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Recurrent miscarriage : unraveling the complex etiology Hanna, Courtney Wood 2013

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RECURRENT MISCARRIAGE: UNRAVELING THE COMPLEX ETIOLOGY  by Courtney Wood Hanna  B.Sc., The University of British Columbia, 2006  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2013  © Courtney Wood Hanna, 2013  ii  Abstract Recurrent miscarriage (RM), defined as 3 or more consecutive spontaneous losses of pregnancy before 20 weeks gestation, affects 1-2% of couples and has a complex etiology.  Half of miscarriages from RM cases are caused by chromosomal abnormalities in the embryo and while there are several associated maternal factors, underlying causes and clinically relevant biomarkers have been elusive.  I hypothesized that genetic and/or epigenetic factors associated with maternal meiotic non-disjunction, reproductive aging and endocrinological profile, or placental functioning will contribute to the etiology of RM.  In these case-control studies, I investigated the association between RM and 1) maternal mutations in synaptonemal complex protein 3 (SYCP3), 2) maternal telomere lengths, 3) maternal polymorphisms in genes in the hypothalamus-pituitary-ovarian (HPO) axis and 4) placental DNA methylation patterns.  The findings suggest that maternal mutations in SYCP3 and polymorphisms in HPO axis genes may not contribute significantly to risk for RM.  No mutations in SYCP3 were identified in women with RM with at least one trisomic conception.  While associations between polymorphisms within the estrogen receptor β, activin receptor 1, prolactin receptor and glucocorticoid receptor genes and RM were identified, these were not significant after correction for multiple comparisons.  Aspects of chromosomal biology may be important factors in the etiology of RM. Women with RM had significantly shorter telomeres compared to controls, suggesting altered rates of biological aging.  In the placental villi of RM samples, there were few differences in DNA methylation at targeted sites when compared to isolated miscarriages and elective terminations.  However, gene ontology analysis showed that imprinted genes and immune response pathways were overrepresented among those sites differentially methylated between RM and elective termination placentas.  The RM group additionally had an increase in the iii  number of outlier cases at a select number of imprinted loci.  Furthermore, several placental samples from both cases and controls showed aberrant DNA methylation profiles at many loci investigated, suggesting these samples may have global dysregulation of DNA methylation and/or differences in placental composition/functioning.  These studies have improved our understanding of mechanisms involved in RM and will contribute to the direction of future research.  iv  Preface A version of Chapter 2 has been published.  Hanna, C.W., Blair, J.D., Stephenson, M.D. and Robinson, W.P. (2012) Absence of SYCP3 mutations in women with recurrent miscarriage with at least one trisomic miscarriage. Reproductive BioMedicine Online. 24(2):251-3.  I generated the hypothesis and study design with W.P. Robinson, directly supervised summer student J.D. Blair, and personally wrote the manuscript.  J.D. Blair contributed equally to this publication by performing data collection, analyzing the results, and editing the manuscript. M.D. Stephenson ascertained patients.  W.P. Robinson supervised the research and edited the manuscript. A version of Chapter 3 has been published.  Hanna, C.W., Bretherick, K.L., Gair, J.L., Fluker, M.R., Stephenson, M.D. and Robinson, W.P. (2009) Telomere length and reproductive aging. Human Reproduction. 24(5):1206-11.  K.L. Bretherick and I contributed equally to the collection of data, analysis of results and preparation of the manuscript.  M.R. Fluker and M.D. Stephenson ascertained patients.  W.P. Robinson supervised this project, and generated the hypothesis for this work with J.L. Gair. A version of Chapter 4 has been published.  Hanna, C.W., Bretherick, K.L., Liu, C.C., Stephenson, M.D. and Robinson, W.P. (2010) Genetic variation in the hypothalamus-pituitary- ovarian axis in women with recurrent miscarriage. Human Reproduction. 25(10):2664-71.  I generated the study design, performed ~80% of data collection, analyzed the results, and wrote the manuscript.  K.L. Bretherick performed 10% of data collection and assisted with hypothesis formation and manuscript editing.   I supervised summer student C.C. Liu in completing the remaining 10% of data collection, in addition to analyzing this subset of the results.  M.D. v  Stephenson ascertained patients.  W.P. Robinson supervised the research and edited the manuscript. A version of Chapter 5 has been published.  Hanna, C.W., McFadden, D.E. and Robinson, W.P. (2013) DNA methylation profiling of placental villi from karyotypically normal miscarriage and recurrent miscarriage. American Journal of Pathology. Epub ahead of print 2013 April 09.  I generated the hypothesis and study design with W.P. Robinson, completed all data collection, analyzed the results, and wrote the manuscript.  D.E. McFadden ascertained patients and did sample collection.  W.P. Robinson additionally supervised the research and edited the manuscript. The collection of the samples for these studies was approved by the University of British Columbia Clinical Research Ethics Board, approval number CO1-0460.  Copyright permission was obtained for all published figures, tables and texts. vi  Table of Contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements ......................................................................................................................xv Chapter  1: Introduction ...............................................................................................................1 1.1 Aneuploidy in miscarriage .............................................................................................. 1 1.1.1 Oogenesis and meiosis ................................................................................................ 2 1.1.2 Maternal risk for chromosome missegregation........................................................... 4 1.2 Maternal factors associated with recurrent miscarriage.................................................. 6 1.2.1 Chromosomal .............................................................................................................. 6 1.2.2 Anatomical .................................................................................................................. 7 1.2.3 Immunological ............................................................................................................ 7 Uterine natural killer cells ................................................................................... 8 Maternal T helper 1 and T helper 2 immune balance ......................................... 9 Placental immunity ........................................................................................... 10 Autoimmunity ................................................................................................... 11 Infection ............................................................................................................ 12 1.2.4 Endocrinological ....................................................................................................... 12 Luteal phase defects .......................................................................................... 12 vii Thyroid dysfunction .......................................................................................... 13 Polycystic ovarian syndrome ............................................................................ 13 Endometrial receptivity ..................................................................................... 14 1.2.5 Thrombophilic........................................................................................................... 15 1.2.6 Psychosocial stress .................................................................................................... 16 1.3 Genetic and epigenetic factors contributing to recurrent miscarriage risk ................... 17 1.3.1 Maternal genetic variants .......................................................................................... 17 Genes involved in meiosis ................................................................................ 17 Genes involved in immune function ................................................................. 18 Genes involved in endocrine function .............................................................. 18 Thrombophilia associated genes ....................................................................... 19 1.3.2 Telomeres .................................................................................................................. 21 1.3.3 Epigenetics in fetal development .............................................................................. 21 1.3.4 Maternal skewed X-chromosome inactivation ......................................................... 23 1.4 Research objectives ....................................................................................................... 23 Chapter  2: Mutational analysis of the SYCP3 gene .................................................................29 2.1 Introduction ................................................................................................................... 29 2.2 Materials and methods .................................................................................................. 29 2.3 Results ........................................................................................................................... 30 2.4 Discussion ..................................................................................................................... 31 Chapter  3: Telomere length and reproductive aging ..............................................................36 3.1 Introduction ................................................................................................................... 36 3.2 Materials and methods .................................................................................................. 38 viii  3.2.1 Samples ..................................................................................................................... 38 3.2.2 Telomere length ........................................................................................................ 39 3.2.3 Statistical analysis ..................................................................................................... 40 3.3 Results ........................................................................................................................... 40 3.4 Discussion ..................................................................................................................... 41 Chapter  4: Genetic polymorphisms in genes involved in the hypothalamus-pituitary- ovarian axis ...................................................................................................................................52 4.1 Introduction ................................................................................................................... 52 4.2 Materials and methods .................................................................................................. 53 4.2.1 Samples ..................................................................................................................... 53 4.2.2 Variant selection ....................................................................................................... 54 4.2.3 Genotyping ................................................................................................................ 54 4.2.4 Statistical analysis ..................................................................................................... 55 4.2.5 Population stratification ............................................................................................ 55 4.3 Results ........................................................................................................................... 55 4.4 Discussion ..................................................................................................................... 57 Chapter  5: Placental DNA methylation associated with pregnancy outcomes .....................70 5.1 Introduction ................................................................................................................... 70 5.2 Materials and methods .................................................................................................. 71 5.2.1 Samples ..................................................................................................................... 71 5.2.2 Array-based quantification of DNA methylation ..................................................... 71 5.2.3 Targeted DNA methylation....................................................................................... 72 5.2.4 Statistical analysis ..................................................................................................... 73 ix  5.3 Results ........................................................................................................................... 74 5.3.1 Array-based quantification of DNA methylation ..................................................... 74 5.3.2 DNA methylation at imprinted genes ....................................................................... 76 5.3.3 ‘Global’ measures of DNA methylation ................................................................... 77 5.4 Discussion ..................................................................................................................... 78 Chapter  6: Discussion .................................................................................................................93 6.1 Summary and significance of findings ......................................................................... 93 6.2 Strengths and limitations............................................................................................... 96 6.3 Future directions ........................................................................................................... 98 6.4 Conclusions ................................................................................................................. 101 References ...................................................................................................................................103 Appendix A: Supplementary tables and figures for Chapter 2 .............................................125 Appendix B: Supplementary tables and figures for Chapter 4 .............................................126 Appendix C: Supplementary tables and figures for Chapter 5 .............................................144  x  List of Tables Table 2.1 Outcomes of 292 pregnancies from 50 women with recurrent miscarriage. ................ 33 Table 2.2 Minor allele frequencies of noncoding single nucleotide polymorphisms within the SYCP3 gene identified in 50 recurrent miscarriage women. ........................................................ 34 Table 3.1 Rate of telomere loss per year in women with evidence of premature reproductive aging and controls ......................................................................................................................... 46 Table 3.2 Raw and age-adjusted mean telomere length ............................................................... 47 Table 4.1 Summary of 35 polymorphisms assessed in this study................................................. 61 Table 4.2 Allele distributions of short tandem repeat polymorphisms. ........................................ 63 Table 4.3 Genotype distributions of single nucleotide polymorphisms. ...................................... 65 Table 5.1 Comparison of demographics for the recurrent miscarriage, miscarriage and elective termination study groups............................................................................................................... 84 Table 5.2 Frequency of outliers at imprinted loci. ........................................................................ 85 Table 5.3 Patterns of DNA methylation among outlier samples. ................................................. 86  xi  List of Figures Figure 1.1 Gametes as the product of meiosis I non-disjunction and meiosis II non-disjunction. 25 Figure 1.2 Number of germ cells with age in females. ................................................................. 26 Figure 1.3 Hormone levels and follicular events during the menstrual cycle. ............................. 27 Figure 1.4 Etiology of recurrent miscarriage. ............................................................................... 28 Figure 2.1 Schematic diagram of the SYCP3 gene and variants. .................................................. 35 Figure 3.1 Telomere-specific qPCR. ............................................................................................ 48 Figure 3.2 Correlation between telomere-specific qPCR and flow-FISH techniques. ................. 49 Figure 3.3 Correlation between telomere length and age in women with evidence of premature reproductive aging and controls. ................................................................................................... 50 Figure 3.4 Correlation between telomere length and age in women with recurrent miscarriage and trisomic pregnancies............................................................................................................... 51 Figure 5.1 Venn diagram of significant Infinium array candidates. ............................................. 87 Figure 5.2 DNA methylation at 4 candidate promoter regions. .................................................... 88 Figure 5.3 Unsupervised clustering of the 20 samples run on the Infinium array. ....................... 89 Figure 5.4 Box plots of DNA methylation at 7 imprinted loci. .................................................... 90 Figure 5.5 Comparison of measures of ‘global’ methylation. ...................................................... 91 Figure 5.6 Principle component plot of all samples. .................................................................... 92  xii  List of Abbreviations ACVR1 activin receptor type 1 ANCOVA analysis of covariance APA  antiphospholipid antibody APC  adenomous polyposis coli APS  antiphospholipid syndrome AXL  AXL receptor tyrosine kinase CD  cluster of differentiation CI  confidence intervals CYP1A2 cytochrome P450, subfamily 1A, polypeptide 2 DEFB1 defensin beta 1 DMR  differentially methylated region ESR1  estrogen receptor α ESR2  estrogen receptor β FDR  false discovery rate FISH  fluorescence in situ hybridization FSH  follicle-stimulating hormone GCCR  glucocorticoid receptor GnRH  gonadotropin releasing hormone HLA  human leukocyte antigen HPO  hypothalamus-pituitary-ovarian HWE  Hardy-Weinberg equilibrium HY  male-specific histocompatibility antigen xiii  IL  interleukin IVF  in vitro fertilization kb  kilobases LD  linkage disequilibrium LINE-1 long interspersed element LH  luteinizing hormone LPD  luteal phase defects MHC  major histocompatibility complex M  miscarriage MI  first meiotic division MII  second meiotic division MLPA  multiple ligation-dependent probe amplification MT  multiple trisomic miscarriages MTHFR methylenetetrahydrofolate reductase NK  natural killer OR  odds ratio PCA  principle component analysis PCOS  polycystic ovarian syndrome POF  premature ovarian failure PRLR  prolactin receptor qPCR  quantitative polymerase chain reaction RM  recurrent miscarriage ROS  reactive oxidative species xiv  SD  standard deviation SLE  systemic lupus erythematosus SNP  single nucleotide polymorphism ST  single trisomic miscarriage STR  short tandem repeat SYCP3 synaptonemal complex protein 3 TA  termination T/S ratio telomere to single copy ratio Th  T helper TH  thyroid hormones uNK  uterine natural killer UTR  untranslated region XCI  X-chromosome inactivation xv  Acknowledgements I would like to acknowledge my supervisor, Dr. Wendy Robinson, for the encouragement to pursue my research interests, invaluable direction and feedback on project design and manuscript generation, and investing time and energy in my professional development.  I would also like to thank my committee, Drs. Carolyn Brown, Michael Kobor and Barbara McGillivray for their advice and support.  I owe a particular thanks to Dr. Mary Stephenson, whose clinical expertise has greatly improved the impact of my research, and Dr. Maria Peñaherrera, whose continual advice and encouragement have been so important to my success.  To all the current and past lab members who have helped me along the way, Karla, Ruby, Luana, Sara, Ryan, Dan, Magda, John, Kirsten and Irina, thank you.  To my family, mom, dad, grandma, grandpa, granny, Shawn, Matt and Amy, thank you for your enduring love and support.  Finally Nick, you have invested so much interest in my work and offered support and encouragement through my highs and lows, thank you for believing in me.  Thank you to the funding organizations for financial support, including the Canadian Institutes of Health Research, University of British Columbia and Interdisciplinary Women’s Reproductive Health program.  1  Chapter  1: Introduction Miscarriage, a spontaneous abortion before 20 weeks gestation, occurs in 15% of clinically-recognized pregnancies, making it the most common complication of pregnancy (Warburton and Fraser 1964).  Recurrent miscarriage (RM), defined as 3 or more consecutive miscarriages, affects 1-2% of couples trying to conceive (Stirrat 1990), which is greater than expected by chance (0.34%).  Approximately 50% of clinically-recognized miscarriages among RM couples have numerical chromosomal abnormalities, with the vast majority being aneuploid (Ogasawara et al. 2000, Stephenson et al. 2002).  This rate is even higher among non-recurring miscarriages, with frequencies reported from 50-70% (Hassold and Chiu 1985, Ogasawara et al. 2000). The etiology of RM is complex with many associated maternal factors.  The diagnostic value of these associated factors is unclear as they are often identified in women with healthy pregnancies and there is currently few recommended therapeutics for women with RM and a coexisting factor (Tang and Quenby 2010).  Consequently, RM is an extremely stressful condition for couples and physicians and is an important area of research.  In the introduction to this thesis, the complex etiology of RM will be described as it is currently understood, with an emphasis on the areas of research that can be expanded upon.  The main topics of discussion will be: 1) aneuploidy in miscarriage, 2) maternal factors associated with RM, and 3) genetic and epigenetic factors contributing to risk for RM. 1.1 Aneuploidy in miscarriage Aneuploidy, the loss or gain of a chromosome, can arise through missegregation of chromosomes during meiosis.  This can occur through non-disjunction of the homologous chromosome pairs or premature separation of the sister chromatids during the first meiotic 2  division (MI), or missegregation of sister chromatids during the second meiotic division (MII) (Figure 1.1).  In addition, errors can arise postzygotically, often in the first few cell divisions after fertilization (Bean et al. 2001).  While the contribution of postzygotic, maternal and paternal meiotic errors for each chromosome differs, among miscarriage specimens most are of maternal meiotic origin (Hassold and Hunt 2001).  In this section, I will provide an overview of oogenesis and discuss the aspects that make this process particularly susceptible to errors in chromosome segregation. 1.1.1 Oogenesis and meiosis During early female fetal development, primordial germ cells migrate to the gonadal ridge, which later develops into the fetal ovaries.  Oogenesis begins with a vast number of mitotic divisions, giving rise to ~7 million cells by the fifth month of gestation (Baker 1971).  At this time, the primary oocytes enter MI and are arrested during the diplotene phase of prophase I. They remain in this state until just before ovulation decades later.  Before birth, each primary oocyte is surrounded by a single layer of granulosa cells, forming a primary follicle.  By the time a female fetus reaches birth, the number of oocytes has decreased to ~2 million through apoptosis and these cells will continue to deplete until puberty (Figure 1.2) (Baker 1971).  Over the course of a woman’s reproductive lifespan, oocytes will be cyclically matured and ovulated until the pool is depleted at menopause.  Tilly and coauthors have recently challenged this central dogma, with studies suggesting that the ovarian capacity for oocyte production continues into adulthood (Johnson et al. 2004, White et al. 2012); however, this is highly contested in the scientific community and has yet to be independently reproduced.  A secondary follicle is a matured primary follicle and is comprised of a single enlarged oocyte and surrounding layers of differentiated granulosa cells and thecal cells.  Granulosa cells 3  are important for generating the protective zona pellucida, providing molecules to the oocyte, and secreting estrogens, while thecal cells have a supportive function and are the ovarian connective tissue (Lunenfeld et al. 1975).  The cyclical maturation of the follicle and its contained oocyte are hormonally controlled by the hypothalamus-pituitary-ovarian (HPO) axis through two phases: the follicular phase and luteal phase.  Once a follicle starts to develop, it will either reach maturity and be ovulated or degenerate and undergo atresia. The HPO axis is a feedback loop that begins with the production of gonadotropin releasing hormone (GnRH) in the hypothalamus, which in turn promotes the release of both luteinizing hormone (LH) and follicle-stimulating hormone (FSH) from the anterior pituitary gland.  During the follicular phase, the action of LH and FSH is to promote the expansion and increased estrogen production of the granulosa cells in a small number of recruited follicles (Figure 1.3) (Sherwood 2004).  The corresponding oocytes undergo rapid enlargement, however only one oocyte-containing secondary follicle will usually develop into a mature antral follicle for ovulation (Sherwood 2004). Initiated by the positive feedback of rising estrogen levels, an LH surge signifies the start of ovulation (Figure 1.3).  As a result, there are several important changes that occur in the ovary.  The follicular cells begin to differentiate into luteal cells.  The maturing oocyte completes MI, extruding a polar body, and then arrests in the metaphase of MII (Sherwood 2004).  The ovarian wall then ruptures, allowing the release of the mature oocyte into the fallopian tube. The luteal phase is characterized by the formation of the corpus luteum from the remaining follicle after rupture and release of the oocyte.  These cells become transformed into an active steroidogenic tissue, producing primarily progesterone (Figure 1.3) (Sherwood 2004). The progesterone secretion promotes morphological and biochemical remodeling 4  (decidualization) of the endometrium in preparation for implantation (Dockery and Rogers 1989).  If the oocyte is not fertilized, the corpus luteum deteriorates and the menstrual cycle begins again. If fertilized, the oocyte completes MII, extruding a second polar body.  The zygote grows and differentiates into the blastocyst, as it travels down the oviduct to the endometrium for implantation.  The corpus luteum produces increasing amounts of progesterone, in response to human chorionic gonadotropin produced by the developing embryo (Sherwood 2004).  The production of progesterone during these early weeks of pregnancy is essential, as removal of the corpus luteum during this stage causes the spontaneous loss of pregnancy (Csapo et al. 1972). After the embryo implants, the extraembryonic cells invade the maternal decidualized endometrium.  These cells aid in remodeling the maternal arteries and generate the placenta, a site for gas, waste and nutrient exchange between the mother and fetus for the remainder of pregnancy (Sherwood 2004).  In the 8 th  week of pregnancy, the production of progesterone is taken over by the placenta, in the luteoplacental transition (Csapo et al. 1972). 1.1.2 Maternal risk for chromosome missegregation There are two primary risk factors for meiotic non-disjunction in females: 1) advanced maternal age and 2) aberrant chromosomal recombination (Nagaoka et al. 2012).  There have been several advances in the past 10 years in understanding the etiology of these risk factors. However, further investigation in human oocytes is needed to validate and elaborate on these hypotheses. It has long been established that advanced maternal age is associated with increased risk for aneuploidy of most autosomal chromosomes (Hassold and Chiu 1985).  This association is hypothesized to be at least partially due to a progressive breakdown of the meiotic machinery 5  during the prolonged prophase arrest, in particular the cohesin protein complexes holding sister chromatids together.  Studies using aging animal models observed reduced cohesin proteins in the oocytes of aged females and consequently increased rates of aneuploidy (Liu and Keefe 2008, Subramanian and Bickel 2008).  However, there has been recent controversy as to whether the cohesin proteins in adult oocytes are in fact those that were established during fetal development or whether they are replenished during a female’s lifetime.  One study found that cohesin proteins are produced in human oocytes during adulthood, suggesting there is the potential for replenishment (Garcia-Cruz et al. 2010).  However, a series of experiments in mice tested whether these proteins were replaced upon destruction and found that there was no rescue of the phenotype (Tachibana-Konwalski et al. 2010).  Furthermore, mice with heterozygous mutations in cohesin genes showed elevated rates of oocyte aneuploidy that increased with maternal age (Murdoch et al. 2013). Aberrant recombination, which can include both achiasmate and poorly located crossovers in MI, have been shown to result in aneuploidy due to chromosome segregation errors (Lamb et al. 2005).  It has been hypothesized that oogenesis may be particularly prone to non- disjunction due to high rates of aberrant recombination in the fetal ovary and low stringency of meiotic check points during oocyte maturation.  While linkage studies of chromosomes 18 and 21 suggested that the frequency of achiasmate MI in oocytes is high (Bugge et al. 1998, Oliver et al. 2008), a study of 31 human fetal ovaries found that only 1.4% of oocytes had an achiasmate chromosome pair (Cheng et al. 2009).  In mice, it has been shown that meiosis progresses in oocytes despite the presence of double strand breaks due to failed repair during recombination (Kuznetsov et al. 2007).  These studies suggest that recombination in fetal oocytes may not be 6  particularly error prone, but those oocytes that do have recombination errors will likely progress through meiosis, thus explaining the maternal origin of many aneuploidies. 1.2 Maternal factors associated with recurrent miscarriage The primary maternal factors associated with RM can be categorized as chromosomal, anatomical, immunological and endocrinological; however, approximately 50% of cases are idiopathic (Figure 1.4) (Clifford et al. 1994, Stephenson 1996).  Variable frequencies and inconsistent associations of these factors and RM are pervasive throughout the literature.  This, in part, is not surprising given the complex etiology of RM; however, other contributors include the lack of consistency between clinical evaluations, underpowered studies and the wide inter- and intra-individual variability of many factors.   In particular, hormone levels and immunological cell populations can change with circadian clock, menstrual cycle, pregnancy, age and tissue type; making it very difficult to match case-control populations.  Furthermore, all of these abnormalities are identified in a substantial proportion of women with uncomplicated pregnancy histories, which suggests that additional environmental and/or genetic factors may contribute to risk for RM.  Despite these difficulties in the study of maternal conditions associated with RM, many factors result in increased risk for pregnancy loss and are important considerations in RM patient management (Rai and Regan 2006). 1.2.1 Chromosomal Approximately 3.5% of couples with RM are carriers of a balanced chromosomal rearrangement (Clifford et al. 1994, Stephenson 1996).  A proportion of the gametes from these couples would thus be unbalanced products of meiosis.  Despite the 50% expected risk of an abnormal conception, empirical evidence shows that the frequency of successful pregnancies among these couples is relatively high, with only one third of miscarriages having unbalanced 7  chromosomal rearrangements (Stephenson and Sierra 2006).  This is likely because some abnormal conceptuses do not survive to implantation. 1.2.2 Anatomical Uterine structural anomalies have been variably associated with RM, with incidence ranging from 1.8 to 16% (Clifford et al. 1994, Stephenson 1996).  These anomalies can include bicornate uterus, septate uterus, intrauterine adhesions and uterine fibroids, as well as rarer abnormalities.  While rates are estimated to be three times higher among women with RM than the general population (Chan et al. 2011), the frequencies reported among RM studies can be erratic due to variable inclusion criteria and imaging technology.  The mechanism of how these anomalies may cause miscarriage is unknown; however, physical impedance of the progression of pregnancy or poor implantation at affected regions has been proposed (Chan et al. 2011). Despite the association between uterine anomalies and RM, many affected women do go on to have successful pregnancies (Clifford et al. 1994) and it has not been determined whether surgical treatment of these conditions improves pregnancy rates (Tang and Quenby 2010). 1.2.3 Immunological Pregnancy is accompanied by dramatic changes in the maternal immune system, to allow the coexistence of a genetically distinct fetus.  Not only are there changes in the mother, but the placental barrier also helps to suppress the maternal immune response.  Both natural killer (NK) cells and T helper (Th) cells at the feto-maternal interface play a particularly important role in regulating inflammation at the time of implantation and throughout the remainder of pregnancy (Granot et al. 2012).  Defects, such as altered feto-maternal immune interactions, autoimmunity and infection, have been suggested to play a role in pregnancy loss, and may be particularly important in women with chromosomally normal RM.  As this field of study progresses, the 8  influence of hormones and stress on immune cell populations will need to be better understood and appropriately controlled for in the study of RM. Uterine natural killer cells Uterine NK (uNK) cells have been proposed to be important specifically in implantation and early pregnancy.  During the luteal phase, increasing numbers of uNK cells are observed, which then apoptose before the next follicular phase (King et al. 1991).  While the cyclic pattern of uNK cell proliferation implies there is hormonal regulation, the controlling factors have not been determined.  Upon fertilization, the number of uNK cells is increased further and maintained throughout the first 20 weeks of pregnancy; with the greatest enrichment at locations of placental invasion (King et al. 1998).  This suggests that they may be important for appropriate regulation of placental invasion and decidualization of the endometrium.  A large proportion of uNK cells also have distinct characteristics; they express different markers [cluster of differentiation (CD) 56 bright , CD16 - ] than those in peripheral circulation (CD56 dim , CD16 bright ), and have reduced cytotoxic potential with increased secretion of angiogenic factors (Hanna et al. 2006, Nishikawa et al. 1991).  The proportion, distribution and number of uNK cells, as well as peripheral NK cells, have been investigated in women with RM.  To summarize, RM has been associated with an elevated proportion of peripheral NK cells in blood (Emmer et al. 2000, King et al. 2010, Kwak et al. 1995), an increase in the proportion of uNK cells in the non-pregnant endometrium (Tuckerman et al. 2007) and lower levels of uNK cells in the decidua (Yamamoto et al. 1999a), when compared to healthy controls.  Another approach has been to compare the decidua from RM women with chromosomally abnormal miscarriages to those with chromosomally normal miscarriages (Quack et al. 2001, Yamamoto et al. 1999b).  The first study found a decrease in 9  uNK cells in the decidua of karyotypically normal miscarriages (Yamamoto et al. 1999b), however the latter found no difference in uNK cells, but an overall increase in activated leukocytes (Quack et al. 2001).  Together these studies suggest that changes in the immune cell composition of the maternal endometrium may be important in the predisposition to chromosomally normal RM, but the exact nature of these changes is unclear. There are many challenges and considerations in the design and interpretation of studies investigating NK cells and reproductive pathologies.  It is known that the prevalence of uNK cells is strongly associated with levels of progesterone, which vary throughout the menstrual cycle and at the cessation of pregnancy (King et al. 1998).  Furthermore, a recent study has found that reproducibility even within the same women from cycle to cycle is poor (Mariee et al. 2012). One group has hypothesized that elevated peripheral NK cells observed among RM women may in fact be due to an acute stress response at the time of blood draw, as levels returned to those consistent with controls upon a second blood draw within 20 minutes; this change was not observed in controls (Shakhar et al. 2006). Maternal T helper 1 and T helper 2 immune balance During pregnancy, there is an essential shift in maternal Th cell balance from cell- mediated to humoral immunity (Wegmann et al. 1993).  The two main players in this balance are Th1 and Th2 cells.  Th1 cells drive the cell-mediated response by producing cytokines, including interleukin 2 (IL-2), interferon gamma and transforming growth factor beta, that improve the killing efficacy of macrophages and cytotoxic T cells; while cytokines produced by Th2 cells (IL- 4, 5, 6, and 10) positively regulate B cells to produce neutralizing antibodies (Laird et al. 2003). A landmark study found that peripheral blood mononuclear cells in 60% of RM women were embryotoxic in vitro, through a cell-mediated Th1 response, and this was not observed in 10  control women (Hill et al. 1995).  Since, many studies have validated this finding, showing a predominant Th1 response in peripheral blood (Kheshtchin et al. 2010, Kwak-Kim et al. 2003, Ng et al. 2002) and decidualized endometrium (Michimata et al. 2003) of RM women compared to controls.  However, concerns have been raised that these studies were confounded by timing of sample collection (Laird et al. 2006), primarily due to the influence of progesterone on these immune cell populations (Check 2002).  There was no evidence of an abnormal increase in cell- mediated immunity in the endometrium of non-pregnant women with RM (Shimada et al. 2004). However, studies in mice support an endometrial shift in Th2 to Th1 immunity resulting in increased susceptibility to spontaneous abortion (Clark and Croitoru 2001) and this effect may be mediated by increased trophoblast apoptosis (Lee et al. 2005c). Placental immunity The placenta has specific adaptations to protect itself from the maternal immune response, primarily involving changes in cell recognition.  The major histocompatibility complexes (MHCs) are antigens on the cell surface that present peptides, originating from endogenous or exogenous proteins, to immune cells.  In the placenta, there is altered expression of the MHC I genes, also known as human leukocyte antigens (HLA) (Kovats et al. 1990).  In normal somatic tissues, the HLA types expressed are the highly variable A, B and C; while the placenta predominantly expresses HLA-E and the non-variable HLA-G (Wei and Orr 1990). Furthermore, the cells that express HLA-G in the extraembryonic tissues are those that come into contact with maternal cells (McMaster et al. 1995), including the invasive extra villous cytotrophoblast, while HLA-E is expressed in all placental cell types, but confined to the cytoplasm (Bhalla et al. 2006).  Decreased expression of HLA-G was observed in the cytotrophoblast of RM cases when compared to terminations (Emmer et al. 2002).  However, 11  this finding was not replicated in independent studies of cytotrophoblast cells (Bhalla et al. 2006) or the decidua/villi interface (Patel et al. 2003) from women with RM.  Polymorphisms in the HLA-G gene have been associated with RM in several studies and are discussed in more detail in section (page 18). Autoimmunity Autoimmunity has been implicated as a risk factor for pregnancy loss, and particularly RM.  While the immunosuppression of pregnancy has been associated with remission of some autoimmune conditions, such as rheumatoid arthritis, others, like systemic lupus erythematosus (SLE), can flare or increase in severity (Buyon 1998).  The strongest association with RM has been antiphospholipid syndrome (APS), defined as the presence of autoantibodies to cell membrane phospholipids (APA), present in 14-20% of RM women (Clifford et al. 1994, Stephenson 1996).  The rates of APS vary among RM populations, possibly due to erroneous false positives associated with recent infection (Ben-Chetrit et al. 2013); hence, two independent positive tests are recommended for diagnosis (Branch et al. 2010).  The typical clinical presentation of APS is an increased incidence of blood clots (thrombosis) with adverse pregnancy outcomes, including miscarriage.  It can occur independently or as a systemic autoimmune response, such as in SLE.  The incidences of SLE, in addition to other autoimmune disorders, are all elevated among RM patients compared to ethnically and age-matched controls (Christiansen et al. 2008).  These autoimmune conditions may cause RM through either thrombotic events in the placental vasculature (De Wolf et al. 1982), or poor placental invasion due to antibodies inhibiting trophoblast function (Yacobi et al. 2002). 12 Infection Ascending infection may disrupt the feto-maternal interface, by inducing a stronger cell- mediated maternal immune response, resulting in poor implantation.  Viral or bacterial infections can cause isolated miscarriage, but there are few chronic infections that are candidates for RM (Nigro et al. 2011).  One such candidate may be bacterial vaginosis, an overgrowth of anaerobic bacteria within the vagina, which has been associated with late RM (Llahi-Camp et al. 1996); although the benefits of treatment on reproductive outcomes have not been shown (Guise et al. 2001). 1.2.4 Endocrinological The hormonal balance in women is maintained by the HPO axis, which regulates maturation and ovulation of the oocyte, implantation and early pregnancy.  In this section, I will discuss several endocrinological conditions, including luteal phase defects (LPD), thyroid dysfunction, and polycystic ovarian syndrome (PCOS) that can increase risk for RM by disrupting this balance of hormones in early pregnancy. Luteal phase defects LPD are characterized by a lack of physiological changes associated with luteal phase progesterone, including reduced secretion from the corpus luteum or poor responsiveness of the endometrium (Smith and Schust 2011).  LPD can be caused by stress, exercise, weight loss and hyperprolactinemia (Arredondo and Noble 2006).  In vitro, the over-expression of prolactin has been shown to inhibit progesterone secretion from granulosa cells (McNatty and Sawers 1975). Among women with RM, LPD have been reported in 17-27% (Li et al. 2000, Stephenson 1996), although the diagnosis of LPD is still controversial.  Currently progesterone treatment is not recommended for women with RM; although a meta-analysis of several trials showed a marginal 13  reduction in miscarriage rates (Haas and Ramsey 2008).  A concern is that the studies were small and inadequately controlled, suggesting a need for a large-scale, randomized, placebo-controlled trial, assessing live birth rate as the primary outcome (Coomarasamy et al. 2011). Thyroid dysfunction Irregular production of thyroid hormones (TH), which in some cases can be caused by thyroid autoimmunity, is associated with RM (Smith and Schust 2011).  Although the exact mechanism of action in early pregnancy is unknown, it has been hypothesized that excess TH can cross the placental barrier and have a direct toxic effect on fetal growth and development (Anselmo et al. 2004).  Contrastingly, reduced levels of TH, due to autoimmunity or underproduction, may impair folliculogenesis by altering granulosa cell function (Wakim et al. 1993).  It has been suggested that the association of altered thyroid function with RM may be merely due to increased incidence of thyroid dysfunction in older women (Kaprara and Krassas 2008). Polycystic ovarian syndrome PCOS is a complex condition that is associated with irregular endocrine profiles, disrupted menstrual cycle, altered metabolic function, and/or obesity.  Approximately 60% of women with PCOS have at least one first trimester miscarriage (Glueck et al. 2002), although the cause of this association is unclear.  While early reports suggested there was an extremely high prevalence of polycystic ovaries among women with RM (Clifford et al. 1994); using the consensus Rotterdam criteria, the incidence only appears to be around 10% (Cocksedge et al. 2009), which is similar to that reported in the general population (Broekmans et al. 2006) . Women with PCOS have an endocrine profile that is characterized primarily by elevated androgens and high levels of LH.  Elevated LH has also been previously identified in women 14  with RM (Regan et al. 1990).  While not extensively studied, elevated androgens and/or LH do not appear to negatively affect folliculogenesis or oocyte quality (Gleicher et al. 2011, Gonen et al. 1990).  Alternatively, women with PCOS, as well as those with RM, often have insulin resistance (Celik et al. 2011, Craig et al. 2002) and increased incidence of obesity (Boots and Stephenson 2011).  Both obesity and metabolic changes have been associated with poor oocyte quality (Purcell and Moley 2011).  Furthermore, women with PCOS have a higher risk for thyroid autoimmunity (Janssen et al. 2004) and thrombophilic disorders (Moini et al. 2012). Taken together, the many features associated with PCOS may increase risk for RM independently or in combination. Endometrial receptivity An interesting hypothesis has recently emerged from one group, suggesting that women with RM may represent a distinct “superfertile” subset of the population and that the cause for recurring miscarriage is impairment in natural embryo selection by the endometrium (Teklenburg et al. 2010a).  In other words, embryos that would otherwise fail to implant, such as those with aneuploidy or other chromosomal abnormalities, are not recognized effectively in women with RM, resulting in implantation and subsequent miscarriage.  Supporting this hypothesis, women with RM were found to have a short time-to-pregnancy interval, with 40% achieving pregnancy in less than 3 months (Salker et al. 2010).  The decidualized endometrium secretes specific factors during the ‘window of implantation’ and these signals are altered in the presence of an arresting blastocyst (Teklenburg et al. 2010b).  This observation led to the postulation that the decidualized endometrium acts as a biosensor for abnormally developing embryos, and that it may be perturbed in women with RM (Teklenburg et al. 2010a).  This group went on to show that the endometrium from women with RM showed altered expression of 15  genes associated with the ‘window of implantation’ and that this could be corrected through in vitro decidualization of the cells (Salker et al. 2010).  This mechanism could also explain some of the immunological and endocrinological associations with RM. 1.2.5 Thrombophilic Thrombophilia is a multifactorial condition that is characterized by an increased risk for the formation of blood clots.  The association and treatment of acquired or inherited thrombophilias among women with RM is controversial (Greer 2011, Krabbendam et al. 2005, McNamee et al. 2012).  Inherited thrombophilias refer to mutations and/or polymorphisms in genes involved in or modulating the activity of the coagulation pathway, while acquired thrombophilias generally describe APS or acquired activated protein C (an anti-coagulant) resistance (McNamee et al. 2012).  Thrombosis may cause late pregnancy loss through disruption of placental vascularization and blood flow to the developing fetus (Vora et al. 2009, Weiner et al. 2003).  Consistently, women with thrombophilias have been found to be at increased risk for stillbirths (Preston et al. 1996). A clear connection between inherited thrombophilias and risk for RM has been elusive (Kovalevsky et al. 2004, Krabbendam et al. 2005, Lund et al. 2010).  The strongest candidate associations are summarized in section (page 19).  Given the rarity of some of these alleles and the inconsistent associations, testing for these variants is currently not recommended as a clinical assessment in the evaluation of RM (Practice Committee of the American Society for Reproductive Medicine 2012). The current recommended therapy for women with thrombophilia, in the form of APS, and RM is a combination of aspirin and heparin during pregnancy (Practice Committee of the American Society for Reproductive Medicine 2012).  However, it has been argued that there is a 16  need for practice of evidence-based medicine in this area, as numerous trials have failed to show the efficacy of treatment (Mantha et al. 2010, Tan et al. 2012).  In fact, it has even been suggested that treatment of inherited thrombophilia may cause maternal harm, due to rare but serious bleeding as a side-effect of anticoagulants, discomfort of daily injections, erroneous treatment of patients with false positive tests, and psychosocial stress (Bradley et al. 2012). 1.2.6 Psychosocial stress Psychological stress has been implicated in both pregnancy loss and RM risk.  Women with increased cortisol, a physiological marker of stress, during the first few weeks of pregnancy were greater than two times more likely to miscarry than those women with levels in the normal range (Nepomnaschy et al. 2006).  Three independent studies found that supportive care improved successful pregnancy rates among women with RM from 30-50% to over 80% (Clifford et al. 1997, Liddell et al. 1991, Stray-Pedersen and Stray-Pedersen 1984).  Women with RM reported higher levels of psychological stress compared to fertile controls, although it was not predictive of positive pregnancy outcomes in these women (Li et al. 2012). One mechanism that has been proposed to link elevated stress to miscarriage is altered immune function.  Reduced cytotoxicity of peripheral blood NK cells was observed among RM women with higher depressive symptoms (Andalib et al. 2006), although this may not reflect uNK cell changes.  Mice with elevated stress (ultrasonic sound exposure) during pregnancy have higher embryo resorption rates, which was associated with an increase in cell-mediated immune response in the endometrium (Joachim et al. 2001).  In addition, psychological stress has been associated with markers of biological aging, such as telomere length and reactive oxidative species (ROS), which will be discussed further in Chapter 3. 17  1.3 Genetic and epigenetic factors contributing to recurrent miscarriage risk RM is likely a multifactorial complex trait, as familial studies have shown that sisters of patients with RM are 6 times more likely to have RM than control women (Christiansen et al. 1990).  Genetic and environmental factors may contribute to the etiology of RM in an additive or synergistic epistatic manner, affecting maternal risk by negatively impacting the progression of oogenesis, implantation or early fetal development.  Extensive studies of maternal genetic variants, in pathways already implicated in the etiology of RM including meiosis, immunological, endocrinological and thrombophilic, have been undertaken to identify reliable biomarkers of risk and further elucidate the pathogenesis of RM (Christiansen et al. 2008). While there has been considerable progress in this area, there are many inconsistent associations and are likely attributable to differences in ethnicities and underpowered studies.  An additional area of study is aspects of chromosome biology, including telomere length, skewed X chromosome inactivation (XCI) and epigenetic modifications.  Aberrant establishment or maintenance of these important processes in the oocyte or embryo may result in miscarriage due to increased risk for non-disjunction or limited cellular capacity for differentiation.  In this section, I will summarize the evidence that genetic and epigenetic factors contribute to risk for RM. 1.3.1 Maternal genetic variants Genes involved in meiosis Genetic variants in genes involved in meiosis may predispose some women to high rates of aneuploidy, due to increased rates of non-disjunction.  The subset of women with RM and recurrent heterotrisomies at a young age may be strong candidates for this genetic predisposition. To date there has only been one gene investigated for mutations in association with RM, 18  synaptonemal complex protein 3 (SYCP3) (Bolor et al. 2009).  SYCP3 is one of several proteins that are essential for tethering of homologous chromosomes together during MI prophase (Yuan et al. 2000).  Further assessment of the ~400 genes encoding meiosis-specific proteins (Feichtinger et al. 2012) may be an area of future study in this subset of RM cases. Genes involved in immune function In addition to the assessment of gene expression patterns of HLA-G in RM, genetic polymorphisms within the gene have been examined in numerous studies.  Reproducible associations, among larger studies, have been identified for HLA-G haplotypes *010103 (synonymous) and *0105N (frame shift), comprised of seven coding SNPs within exons 2 and 3, and a 14bp insertion/deletion in the 3’untranslated region (UTR) of the HLA-G gene (Aldrich et al. 2001, Pfeiffer et al. 2001, Vargas et al. 2011, Zhu et al. 2010).  It has been proposed that HLA-G polymorphisms (14bp in/del, specifically) may also predispose women to secondary RM, if their first liveborn infant was male, by contributing to an altered maternal immune response to the HY (male-specific histocompatibility antigen) (Christiansen et al. 2012).  The pathogenesis of this association needs to be elucidated further, but it is an interesting explanation for the epidemiological finding that secondary RM is more frequent after male births (Ooi et al. 2011). Genes involved in endocrine function While there may be an underlying genetic susceptibility to hormonal imbalance in women with RM, genetic variation in genes involved in the HPO axis has not been widely studied.  A moderate association with a SNP (rs10046) in the aromatase (CYP19A1) gene was identified in a study of the estrogen synthesis pathway (Cupisti et al. 2009, Litridis et al. 2011). In addition, several polymorphisms in the promoter and intron 2, as well as missense mutations with functional consequences, of the chorionic gonadotropin (CGB5/8) genes have been 19  associated with RM (Nagirnaja et al. 2012, Rull et al. 2008).  A recent meta-analysis of polymorphisms in the estrogen receptor α (ESR1) and progesterone receptor (PR) genes found no association (Su et al. 2011).  While these data suggest genetic variation in receptors and regulatory genes may influence risk for RM, a more comprehensive study is needed. Thrombophilia associated genes There has been extensive study of inherited thrombophilias, with the primary focus being on the functional polymorphisms in genes involved in three pathways that influence blood clot formation: coagulation, fibrinolysis, and the folate cycle (Krabbendam et al. 2005).  Two variants, Factor V Leiden (F5) G1691A and prothrombin (F2) G20210A, have been associated with increased risk for late miscarriage (≥10 weeks gestation) in a cohort of more than 32,000 women (Lissalde-Lavigne et al. 2005).  A systematic review and meta-analysis also found that these variants were associated with RM (Bradley et al. 2012, Kovalevsky et al. 2004).  In addition, two studies with cohorts of >500 women with RM, both found associations with plasminogen activator inhibitor 1 (PAI-1 or SERPINE1) 4G and methylenetetrahydrofolate reductase (MTHFR) C677T variants (Goodman et al. 2006, Ozdemir et al. 2012).  While these data suggest that genetic variation in pathways involved in thrombosis susceptibility contributes to risk for RM, environmental factors may also play a role. The folate cycle involves the conversion of dietary folic acid into intermediate molecules contributing to the methylation and nucleotide synthesis pathways.  Numerous variants in this pathway have been studied for their association with disease, particularly in fetal health and survival.  The most influential and widely studied is the MTHFR C677T polymorphism, a nonsynonymous change that causes a dramatic reduction in enzyme activity (Frosst et al. 1995). In addition to causing elevated homocysteine, genetic variation within folate cycle enzymes, 20  including MTHFR, can cause decreased production of methyl donors and nucleotide precursors (DeVos et al. 2008).  As mentioned above, two large studies identified an association between MTHFR C677T and RM (Goodman et al. 2006, Ozdemir et al. 2012); however, a meta-analysis only found an association with RM in Chinese populations, suggesting its effect may be modified by genetic background and environmental factors, such as diet (Ren and Wang 2006). There has been no other consistent evidence of an association between other genetic variants within the folate cycle and RM. MTHFR C677T may contribute to susceptibility of RM through several potential mechanisms: 1) altered establishment and/or maintenance of DNA and/or histone methylation in the developing oocyte or embryo, 2) aberrant DNA synthesis/repair in the developing oocyte or embryo, 3) placental thrombosis due to elevated homocysteine levels, or 4) impaired ovarian function affecting oocyte maturation.  While low folate diet and/or MTHFR C677T polymorphism are known to reduce levels of methyl donors, there have been very few studies in humans demonstrating that maternal diet or polymorphisms alter fetal DNA methylation (Hogg et al. 2012, Park et al. 2008).  In addition, folic acid deficiency in cell culture causes uracil to be mis-incorporated into DNA, leading to point mutations and genomic instability (Duthie and Hawdon 1998), suggesting folate levels are important for genomic integrity.  Low folate diet and/or MTHFR C677T also cause elevated homocysteine (Guttormsen et al. 1996), a nonessential amino acid that is associated with the risk for thrombosis (den Heijer et al. 1996). Independent of the effects seen in the folate pathway, two separate groups have found that MTHFR genotype can influence ovarian activity, specifically decreasing estrogen synthesis from granulosa cells and increasing serum FSH levels (Hecht et al. 2009, Rosen et al. 2007). 21  1.3.2 Telomeres Telomeres are the TTAGGG repeats that associate with complexes of proteins to protect the ends of chromosomes in humans.  Telomere length is a marker of biological aging, as it declines proportionally with number of cell divisions due to the end replication problem (Harley et al. 1990).  Additionally, exposure to oxidative stress within the cell increases the rate of loss (Serra et al. 2000) and may account for telomere length decline in cells that do not replicate, such as oocytes.  Cells that express telomerase are able to elongate telomeres through reverse transcription; however the majority of somatic cells lack this enzyme (Kim et al. 1994). Telomeres are not only important in protecting the chromosomes from degradation, but also for positioning in meiosis, by allowing the chromosome pairs to tether together and align appropriately within the cell for recombination (Cooper et al. 1998).  In mouse, irregular shortening of telomeres is associated with abnormal recombination and synapses in meioses, particularly in females, mimicking the age-related effects (Liu et al. 2004).  This led to the hypothesis that the age-related increase in aneuploidy rates in women may be partly attributable to a decline in telomere length (Keefe et al. 2006).  This group also showed that exposing mice to a compound that reduces the effects of oxidative stress, increased telomere length in the ovaries and improved egg quality (Liu et al. 2012).  In humans, telomere length in sister oocytes from women undergoing in vitro fertilization (IVF), was also a strong predictor of pregnancy outcome (Keefe et al. 2007), suggesting that telomere length may be important for human reproductive health as well. 1.3.3 Epigenetics in fetal development Epigenetics is defined as mitotically heritable chemical changes that influence gene expression, without affecting DNA sequence.  These changes can include DNA methylation, 22  histone modifications, histone variants and non-coding RNAs.  The most extensively studied is DNA methylation, which primarily involves the addition of a methyl group to a cytosine in a CpG dinucleotide.  Generally, it is thought that DNA methylation at promoter regions can limit the accessibility of this DNA to transcription machinery, directly and through crosstalk with epigenetic modifiers, and thus reduce expression of the associated gene (Klose and Bird 2006). Epigenetic patterns are essential in development for tissue differentiation and response to environmental cues (Monk 1995).  Aberrant establishment or maintenance of epigenetic marks in the developing embryo may be a mechanism for pregnancy loss (Messerschmidt et al. 2012, Pliushch et al. 2010, Yin et al. 2012). Developmentally important imprinted genes, those that are mono-allelically expressed in response to parent-of-origin differentially methylated regions have specifically been examined for an association with miscarriage.  The first study to look at DNA methylation in miscarriage samples reported an increase in outliers at several imprinted loci (Pliushch et al. 2010).  Since, there have been two studies investigating DNA methylation at specific loci in RM, both with limited sample size and therefore must be interpreted with caution.  Aberrant gain of allelic DNA methylation at the CGB5 gene, a non-imprinted gene, in placental trophoblast and loss of H19 methylation in sperm was observed from couples with RM (Ankolkar et al. 2012, Uuskula et al. 2011).  Interestingly, a mouse model deficient for an epigenetic modifier gene (Trim28) in oocytes showed preferential loss of DNA methylation at imprinted loci and complete embryonic lethality (Messerschmidt et al. 2012), suggesting that loss of differentially methylation regions in oogenesis may be a mechanism of RM.  A comprehensive analysis of genome-wide and site- specific changes of DNA methylation in miscarriage and RM is needed to evaluate the frequency and nature of epigenetic errors in early pregnancy. 23  1.3.4 Maternal skewed X-chromosome inactivation X-chromosome inactivation (XCI) is the epigenetic silencing of one of the X chromosomes to allow dosage compensation in females.  In humans, there is random XCI in all tissues during development, with each X being inactivated in approximately an equal number of progenitor cells, resulting in a 50:50 distribution (Gartler 1976).  Skewed XCI can occur when there is selection against cells that have inactivated a particular X chromosome, for example if there was a chromosomal aberration or mutation, or due to stochastic events in a small number of progenitor cells (Willard 1996).  Skewed XCI, measured in peripheral blood, has been previously associated with aging (Hatakeyama et al. 2004) and various diseases, in particular RM (Beever et al. 2003).  While there have been conflicting reports in the literature, a recent meta-analysis found a two-fold increased risk for RM among women with skewed XCI (Su et al. 2011).  It has been suggested that cryptic rearrangements or mutations on the X chromosome or a restricted stem cell population early in development may explain this association (Robinson et al. 2001), but this remains to be demonstrated. 1.4 Research objectives The purpose of my thesis is to investigate factors that may contribute to the pathogenesis of RM.  I hypothesize that genetic and/or epigenetic factors associated with meiotic non- disjunction, maternal endocrinological profile, reproductive aging in females and/or placental functioning will contribute to the etiology of RM.  Therefore the objectives of this study are: 1) To determine whether mutations in the synaptonemal complex protein 3 (SYCP3) gene are associated with increased risk for aneuploidy among women with RM. 2) To compare telomere lengths in peripheral blood between women with RM and healthy controls, as an indicator of reproductive aging. 24  3) To evaluate the frequencies of functional polymorphisms within genes involved in the HPO axis among women with RM and those that are reproductively healthy. 4) To investigate patterns of DNA methylation in placental villi from first trimester, karyotypically normal products of conception from women with RM, a single miscarriage, or an elective termination. This study will improve our understanding of mechanisms involved in RM and will identify markers of potential prognostic value for clinical evaluation. 25   Figure 1.1 Gametes as the product of meiosis I non-disjunction and meiosis II non-disjunction.  Diagram shows one pair of homologous chromosomes progressing through the meiotic divisions, missegregating in meiosis I (left) and meiosis II (right). Fertilized gametes would either result in trisomy or monosomy. 26   Figure 1.2 Number of germ cells with age in females.  The number of germ cells increases dramatically through mitotic divisions in the female fetus until the peak at ~6 months gestation. These then undergo apoptosis, depleting to ~2 million by birth.  At the onset of puberty, oocytes are cyclically recruited until their eventual depletion at menopause, occurring at an average age of 50 years old (based on Baker 1971, with permission). 27   Figure 1.3 Hormone levels and follicular events during the menstrual cycle.  The days of the menstrual cycle are divided into three portions: 1) follicular phase, 2) ovulation, and 3) luteal phase.  The corresponding relative hormonal levels and follicular events are shown in the panels below (based on Mader 2006, with permission). 28   Figure 1.4 Etiology of recurrent miscarriage.  Proportion of recurrent miscarriage patients with parental chromosomal rearrangements (balanced translocations), immunological (anti-phospholipid syndrome), uterine anatomical malformations, endocrine (luteal phase defects and thyroid dysfunction) and unknown etiology (based on averages, wherever possible, from Clifford et al. 1994, Stephenson 1996). 29  Chapter  2: Mutational analysis of the SYCP3 gene 2.1 Introduction Women with RM due to aneuploidy likely have a distinct etiology, as these women may represent a subset of the population that is at increased risk for meiotic non-disjunction.  Genetic variation in genes involved in chromosome pairing, recombination and segregation in meiosis may contribute to this increased risk.  SYCP3 is involved in forming the synaptonemal complex in MI, which is a structure that allows for the pairing and recombination of homologous chromosomes.  SYCP3 is an essential component of the axial and lateral elements of this complex that holds the chromosomes together (Page and Hawley 2004). In mice deficient in Sycp3, chromosomes fail to synapse (Yuan et al. 2000).  Male mice deficient in Sycp3 are infertile due to arrest of meiosis, while female mice are fertile but have decreased litter sizes attributable to increased rates of trisomic fetuses due to abnormal pairing of the chromosomes in meiosis in the germ cells (Yuan et al. 2002).  Bolor and coauthors (2009) first suggested a role of mutations in the SYCP3 gene in RM, identifying two of 26 Japanese women with RM with heterozygous mutations within and nearby exon 8.  The present study sought to identify novel and previously observed mutations in SYCP3 in women with RM who had at least one documented trisomic miscarriage, a subset most likely to carry mutations in SYCP3. 2.2 Materials and methods A total of 50 women with RM and at least one trisomic conception, were ascertained in the Recurrent Pregnancy Loss Program at BC Women’s Hospital & Health Centre, Vancouver, British Columbia.  The age at time of pregnancy [mean ± SD (range)] was 36.2 ± 5.2 years (22- 44) with a total of 292 pregnancies, of which 216 (74%) ended in miscarriage. Table 2.1 30  summarizes the pregnancy outcomes within this study population, including the distribution of miscarriage karyotypes.  The number of miscarriages was 4.3 ± 1.5 (3-9); with 34 women having a single trisomic miscarriage and 16 women having multiple heterotrisomic miscarriages. Carriers of structural chromosome rearrangements were excluded from this study. DNA was extracted from whole peripheral blood using conventional methods.  All coding exons (2-9) of SYCP3, including the intron/exon boundaries, were PCR amplified by conventional PCR, using primer sequences shown in Supplementary Table 2.1.  Sequencing was done utilizing a 3130xl genetic analyzer (Applied Biosystems, Melbourne, Australia), with BigDye Terminator sequencing kit version 3.1.  Sequence data were analyzed using Chromas 2.33 (Technylisium, Australia) and SeqDoC (Crowe 2005). 2.3 Results In this study, all coding exons (2-9) of the SYCP3 gene, located on chromosome 12q23.2, were sequenced.  To assess intron/exon boundaries, peripheral sequence surrounding each exon was included in the corresponding PCR amplicon [mean ± SD = 161.2 ± 82.1 base pairs (bp)]. No novel or previously reported mutations within the coding exons or intron/exon boundaries were identified in our study population. Four non-coding single nucleotide polymorphisms (SNPs) were present at variable frequencies (2-29%) among these 50 RM women (Figure 2.1).  The frequencies of these SNPs were comparable to those reported in the world-wide population, obtained from the UCSC Genome Browser (Table 2.2).  However, this finding should be interpreted with caution as the ethnicity of the population and RM groups is likely extremely divergent. 31  2.4 Discussion In the first study to assess SYCP3 in 26 Japanese women with RM, two mutations were identified: a 4 bp deletion within the splice acceptor site of exon 8 resulting in C terminal truncation and a synonymous change at position 657T>C, which disrupted splicing of intron 8 (Figure 2.1) (Bolor et al. 2009).  The C terminal region is of particular importance for SYCP3 function, as it comprises a coil-coiled domain that is highly conserved and has been shown to be necessary in rats for SYCP3 assembly in meiosis (Baier et al. 2007).  An additional homozygous variant (666A>G) was identified in two Japanese female with unexplained infertility among 88 investigated (Figure 2.1) (Nishiyama et al. 2011). In contrast, a more recent study found no association between the SYCP3 657T>C variant and RM in 101 Japanese women, nor when assessing the subset of 47 women with at least one karyotypically abnormal miscarriage, although not strictly trisomic (Mizutani et al. 2011).  The present study supports this latter finding in an ethnically divergent western Canadian population, which is predominantly Caucasian.  Although the sample size is limited, the cohort was well-characterized and included women most likely to be at increased risk of meiotic non-disjunction.  An additional strength of this analysis was the investigation of the entire coding region including the intron/exon boundaries in SYCP3 for variants within this population.  These findings, therefore, suggest that mutations in SYCP3 are not a common factor contributing to risk for meiotic non-disjunction in human maternal gametogenesis.  Given the complexity of meiosis and the many genes involved in this process, it seems unlikely that mutations in a single gene would account for a large number of RM patients. Multiple mutations and/or polymorphisms in a variety of genes may influence risk for non-disjunction and subsequent RM.  Further characterization of RM patients 32  and the application of techniques such as whole exome sequencing, which would allow screening of many genes of interest simultaneously, would help clarify the underlying mechanisms involved. 33  Table 2.1 Outcomes of 292 pregnancies from 50 women with recurrent miscarriage. Miscarriages are further subcategorized by karyotype. Pregnancy Outcome Number Livebirth 56 Termination 15 Ectopic 5 Miscarriage 216  46, XX or 46, XY  17  Trisomy*  73  Triploidy  3  Other  2  Not karyotyped  121 * This includes 22 cases of trisomy 13-15, 14 cases of trisomy 16, 11 cases of trisomy 21-22, and 26 cases of other trisomies. 34  Table 2.2 Minor allele frequencies of noncoding single nucleotide polymorphisms within the SYCP3 gene identified in 50 recurrent miscarriage women.  For each variant, the dbSNP identifier, genic location and heterozygosity are given, if available.  Population frequencies, from the UCSC Genome Browser, were not significantly different from those in our RM study group, using Yates chi-square comparison. Variant Genic Location Heterozygosity Population minor allele frequencies RM women minor allele frequencies rs3751248 intron 2 0.205 +/- 0.246 8.00% 5.10% rs10860779 intron 5 0.444 +/- 0.157 33.29% 29.00% rs145003954 intron 6  not reported 8.29% 5.00% rs17723833 exon 9 (3’UTR)  not reported 2.28% 2.00% 35   Figure 2.1 Schematic diagram of the SYCP3 gene and variants.  Exons of the SYCP3 gene are denoted by grey boxes and numbered, with wider coding exons than those comprising of the untranslated regions.  Introns are marked by the dashed line.  The transcription start site is marked with an arrow, showing direction of gene transcription.  Mutations previously associated with RM are labeled in black, while SNPs found among this RM study population are labeled in red. 36  Chapter  3: Telomere length and reproductive aging 3.1 Introduction Female fertility declines with age due to the combined effects of both a decrease in the rate of conception and an increase in the rate of pregnancy loss due to aneuploidy.  Age-related changes in the human ovary, including depletion of ovarian follicles (Faddy et al. 1992, Faddy 2000) and a decline in oocyte genomic stability leading to aneuploidy (Hassold and Hunt 2001) may contribute to this phenomenon.  The rate of female reproductive aging displays a large amount of inter-individual variability.  This is reflected in the variability in age of reproductive senescence (menopause), which typically occurs anytime between 40 to 60 years of age (Kato et al. 1998, te Velde and Pearson 2002), as well as in the individual variability in risk of conceiving a trisomic pregnancy (Nicolaides et al. 2005, Warburton et al. 2004).  This natural variation in reproductive aging may be the result of environmental and genetic factors that affect individual rates of cellular aging. Both animal models and human epidemiological studies support the suggestion that longevity is associated with an increase in reproductive lifespan.  Mice and flies selectively bred for reproductive longevity have an overall increase in total lifespan when compared to unselected controls (Hutchinson and Rose 1991, Nagai et al. 1995).  Human population studies have reported that higher total fecundity (Manor et al. 2000, Muller et al. 2002), later age at last reproduction (Doblhammer 2000, Helle et al. 2005, Muller et al. 2002, Smith et al. 2002) and older age at menopause (Cooper and Sandler 1998, Jacobsen et al. 1999, Snowdon et al. 1989) are positively correlated with longevity.  A study of female centenarians found that women living to at least 100 are greater than four times more likely to have had a child while in their forties than women living to age 73 (Perls et al. 1997).  There are several possible explanations 37  for the relationship between longevity and age at menopause: 1) prolonged estrogen exposure associated with later menopause may have a positive influence on life expectancy (Perls et al. 1997), 2) effective age of the ovary could directly affect longevity (Cargill et al. 2003, Hsin and Kenyon 1999), or 3) selective pressures to maximize a woman’s reproductive years by slow reproductive aging may have positively selected for women with slower rates of cellular aging (Perls et al. 2002, Perls and Fretts 2001). Telomere length exhibits considerable inter-individual variation (Hastie et al. 1990) and may contribute to the observed variability in reproductive aging.  Telomere variability may be due to differences in telomere length at conception, telomerase activity during early development, rate of cell division and rate of telomere loss per cell division.  Shorter telomeres may limit the mitotic capacity of primordial germ cells during fetal development and therefore restrict the size of the follicular pool (Keefe et al. 2006).  Studies examining telomere length and reproductive aging in humans have produced contradictory results in which telomere length has been both positively and negatively associated with different measures of reproductive aging (Aydos et al. 2005, Dorland et al. 1998a, Keefe et al. 2007). Given the links between reproductive aging and biological aging, and the potential influence of telomere length on oocyte quality, I hypothesized that women who display evidence of premature reproductive aging will have a shorter average telomere length than control women. The objective of this study was to assess telomere length in peripheral blood leukocytes in two groups of women with evidence of premature reproductive aging: 1) patients with idiopathic premature ovarian failure (POF) who experienced menopause before 40 years of age, and 2) women with a history of RM.  While menopause represents a finite end of the reproductive lifespan, it is preceded by a period of subfertility, in which women have increased susceptibility 38  to miscarriage (Broekmans et al. 2009).  These study groups were compared to two control groups: 1) women from the general population not selected on the basis of reproductive history and 2) women who had had a healthy pregnancy after 37 years of age and had not experienced any pregnancy loss.  This latter group may represent women with potentially slower rates of reproductive aging, as they have not experienced difficulties conceiving or maintaining pregnancy despite a relatively advanced reproductive age. 3.2 Materials and methods 3.2.1 Samples  Women with RM (N=95), which includes 47/50 RM cases from Chapter 1, were ascertained through the Recurrent Pregnancy Loss Clinic at Women’s Health Centre of British Columbia.  These 95 women had a total of 458 miscarriages, and of those, 167 were karyotyped. Karyotyped miscarriages consisted of 72 diploid losses, 71 aneuploid losses and 24 other anomalies, including polyploidy, sex chromosome aneuploidies, and translocations.   Of those women with aneuploid losses, there were 32 women who had a single trisomic miscarriage (ST), and 17 women who had multiple trisomic miscarriages (MT).  POF patients (N=34) with idiopathic secondary amenorrhea were ascertained from the POF Clinic at the Women’s Health Centre of British Columbia.  POF diagnosis was made based on the absence of menses for at least 3 months and two serum FSH results of >40 mIU/mL obtained more than one month apart, prior to 40 years of age. Two control groups were used in this study: Control group 1 (N=108) consisted of healthy women of reproductive age, ranging from 17 to 55 years, and Control group 2 (N=41) consisted of women who have had a healthy pregnancy over 37 years of age with no history of infertility or miscarriage.  Control group 1 was comprised of anonymous healthy women from 39  the Vancouver area.  Similarly, Control group 2 had healthy women from the Vancouver area, ascertained specifically at the British Columbia Women’s Hospital on the basis of a healthy pregnancy after the age of 37.  DNA was obtained by standard salt extraction from ~5mL of blood collected in EDTA tubes. 3.2.2 Telomere length  Average relative telomere length was determined by quantitative PCR (qPCR) (Cawthon 2002).  Amplification of the telomeric repeat region was expressed relative to amplification of 36B4, a single copy housekeeping gene on chromosome 12.  This telomere to single copy (T/S) ratio is proportional to the average telomere length of the sample, due to the amplification being proportional to the number of primer binding sites in the first cycle of the PCR reaction (Figure 3.1) (Cawthon 2002).  The protocol was performed as previously described (Cawthon 2002) with several modifications; amplifications were carried out in 20uL reaction with approximately 5ng genomic DNA, 0.5uM ROX Reference Dye (Invitrogen, Carlsbad, USA), and 0.2x SYBR Green I nucleic acid gel stain in DMSO (Invitrogen, Carlsbad, USA).  Samples were run in triplicate on 96-well plates containing a standard curve constructed with reference DNA serially diluted to concentrations from 10ng to 0.625ng.  A no template control and both short- and long-telomere reference samples were run on each plate as quality controls.  Dissociation melting curves were run after each sample to ensure amplification of a single species. Replicates of each plate were done to ensure reliable values were ascertained.  The values between both runs were significantly correlated, with a correlation coefficient of r=0.49 (p<0.0001).  To improve the accuracy of our estimates we averaged the values of the two independent measurements.  When values were discrepant between the two runs by more than 0.2 SDs, subsequent runs were done and an average of all values was used in further data analyses. 40  The telomere-qPCR assay was validated using DNA extracted from leukocyte cell pellets following flow fluorescence in situ hybridization (FISH) (N=12) (Baerlocher et al. 2006).  There was a strong correlation between the qPCR T/S ratio and the flow-FISH telomere lengths (r=0.96) (Figure 3.2).  The strong correlation obtained validates the use of an average measurement of t/s values as an accurate reflection of telomere length. T/S values were converted to kilobases (kb) using the linear equation from this correlation (y = 7.25x + 2.50).  As expected the y-intercept is at 2.5 kb since the flow-FISH assay was calibrated using Southern blot telomere restriction fragment lengths, which includes ~ 2.5 kb of subtelomeric repeat (Baerlocher et al. 2006). 3.2.3 Statistical analysis Rate of telomere decline was determined by linear regression analysis, and one-tailed t- tests were used to determine the significance of the regression because of the a priori hypothesis that telomere length was associated with age.  Yearly rates of telomere decline were compared using two-tailed t-tests for comparison of regression slopes.  Mean telomere length comparisons between sample groups were determined using pair-wise analysis of covariance (ANCOVA) tests to adjust for differences in ages between sample groups. 3.3 Results Telomere length in Control group 1 significantly declined with age (p=0.001,  one-tailed t- test) at a rate of 40 bp per year [95% confidence interval (CI) =14-66 base pairs], although there was significant variability in telomere length at any given age (R 2 =0.081, Table 3.1, Figure 3.3). There was also a weak (R 2 =0.161) but significant negative association between telomere length and age in POF patients (p=0.01 one-tailed t-test), but not in Control group 2 or the RM group as a whole.  Subsets of the RM group who have experienced ST or MT are of particular interest, as 41  incidence of trisomic pregnancy increases with age, contributing to the age-related increase in RM.  There was a weak (R 2 =0.130) but significant relationship between telomere length and age in the ST subset of the RM group (p=0.02, one-tailed t-test) but not the MT subset (Table 3.1, Figure 3.4).  However, in no sample group was the rate of telomere decline significantly different than that of Control group 1 (two-tailed t-tests for comparison of regression slopes), thus ANCOVA was used to adjust for age effects on mean telomere length for further comparisons of mean telomere length between groups.  Mean telomere length and age-adjusted mean telomere length for each sample group are shown in Table 3.2.  Although women in Control group 2 had longer age-adjusted mean telomere lengths than those in Control group 1, this difference was not significant.  The RM group had shorter age-adjusted mean telomere length than Control group 1 (8.46 vs. 8.92 kb, p=0.0004) and this was also apparent in comparison to Control group 2 (9.11 kb, p=0.02). However, short telomeres were not specifically confined to the subset of this group that had had either a single trisomy or multiple trisomic pregnancies.  Contrary to expectation, age-adjusted mean telomere length in the POF patient group was longer than that in Control group 1 (9.58 vs. 8.92 kb, p=0.01), although this was not significant in comparison to Control group 2. 3.4 Discussion  Telomere-specific qPCR was used to assess telomere length in groups of women with a reproductive history suggestive of premature reproductive senescence to determine whether telomere length is associated with reproductive aging.  As hypothesized, women experiencing RM had shorter age-adjusted mean telomere length than control women, although this effect was not specifically confined to women with trisomic pregnancies.  In contrast, POF patients had a longer age-adjusted mean telomere length than that of controls.  The high variability in telomere 42  length at any given age and the rate of telomere length decline with age has been previously reported in many control populations (Benetos et al. 2001, Hastie et al. 1990, Slagboom et al. 1994).  In this study, the relationship between telomere length and age was not significantly different than zero in all sample groups, perhaps reflecting the limited age range in some groups. Regardless, none of the groups had a significantly different rate of telomere decline than that of controls. The observed shorter average telomere length in women with RM and the trend of longer telomere lengths in women in Control group 2, who have had viable pregnancies late in their reproductive life, are consistent with the hypothesis that telomere length is a determinant of the rate of reproductive aging in women.  Previous studies have reported that telomere length is a strong predictor of developmental potential of sister oocytes from women undergoing IVF (Keefe et al. 2007) and is also correlated with reproductive life span in women (Aydos et al. 2005).  Short telomere length in telomerase-deficient mice is associated not only with premature aging but also with reduced fecundity leading to sterility (Liu et al. 2002).  These mice exhibit impaired oogenesis and mimic the human age-related decline in oocyte quality, with increased rates of apoptosis of the oocytes, impaired chromosome synapsis and recombination, and increased likelihood of non-disjunction and aneuploidy (Liu et al. 2004). Young mothers of children with Down syndrome have normal telomere lengths (Dorland et al. 1998b), suggesting that predisposition to non-disjunction may not be the only explanation for the finding of shortened telomeres in women with RM; although, the telomere length of peripheral blood cells may not necessarily reflect that of oocytes or embryos.  Psychological stress indicated by physiological stress markers has also been shown to negatively influence telomere length (Epel et al. 2004) due to an increased rate of cell turnover and increased exposure to ROS.  The shorter 43  telomere lengths in women with RM may therefore reflect higher levels of psychological and physiological stress and/or constitutionally shortened telomeres. The finding of increased telomere length in POF patients does not support the hypothesis that these women have accelerated cellular aging.  As this is a relatively small sample size and the findings were not highly significant, it is possible that these results are due to a Type I error (false positive).  Although care must be taken in conclusions drawn from these data, and the necessity for additional study in this area must be emphasized, these findings are nonetheless intriguing.  One explanation for the increase in telomere length observed in the POF cohort may be a constitutional genetic tendency towards an overall slower rate of cell division, perhaps by predisposing towards a prolonged cell cycle.  A slower cell division rate in the developing ovary could lead to the establishment of a reduced follicular pool during early embryonic development, whereas fewer cell divisions in hematopoietic stem cells could result in longer telomeres measured in peripheral blood.  If longer telomeres in blood reflect fewer mitotic divisions in the initial germ cell pool, this could explain a smaller follicular pool and early menopause in POF patients (Dorland et al. 1998a).  A second possibility is that longer telomeres in the POF patients are a result of autoimmunity in these women.  Autoimmune destruction of the ovaries is a common cause of POF (Goswami and Conway 2005), and autoimmunity could conceivably alter blood cell composition (Josefowic et al. 2012) to a cell type with longer telomeres (Rufer et al. 1999).  However, the limited existing evidence on telomere length and autoimmunity suggests that autoimmune conditions are associated with shorter rather than longer telomeres (Jeanclos et al. 1998), making this a less likely explanation. Alternatively, longer telomeres in the POF patient group may be the result of abnormal hormone exposure in these women.  POF patients in our study may have been exposed to 44  elevated estrogen levels as a result of recruitment of large cohorts of oocytes in menstrual cycles occurring prior to POF onset.  Premature follicular pool exhaustion resulting from the continual recruitment of large cohorts of follicles prior to menopause has been proposed as one mechanism for POF (Pal and Santoro 2002).  Estradiol is secreted from developing follicles (Havelock et al. 2004) and stimulated estrogen level is correlated with the size of the antral follicle cohort (Scheffer et al. 2003).  If abnormally large follicular cohorts were recruited while POF patients were still cycling, this could lead to elevated estrogen levels with a positive influence on telomere length.  This positive influence on telomere length prior to the onset of POF may be reflected later in life in the form of long telomeres after POF diagnosis.  On the other hand, maintenance of telomere length may be a recent phenomenon in these women, resulting from hormone replacement therapy following POF diagnosis.  Although we lack the clinical details to assess this possibility in our POF population, this hypothesis is supported by the finding that long term hormone replacement therapy in postmenopausal women slows the rate of telomere attrition (Lee et al. 2005a).  Two mechanisms by which estrogen may positively regulate telomere length have been proposed: 1) estrogen may ameliorate the negative effects of ROS (Aviv et al. 2005) which reduce telomere length by inducing single strand breaks (von Zglinicki 2000) and 2) estrogen may stimulate telomerase activity (Aviv et al. 2005).  Ovarian telomerase activity is reportedly low in POF patients with follicular depletion, but high in POF patients with ovarian dysfunction and normal follicle counts (Kinugawa et al. 2000).  Since follicle count is correlated with circulating estrogen level (Vital-Reyes et al. 2006) this supports the suggestion that telomerase activity is influenced by estrogen exposure, at least in the ovary. There are several limitations to this study that must be considered.  Telomere length varies among blood cell types (Lansdorp 2006, Rufer et al. 1999); therefore variability between 45  individuals in telomere length measured in peripheral whole blood may be a consequence of differences in blood cell composition.  Furthermore, telomere length measured in peripheral blood may not necessarily reflect telomere length in the ovary or developing embryo.  However, there is a strong correlation between telomere lengths from tissues of a single individual (Butler et al. 1998) suggesting that telomere length measured in whole blood may be an accurate proxy for telomere length at the ovary.  Small sample sizes and limited clinical details (including incomplete karyotype information on the losses of RM group and no details on reproductive history of Control group 1) restrict the ability to subdivide sample groups into more homogeneous phenotypes and negatively impact the power of these analyses. RM, trisomic pregnancy, and POF have all been considered measures of premature reproductive aging.  However, the observation that RM and POF showed opposite associations with telomere length, and trisomic pregnancy showed no evidence of an association, suggests that these different types of reproductive aging are influenced by unique factors.  Further studies are necessary to confirm these findings in larger more precisely defined populations, examine the physiological mechanisms that influence both telomere length and reproductive aging, and investigate the molecular mechanisms responsible for longer telomere lengths in the POF population. 46  Table 3.1 Rate of telomere loss per year in women with evidence of premature reproductive aging and controls   Age range   Rate of telomere decline (bp/year) Sample group N (years) Mean Lower 95% CI Upper 95% CI R 2a  P-value b Control group 1 108 17-55 -40   -66  -14 0.081 0.001 Control group 2 46 37-54  26   -56 107 0.009 0.26 POF patients 34 21-50 -98 -178  -17 0.161 0.01 RM 95 24-45   -3   -40   40 0.000 0.44    Single trisomy 32 24-44 -56 -110   -2 0.130 0.02    Multiple trisomy 17 33-44 -23 -150 105 0.010 0.35 a R 2 is a measure of the goodness of fit of the regression a P values are based on a one-tailed test for significance of the regression based on the t distribution. 47  Table 3.2 Raw and age-adjusted mean telomere length    Mean telomere length  Sample group N Mean age (years) Raw data (± SD) (kb) Age-adjusted  (kb) P-value a,b  Control group 1 108 36.3 8.98±1.15 8.92 Control group 2 46 41.5 8.99±1.03 9.11 0.36 POF patients 34 35.4 9.61±1.38 9.58 0.01, 0.32 RM 95 35.8 8.47±0.92 8.46 0.0004, 0.02    Single trisomy 32 36.3 8.80±0.78 8.80 0.39, 0.26    Multiple trisomy 17 39.3 8.42±0.69 8.52 0.11, 0.06 a P values for comparison to control group 1, and 2, respectively. b ANCOVA (k=2 for comparison to Control group 1 or 2) was used to adjust raw telomere length data by age in comparisons between groups. 48   Figure 3.1 Telomere-specific qPCR.  In summary, primers designed to be complementary to the TTAGGG repeats anneal to the telomere template during the first round of PCR replication. Over consecutive rounds, the preferential amplicon is the shortest possible length, totaling the length of the 2 primers.  Subsequently, the relative quantification of the telomere amplification will be proportional to the number of binding sites (ie. the length of the telomere). 49   Figure 3.2 Correlation between telomere-specific qPCR and flow-FISH techniques. y = 7.2497x + 2.5008 R² = 0.9212 0 2 4 6 8 10 12 14 0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400 F lo w  F IS H  t e l le n g th  ( k b ) qPCR t/s tel length 50   Figure 3.3 Correlation between telomere length and age in women with evidence of premature reproductive aging and controls.  Age compared to telomere length (kb) in (a) control group 1, (b) control group 2, (c) POF patients, and (d) RM. 51    Figure 3.4 Correlation between telomere length and age in women with recurrent miscarriage and trisomic pregnancies.  Age compared to telomere length (kb) in women with RM with (a) ST and (b) MT. 52  Chapter  4: Genetic polymorphisms in genes involved in the hypothalamus- pituitary-ovarian axis 4.1 Introduction  Altered levels of hormones and other factors that are involved in maintaining control of the HPO axis can have negative effects on fertility and pregnancy.  As discussed in section 1.1.1 (page 2), the central components of the HPO feedback loop are GnRH, gonadotropins (LH and FSH), and steroid hormones (estradiol and progesterone).  Elevated levels of gonadotropins and estradiol have been associated with RM (Gurbuz et al. 2003, Gurbuz et al. 2004, Li et al. 2000). Similarly, elevated FSH is seen with advancing maternal age and is indicative of reduced ovarian responsiveness (Fitzgerald et al. 1998).  Furthermore, endocrine disorders such as LPD, thyroid dysfunction and PCOS have also been associated with RM.  Together these findings suggest that regulation of the HPO axis may be altered in these women, possibly due to genetic variation affecting the responsiveness or efficiency of receptors, enzymes and regulatory genes.  As discussed in section (page 18), there has been some evidence that genetic variation may contribute to susceptibility for RM; however there has been no comprehensive study in this area. I therefore hypothesized that genetic polymorphisms in genes involved in regulating the HPO axis would be associated with RM.  To investigate this, we compared allele and genotype frequencies of short tandem repeats (STRs) and SNPs in 20 genes involved in the HPO axis (Table 4.1) among women with RM and controls.  Polymorphisms assayed include those that have been previously reported to affect transcription, hormone levels or reproductive outcome. 53  4.2 Materials and methods 4.2.1 Samples A total of 357 women were recruited from a Western Canadian population at the BC Women’s Hospital & Health Centre in Vancouver, British Columbia.  The case group consisted of 227 women with RM (all evaluated by a single physician, M.D.S.), which includes 49/50 RM cases from Chapter 1 and 90/95 RM cases from Chapter 2 and 88 new cases.  This RM group had a mean age at time of pregnancy (SD; range) of 31.4 (6.1; 15-40) years with a total of 1379 pregnancies, of which 1027 (75%) ended in miscarriage.  The mean number of miscarriages (SD; range) was 4.5 (1.9; 3-13).  Chromosome results were obtained in 208 of these miscarriages, of which 110 (53%) were euploid, with a 46,XX/46,XY ratio of 0.80 (49/61).  Ninety eight (47%) of the miscarriages were karyotypically abnormal, including 70 autosomal trisomies, 16 polyploidies, 3 polyploidies with trisomies, 4 unbalanced translocations, 3 monosomy X (45,X), 1 monosomy X and trisomy 21, and 1 sex chromosome trisomy (47,XXY).  Carriers of a structural chromosome rearrangement were excluded from this study.  Forty (18%) of the 227 women with RM had concurrent infertility. The control group used in this study consisted of 130 women of reproductive age. Proven fertility and/or regular menstrual cycles were known in 67 of these women with a mean (SD, range) menstrual cycle length of 28.4 (2.1, 23-35) days.  Women with a known history of miscarriage, infertility or abnormal cycles were excluded from this study group.  Reproductive history was unknown in the remaining 63 women; however, inclusion of these controls will only marginally reduce the power, as few will have irregular cycles and/or RM.  On the basis of the 357 subjects, with a power of 0.80 and an α of 0.05, an effect size of 0.16 can be observed in this study (Faul et al. 2007). 54  The collection of the samples for this study was approved by the University of British Columbia Clinical Ethics Review Board. 4.2.2 Variant selection Candidate genes in this study were identified through literature search, using the search words ‘recurrent miscarriage’, ‘fertility’, and ‘female reproduction’.  Genes identified to be involved in female fertility through involvement in or modulation of the HPO axis, were further investigated for potential functional polymorphisms (Supplementary Table 4.1).  Polymorphisms chosen are those that have been reported previously to be associated with reduced fertility in women and/or altered HPO axis hormone levels.  In some cases, published polymorphisms could not be utilized due to technical constraints on the applied assay design in the current study (Sequenom iPlex) or due to limited available information. To assess the possibility of population stratification, a difference in ethnic distribution between cases and controls, as a confounding factor in this study, 23 ancestral informative SNPs were chosen to assay in cases and controls, as described by (Kosoy et al. 2009).  Self-reported ethnicity was available for a subset of cases and controls in this study and was comparable, comprising of predominantly Caucasian women with Asian admixture. 4.2.3 Genotyping DNA was extracted from whole peripheral blood using conventional methods.  Thirty- one SNPs and 21 ancestral informative SNPs were successfully assayed using the Sequenom iPlex Assay (Sequenom Inc., San Diego, CA) by the Génome Québec Innovation Centre at McGill University, Montreal, Canada.  STRs near the promoters of, or within, ESR1, ESR2, AR, and SHBG genes were assessed by PCR as previously reported (Bretherick et al. 2008). 55  4.2.4 Statistical analysis Hardy-Weinberg Equilibrium (HWE) was tested for each of the polymorphisms in controls (Supplementary Table 4.2).  Chi-squared analysis was used for comparisons of allele and genotype frequencies for the 35 polymorphisms (31 SNPs and 4 STRs) between the RM cases and controls.  Within the RM cases, the comparison of mean number of miscarriages grouped by genotype for each SNP individually was completed using ANCOVA, which also corrected for differences in maternal age between groups (Pineda et al. 2010).  The Benjamini- Hochberg False Discovery Rate (FDR) model was used to correct for multiple analyses (Benjamini and Hochberg 1995). 4.2.5 Population stratification Twenty-one of the 23 ancestral informative SNPs were genotyped successfully, 1 was excluded as it was not in HWE, suggesting possible genotyping error and the remaining 20 were analyzed for allele frequencies.  There was no significant difference in genotype distribution of the ancestral informative SNPs between control and RM groups (Supplementary Table 4.3) suggesting that population stratification is unlikely to be a confounding factor in this study. 4.3 Results The allele distributions for AR CAG(n), ESR1 TA(n), ESR2 CA(n) and SHBG TAAAA(n) STRs were compared between women with RM (N=227) and controls (N=130) (Table 4.2).  The ESR2 CA(n) allele distribution varied between RM women and controls (p=0.03), however there is no apparent trend based on allele size. The allele and genotype distributions were compared between the RM group and controls for the 31 SNPs assayed in 20 genes (Table 4.3).  The genotypes at a C/T SNP (rs37389) within intron 4 of the prolactin receptor (PRLR) gene differed between the RM group and controls with 56  an excess of heterozygotes and deficiency of homozygotes in the RM group (p=0.03).  The alleles at a G/C SNP (rs41423247) within intron 2 of the glucocorticoid receptor (GCCR) gene also differed (p=0.04), with a minor allele frequency of 33.7% in RM women compared to 41.5% in controls.  The odds ratio (OR) for the GG genotype in the RM group is 1.44 (95% CI, 0.93-2.24). As some effects may be more pronounced among women with multiple miscarriages, we grouped the RM cases by genotype and compared mean number of miscarriages within these groups, correcting for maternal age as a covariant (Supplementary Table 4.4).  For a G/T SNP (rs2033962) within the activin receptor type 1 (ACVR1) gene, the presence of the minor T allele was associated with increased number of miscarriages in an additive fashion (p=0.02), with GG genotypes (N=160) having a mean number of miscarriages (SD) of 4.3 (1.6), GT genotypes (N=61) with 5.0 (2.3) and TT genotypes (N=7) with 5.3 (2.7); however, the OR for the presence of the T allele was not higher (1.04, 95% CI 0.65-1.66). The minor G allele for the -351A/G SNP (rs9340799) within the promoter region of the estrogen receptor α (ESR1) gene was not associated with RM.  Although, there is a non- significant increased frequency in the GG genotype in the RM group (15%) compared to controls (9%) (p=0.11), as well as an increasing number of miscarriages observed with the number of G alleles present (p=0.08) (Supplementary Table 4.4).  No difference was observed for the ESR1 - 397C/T (rs2234693) polymorphisms with RM or number of miscarriages (p=0.23 and p=0.25, respectively), which is in strong linkage disequilibrium (LD) with the -351A/G SNP (van Meurs et al. 2003). After using the Benjamini-Hochberg FDR model to correct for multiple comparisons, none of the associations were found to be statistically significant. 57  4.4 Discussion This study examined 35 polymorphisms within 20 genes that influence the HPO axis (Table 4.1).  I identified several candidate associations; polymorphisms within three genes (ESR2, PRLR and GCCR) were associated with RM and ACVR1 showed an additive trend of increased number of miscarriages with the minor allele.  However, after correction for multiple analyses, these associations were not statistically significant. These candidate genes have previously been suggested to have a role in female fertility; therefore, a potential role in RM required investigation.  Two independent studies reported that prolactin may play a role in miscarriage, with a reduction in prolactin expression in the endometrium in women with RM (Garzia et al. 2004) and the down-regulation of PRLR in women who underwent in vitro fertilization and miscarried compared to those with ongoing pregnancies (Bersinger et al. 2008).  Mouse models have also shown that a lack of Prlr is associated with female infertility due to failure of embryo implantation (Ormandy et al. 1997), suggesting that the PRLR is an essential component for endometrial receptivity. Decreased activin levels have been associated with miscarriage (Prakash et al. 2005).  In addition, the G/T SNP (rs2033962) in the ACVR1 gene has been associated with levels of anti- Mullerian hormone and follicle numbers in women with polycystic ovarian syndrome (Kevenaar et al. 2009), which in turn has been linked to RM (Rai and Regan 2006). The GCCR mediates the activity of cortisol, a marker of elevated stress.  The minor allele of the Bc/I (rs41423247) polymorphism within the GCCR gene has been associated with increased cortisol levels in women on oral contraceptives who underwent psychological stress testing (Kumsta et al. 2007).  Elevated levels of maternal urinary cortisol prior to 6 weeks of gestation were associated with a higher risk of miscarriage (Nepomnaschy et al. 2006).  Lastly, 58  women with self-reported high levels of distress and long menstrual cycles were found to have a higher risk of miscarriage (Hjollund et al. 1999).  We found a tendency towards an increased frequency of the G (major) allele of the rs41423247 polymorphism within the GCCR gene in women with RM, with an OR for the GG genotype of 1.44 (95% CI 0.65-1.66).  This may suggest a difference in responsiveness to stress between control women and those with RM. Estrogen plays an essential role in follicular development and maintenance of early pregnancy.  Esr1 null female mice are infertile, with no corpus luteum formation and altered gonadotropin levels, whereas, Esr2 null female mice have a subfertile phenotype with fewer number of oocytes, which may be due to decreased ovarian responsiveness to gonadotropins (Emmen and Korach 2003).  There have been several studies investigating a potential association between the -397T/C and -351A/G SNPs in ESR1 and RM.  The -397C allele has been associated with increased expression of the ESR1 gene (Zhai et al. 2006).  An effect that may be explained by the creation of a transcription factor binding site or due to the LD with shorter TA(n) alleles in the promoter that may influence expression (Herrington et al. 2002).  Alessio and coauthors (2008) assessed both these ESR1 SNPs and the ESR2 STR in 75 Brazilian women with RM and found no association.  However, a recent study found an association with an increased number of miscarriages and the ESR1 haplotype composed of the -397T and -351A alleles (Pineda et al. 2010).  We did not find such an association, although the observed tendencies in our data suggest that the role of ESR polymorphisms in RM may be of interest to investigate further in a larger study. Contradictory results from these different studies may be due to the differences in ascertainment of women with sporadic and RM.  Historically, miscarriage risks were estimated at 15%, because only clinical pregnancies of 6 weeks or greater were included (Jacobs and 59  Hassold 1987).  With the inclusion of preclinical pregnancies, miscarriage risks approach 30- 50% (Edmonds et al. 1982, Wilcox et al. 1988).  Many cases of a single preclinical miscarriage may be due to chance rather than an increased susceptibility.  This is supported by the finding that rates of chromosome errors, such as trisomy, monosomy and polyploidy, are inversely associated with number of miscarriages (Ogasawara et al. 2000).  Therefore, susceptibility due to genetic variability in hormone regulation may be more likely to play a role in women with strictly defined RM.  In this study, the mean number of miscarriages within the RM group is higher than most other studies, increasing the likelihood of ascertaining women at an exacerbated risk of miscarriage. RM is known to be heterogeneous in etiology.  We did not stratify our sample population for primary (no prior live birth) or secondary (prior live birth) RM, nor for clinical risk factors identified.  In addition, many of the miscarriages were not karyotyped; therefore, we could not compare results stratified for euploid and aneuploid miscarriages. We were unable to obtain information on menstrual cycle length or regularity for a subset of the controls and the women with RM.  Ensuring all women in the control group had regular cycles would strengthen the study, possibly increasing the significance of true associations.  In addition, the role of genetic variation in the HPO axis may be augmented in RM women with irregular menstrual cycles. The selection of only a few polymorphisms for each gene studied in this investigation allows only the assessment of that given site and those in LD with it.  It does not, however, capture all of the genetic variation within these genes; therefore, the potential role of other SNPs and rare mutations in the risk for RM cannot be excluded.  Furthermore, the synergistic effect of combinations of SNPs, particularly in extremely polymorphic genes, and gene-environmental interactions is difficult to appropriately address in association studies.  A more extensive analysis 60  of the genetic variation within these genes is needed in future studies to entirely evaluate the role of the HPO axis in the risk for RM. In conclusion, in this study we investigated the association between genetic polymorphisms affecting the function of genes involved in regulating the HPO axis and RM.  We identified candidate associations between RM and genetic variants in ESR2, PRLR, GCCR, and ACVR1.  However, these associations were not significant after correcting for multiple comparisons.  These findings may suggest that these gene variants have little or no effect on folliculogenesis and/or early maintenance of pregnancy.  However, due to the limitation of sample size in this analysis, future studies in a larger, well-characterized group of women with RM are needed to determine whether these candidate genes are associated with RM. 61  Table 4.1 Summary of 35 polymorphisms assessed in this study. Gene   Description   Polymorphisms assayed ACVR1   Activin receptor type 1   rs2033962 AR   Androgen receptor   CAG repeat, rs6152 CBG   Corticosteroid-binding globulin   rs2281517 CGB5   Chorionic gonadotropin beta polypeptide 5   rs4801789 CYP17   Steroid 17-hydrolase   rs743572 CYP19   Aromatase    rs10046 ESR1   Estrogen receptor α   TA repeat, rs2234693, rs9340799 ESR2   Estrogen receptor β   CA repeat, rs1256049 FBLN1   Fibulin 1   rs9682 FSHR   Follicle-stimulating hormone receptor   rs1394205, rs6166 GCCR   Glucocorticoid receptor   rs41423247, rs6198 INHA   Inhibin α   rs35118453 LHR   Luteinizing hormone receptor   rs2293275, rs12470652 62  Gene   Description   Polymorphisms assayed PAPPA   Pregnancy-associated plasma protein A   rs7020782 PGR   Progesterone receptor   rs518162, rs1042838 PRL   Prolactin    rs1341239, rs2244502 PRLR   Prolactin receptor   rs9292573, rs37389, rs13354826 SHBG   Sex hormone-binding globulin   TAAAA repeat, rs6259, rs1799941, rs6257 THRB   Thyroid hormone receptor β   rs3752874 TSHR   Thyroid stimulating hormone receptor   rs2234919, rs1991517  63  Table 4.2 Allele distributions of short tandem repeat polymorphisms.  Comparison of allele distributions between women with RM (N=227) and controls (N=130) for STR polymorphisms within hormone receptors. Allele RM Observed (frequency) Controls Observed (frequency)  p-value* AR (Androgen receptor) CAG repeat  0.631 ≤20 124 (0.27) 85 (0.33)  21 81 (0.18) 44 (0.17)  22 53 (0.12) 26 (0.10)  23 62 (0.14) 31 (0.12)  ≥24 134 (0.30) 74 (0.28)  ESR1 (Estrogen receptor α) TA repeat  0.250 ≤13 47 (0.10) 24 (0.10)  14 135 (0.30) 87 (0.34)  15 54 (0.12) 19 (0.07)  16 13 (0.03) 11 (0.04)  17-20 46 (0.10) 21 (0.08)  21 45 (0.10) 23 (0.09)  22 28 (0.06) 26 (0.10)  23 37 (0.08) 25 (0.10)  ≥24 49 (0.11) 24 (0.09)  ESR2 (Estrogen receptor β) CA repeat  0.026 ≤18 74 (0.16) 33 (0.13)  19 25 (0.06) 20 (0.08)  20 11 (0.02) 15 (0.06)  21 32 (0.07) 17 (0.07)  64  Allele RM Observed (frequency) Controls Observed (frequency)  p-value* 22 52 (0.12) 45 (0.17)  23 164 (0.36) 76 (0.29)  ≥24 96 (0.21) 54 (0.21)  SHBG (Sex hormone-binding globulin) TAAAA repeat 0.511 ≤6 121 (0.27) 61 (0.23)  7 31 (0.07) 23 (0.09)  8 149 (0.33) 95 (0.38)  9 111 (0.24) 63 (0.24)  ≥10 42 (0.10) 18 (0.07) a Chi-square analysis 65  Table 4.3 Genotype distributions of single nucleotide polymorphisms.  Comparison of genotype distributions between women with RM (N=227 a ) and controls (N=130  a ) for hormone pathway gene polymorphisms. SNP Genotype RM Observed (frequency) Controls Observed (frequency) P genotypes b  P alleles b  ACVR1  (Activin receptor type 1)    rs2033962 GG 159 (0.70) 92 (0.71) 0.896 0.920  GT 61 (0.27) 33 (0.25)   TT 7 (0.03) 5 (0.04)  AR (Androgen receptor)  rs6152 GG 161 (0.71) 95 (0.73) 0.752 0.920  GA 65 (0.29) 33 (0.25)   AA 1 (0.00) 2 (0.02)  CBG (Corticosteroid-binding globulin)  rs2281517 TT 141 (0.62) 79 (0.61) 0.767 0.699  TC 76 (0.34) 43 (0.33)   CC 10 (0.04) 8 (0.06)  CGB5 (Chorionic gonadotropin β polypeptide 5)  rs4801789 CC 123 (0.55) 69 (0.53) 0.803 1.000  CT 72 (0.32) 46 (0.35)   TT 29 (0.13) 15 (0.12)  CYP17 (Steroid 17-hydrolase)  rs743572 AA 77 (0.34) 54 (0.42) 0.323 0.320  AG 105 (0.46) 51 (0.39)   GG 45 (0.20) 25 (0.19)  CYP19 (Aromatase)  rs10046 TT 62 (0.27) 33 (0.25) 0.307 0.663 66  SNP Genotype RM Observed (frequency) Controls Observed (frequency) P genotypes b  P alleles b   TC 108 (0.48) 72 (0.55)   CC 57 (0.25) 25 (0.19)  ESR1 (Estrogen receptor α)  rs2234693 TT 70 (0.31) 43 (0.33) 0.231 0.230  TC 103 (0.45) 66 (0.51)   CC 54 (0.24) 21 (0.16)  rs9340799 AA 101 (0.45) 57(0.44) 0.113 0.450  AG 90 (0.40) 62 (0.48)   GG 35 (0.15) 11 (0.09)  ESR2 (Estrogen receptor β)  rs1256049 GG 199 (0.88) 115 (0.88) 1.000 0.764  GA 24 (0.11) 14 (0.11)   AA 4 (0.02) 1 (0.01)  FBLN1 (Fibulin 1 )  rs9682 CC 91 (0.40) 40 (0.31) 0.208 0.130  CT 109 (0.48) 71 (0.55)   TT 27 (0.12) 19 (0.15)  FSHR (Follicle-stimulating hormone receptor)  rs1394205 GG 112 (0.50) 69 (0.53) 0.677 0.454  GA 93 (0.41) 52 (0.40)   AA 21 (0.09) 9 (0.07)  rs6166 AA 67 (0.30) 41 (0.32) 0.831 0.624  AG 118 (0.52) 68 (0.52)   GG 42 (0.19) 21 (0.16)  GCCR (Glucocorticoid receptor)  67  SNP Genotype RM Observed (frequency) Controls Observed (frequency) P genotypes b  P alleles b  rs41423247 GG 102 (0.45) 47 (0.36) 0.120 0.044  GC 97 (0.43) 58 (0.45)   CC 28 (0.12) 25 (0.19)  rs6198 AA 164 (0.73) 88 (0.69) 0.381 0.269  AG 55 (0.25) 34 (0.27)   GG 5 (0.02) 6 (0.05)  INHA (Inhibin α )  rs35118453 CC 147 (0.65) 81 (0.62) 0.878 0.671  CT 68 (0.30) 41 (0.32)   TT 12 (0.05) 8 (0.06)  LHR (Luteinizing hormone receptor)  rs2293275 GG 92 (0.41) 41 (0.32) 0.148 0.279  GA 90 (0.40) 65 (0.50)   AA 41 (0.18) 23 (0.18)  rs12470652 TT 200 (0.88) 115 (0.88) 1.000 1.000  TC 27 (0.12) 15 (0.12)   CC 0 (0.00) 0 (0.00)  PAPPA (Pregnancy-associated plasma protein A)  rs7020782 AA 100 (0.44) 56 (0.43) 0.947 0.842  AC 105 (0.46) 60 (0.46)   CC 22 (0.10) 14 (0.11)  PGR (Progesterone receptor)  rs518162 CC 190 (0.84) 114 (0.88) 0.387 0.584  CT 36 (0.16) 14 (0.11)   TT 1 (0.00) 2 (0.02)  68  SNP Genotype RM Observed (frequency) Controls Observed (frequency) P genotypes b  P alleles b  rs1042838 GG 176 (0.78) 92 (0.71) 0.409 0.282  GT 46 (0.20) 34 (0.26)   TT 5 (0.02) 3 (0.02)  PRL (Prolactin)  rs1341239 GG 94 (0.42) 52 (0.40) 0.923 0.752  GT 102 (0.45) 59 (0.45)   TT 30 (0.13) 19 (0.15)  rs2244502 AA 105 (0.47) 69 (0.53) 0.340 0.446  AT 104 (0.46) 49 (0.38)   TT 17 (0.08) 11 (0.09)  PRLR (Prolactin receptor)  rs9292573 TT 100 (0.44) 54 (0.40) 0.304 0.842  TC 94 (0.41) 63 (0.48)   CC 33 (0.15) 13 (0.12)  rs37389 CC 178 (0.78) 108 (0.83) 0.028 0.920  CT 45 (0.20) 15 (0.12)   TT 4 (0.02) 7 (0.05)  rs13354826 TT 105 (0.47) 57 (0.44) 0.807 0.572  TC 92 (0.41) 53 (0.41)   CC 28 (0.12) 19 (0.15)  SHBG (Sex hormone-binding globulin)  rs6259 GG 171 (0.75) 96 (0.74) 0.842 0.823  GA 51 (0.23) 31 (0.24)   AA 3 (0.01) 2 (0.02)  rs1799941 GG 138 (0.61) 77 (0.59) 0.733 0.920 69  SNP Genotype RM Observed (frequency) Controls Observed (frequency) P genotypes b  P alleles b   GA 75 (0.33) 47 (0.36)   AA 14 (0.06) 6 (0.05)  rs6257 TT 194 (0.85) 109 (0.84) 0.791 0.617  TC 32 (0.14) 19 (0.15)   CC 1 (0.00) 2 (0.02)  THRB (Thyroid hormone receptor β)  rs3752874 CC 172 (0.76) 91 (0.70) 0.425 0.377  CT 49 (0.22) 36 (0.28)   TT 6 (0.03) 3 (0.02)  TSHR (Thyroid stimulating hormone receptor)  rs2234919 CC 196 (0.86) 118 (0.91) 0.286 0.357  CA 30 (0.13) 11 (0.08)   AA 1 (0.00) 1 (0.01)  rs1991517 CC 184 (0.81) 112 (0.86) 0.277 0.224  CG 39 (0.17) 17 (0.13)    GG 4 (0.02) 1 (0.01)  a N is the total number of samples run on the platform, the number of successful genotype calls may be less for some SNPs. b Chi-square analysis  70  Chapter  5: Placental DNA methylation associated with pregnancy outcomes 5.1 Introduction During development, changes in DNA methylation are associated with the stable differentiation of cell types from a single totipotent zygote to the embryonic and extra-embryonic tissues (Monk 1995).  However, subtle environmental perturbations during embryonic development or gametogenesis can disrupt the setting of these epigenetic marks (Hogg et al. 2012, Velker et al. 2012).  Imprinted genes have been shown to be environmentally sensitive and may be particularly susceptible to disruptions in DNA methylation (Faulk and Dolinoy 2011). As many imprinted genes have an essential role in placental growth and differentiation (Reik and Walter 2001), they are attractive candidates to test for abnormalities associated with poor placental function.  Furthermore DNA methylation may be inherently more variable in the placenta that in other tissues, possibly due to its need to be responsive to a variety of signals in its function as a mediator of exchange between the fetus and mother (Yuen and Robinson 2011). Aberrant DNA methylation, arising during embryo development or gametogenesis, has been suggested as a potential cause of pregnancy loss.  Extreme DNA methylation values at several imprinted loci were more frequent in the muscle tissue of stillborns and spontaneous abortions than in induced abortions (Pliushch et al. 2010).  In addition, aberrant hemi- methylation and mono-allelic expression of the maternal CBG5 gene, a component of the placental hormone human chorionic gonadotropin, was seen in trophoblast from 2 RM cases and one elective termination, but not in healthy pregnancies (Ankolkar et al. 2012, Uuskula et al. 2011).  A comprehensive analysis of genome-wide and site-specific patterns of DNA methylation in miscarriage and RM is needed to evaluate the frequency and nature of epigenetic errors in early pregnancy.  In this study, we evaluated patterns in DNA methylation in chorionic 71  villus samples from the products of conception of women with RM, isolated miscarriage, or elective termination.  We hypothesized that placental villi of karyotypically normal miscarriages, particularly those occurring in women with RM, would exhibit aberrant DNA methylation globally and/or at specific loci.  Using both microarray and targeted approaches we assessed 1) differences at specific candidate loci between groups; 2) overall differences and individual outliers at imprinted loci; and 3) “global” alterations in DNA methylation based on long interspersed element (LINE-1) sequences and all CpG sites interrogated. 5.2 Materials and methods 5.2.1 Samples Placental chorionic villus samples were obtained anonymously from miscarriage samples evaluated through the Embryopathology Laboratory at the BC Children’s and Women’s Hospital.  Cases were comprised of karyotypically normal miscarriage samples from an independent cohort of women with a history of recurrent miscarriage (RM; N=33), and women with a single miscarriage (M; N=21).  As part of routine clinical workup, all miscarriage specimens with culture failure or in which the karyotype was 46,XX, were further assessed with comparative genome hybridization.  First trimester chromosomally normal control samples were separately ascertained from anonymous 8-12 week elective terminations (TA; N=16). Chromosome constitution of TA samples was assessed with multiple ligation-dependent probe amplification.  Table 5.1 describes and compares the demographics for each study group. 5.2.2 Array-based quantification of DNA methylation DNA from placental chorionic villus samples was purified using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany), and 750ng of DNA for each sample was bisulfite converted using the EZ DNA Methylation Kit (Zymo Research Corporation, Irvine, USA).  A 72  subset of 10 RM and 10 TA villi samples were run independently on the Infinium HumanMethylation27 BeadChip array (Illumina Inc., San Diego, USA).  Each probes in this array interrogates DNA methylation at one of 27,578 CpG sites throughout the genome, and is enriched for sites within gene promoters.  Samples and arrays were prepared as per the manufacturer’s specifications (Illumina Inc., San Diego, USA).  Data were normalized to background intensity levels using GenomeStudio Software 2011 (Illumina Inc., San Diego, USA).  Probes on the X and Y chromosomes (N=1092) and with bad detection p-values (p>0.01 in any sample; N=424) were omitted, leaving a total of 26,062 probes for analysis.  The array data from this study are publicly available at NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession number #16704108. 5.2.3 Targeted DNA methylation Candidate CpG sites from 4 significant Infinium array probes, 7 imprinted genes (Table 5.2), and LINE-1 sequences were assayed using bisulfite pyrosequencing.  This was performed on a Pyromark MD machine using the PyroGold SQA reagent kit (Qiagen, Hilden, Germany). Primers were designed using the PSQ Assay Design Software: Version 1.0.6 or selected from published studies (Supplementary Table 5.1).  Each 15 µL PCR reaction contained 1xPCR Buffer (Qiagen, Hilden, Germany), 3 mM Gibco dNTPs (Invitrogen, Carlsbad, USA), 0.9 U HotStart Taq DNA polymerase (Qiagen, Hilden, Germany), 6 µM of each of the forward and reverse primers, and ~15 ng of bisulfite converted DNA.  Cycling conditions were: 95°C for 15 min, 95°C for 30 s, 55°C for 30 s, 72°C for 30 s x40, with a final extension of 72°C for 10 min. The correlation between the Infinium beta values and percent methylation measured by bisulfite pyrosequencing was highly significant for all candidate CpG sites (p<0.0001; Supplementary Figure 5.1).  PCR cycling conditions for LINE-1 primers were those from the commercially 73  available LINE-1 assay (Qiagen, Hilden, Germany).  DNA methylation of LINE-1 sequences has been used extensively in the literature as a proxy for global methylation, as they are CG rich and distributed widely throughout the genome (Price et al. 2012). 5.2.4 Statistical analysis Student’s two-tailed t-test was used to compare study group demographics, including maternal age, gestational age, and number of gestations, parity and miscarriages.  Fisher’s Exact Probability Test was used to compare the male to female ratio among the study groups. For the 26,062 quality Infinium array probes, beta values were corrected using M-value conversion (Du et al. 2010) and colour channel normalization in R Statistical Software 2.12.0 (The R Project for Statistical Computing, Auckland, New Zealand).  Significance Analysis of Microarrays (Stanford University, Stanford, USA) was utilized on M-values to select significant candidate CpG sites.  An FDR of less than 0.05 was used in conjunction with an absolute difference (delta beta) of greater than 0.05 average beta values between the RM and TA groups (Figure 5.1).  Within the candidate probe list, probes containing SNPs and/or showing bimodal distribution of beta values (N=3) or cross-hybridizing to multiple locations within the genome (N=0) were eliminated from analysis (Supplementary Table 5.2). Gene ontology analysis was completed using ermineJ version 2.1.21 (Lee et al. 2005b). A gene score resampling method was done using delta beta values as the probe score for each of the 26,062 Infinium array probes.  The ‘Best’ score for gene replicates was used, with 10,000 iterations to obtain corrected p-values.  In addition to standard biological processes gene ontology groups, custom gene sets included in this analysis were: 1) genes previously associated with RM (Baek 2004, Rull et al. 2012), 2) imprinted genes (http://igc.otago.ac.nz/), which was 74  further subdivided into 2a) maternally expressed imprinted genes and 2b) paternally expressed imprinted genes. Average DNA methylation of target regions, assessed by bisulfite pyrosequencing, was compared between groups using the non-parametric Mann-Whitney (k=2) and Kruskal-Wallis (k=3) tests.  The post hoc Dunn’s Multiple Comparisons Test was used to further assess which pair-wise comparisons were contributing to significance in the Kruskal-Wallis analysis. Correction for multiple comparisons was done using the Benjamini Hochberg FDR method (Benjamini and Hochberg 1995).  The relationship between DNA methylation (%) by pyrosequencing and Infinium average beta values or gestational age was evaluated using linear correlation.  Fisher’s Exact test was used to compare the number of outliers for DNA methylation at imprinted genes between the RM, M and TA groups.  Principle component analysis (PCA) utilizing DNA methylation (%) values for all first trimester placental samples (N=70) at the 12 targeted loci assessed by bisulfite pyrosequencing in this study (Supplementary Table 5.1), including LINE-1 sequences, was done to identify outlier samples. GraphPad Prism 4 (GraphPad Software, Inc., La Jolla, USA), VassarStats (Vassar College, Poughkeepsie, USA) and R Statistical Software 2.12.0 (The R Project for Statistical Computing, Auckland, New Zealand) were used for statistical analyses and graphing. 5.3 Results 5.3.1 Array-based quantification of DNA methylation The Illumina Infinium HumanMethylation27 BeadChip array quantifies DNA methylation of CpG sites within the proximal promoter regions of almost 15,000 genes throughout the genome.  Using a criteria of an FDR<0.05 and delta beta>0.05, 14 differentially methylated candidate CpG sites were identified from the comparison of 10 RM and 10 TA 75  placental samples.  Three of these were omitted due to the presence of a SNP in the probe binding region and/or bimodal distribution of beta values (suggestive of being influenced by a SNP).  Five of the remaining 11 candidates were more highly methylated in cases compared to controls, while 6 were less methylated (Supplementary Table 5.2). Four of these candidates were selected for confirmatory follow up based on functional relevance of the associated gene, including cytochrome P450, subfamily 1A, polypeptide 2 (CYP1A2) cg04968473, defensing β 1 (DEFB1) cg24292612, adenomatous polyposis coli (APC) cg20311501, and AXL tyrosine kinase receptor (AXL) cg14892768.  These sites were further assessed in the larger sample populations of 33 RM, 21 M, and 16 TA samples, with bisulfite pyrosequencing.  There was a significant difference in methylation at CYP1A2 promoter region between groups (p=0.002; Figure 5.2A), which post hoc analysis identified as a significant increase in average methylation in M (64.4%) compared to TA (50.6%; p<0.01); while the RM group was also increased relative to the TA group, this was not significant.  DEFB1 showed a difference in methylation between groups (p=0.008, Figure 5.2B), in which both RM (9.3%) and M (7.9%) had marginally decreased methylation compared to TA (11.3%; p<0.05).  The result for APC was not confirmed in this larger sample set (Figure 5.2C).  Finally, altered methylation was observed at the AXL promoter (p=0.02; Figure 5.2D), with an increase in RM (59.1%) compared to TA (52.0%; p<0.05) in post hoc analysis.  Previous studies have shown that gestational age has a strong influence on DNA methylation at many sites throughout the genome in placental villi (Novakovic et al. 2011). Unsupervised clustering showed that the TA samples do not cluster separately and are thus of similar nature and gestational age as the RM samples (Figure 5.3).  However, as gestational age is not known for the remainder of the follow up group of TAs, the influence of gestational age 76  was considered in further analyses.  Using the RM and M samples (N=54), we identified a significant positive correlation between gestational age and DNA methylation at the CYP1A2 promoter region (r=0.58, p<0.0001; Supplementary Figure 5.2).  It is thus possible that the observed increase in methylation in the RM and M groups compared to the TA group (Figure 5.2A), may be confounded by differences in gestational age.  There was no significant correlation between maternal age and DNA methylation at any of the assayed regions (Supplementary Figure 5.3). ErmineJ gene ontology analysis, of the 26,062 Infinium array probes, was utilized to assess whether certain gene ontologies were enriched among those sites showing the largest delta betas between the RM and TA groups.  In other words, this analysis is not based on the small subset of probes we identified as candidates, but on the distribution of differences between the two groups relative to the gene content on the array.  There was a highly significant enrichment of imprinted genes (p=9.53E-10) and genes previously associated with RM (p=9.51E-06; Supplementary Table 5.3).  When subcategorizing the imprinted genes by parental origin of expression, maternally expressed genes (paternally methylated) were more significantly enriched (p=1.90E-09) than paternally expressed genes (p=7.98E-06).  Notably, there were a higher percentage of gene ontology biological processes involved in immune response among those identified as significantly enriched in this dataset (18.4%). 5.3.2 DNA methylation at imprinted genes  DNA methylation was assessed at imprinted loci using bisulfite pyrosequencing (Figure 5.4), due to the previously reported association with pregnancy loss (Pliushch et al. 2010) and the observed enrichment in the ErmineJ gene ontology analysis.  These 7 loci, including maternally methylated PLAGL1, SGCE, KvDMR1 and SNRPN and paternally methylated H19/IGF2 ICR1, 77  CDKN1C, and MEG3, were selected because they have been previously demonstrated to maintain their imprints in placenta (Bourque et al. 2011, Yuen et al. 2011).  After correction for multiple comparisons, there was significantly increased average methylation in M (51.7%) compared to RM (48.2%) and TA (47.8%) at the H19/IGF2 ICR1 (p<0.0001; Supplementary Figure 5.4).  In addition, there was no correlation between DNA methylation at any of the 7 imprinted loci and gestational age (Supplementary Figure 5.5). As we may expect only a subset of miscarriages to be attributed to aberrant DNA methylation due to the heterogeneous etiology, we sought to identify individual samples that display values outside of the normal range.  It was previously demonstrated that spontaneous abortion and stillbirth were associated with increased number of outliers, defined as greater than 1.5x the inter-quartile range, for DNA methylation at imprinted loci (Pliushch et al. 2010). Using this criterion, we observed a significant increase in the number of outliers for DNA methylation in the RM (3.9%) group compared to M (0.0%) and TA (0.9%; p=0.02; Table 5.2). 5.3.3 ‘Global’ measures of DNA methylation  To assess whether there was overall dysregulation of DNA methylation in any sample, or groups as a whole, two ‘global’ measures, using representative dispersed sequences, were used: 1) the average of the 26,062 Infinium array probes, and 2) the average methylation at consensus LINE-1 sequences.  There were no significant differences in average methylation observed between groups, after correction for multiple comparisons (Figure 5.5).  To investigate individual samples, principle components analysis (PCA) was utilized to identify those that show distinct patterns of DNA methylation at the 12 targeted loci within this study, including LINE-1 sequences.  In a PCA plot comparing the two primary principle components, attributing 44% of the variance within the dataset, outlier samples were identified (Figure 5.6).  The identified 78  outlier samples show altered DNA methylation at multiple loci (Table 5.3), defined as greater or less than one or more standard deviations from the mean measured in all first trimester chorionic villi (N=70), although no consistent pattern of dysregulation was evident. 5.4 Discussion  In this study, we assessed DNA methylation globally and at targeted loci in placental samples from first trimester RM, M and TA pregnancies.  This was used to address whether there were differences between these groups and whether a subset of pregnancies showed distinct epigenetic patterns.  Using both candidacy and gene ontology approaches, several differences in DNA methylation were associated with RM and/or isolated miscarriage.  Two candidate CpG sites, near the promoters of DEFB1 and AXL, were identified as differentially methylated between RM and TA, while DEFB1 and CYP1A2 were differentially methylated between M and TA.  As a subset of the RM and TA groups did not cluster separately using Infinium array profiles, it does not appear that mode of fetal demise is associated with gross differences in cell composition or epigenetic gene regulation in the placenta.  The gene ontology analysis of differential methylation on the Infinium array showed an enrichment of genes previously associated with RM, imprinted loci and immunological pathways.  In addition, there were an increased number of outliers for DNA methylation at 7 imprinted loci among RM placentae. While we did not observe an overall trend of altered ‘global’ DNA methylation in any group, specific placental samples in each of the three comparison groups showed altered methylation at multiple loci. The CYP1A2 gene promoter region showed an increase in DNA methylation in the M placental samples compared to TA.  However, the contribution of gestational age on the observed association cannot be addressed, as information was not available for the TA samples. 79  There is a strong positive correlation between DNA methylation at this site and gestational age in RM and M samples, therefore the association would need to be independently validated in a well-characterized population.  CYP1A2 is of particular interest, given its role in caffeine and drug metabolism.  High CYP1A activity, a combination of both isoform 1 and 2, in first trimester placenta correlates strongly with maternal age and is associated with maternal smoking and alcohol consumption (Collier et al. 2002).  Maternal intake of caffeine in conjunction with genetically altered metabolic activity of CYP1A2 has been associated with both karyotypically normal miscarriage and RM (Sata et al. 2005, Signorello et al. 2001).  Together these findings raise the possibility that altered expression of CYP1A2, may be reflective of genetic and environmental influences that contribute to risk for miscarriage. At the promoter of DEFB1 (hBD-1) there was an incremental decrease in methylation in RM and M placental villi compared to TA.  DEFB1 encodes for an antimicrobial peptide involved in the innate immune response, which is expressed from placental tissues (King et al. 2007).  Increased placental expression of DEFB1 was observed in HIV-positive women (Aguilar-Jimenez et al. 2011), and a trend towards increased levels was observed in women with preterm premature rupture of membranes and chorioamnionitis (Polettini et al. 2011).  The placenta provides an immunological barrier between the mother and fetus, protecting the genetically distinct fetus from the maternal immune system.  Furthermore, the process of implantation is mediated by the immune system and it has been suggested that miscarriage may be a process of an exaggerated inflammatory response by the mother (Christiansen 2012).  There is conflicting evidence as to whether infection during pregnancy is associated with RM (Rai and Regan 2006); however gene expression studies (Baek 2004, Krieg et al. 2012), as well as the 80  observed enrichment of gene ontology groups involved in immune response in our gene ontology analysis, support some role of immune function in risk for RM. CpG sites within the promoter regions of APC and AXL, two putative imprinted genes, were also identified in the candidate analysis (Choufani et al. 2011, Yuen et al. 2011).  In follow up, only AXL showed a consistent increase in methylation in the RM compared to the TA samples.  The paternally methylated AXL functions to promote cell proliferation, although its role in the placenta has not been studied.  Interestingly, a knockout of 3 tyrosine kinases in mice, including Axl, resulted in lupus erythematosus and recurrent fetal loss (Lu and Lemke 2001). A threshold mechanism has been proposed; suggesting that an accumulation of aberrant DNA methylation at developmentally important loci, such as imprinted genes, passed a tolerated threshold may result in the miscarriage of pregnancy (Pliushch et al. 2010).  Supporting this hypothesis, Pliushch and coauthors identified outliers in 4.6% and 1.0% of muscle samples from spontaneous abortions and induced abortions, respectively, at 6 imprinted genes (Pliushch et al. 2010).  Using the same definition of outlier DNA methylation, but placental samples, we report similar differences between the RM (3.9%) and TA (0.9%) groups at 7 imprinted genes (4/6 from the Pluishch study), while we observed no outliers in M group.  As the previous study did not stratify cases based on pregnancy history, it is possible these represent a similar subset of patients at increased risk for miscarriage. Using ErmineJ gene ontology analysis, we additionally observed an enrichment of imprinted genes in those sites showing greater differences in beta values between RM and TA groups.  Deletion of a single gene, Trim28, in the oocytes of female mice resulted in RM with no liveborns and the corresponding fetuses showed widespread loss of DNA methylation at imprinted loci, particularly those which are paternally methylated (Messerschmidt et al. 2012). 81  Interestingly, we also observed a stronger enrichment of paternally imprinted (maternally expressed) genes in the gene ontology analysis.  However, there was no apparent increase in the number of outliers at paternally methylated imprinted loci using a targeted approach (Table 2); however, this may be due to the very small number of outliers identified in this study and the limited analysis of only 3 paternally methylated genes.  It has been hypothesized that maternal protein complexes within the oocyte provide protection of germline differentially methylated regions near imprinted loci, before embryonic transcription initiates (Messerschmidt et al. 2012). Therefore, dysregulation of these maternal effect genes, either genetically or environmentally, may contribute to risk for RM with corresponding DNA methylation abnormalities in the embryo.  These types of genes may be potential candidates for future study in women with RM with no liveborns and evidence for dysregulation of methylation at imprinted genes in these miscarriages.  There are several limitations to this study.  First, there was incomplete information on obstetrical history and clinical investigations of the women with RM.  A well-defined population would allow comparisons of DNA methylation levels with specific clinical features.  Obtaining exact gestational ages for the TA cohort would also improve the study power, allowing statistical correction for this covariate.  Assessment of gene expression corresponding to the observed DNA methylation changes would also strengthen the findings; however, due the complex nature of sample collection from spontaneous or scheduled abortions, the duration of time for placental tissue degradation is extensive and this is detrimental to the integrity of placental RNA (Avila et al. 2010).  Also, a criticism has been that DNA methylation may not be a stable epigenetic mark at imprinted loci in the placenta (Lewis et al. 2004); to address this, we specifically targeted sites that show maintenance of DNA methylation throughout gestation (Bourque et al. 2011). 82  Furthermore, the observed frequency of outliers at imprinted loci was similar between first trimester placenta and fetal somatic tissue (Pliushch et al. 2010). The observed differences in DNA methylation between RM, M and TA groups appear to be limited to specific loci, as ‘global’ DNA methylation was not altered.  This contrasts with a recent study that found decreased average methylation of all genomic CpG sites and altered expression of DNA methyltransferases in placental villi of early pregnancy losses, although with no correction for gestational age (Yin et al. 2012).  The targeted differences combined with findings of the gene ontology analysis suggest that changes in placental DNA methylation of genes involved in environmental adaptation, immune response and imprinted genes, may contribute to the etiology of RM.  It is, however, difficult to determine whether these differences are causal, or a consequence of placental adaptation to an unhealthy embryo.  Evidence suggesting that aberrant establishment or maintenance of DNA methylation in the embryo may contribute to miscarriage is mounting (Messerschmidt et al. 2012, Pliushch et al. 2010, Yin et al. 2012).  Studies from mouse suggest that altered DNA methylation in the embryo may impair implantation and normal growth (Yin et al. 2012).  Alternatively, the demise of pregnancy is marked by declining progesterone and altered uterine immune cell composition (King et al. 1989), suggesting that apoptosis of placental cells and/or a cellular response to the termination of pregnancy is possible and may be reflected by changes in DNA methylation.  Future studies with a larger, well-characterized sample population will allow for a more comprehensive assessment of small differences in DNA methylation between groups. Several samples showed distinct patterns of altered DNA methylation, not only at imprinted loci, but at several of the 12 targeted loci investigated.  A more extensive genomic analysis of these dysregulated samples may further elucidate the nature of these altered patterns. 83  In addition, the clinical relevance of these findings will need to be determined, elucidating whether these differences in DNA methylation are more common in placentae associated with adverse pregnancy outcomes. 84  Table 5.1 Comparison of demographics for the recurrent miscarriage, miscarriage and elective termination study groups.  Student’s two-tailed t-test was used to statistically compare groups for each variable, unless otherwise denoted.  RM (N=33) M (N=21) TA (N=16) P-value Maternal age (years) (Mean±SD)  33.7±5.0  31.1±8.7  NA  0.17 Fetal male:female ratio  17:16  11:10 9:7 0.95 *  Gestational age (weeks) (Mean ±SD) 9.5±2.4  12.6±3.2  1 st trimester 0.001 Gestations [Median (range)] 4 (3-9)  1 (1-4)  NA  <0.0001 Parity [Median (range)] 0 (0-2)  0 (0-2)  NA 0.48 Miscarriages [Median (range)] 3 (3-9)  1 (1-1)  NA  <0.0001 SD = standard deviation; NA = not available * Fisher’s Exact Probability Test. 85  Table 5.2 Frequency of outliers at imprinted loci.  Comparison of the number of outliers (defined as greater than 1.5x the inter- quartile range) for DNA methylation at 7 imprinted loci between RM, M and TA groups (p=0.02). Gene/ Region Location Methylated allele Average methylation (%) (N=70) RM (N=33) M (N=21) TA (N=16) PLAGL1 6q24.2 M 53.7 3 0 0 SGCE 7q12.3 M 48.1 1 0 0 KvDMR1 11p15.5 M 61.4 0 0 0 SNRPN 15q11.2 M 48.7 1 0 0 H19/IGF2 ICR1 11p15.5 P 49.2 2 0 1 CDKN1C 11p15.5 P 24.8 1 0 0 MEG3 14q32.3 P 36.3 1 0 0 Total number of outliers   9/231 0/147 1/112 Percentage    3.9% 0.0% 0.9%  M = maternal; P = paternal 86  Table 5.3 Patterns of DNA methylation among outlier samples.  Variability of DNA methylation observed at 12 loci among samples identified in the Principle Component Analysis (PCA) as outliers. PCA plot # Sample Gestational age (wks) Karyotype DNA methylation range PLAGL1 SGCE KvDMR1 SNRPN H19/IGF2 ICR1 CDKN1C MEG3 CYP1A2 DEFB1 APC AXL LINE1 8 RM27 13.6 46,XY ++ + + ++ N N + + N N N N 16 RM43 11.1 46,XX N N + N + -- ++ N ++ -- -- ++ 35 M2 13.5 46,XY + N + N N N + N N + N N 36 M4 6 46,XX N - N N N - + + + -- -- ++ 39 M18 10 46,XX ++ N ++ N N N N N N + N N 59 TA6 NI 46,XX N N N N N -- + + + -- -- ++ 61 TA9 NI 46,XX N N N - N - + N + -- -- ++ N represents a normal range of methylation (within one standard deviation [SD] of the mean in all first trimester placental samples [N=70]); +/- is more/less than 1 SD from the mean; ++/-- is more/less than 2 SD from the mean. 87   Figure 5.1 Venn diagram of significant Infinium array candidates.  Venn diagram of the Illumina Infinium HumanMethylation27 BeadChip probes identified using either a false discovery rate (FDR) <0.05 or an absolute difference of beta values (Delta beta) >0.05 between the RM (N=10) and TA (N=10) groups, and those in common. 88   Figure 5.2 DNA methylation at 4 candidate promoter regions.  Box plots comparing DNA methylation (%) at the promoter regions of A) CYP1A2 (p=0.002), B) DEFB1 (p=0.008), C) APC (p=0.18), and D) AXL (p=0.02) genes between RM (N=33), M (N=21) and TA (N=16) groups.  The box plot whiskers indicate 1.5x the inter-quartile range, while the open circles are outlier values.  The horizontal bars with asterisk indicate which comparisons were statistically significant in post hoc pair wise analysis (* p<0.05; ** p<0.01). 89   Figure 5.3 Unsupervised clustering of the 20 samples run on the Infinium array. Unsupervised clustering of RM (N=10) and TA (N=10) samples run on the Infinium array. Gestational ages are denoted for RM samples (blue). 90   Figure 5.4 Box plots of DNA methylation at 7 imprinted loci.  Box plots of DNA methylation (%) at 7 imprinted loci for all first trimester placental samples (N=70).  The box plot whiskers indicate 1.5x the inter-quartile range, while outlier values are denoted for each group: RM (circle), M (square), TA (triangle). 91   Figure 5.5 Comparison of measures of ‘global’ methylation.  Comparison of measures of ‘global’ DNA methylation using: A) average methylation at LINE-1 consensus sequences (p=0.03) between RM (N=33), M (N=21), and TA (N=16) groups and B) Infinium array probe average (p=0.19) between RM (N=10) and TA (N=10).  The box plot whiskers indicate 1.5x the inter-quartile range, while the open circles are outlier values. 92   Figure 5.6 Principle component plot of all samples.  PCA plot of component 1 vs. component 2 (44% of variance) for DNA methylation (%) at 12 targeted loci among RM (N=33, blue), M (N=21, green) and TA (N=16, black) placental samples.  Red arrows represent the vectors for each of the 12 assays and outliers are circled. 93  Chapter  6: Discussion  In this thesis, I investigated several genetic and epigenetic factors that may contribute to the etiology of RM.  These included the mutational analysis of the synaptonemal complex gene SYCP3, measurement of telomeres as a representation of biological aging, genotyping of polymorphisms in genes involved in the HPO axis and assessing DNA methylation patterns in placental villi.  In this discussion, I will summarize the main findings and their significance, highlight the strengths and limitations, and discuss future directions for this research. 6.1 Summary and significance of findings  RM is a heterogeneous, multifactorial trait and despite expecting small contributions of genetic and epigenetic factors to risk, identifying associations has proven challenging.  In this thesis, I have found that genetic variants in SYCP3 and HPO axis genes likely do not contribute significantly to the etiology of RM.  However, associations with aspects of chromosome biology, such as maternal telomere length and placental DNA methylation, suggest that biological aging and placental development are important areas of future research. The results from Chapter 2 contradict earlier findings of an association between RM and mutations in the SYCP3 gene, as no mutations were identified among a population of 50 women with RM and an aneuploid loss.  This finding has been further supported by a recent publication that also found no mutations in SYCP3 among 56 women with RM or an aneuploid loss (Lopez- Carrasco et al. 2012).  To date, there have been no examples of mutations leading to aneuploid miscarriage or RM in humans.  This is despite several studies in mouse that have identified candidate genes involved in meiosis, that when mutated result in increased rates of aneuploidy in oocytes (Murdoch et al. 2013, Shin et al. 2010, Yuan et al. 2002).  While further research may 94  reveal this as a contributing risk factor in isolated cases, the present study provides evidence that this is not a common risk factor for RM and is not justified to be assessed routinely in the clinic. The identification of shorter telomeres among women with RM supports the hypothesis that there may be an altered rate of biological aging among these women.  While there was no significant change in the rates of telomere decline, average telomere lengths were shorter among women with RM compared to control women from the general population and those ascertained on the basis of advanced reproductive health.  Although shorter telomere lengths in the oocyte have been suggested to predispose to non-disjunction (Treff et al. 2011), there was not a pronounced effect observed among those women in the RM group with aneuploid losses.  The impact of this study is emphasized by its already 20 citations in the literature.  While maternal telomere length cannot be used as a clinical prognostic test, the observations in this study may hint at underlying factors that may be associated with both shortened telomeres and RM.  These underlying factors may include increased exposure to stress (Epel et al. 2004), altered hormonal profile (Lee et al. 2005a), autoimmunity (Jeanclos et al. 1998) or, in fact, truly reflect reproductive aging (Aydos et al. 2005).  The investigation of 35 functional polymorphisms in genes involved in the HPO axis identified several associations with RM; however there is a need for independent verification, as these were not significant after correction for multiple comparisons.  These weak associations may suggest that disruptions of HPO axis gene function or expression may individually have a small contribution to risk for RM.  The CA(n) STR in the ESR2 gene showed altered allele distribution in RM relative to controls, however no trend was apparent.  The heterozygous C/T genotype in the PRLR gene polymorphism (rs37389) was overrepresented among women with RM.  The G allele in the GCCR gene polymorphism (rs41423247), which has been previously 95  associated with altered response to stress, was also associated with RM.  Finally, the T allele in the ACVR1 gene polymorphism (rs2033962) was associated with increased number of miscarriages, in an additive manner.  This is the first study to perform a more comprehensive investigation of genetic variation in the HPO axis and lays the framework for future studies.  If the candidates identified are validated and assessed for their contribution to changes in hormone levels, these may provide markers that can be tested in conjunction with endocrine profiles to allow for more personalized hormone treatments with improved efficacy.  The assessment of global and targeted DNA methylation in RM, miscarriage and elective termination placental villi in this thesis has been an important scientific contribution.  Several groups have suggested that dysregulation of DNA methylation, especially at imprinted loci, may be a cause of pregnancy loss (Messerschmidt et al. 2012, Pliushch et al. 2010); although no comprehensive study had been done.  Using the Infinium array, 11 candidate loci were identified with differential DNA methylation between RM and elective terminations.  Despite the identification of a limited number of candidates, using gene ontology analysis, I inferred that there may be altered methylation profiles at imprinted genes, genes previously associated with RM and immune response genes in placental villi of RM cases.  While these changes may be indicative of causal factors, more likely they represent changes in vascularization and immune response at the maternal-fetal interface commonly associated with RM and miscarriage. Targeted assaying of imprinted genes showed an increase in the number of outlier methylation values among RM cases, consistent with previous reports in miscarriages and stillbirths (Pliushch et al. 2010).  However, given that these outliers were identified in <5% of cases it may not be a valuable prognostic marker for routine clinical use.  Using ‘global’ measures of DNA methylation, no difference was observed between groups; however, a subset 96  of samples, not restricted to the RM group, showed altered methylation profiles at multiple loci. This may suggest that there is dysregulated growth or development of these samples.  While errors in DNA methylation may not be a significant contributor to chromosomally normal miscarriage, aberrant patterns of DNA methylation are observed in a subset of cases.  Whether these changes are associated with a pathological phenotype is yet to be determined. 6.2 Strengths and limitations Previous studies of RM have used varied patient inclusion criteria, which can contribute to contradictory associations and unclear findings.  All women have a risk of miscarriage and to identify a distinct subset of women at increased risk requires stringent criteria.  In these studies I have used the definition of 3 or more consecutive miscarriages, as recommended by the European Society of Human Reproduction and Embryology (Daya 2005).  The American Society for Reproductive Medicine has recently defined RM as two or more non-consecutive miscarriages (Practice Committee of the American Society for Reproductive Medicine 2013). However, using this criterion, studies are likely including many women who have had two miscarriages by chance rather than due to an underlying predisposition. In support of this view, it has been reported that rates of aneuploidy decrease as the number of consecutive miscarriages increases (Ogasawara et al. 2000); suggesting that women with higher rates of miscarriage have differing contributing etiological factors.  However, this has been contested by a study that found no difference in the frequency of associated factors between those women with two or more, versus three or more miscarriages (Jaslow et al. 2010). To further delineate the differences in etiology, a more comprehensive analysis may be needed using an RM cohort with 5 or more losses, as this appears to be a threshold where a dramatic shift in the rate of aneuploidy occurs (Ogasawara et al. 2000).  An additional consideration is the 97  age of the women; as the rates of aneuploidy among RM patients >35 years old, even with 5 more losses, is more similar to that of women with isolated miscarriage (Christiansen et al. 2008). Our total study cohort is a particular strength of these studies, given the large proportion of women with ≥5 miscarriages (35%) and whose age at first miscarriage was ≤35 (75%).   This therefore enriched our patient cohort with women likely to have chromosomally normal miscarriages, despite only having karyotypic information on 20% of miscarriages.  Furthermore, all patients have been evaluated by one clinician, allowing consistent assessment of associated clinical factors and reproductive histories. In addition to the variable definition of RM, there are many small studies in different populations that have reported conflicting genetic associations.  Our investigation assessing the association between genetic polymorphisms and RM (Chapter 3) was enhanced by the use of ancestral informative SNPs to address population stratification as a confounder.  Minor allele frequencies can vary dramatically depending on the population and given the diversity and admixture of most urban centres, particularly Vancouver, this is an important consideration for any association study using these types of populations.   Furthermore, the power to assess a difference was increased by using control women not only from the general population, but selected to be reproductively healthy, with no history of infertility and/or miscarriage and at least one pregnancy after the age of 37.  As we would expect women from the general population to contain a variety of reproductive profiles, these reproductively healthy women represent the opposing end of a spectrum as our RM cohort. There are however several important limitations to these studies.  Despite our relatively large cohort of women with RM, the sample size limits our ability to subcategorize women based 98  on clinical characteristics for a more refined analysis of interactions between genetic and clinical features.  Such analyses may provide clinically valuable biomarkers for specific risk groups. Furthermore, due to the complex nature of RM, genetic and environmental factors may have small cumulative contributions to risk, and larger cohorts will be needed to assess these and their interactions.  Another limitation with this cohort of women with RM is the incomplete karyotypic information of all miscarriages.  However, this is a common challenge among RM studies, as routine clinical assessment of fetal chromosomal constitution is usually only done after the third miscarriage, if at all.  As women with a miscarriage resulting from meiotic non- disjunction represent a distinct etiological group from those with euploid miscarriages, it is likely that there is misclassification of some women in our case population, reducing the study power. Investigating aspects of chromosome biology, such as telomere length or DNA methylation, among RM patients presents certain challenges, particularly regarding tissue- and cell type-specific differences.  The measurement of telomere length in whole peripheral blood is based on an average of all chromosomes and all cell types in this sample.  Furthermore, I was unable to delineate whether shortened telomere lengths in RM women was indicative of limited oocyte viability due to systemically shortened telomere lengths, elevated stress due to the condition, or associated with a coexisting factor.  Similarly, the changes in DNA methylation in placental villi may be reflecting tissue composition differences, a response to an unhealthy embryo or maternal factor, or a causal epigenetic defect.  Some of these questions may be answered with the analysis of additional tissues at several time points in future studies. 6.3 Future directions Future studies need to be designed specifically to help unravel the maternal versus fetal causes of RM.  While maternal causes are largely speculative at this time, associations with 99  immunological, endocrinological, and thrombophilic factors suggest that the remodeling and maintenance of the maternal endometrium is essential.  Fetal causes generally refer to chromosomal imbalances, such as aneuploidy, but may also include changes in DNA methylation, telomeres, or recessive or de novo lethal mutations.  While fetal factors may be expected to have a larger contribution to the etiology of isolated miscarriage, maternal predisposition may result in recurrence of these errors, leading to RM.  Despite this complexity, in my opinion there are two primary research outcomes that should be strived for: 1) identify women predisposed to RM due to a maternal factor and 2) assess mechanistically how maternal and fetal factors detrimentally impact pregnancy.  There are several considerations and exciting areas of future study that can enable the field to work towards these goals. An important enhancement that would improve the ability to detect genetic associations is refinement of the RM and control cohorts.  Christiansen and coauthors (2008) suggested that the inclusion of women with ≥5 miscarriages and ≤35 years of age would enhance risk estimates by reducing the contribution of miscarriages caused by chromosomal abnormalities and other fetal factors (Christiansen et al. 2008).  While increasing these thresholds would further the homogeneity of this RM group, attaining appropriate sample sizes would become more challenging.  In our study, reproductively healthy controls were defined as no history of miscarriage and a healthy pregnancy late in reproductive life; however, this group could be further refined by selecting only women with regular menstrual cycles and at least two live births.  The addition of these two criterion would help eliminate women with endocrinological conditions and those women who may be susceptible to secondary RM. Much of current genetics research on the etiology of RM is centred on case control studies that are primarily candidate-driven investigations in the areas of thrombophilia, 100  immunology, and endocrinology.  While these have provided valuable insight into the pathways involved in RM, there has been limited progress in identifying genetic biomarkers of risk.  It is likely that many genetic variants may have a small contribution to overall risk for RM and there are challenges in obtaining adequate sample sizes to detect these associations.  Synthesizing findings from larger studies and meta-analyses may lead to the eventual characterization of sets of risk biomarkers (Christiansen et al. 2008).  Progress in this area may be expedited by sub- classifying patients based on clinical features and performing association studies in each subset separately.  Psychosocial stress is a potential maternal risk factor for RM, supported by the associations of RM with shortened telomeres and a glucocorticoid receptor polymorphism identified in this thesis (Chapters 2 and 3).  Several older studies have shown that supportive care among RM patients improves pregnancy success rates (Clifford et al. 1997, Liddell et al. 1991, Stray-Pedersen and Stray-Pedersen 1984).  Managing patient stress is a relatively accessible and non-invasive clinical intervention, thus making this an exciting area for future research.  As there is a complex interaction between environmental, genetic and epigenetic susceptibilities in multifactorial diseases, designing a study to evaluate all three will be important.  Chronic stress can be indirectly measured using hair cortisol measurements (Vanaelst et al. 2012).  The benefits of using this method, as compared to blood, serum or urine measurements, are that it is non- invasive, unlikely to cause acute stress and not susceptible to daily fluctuations (Russell et al. 2012).  Evaluating levels of chronic stress in conjunction with genetic variants among women with RM would be a novel investigation of the impact of stress on pregnancy outcomes in a high risk population.  Furthermore, the evaluation of DNA methylation changes in the maternal 101  endometrium and placentae from miscarriages among these RM women may provide insight into the pathological mechanisms of stress in pregnancy and RM risk.  Alternatively or in addition to being a consequence of stress, shortened telomeres could reflect altered rates of reproductive aging among these RM women.  Recent studies investigating reproductive aging in women with BRCA1/2 mutations found an association with earlier age at natural menopause (Lin et al. 2013), decreased ovarian reserve (Titus et al. 2013) and decreased telomere lengths (Martinez-Delgado et al. 2011).  BRCA proteins are involved in the double strand break (DSB) repair pathway, which is essential for recombination in meiosis and alternative telomere lengthening in the early embryo (Johnson and Keefe 2013).  Together these data further support the link between reproductive aging and telomere attrition.  A new strongly predictive marker of biological aging is the level of DNA methylation at specific genomic loci (Hannum et al. 2013).  These authors identified individuals that have faster or slower aging rates than their chronological age.  These epigenetic markers of aging may be valuable as an independent measure of biological aging rates in women with evidence of premature reproductive aging, such as RM cases. 6.4 Conclusions Recurrent miscarriage is a complex condition, in which almost half of all patients have no associated risk factor and those who do, have limited and often experimental available treatment options.  A subset of women with aneuploid losses late in their reproductive life would benefit from education and planning for families earlier; however, there is a need for improved understanding of maternal factors that contribute to idiopathic RM and identification of genetic biomarkers to direct treatment and counseling for these susceptible groups of women.  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SYCP3 region Primer Sequences Exon 2 F 5’- TCCTTGTTCGATATCTCCTTTGA -3’ R 5’- CCGTGTCAGCAGGTTCTGTA -3’ Exon 3, 4 F 5’- AACCCAGGGAGACTTGAAAAA -3’ R 5’- TGTGAGAACAAGGCATTAAATAACA -3’ Exon 5 F 5’- ACACATTGTTTTGTTTATTAGCTCTTTTT -3’ R 5’- AGGACTATCATACTTAGAGAAAAATCAAGC -3’ Exon 6 F 5’- TTTTGGTTTCCCATCAGAAGA -3’ R 5’- TTTAAAACACATGGCCAGCA -3’ Exon 7 F 5’- GCATTGATTTTTAACACTTTCTTTT -3’ R 5’- TCCCAACAAAACCATTTGAA -3’ Exon 8 F 5’- ACCTATTTCAGCAAATAAAAT -3’ R 5’- CAAATAGATGAGCATTTGAA -3’ Exon 9 F 5’- TGGAAACTGTAAGTGATCATATTGAA -3’ R 5’- ATGTAAAATAGATTTTGTATTCCGTTT -3’ 126  Appendix B: Supplementary tables and figures for Chapter 4 Supplementary Table 4.1 Polymorphisms within genes involved in hypothalamus-pituitary-ovarian axis regulation selected for investigation in this study. Gene Description Polymorphism Location UCSC code Heterozygosity Protein change Associations References ACVR1 Activin receptor type 1 G/T intron rs2033962 0.281 +/- 0.248 Disturbed folliculogenesis in PCOS patients Kevenaar et al. 2009 A/T intron rs1220134* 0.489 +/- 0.074 T/C intron rs10497189* 0.104 +/- 0.203 AR Androgen receptor CAG repeat exon 1 Expanded polyglutamine repeat PCOS; premature sexual maturation Hickey et al. 2002; Lappalainen et al. 2008 1733 G/A exon 1 rs6152 0.383 +/- 0.212 synonymous Recurrent spontaneous abortion; endometrial cancer risk Karvela et al. 2008; Yang et al. 2009 CBG Corticosteroid-binding globulin T/C promoter rs2281517 0.333 +/- 0.236 Upregulated in endometrium of infertile women Misao et al. 1995 CGB5 Chorionic gonadotropin beta polypeptide 5 C/T promoter rs4801789 unknown   SNPs flank region associated with RM Rull et al. 2008 A/G 5’ UTR rs710899* 0.180 +/- 0.240 C/T intron 2 rs34335161* 0.172 +/-0.237  Protective effect toward RM CYP17 Steroid 17-hydrolase A/G 5’ UTR rs743572 0.475 +/- 0.109  Short menstrual cycles  Henningson et al. 2007 CYP19 Aromatase T/C 3' UTR rs10046 0.483 +/- 0.091 Estrodiol levels; Age at natural menopause; risk for miscarriage Dunning et al. 2004; He et al. 2007; Guo et al. 2006; Cupisti et al. 2009 A/G exon 3 rs700518* 0.473 +/- 0.112 synonymous (Val-Val) ESR1 Estrogen receptor α TA repeat promoter    Premature ovarian failure Bretherick et al. 2008 PvuII T/C intron 1 rs2234693 0.497 +/- 0.038   Endometriosis; ovarian hyper- Hsieh et al. 2007; Georgiou et 127  Gene Description Polymorphism Location UCSC code Heterozygosity Protein change Associations References XbaI  A/G intron 1 rs9340799 0.399 +/- 0.205 stimulation response; IVF pregnancy outcome; risk for spontaneous abortion al. 1997; Sundarrajan et al. 1999; Pineda et al. 2009 ESR2 Estrogen receptor β CA repeat intron 5    Breast cancer risk Tsezou et al. 2008 RsaI G/A coding rs1256049 0.276 +/- 0.249 synonymous (Val-Val) Ovulatory dysfunction Sundarrajan et al. 1999 FBLN1 Fibulin 1 C/T exon 9 rs9682 0.417 +/- 0.186 synonymous Abnormal expression of FBLN1 is associated with abnormal placenta in mice Singh et al. 2006 FSHR Follicle-stimulating hormone receptor -29 G/A promoter rs1394205 0.461 +/- 0.134 Disruption of potential TF binding site; serum FSH levels & sensitivity of FSHR in vivo Perez-Mayorga et al. 2000; de Castro et al. 2003 919 A/G exon 10 rs6166 0.473 +/- 0.112 Asn680Ser Severity of clinical features in PCOS  Valkenburg et al. 2009 GNRH Gonadotropin releasing hormone C/G exon 1 rs6185* 0.415 +/- 0.188 Trp16Ser Breast cancer adverse outcomes Piersma et al. 2007 GCCR Glucocorticoid receptor BclI G/C intron B rs41423247 0.441 +/- 0.162 GC sensitivity ; hypothalamus- pituitary-adrenal axis response Kumsta et al. 2007 A/G 3’ UTR rs6198 0.164+/- 0.235 GRβ unable to bind ligand; dom - ve effect; psychological stress is associated with reduced fertility and risk for pregnancy loss Hjollund et al. 1999; Nepomnaschy et al. 2006 INHA Inhibin α 129 C/T promoter rs35118453 unknown linked with TG repeat Premature ovarian failure Woad et al. 2009; Harris et al. 2005; Marozzi et al. 2002 128  Gene Description Polymorphism Location UCSC code Heterozygosity Protein change Associations References LHB Luteinizing hormone β subunit T/C exon 2 rs1800447* 0.419 +/- 0.184 Trp8Arg Hyperfunctional promoter; infertility Themmen and Huhtaniemi. 2000; Huhtaniemi and Themmen. 2005; Nagirnaja et al. 2010; Okuno et al. 2001; Liu et al. 2005 LHR Luteinizing hormone receptor G/A exon 10 rs2293275 0.469 +/- 0.121 Ser312Asn Breast cancer risk Piersma et al. 2007 T/C exon 10 rs12470652 0.022 +/- 0.103 Asn291Ser Increased receptor sensitivity PAPPA Pregnancy-associated plasma protein A A/C exon 14 rs7020782 0.477 +/- 0.105 Tyr/Ser Recurrent pregnancy loss Suzuki et al. 2006 PGR  Progesterone receptor +44 C/T promoter rs518162 0.224 +/- 0.249 Uterine fibroids and endometriosis  Govindan et al. 2007 G/T exon 4 rs1042838 0.144 +/- 0.226 Val660Leu (PROGINS)  Less responsive to progestin; endometriosis Romano et al. 2007; De Carvalho et al. 2007 PRL  Prolactin G/T promoter rs1341239 0.305 +/- 0.244  Altered promoter activity ; PRL levels in plasma; breast cancer risk Stevens et al. 2001; Lee et al. 2007; Vaclavicek et al. 2006 A/T intron 1 rs2244502 0.458 +/- 0.139 PRLR  Prolactin receptor T/C intron 1 rs9292573 0.492 +/- 0.063 Breast cancer risk Vaclavicek et al. 2006 C/T intron 4 rs37389 0.385 +/- 0.210 T/C intron 1 rs13354826 0.262 +/- 0.250 SHBG  Sex hormone-binding globulin TAAAA repeat promoter PCOS; serum SHBG levels Xita et al. 2003; Eriksson et al. 2006 G/A exon 8 rs6259 0.188 +/- 0.242 Asp327Asn SHBG and estradiol levels; Age at menopause Cousin et al. 2004; Eriksson et al. 2006; Xita et al. 2005 G/A 5' UTR rs1799941 0.329 +/- 0.237   SHBG levels Eriksson et al. 2006 129  Gene Description Polymorphism Location UCSC code Heterozygosity Protein change Associations References T/C intron 1 rs6257 0.128 +/- 0.218  Serum SHBG levels Riancho et al. 2008 THRB Thyroid hormone receptor β C/T exon 7 rs3752874 0.192 +/- 0.243 synonymous Higher serum TSH; mutation associated with increased rate of miscarriage Sorenson et al. 2008; Anselmo et al. 2004 TSHR  Thyroid stimulating hormone receptor C/A exon 1 rs2234919 unknown Pro52Thr Reduced receptor function Loos et al. 1995 C/G exon 10 rs1991517 0.184 +/- 0.241 Asp727Glu Lower levels of TSH in plasma Peeters et al. 2003 *Due to technical limitations, assays could not be designed for these SNPs 130  Supplementary Table 4.2 Assessing single nucleotide polymorphisms for Hardy-Weinberg Equilibrium within controls.  Using observed and expected genotype frequencies for controls (N=130), Hardy-Weinberg Equilibrium was calculated for all assessed SNPs; N may be less if genotyping calls failed. Alleles Observed Expected p-value b  HPO Axis Polymorphisms rs10046 CC 25 29  0.644    CT 72 65 TT 33 37 rs1042838 GG 92 92  0.888    GT 34 34 TT 3 3 rs12470652 TT 115 115  1.000    TC 15 14 CC 0 0 rs1256049 GG 115 114  0.863    GA 14 15 AA 1 0 rs13354826 TT 57 54  0.719    CT 53 59 CC 19 16 rs1341239 TT 19 18  0.966    TG 59 61 GG 52 51 rs1394205 GG 69 69  0.995    GA 52 51 AA 9 9 rs1799941 GG 77 78  0.956    GA 47 46 AA 6 7 rs1991517 131  Alleles Observed Expected p-value b  CC 112 112  1.000    CG 17 18 GG 1 1 rs2033962 GG 92 91  0.887 GT 33 36 TT 5 4 rs2234693 CC 43 44 0.951    CT 66 63 TT 21 22 rs2234919 CC 118 117  0.842 AC 11 12 AA 1 0 rs2244502 AA 69 68  0.956 AT 49 51 TT 11 10 rs2281517 TT 79 78  0.919 TC 43 46 CC 8 7 rs2293275 AA 23 24  0.970 AG 65 63 GG 41 42 rs35118453 CC 81 79  0.779 TC 41 45 TT 8 6 rs37389 CC 108 103  0.054 TC 15 26 TT 7 2 rs3752874 CC 91 91 1.000 TC 36 35 TT 3 3 rs41423247 CC 25 22  0.783 132  Alleles Observed Expected p-value b  CG 58 63 GG 47 44 rs4801789 CC 69 65  0.504 CT 46 54 TT 15 11 rs518162 CC 114 113  0.863 CT 14 17 TT 2 1 rs6152 GG 95 96  0.920 GA 33 32 AA 2 3 rs6166 AA 41 43  0.848 AG 68 63 GG 21 23 rs6198 AA 88 86  0.723 GA 34 38 GG 6 4 rs6257 TT 109 108  1.000 CT 19 21 CC 2 1 rs6259 GG 96 96  1.000 GA 31 30 AA 2 2 rs7020782 AA 56 57  0.961 AC 60 58 CC 14 15 rs743572 AA 54 49  0.393 AG 51 62 GG 25 20 rs9292573 CC 13 15  0.856 CT 63 59 133  Alleles Observed Expected p-value b  TT 54 56 rs9340799 AA 57 60 0.726 AG 62 57  GG 11 14  rs9682 CC 40 44 0.592 CT 71 63 TT 19 23 Ancestral Informative Polymorphisms rs4908343 AA 89 86  0.546 AG 34 39  GG 7 4  rs3737576 TT 110 110  0.863 CT 19 19 CC 1 1 rs260690 TT 110 104  0.019 TG 13 24 GG 7 1 rs6548616 TT 65 65  1.000 CT 54 54 CC 11 11 rs10007810 CC 84 86  0.730 CT 43 40 TT 3 5 rs7657799 TT 121 121  0.791 GT 8 8 GG 0 0 rs870347 TT 115 112  0.584 GT 10 17 GG 4 1 rs6451722 GG 73 75  0.737 AG 52 47 AA 5 7 134  Alleles Observed Expected p-value b  rs6422347 TT 106 104  1.000 CT 21 24 CC 3 1 rs1040045 TT 81 79  0.779 CT 41 45 CC 8 6 rs7803075 GG 55 54  0.980 AG 58 59 AA 17 16 rs10108270 CC 49 49  1.000 CA 62 62 AA 19 19 rs2416791 GG 103 103  1.000 AG 25 26 AA 2 2 rs772262 GG 108 109  1.000 AG 22 20 AA 0 1 rs9319336 TT 111 109  0.863 CT 16 20 CC 3 1 rs7997709 TT 109 107  0.863 CT 18 22 CC 3 1 rs9530435 GG 99 98  1.000 AG 28 30 AA 3 2 rs9522149 CC 68 64  0.543 CT 47 54 TT 15 11 rs3784230 AA 39 44  0.403 135  Alleles Observed Expected p-value b  AG 74 63 GG 17 22 rs11652805 AA 86 82  0.393 AG 35 42 GG 9 5 rs4891825 AA 111 110  1.000 AG 17 19 GG 2 1 b Chi-square analysis, genotypes were combined where necessary to meet the analysis requirements. 136  Supplementary table 4.3 Genotype distributions for controls and recurrent miscarriage women for 21 ancestral informative single nucleotide polymorphisms. Genotype RM (N=227) a Controls (N=130)a p-valueb N Frequency N Frequency  rs4908343 AA 136 0.60 89 0.68 0.233 AG 79 0.35 34 0.26 GG 12 0.05 7 0.05  rs3737576 TT 195 0.86 110 0.85 0.863 CT 30 0.13 19 0.15 CC 2 0.01 1 0.01 rs6548616 TT 107 0.48 65 0.50 0.502 CT 104 0.46 54 0.42 CC 13 0.06 11 0.08 rs10007810 CC 146 0.64 84 0.65 0.379 CT 69 0.30 43 0.33 TT 12 0.05 3 0.02 rs7657799 TT 202 0.91 121 0.94 0.462 GT 20 0.09 8 0.06 GG 0 0.00 0 0.00 rs870347 TT 182 0.81 115 0.89 0.071 GT 38 0.17 10 0.08 GG 4 0.02 4 0.03 rs6451722 GG 141 0.62 73 0.56 0.228 AG 72 0.32 52 0.40 137  Genotype RM (N=227) a Controls (N=130)a p-valueb N Frequency N Frequency  AA 14 0.06 5 0.04 rs6422347 TT 178 0.78 106 0.82 0.748 CT 44 0.19 21 0.16 CC 5 0.02 3 0.02 rs1040045 TT 144 0.63 81 0.62 0.869 CT 72 0.32 41 0.32 CC 11 0.05 8 0.06 rs7803075 GG 112 0.49 55 0.42 0.217 AG 80 0.35 58 0.45 AA 35 0.15 17 0.13 rs10108270 CC 115 0.51 49 0.38 0.056 CA 83 0.37 62 0.48 AA 29 0.13 19 0.15 rs2416791 GG 165 0.73 103 0.79 0.368 AG 56 0.25 25 0.19 AA 6 0.03 2 0.02 rs772262 GG 187 0.82 108 0.83 1.000 AG 37 0.16 22 0.17 AA 3 0.01 0 0.00 rs9319336 TT 180 0.79 111 0.85 0.323 CT 37 0.16 16 0.12 CC 10 0.04 3 0.02 rs7997709 138  Genotype RM (N=227) a Controls (N=130)a p-valueb N Frequency N Frequency  TT 174 0.77 109 0.84 0.338 CT 44 0.20 18 0.14 CC 7 0.03 3 0.02 rs9530435 GG 165 0.73 99 0.76 0.472 AG 50 0.22 28 0.22 AA 11 0.05 3 0.02 rs9522149 CC 108 0.48 68 0.52 0.084 CT 71 0.31 47 0.36 TT 47 0.21 15 0.12 rs3784230 AA 90 0.40 39 0.30 0.131 AG 105 0.46 74 0.57 GG 32 0.14 17 0.13 rs11652805 AA 155 0.68 86 0.66 0.470 AG 63 0.28 35 0.27 GG 9 0.04 9 0.07 rs4891825 AA 189 0.83 111 0.85 0.708 AG 37 0.16 17 0.13 GG 1 0.00 2 0.02  *N may be less for individual SNPs if genotype calls failed **Chi-square analysis 139  Supplementary Table 4.4 Mean number of miscarriages within the recurrent miscarriage group subdivided by genotype for each of 31 single nucleotide polymorphisms within genes involved in the hypothalamus-pituitary-ovarian axis. Genotype Age Mean miscarriages Standard Deviation P-value b  rs10046 CC 32.9 4.42 1.58 0.757 CT 34.6 4.53 1.81 TT 33.7 4.68 2.36 rs1042838 GG 33.9 4.49 1.92 0.211 GT 34.0 4.76 1.91 TT 34.5 3.33 0.52 rs12470652 TT 34.1 4.56 1.83 0.438 TC 33.0 4.25 2.32 rs1256049 GG 33.6 4.59  1.97 0.217 GA 35.4 3.92  1.18 AA 41.8 5.00 0.71 rs13354826 CC 33.8 4.46 2.17 0.995 CT 34.2 4.51 1.99 TT 33.8 4.49 1.72 rs1341239 TT 33.5 4.27 1.74 0.106 TG 33.1 4.29 1.60 140  GG 35.1 4.84 2.20 Genotype Age Mean miscarriages Standard Deviation P-value b  rs1394205 GG 33.8 4.43 1.74 0.490 GA 34.1 4.54 1.83 AA 33.5 4.95 2.82 rs1799941 GG 34.2 4.47 1.70 0.627 GA 33.6 4.67 2.33 AA 33.6 4.21 1.12 rs1991517 CC 34.0 4.59 1.98 0.225 CG/GG a  34.0 4.20 1.49 rs2033962 TT 33.9 5.29 2.69 0.021 TG 33.7 5.02 2.34 GG 34.1 4.29 1.62 rs2234693 CC 33.4 4.89 2.10 0.253 CT 34.3 4.39 1.81 TT 33.8 4.44 1.86 rs2234919 CC 33.9 4.48 1.81 0.423 CA/AA a  34.1 4.77 2.40 rs2244502 AA 34.1 4.59 1.77 0.194 141  AT 33.4 4.26 1.66 Genotype Age Mean miscarriages Standard Deviation P-value b  TT 34.5 4.75 2.12 rs2281517 TT 33.9 4.41 1.99 0.541 TC 34.0 4.70 1.71 CC 34.0 4.70 1.95 rs2293275 GG 34.9 4.76 2.05 0.186 GA 33.5 4.28 1.62 AA 33.4 4.56 2.13 rs35118453 TT 32.7 5.08 2.07 0.434 TC 33.8 4.62 2.07 CC 34.1 4.43 1.80 rs37389 TT/TC 33.4 3.65 1.67 0.557 CC 34.1 4.48 1.96 rs3752874 TT 34.5 5.17 3.92 0.694 CT 32.8 4.53 1.94 CC 34.3 4.49 1.80 rs41423247 CC 32.1 4.50 1.95 0.673 CG 34.2 4.64 1.90 GG 34.2 4.40 1.89 142  rs4801789 Genotype Age Mean miscarriages Standard Deviation P-value b  CC 34.1 4.52 1.87 0.676 CT 34.3 4.58 2.05 TT 33.2 4.21 1.47 rs518162 CC 34.0 4.53 1.87 0.779 CT/TT a  33.5 4.43 2.09 rs6152 GG 33.9 4.55 1.90 0.715 GA/AA a  34.1 4.45 1.87 rs6166 AA 33.7 4.43 1.74 0.378 AG 34.3 4.44 1.88 GG 33.4 4.88 2.17 rs6198 GG 32.0 4.00 0.71 0.676 GA 34.3 4.38 1.91 AA 33.9 4.58 1.93 rs6257 CC/CT a  34.9 4.64 1.75 0.723 TT 33.8 4.50 1.93 rs6259 GG 34.0 4.62 1.83 0.220 GA/AA a  33.8 4.25 2.09 rs7020782 143                     aLess than 5 individuals homozygous for the minor allele, therefore combined with heterozygotes for analysis b ANCOVA CC 33.6 4.64 2.57 0.406 Genotype Age Mean miscarriages Standard Deviation P-value b  CA 34.1 4.68 1.94 AA 33.9 4.33 1.67 rs743572 AA 32.7 4.52 1.83 0.963 AG 34.6 4.49 2.02 GG 34.4 4.58 1.78 rs9292573 CC 34.4 4.48 2.00 0.933 CT 33.9 4.47 1.68 TT 33.9 4.57 2.07 rs9340799 AA 33.8 4.21 1.65 0.077 AG 34.5 4.67 1.96 GG 32.9 4.94 2.21 rs9682 CC 33.7 4.37 1.56 0.279 CT 34.4 4.72 2.19 TT 33.0 4.19 1.66 144  Appendix C: Supplementary tables and figures for Chapter 5 Supplementary Table 5.1 Primers used for assessment of DNA methylation by bisulfite pyrosequencing. Gene/Region  Primers Reference (if applicable) APC F TTTTTTGTTTGTTGGGGATTG Avila et al. 2010  R Biotin/AATCCRACAACACCTCCATTCTAT    S TTTGTTGGGGATTGG PLAGL1 F Biotin/GAYGGGTTGAATGATAAATGGTAGATG Bourque et al. 2010  R TCRACRCAACCATCCTCTTAACTAC    S ACRCAACCATCCTCTTA SGCE  F TGGTGTGTGTYGAAGAAATTTGATTG Peñaherrera et al. 2010  R Biotin/CAAACRCRATCTCCACTAAATAC    S TGTGTGTYGAAGAAATTTGAT H19/IGF2 ICR1 F Biotin/ACAATACAAACTCACACATCACAAC Horike et al. 2009  R TGAGTGTTTTATTTTTAGATGATTTT    S GTGGTTTGGGTGATT CDKN1C F Biotin/TATTATATTATGTTAATTGTGGTTGGG N/A  R CAACAAACACTAATACACACTAATA    S AACACTAATACACACTAATACTAAA KvDMR1 F TTAGTTTTTTGYGTGATGTGTTTATTA Bourque et al. 2010  R Biotin/CCCACAAACCTCCACACC    S TTGYGTGATGTGTTTATTA MEG3 F Biotin/GGTTTATATTTGGGAATTAGTTATGT N/A  R CCCCCAAATTCTATAACAAATTA    S AATACTTTTTCCCTAC SNRPN F Biotin/TATGTTTAGGYGGGGATGTGTG Bourque et al. 2010  R AAAAACCACCRACACAACTAACCTTAC    S CAAATACRTCAAACATCT 145  Gene/Region  Primers Reference (if applicable) AXL F TTTGAGGAAAGTTTGGTATTTATG N/A  R Biotin/CACTCACCCCTAAAAACCAT    S TAGGATGGGTAGGGTT CYP1A2 F TGGGGATTTGGGTTGAAAATTAG N/A  R Biotin/AAACTTCTTTCCCACTACACACATAA    S GATTTGGGTTGAAAATTA DEFB1 F GGATTTTAGGAATTGGGGAGA N/A  R Biotin/CCTTAACTATAACACCTCCCTTCA    S AGGTTTTTAGAGGTTGGA LINE-1 F TTTTGAGTTAGGTGTGGGATATA Bollati et al. 2007  R Biotin/AAAATCAAAAAATTCCCTTTC    S AGTTAGGTGTGGGATATAGT 146  Supplementary Table 5.2 List of 14 candidate CpG sites identified by the Illumina Infinium HumanMethylation27 BeadChip analysis, using a false discovery rate<0.05 and a Delta beta>0.05. Probe Gene Gene name Chr CGI Hg18 Location RM TA q-value Delta beta SNP/bimodal Evidence for functional candidacy cg22879515 BTG4 B-cell translocation gene 4 11q23 Y 110888725 0.190 0.280 0.027 -0.090 Y Growth inhibitor; highly expressed in the oocyte & preimplantation embryos in mice (Buanne et al. 2000) cg08775774 CCDC62 Coiled-coil domain-containing protein 62 12q24 N 121824850 0.333 0.397 0.038 -0.064 Y Interacts with ERα and β, modifies expression of ER targets (Chen et al. 2009) cg21783004 LECT2 Leukocyte cell- derived chemotaxin 2 5q31 N 135318187 0.628 0.689 0.011 -0.062 N Inflammatory response (Yamagoe et al. 1996) cg04970352 ALX4 Aristaless-like 4, mouse, homolog of 11p11 Y 44283975 0.399 0.460 0.039 -0.060 N N/A cg05316065 GSDMC Gasdermin C 8q24 N 130868189 0.206 0.263 0.046 -0.057 N N/A cg06812977 RNLS Renalase 10q23 Y 90333016 0.266 0.320 0.050 -0.055 N Regulates blood pressure and cardiac function (Xu et al. 2005) cg13262687 POU4F2 Pou domain, class 4, transcription factor 2 4q31 Y 147779029 0.272 0.325 0.011 -0.053 N N/A cg24292612 DEFB1 Defensin, beta, 1 8p23 N 6722882 0.129 0.180 0.032 -0.052 N Antimicrobial peptide active in epithelia of the female reproductive tract (Bensch et al. 1995); increased expression in the endometrium of women with infertility (Das et a. 2007) 147  Probe Gene Gene name Chr CGI Hg18 Location RM TA q-value Delta beta SNP/bimodal Evidence for functional candidacy cg20311501 APC APC gene 5q22 N 112101401 0.558 0.505 0.039 0.054 N  Putative imprinted gene in placenta (Yuen et al. 2011; Guilleret et al. 2009) cg14892768 AXL AXL receptor tyrosine kinase 19q13 N 46417172 0.671 0.614 0.039 0.058 N Mice deficient in 3 tyrosine kinases, including AXL, had systemic lupus erythematosus and recurrent fetal loss (Lu and Lemke. 2001) cg17836145 VNN2 Vanin 2 6q23 N 133126335 0.539 0.469 0.027 0.071 N Increased expression in autoimmune disease (Bovin et al. 2007) cg19949550 ASB2 Ankyrin repeat- and socs box- containing protein 2 14q23 N 93493457 0.721 0.624 0.048 0.097 Y N/A cg04968473 CYP1A2 Cytochrome P450, subfamily I, polypeptide 2 15q24 N 72827787 0.504 0.382 0.026 0.123 N Caffeine & drug metabolism (Shimada et al. 2004); associated with RM (Sata et al. 2005) cg05294455 MYL4 Myosin, light chain 4, alkali, atrial, embryonic 17q21 N 42641608 0.668 0.538 0.027 0.131 N N/A 148  Supplementary Table 5.3 Significant gene ontology groups from ErmineJ analysis of 10 recurrent miscarriage and 10 elective termination samples using the Illumina Infinium HumanMethylation27 array, listed by corrected p-value. Name ID Probes Number of Genes Raw Score p-value Corrected p-value muscle system process GO:0003012 135 135 0.02775651 1.00E-12 6.35E-10 muscle contraction GO:0006936 122 122 0.0283608 1.00E-12 7.62E-10 Imprinted genes IM Genes 68 68 0.03970521 1.00E-12 9.52E-10 xenobiotic metabolic process GO:0006805 99 99 0.02916292 1.87E-12 1.02E-09 cellular response to xenobiotic stimulus GO:0071466 100 100 0.02907881 2.44E-12 1.16E-09 regulation of heart contraction GO:0008016 64 64 0.03485897 1.00E-12 1.27E-09 Maternally expressed imprinted genes MEG Genes 36 36 0.04346512 1.00E-12 1.90E-09 adenylate cyclase-activating G-protein coupled receptor signaling pathway GO:0007189 31 31 0.03846768 1.00E-12 3.81E-09 circulatory system process GO:0003013 132 132 0.02655087 3.93E-11 1.50E-08 blood circulation GO:0008015 132 132 0.02655087 3.93E-11 1.66E-08 cellular metal ion homeostasis GO:0006875 186 186 0.02389958 1.02E-10 3.53E-08 calcium ion homeostasis GO:0055074 143 143 0.02501162 1.58E-10 5.00E-08 cellular divalent inorganic cation homeostasis GO:0072503 140 140 0.02490388 2.32E-10 6.79E-08 inflammatory response GO:0006954 200 200 0.0234339 6.66E-10 1.69E-07 metal ion homeostasis GO:0055065 197 197 0.02344973 6.26E-10 1.70E-07 divalent inorganic cation homeostasis GO:0072507 149 149 0.02446927 1.06E-09 2.52E-07 regulation of response to external stimulus GO:0032101 198 198 0.02317321 1.84E-09 4.11E-07 cellular calcium ion homeostasis GO:0006874 135 135 0.02533335 2.12E-09 4.48E-07 regulation of muscle system process GO:0090257 76 76 0.02856518 2.50E-09 5.01E-07 leukocyte activation GO:0045321 174 174 0.02329665 3.04E-09 5.26E-07 cell-cell adhesion GO:0016337 170 170 0.02330821 2.91E-09 5.28E-07 leukocyte migration GO:0050900 123 123 0.02561222 2.83E-09 5.38E-07 platelet activation GO:0030168 154 154 0.02377188 4.28E-09 7.08E-07 149  Name ID Probes Number of Genes Raw Score p-value Corrected p-value regulation of epithelial cell proliferation GO:0050678 124 124 0.02525684 8.07E-09 1.28E-06 elevation of cytosolic calcium ion concentration GO:0007204 79 79 0.02804934 8.54E-09 1.30E-06 regulation of homeostatic process GO:0032844 140 140 0.02373767 1.20E-08 1.69E-06 regulation of muscle contraction GO:0006937 68 68 0.02957137 1.26E-08 1.71E-06 response to bacterium GO:0009617 185 185 0.02288847 1.31E-08 1.72E-06 cellular response to cytokine stimulus GO:0071345 166 165 0.02346636 1.19E-08 1.75E-06 negative regulation of multicellular organismal process GO:0051241 166 166 0.02337247 1.62E-08 2.06E-06 digestion GO:0007586 69 69 0.0279473 1.90E-08 2.33E-06 adenylate cyclase-modulating G-protein coupled receptor signaling pathway GO:0007188 76 76 0.02764782 2.16E-08 2.57E-06 cytosolic calcium ion homeostasis GO:0051480 91 91 0.02724974 2.30E-08 2.65E-06 lymphocyte activation GO:0046649 141 141 0.0234194 3.28E-08 3.67E-06 steroid metabolic process GO:0008202 154 154 0.02302849 4.91E-08 5.34E-06 epidermis development GO:0008544 146 146 0.02326541 5.28E-08 5.58E-06 immune effector process GO:0002252 120 120 0.02450512 6.72E-08 6.92E-06 Paternally expressed imprinted genes PEG Genes 32 32 0.03547532 7.97E-08 7.98E-06 G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger GO:0007187 100 100 0.02531593 9.68E-08 9.45E-06 Genes associated with RM RM Genes 60 60 0.02926887 9.99E-08 9.51E-06 regulation of leukocyte activation GO:0002694 200 200 0.02203887 1.11E-07 1.03E-05 regulation of lymphocyte activation GO:0051249 176 176 0.02214319 1.61E-07 1.46E-05 positive regulation of secretion GO:0051047 133 133 0.02380548 1.83E-07 1.62E-05 regulation of neurological system process GO:0031644 132 132 0.02362232 3.00E-07 2.60E-05 regulation of nucleotide metabolic process GO:0006140 184 184 0.02193497 3.14E-07 2.66E-05 regulation of synaptic transmission GO:0050804 114 114 0.02389627 3.38E-07 2.80E-05 cytokine-mediated signaling pathway GO:0019221 131 130 0.0235637 3.51E-07 2.84E-05 gland development GO:0048732 163 162 0.02234582 3.95E-07 3.13E-05 visual perception GO:0007601 116 116 0.02382121 4.10E-07 3.19E-05 150  Name ID Probes Number of Genes Raw Score p-value Corrected p-value epithelial cell differentiation GO:0030855 152 152 0.02255615 4.30E-07 3.28E-05 actin filament-based process GO:0030029 183 183 0.02178972 4.95E-07 3.69E-05 developmental maturation GO:0021700 78 78 0.02618592 5.08E-07 3.72E-05 regulation of MAP kinase activity GO:0043405 150 150 0.02248285 5.30E-07 3.81E-05 regulation of purine nucleotide metabolic process GO:1900542 182 182 0.02175086 5.58E-07 3.94E-05 sensory perception of light stimulus GO:0050953 117 117 0.02369091 5.71E-07 3.96E-05 response to oxidative stress GO:0006979 144 144 0.02244905 5.83E-07 3.96E-05 protein activation cascade GO:0072376 43 43 0.03154828 6.25E-07 4.17E-05 regulation of transmission of nerve impulse GO:0051969 121 121 0.02364677 6.39E-07 4.19E-05 cell junction organization GO:0034330 99 99 0.02444168 7.56E-07 4.88E-05 activation of immune response GO:0002253 159 159 0.02208538 8.41E-07 5.34E-05 secretion by cell GO:0032940 199 199 0.02140264 8.89E-07 5.55E-05 regulation of ion homeostasis GO:2000021 71 71 0.02607518 9.31E-07 5.72E-05 endocytosis GO:0006897 156 156 0.02201122 1.04E-06 6.28E-05 negative regulation of transport GO:0051051 146 146 0.02213919 1.38E-06 8.19E-05 response to nutrient GO:0007584 153 153 0.02190577 1.40E-06 8.20E-05 response to metal ion GO:0010038 151 151 0.0221242 1.43E-06 8.27E-05 regulation of blood pressure GO:0008217 69 69 0.0257222 1.83E-06 1.04E-04 humoral immune response GO:0006959 60 60 0.02748179 2.29E-06 1.28E-04 regulation of membrane potential GO:0042391 111 111 0.02342726 2.40E-06 1.31E-04 positive regulation of neurological system process GO:0031646 33 33 0.03283363 2.38E-06 1.31E-04 cellular response to growth factor stimulus GO:0071363 109 109 0.02340325 2.54E-06 1.36E-04 sensory perception of chemical stimulus GO:0007606 49 49 0.02858786 2.67E-06 1.41E-04 regulation of MAPK cascade GO:0043408 180 180 0.02116248 3.24E-06 1.69E-04 regulation of cytokine production GO:0001817 196 196 0.02097018 3.35E-06 1.72E-04 sodium ion transport GO:0006814 73 73 0.02529259 4.06E-06 2.06E-04 glycerolipid metabolic process GO:0046486 116 116 0.02287256 4.18E-06 2.07E-04 negative regulation of kinase activity GO:0033673 94 94 0.02366534 4.13E-06 2.07E-04 151  Name ID Probes Number of Genes Raw Score p-value Corrected p-value morphogenesis of an epithelium GO:0002009 184 184 0.02104682 4.50E-06 2.20E-04 response to growth factor stimulus GO:0070848 120 120 0.02280165 4.93E-06 2.38E-04 muscle organ development GO:0007517 141 141 0.02161454 5.52E-06 2.63E-04 regulation of peptidyl-tyrosine phosphorylation GO:0050730 83 83 0.02490981 6.08E-06 2.86E-04 positive regulation of MAP kinase activity GO:0043406 108 108 0.02290668 7.59E-06 3.52E-04 cell chemotaxis GO:0060326 58 58 0.02671957 7.77E-06 3.57E-04 positive regulation of cytokine production GO:0001819 104 104 0.0228866 7.92E-06 3.59E-04 positive regulation of protein kinase activity GO:0045860 194 194 0.02066089 8.26E-06 3.70E-04 regulation of hormone levels GO:0010817 113 113 0.02285105 8.55E-06 3.78E-04 positive regulation of protein serine/threonine kinase activity GO:0071902 128 128 0.02226148 8.98E-06 3.93E-04 negative regulation of hydrolase activity GO:0051346 96 96 0.023245 9.87E-06 4.27E-04 cellular defense response GO:0006968 36 36 0.03044358 1.03E-05 4.34E-04 growth GO:0040007 189 189 0.02058592 1.02E-05 4.38E-04 response to corticosteroid stimulus GO:0031960 96 96 0.02321601 1.05E-05 4.38E-04 positive regulation of cell activation GO:0050867 152 152 0.02134234 1.10E-05 4.54E-04 extracellular matrix organization GO:0030198 79 79 0.02456241 1.15E-05 4.69E-04 embryonic organ development GO:0048568 181 181 0.02059873 1.55E-05 6.28E-04 organophosphate metabolic process GO:0019637 121 121 0.02228751 1.57E-05 6.29E-04 respiratory system development GO:0060541 97 97 0.0229996 1.61E-05 6.40E-04 negative regulation of immune system process GO:0002683 94 94 0.02299012 1.64E-05 6.46E-04 response to lipopolysaccharide GO:0032496 115 115 0.02222971 1.78E-05 6.92E-04 potassium ion transport GO:0006813 120 120 0.02220614 1.88E-05 7.21E-04 cell junction assembly GO:0034329 89 89 0.02400723 1.92E-05 7.30E-04 defense response to bacterium GO:0042742 76 76 0.02422679 2.07E-05 7.81E-04 leukocyte chemotaxis GO:0030595 41 41 0.02890583 2.09E-05 7.82E-04 regulation of T cell activation GO:0050863 134 134 0.02183027 2.39E-05 8.75E-04 response to hypoxia GO:0001666 148 148 0.02102689 2.37E-05 8.77E-04 152  Name ID Probes Number of Genes Raw Score p-value Corrected p-value neuropeptide signaling pathway GO:0007218 58 58 0.02595758 2.47E-05 8.88E-04 nucleotide biosynthetic process GO:0009165 109 109 0.02234566 2.45E-05 8.89E-04 response to oxygen levels GO:0070482 158 158 0.02080428 2.54E-05 8.94E-04 glycerophospholipid metabolic process GO:0006650 73 73 0.02424943 2.52E-05 8.96E-04 regionalization GO:0003002 160 160 0.02079875 2.57E-05 8.98E-04 regulation of ion transport GO:0043269 120 120 0.02204689 2.64E-05 8.99E-04 lipid catabolic process GO:0016042 115 115 0.02204796 2.64E-05 9.05E-04 cellular aromatic compound metabolic process GO:0006725 135 135 0.02178681 2.63E-05 9.11E-04 response to molecule of bacterial origin GO:0002237 125 125 0.02202947 2.74E-05 9.24E-04 negative regulation of epithelial cell proliferation GO:0050680 50 50 0.02679098 2.98E-05 9.96E-04  Italics = Custom groups; Bold = gene ontology classifications involved in immune response 153   Supplementary Figure 5.1 Correlation between Infinium average beta values and bisulfite pyrosequencing methylation (%) values for all samples run on the array (N=20) at candidate CpG sites selected for follow up: A) CYP1A2 cg04968473 (r=0.98, p<0.0001), B) DEFB1 cg24292612 (r=0.89, p<0.0001), C) APC cg20311501* (r=0.96, p<0.0001), and D) AXL cg14892768 (r=0.82, p<0.0001).  *Note: the pyrosequencing assay assessed a nearby CpG, but not the exact same site as the Infinium probe. 154   Supplementary Figure 5.2 Correlation between gestational age and DNA methylation (%), as measured by bisulfite pyrosequencing, at each of the candidate regions identified from the Infinium analysis: A) CYP1A2 (r=0.58, p<0.0001), B) DEFB1 (r=-0.05, p=0.74), C) APC (r=0.03, p=0.83), D) AXL (r=0.03, p=0.83) in RM and M placental samples (N=54). 155   Supplementary Figure 5.3 Correlation between maternal age (years) and DNA methylation (%) at 12 targeted loci in RM and M placental samples (N=54): A) H19/IGF2 ICR1 (r=0.02, p=0.87), B) SNPRN (r=-0.06, p=0.64), C) PLAGL1 (r=-0.15, p=0.29), D) SGCE (r=0.08, p=0.55), E) CDKN1C (r=-0.25, p=0.06), F) KvDMR1 (r=-0.21, p=0.13), G) MEG3 (r=0.12, p=0.37), H) APC (r=-0.19, p=0.17), I) AXL (r=-0.13, p=0.33), J) CYP1A2 (r=-0.06, p=0.69), K) DEFB1 (r=0.24, p=0.08), and L) LINE1 (r=0.17, p=0.23). 156   Supplementary Figure 5.4 Comparison of average DNA methylation (%) at 7 imprinted loci between RM (N=33), M (N=21) and TA (N=16) groups: A) PLAGL1 (p=0.34), B) SGCE (p=0.006), C) H19/IGF2 ICR1 (p<0.0001), D) CDKN1C (p=0.77), E) KvDMR1 (p=0.14), F) MEG3 (p=0.93), G) SNRPN (p=0.07). 157   Supplementary Figure 5.5 Linear correlation between gestational age and DNA methylation at imprinted loci among RM and M placental samples (N=54): A) PLAGL1 (r=-0.00, p=0.98), B) SGCE (r=-0.01, p=0.96), C) H19/IGF2 ICR1 (0.04, p=0.75), D) CDKN1C (r=0.13, p=0.35), E) KvDMR1 (0.14, p=0.32), F) MEG3 (r=0.03, p=0.86), G) SNRPN (r=0.11, p=0.46).


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