1. health and fitness
2. disorders
3. mental disorder
4. depression

MATERNAL DEPRESSION DURING PREGNANCY, METHYL NUTRIENT
METABOLISM, AND SEROTONIN TRANSPORTER
by
Julia Jia Wei
B.Sc., The University of British Columbia, 2009
A THESIS SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
in
The Faculty of Graduate Studies
(Experimental Medicine)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
June 2011
© Julia Jia Wei, 2011
Abstract
Depression occurs in 15% of pregnant women and 1/3 are taking selective serotonin
reuptake inhibitors (SSRIs) as antidepressants. The neurotransmitter, serotonin, plays a critical
role in modulating stress responses and early brain development. Serotonin transporter (SLC6A4)
regulates extracellular serotonin levels, and an insertion/deletion variant in the promoter
(5HTTLPR) is associated with depression. Maternal mood and SSRIs may program newborns’
behaviour later in life. The underlying molecular mechanism for developmental programming
may involve DNA methylation, which requires methyl nutrients as enzymatic cofactors. While
low methyl nutrient status (folate and vitamin B12) and a genetic variant in
methylenetetrahydrofolate reductase (MTHFR C677T) have been associated with depression in
adults, the role of methyl nutrient metabolism in depression during pregnancy remains unclear.
Furthermore, little is known about the combined roles of methyl nutrient status and depression in
the epigenetic regulation of SLC6A4.
The experiments in this thesis explored whether disturbances in methyl nutrient
metabolism and depressed mood during the 3rd trimester of pregnancy affect SLC6A4
methylation and expression in mothers and their newborns. Maternal folate status was positively
associated with maternal SLC6A4 methylation at CpGs 1, 4, and 8 (P<0.05). Maternal 3rd
trimester mood was inversely associated with SLC6A4 CpG 10 methylation in both mothers and
newborns (P<0.05). Methylation at SLC6A4 CpG 8 was lower in newborns with mothers with
the MTHFR 677TT genotype, and methylation at CpGs 6 and 10 were lower in newborns with
the MTHFR 677TT genotype (P<0.05). Maternal SLC6A4 mRNA level was positively associated
with mean maternal methylation and methylation at CpGs 5, 7, 8, and 10 (P<0.05). Yet, newborn
SLC6A4 mRNA level was negatively associated with newborn methylation at CpGs 4 and 7
ii
(P<0.05). Homozygosity for the minor allele for MTHFR C677T and 5-HTTLPR
insertion/deletion variants in mothers were not associated with maternal mood (P>0.05).
These results provide evidence to suggest that maternal mood during pregnancy and
methyl nutrient metabolism may program SLC6A4 gene expression through DNA methylation in
both mothers and their newborns. Mood during pregnancy and disturbances in methyl nutrient
metabolism could set up life-long health consequences in the development of the offspring.
iii
Preface
Statement of co-authorship:
I composed this thesis in its entirety, with direction and input from Dr Angela Devlin and
Dr. Tim Oberlander. This thesis was revised by Dr. Angela Devlin, Dr. Tim Oberlander and Dr.
Timothy Green.
This is a retrospective study using archived blood and serum samples collected by
members of the Oberlander Laboratory. Members of the Oberlander Laboratory were also
responsible for administering mood questionnaires. I was responsible for executing monocyte
isolation and all nucleic acid extractions. In addition, I was responsible for conducting the DNA
methylation analyses, gene expression analyses, and genotyping assays. I prepared all serum
samples for Janette King and Roger Dyer (Metabolomics Core Lab of the UBC Nutrition
Research Program, directed by Dr. Sheila Innis), who performed the methyl nutrient analyses. I
conducted statistical analyses for all results.
A manuscript will be prepared for future publication based on results from Chapters 3-5.
The manuscript will include relevant sections of this thesis, including introduction, methods,
results and discussions found in Chapters 1-6.
Statement of research ethics approval:
This thesis was conducted under ethical approval from the University of British
Columbia Research Ethics Board, and the Children’s and Women’s Health Centre of British
Columbia Research Review Committee (Certificate number: H05-70629).
iv
Table of Contents
ABSTRACT....................................................................................................................................... ii
PREFACE ........................................................................................................................................ iv
TABLE OF CONTENTS ..................................................................................................................... v
LIST OF TABLES ........................................................................................................................... viii
LIST OF FIGURES ........................................................................................................................... ix
LIST OF ABBREVIATIONS................................................................................................................ x
ACKNOWLEDGEMENTS................................................................................................................. xii
CHAPTER I: INTRODUCTION ....................................................................................................... 1
1.1
Project Overview .............................................................................................................. 1
1.2
Depression and the Brain ................................................................................................. 5
1.2.1
Depression................................................................................................................. 5
1.2.2
Serotonergic System ................................................................................................. 5
1.2.3
Depression During Pregnancy .................................................................................. 7
1.3
Developmental Programming .......................................................................................... 8
1.3.1
Overview of Developmental Programming .............................................................. 8
1.3.2
Human Epidemiological Evidence ........................................................................... 9
1.3.3
Developmental Programming in Animal Models ................................................... 10
1.4
Epigenetics ..................................................................................................................... 11
1.4.1
Epigenetics Defined ................................................................................................ 11
1.4.2
Epigenetics as a Potential Programming Mechanism ............................................. 11
1.5
Methyl Nutrient Metabolism .......................................................................................... 13
1.5.1
Folate....................................................................................................................... 13
1.5.2
Vitamin B12 ............................................................................................................. 15
1.5.3
Methyl Nutrient Metabolism................................................................................... 15
1.5.4
Methylenetetrahydrofolate Reductase Polymorphism ............................................ 17
1.5.5
Deficiencies in Folate and Vitamin B12 .................................................................. 17
1.5.6
Methyl Nutrients and DNA Methylation ................................................................ 18
1.6
Rationale and Hypothesis ............................................................................................... 19
v
CHAPTER 2: MATERIALS AND METHODS ................................................................................. 22
2.1
Study Population ............................................................................................................ 22
2.2
Maternal Mood Assessments ......................................................................................... 22
2.3
Subject Samples ............................................................................................................. 23
2.4
Nucleic Acid Extraction ................................................................................................. 23
2.4.1
DNA Isolation from Monocytes ............................................................................. 24
2.4.2
RNA Isolation from Whole Blood .......................................................................... 24
2.4.3
DNA Isolation from Whole Blood .......................................................................... 25
2.4.4
Assessment of Nucleic Acid Quality and Quantity ................................................ 25
2.5
DNA Methylation Assay ................................................................................................ 25
2.5.1
Target Gene ............................................................................................................. 25
2.5.2
Bisulphite Pyrosequencing...................................................................................... 26
2.6
Gene Expression Assay .................................................................................................. 30
2.7
Genotyping Assays ......................................................................................................... 30
2.8
Methyl Nutrient Quantification ...................................................................................... 33
2.9
Exclusions ...................................................................................................................... 33
2.10 Statistical Analyses ........................................................................................................ 33
CHAPTER 3: MOTHERS ............................................................................................................. 35
3.1
Maternal Methyl Nutrient Metabolism and Mood ......................................................... 36
3.2
Maternal Methyl Nutrient Metabolism, Mood, and Methylation................................... 40
3.3
Maternal Methyl Nutrient Metabolism, Methylation, and Gene Expression ................. 45
3.4
5HTTLPR ....................................................................................................................... 48
CHAPTER 4: MATERNAL INFLUENCES ON NEWBORNS............................................................ 49
4.1
Maternal Methyl Nutrient Metabolism, Maternal Mood, and Newborn Methylation ... 50
4.2
Maternal Methyl Nutrient Metabolism and Newborn Gene Expression ........................ 55
4.3
Maternal Mood and Newborn Gene Expression ............................................................ 55
vi
CHAPTER 5: NEWBORNS ........................................................................................................... 56
5.1
Maternal and Newborn Methyl Nutrients ...................................................................... 57
5.2
Newborn Methyl Nutrient Metabolism and DNA Methylation ..................................... 60
5.3
Newborn Methyl Nutrient Metabolism, DNA Methylation, and Gene Expression ....... 62
5.4
5HTTLPR ....................................................................................................................... 65
CHAPTER 6: GENERAL DISCUSSION ......................................................................................... 66
6.1
Discussion of Results ..................................................................................................... 66
6.1.1
Methyl Nutrient Status ............................................................................................ 66
6.1.2
Discussion of First Aim – Mothers ......................................................................... 70
6.1.3
Discussion of Second Aim – Maternal Influences on Newborns ........................... 76
6.1.4
Discussion of Third Aim – Newborns .................................................................... 78
6.2
Limitations, Strengths, and Future Directions ............................................................... 80
6.2.1
Limitations .............................................................................................................. 80
6.2.2
Strengths ................................................................................................................. 83
6.2.3
Future Directions .................................................................................................... 83
6.3
Concluding Remarks ...................................................................................................... 84
BIBLIOGRAPHY ............................................................................................................................. 87
vii
List of Tables
Table 2.1 Primers for bisulphite pyrosequencing......................................................................... 29
Table 2.2 Primers for genotyping. ................................................................................................ 32
Table 3.1 Maternal MTHFR C677T genotype and demographic data of the pregnant women. .. 38
Table 3.2 Relationship between maternal folate levels and maternal SLC6A4 methylation
(adjusted for EPDS scores). .......................................................................................................... 41
Table 3.3 Relationship between maternal folate levels and maternal SLC6A4 methylation
(adjusted for HAM-A scores). ...................................................................................................... 42
Table 3.4 Relationship between maternal 3rd trimester EPDS scores and maternal SLC6A4
methylation. .................................................................................................................................. 43
Table 3.5 Relationship between maternal 3rd trimester HAM-A scores and maternal SLC6A4
methylation. .................................................................................................................................. 44
Table 3.6 Estimated difference in maternal SLC6A4 gene expression according to maternal
SLC6A4 methylation sites (unadjusted). ....................................................................................... 46
Table 3.7 Estimated difference in maternal SLC6A4 gene expression according to maternal
SLC6A4 methylation sites (adjusted). ........................................................................................... 47
Table 4.1 Maternal MTHFR C677T genotype and clinical data of their newborns. .................... 51
Table 4.2 Relationship between maternal MTHFR C677T genotype and newborn SLC6A4
methylation (adjusted for EPDS scores). ...................................................................................... 52
Table 4.3 Relationship between maternal MTHFR C677T genotype and newborn SLC6A4
methylation (adjusted for HAM-A scores). .................................................................................. 53
Table 4.4 Relationship between prenatal exposure to maternal 3rd trimester mood and newborn
SLC6A4 methylation. .................................................................................................................... 54
Table 5.1 Methyl nutrient status in mothers and newborns. ........................................................ 58
Table 5.2 Relationship between newborn MTHFR C677T genotype and newborn SLC6A4
methylation. .................................................................................................................................. 61
Table 5.3 Relationship between newborn SLC6A4 methylation and newborn SLC6A4 expression
(unadjusted)................................................................................................................................... 63
Table 5.4 Relationship between newborn SLC6A4 methylation and newborn SLC6A4 expression
(adjusted)....................................................................................................................................... 64
viii
List of Figures
Figure 1.1 Methyl nutrient metabolism.......................................................................................... 3
Figure 1.2 Schematic representation of thesis project. .................................................................. 4
Figure 2.1 Schematic representation of the SLC6A4 promoter region analyzed for methylation
status. ............................................................................................................................................ 28
Figure 3.1 Specific aim 1. ............................................................................................................ 35
Figure 3.2 Relationship between early 3rd trimester EPDS scores and HAM-D scores. ............ 39
Figure 3.3 Relationship between maternal vitamin B12 and holoTC status. ................................ 39
Figure 4.1 Specific aim 2. ............................................................................................................ 49
Figure 5.1 Specific aim 3. ............................................................................................................ 56
Figure 5.2 Relationships between maternal and newborn methyl nutrient levels. ...................... 59
Figure 5.3 Relationship between newborn vitamin B12 and holoTC levels. ................................ 59
ix
List of Abbreviations
5,10-MTHF
5,10-methylenetetrahydrofolate
5-HT
Serotonin
5-HTT
Serotonin transporter
5-HTTLPR
Serotonin transporter linked polymorphic region
5-MTHF
5-methyltetrahydrofolate
AdoHcy
S-Adenosylhomocysteine
AdoMet
S-Adenosylmethionine
Bdnf
Gene for brain derived neurotrophic factor
bp
Base-pair
cDNA
Complementary DNA
DHF
Dihydrofolate
DHFR
Dihydrofolate reductase
DMR
Differentially methylated region
DNA
Deoxyribonucleic acid
DNMT
DNA methyltransferase
dTMP
Deoxythymidine monophosphate
EDTA
Ethylenediaminetetraacetic acid
EPDS
Edinburgh postnatal depression scale
GCP II
Glutamate carboxypeptidase II
GLM
General linear model
GR
Glucocorticoid receptor
H3K9
Histone 3 lysine 9
HAM-A
Hamilton rating scale for anxiety
HAM-D
Hamilton rating scale for depression
Hcy
Homocysteine
HDAC
Histone deacetylase
x
HoloTC
Holotranscobalamin
HPA
Hypothalamic-pituitary-adrenal
IF
Intrinsic factor
Igf2 or IGF2
Gene for insulin-like growth factor 2
LG/ABN
Licking/grooming-arched back nursing
LP
Low protein
LSD
Least significant difference
MAT
Methionine adenosyltransferase
MeCP2
Methyl CpG binding protein 2
MS
Methionine synthase
MTHFD
Methylenetetrahydrofolate dehydrogenase
MTHFR
Methylenetetrahydrofolate reductase
NR3C1 or Nr3c1 Gene for glucocorticoid receptor
NTD
Neural tube defect
PND
Postnatal day
RDA
Recommended dietary allowance
RNA
Ribonucleic acid
RQ
Relative quantifiation
SAHH
S-Adenosylhomocysteine hydrolase
SD
Standard deviation
SEM
Standard error of the mean
SHMT
Serine hydroxymethyltransferase
SLC6A4
Gene for serotonin transporter
SSRI
Selective serotonin reuptake inhibitor
THF
Tetrahydrofolate
TSS
Transcription start site
TYMS
Thymidylate synthase
xi
Acknowledgements
First and foremost, I would like to express my sincere gratitude to my thesis cosupervisors Dr. Angela Devlin and Dr. Tim Oberlander for providing me with the unique
opportunity to explore this remarkable research project. I am indebted to my supervisors for not
only their exceptional scientific guidance, but also for their continuing support and patience. I
would like to thank my supervisory committee member, Dr. Timothy Green for his advice,
insight, and tremendous help in statistics.
I am grateful for the help that Ursula Brain has provided me. She has been a key player in
making sure that I stay organized in maintaining my databases. Further, I would like to thank all
my lab mates at the Child and Family Institute (CFRI) and friends. To Tiffany Ngai, Melissa
Glier, Dian Sulistyoningrum, Eugene Wang, Mihai Cirstea, Rika Aleliunas, Baki Cvijetinovic,
Ashish Sharma, Tina Li, Edgar Chan-Wong, and Roger Jen – a heartfelt appreciation goes to
them for teaching me molecular biology techniques, providing me with invaluable advice in
trouble-shooting experiments and writing the thesis, offering me emotional support, and making
my graduate studies experience enjoyable. Moreover, I would like to recognize Janette King and
Roger Dyer for their technical assistance in methyl nutrient studies.
The completion of this thesis was made possible by studentships from the
Interdisciplinary Women’s Reproductive Health (IWRH) Research Training Program, the
Canadian Institutes of Health Research, NeuroDevNet (NDN), and CFRI. I would also like to
acknowledge IWRH, NDN, CFRI and UBC for providing me with travel funding.
Finally, I would like to extend my deepest gratitude to my family for instilling in me the
value of education and coaching me in so many aspects of life. I’m extremely fortunate to have
their love and support.
xii
1 CHAPTER I: Introduction
1.1 Project Overview
Up to 15% of women experience depression during pregnancy, and one in three are
taking selective serotonin reuptake inhibitors (SSRIs) as antidepressants (1). The
neurotransmitter, serotonin (5-HT) plays a critical role in modulating stress responses and early
brain development. Depressive maternal mood and SSRI usage alter 5-HT levels during
development and may program the newborn’s responses to environmental shifts later in life (24). The molecular mechanisms underlying developmental programming remain poorly
understood, but may involve epigenetic processes such as DNA methylation. DNA methylation
requires several methyl nutrients (Figure 1.1), such as folate and vitamin B12, as enzymatic
cofactors in the production of S-adenosylmethionine (AdoMet), the key methyl donor in the
body. Methyl nutrient metabolism and early postnatal exposure to adverse maternal care giving
are independently associated with gene-specific changes in DNA methylation and expression in
rodents (5-7). Furthermore, methyl nutrient metabolism and prenatal exposure to maternal mood
are independently associated with gene-specific changes in DNA methylation in humans (8-11).
The overall objective of this thesis is to investigate whether disturbances in methyl
nutrient metabolism and depressed mood during pregnancy affect methylation and gene
expression (mRNA levels) of the serotonin transporter in mothers and their newborns. As
steps toward achieving the objective, I performed DNA, RNA, and biochemical analyses on
blood samples obtained from a cohort of pregnant women and their newborn infants. A
schematic representation of my thesis is shown in Figure 1.2, and more details will be provided
in Chapters 3-5. In this introduction, relevant background information and literature review
1
pertaining to depression, serotonin, developmental programming, and methyl nutrient
metabolism will be provided. I will then present a rationale in support of my thesis project.
2
Figure 1.1 Methyl nutrient metabolism.
Abbreviations: THF, tetrahydrofolate; DHF, dihydrofolate; 5-MTHF, 5-methyltetrahydrofolate;
5,10-MTHF, 5,10-methylenetetrahydrofolate; AdoMet, S-adenosylmethionine; AdoHcy, Sadenosylhomocysteine; Met-DNA, methylated DNA; Hcy, homocysteine; B12, vitamin B12;
dTMP, deoxythymidine monophosphate; TYMS, thymidylate synthase; DHFR, dihydrofolate
reductase; MTHFD, methylenetetrahydrofolate dehydrogenase; SHMT, serine
hydroxymethyltransferase; MAT, methionine adenosyltransferase; MTHFR,
methylenetetrahydrofolate reductase; MS, methionine synthase; DNMT, DNA
methyltransferase; SAHH, AdoHcy hydrolase.
3
Figure 1.2 Schematic representation of thesis project.
4
1.2 Depression and the Brain
1.2.1
Depression
Depression is the 4th leading cause of disease worldwide (12), with a life-time prevalence
of 12.2% in Canada (13). This mental illness is characterized by sadness, guilt, lack of pleasure,
low energy, and disturbed sleep or appetite (14). Depression occurs more frequently in women
and in older individuals, and is likely under-reported (13). There are many hypotheses regarding
the etiology of depression, such as dysfunctions in neurotransmitter systems, dysfunctions in the
hypothalamic-pituitary-adrenal (HPA) axis, or imbalances in the immune system (15). However,
it remains unclear how these factors contribute to depression.
Depression is mainly treated by psychiatric medications. The most frequently prescribed
class of antidepressants is SSRI, and the less common classes include tricyclic antidepressants
and monoamine oxidase inhibitors (16). However, inadequate or non-response to treatment of
depression remains a concern (17).
1.2.2
Serotonergic System
One type of neurotransmitter system suggested to be involved in depression is the
serotonergic system. Serotonin is a neurotransmitter that is involved in the modulation of stress
responses, and acts as a trophic factor (cell division, synaptogenesis, etc.) during
neurodevelopment (18). The cell bodies of 5-HT neurons are mainly located in the raphe nuclei
in the brain stem (15). The axons of 5-HT neurons extend throughout the entire brain, where 5HT is released and could be taken up by 16 different subtypes of 5-HT receptors on the postsynaptic membranes (19).
5
Serotonin is also present in the blood, mainly in platelets (20). It has been demonstrated
in rats that elevated levels of 5-HT in the brain are reflected in the peripheral circulation (via
administration of 5-hydroxytryptophan, a precursor of 5-HT) (21). As such, whole blood serves
as an easier and more useful source to measure body 5-HT levels than the brain/neural tissue.
Decreased levels of tryptophan in plasma (22) and 5-hydroxyindoleacetic acid (metabolite of 5HT) in cerebral spinal fluid (23) are often reported in depressed patients. Furthermore, low levels
of whole blood 5-HT have been associated with depression in children (24). This suggests that
having a “serotonergic vulnerability” may lead to depressive symptoms (15).
A modulator of the amount and duration of 5-HT in the synaptic cleft is the serotonin
transporter (5-HTT). Serotonin transporter is a membrane-bound protein that governs the
reuptake of 5-HT, thereby decreasing extracellular 5-HT levels. The transporter is mainly found
on presynaptic neurons and platelets (25). SSRIs target 5-HTT and raise the level of extracellular
5-HT (26).
The gene that encodes 5-HTT, SLC6A4, is located on chromosome 17. Methylation of the
SLC6A4 promoter has been shown to be associated with SLC6A4 expression in lymphoblast cell
lines (27). Although, the methylation status seems to depend on the genotype of a 44 base-pair
(bp) insertion/deletion variant (referred to as the 5-HTT-linked polymorphic region, or 5HTTLPR, variant) in the promoter region (28). The long (l) allele has a frequency of 0.6 in a
Caucasian population, and contains 16 repeats of a 22 base-pair (bp) repeat sequence (29). The
short (s) allele has an allele frequency of 0.4 in a Caucasian population and contains 14 repeats
(29). Homozygosity for the short allele (ss) is associated with increased SLC6A4 promoter
methylation (27). Using luciferase plasmid constructs, it was shown that the 5-HTTLPR s allele
has lower basal promoter activity than the l allele in human placental choriocarcinoma cell lines
6
(28). The ss genotype is associated with anxiety (29), depression in adults after exposure to
stressful life events (such as threat, loss, humiliation, or defeat) (30), depression in adults after
exposure to childhood maltreatment (30), and prolonged salivary cortisol response to stressors
(31,32). As well, the s allele has been shown to unfavorably affect SSRI treatment in the elderly
(33). Intriguingly, cortisol may increase 5-HTT expression (34). However, this increase is
dependent on the 5HTTLPR genotype (34). More recently, a variant in the l allele of the
5HTTLPR has been identified (A→G; rs 25531; presence of the minor G allele in the 5HTTLPR l
variant (Lg) creates a binding site for AP2, a repressor), and shown to be associated with
obsessive-compulsive disorder (35).
1.2.3
Depression During Pregnancy
Antenatal depression occurs in approximately 15% of mothers during pregnancy (1) and
one third of depressed mothers take SSRIs during pregnancy (1). Maternal depression and SSRI
exposure during pregnancy appear to be associated with adverse outcomes in the newborn, and
this raises concerns regarding the long-term consequences of exposure to antenatal depression
and SSRIs in the development of the offspring (2-4). However, it is difficult to distinguish
between the consequences of prenatal exposure to maternal depression and prenatal exposure to
SSRIs.
Mothers who were depressed during pregnancy have higher levels of cortisol and lower
levels of 5-HT in urine than mothers who were not depressed (2). Prenatal exposure to maternal
depression was associated with higher levels of cortisol and lower levels of 5-HT in the urine of
newborns (2). Further, newborns from mothers who were depressed during pregnancy exhibited
less optimal motor, orientation, and habituation scores than infants of mothers who were not
7
depressed (2). Rats that were exposed to adverse maternal care during early postnatal
development [a time period suggested to be similar to the fetus in the 3rd trimester of pregnancy
in humans (26)] showed higher plasma corticosterone (dominant form of glucocorticoid in
rodents) responses to acute stress during adulthood than rats that were not exposed to adverse
maternal care (36).
Infants with prenatal exposure to SSRIs showed reduction of early evening basal salivary
cortisol levels compared to non-SSRI exposed infants at 3 months of age (3). Furthermore,
prenatal SSRI exposure was associated with neurobehavioural disturbances in newborns, such as
increased jitteriness, feeding disturbances, irritability, and respiratory disturbances (4). These
findings suggest that prenatal exposure to “shifts in environment”, such as maternal mood and
SSRIs, may influence offspring 5-HT activity and behaviour.
1.3 Developmental Programming
1.3.1 Overview of Developmental Programming
Developmental programming refers to the concept that environmental exposures during
prenatal and early postnatal development may program an individual’s metabolic and
physiological response to environmental shifts later in life and contribute to the development of
chronic diseases. This theory was postulated by Dr. David Barker in the 1980’s (37). Barker and
colleagues found that geographical regions of the United Kingdom with high rates of low birth
weight in the early 1900’s also had high rates of deaths due to coronary heart disease between
1968 and 1978 (38,39). Many human epidemiological and animal studies thus followed to
support the concept of developmental programming.
8
1.3.2 Human Epidemiological Evidence
Support for the concept of developmental programming came from studies of a cohort
conceived during the World War II Dutch famine. The famine caused severe stress and
nutritional inadequacy, where energy intakes decreased to less than 1000 kcal/day (less than
4200 kJ/day) per person (40). Prenatal exposure to the famine in the 2nd trimester was associated
with affective psychosis in both men and women as adults (40,41), Antisocial Personality
Disorder in men at 18 year of age (42), and addiction in men at ~60 years of age (43). Early
gestational exposure to the Dutch famine was also associated with schizophrenia at 24-48 years
of age in both men and women (44).
Other epidemiological studies showed that early gestational exposure to the 1959-1961
Chinese famine was associated with increased risk for schizophrenia later in life (45). Prenatal
and early postnatal (up to 3 months old infants) exposure to maternal stress imposed by the 1967
Six-Day War in Israel was associated with increased risks for developmental delays and social
withdrawal behaviour in boys at elementary school age (46), and schizophrenia in females after
30 years of age (47). Perhaps the most intriguing studies were those that showed prenatal
exposure to maternal anxiety [often comorbid with depression (48)] have adverse consequences
on the offspring. Children prenatally exposed to maternal anxiety during the 32nd week of
gestation had higher salivary awakening cortisol levels, and displayed behavioural and emotional
problems at 10 years old (49,50). Moreover, a longitudinal study showed that antenatal
depression was associated with antisocial behaviour in the offspring in adolescence (51). Taken
together, in utero exposure to maternal stress suggests that adverse outcomes surface later in life.
9
1.3.3 Developmental Programming in Animal Models
In addition to human studies, many animal studies support the concept of developmental
programming. In rats, numerous researchers have studied neonatal pups exposed to different
maternal care in the early postnatal period. This included mothers that exhibited high levels of
licking/grooming-arched back nursing (LG/ABN) towards their young (assumed good maternal
behaviour), or mothers that exhibited lower LG/ABN (36). In the first 10 days of life, offspring
exposed to low maternal LG/ABN care had higher levels of plasma adrenocorticotropic hormone
and corticosterone in response to stressors (restraint test) than offspring exposed to high maternal
LG/ABN care (36). Early postnatal exposure to low maternal LG/ABN also led to more fearful
responses in novel environments in adult (~postnatal day 100) offspring (52). Female rats
exhibited similar maternal behaviour towards their own offspring as the female rats’ mothers
were to them when they were pups (53). To take it a step further, researchers conducted crossfostering studies and showed that offspring conceived by low licking/nursing dams, but nursed
by high licking/nursing foster dams showed high licking/nursing behaviour towards their own
offspring (54,55). Similarly, offspring from female rhesus monkeys exposed to stressors during
late pregnancy exhibited abnormal social behaviour at adolescence (four years of age) (56).
These epidemiological and animal studies show a clear pattern between prenatal or early
postnatal exposure to shifts in environment, and the possible programming of behaviour that may
have life-long health consequences. The molecular mechanism underlying developmental
programming is not well understood but is thought to be mediated through epigenetic
mechanisms.
10
1.4 Epigenetics
1.4.1 Epigenetics Defined
Epigenetics refers to changes in gene expression that occur without changes in DNA
sequence (57). Epigenetic processes include DNA methylation, chromatin modifications
(acetylations, methylation, phosphorylation, etc.), and miRNA (57). Epigenetic patterns are
heritable, but are responsive to shifts in environment, particularly during development (5,6,55).
DNA methylation takes place at CpG dinucleotides, specifically the 5’ position of
cytosines (58). DNA methyltransferases (DNMTs) are ubiquitous enzymes responsible for DNA
methylation (59). Maintenance methylation is performed by DNMT1, which is responsible for
methylation during somatic cell replication (58). De novo methylation is accomplished by
DNMT3a and DNMT3b during embryonic development (59). DNA methylation in the promoter
is often associated with transcriptional silencing (60). It is responsible for maintaining many
biological roles such as silencing of repetitive elements (60), X-chromosome inactivation (61)
and genomic imprinting (61).
1.4.2 Epigenetics as a Potential Programming Mechanism
Early studies to suggest that epigenetic processes play a role in developmental
programming showed that early postnatal variations in maternal care altered the methylation
status of Nr3cl (gene that encodes the glucocorticoid receptor, GR) and Bdnf (gene that encodes
the brain derived neurotrophic factor) in adult rats (6,55). Neonatal pups were exposed to either
mothers that displayed normal care (high levels of nursing/licking), or adverse care (low levels
of nursing/licking). Offspring that were exposed to adverse maternal care during the early
postnatal period [postnatal day (PND) 1-7] had increased Nr3c1 and Bdnf promoter methylation
11
in the hippocampus or prefrontal cortex (6,55). These DNA methylation patterns were present in
the 1st day of life, were not present in cross-fostered pups, were shown to persist into adulthood
(PND 90), and were associated with elevated corticosterone levels (6).
Gene-specific DNA methylation has been associated with prenatal exposure to stress in
humans. In utero exposure to maternal depression was associated with increased methylation of
NR3C1 promoter in cord blood (9). Interestingly, this region of NR3C1 is analogous to the Nr3c1
region in rats where prenatal exposure to adverse maternal care giving altered methylation in the
brain (6). Further, elevation of infant salivary cortisol in response to stress at 3 months of age
was associated with methylation of NR3C1 promoter (9). Increased methylation has been
observed in NR3C1 in post-mortem hippocampal tissue from suicide victims who had
experienced abuse in childhood, compared to suicide victims with no abuse history, or
hippocampal tissue from adults who had died from other causes (10). NR3C1 mRNA levels in
the hippocampus were also decreased in suicide victims who experienced childhood abuse,
compared to adults who had no history of child abuse and died of accidental causes (10).
Another study showed that maternal 2nd trimester mood was inversely associated with SLC6A4
methylation in the blood of both mothers and their newborns (11). Taken together, these studies
provide evidence that prenatal and early postnatal exposure to adverse environments can alter the
methylation status of DNA, which may program gene expression and contribute to the
development of chronic disease.
12
1.5 Methyl Nutrient Metabolism
DNA methylation is closely linked to the methyl nutrient metabolism (Figure 1.1; page
2). Some components of this metabolism include methyl nutrients (such as folate and vitamin
B12) and the enzyme methylenetetrahydrofolate reductase (MTHFR).
1.5.1 Folate
Folate is a water soluble essential B vitamin (62). The predominant forms of dietary
folate are polyglutamylated, and are found in leafy vegetables, potatoes, and fruits (63).
Naturally occurring folate is also found in the monoglutamylated form, in milk, eggs, and meats
(63). In addition, polyglutamylated forms of folate are synthesized by the bacterial microflora in
the large intestine (64). Following studies that proved the success of folate in reducing rates of
neural tube defects (NTDs) (65,66), the Canadian government mandated folic acid-fortification
of grain products in 1998, and recommended folic-acid-containing prenatal vitamin supplement
usage in pregnant women (67,68). The Recommended Dietary Allowance (RDA) of folate is
400 ug per day for the general population and 600 ug per day for pregnant women (69). Folic
acid is the monoglutamate folate form generally found in fortified foods [white flour, cornmeal
and enriched pasta products (68)] and supplements because it is more stable than naturally
occurring folates (70,71). Since fortification, a typical North American diet in a day contains
roughly 400 ug of folate (72). High folic acid intake after fortification leads to elevated
unmetabolized folic acid in the circulation (73), and this is likely due to low DHFR activity in
humans (74,75). The average Tolerable Upper Intake Level for folic acid is 1 mg per day in
adults (69).
13
In the duodenum and upper jejunum, the polyglutamylated folate is hydrolyzed to its
monoglutamylated form by the enzyme glutamate carboxypeptidase II (GCP II), located at the
intestinal apical brush border membrane (62).These monoglutamylated folates are absorbed by
the proton-coupled folate transporter into the intestinal mucosa. From there, folate, in the form of
tetrahydrofolate (THF) becomes reduced to 5-methyltetrahydrofolate (5-MTHF) within the
enterocyte (76). Folic acid, on the other hand, needs to be first reduced to THF by dihydrofolate
reductase (DHFR) in the enterocyte prior to converting to 5-MTHF (77). The reduction of folate
to 5-MTHF may occur in the liver as well. 5-MTHF is the predominant circulating form of folate
in the bloodstream and is taken into cells via carriers or receptors, such as reduced folate carrier
and folate receptor α (62). In order for folate to remain intracellular and to participate in the
methyl nutrient metabolism, folate becomes polyglutamylated (77).
Rodent studies have shown that during pregnancy, plasma and hepatic folate levels were
much higher in the fetus than the mother (78), suggesting a preferential transfer of folate from
mother to fetus during gestation. Similarly in humans, blood from the umbilical cord taken at
birth had much higher levels of total folate than maternal blood taken 1-12 hours prior to
delivery (75). Most of this difference in folate levels between the mother and the newborn was
due to higher cord blood levels of 5-MTHF, and not unmetabolized folic acid (75).
Although folic acid supplementation is effective at reducing NTD risk (79) and may even
enhance antidepressant response (80), there are also concerns about excess folic acid intake. One
major concern is that excess folate intake may mask vitamin B12 deficiency (81,82).
14
1.5.2 Vitamin B12
Vitamin B12, or cobalamin, is a large cobalt-containing water soluble B vitamin (83,84).
Vitamin B12 is synthesized by bacteria and is mainly found in animal products, such as milk,
eggs, meats, and fish (84). Vitamin B12 has also been fortified in cereals (84). A typical North
American diet in a day contains 3-30 ug of vitamin B12 (85).
Vitamin B12 from the diet is initially bound to a salivary protein, haptocorrin, in the
stomach (86). After migrating to the duodenum, vitamin B12 is released from haptocorrin and
subsequently binds to a gastric protein, intrinsic factor (IF), in the ileum (86). The IF-bound
Vitamin B12 is then absorbed by the intestinal mucosal cells via receptor-mediated endocytosis
(84). Generally, 50% of dietary vitamin B12 is absorbed in healthy adults (84). However,
malabsorption is common in the elderly and in patients with gastric dysfunctions (87). Once in
the enterocyte, vitamin B12 is released from the IF and binds to transcobalamin II, forming a
complex called holotranscobalamin (HoloTC) (88). HoloTC accounts for about 30% of total
circulating vitamin B12 and is the biologically active form taken up by cells (88).
The RDA for vitamin B12 is 2.4 ug per day for adults and 2.6 ug per day for pregnant
women (69). Given that cord blood from newborns and the placenta contain higher levels of
vitamin B12 than blood from mothers at delivery (89), vitamin B12 may be preferentially
transferred to the fetus during pregnancy.
1.5.3 Methyl Nutrient Metabolism
Both folate and vitamin B12 are involved in the methyl nutrient metabolism. As part of
the folate cycle, 5-MTHF functions to donate methyl groups for the remethylation of
homocysteine (Hcy) to methionine, producing THF. This reaction is catalyzed by the
15
ubiquitously expressed enzyme, methionine synthase (MS) (90). MS requires vitamin B12 as an
enzymatic cofactor (90). Serine hydroxymethyltransferase, a vitamin B6 dependent enzyme,
converts THF to 5,10-methyleneTHF using serine as a one-carbon donor (62). Alternatively,
methylenetetrahydrofolate dehydrogenase converts THF through a series of reactions to 5,10methyleneTHF as well (62). The intermediate product of the series of reactions is 10formylTHF, and 10-formylTHF can donate a carbon for purine synthesis (62). 5,10methyleneTHF is a cofactor for thymidylate synthase, which converts deoxyuridine
monophosphate to deoxythymidine monophosphate (dTMP) for thymine and later on pyrimidine
synthesis (62). 5,10-methyleneTHF is subsequently reduced to 5-MTHF by the vitamin B2
dependent enzyme, MTHFR (62). Subsequently, 5-MTHF donates a methyl group for the
formation of methionine (62).
The folate cycle is metabolically linked to the methionine cycle. After the remethylation
of Hcy to methionine, methionine is converted to AdoMet by methionine adenosyltransferase
(62). AdoMet is a universal methyl donor for the methylation reactions of DNA, RNA, lipids,
and proteins, catalyzed by methyltransferases such as DNMTs. S-adenosylhomocysteine
(AdoHcy) is produced following methyl donation by AdoMet, and AdoHcy is then hydrolyzed to
adenosine and Hcy by AdoHcy hydrolase (62). High levels of AdoHcy have been shown to
inhibit methyl transferase activity (91,92). Hcy can be remethylated to methionine by MS,
completing the methionine cycle. Alternatively Hcy can be remethylated by the liver and kidney
specific betaine homocysteine methyltransferase, which uses betaine as the methyl donor (62).
As well, Hcy can partake in the transsulfuration pathway to form cysteine, via vitamin B6
dependent enzymes cystathionine β-synthase and cystathionine γ-lyase (62).
16
1.5.4 Methylenetetrahydrofolate Reductase Polymorphism
The gene that encodes for MTHFR, MTHFR, is located on chromosome 1 and has a
common variant (C677T; rs1801133) in exon 4. The MTHFR variant results in the conversion of
an alanine amino acid to a valine residue in the catalytic domain of the protein at a frequency of
38% (93). This substitution results in a thermolabile enzyme with 70% reduced activity in
subjects with the homozygous TT genotype (93). The T allele has a frequency of approximately
0.38 in Caucasian populations (93).
The MTHFR 677TT genotype has been associated with increased risk for NTDs (94-97),
colorectal carcinomas (when folate is limiting) (98), and stroke (when folate is limiting) (99).
Studies have shown that the genetic variant is associated with an increased risk of psychiatric
disorders such as unipolar depression, schizophrenia, bipolar disorder, and anxiety (100-102).
Most noteworthy is the recent study which found that MTHFR 677TT genotype is associated
with antenatal depression in the 2nd trimester (11).
1.5.5 Deficiencies in Folate and Vitamin B12
Deficiencies in methyl nutrients may disrupt the equilibrium of the methyl nutrient
metabolism and result in adverse consequences in health. Folate deficiency is defined as < 6
nmol/L in serum or plasma, although this value varies (103). The definition of vitamin B12
deficiency varies as well, but is generally around <150 pmol/L in serum or plasma (87). HoloTC
deficiency has been suggested to be <35 pmol/L in plasma (104). Low levels of folate have been
shown to lead to megaloblastic anemia (77) and elevated concentrations of plasma Hcy (a risk
factor for cardiovascular disease) (105). As well, folate supplementation has been shown to
17
decrease the incidences of NTDs (65,66). Importantly, low folate levels have been associated
with depression in older adults (106).
The folate status of Canadians, including women of child-bearing age, has improved
significantly since the commencement of mandatory fortification (107,108). However,
insufficient vitamin B12 status in Canadians had been reported in the elderly and in pregnant
women from Ontario (109,110). Excess folic acid could mask vitamin B12 deficiency, because
MS remains inactive in the absence of vitamin B12 (77). As a consequence, folate becomes
“trapped” in the 5-MTHF form, creating a functional folate deficiency (111). This functional
folate deficiency results in impaired DNA synthesis, the cause of megaloblastic anemia (111).
While vitamin B12 deficiency leads to megaloblastic anemia in the presence of low folate, excess
folic acid could mask the haematological manifestations of low vitamin B12, and delay diagnosis
of vitamin B12 deficiency (77). The autoimmune disease, pernicious anemia, leads to vitamin B12
deficiency due to vitamin B12 malabsorption (112). Vitamin B12 deficiency has been associated
with colorectal cancer (113), NTDs (114), and cognitive deficits (115,116). Notably, vitamin B12
was inversely associated with depression in the elderly (106,117).
1.5.6 Methyl Nutrients and DNA Methylation
Methyl nutrients have been shown to be capable of altering DNA methylation. For
example, in a study, men with hyperhomocysteinaemia (HHcy) exhibited DNA hypomethylation
and bi-allelic expression of the imprinted gene, H19 (expressed only on the maternal allele) in
blood (118). However, after 5-MTHF supplementation, H19 shifted back to monoallelic
expression (118). In mice with HHcy, there was decreased Igf2/H19 differentially methylated
region (DMR) methylation in liver, yet increased Igf2/H19 DMR methylation in the brain and
18
aorta (Igf2 is an imprinted gene expressed by the paternal allele, and encodes for insulin-like
growth factor 2) (119), and methylation-silencing of Fads2 in liver (120). Furthermore, there is a
well established mutation in the Agouti gene caused by the insertion of a retrotransposon,
creating an ectopic promoter for the Agouti gene. Mice supplemented with folic acid, vitamin
B12, choline and betaine during pregnancy had pups with increased methylation at the
retrotransposon, leading to the darkening of offspring coat colour (5). Pregnant rats fed folic acid
in the absence of vitamin B12 showed reduced global DNA methylation in the placenta (121).
Additionally, the effects methyl nutrients have on DNA methylation may be mediated by
genetics. For instance, folate status correlates with total genomic DNA methylation only in
subjects with the MTHFR 677TT genotype, and not the MTHFR 677CC genotype in blood cells
from Italian adults (8).
1.6 Rationale and Hypothesis
Altered methyl nutrient levels, such as low plasma folate and vitamin B 12, and high
plasma Hcy, are associated with depressive disorders in the elderly (100,106,122). The MTHFR
677TT genotype is associated with depression in both the non-pregnant population and pregnant
women in their 2nd trimester (11,101). Although folate status in pregnant women has improved
since 1998 due to folic acid fortification (107,108), poor vitamin B12 status was shown in
pregnant women in Ontario (110). No studies have assessed the role of methyl nutrient status in
the etiology of depression during late pregnancy.
Serotonin not only plays an important role in mood regulation, but also serves as a
trophic signal directing early brain development and function (26). Prenatal exposure to
disturbed maternal mood and SSRI has been associated with adverse outcomes in the offspring
19
(4,49). The serotonin transporter governs the availability of extracellular 5-HT, and SLC6A4
expression have been shown to be influenced by DNA methylation (27). DNA methylation has
been suggested as an underlying mechanism contributing to the phenomenon of developmental
programming. Studies in rodent models have shown differential Nr3c1 methylation and
expression following early postnatal exposure to adverse maternal care giving (6,55). Similarly
in humans, it has been shown that prenatal exposure to maternal depressed mood during the 2 nd
trimester was associated with the methylation status of NR3C1 in blood leukocytes of newborns
and SLC6A4 in blood leukocytes of both the mothers and their newborns (9,11). DNA
methylation is metabolically linked to the methyl nutrient metabolism. Studies showed that
folate, vitamin B12, and the MTHFR 677TT genotype are associated with gene-specific and
global changes in DNA methylation (8,123). However, little is known about the collective roles
of maternal methyl nutrient metabolism and maternal mood on methylation and gene expression
of SLC6A4 in mothers and their newborns.
Based on previous findings, I hypothesize that disturbances in methyl nutrient
metabolism contribute to maternal depressed mood during pregnancy, and prenatal
exposure to maternal depressed mood affects SLC6A4 methylation and expression in
mothers and their newborns.
The hypothesis will be addressed based on the following specific aims:
1. To determine the effects of methyl nutrient metabolism (folate, vitamin B12, holoTC, and
MTHFR C677T genotype) on mood during the 3rd trimester, and to determine whether
maternal mood and methyl nutrient status are associated with SLC6A4 methylation and
expression in blood collected during the 3rd trimester of pregnancy.
20
2. To determine whether prenatal exposure to maternal methyl nutrient metabolism (folate,
vitamin B12, holoTC, and MTHFR C677T genotype) and maternal mood is associated
with SLC6A4 methylation and expression in newborns (cord blood).
3. To determine whether newborn methyl nutrient metabolism (folate, vitamin B12, holoTC
and MTHFR C677T genotype) is associated with SLC6A4 methylation and expression in
newborns (cord blood).
21
2 CHAPTER 2: Materials and Methods
2.1 Study Population
Ninety women were recruited during their early 3rd trimester of pregnancy (26-28 weeks
gestation) between October 2006 and October 2009 at the Women’s and Children’s Health
Center, and family physician offices (Vancouver, BC, Canada). The pregnant women were
recruited as part of a longitudinal cohort study to examine the effects of prenatal SSRI exposure
on neurobehavioural outcomes during infancy and childhood. As such, some women were
receiving SSRIs treatment.
2.2 Maternal Mood Assessments
Mood was assessed in the early third trimester of pregnancy (26-28 weeks gestation).
Clinician administered questionnaires used were the Hamilton Rating Scale for Depression
(HAM-D) (124) and Hamilton Rating Scale for Anxiety (HAM-A) (125). HAM-D, designed to
assess depressive mood, is a 21-item questionnaire with a score range of 0-61. Higher scores on
the HAM-D denote more depressive mood symptoms, and a score of 10 or more indicates
depression. HAM-A is a 14-item questionnaire with a score range of 0-56, with higher scores
indicating more anxious symptoms. HAM-A is designed to measure psychic anxiety
(psychological distress and agitation) and somatic anxiety (physical complaints caused by
anxiety). A HAM-A score of 14 or more indicates anxiety (14-17 indicates mild anxiety, 18-24
indicates moderate anxiety, and 25-30 indicates severe anxiety). In addition, maternal mood was
assessed by the self-reported questionnaire, Edinburgh Postnatal Depression Scale (EPDS) (126).
22
EDPS is a 10-item questionnaire with a score range of 0-30, designed to assess depressive mood
severity. An EPDS score of 10 or more signifies depression.
2.3 Subject Samples
Whole blood samples were collected in EDTA-coated tubes (BD Vacutainer from
Becton, Dickinson and Co., Franklin Lakes, NJ) from approximately 90 women at 33-36 weeks
gestation. Newborn cord blood was also collected in the same manner at birth. EDTA,
(ethylenediaminetetraacetic acid) serves as an anti-coagulant (chelates to the calcium in the
blood) (127). Serum was also collected from the women at delivery and newborn cord blood at
birth. To extract the serum, blood was collected, allowed to coagulate at room temperature for 15
minutes, centrifuged (3,000g for 8 minutes), and the serum (top layer) was collected. Whole
blood and serum samples were stored at -80ºC in the Devlin laboratory at the Child and Family
Research Institute (Vancouver, BC, Canada) for subsequent analyses.
2.4 Nucleic Acid Extraction
Half milliliter of blood was aliquoted for monocyte extraction, and subsequent DNA
extraction (for DNA methylation assays). Another 0.5ml of blood was aliquoted into 1.38ml of
PAXgene blood RNA stabilizing solution (Qiagen Inc., Mississauga, ON) for total RNA
extraction and genomic DNA extraction (for gene expression and genotyping assays,
respectively).
23
2.4.1 DNA Isolation from Monocytes
The relationship between SLC6A4 methylation status and 3rd trimester maternal mood
was determined previously in whole blood, a heterogeneous population of cells (11). To take this
one step further, I focused on cell-specific differences in SLC6A4 methylation status. As such, I
chose to look at SLC6A4 methylation in monocytes, which represent 2-8% of the leukocyte
population (128).
Monocytes were extracted from maternal and newborn cord blood samples using
EasySep Human CD14 (monocyte-specific marker (129)) positive selection kit and EasySep
magnet (StemCell Technologies, Vancouver, BC). Genomic DNA was extracted from the
monocytes using DNeasy Blood & Tissue kit (Qiagen Inc., Mississauga, ON), and treated with
RNase A (Qiagen Inc., Mississauga, ON) to remove contaminating RNA.
2.4.2 RNA Isolation from Whole Blood
Initial attempts at extracting RNA from monocytes using TRIzol (Invitrogen, Carlsbad,
CA) with UltraPure Glycogen (Invitrogen, Carlsbad, CA) as carrier for RNA, or using the
RNeasy Plus Mini kit (Qiagen Inc., Mississauga, ON) were unsuccessful. The trial experiments
revealed that the integrity and amount of RNA extracted from monocytes was insufficient to
accurately quantify mRNA levels. Hence, SLC6A4 mRNA was not quantified in monocytes.
Alternatively, I quantified SLC6A4 mRNA levels in whole blood.
Whole blood was incubated in PAXgene blood RNA stabilizing solution overnight at
room temperature (20 hours) (130). The solution was centrifuged at 3000g for 10 minutes at 4ºC
and the pellet was resuspended with 2ml RNase-free water. Two hundred microlitres of solution
was taken for DNA extraction (see section 2.4.3). The remaining solution was used for RNA
24
extraction following manufacturer’s protocol for PAXgene Blood RNA Kit (Qiagen Inc.,
Mississauga, ON), and treated with DNase I (Qiagen Inc., Mississauga, ON) to remove
contaminating genomic DNA.
2.4.3 DNA Isolation from Whole Blood
DNA was extracted from the blood-PAXgene solution using the DNeasy Blood & Tissue
kit (Qiagen Inc., Mississauga, ON) following manufacturer’s instructions, and treated with
RNase A to remove contaminating RNA.
2.4.4 Assessment of Nucleic Acid Quality and Quantity
Quality of DNA and RNA were assessed on an agarose gel. DNA quality was confirmed
by visualization of high-molecular weight nucleic acid, and RNA quality was confirmed by
visualization of intact 18S and 28S rRNA bands. Concentration and purity of DNA and RNA
were determined using the Nanovue NanoSpec spectrometer (General Electric Inc., Fairfield,
CT). An A260/A280 ratio (ratio of absorbance at 260nm and 280nm) of 1.8-2.1 was deemed an
acceptable purity reading for DNA and RNA, as per manufacturer’s instructions. A lower ratio
value indicates protein contamination (proteins absorb light at 280nm).
2.5 DNA Methylation Assay
2.5.1 Target Gene
I chose SLC6A4 as my target gene for two reasons. Firstly, serotonin transporter plays a
critical role in stress regulation. Secondly, methylation of the promoter region of SLC6A4 had
recently been associated with SLC6A4 mRNA level (27). Philibert et al. had analyzed a CpG rich
25
region surrounding exon 1A. In that region, three CpG sites upstream of the transcription start
site (TSS) were shown to be associated with mRNA levels. I chose to focus on a portion of that
CpG rich region. The region of focus encompasses 10 CpG sites between -471bp and -374bp
upstream of the TSS. The CpG 8 that I analyzed in my study corresponds to one of the three CpG
sites that Philibert et al. had shown to be associated with gene expression (the CpG at location
872 in the Philibert et al. study) (27). My DNA methylation assay was focused on a 203bp
fragment containing the 10 CpG sites (Figure 2.1).
2.5.2 Bisulphite Pyrosequencing
Monocyte-specific genomic DNA (50ng) was bisulphite-treated using EZ DNA
Methylation-Gold kit (Zymo Research, Irvine, CA) following manufacturer’s protocol. The
process of bisulphite conversion converts unmethylated cytosines to uracils through deamination
(131). The CpG-rich region of SLC6A4 promoter (11) was amplified by PCR using customdesigned primers (PyroMark Assay Design software, version 2.0), and HotStar Taq Polymerase
(Qiagen Inc., Mississuaga, ON). The biotin labeled primer was ordered from Integrated DNA
Technologies, Inc. (Coralville, IA) and non-biotin-labeled primer was ordered from Invitrogen
(Carlsbad, CA) (Table 2.1). The mastermix consisted of 1x PCR buffer, 1x Q-solution, 0.2mM
of dNTP, 0.2uM of the forward and reverse primers, and 2 units of Hotstart Taq. The cycling
condition was 95°C for 15 minutes, 45 cycles of 94°C (1 minute), 60°C (1 minute), and 72°C (1
minute), 72°C for 10 minutes, and held infinite at 4°C.
Quantification of methylation was performed using the PyroMark MD Pyrosequencing
System (Qiagen Inc., Mississuaga, ON) (132). Sequencing primers were designed using the
PyroMark Assay Design software (version 2.0) and ordered from Invitrogen (Carlsbad, CA).
26
PCR samples (10-15ul) were prepared for pyrosequencing. Each sample was incubated with 38ul
of binding buffer, 2ul of Streptavidin Sepharose High Performance Beads, and water to make up
a total volume of 80ul. The biotinylated PCR products were isolated using the PyroMark
Vacuum Prep Workstation (Qiagen Inc., Mississuaga, ON). Pyrosequencing reactions were
prepared containing 0.3mM of sequencing primer, annealing buffer, and the purified biotinylated
PCR product. The percent methylation at each CpG site for the gene of interest was quantified
using the Pyro Q-CpG software (version 1.0.9, 2006, Biotage, AB).
27
Figure 2.1 Schematic representation of the SLC6A4 promoter region analyzed for
methylation status.
The CpGs are underlined and numbered. Numbering of the gene sequence is relative to the
transcriptional start site. Adapted from Devlin et al., 2010, with permission (11).
28
Table 2.1 Primers for bisulphite pyrosequencing.
Gene
PCR
Product
Size (bp)
203
Serotonin
Transporter
(SLC6A4)
PCR Primers:
SLC6A4-F
(forward)
5’-biotin-GTATTGTTAGGTTTTAGGAAGAAAGAGAGA-3’
SLC6A4-R
(reverse)
5’-AAAAATCCTAACTTTCCTACTCTTTAACTT-3’
Sequencing
Primer:
SLC6A4-S
5’-AAACTACACAAAAAAACAAAT-3’
29
2.6 Gene Expression Assay
Total RNA (500ng) was reverse transcribed using the High Capacity cDNA Reverse
Transcription Kit (Applied Biosystems, Foster City, CA). The reaction contained 15.8 ul water, 8
ul RT buffer, 3.2 ul dNTP mix, 8 ul primer mix, and 4 ul reverse transcriptase for each sample,
following manufacturer’s instructions. The cDNA was diluted by 1 in 5 for SLC6A4 mRNA
quantification by real-time PCR.
The ΔΔCt method of relative quantification (133) was used to quantify SLC6A4 mRNA
levels with commercially available TaqMan primers and probes for SLC6A4 (FAM-dye labeled;
SLC6A4 assay ID: Hs00169010_m1*) and the ABI 7500 real-time PCR system (Applied
Biosystems, Foster City, CA). The gene, SLC6A4, contains 14 exons (134) and has at least four
spliced variants (135). This Taqman probe spans exons 8-9, and detects all but one minor splice
variant of SLC6A4 (GenBank ID: AY902473.1 not detected). The endogenous control for the
ΔΔCt method was 18s rRNA (Applied Biosystems, Foster City, CA). The genes were quantified
using 10ul Taqman Gene Expression Master Mix, 2x primers, 4ul of water, and 5ul of diluted
cDNA. Calibrator for the maternal samples was comprised of 8 pooled samples from women
who had low early 3rd trimester depressive mood scores (HAM-D and EPDS) and were not using
SSRIs. The calibrator for the newborn samples was comprised of 8 pooled samples from
newborns who were not prenatally exposed to SSRIs and whose mothers had low early 3rd
trimester depressive mood scores (HAM-D and EPDS). Each sample was run in duplicate.
2.7 Genotyping Assays
The 5-HTTLPR insertion/deletion variant was genotyped by PCR and agarose gel
electrophoresis (136) (Table 2.2). Genotyping of the MTHFR C677T and the 5-HTTLPR La/Lg
30
variants were accomplished using TaqMan SNP genotyping assay primers and MGB probes
(FAM-dye and VIC-dye labeled), Taqman Genotyping Mastermix (Applied Biosystems, Foster
City, CA), and the ABI 7500 real-time PCR system (Applied Biosystems, Foster City, CA). The
primers and probes for the MTHFR C677T variant are standard oligonucleotides by Applied
Biosystems (Assay ID: C___1202883_20). The primers and probes for the 5HTTLPR La/Lg
variants were custom-made by Applied Biosystems (35). The genotyping contained 9 ul of
Taqman Genotyping Mastermix, 0.45ul of primers and probes, 6.6ul of water, and 1.5ul of DNA.
7-deaza-dGTP (New England Biolabs, Pickering, ON) was used in a 1:1 ratio with dGTP due to
the nature of the PCR reaction. 7-deaza-dGTP is commonly used when a GC rich region is
amplified by PCR. 7-deaza-dGTP acts like dATP through less hydrogen bonding to the
complementary nucleotide, and this helps prevent compressions. The cycling conditions were 2
minutes at 50°C, 10 minutes at 95°C, followed by 40 cycles of 15s at 96°C and 90s at 62.5°C.
31
Table 2.2 Primers for genotyping.
Gene
Product Size
(bp)
5HTTLPR (l/s)
529 for l
allele;
485 for s
allele
PCR Primers:
5-HTTLPR-F
5’-GGCGTTGCCGCTCTGAATGC-3’
(forward)
5-HTTLPR-R
5’-GAGGGACTGAGCTGGACAACCAC-3’
(reverse)
142
5HTTLPR
(A→G; rs25531)
PCR Primers:
5-HTTLPR-SNP
5’-CTCCTAGGATCGCTCCTGCAT-3’
(forward)
5-HTTLPR-SNP
5’-GATGCTGGAAGGGCTGCA-3’
(reverse)
Reporter Sequences:
Reporter 1 (VIC dye)
5’-CCCCGGCATCCCCCCT-3’
Reporter 2 (FAM dye)
5’-CCCCAGCATCCCCCCT-3’
32
2.8 Methyl Nutrient Quantification
Vitamin B12 was initially quantified in maternal serum samples by a Lactobacillus
delbrueckii microbiological assay (ALPCO Diagnostics, Salem, NH). However, the vitamin B12
levels obtained were much lower than expected. I suspected that this result was caused by
intrapartum antibiotic usage by women at delivery, as confirmed by review of maternal clinical
charts. Therefore I decided to proceed with an alternative method. Folate, vitamin B12, and
HoloTC was quantified using an AxSYM autoanalyzer (Abbott, North Chicago, IL) via a
chemiluminescence method, following manufacturer’s instructions.
2.9 Exclusions
To ensure that all the biochemical analyses performed were from blood or serum taken at
the same time-points for each subject, I chose to only include in my study 1) maternal blood that
was taken at 33-36 weeks of gestation, 2) newborn cord blood taken at delivery, 3) maternal
serum taken between 33 weeks of gestation and delivery, and 4) newborn serum taken at
delivery. As such, I chose to exclude six maternal blood samples that were taken at time of
delivery or post-delivery. A source of newborn blood for a small subset of my cohort was taken
more than 24 hours after birth (from heel pricks). Therefore, these fifteen newborn blood
samples were also excluded. I chose to exclude the methyl nutrient measurements of five women
and three newborns, because they were taken between 24 to 48 hours post-delivery or after birth.
2.10 Statistical Analyses
In order to explore my three specific aims, general linear models (GLMs) were used for
both categorical and continuous variables. Details of statistical analyses are presented in
33
Chapters 3-5. Least Significant Difference (LSD) post-tests were performed to account for
multiple testing. P-values less than 0.05 were chosen to be considered significant. Data was
checked for normality via histograms. Given that the distribution of SLC6A4 mRNA levels and
methyl nutrients had a positive skew, all analyses using mRNA levels, folate, and holoTC as
dependent factors were carried out after natural logarithmic (ln) transformation. All statistical
analyses were conducted using PASW Statistics, version 18.0 (IBM, Armonk, NY) for
Windows.
34
3 CHAPTER 3: Mothers
My first specific aim was to determine the effects of methyl nutrient status (folate,
vitamin B12, HoloTC) and MTHFR C677T genotype on mood during the 3rd trimester, and to
determine whether maternal mood and methyl nutrient status are associated with SLC6A4
methylation and expression during the 3rd trimester of pregnancy. This specific aim was
addressed by the following questions (see Figure 3.1 for schematic representation):
a. What is the relationship between maternal methyl nutrient metabolism and maternal
mood during the 3rd trimester of pregnancy?
b. What is the influence of mood and methyl nutrient metabolism on SLC6A4 methylation
in the 3rd trimester of pregnancy?
c. What are the effects of methyl nutrient status and SLC6A4 methylation on SLC6A4
mRNA levels in the 3rd trimester of pregnancy?
d. Is the 5HTTLPR variant associated with depressive mood during pregnancy, SLC6A4
methylation and SLC6A4 mRNA levels?
Figure 3.1 Specific aim 1.
35
3.1 Maternal Methyl Nutrient Metabolism and Mood
Maternal demographic characteristics were examined according to MTHFR C677T
genotype and the characteristics were found to not vary significantly between the genotypes
(Table 3.1). The genotype frequencies for the MTHFR C677T variant were approximately
52.9% for CC, 33.3% for CT, and 13.8% for TT. This is similar to previous reports in Caucasian
populations (11,93).
Using GLM, I determined that the maternal MTHFR genotype, adjusted for SSRI usage,
was not associated with maternal depressive or anxious mood (dependent variable) during the 3rd
trimester of pregnancy (P>0.05). Given that early 3rd trimester EPDS scores and HAM-D scores
were highly associated (P<0.001) (Figure 3.2), only EPDS scores were used in the subsequent
analyses. EPDS was chosen over HAM-D because a previous study showed that the EPDS score
from the 2nd trimester of pregnancy was positively associated with the MTHFR 677TT genotype
(11).
In order to determine whether disturbances in methyl nutrient status are associated with
depressive mood during the 3rd trimester, GLM was performed with depressive mood as a
continuous dependent variable. The covariates were set as maternal folate status and maternal
holoTC status; the fixed factors were set as maternal MTHFR C677T variant and SSRI usage;
and the dependent factor was set as mood (as represented by EPDS or HAM-A scores). Vitamin
B12 levels were highly associated with holoTC levels in the mothers (P<0.001) (Figure 3.3), and
holoTC has been suggested to be a more biologically useful indicator of vitamin B 12 status (137).
Therefore, maternal holoTC was chosen to represent maternal vitamin B12 status in subsequent
analyses. No significant associations between methyl nutrient status and mood in the 3 rd trimester
were found (P>0.05).
36
A GLM was then conducted to determine whether SSRI usage (adjusted for mood)
influences folate and holoTC levels in the pregnant women in their 3 rd trimester of pregnancy.
No significant differences were found (P>0.05). Furthermore, the women were categorized into a
“depressed” group, and a “non-depressed” group according to their early 3rd trimester EPDS
score. An EPDS score of 10 or higher indicated depressive mood, and 9 or lower indicated nondepressive mood. No differences in methyl nutrient status were detected between the two mood
groups, with or without adjustments for SSRI usage (P>0.05).
37
Table 3.1 Maternal MTHFR C677T genotype and demographic data of the pregnant
women.
Maternal Characteristics
Maternal MTHFR C677T Genotype
CC (n=46)
CT (n=29)
TT (n=12)
Maternal age at birth, years (SD)
33.7 (5.7)
34.1 (4.9)
34 (6.2)
Maternal education, years (SD)
17.7 (3.5)
18 (3.4)
17.9 (4.6)
Delivery type, % caesarian-section
20
24
25
SRI treated during pregnancy, %
32
48
25
Maternal alcohol consumption in single
drinks during whole pregnancy
5.5
2.5
9.5
Tobacco Use, %
4
4
0
SD = Standard Deviation
38
Figure 3.2 Relationship between early 3rd trimester EPDS scores and HAM-D scores.
R2 = 0.657, P<0.001
Figure 3.3 Relationship between maternal vitamin B12 and holoTC status.
R2 = 0.787, P< 0.001
39
3.2 Maternal Methyl Nutrient Metabolism, Mood, and Methylation
GLM was used to determine if mood and methyl nutrient metabolism affect SLC6A4
methylation in the 3rd trimester of pregnancy. The dependent factor was SLC6A4 methylation at
specific CpG sites. The covariates were set as maternal mood (either EPDS scores or HAM-A
scores), maternal folate levels, maternal holoTC levels, and maternal age at delivery. The fixed
factors were set as maternal MTHFR C677T genotype and SSRI usage.
Serum folate levels were associated with SLC6A4 methylation at CpGs 1, 4 and 8 (P<0.05)
(Tables 3.2 and 3.3). In order to display the data in a more biologically significant manner, I
multiplied the estimated increase in percent SLC6A4 methylation (β value) and its 95%
confidence interval (CI) by five, to show the percent methylation increase per 5 nmol/L increase
in folate levels. When adjusted for EPDS scores, every 5 nmol/L increase in folate level was
associated with 0.967, 0.390, and 0.477 % increase in SLC6A4 methylation at CpG sites 1, 4, and
8, respectively (P≤0.05) (Table 3.2). When adjusted for HAM-A scores, every 5 nmol/L increase
in folate level was associated with 0.928, 0.414, and 0.453 % increase in SLC6A4 methylation at
CpG sites 1, 4, and 8, respectively (P<0.05) (Table 3.3).
Maternal 3rd trimester EPDS predicted a decrease in SLC6A4 methylation at CpG 10, such
that every unit increase in EPDS was associated with 0.163 % decrease in methylation (P<0.05)
(Table 3.4). Maternal 3rd trimester HAM-A also predicted a decrease in SLC6A4 methylation at
CpG 10, such that every unit increase in HAM-A was associated with 0.170 % decrease in
methylation (P<0.05) (Table 3.5).
40
Table 3.2 Relationship between maternal folate levels and maternal SLC6A4 methylation
(adjusted for EPDS scores).
Estimated
difference in
percent
SLC6A4 CpG
Methylation (β)
Variable
Serum
folate level
(per 5
nmol/L
increase)
95% CI
0.280
P
Partial
eta
squared
(ƞ2)
CpG 1
0.967
(
, 1.655 )
0.007
0.133
CpG 2
0.153
( -0.125 , 0.430 )
0.276
0.023
CpG 3
0.348
( -0.495 , 1.190 )
0.412
0.013
CpG 4
0.390
(
, 0.780 )
0.050
0.072
CpG 5
0.150
( -0.330 , 0.630 )
0.535
0.007
CpG 6
-0.002
( -0.445 , 0.445 )
0.992
0.000
CpG 7
-0.259
( -0.990 , 0.475 )
0.482
0.010
CpG 8
0.477
(
, 0.875 )
0.019
0.101
CpG 9
-0.285
( -1.000 , 0.430 )
0.428
0.012
CpG 10
0.135
( -0.520 , 0.790 )
0.681
0.003
Mean
CpG
0.207
( -0.050 , 0.460 )
0.109
0.049
0.000
0.080
CI; Confidence Interval
Estimates adjusted for EPDS score, maternal holoTC levels, maternal age at delivery, maternal
MTHFR C677T genotype, and SSRI exposure.
41
Table 3.3 Relationship between maternal folate levels and maternal SLC6A4 methylation
(adjusted for HAM-A scores).
Estimated
difference in
percent
SLC6A4 CpG
Methylation
(β)
Variable
Serum
folate level
(per 5
nmol/L
increase)
95% CI
0.240
P
Partial
eta
squared
(ƞ2)
CpG 1
0.928
(
,
1.620
)
0.009
0.123
CpG 2
0.160
( -0.120 ,
0.440
)
0.260
0.024
CpG 3
0.294
( -0.540 ,
1.125
)
0.482
0.010
CpG 4
0.414
(
,
0.810
)
0.040
0.078
CpG 5
0.144
( -0.350 ,
0.635
)
0.560
0.007
CpG 6
-0.006
( -0.455 ,
0.445
)
0.980
0.000
CpG 7
-0.259
( -1.005 ,
0.485
)
0.488
0.009
CpG 8
0.453
(
,
0.860
)
0.029
0.089
CpG 9
-0.253
( -0.995 ,
0.490
)
0.498
0.009
CpG 10
0.043
( -0.600 ,
0.685
)
0.894
0.000
Mean
CpG
0.192
( -0.060 ,
0.445
)
0.134
0.043
0.020
0.050
CI; Confidence Interval
Estimates adjusted for HAM-A score, maternal holoTC levels, maternal age at delivery, maternal
MTHFR C677T genotype, and SSRI exposure.
42
Table 3.4 Relationship between maternal 3rd trimester EPDS scores and maternal SLC6A4
methylation.
Estimated
difference in
percent
SLC6A4 CpG
Methylation (β)
Variable
EPDS score
(per unit
increase)
95% CI
P
Partial
eta
squared
(ƞ2)
CpG 1
-0.079
( -0.245 ,
0.087
)
0.345
0.017
CpG 2
0.029
( -0.038 ,
0.097
)
0.387
0.014
CpG 3
-0.017
( -0.221 ,
0.187
)
0.871
0.001
CpG 4
0.066
( -0.029 ,
0.160
)
0.170
0.036
CpG 5
-0.078
( -0.194 ,
0.039
)
0.186
0.033
CpG 6
-0.031
( -0.138 ,
0.077
)
0.570
0.006
CpG 7
0.105
( -0.072 ,
0.283
)
0.239
0.027
CpG 8
-0.079
( -0.175 ,
0.017
)
0.103
0.050
CpG 9
0.170
( -0.003 ,
0.343
)
0.054
0.069
CpG 10
-0.163
( -0.321 , -0.004 )
0.045
0.075
Mean
CpG
-0.008
( -0.069 ,
0.807
0.001
0.054
)
CI; Confidence Interval
Estimates adjusted for maternal folate levels, maternal holoTC levels, maternal age at delivery,
maternal MTHFR C677T genotype, and SSRI exposure.
43
Table 3.5 Relationship between maternal 3rd trimester HAM-A scores and maternal
SLC6A4 methylation.
Estimated
difference in
percent
SLC6A4 CpG
Methylation (β)
Variable
HAM-A
score (per
unit
increase)
95% CI
P
Partial
eta
squared
(ƞ2)
CpG 1
-0.069
( -0.207 ,
0.068
)
0.315
0.019
CpG 2
0.010
( -0.046 ,
0.066
)
0.729
0.002
CpG 3
-0.111
( -0.277 ,
0.055
)
0.186
0.033
CpG 4
0.042
( -0.037 ,
0.121
)
0.290
0.022
CpG 5
-0.001
( -0.099 ,
0.097
)
0.984
0.000
CpG 6
-0.003
( -0.092 ,
0.087
)
0.953
0.000
CpG 7
-0.017
( -0.166 ,
0.131
)
0.816
0.001
CpG 8
-0.039
( -0.119 ,
0.042
)
0.341
0.017
CpG 9
0.043
( -0.105 ,
0.191
)
0.559
0.007
CpG 10
-0.170
( -0.298 , -0.042 )
0.010
0.120
Mean
CpG
-0.031
( -0.082 ,
0.216
0.029
0.019
)
CI; Confidence Interval
Estimates adjusted for maternal folate levels, maternal holoTC levels, maternal age at delivery,
maternal MTHFR C677T genotype, and SSRI exposure.
44
3.3 Maternal Methyl Nutrient Metabolism, Methylation, and Gene
Expression
The effects of methyl nutrient status and SLC6A4 methylation on SLC6A4 mRNA levels
in the 3rd trimester of pregnancy were determined. GLMs were conducted to determine whether
maternal folate levels, maternal holoTC levels, maternal MTHFR C677T genotype, mood during
the 3rd trimester, and SSRI usage were independently associated with mRNA levels. I did not
find significant differences in any of the above (P>0.05).
I also used GLMs to determine whether SLC6A4 methylation at each of the CpG sites is
associated with SLC6A4 mRNA levels. For every percent increase in mean CpG methylation and
methylation at CpG sites 5, 7, 8, 10, a unit increase of 0.221, 0.126, 0.059, 0.128, and 0.098 in
SLC6A4 expression was detected (respectively) (P<0.05) (Table 3.6).
To take this a step further, I conducted a GLM with SLC6A4 mRNA level as the
dependent factor. The covariates were set for site-specific maternal SLC6A4 methylation,
maternal folate levels, maternal holoTC levels, and maternal age at delivery. The fixed factor
was maternal MTHFR C677T genotype. After adjusting for methyl nutrient status, I found that
the mean CpG methylation and methylation at CpG sites 5, 7, and 10 predicted increases in
SLC6A4 mRNA levels (P<0.05) (Table 3.7). Every percent increase in the mean level of
SLC6A4 methylation and methylation at CpGs 5, 7, and 10 was associated with 0.221, 0.115,
0.065, and 0.084 unit increase in mRNA expression. No associations were found between folate,
holoTC, MTHFR C677T genotype, or maternal age and SLC6A4 mRNA levels.
45
Table 3.6 Estimated difference in maternal SLC6A4 gene expression according to maternal
SLC6A4 methylation sites (unadjusted).
Unadjusted
Variable
Estimated
difference in
SLC6A4 gene
expression
(β)
CpG 1
-0.005
( -0.067 ,
0.058
CpG 2
0.035
( -0.122 ,
CpG 3
0.025
CpG 4
P
Partial
eta
squared
(ƞ2)
)
0.877
0.000
0.192
)
0.657
0.004
( -0.030 ,
0.080
)
0.364
0.016
-0.072
( -0.182 ,
0.037
)
0.189
0.033
CpG 5
0.126
(
,
0.214
)
0.006
0.137
CpG 6
0.075
( -0.024 ,
0.175
)
0.136
0.042
CpG 7
0.059
(
0.001
,
0.117
)
0.047
0.073
CpG 8
0.128
(
0.027
,
0.229
)
0.014
0.111
CpG 9
0.015
( -0.046 ,
0.075
)
0.627
0.005
CpG 10
0.098
(
0.035
,
0.160
)
0.003
0.160
Mean
CpG
0.221
(
0.062
,
0.380
)
0.007
0.130
95% CI
0.038
CI; Confidence Interval.
β and 95% CI represent ln-transformed SLC6A4 expression values.
46
Table 3.7 Estimated difference in maternal SLC6A4 gene expression according to maternal
SLC6A4 methylation sites (adjusted).
Adjusted
Variable
Estimated
difference
in SLC6A4
gene
expression
(β)
CpG 1
-0.005
( -0.074
CpG 2
0.077
CpG 3
P
Partial
eta
squared
(ƞ2)
, 0.065 )
0.897
0.000
( -0.091
, 0.244 )
0.361
0.018
0.030
( -0.027
, 0.087 )
0.290
0.024
CpG 4
-0.090
( -0.205
, 0.025 )
0.124
0.050
CpG 5
0.115
(
0.023
, 0.206 )
0.015
0.119
CpG 6
0.090
( -0.013
, 0.193 )
0.085
0.062
CpG 7
0.065
(
0.007
, 0.124 )
0.030
0.096
CpG 8
0.108
( -0.006
, 0.221 )
0.062
0.072
CpG 9
0.010
( -0.053
, 0.073 )
0.753
0.002
CpG 10
0.084
(
0.019
, 0.150 )
0.012
0.126
Mean
CpG
0.221
(
0.054
, 0.387 )
0.010
0.131
95% CI
CI; Confidence Interval.
Estimates adjusted for maternal folate levels, maternal holoTC levels, maternal age at delivery,
and maternal MTHFR C677T genotype.
β and 95% CI represent ln-transformed SLC6A4 expression values.
47
3.4 5HTTLPR
The genotype frequencies for the maternal 5HTTLPR insertion/deletion variant in this
study population were determined to be 37.0% for l/l, 49.3% for l/s, and 13.7% for s/s, which is
similar to previous reports in non-pregnant women and men (30). To determine whether the
maternal 5HTTLPR insertion/deletion variant is associated with maternal SLC6A4 mRNA levels,
a GLM was performed with mRNA levels as the dependent variable. SLC6A4 mRNA levels did
not vary with 5HTTLPR insertion/deletion genotype (P>0.05). Unfortunately the effect of the
5HTTLPR A→G variant on SLC6A4 mRNA levels could not be examined in my study
population due to small sample size (n=68). The distribution of the 5HTTLPR A→G genotypes
amongst the pregnant women was 20.6% La/La, 8.8% La/Lg, 45.6% La/S, and 25% S/S. Also,
no women in my cohort had the Lg/Lg or Lg/S genotypes.
In order to determine whether the 5HTTLPR insertion/deletion variant is associated with
SLC6A4 methylation in the 3rd trimester of pregnancy, I conducted a GLM with the maternal
5HTTLPR insertion/deletion variant as the fixed factor, and maternal SLC6A4 CpG sites as
dependent variables. I did not detect any associations between the 5HTTLPR genotype and
methylation at each CpG site (P>0.05).
Furthermore, I conducted a GLM with the maternal 5HTTLPR insertion/deletion variant
as the fixed factor, and the mood score (either EPDS or HAM-A) as the dependent variable. I
also adjusted for SSRI usage. No relationships were found between 3rd trimester EPDS or HAMA scores and the 5HTTLPR insertion/deletion variant (P>0.05).
48
4 CHAPTER 4: Maternal Influences on Newborns
My second specific aim was to determine whether prenatal exposure to maternal methyl
nutrient status (folate, vitamin B12, HoloTC, and MTHFR C677T genotype) and maternal mood
are associated with SLC6A4 methylation and expression in newborns. This specific aim was
addressed by the following questions (see Figure 4.1 for schematic representation):
a. What are the effects of maternal late gestation depressive mood and maternal methyl
nutrient status on SLC6A4 methylation in newborns?
b. What is the relationship between maternal methyl nutrient status and SLC6A4 mRNA
levels in newborns?
c. What is the relationship between maternal mood and SLC6A4 mRNA levels in newborns?
Figure 4.1 Specific aim 2.
49
4.1 Maternal Methyl Nutrient Metabolism, Maternal Mood, and Newborn
Methylation
Newborn clinical characteristics were examined according to their mothers’ MTHFR
C677T genotype, and these clinical characteristics did not vary significantly with maternal
MTHFR C677T genotype (Table 4.1). In addition, maternal SLC6A4 methylation was not
associated with newborn SLC6A4 methylation (P>0.05).
To determine the association between maternal late gestation depressive mood and
maternal methyl nutrient status on SLC6A4 methylation in newborns, I conducted a GLM. The
covariates were set as maternal folate status, maternal holoTC status, maternal age at delivery,
and mood. The fixed factors were set as maternal MTHFR C677T genotype, sex of newborn and
SSRI exposure. The dependent factor was set for the methylation of each individual newborn
SLC6A4 CpG site.
As shown in Table 4.2, after adjustments for maternal methyl nutrient metabolism
(folate, holoTC, and MTHFR genotype), maternal age at delivery, SSRI exposure and EPDS
scores, SLC6A4 methylation at CpG 8 was lower in newborns with mothers with the MTHFR
677TT genotype, but only compared to newborns with mothers with the CT genotype (P<0.05).
Similar results were obtained after adjusting for maternal methyl nutrient metabolism, maternal
age at delivery, SSRI exposure and HAM-A scores, such that percent SLC6A4 methylation at
CpG 8 was lower in newborns with mothers carrying the MTHFR 677TT genotype, but only
compared to newborns with mothers carrying the CT genotype (P<0.05) (Table 4.3).
I also found that prenatal exposure to maternal 3rd trimester EPDS scores predicted a
decrease in newborn SLC6A4 methylation at CpG 10, such that every unit increase in EPDS
score was associated with a 0.254 % decrease in methylation (P<0.05) (Table 4.4).
50
Table 4.1 Maternal MTHFR C677T genotype and clinical data of their newborns.
Newborn Characteristics
Maternal MTHFR C677T Genotype
CC (n=46)
CT (n=29)
TT (n=12)
Prenatal SSRI Exposure, days (SD)*
248.1 (60)
257.9 (47.9)
223.3 (101.6)
Birth weight, g (SD)
3404 (462)
3434 (602)
3400 (371)
Head Circumference, cm (SD)
34.9 (1.4)
34.3 (1.4)
34.7 (1.3)
Birth length, cm (SD)
51.4 (2.4)
51.1 (2.7)
50.6 (2.2)
Gestational age at birth, weeks (SD)
39.5 (1.6)
39.2 (1.7)
39.6 (1.2)
Sex, % Males
57
55
50
Apgar score at 1 minute (SD)
7.9 (1.6)
8.2 (1.4)
8.6 (1.0)
Apgar score at 5 minute (SD)
9.0 (0.4)
9.0 (0.5)
9.0 (0.4)
* Only amongst newborns who were exposed to SSRIs
51
Table 4.2 Relationship between maternal MTHFR C677T genotype and newborn SLC6A4
methylation (adjusted for EPDS scores).
Newborn SLC6A4
Methylation (%)
Maternal MTHFR Genotype
CC (n = 31)
CT (n = 22)
TT (n = 7)
CpG 1
4.87 ± 0.32
4.55 ± 0.40
4.47 ± 0.63
CpG 2
1.70 ± 0.24
1.91 ± 0.31
2.10 ± 0.49
CpG 3
4.82 ± 0.36
4.02 ± 0.45
4.69 ± 0.71
CpG 4
1.49 ± 0.24
1.88 ± 0.31
1.11 ± 0.48
CpG 5
6.00 ± 0.21
6.14 ± 0.26
5.40 ± 0.41
CpG 6
2.12 ± 0.22
1.72 ± 0.28
1.17 ± 0.44
CpG 7
3.38 ± 0.37
3.25 ± 0.47
4.18 ± 0.74
CpG 8
6.40 ± 0.24
6.82 ± 0.31
5.66 ± 0.48*
CpG 9
4.71 ± 0.37
4.74 ± 0.47
4.46 ± 0.74
CpG 10
10.00 ± 0.56
10.01 ± 0.71
9.89 ± 1.11
Mean CpG
4.55 ± 0.13
4.51 ± 0.16
4.31 ± 0.25
* P<0.05 as compared to the CT genotype.
Data represent mean ± Standard Error of the Mean (SEM).
Estimated means are adjusted for maternal folate levels, maternal holoTC levels, maternal age at
delivery, EPDS mood score, sex of newborn, and SSRI exposure.
Corrected for multiple comparisons using LSD.
52
Table 4.3 Relationship between maternal MTHFR C677T genotype and newborn SLC6A4
methylation (adjusted for HAM-A scores).
Newborn SLC6A4
Methylation (%)
Maternal MTHFR Genotype
CC (n = 31)
CT (n = 22)
TT (n = 7)
CpG 1
4.88 ± 0.32
4.56 ± 0.41
4.43 ± 0.63
CpG 2
1.71 ± 0.25
1.88 ± 0.31
2.15 ± 0.49
CpG 3
4.90 ± 0.36
4.01 ± 0.45
4.62 ± 0.71
CpG 4
1.51 ± 0.24
1.88 ± 0.30
1.07 ± 0.47
CpG 5
5.99 ± 0.21
6.16 ± 0.27
5.38 ± 0.41
CpG 6
2.07 ± 0.22
1.75 ± 0.27
1.18 ± 0.42
CpG 7
3.38 ± 0.37
3.25 ± 0.47
4.17 ± 0.73
CpG 8
6.40 ± 0.24
6.83 ± 0.31
5.65 ± 0.48*
CpG 9
4.64 ± 0.38
4.73 ± 0.48
4.56 ± 0.75
CpG 10
10.17 ± 0.59
10.03 ± 0.74
9.64 ± 1.15
Mean CpG
4.56 ± 0.13
4.51 ± 0.16
4.28 ± 0.25
* P<0.05 as compared to the CT genotype.
Data represent mean ± SEM.
Estimated means are adjusted for maternal folate levels, maternal holoTC levels, maternal age at
delivery, HAM-A mood score, sex of newborn, and SSRI exposure.
Corrected for multiple comparisons using LSD.
53
Table 4.4 Relationship between prenatal exposure to maternal 3rd trimester mood and
newborn SLC6A4 methylation.
Estimated
difference in
percent
SLC6A4 CpG
Methylation (β)
Variable
EPDS
score (per
unit
increase)
95% CI
P
Partial
eta
squared
(ƞ2)
CpG 1
-0.043
( -0.157 ,
0.071
)
0.451
0.012
CpG 2
0.039
( -0.049 ,
0.126
)
0.377
0.017
CpG 3
-0.082
( -0.210 ,
0.046
)
0.204
0.035
CpG 4
-0.038
( -0.123 ,
0.047
)
0.375
0.017
CpG 5
-0.017
( -0.091 ,
0.058
)
0.657
0.004
CpG 6
0.025
( -0.054 ,
0.103
)
0.529
0.009
CpG 7
-0.006
( -0.139 ,
0.127
)
0.927
0.000
CpG 8
-0.004
( -0.090 ,
0.083
)
0.935
0.000
CpG 9
0.095
( -0.038 ,
0.228
)
0.157
0.043
CpG
10
-0.254
( -0.454 ,
-0.054 )
0.014
0.124
Mean
CpG
-0.028
( -0.074 ,
0.017
0.215
0.033
)
CI; Confidence Interval
Estimates adjusted for maternal folate levels, maternal holoTC levels, maternal age at delivery,
maternal MTHFR C677T genotype, sex of newborn, and SSRI exposure.
54
4.2 Maternal Methyl Nutrient Metabolism and Newborn Gene Expression
The relationship between maternal methyl nutrient status and SLC6A4 mRNA levels in
newborn whole blood was determined. I conducted a GLM and the covariates were set as
maternal folate levels and maternal holoTC levels. The fixed factors were set as maternal
MTHFR C677T genotype and sex of newborn. The dependent factor was set as newborn SLC6A4
mRNA levels. No associations between maternal methyl nutrient status and SLC6A4 mRNA
levels in cord blood from newborns were detected (P>0.05).
4.3 Maternal Mood and Newborn Gene Expression
Prenatal exposure to maternal depressive mood and SSRIs did not directly influence
SLC6A4 mRNA levels in newborns (P>0.05). This was shown by a GLM with the dependent
variable set as newborn SLC6A4 mRNA levels, the covariate set as mood (EPDS or HAM-A),
and the fixed factor set as SSRI exposure. I also conducted a GLM wherein newborn gene
expression was categorically grouped by mothers who were depressed during pregnancy or
mothers who were not depressed during pregnancy (EPDS scores of 10 or more, and EPDS
scores of 9 or less, respectively). I further adjusted for SSRI exposure in this analysis, and found
no associations (P>0.05).
55
5 CHAPTER 5: Newborns
My third specific aim was to determine whether newborn methyl nutrient metabolism
(folate, holoTC and MTHFR C677T genotype) is associated with SLC6A4 methylation and
mRNA levels in cord blood from newborns. This specific aim was addressed by the following
questions (see Figure 5.1 for schematic representation):
a. Is there an association between maternal methyl nutrient status and newborn methyl
nutrient status?
b. What is the relationship between newborn methyl nutrient status and methylation of
SLC6A4 in newborns?
c. What are the effects of newborn methyl nutrient metabolism and SLC6A4 methylation on
SLC6A4 mRNA levels in newborns?
d. Is the 5HTTLPR variant in newborns associated with newborn SLC6A4 methylation and
mRNA levels?
Figure 5.1 Specific aim 3.
56
5.1 Maternal and Newborn Methyl Nutrients
The methyl nutrient status of mothers at delivery and their newborns at delivery are
shown in Table 5.1. None of the women in my study population were deficient in folate at
delivery (defined as <6 nmol/L). Although, 29.2% of my cohort of women were considered
deficient in vitamin B12 (defined as <150 pmol/L), only 2.8% of my cohort were deficient in
holoTC (defined as <35 pmol/L). I found no associations between maternal and newborn folate
levels (P>0.05) (Figure 5.2A). Maternal vitamin B12 and holoTC levels were closely associated
with newborn vitamin B12 and holoTC levels, respectively (P<0.001 and P=0.001) (Figure 5.2B
and Figure 5.2C). Newborn vitamin B12 levels were also highly associated with newborn
holoTC levels (P<0.001) (Figure 5.3). Therefore, newborn holoTC status was chosen to
represent newborn vitamin B12 status in subsequent analyses.
57
Table 5.1 Methyl nutrient status in mothers and newborns.
n
Median (1st Quartile,
3rd Quartile)
Minimum
Level
Maximum
Level
Maternal Folate
(nmol/L)
72
37.4 (35.8, 39.4)
17.5
65.0
Maternal HoloTC
(pmol/L)
72
78.8 (56.6, 99.8)
26.6
664.9
Maternal Vitamin B12
(pmol/L)
72
197.9 (139.7, 268.5)
59.4
2003.7
Newborn Folate
(nmol/L)
69
37.8 (36.5, 39.6)
32.4
56.6
Newborn HoloTC
(pmol/L)
69
179.2 (117.8, 279.4)
32.5
647.8
Newborn Vitamin B12
(pmol/L)
69
390.4 (253.9, 592.5)
73.4
2352.2
58
A)
B)
C)
Figure 5.2 Relationships between maternal and newborn methyl nutrient levels.
A) Folate; R2 = 0.039, P> 0.05. B) Vitamin B12; R2 = 0.591, P<0.001. C) HoloTC; R2 = 0.429,
P= 0.001.
Figure 5.3 Relationship between newborn vitamin B12 and holoTC levels.
R2 = 0.647, P< 0.001.
59
5.2 Newborn Methyl Nutrient Metabolism and DNA Methylation
A GLM was used to determine the relationship between newborn methyl nutrient
metabolism (folate, holoTC, and MTHFR genotype) and newborn SLC6A4 methylation. The
covariates were set as newborn folate levels and newborn holoTC levels. The fixed factors were
set as newborn MTHFR C677T genotype and sex of newborn. The dependent factor was set as
methylation at each newborn SLC6A4 CpG site. As shown in Table 5.2, newborn MTHFR
C677T genotype was associated with percent SLC6A4 methylation at CpGs 6 and 10 (P<0.05).
Specifically, newborns with the TT genotype had lower SLC6A4 methylation at CpG 6 compared
to newborns with the CC and CT genotypes, and lower methylation at CpG 10 compared to
newborns with the CT genotype.
60
Table 5.2 Relationship between newborn MTHFR C677T genotype and newborn SLC6A4
methylation.
Newborn
SLC6A4
Methylation
(%)
Newborn MTHFR Genotype
CC (n = 26)
CT (n = 24)
TT (n = 11)
CpG 1
4.40
±
0.31
4.64
±
0.32
5.37
±
0.47
CpG 2
1.86
±
0.26
1.27
±
0.27
1.63
±
0.39
CpG 3
4.75
±
0.40
4.61
±
0.41
4.05
±
0.61
CpG 4
1.69
±
0.25
1.46
±
0.26
1.94
±
0.38
CpG 5
5.98
±
0.23
6.13
±
0.24
5.58
±
0.36
CpG 6
1.82
±
0.21
2.21
±
0.22
0.97
±
0.32*,#
CpG 7
3.38
±
0.39
3.30
±
0.41
3.79
±
0.60
CpG 8
6.29
±
0.26
6.64
±
0.27
5.97
±
0.40
CpG 9
4.59
±
0.38
5.05
±
0.39
5.13
±
0.57
CpG 10
10.17
±
0.58
10.74
±
0.60
8.15
±
0.88*
Mean CpG
4.50
±
0.13
4.61
±
0.13
4.26
±
0.20
* P<0.05 as compared to CT genotype.
#
P<0.05 as compared to CC genotype.
Data represent mean ± SEM.
Estimated means are adjusted for newborn folate, newborn holoTC, and sex of newborn.
Corrected for multiple comparisons using LSD.
61
5.3 Newborn Methyl Nutrient Metabolism, DNA Methylation, and Gene
Expression
To examine the effects of newborn methyl nutrient metabolism and SLC6A4 methylation
on SLC6A4 mRNA levels, I first conducted GLMs to determine whether newborn folate levels,
newborn holoTC levels, newborn MTHFR C677T genotype, and sex of newborn were
independently associated with newborn SLC6A4 mRNA level. I did not find significant
differences in any of the above (P>0.05).
Next, I conducted GLMs to determine whether newborn SLC6A4 methylation at each of
the CpG sites was associated with newborn SLC6A4 expression. I found that for every percent
increase in methylation at CpG site 7, there was a decrease in 0.189 unit of newborn SLC6A4
mRNA level (P<0.05) (Table 5.3). Similarly, for every percent increase in methylation at CpG
site 4, there was a 0.207 unit decrease in newborn SLC6A4 mRNA level (P<0.05) (Table 5.3).
Further, I conducted a GLM in order to take into account the effects of newborn methyl
nutrient metabolism (folate, holoTC, and MTHFR genotype), sex of newborn and newborn
SLC6A4 methylation on gene expression. The model had site-specific SLC6A4 methylation,
newborn folate levels, and newborn holoTC levels as covariates, and had newborn MTHFR
C677T genotype and sex of newborn as fixed factors. SLC6A4 mRNA level in newborns was set
as the dependent factor. As shown in Table 5.4, CpG site 7 predicted a decrease in SLC6A4
methylation, such that with every percent increase in SLC6A4 methylation at CpG 7, there was a
0.155 unit decrease in SLC6A4 gene expression (P<0.05).
62
Table 5.3 Relationship between newborn SLC6A4 methylation and newborn SLC6A4
expression (unadjusted).
Unadjusted
Variable
Estimated
difference in
SLC6A4 gene
expression (β)
P
Partial
eta
squared
(ƞ2)
CpG 1
0.099
( -0.076 ,
0.274
)
0.262
0.026
CpG 2
0.079
( -0.127 ,
0.286
)
0.443
0.012
CpG 3
0.092
( -0.042 ,
0.226
)
0.175
0.037
CpG 4
-0.207
( -0.409 , -0.005 )
0.045
0.080
CpG 5
0.076
( -0.159 ,
0.311
)
0.519
0.009
CpG 6
0.027
( -0.230 ,
0.284
)
0.831
0.001
CpG 7
-0.189
( -0.310 , -0.068 )
0.003
0.167
CpG 8
0.104
( -0.110 ,
0.317
)
0.334
0.019
CpG 9
-0.086
( -0.230 ,
0.058
)
0.234
0.029
CpG 10
0.067
( -0.037 ,
0.170
)
0.200
0.033
Mean
CpG
0.009
( -0.419 ,
0.437
)
0.967
0.000
95% CI
CI; Confidence Interval.
β and 95% CI represent ln-transformed SLC6A4 expression values.
63
Table 5.4 Relationship between newborn SLC6A4 methylation and newborn SLC6A4
expression (adjusted).
Estimated
difference in
Variable
SLC6A4 gene
expression (β)
Adjusted
95% CI
P
Partial
eta
squared
(ƞ2)
CpG 1
0.092
( -0.106 ,
0.289
)
0.355
0.020
CpG 2
0.037
( -0.173 ,
0.247
)
0.722
0.003
CpG 3
0.047
( -0.098 ,
0.192
)
0.514
0.010
CpG 4
-0.126
( -0.346 ,
0.094
)
0.256
0.031
CpG 5
0.006
( -0.231 ,
0.243
)
0.961
0.000
CpG 6
-0.009
( -0.275 ,
0.258
)
0.947
0.000
CpG 7
-0.155
( -0.283 , -0.027 )
0.019
0.125
CpG 8
0.108
( -0.104 ,
0.320
)
0.308
0.025
CpG 9
-0.007
( -0.162 ,
0.147
)
0.923
0.000
CpG 10
0.057
( -0.047 ,
0.160
)
0.276
0.028
Mean
CpG
0.030
( -0.394 ,
0.454
)
0.886
0.000
CI; Confidence Interval.
Estimates adjusted for newborn folate, newborn holoTC, newborn MTHFR C677T genotype, and
sex of newborn.
β and 95% CI represent ln-transformed SLC6A4 expression values.
64
5.4 5HTTLPR
Lastly, to determine whether the 5HTTLPR insertion/deletion variant in newborns is
associated with newborn SLC6A4 mRNA levels, a GLM was performed with mRNA levels as
the dependent variable. The newborn 5HTTLPR variant was set as the categorical variable and
newborn mRNA level was set as the dependent factor. Newborn SLC6A4 mRNA level did not
vary with newborn 5HTTLPR genotype (P>0.05). The Genotype frequencies for the newborn
5HTTLPR insertion/deletion variant in my study population were 30.4% for l/l, 45.7% for l/s,
and 16.3% for s/s, similar to that previous reported in adults (30).
SLC6A4 mRNA levels were not grouped by the newborn 5HTTLPR A→G variant due to
small sample size (n=61). The distribution of the 5HTTLPR A→G genotypes amongst the
newborns was 27.9% La/La, 9.8% La/Lg, 41.0% La/S, 1.6% Lg/S, and 19.7% S/S. In my cohort,
no newborns had the Lg/Lg genotypes.
In order to determine whether the 5HTTLPR insertion/deletion variant is associated with
SLC6A4 methylation in newborns, I conducted a GLM with the newborn 5HTTLPR
insertion/deletion variant as the fixed factor, and newborn SLC6A4 CpG sites as dependent
variables. I did not detect any associations between the 5HTTLPR genotype and methylation at
each CpG sites in newborns (P>0.05).
65
6 CHAPTER 6: General Discussion
6.1 Discussion of Results
6.1.1 Methyl Nutrient Status
Results from this thesis indicated that the MTHFR 677TT genotype was not associated
with increased depressive or anxious mood during the 3rd trimester. This is surprising given that
previous findings showed that the MTHFR C677T variant is associated with depression not only
in the general population, but also in pregnant women in their 2nd trimester (Devlin et al.)
(11,101). An explanation for this may simply be small sample size (n=83). Another explanation
may involve gene-environment interactions. The function of the MTHFR enzyme is to reduce
various forms of folate to 5-MTHF. Given that the MTHFR 677TT variant leads to reduced
enzymatic activity (93), less folate is reduced to the 5-MTHF form in individuals with the
variant. However, in a population with high levels of folate such as my study population, the
reduced MTHFR activity may be overcome and adequate levels of 5-MTHF may be produced.
In addition, MTHFR activity is dependent on riboflavin as a cofactor (62). A study had
shown that riboflavin intake is inversely associated with colorectal adenomas, and the
association was more pronounced in adults with the MTHFR 677TT genotype than the 677CC or
677CT genotypes (138). Elderly females with low levels of riboflavin and the MTHFR 677TT
genotype had higher risks for fractures compared to elderly females with low levels of riboflavin
and the MTHFR 677CC genotype (139). The inverse association between plasma riboflavin and
Hcy was much more significant in men with the MTHFR 677TT genotype than the 677CC
genotype (140). On the contrary, high levels of plasma riboflavin (as a result of supplementation)
had been shown to increase the amount of 5-MTHF in mucosal cells of individuals (with
colorectal polyps) with the MTHFR 677CT or 677TT genotypes (141). These studies suggest that
66
riboflavin interacts with the MTHFR C677T genotype, such that the presence of low riboflavin
levels may exacerbate the effects of the MTHFR C677T variant, and high riboflavin levels may
enhance MTHFR activity in individuals with the MTHFR C677T variant. I suspect that
riboflavin status in my cohort of pregnant women may influence any associations between the
MTHFR C677T variant and mood. However, riboflavin was not quantified in my study
population. I do not expect that riboflavin will be low in my study population as riboflavin is
likely included in prenatal supplements (Materna contains 1.4mg) and may be found to be
fortified in cereals, rice, cornmeal, and flour (142). Even though the cohort of pregnant women
from Devlin et al. were also taking prenatal supplements containing folic acid (11), serum
methyl nutrients such as folate or riboflavin were not quantified in that study population.
My study is the first to look at whether folate and vitamin B12 status are associated with
depression during late pregnancy. Folate was not found to be different between mothers who
were depressed (EPDS score of 10 or above) during pregnancy or not depressed (EPDS score of
9 or below) during pregnancy. This is not surprising, because as a consequence of folic acid
supplementation and the usage of prenatal supplements (such as “Materna”) by all the women,
the women in my cohort all had similar but elevated folate status (median = 37.4 nmol/L) at
delivery. Thus, this did not allow me to determine any associations of depression or anxiety in
pregnant women with low folate status. It may be interesting to conduct a similar study in a
population with no government mandated folic acid fortification, such as Malaysia or New
Zealand.
Furthermore, holoTC was not found to be different between mothers who were depressed
during pregnancy or not depressed during pregnancy. This is not what I expected, given that
previous studies in the elderly have shown that vitamin B12 is inversely associated with
67
depressive mood (106,117) However, the elderly are at greater risk for vitamin B12 deficiency
due to malabsorption of vitamin B12 in the intestine (87). Similar to my results, a recent Japanese
study (n = 86) also found no relationships between depression during the 1st trimester of
pregnancy and either serum folate levels or vitamin B12 intake levels (143).
I expected that the vitamin B12 status in my cohort of pregnant women to be low, as a
study had shown previously that Ontario women in early gestation had low vitamin B12 status
(geometric mean = 249 pmol/L; n = 10,622) (110). The vitamin B12 level in my current cohort
of women (median = 198 pmol/L) was lower than the pregnant women from Ontario (110), a
group of non-pregnant 18-25 year old women from Manitoba (median = 400 pmol/L; n = 95)
(144), and populations of non-pregnant women of reproductive age in Vietnam (mean = 494
pmol/L; n = 245) (145) and the Netherlands (median = 245 pmol/L; n = 53) (146). A closer look
at my data indicated that almost 30% of women were deficient in vitamin B12 during delivery.
The vitamin B12 status of the women in my study at delivery was highly associated with their
holoTC status. However, I found that less than 3% were deficient in holoTC.
The women in my cohort had similar holoTC levels (median = 79 pmol/L) compared to
pregnant women in Ontario (geometric mean = 81 pmol/L) (114), non-pregnant women of
reproductive age in Vietnam (mean = 78 pmol/L) (145), and Latino elderly living in California
(median = 80 pmol/L; n = 1209) (104). This discrepancy in the percent of women deficient in
vitamin B12 and percent deficient in holoTC highlights the fact that vitamin B12 may be
preferentially metabolized to holoTC to ensure adequate levels of the active form of vitamin B12
required by cells. A study had shown that 9-80% of the total serum vitamin B12 is in the form of
holoTC in the elderly (147). This proportion was dependent on vitamin B12 status, with higher
percentage of vitamin B12 in the holoTC form when vitamin B12 levels are low (148).
68
My results showed that newborns had similar and even higher levels of folate, vitamin
B12, and holoTC than their mothers at delivery. This provides support that there may be
preferential transfer of folate and vitamin B12 to the fetus during pregnancy (78,89). A
preferential transfer may also explain the slightly lower levels of vitamin B12 in my group of
pregnant women compared to various non-pregnant populations (144-146). A recent study from
Spain showed that the levels of both folate and vitamin B12 in pregnant women gradually
declined with the progression of pregnancy (149), and this may be attributable to hemodilution
during pregnancy (69). Although the decline in folate levels during pregnancy may not be
present in women in North America due to prolonged exposure to folic acid fortification, this
trend may be present in vitamin B12 levels. As such, this may be another explanation as to why
the vitamin B12 status in my cohort of women, taken at delivery, is lower than various nonpregnant populations (144-146).
There were a few women with very elevated vitamin B12 (eg. 1479 pmol/L) and holoTC
(eg. 627 pmol/L) levels at delivery in my study population, compared to the median of 198
pmol/L and 79 pmol/L, respectively. The elevated levels were reflected in the women’s
newborns at birth (holoTC = 296 pmol/L; vitamin B12 = 2352 pmol/L). Although we did not
record dietary intake data, it would be interesting to know how the process of pregnancy may
lead to changes in appetite, and thus preferences for certain diets, in my cohort of women. As
well, mood may affect eating patterns. These could affect nutritional status in this current cohort
and explain some variations. Furthermore, genetic variants in key enzymes may affect absorption
of methyl nutrients into the body. For instance, a variant in the GCP II gene, GCPII, affects
absorption of dietary folate into the intestine (150). There may be a genetic variant in the
69
receptor responsible for absorption of vitamin B12, such that much more vitamin B12 is absorbed
or retained in cells.
Taking it a step further, I examined the characteristics of mothers and their newborns
with lower methyl nutrient levels than the rest of my cohort. I did not see any distinct
characteristics (eg. mood, level of education, number of pregnancies, MTHFR C677T or
5HTTLPR insertion/deletion genotypes, number of alcoholic drinks during pregnancy, etc.) that
distinguished the mothers and their newborns from the rest of the cohort. However, I did see that
the three women with the lowest folate levels (18.8 nmol/L, 18.9 nmol/L, and 20.0 nmol/L) also
were vitamin B12 deficient (59.4 pmol/L, 132.1 pmol/L, and 87.5 pmol/L, respectively) and had
lower levels of holoTC than the rest of the cohort (34.8 pmol/L, 54.2 pmol/L, and 26.63 pmol/L,
respectively). Given that compliance for the consumption of prenatal supplements was not
recorded, this finding could be due to low compliance with supplement usage during pregnancy.
As quantified by the ΔΔCt method of relative quantification (RQ), the SLC6A4 mRNA levels in
the women with low folate levels (no RNA available for one woman, and RQ values were 0.326
and 0.892 for the other two women), were not outliers compared to the rest of the study
population (median = 0.777; mean = 0.935; SD = 0.622). The SLC6A4 % methylation at CpG
sites 1-10 and the mean CpG % methylation in the women with low folate levels were not
outliers compared to the rest of the cohort’s % methylation at corresponding sites.
6.1.2 Discussion of First Aim – Mothers
As a step in determining the connections between maternal mood, maternal methyl
nutrient status, and epigenetic regulation of SLC6A4 during pregnancy, I found that folate was
associated with increases in methylation at CpG sites 1, 4 and 8 for SLC6A4. Given the role of
70
folate in the methyl nutrient metabolism and its connection to the production of AdoMet, my
result seems to support the notion that folate alters gene-specific methylation by the donation of
methyl groups. Taking into account the fact that the folate status in my population of pregnant
women were all very similar (the majority were between 30-40 nmol/L), the association between
folate and methylation suggests that gene-specific methylation may be very sensitive to small
changes in folate levels. The influence of folate in methylation had previously been shown in
imprinted genes (altered methylation of H19 in men treated with 5-MTHF) (118).
North American grain products are fortified with folic acid, and it is now very rare to be
folate-deficient in Canada (107). Concerns have been raised regarding whether high levels of
folate, commonly seen in populations with folic acid fortification (107), may play a role in
current health risks. These concerns include the inactivation of tumour-suppressor genes and the
promotion of the proliferation of pre-neoplasms that could lead to cancers such as colorectal
carcinomas (151,152). Unmetabolized folic acid is also associated with decreased natural killer
cells’ cytotoxicity (153). Furthermore, high folate levels may mask vitamin B12 deficiency
(81,82). Folate-vitamin B12 imbalance has been associated with cognitive impairments in the
elderly (154) and insulin resistance in 6-year old children (155). Altered methylation status may
be a possible mechanism behind disorders associated with high folate levels.
High folate levels may lead to increased levels of 5-MTHF. 5-MTHF serves as the
methyl group donor for the synthesis of methionine (62). Under circumstances of high folate
levels, more Hcy is expected to be remethylated to methionine. This may lead to increased
production of AdoMet and increased capacity for methylation reactions. This could also
potentially decrease the expression of enzymes involved, such as MS or MAT, such that any
decrease in expression of these enzymes could counterbalance the likelihood for elevated levels
71
of folate to increase methylation. The potential increase in methylation capacity imposed by high
folate levels is also dependent on the availability of other components of the methyl metabolism,
such as vitamin B12, riboflavin, or vitamin B6. For instance, MS does not function in the absence
of adequate vitamin B12 (156). This would elevate Hcy in the body. Riboflavin is a cofactor for
MTHFR, and the enzyme (methionine synthase reductase) that maintains the activity of MS (62).
Deficiency in riboflavin in the presence of high folate would not only elevate non-reduced forms
of folate (forms that are not 5-MTHF), but also elevate Hcy in the blood. Hcy may then be
reconverted to AdoHcy. This would decrease the AdoMet/AdoHcy ratio and decrease
methylation capacity (119,120). A portion of Hcy could go through the transulfuration pathway
to form cysteine, but this reaction is vitamin B6 dependent (62). Thus, low levels of vitamin B6 in
the presence high folate could further exacerbate the alteration in methylation capacity through
the synthesis of AdoHcy.
Although my study population consisted of women with high folate levels and
suboptimal vitamin B12 levels, the holoTC status of the women and their newborns were not low.
Given that holoTC represents the proportion of vitamin B12 required by cells (137), the problem
with folate-vitamin B12 imbalance may not be present in my cohort. However, it is important to
quantify other methyl nutrients, such as riboflavin and vitamin B6, in order to better identify the
relationship between high folate levels and methylation.
Interestingly, both EPDS and HAM-A scores were inversely associated with SLC6A4
CpG 10 methylation in monocytes. A previous study from our lab had shown that EPDS was
inversely associated with SLC6A4 methylation in leukocytes at sites which corresponded to
CpGs 1, 4, 5, 6, 7, and 9 in my study (11). However, that study did not control for folate and
holoTC levels. Taken together, these results suggest that methylation levels may be very
72
different between cell populations. Further, these results suggest that the relationship between
EPDS or HAM-A scores and SLC6A4 promoter methylation may be influenced by folate and
holoTC levels.
After adjusting for methyl nutrient status and MTHFR C677T genotype, I found that
SLC6A4 CpGs 5, 7, 8, 10, and mean methylation levels were directly associated with SLC6A4
mRNA levels. This is contrary to what I expected, because a previous study by Philibert et al. in
lymphoblast cell lines demonstrated that methylation of the SLC6A4 promoter was inversely
associated with SLC6A4 mRNA levels (27). The authors looked at 81 CpG sites, of which four
were associated with mRNA levels. CpG 8 in my study corresponds to one of the four sites.
Given that my methylation analyses were performed in monocytes, and my gene expression
analyses were performed in leukocytes, the contradicting results between the studies might be
due to cell-specific differences between lymphoblasts, monocytes, and leukocytes. Another
explanation for this discrepancy is that a different method (bisulphite pyrosequencing) for
quantifying DNA methylation was used in this thesis compared to that used by Philibert et al.
Bisulphite pyrosequencing allows for more sensitive detection of methylation than bisulphite
sequencing using clones.
The s allele of the 5HTTLPR insertion/deletion variant had previously been shown to be
associated with higher SLC6A4 methylation than the l allele by Philibert et al. (27). The
5HTTLPR is located in the promoter of SLC6A4, approximately 1,000bp upstream of the CpGrich region where methylation was analyzed in this thesis. A possible mechanism behind the
relationship between the 5HTTLPR insertion/deletion variant and SLC6A4 methylation may be
via the state of the chromatin (57). Specifically, the s allele may induce methylation at the CpG
sites within the 5HTTLPR due to the shorter length of the region compared to the l allele. This
73
may lead to the binding of methyl CpG binding protein 2 (MeCP2) (157) and the methylation of
lysine 9 on histone 3 (H3K9) (158). Subsequently, there may be recruitment of histone
modifying enzymes such as histone deacetylase (HDAC) (158,159). These changes may cause
positive feedback for CpG methylation and may lead to more condensed chromatin structure in
the 5HTTLPR region (57). It is possible that CpG methylation begins at the 5HTTLPR, but will
extend to surrounding regions (including the SLC6A4 promoter region that I analyzed) (160).
Ultimately, this is all speculative. No studies have been carried out that examined the functional
protein interactions in the SLC6A4 promoter region.
In my current study, I did not find an association between 5HTTLPR genotype and
SLC6A4 methylation. This may be due to two reasons. Firstly, the total number of CpG sites
varied between my study and the study conducted by Philibert et al. (27). Philibert et al.
examined the average methylation level of 81 CpG sites, and found an association between
average methylation and the 5HTTLPR insertion/deletion variant. In my study, I examined 10
CpG sites in the 5’ portion of the region analyzed by Philibert and colleagues. I did not see
associations between the 5HTTLPR insertion/deletion variant and methylation (either at
individual CpG sites or mean CpG methylation). Secondly, the methylation technique conducted
by Philibert et al. and my study differ. Philibert and colleagues calculated methylation via the
average of 10 clones (bisulphite sequencing). I conducted my study via bisulphite
pyrosequencing, which as mentioned previously, is a much more quantitative technology that
allows detection of small changes in DNA methylation. Further studies to examine the
relationship between the 5HTTLPR insertion/deletion variant and SLC6A4 methylation is
warranted via the bisulphite pyrosequencing method.
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The fact that pregnant women homozygous for the short allele of the 5HTTLPR
insertion/deletion variant did not have more depressive mood during pregnancy than women with
the l/l or l/s genotypes in my cohort was not surprising. This is because although previous studies
had shown that the s/s genotype was associated with depression, the association was only present
after exposure to childhood maltreatment or major stressful life events (29,30). In my current
study, I did not collect information regarding early life adversity or occurrence of major life
events (such as loss, humiliation, threat, or defeat). Perhaps a relationship between depression
and the s/s genotype would become evident in the context of previous exposure to adverse life
events. Therefore, obtaining information on the emotional backgrounds of the women in my
study population may be important in influencing the outcomes of my study. A previous study
also did not find an association between 5HTTLPR and women in the late 2nd trimester, however,
they did not control for stressful life events or childhood adversity either (11).
In addition, I did not observe a relationship between 5HTTLPR insertion/deletion
genotype and mRNA levels of SLC6A4. This is unexpected because previous studies had shown
that the 5HTTLPR insertion/deletion variant is associated with SLC6A4 gene expression in
lymphoblast cell lines (161). This could be due to differences in the region of mRNA transcript
detected in my study and the previous study. The commercially available probe used in my study
spans exons 8-9 and detects most splice variants of SLC6A4. Whereas the previously study only
showed the association between 5HTTLPR genotype and SLC6A4 gene expression with a probe
that recognized exon 1A (and not the alternative exon 1B) in SLC6A4. The previous study also
did not find an association between genotype and SLC6A4 mRNA levels when they used a probe
that bridged exons 8-9 (161).
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6.1.3 Discussion of Second Aim – Maternal Influences on Newborns
Newborns with mothers with the MTHFR 677TT genotype had lower methylation at CpG
site 8. This trend, although not significant, was previously shown in leukocytes (11). It is
conceivable that the maternal MTHFR 677TT genotype may decrease the level of 5-MTHF in
mothers, leading to less transfer of 5-MTHF to the newborns. A study showed that the dominant
form of folate in cord blood is 5-MTHF (75). Hence, maternal MTHFR 677TT genotype may
influence the levels of 5-MTHF available in the newborn. Any decrease in 5-MTHF may alter
the methylation capacity of AdoMet in the newborns. Previous literature had shown that altered
methyl nutrient levels during pregnancy led to changes in offspring DNA methylation
(5,162,163). A study in rats showed that dams fed a low protein (LP) diet gave birth to offspring
with decreased gene-specific methylation. However, prenatal exposure to LP diet in conjunction
with folate supplementation did not cause altered methylation profiles in the same genes (162).
In humans, folate supplementation had been shown to be associated with increased IGF2
methylation in the newborn (163). This alludes to the fact that prenatal exposure to imbalances in
the methyl nutrient metabolism may play important roles in gene-specific DNA methylation.
I also analyzed the effects of maternal methyl nutrients and mood on SLC6A4
methylation and mRNA levels in newborns. Prenatal exposure to 3rd trimester EPDS scores was
inversely associated with newborn SLC6A4 methylation at CpG 10. The direction of this
association between EPDS and CpG 10 was the same as the direction of association between
EPDS and maternal SLC6A4 CpG 10 methylation. Given that maternal methylation status of
SLC6A4 was not associated with newborn methylation status of SLC6A4, it is possible that the
effect of EPDS on CpG 10 methylation was due to a “programming” effect of mood, and not the
heritability of methylation patterns from mother to newborn. My results are in agreement with
76
previous literature that had shown that antenatal mood influences newborn DNA methylation of
stress regulatory genes, including SLC6A4 (9,11). Interestingly, HAM-A scores did not have an
effect on the methylation of CpG 10, or any other CpG sites in the newborn. This suggests that
although depression and anxiety are often comorbid (48), they may still differ in their etiology
and have different effects on SLC6A4 methylation.
Prenatal exposure to maternal depression and anxiety may lead to disturbances in brain
development and behaviour, given that 5-HT acts as a trophic factor during early development
(18,49,164). SSRIs function to block 5-HTT, leading to an increase in the level of 5-HT in the
extracellular space (26). In the mothers, SSRIs serve to increase the serotonergic tone. SSRIs
taken by the mothers can also cross the placenta and the blood brain barrier (165), thereby
potentiallyaltering the serotonergic tone in the newborns as well. This may be detrimental in
influencing the development of the sertotonergic system in the newborns. Although not
measured in my cohort, the mothers and newborns with exposure to SSRIs may have altered 5HT levels compared to mothers and newborns without exposure to SSRIs. Any changes in 5-HT
levels caused by SSRI exposure could influence homeostasis mechanisms in the body, such that
components of the serotonergic system (eg. 5-HTT) may be altered. Specifically, SSRI exposure
could potentially alter SLC6A4 methylation. Given that SSRIs block 5-HTTs, perhaps SLC6A4
methylation would subsequently change in a direction such that there is more SLC6A4
expression, and consequently more 5-HTT protein expression to counterbalance the changes
caused by SSRIs.
Studies had shown that fluoxetine (a type of SSRI) increases protein and mRNA levels
of methyl-CpG binding domain 1(MBD1) protein and MeCP2 in the brain (dorsal caudateputamen, frontal cortex and hippocampus) in adult rats (166,167). Fluoxetine also increased
77
HDAC mRNA levels and decreased histone 3 acetylation (associated with open chromatin
structure) in the dorsal caudate-putamen, frontal cortex and hippocampus of adult rats (166). On
the contrary, studies suggested that fluoxetine upregulates histone 3 acetylation in the
hippocampus of traumatic-brain-injured rats (167), and at the Bdnf promoter in the hippocampus
of methylmercury exposed mice (168). This suggests that the direction of alterations in histone
acetylation caused by fluoxetine exposure is context-dependent. Taken together, these rodent
studies provide evidence towards underlying mechanisms for how SSRIs may influence
methylation. However, I did not find an association between SSRI exposure and SLC6A4
methylation in either mothers or newborns in my study. Although, this does not necessarily mean
that SSRIs do not have an effect on SLC6A4 methylation.
Intriguingly, no effects of maternal mood or SSRI exposure on newborn SLC6A4 mRNA
levels were found in my cohort. This is contrary to what was expected because a recent study had
shown that both maternal depression and anxiety were associated with higher SLC6A4 mRNA
levels in the placenta, even when the women were treated with SSRIs (169). Further studies are
necessary to determine whether mood or SSRI exposure could alter SLC6A4 methylation or
mRNA levels in other cell types or tissues.
6.1.4 Discussion of Third Aim – Newborns
As a step in determining the role of newborn methyl nutrient metabolism in newborn
methylation and expression of stress-regulatory genes, I analyzed SLC6A4 methylation and
mRNA levels in newborns. Newborn MTHFR 677TT genotype was inversely associated with
SLC6A4 CpG 6 and 10 methylation levels in newborns. This suggests that methyl nutrient
metabolism in newborns may play a role in DNA methylation during development. In particular,
78
because the MTHFR 677TT genotype results in a decrease in functional activity of the enzyme
MTHFR (93), newborns with the 677TT genotype may not have the capacity to produce as much
5-MTHF as newborns with the 677CC or 677CT genotypes. While the remethylation of
homocysteine to methionine may be accomplished by other methyl donors, such as betaine in
liver, this pathway is not present in monocytes. As such, the MTHFR 677TT genotype may
decrease methylation capacity. Further, less production of 5-MTHF may lead to elevated Hcy
concentrations in serum. A consequence of this may be increased AdoHcy level, because Hcy
can be reconverted back to AdoHcy (62). Elevated levels of AdoHcy have been suggested to be
an inhibitor of methyltransferases, thus further decreasing methylation capacity (91,92). Studies
have shown that individuals with the MTHFR 677TT genotype have lower global DNA
methylation than individuals with the 677CC or 677CT genotypes in several populations
(123,170,171). Furthermore, mice with the targeted disruption of the Mthfr gene (Mthfr +/-) have
lower AdoMet in the liver and higher AdoHcy in the brain than Mthfr +/+ mice (172). Both
Mthfr +/- and Mthfr -/- mice exhibited global DNA hypomethylation in the brain and ovaries
compared to Mthfr +/+ mice (173).
Given that all newborns had adequate folate levels in my cohort, there may be decreased
5-MTHF availability and increased availability of other forms of folate in the cells. For instance,
perhaps more folates are in the forms of 5,10-MethyleneTHF or 10-formylTHF. This may lead to
altered dTMP or purine synthesis (174).
Newborn SLC6A4 CpG 4 and 7 methylation was inversely associated with SLC6A4
mRNA level. However, this inverse relationship only remained for CpG 7 methylation when I
controlled for newborn folate levels, newborn holoTC levels, newborn MTHFR C677T variant,
and sex of newborn. This result is interesting because a previous study had shown that 2nd
79
trimester EPDS score was inversely associated with newborn SLC6A4 methylation at CpGs 4
and 7 in leukocytes (11). If methylation patterns in monocytes reflect that of leukocytes,
decreased SLC6A4 methylation at CpGs 4 or 7 in newborn monocytes could potentially lead to
increased serum 5-HTT levels via increased SLC6A4 mRNA levels in leukocytes. As such, an
increase in 5-HTT levels could effectively decrease the availability of extracellular 5-HT. This
could mean that prenatal exposure to maternal depressive mood may lead to a “serotonergic
vulnerability” in the developing infant (15). Additionally, 5-HT had been shown to induce GR
gene expression in hippocampal cells (175). Lower 5-HT protein levels could lead to less GR
gene expression and elevated cortisol levels in the infant, and thus affect an infant’s stress
response. However, it is not known whether this induction process occurs in monocytes, or other
blood cells.
6.2 Limitations, Strengths, and Future Directions
6.2.1 Limitations
This study has several limitations. Firstly, the mothers’ whole blood and their newborns’
cord blood were not separated into leukocytes, erythrocytes, and platelets prior to storage. This
resulted in difficulty in isolating monocytes. Unfortunately, I was not able to obtain good quality
RNA from monocytes for gene expression analyses. Although DNA for methylation analyses
was isolated from monocytes, RNA was isolated from whole blood, a heterogeneous population
of cells. As a result, this did not allow comparison of DNA methylation and mRNA levels in the
same cell type. Furthermore, it is conceivable that the amount and quality of RNA extracted
heavily depends on the length of time that the whole blood and cord blood were at room
temperature prior to storage in the freezer. This is because RNA is highly unstable and
80
intracellular RNAse is still active at room temperature. In retrospect, perhaps a better way for
RNA storage is aliquoting the blood into an RNA stabilizing solution (such as the PAXgene
solution) prior to freezing.
Secondly, methylation analysis in just one cell type does not allow for an extension of the
observations in my study to other cell or tissue types. In order to determine whether the results
from my study is a cell-specific effect or not, further methylation analyses should be performed
in other cell types, such as lymphocytes, or other tissue types, such as the placenta (important for
fetal development and growth).
Thirdly, a larger sample size would allow for more robust statistical results. It was
previously determined that 90 mother-newborn pairs would be sufficient sample size to detect
clinically important relationships of at least 0.24-0.28 with 80% power and alpha of 0.05. Due to
the exclusions I made (because of when the blood and serum were taken for the mothers and
their newborns, inability to isolate monocyte-specific DNA from small blood volumes, and
inability to extract intact RNA for some samples), my sample size decreased to approximately
75, depending on the type of analyses performed. As such, the categorical variables used in my
analyses, such as the MTHFR or 5-HTTLPR variants have low sample size in certain groups. For
instance, only seven study subjects with MTHFR 677TT genotype were analyzed for the
influence of maternal MTHFR variant on newborn SLC6A4 methylation. In addition, not enough
study subjects represented all genotypes of the 5-HTTLPR A→G substitution variant.
Further, food frequency questionnaires designed to assess folate and vitamin B12 intakes
should be administered to the women during pregnancy, allowing for assessments of methyl
nutrient intake (may be different between the women in response to the side effects of
81
pregnancy). Results from food frequency questionnaires, in combination with serum methyl
nutrient quantification, may be a better indicator of methyl nutrient levels.
In addition, my study required multiple comparisons both within statistical tests and
between statistical models. I conducted the LSD post-test to account for multiple comparisons
within statistical tests. For instance, I conducted the LSD post-test when I determined the
relationship between the MTHFR C677T genotypes and methylation. However, for between
statistical models (eg. determining the effect of mood on SLC6A4 methylation at CpG 1, versus
determining the effect of mood on SLC6A4 methylation at CpG 2), I did not account for multiple
comparisons. As a consequence, there is a possibility that some of the significances I observed in
my results are type I errors. Given that this is a study that focused on exploring the trends
between methyl nutrients, mood, and serotonin transporter methylation and mRNA levels, it was
of more interest to determine 1) any potential associations between the components, and 2) the
direction of any trends. To take it a step further, correction for multiple comparisons between
statistical models is needed. This could be achieved using the Benjamini and Hochberg false
discovery rate calculation (176).
Lastly and most importantly, the level of differences observed in methylation levels (if
truly significant) was very small. One may wonder whether small changes in methylation are
biologically meaningful. Given the complexity and dynamics of the genome (57,177), it is not
clear whether small changes are relevant but it certainly may be. This is because any small
changes in methylation may lead to modifications in chromatin structure (through the
recruitment of transcription factors or through modifications of histones). Hence, any small
changes in DNA methylation may amplify into larger changes. These minute changes in DNA
methylation may also represent the beginning of deviations from normal methylation patterns,
82
and these changes could be exacerbated into larger changes with further exposure to similar
environmental factors. One way to test whether minute changes in SLC6A4 methylation may be
biologically relevant is to quantify whether downstream changes, such as 5-HTT protein levels,
occur due to differences in methylation or mRNA levels. Another way to test whether small
alterations in SLC6A4 methylation are biologically meaningful is to look at protein interactions
with the CpG-rich or surrounding regions. This could be accomplished via chromatin
immunoprecipitation. Putative binding sites for transcription factors could be determined via in
silico search on the MatInspector software (178).
6.2.2 Strengths
Despite the limitations of this study, there are many strengths offered by this project that
made the research remarkable. Firstly, this is the first study of its kind to examine gene-nutrient
interactions through measuring methyl nutrient levels and SLC6A4 methylation and mRNA
levels. This study allowed for the determination of how 5-HTT is influenced by methyl nutrients
and mood on two different genomic levels – SLC6A4 methylation and gene expression.
Secondly, this study permitted the usage of bisulphite pyrosequencing, a state-of-the-art
technology that allows precise quantification of DNA methylation at CpG sites. Lastly, and most
importantly, this study was performed with human subjects and this allows for direct
translational value into the healthcare setting.
6.2.3 Future Directions
To take this project a step further, methylation and expression of NR3C1 will be analyzed
to determine its relationships with mood and methyl nutrient metabolism in mothers and their
83
newborns. Further, this could potentially provide evidence at the gene level for any 5-HTT and
GR interactions in blood cells.
There is plasma currently in storage that would permit future assessments of 5-HTT
protein levels (and GR protein levels) in mothers and their newborns. There is also in storage the
portion of leukocytes that does not contain monocytes. SLC6A4 methylation should be assessed
in lymphocytes to determine whether there are cell-specific differences in SLC6A4 methylation
patterns. Additional studies should also be undertaken to assess alternative CpG-rich regions of
the SLC6A4 promoter where methylation was shown to be associated with gene expression (27).
Furthermore, my thesis project is a branch of a large prospective study looking at the
effects of prenatal exposure to maternal depressive mood and SSRIs on the developmental
outcomes of children. Behaviour in early infancy and childhood could be assessed to determine
whether prenatal exposure to maternal depressive mood, SSRIs, and alterations in the methyl
nutrient metabolism may “program” or impact growth and development of the children.
6.3 Concluding Remarks
The World Health Organization had recognized depression as a major cause of morbidity
worldwide (14). The occurrence of depression is especially concerning during pregnancy, due to
the possibility that altered in utero environment imposed by maternal depressive mood or SSRI
usage have adverse consequences in development of the offspring (2,4). The methyl nutrient
metabolism may be the link between depression and gene expression. In this thesis, I determined
the associations between maternal mood, methyl nutrient status, SLC6A4 methylation and gene
expression of SLC6A4 in mothers and their newborns.
The main findings of this thesis are as follows:
84
1. Maternal folate status was positively associated with maternal 3rd trimester SLC6A4
methylation at CpGs 1, 4, and 8.
2. Maternal 3rd trimester mood was inversely associated with SLC6A4 CpG 10 methylation
in the mothers and their newborns.
3. In newborns, decreased methylation at SLC6A4 CpG 8 was associated with maternal
MTHFR 677TT genotype, and decreased methylation at CpGs 6 and 10 were associated
with newborn MTHFR 677TT genotype.
4. Maternal SLC6A4 mRNA level was positively associated with mean maternal SLC6A4
methylation and methylation at CpG sites 5, 7, 8, and 10. Yet, newborn SLC6A4 mRNA
level was inversely associated with newborn SLC6A4 methylation at CpG sites 4 and 7.
5. Homozygosity for the minor alleles of the MTHFR C677T and the 5-HTTLPR
insertion/deletion variants in mothers were not found to influence maternal 3 rd trimester
mood.
In conclusion, the results in my thesis provide evidence that methyl nutrient metabolism
may influence methylation of SLC6A4, a gene involved in stress-regulation in both mothers and
their newborns. Further, the results suggest that antenatal maternal depressive mood during
pregnancy may affect SLC6A4 methylation in both mothers and their newborns. This may lead
to, or “program” changes in the serotonergic system via DNA methylation. In addition, SLC6A4
methylation may be one type of epigenetic mechanism that affects SLC6A4 gene expression.
Taken together, early life environment, genetics and epigenetics may serve as mechanisms that
influence development. As such, alterations in maternal mood and methyl nutrient metabolism
leading to changes in SLC6A4 expression may set up life-long health consequences in the
85
newborn. My thesis work serves as an important step in providing proof of gene-environment
interactions during pregnancy, and contributes to the exciting field of developmental
programming.
86
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