Gene Expression Proile of
Normal and Compromised Placentas
Vasilis Sitras MD
A dissertation for the degree of Philosophiae Doctor
University of Tromsø
Institute of Clinical Medicine
University Hospital of North Norway
Department of Obstetrics and Gynecology
June 2009
+
Cover pictures
Upper-left:
Human term placenta plastinated by Vasilis Sitras, exhibited at the Department of Human
Anatomy, Faculty of Medicine, University of Tromsø.
Upper-right:
Doppler blood flow velocity waveforms of the uterine artery with protodiastolic “notching”
(arrow) indicating increased utero-placental vascular impedance.
Lower-left:
Volcano-plot showing differentially expressed genes from a microarray experiment. The color
indicates up-(red) or down- regulation (green).
Lower-right:
Microscopic view of chorionic villus demonstrating laeverin immunofluorescence (green) in
the cell-membrane of the syncytiotrophoblasts.
To my sons
Vangelis and Alexandros
“The difficult is a bagatelle, the impossible is a challenge”
Solan Gundersen
Flåklypa Grand Prix (1975)
CONTENTS
ACKNOWLEDGEMENTS……………………………………………………......................
7
ABBREVIATIONS………………………………………………………………………....
9
ABSTRACT…………………………………………………………….............................
10
LIST OF PAPERS………………………………………………………………………….
12
1. INTRODUCTION..................................................................................................
13
1.1 Placenta: The organ of life......………..…………………………………...
13
1.2 Feto-placental-maternal circulation...…………………………………….
14
1.3 Gene expression...………………….………………………………………
16
2. AIMS OF THE THESIS…………………………………………………………………
17
3. MATERIALS AND METHODS………………………………………………………….
17
3.1 Study population ………….………….……………………........................
17
3.2 Hemodynamics……………………………………………………………..
19
3.3 Microarrays………………………………….……………………...............
20
3.4 Real-time reverse-transcriptase polymerase chain reaction…………..
28
3.5 Placental morphology……………………………………………………...
30
3.6 Urinary human chorionic gonadotropin measurement……………........
31
3.7 Leptin immunofluorescence……………………………………………….
31
4. MAIN RESULTS………………………………………………………………………
32
4.1 Paper I………………………………………………………………............
32
4.2 Paper II………………..…………………………………………….............
33
4.3 Paper III…………………...…………………………………………...........
35
5. DISCUSSION…………………………………………………………………………
36
5.1 Gene expression profile of the normal placenta...………………..........
36
5.2 Placental gene expression in parturition…………………………..........
37
5.3 Placental gene expression in pregnancy disorders……………............
38
5.4 Placental angiogenesis in healthy and compromised pregnancies......
40
5.5 Inflammatory pathways in early-onset preeclampsia and IUGR...........
40
5.6 Limitations and strength of the studies……………………………..........
41
6. CONCLUSIONS............................................................................................................
44
7. FUTURE PERSPECTIVES.............................................................................................
44
8. REFERENCE LIST........................................................................................................
45
APPENDIX: PAPERS I-III
7
ACKNOWLEDGEMENTS
This thesis is a result of consistency, inspiration and enthusiasm; values imbibed to me by my
father and my supervisor. They both share two common characteristics: integrity and a warm
heart. I am privileged to have Prof. Ganesh Acharya as my “guru”; he is a role model as a
clinician, a researcher and a human being, but most of all he is “a friend for life”.
I would like to thank Professor Ruth Hracky Paulssen, my second supervisor, for
guiding me through the “simple complexity” of microarrays and for adding a “woman’s
touch” in this project. I would also like to thank the staff at the Microarray Resource Center in
Tromsø (MRCT), Halvor Grønaas, Jørn Leirvik and Thea Charlotte Smedsrud, for their
excellent technical assistance. Especially, Chris Fenton, the newest member of the MRCT
team, has brought fresh ideas and invented novel computational analysis methods. A warm
thank to Åse Vårtun and Mona Nystad who with their smile, positive attitude and kindness
made my working days a “family business”. I am grateful to Prof. Jan Martin Maltau, Prof.
Pål Øian, Dr. Ingard Nilsen, Olaug Kråkmo and Tove Olsen, the “leader-team” of the
Department of Obstetrics and Gynecology, University Hospital of Northern Norway, for
giving me the opportunity to combine research with clinical training. Many thanks to my
colleagues and midwifes that supported me and helped recruiting the study participants.
Special thanks to Dr. Poul Ladehoff, for mentoring during the early stage of my clinical
training; Inger Sperstad for creating the study database, Rod Wolstenholme for editing the
figures and Turid Bakkevoll for editing the text of the thesis. Many thanks to Prof. Raafat ElGewely for stimulating discussions on interesting aspects of molecular biology and to Dr. Tor
Arne Hanssen for valuable input on placental pathology.
The studies were financially supported by a grant from the Northern Norway Regional
Health Authority (Grant No: 721484).
My parents, Voula and Vangelis, and my sister Dina have always been “here” for me
even if we live in the two extremes of Europe. My optimistic attitude in life is a result of a
wonderful childhood they gave me. Kjellaug and Ernst Sneve, my parents in law, accepted me
as “a son” and I am deeply grateful for their love and support.
Oddvar and Helena Bogetvedt were reserve-grandparents for our sons in Tromsø and
helpful with babysitting when work overtook our roles as parents. Oddvar passed away
suddenly on the 15th of January 2009, at the age of sixty. He was my supervisor when I
worked in the Department of Anatomy, University of Tromsø and became a good friend and
family member. He taught me to always choose what has meaning in life, put maximum effort
8
to accomplish it and, most importantly, enjoy every minute of it. I miss his friendship and
guidance.
Monica, my wife, is the ideal partner. Our journey in life is filled with love, mutual
respect and happiness. She is critical when needed but always understanding and caring. She
is the perfect colleague, friend and mother. Together we make a “dream team”.
Tromsø, June 2009
Vasilis Sitras
9
ABBREVIATIONS
CT
threshold cycle
DNA
deoxyribonucleic acid
ENG
endoglin
FOS
v-fos murine osteosarcoma viral oncogene homolog
FOSB
FBJ murine osteosarcoma viral oncogene homolog B
GNGT1
guanidine nucleotide binding protein (G protein), gamma transducing activity
polypeptide 1
hCG
human chorionic gonadotropin
HELLP
hemolysis, elevated liver enzymes, low platelets
hPL
human placental lactogen
HSD17B4
hydroxysteroid (17-beta) dehydrogenase 4
IUGR
intrauterine growth restriction
LEP
leptin
LRAP
leucocyte-derived arginine aminopeptidase
mRNA
messenger ribonucleic acid
PI
pulsatility index
PlGF
placental growth factor
RT-PCR
real-time reverse-transcriptase polymerase chain reaction
sFlt-1
soluble fms-like tyrosine kinase 1
VEGF
vascular endothelial growth factor
10
ABSTRACT
Objective
Placenta has an important function of sustaining life in utero. Role of placental gene
expression in the physiology of parturition and pathogenesis of pregnancy disorders are
poorly understood.
The aims of this thesis were to:
1. Investigate the effect of labor on global gene expression profile of normal placentas
2. Compare placental gene expression profile of uncomplicated pregnancies with that of
pregnancies complicated by severe preeclampsia and intrauterine growth restriction
(IUGR)
Materials and Methods
Hemodynamic assessment of the maternal uterine and feto-placental circulations was
performed ≤72 hours before delivery using Doppler ultrasonography. Global gene expression
profile was investigated using microarrays in placental samples collected after delivery from
women with normal pregnancies (n=34), and pregnancies complicated by preeclampsia
(n=16) and IUGR (n=8). The effect of parturition on gene expression was studied comparing
placentas obtained from healthy women after normal delivery (n=17) with placentas obtained
from women delivered by elective cesarean section (n=17). Placental gene expression profiles
of women with severe preeclampsia (i.e. BP≥160/110 mmHg and proteinuria ≥2+ in dipstick)
and IUGR due to placental insufficiency (i.e. estimated fetal weight <5th percentile for the
gestational age with hemodynamic signs of redistribution of blood flow to the fetal brain)
were compared with healthy controls. Microarray results were validated at the transcript and
protein level by real-time reverse-transcriptase polymerase chain reaction (RT-PCR),
placental immunofluorescence and urinary electrochemiluminescence immunoassay.
Results
Gene expression profile was similar in healthy placentas obtained following normal delivery
and elective cesarean section. Placental genes were differentially expressed in preeclampsia
and IUGR compared to normal controls indicating a central role of the placenta in the
pathogenesis of these pregnancy-specific disorders. In particular, 16 genes were able to
differentiate preeclamptic from normal placentas in our samples after supervised clustering.
Several known (leptin, Flt-1, endoglin) and some novel genes (laeverin) and pathways
(angiogenesis, hypoxia, Alzheimer, Notch) were found to be involved in the pathogenesis of
preeclampsia. Subgroup analysis comparing early- (i.e. ≤34 weeks) with late-onset
11
preeclamptic placentas showed different genetic signatures with oxidative stress,
inflammation and endothelin signaling pathways mainly involved in early-onset disease.
In IUGR, genes involved in glucocorticoid-metabolism and inflammation mediated by
chemokine and cytokine signaling pathway were differentially expressed compared with
controls. None of the known imprinted placental genes were differentially expressed.
Conclusion
Labor does not significantly alter the global gene expression profile in near term placenta.
Placental gene expression profile is altered in severe preeclampsia and IUGR. Early-onset
preeclampsia has a different genetic signature compared with late-onset preeclampsia
supporting the possibility of different pathogeneses. Genes involved in inflammatory
pathways are up-regulated both in early-onset preeclampsia as well as in IUGR, indicating
that they might share common pathogenetic features.
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LIST OF PAPERS
This thesis is based on the following papers, which are referred in the text by Roman
numerals:
I.
Sitras V, Paulssen RH, Grønaas H, Vårtun Å, Acharya G. Gene Expression in
Laboring and Non-laboring Human Placenta Near Term. Mol Hum Reprod. 2008;
14(1):61-65.
II.
Sitras V, Paulssen RH, Grønaas H, Leirvik J, Hanssen TA, Vårtun Å, Acharya G.
Differential Placental Gene Expression in Severe Preeclampsia. Placenta 2009; 30
(5) : 424-433.
III.
Sitras V, Paulssen RH, Leirvik J, Vårtun Å, Acharya G. Placental Gene Expression
in Intrauterine Growth Restriction Due to Placental Insufficiency. Reproductive
Sciences 2009; doi:10.1177/1933719109334256.
13
1. INTRODUCTION
Evolutionary changes, starting 120 million years ago, led to the creation of placenta and the
appearance of new species, the eutherian animals. Although the placenta has similar function
in all species, different species developed placentas of unique structural complexity. Indeed,
from a simple membrane in the inner surface of the egg in reptiles and birds, the placenta has
evolved through several developmental steps to a complex organ in primates. These
phylogenetic changes necessitated drastic reorganization of the morphological and molecular
structure of the placenta, driven by mitochondrial and nuclear genetic rearrangement (Carter
and Mess, 2007). Thus far, however, the molecular mechanisms behind this process are
unknown. Even the developmental processes that occur in the placenta, from conception to
birth, in a single species are still not completely clear. Recent longitudinal genomic studies in
the murine placenta suggest that a transition occurs during mid-gestation, with usage of
different genes by the same cellular populations, in the absence of any major morphological
change (Knox and Baker, 2008). In the human placenta, developmental changes are thought
to be controlled by different factors such as oxygen tension, placental hormones (hPL, hCG),
growth factors (VEGF, PlGF) etc. Unbalanced regulation of these factors could lead to
impaired placental development causing pregnancy complications such as miscarriage,
preeclampsia and IUGR (Lyall and Kaufmann, 2000).
1.1 Placenta: The organ of life
Placenta is a temporary but essential organ in human development. It is highly vascular and
mediates gas, nutrient and waste exchange between the fetal and maternal circulations. It is
also an important endocrine organ that produces and metabolizes hormones in autocrine,
paracrine and endocrine fashion, influencing its own function as well as that of the fetus and
the mother. Additionally, the placenta seems to protect the fetus-allograft from the maternal
immune system. One could argue that the placenta is a living organism itself as it undertakes
some of the functions performed after birth by the lungs, gut, kidneys and endocrine organs.
Indeed, mutations in genes involved in placental development and morphogenesis result in
early fetal loss (Rossant and Cross, 2001).
Recent, evolutionary morphologic analysis presents evidence that the human placenta
has evolved from a common eutherian ancestor (Wildman et al., 2006). From a morphologic
point of view, placentas of different species can be divided in several types depending on the:
(i) Feto-maternal interface, which is the number of maternal tissue-layers penetrated by fetal
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tissue (iii) Feto-maternal interdigitation, which is the form of contact between maternal and
fetal tissue/blood and (iii) Shape. The human placenta has: (i) Hemochorial interface, because
trophoblastic cells are highly invasive and penetrate in the maternal spiral arteries. (ii) Villous
interdigitation and (iii) Discoid shape.
The human placenta undergoes a continuous remodeling throughout the pregnancy in
response to changing functional needs. Formation of the placenta starts after the union
between a sperm and an egg (fertilization) forming the embryo, which undergoes mitotic
divisions to form the blastocyst. This has two different cell populations: the inner cell mass
which develops in the embryo proper and the trophectoderm from which the placenta and the
extraembryonic membranes are formed. Already at this early stage, during implantation of the
blastocyst in the decidua (i.e. the uterine epithelium during pregnancy), the trophectoderm is
responsible for the interconnection of the developing embryo with the mother and thus plays a
crucial role in the successful outcome of pregnancy (Dey et al., 2004). Development of the
placental villi, the functional units of the placenta, starts when cytotrophoblast proliferation
gives rise to branches that protrude into the lacunae containing maternal blood to form the
villous tree (primary villi). At 5 weeks gestation these villi are invaded centrally by extraembryonic mesenchyma, transforming into secondary villi. During the second trimester, fetoplacental capillaries are formed de novo (vasculogenesis) in the central stroma of the villi,
transforming them into tertiary villi which have several branches and sub-branches with
blunt-ended extremities that form the “villous tree”.
At term, the feto-maternal hemochorial interface provides a surface of 15 m2 for
exchange and consists of five distinct layers: (i) Villous syncytiotrophoblast lining directly the
intervillous space which bathes in maternal blood (ii) Basal membrane (iii) Villous core
containing extracellular matrix and pericytes (iv) Basement membrane attached to capillary
endothelial cells and (v) Fetal capillary endothelial cells where fetal blood circulates
(Georgiades et al., 2002).
1.2 Feto-placental-maternal circulation
In the 16th and 17th century, Aranzio and William Harvey described that the uterine and
umbilical vessels are not directly connected in the placenta and that fetal blood is circulated
within fetal tissues and to some extent into the placenta (Fishman and Richards, 1964). The
driving force directing blood in the placenta is probably the pressure gradient between the two
umbilical arteries and vein on the fetal side, and between the uterine arteries and veins on the
maternal side.
15
During embryogenesis, hematopoiesis occurs within the yolk-sac (Pereda and Niimi,
2008) and within the villous capillaries from hematopoietic stem cells (Demir et al., 1989).
The feto-placental circulation is established by seven weeks of gestation. Morphological
studies have demonstrated continuous evolution of the different villous types during gestation
(Kaufmann et al., 1979). In the first and second trimester, trophoblastic sprouts generate new
mesenchymal villi that transform into poorly vascularised immature intermediate villi causing
high placental vascular impedance (Fisk et al., 1988). In the third trimester, mesenchymal villi
transform to mature intermediate villi, which don’t self-replicate but elongate and produce
terminal villi. The decrease in trophoblast proliferation along with a relative increase in
endothelial proliferation from 26 weeks of gestation until term causes a switch from
branching to non-branching angiogenesis (i.e. formation and differentiation of new blood
vessels). This results in the formation of long and slender villous trees containing one or two
poorly branched capillary loops, which in turn, cause a decrease in feto-placental vascular
impedance (Jauniaux et al., 1992). These morphological changes in villous tree development
are reflected in the hemodynamic changes observed in the feto-placental circulation
throughout pregnancy. Indeed, ultrasound assessment of the feto-placental circulation shows a
gradual increase in the fraction of the fetal cardiac output distributed to the placenta in the
second trimester (Vimpeli et al., 2008) with slight decrease towards term (Kiserud et al.,
2006).
The maternal side of the placenta is perfused ~ 83% by the two uterine arteries and
~17% by the ovarian arteries (Wehrenberg et al., 1977), and about 75% of the uterine flow
circulates in the intervillous space (Bartels and Moll, 1962). There is a steady increase in
uterine blood flow with advancing gestation (Thaler et al., 1990), although the uterine blood
flow normalized by fetal weight decreases (Konje et al., 2001). There is an increase in blood
flow in the common iliac artery, but the blood flow in the external iliac artery decreases,
indicating redistribution of lower extremity blood flow to the pregnant uterus and the placenta
(Palmer et al., 1992). Clearly, a carefully orchestrated and balanced maturation of the fetoplacental and utero-placental circulations is necessary for the physiologic function of the
placenta, which is reflected in fetal and maternal wellbeing throughout gestation (Kingdom
and Kaufmann, 1997). However, maternal uterine artery blood flow and oxygen delivery to
the placenta, are not the sole determinants of fetal wellbeing. Indeed, a recent study on
maternal oxygen transport in relation to fetal growth in high altitude pregnancies of two
genetically different populations, concluded that the largest independent determinant of fetal
weight was placental weight (Zamudio et al., 2007). Thus, genetic programming of the
16
placenta might play an essential role not only in determining pregnancy outcome, but also in
the development of disease in adult life (Giussani, 2007).
1.3 Gene expression
The expression of deoxyribonucleic acid (DNA) follows the rules of a central dogma which
occurs in two stages: (i) Transcription, during which DNA is transcribed into messenger RNA
(mRNA) and (ii) Translation, during which mRNA serves as a template for protein synthesis.
The term gene expression refers to the process of decoding the DNA information, from
gene to protein. The expression level of different genes among cells or tissues varies
depending on the function, developmental stage or disease state, resulting in different
phenotypes (i.e. the observable properties of an organism that are produced by the interaction
of the genotype and the environment). Regulation of gene expression is complex, but
schematically it can be divided in four different levels as shown in Figure 1: The first and
most important level is transcription initiation. The next is maturation of pre-mRNA to
mRNA by alternative splicing [removal of non-protein coding DNA sequences (introns) and
putting together coding sequences (exons)]. Further regulation of gene expression occurs at
the translation level, through mRNA editing, and finally, at the protein level through posttranslational modifications that can determine protein activity. Recently, approximately 700
human small-RNAs (consisting of 20-30 nucleotides) have been shown to play an important
role in gene expression (Kim et al., 2009) by interfering with chromatin formation, mRNA
stability and translation control.
DNA
Nucleus
Cytoplasm
Splicing
Pre-mRNA
Transcription
mRNA
Translation
Protein
Figure 1. Schematic description of gene expression in a living cell
17
2. AIMS OF THE THESIS
The main aim of this thesis was to investigate the global gene expression profile in healthy
and compromised human placentas. More specific objectives were to study the effect of:
a) normal labor, b) severe preeclampsia, and c) IUGR due to placental insufficiency on global
placental gene expression profile.
Additional aims were to investigate the differences in placental gene expression profiles
between early- and late-onset preeclampsia, and between IUGR with and without
preeclampsia.
3. MATERIALS & METHODS
3.1 Study population
Pregnant women were recruited from the Department of Obstetrics and Gynecology,
University Hospital of Northern Norway, during the period January 2005 - November 2006.
The study protocol was approved by the Regional Committee for Medical Research Ethics
(REK-Nord 94/2004). A total of 79 pregnant Caucasian women were recruited to the study.
Informed written consent was obtained from all the participants. The flow of participants is
shown in Figure 2.
Total participants
(n= 79)
Healthy controls
(n= 50)
IUGR
Study III
(n= 8)
Severe Preeclampsia
(n= 21)
Selected for
Study I
(n= 34)
Excluded
(poor mRNA quality)
(n= 5)
Normal delivery
(n= 17)
Elective CS
(n= 17)
Randomly
selected controls
Study II
(n= 21)
Severe
Preeclampsia
Study II
(n= 16)
IUGR with
Preeclampsia
(n= 4)
IUGR without
Preeclampsia
(n= 4)
Randomly
selected controls
Study III
(n= 8)
Figure 2. Flow diagram showing the phenotypes of the study participants
18
Gestational age was assigned by ultrasound at 18-20 weeks gestation. Severe
preeclampsia was defined as a blood pressure (BP) of at least 160 mmHg systolic and/or 110
mmHg diastolic, with proteinuria ≥2+ on dipstick, measured on at least two occasions 6
hours apart while the patient was on bed rest, or hemolysis, elevated liver enzymes and low
platelet (HELLP) syndrome, after the 20th week of gestation (Sibai et al., 2005). Women with
pre-existing chronic hypertension, renal disease, lupus erythematosus, diabetes, and
gestational hypertension without proteinuria were excluded. IUGR was defined as: (i) birth
weight below the 5th percentile for the gestational age according to Norwegian nomograms
(Skjaerven et al., 2000) (ii) oligohydramnios (i.e. amniotic fluid index ≤5th percentile for the
gestational age) in the absence of fetal congenital urinary tract abnormality (Phelan et al.,
1987) and (iii) abnormal fetal Doppler parameters (absent or reversed end-diastolic flow in
the umbilical artery or signs of blood flow redistribution to the fetal brain, i.e. middle cerebral
artery pulsatility index (PI) <5th percentile and umbilical artery PI >95th percentile). Women
with small for gestational age fetuses with normal Doppler, known fetal chromosomal or
structural congenital abnormality or infection were excluded.
Physical examination and ultrasonography were performed and blood and urinary
samples were collected ≤72 hours before delivery. Placental and cord blood samples were
collected immediately after delivery, following a standardized technique by two designated
persons. Blood samples from the umbilical artery and vein were separately analyzed for acidbase status using a spectrophotometer ABL 725 Blood Gas Analyzer (Radiometer Medical
AS, Copenhagen, Denmark) within 15 minutes of collection. Samples that had a pCO2
difference of <1 kPa (7.5 mmHg) and pH difference of <0.03 units between umbilical arterial
and venous blood were considered to have mixed blood and were discarded. The neonatal and
placental weights were determined using electronic scales (Seca Model 727, Vogel & Halke,
Hamburg, Germany). Outcome of pregnancy and information on the neonates were
prospectively recorded. All collected data were stored in an electronic database (Figure 3).
19
Figure 3. Microsoft Access data file showing the list of variables recorded for each participant of the study
3.2 Hemodynamics
Maternal uterine and feto-placental hemodynamics was studied using Acuson Sequoia 512
ultrasound system (Mountain view, CA, USA) with a 2.5 - 6 MHz curvilinear transducer.
After a survey of fetal anatomy, biometry was performed to estimate the fetal weight. Blood
flow velocity waveforms were obtained from the maternal uterine arteries, fetal middle
cerebral artery, and umbilical artery and vein at a free-loop of the cord using color directed
pulsed-wave Doppler. A large sample volume (5-10mm) and lowest possible insonation angle
(kept below 20 degrees) were used. Velocity measurements were performed online and angle
correction was used as required. Values used for statistics were an average of at least three
cardiac cycles. The PI was calculated from the arterial blood velocity waveforms as:
PI = (peak systolic velocity − end-diastolic velocity)/time-averaged maximum velocity
Umbilical vein inner diameter was measured at a free-loop of the cord using B-mode
ultrasound. Uterine artery diameter was measured using power Doppler angiography
(Acharya et al., 2007; Konje et al., 2001). The values used for analysis were an average of
three separate measurements. The volume blood flows (Q) of the umbilical vein and uterine
20
arteries were calculated as the product of time-averaged intensity weighted mean velocity
(Vmean) and the cross-sectional area (CSA) of the blood vessel:
Q (ml/min) = Vmean (cm/s) * CSA (cm2)* 60
Assuming that the blood vessel has a circular lumen, CSA was calculated as:
CSA = π * Radius2 = 0.785*Diameter2
3.3 Microarrays
The founding report on microarray technique was based on the genome of the flowering plant
Arabidopsis thaliana containing the smallest genome of any higher eukaryote examined to
date (Schena et al., 1995). It was a pioneering study demonstrating a novel high-capacity
system that could monitor the expression of 45 genes in parallel. Although recent advances in
sequencing and microarray technology have produced high-density microarrays, the basic
methodology of DNA microarray remains similar: RNA samples or targets are hybridized to
known cDNAs/oligo probes on the arrays. One such sequencing effort is the Human Genome
Project. It was established as an international project with the aim to identify the
chromosomal position and genomic organization of all human genes. In 2004, the
International Human Genome Sequencing Consortium completed 99% of the human DNA
sequencing and announced that “the human genome could be regarded as effectively known”
(IHGSC, 2004).
A microarray is a small (approximately 1.3x1.3 cm) glass, silicon slide or chip upon
the surface of which a large number of known human gene sequences (usually from 500 to
35 000) are fixed in defined positions in a matrix-fashion. There are two types of microarrays:
(i) cDNA microarrays where individual DNA sequences are spotted on the array (usually up
to 1500 base pairs) (ii) Oligonucleotide-arrays where oligonucleotides (usually 70-mers) are
either printed or robotically synthesized directly on the array. Microarray technology offers
the potential to analyze the expression state of each individual gene represented in the genome
in a single experiment. Yet, with this method it is impossible to quantify the absolute
expression level of a gene. It is thus necessary to perform comparative studies, for example
normal versus diseased tissue samples, in order to obtain the relative gene expression profile
of a given sample. A typical microarray experiment is executed following a number of
necessary steps (Figure 4).
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Biological
question
Experimental design
Failed
Microarray experiment
Quality
measurement
Image analysis
Pre-processing
Pass
Analysis
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation
Figure 4. Outline of a typical microarray experiment
Experimental design
The first and most crucial step of microarray experiments is the design (Yang and Speed,
2002) which is dependent on the aim of the study. Microarray studies are comparative studies
and the overall results depend on the phenotypic characteristics of the samples that are
compared. Gene expression profiling of a diseased sample, is based on the relative up- or
down-regulation of each gene, compared to an “assumed” healthy sample which is used as
control. Possible confounding factors that would add “noise” to the data should also be taken
into account. Given that y represents a measurement of gene expression in the placenta, the
following linear model may be calculated:
y = parity + maternal age + delivery method + … + healthy or diseased + ε
where ε is an “error-term” representing the inherent biological variation of the sample and the
measurement error of the system.
There are three main types of experimental design: (i) Direct, where each diseased
sample is matched to one healthy control (pair-wise comparison), (ii) Indirect, where each
diseased sample is matched to a pool of several healthy samples (common reference) and (iii)
Loop design, where each sample is compared with the others in consecutive pairs (Figure 5).
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Direct
Indirect
A
A
C
B
D
Loop
B
A
Common Reference
(C+D)
B
C
Figure 5. Graphical representation of microarray experimental designs. The arrows indicate hybridization
between two mRNA samples (A, B, C etc)
Microarray laboratory procedures
There are five laboratory steps during a microarray experiment:
1. RNA extraction and quantity/quality control
2. Labeling
3. Hybridization
4. Washing
5. Image acquisition
RNA extraction and quantity/quality control
Disruption and homogenization of placental samples were performed in lysis buffer using the
MagNa Lyser Instrument (Roche Applied Science, Germany), according to the
manufacturer’s instructions. Isolation of total RNA was performed using the MagNa Pure
Compact RNA isolation kit and the MagNa Pure Compact Instrument (Roche Applied
Science, Germany) (Paulssen et al., 2006). RNA was quantified by measuring absorbance at
260nm, and RNA purity was determined by the ratios OD260nm/280nm and
OD230nm/280nm using the NanoDrop instrument (NanoDrop® ND-1000, Wilmington,
USA). The RNA integrity was determined by electrophoresis using the Agilent 2100
Bioanalyser (Matriks, Norway). Only RNA samples with a ribosomal RNA 28S/18S ratio of
>1 and/or RNA Integrity Number >7.2 were used for microarray experiments (Schroeder et
al., 2006).
23
Two different microarray platforms were used for the studies of this thesis: (i) A twochannel platform, needing two fluorescent dyes (direct labeling) in study I and a one-channel
platform, based on chemiluminescence (indirect labeling), in studies II and III.
Microarray procedures for study I
For the two-channel platform, total RNA was reverse transcribed and labeled directly with
Cy3- and Cy5- attached dendrimer (Figure 6), using the Genisphere 3DNA 350HS kit
(Genisphere, Montvale, NJ) as described in the manufacturer’s protocol. Hybridizations were
carried out in a TECAN HS4800 instrument (TECAN, Austria) using the formamide-based
hybridization buffer from Genisphere containing 5% dextrane sulphate and 5.5 ng/ml human
COT1-DNA (GIBCO-BRL Life technologies) at 37º for 23 hours. 3DNA dendrimer
hybridizations were carried out in formamide-based hybridization buffer alone. Posthybridization washes were carried out at room temperature with 2x saline sodiumcitrate
(SSC) for 1 min, 0.2% sodium dodecylsulphate (SDS) /2xSSC for 1 min and finally with
0.2xSSC for 30 sec. For global gene expression analysis 35K oligo microarrays were used
that were obtained from the Norwegian Microarray Consortium (http://www.microarray.no).
Briefly, the arrays contained spotted 70-mer oligonucleotides obtained from the Human
Array-Ready Oligo Sets (AROS™) v3.0, OPERON, Germany (http://www.operon.com). The
set contained 34,580 probes representing 24,650 human genes and 37,123 gene transcripts.
The
probe
design
is
fully
based
on
the
Ensembl
Human
13.31
Database
(http://www.ensembl.org) and Human Genome Sequencing Project. As external control
system, the Spot Report oligo validation system (Cat# 252170-7) from Stratagene was used.
The arrays were scanned with the GenePix 4000B scanner (Axon Instruments Inc).
24
Figure 6. Flowchart describing the microarray experiment for study I
Microarray procedures for studies II and III
For the one-channel platform, total RNA samples were processed into digoxigenin (DIG)labeled cRNA (Figure 7) using the Applied Biosystems Chemiluminescent NanoAmp RTIVT Labeling Kit. The labeled DIG-cRNA (10 μg per microarray) was then injected into each
microarray hybridization chamber. Following hybridization at 55°C for 16 hours, the unbound
material was washed from the microarrays. Features that retained bound DIG- labeled cRNA
were visualized using the Applied Biosystems Chemiluniescence Detection Kit. Anti-DIG
alkaline phosphatase was used to hydrolyse a chemiluminescence substrate to generate light at
458nm which was than detected by the Applied Biosystems 1700 Chemiluninescent
Microarray Analyzer. The Human Genome Survey Microarray v.2.0 (Applied Biosystems)
with 32878 probes for the interrogation of 29098 genes was used for microarray analysis.
25
Figure 7. Flowchart describing the microarray experiments for studies II and III
Image analysis
The scanned images are then turned into numbers, called raw data. Each spot on the
microarray is given a number depending on its fluorescence intensity. The main steps during
image analysis are normalization, aiming to remove effects from the technical process from
the data (for example different labeling efficiencies, different scanning parameters etc), and
filtering aiming to eliminate spots that will bias or add noise to the results.
In study I, the features were extracted from the arrays using Genepix 6.0 software
(Axon instruments Inc., 2004). The background estimates were calculated using the
morphological opening method (Soille, 2006). Spots that displayed a signal-to-noise ratio of
less than three or that were significantly saturated (more than 20% saturation among
foreground pixels) were filtered out. The median was used as the averaging measure of the
foreground pixels. After quality control, genes that were present in less than 50% of the
arrays were filtered out. Normalization was carried out using Lowess normalization
(Cleveland, 1979).
In study II, the features were extracted from the arrays using the ABI1700 software.
Normalization was carried out using quantile normalization which forces all the slides to have
the same intensity distribution. Spots on the arrays with less than excellent signal and with
26
expression values missing for ≥25% of the samples in the preeclampsia group or ≥35% in the
control group were filtered out.
In study III, the features were extracted from the arrays using the ABI1700 software.
The gene expression data were analyzed using J-Express pro 2.7 (Molmine, Bergen, Norway)
(Dysvik and Jonassen, 2001). The pre-normalized intensity values were extracted per spot
from the data files. Weak spots with signal to noise ratio <3.0 and control spots were filtered
out. All arrays were normalized by the Lowess method (Cleveland, 1979). Missing expression
levels were inferred using the LSimpute method (Bo et al., 2004).
Data analysis
Microarray experiments generate large amount of data and represent a challenge for
conventional statistical methods. Novel computational methods that are able to handle large
datasets are developed. A major problem in microarray experiments is the abundance of false
positive findings and often adjustment with multiple testing and permutation (i.e. reordering)
of the data is necessary in order to reduce type I errors (false positives). Microarray data
analysis aims to find genes with different fluorescent intensity between arrays, i.e.
differentially expressed genes between distinct phenotypic groups. A gene is differentially
expressed between two groups when it is systematically up- or down-regulated in one group
compared to the other. Differentially expressed genes are usually presented at two levels: (i)
statistical level shown by p-values indicating the probability that the difference in gene
expression of a particular gene between two samples is true and (ii) biological level which is
the mean fold-change in relative over- or under-expression of a particular gene between the
samples. The level of p-value and fold-change above which the genes are “significantly”
differentially expressed is arbitrary. In addition, clustering methods and principal component
analysis (PCA) (Raychaunhuri et al., 2000) that organize genes or samples into groups or
hierarchy based on expression similarity are used in order to make microarray results easier to
understand.
More specifically, for the purpose of finding differentially expressed genes in study I,
we applied an empirical Bayes analysis (Smyth, 2004) using the LIMMA package (Smyth,
2000). The data were analyzed using a two-component linear model. The prior guess of the
number of differentially expressed genes was set to 0.01. Multiple testing was accounted for
by estimating the false discovery rate (set at <15-20%) applying the Benjamini-Hochberg
(Benjamini, 1995) and Storeys less conservative Q-value (Storey, 2002) procedure. A 2.5-fold
change in expression of any gene that was present on ≥ 8 arrays was considered significant, if
27
the difference in fluorescence intensity between two matched samples reached a p-value of <
0.01.
For the purpose of finding differentially expressed genes in study II, we applied an
empirical Bayes analysis using the LIMMA package. The data were analyzed using an
ANOVA two-component linear model accounting for preeclampsia versus normal and
nulliparity versus multiparity. We further used the same method in order to identify potential
differentially expressed genes between early- and late-onset preeclampsia. The cut-off timepoint of early- versus late-onset preeclampsia was 34 weeks gestation. The prior guess of the
proportion of differentially expressed genes was set to 0.01. A two-fold change in expression
of any gene was considered significant, if the difference in fluorescence intensity between the
samples reached a p-value < 0.01. A hierarchical cluster analysis was conducted on the 300
most differentially expressed genes. A Predictive Analysis of Microarrays (PAM) (Tibshirani
et al., 2002) was conducted in order to investigate whether any set of genes provides
predictive power between the groups (preeclampsia versus normal).
In study III, statistical significance was assigned to the genes by using the Significance
Analysis of Microarrays (SAM) methodology (Tusher et al., 2001). PCA was performed on
the 20 genes with best t-scores in the main analysis, in order to obtain discrimination between
IUGR and normal placentas.
Database submission of microarray data
The Microarray Gene Expression Data Society (MGED; http://www.mged.org) has
recommended that all publications using microarray technology should be compliant with the
MIAME (Minimal Information About Microarray Experiments) requirements (Brazma et al.,
2001) and raw data should be available in public repositories such as ArrayExpress (Brazma
et al., 2003) or Gene Expression Omnibus (GEO)(Edgar et al., 2002). All our studies are
MIAME
compliant
and
raw
data
can
be
retrieved
in
GEO
(http://www.ncbi.nlm.nih.gov/projects/geo/) with accession numbers GSE 8375, GSE 10588
and GSE 12216 for study I, II and III respectively.
Gene Annotations
The last step of microarray studies is the interpretation of the results. Lists of differentially
expressed
genes
between
groups
are
examined
in
order
to
find
possible
associations/interactions regarding their biological function (cellular events to which the gene
product contributes), cellular component (location of a gene/protein), molecular function (at a
28
biochemical level) or pathways (molecular signaling networks of communication between
genes/proteins). This process is performed via internet-databases with updated, evidencebased information/knowledge on gene/protein function by using software able to handle large
datasets. In other words, the ultimate scope of every microarray experiment is to find answers
on biological questions such as: What is the role of the differentially expressed genes in
determining the disease phenotype? Which cellular/molecular pathways might be involved?
How does the disease process affect cellular/tissue function and phenotype? Or vice versa,
how does cell/tissue remodeling affect pathogenesis of disease? Are there single genes or sets
of genes that could predict disease severity and outcome; and if so, through which pathways?
Ultimately, could the findings be applied in clinical practice?
For the annotation of the genes, PubGene 2.6™ Database and Analysis Software
(Jenssen et al., 2001) and DAVID Bioinformatics Resources 2007 (Dennis et al., 2003) were
used in study I and Protein ANalysis THrough Evolutionary Relationships (PANTHER) (Mi
et al., 2007) in studies II and III.
3.4 Real-time reverse-transcriptase polymerase chain reaction
As a large amount of genes might be differentially expressed in microarray experiments, the
level of expression of selected genes must be confirmed by other independent methods.
RT-PCR is the method of choice to validate microarray data. It was invented by Kary
Mullis (Mullis, 1990) and is a method for amplifying a specific region of a DNA strand, by
mimicking the DNA duplication process occurring in nature. The process requires a DNAtemplate, one or more specific primers, two enzymes (reverse transcriptase and DNA
polymerase) and several deoxynucleotide triphosphates that serve as “bricks” to build a new,
complementary DNA strand (cDNA). The process consists of three steps: (i) Reverse
transcription and denaturation from single stranded DNA-template to cDNA (ii) Primer
annealing and (iii) Primer extension (elongation) with formation of cDNA. This procedure is
repeated several times, in general 20-35 cycles, so that the amount of cDNA increases
exponentially (Freeman et al., 1999).
In RT-PCR there is an additional fourth step during which a fluorogenic probe is
annealed to the cDNA sequence, between the forward and reverse PCR primers. During
extension the fluorescent molecule is cleaved, emitting fluorescence that is measured
continuously (real-time) and is proportional to the amount of PCR product (Figure 8).
29
Figure 8. Outline of RT-PCT technique, reproduced with permission from King and Sinha, JAMA, 2001
An example of an RT-PCR amplification curve of a gene (MML3) is shown in Figure
9. The curves indicate the fluorescent signal of the PCR product (ΔRn) at each time point,
reaching an exponential phase where there is a doubling of the product with each cycle. The
higher the starting copy number of the gene, the earlier the exponential phase starts. A fixed
fluorescence threshold is set by the operator, significantly above the baseline (red line). A
parameter called CT (threshold cycle) is defined as the cycle number at which the fluorescence
emission exceeds the fixed threshold. The lower the CT, the higher the initial amount of target
gene.
30
Figure 9. Example of RT-PCR amplification curve of a gene (MML3)
In RT-PCR the relative expression of a gene is normalized to genes that are
constitutively active, called housekeeping genes. These genes have a constant expression
regardless of the state of the cell. There are several housekeeping genes in the human placenta
(Vandesompele et al., 2002; Meller et al., 2005). By comparing the CT values of the genes in
question with the CT values of housekeeping genes, one can calculate the expression level of
the genes in different states of the samples, by using different formulas (Livak and
Schmittgen, 2001; Pfaffl, 2001).
3.5 Placental morphology
All placentas were macroscopically examined in the labor ward after delivery for
completeness, number of umbilical cord vessels, color of the membranes and gross
morphology of the maternal surface looking for signs of retroplacental clots, calcifications,
infarctions etc. After taking biopsies, whole placenta was immersed in 4% formalin and sent
for histopathologic examination. All placentas were examined according to our hospital’s
standard laboratory procedure. Five sections were examined that were sampled from the
umbilical cord, membranes, central portion of placenta, basal plate and transmural central
part, respectively. One pathologist with special training in placental microscopy, evaluated all
sections of the preeclamptic placentas, without prior knowledge of clinical status, looking for
signs of inflammation as described previously (Redman et al., 1999) in standard hematoxylin
and eosin stained slides.
31
3.6 Urinary human chorionic gonadotropin measurement
Urinary samples were collected from the mother before delivery and stored at –70°C until
further analysis. Urinary hCG concentration was measured in 41 normal pregnancies and 18
pregnancies affected by severe preeclampsia by an automated electrochemiluminescence
immunoassay method using intact hCG + β-subunit kit (Modular E170 Analyser, Roche
Diagnostics) which has previously been validated (Ajubi et al., 2005).
3.7 Leptin immunofluorescence
Tissue sections were fixed in formalin and embedded in paraffin blocks according to standard
procedures. Glass slides were cleaned with 95% ethanol, treated with subbing solution and air
dried. 4–6 micron thick tissue sections were cut and applied to slides. Slides were
deparaffinized in xylene using three changes 5 minutes each. Sections were hydrated
gradually through graded alcohols (100% ethanol twice for 10 minutes each, then 95%
ethanol twice for 10 minutes each). Slides were then washed in de-ionized water for 1 minute
with stirring and excess liquid aspirated from slides.
Antigen unmasking was performed using heat treatment. Slides were placed in a
prewarmed solution of 10 mM sodium citrate buffer, pH 6.0 for 5 min. This step was repeated
once. Slides were let to cool down at room temperature in the buffer for 20 minutes before
they were washed in de-ionized water three times for 2 minutes each and excess liquid was
aspirated from slides.
Slides were incubated with 500 µl 10% normal goat blocking serum (Santa Cruz
Biotechnology, Inc, USA) in 1x phosphate buffered saline (PBS) for 20 minutes at room
temperature. Suction was used to remove reagents (drying of the specimens was avoided
between steps) before slides were washed in 1xPBS two times for 2 minutes each. Primary
antibody for leptin 2.5 µg/ml (Ob (A-20) Santa Cruz Biotechnology, Inc, USA) in PBS with
1.5% normal blocking serum was incubated for 60 minutes. PBS only was used to test the
specificity of the primary antibody (negative control). Slides were washed with three changes
of PBS for 5 minutes each and then incubated for 45 minutes at room temperature with
secondary goat anti-rabbit IgG-FITC 2.5 µg /ml (Santa Cruz Biotechnology, Inc, USA) in
PBS with 1.5% normal blocking serum. Slides were washed with three changes of PBS for 2
minutes each before they were air dried and counterstained with DAPI (4’,6-diamidino-2phenylindole) II (Vysis, Abbott Diagnostics, USA).Images were obtained using a CytoVision
(Applied Imaging, San Jose, CA, USA) digital system equipped with a charged-coupled
device (CCD) camera (Cohu Inc., San Diego, USA).
32
4. MAIN RESULTS
4.1 Paper I
Gene expression profiling was carried out on mRNA extracted from placental samples that
were obtained from 17 women after vaginal delivery (laboring) and 17 women after elective
cesarean section before the onset of labor (non-laboring). All women were healthy and had
uncomplicated pregnancies. There were no significant differences regarding maternal
phenotype, hemodynamic parameters and pregnancy outcome between the groups. In the
group of women delivering vaginally, the mean duration of labor was 7.6 (range, 2-15) hours.
Out of approximately 25 000 genes that were represented on the array, one third (n=8000)
passed stringent quality criteria. An empirical Bayes analysis was performed in order to find
differentially expressed genes. Only three genes, namely FOS, FOSB and GNGT1 were
differentially expressed more than 2-fold. Several other genes captured by microarrays were
related to oxytocin degradation (LRAP) and placental hormone metabolism (HSD17B4), but
did not seem to be significantly affected by the mode of delivery. Furthermore, validation
using RT-PCR (Paper I, Table 2) showed only FOS to be actually down-regulated by 2.2
mean fold-change in laboring compared to non-laboring placentas (Figure 9), suggesting that
the global placental gene expression profile is not significantly altered by labor.
FOS/SDHA
200
150
100
50
0
Placental Samples
Figure 10. Relative expression of FOS normalized for SDHA (housekeeping gene) in for each of the placental
samples estimated by RT-PCR. Red indicates laboring, green indicates non-laboring placentas; mean
FOS/SDHA expression of 19.56 and 43.73 respectively
33
4.2 Paper II
Gene expression profiling was carried out on mRNA extracted from placental samples that
were obtained from 21 women with severe preeclampsia and from 21 controls that were
randomly selected from a group of 50 women with uncomplicated pregnancies. Preeclamptic
women had an average mean arterial pressure of 132mmHg and proteinuria of 5g in a 24-hour
urine specimen. They were not significantly different in terms of age, parity and body mass
index (BMI) from their controls. However, they had significantly higher mean uterine artery
PI and reduced uterine artery volume blood flow, although the fetal circulation was not
compromised as indicated by normal cerebro-placental ratio (middle cerebral PI/umbilical
artery PI) and normalized umbilical vein volume blood flow. Eleven out of sixteen
preeclamptic women were delivered prematurely (at <37 weeks) by cesarean section before
the onset of labor and among them 3 had HELLP syndrome. The method of delivery (vaginal
versus cesarean) was balanced between the groups. The number of small for gestational age
babies was significantly higher and placental weight lower among preeclamptic women
compared with controls. Two preeclamptic women had growth restricted fetuses. However,
the neonatal outcome was not different between the groups.
After RNA isolation, five preeclamptic placentas were excluded due to poor RNA
quality. Thirty-seven hybridizations in a one-channel detection system of chemiluminescence
emitted by the microarrays were performed. A total of 18811 genes on the arrays passed our
quality criteria after image analysis. In preeclamptic placentas 213 genes were significantly
(fold-change≥2 and p≤0.01) up-regulated and 82 were down-regulated, compared with normal
placentas (Paper II, Table 1S). Supervised clustering analysis showed 16 of these genes able
to differentiate preeclamptic from healthy placentas (Paper II, Figure 1). Microarray results
were validated by RT-PCR for 16 selected genes and there was a good correlation for most of
them (Paper II, Table 3S). Different subtypes of hCG were up-regulated in preeclamptic
placentas from 3.5 to 6.4 fold. We validated this result by measuring hCG concentration in
maternal urine. Mean concentration of urinary hCG in the control group was 20034 (95% CI:
13121-27687) IU/L and in preeclamptic patients 68271 (95% CI: 18917-117624) IU/L
(p=0.04). There was no significant correlation between urinary hCG values and placental
weight (r=0.03; p=0.823). Leptin RNA was highly (40 fold) up-regulated in the preeclamptic
placentas both in microarray and RT-PCR. In order to correlate this result with leptin protein
expression levels, immunofluorescence was performed in preeclamptic and normal placentas.
Leptin was expressed in the villous trophoblast and was more abundant in the preeclamptic
placenta (PaperII, Figure 4).
34
Pathway analysis using all differentially expressed genes between 16 preeclamptic and
21 normal placentas showed several differentially expressed genes to be associated with
Alzheimer disease, angiogenesis, Notch-, TGFβ- and VEGF-signaling pathways. Preliminary
biological link analysis showed that hypoxia, apoptosis, angiogenesis and folate metabolism
were altered in the preeclamptic placentas (data not shown).
The preeclamptic placentas were further sub-grouped depending on (i) disease onset
and (ii) level of proteinuria.
Five placentas were delivered before 34 weeks of gestation (early-onset preeclampsia)
and 11 after 34 weeks (late-onset preeclampsia). Microarray data analysis showed 402
differentially expressed genes between these groups (Paper II, table 2S and Figure 2).
Comparison of pathways showed that oxidative stress, inflammation mediated by chemokine
and cytokine and endothelin signaling pathways were mainly involved in early-onset
preeclampsia (Paper II, Figure3). This result was validated for selected genes by RT-PCR
(Paper II, Table 3S).
Preeclamptic placentas were stratified in three different phenotypes, according to the
level of proteinuria: Group A: four patients with proteinuria 1-2 g/l, Group B: five patients
with proteinuria 3-4 g/l and Group C: five patients with proteinuria ≥ 5 g/l.
A three-
component ANOVA was performed that showed significant differences in gene expression
profile between groups A and C (Figure 11).
Figure 11. Volcano plot showing differentially expressed placental genes between preeclamptic women
with proteinuria 1-2 g/l compared to those with proteinuria >5 g/l. Each spot represents a gene and color
intensity represents the expression level. All the genes located on the upper-lateral quadrants are
significantly differentially expressed at a statistical and biological level)
35
4.3 Paper III
Gene expression profiling was carried out on mRNA extracted from placental samples that
were obtained from eight patients with IUGR due to placental insufficiency, and from eight
women with uncomplicated pregnancies that served as controls. Four women with IUGR also
had preeclampsia. There were no significant differences regarding maternal age, parity and
weight gain during pregnancy between the groups. Gestational age, birth weight and placental
weight were significantly lower in IUGR pregnancies. All patients with IUGR had
oligohydramnios and hemodynamic signs of redistribution of blood flow to the fetal brain, as
implicated by the inclusion criteria of the study. Two women in the IUGR group received a
single course of antenatal corticosteroid. The mode of delivery (vaginal versus cesarean) was
balanced between the study groups. Neonatal outcome was favorable in both groups as
indicated by Apgar scores and cord blood acid-base status (Paper III, Table 1).
Microarray data analysis showed that in IUGR placentas 47 genes were up-regulated
and 70 genes were down-regulated at a significant level (fold-change ≥1.5 and p-value≤.05)
compared with normal placentas (Paper III, Tables 2 and 3). PCA for the top 20 differentially
expressed genes showed good separation in terms of gene expression patterns between the
groups (Paper III, Figure 1). Pathway analysis of differentially expressed genes between
IUGR and normal placentas, after Bonferroni correction for multiple testing, showed
significant up-regulation of the inflammation mediated by chemokine and cytokine signaling
pathway (pathway accession number P00031 in http://www.pantherdb.org). Subgroup
analysis comparing IUGR with and without preeclampsia showed few (n=27) differentially
expressed placental genes (Paper III, Table 4). Validation of the microarray results by RTPCR showed good correlation between the two methods (Paper III, Table 5).
36
5. DISCUSSION
This thesis recapitulates three studies on global gene expression profile of healthy (laboring
and non-laboring) and compromised (preeclamptic and IUGR) placentas using genome wide
microarrays. Placental gene expression was altered by disease states but not by normal labor.
Results have been validated at the transcript and protein levels for selected genes. These
results indicate that gene expression profiling of the human placenta is a useful tool in the
effort to understand the role of the placenta in parturition and in the patho-physiology of
pregnancy-specific disorders, such as preeclampsia and IUGR.
5. 1 Gene expression profile of the normal placenta
The human placenta is a complex tissue composed by a broad variety of placental/fetal and
maternal cells. Structural heterogeneity of placental villi depends mainly on the degree of
maturity and on the sampling site of the placenta. Accordingly, global gene expression profile
varies within the normal placenta (Sood et al., 2006). In particular, it appears that hypoxiarelated genes are up-regulated in placental sites distant to the umbilical cord insertion and
towards the basal and chorionic plates (Wyatt et al., 2005), possibly reflecting reduced blood
perfusion at these sites (Matijevic et al., 1995). In fact, these changes in gene expression,
although modest (up to three-fold), correlate well with villous histology, i.e. increased fibrin
deposition and syncitial knots and reduced villous size, mostly observed at the periphery of
the placental disc. Moreover, gene expression profiling of the feto-maternal interface (basal
plate) between midgestation and term, showed dramatic changes regarding angiogenesis, cell
motility, extracellular matrix modulation, gene transcription, signal transduction, immune
response, protein biosynthesis, and lipid metabolism (Winn et al., 2007). Therefore, for the
purpose of this thesis, placental samples were taken from a standardized location,
approximately 2cm beside the umbilical cord insertion (a landmark that is easily
recognizable), and the sample included tissue from the middle layer of placenta midway
between maternal and fetal surfaces. This is a representative site to study placental gene
expression as it includes central villous parenchyma that is well perfused. Additionally, all
samples were collected by two designated persons to ensure technical reproducibility and
washed thoroughly with isotonic saline to avoid contamination with maternal blood.
37
5.2 Placental gene expression in parturition
The molecular mechanisms regulating the duration of pregnancy and initiation of labor are
still incompletely understood. Clearly, interactions of maternal, placental, fetal and
environmental factors cooperate in order to activate the uterus in order to expel the fetus
(Challis et al., 2000b). Deregulation of these factors might cause premature delivery
(Goldenberg et al., 2008). Moreover, there is epidemiological evidence that genetic factors
might play a role in preterm birth (Varner and Esplin, 2005), indicating that regulation of
gene expression in feto-maternal tissues may be involved in the process of parturition. Indeed,
several studies using high-throughput methods (i.e. genomics, trancriptomics, proteomics)
identified genes involved in prostaglandin synthesis and inflammatory response being
differentially expressed in the myometrium, cervix and chorio-amniotic membranes in
laboring compared to non-laboring women (Romero et al., 2006). However, few genomic
studies have focused on the role of the human placenta in labor. A recent microarray study on
placental tissue collected from five laboring and five non-laboring women, using only one
array and indirect design, showed that most placental genes were not differentially expressed
in labor and that a slightly (mean ratio=2.71) increased expression of matrix
metalloproteinase-1 (MMP-1) was seen in laboring women (Vu et al., 2008). Another study
using two 15K, custom-made human cDNA arrays on placental samples from seven women
undergoing labor longer than 15 hours and 10 samples from women undergoing elective
cesarean section, showed 90 transcripts to be significantly altered in their expression levels by
>1.35-fold. Many of the placental genes up-regulated in women with prolonged labor were
involved in oxidative stress (Cindrova-Davies et al., 2007). In our study, Placental FOS was
the only gene that was down-regulated (2.2-fold) in women who delivered vaginally
following normal (not prolonged) labor. Interestingly, in the study by Cindrova-Davies et al.
(2007) placental FOS was 4.96-fold up-regulated in women with prolonged labor but the
confidence interval of the CT values in RT-PCR were wide [6.72 (5.03-7.42) in non-laboring
and 4.70 (2.96-6.93) in women after long labor]. However, ingenuity pathway analysis
showed that FOS might play a role in the regulatory relationships among differentially
expressed transcripts during labor.
Results of our study indicate that placental gene expression profile is not significantly
altered by normal labor. The discrepancies between our study and other studies (CindrovaDavies et al., 2007; Vu et al., 2008) might be due to different sample size, experimental
design and image and data analysis criteria. We have used a larger sample size, direct
experimental design and more stringent image and data analysis criteria compared with
38
previous studies. Uterine contractions cause intermittent reduction of blood flow to the uterus
that might result in oxidative stress during prolonged labor. In our study, however, there were
no significant hemodynamic differences in placental circulation. Moreover, the fetal oxygen
uptake is not altered by normal labor (Acharya and Sitras, 2009). Our results support the
assumption that the fetal genome and the maternal tissues play a key role in parturition, rather
than the placenta (Challis et al., 2000a; Young, 2007). Nevertheless, it would be wise to
balance between laboring and non-laboring women while matching subjects in comparative
placental genomic studies.
5.3 Placental gene expression in pregnancy disorders
There is clinical, epidemiological, genetic and histopathologic evidence that abnormal
placentation plays an important role in the pathogenesis of various pregnancy disorders, such
as preeclampsia, IUGR, preterm birth and miscarriage (Roberts and Hubel, 2008). The results
of this thesis provide evidence, at the transcript and protein level, that placental gene
expression profile is altered in pregnancies complicated by severe preeclampsia and IUGR
due to placental insufficiency. Our results are in concordance with previous microarray
studies that consistently showed differential placental gene expression in preeclampsia
(Founds et al., 2008; Enquobahrie et al., 2008) and IUGR (Roh et al., 2005; McMinn et al.,
2006), and we have made a comprehensive list of differentially expressed genes available
(Paper II, Table 1S).
A set of 16 genes was found to predict preeclampsia phenotype in the study samples
and a set of 20 genes to separate IUGR from normal placentas. Some of these genes in
combination with other biochemical and/or ultrasound markers (e.g. uterine artery Doppler,
flow-mediated vasodilatation of brachial artery) might help in early identification of
pregnancies at risk. In fact, some studies have already shown that the combined use of high
maternal serum markers (e.g. hCG, sFlt1) and presence of notching in the uterine artery wave
form in mid trimester could predict preeclampsia and/or IUGR better (Hershkovitz et al.,
2005; Barkehall-Thomas et al., 2005; Stepan et al., 2007).
Pathway analysis of differentially expressed genes showed angiogenesis, oxidative
stress, Notch and Alzheimer disease signaling pathways to be involved in severe
preeclampsia. Moreover, severe preeclampsia appeared to affect placental gene expression in
different ways depending on the time of disease onset, with inflammation mediated by
cytokines and chemokines pathway involved in early-onset disease. Interestingly, the same
inflammatory pathway seems to be up-regulated in IUGR as well, suggesting common
39
pathogenesis. Disturbance of placental development might cause preeclampsia phenotype in
the mother and alter fetal programming (Myatt, 2006; Ness and Sibai, 2006).
Investigation on how differentially expressed placental genes link together in the
biological context, highlighted possible synergistic effects among hypoxia, oxidative stress,
inflammation,
apoptosis
and
folate
metabolism
in
our
study
population.
6-
pyruvoyltetrahydropterin synthase (PTS), a key enzyme in the synthesis of tetrahydrobiopterin
(H4B) and an essential cofactor of NO synthase, was significantly down-regulated in
preeclamptic placentas. Placental H4B regulates NO synthase activity in late pregnancy
(Kukor et al., 2000)
possibly through stabilization by vitamin C (Toth et al., 2002).
Furthermore, H4B, folic acid, vitamin C and reactive oxygen species are known to interact in
order to maintain adequate endothelial function (Figure 12) and thus unbalanced regulation
might cause vascular diseases (Das, 2003).
Figure 12. Schematic representation of interaction between chemokines, vitamins, reactive oxygen species
(ROS), nitric oxide (NO) and cyclooxygenases (COX) affecting endothelial function. Red arrows indicate
negative and black arrows indicate positive effect
Taken together these findings suggest that vitamins such as H4B, folic acid and
vitamin C might have a role in the pathogenesis of preeclampsia. Indeed, there are ongoing
clinical trials investigating the effect of vitamin and oxygen supplementation in the prevention
of pregnancy disorders (Holmes et al., 2004; Rumbold et al., 2006). Although, several clinical
40
trials have so far failed to prove a clear prophylactic and/or therapeutic effect of antioxidant
therapy (Kontic-Vucinic et al., 2008), it must be underlined that these trials had different
designs regarding participants, intervention schemes and outcomes.
5.4 Placental angiogenesis in healthy and compromised pregnancies
In the course of pregnancy there is a continuous regulation of placental villous vascular
development. This process is not completely understood but it is thought to be controlled by
the expression of several angiogenic growth factors, which is influenced by genetic,
endocrine, metabolic and environmental factors (Charnock-Jones et al., 2004). Oxygen
tension in the intervillous space is a major factor affecting the expression of these angiogenic
factors, and hence, placental vasculo- and angio-genesis (Kaufmann et al., 2004). In fact,
conditions causing fetal hypoxia might be regulated by placental angiogenic factors, including
their receptors and antagonists (Mayhew et al., 2004). In our study population, the uteroplacental blood flow was reduced in preeclamptic pregnancies. This was reflected in placental
gene expression profile with up-regulation of genes involved in angiogenesis, TGF-beta and
VEGF signaling pathways in preeclamptic placentas. Recent studies suggest that an excess of
placentally-derived anti-angiogenic factors (i.e. sFlt1 and ENG) in the maternal circulation
might cause preeclampsia (Tjoa et al., 2007). Levels of VEGF and its receptors (VEGFR1
and 2) and PlGF are reported to be higher, lower or unaltered in late-onset preeclampsia and
IUGR with positive end-diastolic flow in the umbilical arteries (Helske et al., 2001; Tse et al.,
2001). However, less evidence exists for the involvement of angiogenic factors in early-onset
preeclampsia and IUGR with absent end-diastolic flow in the umbilical arteries (Khaliq et al.,
1999; Mayhew et al., 2004). Results of our studies suggest that the interpretation of the
“angiogenic state” of the human placenta during preeclampsia and /or IUGR should be
closely correlated to the phenotype of the disease, i.e. severity, time-onset and degree of
hemodynamic compromise.
5.5 Inflammatory pathways in early-onset preeclampsia and IUGR
The placental genes involved in the inflammation mediated by cytokines and chemokines
pathway were up-regulated in early-onset preeclampsia and IUGR in our study population.
Cytokines include the interleukins, interferons and tumour necrosis factors (TNF) and have
both pro- and anti-inflammatory actions (Jason et al., 2001). They play pivotal role in
intercellular communication, especially between B- and T-cells, and affect neuronal,
haematopoietic and embryonal development (Townsend and McKenzie, 2000). Chemokines,
41
called also chemotactic cytokines, comprise a large family of small (6-14 kDa) proteins. They
mediate their effects through binding with trans-membrane receptors on different leukocyte
lineages with activation of G-protein signaling pathways (Mellado et al., 2001). The final
effect is regulation of chemotaxis of immune cells and regulation of gene expression and
apoptosis (Vlahakis et al., 2002).
Maternal endothelial dysfunction during preeclampsia is linked to a generalized
maternal inflammatory response (Redman et al., 1999). A pathophysiological model of
preeclampsia where placental factors cause the clinical syndrome has been suggested
(Redman and Sargent, 2005). Interactions among T-cells and placental cytokines and
chemokines may alter maternal immune response and cause preeclampsia/IUGR (Visser et
al., 2007). Results of our study show that the inflammation mediated by cytokines and
chemokines pathway is altered in early-onset preeclampsia and IUGR, although it was not
possible to match the gestational age of our patients with their controls. As normal pregnancy
requires immune adaptation in the feto-placental interface, cytokine levels might change
continuously throughout gestation, the validity of these results may be questioned.
Nonetheless, cytokine-receptor microarray on placental samples (Pang and Xing, 2003) and
serum cytokine (LEP, IL-10, TNF, IL-6 and IL-8) assay (Sharma et al., 2007) have also
shown over-expression of inflammatory markers in preeclamptic women compared with
gestational age matched healthy controls. Moreover, a microarray study of placental tissue
obtained from two pregnancies complicated by early-onset preeclamptia with IUGR
compared to a gestational age matched control placenta, showed up-regulation of
inflammatory genes, many of which decode cytokines, in the absence of histopathologic
inflammatory findings (Heikkila et al., 2005).
5.6 Limitations and strengths of the studies
Several approaches were used in order to optimize our experiments. We used direct
comparison design which is more precise than the indirect design because the replicates
available for comparison are doubled in number. Furthermore, it avoids the technical variation
between the microarray slides caused by the instability of the common reference.
Relatively large number (n=50) of “healthy” placentas (biological replicates) from
women with uncomplicated pregnancies was collected in order to match them randomly with
compromised placentas. Randomization is a crucial step in experimental design, performance
and interpretation as it reduces both technical and biological variation in microarray
experiments. Indeed, in an ideal experiment the association of measured variables is
42
true/causative, when randomization ensures that exposed and unexposed experimental units
are interchangeable (Hernan and Robins, 2006). By randomizing, we were able to balance the
possible confounding effects of maternal age, parity, fetal sex and delivery method between
the study groups.
The quality of mRNA was checked by electrophoresis and in study II 5 placentas that
yield mRNA of poor quality even after repeated isolation were discarded. Poor mRNA quality
may be due to apoptotic process that is shown to occur in the preeclamptic placenta (Allaire et
al., 2000).
Microarrays are able to investigate gene expression only at a transcriptional level, i.e.
measure the mRNA levels of a given gene at a given time. This has several drawbacks: (i) a
single gene can give rise to multiple gene products (ii) RNA can be alternatively spliced or
edited to form mature mRNA (iii) mRNA levels do not necessarily correlate with protein
levels, which are the functional endpoints of coding genes (iv) proteins are regulated by
additional mechanisms such as posttranslational modifications, compartmentalization and
proteolysis (v) protein to protein interactions (folding) or cellular environmental conditions,
such as pH and temperature, might affect protein function. Finally, biological function is
determined by the complexity of these processes. All these factors are related to the
functional, dynamic complexity of biological systems (such as cells, organs and organisms)
which cannot be captured by microarrays.
On the other hand, microarrays are a powerful tool of investigating genome-wide
expression profile, providing information not otherwise attainable. Traditionally, biomedical
research has been based on “reductionism” in the sense that complex biological systems were
divided in simple units that were easier to explore and understand. However, as disease
probably affects several molecular mechanisms, one can hypothesize that in certain diseases
sets of genes are affected rather than a single gene. With the help of bioinformatic tools that
analyze the relations among genes forming multiple molecular pathways, microarray
technology provides insights into the mechanistic molecular processes occurring in the
diseased cell/tissue as a whole. Another important related issue is the intercellular signaling,
i.e. the interactions between the different cells in a tissue. These behavioral interactions
between cells are difficult to explore in in vitro studies, making microarray comparison
among diseased and healthy tissues a method that can point out the biological pathways
involved in the development of the disease state. Indeed, whole genome expression profiles
have been successfully applied in cancer research aiming to classify different cancer subtypes
on the basis of their differential genetic signature. Pathologically indistinguishable cancer
43
types with different global gene expression profiles, may share common “genomic-related”
response to therapy, individual metastasizing capabilities and different prognoses (Segal et al.,
2005). Moreover, signatures of differentially expressed sets of genes have been reproduced by
several microarray platforms and laboratories, and validated in large patient populations using
RT-PCR, indicating that results from microarray experiments can provide clinicians with
prognostic and therapeutic tools.
44
6. CONCLUSIONS
Genome-wide expression profiling of the placenta is a powerful tool to detect differences in
gene expression between healthy and compromised pregnancies. From the results of this
thesis, the following conclusions can be drawn:
a. Placental gene expression is not significantly altered by normal labor, indicating a
secondary role of this organ in the initiation and progression of normal parturition.
b. Placental gene expression is profoundly altered in severe preeclampsia,
suggesting that the placenta plays a central role in the pathogenesis of this disease.
c. Placental angiogenic factors – including their receptors and antagonists – are
involved in the pathogenesis of preeclampsia. However, the expression levels of
placental angiogenic factors alone cannot clearly differentiate early- from lateonset preeclampsia.
d. There is no epigenetic modification of imprinted placental genes in placental
insufficiency leading to IUGR.
e. Inflammation mediated by cytokines and chemokines signaling pathway in the
placenta might be involved in the development of early-onset preeclampsia and
IUGR.
7. FUTURE PERSPECTIVES
Early-onset preeclampsia and IUGR due to placental insufficiency might share common
placental pathogenesis. Preeclampsia and IUGR frequently lead to preterm delivery and are
associated with poor perinatal outcome. Therefore, early identification of women at high risk
of developing these complications might improve pregnancy outcome. A set of placental
genes/proteins detected in the maternal blood and/or urine together with the assessment of
maternal, fetal and placental hemodynamics might help clinicians in the prediction and early
diagnosis of these pregnancy-specific disorders. In this respect, laeverin, a placenta specific
protein that was found to be over-expressed in severe preeclampsia in our study population,
deserves further investigation. In particular, it would be interesting to further explore the
downstream effects of this gene on placental development and function.
45
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