THE PRODUCTION AND LOCALIZATION OF LUTEINIZING HORMONE IN THE BRAIN
A thesis submitted to the Kent State University Honors College in partial fulfillment of the requirements for University Honors
by
Ya’el Courtney
May, 2019
Thesis written by
Ya’el Courtney
Approved by
______, Advisor
______, Chair, Department of Biological Sciences
Accepted by
______, Dean, Honors College
ii
TABLE OF CONTENTS
LIST OF FIGURES AND TABLES …..……………………….………………………..iv
ABBREVIATIONS………..…….……………………………………………….………v
ACKNOWLEDGMENTS……………………………………………...……………..vi-vii
CHAPTER
I. INTRODUCTION……………………………………………….………1
II. METHODS…………..………………………………………….….……16
III. RESULTS………………………………………………………………..26
IV. DISCUSSION………………………………………….…….…………..39
REFERENCES……………………………………………………………...…………...47
iii LIST OF FIGURES AND TABLES
Figure 1. Normal and Dysfunctional HPG Axis Feedback Mechanism…………...………..4
Figure 2. Single Cell RNA Sequencing Data Processing Pipeline…………….…………..21
Figure 3. Single Cell RNA Sequencing Quality Assurance Metrics…………..….……22-23
Figure 4. LHβ Probe Validation in Rat Pituitary………………………..………….……...27
Figure 5. LHβ In Situ Hybridization in Cortex………….………….……….…….……….29
Figure 6. LHβ In Situ Hybridization in the Hippocampal Formation……………………..30
Figure 7. LHβ In Situ Hybridization in the Hypothalamus………………………………..31
Figure 8. LHβ In Situ Hybridization in the Amygdala………………………….…………33
Figure 9. Sex Differences in LHβ RNA Expression……………………………….………35
Figure 10. SHAM vs. OVX Differences in LHβ RNA Expression…………………..……37
Table 1. LHβ TPM in Cortex Cells..……………...……………………………….……….29
Table 2. LHβ TPM in Hippocampal Cells…………………………………………………30
Table 3. LHβ TPM in Hypothalamic Cells………………………...………………………31
Table 4. LHβ TPM in Amygdalar Cells…………………………...………………………33
Table 5. Wilcoxon Signed Ranks Test for M v. F LH………………………..……………35
Table 6. Wilcoxon Signed Ranks Test for SHAM v. OVX LH…………………………...37
Table 7. LHβ TPM in Non-Neuronal Cells…………………………………………..……38
iv ABBREVIATIONS
Abbreviation Meaning HPG Hypothalamic-pituitary-gonadal GnRH Gonadotropin-releasing hormone LH Luteinizing hormone FSH Follicle-stimulating hormone hCG Human chorionic gonadotropin GnRHR Gonadotropin-releasing hormone receptor LHR, LHCGR Luteinizing hormone and human chorionic gonadotropin receptor AD Alzheimer’s disease HRT Hormone replacement therapy CGA Glycoprotein hormone alpha polypeptide LHB Luteinizing hormone, beta subunit OVX Ovariectomy HCR-FISH Hybridization chain reaction fluorescence in situ hybridization scRNAseq Single-cell RNA sequencing CTX Cortex HPF Hippocampal Formation HYP Hypothalamus AMYG Amygdala IT Intratelencephalic PT Pyramidal Tract GLU Glutamatergic GABA Gamma-aminobutyric acid PVN Paraventricular nucleus RSC Retrosplenial cortex CNS Central nervous system CSF Cerebrospinal fluid
v ACKNOWLEDGMENTS
With my deepest gratitude, I owe my thanks to everyone who has guided me through my undergraduate scientific career and enabled me to reach my goals. First, I am immensely thankful for my thesis advisor, Dr. Gemma Casadesus. She took me as an undergraduate in her lab when I possessed zero wet-lab skills and has fostered my growth as a scientist through her continuous encouragement, honesty, and unrelenting standards for robust science. I would particularly like to thank Dr. Casadesus for the trust she placed in me and the freedom she gave me to explore novel methods as we seek to understand
Luteinizing Hormone in a deeper way. My time in her lab has refined my skills, both personal and scientific, in ways that will benefit me greatly throughout my pursuit of my
Ph.D. I also thank the members of my thesis committee for their time and counsel: Dr.
Timothy Meyers, Dr. Wilson Chung, and Dr. Joel Hughes.
I would like to thank Megan Mey, a graduate student in the Casadesus lab, for her mentorship and constant sunny encouragement. She taught me wet-lab techniques from square one with unwavering patience and kindness and sacrifices her time on many occasions to help me. I thank Sabina Bhatta for sharing her knowledge and insightful questions throughout the process. I also thank the other members of the Casadesus lab,
Rachel, John, and Spencer for welcoming me into the lab community and helping me with my countless inane questions.
I also owe my thanks to mentors outside of the Casadesus lab who opened for me the door into a world of science. I thank my first undergraduate mentor, Dr. Joel Hughes,
vi and his cardiovascular psychophysiology lab for taking a chance on a freshman and enabling me to get my first summer research experience at Washington University in St.
Louis.
I thank the BP-ENDURE program, Dr. Diana Jose-Edwards, and Dr. Erik Herzog for mentoring me through two summers of research and teaching me the basics of scientific communication. I thank Dr. Todd Braver, Dr. Joset Etzel, and Debbie Yee and the
Cognitive Psychopathology Lab at WashU for teaching me how to learn challenging and frustrating new skills, and when to ask for help. I thank Dr. Sara Newman and Dr. Josh
Pollock and the Electrophysiological Neuroscience Lab at Kent State for letting me lead the development of my own experiment and for facilitating and understanding the evolution of my scientific interests. I thank the Broad Institute Summer Program, especially
Dr. Bruce Birren and Francie Latour, for unabashedly helping me dive into my weaknesses as a person and as a scientist and seek a growth mindset. I thank Dr. Beth Stevens and Dr.
Matthew Johnson at Harvard for their mentorship over the summer of 2018, and for their advocacy in my graduate school applications. Each of these mentors has been an indispensable piece in the puzzle of experiences that allowed me to achieve my dream of admission to a top-tier neurobiology Ph.D. program.
I thank BP-ENDURE, SACNAS, ABRCMS, and Kent State University for their monetary support of conference travel. It is through these avenues that I have been able to accrue experience presenting my research at an international level, and I have not taken these opportunities for granted.
vii My trajectory into, through, and out of Kent State has been unconventional and uniquely challenging. Strong enough words do not exist to convey my gratitude for those named and unnamed who have supported me in every imaginable way. I am thankful for my grandma, Linda Powlison, and her unconditional open arms and open ears. I am thankful for my boss at Bellacino’s of Stow, Dave Segen, who has been supportive and flexible when I take time off for summer research, for conferences, and for graduate school interviews. I am thankful for the customers I serve and bartend for, who ask me about my science and let me re-kindle my excitement for my pursuits with every explanation. I am consistently in awe that science is a real career, and that I can get paid to think about questions that are boundlessly intriguing and exciting. I am thankful for roommates who, throughout the years, have made my home environment a safe, relaxing, and accepting space.
Lastly, I thank all the scientists who have surrounded me in each research experience I’ve had. I thank those who have taken time to answer my questions, to encourage me, to inspire me. I have seen the value in a diverse body of scientists and learned that collaboration will foster better science than competition ever will. These are lessons I will hold in my heart for the rest of my life and implement at every turn in my career.
viii 1
Chapter 1: Introduction
The Importance of Studying Age-Related Cognitive Decline
Over the last 200 years, the world has achieved impressive progress in health that has led to dramatic increases in life expectancy. Since 1900 the global average life expectancy has more than doubled and is now approaching 70 years, and in some countries is as high as 85-90 years. Although this increasing life expectancy generally reflects positive human development, it brings new challenges. These challenges stem from the fact that growing older is still inherently associated with biological and cognitive degeneration, although the progression of cognitive decline, physical frailty, and psychological impairment varies between individuals. Degenerative aging processes underlie a host of diseases including cancer, ischemic heart disease, type 2 diabetes,
Alzheimer's disease, and others (Atwood & Bowen, 2011; Prasad, Sung, & Aggarwal,
2012).
Mental health deterioration due to chronic neurodegenerative diseases represents the largest cause of disability in the world. There are well documented, common patterns of negative effects of aging in the brain. These especially relate to learning and memory that are regulated by brain regions that comprise the memory portion of the limbic system
(Rolls, 2015). These areas include the cingulate, entorhinal, and parahippocampal cortices as well as the hippocampal formation. Generally, visuospatial capabilities, psychomotor speed, and general intelligence decrease with age 2
(Kolanowski et al., 2017; Li et al., 2011; Lindeboom & Weinstein, 2004; Martin, Wittert,
& Burns, 2007; Salthouse, 1996; Shock, 1984). Spatial memory is also impaired with age, especially the ability to form a cognitive map. This ability is highly dependent on the hippocampus, indicating that hippocampal function decreases with age (Bryan et al.,
2010; Jeffery, 2018; Packard & McGaugh, 1996; Ziegler & Thornton, 2010b).
Human aging is a complex process with multiple driving factors. Many scientists have posited theories as to what primarily underlies senescence. Overall, however, these factors all involve events that undercut cellular integrity by damaging proteins, nucleic acids, or membranes. For example, Hayflick’s theory of limited cell replication states that a normal human cell can only replicate and divide 40-60 times before it cannot divide anymore and ultimately dies (Wright & Hayflick, 1975). This is related to the observation that telomeres shorten each time a cell divides, and that telomere shortening is related to stem cell dysfunction in aging and an inability to regenerate certain tissues (Blasco,
2007). Especially in the brain, the effects of aging are also related to free radical damage
(Oliver & Reddy, 2019; Stefanatos & Sanz, 2018) and excess inflammation (Meira et al.,
2008). Each of these inarguably plays a role in bodily and brain aging. However, another physiological factor that changes dramatically through the aging process and underlies, at least partially, CNS deterioration is hormone balance (Bowen & Atwood, 2004; Gibbs,
2010; Hojo et al., 2004; McEwen & Alves, 1999).
The Role of Hormone Balance in Aging
Hormones are signaling molecules that are transported through the circulatory system to produce effects on organs throughout the body. They regulate all physiological 3
functions through their ability to protect cells from external insults and optimize adaptive and metabolic cellular responses. Because they are so integral to basic cell function, imbalances affect total homeostasis and cause functional decline throughout the body.
Reproductive hormones, particularly, are involved in essential processes throughout growth and development. The Reproductive-Cell Cycle Theory considers that these same ubiquitous hormones act in an antagonistic pleiotropic manner to control aging via cell cycle signaling and that their dysregulation in later life drives senescence (Atwood &
Bowen, 2011).
These reproductive hormones are largely tied to the hypothalamic-pituitary-gonadal axis (Atwood et al., 2005; Bowen & Atwood, 2004). The hypothalamic-pituitary-gonadal
(HPG) axis refers to the hypothalamus, pituitary glands, and adrenal glands as acting in concert as a single system. It plays a critical role in development and regulation of several body systems, with historical research emphasis placed on its role in the reproductive and immune systems. Fluctuations in this axis cause changes in hormones produced by each gland and have various local and systemic effects.
In a healthy system, the HPG axis is controlled by a negative feedback loop. The hypothalamus releases gonadotropin-releasing hormone (GnRH) which acts on its receptor (GnRHR) in the anterior pituitary. GnRHR signaling leads to the production and secretion of the gonadotropins, including luteinizing hormone (LH) and follicle- stimulating hormone (FSH). LH proceeds to act in the gonads and stimulate the release of sex steroids, which complete the negative feedback loop by inhibiting the release of
GnRH. 4
Regulation of the HPG axis changes dramatically during the aging process, leading to drops in androgens in males and estrogens in females. In males, this decrease occurs gradually in a process referred to as andropause. Conversely, in females, menopause is characterized by a dramatic and rapid reduction in estrogen. This decrease in estrogen removes negative feedback on gonadotropin production, leading to a 3x increase in peripheral LH (Figure 1; Daniel, Hulst, & Berbling, 2006).
Figure 1: A general overview of the negative feedback mechanism of the HPG axis
functioning normally (A) and after reproductive senescence (B). from Bidinotto 2017.
In women more so than in men, molecular, clinical, and epidemiological evidence suggests that dysregulation of HPG axis hormones underlies age-related diseases such as stroke (Wilson et al., 2008), osteoporosis (Sun et al., 2006), heart disease (Lee et al.,
2009) and cancer. At a CNS level, HPG axis dyshomeostasis is a risk factor for AD.
Females have a higher risk of developing AD and this is linked to the changes in reproductive hormones that occurs during menopause. Importantly, increased prevalence 5
of cognitive disease correlates with the abrupt earlier loss of gonadal function (Gleason,
Cholerton, Carlsson, Johnson, & Asthana, 2005) as well as natural menopause (Atwood et al., 2005; Rapp et al., 2003; Sherwin & McGill, 2003). In support of this relationship, receptors for HPG hormones have been found in areas associated with learning and memory, especially the hippocampus (Ascoli, Fanelli, & Segaloff, 2002; Roepke,
Ronnekleiv, & Kelly, 2011).
Since mice do not undergo menopause in a comparable way to humans, one way to further study these effects is to subject mice to an ovariectomy. Surgical animal models of menopause yield cognitive decline similar to what is observed in menopausal women (Bove et al., 2014; Farrag, Khedr, Abdel-Aleem, & Rageh, 2002; Heikkinen,
Puoliväli, & Tanila, 2004). For example, ovariectomy results in deficits in recognition memory (Daniel et al., 2006; Wallace, Luine, Arellanos, & Frankfurt, 2006), novelty seeking (Baeza, De Castro, Giménez-Llort, & De la Fuente, 2010), and spatial memory
(Bohacek & Daniel, 2010). In parallel with cognitive deficits, several studies show that ovariectomy leads to declines in neuroplasticity, similar to normal aging, namely spine density as well as a reorganization of synapses in the prefrontal cortex and hippocampus
(Bloss et al., 2013; Wallace et al., 2006). Together these data suggest that the HPG axis is clearly relevant to aspects of cognitive function and its dysregulation linked to age- related cognitive decline and Alzheimer’s Disease (AD) pathology.
Hormone Replacement Therapy to Treat Age-Related Cognitive Decline
Ovariectomy-induced cognitive decline is restored by normalizing reproductive steroid hormones (Blair et al., 2016; Bohacek & Daniel, 2010; Bryan et al., 2010; 6
Chlebowski et al., 2010; Daniel et al., 2006; Heikkinen et al., 2004; Palm et al., 2014;
Telegdy, Adamik, Tanaka, & Schally, 2010; Wallace et al., 2006; Ziegler & Thornton,
2010a). Furthermore, women with AD demonstrate lower levels of estrogen, which has encouraged efforts towards hormone replacement therapy to improve cognition and decrease AD risk in postmenopausal women. This has met with limited success: a randomized double-blind study found that 17B-estradiol treatment successfully raised serum estrogens provided no significant improvement in cognition (Polo Kantola et al.,
1999). Furthermore, the Women’s Health Initiative funded a study of 16,000 women that indicated HRT with estrogen and progestin increased risk of dementia (Nelson, Walker,
Zakher, & Mitchell, 2012; Rossouw et al., 2002).
Epidemiological data suggested that benefits of HRT seemed to depend on the time of administration in relation to menopause, where it becomes ineffective 10 years after menopause (Rapp et al., 2003; Sherwin & McGill, 2003). Given this apparent critical period for administration of HRT, it was postulated that there is a bias towards the efficacy of HRT in healthy cells only. That is, estrogens may only be beneficial when administered to healthy neurons, and to protect from neurological damage, estrogens would have to be preemptively administered (Brinton, 2008). Unfortunately, recent clinical data do not seem to support epidemiological findings (Henderson et al., 2016).
While hormone replacement therapy with estrogen can produce dramatic improvements in cognitive function in rodents models of menopause (Blair, Bhatta, &
Casadesus, 2019a; Bohacek & Daniel, 2010; Bryan et al., 2010; Daniel et al., 2006;
Heikkinen et al., 2004; Henderson et al., 2016) and these findings are reflected 7
epidemiologically in women, clinical trials thus far have failed to really support a beneficial role for steroid hormones in humans. Additionally, HRT with estrogen increases the risk of breast cancer, ovarian cancer, heart attack, and stroke (Collaborative
Group On Epidemiological Studies Of Ovarian Cancer et al., 2015; Rossouw et al.,
2002). As such, it is imperative to research other therapeutic candidates that may be beneficial without these deleterious effects.
Luteinizing Hormone: A Paradigm Shift
Luteinizing hormone (LH) is a critical gonadotropin in the regulation of the reproductive system. LH stimulates the production of sex steroids, promotes progesterone secretion in the luteal phase, and initiates oocyte maturation (Baskind &
Balen, 2016). Canonically, LH is produced in the anterior pituitary and its receptor is present in the gonads where it carries out these reproductive functions. Its reproductive functions have been well-characterized in the literature, especially as they relate to increasing fertility (Ezcurra & Humaidan, 2014).
In recent years, the relevance for LH as a CNS active hormone is becoming evident (Blair, Bhatta, Mcgee, & Casadesus, 2015; Blair, McGee, Bhatta, Palm, &
Casadesus, 2015). Abnormally high peripheral LH causes memory impairments in mice
(Casadesus et al., 2007), and elevated peripheral hCG (shares LH receptor) impairs working memory and increases brain amyloid-B40 in a mouse model of AD (Barron,
Verdile, Taddei, Bates, & Martins, 2010). Pharmacological approaches that lower peripheral LH synthesis such as Cetrorelix, antide, or leuprolide acetate, all GnRHR modulators which downregulate LH synthesis, improve cognition in ovariectomized 8
rodents and in rodent models of AD (Barron et al., 2010; Blair et al., 2016, 2019a;
Bryan et al., 2010; Casadesus et al., 2007; Palm et al., 2014; Telegdy et al., 2010;
Ziegler & Thornton, 2010b). Importantly, unlike estrogen replacement in ovariectomized rodents or human epidemiological data (Bohacek & Daniel, 2010;
Chlebowski et al., 2010; Rapp et al., 2003; Wallace et al., 2006), downregulation of peripheral LH rescues ovariectomy-induced cognitive deficits and spine plasticity loss regardless of the timing of treatment onset (Blair et al., 2016).
The role for LH in the CNS is further validated by the wide expression of its receptor in cognition related areas including the hippocampus, midbrain, and cortex (AL-
Hader, Lei, & Rao, 1997; al-Hader, Tao, Lei, & Rao, 1997; Apaja, Harju, Aatsinki,
Petäjä-Repo, & Rajaniemi, 2004; Hostetter, Gallo, & Brownfield, 1981; Lei, Lichtt, &
Hiatt, 1993; Mak et al., 2007).
Luteinizing Hormone Receptor Mechanism
Investigation of the signaling pathways associated with LH receptor modulation suggests a potential role for the luteinizing hormone/human chorionic gonadotropin receptor (LHCGR) in cognition and neuroplasticity (Apaja et al., 2004; Casadesus et al.,
2007; Hou, Arvisais, & Davis, 2010; Mores, Krsmanovic, & Catt, 1996). The LHCGR belongs to the leucine-rich-repeat-containing G-protein coupled receptor subfamily
(LGRs). GPCRs transduce extracellular signals through their seven-helical transmembrane (7TM) domains to activate G-proteins. While most GPCRs have short extracellular N-terminal peptides and bind small molecules, LGRs have unusually large ectodomains containing leucine-rich repeats. This enables the binding of large ligands 9
like hormones. Upon hormone binding, conformational changes in the receptor transduce hormone signals downstream to the inside of the target cell.
LHCGR canonically acts for steroidogenesis via the Gas/cAMP/protein kinase A pathway but has also been shown to activate additional pathways including the
Gaq/inositol phosphate/protein kinase C, protein kinase B, and ERK1/2 pathways
(Ascoli et al., 2002; Narayan, 2015). Several studies have also shown that LHCGR can evoke signaling that is robustly linked to learning and memory via GSK3b (Palm et al
2014), the mTOR cascade (Hou et al., 2010), and its phospholipase C signaling. The mTOR cascade has been intimately associated with synaptic plasticity and ASD
(Donadeu et al., 2011; Hoeffer & Klann, 2010). On the other hand, phospholipase C signaling is known to diminish long term potentiation and facilitate long term depression in the hippocampus (Taufiq et al., 2005) and also regulate neurite outgrowth. The involvement of LH receptor activation is further supported by work showing that modulation of LH receptor signaling leads to changes in cognition, long term potentiation-related signaling, and neuronal plasticity both in terms of spines and neurite outgrowth (Bryan et al., 2010; Palm et al., 2014, Blair et al., 2016; Blair et al., 2019).
However, while the involvement of LHR signaling in cognition and neuroplasticity is becoming abundantly clear, the source of LH is less clear and the focus of this thesis.
Luteinizing Hormone Production
LH is a heterodimeric glycoprotein consisting of CGα (glycoprotein hormone alpha polypeptide) and LHβ. CGα is a common alpha subunit shared between luteinizing hormone (LH), follicle-stimulating hormone (FSH), human chorionic gonadotropin 10
(HCG), and thyroid-stimulating hormone (TSH). The LHβ subunit consists of 120 amino acids that confer its unique biological action and are responsible for the specificity of its interaction with the LH receptor. The beta subunit displays significant homology with hCG and both stimulate the same receptor, however, the hCG beta subunit contains an additional 24 amino acids and differs in its sugar chains.
The primary accepted route of LH production occurs when gonadotropin- releasing hormone (GnRH) is released from the hypothalamus and acts on its receptor
(GnRHR) in the pituitary gland. When the GnRHR is activated, LH is produced in the anterior pituitary and secreted into the bloodstream. It causes the production of estrogen in females and testosterone in males, and estrogen and testosterone to participate in a regulatory negative feedback role by inhibiting further release of GnRH from the hippocampus (Czieselsky et al., 2016). As such, LH levels are commonly measured and reported as serum levels.
The effects of serum LH on cognition are not yet well understood. On one hand, while some hypothesize that LH crosses the blood-brain barrier (Lukacs, Hiatt, Lei, &
Rao, 1995) to activate the LH receptor and produce CNS deficits (Barron et al., 2010;
Bowen et al., 2004; Verdile et al., 2014; Ziegler & Thornton, 2010b) and AD development (Casadesus et al., 2007). LH is a large glycoprotein that has been shown by others not to cross the blood-brain barrier (Jiang, Dias, & He, 2014; Ondo, Mical, &
Porter, 1972). Furthermore, the involvement of LH receptor signaling pathways in cognition/plasticity promotion, make the above hypothesis unlikely. On the other hand, increasing evidence suggests there is an inverse relationship between serum LH levels 11
and brain LHβ levels after ovariectomy (Blair et al., 2019a; N. Emanuele, Oslapas,
Connick, Kirsteins, & Lawrence, 1981; Palm et al., 2014). To this end, LHβ protein has been identified in cognition-modulating areas such as the hippocampus, the cingulate cortex, and midbrain structures such as the thalamus and superior colliculi (Bidinotto,
2017; Blair, Bhatta, et al., 2015). LHβ protein localizes to synaptic vesicles of pyramidal neurons (Blair et al, 2015). Importantly, a study comparing human AD and control brains showed that LHβ mRNA levels are decreased in AD patients even though serum levels are increased (Palm et al 2014).
The presence of LHβ mRNA in the brain, taken in conjunction with the inverse relationship between brain and serum LH, suggests that there is LHβ production independent of the anterior pituitary. Notably, the highest levels of LHβ protein have been repeatedly observed in the hypothalamus (Arrau, Croxatto, & De la Lastra, 1965;
Guillemin, Jutisz, & Sakiz, 1963; Hostetter et al., 1981), with present but much lower levels demonstrated in the cingulate cortex, temporal cortex, hippocampus, prefrontal cortex, basal ganglia/thalamus, and cerebellum (Blair, Bhatta, & Casadesus, 2019b; N.
Emanuele et al., 1981; Nicholas Emanuele et al., 1985). Intriguingly, levels of LH protein in the brain seem to change in some areas after ovariectomy while remaining constant in others. To this end, cognition-related areas follow an inverse relationship between SHAM
(low peripheral LH) vs. OVX conditions (High peripheral LH) but not basal ganglia/thalamus, cerebellum, or hypothalamus, the brain regions with the highest LHβ levels out of all areas examined (Blair et al 2019). This further supports a role of brain
LHβ in regulating learning and memory processes. However, it is imperative to address 12
whether 1) LHβ is peripherally produced or locally produced in the brain as Palm et al.,
2014 would suggest, 2) if locally produced in the brain, whether all brain LHβ is synthesized in the hypothalamus and then differentially transported to other brain regions or 3) whether it is independently produced in various regions within the brain 4) whether
LHβ transcription levels show a similar inverse relationship with serum LH to that between brain LHβ and serum LH, such as during ovariectomy and 5) whether there are general sex differences in LHβ transcription. These questions form the basis for the current study, as it will be useful to resolve where LHβ is transcribed as we move towards understanding its mechanisms and importance in cognitive processes.
Research Aims
We have an elementary understanding of LHCGR in terms of where it is located and some processes it mediates. However, much less has been done in terms of characterizing LH itself. Several pressing questions must be answered to further our understanding of LH regulation and function. The Casadesus lab and others (Nicholas
Emanuele et al., 1985; Kaetzel et al., 1985; Short, O’brien, & Graff-Radford, 2001) have made preliminary steps in identifying LHβ protein localization via immunohistochemistry and sandwich ELISA in several brain regions, but it is not yet conclusively determined whether LH is transcribed within the brain, whether it is primarily produced in one region and transported to others or whether the inverse relationship between peripheral levels of hormone and LHβ protein in the brain hold at the level of transcription. Additionally, it has not been conclusively determined which 13
neuronal or glial subtypes have LH. This study seeks to illuminate these two areas by pursuing the following research aims:
Aim 1: Use in situ hybridization to locate LH mRNA transcripts in a thorough, whole brain assay in male, female, and ovariectomized mice.
Aim 2: Utilize single-cell RNA sequencing to validate the observations from our in-situ work and begin to address colocalization of LHβ with known cell type markers.
By illuminating the localization and transcription of LHβ, both in terms of neural region and cell-type, hopefully we can move closer to understanding its mechanism of action. Then, perhaps more effective therapeutics can be developed using this knowledge.
Aim 1 will be addressed utilizing a newer method called third generation in situ hybridization chain reaction (Choi et al., 2018). Standard in situ hybridization (ISH) allows for the visualization of mRNA location in tissue by using complementary RNA probes and fluorescent tags. However, standard ISH has several weaknesses including high background signal, inability to label several mRNAs at once, and difficulty quantifying results. In situ hybridization based on the mechanism of the hybridization chain reaction (HCR) has addressed multi-decade challenges that impeded imaging of mRNA expression in diverse organisms, offering a unique combination of multiplexing, quantitation, sensitivity, resolution, and versatility.
HCR provides a method of sensitively amplifying the target mRNA. An HCR kit comes not only with traditional DNA probes for in situ hybridization but with amplifier sequences. Each HCR amplifier consists of two types of kinetically trapped DNA 14
hairpins that coexist stably, storing the energy to drive a conditional self-assembly cascade upon exposure to an initiator sequence. This initiator sequence is part of the
DNA probes designed for the target of interest, so only this gene target is amplified strongly (Dirks & Pierce, 2004). The role of HCR in in-situ hybridization is to boost the signal from real mRNA above background autofluorescence inherent to the sample. To further accomplish this, the DNA probes are split into two complementary halves, and both must be bound to the target of interest to achieve amplification, thus reducing nonspecific binding (Choi et al., 2018). Because of its unique background suppression capability and avoidance of nonspecific binding, HCR-FISH was chosen over conventional in situ hybridization to localize LH mRNA transcripts.
Aim 2 requires the identification of specific neuronal subtypes associated with luteinizing hormone and luteinizing hormone receptor. Historically, gene expression studies have been limited to the analysis of pooled populations of cells, which was necessary to obtain sufficient RNA for analysis. This does not enable identification of the cell types that express certain genes but instead provides an average of heterogeneous components. Cell heterogeneity is a key feature of organ development, especially notable in the brain. It is critical to be able to determine with high resolution which neuronal or glial subtypes are involved in various processes.
The past decade has witnessed rapid growth in the analysis of gene expression, allowing it to be carried out at a much higher resolution than previously possible. In fact, the expression level of every gene, even in a single cell, can now be defined. This is known as single-cell RNA sequencing (scRNAseq) and enables rapid determination of 15
precise gene expression patterns of tens of thousands of individual cells. scRNA seq is enabled by first isolating single cells and powerfully amplifying their RNA content
(Potter, 2018). Then, RNA is reverse transcribed into cDNA which then undergoes high- throughput DNA sequencing.
Genes that are highly expressed within a sample produce more RNA, more cDNA, and more DNA sequence reads than genes that are weakly expressed. scRNAseq ultimately provides a digital readout of gene expression, with the number of DNA sequence reads aligning with the expression level of a gene in a sample. During analysis, cells are clustered in groups that appear to be the same type based on their RNA expression and can be identified with known cell type markers. This study colocalized
LH mRNA with known cell type markers to identify which cell types contained these mRNA transcripts.
16
Chapter 2: Methods
Aim 1: HCR-FISH
Mice
Eleven mice were used in total for this work: three males, three females, two ovariectomized females, and three females receiving sham surgery. All were C57BL/6J mice obtained from The Jackson Laboratory. Mice were housed in accordance with the
Kent State University Institutional Animal Care and Use Committee. Housing included water and food ad libitum.
Ovariectomy
3 female WT C57BL/6J mice were ovariectomized and 3 female WT C57BL/6J given SHAM surgeries at 2 months of age. Briefly, once the proper plane of anesthesia was reached using isoflurane, a small incision was made and both ovaries were removed.
Control animals (SHAM) animals underwent identical procedures, but the ovaries were exposed and placed back in the abdominal cavity rather than being resected. Wound clips closed the incision, and the animals were placed in clean cages to recover and subsequently moved back to their housing rooms.
Tissue Collection
The 3 male and 3 female WT C57BL/6J mice were sacrificed at 4 months of age for tissue collection. The 2 ovariectomized females and 3 sham surgery females were given ovariectomies/sham surgeries at 2 months and sacrificed at 4 months. 17
Brain tissue was fresh frozen in OCT within a cryomold and stored at -80°C until cryosectioned.
Brains were sectioned coronally at 10 microns using a Leica CM1950 cryostat. To ensure a representative assay of the whole brain, sections were kept every 200 microns in triplicate, resulting in a total of 45 unique sections per mouse to be stained. This interval was chosen to best capture every possible brain region. Sections were immediately mounted to Fisherbrand Superfrost Plus microscope slides and air-dried for at least 30 minutes before being stored at -20°C.
Slide Preparation
Tissue sections were removed from -20°C storage, OCT removed from the slide with tweezers, and appropriate hydrophobic barriers drawn around tissue samples using the ImmEdge pen from Vector Laboratories. Sections were then fixed by immersion in
4% PFA at room temperature for 15 minutes (per the Allen Institute protocol for in situ hybridization). After fixation, tissue was permeabilized by incubation in 70% EtOH for 2 hours at room temperature.
In Situ Hybridization
The neuroanatomical localization of LH was accomplished through in situ hybridization, specifically in situ hybridization chain reaction (HCR-FISH). Appropriate probes and buffers were obtained from a Molecular Technologies v3.0 HCR kit (Choi et al., 2018). Tissue samples were stained every 200 microns coronally to construct an atlas of 65 sections. HCR-FISH was performed directly on tissue sections mounted on slides. 18
Tissue was pre-hybridized in 30% probe hybridization buffer for 10 minutes inside a 37°C humidified chamber. A probe solution was made using 0.2 uL of 2 uM probe stock per 100 uL of probe hybridization buffer. This mixed solution was added on top of the sample, a coverslip was added, and it was incubated overnight (12-16 h) in the
37°C humidified chamber. After incubation, the excess probe was removed by a series of
15-minute washes with the following ratios of probe wash buffer and SSCT (saline- sodium citrate + tween):
Wash Probe Wash Buffer: SSCT
1 3:1
2 1:1
3 1:3
4 0:4
To achieve amplification, amplification buffer was then added on top of the tissue sample and incubated for 30 minutes at RT. Snap-cooled hairpins were then added to amplification buffer, added on top of the tissue sample and allowed to incubate overnight
(12-16 h) at RT. After incubation, excess hairpins were removed by a series of three washes in 5 x SSCT. The slide was then dried, Invitrogen SlowFade Gold antifade reagent with DAPI added as a mountant, and coverslipped for imaging.
Imaging
Slides were imaged using an Olympus Fluoview 1000 confocal microscope and accompanying Olympus confocal software. Images were taken with Olympus UPLSAPO
Super Apochromat objectives: a 20x objective (UPLSAPO 20x), a 40x oil objective 19
(UPLSAPO 40XS), and a 60x oil objective (UPLSAPO 60XO). DAPI counterstaining was visualized with a 405 nm laser and LHB mRNA (Alexa Fluor 488) with a 488 nm laser.
Quantification
LHβ mRNA was quantified manually using a standard visual ordinal scale from
0-5, with 5 representing the highest level of RNA. For each region of interest, the same field of view was considered at 40x magnification. The number of cells presenting LHβ
RNA was evaluated independently by two researchers for each mouse. Because an ordinal scale was used, the median of the expression level for each experimental group
(M, F, OVX, SHAM) was taken as the central tendency of the data. Wilcoxon Signed
Ranks Tests were administered to the data via IBM SPSS Statistics v24.0 to ascertain whether observed differences were statistically significant.
Aim 2: Single Cell RNA-Sequencing Analysis
Bioinformatics analysis of single-cell RNA sequencing data was used to achieve cell- type localization of LHβ transcripts. The expression data came from two main sources: the Allen Institute “Allen Cell Types Database” (Allen Cell Types Database, 2018) and the Janelia NeuroSeq project. Several other databases were used to validate the existence of LHβ in identified cell types: DropViz (Saunders et al., 2018), the Broad Institute
Single Cell Portal, and data supplied by (Rosenberg et al., 2018).
RNA sequencing
Allen Institute 20
The Allen Institute mouse cell types dataset was created to provide a comprehensive overview of cell types in the primary visual cortex (VISp) as well as a comparison with alternate cortical regions and secondary motor areas. The dataset contains 133 putative cell types, sampled to sufficient depth to capture substantial transcriptomic diversity. The sampling strategy for cortex used four mouse Cre lines to capture pan-neuronal (Snap25-IRES2-Cre;Ai14), glutamatergic (Slc17a7-IRES2-
Cre;Ai14) , and GABAergic (Gad2-IRES-Cre;Ai14, Slc32a1-IRES2-Cre;Ai14) neurons from 4-5 layers of cortex (L1, L2/3, L4, L5, L6) based on relative cell proportions in vivo. The number of cells captured reflects a coverage goal of at least 16 cells of each given type, at a frequency of 0.4% of glutamatergic neurons and 0.1% of GABAergic neurons. Data includes cells from the anterior cingulate (5,028 cells), anterior lateral motor cortex (9,956 cells), lateral geniculate nucleus (1,824 cells), lateral posterior nucleus (86 cells), the primary motor cortex (4,916 cells), and primary visual cortex
(15,205 cells).
Tissue samples were obtained from adult (postnatal day P53-P59) mice, and sequencing executed on the Illumina HiSeq 2500v4 platform with a paired-end 100bp read length.
Janelia
Data gathered from the Janelia NeuroSeq project were also generated from adult mice using the Illumina HiSeq2500 platform with a paired-end 100bp read length.
However, I added this dataset to the Allen Institute data because the NeuroSeq project sampled cells from a larger diversity of neural regions: the olfactory bulbs, other 21
olfactory regions, isocortex and claustrum, hippocampal formation, striatum and related ventral forebrain structures, pallidum, thalamus, hypothalamus, midbrain, medulla, pons, cerebellum, retina, olfactory epithelium, spinal cord, and even some peripheral nervous system and non-neural tissue.
Data Processing and Analysis
Figure 2: scRNA sequencing data analysis pipeline
Raw fastq sequencing data were uploaded to the Galaxy web platform, and I used the public server at usegalaxy.org to perform quality control analyses the data (Afgan et al., 2018). Quality control analyses were run using the package FastQC to validate per base sequence quality, per tile sequence quality, per sequence quality scores, per base sequence content, per sequence GC content, per base N content, sequence length 22
distribution, sequence duplication levels, overrepresented sequences, and adapter content (Figure 3). 23
Figure 3: Quality assurance metrics resulting from FastQC package. A. GC distribution over all sequences. B. N content across all bases. C. Quality scores across all bases. D. Quality score distribution over all sequences.
Illumina sequencing adapters were clipped from the reads using the fastqMCF program (Aronesty et al 2011) for alignment. After clipping and quality control checks, the paired-end reads were mapped using Spliced Transcripts Alignment to a Reference 24
(STAR v2.5.3) (Dobin et al., 2013) using default settings. Files were aligned to the mm10 mouse genome sequence (Genome Reference Consortium, 2011) with the RefSeq transcriptome version GRCm38.p3 and updated by removing duplicate Entrez gene entries from the gtf reference file.
Cells that passed QC criteria were evaluated for similarity using the iterative clustering R package hicat, available via Github (https://github.com/AllenInstitute).
Cells were grouped into very broad categories using known markers, then clustered using high variance gene selection, dimensionality reduction, dimension selection,
Jaccard-Louvain or hierarchical (Ward) clustering, and cluster merging based on the overall significance of differentially expressed genes. This process was repeated within each resulting cluster until no more child clusters met differential gene expression or cluster size termination criteria. The entire clustering procedure was repeated 100 times using 80% of all cells sampled at random, and the frequency with which cells co-cluster was used to generate a final set of clusters, again subject to differential gene expression and cluster size termination criteria.
For dimension reduction, hicat provides two modes: PCA for principal component analysis, and WGCNA, which identified co-expressed gene modules and uses the module eigengene as reduced dimensions. Each of these two approaches has its own strengths: WGCNA mode is good for detecting rare clusters, and provides cleaner cluster boundaries, while PCA mode is more scalable to large datasets, captures combinatorial marker expression pattern, and more sensitive to low-depth datasets. To combine the strengths of both methods, this study ran the whole clustering pipeline 25
using both modes on subsampled cells 100 times and combined the two resulting cell- cell co-clustering matrices to derive the final consensus clusters. The key strength of this approach is to provide fine resolution cell type categorization that withstands rigorous statistical testing to ensure reproducibility and biological relevance of the results.
Typically, this approach enables the identification of additional “marker genes” for each cluster - genes that are uniquely expressed highly in those clusters. However, from clustering, I proceeded to extract information about how highly LHβ is expressed in each clustered cell type.
One limitation commonly faced by single-cell RNA sequencing studies is the inherent technical noise. Each cell is typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts (Murray et al., 2018). The practical result of this is that in single cell expression datasets, many cells will display 0 expression for a given gene. Due to the nature of technical noise and batch effect in sequencing, however, many of the cells that output a reading of 0 may well have RNA that simply wasn’t registered. This bias towards false negatives is greater for genes with generally lower expression, like LHβ. Any analysis pipeline must, therefore, include a way to address these false zeroes systematically. In this study, expression values will be reported in terms of non-zero averages, and the non-zero fraction of cells will also be provided. This approach extracts true expression from cells that certainly express LHβ, with the limitation of perhaps missing some cells with true zero biological expression. However, if all cells of a given type display zero expression, it is likely that this class of cell does not contain LHβ RNA. 26
Chapter 3: Results
LHβ Probe Validation in Rat Pituitary
As the Molecular Instruments HCR-FISH probes were custom-designed and not previously validated, they had to be tested on a positive control tissue before staining the uncharacterized brain sections. A tissue with known LHβ expression patterns is the pituitary gland, where LHβ expression should be abundant in the anterior pituitary (pars distalis), and absent in the intermediate and posterior pituitary (pars intermedia and pars nervosa). Indeed, in a rat pituitary, LHβ expression is shown in the anterior pituitary and no other regions (Figure 4). Several other control experiments were also executed: a negative control where LHβ probe was applied with a non-conjugated fluorophore and an additional positive control where a calcitonin receptor (CalcR) HCR-FISH probe was applied to areas known to express CalcR. 27
LHβ mRNA is Present in Regions of the Brain Where Protein is Observed
The first major question this thesis seeks to address is whether the LHβ protein observed in the brain is produced endogenously, and if so, whether it is produced in a central location and transported to its destination or produced locally. Previous data has shown that LHβ protein is expressed in much higher amounts in the hypothalamus than other brain regions (Blair et al., 2019a; N. Emanuele et al., 1981; Oslapas, Emanuele,
Lawrence, Kirsteins, & Connick, 2008), leading to the idea that perhaps it is produced in the hypothalamus and transported elsewhere. This idea would be supported if HCR-FISH labeled LHβ transcript in the hypothalamus and not in other tissues where LHβ protein 28
has been identified. In fact, LHβ mRNA was labeled in the same limbic and cognition- associated regions where the protein has been identified, suggesting that it is able to be translated locally as needed.
LHβ in the Cortex
LHβ is consistently expressed in layers 2/3, 5, and 6 of the cortex (Figure 5). This is consistent across the visual, motor, and somatosensory cortex. Single-cell RNA sequencing reveals that expression is predominantly in glutamatergic intratelencephalic
(IT) neurons (Table 1). IT-type neurons exist in cortex along with pyramidal tract (PT) neurons and are thought to convey sensory and motor planning information to the striatum. Additionally, IT neurons preferentially innervate direct pathway neurons and
PT neurons preferentially innervate indirect pathway striatal neurons (Reiner, Hart, Lei,
& Deng, 2010). This aligns with previous localization of LHβ protein in cortical layers
2, 3, and 5 (Bidinotto, 2017). 29
Figure 5. A. Allen Institute reference for cortex L1-L6. B. LH in situ staining layers 2, 3, and 5 of the somatosensory cortex at 20x magnification. C. LH in situ staining layers 2,3 of the somatosensory cortex at 40x magnification. Table 1. Cortex LHβ mRNA levels from scRNAseq reported in transcripts per million and LHβ protein presence confirmation.
LHβ in the Hippocampal Formation
LHβ RNA is evident in the hippocampal formation in CA2, CA3, the dentate gyrus (DG), and the subiculum (SUB; Figure 6). Single-cell RNA sequencing reveals that expression is predominantly in glutamatergic neurons (Table 2). This aligns with previous localization of LHβ protein in all of these regions (Bidinotto, 2017; Hostetter et al., 1981). 30
Figure 6. LH in situ staining at 40x magnification in regions of the hippocampal formation – CA2, CA3, the dentate gyrus, and the subiculum. Table 2. Hippocampal LHβ mRNA levels from scRNAseq reported in transcripts per million and LHβ protein presence confirmation.
LHβ in the Hypothalamus
LHβ RNA is observed via HCR-FISH in several hypothalamic nuclei: paraventricular nucleus (PVN), arcuate nucleus (ARH), ventromedial hypothalamus
(VMH), premammillary nucleus (PM), and the medial preoptic area (mPOA; Figure 7).
This aligns with and adds detail to previous localization of LHβ protein in these regions
(Bidinotto, 2017; Hostetter et al., 1981). Single-cell RNA sequencing further supports its presence the mPOA, ARH, and PVN (Table 3). In the mPOA and ARH, LHβ mRNA is found in GABA-ergic neurons, while in the PVN it is found in glutamatergic neurons. 31
Figure 7. LH in situ staining at 40x magnification in regions of the hypothalamus – paraventricular nucleus, arcuate nucleus, ventromedial nucleus, premammillary nucleus, and the medial preoptic area. Table 3. Hypothalamic LHβ mRNA levels from scRNAseq reported in transcripts per million and LHβ protein presence confirmation. 32
LHβ in the Amygdala
LHβ RNA is observed via HCR-FISH in several amygdalar regions: the central amygdalar nucleus (CEA), medial amygdalar nucleus (MEA), and cortical amygdalar area (COA; Figure 8). LHβ protein has not been previously recorded in amygdalar regions. However, the amygdala has been noted to regulate LH secretion under the influences of olfactory signals and stress (Lawton & Sawyer, 1970; Lin et al., 2011;
Rajendren & Moss, 1993; Watanabe et al., 2017). Single cell sequencing further supports the presence of LHβ RNA in the CEA and COA, while the MEA was not included in the single cell datasets that were analyzed (Table 4). 33
Figure 8. LH in situ staining at 40x magnification in regions of the amygdala – central amygdalar nucleus, medial amygdalar nucleus, and cortical amygdalar area. Table 4. Amygdalar LHβ mRNA levels from scRNAseq reported in transcripts per million and LHβ protein presence confirmation.
LHβ RNA is Observably Different in Male and Female Mice
In situ hybridization was performed on a total of 11 mice: 3 males (M), 3 females (F),
2 females two months post-ovariectomy (OVX), and 2 females two months post-sham surgery (SHAM). This selection was made to enable the localization of luteinizing hormone and to observe whether there were clear differences in LHβ mRNA between sexes or in ovariectomized mice, as seen for protein. 34
When levels of LHβ RNA are compared between male and female mice of the same age (4 months, P120), there are several notable differences (Figure 9). Female mice display more LHβ mRNA in the arcuate nucleus, the paraventricular nucleus, the ventromedial nucleus, the medial amygdalar nucleus, and the cortical amygdalar nucleus.
For cortical and hippocampal areas, the levels of mRNA are not significantly different.
Significance of this comparison was determined using the Wilcoxon Signed Ranks Test in IBM SPSS Statistics V24.0, which is designed as a nonparametric test for comparisons between groups of ordinal measurements (Table 5). These differences may be related to several phenomena that will be further explored later. 35
Figure 9. Boxplots of median LHβ RNA quantified on an ordinal scale from 0-5. M represents a region in male mice, F in female. Regions depicted: retrosplenial cortex L 2/3, subiculum, arcuate hypothalamic nucleus, ventromedial hypothalamic nucleus, medial amygdalar nucleus, cortical amygdalar nucleus. Table 5. Wilcoxon Signed Ranks Test results, Asymp. Sig. (2 tailed) is equivalent to P value.
36
LHβ RNA is reduced in the Arcuate Nucleus and Medial Preoptic Area of
Ovariectomized Mice
LH protein levels are markedly reduced in several regions of the mouse brain after ovariectomy, while increased in the pituitary and the periphery (Palm et al., 2014).
This phenomenon is observed in the hypothalamus, cingulate cortex, hippocampus, temporal cortex, and prefrontal cortex (Blair et al., 2019a). By comparing LHβ RNA levels between ovariectomy and sham surgery conditions, we hope to gain insight into whether the protein pattern is mirrored by the RNA.
When levels of LHβ RNA are compared between ovariectomized mice (OVX) and mice that received a sham surgery (SHAM) of the same age (5 months, 2 months post-surgery), there are two notable differences. For most regions where LHβ RNA is observed, the level is not notably different between the two conditions. However, it is lowered in the arcuate nucleus of the hypothalamus and the medial preoptic area (Figure
10). Significance of this comparison was determined using the Wilcoxon Signed Ranks
Test in IBM SPSS Statistics V24.0, which is designed as a nonparametric test for comparisons between groups of ordinal measurements (Table 6). These differences may be related to the observed deficits in hypothalamic LH post-ovariectomy. 37
Figure 10. Boxplots of median LHβ RNA quantified on an ordinal scale from 0-5. S represents a region in SHAM mice, O in OVX. Regions depicted: arcuate hypothalamic nucleus, paraventricular nucleus, anterior hypothalamic nucleus, ventromedial nucleus, medial preoptic area. Table 6. Wilcoxon Signed Ranks Test results, Asymp. Sig. (2 tailed) is equivalent to P value.
38
LHβ RNA in Non-Neuronal Cells
Table 7. Non-neuronal LHβ mRNA levels from scRNAseq reported in transcripts per million and LHβ protein presence confirmation.
In addition to the neuronal populations indicated by both HCR-FISH and single- cell sequencing, LHβ is observed in very high amounts in several non-neuronal cell types: ependymocytes, olfactory epithelium cells, and choroid plexus.
39
Chapter 4: Discussion
LH has increasingly been implicated in processes of cognition and plasticity, and
LHβ protein shown to be expressed in the brain. It has remained unclear whether LHβ is produced endogenously in the brain, and if so, where this occurs. The results presented in this study provide support for endogenous LH production in the brain, based on the presence of transcripts by in situ and further validated through bioinformatics using two major single cell mRNA databases. Additionally, although LHβ protein is observed at dramatically higher levels in the hypothalamus than in other brain regions, it seems that
LH is not produced solely in the hypothalamus for transport to other regions but produced locally everywhere the protein is observed.
We have demonstrated that LHβ mRNA is found in many important areas controlling cognition and function where LHβ protein has previously been identified. To understand the implications of local LH production, the regions where LH mRNA is found must be considered in the context of LHCGR distribution in the brain and the observed effects of LH treatment. LHCGR has been seen most prominently in the hippocampus and dentate gyrus, but also in the cerebellum, the cortex, ependymal cells of all 4 ventricles, choroid plexus, preoptic areas, and the paraventricular and arcuate nuclei of the hypothalamus (Angeles, 1972; Apaja et al., 2004; Lei et al., 1993; Mak et al., 2007). 40
A Role for LH in Spatial and Recognition Memory mediated by the Hippocampus and
Retrosplenial Cortex
Past studies have observed that LHCGR mediates increased neurogenesis in the hippocampal dentate gyrus (Mak et al., 2007), mediates increased activity in the ventral hippocampus after intravenous administration (Angeles, 1972), and mediates improved performance in the Morris water maze via the superior colliculus (Palm et al., 2014). In the hippocampus, then, perhaps cells must produce luteinizing hormone in response to certain stimuli to facilitate increased neurogenesis or dendritic branching. This is supported by the observation that when neural LH levels decrease after menopause or in a parallel ovariectomized condition one sees a decrease in synaptic plasticity and hippocampal function (Blair et al., 2016; Wallace et al., 2006), and why treatments that result in increased brain LH can rescue some plasticity and facilitate neurogenesis (Mak et al., 2007; Palm et al., 2014).
Within the cortex, LH was present in intratelencephalic neurons in layers II/III, V, and VI. This pattern was seen in the motor cortex, visual cortex, somatosensory cortex, and retrosplenial cortex (RSC). The RSC plays a role in several cognitive functions: episodic memory, navigation, and future planning. It is also “consistently compromised in the most common neurological disorders that impair memory” (Vann, Aggleton, &
Maguire, 2009). Interestingly, the RSC also includes reciprocal links with the hippocampal formation, thought to be foundational in learning and navigation. Data has shown that dendritic spine density is reduced in the RSC in an ovariectomized condition, but not in an ovariectomized condition where HCG is administered (Jeffrey Blair et al., 41
2018). Furthermore, dendritic spine density in the RSC is correlated with Morris water maze performance in mice (Jeffrey Blair et al., 2018). This suggests that the presence of
LH in the RSC is important for maintaining dendritic spine density in intratelencephalic neurons and that decreases in LH during aging may be related to impaired spatial memory due to decreased spine density.
A “Short-Loop" Feedback Mechanism for LH
The local production of LH also supports the role of a “short-loop” feedback mechanism for LH secretion. The idea of “short-loop” feedback means that, in addition to the downstream products of LH release (estrogen and testosterone) feeding back to the hypothalamus to inhibit GnRH release, LH itself can act on the hypothalamus in this manner. This idea was first raised in the 1960s but has remained controversial since
(David, Fraschini, & Martini, 1966; N. Emanuele et al., 1981; Molitch, Edmonds, Jones,
& Odell, 1976; Nakazawa, Marubayashi, & McCann, 1991; Oslapas et al., 2008).
However, a vast pool of evidence has accumulated over the years that points to the robustness of this mechanism. Typically, the production of GnRH is regulated by negative feedback loops involving peripheral testosterone and estrogen. However, when
LH is implanted into the median eminence region of the hypothalamus, it effectively reduces pituitary production of LH (David et al., 1966). LH is also most highly expressed in the hypothalamus, and a significant fall in hypothalamic LH coincides with the surge in pituitary and serum LH during proestrus (N. Emanuele et al., 1981). The presence of LH mRNA in the arcuate nucleus and the preoptic nucleus is important to note, as these areas also contain GnRH neurons. The GnRH neurons in these areas 42
express LHCGR receptors, and activation of these receptors inhibits pulsatile neuropeptide release (Mores et al., 1996). This establishes a mechanism for short-loop feedback of LH secretion. It is also likely that LH produced in other areas of the brain can be transported to regions with LH-sensitive GnRH neurons and thus exert its effect on secretion. This transport may be mediated by the ependymocytes that line the various ventricles in the brain. Ependymal tanycytes have been thought to transport molecules between the CSF and CNS (Lei et al., 1993). Since ependymal cells from all four ventricles contain hCG/LH receptors, it is possible that these receptors transport CNS
LH into CSF for transportation to the hypothalamus or median eminence.
A Role for LH in the Amygdala
The presence of LHβ RNA in amygdalar regions suggests that amygdalar translation of LHβ may be utilized as a signaling mechanism. It has been shown that stress exerts profound inhibitory effects on reproduction by suppressing the pulsatile release of GnRH and therefore LH (Lin et al., 2011). While the MEA and CEA have been shown to be key modulators of this effect, the exact mechanisms by which stressors disrupt the hypothalamic GnRH pulse generator neurons have yet to be elucidated. The data presented here suggest that local production of LH occurs in CEA and MEA neurons. These neurons are known to project to GnRH neurons in the hypothalamus. These hypothalamic GnRH neurons have functional LHCGRs that have been shown to inhibit pulsatile neuropeptide release when stimulated with LH (Mores et al., 1996). Further experiments are needed to validate this mechanism, but the presence 43
of LHβ mRNA in applicable amygdalar regions is the first step in demonstrating that LH may be produced in the brain as a messenger to reduce peripheral LH secretion.
Implications of Sex Differences in LH mRNA
Our in-situ data provide evidence of sex-differences in various brain regions.
Particularly, the enhanced presence of LHβ mRNA in female mice is observed in several areas of the accessory olfactory system that, together, are involved in the mating-induced enhancement of lordosis in female mice. The accessory olfactory system is typically understood as including the vomeronasal organ, the accessory olfactory bulb, the bed nucleus of the accessory tract, the medial amygdala, the preoptic nucleus, the ventromedial nucleus, and the premammilary nucleus (Davis, Macrides, Youngs,
Schneider, & Rosene, 1978; Rajendren & Moss, 1993; Rosser, Hökfelt, & Goldstein,
1986; WITKIN, 1987). These are, without exception, areas where either LH, LHCGR, or both have been observed. This suggests that local LH production and use as a chemical messenger is integral in female behavioral responses to male pheromones. This pathway is likely also related to the cognitive effects of LH, as there have been reports of male pheromone-stimulated neurogenesis in the adult female brain (Mak et al., 2007).
The presence of sex differences in LHβ mRNA in the medial amygdala, where females show higher levels than males supports a possible mechanism for stress-induced functional hypothalamic amenorrhea, a type of stress-related infertility. This infertility is currently not linked to any specific biological mechanism, and most treatments are focused on cognitive-behavioral approaches to ameliorate life stress (Lania et al., 2019).
There is evidence in mice that psychogenic stress exerts profound inhibitory effects on 44
reproduction by suppressing the pulsatile release of GnRH and therefore LH, and that these inhibitory effects are selectively mediated by the medial amygdala (Lin et al.,
2011). Taken with the idea of a short-loop feedback system for LH production in the brain, the presence of LH mRNA in the medial amygdala suggests that perhaps amygdalar production of LH acts to suppress GnRH production and therefore peripheral
LH release.
Changes in LHβ mRNA after Ovariectomy
While luteinizing hormone protein levels are significantly different in the ovariectomized condition, LHβ mRNA appears largely unchanged. This suggests that in cortical, hippocampal, and amygdala regions, the differences in the amount of protein are not mediated by the amount of RNA. Rather, it is likely that the translation is inhibited, perhaps by epigenetic modifications. LHβ mRNA levels do appear lower in the medial preoptic area and the arcuate nucleus. These are both hypothalamic regions that are known to be involved in HPG axis regulation, containing GnRH neurons with active
LHCGRs. However, the significance of lowered LHβ mRNA transcripts is unclear. It seems that there is a “short-loop” feedback mechanism for LH, but more work needs to be done to ascertain whether this mechanism remains robust during aging and after menopause, or if its effects are limited to pre-menopausal hormonal modulation.
There is also more work to be done in characterizing the trajectory of changes in LH protein and mRNA after ovariectomy. This work was conducted 2 months after ovariectomy in a mouse. Levels of mRNA observed here may be vastly different from levels observed in the days following surgery, and they may change further still given 45
more time. It will be informative in the future to characterize the full range of LH expression during aging, and whether the canonical negative feedback loop, as well as a short-loop feedback construct, remain functional throughout life.
Conclusion
Results presented in this study provide an improved understanding of the localization of LHβ RNA in the brain, both in terms of regional prevalence and cell-type specificity within each region. Prior to this work, it was unclear whether LH protein was produced locally where expression was evident, or if it was produced centrally and distributed to these locations. This study demonstrated that LHβ RNA is observed in almost every area where LH protein is seen. Thus, LHβ seems to be produced locally where protein is observed and independently of the HPG axis. This study also highlights several novel regions of LH production that require further investigation, especially types of non-neuronal cells with extraordinarily high LHβ RNA contents. The purpose of LHβ in ependymal cells, the olfactory epithelium, and choroid plexus cells is likely to be biologically important and should be probed more deeply in future experiments.
As a whole, the data presented here study strongly support that LHβ is endogenously produced in the brain. While peripheral hormone levels have long been implicated in changes of behavior, the effect of reproductive hormones within the brain was largely ignored for many years. Studies of the modulatory effects of hormones on cognition have led to the discovery that hormone receptors are expressed in the CNS
(Ascoli et al., 2002; Roepke et al., 2011). Ever since, much research has been focused on the effects of steroid hormones testosterone and estrogen and other hormones thought to 46
be secondary or irrelevant. The widespread expression of LHCGR in the brain, combined with the likelihood that LH is produced locally, has tremendous implications for the importance of independent LH and HCG signaling in the brain. It is likely that LH has direct roles and drives processes that were previously not understood or assumed to be regulated by steroid hormones. While this study was inspired by the clear role of LH in the aging brain, it will also be illuminating to investigate the role of LH during development. Because LH has been shown to affect neural plasticity and neuronal differentiation (Donadeu et al., 2011), and given the widespread distribution of the receptor, it is likely intimately involved in development in the hippocampus, hypothalamus, amygdala, and cortex. This work only begins to reveal how much we have yet to discover about the role of the whole hormonal milieu in cognitive function.
Comprehensive further study of the HPG axis hormones is necessary to begin to dissect the complex web of interactions that underlies neural development and aging.
47
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