THE IDENTIFICATION OF TRANSCRIPTIONAL SIGNATURES OF STEROID RESPONSE IN HUMAN NEURONS AND MICRORNA NETWORKS AS A CONTRIBUTOR TO REPRODUCTIVE STEROID PATHOLOGY

A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Global Infectious Diseases

By

Allison Courtney Goff, M.A.

Washington, DC

April 16, 2021 Copyright 2020 by Allison Courtney Goff All Rights Reserved

ii THE IDENTIFICATION OF TRANSCRIPTIONAL SIGNATURES OF STEROID RESPONSE IN HUMAN NEURONS AND MICRORNA NETWORKS AS CONTRIBUTORS TO REPRODUCTIVE STEROID PATHOLOGY

Allison Courtney Goff, M.A.

Thesis Advisors: David Goldman, M.D. and Peter J. Schmidt, M.D.

ABSTRACT

Ovarian steroids change the brain in utero, during puberty, and in adulthood. At the neuronal level, ovarian steroids affect structure, gene networks, and physiological outputs, and at the neurocircuitry level they modulate working memory, reward, and emotion. In reproductive related affective disorders such as premenstrual dysphoric disorder (PMDD), alterations in mood and affect are linked to changes in ovarian steroid levels. However, the pathophysiology of these disorders is just beginning to be characterized. For PMDD, previous work demonstrated intrinsic differences in the ESC/E(Z) complex, a major epigenetic modifier. However, this work showed a discrepancy between transcripts and protein: while transcripts in this complex tend to be upregulated in PMDD, protein quantities were downregulated. The work in this thesis builds directly on this knowledge by investigating the role of microRNAs, a unique class of regulatory molecules that dynamically regulate gene networks and are capable of resolving seemingly paradoxical relationships between transcript and protein levels. Congruently, several of the leading microRNA gene networks that were identified by this objective targeted ESC/E(Z) transcripts. This thesis also uncovered evidence for a key role of the Vascular Epithelial

Growth Factor A gene (VEGFA) in PMDD and began the foundational work of identifying transcriptional signatures of estradiol, progesterone, and allopregnanolone on human

iii neuronal cells across maturation stages, which can be used as markers for typical neuronal response to sex steroids.

iv “None of it was real; nothing was real. Everything was real; inconceivably real, infinitely dear. These and all things started as nothing, latent within a vast energy broth, but then we named them, and, in this way, brought them forth.” ~George Saunders

“And there would be quite a number of things to chat about: around you there is a wild people, provoking one’s curiosity, there is danger every day, extraordinary incidents happen; and no wonder there is occasion to regret that so few of us take notes.” ~Mikhail Lermontov

“The only way to guarantee an outcome is to do nothing.” ~Mark Goff

The research and writing of this thesis are dedicated to my family, my friends, and everyone else who helped make me who I am today.

Many thanks, Allison Goff

v TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION ...... 1

1.0 Sex Steroid Hormone Synthesis and Action ...... 1

1.1 Physiology of the Ovarian Cycle ...... 4

1.2 Sex Steroid Hormone Effects on the Developing Brain ...... 5

1.3 Sex Steroid Hormone Effects on the Adult Brain ...... 8

1.4 Sex Steroid Hormone Effects on Mood and Behavior...... 10

1.5 Pathophysiology of Premenstrual Dysphoric Disorder ...... 12

1.6 Cellular Differences in PMDD ...... 14

1.7 MicroRNAs ...... 16

1.8 Lymphoblastoid Cell Lines ...... 18

1.9 Human Neuronal Cell Lines ...... 20

1.10 Thesis Focus...... 21

1.11 Figures...... 23

CHAPTER 2: GENERAL METHODS FOR ALL CHAPTERS ...... 27

2.0 Differential Gene Expression Analysis ...... 27

2.1 Pathway Analyses ...... 27

CHAPTER 3: TRANSCRIPTIONAL EFFECTS OF SEX STEROID HORMONES IN HUMAN FEMALE NEURONAL CELLS AT DIFFERENT STATES OF CELLULAR MATURATION ...... 29

3.0 Introduction ...... 29

3.1 Specific Methods ...... 33

3.2 Results ...... 39

vi

3.3 Discussion ...... 43

3.4 Tables and Figures ...... 50

CHAPTER 4: MICRORNAS IN PREMENSTRUAL DYSPHORIC DISORDER AND THE ROLE OF VASCULAR ENDOTHELIAL GROWTH FACTOR ...... 62

4.0 Introduction ...... 62

4.1 Specific Methods ...... 64

4.2 Results ...... 69

4.3 Discussion ...... 71

4.4 Tables and Figures ...... 78

CHAPTER 5: CONCLUDING DISCUSSION ...... 92

5.0 Additional Thoughts on Chapter 3 ...... 93

5.1 Long-Term Use of h-iPSCs in PMDD Research ...... 94

5.2 Additional Thoughts on Chapter 4 ...... 95

5.3 Future Work Involving the Results of Both of the Presented Studies ...... 96

5.4 Final Summary ...... 96

BIBLIOGRAPHY ...... 98

vii LIST OF FIGURES

Figure 1.1: Sex steroid hormone synthesis pathway ...... 23

Figure 1.2: Recurrence of symptoms of premenstrual syndrome during the addition of estradiol or progesterone to the Leuprolide regimen ...... 24

Figure 1.3: ESC/E(Z) transcript/protein quantity disparity in PMDD LCLs ...... 25

Figure 1.4: microRNA biosynthesis in mammals ...... 26

Figure 3.1: Principal component analyses show distinctions between cell lines, subtle responses to sex steroid hormone ...... 51

Figure 3.2: Most expressed genes were expressed across NSC, UL, SH, and DL cell lines ...... 53

Figure 3.3: Top neuron-related pathways are present in genes that are shared between NSC, SH cells and NSC, SH and DL cells ...... 54

Figure 3.4: UL and SH cells share the highest number of upregulated and downregulated ALLO-responsive genes (FDR<0.1) ...... 56

Figure 3.5: UL and SH cells share the highest number and most significant upregulated and downregulated ALLO-responsive genes (p<0.05) ...... 58

Figure 3.6: UL and SH cells share the highest number and most significant upregulated and downregulated E2-responsive genes (p<0.05) ...... 60

Figure 3.7: Few P4-responsive (p<0.05) genes shared across NSC, UL, SH, and DL cell lines ...... 61

Figure 4.1: miRNA WGCNA modules are enriched for ESC/E(Z)-associated transcript targets ...... 81

Figure 4.2: Differentially expressed miRs in PMDD and mRNA targets in PMDD vs control LCLs ...... 85

Figure 4.3: Genomics of two miRs dysregulated in PMDD: the miR-503-5p/424-5p cluster on Xq26.3 and the miR195-5p/497-5p cluster on 17p13.1 ...... 87

Figure 4.4: Upregulated MIR15 family miRs in PMDD LCLs target the VEGF signaling pathway ...... 89

Figure 4.5: MIR15 genes implicated in PMDD target the Angiogenesis and VEGF signaling pathways ...... 91

viii CHAPTER 1: INTRODUCTION

Ovarian steroids such as estradiol and progesterone not only change the brain in utero and during puberty but also impact brain function in the adult. At the cellular level, ovarian steroids alter neuronal structure, function, and neuronal transmission, and at the circuitry level they affect working memory, reward, and emotional processing. Given the wide range of effects of ovarian steroids on the brain, it is logical to predict that they would regulate mood and cognition. Recently, this prediction was borne out by the discovery that alterations in mood and affect in reproductive related affective disorders such as premenstrual dysphoric disorder (PMDD) are directly linked to changes in ovarian steroid levels. However, the cellular neuronal mechanisms and origins of interindividual variation in susceptibility to affective illnesses triggered by ovarian steroids are unknown. MicroRNAs (miRs) are a class of regulatory RNAs that serve as genetic switches, each usually regulating scores of genes, and enabling gene networks to respond rapidly and coordinately to changing signals such as the cyclical variation in ovarian steroids throughout the menstrual cycle. I hypothesized that transcriptional signatures of neuronal response to estradiol, progesterone, and allopregnanolone recently observed in PMDD may be partially due to microRNA dysregulation. In this endeavor, it would be key to integrate known aspects of the synthesis and actions of ovarian steroids in the brain.

1.0 Sex Steroid Hormone Synthesis and Action

Human steroid hormones are synthesized within the adrenal gland or gonads from a common precursor molecule: cholesterol, which is an abundant plasma lipid and structural component of plasma membranes (Busillo et al., 2014). The relative concentrations of steroid

1 metabolic enzymes present in the tissue environment determine the final steroid to be synthesized, which is derived from a common precursor, pregnenolone, via cholesterol (Grummer & Carroll,

1988). While there are many steroid hormone derivatives of cholesterol, the focus of this thesis is on 17-estradiol (E2), progesterone (P4), and 3α5α-tetrahydroprogesterone (allopregnanolone, or

ALLO). Cholesterol is converted to pregnenolone, which can then be converted to P4. Via several steps, pregnenolone can be converted to testosterone (T), where, if in the presence of aromatase,

T is converted to E2. Alternatively, if P4 is the presence of 5α-reductase, it will be converted to

ALLO (A. Leslie Morrow, 2007) (Fig. 1.1).

Steroid hormones are secreted into the circulation from their point of synthesis, affording them the capacity to act on distant tissues. While circulating in the plasma, they are bound to a serum binding protein to prevent degradation. At the target tissue, E2 and P4 can modulate cellular action via two mechanisms. “Classical” steroid receptor signaling occurs via nuclear translocating receptors, enacting long-lasting (hours) effects on gene transcription. The lipophilic steroid hormone, in this case E2 or P4, is released from its serum binding protein and diffuses across the plasma membrane into the cell. There are two classes of E2 nuclear receptors that arise from distinct genes: estrogen receptor ɑ and estrogen receptor , collectively “ERɑ/”. There are two

P4 receptor isoforms: PRA and PRB, collectively “PGR.” Each receptor variant has distinctive cellular localization and function (Roberta Diaz Brinton et al., 2008; S. K. Mani et al., 2006). In the cytoplasm, ERɑ/ and PR are bound to chaperone proteins. Once E2 or P4 bind to their cognate receptors, the receptors dissociate from the chaperones, allowing them to dimerize with another ligand-activated receptor. Upon dimerization, the receptors migrate to the nucleus and bind to their respective response elements (REs), along with co-activators or repressors (DeMarzo et al., 1991;

Kuntz & Shapiro, 1997).

2 “Non-classical” steroid receptor signaling occurs at the plasma membrane where binding of ligand to receptor enacts rapid (within minutes) and transient signaling cascades (Revankar et al., 2005). GPER is the non-classical E2 cell membrane receptor (Revankar et al., 2005; Vrtačnik et al., 2014). GPER, a G protein-coupled receptor located in the plasma membrane and endoplasmic reticulum, stimulates cAMP production, Ca2++ mobilization, and activates downstream signaling pathways such as PI3K/Akt and ERK/MAPK (Filardo et al., 2007;

Revankar et al., 2005). P4 membrane receptors are classified into two groups: the b5-like heme/steroid binding protein family, which includes PGRMC1 and its paralog PGRMC2, as well as the class II progestin and adipoQ receptor family (mPRs), which includes the PAQR5/6/7/8/9 genes (mPR//// proteins, respectively). PGRMC1/2 proteins are primarily located in the endoplasmic reticulum membrane (Ryu et al., 2017), are expressed in a range of tissues including brain and ovary (Gerdes et al., 1998; Krebs et al., 2000; S. Petersen et al., 2013), and are involved in a variety of cell pathways, including cholesterol and steroid biosynthesis (Piel et al., 2016; Ryu et al., 2017). The mPRs are a group of G protein coupled receptors located in the plasma membrane

(J. L. Smith et al., 2008; Thomas et al., 2007) that activate the MAPK pathway by inhibiting cAMP production (Thomas et al., 2007; Zhu et al., 2003). While ALLO is able to bind to any of these mPRs, it appears to have the highest affinity for mPR (Thomas & Pang, 2020).

ALLO is an endogenous neurosteroid, synthesized first by the reduction of P4 to 5- dihydroprogesterone (5-DHP) in the presence of 5-reductase, and second by the reduction of

5-DHP when in the presence of 3-hydroxysteroidoxidoreductase (3-HSOR), which also oxidizes ALLO back to 5-DHP (Compagnone & Mellon, 2000). Unlike E2 and P4, ALLO does not appear to exhibit direct genomic effects; rather, ALLO is most known as a -aminobutyric acid

(GABA) agonist. ALLO is a positive allosteric modulator of the GABAA receptor (particularly α4

3 subunits) leading to an influx of GABA-mediated Cl− conductance (Harrison & Simmonds, 1984;

Herd et al., 2007; A. Leslie Morrow, 2007), thereby enhancing the inhibitory effects of GABA, typically resulting in anxiolysis (Stell et al., 2003). However, ALLO also binds to mPRs (S.

Petersen et al., 2013; Thomas & Pang, 2012; Zhu et al., 2003). Evidence from human cells shows moderate to high affinity of ALLO to mPR, , and (Pang et al., 2013; Thomas & Pang, 2012), which are expressed throughout the brain (GTEx Consortium, 2013; Pang et al., 2013).

All three of these ovarian steroid hormones can diffuse through the blood brain barrier

(BBB) and be synthesized endogenously in the brain and impact brain function, making them all potentially neuroactive. The steroidogenesis in brain is conserved across vertebrates (Tsutsui et al., 1999); however, only ALLO is best known for its neuronal effects. E2 and P4, while also neuroactive, have most often associated with reproductive effects, specifically their critical roles in the ovarian cycle.

1.1 Physiology of the Ovarian Cycle

In many mammalian species, eggs suitable for fertilization are released only in certain seasons, or even years. However, most human females of reproductive age and not undergoing hormonal suppression or severe stress, experience an approximately 28-day hormonal cycle. regulating the uterus and ovaries and exquisitely regulated by the hypothalamic-pituitary-gonadal

(HPG) axis. The role of the HPG axis regulation during the ovarian cycle is the following. The ovarian steroids themselves exert a negative feedback loop on hypothalamic gonadotropin- releasing hormone (GnRH) and gonadotropins in the pituitary. At the onset of the follicular phase

GnRH is secreted from the hypothalamus in pulsatile fashion, and traverses through the hypophysial portal bloodstream to the pituitary gland. In the pituitary, GnRH binds to and activates

4 gonadotropic cells, which are stimulated to produce follicle-stimulating hormone (FSH) and luteinizing stimulating hormone (LH). FSH and LH travel through the bloodstream and activate developing ovarian follicles and stimulate them to produce E2 in increasing amounts, leading to ovulation. The newly liberated E2 circulates through the blood to the brain, where, when it exceeds a threshold concentration, increases GnRH pulse rate, contributing to a surge in LH release from the pituitary. The elevated levels of LH induce the mature ovarian follicle to rupture, releasing the oocyte. Ovulation marks the onset of the luteal phase, during which the residual follicular envelope becomes the corpus luteum (under the influence of LH), which produces both E2 and P4, increasing levels from the reduced E2 output seen at ovulation, and the usually low P4 levels seen during the follicular phase.

1.2 Sex Steroid Hormone Effects on the Developing Brain

The modern quest to understand the lasting effects of hormones on the brain consequent to exposures in the peri-natal period can be traced to the late 1950s when female offspring of pregnant guinea pigs with androgens were discovered to resemble males both morphologically and behaviorally (Phoenix et al., 1959). Later studies in rats showed morphological sexual dimorphism in the preoptic area is sensitive to differential hormone exposure and/or male castration in the perinatal period, though that difference was not observed in the adult (Gorski et al., 1978; Pfaff,

1966). Since that time, it has been thought that organizational effects of sex steroid hormones on the brain occur during a period of sexual differentiation, defined as a “process that occurs during a restrictive developmental period and is irreversible.” That is, in primates, sexual differentiation occurs during a sensitive developmental time window, often the mid- to late-gestational period

(M. M. McCarthy, 2008). In mammals, sexual differentiation is a secondary effect of sexual

5 determination, which is the chromosomal or genetic sex of an individual that determines gonadal fate (M. McCarthy et al., 2017). Once gonads are formed during the 13th week of gestation, circulating hormones from testicles or ovaries differentially organize the developing brain (M. M.

McCarthy & Konkle, 2005). Thus, after puberty, differentiated neurons are acted upon by gonadal steroids, thus producing stereotypical sexual behavior/physiology. This construct for brain development is known as the Organizational-Activational hypothesis. It is now generally accepted that early steroidal impact on the developing brain dictates adult brain morphology and sexual behavior as quantified by female/male differences in sexual behavior and cyclical (female) versus pulsatile (male) gonadotropin secretions (Arnold & Breedlove, 1985; M. M. McCarthy, 2008).

Of the sex steroid hormones, E2 appears to be among the most influential in the developing brain. For example, it is actually the presence of E2 that “organizes” the male brain, and the lack of E2 that “organizes” the female brain (M. M. McCarthy, 2008; Phoenix et al., 1959). Two reasons for this are, first, female fetuses produce high levels of -fetoprotein, which binds circulating E2, thus preventing E2 from entering the brain (Tomasi, 1977). Second, the aromatization hypothesis proposes that T crosses into the brain, is converted via aromatase to E2, where it acts on ERs (E2 often mimics the effects of T in low doses) (MacLusky et al., 1987). Furthermore, E2 promotes neurite outgrowth (Toran-Allerand, 1976; Toran-Allerand et al., 1983) and modulates synaptic patterning, particularly dendritic spine formation, phenomena also seen in the adult brain

(hippocampus) (M. M. McCarthy, 2008). E2, aromatase, and E2 receptor levels are very high prenatally and continue to decline until adulthood. ER and ER beta are expressed throughout the brain, but have distinct expression patterns (Mueller & Korach, 2001; Osterlund, Grandien, et al.,

2000; Osterlund, Gustafsson, et al., 2000). E2 is a widespread and multifunctional steroid hormone in the brain, though it is not alone, as P4 also exhibits many effects on developing neurons.

6 High concentrations of circulating P4 in plasma and brains of developing males and females appear to be equal, suggesting a crucial role for brain development (Nguyen et al., 2003; Weisz &

Ward, 1980). Additionally, P4 receptors, classical and membrane, are broadly expressed throughout the brain and across neural cell types (Roberta Diaz Brinton et al., 2008). During fetal development, circulating P4 is thought to originate from maternal serum, the placenta, and the developing nervous system itself (Compagnone & Mellon, 2000; Tuckey, 2005; Wagner &

Quadros‐Mennella, 2017). Both circulating P4 and P4 that is synthesized de novo in neural and glial cells are important for neural development. For example, P4 is thought to promote neurogenesis, dendritic outgrowth, and aid in synaptogenesis during myelin sheath formation

(Sakamoto et al., 2001; Schumacher et al., 2004; TESTAS et al., 1989). More specifically, P4 can promote neural progenitor cell proliferation as seen in sheep and rat hippocampi (Yawno et al.,

2009), as well as increase dopamine transporter expression and dopamine neurogenesis from neural progenitors in mice (S. C. Woolley et al., 2006). Notably, however, many of the neurodevelopmental functions played by P4 are observed along with its metabolite, ALLO.

ALLO is also synthesized in and exerts effects on the developing brain. Like P4, high ALLO concentrations are found in circulating fetal and maternal plasma, and fetal brain (Nguyen et al.,

2003). Like E2 and P4, ALLO can also stimulate neural cell proliferation during development. In neural progenitor cells derived from both rats and human embryonic stem cells, ALLO stimulates progenitor cell proliferation by modulating GABAA receptors (Gago et al., 2004; Grobin et al.,

2006; J. M. Wang et al., 2005). In the adult brain, GABA is an inhibitory neurotransmitter, however, it is excitatory in developing neurons across the neonatal brain because the chloride Cl¯ concentration gradient is reversed by high intracellular Cl¯ concentrations (LoTurco et al., 1995;

Obrietan & van den Pol, 1995), an evolutionary conserved phenomenon (Ben-Ari, 2002).

7 Furthermore, E2 is thought to increase intracellular Cl¯, thereby higher E2 levels in the developing male brain lead to stronger GABA responses in the developing male brain (Nuñez & McCarthy,

2009).

Sex steroid hormones have a clear impact on sexual differentiation, but the signaling pathways they initiate affect changes in the brain throughout the lifespan. It is also important to note that the effects of sex steroid hormones affect the brain via both those that are endogenously made within the central nervous system and those circulating, which freely cross the BBB and induce neuronal differences in steroid sensitive areas at the time of exposure, just as they do in adults.

1.3 Sex Steroid Hormone Effects on the Adult Brain

Sex steroid hormones shape and modify the brain throughout the lifespan. However, how sex steroid hormones impact the brain during early development is distinct from that of adulthood; the response to sex steroid hormones in adults is constrained by brain architecture shaped by exposure in the perinatal sensitive period. Here, the focus will be on three ovarian sex steroids, E2,

P4, and ALLO, that are typical to female reproduction, but are most certainly neuroactive, affecting a wide range of cellular functions and are ultimately associated with many non-reproductive behaviors. The effects of these hormones on adult brains are transient and, as referred to in the previous section, are “activational” (M. McCarthy et al., 2017; Phoenix et al., 1959).

E2 has many roles in adult neuronal function. For example, spine density in the dentate gyrus of the hippocampus is modulated by E2 in aged, but not young, female rats (P. Miranda et al., 1999). Indeed, many preclinical studies show a rapid increase in dendritic spine density in the hippocampus and prefrontal cortex after E2 treatment, potentially explaining the role of E2 enhancement of memory formation (R. D. Brinton, 2001; Cooke & Woolley, 2005; Luine et al.,

8 2003; Luine & Frankfurt, 2012; Morrison et al., 2006). Morphological studies suggest that E2- induced spines tend to be thin, nascent spines, typical of a transient spine and contained NMDA receptors, indicating an increase in functional synapses (Kasai et al., 2010; Srivastava et al., 2008).

These studies are consistent with others suggesting an important role for E2 in neuroplasticity (R.

D. Brinton, 2001; Cooke & Woolley, 2005; C. S. Woolley, 2007). E2 also shows neuroprotective effects after injury, as demonstrated in mice and rats by enhancing neurite outgrowth and protecting against glutamate toxicity and the toxicity induced by -amyloid (R. D. Brinton, 2001;

Nilsen et al., 2002; Rozovsky et al., 2002). Furthermore, E2 and P4 sometimes work together to potentiate these neuroprotective effects (Nilsen & Brinton, 2002), implicating P4 as another important molecule for neural function.

As in development, P4 plays many non-reproductive roles in cells across the brain. In mouse and rat models, P4 protects against neurodegeneration and apoptosis and promotes neuro- and synaptogenesis after injury (Bali et al., 2012; Barha et al., 2011; Gonzalez Deniselle et al.,

2002). These neuroprotective qualities are associated with an upregulation of brain-derived neurotropic factor (BDNF), increased choline acetyltransferase levels, and increased myelination

(Azcoitia et al., 2003; Roberta Diaz Brinton et al., 2008; Koenig et al., 1995).

In the adult brain, ALLO is best known as a positive allosteric modulator of the GABAA receptor and an mPR agonist. As stated above, GABA is an excitatory neurotransmitter during early development, but later becomes the predominant inhibitory neurotransmitter of the brain.

ALLO has a high affinity for the GABAA receptor to enhance affinity for GABA or directly activate GABA receptors, increasing Cl¯ influx and hyperpolarizing the neuronal membrane

(Mingde Wang, 2011). When GABA is inhibitory, ALLO binding can evoke anxiolytic, antidepressant, and anticonvulsant effects, as well as NSC proliferation after injury (Beyenburg et

9 al., 2001; Landgren et al., 1987; Schüle et al., 2014; J. M. Wang et al., 2005). Additionally, ALLO has binding affinity for each of the five mPRs but has the highest affinity for mPR. Most mPRs show broad mRNA expression distribution in cells and tissues throughout the body. However, mPR mRNA appears to be exclusively expressed in the brain, where it is thought that ALLO- mPR binding helps facilitate apoptotic inhibition, the main neuroprotective effect of ALLO

(GTEx Consortium, 2013; Pang et al., 2013; Tang et al., 2005; Thomas & Pang, 2020).

Much of what is known about the effects of sex steroid hormones on the brain come from animal studies, predominantly rat and mouse. Intracellular studies on the effects of E2 and P4 are in humans often conducted on non-neuronal cells such as breast cancer or human embryonic kidney (HEK) cells (Darbre et al., 1983; Perillo et al., 2000; Shen et al., 2006; R. Wang et al.,

2019; Zou et al., 2009), many of which are focused on target genes or pathways unrelated to brain function. Recently, some findings on effects of sex steroid hormones in animal models have been mirrored in human cells. For example, independent studies showed cell proliferation induced by

E2, P4, or ALLO in human derived NSCs (J. M. Wang et al., 2005, 2008; Mingde Wang, 2011).

However, fundamental knowledge of how E2, P4, or ALLO effect gene expression changes in human neurons is still lacking. Understanding the transcriptional mechanisms by which ovarian steroid hormones affect developing and developed female neurons can provide important insights into the trajectory and etiology of neuropsychiatric disorders, particularly those that are sex specific.

1.4 Sex Steroid Hormone Effects on Behavior and Mood

Sex steroid hormones freely cross the BBB and are also synthesized endogenously in the brain, where they influence a broad range of non-reproductive effects. Ovarian steroids, E2, P4,

10 and ALLO, in particular, alter mood and behavior in a sex-specific manner, potentially partly explaining sex differences in some psychiatric disorders. For example, the prevalence of depression in women has been consistently reported as twofold that of men, even across countries

(Bland, 1992; Kessler et al., 1993; Weissman et al., 1993; Weissman & Klerman, 1977). Cellular changes in the brain triggered by circulating ovarian steroids may provide an indication to the increased susceptibility to depression and mood disorders in women. In rats, E2 exerts some of the same cellular actions induced by antidepressant/mood stabilizers: stimulates BDNF (Bath et al., 2013), increases cAMP response element-binding protein (CREB) and neurotrophic tyrosine kinase receptor type 1 (trkA) activity (Jang et al., 2009; Lai et al., 2003), and decreases glycogen synthase kinase-3 activity (Goodenough et al., 2005). Furthermore, individuals with major depression show decreased ALLO concentrations in plasma (Ströhle et al., 1999), which appear to be restored by selective serotonin reuptake inhibitor (SSRI) treatment (Romeo et al., 1998;

Uzunova et al., 1998).

ALLO, a P4 metabolite, is a potent positive allosteric modulator of GABAAR, particularly the ɑ4 subunit, which, in adults, tends to have anxiolytic, antidepressant, antiepileptic, and sedative effects (Bäckström et al., 1990; A. L. Morrow et al., 1995; Paul & Purdy, 1992). This is consistent with the association of GABAAR with anxiolysis and as the effective target for benzodiazapines

(Nuss, 2015; van Rijnsoever et al., 2004). Recently, ALLO was approved for treatment of post- partum depression (PPD) (Meltzer-Brody et al., 2018), and is being used in clinical studies for treatment of major depressive disorder (MDD) (Gunduz-Bruce et al., 2019; Lüscher & Möhler,

2019), demonstrating the potential for ALLO as a target for the development of novel treatments in psychiatry.

11 Several mood disorders are triggered by abnormal reactions to otherwise normal (i.e., physiologic) changes in sex steroids. Mood disorders linked to changes in ovarian steroid levels in women are perimenopausal depression (PMD), post-partum depression (PPD), and premenstrual dysphoric disorder (PMDD). PMDD is the focus of much of this thesis.

1.5 Pathophysiology of Premenstrual Dysphoric Disorder

Premenstrual Dysphoric Disorder (PMDD) is characterized by an abnormal affective and behavioral response during the luteal phase of the menstrual cycle to otherwise normal (i.e., physiologic) changes in steroid hormone levels across the normal menstrual cycle. PMDD affects approximately 2-8% of women of reproductive age (Epperson et al., 2012; Kimberly Ann Yonkers et al., 2008). Clinical studies in women with PMDD show this disorder is not a typical endocrinopathy. For example, women with PMDD exhibit normal hypothalamic-pituitary-gonadal

(HPG) axis function (Schiller et al., 2016) and normal peripheral E2, P4, and ALLO levels

(Epperson et al., 2002; Lombardi et al., 2004; Monteleone et al., 2000; Rapkin et al., 1997; Schiller et al., 2014; P. J. Schmidt et al., 1994; M. Wang et al., 1996). Additionally, when E2 and P4 are suppressed by Lupron, a gonadotropin releasing hormone (GnRH) receptor agonist, ~70% of

PMDD patients experience symptom remission (Helvacioglu et al., 1993; P. J. Schmidt et al.,

1998), whereas symptoms recur when physiologic doses of either E2 or P4 are added back (P. J.

Schmidt et al., 1998). In contrast, asymptomatic matched controls (herein, “controls”) that underwent identical Lupron and hormone treatments experienced no mood changes (Pincus et al.,

2011; P. J. Schmidt et al., 1998) (Fig. 1.2). In particular, the increase in E2 and P4 levels at the beginning of the luteal phase has shown to be crucial for the onset of PMDD symptoms, as a

12 previous study showed the persistence of PMDD symptoms despite blocking the mid- to late-luteal phase (Peter J. Schmidt et al., 1991).

ALLO also is pathophysiologically relevant to PMDD. Inhibiting 5α-reductase prevents the conversion of P4 to ALLO, thus stabilizing ALLO levels across the menstrual cycle, significantly reducing PMDD symptoms (Martinez et al., 2016; P. J. Schmidt et al., 1998).

Manipulation of E2, P4, or ALLO in clinical studies show symptom reduction, but current patient treatments are based on symptom similarity to other mood disorders and have limited efficacy.

Neuroimaging studies have shown differing activity levels in various brain regions of women with PMDD during the luteal phase. Compared to controls, activity levels in the medial orbitofrontal cortex decreased and insula increased during a response inhibition task (Bannbers et al., 2012; Protopopescu, Tuescher, et al., 2008). In anticipation of negatively valanced pictures, increased activity was seen in the prefrontal cortex and decreased activity in the right dorsolateral prefrontal cortex (Gingnell et al., 2013; N. Petersen et al., 2018). These studies suggest network- level alterations in the activity of neuronal circuits underlying reward, social cognition, and affective states.

First-line treatments are selective serotonin reuptake inhibitors (SSRIs), which effectively treat symptoms in ~60% of women with PMDD (Pearlstein & Steiner, 2008). Lupron (a secondary/tertiary line of treatment) is effective in ~70% of women with PMDD (Peter J. Schmidt et al., 2017), but can only be administered for short periods of time due to accompanying side effects related to low estrogen levels (Magon, 2011). Though SSRIs and Lupron are effective in relieving symptoms for some and/or in the short-term, there is a clear need for alternative therapies.

The biological mechanisms underlying PMDD are unknown, but epidemiologically representative twin studies estimate heritability at 56% (Condon, 1993; K. S. Kendler et al., 1992; Kenneth S.

13 Kendler et al., 1998; Treloar et al., 2002; O. B. van den Akker et al., 1995; O. B. A. van den Akker et al., 1987), suggesting the existence of a genetic vulnerability to PMDD. The existence of this genetic vulnerability has not yet been identified, though studies using lymphoblastoid cell lines

(LCLs) from PMDD patients and matched controls have thus far identified several key differences in cellular function in women with PMDD.

1.6 Cellular Differences in PMDD

Clinical studies suggest that there is an intrinsic difference in the way women with PMDD respond to changes in steroid levels compared to controls. To investigate the underlying cellular mechanisms, our lab generated LCLs from PMDD patients and controls using EBV (further description below). Specifically, these lines were derived from women with PMDD and controls who had confirmed clinical diagnosis and had participated in the Lupron clinical study described above, also performed in our lab (P. J. Schmidt et al., 1998). LCLs were generated from whole blood and subsequently expanded at Laboratory of Neurogenetics and the National Institutes of

Health and are available as frozen stocks. These cells were used to recapitulate the clinical Lupron study on the cellular level (Dubey et al., 2017). That is, the LCLs were used as cellular model of

PMDD to test whether there was a genetic predisposition in gene expression at baseline and/or in response to E2 or P4 levels compared to controls. To do this, LCLs from PMDD (n=10) and controls (n=9), were either untreated (UT), or exposed to E2 (100nM), or P4 (100nM) for a total of 24 hours. After collection, RNA was extracted and processed for whole transcriptome sequencing (RNA-seq) to investigate global gene expression differences between UT PMDD and controls, as well as due to hormone exposure.

14 Many genes manifest mRNA expression differences both at baseline and in response to hormone (P-value < 0.05, uncorrected). In particular, multiple genes associated with the ESC/E(Z) complex, a group of genes associated with gene silencing via histone methylation, were shown to be significantly upregulated in women with PMDD compared to controls both at baseline and in response to hormone (Dubey et al., 2017). However, protein analysis of ESC/E(Z) complex genes revealed that women with PMDD exhibit a discrepancy between mRNA levels. That is, mRNA levels were significantly upregulated compared to controls, whereas protein levels were significantly downregulated compared to controls (Fig. 1.3).

Another recent cellular study, again using LCLs from women with PMDD and controls, showed E2 induced aberrations in Ca2+ homeostasis and decreased endoplasmic reticulum (ER) stress response. In particular, four of the top differentially expressed genes in response to E2

(NUCB1, DST, GCC2, GOLGB1) are part of a physically interacting network involved in ER-

Golgi function. These data were used as the basis to test the differences in intracellular stress response in PMDD LCLs. The results showed that in the presence of E2, PMDD LCLs exhibited a significantly (p<0.05) decreased XBP1 splice response to thapsigargin, a sarco/ER Ca2+ inhibitor that rapidly depletes Ca2+ in the ER, thus inducing ER stress (Gupta et al., 2015; Walter & Ron,

2011).

These studies suggest there is something different about the cellular biology in women with

PMDD that causes women with PMDD to exhibit the dissociation between mRNA and protein levels in the ESC/E(Z). There are many potential explanations for this dissociation including missense or nonsense variants that result in differential transcript quantities, post-transcriptional regulation, protein misfolding, or errors in the endoplasmic reticulum (ER) related to vesicle packaging, transport, or secretion of the polypeptide. However, and although polygenic origins of

15 psychiatric diseases are increasingly recognized, it is less likely that there would be genomic variants at each of the ESC/E(Z) genes in a single individual.

1.7 MicroRNAs

MicroRNAs (miRs) are short (~22nt) single-stranded regulatory RNA molecules. They are posttranscriptional regulators of gene expression and are necessary for organismal growth and development (Bak et al., 2008; He et al., 2012; Landgraf et al., 2007; Olson et al., 2009). miRs are transcribed by RNA polymerase II or III and are derived from portions of transcripts that fold back on themselves, forming hairpin loops (Fig. 1.4). The stem of a miR hairpin loop is excised from ongoing transcription by Drosha, an endoribonuclease, and Pasha, an RNA-binding protein. The exportin system transports the miR to the cytoplasm where the hairpin loop is excised by Dicer, leaving a ~22nt RNA duplex with 3 overhangs on each end. This is then loaded on to the

Argonaute protein to form the RNA-induced silencing complex (RISC). Either strand can become the functional mature miR, though it is often 5 end of the precursor. The “passenger strand,” i.e., the non-functional miR, is released and degraded. The mature miR binds to the 3 untranslated region (UTR) of the target mRNA via complementarity to nucleotides 2-8, the miR “seed sequence,” to direct posttranscriptional repression, e.g., translation inhibition, mRNA de- adenylation, and/or decay. miRs have low target specificity: imperfect binding is tolerated outside the seed sequence, allowing each miRNA to target many mRNAs (Baek et al., 2008; Lee et al.,

2004; Selbach et al., 2008). Thus, while there are relatively few human miRNAs compared to mRNAs, it is nevertheless estimated that miRNAs regulate 70-92% of gene transcripts (K. C.

Miranda et al., 2006; Morgan & Bale, 2012). miRs contribute to phenotypes determination of all

16 cells, though the focus here will be on miRNA impact on neuronal regulation and responses to sex steroid hormones.

miRs play an important role in neuronal functioning, including differentiation, neurite outgrowth, neurogenesis, axon guidance, and plasticity (Åkerblom et al., 2012; Cheng et al., 2009;

Gao et al., 2010; Olde Loohuis et al., 2012; Pathania et al., 2012; Schaefer et al., 2007). In murine models, Dicer knockdown results in decreased neuronal size and aberrant axonal pathfinding

(Davis et al., 2008; De Pietri Tonelli et al., 2008), and Pasha knockdown shows synaptic connectivity loss and a reduction in number and size of dendritic spines (Olde Loohuis et al., 2012;

Stark et al., 2008). Specifically, miRNAs such as miR-124, miR-132, and the miR-200 family consistently emerge as neurogenesis regulators (Cheng et al., 2009, 2009; Choi et al., 2008; Gao et al., 2010).

Given the essential role of miRNAs in neuronal function, it is unsurprising miRNAs have emerged as contributors to psychiatric disease pathophysiology. Postmortem neuropathology suggests miRNA dysregulation in psychiatric disorders, including schizophrenia (Moreau et al.,

2011; Perkins et al., 2007; Smalheiser et al., 2014), bipolar disorder (Miller et al., 2012; Shih et al., 2012), and major depressive disorder (MDD) (Belzeaux et al., 2012; Smalheiser et al., 2014).

An individual’s ability to cope with stress is critical in the development of MDD. In murine models of stress-induced depression, expression of some miRNAs is blunted in the frontal cortex and hippocampus compared to healthy mice who show robust adaptive miRNA responses to stress

(Rinaldi et al., 2010; Smalheiser et al., 2011). In particular, many of those miRNAs are known to be enriched in synaptic fractions (Lugli et al., 2008), as well as share 5-seed motifs, suggesting they will target similar or overlapping mRNAs.

17 miRs also modulate transcriptional regulators, adding yet another layer to transcriptional mechanisms. By targeting mRNAs of transcription factors, DNA methyltransferases, histone deacetylases, and polycomb group genes, miRNAs can form feedback regulatory circuits to organize gene expression (Sato et al., 2011). Relatedly, miRNAs are known to regulate and be regulated by E2 and P4 (Klinge, 2009; Yuan et al., 2015). For example, the known neurogenesis regulator mentioned above, miR-200a is also known to regulate PGR (Williams et al., 2012).

Given this previous evidence, it is possible miRNAs play a role in mediating ovarian hormones in relation to psychiatric disorders, PMDD in particular.

Devising a model to understand micro and messenger RNA in humans is a difficult task.

Human models for investigating affective disorders such as PMDD on the cellular level are difficult to obtain. Public biobanks contain multitudes of human cells and tissues, though these are often either derived from tumors, which often possess a host of genomic anomalies, or are obtained post-mortem, ultimately representing a limited range of pathologies and a wide range of pre- mortem exposures and post-mortem intervals impacting gene expression. It is often essential to use live cells or tissue to understand the underlying cellular mechanisms of a disorder, though obtaining specific cells or tissue from a living person is prohibitive. However, there are several methods of creating cellular models from living humans via blood collection. One common cellular model that can be obtain via blood collection is lymphoblastoid cell lines (LCLs)

(Gladkevich et al., 2004).

1.8 Lymphoblastoid Cell Lines

LCLs are immortalized cell lines that can be created from small amounts of whole blood and are an inexpensive and reliable tool for biomedical research since first described in 1963

18 (Benyesh-Melnick et al., 1963). LCLs provide a strong research tool in personalized medicine as they maintain a relatively stable genomic profile of the individual from which they were derived as opposed to cancer cell lines, which are known to undergo genetic rearrangements during cell division. Thus, they can be used to investigate both inherited diseases (Sie et al., 2009) and, as they are known to express a variety of transcripts and proteins found in neurons, neuropsychiatric disorders (Gladkevich et al., 2004; Kakiuchi et al., 2004, 2008). In the study of neuropsychiatric disorders, LCLs have been used to investigate a wide variety of cellular functions including transcriptomics (Hu et al., 2006; Kakiuchi et al., 2008), mitochondrial dysfunction (Dodson &

Guo, 2007), calcium regulation (Kato et al., 2002), proteomics (Caron et al., 2002), and more recently, epigenomic signatures (Brennan et al., 2009) such as microRNAs (miRNAs) (Skalsky et al., 2012). Additionally, as they are living cells, they can be used to investigate phenotypic responses to drug and/or small molecule challenges.

To derive LCLs from whole blood, lymphocytes are separated via density centrifugation.

Once isolated, lymphocytes are infected with the Epstein-Barr virus (EBV), which inhibits apoptosis in these cells. Once introduced, EBV selectively induces transformation of mature B lymphocytes to immature lymphoblastoid cells, and via cell proliferation, removes other cells such as T-lymphocytes from the cell population. Inside the B cell, the EBV genome reportedly exists as distinct episomal copies in the cytosol, but can also nonrandomly integrate into the genome

(Gualandi et al., 1992; Joesch-Cohen & Glusman, 2017; Leenman et al., 2004). The karyotypically normal transformed B cells are then allowed to proliferate, grow rapidly in suspension cultures, and can be maintained in growth medium indefinitely or cryopreserved for later use.

While LCLs provide a diversity of uses for biological investigation, they are not without their limitations. The specifics of how EBV transformation affects epigenetic mark are still

19 developing. Some studies have begun identifying where and what changes EBV makes to epigenetic marks, researchers must remain mindful of this gap in knowledge. EBV-transformed

LCLs actively proliferate, have normal diploid karyotypes, and have no tumorigenic properties.

Additionally, while LCLs are known to express a wide variety of genes, they are, ultimately, B lymphocytes whose phenotype is quite different from that of neuronal cells. However, neuronal cell specific models are potentially able to fill that knowledge gap.

1.9 Human Neuronal Cell Lines

Several tools exist to model psychiatric phenotypes at the cellular level: commercially available neuronal lines and neurons derived via stem cells: embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs). Commercially available neuronal lines are generally derived from blastomas and are useful models for understanding general aspects of neuronal mechanisms, e.g., neuronal response to drug or small molecule perturbation, disease models if the phenotype allows for it, etc. One advantage of using stock neuronal cell lines is that there should be less heterogeneity during analysis than there would be with biological replicates. When plating and expanding stock cell lines, each well or flask should be a technical replicate as all the cells are from the same biological source. Given fewer opportunities for variation, technical replicates provide stronger power with fewer samples than would be needed with biological replicates.

Hence, for a general understanding of how neuronal cells respond under certain conditions, e.g., the transcriptional response profile after small molecule perturbation, commercial neuronal lines provide a suitable model that are easy to attain and generally straightforward to grow. However, these cells tend to be cancerous in origin and do not provide much phenotypic variety. Though

20 more difficult to attain, one can preserve the genomic profile from individuals of a specific phenotype by creating iPSCs and differentiating them to neuronal cell lines.

The first iPSCs were created from mouse fibroblasts in 2006 (Takahashi & Yamanaka,

2006), with the first human iPSCs (Takahashi et al., 2007) and neural differentiation (Wernig et al., 2008) quick to follow. iPSCs are created by collecting fibroblast, blood, or urine samples and reprogramming them after exposure to specific transcription factors associated with pluripotency

(Fusaki et al., 2009; Y. Kim et al., 2016; Takahashi et al., 2007). This process converts the once somatic cells into fully pluripotent cells, resembling embryonic stem cells in their genetic markers of pluripotency and potential to self-renew. The multilineage cell types into which iPSCs can differentiate makes iPSCs a powerful tool for modeling diseases in a specified cell type from an individual or specific population.

1.10 Thesis Focus

Fluctuations in circulating levels of steroid sex hormones produced by the ovaries is a natural phenomenon in women after the onset of puberty up until menopause and during pregnancy. In a subset of women, however, these fluctuations trigger depressive symptoms, the timing of which is distinct and predictable: premenstrual, post-partum, and perimenopausal.

Although it is expected that these symptoms arise through the action of the sex hormones on cells in the brain, the effect of steroid hormone exposure on the behavior of neurons is poorly understood, and neuronal cell lines from patients with relevant mood disorders have been unavailable. Previously in our laboratory, intrinsic cellular differences were identified between lymphoblastoid cell lines (LCLs) from women with premenstrual dysphoric disorder (PMDD) - a form of reproductive endocrine-related mood disorder in which physiologic changes in ovarian

21 steroid or allopregnanolone levels trigger the onset of negative affective symptoms in women at risk but otherwise have no obvious behavioral effects in women without this condition. The focus of this thesis is to build on this prior work by first determining the effects of ovarian steroids (and a neurosteroid metabolite) on gene expression in neuronally-relevant cell lines. The studies reported in this thesis employ both commercially available human neuronal cell lines and human

NSCs differentiated (in-house) from human induced pluripotent stem cells (h-iPSCs) to examine gene expression differences that could provide insight into the potential transcriptional effects of the ovarian steroids estradiol (E2), progesterone (P4) and the neurosteroid metabolite of P4, allopregnanolone (3α-THP [ALLO]) on neurons across maturation stages. Second, work in this thesis builds directly from findings in the aforementioned PMDD study by investigating the role of microRNAs in regulating previously identified differences in mRNA between PMDD and controls in an LCL model.

22 1.11 FIGURES

Figure 1.1: Sex steroid hormone synthesis pathway

Key pathways described in 1.1 are depicted here. Steroids are labeled and in blue boxes, enzymes that catalyze the conversion from steroid to product are labeled and in green circles.

23

Figure 1.2: Recurrence of symptoms of premenstrual syndrome during the addition of

estradiol or progesterone to the Leuprolide regimen

Women with PMDD and matched controls enrolled in a clinical received eight weeks of leuprolide hormone suppressant followed by four weeks of E2 (plus leuprolide) and four weeks of P4 (plus leuprolide). The values displayed in the chart are means and standard errors of seven daily scores on the sadness scale of the Daily Rating Form. A score of 1 indicates the symptom was not present and a score of 6 in was present at an extreme. (P. J. Schmidt et al., 1998)

24

Figure 1.3: ESC/E(Z) transcript/protein quantity disparity in PMDD LCLs

Percent change from control of mRNA expression via RNA-seq for the 13 genes of the ESC/ E(Z) complex (top) and percent change from control of protein expression via ProteinSimple western analysis for the 13 genes of the ESC/E(Z) complex (bottom).

* represents p<0.05

# represents p<0.1

(Dubey et al., 2017)

25

Figure 1.4: microRNA biosynthesis in mammals

A schematic of miR synthesis as described in section 1.7. miRs are transcribed by RNA Pol II or

III, producing the primary miR hairpin loop, which is cleaved by the Drosha-DGCR8 (Pasha) to produce the pre-miR. The exportin complex transports it to the cytoplasm where the hairpin loop is cleaved and the remaining duplex is processed by the Dicer complex and loaded into the RISC, which guides the miR to silence target mRNAs. (Winter et al., 2009)

26 CHAPTER 2: GENERAL METHODS FOR ALL CHAPTERS

2.0 Statistical Differential Gene Expression Analysis

Differential gene expression (DGE) was determined using the Empirical Analysis of

Digital Gene Expression Data in R (edgeR) package (Y. Chen et al., 2016; Robinson et al., 2010;

Robinson & Smyth, 2007) (R environment v3.5.2) for all sequencing comparisons. Raw counts were used as input and groups were assigned by diagnosis and/or hormone treatment. Each comparison group was TMM-normalized and genewise tests resulted in matrix outputs that included log2 fold change (FC), log2 counts per million (CPM), F-statistic, nominal P-value, and

FDR adjusted P-value for each gene. Plots were generated using the ggplot2 package (Wickham,

2016).

2.1 Pathway Analyses

Pathway analyses, transcription factor enrichments, gene ontologies, and disease and cell type enrichments from results in each study were performed using the Enrichr database (E. Y.

Chen et al., 2013; Kuleshov et al., 2016). Enrichr collates data from 159 libraries, including KEGG,

REACTOME, GO, etc., providing the advantage of analyzing many pathway databases at once.

Enrichr results are provided in a matrix output that includes overlap, (# of genes in the input dataset/# genes in an annotated set), P-value (hypergeometric test), adjusted P-value (q-value, or

Benjamini-Hochberg correction for multiple hypotheses), odds ratio (deviation from rank/expected rank), combined score, and genes (genes in the input dataset present in the corresponding annotated set). For the combined score, Enrichr compares a mean rank and standard deviation for the input gene set to a precomputed background expected rank for each gene in the

27 gene set library, the standard deviation is then used to determine the combined score: ln(P-value)

* z, where z is the deviation from an expected rank score (E. Y. Chen et al., 2013).

28 CHAPTER 3: TRANSCRIPTIONAL EFFECTS OF SEX STEROID HORMONES IN

HUMAN FEMALE NEURONAL CELLS AT DIFFERENT STATES OF CELLULAR

MATURATION

3.0 Introduction and Background

As described in Chapter 1, previous literature demonstrates widespread actions of sex steroid hormones on human brain development. Estradiol (E2), progesterone (P4), and allopregnanolone (ALLO) all play roles in both developing and mature neurons across the brain and their levels are influenced by chromosomal sex and quantities of circulating maternal hormones. The effects of these hormones on the fetus are also both time- and area-specific.

However, animal models form the basis for much of what is known. The influence of these hormones on developing and mature human neuronal cells is not fully characterized.

Several neuronal cell lines commonly used in neuronal studies were chosen to characterize steroid hormone response in neurons: Luhmes and SH-SY5Y cell lines, and NSCs. The NSCs here were derived from the differentiation of iPSCs created from asymptomatic control women who enrolled in a clinical study (P. J. Schmidt et al., 1998). NSCs were used in this portion of the study in part because of their early state phenotype, which distinguish these cells from the other cell lines. They were also used because of their high efficiency of differentiation from human iPSCs when compared to other more differentiated neuronal cell types. They were differentiated using a standard, easily reproducible protocol that is widely used in the field (GibcoTM, “Induction of

Neural Stem Cells from Human Pluripotent Stem Cells Using PSC Neural Induction Medium,”

MAN0008031). Advances in the efficiency of differentiation protocols for specific neuronal types will allow these studies to be repeated in the future on more mature cells. Luhmes cells are

29 commercially available, non-cancerous in origin, and are easily differentiated to mature neurons.

Furthermore, they have a well-documented dopaminergic-like phenotype, as will be described in more detail below. This dopaminergic-like phenotype is particularly interesting as E2 has significant modulatory effects on the dopamine system in a sexually dimorphic manner (Becker,

1990; Yoest et al., 2014). These characteristics are unique and extend the potential to understand female neuronal gene response to sex steroid hormones across maturation stages. Finally, SH-

SY5Y cells are cancerous in origin, which is common for commercially available human neuronal cell lines; therefore, results from this study could potentially inform future studies that also utilize cancer-derived neuronal cell lines. Specific details of each cell line are explored below, but importantly, each line is female in origin with a unique neuronal phenotype.

Neural Stem Cells

A commercially available protocol (LifeTech) was followed to differentiate NSCs from human induced pluripotent stem cells (h-iPSCs). The LifeTech protocol (described in the methods section below under the heading “Human Induced Pluripotent Stem Cell Generation: NSC

Differentiation from iPSCs”) provides the instructions and materials for neural stem cell (NSC) differentiation from iPSCs. These cells appeared to be the most nascent compared to the other cell lines used in this study. The definitions of NSCs, neural progenitor cells (NPCs), relatively non- specific as they are often used interchangeably, but of the three cell types, NSCs are generally understood to be the least mature. The following definitions will be used for the purposes of this study. NSCs are multipotent cells capable of unlimited self-renewal, marked by SRY-box transcription factor 2 (SOX2) (Ellis et al., 2004; Graham et al., 2003), SRY-box transcription factor

9 (SOX9) (Scott et al., 2010), and notch receptor 1 (NOTCH1), (Ables et al., 2010; Corbin et al.,

30 2008; Yoon & Gaiano, 2005), which are involved in establishing and/or maintaining NSC self- renewal capacities and are downregulated during differentiation. Additionally, NSCs are capable of differentiating into neurons, astrocytes, and oligodendrocytes (Gage, 2000). In contrast, NPCs do not self-renew and have a more limited ability to proliferate, only developing into neuronal or glial cells (Martínez-Cerdeño & Noctor, 2018). There are several varieties of NPCs, but, in addition to lower expression levels (or absence) of SOX2, SOX9, and NOTCH1, several markers that distinguish NPCs from NSCs include the presence of or higher expression levels of doublecortin (DCX) (Guichet et al., 2013; Klein et al., 2020; Martí‐Mengual et al., 2013; Walker et al., 2007), neurogenic differentiation 1 (NEUROD1) (Kwak et al., 2015; Pataskar et al., 2016), and nestin (NES) (Bernal & Arranz, 2018; Hendrickson et al., 2011; Sunabori et al., 2008; Zhao

& Gage, 2009). Neural precursor cells refer to a collective population of NSCs and NPC (Dibajnia

& Morshead, 2013) and contain varying levels of the above markers. The use of these NSCs along with the other three lines provides multiple cell states of neuronal differentiation in which to study steroid hormone response.

Luhmes Cells

The Lund Human Mesencephalic, or Luhmes, cell line is a conditionally immortalized (via expression of v-myc under the control of a tetracycline responsive element) human neuronal precursor line with dopaminergic features (Scholz et al., 2011) derived as a subclone of

MESC2.10, which originated from 8-week-old female fetal human ventral mesencephalic tissue

(Lotharius et al., 2002, 2005). Due to their non-cancerous origin, it is reported that Luhmes cells have avoided genetic mutations often characteristic of tumor-derived lines (Tong et al., 2017); karyotyping has shown a normal set of chromosomes (Edwards & Bloom, 2019). Furthermore,

31 these cells can be easily differentiated (by withdrawal of tetracycline and subsequent downregulation of v-myc) into morphologically mature dopaminergic-like neurons marked by dopamine transporter (DAT), synaptic vesicle amine transporter (VMAT2), nuclear receptor related 1 (NURR1), and tyrosine hydroxylase (TH), with detectable electrophysiological currents

(Du et al., 2017; Lotharius et al., 2005; Scholz et al., 2011; X.-M. Zhang et al., 2014). Despite their frequent use as an in vitro neuronal model, their responsivity to E2, P4, and ALLO is not yet fully characterized. Furthermore, Luhmes provide a unique source to explore both NSCs and their differentiated dopamine-like mature state, allowing us to understand how a known neuronal cell type differs in sex steroid hormone response from its precursor to its mature state.

SH-SY5Y Cells

SH-SY5Y (ATCC® CRL-2266TM), is a human female neuroblastoma cell line. Originally derived from a metastatic bone marrow tumor biopsy, a neuroblast-like subclone was created and twice more subcloned, creating the current SH-SY5Y (SH) line, first described in 1978

(Biedler et al., 1978). SH cells are thought to exhibit a catecholaminergic phenotype (Biedler et al., 1978; Kovalevich & Langford, 2013; Xicoy et al., 2017) marked by expression of dopamine

β-hydroxylase (DBH) (Kovalevich & Langford, 2013; Thul et al., 2017; Uhlén et al., 2015), which catalyzes the conversion of dopamine to norepinephrine and is expressed in adrenergic and noradrenergic neurons (Ishiguro et al., 1993). They are also able to express a dopaminergic phenotype, marked by moderate DAT and TH expression (Biedler et al., 1978; Kovalevich &

Langford, 2013; Xicoy et al., 2017); however, evidence of this is inconclusive (Kovalevich &

Langford, 2013; Kowalczyk et al., 2009; Ross et al., 1983; Uhlén et al., 2015).

32 Research Question

Although Luhmes and SH-SY5Y cells have been extensively used in neurobiological studies, the effects of E2, P4, and ALLO on gene expression in neuronal cell lines have not been well characterized. These cell lines are used here to uncover the transcriptional effects of these hormones on female neurons and how those effects may differ at different stages of neuronal maturation.

Importantly, the expression of receptors for E2 (ESR1, ESR2, and GPER), P4 (PGRMC1 and PGRMC2), and/or ALLO (GABRA4, and PAQR7) were confirmed via qRT-PCR in each neuronal cell line to validate the use of each line in our study. Of note, the P4 nuclear receptor,

PGR, was not detected in any cell line, and ESR1 and GABRA4 were only detected in the NSCs.

Having confirmed the expression of receptors required for a cellular response to sex steroid hormones in these cell lines, the study moved forward to address the following question: what are the transcriptional effects in response to E2, P4, and ALLO, exposure and do these effects differ across stages of neuronal maturation? To answer this, I characterized sex steroid responsive genes by whole transcriptome profiling in neuronal cell lines by whole transcriptome analysis of NSCs, differentiated and undifferentiated Luhmes cells, and SH-SY5Y cells exposed to Vehicle (DMSO),

E2, P4, or ALLO.

3.1 Specific Methods

Creation of h-iPSCs and NSC differentiation are described below. Luhmes (ATCC® CRL-

2927TM) and SH-SY5Y (ATCC® CRL-2266TM) cells were thawed and plated according to ATCC specifications. “Undifferentiated” Luhmes cells (ULs), “differentiated” Luhmes cells (DLs), and

33 SH-SY5Y cells (SHs) were cultured following ATCC product sheet protocols. All cells were incubated at 37°C, 5% CO2 at all times except during feeding and splitting.

Human Induced Pluripotent Stem Cell Generation: h-iPSC transformation

H-iPSC lines were generated and expanded in the Laboratory of Neurogenetics at the

National Institutes of Health and are available as frozen stocks. H-iPSCs were generated from peripheral mononuclear blood cells (PBMCs), isolated from routine venous blood draws from female controls in a clinical study (P. J. Schmidt et al., 1998). CD4+ cells were isolated from

PBMCs via negative selection and then activated and expanded using CD3/CD28 beads and recombinant IL-2. At expansion, cells were transduced with lentiviral particles capable of inducing the expression of the four Yamanaka factors (Seki et al., 2012). 48 hrs after transduction, the cells were plated on to mouse embryonic fibroblast (MEF) feeder layers. Clones emerged 10-12 days post-infection, which were then picked and expanded 1:3. After 4 expansions, one plate was set aside for immunocytochemistry to confirm pluripotency using SSEA and Tra-1-60 antibodies, one plate was used for differentiation, and two clones were frozen and stored in DMSO at 80°C.

Human Induced Pluripotent Stem Cell Generation: NSC Differentiation from iPSCs

Four independent h-iPSCs lines derived from four individuals were thawed, added to maintanence growth medium, and added to pre-plated mouse embryonic fibroblast (MEF) feeder layers. Cultures were incubated at 37°C, 5% CO2 until colonies formed and pluripotency was re- confirmed. No cultures were maintained for more than 60 passages in order to reduce the impact of accumulated mutations that can arise in long-term cultured stem cells. Once pluripotency was re-confirmed with SSEA and Tra-1-60 antibodies, h-iPSCs were transferred to 6-well, Matrigel®-

34 coated plates for differentiation. Matrigel®-coated plates were made in-house with Matrigel® working solution: Matrigel® [Corning®, #354277] was diluted 1:100 in Neural Basal Media

[ThermoFisher Scientific, #21103-049]). Each well was coated with 1mL of Matrigel® working solution and incubated at 37°C, 5% CO2 for 1 hour before use.

Cells were differentiated to NSCs using an established protocol (GibcoTM, “Induction of

Neural Stem Cells from Human Pluripotent Stem Cells Using PSC Neural Induction Medium,”

MAN0008031), with minor adjustments. Matrigel® working solution was aspirated and replaced with 1.5mL 20% KOSR. Each well was coated with 1mL and incubated at 37°C, 5% CO2 for 1 hour. Matrigel® working solution was aspirated and replaced with 1.5mL 20% KOSR. H-iPSCs were washed twice with 1x DPBS, no CaCl2, no MgCl2 [ThermoFisher Scientific, #14190].

Adhered cells were detached with Versene [ThermoFisher Scientific, #15040066] and transferred to Matrigel®-coated KOSR plates with Complete Neural Induction Media (NIM) (PSC Neural

Induction Medium [GibcoTM, #A1647801], 10% Gentamicin (5mg/mL) [GibcoTM, #12634-10]) for induction. Cells were fed with NIM over 6 days as described in MAN000803, on the 7th day cells were harvested and passaged 1:6 into Complete Neural Expansion Media (NEM) (PSC

Neural Induction Medium [GibcoTM, #A1647801], Advanced DMEM/F12 [ThermoFisher

Scientific, #12632010], 5% Gentamicin [GibcoTM, #12634-10]) with ROCK inhibitor Y27632

[Sigma-Aldrich, #Y0503-1MG], as described in MAN0008031.

Cells were passaged 1:6 or 1:12, depending on confluency, a total of three times. Cells were washed twice with 1x PBS and transferred to plates coated with phenol red-free Matrigel® working solution (phenol red-free Matrigel® [Corning®, #356231], NeurobasalTM Medium, minus phenol red [GibcoTM, #12348017]) and fed with phenol red-free NEM (phenol red-free Advanced

DMEM/F12 [ThermoFisher Scientific, custom-made], and NeurobasalTM Medium, minus phenol

35 red [ThermoFisher Scientific, #12348017]) to avoid estrogen-like mimicking properties known to exist in phenol red (Berthois et al., 1986). Hormone treatments were added 5 days into passage 3.

Cell Culture and Hormone Exposures

Four plates of ULs were seeded at 1.92x105 cells/well onto fibronectin [Sigma-Aldrich,

#F2006] coated poly-L-ornithine [Sigma-Aldrich, #P4957] 6-well plates, within the suggested guidelines (ATCC® CRL-2927TM product sheet). Undifferentiated cells were fed UL stock media

(DMEM/F12, no phenol red [ThermoFisher Scientific, #21041025], 1% L-Glutamine (200nM)

[GibcoTM, #25030-081], 2% GS21TM neural supplement [MTI-GlobalStem, #GSM-3100], and 1%

Gentamycin (5mg/mL) [ThermoFisher Scientific, #15750078]. Cells were fed every day for 4 days and hormone-treated on the 5th day.

To differentiate ULs to DLs, four 6-well plates were seeded at 2.3x105 cells per well onto precoated poly-L-lysine 6-well plates [Corning®, #356515] with additional coating of laminin

[Sigma-Aldrich, #L2020] (5ug/mL) prepared in DPBS with Ca2+ [ThermoFisher Scientific,

#14040133]. DLs were fed UL stock media supplemented with 1:10,000 tetracycline [Sigma-

Aldrich, #T7660], 1:50,000 GDNF [PeproTech, #450-10], and 1:50 dbcAMP [Sigma-Aldrich,

#D0627]. Cells were differentiated for 4 days and hormone-treated on the 5th day.

SH cells were plated onto T25 flasks with SH stock media (DMEM high glucose phenol red-free media [ThermoFisher Scientific, #21041025], 15% knock-out serum (KOSR) (estrogen- depleted) [Gibco, #10828-028], 2% L-Glutamine (200mM) [Gibco, #25030-081], 1mM Sodium

Pyruvate [Gibco, #11360], and 0.4% Pen Strep [Gibco, #15070-063]). SH cells grow as a mixture of floating and adherent cells, though adherent cells detached easily during splitting and did not require an anti-adherent. Cells were split 1:2 every 4 days. At the second split, cells were

36 transferred to 6-well plates 1mL of SH stock media for 4.41x105 cells/well. Cells were fed every day for 4 days and hormone-treated on the 5th day.

E2, P4, and ALLO were dissolved individually in DMSO [Sigma-Aldrich, #D2650]

(vehicle [Veh]) and added to sterile phenol red-free media respective to each cell line. Once cells reached approximately 90% confluence, the growth medium was replaced with fresh medium supplemented with one of E2 (100nM), P4 (100nM), ALLO (100nM), or DMSO vehicle. Vehicle treated cells were exposed to identical concentrations of DMSO as those exposed to hormone.

Concentrations of ovarian steroids and exposure interval were based on physiological levels and published in vivo studies (Dubey et al., 2017; Faroni & Magnaghi, 2011; Villablanca et al., 2002).

Cells were exposed to hormone for 24hrs, after which each well was washed twice with 1x PBS

[Life Technologies, #10010023], collected in TRIzolTM [Life Technologies, #15596018], and stored at -80C.

RNA Extraction, Library Preparation, and Sequencing

Three wells for each individual cell line and hormone treatment were collected from UL,

SH, and DL cells each, considered technical replicates (n=3 for each line and each hormone treatment). One well from each NSC line was collected as a biological replicate (n=4 for each line and each hormone treatment). No technical replicates were included for the NSCs. Total RNA was isolated from using Qiagen© RNeasy Mini Kit [Qiagen, #74106] using standard protocol. RNA quality and quantity were obtained with a spectrophotometer [NanoDropTM Technologies, ND-

1000]. Mean mRNA 260/280 for all samples ranged between 1.98-2.02. cDNA was made using the SuperScriptTM VILOTM cDNA Synthesis Kit [ThermoFisher Scientific, #11754250]. Libraries were constructed with the Ion AmpliSeqTM Human Transcriptome Expression Kit [ThermoFisher

37 Scientific, #A26326] with 100ng RNA input and unfixed RNA options using standard protocol.

Quantities and qualities of amplicon libraries were validated using the Agilent® 2100 Bioanalyzer and analyzed on the Ion ProtonTM sequencing system. Raw data were processed on the Torrent

Server using the AmpliSeqRNA plug-in. This Torrent SuiteTM software automatically used the Ion

Torrent Mapping Alignment Program to align the amplicon reads to the customized AmpliSeq

Transcriptome reference file, a set that contains all transcripts targeted by AmpliSeq

Transcriptome, which is essentially all genes in RefSeq (20,818). Raw count data and aligned reads were extracted from the Torrent Server for analysis (Table 1).

Differential Expression Analyses

Differential gene expression was determined separately for each cell type and its respective hormone treatments. Several samples were discarded after showing a Z-score of >2.5 standard deviations from the mean: one Veh UL, one Veh DL, and one E2 SH, particularly given the stock cell lines should be the equivalent of technical replicates. Given expected variability in biological replicates in NSCs, all samples were retained for analysis. Raw counts for each sample were filtered at a minimum of 6 counts per sample and a total of 15 counts across all samples using the filterByExpr function, and CPM normalized, resulting in 11,879 genes in DLs, 11,115 genes ULs,

11,471 genes in SHs, and 12,465 genes in NSCs. A quantile-adjusted conditional maximum likelihood model was implemented to test for differential gene expression using a gene-wise exact test.

38 Statistical Determination of Gene Overlap

The threshold for genes that are responsive to hormone and subsequently used for comparisons across cell lines are those that are statistically significant (p<0.05) for each neuronal cell type and treatment. Multi-set intersections (n>=3) require more complex computation than the standard hypergeometric test (synonymous with the standard one-tailed Fisher’s exact test [Rivals,

2007]) that is used to calculate two-sample intersections. Therefore, for each multi-set intersection,

SuperExactTest (Wang, 2015) was employed in the R environment to calculate fold enrichments

(FE, the ratio between the observed intersection to that which would be expected randomly), and the statistical significance of each overlap. SuperExactTest corrects for statistical probability testing in multi-set intersections by adjusting the hypergeometric density model to sum the density values from two to n possible overlap sizes.

3.2 Results

Gene Expression for Neuronal Markers Suggests Varying Stages of Maturation

As mentioned above, NSCs are the most nascent cells of those trancsriptomically profiled, and expression of key mRNA markers verified the NSC phenotype. For example, the NSC cultures expressed high levels of NOTCH1, SOX2, and SOX9, all of which are common to NSC, but not

NPCs. The NSCs expressed low levels of DCX, NES, and NEUROD1, all of which are common to NPCs but not NSCs. Further confirming NSC phenotype, NSCs were the only line of the four that expressed oligodendrocyte transcription factors 1 and 2 (OLIG2 and OLIG3), which are necessary for oligodendrocyte development, distinguishing them from NPC phenotype (Dai et al.,

2015; H. Takebayashi et al., 2002). NSCs also expressed various dopamine-related genes: dopamine receptor D4 (DRD4), forkhead box A2 (FOXA2), which is essential for dopaminergic

39 differentiation (T. Kim et al., 2017; Pristerà et al., 2015), DAT, and the GABA receptor subunit to which ALLO is known to bind, GABRA4.

UL gene expression was similar to those typical of early neuronal progenitor cells: they exhibited NPC markers but also retained some NSC markers. ULs also expressed SOX9 and SOX2 albeit at lower levels than those of the NSCs. Several NPC markers expressed by ULs are NES and

DCX, which are associated with neural progenitor cells. Additionally, ULs expressed nuclear receptor related 1 (NURR1) and high levels of the dopamine transporter (DAT), suggesting the tendency toward a dopaminergic phenotype.

In contrast, SHs are the only line that expressed DBH, consistent with its reported catecholaminergic phenotype. SH cells also expressed high levels of achaete-scute family BHLH transcription factor 1 (MASH1), a transcription factor essential for neural differentiation, NES and

DCX, which are all associated with the neural progenitor cell phenotype (Cau et al., 2002; Guichet et al., 2013; E. J. Kim et al., 2011). SHs also expressed high levels of microtubule associated protein 2 (MAP2), a microtubule stabilizing protein typically found in mature neurons (Soltani et al., 2005).

DLs expressed high levels of mature neuron markers (PSD95, SYN, and NEFM) as well as some of the previously documented dopamine markers (DRD2, KCNJ6, and NURR1) (Scholz et al., 2011), confirming the predicted mature neuronal phenotype. Notably, TH was not detected in either DL or SH cells, despite it an often-reported characteristic.

Most Expressed Genes are Common to all Four Cell Lines

Unsupervised analyses show clear gene expression patterns distinctive for each cell type

(Fig. 1a), with DL and SH lines to have the most similar pattern of gene expression. Responses to

40 E2, P4, or ALLO treatment (100nM) only influenced the expression of a small number of genes within any one cell type (Fig. 1b). Analysis of CPM normalized, filtered gene lists of all vehicle- treated (i.e., steroid-free) cells shows 69% overlap in gene expression (Fold enrichment [FE]=4.6, p=0) (Fig. 2).

Using each cell line’s genetic overlap as Enrichr input for pathway analyses, the 455 genes common to NSC and SH cells showed that those two lines to have the largest number of significant

(q<0.1) neuronally-related pathways (Fig. 3.3). In particular, “CHL1 interactions,” a pathway associated with neuronal adhesion (S. Chen et al., 1999) and neurite outgrowth (Herron, 2009), showed high Enrichr combined scores and q<0.01 significance in both BioPlanet and

REACTOME databases. Genes common to NSC, SH, and DL also showed a large number of neuron-related pathways.

Several Genes are ALLO- and/or E2-Responsive in Multiple Neuronal Lines

One gene was responsive (p<0.05) to both ALLO and E2 across all cell lines: Serine and arginine rich splicing factor 2 (SRSF2). SRSF2 encodes a member of the SR-rich pre-mRNA splicing factor family, which is part of the spliceosome. After either ALLO or E2 exposure, SRSF2

-2 was downregulated (p<0.05) in UL, SH, and NSCs (overlap significance phyp=3.64x10 ), but upregulated (FDR<0.01) in DLs (Figs. 3.4, 3.5). One additional gene was also downregulated

(p<0.05) in response to ALLO in UL, SH, and NSCs, but upregulated (p=0.0325) in DLs: small nucleolar RNA, H/ACA box 62 (SNORA62). Furthermore, cAMP responsive element binding protein 3 regulatory factor (CREBRF), a gene encoding a protein known for negative regulation of unfolded protein response during endoplasmic reticulum stress, was upregulated (p<0.05) in

41 response to ALLO in UL, SH, and NSCs but did not respond in DLs. In response to E2, CREBRF was upregulated (p<0.05) in NSCs and SHs but there was no response observed in DLs or ULs.

Overlap in Gene Response to ALLO Most Significant Between UL and SH Cells

While UL, DL, SH, and NSC cells did not share any FDR significant genes in response to

-3 ALLO, UL and SH shared one upregulated (FDR<0.1) gene: SMG6 (padj<4.22x10 ), a nonsense- mediated mRNA decay (NMD) factor and a component of a telomerase ribonucleoprotein complex responsible for the replication and maintenance of chromosome ends (Fig. 3.4). Across DL, UL, and SH lines, one gene (DIP2A) was upregulated (p<0.05), and four genes (GOT2, C2orf29,

UBE2E1, and SPTBN2) were downregulated (p<0.05) in response to ALLO.

Enrichr analysis against the drug signature database (DSigDB) of the 135 downregulated

(p<0.05) genes shared between UL and SH showed enrichment for genes that respond to valproic acid (padj=0.0001), an anticonvulsant that exhibits its effects by increasing GABA concentrations in the brain and acts as an HDAC inhibitor (M. Rahman & Nguyen, 2021). Enrichr pathway analysis of the 42 downregulated (p<0.05) genes shared between UL and SH showed enrichment for the tricarboxylic acid (TCA) cycle in both BioPlanet (padj=0.0009) and Reactome (padj=0.0052).

BCL2 is downregulated in response to ALLO and P4 in DL and SH cells

No genes were up- or downregulated in response to P4 in all cell lines (Fig. 3.7). However, it is notable that P4 induced significant (p<0.05) downregulation of the BCL2 apoptosis regulator

(BCL2) gene in DL and SH cells. BCL2 was also downregulated in response to ALLO in DL and

SH cells, and downregulated in response to E2 in SH cells.

42 Transcripts for ESC/E(Z) Complex Genes Were Present in all Neuronal Cell Lines

Transcripts for the genes that comprise the ESC/E(Z) complex, the histone modifying complex found to be dysregulated in a study in our lab using LCLs as a cellular model for PMDD, were expressed in all neuronal cell lines and at comparable levels to those in LCLs. SH cells showed PHF1 downregulation in response to E2, and RBBP4 downregulation in response to

ALLO.

3.3 Discussion

Sex steroid hormones exert widespread effects on the developing and adult brain. Previous studies have investigated some of these hormone-driven effects but were typically performed on neuronal cells or tissues derived from rodent models. The goal of this chapter was to determine the transcriptional effects of E2, P4, and ALLO exposure on neuronal cells and whether those effects differed across maturation stages. The results showed expression changes in several genes that responded to E2 and/or ALLO across all four cell lines, and the responses in those genes differed in mature neuronal cells compared to cells in earlier stages of development. These results indicate specific transcriptional signatures of hormone response.

Neuronal Cell Lines Showed Unique Maturation Stages

Previously defined cell type-specific transcripts were used to determine that each cell line exhibited gene expression consistent with its documented phenotype. NSCs exhibited the highest expression levels for genes associated with self-renewal, consistent with NSC phenotype. The other three lines showed either low or no expression of those genes, suggesting the NSCs were at the earliest stage of maturation. UL gene expression markers confirmed a neuronal precursor

43 phenotype, therefore at a later maturation stage than NSCs. SHs expressed high levels of NPC markers as well as its well-documented marker for a catecholaminergic neuron, DBH, suggesting them to be NPCs with a catecholaminergic-like phenotype. DLs expressed the high levels of mature and dopaminergic neuron markers, suggesting they were a mature or approaching a mature neuronal state. Each neuronal line showed gene expression that was overall consistent with the literature and/or protocols, though there were several exceptions.

SH cells showed little mRNA expression evidence of a dopaminergic phenotype. The lack of consistency regarding the SH phenotype, both in the literature and in the findings in this study, may be due to several factors. First, SH cells may have some stable characteristics, but it may be that not all subclones are identical, as suggested in Biedler, et al. (Biedler et al., 1978). Second, changes in media composition have been shown to affect SH maturation and phenotypic state

(Kovalevich & Langford, 2013). For example, co-administration of retinoic acid and phorbol esters to the cell culture medium has been shown to induce SH differentiation and upregulate TH and

DAT (Presgraves et al., 2004). Additionally, the SH cells in this study expressed high levels of

MAP2, which is typically associated with mature neurons (Sarnat, 2013). However, this could be due to the cancerous origins of SH cells as MAP2 has also shown to be a marker for neuroblastoma

(Krishnan et al., 2009).

ULs expressed higher mRNA levels of DAT, than DLs, and DLs did not express TH or

VMAT2. DAT is typically localized to mature dopaminergic neurons predominantly in the substantia nigra (GTEx Consortium, 2013; McHugh & Buckley, 2015). It is possible that the higher DAT expression in ULs is due to the presence of the v-myc oncogene. Previous work comparing gene expression differences of dopaminergic marker genes in DLs across differentiation days are consistent with those observed in this study. Using qRT-PCR, Scholz et

44 al. (Scholz et al., 2011) reported only low TH expression at day 0 and did not see an increase until differentiation day 6. Scholz, et al. also did not report VMAT2 expression at day 0, and only low expression at day 6, potentially corroborating why VMAT2 was also undetected in this study.

However, DLs have also shown electrophysiological activity starting at differentiation day 3, which suggests that by day 4, the length of differentiation in this study, is enough time for the DLs to have reached neuronal maturity (Du et al., 2017; Scholz et al., 2011).

Several Potential Maturation-Specific Markers Were Identified in Response to ALLO and E2

SRSF2 and SNORA62 showed the most robust responses to ALLO, suggesting they may be markers for ALLO response in neurons. SRSF2, a member of the serine arginine (SR) protein family and is involved in pre-mRNA splicing (Howard & Sanford, 2015), also responded to E2 across all neuronal cell lines. While literature on the role of SRSF2 in neurons is sparse, some studies demonstrated a decrease in SR protein activity during neuronal differentiation

(Hammarskjold & Rekosh, 2017; Liu & Bossing, 2016). Enrichr analysis showed SRSF2 protein- protein interactions with ESR1 and ESR2 (Protein-protein interaction hub proteins database) but does not show transcription factor binding in ChIP/ChEA/ENCODE databases, thus, the mechanism behind the SRSF2-ERɑ/ protein interaction is unclear. Previous studies have not yet characterized potential SRSF2-ALLO interactions. SNORA62 is a small nucleolar RNA of a class known for modifying uridines to pseudo-uridines (Ruff et al., 1993); the function of SNORA62 in the brain and/or the relationship between SNORA62 and ALLO is difficult to determine as little appears to be known. Nevertheless, perhaps SRSF2 and/or SNORA62 upregulation could be used as a marker for E2 or ALLO response in NSCs and NPCs and SRSF2 and/or SNORA62 downregulation as a marker for E2 or ALLO response in mature neurons.

45 CREBRF was upregulated in NSC, UL, and SH cells in response to ALLO and, and was also upregulated in NSCs and SHs in response to E2, but in DLs, CREBRF did not respond to hormone. CREB transcription factors are a family of proteins that bind to cyclic adenosine monophosphate (cAMP) response elements to activate gene transcription. E2 and P4 act via signaling cascades to phosphorylate CREB and initiate transcription (S. Mani & Oyola, 2012;

McEwen & Alves, 1999). ALLO has also been shown to activate CREB (Irwin et al., 2015).

CREB3 in particular is most highly expressed in the nervous system (Ying et al., 2015) and is known to activate transcription in response to endoplasmic reticulum (ER) stress (Howley et al.,

2018; Liang et al., 2006) and is associated with axonal growth in the brain (Hasmatali et al., 2019).

CREBRF is a negative regulator of CREB3 and CREBRF upregulation is associated with tumorigenesis (Han et al., 2018; Xue et al., 2016). However, a study in gastric cancer cells showed that CREBRF induced AKT signaling, thus promoting cell proliferation (Han et al., 2018).

CREBRF upregulation in NSC, UL, and SH cells in response to ALLO and E2 may serve to promote cell proliferation in NSCs and NPCs.

The ALLO responsive genes that comprise the overlap between UL and SH cells showed enrichment for both valproic acid signature genes and downregulation of TCA genes, potentially suggesting effects associated with GABA modulation. Valproic acid is primarily used as an anticonvulsant, but is also used in the treatment of mood, anxiety, and psychiatric disorders

(Chateauvieux et al., 2010). One of its primary mechanisms of action is via inhibiting GABA degradation (Johannessen & Johannessen, 2003; M. Rahman & Nguyen, 2021). GABA is synthesized via the TCA cycle from α-ketoglutarate (Ghodke-Puranik et al., 2013; Waagepetersen et al., 1999). However, within the TCA cycle α-ketoglutarate is converted to succinyl CoA, shunting it away from GABA synthesis. Aside from inhibiting several GABA degradation-

46 associated genes, valproic acid has also been shown to downregulate several TCA cycle genes, including SUCLG1, which was enriched in the gene overlap (Luís et al., 2014).

While there were several ALLO- and E2-responsive genes and pathways, there were relatively few significant P4-responsive genes. One potential reason for the low responsivity to P4 seen in this study is that PGR, the P4 nuclear receptor, was not expressed in any of the cell lines.

The membrane receptors, PGRMC1/2 and several of the mPRs, were expressed, but membrane receptors may not exert the same effects on transcriptional activity as nuclear receptors.

Furthermore, SRD5A3, the gene encoding 5ɑ-reductase, an enzyme essential for converting P4 to

ALLO, was expressed at similar levels across all cell types. This possibly allowed the exogenous

P4 treatment to be converted to ALLO during the 24hr exposure instead of exerting effects. The most notable gene responsive to P4 was BCL2, which was downregulated in DL and SH cells. It was also downregulated in DL and SH cells response to ALLO and downregulated in SH cells in response to E2. This is opposite of what has been seen in prior studies (Nilsen & Brinton, 2002;

Yao et al., 2005) and inconsistent with the anti-apoptotic effects of P4 and ALLO. However, neither the NSCs nor the ULs show BCL2 responsivity to any hormone treatment. One explanation for this apparent anomaly exists in the v-myc induced oncogene DL and inherent cancerous properties of SH cells described in more detail below. The dataset collected in this study showed high BCL2 expression levels in vehicle treated cells in comparison to the other three cell lines and

BCL2 expression in SH cells is known to further increase after differentiation (Lasorella et al.,

1995; Raguenez et al., 1999), a phenomenon potentially linked to its neuroblastoma properties.

While there are many strengths to this study and its discoveries, there are also limitations. mRNA levels are a good indication of cellular phenotype, but they do not ipso facto determine protein levels, or even the presence of the protein at all. This study would be strengthened by

47 determining protein quantities of the key mRNAs listed in the results. This could be done via immunocytochemistry (ICC) or western blot. ICC may be optimal for the common markers listed for each cell line given the need to verify simply whether the protein is there or not, especially given that the antibodies seem quite common. ICC also allows for double staining, which could be particularly helpful to further understand the UL phenotype. For example, in this study one might expect probing for both SOX2 and DCX would result in a mixture of cell populations: those that are labeled SOX2 would not label DCX, and those that are labeled DCX would not label SOX2.

This would be similar to the mix of NSCs (those that stain for SOX2) and NPCs (those that stain for DCX) characteristic of a neuronal precursor phenotype. Western blotting could help better quantify potential protein differences in hormone responsive mRNAs identified in this study, e.g.,

SRSF2 and CREBRF, for each cell line.

Commercially available human neuronal cell lines are a valuable resource for cellular neuroscience, but they also have several limitations. SH cells are essentially neuroblastoma lines with neuronal qualities; their cancerous origins allow for mutations and gene expression aberrations not observed in a healthy developing brain. Similarly, v-myc oncogene introduction in

ULs can introduce tumorigenic properties, of which the secondary effects are not yet fully understood. However, it is not unreasonable to assume a potential for gene expression modulation.

To repress v-myc induced immortalization, DLs are treated with the antibiotic tetracycline, which may introduce yet another confounding variable of which the effects on gene expression are not well understood. However, despite the variety of potential confounds in these cell lines, SRSF2 and SNORA62 respond to ALLO and E2 across all cell lines, and CREBRF responds to ALLO and

E2 in multiple lines, demonstrating the robustness of these genes to respond to ALLO and/or E2.

48 The results from this study provide a transcriptional signature of neuronal cell responses to

E2 and ALLO. There are many confounding variables that make these gene expression changes in vivo far more complex, e.g., other steroids, like glucocorticoids, were not present in this study but compete for the same hormone receptor, regional-specific neurons respond differently to hormone.

However, controlled, relatively simple methods like those used here must be applied to understand the transcriptional signatures of response to individual hormones before understanding all the confounding factors. Furthermore, the results in this study can now be used as a marker for atypical gene response to hormone in clinically relevant way: the study from which the NSCs in this study were derived also collected samples from women with confirmed PMDD diagnosis, providing a potential NSC model of PMDD. By using NSCs as a model for PMDD and comparing to controls, the results in this study, e.g., SRSF2, SNORA62, and CREBRF can all be tested to determine whether PMDD NSCs elicit a typical gene response to sex steroids. Furthermore, no ESC/E(Z) genes responded to steroid exposure in NSCs. However, after the establishment of a PMDD NSC line, expression level differences in ESC/E(Z) complex genes without hormone exposure can be determine via qRT-PCR between PMDD and control NSCs, determining whether the ESC/E(Z) complex is neuronally relevant in PMDD.

49 3.4 Tables and Figures

Table 3.1: Amplicon library averages per sample Cell line Raw reads Aligned reads Read depth

UL 22,558,599 22,511,854 292x

DL 17,087,473 17,059,115 188x

SH 17,042,302 17,014,727 175x

NSC 17,008,453 16,976,290 195x

UL: Undifferentiated Luhmes cells

DL: Differentiated Luhmes cells

SH: SH-SY5Y cells

NSC: Neural stem cells

50 A

B

Figure 3.1: Principal component analyses show distinctions between cell lines, subtle

responses to sex steroid hormone

51 A principal component analysis (PCA) plot for vehicle-treated neuronal cell lines (A) shows distinction between cell lines and that technical replicates are homogeneous. A PCA plot for all cell lines including their treatments (B) also shows distinctions between cell lines, though within- cell line treatments show only subtle responses. Samples were CPM normalized, outliers omitted, and the gene list was left unfiltered, i.e., all 20,817 genes were included. The x-axis label shows the percent variation attributed to PC1, y-axis the percent variation attributed to PC2.

52

ULUL SHSH

143 354 N P L 65 C D 248 455

237 101 341 934

141 9702 63

416 556

238

Figure 3.2: Most expressed genes were expressed across NSC, UL, SH, and DL cell lines

Of the 20,817 possible genes detected by AmpliSeq Transcriptome, 13,994 genes were expressed in at least one cell line after CPM normalization and gene filtering. 69% of those 13,994 genes are represented in all four V-treated cell lines: DL (n=2), UL (n=2), SH (n=3) (technical replicates), and NSCs (n=4) (biological replicates). The number in each area indicates the number of genes within the overlap.

53 A

BioPlanet

REACTOME

Panther

KEG G

GO CC

MF GO

Enrichr Combined score (log2 PEnrichment * ZExpected Rank)

Figure 3.3: Top neuron-related pathways are present in genes that are shared between

NSC, SH cells and NSC, SH and DL cells cThe 455 genes that overlap between NSC and SH cells were used as Enrichr input and were shown to be enriched for neuron-relevant pathways across several databases: BioPlanet (green),

54 REACTOME (purple), Panther (blue), KEGG (red), GO cellular components (GOCC, orange), and GO molecular function (GOMF, brown). Bars are proportional to the Combined Score, bold, highlighted bars denote padj<0.1 pathways.

55

Upregulated ALLO-responsive genes (FDR<0.1) in NSC, UL, SH, and DL cell lines

Figure 3.4: UL and SH cells share the highest number of upregulated and downregulated

ALLO-responsive genes (FDR<0.1)

Genes that are significantly (FDR<0.1) upregulated in response to ALLO across DL, UL, and SH cells (analysis did not result in FDR<0.1 genes in NSCs). The top number in each area of the five- way Venn diagram refers to the number of genes in that overlap. The numbers in parentheses below are that of the number of genes in the overlap as a percentage of the total number of genes in the diagram. For each cell line intersection, the table displays, the total number of genes significantly (p<0.05) upregulated (total # of overlapping genes), and the statistical output of multi- set intersection analyses: the fold enrichment (FE) and statistical significance (P-value).

56 A

NSC

Genes commonly upregulated (p<0.5) in response to ALLO between NSC, UL, SH, and DL cell lines

B NSC

57 Genes commonly downregulated (p<0.5) in response to ALLO between NSC, UL, SH, and DL cell lines

Figure 3.5: UL and SH cells share the highest number and most significant upregulated

and downregulated ALLO-responsive genes (p<0.05)

Genes that are significantly (p<0.05) upregulated (A) or downregulated (B) in response to ALLO

[100nM] across all four cell lines. The top number in each area of the five-way Venn diagram refers to the number of genes in that overlap. The numbers in parentheses below are that of the number of genes in the overlap as a percentage of the total number of genes in the diagram. The table under each diagram displays, for each cell line intersection, the total number of genes significantly (p<0.05) increased (A) or decreased (B) (total # of overlapping genes) and the output of multi-set intersection statistical analyses: the fold enrichment (FE), and statistical significance

(P-value).

58 A

NSC

Genes commonly upregulated (p<0.5) in response to E2 between NSC, UL, SH, and DL cell lines

B

NSC

59 Genes commonly downregulated (p<0.5) in response to E2 between NSC, UL, SH, and DL cell lines

Figure 3.6: UL and SH cells share the highest number and most significant upregulated

and downregulated E2-responsive genes (p<0.05)

Genes that are significantly (p<0.05) upregulated (A) or downregulated (B) in response to ALLO

[100nM] across all four cells lines. The top number in each area of the five-way Venn diagram refers to the number of genes in that overlap. The numbers in parentheses below are that of the number of genes in the overlap as a percentage of the total number of genes in the diagram. The table under each diagram displays, for each cell line intersection, the total number of genes significantly (p<0.05) increased (A) or decreased (B) (total # of overlapping genes) and the output of multi-set intersection statistical analyses: the fold enrichment (FE), and statistical significance

(P-value).

60

NSC

Figure 3.7: Few P4-responsive (p<0.05) genes shared across NSC, UL, SH, and DL cell lines

All genes that are significantly (p<0.05) responsive to P4 [100nM] (both upregulated and downregulated) across all four cells lines. The top number in each area of the five-way Venn diagram refers to the number of genes in that overlap. The numbers in parentheses below are that of the number of genes in the overlap as a percentage of the total number of genes in the diagram.

61 CHAPTER 4: MICRORNAS IN PREMENSTRUAL DYSPHORIC DISORDER AND

THE ROLE OF VASCULAR ENDOTHELIAL GROWTH FACTOR

4.0 Introduction

As discussed in the introduction, innate differences in gene expression were found in

PMDD by comparing lymphoblastoid cell lines (LCLs) derived from women with PMDD to LCLs of matched controls (Dubey et al., 2017). Multiple transcripts associated with the Extra Sex Combs and Enhancer of Zest (ESC/E(Z)) protein complex were observed to be dysregulated in PMDD.

The Polycomb Repressive Complex 2 (PRC2) comprises the core proteins of the ESC/E(Z) complex, which is primarily known for gene silencing via histone H3 lysine 27 (H3K27) and H3K9 trimethylation (H3K27me3, H3K9me3) (Di Croce & Helin, 2013; Krupke et al., 2017; Margueron

& Reinberg, 2011; C. M. Smith et al., 2019). In addition to the PRC2 genes, ESC/E(Z) contains additional genes transiently associated with gene silencing.

Further analysis revealed that women with PMDD exhibit a seemingly paradoxical dissociation between mRNA and protein levels: ESC/E(Z) transcripts that were significantly upregulated in PMDD also showed significantly downregulated protein levels. There are many potential explanations for this dissociation (as described in the introduction) including missense or nonsense variants that result in differential transcript quantities, post-transcriptional regulation, protein misfolding, or errors in the endoplasmic reticulum (ER) related to vesicle packaging, transport, or secretion of the polypeptide. Although polygenic origins of psychiatric diseases are increasingly recognized, it is less likely that there would be genomic variants at each of the

ESC/E(Z) genes in a single individual. Therefore, trans-acting post-transcriptional regulatory

62 mechanisms, specifically, the role of microRNAs (miRs), were investigated as a potential source of disparity between ESC/E(Z) transcripts and protein levels.

miRs are small non-coding RNAs that target and bind to the 3'-untranslated region (3'-

UTR) of mRNAs, repressing their translation (Selbach et al., 2008). miRs also affect transcription by targeting transcription factors, DNA methyltransferases, and histone deacetylases, and participate in feedback regulatory circuits to organize gene expression. Importantly, miRs also alter the transcription of Polycomb group genes (Sato et al., 2011). miR dysregulation has been implicated by transcriptome and genome association studies of psychiatric disorders including schizophrenia (Moreau et al., 2011; Perkins et al., 2007; Smalheiser et al., 2014), bipolar disorder

(Miller et al., 2012; Shih et al., 2012), and major depressive disorder (MDD) (Belzeaux et al.,

2012; Li et al., 2013; Smalheiser et al., 2014), and modulate the ovarian cycle (Donadeu et al.,

2012; Shukla et al., 2018). In MDD, a single nucleotide polymorphism (SNP) in the 3'-UTR of an

MDD-associated gene, P2RX7, alters the binding site for miR-1302 and miR-625, and two other

MDD-associated SNPs occur in miR pathway genes, AGO1 and DGCR8 (He et al., 2012; O. A.

Rahman et al., 2010). A postmortem expression study found global downregulation of miRs in the dorsolateral prefrontal cortex (DLPFC) of people with MDD and several miR pairs were encoded in the same chromosomal region or had identical seed sequences (Smalheiser et al., 2012).

Because PMDD is sex-limited, it is of interest that studies have shown sex-specific miR expression and targeting in the brain (Morgan & Bale, 2017). The ~25% of X chromosome genes that either escape or exhibit variable patterns of X inactivation include X chromosome miRs that are associated with several disorders, including MDD, that are more common in women (Carrel &

Willard, 2005; Gurwitz, 2019; Hewagama et al., 2013). The high co-morbidity of PMDD and

MDD, ~60% (Fava et al., 1992; D. R. Kim et al., 2004), the shared prominence of affective

63 symptoms, and the impact of ovarian steroids in PMDD ontogeny suggest a role for miRs in

PMDD pathophysiology. This led to the question, are miRs involved in the reported discrepancy between transcript and protein levels in the ESC/E(Z) complex? To answer this, I globally profiled miR expression in an LCL model of PMDD to determine whether miRs are differentially expressed in PMDD LCLs, and if those miRs contributes to the dissociation between ESC/E(Z) transcripts and proteins levels in PMDD. The nature of global profiling added the additional aim of investigating whether individual miRs, or miR networks, could identify new genes or gene pathways that could contribute to PMDD etiology.

4.1 Specific Methods

Previously Attained LCL Transcriptome Data

Demographic information and clinical characteristics for each participant who contributed cell lines (a subset of LCLs from Dubey, et al.) for this study are reported in Tables 4.1 and 4.2.

LCLs were created via Epstein-Barr virus transformation of peripheral blood mononuclear cells

(Oh et al., 2003). Briefly, PBMCs were separated from whole blood samples, transformed with

Epstein-Barr virus (EBV), expanded, and frozen, requiring 4-8 weeks. Selected cell lines (i.e., matched PMDD and controls) were thawed, grown, and maintained for >300 generations (cell doublings) prior to treatment. LCLs were cultured in 15cm2 flasks with RPMI containing 15% fetal bovine serum (FBS), 2% 200-mM glutamine, and 1% gentamicin (5 mg/ml) at 37°C, with

5% CO2. To avoid E2-like activity in phenol red and P4-like activity in FBS, LCLs were cultured in a sex steroid-free growth medium of phenol red-free RPMI 1640 media (Gibco, Cat. no. 11835-

055), supplemented with 15% knock out serum (KOSR) (estrogen-depleted) [Gibco, Cat. no.

10828-028] + 2% glutamine (200-mM), and 1% gentamicin (5 mg/ml) minimum of 3-5 days

64 before collection. At 1×106 cells/mL, cells were seeded from each cell line at 2×105 cells/mL in new T25 flasks, grown again to 1x106 cells/mL, and stored as pellets at -80°C.

LCL Culture and Treatment

LCLs from women with PMDD (n=8) and controls (n=13) were thawed and grown to ~106 cells per flask, cultured, and collected at the same time to avoid batch effects. LCLs were cultured in T25 flasks with RPMI media [GibcoTM, Cat. #11875-119] containing 15% fetal bovine serum

(FBS) [GibcoTM, Cat. #16000-044], 2% 200mM L-Glutamine [Gibco, Cat. #25030-081], and 1%

TM Gentamicin [Gibco , Cat. #15750-060] (5 mg/ml) at 37°C, with 5% CO2. Phenol red has been shown to have estrogen-like activity, and normal FBS contains 8 ng/100 ml progesterone (Garcia-

Gonzalo & Belmonte, 2008). To wash out phenol red and FBS, cells were transferred to new T25 flasks and cultured in a sex steroid-free growth medium of phenol red-free RPMI 1640 media

[Gibco, Cat. #11835-055], supplemented with 15% KnockOut™ Serum Replacement (KOSR)

[Gibco, Cat. #10828-028], 2% L-Glutamine (200mM) [Gibco, Cat. #25030-081], and 1%

Gentamicin (5 mg/ml) [GibcoTM, Cat. #15750-060] 5 days prior to collection to reduce exposure to exogenous steroid-like activity (Dubey, 2017). LCLs from each case and control subject were collected into ~106 cell pellets, frozen on dry ice, and transferred to -80°C for storage.

miRNA Isolation

miRs were isolated using the mirVanaTM PARISTM purification kit [InvitrogenTM, Cat.

#AM1556] according to manufacturer’s instructions. miR quantity was assessed on the Agilent®

2100 Bioanalyzer platform in conjunction with the Agilent® Small RNA Kit [Agilent®, Cat. #5067-

1548]. Size analysis showed that the samples were predominantly composed of small RNAs (i.e.,

65 <200 nucleotides long), ~20-50% of which were estimated to be miR (15-25 nucleotides long), a miR quantity of ~10-20 ng from each sample.

miRNA Library Preparation and Sequencing

Total small RNA was used to prepare cDNA libraries using Ion Total RNA-Seq Kit v2

[ThermoFisher Scientific, Cat. #4475936] following manufacturer’s instructions for small RNA.

Samples were barcoded with Ion Xpress™ RNA-seq Barcode 1-16 Kit [ThermoFisher Scientific,

Cat. #4475485] and size-selected using the Magnetic Bead Purification Module [ThermoFisher

Scientific, Cat. #4475486]. Library quality was assessed with the Agilent® 2100 Bioanalyzer, which showed miR quantities of 1-20 ng/µl. Libraries were sequenced using the Ion Proton™

System to produce an average of 8.5 million single-end reads per sample, well above suggested guidelines for differential expression analysis using miR-seq (Campbell et al., 2015; Metpally et al., 2013).

Read Mapping and Alignment

miRNA-seq reads were aligned and mapped using the Partek® Flow® microRNA pipeline

(Kozomara & Griffiths-Jones, 2014; Langmead et al., 2009). First, Pre-alignment QA/QC trimmed reads with a Phred quality score cut-off of 20 and discarded reads shorter than 15 nucleotides.

Second, Reads were mapped to the miRBase mature miRNA version 21 (hg19) (Kozomara &

Griffiths-Jones, 2014) using Bowtie 1.0.0, which has been shown to be optimal for short, high- quality reads and allows for mismatches (Langmead et al., 2009). Seed length was set to 10 nucleotides and allowed for 1 mismatch. These parameters produced a 22-nucleotide average

66 length, 1.14 million average aligned reads, and an average depth of coverage of 287x across all samples, revealing 2,577 mapped miRNAs.

Computation of Differential Gene Expression

Differential gene expression was determined using edgeR as described in General

Methods. One case sample was discarded after z-score analysis showed it to be >2.5 standard deviations from the mean, leaving a total of n=7 cases and n=13 controls for subsequent analyses.

Raw counts were filtered at a minimum of 4 counts per sample in at least 6 samples, resulting in

2,540 miRNAs. After normalization, a quantile-adjusted conditional maximum likelihood model was implemented to test for differential gene expression using a gene-wise exact test.

Weighted Gene Co-expression Network Analysis (WGCNA) for miRNA-Sequencing

Scale-free gene networks were constructed using WGCNA (Langfelder & Horvath, 2008,

2012; Storey, 2002; B. Zhang & Horvath, 2005) with the WGCNA v1.66 package in R. To weight stronger correlations, a signed hybrid adjacency network was constructed using the lowest power

(β=12) that approximated a scale-free topology (signed R2 threshold=0.9). The signed hybrid adjacency network approach was also used in order to eliminate genes with negative correlations, which can be considered unconnected (Langfelder, 2018), and cut height was set to 0.25

(corresponding to a correlation of 0.75) to merge closely correlated modules. The adjacency matrix was then used to calculate a topological overlap matrix (TOM) to assess connectedness for each gene. Connectivity (degree, or k) is a measure of how correlated (co-expressed) a gene is with all other network genes. A dendrogram based on TOM dissimilarity was used to construct modules, which are clusters of highly connected, positively correlated genes. Minimum module size was set

67 to 10 due to the smaller size of the miRNA transcriptome compared to that of the mRNA transcriptome. Default parameters for the merged dynamic hybrid approach were retained for gene clustering and module eigengene (i.e., the module’s first principal component) dissimilarity calculations. Gene modules were correlated to PMDD diagnosis using default parameters (Pearson correlation coefficients). Intramodular connectivity (kIM), a measure of how connected (co- expressed) each gene is within a module, was calculated for modules significant by diagnosis. kIM was then used to determine hub genes for each module, i.e., genes with the highest amount of intramodular connectivity.

miRNA Target prediction and Pathway Analysis

MiRs showing significant expression differences between PMDD cases and controls

(FDR<0.1 and p<0.05) and that target genes in biological processes previously associated with mood disorders and/or with sex steroid targeting or regulation were used as input to the

MirGeneDB platform (Fromm et al., 2020) to identify gene families. The miRNet 2.0 platform

(Chang et al., 2020; Fan et al., 2016; Fan & Xia, 2018) was used to identify miR transcript targets and their functional enrichment pathways. MirGeneDB complements the miRbase platform, but uses consistent and well-defined criteria, including evolutionary conservation rather than public voting, to identify high confidence miRs. miRNet uses multiple databases: miRTarBase v8.0

(Huang et al., 2020), TarBase v8.0 (Karagkouni et al., 2018), and miRecords (Xiao et al., 2009) to manually curate empirically validated miR targets. KEGG, REACTOME, and GO terms were used on the miRNet platform to analyze pathway enrichment of miR gene targets. miR target transcripts that were significant (p<0.05) by diagnosis were used as input to the Enrichr database (E. Y. Chen

68 et al., 2013; Kuleshov et al., 2016). The network graph was made with Gephi v0.9.2 (Bastian et al., 2009) and Inkscape v0.92.4 (Harrington et al., 2004).

4.2 Results miRNA Co-Expression Network miRNA Genes Target ESC/E(Z) Complex Genes

WGCNA of 2,577 miR genes identified 15 miR co-expression network modules significant for PMDD diagnosis (Fig. 4.1a). Two of those modules were significantly (pnom<0.05) altered in

PMDD and both were downregulated, lightcyan (55 miR genes) and darkgrey (19 miR genes) (Fig.

4.1b).

The 55 miRs in the lightcyan module target 5,719 empirically validated unique transcripts

(miRNet). Of those 5,719 transcripts, 58 were significantly (pnom<0.05) upregulated in PMDD

LCLs compared to controls. Enrichr analysis against the Epigenomics Roadmap HM ChIP-seq database using those 58 genes as input showed enrichment for H3K27ac and H3K9ac sites across multiple cell types (Fig. 4.1c). The 19 miRs in the darkgrey module target 2,126 empirically validated unique mRNA transcripts (miRNet). REACTOME pathway analysis of these targets revealed enrichment for transcript targets in the “PRC2 methylates histones and DNA” pathway

(unbiased empirical sampling, padj.=0.072). Additionally, 3 of the 4 miRs that target ESC/E(Z) complex genes were among the strongest intramodular hub miRs correlated with PMDD (Fig.

4.1d). Thus, this miR analysis independently implicated the role of the ESC/E(Z) complex first identified in Dubey, et al.

69 Differential Expression Analysis Reveals miRNA Candidate Genes

Of 2,577 mature miR transcripts detected across all PMDD and control LCLs, only 237 displayed significant differential expression between cases and controls (pnom<0.05), with approximately equal numbers up- or down-regulated (Fig. 4.2a). Three miRs were differentially expressed at FDR<0.1: hsa-miR-503-5p and hsa-miR-34c-5p were upregulated and hsa-miR-

4738-5p was downregulated in PMDD compared to controls (Fig. 4.2a); however, close inspection indicated that the group effect of has-miR-34c-5p was largely driven by a single PMDD subject.

Hsa-miR-503-5p has 251 empirically validated target genes (miRNet), twelve of which were significantly downregulated in PMDD LCLs (LIX1L, CNKSR3, DNAH17, FOXK1, KIF23,

NUCKS1, MAP4K2, TRAK1, VEGFA, FGFR1, BTN3A3, and ANLN) (Fig. 4.2b). Hsa-miR-4738-

5p has 29 empirically validated target genes (miRNet), one of which was significantly (p<0.05) upregulated (ZFAND4) in PMDD LCLs (Fig. 4.2c). The reciprocal relationship of miR expression to target gene expression (increasing miR expression suppressing target gene expression, and downregulation of miR expression leading to increased target gene expression) was overall consistent for the upregulated hsa-miR-503-5p.

Hsa-miR-503-5p Clusters with MIR15 Family Genes and are Upregulated in PMDD LCLs

Compared to Controls

According to MirGeneDB, hsa-miR-503-5p is part of the MIR15 family, which is comprised of 8 homologous miRs that are highly conserved in vertebrates (Fromm et al., 2020;

Kent et al., 2002). In our dataset, four of the eight miRs from the MIR15 family were significantly

(pnom<0.05) upregulated in PMDD LCLs compared to controls: hsa-miR-503-5p, hsa-miR-424-5p, hsa-miR-195-5p, and hsa-miR-497-5p; herein designated as miR-503-5p/424-5p and miR-195-

70 5p/497-5p, as each pair is transcribed by a single host gene (MIR503HG at the Xq26.3 locus and

MIR497HG at the 17p13.1 locus, respectively) (Fig. 4.3), suggesting transcriptional clustering.

- Additionally, PHF19, a member of the ESC/E(Z) complex that is downregulated (pnom=1.96x10

4) in PMDD LCLs, is a target of hsa-miR-424-5p, hsa-miR-195-5p, and hsa-miR-497-5p.

Upregulated MIR15 Family miRNAs in PMDD LCLs Target the VEGF Signaling Pathway

Collectively, miR-503-5p/424-5p and miR-195-5p/497-5p target 663 empirically validated genes (miRNet) expressed in our LCL dataset. Of these, 58 genes showed nominally significant expression differences (pnom<0.05) between PMDD and control LCLs: 35 downregulated and 23 upregulated. However, only downregulation (p=0.014) was statistically significant (upregulation p=0.055). Of the 35 downregulated genes (pnom<0.05) transcripts, eight are targeted by all four

-32 miRs (padj=7.2x10 ) (CNKSR3, FOXK1, KIF23, NUCKS1, MAP4K2, TRAK1, VEGFA, and

BTN3A3) (Fig. 4.4a). VEGFA, which encodes a heparin-binding protein growth factor active in angiogenesis and endothelial growth, was of particular interest. Enrichr analysis of all 542 downregulated genes (pnom<0.05) in PMDD LCLs found enrichment for the VEGF signaling

-4 pathway in both Panther 2016 (Human) (padj=3.65x10 ) and KEGG 2019 Human (padj=0.019) databases. Restricting the Enrichr input to the 35 down-regulated (pnom<0.05) miR-503-5p/424-5p and miR-195-5p/497-5p targets also identified VEGF signaling (padj.=0.044) and Angiogenesis

(padj=0.002) as top hits (Fig. 4.4b), with eight of the sixteen transcripts overlapping between the two pathways (Fig. 4.5).

71 4.3 Discussion

A previous study in our lab demonstrated that several transcripts encoding members of the

PRC2 complex, part of the ESC/E(Z) complex, were upregulated in LCLs derived from PMDD patients. However, for several of these upregulated transcripts the observed protein level was significantly lower than in controls. This reciprocal relationship between mRNA and protein levels suggested post-transcriptional dysregulation in PMDD. Here, I set out to identify whether miRs and/or miR networks can account for disparity in transcript and protein quantities in the ESC/E(Z) complex as well as to identify new genes and pathways in PMDD via miR expression. I identified miR networks that target and/or are enriched for PRC2 transcripts, supporting the hypothesis that miRs are partly responsible for transcript dysregulation, though whether they are responsible for

ESC/E(Z) protein quantity disparity is still unknown.

One of the two downregulated miR network modules was enriched for miRs that target

PRC2 complex transcripts, three of which (SUZ12, RBBP4, AEBP2) are upregulated in PMDD.

Two of these genes (AEBP2 and SUZ12; protein quantities for RBBP4 were undetermined) exhibited decreased protein quantities (Dubey et al., 2017). SUZ12 and RBBP4 are core members of the complex involved in DNA and histone binding (Morey & Helin, 2010). The accessory protein AEBP2 binds to DNA thereby stabilizing and enhancing the catalytic activity of PRC2

(van Mierlo et al., 2019).

Downregulation of PRC2 proteins seen in PMDD, as previously reported (Dubey et al.,

2017), would putatively lead to decreased H3K27me3, associated with active transcription. The second miR module was enriched for targets typically located near H3K27ac sites, associated with active transcription, across many cell lines. This result implies that that LCLs from women with

PMDD show H3K27 methylation in otherwise common H3K27ac areas due to overall dysfunction

72 in PRC2. Therefore, the genes in those areas are silenced, resulting in downregulation of miRs that would target and regulate transcription. Alternatively, if H3K27ac in these areas are approximately the same in PMDD cells, this result suggests that the downregulation of the miRs that target these genes contributes to aberrant expression.

The upregulated miR-503-5p is a member of the MIR15 family gene cluster, which includes the miR pairs miR-503-5p/424-5p and miR-195-5p/497-5p that are all upregulated in

PMDD LCLs. Amongst the target transcripts of the MIR15 family is the ESC/E(Z) member

PHF19, a finding consistent with the significant transcript and protein downregulation seen in

Dubey, et al. (Dubey et al., 2017). PHF19 is an accessory member of the ESC/E(Z) complex that recruits PRC2 to target genes and modulates histone methyltransferase activity to promote transcriptional repression (Brien et al., 2012; Cai et al., 2013, p. 201; Deng et al., 2018; Hunkapiller et al., 2012). Knock-down of PHF19 is associated with an increase in MTF2 recruitment to the

PRC2 complex as well as a genome-wide increase in PRC2 binding (Jain et al., 2020). MTF2 expression was significantly increased in PMDD LCLs, the resulting protein was significantly decreased (Dubey et al., 2017), pointing to a genome-wide decrease in PRC2 binding.

Dysregulation of this miR family has previously been associated with MDD in humans

(Smalheiser et al., 2012) and in mice that had undergone chronic stress (Cao et al., 2013). Thus, results in this study identified three separate ways in which miRs target PRC2.

The MIR15 target genes showed enrichment for several transcript targets in the vascular endothelial growth factor (VEGF) signaling pathway. Based on the transcriptome data in Dubey, et al. (Dubey et al., 2017), but not previously noted, this VEGF gene network is downregulated in

PMDD compared to controls. VEGF is a family of proteins that stimulate microvascular permeability and angiogenesis. In relation to the ovarian cycle, VEGFA has long been known to

73 have an important role in corpus luteum (CL) angiogenesis, e.g., blocking or suppressing VEGFA receptors, and thereby inhibiting CL development in multiple species and in human cell culture

(Ferrara et al., 1998; Shweiki et al., 1993; Sugino et al., 2000; Zimmermann et al., 2001).

Furthermore, angiogenesis during CL development is essential for P4 secretion (Ferrara et al.,

1998; Fraser & Wulff, 2003) that initiates decidualization of the endometrium, a tissue that also intrinsically expresses VEGFA and its receptor, KDR (Giatromanolaki et al., 2006; Torry et al.,

1996).

Plasma VEGFA levels vary across the menstrual cycle, with lower VEGFA levels during the luteal compared to the follicular phase (Bausero et al., 1998; Benoy et al., 1998). VEGFA transcript and protein are E2-responsive in a wide variety of cells and tissues, e.g., the endothelium, granulosa cells, endometrial stromal cells, and adipose tissue (Applanat et al., 2008; Bausero et al., 1998; Fatima et al., 2017; Sengupta et al., 2003). Also, P4 concentrations are inversely related to VEFGA throughout the menstrual cycle (Benoy et al., 1998; Heer et al., 1998). However, whether VEGFA regulates or is regulated by E2 or P4, or whether P4 and VEGFA are correlated because of a mutually shared regulatory system or secondary to a confounding variable remains unknown. Notably, these cyclical changes in VEGFA concentrations also could modulate blood brain barrier (BBB) permeability (Argaw et al., 2012; Jiang et al., 2014).

VEGFA, which the results in this study implicate in PMDD, influences a variety of neuronal functions, though whether it crosses the BBB or is only endogenously synthesized in brain is unclear. In the brain, VEGFA plays a critical role in embryonic and adult neurogenesis

(Hartog et al., 2009; Louissaint et al., 2002; Mirzadeh et al., 2008; Tavazoie et al., 2008), neuronal growth and axon guidance (Khaibullina et al., 2004; Rosenstein et al., 2003; Schwarz et al., 2004;

Sondell et al., 1999; Vieira et al., 2007), and neuronal survival (Cariboni et al., 2011; K. L. Jin et

74 al., 2000; Kun Lin Jin et al., 2000; Rosenstein et al., 2003), both independently and via its role in angiogenesis. VEGFA is particularly important for the survival of hypothalamic gonadotropin releasing hormone (GnRH) neurons that are essential for menstrual cycle regulation (Cariboni et al., 2011). E2 and P4 act directly and rapidly on GnRH neurons (Abe et al., 2008; Abe & Terasawa,

2005; Bashour & Wray, 2012; Skinner et al., 1998; Sun et al., 2010), though hypothalamic GnRH neuron abnormalities have not been directly linked to PMDD pathology (M. J. Smith et al., 2004).

However, GnRH neurons project to several areas of the brain that exhibit E2 and P4 neuroregulatory effects, including the hippocampus and amygdala (Berman et al., 1997; Spencer et al., 2008; Wilson et al., 2006), that are differentially responsive to ovarian steroids in women with PMDD (Dreher et al., 2007; Protopopescu, Butler, et al., 2008; Protopopescu, Tuescher, et al., 2008).

Given its role in the brain, it is unsurprising VEGF signaling has been implicated in psychiatric disorders. VEGF signaling dysregulation has been associated with MDD in humans and in rodent models of depression (Isung et al., 2012; Smalheiser et al., 2014; M. Takebayashi et al., 2010). Rats with depression-like behavior showed decreased VEGFA expression in the frontal cortex and hippocampus (Elfving et al., 2010). VEGFA is induced by selective serotonin reuptake inhibitors (SSRIs) in hippocampal neuronal and endothelial cells, and VEGFA signaling blockade attenuates anti-depressant effects (Greene et al., 2009; Warner-Schmidt & Duman, 2007). These observations are also of potential relevance to PMDD, for which SSRIs are an effective treatment

(Freeman et al., 1999; Steiner et al., 1995; Kimberly A. Yonkers et al., 2015). Finally, VEGFA signaling may play an essential neurotrophic and synaptogenic role in the anti-depressant effects of ketamine (Deyama et al., 2019).

75 These data suggest a role for the VEGF signaling pathway in PMDD by reporting that four upregulated miRs from the MIR15 family target VEGFA, and transcripts downregulated in PMDD compared to control LCLs are enriched for the VEGF signaling pathway. Previous studies have also shown an inverse correlation between expression of miR-424-5p and VEGFA (Braza-Boïls et al., 2014). miR-503-5p/424-5p are of particular interest for future investigation as they have been observed to be upregulated in response to E2 (Baran-Gale et al., 2016; Pande et al., 2018) and are highly expressed in granulosa cells, with varying expression levels during the menstrual cycle

(Baran-Gale et al., 2016; Moreno et al., 2015; Salilew-Wondim et al., 2014). Evidence has also suggested miR-503-5p/424-5p help coordinate the unfolded protein response during endoplasmic reticulum (ER) stress and are downregulated in response to thapsigargin (Tg), a sarco/ER Ca2+ inhibitor that induces ER stress by depleting Ca2+ in the ER (Gupta et al., 2015; Walter & Ron,

2011). This potentially corroborates preliminary data from our lab demonstrating a decreased ER stress response in PMDD LCLs after Tg challenge.

A limitation in this study is that analysis was restricted to LCLs, which are a valuable cellular model, though not fully representative of the specific cell types relevant to PMDD and may lack some neuron-specific gene expression. Findings here suggest that analysis of the role of

VEGFA in developing GnRH or other neurons in PMDD may be useful and will be a future focus using iPSCs derived from women with PMDD. Although this study identified gene expression differences that distinguish PMDD patients from controls, it focused on differential expression in

LCLs in the absence of hormonal perturbation. Investigation into the MIR15 family and VEGF signaling pathway in the presence of E2 or P4 may expand our results, particularly given the known abilities of E2 and P4 to destabilize mood in PMDD. Further experiments such as ChIP-seq on

H3K27ac and H3K27me3 comparing PMDD and control LCLs could identify H3K27 acetylation

76 and methylation marks. Additionally, crosslinking and immunoprecipitation approaches to show that the miR:mRNA binding is specific to ESC/E(Z) and VEGF signaling transcripts, or specific miR knockdown or knock-in studies followed by quantification of target transcripts and protein quantitation could further support our findings.

These findings buttress the role of the ESC/E(Z) complex in PMDD and are the first to use miR and miR network discovery in a sex hormone-linked mood disorder, implicating VEGFA.

While it is unclear whether miR dysregulation is a cause or effect of PMDD, this study advances the importance of exploring miRs to help identify transcriptional disparities and key pathways in molecular psychiatry. If future studies find support for the role of VEGF signaling pathway, miR-

503-5p/424-5p, and miR-195-5p/497-5p dysregulation, these could represent potential drug targetable risk factors for PMDD.

77 4.4 Tables and Figures

Table 4.1: Demographics of women contributing LCL cell lines for miRNA-sequencing

microRNA-seq Controls (n = 13) PMDD (n = 7) 10 Caucasian 1 Caucasian Race/Ethnicity in # of individuals 2 Black/African- 4 Black/African- American American 1 Hispanic 2 Hispanic Age in years 37.4 (6.6) 36.9 (6.4) BMI in kg/m2 27 (6.8) 28.6 (5.6)a Parity 2 out of 13 5 out of 7 Prior psychiatric history in # of 0 Prior MDE 4 Prior MDE individuals Prior treatment history in # of 0 Prior SSRI use 2 Prior SSRI use individuals a One unreported

Table 4.2: Demographics of women contributing LCL cell lines for RNA-sequencing

RNA-seq Controls (n = 9) PMDD (n = 11) 4 Caucasian 6 Caucasian Race/Ethnicity in # of individuals 5 Black/African- 5 Black/African- American American Age in years 36.9 (6.5) 39.9 (6.3) BMI in kg/m2 27.5 (7.3) 27.1 (4.7)a Parity 1.2 (1.5) 1.7 (1.4) Prior psychiatric history in # of 0 Prior MDE 4 Prior MDE individuals Prior treatment history in # of 0 Prior SSRI use 3 Prior SSRI use individuals a One unreported

Demographic information for women with PMDD and controls from whom the LCLs were derived are reported for each sequencing experiment. Values for age, body mass index (BMI), and parity are presented as mean (standard deviation). Women with PMDD and asymptomatic controls from which the LCLs were derived were recruited as previously described (Schmidt, 1998; Dubey,

78 2017). Briefly, participants were 18-48 years old, had regular menses (21-35 days), and were not medically ill, taking medications, or pregnant. DSM-5 criteria for PMDD were prospectively confirmed through daily symptom ratings over three menstrual cycles. Each woman had symptom ratings (irritability, depression, anxiety, mood lability) ≥30% increased relative to the range of the symptom scale employed by each woman in the week prior to menses compared to the following week. Women with significant symptoms outside the luteal phase were excluded. Absence of symptoms in controls was confirmed using daily ratings over 2 months. Women contributing samples for sequencing underwent a trial of leuprolide and hormone add-back (Schmidt, 1998;

Dubey, 2017). The study protocol was reviewed and approved by the National Institute of Mental

Health Institutional Review Board and all women gave written informed consent.

79 A r-value p-value n

0.30 0.20 14

0.26 0.30 34

0.44 0.05 15

-0.12 0.60 15

-0.03 0.90 38

0.33 0.90 20

0.27 0.30 93

-0.19 0.40 20

-0.35 0.10 124

-0.44 0.05 163

-0.42 0.07 1,032

-0.45 0.05 19

-0.40 0.08 790

-0.52 0.02 55

-0.32 0.20 137

B

p=0.007 p=0.013

80 C

Roadmap Epigenomics

Enrichr Combined score (log2 PEnrichment * ZExpected Rank) D

Figure 4.1: miRNA WGCNA modules are enriched for

ESC/E(Z)-associated transcript targets

81 (A) Module-trait relationship heatmap of the 15 WGCNA modules. Each row corresponds to a

module eigengene. Each cell contains the Pearson’s correlation coefficient, r-value

(eigengene:diagnosis), corresponding to PMDD diagnosis; i.e., a negative r value corresponds

to a negative association with PMDD, the associated Student’s asymptotic p-value, and the

number of miRNAs in each module (n).

(B) Violin plots and associated t-tests comparing control vs PMDD module eigengene (ME)

expression values (y-axis) for each sample show significant downregulation in PMDD-

associated module (lightcyan, which is enriched for transcripts located at H3K27ac sites, and

darkgrey, which is enriched for transcripts associated with PRC2 methylation).

(C) Significantly (p<0.05) upregulated transcripts in PMDD LCLs that are targeted by miRs in the

lightcyan module show enrichment for loci associated with predominantly H3K27ac peaks

(bold, light cyan), per Epigenomics Roadmap HM ChIP-seq via Enrichr.

(D) A scatter plot of the 19 miRs in the darkgrey module shows the relationship between the

Pearson’s correlation coefficient, r-value (PMDD:control), and module membershipa of the

four miRs (labeled and highlighted in red) associated with the “PRC2 methylates histones and

DNA” REACTOME pathway via miRNet enriched in the darkgrey module. Three of the four

show strong correlation with PMDD diagnosis and are of the top five module hub genesb. aModule membership is the Pearson correlation between a gene’s expression profile and the module eigengene (i.e., first principal component). bGenes with high module membership tend to have high intramodular connectivity and are thus referred to as hub genes.

82 A

83 B

C

84 D

Figure 4.2: Differentially expressed miRs in PMDD and mRNA targets in

PMDD vs control LCLs

PMDD (n=7), control (n=13). For all plots, statistical thresholds are depicted by color: no significant difference (grey), pnom<0.05 (pink), FDR<0.1 (red).

(A) All miR genes gathered from the results of miR-seq differential expression analysis comparing

PMDD and control LCLs are shown in an MA, i.e., log transformed ratio (Fold change [FC])

and log transformed mean average (CPM), plot (left), and Volcano plot (right). These plots

combined display the fold change (log2), CPM (log2), and p-value (-log2) distribution of

detected miRNAs. miRs below the FDR<0.1 significance threshold are labeled.

(B) All differentially expressed genes gathered from the results of RNA-seq analysis comparing

PMDD and control LCLs. Genes targeted by miR-503-5p are highlighted in black (no

significant difference) or violet (pnom<0.05, labeled).

85 (C) All differentially expressed genes gathered from the results of RNA-seq analysis comparing

PMDD and control LCLs. Genes targeted by miR-4738-5p are highlighted in black (no

significant difference) or violet (pnom<0.05, labeled).

(D) All differentially expressed genes gathered from the results of RNA-seq analysis comparing

PMDD and control LCLs, with genes involved in the VEGF signaling pathway from Panther

2016 (Homo sapiens) highlighted in black (no significant difference) or violet (pnom<0.05,

labeled).

86 A

chrX (q26.3) p22.2 21.1 q21.1 Xq23 24Xq25 Xq28

Scale 1 kb hg38 chrX: 134,543,500 134,544,000 134,544,500 134,545,000 134,545,500 134,546,000 134,546,500 134,547,000 GENCODE v32 Comprehensive Transcript Set (only Basic displayed by default) MIR503HG MIR503 MIR503HG MIR424 MIR503HG RefSeq gene predictions from NCBI RefSeq Curated H3K27Ac Mark (Often Found Near Regulatory Elements) on 7 cell lines from ENCODE Layered H3K27Ac

B

chr17 (p13.1) 13.3 13.2p13.1 17p12 17p11.2 17q11.2 17q12 21.31 17q22 23.2 24.2q24.3q25.1 17q25.3

Scale 2 kb hg38 chr17: 7,015,500 7,016,000 7,016,500 7,017,000 7,017,500 7,018,000 7,018,500 7,019,000 7,019,500 GENCODE v32 Comprehensive Transcript Set (only Basic displayed by default) AC040977.2 RNASEK-C17orf49 C17orf49 MIR497 C17orf49 C17orf49 C17orf49 C17orf49 MIR497HG MIR497HG MIR195 RefSeq gene predictions from NCBI RefSeq Curated H3K27Ac Mark (Often Found Near Regulatory Elements) on 7 cell lines from ENCODE Layered H3K27Ac

Figure 4.3: Genomics of two miRs dysregulated in PMDD: the miR-503-5p/424-5p cluster

on Xq26.3 and the miR195-5p/497-5p cluster on 17p13.1

UCSC Genome Browser (Kent, 2002) depictions of (A) miR 503 host gene (MIR503HG) on chrXq26.3 with miR-503-5p and miR-424-5p clustered as either an intron (MIR503, highlighted in light blue) and partially overlapping an exonic region (MIR424, highlighted in light blue) in both GENCODE v32 and RefSeq databases. Similarly, MIR497HG is depicted in (B) on chr17p13.1 with miR-497-5p and miR-195-5p (each highlighted in light blue) clustered as introns from both GENCODE v32 and RefSeq databases. Both host genes show H3K27ac enrichment at

87 the promoter region; MIR497HG also shows enrichment within and at the end of the gene.

H3K27ac marks are presented as layered histograms on both (A) and (B), with each colored layer representing a cell line from which ChIP-seq data were collected (Rosenbloom, 2013).

88 A

B

Panther Pathway

Enrichr Combined score (log2 PEnrichment * ZExpected Rank)

Figure 4.4: Upregulated MIR15 family miRs in PMDD LCLs target

the VEGF signaling pathway

89 (A) A five-way Venn Diagram shows the number of distinct and shared transcript targets across

each of the four MIR15 family miRs (Hsa-miR-###) and all total significantly (p<0.05)

downregulated genes in PMDD LCLs (LCL Down). The area of the diagram encompassing all

35 transcripts that are significantly (p<0.05) downregulated in PMDD LCLs and are targeted

by at least one miR is outlined in bold.

(B) Enrichr analysis against the Panther database using the 35 significant (pnom<0.05) transcripts

downregulated in PMDD (that are also targeted by at least 1 of the significantly (pnom<0.05)

upregulated MIR15s in PMDD) were enriched for VEGF signaling and related pathways. The

Angiogenesis pathway, which VEGF signaling is thought to regulate, and the VEGF pathway

are significantly (FDR<0.05) downregulated and targeted by MIR15, (light blue, bold text).

The FGF signaling pathway was also significantly (pnom<0.05) downregulated and targeted by

MIR15 (bold text).

90

Figure 4.5: MIR15 genes implicated in PMDD target the Angiogenesis and VEGF signaling

pathways

A network depiction of the interconnectedness between two Panther 2016 (via Enrichr) pathways that are enriched in all significantly enriched and targeted by the significantly (pnom<0.05) upregulated MIR15 family miRs (plum) in PMDD LCLs: Angiogenesis (padj=0.002) and VEGF signaling (padj=0.044). Transcripts directly targeted by MIR15 family miRs are depicted in dark green, transcripts only part of angiogenesis in yellow, transcripts only part of VEGF signaling in blue, and transcripts part of both pathways in seafoam green. The genes within the superimposed

Venn diagrams populate each pathway (labeled).

91 CHAPTER 5: CONCLUDING DISCUSSION

Scientific studies of sex differences have historically been avoided, and frequently, women have been under-represented in biomedical research studies. Investigators justified the exclusion of females because of the ovarian cycle, which was claimed as a confound due to cyclical changes in hormone levels. Clinical trials have tended to exclude women of child-bearing age because of concerns about teratogenicity. The bias against or overt exclusion of females in research due to ovarian cyclicity was unreasonable, but although sex differences tend to represent a composite of the effects of genetics (chromosomal composition), exposure to sex steroids (organizational- activational effects), and environment (i.e., external stressors, cultural expectations, nutrition), the relevancy of hormonal differences between the sexes is indeed real. Males also exhibit wide variation in physiology and behavior due to fluctuations in arousal, sexual function and underlying physiology. Ovarian-steroid triggered depression represents a unique opportunity to understand the pathophysiology of affective disorders that cannot be exploited without studying females. The importance of sex differences in health and disease led the National Institutes of Health (NIH) in

2015 to require NIH-funded research studies to include the examination of sex as a biological variable.

Sex steroid hormones are powerful neuroregulators that modulate myriad neural processes, ultimately influencing behavior and cognition, making their effects profoundly complex.

However, the cellular mechanisms by which these hormones affect human neurons, a fundamental step in understanding how hormones might ultimately affect cognition, has not been well characterized. Additionally, affective disorders can be restricted by sex, for example in response to ovarian steroids such as the increase in E2 and P4 at the onset of the luteal phase seen in PMDD.

92 Therefore, the goals of this thesis were to first begin the work of understanding human neuronal response to E2, P4, and ALLO by profiling the transcriptional responses to those hormones across neuronal cell lines at varying stages of maturation. The second goal of this thesis was to expand upon previous findings in PMDD using LCLs as a model by investigating a discrepancy seen between transcripts and proteins of the ESC/E(Z) complex.

5.0 Additional Thoughts on Chapter 3

Chapter 3 explores the response to ovarian steroids specifically in human female neurons across stages of maturation. However, a comparable set of male neurons in which to compare these responses was not available in this study, therefore it cannot be claimed that the gene responses reported here are unique to females. In fact, there is nothing obvious about these genes or pathways to claim that the same responses would not appear in males as well, except that, translationally, males are not exposed to cyclical variations in hormones through an ovarian cycle. A future study using a similar set of male neuronal cells could indicate whether the responses seen in this study are common or sexually dimorphic.

Conducting a study to compare transcriptional responses to E2, P4, or ALLO in male neurons to those in females could result in either identifying that the responses are common or divergent. If there are clear commonalities in gene and/or pathway responses to sex steroids, perhaps this means there is a baseline unifying way in which cells respond to these hormones regardless of sex. While E2, P4, and ALLO are often associated with females, they are certainly also present and neuroactive in males; thus, neurons in males and females alike must have a way to respond to their presence. Studies have demonstrated a wide range of mechanisms whereby the same hormone exposure has differed between males and females. For example, the presence of E2

93 in the striatum has been shown to stimulate dopamine response in females, but not in males

(Becker, 1990; Castner et al., 1993). If so, perhaps these hormones elicit the same cellular responses, but their effective differences in brain function (i.e., at the tissue level) throughout the lifespan are the result of the timing and quantity of exposures to steroid hormones. Alternatively, transcriptional responses to E2, P4, and ALLO in neurons could be sexually dimorphic. This could present in numerous ways, such as differential sensitivity steroid signaling. For example, higher hormone sensitivity could result in stronger cellular effects in neurons of females, producing stronger downstream signaling that ultimately induce larger differences across cell populations.

Sexually dimorphic responses could also present as more widespread differences in neuronal composition between males and females. For example, neurons that otherwise share identity between males and females differ on the presence or absence of the requisite steroid receptor.

There is an almost endless list of ways in which male neurons may respond differently than female neurons to E2, P4, and ALLO. Tissue specific modifiers, tissue-specific co-regulators, hormone-hormone competition for ligand binding, multiple isoforms for each hormone receptor, all of these are impacted by the physiological environment. These examples are a reminder that in order to understand the neuronal mechanisms of sex steroid response, first it must be established whether these responses are sexually dimorphic and if so, using male neuronal cells as a comparison would expand the understanding of neuronal responses to sex steroids.

5.1 Long-Term Use of h-iPSCs in PMDD Research

The use of h-iPSCs as a model for understanding the cellular mechanisms of psychiatric disorders holds great potential. Specific neuronal types derived from iPSCs can give a clearer understanding of hormone responses in adults. For example, serotonergic neurons derived from

94 women with PMDD could be used to investigate a remarkable quality observed in many women who suffer from PMDD: a fast-acting response to selective serotonin reuptake inhibitors (SSRIs).

That is, women with PMDD whose symptoms improve with SSRI treatment tend to respond within

48 hours after starting treatment (Steinberg et al., 2012), far shorter than the typical two-week

SSRI response time for depression or anxiety. This suggests a unique response ability of the serotonin and/or GABA system, though an unknown mechanism. Differentiating serotonergic neurons from h-iPSCs could serve a dual purpose of investigating both the underlying cellular mechanisms of the rapid response to SSRIs in women with PMDD compared and also perhaps expand the general knowledge of serotonergic neurons and therapeutic mechanisms of SSRIs.

5.2 Additional Thoughts on Chapter 4

Chapter 4 explores the role of miRs as the potential cause of a previously observed incongruity between transcript and protein levels in the ESC/E(Z) complex in PMDD LCLs. miRs have relatively recently emerged as important transcriptional regulators in the quest to understand the mechanisms driving the pathophysiology of psychiatric syndromes. As discussed above, miRs play a vast role in regulating virtually all aspects of neuronal functioning, and miR dysregulation has been reported in numerous psychiatric disorders including bipolar disorder, schizophrenia, and major depression. miRs have great but to this point untapped potential for therapeutics, given that a single miR can target and downregulate tens to hundreds of protein coding genes or, conversely, the repression of one miR can upregulate hundreds of genes. Therapeutic targeting of miRs is particularly attractive for complex disorders, which arise via pleiotropic effects. One could imagine perhaps identifying a gene co-expression network in complex disorder comprised of genes that are targeted by a single dysregulated miR; treatment could appear as easy as giving someone

95 a dose of that miRs complement. While the use of miRs as a therapeutic target has been suggested, its feasibility is debatable. The ability of a single miR to regulate large quantities of genes is attractive, it also makes targeted interventions very difficult. One could imagine a high probability of off-target effects. Therefore, it is critical to identify widespread gene network consequences of miR function and inhibition, and to identify disorders in which miRs are etiologic. Recently, miR inhibition has been attempted with variable success in mice (Hullinger et al., 2012; Krützfeldt et al., 2007).

5.3 Future Work Involving the Results of Both of the Presented Studies

A follow-up study that combines results from both studies is to further investigate VEGFA and its potential relationship with SRSF2 as a potential contributor to PMDD psychopathology.

Previous work suggests SRSF2 plays a role VEGFA alternative splicing, as SRSF2 is one of three

SR proteins thought to be in control of the terminal splice-site selection (Amin et al., 2011). To date, at least 16 VEGFA isoforms gene have been identified (Peach et al., 2018), each with different physiological activity, the most common being VEGFA165. As stated in the above chapters, there also appear to be interactions between E2 and VEGFA, and E2 and SRSF2. A future study could establish whether women with PMDD typically carry an uncommon VEGFA isoform and/or if SRSF2 contributes to the splicing of that isoform, as well as the nature of the relationship between E2 and VEGFA and/or E2 and SRSF2.

5.4 Final Summary

The data presented in this thesis enable a more complete understanding of the transcriptional effects of sex steroid hormones in neurons and in an ovarian steroid triggered

96 disorder, PMDD. I hypothesized that neurons would exhibit transcriptional signatures in response to E2, P4, and ALLO. The first study used four human female neuronal cell lines across several maturation stages: NSCs, Luhmes neuronal precursor cells, SH-SH5Y neuronal progenitor cells, and Luhmes mature neurons to explore the transcriptional responses to E2, P4, and ALLO exposure. Indeed, several gene signatures were identified in response to E2 and ALLO. I also hypothesized that PMDD pathology would in part be due to microRNA dysregulation. Using LCLs as a model for PMDD, this second study profiled global miR expression to compare miR expression levels to control LCLs. I identified two miRs co-expression networks that target several

ESC/E(Z) complex genes, suggesting miRs play a role in an underlying dysregulation of ESC/E(Z) complex transcripts, and accounting for the paradoxical upregulation of mRNAs and downregulation of proteins in components of this complex. Data from this study also identified several dysregulated miRs from the MIR15 family in PMDD. These miRs target VEGFA and genes involved in the VEGF signaling pathway, implicating both MIR15 family genes and the VEGF signaling pathway as possible contributors to PMDD pathology. Combined, these studies suggest a wide range of future work examining the effects of sex steroids on neuronal cells, particularly as applied to understanding the cellular mechanisms of PMDD.

97

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