The role of folic acid in maintaining colorectal cancer cell DNA methylation patterns and cancer stem cell phenotype in vitro

By

Nathan Farias

A Thesis presented to The University of Guelph

In partial fulfillment of requirements for the degree of Master of Science in Biomedical Sciences

Guelph, Ontario, Canada

© Nathan Farias December, 2013

ABSTRACT

The role of folic acid in maintaining colorectal cancer cell DNA methylation patterns and cancer stem cell phenotype in vitro

Nathan Farias Advisor: University of Guelph, 2013 Dr. B. L. Coomber

Folic acid is a B vitamin involved in DNA CpG methylation. Mandated dietary fortification has led to a subsequent increase in blood folate concentration which has been correlated to a simultaneous spike in colorectal cancer incidence in Canada and the US. Several human colorectal cancer cell lines were cultivated under low (0 mg/L), standard (4 mg/L), and high (16 mg/L) folate conditions for seven days, then assessed for DNA methyltransferase1 protein expression, changes in DNA methylation, and ability to generate colonospheres in culture. Low folic acid levels generally led to reduced DNMT1 protein expression, CpG hypomethylation, and reduced colonosphere yield. High folic acid levels led to increased

DNMT1 protein expression, CpG hypermethylation, and maintained colonosphere yield. This data demonstrates that varying levels of folic acid in vitro can influence the methylation status and cancer stem cell self-renewal ability of human colorectal cancer cells.

ACKNOWLEDGEMENTS

I would like to take this opportunity to acknowledge and thank everyone who contributed and helped me throughout the course of my masters degree. Firstly, I would like to acknowledge and thank Dr. Brenda Coomber for providing me with the opportunity to contribute to such an exciting and rewarding field as cancer research. Her passion and willingness to educate made her an invaluable resource, while her determination and commitment to research were admirable qualities that motivated me throughout my degree. Brenda’s persistence in challenging my ideas and concepts not only ensured that my work was accurate, but it contributed to the development of my critical thinking skills that I will carry on in my future endeavors.

I would also like to thank my committee members Dr. Terry Van Raay and Dr. Marica

Bakovic for being involved in my project and providing me with guidance and instruction throughout the course of my work. Dr. Van Raay assisted me in understanding the Wnt pathway activation in my cell model, while Dr. Bakovic assisted me in understanding the folate cycle in regards to DNA methylation.

I would also like to acknowledge Dr. Bekim Sadikovic for undertaking the data analysis of the Illumina Human Methylation 450k Array. The results that he generated were important contributions to my thesis

Next I would like to thank everyone involved with teaching me how to use equipment or new protocols. I would like to thank Amanda Barber for being my first instructor in a lab setting and teaching me essential techniques and protocols, such as cell culture and western blotting.

Amanda gave me a running start on my project and without her it wouldn’t have been possible.

Next I would like to acknowledge Jodi Morrison for not only being a fantastic technician but also a vital resource throughout the course of my work. Her substantial technical knowledge and

iii patience make her and essential asset in our lab and it would be difficult to picture a smooth running operation without her. In addition to this, I would like to thank Helen Coates, Monica

Antenos, Richard Gilbert and Leanne Delaney for helping me with understanding new protocols and equipment.

I would like to thank all my fellow colleagues and lab mates for providing me with a fun and positive atmosphere both in and outside the lab. I would like to acknowledge Amanda

Barber, Nelson Ho, Amy Richard, Sonja Zours, Richard Gilbert, Fiasal Alibhai, Peter Podobed,

Lindsay Robinson, Stacey Butler, Mai Jarad, Meghan Doerr, Jonathan Asling, and Sean Masson.

I would like to especially acknowledge Leanne Delaney, someone who I could always count on for assistance and reinforcement.

Finally, I would like to acknowledge my friends and family from far and wide for providing me with the support and encouragement that I needed to be successful in my degree. I would like to thank Vovo (Grandma), Anthony Piccolo, Alban Vuktilaj, Alessia Piccolo, Sandra

Piccolo, Jack Piccolo and the entire Delaney Family. Most importantly, I would like to thank my mother, Elvira Farias, for always being there to support and motivate me. Thank you for always showing an interest and listening to me even though you don’t really understand what I do. My mother is the inspiration for everything that I accomplish and I owe all my success to her for raising me into who I am today. I can never thank you enough.

iv DECLARATION OF WORK PERFORMED

I declare that all work reported in this thesis was performed by me, with the exception of the items indicated below.

Illumina HumanMethylation450K BeadChip array was done at the Genetic & Molecular

Epidemiology Laboratory, Hamilton General Hospital, Hamilton, ON, and analyzed by Dr.

Bekim Sadikovic (Assistant Professor Pathology and Molecular Medicine, McMaster

University).

v TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... iii DECLARATION OF WORK PERFORMED ...... v TABLE OF CONTENTS ...... vi LIST OF FIGURES ...... viii LIST OF ABBREVIATIONS ...... ix INTRODUCTION ...... 1 LITERATURE REVIEW ...... 4 Colorectal cancer ...... 4 Normal Colon Homeostasis ...... 6 Hierarchical crypt organization ...... 6 Identifying normal ISC ...... 7 Normal colon stem cell maintenance ...... 8 Wnt ...... 8 Notch ...... 9 BMP ...... 10 Hedgehog ...... 10 Cancer Stem Cells ...... 11 CSC assays ...... 12 Colon CSCs ...... 14 CSC niche ...... 14 CSC markers ...... 16 DNA Methylation ...... 18 DNA methylation associated genetic regulation ...... 18 Aberrant DNA methylation in cancer ...... 20 Hypomethylation in CRC ...... 21 Hypermethylation in CRC ...... 22 Folate, DNA Methylation, and Cancer ...... 23 Folate cycle ...... 24 Folate and CRC incidence ...... 25 RATIONALE ...... 29 MATERIALS AND METHODS ...... 31 Tissue culture ...... 31 Colonosphere formation ...... 31 Limiting dilution analysis ...... 32 Colonosphere formation after folate exposure ...... 32 Protein isolation and quantification ...... 32 SDS-PAGE and western immunoblotting ...... 33 DNA isolation ...... 34 DNA methylation quantification ...... 34 Immunofluorescence ...... 35 Monolayer ...... 35 Colonospheres ...... 35

vi Statistical Analysis ...... 36 RESULTS ...... 38 Cellular growth ...... 38 DNMT1 protein expression ...... 38 DNA methylation ...... 39 Colonosphere formation ...... 40 Colonosphere characterization ...... 40 DISCUSSION ...... 56 IMPLICATIONS AND FUTURE DIRECTIONS ...... 66 LIMITATIONS ...... 68 SUMMARY AND CONCLUSIONS ...... 70 REFERENCES ...... 72 APPENDIX I – Chemical List and Suppliers ...... 87 APPENDIX II – Preparation of Solutions ...... 89 APPENDIX III – Gene regions with Greater than 10 Percent Difference (P < 0.01) in CpG Island Methylation Between Folic Acid Treatments ...... 91

vii LIST OF FIGURES

Figure 1. Folic acid metabolism ...... 28 Figure 2. Effect of folic acid treatment on HCT116 cellular proliferation ...... 42 Figure 3. DNMT1 protein expression following folic acid treatment ...... 43 Figure 4. Global changes to cytosine methylation in response to folate treatment as detected by Illumina HumanMethylation450K BeadChip Array ...... 44 Figure 5. Site specific changes to cytosine methylation in response to folate treatment as detected by Illumina HumanMethylation450K BeadChip Array ...... 45 Figure 6. Global changes to cytosine methylation in response to folate treatment as detected by EpiSeeker methylated DNA Quantification Kit ...... 46 Figure 7. Colonosphere formation in vitro ...... 47 Figure 8. Limiting Dilution Analysis ...... 48 Figure 9. Colonosphere formation assay ...... 49 Figure 10. HCT116 colonosphere β-catenin localization ...... 50 Figure 11. HCT116 colonosphere β-catenin phosphorylation ...... 51 Figure 12. Day 10 colonosphere β-catenin localization ...... 52 Figure 13. Wnt pathway activation in 10 day old SW480 and HCT116 colonospheres ...... 53 Figure 14. Notch pathway activation in 10 day old SW480 and HCT116 colonospheres .....54 Figure 15. DNMT protein expression in 10 day old SW480 and HCT116 colonospheres ....55

viii LIST OF ABBREVIATIONS

APC Adenomatous Polyposis Coli APS Ammonium Persulfate BMP Bone Morphogenic Protein BSA Bovine Serum Albumin CIMP CpG Island Methylator Phenotype CpG Cytosine-phospho-Guanine CRC Colorectal Cancer CSC Cancer Stem Cell DAPI 4’,6-Diamidino-2-Phenylindole DHFR Dihydrofolate reductase DMEM Dulbecco’s Modified Eagle Media DNA Deoxyribonucleic acid DNMT DNA Methyltransferase EGF Epidermal Growth Factor FBS Fetal bovine serum FGF Fibroblast Growth Factor GSK3β Glycogen synthase kinase beta Hh Hedgehog Ihh Indian hedgehog ISC Intestinal Stem Cell Lgr5 Leucine-rich repeat containing G-protein coupled Receptor 5 MAT Methionine adenyltransferase MLH1 MutL Homolog 1 MS Methionine synthase MTHFR Methylenetetrahydrofolate reductase NOD Non-Obese Diabetic NTD Neural Tube Defects p16 Protein16 p53 Protein 53 PBS Phosphate buffered saline PVDF Polyvinylidene fluoride RAS Rat Sarcoma RARB Retinoic Acid Receptor Beta RPMI Roswell Park Memorial Institute PCR Polymerase Chain Reaction Rb Retinoblastoma SAH S-Adenosylhomocysteine SAHH S-Adenylhomocysteine hydrolase SAM S-Adenosylmethionine SCID Severe Combined Immunodeficiency SDS Sodium Dodecyl Sulfate SFRP Secreted Frizzled Related Protein Shh Sonic Hedgehog SHMT Serine hydroxymethyltransferase

ix Sp1 Specificity protein 1 Sp2 Specificity protein 2 SCM Stem Cell Media TBS Tris buffered saline TBS-T Tris buffered saline - tween 20 TEMED N,N,N,N-Tetramethylethylene diamine Tet Ten-eleven translocation THF Tetrahydrofolate Wnt Wingless related integration site

x Introduction

Colorectal cancer (CRC) initiation and development involves the accumulation of multiple genetic and epigenetic alterations, resulting in oncogenic activation and deactivation of tumor suppression in the cell [1]. Colon cell transformation has been well characterized and typically includes activating mutations in Wnt and Ras pathways, inactivating mutations to p53,

SMAD4, and PTEN, and global hypomethylation within the genome [1]. In the case of sporadic

CRC, the acquisition of these genetic changes leading to neoplastic phenotype is estimated to happen over a period of a decade or more [1]. Hence, cellular transformation is most likely to occur within the intestinal stem cells (ISC), which persist for years as opposed to the differentiated cells, which are a more transient population [1, 2].

Transformed ISC retain properties of normal stem cells, which allow them to drive tumor proliferation, differentiation, and migration [3-5]. Deregulated ISC maintenance results in constitutive expression of Wnt, Notch, and Hedgehog pathways, and deactivation of the BMP pathway to promote cellular “stemness” and inhibit differentiation [3, 6, 7]. This model of CRC development follows the hierarchical or cancer stem cell model, which proposes that tumor cells are organized in a hierarchy analogous to normal tissue organization [3, 6, 7]. In this model only a small population of cells, termed cancer stem cells (CSC), are the actual drivers of tumor maintenance and propagation [3, 6, 7]. Recent advances in technology have given us the opportunity to discover the relevance of these cells in tumor biology [4]. Targeting the CSC population and not the bulk of the tumor stands to be a considerable approach towards treating cancer.

Aberrant genomic methylation has been shown to be involved in deregulating stem cell maintenance pathways, oncogene activation, and tumor suppressor inactivation. By altering the

1 epigenetic code, tumors cells can develop apoptotic resistance, increase proliferative capacity, and acquire a dedifferentiated phenotype [8-10]. DNA methylation is an inheritable epigenetic mechanism that can regulate genetic expression without altering the genetic sequence [11, 12].

This genetic regulatory mechanism serves as a vital component for establishing and maintaining cellular fate, differentiation, and multipotency [8-10]. Once established, the genomic methylation pattern is considered very stable, but alterations can occur.

Folate is the generic term for the B9 vitamin found naturally in a variety of foods including green leafy vegetables, eggs, legumes, citrus fruits, and yeast [13]. Folic acid is the oxidized, more stable, synthetic form of folate, which is found in supplements and fortified foods

[13]. Folic acid is currently fortified in all grain products produced in North America to attenuate the occurrence of neuro tube defects [14, 15]. Folate plays pivotal roles in nucleotide biosynthesis and donating methyl groups for DNA methylation maintenance. Folate supplementation in normal tissue maintains epigenetic patterns as well as genomic integrity, protecting against aberrant transcriptional regulation and cellular transformation [16, 17].

However, in the presence of pre-tumorous tissue, folate supplementation may facilitate the increased DNA replication necessary in cancer and promote aberrant hypermethylation patterns subsequently silencing tumor suppression mechanisms.

Although folate fortification has been generally effective at reducing NTD, it may be harmful for subpopulations prone to colon tumor formation. Hence, more clinical and epidemiological studies are required to assess the efficacy of folate supplementation. Such research will hopefully elucidate optimal and effective dosage and timing of for safe folate intervention.

2 With this in mind, I sought to investigate the influence of folic acid supplementation on the CSC phenotype. In particular, we focused on how folate-induced changes to cellular DNA methylation patterns affected in vitro properties of colorectal cancer CSC. In this thesis, I provide evidence that varying levels of folate supplementation can alter CRC cell proliferation,

DNA methylation, and stem cell phenotype in vitro.

3 LITERATURE REVIEW

Colorectal Cancer

Although improved within recent years, colorectal cancer (CRC) is still one of the leading causes of morbidity and mortality worldwide. Globally, CRC is the third and second most diagnosed cancer in males and females, respectively, and in 2008 was responsible for an estimated 608,700 deaths [18]. In Canada, colorectal cancer is the third most commonly diagnosed cancer and the second leading cause of cancer related death [19].

The progression of CRC begins with the formation of a small lesion known as an aberrant crypt focus that expands to form a benign adenoma [20]. Benign tumors can remain undetected for the duration of a person’s lifetime, or eventually develop into an invasive adenocarcinoma

[20]. Progression from adenoma to metastatic cancer in the colon involves multiple genetic and epigenetic alterations resulting in oncogenic pathway activation, while simultaneously deactivating tumor suppression mechanisms in the cell [1]. A variety of genetic and environmental factors have been implicated in CRC incidence, which includes a history of inflammatory bowl disease, a western diet, and hereditary disorders that predispose individuals to intestinal adenomas, such as hereditary non-polyposis CRC (Lynch Syndrome) and familial adenomatous polyposis (FAP) [21]. Although great strides have been made towards understanding how CRC develops, further work is necessary to elucidate the relationship between genetic and environmental factors associated with cellular transformation in the colon.

The current model for CRC development involves a stepwise process requiring the independent accumulation of about 4-6 genetic alterations [1]. Typically, these changes include activating mutations in both Wnt and Ras pathways, inactivating mutations to p53, SMAD4, and

PTEN, and global hypomethylation within the genome [1]. Traditionally, it was hypothesized

4 that these alterations were acquired randomly and clonally selected for in a process known as clonal selection [22, 23]. This process also explained why CRC tumors showed such diverse cancer cell populations and molecular heterogeneity. However, recent experimental evidence suggests an additional layer of complexity exists within this classical model. In addition to clonal selection, heterogeneity may also be explained by different degrees of differentiation within genetically identical clones [3, 4, 24].

The latter model, termed the hierarchical or cancer stem cell model, proposes that tumors mimic normal tissue hierarchy and that only a minor subpopulation of cells (less then ~1 in

1000), termed cancer stem cells are the actual drivers of tumor maintenance and propagation [3,

4, 24]. CSCs have been implicated in an array of tumorigenic processes, including immune evasion, angiogenesis, and chemoresistance [25]. Therefore, targeting the CSC population, and not the bulk of the tumor, is likely an essential step in the development of efficient treatments for

CRC.

In addition to changes in genetic sequences, cancer initiation and progression can also occur through the accumulation of epigenetic changes. Epigenetic changes are inheritable changes in genetic expression or chromatic patterns that are not associated with altering the genetic sequence [11, 12]. Such changes include DNA methylation, histone modifications, chromatin remodeling, and microRNA regulation [26, 27]. The epigenetic foundation is a vital component for establishing and maintaining cellular fate, differentiation, and multipotency [8-

10]. Thus, acquisition of aberrant epigenetic patterns that promote dedifferentiation and phenotypic “stemness” are adaptations acquired by CSCs, allowing them to progress and propagate [8-10].

5 This review will begin by covering normal colon homeostasis to provide context to the cellular deregulation that occurs in CRC. Additionally, the contribution of aberrant DNA methylation to carcinogenesis will be discussed, with particular emphasis on the involvement of folate in influencing these changes.

Normal Colon Homeostasis

Hierarchical crypt organization

The colon is one of the most highly proliferative tissues in the entire body, with an astonishingly high epithelial turnover rate of about one week [28]. The rapid shedding of surface epithelial cells requires a constant supply of functional epithelial cells to replace those that are sloughed off the surface. To accommodate this rapid renewal process, the luminal architecture of the colon is organized into structural units known as crypts. Each crypt represents a hierarchical cellular system that is monoclonal in nature [29-31]. The surface cells at the top of the crypt comprise the intestinal epithelial layer, which consists of several differentiated cell types: enterocytes, goblet cells, and enteroendocrine cells. In contrast, the base of the crypt accommodates the stem cell niche, which contains the somatic stem cells or intestinal stem cells

(ISC) and supporting Paneth cells[32-34].

The intestinal stem cells are a multipotent, self sustaining population at the top of the cellular hierarchy, which rely on supporting signals emanating from surrounding mesenchymal cells and differentiated progeny residing in the stem cell niche for regulation. Stem cells can undergo symmetrical or asymmetrical cellular divisions. Symmetrical or a “self-renewal division” results in two cells with the same uncommitted differential potential as the original, and increases the stem cell pool [30, 35]. Asymmetrical cell divisions, or “differentiation divisions”, results in one daughter cell that will preserve its multipotency, and another daughter

6 cell that will undergo the stepwise process of differentiation into a mature colonocyte [30, 35].

The fate of the two daughter cells is determined by a neutral competition for resources and signals necessary for stem cell maintenance emanating from the surrounding mesenchyme and

Paneth cells [30, 35]. Cells destined for differentiation, also known as transient amplifying (TA) cells, undergo 4-5 rounds of cellular divisions within a period of 48 hours, subsequently differentiating into one of several mature colon epithelial cell types [2, 34, 36]. Hence, mutations within a single stem cell can quickly spread throughout the entire crypt, which has implications for disease.

Identifying Normal ISC

Currently, there is debate regarding the exact location and identification of colon stem cells, due to a paucity of ISC markers and functional stem cell assays. However, extensive research involving tracing techniques and surface markers has accumulated substantial evidence indicating that colon stem cells are, in fact, located at the crypt base adjacent to supportive

Paneth cells. Most of this work has been done looking at Wnt pathway proteins, specifically leucine-rich repeat-containing G protein coupled receptor 5 (Lgr5) [33]. Lgr5 has shown to be restricted to a small pool of basally located pluripotent cells capable of maintaining the intestinal epithelium [33]. This evidence is further supported by the fact that when grown in vitro, these marked cells have the capacity to form crypt-like structures that recapitulate the cellular hierarchy found in vivo [37, 38]. Other evidence suggests that the ISC population instead resides at the +4 position, directly above the Paneth cell region. These cells have been shown to be Bmi-

1 and Tert positive, characterized as a quiescent and radioresistant population. They also have the capacity to give rise to Lgr5+ ISC’s [32, 39, 40]. These attributes suggest that these cells may

7 instead act as “master” stem cells, maintaining ISC populations during periods of extreme stress to the colon [40].

Current research efforts have focused on expanding the pool of ISC surface markers for the detection and isolation of these cells. Markers elucidated thus far have been identified in both mice and human models and seem to be primarily involved in proliferation and differentiation.

They include Musashi-1 (Msi-1), Hes-1, CD133, CD29, Eph-B receptors, Bmi1, Lgr5, aldehyde dehydrogenase-1 (ALDH-1), Tert, and achaete scute-like-2 (Ascl-2) [3, 33, 41-45].

Normal Colon Stem Cell Maintenance

The stem cell niche integrates the molecular signals from many components to produce a sustainable microenvironment specialized to maintain ISC. Current evidence indicates that these signals are primarily involved with Wnt, Notch, BMP, and Hedgehog pathway activation. The balance and interaction between these intercellular pathways is what ultimately regulates stem cell proliferation, differentiation, and the subsequent regulation of normal intestinal architecture.

Wnt

Wnt ligands within the stem cell niche regulate β-catenin localization within the cell via frizzled (FZ) and low density lipoprotein receptor-related (LRP) protein receptors [5, 46]. In the presence of Wnt ligands, these receptors signal the disassembly of the destruction complex consisting of APC, AXIN2, glycogen synthase kinase-3β (GSK3β), and casein kinase 1 (CK1) necessary for targeted β-catenin phosphorylation, ubiquitination and subsequent degradation [5,

46]. Unphosphorylated β-catenin is free to translocate to the nucleus where it interacts with transcription factor (TCF) family and lymphocyte enhancer factor-1 (LEF-1) transcription factors to promote the transcription of Wnt target genes such as cyclin-D1 and c-myc [5, 46].

8 An active Wnt pathway is crucial for stem cell maintenance and proliferation. Nuclear localization of β-catenin, a hallmark of Wnt pathway activation, is exclusively observed at the crypt base in ISC [33]. The presence of Wnt ligands also seems to be targeted at the crypt base, as it has been shown that the main source of intestinal Wnt ligand comes directly from the adjacent mesenchymal and Paneth cells [5]. Experiments have shown that the overexpression of

Wnt activator R-spondin-1 results in colon stem cell proliferation in vitro, while transgenic expression of Wnt inhibitor DKK1 or deletion of TCF4 transcription factor both result in Wnt pathway inactivation and subsequent epithelial layer attrition in vivo [47-49].

Notch

The canonical Notch pathway becomes activated via the direct contact of Notch surface receptors DLL1, DLL3, DLL4, Jagged1, and Jagged 2 found on adjacent cells, however secreted ligands do exist [50]. Delta and Jagged ligands on the surface of neighboring cells interact with

Notch receptors 1-4 to initiate two cleavage events: the first by disintegrin and metalloprotease

(ADAM) proteins, and the second by γ-secretase (GS) [50]. These proteases release the Notch intracellular domain (NICD), which is then free to translocate to the nucleus. In the nucleus,

NICD recombines with transcription factors, binding protein suppressor of hairless (R) and mastermind -like protein 1 (M) to drive the transcription of Notch target genes, such as Hes-1

[50].

There is a considerable amount of crosstalk between the Wnt and Notch signaling pathways to coordinate proliferation as well as differentiation [50]. In the presence of Wnt activation, Notch seems to promote “stemness” as well as proliferation [51]. However, in the absences of Wnt pathway activation Notch seems to promote cellular differentiation [51].

9 In the colon, Notch activation determines lineage decision between enterocyte and secretory cells [50, 51]. Inhibition of Notch pathway results in increased globlet cell production, while increasing Notch activation leads to increased enterocyte production [52]. The Notch pathway shows classical negative feedback regulation, which is helpful to maintain easy fate determination in the colon [50]. Like most other ISC maintenance signals, the Paneth cells supply most Notch ligands.

BMP

Bone morphogenic proteins (BMP) are TGFβ pathway family members [53]. They facilitate their actions through intracellular signals using SMAD protein activation. BMP signaling occurs at the crypt surface where it promotes cellular differentiation [53]. Briefly, BMP ligand binding results in BMPR1 and BMPR2 heterodimerization and subsequent SMAD1,

SMAD5 and SMAD8 protein phosphorylation and activation [53]. These proteins then complex with SMAD4 and translocate to the nucleus, where they drive the expression of BMP-targeted genes assisted by RUNX2 and other cofactors [53].

BMP pathway activation counteracts Wnt activation and halts proliferation while promoting differentiation [53]. BMP ligands are primarily supplied via mesenchymal cells throughout the crypt, but are blocked in the stem cell niche due to the presence of BMP inhibitor noggin [53]. Experimentally it has been shown that both BMP deficiency, as well as noggin overexpression, results in crypt hyperproliferation and crypt fission in vivo [54, 55].

Hedgehog

Hedgehog signaling relays on the interaction between patched (PTCH) 1 and 2 and smoothened (SMO) proteins [56, 57]. PTCH binds and inactivates SMO. However, in the presence of Hh ligands Sonic Hh (Shh), Desert Hh (Dhh), and Indian Hh (Ihh), PTCH releases

10 SMO, which is then free to activate Gli transcription factors [56, 57]. Activated Gli transcription factors are free to regulate targeted gene expression[56, 57].

The role of Hh in ISC maintenance seems to be less obvious than other pathways and seems to depend on the isoform expressed as well as the activation of other pathways. Ihh is expressed in differentiated colon cells, where it signals to the surrounding mesenchyme to release BMP signals [58]. However, +4 crypt cells have been reported to express Shh instead, which may be necessary to promote “stemness”[59].

Cancer Stem Cells

As discussed above, the hierarchical model defines cancer stem cells as the only cells capable of propagating all other cancer cells within a tumor [4, 60]. This is in contrast to the stochastic model, which states that every cell in the tumor has the potential to propagate and that their ability to do so depends merely on the accumulation of random genetic aberrations and divergent microenvironmental influences [4, 60]. Unlike the stochastic model, the hierarchical model argues that the only genetic aberrations, epigenetic changes, and microenvironmental signals that influence clonal evolution of the tumor are those targeted towards the CSC population [4, 60]. Although the hierarchical model has been around for many years, only through recent advances in technology have we been able to identify and demonstrate the existence and relevance of CSCs [4]. The first CSCs were isolated from an acute myeloid leukemia and since then have been identified in tumors from other stem cell containing organ systems including colon, brain, prostate, pancreatic and hepatic [61-66]. However, it seems that not all malignancies follow the CSC model. In melanoma, for example, the CSC model has been challenged, instead suggesting that all cells within the bulk of the tumor have the same potential to initiate new tumors [67].

11 CSC Assays

Perhaps the most significant contribution to CSC research was the development of robust assays capable of identify and isolating the CSC population from the rest of the tumor. Progress in immunodeficient mouse models has enabled the use of limiting dilution xenografting techniques for the detection and quantification of CSC tumor initiating potential in primary tumors [68-70]. Often, CSCs are referred to as cancer initiating cells due to their ability to produce a progressively growing tumor that recapitulates the phenotype and heterogeneity of the original tumor [4]. Tumor CSCs from the primary host are removed and reintroduced into a secondary host to formally confirm that the original CSC had self-renewal ability [4, 66, 71].

These experiments are considered the gold standard in CSC identification and have been used on a multitude of different tumors types, including colon, brain, breast, and hematopoietic malignances [63, 66, 71-74].

For the most part, limitations in these models are those associated with most xenograft models. Firstly, CSCs are reliant on growth and maintenance factors that are not always cross- species active [75, 76]. These inter-species differences may influence the survival, reproduction and degree of differentiation of the CSC. Secondly, the immunodeficiency of the mice, although necessary for engraftment with human cells, removes a now recognized vital component in tumor growth and biology [75, 77, 78]. Civenne et al. demonstrated that the degree of immunosuppression in two mice models dictated whether CD271 (a cancer initiating cell marker) negative melanoma cells could initiate new tumors [79]. In standard NOD/SCID mice, no tumors were formed, however in fully immunodeficient NOD/SCID/IL2rγ , lacking T-, B-, and NK-cells, CD271 negative cells could initiate tumors [79]. Finally, the ~2 year lifespan of mouse models limits the analysis of slow growing tumors and age related factors on tumor

12 growth [80, 81]. This limitation may also be a potential cause for error, as it constrains our ability to distinguish between actual CSCs and transiently amplifying cells that have transient but not permanent self-sustaining potential [81].

Early in vitro assays for culturing CSC were initially hindered due to the lack of understanding of the growth requirements necessary for clonal expansion. Then, in 1992, a novel approach to culturing neural stem cells was established that showed the expansion of primitive, multipotent neural cells in liquid suspension [82-84]. The approach used fibroblast growth factor

(FGF) and epidermal growth factor (EGF) supplementation as well as non-adherent conditions and resulted in floating clonal clusters termed neurospheres [82-84]. The approach maintains multipotency with supplementation, while eliminating adhesion-dependent differentiated cells

[82-84]. This important breakthrough has since been expanded to support the growth of many other types of undifferentiated cells, both normal and malignant, in vitro, including from colon, breast, skin and prostate [62, 71, 85, 86].

The third major contribution to CSC research was the adaptation of fluorescence activated cell sorting (FACS) technology for the fractionation of the CSCs based on the presence of cell surface differentiation markers [4]. The same surface markers used to identify normal stem cells are also frequently used for the identification and isolation of CSCs. In CRC, CSCs have been identified using CD133, CD166, CD44, CD24, EpCAM, and EGR5 cell surface markers [5, 87, 88]. However, inconsistencies do exist when evaluating the threshold expression of these markers to determine true stemness [89]. These discrepancies are discussed in further detail below.

These improved methods for studying CSCs have proven the relevance of these cells in tumor biology and has profoundly influenced interest in targeting these cells clinically. Not only

13 are CSCs considered the drivers of metastasis, but they have also been implicated in a number of cancer related phenomena including multipotency, immune evasion, metastasis, and therapeutic resistance [23, 81, 90, 91].

Colon CSCs

The longevity and replicative capacity seen in ISCs puts the tissue at serious risk of developing cancer. Being long lived, it is assumed that ISCs accumulate the oncogenic mutations necessary for cellular transformation over a period of decades [1, 2]. This is known as the bottom-up theory of CRC development and is preferred to the top-down model, which suggests that instead, the more transient differentiated cells are responsible for tumor initiation [2, 92].

However, despite extensive in vitro and in vivo studies, the cell of origin for CRC remains elusive. Deletion of functional APC using Bmi1, CD133, Lgr5, and Ah-Cre recombinase mice in

ISC and short lived differentiated cells both lead to the development of adenomas [2, 32, 44].

However, adenomas induced in differentiation cells are more rare and occur less rapidly than those induced in ISC [2]. Hence, the ISC seem to be the more likely candidates for CRC initiation. Nonetheless, whichever cell is responsible for the original transformation, once initiated, CRC seems to follow a hierarchical model for cancer progression.

CSC niche

Analogous to normal ISC homeostasis, CRC CSCs reside in a microenvironment, which drives proliferation, differentiation, migration, and renewal [5-7]. The deregulated CSC niche gains tumor-promoting traits and supplies the tumor with Wnt, Notch, TGFβ, and Hedgehog signals to maintain the hierarchical organization of the tumor [6, 7]. In this model, Notch and

Wnt pathways promote cancer cell stemness, while BMP and Hedgehog signals promote tumor cell differentiation [6, 7].

14 Notably, Wnt pathway deregulation is the most common transformation in CRC, and mutations in this pathway are one of the first mutations to occur in the majority of sporadic CRC cases [1]. The substantial role that the Wnt pathway plays in CSC proliferation has been highlighted by the fact that activating mutations and inactivating mutations in either β-catenin or

APC, respectively, are sufficient to drive intestinal hyperplasia [46]. Furthermore, the genetic defect underlying FAP is a heterozygous mutation in APC [93]. FAP patients develop hundreds of colonic polyps early in life, and have a 100% risk of developing CRC [93]. Although Wnt pathway activation is one of the early steps toward CRC development, immunohistochemical observations have revealed that CRC tumors do not harbor β-catenin nuclear localization homogeneously [94]. Thus, it is assumed that only the CSCs, maintained by the tumor microenvironment, show β-catenin activation and use this to drive tumor growth. For example,

Vermeulen et al. demonstrated that high Wnt pathway activation was correlated with both stem cell marker expression as well as increased ability to induce tumor growth in vivo [5].

Additionally, tumor regions showing the highest Wnt pathway activation subsequently show a predominantly invasive character [23].

There are clear data establishing the role of the Wnt pathway with CSC maintenance in

CRC, however strong experimental evidence also exists connecting other microenvironmental drivers to CSC maintenance. Significant upregulation of Notch and Shh signaling is seen in adenocarcinomas, but not in normal differentiated tissue [56, 95]. Conversely, the loss of BMP signaling through loss of Smad4 or BMPR is a common progression of CRC that drives tumor proliferation [96]. Currently, the development of agents to target these critical pathways is underway to serve as potential anti-CSC therapeutics [97].

15 CSC markers

In CRC, CSCs are defined using several surface markers, some of which are upregulated in normal ISC, while others are ubiquitously expressed in adult stem cells. Reliably characterizing CSCs relies on establishing a relationship between the co-expression of these surface markers and the CSC phenotype. CD133, a transmembrane glycoprotein associated with plasma membrane organization, was one of the first markers used to identify CRC CSCs [66,

71]. Limiting dilution xenotransplantation studies using NOD/SCID mice have shown that

CD133+ enriched tumor cells form tumors with increased frequency compared to unfractionated tumor cells, while CD133- cells fail to form tumors at all [66]. Additionally, tumors formed by

CD133+ cells morphologically and heterogeneously resemble the primary tumor even after serial transplantation [66]. In vitro, CD133+ enriched cells grown as colonospheres do not express differentiation markers [71]. However, when deprived of stem cell growth factors, CD133+ cells lose CD133 expression, gain differentiation marker CK20, and begin to acquire adherent morphology [71]. Clinically, high CD133 expression has been correlated with poor prognosis

[98].

However, concerns regarding the significance of CD133 expression have arisen, with some researchers arguing that CD133 is, in fact, not a specific marker of CRC CSCs. Shmelkov et al. showed that CD133 was not restricted to stem cells, but also ubiquitously expressed in differentiated mice and human colon cells [89]. They also showed that both CD133+ and

CD133- CRC cells were able to establish long-term tumorigenesis in NOD/SCID mice xenografts, and that in fact tumors generated from CD133- cells were more aggressive [89].

Horst et al. reported that CD133 knockdown in CRC cell lines did not affect proliferation, migration, invasion, or colony formation [99]. Some have suggested that the discrepancies

16 associated with CD133 expression may actually be a reflection of cell cycle stage, or inaccurate detection by antibodies that target glycosylation-dependent epitopes [100, 101]. Ultimately, the authors concluded that current understanding of CD133’s functional role in CSCs is weak and further research is necessary to understand its reliability as a CSC marker.

Alternatively, it has been suggested that not one but a combination of markers would be a more reliable and necessary method for detecting CRC CSCs. Additional markers include

CD166, CD24, CD44, CD29, CD26, Lgr5, Msi-1, Bmi-1, ALDH-1, and EpCam. The presence of all these markers has been associated with tumorigenesis, clonagenic ability, and multilineage potential in vitro and in vivo. For example, CD44+ primary CRC cells are able to generate colonospheres and xenografted tumors that resemble the original lesion from just a single cell

[102]. Furthermore, cells lacking CD44 and EpCam surface markers fail to form xenograft tumors in NOD/SCID mice [87]. In contrast, primary CRC cells purified for CD44 and EpCaM expression show high frequency tumor formation when injected subcutaneously into NOD/SCID mice [87]. Although it has been shown that CD44 expression is not constrained to the stem sell compartment like CD133, it may be necessary to employ other markers for reliable purification.

ALDH, a detoxifying enzyme responsible for oxidizing intracellular aldehydes, has been shown to enrich the CSC population of CD44+ and CD133+ primary cancer cells [42]. Additional enhance detection techniques may come from markers of Wnt pathway activation, such as the presence of Lgr5 surface protein [103].

Lastly, genetic expression profiles specifically looking at genes that promote pluripotentcey such as Oct-4, Sox-2, Nanog, and Klf-4 also seem to be potential CSC characterizing markers. These genes facilitate a shift towards a stem-like phenotype and their

17 expression has been associated with poor prognosis and resistance to conventional therapy [104,

105].

DNA methylation

DNA methylation associated genetic regulation

Many of the aforementioned stem cell maintenance proteins and transcription regulators show partial or full regulation of their expression through epigenetic mechanisms [8, 106]. DNA methylation occurs at cytosine residues usually associated with CpG dinucleotide rich regions also known as CpG islands. CpG islands encompass promoter gene regions of approximately half of all genes [107-111]. A high density of methylation in these regions is associated with transcriptional silencing [112, 113]. Recent studies using cell line models have shown that approximately 5% of all cytosine residues in the human genome are methylated and that nearly all or most of this methylation occurs within the CpG islands. [114, 115]

Multiple regulatory mechanisms are associated with CpG methylation and transcriptional silencing. Firstly, in vitro studies have shown that methylation directly interferes with transcription factor binding to target sequences [113]. Without intimate genetic interaction, these transcription factors lose their potent transcriptional promoting effects [116]. Additionally, once methylated, methyl-CpG binding domain proteins (MBD’s) bind to methylation regions of DNA, which also interferes with transcription factor binding and alters chromatin structure [112].

Finally, MBDs recruit histone-modifying proteins that alter chromatin density, making it transcriptionally incompetent [113].

Cellular methylation patterns are initially established during embryogenesis and are specific to certain cell types [110-112]. These patterns are very stable and inheritable in somatic differentiated cells. DNA methylation patterns are initially erased and reestablished in the

18 embryo by a group of de novo DNA methyltransferases (DNMT), DNMT3a and DNMT3b, which are expressed in most dividing cell types [117]. They function primarily during mammalian development, and are involved in epigenetic reprograming during differentiation

[117]. DNMT1, the maintenance methyltransferase, uses hemi-methylated DNA as a substrate to restore the symmetrical DNA methylation pattern after DNA replication, thus perpetuating DNA methylation throughout subsequent cellular divisions [110, 111, 117].

Once established, the genomic methylation pattern is considered very stable, however alterations can occur. Loss of DNA methylation can occur passively through the absence of methyl donors or the inactivation of the methylation machinery (DNMTs) during successive rounds of DNA replication [118]. Conversely, active de-methylation is the enzymatic process of removing the methyl group from the DNA [118]. Briefly, Ten eleven translocation (Tet) proteins can oxidize methyl-cytosines to form hydroxyl-methyl-cytosine. Base excision repair (BER) proteins can detect these modified bases and replace them with unmethylated cytosine residues

[119]. TET2 inactivating mutations have been reported in myeloid leukemia, which may contribute to a hypermethylated phenotype [119].

DNA methylation-associated genomic silencing is a vital component in normal cellular homeostasis. DNA methylation is an important mechanism used for the silencing and inactivation of potentially mutagenic repetitive genomic sequences. These sequences are mobile within the genome and are commonly involved in recombination. They include long interspersed elements (LINEs), short interspersed elements (SINEs), retroviral DNA, transposons, retrotransposons, and tandem genomic repeats [116, 120-122]. Additionally, DNA methylation is required for proper genomic imprinting and X-chromosome inactivation [122]. During early female embryogenesis, one of the two X-chromosomes is randomly chosen for inactivation

19 [123]. Hypermethylated CpGs throughout the length of that chromosome contribute to its condensation and subsequent inactivation [123]. Genetic imprinting is another form of mono- allelic gene expression, which results from differentially methylated genes. These differentially expressed parental genes are once again chosen during early embryogenesis and gametogenesis.

Methylation of the CpG regions associated with either the paternal or maternal allele result in transcriptional inactivation and the dominance in the other parental allele [124, 125].

Aberrant DNA methylation in cancer

Three types of altered epigenetic patterns have been associated with cancer development: hypomethylation, hypermethylation and loss of imprinting (LOI). Global hypomethylation and

CpG targeted hypermethylation are considered major defining characteristics of tumorigenesis

[126]. These changes in DNA methylation do not appear to be random due to the similarities seen in methylation patterns from tumors of specific lineages, which significantly differ from other tumors and normal equivalent tissue [126]. Interestingly, the methylation changes observed in cancer cells show substantially greater differences than DNA methylation between types of normal tissue [126].

At a very early stage in CRC, premalignant adenomas show a global loss of DNA methylation that carries over into the carcinoma development [127]. This loss in genetic methylation has significant implications in gene activation, chromosomal instability, and loss of heterozygosity [128-130]. Conversely, site-specific hypermethylation also occurs and is commonly referred to as the CpG island methylator phenotype (CIMP). Commonly hypermethylated genes in CRC include the mismatch repair enzyme MLH1 and tumor suppressors such as retinoblastoma (Rb), P16, retinoic acid receptor beta (RARB), and secreted frizzled related protein (SFRP) [131-134]. LOI refers to the loss of parental alleles, and is usually

20 seen in embryonic tumors [135]. Ultimately, the methylation pattern changes observed in cancerous tissue contribute to chromosomal instability and the inactivation of tumor suppressors.

Hypomethylation in CRC

Hypomethylation of colon tissue is associated with an increased risk of CRC and is a mechanism used to drive neoplastic transformation. Global hypomethylation is associated with aberrant genetic activation as well as chromosome breakage, which can result in aneuploidy

[136]. Hypomethylation of repetitive elements measured by bisulfite sequencing in 722 elderly individuals has been shown to be a predictor for increased risk of developing all cancers and is associated with increased mortality from cancer [137]. Compared to normal adjacent intestinal epithelium, CRC tissue has been shown to commonly display LINE-1 CpG region hypomethylation [120, 138]. Detection of LINE-1 hypomethylation is commonly used as a surrogate marker for global genomic methylation, and is also associated with increased mortality in CRC patients [120, 138].

Hypomethylation within specific CpGs, resulting in aberrant protein activation, also promotes tumorigenesis. The hypomethylation of CDH5 (P-Cadherin) in CRC results in ectopic expression of the protein and subsequent increased carcinogenesis [139-141], although it’s exact role in the tumorigenic pathway is yet to be determined. Additionally, CD133, a marker for CRC

CSCs, is both hypomethylated and highly expressed in more advanced CRC [142]. CD133 seems to be tightly regulated by DNA methylation, elucidating a connection between aberrant DNA methylation and the CSC phenotype in CRC [142].

21 Hypermethylation in CRC

The hypermethylation and subsequent silencing of a number of genes involved in

DNA repair, apoptosis, and tumor suppression has been associated with CRC progression and initiation. Commonly hypermethylated genes include the mismatch repair enzyme MLH1 and tumor suppressors such as retinoblastoma (Rb), P16, RARB and SFRP [131-134]. Promoter hypermethylation of MLH1, MGMT and HIC1 have been shown to actually lead to CRC formation [143, 144]. Additionally, the tumor suppressor gene p16 has been shown to harbor hypermethylation, leading to loss of expression in CRC cells, an alteration commonly observed in more advanced CRCs [145, 146]. Other tumor suppressors repressed in CRC by hypermethylation include p14 and HLTF [147, 148]. IRF8, a transcription factor known to regulate Fas-mediated apoptosis, has been shown to be hypermethylated and transcriptionally repressed in CRC cells. By inactivating the IRF8 promoter, CRC cells evade apoptotic cell death and acquire a metastatic phenotype[149].

This targeted hypermethylation has inspired a demethylating approach for treating CRC carcinogenesis. By reprograming CIMP or aberrant cell methylation phenotypes, cells may once again gain expression of tumor suppressors and potentially reverse their malignant phenotype. In light of this, demethylating agents have been developed. One such compound is 5-aza-2’- deoxycytidine, a cytosine analog that irreversibly binds DNMTs and promotes passive demethylation in vitro [150]. In one study, 5-aza-2’-deoxycytidine was shown to reverse the hypermethylation of the tumor suppressor MLH1 and subsequently regain its expression, even at

10 days post-treatment in vitro [151]. Another study has shown that treatment with 5-aza-2’- deoxycytidine disrupted DNMT protein interactions and sensitized cancer cells to

22 chemotherapeutic agents [150]. However, caution must be used when utilizing these therapies as the potential for activating oncogenes and aggravating established tumors is a possibility [150].

The above epigenetic changes, which lead to transformation, seem to be a natural phenomena influenced by age, diet and environmental signals. It has been well documented that global demethylation in the gastrointestinal track increases with age [152]. Additionally, an increasing number of environmental influences are becoming linked to changes in DNA methylation such as: air pollution, benzene exposure, and particulate pollution [153, 154]. The influence of dietary intake, specifically the availability of methyl donors such as folate, betain and choline seems to modulate methylation patterns in a dose responsive manner [155].

However, the extent of their ability to do so continues to be controversial and ambiguous. The remainder of this review will focus on folate’s role in modulation of cellular DNA methylation.

Folate, DNA Methylation, and Cancer

Folate is the generic term for the B9 vitamin found naturally in a variety of foods including green leafy vegetables, eggs, legumes, citrus fruits, and yeast [13]. Folic acid, the oxidized, more stable, synthetic form of folate is found in supplements and fortified foods [13].

Folate is taken up into the folate cycle where it is used as an intermediate in cellular biosynthesis, and methylation (discussed below). In 1998, sufficient evidence for folate’s protective effects on preventing neural tube defects (NTD) subsequently led to the mandatory fortification of folic acid in grain products within Canada and the USA [14, 15]. Since then, blood, tissue, and serum folate levels within the population have more than doubled, with serum levels increasing from

11.4 nmol/L to 26.9 nmol/L, which has had a substantial beneficial effect on the original target,

NTD [13, 156]. However, concerns have risen regarding potential harmful effects of such high folate levels, especially within certain subpopulations. In 2007, a paper published by Mason et

23 al. showed a temporal association between folic acid fortification and a sudden spike in CRC incidence in Canada and the US [157]. The authors go on to explain that, although folate is generally regarded as a preventative agent against CRC development, these effects may be specific to the stage of cancer, and folate in fact may act as a progressive driver for advanced stage CRC [157]. Thus, further research is required to define the complex relationship between folate and CRC development to ensure safe and responsible fortification practices.

Folate Cycle

The folate cycle is a process that utilizes folate derivatives as substrates for single carbon transfer reactions. Absorbed folate is metabolized to 5-methyltetrahydrofolate (5-MethylTHF) in the intestines or liver [158, 159]. 5-Methyl THF is the primary folate derivative absorbed by non- hepatic tissue. Within the cell, 5-Methyl THF is converted to tetrahydrofolate (THF) via methionine synthase, which serves as the principle vehicle for distributing methyl group moieties

[158, 159]. When folic acid is consumed, it is primarily converted to dihydrofolate via dihydrofolate reductase in the intestines or liver and subsequently to THF, which enters the folate pool [158, 159]. Once THF is formed from either folate or folic acid, it is subsequently enters the folate cycle. The folate cycle is coupled to the methionine cycle, and together these cycles constitute a bicyclic pathway central to one carbon metabolism and distribution throughout the cell (Figure 1).

Briefly, THF is converted to 5,10-methylene THF by the B6 dependent enzyme serine hydroxymethyltransferase (SHMT) using one-carbon units fed into the cycle from serine or glycine catabolism [158, 159]. 5,10-methylene THF is subsequently reduced back to 5-methyl

THF by methylenetetrahydrofolate reductase (MTHFR) to complete the folate cycle [158, 159].

This reaction is important for maintaining a flux of methyl groups for the remethylation of

24 homsysteine to methionine via the B12 dependent enzyme methionine synthase (MS) [158, 159].

This marks the beginning of the methionine cycle. Methionine is then used as a substrate for S- anenosyl methionine (SAM) synthesis via methionine adenyltransferase (MAT) [158, 159]. SAM molecules are used throughout the cell as methyl donors and are vital for the methylation of

DNA, RNA, histones, proteins and other small molecules. SAM substrates are subsequently converted to S-anenosyl homocysteine (SAH) after methyl group donation [158, 159]. SAH is then deadenylated by S–anenosyl homocysteine hydrolase (SAHH) to homocysteine to complete the methionine cycle [158, 159].

Folate and CRC incidence

Low folate status has been associated with increased risk of developing NTD, cardiovascular disease and some types of cancer [160-162]. The mechanism, or mechanisms, by which folate contributes to these pathologies remain unclear. Inadequate cellular folate during division can result in reduced thymidine availability, resulting in increased uracil integration into

DNA [163]. Ultimately, this increases mutagenesis and strand breaks, leading to the formation of

“micro nuclei” in vitro [163]. Therefore, in rapidly proliferating cells, folate seems to be necessary for efficient DNA replication and in fact, interrupting folate metabolism has been shown to inhibit tumor growth [16, 17]. This is the basis for anti-folate chemotherapy with agents such as methotrexate [16, 17]. Hence, low folate levels resulting in CRC incidence seem counterintuitive. However, folate status seems to have the opposite effect in normal tissue [16,

17]. The loss of methyl donors in response to low folate levels contributes to reduced global methylation seen in CRC tissue [16, 17]. Consequently, these effects contribute to genomic instability, mutagenesis, and aberrant transcriptional control [16, 17].

25 In contrast, folic acid supplementation is thought to maintain genomic integrity in normal tissue, but promote development of established tumors. The main mechanisms associated with this are maintaining nucleotide synthesis, and contributing to tumor suppressor methylation. Two animal studies using dimethyl hydrazine (DMH), a well known carcinogen, showed that while low and superphysiological doses of folate promote colonic neoplastic transformation, moderate levels show a protective affect [164, 165]. Thus, optimal folate dosage is fundamental for determining effective safe levels for humans.

Dietary folate’s ability to influence DNA methylation has been well established in vivo.

A study looking at folate depletion on leukocyte genomic methylation levels in elderly women found that moderate reductions in folate were able to significantly reduce DNA methylation in these cells [166]. A similar study also looking at postmenopausal woman found that reduced folate intake resulted in global hypomethylation in lymphocytes. [167]. Furthermore, folate supplementation seems to be able to significantly increase DNA methylation in vivo. Folic acid supplementation at a recommended dose of 400 µg/day for 3-12 months significantly increased colonic mucosa genomic methylation in CRC patients [168]. Another folic acid supplementation study showed that 400 µg/day increased global genomic methylation in lymphocytes and colonic mucosa in CRC patients [169].

Thus, the evidence suggests a dual role for folate in CRC modulation. While folate supplementation maintains genomic integrity and is protective in normal tissue, it may encourage aberrant site-specific hypermethylation that promotes the progression of established neoplasms.

On the other hand, while folate deficiency results in chromosomal instability and hypermethylation predisposing normal tissue to CRC initiation, it may also inhibit the progression of benign colonic tumors. Although folate fortification has been generally effective

26 at reducing NTD, it may be harmful for subpopulations prone to colon tumor formation in the senior population and individuals with genetic predispositions. Hence, more clinical and epidemiological studies are required to assess the efficacy of folate supplementation and to determine optimal and effective dosage and timing of for safe folate intervention.

27

DNA Methylation Methionine Cycle Folate Cycle Folic Acid

DHFR

DHF

DHFR MAT Cytosine SAM Methionine THF

SHMT

Nucleotide DNMT MS 5,10-Methylene THF Biosynthesis MTHFR

Methyl-cytosine SAH Homocyseine 5-Methyl THF SAHH

Folate

Figure 1. Folic acid metabolism. Schematic showing how folate is involved in DNA methylation and nucleotide biosynthesis in the cell. DHFR: Dihydrofolate reductase, DNMT: DNA methylatransferase, MAT: Methionine adenyltransferase , MS: Methionine synthase, MTHFR: Methylenetetrahydrofolate reductase, SAH: S-Adenosylhomocysteine, SAHH: S-Adenylhomocysteine hydrolase, SAM: S-Adenosylmethionine, SHMT: Serine hydroxymethyltransferase, THF: Tetrahydrofolate

28 RATIONALE

DNA methylation is a genetic regulatory mechanism that serves as a vital component for establishing and maintaining cellular fate, differentiation, and multipotency. Aberrant DNA methylation is a common genetic alteration acquired during CRC development to activate oncogenes, inactivate tumor suppressors, and deregulate cellular maintenance pathways. Altering the supply of methyl donors through folate supplementation may be able to modify CRC cell methylation patterns in a way that may modulate or promote tumorigenesis. This model suggests that increased folate supplementation maintains genomic integrity and methylation in normal tissue, but promotes hypermethylation of tumor suppressors in tumorous tissue. Conversely, while folate deficiency in normal tissue will result in global hypomethylation leading to genomic instability and oncogene activation, in neoplastic tissue, it reactivates hypermethylated tumor suppressors and limits genetic replication.

Objective 1: Assess if folic acid, a synthetic form of folate, can affect CRC cell global and CpG specific DNA methylation levels in a dose response manner.

A subpopulation of cancer cells termed CSC has been shown to posses hallmarks of native stem cells, namely the capacity to self renew and have an undifferentiated state. DNA methylation is an important epigenetic regulator of transcriptional activity in stem cells. Therefore, aberrant

DNA methylation may help lock in the activation of stem cell renewal pathways that contribute to self-renewal and tumorigenesis during CRC development. Varying folic acid concentrations may alter methylation patterns in a way that impacts stem cell phenotype during CRC progression.

29

Objective 2: Characterize the stem cell phenotype of CRC cells following treatment with varying levels of folic acid.

Pursuing these objectives may lead to an enhanced understanding of the response of CRC cells to varying levels of folic acid, and determination of whether folic acid-induced changes to genomic methylation is a mechanism by which CRC cells gain a CSC phenotype and increased tumorigenesis.

30 MATERIALS AND METHODS

A list of suppliers for chemicals and reagents is found in Appendix I, and details of solution preparation are found in Appendix II.

Tissue culture

Human colorectal cancer cell lines HCT116 (ATCC), Caco2 (ATCC), LS174T (ATCC)

PC7 (DLD1 sub line)[170], LIIA (DLD1 Sub line)[170] and a normal rat intestinal cell line,

IEC18 (ATCC) were grown in standard cultured conditions. Cells were maintained in a 37°C humidified incubator with 5% CO2 in single plates, 10 cm in diameter. Standard culture media consisted of RPMI 1640 supplemented with 10% FBS, and 1% gentamycin. Treatment media consisted of folate free RPMI 1640 supplemented with 10% dialyzed FBS, and 1% gentamicin.

Folic acid dissolved in 1M NaOH was added to the treatment media to produce a final concentration of 0 mg/L, 4 mg/L or 16 mg/L. Cells were re-supplemented with media every 1-2 days and harvested after 7 days of growth. Harvesting involved treating the cells with 3 mL of trypsin for ~5 minutes to allow for sufficient detachment. Trypsin was deactivated using equal amounts of standard or treatment media (depending on the sample), and cells were pelleted at

350 x g for 4 minutes. Cell pellets were then stored at -80°C.

Colonosphere formation

Cells were diluted to 50 cells/mL in serum free stem cell media (SCM) media containing

DMEM/F12, 10% B27 supplement, 10 ng/mL fibroblast growth factor, 20 ng/mL epidermal growth factor and 1% gentamycin. Cells were plated in 96 well ultra-low adhesion plates

(Corning) at 10 cells (in 200 uL of cell suspension) per well. Wells were re-supplemented with media every 3-4 days for either 10 days or 20 days. After ten days, colonospheres were collected for protein analysis. Approximately 192 (2 x 96 well plates) colonospheres were collected in a

31 1.5 ml Eppendorf tube and mechanically separated into a single cell suspension. Cells were pelleted at 350 x g for 4 minutes and pellets were stored at -80°C. At the end of 20 days, the number of wells showing formation of colonospheres was counted and the frequency of sphere forming cells in a particular cell type was calculated.

Limiting dilution analysis

Single cell suspensions obtained from adherent derived cells were plated at concentrations of 1, 10 and 100 cells per well (in 200 µl SCM) in 96-well ultra-low adhesion plates and incubated for 20 days with media changes as described above. At the end of 20 days, the number of wells showing formation of colonospheres was counted and the frequency of sphere forming cells in each cell type was calculated.

Colonosphere formation after folate exposure

Single cell suspensions obtained from adherent folic acid treated cells were plated at concentrations of 10 cells per well (in 200 µl SCM) in 96-well ultra-low attachment plates and incubated for 20 days with media changes. At the end of 20 days, the number of wells showing formation of colonospheres was counted and the frequency of sphere forming cells in each cell type was calculated.

Protein isolation and quantification

For extraction of whole cell lysate, 50-400 µl lysis buffer was used to re-suspend pelleted cells. Cell lysate was incubated for 10 minutes on ice before centrifugation at 12,000 x g for 15 minutes at 4°C. Supernatant was separated into 20-100 µl aliquots and stored at -80°C. Protein quantification of cell lysates was assessed for western immunoblotting, as described below.

Protein was quantified using the Bio-Rad DC™ Protein Assay Kit, OD630 values of each sample were standardized to control values for 0.2, 0.5, 1, 2.5, and 5 µg/µl BSA.

32 SDS-PAGE and western immunoblotting

Sample proteins were loaded onto 7.5% polyacrylamide gels and separated by electrophoresis at 125 V in electrophoresis running buffer. Separated sample protein was then transferred onto a methanol-activated polyvinylidene difluoride (PVDF) membrane via wet transfer in wet transfer buffer at 100 V for 2 and one and a half hours. Transfer of proteins onto

PVDF membrane was confirmed by staining with amido black, followed by destaining with methanol and Milli-Q water. Membranes were blocked for 1 hour at room temperature with 5%

(w/v) non-fat milk in Tris-buffered saline/Tween 20 (TBS-T) and incubated overnight at 4°C with primary antibodies diluted in 5% (w/v) non-fat milk or 5% (w/v) BSA in TBS-T.

Primary antibodies and their respective concentrations were as follows: mouse-anti-α- tubulin (1:600,000), mouse-anti-DNMT-1 (1:1,000), rabbit-anti-phospho-β-catenin (1:500), rabbit-anti-β-catenin (1:500), rabbit-anti-Notch-1 (1:1,000), rabbit-anti- EpCAM (1:1,000), rabbit-anti-Lgr5 (1:1,000), and rabbit-anti-β-actin (1:1,000). After incubation with primary antibody, membranes were washed (3 x 10 minutes) with TBS-T and incubated with HRP- labeled goat-anti-mouse or goat-anti-rabbit secondary antibodies (1:20,000) in 5% (w/v) non-fat milk in TBS-T for 1 hour at room temperature. After incubation with secondary antibody, membranes were washed (3 x 10 minutes) with TBS-T. Membranes were then incubated with chemiluminescent HRP substrate Luminata™ Forte and bands were detected using Bio-Rad

ChemiDoc™ XRS+ system. Molecular weight of proteins was determined by comparison with

GeneDirex® BLUeye Prestained Protein Ladder. Densitometric analysis was done using the Bio-

Rad Image Lab™ Software.

33 DNA isolation

Genomic DNA was isolated from folic acid treated HCT116 and SW480 pelleted cells using DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s protocol. The concentration and purity were determined by measuring the absorbance at 230, 260 and 280 nm using a Nonodrop ND-1000 (Thermo Scientific).

DNA methylation quantification

Global DNA quantification was detected using EpiSeeker methylated DNA Colormetric

Quantification Kit (Abcam). Briefly, DNA isolated from folic acid treated HCT116 and SW480 cells (described above) was bound to the assay well and captured by a methyl–cytosine specific antibody. Antibodies were detecting using enhancer solution. After ~10 minutes, absorbance was read on a plate reader at 450 nm. Absolute and relative methyl-cytosine content was then calculated using the supplied formula.

Illumina HumanMethylation450K BeadChip array was carried out by our collaborator,

Dr. Bekim Sadikovic. Briefly, DNA from samples was bisulfite converted and hybridized to an

Infinum Human Methylated450 BeadChip. Hybridized bisulfite treated DNA then underwent a single nucleotide extension conjugated to a fluorophore that distinguished between methylated and unmethylated cytosine residues. The array was read using a BeadArray reader, and the level of methylation was determined by calculating the ratio of fluorescent signals from the methylated versus unmethylated sites. The degree of methylation was then analyzed using

Partek® software program (Partech, Munster, Germany).

34 Immunofluorescence

Monolayer

Cells were grown on sterile Fisherbrand Superfrost® Plus slides for 5 days and fixed for

15 minutes with 4% paraformaldehyde (PFA). After fixation, slides were washed (3 x 5 minutes) with phosphate-buffered saline (PBS). Slides were then incubated for 1 hour at room temperature in blocking buffer containing Triton X-100 to permeabilize the cells. Following blocking, slides were incubated overnight at 4°C in anti-β-catenin (1:50) or E-cadherin (1:50) diluted in antibody dilution buffer or just in antibody dilution buffer for a negative control. Slides were then washed washed (3 x 5 minutes) with PBS and incubated at room temperature with a goat-anti-rabbit secondary antibody conjugated to Cy3®, diluted 1:300 in antibody dilution buffer. Slides were washed (3 x 5 minutes) with PBS and nuclei were stained with 30 µM 4’,6-diamidino-2 phenylindole (DAPI) diluted 1:100 in PBS for 8 minutes at room temperature. Cells were then washed (3 x 5 minutes) with PBS. Upon completion of washes, Dako Fluorescent Mounting

Medium was used to apply coverslips prior to visualization.

Colonospheres

Colonosphere grown cells were analyzed either as intact spheres or mechanically separated into single cell suspensions and cytospun onto a plate. For dispersed preparations, colonospheres were collected in 1.5 mL Eppendorf tubes and mechanically separated into a single cell suspension with a 20-gauge syringe needle. Following centrifugation at 350 x g for 4 minutes, supernatant was removed and cells were re-suspended to a final concentration of

1,000,000 cells/ml in PBS. 100 µl was added to a single cytospin funnel attached to a

Fisherbrand Superfrost® Plus slide. To transfer the cell suspension to the slide, cytospins were performed at 700 x g for 6 minutes using a Shandon Cytospin 3 Cytocentrifuge. Slides were then

35 fixed and stained as previously described.

Intact colonospheres were fixed and permeabilized for 3 hours at 4°C in 4% PFA and 1%

Triton X-100 and washed in PBS (3 x 10 minutes). Colonospheres were then dehydrated in an ascending series of methanol at 4°C in PBS, 25%, 50%, 75%, 95%, 30 minutes each and 100% for 7 hours, then rehydrated in the same descending series. Colonospheres were washed in PBS

(3 x 10 minutes) then incubated overnight at 4°C in blocking buffer. Colonospheres were washed in PBS (3 × 10 minutes) then incubated with primary antibodies as previously described for 48 hours at 4°C. Colonospheres were then washed in PBS (4 x 30 minutes) and incubated in appropriate secondary antibodies as previously described for 24 h. Cell nuclei were stained with

30 µM DAPI diluted 1:100 in PBS for 40 minutes at room temperature. Mounting was carried out on a glass slide and a cover slip, using ring page reinforcement stickers as 'spacers' between them. Colonospheres were re-suspended in PBS and allowed to adhere to Fisherbrand

Superfrost® slides. Dako Fluorescent Mounting Medium was used to apply coverslips prior to visualization, and edges of the coverslip were sealed with nail polish.

Visualization of immunofluorescent slides was done using a Leica Optitech microscope with a QImaging™ QICAM Fast 1394 camera, captured using QCapture Pro™ software, and overlaid using Adobe® Photoshop, or captured with Olympus Fluoview 500™

Confocal Microscope and imaged, processed and merged with Fluoview version 5.0™.

Statistical analysis

Statistical analysis was performed using GraphPad Prism® software. A non-parametric

Kruskal-Wallis test was used to determine if differences existed between treatments and an

ANOVA was used to analyze the Illumina HumanMethylation450K. At least three biological replicates were used for each statistical analysis, and treatments were considered significantly

36 different if statistical tests produced a p-value under 0.01 for the Illumina

HumanMethylation450K array and 0.05 for all other data.

37 RESULTS

Cellular growth

An array of CRC cell lines: HCT116, Caco2, SW480, LS174T, DLD subclones PC7 and

LIIA, and an immortalized rat intestinal cell line: IEC18, were treated with deficient (0 mg/L), standard (4 mg/L), and excessive (16 mg/L) levels of folic acid. All cells treated with deficient folic acid showed impaired growth and proliferation, while both standard and excess folic acid treatments were sufficient to allow cellular proliferation. On the first day of treatment, all cells started off at ~10% confluence. After a period of 7 days, both standard and excessive folic acid treatment groups reached ~100%, while deficient folic acid treated cells could only achieved

~50% confluence on their respective growth dishes (Figure 2). No significant differences in floating cells or cellular morphology were detected which suggests that these differences were strictly due to proliferation rate and not cell death.

DNMT1 protein expression

Before genomic methylation analyses was done, DNMT1 protein expression in all aforementioned folic acid treated cells was assessed to determine if varying folic acid concentrations could influence the levels of DNMT1 protein. Although DNMT1 protein expression in IEC18, HCT116, LS174T, PC7 and LIIA cells showed a slight dose responsive affect associated with folate treatment, only HCT116 deficient vs excessive and LIIA deficient vs standard treatments showed statistically significant differences (p < 0.05). Conversely,

SW480 and Caco2 cells showed an inverse association between folic acid concentration; however, statistical significance was not reached (Figure 3).

38 DNA methylation

To assess if altered folate concentration could alter genomic methylation patterns, genomic DNA was isolated from HCT116 and SW480 folic acid treated cells and assessed for global cytosine DNA methylation changes using ELISA and microarray techniques. The

Illumina 450 beadchip array is a high throughput microarray that analyzes over 450,000 cytosine sites specifically targeted toward CpG islands, shores, and shelves, but also includes transcription start sites, genes bodies, and intergenic regions. Detected by microbead array,

ANOVA analysis shows that folate significantly (p < 0.01) altered DNA methylation in a dose responsive manner. HCT116 and SW480 cells treated with deficient (0 mg/L) folic acid levels had reduced total cytosine DNA methylation, while cells treated with excessive (16 mg/L) folic acid levels showed increased cytosine methylation relative to standard (4 mg/L) treated cells.

Additionally, the changes in cytosine methylation seem to be particularly associated with the

CpG regions, especially in deficient (0 mg/L) folic acid treated SW480 cells (Figure 4).

The Illumina 450 beadchip array also generated a list of genes associated CpG regions and their methylation levels in response to folate treatment. Cytosine residues are either 100%

(fully methylated), or 0% (unmethylated) methylated. Therefore, it is important to note when interpreting the data that methylation percentage is a result of the average of methylation measurements between all DNA molecules in a sample. For example a methylation level of 50% may correspond to half of all cells having 100% methylated and the other half having 100% unmethylated cytosines at that site. Genes associated with CpG regions that had significant (p <

0.01) changes between treatments when analyzed by ANOVA were considered to be folic acid

“hot spots”, or regions that may be susceptible to folic acid-mediated methylation changes. A representative sample of five genes showing significant (p < 0.01) differences in CpG

39 methylation between folic acid treatments is displayed for HCT116 and SW480 cells in Figure 5.

In many cases, deficient folic acid levels reduced CpG methylation, while excessive levels either increased or maintained methylation compared to their baseline methylation status in standard folic acid levels.

Global cytosine methylation was then assessed using an ELISA based kit (EpiSeeker methylated DNA Quantification Kit, Abcam). In this method, the DNA is tagged with methyl- cytosine specific enzyme conjugated antibodies, which is then colorimetrically quantified to detect global methyl cytosine levels. Although slight changes in global methyl cytosine content were seen, no significant (p < 0.05) differences were detected between any treatments (Figure 6).

Colonosphere formation

Cell lines were assessed for their ability to be grown as colonospheres in suspension supplemented with SCM (Figure 7). All cell lines showed a high ability for colonosphere formation except for IEC18 and Caco2. IEC18 cells failed to form colonospheres even at relatively high cellular densities (1000 cells/well) while Caco2 colonospheres were small, slow growing and short lived.

The ability of cells to form colonospheres following folic acid treatment was slightly reduced in all deficient-treated cells but was only significantly reduced in HCT116 (Figure 8).

Standard and excessive folate treatments maintained high colonosphere formation capability in all cell lines tested (Figure 8).

Colonosphere characterization

Colonosphere forming cells were subsequently assessed for Wnt and Notch pathway activation as surrogate markers for stem cell phenotype. On the fifth day of colonosphere cultivation, HCT116 cells showed greater β-catenin nuclear localization (Figure 9) and reduced

40 phospho-β-catenin (Figure 10) levels compared to monolayer HCT116 cells. These results suggest that the Wnt pathway is active in colonosphere-derived cells. However, on the tenth day of colonosphere cultivation, HCT116 cells showed heterogeneous β-catenin nuclear localization

(Figure 11) and increased overall phospho-β-catenin levels (Figure 12), suggesting downregulation of the Wnt pathway. Additionally, full length (inactive) Notch protein expression was increased while cleaved (active) Notch protein expression was reduced in ten day-old colonosphere-derived cells compared to monolayer cells (Figure 13). The expression of stem cell surface marker Lgr5 does not seem to alter between monolayer and colonosphere- derived cells on day ten of cultivation (Figure 12). Thus, colonosphere-derived HCT116 cells show a transient stem cell phenotype after 5 days of colonosphere cultivation that is replaced by a more differentiated phenotype at ten days.

SW480 cells showed increased expression of β-catenin, phospho-β-catenin (Figure 12), and cleaved Notch (Figure 13) on day ten of colonosphere cultivation compared to monolayer.

Additionally, immunofluorescencent analysis showed that β-catenin nuclear localization was very strong in both monolayer and colonosphere-derived cells (Figure 11). Altogether, this indicates the activation of both Notch and Wnt pathway in these cells. Lgr5 expression was reduced in ten day-old colonosphere-derived cells compared to monolayer (Figure 12).

Altogether, these data suggest a high CSC phenotype in both colonosphere and monolayer grown

SW480 cells.

In both HCT116 and SW480 cells, DNMT1 protein expression was increased compared to monolayer grown cells (Figure 14).

41 0 mg/L 4 mg/L 16 mg/L

Day 1

100 µm

Day 7

Figure 2. Effect of folic acid treatment on HCT116 cellular proliferation. HCT116 cells treated with a deficient level of folic acid (0 mg/L) show significantly decreased proliferative ability after 7 days. However, cells treated with both standard (4 mg/L) and excessive (16 mg/L) levels of folate showed sufficient proliferation.

42 IEC18 HCT116 SW480 Folic acid (mg/L)

4 - 0 mg/L 4 mg/L 3- 16 mg/L * 2 -

1 - (4 mg/L) DNMT1 levels DNMT1 levels (4 mg/L) * Densitometry normalized to standard standard to normalized Densitometry 0 - DNMT1 (180 kDa)

α-Tubulin (55kDa)

Caco2 LS174T PC7 LIIA

3-

2 -

* * 1 -

(4 mg/L) DNMT1 levels DNMT1 levels (4 mg/L) * *

Densitometry normalized to standard standard to normalized Densitometry 0 - DNMT1 (180 kDa)

α-Tubulin (55kDa)

Figure 3. DNMT1 protein expression following folic acid treatment. IEC18, HCT116, SW480, Caco2, LS174T, PC7, and LIIA cells were treated with 0, 4, or 16 mg/L of folic acid and assessed for DNMT1 protein expression using western blot analysis. Tubulin was used to ensure equal loading of total protein. Significance was detected with three biological replicates. IEC18, HCT116, LS174T, PC7, and LIIA cells show a dose dependent response to folate treatment, while SW480 and Caco2 cells show an inverse response. Only HCT116 and LIIA show significant responses (p < 0.05) indicated by the * and ** respectively, determined using a non-parametric Kruskal-Wallis test; N = 3.

43 HCT116 SW480 Total 0 mg/L 4 mg/L 16 mg/L 0 mg/L 4 mg/L 16 mg/L CpG Islands 0.02 - Non-CpG Islands

0.00 -

-0.02 - relative to standard standard to relative (4 mg/L) folic acid levels levels acid folic (4 mg/L) Cytosine methylation (%) methylation Cytosine

-0.04 - Figure 4. Global changes to cytosine methylation in response to folate treatment as detected by Illumina HumanMethylation450K BeadChip Array. HCT116 and SW480 cells were treated with 0, 4, and 16 mg/L of folic acid for 7 days then assessed for changes to DNA methylation. Standard (4 mg/L) folic acid levels were used as the baseline and deficient (0 mg/L) and excessive (16 mg/L) folic acid levels were compared to this baseline. Within the context of the over 450,000 sites assayed, global DNA methylation changed according to a dose response with folate levels in both cell lines (p < 0.01) N = 4.

44 HCT116 SW480 POU4F1– Chr 13 FAM184B – Chr 4 100 100

50 50

0 0 KAL1– Chr X HIC1 – Chr 17 100 100

50 50

0 0 SLC26A4– Chr 7 SPON2– Chr 4 100 100

50 50

0 0 Hand1– Chr 5 ARHGAP2 – Chr 10 100 100

50 50

0 0 GAD1– Chr 2 RAB38– Chr 11 100 100

50 50

0 0 Legend HCT116 – 0 mg/L HCT116 –4 mg/L HCT116 – 16 mg/L CpG Region SW480 – 0 mg/L SW480 – 4 mg/L SW480 – 16 mg/L Figure 5. Site specific changes to cytosine methylation in response to folate treatment as detected by Illumina HumanMethylation450K BeadChip Array. HCT116 and SW480 cells were treated with 0, 4, and 16 mg/L of folic acid for 7 days then assessed for changes to DNA methylation. A sample of five genes showing significant (p < 0.01) changes to methylation level in response to folic acid treatment in HCT116 (left column) and SW480 (right column) cells is shown above. The y-axis indicates the methylation level (%) and the CpG region of the gene is highlighted by the grey box. Deficient folate levels reduced CpG methylation, while excessive folate levels increased CpG methylation. All genes were analyzed using an ANOVA to detect significant methylation changes between treatments; N=4.

45 HCT116 SW480 Folic acid (mg/L)

0 mg/L 4 mg/L 0.1 - 16 mg/L

0.0 -

relative to standard standard to relative -0.1 - (4 mg/L) folic acid levels levels acid folic (4 mg/L) Cytosine methylation (%) methylation Cytosine

Figure 6. Global changes to cytosine methylation in response to folate treatment as detected by EpiSeeker methylated DNA Quantification Kit (Abcam). HCT116 and SW480 cells were treated with 0, 4, and 16 mg/L of folic acid for 7 days then assessed for changes to DNA methylation. Standard (4 mg/L) folic acid levels were used as the baseline and deficient (0 mg/L) and excessive (16 mg/L) folic acid levels were compared to this baseline. Global DNA methylation did not show any significant changes associated with folic acid treatment (p > 0.05), determined using a non-parametric Kruskal-Wallis test; N = 3.

46 IEC18 Caco2 HCT116

Monolayer

100 µm

No Colonosphere Colonosphere formation

SW480 LS174T LIIA PC7

Monolayer

Colonosphere

Figure 7. Colonosphere formation in vitro. Representative photographs showing established human colorectal cancer cell lines Caco2, HCT116, SW480, LS174T, LIIA (DLD-1 subclone), PC7 (DLD-1 subclone), and rat intestinal epithelial cell line IEC18. Cells were plated under colonosphere conditions at a concentration of 10 cells per well in suspension and grown for 10 days. IEC18 cells fail to survive in suspension, while all cancer cell lines proliferated and formed colonospheres to some degree.

47 HCT116 SW480 100 - HCT116 SW480

colonosphere 50 - formation (%) formation Wells positive for positive Wells

0- 1 10 100 1 10 100 Cells per well

Figure 8. Limiting Dilution Analysis. Cells were plated at 1, 10 and 100 cells per well (in 200 µl SCM) in 96-well ultra-low adhesion plates. After 20 days the number of wells showing colonosphere formation was counted. Cells plated at higher densities had higher colonosphere yields. However no significant differences were detected between cellular concentrations (p > 0.05), determined using a non-parametric Kruskal-Wallis test; N = 3 for HCT116 and N = 2 for SW480.

48 HCT116 SW480 Folic acid (mg/L) 100 - *

0 mg/L

50 - 4 mg/L

formation (%) formation * 16 mg/L

0- LS174T PC7 LIIA 100 - colonosphere

50 - Wells positive for positive Wells 0- Figure 9. Colonosphere formation assay. Cells were treated with 0, 4, and 16 mg/L of folic acid for 7 days then passaged in suspension culture at 10 cells per well. Deficient folic acid concentrations subsequently produced a lower yield of colonospheres, while cells treated with excessive folic acid concentration showed no significant difference in colonosphere forming potential compared to standard treatment. Only HCT116 cells showed significant (p < 0.05) differences in colonosphere formation in response to folate treatment indicated by *, determined using a non-parametric Kruskal-Wallis test; N = 3.

49 DAPI β-catenin Merge

Monolayer

Colonosphere

Figure 10. HCT116 colonosphere β-catenin localization. Immunofluorescence comparing β- catenin localization in HCT116 cells grown in monolayer and colonosphere suspension for 5 days. Cells grown in suspension show increased β-catenin nuclear localization, while cells grown in monolayer show increased membrane associated β-catenin.

50 5 Days 10 Days Protein Monolayer Colonosphere Monolayer Colonosphere kDa

Phospho- 92 β-catenin

β-catenin 92

β-actin 45

Figure 11. HCT116 colonosphere β-catenin phosphorylation. Western blot showing β-catenin phosphorylation in HCT116 cells grown in monolayer and colonosphere suspension for 5 and 10 days. Cells grown in suspension for 5 days show reduced β-catenin phosphorylation while 10 day-old colonospheres show increased β-catenin phosphorylation compared to monolayer grown cells.

51 DAPI β-catenin E-Cadherin Merge

Monolayer

HCT116

Colonosphere

Monolayer

SW480

Colonosphere

Figure 12. Day 10 colonosphere β-catenin localization. Immunofluorescence comparing β- catenin and E-cadherin localization in HCT116 and SW480 cells grown in monolayer and colonosphere suspension for 10 days. HCT116 cells grown in suspension show marginal increases in nuclear localization while no change in observed in SW480 cells.

52 HCT116 SW480 Protein Monolayer Colonosphere Monolayer Colonosphere kDa

Lgr5 100

β-actin 45

Phospho- 92 β-catenin

β-catenin 92

β-actin 45

Figure 13. Wnt pathway activation in 10 day-old SW480 and HCT116 colonospheres. Western blot showing β-catenin phosphorylation and Lgr5 protein expression in SW480 and HCT116 cells grown in monolayer and colonosphere suspension for 10 days. HCT116 and SW480 cells both showed increased β-catenin phosphorylation when grown as colonospheres compared to those grown in monolayer. Lgr5 expression was reduced in SW480 colonosphere grown cells while no significant change was seen in HCT116 cells.

53 HCT116 SW480 Protein Monolayer Colonosphere Monolayer Colonosphere kDa

NFL 300

NTD 120

β-actin 45

Figure 14. Notch pathway activation in 10 day-old SW480 and HCT116 colonospheres. Western blot showing full length (NFL) and cleaved (NTD) Notch protein in SW480 and HCT116 cells grown in monolayer and colonosphere suspension for 10 days. Levels of active Notch (NTD) were reduced in HCT116 colonosphere grown cells, but there was no change observed in colonosphere grown SW480 cells compared to monolayer grown cells.

54 HCT116 SW480 Protein Monolayer Colonosphere Monolayer Colonosphere kDa

DNMT1 180

β-actin 45

Figure 15. DNMT protein expression in 10 day-old SW480 and HCT116 colonospheres. Western blot showing DNMT1 protein expression in SW480 and HCT116 cells grown in monolayer and colonosphere suspension for 10 days. DNMT1 protein expression was increased in colonosphere grown HCT116 and SW480 cells compared to monolayer grown cells

55 DISCUSSION

Findings regarding folate status and cytosine methylation in vivo have been contradictory and inconclusive [16, 171]. Elucidating the influence of folate levels on DNA methylation and cellular transcription is necessary for definitively outlining a mechanism involving folate and

CRC risk. In the present investigation, I show that different levels of folate can significantly alter

CRC cell DNMT1 protein expression, cytosine methylation, and cell growth in suspension, a

CSC property.

Folate is necessary for nucleotide biosynthesis in dividing cells. When folate is taken into the cell it is converted to 5,10-MTHF, which is subsequently used as a substrate for de novo synthesis of thymidine and purines [171]. Inadequate folate levels, especially in rapidly dividing cells, may result in inefficient DNA replication and impaired cellular proliferation [171].

Additionally, reduced folate concentrations can result in reduced methyl donor substrates, leading to genomic hypomethylation [171]. In cancer cells, this may mean transcriptional activation of tumor suppressor genes involved in cell cycle regulation and reduced cellular proliferation [171].

Here, I show that deficient folic acid treatment (0 mg/L) resulted in reduced cellular proliferation in all CRC cells tested, while standard (4 mg/L) and excessive (16 mg/L) folic acid treatments maintained cellular proliferation. These results are consistent with a previous report depriving HCT116 and Caco2 cells of folic acid [172]. In that study, the authors report that in

HCT116 cells, folate deficiency altered transcriptional expression to favor sustained methyl donor levels such as SAM over nucleotide biosynthesis [172]. In contrast, Caco2 cells shuttled folate pools for nucleotide biosynthesis [172]. Therefore, in the presence of folate deficiency, cells may favor conserving methylation patterns or genetic integrity. In the former case,

56 reduction in nucleotide precursors results in inefficient DNA replication and impaired cellular division. However, one study looking at HCT116 and Caco2 cells showed a significant inverse relationship between cellular proliferation and increased levels of folate supplementation [173].

One explanation for this discrepancy may be the differences in basal folate levels. While the previous study used 1 mg/L folic acid as the lowest dose, my study used 0 mg/L. The extreme jump in dosage (between 0 and 4 mg/L), as well as the limited range of doses used in my study may have potentially shielded any inverse association between the thresholds of 0 and 4 mg/L.

The ability of folate to modulate cellular proliferation seems to be consistent with in vivo observations showing that increased folate regulates mucosal cell proliferation in ulcerative colitis patients [174].

DNMTs are responsible for establishing and maintaining cellular DNA methylation patterns. Changes in the activity and expression of any of these enzymes have been associated with aberrant DNA methylation and transcriptional control [110, 175]. DNMT1 usually shows constitutively low levels in normal resting cells, but its expression can be modified by different cell signaling pathways [176-178]. I have shown that altered folic acid exposure can influence

DNMT1 protein expression in CRC cells. In some cells, deficient folic acid treatment results in reduced DNMT1 protein expression, while in others, DNMT1 protein expression is increased.

Conversely, in some cells, excessive folic acid exposure results in increased DNMT1 protein expression, while in others, it was reduced. Few studies have reported this differential dose response with folic acid and DNMT protein expression and currently, little is known about the direct influence of folate or any of its derivatives on DNMT1 expression or activity [172, 179-

181]. However, one investigation that saw significantly increased DNMT3a and 3b expression in glioma cells following 4 mg/L of folic acid supplementation also found a simultaneous increased

57 recruitment of Sp1 and Sp3 transcription factors to the DNMT’s promoters [180]. They suggest that this is the mechanism by which folic acid mediates its effects on DNMT protein expression

[180].

Another explanation may be related to the synchronous regulation of DNMT1 with the cell cycle. DNMT1 is primarily expressed during S phase of the cell cycle where it interacts with the replication fork to maintain genomic methylation patterns in newly synthesized daughter strands of DNA [182]. Reduction in nucleotide biosynthesis due to reduced folate concentrations may have resulted in inefficient DNA synthesis and cell cycle arrest in my cultured cells.

Ultimately, DNMT1 proteins are no longer required in arrested cells and their expression may be reduced. The contrasting expression profiles seen between cell lines may be a result of differential DNMT1 transcriptional regulation due to differential expression and activity of cell cycle DNMT1 regulators, specifically RAS, Rb, and p53.

Briefly, the RAS-AP-1 pathway is known to promote DNMT1 protein expression during the S-phase of the cell cycle [178, 183-185]. The DNMT1 gene encompasses three c-Jun enhancers and multiple AP-1 sites [178, 183-185]. Overexpression of RAS induces a protein kinase cascade that in turn activates AP-1 proteins such as c-Fos/c-June, which results in the transcriptional stimulation of the DNMT1 promoter [178, 183-185]. This may explain why cells harboring KRAS hyperactivating mutations (HCT116, SW480, PC7, LIIA, LS174T) [186] have relatively higher DNMT1 protein levels compared to KRAS wild type expressing cells (Caco2,

IEC18) [186, 187].

Rb/E2F is another well-recognized DMNT1 control pathway that intricately synchronizes

DNMT1 transcription with cell cycle control [177, 188]. During G1 phase of the cell cycle, Rb binds E2F and prevents the transcription of DNMT1 [177, 188]. During S phase, cyclin

58 dependent kinases (CDKs) phosphorylate and disrupt Rb and E2F binding, allowing E2F to act as a transcription factor for DNMT1 protein expression [177, 188]. Dephosphorylation of Rb in the G2 phase subsequently inactivates the DNMT1 gene [177, 188]. Altered DNA replication efficiency in response to folic acid treatment may have an effect on cell cycle progression mediated through Rb activation, which subsequently modulates DNMT1 protein expression. This may explain the dose response of DNMT1 protein expression to folic acid observed in HCT116,

LIIA, PC7, IEC18 and LS174T (Figure 3).

Lastly, p53 colocalizes with Sp1 to consensus sequences within the DNMT1 gene promoter, inhibiting its expression [176, 189]. p53 promoter binding and transcriptional inhibition of DNMT1 seems to be dependent on the presence of DNA damage, posttranslational modifications, and relative co-expression of Sp1 [176, 189]. Differential expression and activation of p53 may explain the differential responses to folate treatment shown by differences in DNMT1 protein expression between cell lines. In p53 wild type HCT116 [186], DNMT1 protein expression shows significant dose response change (p < 0.05) in response to folate treatment, while most p53 deficient cells (SW480, Caco2, PC7) [186] showed no significant effect. In contrast, some of these cells showed a suggestive inverse dose response (SW480,

Caco2), however the effects were not considered significant. These results suggest that folic acid can modulate DNMT1 protein levels in some human colorectal cancer cells, and its ability to do so may depend on the expression and activity of cell cycle regulatory proteins.

By altering methyl substrate quantity, varying folate levels are assumed to alter the degree of DNA methylation and, subsequently, transcriptional control. Reduced folate levels result in reduced SAM production, which reduces the cell's capacity to methylate DNA [171].

On the other hand, high folate levels resulting in an over abundance of methyl donors may

59 facilitate the superfluous methylation of the genome, leading to inadvertent transcriptional control [171]. These same aberrant methylation patterns, suggested to be achievable through changes in folate intake, are common alterations seen in the colon mucosa during CRC development. Firstly, global DNA hypomethylation is a known ubiquitous feature in carcinogenesis, especially in CRC [190]. The hypomethylation of highly repeated sequences such as LINE-1 elements are responsible for a large portion of this hypomethylation [190], although the hypomethylation of transcriptional elements has also been reported. A group comparing patient CRC tissue with matched normal mucosa showed that the hypomethylation of

LINE-1 elements within and upstream of MET, RAB3IP, and CHRM3 proto-oncogene sequences led to their subsequent expression in CRC tissue [191]. Site-specific hypomethylation has also been reported. The CDH3 gene becomes hypomethylated early in CRC development, which results in its ectopic expression, which promotes crypt dysplasia [141].

Here, I show that deficient folate concentrations were able to reduce site specific and, to some extent, global cytosine methylation in HCT116 and SW480 cells. Significant reduction in cytosine methylation was seen in the over 450,000 sites assayed between deficient and standard treatments. Using the ELISA based approach, I also saw slightly reduced global methylation in deficient treated HCT116 and SW480 cells, but the results were not statistically significant.

These differences may be associated with the fact that the greatest change to methylation seems to be specifically associated with the CpG island regions, especially in SW480 cells. Therefore, global analyses that evaluate all cytosines may not be sensitive enough to detect these changes.

Such discrepancies may be why previous studies have failed to significantly detect folate- mediated hypomethylation in HCT116 and SW480 cells [172, 192].

60 Cytosine hypermethylation, specifically within transcriptionally relevant CpG regions, is another common alteration during CRC development. One study that looked at DNA methylation using the 450K array showed that, compared to normal colonic tissue, HCT116 cells have 97.7% increase in DNA methylation within CpG regions [193]. This alteration can result in the inactivation of tumor suppressors and apoptotic control mechanisms. Recently, Gen et al. described a novel CRC tumor suppressor gene, ZIC1, which is hypermethylated and downregulated in cancer cell lines as well as primary CRC tissue compered to normal mucosa

[194]. The authors also showed that ectopic expression of ZIC1 suppresses CRC cell proliferation and induces apoptosis [194]. Currently, the list of commonly hypermethylated tumor suppressors include genes coding for CDK inhibitors (p16INK4a, p15INK4b, Rb, p14ARF), DNA repair proteins (BRCA1, hMLH1, MGMT), carcinogen-metabolism enzymes

(GSTP1), cell-adherence proteins (CDH1, CDH13), and apoptosis proteins (DAPK, TMS1)

[195].

My results show that excessive folic acid can induce significantly increased DNA methylation within the over 450,000 sites analyzed in the methylation array. These results suggest that folate can increase CpG methylation, which agrees with the current literature. A study looking at 31 confirmed CRC patients saw that 400 µg/day of folic acid supplementation subsequently showed a 25% increase in mucosal methylation [169]. In vitro, supra-physiological levels of folate were able to induce both p16 and ESR1 hypermethylation [196].

The Illumina HumanMethylation450 BeadChip array, which has a selective bias towards

CpG islands, shores and shelves, showed significant methylation changes following folate treatment. However, global analysis of cytosine methylation via ELISA showed no significant response. This suggests that folic acid-mediated changes to DNA methylation seem to be

61 primarily targeted towards cytosine residues associated with CpG island regions. These are usually associated with genetic promoters and are involved in genetic control. The Illumina

HumanMethylation450 BeadChip array analysis generated a list of genes that showed significant

(>10%) changes to CpG associated methylation. Many of these genes, such as SPON2, POU4F1,

SLC26A4, HAND1, and GAD1, have not been fully characterized, but their increased methylation and reduced expression have been associated with cancer development [197-202].

Others, such as RAB38 and ARHGAP2, have been linked to oncogenic pathways [203, 204].

The array results also show that tumor suppressors KAL1 and HIC1 are hypermethylated in folate-supplemented cells. KALI is a surface glycoprotein, reduced in prostate tumor cells, that has been shown to suppress metastasis in prostate cancer cells [205, 206]. HIC1 is a zinc-finger transcription factor ubiquitously expressed in normal tissue, but hypermethylated and underexpressed in tumor tissue [207]. Although I did see significantly (p < 0.003) reduced CpG island methylation within the BRCA1 gene in deficiently treated HCT116 cells compared to excessively treated cells (data not shown), no other commonly hypermethylated tumor suppressors were significantly changed. This could be a result of the length of time that the cells were treated. Cells treated for a greater period of time may eventually develop aberrant methylation in these regions. My data suggest that altered folate levels can influence CpG methylation patterns that may promote tumorigenesis, however further validation is necessary to elucidate if changes to methylation are, in fact, leading to changes in gene transcription.

Sphere formation from a single cell in suspension is a hallmark of CSC, and has been successfully used to expand CRC CSC in vitro [71, 81, 88, 208]. In these cultures, the presence of growth factors such as fibroblast growth factor and epidermal growth factor promote the expansion of the stem cell population, while the non-adherent conditions limit the survival of the

62 more differentiated cells [71, 84, 88]. In-depth investigation has provided substantial evidence that suspension cultures can efficiently maintain undifferentiated cells in vitro. Kanwar et al. previously showed that colonosphere-derived HCT116 cells have increased CSC surface marker

CD44 expression, increased Wnt pathway activation, and an increased ability to form subsequent colonospheres compared to the parent monolayer cultures [209]. Additionally, these cells were shown to have reduced alkaline phosphatase activity, a marker of differentiation, and increased

ABCG2 transporter protein expression, resulting in an increased efflux of H33342, suggesting that these cells can exclude drugs, a phenomenon possessed by CSC [209].

Here, I show that all CRC cell lines screened have the ability to form colonospheres from single cells in culture indicating that these cells show stem-like phenotypes in vitro. In contrast, rat IEC18 cells, which are an immortalized normal intestinal cell line, failed to form spheres in culture, even at the relatively large concentration of 1000 cells/well. The capacity to form colonospheres in my CRC cell lines was dependent on the number of cells plated, which may indicate that not every cell within these cultures share the CSC phenotype. However, all cancer cell lines had a colonosphere forming potential that was much greater than what is seen in most primary tumors, which may be indicative of a greater self-renewal potential necessary for immortalization in culture [208, 210, 211]. Previous reports endorse this view, showing that

CRC cell lines in general have a relatively high CSC population [208, 210, 211].

Here, I show that HCT116 cells cultured in deficient folate levels showed a significantly reduced ability to form colonospheres. Potentially, this may be a result of altered transcriptional expression in response to folate-modulated hypomethylation that promotes a cellular differentiated phenotype. However, additional analysis is required to assess significantly modified genes that may contribute to this result.

63 It has been well established that colon CSC utilize analogous ISC pathways to maintain their self-renewal and undifferentiated state [46, 212]. Thus, I assessed colonosphere-derived cells for Wnt and Notch pathway activation. HCT116 colonospheres cultured for 5 days showed reduced β-catenin phosphorylation and increased β catenin nuclear localization. These results have been previously established [209]. However, at ten days, β-catenin phosphorylation increases and nuclear localization becomes more heterogeneous. Contrary to previous studies, I found the Wnt target protein Lgr5 was not significantly increased in the colonosphere derived cells after ten days. Hirsch et al. showed that loss of Lgr5 in SW480 cells resulted in reduced stem like properties including colonosphere formation [213]. Han et al. showed that HCT116 colonosphere grown cells have increased Lgr5 expression compared to monolayer grown cells and that this expression was correlate with both stem like properties and an epithelial to mesenchyme transition phenotype [214]. As well, Notch activation seems to be reduced compared to monolayer. Thus, colonosphere derived HCT116 cells seem to transiently express a stem cell phenotype at the beginning of colonosphere formation that diminishes as the colonosphere proliferates. This may be due to reduced availability of nutrients and growth factors to centrally located cells leading to induced differentiation. It has been previously shown, using immunofluorescent staining for Ki-67, a marker for dividing cells, that the most rapidly dividing cells in HCT116 colonosphere cultures are located at the periphery [211]. This may be an indication that the CSC phenotype observed in HCT116 cells is plastic and reliant on microenvironmental signals [208, 215]. Similar results with HCT116 cells have been reported

[208]. In contrast, SW480 cells have high activation of Wnt and Notch in both monolayer and colonosphere derived cells indicating a very potent intrinsic CSC phenotype. In both cells lines,

DNMT1 protein expression was significantly increased in colonosphere-grown cells. Increased

64 DNMT1 expression has previously been associated with poor tumor differentiation in gastric cancer [216]. Increased DNMT1 protein expression may be necessary to maintain CSC phenotypes in colonosphere grown cells. In summary, HCT116 and SW480 cells grown in suspension and supplemented with stem cell growth factors show a stem-like phenotype, which is in agreement with the current literature. However, HCT116 cells only show a transient stem like phenotype that diminishes over time.

65 Implications and Future Directions

Inconsistency in the literature has consequently led to the accumulation of conflicting reports regarding folate status and CRC development. Although folate or folic acid are widely regarded as a preventative agent against CRC, some have argued that increased folate status may support the development of pre-existing tumors [157]. In this investigation, I have attempted to elucidate folic acid’s role in tumorigenesis by studying its effects on cytosine methylation. I have shown that folic acid is necessary for CRC cell proliferation in vitro. This response is most likely a result of folic acid's fundamental role in nucleotide biosynthesis and efficient DNA synthesis.

These data suggest that excess dietary folate levels may be beneficial for tumor survival and growth. However, more in vivo studies are necessary to confidently ascertain this response.

Additional analysis into the response of folate intermediates to folic acid supplementation is also necessary. By analyzing changes to SAM and SAH levels, we can deduce how folic acid is modulating its cellular effects.

Using high throughput genomic analysis, I have shown for the first time that varying levels of folic acid can significantly modulate DNMT1 protein expression and cytosine methylation, in a dose responsive manner in CRC cells in vitro. Specifically, changes to DNA methylation seem to be associated with CpG island regions involved in transcriptional regulation. Several genes associated with significant changes to CpG island methylation as a response to folate levels have been shown to have tumor suppressor activity, or in some way have aberrant expression during cancer progression. Additional work such as qRT-PCR and western blot analyses are necessary to validate the significance of these methylation changes, however the implications are that varying folic acid concentration can potentially influence genomic expression in CRC cells. Depending on its capacity to do so, folic acid may be

66 promoting expression profiles suitable for CRC progression and maintenance. Future investigation may be focused on providing more applicable models that can benefit from such a high throughput methylation analysis. For example, in vivo studies of primary CRC xenografted into SCID/NOD mice subjected to varying levels of folate in their diet would subsequently allow assessment of tumor response, in a more applicable microenvironment, to folic acid exposure.

I have also shown that folic acid exposure impacted colonosphere-forming potential in

HCT116 cells. These results indicate that folic aced may be influencing the CSC phenotype in this cell line, potentially through modulating cytosine methylation. Further analysis and validation of the methylation profiles seen in the treated cells is required to support this hypothesis. A follow up experiment could be designed to assess the expression of putative CSC surface markers in response to folate treatments.

Altogether, my findings suggest that ample folic acid is required for CRC proliferation, cytosine methylation and in some cells, maintaining a CSC phenotype. My research showed that deficient folic acid impairs CRC cancer cell proliferation, but does not necessarily show that increased folic acid aggravated it. The purpose of this investigation is not to undermine epidemiological claims that dietary folate serves a protective role in CRC development. Instead,

I wish to highlight the complexity of folate’s role in tumorigenesis, and suggest that high administration to susceptible individuals at certain time frames may accelerate growth of established neoplasms. Future projects should focus on characterizing folate’s effects on neoplastic tissue in vivo.

67 Limitations

Following treatment, deficient (0 mg/L) treated cells showed reduced confluence compared to standard and excessive folic acid treated cells. However, these differences were not definitively determined to be as a result of reduced proliferation or increased cell death. This could have been assessed by cytolysis assay using Trypan blue to analyze membrane integrity in the cells, by mitochondrial activity assays that use active metabolism as a surrogate for cellular viability, or by analyzing caspase cleavage via western blot analysis.

A direct association between folic acid level and the various effects noted here could not be established since I did not analyze changes that occurred in the folate cycle. Particular substrates of interest would have been SAM, SAH, MTHFR and 5,10-methTHF. Identifying downstream effects on these substrates in response to altered folate levels may have highlighted potential mechanisms explaining the results to DNA methylation, cellular proliferation, and colonosphere formation.

My results clearly indicate that folic acid significantly altered DNA methylation in a dose responsive manner; however, additional analysis is necessary to validate that these methylation changes are leading to transcriptional alterations. Without qRT-PCR or western blot analysis, I cannot confirm that methylation changes observed in these cells are having any significant affect on gene expression.

Finally, I saw that colonosphere-forming cells showed increased CSC pathway activation at five days of cultivation that reversed at day ten. This may be due to a plastic CSC phenotype in HCT116 cells that is expressed in initial CSC culture but becomes partly differentiated over time. Reduced cellular proliferation and reduced colonosphere-forming ability

68 may be a result of increased tumor suppressor and cellular differentiation protein expression in the deficient treated cell.

69 SUMMARY AND CONCLUSIONS

The purpose for this study was primarily to determine if folic acid has the ability to modulate cytosine methylation in CRC cell line models in vitro. In light of this outcome, my second objective was to investigate how and if these epigenetic alteration translated to the CSC phenotype in these cells. Deficient (0 mg/L) folic acid concentration reduced cellular proliferation, while standard (4 mg/L) and excessive (16 mg/L) folic acid concentrations sustained high cellular proliferation rates for 7 days. This response may be modulated in part by folate’s role in nucleotide biosynthesis; without folate, cells are deprived of DNA monomers and are limited in their proliferative capacity.

DNMT1 is the maintenance methyltransferase responsible for maintaining DNA methylation throughout subsequent cellular divisions in daughter cells. DNMT1 protein expression showed a dose responsive effect to folic acid treatment that was significant for

HCT116 and LIIA cells. Although not statistically significant, an inverse dose response was seen with SW480 and Caco2 cells. Since DNMT1 expression is synchronous with cell cycle, this effect may be a consequence of the reduced cellular proliferation seen in deficient treated cells.

Additionally, the distinct responses between cell lines may be a result of differential expression and activity of the oncogenes and tumor suppressors, particularly RAS Rb, and p53, which are involved in syncing DNMT1 expression with S phase of the cell cycle. Conversely, folic acid or any of its derivatives may be intimately involved in DNMT1 protein expression so as to synchronize DNA methylation with high methyl substrate levels. However, further investigation is necessary to elucidate the mechanism involved to confirm this pathway as a possibility.

Cytosine methylation significantly changed in HCT116 and SW480 cells as a result of varying folic acid levels. After 7 days, deficient (0 mg/L) and excessive (16 mg/L) folic acid

70 treated cells had reduced and increased levels of cytosine methylation, respectively. The responses were significant using the Illumina HumanMethylation450K array and not significant using a global methyl cytosine ELISA. This discrepancy may be a result of the sensitivity difference between the two assays. While the ELISA analyzes all potential cytosine residues in the genome, the Illumina HumanMethylation450K array is enriched in CpG regions involved in transcriptional regulation. This suggests folic acid induced methylation changes may be targeted toward CpG regions, which have greater influence on genetic expression. Various significantly effected CpG regions detected in the array are associated with genes that are potential oncogenes and tumor suppressors.

Finally, folic acid concentrations significantly influenced the ability of HCT116 cells to grow in suspension and generate colonospheres. The capacity to proliferate in suspension supplemented with SCM is a defining characteristic of CSC. Deficient (0 mg/L) folic acid levels significantly impaired HCT116 cells from growing under these conditions while standard (4 mg/L) and excessive (16 mg/L) folic acid concentrations conserved this ability. This may represent an underlying change in genetic expression as a result of altered genetic methylation from varying folate levels. However, additional validation at the transcriptional level is necessary to confirm this. The colonospheres grown in suspension seem to upregulate Wnt pathway activation at five days and then show a subsequent moderation in Wnt, and Notch pathways at day ten. It seems as though HCT116 and SW480 cells transiently upregulate CSC pathways at the beginning of colonosphere growth, then acquire a more heterogeneous expression as the colonosphere enlarges.

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86 APPENDIX I – CHEMICAL LIST AND SUPPLIERS

0.5 M Tris-HCl Buffer pH 6.8 BioRad, Mississauga, ON, Canada 1.5 M Tris-HCl Buffer pH 8.8 BioRad, Mississauga, ON, Canada 4% Paraformaldehyde USB Corporation, Cleveland, OH, USA 40% Acrylamide/Bis solution BioRad, Mississauga, ON, Canada Amido black BioRad, Mississauga, ON, Canada Aprotonin Sigma-Aldrich, Oakville, ON, Canada APS BioRad, Mississauga, ON, Canada B27 Supplement Life Technologies, Burlington, ON, Canada Bio-Rad DC™ Protein Assay Kit BioRad, Mississauga, ON, Canada BLUeye prestained protein ladder FroggaBio, Toronto, ON, Canada BSA Santa Cruz, Santa Cruz, CA, USA Caco2 American Type Culture Collection (ATCC), Manassas, VA DAPI Life Technologies, Burlington, ON, Canada Dialyzed FBS Life Technologies, Burlington, ON, Canada DMEM Sigma-Aldrich, Oakville, ON, Canada DMEM/F12 Life Technologies, Burlington, ON, Canada EGF Sigma-Aldrich, Oakville, ON, Canada EpiSeeker methylated DNA Abcam, Burlington, ON, Canada Quantification Kit Ethanol Fisher Scientific, Nepean, ON, Canada FBS Life Sciences, Burlington, ON, Canada FGF Sigma-Aldrich, Oakville, ON, Canada Fluorescent mounting medium Dako, Carpinteria, CA, USA Folic Acid Free RPMI 1640 Life Technologies, Burlington, ON, Canada Gentamicin Sigma-Aldrich, Oakville, ON, Canada Goat-anti-rabbit Cy3 Jackson Immunoresearch, West Grove, PA, USA HCT116 American Type Culture Collection (ATCC), Manassas, VA HRP-labeled goat anti-mouse Sigma-Aldrich, Oakville, ON, Canada HRP-labeled goat-anti-rabbit Sigma-Aldrich, Oakville, ON, Canada LS174T American Type Culture Collection (ATCC), Manassas, VA Luminata ™ Forte EMD Millipore, Darmstadt, Germany Lysis buffer Cell Signaling Technology, Danvers, MA, USA Methanol Fisher Scientific, Nepean, ON, Canada Mouse-anti-tubulin antibody Sigma-Aldrich, Oakville, ON, Canada Mouse-anti-DNMT1 antibody Cedarlane, Burlington, ON, Canada Normal Goat Serum Life Technologies, Burlington, ON, Canada PBS Sigma-Aldrich, Oakville, ON, Canada PMSF Sigma-Aldrich, Oakville, ON, Canada Protease inhibitor cocktail II Sigma-Aldrich, Oakville, ON, Canada Rabbit-anti-β-actin antibody Cell Signaling Technology, Danvers, MA, USA Rabbit-anit-β-catenin antibody Cell Signaling Technology, Danvers, MA, USA Rabbit-anti Lgr5 antibody Abcam, Burlington, ON, Canada Rabbit-anti-Notch antibody Cell Signaling Technology, Danvers, MA, USA Rabbit-anit-Phospho-β-catenin Cell Signaling Technology, Danvers, MA, USA

87 antibody SDS BioRad, Mississauga, ON, Canada SW480 American Type Culture Collection (ATCC), Manassas, VA TEMED Roche, Mississauga, ON, Canada Tris Base Fisher Scientific, Nepean, ON, Canada Triton X-100 BioRad, Mississauga, ON, Canada Trypan blue Sigma-Aldrich, Oakville, ON, Canada Trypsin Veterinary College, Guelph, ON, Canada Tween 20 Fisher Scientific, Nepean, ON, Canada Ultra-low attachment plates Corning, Tweksbury, MA, USA

88 APPENDIX II – PREPARATION OF SOUTIONS

7.5% Resolving Gel 2 x 1.5 mm Gel H2O 11 ml 40% acrylamide-bis solution 3.75 ml 1.5 M Tris-HCl (pH 8.8) 5 ml 10% SDS 200 µl 10% APS 240 µl TEMED 10 µl

Mix all components together and add 9 ml to space between plates. Top off with isopropanol. Once polymerized (~20 minutes), pour off the isopropanol and add the stacking Gel.

5% Stacking Gel 2 x 1.5 mm Gel H2O 3.2 ml 40% acrylamide-bis solution 1 ml 0.5 M Tris-HCl (pH 6.8) 1.26 ml 10% SDS 50 µl 10% APS 50 µl TEMED 5 µl

Mix all components together and add to top of polymerized resolving gel, add comb, and allow polymerization (~20 minutes). When the gel is polymerized, it can be used right away or stored at 4°C in a humidified chamber for 24 hours.

10 X Electrophorises Buffer Glycine 144.2 g Tris base 30.2 g 20% SDS 50 ml

Mix in H2O to a final volume of 750 mL, bring to a pH of 8.3, Add H20 to a final volume of 1 L. Store at room temperature. Dilute to 1 X in H20 when needed.

Towbin’s Solution Glycine 141.1 g Tris base 30.25 g

Mix in H20 to a final volume of 1 L. Store at room temperature.

89 Wet Transfer Buffer H20 828 mL 2% SDS 2 mL Towbin’s Solution 80 mL Methanol 100 mL

Mix all components and store at 4°C until needed

10 X TBS Tris base 24.25 g NaCl 80 g

Mix in H2O to a final volume of 750 mL and bring to a pH of 7.6. Add H20 to a final volume of 1 L. Store at room temperature and dilute to 1 X in H20 when needed. For TBS-T, add 1 ml Tween 20 to 1 L of 1 X TBS.

Immunofluorescence Blocking Buffer Normal goat serum 1.25 ml Triton X-100 75 µl

Mix in PBS to a final volume of 25 ml, and store at 4°C for up to 2 weeks.

Immunofluorescence Antibody dilution buffer Triton X-100 120 µl BSA 0.4 g

Mix in PBS to a final volume of 40 ml, and store at 4°C for up to 2 weeks.

90 APPENDIX III – GENE REGIONS WITH GREATER THAN 10 PERCENT DIFFERENCE (P < 0.01) IN CPG ISLAND METHYLATION BETWEEN FOLIC ACID TREATMENTS

HCT116 4 mg/L vs. HCT116 0 mg/L

Gene Chromosome Probes in p-value Difference in Methylation Region A1BG (-) chr19 3 0.00138973 -0.19887 ABHD11 (-) chr7 4 0.00130287 0.10124 ACSL5 (+) chr10 3 0.00651438 0.106909 ADAMTS13 (+) chr9 3 0.00434292 0.101354 ADAMTSL3 (+) chr15 5 0.0083384 0.171545 ADARB2-AS1 (+) chr10 8 0.00781725 0.132611 ALOX15 (-) chr17 3 0.0069921 0.126192 ANO8 (-) chr19 3 0.00846869 0.126174 ARX (-) chrX 3 0.00377834 0.11928 ASTN2 (-) chr9 4 0.00529836 0.144433 AUTS2 (+) chr7 6 0.00195431 0.163871 AUTS2 (+) chr7 5 0.000868583 0.130365 BCOR (-) chrX 3 0.00234518 -0.115913 BMP7 (-) chr20 4 0.00325719 0.149738 C3orf55 (+) chr3 6 0.00985842 0.155039 C3orf70 (-) chr3 9 0.00195431 0.155516 C9orf64 (-) chr9 5 0.00555893 0.112979 CCDC120 (+) chrX 3 0.00781725 0.132677 CCDC8 (-) chr19 3 0.000477721 0.140859 CCDC89 (-) chr11 3 0.00364805 0.104062 CFP (-) chrX 3 8.69E-05 0.133886 CREM (+) chr10 3 0.00260575 0.137344 CYB5R4 (+) chr6 3 0.000955442 0.105128 CYP26C1 (+) chr10 3 0.00603665 0.115574 CYP2R1 (-) chr11 3 0.00112916 0.124043 DBX2 (-) chr12 3 0.00560236 0.144646 EBF3 (-) chr10 4 0.0023886 0.183647 EHD3 (+) chr2 4 0.00981499 0.142816 ELOVL4 (-) chr6 5 0.0076001 0.145709 EMX2 (+) chr10 3 0.00152002 0.102786 EN2 (+) chr7 3 0.00234518 0.113777 FAM150B (-) chr2 3 0.000260575 0.128336 FAM155B (+) chrX 3 0.000260575 0.137059 FAM198B (-) chr4 3 0.00225832 0.109293 FAM26D (+) chr6 3 0.00138973 0.111326 FBXO39 (+) chr17 6 0.0076001 -0.106082 FGF13-AS1 (+) chrX 6 0.00173717 0.136929 FLJ33360 (-) chr5 4 0.00325719 -0.106878

91 FLT1 (-) chr13 5 0.00555893 0.115263 FOXD4L1 (+) chr2 5 0.00577608 0.15436 FOXL1 (+) chr16 3 0.00138973 0.120917 GCM2 (-) chr6 6 0.0076001 0.168991 GJA10 (+) chr6 4 0.00586294 0.134194 GNAS (+) chr20 3 0.000173717 0.156053 GPR135 (-) chr14 10 0.00738296 0.151354 GPRASP2 (+) chrX 5 8.69E-05 0.185161 GSX1 (+) chr13 4 0.00212803 0.123742 HAND1 (-) chr5 5 0.00199774 0.105802 HDAC4 (-) chr2 3 0.00147659 0.137818 IGDCC4 (-) chr15 3 0.00217146 0.10219 IGF1R (+) chr15 6 0.0023886 0.110458 IRX5 (+) chr16 4 0.00807783 0.104808 KCNMB2 (+) chr3 3 0.00473378 0.139925 KCNMB3 (-) chr3 4 0.000260575 0.114582 KIF11 (+) chr10 3 0.000608008 -0.119002 KRBA1 (+) chr7 3 0.00225832 -0.113513 LINC00261 (-) chr20 4 0.00859897 0.116061 LINC00574 (+) chr6 3 4.34E-05 0.145632 LOC100506421 (-) chr2 3 0.000130288 0.116082 LPGAT1 (-) chr1 4 0.00434292 0.112866 MAML3 (-) chr4 7 0.000260575 0.216935 MIR138-1 (+) chr3 4 0.00634066 0.142976 MYH2 (-) chr17 3 0.00781725 0.131298 NAPG (+) chr18 3 0.00616694 -0.114293 NEXN (+) chr1 7 0.0023886 0.10642 NKX6-1 (-) chr4 3 0.00777382 0.113807 NLGN4Y-AS1 (-) chrY 4 0.00694867 -0.132159 NLRP12 (-) chr19 3 0.00538522 0.12807 NMNAT2 (-) chr1 4 0.0093807 0.113179 NOS1 (-) chr12 3 0.00195431 0.106572 NPY5R (+) chr4 3 0.00377834 0.130967 NUP210 (-) chr3 5 0.0080344 0.14048 OR6K3 (-) chr1 5 0.00529836 -0.1012 OTUD7A (-) chr15 3 0.00130287 0.123452 P2RX2 (+) chr12 5 0.00130287 0.191005 PDE10A (-) chr6 8 0.00251889 0.114562 PDZRN3 (-) chr3 3 0.00234518 0.10372 PITX2 (-) chr4 4 0.00173717 0.108704 PLOD2 (-) chr3 3 0.00473378 0.109508 POU4F1 (-) chr13 8 0.0076001 0.147924 PRMT2 (+) chr21 3 0.000434292 0.107117 PTPRS (-) chr19 3 0.00243203 0.101022 RIIAD1 (+) chr1 3 8.69E-05 0.131309 RRAGD (-) chr6 3 0.00412577 -0.117197

92 SALL3 (+) chr18 4 0.00195431 0.127366 SAR1B (-) chr5 3 0.00152002 0.113506 SHOX2 (-) chr3 6 0.000434292 0.117992 SHQ1 (-) chr3 5 0.00716581 0.118599 SIM1 (-) chr6 3 0.00529836 0.142586 SLC26A4 (+) chr7 5 0.00981499 0.10694 SOX1 (+) chr13 4 0.00234518 0.118398 SOX17 (+) chr8 3 0.00660123 0.120814 SOX7 (-) chr8 3 0.00542865 0.140177 STAG3 (+) chr7 3 0.00642752 0.106954 TCF21 (+) chr6 3 0.00195431 0.148177 TENM3 (+) chr4 3 0.000477721 0.112725 TFAP2B (+) chr6 4 0.00707895 0.133752 TJP2 (+) chr9 3 0.00555893 0.162443 TRIM36 (-) chr5 4 0.0096847 0.119367 TRIM7 (-) chr5 6 0.000955442 0.107928 TRO (+) chrX 4 0.00182402 -0.13218 TSPYL4 (-) chr6 3 0.00616694 0.132535 TTC34 (-) chr1 3 0.00195431 0.101797 TUBB2B (-) chr6 3 0.00425606 0.100908 VGLL2 (+) chr6 3 0.00130287 0.155265 WDR60 (+) chr7 3 0.00234518 0.105068 WNT6 (+) chr2 5 0.00382177 0.112281 ZC3H12B (+) chrX 4 0.000260575 0.125792 ZFHX3 (-) chr16 3 0.00738296 0.231891 ZIC4 (-) chr3 3 0.00199774 0.116528 ZIC4 (-) chr3 4 0.00143316 0.107046 ZNF254 (+) chr19 3 0.0069921 0.111182 ZNF329 (-) chr19 3 0.00199774 0.104732 ZNF507 (+) chr19 3 0.00651438 0.122804 ZNF521 (-) chr18 3 0.000434292 0.135887 ZNF534 (+) chr19 3 0.0010423 0.107871 ZNF536 (+) chr19 6 0.00994528 0.137959 ZNF879 (+) chr5 5 0.00664466 0.13929

HCT116 0 mg/L vs. HCT116 16 mg/L

Gene Chromosome Probes in p-value Difference in Methylation Region ACAN (+) chr15 5 0.0055155 -0.112757 ALLC (+) chr2 3 0.00130287 -0.111369 ANKAR (+) chr2 5 4.34E-05 -0.154094 AQPEP (+) chr5 3 0.000173717 -0.108573 ASTN2 (-) chr9 5 0.00538522 -0.130087 ATP10A (-) chr15 3 0.00052115 -0.119304 AUTS2 (+) chr7 5 0.000738296 -0.141763

93 AUTS2 (+) chr7 4 0.00304004 -0.146609 BASP1 (+) chr5 3 0.0013463 -0.136856 BDNF (-) chr11 9 0.00786068 -0.134794 BMP7 (-) chr20 6 0.0034309 -0.113726 BMS1 (+) chr10 4 0.00412577 -0.146899 BRCA1 (-) chr17 4 0.0028229 -0.15538 BUD13 (-) chr11 3 0.00112916 -0.113985 C11orf70 (+) chr11 3 0.00868583 -0.157731 C2orf82 (+) chr2 5 0.0083384 -0.111614 C3orf55 (+) chr3 3 0.00499435 -0.15032 C4orf19 (+) chr4 3 0.00859897 -0.141431 CA5B (+) chrX 3 0.0055155 -0.108851 CALCA (-) chr11 4 0.00112916 -0.122908 CBLN2 (-) chr18 5 0.00616694 -0.199937 CCDC146 (+) chr7 4 0.00338747 -0.122876 CCDC160 (+) chrX 3 0.00855554 -0.129264 CDIP1 (-) chr16 3 4.34E-05 -0.123583 CDYL2 (-) chr16 4 0.00651438 -0.119418 CENPC1 (-) chr4 3 0.000347433 -0.126176 CHRM1 (-) chr11 4 0.00460349 -0.10908 CHRM1 (-) chr11 4 0.00694867 -0.113388 CLDN4 (+) chr7 3 0.00738296 0.110802 CLYBL (+) chr13 3 0.00825154 -0.104844 CNOT6L (-) chr4 3 0.000868583 -0.105483 CNR1 (-) chr6 4 0.00191088 -0.170621 CNTN4 (+) chr3 5 0.00295318 -0.107958 COL2A1 (-) chr12 7 0.00981499 -0.108235 CYLD (+) chr16 3 0.00712238 -0.106391 DACT2 (-) chr6 3 0.00686181 -0.114879 DACT2 (-) chr6 3 0.00290975 -0.120298 DCTD (-) chr4 3 0.000651437 -0.113213 DLG2 (-) chr11 3 0.00308347 -0.132027 DLX2 (-) chr2 4 0.000130288 -0.141797 EBF3 (-) chr10 3 0.00781725 -0.107561 EBF3 (-) chr10 3 0.00182402 -0.152644 EDIL3 (-) chr5 9 0.00429949 -0.16413 EFCAB9 (+) chr5 3 0.0034309 -0.142303 EGR3 (-) chr8 5 0.00877269 -0.116213 ELOVL4 (-) chr6 3 0.00564579 -0.113293 ERICH1-AS1 (+) chr8 3 0.00686181 -0.132543 ERICH1-AS1 (+) chr8 3 0.00052115 -0.174386 FAM150B (-) chr2 3 0.00603665 -0.112536 FAM155B (+) chrX 3 0.00221489 -0.106715 FAM26F (+) chr6 3 0.00516807 -0.115591 FAM47C (+) chrX 3 0.00829497 -0.119881 FERMT1 (-) chr20 3 0.00338747 -0.166365

94 FEZF2 (-) chr3 4 0.00725267 -0.171085 FGF13-AS1 (+) chrX 8 0.00568922 -0.147831 FOXB1 (+) chr15 7 0.00651438 -0.108498 FOXI3 (-) chr2 3 0.000738296 -0.150471 FRMD1 (-) chr6 3 0.00351776 -0.130786 FSTL4 (-) chr5 6 0.0093807 -0.117058 GAD1 (+) chr2 4 0.000651437 -0.17757 GATA4 (+) chr8 4 0.00264918 -0.135568 GBX2 (-) chr2 3 0.00690524 -0.133778 GFPT2 (-) chr5 5 0.00112916 -0.11916 GHSR (-) chr3 4 0.00317033 -0.102508 GPC2 (-) chr7 5 0.00898984 -0.106871 GPR150 (+) chr5 3 0.00260575 -0.120167 GRIK2 (+) chr6 3 0.00872926 -0.132231 GRM5-AS1 (+) chr11 5 0.0055155 -0.180998 GSPT2 (+) chrX 4 0.00742639 -0.128725 GSX1 (+) chr13 3 0.00859897 -0.101484 GTDC1 (-) chr2 3 0.0038652 -0.180926 HAND1 (-) chr5 5 0.000173717 -0.170157 HIST1H3D (-) chr6 5 0.0023886 -0.116208 HLA-DPB2 (+) chr6 3 0.00317033 -0.131475 IGF1R (+) chr15 6 0.00872926 -0.116611 IL17RE (+) chr3 3 0.00859897 -0.136068 IL1RL2 (+) chr2 5 0.00647095 -0.112828 INPP5A (+) chr10 3 0.00994528 -0.106088 INSM1 (+) chr20 4 0.0034309 -0.153078 IRF8 (+) chr16 5 4.34E-05 -0.146803 IRX3 (-) chr16 4 0.00299661 -0.137768 ISLR2 (+) chr15 5 0.00125945 -0.168817 JAKMIP1 (-) chr4 5 0.00807783 -0.101247 JAKMIP1 (-) chr4 4 0.00703553 -0.128375 KAL1 (-) chrX 4 0.00977156 -0.203462 KIAA0141 (+) chr5 3 0.00690524 -0.136804 KIF5C (+) chr2 6 0.00820811 -0.135596 KL (+) chr13 4 0.00881612 -0.107784 KRT3 (-) chr12 4 0.00052115 -0.106349 LHFPL4 (-) chr3 3 0.000738296 -0.123715 LHX4 (+) chr1 5 0.00243203 -0.123588 LINC00574 (+) chr6 3 0.000738296 -0.121977 LOC100506421 (-) chr2 3 0.000173717 -0.132799 LOC101054525 (+) chr11 3 0.0013463 -0.112408 LOC255167 (+) chr5 4 0.00764353 -0.107654 LOC284395 (-) chr19 3 0.00438635 -0.104261 LOC91948 (-) chr15 6 0.000130288 -0.154663 LRAT (+) chr4 9 0.00529836 -0.129292 LRRC7 (+) chr1 3 0.00868583 -0.123502

95 MAK (-) chr6 3 0.00112916 -0.117901 MANSC1 (-) chr12 5 0.00660123 -0.141337 MAP4K3 (-) chr2 3 0.00964127 -0.100338 MECOM (-) chr3 3 0.00473378 -0.135755 MEIS1 (+) chr2 3 0.00768696 -0.107088 MIR1182 (-) chr1 3 0.00573265 -0.105463 MIR3191 (-) chr19 3 0.000173717 -0.14114 MIR4436A (+) chr2 3 0.000347433 -0.121906 MIR4683 (-) chr10 8 0.00664466 -0.13601 MIR548B (-) chr6 4 0.00516807 -0.139444 MPHOSPH9 (-) chr12 3 0.00673152 -0.172244 MYO7A (+) chr11 3 0.00955442 -0.101354 NCL (-) chr2 3 0.0031269 -0.106918 NEFL (-) chr8 3 0.0038652 -0.145114 NKAP (-) chrX 4 0.00412577 -0.132342 NKX2-6 (-) chr8 3 0.00508121 -0.126149 NKX3-2 (-) chr4 5 0.0096847 -0.11488 NPAS3 (+) chr14 3 0.0041692 0.119732 NPNT (+) chr4 3 0.00568922 -0.174595 NPY5R (+) chr4 4 0.00399548 -0.112243 NR2F2 (+) chr15 3 0.00308347 -0.107253 NR2F2 (+) chr15 5 0.00243203 -0.1181 NR2F2 (+) chr15 3 0.00829497 -0.140237 NR3C1 (-) chr5 4 0.00868583 -0.118894 NXPH1 (+) chr7 5 0.00395205 -0.130083 OBFC1 (-) chr10 3 0.00525493 -0.102804 OPCML (-) chr11 3 0.00247546 -0.107695 OR51E2 (-) chr11 3 0.00816468 -0.105368 OTP (-) chr5 3 0.0083384 -0.109575 OTP (-) chr5 4 0.0080344 -0.112342 PABPC5 (+) chrX 5 0.00112916 -0.154594 PAK3 (+) chrX 4 0.00590637 -0.10819 PAX8 (-) chr2 5 0.000868583 -0.142185 PCDHGB2 (+) chr5 4 0.0055155 -0.135724 PDE10A (-) chr6 4 0.000955442 -0.139412 PDE4D (-) chr5 3 0.000738296 -0.13449 PDE7B (+) chr6 3 0.00881612 -0.102812 PDIA6 (-) chr2 3 0.00516807 -0.124037 PDX1 (+) chr13 3 0.000738296 -0.123935 PNMT (+) chr17 5 0.00138973 -0.100932 POM121L2 (-) chr6 3 0.00429949 -0.123573 POU4F1 (-) chr13 8 4.34E-05 -0.258893 POU4F2 (+) chr4 3 0.0052115 -0.132496 PPP1R3D (-) chr20 3 0.000738296 -0.151016 PRDM12 (+) chr9 3 0.00508121 -0.13586 PRKAA2 (+) chr1 3 0.000173717 -0.100659

96 PRR5L (+) chr11 3 0.0013463 -0.116857 PTGER1 (-) chr19 3 0.000738296 -0.145438 PTPRD (-) chr9 5 0.000304004 -0.160242 RAB40A (-) chrX 3 0.00486407 -0.11721 RALGAPA2 (-) chr20 3 0.00616694 -0.136152 RBFOX1 (+) chr16 3 0.00660123 -0.13512 RCN3 (+) chr19 5 0.00829497 -0.133057 RFX6 (+) chr6 7 0.00933727 -0.104876 RORB (+) chr9 5 0.000651437 -0.10884 SARM1 (+) chr17 4 0.00286632 -0.168715 SBNO2 (-) chr19 4 0.0086424 -0.126431 SCAND3 (-) chr6 3 0.00786068 -0.111128 SHISA9 (+) chr16 3 0.0055155 -0.124148 SHROOM4 (-) chrX 3 0.0096847 -0.108922 SIX1 (-) chr14 5 0.00112916 -0.151279 SIX3 (+) chr2 4 0.00243203 -0.101497 SLC26A4 (+) chr7 7 0.00260575 -0.14453 SLC2A3 (-) chr12 3 0.000260575 -0.110897 SLC4A4 (+) chr4 3 0.0072961 -0.127017 SLC5A7 (+) chr2 4 0.00529836 -0.120727 SLC5A8 (-) chr12 3 0.0083384 -0.12047 SMYD3 (-) chr1 3 4.34E-05 -0.113862 SNORD5 (-) chr11 3 0.00191088 -0.1035 SOX1 (+) chr13 5 0.000173717 -0.125247 SOX3 (-) chrX 3 0.00981499 -0.141557 SRRM4 (+) chr12 3 0.00820811 -0.108584 ST8SIA5 (-) chr18 7 0.0055155 -0.134939 STAC2 (-) chr17 3 0.000955442 -0.111836 SV2A (-) chr1 3 0.00499435 -0.117881 SVIL (-) chr10 3 0.00290975 -0.10434 SYT9 (+) chr11 5 0.0096847 -0.108725 TBC1D30 (+) chr12 3 0.00286632 -0.108553 TBX3 (-) chr12 3 0.00429949 -0.116402 TDH (+) chr8 3 0.0069921 -0.119204 TFAP2B (+) chr6 4 0.00647095 -0.133427 TGS1 (+) chr8 3 0.000868583 -0.104787 THBS4 (+) chr5 3 0.00964127 -0.104515 TJP2 (+) chr9 5 0.0044732 -0.162244 TLE4 (+) chr9 3 0.00182402 -0.160237 TNFRSF8 (+) chr1 3 0.00964127 -0.101742 TNS3 (-) chr7 3 0.0031269 -0.132047 TNXB (-) chr6 3 0.0013463 -0.119714 TP63 (+) chr3 3 0.00230175 -0.143096 TRIM36 (-) chr5 5 0.000738296 -0.147975 TRIM59 (-) chr3 3 0.00317033 -0.122828 TRPV2 (+) chr17 3 0.00977156 -0.106571

97 TSHZ3 (-) chr19 3 0.000347433 -0.149498 TSPYL4 (-) chr6 3 0.00160688 -0.138308 TTC3P1 (-) chrX 3 0.00768696 -0.118224 TUBB2B (-) chr6 5 0.00851212 -0.10488 UACA (-) chr15 3 0.000217146 -0.121194 UBE2MP1 (-) chr16 3 0.000868583 -0.135241 VIPR2 (-) chr7 4 0.00429949 -0.196405 VLDLR (+) chr9 3 0.00112916 -0.104957 VTRNA2-1 (-) chr5 3 0.000781725 -0.119751 WNT6 (+) chr2 4 0.00573265 -0.111467 WNT7B (-) chr22 4 0.00681838 -0.133807 XKR4 (+) chr8 3 0.00877269 -0.109555 ZBTB16 (+) chr11 3 0.00373491 -0.114144 ZC3H18 (+) chr16 6 0.0023886 -0.111742 ZEB2-AS1 (+) chr2 3 0.00651438 -0.108082 ZIC4 (-) chr3 3 0.00399548 -0.113408 ZIC4 (-) chr3 4 0.000173717 -0.139856 ZNF223 (+) chr19 3 0.00052115 -0.120992 ZNF254 (+) chr19 4 0.00799097 -0.102863 ZNF451 (+) chr6 3 0.00429949 -0.14204 ZNF491 (+) chr19 3 0.00512464 -0.123898 ZNF501 (+) chr3 5 0.00647095 -0.125187 ZNF521 (-) chr18 3 0.000347433 -0.117494 ZNF682 (-) chr19 8 0.00868583 -0.106662

HCT116 4 mg/L vs. HCT116 16 mg/L

Gene Chromosome Probes in p-value Difference in Methylation Region ABHD11 (-) chr7 4 0.000434292 0.108917 ADAMTS2 (-) chr5 3 0.000173717 -0.106321 ADAMTSL3 (+) chr15 5 0.00812125 0.191665 ANKMY1 (-) chr2 3 0.00286632 -0.101034 ANKS1B (-) chr12 4 0.00390862 -0.188101 ARSD (-) chrX 3 0.0013463 -0.101751 ASTN1 (-) chr1 3 0.00382177 0.112534 ATP10A (-) chr15 4 0.00112916 -0.109038 ATP11C (-) chrX 4 0.00786068 -0.104882 BCAT1 (-) chr12 3 0.000217146 0.139943 BDKRB1 (+) chr14 3 0.00243203 -0.100185 BMP6 (+) chr6 3 0.00377834 -0.117736 BMS1 (+) chr10 3 0.000868583 -0.116883 BUD13 (-) chr11 4 0.000651437 -0.116662 C2orf82 (+) chr2 3 0.000998871 -0.144562 C4orf19 (+) chr4 10 0.00390862 -0.121826 CALCA (-) chr11 4 0.00121602 -0.139016

98 CBLN2 (-) chr18 4 0.0013463 -0.166161 CBLN4 (-) chr20 4 0.00473378 -0.131783 CCDC67 (+) chr11 3 0.00243203 -0.145515 CD37 (+) chr19 4 0.00777382 -0.114081 CDH18 (-) chr5 3 0.000173717 -0.162427 CELSR3 (-) chr3 5 0.0044732 -0.10336 CHODL (+) chr21 3 0.00786068 -0.126872 CHRM1 (-) chr11 3 0.00112916 -0.100494 CHST11 (+) chr12 4 0.000173717 -0.101785 CLSTN2 (+) chr3 5 0.00390862 -0.135594 CNR1 (-) chr6 4 0.00186745 -0.141138 COL12A1 (-) chr6 3 0.0031269 -0.106954 CST9L (-) chr20 3 0.00707895 -0.102286 CYP27C1 (-) chr2 5 0.0041692 -0.102929 DNAI2 (+) chr17 5 0.00755668 -0.11403 DNASE1L2 (+) chr16 4 0.00894641 -0.101781 DUSP21 (+) chrX 3 0.00916355 -0.113388 EN2 (+) chr7 3 0.0069921 -0.123136 ERICH1-AS1 (+) chr8 3 0.0013463 -0.128232 FAM169A (-) chr5 4 0.00108573 -0.104118 FBXO39 (+) chr17 9 0.00746982 -0.134811 FOXF2 (+) chr6 3 0.00173717 -0.122001 GPM6B (-) chrX 5 0.00112916 -0.133499 GPR150 (+) chr5 4 0.0044732 -0.12142 GTF2I (+) chr7 3 0.00334405 -0.126687 HNRNPF (-) chr10 4 4.34E-05 0.128183 HOXA1 (-) chr7 3 0.0093807 -0.104796 IER3IP1 (-) chr18 3 0.00538522 -0.121139 IGIP (+) chr5 3 0.00186745 -0.122925 IRAK1BP1 (+) chr6 5 0.00473378 -0.115301 IRX6 (+) chr16 4 0.0072961 -0.106363 JAKMIP3 (+) chr10 3 0.00273604 -0.128682 KIAA1755 (-) chr20 7 0.00877269 -0.158885 KLHL35 (-) chr11 4 0.000390862 -0.11734 LBX2 (-) chr2 8 0.00112916 -0.142062 LINC00837 (-) chr10 5 0.000477721 -0.101278 LOC100507584 (-) chr6 3 0.00890298 -0.114152 LOC731424 (-) chr4 3 0.000130288 -0.114411 LRRC34 (-) chr3 3 0.00621037 0.127622 LYZL1 (+) chr10 3 0.00052115 -0.107509 MEGF10 (+) chr5 4 0.00738296 -0.116276 MGA (+) chr15 3 0.00495092 0.110276 MIR129-1 (+) chr7 4 0.00503778 -0.112668 MIR3156-2 (+) chr18 3 0.0059498 -0.119596 MIR4644 (+) chr6 3 0.000260575 -0.119586 MIR593 (+) chr7 3 0.00495092 -0.113207

99 MTRNR2L4 (-) chr16 3 0.00568922 0.151855 MYOM1 (-) chr18 4 0.00781725 -0.103083 NACC2 (-) chr9 3 0.00647095 -0.110151 NKX2-5 (-) chr5 5 0.00890298 -0.14158 NR0B1 (-) chrX 4 0.00998871 -0.100054 NRG4 (-) chr15 3 0.00382177 -0.136416 NRXN3 (+) chr14 7 0.00460349 -0.136262 OLIG2 (+) chr21 3 0.00816468 -0.109604 OPCML (-) chr11 3 0.000217146 -0.109893 PAPLN (+) chr14 4 0.000217146 -0.128602 PAX6 (-) chr11 3 0.00495092 -0.107552 PDZD4 (-) chrX 4 0.00981499 -0.107112 POU3F4 (+) chrX 3 0.00112916 -0.182749 PPP2R2D (+) chr10 4 0.00438635 -0.173962 PRDM5 (-) chr4 3 0.00712238 0.102551 PTPRD (-) chr9 5 0.00881612 -0.132326 PXT1 (-) chr6 3 0.00642752 -0.147412 RALGAPA2 (-) chr20 3 0.0013463 -0.102559 RBMY2FP (+) chrY 3 0.00382177 -0.334087 RIMBP2 (-) chr12 3 0.00256232 -0.131056 RNF219-AS1 (+) chr13 4 0.00360462 -0.131465 SATB2 (-) chr2 3 0.000955442 -0.175842 SDK1 (+) chr7 5 0.00568922 -0.143023 SEMA6D (+) chr15 3 0.000564579 -0.121683 SIX3 (+) chr2 3 0.00712238 -0.116805 SIX6 (+) chr14 3 0.0038652 0.122274 SLC35G2 (+) chr3 5 0.000998871 -0.101314 SLC8A2 (-) chr19 4 0.0038652 -0.205381 SMAD3 (+) chr15 3 0.000998871 0.111834 TBL1Y (+) chrY 3 0.00755668 0.27738 TMEM204 (+) chr16 3 0.00451663 -0.11103 TMEM215 (+) chr9 3 0.000738296 -0.115207 TOX2 (+) chr20 7 0.00707895 -0.141529 TP63 (+) chr3 3 0.0093807 -0.111816 TRO (+) chrX 4 0.000738296 -0.145581 VAMP5 (+) chr2 4 0.00108573 -0.110188 VSTM2B (+) chr19 3 0.0080344 -0.104978 WNT3 (-) chr17 3 0.0017806 -0.122216 WNT3A (+) chr1 3 0.00894641 -0.129991 XKR7 (+) chr20 3 0.00212803 -0.150217 ZIC5 (-) chr13 4 0.00799097 -0.103135 ZNF788 (+) chr19 3 0.000825154 0.122279

100

SW480 4 mg/L vs. SW480 0 mg/L

Gene Chromosome Probes in p-value Difference in Methylation Region ACOXL (+) chr2 5 0.00117259 0.149752 ACTR3B (+) chr7 8 0.00994528 0.130212 ACTR3C (-) chr7 3 0.000130288 0.215833 ADAMTSL3 (+) chr15 6 0.00290975 0.158852 ADHFE1 (+) chr8 4 0.0065578 0.102084 AJAP1 (+) chr1 6 0.0090767 0.178678 ANKRD33B (+) chr5 3 0.000217146 0.170489 AP3B1 (-) chr5 5 0.00712238 0.178474 APC2 (+) chr19 8 0.00217146 0.232218 ARHGAP22 (-) chr10 7 0.000304004 0.132806 ARHGAP31 (+) chr3 4 0.00777382 0.112074 ARMC3 (+) chr10 4 0.00912013 0.143751 ARRDC2 (+) chr19 4 0.0044732 0.188079 ASB18 (-) chr2 4 0.0044732 0.109889 ATG9B (-) chr7 3 0.00903327 0.108544 BMS1 (+) chr10 4 0.00794754 0.117595 C14orf28 (+) chr14 4 0.00173717 0.16844 C1orf141 (-) chr1 4 0.00642752 0.178533 CA4 (+) chr17 7 0.00994528 0.103578 CALB1 (-) chr8 4 0.00156345 0.116505 CCDC140 (+) chr2 3 0.00464692 0.121971 CCL2 (+) chr17 4 0.00456006 0.145221 CD7 (-) chr17 3 0.0093807 -0.113298 CDH2 (-) chr18 8 0.00829497 0.18964 CDKAL1 (+) chr6 3 0.00143316 -0.118219 CELF4 (-) chr18 3 0.00794754 0.140393 CHST15 (-) chr10 3 0.000694867 0.115242 CHST7 (+) chrX 3 0.00720924 0.187026 CLDN10 (+) chr13 5 0.00929384 0.15081 CNGA3 (+) chr2 5 0.00225832 0.166299 COL24A1 (-) chr1 3 0.00125945 0.109439 COL8A1 (+) chr3 3 0.000651437 0.113839 CORO6 (-) chr17 5 0.000781725 0.133072 CPLX1 (-) chr4 5 0.0052115 0.132261 CR1 (+) chr1 3 0.00673152 0.136945 CUX2 (+) chr12 5 0.000564579 0.185074 CYP26C1 (+) chr10 3 0.0090767 0.143032 DFNA5 (-) chr7 6 0.00217146 0.117507 DGCR10 (+) chr22 3 0.00812125 0.107787 DGKA (+) chr12 3 0.0010423 0.105594 DIP2C (-) chr10 3 0.0069921 0.107949

101 DIP2C (-) chr10 5 0.00334405 0.100445 DIP2C (-) chr10 3 0.00573265 -0.200965 DIP2C (-) chr10 3 0.00707895 -0.273 DKFZp686K1684 (+) chr11 3 0.00495092 0.17098 DLK1 (+) chr14 4 0.00781725 0.200574 DLX1 (+) chr2 3 0.00156345 0.123537 DLX6 (+) chr7 3 0.00825154 0.104497 DNAH11 (+) chr7 3 0.0044732 0.103468 DSG4 (+) chr18 3 0.00903327 0.152414 EDNRA (+) chr4 5 0.00985842 0.148513 EN2 (+) chr7 7 0.00985842 0.126343 ENTPD3 (+) chr3 4 0.00851212 0.101958 EOMES (-) chr3 3 0.00781725 0.119692 ERICH1-AS1 (+) chr8 3 0.000694867 0.212349 ERICH1-AS1 (+) chr8 3 0.000955442 0.151311 ESR1 (+) chr6 5 0.00781725 0.179626 EVX2 (-) chr2 3 0.0041692 0.113701 FAM135B (-) chr8 3 0.000694867 0.11425 FAM153C (+) chr5 4 0.00564579 0.117184 FAM19A4 (-) chr3 10 0.00773039 0.168827 FAM43A (+) chr3 5 0.00994528 0.144669 FBLIM1 (+) chr1 7 0.00716581 0.125019 FCGR1C (+) chr1 3 0.00351776 0.136572 FCGR3A (-) chr1 6 0.00199774 0.156982 FGF14 (-) chr13 5 0.000608008 0.102936 FOXA1 (-) chr14 4 0.00764353 0.158769 FOXA1 (-) chr14 5 0.00234518 0.135223 FOXB1 (+) chr15 8 0.00912013 0.196432 FOXD1 (-) chr5 3 0.0031269 0.131655 FOXF1 (+) chr16 4 0.00920698 0.105422 FOXF2 (+) chr6 4 0.00538522 0.22056 FOXF2 (+) chr6 7 0.0090767 0.102983 FOXG1 (+) chr14 3 0.00859897 0.169185 FSTL1 (-) chr3 3 0.00977156 0.11191 FZD2 (+) chr17 3 0.00994528 0.11172 GABRA5 (+) chr15 4 0.00577608 0.176215 GAD1 (+) chr2 3 8.69E-05 0.13032 GAL3ST2 (+) chr2 3 0.000738296 0.113753 GAP43 (+) chr3 6 0.0083384 0.112097 GATA2 (-) chr3 6 0.000912012 0.138837 GCK (-) chr7 4 0.00277947 0.141875 GCNT2 (+) chr6 3 0.00994528 0.172664 GLS2 (-) chr12 3 0.00651438 0.103445 GLTSCR1 (+) chr19 3 0.00125945 0.131643 GNA15 (+) chr19 7 0.00225832 0.147937

102 GNAL (+) chr18 7 0.00994528 0.103849 GPC6 (+) chr13 3 0.00529836 0.105525 GRIK3 (-) chr1 6 0.00290975 0.146134 GRIN2A (-) chr16 4 0.0010423 0.127823 GSPT2 (+) chrX 3 0.00125945 0.122935 GSTM3 (-) chr1 5 0.00290975 0.159553 HAND2 (-) chr4 4 0.00786068 0.121338 HCN1 (-) chr5 3 0.00408234 0.143192 HFE2 (+) chr1 3 0.00516807 0.151871 HIC1 (+) chr17 3 0.00538522 0.156686 HIST1H3C (+) chr6 5 0.00182402 0.1582 HIST3H2A (-) chr1 4 0.00720924 0.103105 HLA-E (+) chr6 4 0.000608008 0.141796 HOTAIR (-) chr12 5 0.0020846 0.12474 HOXC12 (+) chr12 8 0.00516807 0.136527 HOXC13 (+) chr12 7 0.00130287 0.154952 HOXC5 (+) chr12 4 0.0083384 0.121868 HOXD10 (+) chr2 4 0.00260575 0.12431 HOXD11 (+) chr2 6 0.000694867 0.122005 HS3ST1 (-) chr4 3 0.0049075 -0.118697 HULC (+) chr6 3 0.00794754 0.117017 HUS1B (-) chr6 3 0.00525493 -0.100558 ID4 (+) chr6 6 0.0055155 0.127942 IKZF1 (+) chr7 6 0.00677495 0.16936 IL17D (+) chr13 3 0.0017806 0.140147 IRX3 (-) chr16 4 0.00564579 0.210661 KCNAB1 (+) chr3 4 0.00751325 0.149284 LATS2 (-) chr13 3 0.00152002 0.171069 LHX1 (+) chr17 3 0.00777382 0.109976 LHX2 (+) chr9 4 0.00781725 0.108021 LIFR (-) chr5 6 0.000955442 0.152184 LINC00240 (+) chr6 3 0.00173717 0.138143 LINC00461 (-) chr5 3 0.00186745 0.130113 LINC00518 (-) chr6 5 0.00525493 0.147894 LINC00602 (+) chr6 3 0.00851212 0.141596 LOC100130992 (+) chr10 5 0.00707895 0.158183 LOC100132111 (+) chr1 3 0.00764353 0.167392 LOC100132891 (+) chr8 10 0.00403891 0.146042 LOC100653515 (-) chr17 3 0.000477721 0.109306 LOC283177 (+) chr11 3 0.000564579 0.105928 LOC286094 (+) chr8 4 0.00382177 -0.101788 LOC375295 (-) chr2 3 0.00573265 0.108557 LOC401242 (-) chr6 4 0.00204117 0.115559

103 LOC643623 (+) chr6 4 0.00707895 0.125965 LOC732275 (-) chr16 3 0.00599323 -0.109747 LRFN5 (+) chr14 4 0.00308347 0.113796 LRRN1 (+) chr3 3 0.00977156 -0.101695 MAP3K8 (+) chr10 3 0.00972813 0.100225 MED13 (-) chr17 3 0.0086424 0.125034 MIDN (+) chr19 3 0.00516807 0.118718 MIPEPP3 (+) chr13 3 0.000304004 0.113143 MIR100HG (-) chr11 3 0.00234518 0.109522 MIR4767 (+) chrX 3 0.000694867 0.263794 MKI67 (-) chr10 3 0.000130288 0.109011 MME (+) chr3 4 0.00981499 0.16472 MPV17L (+) chr16 4 0.00898984 0.125117 MT1L (+) chr16 3 0.000781725 0.147065 MTUS2 (+) chr13 3 0.000998871 0.13375 MYOM2 (+) chr8 3 0.00477721 -0.109737 NAV3 (+) chr12 3 0.00125945 0.152307 NEFH (+) chr22 3 0.0052115 0.142961 NEUROD1 (-) chr2 3 0.0076001 0.112707 NFATC1 (+) chr18 4 0.00925041 0.1512 NKX2-4 (-) chr20 3 0.00994528 0.152072 NKX2-6 (-) chr8 3 0.000217146 0.164589 NKX2-6 (-) chr8 5 0.00720924 0.148247 NKX6-1 (-) chr4 3 0.00152002 0.178569 NKX6-1 (-) chr4 6 0.00851212 0.124024 NKX6-1 (-) chr4 3 0.00503778 0.109583 NR1I2 (+) chr3 4 0.00955442 0.101042 NR2E1 (+) chr6 8 0.00955442 0.120802 NR2F2 (+) chr15 3 0.00825154 0.116358 NR2F2 (+) chr15 3 0.00964127 0.104515 NRN1 (-) chr6 6 0.00846869 0.10066 OCA2 (-) chr15 3 0.00912013 0.105934 OR10K2 (-) chr1 4 0.000955442 -0.156204 OR2V2 (+) chr5 4 0.000304004 0.173013 OR6S1 (-) chr14 3 0.0096847 0.114162 OSR1 (-) chr2 5 0.00720924 0.141678 OSR2 (+) chr8 3 0.00308347 0.10103 OTP (-) chr5 3 0.00351776 0.123918 PABPC3 (+) chr13 5 0.00951099 0.127915 PAX6 (-) chr11 7 0.00677495 0.149151 PAX8 (-) chr2 3 0.0044732 0.100171 PCDH10 (+) chr4 6 0.00768696 0.15381 PCDH17 (+) chr13 4 0.0069921 0.135984 PCDH7 (+) chr4 3 0.00577608 0.164523 PCDHGB7 (+) chr5 3 0.00581951 0.108868 PDGFRA (+) chr4 5 0.0052115 0.160931

104 PDZRN4 (+) chr12 4 0.00859897 0.11601 PFKP (+) chr10 3 0.00408234 -0.147958 PKHD1L1 (+) chr8 3 0.000304004 0.138504 PLK1S1 (+) chr20 3 0.0052115 0.101506 POU4F2 (+) chr4 3 0.00964127 0.100961 PRDM13 (+) chr6 6 0.0093807 0.188214 PRKG1-AS1 (-) chr10 3 0.000173717 0.202825 PRPH (+) chr12 4 0.00725267 0.107242 PTF1A (+) chr10 3 0.0044732 0.204415 PTF1A (+) chr10 5 0.00560236 0.19103 PTPRN2 (-) chr7 3 0.0017806 0.135397 PTPRN2 (-) chr7 5 0.00712238 0.133076 RAB38 (-) chr11 7 0.00369148 0.137717 RAPGEF1 (-) chr9 3 0.00338747 0.115247 RHBG (+) chr1 4 0.00634066 0.160167 RIMS2 (+) chr8 5 0.000608008 0.132139 RSPH9 (+) chr6 3 0.00786068 0.139413 RSPO3 (+) chr6 3 0.00846869 0.104221 RUNDC3A (+) chr17 6 0.000260575 0.189661 SEMA6D (+) chr15 3 0.00877269 0.101698 SEPT9 (+) chr17 10 0.0072961 0.100039 SFMBT1 (-) chr3 4 0.00951099 -0.176846 SHC3 (-) chr9 3 0.000955442 0.194327 SHH (-) chr7 8 0.00299661 0.171694 SIM1 (-) chr6 10 0.00204117 0.128698 SIX1 (-) chr14 3 0.0044732 0.154385 SIX6 (+) chr14 3 0.00920698 0.132353 SKOR1 (+) chr15 4 0.000434292 0.121777 SLCO3A1 (+) chr15 3 0.00994528 -0.122976 SMOC2 (+) chr6 5 0.0044732 -0.146687 SNAP91 (-) chr6 5 0.00516807 0.160198 SNTG2 (+) chr2 8 0.00977156 0.105646 SOCS2 (+) chr12 4 0.00273604 0.121009 SOX1 (+) chr13 4 0.00794754 0.100403 SOX11 (+) chr2 4 0.00308347 0.207223 SOX14 (+) chr3 5 0.00290975 0.117785 SOX2-OT (+) chr3 4 0.00668809 0.112061 SOX2-OT (+) chr3 4 0.0080344 0.102776 SOX9 (+) chr17 4 0.00955442 0.130042 SRD5A1 (+) chr5 3 0.00825154 0.119117 STK32B (+) chr4 6 0.00195431 0.143962 SYNE1 (-) chr6 3 0.00616694 0.110472 TACC2 (+) chr10 7 0.00212803 0.15734 TBX18 (-) chr6 3 0.000217146 0.164575 TBX2 (+) chr17 8 0.00234518 0.130638 TBX3 (-) chr12 3 0.00369148 0.122371

105 TBX5-AS1 (+) chr12 5 0.00673152 0.170918 TFAP2A (-) chr6 3 0.00138973 0.122518 TGM2 (-) chr20 4 0.00456006 0.128589 THEGL (+) chr4 5 0.00429949 0.114429 TJP2 (+) chr9 5 0.000217146 0.193608 TLX1 (+) chr10 4 0.00195431 0.102093 TMEM33 (+) chr4 7 0.00616694 0.120612 TRIM71 (+) chr3 6 0.00560236 0.141502 TTTY18 (-) chrY 3 0.00994528 -0.11055 TTTY20 (-) chrY 4 0.00581951 -0.214635 TWIST2 (+) chr2 4 0.00599323 0.115412 ULBP1 (+) chr6 4 0.0010423 0.158396 UNC5D (+) chr8 4 0.00668809 0.192021 UNC80 (+) chr2 8 0.00290975 0.158212 VAX2 (+) chr2 4 0.00351776 0.133887 VGLL2 (+) chr6 3 0.00277947 0.146233 VGLL2 (+) chr6 3 0.00725267 0.12483 VIPR2 (-) chr7 5 0.00538522 0.224617 VIPR2 (-) chr7 3 0.000912012 -0.107292 VSTM2B (+) chr19 3 0.00495092 0.121031 WNT6 (+) chr2 3 0.00147659 0.252535 WNT6 (+) chr2 4 0.0059498 0.116882 WNT7B (-) chr22 3 0.00677495 0.12963 WNT9A (-) chr1 5 0.0052115 0.103018 XKR7 (+) chr20 3 0.00994528 0.143149 ZIC3 (+) chrX 6 0.00508121 0.176626 ZIC4 (-) chr3 4 0.00951099 0.102143 ZIC5 (-) chr13 4 0.00677495 0.102269 ZNF213 (+) chr16 3 0.000130288 -0.120138 ZNF385D (-) chr3 3 0.00985842 -0.14981 ZNF764 (-) chr16 5 0.00138973 0.238106

106 SW480 0 mg/L vs. SW480 16 mg/L

Gene Chromosome Probes in p-value Difference in Methylation Region ABO (-) chr9 4 0.00360462 -0.135982 ACOXL (+) chr2 4 0.000998871 -0.185287 ACTR3C (-) chr7 3 0.000998871 -0.178015 ADAM12 (-) chr10 7 0.00925041 -0.163203 ADAM33 (-) chr20 3 0.0093807 0.123182 ADAMTSL3 (+) chr15 5 0.00421263 -0.159744 AEBP1 (+) chr7 6 0.0083384 -0.104401 AGAP1 (+) chr2 3 0.000260575 -0.109941 AHRR (+) chr5 3 0.00985842 -0.104989 ALLC (+) chr2 3 0.00577608 -0.152143 ALPP (+) chr2 4 0.00985842 -0.147153 AMPH (-) chr7 3 0.00972813 -0.107816 ANO4 (+) chr12 3 0.00264918 -0.118494 AP2A2 (+) chr11 3 0.000651437 -0.1021 AP3B1 (-) chr5 5 0.00564579 -0.182887 AP3B1 (-) chr5 4 0.00182402 -0.184844 APC2 (+) chr19 3 0.00564579 -0.106442 ARHGAP22 (-) chr10 8 0.000564579 -0.153849 ARHGEF4 (+) chr2 4 0.00647095 -0.120247 ARMC3 (+) chr10 4 0.00330062 -0.145342 ARRDC2 (+) chr19 3 0.00990185 -0.179532 ATG9B (-) chr7 3 0.00777382 -0.10767 ATOH7 (-) chr10 3 0.000651437 -0.143526 ATP11A (+) chr13 4 0.00647095 -0.107811 AVPR1A (-) chr12 7 0.0083384 -0.143208 BARHL1 (+) chr9 3 0.00412577 -0.110788 BARHL2 (-) chr1 9 0.00299661 -0.184647 BASP1P1 (-) chr13 4 0.00568922 -0.119478 BEND6 (+) chr6 3 0.00903327 -0.104681 BHLHE22 (+) chr8 3 0.00916355 -0.181507 BIRC8 (-) chr19 3 0.00586294 -0.153652 BTBD19 (+) chr1 4 0.00412577 -0.128272 BTRC (+) chr10 4 0.00668809 -0.107828 C16orf78 (+) chr16 3 0.000825154 -0.116164 C6orf147 (-) chr6 5 0.0072961 -0.147676 C7orf13 (-) chr7 3 0.00825154 -0.153739 C8orf44-SGK3 (+) chr8 3 0.000825154 -0.194811 C9orf129 (-) chr9 3 0.000217146 -0.104905 CA5A (-) chr16 3 0.00182402 0.106717 CACNA1B (+) chr9 3 0.000434292 -0.117438 CACNA2D3 (+) chr3 3 0.00608008 -0.107898 CALCOCO1 (-) chr12 5 0.00621037 -0.129995

107 CALY (-) chr10 3 0.00925041 -0.108611 CARS (-) chr11 5 0.00212803 -0.116135 CCDC140 (+) chr2 3 0.00846869 -0.109549 CCDC144CP (+) chr17 4 0.00412577 -0.108037 CCDC65 (+) chr12 3 0.0017806 -0.128018 CCK (-) chr3 3 0.00317033 -0.158962 CCL2 (+) chr17 4 0.00972813 -0.144611 CD34 (-) chr1 4 0.00634066 -0.150464 CD44 (+) chr11 3 0.00195431 -0.113276 CDH2 (-) chr18 3 0.00668809 -0.199532 CDH3 (+) chr16 6 0.0010423 -0.175727 CDKAL1 (+) chr6 3 0.00581951 0.111092 CDKN3 (+) chr14 3 0.00564579 -0.108041 CELF4 (-) chr18 4 0.00108573 -0.128772 CHL1 (+) chr3 3 0.000347433 -0.122983 CHRNB1 (+) chr17 3 0.00108573 -0.113401 CHST15 (-) chr10 3 0.000825154 -0.126856 CHST7 (+) chrX 4 0.00412577 -0.256945 CLDN10 (+) chr13 3 0.00182402 -0.131988 CLEC3A (+) chr16 4 0.00916355 -0.122524 CORO6 (-) chr17 5 0.00925041 -0.131302 CPLX1 (-) chr4 8 0.0038652 -0.116655 CRB2 (+) chr9 4 0.00182402 -0.13786 CUX2 (+) chr12 6 0.00985842 -0.188502 CYP26C1 (+) chr10 3 0.00673152 -0.111858 CYP26C1 (+) chr10 3 0.00920698 -0.112028 CYP2E1 (+) chr10 3 0.00338747 -0.132966 DBX1 (-) chr11 3 0.00903327 -0.105594 DGCR10 (+) chr22 3 0.0049075 -0.110006 DIP2C (-) chr10 3 0.00985842 0.178228 DIP2C (-) chr10 4 0.00842526 -0.227571 DKFZp686K1684 (+) chr11 3 0.00338747 -0.211311 DLK1 (+) chr14 4 0.00738296 -0.209159 DLX1 (+) chr2 3 0.0059498 -0.122298 DLX4 (+) chr17 7 0.00825154 -0.11694 DMRTA2 (-) chr1 3 0.00586294 -0.101035 DNAH11 (+) chr7 4 0.00916355 -0.112241 DNMT3A (-) chr2 3 0.00421263 -0.101107 DSG4 (+) chr18 3 0.00664466 -0.20463 EFCAB1 (-) chr8 4 0.00738296 -0.117434 EMX2 (+) chr10 7 0.00738296 -0.1146 EN1 (-) chr2 3 0.00881612 -0.115226 EN1 (-) chr2 5 0.00564579 -0.125369 EN2 (+) chr7 5 0.00529836 -0.1439 EN2 (+) chr7 4 0.00191088 -0.150903

108 ENPP7 (+) chr17 4 0.00234518 -0.131838 EPHB3 (+) chr3 5 0.00508121 -0.100644 ERICH1-AS1 (+) chr8 3 0.000347433 -0.185465 ERICH1-AS1 (+) chr8 3 0.000998871 -0.224704 ESPNP (-) chr1 4 0.00599323 -0.107094 ESR1 (+) chr6 9 0.00473378 -0.123313 ESYT2 (-) chr7 3 0.0072961 -0.112815 EVX1 (+) chr7 3 0.00725267 -0.140297 EVX2 (-) chr2 3 0.00599323 -0.12204 EYA4 (+) chr6 5 0.00920698 -0.125751 F7 (+) chr13 3 0.00881612 -0.102155 FAM110B (+) chr8 3 0.0083384 -0.146662 FAM135B (-) chr8 3 0.00738296 -0.10993 FAM153C (+) chr5 4 0.00186745 -0.128174 FAM183A (+) chr1 4 0.000912012 -0.139045 FAM184B (-) chr4 12 0.00634066 -0.249618 FAM19A4 (-) chr3 12 0.00182402 -0.172049 FAM19A5 (+) chr22 6 0.00985842 -0.172771 FBLIM1 (+) chr1 7 0.00299661 -0.139395 FBLN2 (+) chr3 4 0.00677495 -0.160012 FBRSL1 (+) chr12 3 0.00317033 -0.117117 FBXO39 (+) chr17 8 0.00959785 -0.101748 FBXW8 (+) chr12 4 0.00547208 -0.101695 FCGR3A (-) chr1 6 0.00599323 -0.130952 FCGR3B (-) chr1 4 0.00820811 -0.142804 FGF2 (+) chr4 3 0.00599323 -0.118383 FGF4 (-) chr11 3 0.00651438 -0.108798 FLJ31813 (-) chr10 4 0.00790411 -0.1006 FLJ37505 (+) chr12 6 0.00390862 -0.146432 FLJ41350 (+) chr10 7 0.000825154 -0.128863 FOXA1 (-) chr14 4 0.00108573 -0.140459 FOXB1 (+) chr15 5 0.00751325 -0.11048 FOXB1 (+) chr15 9 0.00195431 -0.225634 FOXC1 (+) chr6 3 0.00733953 -0.100531 FOXC2 (+) chr16 4 0.000651437 -0.194103 FOXF2 (+) chr6 3 0.00955442 -0.109831 FOXF2 (+) chr6 4 0.00421263 -0.110857 FOXI2 (+) chr10 10 0.00790411 -0.118836 FOXO3B (-) chr17 3 0.00846869 -0.109836 FRK (-) chr6 3 0.00264918 -0.115557 FRMD1 (-) chr6 3 0.00746982 -0.142472 FSTL1 (-) chr3 4 0.00972813 -0.139922 FUT9 (+) chr6 3 0.00799097 -0.102776 FZD2 (+) chr17 4 0.00564579 -0.180689 FZD7 (+) chr2 3 0.00130287 -0.103446 G0S2 (+) chr1 3 0.00677495 -0.107246

109 GAD1 (+) chr2 3 0.000260575 -0.114665 GAL3ST2 (+) chr2 3 0.00321376 -0.109967 GATA2 (-) chr3 5 0.000217146 -0.164724 GATA4 (+) chr8 3 0.00990185 -0.135479 GBX2 (-) chr2 4 0.00247546 -0.116554 GDF6 (-) chr8 8 0.00264918 -0.140928 GFRA1 (-) chr10 7 0.00503778 -0.158042 GHSR (-) chr3 4 0.0031269 -0.110046 GJD2 (-) chr15 3 0.00812125 -0.100309 GLTSCR1 (+) chr19 3 0.00568922 -0.117582 GNAL (+) chr18 6 0.00421263 -0.149989 GNB4 (-) chr3 8 0.00799097 -0.253079 GPNMB (+) chr7 3 0.00846869 -0.186138 GREB1L (+) chr18 7 0.00204117 -0.11736 GRID1 (-) chr10 10 0.00429949 -0.117066 GRIK3 (-) chr1 7 0.00412577 -0.167173 GRIN2A (-) chr16 4 0.000304004 -0.156174 GRIN2D (+) chr19 3 0.000998871 -0.167734 GSTM3 (-) chr1 4 0.00560236 -0.159142 GSX1 (+) chr13 4 0.00742639 -0.127466 GSX1 (+) chr13 4 0.00373491 -0.127695 GUSBP4 (-) chr6 4 0.00317033 0.158269 HAND2 (-) chr4 3 0.00460349 -0.110608 HFE2 (+) chr1 4 0.0080344 -0.128461 HIC1 (+) chr17 4 0.00820811 -0.147117 HIST1H1A (-) chr6 3 0.00920698 -0.125726 HIST1H3C (+) chr6 8 0.00673152 -0.133662 HIST1H4B (-) chr6 3 0.00360462 -0.166743 HIST1H4L (-) chr6 9 0.00925041 -0.180167 HLA-E (+) chr6 4 0.00117259 -0.15771 HLCS (-) chr21 5 0.00547208 -0.11366 HMX2 (+) chr10 6 0.00786068 -0.148236 HOXC-AS5 (-) chr12 5 0.00894641 -0.14245 HOXC13 (+) chr12 7 0.00668809 -0.139927 HOXC8 (+) chr12 3 0.00147659 -0.114832 HOXD10 (+) chr2 3 0.00894641 -0.115655 HOXD11 (+) chr2 7 0.00308347 -0.110779 HOXD11 (+) chr2 6 0.00651438 -0.179696 HS6ST1 (-) chr2 3 0.00573265 -0.101741 HSD17B1 (+) chr17 3 0.00816468 -0.112266 IER3IP1 (-) chr18 3 0.00738296 -0.156456 IFNG (-) chr12 3 0.00564579 -0.208946 INSIG2 (+) chr2 3 0.00820811 -0.138974 IRAK1BP1 (+) chr6 4 0.0080344 -0.129759 IRX1 (+) chr5 3 0.0034309 -0.145279 IRX4 (-) chr5 4 0.000260575 -0.12413

110 IRX5 (+) chr16 5 0.00885955 -0.151374 JPH3 (+) chr16 4 0.00616694 -0.114581 KCNG3 (-) chr2 3 0.0083384 -0.140532 KLHL23 (+) chr2 4 0.00707895 -0.112248 LAMP5 (+) chr20 6 0.00573265 -0.132303 LATS2 (-) chr13 3 0.00586294 -0.164958 LDHB (-) chr12 3 0.000825154 -0.109365 LGALS7 (-) chr19 3 0.000651437 -0.129759 LHX1 (+) chr17 7 0.0059498 -0.130791 LHX2 (+) chr9 3 0.00647095 -0.106318 LIFR (-) chr5 6 0.00182402 -0.161478 LINC00461 (-) chr5 4 0.00573265 -0.125705 LINC00472 (-) chr6 4 0.00599323 -0.168182 LINC00518 (-) chr6 5 0.00616694 -0.133474 LINC00617 (+) chr14 3 0.00451663 -0.15061 LINC00634 (+) chr22 3 4.34E-05 -0.223077 LINC00869 (+) chr1 5 0.000651437 -0.112486 LINGO3 (-) chr19 4 0.00599323 -0.20349 LOC100130872 (-) chr4 3 0.00399548 -0.106052 LOC100131289 (+) chr6 3 0.00673152 -0.109552 LOC100132111 (+) chr1 10 0.00959785 -0.216408 LOC100653515 (-) chr17 3 0.000434292 -0.118479 LOC286094 (+) chr8 3 0.00052115 0.1128 LOC732275 (-) chr16 4 0.000260575 0.158412 LOC93622 (+) chr4 5 0.00421263 -0.101134 LRRC9 (+) chr14 4 0.00577608 -0.16307 LRRTM3 (+) chr10 3 0.00052115 -0.139771 MANBA (-) chr4 4 0.00790411 -0.102658 MARCH3 (-) chr5 3 0.00204117 -0.123479 MC2R (-) chr18 3 0.00360462 -0.126837 MERTK (+) chr2 3 0.00773039 -0.118263 MGARP (-) chr4 3 0.000825154 -0.128147 MIPEPP3 (+) chr13 4 0.000738296 -0.135599 MIR4273 (+) chr3 3 0.00456006 -0.13375 MIR4669 (+) chr9 4 0.00373491 -0.129062 MIR4759 (+) chr21 3 0.000825154 -0.157673 MKI67 (-) chr10 5 0.00603665 0.115999 MOXD1 (-) chr6 9 0.00846869 -0.180126 MTMR7 (-) chr8 3 0.0017806 -0.159839 MYF6 (+) chr12 3 0.0083384 -0.113265 NACC2 (-) chr9 3 0.0034309 -0.121292 NCL (-) chr2 4 0.00903327 -0.101622 NEUROG2 (-) chr4 4 0.00642752 -0.111493 NKPD1 (-) chr19 3 0.00634066 -0.13536 NKX2-1 (-) chr14 9 0.00790411 -0.118838 NKX2-2 (-) chr20 4 0.00742639 -0.146774

111 NKX2-3 (+) chr10 5 0.00221489 -0.111932 NKX2-6 (-) chr8 5 0.00946756 -0.166437 NKX6-1 (-) chr4 3 0.000738296 -0.122099 NKX6-1 (-) chr4 5 0.00147659 -0.138666 NKX6-1 (-) chr4 3 0.000260575 -0.221036 NKX6-2 (-) chr10 6 0.00586294 -0.239943 NOS1 (-) chr12 5 0.00903327 -0.182558 NOTO (+) chr2 3 0.0059498 -0.121916 NR1I2 (+) chr3 4 0.0083384 -0.113892 NR2E1 (+) chr6 4 0.00525493 -0.139768 NR2F2 (+) chr15 3 0.00247546 -0.10352 NR2F2 (+) chr15 3 0.000998871 -0.105623 NR2F2 (+) chr15 3 0.00799097 -0.114991 NR2F2 (+) chr15 5 0.00108573 -0.130977 NR4A2 (-) chr2 6 0.00738296 -0.117671 NRG1 (+) chr8 4 0.0055155 -0.130314 NYAP2 (+) chr2 3 0.00694867 0.164249 OLFM4 (+) chr13 5 0.0069921 -0.174468 OLIG3 (-) chr6 3 0.00586294 -0.121759 ONECUT1 (-) chr15 5 0.00560236 -0.119922 ONECUT2 (+) chr18 10 0.000825154 -0.170447 OPN4 (+) chr10 3 0.00503778 -0.101257 OR1F1 (+) chr16 4 0.00994528 -0.101727 OR2V2 (+) chr5 3 0.00204117 -0.116824 OR51B2 (-) chr11 3 0.00256232 0.104824 OSR1 (-) chr2 4 0.00399548 -0.173314 OTX2 (-) chr14 6 0.00733953 -0.1621 PABPC3 (+) chr13 4 0.00877269 -0.148463 PALLD (+) chr4 4 0.00412577 -0.110142 PAQR9 (-) chr3 3 0.00825154 -0.176598 PAX6 (-) chr11 6 0.0059498 -0.154685 PAX8 (-) chr2 3 0.00881612 -0.124266 PAX9 (+) chr14 4 0.0059498 -0.147313 PAX9 (+) chr14 6 0.00568922 -0.150176 PAX9 (+) chr14 4 0.0049075 -0.199491 PBX1 (+) chr1 4 0.0034309 -0.173259 PCDH10 (+) chr4 7 0.00855554 -0.170823 PCDHA10 (+) chr5 3 0.00599323 -0.110258 PDE4B (+) chr1 4 0.00260575 -0.119202 PDGFRA (+) chr4 5 0.00599323 -0.169514 PHOSPHO1 (-) chr17 5 0.00881612 -0.117945 PI15 (+) chr8 3 0.00994528 -0.101824 PIP5KL1 (-) chr9 6 0.00421263 -0.11373 PITX1 (-) chr5 4 0.00673152 -0.102785 PITX1 (-) chr5 4 0.00920698 -0.166536 PITX2 (-) chr4 6 0.00951099 -0.122878

112 PITX2 (-) chr4 4 0.00581951 -0.151682 PKHD1L1 (+) chr8 3 0.000825154 -0.135254 PLAG1 (-) chr8 4 0.00421263 -0.169563 PLK1S1 (+) chr20 4 0.00599323 -0.11531 POU3F2 (+) chr6 7 0.00460349 -0.151552 PPFIA4 (+) chr1 4 0.0062538 -0.108134 PRDM14 (-) chr8 3 0.00877269 -0.10335 PRKAG2 (-) chr7 3 0.0034309 -0.120243 PRKG1-AS1 (-) chr10 3 0.000825154 -0.17103 PRPH (+) chr12 4 0.00438635 -0.127112 PRR26 (+) chr10 4 0.00573265 -0.128233 PRSS16 (+) chr6 4 0.0093807 -0.125884 PRTG (-) chr15 4 0.0069921 -0.113904 PRTG (-) chr15 5 0.0055155 -0.148688 PTF1A (+) chr10 4 0.00668809 -0.155546 PTF1A (+) chr10 3 0.00586294 -0.168632 PTF1A (+) chr10 4 0.00994528 -0.183049 PTPRN2 (-) chr7 5 0.0080344 -0.148416 PTPRO (+) chr12 3 0.00825154 -0.138047 QKI (+) chr6 4 0.00264918 -0.130738 RAB38 (-) chr11 6 0.00690524 -0.145285 RGS20 (+) chr8 4 0.00790411 -0.115355 RHOBTB1 (-) chr10 3 0.000651437 -0.171582 RHOD (+) chr11 3 0.00204117 -0.102708 RIMS2 (+) chr8 5 0.000955442 -0.147717 RNH1 (-) chr11 6 0.00403891 -0.101157 RNU6-53P (+) chr10 3 0.00577608 -0.154398 RPP21 (+) chr6 3 0.00516807 -0.167822 RPTOR (+) chr17 5 0.00286632 -0.105456 RPTOR (+) chr17 3 0.00147659 -0.107689 RTN1 (-) chr14 3 0.00686181 -0.154111 RUNDC3A (+) chr17 6 0.00269261 -0.175124 SALL3 (+) chr18 3 0.00568922 -0.144457 SH3PXD2A (-) chr10 4 0.00564579 -0.107306 SHH (-) chr7 9 0.00733953 -0.187464 SHOX2 (-) chr3 7 0.000825154 -0.115535 SHROOM1 (-) chr5 4 0.00577608 -0.146972 SIGLEC12 (-) chr19 3 0.00290975 0.104712 SIM1 (-) chr6 3 0.000955442 -0.120046 SIM1 (-) chr6 7 0.00438635 -0.205265 SIX1 (-) chr14 6 0.00738296 -0.14456 SKOR1 (+) chr15 4 0.000434292 -0.131161 SMIM21 (-) chr18 4 0.0083384 -0.161216 SNTG2 (+) chr2 4 0.00799097 -0.153132 SOCS2 (+) chr12 4 0.00529836 -0.128355 SOD3 (+) chr4 3 0.00647095 -0.142366

113 SOGA3 (-) chr6 5 0.0093807 -0.100011 SOX1 (+) chr13 5 0.000651437 -0.156865 SOX1 (+) chr13 3 0.00429949 -0.207389 SOX11 (+) chr2 3 0.00673152 -0.120955 SOX14 (+) chr3 5 0.00421263 -0.127687 SOX17 (+) chr8 5 0.00421263 -0.144494 SOX17 (+) chr8 4 0.00642752 -0.1598 SOX2-OT (+) chr3 4 0.00738296 -0.125552 SOX2-OT (+) chr3 3 0.00251889 -0.137564 SPACA1 (+) chr6 4 0.00634066 -0.156858 SPON2 (-) chr4 5 0.00955442 -0.110802 SQSTM1 (+) chr5 3 0.00738296 -0.145221 SRCIN1 (-) chr17 3 0.00738296 -0.169372 SRY (-) chrY 3 0.00473378 -0.249424 STK32B (+) chr4 7 0.00577608 -0.142847 SYBU (-) chr8 3 0.00825154 0.13096 SYNE1 (-) chr6 3 0.000998871 -0.142502 SYT10 (-) chr12 5 0.00508121 -0.138411 TBCA (-) chr5 5 0.00686181 -0.15037 TBR1 (+) chr2 5 0.00690524 -0.141979 TBX3 (-) chr12 3 0.00195431 -0.118642 TBX3 (-) chr12 3 0.00634066 -0.131511 TBX5-AS1 (+) chr12 4 0.00825154 -0.192604 TCF21 (+) chr6 3 0.00121602 -0.195568 TCTN3 (-) chr10 3 0.00629723 -0.118972 TET1 (+) chr10 3 0.00147659 -0.129042 TFAP2A (-) chr6 3 0.0023886 -0.121296 TFAP2D (+) chr6 3 0.00825154 -0.118586 THBS4 (+) chr5 9 0.00738296 -0.189163 TJP2 (+) chr9 4 0.00264918 -0.237592 TLX1 (+) chr10 4 0.00751325 -0.132019 TLX1 (+) chr10 6 0.0083384 -0.145738 TLX3 (+) chr5 6 0.0023886 -0.138421 TMEM132C (+) chr12 3 0.00686181 -0.153142 TMEM220-AS1 (+) chr17 3 0.00373491 -0.197777 TMEM30B (-) chr14 3 0.00881612 -0.121125 TMEM33 (+) chr4 8 0.000608008 -0.188624 TNRC6C (+) chr17 5 0.00573265 -0.12981 TOB2P1 (-) chr6 3 0.00269261 -0.142215 TRAPPC9 (-) chr8 3 0.00204117 0.100089 TRHDE-AS1 (-) chr12 3 0.00555893 -0.136762 TRIM71 (+) chr3 5 0.00768696 -0.161962 TRPM1 (-) chr15 3 0.00651438 -0.112502 TRPM2 (+) chr21 3 0.00360462 -0.10231 TTTY23 (+) chrY 3 0.00317033 -0.142511 TTTY23B (+) chrY 3 0.00373491 0.189596

114 TWIST1 (-) chr7 4 0.0023886 -0.142055 UBD (-) chr6 8 0.0093807 -0.114524 UPK1B (+) chr3 3 0.00773039 -0.100318 USP10 (+) chr16 3 0.000825154 -0.114727 USP29 (+) chr19 3 0.00121602 -0.102198 VAX2 (+) chr2 4 0.0083384 -0.166983 VCAN (+) chr5 6 0.000651437 -0.147223 VIPR2 (-) chr7 5 0.00412577 -0.221952 WASF3 (+) chr13 3 0.00052115 -0.115222 WDR67 (+) chr8 6 0.00994528 -0.100831 WDR76 (+) chr15 10 0.00599323 -0.150481 WNT6 (+) chr2 7 0.0083384 -0.173335 WNT7B (-) chr22 3 0.00916355 -0.116016 WNT9A (-) chr1 4 0.00994528 -0.105679 ZADH2 (-) chr18 5 0.00308347 -0.164867 ZFHX3 (-) chr16 3 0.00677495 -0.16133 ZFP42 (+) chr4 3 0.00621037 -0.163296 ZIC1 (+) chr3 4 0.00299661 -0.136592 ZIC1 (+) chr3 4 0.00434292 -0.195352 ZIC3 (+) chrX 4 0.0069921 -0.151264 ZIC4 (-) chr3 4 0.00742639 -0.119968 ZIC4 (-) chr3 3 0.00786068 -0.125181 ZIC4 (-) chr3 4 0.00295318 -0.126504 ZIC4 (-) chr3 3 0.000825154 -0.147824 ZIC5 (-) chr13 4 0.00256232 -0.122175 ZNF114 (+) chr19 3 0.00121602 -0.120196 ZNF213 (+) chr16 3 0.00147659 0.103982 ZNF382 (+) chr19 3 0.00712238 -0.104272 ZNF701 (+) chr19 3 0.00555893 -0.125284 ZNF804A (+) chr2 3 0.00790411 -0.14359 ZNF876P (+) chr4 3 0.00112916 -0.236097 ZRANB2-AS1 (+) chr1 3 0.00894641 -0.134259 ZYG11A (+) chr1 4 0.00790411 -0.127694

SW480 4 mg/L vs. SW480 16 mg/L

Gene Chromosome Probes in p-value Difference in Methylation Region ADAR (-) chr1 3 0.00290975 0.10302 ADCY8 (-) chr8 4 0.00703553 -0.122663 APC2 (+) chr19 3 0.00165031 -0.107772 BRSK2 (+) chr11 3 0.00191088 0.15168 BTBD3 (+) chr20 3 0.00881612 -0.114519 CD300E (-) chr17 3 0.00186745 -0.111304 COL6A4P2 (+) chr3 5 0.00356119 -0.100077 DCLK1 (-) chr13 4 0.000651437 -0.24213

115 DIP2C (-) chr10 6 0.00716581 -0.135181 DIP2C (-) chr10 5 0.0023886 0.171785 DIP2C (-) chr10 4 0.00486407 -0.257144 DIP2C (-) chr10 4 0.00573265 -0.261043 DIP2C (-) chr10 4 0.00138973 -0.280698 DIP2C (-) chr10 3 0.00951099 -0.180279 DNAJA1P5 (+) chr1 3 0.00351776 -0.143205 EN1 (-) chr2 3 0.0041692 -0.129551 ESR1 (+) chr6 3 4.34E-05 0.10222 ESRRB (+) chr14 3 0.00573265 -0.155502 ESRRG (-) chr1 3 0.00768696 -0.101707 FAM53A (-) chr4 3 0.000173717 -0.102678 FERMT3 (+) chr11 4 0.00191088 -0.106574 GABRA5 (+) chr15 3 0.0065578 -0.105653 GSDMC (-) chr8 3 0.00234518 -0.117605 IL21R-AS1 (-) chr16 3 0.00564579 -0.102963 KCTD4 (-) chr13 3 0.0062538 -0.12613 KIAA1217 (+) chr10 3 0.00898984 0.132298 KLF6 (-) chr10 3 0.00621037 -0.109297 KRTCAP3 (+) chr2 4 0.00942413 -0.102816 LINC00221 (+) chr14 4 0.00794754 -0.141009 LINC00634 (+) chr22 3 0.00946756 -0.10997 LOC100287834 (-) chr7 3 0.00112916 -0.165126 LOC100652739 (+) chr6 3 0.00516807 0.101509 LOC283688 (-) chr15 3 0.00117259 -0.100723 LOC401164 (+) chr4 3 0.000304004 -0.107494 LOC643623 (+) chr6 3 0.00621037 0.106254 LOC728724 (-) chr8 3 0.000738296 0.115878 MGC34034 (+) chr6 3 0.00256232 -0.118353 MPP7 (-) chr10 3 0.00660123 -0.111429 MS4A6E (+) chr11 3 0.00112916 -0.25168 MYH13 (-) chr17 3 0.00117259 0.117142 NCKAP5 (-) chr2 3 0.0093807 0.119383 OLFM1 (+) chr9 3 0.00165031 -0.117726 QKI (+) chr6 3 0.00755668 -0.107936 RASSF3 (+) chr12 3 0.00377834 -0.125915 SCCPDH (+) chr1 3 0.00395205 -0.111655 SH3PXD2A (-) chr10 5 0.00421263 -0.113418 SHC2 (-) chr19 3 0.00946756 0.107522 SNURF (+) chr15 4 0.00330062 0.161092 SRY (-) chrY 3 0.00377834 -0.367673 TGM2 (-) chr20 3 0.000217146 0.11112 TNXB (-) chr6 3 0.0028229 -0.10011 TSPY4 (+) chrY 4 0.00182402 -0.175366 TTTY23 (+) chrY 3 0.00204117 -0.263456 WDR60 (+) chr7 4 0.00573265 -0.154263

116 ZBED3 (-) chr5 3 8.69E-05 -0.163916 ZNF423 (-) chr16 3 0.00264918 -0.106061 ZNF556 (+) chr19 3 0.00777382 0.126523

117