GENOMIC REGULATION of CLOCK FUNCTION a Dissertation Submitted

GENOMIC REGULATION OF CLOCK FUNCTION

A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Dissertation written by Jessica L. Vespoli B.S., University of Pittsburgh Johnstown, 2009

Approved by ______Dr. Eric M. Mintz, Ph.D., Department of Biological Sciences, Doctoral Advisor ______Dr. Gail Fraizer, Ph.D., Department of Biological Sciences ______Dr. Olena Piontkivska, Ph.D., Department of Biological Sciences ______Dr. Aaron Jasnow, Ph.D., Department of Psychological Sciences ______Dr. Fayez Safadi, Ph.D., Department of Anatomy & Neurobiology, NEOMED Accepted by ______Dr. Laura Leff, Ph.D., Chair, Department of Biological Sciences ______Dr. James L. Blank, Ph.D., Dean, College of Arts and Sciences

Table of Contents

Page List of Figures……………………………………………………………………………vii List of Tables……………………………………………………………………………..ix List of Abbreviations……………………………………………………………………...x Acknowledgements……………………………………………………………………...xiv

CHAPTER I. Introduction………………………………………………………………. 1 Background on Circadian Rhythms ...... 1 The Molecular Circadian Clock………………………………………………………...3 The Suprachiasmatic Nucleus…………………………………………………………. 6 SCN organization……………………………………………………………………...10 VIP in the SCN……………………………………………………………………….. 12 AVP in the SCN……………………………………………………………………… 13 GRP in the SCN…………………………………………………………………….... 14 Peripheral Clocks……………………………………………………………………...15 Estrogen and the Genomic Circadian Clock…………………………………………. 16 Relevance to Human Health …………………………………………………………. 17 Specific Aims………………………………………………………………………….21

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CHAPTER II. Circadian and subregional gene expression in the suprachiasmatic nucleus……...…………………………………………………………………………… 24 Introduction……………………………………………………………………. ...24 Materials and Methods………………………………………………………… ...26 Animals………………………………………………………………… ...... 26 Tissue Preparation……………………………………………………… ...... 26 Analysis……………..…………………………………………………...... 28 Results………………………………………………………………………….... 29 Microarray analysis of rhythmic gene expression...………………… ...... …..29 Microarray analysis of regional gene expression….………………… ...... ….34 Discussion………………………………………………………………………..36

CHAPTER III. Estrogen response element and the core circadian clock genes: a bioinformatics analysis.…..………………………………………………… …………...44 Introduction…………………………………………… ...... ………………………....44 Materials and Methods……………………………… ...... …………………………...49 Sequences…………………………………………… ...... …………………..49 ERE predictions and alignment……………………… ...... ………………….49 Results………………………………………………… ...... ………………………....53 Predicted EREs……...……………………………… ...... …………………...53 Conservation of EREs……………………………… ...... …………………...55 Discussion…………… ...... …………………………………………………………..56

CHAPTER IV. Estrogen and the peripheral clock………………………………………61 Introduction………… ...... …………………………………………………………....61 Materials and Methods……………… ...... …………………………………………...62

Animals…………………………………………………… ...... ……………..62 Tissue preparation and analysis of gene expression.…………… ...... ……….63 Results ...... 66 Liver ...... 66 Uterus ...... 77 Discussion ...... 90

CHAPTER V. Global Discussion ...... 95 Future Directions ...... 98 References ...... 100

List of Figures

Page

CHAPTER I. Introduction

Fig. 1.1. Molecular Circadian Feedback Loop………………………………………...4

Fig. 1.2. The SCN ……………..………………………………………………………6

Fig. 1.3. The pathway of light through the SCN……...……………………………….8

Fig. 1.4. SCN ssubregions………………………………….…………………………11

Fig. 1.5. The pathway of estrogenic action…………………………………………...17

CHAPTER II. Temporal analysis of the SCN by microarray

Fig. 2.1. Regional gene expression differences……………...………………………..27

Fig. 2.2. qPCR of circadian gene expression………...………………………………..30

Fig. 2.3. Venn diagram of regional expression ……………...………………………..33

Fig. 2.4. Comparison of the Dorsal and Ventral SCN ……………..……………..…..37

Fig. 2.5. Allen Brain atlas images for circadian genes ……………………………….39

CHAPTER III. Estrogen Response Elements and the Core Circadian Clock: a bioinformatics analysis

Fig. 3.1. Scatter Plot of Predicted EREs in core circadian clock genes ...... 54

CHAPTER IV.

Fig. 4.1. Clock expression in the Liver 2 hours after treatment ...... 64

Fig. 4.2. Clock expression in the Liver 24 hours after treatment ...... 65

Fig. 4.3. Bmal1 expression in the Liver 2 hours after treatment ...... 67

Fig. 4.4. Bmal1 expression in the Liver 24 hours after treatment ...... 68

Fig. 4.5. Per1 expression in the Liver 2 hours after treatment ...... 70

Fig. 4.6. Per1 expression in the Liver 24 hours after treatment ...... 71

Fig. 4.7. Per2 expression in the Liver 2 hours after treatment ...... 73

Fig. 4.8. Per2 expression in the Liver 24 hours after treatment ...... 74

Fig. 4.9. Per3 expression in the Liver 2 hours after treatment ...... 75

Fig. 4.10. Per3 expression in the Liver 24 hours after treatment ...... 76

Fig. 4.11. Clock expression in the Uterus 2 hours after treatment ...... 78

Fig. 4.12. Clock expression in the Uterus 24 hours after treatment ...... 79

Fig. 4.13. Bmal1 expression in the Uterus 2 hours after treatment ...... 81

Fig. 4.14. Bmal1 expression in the Uterus 24 hours after treatment ...... 82

Fig. 4.15. Per1 expression in the Uterus 2 hours after treatment ...... 83

Fig. 4.16. Per1 expression in the Uterus 24 hours after treatment ...... 84

Fig. 4.17. Per2 expression in the Uterus 2 hours after treatment ...... 86

Fig. 4.18. Per2 expression in the Uterus 24 hours after treatment ...... 87

Fig. 4.19. Per3 expression in the Uterus 2 hours after treatment ...... 88

Fig. 4.20. Per3 expression in the Uterus 24 hours after treatment ...... 89

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List of Tables

Page

Table 2.1. Known clock-related cycling genes in the SCN ...... 31

Table 2.2. Cycling genes in the SCN of unknown circadian function ...... 32

Table 2.3. Results of DAVID functional analysis ...... 35

Table 2.4. Gene list of the 85 differentially expressed ventral genes ...... 38

Table 3.1. Ensembl genome identification for all species and genes ...... 46-48

Table 3.2. Predicted EREs for Clock related and control genes ...... 50

Table 3.3. Conservation of predicted EREs for all genes ...... 52

Table 4.1 Summary of the effect of estrogen on clock genes in the liver and uterus ...... 91

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List of Abbreviations

ACTH – adrenocorticotropic hormone

AGT – angiotensinogen

ARC – arcuate nucleus

AVP – arginine vasopressin

Bmal1 – brain and muscle aryl hydrocarbon receptor nuclear translocator (ARNT) –like

Brca1 – Breast Cancer 1, Early Onset

BST – bed nucleus of the stria terminalis

CK – casein kinase

Clock – circadian locomotor output cycles kaput

Cox7 – Cytochrome C Oxidase Subunit

Cry1 – cryptochrome 1

Cry2 – cryptochrome 2

CT – circadian time

CTSD – Cathepsin D

DD – constant darkness

EMC – extraskeletal myxoid chondrosarcoma

ER – estrogen receptor

ERE – estrogen response element

ERU – estrogen response unit

Gapdh – Glyceraldehyde-3-Phosphate Dehydrogenase

GHT – geniculohypothalamic tract

GPI – Glucose-6-Phosphate Isomerase

GPR30 – G protein-coupled receptor 30

GRP – gastrin releasing polypeptide i.p. – intraperitoneal

LCM – laser capture microscopy

LD – light-dark

Nt – nucleotide

OVX – ovariectomized

Per1 – period 1

Per2 – period 2

Per3 – period 3

PSMB2 – Proteasome (Prosome, Macropain) Subunit, Beta Type, 2

PVN – paraventricular nucleus qPCR – quantitative realtime polymerase chain reaction

Rev-Erbα – nuclear receptor subfamily 1, group D, member 2

RHT – retinohypothalamic tract

Rorα – RAR-Related Orphan Receptor A

SCN – suprachiasmatic nucleus

Snrpd3 – Small Nuclear Ribonucleoprotein D3 Polypeptide

SON – supraoptic nucleus

TF – transcription factor

Tff1 – Trefoil Factor 1

VIP – vasoactive intestinal polypeptide

VPAC2 – Vasoactive Intestinal Peptide Receptor 2

WT – wildtype

ZT- zeitgeber time

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Acknowledgements

First, I would like to thank Dr. Eric Mintz for giving me the opportunity to work in his lab and explore new avenues of research within the lab. The past five years have been full of ups and downs, but he has been there throughout it all. He gave me a lab to be a part of and grow not only scientifically, but personally as well. I am thankful for the opportunity to have been able to explore avenues of research that were new to me, as well as Dr. Mintz and the lab. Dr. Mintz might have been at the bottom of my list of people to work with at Kent State when applying, but coming out on the other side and throughout this long process he has gone straight to the top of my list and I couldn’t have asked for a better advisor, mentor and sometimes counselor to lead me through this process.

I would like to thank the member of my committee, Drs. Piontkivska, Fraizer,

Jasnow and Safadi for being a part of my committee and helping guide me these past years. Their unique backgrounds and area of expertise helped in making me the scientist that I am today and will continue to grow as. I thank them for their continued willingness to help whenever I needed an ear to listen or a push to get things done and for being available to help train me in techniques that are in their field of expertise.

To the members of the Mintz Lab both past and present Jessica Murphy, Tracey

Toppacio, Amanda Klein, Ashutosh Rastogi, Will Huffman, Ghada Nusair and Erin

Gilbert thank you for being there with me through this long and tedious process of doing rhythms research. I consider each of you a friend and colleague and can’t wait to see what the future holds for you. Each one of you has been an integral part of my degree. I

xiii thank Jessica for being there from beginning to end, teaching me, growing alongside me and becoming a friend. I thank Ghada, Tracey and Will, for helping with many overnight experiments and bringing me coffee and food at all hours of the night. Without each one of you I wouldn’t have made it through.

I’d like to thank my church family at Riverwood Community Chapel, The Young adults and my life group, for helping me focus on the important things and take all of my problems even my dissertation to God. Also, thank you for being there when I needed a friend to talk to, a shoulder to cry on or a mentor to lead me in the right direction. Thank you for helping me root myself in Christ and growing my faith through this difficult time and the many trials that have come along with it. The Ladies, thank you for being my sounding board for all major life issues and decisions and always holding me accountable to the decisions I make. Without you I wouldn’t be where I am today. Bill and Amy thank you for being the spiritual leaders in my life here in Kent and always knowing when I needed a friend or a mentor.

I am ever thankful for the support, love and commitment of my family. To my parents, Brenda and Tim and my sister Monica thank you for believing that I could do this and always being there to lift me up when I thought I couldn’t. And I know that my

Grandparents supported me in this journey up until the very end of their life’s and I love and miss them every day (Leola, January, 2014, Vermona, January, 2015 and Harry,

April, 2015) and I would like to dedicate this dissertation in their memory.

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And last, but never least thank you to my loving husband, Paul. You’ve been there for me through the past 2.5 years of this process and those might have been the most stressful, but you stayed right by my side giving me encouragement and love through it all. I am thankful that you are willing to sacrifice and follow me wherever, to help me pursue my dreams. Thank you for always believing in me, even when I felt like giving up and being my biggest supporter, I will never be able to thank you enough for the constant love, support and leadership that you give me.

CHAPTER I

Introduction

Background on Circadian Rhythms

Organisms are exposed to a 24 hour cycle of light and darkness from the earth’s rotation on its axis. This cycle of light and dark has a major impact on the behavior and physiology of organisms directly exposed to it, which include the majority of terrestrial species. These organisms and those that are exposed to environmental cues are under the influence of an internal circadian clock. “Circadian” is a term of Latin origin; “circa” meaning about and “diem” meaning day, which combines to “about a day”. Thus, circadian rhythms are rhythms that persist in cycles that are about one day or 24 hours long. Most organisms have a circadian rhythm of either slightly less than or greater than

24 hours, with some organisms having a shorter or longer rhythm depending on their lifestyle traits. Circadian rhythms have been discovered in all types of organisms, giving them the ability to adapt to their environment and predict changes in the environment.

Plants, for example, have adapted to this cycle to maximize their ability to

1 photosynthesize and increase their exposure to sunlight by increasing the surface area of their leaves during the day by opening them during the day. This is a true circadian rhythm, as it persists in constant darkness (Moore-Ede et al., 1982). A second example is nocturnal animals avoiding predation by timing their activity to the night and remaining hidden during the daylight. These rhythms, though under the influence of the light/dark cycle, also persist under constant conditions indicating that these rhythms are generated by an endogenous internal circadian clock. These constant conditions are ones in which there are no external timing cues. In the absence of such cues, rhythmic physiology or behavior must be driven by an internal clock. By convention, we refer to circadian time

(CT) 0 as the start of the subjective day under such conditions.

There are several criteria that must be met in order for a rhythm to be considered circadian. The rhythm must persist in constant conditions, maintaining an approximately

24 hour rhythm over several cycles with little variation in timing from one cycle to the next. This shows that the rhythm is endogenous and not just a response to external cues from the environment. The second condition that must be met is the ability to maintain a

24 hour rhythm across varying temperatures, due to daily fluctuations in an organism’s environment. If the clock was temperature dependent we would see a speeding up or slowing down of the rhythm in response to temperature. The maintenance of rhythms across varying temperatures is necessary, because a clock that changes how fast it runs when the temperature changes is not a very good clock. The final criterion that must be met is the ability of the clock to reset in the presence of an external cue. The ability to reset the clock quickly to, for example, external light at night is important to help an animal anticipate the timing of sunrise and sunset in the following cycles. Since most

2 animals have rhythms that are slightly shorter or longer than 24 hours, the difference between the endogenous rhythm and the 24-hr day requires a daily resetting by environmental cues (Moore-Ede et al., 1982, Refinetti, 2007). If a rhythm meets the above criteria we can classify it as an endogenous circadian rhythm.

The clock that drives these changes in physiology and behavior is comprised of clocks contained within individual cells. This clock is a molecular clock that works at the levels of transcription and translation. This clock drives rhythms in physiology and behavior through interactions of a set of core circadian clock genes.

The Molecular Circadian Clock

The molecular circadian clock is a network of positive and negative feedback loops, and operates within most cells of the body. This molecular mechanism in mammals has been well defined and reviewed (Albrecht, 2002, Hastings and Herzog,

2004, Okamura, 2004, Bozek et al., 2009). There are several genes involved in this molecular loop that feed back to inhibit their own expression, with a cycle time of around

24 hours (Whitmore et al., 1998, Bell-Pedersen et al., 2005). The driving force of this pathway is the positive limb of the feedback loop consisting of Clock and Bmal1 (Abe et al., 1998, Gekakis et al., 1998). Once translated, CLOCK and BMAL1 proteins dimerize in the cytoplasm and are transported into the nucleus where they bind to their regulatory

Figure 1.1: Schematic representation of the core circadian molecular clock feedback loop.

4 sequence, known as an e-box. Targets of the CLOCK/BMAL1 dimer include genes that are a part of the negative limb of the feedback loop: period genes (Per) and cryptochrome genes (Cry) (Abe et al., 1998, Gekakis et al., 1998, Hastings and Herzog, 2004).

Transcriptional activation of the Per and Cry genes occurs mostly during the day; these genes are then translated in the cytoplasm and accumulate. The CRY and PER proteins form hetero/homodimers in the cytoplasm; once the hetero/homodimers are formed casein kinase proteins (CKs) phosphorylate the complex (Kume et al., 1999). Once phosphorylated the complex enters the nucleus where it can inhibit the dimerization of

CLOCK and BMAL1 (Darlington et al., 1998, Kume et al., 1999, Bae et al., 2001,

Reppert and Weaver, 2002, Eide et al., 2004, Hastings and Herzog, 2004, Lee et al.,

2004, Antle and Silver, 2005, Akashi et al., 2006, Ko and Takahashi, 2006, Bozek et al.,

2009). This ultimately causes inhibition of the transcription of Per and Cry genes in the nucleus, allowing the cycle to begin again with Clock and Bmal1 transcription

(Darlington et al., 1998, Gekakis et al., 1998, Sangoram et al., 1998, Kume et al., 1999).

This is illustrated in Figure 1.1.

A second part of the negative feedback loop includes two other genes that feedback and either inhibit or enhance the transcription of Bmal1. These genes are

Reverbα and RORα. Reverbα and RORα are regulated by CLOCK/BMAL1 via binding at e-box sites in their promoter regions. Once transcribed in the nucleus, they are translated in the cytoplasm and transported back into the nucleus where they can bind to their regulatory elements in the promoter region of Bmal1, either inhibiting transcription

(REVERBα) or enhancing transcription (RORα). These two proteins can change the expression of Bmal1, thereby changing the speed of the clock and the amplitude of clock

SCN SCN

Optic Chiasm

Figure 1.2: SCN location just above the optic chiasm and on both sides of the third ventrical (3V) in the medial brain. LCM image from a mouse brain section at 12 μm, stained with hemotoxylin at 10x magnification.

output. The molecular circadian clock is outlined in figure 1.1, this figure illustrates the feedback loop from the nucleus to the cytoplasm and back through the cycle. This molecular clock is found throughout the body, but the master circadian pacemaker is found in the suprachiasmatic nucleus of the hypothalamus (SCN).

The Suprachiasmatic Nucleus

The SCN is comprised of paired nuclei along the midline of the hypothalamus, and is located lateral to the third ventricle and just dorsal to the optic chiasm. Figure 1.2 is a hemotoxylin stained 12 µm slice of the mouse brain; the SCN is stained darker that the surrounding tissue. It is composed of approximately 20,000 neurons (Inouye and

Kawamura, 1979) and is considered to be the master circadian pacemaker. Lesions of the

SCN result in the loss of circadian rhythmicity throughout the organism when no external cues are present (Stephan and Zucker, 1972), and rhythmicity is restored upon transplantation of SCN tissue (Lehman et al., 1987, DeCoursey and Buggy, 1989, Ralph et al., 1990, Li et al., 2008). Therefore, the SCN is important in generating and regulating the circadian timing of the organism. The SCN sends efferent signals to other central and peripheral clocks, synchronizing rhythms throughout the body. Rhythms in electrical activity are present in the SCN as well as other regions of the brain. However, if the SCN are removed, the other regions do not show rhythmic electrical activity, but SCN explants continued to show this rhythmicity in vitro, giving evidence that the presence of the SCN is essential for maintenance of rhythmicity in other regions of the brain (Kalsbeek et al.,

2006).

Figure 1.3: An illustration of the pathway of light from the eyes and through the SCN and to the outputs from the SCN to the areas of the brain under direct innervation of the SCN and finally to the peripheral clocks causing changes in physiology and behavior.

When SCN cells are dissociated in media the cells themselves maintain a circadian rhythm without having contact with other SCN cells (Murakami et al., 1991, Maywood et al., 2006). However, when SCN cells are plated together and able to have contact with one another they are able to synchronize rhythms with each other, showing that the cells within the SCN have an intrinsic ability to maintain and synchronize rhythms without innervation from the optic chiasm or other input pathways, giving evidence to the fact that the SCN is the master circadian pacemaker. Therefore, the SCN is an endogenous, master pacemaker within the brain that drives rhythms of physiology and behavior throughout the organism.

The SCN receives light information from the eyes through the retinal hypothalamic tract (RHT), connecting the retina in the eye directly to the SCN. This can be seen in Figure 3 outlining the innervations and outputs of the SCN. The primary neurotransmitter in this pathway is glutamate (Castel et al., 1993). Application of a glutamate receptor antagonist or severing the RHT, prevents the SCN from photic entrainment to the light/dark cycle (Johnson et al., 1988). Another way for the SCN to receive information is through the intergeniculate leaflet (Ribak and Peters, 1975). This input to the SCN serves to transfer information from both photic and non-photic cues using Neuropeptide Y as a neurotransmitter (Moore et al., 1984). A third major input to the SCN is from the median raphe nucleus of the midbrain, through serotonergic innervation (Meyer-Bernstein and Morin, 1996, Meyer-Bernstein et al., 1997, Morin and

Meyer-Bernstein, 1999). This pathway is thought to carry primarily nonphotic information. The SCN has several efferent targets throughout the brain that have been

9 identified through tracing experiments. Output signals from the SCN to other areas of the brain help drive the physiology and behavior of the organism. The regions that receive information from the SCN include the paraventricular nucleus, arcuate nucleus (ARC), supraoptic nucleus, ventral lateral septum, medial preoptic area, anterior hypothalamic area, dorsalmedial hypothalamus, bed nucleus or the stria terminalis (BST) and subparaventricular zone (Watts and Swanson, 1987, Watts et al., 1987, Moga and

Moore, 1997, Abrahamson et al., 2001, Abrahamson and Moore, 2001, Kriegsfeld et al.,

2004). These areas are then able to transfer information to other regions of the brain and also to peripheral organs.

SCN Organization

The SCN can be divided into subregions by several different criteria, which result in similar but not exactly identical subdivisions. The first way that the SCN can be divided is through the differential expression of rhythmicity. For example, the central region of the hamster SCN lacks a rhythm of firing in calbindin neurons and surrounding non-calbindin neurons have rhythmic firing rates (Jobst and Allen, 2002). A second way

SCN organization is examined is through the localization of what is known as the core and shell (Miller et al., 1996, Moore et al., 2002b, Evans et al., 2011). This method characterizes cells within the SCN based on location and unique rhythms of gene expression. The core region of the SCN in rats is defined by the presence of vasoactive intestinal polypeptide (VIP) and gastrin-releasting peptide (GRP); this region is also innervated by the retina, IGL, and the median raphe nucleus and comprises approximately 70% of the SCN (Moore et al., 2002b). The shell surrounds the core, and

AVP 3 AVP

GRP GRP

VIP VIP

Chiasm

Figure 1.4: A schematic representation of the SCN and the subregions represented with the neuropeptides that are localized to each region. These are the regions focused on in Chapter 2.

11 has a higher expression of arginine-vasopressin (AVP) than the core region (Moore et al.,

2002). These two regions show differences in PER2 rhythmic expression within the neurons, with there being less rhythmic activity of PER2 in the core than in the shell

(Evans et al., 2011). The core/shell model defines two specific regions of the SCN but does not completely define the SCN based on expression differences. Another way to define the organization of the SCN is based upon the neuropeptides that are expressed in a particular region. This is a helpful way of looking at the mouse SCN in particular, due to the distinct localization of three neuropeptides. This model aligns three regions of the

SCN throughout the rostral/caudal axis of the SCN, with some regions changing in size across this axis (Morin, 2007), based on these neuropeptides. This model is depicted in figure 1.4. In this model the SCN sub-regions are a ventral region directly above the optic chiasm containing VIP, a central region containing GRP, and a dorsal region containing

AVP (Morin, 2007). This is the model for SCN organization that will be used in this dissertation, to examine regional differences in gene expression across the SCN, as defined by the neuropeptide that is predominant in that region.

VIP in the SCN

VIP is the neuropeptide found in the most ventral region of the mouse SCN, sitting directly on top of the optic chiasm. VIP is released in the SCN as a function of circadian time as well as the intensity of light received by the eyes (Shinohara et al.,

1994, 1995), and plays a critical role in the circadian synchronization of the cells within the SCN (Harmar et al., 2002, Colwell et al., 2003, Aton et al., 2005, Vosko et al., 2007).

If either VIP or its receptor is in some way inhibited there is a loss of rhythmicity in several processes throughout the organism (Harmar et al., 2002, Colwell et al., 2003,

Maywood et al., 2006). VIP also affects the expression of Per1 and Per2 within the SCN, these genes are responsible for the light induced resetting of the clock (Piggins et al.,

1995, Watanabe et al., 2000, Reed et al., 2001, Nielsen et al., 2002).

VIP and its receptor VPAC2 reset the SCN to light (Piggins et al., 1995) and regulate the period of the clock in the SCN (Harmar et al., 2002). This is demonstrated in VIP or

VPAC2 receptor knockout mice, which display disrupted or desynchronized wheel running behavior and sleep (Harmar et al., 2002). Therefore the VIP signaling pathway in the SCN helps in regulating behavioral output as well as timing within the SCN and peripheral organs (Vosko et al., 2007). The pathways and actions of VIP within the SCN and localization of VIP to the ventral SCN give us insight into how VIP in the SCN acts to help control the synchronization of rhythms in the SCN and throughout the peripheral organs.

AVP in the SCN

AVP is widely distributed throughout the body and has extensive roles in behavior (Caldwell et al., 2008). AVP was first localized in the rat SCN (Vandesande et al., 1975). AVP in the central nervous system is released in a circadian manner peaking at mid-day under a light-dark cycle (Reppert et al., 1983). SCN AVP release has peak expression mid-day in the cycle, in rat SCN slices and cultured rat SCN (Gillette and

Reppert, 1987, Murakami et al., 1991), AVP within the SCN also determines the rhythm of AVP release into cerebrospinal fluid; with the ablation of the SCN, there is no rhythmic AVP expression in cerebrospinal fluid (Coleman and Reppert, 1985). Nest building (Bult et al., 1993) and wheel running activity (Gerkema et al., 1994, Wollnik and Bihler, 1996) are regulated by the release of AVP from the SCN and AVP signals to

13 neuroendocrine targets to regulate the timing of the release of hormones in a circadian manner. This includes the release of adrenocorticotropic hormone (ACTH) from the pituitary (Kalsbeek et al., 2008b). The release of AVP from the SCN, inhibits the release of ACTH and corticotrophin releasing hormone (Kalsbeek et al., 2006). The AVP containing neurons, however, do not contain only AVP, but other neurotransmitters or secreted proteins known to be a part of the circadian clock or an output of the circadian clock, such as GABA and prokineticin 2 (PK2) (Antle and Silver, 2005, Masumoto et al.,

2006). Furthermore, there is evidence that the vasopressin V1a receptor aids in the control of the amplitude of rhythmicity of PK2 within the SCN (Li et al., 2009). The V1a receptor when activated controls the rhythmic locomotor activity in rats aiding in the resetting of the clock in response to daily timing cues. (Li et al., 2009, Yamaguchi et al.,

2013). Therefore, the AVP region of the SCN is an integral part of circadian timing outputs.

GRP in the SCN

GRP in mice is found in the center of the SCN between the AVP and VIP regions

(Antle et al., 2005). GRP is responsible for the integration of light signaling through the

RHT to the SCN (Tanaka et al., 1997, Abrahamson et al., 2001). The GRP cells in the

SCN lack rhythmic expression of the clock elements Per1 and Per2, negative regulators of the circadian clock (Karatsoreos et al., 2004). When GRP is applied to the SCN or the third ventricle (near the SCN), GRP resets the clock in the same manner as a light pulse

(Albers et al., 1991, Albers and Thomas, 1991, Piggins et al., 1995, Antle and Silver,

2005, Kallingal and Mintz, 2006, 2010). GRP is also thought to transmit information from the SCN to other areas of the hypothalamus such as the supraoptic nucleus

(Kallingal and Mintz, 2014). GRP is integral in resetting the phase of the SCN in response to light as well as communicating photic information to other regions of the brain. Also, exogenous application of GRP to the SCN, in animals lacking the VPAC2

VIP receptor, aids in the synchronization of clock gene expression in cells that show an asynchronous rhythm of expression (Maywood et al., 2006). Thus GRP, in the absence of

VIP and its signaling pathway, acts in the same manner that VIP would in response to stimuli, mostly light (Piggins et al., 1995, Piggins and Cutler, 2003). Overall, it is believed that the GRP region of the SCN acts as an integration center for light signaling form the VIP region and aides in synchronizing the ventral and dorsal SCN as well as other regions of the hypothalamus and brain.

Peripheral Clocks (Liver and Uterus)

Though the SCN is considered the master circadian clock, several other organs show circadian rhythms of gene expression. These organs include, but are not limited to muscle, adipose, liver, uterus and bone. These peripheral clocks are under the control of the outputs from the SCN, which helps to synchronize the organs in phase with one another (Schibler, 2007), but some organs receive input indirectly from the SCN through other mechanisms such as feeding or responses to stress (Damiola et al., 2000).

Peripheral oscillators show distinct rhythms of clock gene expression as well as other clock-controlled genes (Hughes et al., 2009). In fact, there is evidence that some genes adhere to a 24 hour cycle while others cycle with either a 12 or 8 hour period in the liver

(Hughes et al., 2009). This and other studies done on the liver show that over 1000 transcripts cycle in the liver (Dibner et al., 2010b). In the liver, genes involved in the pathways of metabolism, energy homeostasis, food metabolism and overall detoxification

15 show circadian rhythms of gene expression (Akhtar et al., 2002, Kalsbeek et al., 2008a,

Vujovic et al., 2008). One way that we know for the clock to be reset or altered in the liver is by the presence or absence of food. Anticipation of food changes the clock in the liver and has an effect of timing of expression on several genes (Damiola et al., 2000,

Dibner et al., 2010).

A second peripheral tissue that shows circadian gene expression is the uterus. The uterus is a reproductive organ that is hormone responsive. Though the uterus is hormone responsive it is has circadian rhythms that are synchronized with other organs by the

SCN. One hormone that has an effect on gene expression in the uterus is estrogen. In uterine tissue the presence of estrogen alters the expression of the clock genes Per1 and

Per2, causing a shift in the clock within the uterus with no change on the phase of the clock within the SCN (Sangoram et al., 1998, Nakamura et al., 2005b, Nakamura et al.,

2008b). Thus, the circadian clock in the uterus is affected by the presence of estrogen, however, the mechanism of action is not known.

Estrogen and the Genomic Circadian Clock

Ovarian hormones, such as estrogen, affect the circadian clock function of nocturnal rodents. Estradiol has a direct effect on the expression of light induced genes in the SCN

(Vida et al., 2008a). These light induced genes play a direct role within the circadian clock that are affected by the presence of estrogen suggest that estrogen is important for clock function in response to light. That estrogen affects the timing of gene expression within the SCN raises the question as to which core circadian clock genes are affected by the presence of estrogen. The core circadian clock genes Per1 and Per2 are differentially affected in the SCN in the presence of estrogen; estrogen advances the peak of Per2

Figure 1.5: Illustration of the mechanism of action of Estrogen. Estrogen can work in several different ways within the cell. The 2 outlined here are, a direct interaction through binding of the ERE and an indirect mechanism which acts through 2nd messengers. Both pathways lead to a change in expression of the targeted genes. Estrogen is pictured in purple and can bind either ER (green or orange) which then either binds the ERE or begins a signal cascade through second messengers and transcription factors (TF) altering gene expression.

17 expression in the SCN, but has no effect on Per1 (Nakamura et al., 2005a). Estrogen also affects the expression of some core circadian clock genes in peripheral tissues. For example, in the uterus the expression of Per1 is enhanced, and causes a biphasic (double peak) of both Per1 and Per2 (Nakamura et al., 2005a, Nakamura et al., 2008a, Nakamura et al., 2010). The studies done by Nakamura, et. al. show that the effect of estrogen on expression is tissue specific.

There are two mechanisms by which estrogen may be regulating the expression of circadian clock genes. The first is direct regulation through estrogen response elements.

Estrogen binds to its receptor, which is a hetero/homodimer of α or β subunits. Most tissues contain one or the other of the subunits, while some tissues express both receptors at the same or different times (Teboul et al., 2008). Once the receptor is bound by estrogen, it enters the nucleus and can bind directly to estrogen response elements (EREs) on the DNA (Gruber et al., 2004). The estrogen receptor (ER) can then alter the expression of the target gene, by recruitment of proteins that activate transcription

(Hewitt et al., 2003). Only a few select core circadian clock genes have been examined in relationship to the effect of estrogen on their expression. A second way that estrogen could affect circadian gene expression is through the g-protein coupled receptor for estrogen. Estrogen binds GPR30, its cell surface g-protein coupled receptor (Dun et al.,

2009, Hazell et al., 2009, Prossnitz and Maggiolini, 2009, Xu et al., 2009). Binding to this receptor may activate second messenger pathways that target one or several of the circadian clock genes or proteins, altering their activity or transcription. A change in one of the circadian clock genes could change the feedback loop causing a delay or advance

18 in the circadian timing of the cells affected. These methods of estrogenic action are illustrated in figure 1.5.

These methods of estrogenic action on the circadian clock suggest several unanswered questions about the relationship between estrogen and clock genes. The first is whether estrogen has a direct effect on the transcription of the core circadian clock genes. If estrogen does effect the transcription of the core circadian clock genes how does this occur? Does estrogen bind to its receptor and bind to the ERE either enhancing or inhibiting transcription of these genes? Or does estrogen bind to its receptor activating a pathway that eventually activates a second messenger that effects the expression of the clock genes? These questions are addressed in chapters three and four of this dissertation.

Relevance to Human Health:

Circadian rhythms are a fundamental part of the health of the body. A properly functioning circadian timing system keeps many processes in the body in check. These integral processes stem from sleep (Dijk and Lockley, 2002), cancer (Mormont and Levi,

1997), metabolism (Sahar and Sassone-Corsi, 2009) and addiction (Falcon and McClung,

2009). Though all these processes are not healthy processes within the body, alterations to the function of the molecular clockwork are what leads to a dysregulation in the harmful pathways (Takahashi et al., 2008). These pathways in many ways are directly influenced by the function of the circadian clock. The clock acts to regulate proper functions of sleep and metabolism while the clock is disrupted in cancer and addiction pathways. Two pathway of particular interest are cancer and metabolism.

Cancer

In cancer the circadian timing system that plays a direct role in the mitotic checkpoints is somehow altered or inhibited, thus allowing a potentially cancerous cell continues through mitosis, allowing it to replicate and eventually form a tumor (Gauger and Sancar,

2005, Hunt and Sassone-Corsi, 2007, Kondratov and Antoch, 2007). In one type of cancer in particular, breast cancer, there is a significant decrease in Per1, this decrease is found in what we believe is the inherited form of breast cancer (Winter et al., 2007). In a second type of cancer, ovarian cancer, there is a decrease in the clock genes Cry1 and

Bmal1 (Tokunaga et al., 2008). This decrease dysregulates the circadian feedback loop, causing a delay or advance in the cycle that cannot be compensated for by core feedback mechanisms.

In cancerous tissues or tumors, there is a change in rhythmicity within the groups of cells that are synchronized to one another (Hunt and Sassone-Corsi, 2007). This rhythm is not dependent on the surrounding tissues, or the direct innervation of the tissue from the

SCN. There is direct circadian regulation of cell proliferation in liver cancer; there is also evidence that circadian gene disruption aids proliferating cells in skipping critical checkpoints in the cell cycle (Qu et al., 2003). There are several other studies linking circadian disruption directly to cancer, including the higher incidence of breast cancer in women working swing shifts at hospitals (Schernhammer et al., 2006). These women are exposed to unnatural levels of light at night during what should be their dark phase. There is a decrease in melatonin in response to the excess light at night; this decrease is linked to an increase in estrogen in the women who work night shifts (Schernhammer et al.,

2006). The increase in estrogen and decrease in melatonin is hypothesized to alter the circadian cycle in cells (Stevens, 2005). This increase in estrogen within the system

20 might be directly affecting circadian clock genes, disrupting the circadian cycle.

Therefore, it is necessary to determine the interactions of estrogen with clock genes, to help understand how an increase in estrogen has an effect on the circadian timing of the cancerous tissue.

Feeding/Metabolism

Circadian rhythms influence an animal’s drive to feed or search for food.

However, if there is a disruption within the circadian clock that controls this behavior there can be a loss of these overt rhythms. For mice, dim light in the dark cycle increases overall body weight, compared to mice that were not exposed to the light, suggesting an increase in feeding (Fonken et al., 2009). This has also been seen in shift workers. There is a significant weight gain in people that work the night shift over those who work the day shift (Kubo et al., 2011). These results are based on timing of light issues that are sent from the SCN to the peripheral organs stimulating an increase in hunger at times when a normal metabolism would be asleep.

An overall better understanding of how circadian rhythms work throughout the

SCN and peripheral tissues is needed. Further understanding of this integral system could lead us to finding potential treatments for several different disorders and diseases, including metabolic disorder and cancer. This dissertation aims to gain knowledge of the integral composition of the SCN and rhythmicity within the SCN as well as how estrogen can affect the circadian timing system directly and the peripheral tissues.

Specific Aims:

The above background information shows that there is a fairly extensive understanding of how the circadian system works and how the SCN is able to act as the

21 master circadian pacemaker. However, there are several questions in that still need to be answered in order to understand the molecular circadian clock and the SCN completely.

The goal of this dissertation is to address three questions that relate to the master circadian pacemaker and the molecular circadian clock. First, are there large-scale differences in gene expression across SCN subregions as defined by neuropeptide expression? Through laser capture microdissection and microarray analysis, these three subregions were examined for genes that were differentially expressed along with the neuropeptides. We also looked at the circadian gene expression in these samples across the SCN as a whole. A second unanswered question in rhythms is how estrogen interacts with the circadian clock genes. Here we examine the presence of EREs in circadian clock genes and their promoters. Through the use of sequence analysis of core circadian clock genes, we were able to determine if estrogen can directly affect gene expression of these genes, by binding to an ERE. The final unanswered question that we aim to shed light on is how estrogen affects the expression of core circadian clock genes in peripheral tissues.

The tissue dependent differences of the action of estrogen based on circadian time as well as mechanism of action, either immediate or delayed, will be examined to help determine how estrogen is working within the circadian system. These questions will be addressed through the following aims.

Specific Aim 1: Identify transcriptional patterns and networks in the SCN that differ across SCN subregions.

We hypothesize that there are gene expression differences between SCN subregions that are not confined to known differences in neuropeptide expression, and that differences in expression across region and circadian time will reveal previously unknown details of

SCN neuronal network organization and function.

Specific Aim 2: Identify putative estrogen response elements (EREs) in the promoter regions of core circadian clock genes.

We hypothesize that the promoter regions of core circadian clock genes contain putative EREs that are functional, in a tissue and time dependent manner, only in those genes that are directly affected by the presence or absence of estrogens.

Specific Aim 3: Identify the response of circadian genes in the peripheral clocks.

We hypothesize that a subset of core circadian clock genes will be affected by estrogens, through either direct binding EREs or pathways regulated by estrogens.

Chapter II

Spatial and temporal analysis of gene expression in the suprachiasmatic nucleus

Introduction

The suprachiasmatic nucleus of the hypothalamus (SCN) is the master circadian clock in mammals (Moore, 1997). The SCN functions as an integration center for environmental cues and physiological factors governing output to the peripheral clocks

(Mohawk et al., 2012). The SCN receives daily timing cues from the environment as well as from the body; these cues are reflected in changes in gene expression, which in turn alter the timing of the body’s internal clocks and synchronized to environmental light/dark cycles. In the absence of exogenous cues to the SCN, animals still show an intrinsic circadian rhythm in physiology and behavior, termed free-running rhythms (Aronin et al., 1990, Albrecht, 2002, Naruse et al., 2004). The SCN shows a specific pattern of circadian gene expression of the core circadian clock genes, including, Per1, Per2, Cry1,

Cry2, Bmal1 and Clock. These genes are integral in signaling clock controlled genes and clock regulatory genes, leading to the circadian expression of genes that are responsible for signaling, cell division, feeding etc. (Dardente et al., 2004, Antle and Silver, 2005,

Akashi et al., 2006, Ko and Takahashi, 2006).

Individual neurons of the SCN are capable of acting as independent circadian oscillators

(Hastings and Herzog, 2004), but the SCN exhibits a specific structural organization

(Moore et al., 2002), with subregions that can be defined by a variety of criteria, including neuropeptide expression and cellular responses to stimuli (Dardente et al.,

2004, Evans et al., 2011). Individual genes have been identified that are differentially expressed in specific SCN subregions. Some of these genes are expressed solely in certain parts of the SCN, such as Vip and Avp, while others can be expressed throughout the SCN but differ in temporal pattern or inducibility (Fos) (Kornhauser et al., 1990). As a result there are many different ways of subdividing the SCN into functionally different subregions. In this study, we explored the organization of the SCN as defined by neuropeptide composition, based on a coronal section of the SCN. These regions are the vasoactive intestinal polypeptide (Vip) region (most ventral and in contact with the chiasm), gastrin –releasing polypeptide (Grp) region (central) and a dorsal vasopressin

(Avp) region (Morin et al., 2006). These three regions were examined under free-running conditions in mice where, Avp and Vip show circadian rhythmicity and Grp is arrhythmic

(Dardente et al., 2004). The current study focuses on three regions within the SCN that can be neurochemically determined. These regions include a ventral region identified by the expression of Vip, a medial-lateral region identified by the expression of Grp, and a dorsal region, that expresses Avp.

Here we examined circadian rhythmicity differences in the SCN subregions of

C57BL6/J mice under free-running conditions. A comprehensive microarray analysis to compare these three regions on the basis of gene expression and a comprehensive profile of rhythms after a substantial period of free-running has been assessed. Our analysis

25 focused on those genes that are expressed with a circadian rhythmicity as well as those genes that display differential expression patterns between the regions at a given time point or across time points. These data provide new insight into the role of structural heterogeneity in SCN function and rhythmic gene expression previously unseen in the

SCN.

Materials and Methods

Animals:

C57BL/6J Mice (http://jaxmice.jax.org/strain/000664.html) were housed on a

12:12 LD cycle with running wheels for 14 days. After 14 days the animals were placed in constant darkness for 14 additional days. Free-running activity rhythms were recorded using Clocklab software (Actimetrics) and used to determine circadian time (CT), with

CT 12 being defined as the onset of wheel-running activity. Animals were sacrificed by cervical dislocation at 2-hour intervals relative to their internal clocks, such that data was collected every 2 circadian hours yielding 12 time points across a circadian day (CT

0,2,4,6,8,10,12,14,16,18,20,22).

Tissue preparation:

Brains were extracted, flash frozen and sliced at 12 µm on a cryostat and direct mounted on Colorfrost Plus slides. Slides were rapidly counterstained with Vector hemotoxylin, and then dehydrated in a series of increasing ethanol and finally xylenes.

Dorsal, central, and ventral SCN subregions were captured using an ArcturusXT laser capture microscope, with 6-9 SCN sections per brain pooled from a single sample. Figure

2.1 shows the accuracy of the subregional captures.

Figure 2.1: A: the SCN from a mouse brain sliced at 12μm and stained with hemotoxylin under 10x magnification. B: The same SCN after LCM capture of the three regions. LCM allows for capture of the three regions separately and specifically.

RNA was extracted and total RNA purified using Arcturus PicoPure™ RNA Isolation

Kits with DNase treatment. RNA was amplified and converted to ssDNA using the

Nugen® Ovation® Pico WTA System V2. ssDNA was purified using a MinElute®

Reaction Cleanup Kit and labeled with the Nugen® Encore™ Biotin Module. Samples were then hybridized to Affymetrix Mouse Gene 1.0 ST Arrays using the Affymetrix

Wash, Stain and Hybridization Kit. Arrays were scanned with a GeneChip® Scanner

3000.

Analysis:

Data were analyzed using JMP genomics 5.1 software (JMP®, Version 5.1. SAS

Institute Inc., Cary, NC). Gene-level signal intensities were generated using the RMA method with Loess smoothing. Individual gene plots are shown as average signal intensity across arrays for that region and time point. Three separate statistical methods were used to examine gene expression across time and groups. First, we used JTK_Cycle

(Hughes et al., 2010) to identify transcripts expressing circadian rhythmicity. Secondly, we compared differences in expression across SCN subregions using analyses of variance and controlling the false discovery rate at 0.05. Finally, we performed a 2-way ANOVA to look at the interaction between SCN subregion and circadian expression.

Amplified ssDNA from the above samples were then ran through quantitative

PCR(qPCR) for three genes (Creb3l1, Dusp4, and Sik1) that showed circadian rhythmicity throughout the SCN as a whole or showed differential expression in the subregions with the control gene Gapdh.

Results from the microarray regional analysis were entered into DAVID (Huang da et al.,

2009) (https://david.ncifcrf.gov/) for analysis of gene functional relationships and pathways.

Results:

Microarray analysis of rhythmic gene expression

Microarray data collected from 114 arrays was ran through the JTK-cycle program (Hughes et al., 2010) to determine genes that cycle in a circadian manner across the data set. Table 2.1 shows a list of known cycling genes that are significantly (p<0.05) rhythmic across the entire SCN from our data. These genes include the core circadian clock genes Cry1, Reverbα, Per1, Per2, Rorα, clock regulatory genes Dusp1, Dusp4,

Mapk4 and Rasd1 and clock output genes Dbp, Prok2 and Sik1. Results shown in table

2.2 show a total of 37 additional genes statistically (p<0.05) rhythmic within the SCN.

These genes are expressed with a circadian rhythm across all three SCN subregions and show no differences in expression pattern between regions. Many of these genes have been previously shown to be found in the SCN, but have not been shown to be rhythmic.

The expression of well characterized genes in our sample was compared to their known expression pattern to validate our assay. Per1, Nr1d1 and RORα peaked at CT6-8 and

Per2 peaked at CT12. The expression patterns of Per1 and Per2 are shown in figure 2.2.

We also selected three additional genes for analysis by qPCR to validate our array findings: Creb3l1, Dusp4 and Sik1. These three genes showed peak expression at CT6 on the microarrays and this was confirmed by qPCR analysis (figure 2.1 for Creb3l1 and

Dusp4). For Dusp4, expression peaked around CT4-8 and remained high across several time points, with an approximately 9-fold difference between the highest and lowest

A. Dusp4 - all regions combined

1000

800

600

400

200 Relative expression Relative 0 0 4 8 12 16 20 Circadian time

B. Creb3l1- all regions combined

12000 10000 8000 6000 4000 2000 Relative expression Relative 0 0 4 8 12 16 20 Circadian time

Figure 2.2: A: Dusp4 expression for all three capture regions combined across circadian time from qPCR. Dusp4 expression peaks between CT4 and CT8. B: Creb3l1 expression for all three regions combined across circadian time. Creb3l1 expression peaked around CT8 in the qPCR.

Table 2.1: Rhythmic genes that have known core circadian clock functions.

Table 2.2: Additional rhythmic genes. The importance of a circadian rhythm in these genes, either to core clock function or to circadian physiology, has not been identified.

Figure 2.3. Total number of differentially expressed transcripts for each region and the overlap between regions. The number represents the number of genes that are highest in that region. For example, the “4” in the overlap between ventral and central indicates that there are 4 genes that are higher in the ventral and central regions as compared to the dorsal.

34 expression. For Creb3l1, peak expression is at CT8, with approximately a 6-fold difference between the time points of highest and lowest expression. Similarly with Sik1, peak expression is at CT6, with about a 6-fold difference between highest and lowest expression.

Microarray analysis of regional gene expression:

There are 85 genes (table 2.4) that show significantly (p < 0.1) higher expression in the ventral region than in the central or dorsal. Figure 2.3 shows the distribution of differentially expresses genes. Several of these genes are oligodendrocyte related genes from the innervations and location of the ventral region to the optic chiasm. These genes are found in the upper left corner of figure 2.4. Among the genes that show significantly

(p < 0.05) higher expression in the ventral region, are several with known roles in regulation of SCN function: Calbindin 2, Na/K/Cl co-transporter 12a2 , proprotein convertase subtilisin/kexin type 1, carbonic anhydrase 2, 5-HT2c receptor, prostaglandin

D2 synthase There are no genes that show higher expression in the central region over the ventral and dorsal regions (figure 2.3). The dorsal region has only 4 genes with significantly higher expression over the other two regions (Rasd1 (RAS, dexamethasone- induced 1), Penk (preproenkephalin), T-cadherin, and SST (somatostatin)). Several other genes known to show regional differences in expression show differences in our results that do not quite reach the statistically significant level. These genes include the GRP receptor (Grpr), which has higher expression in the dorsal SCN than in other regions.

DAVID analysis revealed enrichment of genes generally associated with myelin or myelination and homeostasis. The enrichment groups can be found in table 2.3.

Enrichment Group # of genes enriched P-value

ensheathment of neurons 8 2.9E-7

axon ensheathment of 8 2.9E-7 neurons

regulation of action 8 4.1E-7 potential in neurons

regulation of action 8 1.0E-6 potential

ion homeostasis 13 1.3E-6

myelination 7 2.1E-6

cellular ion homeostasis 12 2.9E-6

cellular chemical 12 3.3E-6 homeostasis

cellular homeostasis 13 3.7E-6

transmission of nerve 11 5.6E-6 impulse

chemical homeostasis 13 5.9E-6

regulation of membrane 8 7.1E-5 potential

homeostatic process 14 1.2E-4

Table 2.3: DAVID results for the ventral vs. dorsal regions. These results indicate that there is significantp (p≤0.001) enrichment in these groups in the ventral region. Most of these groups contain myelin related genes. Enhancement of myelin related genes was expected in the ventral region as a result of the location just above the optic chiasm.

Groups considered to be significantly enriched in the ventral region over the dorsal region had a p-value less than or equal to 0.001.

Discussion

This study examined the transcriptome of three subregions of the SCN, across both spatial and temporal dimensions. We identified the three SCN subregions on the from SCN subregions enriched for these peptides, and that the assay of the expression of the mRNA for these peptides conformed to their known profiles (Ghada Nusair, personal communication). The use of laser capture is a critical advantage of this study.

Previously, our laboratory was the first to utilize this technique to isolate the whole SCN from surrounding tissue (Porterfield et al., 2007), but this is the first study to further refine the technique to take neuropeptide-enriched cell populations from within the SCN.

The ability to specifically capture just SCN and not surrounding tissue is integral to this study, allowing the subregions to be free of extra-SCN tissue. Previous studies that have examined the transcriptome of the SCN used tissue punches of the SCN for analysis.

While this technique yields a greater yield of tissue, and therefore total RNA, it also results in the inclusion of extra-SCN tissue. However, the reduced yield in tissue by using the laser capture technique does result in increased sample to sample variability and increased importance to the amplification steps of the microarray hybridization method.

Another unique feature of this study is the capture of samples from mice that were maintained in true free-running conditions. In most studies incorporating microarray analysis of the SCN, animals are maintained on a light-dark cycle and then sacrificed on the first or second day after release into constant darkness. This is done because these animals are not under any kind of monitoring to measure their endogenous circadian

Figure 2.4: Volcano plot comparing gene expression differences between the dorsal and ventral regions of the SCN. The red dashed line is the significance line for p=0.05. The x-axis is the log difference of expression: 0 = no difference and 1 = 2x difference, as you can see here most genes cluster between -1 and 1 thus not showing much difference in expression between regions. The y-axis is the –log10(p-value) with 5 = to 0.05). There is significantly higher expression of the genes (red dots) in the ventral region over the dorsal region.

Arhgap23 Dbndd2 Lrrtm3 Pmp22 Sox10 Arhgef10 Dgki Mag Ppp1r14a Stk32a Arhgap36 Dlx1 Mal Psat1 Synpr Atp1a2 Dock10 Mbp Ptchd1 Syt1 Auts2 Edil3 Mobp Ptgds Tpbg Bcas1 Enpp4 Mog Ptprk Trf Cacna2d3 Ephb1 Mtus2 Rbpms Trpm3 Cadm2 Ermn Neto1 Rhog Ttyh2 Cadps2 Fa2h Nkain2 Rln1 Vip Calb2 Foxn3 Pcdh19 Rnf13 Car2 Foxo1 Pcdh9 S1pr1 Cblc Gatm Pcsk1 Scd2 Cbln2 Gpm6b Phlpp1 Sema3e Ccdc153 Grm3 Pik3r1 Sfrp2 Chn2 Gsbs Pip4k2a Slain1 Cldn11 Hbegf Pkp4 Slc12a2 Cnp Htr2c Plekhb1 Slc44a1 Cryab Irs4 Pllp Slc4a4 Dab1 Lpar1 Plp1 Slc6a9

Table 2.4: List of the 85 genes that show higher expression in the ventral region over the central and dorsal regions from microarray analysis of the three regions.

Figure 2.5: Brain sections from the Alan Brain Atlas (Lein et al., 2007) for 4 genes shown to be rhythmic in the SCN. The SCN for these 4 genes is highlighted in blue and shows that these genes are present within the SCN.

39 rhythms, and therefore tissue must be collected before the natural variation in endogenous circadian period causes the animals to become out of phase with one another.

In addition, it may take several cycles for the effects of the light/dark cycle to damp out.

In our study, at the time of collection mice had been in constant conditions for at least

14days, and therefore any rhythms detected were solely a function of their endogenous clocks and not an aftereffect of the environmental light/dark cycle.

Using very stringent statistical criteria, we identified only 50 genes showing circadian oscillations in our data set. This number is considerably lower than that reported for the SCN in other studies. Panda et al. (2002) reported 337 genes with statistically significant circadian oscillation in the SCN, or, for comparison, 250 oscillating genes in the pituitary gland (Hughes et al., 2007). However, the use of stringent statistical criteria contributes to the finding that virtually all of the genes oscillating in our data set have a high amplitude rhythm. Furthermore, the genes on this list that have been investigated in the context of circadian rhythms have universally turned out to have either important functions in the circadian clock or in connecting the clock to circadian variations in cellular physiology. For example, the gene Rasd1 is responsible for gating the photic responsiveness and resetting of the clock in mice as well as suppressing non photic stimuli (Cheng et al., 2004, Cheng et al., 2006, Cheng and

Obrietan, 2006), while the Dusp1 encodes a phosphatase critical for downregulating the key signaling pathway for light-induced phase shifts of the circadian clock(Butcher et al.,

2003, Doi et al., 2007).

In addition, we used the Allen Brain Atlas (Lein et al., 2007) (http://mouse.brain- map.org) to check on the presence of several of our identified genes. The results in the

40 atlas from in situ hybridization demonstrate highly enriched expression of several genes in the SCN, including Creb3l1, Ptp4a1, Pas11b and Slc2q13 as seen in figure 2.5. It is likely that the additional genes we have identified showing circadian expression will have important physiological functions, and provide specific targets for future investigations in circadian physiology.

One of the primary goals of this study was to address the question of how much variation is present in gene expression across different SCN subregions. Although a number of differences are known, particularly among neuropeptides and their receptors, the SCN as a whole is comprised of nearly uniform-looking neurons that exclusively express GABA as their small molecule neurotransmitter (Moore and Speh, 1993). So the question arose as to whether the SCN neurons differentiate themselves primarily as a function of neuropeptide identity, or whether there are large-scale differences in gene expression that go along with these differences in neuropeptide expression. Our results suggest that the differences between subregions are relatively minor and that neuropeptides are the primary differentiating factor across these regions. It is not surprising that some of the largest differences seen between regions are expression of the neuropeptides VIP, GRP and AVP, since those were the markers used to define our subregions. However, given our large sample size (37 arrays per region), the number of differences detected between regions is quite small. There are four genes that show higher expression in the dorsal region over both central and ventral regions. These genes consist of two neuropeptides (Penk and Sst) with roles in SCN function, one gene that is responsible for the gating of photic input to the circadian clock (Rasd1) (Cheng et al.,

2004, Cheng et al., 2006, Cheng and Obrietan, 2006) and one gene involved in axonal

41 pathway formation (T-cadherin) (Hayano et al., 2014). However, in the ventral SCN there are 85 transcripts that are more highly expressed over the other 2 regions. Several of the genes from the ventral region are related to myelin and may be the result of capture of a small piece of the optic chiasm, or from oligodendrocyte processes that extend into the

SCN. Aside from VIP, two of the genes that are elevated in the ventral SCN are Calb2 and Slc12a2. Calb2 is localized to the ventral most region of the SCN in previous studies

(Silver et al., 1999, Marshall et al., 2000) and is also highest in expression in the ventral region in this study, providing further positive confirmation of the regional specificity of our captures. Slc12a2 encodes a sodium/potassium/chloride transporter and is responsible for the aiding in chloride ion equilibrium (Alamilla et al., 2014) and is expressed in the

VIP region of the SCN in rats (Belenky et al., 2010).

It is important to note that, even disregarding statistical test results, that very few genes showed average levels of expression between SCN subregions that were greater than 2-fold. Of course, this result includes only genes that were above a threshold level of expression necessary for detection by our methods, so there may be some genes that are at very low expression levels that differ highly in expression between subregions.

Nevertheless, these data provide further support for the notion that SCN neurons are, for the most part, relatively uniform in gene expression patterns.

We were unable to detect differences in circadian rhythms across different subregions of the SCN. In constant darkness, most cycling genes in the SCN are in phase with one another across the nucleus, with small differences in timing that are probably not resolvable with 2-hour time point intervals and the variability observed in our samples. While disappointing, this finding also reinforces the notion that regional

42 differences in expression in the SCN are, for the most part, small in either timing or amplitude of expression.

Future Directions

The SCN can be defined by the regional expression of the neuropeptides AVP,

VIP and GRP. Other than these neuropeptides several genes can be seen at higher expression levels in both the dorsal and the ventral regions of the SCN. The genes that are differentially expressed in the three subregions are points for further investigation as to how they contribute to the makeup of the SCN and the differences within the regions.

Some of these genes may work in the processing of information from the ventral SCN to the dorsal SCN or these genes may be part of the output processes from the SCN to target areas that receive input from the SCN.

Chapter III:

Introduction:

The core molecular mechanism of the circadian clock in eukaryotes is a transcriptional translational negative feedback loop (Moore, 1997). The loop begins with the activation and transcription of the genes Clock and Bmal1. Once translated, the Clock and Bmal1 proteins dimerize and bind to E-boxes on their target genes, enhancing transcription (Mohawk et al., 2012). Two sets of target genes include the period (Per1,

Per2, and Per3) and cryptochrome (Cry1 and Cry2) genes. Per and Cry proteins dimerize, translocate back into the nucleus and inhibit the expression of Clock and Bmal1

(Morin et al., 1977, Moore, 1997, Morin et al., 2006). The Clock/Bmal1 dimer also binds

E-boxes in the promoter region of Rorα and Reverbα. Reverbα acts to inhibit the transcription of Bmal1, while Rorα enhances the transcription of Bmal1. Another key player in the feedback loop is casein kinase I epsilon (CKIԑ), acts to phosphorylate Per protein and targets it for degradation, directs the protein to the nucleus, and binds the

Per/Cry heterodimer making it active (Nakamura et al., 2005b, Mohawk et al., 2012).

This intricate feedback loop is present in SCN neurons and almost all other cells that contain a functional circadian clock, and is responsible for driving circadian patterns of gene expression in a wide array of genes (Nakamura et al., 2010b).

An interesting question in chronobiology is how steroid hormones exert their influence on circadian rhythms (Albers et al., 1981, Labyak and Lee, 1995). In this study, the focus of investigation is estrogen. Estrogen binds to an estrogen receptor

(ER), a hetero/homodimer of α or β subunits (Cowley et al., 1997, Pettersson et al., 1997,

Saunders et al., 2002, Li et al., 2004). Most tissues contain one or the other of the subunits, while some tissues express both receptors at the same time or different times

(Couse et al., 1997, Vida et al., 2008b). The SCN of the mouse expresses immunoreactivity for ER β, primarily in the dorsal and lateral portions of the nucleus

(Vida et al., 2008b). ER α is present in only a small number of cells, however, the SCN receives input from a variety of brain regions expressing estrogen receptor α (De La

Iglesia et al., 1999). Once the receptor is bound by estrogen, it enters the nucleus and can bind directly to estrogen response elements (EREs) on the DNA. The ER can then regulate transcription of the target gene through recruitment of proteins that activate transcription (Shang et al., 2000). Thus, the estrogen signaling pathway can directly alter transcription of genes containing EREs, and previous research shows that estrogen affects the transcription of a few core circadian clock genes (Nakamura et al., 2001, Nakamura et al., 2005b, Nakamura et al., 2008b). Furthermore, estrogen has a direct effect on clock regulatory elements in the SCN (Abizaid and Mezei, 2004). Therefore, it is possible that estrogen plays a role in regulating sensitivity to photic stimuli (Abizaid and Mezei,

2004). The fact that estrogen has an effect within the SCN on the timing of the clock raises the question as to the mechanism by which core circadian clock genes are affected by the presence of estrogen. Evidence in support of this idea includes the finding that expression of the core circadian clock genes Cry1, Per1 and Per2 in the SCN are affected by estrogen (Nakamura et al., 2001, Nakamura et al., 2005b). Estrogen also affects the expression of some core circadian clock genes in peripheral tissues in a tissue-dependent

TABLE 3.1. ACCESSION NUMBERS OF BMAL1 CLOCK CK1Δ CK1Ε CLOCK GENES USED IN THE STUDY, BY SPECIES HUMAN (HOMO SAPIENS) ENSG00000133794 ENSG00000134852 ENSG00000141551 ENSG00000213923

KANGAROO RAT (DIPODOMYS ORDII) ENSDORG00000015539 ENSDORG00000008014 ENSDORG00000009978 ENSDORG00000011036

MACAQUE (MACACA MULATTA) ENSMMUG00000009690 ENSMMUG00000001919 ENSMMUG00000018329 ENSMMUG00000010122

MARMOSET (CALLITHRIX JACCHUS) ENSCJAG00000010162 ENSCJAG00000018261 ENSCJAG00000017987 ENSCJAG00000011879

MOUSE (MUS MUSCULUS) ENSMUSG00000055116 ENSMUSG00000029238 ENSMUSG00000025162 ENSMUSG00000022433

MOUSE LEMUR (MICROCEBUS MURINUS) ENSMICG00000001229 ENSMICG00000000320 ENSMICG00000001820 ENSMICG00000011583

OLIVE BABOON (PAPIO ANUBIS) ENSPANG00000006538 ENSPANG00000006882 ENSPANG00000018853 ENSPANG00000016597

ORANGUTAN (PONGO ABELII) ENSPPYG00000003473 ENSPPYG00000014762 ENSPPYG00000008771 ENSPPYG00000011818

PIKA (OCHOTONA PRINCEPS) ENSOPRG00000004853 ENSOPRG00000005292 ENSOPRG00000004698 ENSOPRG00000016339

RABBIT (ORYCTOLAGUS CUNICULUS) ENSRNOG00000014448 ENSOCUG00000022350 ENSOCUG00000007415 ENSOCUG00000002004

RAT (RATTUS NORVEGICUS) ENSRNOG00000014448 ENSRNOG00000002175 ENSRNOG00000036676 ENSRNOG00000013076

SQUIRREL (ICTIDOMYS ENSSTOG00000022144 ENSSTOG00000016179 ENSSTOG00000001725 ENSSTOG00000005334 TRIDECEMLINEATUS)

TARSIER (TARSIUS SYRICHTA) ENSTSYG00000000577 ENSTSYG00000003546 ENSTSYG00000007015 ENSTSYG00000007015

VERVET-AGM (CHLOROCEBUS SABAEUS) ENSCSAG00000004681 ENSCSAG00000005019 ENSCSAG00000002142 ENSCSAG00000006688

OPOSSUM (MONODELPHIS DOMESTICA) ENSMODG00000008708 ENSMODG00000020678 ENSMODG00000003587 ENSMODG00000018728

Table 3.1: Ensemble ID’s for all genes and species collected.

TABLE 3.1. ACCESSION NUMBERS OF CK2Α CRY1 CRY2 PER1 CLOCK GENES USED IN THE STUDY, BY SPECIES (CONT.) HUMAN (HOMO SAPIENS) ENSG00000101266 ENSG00000008405 ENSG00000121671 ENSG00000179094

KANGAROO RAT (DIPODOMYS ORDII) ENSDORG00000005082 ENSDORG00000008719 ENSDORG00000015496 ENSDORG00000003870

MACAQUE (MACACA MULATTA) ENSMMUG00000031224 ENSMMUG00000021808 ENSMMUG00000002453 ENSMMUG00000021798

MARMOSET (CALLITHRIX JACCHUS) ENSCJAG00000020853 ENSCJAG00000015099 ENSCJAG00000011551 ENSCJAG00000014556

MOUSE (MUS MUSCULUS) ENSMUSG00000074698 ENSMUSG00000020038 ENSMUSG00000068742 ENSMUSG00000020893

MOUSE LEMUR (MICROCEBUS MURINUS) ENSMICG00000013515 ENSMICG00000006849 ENSMICG00000014177 ENSMICG00000013724

OLIVE BABOON (PAPIO ANUBIS) ENSPANG00000018784 ENSPANG00000026255 ENSPANG00000013724 ENSPANG00000023840

ORANGUTAN (PONGO ABELII) ENSPPYG00000010866 ENSPPYG00000004911 ENSPPYG00000003329 ENSPPYG00000007949

PIKA (OCHOTONA PRINCEPS) ENSOPRG00000005138 ENSOPRG00000016561 ENSOPRG00000009369 ENSOPRG00000016943

RABBIT (ORYCTOLAGUS CUNICULUS) ENSOCUG00000005070 ENSOCUG00000013721 ENSOCUG00000025433 ENSOCUG00000026333

RAT (RATTUS NORVEGICUS) ENSRNOG00000005276 ENSRNOG00000006622 ENSRNOG00000007478 ENSRNOG00000007387

SQUIRREL (ICTIDOMYS ENSSTOG00000008643 ENSSTOG00000020498 ENSSTOG00000011521 ENSSTOG00000005366 TRIDECEMLINEATUS)

TARSIER (TARSIUS SYRICHTA) ENSTSYG00000003464 ENSTSYG00000010048 ENSTSYG00000013868 ENSTSYG00000012377

VERVET-AGM (CHLOROCEBUS SABAEUS) ENSCSAG00000010463 ENSCSAG00000002927 ENSCSAG00000005615 ENSCSAG00000010737

OPOSSUM (MONODELPHIS DOMESTICA) ENSMODG00000019523 ENSMODG00000001579 ENSMODG00000019881 ENSMODG00000007769

TABLE 3.1. ACCESSION NUMBERS OF CLOCK GENES USED IN PER2 REVERBΑ RORΑ THE STUDY, BY SPECIES (CONT.) HUMAN (HOMO SAPIENS) ENSG00000132326 ENSG00000126368 ENSG00000069667

KANGAROO RAT (DIPODOMYS ORDII) ENSDORG00000011263 ENSDORG00000015568 ENSDORG00000001826

MACAQUE (MACACA MULATTA) ENSMMUG00000018930 ENSMMUG00000002933 ENSMMUG00000018192

MARMOSET (CALLITHRIX JACCHUS) ENSCJAG00000000428 ENSCJAG00000015973 ENSCJAG00000003989

MOUSE (MUS MUSCULUS) ENSMUSG00000055866 ENSMUSG00000020889 ENSMUSG00000032238

MOUSE LEMUR (MICROCEBUS MURINUS) ENSMICG00000004716 ENSMICG00000003310 ENSMICG00000010861

OLIVE BABOON (PAPIO ANUBIS) ENSPANG00000008425 ENSPANG00000021704 ENSPANG00000017172

ORANGUTAN (PONGO ABELII) ENSPPYG00000013324 ENSPPYG00000008469 ENSPPYG00000006528

PIKA (OCHOTONA PRINCEPS) ENSOPRG00000015772 ENSOPRG00000012121 ENSOPRG00000012364

RABBIT (ORYCTOLAGUS CUNICULUS) ENSOCUG00000000215 ENSOCUG00000009732 ENSOCUG00000012900

RAT (RATTUS NORVEGICUS) ENSRNOG00000020254 ENSRNOG00000009329 ENSRNOG00000027145

SQUIRREL (ICTIDOMYS TRIDECEMLINEATUS) ENSSTOG00000008549 ENSSTOG00000006333 ENSSTOG00000012400

TARSIER (TARSIUS SYRICHTA) ENSTSYG00000013592 missing from assembly ENSTSYG00000001170

VERVET-AGM (CHLOROCEBUS SABAEUS) ENSCSAG00000006029 ENSCSAG00000002772 ENSCSAG00000012081

OPOSSUM (MONODELPHIS DOMESTICA) ENSMODG00000010664 ENSMODG00000013032 ENSMODG00000011532

48 manner (Nakamura et al., 2001, Nakamura et al., 2005a, Nakamura et al., 2008a,

Nakamura et al., 2010a). Though estrogen has an effect on core circadian clock genes

Per1, Per2 and Cry1, the mechanism of action is not known and there is likely to be other estrogen responsive clock genes. Thus, in this study we look at the distribution of ERE in the genomic sequences of core circadian clock genes.

Methods and Materials

Sequences:

Sequences were collected from the Ensembl genome browser (Karolchik et al.,

2003) for 20 species where sequences were available (for species list see table 1). The genes were collected with 5000 base pairs upstream of the transcriptional start site to include the normal size of known promoter regions. Sequences were collected for Clock,

Bmal1, Per1, Per2, Per3, Cry1, Cry2 CK1ԑ, CK1ɗ, CK2α, Reverbα and RORα. Exact sequences collected from Ensembl are listed in table 1.

ERE prediction and multiple sequence alignment:

Dragon ERE Finder was used to determine the presence of EREs (Bajic et al.,

2003). This program identifies potential EREs in a given sequence; however, a potential

ERE site cannot be determined as functional just by in-silico discovery alone. Several other criteria must be met in order for the ERE to be functional and even then only binding studies can confirm actual direct binding to the ERE by the estrogen receptor

(Klinge et al., 1992, Klinge, 2001). Dragon ERE Finder takes several things into account when predicting EREs in a control sequence with 83% sensitivity (Bajic et al., 2003).

Using the Dragon ERE Finder on a random nucleotide (nt) sequence only one ERE was

Gene Gene category Gene Length # of predicted EREs forward strand # of predicted EREs reverse strand # of clustered EREs within 500 bp Bmal1 clock gene 75465 6 6 4 Clock clock gene 119236 8 6 5 CK1ε clock gene 61746 7 4 4 CK1δ clock gene 31327 4 5 0 CK2α clock gene 36802 0 3 0 Cry1 clock gene 39870 1 1 1 Cry2 clock gene 30220 3 2 1 Per1 clock gene 19059 2 0 0 Per2 clock gene 40870 2 3 1 Rorα clock gene 523767 30 27 6 Reverbα clock gene 14453 3 1 0 Agt estrogen responsive 13679 3 1 4 Brca1 estrogen responsive 43043 2 0 0 Cox7 estrogen responsive 51762 4 5 2 Ctsd estrogen responsive 17710 4 4 2 Tff1 estrogen responsive 7759 1 2 1 Emc House Keeping 15349 0 1 0 Gapdh House Keeping 11829 3 1 2 Gpi House Keeping 25624 3 7 3 Psmb2 House Keeping 28348 1 0 0 Snrpd3 House Keeping 33708 6 6 4

Table 3.2: Gives the total number of predicted EREs per gene, per strand and the cluster of EREs within 500bp of each other. EREs clustered in a 500bp region may make up a functional estrogen response unit (ERU).

50 found in 13,300 nt (Bajic et al., 2003). In the promoter region of estrogen responsive genes there is a 2.8-fold higher total number of EREs found than in genes that are not responsive to estrogen (Bajic et al., 2003). Thus, Dragon ERE finder was the program used in this study.

Once the genic sequences were extracted from the human genome assembly

(version GRCh38.p3) in the Ensembl (see above), the sequences were run through the

Dragon ERE finder v. 3 (Bajic et al., 2003) tool on the web (http://datam.i2r.a- star.edu.sg/ereV3/) to determine the presence of EREs in human genes.

Genic sequences from human and other species were aligned using

MultiPipMaker (http://pipmaker.bx.psu.edu/pipmaker/). Sequence alignments across species were then analyzed using MEGA 6 (Tamura et al., 2013) with human as the reference sequence, and predicted EREs (if any) were mapped onto the multiple sequence alignment. Identified EREs were examined across all species in the alignment, to determine the extent of sequence conservation across all 20 species. However, because of the draft status of several mammalian genomes, not all genes had all 20 species representatives. It should be noted that in addition to human sequence, chimpanzee and gorilla sequences were present across all genes examined.

The extent of sequence conservation was evaluated for each individual predicted

ERE. In particular, we recorded whether the non-human sequences in the respective aligned position of a predicted ERE were (a) a complete match (to the predicted human

ERE), (b) had no identifiable sequence conservation (in other words, contained a lot of mismatches and/or insertions/deletions), (c) had a certain number of mismatched

GeneType GeneName # predicted # of conserved in # of conserved EREs # of conserved EREs in EREs 2 species, in 3 species, Human- 5 species, Human- Human-Chimp Chimp-Gorilla Chimp-Gorilla-Gibbon- Macaque Clock Bmal 12 11 7 3 Clock CSK2a 3 2 1 1 Clock Clock 13 3 1 0 Clock Cry1 2 2 1 1 Clock Cry2 5 5 3 0 Clock Nr1d1 4 2 2 2 Clock Per1 1 1 1 0 Clock Per2 5 2 0 0 Clock Rora 57 7 7 3 PosControl AGT 4 0 0 0 PosControl BRCA1 2 1 0 0 PosControl Cox7 9 2 2 0 PosControl CTSD 7 0 4* 0 NegControl EMC 1 0 1* 0 NegControl GPI 10 4 3 0 NegControl Gapdh 4 3 1 0 NegControl PSMB2 1 1 0 0 NegControl TFF1 3 3 3 1

Table 3.3: Over all conservation of the clock genes and the controls. Bmal1 is conserved even among distantly related species; may indicate shared clock properties among a larger group of taxa. Per1 is conserved among closely related species; may indicate clock peculiarities in higher primates. * EREs conserved in gorilla, but not chimp, may be related to assembly quality/missing genomic regions

52 nucleotides (compared to human reference ERE), or (d) contained an insertion or deletion within the ERE (compared to human reference ERE). Some alignments consisted of less than 20 species because the sequences for these genes were not complete in Ensembl for the particular species. Sequence identifiers for each gene for each species can be found in table 3.1.

Results

Predicted EREs

Table 3.2 shows the total number of predicted EREs in the forward and reverse strands of 11 core circadian clock genes, as well as 5 known estrogen responsive non- clock genes and 5 house-keeping genes. Rorα is among the longest genes in the genome

(Lander et al., 2001), and appears to have the largest number of EREs. Rorα has 57 predicted EREs, with 30 on the positive strand and 27 on the negative strand of DNA. No other gene in this study approaches this total number of EREs. On the other hand, the smallest number of predicted EREs are in Emc and Psmb2, with only 1 ERE each. Both genes are considered house-keeping genes and were used as controls that are not presumably affected by estrogen. Interestingly, the second smallest number of EREs was in two genes that are known to be estrogen responsive: Per1 (Nakamura et al., 2005a) and Brca1 (Xu et al., 1997). Further, Per1, Brca1 and Psmb2 have predicted only EREs in the forward strand of DNA, whereas Emc and CK2α contain predicted EREs only in the reverse strand. On average, known estrogen responsive genes have 2.8 forward strand and 2.4 reverse strand predicted EREs. Clock genes have a slightly elevated number of average EREs in both categories compared to these averages, with 3.6 forward and 3.1

Figure 3.1: All EREs mapped by their absolute position. The x axis is the position of EREs and the Y axis represents the genes of interest based upon number. Blue are EREs on the forward strand and red are EREs on the reverse strand. This figure gives a visualization of the clustering occurring for each gene.

54 reverse predicted EREs, (Rorα was removed from these calculations). The negative control, house-keeping genes have 2.6 forward and 3.0 reverse predicted EREs. The clock genes with the largest number of EREs besides Rorα are Clock (14), Bmal1 (12),

CK1ε (11), and CK1δ (9).

Clustering of EREs

EREs within 500 bps of each other were considered clustered. Table 3.2 shows that core circadian clock genes have an average of 1.6 clustered EREs, while estrogen responsive genes and house-keeping have 1.8 clustered EREs. Of clock genes, Rorα has the largest number of clusters, Cry1, Cry2 and Per2 have the smallest number of clusters and CK1δ, CK2α, Per1 and Reverbα have no clusters. Similarly, estrogen responsive genes and house-keeping have a wide variety of clusters as well ranging from 0 to 4 clusters. This pattern of clustering is seen in Figure 3.1.

Conservation of EREs

Table 3.3 shows the conservation of predicted EREs for 11 core circadian clock genes, 5 known estrogen responsive gens and 5 house-keeping genes. For three genes with predicted EREs (CK1ε, CK1δ and Snrpd3) in humans there was no sequence alignment for the EREs across any of the species examined. There were no EREs completely conserved across all species. For the clock genes the most conserved ERE is found in Reverbα, where this ERE is shared across genomic sequences of 13 of the 20 species. Among the known estrogen responsive genes the most conserved ERE is in Tff1, this ERE is conserved in 7 of the 20 species and for the house-keeping genes the highest conserved ERE is found in Psmb2 and is conserved in 4 of the 20 species. Overall, the

55 lowest conservation is found in the house-keeping genes. For Bmal1, 11 of the 12 predicted EREs show some conservation between species. For Bmal1 there is no conservation of EREs in species that are not primates. Predicted EREs for Clock, lack conservation for 12 of the 13 predicted EREs. The 1 predicted ERE for clock shows conservation in 4 primate species as well as rabbit. For Cry1, the three predicted EREs all show conservation across several species, but all species are a part of the primate family.

Cry2 predicted EREs all show conservation in the primates, but in no other species, with some primates lacking a conserved ERE. CK2α contains three EREs that show conservation across several species, with highest number of species being a part of the primate family. Reverbα has four EREs that show conservation across several species, but again mostly in primates. However, the most conserved ERE is conserved in primates as well as guinea pig, bushbaby, pika and rabbit with only 1 substitution in the ERE in mouse, rat, squirrel and opossum. In Rorα, there are only 6 of the 57 EREs that are conserved and these are only conserved across primate species. The one ERE found in

Per1 is conserved across only primate species and not in all primate species. For Per2, 2 of the 3 EREs show some conservation, one ERE shows conservation with only gorilla and orangutan and the other shows conservation with chimp and orangutan.

Discussion

Our current data shows that all core circadian clock genes contain predicted EREs, although the number of EREs that are evolutionary conserved across at least 2 closest species (such as human-chimpanzee) varies between gene categories.

Further, many EREs are conserved only among the closest primate genomes (human, chimpanzee and gorilla), but not other taxa, which may indicate that certain regulatory

56 properties of clock-related genes may be shared at different taxonomic levels. We see the most conserved ERE in the Reverbα gene; this ERE is conserved across 13 of the 20 species. This extent of evolutionary conservation is not found in any of our control estrogen responsive genes, where at most the ER are shared by 5 species. This suggests that this Reverbα ERE is highly conserved and possibly functional, if not important in regulation. No other EREs in this study show conservation in more than 7 species, this amount of conservation in Reverbα may point to a functional ERE. Though this is the most conserved ERE, there is no reason to believe that the other predicted EREs are not functional. There are several EREs that are conserved across primate species, thus pointing to a possible conserved function in primates, but not lower order mammals.

Predicted EREs show 1-3 sequence differences from the human predictions, these differences do not negate the possibility of function (Klinge et al., 1992, Loven et al.,

2001, Wood et al., 2001). Several EREs show 1-3 sequence differences from a consensus

ERE and are still functional EREs (Klinge et al., 2001). Thus these predicted EREs, though not highly conserved may still be functional for human, but are just not conserved from species to species.

The EREs found in Clock, Bmal1, CK2α, Cry1, Cry2, Per1, Per2, and Rorα show little or no conservation except through the primate family, with some EREs showing no conservation across any species. For Rorα, it was expected that the number of conserved

EREs would be greater than any of the other genes that were examined due to the total number of predicted EREs. This, however, is not the case. Only 6 of the 57 EREs were conserved. Thus, there is no increase in conservation in the gene with the largest total

57 number of EREs. For two (CK1ε and CK1ɗ) of the core circadian clock genes there was no conservation of the predicted EREs.

Another criterion for estrogen responsiveness is the clustering of EREs several hundred base pairs apart (Beekman et al., 1991). The clustering of several imperfect

EREs is an estrogen response unit (ERU). ERUs can have an effect on the transcription of the gene they are associated with (Beekman et al., 1991). This is seen in the TFFα gene, where two EREs are clustered and thus increases the expression of TFFα (El-Ashry et al.,

1996). Here we examined the core circadian clock genes for clustered EREs. The largest number of clusters was found in the gene (Rorα) which also has the highest number of predicted EREs. Within these clustered EREs there is little or no conservation across species, however, clustering alone may indicate function (Beekman et al., 1991). A total of 7 (Clock, Bmal, Per2, Cry1, Cry2, Reverbα, Rorα and CK1ε) of the clock genes showed clustered EREs. Even though conservation is limited, these genes may be regulated through the ER binding to EREs among clustered EREs in an ERU. Thus the function of the clustered EREs need to be further experimentally examined.

Examination of predicted EREs in the core circadian clock genes enables further understanding of the circadian cycle. Genes on both limbs of the feedback loop contain

EREs that are conserved across the primates. However, there are higher numbers of EREs in genes that would act to promote the positive limb of the feedback loop. Thus, it appears that the circadian cycle can be effected in both an enhancing/inhibiting manner as of the control of the cycle itself. The current data for clock genes in humans suggests that there is higher conservation of EREs across the primate family. We see little conservation once we move out of the primates and into the rodents. This is interesting because most

58 studies done on the interaction of estrogen and the circadian clock have been done in rodents. Rodents serve as a great model for human based studies, but they don’t always give us the exact transferrable data that we hope to get. Rodent studies aid in the discovery of possible pathways of action for further exploration in humans. This study shows directly that some genes may not be under estrogen control (or maybe through a different pathway) through direct binding in the rodents where sites are found to be conserved and present in the primate. Thus, further investigation needs to be done to determine if these EREs are bound directly by estrogen in the primates.

Future Directions:

The data presented in this study is the beginning of understanding how estrogen can have a direct effect on the circadian system in humans and primates and possibly some rodents. Binding affinity studies in a natural circadian system needs to be assessed to determine if the EREs discovered in the above are actually being bound by the estrogen receptor or not. It is imperative that these studies be done in several different tissue types across the organism as well as across circadian time. Once binding is established as true or false, and then the genes will need to be examined for response across circadian time and tissue type. This will give us a total picture of how estrogen is able to effect the circadian clock and how exactly the direct mechanism occurs, which genes, what circadian time and what tissues.

The knowledge of EREs in circadian clock genes and the data presented here as well as data acquired from the use of this data in future studies will give us target genes for estrogen therapies. This data may also be important in the function and control of the

59 cell cycle timing in a circadian manner, thus leading to new discoveries in the treatment of breast, cervical and uterine cancers. The general knowledge of how estrogen directly affects the circadian clock can aid in understanding further how the female and male body works and lead to future discoveries as to how fluctuating estrogen levels in women are affecting the body.

Chapter IV

Estrogen and Peripheral Clocks

Introduction:

The molecular circadian clock is a tightly regulated transcription-translation feedback loop, with individual components that can have their expression levels altered by a variety of hormones. One steroid hormone that alters the circadian timing of gene expression and behavior is estrogen. Estrogen shortens the period of the free-running activity rhythm in ovariectomized (OVX) female hamsters (Morin et al., 1977). In OVX rats there is a decrease in the rhythmicity of running and drinking behavior under constant light conditions (Thomas and Armstrong, 1989) and the presence of estrogen changes the expression pattern of the period genes Per1 and Per2 (Nakamura et al.,

2005a, Nakamura et al., 2008a, Nakamura et al., 2010a). Chronic administration of estrogen via subcutaneous implants advances the peak expression of Per2 in the SCN

(Nakamura et al., 2005a). Estrogen also delays the expression, but increases the peak expression of Per1 in the liver (Nakamura et al., 2005a). In uterine tissue, estrogen causes a biphasic, or double peak expression of Per1 and Per2 in rats implanted with an estrogen capsule (Nakamura et al., 2005a). Also, in pregnant rats Per1 is induced by estrogen in uterine cell cultures, with the increase in Per1 depending on the tissue within the uterus

(He et al., 2007). When estrogen is applied to explanted mouse uterine tissue there is a shift in the peak expression of Per2 (Nakamura et al., 2008a), but not when applied to

61 explanted uterus endometrial stromal cells (Hirata et al., 2009). In an intact animal there is a fluctuation of estrogen based on the time of day (Perlow et al., 1982) or the point in the reproductive cycle that an animal is in (Legan et al., 1975); therefore, constant levels of estrogen do not represent a normal physiological state. However, giving a single injection to an animal that has been exposed to estrogen; we can examine the acute effects of estrogen on gene expression. . With this approach the timing of injection can also be varied to target changes in gene expression in different tissues across the circadian cycle and aid in determining whether responses to estrogen are dependent on circadian time. Estrogen has an effect on circadian timing of wheel running behavior

(Morin, 1980, Takahashi and Menaker, 1980, Hajszan et al., 2010), but few studies have focused on the differential effects that estrogen can have as a function of the circadian time of day (Nakamura et al., 2005a, He et al., 2007, Nakamura et al., 2008a, Nakamura et al., 2010a), length of exposure or tissue type (Nakamura et al., 2005a, He et al., 2007,

Nakamura et al., 2008a). It is important to study hormonal effects throughout the entire circadian cycle to determine if certain genes are more responsive to estrogen at certain times of the day. This study focuses on determining the impact of estrogen on clock gene expression in liver and uterus.

Methods and Materials

Animals:

Female mice (C57BL/6J strain) aged 60 days were housed in a 12/12 LD cycle and ovaries removed via ovariectomy (OVX) under ketamine (14.12mg/ml)/xylazine

(1.18mg/ml) anesthesia with each mouse receiving a dosage of .08ml. Animals were

62 returned to the 12/12 LD cycle after surgery and allowed to heal for 21 days. On day 21 animals were given an intraperitoneal injection of either estradiol (4 µg/g body mass in

2ml) dissolved in sunflower oil, or sunflower oil vehicle alone. Injections were given at 6 hour intervals throughout the 24-hour day. The time of lights on is defined as zeitgeber time (ZT) zero. Injections were given at ZT 22, ZT 4, ZT 10 and ZT 16 and animals were sacrificed by cervical dislocation under dim red light, two hours later at ZT 0, ZT 6, ZT

12 and ZT 18. This experiment tested the short-term effects of estrogen on clock gene expression. A second set of animals were OVX as above and kept in a 12/12 LD cycle for

21 days. These animals were given injections 24 hours before sacrifice at ZT 0, ZT 6, ZT

12 and ZT 18. Animals were sacrificed by cervical dislocation and liver and uterus were removed and flash frozen. Five animals per time point per treatment were used for a total of 80 animals. All procedures were approved by the Institutional Animal Care and Use committee at Kent State University.

Tissue preparation and analysis of gene expression:

Once the tissues were removed they were stored at -80°C for no more than one month. Tissues were homogenized and total RNA was extracted and purified using

Qiagen Rneasy kits with Dnase treatment. Total RNA was then converted into cDNA for use in real-time quantitative PCR (qPCR) using a reverse transcription kit from Qiagen and a heated lid thermocycler All samples analyzed using qPCR for the expression of five genes using stock primer/probe sets from IDT (Clock, Bmal1, Per1, Per2 and Per3).

18S ribosomal RNA expression was used as the control gene. Relative gene expression was determined by converting all samples to a relative expression value through the use of a 2-ΔCT, with ΔCT being the average expression of the triplicates from the output from

Figure 4.1: 2 hour effects of estrogen injections on Clock gene expression as measured with qPCR for the liver. There is a significant effect of time of day for Clock. There is also a significant treatment effect at ZT 6 (*) with estrogen decreasing the expression of clock and shifting the peak of expression to ZT12 instead of ZT6.

1.8 Clock - Liver 24 hour

1.6

1.4

1.2

1 Estradiol 0.8 Relative Relative expression Oil 0.6

0.4

0.2

0 ZT0 ZT6 ZT12 ZT18

Figure 4.2: 24 hour effects of estrogen injections on Clock gene expression as measured with qPCR for the liver. There is no significant effect of time of day or treatment and there is no interaction between treatment and time.

65 the qPCR minus the average for 18S for the triplicates. These were then averaged and normalized to the average of ZT 0 oil samples for each gene. Significant differences in gene expression were evaluated using two-way ANOVAs, with time of sacrifice and treatment as factors. Interactions between factors were analyzed using a Tukey-Kramer

Multiple Comparison Test. Significance was ascribed for all statistical tests if p < 0.05.

Results:

Liver:

Clock – 2 hours after injection

For Clock, in the liver, there were significant effects of time (F3,32 = 18.15, p <

0.001), treatment (F1,32 = 5.65, p = 0.024), and a significant interaction between time and treatment (F3,32 = 2.96, p = 0.047). Analysis of the interaction revealed that Clock expression was significantly reduced after estradiol administration as compared to oil for injections at ZT 6 (p < 0.05). Clock expression peaked between ZT6 and ZT12 with about an average of a 6-fold difference over the ZT0 and ZT18 time points. Figure 4.1 illustrates these results.

Clock – 24 hours after injection

In the 24-hour group peak Clock expression occurred in the dark phase at ZT18 and ZT0 with less than a 2-fold difference in expression. There was no significant effect of time of day (F3,32 = 2.10, p = 0.12) or treatment (F1,32 = 0.11, p = 0.75), nor was there a significant interaction (F3,32 = 0.11, p = 0.95) between time and treatment.

Bmal1- 2hours after injection

Bmal1 - Liver 2 hour 4

3.5

2.5

2 Estradiol Oil 1.5

Relative Expression Relative 1

0.5

0 ZT0 ZT6 ZT12 ZT18

Figure 4.3: 2 hour effects of estrogen injections on Bmal1 gene expression as measured with qPCR for the liver. There is a significant effect time of day and no significant effect of treatment for Bmal1.

Bmal1 - Liver 24 hour 1.6

1.4

1.2

0.8 Estradiol Oil

0.6 Relative Expression Relative 0.4

0.2

0 ZT0 ZT6 ZT12 ZT18 Figure 4.4: 24 hour effects of estrogen injections on Bmal1 gene expression as measured with qPCR for the liver. There is a significant effect of time of day that is not dependent on treatment for Bmal1. There is also no significant treatment effect.

For Bmal1 in the liver, there was a significant effect based on time of day (F3,32

= 6.09, p = 0.002) but, no significant effect of treatment (F3,32 = 0.34, p = 0.562) or the interaction of time and treatment (F3,32 = 0.22, p = 0.882). Bmal1 expression peaked at

ZT12 for the 2-hour group with about a 3-fold difference between time points of highest and lowest expression. Figure 4.3 illustrates these results.

Bmal1-24hours after injection

In the liver, for Bmal1 expression, there was a significant effect of time of day

(F3,32 = 31.87, p <0.001), but no significant interaction between treatment (F3,32 = 0.34, p

= 0.562), nor between time and treatment (F3,32 = 0.54, p = 0.659). Bmal1 expression peaked at ZT0 and then at ZT12 with about an 4-fold difference between the time points of highest and lowest expression. The peak at ZT12 for the 2-hour data is approximately a 10-fold difference than either of the peaks for the 24-hour group. Figure 4.4 illustrates these results. Figure 4.4 illustrates these results.

Per1-2 hours after injection

Per1 expression in the liver was significantly affected based on the time of day

(F3,32 =9.42 , p < 0.001) but, no interaction between treatment (F3,32 = 0.60, p = 0.445), nor was there a significant interaction between time and treatment (F3,32 = 1.29, p =

0.295). . Peak expression of Per1 occurred at ZT6, and had an approximately 4 fold difference in expression between the time points of highest and lowest expression.

Per1- 24 hours after injection

30 Per1: Liver 2 hour

15 Estradiol Oil

10 Relative Expression Relative 5

0 ZT0 ZT6 ZT12 ZT18

Figure 4.5: 2 hour effects of estrogen injections on Per1 gene expression as measured with qPCR for the liver. There is a significant effect of time of day that is not dependent on treatment for Per1. There is also no significant treatment effect.

Per1 - Liver 24 hour 9 8

6 Estradiol 5 Oil 4 3

Relative Expression Relative 2 1 0 ZT0 ZT6 ZT12 ZT18

Figure 4.6: 24 hour effects of estrogen injections on Per1 gene expression as measured with qPCR for the liver. There is a significant effect of time of day that is not dependent on treatment for Per1. There is also no significant treatment effect.

For Per1 expression in the liver, there was a significant effect of the time of day

(F3,32 = 24.27, p < 0.001), no significant effect of treatment (F3,32 = 0.08, p = 0.786), but a significant interaction between time and treatment (F3,32 = 3.36, p < 0.05). Per 1 expression peaked at ZT6 for the 24-hour group had about a 2 fold difference between the time points of highest and lowest expression.

Per2 - 2 hours after injection

For Per2, there was a significant effect of the time of day (F3,32 = 14.85, p <

0.001) and no significant interaction between treatment (F3,32 = 0.09, p = 0.761) and no significant interaction between time and treatment. Per2 expression peaked at ZT12 with a 2-fold difference between time points of the highest and lowest expression.

Per2 – 24 hours after injection

For Per2 in the liver 24-hour after injection there was a significant effect of the time of day (F3,32 = 34.10, p < 0.001) and a significant interaction between treatment

(F3,32 = 13.47, p < 0.001) and between time and treatment (F3,32 = 7.63, p < 0.001).

Analysis of the interaction revealed that Per2 expression is significantly increased at ZT

12 by and injection of estrogen 24-hours prior. This interaction strengthened the peak expression of Per2 at ZT 12.

Per3 – 2 hours after injection

For Per3 in the liver, there was a significant effect of time of day (F3,32 = 8.01, p <

0.001, ), but no significant interaction between treatment (F3,32 = 0.02, p = 0.879) and between time and treatment (F3,32 = 0.40, p = 0.757). Per3 expression peaked at ZT 6

Per2 - Liver 2 hour 16

8 Estradiol Oil 6

Relative Expression Relative 4

0 ZT0 ZT6 ZT12 ZT18 Figure 4.7: 2 hour effects of estrogen injections on Per2 gene expression as measured with qPCR for the liver. There is a significant effect of time of day that is not dependent on treatment for Per2. There is also no significant treatment effect.

Per2 - Liver 24 hour 14

12 *

8 Estradiol 6 Oil

Relative Expression Relative 4

0 ZT0 ZT6 ZT12 ZT18 Figure 4.8: 24 hour effects of estrogen injections on Per2 gene expression as measured with qPCR for the liver. There is a significant difference with reference to time of day that is not dependent on treatment for Per2. There is also a significant treatment effect with estradiol enhancing Per2 expression at ZT12 (*).

50 Per3 - Liver 2 hour 45

35 30 Estradiol 25 Oil 20

15 Relative Expression Relative 10 5 0 ZT0 ZT6 ZT12 ZT18

Figure 4.9: 2 hour effects of estrogen injections on Per3 gene expression as measured with qPCR for the liver. There is a significant difference with reference to time of day that is not dependent on treatment for Per3. There is no significant treatment effect for Per3.

Per3 - Liver 24 hour 12

6 Estradiol Oil

4 Relative Expression Relative

0 ZT0 ZT6 ZT12 ZT18 Figure 4.10: 24 hour effects of estrogen injections on Per3 gene expression as measured with qPCR for the liver. There is a significant difference with reference to time of day that is not dependent on treatment for Per3. There is no significant treatment effect for Per3.

76 with approximately a 2-fold difference in expression between the time point of highest and lowest expression.

Per3 – after 24 hour injection

For Per3 in the liver, at 24 hours after injection, there was a significant effect of time of day (F3,32 = 52.86, p < 0.001) and no significant interaction between treatment

(F3,32 = 2.53, p = 0.121), nor between treatment and time (F3,32 = 1.76, p = 0.0175). Per3 expression peaked at ZT 6 with approximately a 2-fold difference between time points of the highest and lowest expression.

Uterus:

Clock - 2 hours after injection

For Clock, in the uterus, there was a significant effect of time of day (F3,32

= 75.00, p < 0.001, ) and a significant interaction between treatments (F3,32 = 6.07, p =

0.020) and between time and treatment (F3,32 = 9.19, p < 0.001). Analysis of the interaction revealed that Clock expression was significantly increased at ZT 0 (p<0.05).

Clock expression peaked at ZT 0 with about an 8-fold difference between the time points of highest and lowest expression. Figure 4.1 illustrates these results.

Clock – 24 hours after injection

For Clock, there was a significant effect of time (F3,32 = 25.95, p<0.001) and a significant interaction between treatment (F3,32 = 7.91, p = 0.008) and between time and treatment (F3,32 = 3.30, p = 0.033). Analysis of the interaction revealed that estrogen increased the expression of Clock at ZT 12. Clock expression peaked at ZT12 with about

Clock - Uterus 2 hour 2.5 *

1.5 Estradiol

1 Oil Relative Expression Relative 0.5

0 ZT0 ZT6 ZT12 ZT18

Figure 4.11: 2 hour effects of estrogen injections on Clock gene expression as measured with qPCR for the uterus. There is a significant effect of time of day that is not dependent on treatment for Clock. There is also a significant treatment effect at ZT 0 (*) with estrogen increasing the expression of Clock.

Clock - Uterus 24 hour 16 14 * 12 10 8 Estradiol 6 Oil

Relative Expression Relative 4 2 0 ZT0 ZT6 ZT12 ZT18

Figure 4.12: 24 hour effects of estrogen injections on Clock gene expression as measured with qPCR for the uterus. There is a significant effect of time of day that is not dependent on treatment for Clock. There is also a significant treatment effect at ZT 12 (*) with estrogen increasing the expression of Clock.

79 a 6 fold difference between time points of highest and lowest expression. There is overall higher expression of Clock in the 24-hour group for all time points, but the highest expression differences occur at ZT12 with about a 10-fold difference of expression compared to the 2-hour group.

Bmal1- 2 hours after injection

For Bmal1 expression in the uterus, there was a significant difference based on time of day (F3,32 = 34.30, p <0.001), no significant interaction between treatment (F3,32 =

0.44, p = 0.514), but a significant interaction between time and treatment (F3,32 = 3.94, p

= 0.017). Bmal1 expression peaked at ZT0 for the 2-hour group with about a 6-fold difference between time points of highest and lowest expression.

Bmal1 – 24 hours after injection

For Bmal1, there was a significant effect of time (F3,32 = 21.07, p < 0.001), a significant interaction between treatment (F3,32 = 12.20, p < 0.001) and between time and treatment

(F3,32 = 10.37, p < 0.001). Analysis of the interaction revealed that estrogen increased the expression of Bmal1 at ZT 12. For the 24-hour group expression peaked at ZT0 with about a 10-fold difference between time points of highest and lowest expression.

Per1- 2 hours after injection

Per1 expression in the uterus was significantly affected based on the time of day

(F3,32 =16.97 , p < 0.001, DF=3), there was no significant interaction between treatment

(F3,32 = 0.01, p = 0.917), but a significant interaction between time and treatment (F3,32 =

Bmal1 - Uterus 2 hour 1.6

1.4

1.2

0.8 Estradiol 0.6 Oil

Relative Relative Expression 0.4

0.2

0 ZT0 ZT6 ZT12 ZT18 Figure 4.13: 2 hour effects of estrogen injections on Bmal1 gene expression as measured with qPCR for the uterus. There is a significant effect of time of day that is not dependent on treatment for Bmal1. There is no significant treatment effect for Bmal1.

Bmal1 - Uterus 24 hour 2.5 *

1.5 Estradiol 1 Oil

Relative Expression Relative 0.5

0 ZT0 ZT6 ZT12 ZT18 Figure 4.14: 24 hour effects of estrogen injections on Bmal1 gene expression as measured with qPCR for the uterus. There is a significant effect of time of day that is not dependent on treatment for Bmal1. There is a significant treatment effect of estrogen at ZT12 (*) for Bmal1.

Per1 - Uterus 2 hour 2.5

2 Estradiol 1.5 Oil

Relative Expression Relative 0.5

0 ZT0 ZT6 ZT12 ZT18 Figure 4.15: 2 hour effects of estrogen injections on Per1 gene expression as measured with qPCR for the uterus. There is a significant difference with reference to time of day that is not dependent on treatment for Per1.

Per1 - Uterus 24 hour 9 8 Estradiol 7 Oil 6 5 4 3

Relative Expression Relative 2 1 0 ZT0 ZT6 ZT12 ZT18

Figure 4.16: 24 hour effects of estrogen injections on Per1 gene expression as measured with qPCR for the uterus. There is a significant effect of time of day that is not dependent on treatment for Per1.

5.08, p = 0.006). Peak expression of Per1 occurred at ZT18 for the 2-hour experimental group.

Per1 – 24 hours after injection

For Per1 there was a significant effect of time (F3,32 = 10.38 p < 0.001), a significant interaction between treatment (F3,32 = 5.95, p = 0.020) and a significant interaction between time and treatment (F3,32 = 4.82, p = 0.007). Analysis of the interaction revealed that estrogen increases the expression of Per1 at ZT 12. Peak expression in the 24-hour experimental group is a broad beak across ZT6, ZT12 and ZT18, with about a 3-fold difference between the time points of highest and lowest expression.

Per2- 2 hours after injection

For Per2, there was a significant effect of the time of day (F3,32 = 5.07, p =0.006), a significant interaction between treatment (F3,32 = 12.16, p = 0.001) and a significant interaction between time and treatment (F3,32 = 5.28, p = 0.005). Analysis of the interaction revealed that Per2 expression was significantly increased after administration of estrogen. Per2 expression peaked at ZT 12 with approximately an 8-fold difference between time points of highest and lowest expression.

Per2 – 24 hours after injection

For Per2 in the uterus, there was a significant effect of time (F3,32 = 34.10, p <

0.001), no significant interaction between treatment (F3,32 = 1.99, p = 0.167), but a significant interaction between time and treatment (F3,32 = 6.89, p = 0.001). Expression

* Per2 - Uterus 2 hour 7 Estradiol

6 Oil

2 Relative Expression Relative 1

0 ZT0 ZT6 ZT12 ZT18 Figure 4.17: 2 hour effects of estrogen injections on Per2 gene expression as measured with qPCR for the uterus. There is a significant difference with reference to time of day that is not dependent on treatment for Per2. There is a significant effect of treatment at ZT0 (*) with estradiol enhancing the expression of Per2 in the uterus.

Per2 - Uterus 24 hour Estradiol 10 Oil 9

8 7 6 5 4 3

Relative Expression Relative 2 1 0 ZT0 ZT6 ZT12 ZT18 Figure 4.18: 24 hour effects of estrogen injections on Per2 gene expression as measured with qPCR for the uterus. There is a significant effect of time of day that is not dependent on treatment for Per2.

Per3 - Uterus 2 hour Estradiol 1.8 Oil 1.6

1.4 1.2 1 0.8 0.6

Relative Expression Relative 0.4 0.2 0 ZT0 ZT6 ZT12 ZT18

Figure 4.19: 2 hour effects of estrogen injections on Per3 gene expression as measured with qPCR for the uterus. There is a significant difference with reference to time of day that is not dependent on treatment for Per3.

Per3 - Uterus 24 hour 12

10 Estradiol 8 Oil

4 Relative Expression Relative 2

0 ZT0 ZT6 ZT12 ZT18 Figure 4.20: 24 hour effects of estrogen injections on Per3 gene expression as measured with qPCR for the uterus. There is a significant difference with reference to time of day that is not dependent on treatment for Per3.

89 peaked for and Per2 at ZT12 with approximately a 3-fold difference for the 24-hour group between time points of highest and lowest expression.

Per3 – 2 hours after injection

For Per3, there was a significant effect of time of day in the (F3,32 = 5.25, p = 0.005), a significant interaction between treatment (F3,32 = 13.31, p < 0.001) and a significant interaction between time and treatment (F3,32 = 6.79, p = 0.001). Analysis of Per3 expression revealed a significant increase in expression at ZT 0. Per2 expression peaked at ZT 18 with approximately a 2-fold difference between the time points of highest and lowest expression.

Per3 – 24 hours after injection

For Per3, there was a significant effect of timer (F3,32 = 24.36, p < 0.001, ), but no significant interaction between treatment (F3,32 = 0.42, p = 0.523), nor between time and treatment (F3,32 = 0.97, p = 0.418). Expression peaked for Per3 at ZT12 for the 24-hour group with about a 9-fold difference between the time points of highest and lowest expression.

These results are summarized in table 4.1.

Discussion:

Estrogen is one of the main gonadal hormones of the reproductive system in mammals. It is therefore important to understand the influence of estrogen on the core mechanisms of an organism’s daily timing system. Previous studies show a link between estrogen and circadian clock genes

Table 4.1: Summary of the effect of estrogen on circadian clock gene expression by treatment, time and organ. Results are shown as a (-) if there was no significant effect of estrogen over the control, (decrease) if expression of the gene was decreased in response to estrogen and (increase) if estrogen increased the expression over the control group.

(Nakamura et al., 2005, He et al., 2007, Nakamura et al., 2008, Nakamura et al., 2010).

The previous studies focus on circadian gene expression after the implantation of an estrogen capsule or application of estrogen directly to the tissue. In contrast, this study examined the expression of core circadian clock genes in the uterus and the liver in response to single injections of estrogen at different times of day. Gene expression in these tissues was examined at four time points to test whether any effects of expression were dependent on the time of day. Two different time courses were examined. The first was single injections 24 hours before sacrifice, to determine the effect of estrogen after one entire cycle, and the second was an injection 2 hours before sacrifice, to determine the more immediate effects of estrogen on clock genes. Presumably, any effects 2 hours after injection would reflect genes directly induced (or suppressed) by estrogen, while effects 24 hours after injection would reflect changes in underlying clock timing or amplitude.

There is a significant effect of time for all genes and treatments in both the liver and uterus, except for Clock 24 hours after injection. This time of day effect has been shown in both tissues for Per1, Per2 and Bmal1 in 4 hour intervals (Nakamura et al.,

2005a, Nakamura et al., 2008a, Nakamura et al., 2010a) and reflects a circadian pattern of gene expression for Clock, Bmal1, Per1, Per2 and Per3 in the liver and uterus. The timing of peak expression is dependent on the tissue type. One immediate concern with the results is that in both the liver and uterus the rhythm of expression for Clock and

Bmal1 is inverted between the 2 hour and 24 hour group. Because these two experiments were performed under similar conditions, there is no reason why such a difference should exist. In the liver, Bmal1 expression should peak around ZT 0 (Korencic et al., 2014),

92 which is consistent with the 24-hour group but not the 2-hour group. For the uterus, peak expression of Clock and Bmal1 should be around ZT6 and ZT0 respectively (Kennaway et al., 2003). Our results suggest that the 2 hour after injection group is closest to this known expression pattern. There is a significant effect of estrogen on these genes based on the tissue and timing of injection. All other time of day results are consistent with clock gene expression patterns for peripheral tissues sampled in a light-dark cycle under the influence of estrogen(Nakamura et al., 2005a, Nakamura et al., 2010a), except for

Bmal1 showing a biphasic expression in the liver for the 2 hour treatment, with a small peak in expression at ZT0 and the main peak at ZT12.

Previous studies show that clock gene expression in the liver and uterus is changed in the presence of estrogen. One study shows that there is an effect of estrogen on Per2 in the liver (Nakamura et al., 2008a). Our current study shows that estrogen has a modest effect on circadian gene expression in both the liver and the uterus. At ZT0 there is an increase in the expression of Per2 and Clock 2 hours after administration of estrogen in the uterus compared to vehicle. This result indicates that estrogen affects both the negative and positive limb of the molecular clock feedback loop. This effect is seen 2 hours after the administration of estrogen suggesting that estrogen is directly altering gene expression, either through estrogen receptor binding at ERE sites or via an estrogen- binding g-protein coupled receptor acting through a second messenger system. In the liver, there is a limited effect of estrogen on circadian gene expression, only one gene expression difference is found at ZT12 and that is in Per2 expression. Per2 is one of the few circadian clock genes that is known to be directly enhanced by the presence of estrogen through the estrogen receptor (Gery et al., 2007). Therefore, it is not surprising

93 that Per2 expression in the liver is changed with the administration of estrogen. However,

Per2 expression was not effected in the liver, in rats that were implanted with estrogen capsules (Nakamura et al., 2005a). In the uterus there were three genes enhanced by the presence of estrogen in the 24 hour treatment, Clock, Bmal1 and Per1. Clock and Bmal1 being the positive regulators of the circadian clock and Per1 helping to make up the negative limb of the feedback loop (Ko and Takahashi, 2006). Thus, control by estrogen can occur in either limb of the clock in the uterus. These results are consistent with the results in chapter 3. Clock, Bmal1, Per1 and Per2 all contain EREs that are conserved across several species. Though, it cannot be concluded that these EREs are functional from this study, this data provides evidence that estrogen affects circadian clock genes differently depending on the tissue.

Conclusions

It can be concluded that estrogen has an effect on the circadian clock on peripheral tissues, and that the effect is tissue and treatment dependent. Our results demonstrate novel effects of estrogen on clock gene expression; however, the exact interaction is still not understood. Further investigation will need to be done to determine the mechanism of action of estrogen on circadian clock genes. A good approach would be to use the results in chapter 3 of this dissertation to inform future empirical studies examining transcriptional regulation of clock genes by estrogen.

Chapter V

Global Discussion

This dissertation focusses on two aspects of the circadian clock and its function.

The first is the spatial and temporal distribution of gene expression across the SCN, and the second is to examine the potential and empirical effects of estrogen on circadian clock function. In Chapter II I investigated the expression differences in three subregions of the

SCN and demonstrated differences in gene expression across the three regions. I identified genes that show circadian expression differences in circadian time across the

SCN. Under conditions of constant darkness circadian expression of several genes previously known to be cycling within the SCN was seen. These genes cycle throughout the SCN as a whole, but there is no difference in gene expression of cycling genes based on the region. These genes give us a new way to look at the circadian clock in the SCN.

These genes will be integral in the future research on the clock within the SCN and how the clock communicates with other regions of the brain or to the peripheral clocks.

Through further understanding of the genes that are cycling within the SCN, there is new information on how the clock in the SCN works and communicates with the peripheral organs.

Chapter II shows that the differential expression of the neuropeptides vasopression (AVP), vasointestinal polypeptide (VIP) and gastrin releasing polypeptide

(GRP), in the three subregions of the SCN, are what make each region unique. The SCN divisions are found in mice, rats and hamsters (Moore et al., 2002a, Dibner et al., 2010a), but previous studies fail to show what defines these regions as different from one another from a genomic point of view. This was the first attempt to capture regions of the SCN based on their neuropeptide expression, other microarray studies focus on whole SCN capture (Porterfield et al., 2007) or SCN punches (Heppel et al., 1947, Panda et al., 2002,

Menger et al., 2005), which pick up surrounding tissue and do not give the definition of regions that are examined here.

The second important finding in chapter II is that there are subsets of genes that are cycling in a circadian manner throughout the SCN, that have not been shown to be an integral part of the circadian clock. These genes give us new set of genes to examine for their direct role in the circadian clock. . Some of these genes may play a direct role in determining the timing of the clock, controlling transcription or signaling to the peripheral organs. With this newly discovered list of genes, there are new avenues to research the molecular clock and its function, as well as the inputs/outputs of the circadian clock. Each of these genes needs to be examined to determine if they are necessary for clock function. The ability to capture out these three regions specifically aids in understanding the gene expression differences between the regions and will lead to further knowledge of how the regions communicate and how the SCN as a whole works. Though our lists of differentially regulated genes and circadian genes are small, it is comprehensive for the SCN.

In Chapter III, my data sheds some light on the possibility of estrogen directly affecting the circadian timing system through the EREs in core circadian clock genes.

Estrogen effects the running behavior of hamsters and other rodents, but no one has been able to answer the question of “Where does estrogen effect the system and is it a direct interaction or a pathway change?” All of the core circadian clock genes examined contain estrogen response elements. Several genes (Bmal1, Clock, Cry1, Cry2, CK2α, Rorα and

Per1) have conserved EREs across the primates. While CK1ε and CK1δ have no conserved EREs across species examined. Genes containing the most conserved EREs are predicted to be under the direct control of the presence of estrogen within the system.

The data from this chapter gives new targets for investigation of estrogen response of the core circadian clock genes, leading to further knowledge of how the clock functions under hormonal control.

Chapter IV took on the challenge of exploring the effect of estrogen on 5 core circadian clock genes. This study examined estrogens effect after a single injection 2 or

24 hours before sacrifice in two peripheral tissues. Chapter IV addresses the question,

“Does estrogen have a quick or lasting effect on changes of gene expression in the peripheral tissues?” Several studies have examined the effects of estrogen on circadian clock genes in the peripheral tissues, these studies were done using implanted estrogen caplets that release estrogen over a long period of time rather constantly (Nakamura et al.,

2005a, Nakamura et al., 2010a). This study focused on the direct administration of a physiological level of estrogen, once before sacrifice. This allowed us to look at the effect of the exposure to estrogen has on the circadian clock genes either immediately (2 hour) or one full cycle later. Our results show that there is a differential effect of the

97 administration of estrogen on the liver and the uterus based on the time of day and the treatment. This gene expression difference between the two tissues gives insight into when the appropriate timing of injection might be to alter a particular gene based on the tissue as well as new insights into the effect of estrogen through possibly direct interaction or second messengers (these pathways are outlined in Chapter 1, figure 1.5).

Future Directions:

The studies done in this dissertation focused on the SCN and estrogens effect on the circadian clock. The SCN studies provide a list of differentially expressed genes based on subregion, this list can be used to further study these genes and their role in the

SCN and the clock. Several studies need to be done to determine where the proteins for these genes are found in the SCN and the Brain, to determine where these genes are having an effect. Studies also need to be done to determine the role of these genes in the timing of the circadian clock within the SCN. The gene list that was generated for circadian genes will also need further investigation to determine the control of these genes in a circadian manner within the SCN. These gene should also be examined in other tissues to determine if their rhythmicity is SCN specific or not. Chapter III results show the potential sites for estrogen to have a direct effect on the expression of the circadian clock genes, this information is necessary to further determine if these sites are bound by estrogen or not. Studies need to be done to determine the binding of estrogen to the EREs that are conserved as well as those that are not conserved to determine functionality of the predicted EREs. Chapter IV gives insight into the effect of estrogen on the circadian clock genes. This data provides an avenue for determining the mechanism through which estrogen is effecting the expression of the clock genes through

98 either a direct effect or a second messenger system. Further investigation needs to be done to determine the exact mechanism of action of estrogen on the circadian clock.

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