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Epigenetic regulation of endocrine aging transitions of the perimenopausal and menopausal brain
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Epigenetic regulation of endocrine aging transitions of the perimenopausal and menopausal brain
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UNIVERSITY OF SOUTHERN CALIFORNIA
Epigenetic Regulation of Endocrine Aging Transitions of the
Perimenopausal and Menopausal Brain
By
Eliza Read Bacon
DOCTORAL DISSERTATION
SUBMITTED TO THE FACULTY OF THE USC
GRADUATE SCHOOL IN PARTIAL FULLFILLMENT OF
THE REQUIREMENTS
for the degree
DOCTOR OF PHILOSOPHY (NEUROSCIENCE)
December 2017
Advisory Committee:
Roberta Diaz Brinton, PhD
Christian Pike, PhD
Alan Watts, PhD
1
DEDICATION
To my husband Nicholas,
None of this would have been possible without you.
Your love and encouragement has been what has kept me going.
Thank you for being patient with me and for being my support during the challenging moments.
I can’t wait to see what the future has in store for us.
To my newborn son Warren,
None of this would have been as meaningful without you.
Even before you were born, you were already the reason I strive to be my very best.
These last few months have not been easy for either of us,
but thank you for putting up with such a stressed out mommy.
2
ACKNOWLEDGEMENTS
I would like to thank Dr. Roberta Diaz Brinton for her support, direction, and mentorship.
I am truly grateful for the opportunity to have conducted my doctoral research under her
leadership. Thank you to the members of my qualifications and dissertation committees: Dr.
Caleb Finch, Dr. Andrew Smith, Dr. Chein-Ping Ko, Dr. Alan Watts, and Dr. Christian Pike,
who’s guidance taught me how to critically evaluate and plan experiments. Many thanks to my
committee chair, Dr. Christian Pike, and the Neuroscience Graduate Program Director, Dr. Pat
Levitt, who made sure that I remained on-track in my completion of the program requirements. I
would also like to thank my labmates and colleagues in both the Brinton and Finch labs for their
friendship and support throughout this journey. Lastly, I would like to acknowledge the National
Institute on Aging for the financial support of my doctoral research and education.
3
ABSTRACT
Epigenetic Regulation of Endocrine Aging Transitions of the Perimenopausal
and Menopausal Brain
Loss of estrogen at menopause has profound effects in nearly all tissues, and in humans,
is marked by an increased risk for stroke, coronary heart disease, and neurological dysfunction in
some women. Furthermore, earlier menopause is linked to adverse cognitive outcomes later in
life and is considered to be a risk factor for Alzheimer’s disease (AD). Heritability of menopause
timing is only 44-66% and variability is present in monozygotic twins and inbred rat strains. In
humans, menopause is associated with accelerated epigenetic age, with post-menopausal women
being “epigenetically” and “biologically” older than pre-menopausal women of the same
chronological age. However, the cause-effect relationship between epigenetic and menopause
age remains undetermined. To address this, we conducted RNA-seq and RRBS methylation
profiling in both the hypothalamus and hippocampus across the perimenopause transition in
young regular cycling, middle-aged regular cycling, middle-aged irregular cycling, and middle-
aged acyclic animals to understand how gene expression and DNA methylation changes with
endocrine status. We also interrogated mechanisms regulating onset of irregular and acycling by
perturbing the methylome in young regular cycling animals to investigate underlying factors
responsible for early menopause.
The first chapter discusses how epigenetic mechanisms regulate and respond to critical
transition periods during development and explores data supporting the hypothesis that
reproductive aging is epigenetically regulated. The second chapter builds upon and tests this
hypothesis by profiling DNA methylation and gene expression changes in the hypothalamus
4
across the perimenopause transition. Using methylome-modifying agents we show that DNA
methylation directly influences perimenopause timing. The third chapter investigates RNA
expression and DNA methylation changes in gene networks related to glucose and myelin
metabolism in the hippocampus, and discusses how changes in these pathways are relevant to
age-related disease etiology and cognitive decline. Finally, the fourth chapter explores the
implications of our findings and how they relate to women’s health in the clinic.
The goal of this project is to better understand factors contributing to individual
differences in perimenopause timing and to identify biological mechanisms responsible for the
increased risk of cognitive impairment during and after the perimenopause transition in the hopes
of developing strategies to identify women at risk and as well as therapeutic interventions.
5
LIST OF ABBREVIATIONS
5-aza, 5-aza-2’-deoxycitidine
5-mC,5-methylcytosine
Acyc, Acyclic
AD, Alzheimer’s Disease
ADHD, Attention-deficit/hyperactivity disorder
APP, Amyloid Precursor Protein
ARTs, Assisted Reproductive Technologies
BDNF, Brain-Derived Neurotrophic Factor
BPA, Bisphenol A
Cbx7, Chromobox 7
D, Diestrus
DES, diethylstilbestrol
DNMT, DNA methyltransferase
E, Estrus
E2, beta-estradiol
EDCs, Endocrine Disrupting Chemicals
EE2, Ethinylestradiol
Eed, Embryonic Ectoderm Development
Esr2, Estrogen receptor β gene
FA, Folic Acid
FSH, Follicle Stimulating Hormone
GnRH, Gonadotropin-Releasing Hormone
GR, glucocorticoid receptor
H3K27me3, Histonr 3 Lysine 27 tri-methylation
H3K4me3, histone 3 lysine 4 tri-methylation
H3K9me2, Histone 3 Lysine 9 di-methylation
Hcy, Homocycstein
HDAC1
HMFA, High Maternal Folic Acid
HPG, Hypothalamic-Pituitary-Gondal
HT, Hormone Therapy
HYPER, Hypermethylated
HYPO, Hypomethylated
IGF2, Insulin Like Growth Factor 2
IPA, Ingenuity Pathway Analysis
Irreg, Irregular Cycling
IUGR, Intrauterine Growth Restriction
KISS1, Kisspeptin1
LH, Luteinizing Hormone
M, Metestrus
OXT, Oxytocin
P, Proestrus
PAM, Perimenopause Animal Model
6
Reg, Regular cycling
RRBS, Reduced Representative Bisulfite Sequencing
S HYPER, Strongly Hypermethylated
S HYPO, Strongly Hypomethylated
SAM, S-adenosylmethionine
Tet, Ten-eleven translocation methylcytosine dioxygenase
7
TABLE OF CONTENTS
1. TRANSITION STATE EPIGENETICS OF THE DEVELOPING AND
AGING BRAIN: EPIGENETIC MECHANISMS REGULATE ONSET AND
OUTCOMES OF BRAIN REORGANIZATION THROUGHOUT LIFE…11
1.1 Abstract……………………………………………………………………...11
1.2 Introduction…………………………………………………………………12
1.3 Methyl-donor Molecules and One-Carbon Metabolism Build and
Maintain the Epigenome...…………………………………………………..13
1.3.1 Epigenetics and Learning and Memory………………………………………..15
1.3.2 One-carbon metabolism Regulates the epigenome Throughout Life………16
1.4 The First Epigenetic Transition: from gametes to zygote…………..…....17
1.4.1 Offspring Health is Impacted before Conception…………………………….17
1.4.2Early Embryonic Epigenetic Reorganization………………………………….18
1.5 Embryonic Environment and Implications Later in Life: Maternal
Nutrition, the Epigenome, and Long Term Implications…………………19
1.5.1 Epigenetic Patterns Established in utero Remain Sensitive to Postnatal
Environment……………………………………………………………………………...19
1.5.2 Toxic in utero Exposures and Negative Health Risks………………………..20
1.6 Puberty is a Window Period for Long Term Neurocognitive Health
Consequences and is Regulated by Epigenetics…………………………..22
1.6.1 Pubertal Timing and Health Outcomes……………………………………….23
1.6.2 Puberty Onset is Epigenetically Controlled………………………………….24
1.6.3 The Transition Period into Puberty is Hypersensitive to Environmental
Insult……………………………………………………………………………….25
1.7 Aging and Reproductive Senescence: Adult Transitions and Age-Related
Diseases……………………………………………………………………...27
1.7.1 Normal aging………………………………………………………………………27
1.7.2 Reproductive Senescence: Menopause and Andropause…………………….28
1.7.2.1 Epigenetic Regulation of GnRH Releasing Neurons……………………………....30
1.7.2.2 Dysregulation of the HPG Axis and Neurodegeneration………………………...31
1.7.2.3 Epigenetic Regulation of Menopause Transition and Timing…………………....32
1.7.3 Epigenetic Consequences of Menopause………………………………………33
1.7.3.1 Epigenetic Consequences of Endocrine Changes…………………………………33
1.7.3.2 Epigenetic Consequences of Deficient One-carbon Metabolism during
Perimenopause………………………………………………………………………...34
1.7.3.3 Epigenetics, Menopause, and Neurodegeneration………………………………..36
1.8 Conclusion…………………………………………………………………...37
2. NEUROLOGICAL AND ENDOCRINE AGING BEINGS BEFORE
PERIMENOPAUSE AND IS DRIVEN BY DNA METHYLATION……….38
2.1 Abstract……………………………………………………………………...38
2.2 Introduction………………………………………………………………....39
2.2.1 Evidence for Epigenetic Regulation of Endocrine Aging…………………...39
8
2.2.2 Epigenetic Consequence of Menopause………………………………………..41
2.3 Methods………………………………………………………………………42
2.3.1 Animals……………………………………………………………………………..42
2.3.2 5-aza-2’-deoxycytidine Treatment……………………………………………....43
2.3.3 Methionione Treatment…………………………………………………………...44
2.3.4 Tissue Collection…………………………………………………………………..44
2.3.5Nucleic Acid Extraction…………………………………………………………...44
2.3.6 RNA-seq…………………………………………………………………………….45
2.3.7 RNA-seq Bioinformatic Analysis by Ingenuity Pathway Analysis (IPA)…..45
2.3.8 Global DNA Methylation Analysis……………………………………………...46
2.3.9 Genome-Wide DNA Methylation Profiling…………………………………….46
2.3.10 RRBS Sequence Alignments and Data Analysis……………………………..47
2.3.11 RRBS Pathway Analysis by Metascape……………………………………....47
2.4 Results……………………………………………………………………….48
2.4.1 The Perimenopause Animal Model (PAM)…………………………………….48
2.4.2 RNA-seq…………………………………………………………………………….49
2.4.3 Epigenetic changes across perimenopause…………………………………....56
2.4.4 GnRH signaling & the HPG Axis……………………………………………….75
2.4.5 Increase in Prolactin Production……………………………………………….75
2.4.6 Other Hormones and Receptors………………………………………………...77
2.4.7 Changes in Glutamate Signaling………………………………………………..77
2.4.8 Changes in GABA Signaling…………………………………………………….78
2.4.9 Melatonin and Circadian Rhythm signaling…………………………………..78
2.4.10 Epigenome Maintenance and One Carbon Metabolism……………………79
2.4.11 Onset and Duration of Perimenopause……………………………………....80
2.4.12 Changes in hypothalamic global DNA methylation………………………...82
2.4.13 DNA methylation regulates menopause timing……………………………...82
2.4.13.1 5-aza-2’-deoxycitidine Treatment Accelerates Perimenopause………………..82
2.4.13.2 Methionine Treatment Delays Loss of Cycling…………………………………..85
2.5 Discussion……………………………………………………………………86
2.5.1 Endocrine and Neurological Aging in Perimenopause……………………...86
2.5.1.1 Endocrine Aging Beings before Perimenopause…………………………………..86
2.5.1.2 Neurological Aging Beings before Perimenopause……………………………….89
2.5.2 Epigenetics and Perimenopause………………………………………………...90
2.5.2.1 Accelerated Epigenomic Aging in Early Transitioners…………………………...90
2.5.3 Perimenopause vs. Menopause………………………………………………….92
2.6 Conclusion…………………………………………………………………...92
3. EPIGENETIC CHANGES IN GENES REQUIRED FOR GLUCOSE AND
MYELIN METABOLISM IN THE BRAIN…………………………………. 93
3.1 Abstract……………………………………………………………………...93
3.2 Introduction…………………………………………………………………94
3.3 Methods……………………………………………………………………...96
3.3.1 Animals……………………………………………………………………………..96
3.3.2 Tissue Collection……………………………………………………………….....97
9
3.3.3 Nucleic Acid Extraction…………………………………………………………..97
3.3.4 RNA-seq…………………………………………………………………………….98
3.3.5 RNA-seq Bioinformatic Analysis by Ingenuity Pathway Analysis (IPA)…..98
3.3.7 Genome-Wide DNA Methylation Profiling…………………………………….99
3.3.8 RRBS Sequencing Alignments and Data Analysis…………………………...100
3.4 Results………………………………………………………………………101
3.4.1 The Regular-Irregular Transition Shows the Most Differentially
Expressed Genes…………………………………………………………………101
3.4.2 Genes Responsible for Glucose Uptake Increase and Diabetes Risk
Decreases in Regular and Irregular Cycling Animals……………………..101
3.4.3 Insulin Signaling and Glucose Tolerance are Perturbed in Irregular
Cycling Animals……………………………………………….…………………102
3.4.4 Fatty Acid Metabolism Changes with Age in Regular Cycling Animals
and Again at Regular-Irregular Transition….……………………………….104
3.4.5 Genes Involved in both Myelin Catabolism and Myelin Generation
and Repair are Up-regulated between 6-9 Month in Regular Cycling
Animals……………………………………………………………………………108
3.4.6 Gene Pathways Promoting Alzheimer’s Pathology are Down-
regulated between 6-9 Months and are Up-regulated during Regular-
Irregular Transition………..……………………………………………………111
3.4.7 Gene Changes Suggest that Glucose Metabolism Declines and
Glycogen Stores are Depleted during Regular-Irregular Transition….….113
3.4.8 Mitochondrial Bioenergetics Decline during Regular-Irregular
Transition…………………………………………………………………….…...114
3.4.9 Genome-Wide Changes in DNA Methylation Occur Mostly During
the Regular-Irregular Transition; Pathway Specific Changes Occur
Mostly Between 6-9 Months…………………………………………………….115
3.4.10 Changes in DNA Methylation Occur Predominantly in Insulin,
Diabetes, Glucose Metabolism, Phospholipase, and Myelin
Catabolism Pathways Prior to Irregular Cycling…...………………………117
3.4.11 Mitochondrial Dysfunction Is Not Regulated by DNA Methylation…….119
3.5 Discussion…………………………………………………………………..119
4. CONCLUSIONS……………………………………………………………124
4.1 Hypothalamic Aging Precedes Aging in the Hippocampus…..…………124
4.2 Fold Differences of Age-Related Gene Expression Changes are
Relatively Small………………..…………………………………………...126
4.3 Do 5-aza and Methionine Treatments Alter Gene Networks that
Exhibit Change during Perimenopause.………………………………….128
4.4 Menopause, Aging, and Alzheimer’s Disease are all Associated
with DNA Hypomethylation ………………………………………………….129
4.5 Earlier Intervention is Needed in Preventing Menopause-Associated
Health Consequences…………….………………………………………...130
10
4.6 Hormone Therapies and Interventions for Pre-, Peri-, and Post-
Menopausal Women are Not Enough to Prevent Cognitive Decline……………....131
REFERENCES………………………………………………………………...133
11
1. TRANSITION STATE EPIGENETICS OF THE DEVELOPING AND
AGING BRAIN: EPIGENETIC MECHANISMS REGULATE ONSET AND
OUTCOMES OF BRAIN REORGANIZATION THROUGHOUT LIFE
Abstract
Brain development is a life-long process that encompasses several critical periods of
transition, during which dramatic and widespread cognitive changes occur. Development in
utero, puberty, and reproductive senescence are all periods of transition that are hypersensitive to
environmental factors. Rather than isolated episodes, each transition builds upon the last and is
influenced by consequential changes that occur in the transition before it. Epigenetic marks, such
as deoxyribonucleic acid (DNA) methylation and histone modifications, provide a mechanism by
which early events can influence development, health, and aging outcomes later in life. For
example, parental environment influences imprinting patterns in gamete cells, which ultimately
impacts gene expression in the embryo; poor maternal nutrition during pregnancy increases the
risk of metabolic disorders in adult offspring; exposure to endocrine modifying molecules in
utero can disrupt puberty timing; precocious puberty is linked to a variety of disorders and
diseases; and early menopause is associated with increased risk for neurodegenerative and other
age-related diseases. This review explores how epigenetics induce and regulate critical periods,
and also discusses how early environmental interactions ultimately not only prime a system
towards a particular health outcome, but also influence the risk of disease or cognitive
impairment later in life.
12
Introduction
Transition states represent critical periods during development and aging when systems
undergo dramatic and widespread changes. These “critical periods” are highly dynamic and often
span several years, such as in the case of puberty and reproductive senescence (perimenopause in
women and andropause in men) [1]. These transition periods are not limited to peripheral tissues,
but are also accompanied by a neurological component, suggesting that the two are intimately
linked [2]. Brain development is a life-long process that is sensitive to both genetic and
environmental factors, and although the underlying deoxyribonucleic acid (DNA) remains
relatively unchanged throughout life, environmental perturbations during critical periods can
result in lasting epigenetic alterations in the brain that may lie dormant before playing out later in
life [3-8].
The epigenome is constantly changing. While a single epigenetic change may not be
enough to significantly influence an entire network to induce an altered phenotype, accumulation
of many changes over time has the potential to alter health trajectories and outcomes. With time
and continuous exposure, diverse environmental insults can sensitize a brain and raise the risk of
subsequent environmental triggers inducing a disease phenotype [2, 9]. Dysregulation of the
brain epigenome has been linked to a number of neurological dysfunctions. Indeed, aberrant
DNA methylation and histone modifications have been seen in autism [10, 11], Alzheimer’s
disease (AD) [12-17], schizophrenia [18, 19], and post traumatic stress disorder (PTSD) In this
review, we refer to epigenetics broadly, but focus mainly on DNA methylation and histone
modifications, which are the best-characterized mechanisms of epigenetic regulation. The
blossoming field of epigenetics stands to provide many answers as to how environmental
13
conditions influence gene expression, disease pathology, and overall phenotypes in the
developing and aging brain.
Methyl-donor Molecules and One-carbon Metabolism Build and Maintain the
Epigenome
Establishment, maintenance, and reorganization of the epigenome rely on the availability
of methyl-donor molecules that are produced in the one-carbon cycle. One-carbon metabolism
utilizes co-factors such as folate, choline, and various other B vitamins (B
6
, B
12
, riboflavin), to
produce S-adenosylmethionine (SAM), the universal methyl-donor that provides methyl-groups
used for DNA, histone, and other protein methylation (Fig. 1). One-carbon metabolism and SAM
production may be impaired by a B-vitamin deficient diet and can result in epigenome
dysregulation, setting the stage for long-term health consequences.
B-vitamin micronutrients are especially critical during embryonic development. In
Humans, maternal folic acid (FA) and choline deficiencies are linked to neural tube defects
(spina bifida) and an increased risk for autism [22-25]. In rats, choline deficiency is linked to a
reduction of neural progenitor cell proliferation and an increase in apoptosis in developing brain
tissues [26, 27]. Furthermore, choline-deprived offspring also show diminished visuospatial and
auditory memory that persists throughout life [28]. Conversely, offspring of choline-
supplemented mothers exhibit an accelerated rate of neurogenesis, a reduction in apoptosis [26,
29], and an increase in visuospatial and auditory memory that is not subject to decline during
normal aging, suggesting that supplemental choline in utero may protect against age-related
cognitive decline later in life [30-34]. These effects are likely mediated through epigenetic
14
mechanisms, as choline deficiency in utero has been associated with changes in the DNA
methylation of genes related to the cell cycle [35, 36].
The agouti mouse model serves as an excellent “epigenetic sensor,” allowing for the easy
visualization of an altered epigenome. All mammals possess the agouti gene, which specifically
determines coat color in the agouti mouse. Hypomethylation of the agouti gene results in a
yellow coat, and hypermethylation in a brown coat. Intermediate levels of methylation in the
agouti gene result in a mottled coat, with the degree of methylation directly corresponding to
mottle intensity [37]. In several studies, pregnant mothers fed diets supplemented with one-
carbon metabolites, such as FA, B12, and choline, gave birth to mice with altered coat color
indicating that these molecules directly influence DNA methylation patterns [38-40]. The
offspring of supplemented mothers were also less prone obesity and other diseases [39].
Transgenerational effects that persisted to the F2 generation were also observed [41].
Maternal folic acid supplementation or restriction alters DNA methylation in many genes
in a sex-specific manner [22]. In males, high maternal folic acid (HMFA) is linked to
hypomethylation and reduced expression of Ror2 (receptor tyrosine kinase like orphan receptor
2)[22], a gene involved in neurogenesis and development of the neocortex [42]. [42]. In HMFA
females, the gene Mtap4 (microtubule-associated protein 4), which plays a role in the central
nervous system (CNS) and in microtubule-dependent transport [43], was overexpressed and
hypomethylated in the promoter region. The study also identified imprinted genes that differed in
DNA methylation status depending on FA diet. Dio3 (Deiodinase, Iodothyronine, Type III),
which has clinical implications in insulin-related diseases, showed differential methylation in
female HMFA mice. In males however, an HMFA diet had no effect on Dio3. Interestingly, in
both male and females, HMFA was associated with the promoter hypermethylation of several
15
autism candidate genes, suggesting that FA’s protective effects against autism is mediated
through epigenetic mechanisms [23-25]. However, the specific subset of autism candidate genes
differed by sex.
Fig. 1. One-carbon metabolism utilizes co-factors such as folate, choline, and various other B vitamins
(B6, B12, riboflavin), to produce S-adenosylmethionine (SAM), the universal methyl-donor that provides
methyl-groups used for DNA, histone, and other protein methylation. Impaired one-carbon metabolism
results in loss of SAM production, an accumulation of homocysteine, and can lead to dysregulation of the
epigenome.
Epigenetics and learning and memory
Dynamic regulation of DNA methylation is required for the transcription changes
involved in successful learning and memory in neurons [44, 45] [44, 45] – a process that can be
16
impaired by methyl-donor deficiency. Indeed, mice with methyl-donor deficiency show reduced
memory consolidation, poor performance in novel object recognition memory tests, and
differential methylation and expression of the glutamate receptor gene, Gria1 [46].
In addition to one-carbon metabolites, fetal deficiency of other micronutrients has
epigenetic consequences linked to learning and memory impairments that persist into adulthood
[47]. Iron deficiency is associated with reduced brain-derived neurotrophic factor (BDNF)
expression in the hippocampus that is accompanied by a loss of DNA methylation, increase in
histone deacetylase 1 (HDAC1) binding, and decreased ribonucleic acid (RNA) polymerase II
binding at the BDNF promoter [47]. These effects, however, can be reversed by choline
supplementation during late gestation, implicating one-carbon metabolism as essential for neuro-
development and long-term health.
One-carbon metabolism regulates the epigenome throughout life
The negative health consequences of impaired one-carbon metabolism are documented
throughout life stages and are particularly apparent during periods of transition. Unfortunately,
the only period during which supplementation is encouraged is during pregnancy (and shortly
after breastfeeding). Although an increased intake of folate during pregnancy has the potential to
prevent the miscarriage and birth defects associated with deficiency, this practice has fostered an
increase in individuals harboring genetic polymorphisms that compromise folate usage [48].
These individuals in particular have an increased requirement for additional folate that is usually
not met during adolescence and adulthood and may predispose them to early aging and cognitive
impairments later in life. Just as all mothers are encouraged to increase their folate intake during
pregnancy, B-vitamin supplements may be beneficial for all individuals throughout life to
17
maintain a healthy epigenome and reduce the risk of age-related diseases. Further research is
necessary to identify specific gene networks that are impacted by methyl-donor deficiency at
different stages of life, and how this relates to disease pathogenesis down the line.
The First Epigenetic Transition: from Gametes to Zygote
Offspring health is impacted before conception
Despite the widespread loss of DNA methylation during early fertilization, some
epigenetic information must be maintained from the original egg and sperm in order to initiate
the subsequent re-programming of the methylome. The retained epigenetic characteristics are
referred to as being “imprinted” onto the genome. Imprinted control regions (ICR) that survive
reprogramming are retained through cellular differentiation and are ubiquitously present in the
adult organism’s somatic tissues. Only primordial germ cells are exempt from the process of
imprinting Imprinted genes set the stage for subsequent epigenetic responses to environmental
perturbations and influence epigenetic remodeling incorrect imprinting during fertilization has
been associated with a number of cognitive and developmental disorders that emerge later in life,
including schizophrenia [18, 52], Angelman syndrome [53], Beckwith-Wiedemann syndrome
[54], Prader-Willi syndrome [55], as well as multiple types of cancers [56, 57] including
breast[58], ovarian [59], and colorectal cancers [60, 61], and leukemia [62].
Imprinted alleles ensure that although a zygote inherits two genetic copies of each gene,
only one copy will be actively expressed in certain genes. This is particularly important in X
inactivation in females, as well as in other autosomal genes where a double dose is toxic [63, 64].
For example, the insulin-like growth factor 2 gene (IGF2) is maternally imprinted (silenced), and
18
only the paternal copy is actively expressed. Re-activation of the maternally inherited IGF2, due
to loss of imprinting, can impact mitochondrial function [65], and is often observed in tumors
[66, 67]. In another example, the improper embryonic imprinting of the Insulin (INS) and
Guanine Nucleotide Binding Protein (G Protein), Alpha Stimulating Activity antisense RNA
(GNASAS) genes are associated with an increased risk in coronary heart disease in adulthood
[68].
Epigenetic mechanisms are well known contributors to developmental disorders, and
gamete imprinting is sensitive to a variety of environmental factors, such as alcohol [69-72],
parental age [19, 73-78], endocrine disrupting chemicals (EDCs) [5, 6, 79-81], as well as fertility
treatments, such as assisted reproductive technologies (ARTs) [82-87]. Thus, parental health,
behavior, and lifestyle before conception leave a lasting impact on offspring health and
development.
Early embryonic epigenetic reorganization
Undeniably, early embryonic development is a period of dramatic change and cellular
reprogramming. Although each differentiated cell has a unique epigenetic signature that cannot
be easily reversed, successful reproduction requires a “reset” of this signature to allow for
totipotency to be restored to a fertilized zygote. Genome-wide studies mapping the DNA
methylation patterns of mouse oocytes, sperm, and fertilized gamete cells through early
development provide evidence for two major epigenetic reprogramming phases during early
embryonic development [88]. Upon fertilization, the zygote undergoes global de-methylation of
the genome. DNA methylation levels continue to drop during the first few rounds of cellular
division before genome re-methylation begins and global levels stabilize. In contrast to somatic
19
tissues, where high CpG-density is correlated with low DNA methylation, the early pre-
implantation phase of a developing embryo is a period during which DNA methylation is
differentially positioned and maintained [89]. During early development, non-CpG methylation
and higher levels of hydroxymethylation have been observed [90]. Little is known about the
regulatory role of non-CpG methylation and hydroxymethylation; however, this may suggest that
the pre-implantation phase is highly plastic and hypersensitive to environmental perturbations
that have an increased potential to influence the health outcomes of the developing organism.
The Embryonic Environment and Implications Later in Life: Maternal
Nutrition, the Epigenome, and Long-term Implications
Epigenetic patterns established in utero remain sensitive to the postnatal environment
Environmental perturbations during embryonic development can result in epigenetic
modifications that correlate with alterations in the gene expression profiles seen in many adult-
onset diseases [4, 8]. Adverse maternal environments, such as poor nutrition, substance abuse,
diabetes, and poor mental health, can also have life-long consequences for offspring [3, 22].
Maternal under-nutrition is known to cause intrauterine growth restriction (IUGR) and low birth
weight [91]. Conversely, maternal over-nutrition, as well as gestational diabetes, has been linked
to macrosomia or high birth weight [91]. Both IUGR and macrosomia have been linked to an
increased risk of adult-onset obesity and metabolic disorders [92-94]. Moreover, the fetal
programming of the hypothalamus, which controls food intake and energy expenditure, is also
influenced by maternal nutrition. Over- or under- nutrition can negatively impact the
20
hypothalamic appetite regulatory systems and predispose offspring to metabolic disorders in
adulthood [95-97].
Mechanistically, the loss of the pancreatic and duodenal homeobox (Pdx1) gene
expression in IUGR is associated with adult-onset diabetes. Under conditions of poor maternal
nutrition during fetal development the histone deacetylase complex mSin3/HDAC is recruited to
the Pdx1 promoter in the pancreas. The loss of the histone acetylation at the Pdx1 promoter
results in the loss of the transcription factor binding required for Pdx1 gene expression [98].
Postnatally, a loss of the activating histone mark on histone 3 (H3) lysine 4 (K4) tri-methylation
(me3) (H3K4me3), and an increase of the repressive H3 Lysine 9 (K9) di-methylation (me2)
(H3K9me2), are seen at the Pdx1 gene promoter. While at this point, repression is still
considered to be “reversible,” the accumulating H3K9me2 marks soon recruit the DNA
methyltransferase DNMT3A, which then methylates and permanently silences Pdx1 expression
[8].
Although IUGR does not immediately result in Pdx1 gene silencing through DNA
methylation in utero, it establishes a chromatin state that is sensitive to further environmental
impact, and raises the likelihood that the gene will be silenced later in life. This same principle
can be applied to neurodevelopment, where the early environment can influence the likelihood of
a particular cognitive outcome and modify the risk of neurodegenerative diseases later in life.
Toxic in utero exposures and negative health risks
EDCs are chemicals that can interfere with the endocrine system and produce adverse
effects on health, reproduction, and development. EDCs are mostly synthetic and have been used
21
widely in industry to produce plastics, pesticides, and oral contraceptive birth control. In recent
years, EDCs have come under intense scrutiny as they have been linked to birth defects,
behavioral issues, cancer, and immune system and metabolic dysfunction [36]. EDCs are
harmful to organisms both pre- and post-natally, and are able to disrupt proper genomic
imprinting. Maternal exposure to bisphenol A (BPA) during late oocyte and early embryo
development has been shown to modify imprinted gene expression in mouse embryos and
placentas through the alteration of DNA methylation patterns and contribute to abnormal
placental development [37, 38]. The transgenerational effects of in utero EDC exposure are well
documented [5, 6, 79-81]. Between the years 1958 and 1976, diethylstilbestrol (DES) was a
common medicine given to pregnant women to prevent miscarriage. Both male and female
offspring exposed to this chemical have been documented as having a dramatically increased risk
for cancers and reproductive issues as adults [99], and a number of studies have also observed
third generation health risks [100-104]. Interestingly, these adverse health effects appear during
puberty, suggesting that DES may alter the epigenetic mechanisms involved in puberty
programming.
There is also growing concern regarding the endocrine disrupting effects of
ethinylestradiol (EE2), one of the primary components of combined oral contraceptives. Use of
this compound has exponentially increased over the last few decades. Increasing in parallel with
oral contraceptive use is the prevalence of cognitive impairments such as Autism Spectrum
Disorder and attention deficit hyperactivity disorder ADHD [36]. Unprocessed amounts of EE2
in oral contraceptives are excreted from the body and, when inadequately removed during
wastewater treatment, traces of the chemical can be found in local surface and ground water [39].
EE2 is known to alter gene expression through epigenetic mechanisms and many environmental
22
groups are becoming increasingly worried as to how this pervasive contamination may influence
the risk of neurodevelopmental disorders [36]. Although used by millions of women to prevent
pregnancy, a thorough study of long-term, or possibly permanent, influences on oocyte
epigenetics has not been conducted.
Finally, women born to mothers who smoked during pregnancy have altered DNA
methylation patterns throughout their genome, and an increased risk for developing gestational
diabetes, obesity, and hypertension as adults [105, 106]. In animal models, prenatal alcohol
exposure has also been linked to adiposity, beta cell dysfunction, and glucose intolerance in
adulthood [69, 70]. Experimentally reducing histone deacetylase expression has been shown to
reverse glucose intolerance in alcohol-exposed offspring, highlighting the potential for disease
intervention if the specific epigenetic perturbations can be identified [107].
Puberty is a Window Period for Long-term Neurocognitive Health
Consequences and is Regulated by Epigenetics
In mammals, initiation of puberty by activation of the hypothalamic-pituitary-gonadal
(HPG) system relies on the functional organization of the hypothalamic GnRH neural network -
a process that takes place early in development [79], suggesting that the transition to puberty is a
process that begins in utero and is manifested later in life [108]. Indeed, neonatal exposure to
EDCs can alter the activation and function of the HPG axis [109], as well as the timing of the
hypothalamic expression of kisspeptin (Kiss1) [110, 111], and ultimately the onset of puberty,
demonstrating how early environments can influence future transition periods and health
outcomes later in life.
23
Pubertal timing and health outcomes
Timing of puberty varies between individuals as well as by sex, with females reaching
sexual maturity earlier than males [112]. Females are more likely to experience precocious, or
early, puberty, while males are more likely to transition [7, 112]. The age of puberty onset can be
modified by environmental factors, and is associated with multiple health and cognitive
outcomes later in life. In girls, early menarche has been associated with an increased risk of
breast cancer, cardiovascular disease, depression, eating and behavioral disorders, diabetes and
obesity, as well as an overall increase risk of mortality [7]. Conversely, late menarche has been
associated with a decreased risk of ischemic heart disease, but an increased risk of osteoporotic
fracture [7]. In boys, initiation of puberty can be difficult to identify due to lack of a quantitative
measurements, such as age of menarche in girls; however, age of voice breaking is commonly
used as a qualitative measurement of puberty onset in males [113]. In boys, early puberty has
been linked to an increased risk for testicular cancer, whereas late puberty is associated with
depression and low self-esteem [7, 113, 114]. In both sexes, it is difficult to determine whether
mistiming in puberty directly causes the associated health conditions, or if these health
conditions are due to the underlying factors controlling puberty onset in the first place.
Nevertheless, the transition through puberty is a critical period when environmental factors can
permanently alter developmental and health trajectories.
24
Puberty onset is epigenetically controlled
As many as 106 distinct parent-of-origin alleles have been implicated in the regulation of
puberty, suggesting that puberty is at least partly controlled by classic, Mendelian genetics [115].
However, variable onset is present in monozygotic twins, as well as inbred rodent strains raised
in similar environments, suggesting that epigenetics may be involved. Indeed, several groups
have identified the KISS1 gene, which regulates puberty in all mammals, to be under epigenetic
control [116-121].
The initiation of puberty begins with the expression of KISS1’s protein product
kisspeptin. Subsequent signaling through the Kiss1 receptor in gonadotrophin-releasing hormone
(GnRH) neurons activates the neuroendocrine reproductive and HPG axes. Mutations in KISS1,
or its receptor KISS1R, can result in a failure to transition into puberty [117]. Differential
expression of KISS1 is thought to contribute to sex differences in the timing of puberty and in
the secretion of Luteinizing hormone (LH) in adulthood [118]. Indeed, studies have found sex-
specific differences in KISS1 messenger RNA (mRNA) expression as well as differences in
DNA methylation along the KISS1 promoter [122-124]. Furthermore, neonatal exposure to
EDCs can alter the activation and function of the HPG axis [109] and the timing of the
hypothalamic expression of Kiss1 [110, 111] demonstrating how early environment can
influence transition periods and health outcomes later in life.
Prior to puberty, the hypothalamus expresses the genes embryonic ectoderm development
(Eed) and chromobox 7 (Cbx7), which bind to the KISS1 promoter and recruit polycomb
repressive complex 2 (PRC2) [116, 117]. PRC2 is responsible for silencing the KISS1 promoter
through chromatin reorganization by trimethylating H3 on lysine 27 (H3K27me3) [116]. At the
onset of puberty, DNA methylation increases at the Eed and Cbx7 gene promoter regions. The
25
increase of DNA methylation is accompanied by a decrease in the expression of the two genes,
and a subsequent loss of their binding at the KISS1 promoter [116, 117]. Loss of Eed and Cbx7
binding, resulting in a loss of KISS1 repression, is accompanied by an increase in transcription-
activating histone marks, H3K4me3, H3K9ac, and H3K14ac along the KISS1 promoter [116].
Thus the KISS1-mediated onset of puberty is set into motion through the epigenetic silencing of
repressive factors [116]. Over-expression of Eed or treatment with 5-azadine, a DNA
methylation inhibitor, is able to block puberty in female rats, further supporting the role of
epigenetic programming in the pubertal transition [116].
Considering the complexity of puberty and the wide range of systemic changes associated
with the transition, it is probable that epigenetic mechanisms are involved in orchestrating the
cooperation of many gene networks during this period. Lominiczi et al. surveyed genome-wide
changes in DNA methylation and RNA transcription across different points of the female
puberty transition and found that several genes with changes in expression were involved in
chromatin and histone modification [116]. Expression changes of many of these genes were also
accompanied by changes in DNA methylation. However, hormones are also known modifiers of
the chromatin landscape. Estrogen induces changes in KISS1 promoter histone acetylation,
possibly contributing to the positive feedback that is involved in generating the preovulatory
surge in females [125]. More research is needed to explore the details of the cause-effect
relationship between epigenetic programing and hormone signaling during puberty.
The transition period into puberty is hypersensitive to environmental insult
In addition to the maturation of reproductive tissues, puberty is a time of widespread
organizational changes in the brain. White matter volume in the frontal and parietal lobes peak at
26
puberty (and subsequently declines thereafter) [126-128], the limbic areas finish developing
[129, 130], and task-dependent brain activity changes [131, 132]. Several groups have found that
adverse life experiences or chronic stress before or during puberty are able to permanently alter
behavioral patterns in a sex-specific manner. Alcohol use during adolescence is associated with
an increased risk of alcohol abuse in adulthood [133]. Dewit et al. found that children who first
used alcohol at ages 11-14 had the highest risk for developing alcohol disorders later in life
[133]. Curiously, children who first used alcohol at an older or younger age did not carry this
same risk, suggesting that ages associated with puberty might be more sensitive to environmental
perturbations. Additionally, Barha et al. found that female rats exposed to chronic stress during
puberty had blunted neurogenesis in the dentate gyrus, resulting in changes in hippocampal
plasticity in adulthood [134]. They did not find similar results in male rats, arguing that neural
system vulnerabilities during puberty may be sex-specific.
Tran et al. investigated the involvement of epigenetic mechanisms in mediating the long-
term influences of adverse environmental encounters during puberty. Chronic stress during
puberty increased visceral pain behaviors in rats and led to an increase in the DNA methylation
of the glucocorticoid receptor and a decrease in the DNA methylation of corticotrophin-releasing
factor in the amygdala [135]. Additionally, Ceccarelli et al. showed that exposure to estrogenic
compounds during early puberty altered the population of neurons expressing estrogen receptor
alpha in the female rat hypothalamus [136].
Initiation of sexual maturation is tightly regulated by epigenetic mechanisms, and the
transition through puberty is a dynamic process that is influenced by environmental factors. If
early events in utero are able to alter the HPG axis to impact puberty onset, it is likely that
environmental conditions during the pubertal transition continue to influence other still-
27
developing brain networks. Alterations in histone modifications and DNA methylation are
mechanisms by which environmental factors during puberty can sensitize the brain towards
specific neurological outcomes and aging phenotypes later in life.
Aging and Reproductive Senescence: Adult Transitions and Age-Related
Diseases
Normal aging
Only 20-30% of the average human life span can be attributed to genetic variation,
implying that longevity is largely due to environmental factors [137-139]. At birth, monozygotic
twins have nearly identical epigenomes. Over time, however, their epigenomes diverge due to
environmental interactions and spontaneous errors in epigenetic maintenance [139]. At older
ages, the epigenomes of monozygotic twins can be dramatically different, explaining why many
have such different medical histories and health outcomes [139-141]. This kind of alteration in
epigenetic patterning is referred to as epigenetic drift.
In contrast to epigenetic drift, which is a seemingly random accumulation of epigenetic
changes over time, there is strong evidence for the existence of an “epigenetic clock,” suggesting
that many age-related changes in the epigenome may be “programmed” as a natural part of
aging. Many groups have identified trends in DNA methylation that changes in predictable
manners with increasing age [142-144]. In general, global DNA methylation declines with age,
with region specific hypermethylation. Global DNA hypomethylation is mainly associated with
repeating regions in the genome, such as Long interspersed nuclear elements (LINEs) and Alu
elements [145-148]. Global hypomethylation results in the loss of chromatin regulatory proteins
28
such as polycomb repression complexes and histone modifications, which subsequently results in
the global remodeling of chromatin and genome instability [149, 150]. However, DNA
hypermethylation at CpG islands (CGIs) results in reduced expression in many genes involved in
tumor suppression, genomic stability and repair, metabolism, cell differentiation and growth, and
regulation of the immune system [139]. Over time, accumulated changes in DNA methylation
and gene expression in networks involved in age-related diseases predispose an individual to
either susceptibility or resiliency to disease pathogenesis [139, 151-154].
Reproductive senescence: menopause and andropause
Perimenopause refers to the transition into female reproductive senescence. Menopause,
the completion of the perimenopause transition, is characterized by the exhaustion of oocytes,
amenorrhoea, and the loss of circulating estrogen [2, 155]. Loss of estrogen during female
reproductive aging has profound effects in nearly all tissues, including breast, bone,
cardiovascular, and brain. Menopause in humans is also marked by an increased risk for stroke,
coronary heart disease, and neurological dysfunction in some women [156-159]. Although a
majority of women have no long-term health consequences, many women experience
neurological symptoms during and after the perimenopause transition [2]. Furthermore, early
menopause has been associated with adverse cognitive outcomes later in life, and the loss of
estrogen during the perimenopause transition is considered to be a risk factor for developing AD
[160-162].
In males, an age-related decrease in plasma testosterone has been associated with both
physical and cognitive changes, including changes in bone architecture, body composition,
muscle strength, hair and skin, and mood [163]. The gradual loss of testosterone with age has
29
been called by several names, but is generally referred to as “andropause” or “late onset
hypogonadism.” The age-associated loss of circulating testosterone is due to both decreased
synthesis in a shrinking population of Leydig’s cells in the testes and disrupted HPG signaling. It
is estimated that in humans, testosterone levels decline at a rate of 1% a year after the age of 30
[164, 165] However, decline does not always occur evenly with age, and is highly variable
among individuals. Although up to 70% of men over the age of 60 may suffer from low levels of
testosterone [164], not all men exhibit symptoms and there is no universal clinically recognized
lower threshold by which andropause can be clearly diagnosed [163].
However, in both males and females, reproductive senescence is not merely characterized
by the loss of sex steroids, but instead is a function of both gonadal failure and hypothalamic-
pituitary aging. The HPG axis, which is activated during puberty, is a negative feedback system
in which pulsatile GnRH, produced in the hypothalamus, stimulates LH and follicle-stimulating
hormone (FSH) production and secretion by the pituitary. LH and FSH stimulate estrogen and
testosterone production in the ovaries and testes respectively. Systemic estrogen and testosterone
then feedback onto the pituitary and hypothalamus and modulate GnRH, LH, and FSH
production and secretion [166]. The pituitary response to GnRH, and the gonadal response to LH
and FSH simultaneously decline with age resulting in the diminished sex steroid production
characteristic of menopause and andropause, and a loss of negative feedback resulting in
increased GnRH, LH, and FSH production (Fig. 2) [166-169].
30
Fig. 2. The hypothalamic-pituitary-gonadal axis is activated during puberty by the epigenetic silencing of
repressive factors. Kisspeptin expression in the hypothalamus activates GnRH-releasing neurons that
signal to the pituitary to synthesize and release LH and FSH. LH and FSH then stimulate estrogen and
testosterone production in the ovaries and testes, respectively. During reproductive senescence, pituitary
responsiveness to GnRH decreases and LH pulses become desynchronized leading to an impaired sex
steroid production and a loss of negative feedback onto the hypothalamus and pituitary. Luteinizing
hormone (LH), Follicle stimulating hormone (FSH), Gonadotropin-releasing hormone (GnRH).
Epigenetic regulation of GnRH-releasing neurons
A series of studies have demonstrated that epigenetic mechanisms directly regulate
GnRH transcription, both in vitro and in vivo [170-172]. During puberty, a rise in GnRH mRNA
is accompanied by a change in the DNA methylation status of the gene promoter [170].
Additionally, specific patterns of histone modifications at the GnRH gene are associated with
differential levels of transcription [173]. Immature GnRH neuronal cells, which do not yet
produce GnRH, possess mostly repressive H3K9me2 histone marks along the GnRH gene, while
31
mature GnRH neuronal cells possess permissive H3K9ac and H3K4me3 histone marks [173].
Although epigenetic modifications in GnRH neurons have not yet been investigated in the
context of reproductive senescence, it is likely that the mechanisms involved in the activation of
the HPG axis are similar to those involved in its aging and ultimate deactivation.
Dysregulation of the HPG axis and neurodegeneration
LH and GnRH receptors are present in neurons throughout the brain, and changes in
concentrations of these gonadal hormones during reproductive senescence have the potential to
influence the structure and function of their neuronal targets. In the hippocampus, LH receptors
are highly expressed [167], and GnRH receptor concentrations increase with age and castration
[174, 175]. Additionally, increased levels of LH and GnRH have been observed in the
hippocampal pyramidal neurons of AD patients [167, 176, 177], suggesting that their altered
signaling activity during reproductive senescence might be involved in disease pathogenesis.
Increased LH levels are seen during the same time period that age-related cell cycle
alterations and the upregulation of oxidative markers are observed [178, 179]. LH has also been
shown to re-activate the mitotic signaling pathways seen in early AD pathogenesis [180, 181].
Furthermore, LH promotes the amyloidogenic pathway in amyloid precursor protein; (APP)
processing [167], providing a direct link to the progression of AD pathology. Lastly, men who
have undergone GnRH agonist therapy for prostate cancer (which ultimately reduces LH
production) show a decreased incidence of neurodegenerative diseases [182, 183]
AD patients exhibit a 2-fold increase in GnRH expression [176, 177]. Activation of
GnRH receptors in hippocampal pyramidal neurons results in a long-lasting enhancement of
32
synaptic transmission via glutamate receptors in CA1 & CA3 [184, 185]. GnRH receptor
activation is modulated by estrogen [186] and the loss of HPG estrogen negative feedback,
resulting in an increased GnRH that may play a role in neurodegeneration in AD [169, 174, 175].
Epigenetic regulation of menopause transition and timing
The heritability of menopause timing is 44-66% and similar to puberty, and variability is
present in monozygotic twins and inbred rat strains, suggesting that epigenetics and
environmental factors may be involved [12]. While recent studies have begun to compare the
pre- and post- menopause epigenome, no studies have directly investigated the epigenetic
mechanisms involved in driving the transition itself.
In humans, menopause has been associated with the accelerated epigenetic patterns of
aging in blood [187]. Women with an earlier age of menopause onset were found to be
“epigenetically older” than women with a later onset (Fig. 3). However, the cause-effect
relationship between epigenetics and menopause age remains undetermined. Untangling this
relationship will prove challenging as differences in epigenetic patterns seen across the
perimenopause transition most likely consist of both age-related changes that initiate onset of
reproductive senescence, as well as changes that occur as a direct result of endocrine status and
loss of circulating sex hormones. Further research is particularly important, as differential
outcomes of menopause have been associated with a risk for neurodegenerative and autoimmune
diseases [188, 189].
33
Epigenetic consequences of menopause
Epigenetic consequences of endocrine changes
In the brain, estrogen is proposed to play a role in synaptic function and is thought to be
neuroprotective against a number of age-related health issues [190-193]. Hormone therapy (HT)
has been proposed as a possible treatment in reducing the risk and symptoms of AD [194, 195].
However, cellular response and sensitivity to estrogen has been shown to decline following long-
term hormonal deprivation [196], and to date no studies have shown HT to be beneficial once
AD symptoms have already presented [197, 198]. Recent meta-analyses have suggested that HT
may actually worsen cognitive dysfunction if initiated too late [199, 200], suggesting that there
may be a window of opportunity after menopause during which HT may be effective.
34
Fig. 3. In humans, menopause is strongly associated with the accelerated epigenetic patterns of aging in
blood. Post-menopausal women are “biologically and epigenetically” older than pre-menopausal women
of the same chronological age. Epigenetic changes prior to and during the perimenopause transition may
provide an explanation for the age-related negative health and cognitive outcomes associated with early
menopause.
Recently, studies have shown that nuclear estrogen receptors are able to modify
chromatin structure and induce genome-wide epigenetic changes [201, 202]. In mouse mammary
tissue, co-activation of both an estrogen receptor (ER) and the glucocorticoid receptor (GR) is
involved in chromatin structure remodeling and is required to induce an accessible chromatin
state that allows GR to bind target DNA regions [202]. Estrogen receptors target hundreds of
genes throughout the genome, in multiple tissues [203-205], and it is possible that similar ER-
GR interacting mechanisms are at work in the brain. Furthermore, glucocorticoids are involved
in regulating the immune response and suppressing the immune response [206-213]. Aging has
been associated with aberrant inflammatory responses in human brains, and many
neurodegenerative diseases are characterized by an increased in chronic inflammation [206-213].
In rats, the onset of acyclicity is also marked by a sudden increase of inflammation in the brain
[214]. It is possible that changes in estrogen signaling and ER activation may trigger global re-
organization of chromatin and perturb the glucocorticoid regulation of the immune response in
the brain.
Epigenetic consequences of deficient one-carbon metabolism during perimenopause
One-carbon metabolism has the potential to modify the relationship between sex
hormones and methylation [215], further contributing to the complexity of endocrine aging.
35
Dietary supplementation of folate has been shown to increase luteal progesterone levels in pre-
menopausal women and to decrease the risk of sporadic anovulation [216], suggesting that folate
may be able to regulate the initiation of the perimenopause transition. Dietary differences in
folate and other one-carbon metabolites, in addition to individual differences in sex hormone
levels, may explain some of the differences seen in menopausal age, risk for cognitive
impairment, and response to intervention therapies.
The efficiencies of one-carbon metabolism vary among individuals, and can fluctuate
over time and with menopause status [217]. Estrogen stimulates the expression of
phosphatidylethanolamine N-methyltransferase (PEMT), a gene involved in the endogenous
production of choline [218]. The loss of estrogen during menopause results in a decreased ability
to produce choline and dramatically increases the need for exogenous choline intake.
Postmenopausal women are thus much more sensitive to choline deficiency and are more likely
to suffer from deficiency-induced fatty liver and muscle damage than are premenopausal women
[219, 220].
Thus, hormonal changes during menopause can negatively impact one-carbon
metabolism and lead to a deficiency in the methyl-donors required to properly maintain the
epigenome. Other evidence of an impaired one-carbon cycle, due to decreased enzymatic activity
or co-factor deficiency, is the accumulation of the intermediate molecule homocysteine (Hcy)
[13, 46]. Methyl-donor synthesis from Hcy relies on the bioavailability of the one-carbon co-
factors folate, choline, and various other B vitamins (Fig. 1). Elevated plasma Hcy has been
observed in post-menopausal women [221], as well as in AD patients, and the dysregulation of
the epigenome in AD is well established [14, 15, 17, 222-226]. Additionally, high levels of Hcy
are associated with an increased risk for developing AD [227, 228] and impaired one-carbon
36
metabolism has been linked to AD, Parkinson’s disease, and other psychiatric disorders [229-
241].
Impaired one-carbon metabolism, resulting in the dysregulation of the epigenome,
provides a link between the perimenopause transition and a risk for cognitive impairment later in
life. Systemic decline in estrogen combined with nutrient deficiencies resulting in impaired
epigenetic maintenance creates a hyper-plastic state that sensitizes the perimenopausal brain to
environmental insults and modifies an individual’s health trajectory.
Epigenetics, menopause, and neurodegeneration
The neurological consequences of menopause have been largely believed to be due to the
dramatic loss of circulating estrogen. Therapies targeting estrogen receptors have been used to
treat many diseases, including breast, uterine, and ovarian cancers, as well as neurodegenerative
diseases. Epigenetic-estrogen signaling interactions are well documented in reproductive
cancers; however, little research has been devoted to understanding the role of epigenetics in
modulating the perimenopause transition. It is likely that in addition to declining estrogen,
epigenetic mechanisms may also share responsibility for cognitive changes and increased risk for
neurodegenerative diseases. Decoding what factors increase the risk for developing neurological
disorders before, during, and after the perimenopause transition will lead to a better
understanding of the “aging” process, and will aid in the development of strategies to prevent
cognitive decline later in life.
37
Conclusion
Brain development is a life-long process that is sensitive to both genetic and
environmental factors. While the genetic code is mostly invariant, environmentally-induced
epigenetic alterations in the brain can cause phenotypes that manifest themselves later in life,
during critical periods of transition. While traditionally only associated with early brain
development, “critical periods” occur throughout life: development in utero, puberty, and
reproductive senescence are all periods of transition that are hypersensitive to environmental
perturbations. Changes that occur during these sensitive periods are then able to influence
subsequent critical periods. For example, in utero exposure to endocrine-disrupting chemicals is
a known cause of precocious puberty, which itself is associated with an increased risk for breast
cancer and other diseases later in life. Rather than being isolated episodes, each transition builds
upon the last. Ultimately, “aging,” neurodegeneration, and other age-related health outcomes are
merely a result of the culmination of these complex series of events that begin in utero and
progress throughout life.
38
2. NEUROLOGICAL AND ENDOCRINE AGING BEINGS BEFORE
PERIMENOPAUSE AND IS DRIVEN BY DNA METHYLATION
Abstract
To better understand the potential underlying mechanisms of neurological symptoms
associated with perimenopause, as well as the control of age of perimenopause onset and
completion, the current study aims to characterize the transcriptional and epigenomic changes
that occur during the transition using a rat model recapitulating fundamental characteristics of
the human perimenopause. RNA-seq analysis showed that the majority of differentially
expressed genes occurred between 6-9 months, prior to perimenopause, when animals were still
regularly cycling. E2, GnRH, and KISS1 were identified as upstream regulators involved in the
transition between each endocrine status. One-carbon key players SAM and homocysteine were
identified as regulators of transcriptional changes during 6-9 months, suggesting that impaired
one carbon metabolism precedes irregular cycling. Pathway analysis of RNA-seq and RRBS
genome wide DNA methylation data identified GnRH signaling and the HPG axis, prolactin,
glutamate and GABA signaling, melatonin and circadian rhythm, as well as epigenome
maintenance and one-carbon metabolism, as pathways which undergo dramatic changes during
perimenopause. Hypothalamic global DNA methylation declined at the onset of irregular
cycling in 9 month animals, and two distinct populations with statistically different levels were
observed at 6 months of age. Treatment with 5-aza-2’-deoxycytidine significantly accelerated the
completion, but not initiation, of perimenopause, as characterized by acyclicity. Conversely,
methionine supplementation delayed the initiation of perimenopause, and resulted in a larger
proportion of animals still cycling regularly at 9 months of age. These data together provide
evidence that endocrine aging begins before the onset of irregular cycling and physical
39
manifestation of perimenopause and that epigenetics, including DNA methylation, regulate the
onset and completion of the perimenopause transition.
Introduction
Perimenopause marks the initiation of the transition into female reproductive senescence.
Similar to puberty, onset and completion of perimenopause varies among individuals and is
influenced by environmental factors. Menopause timing is only 44-66% heritable and variability
is present in monozygotic twins and inbred rat strains, suggesting that epigenetics and
environmental factors play an important role in reproductive aging. While recent studies have
begun to compare the pre- and post- menopause epigenome, no studies have directly investigated
the epigenetic mechanisms involved in driving the transition itself. Furthermore, the cause-effect
relationship of post menopausal epigenetic differences is still unclear. It is likely that the
epigenetic changes which occur during perimenopause consist of both programmed changes,
which drive the transition, as well as consequential changes that result from endocrine
fluctuations associated with reproductive aging and the loss of circulating sex hormones. Despite
the lack of direct investigation, studies suggest that reproductive aging is, at least in part,
epigenetically regulated.
Evidence for Epigenetic Regulation of Endocrine Aging
The epigenome is established and maintained by one-carbon metabolism, a process by
which B vitamins are used to generate the necessary methyl-groups used in DNA, histone, and
40
other protein methylation. Bioavailability of one-carbon key players may modify the relationship
between sex hormones and epigenetics [215]. Dietary supplements of folate (vitamin B9) have
been shown to increase luteal progesterone levels in pre-menopausal women and decrease risk
for sporadic anovulatory cycles [216], suggesting that folate (and other one-carbon metabolites)
may be able to regulate the estrus cycle and timing of perimenopause initiation through
alterations in the epigenome.
Both early and late menarche are associated with early natural menopause in women
[113] and perinatal exposure to endocrine disrupting chemicals (EDCs) can induce precocious
puberty and pre-mature reproductive aging in rats [242-244]. The epigenetic modifying
properties of EDCs are well established, and the influence EDCs impose on reproductive
maturation and aging is likely mediated through these epigenetic mechanisms.
EDC exposure in rats has also been shown to result in decreased expression of the
puberty-initiating Kisspeptin1 (KISS1) peptide later in adult life [6]. KISS1 expression is
required for initiating the pubertal transition and its’ transcription is tightly regulated by
epigenetic factors [116]. The role of KISS1 signaling in reproductive senescence is not clear, but
studies in human and primates have shown increases in KISS1 expression and secretion after
menopause and ovaryectomy [245, 246]. Knockout of endogenous progesterone in kisspeptin
neurons results in disrupted LH surge and impaired fertility in mice, exemplifying the
importance of KISS1 and KISS1-neuron signaling in estrus cycle regulation [247]. It is arguable
that like puberty, epigenetics may play an important role in regulating KISS1 expression and
signaling during perimenopause.
41
Epigenetic Consequence of Menopause
In women, the perimenopause transition spans several years and the resulting loss of
estrogen has profound effects in nearly all tissues, including breast, bone, cardiovascular, and
brain. Menopause in humans is also marked by an increased risk for stroke, coronary heart
disease, and neurological disorders [156-159]. Although the majority have no serious long-term
health consequences, many women suffer neurological symptoms during and after the
perimenopause transition. Individual differences in epigenetics combined with individual
differences of sex hormone levels may explain differences seen in menopausal age, response to
hormone therapy, and incidence of cognitive impairment and neurological diseases later in life.
One-carbon metabolism varies among individuals and can fluctuate over time as well as
with menopause status [217]. Estrogen stimulates expression of Phosphatidylethanolamine N-
methyltransferase (PEMT), a gene involved in endogenous production of the B vitamin choline
[218]. The loss of estrogen during menopause, resulting in impaired ability to produce choline,
dramatically increases the need for exogenous choline intake. Postmenopausal women are thus
much more sensitive to choline deficiency and are more likely to suffer from fatty liver and
muscle damage than premenopausal women due to deficient levels of choline [219, 220]. Shifts
in one-carbon metabolism during perimenopause have the potential to disrupt the production of
methyl-donors needed to properly maintain the epigenome. Alteration of epigenetic patterns due
to perimenopause-induced impairment in one-carbon metabolism might predispose a particular
individual to age-related diseases and cognitive impairment later in life.
Homocycstein is an intermediate in the one-carbon cycle and its’ accumulation signifies
impairment of the one-carbon cycle, due to decreased enzymatic activity or co-factor deficiency
42
[13, 46]. Methly-donor synthesis from homocycstein relies on bioavailability of one-carbon co-
factors folate, choline, and various other B vitamins. Elevated plasma homocycstein has been
observed in post-menopausal women [221] as well as Alzheimer’s disease (AD) patients, and is
associated with an increased risk for developing AD [227, 228]. Additionally, impaired one-
carbon metabolism has been linked to AD, Parkinson’s disease, and other psychiatric disorders
[229-241]. Changes in DNA methylation and dysregulation of the epigenome in AD are well
established [14, 15, 17, 222-226]. Impaired one-carbon metabolism, resulting in dysregulation of
the epigenome provides a link between menopause and risk for cognitive impairment later in life.
Investigation of epigenetic changes across the perimenopause transition is needed to better
understand the driving factors of perimenopause as well as the long-term cognitive consequences
associated with menopause.
Methods
Animals Animal studies were performed following National Institutes of Health guidelines on
use of laboratory animals; protocols were approved by the University of Southern California
Institutional Animal Care and Use Committee. A total of 201 young or middle-aged female
Sprague-Dawley rats were obtained from Envigo Laboratories. Regular 6 month group was
cycled from 5 month of age using rats that had given birth to at least one litter. Rats for all other
groups were aged from 8–9 month old breeders. One week after arrival, ovarian-functioning
statuses were evaluated daily by the cytology of uterine cells obtained from lavage at 11am. The
smear was morphologically characterized based on the four stages of the cycle: Estrus (E),
Metestrus (M), Diestrus (D) and Proestrus (P). The regular 4-5 day estrus cycle is defined as the
43
period between successive estrus smears (E, M, D, P, E, M, D, P, E). In addition to regular
cycling animals, selected groups included middle-aged rodents at defined stages of
perimenopause (Fig. 1B). The irregular group was defined as 2 contiguous cycles of >5 days
characterized by prolonged diestrus stages. The acyclic (constant estrus) group was defined as
persistent vaginal cornification lasting > 8 days. Rats at designated age (6m or 9–10m) and
cycling status were euthanized at estrus or constant estrus. Five sets of animals were used in this
study. The first set (85 rats in total) included all 4 experimental groups (Reg-6m, Reg-9–10m,
Irreg-9–10m, and Acyc-9–10m). Of this set, N = 5–6 per group were used for analyses of steroid
levels and RRBS data. A second set of rats, containing all four groups, (40 rats in total) was used
for RNA-seq analysis. A third set of rats (30 rats in total) was used to assess timing of
perimenopause onset, duration, and completion. Cycling status of these animals were monitored
from 9 months regular cycling until they reached constant estrus. The fourth set of rats,
containing only the Reg-6m group, (20 rats in total) was used for 5-aza-2’-deoxycitidine
treatment. A fifth set of rats, containing only the Reg-6m group, (26 rats in total) was used for
methionine treatment. Rats that did not meet the endocrine criteria for each group were excluded
from analyses for this study.
5-aza-2’-deoxycytidine Treatment 0.1mg/ml 5-aza-2’-deoxycitadine-saline solution was
prepared fresh for each use and stored on ice (Sigma Aldrich). Animals (N=6 per group) were
subcutaneously injected with 0.25mg/kg drug, or an equivalent volume of saline, three times
weekly (M,W,F) beginning at 6 months of age. Drug treated animals were euthanized upon
entering into constant estrus at 9 months. Saline treated animals were euthanized on estrus at 9
months. Tissues were collected and brains were perfused for immunohistochemical analysis.
44
Methionione Treatment 50 mg/ml Methionine-H2O stock solution will be prepared and stored
(Sigma Aldrich). Regularly cycling animals were subcutaneously injected with 50mg/kg,
100mg/kg, 200mg/kg methionine, or an equivalent volume of H2O, three times weekly (M,W,F)
beginning at 6 months of age. All three methionine treatment groups were combined for analysis
(n = 7) against the vehicle group (n = 4). Animals were euthanized at 10 months and tissues were
collected.
Tissue Collection For the first two sets of animals, rats were euthanized and the brains rapidly
dissected on ice. Cerebellum, brainstem, and hypothalamus were removed from each brain and
the two hemispheres were separated. The cortical hemisphere was fully peeled laterally and
hippocampus was then separated. Cerebellum, midbrain, brainstem, hypothalamus, and both
cortexes and hippocampi were harvested and frozen at −80°C for subsequent analyses. Ovaries
and uterus were harvested and frozen at −80°C for subsequent analyses.
Nucleic Acid Extraction Total RNA was extracted from tissue homogenized in trizol and
purified using the PureLink® RNA Mini Kit (Thermo Fisher Scientific). RNA was DNase
treated on column during purification (Thermo Fisher Scientific). DNA was extracted from
tissue homogenized in lysis buffer [10mM Tris-HCl(pH 8.0), 1mM EDTA, 0.1% SDS], RNase
treated (Zymo Research Corp., Irvine, CA), purified using phenol/chloroform/isoamyl alcohol,
and then precipitated in isopropanol.
45
RNA-seq Total RNA-seq libraries were constructed from RNA extracted from the hypothalamus
of female rats. Samples were run on the Illumina HiSeq 2500 using 50bp PE to obtain a total
read depth of roughly 50 million read pairs per sample. Raw data files in the FASTQ format
underwent QA/QC and trimming procedure in the cloud-based Partek Flow environment
(http://www.partek.com/). The paired end reads for each sample were then aligned using TopHat
to the rat reference genome rn6 (Ensembl 80). Transcript assembly and quantification of aligned
reads were carried out using Cufflinks. The Cufflinks output consisted of a list of differentially
expressed genes (DEG) for each comparison.
RNA-seq Bioinformatic Analysis by Ingenuity Pathway Analysis (IPA) Expression data for
genes with the p-value < 0.5 were analyzed by IPA core analysis composed of a network analysis
and an upstream regulator analysis. We used these relaxed criteria to maximize the coverage of
the gene array results in the bioinformatic analyses. The network analysis identified biological
connectivity among molecules in the dataset that were up- or down-regulated in a comparison
(focus molecules that serve as “seeds” for generating networks) and their interactions with other
molecules present in the Ingenuity Knowledge Base. Focus molecules were combined into
networks that maximized their specific connectivity. Additional molecules from the Ingenuity
Knowledge Base (interacting molecules) were used to specifically connect two or more smaller
networks to merge them into a larger one. A network was composed of direct and indirect
interactions among focus molecules and interacting molecules, with a maximum of 70 molecules
per network. Generated networks were ranked by the network score according to their degree of
relevance to the network eligible molecules from the dataset. The network score was calculated
with Fisher’s exact test, taking into account the number of network eligible molecules in the
46
network and the size of the network, as well as the total number of network eligible molecules
analyzed and the total number of molecules in the Ingenuity Knowledge Base that were included
in the network. Higher network scores are associated with lower probability of finding the
observed number of network eligible molecules in a given network by chance.
The Ingenuity’s Upstream Regulator Analysis in IPA is a tool that predicts upstream
regulators from gene expression data based on the literature and compiled in the Ingenuity
Knowledge Base. A Fisher’s exact test p-value was calculated to assess the significance of
enrichment of the gene expression data for the genes downstream of an upstream regulator. A z-
score was given to indicate the degree of consistent agreement or disagreement of the actual
versus the expected direction of change among the downstream gene targets. A prediction about
the state of the upstream regulator, either activated or inhibited, was made based on the z-score.
Global DNA methylation Analysis 100ng of genomic DNA from hypothalamus, hippocampus,
or blood was used to determine total 5-methylcytosine (5-mC) using the 5-mC DNA ELISA kit
(Zymo Research Corp., Irvine, CA, USA) as per the manufacturer’s instruction.
Genome-Wide DNA Methylation Profiling A modified reduced representative bisulfite
sequencing (RRBS) protocol (Methyl-MiniSeq
TM
) was used to prepare libraries from 200-500 ng
of genomic DNA digested with 60 units of TaqαI and 30 units of MspI (NEB) sequentially and
then extracted with Zymo Research (ZR) DNA Clean & ConcentratorTM-5 kit (Cat#: D4003).
Fragments were ligated to pre-annealed adapters containing 5’-methyl-cytosine instead of
47
cytosine according to Illumina’s specified guidelines (www.illumina.com). Adaptor-ligated
fragments of 150–250 bp and 250–350 bp in size were recovered from a 2.5% NuSieve 1:1
agarose gel (ZymocleanTM Gel DNA Recovery Kit, ZR Cat#: D4001). The fragments were then
bisulfite-treated using the EZ DNA Methylation-LightningTM Kit (ZR, Cat#: D5020).
Preparative-scale PCR was performed and the resulting products were purified (DNA Clean &
ConcentratorTM - ZR, Cat#D4005) for sequencing on an Illumina HiSeq.
RRBS Sequence Alignments and Data Analysis Sequence reads from bisulfite-treated
MiniSeq
TM
libraries were identified using standard Illumina base- calling software and then
analyzed using a Zymo Research proprietary analysis pipeline, which is written in Python and
used Bismark (http://www.bioinformatics.babraham.ac.uk/projects/bismark/) to perform the
alignment to the rn6 genome. Index files were constructed using the
bismark_genome_preparation command and the entire reference genome. The --non_directional
parameter was applied while running Bismark. All other parameters were set to default. Filled-in
nucleotides were trimmed off when doing methylation calling. The methylation level of each
sampled cytosine was estimated as the number of reads reporting a C, divided by the total
number of reads reporting a C or T. Fisher’s exact test or t-test was performed for each CpG site
which has at least five reads coverage, and promoter, gene body and CpG island annotations
were added for each CpG included in the comparison.
RRBS Pathway Analysis by Metascape The top 3,000 sites, sorted by p-vaue and irrespective
of methylation change, were used to generate a list of gene promoters (defined by +/- 1 kb of the
48
transcription start site (TSS)) and gene bodies (annotated exons and introns). This list was then
analyzed by Metascape’s Meta-analysis gene pathway enrichment. Each gene within the list is
first given an Entrez Gene ID. The gene ID list is then converted to Human gene IDs to take
advantage of knowledgebases which are most complete for the human genome. The analysis is
then performed as if the input list is human genes and compared against Metascape’s data
sources, which are updated monthly. Given a gene list, pathway/process enrichment analysis
applies the standard accumulative hypergeometric statistical test to identify ontology terms,
where input genes show significant presence. In the case of overlapping ontology terms,
redundant hits are automatically clustered into groups based on their similarities. A heuristic
algorithm samples the 20 top-score clusters and selects up to 10 best terms within each and then
connects all term pairs with Kappa similarity above 0.3. The resultant network is visualized with
Cytoscape, where each node represents one enriched term.
Results
The Perimenopause Animal Model (PAM)
9–10 month-old female Sprague-Dawley rats were stratified into groups according to the
stage of ovarian senescence that followed the classification of the human perimenopause-
menopause transition as per Stages of Reproductive Aging Workshop (STRAW) [248, 249]. The
endocrine aging groups included regular cyclers (4–5 day cycles), irregular cyclers (5–8 day
cycles), and acyclic (no cycling within 9 days) at 9–10 months. At 6 months, 82% of females
were regular cyclers (reg 6m), 18% were irregular cyclers at 8–9 months, 62% were regular
cyclers and the percentage of irregular cyclers increased to 43%, with the first appearance of
49
acyclicity in 5%; at 9–10 months, the percent of regular cyclers declined to 37% (reg 9m), the
percent of irregular cyclers decreased to 30% (irreg 9m), and the remaining 33% were acyclic
(acyc 9m) (Fig. 1A). Increase in body weight was observed between the 6 month- and the 9–10
month-old regular cyclers (p < 0.0001) and was constant for all subsequent stages (Fig. 1D).
There were no differences in body weight among 9–10 month-old animals at different endocrine
stages (p = 0.0515). Uterine weight did not differ across the 5 endocrine phenotypes (p = 0.30)
and was consistent with all animals being euthanized at estrus or constant estrus (Fig. 1B).
RNA-seq
A total of 20877 transcripts in the hypothalamus were identified, sequenced, and
analyzed. Between 6-9 months, 2094 (10%) genes were significantly different (p< 0.05).
Between 9 month regular and irregular, and irregular and acyclic groups, 374 (2%) and 442 (2%)
genes, respectively, were significantly different (p< 0.05) (Fig 2). Ingenuity Pathway Analysis
(IPA) was used to identify canonical signaling pathways associated with changes in gene
expression between endocrine groups. 446 significant signaling pathways were identified within
reg 6m – reg 9m, 256 within reg 9m – irreg 9m, and 195 within irreg 9m – acyc 9m group
comparisons. Selected top canonical pathways, and the genes involved, are summarized in table
1.
The top upstream transcriptional regulator across all group comparisons was beta-
estradiol (E2) (table 2). Surprisingly, E2 signaling was predicted to be already involved in the
down regulation of genes between 6-9 months, prior to perimenopause onset of irregular cycling.
GnRH and KISS1 were also identified as regulators across all group comparisons. SAM and
50
homocysteine were predicted to down regulate related genes between reg 6m and reg 9m groups,
suggesting that impaired one carbon metabolism may be responsible for transcriptional changes
seen during this time, possibly through epigenetic mechanisms. S-adenoslyhomocysteine, which
is formed by the demethylation of SAM during methyl-group donating, was identified as a
regulator during reg 9m – irreg 9m and irreg 9m – acyc 9m transitions. However IPA could not
predict in which direction SAH influenced gene transcription.
Fig 1. A) Transition of cycling stages with age: percentage of aging rats by cycling status from a cohort
of 85 rats. B) Body and uterine weight of animals with different age and cycling status. Data were
presented as average ± SEM, *p ≤ 0.0001, n = 9-20.
A
B
51
Fig 2. A total of 20877 transcripts in the hypothalamus were identified and sequenced. Of the total
number of transcripts sequenced, 2094 (10%), 374 (2%), 442 (2%) genes were significantly different
between the 6 and 9 months regular cycling, regular and irregular cycling 9 month, and irregular and
acyclic 9 month groups, respectively (p< 0.05).
52
Ingenuity Canonical Pathways
-log(p-
value)
z-score Genes
Reg 6m – Reg 9m
Dopamine-DARPP32 Feedback in cAMP Signaling 14.1 -4.33
NOS1,PRKACB,GRIN2A,CAMK4,GRIN2D,KCNJ16,PPP1CB,PRKG2,ATP2A2,PPP1R14B,KCNJ11,PLCD1,
KCNJ4,CACNA1E,PPP3CB,CREB1,PPM1L,PRKAR1B,PRKCE,PLCB1,PPP2R2C,ADCY8,GUCY1B3,KCNJ
12,GRIN2B,GUCY1A3,PPP1R1B,CSNK1G3,ADCY3,GNAQ,PRKAR2A,GNAI1,DRD5,CACNA1C,KCNJ3,P
LCL2,DRD2,ATF2,GRIN3A,ADCY9,PLCB4,PRKCD,ADCY1,ITPR3,GUCY1A2,PLCB3,PRKCH,KCNJ6,A
DCY7,PRKCB
Synaptic Long Term Potentiation 11.9 -3.781
PRKACB,GRIN2A,CAMK4,PPP1R1A,GRM3,GRIA1,GRIN2D,PPP1CB,KRAS,PPP1R14B,GRIA4,PLCD1,C
AMK2D,PPP3CB,MAPK3,CREB1,PRKAR1B,PRKCE,PLCB1,ADCY8,GRIN2B,RRAS,GRM1,PRKAR2A,G
NAQ,CACNA1C,PLCL2,GRIN3A,ATF2,GRM5,GRM7,PLCB4,PRKCD,ADCY1,ITPR3,PLCB3,PRKCH,GRI
A3,PRKCB
Synaptic Long Term Depression 11.4 -2.897
PAFAH1B2,NOS1,GRM3,GRIA1,GRID2,KRAS,PRKG2,GRIA4,PLCD1,LCAT,MAPK3,PLA2G5,PPM1L,IG
F1R,PRKCE,PLCB1,PPP2R2C,RYR1,GUCY1B3,PNPLA8,GUCY1A3,RRAS,GRM1,RYR2,GNAI1,CRH,GN
AQ,PLCL2,CRHR1,PLA2G2A,GRM5,GRM7,PLCB4,PRKCD,ITPR3,GUCY1A2,PLCB3,PRKCH,PAFAH1B
1,NPR2,GNAL,GRIA3,PRKCB
Endothelin-1 Signaling 10.9 0.289
PAFAH1B2,NOS1,NAPEPLD,PLD2,MAPK15,PIK3R5,SHC3,KRAS,CASP4,MAPK13,PLCD1,SHC1,LCAT,
MAPK3,FGFR4,PLA2G5,PRKCE,PLCB1,ECE1,ADCY8,CASP8,FRS2,GUCY1B3,PNPLA8,GUCY1A3,RRA
S,ADCY3,MAPK8,GNAI1,GNAQ,MAPK9,PLCL2,PLD1,PLA2G2A,PLD4,PIK3R3,ADCY9,FOS,PLCB4,PR
KCD,ADCY1,ITPR3,GUCY1A2,PLCB3,PRKCH,PAFAH1B1,ADCY7,GNAL,PRKCB
G-Protein Coupled Receptor Signaling 10.7
PRKACB,GRM3,PDPK1,KRAS,CAMK2D,FGFR4,MAPK3,PLCB1,SMPDL3B,FRS2,ADRA1B,HRH2,PDE1
0A,GRM1,RRAS,OPRM1,CNR1,NFKB2,STAT3,DRD2,MC4R,ATF2,PIK3R3,GRM7,ADCY9,RAP1GAP,PD
E1B,PLCB3,DUSP4,GNAL,NAPEPLD,CAMK4,PTGDR,PIK3R5,CHRM3,HRH3,OPRL1,HTR2C,SHC1,CRE
B1,PRKAR1B,PRKCE,PDE4D,ADCY8,ADORA1,PDE9A,ADCY3,PRKAR2A,GNAI1,RGS16,GNAQ,DRD5,
CRHR1,GRM5,PLCB4,LPAR1,RASGRP1,ADCY1,OPRK1,CALCR,ADCY7,PRKCB
Neuropathic Pain Signaling In Dorsal Horn Neurons 10.7 -4
PRKACB,GRIN2A,CAMK4,GRM3,GRIA1,GRIN2D,PIK3R5,GRIA4,PLCD1,TACR1,CAMK2D,MAPK3,FG
FR4,CREB1,PRKAR1B,PRKCE,PLCB1,KCNQ3,FRS2,GRIN2B,GRM1,PRKAR2A,PLCL2,GRIN3A,GRM5,
GRM7,PIK3R3,FOS,PLCB4,KCNQ2,PRKCD,ITPR3,PLCB3,PRKCH,GRIA3,PRKCB
CREB Signaling in Neurons 10.6 -4.117
PRKACB,GRIN2A,CAMK4,GRM3,GRIA1,GRIN2D,GRID2,PIK3R5,KRAS,GRIA4,PLCD1,SHC1,CAMK2D
,GNB3,MAPK3,FGFR4,CREB1,PRKAR1B,PRKCE,PLCB1,ADCY8,FRS2,GRIK1,GNG4,GRIN2B,GRM1,RR
AS,ADCY3,GNAI1,PRKAR2A,GNAQ,PLCL2,ATF2,GRM5,GRM7,PIK3R3,ADCY9,PLCB4,POLR2E,PRKC
D,ADCY1,ITPR3,PLCB3,PRKCH,ADCY7,GNAL,GRIA3,PRKCB
Axonal Guidance Signaling 9.8
DPYSL2,PRKACB,SHH,ECEL1,GLI2,ITSN1,FZD3,CXCL12,WNT6,KRAS,LIMK1,GNB3,FGFR4,MAPK3,
UNC5D,PLCB1,ADAM23,PLXNB2,GSK3B,FRS2,ACTR2,KALRN,RRAS,TUBB2A,ITGA5,PLCL2,HHIP,PI
K3R3,HERC2,EPHA6,PRKCD,FZD6,PLCB3,PRKCH,FZD5,PDGFD,GNAL,ENPEP,RND1,SLIT1,ARPC1B,P
IK3R5,EPHA4,PDGFC,ABLIM1,ROBO1,EPHB6,PLCD1,NTNG1,SHC1,EFNB2,WNT7A,PPP3CB,SRGAP1,
NGFR,EFNB1,MKNK1,NTRK1,PRKAR1B,DCC,SMO,PRKCE,SEMA3B,ROBO2,ERBB2,MYL12B,BMP1,S
EMA3E,GNG4,PLXNC1,ARHGEF12,GNAI1,PRKAR2A,GNAQ,NFATC4,BMP5,EFNA4,PLCB4,LINGO1,E
PHA5,BMP7,BMP6,WNT5A,FZD7,PRKCB
Corticotropin Releasing Hormone Signaling 9.07 -3.536
NOS1,PRKACB,SHH,GLI2,CAMK4,POMC,MAPK13,MAPK3,CREB1,PRKAR1B,SMO,PRKCE,ADCY8,G
UCY1B3,GUCY1A3,CNR1,ADCY3,GNAQ,GNAI1,CRH,PRKAR2A,CRHR1,ATF2,ADCY9,FOS,PRKCD,A
DCY1,ITPR3,GUCY1A2,PRKCH,NPR2,ADCY7,PRKCB
GNRH Signaling 8.4 -4.564
PRKACB,KRAS,MAPK13,CAMK2D,MAPK3,CREB1,PRKAR1B,PLCB1,PRKCE,ADCY8,EGFR,MAP3K9,
RRAS,EGR1,ADCY3,GNAQ,MAPK8,GNAI1,PRKAR2A,DNM3,MAPK9,NFKB2,ATF2,ADCY9,FOS,PLCB
Table 1
53
4,PRKCD,ADCY1,ITPR3,GNRH1,PLCB3,PRKCH,DNM1L,ADCY7,PRKCB
Protein Kinase A Signaling 7.52 -0.492
PRKACB,SHH,SMAD3,PPP1R14B,PTEN,CAMK2D,GNB3,MAPK3,PLCB1,SMPDL3B,GSK3B,PTPRG,AD
D2,PDE10A,PPP1R1B,PTPN18,NFKB2,PLCL2,PTPN3,ATF2,ADCY9,PTPRB,PRKCD,PDE1B,ITPR3,PLCB
3,PRKCH,DUSP4,NAPEPLD,CAMK4,PPP1CB,UBASH3B,AKAP11,PLCD1,PTPN4,PPP3CB,PTPRJ,DUSP2
6,NGFR,CREB1,SMO,PRKAR1B,DCC,PRKCE,SMAD4,PDE4D,RYR1,ADCY8,MYL12B,GNG4,PDE9A,RY
R2,ADCY3,PRKAR2A,GNAI1,GNAQ,PYGL,AKAP6,TCF7L1,NFATC4,DHH,PLCB4,PTPRU,CDC14B,AD
CY1,KDELR3,Ptprt,ADCY7,PTPN22,TCF7L2,PRKCB
Calcium Signaling 7.46 -3.657
PRKACB,GRIN2A,CAMK4,CHRNA6,ATP2B1,GRIN2D,GRIA1,TNNT2,TRPC5,GRIA4,MYH7B,ATP2A2,S
LC8B1,CAMK2D,PPP3CB,MAPK3,CREB1,PRKAR1B,CHRNA7,RYR1,GRIK1,GRIN2B,LETM1,CHRNA4,
ATP2C1,MYH14,RYR2,HDAC1,SLC8A3,PRKAR2A,NFATC4,ATP2B2,HDAC5,GRIN3A,ATF2,ATP2B3,C
HRNB2,ITPR3,SLC8A1,RCAN2,GRIA3
CDK5 Signaling 7.28 -1.512
PRKACB,MAPK15,PPP1CB,KRAS,MAPK13,PPP1R14B,CDK5R1,MAPK3,NGFR,PPM1L,PRKAR1B,LAM
B1,PPP2R2C,ADCY8,LAMA5,RRAS,PPP1R1B,EGR1,ADCY3,MAPK8,PRKAR2A,MAPK9,DRD5,FOSB,A
DCY9,ADCY1,ADCY7,GNAL
Glutamate Receptor Signaling 7.07 -3.317
SLC17A8,GRIN2B,GRIN2A,CAMK4,GRM3,GRM1,GRID2,GRIN2D,GRIA1,SLC38A1,GRIA4,GRIN3A,GR
M5,GRM7,GNB3,SLC17A7,DLG4,SLC1A2,GRIA3,GRIK1
GABA Receptor Signaling 6.44
SLC32A1,GABRA5,ABAT,ADCY3,SLC6A13,AP2A2,ALDH9A1,GABRB2,ADCY9,SLC6A11,GABRG3,GA
D2,GABRG2,KCNQ2,ADCY1,KCNQ3,GABRD,ADCY8,GABRA1,ADCY7,SLC6A12
Leptin Signaling in Obesity 5.73 0.632
PRKACB,SOCS3,LEPR,ADCY3,PRKAR2A,PIK3R5,POMC,PLCL2,STAT3,PIK3R3,PLCD1,ADCY9,PLCB4
,FGFR4,MAPK3,ADCY1,PRKAR1B,PLCB3,PLCB1,AGRP,ADCY8,ADCY7,FRS2
Prolactin Signaling 4.79 -0.447
STAT5A,SOCS3,PRL,RRAS,PIK3R5,PDPK1,KRAS,STAT3,NR3C1,IRF1,PIK3R3,SHC1,FOS,FGFR4,PRKC
D,MAPK3,PRKCE,PRKCH,PRLR,FRS2,PRKCB
Dopamine Receptor Signaling 4.23 -2.324
PRKACB,PPP1R1B,PRL,DDC,ADCY3,PRKAR2A,PPP1CB,DRD5,DRD2,PPP1R14B,ADCY9,MAOB,COMT
,PPM1L,ADCY1,PRKAR1B,PPP2R2C,ADCY8,ADCY7
Melatonin Signaling 4.21 -2.357
PRKACB,CAMK4,GNAQ,GNAI1,PRKAR2A,PLCL2,PLCD1,PLCB4,CAMK2D,MAPK3,PRKCD,PRKAR1B
,GNRH1,PLCB3,PLCB1,PRKCE,PRKCH,PRKCB
Circadian Rhythm Signaling 3.24 PER3,GRIN2B,AVP,GRIN2A,GRIN2D,BHLHE40,CREB1,VIP,GRIN3A,ATF2
Reg 9m – Irreg 9m
Intrinsic Prothrombin Activation Pathway 4 -1 COL1A1,F13A1,THBD,COL18A1,COL3A1
Acute Phase Response Signaling 3.93 2 C1R,FOS,SERPING1,TTR,NFKBIA,C1S,SERPINF1,MAPK13,RBP1,CRABP1,RBP4
Neuroprotective Role of THOP1 in Alzheimer's
Disease
3.32 MME,HLA-A,NTS,GNRH1,SST
Wnt/β-catenin Signaling 3.29 0.707 SOX7,CDH1,SFRP2,DKK3,SOX14,WNT16,WNT6,SFRP1,UBC,FZD7
Tryptophan Degradation X 3.16 MAOB,ALDH1A1,DDC,ALDH1A2
GABA Receptor Signaling 3.09 SLC32A1,GABRA5,GAD2,SLC6A13,UBC,SLC6A12
Circadian Rhythm Signaling 2.69 AVP,GRIN2D,BHLHE40,CREB5
Type I Diabetes Mellitus Signaling 2.64 CD247,GAD2,NFKBIA,HLA-A,HLA-DQA1,HLA-DOB,MAPK13
Corticotropin Releasing Hormone Signaling 2.61 1.89 FOS,GUCY1A3,CRH,NR4A1,POMC,MAPK13,CREB5
Dopamine Degradation 2.6 MAOB,ALDH1A1,ALDH1A2,Sult1d1
Nur77 Signaling in T Lymphocytes 2.55 CD247,HLA-A,HLA-DQA1,NR4A1,HLA-DOB
Complement System 2.51 C1R,SERPING1,C1S,C7
Antigen Presentation Pathway 2.46 HLA-A,HLA-DQA1,HLA-DOB,TAP1
PCP pathway 2.42 -1.342 ROR2,WNT16,WNT6,JUNB,FZD7
54
Dendritic Cell Maturation 2.36 0.333 COL1A1,NFKBIA,HLA-A,HLA-DQA1,HLA-DOB,MAPK13,COL18A1,CREB5,COL3A1
Calcium-induced T Lymphocyte Apoptosis 2.33 0.447 CD247,HLA-A,HLA-DQA1,NR4A1,HLA-DOB
GNRH Signaling 2.25 1.89 FOS,EGR1,GNRH1,FSHB,MAPK13,GNA14,CREB5
Putrescine Degradation III 2.21 MAOB,ALDH1A1,ALDH1A2
Toll-like Receptor Signaling 2.12 FOS,NFKBIA,MAPK13,TIRAP,UBC
Dopamine Receptor Signaling 2.05 MAOB,PPP1R1B,PRL,DDC,SLC18A2
Irreg 9m – Acyc 9m
Atherosclerosis Signaling 6.08
ALOX15,APOA4,PLA2G3,ALOX12,PLA2G2A,ALOXE3,SELPLG,COL1A1,LCAT,S100A8,APOD,COL3A1
,RBP4
Antigen Presentation Pathway 4.15 HLA-DRA,HLA-DQA1,HLA-DQB1,CD74,TAP1,HLA-DRB5
LXR/RXR Activation 3.32 1.667 APOA4,TF,LCAT,NR1H4,ITIH4,SERPINF1,S100A8,RBP4,APOD
Fatty Acid α-oxidation 3.16 ALDH1L1,ALDH1A1,ALDH1A2,ALOXE3
Eicosanoid Signaling 2.79 ALOX15,LCAT,PLA2G3,ALOX12,PTGDS,PLA2G2A
Autoimmune Thyroid Disease Signaling 2.74 HLA-DRA,HLA-DQA1,HLA-DQB1,CGA,HLA-DRB5
Intrinsic Prothrombin Activation Pathway 2.7 COL1A1,F13A1,THBD,COL3A1
Basal Cell Carcinoma Signaling 2.63 1.633 BMP4,GLI2,WNT6,BMP7,BMP6,BMP5
FXR/RXR Activation 2.58 APOA4,TF,LCAT,NR1H4,ITIH4,SERPINF1,RBP4,APOD
Extrinsic Prothrombin Activation Pathway 2.51 F13A1,TFPI,THBD
Coagulation System 2.39 0 F13A1,THBD,TFPI,A2M
B Cell Development 2.39 HLA-DRA,HLA-DQA1,HLA-DQB1,HLA-DRB5
Acute Phase Response Signaling 2.32 FOS,SERPING1,FN1,TF,ITIH4,SERPINF1,A2M,CRABP1,RBP4
Nur77 Signaling in T Lymphocytes 2.3 HLA-DRA,HLA-DQA1,NR4A1,HLA-DQB1,HLA-DRB5
Histamine Degradation 2.29 ALDH1L1,ALDH1A1,ALDH1A2
Oxidative Ethanol Degradation III 2.17 ALDH1L1,ALDH1A1,ALDH1A2
MIF Regulation of Innate Immunity 2.14 1 FOS,PLA2G3,CD74,PLA2G2A
Circadian Rhythm Signaling 1.63 GRIN2A,GRIN2D,VIP
OX40 Signaling Pathway 1.58 H2-T24,HLA-DRA,HLA-DQA1,HLA-DQB1,HLA-DRB5
GNRH Signaling 1.02 -2.236 FOS,CAMK2A,EGR1,GNRH1,FSHB
Table 1: Top canonical pathways identified by Ingenuity Pathway Analysis (IPA). Positive and negative z-scores indicate predicted pathway
activation, or predicted inhibition, respectively. Z-scores are not available for pathways where no prediction can currently be made.
55
Symbol Gene Name
Reg 6m – Reg 9m Reg 9m – Irreg 9m Irreg 9m – Acyc 9m
z-score p-value z-score p-value z-score p-value
GnRH gonadotropin releasing hormone 1 -0.286 5.87E-05 1.647 4.36E-07 -0.971 8.47E-05
KISS1 Kisspeptin -1.969 0.153 1.969 0.000456 -1.969 0.000779
SAH S-adenosylhomocysteine 0.0334 0.0385
SAM S-adenosylmethionine -1.287 0.0336 0.0258
Hcy homocysteine -0.043 0.0206
PRL prolactin 3.936 8.25E-15
E2 beta-estradiol -2.08 8.65E-25 0.569 4.7E-18 -0.042 1.05E-19
Table 2: Upstream regulators identified by Ingenuity Pathway Analysis (IPA). Activation z ‐score infers the activation states of predicted
transcriptional regulators. Dependent on the observed expression of a gene in the dataset, the activation state of a regulator is determined by the
direction associated with the relationship to the regulator. Positive and negative z-scores indicate predicted activation or inhibition, respectively. Z-
scores are not available for when no directional prediction can be made.
Table 2
56
Epigenetic changes across perimenopause
A total of 23 hypothalamic samples were used (n = 5 – 6). An average of 36 million (M)
read pairs were obtained for each sample (range = 31 – 46M read pairs). The percentage of the
raw reads which were mapped to the rn6 genome ranged from 44 – 58%. An average of 4.7
million unique CpG sites were sequenced for each sample (range = 3.8 – 5.4M) at an average
depth of 13X (range = 10 – 16X).
Considering the highly regulatory role of CpG sites within the genome and the inbred
genetic background of the animals used in this study, it is expected that the correlation (r) of
CpG methylation patterns between two groups be very close to 1 for the same tissue type.
Despite the high correlations between groups, we still observed thousands of significantly
differentially methylated CpG sites, suggesting that these sites may play an important role in
regulating the transcriptional differences in endocrine statuses. Conversely, the regulatory role of
non-CpG methylation is less established and CHG and CHH sites are more prone to random,
individual mutations (“H” refers to different cytosine methylation contexts, namely CpG, CHG,
and CHH, where H means “not G” (A,T, or C)). This is reflected in our data by a lower r-value
for CHG and CHH comparisons between groups (table 3), showing that CHG and CHH patterns
are less similar between groups. However, heatmaps displaying hierarchical clustering of the top
100 significant CHG and CHH sites show very little differences between groups, implying that
the genome-wide differences observed are largely on an individual basis and only very few sites
correlate with endocrine status (Fig 3-5).
Metascape’s Meta-analysis gene pathway enrichment was used to identify pathway
clusters associated with the genes, or gene promoters, containing methylation differences
57
between endocrine groups. Genes and gene promoters were analyzed separately. Selected top
clusters, and the involved genes, are summarized in table 4 and table 5.
Cytosine
context
Reg 6m – Reg 9m Reg 9m – Irreg 9m Irreg 9m – Acyc 9m
r-value
CpG 0.9531 0.9521 0.9558
CHG 0.4441 0.4552 0.4978
CHH 0.4711 0.4760 0.5240
Table 3: Correlation of Cytosine Methylation (r-value) – CpG, CHG, and CHH correlations between
endocrine groups. For replicates, one would expect r = 1, demonstrating perfect 1:1 correlation.
Considering regulatory role of CpGs and the inbred genetic background of the, it is expected that CpG
methylation between two groups be close to r= 1. CHG and CHH correlations have a lower r-value
between groups.
Table 3
58
Pathway Description -log(p-value) Log(q-value) Genes
Reg 6m – Reg 9m
regulation of nervous system
development
-8.466 -4.220
Epo,Epor,Grin1,Met,Prkca,Reln,Snap25,Myo5b,Notch1,Tnr,Ywhah,Agrn,Marcks,Pparg,Igf1r,Pak3,Atn1,Pitx3,Pacsin1,M
tor,Notch3,Robo1,Nrxn1,Dusp10,Adcy5,Unc13a,Slit1,Cdh1,Nme2,Synj1,Nrxn3,Nsmf,Lhx5,Brinp1,Clstn2,Dnm3,Fat3,Ne
ctin1,Prpf19,Ube2v2,Ephb3,Plxna2,Nedd4l,Katnb1,Lrp1,Bcl6,Tiam1,Eif4enif1,Kif13b,Unc5d,Trim67,Tenm4,Inpp5f,Sem
a5a,Lrig2,Ezh2,Plxnd1,Adgrb2,Dock7,Srpx2,RGD1305733,Srgap2,Stk24,Sufu,Nrcam,Dtnbp1,Aatk,Ezr,Espn,Prkcd,Rab8
b,Trpm2,Fuz,Pls1,Tbc1d10c,Hdac4,Epha2,Rac2,Tbc1d8,Nefh,Cd44,Lamb2,Cspg5,Llgl1,Lst1,Mmp2,Grip1,Fez2,Ctnnd2,
Myo16,Foxp1,Fry,Bicdl1,Adcy1,Kif5c,Etv4,Tbce,Unc5c,Sparc,Csnk1g2,Mapk14,Pacsin2,Bcl9l,Shroom3,Dock1,Lamc3,
Epha1,Ttbk1,Cdc7,Parvb,Fbxo22,Poldip2,Rack1,Lgals3,Tie1,Vegfb,Phldb1,Prok2,Gsn,Pax9,Ankrd6
synapse organization -6.704 -3.157
Drd1,Grin1,Prkca,Reln,Snap25,Lamb2,Tnr,Agrn,Pclo,Nrxn1,Unc13a,Dlgap1,Slit1,Cdh1,Ctnnd2,P2rx2,Nrxn3,Clstn2,Dn
m3,Nectin1,Ube2v2,Ephb3,Tiam1,Sez6l2,Plxnd1,Adgrb2,Srpx2,Sdk2,Chga,Myo5b,Notch1,Pak3,Psmc3,Ezr,Espn,Mtor,P
ex14,Myo1c,Thra,Rack1,Fez2,Prkcd,Phldb1,Tfip11,Dnajc15,Riok1,Nedd4l,Trpm2,Gsn,Baiap2l1,Fuz,Epha1,Nphp4,Ncka
p1l,Tbc1d10c,Nprl2,Hdac4,Epha2,Rac2,Pmaip1,Tbc1d8,Capza1,Fhod3
regulation of response to external
stimulus
-6.025 -2.871
C4bpb,Drd1,Grin1,Met,Ppm1b,Prkca,Cd44,Notch1,Tnr,Agrn,Pparg,Igf1r,Alox5ap,Anxa2,Mtor,Gas6,Robo1,Nrxn1,Dusp1
0,Mta1,Slit1,Abat,Mapk14,Vegfb,Bbs2,Tnfrsf1b,Prkcd,Zc3hav1,Shpk,Plxna2,Cuedc2,Pik3ap1,Foxp1,Bcl6,Tiam1,Il17rb,
Ccr6,Inpp5f,Sema5a,Lrig2,Ano6,Nckap1l,Rab34,Stk24,Havcr2,Rac2,Tnfrsf11a,Igf2,Vav1,Adar,Mmp2,Nr1d1,Irak4,Polr3
d,Cd300a
single-organism cellular localization -5.798 -2.782
Chga,Drd1,Nefh,Ppm1b,Slc9a1,Snap25,Myo5b,Dpp6,Pacsin1,Ezr,Anxa2,Mtor,Pclo,Gas6,Lmna,Nrxn1,Pex14,Unc13a,Ad
ar,Mapk14,Jup,Thra,Rack1,Cdh1,Hdac3,Synj1,Bbs2,Pom121,Ap3m2,Prkcd,Klc1,Dnm3,Atp5o,Rab8b,Rangrf,Katnb1,Gsn
,Bcl6,Ift57,Kif13b,Timm21,Rab11fip2,Sema5a,Kif5c,Dennd1a,Pls1,Trak1,Rab34,Tmem110,Tbc1d10c,Sufu,Epha2,Rac2,
Pmaip1,Sytl3,Cd300a,Dtnbp1,Myo1c,Polr1a,Nedd4l,Dnajc27,Srebf2,Cyb5r1,Gbf1,Lrig2,Ralgapb,Tsga13,Epo,Fkbp1a,Bir
c5,Adcy5,Nr1d1,Nat8l,Tiam1,Itpr3,Llgl1,Pik3c3,Abat,Stxbp5l,Cidea,Wwp2,Trpm2,Foxp1,Lrp1,Il17rb,Llgl2,Havcr2,Cub
n,Pparg,Banp,Sumo3
regulation of membrane potential -5.725 -2.754
Atp1a3,Cacna1c,Drd1,Grin1,Met,Reln,Scn2a,Slc9a1,Gclc,Ywhah,Agrn,Dpp6,Gria3,Cacna1d,Cacna1g,Gria1,Grik2,Nrxn1
,Chrna9,Kcnh1,Abat,Jup,Rack1,Cngb1,P2rx2,Kcnh6,Tpcn1,Kcnh8,Rangrf,Nedd4l,Wwp2,Pmaip1,Nrcam,Dgki,Tnr,Itpr3
regulation of cellular component
movement
-5.497 -2.551
Cacna1c,Drd1,Met,Nefh,Prkca,Reln,Sparc,Notch1,Agrn,Igf1r,Pak3,Gas6,Robo1,Lmna,Slit1,Myo1c,Mapk14,Jup,Mmp2,R
ack1,Cdh1,Lgals3,Tie1,Vegfb,Bbs2,Nsmf,Rangrf,Plxna2,Gsn,Foxp1,Lrp1,Smurf2,Bcl6,Tiam1,Unc5d,Ccr6,Fuz,Inpp5f,Do
ck1,Sema5a,Lrig2,Epha1,Plxnd1,Dock7,Ano6,Nckap1l,Srpx2,RGD1305733,Srgap2,Stk24,Unc5c,Hdac4,Epha2,Rac2,Cd3
00a,Chga,Vav1,Lamb2,Fez2,Prkcd,Nectin1,Prok2,Ephb3,Trpm2,Gbf1,Plekhg5,Kif5c,Etv4,Nrcam,Mid2,Alox5ap,Zc3hav1
,Il17rb,Rab34,Havcr2,Tnfrsf11a
endocytosis -5.390 -2.466
Prkca,Snap25,Vav1,Grk2,Cebpe,Pparg,Itpr3,Clcn5,Plcg2,Pacsin1,Gria1,Rabep1,Ezr,Anxa2,Gas6,Csnk1g2,Cubn,Rack1,L
gals3,Synj1,Pacsin2,Dnm3,Hip1,Nedd4l,Pla2r1,Gsn,Lrp1,Inpp5f,Loxl4,Cd5l,Dennd1a,Necap1,Ano6,Nckap1l,Rab34,Cdc
7,Elmo1,Mrc2,Cd300a,Dtnbp1,Myo5b,Notch1,Cacna1g,Llgl1,Pclo,Fez2,Stxbp5l,Llgl2,Tbc1d10c,Rac2,Sytl3,Tbc1d8,Agr
n,Gria3,Nr1d1
response to amino acid -5.265 -2.390
Drd1,Epor,Reln,Sds,Myo5b,Gclc,Igf1r,Gria1,Mtor,Pik3c3,Mmp2,Cdh1,Nsmf,Prkcd,Aars,Cpeb4,Sesn3,Pck1,Ubr1,Atp1a3
,Sparc,Igf2r,Abcc2,Pparg,Slc14a2,Acaca,Nme2,Synj1,Tie1,Dusp1,Aldh1a2,Brinp1,Gsn,Foxp1,Tead2,Tmem161a
Focal adhesion -5.251 -2.390
Met,Ppp1ca,Prkca,Reln,Vav1,Comp,Lamb2,Tnr,Igf1r,Itgb4,Pak3,Vegfb,Shc3,Ppp1r12b,Lamb3,Col9a1,Dock1,Pak7,Lamc
3,Parvb,Rac2,Epo,Epor,Ywhah,Mtor,Rbl2,Cdk6,Ppp2r2c,Fgf22,Fgf6,Pik3ap1,Gnb4,Ppp2r5e,Pck1,Epha2,Creb5,Cd44,Ag
rn
Circadian entrainment -5.205 -2.390
Cacna1c,Grin1,Prkca,Itpr3,Plcb3,Gria3,Cacna1d,Cacna1g,Gria1,Adcy4,Adcy5,Gnb4,Adcy1,Rps6ka5,Grk2,Grik2,Dlgap1,
Drd1,Ppp1ca,Mapk14,Ppp2r2c,Ppp2r5e,Kif5c,Creb5,Atp1a3,Snap25,Pclo,Slc9a1,Vav1,Cngb1,Abcc4,Tiam1,Rac2,Mmp2,
Prkcd,Ywhah,Igf1r,Smc1a,Mad2l1,Cpeb4,Anapc1,Ezr,Gad1,Abat,Slc38a5,Pla2g1b,Ppp1r12b,Mtor,Notch3,Pik3c3,Cdh1,
Prkab1,Hdac4,Camkk2,Trpm2,Grk6,Shc3,Ccr6,Elmo1,Pde8a,Plcg2
Table 4
59
Glutamatergic synapse -5.045 -2.296 Cacna1c,Grin1,Prkca,Grk2,Itpr3,Plcb3,Gria3,Cacna1d,Gria1,Adcy4,Grik2,Adcy5,Dlgap1,Gnb4,Adcy1
Dopaminergic synapse -4.975 -2.296 Cacna1c,Drd1,Ppp1ca,Prkca,Itpr3,Plcb3,Gria3,Cacna1d,Gria1,Adcy5,Mapk14,Ppp2r2c,Gnb4,Ppp2r5e,Kif5c,Creb5
Insulin secretion -4.487 -1.956 Atp1a3,Cacna1c,Prkca,Snap25,Itpr3,Plcb3,Cacna1d,Adcy4,Pclo,Adcy5,Adcy1,Creb5
GnRH signaling pathway -3.509 -1.356 Cacna1c,Prkca,Itpr3,Plcb3,Cacna1d,Adcy4,Adcy5,Mapk14,Mmp2,Prkcd,Adcy1
GABAergic synapse -2.988 -1.049 Cacna1c,Gad1,Prkca,Cacna1d,Adcy4,Adcy5,Abat,Slc38a5,Gnb4,Adcy1
Long-term potentiation -2.767 -0.930 Cacna1c,Grin1,Ppp1ca,Prkca,Itpr3,Plcb3,Gria1,Adcy1
Oxytocin signaling pathway -2.610 -0.845 Cacna1c,Ppp1ca,Prkca,Itpr3,Plcb3,Cacna1d,Adcy4,Adcy5,Camkk2,Prkab1,Trpm2,Ppp1r12b,Adcy1
Chemokine signaling pathway -2.578 -0.826 Vav1,Grk2,Plcb3,Adcy4,Grk6,Adcy5,Shc3,Prkcd,Gnb4,Tiam1,Adcy1,Ccr6,Elmo1,Rac2
Thyroid hormone synthesis -2.525 -0.793 Atp1a3,Prkca,Itpr3,Plcb3,Adcy4,Adcy5,Adcy1,Creb5
Estrogen signaling pathway -2.304 -0.659 Itpr3,Plcb3,Adcy4,Adcy5,Mmp2,Shc3,Prkcd,Adcy1,Creb5
Inflammatory mediator regulation of TRP
channels
-2.263 -0.634 Ppp1ca,Prkca,Itpr3,Plcb3,Plcg2,Adcy4,Adcy5,Mapk14,Prkcd,Adcy1
Cholinergic synapse -2.263 -0.634 Cacna1c,Prkca,Itpr3,Plcb3,Cacna1d,Adcy4,Adcy5,Gnb4,Adcy1,Creb5
neuron death -5.126 -2.327
Chga,Epo,Epor,Grin1,Scn2a,Slc9a1,Gclc,Agrn,Pak3,Atn1,Pitx3,Grik2,Mtor,Birc5,Rack1,Hdac3,Vegfb,Nsmf,Ube2v2,Dia
blo,Tox3,Aars,Trpm2,Srpk2,Lrp1,Cpeb4,Hdac4,Pmaip1,Dtnbp1,Nrbp2,Aatk
Axon guidance -4.974 -2.296
Met,Prkca,Plcg2,Pak3,Limk2,Robo1,Slit1,Trpc4,Ephb3,Plxna2,Sema4b,Unc5d,Sema5a,Pak7,Epha1,Srgap2,Unc5c,Epha2,
Rac2
positive regulation of protein localization
to cell periphery
-4.858 -2.214
Myo5b,Dpp6,Ezr,Nrxn1,Rack1,Rangrf,Rab11fip2,Pls1,Epha2,Myo1c,Pmaip1,Agrn,Pacsin1,Anxa2,Gas6,Jup,Cdh1,Bbs2,
Pacsin2,Prkcd,Rab8b,Baiap2l1,Myof,Ano6,Rab34,Reln,Slc9a1,Snap25,Pparg,Lmna,Dlg2,Ykt6,Tie1,Bag6,Dnm3,Gsn,Chc
hd6,Gbf1,Abca4,Ubxn2b,Nckap1l,Elmo1,Tbc1d10c,Nrcam,Sytl3,Cd300a,Tbc1d8,Sec16a,Mta1,Pik3c3,Synj1,Hook2,Cog
4,Dtnbp1,Grik2,Tiam1
regulation of catabolic process
-
4.76784527
3
-2.155
C4bpb,Ppp1ca,Gclc,Nrdc,Psmc3,Ezr,Anxa2,Mtor,Dap,Dlgap1,Thra,Rack1,Abcd2,Fez2,Bag6,Chek2,Acacb,Pfkfb3,Tnfrsf
1b,Prkcd,Tnrc6b,Tpcn1,Zc3hav1,Poldip2,Ube2v2,Cidea,Nedd4l,Banp,Rnf217,Mad2l1,Srebf2,Fbxo22,Osbpl7,Depdc5,Tri
m67,Lrig2,Lrpprc,Sufu,Nprl2,Hdac4,Mid2,Sumo3,Nrbp2,Sumo2
cation transmembrane transport -4.682 -2.079
Atp1a3,Cacna1c,Drd1,Epo,Grin1,Reln,Scn2a,Slc9a1,Snap25,Slc30a2,Ywhah,Agrn,Fkbp1a,Itpr3,Dpp6,Plcg2,Rasa3,Cacna
1d,Cacna1g,Grik2,Slc24a1,Gas6,Nrxn1,Kcnt1,Chrna9,Kcnh1,Cngb1,Atox1,Trpc4,Slc24a3,P2rx2,Kcnh6,Atp5o,Tpcn1,Kc
nh8,Rangrf,Slc39a11,Atp13a1,Nedd4l,Wwp2,Trpm2,MGC105649,Slc25a29,Ano6,Tmem110,Slc41a2,Slc9a9,Trpm5,Kcn
k18,Mtor,Abcc3,Epor,Met,Prkca,Hk1,Gclc,Abcc2,Igf1r,Txnrd2,Gria1,Txnrd1,Birc5,Adcy5,Nell2,Rack1,Nme2,Txnl1,Nr1
d1,Txndc12,Tiam1,Rab11fip2,Ccr6,Rab34,Dnajc16,Letmd1,Clcn5,Grk6,Lgals3,Nectin1,Slc10a7,Abat,Prkcd,Nat8l,Pla2r1
,Tnfrsf11a,Cd300a,Dtnbp1,Anpep
regulation of synaptic plasticity -3.054 -1.079 Drd1,Grin1,Reln,Snap25,Tnr,Itpr3,Gria1,Grik2,Slc24a1,Camk2n2,Unc13a,Bhlhe40,Ctnnd2,Nsmf,Rnf39,Dgki
learning or memory -4.062 -1.691
Atp1a3,Cacna1c,Drd1,Grin1,Igf2,Prkca,Reln,Snap25,Tnr,Itpr3,Cacna1d,Gria1,Mtor,Nrxn1,Thra,Synj1,Ctnnd2,Shc3,Nrxn
3,Brinp1,Adcy1,Pak7,Dgki
Reg 9m – Irreg 9m
regulation of cell development -9.820 -5.574
Met,Ptn,RT1-A2, Atxn1,Ntrk2,Cntn2,Cacna1a,Il1rap,Man2a1,Shox2,Agrn,Fn1,Ifng,Igf1r,Nf2,Afdn,Itgb3,Serpine2,Trpv2,
Mapt,Sema3a,Runx1,Gsk3a,Srcin1,Notch3,Adam17,Isl2,Csnk1e,Tgfb1,Clock,Pdlim5,Adcy5,Akap6,Slit1,Ncs1,Clip1,Sha
nk1,Cdk5rap3,Nr2f1,Cdh2,Vegfa,Kalrn,Dll1,Gsk3b,Rims1,Hdac2,Synj1,Megf8,Six1,Eif2ak4,Npr2,Efna5,Cntn1,Brinp1,I
npp5j,Dscam,Ptprg,Unc13d,Sipa1l1,Fzd3,Ephb3,Plxna2,Nedd4l,Rit2,Tnik,Snapin,Rap1a,Foxp1,Pin1,Flrt2,Sf3a2,Fbxo22,
Adgrb3,Bcl6,Tiam1,Kif13b,Tenm4,Dock1,Kank1,Sema3f,Rasal1,Idh2,Iqgap1,Ect2,Spen,Prkca,Prkaca,Eif2ak3,Adgrl1,Nr
xn3,Clstn2,Pax2,Atp2b2,Nefh,Vdr,Cnp,Camk2a,Pecam1,Nr4a2,Ezr,Csnk1g2,Itga7,Nrp2,Mkln1,Sh3kbp1,Col18a1,Cdh23,
Ntn3,Epb41l3,Lama5,Cfdp1,Fndc3b,Syne3,Cyfip2,Sema5b,Tfcp2l1,Adcy1,Vcl,Myo10,Stk4,Prex2,Ttbk1,Ptprq,Tbce,Unc
93b1,Cadm1,Rp2,Brsk1,Pcdh15,Cacna1c,Stat5a,Ccnb1,Gamt,Pdgfra,Acvr2b,Pkm,Hnf1b,Il7,Areg,Por,Myh6,Prkg1,Tec,H
dac3,Ptch1,Bbs2,Nek1,Sirt6,Atm,Col9a1,Sos1,Chst11,Rdh10,Large1,Ehmt2,Sik3,Serpina3c,Anxa3,Dmp1,Cd36,Pde3b,M
60
mp9,Tie1,Flt4,Erg,Pax9,Xdh,Tmem100,Stard13,Ppp1r16b,Prnp,Wnt1,Klf5,Cln3,Cenpj,Eps8l1,Bcas3,Tbc1d5,Vcan,Bicdl
1,Camsap3,Acvr1b,Adam10,Pttg1,Tkt,Mmp14,Osgin1,Nod2,Irf8,Armc10,Ppp1r1c,Prom1,Trpm4,Mecom,Scin
trans-synaptic signaling -7.708 -4.479
Atp2b2,Cacna1c,Met,Prnp,Ptn,Glul,Atxn1,Ntrk2,Cntn2,Cacna1a,Camk2a,Glrb,Agrn,Prkaca,Itpr3,Ifng,Cadps,Serpine2,Ma
pt,Atp2a2,Cacna1g,Grik2,Pclo,Nsf,Chrna9,Dlgap1,Adgrl1,Shank1,Abat,Mmp9,Stx4,Psmc5,Cdh2,Kalrn,Gsk3b,Rims1,Sy
nj1,Eif2ak4,Nrxn3,Cplx2,Dlgap2,Ppfia3,Clstn2,Sipa1l1,Cacna1b,Grid2ip,Snapin,Rap1a,Grik3,Lynx1,Sytl5,Celf4,Slc12a7
,Fchsd2,Dagla,Brsk1,Dgki,Shisa7,Atp1a3,Gad1,Pomc,Prkca,Cnp,Cpt1a,Cd36,Lsamp,Kcnab1,Runx1,Nr4a2,Csnk1e,Asl,A
plp2,Adcy3,Adcy5,Ncoa2,Hdac2,Bbs2,Cdh23,Gpr176,Brinp1,Dscam,Cln3,Abca7,Myg1,Adgrb3,Adcy1,Sez6l2,Prex2,Tbc
e,Ehmt2,Pax5,Pcdh15
regulation of synaptic plasticity -4.542 -2.278
Atp2b2,Prnp,Ptn,Ntrk2,Cntn2,Camk2a,Itpr3,Serpine2,Mapt,Grik2,Mmp9,Stx4,Kalrn,Gsk3b,Rims1,Eif2ak4,Cplx2,Ppfia3,
Sipa1l1,Grid2ip,Dgki,Shisa7
learning or memory -2.982 -1.291
Atp1a3,Cacna1c,Prkca,Prnp,Ptn,Atxn1,Ntrk2,Cntn2,Itpr3,Mapt,Kcnab1,Adcy3,Shank1,Kalrn,Synj1,Eif2ak4,Nrxn3,Brinp
1,Cln3,Abca7,Adgrb3,Adcy1,Ehmt2,Dgki
long-term synaptic potentiation -2.358 -0.861 Prnp,Ptn,Ntrk2,Itpr3,Serpine2,Stx4,Kalrn,Rims1,Eif2ak4
secretion by cell -5.766 -3.047
Cacna1c,Pomc,Prkca,Glul,Ntrk2,Inhbb,Itpr1,Anxa3,Acvr2b,Cacna1a,Camk2a,Il1rap,Hnf1b,Fn1,Itpr3,Ifng,Cpt1a,Cadps,C
d36,Lrp2,Serpine2,Itsn1,Pde3b,Atp2a2,Cacna1g,Runx1,Prkg1,Ezr,Srcin1,Pclo,Tgfb1,Nsf,Clock,Adcy5,Adgrl1,Ncs1,Abat,
Stx4,Kalrn,Rims1,Synj1,Hmgn3,Mtnr1a,Nrxn3,Cplx2,Efna5,Ppfia3,Zfp384,Trpm4,Unc13d,Nr1d1,Cacna1b,Tfr2,Fam3d,
Nod2,Rfx6,Snapin,Rap1a,Foxp1,Sytl5,Tiam1,Sptbn1,Idh2,Unc93b1,Cadm1,Ufm1,Bank1,Slc8b1,Brsk1,Cd274,Scrn1,Dgk
i
insulin secretion -5.298 -2.768
Cacna1c,Glul,Inhbb,Itpr1,Acvr2b,Hnf1b,Itpr3,Ifng,Cpt1a,Pde3b,Pclo,Clock,Adcy5,Abat,Stx4,Hmgn3,Mtnr1a,Efna5,Trpm
4,Nr1d1,Fam3d,Rfx6,Tiam1,Ufm1,Slc8b1
regulation of peptide transport -4.320 -2.134
Pkd1,Prnp,Glul,Inhbb,Itpr1,Il1rap,Prkaca,Fn1,Itpr3,Ifng,Cpt1a,Ogt,Cd36,Pde3b,Gsk3a,Nup54,Kcnn3,Ezr,Srcin1,Tgfb1,Cl
ock,Adcy5,Akap6,Nfkbib,Abat,Stx4,Kalrn,Gsk3b,Hdac3,Hmgn3,Mtnr1a,Efna5,Zfp384,Trpm4,Nr1d1,Tfr2,Fam3d,Ndfip1
,Nup93,Nod2,Rfx6,Foxp1,Apc2,Srebf2,Fbxw11,Tiam1,Sptbn1,Kank1,Tmem110,Idh2,Unc93b1,Ect2,Cadm1,Bcas3,Ufm1
,Bank1,Rnase2,Slc8b1,Pkd2,Cd274
hormone transport -4.226 -2.073
Cacna1c,Pomc,Glul,Inhbb,Itpr1,Acvr2b,Hnf1b,Itpr3,Ifng,Cpt1a,Lrp2,Pde3b,Runx1,Pclo,Clock,Adcy5,Abat,Stx4,Kalrn,Sl
co1c1,Hmgn3,Mtnr1a,Efna5,Abcb1a,Trpm4,Nr1d1,Tfr2,Fam3d,Rfx6,Tiam1,Ufm1,Slc8b1
regulation of hormone levels -3.754 -1.752
Cacna1c,Pomc,Glul,Inhbb,Itpr1,Pdgfra,Acvr2b,Cacna1a,Hnf1b,Itpr3,Ifng,Igf1r,Cpt1a,Lrp2,Por,Pde3b,Runx1,Pclo,Clock,
Lrat,Adcy5,Abat,Stx4,Kalrn,Hsd17b12,Slco1c1,Hmgn3,Mtnr1a,Efna5,Abcb1a,Trpm4,Nr1d1,Tfr2,Hsd17b11,Fam3d,Rfx6
,Tiam1,Rdh10,Ufm1,Slc8b1
response to carbohydrate -3.061 -1.331
Gstp1,Met,Prkca,Glul,Acvr2b,Prkaca,Hnf1b,Fn1,Igf1r,Ogt,Acan,Tgfb1,Adcy5,Nfkb1,Stx4,Hmgn3,Efna5,Trpm4,Nr1d1,P
ax2,Rfx6,Rap1a,Tiam1,Col6a2,Ufm1,Si
glucose homeostasis -3.025 -1.315
Cacna1c,Met,Pomc,Prkaca,Fn1,Igf1r,Ogt,Cd36,Pde3b,Adcy5,Stx4,Ptch1,Hmgn3,Efna5,Trpm4,Nr1d1,Pax2,Rfx6,Rap1a,S
irt6,Tiam1,Ncor2,Ufm1,Slc8b1
synapse organization -7.082 -4.091
Atp2b2,Prkca,Ptn,Ntrk2,Cntn2,Cacna1a,Glrb,Il1rap,Agrn,Utrn,Mapt,Pclo,Pdlim5,Dlgap1,Slit1,Adgrl1,Shank1,Cdh2,Kalr
n,Six1,Nrxn3,Efna5,Pcdhgc3,Dscam,Clstn2,Ephb3,Snapin,Pin1,Flrt2,Adgrb3,Tiam1,Sez6l2,Cadm1
cell-cell adhesion -6.512 -3.608
Pkd1,Prkca,Prnp,Wnt1,Stat5a,Pdgfra,Cd80,Cdh6,Il7,Ifng,Nf2,Cd36,Itgb3,Serpine2,Prkg1,Ezr,Anxa2,Tgfb1,Cd55,Pdlim5,
Adgrl1,Itga7,Abat,Cdh2,Vegfa,Cdh23,Efna5,Dscam,Il12rb1,Ptprg,Clstn2,Pcdhga9,Pcdhga11,Ephb3,Sox13,Ndfip1,Pcdhg
a7,Pcdhga5,Icam4,Tmod3,Cyfip2,Bcl6,Baiap2l1,Vcl,Cdh16,Kifc3,Dchs1,Drosha,Myo10,Sos1,Lmo7,Cadm1,Pcdhga8,Pcd
hgb7,Pcdhgb8,Pcdhga2,Pcdhga3,Pcdhga10,Cd274,Pcdhga1,Camsap3,Pcdh15,Ptn,Dmp1,Utrn,Fn1,Pde3b,Adam10,Srcin1,
Mmp14,Stx4,Dll1,Hsd17b12,Gsk3b,Cdk6,Lama5,Unc13d,Dock1,Kank1,Iqgap1,Bcas3,Prkx,Jam2,Fndc3b
regulation of cellular component
biogenesis
-6.069 -3.270
Add2,Prkca,Wnt1,Ntrk2,Il1rap,Agrn,Prkaca,Hnf1b,Ifng,Nf2,Cd36,Mapt,Pecam1,Psmc3,Arpc1b,Ezr,Tgfb1,Pdlim5,Slit1,A
dgrl1,Clip1,Shank1,Mmp14,Psmc5,Vegfa,Gsk3b,Klf5,Rims1,Hdac2,Six1,Nrxn3,Efna5,Pip4k2a,Hrk,Inpp5j,Clstn2,Ephb3,
Rab3gap2,Nedd4l,Irf8,Cln3,Rap1a,Scin,Flrt2,Sirt6,Atm,Tmod3,Adgrb3,Arhgap28,Baiap2l1,Lmod1,Sptbn1,Cenpj,Fchsd2,
Kank1,Myo10,Lrsam1,Nphp4,Sptb,Zfp451,Terf2,Eps8l1,Iqgap1,Ehmt2,Bcas3,Spidr,Tbc1d5
61
homophilic cell adhesion via plasma
membrane adhesion molecules
-5.822 -3.081
Cdh6,Cdh2,Cdh23,Dscam,Ptprg,Clstn2,Pcdhga9,Pcdhga11,Pcdhga7,Pcdhga5,Cdh16,Dchs1,Cadm1,Pcdhga8,Pcdhgb7,Pcd
hgb8,Pcdhga2,Pcdhga3,Pcdhga10,Pcdhga1,Pcdh15,Cd36,Adgrl1
regulation of neurotransmitter levels -5.678 -3.011
Gad1,Glul,Ntrk2,Cacna1a,Camk2a,Cadps,Atp2a2,Pclo,Nsf,Adgrl1,Abat,Stx4,Rims1,Synj1,Nrxn3,Cplx2,Ppfia3,Cacna1b,
Cln3,Snapin,Rap1a,Sytl5,Dagla,Brsk1,Dgki,Slc6a6,Slc6a8,Cacna1c,Cacna1g,Unc13d,Cdh2,Dennd1a
ion transport -5.604 -2.949
Adcyap1r1,Atp1a3,Atp2b2,Atp4b,Cacna1c,Pde4b,Pkd1,Prnp,Slc9a3,Vdr,Itpr1,Cacna1a,Cacna2d1,Camk2a,Kcnj8,Agrn,It
pr3,Kcnh3,Dpp6,Itgb3,Plcg2,Serpine2,Trpv2,Atp2a2,Cacna1g,Kcnab1,Slc6a8,Grik2,Kcnn3,Prkg1,Tgfb1,Nsf,Slc22a4,Aka
p6,Chrna9,Ncs1,Kcnh1,Cngb1,Slc24a3,Cdh23,Kcnh2,Cntn1,Trpm4,Cacna1b,Tfr2,Nedd4l,Ndfip1,Cnnm2,Slc4a5,Grik3,Sl
c30a6,Slc25a37,Cacna2d3,Slc12a7,Cnksr3,Tpcn2,Pacsin3,Slc9a8,Cacna2d4,Tmem110,Trpm1,Cdk2,Slc9a9,Sfxn1,Slc8b1,
Pkd2,Slc1a1,Shank1,Mmp9,Atp5o,Atp6v0d1,Trpm3,Tmem63c,Met,Prkca,Pdgfra,Prkaca,Fn1,Igf1r,Alas2,Ogt,Cd36,Cd55,
Aplp2,Adcy5,Stx4,Hmgn3,Efna5,Inpp4b,Nr1d1,Pde6b,Cln3,Pax2,Rfx6,Snapin,Rap1a,Tiam1,Ufm1,Tmtc2,Prdx6,Large1,
Glrb,Lipg,Pkib,Stat5a,Acvr1b,Adam17,Abat,Vegfa,Dll1,Cdk6,Sirt6,Srebf2,Atm,Terf2
axon extension involved in axon
guidance
-5.344 -2.797
Sema3a,Slit1,Nrp2,Vegfa,Megf8,Dscam,Plxna2,Sema3f,Agrn,Gstp1,Met,Pde4b,Prkca,Pdgfra,Cntn2,Fn1,Ifng,Itgb3,Adam
10,Adam17,Isl2,Tgfb1,Nr4a1,Stx4,Ntn3,Efna5,Pik3c2g,Trpm4,Fzd3,Hoxb9,Ephb3,Nod2,Foxp1,Flrt2,Sema5b,Tiam1,Plek
hg5,Rnase2,Arhgef16,Trpv2,Mapt,Gsk3b,Vcl,Stat5a,Igf1r,Cd36,Serpine2,Alox5ap,Prkg1,Anxa2,Clock,Cd55,Abat,Nfkb1,
Sharpin,Tec,Bbs2,Eif2ak4,Il12rb1,Susd4,Ndfip1,Fam46a,Bcl6,Drosha,Lrsam1,Tkfc,Atp2b2,Ogt,Ezr,Slc12a7
organophosphate metabolic process -5.223 -2.733
Adcyap1r1,Atp2b2,Pde4b,Pgk1,Ntrk2,Ampd3,Pdgfra,Cnp,Pkm,Ifng,Ogt,Plcg2,Pde3b,Myh6,Gsk3a,Prkg1,Tgfb1,Prpsap1,
Adcy3,Tkt,Adcy5,Akap6,Inpp4a,Adcy2,Vegfa,Synj1,Cds2,Mtnr1a,Npr2,Dlgap2,Inpp4b,Pik3c2g,Pip4k2a,Gpr176,Inpp5j,
Atp5o,Gucy2g,Pigp,Upp1,Pde6b,Isyna1,Rit2,Tk2,Nod2,Kars,Cln3,Pank1,Nudt3,Lpcat4,Pigt,Dpm1,Ola1,Gars,H6pd,Sirt6,
Plppr2,Atm,Adcy1,Plppr3,Mtmr1,Ptprq,Naxd,Cbfa2t3,Idh2,Nmnat3,Nphp3,Pik3r4,Oas2,LOC365985,Pkd2,Atp5j2,Hint1,
Gamt,Aldh6a1,Xdh
peptidyl-tyrosine phosphorylation -5.159 -2.676
Met,Prkca,Prnp,Stat5a,Ntrk2,Pdgfra,Cd80,Agrn,Ifng,Igf1r,Nf2,Areg,Cd36,Itgb3,Pecam1,Srcin1,Adam17,Tgfb1,Insrr,Aplp
2,Vegfa,Tec,Hdac2,Tie1,Flt4,Efna5,Sh3bp5,Cntn1,Il12rb1,Ehd4,Ephb3,Nod2,Dyrk3,Iqgap1,Pkd1,Wnt1,Inhbb,Ccnb1,Acs
l1,Acvr2b,Ogt,Acvr1b,Itsn1,Gsk3a,Anxa2,Akap6,Mmp9,Cdh2,Gsk3b,Hdac3,Rplp1,Dscam,Avpi1,Rit2,Cln3,Tnik,Rap1a,
Pin1,Abca7,Bmper,Atm,Tiam1,Stk4,Traf7,Ect2,Pik3r4,Bank1,Xdh,Spdye4,Smyd3,Pkd2,Atxn1,Myh6,Eif2ak3,Ltbp2,Nrp
2,Dll1,Megf8,Npr2,Fgf22,Hip1,Sipa1l1,Nup93,Akap2,Flrt2,Rnf111,Wdr6,Cyfip2,Baiap2l1,Sptbn1,Kank1,Fbxl15,Sos1,C
hst11,Zfp451,Csrnp1,Twsg1,Tmem100,Gstp1,Ptn,Fn1,Runx1,Csnk1e,Acan,Nr4a1,Nfkb1,Has1,Pax2,Pax9,Anxa3
regulation of cellular localization -4.978 -2.551
Adcyap1r1,Nefh,Pkd1,Prnp,Ptn,Ntrk2,Cacna1a,Camk2a,Prkaca,Ifng,Nf2,Ogt,Cd36,Dpp6,Itgb3,Mapt,Atp2a2,Eif2ak3,Gsk
3a,Nup54,Kcnn3,Ezr,Anxa2,Csnk1e,Tgfb1,Adcy5,Akap6,Cdk5rap3,Nfkbib,Stx4,Cdh2,Vegfa,Kalrn,Gsk3b,Rims1,Hdac3,
Hmgn3,Efna5,Inpp4b,Trpm4,Unc13d,Nr1d1,Cacna1b,Nedd4l,Nup93,Rfx6,Nus1,Rap1a,Pin1,Apc2,Srebf2,Fbxw11,Tiam1,
Sptbn1,Kank1,Tmem110,Iqgap1,Ect2,Tp53inp2,Bcas3,Ufm1,Rnase2,Spidr,Pkd2,Arhgef16,Trim8
Irreg 9m – Acyc 9m
cell morphogenesis -7.301 -3.055
Ar,Atp2b2,Erbb2,Ret,Tpm1,Dcc,Cntn2,Marcks,Vldlr,Itgb7,Igf1r,Hck,Numb,Gap43,Mapt,Slc1a3,Dlg4,Erbb3,Pitpna,Ptpr
m,Chrnb2,Isl2,Dlc1,Kit,Unc13a,Limk1,Fgr,Lrp4,Cit,Dclk1,Kalrn,Sh3kbp1,Gfra3,Rims1,Ctnnd2,St14,Eif2ak4,Tenm2,Ns
mf,Gli3,Opa1,Nectin1,Nptx1,Sarm1,Lmx1a,Nedd4l,Pgrmc1,Map7,Sema3e,Washc2c,Ttc8,Syne3,Rab21,Grhl2,Bcl6,Fry,T
ctn1,Fbxw8,Lats2,Kif13b,Dock1,Fermt3,Sema5a,Plekho1,Plxnd1,Upk3a,Wnt7b,Epha4,Ptprq,Efnb3,Enah,Ank3,Raph1,Fo
xb1,Notch4,Myh14,Gfi1,Jak2,Stx1b,Cd44,Mtor,Phgdh,Negr1,Grid2,Ikbkb,Magi2,Klf4,Inpp5j,Ptprg,Slc12a5,Cbfa2t2,Map
4k4,Lrig2,Drd3,Prkch,Prpf19,Hoxd3,Mycl,Sorl1,Olig2,Pbx1,Mib1,Tenm4,Tlr2,Sirt2,Gsx2,Clcf1,Nrxn3,Myrf,Syndig1,Ax
in2,Akap6,Hdac4,Pld1,Espn,Bcas3,Washc1,Tbc1d8,Plcb1,Tph1,Syk,Itpkb,Gpc1,Prom1,Kitlg,Mmp14,Ltbp3,Xbp1,Hax1,
Trim16,Cd101,Zbtb16,Edn3,Tmem100,Fbn2
protein autophosphorylation -6.317 -2.673
Erbb2,Jak2,Pdgfb,Syk,Pdgfra,Jak3,Igf1r,Hck,Eif2ak1,Mapk3,Stk39,Mtor,Kit,Mvp,Fgr,Eif2ak4,Wnk1,Ripk3,Mapk15,Atm
,Lmtk2,Epha4,Itk,Aak1,Map3k9,Prkx,Ulk3,Tyk2,Pkd1,Pkib,Cd44,Marcks,Vldlr,Axin2,Erbb3,Map2k5,Kitlg,Akap6,Ajuba
,Klf4,Cdc25b,Rptor,Nvl,Tnks,Ube2s,Vav3,Grhl2,Sorl1,Shc2,Wnt7b,Slc11a1,Edn3,Lpar2,Pot1,Gcn1l1,Ar,Plcb1,Dcc,Fgf9,
Bok,Mid1,Itpkb,Phlpp1,Bcl2l11,Golph3,Akap12,Mcf2l,Rasd2,Serpina12,Xbp1,Hax1,Cxxc5,Dnajc27,Myd88,Sema4c,Tsp
an6,Flcn,Sema5a,Tlr2,Ube3a,Prr5l,Ikbke,Rnf31,Clcf1,Bank1,Slc35b2,Chd5,Mapt,Pik3r3,Prpsap1,Limk1,Lats2,Vac14,Ld
62
b1,Midn,Washc1,Gbas,Ret,Ghrhr,Myo1e,Ogt,Grb14,Gpc1,Sort1,Ptpn12,Epha10,Ofd1,Adamts3,Lrig2,Efnb3,Sirt2,Cd80,L
rp4
brain development -6.172 -2.625
Atp1a3,Atp2b2,Plcb1,Pdgfra,Ghrhr,Cntn2,Fgf9,Marcks,Vldlr,Igf1r,Ogt,Phlda1,Numb,Mapt,Atp2b1,Cyp11a1,Gabra5,Bok
,Chrnb2,Mtor,Dlc1,Phlpp1,Bcl2l11,Grid2,Ncoa2,Dclk1,Ssbp3,Pitx1,Pafah1b3,Ncoa6,Gli3,Sdf4,Abcb1a,Shank2,Ptprg,Nm
e7,Pou3f3,Slc17a8,Gsx1,Lmx1a,Gnb4,Ttc8,Grhl2,Atm,Efhc1,Sema4c,Olig2,Tctn1,Tbx19,Pomk,Rpgrip1l,Tacc2,Ldb1,Se
ma5a,Sema6d,B4galt2,Wnt7b,Ogdh,Sirt2,Wls,Gsx2,Foxb1,Hesx1,Rrm1,Chd5,Mapk3
learning or memory -4.687 -2.009
Atp1a3,Plcb1,Cntn2,Fosl1,Itpr3,Drd3,Mapt,Gabra5,Chrnb2,Mtor,Kit,Kcnk10,Calb1,Kalrn,Ctnnd2,Eif2ak4,Nrxn3,Nrxn2,
Shank2,Slc12a5,Lmx1a,Pomk,Tanc1,B4galt2,Ehmt2,Foxb1,Jph4
inositol lipid-mediated signaling -5.893 -2.575
Erbb2,Jak2,Pdgfb,Pld1,Pdgfra,Igf1r,Ogt,Erbb3,Pik3c3,Fgr,Pik3cb,Klf4,Serpina12,Xbp1,Hax1,Gsn,Sirt2,Ube3a,Prr5l,Ppp
1r16b
cation transmembrane transport -4.939 -2.177
Atp1a3,Atp2b2,Cp,Grin2c,Pkd1,Slc9a2,Itpr1,Cacna2d1,Slc34a1,Itpr3,Capn3,Drd3,Dlg4,Scn10a,Atp2b1,Scn11a,Atp6v0a1
,Slc12a3,Stk39,Slc13a1,Hcn4,Slc18a3,Kcnt1,Akap6,Atp5a1,Kcnk10,Scn9a,Ikbkb,Slc5a5,Stim2,Cacng4,Atp12a,Slc12a5,
Ero1a,Slc6a15,Pex5l,Catsper3,Loxhd1,Nedd4l,Pkd2l2,Atp6v0a4,Cox7a2l,Sumo1,Scara5,Slc12a7,Ano1,Oxsr1,Slc11a1,Pi
ezo1,Trpm1,Ank3,Slc41a1,Edn3,Slc8b1,Uqcr11,Slc12a6
cellular homeostasis -3.174 -1.063
Atp1a3,Atp2b2,Cp,Jak2,Pkd1,Plcd1,Pygm,Slc9a2,Itpr1,Pdgfra,Ghrhr,Jak3,Cacna2d1,Slc34a1,Nr3c2,Itpr3,Igf1r,Ogt,Capn
3,Drd3,Dlg4,Pdx1,Atp2b1,Atp6v0a1,Bok,Cd55,Akap6,Wfs1,Calb1,Pik3cb,Herpud1,Ncoa6,Ptprn,Stim2,Slc12a5,Ero1a,N
ptx1,Xbp1,Tm9sf4,Atp6v0a4,Fggy,Map4k4,Scara5,Slc12a7,Ano1,Peo1,Slc11a1,Gtf2i,Large1,Trpm1,Ttc7a,Slc41a1,Edn3
,Slc8b1,Ormdl1
protein dephosphorylation -4.994 -2.177
Jak2,Ptprm,Dlc1,Phlpp1,Akap6,Ikbkb,Magi2,Ptpn12,Nsmf,Ptpn21,Cdc25b,Ptprg,Ppp4c,Ptpn7,Ptpn9,Eya4,Fbxw11,Ptpn1
4,Ppp6r3,Ppp1r3c,Cdc14a,Ppa2,Ppp1r8,Ptprq,Ppp1r16b,Pdxp,Dlg2,Wnk1,Inpp5j,Ripk3,Rimbp2,Plppr2,Sfi1,Pcif1
negative regulation of endoplasmic
reticulum stress-induced intrinsic
apoptotic signaling pathway
-4.958 -2.177
Pdx1,Wfs1,Herpud1,Opa1,Hyou1,Xbp1,Syvn1,Bok,Itpr1,Ero1a,Ubxn1,Tmem259,Jak2,Igf1r,Mtor,Sort1,Gsn,Sema5a,Eif2
ak4,Creb3l3,Cd44,Bcl2l11,Ripk3,Flcn,Noc2l,Mapt,Atm,Ikbke,Zmat4
glial cell differentiation -4.927 -2.177
Erbb2,Pdgfb,Cntn2,Drd3,Gap43,Erbb3,Cyp11a1,Bok,Mapk3,Mtor,Phgdh,Gpc1,Nab1,Gli3,Prpf19,Myrf,Gsn,Olig2,Tenm4
,Tlr2,Epha4,Sirt2,Gsx2,Clcf1,Mmp14,Prkch
oligodendrocyte differentiation -3.522 -1.306 Erbb2,Cntn2,Drd3,Bok,Mtor,Gli3,Myrf,Gsn,Olig2,Tenm4,Tlr2,Sirt2,Gsx2
axon ensheathment in central nervous
system
-2.509 -0.728 Cntn2,Myrf,Tenm4,Tlr2
gamma-aminobutyric acid metabolic
process
-4.918 -2.177 Gad1,Slc1a3,Phgdh,Aldh5a1,Gad2,Ggt7
cell-cell adhesion -4.666 -2.000
Erbb2,Jak2,Pkd1,Ret,Syk,Pdgfra,Jak3,Cd44,Cd80,Itgb7,Ppara,Cd47,Map2k5,Ptprm,Itpkb,Cnn3,Negr1,Kit,Cd55,Fat2,Gol
ph3,Grid2,Cdh8,Pik3cb,Ajuba,Ctnnd2,Klf4,Tenm2,Gli3,Ptprg,Nectin1,Ripk3,Pcdhga9,Itgb5,Fibp,Scarf2,Xbp1,Gsn,Trim2
9,Mpzl2,Bcl6,Igsf5,Dennd6a,Zfp35,Fermt3,Cdh26,Wnt7b,Gldn,Zbtb16,Efnb3,Piezo1,Ank3,Fblim1,Tpm1,Utrn,Erbb3,Dlc
1,Mmp14,Vav3,Sema3e,Washc2c,Flcn,Dock1,Ldb1,Sema5a,Plxnd1,Tfe3,Bcas3,Col16a1,Prkx,Pdgfb,Capn3,Chrnb2,Fgr,P
agr1,Myd88,Clcf1
regulation of autophagy -4.453 -1.842
Mapt,Atp6v0a1,Bok,Mapk3,Mtor,Dap,Sh3bp4,Eif2ak4,Rptor,Xbp1,Rasip1,Srebf2,Atm,Tbc1d25,Flcn,Depdc5,Usp10,Tlr2
,Lrsam1,Tfeb,Sirt2,Ehmt2,Washc1,Grin2c,Pkd1,Ppara,Ogt,Herpud1,Acacb,Mapk15,Nedd4l,Cnot1,Ubxn1,Tmem259,Sorl
1,Sumo1,Osbpl7,Ppp1r3c,Lrig2,Hnrnpr,Cbfa2t3,Ube3a,Prr5l,Rnf114,Hdac4,Tysnd1,Azin2,Oaz2,Pik3c3,Pik3cb,Tecpr1,W
dfy3,Arsa,Ulk3
synapse organization -4.445 -1.842
Atp2b2,Erbb2,Pdgfb,Fnta,Cntn2,Utrn,Mapt,Dlg4,Chrnb2,Unc13a,Lrp4,Kalrn,Magi2,Ctnnd2,Nrxn3,Nrxn2,Il10ra,Shank2,
Nectin1,Lmx1a,Sema3e,Pdzrn3,Plxnd1,Wnt7b,Ank3,Syndig1
positive regulation of telomerase activity -4.164 -1.617
Pkib,Mapk3,Klf4,Mapk15,Nvl,Tnks,Grhl2,Pot1,Atm,Pdgfb,Pdgfra,Igf1r,Kitlg,Eya4,Slx1b,Pagr1,Dnmt3l,E2f7,Clcf1,Spidr
,Slf2,Axin2,Ppp4c,Recql5,Flcn,Bcl6,Zbtb38,Ehmt2,Gnl3l,Smg7,Gfi1,Ccnb1,Ogt,Cit,Xbp1,Tpr,Noc2l,Phf8,Tada2a,Fendrr
regulation of telomerase activity -3.655 -1.355 Pkib,Mapk3,Klf4,Mapk15,Nvl,Tnks,Grhl2,Atm,Pot1
63
telomere maintenance via telomerase -2.849 -0.914 Pkib,Mapk3,Mapk15,Tnks,Atm,Smg7,Pot1,Gnl3l
regulation of chromosome organization -2.521 -0.731
Gfi1,Pkib,Ccnb1,Ogt,Axin2,Mapk3,Cit,Mapk15,Xbp1,Tnks,Slx1b,Atm,Flcn,Bcl6,Tpr,Noc2l,Phf8,Tada2a,Ehmt2,Slf2,Pot
1,Gnl3l,Fendrr
Table 4: Metascape Pathway Analysis of DNA Methylation – genes
Pathway Description -log(p-value) Log(q-value) Genes
Reg 6m – Reg 9m
mitotic cell cycle process -3.55825 0 Pdgfb,Ube2i,Mnat1,Ppm1d,Ercc3,Pds5a,Nek6,Tada3,Tcf19,Ppp2r5c
cellular response to DNA damage stimulus -2.83522 0 Xrcc1,Mnat1,Poli,Ercc3,Pds5a,Fan1,Fnip2,Yap1,Ppp2r5c,Ube2i
ribonucleoprotein complex assembly -2.67192 0 Sf3b1,Prpf18,Nip7,Pih1d1,Snrpd2
negative regulation of apoptotic process -2.5362 0 Cryab,Adora1,Arf4,Wnt11,Tax1bp1,Mnat1,Nuak2,Hax1,Pih1d1,Yap1
ERBB signaling pathway -2.07189 0 Adora1,Arf4,Nrg2
regulation of cellular component
biogenesis -2.16487 0 Myo5b,Cryab,Tmsb4x,Wnt11,Hax1,Pih1d1,Fnip2,Ephb2,Tbc1d2b
behavior -2.14563 0 Ptgds,Adora1,Slc22a5,Kcnab1,Arf4,Pyy,Mmp17,Ephb2
vesicle organization -2.07497 0 Doc2g,Tbc1d25,Tbc1d2b,Snx12,Zpbp
dendritic spine development -2.02678 0 Myo5b,Arf4,Ephb2
Reg 9m – Irreg 9m
regulation of metaphase/anaphase
transition of cell cycle -4.360 -0.510 Mos,Ube2c,Tpr,Nek6,Susd2
dicarboxylic acid transport -4.154 -0.510 Htt,P2rx7, Slc6a8, Slc21a4,Slc17a6,Slc25a21,Slc6a8
sodium ion transport -3.877 -0.330 Slc9a3,Nedd4,P2rx7,Slc6a8,Kcnk1,Slc17a6,Nkain4,Mfsd3,Gata2,Itpr3,Lgals3bp,Snx8,Susd2
cellular response to heat -2.592 0.000 Clpb,Tpr,Chordc1
axo-dendritic transport -2.569 0.000 Htt,Spg7,Cnih2,Gata2,P2rx7,Clip1,Tek,Tpr,Kpna6
positive regulation of cytoskeleton
organization -2.473 0.000 P2rx7,Clip1,Tek,Nck1,Lmod1,Fgb,Ube2c,Tpr,Chordc1,Htt
circadian regulation of gene expression -2.441 0.000 Ppp1ca,Mybbp1a,Bhlhe40
positive regulation of phosphatidylinositol
3-kinase signaling -2.364 0.000 Nedd4,Unc5b,Tek,Fgb,Mos,Htt,P2rx7,Nck1,Gcnt2,Nek6,Lamtor3
extrinsic apoptotic signaling pathway -2.256 0.000 Fgb,Ppp1ca,Htt,P2rx7,Unc5b,Itpr3
mTOR signaling pathway -2.075 0.000 Clip1,Dvl2,Lamtor3,Nprl2
nucleobase-containing compound transport -2.047 0.000 Htt,P2rx7,Thoc3,Tpr
mRNA transport -2.041 0.000 Htt,Thoc3,Tpr
positive regulation of exocytosis -2.013 0.000 Fgb,Gata2,Sept5
Irreg 9m – Acyc 9m
Table 5
64
dopamine metabolic process -3.196 0 Npr1,Chrna7,Sncb,Gpr37
carbohydrate derivative biosynthetic
process -3.007 0
Cad,Npr1,Pdgfb,Acot7,Phlda1,Dpm2,Cltc,Nme3,Vcp,Hs3st5,Ube2j1,LOC314140,Impdh1,Mgat4a,Ccdc126,Chka,Nmn
at1
response to endoplasmic reticulum stress -2.965 0 Pdx1,Apaf1,Vcp,Ero1a,Ube2j1,Txndc11,Ppp1r15b,Pmaip1,Ywhaz,Cyr61,Uaca,Socs4,Arih2,Rnf14
dicarboxylic acid transport -2.908 0 Agxt,Slc1a1,Slc19a1,Slc21a4,Slc17a6
protein N-linked glycosylation -2.813 0 Vcp,Ube2j1,Mgat4a,Ccdc126,Slc35d3
sulfur compound catabolic process -2.743 0 Agxt,Acot7,Pcyox1,Cad,Ero1a,Nmnat1,Sephs2,Prodh2,Pcca
protein autophosphorylation -2.668 0 Pdgfb,Mvp,Fgr,Prkd1,Uhmk1,Tnk2,Irak3,Prkx
proteasomal protein catabolic process -2.666 0 Agxt,Vcp,Ube2j1,Socs4,Fbxl3,Arih2,Cul4a,Ubr2,Pmaip1,Rnf14
intracellular protein transport -2.595 0
Nfkbia,Ywhaz,Syngr1,Cltc,Ap2s1,Vti1a,Prkd1,Vcp,Uhmk1,Hspa4,Ube2j1,Slu7,Slc35d3,Snip1,Uaca,Arih2,Tom1l2,Grt
p1,Syndig1,Rassf5,Rab34,Pmaip1
Deubiquitination -2.463 0 Nfkbia,Smad2,Vcp,Mdm4,Igfbp2,Rps3a,Rpl19,Ero1a,Rps18,Gpr119
SLC-mediated transmembrane transport -2.401 0 Slc1a1,Slc5a1,Slc21a4,Slc22a7,Slc6a15
Transport of glucose and other sugars, bile
salts and organic acids, metal ions and
amine compounds -2.236 0 Slc5a1,Slc22a7,Slc6a15
positive regulation of synapse assembly -2.371 0 Clstn2,Syndig1,Lingo4,Lrtm2,Pdgfb,Chrna7,Fnta,Sncb
HTLV-I infection -2.171 0 Pdgfb,Fosl1,Nfkbia,Smad2,Wnt9b,Anapc7,RT1-M4,Atf1
negative regulation of response to
endoplasmic reticulum stress -2.146 0 Pdx1,Ube2j1,Ppp1r15b
response to dsRNA -2.110 0 Nfkbia,Snip1,Irak3,Pmaip1
Neutrophil degranulation -2.101 0 Syngr1,Gdi2,Apaf1,Fgr,Vcp,A1bg
Table 5: Metascape Pathway Analysis of DNA Methylation– Promoter
65
Fig 3. RC6-RC9 Hierarchical Clustering – Samples
are grouped based on similarity for the top 100
differentially methylated A) CpG, B) CHG, and C)
CHH sites covered in the assay. Yellow represents high
levels of DNA methylation, and red represents low
levels of DNA methylation.
A
B
C
66
A
C
B
Fig 4. RC9-IR9 Hierarchical Clustering – Samples
are grouped based on similarity for the top 100
differentially methylated A) CpG, B) CHG, and C)
CHH sites covered in the assay. Yellow represents high
levels of DNA methylation, and red represents low
levels of DNA methylation.
67
A
C
B
Fig 5. IR9-AC9 Hierarchical Clustering – Samples
are grouped based on similarity for the top 100
differentially methylated A) CpG, B) CHG, and C)
CHH sites covered in the assay. Yellow represents high
levels of DNA methylation, and red represents low
levels of DNA methylation.
68
Reg 6m – Reg 9m
Reg 9m – Irreg 9m
Irreg 9m – Acyc 9m
Gene Symbol RNA DM
RNA DM
RNA DM
GNRH1 -1.7 (0.0368) - 1.9 (0.0114) - -1.6 (0.0469) -
FSHB - - 1.8 (0.0124) - -1.8 (0.0157) -
PRL 73.5 (0.0106) - -3.3 (0.00085) - - -
PRLHR -1.6 (0.00655) - - - - -
PRLR -1.4 (0.0223) - - (1) Intron HYPER - -
TRH -1.4 (0.00005) - 1.3 (0.00005) - -1.2 (0.0007) -
TSHR 1.6 (0.00005) - - (1) Intron HYPER - -
DRD5 -1.4 (0.0464) (1) Exon HYPO - - - -
DRD2 -1.3 (0.0011) - - - - -
ESR2 -1.5 (0.0138) - - - - -
ESRRG -1.3 (0.00035) (15) Intron (8) HYPO
(7) HYPER
- (6) Intron (5) HYPO
(1) HYPER
- (4) Intron (1) HYPO
(3) HYPER
OXT -3.5 (0.00005) - 3.5 (0.00005) - - -
CRH -1.9 (0.00005) (1) Promoter HYPO 1.4 (0.00845) - - -
CRHBP -1.3 (0.0216) (1) Intron HYPER - - - -
KISS1 1.4 (0.00805) - - - - -
Table 6: HPG-Axis and Hormone Signaling. Fold changes and their respective p-values (RNA column) are listed for each endocrine group
comparison. DM column at what endocrine status differential cytosine methylation was observed, within what region (promoter, exon, or intron),
and whether the site(s) were hypo- or hyper-methylated. Hyper- or hypo-methylated is defined as 0 ‐33% more, or less respectively, methylated
than reference (p ‐value < 0.05).
69
Reg 6m – Reg 9m
Reg 9m – Irreg 9m
Irreg 9m – Acyc 9m
Gene Symbol RNA DM RNA DM RNA DM
GRIA1 -1.2 (0.0035) - - (2) Intron S HYPER - -
GRIA3 -1.1 (0.043) - - - - -
GRIA4 -1.3 (0.00005) - - - - (1) Promoter HYPER
Grid1 -1.1 (0.0331) - - (6) Intron (5) HYPO
(1) HYPER
- (6) Intron (3) HYPO
(3) HYPER
GRID2 -1.2 (0.0323) (2) Intron HYPER - (4) Intron (3) HYPO
(1) HYPER
- (2) Intron (1) HYPO
(1) HYPER
GRID2IP -1.6 (0.0002) - - (1) Exon HYPO -
GRIK1 -1.2 (0.0071) (1) Intron
(1) Exon
HYPER - (1) Intron HYPER - (1) Intron HYPO
GRIK3 - (4) Intron (2) HYPO
(2) HYPER
- (4) Intron HYPER -1.2 (0.0432) (2) Intron HYPO
GRIN2A -1.7 (0.00005) - - (1) Intron HYPO -1.3 (0.0361) (2) Intron (1) HYPO
(1) HYPER
GRIN2B -1.2 (0.0035) (6) Intron (4) HYPO
(1) HYPER
(1) S HYPER
- - - -
GRIN2D -1.2 (0.00595) - 1.1 (0.0192) - -1.2 (0.0222) -
GRIN3A -1.3 (0.00065) - - - - -
GRM1 -1.2 (0.0003) - - - - (1) Intron HYPER
GRM3 -1.2 (0.00575) - - - - (1) Intron HYPO
GRM5 -1.2 (0.0086) - - - - (1) Promoter HYPER
GRM7 -1.2 -0.0082 - - (3) Intron (1) S HYPO
(2) HYPO
- (1) Intron HYPO
VGLUT1 -3.0 -0.00945 - - - - -
VGLUT3 -1.4 -0.0236 (1) Promoter HYPO - - - -
GLT1 -1.2 -0.0006 (2) Intron (1) HYPO
(1) HYPER
- - - -
NAT2 -1.2 -0.00105 - - - - (1) Intron HYPER
Table 7: Changes in Glutamate Signaling. Fold changes and their respective p-values (RNA column) are listed for each endocrine group
comparison. DM column at what endocrine status differential cytosine methylation was observed, within what region (promoter, exon, or intron),
and whether the site(s) were hypo-, strongly(S) hypo, hyper-, or strongly hyper-methylated. Hyper- or hypo-methylated is defined as 0 ‐33% more,
70
or less respectively, methylated than reference (p ‐value < 0.05). Strongly hyper- or strongly hypo-methylated is defined as 33-100% more, or less
respectively, methylated than reference (p ‐value < 0.05).
Reg 6m – Reg 9m
Reg 9m – Irreg 9m
Irreg 9m – Acyc 9m
Gene Symbol RNA DM RNA DM RNA DM
DBI 1.2 (0.00005) - - - - -
GABRA1 -1.3 (0.00005) - - - - -
GABRA5 1.2 (0.00005) - -1.1 0.0443 - - -
GABRB2 -1.2 (0.0041) - - - - -
GABRD 1.1 (0.008) - - - - -
GABRG2 -1.3 (0.00005) - - - - -
GABRG3 -1.8 (0.00015) (1) Intron HYPER - - - (1) Intron HYPO
SLC32A1 -1.4 (0.00515) - 1.2 0.00035 - -1.2 0.00145 -
Table 8: Changes in GABA Signaling. Fold changes and their respective p-values (RNA column) are listed for each endocrine group
comparison. DM column at what endocrine status differential cytosine methylation was observed, within what region (promoter, exon, or intron),
and whether the site(s) were hypo- or hyper-methylated. Hyper- or hypo-methylated is defined as 0 ‐33% more, or less respectively, methylated
than reference (p ‐value < 0.05).
71
Reg 6m – Reg 9m Reg 9m – Irreg 9m Irreg 9m – Acyc 9m
Gene
Symbol
RNA DM RNA DM RNA DM
ABAT -1.1 0.0358 (1) Intron - - - -
ADCY1 -1.7 0.00005 (3) Intron (1)HYPO
(2) HYPER
- - - (1) Exon HYPER
ADCY3 -1.1 0.0276 (1) Intron HYPO - - - (1) Intron HYPER
ATF2 -1.2 0.00675 - - - - (1) Intron HYPER
AVP -1.4 0.00005 (1) Promoter S HYPER 1.7 0.00005 - - -
BHLHE40 1.1 0.0142 - -1.2 0.0183 - - -
CAMK2D -1.1 0.028 (1) Intron S HYPER - (1) Intron S HYPER - (2) Intron S HYPO
CAMK4 -1.3 0.0216 (3) Intron (2) HYPO
(1) HYPER
- - - (1) Intron HYPO
CACNA1C -1.1 0.0234 (2) Exon
(2) Intron
(3)HYPO
(1)HYPER
- (4) Exon
(3) Intron
(3) HYPO
(4) HYPER
- (2) Exon
(2) Promoter
(1) HYPO
(3) HYPER
CDH1 2.1 0.00005 (1) Exon HYPER -1.7 0.00005 (1) Intron HYPO - -
CREB1 -1.3 0.0491 - - - - -
CREB5 - - 1.2 0.0365 (5) Intron (2) HYPO
(3) HYPER
- (2) Intron HYPER
ELMO1 -1.2 0.0142 - - (2) Intron (1) HYPO
(1) HYPER
- (2) Intron (1) HYPO
(1) HYPER
EZR 1.3 0.00005 (2) Intron HYPO - (1) Exon
(4) Intron
(3) HYPO
(2) HYPER
- (2) Exon
(3) Intron
(4) HYPO
(1) HYPER
GNAI1 -1.1 0.0189 - - - - -
GNAQ -1.2 0.00425 (1) Intron HYPO - - - (1) Intron HYPO
GRIA1 -1.2 0.0035 (1) Intron HYPO - (2) Intron (1) S HYPER
(1) HYPER
- -
GRIA3 -1.1 0.043 - - - - -
GRIN2A -1.7 0.00005 - - (1) Intron HYPO -1.3 0.0361 (2) Intron (1) HYPO
(1) HYPER
GRIN2B -1.2 0.0035 (6) Intron (1) S HYPO
(4) HYPO
(1) HYPER
- - - -
GRIN2D -1.2 0.00595 - 1.2 0.0192 - -1.2 0.0222 -
GRIN3A -1.3 0.00065 - - - - -
IGF1R -1.2 0.00685 - - (2) Intron (1) HYPO
(1) HYPER
- (1) Exon
(8) Intron
(4) HYPO
(5) HYPER
ITPR3 1.3 0.00655 (2) Intron (1) HYPO
(1) HYPER
- (4) Intron (3) HYPO
(1) HYPER
- (1) Intron HYPO
MAPK3 1.1 0.0417 - - - - -
72
NOS1 -1.2 0.0172 (2) Intron HYPO - (2) Intron (1) HYPO
(1) HYPER
- (1) Intron HYPER
PCLO -1.2 0.0002 - 1.1 0.0137 (1) Intron HYPER - (1) Exon HYPO
PER3 1.1 0.014 (1) Exon
(1) Intron
(1) HYPO
(1) HYPER
- (1) Exon
(1) Intron
HYPO - -
PLCB1 -1.2 0.00215 (2) Intron (1) HYPO
(1) HYPER
- (2) Intron HYPO - (2) Intron (1) HYPO
(1) HYPER
PLCB3 1.2 0.004 - - - - -
PLCB4 -1.1 0.0376 (3) Intron (2) HYPO
(1) HYPER
- (3) Intron (1) HYPO
(2) HYPER
- (2) Intron HYPO
PLCD1 1.2 0.0253 - - - - -
PLCL2 -1.1 0.0282 - - - - (1) Intron HYPO
PPP2R2C -1.2 0.0004 (1) Intron HYPO - - - -
PRKACB -1.3 0.00005 (1) Promoter
(1) Intron
(1) HYPO
(1) HYPER
- - - -
PRKAR1B -1.2 0.00485 - - - - (2) Intron HYPER
PRKAR2A -1.2 0.00245 - - - - -
PRKCB -1.3 0.00005 (4) Intron (1) HYPO
(3) HYPER
- - - (1) Intron HYPER
PRKCD -1.6 0.00005 - - - - -
PRKCE -1.2 0.0104 (6) Intron (5) HYPO
(1) HYPER
- (8) Intron (4) HYPO
(4) HYPER
- (5) Intron (2) HYPO
(1) S HYPER
(2) HYPER
PRKCH -1.3 0.04 (5) Intron (1) HYPO
(4) HYPER
- (3) Intron (1) HYPO
(2) HYPER
- (4) Intron HYPO
PRKG2 -1.2 0.025 - - - - -
SHC3 -1.3 0.0019 (2) Intron HYPER - - - (1) Intron HYPO
VIP -1.4 0.00005 - - - 1.4 0.0024 -
Table 9: Melatonin Signaling and Circadian Rhythm. Fold changes and their respective p-values (RNA column) are listed for each endocrine
group comparison. DM column denotes at what endocrine status differential cytosine methylation was observed, how many sites within what
regions (promoter, exon, or intron), and whether the site(s) were hypo-, strongly(S) hypo, hyper-, or strongly hyper-methylated. Hyper- or hypo-
methylated is defined as 0 ‐33% more, or less respectively, methylated than reference (p ‐value < 0.05). Strongly hyper- or strongly hypo-
methylated is defined as 33-100% more, or less respectively, methylated than reference (p ‐value < 0.05).
73
Reg 6m – Reg 9m
Reg 9m – Irreg 9m
Irreg 9m – Acyc 9m
Gene Symbol RNA DM RNA DM RNA DM
DNMT3A - - - - -1.1 0.0392 -
DNMT3B - - - - -1.5 0.0212 -
TET1 -1.4 0.00565 - -1.1 0.0443 - - -
TET3 - - - - -1.2 0.0232 -
MECP2 -1.2 0.0106 - - - - -
EHMT1 -1.2 0.0262 - - - -
PRMT8 -1.2 0.041 (1) Intron HYPER - (1) Intron HYPO - (1) Intron HYPO
METTL7A 1.2 0.00185 - 1.2 0.00035 - -
METTL8 -1.3 0.0311 - - - -
HDAC1 1.2 0.0089 - - - -
HDAC5 -1.2 0.00025 (1) Intron HYPO - (1) Intron HYPER -
KDM6B 1.1 0.0137 - - - -
KMT2A -1.1 0.0126 - 1.15 0.00615 - -1.16 0.0079
KMT2B - - - - -1.12 0.038
Table 10: Changes in Epigenome Regulators Fold changes and their respective p-values (RNA column) are listed for each endocrine group
comparison. DM column at what endocrine status differential cytosine methylation was observed, within what region (promoter, exon, or intron),
and whether the site(s) were hypo- or hyper-methylated. Hyper- or hypo-methylated is defined as 0 ‐33% more, or less respectively, methylated
than reference (p ‐value < 0.05).
74
Reg 6m – Reg 9m
Reg 9m – Irreg 9m
Irreg 9m – Acyc 9m
Gene Symbol RNA DM RNA DM RNA DM
MTHFR - - - - -1.2 0.0481 -
SLC44A2 1.1 0.0314 - - (1) Intron HYPO - (1) Intron HYPER
FOLR2 2.5 0.003 - - (1) Intron HYPO - (1) Intron HYPER
TYMS -1.4 0.0067 - - - - -
MUT -1.1 0.0462 - - - - -
PLD1 1.2 0.0144 - - (2) Intron HYPO - (2) Intron HYPER
PLD2 1.2 0.00605 - - - - (1) Exon
(1) Intron
(1) HYPO
(1) HYPER
PLD4 1.2 0.0273 - - - - -
NAPEPLD -1.3 0.0252 - - - - -
Table 11: Changes in One Carbon Metabolism Fold changes and their respective p-values (RNA column) are listed for each endocrine group
comparison. DM column at what endocrine status differential cytosine methylation was observed, within what region (promoter, exon, or intron),
and whether the site(s) were hypo- or hyper-methylated. Hyper- or hypo-methylated is defined as 0 ‐33% more, or less respectively, methylated
than reference (p ‐value < 0.05).
75
GnRH signaling & the HPG Axis
Reproductive senescence is not only characterized by the loss of sex steroids, but is a
function of both gonadal failure and hypothalamic-pituitary aging. The hypothalamic-pituitary-
gondal (HPG) axis, which is activated during puberty, is a negative feedback system in which
pulsatile gonadotropin-releasing hormone (GnRH) produced in the hypothalamus stimulates
luteinizing hormone (LH) and follicle stimulating hormone (FSH) production and secretion by
the pituitary. LH and FSH then stimulate estrogen and production in the ovaries. Systemic
estrogen then feeds back onto the pituitary and hypothalamus to modulate GnRH, LH and FSH
production and secretion [166]. Pituitary response to GnRH, and gonadal response to LH and
FSH simultaneously decline with age resulting in the diminished sex steroid production
characteristic of menopause, and a loss of negative feedback resulting in increased GnRH, LH
and FSH production [166, 168].
In the hypothalamus, GnRH transcripts decreased 1.7 fold from 6 to 9 months, increased
again (1.9 fold) at the onset of irregular cycling (irreg 9), and finally decreased once again (1.5
fold) in the acyclic animals (acyc 9) (table 6). Hypothalamic expression of the follicle
stimulating hormone subunit B (FSHB) was unchanged between 6 to 9 months, increased in the
at the onset of irregular cycling, and was decreased in the acyclic group. No significant
methylation differences in the GnRH1 or FSHB genes were observed. Expression and DNA
methylation changes are summarized in table 6.
Increase in Prolactin Production
Prolactin is a hormone that plays a role in fertility by negatively regulating FSH and
GnRH. Although primarily produced in the pituitary, the hypothalamus as also been shown to
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produce significant amounts [250]. In the pituitary, activation of dopamine receptor D2 (DRD2)
suppresses prolactin gene expression as well as secretion [251]. Furthermore, thyropropin-
releasing hormone (TRH) produced in the hypothalamus acts on the pituitary to increase
prolactin expression and secretion [251]. However, little is known about how prolactin is
regulated in the hypothalamus.
Prior to perimenopause, between 6 to 9 months, there was a dramatic increase in prolactin
expression (73.5 fold) in the hypothalamus. Accompanying this increase were modest decreases
in the prolactin releasing hormone receptor (PRLHR) (-1.6 fold) and prolactin receptor (PRLR)
(-1.4 fold) genes, which both normally suppress prolactin secretion. TRH declined -1.4 fold, and
dopamine receptors D2 and D5 declined -1.3 and -1.4 fold respectively. During the transition
from regular to irregular cycling, prolactin decreases slightly and remained unchanged through
the transition to acycling. However, prolactin remained elevated compared to pre-perimenopause
levels. PRLHR, PRLR, DRD2, and DRD5 did not show any further changes in expression. TRH
increased 1.2 fold from regular to irregular cycling and showed no significant changes during the
irregular to acycling transition.
There were no observed DNA methylation alterations in PRL or its’ receptors PRLHR
and PRLR that correlated to changes in expression levels. However, hypomethylation of DRD5
was observed during 6-9 months when DRD5 expression declined. Expression and DNA
methylation changes are summarized in table 6.
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Other Hormones and Receptors
Estrogen receptor β (Esr2) expression decreased -1.5 fold from 6-9 months and remained
unchanged through perimenopause. Oxytocin (OXT) expression temporarily dropped during 6-9
months before recovering between RC9-IR9. Corticotorpin releasing hormone (CRH) and
corticotorpin releasing hormone binding protein (CRHBP) expression decreased -1.9 and -1.3
fold respectively. A single site within the CRH promoter region was hypomethylated, and a sigle
site within a CRHBP intron was hypermethylated between 6-9 months. No further changes in
DNA methylation occurred in the two genes. CRH expression rebounded slightly (1.4 fold)
between RC9-IR9. Estrogen Related Receptor Gamma (ESRRG) transcriptionally activates DNA
cytosine-5-methyltransferases 1 (Dnmt1) via binding to estrogen response elements in the
DNMT1 promoters. Between 6-9 months, ESRRG transcription drops -1.3 fold and remains
unchanged through the perimenopause transition. The decreased transcription was accompanied
by changes in 15 DNA methylation sites throughout the gene. Although ESRRG transcription
remained unchanged through the transition, DNA methylation continued to change throughout
perimenopause. Expression and DNA methylation changes are summarized in table 6.
Changes in Glutamate Signaling
The data showed a surprising and dramatic decrease of glutamate receptor signaling
between 6-9 months which did not recover during perimenopause. 15 glutamate receptors and 4
glutamate transporter genes were all significantly down-regulated in the regular cycling 9 month
group compared to 6 month. Of these, 3 receptors and 2 solute carriers also underwent changes
in DNA methylation between 6-9 months. Only 1/15 receptors (GRIND2) rebounds at the onset
of irregularity before decreasing again at ayclic. Including GRIND2, two additional glutamate
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receptors continue to drop at acyclicity. An additional glutamate receptor (GRIK3) had
unchanged expression until the onset of acyclicity, at which it declined. Several genes saw
changes in DNA methylation across perimenopause that was not associated with changes in gene
transcription. Expression and DNA methylation changes are summarized in table 7.
Changes in GABA Signaling
4 GABA receptors (GABRA1, GABRB2, GABRG2, and GABRG3) and 1 transporter
gene (SLC32A1) were significantly down regulated in between 6-9 months. SLC32A1
expression increased at irregular cycling before decreasing again at the onset of acyclicity.
Another 3 GABA receptors (DBI, GABRA5, and GABRD) were up regulated between 6-9
months. GABRA5 expression decreased at irregular cycling and did not change further. A single
site was hypermethylated and associated with decreased expression at 6-9 months in GABRG3.
The remaining receptors and transporters saw no changes in DNA methylation across the
perimenopause transition. Expression and DNA methylation changes are summarized in table 8.
Melatonin and Circadian Rhythm signaling
Post-menopausal women exhibit a loss of circadiam rhythm robustness [252] and are
more likely to complain about sleep disturbances such as insomnia or poor sleep quality
compared to pre-menopausal women [253]. Both RNA and DNA methylation pathway analysis
identified melatonin signaling and circadian rhythm as systems undergoing change during the
perimenopause transition. Consistent with our data trends, alterations in these systems first
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appear during early hypothalamic aging, between 6-9 months. A majority (36/44) of the genes
showed decreased expression while DNA methylation changes consisted of both hypo- and
hyper- methylation in primarily intron regions. While transcriptional changes occured
predominantly prior to onset of irregular cycling, changes in the DNA methylation continued to
accumulate throughout the transition. Expression and DNA methylation changes are summarized
in table 9.
Epigenome Maintenance and One Carbon Metabolism
Several genes involved in epigenetic maintenance of DNA methylation and histone
modifications change in expression across the perimenopause transition. In total, 15 epigenetics
genes were identified, including two DNA methyltransferases (DNMT), two ten-eleven
translocation methylcytosine dioxygenases (Tet), seven histone methyltransferases, two histone
deacetylases, and a histone demethylase. The majority of the changes seen occurred between 6-9
months. During this time, 11 genes change in various directions (summarized in table 10). Only
one gene, a histone methyltransferase, changed at the onset of irregular cycling, and a total of
five genes were down regulated in the acyclic group. The temporal pattern of expression changes
would suggest that an epigenetic reorganization even begins between 6 to 9 months, before the
onset of perimenopause.
Maintenance and reorganization of the epigenome depends upon one-carbon metabolism
to produce S-adenosylmethionine (SAM), the universal methyl-donor which provides the
methyl-groups used for DNA and histone methylation. Our data showed a total of nine genes
involved in one-carbon metabolism and SAM production that were significantly changed across
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the groups (table 11). Eight of these genes were identified as changing between 6-9 months.
None of the identified genes were found to change during the transition to irregular cycling, and
only one gene was found to change at the onset of acyclicity. Again, the data suggests that
systems involved in epigenetic maintenance and reorganization are altered before the physical
manifestations of perimenopause.
Onset and Duration of Perimenopause
We define duration of perimenopause as the number of days between initiation of
irregular cycling and first day of acyclicity. The ROUT method was used to identify outliers and
distinguish three clear populations, which had short, average, and long perimenopause transitions
that were significantly different from each other (Fig. 4A). Average age of perimenopause
initiation, characterized by the onset of irregular cycles, was 284 ± 10.4 days with 95% of
animals entering perimenopause within an 8 day window between 280-288 days (Fig. 4B). There
was no correlation between age to enter perimenopause and duration of the transition period
(R2= 0.00114) (Fig. 4C). Average age of perimenopause completion, characterized by persistent
vaginal cornification of cells, was 339 ± 33.5 days with 95% of animals entering perimenopause
within an 23 day window between 328-351 days (Fig. 4D). Perimenopause completion at an
older age was significantly correlated with longer overall duration of the transition (R2= 0.8942)
(Fig. 4E).
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Fig. 4. A) Duration of the perimenopause transition (time spent cycling irregularly before
acyclicity) can be separated into three groups: short, average, and long. An unpaired T test with Welch’s
correction was used to determine significance (*p= 0.024, **p=0.0042, ****p= <0.0001). Average length
of duration across all groups was 54 days with a range of 0-120 days. B) 95% of animals enter
perimenopause within an 8 day window (280-288 days) of with an average age of 284 ± 10.4 days. C)
There was no significant correlation between age to enter perimenopause and duration of the transition
period (R2= 0.00114). D) 95% of animals exit perimenopause within a 23 day window (328-351 days) of
A
B
C
D E
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with an average age of 339 ± 33.5 days. E) Perimenopause completion at older ages correlate with a
longer transition durations (R2= 0.8942).
Changes in Hypothalamic Global DNA Methylation
In the hypothalamus, global DNA methylation dramatically declines (p=0.0035) (Fig.
3A) at the onset of irregular cycling, which marks the beginning of perimenopause. Decrease
DNA methylation levels are sustained through the transition (p=0.0051) (Fig. 5A). From 6-9
months, while animals are still regular cycling, global DNA methylation shows a non-significant
increase, with two distinct populations, which were statistically different from each other
(p=0.0026) (Fig. 5B). 33% of animals had global DNA methylation levels similar to 9 month
regular cycling animals, and 66% had levels similar to irregular and acyclic animals, suggesting
that individual differences in epigenetic profile may contribute to individual differences seen
during normal endocrine aging. When the two groups were analyzed separately, the increase
between the majority 66% of 6 month (RC 6 low) and 9 month animals became statistically
significant (p=0.0028) (Fig. 5B). No significant different was seen in the remaining 33% of
animals (RC 6 high).
DNA methylation regulates menopause timing
5-aza-2’-deoxycitidine Treatment Accelerates Perimenopause
To determine if DNA methylation directly influences onset and progression of
perimenopause, we treated animals with 5-aza-2’-deoxycitidine (5-aza), a de-methylating agent,
and assed cyclicity and perimenopause timing. 5-aza hypomethylates DNA by effectively
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depleting DNMT’s which prevents the methylation of cytosines. Long-term treatment should
prevent proper epigenome reorganization associated with the perimenopause transition. If
perimenopause is indeed an epigenetic event, treatment with 5-aza should disrupt timing of onset
and/or completion of the perimenopause transition. Since RNA-seq and global DNA methylation
analysis suggested that hypothalamic aging begins during 6-9 months, before the onset of
irregular cycling, treatment was initiated at 6 months, in the likelihood of causing the greatest
epigenomic perturbation. Regular cycling animals were injected with 5-aza or vehicle three times
a week for three months. Because hypothalamic DNA methylation dramatically declined at the
onset of perimenopause (Fig. 5), we hypothesized that 5-aza-induced hypomethylation might
induce premature of onset and/or completion of the perimenopause transition.
Fig. 5. A) Hypothalamic DNA methylation declines at the onset of perimenopause (p= 0.0035) and
remains low through the completion of the transition (p=0.0051). B) Two statistically different
populations exist within the 6 month regular group (p=0.0026). When compared separately, the 6 month
“low” population has statistically decreased levels of global DNA methylation (p= 0.0028).
A B
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At 9 months of age a significantly higher proportion of 5-aza-treated animals had
transitioned into constant estrus and were no longer cycling (acyc), as assessed by a chi-square
test (p = 0.033). In contrast, all of the vehicle treated animals were still cycling (regularly or
irregularly) (Fig. 6). Survival curve analysis, using log-rank (Mantel-Cox) test followed by
Gehan-Breslow-Wilcoxon test, shows a non-significant trend towards a slight acceleration of
perimenopause onset between the two groups (Fig. 7A). However, the age to exit perimenopause
and enter into menopause was strikingly, and significantly, accelerated in the 5-aza-treated group
(Mantel-Cox p= 0.0043 ; Gehan-Brewslow-Wilcoxon p= 0.006) (Fig. 7B). These results suggest
that perimenopause duration and menopause timing is regulated by epigenetic mechanisms
including DNA methylation.
Fig. 6. At 9 months of age, the 5-aza treated group had a significantly higher proportion of acyclic
animals, whereas the vehicle treated group had a significantly higher proportion of irregular cycling
animals (p= 0.033) by chi-square test.
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Fig. 7. A) Survival curve analysis show a non-significant acceleration of perimenopause initiation in 5-
aza-treated animals. B) 5-aza significantly accelerated progression through the transition and
perimenopause completion (Mantel-Cox p= 0.0043 ; Gehan-Brewslow-Wilcoxon p= 0.006).
Methionine Treatment Delays Loss of Cycling
RNA-seq and genome-wide DNA methylation analysis suggested that impaired one-
carbon metabolism may be involved in the onset of perimenopause. Impaired one-carbon
metabolism, resulting in a decrease of SAM production may be responsible for the loss of DNA
methylation observed in perimenopausal animals. To test whether we could delay perimenopause
onset and/or completion, we treated animals with methionine (a precursor of SAM), in an
attempt to supplement the aging epigenome and prevent DNA hypomethylation. Treatment was
initiated at 6 months and continued until 10 months at which time cyclicity was assessed. At 10
months a significantly larger proportion of methionine treated animals (86%) were still regularly
A B
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cycling compared to the control group (25%) as assessed by chi-square test (p = 0.044). There
were no irregularly cyclers and all remaining animals were acyclic (Fig. 8).
Fig. 8. At 10 months 86% of methionine treated animals were still regularly cycling compared to 25% of
the vehicle treated animals (p = 0.044). Remaining animals were all acyclic.
Discussion
Endocrine and Neurological Aging in Perimenopause
Endocrine Aging Beings before Perimenopause
Multiple systems contribute to the onset of perimenopause and decline of ovarian
function, including both hypothalamic and functional ovarian aging, environmental, genetic and
lifestyle factors, as well as systemic diseases. Hypothalamic aging leads to desynchronized
GnRH production and an impaired surge of LH and FSH release from the pituitary gland. These
central nervous system changes, together with ovarian ageing, impair ovarian follicle maturation,
hormone production and ovulation. This leads to cycle irregularities and upregulation of GnRH
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and FSH. As systemic ageing progresses, anovulatory cycles become more frequent and finally,
cycling ceases altogether (Fig. 9).
Surprisingly, we saw the most significant changes in gene transcription occur during the
6- to 9-month period when animals were still cycling regularly (Fig2). Although prior to
perimenopause onset, our data indicate that hypothalamic aging begins during this time period.
Activation of the hypothalamic-pituitary-adrenal (HPA) axis in puberty is initiated by KISS1-
signaling, which requires the release of epigenetic suppression. KISS1, together with GnRH and
E2, were identified as upstream regulators of the transcriptional changes seen between 6-9
months as well as throughout the perimenopause transition (Table 2). We also observed
expression changes in both GnRH and KISS1 prior to perimenopause (Table 6). The function of
KISS1 signaling in reproductive senescence is not well characterized; however it seems likely
that it may play an important part in the progression of hypothalamic aging.
Hyperprolactinemia is a known reproductive inhibitor in both males and females and is
responsible for loss of libido as well as infertility [254]. We propose that hyperprolactinemia
plays a similar role in menopause as it does in infertility - by negatively regulating GnRH and
FSH. We saw no changes in the DNA methylation patterns in the GnRH gene, revealing that
other upstream factors are responsible for the altered expression levels seen prior to and during
perimenopause. The dramatic increase in expression prior to perimenopause makes prolactin a
likely candidate as a perimenopause initiator. However, we saw no changes in DNA methylation
of prolactin or its' receptors that correlated with altered expression suggesting that yet another
upstream factor is involved. Prolactin secretion and gene expression are regulated by various
hormones including dopamine and TRH and their receptors [251, 255]. Our data show changes
in TRH expression occur across perimenopause – although we see no changes in DNA
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methylation. Decreased expression of dopamine receptor D5 (DRD5) is associated with
hypomethylation of a single site within its exon. It is possible that this site serves as a regulatory
signal for transcription; however, further studies are needed to investigate the relationship
between DNA methylation status and DRD5 expression.
Fig. 9. Endocrine Aging in Perimenopause
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Neurological Aging Beings before Perimenopause
Our data shows that prior to perimenopause, many genes involved in the one-carbon
cycle and epigenetic regulation have altered transcriptional expression. These changes likely
influence timing through the perimenopause transition as well as establish the groundwork for
increased risk of neurodegenerative diseases later in life. For example, changes in one-carbon
metabolism have been directly linked to alterations of pathways involved in AD etiology. A diet
deficient in B-vitamins has been shown to induce hypomethylation in the Presenilin 1 (PSEN1)
gene promoter, resulting in protein overexpression and a subsequent increase in amyloid-beta
(A β) deposition both in in vitro and in vivo models [240, 256]. SAM supplementation reverses
these effects, demonstrating the importance of the one-carbon cycle in maintaining cognitive
health [239]. Furthermore, hormonal regulation of one-carbon metabolism means that an earlier
menopausal age provides an enhanced opportunity for epigenetic dysregulation leading to further
neurological decline.
We see dramatic fluctuations in both glutamatergic and GABAergic signaling pathways.
Between 6-9 months, every glutamate receptor and transporter is down regulated and GABA
receptors show expression changes in both directions. Glutamate-mediated excitotoxicity has
been linked to several neurodegenerative disorders such as amyotrophic lateral sclerosis,
multiple sclerosis, as well as Parkinson's and Alzheimer’s disease [257, 258], and impaired
GABAergic signaling is believed to be a key feature of all neurodegenerative etiologies [259].
Glutamate transporters are responsible for removing glutamate from the
extracellular/synaptic space to prevent tonic activation of post-synaptic glutamate receptors and
excitotoxicity. In particular, the transporter GLT1 is so crucial to glutamate re-uptake that it is
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believed to represent a total 1% of the brain’s protein[260]. We observe a significant decrease in
four glutamate transporter genes, including GLT1 between 6-9 months (Table 7). Expression
changes were accompanied by differential DNA methylation in two of the transporters,
VGLUT3 and GLT1. It’s possible that the changes in glutamate and GABA signaling prior to
perimenopause sensitize the brain to environmental or hormonal insult and increase the risk of
neuronal death and cognitive impairment with age.
Epigenetics and Perimenopause
Accelerated Epigenomic Aging in Early Transitioners
Global DNA methylation is reported to decline with age [261, 262]. In the hypothalamus,
we do not observe a significant change in DNA methylation between 6-9 months (Fig 5A).
However, within the regular 6 month animals, we see two distinct populations – with “high” and
“low” global DNA methylation levels that are significantly different from each other.
Unsurprisingly, individual animals appear to be aging at various rates and a subset of the 6
month animals’ epigenomes appear to be biologically and endocrinologically “older”. Indeed, at
6 months two-thirds of the animals showed low DNA methylation levels equivalent to 9 month
irregular and acyclic groups and the remaining one-third had DNA methylation levels similar to
regular cycling 9 month animals (Fig 2B). Furthermore, this ratio of 1:3/2:3 matches the
percentage of 9 month animals regular cycling (37) versus irregular (30%) or acyclic (33%) (Fig.
1A), suggesting that if left undisturbed, animals with lower global DNA methylation levels at 6
month would become irregular or acyclic and animals with higher levels would continue to cycle
regularly at 9 months of age. This data heavily suggests that individual epigenetic differences
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that are present prior to perimenopause predispose an individual towards a particular
perimenopause outcome (late vs. early).
In humans, menopause has been shown to accelerate epigenetic patterns of aging in blood
[187]. Women with an earlier age of menopause onset have been found to be “epigenetically
older” than women with a later onset [187]. Here we show that, in rats, global DNA methylation
declines at the onset of perimenopause supporting the hypothesis that menopause and epigenetic
age are inversely correlated. However, the cause-effect relationship of epigenetic and
menopausal age remains ambiguous. To better understand this relationship, we sought to perturb
the epigenome in our animals and assess if perimenopause timing was altered. We used 5-aza,
which has been shown to shorten the lifespan of cells via hypomehtylation of DNA [263], to
induce an accelerated aging phenotype. In doing so, we were able to accelerate the
perimenopause transition, bringing on an early “menopause” status in our animals (Fig. 7B).
Since an “older” epigenome is correlated with earlier menopause and associated with
impaired one carbon metabolism and loss of SAM, we also supplemented animals with
methionine in an attempt to slow epigenetic aging and prolong reproductive competency.
Methionine, a precursor to SAM, was chosen rather than SAM itself because it is much less
volatile, has a greater half-life, and a lower dose is required to obtain systemic effects [264].
Regular cycling 6 month animals that were regularly supplemented with methioinine remained
reproductively competent longer than their vehicle treated counterparts (Fig. 8). Together these
data further clarify the cause-effect relationship of epigenetic and menopause age and provide
evidence that epigenetic mechanisms regulate the perimenopause transition.
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Perimenopause vs. Menopause
Studies have yet to address the relationship between negative health outcomes and
perimenopause timing vs. menopausal age. In humans, negative health outcomes are associated
with early menopause. However, menopause is merely the completion of a complicated
transition period, which can span several years. It is unknown whether these health outcomes are
only associated with early menopause, or with early perimenopause onset or longer overall
duration. We observed a very short window of time during which animals entered into the
perimenopause transition, and a much larger window of time during which animals exited. Since
animals all begin perimenopause at roughly the same age, it is likely that negative health
outcomes are related to an accelerated transition that results in an early menopause status.
Conclusion
Early menopause is associated with adverse cognitive outcomes later in life. Current
interventions focus on peri- or post- menopausal women, however our data strongly indicates
that hypothalamic aging begins before the onset of perimenopause. Environmental perturbations
during this sensitive time period can influence menopausal outcome via epigenetic mechanisms,
as demonstrated by 5-aza and methionine treatments. Thus, it may be necessary to initiate
preventative care rather than intervention therapy when treating perimenopause symptoms and
neurological deficits associated with menopause.
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3. EPIGENETIC CHANGES IN GENES REQUIRED FOR GLUCOSE AND
MYELIN METABOLISM IN THE BRAIN
Abstract
Glucose hypometabolism and reduced bioenergetic capacity in the brain is associated with the
cognitive impairments seen in Alzheimer's Disease (AD). In the aging female brain, catabolism
of endogenous myelin brain lipids to generate ketone bodies is a systems level adaptive response
to satisfy brain energy demand in the wake of glucose hypometabolism. We investigate
hippocampal changes in RNA transcription and epigenetics of genes involved in these pathways
in efforts to elucidate cause and timing of glucose metabolic deficiency during the
perimenopause transition. The transition from regular to irregular cycling showed the most wide-
spread changes in both transcription and global DNA methylation patterns. Changes in glucose
uptake, fatty acid biosynthesis, and myelin metabolism pathways were first detected in regular
cycling animals, between 6-9 months of age. At the transition from regular to irregular cycling,
gene expression patterns suggest that insulin signaling, glucose metabolism, and mitochondrial
function begin to decline. Gene pathways promoting Alzheimer’s pathology are down-regulated
between 6-9 months and are up-regulated during the regular to irregular transition. Changes in
DNA methylation was observed in insulin/diabetes, glucose metabolism, phospholipase, and
myelin catabolism pathways but not in mitochondrial dysfunction. Animals that transition to
irregular cycling earlier experience wide-spread epigenetic changes sooner than late transitioners
and are likely epigenetically "older". These epigenetic differences establish a basis for different
trajectory outcomes between early- and late- transitioners and may predispose early transitioners
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to AD-associated pathways such as brain hypometabolism, mitochondrial dysfunction, Aβ
generation, and white matter degeneration.
Introduction
Glucose hypometabolism and reduced bioenergetic capacity in the brain is associated
with the cognitive impairments seen in Alzheimer's Disease (AD) [265-269]. Importantly, these
brain bioenergetic systems are compromised early, during the prodromal stages of
neurodegeneration and before the disease can be recognized clinically [2, 265, 266, 270-276].
Decreased glucose utilization in the entorhinal cortex and hippocampus, correlates with cognitive
deficits over time and can predict cognitive decline in healthy individuals [270, 277-279].
Additionally, individuals with AD exhibit cerebral glucose hypometabolism that correlate to
disease progression [2, 270-274, 278, 280-289].
Under conditions of diminished glucose availability, the brain will progressively utilize
fatty acids as an alternative energy source [290, 291]. In the aging female brain, catabolism of
endogenous myelin brain lipids to generate ketone bodies is a systems level adaptive response to
satisfy brain energy demand in the wake of glucose hypometabolism. Furthermore, shift to
ketone utilization in the brain is associated with female reproductive senescence [214, 267, 292]
suggesting that brain aging and menopause are intimately linked. Loss of estrogenic control of
glucose metabolism can lead to decreased glucose utilization, diminished aerobic glycolysis and
altered oxidative phosphorylation, which together generate a hypometabolic phenotype in the
menopausal female brain [214, 265, 267, 293, 294]. Animal models of female endocrine aging
and AD, demonstrate declining mitochondrial bioenergetics and a generalized shift away from
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glycolytic pathways towards use of ketone bodies during the transition to reproductive
senescence and early in the development of AD pathologies [265, 292-294]. In humans, although
a majority of women have no long-term health consequences after menopause, many experience
neurological symptoms during and after the perimenopause transition [2] and early menopause
has been associated with adverse cognitive outcomes, including AD [160-162]. It is possible that
glucose hypometabolism, mitochondrial dysfunction, and the subsequent shift to ketone
metabolism during perimenopause is involved in these cognitive symptoms and in the
development of neurodegenerative diseases.
White matter degeneration is a hallmark characteristic of many neurodegenerative
diseases, including AD. Regions most vulnerable to white matter degeneration map onto regions
preferentially affected in the pathological trajectory of AD [295], suggesting a link between
mechanistic pathways affected in the early stages of AD and late stage white matter degeneration
and cognitive deficits [296]. In the aging female brain, white matter degeneration is associated
with a decline in mitochondrial respiration, increased mitochondrial hydrogen peroxide
production and cytosolic-phospholipase-A2 sphingomyelinase pathway activation [296]. An
increase in fatty acids and mitochondrial fatty acid metabolism machinery is coincident with a
rise in brain ketone bodies and decline in plasma ketone bodies, linking mitochondrial
dysfunction early in aging with later age development of white matter degeneration [296].
Glucose hypometabolism, ketone utilization, and onset of AD pathologies are all
associated with reproductive senescence [2, 214, 296]. Our studies in rats suggest that endocrine
aging begins before onset of perimenopause, while animals are still regularly cycling (Bacon et
al.), and that the period prior to irregular cycling may be sensitive to environmental impact
which can protect against or exacerbate vulnerability to AD and other neurodegenerative
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diseases [2]. Prior to irregular cycling, the hypothalamic epigenome is subject to widespread
reorganization which is associated with expression changes of many genes (Bacon et al.).
Epigenetic aging and aberrant DNA methylation patterns could predispose an individual to
hypometabolism and increase risk of cognitive impairment later in life if these changes occur in
genes involved in glucose metabolism, myelin catabolism, or fatty acid metabolism. Although
decreased glucose metabolism and the compensatory increase in ketone body utilization are
associated with endocrine aging, the upstream mechanisms that regulate these systems are
relatively unknown. We investigate hippocampal changes in RNA transcription and DNA
methylation of genes involved in these pathways in efforts to elucidate cause and timing of
glucose metabolic deficiency during the perimenopause transition.
Methods
Animals Animal studies were performed following National Institutes of Health guidelines on
use of laboratory animals; protocols were approved by the University of Southern California
Institutional Animal Care and Use Committee. A total of 125 young or middle-aged female
Sprague-Dawley rats were obtained from Envigo Laboratories. Regular 6 month group was
cycled from 5 month of age using rats that had given birth to at least one litter. Rats for all other
groups were aged from 8–9 month old breeders. One week after arrival, ovarian-functioning
statuses were evaluated daily by the cytology of uterine cells obtained from lavage at 11am. The
smear was morphologically characterized based on the four stages of the cycle: Estrus (E),
Metestrus (M), Diestrus (D) and Proestrus (P). The regular 4-5 day estrus cycle is defined as the
period between successive estrus smears (E, M, D, P, E, M, D, P, E). In addition to regular
cycling animals, selected groups included middle-aged rodents at defined stages of
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perimenopause. The irregular group was defined as 2 contiguous cycles of >5 days characterized
by prolonged diestrus stages. The acyclic (constant estrus) group was defined as persistent
vaginal cornification lasting > 8 days. Rats at designated age (6m or 9–10m) and cycling status
were euthanized at estrus or constant estrus. Two sets of animals were used in this study. The
first set (85 rats in total) included all 4 experimental groups (Reg-6m, Reg-9–10m, Irreg-9–10m,
and Acyc-9–10m). Of this set, N = 5–6 per group were used for analyses of RRBS data. A
second set of rats, containing all four groups, (40 rats in total) was used for RNA-seq analysis.
Cycling status of these animals were monitored from 9 months regular cycling until they reached
constant estrus. Rats that did not meet the endocrine criteria for each group were excluded from
analyses for this study.
Tissue Collection For the first two sets of animals, rats were euthanized and the brains rapidly
dissected on ice. Cerebellum, brainstem, and hypothalamus were removed from each brain and
the two hemispheres were separated. The cortical hemisphere was fully peeled laterally and
hippocampus was then separated. Cerebellum, midbrain, brainstem, hypothalamus, and both
cortexes and hippocampi were harvested and frozen at −80°C for subsequent analyses. Ovaries
and uterus were harvested and frozen at −80°C for subsequent analyses.
Nucleic Acid Extraction Total RNA was extracted from tissue homogenized in trizol and
purified using the PureLink® RNA Mini Kit (Thermo Fisher Scientific). RNA was DNase
treated on column during purification (Thermo Fisher Scientific). DNA was extracted from
tissue homogenized in lysis buffer [10mM Tris-HCl(pH 8.0), 1mM EDTA, 0.1% SDS], RNase
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treated (Zymo Research Corp., Irvine, CA), purified using phenol/chloroform/isoamyl alcohol,
and then precipitated in isopropanol.
RNA-seq Total RNA-seq libraries were constructed from RNA extracted from the hypothalamus
of female rats. Samples were run on the Illumina HiSeq 2500 using 50bp PE to obtain a total
read depth of roughly 50 million read pairs per sample. Raw data files in the FASTQ format
underwent QA/QC and trimming procedure in the cloud-based Partek Flow environment
(http://www.partek.com/). The paired end reads for each sample were then aligned using TopHat
to the rat reference genome rn6 (Ensembl 80). Transcript assembly and quantification of aligned
reads were carried out using Cufflinks. The Cufflinks output consisted of a list of differentially
expressed genes (DEG) for each comparison.
RNA-seq Bioinformatic Analysis by Ingenuity Pathway Analysis (IPA) Expression data for
genes with the p-value < 0.5 were analyzed by IPA core analysis composed of a network analysis
and an upstream regulator analysis. We used these relaxed criteria to maximize the coverage of
the gene array results in the bioinformatic analyses. The network analysis identified biological
connectivity among molecules in the dataset that were up- or down-regulated in a comparison
(focus molecules that serve as “seeds” for generating networks) and their interactions with other
molecules present in the Ingenuity Knowledge Base. Focus molecules were combined into
networks that maximized their specific connectivity. Additional molecules from the Ingenuity
Knowledge Base (interacting molecules) were used to specifically connect two or more smaller
networks to merge them into a larger one. A network was composed of direct and indirect
99
interactions among focus molecules and interacting molecules, with a maximum of 70 molecules
per network. Generated networks were ranked by the network score according to their degree of
relevance to the network eligible molecules from the dataset. The network score was calculated
with Fisher’s exact test, taking into account the number of network eligible molecules in the
network and the size of the network, as well as the total number of network eligible molecules
analyzed and the total number of molecules in the Ingenuity Knowledge Base that were included
in the network. Higher network scores are associated with lower probability of finding the
observed number of network eligible molecules in a given network by chance.
The Ingenuity’s Upstream Regulator Analysis in IPA is a tool that predicts upstream
regulators from gene expression data based on the literature and compiled in the Ingenuity
Knowledge Base. A Fisher’s exact test p-value was calculated to assess the significance of
enrichment of the gene expression data for the genes downstream of an upstream regulator. A z-
score was given to indicate the degree of consistent agreement or disagreement of the actual
versus the expected direction of change among the downstream gene targets. A prediction about
the state of the upstream regulator, either activated or inhibited, was made based on the z-score.
Genome-Wide DNA Methylation Profiling A modified reduced representative bisulfite
sequencing (RRBS) protocol (Methyl-MiniSeq
TM
) was used to prepare libraries from 200-500 ng
of genomic DNA digested with 60 units of TaqαI and 30 units of MspI (NEB) sequentially and
then extracted with Zymo Research (ZR) DNA Clean & ConcentratorTM-5 kit (Cat#: D4003).
Fragments were ligated to pre-annealed adapters containing 5’-methyl-cytosine instead of
cytosine according to Illumina’s specified guidelines (www.illumina.com). Adaptor-ligated
100
fragments of 150–250 bp and 250–350 bp in size were recovered from a 2.5% NuSieve 1:1
agarose gel (ZymocleanTM Gel DNA Recovery Kit, ZR Cat#: D4001). The fragments were then
bisulfite-treated using the EZ DNA Methylation-LightningTM Kit (ZR, Cat#: D5020).
Preparative-scale PCR was performed and the resulting products were purified (DNA Clean &
Concentrator
TM
- ZR, Cat#D4005) for sequencing on an Illumina HiSeq.
RRBS Sequence Alignments and Data Analysis Sequence reads from bisulfite-treated
MiniSeq
TM
libraries were identified using standard Illumina base- calling software and then
analyzed using a Zymo Research proprietary analysis pipeline, which is written in Python and
used Bismark (http://www.bioinformatics.babraham.ac.uk/projects/bismark/) to perform the
alignment to the rn6 genome. Index files were constructed using the
bismark_genome_preparation command and the entire reference genome. The --non_directional
parameter was applied while running Bismark. All other parameters were set to default. Filled-in
nucleotides were trimmed off when doing methylation calling. The methylation level of each
sampled cytosine was estimated as the number of reads reporting a C, divided by the total
number of reads reporting a C or T. Fisher’s exact test or t-test was performed for each CpG site
which has at least five reads coverage, and promoter, gene body and CpG island annotations
were added for each CpG included in the comparison.
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Results
The Regular-Irregular Transition Shows the Most Differentially Expressed Genes
A total of 19748 transcripts in the hippocampus were identified, sequenced, and
analyzed. Between 6-9 months, 1275 (6%) genes were significantly different (p< 0.05). Between
9 month regular and irregular, and irregular and acyclic groups, 1873 (9%) and 834 (4%) genes,
respectively, were significantly different (p< 0.05) (Fig 1). The transition from regular to
irregular cycling showed the most differentially expressed genes, suggesting that this transition
stage is a critical period of change in the hippocampus. Among differentially expressed genes,
the majority showed fold change differences between 10-20%. In the 6-9 month comparison,
149 genes (12%) had a fold change of <10%, 700 (55%) between 10-20%; 360 (28%) between
20-50%; 55 (4%) between 50-100%; and 11 genes (1%) had a fold change greater than 100%. In
the regular-irregular comparison, 179 genes (10%) had a fold change of <10%, 1278 (68%)
between 10-20%; 365 (19%) between 20-50%; 36 (2%) between 50-100%; and 14 genes (1%)
had a fold change greater than 100%. In the irregular-acyclic comparison, 157 genes (19%) had a
fold change of <10%, 452 (54%) between 10-20%; 165 (20%) between 20-50%; 31 (4%)
between 50-100%; and 27 genes (3%) had a fold change greater than 100%.
Genes Responsible for Glucose Uptake Increase and Diabetes Risk Decreases in
Regular and Irregular Cycling Animals
Adiponectin Receptor 2 (ADIPOR2), a gene involved in glucose uptake, was up-
regulated between 6-9 months (p= 0.02) (Table 1). ADIPOR2 is a receptor for Adiponectin, a
hormone that regulates glucose and lipid metabolism and homeostasis [297, 298]. Adiponectin-
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binding activates a signaling cascade that leads to increased PPARA activity, and ultimately to
increased fatty acid oxidation and glucose uptake. Surprisingly, genes related to type I diabetes
were down regulated (Table 2). Glutamate Decarboxylase 1 and 2 (GAD1 and GAD2),
autoantigens in insulin-dependent diabetes, were down-regulated between 6-9 months (p=
0.00005 and p= 0.008). In irregular animals CD3e Antigen, Epsilon Polypeptide (CD3E), a
protein associated with susceptibility to TID in women, was also down-regulated (p= 0.001).
Insulin Signaling and Glucose Tolerance are Perturbed in Irregular Cycling
Animals
Several genes involved in insulin response and signaling changed at the onset of
irregular cycling (Table 2). At the transition to irregular cycling, several genes involved in
insulin signaling were up-regulated: Phosphoinositide-3-Kinase Regulatory Subunit 1 and 3
(PIK3R1 and PIK3R3) (p= 0.03 and p= 0.02), Protein Kinase AMP-Activated Catalytic Subunit
Alpha 2 (PRKAA2) (p= 0.03), Insulin Receptor (INSR) (p= 0.04), and Glycogen Synthase
Kinase 3 Beta (GSK3B) (p= 0.02). PIK3R1 is predominantly found in the liver where it plays a
role in glucose tolerance improvement [299], however it likely has a similar function in the
brain. PRKAA2 is the catalytic subunit of the AMP-activated protein kinase(AMPK) which
regulates lipid and glucose metabolism through a variety of mechanisms including:
phosphorylation of insulin signaling molecule Insulin Receptor Substrate 1 (IRS1), glycolytic
enzymes PRKFB2 and PRKFB3, and Acetyl-CoA Carboxylase (ACC) and Beta-Hydroxy Beta-
Methylglutaryl-
103
A
B
Fig 1. A) A total of 20877 transcripts in the hippocampus were identified and sequenced. Of the total
number of transcripts sequenced, 1274 (6%), 1872 (9%), 832 (4%) genes were significantly different
between the 6 and 9 months regular cycling, regular and irregular cycling 9 month, and irregular and
acyclic 9 month groups, respectively (p< 0.05). B) In all comparisons, the majority of differentially
expressed genes showed a modest 10-20% fold change.
104
CoA Reductase (HMGCR), key enzymes involved in regulating de novo biosynthesis of fatty
acid and cholesterol; INSR internalization; translocation of SLC2A4 to the cell membrane [300].
Lastly, GSK3A and GSK3B phosphorylate and inactive glycogen synthases in response to
insulin. Although GSK3A was down-regulated in irregular animals, GSK3B was up-regulated
during the transition.
Down-regulated during this time were Protein Tyrosine Phosphatase, Receptor Type N
(PTPRN) (p= 0.02), a protein required for maintaining the levels of insulin-containing vesicles,
Serine/Threonine Kinase 1 (AKT1) (p= 0.02), which mediates insulin-induced translocation of
the SLC2A4 glucose transporter to the cell surface [301], PIK3R2 (p= 0.048), and GSK3A (p=
0.02). Finally, the only change we observed in acycling animals was an up-regulating in AKT1
(p= 0.01).
These data indicate that insulin signaling pathways are primarily perturbed between
regular and irregular cycling when endocrine function begins to decline. That further
perturbations in insulin signaling do not occur at the onset of acyclicity, when gonadal hormone
production ceases completely, suggest that loss of endocrine function is not a primary initiator.
Fatty Acid Metabolism Changes with Age in Regular Cycling Animals and Again at
Regular-Irregular Transition
Phospholipases are enzymes which catalyze the hydrolysis of phospholipids into fatty
acids and other lipophilic molecules. The fatty acids generated by this process can then undergo
beta oxidation to produce acetyl –CoA which feeds directly into the Krebs cycle and provide
105
energy to the cell. We observed modest increases in several phospholipases prior to and across
the perimenopause transition, suggesting an increase in cellular demand for fatty acids (Table 3).
Between 6-9 months in regular cycling animals, nine phospholipases were up-regulated
(Abhydrolase Domain Containing 3 (ABHD3), p=0.02; Glycosylphosphatidylinositol Specific
Phospholipase D1 (GPLD1), p= 0.001; Phospholipase A2 Group XVI (PLA2G16), p= 0.01;
Phospholipase A2 Group III (PLA2G3), p= 0.03; Phospholipase A2 Group VII (PLA2G7), p=
0.0004; Phospholipase C Beta 1 (PLCB1), p= 0.01; Phospholipase C Beta 2 (PLCB2),
p= 0.0001; Phospholipase D1 (PLD1), p= 0.01; Phospholipase D4 (PLD4), p= 0.0001) (Table 2).
Between the regular to irregular transition, GPLD1 (p= 0.03) and PLCB4 (p= 0.04) were further
up-regulated, as were Phospholipase A2 Group IVE (PLAG2G4E) (p= 0.01) and Patatin Like
Phospholipase Domain Containing 8 (PNPLA8) (p= 0.03). PLA2G3 (p= 0.02), PLD3 (p= 0.03),
and PLD4 (p= 0.002) were down-regulated in irregular animals. In acyclic animals, PLA2G3
(p=0.04), PLD3 (p= 0.02), and Phospholipase C Eta 2 (PLCH2) (p= 0.03) were up-regulated and
PLA2G7 (p= 0.005) was down-regulated. Other genes involved in fatty acid biosynthesis and
production changed across the
different comparison groups (Table 3). Between 6-9 months, N-Acylethanolamine Acid Amidase
(NAAA), which makes fatty acids from fatty acid amides was up-regulated (p= 0.04). Acyl-CoA
Thioesterase 7(ACOT7) hydrolyzes Acyl-CoAs to free fatty acids and coenzyme A and thus
regulates intracellular levels of the three molecules, participating in lipid homeostasis. ACOT7
was down-regulated during the regular to irregular transition (p= 0.02) suggesting that lipid
homeostasis may be dysregulated during this time. Lastly, Fatty Acid Synthase (FASN) was up-
regulated (p= 0.05) and Peroxiredoxin 6 (PRDX6) was down-regulated (p= 0.04). In contrast,
Solute Carrier Family 27 Member 2 (SLC27A2) belongs to a family of enzymes which
106
synthesize lipids by converting free long-chain fatty acids into fatty acyl-CoA esters. SLC27A2
was nearly 2-fold up-regulated between regular and irregular animals (p= 0.0004) and down
regulated between irregular and acyclic animals (p= 0.01).
Reg6-Reg9 Reg9-Irreg9 Irreg9-Acyc9
Glucose Metabolism Fold Change p-value Fold Change p-value Fold Change p-value
AGL
1.11 0.0194
GAA
1.14 0.001
PGM3
1.12 0.05
CEBPB
-1.16 0.01
PPP1CA
-1.10 0.02
PPP1R3C
-1.10 0.04
Glycogen Metabolism
ALDOA
-1.11 0.02
ENO3
1.26 0.003
GPD1
1.1 0.03 1.09 0.03
GPI
-1.10 0.03
H6PD
1.13 0.05
PEBP1
-1.18 0.00005 1.09 0.04
PFKL
1.09 0.04
PGAM1
-1.09 0.05
PGLS
-1.18 0.003
Table 1. Glucose and Glycogen Metabolic Genes - Fold changes and respective p-values of
significant genes are listed for each endocrine group comparison. Glucose metabolism and
glycogen breakdown pathways are altered beginning at irregular cycling.
107
Reg6-Reg9 Reg9-Irreg9 Irreg9-Acyc9
Insulin Signaling Fold Change p-value Fold Change p-value Fold Change p-value
ADIPOR2 1.121 0.02
AKT1
-1.11 0.02
INSR
1.10 0.04
GSK3A
-1.14 0.005
GSK3B
1.12 0.02
PIK3R1
1.12 0.03
PRKAA2
1.15 0.03
PIK3R2
-1.09 0.05
PRKACA
-1.10 0.02
PTPRN
-1.10 0.02 1.11 0.01
Fatty Acid Crosstalk
PRKCQ
-1.19 0.004
SLC27A4
-1.10 0.03
Diabetes Risk
CD3E
-1.27 0.001 1.37 0.00005
GAD1 -1.17 0.00005
GAD2 -1.13 0.001
PIK3R3
1.14 0.02
During perimenopause, fat-induced insulin resistance pathways were inhibited. Protein
Kinase C Theta (PRKCQ) is believed to mediate inhibitory effects of free fatty acids on insulin
signaling by phosphorylating IRS1, inhibiting downstream activation of the PI3K/AKT pathway
Table 2. Insulin-signaling and Diabetes Risk – Associated Genes - Fold changes and
respective p-values of significant genes are listed for each endocrine group comparison.
ADIPOR2 plays a role in glucose uptake and was up-regulated between 6-9 months. Insulin
signaling pathways saw the most changes at the transition from regular to irregular cycling.
Genes associated with diabetes risk were down-regulated between in regular and irregular
animals.
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[302]. Furthermore, PRKCQ knockout mice are protected from fat-induced insulin resistance
[303]. PRKCQ was down regulated in acyclic animals (p= 0.004)(Table 2). Additionally, the
fatty acid transporter, Solute Carrier Family 27 Member 4 (SLC27A4) was down regulated in
irregular animals (p= 0.03) (Table 2). In mice, inactivation of SLC27A4 has been shown to
prevent fat-induced insulin resistance in skeletal muscle [304]. In humans, SLC27A4 expression
is up-regulated in acquired obesity and correlates with insulin resistance [305].
Acyl-CoA Thioesterase 8 (ACOT8), a gene involved in oxidation of fatty acids, was
down-regulated in irregular animals (p= 0.02). 3-Hydroxy-3-Methylglutaryl-CoA Synthase 2
(HMGCS2), which catalyzes the first reaction of ketogenesis, was down-regulated between 6-9
months (p= 0.003).
Genes Involved in both Myelin Catabolism and Myelin Generation and Repair are
Up-regulated between 6-9 Month in Regular Cycling Animals
Several genes related to myelin generation and repair showed modestly increased
expression between 6-9 months, in regularly cycling animals (Proteolipid Protein 1 (PLP1), p=
0.0261; Myelin Associated Glycoprotein (MAG), p= 0.00005; Myelin Oligodendrocyte
Glycoprotein (MOG), p= 0.02; Claudin 11 (CLDN11), p= 0.001; myelin basic protein (MBP),
p= 0.00005; Myelin-Associated Oligodendrocyte Basic Protein (MOBP), p= 0.02; 2',3'-Cyclic
Nucleotide 3' Phosphodiesterase (CNP), p= 0.0002) (Table 4).
Tumor necrosis factor receptor superfamily,member 1A (TNFRSF1A) (p= 0.04) and
Alkaline Ceramidase 2 (ACER2) (p= 0.00005), two genes involved in myelin catabolism into
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fatty acids, were up-regulated between 6-9 months. TNFRSF1A is involved in TNF-activation of
acid sphingomyelinase which is responsible for catalyzing the conversion of sphingomyelin to
ceramide [306, 307]. ACER2 hydrolyzes very long chain ceramides to generate sphingosine and
fatty acids [308].
At the onset of perimenopause the myelin generating/repair genes CLDN11 (p= 0.001),
CNP (p= 0.0002), MAG (p= 0.00005), and Oligodendrocyte Transcription Factor 1(OLIG1) (p=
0.01) were down-regulated. However, Sphingomyelin Synthase 2 (SGMS2) was up-regulated
during this time. Two genes, Sphingomyelin Phosphodiesterase 1(SMPD1) (p= 0.03) and
Sphingomyelin Phosphodiesterase 3 (SMPD3) (p= 0.02), which are involved in myelin
catabolism were also down-regulated in irregular animals. In acyclic animals, myelin
generating/repair genes PLP1 (p= 0.002) and myelin and lymphocyte protein (MAL) (p= 0.0004)
were down-regulated.
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Reg6-Reg9 Reg9-Irreg9 Irreg9-Acyc9
Beta oxidation Fold Change p-value Fold Change p-value Fold Change p-value
ACOT8
-1.16 0.02
Ketogenesis
HMGCS2 -1.20 0.003
Fatty Acid Biosynthesis
ACOT7
-1.10 0.02
FASN
1.09 0.05
NAAA 1.13 0.03
PRDX6
-1.09 0.04
Lipid Biosynthesis
SLC27A2
1.74 0.0004 -1.45 0.01
Phospholipases
ABHD3 1.09 0.02
GPLD1 1.14 0.001 1.10 0.03
PLA2G16 1.12 0.01
PLA2G3 1.24 0.03 -1.29 0.02 1.24 0.04
PLA2G4E
1.21 0.01
PLA2G7 1.15 0.0004
-1.13 0.005
PLCB1 1.14 0.008
PLCB4 1.19 0.0001 1.12 0.04
PLCH2
1.10 0.03
PLD1 1.14 0.008
PLD3
-1.09 0.03 1.10 0.02
PLD4 1.19 0.0001 -1.18 0.002
PNPLA8
1.10 0.03
Table 3. Fatty Acid Metabolic – Associated Genes - Fold changes and respective p-values of
significant genes are listed for each endocrine group comparison. Genes involved in fatty acid
biosynthesis were up-regulated between 6-9 months. Fatty acid metabolic pathways continued
to see change during the regular-irregular and irregular-acyclic transitions.
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Reg6-Reg9 Reg9-Irreg9 Irreg9-Acyc9
Myelin Generation and Repair Fold Change p-value Fold Change p-value Fold Change p-value
CLDN11 1.12 0.001 -1.10 0.02
CNP 1.15 0.00015 -1.10 0.03
MAG 1.19 0.00005 -1.12 0.007
MAL
-1.16 0.0004
MBP 1.18 0.00005
MOBP 1.13 0.02
MOG 1.11 0.02
OLIG1
-1.12 0.006
PLP1 1.09 0.03
-1.17 0.002
PMP22
SGMS2
1.32 0.02
Meylin Catabolism
ACER2 1.31 0.00005
SMPD1
-1.10 0.03
SMPD3
-1.11 0.02
TNFRSF1A 1.13 0.04
Gene Pathways Promoting Alzheimer’s Pathology are Down-regulated between 6-9
Months and are Up-regulated during Regular-Irregular Transition
AD-related genes and their expression changes are listed in table 5. Beta-Site APP-
Cleaving Enzyme 2 (BACE2) cleaves amyloid precursor protein (APP) into amyloid beta (Aβ)
Table 4. Myelin Metabolic – Associated Genes - Fold changes and respective p-values of
significant genes are listed for each endocrine group comparison. Both myelin generation and
catabolic-related genes were up-regulated between 6-9 months in regular cycling animals. Both
pathways saw further changes throughout the perimenopause transition.
112
peptide, a critical player in AD pathology [309]. In regular cycling animals, BACE2 is down-
regulated between 6-9 months (p= 0.02). Also down regulated during this time is Cyclin
Dependent Kinase 5 Regulatory Subunit 1(CDK5R1) (p= 0.01), a protein associated with
abnormally phosphorylated tau. Two AD protective genes Clusterin (CLU), a secreted chaperone
which inhibits the formation of amyloid fibrils, and Apolipoprotein E (APOE) were up-regulated
(CLU, p= 0.01; APOE, p= 0.01) at 9 months compared to 6 month regularly cycling animals.
APOE expression then decreased at irregular cycling (p= 0.004).
Cathepsin D (CTSD), a gene involved in APP processing and associated with decreased
Aβ42 [310], is down-regulated during the regular to irregular transition (p= 0.007). Glycogen
Synthase Kinase 3 Beta (GSK3B), a protein kinase involved in abnormal tau phosphorylation
[311] was up-regulated in irregularly cycling animals (p= 0.02). 24-Dehydrocholesterol
Reductase (DHCR24) expression was decreased in irregular animals (p= 0.3) and increased in
acyclic animals (p= 0.2). Reduced expression of DHCR24 is seen in the temporal cortex of AD
patients and is associated with increased beta-secretase 1 (BACE1) activity [312]. BACE2
expression was up-regulated between in irregularly cycling animals (p= 0.02).
Reg6-Reg9 Reg9-Irreg9 Irreg9-Acyc9
Alzheimer’s Pathology Fold Change p-value Fold Change p-value Fold Change p-value
APOE 1.11 0.01 -1.16 0.004
BACE2 -1.20 0.02 1.22 0.02
CDK5R1 -1.10 0.01
CLU 1.10 0.01
CTSD
-1.12 0.01
DHCR24
-1.10 0.03 1.10 0.02
GSK3B
1.12 0.02
Table 5. Alzheimer’s Pathology – Associated Genes - Fold changes and respective p-values of
significant genes are listed for each endocrine group comparison. Pathways involved in the
progression of AD pathology were inhibited between 6-9 months in regular cycling animals and
activated during the regular-irregular transition.
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Gene Changes Suggest that Glucose Metabolism Declines and Glycogen Stores are
Depleted during Regular-Irregular Transition
Evidence of glucose hypometabolism in the hippocampus first appears during the
transition from regular to irregular cycling (Table 1). The catalytic enzymes driving steps 2, 4,
and 8 (Glucose-6-Phosphate Isomerase (GPI), p= 0.03; Aldolase, Fructose-Bisphosphate A
(ALDOA), p= 0.02; and Phosphoglycerate Mutase 1 (PGAM1), p= 0.05) of the glycolysis
pathway were down-regulated by roughly 10% in irregular compared to regular cycling animals.
These findings are consistent with our previous reports of decreased glucose metabolism in
irregular cycling animals [214]. In ayclic animals, enzymes responsible for glycolysis steps 3 and
9 (Phosphofructokinase, Liver Type (PFKL), p= 0.04; and Enolase 3 (ENO3), p= 0.003) were
up-regulated, possibly in an attempt to compensate for declining glucose metabolism. We also
observed an increase in genes involved in glycogen breakdown (Amylo-Alpha-1, 6-Glucosidase,
4-Alpha-Glucanotransferase (AGL), p= 0.02; Glucosidase Alpha, Acid (GAA), p= 0.001; and
Phosphoglucomutase 3 (PGM3), p= 0.05) concomitantly with a decrease in glycogenesis genes
(CCAAT/Enhancer Binding Protein Beta (CEBPB), p= 0.01; and Protein Phosphatase 1
Regulatory Subunit 3C (PPP1R3C), p=0.04) across the perimenopause transition. Unpacking and
utilization of glycogen energy stores may be in response to declining glycolytic activity and an
attempt to restore metabolic homeostasis.
114
Mitochondrial Bioenergetics Decline during Regular-Irregular Transition
Consistent with our previous reports, changes in the brain’s bioenergetic profile in the
hippocampus is associated with the endocrine transition state manifested by irregular cycling
[214] (Table 6). Between 6-9 months, the mitochondrial Uncoupling Protein 2 (UCP2) is the first
and only mitochondrial gene observed changing. UCP2 is briefly up-regulated by 30% between
6-9 months (p=0.00005) before falling to pre-perimenopausal levels at irregular cycling (p=
0.00005). At the onset of irregular cycling, several genes involved in electron transport were
downregulated (ATP Synthase, H+ Transporting, Mitochondrial F1 Complex, Delta Subunit
(ATP5D), p= 0.0008; Cytochrome C Oxidase Subunit 4I1 (COX4I1), p= 0.03; Cytochrome C
Oxidase Subunit 6A1 (COX6A1), p= 0.03; Cytochrome C Oxidase Subunit 8B (COX8B), p=
0.01; Cytochrome C1 (CYC1), p= 0.01; NADH:Ubiquinone Oxidoreductase Subunit A7
(NDUFA7), p= 0.03; NADH:Ubiquinone Oxidoreductase Subunit S7 (NDUFS7), p= 0.03;
NADH:Ubiquinone Oxidoreductase Subunit V1(NDUFV1), p= 0.05) COX8B expression was
restored in acyclicing animals, but the remaining electron transport genes remained down-
regulated. The mitochondrial stress response genes, Glutathione Peroxidase 4 (GPX4) (p= 0.03)
and PTEN Induced Putative Kinase 1 (PINK) (p= 0.03) were also down-regulated in irregular
animals. Lastly, Malate Dehydrogenase 2 (MDH2) (p= 0.03), the Krebs cycle gene which codes
for the enzyme responsible for the conversion of malate to oxaloacetate, was down-regulated in
irregular animals.
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Genome-Wide Changes in DNA Methylation Occur Mostly During the Regular-
Irregular Transition; Pathway Specific Changes Occur Mostly Between 6-9 Months
A total of 24 hippocampal samples were used (n = 6). An average of 38.5 million (M)
read pairs were obtained for each sample (range = 27 – 64 M read pairs). The percentage of the
raw reads which were mapped to the rn6 genome ranged from 35- – 56%. An average of 4.3
million unique CpG sites were sequenced for each sample (range = 2.9 – 5.4M) at an average
depth of 13X (range = 9 – 20X).
Reg6-Reg9 Reg9-Irreg9 Irreg9-Acyc9
Electron Transport Chain Fold Change p-value Fold Change p-value Fold Change p-value
ATP5D
-1.16 0.001
COX4I1
-1.09 0.03
COX6A1
-1.10 0.03
Cox8b
-2.05 0.01 2.33 0.003
CYC1
-1.12 0.01
NDUFA7
-1.11 0.03
NDUFS7
-1.10 0.03
NDUFV1
-1.09 0.05
UCP2 1.29 0.00005 -1.28 0.00005
Stress Response
GPX4
-1.11 0.03
PINK1
-1.10 0.03
Krebs Cycle
MDH2
-1.10 0.03
Table 6. Mitochondrial Function – Associated Genes - Fold changes and respective p-values of significant
genes are listed for each endocrine group comparison. Genes involved in the electron transport chain, stress
response, and Krebs cycle were all down-regulated at the regular-irregular transition.
116
Gene transcription is regulated by a myriad of epigenetic factors, including DNA
methylation, histone modification, chromatin structure, and non-coding RNAs. In normal cells,
active gene transcription is associated with promoters that are unmethylated and contain
"activating" histone marks such as H3K4me3. Conversely, the promoters of silenced genes are
associated with repressive histone marks (H3K27me3 and H3K9me3) and higher levels of CpG
methylation. However, promoter methylation is not the only means by which DNA methylation
regulates gene expression - DNA methylation of distal enhancers, which bind activating proteins
and bring them to promoter regions through chromosomal looping, also play an important role in
cell type specific gene expression [313]. Enhancers regions are located throughout the genome
including within intronic regions of the genes that they regulate [314]. The majority of the
methylation changes occurred within intron regions and may explain why the majority of genes
were differentially expressed genes by a modest 10-20%.
Although DNA methylation can occur at any cytosine, "CpG" methylation (where a
cytosine is immediately followed by a guanine) has historically been considered to the more
important regulatory mark, due to its' evolutionarily conserved nature, as opposed to CHG or
CHH methylation (“H” refers to different cytosine methylation contexts, namely CpG, CHG, and
CHH, where H means “not G” (A,T, or C)), which is more prone to random, individual
mutations. However, recent studies show that in the brain non-CpG methylation likely plays an
equally important role [90]. Considering the conserved nature of CpG patterns along with the
inbred genetic background of the animals used in this study, it is unsurprising that we observe an
r-correlation value very close to "1" in each comparison. In contrast, CHG and CHH
comparisons show a lower r-value between groups signifying that CHG and CHH patterns are
less similar than CpG patterns between groups.
117
The regular to irregular transition showed the most wide-spread changes in DNA
methylation as evidenced by a lower r-value for CpG, CHG, and CHH comparisons (Fig. 2).
However, in the pathways discussed in this paper, the most significant changes occurred between
6-9 months in regular cycling animals, with a total of 18 genes showing change in at least one
methylation site between 6-9 months, followed by followed by the irregular-acyclic transition
with 12 genes, and finally the regular-irregular transition with 10 genes.
Changes in DNA Methylation Occur Predominantly in Insulin/Diabetes,
Phospholipase, and Myelin Catabolism Pathways Prior to Irregular Cycling
If perimenopause-associated changes in expression are in direct response to declining sex
hormones, we would expect expression to be predominantly affected during the irregular to
acycling transition when changes in hormone signaling are most dramatic. Instead, our RNA-seq
data demonstrates that the majority of transcriptional changes occur during regular and irregular
cycling - in the beginning stages of endocrine aging. This suggests that declining hormone
signaling due to endocrine aging is not the only factor driving the transcriptome changes seen in
perimenopause. We hypothesize that epigenetics, specifically DNA methylation, may regulate
transcriptional changes during perimenopause.
In our analysis we only considered genes which had differential methylation patterns that
correlated to a coincident or subsequent change in expression, not genes in which differential
DNA methylation followed a change in expression (Table 7). Differential methylation was
identified in three genes involved in glucose metabolism across the different groups. H6PD
showed hypo-methylation of a single intron cytosine between 6-9 months which correlated with
up-regulated expression in irregular animals. GPD1 and PPP1R3C exhibited differential
118
expression and DNA methylation during the irregular-acyclic transition. In insulin signaling
pathways 5 genes in total were identified with DNA methylation changes that correlated to
changes in expression. INSR, PIK3R1, and PIK3R3 showed differentially methylated sites
during 6-9 months with expression changes that followed in the next transition period, and
acyclic-associated expression changes in PRKCQ and PTPRN were associated with earlier
changes in DNA methylation. Myelin maintenance genes CNP and MBP showed changes
in both DNA methylation and RNA transcription between 6-9 months. All four myelin
Reg 6 - Reg 9
Reg 9 - Irreg
9 Irreg 9 - Acyc 9
CHH 0.4369 0.3040 0.4624
CHG 0.4365 0.3039 0.4159
CpG 0.9512 0.9256 0.9559
Regular 6 – Regular 9 Regular 9 – Irregular 9 Irregular 9 – Acyclic 9
Fig. 2 Correlation of DNA methylation marks. A) CpG Scatter plots indicate that the regular to irregular comparison
shows the least correlation as indicated by a lower r-value. B) Table displaying CHH, CHG, and CpG r values across all
comparisons.
A
B
119
catabolism genes which were identified to have altered expression also showed correlating
changes in DNA methylation. Fatty acid synthesis genes, NAAA and FASN, also showed
expression changes that were accompanied or preceded by changes in DNA methylation.
Phospholipases PLA2G7, PLCB1, and PLCB4 were up-regulated between 6-9 months with
altered methylation patterns. PLA2G7 and PLCB4 expressions continued to change alongside
DNA methylation patterns through subsequent endocrine transitions. Lastly, APP processing
genes BACE2 and CTSD exhibited changes in DNA methylation with correlated with altered
expression levels between 6-9 months (BACE2) and during the regular to irregular transition
(BACE2 and CTSD).
Mitochondrial Dysfunction Is Not Regulated by DNA Methylation
Expressions of several mitochondrial genes were observed changing across the
perimenopause transition. However, we did not observe any changes in DNA methylation that
directly correlated to changes in transcription levels, indicating that the expression of these genes
are regulated by alternative mechanisms or factors. Changes in mitochondrial bioenergetics
during perimenopause may be a response to the loss of circulating sex hormones, rather than an
epigenetically programmed phenomenon.
Discussion
Epigenetic changes in DNA methylation occurred in pathways involved in brain glucose
metabolism, insulin signaling, myelin generation and catabolism, fatty acid biosynthesis and
phospholipase expression. We did not observe DNA methylation changes in mitochondrial
120
function, beta oxidation or ketogenic pathways. Our data here suggest that myelin catabolism
and increased phospholipase expression proceeds decreased transcription of glucose metabolic
genes. We hypothesize that an increase in fatty acids biosynthesis between 6-9 months leads to
perturbed insulin signaling and eventually glucose hypometabolism during the regular to
irregular transition, as free fatty acids inhibit insulin-stimulated glucose uptake [315].
Mitochondrial dysfunction, which also occurs during this time, likely further exacerbates glucose
hypometabolism and utilization of alternative fuel sources.
The transition from regular to irregular cycling shows the largest change in genome-wide
DNA methylation, demonstrating that early transitioners experience wide-spread epigenetic
changes sooner than late transitioners. However, methylation changes in myelin catabolism,
phospholipase, glucose metabolism, and insulin signaling pathways occur prior to irregular
cycling, between 6-9 months. This suggests to us that in these specific pathways, trajectory
outcomes are already established before perimenopause onset. By 9 months of age only 37% of
animals were regular cycling. The remaining animals were irregular (30%) or acyclic (33%),
illustrating that although all animals were regular cycling at 6 months, their endocrine
trajectories were already very different. Given the wide-spread methylation differences in
irregular animals, it is likely that early transitioners were already epigenetically "older" as 6
months of age. These epigenetic differences establish a basis for different trajectory outcomes
between early- and late- transitioners and may predispose early transitioners to AD-associated
pathways such as brain hypometabolism, mitochondrial dysfunction, Aβ generation, and white
matter degeneration.
121
Reg6-Reg9 Reg9-Irreg9 Irreg9-Acyc9
RNA DNA Methylation RNA DNA Methylation RNA DNA Methylation
Glucose
Metabolism Direction Location Status
Fold
Difference p-value Direction Location Status
Fold
Difference p-value Direction Location Status
Fold
Difference p value
PPP1R3C
↓ INTRON HYPO -0.12 0.02
EXON HYPO -0.31 0.05
Glycogen
Metabolism
GPD1
↑
↑ PROMOTER HYPO -0.29 0.04
H6PD
INTRON HYPO -0.16 0.001 ↑
Insulin Signaling
ADIPOR2 ↑
EXON S.HYPO -0.36 0.01
INSR
INTRON HYPER 0.33 0.01 ↑
INTRON HYPO -0.23 0.03
PIK3R1
INTRON HYPO -0.13 0.0002 ↑
INTRON HYPO -0.13 0.04
PROMOTER HYPO -0.29 0.02
PROMOTER HYPO -0.29 0.03
INTRON HYPO -0.28 0.01
PTPRN
↓
↑ INTRON HYPER 0.23 0.04
Insulin Fatty Acid
Cross talk
PRKCQ
INTRON HYPER 0.26 0.01
INTRON HYPO -0.29 0.02 ↓
PIK3R3
INTRON HYPO -0.19 0.03 ↑
Fatty Acid
Biosynthesis
NAAA ↑ INTRON HYPO -0.13 0.03
FASN
INTRON HYPER 0.26 0.04 ↑
Myelin Generation
Repair
122
CNP ↑ CPG ISLAND HYPO -0.33 0.003 ↓
CPG ISLAND HYPO -0.29 0.0005
MBP ↑ INTRON HYPO -0.3 0.04
INTRON HYPO -0.14 0.01
INTRON HYPER 0.14 0.0
MOBP ↑
INTRON S. HYPER 0.37 0.0001
INTRON HYPO -0.12 0.04
PMP22
INTRON HYPO -0.2 0.006
INTRON HYPO -0.19 0.03
Myelin Catabolism
ACER2 ↑ INTRON HYPER 0.11 0.04
INTRON S.HYPER 0.36 0.003
SMPD1
↓ EXON HYPER 0.2 0.03
SMPD3
CPG ISLAND HYPER 0.1 0.001 ↓ EXON HYPER 0.25 0.02
EXON HYPO -0.14 0.05
TNFRSF1A ↑ INTRON HYPO -0.3 0.03
INTRON HYPER 0.12 0.04
Phospholipases
GPLD1 ↑
↑
PROMOTER HYPO -0.11 0.01
PLA2G7 ↑ INTRON HYPER 0.14 0.002
INTRON HYPO -0.12 0.04 ↓
PLCB1 ↑ INTRON HYPO -0.21 0.02
INTRON HYPO -0.19 0.05
INTRON HYPER 0.12 0.03
INTRON HYPER 0.19 0.02
PLCB4 ↑ INTRON HYPO -0.18 0.03 ↑ INTRON HYPER 0.2 0.04
INTRON HYPO -0.12 0.01
INTRON HYPO -0.11 0.0002
INTRON HYPO -0.23 0.02
INTRON HYPER 0.32 0.01
PLD1 ↑
INTRON HYPO -0.29 0.00004
EXON HYPO -0.16 0.02
INTRON HYPO -0.18 0.05
INTRON HYPER 0.14 0.02
PLD4 ↑
↓
INTRON HYPO -0.13 0.02
Alzheimer’s
Pathology
BACE2 ↓ INTRON HYPER 0.1 0.03 ↑
CTSD
INTRON HYPO -0.13 0.008 ↓
123
Table 7. DNA Methylation Changes Associated with Altered Expression. RNA column
denotes the direction of transcription regulation. DM column denotes at what endocrine
status differential cytosine methylation was observed, how many sites within what regions
(promoter, exon, or intron), and whether the site(s) were hypo-, strongly(S) hypo, hyper-, or
strongly hyper-methylated. Hyper- or hypo-methylated is defined as 0 ‐33% more, or less
respectively, methylated than reference (p ‐value < 0.05). Strongly hyper- or strongly hypo-
methylated is defined as 33-100% more, or less respectively, methylated than reference
(p ‐value < 0.05).
124
4. CONCLUSIONS
Hypothalamic Aging Precedes Aging in the Hippocampus
Hypothalamic aging appears to precede hippocampal aging, as demonstrated by
RNA-seq and DNA methylation profiling. In the hypothalamus, the largest change (10%)
in global transcription occurred between 6-9 months in regularly cycling animals,
compared to hippocampus which saw its’ greatest change during the regular to irregular
transition (Table 1). Similarly in DNA methylation, global hypothalamic changes
occurred predominantly between 6-9 months as indicated by lower r-values (Table 1).
The greatest change in hippocampal DNA methylation occurred between regular and
irregular groups (Fig 1B). We observed larger changes in DNA methylation, as indicated
by larger r-value differences, in hippocampal compared to hypothalamic tissue. Together
this data provides evidence that DNA methylation is strongly correlated to the
transcriptional changes that occur across the perimenopause transition.
Reg 6 - 9 Reg - Irreg Irreg - Acyc
Hypothalamus 10% 2% 2%
Hippocampus 6% 9% 4%
Table 1. Significantly changes genes in hypothalamic and hippocampal tissue across
perimenopause. In the hypothalamus, the period between 6-9 months in regular cycling animals showed
the most drastic changes in gene expression. In the hippocampus, the transition from regular to irregular
cycling exhibited the most widespread changes.
125
In both hypothalamus and hippocampus there are a number of pathways affected
in each critical period during perimenopause. However, pathways related to hormone
signaling were present in hypothalamic analyses and not hippocampal. Conversely,
learning and memory, neurodegeneration, and glucose and lipid metabolism dominated
the top pathways affected in the hippocampus but were not as well represented in
hypothalamic analyses. This suggests to us that our RNA-seq/DNA methylation analysis
is an accurate “snapshot” of changes occurring in these tissues during each critical period
of perimenopause.
Hypothalamus
Hippocampus
Reg 6 - 9 Reg - Irreg Irreg - Acyc Reg 6 - 9 Reg - Irreg Irreg - Acyc
cytosine context
CpG 0.9531 0.9521 0.9558
0.9512 0.9256 0.9559
CHG 0.4441 0.4552 0.4978
0.4365 0.3039 0.4159
CHH 0.4711 0.476 0.524
0.4369 0.304 0.4624
Table 2. Correlation of cytosine methylation across perimenopause in hypothalamus and
hippocampus. R-values for different cytosine contexts (CpG, CHG, and CHH) are listed for each
transition period during perimenopause. The lowest r-values are highlighted in yellow. In the
hypothalamus, the period between 6-9 months in regular cycling animals shows the least correlation
(most difference) for cytosines in the CHG and CHH context. The largest difference in CpG
methylation was seen during the regular to irregular transition. In hippocampus, the regular to
irregular transition showed the least correlation and the most difference across all cytosine contexts.
126
In both hypothalamic and hippocampal tissues, transcriptional changes associated
with aging are both accompanied by changes in global DNA methylation. However,
considering that hypothalamic aging precedes hippocampal again, it is likely that the
transcriptional changes seen in the hippocampus are a consequence of both endocrine
aging set into motion by the hypothalamus and changes in DNA methylation. Sex
hormones are known modifiers of chromatin landscape and the details of the complicated
relationship between hormone signaling, epigenetic regulation, and gene transcription
remain to be clarified.
Fold Differences of Age-Related Gene Expression Changes are
Relatively Small
Compared to cancer and other disease states, expression changes that occur with
age and across the perimenopause transition are relatively small. In the hypothalamus,
roughly half of the significantly change genes only showed 1.2 - 1.5 fold (20-50%)
differences. In the hippocampus, the average fold difference was even smaller, with more
than half of genes changing by only 1.1 - 1.2 fold (10-20%) (Fig 1.). In many RNA-seq
pathway analysis protocols, default inclusion criteria include >1.2 fold change and a p-
value of < 0.05. Because these default criteria would have excluded a significant portion
of our data, we did not set a fold change cut off and included all changes with a p-value
>0.05. Although few genes exhibiting dramatic change were identified, many gene
networks were found to contain several genes with moderate differences in expression
that could cumulatively result in significant dysregulation of a given molecular pathway.
127
Also worth noting is that most pathway analysis programs are based on literature
databases which are very heavy with cancer research. Pathways built using disease state
relationships may not always appropriately apply to normal aging.
Fig 1. Fold Changes of Significant Differentially Expressed Genes in Hypothalamus and
Hippocampus. In the hypothalamus, roughly half of all differentially expressed genes showed 20-50%
changes in either direction. In the hippocampus, more than half of all genes only changed by 10-20%.
Hypothalamus
Hippocampus
128
Do 5-aza and Methionine Treatments Alter Gene Networks that Exhibit
Change during Perimenopause?
We have shown that 5-aza or methionine treatment is able to shift the timing of
acyclicity onset. Subsequent experiments should address whether these treatments also
influence the same gene networks that exhibit change across the perimenopause
transition. For example, do methinonine-treated animals have DNA methylation and gene
expression profiles similar to non-treated, young and regularly cycling animals? More
specifically, does methinonine treatment prevent shifts in DNA methylation and gene
expression patterns in glucose and myelin metabolism gene networks that are associated
with each endocrine status?
Repeating the RNA-seq and DNA methylation profiling conducted in this study
using 5-aza and methionine treated animals would shed light on what epigenetic changes
are driving factors and which are consequences of the perimenopause transition, and will
help identify which menopause-associated symptoms are most likely to respond to early
therapeutic interventions. Furthermore, understanding how glucose and myelin
metabolism gene networks change across the perimenopause transition in late or early
transitioners would provide further insight as to why some women emerge from
menopause with cognitive symptoms and an increased risk for neurodegenerative
diseases. Additionally, these data may also provide candidate biomarkers to assess risk
for early menopause and future cognitive decline.
The underlying mechanisms which link early menopause to increased risk for
neurodegenerative diseases are not well understood. Nor is it understood why some
129
women suffer significant cognitive symptoms during and after menopause while other
women seem to emerge relatively unscathed. These mechanisms could be interrogated by
using the same PAM model and following animals through perimenopause into
acyclicity, assessing them at an older age to identify cognitive differences between
“early” and “late” transitioners. For example, 12 month acyclic animals could be divided
into groups based on the age at which they began cycling irregularly or stopped cycling.
Differences between these two groups would provide insight as to what pathways are
affected by early menopause and how they might influence cognitive decline in women.
Menopause, Aging, and Alzheimer’s Disease are all Associated with
DNA Hypomethylation
Decline of global DNA methylation is a shared characteristic of menopause,
chronological aging, and AD. Additionally, early menopause is associated with
accelerated biological age, and increased risk for AD development. It is unlikely that
hormonal changes during early menopause are solely responsible for increased AD risk.
We speculate that in addition to hormonal influence, both early menopause and risk for
AD are consequences of accelerated epigenetic/biological aging. Furthermore, one-
carbon metabolism has been indicated in a key player in AD etiology [316-318] and
treatment with SAM, methionine, and other b-vitamins has been shown to attenuate
progression disease pathologies in AD models [239, 319]. Similarly, we show here that
methionine treatment delayed menopause, providing evidence that menopause, aging, and
130
neurodegeneration share the mechanistic link of DNA hypomethylation due to impaired
one-carbon metabolism.
Individuals with genetic polymorphisms that compromise folate usage in the one
carbon metabolism have increased in recent decades due to the use of folic acid during
pregnancy. These individuals require a lifetime need for additional dietary folate that is
often not met after birth and may predispose them to accelerated biological aging, early
menopause in women, and age-related cognitive decline [48]. Although no studies have
focused on folate utilization and menopausal age, it is worth investigating as it may
provide some answers as to why some women experience early menopause and increased
risk of neurodegeneration.
Earlier Intervention is Needed in Preventing Menopause-Associated
Health Consequences
Although hypothalamic aging begins before onset of perimenopause, treatment is
not usually offered until well into perimenopause, when symptoms have become
unmanageable. Earlier interventions are needed. Because early menopause is liked to AD
and hypothalamic aging begins before onset of irregular cycling, preventative measures
should be taken prior to onset of perimenopause in order to protect women against
menopause-associated neurodegeneration to treat women. Unfortunately, in the clinic it is
difficult to identify which women are at risk for early menopause. Besides a family
history, few biomarkers exist that provide meaningful information as to when a woman
might expect to enter the transition. However, post-menopausal women are
131
epigenetically/biologically “older” than pre-menopausal women of the same
chronological age, spotlighting DNA methylation as a promising biomarker that may be
suitable for and easily implemented in the clinic. Commercially available tests, which use
free floating DNA in blood or urine to determine “biological age”, offer promising
clinical applications and may be able to identify women at risk for early menopause due
to accelerated epigenetic age. Other potential biomarkers include homocysteine or s-
adenosylhomocysteine, as these are up-regulated when one-carbon metabolism is
impaired.
Hormone Therapies and Interventions for Pre-, Peri-, and Post-
Menopausal Women are Not Enough to Prevent Cognitive Decline
Bioidentical hormone therapies are becoming increasingly popular and are
marketed to both pre- and perimenopausal women as a way to “optimize” hormone levels
and improve quality of life. However, these interventions are not FDA approved, have
not gone through clinical trials, and may not be suitable or beneficial for all women. In
fact, similar to standard hormone replacement therapy, it is likely that while these types
of interventions may be beneficial to some women, they may be detrimental to the long
term cognitive health of others.
In clinical trials, hormone therapies (HTs) for menopausal women have been
largely unsuccessful in preventing or delaying AD in the general population. Although
some women respond positively, many do not and some even fare worse in the long run.
These outcomes support the growing opinion that menopause-associated endocrine
132
changes are not solely responsible for disease development or increased risk. As we have
shown here, both menopause and AD are consequences of epigenetic aging and therefore
it is not surprising that HT alone is unable to influence the mechanisms driving these
conditions. The relationship between epigenetic and endocrine is complicated and
involves several systems and mechanisms which share crosstalk at several points during
biological aging. DNA methylation may drive the perimenopause transition; however, it
is likely that the resulting loss of hormones that occurs with endocrine aging further
impacts epigenetic aging. This complex relationship may also explain why HT is
successful in some, but not all women and why some menopausal symptoms (such as hot
flush) respond well to HT and others do not. Going forward, effective therapies will need
to target both hormonal and epigenetic aspects of biological aging.
133
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Abstract (if available)
Abstract
Loss of estrogen at menopause has profound effects in nearly all tissues, and in humans, is marked by an increased risk for stroke, coronary heart disease, and neurological dysfunction in some women. Furthermore, earlier menopause is linked to adverse cognitive outcomes later in life and is considered to be a risk factor for Alzheimer’s disease (AD). Heritability of menopause timing is only 44-66% and variability is present in monozygotic twins and inbred rat strains. In humans, menopause is associated with accelerated epigenetic age, with post-menopausal women being “epigenetically” and “biologically” older than pre-menopausal women of the same chronological age. However, the cause-effect relationship between epigenetic and menopause age remains undetermined. To address this, we conducted RNA-seq and RRBS methylation profiling in both the hypothalamus and hippocampus across the perimenopause transition in young regular cycling, middle-aged regular cycling, middle-aged irregular cycling, and middle aged acyclic animals to understand how gene expression and DNA methylation changes with endocrine status. We also interrogated mechanisms regulating onset of irregular and acycling by perturbing the methylome in young regular cycling animals to investigate underlying factors responsible for early menopause. ❧ The first chapter discusses how epigenetic mechanisms regulate and respond to critical transition periods during development and explores data supporting the hypothesis that reproductive aging is epigenetically regulated. The second chapter builds upon and tests this hypothesis by profiling DNA methylation and gene expression changes in the hypothalamus across the perimenopause transition. Using methylome-modifying agents we show that DNA methylation directly influences perimenopause timing. The third chapter investigates RNA expression and DNA methylation changes in gene networks related to glucose and myelin metabolism in the hippocampus, and discusses how changes in these pathways are relevant to age-related disease etiology and cognitive decline. Finally, the fourth chapter explores the implications of our findings and how they relate to women’s health in the clinic. ❧ The goal of this project is to better understand factors contributing to individual differences in perimenopause timing and to identify biological mechanisms responsible for the increased risk of cognitive impairment during and after the perimenopause transition in the hopes of developing strategies to identify women at risk and as well as therapeutic interventions.
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Asset Metadata
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Bacon, Eliza Read
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Core Title
Epigenetic regulation of endocrine aging transitions of the perimenopausal and menopausal brain
School
School of Pharmacy
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Doctor of Philosophy
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Neuroscience
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11/14/2017
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10/05/2017
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aging,DNA methylation,epigenetics,glucose,hippocampus,Hypothalamus,Menopause,myelin,OAI-PMH Harvest,perimenopause
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Brinton, Roberta (
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glucose
hippocampus
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perimenopause