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Analysis of placental gene expression profile in
pregnancies affected by fetal growth restriction
and macrosomia
A thesis submitted in fulfilment of the requirements for the degree of
Master of Philosophy in Medicine
Amin Sabri
SID: 311081479
Queen Elizabeth II Research Institute for Mothers and Infants
Department of Obstetrics, Gynaecology and Neonatology
Sydney Medical School
The University of Sydney
Declaration
I hereby declare that this thesis consists of original work carried out by the author unless
otherwise stated and duly acknowledged. To the best of my knowledge no part of this thesis
has been submitted totally or partially for the award of any other degree of the University or
other institution.
Amin Sabri
August 2013
I
Acknowledgements
I would like to express the deepest appreciation to all those who provided me the possibility
to complete this thesis. A special gratitude I give to my supervisor, Clinical Professor Jon
Hyett, for the useful comments, remarks and engagement through the learning process of this
thesis. Without his guidance and persistent help, this thesis would not have been possible
within the limited time frame.
I would also like to acknowledge with much appreciation the crucial role of Dr Donna Lai
who gave the permission to use all required equipment in the Bosch Institute Molecular
Biology Facility in the Anderson Stuart and Blackburn buildings at the University of Sydney.
Without the support and guidance of Dr Lai and Mr Sheng Hua in performing laboratory
experiments this thesis would not have materialised.
Furthermore, I would like to thank all the students and staff at Queen Elizabeth II Research
Institute for Mothers and Infants and Royal Prince Alfred Hospital for their friendship and
encouragement. I want to thank my loved ones, Dr Cecilia Ng, Miss Arlene D’Silva and Dr
Jasjot Kaur who have supported me throughout the entire process, both by keeping me
harmonious and helping me put the pieces together. I will be grateful forever for your love.
I appreciate the feedback offered by my associate supervisor, Dr Sean Seeho, which resulted
in the improvement of my thesis writing. I have greatly benefited from advice and comments
given by Professor Ian Fraser, Dr Robert Markham, Dr Frank Manconi and Dr Emily Miller
especially in my project presentation that has improved my presentation skills.
II
Last but not least, I would like to show my greatest appreciation to my parents for their
endless love and support. I would not be able to do a course in Australia without their
generous and wholehearted support.
III
Abstract
Objective: Extremes of fetal growth are associated with increased perinatal mortality and
morbidity and a higher prevalence of cardiovascular disease, obesity and diabetes in later life.
The placenta plays a pivotal role in fetal growth and development. We aimed to identify
changes in placental gene expression in term pregnancies with evidence of growth
dysfunction and to identify candidate genes that may be used to identify abnormal patterns of
growth prior to delivery.
Materials and methods: Placenta samples were collected and classified from pregnancies
that affected by fetal growth restriction (birth weight <10th percentile; n=4), macrosomia
(birth weight >90th percentile; n=6) or normal pregnancies (birth weight between 40th and 60th
percentile; n=5). All pregnancies were 39 weeks’ gestation, with male infants delivered by
Caesarean section. RNA was extracted from the placental samples prior to microarray gene
expression analysis using Affymetrix HG-U219 array strip kits. Microarray data were
analysed using Partek Express, Ingenuity Pathway Analysis software, Visualization and
Integrated Discovery (DAVID) online tool, and Protein ANalysis THrough Evolutionary
Relationships (PANTHER) online tool. Significant differential gene expression was defined
by >2 fold-change with p<0.05.
Results: Significant changes in gene expression between fetal growth restriction and control
samples were seen with 242 transcripts (representing 199 genes) up-regulated and 191
transcripts (139 genes) down-regulated. Fewer significant changes were found when
comparing macrosomic and control specimens with 42 transcripts (representing 33 genes) and
10 transcripts (representing 8 genes) being up-regulated and down-regulated respectively.
IV
Potential biomarkers for fetal growth restriction including TXNDC5, LRRFIP1, LRP2,
ENPEP, PPFIBP1, CPXM2, CLDN1, ST3GAL1, TRPV6, and CD200 were identified. In
macrosomic newborns leptin with 6.5 fold-change down-regulation (p=0.01) together with
COL17A1, GCH1, PHLDB2, KATNBL1, CLDN1, and LPHN3 were candidate genes as
biomarkers.
Conclusions: Dysfunctional growth is associated with differential placental gene expression
and affects genes involved with a whole spectrum of developmental and cellular functions. In
fetal growth restriction, important pathways with wide range of functions including
embryonic development, angiogenesis, amino acids metabolism, transmembrane transport of
small molecules, Notch signalling, and p53 signalling pathway were identified, indicating the
complex pathophysiology of the fetal growth restriction. Leptin which plays a crucial role in
energy homeostasis and control of food intake appears to be decreased in macrosomic
newborns and might be of significance for long term outcome.
V
Table of Contents
Declaration .................................................................................................................................. I
Acknowledgements ................................................................................................................... II
Abstract .................................................................................................................................... IV
Table of Contents ..................................................................................................................... VI
List of Figures .......................................................................................................................... IX
List of Tables ............................................................................................................................. X
Abbreviations ........................................................................................................................... XI
Chapter 1 ................................................................................................................................. 1
Placental development and physiology ......................................................................... 1
1.1. Introduction ..................................................................................................................... 1
1.2. Development of the placenta ........................................................................................... 2
1.3. The roles of the placenta .................................................................................................. 3
1.3.1. Nutrient transfer and metabolic role ......................................................................... 5
1.3.1.1. Oxygen metabolism............................................................................................ 7
1.3.1.2. Glucose metabolism ........................................................................................... 8
1.3.1.3. Amino acids metabolism .................................................................................... 9
1.3.1.4. Lipid metabolism.............................................................................................. 10
1.3.2. Endocrine role ......................................................................................................... 11
1.3.2.1. Human placental lactogen (hPL) ...................................................................... 12
1.3.2.2. Human placental growth hormone (hPGH) ..................................................... 13
1.3.2.3. Adipocytokines................................................................................................. 13
1.3.2.4. Insulin-like growth factors (IGF) ..................................................................... 14
1.3.2.5. Human chorionic gonadotropin (hCG) ............................................................ 15
1.3.2.6. Placental hormones and prenatal diagnosis ...................................................... 15
1.3.3. Immunologic role .................................................................................................... 16
Chapter 2 ............................................................................................................................... 18
Placenta, fetal growth, and molecular mechanisms ................................................ 18
2.1. Fetal growth restriction (FGR) ...................................................................................... 18
2.1.1. Aetiology of FGR ................................................................................................... 19
VI
2.1.1.1. Fetal factors ...................................................................................................... 20
2.1.1.2. Maternal factors................................................................................................ 20
2.1.1.3. Placental factors ............................................................................................... 23
2.1.2. Placental adaptation to the maternal environment .................................................. 23
2.1.3. Placenta and pathogenesis of FGR ......................................................................... 26
2.1.3.1. Placental blood flow ......................................................................................... 28
2.1.3.2. Placental nutrient transport in FGR .................................................................. 29
2.1.3.3. Placental ion transport in FGR ......................................................................... 31
2.1.3.4. Placental transport function in fetal overgrowth .............................................. 32
2.1.4. Consequences of FGR ......................................................................................... 33
2.1.4.1. Short term consequences .................................................................................. 33
2.1.4.2. Long term consequences .................................................................................. 35
Chapter 3 ............................................................................................................................... 38
Aims and hypothesis .......................................................................................................... 38
Chapter 4 ............................................................................................................................... 40
Methodology ......................................................................................................................... 40
4.1. Sample collection and processing.................................................................................. 40
4.2. Description of samples used in the experiments ........................................................... 41
4.3. RNA extraction from placenta ....................................................................................... 42
4.4. Total RNA purity and concentration assessments ......................................................... 43
4.5. Total RNA quality assessment ...................................................................................... 45
4.6. Microarrays .................................................................................................................... 47
4.6.1. Reverse transcription to synthesize first-strand cDNA ........................................... 49
4.6.2. Second-strand cDNA synthesis............................................................................... 49
4.6.3. In vitro transcription to synthesize labelled aRNA ................................................. 50
4.6.4. aRNA purification ................................................................................................... 50
4.6.5. Analysis of aRNA concentration and size .............................................................. 51
4.6.6. Fragmentation of labelled aRNA ............................................................................ 52
4.6.7. Hybridization .......................................................................................................... 52
4.6.8. Fluidics (wash and stain) and imaging.................................................................... 53
4.7. Microarrays quality control ........................................................................................... 55
VII
4.7.1. Signal value ............................................................................................................. 56
4.7.2. Hybridization controls ............................................................................................ 56
4.7.3. Labelling controls ................................................................................................... 57
4.7.4. Sample quality (housekeeping genes) ..................................................................... 57
4.8. Gene significance estimate ............................................................................................ 57
4.8.1. Principle component analysis (PCA) ...................................................................... 58
4.8.2. Effect size................................................................................................................ 58
4.9. Data analysis .................................................................................................................. 58
Chapter 5 ............................................................................................................................... 60
Results ................................................................................................................................... 60
5.1. Description of patients ................................................................................................... 60
5.2. NanoDrop results ........................................................................................................... 60
5.3. Bioanalyzer results ........................................................................................................ 62
5.4. Microarray gene results ................................................................................................. 66
5.4.1. Quality control ........................................................................................................ 66
5.4.2. Principle components analysis ................................................................................ 69
5.4.3. Effect size................................................................................................................ 70
5.4.4. Microarray gene expression analysis ...................................................................... 70
Chapter 6 ............................................................................................................................... 86
Discussion ............................................................................................................................. 86
6.1 Summary......................................................................................................................... 86
6.2 Optimising the placenta sample collection and RNA extraction .................................... 86
6.3 Investigation of the microarray findings ........................................................................ 87
6.4 Other microarray studies on the placenta ..................................................................... 100
6.5 Limitations of this study ............................................................................................... 107
6.6 Future directions ........................................................................................................... 107
6.7 Conclusion .................................................................................................................... 108
References ......................................................................................................................... 110
VIII
List of Figures
Figure 4.1 Structure of eukaryotic ribosomes. ...................................................................... 46
Figure 4.2 Principle of injection and separation of RNA in bioanalyzer 2100. .................... 47
Figure 4.3 Overview of the GeneAtlas 3’ IVT Express Kit Labelling Assay. ...................... 48
Figure 4.4 Magnetic stand for the paramagnetic bead precipitation from standard 96-well Ubottom plate. ............................................................................................................................. 51
Figure 4.5 Tray maps used in the fluidics station.................................................................. 54
Figure 4.6 The GeneAtlas fluidics station (A) and imaging station (B). .............................. 55
Figure 5.1 NanoDrop report for control samples. UV absorption plot has been shown for
two sequential elutions. The top five curves indicate the first RNA elutions which were used
in the microarray analysis while the lower ones are for the second RNA elutions. ................. 62
Figure 5.2 Electropherogram of samples included in microarray assay. .............................. 63
Figure 5.3 Bioanalyzer result for two of the placenta samples from vaginal delivery. ......... 64
Figure 5.4 Comparison of the RNA quality in samples stored in saline with those stored in
RNALater. The electropherograms in each row are for the same sample. .............................. 65
Figure 5.5 Labelling controls. ............................................................................................... 66
Figure 5.6 Hybridization controls. ........................................................................................ 67
Figure 5.7 3’/5’ ratio metrics. ................................................................................................ 68
Figure 5.8 PCA plot in three weight groups. ......................................................................... 69
Figure 5.9 The effect size of the fetal birth weight on transcriptome overall. ...................... 70
Figure 5.10 Dot plots for some of the dysregulated genes in FGR. ...................................... 77
Figure 5.11 Dot plots for some of the dysregulated genes in macrosomia. .......................... 82
Figure 6.1 Adipocytokine signalling based on the KEGG pathways in human. ..................... 89
Figure 6.2 Network of molecules including leptin which play a role in digestive system
development. Focused molecules in this study have been highlighted. Red colour indicates
up-regulated and green colour indicates down-regulated genes in macrosomia. ..................... 92
Figure 6.3 Serotonin receptor signalling. GCH1 catalyses the production of BH4, a required
cofactor in the synthesis of serotonin. GCH1 had decreased level of expression in both FGR
and macrosomia........................................................................................................................ 94
Figure 6.4 Notch signalling pathway and dysregulated genes in FGR. ................................ 97
Figure 6.5 Pathways affected in FGR based on PANTHER database. ................................. 99
IX
List of Tables
Table 2.1 Maternal, fetal, and placental causes of FGR ........................................................ 22
Table 4.1 Inclusion and exclusion criteria for the samples used in the experiment .............. 42
Table 4.2 Hybridization Cocktail Setup. ............................................................................... 52
Table 5.1 Characteristics of patients...................................................................................... 60
Table 5.2 NanoDrop results. .................................................................................................. 61
Table 5.3 Down-regulated genes in FGR. ............................................................................. 72
Table 5.4 Up-regulated genes in FGR. .................................................................................. 73
Table 5.5 Down-regulated genes in macrosomia. ................................................................. 75
Table 5.6 Up-regulated genes in macrosomia. ...................................................................... 76
Table 5.7 Potential biomarkers that are up or down regulated in placental tissue from term
growth restricted newborns. ..................................................................................................... 85
Table 5.8 Potential biomarkers that are up or down regulated in placental tissue from term
macrosomic newborns. ............................................................................................................. 85
Table 6.1 Findings of the other studies showing the changes in leptin level in maternal
blood, cord blood, and placenta in FGR................................................................................... 90
Table 6.2 Findings of the other studies showing the changes in leptin level in maternal
blood, cord blood, and placenta in macrosomia. ...................................................................... 90
Table 6.3 Significant cluster of pathways in three different categories related to growth
retardation................................................................................................................................. 96
Table 6.2 Genes associated with Notch signalling in FGR. .................................................. 96
Table 6.4 Data from other microarray studies on placenta showing up-regulated and downregulated genes in FGR. ......................................................................................................... 104
X
Abbreviations
ACOG
American college of obstetrics and gynaecology
ACTH
Adrenocorticotropic hormone
ANOVA
Analysis of variance
APH1B
Anterior pharynx defective 1 homolog B
AREG
Amphiregulin
aRNA
Amplified RNA (Biotin-congugated nucleotide)
ATP
Adenosine triphosphate
ATRX
Alpha thalassemia/mental retardation syndrome X-linked
BCL2
B-cell CLL/lymphoma 2
BH4
Tetrahydrobiopterin
BM
Basal plasma membrane
BTNL9
Butyrophilin-like 9
Ca
Calcium
CASP4
Caspase 4, apoptosis-related cysteine peptidase
CCNG1
Cyclin G1
cDNA
Complementary DNA
CGB
Chorionic gonadotropin, beta polypeptide
CLDN1
Claudin 1
COL17A1
Collagen, type XVII, alpha 1
CPXM2
Carboxypeptidase X (M14 family), member 2
CRH
Corticotropin releasing hormone
CTNNB1
Catenin beta-1
DAVID
Database for Annotation, Visualization and Integrated Discovery
DLAT
Dihydrolipoamide S-acetyltransferase
DNA
Deoxyribonucleic acid
EGF
Epidermal growth factor
EGFR
Epidermal growth factor receptor
EGR1
Early growth response 1
ELISA
Enzyme-linked immunosorbent assay
ENG
Endoglin
ENPEP
Glutamyl aminopeptidase (aminopeptidase A)
XI
ERBB2
V-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma
derived oncogene homolog (avian)
ETS1
V-Ets erythroblastosis virus E26 oncogene homolog 1 (avian)
ETS2
V-Ets erythroblastosis virus E26 oncogene homolog 2 (avian)
F
Fold change
F5
Coagulation factor V (proaccelerin, labile factor)
FGF
Fibroblast growth factor
FGFR
Fibroblast growth factor receptor
FGR
Fetal growth restriction
FLI1
Friend leukemia virus integration 1
FLT1
Fms related tyrosine kinase 1
FOS
Proto-oncogene c-fos
FOXP1
Forkhead box P1
FRZB
Frizzled-related protein
FSH
Follicle-stimulating hormone
FURIN
Furin (paired basic amino acid cleaving enzyme)
FZD5
Frizzled family receptor 5
G6Pase
Glucose-6-phosphatase
GAPDH
Glyceraldehyde-3-phosphate dehydrogenase
GCH1
Guanosine triphosphate cyclohydrolase I
GDM
Gestational diabetes mellitus
GFRA2
GDNF family receptor alpha 2
GH
Growth hormone
GHRH
Growth hormone releasing hormone
GLUT
Glucose transporter
GNGT1
Guanine nucleotide binding protein (G protein), gamma transducing activity
polypeptide 1
GnRH
Gonadotropin releasing hormone
H
Hydrogen
hCG
Human chorionic gonadotropin
HHEX
Hematopoietically expressed homeobox
HIFs
Hypoxia-inducible factors
HIST1H1C Histone cluster 1, h1c
XII
HLA
Human leukocyte antigen
hPGH
Human placental growth hormone
hPL
Human placental lactogen
IDDM
Insulin-dependent diabetes mellitus
IGF
Insulin-like growth factor
IGF1R
Insulin-like growth factor 1 receptor
IGF2BP2
Insulin-like growth factor 2 mrna binding protein 2
IGF2R
Insulin-like growth factor 2 receptor
IGFBP
Insulin-like growth factor binding protein
IPA
Ingenuity pathways analysis
ITGB8
Integrin, beta 8
IVT
In vitro transcription
JAG1
Jagged 1
K
Potassium
KATNBL1 Katanin p80 subunit B-like 1
KEGG
Kyoto Encyclopedia of Genes and Genomes
LDHC
Lactate dehydrogenase C
LEP
Leptin
LGR5
Leucine-rich repeat-containing G protein-coupled receptor 5
LH
Luteinizing hormone
LIMS1
LIM and senescent cell antigen-like domains 1
LPHN3
Latrophilin 3
LRP2
Low density lipoprotein receptor-related protein 2
LRRFIP1
Leucine rich repeat (in FLII) interacting protein 1
MAD2L1
MAD2 mitotic arrest deficient-like 1
MET
Met proto-oncogene (hepatocyte growth factor receptor)
MHC
Major histocompatibility complex
MTHFD2
Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2,
methenyltetrahydrofolate cyclohydrolase
MVM
Microvillous membrane
MYCN
V-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian)
Na
Sodium
NCBI
National Center for Biotechnology Information
XIII
NEC
Necrotising enterocolitis
NOTC2
Neurogenic locus notch homolog protein 2
NTRK2
Neurotrophic tyrosine kinase, receptor, type 2
NUSAP1
Nucleolar and spindle associated protein 1
OGDH
Oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide)
OMIM
Online Mendelian inheritance in man
P3C2B
Phosphatidylinositol 4-phosphate 3-kinase C2 domain-containing subunit beta
p53
Tumour protein p53 (TP53)
PAI-1
Plasminogen activator inhibitor type-1
PAK3
P21 protein-activated kinase 3
PANTHER Protein analysis through Evolutionary Relationships
PAPP-A
Pregnancy-associated plasma protein A, pappalysin 1
PAPP-A2
Pappalysin 2
PARP1
Poly (ADP-ribose) polymerase 1
PCA
Principle component analysis
PDGF
Platelet derived growth factor
PFKFB2
6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2
PGM3
Phosphoglucomutase 3
PHIP
Pleckstrin homology domain interacting protein
PHLDB2
Pleckstrin homology-like domain, family B, member 2
PIK3C2B
Phosphatidylinositol-4-phosphate 3-kinase, catalytic subunit type 2 beta
PMAIP1
Phorbol-12-myristate-13-acetate-induced protein 1
pO2
Pressure of oxygen
PPFIBP1
PTPRF interacting protein, binding protein 1 (liprin beta 1)
PPIG
Peptidylprolyl isomerase G (cyclophilin G)
PPP3R1
Protein phosphatase 3, regulatory subunit B, alpha
PRL
Prolactin
PTH-rP
Parathyroid hormone-related protein
PTPRB
Receptor-type tyrosine-protein phosphatase beta
PYGM
Phosphorylase, glycogen, muscle
QC
Quality control
RAB3B
RAB3B, member RAS oncogene family
RECK
Reversion-inducing-cysteine-rich protein with kazal motifs
XIV
RefSeq
The reference sequence
RIN
RNA integrity number
RNA
Ribonucleic acid
rpm
Revolutions per minute
S
Svedberg
SBNO2
Notch homolog 2
SGA
Small for gestational age
SIGLEC6
Sialic acid binding Ig-like lectin 6
SLC
Solute carrier
SMC3
Structural maintenance of chromosomes 3
SOX4
SRY (sex determining region Y)-box 4
SPTBN1
Spectrin, beta, non-erythrocytic 1
ST3GAL1
ST3 beta-galactoside alpha-2,3-sialyltransferase 1
TCF7L2
Transcription factor 7-like 2 (T-cell specific, HMG-box)
TF7L2
Transcription factor 7-like 2
TNF
Tumor necrosis factor
TP63
Tumor protein p63
TRPV6
Transient receptor potential cation channel, subfamily V, member 6
TSH
Thyroid-stimulating hormone
TXNDC5
Thioredoxin domain containing 5 (endoplasmic reticulum)
UCSC
University of california santa cruz
WHO
World health organization
αFP
Alpha fetoprotein
XV
Chapter 1
Placental development and physiology
1.1. Introduction
The name ‘placenta’ derives from the Greek word ‘plakuos’ which means ‘flat cake’. The
placenta is the interface between the mother and the fetus and regulates the amount and
composition of nutrients transported. It is a haemochorial, villous type organ where the
maternal blood enters the intervillous space via the spiral arteries and flows directly around
the terminal villi of the fetal circulation without any intervening maternal vessel wall
(Haggarty, 2005). The chorioallantoic placenta is the defining organ of eutherians that has
enabled prolonged intrauterine gestation. As such, normal placental development and function
are essential for mammalian reproductive success. The characteristic cell type that forms
substantial parts of the placenta is called the ‘trophoblast’ (from Greek trephein ‘to feed’ and
blastos ‘germinator’). In addition to regulating nutrient supply, the placenta also has a number
of other pivotal functions highlighting the importance of normal trophoblast differentiation in
ensuring a successful pregnancy (John and Hemberger, 2012). The earliest trophoblast cells,
which differentiate into various placental cell types, are under tight genetic and epigenetic
control (Fowden et al., 2011).
Varying degrees of abnormal placental development and secondary pathological processes
underpin complications of pregnancy (Chaddha et al., 2004). Failure of one or more key
components of placentation may result in a wide range of complications for the mother and/or
the fetus, including embryonic and second-trimester fetal death, preeclampsia, fetal growth
restriction (FGR) and abruption (Chelbi and Vaiman, 2008).
1
1.2. Development of the placenta
Placentation is a multi-step process which begins with implantation of the blastocyst into the
uterine tissue. The placenta is composed of fetal and maternal components; the fetal part
consists of trophoblast and extraembryonic mesoderm (chorionic plate) while the
endometrium gives rise to the maternal part. Approximately three to four days after
fertilization, the morula enters the uterus and a cavity appears within the cell mass forming
the blastocyst. The inner cell mass differentiates into the tissues of the embryo. The
surrounding epithelial cells, the trophectoderm, give rise to the placenta. Six to seven days
after conception, while the endometrium is in the secretory phase, the microvilli on the
trophoblastic cells begin to interact with the microvilli of pinocytes on the endometrial cells.
Meanwhile, decidualization takes place in the endometrium which includes secretory
alteration of the endothelial gland, inflow of specialised uterine natural killer cells, and
vascular remodelling. This process is largely controlled by the ovarian hormones oestrogen
and progesterone. Finally, trophoblast cells, influenced by matrix metalloproteinases, invade
the decidua and the trophectoderm differentiates into the cytotrophoblast. The invasion
process involves vascular remodelling of the placental bed controlled by variety of molecules
and maternal endocrine signals. These changes promote blood flow to the implantation site
and mediate the maternal adaptive responses to pregnancy. This final human-specific step
leads to the formation of a haemochorial placenta at the end of the first trimester (Chaddha et
al., 2004, Chelbi and Vaiman, 2008).
During
placentation,
cytotrophoblasts
fuse
together
to
form
the
multinucleated
syncytiotrophoblast which is responsible for secretion of human chorionic gonadotropin
(hCG) and progesterone; hormones necessary for the continuation of pregnancy. The
syncytiotrophoblast also delivers oxygen and nutrients to the fetus and removes waste from it.
2
The proximal columns of cytotrophoblast are penetrated centrally by mesodermal cells and
consequently haemangioblasts enter and fetoplacental blood vessels form by vasculogenesis
(Chaddha et al., 2004). Morphologically, the outermost layer of villi is formed from the
syncytiotrophoblast overlying cytotrophoblastic cells that cover a core of vascular mesoderm.
Cytotrophoblasts that penetrates beyond the syncytiotrophoblast shell contribute to the
formation of the extravillous cytotrophoblast. Vacuolar spaces begin to appear within the
mantle and steadily these spaces come together to constitute larger lacunae which develop to
the intervillous space. Expansion of lacunae reduces the thickness of the syncytiotrophoblast
and results in the formation of trabeculae. As cytotrophoblasts reach the distal part of
trabeculae, the cells begin to spread latterly and make connection with those from other
trabeculae to form the cytotrophoblastic shell.
By 20 weeks’ gestation, large numbers of decidual septa with a maternal core grow into the
intervillous space without touching the chorionic plate. However, these decidual septa rest in
a layer of syncytial cells. As a result of septal formation, macroscopic compartments appear in
the placenta called cotyledons. Approximately 100 spiral arteries pass through the cotyledons
and penetrate the decidual plate and supply blood to the intervillous spaces. A term placenta is
characterised by its discoid shape measuring approximately 15 to 25 cm in diameter, 3cm in
thickness, and 500 - 600 gm in weight.
1.3. The roles of the placenta
In mammals, placental function is the cornerstone of reproductive success playing a pivotal
role in fetal growth and developmental programming. The birth of a healthy infant at term
requires a coordinated series of events ensuring the normal development of both embryo and
placenta. To achieve this goal, the placenta must integrate signals from the fetus and the
3
mother in order to match fetal demand with maternal substrate supply. At the same time the
placenta must meet its own high metabolic demands, while also enabling excretion of various
metabolites and carbon dioxide (Chaddha et al., 2004, Roland et al., 2012). The placenta
mediates the active transport of nutrients and metabolic waste across the barrier separating the
maternal and fetal compartments, and modifies the composition of some nutrients through its
own metabolic activity (Garnica and Chan, 1996). It also synthesizes a wide range of
structural proteins and enzymes and a range of products with putative endocrine, metabolic,
haemostatic and immunological functions (Carter, 2000). The placenta also plays a role in the
exposure of the fetus to potentially toxic xenobiotics via the maternal circulation (Prouillac
and Lecoeur, 2010).
Changes in both the gross morphology and ultrastructure of the placenta occur with increasing
gestational age in response to nutritional and endocrine manipulations (Sibley et al., 1997,
Sibley et al., 2005). These changes lead to alterations in vascularity and the surface area
available for exchange of factors. The thickness of the interhaemal membrane and the density
of transporter proteins also affect placental transport efficacy (Burton and Fowden, 2012).
Coordinated development of the fetal villous tree and the vasculature of the placenta are
therefore necessary for normal fetal growth and well-being. Blood vessels develop by
branching angiogenesis growing into immature intermediate villi and then forming large
numbers of gas-exchanging terminal villi. This process involves non-branching angiogenesis
in mature intermediate villi (Kingdom et al., 2000).
Another role of the placenta is providing a low oxygen environment to ensure the protection
of the fetus from free oxygen radicals in the first trimester. The early embryo and developing
placental villi are vulnerable to oxygen-mediated injury (Rodesch et al., 1992, Jauniaux et al.,
4
2000). The occlusion of the tips of the spiral arteries by plugs of endovascular trophoblast
cells occurs in during the first 10 to 12 gestational weeks. Maternal blood flow does not occur
within the intervillous space during this time, resulting in an oxygen poor environment which
is important for the developing embryo. Non-physiological levels of oxygen tension in the
first trimester may impair metabolism and cause tissue damage (Hustin and Schaaps, 1987,
Hempstock et al., 2003). An abnormal increase in intervillous circulation has been proposed
to cause miscarriage (Merce et al., 2009). From the sixth gestational week onwards maternal
intervillous blood flow occurs with a gradual increase during the first trimester (Merce et al.,
2009).
Aspects of placental function can be divided into three main components: (i) transfer of
nutrients and waste products, (ii) a metabolic role with endocrine functions, and (iii)
immunologic functions.
1.3.1. Nutrient transfer and metabolic role
In mammals, the major determinant of intrauterine growth is the placental nutrient supply,
which, in turn, depends on the size, morphology, blood supply, and transporter abundance of
the placenta. It also depends on the synthesis and metabolism of nutrients and hormones in the
uteroplacental tissues (Fowden et al., 2006). In the second and third trimesters, the noticeable
expansion of the placenta through the proliferation of different cell types maintains the high
level of nutrient supply. The nutrients are basically used for the purposes of generating
cellular energy, synthesizing DNA and RNA, proteins, membranes, and other components
crucial for cellular growth. Simple diffusion, facilitated diffusion, active transport and
receptor-mediated endocytosis are the common pathways for nutrient transfer across the
placenta.
5
The syncytiotrophoblast and the fetal capillary endothelium express transporters (Vahakangas
and Myllynen, 2009). In syncytiotrophoblast, various transporters have been found both in the
brush-border (apical membrane) facing maternal blood and in the basolateral membrane close
to fetal capillaries where they transfer compounds in and out of the syncytiotrophoblast
(Vähäkangas and Myllynen, 2009).
In order to warrant the demand of the growing fetus, complex interactions between the
developing microvillous and basal membrane within the trophoblast and between the maternal
and fetal circulations occur to facilitate an increase in nutrient delivery (Regnault et al., 2005).
The placenta is a metabolically active interface and has a dynamic regulatory function,
sensing the concentration of available nutrients (Jansson and Powell, 2006). The placenta
adapts to increase its transport capacity under conditions when the maternal nutrient supply or
placental size may be limiting for normal fetal growth (Burton and Fowden, 2012). Placental
transporters are subjected to regulation by hormones. For example, insulin up-regulates
several key placental transporters and maternal insulin may represent a ‘good nutrition’ signal
to increase placental nutrient transfer and the growth of the fetus (Jansson and Powell, 2006).
The placenta is thus an active and crucial partner with both the fetus and mother in
determining fetal growth.
Various factors affect nutrient transfer across the syncytiotrophoblast. These include: (1)
maternal and fetal blood flow; (2) the nutrient concentration gradient across the
syncytiotrophoblast; (3) the concentration of transporter proteins to facilitate or actively
transport nutrients; (4) the exchangeable surface area; (5) the rate of diffusion of some
nutrients across membranes without the intervention of transport proteins; (6) metabolism
6
(utilization and de novo synthesis) within the placenta; and (7) the rate of nutrient utilization
by the fetal tissues (Haggarty, 2005).
Many of the common pregnancy pathologies are associated with abnormalities in the transport
and metabolism of important compounds such as glucose, oxygen, and amino acids.
1.3.1.1. Oxygen metabolism
The placenta has a high rate of oxygen consumption and despite low intervillous partial
pressure of oxygen (pO2), about 40% cent of total uterine oxygen uptake is consumed by the
placenta (Bonds et al., 1986). Most of the oxygen consumption in the placenta is likely to be
used for oxidative phosphorylation. Protein synthesis also accounts for a substantial fraction
of placental oxygen consumption, as in vitro experiments have revealed that at physiological
pO2, placenta has a protein synthesis rate of 40% per day (Carroll and Young, 1987). Cation
transport such as the Na+/K+-pump and Calcium (Ca2+)-ATPase makes also a significant
contribution to placental oxygen consumption (Clausen et al., 1991, Rolfe and Brown, 1997).
Phosphoenolpyruvate carboxykinase activity and probably glucose production are the other
important reactions that consume oxygen in the placenta (Diamant et al., 1975, Prendergast et
al., 1999). These functions are cut back when the oxygen supply is reduced, perhaps in favour
of preserving structural integrity (Schneider, 2000).
Reduced fetoplacental oxygenation is an underlying cause of pregnancy pathologies,
including FGR and preeclampsia, which are more common at high altitude because of the
chronic hypoxia in these areas (Zamudio et al., 2010). The prevalence of low birth weight
infants is higher among the women who live at high altitudes (Jensen and Moore, 1997, Julian
et al., 2008).
7
In mammals, the primary transcriptional response to hypoxic stress is mediated by the
hypoxia-inducible factors (HIFs). oxygen deprivation leads to the up-regulation of genes
mainly by the HIFs (Majmundar et al., 2010). Under conditions of reduced oxygen supply
such as those occurring in preeclampsia or FGR, hypoxia responses will be activated in the
placenta.
1.3.1.2. Glucose metabolism
Glucose is the primary substrate for fetal growth, a source of carbon skeletons for synthetic
purposes, and the only one that can be metabolised anaerobically (Zamudio et al., 2010). A
complex set of mechanisms is involved in maintaining the relatively constant glucose supply
to the fetus. The facilitated-diffusion glucose transporters (GLUT) family regulates glucose
uptake into fetal tissue. GLUT1 is embedded in both the microvillus and basal membranes of
the syncytiotrophoblast (Barros et al., 1995) and GLUT3 and GLUT4 are present on the
vascular endothelium and the intervillous stromal cells, respectively (Jansson et al., 1995,
Xing et al., 1998). Immunoblotting and immunocytochemistry experiments have failed to
detect GLUT2 protein in placental homogenates, microvillous or basal membranes (Jansson et
al., 1995). Although the fetus has the potential for gluconeogenesis, the actual formation of
glucose from pyruvate is not apparent until after birth because the rate limiting enzyme
phosphoenolpyruvate carboxykinase appears only after birth in the immediate newborn
period. Therefore, the maternal circulation is the only verified source for fetoplacental glucose
during pregnancy (Kalhan and Parimi, 2000). Glucose is mainly metabolised via the
glycolytic pathway in the placenta, however glycogen synthesis and pentose pathways have
also been described in placenta (Moe et al., 1991, Hahn et al., 2001). The human placenta at
term is thought to be capable of glucose production since glucose-6-phosphatase (G6Pase;
present in the placenta), is required for the final step in the synthesis of glucose (Prendergast
8
et al., 1999). However, G6Pase in the placenta may be the product of a different gene from
conventional hepatic G6Pase (Leonce et al., 2006).
Oxidative and non-oxidative fetal glucose metabolism is dependent on the additive effects of
fetal plasma glucose and insulin concentrations, although there is little effect of basal insulin
concentration on this metabolism. Glucose-stimulated insulin secretion increases over
gestation, can be down-regulated by constant, high concentrations of glucose, but enhanced
by pulsatile hyperglycemia (Hay, 2006).
1.3.1.3. Amino acids metabolism
During pregnancy, amino acids are essential for the development of the fetus as they are the
components of proteins and are vital precursors for the synthesis of non-protein substances,
such as nitric oxide, polyamines, neurotrasmitters, purine and pirimidine nucleotides (Grillo et
al., 2008).
The transport of amino acids across the placenta is a complex process mediated by
transporters located on the microvillus plasma membrane and basal membrane of the
syncytiotrophoblast (Cleal and Lewis, 2008, Grillo et al., 2008, Avagliano et al., 2012). Two
main classes of amino acid transporters are Na+-dependent systems and Na+-independent
systems. Na+-dependent transporters include: system A, ASC, N, XGA, β, B0,+, and GLY.
Cationic amino acid transport systems such as y+ and glycoprotein associated transport
systems involving ASC, b0,+, L, y+L, Xc¯, and T are also described (Grillo et al., 2008).
Na+-dependent transport systems have a crucial role in the microvillus plasma membrane
leading to increased concentrations of amino acids within the cell that require energy to act
9
against the concentration gradient. In contrast, Na+-independent systems are essential in the
outflux of amino acids across the basal membrane (Avagliano et al., 2012).
The genes encoding the different transporters are divided into different solute carrier (SLC)
families (Desforges and Sibley, 2010). For example, SLC38 encodes systems A and N and the
Na+-coupled neutral amino acid transporter family and SLC43 encodes the Na+-independent,
system-L-like amino acid transporter family (Hediger et al., 2004).
All amino acids, except tryptophan, are present in at least 186% higher concentrations in the
intervillous space than in maternal venous blood (Camelo et al., 2004). For many amino acids
the concentration is higher within the placenta than the fetal circulation (Philipps et al., 1978).
The difference in concentration of amino acids between the intervillous space and maternal or
fetal circulations suggests active transport of amino acids across the microvillus plasma
membrane. The placenta therefore plays an active role in determining the flux of amino acids
into the fetal circulation (Avagliano et al., 2012).
1.3.1.4. Lipid metabolism
Maternal plasma lipoproteins can be taken up by the placenta via lipoprotein or other
scavenger receptors. Lipoprotein triglycerides may also be hydrolysed by placental lipases,
mainly lipoprotein lipase, localised in the microvillous membrane (MVM) (Lindegaard et al.,
2005). Free fatty acids are released by this process and diffuse across the plasma membrane.
Transfer of non-esterified fatty acids is facilitated by a transporter-mediated mechanism
involving plasma membrane fatty acid binding proteins, fatty acid translocase, and a family of
fatty acid transport proteins (Haggarty et al., 2002).
10
1.3.2. Endocrine role
A special role of the placenta is the neuroendocrine function producing a wide range of
hypothalamic, pituitary, and end organ analogues. Numerous physiological changes in the
mother are required to facilitate fetal growth, parturition, lactation and maternal behaviour.
Hormonal fluctuations in pregnancy are the main reason for these maternal adaptations which
include changes in brain function and are essential to ensure a successful pregnancy outcome
(Russell et al., 2001). Indeed, dysregulated placental hormone secretion can lead to FGR.
These products act as endocrine, paracrine and autocrine factors to control the secretion of
other regulatory molecules, including the pituitary hormones of both mother and fetus and
their placental counterparts. Furthermore, they contribute to the regulation of maternal and
fetal physiology during pregnancy, ranging from the control of placental development to fetal
growth and maturation, regulation of uterine blood flow and initiation of labor (Reis et al.,
2001).
Cytotrophoblasts and the syncytiotrophoblast are the main sources of synthesis and secretion
of many placental hormones during pregnancy (Nelson et al., 1986, Kovalevskaya et al.,
2002). Villous stromal cells and Hofbauer cells (placental macrophages that are present in the
villus across gestation) are also a source of hormones and growth factors (Tang et al., 2011).
Most placental hormones are protein hormones, but oestrogen and progesterone are steroid
hormones. Placental proteins have taken a new role in recent years as screening tests for the
prenatal diagnosis of fetal aneuploidy. Placental proteins are secreted primarily into the
maternal circulation and their effects are probably confined to the mother (Sorensen et al.,
1995).
11
Hormones that regulate maternal metabolism and fetal growth include human placental
lactogen (hPL), prolactin (PRL), human placental growth hormone (hPGH), growth hormone
releasing hormone (GHRH), parathyroid hormone-related protein (PTH-rP), adipocytokines,
and insulin-like growth factors (IGF). Other hormones secreted by the placenta serve a wide
range of functions. Some target maternal reproductive organs like hCG, oestrogen,
progesterone, gonadotropin releasing hormone (GnRH), oxytocin, inhibin A, activin A, and
follistatin. Corticotropin releasing hormone, urocortin, and adrenocorticotropin regulate stress
response and vascular endothelial growth factor regulates vascular development. Below some
of these hormones have been discussed in more detail:
1.3.2.1. Human placental lactogen (hPL)
hPL is structurally and functionally similar to human growth hormone (GH) and PRL, which
acts in a prolactin-like manner on PRL receptor on neuroendocrine dopaminergic neurones to
stimulate dopamine synthesis and release, thus inhibiting PRL secretion from the maternal
pituitary gland (Freemark, 2006, Grattan et al., 2008).
hPL and PRL might serve an adaptive role in nutrient homeostasis and stimulating maternal
food intake in pregnancy by induction of central leptin resistance and promoting maternal
pancreatic beta-cell proliferation and insulin production to defend against the development of
gestational diabetes mellitus (Newbern and Freemark, 2011). Lactogen signalling through the
PRL receptor is essential for the establishment and maintenance of beta cell reserve and the
pancreatic adaptation to maternal insulin resistance which is caused by placental GH in order
to mobilise maternal nutrients for fetal growth. PRL and hPL also act in concert with
placental PTH-rP to stimulate gastrointestinal Ca2+ absorption, which increases two folds
during pregnancy (Freemark, 2006).
12
1.3.2.2. Human placental growth hormone (hPGH)
The syncytiotrophoblast layer of the human placenta produces a GH specific to this organ.
hPGH is the product of the GH2 gene and is structurally distinct from pituitary GH, from
which it differs by 13 amino acids and an N-glycosylation site (Alsat et al., 1998, Lacroix et
al., 2002). From 12–20 weeks to term hPGH in the maternal circulation gradually substitutes
for pituitary GH which becomes undetectable after 20-24 weeks of gestation. hPGH is
secreted by the placenta in a non-pulsatile manner and not regulated by GHRH (Lacroix et al.,
2002, Freemark, 2006). It is a major determinant of maternal insulin resistance in pregnancy.
The continuous secretion of hPGH appears to have important implications for physiological
adjustment to gestation and especially in the control of maternal IGF1 levels. PGH secretion
is regulated in vitro and in vivo by glucose.
Reduced maternal levels of PGH are observed in pregnancies with fetal growth retardation
(Lacroix et al., 2002). It has been suggested that hPGH has an autocrine/paracrine role in the
regulation of trophoblast invasion and interestingly, it is more efficient than pituitary GH in
stimulating extravillous cytotrophoblast invasiveness (Lacroix et al., 2005).
1.3.2.3. Adipocytokines
Adipose tissue secretes a number of hormones, called adipocytokines, which are important in
modulating maternal and fetal metabolism. Adipocytokines are produced by the placenta and
include different hormones such as leptin, adiponectin, ghrelin, tumor necrosis factor (TNF),
interleukin 6, visfatin, resistin, and apelin (Briana and Malamitsi-Puchner, 2009).
Adiponectin is an adipose tissue–derived plasma protein that is involved in regulation of
insulin resistance and glucose homeostasis and consequently is a plausible candidate for
13
regulation of intrauterine fetal development. Furthermore, conditions associated with
increased insulin resistance, such as gestational diabetes and preeclampsia, may be influenced
by this hormone (Mazaki-Tovi et al., 2005b). Serum adiponectin concentration was reported
to be significantly lower in the growth restricted fetuses compared with macrosomic and
appropriate fetuses. The lower serum adiponectin concentrations and the positive correlation
with birth weight in the FGR, suggest that this hormone may participate in the regulation of
fetal growth and may be a biological marker for this process (Tovi et al., 2004).
1.3.2.4. Insulin-like growth factors (IGF)
IGF system plays a critical role in fetal and placental growth throughout gestation. In
mammals, the IGF system consists of two ligands (IGF1 and IGF2), two receptors (IGF1R
and IGF2R) and six binding proteins (IGFBPs). The IGF1 receptor has a high degree of
homology with the insulin receptor and is the only IGF receptor to definitively have IGFmediated signalling functions. The IGF2R is identical to the cation independent mannose 6phosphate receptor, which functions in the trafficking of lysosomal enzymes (Jones and
Clemmons, 1995). IGF2 is the primary growth factor and nutrient transfer supporting
embryonic growth while IGF1, which is particularly sensitive to poor nutrition, plays an
important role in late gestation to ensure normal fetal growth and appropriate birth weight at
delivery. Disruption of the IGF1, IGF2 or IGF1R gene retards fetal growth, whereas
disruption of IGF2R or overexpression of IGF2 enhances fetal growth (Gicquel and Le Bouc,
2006).
Imprinting plays a key role in the IGF system. For instance, the IGF2 gene is paternally
expressed so in heterozygous carriers following disruption of the paternal gene the growth
retardation phenotype is expressed, whereas the IGF2R gene is maternally expressed. Some
14
growth disorders such as Beckwith-Wiedemann syndrome and Silver-Russell syndrome are
linked with disruption of this imprinting (Gicquel and Le Bouc, 2006).
1.3.2.5. Human chorionic gonadotropin (hCG)
hCG is a glycoprotein composed of alpha and beta subunits. The alpha subunit is shared with
luteinizing hormone (LH), follicle-stimulating hormone (FSH), and thyroid-stimulating
hormone (TSH), while the beta subunit is unique. After implantation, the syncytiotrophoblast
produces hCG which enters the maternal blood and maintains the activity of the progesteroneproducing corpus luteum in the ovary during the first seven weeks of pregnancy (Vause and
Saroya, 2005). Additionally, hCG stimulate production of testosterone by fetal leydig cells
during the first and second trimesters of gestation to affect male sexual development before
fetal LH production occurs (Huhtaniemi et al., 1977). hCG may play a role in the maintenance
of the later stages of pregnancy by uterine vessels vasodilation and myometrium smooth
muscle relaxation (Kurtzman et al., 2001). Similar to TSH, hCG stimulates thyroxine
production within the thyroid (Reis et al., 2001). hCG secretion declines after 11 weeks’
gestation but oestrogen and progesterone secretion increase until parturition (Vause and
Saroya, 2005).
1.3.2.6. Placental hormones and prenatal diagnosis
Some placental hormones have been described as useful biochemical markers for prenatal
screening in the first and second trimesters. The most commonly used maternal serum
analytes for first trimester screening include hCG and pregnancy-associated plasma protein A
(PAPP-A), which are usually used in combination with the fetal nuchal translucency
measurement. Second trimester screening include the measurement of blood level of alpha
15
fetoprotein (αFP), free βhCG, unconjugated estriol, and in some laboratory, inhibin A
(Gagnon et al., 2008, Ndumbe et al., 2008).
PAPP-A is a glycoprotein produced in the placenta. It is present in the maternal circulation in
increasing concentrations during pregnancy. Low maternal serum levels are associated with
trisomy 21, 18, and 13 and adverse pregnancy outcomes such as preterm delivery, FGR,
preeclampsia, and stillbirth (Kirkegaard et al., 2010).
In pregnancies complicated by fetal trisomy 13 and 18, first trimester maternal serum levels
of free βhCG are decreased while it is elevated in trisomy 21. In those complicated by fetal
sex chromosome abnormalities, free βhCG is normal (Gagnon et al., 2008).
1.3.3. Immunologic role
During pregnancy, intimate positioning of maternal and fetal cells and molecules which are
genetically different would be expected to produce an immune rejection response by the
maternal immune system. In immunological terms, the placenta is a semi-allograft or, as in
some cases of surrogate pregnancies, a complete allograft. Several strategies are used by the
trophoblast to protect the fetus from maternal immune system rejection (Bowen et al., 2002).
The trophoblast cells are negative for classical major histocompatibility complex (MHC) class
I or class II molecules that are the main targets of killer cytotoxic lymphocytes and therefore a
T cell-mediated response is unlikely. Instead, they express a non-classical class MHC protein,
human leukocyte antigen (HLA)-G, which is postulated to suppress maternal immune cells
(Lefebvre et al., 2000). Moreover, the syncytiotrophoblast contains inhibitors of complement
(Kelly et al., 1995). In addition, trophoblast cells evade the maternal immune system by
16
secretion of Fas ligand which induces immune cell apoptosis and trigger the death of Fas
positive maternal T cells (Abrahams et al., 2004).
Experiments in transgenic mice have suggested that dysfunction of both the innate (natural
killer cells) and adaptive (T-cells and T regulatory cells) immune systems result in higher
rates of fetal loss (Christiansen, 2013).
17
Chapter 2
Placenta, fetal growth, and molecular mechanisms
2.1. Fetal growth restriction (FGR)
FGR also known as intrauterine growth restriction American College of Obestetricians and
Gynecologists (2013) remains one of the main challenges in maternity care. A wide range of
definitions of FGR can be found in the literature. FGR is usually defined as a failure of the
fetus to reach its genetically predetermined growth potential. However, in practice, FGR is
suspected if sonographic estimates of size, symmetry or fetal weight are abnormal (Miller et
al., 2008). A diagnosis cannot usually be made by observation at a single point in time and
requires serial measurements, but this may be of limited value from a clinical perspective.
Current mechanisms of fetal surveillance are focused on recognition of early onset FGR and
late onset FGR resulting in stillbirth towards the end of pregnancy remains a significant
problem.
The use of the terms ‘small for gestational age’ (SGA) and ‘FGR’ can be confusing, and the
terms often are used interchangeably. The World Health Organization (WHO) defines FGR
when the fetus is <10th percentile, whereas the American College of Obstetricians and
Gynecologists (ACOG) defines FGR when the fetus is <10th percentile on the curve of weight
for gestational age, and is frequently associated with placental insufficiency (Nardozza et al.,
2012). According to the recent ACOG bulletin (American College of Obestetricians and
Gynecologists, 2013), SGA exclusively refers to the ‘newborns’ whose birth weight is less
than the tenth percentile for gestational age. Nonetheless, the 10th birth weight percentile for a
given gestational age is the most commonly used cut-off for the definition of FGR (De Jong et
al., 2000). Fetuses below 10th birth weight percentile might be constitutionally small without
18
any pathologic condition. Customised standards for fetal growth and birth weight have
improved the detection of FGR by better distinction between physiological and pathological
smallness and have led to internationally applicable norms (Ego et al., 2006, Figueras and
Gardosi, 2011). In a customised or individualised birth weight standard, birth weight is
adjusted for physiologic factors such as birth order (parity), fetal gender, maternal height,
weight and ethnic origin. Pathologic factors such as smoking, alcohol, chronic hypertension,
and diabetes mellitus are not considered for this adjustment.
Pathological FGR can often be distinguished from constitutional smallness by assessment of
number of factors including prior history, serum biochemical markers such as low PAPP-A,
raised inhibin, elevated αFP and sonographic examination, monitoring fetal weight,
disproportionate asymmetric growth (increased head to abdominal circumference ratio),
reductions in amniotic fluid volume and abnormal Doppler waveforms in the umbilical artery,
ductus venosus and middle cerebral artery.
2.1.1. Aetiology of FGR
Fetal growth is the result of a complex cascade of processes which require coordination of
components within the maternal, placental and fetal compartments. Fetal growth and
development, and placental structure and function, are the result of the dynamic integration of
cell proliferation, differentiation and migration. There are numerous perturbations that
modulate these processes including hormones, growth factors, extracellular matrix proteins,
cell surface receptors, post-receptor signalling pathways, cell cycle control proteins, and
apoptosis.
19
The most common cause of FGR (80–90% of all cases) is a compromised supply of nutrients
and oxygen to the fetus, which results in the disproportionate growth of the brain in relation to
other fetal organs like liver, kidneys, pancreas, skeletal muscles, and fatty tissues (Bauer et
al., 2003).
2.1.1.1. Fetal factors
1. Chromosomal alterations: primarily trisomy of chromosomes 13, 18 and 21 (Lin and
Santolaya-Forgas, 1998).
2. Inborn error of metabolism and genetic syndromes: gene mutations, such as the mutations
determine the production or activity of insulin-like growth factor (Abuzzahab et al., 2003).
3. Intrauterine infections: viral infections causing placentitis, lesions of the vascular
endothelium, and fetal viraemia, with direct inhibition of cellular multiplication, obliterating
angiopathy, and cytolysis (Neerhof, 1995).
4. Multiple gestations: an important cause of FGR; about 15–30% of these pregnancies may
have growth restriction, and it is more common in monochorionic gestations. Until 28–30
weeks’ gestation, the fetus in a multiple gestation has growth very similar to that of a single
gestation, but from this time on, there is a 15–20% decrease in growth velocity (Blickstein,
2004).
2.1.1.2. Maternal factors
1. Clinical conditions: hypertensive disorders of pregnancy increase the incidence of FGR due
to decreased uterine–placental perfusion. There is a strong association between preeclampsia
20
and FGR due to deficient trophoblastic invasion (Lyall et al., 2001). Other diseases, such as
insulin-dependent diabetes mellitus with vasculopathy, severe renal conditions, autoimmune
diseases (collagenoses, antiphospholipid antibody syndrome), hereditary or acquired
thrombophilia, hyperhomocysteinemia, and severe anaemia are also associated with FGR
(Howley et al., 2005, Carolis et al., 2012).
2. Nutritional disorders: Maternal nutrition plays a crucial role in influencing fetal growth and
birth outcomes (Abu-Saad and Fraser, 2010). There is growing evidence that maternal
nutritional status can alter the epigenetic state (stable alterations of gene expression through
DNA methylation and histone modifications) of the fetal genome. This may provide a
molecular mechanism for the impact of maternal nutrition on both fetal programming and
genomic imprinting. Promoting optimal nutrition will not only ensure optimal fetal
development, but will also reduce the risk of chronic diseases in adults (Wu et al., 2004).
3. The use of drugs: Alcohol and drug use by pregnant women are harmful to the developing
embryo and fetus, and in the majority of cases also cause FGR. Exposure to carbon monoxide
reduces the capacity of fetal haemoglobin to carry oxygen. Cigarette smoking reduces plasma
leptin concentration in vivo, whereas nicotine has been shown to have no direct effect on
leptin expression in vitro. Nicotine possibly reduces leptin secretion indirectly via enhanced
plasma catecholamine concentration (Salmasi et al., 2010) by increasing the maternal
catecholamines, nicotine also reduces placental perfusion. Equally, passive smoking, use of
drugs (such as cocaine, heroin and others), alcohol, the inadvertent use of teratogenic
substances (anticonvulsants, warfarin anticoagulants, antineoplastic agents, and folic acid
antagonists), exposure to radiation, and living at high altitudes comprise aetiologic factors of
FGR (Streissguth et al., 1981).
21
4. Other factors: Constitutional factors such as fetal gender, parity, maternal age, and maternal
height can affect the birth weight. Observations have shown birth weight distribution was
shifted to lower birth weight in ethnic minority groups; however, it is debateable whether
such differences are merely due to ethnicity or other factors such constitutional and
environmental (smoking, drinking, depression, stress, etc.) factors play a role in this condition
(Goedhart et al., 2008).
Table 2.1 Maternal, fetal, and placental causes of FGR
Maternal
Fetal
Placental
•Reduction in uteroplacental blood
flow
•Genetic factors
•Uteroplacental vascular
insufficiency
•Diminished caloric intake
•Multiple gestation
•Uterine malformations
•Infections: cytomegalovirus,
syphilis, rubella, varicella,
toxoplasmosis, tuberculosis, HIV,
congenital malaria
•Previous pregnancy with FGR
•Low pre-pregnancy weight
•Low socioeconomic status
•Smoking, alcohol, illicit drugs
•Maternal age: <16 yrs, >35 yrs
•Assisted reproductive technology
•Teratogens: anticonvulsants,
methotrexate, warfarin
•Congenital anomalies
•Aneuploidies: trisomy 13, 18, 21
•Chorionic separation (partial
abruption, haematoma)
•Extensive villous infarction
•Marginal or velamentous cord
insertion (chorion regression)
•Confined placental mosaicism
•Chromosomal microdeletions
•Imprinting: Russell-Silver
syndrome
•Vascular disease: chronic
hypertension, pre-gestational
diabetes, antiphospholipid antibody
syndrome, collagen vascular disease
(e.g., systemic lupus erythematosus,
thrombophilia, renal disease,
Crohn’s disease, ulcerative colitis)
•Hypoxemia, high altitude
•Anaemia including
haemoglobinopathies
22
2.1.1.3. Placental factors
The most common pathological finding at delivery in severe FGR pregnancies and in third
trimester ‘unexplained’ stillbirths is gross and histopathologic evidence of uteroplacental
vascular insufficiency which is morphologically characterised by placental infarction (Laurini
et al., 1994, Lausman et al., 2012). Progressive uteroplacental dysfunction leads to placental
respiratory failure and is associated with an increased incidence of perinatal asphyxia and
neurodevelopmental disorders (Burke et al., 2006).
2.1.2. Placental adaptation to the maternal environment
The placenta is not just a passive participant in pregnancy supplying maternal substrates to the
fetus. It also has a capacity to supply nutrients in response to intrinsic and extrinsic variations
in the maternal-fetal environment in order to maximise fetal growth and viability at birth. The
various homeostatic mechanisms within the placenta and their interaction with maternal
physiological adaptations during pregnancy act to ensure a constant supply of substrate to the
fetus, free of large diurnal fluctuations corresponding to the timing of maternal meals, and to
protect the fetus against a transiently poor intake during critical periods of fetal growth
(Haggarty, 2005). These adaptations help the mother to meet the full fetal requirement for
nutrients; however, some of these adaptations may also affect the development of individual
fetal tissues, with pathophysiological consequences long after birth (Sandovici et al., 2012). If
these responses are truly adaptive, then they should be detectable within the physiological
range. Indeed, in more pathological situations the adaptations may be compromised by severe
stress responses aimed at ensuring placental cell survival and conserving energy levels by all
the maternal and feto-placental tissues (Burton and Fowden, 2012).
23
Adaptation in placental supply capacity might occur in three components: blood flow,
exchange barrier surface area and transporter activity and expression. Changes in blood flow
will predominantly affect transfer of small hydrophobic solutes such as the respiratory gases.
Changes in exchange surface area will have predominant effects on hydrophilic solutes such
as glucose and amino acids while transporter activity adaptations will have highly specific
effects on transfer of particular solutes (Sibley et al., 2010). For instance, the activity of
placental transporters for cationic and neutral amino acids is reduced in FGR; whereas,
Placental System A activity can be up-regulated in small normal babies to meet the demands
of fetal growth, its reduced activity in FGR result in impaired leucine transport to the fetus
(Jansson et al., 1998). However, activity of the Ca2+-ATPase goes up (Strid et al., 2003) and
some other transporters, such as the GLUT1 do not change at all (Jansson et al., 1993).
Interestingly, the activity of some placental transporters such as System A increases over the
course of gestation, whilst that of others like Na+/H+ exchanger decreases. These variations in
placental transporters suggest adaptive responses to different fetal demands on the placenta as
it develops and grows (Mahendran et al., 1994).
Abnormal stimuli that alter placental development include hypoxia, oxidative and nitrative
stress, and abnormal maternal nutrient status. These changes lead to alterations in placental
transporter expression and activity or to epigenetic regulation of placental gene expression to
maintain fetal growth. In addition, these stresses may be a general underlying mechanism that
links altered placental function to fetal programming. Hypoxia is physiological for
organogenesis and placental tissue normally exists in a relatively hypoxic environment, but
FGR and preeclampsia are associated with a greater degree of trophoblast hypoxia (Myatt,
2006). Cultured term human trophoblasts have shown reduced expression and activity of
24
System A amino acid transporter and increased expression of GLUT in hypoxic condition
(Esterman et al., 1997, Nelson et al., 2003).
Observations in the human placenta at high altitude have shown that changes in placental
metabolism occur due to a reduction in mitochondrial activity to conserve the supply of
oxygen to the fetus; but at the cost of less glucose availability because of increased anaerobic
glucose consumption which result in hypoglycemia-mediated decrease in fetal growth (Illsley
et al., 2010).
Short term maternal food deprivation in the mouse has been linked to changes in gene
expression related to autophagy and ribosomal turnover in the placenta in order to maintain
the flow of nutrients to the developing hypothalamus. The fetus controls its own destiny in
times of acute starvation by short-term sacrifice of the placenta to preserve brain development
(Broad and Keverne, 2011).
Further support for the concept of adaptation in placental supply capacity to meet fetal
demand comes from analysis of the relationship between placental and fetal weight. Analysis
of placental weight, birth weight and their ratio in 1,569 FGR and 15,047 normal infants
showed that growth restricted infants had smaller placentae than normal infants when
gestational age was used as a covariate, suggesting that fetal growth depends on the actual
weight of the placenta and also that the placental transfer efficiency seems to be actually
greater in growth restricted infants (Heinonen et al., 2001). Others report that the ratio of
placental weight to birth weight is not an accurate marker of fetal growth (Williams et al.,
1997). This reflects the role of multiple and complex determinants of placental supply
capacity in order to meet fetal demand.
25
2.1.3. Placenta and pathogenesis of FGR
As the placenta is the regulator of nutrient composition and supply from mother to fetus and
the source of hormonal signals that affect maternal and fetal metabolism, appropriate
development of the placenta is crucial for normal fetal development. Placental function
evolves in a carefully orchestrated developmental cascade throughout gestation. Interactions
between maternal and fetal placental circulations are fundamental for an adequate exchange
of oxygen and nutrients Abnormal development of the trophoblast or placental vasculature
and disruption of this cascade may lead to abnormal fetal development (Myatt, 2006).
As discussed in section 2.1.1 the aetiologies of FGR are diverse which make understanding of
the pathophysiology difficult. Defects in all the trophoblast-differentiating pathways
(endovascular, interstitial and chorionic villous) play a role in the pathogenesis of early onset
FGR. Since uteroplacental insufficiency is a common cause of FGR and may be amenable to
therapy in the future, particular focus is placed on the mechanisms involved in the placenta
(Sankaran and Kyle, 2009).
Between the sixth and twelfth weeks of pregnancy, cytotrophoblast invasion is established in
the decidual tissues, including within intradecidual segments of the spiral arteries. A second
wave of trophoblast invasion occurs between 16 and 18 weeks, when endovascular invasion is
extended to the intramyometrial portion of the spiral arteries. These vessels lose their muscle
and elastic layers (replaced by a fibrin matrix), resulting in a significant drop in resistance to
flow and less responsiveness to local vasoconstrictive agents (Nardozza et al., 2012).
Incomplete trophoblast migration will result in failure in the destruction of the muscle and
elastic portion of the spiral arteries and this in turn will result in maintenance of a high
resistance rather than a low resistance circulation. A failure in trophoblast invasion is also
26
associated with passage of nutritients across the intervillous space, and the possibility of
greater vasoconstrictive action, since innervation is not affected (Nardozza et al., 2012).
27
2.1.3.1. Placental blood flow
Both maternal and fetal blood flows and their distribution will influence placental exchange.
Doppler velocimetry analysis has shown reduced blood flow in both the uterine and umbilical
arteries in FGR, that is the reduced blood flow on both sides of the placental exchange barrier.
For exchange to be optimal, placental blood flows should match the permeability of the
barrier. If permeability is low, then the amount of substance transferred across the barrier will
depend largely on the permeability characteristics of the membrane and will be little
influenced by blood flows. Such substances are said to be diffusion limited and many
hydrophilic substances belong to this group. If, on the other hand, one or both blood flows are
low and permeability is high, net flux depends on flow and is little influenced by the
permeability characteristics of the membrane. Therefore, the transfer of a molecule across a
barrier may be limited by the actual diffusion (diffusion-limitation) or by the rate of blood
flow (blood flow-limitation). Examples of molecules subjected to blood flow-limited
transport are carbon dioxide, oxygen and antipyrine (Schröder, 1995). Thus, it is likely that
the reduction in placental blood flows contribute to fetal hypoxia in FGR. Indeed, in utero
cord blood sampling has demonstrated that FGR is associated with hypoxemia. In contrast,
transplacental transport of nutrients such as glucose and amino acids will be less affected by
changes in blood flow since the transport across the barrier is the primary limiting factor for
the transfer of these molecules (Jansson and Powell, 2006).
In FGR, uterine venous samples show significantly higher oxygen content (larger
transplacental gradients) and lower coefficients of uterine oxygen extraction which means that
in FGR pregnancies the placenta simulates a relatively inefficient venous equilibrator, and
both perfusion pattern and placental structure might be affected (Pardi et al., 1992). This
28
suggests that the impairment of the fetoplacental circulation in the villi is of considerably
greater importance etiologically than changes in uterine blood flow.
2.1.3.2. Placental nutrient transport in FGR
The two polarised plasma membranes of the syncytiotrophoblast represent the primary barrier
for transplacental transfer of nutrients and most ions. The plasma membrane directed towards
the maternal compartment is the MVM whereas the basal plasma membrane (BM) faces the
fetal capillaries. It has been hypothesized that FGR is associated with alterations in the
activity of placental transporters which may contribute to the growth restriction.
Transport of amino acids across the human placenta is an active process resulting in amino
acid concentrations in the fetal circulation that are substantially higher than those in maternal
plasma. The System A transporter (expressed by genes SLC38-A1, -A2 and, -A4), which is a
Na+-dependent transporter, is critical in mediating the uptake of non-essential neutral amino
acids (glycine, serine, alanine) across the MVM (Jansson, 2001). In normal pregnancies,
smaller babies with a lower abdominal circumference but within normal range of birth weight
have higher MVM System A activity (Godfrey et al., 1998). By contrast, MVM System A
activity is markedly reduced in FGR especially in those babies who are delivered prematurely
(Mahendran et al., 1993, Glazier et al., 1997, Jansson et al., 2002). Furthermore, system A
activity is related to the severity of FGR and the most severe cases of FGR is associated with
the most pronounced decreases in MVM System A activity (Glazier et al., 1997). In addition,
the activity of a number of placental transport systems for essential amino acids, such as
lysine, leucine (Jansson et al., 1998) and taurine (Norberg et al., 1998) is reduced in MVM
and BM isolated from FGR placentas. These in vitro findings are compatible with an in vivo
29
study in pregnant women, using stable isotopes, that placental transfer of the essential amino
acids leucine and phenylalanine is reduced in FGR (Paolini et al., 2001).
The down-regulation of placental amino acid transporters in FGR results in a decreased
delivery of amino acids to the fetus and is likely to be an important factor causing the low
fetal plasma concentrations of certain amino acids in this pregnancy complication. Amino
acids, together with glucose, are the primary stimulus for secretion of the most important
growth promoting hormone, insulin, which may affect placental nutrient transfer by
increasing the activity of the System A transporter (Jansson et al., 2003). It appears that the
combination of decreased fetoplacental amino acid uptake and disrupted insulin/IGF
signalling in liver and muscle account for decreased fetal growth in FGR (Regnault et al.,
2005).
Transport of other solutes is also affected. FGR fetuses typically have decreased subcutaneous
fat depots, contributing to the thin appearance of these infants. This may be due to decreased
fetal fat synthesis or reduced placental transfer of free fatty acids. The activity of MVM
lipoprotein lipase is critical in releasing free fatty acids incorporated in lipoproteins
circulating in maternal blood. Indeed, reduced activity of MVM lipoprotein lipase and
increased expression of L-fatty acid binding proteins may contribute to the altered lipid
deposition and metabolism in FGR and diabetic pregnancies (Magnusson et al., 2004).
Comparison of growth restricted and normal fetuses has shown differences in relative
maternal and fetal concentrations of fatty acids such as linoleic acid and docosahexaenoic
acid. It has been suggested that these differences may either be due to inadequate
transplacental supply or alternatively due to the fact that the fetus lacks the enzymes
necessary for fatty acid metabolism. These changes would then affect the feto-placental
30
biochemical environment, potentially leading to the neural and vascular complications
associated with FGR (Cetin et al., 2002). It is important to note that some transporters do not
show altered activity in FGR. For example, both the expression and activity of the GLUT1 in
MVM and BM remain unchanged in FGR (Jansson et al., 1993, Jansson et al., 2002).
2.1.3.3. Placental ion transport in FGR
Placental ion transport is either correlated with fetal growth or appears to be regulated in a
compensatory manner relative to fetal growth. Placental expression and activity of Na+/H+
ATPase are reduced in the syncytiotrophoblast in FGR (Hughes et al., 2000). This alteration
might be responsible for in utero acidosis in a subgroup of FGR fetuses (Nicolaides et al.,
1989). Furthermore, Na+/K+ ATPase activity is reduced in the MVM of placentas from FGR
pregnancies. These changes might impair the function of Na+-coupled transporters for vital
nutrients such as amino acids and contribute to the reduced growth of these fetuses
(Johansson et al., 2003). Interestingly, placental Ca2+ pump is activated which may be
secondary to increased fetal Ca2+ demand. Mid-molecular fragment of parathyroid hormone
related peptide (PTHrP mid-molecule) could be one of the mechanisms involved in the
activation of BM Ca2+ ATPase in FGR (Strid et al., 2003).
In summary, alteration to human placental transporters in FGR is classified into three groups:
1) those transporters that are unaffected by FGR such as glucose; 2) transporters with
decreased activity such as System A, Na+/H+ ATPase, and Na+/K+ ATPase; 3) transporters
with increased activity such as Ca2+.
31
2.1.3.4. Placental transport function in fetal overgrowth
Fetal overgrowth represents a risk factor for operative delivery, traumatic birth injury and
developing diabetes and obesity later in life. Since transplacental transport of glucose is a
facilitated process, its net transfer is dependent on the concentration gradient between
maternal and fetal blood. Pedersen’s hypothesis states that maternal hyperglycemia increases
net transfer of glucose to the fetus and consequently increased substrate availability contribute
to elevated fetal insulin secretion and fetal overgrowth (Pedersen, 1954). However, incidence
of macrosomic babies remains surprisingly high despite the rigorous glycemic control of the
pregnant woman with diabetes. In fact, this poor correlation between maternal glucose level
and the size of baby suggests the role of other factors in fetal growth, possibly changes in
placental transport function because of maternal diabetes. Indeed, placental activity of glucose
transport and lipoprotein lipase have been reported to be increased in insulin-dependent
diabetes mellitus (IDDM) associated with accelerated fetal growth, but not in gestational
diabetes mellitus (GDM) (Jansson et al., 1999, Jansson et al., 2001, Magnusson et al., 2004).
Thus, up-regulation of placental GLUT in IDDM may contribute to increased placental
glucose transfer and stimulate fetal growth even if the mother is normoglycemic.
Concerning amino acid transfer, diabetes in pregnancy is associated with an increased system
A activity in MVM regardless of fetal size. It has been also reported that in pregnant women
with GDM who have macrosomic fetus, leucine transport is increased. These changes result
in an increased uptake of neutral amino acids across MVM, which may be used in placental
metabolism or be delivered to the fetus (Thomas et al., 2002). Although sometimes there are
completely different results, (Kuruvilla et al., 1994) findings in general suggest the enhanced
placental capacity for nutrient transfer in pregnancies with diabetes.
32
2.1.4. Consequences of FGR
FGR which accounts for almost 10% of all pregnancies is associated with distinct short- and
long-term morbidities (Diplas et al., 2009). It is a major contributor to perinatal morbidity and
mortality, being responsible for 50% of perinatal deaths occurring <37 weeks’ gestation and
20% of perinatal deaths >37 weeks (Figueras et al., 2007). Immediate morbidities include
those associated with prematurity and inadequate nutrient reserve, while childhood
morbidities relate to impaired maturation and disrupted organ development (Christou and
Brodsky, 2004). Long-term sequelae include metabolic and cardiovascular complications in
later life.
2.1.4.1. Short term consequences
FGR has serious consequences for babies. It is associated with increased risk of premature
birth and increased morbidity related to prematurity. A large meta-analysis ⁄ meta-regression
demonstrated that FGR is associated with low Apgar scores at 5 min, sepsis, intracranial
haemorrhage, intrauterine and neonatal death, necrotising enterocolitis (NEC) and respiratory
complications such as respiratory distress compared with a normally grown cohort (Edwards
and Ferguson-Smith, 2007, Damodaram et al., 2011). Although antenatal steroids have a
beneficial effect on reducing some of these morbidities, the adrenal gland in FGR fetuses is
already stimulated due to intrauterine stress these patients may not respond to exogenous
steroid administration (Longo et al., 2012). However, exposure to glucocorticoids either
antenatally to accelerate lung maturation or postnatally to facilitate weaning from mechanical
ventilation may result in later adverse renal or cardiovascular outcomes (Bensky et al., 1996,
Wintour et al., 2003).
33
Abnormalities of the fetal mesenteric circulation as a part of changes associated with
circulatory redistribution, combined with fetal hypoxia, could result in hypoxic-ischaemic
injury of the bowel and development of NEC. Retinopathy of prematurity also seems to occur
due to intrauterine hypoxia, altered levels of growth factors and diminished antioxidant
capacity (Longo et al., 2012).
Babies with growth restriction develop ‘fetal distress’ and metabolic acidosis more frequently,
and are more likely to develop hypoxic encephalopathy and cerebral palsy (Jarvis et al., 2003,
M. Kady and Gardosi, 2004).
Neonatal polycythaemia and thrombocytopenia are more evident in growth restricted
newborns. Chronic intrauterine hypoxia is assumed to be responsible for the increase of
erythropoietin and impairment of thrombopoiesis in these newborns (Wasiluk et al., 2009).
FGR infants are at an increased risk for thermoregulatory disturbances. Ideally, these infants
can increase heat production to compensate for excessive heat loss because brown adipose
tissue stores are not usually depleted. If heat loss, exacerbated by a relatively large body
surface area and decreased subcutaneous tissue, is allowed to continue, hypothermia will
ensue. Both hypoxia and hypoglycaemia can interfere with heat production and may be
pathogenically implicated in the disrupted thermoregulatory response after FGR (Rosenberg,
2008).
Fasting hypoglycemia is another issue of major concern for the FGR infant. The
hypoglycemia in the FGR infant is due primarily to decreased glycogen stores (the
predominant source of glucose in the first hours after birth) with a possible contribution from
34
diminished glucose production in the liver from alanine and lactate via gluconeogenesis
(Rosenberg, 2008). In early postnatal life, FGR infants display increased insulin sensitivity
with respect to glucose disposal but not with respect to suppression of lipolysis, ketogenesis,
and hepatic production of IGF-binding protein-1 (IGFBP-1) (Bazaes et al., 2003).
2.1.4.2. Long term consequences
Intrauterine programming is defined as a permanent or long-term change to the physiology,
morphology or metabolism of a fetus or neonate in response to a specific insult or stimulus at
a critical period in development (Barker, 2001). A reduction in nutrients below that required
for optimal fetal growth ‘reprograms’ the offspring via permanent structural and functional
changes that, in the context of postnatal nutrient surfeit, predispose to disease syndrome
(Chernausek, 2012). Changes in the concentrations of metabolites such as amino acids,
glucose, and Ca2+ in body fluid compartments will alter homeostatic set points with life-long
effects on how the individual reacts to changes in his or her environment (Sibley et al., 2010).
The ‘fetal origins of adult disease’ hypothesis proposes that type 2 diabetes mellitus, obesity,
hypertension, dyslipidemia, and insulin resistance (the metabolic syndrome), as well as
coronary heart disease and stroke originate in developmental plasticity, in response to undernutrition during fetal life and infancy (Barker, 2004). Animal models suggest that leptin
deficiency or, alternatively, leptin resistance may contribute to the pathogenesis of the
metabolic syndrome in the FGR. Leptin is a 16-kilodalton hormone synthesized by and
secreted from the adipose tissue. It acts on the nervous system, adipose tissue, respiratory and
immune systems, and skeletal growth control. It circulates in direct proportion to body fat
stores, enters the brain and regulates food intake and energy expenditure. Deficient leptin
35
neuronal signalling favours weight gain by affecting the central homeostatic circuitry (Bar-El
Dadon et al., 2011, Coupé et al., 2012).
FGR may permanently alter the endocrine–metabolic status of the fetus, driving an insulin
resistance state that can promote survival at the short term but that facilitates the development
of type 2 diabetes mellitus in adult life (Kanaka-Gantenbein, 2010). A significant fetal
adaptation to poor nutrition is an up-regulation of the insulin receptor without the upregulation of insulin signalling in fetal skeletal muscle. After birth, however, there is an upregulation of both the insulin receptor and the insulin signalling pathway in the FGR infant. In
suboptimal nutritional environment, adaptive responses result in the development of a ‘thrifty
phenotype’ in which there is a relative decrease in overall body growth and in the growth of
organs such as the gut and liver, while there is differential sparing of the growth of key organs
such as the brain and heart (Longo et al., 2012). Brenner and colleagues hypothesized that
FGR may cause a low nephron number, thus predisposing to later hypertension and renal
disease (Brenner et al., 1988). Subsequent studies showed that FGR were associated with mild
to moderate elevations of blood pressure, reduced numbers of compensatory hypertrophied
glomeruli, lower glomerular filtration rate and higher urine albumin-to-creatinine ratio (Chan
et al., 2010). Moreover, there are strong pointers to the importance of maternal body
composition and dietary balance during pregnancy that results in impaired fetal development
and consequently programme adult cardiovascular disease (Godfrey and Barker, 2001).
Fibrinolysis disorders have been proposed as one putative link between metabolic syndrome
and cardiovascular disease. Plasminogen activator inhibitor type-1 (PAI-1) is strongly
associated with metabolic syndrome and identified as a predictive marker for myocardial
infarction. Meas et al., found that elevation of PAI-1 plasma concentrations are significantly
36
increased in adults born SGA independently of the presence of metabolic syndrome or
insulin-resistance (Meas et al., 2010).
Other adverse outcomes of FGR include short stature in children and adults, premature
adrenarche, ACTH-adrenal gland axis disturbance, polycystic ovarian syndrome, neurological
sequelae, and motor and cognitive delay (Baschat, 2011, Roth and Sathyanarayana, 2012).
Reduced bone mineralization both in childhood and in adult age osteoporosis later in life has
also been reported (Jansson and Powell, 2006). On the other hand, epidemiological studies
has shown that adverse pregnancy outcomes such as preeclampsia, preterm delivery, and low
birth weight are associated with increased risk for the affected women for cardiovascular
disease in later life (Smith et al., 2001). Therefore, FGR is an important condition that
requires clinical awareness and health service focus on developing better strategies for the
early detection of FGR.
37
Chapter 3
Aims and hypothesis
Abnormal fetal growth is a common complication of pregnancy. Suboptimal fetal growth
occurring in FGR fetuses is an important cause of perinatal mortality and morbidity.
Moreover, an association has been demonstrated between low birth weight percentile and late
onset adult diseases. We hypothesize that abnormal rates of fetal growth are a reflection of
placental function and that this is associated with changes in placental gene expression.
One of the most popular applications of microarrays is to compare expression of a set of
genes from a sample maintained in a particular condition to the same set of genes from a
reference sample. Genome-wide expression analysis of placental tissue may provide valuable
insight regarding genes and pathways influencing fetal growth.
This study aims to identify changes in placental gene expression in term pregnancies with
abnormal patterns of fetal growth using GeneAtlas 3’ IVT Express Kit (Affymetrix HG-U219
array strip) which provides wide coverage of the transcribed genome with total probes
number of 49411 derived from UniGene, RefSeq, NCBI genome version, UCSC, Ensembl,
GenBank, and Entrez data sources (25-mer probes interrogating 9 to 11 unique sequences of
each transcript - interrogate up to 275 bases per transcript).
Results of this study may be of future value in defining pregnancies at risk of fetal morbidity
and mortality related to growth dysfunction. In addition, findings of this research might help
to identify novel biomarkers for the screening, early detection, and management of FGR
which is difficult to predict using current techniques.
38
Hypothesis 1:
There is a difference in the placental gene expression profile and signalling between FGR and
normal infants.
Hypothesis 2:
There is a difference in the placental gene expression profile and signalling pathways between
macrosomic and normal infants.
Hypothesis 3:
Differentially expressed genes involved in abnormal fetal growth (either FGR or macrosomia)
have molecular and biological functions associated with adult-onset disease.
39
Chapter 4
Methodology
4.1. Sample collection and processing
This study was approved by the Human Ethics Committees of Royal Prince Alfred Hospital
(RPAH) (protocol no. X11-0252 & HREC/11/RPAH/385) and the University of Sydney
(project no. 2013/116). Written, informed consent was obtained from the mother of each
newborn.
Placentas and maternal blood samples were collected from 194 pregnant women being
admitted for delivery at RPAH between 1 February 2012 to 31 August 2012. These samples
were collected from elective Caesarean section and vaginal deliveries between 23 to 40
weeks’ gestation. Samples were collected from normal pregnancies and those complicated by
FGR, preeclampsia, and gestational diabetes. The placentas collected during this period
included both male and female infants with birth weights ranging from 990gm to 5079gm
(gestational adjusted birth weights from 1st to 99th percentile). The vast majority of samples
were obtained from singleton pregnancies however samples from some twins and triplets
were also collected.
Tissue samples from Caesarean section were collected and processed immediately after
delivery but those from vaginal delivery were initially stored at 4°C after delivery before
being collected and processed. A 1cm × 1cm piece of tissue was removed from the central
part of each placenta, directly behind the cord insertion site, and placed in cold normal saline.
Placental tissue was excised from the fetal surface of the placenta after removing the maternal
surface and visible vessels. The sample was rinsed extensively with sterile saline solution to
40
minimize maternal blood contamination. The tissue sample was then divided into sixteen
smaller pieces (~ 100mg). Eight were transferred to 2mL tubes and immersed in 1.5mL
volume of RNALater (Ambion, USA), an RNA stabilisation reagent. These were incubated at
4°C for 24 hours and then transferred to -80°C. The remaining eight pieces were stored frozen
at -80°C, in sterile normal saline.
4.2. Description of samples used in the experiments
A series of placental samples were selected from a placental tissue bank to provide
representative examples of normal newborns, FGR and macrosomia.
The control group included infants born with a birth weight centile between 40% and 60%, in
the FGR group, infants were defined with birth weight centile <10% and the macrosomic
group was defined as a birth weight centile >90% (Sacks, 2007). Birth weight centiles were
calculated based on gestational age, the newborn’s sex and birth weight with a tool provided
on the Royal Prince Alfred Hospital Neonatal Unit website (http://www.sswahs.nsw.gov.au/RPA/neonatal/default.htm) (Beeby et al., 1996).
For consistency and to reduce the number of biological variables, we selected placental
samples from pregnancies with male infants that were delivered at 39 weeks’ gestation by
elective Caesarean section for the remaining work. None of the mothers smoked or had a
history of alcohol / illicit drug use during pregnancy.
41
Table 4.1 Inclusion and exclusion criteria for the samples used in the experiment
Exclusion criteria
Inclusion Criteria
Female babies
Male babies
Fetuses with prenatal diagnosis of chromosomal
abnormality
Term pregnancy (39 weeks)
Malformation syndromes
Normal PAPP-A level
Normal vaginal delivery
Non-smoker mothers
Caesarean section delivery
*
Preterm delivery
Multiple pregnancy
Mothers with chronic hypertension
Smoking during pregnancy
*
PAPP-A, Pregnancy-associated plasma protein A measured in first trimester.
4.3. RNA extraction from placenta
Total RNA was extracted from all samples using TRIzol reagent and PureLink RNA Mini
Kits (Life Technologies Australia Pty Ltd, VIC, Australia) according to the manufacturer’s
specifications. In brief, one tube of each sample, containing 100-150 mg tissue was used. The
RNALater was discarded, the tissue divided into two approximately equal parts using a sterile
scalpel and placed into 2 x 2mL RNase-free microfuge tubes, each containing 1mL TRIzol
reagent. Mechanical disruption of tissues was performed using 5mm stainless steel beads and
the tissue lyser system (TissueLyser LT, Qiagen, Germany) for 5mins at 50 Hertz oscillation
frequency.
50μl 4-bromoanisole per 1mL TRIzol reagent was added and mixed vigorously for 15secs.
Following incubation at room temperature for 3mins the lysate was centrifuged at 12,000g at
4°C for 15mins. Approximately 400μl of the clear supernatant containing the RNA was
transferred to a fresh 2mL RNase–free tube and an equivalent volume of 70% (v/v) ethanol
was added and vortexed for 1min until the ethanol was completely homogenised with the
42
lysate and any precipitates were dissolved. Afterwards, the binding, washing, and elution
steps were performed.
Up to 700μl, at a time, of the new solution were transferred to a spin cartridge (PureLink
RNA Mini Kit, Invitrogen) and centrifuged at 12,000g for 15-20 secs at room temperature and
the filterate was discarded. These steps were repeated until the whole sample was spun
through the spin column. Next, 700μl of wash buffer I (7-13% guanidine hydrochloride
solution) was added to the spin cartridge - with the extracted total RNA bound to the silica
filter and centrifuged at 12,000 g for 15-20 secs.
The spin cartridge was then transferred to a new collection tube and centrifuged at 12,000g
for 15-20 secs with 500μl of wash buffer II. This process was repeated, the wash discarded
and the filter centrifuged at 13,000g for 2mins to dry the membrane and eliminate as many
contaminants carried over during the purification process.
For elution of total RNA, the spin cartridge was incubated in a new collection tube with 60μl
of RNase-free water for 2mins at room temperature, centrifuged at 15,000g for 2mins and the
flow through was immediately stored on ice and transferred to -80°C. This step was repeated
in a new collection tube with 60μl of RNase-free water to retrieve the maximum amount of
RNA as possible.
4.4. Total RNA purity and concentration assessments
Total RNA was assessed using NanoDrop (Thermo Fisher Scientific Australia Pty Ltd, VIC,
Australia) to evaluate and determine the RNA concentration (ng/μl) and the presence of
contaminants carried over in the sample (based on 260/230 and 260/280 ratios). 1.5µl of
43
RNase-free water was added to register a blank measurement as the reference parameter.
1.5μl of each of the elutions was placed in the NanoDrop ND1000 spectrophotometer and the
measurements were recorded and then stored for further analysis.
Nucleic acids are traditionally quantified by measuring UV absorption using a
spectrophotometer. The spectrophotometer registers the absorbance of all the molecules
present in the sample. The concentration of nucleic acid can be determined using the BeerLambert law, which predicts a linear change in absorbance with concentration. RNA has its
absorption maximum at 260nm and the ratio of the absorbance at 260nm and 280nm is used
to assess the RNA purity of an RNA preparation. A 260/280 ratio of ~2.0 is generally
accepted as ‘pure’ for RNA. If the ratio is appreciably lower it may indicate either the
presence of protein, phenol or other contaminants that absorb strongly at ~280nm or a very
low concentration (less than 10ng/µl) of nucleic acid. High 260/280 ratios are not indicative
of an issue.
The 260/230 ratio is used as a secondary measure of nucleic acid purity. This value for ‘pure’
nucleic acid is often higher than the respective 260/280 value. Expected 260/230 values are
commonly in the range of 2.0 to 2.2. If the ratio is appreciably lower than expected, it may
indicate the presence of contaminants which absorb at 230nm such as phenol and
ethylenediaminetetraacetic acid. The TRIzol reagent is a phenolic solution which absorbs in
the UV both at 230nm and 270nm. Guanidine isothiocyanate, used for RNA isolations will
absorb at 260nm [Thermo Scientific; T042 & T009 - Technical bulletin].
44
4.5. Total RNA quality assessment
Aliquots from each sample were analysed for RNA preservation with the 2100 Bioanalyzer
assay (Agilent Technologies, VIC, Australia). Under aluminium foil to ensure minimal loss of
fluorescence, the RNA 6000 Nano Gel Matrix was spun through a filter at 1,500g for 10mins
at room temperature. 1μl of RNA Nano Dye Concentrate was then added to 65μl of filtered
gel and the mixture was vortexed and centrifuged at 13,000g for 10mins.
Prior to loading, 2μl of Agilent RNA 6000 Nano Ladder and 2μl of each sample were heated
at 70°C for 2mins in a pre-heated PCR thermal cycler (BioRad T100 gradient thermal cycler)
to induce nucleotide denaturation and immediately placed on ice to avoid reformation of
bonds between nucleotide strands.
The RNA Nano 6000 Labchip was loaded into the chip priming station (set in the C position)
with the syringe loaded with 1mL of air. 9μl of gel-dye mix was added to the well. The air in
the syringe was injected into the chip; the plunger was secured at the highest syringe clip
position for 30secs and released, allowing 5secs for the plunger to return to its original
position. The gel-dye mix, RNA Nano Marker, RNA Nano Ladder and 1μl of each sample
were added to the chips. After pressurisation, the overall loading of the chip was completed
within 5mins.
Once loading was completed, the chip was vortexed using a Vortex Mixer (Model MS2S8/MS2-S9) at 2,400rpm for 1min, loaded onto the Bioanalyzer and examined with the 2100
Expert software (Agilent, 2005).
45
The RNA integrity of the extracted total RNA was assessed using the Agilent RNA 6000
nano kit of the Bioanalyzer 2100 system (Agilent Technologies, VIC, Australia). Agilent
2100 Bioanalyzer uses a combination of microfluidics, capillary electrophoresis, and
fluorescence to evaluate both RNA concentration and integrity.
Messenger RNA (mRNA) comprises only 1-3% of total RNA samples and is therefore not
readily detectable, even with the most sensitive of methods. Ribosomal RNA (rRNA), on the
other hand, makes up >80% of total RNA samples. The eukaryotic ribosome (80 Svedberg
[80S]) is consisting of a small 40S subunit and a large 60S subunit. The 60S subunit contains
5S, 5.8S, and 28S rRNAs (120, 160, and 4800 bases, respectively) in addition to 50 proteins.
The 40S subunit has 18S rRNAs (1900 bases) and 33 proteins (Figure 4.1). In mammalian
systems the majority of rRNA is comprised of the 28S and 18S rRNA species. The
Bioanalyzer system is an indirect approach to assess the mRNA integrity. Indeed, this method
relies on the assumption that rRNA quality and quantity reflect that of the underlying mRNA
population.
Figure 4.1 Structure of eukaryotic ribosomes.
Proteins
Source: http://www.mun.ca/biology/scarr/MGA2_03-23.html
46
The theoretical 28S:18S ratio is approximately 2.7:1; but a 2:1 ratio or higher has long been
considered the benchmark for intact RNA. The Bioanalyzer 2100 system separates the RNA
molecules using electrophoresis and registers the fluorescence trace, creating an
electropherogram and a gel image (Figure 4.2). Using this tool, RNA Integrity Number (RIN)
is determined by the ratio of the ribosomal bands as well as the RNA area under the sample
peaks and also entire electrophoretic trace of the RNA sample, including the presence or
absence of degradation products (Schroeder et al., 2006).
Figure 4.2 Principle of injection and separation of RNA in Bioanalyzer 2100.
1
2
3
4
1. The sample moves through the microchannels from the sample well.
2. The sample is injected into the
separation channel.
3. Sample components are
electrophoretically separated.
4. Components are detected by their
fluorescence.
4.6. Microarrays
Microarray analysis was performed using Affymetrix HG-U219 array strips (~530,000 probes
containing 49,411 probe sets which cover more than 36,000 transcripts and variants through
UniGene or via RefSeq annotation) following RNA sample preparation with a GeneChip 3’
IVT Express Kit (Manufactured for Affymerix, Inc. by Ambion Inc., USA) according to the
manufacturer’s guidelines. All samples were diluted with RNAse free water (MiliQ) to an
RNA concentration of approximately 250ng/µl. They all met the required A260 / A280 ratio
(1.7 - 2.1). The steps of complementary DNA (cDNA) synthesis, in vitro transcription (IVT),
47
fragmentation, labelling, hybridisation, fluidics, and imaging were performed as the
manufacturer’s instruction (Figure 4.3).
Figure 4.3 Overview of the GeneAtlas 3’ IVT Express Kit Labelling Assay.
Source: GeneAtlas 3’ IVT Manual
48
4.6.1. Reverse transcription to synthesize first-strand cDNA
Reagents were removed from the freezer 20mins prior to synthesizing first-strand cDNA to
thaw at room temperature before being placed on ice. Poly-A RNA control was diluted with
Poly-A control dilution buffer. Poly-A controls contains probe sets for several polyadenylated
transcripts for the Bacillus subtilis genes that are absent in eukaryotic samples (lys, phe, and
dap).They provide exogenous positive controls to monitor the target labelling process from
start to finish. Their resultant signal intensities on arrays serve as indicators of the labelling
reaction efficiency. The serial dilution of Poly-A RNA controls were done in non-stick
RNase-free microfuge tubes to final concentrations of 1:100,000 (lys), 1:50,000 (phe), and
1:6,667 (dap). 1.5µl of diluted poly-A RNA controls were added to 250ng (1µl) RNA sample.
Reverse transcription in GeneAtlas 3’ IVT Express Kit involved using T7 oligo (dT) primer to
synthesize first-strand cDNA containing a T7 promoter sequence. First-strand master mix was
prepared in a nuclease-free tube on ice for four samples using 8.4µl first-strand buffer mix
and 2.1µl first-strand enzyme mix. First-strand cDNA synthesis was performed with addition
of 2.5µl first-strand master mix in individual tubes and incubation for 2 hours at 42°C in a
thermal cycler.
4.6.2. Second-strand cDNA synthesis
To prepare second-strand master mix for four samples, 27.3µl nuclease-free water was mixed
with 10.5µl second-strand buffer mix and 4.2µl second-strand enzyme mix on ice. After
vortexing the assembly, 10µL of second-strand master mix was added to each (5µL) cDNA
sample. After mixing thoroughly and brief centrifugation, the reaction was placed on a
thermal cycler block (pre-cooled to16°C) and incubated for 1 hour at 16°C followed by
49
10mins at 65°C. Next, the tubes were centrifuged to collect 15µl double-stranded cDNA at
the bottom of the tube and were placed on ice to proceed in vitro transcription.
4.6.3. In vitro transcription to synthesize labelled aRNA
IVT master mix was assembled by mixing 8.4µl IVT biotin label, 42µl IVT labelling buffer,
and 12.6 µl IVT enzyme mix in a nuclease-free tube at room temperature. After vortexing and
brief centrifugation, 15µl of IVT master mix was transferred to each (15µl) double-stranded
cDNA sample and mixed thoroughly. In the next step, the IVT reaction was incubated for 16
hours at 40°C in a thermal cycler. The reaction was then removed from thermal cycler and
placed on ice, to perform aRNA purification.
4.6.4. aRNA purification
This step removes enzymes, salts, and unincorporated nucleotides. The tube containing RNA
binding beads was flicked several times to ensure all beads had become suspended. 22µl
‘RNA binding beads’ and 110µl ‘aRNA binding buffer concentrate’ were mixed to prepare
‘aRNA binding mix’ for four samples. 15µl aRNA binding mix was added to each sample and
the reaction transferred to the ‘U-bottom plate’ provided with the kit. Next, 60µl 100%
ethanol was added to each sample, and placed on a plate shaker for 5mins at 500rpm.
To capture RNA binding beads, the plate was moved onto the magnetic stand (Figure 4.4) for
5mins. When the mixture became transparent and the RNA binding beads formed aggregate
against the magnets in the magnetic stand, the supernatant was carefully removed and
discarded and then the plate was removed from the magnetic stand.
50
Figure 4.4
Magnetic stand for the
paramagnetic bead precipitation from
standard 96-well U-bottom plate.
The beads were washed 2 times. For this purpose, 80µl aRNA wash solution was added to
each sample and the U-bottom plate was placed on a plate shaker for 1min at 900rpm. In
order to capture the RNA binding beads, the plate was moved to a magnetic stand for 5mins.
Then the supernatant was aspirated and discarded carefully without disturbing the RNA
binding beads. The plate was next placed on a plate shaker for 1min at 1,200rpm to allow the
residual ethanol to evaporate from the beads.
In order to elute the purified aRNA from the RNA binding beads 30µl aRNA elution solution
(preheated for 10mins at 60°C) was added to each sample and shaken vigorously for 3mins at
1,200rpm. When the RNA binding beads were fully dispersed they were captured by moving
the plate onto the magnetic stand for 5mins. The supernatant containing the eluted aRNA was
transferred to RNAse free 0.2mL PCR tube and stored on ice ready to proceed with
quantitation and fragmentation.
4.6.5. Analysis of aRNA concentration and size
In order to analyse the concentration of unfragmented aRNA and size distribution of
unfragmented aRNA we used the NanoDrop and 2100 Bioanalyzer assays; as described in
sections 4.4 and 4.5 of this chapter. All the samples met the required aRNA profile which was
51
a distribution of sizes from 250–5,500 nucleotides with most of the aRNA between 600–1,200
nucleotides.
4.6.6. Fragmentation of labelled aRNA
A fragmentation mixture was prepared by adding 5µl of ‘5x array fragmentation buffer’ to
10µg aRNA (the volume of aRNA was dependent on its resultant concentration as determined
using the NanoDrop). RNAse free water was added to this mixture to achieve 25µl total
volume and incubated at 94ºC for 35mins. The fragmented aRNA was then analysed using the
2100 Bioanalyzer. All samples had a distribution of 35–200 nucleotides aRNA fragments
with a peak at approximately 100-120 nucleotides.
4.6.7. Hybridization
The array strip and pre-hybridization buffer were equilibrated to room temperature. The
barcode of the Affymetrix array strip was scanned by the GeneAtlas system and registered in
the GeneAtlas software and sample names were accordingly assigned on the array strip. The
temperature of the GeneAtlas hybridization station was set to 45°C. ‘Hybridization cocktail
master mix’ was prepared according to the table 4.2.
Table 4.2 Hybridization Cocktail Setup.
Component
Master Mix for 4 Arrays
Control Oligonucleotide B2 (3nM)
11μl
20X Hybridization Controls (bioB, bioC, bioD, cre)
33μl
1.3X Hybridization Solution A
176μl
1.3X Hybridization Solution B
330μl
Nuclease-free Water
27.3μl
52
The 20X hybridization controls preheated to 65°C for 5mins. The 20X eukaryotic
hybridization controls are comprised of a mixture of biotinylated and fragmented cRNA of
bioB, bioC, bioD, and cre in staggered concentrations to evaluate sample hybridization
efficiency. Control Oligo B2 is a pre-labelled DNA spike-in control required for hybridization
to the control probes on the array utilized for grid alignment by GeneChip Software.
To prepare the hybridization cocktail, 131.2µl of the hybridization cocktail master mix was
added to 18.8µl of fragmented and labelled aRNA, thoroughly mixed and placed on ice. 120µl
pre-hybridization buffer was pipetted into the appropriate wells of the hybridization tray and
the array strip was placed into the tray and incubated on the hybridization station at 45°C for
15mins. During the pre-hybridization, 120µl of the hybridization cocktail was transferred into
a 0.2mL PCR tube and incubated at 96°C for 10mins in a thermal cycler, followed by 2mins
at 45°C.
The array strip was the removed and immersed in the pre-hybridization solution and 120µl of
pre-heated hybridization cocktail applied to the middle of the appropriate wells of a new
hybridization tray. The array strip was removed from the hybridization tray containing the
pre-hybridization buffer and placed into the hybridization tray containing the hybridization
cocktail samples. The array strip was then placed and locked in place by clamping inside the
hybridization station. Overnight, 16 hours incubation at 45°C hybridization was performed
using the GeneAtlas software.
4.6.8. Fluidics (wash and stain) and imaging
Following the completion of overnight hybridization, 850µl of ‘wash B’ reagent was added to
the corresponding positions on the wash B tray and 200µl of ‘array holding buffer’ to the
53
corresponding positions in the imaging tray. Likewise, 850µl of ‘wash A’ reagent, 350µl of
‘stain cocktail 1’ and 350µl of ‘stain cocktail 2’ were added to the corresponding positions in
the wash A/Strain tray (Figure 4.5).
Figure 4.5 Tray maps used in the fluidics station.
After loading the prepared trays to the GeneAtlas fluidics station (Figure 4.6-A) the bar code
of the array strip was again scanned, loaded onto the appropriate deck of the fluidics station
and the fluidics stage started.
54
Figure 4.6 The GeneAtlas fluidics station (A) and imaging station (B).
A
B
When the wash and stain process was completed, after approximately 1.5 hours, the imaging
tray and array strip were directly placed in the imaging station (after scanning the bar code of
the array strip) and imaging of the array was performed (Figure 4.6-B).
4.7. Microarrays quality control
Once the imaging step was completed, the quality control (QC) report in the Affymetrix
GeneAtlas operating software was assessed to ensure all samples had passed the quality
metrics for signal, hybridization controls, labelling controls, and sample quality. Files from
scanned chips (CEL files) were imported to the Partek Express (Affymetrix Edition) software
using Robust Multichip Average algorithm which includes background correction, quantile
normalisation, and median polish summarisation (Bolstad et al., 2003).
55
The QC metrics provides QC information from the control and experimental probes on the
Affymetrix chips to provide confidence in the quality of the microarray data or to identify
samples that do not pass the QC metrics. The QC metrics used for hybridization and labelling
controls were in Log 2 intensity scale and for 3’/5’ controls in ratios.
Several controls are available to monitor assay data quality, which mainly include:
hybridization controls, labelling controls, and internal control genes.
4.7.1. Signal value
This is a measure of the average brightness of the probe set on the array, minus the
background noise. The signal to noise ratio must be above a certain value for the array to pass
QC.
4.7.2. Hybridization controls
Four exogenous biotin labelled molecules are spiked into the hybridization cocktail before
hybridization, but after sample labelling. These high-quality controls (the 20X eukaryotic
hybridization controls) which are composed of a mixture of biotinylated and fragmented
aRNA of bioB, bioC, bioD, and cre. from E. coli in staggered concentrations are for
monitoring array hybridization, washing, and staining for reproducible results. The 20X
eukaryotic hybridization controls are spiked into the hybridization cocktail, independent of
RNA sample preparation, and are thus used to evaluate sample hybridization efficiency on
eukaryotic gene expression arrays.
56
4.7.3. Labelling controls
Poly-A controls are added to RNA sample prior to using the IVT express kit. Three
independent poly-A RNA controls, derived from the Lys, Phe, and Dap genes of Bacillus
subtilis, are provided at increasing concentrations (Lys < Phe < Dap). The spikes are inserted
into the sample prior to labelling and their resulting detection is dependent on the labelling
reaction that labels the biological sample. After spiking directly into eukaryotic total RNA
samples, labelled aRNA targets are prepared and hybridized onto GeneChip expression
arrays. The resultant signal intensities for the poly-A RNA controls serve as sensitive
indicators of the efficiency of the labelling reaction and are independent of input sample RNA
quality.
4.7.4. Sample quality (housekeeping genes)
Housekeeping genes are gene transcripts that are constitutively expressed on most samples.
These transcripts serve as internal controls, are useful for monitoring the quality of the
starting sample, and are subject to any variability in the labelling of the sample and
hybridization for the array. For 3’ expression arrays, GAPDH is used to assess RNA sample
and assay quality. GAPDH has separate probe sets at the 3’ and 5’ end of the gene. In highquality samples, reverse transcriptase should process from the 3’ through towards the 5’ end.
The 3’/5’ ratio compares the abundance of the signal at the 3’ end over the abundance at the
5’ end. A ratio of 3 or less is considered acceptable.
4.8. Gene significance estimate
Affymetrix GeneAtlas operating software was used to first check all samples had passed all
QC metrics. The chips were scanned and files were imported to Partek Express (Affymetrix
Edition) software for further evaluation (Bolstad et al., 2003). Gene expression between FGR
57
or macrosomic placentas and normal controls was compared and defined statistically by
Analysis of Variance (ANOVA) and false discovery rate. Genes with >2 fold change and pvalue <0.05 were considered to be statistically significant and differentially expressed.
4.8.1. Principle component analysis (PCA)
PCA is an excellent method for visualising high dimensional data by reducing the variation
across all of the many thousands of probes being interrogated on the chip into a two or three
dimensional representation. In a PCA plot, each point represents a sample (microarray) and
their position are relative to each other. Those dots that are closer to each other represent
samples in which the transcriptome measurements over the whole chip are similar. Those dots
that are further away from each other represent samples in which the transcriptome
measurements over the whole chip are more dissimilar. Samples that have similar overall
gene expression levels will group together into clusters.
4.8.2. Effect size
Effect size provides information on the importance of each experimental factor to the
transcriptome. Applying ANOVA, Partek Express software was used to test for the difference
in means of a response variable between different groups.
4.9. Data analysis
In order to further investigate the gene ontology and biological function of the gene sets that
emerged from the filtering step we utilised Ingenuity Pathways Analysis (IPA) software
(Ingenuity System, Inc., 2013) with a biomarker filter for human placenta. IPA software was
also used to cross check the list of differentially expressed candidate genes and to identify
specific pathways and explore networks relevant to fetal growth and development. Fisher’s
58
exact test (p-value 0.05) was used for canonical pathway analysis. Other bioinformatics tools
including Database for Annotation, Visualization and Integrated Discovery (DAVID), and
Protein ANalysis THrough Evolutionary Relationships (PANTHER) (Thomas et al., 2006)
were used to annotate gene ontology and network. Significant changes in gene and potential
biomarker expression that were consistent in all indexes are reported.
59
Chapter 5
Results
5.1. Description of patients
Total number of 15 placenta samples were analysed in this study. Compared to control group,
the mean of maternal age was less in FGR and macrosomic groups but the p-value was not
significant for these differences. The birth weight and birth weight centiles were significantly
different in the three groups (p<0.001). On average, mothers of growth restricted babies
seemed to have a less pre-pregnancy weight compared to control babies while mothers of the
macrosomic babies had greater pre-pregnancy weight. However, these differences were not
statistically significant (Table 5.1).
Table 5.1 Characteristics of patients.
Maternal age (years)
Gestational age (weeks)
Birth weight (gm)
Birth weight centile
p-value
p-value
(FGR)
(Macrosomia)
34.5 ± 5.0
0.23
0.25
39
-
-
4367.3 ± 349.9
<0.001
<0.001
FGR
Control
Macrosomia
34.4 ± 4.6
38 ± 4.2
39
39
2812.5 ± 22.4
3443.6 ± 69.7
th
th
th
th
<10
40 – 60
>90
<0.001
<0.001
GDM/non-GDM ratio
0/4
2/5
1/6
-
-
Maternal weight (kg)
54.8 ± 6.1
58.5 ± 5.7
74.3 ± 19.2
0.37
0.11
Values are shown as mean ± SD. GDM, gestational diabetes mellitus.
5.2. NanoDrop results
The concentration and purity of the total RNA extracted from the samples included in the
microarray analyses were assessed using NanoDrop ND1000 spectrophotometer. The
concentrations and 260/280 and 260/230 ratios of the first elutions obtained from each sample
60
are reported in Table 5.2. All the samples included in the microarray assay had 260/280 ratios
between 2 and 2.21 and most samples showed similar UV light absorption spectral patterns.
Table 5.2 NanoDrop results.
Sample ID
Concentration
(ng/µl)
260nm
280nm
260/280
260/230
absorbance
absorbance
Ratio
Ratio
PLPP-007
835.91
20.898
9.74
2.15
2.21
PLPP-026
761.79
19.045
8.792
2.17
2.17
PL12-056
1315.85
32.896
15.313
2.15
2.22
PL12-085
1404.66
35.117
16.18
2.17
2.25
PL12-132
1330.75
33.269
15.297
2.17
2.13
PLPP-001
1269.62
31.741
14.848
2.14
2.25
PLPP-002
1444.2
36.105
16.73
2.16
2.01
PLPP-032
818.54
20.463
9.508
2.15
2.23
PLPP-040
849.61
21.24
9.974
2.13
2
PLPP-063
1088.13
27.203
12.937
2.1
2.21
PLPP-013
961.26
24.032
11.108
2.16
2.09
PLPP-25
785.04
19.626
9.074
2.16
1.64
PLPP-51
1692.65
42.316
19.736
2.14
2.23
PLPP-52
1261.48
31.537
14.807
2.13
2.25
PLPP-60
663.22
16.58
7.646
2.17
2.2
PLPP-124
860.03
21.501
9.738
2.21
2.26
61
Figure 5.1
NanoDrop report for control samples. UV absorption plot has been shown for
two sequential elutions. The top five curves indicate the first RNA elutions which were used
in the microarray analysis while the lower ones are for the second RNA elutions.
5.3. Bioanalyzer results
All the samples included in the microarray analysis had RINs ranging from 5.70 to 7.30
(Figure 5.2).
62
Figure 5.2 Electropherogram of samples included in microarray assay.
63
The placenta samples which were collected after vaginal delivery with a 1-6 hour delay until
collection showed poor RNA quality despite using the same methodology in processing the
samples and RNA extraction. The RIN for all these samples was below 5 (Figure 5.3) –
therefore, these were not suitable for further evaluation.
Figure 5.3 Bioanalyzer result for two of the placenta samples from vaginal delivery.
The importance of using RNALater in maintaining the quality of RNA was illustrated when
we compared the electropherograms of placental samples which were stored at -80°C in
RNALater (n=4) to samples which were only stored at -80°C in normal saline (n=4). An
example of this is illustrated in Figure 5.4.
64
Figure 5.4 Comparison of the RNA quality in samples stored in saline with those stored in
RNALater (kept at -80°C). The electropherograms in each row are for the same sample.
Saline
RNALater
Moreover, in our experiment the RNA concentration and quality was better when the RNA
extraction was performed using TRIzol compared to a lysis buffer which was provided in the
PureLink RNA Mini Kit.
65
5.4. Microarray gene results
5.4.1. Quality control
To assess the sample labelling reaction we applied the labelling metrics which showed
increasing concentrations of lys, phe, and dap. This pattern ensures the correct labelling of the
biological samples (Figure 5.5).
Figure 5.5 Labelling controls.
log 2 expression
QC Metrics
Sample
66
BioB, BioC, BioD and cre, used as hybridization controls, showed increasing concentrations,
indicating that hybridization had occurred correctly in all arrays (Figure 5.6).
log 2 expression
Figure 5.6 Hybridization controls.
67
The abundance of signal at 3’ end over the abundance of signal at 5’ end for GAPDH was less
than 2 in all samples. A ratio less than 3 ensures the high quality of the samples included in
microarray (Figure 5.7).
Figure 5.7 3’/5’ ratio metrics.
3’/5’ ratio
QC Metrics
Sample
68
5.4.2. Principle components analysis
PCA was applied to have a visual measurement of the transcriptomic variation with regards to
birth weight. There was relatively clear separation between FGR and control groups whereas
macrosomic samples did not show a strict separation with either FGR or control groups
(Figure 5.8).
Figure 5.8 PCA plot in three weight groups.
Red: FGR; Blue: Control; Green: Macrosomia.
69
5.4.3. Effect size
A comparison of the amount of sample noise to absolute difference in gene expression was
made using the ANOVA test to calculate p-values and the F-Ratio for effect sizes (Figure
5.9). On the basis of differences in birth weight, absolute variance in gene expression was
twice the level of background noise variance.
Figure 5.9 The effect size of the fetal birth weight on transcriptome overall.
5.4.4. Microarray gene expression analysis
Transcriptomic profiling was performed in fifteen human placental samples collected from
four FGR, six macrosomic and five control pregnancies. Quantitative microarray analysis of
the gene expression dynamic in the three study groups was accomplished by applying
ANOVA-based analysis. Significant changes in gene expression between FGR and control
samples were seen with 242 transcripts (representing 199 genes) being up-regulated and 191
transcripts (139 genes) down-regulated. Fewer significant changes were found when
comparing macrosomic and control specimens with 42 transcripts (representing 33 genes) and
10 transcripts (representing 8 genes) being up-regulated and down-regulated respectively.
70
The details of altered genes in each group and their changes in the opposite group are shown
in tables 5.3 to 5.6. The most significant down-regulated genes in FGR pregnancies were
TXNDC5 (fold change, F= -7.17; p=0.02), AREG (F= -3.72; p=0.01), LRRFIP1 (F= -3.56;
p<0.01) and LRP2 (F= -3.35; p<0.01) (Table 5.3). Genes with significantly increased
expression in placentas of FGR were also detected including CPXM2 (F= 6.2; p<0.01),
RAB3B (F= 5.6; p=0.02), and GNGT1 (F= 5.6; p<0.01) (Table 5.4). In macrosomic newborns
leptin (LEP) showed 6.5 fold down-regulation (p=0.01) and PHLDB2 showed 3.5 fold upregulation (p=0.02) (Table 5.5). Variants of GCH1 were also down-regulated (F= -2.4;
p=0.02) (Table 5.6).
In order to see the data spread in groups and provide a visual comparison between groups
based on the signal intensity of individual samples, dot plots are shown for some selected
genes (Figures 5.10 and 5.11).
71
Table 5.3 Down-regulated genes in FGR.
Gene
Symbol
Gene Title
MUTED
/TXNDC5
muted homolog (mouse) / thioredoxin domain containing
5 (endoplasmic reticulum
AREG
FoldChange
p-value
FoldChange
p-value
(Macrosomia)
(FGR)
(Macrosomia)
-7.2
0.021
-1.3
0.729
amphiregulin
-3.7
0.012
-1.6
0.242
LRRFIP1
leucine rich repeat (in FLII) interacting protein 1
-3.6
0.000
-1.3
0.197
LRP2
low density lipoprotein receptor-related protein 2
-3.4
0.003
-1.8
0.074
FAM46C
family with sequence similarity 46, member C
-3.2
0.013
-2.3
0.040
FRAS1
Fraser syndrome 1
-3.1
0.001
-1.6
0.065
HLA-DPB1
major histocompatibility complex, class II, DP beta 1
-3.0
0.048
-2.1
0.121
SIPA1L2
signal-induced proliferation-associated 1 like 2
-3.0
0.009
-1.6
0.183
ENPEP
glutamyl aminopeptidase (aminopeptidase A)
-3.0
0.001
-1.3
0.187
FCGR3A
Fc fragment of IgG, low affinity IIIa, receptor (CD16a)
-2.9
0.031
-1.5
0.299
PPFIBP1
PTPRF interacting protein, binding protein 1 (liprin beta 1)
-2.8
0.001
-1.4
0.150
PTPRB
protein tyrosine phosphatase, receptor type, B
-2.8
0.002
-1.9
0.017
TMEM97
transmembrane protein 97
-2.8
0.001
-1.5
0.087
EGR1
early growth response 1
-2.8
0.047
-1.3
0.593
ADAMTS19
ADAM metallopeptidase with thrombospondin type 1
motif, 19
-2.7
0.002
-1.1
0.704
SOX4
SRY (sex determining region Y)-box 4
-2.7
0.036
-1.5
0.304
SPATS2
spermatogenesis associated, serine-rich 2
-2.7
0.006
-1.4
0.235
GCH1
GTP cyclohydrolase 1
-2.7
0.021
-2.3
0.029
MAD2L1
MAD2 mitotic arrest deficient-like 1 (yeast)
-2.7
0.029
-1.3
0.438
MTHFD2
methylenetetrahydrofolate dehydrogenase (NADP+
dependent) 2, methenyltetrahydrof
-2.6
0.015
-1.3
0.358
ITGA6
integrin, alpha 6
-2.6
0.021
-1.3
0.425
CHN1
chimerin (chimaerin) 1
-2.6
0.028
-1.4
0.347
TNFSF10
tumor necrosis factor (ligand) superfamily, member 10
-2.6
0.006
-1.4
0.183
GPD1L
glycerol-3-phosphate dehydrogenase 1-like
-2.5
0.019
-1.7
0.130
PLK2
polo-like kinase 2
-2.5
0.017
-1.5
0.176
BIN2
bridging integrator 2
-2.5
0.008
-1.6
0.116
MAL2
mal, T-cell differentiation protein 2
-2.5
0.007
-1.4
0.264
KIAA0101
KIAA0101
-2.5
0.015
-1.4
0.283
JAG1
jagged 1
-2.5
0.037
-1.8
0.121
ATP7B
ATPase, Cu++ transporting, beta polypeptide
-2.5
0.017
-1.1
0.690
(FGR)
72
Table 5.4 Up-regulated genes in FGR.
Gene
Symbol
Gene Title
CPXM2
carboxypeptidase X (M14 family), member 2
RAB3B
FoldChange
p-value
FoldChange
p-value
(Macrosomia)
(FGR)
(Macrosomia)
6.2
0.003
2.0
0.158
RAB3B, member RAS oncogene family
5.6
0.015
2.3
0.152
GNGT1
guanine nucleotide binding protein (G protein), gamma
transducing activity polyp
5.6
0.005
3.3
0.022
HERC2P2
/HERC2P3
hect domain and RLD 2 pseudogene 2 / hect domain and
RLD 2 pseudogene 3
4.3
0.044
2.4
0.168
CLDN1
claudin 1
4.2
0.013
2.9
0.035
SH3TC2
SH3 domain and tetratricopeptide repeats 2
3.9
0.008
3.5
0.007
ST3GAL1
ST3 beta-galactoside alpha-2,3-sialyltransferase 1
3.5
0.011
2.2
0.062
ZNF117
zinc finger protein 117
3.4
0.019
3.0
0.019
C11orf90
chromosome 11 open reading frame 90
3.3
0.037
1.9
0.202
MUC1
mucin 1, cell surface associated
3.3
0.006
-1.0
0.919
FGF12
fibroblast growth factor 12
3.2
0.020
2.2
0.063
LOC388948
hypothetical LOC388948
3.2
0.030
2.7
0.038
TRPV6
transient receptor potential cation channel, subfamily V,
member 6
3.1
0.023
2.3
0.062
NIPAL1
NIPA-like domain containing 1
3.1
0.042
2.2
0.109
MUC15
mucin 15, cell surface associated
3.1
0.036
2.1
0.111
ARHGEF37
Rho guanine nucleotide exchange factor (GEF) 37
3.1
0.039
1.6
0.325
FHL1
four and a half LIM domains 1
3.0
0.045
1.5
0.413
CD200
CD200 molecule
3.0
0.047
1.8
0.229
RAB6B
RAB6B, member RAS oncogene family
3.0
0.048
1.4
0.428
SLC2A11
solute carrier family 2 (facilitated glucose transporter),
member 11
2.9
0.050
2.1
0.118
RAB11FIP5
RAB11 family interacting protein 5 (class I)
2.9
0.042
1.5
0.331
AFF1
AF4/FMR2 family, member 1
2.9
0.017
1.9
0.083
FRZB
frizzled-related protein
2.9
0.037
2.6
0.038
FAM47E
family with sequence similarity 47, member E
2.9
0.046
2.0
0.135
AHR
aryl hydrocarbon receptor
2.9
0.023
1.7
0.178
PAPP-A
pregnancy-associated plasma protein A, pappalysin 1
2.9
0.018
2.0
0.071
HSD11B2
hydroxysteroid (11-beta) dehydrogenase 2
2.9
0.046
2.2
0.085
STEAP4
STEAP family member 4
2.8
0.034
1.5
0.285
TRAJ17
T cell receptor alpha joining 17
2.8
0.023
1.5
0.258
MORN3
MORN repeat containing 3
2.8
0.046
1.4
0.397
STAT5B
signal transducer and activator of transcription 5B
2.7
0.029
1.8
0.142
FURIN
furin (paired basic amino acid cleaving enzyme)
2.7
0.016
1.7
0.115
(FGR)
Continued
73
Table 5.4 Continued
Gene
Symbol
Gene Title
PLAC4
placenta-specific 4
PHLDB2
FoldChange
p-value
FoldChange
p-value
(Macrosomia)
(FGR)
(Macrosomia)
2.7
0.033
2.2
0.053
pleckstrin homology-like domain, family B, member 2
2.7
0.048
2.2
0.076
SLC43A2
solute carrier family 43, member 2
2.7
0.000
1.8
0.005
TMEM54
transmembrane protein 54
2.7
0.007
1.2
0.442
SCNN1B
sodium channel, nonvoltage-gated 1, beta
2.7
0.031
1.4
0.410
MMP11
matrix metallopeptidase 11 (stromelysin 3)
2.7
0.041
1.9
0.109
ERBB2
v-erb-b2 erythroblastic leukemia viral oncogene homolog
2, neuro/glioblastoma
2.7
0.040
1.7
0.205
HIST1H1C
histone cluster 1, H1c
2.6
0.036
1.7
0.169
CAPN6
calpain 6
2.6
0.013
1.5
0.218
SLC7A5
solute carrier family 7 (cationic amino acid transporter, y+
system), member 5
2.6
0.047
1.5
0.287
IGHA1 /
IGHA2
immunoglobulin heavy constant alpha 1 /
immunoglobulin heavy constant alpha 2
2.6
0.044
1.6
0.219
C2orf72
chromosome 2 open reading frame 72
2.6
0.047
1.8
0.145
SLC23A2
solute carrier family 23 (nucleobase transporters),
member 2
2.6
0.042
1.8
0.134
CORO6
coronin 6
2.5
0.021
1.6
0.150
DUSP4
dual specificity phosphatase 4
2.5
0.013
1.6
0.139
ELF3
E74-like factor 3 (ets domain transcription factor,
epithelial-specific )
2.5
0.025
1.5
0.253
KIR2DL1 /
KIR2DL2
killer cell immunoglobulin-like receptor, two domains,
long cytoplasmic tail, 1
2.5
0.030
1.4
0.302
STRA6
stimulated by retinoic acid gene 6 homolog (mouse)
2.5
0.050
1.9
0.125
PCDH1
protocadherin 1
2.5
0.021
1.4
0.282
ITPRIP
inositol 1,4,5-triphosphate receptor interacting protein
2.5
0.017
1.5
0.213
PAPP-A2
pappalysin 2
2.5
0.040
1.0
0.902
TTBK2
tau tubulin kinase 2
2.5
0.020
1.5
0.181
BLID
BH3-like motif containing, cell death inducer
2.5
0.009
1.7
0.057
KIAA1549
KIAA1549
2.5
0.010
1.1
0.808
PLEKHA6
pleckstrin homology domain containing, family A member
6
2.5
0.050
1.6
0.252
(FGR)
74
Table 5.5 Down-regulated genes in macrosomia.
Gene
Symbol
Gene Title
LEP
Fold-Change
p-value
Fold-Change p-value
(Macrosomia) (Macrosomia) (FGR)
(FGR)
leptin
-6.5
0.014
4.3
0.065
COL17A1
collagen, type XVII, alpha 1
-4.3
0.049
-1.8
0.458
HTRA4
HtrA serine peptidase 4
-3.1
0.024
-2.0
0.166
BTNL9
butyrophilin-like 9
-2.9
0.022
-2.5
0.065
HLA-DPB1
major histocompatibility complex, class II, DP -2.4
beta 1
0.025
-1.9
0.119
FAM46C
family with sequence similarity 46, member C -2.3
0.040
-3.2
0.013
GFRA2
GDNF family receptor alpha 2
-2.3
0.011
-2.2
0.021
GCH1
GTP cyclohydrolase 1
-2.3
0.029
-2.7
0.021
75
Table 5.6 Up-regulated genes in macrosomia.
FoldChange
p-value
FoldChange
p-value
Gene
Symbol
Gene Title
PHLDB2
pleckstrin homology-like domain, family B, member 2
3.5
0.023
2.4
0.123
SH3TC2
SH3 domain and tetratricopeptide repeats 2
3.5
0.007
3.9
0.008
GNGT1
guanine nucleotide binding protein (G protein), gamma
transducing activity polyp
3.3
0.022
5.6
0.005
C15orf29
chromosome 15 open reading frame 29
3.1
0.004
2.0
0.077
ZNF117
zinc finger protein 117
3.1
0.011
2.6
0.039
CLDN1
claudin 1
2.9
0.035
4.2
0.013
LPHN3
latrophilin 3
2.7
0.025
2.4
0.060
LOC388948
hypothetical LOC388948
2.7
0.038
3.2
0.030
FRZB
frizzled-related protein
2.6
0.038
2.9
0.037
FAM164A
family with sequence similarity 164, member A
2.6
0.005
1.4
0.248
C10orf118
chromosome 10 open reading frame 118
2.5
0.049
2.1
0.125
USP16
ubiquitin specific peptidase 16
2.4
0.007
2.2
0.022
GOLGA4
golgin A4
2.4
0.033
1.9
0.151
MALAT1
metastasis associated lung adenocarcinoma transcript 1
(non-protein coding)
2.4
0.025
2.4
0.043
FGF7
fibroblast growth factor 7
2.4
0.020
1.6
0.217
C14orf106
chromosome 14 open reading frame 106
2.3
0.029
1.4
0.423
SMC3
structural maintenance of chromosomes 3
2.3
0.008
1.6
0.115
ZNF554
zinc finger protein 554
2.3
0.014
2.2
0.030
DEPDC1B
DEP domain containing 1B
2.2
0.048
2.2
0.076
FER1L4
fer-1-like 4 (C. elegans) pseudogene
2.2
0.014
2.1
0.036
STRA6
stimulated by retinoic acid gene 6 homolog (mouse)
2.2
0.050
2.5
0.039
PPIG
peptidylprolyl isomerase G (cyclophilin G)
2.2
0.040
1.3
0.444
CASP4
caspase 4, apoptosis-related cysteine peptidase
2.1
0.028
1.2
0.595
DST
dystonin
2.1
0.016
1.3
0.401
ATRX
alpha thalassemia/mental retardation syndrome X-linked
2.1
0.034
1.5
0.308
GABRE
gamma-aminobutyric acid (GABA) A receptor, epsilon
2.1
0.006
1.9
0.020
ISM1
isthmin 1 homolog (zebrafish)
2.1
0.007
1.4
0.191
ZNF644
zinc finger protein 644
2.1
0.003
1.7
0.028
PHIP
pleckstrin homology domain interacting protein
2.1
0.024
1.4
0.263
USP15
ubiquitin specific peptidase 15
2.0
0.018
2.3
0.013
GCC2
GRIP and coiled-coil domain containing 2
2.0
0.015
1.7
0.075
SMC4
structural maintenance of chromosomes 4
2.0
0.026
-1.0
0.981
(Macrosomia)
(Macrosomia)
(FGR)
76
(FGR)
Figure 5.10 Dot plots for some of the dysregulated genes in FGR.
Continued
77
Figure 5.10 Continued
Continued
78
Figure 5.10 Continued
Continued
79
Figure 5.10 Continued
Continued
80
Figure 5.10 Continued
81
Figure 5.11 Dot plots for some of the dysregulated genes in macrosomia.
Continued
82
Figure 5.11 Continued
83
The bioinformatics analyses of the data suggested the disturbance of various molecular and
cellular functions and their links to diseases. Differential gene expression in FGR was most
strongly associated with nervous system development, organismal development, cell-to-cell
signalling and interaction network functions. Differential gene expression in macrosomic
newborns was most strongly associated with nutritional disease, behaviour, digestive system,
lipid metabolism development and function and cell-to-cell signalling and interaction network
functions. Genes that were significantly differentially expressed in FGR included groups
associated with gastrointestinal disease (p<0.001), haematological disease (p<0.001),
endocrine system disorders (p<0.001), and neurological disease (p<0.001). Genes that were
significantly differentially expressed in macrosomic newborns included groups associated
with nutritional disease (p<0.001), psychological disorders (p<0.001), cardiovascular disease
(p<0.001) and connective tissue disorders (p<0.001).
Lipid metabolism was altered in both groups with significant changes in 34 molecules in FGR
(p<0.001) and in 3 molecules (p<0.001) in the macrosomic group. Significant dysregulation
of the canonical epithelial-mesenchymal transition pathway (p<0.001) was seen in FGR.
Genes involved in this group included FGFR3, MET, ETS1, PIK3C2B, APH1B, EGR1,
FGF12, FGFR2, FZD5, JAG1, TCF7L2, and EGFR. The Notch signalling pathway also
showed significant changes in FGR (p<0.001) with alterations in the expression of NOTCH2,
FURIN, APH1B, and JAG1. Upstream analysis in FGR indicated that p53 (activation zscore=2.002; p<0.001) was activated. As there were fewer up- / down-regulated genes defined
in the macrosomic group, pathway analysis showed no significant changes. At a single gene
level GCH1, part of the tetrahydrobiopterin biosynthesis pathway showed significant downregulation (p<0.001). Potential biomarkers identified through bioinformatics analysis are
shown in table 5.7 and 5.8.
84
Table 5.7 Potential biomarkers that are up- or down-regulated in placental tissue from term
growth restricted newborns.
Symbol
Location / Family
Affymetrix Probe ID
Folds Change
p-value
Potential biomarkers that are up-regulated
CPXM2
Extracellular space / Peptidase
11719536_at
6.2
<0.01
CLDN1
Plasma membrane
11728234_a_at
4.2
0.001
ST3GAL1
Cytoplasm / Enzyme
11718738_a_at
3.5
0.001
TRPV6
Plasma membrane / Ion channel
11752375_a_at
3.1
0.02
CD200
Plasma membrane
11742863_a_at
3.0
0.05
Potential biomarkers that are down-regulated
TXNDC5
Cytoplasm / Enzyme
11715395_a_at
-7.2
0.02
LRRFIP1
Cytoplasm
11739298_a_at
-3.6
<0.001
LRP2
Plasma membrane / Transporter
11727123_at
-3.4
<0.01
ENPEP
Plasma membrane / Peptidase
11721126_a_at
-3.0
<0.001
PPFIBP1
Plasma membrane
11734874_x_at
-2.8
<0.001
Table 5.8 Potential biomarkers that are up- or down-regulated in placental tissue from term
macrosomic newborns.
Symbol
Location / Family
Affymetrix Probe ID
Folds Change
p-value
Potential biomarkers that are up-regulated
PHLDB2
Cytoplasm
11737039_a_at
3.5
0.02
C15orf29
Nucleus
11761552_at
3.1
<0.01
CLDN1
Plasma membrane
11728234_a_at
2.9
0.03
LPHN3
Plasma membrane
11726897_s_at
2.7
0.02
Potential biomarkers that are down-regulated
LEP
Extracellular space / Growth factor
11735095_at
-6.5
0.01
COL17A1
Plasma membrane
11726564_a_at
-4.3
0.05
GCH1
Cytoplasm / Enzyme
11721733_a_at
-2.3
0.03
85
Chapter 6
Discussion
6.1 Summary
We aimed to determine whether there was evidence of functional placental pathology
underlying abnormal fetal growth and development by performing global gene expression
analysis. We have demonstrated that there are a significant number of genes that are
differentially expressed in either FGR or macrosomic pregnancies. Genes associated with
abnormal fetal growth that may play a role in the fetal origin of adult onset diseases were
described. Genes that were differentially expressed differed between FGR and macrosomia so
the developmental mechanisms associated with these changes are unlikely to merely represent
opposite ends of a spectrum.
6.2 Optimising the placenta sample collection and RNA extraction
Experiments that were performed to define the optimal method for collecting and storing
placental samples for RNA extraction and gene expression analysis showed that whilst tissues
collected at the time of elective Caesarean section were of good quality, those collected at the
time of vaginal delivery were of poor quality and were not suitable for further analysis. One
significant difference in the collection protocols was that the researcher was present at the
time of Caesarean section and all samples were processed in <20 minutes. In contrast, the
researcher was telephoned to attend after vaginal delivery, leading to a delay of 1-6 hours in
sample preparation. Further work is needed to demonstrate whether this time delay is the
major reason for sample failure or whether the poor sample quality is inherent in the process
of vaginal delivery. This will require the presence of research staff on the labour ward at the
time of delivery. This is important to assess, as in the long term it is important to determine
86
placental function in a cohort (the majority of pregnancies) that have vaginal delivery as well
as from those women having a Caesarean section.
Sitras et al. investigated the effect of the mode of labouring on gene expression in near term
human placentas. The analysis 27 placenta samples after vaginal delivery and 17 samples
after elective Caesarean section showed 92 genes were down-regulated and 94 genes were upregulated in the placentas obtained from vaginal delivery compared to those from Caesarean
section. However, all these genes expressed at a level of <2.5 fold-change and p>0.01 (Sitras
et al., 2008).
Fajardy et al. evaluated the RNA quality by performing a set of experiment on human
placenta over a progressive time course of 4 days. They used two different protocols which
included direct transfer of tissue into RNALater and a dissection of placenta villosities before
bio-banking. The findings showed that placental samples can be stored at 4°C up to 48 hours
without alteration in RNA quality. The result of RNA integrity and intensity testing was better
in those tissues that were directly transferred to RNALater solution (RIN ranged from 6.44 to
7.22) compared with those that underwent a dissection before RNA processing (RIN ranged
from 4.5 to 6.05). Long term storage of the placenta did not affect RNA integrity except for
changes in the transcript levels of stress-responsive genes such as TNF-alpha and COX2 after
48 hours (Fajardy et al., 2009).
6.3 Investigation of the microarray findings
The 41 genes found to be differentially expressed in macrosomic newborns contribute to
various molecular and biological functions with possible long term implications. The
expression of leptin (LEP) was six times lower in macrosomic placentas compared to
87
controls. Sialic acid binding Ig-like lectin 6 (SIGLEC6), that binds to and modulates leptin,
was also marginally down-regulated (F= -1.9, p=0.085). Leptin, a 167-amino-acid peptide, is
mainly produced in adipose tissue and is involved in the control of hypothalamic-pituitarygonadal axis (Zhang et al., 1994). Most leptin effects seem to be mediated through specific
counteraction of neuropeptide Y (NPY) in the hypothalamus (Wiznitzer et al., 2000). The
leptin receptor is abundantly expressed in the arcuate nucleus of the hypothalamus which
makes it a target site for leptin induced effects (Figure 6.1) (Kageyama et al., 2012). This
protein plays a crucial role in the endocrine regulation of appetite, energy homeostasis and
insulin secretion. In adults, the serum concentration of leptin is inversely proportional to body
fat mass and is sometimes referred to as the obesity gene (Considine et al., 1996). Leptin is
also associated with control of blood pressure, bone formation, immunity and reproductive
developmental changes. Dysregulation in fetal life has the potential to have a significant
impact on a child’s long term development (Shekhawat et al., 1998).
88
Figure 6.1 Adipocytokine signalling based on the KEGG pathways in human.
Source: http://www.kegg.jp
Some studies have measured leptin levels in maternal and cord blood with regards to fetal
growth (Table 6.1 and 6.2) but there are a few studies on global gene expression analysis of
placenta in the pregnancies affected by FGR or macrosomia. Despite some variances in the
results of different studies the consensus appears to be that cord blood leptin increases in
macrosomia but decreases in FGR while there are no significant changes in the maternal
blood of macrosomic and growth restricted newborns. Interestingly, the changes seen in leptin
89
gene expression oppose those seen in the concentration of leptin in cord blood, suggesting
that the regulatory mechanisms involving in translating gene into protein expression are
complex. Investigation of leptin expression has shown that the leptin levels in macrosomic
infants is higher than in controls and that there are higher levels in infants of diabetic mothers
when compared to non-diabetic mothers (Vela-Huerta et al., 2008).
Table 6.1 Findings of other studies showing the changes in leptin level in maternal blood,
cord blood, and placenta in FGR.
Maternal Cord
blood
blood
Placental
gene
expression
Reference
Assay
(Struwe et al., 2010)
Microarray, Radioimmunoassay
-
=
↑
(Valuniene et al., 2007)
Radioimmunoassay
-
↓
-
(Martinez-Cordero et al., 2006)
Immunoradiometric assay
-
↓
-
(Weyermann et al., 2006)
ELISA
=
↓
-
(Grisaru-Granovsky et al., 2003)
Immunoradiometric assay
=
=
-
(Pighetti et al., 2003)
Immunoradiometric assay
↑
↓
-
(Yildiz et al., 2002)
Radioimmunoassay
↓
↓
-
(Varvarigou et al., 1999)
Radioimmunoassay
=
↓
-
(Koistinen et al., 1997)
Radioimmunoassay
-
↓
-
Table 6.2 Findings of other studies showing the changes in leptin level in maternal blood,
cord blood, and placenta in macrosomia.
Reference
Assay
Maternal
blood
Cord
blood
Placental gene
expression
(Ategbo et al., 2006)
ELISA
↑
-
-
(Weyermann et al., 2006)
ELISA
↓
=
-
(Mazaki-Tovi et al., 2005a)
Radioimmunoassay
-
↑
-
(Yang and Kim, 2000)
Immunoradiometric assay
-
↑
-
(Varvarigou et al., 1999)
Radioimmunoassay
↑
↑
-
(Shekhawat et al., 1998)
Radioimmunoassay
-
↑
-
(Koistinen et al., 1997)
Radioimmunoassay
-
↑
-
90
Leptin together with ten other focussed molecules including ATRX, CASP4, FGF7, FRZB,
GCH1, GFRA2, GNGT1, PHIP, PPIG, and SMC3 was involved in a network that contributes
to digestive system development and function (Figure 6.2). Disturbance of this network
postulates a higher risk for nutritional disease and behavioural disorders in the macrosomic
group.
In our dataset, while leptin was significantly increased in macrosomic newborns, a
comparison between diabetic and non-diabetic mothers did not show any difference in gene
expression. This finding is conflictive with the literature, and may be due to the small number
of samples available in this series. Alternatively there may be no difference due to the high
standard of blood glucose control documented in these pregnancies.
91
Figure 6.2
Network of molecules including leptin which play a role in digestive system
development. Focused molecules in this study have been highlighted. Red colour indicates the
up-regulated and green colour indicates the down-regulated genes in macrosomia.
Guanosine triphosphate cyclohydrolase I (GCH1) was shown to be down-regulated in both
FGR (F= -2.7, p=0.02) and macrosomia (F= -2.3, p=0.03). The protein encoded by GCH1 is
the first and rate-limiting enzyme in tetrahydrobiopterin (BH4) biosynthesis which is an
essential cofactor required by aromatic amino acid hydroxylases and nitric oxide synthases.
Inhibition or silencing of GCH1 reduces the tube formation of human umbilical vein
92
endothelial cells (Pickert et al., 2013). Therefore, decreased GCH1 production in FGR can
result in reduced levels of nitric oxide and hence, disturbance of endothelial cell formation of
the umbilical cord leading to abnormal fetal growth due to improper nutrient and waste
transfer. BH4 is essential for synthesis of dopamine, norepinephrine, epinephrine, and
serotonin (Kapatos, 2013). BH4 deficiency has been described in severe neurological
disorders and is also implicated in Parkinson's disease, Alzheimer's disease and depression
(Figure 6.3) (Thony et al., 2000, Blau et al., 2001).
93
Figure 6.3 Serotonin receptor signalling. GCH1 catalyses the production of BH4, a required
cofactor in the synthesis of serotonin. GCH1 had decreased level of expression in both FGR
and macrosomia.
94
In FGR, 73 dysregulated genes had a role in embryonic development. Development of
sensory organs (activation z-score=1.13, p<0.001), structures of the head and neck (activation
z-score= 1.20, p<0.001), the size of the embryo (activation z-score= -3.18, p<0.001), and eye
development (activation z-score=1.13, p<0.001) were the top physiological functions affected
in FGR. 21 genes with potential effect on the size of embryo were involved in this group.
Based on the direction of the changes, eighteen were predictive for FGR, including CTNNB1,
ETS2, F5, FGFR2, FLI1, FOXP1, ITGB8, LIMS1, LRP2, MAD2L1, MTHFD2, MYCN,
NUSAP1, PARP1, PPP3R1, RECK, SPTBN1, and SOX4 while three were predictive of
macrosomia; FURIN, EGFR, and HIST1H1C.
The DAVID analysis for disease (online Mendelian inheritance in man (OMIM) category)
identified higher risk for type 2 diabetes mellitus in FGR based on a meta-analysis of
genome-wide
association
(p=0.01).
Notch
homolog
2
(Drosophila)
(SBNO2),
hematopoietically expressed homeobox (HHEX), leucine-rich repeat-containing G proteincoupled receptor 5 (LGR5), transcription factor 7-like 2 (T-cell specific, HMG-box)
(TCF7L2) were recognised as genes related to a susceptibility for type 2 diabetes. The
DAVID pathway viewer identified significant clusters of Kyoto Encyclopaedia of Genes and
Genomes (KEGG), PANTHER, and REACTOME pathways in FGR (Table 6.3) but because
of the limited number of genes involved in the macrosomic group no significant biological
pathways were defined.
95
Table 6.3
Significant cluster of pathways in three different categories related to growth
retardation.
Category
Term
Count
p-value
KEGG_PATHWAY
hsa04320: Dorso-ventral axis formation
5
<0.01
KEGG_PATHWAY
hsa04520: Adherens junction
6
0.02
PANTHER_PATHWAY
P00005: Angiogenesis
11
<0.01
PANTHER_PATHWAY
P04398: p53 pathway feedback loops 2
5
0.04
PANTHER_PATHWAY
P00004: Alzheimer disease-presenilin pathway
8
0.04
REACTOME_PATHWAY
REACT_299: Signalling by Notch
4
<0.01
REACTOME_PATHWAY
REACT_578: Apoptosis
8
0.03
REACTOME_PATHWAY
REACT_13: Metabolism of amino acids
9
0.03
REACTOME_PATHWAY
REACT_15518: Transmembrane transport of small molecules
4
0.07
Notch Signalling was one of the top canonical pathways affected in FGR (p<0.01). Four
genes in this network had disturbed levels of expression; two being up-regulated and two
down-regulated (Table 6.2 and Figure 6.4).
Table 6.2 Genes associated with Notch signalling in FGR.
Symbol
Fold Change
p-value
Location/Type
FURIN
2.7
0.02
Cytoplasm/Peptidase
NOTCH2
2.0
0.01
Plasma membrane/Transcription regulator
APH1B (Gamma-Secretase)
-2.1
0.03
Plasma membrane/Peptidase
JAG1
-2.5
0.04
Extracellular space/Growth factor
The Notch pathway is involved in cell fate choices in various developing tissues and it is an
evolutionarily conserved mechanism for cell-cell interaction. Mouse models have
demonstrated this pathway is crucial for various processes happening during placental
96
development such as fetal angiogenesis and formation of maternal blood vessels in the
placenta (Gasperowicz and Otto, 2008).
Figure 6.4 Notch signalling pathway and dysregulated genes in FGR.
ErbB signalling was also affected in FGR with involving six genes including AREG/AREGB,
EGFR, ERBB2, FOS, PAK3, and PIK3C2B (p<0.001). This pathway plays an important role
during growth and development of a number of tissues including the heart, mammary gland
and the central nervous system.
Abnormal level of the expression of BCL2, CCNG1, CTNNB1, PIK3C2B, PMAIP1, and TP63
resulted in disturbance of p53 signalling in FGR (p<0.001). Upstream analysis in this group
97
also showed the activation of p53. The p53 tumour suppressor protein is a transcriptional
regulator that plays a crucial role in DNA repair, cell-cycle progression, angiogenesis, and
apoptosis.
In order to obtain functional classification of the dysregulated genes, we used the PANTHER
online tool to investigate the pathways and biological processes that these genes contribute to
(Mi and Thomas, 2009, Mi et al., 2010). In FGR, the most significant pathway distinguished
based on PANTHER database was angiogenesis (Figure 6.5) with involvement of 11 genes
including protein jagged-1 (JAG1), neurogenic locus notch homolog protein 2 (NOTC2), p21
protein-activated kinase 3 (PAK3), receptor-type tyrosine-protein phosphatase beta (PTPRB),
transcription factor 7-like 2 (TF7L2), frizzled-5 (FZD5), v-Ets avian erythroblastosis virus
E26 oncogene homolog 1 (ETS1), frizzled-related protein (FRZB), catenin beta-1 (CTNNB1),
phosphatidylinositol 4-phosphate 3-kinase C2 domain-containing subunit beta (P3C2B),
proto-oncogene c-fos (FOS).
98
Genes
Figure 6.5 Pathways affected in FGR based on PANTHER database.
Category
EGF, epidermal growth factor; FGF, fibroblast growth factor; GnRH, gonadotropin releasing hormone, PDGF, platelet derived growth factor.
99
6.4 Other microarray studies on the placenta
Different microarray studies have targeted the role of placenta in FGR and preeclampsia.
Sitras et al. investigated the placental gene expression profile in FGR due to placental
insufficiency. The study population included eight women with uncomplicated pregnancies
and eight women with FGR. Three criteria were considered for the definition of FGR in this
study: (a) birth weight below the ≤5th centile for gestational age; (b) oligohydramnios
(amniotic fluid index 5th centile for gestational age); and (c) abnormal fetal Doppler
parameters (absent or reversed end-diastolic flow in the umbilical artery or signs of blood
redistribution). One of the women with FGR and two controls had their babies delivered
vaginally. Women with growth restricted fetuses but normal Doppler were excluded. Four of
the FGR pregnancies were also affected by preeclampsia. The microarray procedure involved
the ‘Applied Biosystems Chemiluminescent NanoAmp RT-IVT Labelling Kit’ followed by
RT-PCR validation. The results of this study demonstrated 47 up-regulated and 70 downregulated genes with ≥1.5 fold changes and p≤0.05 compared to normal placentas (Table 6.4 Study 1). Pathway analysis in this study showed up-regulation of inflammation mediated by
the chemokine and cytokine signalling pathway. Transcription factors Fos and FosB which
might regulate angiogenesis through mediators such as vascular endothelial growth factor and
placental growth factor were up-regulated in FGR placenta by 2.5 fold. Down-regulated genes
were mostly ribosomal protein coding genes, indicating reduced gene translation in FGR
pregnancies. There was no significant difference in subgroup analysis between FGR placentas
of pregnancies with and without preeclampsia, however common pathogenic mechanisms
were observed with severe early-onset preeclampsia (Sitras et al., 2009).
Struwe et al. performed a microarray study on the placenta of ten normal and ten growth
restricted newborns. Birth weight <10th percentile and at least one pathologic finding in
100
prenatal Doppler ultrasound were considered as FGR definition and infants with birth weight
between 25 and 75 percentile were considered as controls. Both male and female infants were
included in this multicentre study. Considering ≥2 folds change as being significant; 132
genes were up-regulated and 25 were down-regulated. Four up-regulated genes with possible
roles in prenatal and postnatal growth and with subsequent metabolic consequences were
validated by RT-PCR including IGFBP-1, prolactin, leptin, and corticotropin releasing
hormone (CRH). RT-PCR confirmed the higher expression of IGFBP-1, leptin, and CRH.
However, on the protein level using western blot analyses, only IGFBP-1 and leptin showed
higher expression in FGR. It has been suggested that leptin was positively correlated with
feto-placental insufficiency in growth restricted newborns (Struwe et al., 2010).
Nishizawa et al. performed a microarray study to investigate the mechanisms underlying
preeclampsia and FGR using the Affymetrix GeneChip system. In this study, FGR was
defined as birth weight <10th percentile for gestational age. The analysis of eight
preeclamptic, eight normotensive without FGR, and eight normotensive with FGR placentas
suggested a high correlation coefficient between subset of FGR and preeclampsia but lack of
concordance between normotensive and disease samples, indicating significant gene
expression alteration upon the onset of preeclampsia and FGR. Notably, compared with
preeclampsia, less FGR sample pair correlation was observed which reflects the possibility of
heterogeneous pathophysiology of FGR. Changes in expression were defined by a 1.5 foldchange and p<0.05; comparison between the control pregnancy and FGR group identified a
total of 252 genes, including 62 genes in common with the gene set for preeclampsia (Table
6.4 - Study 2). Gene set enrichment analysis revealed the activation of p53 pathway and
placental anti-angiogenic factors in preeclampsia and FGR. Genes such as LEP, CGB, CRH,
and PAPP-A2 were up-regulated in both FGR and preeclampsia (Nishizawa et al., 2011).
101
Interestingly, our data also showed a degree of the up-regulation of these genes in FGR as
LEP (F=4.3, p=0.07), CGB (F=2.8, p=0.05), CRH (F=2.9, p=0.09), PAPP-A (F=2.9, p=0.02),
and PAPP-A2 (F=2.5, p=0.04). Our findings confirmed p53 signalling pathway activation in
FGR.
Other studies have focused on the role of specific placental networks or metabolic processes
in terms of fetal growth and development. Lee et al. performed gene expression analysis of
the placenta to investigate the changes occurring in glucose metabolism in FGR using a
‘Whole Human Genome Oligo Microarray Kit’ (Agilent). Tissue from six normal and six
FGR from pregnancies >37 weeks’ gestational age delivered by Caesarean section were
included. FGR pregnancies were diagnosed based on the findings of ultrasound scanning.
2,012 genes had disturbed level of expression with ≥2 fold-change, of which seven had
possible role in the metabolism of glucose including LDHC, DLAT, PFKFB2, OGDH, PYGM,
PGM3, and IGF2 (Table 6.4 - Study 3) (Lee et al., 2010). Our results did not show significant
changes in these genes, however other similar genes related to glucose metabolism such as
IGF1R (F= -1.9, p<0.01), IGF2 (F= -1.9, p=0.05), IGF2BP2 (F=1.8, p=0.04), and IGFBP-7
(F= -2, p=0.06) were observed.
Toft et al. demonstrated changes in placental angiogenic genes in pregnancies affected by
preeclampsia and FGR. Study groups included preeclampsia (n=10), FGR (n=8), and
preeclampsia + FGR (n=10). Microarray results did not show significant difference however,
two anti-angiogenic genes, endoglin (ENG) and fms related tyrosine kinase 1 (FLT1) have
been reported to be increased in RT-PCR experiments in preeclampsia with FGR (Toft et al.,
2008). This study considered comparing preeclampsia and FGR without having normal
control placentas and therefore it would be expected to see no significant difference in gene
102
expression pattern since these two pathological conditions might share similar
pathophysiology. However, the authors have concluded that microarrays might not be
sensitive enough to detect subtle changes in gene expression.
Another study by Dunk et al. showed up-regulation of BTNL9 and NTRK2 in placental microvascular endothelial cells in severe FGR placentas. Three preterm control placenta and six
placentas from pregnancies with severe FGR from pregnancies between 24 to 34 weeks
gestational age delivered vaginally or by Caesarean section were analysed using ‘Affymetrix
U133 Plus 2 GeneChips’. Preterm controls were appropriately grown fetuses while severe
FGR were defined as fetal weight ≤10th percentile with absent or reversed end-diastolic
velocities in the umbilical artery (Dunk et al., 2012). Our results showed marginally
significant down-regulation of BTNL9 (F= -2.4, p=0.06) in the placentas of macrosomic
babies.
Winn et al. suggested that leptin, CRH, inhibin, SIGLEC6, and PAPP-A2 are differentially
expressed in severe preeclampsia. These findings were obtained from an array-based study
using the HG-U133 Affymetrix GeneChip platform on 11 women who experienced
preeclampsia and 12 preterm patients. 55 genes were identified as being differentially
expressed and statistical analysis showed the method of delivery (vaginal delivery vs.
Caesarean section) was not a confounding factor (Winn et al., 2009). Interestingly, the results
of our study showed down-regulation of SIGLEC6 and LEP in macrosomic groups which
support the possible common pathophysiology of abnormal fetal growth and preeclampsia
through at least in some stages / forms of the diseases’ development.
103
Table 6.4 Data from other microarray studies on placenta showing up-regulated and downregulated genes in FGR.
Up-regulated Genes
Gene Name
Down-regulated Genes
Fold-Change
p-value
Study 1 (Sitras et al., 2009)
Gene Name
Fold-Change
p-value
Study 1 (Sitras et al., 2009)
CYTL1
3.0
0.01
DHRS2
-2.9
0.05
C7
2.9
<0.01
TRIM28
-2.8
0.02
FOS
2.6
0.01
MGAT4B
-2.7
<0.01
FOSB
2.5
0.02
MBD3
-2.7
0.01
EVI5
2.5
<0.01
PAQR7
-2.4
0.01
HSD11B1
2.3
<0.01
HYAL2
-2.4
0.04
LGALS14
2.3
0.02
ENG
-2.3
0.01
FKHL18
2.2
0.03
UNC13D
-2.3
<0.01
DKFZP564O0823
2.1
<0.01
MICAL-L1
-2.2
0.01
FLJ20032
2.1
<0.01
TACSTD2
-2.2
0.03
THOC2
2.0
<0.01
FBXW5
-2.2
<0.01
RBP1
1.9
0.01
unassigned
-2.1
0.03
ADCY3
1.9
<0.01
MRPL39
-2.0
0.02
ADAMTSL3
1.9
<0.01
PCQAP
-2.0
<0.01
B PTPRB
1.9
<0.01
TMEM16A
-2.0
0.01
RORA
1.9
<0.01
UBXD1
-2.0
<0.01
ACTR2
1.8
0.02
PLEKHM2
-2.0
<0.01
C8orf70
1.8
0.03
SRCAP
-2.0
<0.01
ALOX5AP
1.8
0.01
RAI16
-2.0
<0.01
RTP2
1.8
0.03
S100A6
-2.0
<0.01
SPRR2A
1.8
0.04
BCAR1
-2.0
<0.01
VPREB3
1.8
0.04
YEATS4
-2.0
<0.01
BRAP
1.8
0.01
CHPT1
-1.9
<0.01
FAM26C
1.7
0.02
CSRP2
-1.9
<0.01
LOC157860
1.7
0.03
DPP9
-1.9
0.02
ZNF650
1.7
0.04
RPS10
-1.9
<0.01
PTGER2
1.7
0.01
AREG
-1.9
0.01
GSTA5
1.7
0.01
SYDE1
-1.9
0.02
PLCB1
1.7
0.03
ENTPD6
-1.9
<0.01
Continued
104
Table 6.4 Continued
Up-regulated Genes
Gene Name
Down-regulated Genes
Fold-Change
p-value
Study 1 (Continued)
Gene Name
Fold-Change
p-value
Study 1 (Continued)
LTBP2
1.7
0.02
MOSC1
-1.9
<0.01
ACTC
1.7
0.02
unassigned
-1.9
0.02
RRAS
1.7
0.01
RPL36A
-1.9
<0.01
KCNJ8
1.7
0.01
CHUK
-1.9
0.01
ETV3
1.7
0.01
CLDN23
-1.8
0.01
TPRXL
1.6
0.01
BCORL1
-1.8
0.01
CXCR4
1.6
0.01
ADORA2B
-1.8
0.01
RNF4
1.6
0.03
18 SLC22A18
-1.8
0.02
PSCD3
1.6
0.03
LOC387758
-1.8
0.02
GPX7
1.6
0.01
HNRPL
-1.8
<0.01
OSBPL3
1.6
0.01
NFE2L3
-1.8
0.01
SAA3P
1.6
0.04
MTA2
-1.8
0.01
ANKRD33
1.6
0.04
FAM3A
-1.8
0.02
DEFB1
1.6
0.01
PVRL2
-1.8
0.01
FLJ16542
1.6
0.03
SCPEP1
-1.8
0.01
NUDT10
1.6
0.02
SLITL2
-1.7
0.01
SMOC2
1.5
0.03
ADAMTS19
-1.7
0.01
MYST2
1.5
0.04
TMEM9
-1.7
0.01
ACADS
-1.7
0.01
Study 2 (Nishizawa et al., 2011)
CXCL9
2.44
0.04
DDR1
-1.7
0.01
KRTAP26
1.91
0.04
MLLT1
-1.7
0.01
LOX
1.88
<0.01
SUPT5H
-1.7
0.01
CXCL13
1.74
0.02
SLC39A7
-1.7
0.04
FPR3
1.73
0.04
RAB17
-1.7
0.01
FLJ11827
1.71
0.01
SLC39A6
-1.7
0.03
IFNG
1.70
0.02
CPOX
-1.7
0.01
RGS1
1.69
0.01
AP2A1
-1.7
0.02
THBS1
1.90
<0.01
CDC20
-1.6
0.04
GZMK
1.67
0.02
GPR157
-1.6
0.01
CYR61
1.65
0.02
DUSP9
-1.6
0.02
Continued
105
Table 6.4 Continued
Up-regulated Genes
Gene Name
Down-regulated Genes
Fold-Change
p-value
Study 2 (Continued)
Gene Name
Fold-Change
p-value
Study 1 (Continued)
MSR1
1.63
0.04
CD151
-1.6
0.02
IFI44L
1.63
0.03
CHST2
-1.6
0.03
TIMP1
1.62
0.03
COMMD8
-1.6
0.02
C1QB
1.60
0.04
SIL1
-1.6
0.04
GLB1L
-1.6
0.03
Study 3* (Lee et al., 2010)
LDHC
2.6
<0.01
C20orf22
-1.5
0.02
OGDH
2.5
<0.05
LXN
-1.6
0.04
IGF2
2.4
<0.05
WDR42A
-1.6
0.02
PFKFB2
2.0
<0.01
FLJ20718
-1.6
0.04
PYGM
2.0
<0.01
DOCK6
-1.5
0.04
DLAT
1.9
<0.05
ACPP
-1.5
0.04
PGM3
1.8
<0.01
Study 2 (Nishizawa et al., 2011)
OR4F16
-1.85
0.02
RPS6KA6
-1.82
0.00
PCDH11Y
-1.69
0.02
GABRE
-1.64
0.03
MGC3536
-1.64
0.01
KIAA1467
-1.63
0.01
HIST1H2BK
-1.61
0.02
GABRA4
-1.58
<0.01
LOC441233
-1.56
0.03
PGAP1
-1.55
<0.01
LRP2
-1.55
0.01
AGL
-1.53
0.01
CCDC125
-1.53
<0.01
HYAL4
-1.53
0.01
C9orf131
-1.52
<0.01
*This study focused on glucose metabolism-related genes.
106
6.5 Limitations of this study
This study was based on a relatively small number of samples and was performed as a means
to develop the technique with a view to allowing more involved assessment, using tissues
from a larger cohort of pregnancies at a later date. Although significant changes in gene
expression are seen we are mindful of the fact that the relatively small number of samples
used here mean that the study is prone to bias. Some of this bias was removed by ensuring
similarity in samples used in the experiments; controlling for mode of the delivery, sex of the
babies, and gestational age to avoid the effect of some known confounders. Moreover, based
on power analysis, only genes with two or more fold changes were selected for downstream
analysis to achieve more reliable results.
Defining the study groups especially FGR was the other challenging issue in this study. It is
questionable whether considering 10th centile is able to distinguish pathological and
physiological smallness of the fetuses effectively. Adding the findings of ultrasound scanning
during pregnancy could increase the sensitivity and specificity of the birth weight centiles in
definition of study groups. Moreover, heterogeneous pathophysiology of FGR is a
challenging problem to achieve a similar profile of gene expression in different studies. In
future work we aim to use measurement of % body fat at birth as a means of defining
nutritionally under- and over-nourished cohorts more effectively.
6.6 Future directions
Although we have proven the value of these research methods in examination of placental
gene expression, further work is needed to define the reliability of sample collection and
processing after vaginal delivery. Time course analysis of RNA quality and optimisation of
107
the method would allow the work to be expanded to pregnancies delivering by labour as well
as by Caesarean section.
Different gene expression profiles observed in FGR and macrosomia need to be further
validated by RT-PCR to achieve more robust results. Focussed experiments on specific
pathways and networks, will discover molecular basis of fetal growth abnormalities and the
possible role of these molecular changes in fetal programming. The possible link between
intrauterine life and childhood or adulthood life is yet to be proven through comprehensive
studies.
Differentially expressed genes can be used for early detection of FGR and macrosomia before
the establishment of irreversible pathophysiologic events and also for therapeutic purposes in
the future. Further investigations are required to provide sufficient evidence for clinical use of
these genes.
6.7 Conclusion
This study has shown that a significant number of genes are differentially expressed in the
placentas of term fetuses that are affected by either fetal growth restriction or macrosomia.
These genes are linked in a variety of developmental pathways that have a potential impact on
fetal and childhood development. Important pathways that have been identified as been
affected include those affecting embryonic development, angiogenesis, amino acid
metabolism and the transmembrane transport of small molecules. Notch and p53 signalling
pathways were also significantly affected in the placentas of fetuses that were growth
restricted. These findings highlight the complex pathophysiology of fetal growth restriction.
108
In the macrosomic group, down-regulation of GCH1 which plays a role in umbilical cord
formation and blood flow regulation together with LEP which plays a crucial role in energy
homeostasis and control of food intake were noticeable. Considering the long term
implications of leptin, this is an alarming issue for mothers and health service providers.
Excessive fetal weight gain during intrauterine life might initiate some adverse consequences
such as obesity, hypertension, and cardiovascular disease.
The differential gene expression seen in these placental tissues suggest that both growth
restricted and macrosomic newborns are susceptible to future health issues as a consequence
of in-utero growth dysfunction. The concept that these differences could first be used to
identify fetuses at risk of adverse obstetric outcome but could also be manipulated to improve
short and long term health outcomes is attractive and deserves further attention.
109
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