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DNA methylation studies of preeclampsia and related conditions Blair, John 2013

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   DNA METHYLATION STUDIES OF PREECLAMPSIA AND RELATED CONDITIONS  by John Blair  B.Sc, McGill University, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  August 2013  ? John Blair, 2013  ii Abstract  Preeclampsia is a leading cause of maternal and fetal death throughout the world. It is caused by placental dysfunction and clinically characterized by hypertension and other adverse outcomes. Early-onset preeclampsia (EOPET) is a severe form of the disorder. Despite much investigation, the underlying biology of EOPET is unclear. It is known that disrupted oxygen delivery and altered cellular differentiation are characteristics of preeclampsia placentas, and that this likely has an effect on the placental molecular profile. This thesis primarily investigates DNA methylation, a key component in regulating gene expression, in placentas and cellular states related to EOPET. Investigating placental cells exposed to hypoxia, we found 147 CpG sites in cytotrophoblast whose DNA methylation was significantly altered by exposure to hypoxia for 24 hours. Many of these sites overlapped with the 223 CpG sites that were altered between normoxic cytotrophoblast and syncitiotrophoblast, however the change was in the opposite direction (hypomethylated vs. hypermethylated), implying hypoxia can molecularly prevent differentiation in trophoblast cells. Expanding on these findings to look at DNA methylation in placental tissue from preeclampsia pregnancies, we found significant differences at 282 CpG sites. Several of these differences occurred in genes that have functional relevance for the development of EOPET. Many of the candidate genes also showed differential gene expression in preeclampsia placentas. To investigate the utility of these candidate CpGs as 1st trimester EOPET biomarkers, placentas with increased susceptibility to preeclampsia (trisomy 16) were investigated across gestational ages. There were many DNA methylation differences in 3rd trimester trisomy 16 placentas that were shared with chromosomally normal 3rd trimester EOPET placentas, suggesting a common molecular profile of preeclampsia prone placentas,  regardless of etiology. Comparing 1st trimester trisomy 16 against 3rd trimester trisomy 16, we   iii found 77 CpG sites differentially methylated in both conditions, and further found 3 changes in first trimester trisomy 16 shared with 3rd trimester EOPET. Overall, these studies have identified several molecular changes in EOPET and related conditions that provide insight into the biology of the disorder while also providing novel candidates to investigate further in a clinical setting.        iv Preface  A version of chapter 2 has been published. Yuen, R.K., Chen, B., Blair, J.D., Robinson, W.P., Nelson, D.M. (2013) Hypoxia alters the epigenetic profile in cultured human placental trophoblasts. Epigenetics. 8(2):192-202. R.K.C.Y. and W.P.R. conceived the experimental design. B.C performed all cell culture protocols and extracted genetic material. R.K.C.Y. and I processed the chips and analyzed all of the methylation array data. R.K.C.Y. and I analyzed the expression array data. I designed, performed and analyzed the follow up qRT-PCR and pyrosequencing experiments. R.K.C.Y. and W.P.R. wrote the draft manuscript. All authors contributed to and edited the manuscript.    A version of chapter 3 has been accepted by Molecular Human Reproduction. Blair, J.D., Yuen, R.K.C., Lim, B.K., McFadden, D.E., von Dadelszen, P., Robinson, W.P. Widespread DNA hypomethylation at gene enhancer regions in placentas associated with early-onset preeclampsia. The study design was conceived by R.K.C.Y., W.P.R. and myself. P.V.D. and D.E.F. ascertained study samples. I (with R.K.C.Y.) processed the methylation array chips. I analyzed all of the methylation and expression array data. I (with B.K.L.) performed all the pyrosequencing experiments. W.P.R. and I wrote the draft manuscript. All authors contributed to and edited the manuscript.       v Table of Contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents .......................................................................................................................... v List of Tables ................................................................................................................................. x List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiv Acknowledgements ................................................................................................................... xvii Chapter  1: Introduction .............................................................................................................. 1  Overview ..................................................................................................................................... 1  Preeclampsia ................................................................................................................................ 2  Clinical characteristics ............................................................................................................ 2  Related disorders ..................................................................................................................... 4  Predisposition to preeclampsia ................................................................................................ 4  Biomarkers for the prediction of preeclampsia ....................................................................... 6  Treatment of preeclampsia ...................................................................................................... 7  The placenta ................................................................................................................................. 8  Placental development............................................................................................................. 8  Altered placental development can lead to preeclampsia ..................................................... 10  Hypoxia ..................................................................................................................................... 11  Gene expression in the placenta ................................................................................................ 12  Epigenetic alterations ............................................................................................................ 13   vi  DNA methylation and gene expression ................................................................................. 14  Quantifying DNA methylation .............................................................................................. 16  Goals of this thesis ..................................................................................................................... 18  Hypothesis ............................................................................................................................. 18  Objectives .............................................................................................................................. 19 1.6.2.1 Objective 1: Identification of DNA methylation profile altered by cell differentiation and hypoxia in cultured trophoblast ..................................................................................................................... 19 1.6.2.2 Objective 2: Identification of DNA methylation profile altered by presence of preeclampsia in 3rd trimester placenta ..................................................................................................................................... 19 1.6.2.3 Objective 3: Identification of DNA methylation markers in Trisomy 16 placentas ................... 20 Chapter  2: DNA methylation profile of cultured human placental trophoblasts ................ 28  Introduction ............................................................................................................................... 28  Methods ..................................................................................................................................... 29  Isolation and culture of primary human trophoblasts ............................................................ 29  Genomic DNA and total RNA extraction ............................................................................. 30  Illumina HumanMethylation450 array .................................................................................. 30  Bisulfite pyrosequencing ....................................................................................................... 31  Illumina HumanHT-12 v4 expression array .......................................................................... 31  Real time RT-PCR ................................................................................................................ 32  Bioinformatic analysis .......................................................................................................... 33  Statistical analysis of genomic features................................................................................. 33  Results ....................................................................................................................................... 34  Genome-wide DNA methylation analysis ............................................................................. 34  DNA methylation changes with oxygen exposure ................................................................ 34  DNA methylation changes with cell phenotype .................................................................... 35   vii  Gene ontology analysis of DNA methylation changes ......................................................... 37  Genomic characteristics and motif analysis of DNA methylation changes .......................... 37  Genome-wide expression analysis of cytotrophoblasts exposed to hypoxia ......................... 38  Confirmation of array results by bisulfite pyrosequencing and reverse-transcription real-time PCR 39  Discussion .................................................................................................................................. 40 Chapter  3: DNA methylation profile of early-onset preeclampsia placentas ....................... 52  Introduction ............................................................................................................................... 52  Methods ..................................................................................................................................... 53  Sample collection .................................................................................................................. 53  Illumina HumanMethylation450 BeadChip .......................................................................... 54  Data processing and analysis ................................................................................................. 54  Gene expression microarray .................................................................................................. 55  Pyrosequencing ..................................................................................................................... 56  Bioinformatics ....................................................................................................................... 56  Results ....................................................................................................................................... 57  Extensive DNA methylation profile changes in early-onset preeclampsia ........................... 57  EOPET, but not associated clinical features, presents a distinct DNA methylation profile.. 58  Differentially methylated loci showed negative correlation with gene expression ............... 59  Differences in DNA methylation are regional and different in related disorders ................. 60  EOPET shows differential hypoxia-related DNA methylation status ................................... 62  Genomic regional and functional enrichments of differentially methylated CpG sites ........ 63  Discussion .................................................................................................................................. 64   viii Chapter  4: Trisomy 16 placentas share a similar DNA methylation profile with early-onset preeclampsia placentas ............................................................................................................... 78  Introduction ............................................................................................................................... 78  Methods ..................................................................................................................................... 80  Samples ................................................................................................................................. 80  Illumina Infinium HumanMethylation450 BeadChip ........................................................... 81  Statistical analysis ................................................................................................................. 81  Bioinformatics ....................................................................................................................... 82  Results ....................................................................................................................................... 82  Methylation changes associated with CPM16....................................................................... 82  Overlap of methylation changes associated with CPM16 and EOPET ................................. 83  There are few methylation differences between CPM16 placentas that develop EOPET and those that do not ................................................................................................................................. 85  DNA methylation changes associated with 1st trimester Trisomy 16 placentas overlap those associated with 3rd trimester CPM16 placentas ................................................................................. 85  Gestational age is a major determinant for placental DNA methylation ............................... 86  Discussion .................................................................................................................................. 87 Chapter  5: Discussion .............................................................................................................. 100  Summary and significance of findings .................................................................................... 100  Strengths and limitations ......................................................................................................... 103  Future directions ...................................................................................................................... 107  Conclusion ............................................................................................................................... 110 References .................................................................................................................................. 111 Appendices ................................................................................................................................. 134   ix Appendix A : Supplementary material for chapter 2 ............................................................................ 134 Appendix B : Supplementary material for chapter 3 ............................................................................ 150 Appendix C : Supplementary material for chapter 4 ............................................................................ 184    x List of Tables Table 1.1:Summary of a subset of gene expression studies comparing preeclampsia and control placentas. Adapted from Louwen et al., 20126 ............................................................................. 23 Table 1.2: Selective summary of current methods for assessing DNA methylation. ................... 25 Table 2.1:Functions of differentially methylated loci between 8% and 20% versus low (<1%) oxygen levels. ............................................................................................................................... 44 Table 2.2: Functions of genes associated in common between hypoxia- and cell-phenotype-differentially methylated loci ........................................................................................................ 44 Table 2.3:Functions enriched for hypoxia-upregulated genes. ..................................................... 45 Table 2.4:Functions enriched for hypoxia-downregulated genes. ................................................ 45 Table 3.1: Clinical information of samples used in Illumina methylation array .......................... 70 Table 3.2: Number of candidate genes, without removing cross-mapping probes, obtained using different cut-off parameters for ?? and FDR................................................................................ 70 Table 3.3:Gene ontology top 10 hits for genes with altered DNA methylation. .......................... 71 Table 4.1: Clinical information for CPM16 samples used in this study ....................................... 91 Table 4.2:Number of candidate probes obtained using different cut-offs for 3rd Trimester CPM16 vs. Controls ...................................................................................................................... 91 Table 4.3: Gene ontology of the candidate CpGs altered in 3rd trimester trisomy 16 ................. 92 Table 4.4: Number of candidate probes obtained using different cut-offs for 1st Trimester T16 vs. Controls ......................................................................................................................................... 93 Table 4.5: Number of candidate probes obtained using different cut-offs for comparing 1st trimester T16 vs. 3rd trimester CPM16 ......................................................................................... 93   xi Table 4.6: Number of candidate probes obtained using different cut-offs for comparing 1st trimester control vs. 3rd trimester control ...................................................................................... 93 Table 4.7: Top 10 gene ontology terms for gestational-age specific probes ................................ 94    xii List of Figures Figure 1.1: Diagram of the developed placenta ............................................................................ 26 Figure 1.2: Schematic of probe binding in the Illumina HumanMethylation450 BeadChip. Adapted from Bibikova, et al. 2011112 .......................................................................................... 27 Figure 2.1:Cell culture condition for placental trophoblast samples ............................................ 46 Figure 2.2: DNA methylation patterns at each differentially methylated CpG and nearby sites . 47 Figure 2.3:Genomic characteristics of differentially methylated regions ..................................... 48 Figure 2.4: Motif analysis of hypoxia differentially methylated loci ........................................... 49 Figure 2.5: Gene expression and DNA methylation correlation of candidate genes .................... 50 Figure 2.6: Proposed model of hypoxia effect on cytotrophoblast ............................................... 51 Figure 3.1: Principle component analysis based upon values of the 282 significant differentially methylated probes ......................................................................................................................... 72 Figure 3.2: Boxplots from pyrosequencing follow-up .................................................................. 73 Figure 3.3: DNA methylation values between cases and controls according to genomic location........................................................................................................................................................ 74 Figure 3.4: Methylation vs. gene expression in four candidate genes .......................................... 75 Figure 3.5: Principle component analysis using valid CpG sites that are significantly altered in cultured cytotrophoblast exposed to hypoxic conditions for 24 hrs ............................................. 76 Figure 3.6: The candidate CpGs from the array fall into a variety of genomic elements ............. 77 Figure 4.1: Schematic of the comparisons in this study ............................................................... 95 Figure 4.2: Distribution of 2254 significant probes significantly different in CPM16 as compared to the whole array.......................................................................................................................... 96   xiii Figure 4.3: Principal component analysis using the 282 candidate CpG probes significantly different in EOPET vs. controls (chapter 3) ................................................................................. 97 Figure 4.4: Histogram showing the distribution of ??s from controls in the two groups of CPM16 samples. ........................................................................................................................... 98 Figure 4.5: Line plot of DNA methylation in JUNB (A) and ARHGEF37 (B), two genes that maintain differences across gestational age and EOPET .............................................................. 99      xiv List of Abbreviations ADAM12 Disintegrin and metalloproteinase domain-containing protein 12 ANCOVA Analysis of co-variance ALT  Alanine transaminase AP-1  Activator protein 1 AST  Aspartate transaminase BLAT  Basic local alignment tool bp  Base pairs BP  Blood pressure cffDNA Cell-free fetal DNA ChIP  Chromatin immunoprecipition CpG  Cytosine-phosphate-guanine dinucleotide CPM  Confined Placental Mosaicism CT  Cycle threshold DAVID Database for annotation, visualization and integrated discovery DNA  Deoxyribonucleic acid DNMT DNA methyltransferase ENCODE Encyclopedia of DNA elements ENG  Endoglin EOPET Early onset preeclampsia FDR  False discovery rate GDM  Gestational diabetes mellitus GO  Gene ontology   xv HC  High density CpG   hCG  Human chorionic gonadotropin HELLP Hemolysis elevated liver enzymes low platelet count HIF  Hypoxia inducible factor HLA  Human leukocyte antigen HPLC   High performance liquid chromatography HRE  Hypoxia response element IC  Intermediate density CpG  IUGR  Intrauterine growth restriction kb  Kilobases LC  Low density CpG area LDH  Lactic dehydrogenase LEP  Leptin LOPET  Late onset preeclampsia Mb  Megabases MeDIP Methylated DNA immunoprecipitation MEME Multiple em for motif elicitation O/E  Observed/Expected PAPP-A Pregnancy associated pregnancy protein A PCA  Principal component analysis PCR   polymerase chain reaction PET  Preeclampsia PlGF  Placental growth factor   xvi qRT-PCR quantitative reverse transcription polymerase chain reaction RNA  Ribonucleic Acid RRBS  Reduced representation bisulfite sequencing SAM  Significance analysis of microarrays sFLT1  soluble fms-related tyrosine kinase 1 SWAN Subset-quantile within array normalization T  Trisomy TF  Transcription factor UCSC  University of California, Santa Cruz VEGF  Vascular endothelial growth factor xvii Acknowledgements  I would first like to acknowledge my supervisor, Dr. Wendy Robinson, for taking a chance on me as a summer student and supporting me throughout my graduate studies. Thank you to my committee Dr. Matt Lorincz and to Dr. Sylvie Langlois, who recommended me to Dr. Robinson in the first place. Thank you to all Robinson Lab members, past and present, who have assisted me through this research with helpful suggestions and timely advice: Courtney, Magda, Ryan, Dan, Maria, Ruby, Irina, Kirsten, Sam, Sean, Brendan, Joanna and Kristal. Also thank you to my parents for encouraging my interest in science.  1 Chapter  1: Introduction   Overview  Preeclampsia (PET) is one of the most common pregnancy disorders, occurring at a rate of 3-5% of pregnancies1. Currently there is no cure except delivery, and it is potentially fatal to both the mother and developing fetus. It leads to thousands of deaths per year, primarily in developing countries2. While much work has been done to understand the clinical aspects of the disorder, the underlying etiology is still poorly understood. It is known that pregnancies affected by preeclampsia have placental insufficiency, characterized by shallow invasion of the maternal uterus and reduced remodeling of the maternal blood vessels that provide nutrients and oxygen3. It is suspected that poor placentation prolongs the hypoxic state experienced by the early developing embryo, which is followed by a rapid reoxygenation that can cause cellular damage and placental microparticle shedding4. This hypoxia/reoxygenation also deregulates many important processes in the placenta, including differentiation of cytotrophoblast to syncytiotrophoblast, necessary for hormone production and nutrient regulation. The placental microparticles that enter the maternal bloodstream create an inflammatory effect and may result in the characteristic clinical symptoms of hypertension and proteinuria5.  Many studies have looked at the altered expression of genes in preeclampsia affected pregnancies6, but few have looked at DNA methylation changes7,8. DNA methylation may be an easier to study representation of the altered gene expression in the placenta due to its stability9, providing a more consistent picture of the altered molecular state. In this thesis, I used the Illumina Infinium HumanMethylation450 BeadChip to assess DNA methylation in preeclamptic  2 placentas and other related conditions. Furthermore, investigating DNA methylation in different types of trophoblast cells that are affected by varying degrees of hypoxia may detect patterns that are consistent with the DNA methylation in preeclamptic placentas. This comparison may elucidate the role of hypoxia and cell type in driving the molecular changes in preeclampsia. Once identified, we can investigate the consistency of those changes across gestational ages in placentas that are more prone to developing preeclampsia. This information can be used to 1) classify placentas based on their underlying etiology or 2) identify potential biomarkers for serum protein or DNA methylation based testing of cell-free DNA in the maternal blood that can identify at risk pregnancies in the first trimester. Early identification of at-risk pregnancies can potentially mitigate the drastic effects of preeclampsia through proper management.    Preeclampsia   Clinical characteristics  The clinical definition of preeclampsia can vary depending on guidelines from different international organizations10. The one consistent characteristic is severe hypertension; beyond that there are several symptoms that can separate preeclampsia from other forms of ?pregnancy induced hypertension?. In this thesis, the clinical guidelines described by the Society of Obstetrics and Gynaecology of Canada will be used to identify cases of preeclampsia11, defined as a combination of: i) hypertension (systolic BP ?140mmHg and/or diastolic BP ?90mmHg measured twice, >4 hours apart) manifesting after 20 weeks gestation, and ii) new-onset proteinuria (?0.3 g/day or ?2+ dipstick) or one or more adverse conditions including maternal  3 symptoms (persistent or new/unusual headache, visual disturbances, persistent abdominal or right upper quadrant pain, severe nausea or vomiting, chest pain or dyspnea), maternal signs of end-organ dysfunction (eclampsia, severe hypertension, pulmonary edema, or suspected placental abruption), abnormal maternal laboratory testing (elevated serum creatinine; elevated AST, ALT or LDH with symptoms; platelet count <100x109/L; or serum albumin < 20 g/L), or fetal morbidity (oligohydramnios, intrauterine growth restriction, absent or reversed end-diastolic flow in the umbilical artery by Doppler velocimetry, or intrauterine fetal death).  Preeclampsia can be further classified by several traits, particularly by gestational age of onset. Early onset preeclampsia (EOPET) occurs before 34 weeks gestation while late-onset (LOPET) occurs after 34 weeks12. While they both may present with the same clinical features, EOPET is often associated with more severe maternal and fetal outcomes in particular intrauterine growth restriction (IUGR)13 and by definition, premature birth. There is increasing evidence that the underlying etiology of EOPET and LOPET are distinct. This is supported by the finding that placental pathologies, such as advanced villous maturation and altered placental gene expression are more prevalent in EOPET14. LOPET on the other hand, shows less severe placental pathology15,16 suggesting complications stem from maternal risk factors such as obesity17.  Preeclampsia can also be sub-classified by the severity of proteinuria. Studies and clinics may use a proteinuria threshold of >2g/day (heavy), which when combined with more severe hypertension (>160/110 mmHg), is one definition of severe preeclampsia18, however the degree of proteinuria is not associated with a difference in outcome19. Furthermore, the proteinuria dependent classification is hampered by the inaccuracy of current methods of measurement20.  4  Related disorders  Other disorders may be related to preeclampsia, as they present with overlapping symptoms and placental pathologies and often occur jointly. Hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome is one such disorder, and it rarely occurs independently of preeclampsia21. HELLP syndrome can cause severe liver damage and even be fatal for the mother. Another disorder, intrauterine growth restriction (IUGR) can exist independently (normotensive IUGR), but occurs more often with preeclampsia. IUGR is a failure of the fetus to achieve the expected growth parameters and in Canada is clinically defined as 1) birthweight below the 3rd percentile for sex and gestational age or 2) birthweight below the 10th percentile with persistent uterine artery notching at 22-25 weeks, indicating impaired uterine circulation, or absent or reversed end diastolic velocity on umbilical artery Doppler, indicating increased fetal vascular stress or oligohydramnios. Normotensive IUGR often shares clinical characteristics with preeclampsia such as altered Doppler ultrasound and oligohydramnios. Pathologically, IUGR can be caused by poor placentation, among other etiologies, resulting in a reduced capacity of the placenta to deliver nutrients to the developing fetus, leading to restricted growth22.    Predisposition to preeclampsia   While the underlying cause of preeclampsia remains unknown in most individuals, there are several factors that can increase one?s risk of developing the disorder. These factors can range from the maternal environment to the nature of the pregnancy itself.  Chronic hypertension  5 is one of the biggest factors causing an increased rate of preeclampsia in affected individuals, estimated at 15-25%23. The maternal vasculature is already stressed, making it more sensitive to aberrant artery remodeling during early pregnancy. Other strong maternal predictors include a previous pregnancy with preeclampsia24, obesity25, maternal age26,27, autoimmune disorders28 and polycystic ovarian syndrome29.   Fetal/placental factors in pregnancy can also increase or decrease the risk of developing preeclampsia. Large chromosomal abnormalities, such as trisomy, often miscarry in the first trimester, making it difficult to establish their impact on risk; however some chromosomal disorders can carry to term or be confined to the placenta and are viable. Pregnancies with an excess paternal contribution, as can occur in triploid pregnancies or androgenetic conceptions, have an increased risk of developing preeclampsia30,31. Other chromosomal abnormalities may also result in increased incidence, including trisomy 13 and 16.  Individuals with full trisomy 13 that carry to term are born with Patau Syndrome and most do not survive beyond the neonatal period. Several genes linked to preeclampsia, such as FLT1, are located on chromosome 13, which could contribute to the 4-5 times increased risk of developing preeclampsia32 in these pregnancies. Trisomy 16 is the most commonly detected autosomal aneuploidy33. While pregnancies with full trisomy 16 miscarry in the first trimester, trisomy 16 that is confined to the placenta (confined placental mosaicism or CPM16) often carry to term. Such pregnancies are associated with fetal IUGR and roughly 25% of these pregnancies are affected by preeclampsia, a rate that is 5-10 times higher than the population average34. In contrast, trisomy 21 has a decreased risk of preeclampsia, and is associated with a deficiency in differentiation of cytotrophoblast to syncytiotrophoblast35. This deficiency may reduce the amount of placental microparticles entering the maternal blood stream, which is implicated in onset of preeclampsia.  6  Biomarkers for the prediction of preeclampsia   Detecting preeclampsia before the onset of symptoms should be an integral step to managing a potential preeclampsia pregnancy as early prediction will help lead to better management of patients. Biomarkers can provide important information to identify at-risk pregnancies. The least invasive screening method is to utilize the serum of maternal blood to measure altered protein levels. Many genes are over-expressed in the preeclampsia placenta as compared to a normal pregnancy. As with any pregnancy, factors that are abundant in the placenta may enter the maternal bloodstream and be detectable over baseline levels.  Markers that screen for increased risk of chromosomal abnormalities are the most commonly studied for applicability to predicting preeclampsia risk e.g. human chorionic gonadotropin (hCG) and Pregnancy Associated Placental Protein ? A (PAPP-A). Other proteins such as Inhibin, Activin, Disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), Placental Growth Factor (PlGF), and Placental Protein 13 (PP13) have also been indicative of preeclampsia in some studies36. Individually, these markers are not sensitive or specific enough to detect cases that will develop into EOPET, however when combined, especially with Doppler ultrasound, the detection rate is higher, but still not clinically acceptable37,38. There are other maternal serum markers, although not well studied yet, that show promise for clinical implementation such as sFLT-1, Leptin and Endoglin39,40.  Alternatively, several non-protein markers may also indicate increased risk of preeclampsia. Circulating cell-free fetal DNA (cffDNA) is a phenomenon common to all pregnancies. As placental cells undergo apoptosis, they lyse and their DNA is released into the maternal bloodstream. During a normal pregnancy, the level of fetal cell-free DNA in the  7 maternal blood is close to 10%, however in preeclampsia pregnancies this can rise41-44. The level of cffDNA itself can be used as a biomarker or one can test aspects of the cffDNA45. DNA methylation-based methods have previously been able to determine sex46 and chromosomal abnormalities47 in the developing fetus based upon individual methylation markers. Certain loci have been identified as potentially good markers for DNA methylation-based diagnosis of preeclampsia based on candidate differences in preeclampsia placentas as compared to control placentas and maternal blood7,48.    Treatment of preeclampsia  The only complete cure for preeclampsia is delivery of the fetus (and subsequently the placenta). As the placenta is the primary agitator in preeclampsia, removing it from the mother allows her to recover from the burden of placental microparticles shedding into the bloodstream. Encouragingly, there are ways of managing the symptoms of the disorder, including placing the mother on antihypertensive therapy such as labetalol, although this is only recommended for cases with severe hypertension (>160/110 mmHg)2.  If the fetus is not delivered soon after onset of symptoms and preeclampsia is allowed to progress, the mother can suffer from eclamptic seizures. This is the most common preeclampsia-related cause of death, as cranial hemorrhage may happen during episodes.  If preeclampsia is early onset, there is a fine line between managing the symptoms to mitigate the complications in the mother all while postponing premature delivery. If the child must be delivered early, as in most cases, corticosteriod treatments are given to progress fetal lung maturation, but these may have significant molecular  8 effects on the placenta49. Furthermore, premature birth could have serious health implications for the child later in life50.   The placenta  The placenta is the mediator between the developing fetus and the mother, and is necessary for gas and nutrient exchange, hormone secretion and blood cell production. In humans, the placenta is discoid in shape and weighs between 500-600 g at term. The multi-lobed villous trees of the placenta are the site of nutrient exchange as they are bathed in the blood from the maternal arteries flowing into the space between them. These villi are composed of a mesenchymal core, surrounded by trophoblast. The villi are anchored to the endometrial wall by cytotrophoblast columns extending into the maternal decidua (Fig. 1.1).   Placental development  Development of the placenta (placentation) occurs after implantation of the embryo into the uterine wall.  Trophoblast cells, originating from the trophectoderm (the outer cell layer of the blastocyst), are exposed to the hypoxic conditions of the uterus and begin to invade the endometrium51.  During the initial stages of invasion, the trophoblast secretes metalloproteinases (MMP-9 and MMP-2), which erode the extracellular matrix of the endometrium52. Trophoblast is composed of two different types of cells, cytotrophoblast and syncytiotrophoblast. Cytotrophoblast cells begin to form primary villous columns behind the advancing synctiotrophoblast53. A subset of cytotrophoblast cells differentiate into the extravillous  9 trophoblast (EVT) and move beyond the villi to encounter the maternal spiral arteries54. Much like a metastasizing cancer, the EVT cells express angiogenic factors (Placental Growth Factor, PlGF ; Vascular Endothelial Growth Factor, VEGF and others) to remodel these arteries, initiating blood flow, bringing oxygen and nutrients to the developing embryo54. The remodeling replaces the thick walls of the arteries with weaker, loose tissue to allow for increased blood flow during pregnancy.  As placentation continues, the cytotrophoblast columns move through the syncytiotrophoblast and connect at their ends, forming the cytotrophoblast shell54. The creation of the shell forms empty spaces between the cytotrophoblast columns called lacunar spaces, which are lined with syncytiotrophoblast. These multinucleated cells now help mediate nutrient flow and produce hormones, including human chorionic gonadotropin (hCG), a hormone that prolongs the viability of the corpus luteum. This enables the continued production of progesterone, a hormone necessary for the maintenance of the pregnancy, until the placenta can take over its production. As development proceeds, the primary villous columns develop into chorionic villi, structures containing the placental arteries and veins. These villi develop clonally, as ?trees?53. The cytotrophoblast shell regresses and now makes up the basal plate, to which the anchoring villi are attached (Fig. 1.1). As the placenta takes its familiar discoid shape and slows its development at 10-12 weeks gestation, the fetus begins to utilize its increased nutrient capacity.   Throughout the process of placentation, there are maternal immunological processes that must be altered to prevent an immune response to the genetically distinct placental cells interacting with the endometrium at the maternal-fetal interface. Invasive trophoblast cells express a different subset of recognition antigens than most cells, suppressing HLA-A and HLA- 10 B regions and instead expressing HLAs-C, E and G55. These cells encounter a unique set of maternal immune cells known as uterine natural killer (uNK) cells. These cells have limited cytotoxic ability, instead secreting cytokines upon recognition of the antigens from trophoblast cells56. Some of these cytokines, such as Leukemia Inhibitory Factor57 and Galectin-158,59, are autoimmunosuppressive while others, like MMP-2, assist with EVT invasion in early gestation60,61. Furthermore, hormones secreted by the syncytiotrophoblast and corpus luteum such as hCG62 and progesterone63 have immunosuppressive properties themselves. Working together, these molecules allow for the implantation and continued maintenance of a pregnancy throughout gestation.    Altered placental development can lead to preeclampsia  EOPET is a disorder of placental insufficiency. The insufficiency stems from poor invasion and spiral artery remodeling3. One of the leading hypotheses for the source of shallow placentation is an increased maternal immune response from altered trophoblast antigen expression or recognition. Disruptions of HLA recognition or expression can initiate cytotoxicity of the uNKs, preventing trophoblast invasion64,65. These disruptions may be caused by paternal or de novo polymorphisms in the embryo?s HLA genomic region66,67. Immunorecognition of these altered antigens through pre-pregnancy exposure to the paternal HLA antigens present in seminal fluid has been shown to decrease incidence of preeclampsia68,69. Shallow placentation results in restricted blood flow, leading to a state of semi-permanent hypoxia, or low oxygen tension. Hypoxia is a natural part of placentation, as hypoxic conditions in the uterus cause the early trophoblast to release angiogenic factors to develop blood vessels70.  11 A more permanent hypoxic state may prevent trophoblast differentiation from cytotrophoblast to syncytiotrophoblast through hypoxia induced transcription factors71,72. As mentioned before, the syncytiotrophoblast is essential for the production of hormones and nutrient regulation that maintain the pregnancy and promote growth. The cellular stress placed on the trophoblast from prolonged exposure to the hypoxic enivronment may cause a degree of apoptosis73, releasing microparticles into the maternal bloodstream which may lead to a inflammatory response and manifestation of the clinical symptoms of preeclampsia5. The poor spiral artery remodeling restricts blood flow and oxygen to the placenta and in turn the embryo, which may be a reason for the increased incidence of IUGR in preeclampsia-affected pregnancies74.  Stress induced apoptosis stemming from reoxygenation following hypoxia is another contributing factor to the etiology of preeclampsia4. The hypoxia-reoxygenation process is often repeated and may cause more damage with each cycle, increasing reactive oxygen species which subsequently increases oxidation induced apoptotic factors75, again leading to microparticle shedding of the trophoblast.    Hypoxia  Hypoxia is a common biological stressor. A normoxic cellular environment for somatic tissues is approximately 8% oxygen, while severe hypoxia, like that experienced by the first trimester placenta, is a 2% oxygen environment76. After invasion and arterial remodeling, normal oxygen levels in the placenta exist at around 8%.  Hypoxic conditions occur naturally, and eukaryotes have evolved many pathways to compensate77. The key hypoxia response involves the hypoxia inducible factor 1 (HIF1) pathway. Exposure to hypoxia increases expression of  12 HIF1-? (HIF1A)78. HIF1-? binds with HIF1-? (ARNT) in a dimer to the genomic ?hypoxia response element? (HRE), represented by an -RCGTG sequence motif, in the promoter region of several genes79,80.  The binding of HIF1 upregulates expression of  target genes, many of which are angiogenic factors78.  There are numerous other hypoxia-induced factors that work in the same or related pathways including: HIF2-? (EPAS1) and HIF3-? (HIF3A). When the hypoxia response pathway is initiated, its main targets are transcription factors that increase the cells ability to acquire more oxygen. These include angiogenic factors that create new blood vessels such as VEGF, and VEGF receptor (FLT1), as well as factors that increase molecular transport and direct cell differentiation and growth.     Gene expression in the placenta  Gene expression in the placenta is very unique because it is a transient organ and has often been compared to a metastasizing cancer81. The rapid growth of the placenta in the first trimester is dependant on the expression of a variety of growth factors. EVT is a key cell population that expresses Placental Growth Factor (PGF)82 and Vascular Epidermal Growth Factor (VEGFB and VEGFC)83 to promote EVT cell proliferation, migration and invasion. These factors interact with the VEGF receptor (FLT1), which is also expressed by the EVT. As the EVT invades and the maternal spiral arteries are remodeled, numerous angiogenic factors in addition to VEGF, PlGF and VEGFR are expressed including, fibroblast growth factors (FGFs) and angiopoetins84.  Studies investigating the differences in expression between preeclampsia and control placentas have repeatedly found a subset of several genes that are over-expressed in preeclampsia (PET) (Table 1.1)6.  Proteins used as maternal serum biomarkers are among those  13 genes identified, including INHA (Inhibin A), FLT1, LEP (Leptin), PAPPA2, and CGA and CGB (hCG). The overexpression of these genes, important for placental development, implies that altered placentation is a key factor in the manifestation of preeclampsia.  In addition to gestational age as an important confounding factor, concerns with studies comparing gene expression in preeclampsia versus controls are the rapid degradation of placental RNA post-delivery9 and the effect of mode of delivery85. There are a multitude of  RNases in the placenta working in a non-predictive manner post-delivery86, making interpretation of results from expression studies difficult. As an alternative, one can analyze DNA methylation. DNA methylation is stable post-delivery and can thus provide a reasonably accurate portrait of the molecular state of the placenta in utero9.    Epigenetic alterations  Epigenetic marks are non-sequence based changes to the genome, including histone modifications and DNA methylation. Differences in these epigenetic marks can correlate with differential gene expression and are often tied to gene regulation. Histones form octameric protein complexes that are the structural base of compacted DNA, chromatin87. When the polypeptide tail of a histone is modified with the addition or removal of a chemical group (e.g. acetylation, phosphorylation and methylation), this can affect the chemical or physical interaction with its surroundings. These interactions can open the chromatin, exposing it to various transcription factors and DNA polymerase (euchromatic state), or they can close the chromatin effectively shutting off transcription (heterochromatic state)87.   14 DNA methylation is the addition of a methyl group (-CH3) to the 5? position of a cytosine base in the genomic sequence, creating 5?-methylcytosine (5?mC). The presence of the methyl group affects protein interactions with the genome through physical blockage or recruitment of methyl binding proteins. The addition of the methyl group is performed by the DNA methyltransferase (DNMT) enzymes and occurs predominantly at CpG dinucleotides88,89. During cell division, DNMT1 recognizes ?hemimethylated? CpG sites on newly replicated DNA strands and adds a methyl group to the corresponding cytosine on the newly replicated DNA strand, maintaining the methylated state. DNMT3a and b act as de novo methylators, targeting completely unmethylated CpGs.  Investigating histone modifications can be useful for determining a comprehensive gene regulation profile for a given cell type90, however the labour intensive analysis is difficult in heterogenous cell populations like whole chorionic villus samples. DNA methylation is relatively inexpensive to quantify and identifying biomarkers may have more immediate clinical utility45. Both epigenetic marks can indicate gene expression and promoter activity. However, using publicly available resources90, one can integrate consensus histone marks into DNA methylation data analysis, providing a potentially more complete regulation profile in a given sample.    DNA methylation and gene expression  DNA methylation of CpG sites has been repeatedly associated with gene regulation. CpG sites are concentrated in generally unmethylated genomic elements called ?CpG islands?91, as methylated cytosines have a tendency to deaminate and mutate into thymines over evolutionary time92. In this thesis I refer to two subgroups of CpG Islands, which vary in their behaviour:  15 high-CpG (55% GC-content, 0.75 observed/expected and >500bp long) and intermediate-CpG (50% GC-content, 0.48 O/E and >200 bp long) density93. CpG islands are commonly located near the promoter region of genes and epigenetic modifications of them can play a role in gene expression92. A plethora of studies have shown a correlation between gain of DNA methylation in the promoter region (and associated CpG island) and decreased gene expression (although many studies have also shown no correlation). This is caused by either physically preventing the binding of transcription factors or by recruiting methyl binding proteins, which act as repressors of gene transcription94. However, not all methylated promoters are paired with inactive genes and not all unmethylated promoters are actively transcribed, indicating that DNA methylation at promoter regions is not the only factor regulating gene expression36.  In non-promoter regions, altered DNA methylation may be a representative mark of altered expression. This is reflected in the observation that many of the largest changes in DNA methylation may occur in areas known as the CpG island shores95, the area immediately adjacent to the CpG island. Decreased methylation in these areas may be indicative of DNA polymerase activity or transcription factor binding on the adjacent CpG island. Alternatively, increased CpG-methylation in the gene body can be associated with increased transcription96.  The DNA methylation profile of any individual is dynamic, especially when compared to the static sequence of the genome. During development, the DNA methylation profile of the fetus and placenta can change drastically in a matter of weeks to allow for differentiation of cell types and response to environmental cues97. After birth, change in DNA methylation over time has been demonstrated in the brain tissue and blood of normal individuals taken at different ages98. This has led to the understanding that over time, epigenetic changes in an individual are a common occurrence that may be influenced by environmental exposures99. Chemical  16 exposure100,101 and physical interaction102 are examples of environmental events that have been shown to alter DNA methylation.  DNA methylation changes in disease have also been well-documented. In many cancers, methylation at the promoter region of a tumor suppressor is directly correlated with repression of that gene and subsequent cancer development103-106. Loss of imprinting disorders, such as Beckwith-Wiedemann Syndrome, often show altered methylation of the imprinted (methylated) loci, causing loss of or biallelic gene expression, resulting in disease107.    Quantifying DNA methylation  Quantification of DNA methylation has advanced significantly in the past decade, with the advent of rapid and highthroughput techniques (Table 1.2). For any given study, the experimental approach depends on whether one wants to measure DNA methylation 1) globally (a genomic average), 2) genome wide (i.e. the distribution of methylation across the genome) or 3) at targeted regions including specific CpG sites.  To obtain an average measurement of DNA methylation across the genome one can use fluorescently labeled antibodies which attach to regions of methylated DNA, then the overall fluorescence can be detected by an ELISA reader108. Alternatively, one can run high-performance liquid chromatography (HPLC) to assay the proportion of unmethylated cytosines vs. methylated cytosines109. This allows one to determine whether there is a large non-specific loss of DNA methylation in a given condition and is expressed as a percentage of methylated cytosines out of total cytosine bases.   17 Genome-wide measurements of DNA methylation can be done using array-based or sequence-based technology. For array-based technologies, the level of resolution depends on the probes used which may range from single CpGs to larger (1kb). Regional array-based  measures of DNA methylation can employ methylated DNA immunoprecipiation (MeDIP) to pull down methylated fragments of DNA, followed by hybridization to a microarray (MeDIP-Chip)110. In this thesis, to look for single CpG DNA methylation differences throughout the genome, the Illumina HumanMethylation450 BeadChip platform is used. This microarray interrogates 482,421 CpG sites covering 99% of known genes111. Individual CpGs are targeted by adding fluorescently labeled nucleotides to probes bound with bisulfite converted DNA (Fig. 1.2). Each probe is replicated thousands of times, resulting in a continuous variable representing DNA methylation. Sequencing technology is also useful for assessing DNA methylation. Quantifying DNA methylation at a single base resolution is key for identifying DNA methylation mediated changes at transcription factor binding sites and identifying the separation between fully, partially and unmethylated DNA, all of which may have indications for gene expression. The most comprehensive method for single base resolution DNA methylation analysis is full genome bisulfite sequencing. For large scale population studies the cost is often prohibitive due to the large number of reads required to provide a reasonably quantitative value. Reduced representation bisulfite sequencing (RRBS) is a popular method using restriction enzymes to exclusively isolate CpG rich regions, which are then bisulfite sequenced, greatly reducing the volume of DNA that needs to be sequenced and theoretically increasing the resolution at each locus112. Sequencing technology can also be used in conjunction with MeDIP (MeDIP-seq)110,  18 which sequences regions in the pulled down fraction of DNA and maps them to the genome, with highly methylated domains being overrepresented.  In this thesis, bisulfite pyrosequencing is used for targeted assessment of DNA methylation113. This method is highly sensitive and inexpensive, however it can only interrogate targeted regions of ~100 bp one at a time. Briefly, bisulfite converted DNA from a region of interest is amplified by PCR and the products are then sequenced in real time. The polymerase is conjugated to a phosphorescent enzyme, producing light with every reaction. The amount of light detected by the sequencing machine corresponds to the number of nucleotides being incorporated at the time. When approaching a methylated cytosine (equivalent to a C/T SNP after bisulfite conversion), cytosine and thymine are dispensed sequentially, and the ratio of light with each reaction is interpreted as the percent methylation at that locus.     Goals of this thesis   Hypothesis  Exposure of placental cells to different levels of oxygen, early stages of preeclampsia and trisomy 16 results in stable changes in DNA methylation, reflecting altered gene expression. These alterations in DNA methylation can be used to improve early screening tools for preeclampsia.    19   Objectives  1.6.2.1 Objective 1: Identification of DNA methylation profile altered by cell differentiation and hypoxia in cultured trophoblast  The first objective of this thesis is to characterize molecular changes in cultured cytotrophoblast and syncytiotrophoblast cells exposed to hypoxia for 24 hours. DNA and RNA will be analyzed using the Illumina HumanMethylation450 BeadChip and the Illumina HT-12v4 Expression BeadChip respectively. Altered DNA methylation and biological processes in each cell type will be characterized through gene ontology and bioinformatics. Common patterns between the cell types will also be characterized.   1.6.2.2 Objective 2: Identification of DNA methylation profile altered by presence of preeclampsia in 3rd trimester placenta  A distinct molecular profile of EOPET will be identified by running placental DNA and RNA samples from EOPET pregnancies on the Illumina HumanMethylation450 BeadChip and the Illumina HT-12v4 Expression BeadChip respectively. This profile will be used to distinguish EOPET placentas from other related disorders as well as separate samples into clusters based on concurrent issues or severity of the disorder. Furthermore, this profile will include specific altered genes that can help elucidate the biological processes that are driving the preeclamptic state. Additionally, DNA methylation changes that may be useful for prenatal screening will also  20 be identified. I will determine whether the genes affected by hypoxic conditions are also significantly different in EOPET placentas.   1.6.2.3 Objective 3: Identification of DNA methylation markers in Trisomy 16 placentas   I will investigate the DNA methylation in placentas that are prone to developing preeclampsia (trisomy 16) at several gestational ages using the Illumina HumanMethylation450 BeadChip. I will determine whether any of the DNA methylation markers identified in Chapter 3 are also affected by these chromosomally abnormal conditions. I will determine whether any of the identified changes are consistent throughout gestation, allowing for the potential use as an early screening tool for preeclampsia.  21 Study Samples Average gestational age Expression of genes Jarvenpaa, 2007114 2 PET + IUGR vs. 3 ctrl 35.7 vs. 38.5  Up: EPAS1, FLT1, SIGLEC10, ANG4        Down: ECGF1, JAG1, Palladin, COL18A1, TNFSF12, VEGF, ANPEP, PDGFA, SERPIN12  Enquobahrie, 2008115 18 PET vs. 18 ctrl  35.8 vs. 38.9  Up: LEP, FLT1, PCDHA3, CYP11A, F2R, IL9, FCGR2B, CDO1, VGLL1, EBI3, INSL4, BCL6,INHA    Down: MGC1132, NR4A2  Winn, 2009116 12 PET vs. 11ctrl  32.1 vs. 31.0 Up: FLT1, LEP, CRH, SIGLEC6, PAPPA2, INHA, ENG, HTRA1  Sitras, 2009117 16 severe PET vs. 21 ctrl  34.0 vs. 39.6  Up: LEP, FLT1, FLT4, CGB, ENG, LAEVERIN, BCL6, INHA, MMP14, PAPPA2    Down: PDGFD  Founds, 2009118 4 PET vs. 8 ctrl (1st trimester CVS)  11.4 vs. 11.3  Up:CCK, CTGA2    Down: FSTL3, MMP12,  22 Study Samples Average gestational age Expression of genes LAIR2, S100A8  Lee, 2010119 13 severe PET vs. 13 ctrl  35.93 vs. 38.48 Up: CXCR6, CXCL3, OSM, LEP, FLT1, VEGFA, SMOX, CYP26A, EGLN3, LDHA, CRY2L1  Va?rkonyi, 2011120 6 PET vs. 6 PET + HELLP vs. 5 ctrl preterm vs. 5 ctrl term  32.4 vs. 30.7 vs. 31.0 vs. 38.9  Up: LEP, CGB, TREM1, LHB, SIGLEC6, PAPPA2     Down: KRT81, OPRK1  Tsai, 2011121 23 PET vs. 37 ctrl  33.6 vs. 37.6 Up: ENG, PAPPA2, RDH13, INHA, LEP, FLT1, SIAE, SIGLEC6  Chang, 2011122 13 PET vs. 10 ctrl vs. superimposed PET 33.5 vs. 38.8 vs. 34.2 Up: HSPA1B, LIMS1, PLAGL1, TRIM31, PPP2R2C     Down: RNF128, ADM, ARFIP1  Kang, 2011123 16 PET vs. 17 ctrl  36.1vs. 39.0 Up: FLT1, LEP ITGA5, EBI3 SIGLEC6, HTRA1  Nishizawa, 201118 8 severe PET vs. 8 ctrl vs. 8 FGR  34.4 vs. 38.1 vs. 37.3  Up: INHBA, INHA, FLRG, BCL6, LEP, UP:PAPPA2, FLT1, ENG, CGB, CRH  23 Study Samples Average gestational age Expression of genes    Down: GSTA3  Mayor-Lynn, 2011124 7 PET vs. 7 preterm ctrl vs.  7 term ctrl  35 vs. 28  vs. 38  Up: CRH,SOCS1, MMP1, MMP9, ADAM17, ADAM30, TIMP3, STC2, CRHBP, EDN2  Junus, 2012125  8 EOPET vs. 4 ctrl  29.3 vs.24.4 Down in early: ACVRL1, EGFL7, ROBO4, IDO1   7 LOPET vs. 6 ctrl  39.9 vs. 40.2  Meng, 2012126 6 PET vs. 6 ctrl  36.4 vs. 39.0  Up: BTNL9. HMBS. ULBP1, CHRNA1, RMRP    Down: INSL6, CXCL9, TMCC1, PAGE2  Lapaireg, 2012127 9 severe PET vs. 7 ctrl 34.6 vs. 38.6 Up: CGB, HTRA4, CRHBP, LHB, QPCT, CD97, MMP19, ADAM2, INHBC     Down: CCL3, NOX4, VCAM1, FOSB, CX3CR1  Table 1.1:Summary of a subset of gene expression studies comparing preeclampsia and control placentas. Adapted from Louwen et al., 20126 24 Method Resolution Spread Strengths Limitations HPLC Whole methylated fraction Genome-wide Accurate; true measure of methylated fraction of DNA Sophisticated equipment necessary ELISA Whole methylated fraction Genome-wide True measure of methylated fraction of DNA; Simple and cheap Prone to user error MeDip-Chip >500 bp Depends on chip Can target quantification to regions of interest only Prone to user error; Only relative DNA methylation to other samples is known Illumina 450k array Single CpG >480,000 CpGs True continuous measure of methylation at single CpGs; high-throughput Cost; Limited choice of targets to investigate MeDIP-Seq <100 bp Genome-Wide Wide spread; small regions of methylation assesment Relative quantification only; cost of sequencing  25 Method Resolution Spread Strengths Limitations RRBS Single CpG Depends on restriction enzyme Increased resolution and spread over traditional sequencing  Less choice of targets; non-continuous methylation values; cost of sequencing Bisulfite Pyrosequencing Single CpG Targeted of ~100bp Continuous methylation values; fast and simple; choice of target Cost of equipment; useful only for predetermined targets Whole genome bisulfite sequencing Single CpG Genome-wide fully comprehensive if sequenced deep enough Cost; Overwhelming amount of data; non-continuous methylation values if sequencing depth too shallow Table 1.2: Selective summary of current methods for assessing DNA methylation.  26  Figure 1.1: Diagram of the developed placenta  27  Figure 1.2: Schematic of probe binding in the Illumina HumanMethylation450 BeadChip. Adapted from Bibikova, et al. 2011111   28 Chapter  2: DNA methylation profile of cultured human placental trophoblasts   Introduction   In this chapter, epigenetic effects on the placenta are investigated by controlling oxygen tension as the independent variable, which has been implicated in the placental dysfunction typical of adverse pregnancy outcomes. The trophoblast bi-layer regulates gene expression and metabolism in response to genetic and environmental signals, including levels of oxygen, nutrients and hormones. Oxygen tension plays a key role in placental development, where the pO2 is < 20 mmHg at the implantation site prior to 8?10 weeks but rises to 40?60 mmHg (5?8% oxygen) after 12 weeks gestation to continue at this level for the second and third trimesters51,128. This rise in pO2 coincides with entry of maternal blood into the intervillous space between 10?12 weeks and perfusion of the tree-like chorionic villi that mediate maternal-fetal exchange. Villi are surrounded by terminally differentiated syncytiotrophoblast, a true syncytium with multiple nuclei in the same cytoplasmic mass. A subjacent mononucleated cytotrophoblast layer is mitotically active and fuses to replenish the syncytiotrophoblast in a highly regulated process under the influence of oxygen tension. The villous core is bounded by the trophoblast basement membrane and consists of a mesenchymal connective tissue containing the fetal blood vessels.  Underperfusion with hypoxia, re-perfusion with oxidative stress, or both are associated with the placentas of women with preeclampsia and fetal IUGR, yielding altered gene expression and placental pathology in these clinical maladies129. Epigenetic alterations play a central role in the cell?s response pathways that are critical for adaptation to hypoxia130. Hypoxia causes global  29 hypomethylation in skin fibroblasts and in tumors (colorectal and melanoma cancer)131. In contrast, hypoxia in prostate cells cultured > 24 h yields a global increase in DNA methylation, including altered DNA methylation at many imprinted loci132. The role of epigenetic alterations upon hypoxia treatment in placenta has been shown in the mouse133. The hypothesis that oxygen levels modulate DNA methylation in cytotrophoblasts, syncytiotrophoblasts, or both phenotypes during primary culture of human trophoblasts was tested. We further compared these to the DNA methylation differences arising as a consequence of differentiation from cytotrophoblast into syncytiotrophoblast in culture.    Methods    Isolation and culture of primary human trophoblasts    This study was approved by the Institutional Review Board of Washington University School of Medicine in St. Louis, MO and University of British Columbia, Vancouver, BC. Primary human trophoblasts (n=5) were isolated from uncomplicated singleton pregnancies delivered by repeat cesarean section at 39-40 weeks gestation using the trypsin-deoxyribo-nuclease-dispase/Percoll method, as previously described134-136. All experiments were carried out in triplicate (i.e. each placenta was split into three samples for each treatment). As there was a very high degree of overlap of hypoxia associated changes, replicate experiments were used to reduce risk of false positive results.  Cultures were plated at a density of 300,000 cells/cm2 and maintained in Dulbecco's modified Eagle's medium (Sigma, St. Louis, MO) containing 10% fetal bovine serum (Gibco, Grand Island, NY), 20 mmol/liter HEPES pH 7.4 (Sigma), penicillin (100  30 units/ml), streptomycin (100 ?g/ml), and fungizone (0.25 ?g/ml; all from Washington University Tissue Culture Support Center) at 37?C in a 5% carbon dioxide/air atmosphere (20% oxygen, standard culture conditions). After 4 h to allow attachment, as illustrated in Fig. 2.1, half of the cells (cytotrophoblasts) were exposed to conditions of < 1% oxygen (<1% O2/5%CO2/10%H2/balance N2), 8% oxygen (8% O2/5%CO2/10%H2/balance N2) or standard culture conditions (20% O2/5%CO2), for 24 h. The other half of the cells from each placenta were continued in standard conditions for 48 h, to form syncytiotrophoblasts. Confirmation of syncytiotrophoblast formation (>85% of cells are multinucleated137) was achieved using immunofluorescence staining for E-cadherin, which stains plasma membranes among trophoblasts137-139. These syncytiotrophoblasts were then exposed to < 1% oxygen, 8% oxygen or standard culture conditions (5%CO2 in air with 20% oxygen) for 24 hours.   Genomic DNA and total RNA extraction    Genomic DNA was purified from cultured trophoblasts using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI), and total RNA was purified using TriReagent (Molecular Research Center, Inc., Cincinnati, OH), according to the manufacturer's instructions.   Illumina HumanMethylation450 array     The Illumina HumanMethylation450 array quantifies methylation at 482,421 CpGs in >20,000 genes. All samples were run on the array in the same batch to avoid any potential bias due to batch effect. Raw data was subject to 1) background subtraction; 2) elimination of probes  31 with detection p value >0.01; 3) M-value conversion (log2 ratio of the intensities of methylated to unmethylated probes)140 ; 4) colour channel bias adjustment; and 5) quantile normalization.  Candidate sites were prioritized by 1) p-value <0.05 in each of three replicate experiments; 2) magnitude of difference between group means >10% (i.e. ??>0.1)141; 3) the detection of multiple altered CpGs associated with the same gene; and 4) potential role in trophoblast function and/or hypoxia response.    Bisulfite pyrosequencing    DNA methylation of selected hypoxia differentially methylated loci was confirmed using bisulfite pyrosequencing. DNA was bisulfite converted and pyrosequencing was performed on a Biotage Pyromark Q96 MD Pyrosequencer. The quantitative levels of methylation for the CpG dinucleotide were evaluated with the Pyro Q-CpG software (Biotage, Uppsala, Sweden). Pyrosequencing assays were designed for FOS, JUN, CD59, CFB, GRAMD3 and ZNF217 (Table S2.1). Pyrosequencing primers were designed to include the same CpG sites that were interrogated by the Illumina probes, and each assay resulted in methylation levels that were highly correlated with those estimated by the Illumina array (R>0.9).    Illumina HumanHT-12 v4 expression array   RNA extracted from cytotrophoblast exposed to different oxygen conditions was used for the expression array study. RNA was assessed for quality using a 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA), comparing the quantities of ribosomal componentsRW.ERROR - Unable to find  32 reference:335. Fifteen cDNA samples (5 in <1%, 6 in 8% and 4 in 20% oxygen exposure group from 2 different placental samples) that passed quality control were hybridized to the Illumina HumanHT-12 v4 Expression BeadChip (Illumina, San Diego, CA), which assessed expression of >24,000 genes using 47,231 probes. Raw data was processed with background subtraction and quantile normalization. Probes with a detection p value >0.01 were eliminated from analysis.   Real time RT-PCR    The gene expression of CD59 (Hs00174141_m1), JUN (Hs99999141_s1), FOS (Hs01119267_g1) and CFB (Hs01006494_g1) was validated using targeted Taqman Gene Expression assays (Applied Biosystems, Melbourne, Australia). YWHAZ (Hs03044281_g1) was used as an endogenous control, as it is known to be highly expressed in placenta whilst not differing with gestational age142. For each sample, 500 ng of RNA was reverse transcribed using the QuantiTect Reverse Transcription Kit (Qiagen, Hilden, Germany). Each assay was run with cDNA samples, non-template control and calibrator in triplicate using an ABI7500 Real time Thermal Cycler (Applied Biosystems, Melbourne, Australia). A total PCR reaction volume of 20 ul in each well was run with a 1:10 cDNA dilution. Relative quantification was calculated using the ??CT method. Briefly, the difference between CT values (?CT) for the target genes and endogenous control was compared to the calibrator for each plate. The calibrator was composed of pooled cDNA from the 8% oxygen exposure group. While FOS, JUN and CFB have a good correlation of gene expression between values from qPCR and array (R>0.7), the correlation between the two methods was low for CD59 (R=0.33), which may due to the milder change of expression compared to other genes.  In addition to the 15 samples run on the expression array,  33 21 more RNA samples from cytotrophoblasts of 2 other placentas run in the methylation array were assessed for expression of the 4 genes.   Bioinformatic analysis    The Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov/) was used for gene ontology (GO) analysis143. Functions of genes with differential methylation or expression were compared against those represented on the array. Enriched GO terms were identified using a cut-off of FDR<10%. The Multiple Em for Motif Elicitation (MEME) site (http://meme.sdsc.edu/) was used to identify motifs enrichment on DNA sequences. Motif search was performed on a 100 bp window with the CpG site of interest at centre. Enriched sequences were then compared to all known motifs in a database using the TOMTOM tool (http://meme.nbcr.net/meme/cgi-bin/tomtom.cgi).   Statistical analysis of genomic features    Cross-hybridization of probes was investigated by the BLAT function in UCSC genome browser. Information on SNPs, repetitive elements, promoter, enhancer and CpG island for each probe was extracted from Illumina annotation. A Chi-square test was performed to determine the enrichment or reduction for candidate loci on different genomic features.    34  Results   Genome-wide DNA methylation analysis    Ninety trophoblast genomic DNA samples, as illustrated in Fig. 2.1, were obtained and DNA methylation profiles were assessed using the Illumina Infinium Human Methylation 450 Bead Chip. To get an overall sense of the relationship of samples to each other we first used unsupervised hierarchical clustering of the samples based on their overall DNA methylation profiles. Samples clustered by the placenta of origin rather than by culture conditions (<1%, 8% and 20% O2 ) or cyto/syncytiotrophoblast differentiation (Fig. S2.1), suggesting that placenta-specific genetic and/or epigenetic differences (such as pregnancy-specific in utero effects on the epigenome) were the main source of variance among samples. The cluster pattern was unaltered by eliminating probes with known genetic polymorphisms at the target CpG. Using the array average as a measure of global methylation, we also found no difference in average methylation level among the exposure groups (Fig. S2.2), demonstrating that there are not global effects on the level of DNA methylation by treatment.   DNA methylation changes with oxygen exposure    Given the tight clustering of samples by placenta of origin, we predicted that only a small proportion of loci would be altered due to oxygen tension or cell phenotype. To reduce the possibility of false positives, we used a relatively stringent cut-off of >10% absolute average methylation difference (?? > 0.1 from Illumina array) and also required loci to be significant (p  35 < 0.05) and show ?? > 0.1 in each of three independent replicate experiments. We observed no significant difference in methylation for any CpG site by our criteria (p < 0.05 and ?? > 0.1) when comparing 8% to 20% oxygen levels in either cytotrophoblast or syncytiotrophoblast. However, we found a set of loci with altered DNA methylation in low oxygen (< 1%) compared to either 8% or 20% in both cell types. As many similar sites were significant in the 8% and the 20% vs. < 1% oxygen comparisons, we decided to reduce the potential for false positive findings by considering as ?hypoxia-associated changes? only those methylation changes that were (1) significant when comparing < 1% to both 8% and 20% oxygen and (2) shared in common between the three replicate experiments. Doing this, we identified 147 CpG loci among 83 genes that were consistently (in each of the three replicates) hypermethylated (average ??>0.1; p<0.05) in cytotrophoblasts after 24 h exposure to <1% oxygen level (Table S2.2). In contrast, there were no differentially methylated loci related to oxygen exposure in syncytiotrophoblast cultures using the same criteria. Thus, the epigenetic response to hypoxia depends on trophoblast phenotype.   DNA methylation changes with cell phenotype     As hypoxia exposure can cause delayed differentiation of cytotrophoblasts into syncytiotrophoblast in culture, it was of interest to compare DNA methylation profile by cell phenotype to changes we observed with hypoxia exposure. Cellular differentiation is normally accompanied by epigenetic alterations, with a general tendency to acquire de novo DNA methylation marks. Considering only cells cultured at 8% oxygen (representing in vivo oxygen level for a term placenta), we found 223 loci that consistently showed a decreased methylation (average ??>0.1) in syncytiotrophoblast, as compared to cytotrophoblasts, while no CpG site  36 showed increased levels of methylation during this change in cellular phenotype (Table S2.3). Intriguingly, 70 out of 147 hypoxia-associated CpGs, which became hypermethylated in low oxygen, overlapped with CpG sites that became hypomethylated upon differentiation of cytotrophoblasts into syncytiotrophoblasts. When comparing the average methylation values for these overlapping sites, there was a trend towards less methylation in cells exposed to 20% as compared to 8% but with the lowest methylation values for syncytiotrophoblast (1% cytotrophoblast >8% cytotrophoblast >20% cytotrophoblast >8% syncytiotrophoblast). While the methylation at 20% oxygen was consistently lower than that for 8% oxygen, the differences were all <10% explaining why these sites were not initially identified as significantly altered by our criteria requiring an average ??>0.1 (Fig.  2.2).   Possible confounders for results from array-based methylation technologies include the cross-hybridization of probes to multiple regions of the genome or the interference of hybridization due to probes being located on or near polymorphisms144,145. However, only two probes in the differentially methylated regions identified as specific to cytotrophoblasts were likely to cross-hybridize into other regions of the genome (data not shown). We also found no enrichment of SNPs in the differentially methylated regions in either the phenotype-specific or the hypoxia-specific CpGs (Fig.  2.3). These findings excluded the possibility that the observed methylation changes were caused by DNA sequence bias in the probes. Moreover, DNA sequence bias was unlikely to play a role as the samples were genetically matched for all comparisons.    37  Gene ontology analysis of DNA methylation changes    Ontology of the genes associated with the 147 hypoxia-associated CpGs in cytotrophoblasts showed enrichment for genes with functions involved in ?signaling? and ?phosphoproteins? (FDR <10%; Table 2.1). Possibly, phosphorylation based signalling networks are affected by oxygen tension. Ontology analysis of the genes with altered DNA methylation by cell-phenotype showed enrichment for genes that functioned in the plasma membrane (Table 2.2). This is consistent with the observation that hypoxia delays cytotrophoblast fusion134, and suggests that this delay involves silencing of genes via DNA methylation that otherwise would be expressed upon differentiation.   Genomic characteristics and motif analysis of DNA methylation changes    Sites of altered DNA methylation were investigated for their association with repetitive elements, promoters, enhancers and CpG islands. The probe distribution for these genomic features was very similar between phenotype-specific and hypoxia-specific differentially methylated regions (Fig.  2.3), which was consistent with the substantial overlap between the two groups. While there was no enrichment of repetitive elements, we found that the hypoxia associated probes were significantly less likely to be in promoter regions (p<0.0005) and CpG islands (p<0.0005), but they were significantly more likely to be in enhancer regions (p<0.0005; Fig. 2.3).  To gain a better knowledge of the mechanism involved in DNA methylation alteration upon hypoxia treatment, we performed a DNA motif search based on a 100 bp window on either  38 side of the differentially methylated cytosine nucleotides. We found 99 of the 147 hypoxia-associated loci (~70%) were located within 100 bp of the consensus DNA sequence TGACTCA (enrichment or E-value = 4.2e-48). Similarly, the TGACTCA motif was also enriched in the cell-phenotype differentially methylated regions (42%, E-value=4.3e-51). This sequence is the transcriptional binding motif of Activator Protein 1 (AP-1; Fig.  2.4), a transcription factor composed of proteins from the c-Fos, c-Jun, ATF and JDP families.    Genome-wide expression analysis of cytotrophoblasts exposed to hypoxia     Although we assume a functional significance of AP-1 binding sites in hypoxia, the relationship between DNA methylation and gene expression of the sites remained to be determined. We thus performed a genome-wide expression study using quality mRNA extracted from the same cultures as used for DNA methylation studies. This included 15 cytotrophoblast samples with the three oxygen paradigms studied. We used a p<0.05 and average absolute fold change of 20% to identify differentially expressed genes, given the small number of differentially methylated sites found and the relatively low absolute levels of DNA methylation. We identified 2034 genes differentially expressed among the samples < 1% vs. 8% and <1% vs. 20% (data not shown).  Gene co-expression analysis showed that the 1022 genes upregulated by hypoxia were also enriched in ?phosphoproteins? (Table 2.3), while the 1012 downregulated genes were enriched in ?acetylation? (Table 2.4). However, only five of the 83 hypoxia-associated, hypermethylated genes showed the expected decreased expression using our criteria for altered expression (p<0.05 and fold change of 20%). Collectively, these observations suggest that a  39 change in DNA methylation is not generally the primary trigger of global gene expression changes observed with exposure of trophoblasts to hypoxia. Since AP-1 binding sites were enriched in the vicinity of differentially methylated CpGs in response to hypoxia, the gain of DNA methylation may be mediated by increased expression of AP-1 genes. Indeed, both JUN and FOS showed significantly higher expression in the <1% oxygen group compared to the other two oxygen levels (JUN p<0.0005, FOS p<0.005) (Fig. 2.5A-B), while HIF1A expression did not increase significantly.    Confirmation of array results by bisulfite pyrosequencing and reverse-transcription real-time PCR    We selected six genes that were functionally relevant to placenta from the array data for confirmatory studies using targeted approaches. Using bisulfite pyrosequencing, we observed no DNA methylation at the promoter regions of JUN and FOS in any trophoblast sample (Fig. S2.3 A-B), while real-time reverse-transcription PCR confirmed the increased gene expression of JUN and FOS at <1% vs. 8% and 20% oxygen (Fig 2.5A-B). Thus, expression levels of these genes do not appear to be regulated by promoter DNA methylation in cultured trophoblasts.  Pyrosequencing confirmed that the gene promoters GRAMD3, CFB, CD59 and ZNF217 showed increased methylation in <1% vs. 8% oxygen (p<0.01 for all four loci) and significantly lower methylation in 20% vs. 8% oxygen (p<0.01 for all except p<0.05 for CFB) (Fig. S2.3 C-F). The expression of CFB and CD59 were decreased in <1% and 8% vs. 20% oxygen (Fig.  2.5 C-D) and their expression levels were inversely correlated with DNA methylation levels (Fig.   40 2.5 E-F). Thus DNA methylation in the promoter is associated with the expression levels of only a subset of genes with altered expression, as expected.   Discussion  This is the first genome-wide analysis of the effect of phenotype and oxygen concentration on DNA methylation and gene expression of human villous cytotrophoblasts and syncytiotrophoblasts. The data show that cytotrophoblasts differ from syncytiotrophoblasts in their methylation profile under standard culture conditions and in response to hypoxia. There are substantial changes of DNA methylation at enhancers of genes responsible for signaling in cytotrophoblast upon hypoxic exposure. However, these responses are unlikely to be the primary mechanism for the majority of gene expression changes observed. Instead, the methylation changes can be attributed to upregulation of genes coding for proteins that comprise the transcription factor AP-1. We speculate that binding of AP-1 to specific sequences can increase DNA methylation and inhibit transcription at these regions and that the methylation of gene promoters or enhancers may play a role in impeding differentiation of villous trophoblasts. Hypoxia causes global hypomethylation in skin fibroblasts, colorectal tumors and melanomas131. In contrast, hypoxia in cultured prostate cells yields global increases in DNA methylation and altered DNA methylation at imprinted loci, possibly as a result of increased expression of DNMT3b132. We observed a much more limited effect on DNA methylation in human trophoblast cultures and specifically, there was no significant change in global DNA methylation as assessed by overall average DNA methylation in the Illumina array. Similarly, there was a relatively small set of loci that showed loss of DNA methylation in the process of  41 differentiation from cytotrophoblast to syncytiotrophoblast. The evidence for DNA demethylases is controversial and loss of DNA methylation is normally thought to occur by passive, replication-dependent manner (i.e. failure to methylate hemi-methylated DNA after replication)146. However, differentiation of cytotrophoblast to syncytiotrophoblast occurs through cell fusion, not cell division, and hence, the altered DNA methylation must have occurred through a replication-independent mechanism.  Interestingly, all loci with significantly altered DNA methylation at low levels of oxygen showed an increase of DNA methylation, while no locus showed significant decreases in methylation. The effects were greatest between <1% and 8% oxygen, but there was also a trend for less methylation in 20% as compared to 8% oxygen at these same loci. The hypoxia changes observed in cytotrophoblasts included regions associated with several genes relevant to the placenta. For example, CP, coding for ceruloplasmin, and ITGA5, coding for alpha-5 integrin, are reported to show increased expression in placentas from preeclampsia and in trophoblast cultures grown in low oxygen147,148. Other genes in the vicinity of the CpGs with altered methylation are reported to show altered gene expression in response to hypoxia, including SOD2149, XDH150,151 and ZNF217152. Collectively, these hypoxia-induced, differentially methylated genes are enriched for signalling function (Table 2.1), which is not a feature shared with cell-phenotype-differentially methylated loci. This suggests that genes associated with differentially methylated loci in response to hypoxia may have secondary effects on genome-wide expression. We observed an extensive change of genome-wide gene expression in cytotrophoblasts exposed to hypoxia (using a 20% change cut-off), which is consistent with previous studies and more prominent than that observed for DNA methylation. Notably, many of the upregulated  42 genes were associated with cell death, a response known to occur in trophoblasts exposed to very low oxygen tensions135. Among the genes upregulated >4-fold in <1% vs. 8% oxygen were VEGFA and the transcription factors JUN and FOS. Moreover, genes exhibiting decreased expression (< 25%) in response to <1% vs. 8% oxygen were genes from a number of histone proteins, such as HIST1H2BG, HIST1H3D, HIST1H4, and HIST2H2BE. This may explain why gene ontology analysis revealed ?acetylation? as being prominent in the hypoxia-down regulated genes. Interestingly, many regions that showed increased methylation after hypoxia exposure corresponded to regions that showed decreased methylation upon differentiation into syncytiotrophoblast. As cytotrophoblast cultured in hypoxic conditions show delayed fusion to syncytiotrophoblast134, we speculate that this is in part due to active suppression of genes by DNA methylation that would otherwise be up regulated upon differentiation. Intriguingly, such inhibition of villous trophoblast differentiation has been observed in placentas from preeclamptic women where malperfusion is common153. Chronic hypoxia in prostate cells was associated with increased expression of the DNA-methyltransferase, DNMT3B, which was argued to explain gene-specific changes in DNA methylation132. Nonetheless, we suggest that the observed methylation changes in trophoblast cells may occur as a consequence of chromatin alterations occurring as a consequence of altered gene expression, such as changes to chromatin resulting from binding of proteins to DNA. The hypoxia-associated loci tend to be located in enhancer rather than promoter or CpG island regions. Through motif analysis, we found that 70% of the hypoxia-associated loci were located in the transcriptional binding motif of AP-1, which is composed of transcriptional binding proteins such as c-Fos and c-Jun (Fig.  2.6). AP-1 is known to regulate gene expression in response to a variety of stimuli, including cytokines, growth  43 factors, stress, and bacterial and viral infections, which in turn control a number of cellular processes including differentiation, proliferation, and apoptosis. AP-1 has previously been reported to down-regulate genes through recruitment of histone deacetylases154,155. We propose that expression of AP-1 is triggered by hypoxia, and this may either interact with DNA methyltransferases (DNMTs) to target methylation at specific sites in the genome, or DNA methylation occurs subsequent to histone deactylation; which then causes suppression of the associated genes that are responsible for syncytiotrophoblast differentiation (Fig.  2.6). Further molecular studies, such as chromatin immunoprecipitation sequencing, to confirm AP-1 and enhancer sequence interaction for the associated genes are needed to resolve these questions. 44   GO Term Count P-Value FDR (%) signal 24 0.0067 8 phosphoprotein 43 0.0067 8 signal peptide 24 0.0072 9.4 cytokine binding 4 0.0099 12 zinc finger region:RING-type 5 0.012 15 duplication 5 0.016 18 disulfide bond 21 0.017 19 Table 2.1: Functions of differentially methylated loci between 8% and 20% versus low (<1%) oxygen levels. GO Term Count P-Value FDR (%) phosphoprotein 25 0.0035 4.1 compositionally biased region:Poly-Lys 4 0.009 11 Reversal of Insulin Resistance by Leptin 2 0.029 19 cell-substrate junction assembly 2 0.043 45 plasma membrane 14 0.044 40 anchored to plasma membrane 2 0.047 43 Table 2.2: Functions of genes associated in common between hypoxia- and cell-phenotype-differentially methylated loci     45  GO Term Count P-Value FDR (%) phosphoprotein 388 2.8E-20 4E-17 translational elongation 28 8.8E-16 1.6E-12 ribosome 23 7.4E-15 1.1E-11 protein biosynthesis 30 6.9E-11 0.000000099 cytosol 101 8.7E-11 0.00000012 translation 40 4.2E-10 0.00000076 death 65 7.8E-10 0.0000014 Table 2.3:Functions enriched for hypoxia-upregulated genes. Redundant GO terms related to ?ribosome?, ?cytosol? and ?cell death? were excluded. GO Term Count P-Value FDR (%) acetylation 261 3.3E-39 4.8E-36 intracellular organelle lumen 157 2.3E-17 3.3E-14 mitochondrion 104 5.9E-14 8.4E-11 ribonucleoprotein complex 65 7.2E-14 1E-10 rna-binding 65 6.2E-13 8.9E-10 phosphoprotein 420 1.6E-12 2.3E-09 RNA processing 68 2.1E-12 3.7E-09 nucleus 274 3.2E-12 4.6E-09 nucleolus 72 9.1E-11 0.00000013 transit peptide:Mitochondrion 52 1.9E-09 0.0000033 Table 2.4:Functions enriched for hypoxia-downregulated genes. Redundant GO terms related to ?organelle lumen?, ?mitochondrion? and ?RNA-binding? were excluded.  46  Figure 2.1:Cell culture condition for placental trophoblast samples Cell cultures were plated and maintained in a culture support center at 37?C in a 5% carbon dioxide/air atmosphere (20% oxygen, standard conditions). After 4 h to allow attachment, half of the cells from each placenta were continued in standard conditions of 5% carbon dioxide/air for 48 h until exposure to <1% oxygen conditions, 8% oxygen conditions or standard conditions, while the other half of the cells were exposed to <1% oxygen, 8%, or standard conditions for 24 h.  47  Figure 2.2: DNA methylation patterns at each differentially methylated CpG and nearby sites. Examples illustrated are A) CD59, B) GRAMD3, C) CFB and D) ZNF217. Although only the CpG at the site of interest met the criteria for a statistically significant methylation difference (p<0.05 and ?? >0.1), methylation differences are also observed at the nearby sites.  48  Figure 2.3: Genomic characteristics of differentially methylated regions.The distribution of CpG loci that were targeted by the methylation array (Array targeted), cytotrophoblast-specifically methylated (Cyto-specific) or hypoxia-specifically methylated (Hypoxia-specific) are shown with respect to different genomic characteristics. RE: repetitive element, *P<0.0005.  49  Figure 2.4: Motif analysis of hypoxia differentially methylated loci . A) Position weight matrix description of DNA sequence enriched in hypoxia differentially methylated loci. A consensus DNA sequence of TGACTCA is enriched in 99 out of the 147 loci. B) The enriched DNA sequence matches the consensus sequence of AP-1 binding region.  50  Figure 2.5: Gene expression and DNA methylation correlation of candidate genes.  Gene expression as validated by Realtime RT-PCR was shown for A) JUN, B) FOS, C) CFB and D) CD59. Since the promoters of JUN and FOS are unmethylated, DNA methylation correlation with gene expression was shown only for E) CFB and F) CD59. 51  Figure 2.6: Proposed model of hypoxia effect on cytotrophoblast. Hypoxic exposure may trigger A) increased expression of JUN and FOS (and perhaps a genome-wide change of gene expression) which encodes Jun and fos to form AP-1 proteins. B) AP-1 may then recruit DNA methylation machinery (such as DNMTs) for de novo methylation of CpGs at the enhancer regions of various genes. C) This may cause a suppression of expression for genes that are responsible for syncytiotrophoblast differentiation, which results in depletion of syncytiotrophoblast formation.  52 Chapter  3: DNA methylation profile of early-onset preeclampsia placentas   Introduction   In this chapter, DNA methylation changes in preeclampsia placentas are investigated, providing a useful adjunct to characterize the pathological changes and to identify clinical subgroups of patients with differing underlying etiology. DNA methylation may also be useful in the development of new early screening approaches to identify at-risk pregnancies45. Lack of DNA methylation at gene promoters is often associated with the potential for gene transcription93. However, gene expression levels may be further modulated by DNA methylation at upstream enhancer sites156, which can affect the binding of transcription factors, and/or at the shores of CpG islands157.  A previous study using an overlapping set of samples as this chapter, showed that EOPET associated placentas exhibit hypomethylation at a number of gene promoter regions compared to controls7; however, it was based on a smaller number of placentas (N=4 EOPET placentas) and relatively small number of CpG sites tested genome-wide (N=1506)7. Another study also showed altered methylation in preeclampsia placentas (N=9) using a genome-wide methylated DNA immunoprecipitation (MeDIP) approach, but did not distinguish clinical subtypes of preeclampsia158. Furthermore, these studies did not compare these findings to gene expression of the complete candidate gene set. In the present study, we sought to follow-up our initial findings7 with a larger set of EOPET and control placentas (N=20 in each group) using the Illumina 450k array.  53 Importantly, this newer array includes sites in enhancers and promoter ?shore? regions, which were underrepresented in previous arrays and may be preferentially indicative of changes in gene expression.  We used these data to 1) evaluate the ability of DNA methylation to distinguish clinical subgroups of preeclampsia and 2) characterize the underlying biological pathways involved in these molecular changes and their potential relationship to changes observed in association with hypoxia exposure.     Methods   Sample collection   Whole chorionic villi were sampled from placentas delivered at Women?s Hospital in Vancouver, Canada. Ethics approval for sample collection was obtained through University of British Columbia/ Children?s & Women?s Health Centre of British Columbia Research Ethics Board. Clinical criteria for EOPET were defined using the Canadian guidelines11. For DNA methylation analysis, samples were further sub-classified by presence/absence of severe proteinuria (>3 g/day), coincident IUGR, gestational diabetes, and HELLP syndrome. IUGR was defined as 1) birthweight below the 3rd percentile for gender and gestational age using Canadian population parameters or 2) birthweight below the 10th percentile with persistent uterine artery notching at 22-25 weeks, absent or reversed end diastolic velocity on umbilical artery Doppler, or oligohydramnios. Placentas from a diversity of pathologically and chromosomally normal preterm births were used as gestational-age matched controls (Table 3.1;S3.1). DNA was  54 extracted from at least two chorionic villi (center and perimeter of placenta) on the fetal side of the placenta and combined in equal quantities for better representation of placenta-wide changes. It should be noted that four of the samples in this study have been previously assessed for methylation changes (using less-comprehensive microarrays)7,159,160.   Illumina HumanMethylation450 BeadChip   Twenty EOPET samples and 20 controls in two batches (2 batches of 10 EOPET vs.10 controls) were used for the Illumina HumanMethylation450 BeadChip111 (Illumina inc., San Diego, CA). 750 ng of DNA was bisulfite converted using the EZ DNA Methylation Kit (Zymo Research, Irvine, CA).  Hybridization of samples was performed following manufacturer?s protocol. Chips were scanned by a HiScan 2000 or iScan (Illumina). Raw and processed data has been deposited into GEO (GSE44667).   Data processing and analysis   Raw data were read into GenomeStudio 2011 (Illumina) where background subtraction and initial processing was performed. Probes targeting a CpG with a documented SNP in the C or G were removed from analysis161. Probes targeting the sex chromosomes were also removed. Any probe that had a missing ?-value or >0.01 detection P-value (probes whose signal intensity was lower than background levels) in one sample was also removed from analysis (n=25,804). This left 430,685 probes for  55 analysis.  Signal intensities were read into R v2.14162 using methylumi163 to create M-values (log transformations proportional to the level of methylation). Values from remaining probes were color channel corrected140, separated into type I and type II probes and normalized by SWAN164. Statistically significant probes were determined using the false discovery rate (FDR). For this, Significance Analysis of Microarrays (SAM) was performed on M-values in R using the siggenes package165. M-values were transformed back to ?-values (a value between 0 and 1 representing the fraction of methylated DNA) for genome-wide analysis.  To enrich for biological relevance, highly significant probes were determined by ?? (difference in DNA methylation between cases and controls) in conjunction with an FDR cutoff. Finally, probes that map away from their intended target were removed161.   Gene expression microarray   Gene expression data were obtained using a subset of EOPET samples and controls (n=8 each) that were also run on the 450K array. RNA was extracted from placental villi stored in RNAlater at -80o C using an RNeasy kit (Qiagen). RNA was assessed for quality on a Bioanalyzer 2100 (Agilent, Santa Clara, California), reverse-transcribed to cDNA and hybridized to HT-12v4 Expression BeadChip (Illumina) following the manufacturer?s protocol. This chip interrogates over 47,000 transcripts genome-wide. Raw data were quantile normalized in GenomeStudio 2011. Probes with bad detection p-values (>0.01) in all samples were removed from analysis (n=13125). Data points from valid probes with a bad detection p-value were removed and replaced  56 with a value of one to represent a lack of expression. Values were log2-transformed and input into SAM.  Significant probes were identified by using an FDR cut-off of 0.05 combined with a fold change of ?1.2. Raw and processed data has been deposited into GEO (GSE44711).   Pyrosequencing  A subset of sites with highly significant differences from the array and/or relevance for prenatal screening or hypoxia was followed up by bisulfite pyrosequencing of EOPET samples (n=20), other disease states (LOPET (n=21) and nIUGR (n=17)) and a larger set of controls covering a broad range of gestational ages (n=93). Assays (Table S3.2) were designed using PSQ Assay design (Biotage, Upsalsa, Sweden) and run on a Qiagen Pyromark Q96 MD (Qiagen, Heiden, Germany). The methylation level detected by pyrosequencing was concordant to that by Illumina methylation array (average r=0.80). Spearman?s correlation was used to assess if methylation change was affected by gestational age. If so, statistical significance was assessed by ANCOVA, with gestational age as the covariate. Bonferroni corrected pair-wise post-hoc analysis was used to determine significance between individual groups.     Bioinformatics   DAVID was used to conduct gene ontology analysis143. Significant biological processes were elucidated by over-representation of genes in gene ontology categories.  57 Next, genomic sequences spanning 50 bp up- and downstream of significant CpG sites were extracted using Galaxy (https://main.g2.bx.psu.edu). These sequences were submitted for recurrent motif discovery using MEME suite166. Recurrent motifs were then queried for known transcription factor binding motifs using the Tomtom tool in MEME suite.    Results   Extensive DNA methylation profile changes in early-onset preeclampsia  Principle component analysis (PCA) was performed using all 430,685 probes (Figure S3.1) to determine the differences in the overall DNA methylation profile between cases and controls. Samples clustered roughly according to presence or absence of preeclampsia rather than by gestational age, which was unexpected, as gestational age has been shown to drive dramatic differences in DNA methylation97, implying DNA methylation changes in EOPET are very dramatic and widespread throughout the genome.  However, it should be noted that the majority (65%) of cases were within a narrow GA age range of 31-34 weeks. Overall, an extremely large number of CpG sites (over 100,000) showed statistically significant differences between groups at an FDR of <0.05, suggesting dramatic epigenetic effects of EOPET on the placenta. Most array studies use a combination of significance (e.g. FDR of <0.01 or 0.05) in combination with a cutoff for mean difference (??) to detect the most biologically meaningful changes. We considered  58 various combinations of ?? values and FDRs to establish values that would allow us to focus on the most significant probes for follow-up (Table 3.2), as there is no standard. Changing the requirement for ?? has a dramatic effect on the number of significant candidates, with 1,312 candidates using ?? of 0.1 and only 87 candidates with ?? of 0.15. Based on this analysis we opted to focus our analysis using a relatively stringent cutoff of 12.5% ?? and 1% FDR, providing a list of 286 CpGs that were differentially methylated in placental samples between EOPET and controls. After elimination of 4 probes that map to multiple locations in the genome, 282 candidate CpGs located in or near 248 genes remained.  Of the candidate CpGs, 74.5% were hypomethylated in EOPET as compared to controls (Table S3.3).  Amongst the genes associated with hypomethylated CpGs were several coding for proteins suggested to be useful in maternal serum screening for preeclampsia including INHBA (2 CpGs), ADAM12, PAPPA2 (3 CpGs), and FLT1, as well as several genes we previously reported as exhibiting hypomethylation in preeclampsia (e.g. TIMP3, MEST, CXCL9)7. However, many of the altered CpGs were associated with genes of unknown placental function.   EOPET, but not associated clinical features, presents a distinct DNA methylation profile   To assess the discriminatory power of the identified EOPET-associated DNA methylation changes, PCA was repeated on all samples using only the 282 candidate CpGs (Figure 3.1).  This revealed a distinct clustering of EOPET samples separate from  59 controls along principal component 1 (40% of the variance).  However, there was more variability amongst the EOPET samples than the controls. In order to determine if some of this variability was due to clinical variation in samples, we further coded samples by presence or absence of IUGR, severe proteinuria, diabetes, or HELLP syndrome. However, there were no distinct clusters of these clinical subtypes using this set of probes (Figure 3.1).  We also compared DNA methylation profiles between EOPET placentas with (n=12) and without (n=8) concurrent IUGR. No sites were found to be significant using modest criteria of an FDR ?0.05 and ???5%, in contrast to the large number of sites significant using such criteria when comparing EOPET vs. controls.  Separately comparing each subset of EOPET samples depending on presence/absence of IUGR, against corresponding gestational-age matched controls produced similar sets of significant probes as the EOPET group as a whole (data not shown). Hence we find no evidence for a major methylation difference between EOPET with and without associated IUGR.    Differentially methylated loci showed negative correlation with gene expression    A subset of placental samples run on the DNA methylation array (8 EOPET vs. 8 controls) was deemed of sufficient quality to be run on an Illumina HT-12v4 Expression BeadChip array. There were 195 probes representing 182 genes that were significantly changed using a cutoff of ?0.05 FDR and ?1.2 fold-change (Table S3.4).  Similarly,  60 17.4% of 282 candidate CpGs from the 450K methylation array (87.9% of which were represented on the expression array) were associated with significantly altered gene expression using a nominal p-value of 0.05 (Table S3.5). There were multiple genes significantly over-expressed in this study that have been shown as over expressed in other preeclampsia genome-wide expression studies despite differing sample criteria and methods6. These common genes include: INHA, LEP, PAPPA2, CGB, CRH, HTRA4, TREM1, CXCR6, EBI3, BTNL9, ULBP1, SIGLEC6, and QPCT. In addition, 23 of the 282 candidate CpGs showed negative correlation between DNA methylation and gene expression, (p<0.05) (Table S3.5), although some of the associated genes did not reach genome-wide statistical significance for differential expression between cases and controls. These genes included EPAS1, FLT1 and FLNB, which are known to be responsible for angiogenesis. This is in contrast to only one CpG showing positive correlation between methylation and expression (ANTXR1). Small sample size likely caused an underestimation of altered methylation sites associated with altered expression. Changes in gene expression can also be regulated by other factors (e.g. availability of transcription factors) in addition to DNA methylation, and can change at term in response to labor and delivery, confounding these comparisons.    Differences in DNA methylation are regional and different in related disorders  The ability to determine molecular differences specific to disease type is important for establishing accurate prediction methods and targeted treatment. Thus,  61 bisulfite pyrosequencing of four altered CpG sites was performed on placentas from EOPET, LOPET, nIUGR, and controls (Figure 3.2). These four sites were chosen for further investigation due to their large ?? and previous evidence that their expression can be affected by hypoxia (BHLHE40167 and SLC2A1168) or is relevant to prenatal screening (INHBA and ADAM12)36. While in some of these genes only one of the associated CpGs fulfilled the criteria of FDR<0.01 and ?12.5% ??, examining all array targets in these genes also showed DNA methylation differences between EOPET placentas and controls in nearby targets (Figure 3.3). Comparing the DNA methylation at the significant CpG sites to expression in each gene revealed statistically significant negative correlation in BHLHE40, and trends in SLC2A1 and ADAM12 (Figure 3.4). The increased expression reflects the expected association of decreased methylation near the promoter region of these genes.  DNA methylation for all four genes showed a small but significant negative correlation with increasing gestational age; therefore all statistical tests were performed using gestational age as a co-variate. It should be noted that EOPET showed decreased DNA methylation at these loci, the opposite direction of that expected if gestational age influenced these changes. Each gene showed a statistically significant difference (p<0.05) between groups, with EOPET being significantly different from the controls for all genes, confirming the array findings. LOPET showed significant alterations compared to controls for INHBA, BHLHE40, and SLC2A1, while only BHLHE40 showed a statistical difference between nIUGR and controls.    62  EOPET shows differential hypoxia-related DNA methylation status   EOPET is associated with placental hypoxia and reoxygenation stress4; thus, we wanted to assess whether sites altered by hypoxia exposure in culture would also be altered in preeclampsia. We recently used the 450K array to assess DNA methylation changes in cultured trophoblast cells exposed to hypoxia169. Of the strictly defined subset of 282 altered CpGs associated with EOPET, only 5 overlapped with those identified as significant changes upon exposure of cells to hypoxia. However, 78.3% of probes identified as significant in the hypoxia study are considered significantly different between EOPET and control samples when using a less strict t-test p-value cutoff of 0.05.  However, these CpGs all demonstrated increased DNA methylation upon exposure to hypoxia in cytotrophoblast, whereas these same loci showed decreased DNA methylation in preeclampsia. As many of these same loci are hypermethylated in syncytiotrophoblast as compared to cytotrophoblast169, these results could also reflect an alteration in trophoblast cell populations in EOPET placentas (i.e. reduced cytotrophoblast:syncytiotrophoblast ratio). We further performed PCA using just the 147 hypoxia-associated loci to determine if EOPET and control placentas would cluster into separate groups using this subset of loci. A separation of EOPET and control placentas was achieved with few outliers (Figure 3.5). Again, there was no further separation of cases based on clinical subclassification of EOPET.    63  Genomic regional and functional enrichments of differentially methylated CpG sites  Investigating whether significant CpGs are located in certain genomic elements such as CpG islands or enhancers can help elucidate the nature of the biological processes that are disrupted in EOPET (Figure 3.6). Significantly more probes fell into enhancer regions from amongst our candidate sites than expected at random from the array. There are also proportionally fewer probes falling into high-density CpG islands, typically unmethylated and associated with gene promoters, than non-CpG islands. Enhancers are often located outside of CpG islands, explaining the enrichment of significant probes in low CpG density areas. There is no significant difference from the expected ratios in probes that fall into intermediate density CpG islands or CpG island shores.  Of significant CpG sites, 29.8%  are located in ?intergenic regions?, which are defined using the Illumina annotation as being further than 2kb from the transcription start or end site of a gene; however upstream enhancers could be located in such intergenic regions170. As it is unclear without functional studies on placental tissue whether an upstream enhancer has an effect on expression or not, we associated all unannotated significant CpGs with the closest transcription start site to a RefSeq gene161 to expose potential transcriptionally important regions for future studies.    Gene ontology analysis for DNA methylation returned a list of enriched biological processes (Table 3.3, Table S3.5). The top significant gene ontology clusters were involved in regulation of gene transcription, implying an overall disregulation of transcription factors affecting gene expression in EOPET placentas. Consistent with this,  64 invocation of the hypoxia response pathway targets many transcription factors79. Many of the genes associated with these gene ontology terms are affected by oxygen levels or directly involved in the hypoxia response pathway (e.g. EPAS1). Differentiation and morphogensis were also significant ontology terms, consistent with the observed altered development of trophoblast in preeclampsia171. Gene ontology analysis for gene expression returned only one hit of p<0.01: ?female pregnancy? (GO:0007565). Consistent with this, many of the genes in this category have been shown to be over-expressed in preeclampsia as detectable in maternal serum (e.g. PAPPA)36.   MEME identified two recurrent motifs in proximity to the significant CpGs. These two motifs each occurred in 28 of 282 sites. To determine if they were associated with a transcription factor binding site, the sequences were submitted to TOMTOM, where the transcription factor databases JASPAR and uniprobe, were searched for corresponding defined motifs. There were no significant occurrences of specific transcription factor motifs, consistent with a wide range of transcription factors that may be affected by the preeclamptic state.    Discussion  In this study we demonstrate widespread alterations in DNA methylation in placentas from pregnancies complicated EOPET. This expands upon and confirms our previous report of altered methylation in EOPET7 with much larger sample size and number of analyzed loci.  EOPET cases can be distinguished from controls based on this methylation profile, regardless of presence/absence of additional features (e.g. IUGR,  65 severe proteinuria, HELLP). These findings support the concept of EOPET being a distinct clinical entity that includes various presentations including HELLP syndrome.   Clinical criteria for the definition of ?severe? preeclampsia vary10 with some based on a gestational age cut-off (i.e. EOPET), and others based on levels of proteinuria (e.g.>2 g/day)18. In our study, some cases classified as EOPET did not have documented proteinuria (i.e. diagnosis was based on the presence of hypertension in combination with other findings such as fetal IUGR as per Canadian guidelines11) while others had heavy proteinuria (>3g/day); however no relationship between proteinuria level and methylation profile was identified. Other conditions concurrent with EOPET in this study (IUGR, gestational diabetes, and HELLP syndrome) did not cluster separately from other EOPET samples. Consistent with this observation, HELLP syndrome has been shown to have a similar transcription profile as preeclampsia120. Thus, there may be consistent molecular features of EOPET placentas despite variability in clinical symptoms; this variability may be reflective of other factors such as maternal health.  When comparing LOPET and nIUGR for a subset of loci, EOPET showed the largest differences from controls, but LOPET and nIUGR trended towards EOPET for these genes. In a separate study using these samples, but focusing on stress and other hormonal pathway genes, LOPET samples similarly tended to exhibit levels of methylation overlapping EOPET and control values, suggesting this may be a heterogeneous group with a subset of cases possibly fitting an ?EOPET-like? pattern and the remainder not.49  Furthermore, previous studies have shown that genes associated with altered DNA methylation or expression in EOPET show fewer and smaller changes in LOPET and/or nIUGR7,14,172. It has been suggested that LOPET and EOPET have  66 distinct etiologies12,173, with EOPET having greater placental disfunction174. The present data support the concept of greater placental disregulation in EOPET, though this needs to be followed up in a more comprehensive manner. Detecting the future onset of preeclampsia early in pregnancy could lead to improved management and help mitigate the effects of the disorder. Ideally the detection method would be non-invasive, likely investigating components of a first trimester maternal serum sample. Altered maternal serum concentrations of a number of proteins have been reported (PAPP-A, ADAM12, hCG, Inhibin, PP13, sFLT1, Endoglin, PlGF, Leptin36,39,40,172) prior to the onset of preeclampsia, with the placenta being the likely source of many of these. Expression of several genes coding for upregulated proteins is increased in preclampsia placentas from the 3rd trimester (ADAM12, LEP, CGB, PAPPA, INHA) as indicated in this and other studies 6. Altered DNA methylation in many of the genes coding for these proteins was observed in the present study including ADAM12, CGA, FLT1, INHBA and, using a lower ?? cut-off of 8%, PAPPA and LEP. We also found a correlation of DNA methylation and gene expression for most of these genes in this study. Hence other genes we identified as showing altered DNA methylation, could perhaps offer candidates to investigate as potential biomarkers in maternal serum for the enhanced prediction of preeclampsia.  Furthermore, methylation differences could be utilized for early screening using an approach based on circulating cell-free fetal DNA (cffDNA), which is increased in mothers predisposed to preeclampsia41. We previously suggested that methylation of TIMP3 was a candidate for DNA methylation-based diagnosis based upon the differential DNA methylation patterns between blood and placenta, with more extreme values in  67 EOPET-associated placentas 7; a finding confirmed by others48. Some sites showing similar properties to those in the present study include BHLHE40 and INHBA. Further evaluation of such sites for their ability to enhance the early detection of EOPET over simply quantifying the cffDNA fraction45 would be of interest. There are several potential drivers for the altered DNA methylation observed in this study. One driver is hypoxia, a key etiological factor in the development of preeclampsia175. The hypoxia response pathway involves the expression of many genes, including the Hypoxia Inducible Factors (HIFs), transcription factors designed to activate genes whose expression can help reestablish oxygen supply. EPAS1, which encodes for HIF2?, is an early factor in the hypoxia response pathway and has been shown to be active in preclampsia placentas176. CpGs in the shore of the promoter CpG Island of this gene were significantly hypomethylated in preclampsia placentas in this study, indicating that there is increased binding of transcription factors such as RNA polymerase95. Supporting this, EPAS1 was over-expressed in this and a previous study114. Frequent targets of HIF2? are BHLHE40 and INHBA79, both of which code for proteins that can prevent trophoblast differentiation177,178. BHLHE40 was shown to have significantly decreased DNA methylation in the shore of the CpG island promoter and increased expression in preclampsia placentas, while INHBA showed decreased DNA methylation directly in the promoter region and has demonstrated increased expression in several studies18,115,117,121,179. In chapter 2 we demonstrated that a subset of genes underwent hypermethylation upon exposure of cytotrophoblast cells to 24 hours of <1% oxygen 169. Many of these same genes became hypomethylated upon differentiation of cytotrophoblast to  68 syncytiotrophoblast, consistent with the observation that hypoxia can supress the differentiation of cytotrophoblast cells into syncytiotrophoblast180.  In the present study, we found that these hypoxia/differentiation sites tended to be hypomethylated in EOPET. As this finding is the opposite of what we expected due to hypoxic exposure, it may be re-oygenation stress, also proposed to occur in the development of preeclampsia, may result in hypomethylation of these same loci. It has been hypothesized that this sudden change in physiological state later in pregnancy can induce a stress response in the trophoblast4, potentially changing the DNA methylation of the chorionic villi. It is possible that the short-term effects of reduced oxygen level in culture do not accurately reflect the long-term effects in vivo. Reduced DNA methylation is in fact observed with long-term hypoxia exposure in some cancer tissues131.  Hypomethylation of these hypoxia-associated loci could be evidence of altered cell composition as many of these CpG sites were significantly hypomethylated in syncytiotrophoblast compared to cytotrophoblast; however, an excess of cytotrophoblast is generally observed181 in preeclampsia, making this explanation less likely. Other potential drivers of altered DNA methylation changes include effects of common treatments given to these women such as the prescription of anti-hypertensive medication or corticosteriod treatments, the latter given for fetal lung maturation before delivery. The effects of anti-hypertensive medicine on DNA methylation in the placenta are unstudied; however there are clear disruptions in the corticosteroid pathways in these samples49, perhaps from the use of antenatal corticosteriods. As these are not individual events, but logically succeed one another, a mix of these factors driving the DNA methylation differences is plausible.   69 There are limitations to this study. To avoid confounding by gestational age ? associated methylation changes 97, control placentas were also obtained from premature births. DNA methylation changes could theoretically be associated with preterm labor or preterm rupture of membranes, which were some causes of premature births in these controls. Furthermore, preeclampsia might have developed in some of these pregnancies had they continued further to term. Nonetheless, the tight clustering of control placentas relative to the EOPET ones, suggests they are a relatively homogenous group. Another limitation comes from the difficulty in obtaining high-quality RNA from placentas. We were only able to obtain quality RNA from 8 of the 20 EOPET samples, each matched to a control, resulting in reduced power when studying expression vs. DNA methylation in this sample set.  By investigating the DNA methylation profile of preclampsia placentas, this study has laid the groundwork for future studies. As many altered CpG sites located in upstream enhancer sites were identified, functional studies investigating DNA methylation-dependent regulation at these sites would help determine the biological significance of these findings. Furthermore, proteins associated candidate genes could be investigated for altered quantities in maternal blood or for properties appropriate for development of DNA methylation based non-invasive prenatal screening methods. Additionally, a more thorough genome-wide investigation of DNA methylation differences in LOPET and nIUGR samples would help elucidate the nature of any potential alterations in placental methylation profile and the nature of the overlap with EOPET.     70  EOPET (n=20) (range) CONTROL (n=20) (range) p-value Gestational Age (wks) 31.8 (24.9 - 37.3) 31.8 (25.0 - 37.3) 0.931 Maternal age (yrs)  33.5 (19.7-42.9) 31.5 (22.2 - 38.7) 0.289 Birthweight (g)  1451 (440 - 3685) 1940 (758-3470) 0.052 Table 3.1: Clinical information of samples used in Illumina methylation array  FDR ?? cutoff Cutoff 0% 10% 12.5% 15% 1 -- 1333 313 88 0.1 154604 1328 312 87 0.05 102343 1312 311 87 0.01 38840 1175 286 82 Table 3.2: Number of candidate genes, without removing cross-mapping probes, obtained using different cut-off parameters for ?? and FDR    71  GO Number Term Count p-value GO:0006355* regulation of transcription, DNA-dependent 43 4.35E-05 GO:0051252* regulation of RNA metabolic process 43 7.32E-05 GO:0030182** neuron differentiation 16 5.05E-04 GO:0010628* positive regulation of gene expression 18 1.26E-03 GO:0045941* positive regulation of transcription 17 2.39E-03 GO:0010604* positive regulation of macromolecule metabolic process 22 3.09E-03 GO:0006928** cell motion 15 3.16E-03 GO:0045449* regulation of transcription 49 3.93E-03 GO:0000904** cell morphogenesis involved in differentiation 10 4.13E-03 GO:0010557* positive regulation of macromolecule biosynthetic process 18 4.30E-03 Table 3.3: Gene ontology top 10 hits for genes with altered DNA methylation. Terms are separated into two groups, those that are related to gene transcription denoted by * and those that are involved in cell differentiation denoted by ** 72  Figure 3.1: Principle component analysis based upon values of the 282 significant differentially methylated probes 73  Figure 3.2: Boxplots from pyrosequencing follow-up  Significance calculated by ANCOVA adjusting for gestational age. Bonferroni corrected pair-wise post-hoc analysis was used to determine significance between individual groups. Every CpG site showed significant DNA methylation difference between EOPET and controls. *P<0.05 ** P <0.01, *** P<0.001. Only results from successful assays are included.  74  Figure 3.3: DNA methylation values between cases and controls according to genomic location.   Distance between each data point does not reflect true genomic distance. CpG islands are assigned according to UCSC Genome Browser delineations. Each plot represents one candidate gene: INHBA (A), BHLHE40 (B), SLC2A1 (C), ADAM12 (D). Asterisk (*) indicates candidate CpG site. This graph demonstates that candidate CpGs fell into a variety of genomic regions relative to the gene structure.  75  Figure 3.4: Methylation vs. gene expression in four candidate genes INHBA (A), BHLHE40 (B), SLC2A1 (C), ADAM12 (D). The expected negative correlation in methylation and gene expression exists for BHLHE40, with trends in SLC2A1 and ADAM12. 76  Figure 3.5: Principle component analysis using valid CpG sites that are significantly altered in cultured cytotrophoblast exposed to hypoxic conditions for 24 hrs 77  Figure 3.6: The candidate CpGs from the array fall into a variety of genomic elements Included are high CpG density islands (HC), intermediate CpG density islands (IC), intermediate CpG density regions that are on the shore of an HC (ICshore), low CpG density regions (LC) and annotated genomic enhancers. ** P <0.01, *** P<0.001. Percentage marker (%) indicates proportion of probes in each category.  78 Chapter  4: Trisomy 16 placentas share a similar DNA methylation profile with early-onset preeclampsia placentas   Introduction  In the previous chapter I demonstrated that many DNA methylation changes are present in the placenta from pregnancies affected by early onset preeclampsia (EOPET). While 3rd trimester placentas were investigated in that chapter, some of the observed methylation changes may originate from the 1st trimester of pregnancy, when defective invasion and remodeling of maternal vessels occurs, and hence be useful for early prediction of at-risk pregnancies. Other methylation changes may reflect altered syncytiotrophoblast development or occur after presentation of clinical symptoms. To try and distinguish early from late methylation changes in EOPET placentas, and to further delineate common changes, regardless of etiology, we utilized trisomy 16 (T16), a genetic defect that is highly predisposing to preeclampsia, as a model. Trisomy 16 is the most common aneuploidy in humans, occurring in over 1% of clinical pregnancies33. Complete T16 pregnancies most often miscarry in the first trimester and never survive to term182. In a phenomenon known as confined placental mosaicism (CPM), trisomy is present only or predominantly in the placenta, while the fetus is chromosomally normal. Pregnancies with trisomy 16 confined to the placenta (CPM16), often continue to term with relatively normal fetal outcomes. CPM16 pregnancies typically show IUGR and are at increased risk of fetal malformation, however postnatal development is usually unaffected183.  An estimated 25%-30% of these  79 pregnancies are affected by preeclampsia, generally the early-onset form (EOPET)184. The cause of this increased incidence of preeclampsia is unknown, and mostly unstudied. There are few genes on chromosome 16 that have been directly associated with preeclampsia; however, many are associated with placental development, including cadherin genes such as E-cadherin (CDH1) whose downregulation is critical for trophoblast differentiation185, and ERK1 (MAPK3), which promotes trophoblast invasion186 and has been shown to be over-expressed in cultured mesenchyme from trisomy 16 placentas187. Trisomy for several different chromosomes is associated with genome-wide alterations of gene expression188; however, few studies have looked at DNA methylation. Our lab previously found few methylation changes in different somatic tissues of trisomy 18 or 21 fetuses compared to chromosomally normal controls189, while another group showed marginally more differences between trisomy 21 leukocytes and controls190.  My hypothesis is that altered placental growth in trisomy 16 predisposes such pregnancies to preeclampsia, and that a subset of DNA methylation changes associated with trisomy 16 will overlap those observed for EOPET.  The first goal was to identify DNA methylation differences in CPM16 placentas compared to 3rd trimester controls and determine if any of these differences were shared with EOPET. To then determine if EOPET-associated changes are likely to be present early in affected pregnancies, I wanted to identify which of these common DNA methylation changes are present in the 1st trimester. For this, miscarried T16 placentas can be used as a model for 1st trimester pregnancies that are at high risk for developing EOPET. To provide further insight into  80 the stability of potential candidate DNA methylation marks over gestation, control placentas from the 1st and 3rd trimesters were also compared.   Methods   Samples   Four groups of placentas were tested in this study (Figure 4.1): 1) five chromosomally normal placentas from 1st trimester spontaneous abortions, 2) five 1st trimester placentas from full trisomy 16 pregnancies that miscarried in the 1st trimester, 3) ten chromosomally normal placentas from pregnancies that delivered in the 3rd trimester and 4) ten 3rd trimester placentas with CPM16. Miscarriage samples were anonymously obtained from specimens undergoing routine clinical evaluation through the Embryofetopathology Unit at the Children's & Women's Health Centre of British Columbia.  Placentas with CPM16 were obtained from a variety of sources. It should be noted that several manuscripts have been previously published using this cohort183,184,187. All trisomic cases were previously molecularly determined to originate from maternal meiotic errors with predominantly only trisomic cells. Preeclampsia was present in five of the 10 CPM16 cases, with the remainder being normotensive (Table 4.1). Third trimester control placentas were collected from deliveries at BC Women?s Hospital; some of these samples have been used in previous publications7,49,159, including Chapter 3 of this thesis, and were all verified to be chromosomally normal. DNA was extracted from a sample of chorionic villi in each case.  81  Illumina Infinium HumanMethylation450 BeadChip  DNA (1000 ng) was bisulfite converted and run on the Illumina Infinium HumanMethylation450 BeadChip (450K array) according to the manufacturer?s protocol. The chips were scanned with an iScan reader (Illumina) and background normalized in GenomeStudio (Illumina). Quality control was performed using built in controls. All samples passed quality control criteria (<5% of probes had invalid values). Probes that hybridized to the sex chromosomes and/or interrogated known single nucleotide polymorphisms were removed from the analysis (n=30670). Probes that had a detection p-value of >0.01 in one or more sample were also removed (n=28465). ?-values were extracted from GenomeStudio, transformed to M-values and colour-corrected using the Methylumi163 package in R 2.14162. Probe type correction was performed using the SWAN164 package in R 2.14. Values were then converted back into biologically relevant ?-values.    Statistical analysis  M-values were imported into R 2.14 and analyzed using the Significance Analysis of Microarrays program in the siggenes165 package. This program corrects for multiple comparisons and outputs a set of significant probes below a certain false discovery rate (FDR) cutoff. The list of significant probes was limited to probes that had a certain ?? (representing mean methylation difference between groups) cutoff. For this study we  82 compared FDRs of 0.01, 0.05 and 0.10 combined with ?? cutoffs of 5%, 10% and 15% for each comparison.    Bioinformatics   Candidate gene lists for each comparison were submitted to DAVID143 for gene ontology analysis.    Results   Methylation changes associated with CPM16   As we previously observed comparing EOPET to control placentas, there were a large number of sites exhibiting differential methylation between CPM16 and 3rd trimester control placentas using minimal statistical cutoffs (Table 4.2).  The most stringent cutoff (FDR <0.01 and ?? >0.15) identified 2254 candidate CpG sites, 46.8% of which were hypomethylated. The 100 CpGs with the largest ??s are listed in Table S4.1.  Gene ontology analysis of the 2254 candidates revealed an overrepresentation of genes involved in cell differentiation, among other categories (Table 4.3).  Comparing the distribution of significant CpGs across annotated genomic elements revealed an overrepresentation of significant probes in enhancers and intermediate CpG density (IC) shores and an underrepresentation of probes in high CpG density islands (Figure 4.2).   Generally, this is the same pattern observed previously in comparing EOPET placentas vs  83 controls with the exception that EOPET placentas did not show increased involvement of IC shore regions.  Increased expression of only a subset of genes from supernumerary chromosomes has been shown in previous studies of trisomy188, suggesting that there may be compensation for expression of some genes such that not all are 50% greater expressed.  I therefore addressed whether there was an overrepresentation of CpG sites from chromosome 16 in our significant candidates. I found no enrichment for CpG sites on chromosome 16 (chi-square test p-value=0.72), which is consistent with other studies of DNA methylation change in trisomy190.    Overlap of methylation changes associated with CPM16 and EOPET  Because of the frequent occurrence of preeclampsia in CPM16 pregnancies, we hypothesized that CPM16 would show similar DNA methylation differences as we had previously identified in EOPET. Using the 282 differentially methylated candidate CpGs identified in chapter 3, we discovered 30 overlapping significant CpG sites in CPM16 using an FDR of <0.01 and ?? >0.15 (Table S4.2). When a less stringent cutoff is used (a nominal p-value of <0.05 and ?? >0.05), 68.8% of the 282 candidate CpG sites are significantly different from controls in CPM16. To determine whether the candidate CpG methylation profiles of CPM16 and EOPET placentas are similar, we analyzed the correlation of the EOPET candidate probes between the two groups. This revealed a highly significant relationship (r=0.837, p<0.0001;Figure S4.1). We also compared the distribution of delta beta values for the probes hypo and hyper methylated in EOPET (Fig  84 S4.2). Of 206 probes previously determined to be hypomethylated in EOPET, 160 (76%) showed significant hypomethylation (p<0.05) in CPM16 placentas. Of 70 probes previously identified as hyper-methylated in EOPET, 34 (49%) were significantly hypermethylated in CPM16 placentas.  While similarities between EOPET and CPM16 can be identified, we also wanted to evaluate significant differences between them that may reflect Trisomy 16 specific changes. Only 35.9% of the EOPET candidate CpGs were significantly different from CPM16 using non-stringent criteria (p<0.05 and ?? >0.05). Many of the sites (81.2%) identified as different between EOPET and CPM16 were in fact differentially methylated in a common direction (hypo- or hypermethylated) in both groups as compared to controls, differing only in magnitude of the change. When such probes are removed from consideration, only 6.9% of CpG sites were significantly different between the two groups (p<0.05).  To visualize the DNA methylation changes shared between EOPET and CPM16, PCA using the candidate CpGs from chapter 3 was plotted (Figure 4.3). The CPM16 placentas clustered closer to the EOPET samples than the controls along the first principal component, which appears to represent preeclampsia status (EOPET or at risk of EOPET). The second principal component segregates samples according to presence/absence of CPM16.    85  There are few methylation differences between CPM16 placentas that develop EOPET and those that do not  As we saw that many of the changes in EOPET are also present in CPM16, determining if there were further differences in the CPM16 placentas that developed preeclampsia compared to those that did not may provide insight into which genes are essential for development of the disorder.  The CPM16 placentas with and without preeclampsia (n=5 in each group) were compared, and I found no significant differences using cut-off criteria of FDR<0.05 and ?? of >5%. However, if we use an uncorrected p<0.05 (rather than an FDR cutoff) combined with ?? >0.05, then 20.3% of candidate probes from chapter 3 were significant between CPM16 samples with and without preeclampsia. Plotting the ?? differences from controls for these probes (Figure 4.4) revealed a shift in the distribution towards larger methylation differences in CPM16 samples with preeclampsia than those without.    DNA methylation changes associated with 1st trimester Trisomy 16 placentas overlap those associated with 3rd trimester CPM16 placentas  To identify changes in DNA methylation that were specific to trisomy 16 regardless of gestational age, we also compared 1st trimester T16 to 1st trimester chromosomally normal miscarriage samples. There were much fewer significant sites than in the 3rd trimester, with none reaching an FDR cutoff of <0.01 (Table 4.4). Using an  86 FDR <0.05 and ?? >0.15, we identified 262 differentially methylated CpG sites (Table S4.3).  To identify CpG sites that were consistently different in trisomy 16 placentas across gestational ages, we compared the 262 candidate CpGs from the 1st trimester and the 2254 candidate CpGs from the 3rd trimester and identified 77 CpG sites in 43 genes that overlapped (Table S4.3). Submitting the 43 genes represented by the 77 CpG sites to gene ontology analysis revealed a top gene ontology hit of ?regulation of growth? (GO:0040008; p <0.01). We further investigated if the 77 CpGs that were consistently associated with trisomy 16 in both the first and third trimester overlapped the EOPET associated changes. Using a low-stringency criteria of p <0.05 and ?? >0.05, only 3 of 282 candidate CpG sites from chapter 3 were also altered in first trimester trisomy 16 in the same direction as the changes in EOPET (cg16699715 and cg00615537; ARHGEF37, cg22996170; JUNB, Figure 4.5).    Gestational age is a major determinant for placental DNA methylation  To determine stability of DNA methylation marks across gestational age, we compared 1st trimester to 3rd trimester samples of trisomy 16 and 1st trimester to 3rd trimester samples of chromosomally normal placentas. There were 19,987 CpG sites on the array that were significantly altered (FDR< 0.01, ??>0.15; Table 4.5) from 1st trimester to 3rd trimester in T16 and 30,729 CpG sites that are changed from 1st trimester to 3rd in chromosomally normal placentas using the same criteria (Table 4.6). The  87 majority of these gestational age related changes were shared in common between the two conditions, with 14,986 CpG sites that consistently had >15% ?? and FDR <0.01 regardless of chromosomal content. Many of these changes had already been identified in a previous study looking at gestational age dependent DNA methylation changes in placenta97 (38.8% of candidate CpG sites from the previous study were significant in this study). Using gene ontology, many of these gestational-age dependent changes are associated with standard development processes such as cell motion and morphogenesis (Table 4.7). Another approach to identify potential early methylation changes associated with risk of preeclampsia is to identify which of the 282 EOPET-associated methylation changes found in third trimester placentas are stable across gestational ages. We found 12 CpGs that were consistent across gestational ages in both control and trisomy 16 pregnancies (p<0.05 and ?? >0.05) (Table S4.2).   Discussion  While the underlying cause of preeclampsia is unknown, an effect of fetal genotype on preeclampsia risk is demonstrated by the high risk associated with some human trisomies191,192  and rare mutations in CDKN1C193 and STOX1194. It is unknown though how closely such situations would mimic the molecular changes that occur in sporadic, chromosomally normal EOPET.  In this study I focused on a specific placental abnormality that predisposes to preeclampsia, CPM16, and demonstrate many similar changes in DNA methylation as observed in chromosomally normal EOPET placentas.  88 While very few DNA methylation differences were conserved across gestational ages, those that were provide candidate biomarkers for early identification of EOPET susceptibility.   Overall T16 showed more significant methylation changes as compared to gestationally age matched controls than the chromosomally normal EOPET group. For example, using an FDR of 0.01 and ?? of 15%, 2254 sites were significant in CPM16, as compared to 282 in chapter 2 using similar criteria. While this could indicate a greater perturbation in development in CPM16, it may also reflect the more uniform etiology of the CPM16 placentas. While there are few distinguishing pathological features of a T16 placenta, placental size tends to be reduced195 and first trimester T16 trophoblast shows a deficiency in EVT proliferation in vitro196, which may contribute to susceptibility to preeclampsia. Interestingly, among the CpGs with the greatest alterations in methylation inCPM16 (?? >35%), were 2 sites each associated with SMAD3 (part of the TGF-beta signaling pathway, important for epithelial to mesenchymal transition197 as happens in differentiation from cytotrophoblast to EVT) and RHOB (a RHO GTPase, involved in EVT migration198). Remarkably, there were few significant DNA methylation differences between CPM16 placentas that did or did not have PET. The finding of similar methylation profiles between EOPET placentas and CPM16 placentas, whether PET is present or not, implies all CPM16 pregnancies are molecularly susceptible to EOPET. As gene ontology analysis implied, this could be reflective of poor placental growth and differentiation of EVT in CPM16 placentas, resulting in similar reduced trophoblast invasion of maternal endometrium as observed in chromosomally normal placentas with EOPET. Definitive  89 causes of EOPET have yet to be discovered, meaning the same conditions that prevent its development in chromosomally normal pregnancies could also explain its lack of development in CPM16 pregnancies. Maternal health could provide a second hit, as women who are obese25, older26 or have a predisposition to hypertension23 are more at risk of developing preeclampsia.  Relatively few DNA methylation changes in trisomy 16 placentas associated with EOPET appear to be consistent through extremes of gestational age. This may be because of extensive epigenetic remodeling that occurs from the 1st to 3rd trimester in placenta, overwhelming the differences. Regardless, we did find three CpGs in or near two genes (ARHGEF37 and JUNB) that showed consistent differences in 1st trimester T16, 3rd trimester CPM16 and EOPET compared to controls. While there is limited information on ARHGEF37, JUNB is a component of the AP-1 transcription factor complex which is implicated in hypoxia169,199, a key condition in the development of preeclampsia. Furthermore, JUNB is an essential transcription factor for the vascularization of the early placenta, disruption of which leads to poor placentation200, another key condition in the development of preeclampsia.  The limited number of CPM16 placentas analyzed in this study restricts the ability to discover significant differences between groups. Furthermore, as many of our CPM16 cases are not collected locally, samples appropriate for gene expression studies were not available. To further the goal of identifying methylation biomarkers that indicate an increased risk of EOPET in the first trimester, a future sample group for analysis could be chorionic villus samples (10-12 weeks gestation) from chromosomally normal pregnancies that go on to develop preeclampsia. These samples have previously been  90 used to analyze gene expression between future preeclampsia villi and gestational-age matched controls118, finding differences in several genes identified as differentially methylated in EOPET in chapter 3 such as EPAS1 and FN1. This study identified multiple DNA methylation differences in a first trimester placental model of increased preeclampsia risk. These marks may be of future clinical use, identifying pregnancies at risk of developing preeclampsia through non-invasive techniques45  91 Case PET GA at PET onset Pregnancy Outcome Sex GA (weeks) BW BW SD 16-09 Y 21 TA F 25 410g -4.4 16-60 Y <25 Livebirth F 28 722g -2.44 16-25 Y <33 Livebirth M 33 1498g -1.61 16-09 Y NA Livebirth M 36 1559g -2.94 16-51 Y 32 Livebirth F 36 1530g -3.12 16-55 N  Livebirth F 34 1278g -3.43 16-57 N  Livebirth M 36 1558g -2.95 16-47 N  Livebirth F 39 3572g 0.87 16-54 N  Livebirth F 37 1920g -2.54 16-56 N  Livebirth F 37 2244g -1.66 Table 4.1: Clinical information for CPM16 samples used in this study FDR Cutoff ?? Cutoff 0% 5% 10% 15% 1 -- 63108 14534 3367 0.1 52846 36073 12739 3261 0.05 31674 24621 10537 3071 0.01 11667 10290 5927 2254 Table 4.2:Number of candidate probes obtained using different cut-offs for 3rd Trimester CPM16 vs. Controls    92 Term Description Count p-value GO:0030182 neuron differentiation 46 3.23E-06 GO:0051094 positive regulation of developmental process 30 1.38E-04 GO:0050954 sensory perception of mechanical stimulus 16 1.73E-04 GO:0048666 neuron development 34 1.86E-04 GO:0007605 sensory perception of sound 15 3.11E-04 GO:0051056 regulation of small GTPase mediated signal transduction 27 3.59E-04 GO:0006357 regulation of transcription from RNA polymerase II promoter 58 4.44E-04 GO:0048839 inner ear development 13 5.12E-04 GO:0051270 regulation of cell motion 22 6.48E-04 GO:0048667 cell morphogenesis involved in neuron differentiation 23 7.64E-04 Table 4.3: Gene ontology of the candidate CpGs altered in 3rd trimester trisomy 16    93  FDR Cutoff ?? Cutoff 0% 5% 10% 15% 1 -- 97419 24962 4957 0.1 3982 3316 2087 1005 0.05 770 677 414 262 0.01 0 0 0 0 Table 4.4: Number of candidate probes obtained using different cut-offs for 1st Trimester T16 vs. Controls FDR Cutoff ?? Cutoff 0% 5% 10% 15% 1 -- 128474 54302 23065 0.1 144001 92273 50958 22922 0.05 99893 74218 46575 22457 0.01 53448 46748 35031 19987 Table 4.5: Number of candidate probes obtained using different cut-offs for comparing 1st trimester T16 vs. 3rd trimester CPM16 FDR Cutoff  ?? Cutoff 0% 5% 10% 15% 1 -- 137584 65600 34025 0.1 171896 113178 63104 33742 0.05 131373 98299 60049 33280 0.01 78221 67978 49658 30729 Table 4.6: Number of candidate probes obtained using different cut-offs for comparing 1st trimester control vs. 3rd trimester control    94 Table 4.7: Top 10 gene ontology terms for gestational-age specific probes  Term Description Count p-value GO:0006928 cell motion 82 1.25E-06 GO:0001501 skeletal system development 60 2.69E-06 GO:0007160 cell-matrix adhesion 24 1.63E-05 GO:0009891 positive regulation of biosynthetic process 105 1.97E-05 GO:0016477 cell migration 51 2.70E-05 GO:0051173 positive regulation of nitrogen compound metabolic process 98 2.84E-05 GO:0031328 positive regulation of cellular biosynthetic process 103 2.84E-05 GO:0051674 localization of cell 55 3.07E-05 GO:0048598 embryonic morphogenesis 55 3.07E-05 GO:0048870 cell motility 55 3.07E-05  95  Figure 4.1: Schematic of the comparisons in this study 96  Figure 4.2: Genomic elements in which the 2254 significant probes significantly different in CPM16 are distributed compared to the whole array. The distribution of 282 probes significant in EOPET is shown for comparison. HC Island - high CpG density island; IC island ? intermediate CpG density island; IC shore ? intermediate CpG density shore. *** - p<0.0001. Percentage marker (%) represents proportion of probes for each study group.  97   Figure 4.3: Principal component analysis using the 282 candidate CpG probes significantly different in EOPET vs. controls (chapter 3) 98  Figure 4.4: Histogram showing the distribution of ??s from controls in the two groups of CPM16 samples. Graphs depict the ??-values in CPM16 with and without PET compared to controls. Values from 210 hypomethylated (A) and 72 hypermethylated (B) candidate probes from chapter 3 are represented. Notable genes in the most different groups are indicated. 99  Figure 4.5: Line plot of DNA methylation in JUNB (A) and ARHGEF37 (B), two genes that maintain differences across gestational age and EOPET  100 Chapter  5: Discussion   In this thesis I investigated DNA methylation of placental samples that were affected by conditions related to the pregnancy disorder preeclampsia. By studying placental cells exposed to different oxygen levels, preeclampsia-associated placental chorionic villi and chorionic villi from other pathological pregnancies, I found DNA methylation differences that helped elucidate the biology that connects these conditions.    Summary and significance of findings   Hypoxia in the placenta has been implicated in the pathogenesis of preeclampsia, and multiple studies have investigated gene expression in hypoxia-exposed placental cell cultures134,147. For the studies in chapter two, cytotrophoblast cells from term placentas were isolated, cultured and assessed for DNA methylation and gene expression changes. Collaborators in Dr.David Nelson?s lab at Washington University performed the cell culture experiments. Half of the cells were immediately cultured in <1%, 8% and 20% oxygen conditions for 24 hours, while the other half cultured in normoxic conditions for 72 hours, fusing to become multinucleated trophoblast cells (syncytiotrophoblast), before being exposed to the same conditions as above.  The 450k methylation array revealed significant hypermethylation at many CpG sites in cytotrophoblast cells exposed to hypoxia. Syncytiotrophoblast cells exposed to the 8% oxygen compared to cytotrophoblast in the same conditions also showed a significant hypomethylation at numerous CpG sites, many of which overlapped with the hypoxia-specific sites.  A large portion  101 of the significantly altered CpG sites in hypoxic cytotrophoblast were located within 50 bp of an AP-1 binding site. Expression of JUN and FOS, two components of the AP-1 complex, were shown by qPCR to be increased in cytotrophoblast cells grown in hypoxic conditions. From these data we hypothesized that as expression of JUN and FOS increases with hypoxia, they bind to AP-1 sites and recruit DNMTs that methylate and repress nearby cell differentiation genes.    This is the first study to investigate genome-wide DNA methylation of placental cells exposed to hypoxia. The DNA methylation differences identified in this chapter in response to hypoxia may help regulate trophoblast cell differentiation and thus are important targets for understanding preeclampsia development.   For chapter three, I analyzed the DNA methylation and gene expression of preeclampsia placentas compared to controls. Using the 450k methylation array, I found thousands of significant differentially methylated CpG sites. I chose to focus on 282 CpG sites associated with 249 genes that exhibited the largest significant differences in DNA methylation between preeclampsia and controls. Many of these genes were also differentially expressed in a subset of the same samples run on the 450k array. Using the 282 significant CpG sites and plotting the first two principal components, samples were clearly separated by presence of EOPET, but did not stratify based on associated condition (IUGR, HELLP, GDM) or severity of proteinuria. Following up 450k candidates by expanding analysis to include samples from related placental disorders (LOPET and nIUGR), we found differences in DNA methylation at four genes based on severity of disease state, with EOPET exhibiting the largest differences from controls. This study identified multiple candidate genes that have previously been implicated in EOPET, including those that are involved in the hypoxia response pathway and trophoblast cell differentiation.  102  In chapter three, I expanded upon previous similar studies6,7,158 analyzing DNA methylation and gene expression in preeclampsia.  The strengths of this study are the comprehensive genome-wide approach to asses DNA methylation; the matching of samples between 450k methylation and expression arrays, allowing assessment of differential DNA methylation on gene expression; and the restriction of analysis to EOPET, a more severe and homogenous subset of preeclampsia. Furthermore, analyzing the DNA methylation between EOPET associated with different conditions was a novel analysis that revealed a lack of differing molecular etiologies in EOPET. The differences identified in this study further confirmed placental involvement in the etiology of preeclampsia and identified novel candidates for further investigation. Among these are enhancers and non-promoter regions that demonstrated an inverse correlation between DNA methylation and gene expression.   In chapter four, I built on my chapter three findings by looking for DNA methylation changes in placentas from trisomy 16 pregnancies, a condition susceptible to developing preeclampsia34. Third trimester CPM16 placentas shared many DNA methylation differences with chromosomally normal EOPET placentas when compared against controls, suggesting a common molecular profile behind their increased susceptibility to the disorder. Interestingly, comparing CPM16 placentas that did and did not develop preeclampsia, few methylation differences were identified. However, the CPM16 placentas with preeclampsia exhibited an overall shift towards larger differences from controls at the EOPET candidate CpGs. In first trimester T16 there was no such shift. There were however many changes that appeared to be T16 related in the 1st and 3rd trimesters. To identify preeclampsia-related DNA methylation differences that are present in the 1st trimester we looked for common changes in the 1st and 3rd trimester T16 placentas. This revealed 77 CpG sites that were significantly altered in both.  103 Investigating which of these changes were involved in EOPET, analysis revealed three DNA methylation differences shared with EOPET when using relaxed significance cutoff criteria.   This study is the first to investigate DNA methylation in trisomy 16 placentas and the first to use a specific genetic abnormality as a model to find biomarkers that may be indicative of preeclampsia development. The developmentally stable DNA methylation differences identified in this study and observed in EOPET may be useful for identifying pregnancies at risk for preeclampsia.   In this thesis I demonstrate how Illumina 450K analysis can be applied to different placental conditions to identify alterations at specific loci and patterns of change. These findings provide a first glimpse into the patterns of changes that can occur in placenta and are important for understanding the underlying placental biology of preeclampsia,. Furthermore, new technologies for genome-wide methylation analysis, such as the Illumina 450K array, have only recently been developed and this work demonstrates different approaches to apply this new technology for the study of disease.   Strengths and limitations   Comprehensively evaluating DNA methylation in human tissue for this thesis explores new clinical potential in the diagnosis and treatment of preeclampsia, however restricted study samples and a lack of bioinformatic information hinders conclusions on the basic biology of the disorder.  The modern study of disease has seen large and rapid advances in methods to mimic disease states. Model organisms are essential for studying complex disorders in vivo.  104 Unfortunately, the molecular changes in these organisms may not be reflective of the molecular conditions in the humans they are modeled after. High-quality human tissues for the study of many disorders are difficult to obtain due to the complexity in obtaining post-mortem samples. Studying preeclampsia, a disorder for which the dysfunctional tissue is the placenta, allows the consistent acquisition of human samples, as the placenta has ceded its function immediately after birth and is often discarded. Many individuals are willing to donate this organ after birth and basing the lab at a maternity hospital provides access to an abundance of fresh samples.  However, obtaining placentas postnatally only permits a snapshot of their molecular profile. This may be after the administration of anti-hypertensive or corticosteriod treatments which may alter the molecular profile away from the true placental state49. Furthermore, depending on the condition, whole placental tissue may be comprised of varying populations of cell types. These different cell types may convey different molecular properties, leading to the detection of the altered cell populations rather than molecular alterations within a cell itself.  Culturing trophoblast cells, as in chapter two, or employing a choriocarcinoma cell line allows for manipulation through treatment (e.g. hypoxia), however they may not accurately reflect the in vivo conditions of the placenta. Ideally I would obtain preeclampsia-associated placental samples at earlier gestational ages (through chorionic villus sampling in the first trimester, before the onset of symptoms) to observe the profile of preeclampsia throughout development.   This field would benefit from the development of a consistent mammalian model of preeclampsia, one that could be analyzed and manipulated throughout gestation. Several different rodent models have been used201,202, however confounding factors in rodent pregnancy such as multiple gestations, reduced relative gestation time and overall differing placental morphology limits the human applicability of such studies.    105  The study of altered DNA methylation affecting preeclampsia-associated placentas is ideally complemented by a study of gene expression. Unfortunately, this is difficult as the placenta has abundant RNAses, which degrade mRNA post-delivery in a non-predictable manner9. Microarray data from RNA must be normalized to adjust for inter-placental variation in degradation of RNA, however due to the non-uniformity of degradation, it is not completely reliable. DNA methylation however is stably maintained following delivery. Thus to provide a more reliable picture of gene disregulation in pathological placentas and their cells, both approaches were used in this thesis.   I found altered DNA methylation of some gene promoters was correlated with altered gene expression. However, many genes (such as JUN and FOS in chapter two) have consistently unmethlyated promoters despite differing levels of expression.  Thus, analyzing promoter DNA methylation is useful for inferring gene expression changes only in a subset of genes. There are many genes whose expression may be regulated by other regions of the genome (i.e. enhancers) that may not even be proximal to the gene170. A strength of this thesis is the use of the Infinium HumanMethylation450 BeadChip to assess DNA methylation in our samples. Microarrays enable a controlled and continuous assessment of DNA methylation at any CpG site provided it is targeted by the array. For this thesis it was more advantageous than sequence based methods, which are untargeted and discrete. Depending on the depth of sequencing, one may only be able to identify large DNA methylation differences, missing out on potentially important regulatory changes. While previous DNA methylation microarrays (the GoldenGate Cancer Panel I and the Infinium HumanMethylation27 BeadChip) focused primarily on gene promoters141,203, the 450k array has ~485,000 probes spread across the genome, including in intergenic regions and specifically annotated enhancer regions111. This wealth of data enables expanded analysis over  106 the previous arrays. Using published expanded annotations of the 450k array161, I was able to adjust for confounding factors such as probes that cross-hybridize to locations other than the target site or have a SNP at the target CpG, adjustments that are often missed144. The expanded annotations also include information about any genomic elements a probe targets, overlapping repetitive elements and proximal genes161. Using the data itself, I established significance using pair-wise comparisons using highly stringent cut-offs. When these cut-offs were determined to be too stringent or not enough, I adjusted the analysis likewise. In chapter four I analyzed the distribution of methylation for a set of probes, establishing a pattern of DNA methylation differences between CPM16 placentas with and without preeclampsia. In chapter two we combined study groups and used replicates to confirm our nominally significant DNA methylation differences, which is in contrast to chapter three where I used a large ?? difference to restrict the significant probes. These varied methods of 450k array analysis provide a framework for other 450k array studies dealing with the same significance issues.  DNA methylation has been shown to affect transcription factor binding at enhancer sites204, and by identifying large DNA methylation changes in these regions, we can infer changes in gene expression at downstream genes. Currently this analysis is limited by the non-placental cell types that the ENCODE (and 450k array) enhancer annotation is based upon90.  Without functional studies in cultured placenta cells or placentas from model organisms, it is difficult to determine how aberrant enhancer methylation may affect downstream gene expression. Additionally, it is estimated that the majority of enhancers do not even affect expression in the most immediate downstream gene170, providing further justification for future functional studies.       107  Future directions   The research presented in this thesis opens numerous new questions for study. Functional analysis, post-hoc bioinformatics, cellular manipulations, and diagnostic clinical tests are among the most promising future directions.   The revelation that AP-1 binding sites are overrepresented near differentially methylated CpG sites in cytotrophoblast cells exposed to hypoxia helps highlight the potential regulatory function that DNA methylation changes in enhancer regions might have in the placenta. A similar pattern was observed in chapter three, although there was no dominant transcription factor binding site proximal to differentially methylated CpGs, but rather a variety of affected binding sites. In order to determine what effect these DNA methylation changes have on biological processes and the disease state, there are several applicable experiments that could be performed. Specific to the findings in chapter two, siRNA could be used to knock down JUN, FOS or both, in cultured placental cells. DNA methylation could then be assessed near AP-1 sites of candidate hypoxia-affected genes. If there is no longer a DNA methylation difference observed at these sites after exposure to hypoxia, this implies a function of AP-1-mediated DNA methylation at this site. This model could be extrapolated to further explore the results of my other studies. For instance, in chapter three, numerous transcription factors were indicated as being differentially expressed and regulated in EOPET placentas. I speculate that these factors may bind to elements upstream of genes, affecting their expression and promoting the EOPET phenotype. If I knock down these transcription factors in placental cells and observe the effect on the genes they regulate, I can assess the functionality of the candidate transcription factor in the placenta.   108  In an ideal situation, an analysis of histone modifications would have been included in this thesis to better understand the epigenetic landscape in EOPET cases. Histone modifications affect DNA methylation through dynamic chromatin remodeling, which contributes to the ability of DNMTs to access CpG sites. Analyzing histone marks in placentas with preeclampsia, hypoxia or trisomy 16 using ChIP-seq would be advantageous for understanding the broader context of altered epigenetic regulation in these conditions and of the developing placenta in general. Using tools like ENCODE90, active promoters, enhancers, and transcribed areas can be predicted using combinations of histone marks.  However, ChIP-seq for multiple histone types is currently prohibitively expensive for any one experiment. Instead, a targeted approach could be used such as, ChIP-chip, which pulls down one histone modification, for example H3K4me1 (commonly associated with enhancers90), and hybridizes those DNA fragments associated with the mark to a microarray with predetermined sequence targets. For this example, the targets included on the microarray could represent putative enhancers as identified by ENCODE. This approach could add to the identification of active enhancers in placenta, of which there is little data currently.     Gene network analysis is useful for many studies using large microarrays, however interactions between genes and proteins in the placenta are not well characterized, making it less effective in studies of this tissue. Despite published connections between certain genes (e.g. BHLHE40 and HIF1A79), software such as Ingenuity Pathway Analysis (Inguenity Systems Inc., Redwood City, CA) and String-db205 have yet to associate them in their databases. Using bioinformatic tools and data-mining, protein and gene interaction networks for preeclampsia could be developed that would be of significant use in future studies. DNaseI hypersensitivity sites (indicating a euchromatic region) from the Roadmap Epigenomics project206 have been  109 mapped using early placental tissue. Using this data, active enhancers in placenta can be identified, along with transcription factors that readily bind these enhancers. Functional studies would still be necessary to confirm associations, but bioinformatics can provide an excellent starting point for understudied placental gene interactions.  The isolation of cytotrophoblast and differentiation to synctiotrophoblast, as demonstrated in chapter two, provides a strong model for additional testing. Treating cells with reagents such as anti-hypertensive drugs or corticosteroids and subsequently observing gene expression and DNA methylation changes may help determine the molecular effects of these common drugs given to preeclampsia patients on the placenta and elucidate the true molecular effects of preeclampsia itself.   While the results of this thesis have provided a step forward to identify the underlying causes of preeclampsia, I have also identified several candidate genes that should be followed up clinically for potential diagnostic purposes. The discovery of several significant candidate genes that have already been studied extensively highlights the importance of assessing the understudied genes identified here. The genes BHLHE40 and EPAS1 were among those differentially methylated and differentially expressed in preeclampsia placentas. Previous studies have highlighted the importance of these genes in trophoblast differentiation and hypoxia, thus measuring their protein levels in the serum of first trimester pregnancies could lead to more accurate predictions of preeclampsia risk. Further assessing the DNA methylation and gene expression status of placentas from LOPET and nIUGR will help to distinguish to the commonalities and differences between those identified with EOPET and which indicate a broader disregulation of placental function. Assessing much larger numbers of EOPET-associated placentas may also identify sub groups within this clinical classification that were not  110 apparent in my sample size of 20 cases. Identifying distinct clinical subsets of patients with a common etiology is important for diagnosis and treatment, as different predictive markers and different treatments may be appropriate to each subgroup.  Beyond this, identifying altered DNA methylation in these candidates may allow for the development of DNA methylation based testing in the cell-free fetal DNA component of maternal blood45. Comparing the DNA methylation of loci identified in affected vs. control placentas against maternal blood has already identified candidates like TIMP3 that are differentially methylated from each other in all three7.    Conclusion   Preeclampsia is a condition that plagues pregnancies throughout the world. Understanding the molecular background as well as establishing novel detection methods will help progression towards prevention of this disorder. 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Nat Biotechnol. 2010;28(10):1045-1048.   134 Appendices Appendix A  : Supplementary material for chapter 2 Gene Primer Sequence (5' to 3') Product length (bp) JUN Forward GAAGAATTTTTTAGGGTGGAGTTT 87   Reverse (Biotin)-ACCCCAAAACCTTCCCATTA     Sequencing TTTAGGGTGGAGTTTTTAT   FOS Forward   AGGTTAGGGGATGAAGGTTTGTT 471   Reverse (Biotin)-ACCCCCCAAAATAAAAAATTTC     Sequencing GTATAAGGGTAAAAAGG   CFB Forward GGGTTTTTAGGATGTTAGAGGT 236   Reverse (Biotin)-CAACCCTACCTAACCTCCAAATAA     Sequencing GGGTTTTTAGGATGTTAGAG   CD59 Forward TTGGGTAAAGTAGGGTTGGAGGTA 409   Reverse (Biotin)-AACACTATCTTCCCCATCAAAATC     Sequencing GATTTATTTAGTGTTGTGGT   ZNF217 Forward GGATTTTAATTGGATGAAATTTTG 271   Reverse (Biotin)-CAAAAAATCAATCCCAACAACCTA     Sequencing TATGATTTATTTATATTAGTTT   GRAMD3 Forward (Biotin)-TTGAGGATTATTTGGGGTGAATTA 359   Reverse CAAATCTACCCAACCACAACTCTT     Sequencing ATCACTAACCCTTTACTCAT   Supplementary Table 2.1: Pyrosequencing primers for Chapter 2 experiments     135 Probe ID Chr Gene name Cyto 1%(N=5) -Avg ?  (SD) Cyto 8% (N=5)-Avg ?  (SD) Cyto 20% (N=5)-Avg ?  (SD) Cyto 1% vs 8%-hyper(?) hypo(?) Cyto 1% vs 20%-hyper(?) hypo(?) Sync 8% vs Cyto 8%-hyper(?) hypo(?) cg04350215 1 ABCA4 0.78  (0.06) 0.59  (0.09) 0.62  (0.08) ? ? NA cg17941109 19 ABHD8 0.54  (0.04) 0.4  (0.06) 0.38  (0.05) ? ? ? cg03502889 11 ABTB2 0.55  (0.06) 0.42  (0.07) 0.4  (0.08) ? ? NA cg15342452 10 ADAM12 0.52  (0.06) 0.36  (0.08) 0.39  (0.06) ? ? NA cg10063575 12 AMHR2 0.71  (0.05) 0.56  (0.05) 0.56  (0.04) ? ? NA cg08554554 20 ARFRP1 0.78  (0.04) 0.63  (0.08) 0.61  (0.08) ? ? NA cg11229273 3 ARHGEF3 0.67  (0.1) 0.23  (0.11) 0.24  (0.11) ? ? NA cg26193418 7 BMPER 0.63  (0.04) 0.51  (0.03) 0.51  (0.04) ? ? NA cg19678111 1 C1orf226 0.67  (0.14) 0.51  (0.12) 0.49  (0.12) ? ? ? cg07880384 6 C2, CFB 0.55  (0.03) 0.4  (0.05) 0.38  (0.06) ? ? NA cg23538064 6 C2, CFB 0.84  (0.02) 0.74  (0.06) 0.7  (0.06) ? ? NA cg23903301 11 CD59 0.65  (0.06) 0.52  (0.07) 0.46  (0.07) ? ? ? cg26366091 1 CHI3L2 0.83  (0.04) 0.57  (0.12) 0.65  (0.1) ? ? NA cg14630032 3 CP 0.62  (0.05) 0.5  (0.05) 0.52  (0.04) ? ? ? cg01875467 5 CSF1R 0.34  (0.03) 0.22  (0.02) 0.2  (0.03) ? ? NA cg11911769 7 CUX1 0.66  (0.07) 0.49  (0.12) 0.46  (0.11) ? ? ? cg05546575 7 DAGLB 0.85  (0.05) 0.56  (0.13) 0.47  (0.13) ? ? ? cg09597022 6 DAXX 0.83  (0.09) 0.66  (0.14) 0.63  (0.13) ? ? ? cg24260359 3 DNASE1L3 0.36  (0.13) 0.2  (0.09) 0.22  (0.1) ? ? NA cg16022049 12 EMP1 0.38  (0.07) 0.28  (0.08) 0.26  (0.08) ? ? NA cg26746309 10 ERLIN1 0.42  (0.08) 0.29  (0.08) 0.3  (0.07) ? ? NA cg26877720 10 FAM107B 0.34  (0.08) 0.17  (0.06) 0.17  (0.05) ? ? NA cg04785903 10 FAM107B 0.75  (0.07) 0.62  (0.08) 0.62  (0.08) ? ? NA cg04231677 1 FAM129A 0.49  (0.09) 0.35  (0.11) 0.36  (0.07) ? ? NA cg13931250 1 FAM163A 0.66  (0.06) 0.54  (0.06) 0.52  (0.04) ? ? ? cg25072336 17 FMNL1 0.43  (0.06) 0.32  (0.09) 0.29  (0.08) ? ? NA cg08804626 6 GMDS 0.84  (0.05) 0.71  (0.07) 0.63  (0.05) ? ? ? cg04915300 2 GPD2 0.75  (0.07) 0.42  (0.09) 0.46  (0.08) ? ? NA  136 Probe ID Chr Gene name Cyto 1%(N=5) -Avg ?  (SD) Cyto 8% (N=5)-Avg ?  (SD) Cyto 20% (N=5)-Avg ?  (SD) Cyto 1% vs 8%-hyper(?) hypo(?) Cyto 1% vs 20%-hyper(?) hypo(?) Sync 8% vs Cyto 8%-hyper(?) hypo(?) cg25426716 5 GRAMD3 0.87  (0.06) 0.64  (0.13) 0.6  (0.11) ? ? ? cg23909079 10 GRID1 0.66  (0.07) 0.47  (0.05) 0.42  (0.05) ? ? ? cg11606195 12 GTF2H3 0.81  (0.07) 0.61  (0.12) 0.55  (0.1) ? ? ? cg19368383 10 HPS1 0.75  (0.04) 0.64  (0.07) 0.56  (0.06) ? ? ? cg16745930 10 HPSE2 0.58  (0.04) 0.39  (0.05) 0.33  (0.06) ? ? ? cg00489394 11 HRASLS5 0.34  (0.05) 0.17  (0.04) 0.17  (0.02) ? ? NA cg06474225 10 HTRA1 0.88  (0.11) 0.5  (0.28) 0.51  (0.24) ? ? NA cg27634991 6 IFNGR1 0.85  (0.05) 0.69  (0.07) 0.62  (0.09) ? ? ? cg07379893 1 IGSF9 0.71  (0.03) 0.57  (0.05) 0.59  (0.03) ? ? NA cg23813257 16 IL32 0.37  (0.07) 0.26  (0.07) 0.28  (0.05) ? ? NA cg20909017 12 ITGA5 0.45  (0.08) 0.25  (0.08) 0.24  (0.07) ? ? ? cg08169341 8 KIAA0146 0.81  (0.06) 0.51  (0.13) 0.58  (0.08) ? ? NA cg12838007 14 KIAA0247 0.43  (0.04) 0.3  (0.04) 0.32  (0.03) ? ? NA cg00101629 1 KIAA1026 0.48  (0.11) 0.33  (0.12) 0.28  (0.11) ? ? ? cg22809683 1 LAMC1 0.6  (0.07) 0.37  (0.08) 0.27  (0.08) ? ? ? cg14615559 9 LCN2 0.82  (0.03) 0.71  (0.05) 0.74  (0.04) ? ? NA cg15466952 1 LEPR 0.4  (0.07) 0.25  (0.06) 0.2  (0.04) ? ? ? cg04222933 1 LY9 0.47  (0.03) 0.32  (0.05) 0.29  (0.04) ? ? ? cg17875657 2 MAP2 0.63  (0.08) 0.42  (0.09) 0.34  (0.06) ? ? ? cg25142283 18 MBD2 0.55  (0.04) 0.4  (0.04) 0.39  (0.04) ? ? ? cg21613620 13 MBNL2 0.42  (0.09) 0.28  (0.05) 0.25  (0.05) ? ? NA cg07351322 7 MLL5 0.65  (0.11) 0.52  (0.13) 0.57  (0.12) ? ? NA cg18700744 12 NAA25 0.56  (0.06) 0.36  (0.09) 0.28  (0.08) ? ? ? cg09776041 11 NADSYN1 0.84  (0.03) 0.71  (0.07) 0.72  (0.06) ? ? NA cg13511777 11 NAV2 0.57  (0.07) 0.33  (0.06) 0.31  (0.07) ? ? ? cg00382999 3 NCK1 0.51  (0.08) 0.3  (0.08) 0.32  (0.07) ? ? NA cg25250968 6 NEDD9 0.47  (0.09) 0.31  (0.09) 0.3  (0.1) ? ? NA cg23824762 2 NEURL3 0.37  (0.06) 0.23  (0.05) 0.22  (0.05) ? ? NA  137 Probe ID Chr Gene name Cyto 1%(N=5) -Avg ?  (SD) Cyto 8% (N=5)-Avg ?  (SD) Cyto 20% (N=5)-Avg ?  (SD) Cyto 1% vs 8%-hyper(?) hypo(?) Cyto 1% vs 20%-hyper(?) hypo(?) Sync 8% vs Cyto 8%-hyper(?) hypo(?) cg14247287 2 NEURL3 0.27  (0.04) 0.14  (0.03) 0.15  (0.04) ? ? NA cg06560379 6 NFKBIE 0.75  (0.05) 0.53  (0.09) 0.54  (0.07) ? ? NA cg13585930 10 NPFFR1 0.7  (0.04) 0.54  (0.1) 0.47  (0.07) ? ? ? cg18442362 7 OGDH 0.49  (0.11) 0.21  (0.09) 0.17  (0.07) ? ? ? cg09075968 13 PCID2 0.55  (0.05) 0.3  (0.08) 0.23  (0.07) ? ? ? cg08272559 16 PKD1L2 0.46  (0.05) 0.29  (0.04) 0.3  (0.04) ? ? ? cg22234930 15 PKM2 0.63  (0.13) 0.47  (0.12) 0.46  (0.11) ? ? ? cg13564459 14 PRKCH 0.5  (0.09) 0.36  (0.09) 0.31  (0.09) ? ? ? cg25769469 5 PTCD2 0.46  (0.09) 0.26  (0.08) 0.24  (0.07) ? ? NA cg18672030 4 RELL1 0.44  (0.15) 0.3  (0.2) 0.31  (0.19) ? ? NA cg10863207 17 RFFL 0.78  (0.05) 0.67  (0.06) 0.64  (0.06) ? ? NA cg23825057 12 RILPL1 0.5  (0.06) 0.36  (0.07) 0.37  (0.06) ? ? NA cg26247064 11 RNF169 0.42  (0.12) 0.17  (0.09) 0.12  (0.07) ? ? NA cg09680007 1 S100A3 0.48  (0.05) 0.33  (0.05) 0.28  (0.06) ? ? ? cg03495084 3 SH3BP5 0.64  (0.06) 0.51  (0.07) 0.5  (0.05) ? ? NA cg16572224 5 SH3PXD2B 0.81  (0.04) 0.71  (0.06) 0.65  (0.07) ? ? ? cg13410614 9 SLC2A6 0.35  (0.12) 0.22  (0.07) 0.24  (0.08) ? ? NA cg03851835 11 SLC35F2 0.47  (0.12) 0.35  (0.08) 0.34  (0.07) ? ? ? cg20515580 11 SYT12 0.4  (0.05) 0.29  (0.05) 0.26  (0.05) ? ? ? cg01723031 4 TBC1D1 0.57  (0.07) 0.44  (0.06) 0.45  (0.06) ? ? ? cg20629315 2 THADA 0.65  (0.1) 0.47  (0.12) 0.45  (0.08) ? ? ? cg05708073 1 TMEM167B 0.71  (0.04) 0.59  (0.05) 0.57  (0.03) ? ? ? cg23097499 1 TNFRSF1B 0.73  (0.03) 0.58  (0.05) 0.58  (0.05) ? ? NA cg16338046 14 TRAF3 0.6  (0.08) 0.47  (0.1) 0.39  (0.09) ? ? ? cg19400926 6 TRIM27 0.52  (0.04) 0.39  (0.04) 0.39  (0.04) ? ? NA cg27410595 11 TRIM29 0.28  (0.04) 0.15  (0.04) 0.16  (0.04) ? ? NA cg25124300 2 XDH 0.71  (0.1) 0.45  (0.11) 0.42  (0.07) ? ? ? cg20979153 20 ZNF217 0.72  (0.1) 0.53  (0.12) 0.5  (0.12) ? ? ?  138 Probe ID Chr Gene name Cyto 1%(N=5) -Avg ?  (SD) Cyto 8% (N=5)-Avg ?  (SD) Cyto 20% (N=5)-Avg ?  (SD) Cyto 1% vs 8%-hyper(?) hypo(?) Cyto 1% vs 20%-hyper(?) hypo(?) Sync 8% vs Cyto 8%-hyper(?) hypo(?) cg22164891 20 ZNF217 0.65  (0.08) 0.47  (0.08) 0.44  (0.08) ? ? NA cg25722029 19 ZSWIM4 0.52  (0.04) 0.38  (0.05) 0.37  (0.05) ? ? NA cg09354692 6  0.57  (0.08) 0.24  (0.12) 0.21  (0.1) ? ? ? cg24401262 1  0.68  (0.12) 0.38  (0.17) 0.33  (0.14) ? ? ? cg23426156 8  0.56  (0.08) 0.29  (0.07) 0.23  (0.06) ? ? ? cg19034132 10  0.52  (0.11) 0.29  (0.09) 0.24  (0.07) ? ? ? cg07586235 1  0.72  (0.12) 0.52  (0.15) 0.45  (0.15) ? ? ? cg26140475 8  0.38  (0.08) 0.19  (0.06) 0.16  (0.06) ? ? ? cg08291435 14  0.93  (0.03) 0.74  (0.1) 0.67  (0.11) ? ? ? cg25568241 3  0.65  (0.11) 0.47  (0.16) 0.43  (0.13) ? ? ? cg10808934 6  0.76  (0.07) 0.58  (0.11) 0.54  (0.09) ? ? ? cg03893663 11  0.58  (0.11) 0.4  (0.13) 0.36  (0.13) ? ? ? cg21121257 11  0.58  (0.08) 0.41  (0.1) 0.4  (0.07) ? ? ? cg26187205 3  0.44  (0.08) 0.27  (0.06) 0.23  (0.02) ? ? ? cg03354554 11  0.66  (0.09) 0.49  (0.09) 0.44  (0.08) ? ? ? cg15459774 10  0.66  (0.05) 0.5  (0.08) 0.44  (0.09) ? ? ? cg12341429 2  0.38  (0.06) 0.22  (0.03) 0.2  (0.04) ? ? ? cg24178897 14  0.71  (0.05) 0.56  (0.07) 0.51  (0.07) ? ? ? cg08824847 11  0.66  (0.07) 0.51  (0.08) 0.46  (0.1) ? ? ? cg08141959 13  0.79  (0.05) 0.65  (0.07) 0.61  (0.05) ? ? ? cg26568031 10  0.58  (0.09) 0.44  (0.11) 0.37  (0.09) ? ? ? cg25729350 15  0.85  (0.06) 0.71  (0.11) 0.62  (0.13) ? ? ? cg05418915 2  0.69  (0.08) 0.55  (0.12) 0.49  (0.14) ? ? ? cg09621958 3  0.73  (0.06) 0.59  (0.08) 0.55  (0.07) ? ? ? cg14746387 5  0.8  (0.07) 0.67  (0.09) 0.65  (0.08) ? ? ? cg15374518 6  0.81  (0.03) 0.68  (0.04) 0.69  (0.03) ? ? ? cg04654299 17  0.83  (0.07) 0.71  (0.09) 0.69  (0.1) ? ? ? cg00234613 4  0.64  (0.03) 0.51  (0.06) 0.46  (0.08) ? ? ?  139 Probe ID Chr Gene name Cyto 1%(N=5) -Avg ?  (SD) Cyto 8% (N=5)-Avg ?  (SD) Cyto 20% (N=5)-Avg ?  (SD) Cyto 1% vs 8%-hyper(?) hypo(?) Cyto 1% vs 20%-hyper(?) hypo(?) Sync 8% vs Cyto 8%-hyper(?) hypo(?) cg18257574 2  0.6  (0.05) 0.48  (0.07) 0.47  (0.06) ? ? ? cg09187338 7  0.81  (0.04) 0.69  (0.07) 0.64  (0.07) ? ? ? cg20675173 3  0.79  (0.04) 0.68  (0.08) 0.61  (0.07) ? ? ? cg02272851 10  0.9  (0.03) 0.79  (0.08) 0.73  (0.07) ? ? ? cg12394201 11  0.47  (0.14) 0.16  (0.08) 0.18  (0.07) ? ? NA cg18202205 7  0.78  (0.07) 0.52  (0.11) 0.53  (0.07) ? ? NA cg15729439 11  0.34  (0.08) 0.11  (0.05) 0.11  (0.04) ? ? NA cg21723861 17  0.43  (0.03) 0.22  (0.07) 0.23  (0.06) ? ? NA cg12232146 12  0.56  (0.11) 0.36  (0.1) 0.39  (0.08) ? ? NA cg20510033 1  0.72  (0.05) 0.52  (0.09) 0.52  (0.08) ? ? NA cg18564881 11  0.48  (0.04) 0.29  (0.07) 0.3  (0.06) ? ? NA cg06122825 1  0.66  (0.08) 0.47  (0.12) 0.46  (0.1) ? ? NA cg05162166 22  0.55  (0.08) 0.38  (0.06) 0.38  (0.05) ? ? NA cg19158754 11  0.69  (0.04) 0.52  (0.05) 0.54  (0.05) ? ? NA cg01312394 2  0.63  (0.06) 0.47  (0.07) 0.42  (0.06) ? ? NA cg03515829 7  0.42  (0.04) 0.27  (0.04) 0.28  (0.03) ? ? NA cg10818657 2  0.33  (0.16) 0.17  (0.08) 0.18  (0.08) ? ? NA cg06093152 1  0.28  (0.05) 0.13  (0.04) 0.11  (0.03) ? ? NA cg02235918 6  0.27  (0.05) 0.12  (0.05) 0.13  (0.03) ? ? NA cg08729279 2  0.71  (0.06) 0.56  (0.06) 0.55  (0.05) ? ? NA cg21994818 8  0.45  (0.08) 0.31  (0.08) 0.3  (0.07) ? ? NA cg25375162 3  0.44  (0.1) 0.3  (0.1) 0.29  (0.11) ? ? NA cg13518625 8  0.27  (0.07) 0.14  (0.04) 0.14  (0.04) ? ? NA cg03922381 10  0.4  (0.07) 0.26  (0.08) 0.26  (0.07) ? ? NA cg02033582 17  0.31  (0.08) 0.18  (0.06) 0.13  (0.04) ? ? NA cg10005565 2  0.46  (0.02) 0.33  (0.05) 0.36  (0.04) ? ? NA cg03481488 10  0.73  (0.05) 0.59  (0.06) 0.6  (0.05) ? ? NA cg01645729 2  0.49  (0.05) 0.36  (0.05) 0.35  (0.05) ? ? NA  140 Probe ID Chr Gene name Cyto 1%(N=5) -Avg ?  (SD) Cyto 8% (N=5)-Avg ?  (SD) Cyto 20% (N=5)-Avg ?  (SD) Cyto 1% vs 8%-hyper(?) hypo(?) Cyto 1% vs 20%-hyper(?) hypo(?) Sync 8% vs Cyto 8%-hyper(?) hypo(?) cg14188401 3  0.41  (0.04) 0.29  (0.06) 0.3  (0.05) ? ? NA cg12535109 2  0.75  (0.06) 0.63  (0.07) 0.6  (0.05) ? ? NA cg03618113 12  0.23  (0.04) 0.12  (0.03) 0.13  (0.03) ? ? NA cg17654419 2  0.85  (0.03) 0.73  (0.04) 0.7  (0.05) ? ? NA cg25773259 12  0.69  (0.05) 0.58  (0.07) 0.56  (0.06) ? ? NA cg03254336 10  0.84  (0.04) 0.73  (0.07) 0.71  (0.07) ? ? NA cg18372930 5  0.67  (0.03) 0.57  (0.03) 0.58  (0.03) ? ? NA Supplementary Table 2.2: Differentially methylated loci between normal (8% and 20%) and low (1%) oxygen levels  141 Probe ID Chr Gene name Cyto 8% -Avg ? (SD) Sync 8% -Avg ? (SD) Sync 8% vs Cyto 8% -hyper(?) hypo(?) cg17941109 19 ABHD8 0.4 (0.06) 0.23  (0.07) ? cg02697979 11 ABTB2 0.75 (0.14) 0.61  (0.14) ? cg07974833 4 ADAMTS3 0.45 (0.05) 0.32  (0.04) ? cg21033632 5 ADAMTS6 0.82 (0.06) 0.62  (0.09) ? cg13670531 10 AFAP1L2 0.64 (0.06) 0.51  (0.07) ? cg02573152 10 ANK3 0.6 (0.08) 0.39  (0.09) ? cg02927618 12 ANKRD13A 0.74 (0.09) 0.62  (0.11) ? cg04228042 12 ART4 0.66 (0.08) 0.49  (0.11) ? cg15733114 12 ART4 0.34 (0.1) 0.21  (0.1) ? cg00253681 12 ART4 0.7 (0.09) 0.58  (0.09) ? cg01715680 14 BTBD7 0.51 (0.06) 0.37  (0.09) ? cg05355436 10 C10orf107 0.53 (0.09) 0.4  (0.11) ? cg10334127 11 C11orf41 0.62 (0.07) 0.49  (0.06) ? cg00388197 14 C14orf183 0.84 (0.07) 0.72  (0.11) ? cg09511177 1 C1orf150 0.51 (0.06) 0.4  (0.08) ? cg19678111 1 C1orf226 0.51 (0.12) 0.39  (0.11) ? cg14189116 12 CAPRIN2 0.82 (0.06) 0.7  (0.07) ? cg26617272 1 CCDC23 0.53 (0.05) 0.39  (0.04) ? cg23903301 11 CD59 0.52 (0.07) 0.33  (0.09) ? cg26734579 19 CDC37 0.8 (0.05) 0.68  (0.07) ? cg17934775 15 CHD2 0.4 (0.08) 0.26  (0.09) ? cg14630032 3 CP 0.5 (0.05) 0.4  (0.05) ? cg12974258 5 CSF1R 0.27 (0.02) 0.15  (0.05) ? cg18940274 7 CUL1 0.82 (0.08) 0.68  (0.08) ? cg11911769 7 CUX1 0.49 (0.12) 0.36  (0.12) ? cg09244071 7 CUX1 0.54 (0.08) 0.42  (0.1) ? cg00331677 16 CX3CL1 0.73 (0.03) 0.59  (0.02) ? cg05546575 7 DAGLB 0.56 (0.13) 0.21  (0.07) ? cg09597022 6 DAXX 0.66 (0.14) 0.42  (0.18) ? cg09365002 6 DAXX 0.79 (0.13) 0.59  (0.21) ? cg22904406 6 DAXX 0.5 (0.07) 0.35  (0.1) ? cg24498636 6 DAXX 0.67 (0.09) 0.54  (0.1) ? cg26500914 6 DAXX 0.6 (0.07) 0.47  (0.1) ? cg23189692 3 EIF4G1 0.87 (0.07) 0.67  (0.13) ? cg13931250 1 FAM163A 0.54 (0.06) 0.41  (0.07) ? cg04697056 4 FAT1 0.53 (0.09) 0.41  (0.09) ? cg23000950 15 FBN1 0.78 (0.05) 0.62  (0.06) ? cg08804626 6 GMDS 0.71 (0.07) 0.52  (0.07) ? cg08698835 16 GNAO1 0.8 (0.05) 0.69  (0.07) ? cg14105536 6 GPR115 0.49 (0.04) 0.35  (0.05) ? cg25426716 5 GRAMD3 0.64 (0.13) 0.35  (0.12) ? cg26107055 5 GRAMD3 0.83 (0.08) 0.56  (0.11) ? cg05593759 5 GRAMD3 0.85 (0.05) 0.64  (0.1) ? cg06547034 10 GRID1 0.72 (0.09) 0.5  (0.08) ?  142 Probe ID Chr Gene name Cyto 8% -Avg ? (SD) Sync 8% -Avg ? (SD) Sync 8% vs Cyto 8% -hyper(?) hypo(?) cg23909079 10 GRID1 0.47 (0.05) 0.31  (0.05) ? cg11606195 12 GTF2H3 0.61 (0.12) 0.38  (0.1) ? cg05856951 16 HMOX2 0.92 (0.05) 0.76  (0.11) ? cg10458734 16 HN1L 0.76 (0.04) 0.63  (0.07) ? cg19368383 10 HPS1 0.64 (0.07) 0.38  (0.06) ? cg16745930 10 HPSE2 0.39 (0.05) 0.19  (0.03) ? cg27634991 6 IFNGR1 0.69 (0.07) 0.4  (0.09) ? cg09049982 20 ITCH 0.56 (0.07) 0.43  (0.06) ? cg20909017 12 ITGA5 0.25 (0.08) 0.11  (0.04) ? cg02225720 17 ITGAE 0.6 (0.07) 0.43  (0.07) ? cg07002540 2 KCMF1 0.68 (0.09) 0.56  (0.1) ? cg24464500 1 KCNK2 0.59 (0.1) 0.42  (0.11) ? cg00101629 1 KIAA1026 0.33 (0.12) 0.2  (0.11) ? cg16429725 16 KIFC3 0.6 (0.07) 0.49  (0.08) ? cg23090046 14 KLC1 0.46 (0.04) 0.34  (0.06) ? cg22809683 1 LAMC1 0.37 (0.08) 0.14  (0.03) ? cg15466952 1 LEPR 0.25 (0.06) 0.09  (0.03) ? cg24321971 11 LGR4 0.37 (0.07) 0.22  (0.08) ? cg24972816 3 LOC100302640 0.46 (0.05) 0.29  (0.07) ? cg07092135 4 LOC285419 0.64 (0.09) 0.53  (0.13) ? cg16336556 2 LTBP1 0.79 (0.05) 0.64  (0.07) ? cg25298596 12 LUM 0.73 (0.08) 0.59  (0.12) ? cg04222933 1 LY9 0.32 (0.05) 0.19  (0.04) ? cg03652336 4 MAML3 0.6 (0.07) 0.47  (0.09) ? cg17875657 2 MAP2 0.42 (0.09) 0.24  (0.05) ? cg26814100 6 MAP7 0.37 (0.08) 0.27  (0.07) ? cg25142283 18 MBD2 0.4 (0.04) 0.27  (0.02) ? cg18700744 12 NAA25 0.36 (0.09) 0.13  (0.05) ? cg13511777 11 NAV2 0.33 (0.06) 0.21  (0.05) ? cg17178175 2 NFE2L2 0.59 (0.06) 0.43  (0.08) ? cg03128029 2 NOP58 0.59 (0.08) 0.41  (0.11) ? cg13585930 10 NPFFR1 0.54 (0.1) 0.35  (0.08) ? cg11063299 2 OBFC2A 0.71 (0.06) 0.45  (0.09) ? cg18442362 7 OGDH 0.21 (0.09) 0.06  (0.02) ? cg08858295 1 OTUD7B 0.34 (0.03) 0.2  (0.04) ? cg07035454 8 OXR1 0.88 (0.06) 0.72  (0.11) ? cg15745401 11 P2RY6 0.63 (0.1) 0.43  (0.13) ? cg18785787 1 PBX1 0.43 (0.05) 0.31  (0.08) ? cg12845808 5 PCDH12 0.54 (0.05) 0.4  (0.07) ? cg09075968 13 PCID2 0.3 (0.08) 0.07  (0.03) ? cg01618928 17 PCYT2 0.29 (0.04) 0.15  (0.05) ? cg14440664 9 PDCD1LG2 0.57 (0.07) 0.45  (0.07) ? cg26366616 8 PDLIM2 0.6 (0.05) 0.49  (0.06) ? cg09026722 1 PEAR1 0.7 (0.1) 0.59  (0.1) ?  143 Probe ID Chr Gene name Cyto 8% -Avg ? (SD) Sync 8% -Avg ? (SD) Sync 8% vs Cyto 8% -hyper(?) hypo(?) cg22043667 6 PECI 0.72 (0.06) 0.6  (0.05) ? cg08272559 16 PKD1L2 0.29 (0.04) 0.18  (0.07) ? cg22234930 15 PKM2 0.47 (0.12) 0.34  (0.12) ? cg25016070 15 PKM2 0.5 (0.07) 0.38  (0.09) ? cg13564459 14 PRKCH 0.36 (0.09) 0.19  (0.08) ? cg27006252 14 PRMT5 0.53 (0.12) 0.37  (0.07) ? cg05301494 19 RAB4B 0.73 (0.08) 0.57  (0.13) ? cg06913345 2 RBMS1 0.52 (0.12) 0.37  (0.11) ? cg18172516 2 RBMS1 0.69 (0.16) 0.55  (0.17) ? cg17824540 2 RBMS1 0.53 (0.07) 0.39  (0.07) ? cg12592365 17 RPTOR 0.71 (0.1) 0.53  (0.12) ? cg26949055 15 RYR3 0.72 (0.08) 0.45  (0.14) ? cg13997435 1 S100A2 0.67 (0.06) 0.54  (0.06) ? cg09680007 1 S100A3 0.33 (0.05) 0.17  (0.04) ? cg20938708 17 SAP30BP 0.65 (0.07) 0.53  (0.07) ? cg09811510 3 SCHIP1 0.79 (0.09) 0.67  (0.11) ? cg04761746 4 SCOC 0.71 (0.05) 0.6  (0.09) ? cg10329928 2 SDC1 0.65 (0.06) 0.48  (0.06) ? cg00159243 12 SELPLG 0.77 (0.11) 0.63  (0.14) ? cg16572224 5 SH3PXD2B 0.71 (0.06) 0.54  (0.12) ? cg23684410 11 SIK3 0.4 (0.05) 0.27  (0.07) ? cg11875744 4 SLC10A7 0.53 (0.09) 0.35  (0.11) ? cg27391816 1 SLC35E2 0.5 (0.09) 0.35  (0.07) ? cg03851835 11 SLC35F2 0.35 (0.08) 0.22  (0.08) ? cg08467103 2 SPRED2 0.8 (0.07) 0.63  (0.12) ? cg07094169 3 SRGAP3 0.81 (0.1) 0.66  (0.12) ? cg10732611 13 STARD13 0.48 (0.11) 0.34  (0.06) ? cg10110335 3 SYN2 0.66 (0.12) 0.54  (0.11) ? cg09366982 14 SYNE2 0.83 (0.06) 0.64  (0.07) ? cg20515580 11 SYT12 0.29 (0.05) 0.18  (0.04) ? cg13005635 12 TAS2R14 0.72 (0.06) 0.49  (0.11) ? cg01723031 4 TBC1D1 0.44 (0.06) 0.3  (0.08) ? cg08692676 6 TBPL1 0.25 (0.05) 0.13  (0.05) ? cg24079727 15 TCF12 0.78 (0.07) 0.62  (0.1) ? cg20629315 2 THADA 0.47 (0.12) 0.33  (0.12) ? cg05708073 1 TMEM167B 0.59 (0.05) 0.46  (0.05) ? cg08919597 6 TNFAIP3 0.79 (0.08) 0.59  (0.14) ? cg16338046 14 TRAF3 0.47 (0.10) 0.24  (0.08) ? cg22644321 8 TRIB1 0.72 (0.07) 0.52  (0.11) ? cg02735733 11 TRIM29 0.66 (0.06) 0.52  (0.02) ? cg24680646 13 TRPC4 0.69 (0.08) 0.48  (0.09) ? cg03704215 13 TRPC4 0.56 (0.07) 0.41  (0.11) ? cg17714703 19 UHRF1 0.79 (0.12) 0.62  (0.19) ? cg24383467 11 USP47 0.59 (0.11) 0.34  (0.13) ?  144 Probe ID Chr Gene name Cyto 8% -Avg ? (SD) Sync 8% -Avg ? (SD) Sync 8% vs Cyto 8% -hyper(?) hypo(?) cg06942111 1 VAV3 0.78 (0.04) 0.67  (0.08) ? cg10460033 3 WNT7A 0.4 (0.07) 0.23  (0.06) ? cg25124300 2 XDH 0.45 (0.11) 0.33  (0.08) ? cg10700483 X XKRX 0.56 (0.12) 0.44  (0.1) ? cg20821170 11 YPEL4 0.56 (0.06) 0.41  (0.07) ? cg18440774 4 YTHDC1 0.48 (0.06) 0.26  (0.06) ? cg04884025 1 ZFYVE9 0.59 (0.06) 0.35  (0.1) ? cg20979153 20 ZNF217 0.53 (0.12) 0.36  (0.12) ? cg08291435 14  0.74 (0.10) 0.43  (0.14) ? cg25729350 15  0.71 (0.11) 0.42  (0.12) ? cg20675173 3  0.68 (0.08) 0.4  (0.1) ? cg07586235 1  0.52 (0.15) 0.26  (0.11) ? cg00596833 4  0.50 (0.05) 0.27  (0.08) ? cg00754989 15  0.66 (0.07) 0.43  (0.1) ? cg12839363 11  0.83 (0.07) 0.59  (0.13) ? cg03354554 11  0.49 (0.09) 0.26  (0.09) ? cg15459774 10  0.5 (0.08) 0.27  (0.08) ? cg25765315 12  0.71 (0.08) 0.48  (0.1) ? cg05418915 2  0.55 (0.12) 0.32  (0.14) ? cg21121257 11  0.41 (0.1) 0.21  (0.08) ? cg24401262 1  0.38 (0.17) 0.18  (0.13) ? cg03893663 11  0.4 (0.13) 0.21  (0.08) ? cg08824847 11  0.51 (0.08) 0.32  (0.09) ? cg09187338 7  0.69 (0.07) 0.5  (0.07) ? cg26568031 10  0.44 (0.11) 0.25  (0.09) ? cg25568241 3  0.47 (0.16) 0.29  (0.16) ? cg00446046 3  0.7 (0.07) 0.52  (0.1) ? cg09354692 6  0.24 (0.12) 0.06  (0.04) ? cg02272851 10  0.79 (0.08) 0.62  (0.14) ? cg24178897 14  0.56 (0.07) 0.39  (0.08) ? cg04965200 12  0.74 (0.06) 0.57  (0.05) ? cg00565679 12  0.82 (0.07) 0.65  (0.07) ? cg20463995 15  0.85 (0.05) 0.68  (0.12) ? cg01009920 8  0.67 (0.11) 0.5  (0.11) ? cg07970146 2  0.5 (0.14) 0.34  (0.16) ? cg22112587 5  0.59 (0.07) 0.43  (0.07) ? cg15887927 13  0.74 (0.1) 0.58  (0.15) ? cg23426156 8  0.29 (0.07) 0.13  (0.05) ? cg05243629 7  0.74 (0.07) 0.58  (0.11) ? cg20127496 8  0.78 (0.05) 0.64  (0.05) ? cg09621958 3  0.59 (0.08) 0.45  (0.08) ? cg04654299 17  0.71 (0.09) 0.56  (0.13) ? cg06316121 1  0.76 (0.09) 0.62  (0.09) ? cg08141959 13  0.65 (0.07) 0.5  (0.06) ?  145 Probe ID Chr Gene name Cyto 8% -Avg ? (SD) Sync 8% -Avg ? (SD) Sync 8% vs Cyto 8% -hyper(?) hypo(?) cg01432692 1  0.51 (0.05) 0.37  (0.04) ? cg13413719 6  0.51 (0.08) 0.37  (0.1) ? cg21715896 15  0.32 (0.05) 0.18  (0.07) ? cg14746387 5  0.67 (0.09) 0.53  (0.12) ? cg04918770 6  0.4 (0.07) 0.26  (0.05) ? cg09410512 2  0.71 (0.11) 0.57  (0.16) ? cg10808934 6  0.58 (0.11) 0.44  (0.11) ? cg15835664 1  0.41 (0.04) 0.28  (0.03) ? cg23981702 1  0.75 (0.11) 0.62  (0.16) ? cg12593541 1  0.26 (0.06) 0.13  (0.05) ? cg06500792 2  0.61 (0.08) 0.47  (0.09) ? cg19907968 7  0.86 (0.04) 0.73  (0.07) ? cg19034132 10  0.29 (0.09) 0.16  (0.06) ? cg17094249 1  0.51 (0.1) 0.37  (0.07) ? cg07443710 10  0.69 (0.09) 0.56  (0.1) ? cg23751171 12  0.65 (0.08) 0.52  (0.1) ? cg00234613 4  0.51 (0.06) 0.39  (0.06) ? cg13306815 6  0.83 (0.05) 0.71  (0.09) ? cg22906553 2  0.47 (0.09) 0.34  (0.07) ? cg25336892 2  0.46 (0.04) 0.33  (0.05) ? cg15374518 6  0.68 (0.04) 0.56  (0.05) ? cg11819702 5  0.74 (0.04) 0.61  (0.04) ? cg18257574 2  0.48 (0.07) 0.35  (0.08) ? cg18756657 13  0.48 (0.1) 0.35  (0.09) ? cg20596543 8  0.71 (0.15) 0.59  (0.11) ? cg26187205 3  0.27 (0.06) 0.14  (0.03) ? cg24522654 12  0.83 (0.07) 0.71  (0.09) ? cg03897489 8  0.28 (0.07) 0.16  (0.07) ? cg16581103 1  0.7 (0.05) 0.58  (0.07) ? cg27303746 16  0.62 (0.05) 0.5  (0.03) ? cg08241401 20  0.77 (0.05) 0.65  (0.07) ? cg13714378 11  0.8 (0.04) 0.68  (0.05) ? cg22786667 7  0.71 (0.1) 0.59  (0.13) ? cg16762030 15  0.65 (0.09) 0.53  (0.08) ? cg26140475 8  0.19 (0.06) 0.07  (0.03) ? cg26829395 15  0.55 (0.05) 0.44  (0.05) ? cg02936493 7  0.72 (0.06) 0.6  (0.09) ? cg15867626 3  0.41 (0.06) 0.3  (0.05) ? cg10595785 6  0.8 (0.04) 0.69  (0.07) ? cg17715419 6  0.67 (0.04) 0.56  (0.04) ? cg00872435 2  0.75 (0.05) 0.63  (0.05) ? cg26232553 8  0.66 (0.08) 0.55  (0.08) ? cg00901982 2  0.37 (0.05) 0.26  (0.06) ? cg02756683 10  0.78 (0.06) 0.67  (0.09) ?  146 Probe ID Chr Gene name Cyto 8% -Avg ? (SD) Sync 8% -Avg ? (SD) Sync 8% vs Cyto 8% -hyper(?) hypo(?) cg12341429 2  0.22 (0.03) 0.11  (0.05) ? cg13410764 9  0.66 (0.07) 0.56  (0.08) ? cg26310285 7  0.62 (0.03) 0.52  (0.05) ? Supplementary Table 2.3: Differentially methylated loci between cytotrophoblast and syncytotrophoblast 147  Supplementary Figure 2.1: Unsupervised clustering of all placental tissue samples of different treatments. Samples were clustered by hierarchical clustering of beta values based on their overall similarity in DNA methylation profile. Clustering is predominantly by placenta of origin rather than by treatment or cyto/syncytiotrophoblast.  148  Supplementary Figure 2.2: Comparison of average methylation level (methylation index) in each treatment group of cytotrophoblast. There is no difference in overall average methylation level between different treatment groups. Therefore, the DNA methylation difference upon hypoxia treatment is local rather than global.  149  Supplementary Figure 2.3:DNA methylation of candidate genes. Validation of DNA methylation by bisulfite pyrosequencing for A) FOS, B) JUN, C) CFB, D) CD59, E) ZNF217 and F) GRAMD3  150 Appendix B  : Supplementary material for chapter 3 Sample Group Gestational Age (weeks)  Birth Weight (g) SD of birthweight Sex Additional Clinical Info Proteinuria* Methylation Array Expression Array PM12 EOPET 31.72 1305 -1.5 MALE IUGR Light Y Y PM15 EOPET 32.86 1480 -2.03 FEMALE HELLP Light Y  PM21 EOPET 33.72 1650 -1.87 MALE IUGR Light Y Y PM39 EOPET 32 1700 0.31 MALE IUGR Heavy Y Y PM43 EOPET 31.72 1440 -0.88 FEMALE AVM Heavy Y Y PM51 EOPET 34 1400 -2.92 FEMALE IUGR, oligohydramnios, AVM Light Y Y PM86 EOPET 24.86 545 -1.64 MALE IUGR Heavy Y Y PM97 EOPET 26 440 -2.2 MALE IUFD, IUGR, AVM Light Y  PM116 EOPET 32.43 1480 -0.7 MALE IUGR Light Y Y PM129 EOPET 37.28 1840 -2.17 MALE HELLP, AVM Light Y Y PM67 EOPET 33.86 1560 -2.24 MALE Gest. Diab., IUGR, AVM Light Y  PM80 EOPET 28.57 1095 -1.47 MALE Gest. Diab, AVM Light Y  PM6 EOPET 32.72 1160 -3.45 MALE IUGR, oligohydramnios, AVM No Y  PM36 EOPET 37.28 3170 0.41 FEMALE Gest. Diab Light Y  PM49 EOPET 25.43 605 -1.32 FEMALE oligohydramnios Heavy Y  PM64 EOPET 33.28 1728 -0.94 FEMALE HELLP, AVM Heavy Y  PM99 EOPET 26.86 595 -3.44 MALE IUGR, oligohydramnios, AVM No Y  PM138 EOPET 34 3685 6.68 MALE Gest. Diab, AVM Heavy Y  PL-130 EOPET 32.57 820 -8.19 MALE HELLP, IUGR, Oligohydramnios Unavailable Y  PL-131 EOPET 33.57 1331 -3.2 FEMALE IUGR Unavailable Y  PM87 Control 37.28 3470 0.99 FEMALE  Y  PL-112 Control 28 1245 0.86 MALE PROM  Y  PL-11 Control 33.72 2495 1.68 MALE Abruption  Y Y PL-25 Control 32.43 NA NA MALE   Y  PL-26 Control 32 1810 0.82 MALE   Y  PL-33 Control 32.86 2230? 1.26 MALE   Y Y  151 Sample Group Gestational Age (weeks)  Birth Weight (g) SD of birthweight Sex Additional Clinical Info Proteinuria* Methylation Array Expression Array PL-56 Control 32.57 2100 0.69 FEMALE PPROM  Y Y PL-64 Control 26 758 -0.5 FEMALE  Y Y PL-76 Control 36 2630 0.48 MALE Chorioamnionitis, PPROM  Y Y PL-96 Control 34 2040 -0.23 MALE PPROM  Y Y PL-113 Control 25 978 0.66 MALE   Y Y PL-104 Control 32.57 1685 -1.14 FEMALE PPROM  Y  PL-38 Control 32.72 2025 0.36 MALE   Y  PL-58 Control 28 1200 0.56 FEMALE PPROM  Y  PL-59 Control 32.43 1795 0.74 MALE PPROM, AVM  Y  PL-32 Control 33.72 2295 0.84 FEMALE Chorioamnionitis  Y  PL-43 Control 33.57 2220 0.52 MALE   Y  PL-102 Control 33.72 2865 3.23 MALE PPROM  Y  PL-21 Control 28.57 1455 1.7 MALE Abruption  Y  PL-65 Control 31.43 1849 0.99 MALE   Y Y PL-103 Control 29.43 1440 1.58 MALE PPROM    PL-105 Control 33.14 2000 0.25 FEMALE    PL-106 Control 32 1820 0.86 FEMALE Placenta Previa    PL-107 Control 30.72 1094 -2.46 FEMALE SROM    PL-108 Control 30.43 1530 2.38 FEMALE Abruption    PL-111 Control 30.43 1245 -0.14 MALE PPROM    PL-115 Control 29.43 1150 -0.98 MALE Abruoption, PPROM    PL-117 Control 29 1400 1.23 FEMALE Irritable Uterus    PL-122 Control 32 1820 0.86 FEMALE    PL-123 Control 32.86 2285 1.50 MALE     PL-129 Control 29.57 1435 1.50 FEMALE    PL-14 Control 33.14 2375 1.90 MALE Abruption    PL-17 Control 33.28 2025 0.36 MALE     PL-18 Control 29 1135 -1.12 FEMALE     152 Sample Group Gestational Age (weeks)  Birth Weight (g) SD of birthweight Sex Additional Clinical Info Proteinuria* Methylation Array Expression Array PL-19 Control 29.43 1030 -2.04 FEMALE    PL-20 Control 28.72 1276 0.13 MALE Abruption    PL-23 Control 30.57 1600 NA FEMALE PPROM    PL-27 Control 32.57 1965 -0.09 FEMALE    PL-28 Control 33.14 2030 0.38 FEMALE    PL-29 Control 32 2050 1.90 FEMALE    PL-30 Control 32.72 1982 0.17 MALE     PL-31 Control 29 1250 -0.09 FEMALE PPROM, Chorioamnionitis, Funistis, Stage Inflamatory response  PL-34 Control 30.72 1404 -1.04 FEMALE SROM, Chorioamnionitis    PL-4 Control 30.43 1535 2.42 MALE PPROM    PL-42 Control 29.14 1165 -0.85 MALE PPROM, oligohydramnios    PL-51 Control 30.14 1065 -1.73 FEMALE    PL-55 Control 28.86 1597 2.90 MALE Uterine Rupture    PL-57 Control 31 1750 0.54 MALE Abruption    PL-62 Control 28.43 1575 3.05 FEMALE Placental Previa, Chortionamnionitis, inflammatory response  PL-63 Control 29.43 1300 0.34 FEMALE    PL-66 Control 29 1365 0.92 MALE     PL-7 Control 30.14 1615 3.13 MALE     PL-73 Control 29 1205 -0.49 FEMALE Abruption    PL-74 Control 30.28 1725 4.10 FEMALE PPROM, Chorioamnionitis    PL-77 Control 32.72 1765 -0.78 FEMALE PPROM    PL-79 Control 28 1000 -0.76 MALE PROM    PL-81 Control 30 1210 -0.45 MALE PPROM    PL-91 Control 31.86 1455 -0.81 FEMALE PPROM    PM5 Control 37.71 3270 0.1 MALE     PM17 Control 36.57 2305 -1.27 FEMALE    PM73 Control 38.07 3510 0.71 MALE     PM74 Control 37.86 3460 0.59 MALE      153 Sample Group Gestational Age (weeks)  Birth Weight (g) SD of birthweight Sex Additional Clinical Info Proteinuria* Methylation Array Expression Array PM78 Control 38 3610 0.97 MALE Failure to progress    PM90 Control 38.28 3505 0.7 MALE     PM101 Control 38 2885 -0.88 MALE     PM112 Control 38.71 3495 0.43 FEMALE    PM114 Control 38.36 3070 -0.41 MALE     PM120 Control 38 3215 -0.04 FEMALE    PM122 Control 37 2730 -0.45 MALE     PM124 Control 40 3375 -0.22 MALE     PM127 Control 38 3475 0.62 FEMALE    PM134 Control 39.57 3595 0.26 FEMALE    PM135 Control 39 3740 1 MALE     PM136 Control 38 3145 -0.22 FEMALE    PM148 Control 40 2995 -1.05 FEMALE    PM150 Control 39 3435 0.29 FEMALE    PM151 Control 39 4120 1.88 FEMALE    PM161 Control 36.36 3205 2.37 FEMALE placenta previa    PM165 Control 39 3070 -0.56 FEMALE    PM167 Control 39 3350 0.09 FEMALE    PM171 Control 38.14 3605 0.96 FEMALE choriod plexius cyst    PM181 Control 39 4470 2.69 MALE     PM131 Control 40.28 3095 -0.83 MALE Gest. Diab.    PM132 Control NA 2940 -1.19 MALE oligohydramnios    PM133 Control 41 4195 1.26 MALE     PM152 Control 41.14        PM155 Control 41.57 4170 1.75 FEMALE    PM156 Control 40.71 3950 0.78 MALE     PM158 Control 37.51 3265 0.09 MALE     PM182 Control 39.86 2885 -1.28 FEMALE     154 Sample Group Gestational Age (weeks)  Birth Weight (g) SD of birthweight Sex Additional Clinical Info Proteinuria* Methylation Array Expression Array PM183 Control 38 3840 1.56 MALE Gest. Diab.    PM192 Control 40 3510 0.07 FEMALE    PM198 Control 41 3590 0.08 MALE     PM31 LOPET 36 1480 -3.28 FEMALE IUGR    PM32 LOPET 34.71 1630 -1.75 FEMALE IUGR, oligohydramnios    PM38 LOPET 36 2225 -0.84 MALE IUGR    PM40 LOPET 38.28 2565 -1.7 FEMALE IUGR    PM44 LOPET 39.28 2730 -1.34 MALE     PM46 LOPET 40 3385 -0.2 FEMALE    PM52 LOPET 35.36 1840 -1.26 MALE oligohydramnios    PM53 LOPET 38.57 4400 2.52 FEMALE Gest. Diab.    PM54 LOPET 34.57 2270 -0.26 FEMALE    PM56 LOPET 34 2615 2.18 FEMALE    PM58 LOPET 37 3010 0.09 FEMALE    PM66 LOPET 35 1790 -1.38 MALE IUGR    PM71 LOPET 38.86 2675 -1.47 FEMALE    PM98 LOPET 37.36 3310 0.68 MALE     PM115 LOPET 41.28 3465 -0.16 MALE     PM119 LOPET 37 2530 -0.84 MALE     PM27 LOPET 35.36 1700 -1.59 MALE IUGR, oligohydramnios    PL132 LOPET 36.86 1710 -3.2 FEMALE IUGR, Cholestasis    PL135 LOPET 36.71 1980 -1.9 MALE IUGR    PL142 LOPET 39.17 2565 -2.08 MALE oligohydramnios    PL144 LOPET 37 1845 -2.78 MALE IUGR, Gest. Diab.    PM4 nIUGR 37.7 2340 -1.2 FEMALE    PM29 nIUGR 36 2600 0.39 FEMALE oligohydramnios    PM35 nIUGR 37 2345 -1.19 FEMALE    PM41 nIUGR 37 1725 -2.39 FEMALE oligohydramnios     155 Sample Group Gestational Age (weeks)  Birth Weight (g) SD of birthweight Sex Additional Clinical Info Proteinuria* Methylation Array Expression Array PM47 nIUGR 38 2645 -1.5 FEMALE oligohydramnios    PM72 nIUGR 34 1445 -2.18 FEMALE oligohydramnios    PM121 nIUGR 40 2930 -1.19 MALE     PM123 nIUGR 35.36 1565 -1.9 FEMALE oligohydramnios, Chorioamnionitis    PM128 nIUGR 30.71 1390 -1.11 FEMALE    PM130 nIUGR 36.86 2090 -1.69 MALE oligohydramnios    PM139 nIUGR 35.71 1740 -2.43 MALE oligohydramnios    PM37 nIUGR 24.57 360 -2.62 MALE oligohydramnios, IUFD    PM42 nIUGR 26 450 -2.14 FEMALE oligohydramnios, Chorionic abruption    PM189 nIUGR 29.71 TA  MALE     PL36 nIUGR 33.86 1720 -1.58 FEMALE  Uneven perfusion, excessive coiling of umbilical cord, solitary intervillus thrombus.  PL72 nIUGR 37 2220 -1.44 FEMALE    PL86 nIUGR 38 2235 -2.55 MALE     Supplementary Table 3.1: Extended clinical information  156 Gene Primer Sequence (5' to 3') Product length (bp) Annealing Temp. Pearson Correlation with Array (r) ADAM12_cg02494582 F TGTGTTAATGATGGGGTTAAGTTG 449 55 0.886   R Biotin-TCCAAAAAACAAACTCTCCCTACT         S TTAATATTTATTTTTAGAAG       BHLHE40_cg20971407 F GGGGAATTGTAGATTAGATTGG 466 57 0.687   R Biotin-ACTTCTTTCCTTACACCCTTTCAA         S TTAGTTTTTTTTATTTAAAG       INHBA_cg11079619 F Biotin-GTGTTAATATTATGAAGAGGAGTTTAG 86 55 0.880   R TACCTTTCTAATCCCCACTCT         S CCCCACTCTTCCACC       SLC2A1_cg01924561 F Biotin-TGGGATTTAAATGTGTTTGTTGAG 286 55 0.765   R CCCCTAAACTAACTACCCAACATT         S AAACCACATCCTCCC       Supplementary Table 3.2: Pyrosequencing primers for chapter 3    157 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg26625897 KRT15 17 39678679 0.547 0.810 -0.263 Intergenic -3.4 Yes cg00592695 ST3GAL1 8 134545685 0.567 0.820 -0.253 5'UTR 38.5 Yes cg12436772 FN1 2 216402384 0.513 0.743 -0.23 Intergenic -101.6 Yes cg20971407 BHLHE40 3 5022392 0.403 0.607 -0.204 Body 1.3  cg17252884 SH3BP5 3 15369681 0.397 0.601 -0.204 Body;5'UTR 4.4  cg15831875 SMYD3 1 245986694 0.586 0.788 -0.202 Body 107.4 Yes cg01924561 SLC2A1 1 43416103 0.355 0.549 -0.194 Body -8.6 Yes cg11079619 INHBA 7 41742630 0.460 0.653 -0.193 5'UTR 0.1  cg19308497 ATP1A4 1 160147442 0.331 0.519 -0.189 Body 0.2  cg13754437 FHL2 2 106016110 0.580 0.767 -0.187 Promoter;5'UTR 0.0  cg18236464 PAPPA2 1 176528783 0.327 0.514 -0.186 Body 96.5  cg14854503 CSGALNACT1 8 19540272 0.521 0.706 -0.185 Promoter 0.0  cg22996170 JUNB 19 12895529 0.507 0.692 -0.184 Intergenic -6.8 Yes cg19140548 SH3PXD2A 10 105552406 0.551 0.734 -0.183 Body 62.7 Yes cg10893014 TEAD3 6 35461236 0.412 0.596 -0.183 5'UTR 3.6  cg22193385 KRT7 12 52638005 0.506 0.688 -0.182 Body 11.0 Yes cg01228410 SLC9A3 5 493893 0.492 0.672 -0.181 Body 30.7  cg02766259 AACS 12 125626809 0.407 0.587 -0.18 3'UTR 37.1  cg14143441 NDRG1 8 134387493 0.636 0.814 -0.178 Intergenic -77.9 Yes cg03983223 WIPF1 2 175499283 0.425 0.602 -0.177 5'UTR 0.0  cg09365002 DAXX 6 33288329 0.440 0.616 -0.176 Body 1.4 Yes cg21245975 C1orf52 1 85725523 0.103 0.276 -0.173 Promoter -0.2  cg17417693 CLIP4 2 29335996 0.479 0.651 -0.172 Intergenic -2.3  cg25245338 TIMP3 22 33196112 0.479 0.650 -0.171 Promoter -0.7  cg00345704 KRTAP2-3 17 39216495 0.570 0.741 -0.17 Promoter -0.2  cg22234930 PKM2 15 72519739 0.587 0.756 -0.169 5'UTR;Body 1.3  cg03728457 CCDC33 15 74619595 0.590 0.757 -0.167 Body 8.7 Yes cg01180628 BHLHE40 3 5023394 0.409 0.575 -0.166 Body 2.3   158 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg19883388 AZIN1 8 103909930 0.523 0.688 -0.165 Intergenic -33.5 Yes cg16568084 BET1 7 93811563 0.390 0.556 -0.165 Intergenic -177.9 Yes cg11327657 FAM207A 21 46388162 0.330 0.494 -0.165 Body 28.2  cg07351322 MLL5 7 104738380 0.348 0.513 -0.165 Intergenic -3.5 Yes cg19417526 NTM 11 131895599 0.359 0.524 -0.165 Body 114.9 Yes cg07950244 PHLDA3 1 201448731 0.705 0.871 -0.165 Intergenic -10.4  cg09182455 CORO1C 12 109116994 0.560 0.724 -0.164 5'UTR 8.3 Yes cg23730027 FLNB 3 57995180 0.269 0.433 -0.164 Body 1.1  cg20576064 FAM160B2 8 21948560 0.523 0.686 -0.163 Body 1.8  cg03777414 TVP23A 16 10868974 0.491 0.653 -0.162 Body 43.5  cg16301004 ITPRIP 10 106082537 0.329 0.491 -0.162 5'UTR 6.1 Yes cg24097814 KRT8 12 53320632 0.473 0.635 -0.162 Promoter;5'UTR -0.4  cg23843484 MAFK 7 1560248 0.426 0.587 -0.162 Intergenic -10.1  cg14704980 LOC285954 7 41607500 0.608 0.767 -0.16 Intergenic -126.0 Yes cg26509870 PHYHIP 8 22076166 0.633 0.794 -0.16 Intergenic 13.7  cg20352351 NCOR2 12 124870274 0.704 0.863 -0.159 Body -45.5 Yes cg02770406 FLNB 3 57991450 0.628 0.787 -0.158 Intergenic -2.7  cg25298189 ARID3A 19 935259 0.165 0.323 -0.157 Body 9.2  cg10246581 CMIP 16 81536171 0.454 0.610 -0.157 Body 7.2  cg03822934 LIMCH1 4 41505964 0.669 0.825 -0.157 Body -34.2 Yes cg10047173 ART4 12 14996587 0.431 0.587 -0.156 Promoter -0.2  cg20317872 DENND2D 1 111743202 0.572 0.728 -0.156 5'UTR;Body 0.1  cg02029908 DUSP1 5 172195602 0.568 0.724 -0.156 Intergenic 2.0  cg05452692 LINC00284 13 44642515 0.629 0.785 -0.156 Intergenic 46.0 Yes cg10586672 SLC6A6 3 14514657 0.464 0.620 -0.156 Body 70.6 Yes cg00736681 ST3GAL1 8 134546052 0.439 0.595 -0.156 5'UTR 38.1 Yes cg26651514 STARD13 13 33864734 0.631 0.787 -0.156 Intergenic -4.8 Yes cg12779575 ERN1 17 62208434 0.655 0.810 -0.155 Promoter -0.9   159 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg12361046 CHSY1 15 101753877 0.517 0.671 -0.154 Intergenic -25.6 Yes cg11811391 HTR1D 1 23520083 0.587 0.741 -0.154 Body 1.1  cg10187713 PARD6B 20 49345687 0.669 0.822 -0.154 Intergenic -2.4  cg10668363 ZNF175 19 52076691 0.592 0.746 -0.154 Body 2.2  cg17115419 CALML3 10 5593926 0.619 0.772 -0.153 Intergenic 27.0  cg13553455 COL17A1 10 105846002 0.319 0.472 -0.153 Promoter -0.4  cg22626683 TNFSF18 1 172903051 0.368 0.521 -0.153 Intergenic 117.1 Yes cg15903956 CYP11A1 15 74676231 0.572 0.724 -0.152 Intergenic -16.2 Yes cg03653726 GNA12 7 2769253 0.428 0.580 -0.152 3'UTR 114.9 Yes cg12632411 RRBP1 20 17595355 0.337 0.489 -0.152 3'UTR 5.1  cg03339817 SFT2D3 2 128458281 0.639 0.791 -0.152 Promoter -0.3  cg00713022 SYDE1 19 15215943 0.614 0.766 -0.152 Intergenic -2.3  cg27328839 DLG5 10 79679462 0.569 0.719 -0.15 Body 6.9 Yes cg17850498 ECE1 1 21565755 0.729 0.879 -0.15 Body 40.5 Yes cg10108710 TTC7B 14 90990086 0.647 0.797 -0.15 Intergenic 121.0 Yes cg16282339 PTPRJ 11 47927019 0.460 0.609 -0.149 Intergenic -76.7  cg27182012 LMNA 1 156095157 0.667 0.815 -0.148 Body;Promoter -0.8  cg06821199 CPLX1 4 779230 0.345 0.492 -0.147 3'UTR 40.0  cg21913652 FJX1 11 35651841 0.357 0.503 -0.147 Intergenic 12.1 Yes cg25032603 LINC00310 21 35554811 0.602 0.749 -0.147 3'UTR 1.8  cg05971934 NTRK3 15 88307922 0.388 0.535 -0.147 Intergenic 493.0 Yes cg01713086 ZNF395 8 28268413 0.284 0.431 -0.147 Intergenic -24.4  cg07158065 MIR3150A 8 96084821 0.547 0.694 -0.146 Promoter -0.3  cg18190824 ANKH 5 14721026 0.652 0.797 -0.145 Body 151.0 Yes cg03024478 UNC5C 4 96090330 0.446 0.591 -0.145 3'UTR 373.0  cg26813604 ERGIC1 5 172256357 0.672 0.816 -0.144 Intergenic -4.9 Yes cg09394306 LAPTM4A 2 20330742 0.672 0.816 -0.144 Intergenic -79.0 Yes cg10994126 PAPPA2 1 176432498 0.471 0.615 -0.144 5'UTR 0.2   160 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg13547665 POLE4 2 75229455 0.487 0.631 -0.144 Intergenic 43.7 Yes cg18672030 RELL1 4 37668528 0.238 0.383 -0.144 Body 19.5  cg16616993 RHOBTB3 5 95129519 0.439 0.583 -0.144 3'UTR 62.0 Yes cg04897892 CMIP 16 81564314 0.536 0.679 -0.143 Body 35.4  cg03099780 MBNL2 13 97864475 0.421 0.565 -0.143 Intergenic -10.1 Yes cg17966362 MSH4 1 76261799 0.534 0.677 -0.143 Promoter -0.8  cg26366616 PDLIM2 8 22439831 0.305 0.448 -0.143 Body 1.8  cg15765546 SLCO2A1 3 133776357 0.443 0.586 -0.143 Intergenic -27.4 Yes cg14605117 AMZ1 7 2766214 0.679 0.821 -0.142 Intergenic -11.4  cg16606561 FAM110A 20 824641 0.656 0.798 -0.142 Promoter -0.6 Yes cg18638180 FAM207A 21 46379089 0.432 0.574 -0.142 Body 19.1 Yes cg15282973 SCARB1 12 125346253 0.504 0.646 -0.142 Body 2.3  cg00834537 APOPT1 14 104047751 0.592 0.733 -0.141 Body 18.5  cg15887927 FLT1 13 29148952 0.470 0.611 -0.141 Intergenic -90.0  cg12261055 INHBA 7 41860801 0.425 0.566 -0.141 Intergenic -118.1 Yes cg12979992 INSIG1 7 155100559 0.734 0.875 -0.141 3'UTR 11.1  cg12804791 ST3GAL4 11 126286828 0.241 0.382 -0.141 Intergenic 9.7 Yes cg03764092 TBXAS1 7 139690853 0.660 0.801 -0.141 Body 176.0  cg19861486 STK24 13 99231630 0.421 0.561 -0.14 Intergenic -2.2  cg24431161 TBXAS1 7 139484351 0.655 0.795 -0.14 Body 6.3 Yes cg20340720 WBP1L 10 104512523 0.450 0.589 -0.14 Body 8.8 Yes cg12647920 CORO1C 12 109144744 0.606 0.745 -0.139 Intergenic -19.4 Yes cg13062627 CSRNP1 3 39208314 0.526 0.665 -0.139 Intergenic -12.3 Yes cg21496948 KLC1 14 104153614 0.365 0.504 -0.139 Body 58.0 Yes cg07981495 CGA 6 87804765 0.503 0.641 -0.138 5'UTR 0.1  cg20586124 CTAGE1 18 19998018 0.395 0.533 -0.138 Promoter -0.1  cg13467628 FAM150B 2 365559 0.367 0.505 -0.138 Intergenic -77.3  cg24204017 FLNB 3 57999988 0.325 0.463 -0.138 Body 5.9 Yes  161 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg11034318 LOC728743 7 150097654 0.525 0.664 -0.138 Intergenic -5.2 Yes cg08900316 PRKCE 2 46301083 0.423 0.561 -0.138 Body 423.0 Yes cg23400222 ZNF608 5 124672706 0.607 0.746 -0.138 Intergenic -588.2 Yes cg02494582 ADAM12 10 128061835 0.428 0.565 -0.137 Body 15.3 Yes cg21823502 ANAPC11 17 79856430 0.331 0.468 -0.137 Body 6.7  cg16104384 c22orf34 22 49935689 0.532 0.669 -0.137 Intergenic 115.5  cg18474072 DIP2C 10 664762 0.396 0.533 -0.137 Body 71.0  cg15129144 EPAS1 2 46527958 0.451 0.589 -0.137 Body 3.4  cg23677911 GALNT2 1 230256394 0.603 0.740 -0.137 Body 53.4 Yes cg08799766 GNA12 7 2801832 0.518 0.654 -0.137 Body 53.1  cg24617203 IL1R2 2 102623381 0.536 0.672 -0.136 Promoter -1.6  cg27307465 JAK1 1 65472802 0.639 0.775 -0.136 Intergenic -40.6  cg13467459 KIAA1614 1 180919564 0.509 0.645 -0.136 Intergenic 22.0  cg11865119 MEST 7 130125932 0.486 0.622 -0.136 Promoter -0.1  cg02681842 PLEC1 8 145033310 0.521 0.657 -0.136 Intergenic -5.2 Yes cg06837325 RGMB 5 97445281 0.424 0.560 -0.136 Intergenic -660.0 Yes cg21560697 TAF1B 2 10054883 0.484 0.620 -0.136 Body 71.0 Yes cg05131266 c14orf159 14 91591888 0.576 0.710 -0.135 Body 11.5  cg01288184 CABLES1 18 20811408 0.468 0.603 -0.135 Body 75.6 Yes cg03730249 FOXJ3 1 42692013 0.514 0.649 -0.135 Body 109.6 Yes cg11355603 HIPK2 7 139256247 0.674 0.809 -0.135 3'UTR 221.0  cg16854917 LINC0085 19 52206691 0.243 0.378 -0.135 3'UTR 0.5  cg04981492 SYDE1 19 15218713 0.367 0.502 -0.135 Body 0.5  cg26166804 H2AFY2 10 71812612 0.181 0.315 -0.134 5'UTR 0.3  cg00622655 MORC3 21 37667639 0.666 0.800 -0.134 Intergenic -24.8  cg27479162 PIK3AP1 10 98450737 0.480 0.614 -0.134 Intergenic -21.4 Yes cg22344841 SDC1 2 20421656 0.489 0.623 -0.134 Body 1.5 Yes cg25500616 ARHGEF4 2 131800227 0.514 0.647 -0.133 Body 3.1   162 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg10007534 FAM207A 21 46378354 0.585 0.718 -0.133 Body 18.4  cg11637968 FNIP2 4 159732036 0.521 0.654 -0.133 Body 41.9 Yes cg05521767 GDPD5 11 75230135 0.710 0.843 -0.133 5'UTR 6.5 Yes cg00415333 IL22RA2 6 137493017 0.631 0.763 -0.133 5'UTR 1.8 Yes cg07564598 MIR4522 17 25619946 0.508 0.641 -0.133 Promoter -1.1  cg06665333 SLC7A5 16 87873837 0.222 0.355 -0.133 Body 29.3  cg25592413 STC2 5 172753646 0.258 0.391 -0.133 Body 1.1  cg01723031 TBC1D1 4 37975326 0.585 0.718 -0.133 Intergenic -3.7  cg21906866 C14orf159 14 91592679 0.517 0.649 -0.132 5'UTR 12.9  cg04038163 CXCL9 4 76925155 0.494 0.626 -0.132 Body 3.5  cg26585416 DAPK3 19 3969745 0.506 0.638 -0.132 5'UTR 0.1  cg02802634 FOXL1 16 86772171 0.402 0.534 -0.132 Intergenic -159.0  cg08018143 FSTL5 4 162110754 0.288 0.420 -0.132 Intergenic 982.0 Yes cg01839603 GNA12 7 2801424 0.428 0.560 -0.132 Body 53.5  cg22514112 IRX4 5 1859764 0.587 0.719 -0.132 Intergenic 23.1  cg16090790 MARCKS 6 113674867 0.588 0.720 -0.132 Intergenic -503.7 Yes cg08536358 PDHA2 4 96760014 0.407 0.539 -0.132 Promoter -1.2 Yes cg11622516 FSTL1 3 120165600 0.520 0.651 -0.131 Body 4.3 Yes cg04573500 MSI2 17 55444427 0.374 0.505 -0.131 Body 105.0 Yes cg00496126 NEBL 10 21569823 0.700 0.831 -0.131 Intergenic -106.7 Yes cg04413904 SSTR5 16 1088479 0.458 0.589 -0.131 Intergenic -34.3  cg12833765 ANTXR1 2 69470878 0.485 0.616 -0.13 Body 230.0 Yes cg05663031 BCL6 3 187453721 0.438 0.568 -0.13 5'UTR 0.6  cg01140247 C10orf11 10 77784369 0.419 0.549 -0.13 Body 244.0  cg12792952 CACHD1 1 64857387 0.643 0.772 -0.13 Intergenic -79.1 Yes cg22860917 GALM 2 38919351 0.549 0.679 -0.13 Body 26.3 Yes cg14384014 ID4 6 19670306 0.555 0.685 -0.13 Intergenic -166.9 Yes cg19674091 IDH2 15 90643766 0.425 0.555 -0.13 Body 1.9 Yes  163 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg26877565 LY6G6C 6 31689610 0.557 0.687 -0.13 Promoter -0.1  cg03387092 MYLK 3 123493248 0.428 0.558 -0.13 Body 19.4 Yes cg20421295 NGEF 2 233749913 0.611 0.741 -0.13 Body 43.0  cg25103772 PAPPA2 1 176513705 0.434 0.564 -0.13 Body 81.4 Yes cg05364179 PCGF3 4 760039 0.502 0.632 -0.13 3'UTR 60.0 Yes cg04858987 SH3BP5 3 15359338 0.724 0.854 -0.13 Body 14.8 Yes cg26188621 VDAC2 10 76983413 0.442 0.572 -0.13 Body;5'UTR 13.0 Yes cg19694404 C1orf98 1 200306620 0.579 0.708 -0.129 Intergenic 36.3 Yes cg04577249 DUSP1 5 172179102 0.375 0.504 -0.129 Intergenic 18.5  cg23963517 BICC1 10 60473872 0.564 0.693 -0.129 Promoter -0.9  cg15429134 LOH12CR1 12 12619103 0.708 0.837 -0.129 3'UTR 111.0  cg14350701 NACC2 9 138969712 0.543 0.672 -0.129 5'UTR 17.4  cg18564881 PPFIA1 11 70091067 0.428 0.557 -0.129 Intergenic -25.7 Yes cg02766770 YPEL5 2 30372795 0.316 0.446 -0.129 5'UTR 2.4 Yes cg03752885 DAPK3 19 3969736 0.500 0.629 -0.128 5'UTR 0.1  cg17843487 EFHD2 1 15756557 0.669 0.797 -0.128 3'UTR 20.2  cg09049982 ITCH 20 32950073 0.544 0.672 -0.128 Promoter -1.0  cg26072254 KIF26B 1 245710355 0.361 0.489 -0.128 Body 36.1 Yes cg06480942 STX1A 7 73116029 0.541 0.668 -0.128 Body 18.0  cg10409560 C4orf26 4 76481299 0.428 0.554 -0.127 5'UTR;Body 0.0  cg25161868 CACNA2D1 7 81579463 0.431 0.558 -0.127 3'UTR 20.9 Yes cg04098985 EXTL2 1 101358409 0.595 0.722 -0.127 5'UTR 2.0  cg22572071 GPR110 6 47074382 0.483 0.610 -0.127 Intergenic -64.3 Yes cg09174601 HS3ST3A1 17 13442632 0.492 0.619 -0.127 Body 62.6 Yes cg26531076 KLF5 13 73743377 0.484 0.611 -0.127 Intergenic 110.2 Yes cg25740652 LIMCH1 4 41361623 0.646 0.772 -0.127 Promoter -1.2  cg21660452 NRXN2 11 64397807 0.253 0.380 -0.127 Intergenic -6.3  cg18743287 RDH13 19 55575837 0.476 0.603 -0.127 Promoter -1.3   164 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg25162927 ANO6 12 45736875 0.536 0.662 -0.126 Body 50.4 Yes cg10235741 DNASE1L3 3 58200326 0.446 0.572 -0.126 Intergenic -3.6  cg10499102 EXOC2 6 564040 0.489 0.615 -0.126 Body;5'UTR 129.0  cg09278187 FOXJ3 1 42644032 0.388 0.515 -0.126 5'UTR 156.0 Yes cg16677191 GLRX 5 95159423 0.500 0.625 -0.126 Promoter -0.8 Yes cg04103490 HIST3H3 1 228614118 0.579 0.705 -0.126 Promoter -1.1  cg17464043 KRT39 17 39115451 0.667 0.793 -0.126 Body 7.7 Yes cg25632577 PCAT1 8 128048403 0.568 0.694 -0.126 Intergenic 23.0 Yes cg00453717 POLD3 11 74344629 0.467 0.593 -0.126 Body 41.0 Yes cg07776698 CPED1 7 120903952 0.584 0.708 -0.125 Body 275.0 Yes cg14072140 DPYD 1 98031839 0.424 0.548 -0.125 Body 354.8 Yes cg16296417 NSMCE2 8 126285443 0.534 0.658 -0.125 Body 181.7 Yes cg17082405 PLEC1 8 145033328 0.605 0.730 -0.125 Intergenic -5.2 Yes cg21857190 TNP1 2 217913299 0.482 0.607 -0.125 Intergenic -189.0 Yes cg06324373 CRTAC1 10 99734805 0.556 0.431 0.125 Intergenic -38.7 Yes cg00970361 FOXB1 15 60288348 0.574 0.449 0.125 Intergenic -8.1 Yes cg25078444 FOXG1 14 29235193 0.640 0.515 0.125 Promoter -1.1 Yes cg06231385 FOXK1 7 4743056 0.688 0.563 0.125 Body 21.1  cg11246563 HLA-H 6 29855406 0.474 0.349 0.125 5'UTR 0.0  cg25366315 BTNL3 5 180408809 0.803 0.677 0.126 Intergenic -7.0  cg21464565 HOXA2 7 27141139 0.532 0.406 0.126 Body 1.2 Yes cg25609528 POU3F1 1 38510182 0.711 0.585 0.126 3'UTR 2.3  cg14650610 SPOCK1 5 136834492 0.508 0.382 0.126 Promoter -0.1  cg01879273 AGAP1 2 236506413 0.713 0.586 0.127 Body 103.7  cg00684116 CERS3 15 101094461 0.852 0.725 0.127 Intergenic -10.0  cg10566121 MPPED2 11 30606026 0.434 0.307 0.127 Promoter -0.3  cg01663018 ONECUT1 15 53097777 0.654 0.527 0.127 Intergenic -15.6  cg03958798 SORCS3 10 106400686 0.537 0.410 0.127 Promoter -0.2   165 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg00317585 LOC339975 4 188156703 0.673 0.546 0.128 Intergenic 270.1  cg00870753 UBE2MP1 16 34404772 0.560 0.432 0.128 Promoter 0.0  cg21253459 FOXB1 15 60288404 0.564 0.434 0.129 Intergenic -8.0 Yes cg06335867 NXPH1 7 8482325 0.452 0.323 0.129 Body 8.7  cg13273396 ACSS3 12 81471867 0.389 0.259 0.13 5'UTR 0.0  cg08283882 EBF2 8 25901017 0.282 0.152 0.13 Body 1.6 Yes cg15715477 LOC100130155 8 65283042 0.710 0.580 0.13 Intergenic -2.7  cg04609163 DDX25 11 125774090 0.269 0.138 0.131 Promoter -0.2  cg18063312 FOXD1 5 72740737 0.669 0.538 0.131 Intergenic 3.6  cg23774717 APBB1IP 10 26856095 0.555 0.423 0.132 Body 129.0  cg21571339 FLRT2 14 86001111 0.439 0.307 0.132 5'UTR 4.5  cg01287975 TAC1 7 97361241 0.469 0.337 0.132 Promoter 0.0  cg23179456 ADCY4 14 24803873 0.578 0.445 0.133 5'UTR 0.4  cg19848629 LOC100130155 8 65286244 0.606 0.473 0.133 5'UTR 0.5  cg14005139 NKX2-1 14 36989550 0.325 0.192 0.133 Promoter -0.1 Yes cg03534453 PCDHB17 5 140537884 0.585 0.452 0.133 Intergenic 2.3  cg04550737 TBX15 1 119530600 0.415 0.282 0.133 5'UTR 1.6 Yes cg10222027 CSDAP1 16 31580590 0.469 0.335 0.134 5'UTR 0.3  cg04241652 FLJ42875 1 2984830 0.446 0.312 0.134 Promoter -0.5  cg16171281 C18orf62 18 73167422 0.712 0.576 0.136 Intergenic -27.8 Yes cg07576142 GPC6 13 93879769 0.476 0.339 0.136 Body 0.7  cg11782635 KCNIP1 5 169930919 0.391 0.255 0.136 Promoter -0.1  cg27314998 PCDH7 4 30720246 0.502 0.366 0.136 Promoter -1.8  cg04965934 EMX1 2 73151201 0.516 0.378 0.137 Body 6.6  cg03418136 PRKXP1 15 101095730 0.647 0.510 0.137 Body 7.9  cg16886987 DSCR6 21 38378634 0.441 0.303 0.138 Promoter -0.2  cg23160016 GABRA2 4 46391929 0.601 0.463 0.138 5'UTR 0.1  cg21479226 C8orf34 8 69244510 0.528 0.389 0.139 Body 1.5   166 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg19291576 DMRT3 9 969530 0.610 0.471 0.139 Intergenic -7.4  cg14636534 LINC00461 5 87968528 0.674 0.535 0.139 5'UTR 0.6  cg10130155 LOC401242 6 28833480 0.695 0.556 0.139 Promoter -2.0  cg01269620 PDE10A 6 166074191 0.435 0.296 0.139 Intergenic 1.4  cg17073273 POU3F2 6 99279795 0.350 0.210 0.139 Intergenic -2.8  cg14118515 EVX2 2 176948728 0.469 0.329 0.14 Promoter 0.0  cg15335669 REREP3 15 22546908 0.606 0.465 0.14 5'UTR 0.3  cg10331073 APBB1IP 10 26856128 0.538 0.398 0.141 Body 128.0  cg20072171 FEZF2 3 62356962 0.417 0.276 0.141 Body 1.8  cg06951122 PCDHA8 5 140222653 0.612 0.471 0.141 Body 1.7  cg18077971 PAX3 2 223164867 0.637 0.495 0.142 Promoter -1.2 Yes cg00615537 ARHGEF37 5 148961011 0.568 0.424 0.143 Promoter -0.1 Yes cg25599538 UCP1 4 141490075 0.334 0.191 0.143 Promoter -0.1  cg10196720 PCDH10 4 134069593 0.433 0.290 0.144 Promoter -0.9  cg16699715 ARHGEF37 5 148961007 0.501 0.356 0.145 Promoter -0.1 Yes cg01414185 HOXD4 2 177017449 0.658 0.513 0.145 Body 1.3  cg26536949 DOC2B 17 57053 0.690 0.540 0.149 Intergenic -25.6 Yes cg09735723 SORCS3 10 106402042 0.489 0.339 0.149 Body 1.2  cg02268229 UCP1 4 141490037 0.430 0.281 0.149 Promoter -0.1  cg18204675 ARHGEF37 5 148961004 0.398 0.248 0.15 Promoter -0.1 Yes cg27262412 TBX15 1 119530702 0.541 0.390 0.151 5'UTR 1.5 Yes cg16492341 APBB1IP 10 26856105 0.603 0.451 0.152 Body 128.0  cg05864326 HOXD3 2 177030150 0.687 0.535 0.152 5'UTR 1.3  cg01729717 SLITRK1 13 84456486 0.458 0.306 0.152 5'UTR 0.0  cg24453699 BARHL2 1 91190891 0.626 0.468 0.158 Intergenic -8.1 Yes cg12405754 EFNA5 5 107005487 0.624 0.456 0.169 Body 1.1  cg19797516 APBB1IP 10 26856454 0.622 0.441 0.181 3'UTR 128.0  cg05452524 HLF 17 53343029 0.502 0.322 0.181 Promoter -0.1   167 TargetID Gene Name Chr Map Position EOPET Control Difference Genomic Area Distance to TSS (kb) Enhancer cg25363445 ALX4 11 44326516 0.511 0.326 0.185 Body 5.2 Yes cg10327440 CDC42BPA 1 227177885 0.794 0.588 0.207 3'UTR 90.0  Supplementary Table 3.3: DNA methylation information for 282 candidate CpG sites  168 Probe ID Gene Chr Avg. EOPET Avg. CTL Fold change ILMN_2207504 LEP 7 2055.1 6.1 336.47 ILMN_2296450 GH2 17 261.3 3.1 84.4 ILMN_1792002 PAPPA2 1 217.3 4.4 49.59 ILMN_1778237 FN1 2 67.2 1.7 38.66 ILMN_2044645 CGB1 19 3337.4 87.2 38.29 ILMN_1785393 ADAM12 10 97.1 2.9 33.53 ILMN_1754126 SH2D5 1 71.3 2.3 30.83 ILMN_1668035 CRH 8 1260.5 41.5 30.4 ILMN_1714067 NTRK2 9 36.1 1.2 29.58 ILMN_3249571 ARMS2 10 151.1 6.0 25.1 ILMN_1799105 COL17A1 10 76.6 3.3 23.44 ILMN_2371911 MUC1 1 44.3 1.9 23.3 ILMN_2099277 HTRA4 8 2103.0 91.2 23.05 ILMN_2109489 GZMB 14 26.3 1.2 22.17 ILMN_1746362 LOC440895 2 31.2 1.6 20.04 ILMN_1688231 TREM1 6 29.3 1.6 18.12 ILMN_1740864 TREML2 6 19.3 1.1 17.77 ILMN_1674696 OLAH 10 91.3 5.2 17.64 ILMN_1737650 DIO2 14 97.3 5.8 16.74 ILMN_1749044 PVRL4 1 79.7 4.8 16.61 ILMN_1862070  13 172.0 10.5 16.45 ILMN_1801442 KRT81 12 19.0 1.2 16.37 ILMN_1698666 CST6 11 125.1 7.9 15.86 ILMN_1675519 LOC644611 1 37.9 2.4 15.53 ILMN_1760315 VWCE 11 48.8 3.2 15.24 ILMN_2325837 CD3D 11 14.6 1.0 14.59 ILMN_1767842 SLC17A8 12 14.5 1.0 14.47 ILMN_1772768 PSG7 19 1233.0 86.4 14.27 ILMN_1758229 QSOX1 1 16.2 1.2 13.82 ILMN_1674640 CXCR6 3 67.0 5.0 13.38 ILMN_1721770 PAPPA 9 3058.8 229.1 13.35 ILMN_2406035 LAMA3 18 27.1 2.1 13.19 ILMN_1659597 LOC649037  205.7 16.0 12.88 ILMN_1726266 ADAM12 10 3299.6 259.1 12.73 ILMN_1651656 PSG7 19 1728.9 136.4 12.67 ILMN_1764483 PSG2 19 1733.4 140.2 12.36 ILMN_2067408 CLRN3 10 18.8 1.6 11.48 ILMN_2309615 PSG6 19 2721.2 238.6 11.4 ILMN_1802653 EBI3 19 2044.8 183.5 11.14 ILMN_1756443 INHA 2 134.5 12.4 10.85 ILMN_1801776 PSG9 19 1234.1 120.1 10.28 ILMN_1771123 LOC653492 19 22.9 2.2 10.27 ILMN_2188333 CD69 12 31.4 3.2 9.92 ILMN_1684349 IL2RB 22 125.4 12.8 9.79 ILMN_1782141 GRHL3 1 18.2 1.9 9.38  169 Probe ID Gene Chr Avg. EOPET Avg. CTL Fold change ILMN_1772976 BTNL9 5 49.1 5.3 9.32 ILMN_3275206 LOC100132240  20.3 2.2 9.23 ILMN_1728734 PSG5 19 1508.8 164.7 9.16 ILMN_1651282 COL17A1 10 778.3 85.0 9.15 ILMN_2109416 NAPSB 19 16.0 1.9 8.59 ILMN_1726624 YPEL4 11 10.4 1.2 8.47 ILMN_2261416 CD3D 11 14.1 1.7 8.46 ILMN_1813386 CORO6 17 196.1 23.4 8.37 ILMN_1709870 HES2 1 40.9 5.0 8.14 ILMN_1749667 OBSCN 1 30.1 3.7 8.12 ILMN_1729314 PRG2 11 9369.1 1169.6 8.01 ILMN_1712894 FUT1 19 12.3 1.6 7.78 ILMN_1686989 INSIG1 7 11.6 1.5 7.77 ILMN_1744455 NOTUM 17 103.4 13.7 7.56 ILMN_3273229 LOC100129781 16 115.3 15.3 7.55 ILMN_2405324 IL28RA 1 11.3 1.5 7.45 ILMN_1741021 CH25H 10 60.2 8.1 7.41 ILMN_1788017 HSH2D 19 7.4 1.0 7.38 ILMN_2366463 FN1 2 58.5 7.9 7.36 ILMN_1731233 GZMH 14 15.7 2.2 7.29 ILMN_1729287 NMUR1 2 9.1 1.3 7.22 ILMN_1787658 MTMR7 8 10.7 1.5 7.2 ILMN_1705231 SLCO2A1 3 827.6 115.5 7.16 ILMN_2139761 LIMCH1 4 464.4 66.1 7.03 ILMN_1700203 KIAA1984 9 49.9 7.2 6.95 ILMN_2061043 CD48 1 44.8 6.5 6.89 ILMN_1762231 ZEB1 10 11.1 1.6 6.84 ILMN_1772951 ST6GALNAC1 17 20.2 3.0 6.79 ILMN_2251766 IL1R2 2 16.4 2.4 6.77 ILMN_1678531 N4BP3 5 16.2 2.4 6.64 ILMN_1688892 LAMA3 18 21.1 3.2 6.62 ILMN_2211263 RFK 9 22.6 3.4 6.62 ILMN_1810159 FLJ90650 5 343.6 52.3 6.57 ILMN_3246826 LVRN 5 186.5 28.4 6.57 ILMN_1859127   84.2 13.0 6.49 ILMN_2384857 DHRS2 14 60.1 9.4 6.37 ILMN_1675584 HTRA4 8 62.6 10.0 6.28 ILMN_1693985 JPH1 8 25.8 4.1 6.28 ILMN_1714848 ZNF354A 5 10.6 1.7 6.15 ILMN_1799030 CMTM2 16 15.1 2.5 6.08 ILMN_1756417 ANKRD37 4 227.0 37.4 6.06 ILMN_2387860 CYP19A1 15 671.4 112.0 5.99 ILMN_1779324 GZMA 5 38.4 6.4 5.97 ILMN_1862043 LOC731042  6.1 1.0 5.94 ILMN_1782863 FAM83B 6 37.7 6.5 5.81  170 Probe ID Gene Chr Avg. EOPET Avg. CTL Fold change ILMN_2322498 RORA 15 32.5 5.8 5.63 ILMN_1714592 CDA 1 25.9 4.7 5.55 ILMN_2384745 PSG4 19 7888.9 1425.1 5.54 ILMN_2357855 NTRK2 9 13.3 2.4 5.49 ILMN_1668865 SLC2A14 12 19.7 3.7 5.38 ILMN_1694810 PANX2 22 6.9 1.3 5.33 ILMN_1695157 CA4 17 605.8 118.7 5.1 ILMN_1676924 CD247 1 40.6 8.4 4.82 ILMN_2166275 NOTUM 17 207.0 43.4 4.77 ILMN_1763972 TJP2 9 9.3 2.0 4.75 ILMN_2093343 PLAC8 4 373.9 79.8 4.69 ILMN_1770612 KRT15 17 9.0 1.9 4.67 ILMN_1731433 ABP1 7 1379.2 297.5 4.64 ILMN_1697733 CST6  114.7 25.7 4.46 ILMN_1658483 IL1A 2 10.1 2.3 4.45 ILMN_1790918 ZNF236 18 6.7 1.5 4.44 ILMN_1841564  10 5.8 1.3 4.43 ILMN_1764455 LOC653520 17 44.9 10.1 4.42 ILMN_2413041 TEAD4 12 7.2 1.7 4.38 ILMN_1679979 PLK3 1 8.0 1.9 4.3 ILMN_1658398 C3orf41 3 28.3 6.6 4.28 ILMN_1725726 DHRS2 14 27.2 6.5 4.21 ILMN_2166457 HPGD 4 680.2 162.0 4.2 ILMN_1777250 ZNF268  4.9 1.2 4.15 ILMN_1770818 LOC642342 9 17.1 4.2 4.11 ILMN_1724832 OVOL2 20 11.2 2.8 4.04 ILMN_1723412 ASCL2 11 329.9 82.8 3.98 ILMN_1700728 KRTCAP3 2 24.0 6.1 3.96 ILMN_3247286 LOC100134794 7 10.4 2.6 3.95 ILMN_2100287 CCDC147 10 10.1 2.6 3.93 ILMN_1740276 CLDN9 16 8.9 2.3 3.93 ILMN_3238435 SNORA12 10 158.2 40.3 3.93 ILMN_1672624 LOC646110 14 20.3 5.2 3.88 ILMN_1774390 LOC441054  5.7 1.5 3.84 ILMN_1696029 FLJ36031 7 4.6 1.2 3.83 ILMN_1744381 SERPINE1 7 521.8 137.9 3.78 ILMN_1778964 CLIC5 6 112.3 30.1 3.74 ILMN_1709434 VIT 2 5.7 1.5 3.73 ILMN_1690174 DLL4 15 14.6 4.0 3.66 ILMN_1738407 ULBP1 6 17.6 4.8 3.64 ILMN_1716758 PCDH1 5 6.8 1.9 3.63 ILMN_1751474 ARHGAP29 1 13.0 3.6 3.62 ILMN_1709257 DSCR6 21 12.3 3.4 3.62 ILMN_2111237 MN1 22 60.2 16.7 3.6 ILMN_1772791 LOC650534  18.9 5.3 3.59  171 Probe ID Gene Chr Avg. EOPET Avg. CTL Fold change ILMN_1730794 SERTAD4 1 92.5 26.3 3.52 ILMN_2299095 SIGLEC6 19 1404.0 398.8 3.52 ILMN_2299661 TNFRSF25 1 7.8 2.2 3.52 ILMN_1747197 SLC41A2 12 24.2 6.9 3.49 ILMN_2048507 KLF3 4 20.4 5.9 3.47 ILMN_3276713 LOC100131094 19 3.5 1.0 3.47 ILMN_1741727 QPCT 2 142.0 41.0 3.46 ILMN_2405156 PPAP2C 19 9.5 2.8 3.35 ILMN_1653026 PLAC8 4 227.5 68.1 3.34 ILMN_3248202 PEG3AS  6.9 2.1 3.3 ILMN_3247458 LOC652750  7.1 2.1 3.29 ILMN_1776925 PRSS22 16 9.8 3.0 3.29 ILMN_2160209 TACSTD1 2 23.2 7.1 3.28 ILMN_1684227 GPR146 7 53.2 16.3 3.26 ILMN_1787115 WWTR1 3 19.6 6.0 3.26 ILMN_1780306 DDHD1 14 7.7 2.4 3.22 ILMN_1781465 LOC652134  6.1 1.9 3.21 ILMN_1680145 SART1 11 3.2 1.0 3.21 ILMN_1730777 KRT19 17 1745.6 553.2 3.16 ILMN_1662482 LPPR4 1 14.5 4.7 3.08 ILMN_1797753 ARHGAP28 18 14.0 4.6 3.07 ILMN_2371053 EFNA1 1 356.4 116.5 3.06 ILMN_1795767 GRHL1 2 70.8 23.4 3.02 ILMN_1667994 AMD1 6 383.4 127.6 3 ILMN_1769013 ASGR1 17 29.1 9.7 3 ILMN_3241505 FLJ43390 14 6.5 2.2 2.99 ILMN_1672417 PTPRCAP 11 9.4 3.1 2.98 ILMN_1731714 CREB5 7 94.4 31.8 2.97 ILMN_2094952 NUAK2 1 36.3 12.2 2.97 ILMN_1727098 PPP1R16B 20 51.8 17.7 2.93 ILMN_1795838 C4orf19 4 94.0 33.2 2.83 ILMN_1658847 MGC61598  271.5 97.1 2.8 ILMN_1687751 BAALC 8 78.4 28.4 2.76 ILMN_2367233 ZNF654 3 20.9 7.7 2.72 ILMN_1659913 ISG20 15 304.9 113.0 2.7 ILMN_1800181 MCMDC1 6 4.9 1.8 2.67 ILMN_1673113 F2RL1 5 36.1 13.7 2.64 ILMN_1735548 HIVEP1 6 20.1 7.8 2.56 ILMN_2219767 MYCN 2 517.2 202.2 2.56 ILMN_2063168 MALL 2 266.7 105.8 2.52 ILMN_3237941 LOC645159 1 27.5 10.9 2.51 ILMN_3196019 FAM60A 12 90.7 37.0 2.45 ILMN_1788462 AMD1 6 1395.8 577.7 2.42 ILMN_1776857 AQP1 7 17.4 7.2 2.42 ILMN_1672022 EPHA4 2 18.7 7.8 2.39  172 Probe ID Gene Chr Avg. EOPET Avg. CTL Fold change ILMN_1743714 CARD10 22 159.2 67.1 2.37 ILMN_1713751 ADAM19 5 237.6 101.9 2.33 ILMN_1721316 TNFRSF10A 8 43.8 19.0 2.31 ILMN_1727671 SSH1 12 39.9 17.5 2.29 ILMN_1703593 BAIAP2L1 7 105.1 47.5 2.21 ILMN_1741422 FUT8 14 146.3 67.9 2.16 ILMN_1774330 WSCD1 17 79.1 39.5 2 ILMN_1795128 C13orf23 13 320.9 166.4 1.93 ILMN_3243366 C2orf55 2 178.3 93.2 1.91 ILMN_1721833 IER5 1 169.1 89.1 1.9 ILMN_2261784 CCNY 10 967.6 511.9 1.89 ILMN_1655429 TNFAIP1 17 424.6 245.5 1.73 ILMN_1722034 KIAA1586 6 41.6 24.3 1.71 ILMN_1798030 XPR1 1 369.0 228.0 1.62 Supplementary Table 3.4: List of probes significantly different by an FDR of <0.05 and fold change of >1.2 between preeclamptic and control placentas  173 TargetID Gene Name Fold EOPET/CTL p-value Correlation with methylation p-value cg26625897 KRT15 4.67 0.00 -0.71 0.027 cg00592695 ST3GAL1 1.14 0.28 -0.31 0.437 cg12436772 FN1 9.07 0.00 -0.87 0.001 cg20971407 BHLHE40 2.98 0.04 -0.75 0.014 cg17252884 SH3BP5 1.32 0.23 -0.38 0.331 cg15831875 SMYD3 0.64 0.11 0.43 0.261 cg01924561 SLC2A1 1.67 0.00 -0.62 0.073 cg11079619 INHBA 3.53 0.20 -0.43 0.266 cg19308497 ATP1A4 1.29 0.48 -0.20 0.618 cg13754437 FHL2 1.17 0.05 -0.50 0.176 cg18236464 PAPPA2 46.76 0.14 -0.65 0.056 cg14854503 CSGALNACT1 0.80 0.14 0.32 0.430 cg22996170 JUNB 1.50 0.22 -0.53 0.150 cg19140548 SH3PXD2A 1.37 0.19 -0.48 0.196 cg10893014 TEAD3 1.01 0.96 -0.15 0.712 cg22193385 KRT7 1.13 0.68 -0.28 0.479 cg01228410 SLC9A3 0.51 0.18 0.34 0.396 cg02766259 AACS 0.65 0.13 0.27 0.505 cg14143441 NDRG1 1.60 0.23 -0.49 0.188 cg03983223 WIPF1 0.85 0.37 0.10 0.814 cg09365002 DAXX 1.12 0.57 -0.63 0.068 cg21245975 C1orf52 0.92 0.51 -0.28 0.490 cg17417693 CLIP4 0.97 0.91 -0.02 0.966 cg25245338 TIMP3 1.28 0.12 -0.48 0.203 cg00345704 KRTAP2-3 NA NA NA NA cg22234930 PKM2 1.12 0.61 -0.34 0.387 cg03728457 CCDC33 0.86 0.68 0.22 0.597 cg01180628 BHLHE40 2.98 0.04 -0.67 0.046 cg19883388 AZIN1 1.52 0.00 -0.72 0.023 cg16568084 BET1 1.01 0.96 0.24 0.554 cg11327657 FAM207A NA NA NA NA cg07351322 MLL5 1.37 0.08 -0.55 0.128 cg19417526 NTM 0.21 0.06 0.56 0.122 cg07950244 PHLDA3 1.34 0.32 -0.36 0.361 cg09182455 CORO1C 1.21 0.06 -0.61 0.079 cg23730027 FLNB 1.88 0.01 -0.72 0.025 cg20576064 FAM160B2 1.50 0.15 -0.43 0.261 cg03777414 TVP23A NA NA NA NA cg16301004 ITPRIP 1.78 0.04 -0.63 0.066 cg24097814 KRT8 1.55 0.09 -0.47 0.210 cg23843484 MAFK 0.83 0.55 0.02 0.969 cg14704980 LOC285954 NA NA NA NA cg26509870 PHYHIP 2.89 0.28 -0.38 0.332 cg20352351 NCOR2 1.03 0.87 -0.35 0.371  174 TargetID Gene Name Fold EOPET/CTL p-value Correlation with methylation p-value cg02770406 FLNB 1.88 0.01 -0.75 0.014 cg25298189 ARID3A 1.39 0.14 -0.46 0.224 cg10246581 CMIP 1.11 0.78 -0.06 0.879 cg03822934 LIMCH1 7.03 0.03 -0.82 0.004 cg10047173 ART4 1.00 0.99 0.24 0.555 cg20317872 DENND2D 0.51 0.05 0.38 0.335 cg02029908 DUSP1 1.83 0.01 -0.66 0.052 cg05452692 LINC00284 NA NA NA NA cg10586672 SLC6A6 4.11 0.01 -0.61 0.078 cg00736681 ST3GAL1 1.14 0.28 -0.33 0.400 cg26651514 STARD13 1.04 0.82 -0.27 0.507 cg12779575 ERN1 1.35 0.22 -0.18 0.660 cg12361046 CHSY1 1.21 0.29 -0.42 0.271 cg11811391 HTR1D 0.99 0.33 0.34 0.390 cg10187713 PARD6B 1.59 0.05 -0.32 0.424 cg10668363 ZNF175 2.05 0.01 -0.56 0.121 cg17115419 CALML3 1.87 0.17 -0.11 0.795 cg13553455 COL17A1 9.68 0.00 -0.81 0.005 cg22626683 TNFSF18 0.39 0.28 0.18 0.654 cg15903956 CYP11A1 3.95 0.02 -0.65 0.057 cg03653726 GNA12 0.90 0.54 0.05 0.899 cg12632411 RRBP1 0.80 0.17 0.39 0.322 cg03339817 SFT2D3 1.21 0.45 0.00 1.000 cg00713022 SYDE1 3.96 0.03 -0.56 0.124 cg27328839 DLG5 1.28 0.27 -0.39 0.312 cg17850498 ECE1 2.02 0.00 -0.62 0.073 cg10108710 TTC7B 0.77 0.32 -0.19 0.638 cg16282339 PTPRJ 0.83 0.70 -0.01 0.979 cg27182012 LMNA 0.94 0.37 0.21 0.607 cg06821199 CPLX1 0.87 0.68 0.54 0.142 cg21913652 FJX1 1.30 0.12 -0.37 0.346 cg25032603 LINC00310 NA NA NA NA cg05971934 NTRK3 NA NA NA NA cg01713086 ZNF395 1.27 0.09 -0.50 0.175 cg07158065 MIR3150A NA NA NA NA cg18190824 ANKH 0.88 0.74 0.02 0.958 cg03024478 UNC5C 1.10 0.74 -0.18 0.669 cg26813604 ERGIC1 0.96 0.90 -0.23 0.573 cg09394306 LAPTM4A 1.00 0.97 0.14 0.736 cg10994126 PAPPA2 46.76 0.14 -0.71 0.027 cg13547665 POLE4 0.97 0.83 -0.08 0.848 cg18672030 RELL1 1.30 0.11 -0.59 0.091 cg16616993 RHOBTB3 1.19 0.37 -0.35 0.371 cg04897892 CMIP 1.11 0.78 -0.42 0.282  175 TargetID Gene Name Fold EOPET/CTL p-value Correlation with methylation p-value cg03099780 MBNL2 1.24 0.08 -0.61 0.078 cg17966362 MSH4 1.15 0.67 -0.06 0.882 cg26366616 PDLIM2 NA NA NA NA cg15765546 SLCO2A1 7.16 0.01 -0.79 0.007 cg14605117 AMZ1 NA NA NA NA cg16606561 FAM110A 1.13 0.67 -0.46 0.223 cg18638180 FAM207A NA NA NA NA cg15282973 SCARB1 2.31 0.00 -0.47 0.213 cg00834537 APOPT1 NA NA NA NA cg15887927 FLT1 4.69 0.06 -0.68 0.039 cg12261055 INHBA 3.53 0.20 -0.37 0.352 cg12979992 INSIG1 2.17 0.01 -0.66 0.048 cg12804791 ST3GAL4 1.16 0.55 -0.20 0.625 cg03764092 TBXAS1 0.45 0.03 0.29 0.476 cg19861486 STK24 1.21 0.04 -0.68 0.041 cg24431161 TBXAS1 0.45 0.03 0.31 0.445 cg20340720 WBP1L 1.34 0.18 -0.30 0.460 cg12647920 CORO1C 1.21 0.06 -0.60 0.087 cg13062627 CSRNP1 NA NA NA NA cg21496948 KLC1 0.91 0.33 0.24 0.561 cg07981495 CGA 1.50 0.14 -0.20 0.623 cg20586124 CTAGE1 0.87 0.67 0.32 0.419 cg13467628 FAM150B 0.95 0.90 -0.12 0.771 cg24204017 FLNB 1.88 0.01 -0.67 0.045 cg11034318 LOC728743 0.64 0.32 0.00 1.000 cg08900316 PRKCE 1.72 0.01 -0.60 0.091 cg23400222 ZNF608 0.87 0.58 0.03 0.934 cg02494582 ADAM12 12.24 0.04 -0.61 0.083 cg21823502 ANAPC11 0.92 0.22 0.41 0.291 cg16104384 c22orf34 0.42 0.47 0.24 0.546 cg18474072 DIP2C 0.75 0.03 0.38 0.338 cg15129144 EPAS1 1.49 0.00 -0.79 0.007 cg23677911 GALNT2 0.82 0.33 0.08 0.851 cg08799766 GNA12 0.90 0.54 0.38 0.330 cg24617203 IL1R2 3.49 0.01 -0.77 0.010 cg27307465 JAK1 1.19 0.40 -0.28 0.492 cg13467459 KIAA1614 0.93 0.86 0.04 0.928 cg11865119 MEST 0.82 0.50 0.41 0.288 cg02681842 PLEC1 1.10 0.64 -0.15 0.711 cg06837325 RGMB 1.68 0.05 -0.38 0.326 cg21560697 TAF1B 1.27 0.11 -0.34 0.397 cg05131266 c14orf159 1.05 0.39 0.33 0.414 cg01288184 CABLES1 0.85 0.42 -0.08 0.847 cg03730249 FOXJ3 1.66 0.07 -0.68 0.038  176 TargetID Gene Name Fold EOPET/CTL p-value Correlation with methylation p-value cg11355603 HIPK2 1.23 0.38 -0.37 0.339 cg16854917 LINC0085 NA NA NA NA cg04981492 SYDE1 3.96 0.03 -0.65 0.055 cg26166804 H2AFY2 0.62 0.14 0.12 0.767 cg00622655 MORC3 1.77 0.02 -0.44 0.247 cg27479162 PIK3AP1 1.04 0.85 -0.13 0.747 cg22344841 SDC1 2.53 0.11 -0.48 0.205 cg25500616 ARHGEF4 15.03 0.14 -0.50 0.180 cg10007534 FAM207A NA NA NA NA cg11637968 FNIP2 1.14 0.34 -0.31 0.443 cg05521767 GDPD5 3.04 0.11 -0.22 0.587 cg00415333 IL22RA2 0.95 0.82 0.18 0.652 cg07564598 MIR4522 NA NA NA NA cg06665333 SLC7A5 2.62 0.01 -0.61 0.079 cg25592413 STC2 1.53 0.09 -0.28 0.489 cg01723031 TBC1D1 1.08 0.75 0.04 0.915 cg21906866 C14orf159 1.05 0.39 0.25 0.540 cg04038163 CXCL9 42.84 0.18 -0.53 0.153 cg26585416 DAPK3 0.86 0.73 0.25 0.532 cg02802634 FOXL1 0.78 0.61 0.30 0.462 cg08018143 FSTL5 2.61 0.08 -0.16 0.704 cg01839603 GNA12 0.90 0.54 -0.35 0.381 cg22514112 IRX4 0.73 0.53 0.24 0.555 cg16090790 MARCKS 0.89 0.16 0.34 0.387 cg08536358 PDHA2 0.96 0.84 0.49 0.195 cg11622516 FSTL1 1.25 0.04 -0.36 0.364 cg04573500 MSI2 0.94 0.75 0.07 0.871 cg00496126 NEBL 1.10 0.66 -0.10 0.814 cg04413904 SSTR5 NA NA NA NA cg12833765 ANTXR1 0.57 0.01 0.77 0.011 cg05663031 BCL6 2.20 0.08 -0.48 0.205 cg01140247 C10orf11 0.81 0.27 0.22 0.593 cg12792952 CACHD1 1.45 0.10 -0.19 0.638 cg22860917 GALM 0.73 0.07 0.41 0.285 cg14384014 ID4 0.80 0.53 0.21 0.603 cg19674091 IDH2 0.95 0.74 0.17 0.672 cg26877565 LY6G6C 1.67 0.32 -0.37 0.350 cg03387092 MYLK 0.88 0.39 0.13 0.755 cg20421295 NGEF 0.99 0.96 0.17 0.682 cg25103772 PAPPA2 46.76 0.14 -0.70 0.032 cg05364179 PCGF3 1.11 0.73 -0.12 0.777 cg04858987 SH3BP5 1.32 0.23 -0.26 0.528 cg26188621 VDAC2 1.22 0.56 -0.13 0.761 cg19694404 C1orf98 NA NA NA NA  177 TargetID Gene Name Fold EOPET/CTL p-value Correlation with methylation p-value cg04577249 DUSP1 1.83 0.01 -0.68 0.041 cg23963517 BICC1 0.85 0.13 0.39 0.322 cg15429134 LOH12CR1 2.54 0.09 -0.41 0.285 cg14350701 NACC2 1.27 0.20 -0.40 0.299 cg18564881 PPFIA1 0.96 0.59 0.23 0.572 cg02766770 YPEL5 1.17 0.01 -0.63 0.067 cg03752885 DAPK3 0.86 0.73 0.17 0.674 cg17843487 EFHD2 1.07 0.55 -0.34 0.388 cg09049982 ITCH 1.17 0.32 -0.24 0.556 cg26072254 KIF26B 0.46 0.01 0.03 0.944 cg06480942 STX1A 1.09 0.77 -0.02 0.958 cg10409560 C4orf26 2.60 0.03 -0.67 0.045 cg25161868 CACNA2D1 0.84 0.68 -0.04 0.917 cg04098985 EXTL2 0.93 0.64 0.02 0.969 cg22572071 GPR110 1.04 0.85 0.10 0.811 cg09174601 HS3ST3A1 0.97 0.89 0.32 0.430 cg26531076 KLF5 1.66 0.06 -0.61 0.081 cg25740652 LIMCH1 7.03 0.03 -0.47 0.211 cg21660452 NRXN2 1.33 0.30 0.13 0.756 cg18743287 RDH13 4.71 0.08 -0.57 0.113 cg25162927 ANO6 1.07 0.61 0.05 0.912 cg10235741 DNASE1L3 1.82 0.15 -0.28 0.480 cg10499102 EXOC2 1.37 0.00 -0.74 0.016 cg09278187 FOXJ3 1.66 0.07 -0.62 0.073 cg16677191 GLRX 1.76 0.00 -0.75 0.015 cg04103490 HIST3H3 1.01 0.98 -0.22 0.582 cg17464043 KRT39 1.08 0.33 0.00 0.995 cg25632577 PCAT1 NA NA NA NA cg00453717 POLD3 1.32 0.17 -0.32 0.415 cg07776698 CPED1 1.16 0.65 -0.27 0.509 cg14072140 DPYD 0.89 0.67 0.38 0.337 cg16296417 NSMCE2 1.06 0.66 0.10 0.801 cg17082405 PLEC1 1.10 0.64 -0.30 0.447 cg21857190 TNP1 0.36 0.15 0.10 0.812 cg06324373 CRTAC1 NA NA NA NA cg00970361 FOXB1 1.14 0.18 0.35 0.381 cg25078444 FOXG1 3.27 0.16 0.39 0.310 cg06231385 FOXK1 1.32 0.09 0.39 0.322 cg11246563 HLA-H 0.84 0.42 -0.39 0.319 cg25366315 BTNL3 1.28 0.32 0.10 0.805 cg21464565 HOXA2 0.60 0.14 -0.27 0.506 cg25609528 POU3F1 0.71 0.38 0.18 0.658 cg14650610 SPOCK1 1.73 0.42 0.12 0.765 cg01879273 AGAP1 0.99 0.97 -0.19 0.647  178 TargetID Gene Name Fold EOPET/CTL p-value Correlation with methylation p-value cg00684116 CERS3 NA NA NA NA cg10566121 MPPED2 0.91 0.80 0.35 0.376 cg01663018 ONECUT1 0.91 0.72 0.14 0.739 cg03958798 SORCS3 0.75 0.43 -0.21 0.598 cg00317585 LOC339975 NA NA NA NA cg00870753 UBE2MP1 1.08 0.73 -0.02 0.958 cg21253459 FOXB1 1.14 0.18 0.40 0.303 cg06335867 NXPH1 1.42 0.36 0.00 0.991 cg13273396 ACSS3 0.53 0.09 0.28 0.480 cg08283882 EBF2 0.79 0.42 -0.22 0.598 cg15715477 LOC100130155 0.75 0.32 -0.32 0.422 cg04609163 DDX25 NA NA NA NA cg18063312 FOXD1 0.57 0.24 -0.14 0.742 cg23774717 APBB1IP 0.81 0.29 -0.23 0.573 cg21571339 FLRT2 0.41 0.06 -0.38 0.327 cg01287975 TAC1 1.53 0.11 0.34 0.393 cg23179456 ADCY4 1.01 0.98 0.14 0.739 cg19848629 LOC100130155 0.75 0.32 -0.12 0.781 cg14005139 NKX2-1 0.99 0.33 -0.40 0.297 cg03534453 PCDHB17 0.87 0.65 -0.14 0.741 cg04550737 TBX15 0.74 0.26 -0.24 0.547 cg10222027 CSDAP1 NA NA NA NA cg04241652 FLJ42875 1.76 0.15 0.00 1.000 cg16171281 C18orf62 0.77 0.58 -0.08 0.843 cg07576142 GPC6 0.64 0.05 -0.29 0.474 cg11782635 KCNIP1 0.22 0.05 -0.52 0.155 cg27314998 PCDH7 0.29 0.01 -0.40 0.298 cg04965934 EMX1 0.66 0.36 -0.05 0.906 cg03418136 PRKXP1 NA NA NA NA cg16886987 DSCR6 3.62 0.00 0.54 0.137 cg23160016 GABRA2 NA NA NA NA cg21479226 C8orf34 0.58 0.27 -0.13 0.753 cg19291576 DMRT3 1.12 0.33 0.00 1.000 cg14636534 LINC00461 NA NA NA NA cg10130155 LOC401242 0.49 0.18 -0.24 0.556 cg01269620 PDE10A 0.69 0.42 -0.35 0.371 cg17073273 POU3F2 0.73 0.40 -0.26 0.514 cg14118515 EVX2 1.49 0.18 0.56 0.117 cg15335669 REREP3 NA NA NA NA cg10331073 APBB1IP 0.81 0.29 -0.13 0.760 cg20072171 FEZF2 0.68 0.35 -0.16 0.694 cg06951122 PCDHA8 1.09 0.33 0.00 1.000 cg18077971 PAX3 0.81 0.18 -0.36 0.361 cg00615537 ARHGEF37 NA NA NA NA  179 TargetID Gene Name Fold EOPET/CTL p-value Correlation with methylation p-value cg25599538 UCP1 1.22 0.65 -0.03 0.946 cg10196720 PCDH10 0.43 0.03 -0.34 0.392 cg16699715 ARHGEF37 NA NA NA NA cg01414185 HOXD4 1.34 0.31 0.53 0.151 cg26536949 DOC2B 1.33 0.46 0.48 0.197 cg09735723 SORCS3 0.75 0.43 0.27 0.510 cg02268229 UCP1 1.22 0.65 0.10 0.809 cg18204675 ARHGEF37 NA NA NA NA cg27262412 TBX15 0.74 0.26 -0.26 0.512 cg16492341 APBB1IP 0.81 0.29 -0.18 0.659 cg05864326 HOXD3 0.79 0.49 0.08 0.846 cg01729717 SLITRK1 1.01 0.95 0.20 0.631 cg24453699 BARHL2 NA NA NA NA cg12405754 EFNA5 NA NA NA NA cg19797516 APBB1IP 0.81 0.29 -0.14 0.725 cg05452524 HLF 1.87 0.13 0.45 0.236 cg25363445 ALX4 0.27 0.07 -0.63 0.070 cg10327440 CDC42BPA 0.72 0.27 -0.22 0.590 Supplementary Table 3.5: Gene expression information for 282 Candidate CpGs (249 Genes)  180 Term Description Count p-value Genes GO:0006355 regulation of transcription, DNA-dependent 43 4.35E-05 IRX4, HLF, FOXK1, ONECUT1, EVX2, FHL2, PAX3, DAXX, ZNF175, HOXA2, MLL5, CSDAP1, NKX2-1, POU3F2, BCL6, ZNF395, POU3F1, FOXB1, BHLHE40, ALX4, FOXD1, KLF5, TBX15, FOXL1, EPAS1, EMX1, DMRT3, BARHL2, ARID3A, TNP1, TEAD3, FOXJ3, MAFK, JUNB, INHBA, CSRNP1, HOXD3, EBF2, HIPK2, FOXG1, HOXD4, ID4, NCOR2 GO:0051252 regulation of RNA metabolic process 43 7.32E-05 IRX4, HLF, FOXK1, ONECUT1, EVX2, FHL2, PAX3, DAXX, ZNF175, HOXA2, MLL5, CSDAP1, NKX2-1, POU3F2, BCL6, ZNF395, POU3F1, FOXB1, BHLHE40, ALX4, FOXD1, KLF5, TBX15, FOXL1, EPAS1, EMX1, DMRT3, BARHL2, ARID3A, TNP1, TEAD3, FOXJ3, MAFK, JUNB, INHBA, CSRNP1, HOXD3, EBF2, HIPK2, FOXG1, HOXD4, ID4, NCOR2 GO:0030182 neuron differentiation 16 5.05E-04 PARD6B, EMX1, BARHL2, PAX3, DAPK3, NTRK3, SLITRK1, FEZF2, HOXA2, FOXG1, NKX2-1, POU3F2, EFNA5, ID4, UNC5C, NTM GO:0010628 positive regulation of gene expression 18 1.26E-03 KLF5, FOXK1, EPAS1, FHL2, TEAD3, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, POU3F1, ALX4 GO:0045941 positive regulation of transcription 17 2.39E-03 KLF5, FOXK1, EPAS1, FHL2, TEAD3, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4 GO:0010604 positive regulation of macromolecule metabolic process 22 3.09E-03 KLF5, FOXK1, EPAS1, BARHL2, FHL2, TEAD3, ANAPC11, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, ECE1, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, ITCH, POU3F1, ALX4 GO:0006928 cell motion 15 3.16E-03 FLT1, BARHL2, ATP1A4, TNP1, SPOCK1, PAX3, FEZF2, HOXA2, FOXG1, NKX2-1, SCARB1, EFNA5, POU3F2, UNC5C, FN1 GO:0045449 regulation of transcription 49 3.93E-03 TAF1B, HLF, FOXK1, EVX2, PAX3, DAXX, MLL5, PCGF3, CSDAP1, ZNF395, FOXB1, ALX4, TBX15, EMX1, DMRT3, BARHL2, FOXJ3, JUNB, INHBA, HOXD3, FOXG1, HOXD4, HIPK2, ERN1, IRX4, ONECUT1, FHL2, ZNF175, RGMB, HOXA2, NKX2-1, POU3F2, BCL6, POU3F1, BHLHE40, FOXD1, KLF5, FOXL1, NACC2, EPAS1, ARID3A, TNP1, TEAD3, MAFK, FEZF2, EBF2, CSRNP1, ID4, NCOR2 GO:0000904 cell morphogenesis involved in differentiation 10 4.13E-03 SLITRK1, FEZF2, PARD6B, HOXA2, FOXG1, NKX2-1, EFNA5, ANTXR1, UNC5C, FN1 GO:0010557 positive regulation of macromolecule biosynthetic process 18 4.30E-03 KLF5, FOXK1, EPAS1, BARHL2, FHL2, TEAD3, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4  181 Term Description Count p-value Genes GO:0008037 cell recognition 5 5.30E-03 CSGALNACT1, FEZF2, FOXG1, SCARB1, NTM GO:0006350 transcription 41 5.38E-03 TAF1B, HLF, FOXK1, ONECUT1, FHL2, PAX3, DAXX, ZNF175, HOXA2, MLL5, PCGF3, CSDAP1, NKX2-1, POU3F2, BCL6, ZNF395, POU3F1, FOXB1, BHLHE40, ALX4, FOXD1, KLF5, TBX15, FOXL1, EPAS1, DMRT3, BARHL2, ARID3A, TEAD3, FOXJ3, MAFK, JUNB, FEZF2, CSRNP1, HOXD3, EBF2, HIPK2, FOXG1, HOXD4, ERN1, NCOR2 GO:0006357 regulation of transcription from RNA polymerase II promoter 19 5.49E-03 FOXK1, EPAS1, ONECUT1, BARHL2, FHL2, TEAD3, PAX3, JUNB, INHBA, HOXA2, CSDAP1, CSRNP1, HIPK2, NKX2-1, POU3F2, BCL6, ID4, ALX4, NCOR2 GO:0048704 embryonic skeletal system morphogenesis 5 6.02E-03 HOXA2, TBX15, HOXD3, HOXD4, ALX4 GO:0000902 cell morphogenesis 12 6.10E-03 SLITRK1, FEZF2, PARD6B, HOXA2, ONECUT1, FOXG1, NKX2-1, EFNA5, BCL6, ANTXR1, UNC5C, FN1 GO:0045935 positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 17 6.32E-03 KLF5, FOXK1, EPAS1, FHL2, TEAD3, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4 GO:0003006 reproductive developmental process 10 6.59E-03 CGA, INHBA, SDC1, CSDAP1, DMRT3, DDX25, MSH4, NKX2-1, TNP1, JUNB GO:0048568 embryonic organ development 8 6.63E-03 HOXA2, TBX15, EPAS1, HOXD3, HOXD4, FOXG1, ALX4, JUNB GO:0031328 positive regulation of cellular biosynthetic process 18 6.77E-03 KLF5, FOXK1, EPAS1, BARHL2, FHL2, TEAD3, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4  182 Term Description Count p-value Genes GO:0045664 regulation of neuron differentiation 7 7.23E-03 NTRK3, HOXA2, HOXD3, FOXG1, BARHL2, POU3F2, ID4 GO:0009891 positive regulation of biosynthetic process 18 7.77E-03 KLF5, FOXK1, EPAS1, BARHL2, FHL2, TEAD3, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4 GO:0045893 positive regulation of transcription, DNA-dependent 14 8.32E-03 FOXK1, EPAS1, FHL2, PAX3, TEAD3, JUNB, INHBA, MLL5, HOXA2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4 GO:0051173 positive regulation of nitrogen compound metabolic process 17 8.45E-03 KLF5, FOXK1, EPAS1, FHL2, TEAD3, PAX3, JUNB, INHBA, MLL5, RGMB, HOXA2, EBF2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4 GO:0051254 positive regulation of RNA metabolic process 14 8.89E-03 FOXK1, EPAS1, FHL2, PAX3, TEAD3, JUNB, INHBA, MLL5, HOXA2, CSRNP1, HIPK2, NKX2-1, POU3F2, ALX4 GO:0007584 response to nutrient 7 9.20E-03 MLL5, DUSP1, CYP11A1, STC2, AACS, TIMP3, MEST GO:0030878 thyroid gland development 3 9.86E-03 CGA, HOXD3, NKX2-1 Supplementary Table 3.6: List of all Gene Ontology categories that are significant for p<0.01 for 282 candidate genes using DAVID 183  Supplementary Figure 3.1: Principal component analysis using all valid probes   184 Appendix C  : Supplementary material for chapter 4 TargetID Gene Name Chr Location Avg. ? CPM16 Avg. ? CTL ?? cg23731272 SMAD3 15 67356838 0.27 0.75 -0.48 cg02925367 PRRT1 6 32116905 0.70 0.24 0.45 cg25432336 RHOB 2 20648580 0.73 0.30 0.43 cg16709294 SFRS5 14 70235567 0.79 0.36 0.43 cg02486855 SMAD3 15 67356942 0.21 0.63 -0.42 cg08544192 ZIC4 3 147656523 0.67 0.26 0.41 cg14644001 PRRT1 6 32116653 0.63 0.23 0.40 cg04364261 c2orf62 2 219233650 0.18 0.58 -0.40 cg06627617 ASAP2 2 9471179 0.42 0.81 -0.39 cg11238995 SYNPR 3 63295625 0.23 0.62 -0.39 cg16258854 RHOB 2 20648194 0.65 0.26 0.39 cg19192280 PRRT1 6 32116893 0.74 0.36 0.38 cg05719164 LHX4 1 180204221 0.54 0.17 0.37 cg08750028 RNF41 12 56616161 0.16 0.53 -0.37 cg06611109 ZIC4 3 147680482 0.73 0.37 0.36 cg04846203 MPZL1 1 167690438 0.21 0.57 -0.36 cg13575271 DPP7 9 140008980 0.44 0.08 0.36 cg12886634 RNF41 12 56616029 0.09 0.45 -0.36 cg13916516 EXD3 9 140268774 0.72 0.36 0.36 cg14757228 PRRT1 6 32116858 0.85 0.49 0.36 cg27436995 FBXL16 16 743998 0.52 0.16 0.35 cg14531663 PRRT1 6 32116933 0.83 0.48 0.35 cg26353296 USP4 3 49378124 0.34 0.69 -0.35 cg25948255 CADM2 3 85512134 0.78 0.44 0.35 cg17735983 MZF1 19 59074482 0.49 0.15 0.34 cg15262954 PRIC285 20 62198872 0.59 0.25 0.34 cg06546806 GBX2 2 237078704 0.57 0.23 0.34 cg16896687 CRYL1 13 20966332 0.41 0.75 -0.34 cg13105599 SMAD3 15 67356641 0.27 0.60 -0.34 cg22379708 AGRN 1 982918 0.81 0.47 0.34 cg23999861 DPP7 9 140009395 0.40 0.06 0.33 cg17171215 GBX2 2 237078223 0.61 0.28 0.33 cg12157614 AGAP1 2 236890427 0.37 0.70 -0.33 cg22466425 HSPA1L 6 31782711 0.60 0.26 0.33 cg07229186 MT1M 16 56666575 0.53 0.20 0.33 cg23279756 ARMC2 6 109159348 0.53 0.86 -0.33 cg01943577 WDR60 7 158741284 0.76 0.43 0.33 cg05552874 IFIT1 10 91153143 0.33 0.66 -0.33 cg17165303 AGRN 1 982308 0.84 0.52 0.33 cg16080427 FKBP10 17 39975631 0.88 0.56 0.33 cg00335735 EPHA4 2 222355484 0.31 0.63 -0.32 cg07530063 DEGS2 14 100613692 0.77 0.45 0.32 cg22627753 AGRN 1 988623 0.94 0.62 0.32 cg26140007 ADAMTS6 5 64395544 0.48 0.80 -0.32 cg11316146 LOC100216546 7 104624178 0.58 0.26 0.32 cg26010734 EPHX3 19 15344046 0.64 0.32 0.32 cg13356427 ESPN 1 6520354 0.49 0.17 0.32  185 TargetID Gene Name Chr Location Avg. ? CPM16 Avg. ? CTL ?? cg02717454 CREBBP 16 3928799 0.68 0.36 0.32 cg00909062 ID4 6 19835100 0.44 0.76 -0.32 cg03020181 CADM2 3 85938637 0.79 0.48 0.32 cg01106989 GSTA4 6 52858459 0.65 0.34 0.32 cg16396866 MCTP1 5 94616672 0.42 0.74 -0.32 cg14139931 EXD3 9 140268639 0.88 0.57 0.32 cg07166333 WDR60 7 158741244 0.70 0.39 0.31 cg01610979 SH3TC1 4 8193310 0.15 0.47 -0.31 cg07699351 FAN1 15 31175400 0.55 0.87 -0.31 cg00785831 ABCA2 9 139913436 0.92 0.61 0.31 cg21363050 PDGFRA 4 55144066 0.20 0.51 -0.31 cg22847691 HSPA1A 6 31783344 0.49 0.17 0.31 cg10598353 HSPA1A 6 31783466 0.48 0.17 0.31 cg03393602 ABHD3 18 19285336 0.56 0.25 0.31 cg23006567 ZNF813 19 53966842 0.86 0.56 0.31 cg06786804 VWA1 1 1377938 0.84 0.53 0.31 cg01336390 HCG4B 6 29895059 0.56 0.25 0.31 cg18243760 PRRT1 6 32116780 0.85 0.55 0.31 cg00177797 LHX4 1 180203837 0.57 0.27 0.30 cg04933829  14 84178956 0.77 0.46 0.30 cg16854917 NCRNA00085 19 52206691 0.60 0.29 0.30 cg08447733 MZF1 19 59074308 0.50 0.20 0.30 cg17494781 HSPA1A 6 31783482 0.47 0.17 0.30 cg10903903 tRNA_val 6 27647843 0.48 0.18 0.30 cg00773902 GDF1 19 19007324 0.47 0.17 0.30 cg03085549 LDHD 16 75150819 0.32 0.62 -0.30 cg14280283 ABCA2 9 139903861 0.93 0.63 0.29 cg19999567 C1orf159 1 1022900 0.69 0.40 0.29 cg25352836 TSSK6 19 19625264 0.42 0.13 0.29 cg17598339 SSB 2 170624727 0.70 0.41 0.29 cg04579966 SPATA13 13 24846221 0.25 0.54 -0.29 cg22508391 LOC100216546 7 104624890 0.58 0.29 0.29 cg17186760 HSPA1L 6 31782374 0.39 0.10 0.29 cg26337070 ATOH8 2 85999873 0.23 0.52 -0.29 cg11707067 MZF1 19 59073430 0.74 0.45 0.29 cg22419075 DPP7 9 140008995 0.42 0.13 0.29 cg24812442 GBX2 2 237079690 0.62 0.34 0.29 cg27067781 PRRT1 6 32116853 0.83 0.54 0.29 cg07172280 DPP7 9 140009455 0.51 0.22 0.29 cg12905114 DPP7 9 140008784 0.49 0.21 0.29 cg13139843 KRTAP19-1 21 31852793 0.71 0.42 0.29 cg15931205 HCG4P6 6 29894820 0.36 0.08 0.29 cg25916135 FLJ30838 2 59071761 0.34 0.62 -0.28 cg17626960 PRRT1 6 32116918 0.85 0.57 0.28 cg05181301 ASAP2 2 9459596 0.58 0.86 -0.28 cg22841810 EBF2 8 25902284 0.42 0.13 0.28 cg18846140 HSPA1A 6 31783000 0.35 0.07 0.28 cg13413286 HSPA1A 6 31783468 0.53 0.24 0.28 cg00381745 ZNF394 7 99096450 0.68 0.40 0.28  186 TargetID Gene Name Chr Location Avg. ? CPM16 Avg. ? CTL ?? cg24867302 KIT 4 55534703 0.50 0.78 -0.28 cg09874271 LOC100216546 7 104624356 0.65 0.37 0.28 cg08216000 ABCA2 9 139906382 0.85 0.57 0.28 cg13710542 EXD3 9 140261971 0.89 0.61 0.28 Supplementary Table 4.1: 100 CpGsites with the largest ?? and FDR <0.01 between CPM16 and 3rd trimester controls  187 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg26625897 KRT15 17 39678679 0.54 0.61 0.80 -0.19 0.00 N.S. 0.57 0.66 -0.09 N.S. cg00592695 ST3GAL1 8 134545685 0.56 0.73 0.81 -0.08 0.00 0.00 0.67 0.78 -0.12 0.01 cg12436772 FN1 2 216402384 0.50 0.48 0.75 -0.26 0.00 N.S. 0.41 0.56 -0.15 0.05 cg17252884 SH3BP5 3 15369681 0.39 0.39 0.59 -0.20 0.00 N.S. 0.34 0.44 -0.10 N.S. cg18236464 PAPPA2 1 176528783 0.31 0.34 0.52 -0.18 0.00 N.S. 0.27 0.40 -0.14 N.S. cg20971407 BHLHE40 3 5022392 0.40 0.48 0.59 -0.12 0.00 0.01 0.46 0.49 -0.03 N.S. cg01924561 SLC2A1 1 43416103 0.35 0.41 0.54 -0.13 0.00 0.03 0.38 0.44 -0.06 N.S. cg15831875 SMYD3 1 245986694 0.58 0.56 0.77 -0.21 0.00 N.S. 0.54 0.58 -0.04 N.S. cg25245338 TIMP3 22 33196112 0.47 0.52 0.65 -0.13 0.00 N.S. 0.43 0.62 -0.19 0.03 cg11079619 INHBA 7 41742630 0.45 0.48 0.64 -0.16 0.00 N.S. 0.45 0.51 -0.06 0.04 cg13754437 FHL2 2 106016110 0.57 0.62 0.76 -0.14 0.00 N.S. 0.59 0.65 -0.06 N.S. cg23730027 FLNB 3 57995180 0.26 0.45 0.45 0.00 N.S. 0.00 0.42 0.47 -0.05 N.S. cg09365002 DAXX 6 33288329 0.41 0.38 0.60 -0.21 0.01 N.S. 0.45 0.32 0.13 N.S. cg22234930 PKM2 15 72519739 0.58 0.58 0.76 -0.18 0.00 N.S. 0.50 0.66 -0.16 N.S. cg02766259 AACS 12 125626809 0.39 0.45 0.57 -0.12 0.01 N.S. 0.44 0.46 -0.03 N.S. cg14854503 CSGALNACT1 8 19540272 0.51 0.49 0.69 -0.20 0.00 N.S. 0.45 0.53 -0.08 N.S. cg00345704 KRTAP2-3 17 39216495 0.56 0.67 0.74 -0.07 0.04 0.00 0.63 0.71 -0.08 N.S. cg03983223 WIPF1 2 175499283 0.42 0.38 0.59 -0.21 0.00 N.S. 0.37 0.40 -0.03 N.S. cg22996170 JUNB 19 12895529 0.50 0.58 0.68 -0.09 0.00 N.S. 0.58 0.58 0.00 N.S. cg19417526 NTM 11 131895599 0.35 0.55 0.52 0.03 N.S. 0.00 0.64 0.46 0.18 N.S. cg19883388 AZIN1 8 103909930 0.52 0.66 0.69 -0.03 N.S. 0.00 0.62 0.70 -0.08 N.S. cg14143441 ST3GAL1 8 134387493 0.63 0.66 0.80 -0.14 0.00 N.S. 0.62 0.69 -0.06 N.S. cg01228410 SLC9A3 5 493893 0.48 0.74 0.65 0.09 N.S. 0.00 0.69 0.79 -0.10 N.S. cg19140548 SH3PXD2A 10 105552406 0.55 0.62 0.72 -0.10 0.00 0.04 0.58 0.65 -0.07 N.S. cg07950244 CSRP1 1 201448731 0.70 0.75 0.87 -0.12 0.00 N.S. 0.72 0.77 -0.05 N.S. cg10668363 ZNF175 19 52076691 0.58 0.80 0.75 0.05 0.03 0.00 0.77 0.83 -0.05 N.S. cg10893014 TEAD3 6 35461236 0.41 0.51 0.58 -0.07 0.04 0.01 0.46 0.56 -0.10 0.05 cg18672030 RELL1 4 37668528 0.23 0.26 0.39 -0.13 0.01 N.S. 0.26 0.25 0.01 N.S. cg02770406 FLNB 3 57991450 0.62 0.61 0.79 -0.18 0.00 N.S. 0.58 0.63 -0.06 N.S.  188 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg03728457 CCDC33 15 74619595 0.58 0.62 0.75 -0.12 0.00 N.S. 0.60 0.64 -0.04 N.S. cg03777414 FAM18A 16 10868974 0.48 0.60 0.65 -0.05 0.02 0.00 0.59 0.61 -0.02 N.S. cg22193385 KRT7 12 52638005 0.50 0.52 0.67 -0.15 0.00 N.S. 0.50 0.53 -0.02 N.S. cg20352351 NCOR2 12 124870274 0.69 0.75 0.86 -0.12 0.00 N.S. 0.72 0.77 -0.05 N.S. cg10246581 CMIP 16 81536171 0.45 0.44 0.61 -0.18 0.00 N.S. 0.41 0.47 -0.06 0.04 cg12632411 RRBP1 20 17595355 0.33 0.46 0.49 -0.03 N.S. 0.00 0.38 0.55 -0.17 0.02 cg17417693 CLIP4 2 29335996 0.47 0.56 0.64 -0.08 0.03 0.01 0.55 0.56 -0.01 N.S. cg01180628 BHLHE40 3 5023394 0.40 0.48 0.57 -0.09 0.00 0.00 0.46 0.50 -0.03 N.S. cg26509870 PHYHIP 8 22076166 0.63 0.72 0.79 -0.07 0.00 0.00 0.70 0.75 -0.05 N.S. cg17115419 CALML3 10 5593926 0.61 0.66 0.77 -0.12 0.00 N.S. 0.59 0.73 -0.14 0.01 cg03822934 LIMCH1 4 41505964 0.66 0.61 0.82 -0.21 0.00 N.S. 0.59 0.63 -0.04 N.S. cg14704980 INHBA 7 41607500 0.60 0.58 0.76 -0.18 0.00 N.S. 0.52 0.64 -0.12 N.S. cg16568084 COL1A2 7 93811563 0.38 0.43 0.54 -0.11 0.00 N.S. 0.41 0.46 -0.06 N.S. cg26651514 STARD13 13 33864734 0.62 0.63 0.78 -0.15 0.00 N.S. 0.59 0.68 -0.09 0.01 cg17850498 ECE1 1 21565755 0.72 0.69 0.88 -0.19 0.00 N.S. 0.63 0.76 -0.14 0.02 cg25592413 STC2 5 172753646 0.25 0.54 0.41 0.13 0.00 0.00 0.53 0.54 -0.01 N.S. cg11355603 HIPK2 7 139256247 0.66 0.66 0.82 -0.16 0.00 N.S. 0.63 0.69 -0.06 N.S. cg04897892 CMIP 16 81564314 0.53 0.54 0.69 -0.14 0.00 N.S. 0.50 0.58 -0.08 0.03 cg05971934 NTRK3 15 88307922 0.38 0.48 0.53 -0.05 N.S. 0.02 0.55 0.42 0.13 N.S. cg12779575 ERN1 17 62208434 0.65 0.65 0.81 -0.16 0.00 N.S. 0.58 0.72 -0.15 0.00 cg27182012 LMNA 1 156095157 0.66 0.67 0.81 -0.14 0.00 N.S. 0.62 0.73 -0.10 0.02 cg00736681 ST3GAL1 8 134546052 0.43 0.60 0.59 0.02 N.S. 0.00 0.55 0.66 -0.11 0.00 cg16301004 ITPRIP 10 106082537 0.32 0.37 0.48 -0.11 0.00 N.S. 0.37 0.36 0.01 N.S. cg10187713 PARD6B 20 49345687 0.66 0.72 0.82 -0.09 0.00 0.04 0.70 0.75 -0.04 N.S. cg24097814 KRT8 12 53320632 0.47 0.52 0.62 -0.10 0.00 N.S. 0.50 0.53 -0.03 N.S. cg10994126 PAPPA2 1 176432498 0.46 0.54 0.61 -0.08 0.00 0.01 0.50 0.57 -0.07 N.S. cg23843484 MAFK 7 1560248 0.42 0.46 0.57 -0.11 0.00 N.S. 0.47 0.45 0.02 N.S. cg03339817 SFT2D3 2 128458281 0.63 0.61 0.78 -0.17 0.00 N.S. 0.56 0.66 -0.10 N.S. cg08900316 PRKCE 2 46301083 0.41 0.43 0.56 -0.13 0.00 N.S. 0.41 0.45 -0.04 N.S.  189 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg24204017 FLNB 3 57999988 0.32 0.40 0.47 -0.07 0.00 0.00 0.37 0.44 -0.06 N.S. cg10586672 SLC6A6 3 14514657 0.46 0.49 0.61 -0.12 0.00 N.S. 0.44 0.54 -0.11 0.04 cg07351322 MLL5 7 104738380 0.34 0.36 0.49 -0.13 0.00 N.S. 0.34 0.39 -0.05 N.S. cg22514112 IRX4 5 1859764 0.57 0.75 0.72 0.03 N.S. 0.00 0.84 0.67 0.18 0.00 cg13553455 COL17A1 10 105846002 0.32 0.30 0.47 -0.16 0.00 N.S. 0.27 0.33 -0.06 N.S. cg09182455 CORO1C 12 109116994 0.55 0.62 0.70 -0.08 0.02 0.05 0.59 0.66 -0.06 N.S. cg01713086 ZNF395 8 28268413 0.28 0.32 0.43 -0.11 0.00 N.S. 0.28 0.36 -0.07 N.S. cg15282973 SCARB1 12 125346253 0.50 0.60 0.65 -0.04 N.S. 0.00 0.55 0.66 -0.11 0.01 cg03764092 TBXAS1 7 139690853 0.65 0.70 0.80 -0.10 0.00 N.S. 0.64 0.75 -0.11 N.S. cg15887927 FLT1 13 29148952 0.46 0.50 0.61 -0.11 0.00 N.S. 0.48 0.52 -0.04 N.S. cg03099780 MBNL2 13 97864475 0.41 0.45 0.56 -0.11 0.00 N.S. 0.38 0.53 -0.15 0.00 cg14605117 GNA12 7 2766214 0.67 0.67 0.82 -0.15 0.00 N.S. 0.63 0.72 -0.09 0.04 cg16606561 FAM110A 20 824641 0.65 0.75 0.80 -0.05 0.02 0.00 0.69 0.80 -0.11 0.02 cg26585416 DAPK3 19 3969745 0.50 0.43 0.65 -0.22 0.00 0.02 0.42 0.44 -0.03 N.S. cg25298189 ARID3A 19 935259 0.16 0.21 0.31 -0.09 0.00 0.02 0.17 0.26 -0.09 0.00 cg24431161 TBXAS1 7 139484351 0.65 0.69 0.79 -0.11 0.00 N.S. 0.66 0.71 -0.05 N.S. cg04981492 SYDE1 19 15218713 0.36 0.43 0.51 -0.08 0.00 0.02 0.40 0.46 -0.06 N.S. cg15903956 CYP11A1 15 74676231 0.56 0.59 0.71 -0.12 0.00 N.S. 0.56 0.62 -0.06 N.S. cg07981495 CGA 6 87804765 0.49 0.61 0.64 -0.03 N.S. 0.00 0.65 0.58 0.07 N.S. cg20576064 FAM160B2 8 21948560 0.52 0.58 0.67 -0.08 0.01 N.S. 0.57 0.60 -0.03 N.S. cg03653726 GNA12 7 2769253 0.42 0.47 0.57 -0.10 0.00 N.S. 0.42 0.53 -0.11 0.02 cg12647920 CORO1C 12 109144744 0.60 0.71 0.75 -0.04 0.04 0.00 0.66 0.75 -0.10 0.01 cg26813604 ERGIC1 5 172256357 0.66 0.69 0.81 -0.12 0.00 N.S. 0.64 0.75 -0.11 0.01 cg22626683 TNFSF18 1 172903051 0.36 0.38 0.51 -0.13 0.00 N.S. 0.37 0.39 -0.02 N.S. cg20317872 DENND2D 1 111743202 0.57 0.63 0.71 -0.08 0.00 0.05 0.59 0.67 -0.09 N.S. cg06837325 RGMB 5 97445281 0.42 0.41 0.56 -0.15 0.00 N.S. 0.39 0.43 -0.04 N.S. cg10108710 TTC7B 14 90990086 0.64 0.64 0.79 -0.15 0.00 N.S. 0.60 0.68 -0.08 0.01 cg11811391 HTR1D 1 23520083 0.58 0.64 0.72 -0.08 0.00 0.04 0.62 0.67 -0.05 N.S. cg02029908 DUSP1 5 172195602 0.56 0.64 0.71 -0.07 0.01 0.01 0.62 0.66 -0.04 N.S.  190 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg23400222 GRAMD3 5 124672706 0.60 0.60 0.74 -0.14 0.00 N.S. 0.57 0.64 -0.07 N.S. cg21245975 C1orf52 1 85725523 0.10 0.09 0.25 -0.16 N.S. 0.01 0.09 0.09 0.00 N.S. cg10047173 ART4 12 14996587 0.43 0.50 0.57 -0.07 0.03 0.04 0.45 0.55 -0.10 0.02 cg09394306 LAPTM4A 2 20330742 0.66 0.73 0.81 -0.08 0.00 0.01 0.69 0.77 -0.08 N.S. cg27328839 DLG5 10 79679462 0.57 0.62 0.71 -0.09 0.00 N.S. 0.58 0.65 -0.06 N.S. cg11327657 FAM207A 21 46388162 0.33 0.43 0.47 -0.04 N.S. 0.01 0.41 0.46 -0.06 N.S. cg00713022 SYDE1 19 15215943 0.61 0.67 0.75 -0.08 0.00 N.S. 0.64 0.71 -0.07 N.S. cg05452692 LINC00284 13 44642515 0.63 0.67 0.77 -0.10 0.00 N.S. 0.62 0.73 -0.11 N.S. cg23677911 GALNT2 1 230256394 0.60 0.59 0.74 -0.15 0.00 N.S. 0.55 0.63 -0.08 0.05 cg18474072 DIP2C 10 664762 0.39 0.35 0.53 -0.17 0.00 N.S. 0.26 0.45 -0.19 0.00 cg25032603 LINC00310 21 35554811 0.60 0.63 0.74 -0.10 0.00 N.S. 0.61 0.66 -0.05 N.S. cg00496126 NEBL 10 21569823 0.69 0.73 0.83 -0.11 0.00 N.S. 0.67 0.78 -0.11 0.02 cg15765546 SLCO2A1 3 133776357 0.44 0.48 0.58 -0.10 0.00 N.S. 0.45 0.51 -0.06 N.S. cg13547665 POLE4 2 75229455 0.48 0.56 0.62 -0.06 0.01 0.01 0.50 0.62 -0.12 0.01 cg08799766 GNA12 7 2801832 0.51 0.53 0.65 -0.12 0.00 N.S. 0.47 0.59 -0.12 0.01 cg12361046 CHSY1 15 101753877 0.51 0.61 0.65 -0.04 N.S. 0.01 0.57 0.66 -0.10 0.03 cg23963517 BICC1 10 60473872 0.55 0.56 0.69 -0.13 0.00 N.S. 0.52 0.60 -0.08 N.S. cg04038163 CXCL9 4 76925155 0.49 0.66 0.62 0.04 N.S. 0.00 0.60 0.73 -0.14 N.S. cg12792952 CACHD1 1 64857387 0.63 0.61 0.77 -0.16 0.00 N.S. 0.61 0.62 -0.01 N.S. cg18743287 RDH13 19 55575837 0.47 0.48 0.60 -0.13 0.00 N.S. 0.42 0.54 -0.12 0.00 cg12261055 INHBA 7 41860801 0.42 0.48 0.56 -0.08 0.01 0.02 0.49 0.47 0.02 N.S. cg07158065 MIR3150A 8 96084821 0.54 0.58 0.68 -0.10 0.00 N.S. 0.54 0.61 -0.07 N.S. cg05521767 GDPD5 11 75230135 0.70 0.71 0.84 -0.13 0.00 N.S. 0.67 0.75 -0.08 N.S. cg11637968 FNIP2 4 159732036 0.52 0.51 0.65 -0.15 0.00 N.S. 0.48 0.53 -0.05 N.S. cg16282339 PTPRJ 11 47927019 0.45 0.54 0.59 -0.05 N.S. 0.04 0.50 0.58 -0.08 N.S. cg19861486 STK24 13 99231630 0.42 0.48 0.55 -0.08 0.00 0.03 0.43 0.52 -0.09 0.02 cg14384014 ID4 6 19670306 0.55 0.51 0.68 -0.18 0.00 N.S. 0.50 0.51 -0.01 N.S. cg21823502 ANAPC11 17 79856430 0.32 0.45 0.46 -0.01 N.S. 0.00 0.44 0.46 -0.03 N.S. cg05131266 C14orf159 14 91591888 0.57 0.53 0.71 -0.18 0.00 N.S. 0.51 0.56 -0.05 N.S. cg12979992 INSIG1 7 155100559 0.73 0.77 0.86 -0.09 0.00 N.S. 0.76 0.79 -0.02 N.S.  191 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg00415333 IL22RA2 6 137493017 0.62 0.55 0.76 -0.21 0.00 0.01 0.53 0.58 -0.06 0.04 cg08018143 FSTL5 4 162110754 0.28 0.38 0.42 -0.04 N.S. 0.02 0.38 0.38 0.00 N.S. cg19674091 IDH2 15 90643766 0.42 0.41 0.55 -0.14 0.00 N.S. 0.31 0.51 -0.20 0.00 cg15129144 EPAS1 2 46527958 0.45 0.54 0.58 -0.04 N.S. 0.00 0.51 0.58 -0.07 0.02 cg03752885 DAPK3 19 3969736 0.49 0.42 0.63 -0.21 0.00 0.00 0.39 0.44 -0.05 N.S. cg20421295 NGEF 2 233749913 0.60 0.76 0.74 0.02 N.S. 0.02 0.76 0.75 0.02 N.S. cg03024478 UNC5C 4 96090330 0.44 0.51 0.58 -0.07 0.01 N.S. 0.48 0.53 -0.05 N.S. cg18190824 ANKH 5 14721026 0.65 0.67 0.78 -0.11 0.00 N.S. 0.63 0.72 -0.09 0.02 cg03387092 MYLK 3 123493248 0.42 0.40 0.56 -0.16 0.00 N.S. 0.36 0.44 -0.08 N.S. cg05364179 PCGF3 4 760039 0.50 0.56 0.63 -0.07 0.01 0.01 0.51 0.61 -0.10 0.01 cg00622655 DOPEY2 21 37667639 0.66 0.74 0.80 -0.05 0.01 0.00 0.71 0.78 -0.07 N.S. cg02494582 ADAM12 10 128061835 0.42 0.53 0.55 -0.02 N.S. 0.00 0.48 0.59 -0.11 0.03 cg04858987 SH3BP5 3 15359338 0.72 0.77 0.85 -0.09 0.00 N.S. 0.73 0.80 -0.08 0.04 cg24617203 IL1R2 2 102623381 0.53 0.60 0.66 -0.06 0.00 0.01 0.56 0.64 -0.08 0.02 cg27307465 JAK1 1 65472802 0.63 0.67 0.77 -0.09 0.00 N.S. 0.63 0.72 -0.09 N.S. cg01723031 TBC1D1 4 37975326 0.58 0.62 0.71 -0.10 0.00 N.S. 0.58 0.66 -0.08 N.S. cg16616993 RHOBTB3 5 95129519 0.43 0.53 0.57 -0.04 N.S. 0.00 0.50 0.56 -0.05 N.S. cg25103772 PAPPA2 1 176513705 0.43 0.44 0.56 -0.12 0.00 N.S. 0.39 0.50 -0.11 N.S. cg07776698 CPED1 7 120903952 0.57 0.60 0.71 -0.11 0.01 N.S. 0.54 0.65 -0.10 N.S. cg26166804 H2AFY2 10 71812612 0.19 0.20 0.32 -0.12 N.S. N.S. 0.15 0.25 -0.10 N.S. cg20340720 WBP1L 10 104512523 0.44 0.50 0.58 -0.08 0.01 N.S. 0.46 0.53 -0.08 N.S. cg16090790 MARCKS 6 113674867 0.58 0.57 0.71 -0.14 0.00 N.S. 0.52 0.62 -0.10 0.05 cg17966362 MSH4 1 76261799 0.53 0.60 0.66 -0.06 0.02 0.01 0.57 0.64 -0.08 N.S. cg00834537 APOPT1 14 104047751 0.59 0.57 0.72 -0.15 0.00 N.S. 0.53 0.60 -0.06 N.S. cg26366616 PDLIM2 8 22439831 0.30 0.37 0.43 -0.07 0.00 0.00 0.33 0.40 -0.06 0.03 cg18564881 PPFIA1 11 70091067 0.42 0.48 0.55 -0.07 0.00 0.02 0.45 0.52 -0.07 0.03 cg13062627 CSRNP1 3 39208314 0.52 0.59 0.65 -0.07 0.03 0.04 0.57 0.60 -0.03 N.S. cg01288184 CABLES1 18 20811408 0.46 0.50 0.60 -0.10 0.00 N.S. 0.49 0.50 -0.01 N.S. cg21913652 TRIM44 11 35651841 0.35 0.42 0.48 -0.06 N.S. 0.04 0.39 0.46 -0.06 N.S.  192 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg04098985 EXTL2 1 101358409 0.59 0.66 0.72 -0.06 0.03 0.02 0.60 0.71 -0.10 0.01 cg12804791 ST3GAL4 11 126286828 0.24 0.22 0.37 -0.15 0.00 N.S. 0.15 0.30 -0.15 0.04 cg22860917 GALM 2 38919351 0.54 0.59 0.67 -0.08 0.00 N.S. 0.57 0.62 -0.05 N.S. cg05663031 BCL6 3 187453721 0.43 0.42 0.56 -0.14 0.00 N.S. 0.36 0.48 -0.12 0.01 cg01839603 GNA12 7 2801424 0.42 0.47 0.55 -0.08 0.02 N.S. 0.43 0.52 -0.09 N.S. cg02681842 PLEC1 8 145033310 0.52 0.57 0.65 -0.08 0.00 N.S. 0.54 0.60 -0.07 N.S. cg27479162 PIK3AP1 10 98450737 0.47 0.47 0.60 -0.13 0.00 N.S. 0.47 0.48 0.00 N.S. cg22572071 GPR110 6 47074382 0.47 0.57 0.60 -0.04 N.S. 0.01 0.49 0.64 -0.15 0.02 cg12833765 ANTXR1 2 69470878 0.48 0.46 0.61 -0.15 0.00 N.S. 0.42 0.50 -0.09 N.S. cg25500616 ARHGEF4 2 131800227 0.51 0.58 0.63 -0.05 0.05 0.01 0.55 0.61 -0.05 N.S. cg14072140 DPYD 1 98031839 0.42 0.49 0.55 -0.06 N.S. N.S. 0.52 0.45 0.07 N.S. cg17843487 EFHD2 1 15756557 0.66 0.76 0.79 -0.03 N.S. 0.00 0.72 0.79 -0.07 N.S. cg03730249 FOXJ3 1 42692013 0.51 0.61 0.63 -0.02 N.S. 0.00 0.56 0.67 -0.10 0.04 cg10007534 C21orf70 21 46378354 0.57 0.61 0.70 -0.09 N.S. N.S. 0.60 0.62 -0.02 N.S. cg25740652 LIMCH1 4 41361623 0.64 0.71 0.77 -0.06 0.01 0.01 0.68 0.75 -0.07 0.03 cg13467459 KIAA1614 1 180919564 0.50 0.54 0.63 -0.09 0.00 N.S. 0.51 0.57 -0.06 N.S. cg19694404 C1orf98 1 200306620 0.58 0.54 0.70 -0.16 0.00 N.S. 0.52 0.56 -0.04 N.S. cg21906866 C14orf159 14 91592679 0.51 0.56 0.64 -0.07 0.01 N.S. 0.54 0.59 -0.06 N.S. cg26188621 VDAC2 10 76983413 0.44 0.53 0.56 -0.03 N.S. 0.00 0.52 0.55 -0.03 N.S. cg13467628 FAM150B 2 365559 0.36 0.49 0.49 0.00 N.S. 0.00 0.44 0.54 -0.10 N.S. cg06480942 STX1A 7 73116029 0.53 0.51 0.66 -0.15 0.00 N.S. 0.46 0.56 -0.10 N.S. cg17464043 KRT39 17 39115451 0.66 0.68 0.79 -0.11 0.00 N.S. 0.66 0.70 -0.04 N.S. cg06665333 SLC7A5 16 87873837 0.22 0.28 0.35 -0.07 0.01 0.01 0.29 0.26 0.03 N.S. cg11034318 ZNF775 7 150097654 0.52 0.53 0.65 -0.11 0.00 N.S. 0.50 0.56 -0.06 N.S. cg21560697 TAF1B 2 10054883 0.48 0.52 0.60 -0.09 0.00 N.S. 0.51 0.52 -0.02 N.S. cg04413904 SSTR5 16 1088479 0.45 0.65 0.57 0.08 N.S. 0.00 0.56 0.74 -0.17 N.S. cg22344841 SDC1 2 20421656 0.48 0.53 0.61 -0.08 0.00 N.S. 0.51 0.55 -0.04 N.S. cg21496948 KLC1 14 104153614 0.36 0.43 0.49 -0.05 N.S. N.S. 0.44 0.42 0.02 N.S. cg26877565 LY6G6C 6 31689610 0.55 0.63 0.68 -0.05 N.S. 0.01 0.61 0.65 -0.03 N.S.  193 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg02766770 YPEL5 2 30372795 0.31 0.37 0.43 -0.07 0.01 0.02 0.33 0.41 -0.08 N.S. cg25161868 CACNA2D1 7 81579463 0.42 0.47 0.54 -0.07 0.05 N.S. 0.45 0.50 -0.05 N.S. cg04103490 HIST3H3 1 228614118 0.58 0.67 0.70 -0.03 N.S. 0.00 0.65 0.70 -0.05 N.S. cg11622516 FSTL1 3 120165600 0.51 0.58 0.63 -0.06 N.S. 0.04 0.56 0.60 -0.04 N.S. cg04573500 MSI2 17 55444427 0.37 0.45 0.49 -0.04 N.S. 0.01 0.39 0.52 -0.13 0.04 cg17082405 PLEC1 8 145033328 0.60 0.65 0.72 -0.07 0.00 0.05 0.63 0.67 -0.04 N.S. cg16677191 GLRX 5 95159423 0.50 0.44 0.62 -0.17 0.00 0.05 0.43 0.46 -0.03 N.S. cg15429134 LOH12CR1 12 12619103 0.70 0.77 0.82 -0.05 0.03 0.03 0.74 0.79 -0.05 N.S. cg26072254 KIF26B 1 245710355 0.37 0.40 0.48 -0.09 0.03 N.S. 0.37 0.42 -0.05 N.S. cg16104384 c22orf34 22 49935689 0.53 0.62 0.65 -0.03 N.S. N.S. 0.54 0.70 -0.17 N.S. cg07564598 WSB1 17 25619946 0.52 0.54 0.64 -0.10 0.00 N.S. 0.54 0.55 -0.01 N.S. cg09278187 FOXJ3 1 42644032 0.38 0.44 0.50 -0.06 0.02 0.05 0.40 0.48 -0.09 N.S. cg14350701 NACC2 9 138969712 0.54 0.56 0.66 -0.10 0.00 N.S. 0.52 0.60 -0.08 0.03 cg18638180 FAM207A 21 46379089 0.43 0.48 0.54 -0.06 N.S. N.S. 0.46 0.50 -0.03 N.S. cg00453717 POLD3 11 74344629 0.46 0.53 0.58 -0.05 N.S. N.S. 0.49 0.57 -0.08 N.S. cg11865119 MEST 7 130125932 0.48 0.62 0.60 0.01 N.S. 0.00 0.58 0.66 -0.08 N.S. cg21660452 NRXN2 11 64397807 0.25 0.38 0.37 0.01 N.S. 0.00 0.34 0.42 -0.08 N.S. cg09049982 ITCH 20 32950073 0.54 0.59 0.66 -0.07 0.02 N.S. 0.57 0.60 -0.03 N.S. cg09174601 HS3ST3A1 17 13442632 0.49 0.56 0.60 -0.04 N.S. 0.01 0.50 0.62 -0.12 0.02 cg21857190 TNP1 2 217913299 0.48 0.55 0.59 -0.04 N.S. 0.04 0.46 0.65 -0.18 0.02 cg01140247 C10orf11 10 77784369 0.42 0.45 0.53 -0.09 0.00 N.S. 0.39 0.50 -0.10 N.S. cg16854917 LINC0085 19 52206691 0.24 0.60 0.36 0.24 0.00 0.00 0.68 0.52 0.16 N.S. cg26531076 KLF5 13 73743377 0.48 0.54 0.60 -0.06 0.04 0.05 0.52 0.56 -0.04 N.S. cg08536358 PDHA2 4 96760014 0.41 0.43 0.52 -0.09 0.03 N.S. 0.42 0.45 -0.03 N.S. cg02802634 FOXL1 16 86772171 0.40 0.46 0.52 -0.05 N.S. N.S. 0.46 0.47 0.00 N.S. cg25162927 ANO6 12 45736875 0.53 0.61 0.65 -0.04 N.S. 0.01 0.58 0.64 -0.06 N.S. cg06821199 CPLX1 4 779230 0.35 0.38 0.46 -0.08 N.S. N.S. 0.31 0.45 -0.15 N.S. cg10499102 EXOC2 6 564040 0.49 0.50 0.60 -0.09 0.01 N.S. 0.42 0.59 -0.17 0.01 cg25632577 PCAT1 8 128048403 0.56 0.55 0.67 -0.13 0.00 N.S. 0.50 0.60 -0.10 0.01  194 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg10409560 C4orf26 4 76481299 0.43 0.50 0.54 -0.04 N.S. 0.02 0.50 0.50 0.00 N.S. cg16296417 NSMCE2 8 126285443 0.53 0.64 0.63 0.01 N.S. 0.00 0.62 0.67 -0.05 N.S. cg06231385 FOXK1 7 4743056 0.69 0.65 0.58 0.07 0.02 N.S. 0.67 0.64 0.03 N.S. cg25078444 FOXG1 14 29235193 0.64 0.60 0.53 0.07 N.S. N.S. 0.62 0.59 0.03 N.S. cg10566121 MPPED2 11 30606026 0.44 0.36 0.33 0.03 N.S. N.S. 0.40 0.32 0.08 N.S. cg06335867 NXPH1 7 8482325 0.45 0.47 0.34 0.13 0.01 N.S. 0.49 0.44 0.05 N.S. cg25609528 POU3F1 1 38510182 0.71 0.69 0.60 0.09 N.S. N.S. 0.70 0.68 0.03 N.S. cg14636534 LINC00461 5 87968528 0.68 0.64 0.56 0.07 N.S. N.S. 0.66 0.62 0.04 N.S. cg01879273 AGAP1 2 236506413 0.71 0.67 0.60 0.07 N.S. N.S. 0.76 0.57 0.19 N.S. cg06324373 CRTAC1 10 99734805 0.56 0.55 0.45 0.10 N.S. N.S. 0.56 0.54 0.02 N.S. cg03958798 SORCS3 10 106400686 0.54 0.48 0.43 0.05 N.S. N.S. 0.51 0.44 0.07 N.S. cg26536949 DOC2B 17 57053 0.69 0.65 0.57 0.08 N.S. N.S. 0.69 0.61 0.08 N.S. cg18063312 FOXD1 5 72740737 0.67 0.76 0.56 0.20 0.00 0.03 0.76 0.76 0.00 N.S. cg03534453 PCDHB17 5 140537884 0.58 0.47 0.47 0.01 N.S. 0.05 0.50 0.45 0.04 N.S. cg21253459 FOXB1 15 60288404 0.57 0.66 0.45 0.21 0.00 0.04 0.67 0.64 0.03 N.S. cg10130155 LOC401242 6 28833480 0.70 0.78 0.58 0.20 0.00 0.01 0.75 0.81 -0.06 N.S. cg04609163 DDX25 11 125774090 0.26 0.21 0.15 0.06 N.S. N.S. 0.17 0.25 -0.07 N.S. cg00317585 LOC339975 4 188156703 0.67 0.63 0.56 0.07 N.S. N.S. 0.66 0.59 0.08 N.S. cg13273396 ACSS3 12 81471867 0.39 0.44 0.28 0.17 0.00 N.S. 0.47 0.41 0.06 N.S. cg04550737 TBX15 1 119530600 0.42 0.39 0.30 0.09 N.S. N.S. 0.39 0.38 0.02 N.S. cg21571339 FLRT2 14 86001111 0.44 0.42 0.32 0.10 0.05 N.S. 0.49 0.36 0.13 N.S. cg23774717 APBB1IP 10 26856095 0.56 0.58 0.44 0.14 0.00 N.S. 0.62 0.54 0.08 N.S. cg00970361 FOXB1 15 60288348 0.58 0.56 0.46 0.10 0.00 N.S. 0.55 0.58 -0.02 N.S. cg17073273 POU3F2 6 99279795 0.35 0.41 0.23 0.18 0.00 N.S. 0.45 0.37 0.08 N.S. cg14650610 SPOCK1 5 136834492 0.51 0.56 0.39 0.17 0.00 N.S. 0.57 0.55 0.02 N.S. cg14005139 NKX2-1 14 36989550 0.32 0.28 0.20 0.08 N.S. N.S. 0.33 0.23 0.09 N.S. cg21479226 C8orf34 8 69244510 0.52 0.44 0.40 0.04 N.S. N.S. 0.43 0.46 -0.02 N.S. cg11246563 HLA-H 6 29855406 0.48 0.51 0.36 0.15 0.00 N.S. 0.50 0.52 -0.02 N.S. cg04965934 EMX1 2 73151201 0.51 0.51 0.39 0.12 0.00 N.S. 0.53 0.48 0.05 N.S.  195 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg21464565 HOXA2 7 27141139 0.53 0.55 0.41 0.14 0.00 N.S. 0.52 0.58 -0.05 N.S. cg00615537 ARHGEF37 5 148961011 0.57 0.61 0.45 0.16 0.01 N.S. 0.62 0.59 0.04 N.S. cg01269620 PDE10A 6 166074191 0.44 0.48 0.32 0.16 0.02 N.S. 0.52 0.43 0.08 N.S. cg01663018 ONECUT1 15 53097777 0.66 0.52 0.53 -0.01 N.S. 0.01 0.57 0.48 0.09 N.S. cg27314998 PCDH7 4 30720246 0.50 0.56 0.38 0.18 0.00 N.S. 0.50 0.62 -0.12 N.S. cg15335669 REREP3 15 22546908 0.60 0.54 0.47 0.07 N.S. N.S. 0.49 0.59 -0.10 N.S. cg16171281 C18orf62 18 73167422 0.71 0.70 0.59 0.11 0.00 N.S. 0.72 0.67 0.06 N.S. cg10222027 CSDAP1 16 31580590 0.47 0.50 0.35 0.15 0.00 N.S. 0.50 0.50 0.00 N.S. cg15715477 LOC100130155 8 65283042 0.71 0.68 0.59 0.09 N.S. N.S. 0.65 0.71 -0.06 N.S. cg01287975 TAC1 7 97361241 0.48 0.54 0.35 0.19 0.00 N.S. 0.57 0.52 0.06 N.S. cg16886987 DSCR6 21 38378634 0.45 0.56 0.32 0.24 0.00 0.03 0.53 0.59 -0.06 N.S. cg23160016 GABRA2 4 46391929 0.60 0.72 0.48 0.24 0.00 0.01 0.71 0.72 -0.01 N.S. cg04241652 PRDM16 1 2984830 0.45 0.42 0.32 0.10 0.02 N.S. 0.43 0.41 0.02 N.S. cg11782635 KCNIP1 5 169930919 0.40 0.38 0.27 0.11 0.05 N.S. 0.39 0.38 0.01 N.S. cg16699715 ARHGEF37 5 148961007 0.51 0.55 0.38 0.17 0.00 N.S. 0.57 0.53 0.04 N.S. cg19848629 LOC100130155 8 65286244 0.60 0.52 0.48 0.04 N.S. 0.02 0.49 0.55 -0.06 N.S. cg06951122 PCDHA8 5 140222653 0.63 0.56 0.50 0.06 N.S. N.S. 0.57 0.56 0.02 N.S. cg23179456 ADCY4 14 24803873 0.58 0.56 0.45 0.11 0.01 N.S. 0.58 0.54 0.04 N.S. cg25599538 UCP1 4 141490075 0.33 0.33 0.21 0.12 N.S. N.S. 0.35 0.31 0.05 N.S. cg25366315 BTNL3 5 180408809 0.80 0.75 0.67 0.08 N.S. 0.05 0.74 0.76 -0.02 N.S. cg02268229 UCP1 4 141490037 0.43 0.41 0.30 0.11 N.S. N.S. 0.46 0.35 0.11 N.S. cg01414185 HOXD4 2 177017449 0.65 0.63 0.52 0.11 0.01 N.S. 0.61 0.65 -0.03 N.S. cg16492341 APBB1IP 10 26856105 0.60 0.62 0.47 0.15 0.01 N.S. 0.66 0.58 0.08 N.S. cg00870753 UBE2MP1 16 34404772 0.55 0.53 0.42 0.11 0.04 N.S. 0.51 0.54 -0.03 N.S. cg18204675 ARHGEF37 5 148961004 0.41 0.47 0.27 0.19 0.00 N.S. 0.48 0.45 0.03 N.S. cg00684116 CERS3 15 101094461 0.87 0.80 0.73 0.06 N.S. N.S. 0.78 0.82 -0.04 N.S. cg05864326 HOXD3 2 177030150 0.69 0.63 0.55 0.07 N.S. 0.02 0.66 0.60 0.06 N.S. cg09735723 SORCS3 10 106402042 0.49 0.42 0.35 0.07 N.S. N.S. 0.35 0.50 -0.15 N.S. cg20072171 FEZF2 3 62356962 0.43 0.36 0.29 0.07 N.S. N.S. 0.37 0.35 0.02 N.S.  196 TargetID Gene Name Chr Location Avg ? EOPET Avg ? CPM16 Avg ? CTL ?? CPM16 vs. CTL T-Test CPM16 vs CTL T-Test CPM16 vs PET Avg ? CPM16 (with PET) Avg ? CPM16 (no PET) ?? T-Test cg14118515 EVX2 2 176948728 0.48 0.45 0.34 0.11 N.S. N.S. 0.42 0.49 -0.07 N.S. cg24453699 BARHL2 1 91190891 0.63 0.63 0.49 0.14 0.01 N.S. 0.60 0.66 -0.07 N.S. cg03418136 PRKXP1 15 101095730 0.66 0.60 0.52 0.08 N.S. N.S. 0.62 0.57 0.04 N.S. cg07576142 GPC6 13 93879769 0.48 0.41 0.35 0.06 N.S. N.S. 0.38 0.44 -0.06 N.S. cg12405754 EFNA5 5 107005487 0.62 0.48 0.48 -0.01 N.S. 0.01 0.56 0.39 0.16 N.S. cg08283882 EBF2 8 25901017 0.29 0.39 0.15 0.24 0.00 N.S. 0.36 0.42 -0.06 N.S. cg19291576 DMRT1 9 969530 0.61 0.49 0.47 0.03 N.S. 0.03 0.55 0.43 0.12 N.S. cg27262412 TBX15 1 119530702 0.54 0.54 0.40 0.14 N.S. N.S. 0.52 0.57 -0.05 N.S. cg01729717 SLITRK1 13 84456486 0.46 0.43 0.31 0.12 0.01 N.S. 0.39 0.47 -0.08 N.S. cg18077971 PAX3 2 223164867 0.64 0.54 0.49 0.05 N.S. N.S. 0.59 0.49 0.11 N.S. cg19797516 APBB1IP 10 26856454 0.62 0.65 0.46 0.19 0.00 N.S. 0.70 0.61 0.09 N.S. cg05452524 HLF 17 53343029 0.51 0.46 0.34 0.11 N.S. N.S. 0.46 0.46 0.00 N.S. cg25363445 ALX4 11 44326516 0.52 0.44 0.34 0.10 N.S. N.S. 0.47 0.42 0.06 N.S. cg10327440 CDC42BPA 1 227177885 0.80 0.51 0.62 -0.11 N.S. 0.00 0.40 0.62 -0.22 N.S. Supplementary Table 4.2: Comparison of values from CPM16 samples to EOPET samples using only candidate CpGs from chapter 3; Bold - Significant using stringent criteria in CPM16 study (FDR <0.01, ?? >0.15); Underline- Does not change with gestational age in trisomy 16 or controls (p<0.05 and ??>0.15); N.S. - Not Significant  197 TargetID Gene Name Chr Location Avg. ? T16 1ST Avg. ? CTL 1ST ?? cg21847720 MYOM2 8 2075777 0.05 0.44 -0.39 cg15892115 ONECUT2 18 55102922 0.10 0.47 -0.36 cg16924822 TSPAN9 12 3309264 0.11 0.47 -0.36 cg01111358 ONECUT2 18 55102627 0.14 0.49 -0.35 cg10416814  13 23422850 0.25 0.59 -0.34 cg14345128 ONECUT2 18 55102849 0.06 0.39 -0.33 cg26839512 SPATA18 4 52918489 0.39 0.72 -0.32 cg16896687  13 20966332 0.58 0.90 -0.32 cg08768621 ONECUT2 18 55102916 0.20 0.51 -0.31 cg16547579 SLC23A2 20 4954333 0.33 0.65 -0.31 cg06937717  7 151145554 0.32 0.63 -0.31 cg21303763 TSPAN9 12 3309708 0.12 0.43 -0.31 cg12060422 KAZALD1 10 102821684 0.28 0.57 -0.30 cg12737152  11 129565909 0.12 0.42 -0.29 cg01549929  16 54683714 0.51 0.81 -0.29 cg01295646 MYOM2 8 2075820 0.11 0.40 -0.29 cg10236239 SULT1C4 2 108994514 0.31 0.59 -0.28 cg14854315 SSH1 12 109246503 0.53 0.81 -0.28 cg06509223  13 23422746 0.56 0.84 -0.28 cg07435237 ARMC5 16 31469562 0.19 0.46 -0.28 cg20358834 LRFN4 11 66624256 0.05 0.32 -0.27 cg09621330  16 54685651 0.53 0.79 -0.27 cg15736127 GPD2 2 157292127 0.12 0.37 -0.26 cg27369286 ARMC5 16 31469821 0.05 0.30 -0.25 cg18156204  17 71948613 0.16 0.41 -0.25 cg05152479 SSH1 12 109246508 0.57 0.81 -0.25 cg13412834 FNBP1 9 132652770 0.44 0.68 -0.24 cg03085549 LDHD 16 75150819 0.55 0.79 -0.24 cg13825574 RNF208 9 140117257 0.12 0.36 -0.24 cg19563510 MAFG 17 79881483 0.18 0.40 -0.23 cg14052511 LOC389705 9 14993584 0.36 0.59 -0.23 cg22033586 GPD2 2 157292113 0.13 0.35 -0.23 cg14073497  11 68611479 0.27 0.50 -0.22 cg21400015 ONECUT2 18 55102461 0.08 0.29 -0.22 cg12838644  14 77413642 0.20 0.42 -0.22 cg14069088 CDKN2BAS 9 21996207 0.54 0.75 -0.22 cg15572779  7 151145683 0.11 0.33 -0.22 cg17172308  6 168533631 0.12 0.34 -0.21 cg01692757  16 54211537 0.63 0.85 -0.21 cg06433658 FAM63A 1 150979092 0.19 0.40 -0.21 cg09269891 CREBBP 16 3843415 0.60 0.81 -0.21 cg17016000 RIN2 20 19869644 0.57 0.78 -0.20 cg07965640 SPAG16 2 214149735 0.25 0.45 -0.20 cg26870803 PC 11 66624853 0.06 0.26 -0.20 cg06431702 PC 11 66624841 0.06 0.26 -0.20 cg20143982 PEX5 12 7341252 0.18 0.38 -0.20 cg18419175 USP42 7 6199980 0.40 0.60 -0.20 cg09451427 CACNA2D2 3 50488230 0.62 0.82 -0.20 cg20168751 FAM149A 4 187066279 0.21 0.40 -0.20  198 TargetID Gene Name Chr Location Avg. ? T16 1ST Avg. ? CTL 1ST ?? cg10005224 HOXC4 12 54424964 0.34 0.54 -0.20 cg05072008 FIGNL1 7 50518647 0.51 0.71 -0.20 cg18182216 FAM63A 1 150978385 0.51 0.70 -0.19 cg00107682  15 96888959 0.49 0.68 -0.19 cg12741645 AEN 15 89167736 0.30 0.49 -0.19 cg19908768 SULT1C4 2 108994325 0.14 0.32 -0.19 cg26065488 TNIK 3 171176016 0.32 0.50 -0.19 cg03992114 ATP11A 13 113343376 0.46 0.65 -0.19 cg21616405 SSH1 12 109246512 0.54 0.72 -0.18 cg19630681  3 64429607 0.42 0.60 -0.18 cg17966192 SULT1C4 2 108994116 0.37 0.55 -0.18 cg03653601 ABCC1 16 16156039 0.62 0.80 -0.18 cg25007283 ZIC4 3 147112143 0.65 0.83 -0.18 cg21812277 PARP4 13 25085776 0.56 0.73 -0.17 cg00751501 WFDC1 16 84342709 0.72 0.89 -0.17 cg18543993 DCAF4L2 8 88886320 0.75 0.92 -0.17 cg19145858  7 129781681 0.30 0.47 -0.17 cg07086592 DCAF4L2 8 88886306 0.76 0.93 -0.17 cg11176694 CXXC5 5 139050415 0.55 0.72 -0.17 cg19282742 ARMC5 16 31469983 0.09 0.25 -0.17 cg24237081 SOX5 12 24102807 0.20 0.36 -0.17 cg24071500 CAMK2D 4 114683176 0.17 0.34 -0.16 cg10535132 PKD2L2 5 137224284 0.69 0.85 -0.16 cg21037265 DENND1C 19 6481819 0.15 0.32 -0.16 cg17607973 MEPCE 7 100027408 0.08 0.24 -0.16 cg10086328 PEX5 12 7341276 0.31 0.47 -0.16 cg25146434  3 16577542 0.62 0.78 -0.16 cg13972124  9 132539744 0.22 0.38 -0.16 cg23486853 LIF 22 30642980 0.17 0.33 -0.16 cg03991512 LDHD 16 75150456 0.41 0.56 -0.16 cg06011334 KHDC1 6 73973128 0.18 0.34 -0.16 cg21141329  17 48290459 0.50 0.66 -0.16 cg11406453  1 101602562 0.70 0.85 -0.15 cg09162146 ARHGAP29 1 94703507 0.22 0.37 -0.15 cg01220680 ANGPT4 20 866011 0.07 0.22 -0.15 cg22512367  6 37517638 0.74 0.89 -0.15 cg00788025 DGKQ 4 962749 0.84 0.69 0.15 cg24178621 SLC6A3 5 1430526 0.86 0.71 0.15 cg15882305  1 933388 0.88 0.73 0.15 cg22657015 MICALL2 7 1482163 0.88 0.73 0.15 cg12792157 MYCBP 1 39339004 0.44 0.29 0.15 cg13299707 SLC12A7 5 1064133 0.79 0.64 0.15 cg02808528 B3GAT1 11 134274713 0.63 0.48 0.15 cg19764541 SLC12A7 5 1063554 0.86 0.71 0.15 cg00697639 SLC12A7 5 1052540 0.70 0.54 0.16 cg11642412 MACROD1 11 63768412 0.50 0.34 0.16 cg01706566 BNIP3L 8 26239922 0.60 0.44 0.16 cg22536770 CRTC1 19 18870922 0.94 0.78 0.16 cg08351607 SLC12A7 5 1056410 0.73 0.57 0.16  199 TargetID Gene Name Chr Location Avg. ? T16 1ST Avg. ? CTL 1ST ?? cg08604181 EXOC4 7 133040186 0.63 0.48 0.16 cg09134969 AGRN 1 980304 0.83 0.67 0.16 cg26143732 COLEC11 2 3688910 0.87 0.71 0.16 cg17759816  19 1313033 0.75 0.59 0.16 cg15925365 GSTA4 6 52858998 0.26 0.10 0.16 cg10489284 SLC12A7 5 1088244 0.93 0.77 0.16 cg04640975  19 3464991 0.75 0.59 0.16 cg04627093  4 14098971 0.77 0.61 0.16 cg07982082 SIPA1 11 65414261 0.63 0.47 0.16 cg19867917 COLEC11 2 3642629 0.23 0.06 0.16 cg20741620 AGRN 1 972225 0.78 0.62 0.16 cg03336167 SLC22A18 11 2930995 0.26 0.10 0.16 cg22217678 RFPL3S 22 32767005 0.70 0.54 0.16 cg11577329 SLC12A7 5 1093738 0.83 0.67 0.16 cg18543991 PLOD2 3 145871392 0.88 0.71 0.16 cg03620975 DYNC1LI1 3 32611110 0.34 0.18 0.17 cg04213775 SLC12A7 5 1063087 0.81 0.65 0.17 cg09325679 SLC27A1 19 17597534 0.93 0.76 0.17 cg26445928 FLJ43663 7 130740829 0.78 0.61 0.17 cg07461111  18 77312717 0.79 0.63 0.17 cg16191307  12 63754169 0.78 0.62 0.17 cg15462530 KIF26A 14 104646474 0.74 0.58 0.17 cg03975652 MIB2 1 1559155 0.70 0.53 0.17 cg00078456 MIB2 1 1564422 0.87 0.71 0.17 cg25554751 KCNQ1 11 2548989 0.87 0.70 0.17 cg20388462  2 3696678 0.80 0.63 0.17 cg14589553 EXD3 9 140275263 0.78 0.61 0.17 cg05392293 FRMD4A 10 13911331 0.86 0.69 0.17 cg17708995 TNPO2 19 12832077 0.32 0.15 0.17 cg09974661 COLEC11 2 3642634 0.26 0.09 0.17 cg22865215 MEF2B 19 19260102 0.86 0.69 0.17 cg05120501 UCRC 22 30165841 0.68 0.51 0.17 cg07007312 DGKQ 4 961505 0.87 0.70 0.17 cg23351738 SNORA38 6 31589926 0.34 0.17 0.17 cg10523494 CD300LB 17 72528386 0.51 0.34 0.17 cg26237003 CCL28 5 43413209 0.66 0.48 0.17 cg08480609 AGRN 1 986560 0.87 0.70 0.18 cg23223533 ARHGEF10 8 1850463 0.88 0.70 0.18 cg11713480 SLC12A7 5 1081913 0.85 0.67 0.18 cg20705882 TOM1 22 35695066 0.91 0.73 0.18 cg18039933 FBRSL1 12 133132641 0.88 0.71 0.18 cg15958424 ACPP 3 132036067 0.40 0.22 0.18 cg02281662 KIF26A 14 104643907 0.80 0.62 0.18 cg12993807 SLC12A7 5 1075045 0.92 0.74 0.18 cg08277679 KIF26A 14 104618028 0.84 0.66 0.18 cg00063291 SLC12A7 5 1055962 0.90 0.72 0.18 cg06176471 ATP1B1 1 169088807 0.50 0.32 0.18 cg08344943 SLC12A7 5 1064462 0.81 0.63 0.18 cg21657043  6 44035552 0.80 0.62 0.18  200 TargetID Gene Name Chr Location Avg. ? T16 1ST Avg. ? CTL 1ST ?? cg23666945 TRIM71 3 32861150 0.33 0.15 0.18 cg02011723 TNPO2 19 12831888 0.34 0.16 0.18 cg03102073  8 333662 0.83 0.65 0.18 cg20817073  21 15071306 0.52 0.33 0.18 cg27178677 PLCB1 20 8834803 0.48 0.30 0.18 cg22742060  2 140126832 0.75 0.57 0.18 cg16690972  14 36289035 0.79 0.60 0.18 cg01241798 GALNS 16 88904469 0.88 0.70 0.18 cg24875595 REG1P 2 79363761 0.72 0.54 0.18 cg09115026 PRSS35 6 84232241 0.68 0.50 0.18 cg05642789 MIB2 1 1564482 0.83 0.64 0.18 cg14817049 SLC12A7 5 1074718 0.63 0.44 0.19 cg16193717 SLC12A7 5 1068999 0.88 0.70 0.19 cg19414302 ACAP3 1 1234354 0.90 0.72 0.19 cg08530610  1 991567 0.79 0.60 0.19 cg21516614 SLC12A7 5 1084110 0.89 0.70 0.19 cg10533746 C2orf81 2 74645626 0.52 0.33 0.19 cg05957393 PWWP2B 10 134219894 0.82 0.63 0.19 cg06925984  17 77767242 0.29 0.11 0.19 cg18951125  12 9720090 0.42 0.23 0.19 cg09944400 TMCC3 12 95043227 0.84 0.65 0.19 cg05361553  8 584986 0.83 0.64 0.19 cg08486065  19 3464875 0.71 0.52 0.19 cg09681675  16 3481785 0.74 0.55 0.19 cg26603598 COX7C 5 85913298 0.59 0.40 0.19 cg09003758 MICB 6 31468816 0.71 0.52 0.19 cg23918296 AMN 14 103397019 0.34 0.15 0.19 cg21753682 GALNT9 12 132693557 0.78 0.59 0.19 cg02967813 COLEC11 2 3681328 0.86 0.67 0.19 cg24570624 C14orf4 14 77493278 0.43 0.23 0.19 cg22955595 SLC12A7 5 1052903 0.69 0.49 0.19 cg03041030 C1orf70 1 1470905 0.82 0.62 0.19 cg01039508  15 39179801 0.81 0.62 0.20 cg10876737 SLC12A7 5 1052223 0.86 0.66 0.20 cg25386676  5 172175721 0.30 0.11 0.20 cg07739758 NCRNA00181 19 58865367 0.76 0.56 0.20 cg01664864 DIO3 14 102027677 0.31 0.11 0.20 cg05987564  2 156981620 0.86 0.66 0.20 cg13644182  19 12747889 0.66 0.46 0.20 cg11803843  10 134834817 0.79 0.58 0.20 cg13740135 PHLDB3 19 44006432 0.40 0.20 0.20 cg15412971 DGKQ 4 956119 0.89 0.68 0.20 cg07459121 SLC12A7 5 1092793 0.76 0.56 0.20 cg15341124 DIO3 14 102027734 0.30 0.09 0.20 cg21491609  4 129224165 0.65 0.44 0.20 cg26825544  5 169539492 0.84 0.64 0.21 cg18565268 PAX9 14 37146463 0.67 0.46 0.21 cg18081515 KIF26A 14 104630064 0.67 0.46 0.21 cg06012269 SLC27A1 19 17608374 0.82 0.62 0.21  201 TargetID Gene Name Chr Location Avg. ? T16 1ST Avg. ? CTL 1ST ?? cg19123465 FBRSL1 12 133131971 0.88 0.68 0.21 cg13305444 ENAH 1 225838196 0.44 0.23 0.21 cg06344195 SLC12A7 5 1079312 0.81 0.60 0.21 cg16756508 FBRSL1 12 133132798 0.80 0.59 0.21 cg21655923 WDR54 2 74647813 0.69 0.48 0.21 cg24082174 SNORA63 3 186503643 0.83 0.62 0.21 cg27585120 SLC12A7 5 1076166 0.77 0.57 0.21 cg18358869  10 102808077 0.35 0.14 0.21 cg19854293 SLC12A7 5 1052919 0.76 0.55 0.21 cg18756954 SLC12A7 5 1074733 0.95 0.74 0.21 cg25513635 GDF1 19 18981378 0.53 0.32 0.21 cg08715837 TUBA8 22 18593609 0.31 0.10 0.21 cg23115083 SLC12A7 5 1054427 0.70 0.49 0.21 cg13563298 WNK2 9 95948059 0.84 0.62 0.21 cg18366919 EPHX3 19 15344364 0.39 0.18 0.22 cg20712955 ZFPM2 8 106810999 0.81 0.59 0.22 cg10598596 KLK10 19 51517212 0.49 0.27 0.22 cg03042281 SLC27A1 19 17608394 0.80 0.59 0.22 cg03120284 AGRN 1 986894 0.69 0.48 0.22 cg01663696 NANOG 12 7942817 0.43 0.21 0.22 cg21693437  11 31851924 0.43 0.21 0.22 cg04337056 GFM1 3 158361858 0.65 0.43 0.22 cg00577109 MZF1 19 59074507 0.42 0.20 0.22 cg05005217 CDCA2 8 25317638 0.41 0.18 0.22 cg20070659  4 188736336 0.60 0.37 0.23 cg08574227 SPATA18 4 52944959 0.38 0.16 0.23 cg08442149 ADARB2 10 1767484 0.91 0.68 0.23 cg16997104 SLC12A7 5 1053643 0.86 0.62 0.23 cg22098317 ANO9 11 431659 0.62 0.38 0.24 cg11969813 P4HB 17 79816559 0.69 0.45 0.24 cg19739906 HNMT 2 138722326 0.45 0.22 0.24 cg04478684 SIPA1 11 65414099 0.75 0.51 0.24 cg27052781 WDR18 19 991359 0.56 0.32 0.24 cg05082376  22 42548792 0.72 0.48 0.24 cg14043049 GMIP 19 19748575 0.86 0.61 0.25 cg00083072 SIPA1 11 65415190 0.71 0.46 0.25 cg07002902 DGKQ 4 960520 0.85 0.60 0.25 cg08265387  14 36288987 0.55 0.30 0.25 cg25984458  22 49800171 0.70 0.44 0.25 cg05983045 TTC23L 5 34842637 0.59 0.34 0.26 cg02876221  4 134545513 0.76 0.51 0.26 cg09660043 PNLIPRP2 10 118395444 0.84 0.59 0.26 cg23514135 SLC12A7 5 1110315 0.88 0.62 0.26 cg08882341  6 166470330 0.72 0.46 0.26 cg11792470 KIF26A 14 104630673 0.81 0.55 0.26 cg08232572  10 134596989 0.66 0.39 0.27 cg01686522 GALNS 16 88905165 0.87 0.59 0.28 cg27024386  3 150238208 0.47 0.19 0.28 cg07816687 HSF4 16 67197186 0.50 0.21 0.29  202 TargetID Gene Name Chr Location Avg. ? T16 1ST Avg. ? CTL 1ST ?? cg26482665 MZF1 19 59073902 0.37 0.08 0.29 cg00177797 LHX4 1 180203837 0.38 0.08 0.29 cg07567497 SLC12A7 5 1060334 0.81 0.51 0.29 cg19363466 MZF1 19 59074265 0.37 0.08 0.30 cg04380229 SLC12A7 5 1084931 0.74 0.44 0.30 cg24626554 LOC389458 7 5111635 0.47 0.18 0.30 cg18848287 LOC389458 7 5111641 0.48 0.18 0.30 cg01106989 GSTA4 6 52858459 0.67 0.37 0.30 cg08447733 MZF1 19 59074308 0.52 0.21 0.31 cg04941630  16 54962001 0.42 0.11 0.31 cg00215425 SLC12A7 5 1054795 0.74 0.41 0.33 cg16709294 SFRS5 14 70235567 0.87 0.53 0.34 cg18391209 CAPN8 1 223747670 0.82 0.48 0.34 cg05014211 MZF1 19 59074005 0.50 0.15 0.35 cg05719164 LHX4 1 180204221 0.42 0.06 0.36 cg25547848  5 178266060 0.62 0.25 0.38 cg17735983 MZF1 19 59074482 0.53 0.13 0.40 Supplementary Table 4.3: Significant CpG sites comparing 1st trimester T16 vs. 1st trimester controls. Bold - Also significantly different in 3rd Trimester T16 vs. controls  203  Supplementary Figure 4.1: Correlation of CPM16 vs. EOPET (A) and controls (B) for 282 CpG sites identified as highly significant in chapter 3. 204  Supplementary Figure 4.2: Histogram showing the distribution of ??s between CPM16 samples and controls for candidate probes from Chapter 3. Notable genes in the most different groups are indicated.   

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