., 2016). To understand this possible mechanistic role of DNMT3B, we performed ChIP for DNMT3B in invasive MCF10CA1a breast cancer cells upon 9-day treatment with PTS at 7μM concentration. This concentration was previously determined to decrease cell proliferation by 50%, with less than 10% of cell death (IC50) (Lubecka et al., 2016). ChIP was followed by next generation sequencing. Upon analysis of DNMT3B ChIP-sequencing (ChIP-seq) data, we  33 identified changes in DNMT3B binding in 3,314 peaks throughout the genome in response to PTS (P < 0.05). Of those peaks, 1,939 peaks were enriched with DNMT3B upon PTS (Figure 3.1A), as shown in the Integrative Genomic Viewer (IGV) chromosomal map (Figure 3.1B). Each bar represents each of the 1,939 DNMT3B-enriched peaks as analyzed by MACS2 peak calling method.   Figure 3.1. Overview of ChIP-sequencing analysis of DNMT3B binding in highly invasive MCF10CA1a breast cancer cells in response to pterostilbene (PTS). (A) Distribution of statistically significant ChIP peaks, as determined by chromatin immunoprecipitation sequencing (ChIP-seq), in cells treated with 7μM PTS for 9 days as compared with control cells (Ctrl, treated with ethanol as vehicle) (P< 0.05). (B) Chromosomal view of DNMT3B-enriched peaks upon PTS treatment (PTS>Ctrl). Each bar indicates a single peak that was generated using MACS2 peak calling method. (C) Broad ChromHMM HMEC sequencing data available on USCS Genome Browser (hg19) was used to assign chromatin states to DNMT3B-enriched peaks. The identified peaks corresponded to different chromatin states. (D) DAVID knowledgebase indicates biological functions and signaling pathways associated with genes corresponding to 1,939 DNMT3B-enriched peaks.  34  Using the Broad ChromHMM track associated with human mammary epithelial cells (HMEC) available on the USCS Genome Browser, DNMT3B-enriched peaks were annotated to corresponding chromatin states. The peaks were mostly located in repetitive elements (40% of peaks), which is in accordance with well-established DNMT3B function in transcriptional repression of these elements, crucial for genomic stability (Gagliardi et al., 2018).  While 27% of peaks were located in regions with unspecified chromatin state, the remaining 33% (647 peaks) were found in regions important for regulation of gene transcription, including promoters, enhancers, and insulators (Figure 3.1C). These 647 DNMT3B-enriched peaks within gene regulatory regions corresponded to 268 unique genes.  Using gene ontology (GO) and KEGG tools in DAVID knowledgebase database, we performed functional and signaling pathway analyses of the 268 DNMT3B-enriched genes. We found that these genes are implicated in signaling pathways commonly upregulated in cancer (Wnt, MAPK, BMP, mTOR, NOTCH, PI3K/Akt), in DNA replication, recombination and repair, cell junction, actin cytoskeleton, regulation of transcription and calcium ion transmembrane transport (Figure 3.1D). Please note that statistical significance of the enrichment in each functional category, as depicted in Figure 3.1D, will depend on the total number of genes assigned to each category in the database.   Thorough analysis of the DNMT3B-enriched target genes revealed candidates with oncogenic functions, including NOTCH2NL, PANX1, PVT1, and JAK2. Of note, NOTCH2NL is an oncogene activating Notch signaling by direct interaction with NOTCH2; thereby promoting proliferation and self-renewal (Suzuki et al., 2018). PANX1 overexpression has been associated with worse prognosis in breast cancer and hepatocellular carcinoma (HCC) patients, which mechanistically is  35 linked to PANX1-dependent enhancement of epithelial-mesenchymal transition and thus cell invasion (Jalaleddine et al., 2019; Shi et al., 2019). PVT1 is a long non-coding RNA that is commonly overexpressed in breast cancer and has been implicated in regulation of MYC oncogene (Sarver et al., 2016; Wang et al., 2017), while JAK2 is a tyrosine kinase activating cancer-driving JAK/STAT signaling pathway (Perner et al., 2019). Of note, among genes associated with DNMT3B-reduced peaks within gene regulatory regions, we found strong candidates associated with inhibition of cancer proliferation, migration and metastasis, and activation of apoptosis. For instance, CHRDL1 encodes for a secreted protein that acts as an antagonist of bone morphogenetic proteins BMPs – promoters of carcinogenesis and metastasis (Pei et al., 2017). Downregulation of CHRDL1 in gastric cancer was shown to be associated with poor survival and mechanistically with cancer progression and metastasis, indicating tumor suppressor role of CHRDL1 (Pei et al., 2017). Furthermore, downregulation of CHRDL1 was linked to promoter hypermethylation (Pei et al., 2017). Hence, our current findings suggest DNMT3B as potentially involved in this DNA hypermethylation event. Another interesting candidate is NOX5, which encodes calcium-dependent NADPH oxidase that generates superoxide (Kim et al., 2019). NOX5 activation was demonstrated to inhibit cancer stem cell formation through ROS generation (Kim et al., 2019). SALL3 was also among genes with DNMT3B-reduced peaks. SALL3 was previously reported to directly inhibit DNMT3A activity and consequently cause DNA hypomethylation and activation of tumor suppressor genes (Shikauchi et al., 2009). Interestingly, we earlier reported upregulation of SALL3 in response to stilbenoids, including PTS, and suggested that this may be one of the mechanisms of stilbenoid-mediated upregulation of tumor suppressor genes (Beetch et al., 2019b). Current findings would imply that an epigenetic DNMT3B-dependent mechanism is involved in SALL3 upregulation  36 mediated by stilbenoids.  Altogether, our findings are in line with our group’s previous studies which have suggested epigenetic regulation of transcriptional activity of tumor suppressor genes and oncogenes in response to stilbenoids in breast cancer (Lubecka et al., 2016; Beetch et al., 2019b). We mechanistically demonstrated the involvement of DNMT3B as a vital player in polyphenol-mediated hypermethylation and silencing of genes with oncogenic functions (Lubecka et al., 2016). We further showed that one third of hypermethylation events in response to stilbenoids occur in gene bodies and majority of those loci within gene bodies is located in gene enhancers (Lubecka et al., 2016). For this reason and to address a research gap in regulatory mechanisms of the activity of enhancers, I have closely examined DNMT3B-enriched peaks that are within gene enhancers. Among 647 DNMT3B-enriched peaks within gene regulatory regions, we found 170 peaks within predicted enhancers that corresponded to 89 unique genes (Figure 3.2). Thirty genes had more than one DNMT3B binding site within their enhancer regions.   3.2 DNMT3B recruitment coincides with decrease in OCT1 binding in response to PTS  Our previous genome-wide DNA methylation study showed that 80% of CpG dinucleotides whose DNA methylation state increases in response to stilbenoids, contain a putative OCT1 binding site (Lubecka et al., 2016). Most importantly, increased binding of DNMT3B was associated with decreased occupancy of OCT1 at the hypermethylated MAML2 enhancer region in response to PTS (Lubecka et al., 2016). We therefore hypothesized that DNMT3B is recruited to OCT1 occupied loci, which gain DNMT3B binding and lose OCT1 binding in response to PTS. To understand these events, we performed ChIP-seq following ChIP with OCT1-specific antibody.  37 OCT1 binding changed in 7,085 peaks throughout the genome in response to PTS (P < 0.05). Decrease in OCT1 binding upon PTS was observed in 4,605 of those differential peaks. Among decreased peaks, 3,204 were assigned to chromatin states reflecting regulatory gene regions. Among these 3,204 peaks, 515 peaks were located within enhancer regions and corresponded to 207 unique genes (Figure 3.2). Interestingly, 41 out of those 207 genes also contained DNMT3B-enriched peaks within enhancers. The majority of the 41 overlap genes fall into a category of oncogenes and pro-metastatic genes, and include NOTCH2NL, PVT1 and others described in the previous paragraph.  3.3 H3K36me3 marks the genes targeted by DNMT3B for methylation at OCT1-occupied loci in response to PTS  As DNMT3B was suggested to recognize and bind H3K36me3-occupied regions followed by catalyzing DNA methylation at these regions (Baubec et al., 2015; Rinaldi et al., 2016), we hypothesized that regions recognized by DNMT3B in response to PTS are occupied by H3K36me3 and this active histone mark is reduced upon the treatment in connection with gene downregulation. We therefore performed ChIP-seq using H3K36me3-specific antibody. We found that nearly 65% of the 647 DNMT3B-eriched peaks within gene regulatory regions demonstrate reduced H3K36me3 occupancy. Decrease in H3K36me3 binding at these regions may reflect downregulation of corresponding gene expression. Among 44 genes that corresponded to 60 H3K36me3-reduced peaks assigned to enhancers, 20 genes contained DNMT3B-enriched peaks and OCT1-reduced peaks assigned to enhancers (Figure 3.2).  Of note, 16 genes out of the 20 overlap genes contained differential peaks for DNMT3B and OCT1 exactly in the same enhancer region. For 10 out of the 16 genes, reduced H3K36me3 occupancy  38 was also detected in the same enhancer.  As mentioned in paragraph 3.1, to address a research gap in regulatory mechanisms of the activity of enhancers, we focus on the 20 genes with targeted enhancers for further studies and refer to these genes as ‘candidate genes with epigenetically targeted enhancers’.     Figure 3.2. Peaks and genes associated with increased binding of DNMT3B, decreased occupancy of OCT1 and decreased enrichment of H3K36me3 in response of MCF10CA1a breast cancer cells to pterostilbene (PTS). ChIP-seq analysis revealed the presence of 1,939 peaks with increased occupancy of DNMT3B in response to PTS, of which 170 were assigned to enhancers and corresponded to 89 unique genes. Decreased occupancy of OCT1 in response to PTS was determined within 4,605 peaks, of which 515 were assigned to enhancers and corresponded to 207 unique genes. Reduced enrichment of H3K36me3 was found in 683 peaks in PTS-treated cells, of which 60 peaks were located at enhancers and corresponded to 44 unique genes. The analysis of an overlap between genes with increased DNMT3B binding, decreased OCT1 binding and reduced occupancy of H3K36me3 at enhancers revealed 20 unique genes.    Peaks at Enhancer170DNMT3BPTS>CtrlTotal Peak Enrichments1,939Peaks at Regulatory Regions647Total Peak Enrichments4,605Peaks at Regulatory Regions3,204Peaks at Enhancer515OCT1Ctrl>PTSTotal Peak Enrichments683Peaks at Regulatory Regions359H3K36me3Ctrl>PTSPeaks at Enhancer60Unique Genes20Unique Genes44Unique  Genes207Unique Genes89 39  3.4 Candidate genes with enhancers, that are characterized by increased DNMT3B and reduced OCT1 and H3K36me3 occupancy, are hypermethylated and downregulated upon exposure to PTS  ‘Candidate genes with epigenetically targeted enhancers’ fell into several functional categories: 1) oncogenes (e.g., PITPNC1 (Halberg et al., 2016), NOTCH2NL (Suzuki et al., 2018), TNNT2 (Johnston et al., 2018), and ZP4 (Costa et al., 2018)), 2) long non-coding RNAs (lncRNAs) (e.g., DANT2, LINC00910, and LOC102724511), 3) microRNAs (miR4477A and miR4477B), 4) small non-coding RNAs (RNVU1-18, SNAR-A14), 5) pseudogenes (LOC100130331, LOC102724580), and 6) epigenetic regulator SMARCA4, which is the ATPase of the chromatin remodeling SWI/SNF complexes, and is associated with poor prognosis in many tumors (Guerrero-Martinez et al., 2018). Of those 20 candidate genes with epigenetically targeted enhancers, we selected 4 genes for further analyses (i.e., PITPNC1, TNNT2, DANT2 and LINC00910), taking into account the magnitude of differential binding and the highest proximity of peaks (i.e., within 500 base pairs) with increased binding of DNMT3B, decreased occupancy of OCT1 and decreased enrichment of H3K36me3 (Figure 3.3). To test the functional relevance of detected changes in the occupancy of DNMT3B, OCT1 and H3K36me3, we first assessed gene expression of the candidate genes following 9-day treatment of MCF10CA1a breast cancer cells with 7μM PTS. Using QPCR, we detected significant downregulation of the 4 selected genes. PTS treatment led to a robust decrease in expression of PITPNC by 84%, TNNT2 by 87%, DANT2 by 41% and LINC00910 by 92%, compared to control (Figure 3.3). Among the downregulated candidate genes, PITPNC1 has been shown to be overexpressed in metastatic breast, colon and melanoma cancers (Halberg et al., 2016). Furthermore, TNNT2 from troponin family, is a well-known gene responsible for  40 coordinating normal cardiac muscle contraction in response to fluctuations in calcium ion concentration (Takeda et al., 2003). Interestingly, overexpression of this gene has been shown to be aberrantly amplified in neuroendocrine prostate cancer and breast cancer (Johnston et al., 2018).   Figure 3.3. Changes in gene expression in response to pterostilbene (PTS). The effect of 9 day-treatment with 7μM PTS on expression of PITPNC1, TNNT2, DANT2 and LINC00910 in highly invasive MCF10CA1a breast cancer cells, as determined by QPCR. REF is a reference gene factor consisting of the geometric mean of expression of 2 reference genes, GAPDH and 18S. All results represent mean ± SD of three independent experiments; ***P < 0.001, **P < 0.01.       41 DANT2, a lncRNA, is identified to be one of the DXZ4 associated non-coding transcripts (Figueroa et al., 2015). DXZ4 is one of the largest CpG-rich regions in the human genome (Chadwick, 2008). Limited research has explored the biological roles of DANT2 and its effect in disease models. Lastly, our understanding of functions of lncRNAs such as LINC00910 in carcinogenesis is limited compared to protein-coding genes. However, lncRNAs have been shown to impact a wide range of biological processes such as metabolism (Zhao et al., 2017), immune response (Chen et al., 2017) and development (Perry et al., 2016), all of which have been found to be dysregulated during carcinogenesis.  Genome browser tracks illustrating fold enrichment (FE) corresponding to DNMT3B (PTS>Ctrl), OCT1 (Ctrl> PTS) and H3K36me3 (Ctrl> PTS) peaks within enhancer regions of the downregulated candidate genes are presented in Figures 3.4A and 3.5A (blue = control: vehicle-treated, red = PTS-treated). Detailed information on peak location, fold enrichment values and statistical p-values corresponding to the peaks is shown in Table 3. In order to understand whether binding of DNMT3B coincides with DNA methylation of the enhancer region, we assessed DNA methylation status of the downregulated candidate genes at specific CpG sites within the affected enhancer regions using pyrosequencing. Gene maps show the exact positions of CpG sites in regions of differential occupancy of DNMT3B, OCT1 and H3K36me3 relative to the transcription start site (+1) (Figures 3.4B and 3.5B). The results confirmed that the enhancer regions of PITPNC1 with 5 CpG sites and TNNT2 encompassing 3 CpG sites were significantly hypermethylated by 8-16% and 6-19% upon PTS treatment, respectively. DANT2, a lncRNA, was hypermethylated by 3-7% across 7 CpG sites and LINC00910, encompassing 8 CpG sites, demonstrated 2-28% hypermethylation across 8 CpG loci (Figures 3.4C and 3.5C).  42            Gene Name   Peak Chromosome   Peak Start   Peak Stop   Fold Enrichment   P-value   Peak Distance from Gene    Gene Strand    PITPNC1                 DNMT3B   chr17 65421176 65421513 10.45933 0.00057 0 + OCT1   chr17 65421180 65421514 6.223 0.027 0 + H3K36me3   chr17 65421176 65421513 6.85267 0.026 0 +    TNNT2                 DNMT3B   chr1 201316112 201316452 12.41657 0.000050 0 - OCT1   chr1 201316110 201316449 7.5565 0.0071 0 - H3K36me3   chr1 201316107 201316451 8.60896 0.0053 0 -    DANT2                 DNMT3B   chrX 115004522 115005259 13.06899 0.000029 0 - OCT1   chrX 115004437 115005280 19.1135 0.0000000031 0 - H3K36me3   chrX 115004525 115005260 8.44884 0.0050 0 -    LINC00910                 DNMT3B   chr17 41466032 41466396 11.98163 0.000087 0 - OCT1   chr17 41465923 41466516 14.44625 0.0000020 0 - H3K36me3   chr17 41466008 41466783 6.00975 0.049 0 -   Table 3. Detailed characteristics of DNMT3B-enriched, OCT1- and H3K36me3-reduced peaks associated with enhancers of the selected candidate oncogenes in highly invasive MCF10CA1a breast cancer cells upon treatment with pterostilbene (PTS).           43  Figure 3.4. Quantitative analysis of DNA methylation status of PITPNC1 and TNNT2 oncogenes as determined by pyrosequencing. (A) Genome browser tracks depicting fold enrichment (FE) of binding of DNMT3B, OCT1 and H3K36me3 in control vehicle-treated (blue) and PTS-treated (red) MCF10CA1a breast cancer cells. (B) A gene map with the exact position of the CpG sites relative to the transcription start site (TSS) within the tested enhancer region. The pyrosequenced CpG sites are numbered and circled. The putative transcription factor binding sites, as predicted by TransFac, are shown in trapezoids. (C) The average DNA methylation status at each of the CpG sites within the enhancer region as determined by pyrosequencing in control vehicle-treated cells (vehicle- ethanol) and MCF10CA1a cells treated with 7μM PTS for 9 days. All results represent mean ± SD of three independent experiments; ***P<0.001, **P<0.01, *P<0.05.       44  Figure 3.5. Quantitative analysis of DNA methylation status of DANT2 and LINC00910 long non-coding RNAs as determined by pyrosequencing. (A) Genome browser tracks depicting fold enrichment (FE) of binding of DNMT3B, OCT1 and H3K36me3 in control vehicle-treated (blue) and PTS-treated (red) MCF10CA1a breast cancer cells. (B) A gene map with the exact position of the CpG sites relative to the transcription start site (TSS) within the tested enhancer region. The pyrosequenced CpG sites are numbered and circled. The putative transcription factor binding sites, as predicted by TransFac, are shown in trapezoids. (C) The average DNA methylation status at each of the CpG sites within the enhancer region as determined by pyrosequencing in control vehicle-treated cells (vehicle- ethanol) and MCF10CA1a cells treated with 7μM PTS for 9 days. All results represent mean ± SD of three independent experiments; ***P<0.001, **P<0.01, *P<0.05.      45 Chapter 4: Discussion  Several studies have suggested an important role of enhancers during carcinogenesis (Sur et al., 2016). The crucial role of enhancers in regulating gene transcription has long been an area of interest, with a more recent focus on the contribution of epigenetic components in regulating the activity of enhancer regions (Luo et al., 2016). Dysregulation of DNA methylation patterns and aberrant expression of DNMTs have been observed across multiple cancer types (Zeng et al., 2017; Zhang et al., 2017). For instance, tumor suppressor genes are often methylated and silenced during carcinogenesis, whereas oncogenes lose methylation within their regulatory regions, including enhancers, and become actively transcribed (Baylin et al., 2016; Lubecka et al., 2016). Our group have shown that stilbenoids mediate transcriptional repression of MAML2 through altering epigenetic patterns at the enhancer region of this oncogene (Lubecka et al., 2016). DNMT3B was suggested to be a key player in driving stilbenoid-mediated silencing of genes with oncogenic functions. Increased binding of DNMT3B at MAML2 enhancer coincided with decreased occupancy of OCT transcription factor, which raised a possibility that DNMT3B is recruited to OCT1-occupied loci within regulatory regions of oncogenes and pro-metastatic genes in response to stilbenoids. Indeed, pro-tumorigenic function of OCT1 has been demonstrated in different cancer types and OCT1 has been shown to regulate genes associated with cell metabolic function, proliferation, oxidative stress and immune modulation, all of which are interconnected with a process of tumorigenesis (Garcia-Cosio et al., 2004; Shakya et al., 2009; Kang et al., 2015; Vazquez-Arreguin et al., 2016). DNMT3B- and OCT1-mediated mechanisms through which stilbenoids modify DNA methylation and impact the expression of genes involved in cancer progression are an emerging research area.   46 In the present study, following ChIP-seq analysis, we found 170 DNMT3B-enriched peaks within predicted enhancer regions corresponding to 89 unique genes in MCF10CA1a breast cancer cells treated with PTS (Figure 3.2). Interestingly, 41 of those genes overlapped with genes that lose OCT1 binding in response to PTS. The majority of the overlap genes fell into a category of oncogenes and pro-metastatic genes. Indeed, several pieces of evidence indicate that DNMT3B recruitment may be directed by recognition of transcription factors (Gagliardi et al., 2018; Hervouet et al., 2018). It was reported that certain transcription factors, for example E2F6 (Velasco et al., 2010), NR6A1 (Sato et al., 2006), and PU.1 (de la Rica et al., 2013), act as positive regulators of DNMT3B recruitment, leading to silencing to their target genes (Hervouet et al., 2018). On the other hand, CTCF (Wang et al., 2012) and SP1 (Brandeis et al., 1994) transcription factors were shown to block de novo DNA methylation at the target regions (Hervouet et al., 2018). Our findings suggest that peaks enriched with DNMT3B upon PTS treatment are also marked in cancer cells by H3K36me3, a histone mark established solely by SETD2 histone lysine methyltransferase (McDaniel et al., 2017). OCT1 transcription factor occupies those peak sites along with H3K36me3. Nearly 65% of the peaks within gene regulatory regions, that gain DNMT3B binding in response to PTS, demonstrate reduced H3K36me3 occupancy. Decrease in H3K36me3 binding at these regions may reflect downregulation of gene expression. Among 41 genes that contained DNMT3B-enriched peaks and OCT1-reduced peaks within enhancer regions, there were 20 genes with H3K36me3-reduced peaks (Figure 3.2). Interestingly, 11 of them were categorized as highly enriched with H3K36me3 occupancy upon comparison of binding intensity (bound/input) throughout the genome in untreated MCF10CA1a cells. Indeed, DNMT3B recruitment has been suggested to be directed by histone modifications, including H3K36me3 (Baubec et al., 2015; Rinaldi et al., 2016). DNMT3B selectively bound the bodies of transcribed  47 genes and led to their preferential methylation which was mediated through recognition of H3K36me3 in mouse embryonic stem cells (Baubec et al., 2015). Recruitment of DNMT3B to cell-type specific actively transcribed enhancers, followed by their hypermethylation, was also demonstrated to be mediated by recognition of H3K36me3 in human epidermal stem cells (Rinaldi et al., 2016). Depletion of SETD2 particularly affected DNA methylation at H3K36me3 sites implying that SETD2-mediated H3K36me3 is key to guide loci-specific recruitment of DNMT3B (Baubec et al., 2015). Interestingly, SETD2-mediated H3K36me3 has been shown to recruit chromatin-associated proteins, such as PHF19 of the polycomb repressive complex PRC2 (Ballare et al., 2012; McDaniel et al., 2017), which constitute an additional level of complexity and further suggests that the crosstalk between the chromatin remodeling complexes, histone modifications, and transcription factors may eventually be key in loci-specific recruitment of DNMT3B. Among 20 genes with enhancers containing DNMT3B-enriched peaks and OCT1- and H3K36me3-reduced peaks (Figure 3.2), which we refer to as ‘candidate genes with epigenetically targeted enhancers’, there were oncogenes (Halberg et al., 2016; Costa et al., 2018; Johnston et al., 2018; Suzuki et al., 2018), lncRNAs, microRNAs, small non-coding RNAs, pseudogenes, and epigenetic regulators (Guerrero-Martinez et al., 2018). We selected 4 of the 20 candidate genes for further analyses (i.e., PITPNC1, DANT2, TNNT2, and LINC00910), taking into account the magnitude of the differential binding and the highest proximity of peak regions for all three binding events. QPCR and pyrosequencing confirmed downregulation (Figure 3.3) and increased DNA methylation at enhancer regions of these genes (Figures 3.4 and 3.5). At each step, our results support our hypothesis that stilbenoids-induced changes in the occupancy of DNMT3B, OCT1 and H3K36me3 mediate epigenetic changes at enhancer regions of oncogenes which contributes to their silencing.    48 Altogether, our study delivers new knowledge and insights into the epigenetic mechanisms of dietary polyphenols from stilbenoid class in regulation of the activity of enhancers. Our findings show that PTS-mediated recruitment of DNMT3B to enhancer regions leads to epigenetic silencing of oncogenes and non-coding RNAs with potential oncogenic functions. We have shown that PTS treatment impaired binding of oncogenic transcription factor OCT1 which was accompanied by DNMT3B enrichment at these regions. In addition, we propose that H3K36me3 may play a crucial role in increased DNMT3B enrichment at enhancer regions and depletion of H3K36me3 may further contribute to condensed chromatin structure, making regions less accessible for transcriptional machinery. Investigating mechanisms through which bioactive compounds reverse aberrant epigenetic activation of genes with oncogenic and pro-metastatic functions could contribute to development of novel therapeutic and preventive strategies against cancer.               49 Overall Conclusions Aberrant DNA methylation has been shown to contribute to cancer biology, ranging from proliferation, differentiation and metastasis. The epigenetic silencing of tumor suppressor genes is believed to be an early, driving event in cancer development and reversal of repression of these genes results in anti-cancer effects (Mishra et al., 2010; Fernandez et al., 2012). Hence, over the past few decades, epigenetic pharmacology has heavily focused on developing therapeutic agents to reverse methylation-mediated silencing of tumor suppressor genes.  However, genome-wide studies of the DNA methylation landscape in cancer have shown that hypomethylation of cancer-promoting genes is the almost constant companion to hypermethylation of tumor suppressor genes (Ehrlich et al., 2002; Stefanska et al., 2011). Several studies suggest a comparable number of hypomethylated and hypermethylated genes in different cancer models (Brennan et al., 2012; Mayol et al., 2012). Interestingly, hypomethylated genes often share similar functions and are particularly enriched in pathways involved in cancer cell proliferation, migration and metastasis (Stefanska et al., 2011). Studies in breast and prostate cancers have shown loss of methyl marks at several candidate genes, such as uPA, CXCR4, and MMP-2, that are associated with cancer proliferation and metastasis (Pakneshan et al., 2004; Shukeir et al., 2006; Ateeq et al., 2008). Furthermore, through DNA hypomethylation signature analyses of cancer cells from different origins, including breast, prostate and liver, Chidkalk et al. identified common hypomethylated candidate genes, such as G0S2, SHISA2 and TMEM156, all of which were reported to enhance tumor invasion (Cheishvili et al., 2015). These studies further confirm that DNA hypomethylation may be linked to the progression of tumor growth and thus  50 targeting loci-specific hypomethylation of DNA appears to represent a beneficial anti-cancer strategy. Despite that, to date, there are no therapeutic approaches that can be clinically used to target loci-specific hypomethylation, implying a need for effective therapeutic strategies to reverse demethylation of oncogenes in cancer.  Remarkably, stilbenoids, including pterostilbene (PTS), are naturally occurring bioactive compounds that bring about subtle changes in the DNA methylation patterns, which at least partially contributes to the anti-cancer action of these compounds. Previous studies by our group demonstrate that stilbenoids induce bidirectional effects on DNA methylation in cancer without affecting normal cells, suggesting their benefits over standard epigenetic therapies (Lubecka et al., 2016; Beetch et al., 2018; Beetch et al., 2019a). Over the past years, the DNA methylation-modifying effects of stilbenoids in the activation of tumor suppressor genes have been successfully established (Stefanska et al., 2010; Stefanska et al., 2012b; Beetch et al., 2019a). However, research surrounding the role of stilbenoids on re-methylation and silencing of oncogenes and pro-metastatic genes is in its infancy. Many studies have also failed to investigate the underlying mechanisms through which stilbenoids regulate transcriptional activity of cancer-related genes. Therefore, identifying novel targets at the molecular level to evaluate the effectiveness and mechanisms of these bioactive compounds is essential.  In research presented in this thesis, I assessed the effects of PTS on protein-DNA interactions resolving mechanistic players involved in PTS-mediated hypermethylation of enhancers of oncogenes. In this study, we proposed a mechanism whereby PTS leads to DNA hypermethylation at specific CpG loci in enhancer regions of potential oncogenes followed by their transcriptional silencing in highly invasive MCF10CA1a breast cancer cells. We identified DNMT3B and oncogenic transcription factor OCT1 as candidates that play a role in hypermethylation and  51 silencing of oncogenes in response to PTS.  For the first time, we showed that enrichment of H3K36me3, a mark indicating active transcription, was diminished upon PTS treatment, proving a crosstalk between DNA methylation machinery and histone modifications. Interestingly, a study by Jafek et al. showed that OCT1 recruits Jmjd1a, a co-factor which demethylates H3K9 and inhibits DNMT binding to specific DNA loci (Jafek et al., 2019). This finding suggests unique interactions between transcription factors and different histone modifying enzymes, both of which can impact DNMTs recruitment and subsequent gene transcriptional activity.  There are several strengths of the in vitro model used in studies presented in this thesis. Highly invasive breast cancer cells MCF10CA1a were derived from xenografts of MCF10A cells transformed with constitutively active Harvey-ras oncogene, and represent poorly-differentiated highly invasive malignant tumors. These engineered cell lines are an excellent isogeneic models for investigating epigenetic changes during carcinogenesis (Lubecka et al., 2016). Additionally, we used PTS, a methoxylated RSV analog, which has been shown to function as an anti-cancer compound with negligible toxicity. Despite the fact that PTS has higher bioavailability compared to RSV, limited studies have used PTS alone in relation to epigenetic mechanisms contributing to anti-cancer effects in cancer models. Unlike other studies that often evaluate anti-cancer effects of dietary compounds after 48-72 hours of treatment, we assessed the effects of prolonged exposure (9-day treatment) to mimic chronic exposure from dietary intake in humans. We also employed physiologically relevant and attainable concentration of PTS (7 μM). Other in vitro studies often use high concentrations [e.g., 50 μM (Hagiwara et al., 2012) and 100 μM (Takashina et al., 2017)] of bioactive compounds to study anti-cancer effects. In addition, evidence supporting the role of stilbenoids in alterations of DNA methylation patterns at enhancer regulatory regions is severely lacking.  From the biological point of view, enhancers serve as a critical element of regulation of  52 gene expression. To date, most research has been focused on the effects of stilbenoids in inducing epigenetic modifications mainly in gene promoters. However, whether the same epigenetic alterations happen at enhancer regions remain unclear.  While emphasizing the strengths of our work, we recognize several limitations, including testing only one cell line and investigating mechanisms only in an in vitro model. In addition, we focus on parent compounds from stilbenoid class. However, it needs to be acknowledged that stilbenoids are metabolized in the human body by human enzymes and gut microbial biotransformation, and resulting metabolites may be biologically active and contribute to the observed effects (Miksits et al., 2005; Brill et al., 2006; Shao et al., 2010; Bode et al., 2013). Although it is evident that the metabolites of stilbenoids contribute to biological effects, whether the parent compound or the metabolites are responsible for the observed beneficial anti-cancer effects needs to be elucidated in future experiments. To add another layer of complexity, inter-individual variability and sex-differences in metabolizing stilbenoids were observed (Dellinger et al., 2014). Stilbenoids are mainly metabolized through glucuronidation and sulfation reactions (Miksits et al., 2005; Brill et al., 2006). UDP-glucuronosultransferases (UGT) are the main enzymes catalyzing glucuronidation (Miksits et al., 2005; Brill et al., 2006). In a study on pooled human liver microsomes, UGT1A1 expression, the most abundant UGT in human liver, was significantly higher in female than male, which may explain more efficient glucuronidation of stilbenoids in females (Dellinger et al., 2014).  Future studies should also assess the temporal sequence of DNMT3B and OCT1 binding as well as the occupancy of H3K36me3 upon exposure to stilbenoid compounds. Understanding the time-based recruitment of these mechanistic players will increase our understanding of how DNA methylation, histone modifications and transcription factors work together to regulate gene  53 expression. Another opportunity for future work is to illustrate antioxidant role of PTS in modulating DNA methylation. It is apparent that oxidative stress induces alterations in DNA methylation through activation of DNA repair mechanisms (O'Hagan et al., 2008; O'Hagan et al., 2011). Hence, compounds that decrease oxidative stress could consequently alter DNA methylation patterns. However, the mechanisms behind this potential relationship remain to be elucidated. Altogether, the present findings unravel a vast array of promising therapeutic opportunities for dietary polyphenols as an adjunctive treatment to chemotherapy. Since these bioactive compounds can target and modulate different biological processes, they may be used in combination with traditional chemotherapy to achieve a better outcome. While the body of evidence supporting the effectiveness of these adjunctive treatments is limited, the benefits of the combination therapies should not be underestimated.  The combination therapies can also be used to reduce adverse side effects associated with high doses of chemotherapeutic drugs while improving therapeutic efficacy. 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