RECURRENT MISCARRIAGE: UNRAVELING THE COMPLEX …

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

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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.

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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.

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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

1.2.3.1 Uterine natural killer cells ................................................................................... 8

1.2.3.2 Maternal T helper 1 and T helper 2 immune balance ......................................... 9

1.2.3.3 Placental immunity ........................................................................................... 10

1.2.3.4 Autoimmunity ................................................................................................... 11

1.2.3.5 Infection ............................................................................................................ 12

1.2.4 Endocrinological ....................................................................................................... 12

1.2.4.1 Luteal phase defects .......................................................................................... 12

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1.2.4.2 Thyroid dysfunction .......................................................................................... 13

1.2.4.3 Polycystic ovarian syndrome ............................................................................ 13

1.2.4.4 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

1.3.1.1 Genes involved in meiosis ................................................................................ 17

1.3.1.2 Genes involved in immune function ................................................................. 18

1.3.1.3 Genes involved in endocrine function .............................................................. 18

1.3.1.4 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


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


4.2.1 Samples ..................................................................................................................... 53

4.2.2 Variant selection ....................................................................................................... 54

4.2.3 Genotyping ................................................................................................................ 54


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


5.2.1 Samples ..................................................................................................................... 71

5.2.2 Array-based quantification of DNA methylation ..................................................... 71

5.2.3 Targeted DNA methylation....................................................................................... 72


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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.

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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 8th

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.

1.2.3.1 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) 56bright

, 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).

1.2.3.2 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).

1.2.3.3 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 1.3.1.2 (page 18).

1.2.3.4 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

1.2.3.5 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.

1.2.4.1 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).

1.2.4.2 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).

1.2.4.3 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.

1.2.4.4 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 1.3.1.4 (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

1.3.1.1 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.

1.3.1.2 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).

1.3.1.3 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.

1.3.1.4 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.


3.2.1 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 (R2=0.081, Table 3.1, Figure 3.3).

There was also a weak (R2=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 (R2=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 R2a

P-valueb

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

aR

2 is a measure of the goodness of fit of the regression

aP 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-valuea,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

aP values for comparison to control group 1, and 2, respectively.

bANCOVA (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

Flo

w F

ISH

tel

len

gth

(kb

)

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 1.3.1.3 (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


4.2.1 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


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

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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)

aChi-square analysis

65

Table 4.3 Genotype distributions of single nucleotide polymorphisms. Comparison of

genotype distributions between women with RM (N=227a) and controls (N=130

a) for hormone

pathway gene polymorphisms.

SNP Genotype

RM Observed

(frequency)

Controls

Observed (frequency)

P

genotypesb

P

allelesb

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


P

genotypesb

P

allelesb

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


P

genotypesb

P

allelesb

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


P

genotypesb

P

allelesb

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


P

genotypesb

P

allelesb

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)

aN is the total number of samples run on the platform, the number of successful genotype calls

may be less for some SNPs.

bChi-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.


5.2.1 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

http://www.ncbi.nlm.nih.gov/geo

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).


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

http://igc.otago.ac.nz/

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,

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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

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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.

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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

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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).

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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).

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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.

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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.

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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 1st 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.

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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

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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.

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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.

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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).

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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).

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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).

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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.

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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.

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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

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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

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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

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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

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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

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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

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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,

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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

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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. While the

genetic and epigenetic factors associated with RM in this thesis cannot be directly used in the

102

clinic, this work lays the framework for future directions and furthers our understanding of the

pathogenesis of RM.

103

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Yuan L, Liu JG, Hoja MR, Wilbertz J, Nordqvist K and Hoog C. Female germ cell aneuploidy

and embryo death in mice lacking the meiosis-specific protein SCP3. Science 2002:296:1115-

1118.

Yuan L, Liu JG, Zhao J, Brundell E, Daneholt B and Hoog C. The murine SCP3 gene is required

for synaptonemal complex assembly, chromosome synapsis, and male fertility. Mol Cell

2000:5:73-83.

Yuen RK, Jiang R, Penaherrera MS, McFadden DE and Robinson WP. Genome-wide mapping

of imprinted differentially methylated regions by DNA methylation profiling of human placentas

from triploidies. Epigenetics Chromatin 2011:4:10.

Yuen RK and Robinson WP. Review: A high capacity of the human placenta for genetic and

epigenetic variation: implications for assessing pregnancy outcome. Placenta 2011:32 Suppl

2:S136-41.

Zhai Y, Zhou G, Deng G, Xie W, Dong X, Zhang X, Yu L, Yang H, Yuan X, Zhang H et al.

Estrogen receptor alpha polymorphisms associated with susceptibility to hepatocellular

carcinoma in hepatitis B virus carriers. Gastroenterology 2006:130:2001-2009.

Zhu Y, Huo Z, Lai J, Li S, Jiao H, Dang J and Jin C. Case-control study of a HLA-G 14-bp

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2010:71:52-54.

125

Appendix A: Supplementary tables and figures for Chapter 2

Supplementary Table 2.1 Primer sequences used for sequencing analysis of the coding

exons (2-9) of the SYCP3 gene.

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


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


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


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-valueb

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


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


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


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


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


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

bChi-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



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



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-valueb

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


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/GGa 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/AAa 34.1 4.77 2.40

rs2244502

AA 34.1 4.59 1.77 0.194

141

AT 33.4 4.26 1.66


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


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/TTa 33.5 4.43 2.09

rs6152

GG 33.9 4.55 1.90 0.715

GA/AAa 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/CTa 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/AAa 33.8 4.25 2.09

rs7020782

143

aLess than 5 individuals homozygous for the minor allele, therefore combined with heterozygotes

for analysis

bANCOVA

CC 33.6 4.64 2.57 0.406


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


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


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


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


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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