application of minimally-invasive uterine fluid aspiration ...€¦ · penultimate thanks go out to...
TRANSCRIPT
Application of Minimally-Invasive Uterine Fluid Aspiration to Identify Candidate Biomarkers of Endometrial Receptivity through a Transcriptomic Approach
by
Crystal Chan
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Science University of Toronto
© Copyright by Crystal Chan 2012
ii
Application of Minimally-Invasive Uterine Fluid Aspiration to Identify Candidate Biomarkers of Endometrial Receptivity through
a Transcriptomic Approach
Crystal Chan
Master of Science
Institute of Medical Science
University of Toronto
2012
Abstract
The endometrium is receptive to the embryo during a restricted window in the mid-secretory
phase. My objectives were to develop a minimally-invasive endometrial sampling method for
gene expression profiling, and to identify genes differentially expressed in the receptive phase.
Twenty-three normo-ovulatory women underwent uterine fluid aspiration during the pre-
receptive (LH+2) and receptive (LH+7) phase of the same natural cycle. RNA was extracted,
reverse transcribed, amplified and hybridized to whole-genome microarrays. Unsupervised
hierarchical clustering revealed self-segregation of pre-receptive and receptive samples.
Importantly, profiling by uterine fluid aspiration was representative of biopsy. An unpaired t-test
with a false discovery rate of 0.05 and a Δ threshold of 4-fold identified 245 unique transcripts as
differentially expressed in the receptive phase. NanoString analysis validated 96% of these
genes. This approach will now allow us to correlate expression of these candidate biomarkers to
implantation outcomes, towards the development of clinical assays predictive for endometrial
receptivity.
iii
Acknowledgments
I would first and foremost like to thank Dr. Ted Brown and Dr. Ellen Greenblatt, supervisors
extraordinaire, who gave me the resources, inspiration, and unconditional support to transform
this research from a pipe dream to reality. Early on in my residency training, I did not envision
taking two years off my clinical training to pursue a Master’s degree. I also could not foresee that
I would discover such a passionate interest in Reproductive Endocrinology and Infertility. Dr.
Greenblatt and Dr. Brown were instrumental in helping me to define my academic goals, explore
my interests, and complete a successful research project. Their supervision was invaluable, and
words cannot fully express my gratitude.
My sincere appreciation goes out to my Program Advisory Committee members, Dr. Robert
Casper and Dr. Stephen Lye, who were supportive yet challenging enough to help spur my
research forward. Thank you for asking me tough questions and training me to think on my feet.
I am also grateful to Dr. Lye for believing in my research and contributing to the final validation
steps.
I would also like to thank the “Genome Canada” crew – Carl Virtanen, Neil Winegarden, Natalie
Stickle, Julie Tsao, Dr. Allan King, and Dr. Pavneesh Madan. The level of bioinformatic support
from Carl was absolutely above and beyond, except for the time BBM was down. Carl made
bioinformatics make sense, and though the learning curve was steep and slippery, I am extremely
grateful for all his teaching. Neil provided much needed clarity and guidance on everything “high
throughput”, “multiplexed”, “-omic”, and really anything that needed to be translated from
Science Jargon to English. Our Genome Canada grant-writing sessions will always have a
special place in my heart.
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I truly appreciate the time and effort that Dr. Terence Colgan contributed to this project, despite a
busy clinical practice. His expertise in gynecologic pathology was integral to the success of this
project. My thanks also go out to Dr. Robert Riddell for his expertise in gastrointestinal
pathology. Thanks to these pathologists, I no longer fear the microscope.
Special thanks go out to the heart and soul of the Rogers Lab and Brown Lab, i.e. Dr. Shawn
Chua and Dr. Alexandra Kollara. They trained me for hours in the fundamentals of molecular
biology, while expecting nothing in return. Their teaching and support were crucially important,
and the skills I acquired from them helped me thrive in the lab. In fact, I believe I can now out-
animate Dr. Chua on Microsoft Powerpoint.
In the Department of Obstetrics and Gynaecology, there are many to thank. Thank you to the
staff of the Mount Sinai Centre for Fertility and Reproductive Health, who made patient
recruitment and sampling possible. I would like to thank Dr. Alan Bocking and Dr. Heather
Shapiro, who whole-heartedly endorsed my decision to enroll in the Clinician Investigator
Program and were supportive and understanding throughout. I would also like to thank all the
gynecologic surgeons who welcomed me into their ORs on a weekly - biweekly basis and never
stigmatized me for being “the one doing research”. The fact that I still feel confident about my
surgical skills after two years is a testament to their teaching and mentorship.
Penultimate thanks go out to my family. Thank you for watching me regress into a grad student,
with grad student hours and habits, without too much criticism. Thank you for adopting Daisy,
especially during the hard times of thesis-writing. Thank you for the prepared food, the shoulder
to cry on, and for not saying “I told you you should have gone into Dentistry.”
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Finally, none of this would have been possible without the contributions of the patients enrolled
in this study. It always amazes and inspires me to see patients contribute to science out of pure
altruism. I hope that this research comes to fruition and ultimately benefits couples suffering
from infertility.
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Table of Contents
Acknowledgments .......................................................................................................................... iii
Table of Contents ........................................................................................................................... vi
List of Tables ................................................................................................................................. ix
List of Figures ................................................................................................................................. x
List of Abbreviations ..................................................................................................................... xi
Chapter 1 INTRODUCTION .......................................................................................................... 1
1.1 INFERTILITY .................................................................................................................... 1
1.1.1 Assisted reproductive technology ........................................................................... 2
1.1.2 Embryo transfer practices and multi-foetal pregnancy in ART .............................. 4
1.2 IMPLANTATION .............................................................................................................. 7
1.2.1 Apposition and adhesion ......................................................................................... 8
1.2.2 Invasion and placentation ..................................................................................... 10
1.3 ENDOMETRIAL RECEPTIVITY ................................................................................... 12
1.3.1 The window of implantation ................................................................................. 13
1.3.2 Current methods of assessing endometrial receptivity ......................................... 14
1.3.3 The role of hormones in endometrial receptivity .................................................. 18
1.3.4 Molecular factors implicated in endometrial receptivity ...................................... 20
1.3.4.1 Cytokines ................................................................................................ 20
1.3.4.2 Growth factors ........................................................................................ 23
1.3.4.3 Molecules involved in cell adhesion ...................................................... 25
1.3.4.4 Cell cycle regulators ............................................................................... 28
1.3.4.5 Glycodelin-A .......................................................................................... 29
vii
1.4 GENE EXPRESSION STUDIES OF ENDOMETRIAL RECEPTIVITY ...................... 31
1.4.1 Endometrial sampling techniques ......................................................................... 33
1.5 THESIS HYPOTHESIS AND RATIONALE .................................................................. 35
1.6 OBJECTIVES ................................................................................................................... 37
1.7 DESCRIPTION OF COLLABORATIONS AND ROLES .............................................. 37
Chapter 2 MATERIALS AND METHODS ................................................................................. 39
2.1 PATIENT SELECTION ................................................................................................... 39
2.2 TISSUE COLLECTION ................................................................................................... 40
2.3 RNA EXTRACTION ........................................................................................................ 43
2.4 RNA INTEGRITY TESTING .......................................................................................... 43
2.5 REVERSE TRANSCRIPTION, AMPLIFICATION OF cDNA, HYBRIDIZATION
TO WHOLE-GENOME MICROARRAY ....................................................................... 45
2.6 MICROARRAY DATA ANALYSIS............................................................................... 46
2.7 VALIDATION STUDIES ................................................................................................ 48
2.7.1 NanoString analysis .............................................................................................. 48
2.7.1.1 Target gene selection for NanoString validation .................................... 49
2.7.1.2 NanoString probe hybridization, immobilization and detection ............ 65
2.7.1.3 NanoString data analysis ........................................................................ 65
2.7.2 Immunohistochemistry ......................................................................................... 66
2.8 MULTIPLEX CYTOKINE IMMUNOASSAY ............................................................... 69
Chapter 3 RESULTS ..................................................................................................................... 70
3.1 DEVELOPMENT OF UFA TECHNIQUE ...................................................................... 70
3.2 CYTOLOGICAL ANALYSIS OF UFA SAMPLES ....................................................... 73
3.3 MICROARRAY ANALYSIS ........................................................................................... 74
3.3.1 Unsupervised hierarchical clustering .................................................................... 74
3.3.2 Differential gene expression analysis of microarray data ..................................... 76
viii
3.4 VALIDATION STUDIES ................................................................................................ 82
3.4.1 NanoString analysis .............................................................................................. 82
3.4.1.1 Validation of differentially expressed genes .......................................... 82
3.4.1.2 NanoString analysis of gastrin exons and “gastrin-related genes” ......... 93
3.4.2 Immunohistochemistry ......................................................................................... 94
3.5 MULTIPLEX CYTOKINE ASSAY ................................................................................ 99
Chapter 4 DISCUSSION ............................................................................................................ 102
4.1 STUDY LIMITATIONS ................................................................................................ 109
4.2 CONCLUSIONS AND FUTURE DIRECTIONS .......................................................... 111
References ................................................................................................................................... 114
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List of Tables
Table 1: RIN scores of samples .................................................................................................... 44
Table 2: List of genes selected for NanoString validation ............................................................ 52
Table 3: Comparison of catheters trialed in UFA development ................................................... 72
Table 4: Differentially expressed genes between UFA LH+7 and Biopsy LH+7 (> 4-fold, FDR <
0.05) .............................................................................................................................................. 81
Table 5: NanoString validation of differentially expressed genes between UFA LH+2 and UFA
LH+7 ............................................................................................................................................. 83
Table 6: Cytokine levels in UFA LH+2 vs. UFA LH+7 supernatants ....................................... 100
x
List of Figures
Figure 1: Timing of endometrial sampling by UFA and Biopsy .................................................. 42
Figure 2: UFA sampling instruments: Tomcat intrauterine catheter attached to syringe ............. 42
Figure 3: Representative cytological smear of UFA sample ........................................................ 73
Figure 4: Heatmap representation of unsupervised hierarchical clustering of UFA LH+2, UFA
LH+7 and Biopsy LH+7 samples ................................................................................................. 75
Figure 5: Pie chart representing GO breakdown of genes ............................................................ 77
Figure 6: Intersection of > 4-fold gene lists .................................................................................. 79
Figure 7: Volcano plot representing the validation of differentially expressed genes between
UFA LH+2 and UFA LH+7 by NanoString analysis ................................................................... 92
Figure 8: NanoString probe design for gastrin exons ................................................................... 93
Figure 9: Endometrial biopsies from receptive phase (LH+7) stained positive for gastrin .......... 96
Figure 10: Biopsies from endometrium in receptive phase and stomach antrum stained positive
for gastrin ...................................................................................................................................... 97
Figure 11: Immunofluorescence staining of endometrial biopsies from receptive phase (LH+7) 98
Figure 12: Endometrial tissue from proliferative phase (Ab 53085 1/2000) ................................ 99
Figure 13: Secreted levels of IL8 in UFA LH+2 vs. UFA LH+7 supernatants .......................... 101
xi
List of Abbreviations
aa amino acid
AHRC Assisted Human Reproduction Canada
ANOVA analysis of variance
ART assisted reproductive technology
β-hCG β- human chorionic gonadotropin
BCA bicinchoninic acid
BMP-2 bone morphogenetic protein 2
C degrees Celsius
CAM cell adhesion molecule
cAMP cyclic adenosine monophosphate
CARTR Canadian Assisted Reproductive Technologies Register
cc cubic centimetre
CCKBR cholecystokinin B receptor
cdk cyclin-dependent kinase
CFAS Canadian Fertility and Andrology Society
CFRH Centre for Fertility and Reproductive Health
CIHI Canadian Institute for Health Information
cm centimetre
COH controlled ovarian hyperstimulation
CPE carboxypeptidase E
CSV comma separated value
Cy3 cyanine 3
DAB diaminobenzidine
xii
DABCO 1,4-diazabicyclo[2.2.2]octane
DAP DASL assay pool
DAPI 4',6-diamidino-2-phenylindole
DASL cDNA mediated Annealing, Selection, extension and Ligation
DNA deoxyribonucleic acid
DNase deoxyribonuclease
dT deoxy-thymine
ECM extra cellular matrix
EGF epidermal growth factor
EGF-R EGF receptor
ER estrogen receptor
eSET elective single embryo transfer
FDR false discovery rate
FFPE formalin-fixed paraffin-embedded
Fr french
FSH follicle stimulating hormone
g gram
G17 gastrin-17
G34 gastrin-34
GO gene ontology
gp130 glycoprotein 130
GRP gastrin-releasing peptide
GRPR gastrin-releasing peptide receptor
H2O2 hydrogen peroxide
HB-EGF heparin-binding EGF-like growth factor
hCG human chorionic gonadotropin
xiii
IL-6 interleukin-6
IL-8 interleukin -8
IL-11 interleukin-11
IL11-R IL11 receptor
IUD intrauterine device
IVF in vitro fertilization
JAK janus kinase
LH luteinizing hormone
LH+2 2 days after the LH surge
LH+7 7 days after the LH surge
LIF leukemia inhibitory factor
LIF-R LIF receptor
LPD luteal phase deficiency
MAPK mitogen-activated protein kinase
min minute
MFP multi-foetal pregnancy
MFPR multi-foetal pregnancy reduction
ml mililitre
MUC-1 mucin-1
NBF 10% neutral buffered formalin
ng nanogram
NGS normal goat serum
NK natural killer
nm nanometre
nt nucleotide
OPN osteopontin
xiv
PAEP progesterone-associated endometrial protein
PAI-1 plasminogen activator inhibitor-1
PAM peptidylglycine alpha-amidating monooxygenase
PBS phosphate-buffered saline
PC prohormone convertase
PCOS polycystic ovarian syndrome
PP14 placental protein 14
PR progesterone receptor
r-hLIF recombinant human LIF
RCC reporter code count
RIN RNA integrity number
RNA ribonucleic acid
RNase ribonuclease
RT room temperature
RT-PCR reverse transcription-polymerase chain reaction
RU-486 Roussel Uclaf 486 (mifepristone)
sec second
SEM standard error of the mean
SMAD mothers against decapentaplegic homolog
SOGC Society of Obstetricians and Gynaecologists of Canada
SPP1 secreted phosphoprotein 1
STAT signal transducer and activator of transcription
TGF-α transforming growth factor-α
TGF-β transforming growth factor-β
TIMP tissue inhibitors of metalloproteinases
TPST1/2 tyrosylprotein sulfotransferase 1/2
xv
UHN University Health Network
UFA uterine fluid aspiration
µL microlitre
VEGF vascular endothelial growth factor
WOI window of implantation
1
Chapter 1 INTRODUCTION
1.1 INFERTILITY
Infertility, defined as the failure to conceive after one year of unprotected intercourse, affects
approximately 10-15% of couples (Greenhall and Vessey 1990; Mosher and Pratt 1991). The
etiology of infertility is multi-factorial and reflects the complexity of the human reproductive
process. For successful pregnancy to occur, several critical events must happen in a coordinated
fashion. Regular ovulation of a mature oocyte must occur; competent sperm with the ability to
fertilize the oocyte must reach the cervix near the time of ovulation; the cervix must be
permissive for sperm to ascend to the upper genital tract; the fallopian tubes must capture the
mature oocyte after ovulation and facilitate sperm and embryo transport; finally, the uterus must
be receptive to embryo implantation and development. Any perturbation of these fundamental
components can lead to impaired fertility. Indeed, the major causes of infertility are classified as
ovulatory dysfunction (15%), male factor (35%), and tubal and pelvic pathology (35%). The
remaining 15% of cases are attributed to rare causes or to unexplained infertility as the diagnosis
of exclusion (Speroff and Fritz 2005).
Societal attitudes toward women and childbearing have changed dramatically over the past
several decades, which has contributed to increasing concerns about infertility. With more
women pursuing higher education and careers, the average age of childbirth has steadily
increased. Statistics Canada data indicate that in 1974, 19.5% of births were to mothers 30 years
of age or older; by 2005, this figure had risen to 48.9% (http://www4.hrsdc.gc.ca/). This trend of
delayed childbearing is significant as several studies have established that female fertility
declines with advancing age. One such study was performed on the Hutterites, a religious sect
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that originated in Western Europe and settled in communal colonies in South Dakota in the late
nineteenth century (Tietze 1957). The Hutterites served as a “natural experiment” for examining
the effects of aging on fertility as their society eschews the use of contraception. In this
population, only 3.5% of women were found to be infertile under the age of 25, but the rates of
infertility increased to 7% by age 30, 11% by age 35, 33% by age 40, and 87% by age 45. Other
studies of women trying to conceive by donor insemination, which control for confounders such
as male factor infertility and frequency of coitus, have shown similar declines in pregnancy rates
with increasing age (Schwartz and Mayaux 1982). The age-associated reduction in female
fertility is attributed to ovarian follicular depletion and the accumulation of abnormalities in
aging oocytes (ASRM 2006). Uterine factors do not appear to contribute to age-related infertility
as age does not affect the histologic response of the endometrium to steroid stimulation, nor the
ability of women to conceive by oocyte donation (Navot, Drews et al. 1994; Noci, Borri et al.
1995). As awareness about infertility grows and more couples seek assistance to conceive, the
need for effective diagnostic tools and treatment options for infertility becomes greater.
1.1.1 Assisted reproductive technology
The modern era of infertility treatment began in 1978, when the first child resulting from in vitro
fertilization (IVF) was born. IVF is the most common form of assisted reproductive technology
(ART), which refers to all techniques in which oocytes are manipulated outside a woman’s body.
IVF was originally designed to overcome infertility from bilateral tubal obstruction, but has since
proven to be the most effective treatment for almost all causes of infertility. This procedure
typically involves administration of exogenous gonadotropins to induce controlled ovarian
hyperstimulation (COH) in the patient, followed by oocyte retrieval, fertilization of oocytes with
3
sperm in the laboratory, and transfer of the resultant embryo(s) back to the patient’s uterus with
the goal of achieving a successful pregnancy (Speroff and Fritz 2005).
Increased public awareness about infertility and the growing availability of infertility services
have led to greater usage of ART. According to data collected from all Canadian fertility clinics
by the Canadian Assisted Reproductive Technologies Register (CARTR), the number of ART
cycles has steadily increased in recent years. From 2003 to 2007, there was a 27% increase in the
total number of ART cycles reported, from 10,656 to 13,482 (Gunby and Daya 2007; Gunby,
Bissonnette et al. 2011). In 2007, 1.6 ART cycles were performed per 1,000 Canadian women of
reproductive age. ART now accounts for 1% of all live births in Canada and the United States,
and as many as 4% in some European nations (Andersen, Gianaroli et al. 2005; Gunby,
Bissonnette et al. 2011).
Despite advances in ART, pregnancy rates following the transfer of embryos produced in vitro
remain suboptimal (Edwards and Beard 1999). Preimplantation embryo development follows a
predictable sequence of events which includes fertilization of the oocyte, formation of the two
pronuclear zygote, serial cleavage divisions of the embryo from the 2-cell to morula stage, and
compaction and cavitation to form a blastocyst. In IVF cycles, embryos were traditionally
cultured to the cleavage stage (2-3 days after fertilization) before being transferred to the
patient’s uterus, which yields an implantation rate of <30% per embryo transferred (Porat,
Boehnlein et al. 2010). Improvements in embryo culture have allowed embryos to be cultured to
the blastocyst stage, which improves implantation potential to 50-60% (Gardner, Surrey et al.
2004). However, only 50% of embryos survive the extended culture to the blastocyst stage, thus
many patients do not have sufficient quantity or quality of embryos to be candidates for
blastocyst transfer (Gardner and Lane 1998). The low implantation and high embryo wastage
4
rates observed are reflected by poor overall success rates in IVF/ART (Kovalevsky and Patrizio
2005). According to the CARTR database, approximately 2 out of 3 ART cycles fail to achieve a
pregnancy (Gunby, Bissonnette et al. 2011).
1.1.2 Embryo transfer practices and multi-foetal pregnancy in ART
Due to the poor likelihood of embryo implantation in ART, the practice of transferring more than
one embryo per cycle to try to improve pregnancy rates has become routine in most countries.
This practice has been associated with a dramatic rise in the number of multi-foetal pregnancies
(MFP), including twins, triplets and above. Prior to the birth of the first IVF-conceived Canadian
child in 1982, the naturally occurring multi-foetal birth rate was 1.8%; by 2004, multi-foetal
births had increased to 3% of total births (Prevention of Multiple Births, 2009). There are several
reasons for this increase, such as advanced maternal age and non-ART ovulation induction
practices; however, much of this increase can be attributed directly to ART. In 2007, an average
of 2.3 embryos were transferred per fresh (non-cryopreserved embryo) ART cycle, and as a
consequence, 30.2% of these cycles resulted in multi-foetal births, a proportion 15 times higher
than the spontaneous rate (Gunby, Bissonnette et al. 2011).
MFP is widely considered to be the most significant complication of ART, and has considerable
public health consequences. There are well-documented increased risks of adverse maternal,
fetal and neonatal outcomes in MFP compared with singleton pregnancies (Bergh, Ericson et al.
1999). Mothers of MFP are at greater risk of developing almost every known maternal
complication of pregnancy, including pre-eclampsia, gestational diabetes, venous
thromboembolism, postpartum haemorrhage, and hysterectomy (Walker, Murphy et al. 2004).
5
Perhaps more concerning from a public health point of view; however, is the increased risk of
preterm birth. Over 50% of twins and virtually 100% of triplets are born before term (less than
37 weeks gestation). Preterm birth accounts for 70% of all neonatal deaths and 75% of neonatal
morbidity including low birth weight, pulmonary dysfunction, necrotizing enterocolitis, and
neurological complications that can have life-long neurodevelopmental sequelae (Wen, Smith et
al. 2004). The risk of cerebral palsy alone is 47-fold higher in triplet pregnancies and 8-fold
higher in twins compared with singletons (Petterson, Nelson et al. 1993).
The provision of neonatal intensive care for multi-foetal births resulting from ART is a large
burden on the Canadian health care system. A study by the Canadian Institute for Health
Information (CIHI) found that the preterm birth rate had increased from 6% in the early 1980s to
8% in more recent years, and attributed this trend to several factors including the use of ART.
This study determined that the preterm birth rate was 9 times higher in multi-foetal compared to
singleton pregnancies, and that the average in-hospital cost for preterm multi-foetal babies was
12 times higher than the cost for term singleton babies (CIHI). The medical, social and economic
impact of MFP resulting from ART in Canada is tremendous, and efforts are being made to
correct this problem. One strategy is the use of multi-foetal pregnancy reduction (MFPR) to
reduce the number of foetuses in higher order MFP (triplets and above) down to twins (Evans,
Ciorica et al. 2004). As the risk of adverse outcomes is directly proportional to the number of
foetuses (Wen, Demissie et al. 2004), the rationale behind MFPR is that sacrificing some of the
foetuses will improve overall maternal and neonatal outcomes. However, MFPR is an ethically-
charged and controversial procedure that can be difficult for both patients and practitioners.
The best way to decrease the incidence of MFP from ART is to change embryo transfer
practices. There is a clear relationship between the number of embryos transferred per ART
6
cycle and the risk of MFP. Countries with the highest rates of MFP from ART report the highest
numbers of embryos per transfer (Cook, Collins et al. 2011). The average number of embryos
transferred in Canada is among the highest in the world, and even though our clinical pregnancy
rates are among the best internationally, Canada is tied with the United States for the highest
twin rates. The factors that influence embryo transfer practices are complex, but one of the main
reasons for the pervasiveness of multiple embryo transfer in North America is that ART is
privately funded and unregulated, and patients and physicians are motivated to transfer more
than one embryo per cycle to overcome low implantation rates and maximize the chance of
pregnancy.
A shift towards elective single embryo transfer (eSET), a practice in which only one embryo is
selected for transfer despite the availability of additional embryos, is necessary to decrease the
risk of MFP. In certain European countries such as Belgium and Sweden, where ART is publicly
funded, government subsidization is contingent on the preferential use of eSET when
appropriate, i.e. in younger women with good prognosis. Approximately 70% of all embryo
transfers in Sweden are eSET, and as a result, only 6% of ART cycles in Sweden result in MFP
(Cook, Collins et al. 2011). A similar effect was recently demonstrated in the province of
Quebec. In August 2010, the Quebec provincial government introduced public funding of ART
tied to a program of eSET for good prognosis patients. In the first three months of this program,
the proportion of eSET cycles increased from 1.6% to 50% and the MFP rate dropped from 26%
to 3.7% (Bissonnette, Phillips et al. 2011). Although the decrease in MFP with increased
adoption of eSET in Quebec is encouraging, critics have challenged this program of regulated
eSET because of the associated decline in clinical pregnancy rates, from 43% to 31%.
7
eSET is employed in only 4% of ART cycles in the rest of Canada (Gunby, Bissonnette et al.
2011). Taking into consideration the private funding model of ART and the inherent drive to
improve success rates, it is unlikely that patients or physicians will voluntarily increase the use of
eSET unless improvements in clinical pregnancy rates are seen. The Canadian Fertility and
Andrology Society (CFAS), the Society of Obstetricians and Gynecologists of Canada (SOGC)
and Assisted Human Reproduction Canada (AHRC) convened a joint meeting in 2009 to address
the problem of multi-foetal births from ART. The proceedings of this meeting promoted the use
of eSET in good prognosis patients, with the goal of decreasing the twin rate from 30% to 25%
by 2012 and to 15% by 2015. An important strategy to encourage the adoption of eSET is a
commitment to research on implantation. Improving our current understanding of implantation
and discovering predictors of successful pregnancy may optimize implantation rates in ART and
reduce the motivation to transfer multiple embryos, ultimately leading to decreased rates of MFP
and better outcomes for both mothers and babies.
1.2 IMPLANTATION
Implantation is the process by which a blastocyst attaches to the endometrial lining of the uterus,
penetrates the epithelium, invades the stroma and establishes the placenta. These early events
are critical in the establishment of pregnancy, but the underlying molecular mechanisms are
poorly understood. The process of implantation in humans is relatively inefficient as compared
with other species. Epidemiological studies of women trying to conceive in natural cycles
indicate that reproductive efficiency in vivo is low in general, as only 20% of women conceive
per cycle even when the couple is fertile. In assisted reproduction cycles, with controlled in vitro
8
conditions that optimize the chance of pregnancy, overall implantation rates are still less than
30%. By contrast, captive baboons have a 70% implantation rate per cycle, and demonstrate
much more reproductive efficiency than humans despite similar cyclic hormone profiles and
ovarian cycles (Henson 1998; Edwards and Beard 1999). Elucidating the process of implantation
in humans is important not only because implantation is generally inefficient in our species, but
also because disordered implantation may be a significant cause of unexplained infertility.
Due to ethical constraints, studying the process of human implantation in vivo is unfeasible.
Therefore, most of the existing information on implantation has been derived from either animal
models or in vitro experiments using human blastocysts co-cultured with polarized endometrial
epithelial cells (Sharkey and Smith 2003). These studies have revealed implantation to be a
highly dynamic process involving the interplay of many autocrine, paracrine and endocrine
factors. Although the molecular mechanisms of implantation are incompletely understood, the
cellular events involved have been described and are conceptualized as a three-step process,
including embryo apposition, adhesion, and invasion.
1.2.1 Apposition and adhesion
In humans, fertilization naturally occurs in the antrum of the fallopian tube, within 24 hours of
ovulation (Speroff and Fritz 2005). Several cleavage divisions occur as the early embryo moves
down the fallopian tube towards the uterus. By the third or fourth day after fertilization, the
embryo enters the uterine cavity as a morula, a solid ball of 16 or more cells. The morula
undergoes compaction and cavitation, forming a central, fluid-filled cavity known as the
blastocoel. At this point, the embryo is referred to as a blastocyst and consists of an outer layer of
9
trophoblast cells known as the trophectoderm, an inner cell mass and the blastocoel. The fate of
the trophectoderm is to make contact with and invade the endometrium, and eventually develop
into the extraembryonic tissues including the placenta. The inner cell mass is pluripotent and will
eventually give rise to the future cell lineages of the embryo proper (Wang and Dey 2006).
The early blastocyst is still surrounded by the zona pellucida, an acellular layer of glycoproteins
that surrounds the oocyte at the time of ovulation. The blastocyst remains free-floating in the
uterine secretions for 1-3 days, during which time it “hatches” from the zona pellucida in
preparation for implantation. Studies of in vitro preimplantation embryo development suggest
that blastocyst hatching occurs by a combination of blastocoel expansion and contraction, and
penetration of the zona pellucida by cytoplasmic extensions of the trophoblast cells known as
trophectoderm projections (Gonzales, Jones et al. 1996). In vivo, components of the uterine fluid
may also contribute to zona hatching.
The factors that determine the site of implantation are not fully understood, but the usual location
is in the upper half of the uterus (Fried 1978). The luminal epithelium is the first surface the
embryo encounters, and consists of a sheet of specialized epithelial cells, distinct from the
glandular cells of the endometrium and the underlying stroma. After hatching from the zona
pellucida around 5-6 days post-fertilization, the blastocyst is able to initiate the process of
implantation. The first step is apposition, where the trophectoderm of the blastocyst comes into
contact with the receptive luminal epithelium of the endometrium. In primates, the blastocyst is
oriented during apposition so that the embryonic pole, the side where the inner cell mass is
situated, makes first contact with the endometrium (Bentin-Ley and Lopata 2000). It has been
suggested that embryo apposition is facilitated by microscopic cytoplasmic protrusions of the
apical surface of the luminal epithelium known as pinopodes (Singh, Chaudhry et al. 2011).
10
However, the preferential attachment of blastocysts to areas presenting pinopodes has only been
demonstrated in a small in vitro assay using cultured endometrial epithelial cells (Bentin-Ley and
Lopata 2000), and a follow-up study failed to corroborate this finding (Petersen, Bentin-Ley et
al. 2005). Furthermore, direct blastocyst-pinopode attachment has never been demonstrated in
humans, nor is there sufficient evidence to correlate pinopode expression in women to
implantation success (Sharkey and Smith 2003; Quinn and Casper 2009).
After initial contact is made, local paracrine signaling between the blastocyst and endometrium
allows for a more stable adhesion to form. In mice, the first sign of adhesion occurs in the
evening of day 4 of pregnancy, and is associated with a localized increase in stromal vascular
permeability at the site of blastocyst attachment. As the blastocyst becomes more intimately
associated with the endometrium, its surface microvilli interdigitate with those on the luminal
epithelium. The adhesion stage involves an array of cell adhesion molecules (CAMs), including
integrins, selectins, lectins and cadherins. The endometrium and the blastocyst also express
extracellular matrix (ECM) components, including laminin and fibronectin, which serve as
ligands for CAMs and mediate cell adhesion. The attachment that results is so firm that the
blastocyst cannot be dislodged from the endometrium by flushing the uterine cavity (Wang and
Dey 2006).
1.2.2 Invasion and placentation
In primates and rodents, the final step of implantation requires the embryo to invade through the
luminal epithelium of the endometrium into the stroma, and establish a vascular relationship with
the mother. Trophoblast cells invade into maternal blood vessels, leading to the formation of a
11
haemochorial placenta that is the site of fetal-maternal interchange during pregnancy (Gonzales,
Jones et al. 1996). The initiation of invasion varies in different species: in mice and rats,
trophoblast cells breach the luminal epithelium by causing epithelial apoptosis; in humans and
other primates, the trophectoderm differentiates into cytotrophoblast and syncytiotrophoblast
cells, the latter of which intrude between luminal epithelial cells and penetrate the basal lamina
(Wang and Dey 2006). As with the preceding steps of implantation, molecular signaling between
the embryo and endometrium is necessary to ensure normal penetration and survival of the
embryo. The mechanism by which maternal tissues can recognize and reject a genetically
abnormal embryo may have to do with aberrant signaling, but this process is poorly understood
(Speroff and Fritz 2005).
At the time of implantation, the endometrial stroma undergoes the process of decidualization
under the influence of progesterone stimulation. During this process, the stromal cells and ECM
are remodeled and form the decidua, which is the maternal interface to the embryo and an
important structural and biochemical tissue in pregnancy. Fibroblast-like stromal cells transform
into glycogen-rich cells, natural killer (NK) cells are recruited to the endometrium, and vascular
remodeling occurs with decidualization (Singh, Chaudhry et al. 2011).
Embryonic invasion is mediated by serine proteases and metalloproteinases (Speroff and Fritz
2005). Plasmin is a serine protease that facilitates degradation of the ECM and activates the
metalloproteinase family, which also has a proteolytic function. Trophoblast cells express
plasminogen activators which convert the precursor plasminogen to active plasmin. The extent of
embryonic invasion is largely controlled by the decidua. The decidua forms a physical barrier
and creates a microenvironment of growth factors, cytokines, and enzymes that limits the
invasiveness of the trophoblast. Plasminogen activator inhibitor-1 (PAI-1) and tissue inhibitors
12
of metalloproteinases (TIMP) are decidual factors that inhibit the degradation of the ECM. PAI-1
and TIMP expression levels are regulated by the cytokine microenvironment of the decidua.
Transforming growth factor-β (TGF-β) is a key factor that not only induces the expression of
both PA1-1 and TIMP, but also promotes integrin expression by trophoblast cells, making the
embryo more adherent to the ECM, thereby slowing migration and limiting invasion (Irving and
Lala 1995).
1.3 ENDOMETRIAL RECEPTIVITY
As described above, implantation is a complex and tightly coordinated event, and is contingent
on the synchronized development of a normal blastocyst and a receptive endometrium. A
molecular dialogue between the embryo and the endometrium is necessary for establishing
successful implantation. Research on implantation has previously been more focused on
embryonic factors, but attention is now shifting to the endometrium as an equally critical
determinant. More interest is being paid to the endometrium due to studies that suggest that the
administration of exogenous gonadotropins and drugs to prevent premature luteinization in IVF
may have a negative effect on implantation by disrupting endometrial development (Hoozemans,
Schats et al. 2004). The interaction between the embryo and the endometrium during
implantation is complex, and the cellular and molecular processes on both sides are highly
interconnected. As the focus of this thesis is to elucidate endometrial factors involved in
implantation, the following review is a summary of the existing literature on endometrial
receptivity. A detailed description of implicated embryonic factors is outside the scope of this
13
work, but it is important to recognize that the embryo does contribute to the establishment of
implantation.
1.3.1 The window of implantation
The menstrual cycle is divided into three components: the follicular phase, ovulation, and the
luteal phase. The typical menstrual cycle is 28 days in length, but considerable variation in
timing exists between cycles and among different women, and normal cycle length can vary
between 26-35 days (Mihm, Gangooly et al. 2011). The follicular phase precedes ovulation and
can be more variable in duration than the luteal phase, which lasts approximately 14 days
following ovulation. The follicular phase is characterized by the development of a dominant
follicle containing a mature oocyte in the ovary and increased estrogen production, which leads
to proliferation of the endometrium. Increased estrogen production leads to a surge in the
production of luteinizing hormone (LH) from the anterior pituitary, and this induces mid-cycle
ovulation of the dominant follicle 34-36 hours after the LH surge (Speroff and Fritz 2005). The
luteal phase is controlled by the corpus luteum, a steroidogenic structure formed by the
remaining granulosa and thecal cells of the ovulated follicle. Progesterone secreted by the corpus
luteum causes endometrial differentiation and secretory changes in preparation for embryo
implantation.
The endometrial environment is not permissive to embryo implantation throughout most of the
menstrual cycle. The endometrium can only be receptive during a narrow “window of
implantation” (WOI) that temporally coincides with the development of an implantation-
competent blastocyst. Endometrial receptivity generally refers to the receptivity of the
14
endometrial epithelium to embryo apposition and adhesion, although the stroma likely also has a
defined period of receptivity to embryo invasion. In humans, the WOI occurs during the mid-
luteal phase of the menstrual cycle (Giudice 1999). The timing of the onset of the WOI was
elucidated by a classic study of uterine samples from women attempting pregnancy before
hysterectomy (Hertig, Rock et al. 1956). The authors identified embryos in the earliest stages of
attachment and invasion and determined that implantation occurred only after cycle day 20,
suggesting that the WOI begins approximately 6-7 days after ovulation. This work was
complemented by a later study on women trying to conceive naturally which demonstrated that
the first appearance of human chorionic gonadotropin (hCG), a molecular indicator of the
establishment of implantation, occurred 6 to 12 days after ovulation, with the majority occurring
on post-ovulation day 8, 9, or 10 (Wilcox, Baird et al. 1999). The rate of early pregnancy loss
rose dramatically with late implantation, after post-ovulation day 10. These findings suggest that
endometrial receptivity peaks in the mid-luteal phase and diminishes in the late luteal phase,
although embryonic factors could also play a role, as unhealthy embryos may implant slowly or
abnormally, resulting in delayed production of hCG. Nonetheless, successful implantation in the
endometrium appears to be limited to a WOI lasting from day 6 to 10 after ovulation (or day 7 to
11 after the LH surge). This temporal restriction imposed by the endometrium may provide a
screening mechanism to exclude abnormal embryos.
1.3.2 Current methods of assessing endometrial receptivity
Although the WOI in humans has been delineated and it is known that the endometrium can only
accept an embryo during a limited period of time, the factors that confer endometrial receptivity
are poorly understood. Therefore, clinical tests for endometrial receptivity are lacking, and the
15
poor ability to determine if the endometrium is in a receptive state during an embryo transfer
cycle contributes to low implantation rates in ART.
Historically, the “gold standard” of endometrial assessment was the histological evaluation of
endometrial tissue acquired by endometrial biopsy in the luteal phase. Over six decades ago,
Noyes et al. analyzed more than 8000 endometrial biopsies from across the menstrual cycle and
defined criteria for dating the endometrium, based on specific morphological features of the
endometrial glands and stroma in different phases (Noyes, Hertig et al. 1975). They identified
distinct histological appearances of the endometrium that are now recognized as the menstrual
phase; the early-, mid-, and late-proliferative phase; and the early-, mid-, and late-secretory
phase. The menstrual endometrium is notable for cellular apoptosis and breakdown of the
glandular, stromal and vascular components. During the proliferative phase of the endometrium,
which corresponds to the follicular phase of the menstrual cycle, estrogen-induced glandular
mitoses and pseudostratification of nuclei are prominent. After ovulation, luteinization of the
ovarian follicle and progesterone secretion by the corpus luteum induce the secretory phase of
the endometrium, which corresponds to the luteal phase of the menstrual cycle. The beginning of
the WOI coincides with the mid-secretory phase, and the predominant morphological features
are glandular secretion (maximal on post-ovulatory day 6-7) and stromal edema (maximal on
post-ovulatory day 8). The latter part of the mid-secretory phase and the late-secretory phase are
characterized by stromal decidualization and the marked infiltration of leukocytes (Murray,
Meyer et al. 2004; Talbi, Hamilton et al. 2006).
The classic features identified by Noyes et al. became the standard for assessing endometrial
normality, and histological abnormalities of the endometrium were linked to the related concept
of luteal phase deficiency (LPD) (Jones 1976). LPD is diagnosed by a luteal phase endometrial
16
biopsy and manifests as delayed histological dating, inconsistent with the chronological date of
the menstrual cycle based on the woman’s next menses. This clinical entity is hypothesized to
result from inadequate progesterone secretion from the corpus luteum or failure of the
endometrium to respond appropriately, and has been proposed as a cause of unexplained
infertility and early pregnancy loss (Murray, Meyer et al. 2004). However, the diagnostic criteria
for LPD and the clinical relevance of this diagnosis are controversial. The first issue that causes
diagnostic uncertainty is the timing of the endometrial biopsy, as some authors advocate for
sampling during the WOI, whereas others recommend sampling of the late-secretory
endometrium to assess the cumulative effects of progesterone. The definition of histological
delay is also a contentious issue, as some authors consider a ≥2-day inconsistency with
chronological dating to be diagnostic of LPD, while others use a less stringent ≥3-day cut-off
(Fadare and Zheng 2005). Due to these diagnostic ambiguities, the prevalence of LPD has been
very difficult to estimate, and has been reported anywhere from <5% to as high as 50% in fertile
women. Furthermore, whether diagnosed in the mid- or late-secretory phase, the prevalence of
histological delay has not been shown to be different in fertile versus infertile women
(Coutifaris, Myers et al. 2004).
Recently, the traditional histological dating criteria have also come under scrutiny, and have
been found to be less temporally specific than previously thought. Moreover, histological
assessment is subject to significant intersubject, intrasubject, and interobserver variability, and
has been shown to be unreliable in distinguishing specific cycle day (Murray, Meyer et al. 2004).
In summary, the diagnostic criteria for LPD are non-standardized, LPD may not be predictive of
fertility status, and the histological method of diagnosis itself is inaccurate and imprecise. An
additional drawback of histological assessment is the requirement of an endometrial biopsy,
which disrupts the endometrium and cannot be performed in actual ART cycles. Thus,
17
histological dating can only be performed in a previous cycle and provides a limited snapshot of
endometrial development. Significant intercycle variability in histological development can
occur within the same patient, and assessment of the endometrium in one cycle cannot be
reliably extrapolated to another (Li, Dockery et al. 1989); therefore, histologic dating is not a
useful clinical assay to determine if and when the endometrial is receptive to embryo
implantation, and cannot be used to guide embryo transfer decisions.
An alternate method of endometrial assessment that can be performed during an ongoing ART
cycle is transvaginal ultrasonography. This technique is routinely used clinically as it offers a
minimally-invasive means of monitoring endometrial development. The thickness and pattern of
the endometrium on ultrasound have some value in predicting readiness for implantation. In a
study of 123 women undergoing IVF, endometrial thickness on ultrasound the day prior to
oocyte retrieval was found to be significantly greater in women who went on to achieve
successful implantation and pregnancy than in women who did not (8.7 ± 0.4 vs. 7.5 ± 0.2 mm, p
< 0.01). On further analysis, no pregnancy occurred when the endometrium was less than 6 mm
thick (Gonen and Casper 1990). The sonographic pattern of endometrial development in the late-
proliferative phase just prior to oocyte retrieval also has prognostic value and can be categorized
into three different types: (A) a homogeneous, hyperechogenic endometrium compared to the
surrounding myometrium, without a central echogenic line; (B) an intermediate, isoechogenic
endometrium; and (C) a multi-layered “triple-line” endometrium with hyperechogenic outer and
middle lines and hypoechogenic inner regions. A type C endometrium is thought to represent
normal endometrial development in the late-proliferative phase and likely reflects morphologic
changes of the endometrium in response to estrogen. The endometrial pattern has been shown to
be a good negative predictor for endometrial receptivity and implantation; when a type A or B
pattern is seen, the negative predictive value for pregnancy is 90.5%. However, endometrial
18
pattern is not a reliable positive predictor of pregnancy; even though a type C endometrium is
considered more favourable for implantation, its positive predictive value is only 30%. Even
when endometrial thickness is taken into consideration, women with a ≥ 6 mm thick, type C
endometrium have only a 39% pregnancy rate. In the scenario of negative pregnancy despite
grossly adequate endometrial thickness and pattern, embryonic factors are often blamed, but
more than likely, ultrasound is too imprecise a tool to pick up subtle abnormalities in endometrial
receptivity in many cases.
Both histological dating and transvaginal ultrasonography are tools that involve subjective,
morphological assessment of the endometrium. Histological dating is limited by methodological
inconsistencies and the required endometrial biopsy renders it unfeasible for use in ART cycles
to predict endometrial receptivity. Ultrasonography is minimally-invasive and can be performed
during an ART cycle to detect gross abnormalities in endometrial development, but is a poor
positive predictor of endometrial receptivity. A better understanding of the molecular factors that
confer endometrial receptivity is necessary to design clinically useful assays to accurately assess
the readiness of the endometrium for implantation.
1.3.3 The role of hormones in endometrial receptivity
The ovarian steroids, estrogen and progesterone, are the primary hormones that control the cyclic
growth and development of the endometrium. They coordinate a cascade of endocrine and
paracrine signal transduction pathways that prepare the endometrium for implantation. Although
many substances that influence endometrial receptivity have been identified, studies on women
with ovarian failure undergoing donor oocyte IVF demonstrate that exogenous estrogen and
19
progesterone supplementation alone is sufficient to induce a receptive endometrium (de Ziegler,
Fanchin et al. 1998). The endometrial effects of estrogen and progesterone are primarily
mediated by nuclear estrogen (ER) and progesterone (PR) receptors. During the proliferative
phase, estrogen induces estrogen and progesterone receptor expression in the endometrial
epithelium and stroma (Garcia, Bouchard et al. 1988). Estrogen exerts a proliferative effect on
the endometrium and primes it for secretory transformation by progesterone. Progesterone is
crucial for implantation and pregnancy in all mammals. In humans, progesterone is the dominant
hormone in the secretory phase and regulates the expression of several molecular modulators of
endometrial receptivity in a spatiotemporal manner, defining the WOI. Progesterone also down-
regulates ER and induces the production of 17-β-hydroxylase-dehydrogenase II, which converts
estradiol to the less active estrone, thereby decreasing estrogen activity in the secretory phase
(Hoozemans, Schats et al. 2004).
The expression of receptors for progesterone in the endometrium is complex and has been
suggested as a potential marker to monitor endometrial development. Two distinct isoforms of
PR derived from the same gene have been identified, type A and B (PRA and PRB). These
receptors are present in both endometrial epithelium and stroma and have a dynamic pattern of
expression across the menstrual cycle. Epithelial PR levels are highest in the proliferative phase
and decrease around the time of implantation. Stromal PR levels persist from the proliferative
phase through the secretory phase and into early pregnancy, reflecting the role of progesterone in
decidualization of the endometrium (Lessey 2003). While both PRA and PRB are expressed in
the stroma during the proliferative phase, stromal PRB levels wane in the secretory phase,
leaving PRA as the predominant isoform (Wang, Critchley et al. 1998). Progesterone exerts its
effects on endometrial receptivity via two proposed pathways, either directly on the epithelium
20
(endocrine pathway) or indirectly through the induction of stromal factors that regulate epithelial
gene expression (paracrine pathway) (Lessey 2003).
1.3.4 Molecular factors implicated in endometrial receptivity
Molecular biology techniques including thin-layer chromatography, immunohistochemistry,
reverse transcription-polymerase chain reaction (RT-PCR), ELISA, and mass spectrometry have
been applied to studying the endometrium in normal and abnormal conditions, to improve our
understanding of endometrial receptivity (Lessey 2011). Such studies have identified a number
of endometrial proteins whose expression is temporally associated with the period of
implantation, and regulated by estrogen or progesterone levels. These include several cytokines,
growth factors, molecules involved in cell adhesion, cell cycle regulators and other factors which
may be important in endometrial-embryo interaction or preparing the endometrium for
implantation (Giudice 1999).
1.3.4.1 Cytokines
Cytokines are a family of glycoproteins or peptides involved in intercellular communication and
cell signaling. These small molecules are produced in most tissues of the body and have been
implicated in diverse physiologic processes, including immunity and inflammation. Cytokines
are characterized by significant pleiotropy and redundancy, and members of this family often
have overlapping or even antagonistic roles. Several cytokines exhibit cycle-specific expression
in the endometrium and have been implicated in the implantation process (Dimitriadis, White et
21
al. 2005). A variety of cytokines are expressed by endometrial epithelial, stromal and decidual
cells; trophoblast cells of the implanting embryo; and leukocytes present around the time of
implantation, particularly macrophages and NK cells. This section will provide a focused review
of key cytokines produced by the endometrium that have been associated with implantation.
Leukemia inhibitory factor (LIF) is a pleiotropic glycoprotein that was originally described to
inhibit proliferation and induce differentiation in a murine leukemic cell line (Williams, Hilton et
al. 1988). LIF is a member of the interleukin-6 (IL-6) family of cytokines, which shares a
common accessory signal transduction subunit, glycoprotein 130 (gp130). Binding of LIF to its
receptor (LIF-R) leads to dimerization with gp130, followed by the activation of several possible
pathways, including the janus kinase/signal transducer and activator of transcription (Jak/STAT)
and mitogen-activated protein kinase (MAPK) pathways (Duval, Reinhardt et al. 2000). LIF was
the first cytokine found to be essential for implantation. In mice, the expression of LIF increases
in endometrial glands just prior to implantation, and in stromal cells that surround the blastocyst
at the time of attachment (Bhatt, Brunet et al. 1991; Song, Lim et al. 2000). This suggests that
LIF may be involved in endometrial preparation for implantation as well as further embryonic
attachment. LIF may also be involved in the process of stromal cell decidualization (Shuya,
Menkhorst et al. 2011). Lif -/-
knockout mice ovulate and produce normal embryos, but
demonstrate failed implantation. However, transfer of embryos from Lif -/-
mice to wild-type
pseudopregnant females results in successful implantation, which indicates that it is the
endometrial expression of LIF that is critical for implantation (Stewart, Kaspar et al. 1992). The
mechanism of action is still unclear, but LIF may signal to both endometrial and embryonic cells.
Preimplantation embryos normally express LIF-R, and Lif-R -/-
knockout mouse embryos can
initiate implantation but exhibit abnormal placentation and die in the perinatal period (Ware,
Horowitz et al. 1995).
22
In humans, LIF is also expressed in the endometrium with peak levels in the mid- to late-
secretory phase, coinciding with the period of implantation and progesterone-dominance
(Charnock-Jones, Sharkey et al. 1994). This timing suggests that progesterone is a regulator of
LIF expression and indeed, treatment of women with the progesterone receptor antagonist
mifepristone (RU486) after ovulation decreases endometrial LIF immunoreactivity during the
WOI (Danielsson, Swahn et al. 1997). LIF may be important for human fertility as LIF protein
levels are reduced in endometrial flushings from women with unexplained infertility compared to
normal fertile women (Laird, Tuckerman et al. 1997). Furthermore, the concentration of LIF
during the late-secretory phase has been shown to be predictive of implantation in a subsequent
cycle in a small study (Ledee-Bataille, Lapree-Delage et al. 2002). Mutations or polymorphisms
in the Lif gene have been identified in women with unexplained infertility and recurrent
implantation failure, but screening for these gene alterations is not justified due to low incidence
(Steck, Giess et al. 2004). Based on the putative involvement of LIF in endometrial receptivity
and implantation, treatment with recombinant human LIF (r-hLIF) has been investigated in
patients with recurrent implantation failure (Brinsden, Alam et al. 2009). However, subcutaneous
administration of r-hLIF in these patients after embryo transfer did not improve implantation or
pregnancy rates.
Interleukin-11 (IL11) is another member of the gp130 family of cytokines whose expression in
the endometrium is critical for implantation in the mouse model (Robb, Li et al. 1998). IL11 has
been described to have thrombocytopoietic and anti-inflammatory effects in different cell types
(Sands, Bank et al. 1999). IL11 is expressed by all cell types of the endometrium, but a
prominent increase in expression occurs in the decidualized stromal cells during the late-
secretory phase (Cork, Li et al. 2001). The IL11 receptor (IL11-R) co-localizes with IL11 in the
decidualized stromal cells, suggesting a local, autocrine involvement in decidualization (Cork,
23
Tuckerman et al. 2002; Dimitriadis, Robb et al. 2002). In a study of human endometrial stromal
cells induced with progesterone or cyclic AMP (cAMP) to decidualize in vitro, IL11 mRNA
levels were up-regulated on gene array (Popovici, Kao et al. 2000). Mice deficient in IL11-R
exhibit failed implantation due to deficient decidualization (Robb, Li et al. 1998). IL11 is also
expressed in the luminal and glandular epithelium, but studies disagree on the timing of maximal
expression, perhaps due to variations in immunohistochemistry protocols (Singh, Chaudhry et al.
2011). The specific activities of endometrial IL11 are still unclear, although a study on IL11-R
deficient mice has demonstrated that IL11 signaling is critical for the differentiation of uterine
NK cells (Ain, Trinh et al. 2004). In humans, there is some evidence for the importance of
endometrial IL11 in implantation. Epithelial IL11 levels during the WOI have been found to be
lower in women with recurrent miscarriage compared with normal fertile controls (Linjawi, Li et
al. 2004). Further studies are needed to elucidate the role and therapeutic implications of IL11
and other cytokines in human implantation.
1.3.4.2 Growth factors
Growth factors are secreted signaling molecules capable of stimulating cellular proliferation and
differentiation. Their cell surface receptors have a tyrosine kinase domain which, upon ligand
binding, can initiate various signal transduction pathways, including the MAPK and mothers
against decapentaplegic homolog (SMAD) pathways (Singh, Chaudhry et al. 2011). A classic
growth factor that has been implicated in implantation is TGF-β, which has three isoforms in
mammals, TGF-β1, TGF-β2, TGF-β3. TGF-β is known to be involved in regulating the
biosynthesis, degradation and remodeling of ECM components (Godkin and Dore 1998). In the
24
human endometrium, all three isoforms of TGF-β are expressed by epithelial, stromal and
decidual cells. TGF-β levels are greatest in the luminal and glandular epithelium during the late
proliferative and early-mid secretory phase, and diminished in the late-secretory phase,
suggesting that epithelial TGF-β expression is induced by progesterone (Chegini, Zhao et al.
1994). Within the stroma, TGF-β2 specifically increases during the secretory phase, and in vitro
stimulation of endometrial explants with progesterone induces TGF-β2 expression (Gold, Saxena
et al. 1994; Bruner, Rodgers et al. 1995). With regards to its role in implantation, TGF-β has
been shown to regulate factors that inhibit the invasiveness of the trophoblast, including PAI-1
and TIMP (Irving and Lala 1995). TGF-β has also been shown to stimulate the synthesis of
fibronectin and vascular endothelial growth factor (VEGF) by trophoblast cells in vitro, but the
in vivo relevance of this and its importance in implantation is unclear (Feinberg, Kliman et al.
1994; Chung, Yelian et al. 2000).
The epidermal growth factor (EGF) family includes EGF and similar molecules such as heparin-
binding EGF-like growth factor (HB-EGF) and transforming growth factor-α (TGF-α), which
interact with a common EGF receptor (EGF-R). HB-EGF is a transmembrane protein that
requires heparin sulfate proteoglycan as a cofactor to bind to its receptor in a juxtacrine manner
(no, Raab et al. 1994). In rodents and humans, HB-EGF is expressed by the endometrium in a
cycle-dependent manner with peak levels during the WOI (Das, Wang et al. 1994; Birdsall,
Hopkisson et al. 1996). HB-EGF is regulated by estrogen and progesterone and expressed by
both stromal and epithelial cells (Lessey, Gui et al. 2002). In the mouse, HB-EGF is also
regulated by LIF, and Lif -/-
knockout mice lose HB-EGF expression in the endometrium (Song,
Lim et al. 2000). A study on the effect of HB-EGF on the endometrium demonstrated that HB-
EGF-coated beads induced a local implantation-like response in the mouse, including increased
vascular permeability, decidualization, and stromal expression of bone morphogenetic protein-2
25
(BMP-2) (Paria, Ma et al. 2001). The importance of HB-EGF in implantation is underscored by
the fact that Hbegf -/-
knockout mice are subfertile, with reduced number of implantation sites and
litter size (Xie, Wang et al. 2007). The maximal expression of HB-EGF in the apical surface of
the luminal epithelium during the WOI suggests a potential role in endometrial-embryonic
interaction (Yoo, Barlow et al. 1997). Indeed, animal studies demonstrate that EGF-R is
expressed by the preimplantation embryo, and HB-EGF promotes blastocyst growth, zona-
hatching and trophoblast outgrowth in vitro (Wiley, Wu et al. 1992; Das, Wang et al. 1994;
Paria, Das et al. 1994). Further studies are needed to evaluate the role of HB-EGF and other
growth factors in the molecular dialogue between the endometrium and embryo in human
implantation.
1.3.4.3 Molecules involved in cell adhesion
Mucin-1 (MUC-1) is an extensively glycosylated, membrane-bound protein expressed on the
luminal aspect of epithelial cells in many parts of the body, where it forms a protective layer
against infection and toxic substances, and may help maintain the lumen by preventing the
adhesion of opposite membranes (Hilkens, Ligtenberg et al. 1992). MUC-1 is expressed by the
endometrium in a cycle-dependent manner, and its large, anti-adhesive extracellular domain is
thought to be a barrier to implantation in many mammals. In mice, MUC-1 expression is down-
regulated at the time of implantation, which suggests that loss of MUC-1 is required for the
endometrium to attain a receptive state, perhaps by exposing adhesion molecules that mediate
embryo attachment (DeSouza, Surveyor et al. 1999). However, in humans, rabbits and baboons,
MUC-1 protein and mRNA levels do not decline during the WOI; in fact, human MUC-1
transcript levels increase 6-fold in the secretory phase and implantation period compared to the
26
proliferative phase (Hey, Graham et al. 1994). Progesterone has been shown to stimulate MUC-1
expression in in vitro studies (Hoffman, Olson et al. 1998). Studies on women with recurrent
miscarriage have demonstrated decreased MUC-1 expression in endometrial biopsies and
flushings performed during the mid-secretory phase (Hey, Li et al. 1995; Aplin, Hey et al. 1996).
The contradictory expression pattern of MUC-1 in different mammals has led to a closer
examination of its adhesion and anti-adhesion properties. Despite the fact that the classic role of
MUC-1 is to provide steric hindrance to cell-to-cell interactions, MUC-1 also has some adhesive
features. One possible explanation for its increased expression in the human WOI is that MUC-1
can carry carbohydrate moieties known as Sialyl-Lewis x and Sialyl-Lewis a, that serve as
ligands for cell adhesion molecules known as selectins, which are expressed by the embryo (Hey
and Aplin 1996; Genbacev, Prakobphol et al. 2003). Specific alterations in MUC-1 glycosylation
may also occur during the WOI to facilitate embryo attachment (DeLoia, Krasnow et al. 1998).
Although the interaction between the embryo and MUC-1 is incompletely understood, the weight
of the evidence suggests that MUC-1 is an important factor in mediating implantation, and
MUC-1 polymorphisms have been associated with unexplained infertility (Horne, White et al.
2001).
The integrins are a family of transmembrane glycoprotein receptors that mediate cell-cell and
cell-ECM adhesion (Lessey and Castelbaum 2002). Each integrin molecule is a heterodimer that
results from the non-covalent binding of an α and β subunit, and with at least 14 known α
subunits and 8 β subunits encoded by separate genes, many distinct integrin molecules are
possible (Bronson and Fusi 1996). The cycle-dependent expression of integrins in the
endometrium was first described by Lessey (Lessey, Damjanovich et al. 1992). Three specific
integrins, α1β1, α4β1 and αvβ3, have been found to be expressed during the WOI (Lessey 1998).
The best characterized of the three is αvβ3 integrin, which is also the most promising as a marker
27
of endometrial receptivity as its appearance in the epithelium coincides with the beginning of the
WOI. An immunohistochemical study of endometrial tissue taken from normal cycling women
demonstrated weak staining of the αv subunit in the proliferative phase epithelium, and this
staining increased gradually during the secretory phase to peak during the period of implantation.
During the proliferative phase, staining of the β3 subunit was absent in the epithelium, but β3
abruptly appeared on luminal and glandular epithelial cells on cycle day 20, heralding the WOI
(Lessey, Damjanovich et al. 1992). A functional ligand for αvβ3 integrin is osteopontin (OPN),
also known as secreted phosphoprotein 1 (SPP1), an ECM component whose expression in the
endometrium parallels that of αvβ3 integrin and peaks in the mid- to late-secretory phase. In situ
hybridization studies have identified OPN mRNA in endometrial epithelial cells, and
immunostaining for OPN has demonstrated its expression on the apical surface of luminal
epithelial cells and in glandular secretions. In vitro studies have determined that αvβ3 integrin and
OPN are differentially regulated. While OPN is up-regulated by progesterone, αvβ3 integrin
(specifically the β3 subunit) is induced by HB-EGF through EGF-R signaling (Apparao, Murray
et al. 2001). Like the endometrium, the trophoblast and preimplantation embryo have also been
found to express both αvβ3 integrin and OPN. Integrin mediated cell-cell interaction may be
important in embryo-endometrial attachment and implantation (Hoozemans, Schats et al. 2004).
In humans, absent or decreased endometrial expression of the β3 subunit has been implicated in
unexplained infertility and conditions associated with impaired endometrial receptivity,
including polycystic ovarian syndrome, mild endometriosis, and hydrosalpinx (Lessey 2011).
Furthermore, a lack of β3 subunit expression may be associated with histological delay in
endometrial development (Lessey, Damjanovich et al. 1992). Pharmacological or surgical
treatment of endometriosis has been shown to restore αvβ3 integrin expression in the eutopic
endometrium, as has treatment of hydrosalpinx by salpingectomy (Giudice 1999). Due to the
28
interest in αvβ3 integrin as a potential marker of endometrial receptivity, a commercial test has
been developed to measure the epithelial expression of the more dynamic β3 subunit (E-tegrity®
β3 integrin analysis, Innovative Reproductive Solutions, Boston, MA). However, this is a
histology-based assay which requires an invasive endometrial biopsy to be performed during the
WOI. Therefore, this test cannot be performed in an ongoing conception or ART cycle to predict
endometrial receptivity and has limited clinical value.
1.3.4.4 Cell cycle regulators
The control of a cell’s progression through the mitotic cycle depends on regulatory proteins
known as cyclins, cyclin-dependent kinases (cdk), and cyclin-dependent kinase inhibitors. Cyclin
E is an activator of the mitotic G1 to S phase transition, and p27 is a cyclin-dependent kinase
inhibitor of this progression. The levels of cyclin E and p27 in the endometrial glands are cycle-
dependent and frame the WOI (Dubowy, Feinberg et al. 2003). As such, these two cell cycle
regulators in combination have been suggested as potential markers of endometrial receptivity.
Over the menstrual cycle in normal, fertile women, immunostaining for cyclin E and p27
changes in intensity and localization. Glandular cyclin E expression progresses from lateral
cytoplasmic staining in the mid-proliferative phase, to strong nuclear staining on post-ovulatory
day 4, and finally to a loss of immunoreactivity after post-ovulatory day 6, coinciding with the
beginning of the WOI. In contrast, p27 staining abruptly appears between post-ovulatory day 3
and 5, and only in the nuclei of glandular cells. This expression pattern has been shown to be
dysregulated in some cases of infertility, with cyclin E persisting beyond post-ovulatory day 6
more frequently in infertile patients than in fertile controls. A histology-based commercial assay
for cyclin E and p27 has been developed that requires an endometrial biopsy to be performed in
29
the late secretory phase (Endometrial Function Test®, Yale University, New Haven, CT).
Because of the invasiveness of this test, it cannot be used to assess the endometrium in an active
ART cycle. Furthermore, the value of cyclin E and p27 as markers of endometrial receptivity is
uncertain due to the paucity of clinical data correlating their expression to implantation
outcomes.
1.3.4.5 Glycodelin-A
Glycodelin, also referred to as progesterone-associated endometrial protein (PAEP) or placental
protein 14 (PP14), among other names, is a secreted glycoprotein produced by various
reproductive tissues and found in the endometrium, placenta, gestational decidua, amniotic fluid,
ovary, as well as the seminal plasma of men (Seppala, Bohn et al. 1998). There are two forms of
this protein with different glycosylation patterns, glycodelin-A (endometrial form) and
glycodelin-S (seminal form) (Morris, Dell et al. 1996). In the endometrium, glycodelin-A is
expressed in a cycle-dependent manner, and is quantitatively the major secreted protein of the
late secretory endometrium and early gestational decidua. Glycodelin-A synthesis has been
localized to the glandular epithelium by immunohistochemical studies, and the major route of
secretion is into the uterine lumen, allowing detection by endometrial flushing (Waites and Bell
1989). Glycodelin-A levels are undetectable in the proliferative and peri-ovulatory endometrium,
but increase significantly in the secretory phase starting from post-ovulatory day 4 and peaking
in the late secretory phase. If implantation occurs, levels rise even more rapidly, and glycodelin-
A accounts for 4-10% of total protein synthesized by the early gestational decidua (Morris, Dell
et al. 1996). Glycodelin-A is also detectable in serum, and serum concentrations are cycle-
30
dependent and reflective of the pattern of expression in the endometrium (Westergaard, Wiberg
et al. 1998).
Glycodelin-A has been shown to have contraceptive and immunosuppressive features. In vitro
studies demonstrate a dose-dependent inhibitory effect on sperm binding to the zona pellucida
(Oehninger, Coddington et al. 1995). Glycodelin-A also suppresses the activity of NK cells, and
this function has been hypothesized to prevent maternal immune rejection of the conceptus
(Okamoto, Uchida et al. 1991). Together, this evidence suggests that low glycodelin-A levels in
the peri-ovulatory endometrium permit fertilization, and increased levels in the mid- to late-
secretory phase allow implantation by reducing the maternal immune response. The regulation of
glycodelin-A expression is unclear. Progesterone is unlikely to be a regulatory factor as it does
not induce glycodelin-A production by decidual cells in vitro, and administration of mifepristone
does not inhibit production (Borri, Noci et al. 1998).
The involvement of glycodelin-A in fertility has been examined by several clinical studies.
Glycodelin-A concentrations in late-secretory endometrial flushings were found to be lower in
women with unexplained infertility compared to fertile controls (Mackenna, Li et al. 1993). In a
subsequent study by the same group, endometrial glycodelin-A levels were also found to be
lower in women with recurrent miscarriage (Dalton, Laird et al. 1998). However, studies that
have measured glycodelin-A in the serum and correlated these levels to fertility outcomes have
produced inconsistent or contradictory results (Mackenna, Li et al. 1993; Westergaard, Wiberg et
al. 1998). Glycodelin-A is promising as a potential marker of endometrial receptivity, but further
research is needed to establish the best way to measure its expression and to determine if its
presence is predictive of implantation.
31
1.4 GENE EXPRESSION STUDIES OF ENDOMETRIAL
RECEPTIVITY
Previous research efforts have typically studied a small number of factors at a time and have
failed to identify any clinically useful biomarkers of endometrial receptivity, which are not only
differentially expressed in the WOI, but also predictive of implantation. Continuing towards this
goal, many groups have started to examine the period of endometrial receptivity from a global
genomic perspective. Advances in gene expression profiling using microarray technology have
enabled the rapid, high throughput and cost-effective transcriptomic analysis of multiple samples
simultaneously, with coverage of the whole human genome. The endometrium has been studied
on a global gene expression level in a variety of conditions, including natural cycles (Carson,
Lagow et al. 2002; Kao, Tulac et al. 2002; Borthwick, Charnock-Jones et al. 2003; Riesewijk,
Martin et al. 2003; Mirkin, Arslan et al. 2005; Talbi, Hamilton et al. 2006; Haouzi, Mahmoud et
al. 2009), COH cycles (Simon, Oberye et al. 2005; Haouzi, Assou et al. 2009), conditions that
render the endometrium non-receptive such as the presence of an intrauterine device (IUD)
(Horcajadas, Sharkey et al. 2006) or treatment with mifepristone (Catalano, Yanaihara et al.
2003), and pathologic conditions such as endometriosis and endometrial cancer (Kao, Germeyer
et al. 2003; Ferguson, Olshen et al. 2005; Matsuzaki, Canis et al. 2005).
Focusing on the process of endometrial receptivity, several studies have compared endometrial
samples taken from normo-ovulatory women in the putative receptive phase (during the WOI) to
the pre-receptive phase, ostensibly to determine candidate genes whose activation or repression
leads to the acquisition of receptivity. However, many of these studies had methodologic
challenges that cause one to question the validity of the genes they implicated in endometrial
32
receptivity. For example, most of these studies did not compare paired receptive and pre-
receptive samples from the same patient, but instead compared samples from different patients,
which could have introduced confounding patient variables (Carson, Lagow et al. 2002; Kao,
Tulac et al. 2002; Borthwick, Charnock-Jones et al. 2003; Mirkin, Arslan et al. 2005; Talbi,
Hamilton et al. 2006). In addition, some of these studies performed microarray analysis on
pooled RNA from different women sampled in the same phase instead of analyzing each sample
individually, which could mask transcriptomic differences between the two phases (Carson,
Lagow et al. 2002; Borthwick, Charnock-Jones et al. 2003). Furthermore, one of these studies
compared endometrial samples obtained by two completely different methods (elective
hysterectomy and endometrial biopsy), and differences in the cellular composition of these
samples could confound the gene expression profiling results (Talbi, Hamilton et al. 2006).
These methodologic issues as well as variations in experimental design and bioinformatic
analysis of data have led to very poor consensus among previous studies attempting to identify
potential genes involved in endometrial receptivity. In fact, among five studies that each
identified hundreds of candidate genes, only one gene was consistently found to be up-regulated
in the receptive phase of the endometrium. This gene was OPN, the ligand to αvβ3 integrin
(Horcajadas, Pellicer et al. 2007). OPN is a cell-adhesion protein that has been hypothesized to
be involved in endometrial-embryonic interaction (Hoozemans, Schats et al. 2004). However,
Opn-/-
knockout mice are fertile, and the specific role of OPN in endometrial receptivity remains
to be determined (Liaw, Birk et al. 1998).
33
1.4.1 Endometrial sampling techniques
Another limitation of previous gene expression studies of endometrial receptivity is the use of
endometrial biopsy to sample tissue for microarray analysis. This invasive procedure involves
inserting a polypropylene curette known as an endometrial pipelle into a patient’s uterus, and
applying suction and a repeated back-and-forth motion to obtain a generous amount of tissue.
This procedure is not only uncomfortable for the patient, but also causes local injury to the
endometrium and can have a negative impact on implantation if performed during a cycle in
which a patient is trying to conceive. A recent study tested the effect of endometrial biopsy on
the day of oocyte retrieval on IVF outcomes and found that implantation rates were dramatically
reduced in the biopsied cohort compared to non-biopsied controls (7.9 vs. 22.9%, p=0.002)
(Karimzade, Oskouian et al. 2010). Because of the disruptive nature of endometrial biopsies,
previous studies could not sample the endometrium during actual conception cycles, and were
thus unable to correlate gene expression during the WOI with implantation outcomes. Therefore,
the ability of previously identified candidate genes to predict implantation could not be
determined.
Another drawback of sampling with endometrial biopsy is that local injury to the endometrium
has been shown to alter the expression of many genes and cause an increased recruitment of
immune cells and pro-inflammatory cytokines to the endometrium (Kalma, Granot et al. 2009;
Gnainsky, Granot et al. 2010). When comparing the receptive to the pre-receptive phase of the
endometrium, the best way is to analyze paired samples from the same patient, to avoid
introducing inter-subject variability. However, if both samples are obtained by biopsy from the
same patient, performing the first biopsy could alter the transcription of many genes which
would confound the discovery of true gene expression differences between the two phases. This
34
limitation must be considered when interpreting the results from previous studies that used this
approach (Riesewijk, Martin et al. 2003; Haouzi, Assou et al. 2009; Haouzi, Mahmoud et al.
2009).
In contrast to endometrial biopsy, uterine fluid aspiration (UFA) is a new sampling method that
has been developed to avoid disruption of the endometrium while enabling molecular analysis of
the endometrial milieu. Several variations of this technique have been described, but every
version involves using a narrow endometrial catheter to obtain secretions from the endometrial
cavity with gentle suction, without causing injury to the lining (Beier-Hellwig, Sterzik et al.
1994; van der Gaast, Beier-Hellwig et al. 2003; Boomsma, Kavelaars et al. 2009). The
minimally-invasive nature of this technique makes it an ideal means of assessing the
endometrium, and it can be performed immediately prior to embryo transfer without affecting
implantation or pregnancy rates (van der Gaast, Beier-Hellwig et al. 2003; Boomsma, Kavelaars
et al. 2009). The use of UFA has so far been confined to the domain of research, but it could be
easily and cost-effectively applied to clinical diagnostics in ART cycles once reliable biomarkers
of endometrial receptivity are identified. To date, investigations on UFA samples have been
limited to immunoassay and gel electrophoresis studies of secreted proteins. To my knowledge,
this present study is the first to use this technique to obtain cellular material from the
endometrium, to study global gene expression changes in the receptive phase.
35
1.5 THESIS HYPOTHESIS AND RATIONALE
Based on the results of previous studies and known morphologic changes in the endometrium
across the menstrual cycle, I hypothesized that a set of differentially expressed genes (and
gene products) exists that can distinguish the receptive phase from the pre-receptive phase
of the endometrium. To test this hypothesis, my preliminary approach was to compare the gene
expression profile of paired endometrial samples taken from the receptive and pre-receptive
phase of the same natural cycle in the same patient. For the receptive timepoint, I elected to
sample patients in the mid-luteal phase of the menstrual cycle, 7 days after the LH surge
(designated as day “LH+7”). This timepoint was chosen because it represents the opening of the
WOI (LH+7 to LH+11), and from the perspective of developing clinical tests for endometrial
receptivity, it is important to detect the early factors involved in the acquisition of the receptive
state. For the pre-receptive timepoint, I sampled patients in the early-luteal phase, 2 days after
the LH surge (LH+2). This timepoint was selected because I wanted a comparison period in the
luteal phase just before the endometrium acquired a receptive state, but not too close to the WOI
so putative receptive factors would not yet be activated. Also, this was a common timepoint
considered as a pre-receptive benchmark for comparison in most previous studies on endometrial
receptivity (Carson, Lagow et al. 2002; Riesewijk, Martin et al. 2003; Mirkin, Arslan et al. 2005;
Haouzi, Mahmoud et al. 2009). A few of the previous studies did sample the proliferative
endometrium instead of the early-luteal phase as a pre-receptive comparison (Kao, Tulac et al.
2002; Borthwick, Charnock-Jones et al. 2003), but this would capture many genes that are
differentially expressed due to the shift from estrogen- to progesterone-dominant regulation,
instead of genes specifically involved in conferring endometrial receptivity.
36
Another of my objectives was to develop a minimally-invasive technique of UFA to sample the
endometrium for gene expression profiling with whole-genome microarrays. In this study, each
patient underwent a UFA sampling in the pre-receptive phase, followed by another UFA
sampling in the receptive phase of the same cycle to determine differentially expressed genes, as
described above. However, I also wanted to validate my novel technique of UFA against the
existing standard method of endometrial sampling. Therefore, on the second day of sampling
(LH+7), each patient also underwent an endometrial biopsy only after the receptive phase UFA
was done, so as to avoid disrupting the endometrium and altering the gene expression profiling
results. The endometrial biopsy and UFA samples taken from the same day were compared to
determine if transcriptomic profiling by UFA is representative of the more invasive biopsy
method.
The ultimate goal of my research is to identify biomarkers of endometrial receptivity that can be
assayed clinically to determine if the endometrium is receptive to implantation in a given cycle.
Towards this goal, my initial studies described in this dissertation will identify a set of genes
differentially expressed in the receptive phase of the endometrium, during the WOI. This set will
include a subset of genes whose activation or repression is essential in generating an
endometrium that is receptive to embryo implantation. In follow-up studies, the minimally-
invasive UFA technique will allow me to identify these specific genes by enabling the profiling
of the endometrium during an active conception cycle, and the correlation of gene expression to
implantation outcomes. The subset of genes critical to endometrial receptivity and predictive of
implantation may inform the development of minimally-invasive clinical assays which will guide
embryo transfer decisions and ideally decrease the tendency towards multiple embryo transfer in
receptive patients.
37
1.6 OBJECTIVES
1. To optimize the minimally-invasive technique of UFA to sample endometrial cells for
gene expression profiling.
2. To identify genes differentially expressed during the receptive phase vs. pre-receptive
phase of the endometrium.
3. To compare the gene expression profile of endometrial samples obtained by UFA vs.
endometrial biopsy.
1.7 DESCRIPTION OF COLLABORATIONS AND ROLES
I developed the study hypothesis, objectives and design of this thesis project, after an extensive
review of the literature. After conception of the research proposal, I obtained institutional ethics
approval and research funding, then proceeded to develop the minimally-invasive technique of
UFA on recruited subjects, under the clinical supervision of Dr. Ellen Greenblatt. As I had broad
clinical experience with endometrial sampling, I had a large degree of independence in
developing this technique. I was solely responsible for recruiting study subjects from the clinic,
and I monitored their cycles and performed sample collection. Sample processing and RNA
extraction was performed by me. I also performed cytological assessment of these samples,
under the guidance of Dr. Terence Colgan, a gynaecologic pathologist at Mount Sinai Hospital.
The high-throughput transcriptomic and proteomic assays, including microarray, NanoString and
multiplex cytokine analysis, were run by the University Health Network (UHN) Microarray
Centre due to the specialized nature of these technologies and the limited availability of these
38
platforms. However, I conducted the subsequent statistical analysis and interpretation of the
results, in close consultation with Carl Virtanen, the bioinformatics manager at the Microarray
Centre. I was also prepared to handle these analyses after attending a two-day Canadian
Bioinformatics Workshop on “Interpreting Gene Lists from -omics Studies”. Due to the
complexity of these analyses, Mr. Virtanen’s expertise was invaluable. Finally, I performed the
embedding, sectioning and immunohistochemical analysis of endometrial tissue for validation
purposes. From conception to conclusion, I was intimately involved in each step of this project,
and plan to continue follow-up studies during my clinical fellowship.
39
Chapter 2 MATERIALS AND METHODS
2.1 PATIENT SELECTION
This study received approval from the Mount Sinai Hospital Research Ethics Board prior to
initiation. For objective 1, a preliminary set of 12 women was recruited with informed consent,
to develop and optimize the technique of UFA for endometrial cell isolation and gene expression
profiling. For the remaining objectives,the study participants included 23 women (mean ± SEM
age: 34.9 ± 0.7 years), also enrolled voluntarily with written informed consent. Healthy women
with no underlying medical conditions were recruited, and the inclusion criteria were:
Age ≤ 40 years
Normal, regular menstrual cycles (26-35 days with intercycle variability of < 5 days)
Normal serum follicle stimulating hormone (FSH), LH and estradiol levels on cycle day 3
Normal uterine cavity on imaging, i.e. no intrauterine pathology
The following exclusion criteria were stipulated:
Pregnancy
History of female factor infertility
Hormonal contraceptive or intrauterine device use
40
As my patient population was recruited from the Mount Sinai Hospital Centre for Fertility and
Reproductive Health (CFRH) and one of the exclusion criteria was female factor infertility,
enrolled patients fell into one of two categories:
Single women or women in same-sex relationships referred for donor insemination
Female partners of couples referred for treatment of male factor infertility
2.2 TISSUE COLLECTION
Each patient was sampled during a natural menstrual cycle, and asked to abstain from
unprotected intercourse during the study cycle. A serum β-hCG level was also measured to rule
out pregnancy. Patients were given urine ovulation tests (Clearblue, Petit Lancy, Switzerland)
and instructed to test their urine starting 17 days before their next anticipated period in order to
detect the day of their LH surge. Two UFA samples were then obtained within the same cycle, 2
days after the LH surge (LH+2) and 7 days after the LH surge (LH+7) [Figure 1]. Due to patient
scheduling issues, one patient’s (Pt 3) second sampling day was LH+8 instead of LH+7, but as
this was within the early WOI and only one day delayed, we included this sample. For similar
reasons, another patient’s (Pt 24) second sampling day was LH+6 instead of LH+7, and as this
was only one day prior to the WOI and transcriptomic changes associated with endometrial
receptivity may have been starting, we also included this sample. I found that the most effective
commercial intrauterine catheter for obtaining an adequate sample of endometrial cells by UFA
was the Tomcat insemination catheter (Kendall, Mansfield, MA), due to its lumen size (3.5 Fr)
and its relative rigidity [Figure 2]. My UFA technique was as follows: with the patient lying in
41
the dorsal lithotomy position, a vaginal speculum was inserted and the cervix was cleansed with
saline. A 3cc syringe was connected to a Tomcat catheter, which was gently introduced into the
uterine cavity, to a point 1-2cm from the uterine fundus. Gentle suction was then applied to
aspirate the fluid contents of the uterine cavity. The catheter was then withdrawn from the
uterine cavity, and the outside of the catheter was wiped to remove any potential cervical mucus.
Each UFA sample contained a small amount of viscous material (<10µL), which was
immediately placed in 1mL of phosphate-buffered saline (PBS) and centrifuged (300 x g for 10
min). The cellular fraction was resuspended in 5-10X volume of RNAlater (Ambion, Austin,
TX) to stabilize the RNA and stored at -80˚C, for later RNA extraction and gene expression
analysis. The supernatant was snap frozen in dry ice and stored at -80 ˚C. A small portion of the
first few UFA samples obtained from an independent study cohort was sent to Pathology for
cytopathologic review to determine cellular composition.
On day LH+7, an endometrial biopsy was also performed as per standard protocol using an
Endocell pipelle (Wallach Surgical, Trumbull, CT), after the second UFA was completed [Figure
1]. Each endometrial biopsy contained 500-1000 µL of tissue and was partitioned into 3 aliquots.
The first portion was snap frozen in dry ice and stored at -80 ˚C for later RNA extraction and
gene expression analysis. Another portion was fixed in 10% neutral buffered formalin (NBF) for
future immunohistochemical validation studies. The final portion was fixed in 10% NBF and
sent to Pathology for review. The histopathology slides from each patient’s biopsy were
reviewed with Dr. Terence Colgan, a gynecologic pathologist, who dated each specimen
according to the classic criteria of Noyes et al. (Noyes, Hertig et al. 1975). Two patients had
endometrial biopsies that were out of phase, defined as >2 days discordance between histological
and chronological dating, and these patients were eliminated from further analysis. The
42
histological dating for the remainder of the patients (n=21) was consistent with the phase of the
cycle they were sampled in (LH+7).
Figure 1: Timing of endometrial sampling by UFA and Biopsy
Figure 2: UFA sampling instruments: Tomcat intrauterine catheter attached to syringe
43
2.3 RNA EXTRACTION
Each UFA (LH+2, LH+7) and biopsy sample (LH+7) from the 21 included patients was
processed individually. Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Valencia,
CA) according to the manufacturer’s instructions. Following adherence of the RNA to the silica-
based membrane, 30 µL of RNase-free water was added to each spin column and the RNA was
eluted. Each RNA sample was then treated with RNase-free recombinant DNase I (DNA-Free,
Ambion, Austin, TX) for 30 min at 37˚C to eliminate genomic DNA contamination. DNase
Inactivation Reagent was then added, the sample was centrifuged (10,000 x g for 90 sec), and the
supernatant containing the RNA was transferred to a fresh tube and stored at -80 ˚C pending the
next steps.
2.4 RNA INTEGRITY TESTING
The extracted RNA samples were analyzed at the UHN Microarray Centre. RNA was quantified
with a NanoDrop ND-1000 Spectrophotometer (Thermo Scientific, Wilmington, DE). To ensure
that the RNA samples were free of contaminants including proteins, phenols, and other organic
solvents, the NanoDrop Spectrophotometer also measured the ratio of absorbance at 260nm and
280nm. All samples had acceptable A260:A280 ratios and went on for further analysis. The
integrity of the RNA samples was also tested using the Agilent 2100 Bioanalyzer (Agilent
Technologies, Santa Clara, CA). The Agilent 2100 Bioanalyzer RNA 6000 Nano Kit was used
for the endometrial biopsy RNA samples (1400 – 9000 ng total RNA/sample), and the RNA
6000 Pico Kit was used for the UFA RNA samples (100 – 4000 ng total RNA/sample). The
bioanalyzer calculates the RNA integrity number (RIN) for each sample, which classifies total
44
RNA based on a numbering system from 1 to 10, with 1 being the most degraded, and 10 being
the most intact. For the purposes of this study, RNA samples with a RIN score < 2 were
eliminated from further analysis since amplification of extremely degraded RNA may skew
microarray results. The samples that went on for microarray analysis had a large distribution of
RIN scores [Table 1], with the majority having good, intact RNA (RIN score 7.0-10.0).
Table 1: RIN scores of samples
Sample ID RIN score
Pt 2 UFA LH+2 +++
Pt 2 UFA LH+7 +++
Pt 3 UFA LH+2 +++
Pt 3 UFA LH+8 ++
Pt 4 UFA LH+2 +++
Pt 4 UFA LH+7 +++
Pt 5 UFA LH+2 +++
Pt 5 UFA LH+7 +++
Pt 6 UFA LH+2 +++
Pt 6 UFA LH+7 +++
Pt 7 UFA LH+2 +++
Pt 7 UFA LH+7 +++
Pt 8 UFA LH+2 +++
Pt 8 UFA LH+7 ++
Pt 9 UFA LH+2 +++
Pt 9 UFA LH+7 +
Pt 10 UFA LH+2 +++
Pt 10 UFA LH+7 +++
Pt 12 UFA LH+2 +++
Pt 12 UFA LH+7 +++
Pt 13 UFA LH+2 +++
Pt 13 UFA LH+7 +
Pt 14 UFA LH+2 +++
Pt 14 UFA LH+7 ++
Pt 15 UFA LH+2 +
Pt 15 UFA LH+7 +
Pt 17 UFA LH+2 ++
Pt 17 UFA LH+7 ++
Pt 18 UFA LH+2 +++
Pt 18 UFA LH+7 +++
45
Pt 20 UFA LH+2 +
Pt 20 UFA LH+7 +++
Pt 21 UFA LH+2 +++
Pt 21 UFA LH+7 +
Pt 24 UFA LH+2 +++
Pt 24 UFA LH+6 +
Pt 2 biopsy LH+7 +++
Pt 3 biopsy LH+8 +++
Pt 4 biopsy LH+7 +++
Pt 5 biopsy LH+7 +++
Pt 6 biopsy LH+7 +++
Pt 7 biopsy LH+7 +++
Pt 8 biopsy LH+7 +++
Pt 9 biopsy LH+7 +++
Pt 10 biopsy LH+7 +++
Pt 11 biopsy LH+7 +++
Pt 12 biopsy LH+7 +++
Pt 13 biopsy LH+7 +++
Pt 14 biopsy LH+7 +++
Pt 15 biopsy LH+7 +++
Pt 16 biopsy LH+7 +++
Pt 17 biopsy LH+7 +++
Pt 18 biopsy LH+7 +++
Pt 20 biopsy LH+7 +++
Pt 21 biopsy LH+7 +++
Pt 24 biopsy LH+6 +++
RIN scores determined by bioanalyzer (+ = RIN 2.0-5.9; ++ = RIN 6.0-6.9; +++ = RIN 7.0-10.0)
2.5 REVERSE TRANSCRIPTION, AMPLIFICATION OF cDNA,
HYBRIDIZATION TO WHOLE-GENOME MICROARRAY
The Human Whole-Genome cDNA mediated Annealing, Selection, extension and Ligation
(DASL) HT assay (Illumina, San Diego, CA) is a sensitive method of amplifying low abundance
and partially degraded RNA samples for gene expression profiling. This assay was performed at
46
the UHN Microarray Centre. To begin the assay, 50ng of each RNA sample was reverse
transcribed to cDNA using biotinylated oligo dT and random nonamer primers. The biotinylated
cDNA was then annealed to the Whole-Genome DASL Assay Pool (DAP) probe groups, which
consist of 29,285 assay-specific oligonucleotides designed to query continuous 50-base
sequences on each cDNA. In addition to gene-specific sequences, the probe groups also contain
primer sites for subsequent PCR amplification and an address sequence for microarray
hybridization. The annealing step involved a 16-hour temperature gradient incubation (70 to
30˚C). The gaps between query oligos were then enzymatically extended and ligated to generate
a PCR template. A pair of universal PCR primers coupled with Cy3 fluorescent dye was used for
amplification, after which dye-labeled, PCR-amplified strands were isolated and hybridized to
Illumina HumanHT-12 v4 BeadChip microarrays. Hybridization proceeded at 58 ˚C for 16
hours. After hybridization, the BeadChips were washed and scanned on an iScan system, and
fluorescence intensities were read and images extracted. The data were then uploaded into
GenomeStudio (v.2010.1, Illumina) via the gene expression module WG-DASL assay for data
quantification.
2.6 MICROARRAY DATA ANALYSIS
The raw expression data files containing probe level intensities were imported into R (v.2.10.0)
with the Biconductor framework and LUMI package installed. After importing the data, a
number of statistical plots (histograms and bar plots) and quality control metrics were applied to
assess data quality and check for outliers. Samples that did not fall within standard quality
thresholds were discarded prior to further analysis. In all, 17 samples from UFA LH+2, 17
47
samples from UFA LH+7, and 20 samples from Biopsy LH+7 passed quality control. Data were
imported into Genespring (v11.5.1, Agilent) for further analysis. During import, the data were
normalized using a quantile normalization function. A “per probe” median-centered
normalization for visualization purposes was also used to help see differences in classes when
clustering. All data analysis was performed on log2-transformed data. After normalization,
filtering was performed on the data to remove probes that showed no signal at all in any sample
group (UFA LH+2, UFA LH+7, Biopsy LH+7), to prevent a confounding effect on subsequent
analysis. Only probes that were in the upper 80th percentile of the distribution of intensities in
80% of at least one of the groups were allowed to pass through this filtering. This left 22,333
probes, out of a total of 29,377 probes on the Illumina HumanHT-12 v4 array.
An unsupervised hierarchical clustering was first performed to assess the degree of similarity
among different samples based on gene expression. Then, supervised statistical testing was
performed to determine probes that were differentially expressed between the sample groups.
The Student’s t-test and one-way ANOVA were used for this directed statistical analysis. An
unpaired t-test was used to determine probes differentially expressed in UFA LH+2 vs. UFA
LH+7. An unpaired test was chosen over a paired test because some samples had been dropped
from analysis due to RNA degradation or failed quality control metrics, and the marginal benefit
from performing a paired analysis would be at the expense of decreased power due to decreased
sample size (patients that had either an inadequate UFA LH+2 or UFA LH+7 sample would be
completely excluded from a paired analysis). One-way ANOVA was used to test for differential
probe expression among the three sample groups, and a post-hoc Tukey test was used to
specifically compare UFA LH+7 vs. Biopsy LH+7. With a large number of genes on the array,
multiple testings of the hypothesis could lead to a large number of false positives; therefore, the
test statistics were corrected using a Benjamini-Hochberg multiple testing correction to generate
48
a false discovery rate (FDR). This method of multiple testing correction was selected as the FDR
calculation is less conservative than the traditional Bonferroni correction, which multiplies the p-
value of each gene by the total number of genes, and can lead to an overly stringent p-value and
a higher false negative rate. The gene lists resulting from each pair-wise comparison included
genes that had a fold-change above a pre-defined Δ threshold of 2-fold, with a FDR of p < 0.05.
Gene ontology (GO) terms associated with differentially expressed genes were determined using
GeneSpring GX software. The GeneCards website (www.genecards.org) and the UHN
Microarray Centre-hosted website known as Gene-fu (http://data.microarrays.ca/genefu/) were
used to obtain more detailed information on individual differentially expressed genes.
2.7 VALIDATION STUDIES
Top candidate genes identified by the microarray screen as differentially expressed between the
UFA LH+2 and UFA LH+7 groups were validated using the NanoString nCounter gene
expression system (NanoString technologies, Seattle, WA). Selected gene products were
internally validated at the protein level by immunohistochemistry.
2.7.1 NanoString analysis
Given the small quantities of genetic material obtained by minimally-invasive UFA (100 – 4000
ng total RNA/sample), a method of validation was chosen that could extract the maximum
amount of expression data from a given sample by quantifying several hundred transcripts
49
simultaneously. NanoString technology is based on the molecular barcoding and detection of up
to 800 target transcripts in a single sample through the use of a colour-coded pair of probes. A
library of probe pairs is designed with two sequence-specific probes for each transcript of
interest. The first probe is a biotinylated capture probe that contains a 50 nt sequence
complementary to the target RNA. The second probe is a reporter probe that contains a second
50 nt sequence complementary to the target RNA, coupled to a colour-coded fluorescent tag that
provides the detection signal. All probes are combined with total RNA samples in a single
solution-based hybridization reaction. Hybridization of capture probes, reporter probes and their
specific target RNA results in tripartite complexes that are then washed across a streptavidin-
coated surface and immobilized via the biotinylated capture probe. An electrical current is
applied across the surface and orients each immobilized complex in the same direction in an
elongated state. This surface is imaged, and the absolute level of expression of each transcript of
interest is quantified by counting the colour-coded tags. The NanoString nCounter system is
advantageous over quantitative RT-PCR because it enables the validation of multiple candidate
genes in a single experiment, without cDNA synthesis or enzymatic reactions, and with the same
level of sensitivity as PCR (Fortina and Surrey 2008). This technology was made available to me
through collaborations with the UHN Microarray Centre, which had one of only two such
systems in Canada at the time.
2.7.1.1 Target gene selection for NanoString validation
When selecting target genes to validate by NanoString, I focused on candidate genes identified
by microarray to be differentially expressed between UFA LH+2 and UFA LH+7 that would
make useful biomarkers from a diagnostic perspective. My ultimate goal is to identify
50
biomarkers of endometrial receptivity whose levels can unambiguously determine if the
endometrium is receptive. Clinically useful biomarkers may or may not be directly involved in
the physiologic process of implantation; however, they should be reliably differentially
expressed in the receptive phase and easily detectable. For this reason, I selected genes that were
robustly differentially expressed in the receptive phase, as well as genes that exhibited an
“on/off” pattern of expression, i.e. not expressed in the pre-receptive phase but expressed in the
receptive phase, or vice versa. The results of the microarray analysis were prioritized according
to fold-change and pattern of expression to identify these genes. An unpaired t-test with a
Benjamini-Hochberg multiple testing correction FDR of p < 0.05 was performed to identify
probes differentially expressed in UFA LH+2 vs. UFA LH+7. This resulted in 8308 significant
probes. 2049 probes were at least 2-fold different between the two groups and 288 probes
(representing 245 distinct genes) were at least 4-fold. To find genes that had an "on/off"
expression pattern, a filter was applied such that only probes in the lower 20th percentile of
expression intensity (i.e. not expressed or “off”) in 80% of the samples in either group (UFA
LH+2 or UFA LH+7) were allowed to pass through. This identified 277 “on/off” probes that
were 2 to 3-fold different, 58 “on/off” probes (representing 42 distinct genes) that were 3 to 4-
fold different, and 61 “on/off” probes that were at least 4-fold different between the two groups.
My final target gene list for NanoString validation was limited to approximately 300 genes due
to cost considerations, as the price for each NanoString assay increases with the number of target
genes. In a non-biased fashion, I generated a prioritized gene list including the 245 genes that
were at least 4-fold differentially expressed and the 42 “on/off” genes that were 3 to 4-fold
differentially expressed in LH+2 vs. LH+7. In addition, 5 housekeeping genes were included for
normalization purposes [Table 2].
51
One of the genes selected for more extensive validation was gastrin, which was the top candidate
gene identified, with the highest fold-change in LH+7 (28.8-fold up-regulated). Gastrin was the
“lowest hanging fruit” of my microarray discovery approach. Gastrin is a known gastrointestinal
hormone that is secreted by G-cells in the antrum of the stomach and is involved in gastric acid
secretion and mucosal cell growth (Rehfeld, Zhu et al. 2008). The human gastrin gene consists of
three exons, two of which contain protein coding regions (exon 2 and 3). The initial product of
mRNA translation is the 101 aa precursor protein known as preprogastrin. In the stomach,
preprogastrin is extensively modified by several processing enzymes, including tyrosylprotein
sulfotransferases, carboxypeptidase E, prohormone convertases, and the amidating enzyme
complex peptidylglycine alpha-amidating monooxygenase (PAM) (Varro and Ardill 2003). This
canonical processing pathway leads to the production of amidated gastrin-17 (G17) and gastrin-
34 (G34), which are respectively the 17 aa and 34 aa bioactive forms of gastrin in the
gastrointestinal system. The bioactive gastrins mediate their actions through the
gastrin/cholecystokinin B receptor (CCKBR) in the stomach. Gastrointestinal expression of
gastrin is regulated by gastrin-releasing peptide (GRP) and its cognate receptor gastrin-releasing
peptide receptor (GRPR). My identification of gastrin in the receptive phase of the endometrium
is a novel finding, and to further explore gastrin’s production and role in the endometrium, I
included probes to all three exons of gastrin, the canonical gastrin processing enzymes, and
related genes in the NanoString assay. Of note, none of the gastrin-related genes had been
significant on the microarray screen. The final target gene list [Table 2] was submitted to
NanoString for probe design.
52
Table 2: List of genes selected for NanoString validation
245 genes with > 4-fold change in LH+7 vs. LH+2
Entrez Gene
Symbol Gene Name
Fold change
on microarray
(LH+7/LH+2)
GAST* Gastrin 28.753675
MT1H metallothionein 1H 21.494686
C4BPA complement component 4 binding protein, alpha 20.304976
HAP1 huntingtin-associated protein 1 (HAP1) 18.934916
FKBP1A-
SDCBP2, syndecan binding protein (syntenin) 2 17.02225
COMP cartilage oligomeric matrix protein 16.096504
MFSD4 major facilitator superfamily domain containing 4 16.034422
TRPM8 transient receptor potential cation channel, subfamily M, member 8 15.9251795
ART3 ADP-ribosyltransferase 3 -14.424864
CALB2 calbindin 2 -14.141429
MT1M metallothionein 1M 13.046927
SCGB2A2 secretoglobin, family 2A, member 2 12.561066
ERMN ermin, ERM-like protein -12.479329
PENK Proenkephalin -12.075802
SLC15A1 solute carrier family 15 (oligopeptide transporter), member 1 11.937685
S100P S100 calcium binding protein P 11.735441
PAEP progestagen-associated endometrial protein (PAEP) 11.24312
ABCC3 ATP-binding cassette, sub-family C, member 3 10.850482
KIF20A kinesin family member 20A -10.809479
RIMKLB family with sequence similarity 80, member B 10.278368
GPR110 G protein-coupled receptor 110 10.262838
MMP9 matrix metallopeptidase 9 9.698598
TEX101 testis expressed 101 9.640955
53
C1orf116 chromosome 1 open reading frame 116 9.53697
RRM2 ribonucleotide reductase M2 polypeptide -9.18264
SPINK1 serine peptidase inhibitor, Kazal type 1 8.926406
NNMT nicotinamide N-methyltransferase 8.8751955
SLC1A1 solute carrier family 1, member 1 8.708015
ANG ribonuclease, RNase A family, 4 8.693736
C2CD4A C2 calcium-dependent domain containing 4A 8.518349
PLA2G2A phospholipase A2, group IIA 8.461064
ARG2 arginase, type II (ARG2) 8.347169
CYP3A5 cytochrome P450, family 3, subfamily A, polypeptide 5 8.199721
FAM64A family with sequence similarity 64, member A -8.197979
CXCL14 chemokine (C-X-C motif) ligand 14 8.150801
KIF4A kinesin family member 4A -8.095105
CDA cytidine deaminase 8.023994
S100A1 S100 calcium binding protein A1 7.972876
CKAP2L cytoskeleton associated protein 2-like -7.9627595
SLC37A2 PREDICTED: Homo sapiens hypothetical protein LOC731486 7.8954663
BC069212 G-2 and S-phase expressed 1 (GTSE1) -7.7812605
ANO3 anoctamin 3 -7.7677684
KRT80 keratin 80 7.7665067
MKI67 antigen identified by monoclonal antibody Ki-67 -7.728911
COL17A1 collagen, type XVII, alpha 1 7.6887474
DLGAP5 discs, large (Drosophila) homolog-associated protein 5 -7.685664
PKMYT1 protein kinase, membrane associated tyrosine/threonine 1 -7.630767
RNF39 ring finger protein 39 7.6107535
C12orf34 chromosome 12 open reading frame 34 -7.5964627
54
PBK PDZ binding kinase -7.596312
PTPRR protein tyrosine phosphatase, receptor type, R 7.5629663
GPX2 glutathione peroxidase 2 7.5268064
EDN3 endothelin 3 -7.437358
C20orf103 chromosome 20 open reading frame 103 -7.369343
MFAP5 microfibrillar associated protein 5 7.325357
HIST1H1A histone 1, H1a -7.3049774
LIF leukemia inhibitory factor (cholinergic differentiation factor) 7.2122912
ANKRD55 ankyrin repeat domain 55 7.176732
FAM83D family with sequence similarity 83, member D -7.1476574
PRB3 proline-rich protein BstNI subfamily 3 7.108714
IRX3 iroquois homeobox 3 7.09259
TRH thyrotropin-releasing hormone -7.053153
APOBEC3B apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like
3B -7.043939
TMEM140 transmembrane protein 140 7.0175085
HPSE Heparanase 7.0089493
UHRF1 ubiquitin-like with PHD and ring finger domains 1 -6.9316854
IL8 interleukin 8 6.8617697
ATOH8 atonal homolog 8 6.7713466
NNAT Neuronatin -6.7668653
IDO2 indoleamine 2,3-dioxygenase 2 6.7513657
ANG angiogenin, ribonuclease, RNase A family, 5 6.704413
IL1RN interleukin 1 receptor antagonist 6.6747627
PTTG3P pituitary tumor-transforming 3 (pseudogene) -6.6717587
CDC20 cell division cycle 20 homolog -6.65181
55
KIFC1 kinesin family member C1 -6.5940156
NCAPG non-SMC condensin I complex, subunit G -6.5759444
HJURP Holliday junction recognition protein -6.560167
KLK4 kallikrein-related peptidase 4 -6.506457
BCMO1 beta-carotene 15,15'-monooxygenase 1 6.4839597
CDCA3 cell division cycle associated 3 -6.471794
MARCO macrophage receptor with collagenous structure (MARCO) 6.375495
GALNTL2 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-
acetylgalactosaminyltransferase-like 2 6.2830462
SFRP4 secreted frizzled-related protein 4 -6.2824078
IRX5 iroquois homeobox 5 6.2741904
PROL1 proline rich, lacrimal 1 6.2442594
PLA1A phospholipase A1 member A -6.242309
KCNG1 potassium voltage-gated channel, subfamily G, member 1 -6.2004895
OLFM1 olfactomedin 1 (OLFM1), transcript variant 1 -6.159311
UBE2C ubiquitin-conjugating enzyme E2C -6.1345243
C1orf64 chromosome 1 open reading frame 64 -6.1103406
CEP55 centrosomal protein 55kDa -6.0806646
KRT23 keratin 23 (histone deacetylase inducible) 6.061944
EMILIN3 elastin microfibril interfacer 3 -5.9881186
ATP12A ATPase, H+/K+ transporting, nongastric, alpha polypeptide 5.9748807
CDC45 CDC45 cell division cycle 45-like -5.934683
PHACTR3 phosphatase and actin regulator 3 (PHACTR3), transcript variant 3 -5.9029403
CDCA2 cell division cycle associated 2 -5.8533797
KIF11 kinesin family member 11 -5.846372
TPX2 TPX2, microtubule-associated, homolog -5.820074
56
CCNE2 cyclin E2 (CCNE2), transcript variant 2 -5.7963657
TSPAN8 tetraspanin 8 5.711741
CDC25C cell division cycle 25 homolog C -5.7068057
C2CD4B PREDICTED: Homo sapiens nuclear localized factor 2 5.702463
C9orf140 chromosome 9 open reading frame 140 -5.664523
SGOL1 shugoshin-like 1 -5.6633263
LINGO4 leucine rich repeat and Ig domain containing 4 5.6257677
DNER delta/notch-like EGF repeat containing 5.603158
HABP2 hyaluronan binding protein 2 5.583786
CENPM centromere protein M -5.5754337
SPHK1 sphingosine kinase 1 5.544253
DLG2 discs, large homolog 2, chapsyn-110 -5.532746
GLI1 glioma-associated oncogene homolog 1 (zinc finger protein) -5.5215254
OIP5 Opa interacting protein 5 -5.481177
GDF15 growth differentiation factor 15 5.480879
PTH2R parathyroid hormone 2 receptor -5.4506636
C9orf100 chromosome 9 open reading frame 100 -5.4306107
LRP4 low density lipoprotein receptor-related protein 4 -5.404816
GINS2 GINS complex subunit 2 (Psf2 homolog) -5.35435
TMEM45B transmembrane protein 45B 5.3526692
TYMP thymidine phosphorylase 5.349547
EFNA1 ephrin-A1 5.344927
TMEM154 transmembrane protein 154 5.3395333
SLC47A1 solute carrier family 47, member 1 -5.328014
SLC26A4 solute carrier family 26, member 4 -5.3269844
C16orf59 chromosome 16 open reading frame 59 -5.3134747
57
FOXM1 forkhead box M1 (FOXM1) -5.306359
GBP2 guanylate binding protein 2, interferon-inducible 5.2898827
FGB fibrinogen beta chain 5.282313
CENPE centromere protein E, 312kDa -5.2430067
SLCO4A1 solute carrier organic anion transporter family, member 4A1 5.228655
LAMA1 laminin, alpha 1 -5.2160726
KIF15 kinesin family member 15 -5.1784863
CENPA centromere protein A -5.160643
C15orf62 chromosome 15 open reading frame 62 5.1599684
PSRC1 proline/serine-rich coiled-coil 1 -5.148875
HMMR hyaluronan-mediated motility receptor -5.136003
INHBB inhibin, beta B (activin AB beta polypeptide) 5.1295366
TMEM119 transmembrane protein 119 -5.102878
FXYD3 FXYD domain containing ion transport regulator 3 5.0909114
PPP2R1B protein phosphatase 2 (formerly 2A), regulatory subunit A, beta
isoform 5.080935
SPC25 SPC25, NDC80 kinetochore complex component, homolog -5.0612283
TCN1 transcobalamin I (vitamin B12 binding protein, R binder family) 5.0350447
NCCRP1 non-specific cytotoxic cell receptor protein 1 homolog (zebrafish) 5.0348268
NOS3 nitric oxide synthase 3 (endothelial cell) 5.0291467
HAL histidine ammonia-lyase 5.0134335
IER3 immediate early response 3 5.008753
NLGN3 neuroligin 3 -4.9990225
POSTN periostin, osteoblast specific factor -4.989173
C9orf71 chromosome 9 open reading frame 71 4.961908
PPP1R1B protein phosphatase 1, regulatory (inhibitor) subunit 1B (dopamine
and cAMP regulated phosphoprotein, DARPP-32) -4.9493384
58
AURKB aurora kinase B -4.939391
LAMB3 laminin, beta 3 4.9287744
B3GNT3 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 3 4.9224806
SLC16A10 solute carrier family 16, member 10 (aromatic amino acid
transporter) 4.9019904
CDKN2B cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) 4.8898644
RBP4 retinol binding protein 4, plasma 4.8870144
EFNB3 ephrin-B3 -4.870514
VWC2 von Willebrand factor C domain containing 2 -4.870358
HIST1H3B histone cluster 1, H3b -4.85691
ASF1B ASF1 anti-silencing function 1 homolog B (S. cerevisiae) -4.8542657
THBD Thrombomodulin 4.844184
FHDC1 FH2 domain containing 1 4.83962
CSF2RA colony stimulating factor 2 receptor, alpha, low-affinity
(granulocyte-macrophage) 4.8346057
IL1B interleukin 1, beta 4.828273
CENPF centromere protein F, 350/400ka (mitosin) -4.828179
PAK7 p21(CDKN1A)-activated kinase 7 -4.79186
SIK1 salt-inducible kinase 1 4.759234
PANX2 pannexin 2 4.735119
LRRC17 leucine rich repeat containing 17 -4.7245154
TMSB15A thymosin beta 15a -4.72413
SLC11A1 solute carrier family 11 (proton-coupled divalent metal ion
transporters), member 1 4.6798015
NDC80 NDC80 homolog, kinetochore complex component (S. cerevisiae) -4.670804
KIF23 kinesin family member 23 (KIF23) -4.6635942
ISLR immunoglobulin superfamily containing leucine-rich repeat -4.626056
59
DTL denticleless homolog (Drosophila) -4.580548
OVGP1 oviductal glycoprotein 1, 120kDa -4.579162
ZNF367 zinc finger protein 367 -4.57436
CDT1 chromatin licensing and DNA replication factor 1 -4.56902
KSR1 kinase suppressor of ras 1 4.5514007
C10orf10 chromosome 10 open reading frame 10 4.54477
KIAA0101 KIAA0101 -4.5424848
CD68 CD68 molecule (CD68) 4.539757
APOBEC3A apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like
3A 4.5302052
GRAMD1C GRAM domain containing 1C 4.5268345
GDF5 growth differentiation factor 5 -4.516892
TMED6 transmembrane emp24 protein transport domain containing 6 -4.5101123
FAP fibroblast activation protein, alpha -4.504968
KCNIP1 Kv channel interacting protein 1 -4.497031
CDKN3 cyclin-dependent kinase inhibitor 3 (CDK2-associated dual
specificity phosphatase) -4.491115
OLR1 oxidized low density lipoprotein (lectin-like) receptor 1 4.484317
HBEGF heparin-binding EGF-like growth factor (HBEGF) 4.4748936
GPR64 G protein-coupled receptor 64 (GPR64) -4.4646864
CBLN1 cerebellin 1 precursor -4.439539
TNFSF10 tumor necrosis factor (ligand) superfamily, member 10 4.4188414
C1orf133 chromosome 1 open reading frame 133, non-coding RNA 4.4044914
EXO1 exonuclease 1 -4.3930244
C15orf48 chromosome 15 open reading frame 48 4.3796554
AK022746 hypothetical protein FLJ12684 -4.3720446
GREM2 gremlin 2, cysteine knot superfamily, homolog (Xenopus laevis) -4.360801
60
DSC2 desmocollin 2 (DSC2) 4.3449693
HIST1H1B histone cluster 1, H1b -4.341674
SLC30A2 solute carrier family 30 (zinc transporter), member 2 4.327846
PLA2G16 phospholipase A2, group XVI 4.314326
MFAP2 microfibrillar-associated protein 2 -4.303573
GLT8D2 glycosyltransferase 8 domain containing 2 -4.2869987
FAM101B family with sequence similarity 101, member B -4.271061
SPP1 secreted phosphoprotein 1, osteopontin (OPN) 4.2577085
CCNA2 cyclin A2 -4.247754
TK1 thymidine kinase 1, soluble -4.2467895
FAM124B family with sequence similarity 124B -4.241369
CCL3L3 chemokine (C-C motif) ligand 3-like 3 4.2278295
ELK4 ELK4, ETS-domain protein (SRF accessory protein 1) 4.224526
TUBA4A tubulin, alpha 4a 4.2241554
SFRP1 secreted frizzled-related protein 1 -4.2214627
ANKRD22 ankyrin repeat domain 22 4.195845
AOX1 aldehyde oxidase 1 4.1843967
B3GNT8 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 8 4.1808686
IL4I1 interleukin 4 induced 1 (IL4I1) 4.1806884
FAM111B family with sequence similarity 111, member B -4.17777
ACCN1 amiloride-sensitive cation channel 1, neuronal -4.177352
AK075287 SPC24, NDC80 kinetochore complex component, homolog (S.
cerevisiae) -4.174586
AHNAK2 AHNAK nucleoprotein 2 4.1705294
SOD2 superoxide dismutase 2, mitochondrial (SOD2), nuclear gene
encoding mitochondrial protein 4.158651
HIST1H2BM histone cluster 1, H2bm -4.156219
61
SDK2 sidekick homolog 2 (chicken) -4.148457
CRISPLD1 cysteine-rich secretory protein LCCL domain containing 1 -4.1452107
TACC2 transforming, acidic coiled-coil containing protein 2 4.1287746
TMEM63C transmembrane protein 63C 4.1267567
CASC5 cancer susceptibility candidate 5 -4.123829
GALNT12 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-
acetylgalactosaminyltransferase 12 (GalNAc-T12) -4.122349
TNFAIP6 tumor necrosis factor, alpha-induced protein 6 4.1109304
WDR76 WD repeat domain 76 -4.105296
TOP2A topoisomerase (DNA) II alpha 170kDa -4.0994887
ATG9B ATG9 autophagy related 9 homolog B (S. cerevisiae) 4.0973053
SERPINB8 serpin peptidase inhibitor, clade B (ovalbumin), member 8 4.0807185
MEGF6 multiple EGF-like-domains 6 4.079654
MT1G metallothionein 1G 4.048484
PGR progesterone receptor (PGR) -4.0428977
GRB7 growth factor receptor-bound protein 7 4.0407047
SLC5A1 solute carrier family 5 (sodium/glucose cotransporter), member 1 4.0376654
SLC7A2 solute carrier family 7 (cationic amino acid transporter, y+ system),
member 2 4.0372887
DYNLT3 dynein, light chain, Tctex-type 3 4.0335155
ANGPTL4 angiopoietin-like 4 (ANGPTL4) 4.021787
LRRC3B leucine rich repeat containing 3B -4.0089254
CCNB2 cyclin B2 -4.008511
42 genes with 3 to 4-fold change in LH+7 vs. LH+2
(on/off expression)
Entrez Gene
Symbol Gene Name
Fold change
on array
(LH+7/LH+2)
FGA fibrinogen alpha chain, transcript variant alpha -3.9709175
62
E2F8 E2F transcription factor 8 3.8893256
CEACAM6 carcinoembryonic antigen-related cell adhesion molecule 6 (non-
specific cross reacting antigen) -3.7183986
FETUB fetuin B (FETUB) -3.699626
ESCO2 establishment of cohesion 1 homolog 2 (S. cerevisiae) 3.6936746
MCM10 minichromosome maintenance complex component 10 3.673121
FXYD2 FXYD domain containing ion transport regulator 2 -3.6537719
TROAP trophinin associated protein (tastin) 3.632155
CCL13 chemokine (C-C motif) ligand 13 -3.6286962
OSCAR osteoclast associated, immunoglobulin-like receptor -3.5839312
PLEKHN1 pleckstrin homology domain containing, family N member 1 -3.5762053
CLEC5A C-type lectin domain family 5, member A -3.5730486
WFDC8 WAP four-disulfide core domain 8 3.5393803
CYP24A1 cytochrome P450, family 24, subfamily A, polypeptide 1 -3.5200493
ANO4 transmembrane protein 16D 3.4780014
ENTPD8 ectonucleoside triphosphate diphosphohydrolase 8 -3.4595578
FABP3 fatty acid binding protein 3, muscle and heart (mammary-derived
growth inhibitor) -3.4248986
G6PC glucose-6-phosphatase, catalytic subunit 3.3653238
BCL2A1 BCL2-related protein A1 -3.3652854
LILRA3 leukocyte immunoglobulin-like receptor, subfamily A (without TM
domain), member 3 -3.3436053
KLKP1 kallikrein pseudogene 1, non-coding RNA. 3.331513
S100A3 S100 calcium binding protein A3 -3.2568588
ST8SIA2 ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 2 3.2562099
MMP10 matrix metallopeptidase 10 (stromelysin 2) -3.2504537
FBN3 fibrillin 3 3.2463682
63
CENPI centromere protein I 3.241103
CDHR1 protocadherin 21 -3.2217069
SLAMF8 SLAM family member 8 -3.2066483
KNG1 kininogen 1 -3.190973
ZCCHC12 zinc finger, CCHC domain containing 12 3.1763084
MT1A metallothionein 1A -3.120497
CDK1 cell division cycle 2, G1 to S and G2 to M (CDC2) 3.1142464
FBXL16 F-box and leucine-rich repeat protein 16 -3.1134303
WISP3 WNT1 inducible signaling pathway protein 3 -3.1070502
SKA1 spindle and kinetochore associated complex subunit 1 3.0991979
SKA3 chromosome 13 open reading frame 3 3.0795465
CSF2RB Homo sapiens colony stimulating factor 2 receptor, beta, low-
affinity (granulocyte-macrophage) (CSF2RB), mRNA. -3.057567
LBP lipopolysaccharide binding protein -3.047222
PCSK6 proprotein convertase subtilisin/kexin type 6 3.044487
SLC2A5 solute carrier family 2 (facilitated glucose/fructose transporter),
member 5 -3.0433705
VNN3 vanin 3 (VNN3) -3.026543
FXYD4 FXYD domain containing ion transport regulator 4 -3.0094652
Gastrin-related genes
Entrez Gene
Symbol Gene Name
Fold change
on array
(LH+7/LH+2)
*GAST_ex1-2 gastrin exon 1-2 N/A
*GAST_ex2 gastrin exon 2 N/A
*GAST_ex3 gastrin exon 3 N/A
CCKBR cholecystokinin B receptor N/A
GRP gastrin-releasing peptide N/A
64
GRPR gastrin-releasing peptide receptor N/A
PAM peptidylglycine alpha-amidating monooxygenase N/A
PCSK1 proprotein convertase subtilisin/kexin type 1 N/A
CPE carboxypeptidase E N/A
TPST1 tyrosylprotein sulfotransferase 1 N/A
TPST2 tyrosylprotein sulfotransferase 2 N/A
Housekeeping genes
Entrez Gene
Symbol Gene Name
Fold change
on array
(LH+7/LH+2)
ACTB actin, beta N/A
GAPDH glyceraldehyde-3-phosphate dehydrogenase N/A
LDHA lactate dehydrogenase A N/A
RPL19 ribosomal protein L19 N/A
TUBA1B tubulin, alpha 1b N/A
65
2.7.1.2 NanoString probe hybridization, immobilization and detection
100ng of total RNA from each study sample was combined with 10µL of the reporter probe
CodeSet and 10µL of the capture probe CodeSet designed by NanoString according to the
specifications discussed above [Table 2]. The hybridization reaction proceeded at 65˚C for 12
hours. Post-hybridization processing was performed as per the manufacturer’s instructions to
remove excess unhybridized reporter and capture probes by affinity purification. The purified
hybridized complexes were then eluted and immobilized on the NanoString nCounter cartridge.
Data collection was performed with the automated nCounter Digital Analyzer, which processes
digital images and tabulates the colour-coded tags in a comma separated value (CSV) format.
2.7.1.3 NanoString data analysis
NanoString reporter code count (RCC) files from UFA LH+2 and UFA LH+7 samples were
imported into the RCC collector template (v1.6.0) for further analysis in Microsoft Excel. All
counts including internal negative controls and housekeeping genes were first normalized to a
scaling factor using each sample’s average of six internal positive control counts compared to the
overall average positive control count. This normalization accounted for variability in sample
preparation and detection. The overall average of the geometric means for the five housekeeping
genes was then used to calculate a further housekeeping normalization factor for each sample to
account for RNA input differences. To determine if a gene could be counted as expressed or
“present”, the average plus 2 times the standard deviation of the negative control counts was
considered “background”. Overall, a gene was classified as being present in LH+2 or LH+7 if it
66
was above background in at least 50% of the samples for either group. To calculate significance,
a Student’s t-test assuming unequal variance was used to compare LH+2 and LH+7 samples. In
order to visualize these results, data on all target genes (not including housekeeping and gastrin-
related genes) were imported into R (v2.13.0). P-values were again calculated along with the
fold-change between LH+2 and LH+7, and a volcano plot was generated to demonstrate the level
of significance and directionality of fold-change for each gene. As part of the validation process,
the fold-change of statistically significant probes in the NanoString dataset was compared to the
original microarray data.
2.7.2 Immunohistochemistry
The expression of gastrin is up-regulated in the receptive phase of the endometrium, according to
the microarray analysis and NanoString validation. I performed a validation of this at the protein
level by immunohistochemistry, using three different gastrin-specific primary antibodies:
Unconjugated rabbit polyclonal anti-gastrin antibody, ab53085 (Abcam, Cambridge,
MA). Immunogen: Synthetic peptide derived from human gastrin
Unconjugated rabbit polyclonal anti-gastrin antibody, ab2716 (courtesy of Dr. Jens
Rehfeld, University of Copenhagen, Denmark). Immunogen: Alpha-amidated C-terminus
of G17
Unconjugated rabbit polyclonal anti-gastrin antibody, ab94023 (courtesy of Dr. Jens
Rehfeld, University of Copenhagen, Denmark). Immunogen: N-terminus of human
progastrin
67
Endometrial biopsies sampled on LH+7 were fixed in 10% NBF overnight at 4C, washed with
PBS (45 min) and then placed in 70% ethanol at 4C. For paraffin embedding, the tissues were
first dehydrated by placing them in increasing concentrations of ethanol: 80% for 1 hour, 95%
for hour, 100% for 2 hours. The biopsies were placed in toluene for 2 hours followed by
immersion in paraffin wax for 2 hours after which they were placed in molds and embedded in
paraffin wax. Serial sections (5 µm thick) of paraffin embedded biopsies were cut using a
microtome and placed on Superfrost slides (Fisher Scientific, Hampton, NH). The sections were
allowed to dry onto the slide overnight.
Paraffin-embedded stomach antrum biopsy tissue was also obtained from the Pathology
department for use as a positive control for gastrin staining. The microarray and NanoString
analyses suggested that gastrin is minimally expressed in the LH+2 endometrium. Unfortunately,
I was unable to obtain endometrial biopsy tissue from LH+2 or the early-luteal phase to use as a
negative control for gastrin expression. However, archived formalin-fixed paraffin-embedded
(FFPE) endometrial tissue from hysterectomies performed in the proliferative phase was
obtained for comparison. Stomach antrum biopsies and proliferative endometrial tissue were
serially sectioned (5 µm thick) and placed on Superfrost slides. In preparation for staining, all
sections were de-paraffinized with xylene and rehydrated in decreasing concentrations of
ethanol: 100% for 5 min, 95% for 5 min, 80% for 5 min, 70% for 5 min. The slides were then
rinsed in ddH20 and placed in PBS.
For immunostaining, the slides were placed in PBS/0.3% TritonX-100 for 10 min and then
washed in PBS (35 min). The slides were incubated for 20 min in methanol/0.3% H2O2. Next,
the sections were washed in PBS (35 min) and incubated with 10% normal goat serum (NGS)
in PBS for 1 hour. Slides were drained, being careful not to allow the sections to completely dry
68
and primary anti-gastrin antibody (1:2000 in PBS + 1% NGS) was added to all the sections
except the secondary antibody-only controls. The slides were placed in a humidified chamber
overnight at 4C. These were then washed with PBS (35 min). The sections were incubated
with a biotinylated goat-anti-rabbit secondary IgG antibody (1:200 in PBS + 1% NGS) for 1
hour, washed with PBS (35 mins) and incubated with a streptavidin-peroxidase complex (1:400
in PBS) for 30 mins at room temperature (RT). The slides were washed (35 min) and then
incubated with diaminobenzidine (DAB)/H2O2 0.03% for 10 min at RT. Next, they were dipped
in ddH2O, placed in hematoxylin (Gill’s formula, Vector Labs, Burlingame, CA) for 2 min, and
rinsed in H2O for 2 min. The sections were dehydrated in increasing concentrations of ethanol:
70% for 5 min, 95% for 10 min, 100% for 10 min and xylene for 10 min. The slides were then
coverslipped with Permount. Pictures were taken with an Olympus BX61 upright, motorized
microscope with Olympus DP72 digital colour camera run by CellSens Standard proprietary
acquisition software (Olympus Canada, Markham, ON).
For immunofluorescence, the slides were blocked in 10% NGS in PBS for 3 hours and rinsed
with PBS (3×5 min). Primary anti-gastrin antibody (1:2000 in PBS + 1% NGS) was added and
slides incubated at 4°C overnight. The slides were washed with PBS (5×5 min) and then
incubated with Alexa Fluor 488 goat anti-rabbit IgG highly cross adsorbed secondary antibody
(1:500 in PBS + 1% NGS) for 1 hour at RT. Next the slides were washed in PBS (5×10 min) and
then placed in a DAPI solution (2g/ml) for 2 minutes. The slides were washed again in PBS and
coverslip mounted using a 10% 1,4-diazabicyclo[2.2.2]octane (DABCO) solution in 50%
glycerol/PBS. Slides were examined on a Zeiss Axioplan Photomicroscope (Zeiss, Oberkochen,
Germany). Pictures were taken on a Quorum (Guelph, ON) WaveFX Spinning Disc Confocal
System with optimized Yokogawa CSU X1, Hamamatsu EM-CCD ImagEM digital camera
69
(C9100-13), and Leica DMI6000B inverted research grade motorized microscope run by
Volocity 5.2.2 Acquisition software (Improvision/PerkinElmer, Waltham, MA).
2.8 MULTIPLEX CYTOKINE IMMUNOASSAY
Several cytokines have been reported to have cycle-specific expression in the endometrium and
have been implicated in the implantation process (Dimitriadis, White et al. 2005). The Milliplex
39-plex Human Cytokine/Chemokine Assay (Millipore, Billerica, MA) is a bead-based multiplex
cytokine immunoassay that measures the concentration of 39 different cytokines in a given
biological sample. I analyzed archived UFA LH+2 and UFA LH+7 supernatant samples with this
assay to compare the secreted cytokine profile of the pre-receptive phase endometrium to the
receptive phase. 10 samples from each group were analyzed as we only had access to 40 assay
reactions (20 paired samples run in duplicate). Total protein content of each UFA supernatant
sample was measured for normalization purposes using a Bicinchoninic Acid (BCA) Protein
Assay Kit (Thermo Scientific, Wilmington, DE), as per the manufacturer’s instructions. 25µL of
each supernatant sample was then run in duplicate on the cytokine assay, as per the
manufacturer’s instructions. The concentrations of 39 different cytokines were calculated for
each sample in pg/µg of total protein. A paired two-tailed t-test was performed for each cytokine,
to determine which cytokines were differentially expressed (p < 0.05) between the two groups.
70
Chapter 3 RESULTS
3.1 DEVELOPMENT OF UFA TECHNIQUE
My technique of UFA was developed with a preliminary cohort of 12 women. Several different
permutations of this technique have been described in the literature, for the purpose of obtaining
endometrial secretions for protein analysis. Previous studies have used a variety of insemination
or embryo transfer catheters to obtain endometrial secretions, either by lavage or straight
aspiration. As insemination catheters have a wider lumen than embryo transfer catheters, some
authors have noted that using an insemination catheter increases the volume of secretions
obtained (van der Gaast, Beier-Hellwig et al. 2003). In contrast to previous work, my technique
was developed specifically for the isolation of endometrial cells for gene expression profiling.
My goal was to select a catheter and method which would allow me to obtain as many viable
endometrial cells as possible, to maximize the amount of total RNA that could be extracted for
subsequent analysis. Cytological smears of UFA samples collected from the preliminary cohort
were reviewed with a gynaecologic pathologist to determine cellular composition.
For the first 7 patients, both straight aspiration and lavage with 2-10cc of normal saline were
trialed. With lavage, the majority of the saline injected would flush through but not be
retrievable, and the resultant samples contained less cellular material than the aspirations. We
subsequently abandoned lavage as a technique and focused on aspiration of endometrial
secretions. Each patient was sampled with one catheter, and in total, four different types of
catheters were trialed, including the Tomcat insemination catheter, the Sydney embryo transfer
catheter (Cook Medical, Bloomington, IN), the Coaxial insemination catheter (Cook Medical,
Bloomington, IN), and a 20-gauge angiocatheter (BD Medical, Franklin Lakes, NJ).
71
The characteristics of the different catheters are summarized in Table 3. The Sydney catheter and
the Coaxial catheter are double lumen catheters that have an outer catheter to shield the sample
from contamination by cervical mucus. However, their flimsy inner catheters made obtaining an
adequate sample of endometrial secretions difficult. Even though the Coaxial catheter has a wide
lumen, it was not rigid enough for this purpose. On cytological assessment, both these catheters
yielded scant numbers of endometrial cells. Similarly, the 20-gauge angiocatheter was too flimsy
to insert easily into the endometrial cavity, and scant numbers of endometrial cells were
obtained. Compared to the other catheters, the Tomcat catheter was more rigid and enabled more
endometrial cells to be obtained. Cervical mucus contamination was avoided by wiping the
outside of the Tomcat catheter after withdrawing it from the endometrial cavity. As it was the
most high-yield approach, aspiration with a Tomcat catheter was selected as the best technique
for UFA, and utilized for the remainder of the study.
72
Table 3: Comparison of catheters trialed in UFA development
Type of catheter
Single or
double
lumen
Lumen
diameter
(Fr)
Advantages Disadvantages
Tomcat IUI catheter Single 3.5
Rigid yet flexible;
Economical;
Good yield of
endometrial cells
No outer sheath to
shield inner catheter
from cervical canal
Cook® Sydney
embryo transfer
catheter
Double 2.8
Outer sheath shields
inner catheter from
cervical canal
Narrow, flimsy inner
catheter;
Poor yield of
endometrial cells
Cook® coaxial
insemination catheter Double 4.4
Outer sheath shields
inner catheter from
cervical canal
Flimsy inner catheter;
Poor yield of
endometrial cells
20-gauge
angiocatheter Single 3.3 Economical
Flimsy catheter;
Poor yield of
endometrial cells
73
3.2 CYTOLOGICAL ANALYSIS OF UFA SAMPLES
Cytological smears of representative UFA samples were reviewed with a gynaecologic
pathologist. There were 40,000 – 60,000 viable cells per UFA sample. Based on morphological
assessment, the predominant cell type was found to be endometrial epithelial cells [Figure 3]. As
histological architecture is not captured with cytological smears of aspirated cells, it could not be
determined if these epithelial cells were luminal or glandular in origin. Leukocytes comprised <
2 – 5% of UFA samples.
Figure 3: Representative cytological smear of UFA sample
74
3.3 MICROARRAY ANALYSIS
3.3.1 Unsupervised hierarchical clustering
To compare the gene expression profiles of all samples, an unsupervised classification with
hierarchical clustering was performed, which revealed a distinct self-segregation of samples by
gene expression into two major branches [Figure 4]. The first branch consisted almost
exclusively of UFA LH+2 samples, whereas the second branch contained only UFA LH+7 and
Biopsy LH+7 samples. This clustering indicates that the phase of sampling is the predominant
variable influencing gene expression, rather than individual patient differences or method of
sampling. This suggests that gene expression signatures can distinguish the receptive phase from
the pre-receptive phase. Within the second branch, three sub-clusters of LH+7 samples emerged:
two containing mostly Biopsy samples with some interspersed UFA samples, and one containing
UFA samples only. Although this suggests that there are key signatures defining samples taken
by UFA or Biopsy, the number of genes shared in the LH+7 group that are different from the
LH+2 group is large enough to allow for a wide array of potential biomarkers that may
distinguish the receptive from the pre-receptive phase. The output data are displayed graphically
as a heatmap, based on the normalized intensity values of each gene, and are represented with
dendrograms that show the clustering relationships between samples [Figure 4].
75
Figure 4: Heatmap representation of unsupervised hierarchical clustering of UFA LH+2,
UFA LH+7 and Biopsy LH+7 samples
76
3.3.2 Differential gene expression analysis of microarray data
An unpaired t-test with a Benjamini-Hochberg multiple testing correction FDR of p < 0.05
identified 2049 probe sets (representing 1748 distinct genes) that were differentially expressed in
UFA LH+2 vs. UFA LH+7, using a pre-defined Δ threshold of > 2-fold. Of the 1748 genes, 839
were up-regulated and 909 were down-regulated in the receptive phase. To obtain a more
manageable and prioritized gene list, I applied a more stringent fold-change criterion of > 4-fold,
which yielded 288 probe sets (representing 245 distinct genes) that were robustly differentially
expressed in the two groups. Of the 245 genes, which were ultimately included in the NanoString
validation, 126 were up-regulated and 119 were down-regulated in the receptive phase [Table 2].
Analysis of the probe sets found to be > 2-fold differentially expressed in UFA LH+2 vs. UFA
LH+7 using the GO Database (www.geneontology.org) and GeneSpring revealed interesting
results. Of the genes up-regulated in LH+7, the main ontological categories were membrane
component (27%), signaling (13%), cellular component (8%), and immunity and inflammation
(7%) [Figure 5a]. Of the genes down-regulated in LH+7 (or up-regulated in LH+2), the main
ontological categories were cell cycle/mitosis (26%), cellular/organelle component (20%),
chromosome/chromatin (13%), and cytoskeleton (7%) [Figure 5b].
77
Figure 5: Pie chart representing GO breakdown of genes
a) GO breakdown of genes up-regulated in UFA LH+7 (> 2-fold) and b) GO breakdown of genes
down-regulated in UFA LH+7 (> 2-fold).
8%
27%
4%
7% 13%
4%
7%
30%
GO Breakdown of Up-regulated Genes in LH+7
cellular component
membrane component
protein binding
biological regulation
signaling
extracellular
immunity/inflammatory
other
6%
20%
7%
26%
13%
28%
GO Breakdown of Down-regulated genes in LH+7
extracellular region
cellular/organelle component
cytoskeleton
cell cycle/mitosis
chromosome/chromatin
other
a
b
78
The unsupervised hierarchical clustering showed UFA LH+7 and Biopsy LH+7 samples
clustering together and self-segregating away from the UFA LH+2 samples. However, there still
appeared to be some differences in the gene expression signatures of UFA LH+7 and Biopsy
LH+7 leading to sub-clustering. A one-way ANOVA with a Benjamini-Hochberg multiple
testing correction FDR of p < 0.05 was used to test for differential probe expression among the
three samples groups, and a post-hoc Tukey test was used to specifically determine which genes
were differentially expressed between UFA LH+2 and UFA LH+7. To explore the issue of gene
expression differences in UFA LH+7 vs. Biopsy LH+7 and to see if including Biopsy samples in
the analysis would significantly affect the gene list, I also performed a t-test (FDR < 0.05) on
pre-receptive samples (UFA LH+2) vs. all receptive samples (UFA LH+7 and Biopsy LH+7). A
similar (paired) t-test was then performed on UFA LH+2 vs. UFA LH+7, which excluded the
Biopsy samples.
The post-hoc ANOVA test comparing UFA LH+2 vs. UFA LH+7 identified 296 differentially
expressed probe sets (> 4-fold). The t-test comparing UFA LH+2 vs. all receptive phase samples
(UFA LH+7 and Biopsy LH+7) identified 213 differentially expressed probe sets (> 4-fold). The
t-test comparing UFA LH+2 vs. UFA LH+7 identified 296 differentially expressed probe sets (>
4-fold). The gene lists obtained from these three statistical tests were overlapped [Figure 6], and
a significant amount of agreement was demonstrated among the three gene lists, whether or not
Biopsy LH+7 samples were included in the analysis.
79
Figure 6: Intersection of > 4-fold gene lists
80
Finally, to determine which specific genes account for the differences between UFA LH+7 and
Biopsy LH+7 samples, a one-way ANOVA (FDR < 0.05) with a post-hoc Tukey test was used to
compare the two groups. This identified 731 probes that were differentially expressed in UFA
LH+7 vs. Biopsy LH+7, using a pre-defined Δ threshold of > 2-fold. Imposing a more stringent
fold-change criterion of > 4-fold yielded 28 probes (representing 28 distinct genes) that were
differentially expressed in the two groups. Of these 28 genes, 27 were up-regulated and 1 was
down-regulated in the UFA samples compared to the Biopsy samples [Table 4].
81
Table 4: Differentially expressed genes between UFA LH+7 and Biopsy LH+7 (> 4-fold,
FDR < 0.05)
Gene
Symbol Gene Name
Fold change
(Δ)
Δ direction
(in UFA
vs. Biopsy)
MMP9 matrix metallopeptidase 9 9.015427 up
SPRR3 small proline-rich protein 3 8.909313 up
IL8 interleukin 8 8.298117 up
C20orf114 chromosome 20 open reading frame 114 7.279231 up
ALAS2 aminolevulinate, delta-, synthase 2 7.063703 up
PI3 peptidase inhibitor 3, skin-derived 7.029795 up
HBA1 hemoglobin, alpha 1 7.021571 up
S100A8 S100 calcium binding protein A8 6.590037 up
HBG1 hemoglobin, gamma A 6.558582 up
IL1B interleukin 1, beta (IL1B) 6.52369 up
FPR1 formyl peptide receptor 1 6.377968 up
IL1RN interleukin 1 receptor antagonist 6.342028 up
AQP9 aquaporin 9 6.188223 up
CRCT1 cysteine-rich C-terminal 1 5.841094 up
HBG2 hemoglobin, gamma G 5.760729 up
MARCO macrophage receptor with collagenous structure 5.27722 up
S100A12 S100 calcium binding protein A12 4.922712 up
S100A9 S100 calcium binding protein A9 (calgranulin B) 4.690746 up
NFE2 nuclear factor (erythroid-derived 2), 45kDa 4.618503 up
SELL selectin L (SELL) 4.564728 up
CMTM2 CKLF-like MARVEL transmembrane domain containing
2 4.463345 up
CEACAM6 carcinoembryonic antigen-related cell adhesion molecule
6 (non-specific cross reacting antigen) 4.44582 up
CNTFR ciliary neurotrophic factor receptor -4.36937 down
SNORA55 small nucleolar RNA, H/ACA box 55 4.194885 up
APOBEC3A apolipoprotein B mRNA editing enzyme, catalytic
polypeptide-like 3A 4.172074 up
PROK2 prokineticin 2 4.166853 up
SCGB3A1 secretoglobin, family 3A, member 1 4.099197 up
MUC4 mucin 4, cell surface associated (MUC4) 4.091254 up
82
3.4 VALIDATION STUDIES
3.4.1 NanoString analysis
To validate the genes differentially expressed by the receptive vs. pre-receptive phase
endometrium on the microarray screen, a prioritized list of 287 genes was selected for
NanoString analysis and probes were designed accordingly. This included all 245 genes that
were > 4-fold differentially expressed and 42 genes that were 3 to 4-fold differentially expressed
with an “on/off” pattern in UFA LH+2 vs. UFA LH+7. The most robustly differentially
expressed of these 287 genes was gastrin, and 3 separate probes were designed to each of
gastrin’s exons. An additional 8 probes were designed to genes that were not significant on the
microarray screen but are known to be involved in canonical gastrin processing or signaling in
the stomach. The results of the NanoString analysis for the 287 validation genes will be
discussed separately from the “gastrin-related genes”.
3.4.1.1 Validation of differentially expressed genes
An initial survey of the NanoString results revealed that 37 genes were “absent”, or expressed
below background levels in both UFA LH+2 and UFA LH+7. This finding may represent a true
lack of expression of these genes, but may also be due to technical problems with the assay or
probe design issues. These absent genes were discarded prior to further analysis. Of the
remaining “present” target genes, the vast majority (239/250 or 95.6%) were validated by the
NanoString analysis, i.e. confirmed to be significantly differentially expressed between UFA
LH+2 and UFA LH+7 by t-test (p < 0.05). Only 11/250 (4.4%) of the target genes were not
83
validated, i.e. not differentially expressed between UFA LH+2 and UFA LH+7 by NanoString
analysis despite significance on microarray screen [Table 5].
Table 5: NanoString validation of differentially expressed genes between UFA LH+2 and
UFA LH+7
239 “present” genes, differentially expressed between LH+2 and LH+7
Gene Symbol Present(1) or
absent (0)
Counts in
LH+2/LH+7 P-VALUE P < 0.05
ABCC3 1 0.061262 0.00203 TRUE
AHNAK2 1 0.287297 0.000213 TRUE
AK022746 1 3.380889 6.26E-06 TRUE
AK075287 1 4.731499 3.36E-06 TRUE
ANG 1 0.078415 0.003835 TRUE
ANG 1 0.111937 0.00603 TRUE
ANGPTL4 1 0.37453 0.002951 TRUE
ANO4 1 2.696733 0.000716 TRUE
AOX1 1 0.069923 0.024588 TRUE
APOBEC3A 1 0.598763 0.031894 TRUE
APOBEC3B 1 2.16434 0.000506 TRUE
ARG2 1 0.057495 1.13E-05 TRUE
ASF1B 1 5.670216 3.42E-08 TRUE
ATG9B 1 0.07888 0.025851 TRUE
ATOH8 1 0.557224 0.009148 TRUE
ATP12A 1 0.094108 0.02125 TRUE
AURKB 1 5.717285 5.98E-08 TRUE
B3GNT3 1 0.074677 2.19E-06 TRUE
BC069212 1 5.265788 5.21E-06 TRUE
BCL2A1 1 0.254583 0.013048 TRUE
BCMO1 1 0.244186 0.001279 TRUE
C10orf10 1 0.039851 0.000379 TRUE
C12orf34 1 2.036277 0.006057 TRUE
C15orf48 1 0.135254 0.002805 TRUE
C15orf62 1 0.189315 0.001092 TRUE
C16orf59 1 2.755497 0.000388 TRUE
C1orf116 1 0.144675 0.003092 TRUE
C1orf133 1 0.354328 0.000953 TRUE
84
C1orf64 1 7.07108 0.006559 TRUE
C20orf103 1 7.227605 3.38E-07 TRUE
C2CD4A 1 0.009116 0.001184 TRUE
C2CD4B 1 0.055555 0.000353 TRUE
C4BPA 1 0.0225 0.000939 TRUE
C9orf100 1 2.264256 0.000273 TRUE
C9orf140 1 4.95253 1.82E-07 TRUE
C9orf71 1 0.142099 0.013458 TRUE
CALB2 1 7.778771 0.000239 TRUE
CASC5 1 3.759276 6.61E-07 TRUE
CBLN1 1 2.532622 0.024918 TRUE
CCL3L3 1 0.380972 0.039196 TRUE
CCNA2 1 7.599714 8.71E-07 TRUE
CCNB2 1 7.02658 7.66E-07 TRUE
CCNE2 1 5.981374 9.83E-06 TRUE
CD68 1 0.153069 0.005902 TRUE
CDA 1 0.143969 0.000915 TRUE
CDC20 1 6.545292 5.36E-07 TRUE
CDC25C 1 3.769771 1.52E-08 TRUE
CDC45 1 6.537058 2.28E-06 TRUE
CDCA2 1 5.775878 1.4E-07 TRUE
CDCA3 1 6.416518 3.15E-08 TRUE
CDHR1 1 0.3399 0.000691 TRUE
CDK1 1 10.50378 1.12E-06 TRUE
CDKN2B 1 0.214704 0.001732 TRUE
CDKN3 1 10.38952 7.51E-06 TRUE
CDT1 1 2.659329 4.59E-06 TRUE
CENPA 1 6.81582 3.81E-06 TRUE
CENPE 1 3.083617 2.42E-06 TRUE
CENPF 1 5.948414 1.72E-07 TRUE
CENPI 1 3.658049 1.61E-07 TRUE
CENPM 1 5.015081 5.94E-06 TRUE
CEP55 1 3.486513 2.76E-08 TRUE
CKAP2L 1 5.972617 8.15E-07 TRUE
CLEC5A 1 0.38471 0.012694 TRUE
COL17A1 1 0.073957 0.00485 TRUE
COMP 1 0.056386 0.027641 TRUE
CPE 1 2.715045 0.000206 TRUE
CRISPLD1 1 5.166439 7.66E-06 TRUE
CSF2RA 1 0.173526 0.00201 TRUE
CSF2RB 1 0.317362 0.034429 TRUE
85
CXCL14 1 0.004665 0.004206 TRUE
CYP24A1 1 0.31927 0.020788 TRUE
CYP3A5 1 0.333019 0.04108 TRUE
DLGAP5 1 4.496501 2.93E-08 TRUE
DNER 1 0.215968 0.019435 TRUE
DSC2 1 0.131359 5.53E-06 TRUE
DTL 1 4.392335 1.12E-06 TRUE
DYNLT3 1 0.119529 4.98E-05 TRUE
E2F8 1 2.584066 0.000145 TRUE
EDN3 1 12.40501 4.1E-05 TRUE
EFNA1 1 0.116763 6.58E-05 TRUE
EFNB3 1 2.487074 0.000965 TRUE
EMILIN3 1 3.900179 0.000257 TRUE
ERMN 1 11.21443 0.000114 TRUE
ESCO2 1 6.267918 3.74E-09 TRUE
EXO1 1 3.880567 1.67E-05 TRUE
FAM101B 1 6.852728 6.51E-06 TRUE
FAM111B 1 8.485139 2.29E-05 TRUE
FAM124B 1 1.91802 0.002292 TRUE
FAM64A 1 4.534886 1.54E-05 TRUE
FAM83D 1 5.575393 2.94E-06 TRUE
FAP 1 1.84998 0.001247 TRUE
FETUB 1 0.237502 0.000626 TRUE
FHDC1 1 0.211296 0.000321 TRUE
FKBP1A-
SDCBP2 1 0.395615 0.00124 TRUE
FOXM1 1 7.059268 9.22E-08 TRUE
FXYD2 1 0.25817 0.047173 TRUE
FXYD4 1 0.101706 0.014983 TRUE
GALNT12 1 6.282616 2.16E-05 TRUE
GALNTL2 1 0.270023 0.006024 TRUE
GAST_ex1-2 1 0.029839 0.017907 TRUE
GAST_ex2 1 0.164237 0.003983 TRUE
GAST_ex3 1 0.007074 0.022423 TRUE
GBP2 1 0.077707 1.5E-05 TRUE
GDF15 1 0.248786 0.000616 TRUE
GINS2 1 7.120504 5.6E-06 TRUE
GLI1 1 2.978204 0.003625 TRUE
GLT8D2 1 5.711023 2.38E-05 TRUE
GPR110 1 0.070522 0.02507 TRUE
GPR64 1 7.336079 6.94E-05 TRUE
GRAMD1C 1 0.043089 0.002132 TRUE
86
GRB7 1 0.116601 0.000287 TRUE
GREM2 1 5.784208 4.17E-05 TRUE
HABP2 1 0.12346 0.002193 TRUE
HAL 1 0.07512 0.003075 TRUE
HAP1 1 0.067725 0.000369 TRUE
HIST1H1A 1 16.78102 0.000372 TRUE
HIST1H1B 1 11.68784 9.83E-06 TRUE
HIST1H2BM 1 6.786156 1.59E-05 TRUE
HIST1H3B 1 14.56885 2.16E-05 TRUE
HJURP 1 7.553779 3.99E-07 TRUE
HMMR 1 3.398351 0.00013 TRUE
HPSE 1 0.05302 0.000192 TRUE
IDO2 1 0.455405 0.007676 TRUE
IER3 1 0.10718 0.000535 TRUE
INHBB 1 0.150463 0.002451 TRUE
IRX3 1 0.061204 0.003196 TRUE
IRX5 1 0.322278 0.011041 TRUE
ISLR 1 2.657384 0.001225 TRUE
KCNG1 1 2.600908 0.00541 TRUE
KCNIP1 1 2.736609 0.009375 TRUE
KIAA0101 1 2.610623 6.83E-05 TRUE
KIF11 1 4.409057 1.85E-08 TRUE
KIF15 1 4.588997 5.99E-08 TRUE
KIF23 1 7.381325 1.02E-07 TRUE
KIF4A 1 4.311626 8.51E-07 TRUE
KIFC1 1 6.525817 1.64E-08 TRUE
KLK4 1 3.648431 0.018836 TRUE
KLKP1 1 2.516239 0.000985 TRUE
KRT23 1 0.120222 0.000743 TRUE
KRT80 1 0.07628 5.58E-07 TRUE
KSR1 1 0.251553 0.001282 TRUE
LAMA1 1 2.926105 7.36E-06 TRUE
LAMB3 1 0.114098 0.000548 TRUE
LBP 1 0.145958 0.008247 TRUE
LIF 1 0.104103 0.008266 TRUE
LRP4 1 14.27916 0.00012 TRUE
LRRC17 1 7.958573 0.000232 TRUE
LRRC3B 1 2.721395 0.008745 TRUE
MARCO 1 0.048552 0.031733 TRUE
MCM10 1 4.20938 3.38E-09 TRUE
MEGF6 1 0.400626 0.004328 TRUE
87
MFAP2 1 4.935555 0.000161 TRUE
MFAP5 1 0.120237 0.000147 TRUE
MFSD4 1 0.021538 0.000243 TRUE
MKI67 1 5.125079 6.93E-08 TRUE
MMP9 1 0.237089 0.014883 TRUE
MT1A 1 0.208848 0.000961 TRUE
MT1G 1 0.007404 1.3E-05 TRUE
MT1H 1 0.012094 0.000133 TRUE
MT1M 1 0.029175 1.53E-06 TRUE
NCAPG 1 4.878529 1.47E-08 TRUE
NCCRP1 1 0.555222 0.025488 TRUE
NDC80 1 2.747122 5.38E-07 TRUE
NLGN3 1 1.761802 0.01431 TRUE
NNMT 1 0.182383 0.005781 TRUE
NOS3 1 0.238086 0.000626 TRUE
OIP5 1 4.762712 5.62E-06 TRUE
OLFM1 1 13.51491 0.000105 TRUE
OLR1 1 0.246321 0.010609 TRUE
PAEP 1 0.003701 0.023825 TRUE
PAK7 1 1.982004 0.008873 TRUE
PANX2 1 0.282386 1.16E-05 TRUE
PBK 1 9.253074 2.99E-06 TRUE
PCSK6 1 5.783798 0.000501 TRUE
PENK 1 7.547161 0.008518 TRUE
PGR 1 7.179539 0.000127 TRUE
PKMYT1 1 3.057381 1.26E-05 TRUE
PLA1A 1 6.551338 0.001224 TRUE
PLA2G16 1 0.10219 0.000159 TRUE
PLA2G2A 1 0.323553 0.009771 TRUE
PLEKHN1 1 0.229147 0.005456 TRUE
POSTN 1 9.240716 9.58E-06 TRUE
PPP1R1B 1 5.666964 0.002448 TRUE
PPP2R1B 1 0.407126 7.74E-05 TRUE
PRB3 1 0.297804 0.001165 TRUE
PROL1 1 0.088691 0.030561 TRUE
PSRC1 1 3.607141 3.85E-08 TRUE
PTH2R 1 2.412974 0.0011 TRUE
PTPRR 1 0.160213 0.003609 TRUE
PTTG3P 1 7.095395 4.05E-07 TRUE
RBP4 1 0.05319 0.021844 TRUE
RIMKLB 1 0.053182 0.000456 TRUE
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RNF39 1 0.327354 0.004834 TRUE
RRM2 1 11.0504 4.82E-06 TRUE
S100A1 1 0.096481 3.43E-05 TRUE
S100P 1 0.007492 0.005682 TRUE
SCGB2A2 1 0.021307 0.001615 TRUE
SDK2 1 3.463934 0.006885 TRUE
SERPINB8 1 0.244613 8.19E-06 TRUE
SFRP1 1 5.454449 0.001826 TRUE
SFRP4 1 36.55166 0.000129 TRUE
SGOL1 1 3.713596 5.85E-08 TRUE
SIK1 1 0.178826 4E-08 TRUE
SKA1 1 4.276862 1.95E-05 TRUE
SKA3 1 3.17418 8.89E-05 TRUE
SLC11A1 1 0.426714 0.027316 TRUE
SLC15A1 1 0.014873 0.006964 TRUE
SLC1A1 1 0.031389 0.006589 TRUE
SLC26A4 1 5.268102 2.37E-05 TRUE
SLC30A2 1 0.106482 8.59E-07 TRUE
SLC37A2 1 0.211868 0.027277 TRUE
SLC47A1 1 11.02891 0.012915 TRUE
SLC5A1 1 0.142281 7.1E-05 TRUE
SLC7A2 1 0.132279 3.99E-05 TRUE
SLCO4A1 1 0.116881 8.09E-05 TRUE
SOD2 1 0.09267 0.000783 TRUE
SPC25 1 5.727666 5.75E-06 TRUE
SPHK1 1 0.201224 0.001654 TRUE
SPP1 1 0.080192 5.32E-05 TRUE
TACC2 1 0.245941 2.78E-05 TRUE
TCN1 1 0.070803 0.040729 TRUE
THBD 1 0.114004 0.006192 TRUE
TK1 1 4.120393 1.76E-05 TRUE
TMED6 1 5.492464 0.004528 TRUE
TMEM119 1 5.832685 6.73E-05 TRUE
TMEM140 1 0.088414 0.000186 TRUE
TMEM45B 1 0.156223 0.001071 TRUE
TMSB15A 1 6.085504 0.002163 TRUE
TNFSF10 1 0.128544 0.003067 TRUE
TOP2A 1 8.540936 4.59E-08 TRUE
TPX2 1 6.411109 1.12E-07 TRUE
TRH 1 41.29464 0.0011 TRUE
TROAP 1 3.968722 1.43E-05 TRUE
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TSPAN8 1 0.051207 0.038857 TRUE
TUBA4A 1 0.280018 0.002341 TRUE
TYMP 1 0.182886 0.000909 TRUE
UBE2C 1 3.515165 1.06E-06 TRUE
UHRF1 1 7.323899 1.55E-08 TRUE
VWC2 1 4.296332 0.000651 TRUE
WDR76 1 2.102928 0.000188 TRUE
ZCCHC12 1 2.850802 0.005363 TRUE
ZNF367 1 4.501526 5.77E-06 TRUE
11 “present” genes, not differentially expressed between LH+2 and LH+7
Gene Symbol Present(1) or
absent (0)
Counts in
LH+2/LH+7 P-VALUE P < 0.05
ACCN1 1 1.413652 0.083278 FALSE
CEACAM6 1 0.819266 0.329137 FALSE
GPX2 1 0.666786 0.222521 FALSE
IL1B 1 0.21489 0.070579 FALSE
IL8 1 0.190594 0.107072 FALSE
LILRA3 1 0.836839 0.299034 FALSE
MMP10 1 0.706629 0.305773 FALSE
OVGP1 1 157.8342 0.068856 FALSE
SPINK1 1 0.067321 0.087273 FALSE
TRPM8 1 0.686299 0.099016 FALSE
WFDC8 1 1.679155 0.057645 FALSE
37 “absent” genes
Gene Symbol Present(1) or
absent (0)
ANKRD22 0
ANKRD55 0
ANO3 0
ART3 0
B3GNT8 0
CCL13 0
DLG2 0
ELK4 0
ENTPD8 0
FABP3 0
FBN3 0
FBXL16 0
FGA 0
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FGB 0
FXYD3 0
G6PC 0
GDF5 0
HBEGF 0
IL1RN 0
IL4I1 0
KNG1 0
LINGO4 0
NNAT 0
OSCAR 0
PHACTR3 0
S100A3 0
SLAMF8 0
SLC16A10 0
SLC2A5 0
ST8SIA2 0
TEX101 0
TMEM154 0
TMEM63C 0
TNFAIP6 0
VNN3 0
WISP3 0
In order to visualize these results, all 287 non-redundant probes were imported into R (v2.13.0).
P-values were again calculated along with the fold-change between the groups, and a volcano
plot was generated [Figure 7]. The dashed blue lines indicate a cut-off p-value of < 0.05 and
fold-changes of greater than 2. Probes coloured in red fall outside of these boundaries, and are
therefore significantly differentially expressed in LH+2 vs. LH+7. As the x axis represents the
log base 2 of the fold-change of LH+2/LH+7, the probes left of the dashed blue lines are up-
regulated in LH+7 and the probes right of the dashed blue lines are down-regulated in LH+7.
This volcano plot illustrates that the vast majority of the probes have been validated to be
differentially expressed in the receptive phase. Comparison of the fold changes of statistically
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significant probes in the NanoString data set with the original microarray data resulted in a 100%
overlap in directionality of the fold changes, i.e. all the validated probe sets that were up-
regulated in the NanoString analysis were also up-regulated on the microarray screen, and all the
validated probe sets down-regulated on NanoString were also down-regulated on microarray.
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Figure 7: Volcano plot representing the validation of differentially expressed genes between
UFA LH+2 and UFA LH+7 by NanoString analysis
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3.4.1.2 NanoString analysis of gastrin exons and “gastrin-related genes”
Three separate NanoString probes were designed to each of gastrin’s three exons [Figure 8]. The
first probe (GAST_ex1-2) recognized exon 1 and a portion of exon 2. The second probe
(GAST_ex2) was specific to exon 2. The third probe was specific to exon 3.
Figure 8: NanoString probe design for gastrin exons
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All three probes validated the differential expression of gastrin in the receptive phase compared
to the pre-receptive phase; however, the fold-change observed between UFA LH+7 and UFA
LH+2 was different depending on the specific probe. The expression measured by GAST_ex1-2
was 34-fold higher in LH+7 vs. LH+2 (p = 0.02). The expression measured by GAST_ex2 was
6-fold higher in LH+7 vs. LH+2 (p = 0.004). The expression measured by GAST_ex3 was
141-fold higher in LH+7 vs. LH+2 (p = 0.02). This finding may reflect variations in probe
affinity but one may also consider the possibility of alternative splicing events.
With regards to the 8 “gastrin-related genes” included in the NanoString analysis, 4 were
“absent” or not expressed in LH+2 or LH+7, including cholecystokinin B receptor, gastrin-
releasing peptide, gastrin-releasing peptide receptor, and proprotein convertase subtilisin/kexin
type 1. Peptidylglycine alpha-amidating monooxygenase was “present” but not differentially
expressed between LH+2 and LH+7. Only three of these genes were found to be differentially
expressed, including carboxypeptidase E (2.7-fold down-regulated in LH+7, p = 0.0002),
tyrosylprotein sulfotransferase 1 (1.9-fold down-regulated in LH+7, p = 0.003) and
tyrosylprotein sulfotransferase 2 (1.6-fold up-regulated in LH+7, p = 0.003).
3.4.2 Immunohistochemistry
Individually, all gastrin antibodies were able to stain gastin equally well in both endometrial and
stomach tissue [Figure 9, 10]. In endometrial biopsies from the receptive phase (LH+7), punctate
staining was observed in the cytoplasm of a subpopulation of glandular epithelial cells [Figure
9]. This punctate staining of gastrin in the endometrium was further confirmed by
immunofluorescence [Figure 11]. Minimal stromal staining was observed in the endometrium. In
stomach antrum biopsies (positive control), punctate staining was observed in the G cells of the
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gastric epithelium, as expected [Figure 10]. In endometrial tissue obtained by hysterectomy in
the pre-receptive, proliferative phase, gastrin immunoreactivity was not detected in the
epithelium [Figure 12].
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Figure 9: Endometrial biopsies from receptive phase (LH+7) stained positive for gastrin
Biopsies were stained with a) Ab 94023 1/2000 (brown); b) Ab 53085 1/2000 (brown);
c) secondary antibody only control.
a
b
c
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Figure 10: Biopsies from endometrium in receptive phase and stomach antrum stained
positive for gastrin
a) Endometrial biopsy from receptive phase (LH+7) and b) stomach antrum biopsy were stained
with Ab 53085 1/2000 (brown)
a
b
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Figure 11: Immunofluorescence staining of endometrial biopsies from receptive phase
(LH+7)
Biopsies were stained with a) Ab 53085 1/2000 (green), DAPI (blue); b) secondary only control
a b
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Figure 12: Endometrial tissue from proliferative phase (Ab 53085 1/2000)
3.5 MULTIPLEX CYTOKINE ASSAY
The multiplex cytokine assay measured the concentrations of 39 different cytokines in the UFA
LH+2 and UFA LH+7 supernatant samples (n=10). The concentrations of 19 cytokines in these
samples were below the detection threshold of the assay; therefore, these cytokines were
excluded from further analysis. Out of the remaining 20 cytokines that were detected, 19 were
not differentially expressed in the two groups [Table 6]. However, one cytokine, interleukin 8
(IL8), was found to be significantly increased in the UFA LH+7 group compared to the UFA
LH+2 group [Figure 13].
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Table 6: Cytokine levels in UFA LH+2 vs. UFA LH+7 supernatants
Cytokine
Average concentration in
LH+2
(pg/µg of total protein)
Average concentration in
LH+7
(pg/µg of total protein)
p - value
EGF 0.13 0.09 0.27
Eotaxin 0.03 0.02 0.56
FGF-2 2.60 2.00 0.18
Fractalkine 0.47 0.26 0.10
G-CSF 0.65 0.72 0.83
GM-CSF 0.07 0.07 0.87
GRO 1.40 1.04 0.50
IFNa2 0.22 0.14 0.18
IFNy 0.03 0.01 0.10
IL-7 0.05 0.04 0.50
IL-8 0.05 0.17 0.02*
IL-12 (p70) 0.03 0.02 0.68
IP-10 0.35 0.83 0.15
MCP-1 0.11 0.17 0.45
MIP-1a 0.02 0.02 0.79
MIP-1b 0.07 0.11 0.13
sCD40L 0.04 0.05 0.42
TGFa 0.02 0.03 0.54
TNFa 0.02 0.02 0.98
VEGF 0.27 0.17 0.20
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Figure 13: Secreted levels of IL8 in UFA LH+2 vs. UFA LH+7 supernatants
0
0.05
0.1
0.15
0.2
0.25
0.3
Co
nce
ntr
atio
n o
f IL
-8
(p
g/µ
g to
tal p
rote
in)
LH+2 LH+7
*P=0.02
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Chapter 4 DISCUSSION
There have been marginal advancements in the understanding of endometrial receptivity since
Noyes et al. first published their observations on the cyclic morphological changes of the
endometrium in the 1950s. Molecular factors that define the temporally-restricted WOI and
confer a state of receptivity to the endometrium have not been clearly established; as a result, the
only clinical modalities available to assess the endometrium are subjective and insensitive tools
such as ultrasound and histological dating. Neither of these methods is a good predictor of
implantation, and endometrial sampling for histological assessment in particular is not feasible
during a cycle in which a patient is trying to conceive. Two things are needed to improve clinical
assessment of endometrial receptivity: the first is the identification of robust biomarkers that can
reliably distinguish the receptive phase and predict the likelihood of implantation, and the second
is the development of a minimally-invasive method of measuring these biomarkers that is safe to
perform in a conception cycle. Improving our ability to assess the endometrium could inform
clinical decisions in ART cycles; for example, if the endometrium is determined to be non-
receptive, embryos could be cryopreserved for transfer at a later date, or if the endometrium is
found to be receptive in a good-prognosis patient, fewer embryos could be transferred to avoid
the risk of MFP. Progress in this field would likely lead to improved outcomes and decreased
complications from ART. Furthermore, the identification of biomarkers of endometrial
receptivity may provide diagnostic insight for patients with early pregnancy loss or recurrent
failed implantation.
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In this study, I set out to address the two needed areas of research on endometrial receptivity. I
first focused on the development of a minimally-invasive sampling approach. In general, a good
clinical assay is one that can be performed on readily accessible biological samples, preferably
tissue or fluids that can be obtained by minimally-invasive means (Campbell and Rockett 2006).
Urine, saliva and serum are fluids that can be easily obtained from patients, and can be used in
the detection of certain biomarkers, for example hormones indicative of ovulation or pregnancy.
However, there have not been any factors found in these fluids that reflect the receptive state of
the endometrium and predict fertility reliably. The transition of the endometrium into a receptive
state is a complex process that involves many local autocrine, paracrine and endocrine factors.
To assess these local factors, several groups have proposed the use of uterine flushings or
aspirations to sample the endometrium. Both of these techniques are less invasive than
endometrial biopsy and neither is detrimental to implantation even if performed just prior to
embryo transfer in ART cycles (van der Gaast, Beier-Hellwig et al. 2003; Berkkanoglu, Isikoglu
et al. 2006). UFA sampling has been used in previous research to measure the levels of a small
number of proteins. I have been successful in adapting its use in a novel way, for the isolation of
endometrial cells for global gene expression profiling. This technique has allowed me to reliably
examine the endometrial transcriptome during the WOI, but its real potential is in studying the
endometrium during a conception cycle to determine molecular predictors of endometrial
receptivity and implantation. Furthermore, this sampling method could be easily and safely
implemented into clinical practice once biomarkers are identified.
My other main objective was to identify molecular biomarkers that can distinguish the receptive
phase of the endometrium. To this end, I compared the gene expression profiles of UFA samples
taken from the same patients, on LH+2 and LH+7 of the same natural menstrual cycle. This
study design was superior to many previous studies on endometrial receptivity because the
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paired sampling reduced the effects of inter-subject variability. The unsupervised hierarchical
clustering performed on all samples revealed an important proof of this principle. Samples taken
during the pre-receptive and receptive phase clustered in their respective groups and segregated
away from each other. This confirms that molecular signatures could discriminate the receptive
endometrium from its pre-receptive state, and that these differences were greater than subject
variability. Supervised statistical testing identified the specific differentially expressed genes that
distinguished the receptive phase. Many of these genes exhibited very robust changes in
expression, which is a desirable characteristic in a potential biomarker. To make the gene list
more manageable, it was prioritized by fold-change, to focus on those genes with the most robust
change. A survey of the prioritized gene list [Table 2] revealed some interesting findings. Firstly,
the “lowest hanging fruit” with the most robust fold-change in the receptive phase was gastrin, a
secreted hormone previously thought to be exclusive to the gastrointestinal system. Secondly,
several genes identified on this gene list have been previously associated with the WOI and are
putatively involved in endometrial receptivity, including glycodelin-A (PAEP), LIF, HBEGF,
and OPN (SPP1) (Charnock-Jones, Sharkey et al. 1994; Westergaard, Wiberg et al. 1998;
Apparao, Murray et al. 2001; Lessey, Gui et al. 2002). Confirming the up-regulation of these
genes in the receptive phase by my approach is an important validation. Finally, the ontological
categories of the differentially expressed genes suggest that these findings are biologically
relevant. In the mid-secretory phase of the endometrium, markers of proliferation are known to
decrease as the endometrium prepares for implantation at both the epithelial and stromal level.
Reflecting this physiological process, the genes I found to be down-regulated in the receptive
phase were enriched for GO categories such as cell cycle, mitosis, chromosomal and
cytoskeleton components. Up-regulated genes were involved in membranes, signaling, cell
components, and immunity/inflammation.
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As my technique was novel, it was important to compare UFA sampling against the standard
method of sampling, by endometrial biopsy. The overall clustering of receptive samples in the
unsupervised analysis, regardless of method of sampling, indicates that gene expression profiling
by UFA is representative of biopsy. However, closer examination of the receptive cluster
revealed sub-clustering, suggesting that subtle gene expression differences do exist between
samples obtained by UFA and biopsy. To show that these differences did not carry much weight,
I demonstrated that the list of differentially expressed genes between LH+2 and LH+7 was not
significantly altered when biopsy samples were included in the analysis. The only effect of
including biopsy samples in the analysis was that slightly fewer probes were identified than
when comparing UFA alone (213 vs. 296), but there was still very good agreement of the
differentially expressed probes identified. When receptive phase samples obtained by UFA or
biopsy were directly compared, there was a cohort of genes that were differentially expressed.
This difference is not unexpected; we know that UFA samples are comprised mostly of epithelial
cells and have minimal stromal or leukocytic content, whereas biopsy samples are much more
heterogeneous in cell composition. However, regardless of the specific cell types obtained, the
priority of this research is to detect genes that are differentially expressed in the receptive phase
in a robust and reproducible manner, and this can technically be done with either sampling
method. Overall, UFA is advantageous because it is minimally-invasive, and will allow me to
correlate the expression of potential biomarkers of endometrial receptivity to implantation
outcomes in follow-up studies. Another potential advantage that I highlighted is that sampling by
UFA may detect the differential expression of slightly more genes than the traditional biopsy
approach. In this preliminary study, I have optimized and validated this technique for global gene
expression profiling of the endometrium, which I will be able to use as my standard approach in
the future.
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The validation of differentially expressed genes using the NanoString platform demonstrated a
high level of consensus with the microarray screen. However, one of the genes that was
“present” on the assay but surprisingly not validated was IL8. There was a strong trend towards
IL8 being up-regulated in the receptive phase, but this was non-significant. IL8 is a chemokine
thought to be involved in the recruitment of T-cells and neutrophils to the endometrium. Besides
chemotaxis, its pleiotropic functions include angiogenesis, and mitogenesis of epidermal,
melanoma, and vascular smooth muscle cells (Mulayim, Palter et al. 2003). Endometrial IL8
mRNA levels have been shown to fluctuate throughout the menstrual cycle, with a nadir in the
late proliferative phase, rising levels after ovulation, and peak expression in the late secretory
phase. Peak expression of IL8 coincides with the period of marked accumulation of leukocytes in
the endometrium. Immunohistochemical experiments have shown that IL8 protein is found in the
luminal and glandular epithelium throughout the menstrual cycle, but is not detectable in the
stroma (Arici, Seli et al. 1998). IL8 mediates its effects through the chemokine receptors CXCR1
and CXCR2. CXCR1 immunoreactivity in epithelial and stromal cells is cycle-dependent and
peaks during the mid-secretory phase, whereas immunostaining for CXCR2 in the same cell
types exhibits minimal variation across the cycle (Mulayim, Palter et al. 2003).
My study demonstrated inconsistent findings with respect to IL8 mRNA and protein levels in the
receptive phase. The NanoString analysis indicated a trend was present but was not statistically
significant [Table 5], while the microarray screen found transcript levels of IL8 to be markedly
increased (6.9-fold) in UFA LH+7 compared to UFA LH+2 [Table 2]. However, to further
complicate matters, when the biopsy samples were included in the microarray analysis and a t-
test comparing pre-receptive samples (UFA LH+2) to all receptive samples (UFA LH+7 and
Biopsy LH+7) was performed, IL8 was no longer significant [data not shown]. Also, when UFA
LH+7 and Biopsy LH+7 were compared with a posthoc ANOVA test to determine gene
107
expression differences due to sampling method, IL8 was found to be significantly up-regulated
(8.3-fold) in UFA samples over biopsy [Table 4]. This perhaps reflects the localization of IL8
expression to the epithelium, as UFA captures predominantly epithelial cells, whereas
endometrial biopsy collects a complex mix of cells, including stromal cells and leukocytes.
Finally, on the multiplex cytokine assay, IL8 was the only cytokine out of 39 whose secreted
levels in the UFA supernatants were significantly higher in the receptive phase than the pre-
receptive phase [Figure 11]. Despite some inconsistencies, when my results are weighed along
with previous studies, IL8 still seems promising as a potential marker of endometrial
development. IL8 likely does exhibit cycle-dependent expression, although the exact pattern of
expression is unclear and requires further study.
NanoString validated the vast majority of my gene list, and is a powerful tool in the study of
complex physiological processes. It is as sensitive as quantitative RT-PCR for measuring gene
expression, and more focused and cost-effective than microarray. This study is the first I know of
to have applied the NanoString platform to validate genes identified as differentially expressed
during the receptive phase of the endometrium. I used NanoString not only to validate my
microarray screen, but also to perform a focused analysis on gastrin and its related genes at the
same time. Designing several specific probes to different regions of the gastrin gene allowed me
to assess the differential transcription patterns of its three exons. The transcript levels of exon 3
were found to be markedly increased (141-fold) in the receptive phase, much more so than the
levels of exon 2 (6-fold). This variation suggests the possibility of alternative splicing of gastrin
mRNA leading to the overexpression of one exon over the others. However, there are other
explanations for this observation; namely, the apparent predominance of certain exons could be
due to different binding affinities of the probes. Whether splice variants of gastrin are being
108
produced in the endometrium, and whether different isoforms are being synthesized at the
protein level remain to be determined.
The identification of gastrin in the receptive phase endometrium at the mRNA level by
microarray and NanoString, and at the protein level by immunohistochemistry, is a novel
finding. The regulation and functions of gastrin in the stomach are well-established, as are the
extensive post-translational modifications that convert the precursor preprogastrin to progastrin,
to the bioactive G34 and G17 forms (Rehfeld, Zhu et al. 2008). Gastrin production is regulated
by gastrin-releasing peptide, which binds to gastrin-releasing peptide receptor on G cells in the
stomach. The precursor preprogastrin has a typical N-terminal signal sequence that is cleaved to
yield progastrin. Progastrin then progresses to the Golgi complex, where tyrosylprotein
sulfotransferases (TPST1/2) mediate tyrosine-sulfation. Cleavage of progastrin to shorter forms
is mediated by prohormone convertases (PC). Endoproteolytic cleavage by PC produces the
substrate for carboxypeptidase E (CPE) (i.e. C-terminal Phe-Gly-Arg). Carboxypeptidase
removal of the C-terminal basic residues leads to the C-terminal Gly-extended form of gastrin,
which is then converted to the final, bioactive amidated G17 or G34 form by peptidylglycine
alpha-amidating monooxygenase (PAM).
Of all the genes I tested that are involved in gastrin processing or signaling in the stomach, only
CPE, TPST1 and TPST2 were differentially expressed in the receptive phase endometrium,
along with gastrin. However, while gastrin was significantly up-regulated, CPE and TPST1 were
down-regulated, and TPST2 was only slightly up-regulated. The biological relevance of this is
unclear, but the fact that the majority of canonical “gastrin-related genes” were not recruited
despite a dramatic up-regulation of gastrin itself suggests that gastrin production and processing,
and perhaps even its function, may be different in the endometrium than described in the
109
gastrointestinal system. Future studies on gastrin will include elucidation of putative splice
variants with RT-PCR, and studies using in vitro models to determine the regulation and function
of gastrin in the endometrium.
4.1 STUDY LIMITATIONS
As is often the case in studies involving human samples, availability of sample material was a
challenge in this study. Despite the overall success of patient recruitment and sampling, I did not
have an excess of either study participants or sample quantity. To have sufficient power for a
microarray discovery analysis, I aimed to enroll approximately 25 patients. I was successful in
recruiting and sampling 23 patients, and 2 of them were excluded from further analysis due to
abnormal histological dating. As the priority was to identify a discovery panel of genes
differentially expressed in the receptive phase endometrium, all samples were processed and
analyzed by whole-genome microarray. From this analysis, I generated a prioritized gene list for
validation by NanoString [Table 2]. However, due to limitations in patient samples, I did not
have a new validation cohort to externally validate this panel of genes. The validation that I
performed was on the original discovery cohort of patient samples, which is an internal
validation of my technique and showed excellent consensus between the microarray screen and
NanoString analysis. Recruiting more patients and performing an external validation of this gene
list is my next priority in follow-up studies.
Another limitation related to the availability of human samples was my inability to obtain early-
secretory (LH+2) endometrial biopsies for comparison in my immunohistochemistry
experiments on gastrin. The microarray and NanoString analysis indicated that gastrin mRNA
110
levels were up-regulated in the mid-secretory phase compared to the early secretory phase. The
ideal immunohistochemistry validation experiment would be to compare gastrin
immunoreactivity in endometrial biopsies taken from both of these phases. I had archived mid-
secretory endometrial biopsy tissue from my study patients. However, I did not collect early
secretory tissue as I had only biopsied the patients once during the receptive phase for
comparison with UFA, to avoid disrupting the endometrium and altering its gene expression
profile. As a substitute for early secretory endometrium, I was able to obtain archived FFPE
endometrial tissue from the proliferative phase for comparison (from Pathology).
Immunoreactivity for gastrin was not detected in this tissue. However, this was not the ideal
negative control tissue as it came from formalin-fixed hysterectomy samples processed
differently from my endometrial biopsy samples, which could have led to destruction of gastrin
epitopes. Furthermore, this “pre-receptive control” tissue was not from the same phase of the
menstrual cycle that was compared in the gene expression studies. However, the immunostaining
of gastrin in the mid-secretory endometrial biopsy tissue was very evident, and this was a clear
validation at the protein level of my gene expression profiling results.
In my study protocol, I processed the UFA samples by placing them in 1mL of PBS and
centrifuging the resulting solution. The pellet was reserved for RNA extraction and gene
expression profiling, and the supernatant was archived for secreted protein analysis. I decided to
dilute the samples with PBS due to the small amount and viscosity of the aspiration fluid, and to
flush out the Tomcat catheter. One of the limitations of this study was that half of the cytokines
on the multiplex cytokine assay panel were not detected in the supernatant samples. A likely
explanation for this is an over-dilution of the samples in PBS. Some of the cytokines that were
below detection threshold have been associated with the peri-implantation window in previous
studies, including IL1 and IL6, and it would have been interesting to observe their pattern of
111
secretion in this study. With my future studies, I will modify my protocol accordingly, and dilute
UFA samples in a much lower volume of buffer.
4.2 CONCLUSIONS AND FUTURE DIRECTIONS
I now have a reliable, minimally-invasive method of sampling the endometrium, as well as a
prioritized list of potential biomarkers of endometrial receptivity, which were the two main
objectives of my preliminary study. My list of potential biomarkers consists of genes
differentially expressed in the receptive phase of the endometrium during the natural cycle. I
have internally validated this panel of genes using my discovery cohort of patients; however, I
have yet to validate their expression in a new cohort of patients.
In follow-up studies, I plan to validate the expression of this discovery panel of genes in a new
cohort of patients, both in natural and COH cycles. There is evidence that COH has a detrimental
effect on endometrial maturation and receptivity due to the supraphysiologic exposure to steroid
hormones and the use of drugs to prevent premature luteinization (Valbuena, Jasper et al. 1999).
Therefore, the molecular milieu of the endometrium in women undergoing COH and IVF may be
different than in naturally cycling women. To understand the molecular differences between
natural and COH cycles, I will sample the same patients in a natural cycle preceding a COH
cycle, and then during the COH cycle to compare the gene expression profiles. A similar
transcriptomic study has been performed which compared the receptive and pre-receptive phases
in natural and COH/IVF cycles and demonstrated differences in gene expression profiles;
however, this study relied on multiple biopsies of the same patients, and the resultant disruption
of the endometrium may have confounded the results (Haouzi, Assou et al. 2009). Furthermore,
112
performing endometrial biopsies on patients in active IVF cycles has been shown to have a
deleterious effect on pregnancy rates, and is ethically unsound (Karimzade, Oskouian et al.
2010).
One of the main priorities of my future studies is to determine which genes in my discovery
panel of genes that are differentially expressed in the receptive phase can predict for actual
endometrial receptivity and implantation. The distinct advantage of my minimally-invasive
approach is that I will be able to sample women safely in conception cycles and not only study
the effects of COH compared to natural cycles, but also correlate gene expression to implantation
outcomes. The identification of candidate biomarkers that are differentially expressed during the
receptive phase and predictive of implantation may inform the development of clinical assays to
assess for endometrial receptivity in ART cycles and improve outcomes.
This minimally-invasive approach will also allow me to study other timepoints in the cycle of
endometrial development while avoiding injury or disruption of the endometrium. The same
patient may be sampled at different timepoints in the menstrual cycle, and the gene expression of
the endometrium in other phases may be studied. I will compare the gene expression profile of
the post-receptive phase to the receptive phase, to see which genes are activated or repressed
after the WOI is over. This will allow the clear delineation of genes that frame the WOI and are
pertinently only differentially expressed during the receptive phase.
Another exciting future direction is the application of my UFA approach to interrogate the
endometrium and study its gene expression profile in a variety of conditions. Pathologic
conditions that have been associated with altered endometrial receptivity include unexplained
infertility, polycystic ovarian syndrome (PCOS), endometriosis, and the presence of
hydrosalpinges. IUD use is a non-pathologic condition that diminishes the receptivity of the
113
endometrium. Studying these pathologic and non-pathologic conditions of the endometrium may
lead to improvements in diagnosis, and will contribute to our understanding of the process of
endometrial receptivity.
114
References
Ain, R., M. L. Trinh and M. J. Soares (2004). "Interleukin-11 signaling is required for the
differentiation of natural killer cells at the maternal-fetal interface." Developmental
dynamics : an official publication of the American Association of Anatomists 231(4):
700-708.
Andersen, A. N., L. Gianaroli, R. Felberbaum, J. de Mouzon and K. G. Nygren (2005). "Assisted
reproductive technology in Europe, 2001. Results generated from European registers by
ESHRE." Human reproduction 20(5): 1158-1176.
Aplin, J. D., N. A. Hey and T. C. Li (1996). "MUC1 as a cell surface and secretory component of
endometrial epithelium: reduced levels in recurrent miscarriage." American journal of
reproductive immunology 35(3): 261-266.
Apparao, K. B., M. J. Murray, M. A. Fritz, W. R. Meyer, A. F. Chambers, P. R. Truong and B.
A. Lessey (2001). "Osteopontin and its receptor alphavbeta(3) integrin are coexpressed in
the human endometrium during the menstrual cycle but regulated differentially." The
Journal of clinical endocrinology and metabolism 86(10): 4991-5000.
Arici, A., E. Seli, L. M. Senturk, L. S. Gutierrez, E. Oral and H. S. Taylor (1998). "Interleukin-8
in the human endometrium." The Journal of clinical endocrinology and metabolism
83(5): 1783-1787.
ASRM (2006). "Aging and infertility in women." Fertility and sterility 86(5 Suppl 1): S248-252.
Beier-Hellwig, K., K. Sterzik, B. Bonn, U. Hilmes, M. Bygdeman, K. Gemzell-Danielsson and
H. M. Beier (1994). "Hormone regulation and hormone antagonist effects on protein
patterns of human endometrial secretion during receptivity." Annals of the New York
Academy of Sciences 734: 143-156.
Bentin-Ley, U. and A. Lopata (2000). "In vitro models of human blastocyst implantation."
Bailliere's best practice & research. Clinical obstetrics & gynaecology 14(5): 765-774.
Bergh, T., A. Ericson, T. Hillensjo, K. G. Nygren and U. B. Wennerholm (1999). "Deliveries and
children born after in-vitro fertilisation in Sweden 1982-95: a retrospective cohort study."
Lancet 354(9190): 1579-1585.
Berkkanoglu, M., M. Isikoglu, M. Seleker and K. Ozgur (2006). "Flushing the endometrium
prior to the embryo transfer does not affect the pregnancy rate." Reproductive
biomedicine online 13(2): 268-271.
Bhatt, H., L. J. Brunet and C. L. Stewart (1991). "Uterine expression of leukemia inhibitory
factor coincides with the onset of blastocyst implantation." Proceedings of the National
Academy of Sciences of the United States of America 88(24): 11408-11412.
115
Birdsall, M. A., J. F. Hopkisson, K. E. Grant, D. H. Barlow and H. J. Mardon (1996).
"Expression of heparin-binding epidermal growth factor messenger RNA in the human
endometrium." Molecular human reproduction 2(1): 31-34.
Bissonnette, F., S. J. Phillips, J. Gunby, H. Holzer, N. Mahutte, P. St-Michel and I. J. Kadoch
(2011). "Working to eliminate multiple pregnancies: a success story in Quebec."
Reproductive biomedicine online.
Boomsma, C. M., A. Kavelaars, M. J. Eijkemans, K. Amarouchi, G. Teklenburg, D. Gutknecht,
B. J. Fauser, C. J. Heijnen and N. S. Macklon (2009). "Cytokine profiling in endometrial
secretions: a non-invasive window on endometrial receptivity." Reproductive
biomedicine online 18(1): 85-94.
Borri, P., I. Noci, B. Fuzzi, A. Rice and T. Chard (1998). "The ovary is not a major source of
placental protein 14 (glycodelin)." Human reproduction 13(12): 3418-3420.
Borthwick, J. M., D. S. Charnock-Jones, B. D. Tom, M. L. Hull, R. Teirney, S. C. Phillips and S.
K. Smith (2003). "Determination of the transcript profile of human endometrium."
Molecular human reproduction 9(1): 19-33.
Brinsden, P. R., V. Alam, B. de Moustier and P. Engrand (2009). "Recombinant human leukemia
inhibitory factor does not improve implantation and pregnancy outcomes after assisted
reproductive techniques in women with recurrent unexplained implantation failure."
Fertility and sterility 91(4 Suppl): 1445-1447.
Bronson, R. A. and F. M. Fusi (1996). "Integrins and human reproduction." Molecular human
reproduction 2(3): 153-168.
Bruner, K. L., W. H. Rodgers, L. I. Gold, M. Korc, J. T. Hargrove, L. M. Matrisian and K. G.
Osteen (1995). "Transforming growth factor beta mediates the progesterone suppression
of an epithelial metalloproteinase by adjacent stroma in the human endometrium."
Proceedings of the National Academy of Sciences of the United States of America
92(16): 7362-7366.
Campbell, K. L. and J. C. Rockett (2006). "Biomarkers of ovulation, endometrial receptivity,
fertilisation, implantation and early pregnancy progression." Paediatric and perinatal
epidemiology 20 Suppl 1: 13-25.
Carson, D. D., E. Lagow, A. Thathiah, R. Al-Shami, M. C. Farach-Carson, M. Vernon, L. Yuan,
M. A. Fritz and B. Lessey (2002). "Changes in gene expression during the early to mid-
luteal (receptive phase) transition in human endometrium detected by high-density
microarray screening." Molecular human reproduction 8(9): 871-879.
Catalano, R. D., A. Yanaihara, A. L. Evans, D. Rocha, A. Prentice, S. Saidi, C. G. Print, D. S.
Charnock-Jones, A. M. Sharkey and S. K. Smith (2003). "The effect of RU486 on the
gene expression profile in an endometrial explant model." Molecular human reproduction
9(8): 465-473.
116
Charnock-Jones, D. S., A. M. Sharkey, P. Fenwick and S. K. Smith (1994). "Leukaemia
inhibitory factor mRNA concentration peaks in human endometrium at the time of
implantation and the blastocyst contains mRNA for the receptor at this time." Journal of
reproduction and fertility 101(2): 421-426.
Chegini, N., Y. Zhao, R. S. Williams and K. C. Flanders (1994). "Human uterine tissue
throughout the menstrual cycle expresses transforming growth factor-beta 1 (TGF beta
1), TGF beta 2, TGF beta 3, and TGF beta type II receptor messenger ribonucleic acid
and protein and contains [125I]TGF beta 1-binding sites." Endocrinology 135(1): 439-
449.
Chung, I. B., F. D. Yelian, F. M. Zaher, B. Gonik, M. I. Evans, M. P. Diamond and D. M.
Svinarich (2000). "Expression and regulation of vascular endothelial growth factor in a
first trimester trophoblast cell line." Placenta 21(4): 320-324.
Cook, J. L., J. Collins, W. Buckett, C. Racowsky, E. Hughes and K. Jarvi (2011). "Assisted
reproductive technology-related multiple births: Canada in an international context."
Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et
gynecologie du Canada : JOGC 33(2): 159-167.
Cork, B. A., T. C. Li, M. A. Warren and S. M. Laird (2001). "Interleukin-11 (IL-11) in human
endometrium: expression throughout the menstrual cycle and the effects of cytokines on
endometrial IL-11 production in vitro." Journal of reproductive immunology 50(1): 3-17.
Cork, B. A., E. M. Tuckerman, T. C. Li and S. M. Laird (2002). "Expression of interleukin (IL)-
11 receptor by the human endometrium in vivo and effects of IL-11, IL-6 and LIF on the
production of MMP and cytokines by human endometrial cells in vitro." Molecular
human reproduction 8(9): 841-848.
Coutifaris, C., E. R. Myers, D. S. Guzick, M. P. Diamond, S. A. Carson, R. S. Legro, P. G.
McGovern, W. D. Schlaff, B. R. Carr, M. P. Steinkampf, S. Silva, D. L. Vogel and P. C.
Leppert (2004). "Histological dating of timed endometrial biopsy tissue is not related to
fertility status." Fertility and sterility 82(5): 1264-1272.
Dalton, C. F., S. M. Laird, S. E. Estdale, H. G. Saravelos and T. C. Li (1998). "Endometrial
protein PP14 and CA-125 in recurrent miscarriage patients; correlation with pregnancy
outcome." Human reproduction 13(11): 3197-3202.
Danielsson, K. G., M. L. Swahn and M. Bygdeman (1997). "The effect of various doses of
mifepristone on endometrial leukaemia inhibitory factor expression in the midluteal
phase--an immunohistochemical study." Human reproduction 12(6): 1293-1297.
Das, S. K., X. N. Wang, B. C. Paria, D. Damm, J. A. Abraham, M. Klagsbrun, G. K. Andrews
and S. K. Dey (1994). "Heparin-binding EGF-like growth factor gene is induced in the
mouse uterus temporally by the blastocyst solely at the site of its apposition: a possible
ligand for interaction with blastocyst EGF-receptor in implantation." Development
120(5): 1071-1083.
117
de Ziegler, D., R. Fanchin, B. de Moustier and C. Bulletti (1998). "The hormonal control of
endometrial receptivity: estrogen (E2) and progesterone." Journal of reproductive
immunology 39(1-2): 149-166.
DeLoia, J. A., J. S. Krasnow, J. Brekosky, A. Babaknia, J. Julian and D. D. Carson (1998).
"Regional specialization of the cell membrane-associated, polymorphic mucin (MUC1)
in human uterine epithelia." Human reproduction 13(1O): 2902-2909.
DeSouza, M. M., G. A. Surveyor, R. E. Price, J. Julian, R. Kardon, X. Zhou, S. Gendler, J.
Hilkens and D. D. Carson (1999). "MUC1/episialin: a critical barrier in the female
reproductive tract." Journal of reproductive immunology 45(2): 127-158.
Dimitriadis, E., L. Robb and L. A. Salamonsen (2002). "Interleukin 11 advances progesterone-
induced decidualization of human endometrial stromal cells." Molecular human
reproduction 8(7): 636-643.
Dimitriadis, E., C. A. White, R. L. Jones and L. A. Salamonsen (2005). "Cytokines, chemokines
and growth factors in endometrium related to implantation." Human reproduction update
11(6): 613-630.
Dubowy, R. L., R. F. Feinberg, D. L. Keefe, G. F. Doncel, S. C. Williams, J. C. McSweet and H.
J. Kliman (2003). "Improved endometrial assessment using cyclin E and p27." Fertility
and sterility 80(1): 146-156.
Duval, D., B. Reinhardt, C. Kedinger and H. Boeuf (2000). "Role of suppressors of cytokine
signaling (Socs) in leukemia inhibitory factor (LIF) -dependent embryonic stem cell
survival." The FASEB journal : official publication of the Federation of American
Societies for Experimental Biology 14(11): 1577-1584.
Edwards, R. G. and H. K. Beard (1999). "Is the success of human IVF more a matter of genetics
and evolution than growing blastocysts?" Human reproduction 14(1): 1-4.
Evans, M. I., D. Ciorica and D. W. Britt (2004). "Do reduced multiples do better?" Best practice
& research. Clinical obstetrics & gynaecology 18(4): 601-612.
Fadare, O. and W. Zheng (2005). "Histologic dating of the endometrium: accuracy,
reproducibility, and practical value." Advances in anatomic pathology 12(2): 39-46.
Feinberg, R. F., H. J. Kliman and C. L. Wang (1994). "Transforming growth factor-beta
stimulates trophoblast oncofetal fibronectin synthesis in vitro: implications for
trophoblast implantation in vivo." The Journal of clinical endocrinology and metabolism
78(5): 1241-1248.
Ferguson, S. E., A. B. Olshen, A. Viale, R. R. Barakat and J. Boyd (2005). "Stratification of
intermediate-risk endometrial cancer patients into groups at high risk or low risk for
recurrence based on tumor gene expression profiles." Clinical cancer research : an official
journal of the American Association for Cancer Research 11(6): 2252-2257.
118
Fortina, P. and S. Surrey (2008). "Digital mRNA profiling." Nature biotechnology 26(3): 293-
294.
Fried, A. M. (1978). "Distribution of the bulk of the normal placenta. Review and classification
of 800 cases by ultrasonography." American journal of obstetrics and gynecology 132(6):
675-680.
Garcia, E., P. Bouchard, J. De Brux, J. Berdah, R. Frydman, G. Schaison, E. Milgrom and M.
Perrot-Applanat (1988). "Use of immunocytochemistry of progesterone and estrogen
receptors for endometrial dating." The Journal of clinical endocrinology and metabolism
67(1): 80-87.
Gardner, D. K. and M. Lane (1998). "Culture of viable human blastocysts in defined sequential
serum-free media." Human reproduction 13 Suppl 3: 148-159; discussion 160.
Gardner, D. K., E. Surrey, D. Minjarez, A. Leitz, J. Stevens and W. B. Schoolcraft (2004).
"Single blastocyst transfer: a prospective randomized trial." Fertility and sterility 81(3):
551-555.
Genbacev, O. D., A. Prakobphol, R. A. Foulk, A. R. Krtolica, D. Ilic, M. S. Singer, Z. Q. Yang,
L. L. Kiessling, S. D. Rosen and S. J. Fisher (2003). "Trophoblast L-selectin-mediated
adhesion at the maternal-fetal interface." Science 299(5605): 405-408.
Giudice, L. C. (1999). "Potential biochemical markers of uterine receptivity." Human
reproduction 14 Suppl 2: 3-16.
Gnainsky, Y., I. Granot, P. B. Aldo, A. Barash, Y. Or, E. Schechtman, G. Mor and N. Dekel
(2010). "Local injury of the endometrium induces an inflammatory response that
promotes successful implantation." Fertility and sterility 94(6): 2030-2036.
Godkin, J. D. and J. J. Dore (1998). "Transforming growth factor beta and the endometrium."
Reviews of reproduction 3(1): 1-6.
Gold, L. I., B. Saxena, K. R. Mittal, M. Marmor, S. Goswami, L. Nactigal, M. Korc and R. I.
Demopoulos (1994). "Increased expression of transforming growth factor beta isoforms
and basic fibroblast growth factor in complex hyperplasia and adenocarcinoma of the
endometrium: evidence for paracrine and autocrine action." Cancer research 54(9): 2347-
2358.
Gonen, Y. and R. F. Casper (1990). "Prediction of implantation by the sonographic appearance
of the endometrium during controlled ovarian stimulation for in vitro fertilization (IVF)."
Journal of in vitro fertilization and embryo transfer : IVF 7(3): 146-152.
Gonzales, D. S., J. M. Jones, T. Pinyopummintr, E. M. Carnevale, O. J. Ginther, S. S. Shapiro
and B. D. Bavister (1996). "Trophectoderm projections: a potential means for
locomotion, attachment and implantation of bovine, equine and human blastocysts."
Human reproduction 11(12): 2739-2745.
119
Greenhall, E. and M. Vessey (1990). "The prevalence of subfertility: a review of the current
confusion and a report of two new studies." Fertility and sterility 54(6): 978-983.
Gunby, J., F. Bissonnette, C. Librach and L. Cowan (2011). "Assisted reproductive technologies
(ART) in Canada: 2007 results from the Canadian ART Register." Fertility and sterility
95(2): 542-547 e541-510.
Gunby, J. and S. Daya (2007). "Assisted reproductive technologies (ART) in Canada: 2003
results from the Canadian ART Register." Fertility and sterility 88(3): 550-559.
Haouzi, D., S. Assou, K. Mahmoud, S. Tondeur, T. Reme, B. Hedon, J. De Vos and S. Hamamah
(2009). "Gene expression profile of human endometrial receptivity: comparison between
natural and stimulated cycles for the same patients." Human reproduction 24(6): 1436-
1445.
Haouzi, D., K. Mahmoud, M. Fourar, K. Bendhaou, H. Dechaud, J. De Vos, T. Reme, D.
Dewailly and S. Hamamah (2009). "Identification of new biomarkers of human
endometrial receptivity in the natural cycle." Human reproduction 24(1): 198-205.
Henson, M. C. (1998). "Pregnancy maintenance and the regulation of placental progesterone
biosynthesis in the baboon." Human reproduction update 4(4): 389-405.
Hertig, A. T., J. Rock and E. C. Adams (1956). "A description of 34 human ova within the first
17 days of development." The American journal of anatomy 98(3): 435-493.
Hey, N. A. and J. D. Aplin (1996). "Sialyl-Lewis x and Sialyl-Lewis a are associated with
MUC1 in human endometrium." Glycoconjugate journal 13(5): 769-779.
Hey, N. A., R. A. Graham, M. W. Seif and J. D. Aplin (1994). "The polymorphic epithelial
mucin MUC1 in human endometrium is regulated with maximal expression in the
implantation phase." The Journal of clinical endocrinology and metabolism 78(2): 337-
342.
Hey, N. A., T. C. Li, P. L. Devine, R. A. Graham, H. Saravelos and J. D. Aplin (1995). "MUC1
in secretory phase endometrium: expression in precisely dated biopsies and flushings
from normal and recurrent miscarriage patients." Human reproduction 10(10): 2655-
2662.
Hilkens, J., M. J. Ligtenberg, H. L. Vos and S. V. Litvinov (1992). "Cell membrane-associated
mucins and their adhesion-modulating property." Trends in biochemical sciences 17(9):
359-363.
Hoffman, L. H., G. E. Olson, D. D. Carson and B. S. Chilton (1998). "Progesterone and
implanting blastocysts regulate Muc1 expression in rabbit uterine epithelium."
Endocrinology 139(1): 266-271.
Hoozemans, D. A., R. Schats, C. B. Lambalk, R. Homburg and P. G. Hompes (2004). "Human
embryo implantation: current knowledge and clinical implications in assisted
reproductive technology." Reproductive biomedicine online 9(6): 692-715.
120
Horcajadas, J. A., A. Pellicer and C. Simon (2007). "Wide genomic analysis of human
endometrial receptivity: new times, new opportunities." Human reproduction update
13(1): 77-86.
Horcajadas, J. A., A. M. Sharkey, R. D. Catalano, J. R. Sherwin, F. Dominguez, L. A. Burgos, A.
Castro, M. R. Peraza, A. Pellicer and C. Simon (2006). "Effect of an intrauterine device
on the gene expression profile of the endometrium." The Journal of clinical
endocrinology and metabolism 91(8): 3199-3207.
Horne, A. W., J. O. White, R. A. Margara, R. Williams, R. M. Winston and E. Lalani (2001).
"MUC 1: a genetic susceptibility to infertility?" Lancet 357(9265): 1336-1337.
Irving, J. A. and P. K. Lala (1995). "Functional role of cell surface integrins on human
trophoblast cell migration: regulation by TGF-beta, IGF-II, and IGFBP-1." Experimental
cell research 217(2): 419-427.
Jones, G. S. (1976). "The luteal phase defect." Fertility and sterility 27(4): 351-356.
Kalma, Y., I. Granot, Y. Gnainsky, Y. Or, B. Czernobilsky, N. Dekel and A. Barash (2009).
"Endometrial biopsy-induced gene modulation: first evidence for the expression of
bladder-transmembranal uroplakin Ib in human endometrium." Fertility and sterility
91(4): 1042-1049, 1049 e1041-1049.
Kao, L. C., A. Germeyer, S. Tulac, S. Lobo, J. P. Yang, R. N. Taylor, K. Osteen, B. A. Lessey
and L. C. Giudice (2003). "Expression profiling of endometrium from women with
endometriosis reveals candidate genes for disease-based implantation failure and
infertility." Endocrinology 144(7): 2870-2881.
Kao, L. C., S. Tulac, S. Lobo, B. Imani, J. P. Yang, A. Germeyer, K. Osteen, R. N. Taylor, B. A.
Lessey and L. C. Giudice (2002). "Global gene profiling in human endometrium during
the window of implantation." Endocrinology 143(6): 2119-2138.
Karimzade, M. A., H. Oskouian, S. Ahmadi and L. Oskouian (2010). "Local injury to the
endometrium on the day of oocyte retrieval has a negative impact on implantation in
assisted reproductive cycles: a randomized controlled trial." Archives of gynecology and
obstetrics 281(3): 499-503.
Kovalevsky, G. and P. Patrizio (2005). "High rates of embryo wastage with use of assisted
reproductive technology: a look at the trends between 1995 and 2001 in the United
States." Fertility and sterility 84(2): 325-330.
Laird, S. M., E. M. Tuckerman, C. F. Dalton, B. C. Dunphy, T. C. Li and X. Zhang (1997). "The
production of leukaemia inhibitory factor by human endometrium: presence in uterine
flushings and production by cells in culture." Human reproduction 12(3): 569-574.
Ledee-Bataille, N., G. Lapree-Delage, J. L. Taupin, S. Dubanchet, R. Frydman and G. Chaouat
(2002). "Concentration of leukaemia inhibitory factor (LIF) in uterine flushing fluid is
highly predictive of embryo implantation." Human reproduction 17(1): 213-218.
121
Lessey, B. A. (1998). "Endometrial integrins and the establishment of uterine receptivity."
Human reproduction 13 Suppl 3: 247-258; discussion 259-261.
Lessey, B. A. (2003). "Two pathways of progesterone action in the human endometrium:
implications for implantation and contraception." Steroids 68(10-13): 809-815.
Lessey, B. A. (2011). "Assessment of endometrial receptivity." Fertility and sterility 96(3): 522-
529.
Lessey, B. A. and A. J. Castelbaum (2002). "Integrins and implantation in the human." Reviews
in endocrine & metabolic disorders 3(2): 107-117.
Lessey, B. A., L. Damjanovich, C. Coutifaris, A. Castelbaum, S. M. Albelda and C. A. Buck
(1992). "Integrin adhesion molecules in the human endometrium. Correlation with the
normal and abnormal menstrual cycle." The Journal of clinical investigation 90(1): 188-
195.
Lessey, B. A., Y. Gui, K. B. Apparao, S. L. Young and J. Mulholland (2002). "Regulated
expression of heparin-binding EGF-like growth factor (HB-EGF) in the human
endometrium: a potential paracrine role during implantation." Molecular reproduction
and development 62(4): 446-455.
Li, T. C., P. Dockery, A. W. Rogers and I. D. Cooke (1989). "How precise is histologic dating of
endometrium using the standard dating criteria?" Fertility and sterility 51(5): 759-763.
Liaw, L., D. E. Birk, C. B. Ballas, J. S. Whitsitt, J. M. Davidson and B. L. Hogan (1998).
"Altered wound healing in mice lacking a functional osteopontin gene (spp1)." The
Journal of clinical investigation 101(7): 1468-1478.
Linjawi, S., T. C. Li, E. M. Tuckerman, A. I. Blakemore and S. M. Laird (2004). "Expression of
interleukin-11 receptor alpha and interleukin-11 protein in the endometrium of normal
fertile women and women with recurrent miscarriage." Journal of reproductive
immunology 64(1-2): 145-155.
Mackenna, A., T. C. Li, C. Dalton, A. Bolton and I. Cooke (1993). "Placental protein 14 levels in
uterine flushing and plasma of women with unexplained infertility." Fertility and sterility
59(3): 577-582.
Matsuzaki, S., M. Canis, C. Vaurs-Barriere, O. Boespflug-Tanguy, B. Dastugue and G. Mage
(2005). "DNA microarray analysis of gene expression in eutopic endometrium from
patients with deep endometriosis using laser capture microdissection." Fertility and
sterility 84 Suppl 2: 1180-1190.
Mihm, M., S. Gangooly and S. Muttukrishna (2011). "The normal menstrual cycle in women."
Animal reproduction science 124(3-4): 229-236.
Mirkin, S., M. Arslan, D. Churikov, A. Corica, J. I. Diaz, S. Williams, S. Bocca and S.
Oehninger (2005). "In search of candidate genes critically expressed in the human
122
endometrium during the window of implantation." Human reproduction 20(8): 2104-
2117.
Morris, H. R., A. Dell, R. L. Easton, M. Panico, H. Koistinen, R. Koistinen, S. Oehninger, M. S.
Patankar, M. Seppala and G. F. Clark (1996). "Gender-specific glycosylation of human
glycodelin affects its contraceptive activity." The Journal of biological chemistry
271(50): 32159-32167.
Mosher, W. D. and W. F. Pratt (1991). "Fecundity and infertility in the United States: incidence
and trends." Fertility and sterility 56(2): 192-193.
Mulayim, N., S. F. Palter, U. A. Kayisli, L. Senturk and A. Arici (2003). "Chemokine receptor
expression in human endometrium." Biology of reproduction 68(5): 1491-1495.
Murray, M. J., W. R. Meyer, R. J. Zaino, B. A. Lessey, D. B. Novotny, K. Ireland, D. Zeng and
M. A. Fritz (2004). "A critical analysis of the accuracy, reproducibility, and clinical
utility of histologic endometrial dating in fertile women." Fertility and sterility 81(5):
1333-1343.
Navot, D., M. R. Drews, P. A. Bergh, I. Guzman, A. Karstaedt, R. T. Scott, Jr., G. J. Garrisi and
G. E. Hofmann (1994). "Age-related decline in female fertility is not due to diminished
capacity of the uterus to sustain embryo implantation." Fertility and sterility 61(1): 97-
101.
no, M., G. Raab, K. Lau, J. A. Abraham and M. Klagsbrun (1994). "Purification and
characterization of transmembrane forms of heparin-binding EGF-like growth factor."
The Journal of biological chemistry 269(49): 31315-31321.
Noci, I., P. Borri, O. Chieffi, G. Scarselli, R. Biagiotti, D. Moncini, M. Paglierani and G. Taddei
(1995). "I. Aging of the human endometrium: a basic morphological and
immunohistochemical study." European journal of obstetrics, gynecology, and
reproductive biology 63(2): 181-185.
Noyes, R. W., A. T. Hertig and J. Rock (1975). "Dating the endometrial biopsy." American
journal of obstetrics and gynecology 122(2): 262-263.
Oehninger, S., C. C. Coddington, G. D. Hodgen and M. Seppala (1995). "Factors affecting
fertilization: endometrial placental protein 14 reduces the capacity of human spermatozoa
to bind to the human zona pellucida." Fertility and sterility 63(2): 377-383.
Okamoto, N., A. Uchida, K. Takakura, Y. Kariya, H. Kanzaki, L. Riittinen, R. Koistinen, M.
Seppala and T. Mori (1991). "Suppression by human placental protein 14 of natural killer
cell activity." American journal of reproductive immunology 26(4): 137-142.
Paria, B. C., S. K. Das, R. A. Mead and S. K. Dey (1994). "Expression of epidermal growth
factor receptor in the preimplantation uterus and blastocyst of the western spotted skunk."
Biology of reproduction 51(2): 205-213.
123
Paria, B. C., W. Ma, J. Tan, S. Raja, S. K. Das, S. K. Dey and B. L. Hogan (2001). "Cellular and
molecular responses of the uterus to embryo implantation can be elicited by locally
applied growth factors." Proceedings of the National Academy of Sciences of the United
States of America 98(3): 1047-1052.
Petersen, A., U. Bentin-Ley, V. Ravn, K. Qvortrup, S. Sorensen, H. Islin, A. Sjogren, S.
Mosselmann and L. Hamberger (2005). "The antiprogesterone Org 31710 inhibits human
blastocyst-endometrial interactions in vitro." Fertility and sterility 83 Suppl 1: 1255-
1263.
Petterson, B., K. B. Nelson, L. Watson and F. Stanley (1993). "Twins, triplets, and cerebral palsy
in births in Western Australia in the 1980s." BMJ 307(6914): 1239-1243.
Popovici, R. M., L. C. Kao and L. C. Giudice (2000). "Discovery of new inducible genes in in
vitro decidualized human endometrial stromal cells using microarray technology."
Endocrinology 141(9): 3510-3513.
Porat, N., L. M. Boehnlein, M. A. Barker, P. Kovacs and S. R. Lindheim (2010). "Blastocyst
embryo transfer is the primary determinant for improved outcomes in oocyte donation
cycles." The journal of obstetrics and gynaecology research 36(2): 357-363.
Quinn, C. E. and R. F. Casper (2009). "Pinopodes: a questionable role in endometrial
receptivity." Human reproduction update 15(2): 229-236.
Rehfeld, J. F., X. Zhu, C. Norrbom, J. R. Bundgaard, A. H. Johnsen, J. E. Nielsen, J. Vikesaa, J.
Stein, A. Dey, D. F. Steiner and L. Friis-Hansen (2008). "Prohormone convertases 1/3
and 2 together orchestrate the site-specific cleavages of progastrin to release gastrin-34
and gastrin-17." The Biochemical journal 415(1): 35-43.
Riesewijk, A., J. Martin, R. van Os, J. A. Horcajadas, J. Polman, A. Pellicer, S. Mosselman and
C. Simon (2003). "Gene expression profiling of human endometrial receptivity on days
LH+2 versus LH+7 by microarray technology." Molecular human reproduction 9(5):
253-264.
Robb, L., R. Li, L. Hartley, H. H. Nandurkar, F. Koentgen and C. G. Begley (1998). "Infertility
in female mice lacking the receptor for interleukin 11 is due to a defective uterine
response to implantation." Nature medicine 4(3): 303-308.
Sands, B. E., S. Bank, C. A. Sninsky, M. Robinson, S. Katz, J. W. Singleton, P. B. Miner, M. A.
Safdi, S. Galandiuk, S. B. Hanauer, G. W. Varilek, A. L. Buchman, V. D. Rodgers, B.
Salzberg, B. Cai, J. Loewy, M. F. DeBruin, H. Rogge, M. Shapiro and U. S.
Schwertschlag (1999). "Preliminary evaluation of safety and activity of recombinant
human interleukin 11 in patients with active Crohn's disease." Gastroenterology 117(1):
58-64.
Schwartz, D. and M. J. Mayaux (1982). "Female fecundity as a function of age: results of
artificial insemination in 2193 nulliparous women with azoospermic husbands.
Federation CECOS." The New England journal of medicine 306(7): 404-406.
124
Seppala, M., H. Bohn and Y. Tatarinov (1998). "Glycodelins." Tumour biology : the journal of
the International Society for Oncodevelopmental Biology and Medicine 19(3): 213-220.
Sharkey, A. M. and S. K. Smith (2003). "The endometrium as a cause of implantation failure."
Best practice & research. Clinical obstetrics & gynaecology 17(2): 289-307.
Shuya, L. L., E. M. Menkhorst, J. Yap, P. Li, N. Lane and E. Dimitriadis (2011). "Leukemia
inhibitory factor enhances endometrial stromal cell decidualization in humans and mice."
PloS one 6(9): e25288.
Simon, C., J. Oberye, J. Bellver, C. Vidal, E. Bosch, J. A. Horcajadas, C. Murphy, S. Adams, A.
Riesewijk, B. Mannaerts and A. Pellicer (2005). "Similar endometrial development in
oocyte donors treated with either high- or standard-dose GnRH antagonist compared to
treatment with a GnRH agonist or in natural cycles." Human reproduction 20(12): 3318-
3327.
Singh, M., P. Chaudhry and E. Asselin (2011). "Bridging endometrial receptivity and
implantation: network of hormones, cytokines, and growth factors." The Journal of
endocrinology 210(1): 5-14.
Song, H., H. Lim, S. K. Das, B. C. Paria and S. K. Dey (2000). "Dysregulation of EGF family of
growth factors and COX-2 in the uterus during the preattachment and attachment
reactions of the blastocyst with the luminal epithelium correlates with implantation
failure in LIF-deficient mice." Molecular endocrinology 14(8): 1147-1161.
Speroff, L. and M. A. Fritz (2005). Clinical gynecologic endocrinology and infertility.
Philadelphia, Lippincott Williams & Wilkins.
Steck, T., R. Giess, M. W. Suetterlin, M. Bolland, S. Wiest, U. G. Poehls and J. Dietl (2004).
"Leukaemia inhibitory factor (LIF) gene mutations in women with unexplained infertility
and recurrent failure of implantation after IVF and embryo transfer." European journal of
obstetrics, gynecology, and reproductive biology 112(1): 69-73.
Stewart, C. L., P. Kaspar, L. J. Brunet, H. Bhatt, I. Gadi, F. Kontgen and S. J. Abbondanzo
(1992). "Blastocyst implantation depends on maternal expression of leukaemia inhibitory
factor." Nature 359(6390): 76-79.
Talbi, S., A. E. Hamilton, K. C. Vo, S. Tulac, M. T. Overgaard, C. Dosiou, N. Le Shay, C. N.
Nezhat, R. Kempson, B. A. Lessey, N. R. Nayak and L. C. Giudice (2006). "Molecular
phenotyping of human endometrium distinguishes menstrual cycle phases and underlying
biological processes in normo-ovulatory women." Endocrinology 147(3): 1097-1121.
Tietze, C. (1957). "Reproductive span and rate of reproduction among Hutterite women."
Fertility and sterility 8(1): 89-97.
Valbuena, D., M. Jasper, J. Remohi, A. Pellicer and C. Simon (1999). "Ovarian stimulation and
endometrial receptivity." Human reproduction 14 Suppl 2: 107-111.
125
van der Gaast, M. H., K. Beier-Hellwig, B. C. Fauser, H. M. Beier and N. S. Macklon (2003).
"Endometrial secretion aspiration prior to embryo transfer does not reduce implantation
rates." Reproductive biomedicine online 7(1): 105-109.
Varro, A. and J. E. Ardill (2003). "Gastrin: an analytical review." Annals of clinical biochemistry
40(Pt 5): 472-480.
Waites, G. T. and S. C. Bell (1989). "Immunohistological localization of human pregnancy-
associated endometrial alpha 2-globulin (alpha 2-PEG), a glycosylated beta-lactoglobulin
homologue, in the decidua and placenta during pregnancy." Journal of reproduction and
fertility 87(1): 291-300.
Walker, M. C., K. E. Murphy, S. Pan, Q. Yang and S. W. Wen (2004). "Adverse maternal
outcomes in multifetal pregnancies." BJOG : an international journal of obstetrics and
gynaecology 111(11): 1294-1296.
Wang, H., H. O. Critchley, R. W. Kelly, D. Shen and D. T. Baird (1998). "Progesterone receptor
subtype B is differentially regulated in human endometrial stroma." Molecular human
reproduction 4(4): 407-412.
Wang, H. and S. K. Dey (2006). "Roadmap to embryo implantation: clues from mouse models."
Nature reviews. Genetics 7(3): 185-199.
Ware, C. B., M. C. Horowitz, B. R. Renshaw, J. S. Hunt, D. Liggitt, S. A. Koblar, B. C. Gliniak,
H. J. McKenna, T. Papayannopoulou, B. Thoma and et al. (1995). "Targeted disruption of
the low-affinity leukemia inhibitory factor receptor gene causes placental, skeletal, neural
and metabolic defects and results in perinatal death." Development 121(5): 1283-1299.
Wen, S. W., K. Demissie, Q. Yang and M. C. Walker (2004). "Maternal morbidity and obstetric
complications in triplet pregnancies and quadruplet and higher-order multiple
pregnancies." American journal of obstetrics and gynecology 191(1): 254-258.
Wen, S. W., G. Smith, Q. Yang and M. Walker (2004). "Epidemiology of preterm birth and
neonatal outcome." Seminars in fetal & neonatal medicine 9(6): 429-435.
Westergaard, L. G., N. Wiberg, C. Y. Andersen, S. B. Laursen, A. Kliem, J. G. Westergaard and
B. Teisner (1998). "Circulating concentrations of placenta protein 14 during the natural
menstrual cycle in women significantly reflect endometrial receptivity to implantation
and pregnancy during successive assisted reproduction cycles." Human reproduction
13(9): 2612-2619.
Wilcox, A. J., D. D. Baird and C. R. Weinberg (1999). "Time of implantation of the conceptus
and loss of pregnancy." The New England journal of medicine 340(23): 1796-1799.
Wiley, L. M., J. X. Wu, I. Harari and E. D. Adamson (1992). "Epidermal growth factor receptor
mRNA and protein increase after the four-cell preimplantation stage in murine
development." Developmental biology 149(2): 247-260.
126
Williams, R. L., D. J. Hilton, S. Pease, T. A. Willson, C. L. Stewart, D. P. Gearing, E. F.
Wagner, D. Metcalf, N. A. Nicola and N. M. Gough (1988). "Myeloid leukaemia
inhibitory factor maintains the developmental potential of embryonic stem cells." Nature
336(6200): 684-687.
Xie, H., H. Wang, S. Tranguch, R. Iwamoto, E. Mekada, F. J. Demayo, J. P. Lydon, S. K. Das
and S. K. Dey (2007). "Maternal heparin-binding-EGF deficiency limits pregnancy
success in mice." Proceedings of the National Academy of Sciences of the United States
of America 104(46): 18315-18320.
Yoo, H. J., D. H. Barlow and H. J. Mardon (1997). "Temporal and spatial regulation of
expression of heparin-binding epidermal growth factor-like growth factor in the human
endometrium: a possible role in blastocyst implantation." Developmental genetics 21(1):
102-108.