White Adipose Tissue Beiging in Mice with Increased Growth Hormone Action

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White Adipose Tissue Beiging in Mice With Increased Growth Hormone Action

A thesis presented to

the faculty of

the College of Health Sciences and Professions of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Katie M. Troike

August 2017

This thesis titled

White Adipose Tissue Beiging in Mice With Increased Growth Hormone Action

KATIE M. TROIKE

has been approved for

the School of Applied Health Sciences and Wellness

and the College of Health Sciences and Professions by

Darlene E. Berryman

Adjunct Research Professor of Food and Nutrition Sciences

Randy Leite

Dean, College of Health Sciences and Professions 3

Abstract

TROIKE, KATIE M., M.S., August 2017, Food and Nutrition Sciences

White Adipose Tissue Beiging in Mice With Increased Growth Hormone Action

Director of Thesis: Darlene E. Berryman

White adipose tissue (WAT) is a complex and dynamic endocrine organ that is most commonly recognized for its energy storage capacity. Brown adipose tissue (BAT) functions to dissipate stored energy in the form of heat through a process known as nonshivering thermogenesis. This process is aided by the mitochondrial matrix protein, uncoupling protein 1 (UCP1), which creates a proton leak across the inner mitochondrial membrane causing chemical energy to be released as heat. More recently, clusters of brown-like or “beige” adipocytes have also been identified in WAT. These adipocytes have the capacity to interconvert between the two phenotypes, in part, through increasing their expression of UCP1. This “beiging” is dependent on environmental and chemical conditions present within the cell. Growth hormone (GH), a protein secreted from the anterior pituitary, has been positively correlated with increased BAT mass. However, this evidence is controversial, and the effects of GH on BAT and WAT beiging are not well defined. Bovine growth hormone transgenic (bGH) mice have increased GH action, are giant and lean, yet develop insulin resistance and have shortened lifespans compared to their wild-type (WT) littermates. The purpose of the current study was to compare the expression of beiging-associated factors at both the RNA and protein levels in bGH mice and WT littermate controls. To accomplish this, several different methods were used to measure expression of beiging-associated gene expression in the WAT and BAT depots 4

of these mice. A previously collected RNA-Seq dataset revealed significant genotype and depot differences, with the greatest number of expression changes detected between genotype in the subcutaneous depot and between depot in the bGH mice. Additionally, qPCR, Western blot analysis, and immunohistochemistry with confocal imaging revealed that UCP1 RNA and protein expression were undetectable in the WAT depots of these mice. No significant differences in UCP1 expression were observed between genotype or age in BAT depots. Basal oxygen consumption rate (OCR) in subcutaneous WAT, but not BAT, exhibited a significant effect of genotype and age. Our results indicate that, at a basal level, GH may affect the expression of certain beiging-associated genes and OCR in a depot-dependent manner, but does not appear to have a direct effect on UCP1 expression.

Dedication

For my grandpa Harry.

Acknowledgments

I would first like to thank my advisor, Dr. Darlene Berryman, for her encouragement, support, and guidance over the years. She is truly an outstanding role model and an inspiration to her students. I would also like to thank my committee members Dr. Cheryl Howe and Dr. Ed List, for their insight and help throughout this process.

This thesis would not have been possible without the help of everyone in the

Kopchick lab “family.” I would like to specifically thank Silvana Duran-Ortiz, who allowed me to use her RNA-Seq data, Brooke Henry for her invaluable contributions to my research, and Jon Young for always being there to answer my questions and help me troubleshoot. Also, a big thank you to Dr. John Kopchick for fostering a uniquely collaborative environment of exceptional scientists and for making Athens feel like home.

Finally, thank you to my parents John and Sharon, and my husband Adam, for loving and encouraging me. I couldn’t have done it without you.

Table of Contents

Page

Abstract ...... 3

Dedication ...... 5

Acknowledgments...... 6

List of Tables ...... 12

List of Figures ...... 13

Chapter 1: Introduction ...... 15

Statement of Problem ...... 18

Research Questions ...... 19

Hypotheses ...... 19

Significance ...... 20

Limitations/Delimitations ...... 20

Definition of Terms ...... 21

Chapter 2: Review of the Literature ...... 23

Adipose Tissue ...... 24

White adipose tissue...... 24

Function...... 25

White adipose tissue depots...... 27

Depot-specific differences...... 28

Obesity and white adipose tissue remodeling...... 30

Brown adipose tissue...... 32 8

Uncoupling protein 1...... 35

Regulation of thermogenesis...... 37

Beiging of White Adipose Tissue ...... 38

Beige adipocytes...... 38

Factors affecting beiging...... 41

Beiging as a potential therapeutic target...... 49

Growth Hormone ...... 50

Structure and regulation...... 50

Growth hormone-induced signaling...... 52

GH function...... 54

Acromegaly...... 55

Bovine growth hormone transgenic mouse...... 56

Laron Syndrome...... 59

Growth hormone receptor knockout mouse...... 59

Growth Hormone and Adipose Tissue ...... 63

Growth hormone and adipokines...... 64

Depot-dependent growth hormone action...... 65

Growth hormone and brown adipose tissue or beiging...... 66

Summary ...... 67

Chapter 3: Materials and Methods ...... 68

Animals ...... 68

Body Weight and Body Composition ...... 69 9

Adipose Tissue Depots ...... 69

RNA-Seq Analysis ...... 70

Reverse Transcription Quantitative PCR ...... 70

Mitochondrial Isolation ...... 71

Immunohistochemistry and Confocal Imaging ...... 72

Protein Isolation and Bradford Assay ...... 73

Western Blot ...... 74

Seahorse Assay ...... 75

Statistical Analysis ...... 75

Chapter 4: Results ...... 77

Cohort 1 ...... 77

RNA expression...... 77

Cohort 2 ...... 88

Body weight and body composition...... 88

Adipose tissue weight...... 90

RNA expression...... 91

Cohort 3 ...... 91

Body weight and body composition...... 91

UCP1 protein expression...... 94

Cohort 4 ...... 96

UCP1 protein expression...... 96

Cohort 5 ...... 98 10

Body weight and body composition...... 98

Adipose tissue weight...... 101

UCP1 protein expression...... 103

Oxygen consumption rate...... 104

Chapter 5: Discussion ...... 106

Body Weight and Body Composition ...... 106

Adipose Tissue Weights ...... 107

Beiging-Associated RNA Expression ...... 109

Ucp1 RNA Expression ...... 113

UCP1 Protein Expression ...... 114

Oxygen Consumption Rate ...... 116

Future Directions ...... 117

Conclusions ...... 118

References ...... 120

Appendix A: Beiging-Associated Molecules for RNA-Seq Analysis ...... 154

Appendix B. qPCR Plate Design ...... 179

Appendix C: Mitochondrial Isolation ...... 180

Appendix D: Immunohistochemistry ...... 182

Appendix E: Protein Isolation ...... 183

Appendix F: Bradford Assay ...... 184

Appendix G: Western Blotting Protocol ...... 185

Appendix H: Western Blot Recipes ...... 188 11

Appendix I: Seahorse Assay ...... 192

List of Tables

Page

Table 1: Summary of bGH Characteristics...... 57

Table 2: Summary of GHR-/- Characteristics ...... 61

Table 3: Cohorts of bGH and WT Littermate Control Mice Used in This Thesis...... 69

Table 4: Antibodies for Immunohistochemistry ...... 73

Table 5: Genotype Comparison of Significantly Altered Genes in Subcutaneous

WAT ...... 80

Table 6: Genotype Comparison of Significantly Altered Genes in Epididymal WAT ..83

Table 7: Depot Comparison of Significantly Altered Genes in WT Mice ...... 84

Table 8: Depot Comparison of Significantly Altered Genes in bGH Mice ...... 85

Table 9: Beiging Molecules for Gene Expression ...... 154 13

List of Figures

Page

Figure 1: WAT structure ...... 25

Figure 2: WAT depots in mice ...... 28

Figure 3: The electron transport chain and uncoupling protein 1 ...... 33

Figure 4: UCP1 structure ...... 36

Figure 5: Adipocyte origins ...... 40

Figure 6: Transcriptional regulators of beiging and their cofactors ...... 43

Figure 7: Secreted factors for brown and beige adipocyte recruitment ...... 46

Figure 8: Immune cells involved in the regulation of beiging ...... 48

Figure 9: Growth hormone action and regulation ...... 52

Figure 10: Growth hormone-induced signal transduction ...... 54

Figure 11: Significantly altered genes in the subcutaneous and epididymal depots of bGH and WT mice ...... 79

Figure 12: Average body weight and body composition comparisons of 7-month- old bGH and WT mice ...... 89

Figure 13: Adipose tissue depot weights for male bGH and WT mice at 7 months

of age ...... 90

Figure 14: Ucp1 mRNA expression in the BAT depots of 7-month-old male bGH and

WT mice...... 91

Figure 15: Average body weight and body composition comparisons of male bGH and

WT mice at 6 months of age ...... 93 14

Figure 16: UCP1 protein expression from isolated mitochondria in the WAT and BAT depots of 6-month-old bGH and WT mice ...... 95

Figure 17: Immunohistochemical analysis of subcutaneous and BAT depots of bGH and WT mice at 12 months of age ...... 97

Figure 18: Average body weight and body composition comparisons of male bGH and

WT mice at 4 and 11 months of age ...... 99

Figure 19: Adipose tissue depot weights for male bGH and WT mice at 4 and 11 months of age ...... 102

Figure 20: UCP1 protein expression in the BAT depots of male bGH and WT mice at 4 and 11 months of age ...... 103

Figure 21: Comparison of oxygen consumption rate in adipose depots of male bGH and

WT mice at 4 and 11 months of age ...... 105

Chapter 1: Introduction

Adipose tissue is a complex endocrine organ with various functions such as protection and support of tissues, thermoregulation, and energy metabolism. Two distinct forms of adipose tissue have been identified and include white adipose tissue (WAT) and brown adipose tissue (BAT). WAT is most commonly recognized as the site of lipid storage, but also secretes a number of hormones and cytokines that regulate appetite, insulin sensitivity, and immune function (Kershaw & Flier, 2004). WAT is distributed throughout the body and exhibits depot differences such as differing levels of protein expression and insulin sensitivity (Sackmann-Sala, Berryman, Munn, Lubbers, &

Kopchick, 2012). Four commonly studied WAT depots in mice include subcutaneous

(located under the skin), retroperitoneal (located behind the kidneys), mesenteric (lining the intestine), and perigonadal (located near the reproductive organs). The majority of

BAT in mice is located in the interscapular region (Berryman et al., 2011). BAT is strikingly dissimilar from WAT, functioning to produce heat through the uncoupling of oxidative phosphorylation in mitochondria, a process known as nonshivering thermogenesis. This uncoupling is aided by a mitochondrial membrane protein, uncoupling protein 1 (UCP1), which catalyzes a proton leak across the inner mitochondrial membrane, releasing stored energy in the form of heat.

While BAT and WAT are the two most prominent forms of adipose, clusters of brown-like, or “beige,” adipocytes have also been identified in white adipose depots.

Under basal conditions, these cells exhibit white adipocyte morphology, but can be converted to beige adipocytes by increasing their UCP1 expression and becoming 16

multilocular. The capacity of these cells to interconvert between the two phenotypes is dependent on the environmental and chemical conditions present. For example, during cold exposure, beige cells can convert to a brown-like state by upregulating UCP1, which produces heat through nonshivering thermogenesis (Rosenwald, Perdikari, Rulicke, &

Wolfrum, 2013). This process results in a phenotypic switch from energy-storing adipocytes to energy-burning ones. Many different molecules can also influence beiging and are secreted in response to conditions such as cold and exercise (Harms & Seale,

2013). Beige adipocytes are not uniformly located throughout all WAT, and vary in number according to adipose tissue depot. Generally, the greatest numbers are found in the subcutaneous and mesenteric depots, while the least amounts reside in the perigonadal depot (Rosen & Spiegelman, 2014).

WAT is a dynamic tissue that is able to remodel in response to various signals.

Growth hormone (GH) has been shown to have profound effects on adipose tissue, the most apparent being a decrease in fat mass, which is mediated through stimulation of lipolysis and inhibition of lipogenesis (List et al., 2013). GH also promotes longitudinal bone growth (Rosenfeld & Hwa, 2009) and increases lean mass (Perrini et al., 2010). GH exerts these effects through a complex feedback system regulated by the stimulatory actions of growth hormone releasing hormone (GHRH) and inhibitory actions of somatostatin (Kato, Murakami, Sohmiya, & Nishiki, 2002). The binding of GH to its receptor on various tissues causes synthesis and release of insulin-like growth factor-1

(IGF-1), which then travels throughout the body and acts as an effector for many of GH’s actions (Lanning & Carter-Su, 2006). 17

The effects of GH on the body can be observed through the comparison of different diseases related to GH absence or excess. Acromegaly is a condition in which hypersecretion of GH from the pituitary confers various physical and metabolic consequences, such as enlarged hands, feet, and facial bones, reduced fat mass, and insulin resistance (Møller, Butler, Antsiferov, & Alberti, 1989; Møller et al., 1992).

Conversely, Laron Syndrome results from a mutation of the growth hormone receptor

(GHR), causing short stature, increased adiposity, and insulin sensitivity (Guevara-

Aguirre et al., 2011). Different mouse models that are designed to mimic these conditions, such as the bovine GH transgenic (bGH) and GHR knockout (GHR-/-) mice, allow us to study GH-related disease. From the few studies in humans with acromegaly or Laron Syndrome as well as the comprehensive studies in these mice, it is clear that GH has profound effects on adipose tissue, and these effects are also depot dependent.

Subcutaneous WAT is usually the most responsive to the actions of GH, while perigonadal depots are the least (Berryman et al., 2011). It is important to consider these differences, as only studying one depot could potentially alter results. Very little research has been conducted regarding the effects of GH on BAT, and different studies provide conflicting evidence. Both increases and decreases in UCP1 expression in response to

GH have been reported (Hioki et al., 2004; Li, Knapp, & Kopchick, 2003; Olsson et al.,

2005; Swaminathan, 2008).

Due to its capacity for thermogenesis (ability to release stored energy in the form of heat), BAT has been widely considered as a therapeutic target for the treatment of obesity and diabetes. The beiging of WAT could offer even greater potential for 18

therapeutic success, as it is present in larger quantities in the human body than BAT (Xu et al., 2003). While a great deal of new research has emerged regarding the beiging of

WAT, the effects of GH on this phenomenon have yet to be explored.

Statement of Problem

According to the Centers for Disease Control and Prevention (CDC), 34.9% of

U.S. adults were considered obese in 2011 and 2012 (Ogden, Carroll, Kit, & Flegal,

2014). The persistence of the obesity epidemic has resulted in an expansion of research efforts both to understand obesity and to aid in the development of novel therapies.

Obesity increases the risk of developing other diseases such as type 2 diabetes, hypertension, and certain types of cancer. Thus, treating obesity also aids in the prevention of dangerous and life-threatening complications. The financial burden of this disease must also be considered, as an estimated $147 billion was spent in 2008 on obesity-related medical costs (Finkelstein, Trogdon, Cohen, & Dietz, 2009). Therefore, a thorough understanding of adipose tissue and the contribution of WAT beiging to energy expenditure is crucial for the prevention and treatment of obesity and its associated conditions.

Because the beiging process essentially converts energy storing cells to energy burning ones, it has garnered a great deal of interest in recent years as a potential target for the treatment of obesity. WAT has been studied extensively and the lipolytic and antilipogenic effects of GH on this tissue are relatively well understood. However, despite much research effort in the field of WAT beiging, the effect of GH on these unique adipocytes has not been well documented. Additionally, though several studies 19

have examined the properties and induction of beige adipocytes, many of the recently discovered beiging factors have not been studied within the context of GH. It is necessary to fully understand the mechanisms by which beige adipocytes develop so that safe and efficacious therapies can be developed for the treatment of obesity and diabetes.

Research Questions

In this study, male bGH transgenic mice, as well as littermate controls of the

C57Bl/6J background strain were used to examine WAT beiging in two different adipose tissue depots. Research questions addressed were:

1. Using RNA-Seq data, will the gene expression of beiging factors differ

between genotype (bGH and WT mice) and depot (subcutaneous and

epididymal)?

2. Using a Western blotting technique, will protein expression levels of UCP1

differ between genotype and depot in four WAT depots (subcutaneous,

epididymal, mesenteric, and retroperitoneal) and one BAT depot

(interscapular)?

Hypotheses

The following hypotheses concerning genotype are based on previous findings that suggest a positive impact of GH on BAT mass and UCP1 expression (Hioki et al.,

2004; Olsson et al., 2005; Zhang et al., 2016) and downregulation of UCP1 in the subcutaneous WAT of mice lacking GH action (GHR-/- mice) (Swaminathan, 2008). In terms of depot differences, previous studies have shown higher numbers of beige adipocytes in the subcutaneous and retroperitoneal depots of WAT, as well as resistance 20

of the epididymal depot to beiging (Rosen & Spiegelman, 2014; Rosenwald et al., 2013).

1. RNA expression of beiging markers will be greater in the WAT of bGH mice

compared to WT controls and greater in the subcutaneous depot compared to

epididymal WAT.

2. Protein expression of UCP1 will be greater in bGH mice relative to WT

controls and greater in the subcutaneous and retroperitoneal depots compared

to epididymal WAT.

Significance

To date, no published data exist regarding factors associated with beiging of

WAT in mice with altered GH action. One study within this laboratory examined UCP1 expression in the BAT of bGH mice; however, beiging of WAT was not addressed (Li et al., 2003). The use of four WAT depots for protein quantification is important because the existence of depot-specific differences could significantly impact the abundance of

UCP1+ adipocytes and other factors associated with their presence in this tissue.

Additionally, the use of BAT as a positive control ensures accurate identification of

UCP1 protein. Quantification of beiging marker expression at the RNA and protein levels will help further characterize and unravel the complex nature of WAT. The understanding of these mechanisms could also be invaluable for future drug development for the treatment of obesity and other metabolic disorders.

Limitations/Delimitations

1. Data from these mice cannot be fully extrapolated to human conditions, as

differences in fat deposition and cellular composition exist. Thus, results from 21

this study cannot be entirely generalized to human populations.

2. Error in measurement due to human manipulation of equipment or reagents

cannot be eliminated entirely. Variations between measurements may occur.

3. Chronological and biological age differ within these models, as bGH mice

tend to age prematurely. Thus, these mice will differ in their biological age

despite having similar chronological age. This must be considered when

interpreting the results.

4. Cell composition between depot and genotype may differ, with some samples

containing fewer or greater numbers of cells, thereby influencing the amount

of UCP1 measured.

Definition of Terms

 Acromegaly. A condition characterized by excessive growth caused by prolonged

hypersecretion of GH in the body (Adelman, Liebert, Nachtigall, Lamerson, &

Bakker, 2013).

 Adipocyte. A unilocular cell comprising WAT.

 Akipokine. A protein, usually a hormone, secreted by adipose tissue.

 Beige adipocyte. A multilocular, UCP1 expressing adipocyte with thermogenic

potential in WAT (Giralt & Villarroya, 2013; Harms & Seale, 2013).

 Beiging. The process by which white adipocytes convert to a brown-like, or

beige, state by upregulating UCP1 expression and becoming multilocular.

 Beiging factor. A molecule that impacts WAT beiging. 22

 Bovine growth hormone transgenic (bGH) mice. Mice that produce excess GH

due to expression of a bovine GH transgene and therefore mimic the acromegalic

condition (Berryman et al., 2004).

 Brown adipocyte. A multilocular adipocyte with densely packed mitochondria

and thermogenic capacity that comprises BAT (Harms & Seale, 2013; Lidell,

Betz, & Enerbäck, 2014).

 Brown adipose tissue (BAT). A distinct type of adipose tissue that is responsible

for converting chemical energy into heat through dissipation of the proton

gradient in oxidative phosphorylation (Lidell et al., 2014).

 Laron Syndrome. A type of dwarfism caused by a defective GH receptor (Laron,

Pertzelan, & Mannheimer, 1966; Zhou et al., 1997).

 Multilocular. Containing several lipid droplets.

 Uncoupling protein 1 (UCP1). A protein embedded in the inner mitochondrial

membrane that uncouples the proton gradient during oxidative phosphorylation,

releasing chemical energy as heat.

 Unilocular. Containing a single lipid droplet.

 White adipose tissue (WAT). Loose connective tissue composed of adipocytes

and various other cell types including preadipocytes, connective tissue,

endothelial cells, immune cells, and connective matrix (Berryman et al., 2011;

Sethi & Vidal-Puig, 2007).

 Western blot. A method used to detect and quantify a specific protein in a sample.

Chapter 2: Review of the Literature

Energy homeostasis relies on the complex interplay of numerous mechanisms throughout the body to balance energy intake and expenditure. Within adipose tissue, the synthesis and breakdown of lipids is orchestrated to provide energy in times of need and store energy in times of excess nutrient availability. A hallmark of obesity is excess fat accumulation, which results when genetic and environmental influences shift the balance toward energy storage within fat cells. According to recent reports from the CDC, obesity now affects over one-third of adults in the United States (Ogden et al., 2014). Obesity increases the risk for developing other diseases such as type 2 diabetes, hypertension, and cancer. Therefore, it is imperative that researchers work to elucidate the underlying mechanisms of obesity and identify therapeutic targets that will aid in the treatment of this disease.

Although WAT is often disparaged for its contribution to overweight and obesity, it serves many other functions through its role as an endocrine organ. BAT, which is completely different from WAT in both function and anatomical location, acts as a thermoregulator by releasing stored energy in the form of heat. Much recent research effort has focused on beige adipocytes within WAT that possess the ability to change their phenotype and behave like brown fat while remaining embedded in the WAT depot.

GH is known to have profound effects on WAT, although its impact on BAT function and beige fat development are not as well understood. This literature review will provide an overview of adipose tissue (AT), GH, and the beiging effect as well as introduce the mouse line that was used in this thesis. 24

Adipose Tissue

While AT was once regarded as merely a simple connective tissue, it is now understood to be a highly complex organ with profound implications in whole-body metabolism and energy homeostasis. The discovery of leptin in 1994 allowed for the recognition of AT as an endocrine organ, and, as a result, interest in the field of adipose tissue research has greatly increased (Halaas et al., 1995; Zhang et al., 1994). Adipose tissue has several functions, with the most widely recognized being lipid storage.

However, it also provides mechanical support, helps maintain glucose homeostasis, and acts as a thermoregulator (Rosen & Spiegelman, 2014).

White adipose tissue. Adipose tissue is usually classified as one of two distinct types: white or brown. WAT and BAT are very different, and are antitheses of one another in both structure and function. WAT is present in much higher amounts than

BAT and is traditionally what is thought of as “fat.” WAT is characterized by large, unilocular adipocytes that range from 30-130 µm in diameter (Wronska & Kmiec, 2012).

The lipid droplet contained within these cells determines the size of the adipocyte and constitutes over 95% of its volume (Lee, Wu, & Fried, 2013; Wronska & Kmiec, 2012).

Although white adipocytes occupy approximately 90% of the tissue volume, they are outnumbered by other cell types in WAT (Lee et al., 2013). The stromal vascular fraction (SVF) is composed of various cell types, including preadipocytes, fibroblasts, immune cells, and vascular cells (Lee, Mottillo, & Granneman, 2014; Ouchi, Parker,

Lugus, & Walsh, 2011). Another important nonadipocyte component of WAT is the extracellular matrix (ECM), which plays a structural role as well as aiding in 25

adipogenesis and the formation of tissue structure (Divoux & Clément, 2011). Figure 1 provides a depiction of white adipose tissue structure.

Figure 1. WAT structure. WAT is a heterogeneous tissue comprised of many different cell types, including preadipocytes, fibroblasts, immune cells, and vascular cells, and extracellular matrix proteins.

Function. At its most basic level, adipose tissue functions as a thermal insulator and provides protection for internal organs (Wronska & Kmiec, 2012). The main molecular function of WAT is metabolic regulation and storage of excess energy in the form of triglycerides. As previously stated, in times of excessive caloric intake, fatty acids and glucose are taken up by cells, converted to triglycerides, and stored in the lipid droplet of the adipocytes. If a shift toward negative balance occurs and energy is required, stored triglycerides are mobilized to form free fatty acids (FFAs) and subsequently transported to tissues to be used for fuel (Wronska & Kmiec, 2012).

The ability of adipose tissue to function as an endocrine organ through the 26

secretion of hormones and cytokines, referred to as “adipokines,” has important implications in body weight homeostasis. Two well-characterized adipokines, leptin and adiponectin, are important in metabolism and energy balance. Leptin, an anorexigenic hormone encoded by the obese (ob) gene, is produced primarily by adipose tissue and secreted in direct proportion to energy availability and adipose tissue mass (Masuzaki et al., 1995). Leptin acts to decrease energy intake and increase expenditure by binding to receptors in the hypothalamus that regulate energy balance and satiety. Additionally, it has been shown that leptin levels are higher in subcutaneous depots than visceral

(Kershaw & Flier, 2004; Wajchenberg, 2000). Leptin has also been linked to the vascular and immune systems, stimulating angiogenesis, promoting hematopoietic stem cell differentiation and proliferation, and decreasing wound healing time (Adya, Tan, &

Randeva, 2015; Lam & Lu, 2007; Tahergorabi & Khazaei, 2015). Adiponectin is also secreted primarily by adipocytes, but unlike leptin, exhibits an inverse relationship with adiposity (Coelho, Oliveira, & Fernandes, 2013). Adiponectin functions as an insulin sensitizer by activating genes that increase lipid breakdown by the liver and decrease triglyceride storage in WAT (Vazquez-Vela et al., 2008). Decreased levels of adiponectin are seen in obesity, type 2 diabetes, and atherosclerosis (Chandran, Phillips, Ciaraldi, &

Henry, 2003). In addition to these two well-characterized adipokines, several proinflammatory cytokines secreted by WAT, such as interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), and monocyte chemoattractant protein-1 (MCP-1), are believed to be responsible for the chronic, low-grade inflammation seen in obesity and may play a role in insulin resistance (Xu et al., 2003). 27

White adipose tissue depots. Historically, WAT has been classified as either subcutaneous or visceral. However, as knowledge of adipose tissue continues to expand, it becomes increasingly evident that this distinction may be too simplistic (Rosen &

Spiegelman, 2014). Additionally, there exists a lack of uniformity among authors regarding classification of both the subcutaneous and visceral depots. Some authors use the general classification of visceral to reference all intra-abdominal depots, while others use a stricter definition for visceral WAT to include only those intra-abdominal WAT depots that drain into the portal vein. The only WAT depots that are consistently recognized as true visceral depots are mesenteric and omental, the latter of which is not typically found in mice (Berryman et al., 2011). Four WAT depots in mice are most consistently studied in our laboratory and include inguinal, mesenteric, perigonadal, and retroperitoneal. The inguinal depot, also referred to as subcutaneous, is located just beneath the skin. The mesenteric depot lines the intestines and is considered a true visceral depot. Perigonadal WAT, also referred to as epididymal in males and paraovarian in females, is located near the reproductive organs. Finally, the retroperitoneal depot is located behind the kidneys and is an intra-abdominal, nonvisceral fat pad. The most commonly studied WAT depots in mice are depicted in Figure 2.

Additionally, there is one BAT depot, prominent in mice, which is located in the interscapular region and is shown in the right panel of Figure 2. 28

Figure 2. WAT depots in mice. Four prominent WAT depots and the interscapular BAT depot in a male mouse are shown. WAT depots are shown in the left panel, including the four used in this study. The inguinal depot (bottom left) is classified as subcutaneous and resides just beneath the skin. The epididymal depot (bottom right) is associated with the testes and is considered an intra-abdominal depot but is not classified as visceral by the stricter definition of drainage into the portal vein. The retroperitoneal depot (top right) is situated behind kidney (K) and is intra-abdominally located. The mesenteric depot (top left) lines the intestine and is considered the only true visceral depot, as it is drained by the portal vein. The interscapular BAT depot is shown on the right. This is the only brown fat pad that can be dissected from mice. From “Depot-Specific Differences in White Adipose Tissue of Wild-Type and GHR-/- Mice of Different Ages,” by L. Sackmann-Sala, 2010, Doctoral Dissertation, Ohio University, Athens, OH; “Growth Hormone and Adipose Tissue: Beyond the Adipocyte,” by D. E. Berryman, E. O. List, L. Sackmann-Sala, E. Lubbers, R. Munn, and J. J. Kopchick, 2011, Growth Hormone and IGF-1 Research, 21, p. 20. Copyright 2011 by Elsevier; and from “Impact of Growth Hormone on Regulation of Adipose Tissue,” by K. M. Troike, B. E. Henry, E. A. Jensen, J. A. Young, E. O. List, J. J. Kopchick, and D. E. Berryman, in press, Comprehensive Physiology. Copyright by Wiley. Reprinted with permission.

Depot-specific differences. Many depot-specific differences have been observed within the WAT of human subjects. When simply comparing visceral and subcutaneous depots, visceral fat has been positively correlated with metabolic diseases such as type 2 diabetes and certain cancers, while subcutaneous does not appear to carry the same risks and may play a role in improving glucose metabolism (Lee et al., 2013). For example, IL- 29

6 has been shown to be higher in the visceral fat depot than subcutaneous, implicating the case for its role in metabolic disease (Fontana, Eagon, Trujillo, Scherer, & Klein, 2007).

It has also been theorized that different WAT depots arise from distinct developmental origins. That is, a study comparing gene expression of cultured human preadipocytes from omental, subcutaneous, and mesenteric origins found differences in several developmental genes that are indicative of distinct adipocyte progenitors (Tchkonia et al.,

2007). Gene expression analysis comparing intra-abdominal and subcutaneous adipose tissue in mice have found stark differences in the expressions of several genes, including some involved in embryonic development (Gesta et al., 2006). These gene patterns also translate into human subjects when comparing subcutaneous and visceral adipose depots, with many of the differences in gene expression being related to body mass index and waist-to-hip ratio (Gesta et al., 2006). Studies have shown depot-specific differences in adipocyte cell size, which may influence metabolism and overall health parameters. For example, visceral adipocyte size is associated with increased LDL and total cholesterol, as well as increased insulin resistance, while subcutaneous adipocyte size has no association or is positively correlated with increased insulin sensitivity (Hoffstedt et al.,

2010; O'Connell et al., 2010).

Depot differences are not limited to adipose tissue in humans, however. Rodent models also exhibit adipose tissue heterogeneity. For example, WT mice have smaller adipocytes in the subcutaneous and mesenteric depots compared to the perigonadal depot

(Sackmann-Sala et al., 2012). Many other differences, such as variations in protein expression, have been observed when comparing mouse WAT depots. For example, 30

protein expression in the mesenteric fat of mice has been shown to be twice as high when compared other depots, with subcutaneous having the lowest oxidative stress and triglyceride turnover (Sackmann-Sala et al., 2012). These differences may be simply due to the locations and innervations of the different tissues or through cell-autonomous mechanisms (Rosen & Spiegelman, 2014). Regardless, when reporting and interpreting data from adipose studies, it is important to recognize the complexity and heterogeneity of the tissue and its differing metabolic parameters.

Obesity and white adipose tissue remodeling. As previously mentioned, 34.9% of

U.S. adults were considered obese in 2011 and 2012 (Ogden et al., 2014). The persistence of the obesity epidemic has resulted in an increase of research efforts to understand and combat obesity. Many comorbid conditions accompany obesity, including type 2 diabetes, hypertension, cardiovascular disease, and certain forms of cancer (Khaodhiar,

McCowen, & Blackburn, 1999). Obesity is classically defined as an excessive accumulation of fat mass, but it is becoming increasingly evident that a complicated interplay of various factors within WAT contribute to this state and increase risk for metabolic complications (Lee et al., 2013).

WAT expansion can occur in two ways during states of positive energy balance.

The first is through hypertrophy, which refers to the increase in volume of existing adipocytes, and the second is hyperplasia, which is the recruitment of new adipocytes through the proliferation and differentiation of existing preadipocytes (Rosen &

Spiegelman, 2014). Hypertrophy occurs when cell size is increased through “filling” of the lipid droplet. The threshold for lipid storage is estimated to be 0.7-0.8 ug/cell, at 31

which point proliferation and differentiation occur (Krotkiewski, Bjorntorp, Sjostrom, &

Smith, 1983). Hyperplasia can also occur independently of hypertrophy, and data suggest that remodeling strategies may differ depending on the depot. A study by Tchoukalova et al. (2010) revealed that, in times of excess nutrient availability, lower-body depots undergo more hyperplastic remodeling, while subcutaneous depots in the upper body undergo hypertrophy. Thus, white adipocytes have remodeling ability due to their expansion potential in times of excess caloric intake.

The term “remodeling” refers to the continuous process of cell turnover and ECM alteration that occurs within adipose tissue (Lee et al., 2010; Mariman & Wang, 2010).

As previously mentioned, adipose tissue can undergo expansion during times of nutrient excess, which contributes to states of obesity. It is important to differentiate between

“healthy” and “pathological” adipose tissue expansion. In “healthy” expansion, adipose tissue expands through the recruitment and differentiation of preadipocytes, minimal macrophage infiltration and subsequent inflammation, adequate vascularization, and marginal ECM deposition (Sun, Kusminski, & Scherer, 2011). During pathological states of expansion, the tissue grows rapidly through hypertrophy with little angiogenesis, increased inflammation, and fibrosis (Sun et al., 2011). Under these conditions, remodeling is altered and the tissue becomes metabolically dysfunctional (Sun et al.,

2011). Much of this dysfunction is attributed to physiological changes within the tissue, including macrophage infiltration, inflammation, hypoxia, and adipocyte death.

Depot differences again become important when discussing WAT remodeling, as visceral depots tend to exhibit greater macrophage infiltration, inflammation, and 32

angiogenic capacity when compared to subcutaneous depots (Sun et al., 2011).

Additionally, adipose tissue fibrosis can present differently depending on depot and weight status.

Brown adipose tissue. BAT is characterized by small, multilocular adipocytes that contain relatively large quantities of mitochondria. The iron present in the mitochondria gives BAT a characteristic reddish-brown color that is visually distinct from white-yellow WAT. BAT has thermoregulatory properties and can produce heat during periods of cold exposure. Unlike WAT, which stores chemical energy, BAT dissipates stored energy in the form of heat. This is accomplished through uncoupling protein-1 (UCP1), also known as thermogenin, which is present in the inner mitochondrial membrane. UCP1 catalyzes a leak in the proton gradient, causing chemical energy to be released as heat (Park, Kim, & Bae, 2014). BAT is highly vascularized, which aids in oxygen delivery to the tissue and the transfer of heat to the rest of the body

(Lidell et al., 2014). As seen in the case of WAT, brown adipocytes make up much of the tissue volume; however, they are likely outnumbered by other cell types, including endothelial cells, preadipocytes, and immune cells (Cannon & Nedergaard, 2004).

Adenosine triphosphate (ATP) is a high-energy molecule which is often referred to as the molecular “currency” of the cell. ATP synthesis occurs in a process called oxidative phosphorylation, which takes place in the mitochondria. Mitchell (1961) first proposed a system in which oxidation and hydrogen ion transfer is coupled to the synthesis of ATP. Electrons are passed through a series of protein complexes, which results in the pumping of hydrogen ions across the inner mitochondrial membrane. The 33

resulting gradient is maintained in this way, as dissipation of the gradient also occurs by the movement of ions back into the mitochondrial matrix through the ATP synthase complex, which catalyzes the formation of ATP from adenosine diphosphate (ADP) and inorganic phosphate (Azzu & Brand, 2010). UCP1 can also cause dissipation of this gradient, resulting in the formation of heat instead of ATP (Azzu & Brand, 2010). Figure

3 depicts the electron transport chain and UCP1.

Figure 3. The electron transport chain and uncoupling protein 1. In oxidative phosphorylation, transfer of electrons through four protein complexes is coupled to the pumping of hydrogen ions across the inner mitochondrial membrane and the creation of an ionic gradient. This gradient is then used by ATPase to generate ATP from ADP and inorganic phosphate. In the presence of UCP1, hydrogen ions can readily flow back through the membrane, thus dissipating the proton gradient and causing heat to be released. From “Role of Uncoupling Proteins in Cancer,” by A. Valle, J. Oliver, and P. Roca, 2010, Cancers, 2, p. 567. Copyright 2013 by the authors.

As previously mentioned, BAT is far less abundant than WAT and is found in greatest quantities in the interscapular region of mice and human infants (Nedergaard,

Bengtsson, & Cannon, 2007). BAT develops prenatally in most mammals and is believed to have evolved as a protective mechanism to guard against cold stress after birth 34

(Cannon & Nedergaard, 2004). Previously, it was believed that adult humans possessed only insignificant amounts of brown fat. However, recent imaging studies have identified metabolically active BAT depots mainly in the supraclavicular region in adult humans

(Nedergaard et al., 2007; Saito et al., 2009). This discovery has expanded the potential of

BAT research as a possible therapeutic target for obesity. When discussing BAT in adult humans, it is important to understand that while the interscapular BAT of human infants appears to consist of classical brown adipocytes and is likely equivalent to that found in rodents, the supraclavicular BAT in adult humans contains mostly beige adipocytes

(Lidell et al., 2013).

Although the endocrine properties of BAT remain largely ambiguous, its ability to produce and respond to certain hormone molecules suggest an endocrine role for BAT.

For example, it has long been known that BAT produces the thyroid hormone triiodothyronine (T3) by virtue of its type II thyroxine 5′-deiodinase (Dio2), which converts thyroxine to T3 (Wang, Zhao, & Lin, 2015). Dio2 ablated mice demonstrated hypothermia upon cold exposure despite normal plasma T3 levels (de Jesus et al., 2001).

From the study by de Jesus (2001), it was inferred that local production of T3 is important as an activator of thermogenesis. More recently, it was discovered that BAT secretes neuregulin 4 (Nrg4), which is part of the epidermal growth factor family (EGF)

(Pfeifer, 2015). Not only is Nrg4 secreted in the greatest amounts by BAT, it was also found to be enriched in subcutaneous WAT following cold exposure, labeling it as an important beiging factor (Rosell et al., 2014). After a series of experiments, Wang et al.

(2014) identified liver as the target tissue of Nrg4 and found that it acts to inhibit de novo 35

lipogenesis through activation of Erb-B2 Receptor Tyrosine Kinase 3 and 4 (ErbB3 and

ErbB4) signaling in hepatocytes. Following high-fat feeding in mice overexpressing

Nrg4, reduction of hepatic steatosis is accompanied by decreased weight gain, improved glucose tolerance, and increased insulin sensitivity (Wang et al., 2014). In addition to its secretory function, BAT also houses receptors for various hormones, including GH, IGF-

1, prolactin, androgen, estrogen, and insulin (Reddy, Tan, Barber, & Randeva, 2014).

These characteristics of BAT, supplemented by the new evidence of BAT-liver crosstalk, further bolster the argument for this tissue as an endocrine organ.

Uncoupling protein 1. Uncoupling protein 1 (UCP1) is a 33 kDa purine nucleotide binding protein that is a member of the mitochondrial transporter family. It shares a great deal of homology with other members of the family, including its three repeated domains, each consisting of about 100 amino acids (Cannon & Nedergaard,

2004). These domains contain three hyperconserved residues that are present in all UCPs and include prolines at positions 32, 132, and 231 (Nedergaard et al., 2001). Figure 4 illustrates the structure of UCP1 and indicates these residues. Despite sharing 57% and

59% homology with close family members UCP2 and UCP3, respectively, UCP1 remains the only protein with significant thermogenic capacity (Krauss, Zhang, &

Lowell, 2005; Nedergaard et al., 2001). This function of UCP1 may be due, in part, to two unique sequences that reside in the central loop of the protein and are not found in any other mitochondrial carrier (Cannon & Nedergaard, 2004). Additionally, these sequences are conserved throughout all species that express UCP1 (Cannon &

Nedergaard, 2004). The first, 144SHLHGIKP, contains histidines which are believed to 36

be necessary for proton transport across the membrane (Bienengraeber, Echtay, &

Klingenberg, 1998; Nedergaard et al., 2001). The second, 299RQTXDC(T/A)T, is a C- terminal sequence (Nedergaard et al., 2001).

Figure 4. UCP1 structure. UCP1, like many mitochondrial carriers, is a tripartite structure. Proline residues at positions 32, 132, and 231 in the 100-residue repeats are indicated. These residues are fully conserved across all species in UCP1, UCP2, and UCP3. Additionally, two sequences unique to UCP1, 144SHLHGiKP and 299RqTxDCxT, are shown. From “UCP1: The Only Protein Able to Mediate Adaptive Nonshivering Thermogenesis and Metabolic Insufficiency,” by J. Nedergaard, V. Golozoubova, A. Matthias, A. Asadi, A. Jacobsson, and B. Cannon, 2001, Biochimica et Biophysica Acta, 1504, p. 82, and from “Brown Adipose Tissue: Function and Physiological Significance,” by B. Cannon and J. Nedergaard, 2004, Physiological Reviews, 84, p. 277. Copyright 2004 by the Physiological Society. Reprinted with permission.

UCP1 is regulated, in part, by diphosphate and triphosphate purine nucleotides, such as ADP, ATP, GDP, and GTP, which inhibit the uncoupling action of UCP1

(Symonds, 2011). In contrast, UCP1 is activated by long chain fatty acids that are 37

produced as a result of β-adrenergic receptor activation (Cannon & Nedergaard, 2004).

Although many models have been proposed to explain the mechanism behind UCP1’s regulation by FFAs, results from a recent study suggest that UCP1 acts as a long chain fatty acid/hydrogen ion (LCFA/H+) symporter (Fedorenko, Lishko, & Kirichok, 2012).

When exposed to cold, mice will regulate body temperature through two mechanisms. The first is through the mechanical action of shivering and the second is through activation of UCP1, also known as nonshivering thermogenesis (Symonds,

2011). Studies in UCP1-ablated mice have shown that BAT, in the absence of UCP1, has no thermogenic capacity. It was also noted that this loss of thermogenesis is not due to a lack of metabolic activity, as the same cAMP activation and lipolysis are observed in both the UCP1-ablated and WT mice (Matthias et al., 2000).

Regulation of thermogenesis. The most important and best understood mechanism of thermogenic control is norepinephrine signaling through β3 adrenergic receptors (Cannon & Nedergaard, 2004). In response to cold stimulus, sympathetic neurons release catecholamines that bind β-adrenergic receptors and trigger downstream signaling cascades that ultimately activate protein kinase A (PKA). PKA induces lipolysis of lipid droplets, which can then be oxidized by mitochondria to produce heat.

During periods of prolonged cold exposure, BAT mass is increased through differentiation of precursor cells, thereby increasing thermogenic capacity (Bukowiecki,

Collet, Follea, Guay, & Jahjah, 1982). This thermoregulatory property of BAT becomes apparent when studying mice who lack functional BAT activity become cold-intolerant

(Harms & Seale, 2013). 38

Although norepinephrine signaling is the most commonly described thermogenic mechanism, many other molecules and pathways have been identified as important regulators of thermogenesis. Obesity research, which has expanded in proportion to waistlines, has led to the understanding that this disease is much more complex than previously described by the popular “calories in, calories out” mantra. This realization has pushed obesity research beyond the limits of diet and exercise studies and naturally has led researchers to investigate BAT activation as a potential therapy. Thus, the field of

BAT research is fast-paced and constantly evolving. As such, this review is not intended to provide an exhaustive description of BAT or its activation, as the focus of this thesis project pertains to the beiging of WAT, a topic which is described in the subsequent section.

Beiging of White Adipose Tissue

The “beiging” or “browning” of WAT refers to the ability of some adipocytes within WAT to take on brown-like characteristics. This phenomenon transforms energy- storing white adipocytes to energy-burning “beige” adipocytes.

Beige adipocytes. The dichotomy of white and brown adipocytes becomes considerably less clear when considering white adipocytes that take on brown-like properties. These cells, referred to as “beige” or “brite” adipocytes, can convert to a brown-like state during periods of prolonged cold exposure by increasing UCP1 expression and becoming multilocular (Rosenwald & Wolfrum, 2014). In basal states, these adipocytes exhibit classical features of WAT, having unilocular morphology and similar gene expression. The possibility that these cells can interconvert between white 39

and brown phenotypes in a bidirectional manner has been suggested, although this theory is still controversial (Barbatelli et al., 2010; Sanchez-Gurmaches & Guertin, 2014). It is now more widely accepted that beige adipocytes arise from unique precursor cells within

WAT (Wu et al., 2012). Beige adipocytes also seem to exhibit regional depot differences, with the highest number in subcutaneous and retroperitoneal depots and the least in perigonadal (Rosen & Spiegelman, 2014). In fact, the perigonadal fat pad has been referred to as a “pure white” depot due to its low number of beige adipocytes (Rosenwald et al., 2013) .

Because they share the phenotypic features of both white and brown adipocytes, beige adipocytes are difficult to classify. Ongoing debate persists as to whether beige adipocytes arise from pre-existing cells of white origin or whether they develop from unique precursor cells. Evidence for the former comes from the observation of some studies that cold induction does not result in proliferation of adipocytes, merely a conversion from one cell type to another (Rosen & Spiegelman, 2014). Conversely, another study uses a pulse-chase mapping technique to label mature adipocytes and found that most subcutaneous beige adipocytes stemmed from newly differentiated adipocytes

(Wang, Tao, Gupta, & Scherer, 2013). It is entirely possible that these two schools of thought are not mutually exclusive and a unified theory could potentially be developed.

However, the existence of a unique beige adipocyte precursor is now a well-accepted concept.

Both white and brown adipocytes derive from mesenchymal stem cells, but have different precursors. Mesenchymal stem cells commit to either an adipogenic or 40

myogenic lineage during differentiation (Park et al., 2014). Brown adipocyte precursors express both the Myf5 and Pax7 genes, which developmentally links them with muscle cells as they share a common precursor. White and beige adipocytes derive from Myf5-

/Pax7- precursors. This provides further evidence that beige adipocytes are a unique cell type, independent of brown adipocytes (Rosen & Spiegelman, 2014). Figure 5 illustrates the developmental origins of brown, white, and beige adipocytes.

Figure 5. Adipocyte origins. Brown adipocytes muscle cells arise from a common Pax7+/Myf5+ precursor. In contrast, white and beige adipocytes derive from a Pax7+/Myf5+ lineage. This suggests that beige adipocytes are a discrete cell type and are not developmentally linked to classical brown adipocytes. It is still unclear whether beige adipocytes arise from a unique precursor or through transdifferentiation of preexisting white adipocytes. From “What We Talk About When We Talk About Fat,” by E. D. Rosen and B. M. Spiegelman, 2014, Cell, 156, p. 20. Copyright 2014 by Elsevier. Reprinted with permission. 41

Factors affecting beiging. The beiging of WAT requires both the suppression of

WAT genes and induction of BAT genes (Lo & Sun, 2013). This is accomplished through a complex series of interactions between various transcription factors and coregulators. Three core transcriptional regulators act as the foundation for induction of the brown fat program and include peroxisome proliferator-activated receptor gamma

(PPARγ), PR domain containing 16 (PRDM16), and peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PGC-1α) (Lo & Sun, 2013). PPARγ is a transcription factor necessary for brown and white adipocyte survival (Imai et al., 2004).

It plays a role in both WAT gene suppression and BAT gene expression. PRDM16 is a zinc-finger protein that is expressed selectively in brown fat and is important for the increased mitochondrial and UCP1 expression seen in authentic brown adipocytes (Seale et al., 2007). PGC-1α is necessary for thermogenesis and acts to regulate mitochondrial respiration (Lo & Sun, 2013). These transcriptional regulators form complexes with other coactivators and repressors to induce beiging (Lo & Sun, 2013). For example, PRDM16 forms a complex with CCAAT/enhancer-binding protein α (C/EBPα), as well as two cofactors, C-terminal-binding proteins 1 and 2 (CtBP 1 and 2) in order to bind the promoter sites of various WAT genes, inhibiting their expression (Lo & Sun, 2013;

Vernochet et al., 2009). PRDM16 also acts in tandem with PPARγ and PGC-1α to initiate the transcription of BAT genes, including UCP1. Sirtuin-1 (SIRT1) is necessary for deacetylation and recruitment of PRDM16 to the transcriptional complex, while early B cell factor-2 (EBF2) acts as a cofactor that binds PPARγ to brown fat genes rather than 42

white (Qiang et al., 2012; Rajakumari et al., 2013). A brief summary of these interactions is depicted in Figure 6.

Figure 6. Transcriptional regulators of beiging and their cofactors. A. Activation of WAT beiging requires simultaneous induction of BAT genes and repression of WAT genes. Three core transcriptional regulators, PPARγ, PRDM16, and PGC-1α, are necessary for both of these processes. Additionally, a number of coactivators and corepressors can act on one or more transcriptional regulators to activate or inhibit beiging. Cox2 and SIRT1 act on PPARγ to activate beiging. Cox2 is a downstream effector of the β-adrenergic signaling pathway and ultimately leads to increased PPARγ, while SIRT1 acts to deacetylate PPARγ and recruit PRDM16 to the transcriptional complex. TLE3 inhibits beiging by disrupting the interaction of PPARγ and PRDM16, while mir-133 decreases the amount of PRDM16 in WAT. TRPV4 and 4E-BP1 inhibit beiging through their negative regulation of PGC-1α, while BMP7 and FGF21 promote beiging by increasing levels of PGC-1α. PGC-1α has also been shown to stimulate increased expression of irisin, another positive beiging regulator. B. Suppression of WAT genes requires the interaction of PRDM16 with C/EBPα and CtBP 1 and 2. C. Induction of BAT genes involves the interaction of all three core regulators, as well as cofactor EBF2, which directs the binding of PPARγ to the promoter of BAT genes. From “Turning WAT into BAT: A Review on Regulators Controlling the Browning of White Adipocytes,” by K.A. Lo and L. Sun, 2013, Bioscience Reports, 33, p. 711. Copyright 2013 by the authors. Reprinted with permission.

As previously discussed, chronic cold exposure can induce beiging. In subcutaneous WAT, it may be possible for cold temperature to directly activate thermogenesis independently of the sympathetic nervous system (SNS), though the mechanism has not yet been elucidated (Park et al., 2014). Indirect activation of thermogenesis through the SNS is aided by many different factors, such as immune cells, which have been proposed to play a role in WAT beiging response. A study in mice suggests that cold-induced activation of M2 macrophages results in a catecholamine release and subsequent local induction of beige adipocytes (Nguyen et al., 2011). Rapid temperature-dependent interconversion of beige adipocytes has been observed; one study in mice showed that cold-induced beiging can be reversed within a 5-week period

(Rosenwald et al., 2013). The same study showed that upon cold stimulation, an increase in UCP1 expression was observed within 5 days (Rosenwald et al., 2013). Other studies, however, have revealed more rapid interconversion of adipocytes. A study investigating rewarming of mice after prolonged cold exposure results in measurable decreases in

UCP1 within 1 day and a return to baseline levels of UCP1 after 21 days (Gospodarska,

Nowialis, & Kozak, 2015).

While cold seems to be the main signal for beiging, a number of other factors have been implicated in the process as well. Many of these molecules act directly on transcriptional regulators or their cofactors to promote beiging, while others alter factors upstream of the β-adrenergic signaling pathway (Lo & Sun, 2013). Examples include irisin, fibroblast growth factor 21 (FGF21), and natriuretic peptides (Harms & Seale,

2013). Irisin is secreted from the muscle during exercise and has been shown to increase 45

beiging in subcutaneous WAT, while having little effect on interscapular BAT (Boström et al., 2012). It was also noted that even very small increases in serum irisin resulted in both beiging and improved glucose tolerance as well as decreased weight gain (Bostrom et al., 2012). There has been a great deal of recent controversy surrounding the detection and measurement of irisin. A study by Albrecht et al. (2015) reported that commercial

ELISA kits did not appear to be measuring irisin and, additionally, found no evidence to support the presence of irisin in humans. However, these results were quickly refuted after tandem mass spectrometry was used to identify and quantify circulating irisin from human plasma (Jedrychowski et al., 2015). Therefore, caution should be used when considering the importance of irisin. FGF21 is primarily a hepatic fasting-induced endocrine hormone that has been implicated in resistance to diet-induced obesity and is increased in BAT during cold exposure (Harms & Seale, 2013; Müller & Tschöp, 2014;

Ohta & Itoh, 2014). It exerts its beiging effect by increasing levels of PGC-1α, which induces UCP1 expression. FGF21 is also increased in response to exercise, and it has been suggested that FGF21 may work synergistically with irisin to increase thermogenic response in BAT after both cold exposure and exercise (Fenzl & Kiefer, 2014; Lee,

Werner, Kebebew, & Celi, 2014). Therefore, exercise is another important regulator of

WAT beiging and thermogenesis. Atrial natriuretic peptides are secreted by the heart and activate protein kinase G (PKG) to cause lipolysis and beiging in WAT. Increases in these peptides are observed after cold exposure (Harms & Seale, 2013). Figure 7 depicts these and other molecules believed to influence beige adipocyte development.

Figure 7. Secreted factors for brown and beige adipocyte recruitment. Various secreted molecules are believed to contribute to BAT activation and development of beige adipocytes. These factors exhibit endocrine, autocrine, and paracrine action. Both liver and BAT produce FGF21; skeletal muscle secretes irisin; the thyroid produces T4, which is subsequently cleaved to T3; the heart secretes natriuretic peptides; and neurons, as well as classically activated macrophages, secrete norepinephrine. BAT produces bone morphogenetic protein 8b (Bmp8b) and vascular endothelial growth factor (Vegf), which behave in an autocrine manner. Orexin and bone morphogenetic protein 7 (Bmp7) are involved in BAT development, although their origins are still unknown. From “Brown and Beige Fat: Development, Function and Therapeutic Potential,” by M. Harms and P. Seale, 2013, Nature Medicine, 19, p. 1252. Copyright 2014 by Elsevier. Reprinted with permission.

Due to the extensive and complex crosstalk between WAT and the immune system, it comes as no surprise that immune cells are also beginning to emerge as important regulators of beiging. In the context of chronic cold exposure, immune cells can act as beiging mediators. For example, under conditions such as cold temperature, 47

WAT produces the adipokine meteorin-like, which leads to increased eosinophil accumulation and subsequent production of interleukin 4 (IL-4), a stimulator of alternatively activated macrophages (AAMacs) (Brestoff & Artis, 2015). These AAMacs produce norepinephrine thereby eliciting a beiging response (Brestoff & Artis, 2015). IL-

4 can also act directly on preadipocytes to promote beige adipocyte development

(Brestoff & Artis, 2015). Additionally, interleukin 33 (IL-33) elicits WAT beiging by stimulating group 2 innate lymphoid cells (ILC2s) (Brestoff et al., 2015). These ILC2s can also produce factors, such as methionine enkephalin (MetEnk), that act directly to regulate beiging (Brestoff et al., 2015). Figure 8 illustrates the complex interactions between WAT and immune cells.

While the beiging factors listed are currently the most extensively studied, this is by no means meant to be a comprehensive list. Adipose tissue beiging is a very popular field of study and is constantly evolving as more information is gathered on the subject.

Therefore, knowledge is ever-evolving and new beiging factors are constantly being described. It is important to appreciate the timeline of this work and understand that it is impossible to develop a complete list of all molecules that influence beiging. 48

Figure 8. Immune cells involved in the regulation of beiging. Under conditions of chronic cold or exercise, WAT produces the adipokine meteorin-like, which leads to increased accumulation of eosinophils. IL-4 is produced by eosinophils and is important for AAMac response. AAMacs subsequently produce norepinephrine, which elicits beiging through differentiation and, potentially, transdifferentiation pathways. IL-4 can also act on preadipocytes in a direct manner to stimulate beige adipocyte development. WAT secretes IL-33, which stimulates the production of IL-5, IL-13, and MetEnk from ILC2s. IL-5 and IL-13 maintain eosinophil and AAMac responses, respectively. IL-13 and MetEnk, similarly to IL-4, can directly stimulate beiging via their action on preadipocytes. From “Immune Regulation of Metabolic Homeostasis in Health and Disease,” by J. Brestoff and D. Artis, 2015, Cell, 161, p. 146. Copyright 2015 by Elsevier. Reprinted with permission. 49

Beiging as a potential therapeutic target. As rates of obesity and type 2 diabetes mellitus steadily rise, brown fat has been explored for its potential as a therapeutic agent.

In the 1930s, a chemical uncoupler, 2,4-dinitrophenol (DNP), was used as a weight loss drug. DNP mimics the actions of UCP1 by creating a proton leak across the mitochondrial membrane, resulting in energy expenditure and weight loss. However, many dangerous side effects, such as hyperthermia, were encountered that resulted in fatalities (Harms & Seale, 2013; Harper, Dickinson, & Brand, 2001). It has been theorized that these toxic effects are a result of DNP’s actions in tissues other than BAT, which has led to an increased push for natural therapies using the induction of brown and beige fat (Harms & Seale, 2013; Rosen & Spiegelman, 2014). Another trial in humans used β3 agonists in an attempt to activate brown fat; however, these were either unsuccessful or were only successful for a short period of time (Cypess & Kahn, 2010).

Experiments in animal models have garnered interest in several specific brown and beige targets for treatment of type 2 diabetes. These include FGF21, irisin, and natriuretic peptides. Clinical trials to demonstrate the efficacy of FGF21 in human subjects are already underway. After initial safety and tolerability assessments, LY2405319, an

FGF21 variant, was administered through subcutaneous injection in 46 individuals with type 2 diabetes in doses of 3, 10, and 20 milligrams (mg)/day (Gaich et al., 2013).

Clinically meaningful reductions in total cholesterol, HDL, LDL, triglycerides, and body weight were observed, and although dose-dependent decreases in fasting glucose did occur, none of the values reached statistical significance (Gaich et al., 2013). Further investigation of the LY variant is needed to confirm its efficacy in the treatment of type 2 50

diabetes.

Other challenges exist in regard to the development of therapies using the aforementioned markers. Irisin and natriuretic peptides were only recently discovered to have an effect on beiging of WAT and much more study is needed to validate these early findings and to ensure there are no other effects of these molecules. Endogenous FGF21 secretion is believed to be augmented by a low-protein diet, although the contribution of individual amino acids and the type of protein eliciting this effect have yet to be investigated (Laeger et al., 2014). Therefore, external factors such as diet may play a role.

Additionally, the mouse models used in beiging studies differ from human subjects in many ways and the similarities and differences (such as depot differences) must be identified to better study beiging in human WAT. Energy balance must also be taken into account, as weight loss achieved through the use of these targets may be counteracted by increases in hunger signals or compensatory metabolic mechanisms in other tissues, such as muscle (Harms & Seale, 2013). Thus, further research is warranted to fully understand all of the factors involved in beiging.

Growth Hormone

Growth hormone (GH) was first isolated from the pituitary in 1956, and its biochemical structure was discovered in 1972 (Ayyar, 2011). Despite concerted research efforts, much is still unknown about GH and its effects throughout the body.

Structure and regulation. GH is a 191 amino acid protein that is a member of both the prolactin (PRL) and placental lactogen (PL) families (Abdel-Meguid et al.,

1987). GH is secreted by the anterior pituitary and has two isoforms, a 20 kDa and a 22 51

kDa protein, with the latter being the primary pituitary-derived GH isoform (Abdel-

Meguid, et al., 1987; Baumann, 2009). GH secretion is regulated through a series of feedback systems mediated by the hypothalamus, which releases two neuropeptides with opposing functions: growth hormone releasing hormone (GHRH) and somatostatin

(Bideci & Çamurdan, 2009; Kato, et al., 2002). GHRH, true to its name, stimulates GH release while somatostatin acts to inhibit GH secretion from the pituitary (Bideci &

Çamurdan, 2009). Fasting and states of hypoglycemia are known to be associated with an increase in GH (Bideci & Çamurdan, 2009). Similarly, GH secretion is positively influenced by sleep and exercise (Kato, et al., 2002). Figure 9 depicts the secretion, action, and regulation of GH.

Figure 9. Growth hormone action and regulation. GH secretion is regulated by GHRH and somatostatin, which are produced in the hypothalamus. Somatostatin inhibits GH secretion while GHRH stimulates release from the anterior pituitary. GHRH can be positively influenced by conditions such as stress, sleep, and exercise, which cause a subsequent rise in GH levels. GH is released in a pulsatile manner and binds the GHR on target tissues, such as liver, muscle, adipose tissue, and bone. This ultimately leads to an increase in IGF-1 production, which can feed back to inhibit GHRH and GH secretion. From “Growth Hormone (GH), GH Receptor, and Signal Transduction,” by J. J. Kopchick, and J. M. Andry, 2000, Molecular Genetics and Metabolism, 71(1-2), p. 293. Copyright 2011 by Elsevier. Reprinted with permission.

Growth hormone-induced signaling. Figure 10 illustrates the complex nature of the GH signaling pathways. The initiation of GH signaling is dependent upon GH binding its growth hormone receptor (GHR) on the cell surface. GHR belongs to the class I 53

hematopoietin, cytokine/growth hormone/prolactin receptor superfamily. The GHR contains two extracellular domains as well as a hydrophobic transmembrane domain

(Kopchick & Andry, 2000). These two domains join to make a preformed dimer, which binds GH at two different sites. This binding causes intracellular domain rotation and activation of Janus Kinase (JAK2) (Kopchick & Andry, 2000; Lanning & Carter-Su,

2006). JAK2 then phosphorylates tyrosine residues on the GHR, which allows signal transducer and activator of transcription 5 (STAT5) to bind. JAK2 subsequently activates

STAT5 through phosphorylation. This signal cascade is referred to as the JAK/STAT pathway and is one of the main signaling pathways activated when GH binds GHR, though other pathways such as the mitogen activated map kinase (MAPK) and phosphotidylinositol 3’ kinase (PI3K) are also activated (Kopchick & Andry, 2000;

Lanning & Carter-Su, 2006). Ultimately, this signaling cascade results in the alteration of gene expression in the cell. One of the most significantly altered genes expressed with

GH signaling is insulin like growth factor 1 (1GF-1).

GH exerts its effects on tissues both directly and indirectly, through the production of IGF-1 (Berneis & Keller, 1996). IGF-1 is a hormone produced mainly in the liver in response to GH signaling and binds its receptor on various tissues, where it exhibits anabolic and lipolytic effects (Perrini, et al., 2010). GH can also induce local production of IGF-1, which then acts in an autocrine or paracrine manner (Berneis &

Keller, 1996). STATs are a family of cytoplasmic proteins that aid in the transcription of various genes after becoming activated (Kopchick & Andry, 2000). Stat5 has been shown to regulate the transcription of IGF-1 (Lanning & Carter-Su, 2006). 54

Figure 10. Growth hormone-induced signal transduction. GH binding to the preformed GHR dimer results in a conformational change of the dimer and subsequent activation of a complex downstream signaling cascade. The activation of these pathways, including the Janus Kinase-Signal Transducer and Activator of Transcription (JAK-STAT) pathway, mitogen-activated protein kinases (MAPK) pathway, and phosphatase trioxide PI3-kinase (PI3K) pathway, results in the regulation of gene expression through transcription factor activation. From “The Growth Hormone Receptor: Mechanism of Activation and Clinical Implications,” by A. J. Brooks and M. J. Waters, 2010, Nature Reviews Endocrinology, 6, p. 515. Copyright 2010 by Nature Publishing Group. Reprinted with permission.

GH function. GH has many effects on the body, both directly and indirectly through the action of IGF-1 and has been shown to result in a lean phenotype by increasing bone growth, decreasing fat mass, and increasing lean (muscle) mass (Perrini, et al., 2010). One of the most well studied aspects of GH function is its stimulatory effect on longitudinal bone growth (Isaksson, Jansson, & Gause, 1982). The process of bone growth is characterized by chondrocyte proliferation and ossification in the epiphyseal 55

growth plates. Both GH and IGF-1 stimulate long bone growth but act on different target cells within the growth plate (Nilsson, Ohlsson, Isaksson, Lindahl, & Isgaard, 1994). Due to the promotion of a lean body type through its stimulatory effect on muscle and inhibition of fat accumulation, GH is often used as a performance-enhancing drug. It is believed that the observed increase in muscle mass may be attributed to increased protein synthesis and decreased breakdown (Møller & Jørgensen, 2009). Additionally, this increase is attributed to both hypertrophy and hyperplasia of the myofibers and is thought to be mediated mainly through the actions of IGF-1 (Velloso, 2008). GH also exhibits lipolytic and antilipogenic effects, increasing circulating levels of FFA, which may indicate a switch from utilization of carbohydrate and protein to lipid oxidation for fuel

(Adamafio & Ng, 1984; Møller & Jorgensen, 2009). GH plays an important role in glucose metabolism and has been identified as a diabetogenic hormone (Møller &

Jørgensen, 2009). GH-induced insulin resistance appears to be the result of both increased endogenous glucose production as well as decreased glucose uptake by muscle

(Møller & Jørgensen, 2009). It has been suggested that this decrease in glucose uptake may result from inhibition of signaling molecules, such as PI3K, which are key regulators of glucose transport into muscle and fat (Clemmons, 2004).

Acromegaly. Acromegaly is a medical condition characterized by excessive GH secretion, resulting in gigantism or excessive growth. Individuals with this condition suffer many physical abnormalities such as enlarged hands, feet, lips, tongue and lower jaw protrusion. Various comorbidities also accompany acromegaly, including cardiovascular disease, impaired lung function, and impaired glucose metabolism and 56

diabetes (Adelman et al., 2013). Usually, acromegaly is caused by a tumor in the pituitary, called a pituitary adenoma, which results in chronically high levels of GH and, subsequently, IGF-1 (Melmed, 2011). This condition can be treated with surgery or radiation therapy to reduce hypersecretion of GH or with various drugs such as octreotides (Sandostatin), lanreotides (Somatuline), and GHR antagonists (Somavert)

(Giustina et al., 2014; Kopchick, 2003; Trainer et al., 2000).

Bovine growth hormone transgenic mouse. Bovine growth hormone transgenic

(bGH) mice are genetically engineered to have excess levels of circulating GH and IGF-1 and mimic the conditions seen in acromegalic individuals (Berryman et al., 2004). These mice are giant and lean, owing greatly to a reduction in fat mass relative to their WT counterparts (Berryman et al., 2004). Although normalized fat mass is greater in younger bGH animals, this trend begins to reverse by the ages of 4 and 6 months for males and females, respectively, and eventually is significantly reduced relative to WT controls

(Palmer et al., 2009). bGH mice are also resistant to diet-induced obesity, yet are hyperinsulinemic and can develop diabetes. These mice have considerably shorter lifespans compared to their littermate controls, which is explained, in part, by organ damage caused by excessive growth (Kopchick et al., 2014; Wolf et al., 1993). Table 1 provides a summary of bGH characteristics. 57

Table 1

Summary of bGH Characteristics

Phenotype Summary Reference

GH and IGF-1 levels  Significantly increased circulating GH and IGF-1 Berryman et al., 2004; compared to controls Palmer et al., 2009

Body size and length  Significantly larger than controls and increased body Berryman et al., 2004; length Ding, Sackmann-Sala, & Kopchick, 2013

Body composition  Adult mice exhibit decreased fat mass and increased Berryman et al., 2004; lean mass Palmer et al., 2009  Mice younger than 4 months have greater fat mass than controls but become significantly leaner by 4 months of age for males and 6 months of age for females

Longevity  Significantly decreased lifespans Wolf et al., 1993

Insulin sensitivity  Hyperinsulinemic and become insulin resistant when Olsson et al., 2005 fed a normal diet (ND) or HFD Glucose tolerance  Depending on age, normal or impaired glucose Kopchick, Bellush, & Coschigano, tolerance 1999 Olsson et al., 2005

Table 1 (continued) Adipokine profile  Increased resistin Berryman et al., 2004;  Decreased leptin and adiponectin Wang, Masternak, Al-Regaiey, & Bartke, 2013

Effects of dietary  Resistant to diet-induced obesity Berryman et al., 2006; manipulation  Hyperphagia and increased EE on HFD Olsson et al., 2005  Exhibit dyslipidemia and become diabetic on HFD  Less fat mass gain on HFD compared to controls and increase in lean mass

Disease  Develop hypertension and have left ventricular Andersson et al., 2006; susceptibility hypertrophy accompanied by impaired cardiac function Baeza, & Garcia-Ruiz, 2000;  Endothelial dysfunction Bohlooly-Y et al., 2013;  Glomerulosclerosis Bollano et al., 2000;  Develop arthritis Ogueta, Olazabal, Santos, Delgado- Yang et al., 1993 59

Laron Syndrome. Laron Syndrome is a condition that results in dwarfism and is most commonly caused by a mutation of the GHR gene (Kopchick, et al., 2014). This mutation results in a dysfunctional GH receptor and so these patients have GHR and IGF-

1 deficiencies accompanied by high circulating levels of GH (Laron et al., 1966). Despite the fact that patients with Laron Syndrome have a high prevalence of obesity, they show enhanced insulin sensitivity and resistance to diabetes and cancer (Guevara-Aguirre et al.,

2011). These individuals also tend to reach old ages, which may be due to their protection from cancer, although the longevity effects of IGF-1 deficiency in human subjects is not fully understood (Laron, 2008).

Growth hormone receptor knockout mouse. The growth hormone gene disrupted knockout mouse (GHR -/-) was generated in the 1990s to mimic Laron

Syndrome. Much like their human counterparts, they exhibit stunted growth and low circulating IGF-1 levels, despite high levels of GH (Zhou et al., 1997). Interestingly, these mice are very insulin sensitive, despite having increased adiposity but have impaired glucose tolerance (Liu et al., 2004; Dominici, Diaz, Bartke, Kopchick, & Turyn,

2000). Because these animals have reduced insulin requirements for maintenance of glucose homeostasis, the impairments in glucose tolerance may be due to decreased insulin production and release by β-cells (Dominici et al., 2000). Insulin sensitivity in the face of obesity has led to these mice being described as having a “healthy obese” phenotype, which may be attributable to the preferential increase in the subcutaneous fat pad relative to visceral (Berryman et al., 2004). These mice also have extremely long 60

lifespans and currently hold the title for the world’s longest lived mouse (Kopchick, et al., 2014). Table 2 provides a summary of GHR-/- mouse characteristics. 61

Table 2

GHR-/- Characteristics

Phenotype Findings Reference

GH and IGF-1 levels  Increased circulating GH and reduced IGF-1 Zhou et al., 1997

Body size and length  significantly smaller and shorter than controls Zhou et al., 1997

Body composition  Significantly increased body fat percentage compared Berryman et al., 2004; to controls with preferential enlargement of the Berryman et al., 2010 subcutaneous depot  No significant difference in lean mass

Longevity  significantly longer lifespans than controls Coschigano et al., 2003; Ding et al., 2013

Insulin sensitivity  decreased insulin levels and increased insulin Berryman et al., 2004; sensitivity Liu et al., 2004; Olsson et al., 2005

Table 2 (continued) Glucose tolerance  decreased glucose levels and impaired glucose Dominici et al., 2000); tolerance Hauck, Hunter, Danilovich, Kopchick, & Bartke, 2001 Adipokine profile  Increased resistin, leptin, and adiponectin Berryman et al., 2004; Egecioglu et al., 2006; List et al., 2013

Effects of dietary  Increased susceptibility to diet-induced obesity Berryman et al., 2004 manipulation  Excess energy intake on a LF diet  Preferential increase of the subcutaneous depot

Disease  Reduced tumor incidence Bartke, Sun, & Longo, 2013; susceptibility  Reduced diabetes incidence Bellush et al., 2000;  Protection from diabetes-induced nephropathy Ikeno et al., 2009; List et al., 2013 63

Growth Hormone and Adipose Tissue

Early studies of GH revealed decreases in fat mass (Bray, 1969). Over the course of many years, and in many different species, GH has exhibited the same effect. GH stimulates lipolysis in part through the action of hormone-sensitive lipase (HSL), which results in an increased free fatty acid level in the serum. Visceral adipose tissue seems to be more susceptible to this action of GH than subcutaneous (Chaves, Júnior, & Bertolini,

2013). In rat adipocytes, incubation with GH over a period of 24 hours increases the function of key molecules involved in the lipolytic cascade, including adenylyl cyclase, beta adrenergic receptors b1 and b3, and HSL (Yang, Mulder, Holm, & Edén, 2004). GH also appears to decrease fat mass through the inhibition of lipogenesis. This effect of GH has been postulated to occur through the regulation of many different molecules, the most notable of which is lipoprotein lipase (LPL). LPL is involved in the uptake of FFA in adipose tissue and therefore positively contributes to fat deposition (Richelsen et al.,

2000). Additionally, pigs treated with GH demonstrate reduced fatty acid synthesis as well as a reduction in glucose transport rates, with no effect on insulin binding or receptor activity (Magri, Adamo, Leroith, & Etherton, 1990). The activity of several lipogenic enzymes, such as fatty acid synthase, are also reduced in these animals (Magri et al.,

1990). Amounts of glucose transporters 1 and 4 (GLUT-1 and GLUT-4, respectively) present in the plasma membrane have been shown to be negatively regulated by GH, suggesting a mechanism for the decrease in glucose uptake observed in the pig model

(Kilgour, Baldwin, & Flint, 1995). It is known that the GH/IGF-1 axis exerts effects on adipogenesis; however, the mechanism and magnitude of these effects are a subject of 64

great debate. For example, GH has been shown to promote adipocyte proliferation in rats but decrease proliferation in pigs (Gerfault, Louveau, & Mourot, 1999; Wabitsch et al.,

1996). IGF-1 has also been shown to play a dual role: both promoting and reducing preadipocyte proliferation (Gerfault et al., 1999; Ramsay, White, & Wolverton, 1989). It has also been suggested that GH can serve as a promoter of adipocyte differentiation

(Doglio, Dani, Grimaldi, & Ailhaud, 1986). Although the exact mechanisms of this have not been investigated, it is thought to be due to the activation of PPARγ by STAT 5a and

5b, which are activated subsequent to GH binding (Chaves et al., 2013). It was also reported, however, that in 3T3L1 cells, GH can have an inhibitory effect depending on the stage of differentiation (Tominaga, Morikawa, & Osumi, 2002). Thus, the effect of the GH/IGF-1 axis on adipogenesis is highly variable and may be determined by multiple factors.

Growth hormone and adipokines. GH influences adipokine secretion, with higher levels of GH being associated with less circulating leptin and adiponectin

(Berryman, et al., 2011). Acromegalic individuals tend to have decreased leptin levels while individuals with Laron Syndrome have high leptin levels (Laron, Silbergeld, Lilos,

& Blum, 1998; Silha et al., 2003). These observations are consistent with expectations, as leptin is positively correlated with fat mass. Adipokine trends in mouse models with altered GH action mimic those observed in clinical settings, with GH-deficient mice showing increased leptin and decreased leptin in the bGH mice (Berryman, et al., 2011).

Additionally, mice with decreased growth hormone action (GHR -/- mice) demonstrate increased circulating levels of adiponectin compared to WT controls, while bGH mice 65

showed the opposite trend (Lubbers et al., 2013). Interestingly, growth hormone deficient

(GHD) individuals have lower adiponectin levels, while a study of Laron patients revealed high circulating adiponectin (Kanety et al., 2009; Lanes et al., 2006). Studies in acromegalic individuals provide conflicting results in terms of adiponectin levels. Some groups have found decreased adiponectin, while others observed increased adiponectin compared to controls (Fukuda et al., 2004; Sucunza et al., 2009).

Depot-dependent growth hormone action. GH does not affect all adipose tissue depots uniformly and depot-specific differences have been observed with respect to its effects. In mice, the subcutaneous depot is usually the most responsive to the effects of

GH, while epididymal is the least altered with GH (Berryman et al., 2004, 2011; List et al., 2009). The subcutaneous and mesenteric depots undergo the most dramatic reductions in mass when obese mice are treated with exogenous GH (List et al., 2009). GHR -/- tend to show preferential increases in the subcutaneous fat pad while the epididymal depot remains proportional to the dwarf size of the mouse (Berryman et al., 2004). Adipose depots also respond differently to GH in terms of remodeling. For example, collagen deposition has been shown to be greatest in the subcutaneous depot of bGH mice

(Householder, 2013). Other cell types within WAT, such as immune cell populations, can also be influenced by GH in a depot-dependent manner. Increases in SVF cell numbers have been observed in the subcutaneous and mesenteric depots of bGH mice compared to their WT counterparts (Benencia et al., 2015). Additionally, macrophages expressing M2 markers are increased to a greater extent in the subcutaneous and mesenteric depots than the epididymal depot in the bGH animals (Benencia et al., 2015). Conversely, a study 66

using macrophage-specific GHR knockout mice on a HFD showed increased accumulation of macrophages in the epididymal fat pad, accompanied by significant increases in the M1/M2 ratio (Lu et al., 2013). T regulatory cells were also observed to be higher in the subcutaneous depot of bGH mice compared to controls, with little changes seen in the epididymal depot (Benencia et al., 2015). Thus, it is important to consider depot differences when studying adipose tissue in these GH-modified mouse lines.

Growth hormone and brown adipose tissue or beiging. Very few studies have assessed the effects of GH on BAT and existing literature is contradictory. One study observed interscapular BAT depot enlargements in GHR-/- mice and negative regulation of UCP1 expression by GH (Li et al., 2003). However, other studies found increases in the brown fat pad in bGH mice and increases in UCP1 expression of BAT after GH treatment (Hioki et al., 2004; Olsson et al., 2005). GH replacement of 1 mg/kg/day and

3.5 mg/kg/day increased UCP1 expression in BAT by 2.0- and 2.8-fold, respectively, although no significant difference in BAT mass was observed with treatment (Hioki et al., 2004). Additionally, microarray analysis used to measure mRNA differences in the subcutaneous fat pad revealed UCP1 to be the second most highly downregulated genes in the GHR-/- mouse (Swaminathan, 2008). This would suggest that beiging of the subcutaneous depot is diminished, although this was not directly explored. The BAT status of GHD patients and acromegalic individuals is still unknown (Reddy et al., 2014).

Therefore, more studies are needed to understand the relationship between GH and BAT or beiging of WAT. 67

Summary

In conclusion, adipose is a diverse and complicated tissue with many functions.

The depot differences and cellular makeup of WAT contribute to its complexity, making further study imperative. BAT is a second kind of adipose tissue with very different functions. Unlike the lipid and energy storage ability seen in WAT, BAT dissipates stored energy in the form of heat. Despite their opposing functions, these two adipose types are not as compartmentalized as once thought. Brown-like adipocyte clusters have been noted among WAT, indicating that certain factors can lead to a “beiging” effect in some adipocytes. Thus, it is apparent that WAT has the ability to remodel in response to various signals. Among these signals is the growth hormone molecule. GH is able to cause lipolysis of adipose tissue, contributing to the lean profile seen in bGH transgenic mice. The bGH transgenic model is long and lean, yet has reduced insulin sensitivity and shortened lifespan. These mice serve as models of acromegaly, a form of gigantism, in humans. In contrast, the GHR -/- mouse is small and obese, yet is insulin sensitive and long-lived. These mice mimic the phenotype of Laron Syndrome, a type of dwarfism.

These two models may help develop a better understanding of the beiging seen in WAT and the different factors responsible.

Chapter 3: Materials and Methods

The primary goal of this study was to investigate the expression of beiging markers in adipose tissue from bGH and WT littermate control mice. To accomplish this, body weight and body composition measurements were taken prior to dissection of adipose tissue. Previously collected RNA-Seq data were used to compare RNA expression values of various beiging markers. UCP1 RNA and protein expression were then quantified by qPCR and Western blot analysis, respectively. Immunohistochemistry

(IHC) staining and confocal imaging were performed to enable visual examination of

UCP1 protein. Additionally, a Seahorse XF analyzer was used to measure basal oxygen consumption rate (OCR) from whole tissue. Details of these various measures are provided below.

Animals

Mice of the C57BL/6J background were bred at the Edison Biotechnology

Institute of Ohio University. The bGH mice were generated through pronuclear microinjection as previously described (Berryman et al., 2004). Several cohorts of male bGH and WT littermate controls at different ages were used for the various analyses. A description of cohorts can be found in Table 3. For all studies, 2 to 4 mice were housed per cage in rooms with controlled light cycles (14-hour light/10-hour dark) and temperature (22 ± 2 °C). A standard chow diet and water were given ad libitum. All procedures used in this thesis were approved by the Ohio University Institutional Animal

Care and Utilization Committee.

Table 3

Cohorts of bGH and WT Littermate Control Mice Used in This Thesis

Cohort n Age (in months) Sex Analysis

Cohort 1 WT: 3 6 Male RNA-Seq bGH: 3

Cohort 2 WT: 10 7 Male qPCR bGH: 9

Cohort 3 WT: 3 6 Male Western blot bGH: 3

Cohort 4 WT: 1 12 Male IHC and bGH: 1 confocal

Cohort 5 WT: 5 4 Male Western blot bGH: 4 and OCR

WT: 4 11 Male Western blot bGH: 3 and OCR

Body Weight and Body Composition

Body weight and body composition measurements were recorded the day before sample collection. Body weight was measured using a Mettler Toledo PL 202-S balance.

Body composition measurements were taken using a quantitative nuclear magnetic resonance (NMR) analyzer (Minispec, Bruker Optics, Billerica, MA) as previously described (Palmer et al., 2009). Fat, free fluid, and lean masses were recorded in grams.

Adipose Tissue Depots

Five separate fat pads were used. These include four WAT depots: inguinal subcutaneous, epididymal, mesenteric, and retroperitoneal, as well as interscapular BAT. 70

Specific depots used in each experiment will be described in later sections. Mice were sacrificed by one of two methods. Cohorts 1, 2, and 5 were bled and sacrificed by cervical dislocation prior to dissection. Cohorts 3 and 4 were anesthetized using CO2 and euthanized via exsanguination by cardiac perfusion with solutions of 1x phosphate buffered saline (PBS) and 4% paraformaldehyde (PFA). Methods for preservation and storage of dissected adipose depots varied according the experiment performed and will be discussed in subsequent sections.

RNA-Seq Analysis

Subcutaneous and epididymal WAT depots used for RNA-Seq analysis were dissected, snap frozen in liquid nitrogen, and stored at -80 °C. RNA isolation and RNA-

Seq analysis were performed as recently described (Benencia et al., 2015; Duran-Ortiz,

2014). Briefly, total RNA was isolated using TRIzol reagent and mRNA was purified using a Dynabeads mRNA Purification kit (Life Technologies™). A cDNA library was then produced and sequenced using the Ion Total RNA-Seq kit and an Ion Torrent

Personal Genome Machine (Life Technologies™), respectively. A list of beiging- associated genes analyzed and their proposed role in beiging can be found in Appendix

Reverse Transcription Quantitative PCR

Ucp1 RNA expression levels were measured in subcutaneous and epididymal

WAT and interscapular BAT. After dissection, tissues were snap frozen in liquid nitrogen and stored at -80 °C. RNA isolation, cDNA synthesis, and qPCR were performed as recently described (Brooks et al., 2016). Total RNA was extracted using TRIzol reagent 71

(Life Technologies™) and cDNA was synthesized using a Maxima First Strand cDNA

Synthesis Kit (Thermo Fisher Scientific). qPCR was carried out using Maxima SYBR

Green/Fluorescein Master Mix (Thermo Fisher Scientific) in a Bio-Rad iCycler® machine. A sample plate design is shown in Appendix B. Data were normalized to housekeeping genes and interplate control samples using qBase software. Primer sequences for housekeeping genes are as follows: Eef2 forward

AGAACATATTATTGCTGGCG, Eef2 reverse CAACAGGGTCAGATTTCTTG, Rps3 forward CAAGAAGAGGAAGTTTGTAGC, Rps3 reverse

GTCCTGGTGGCTAAAATAATG. Primer sequences for the target gene are as follows:

Ucp1 forward GATGGTGAACCCGACAACTT, Ucp1 reverse

CTGAAACTCCGCTGAGAAG

Mitochondrial Isolation

Mitochondria were isolated from four WAT depots (subcutaneous, epididymal, mesenteric, and retroperitoneal) as well as interscapular BAT. Cardiac perfusion was performed prior to dissection. Two small slices (~0.5 x 0.5 cm) of tissue were taken from whole fat pads and fixed in 4% PFA for future histological study. The larger portions were placed in 5 mL of mitochondrial isolation buffer (MIB) on ice and minced with scissors. After mincing, homogenization was performed in a 50 mL Pyrex® homogenizer using a Teflon® pestle attached to an 18-volt cordless power drill. Homogenized samples were transferred to 50 mL conical tubes and spun in a Beckman Coulter Avanti® J-E

Series centrifuge at 8,400 rpm. After three rounds of the first centrifugation, the resulting pellet was resuspended in MIB and spun at 2,400 rpm. The supernatant was then 72

transferred to a clean tube and spun again at 8,400 rpm. The final resulting pellet was resuspended in mitochondrial assay solution (MAS) and stored at -80 °C. A detailed protocol for mitochondrial isolation can be found in Appendix C.

Immunohistochemistry and Confocal Imaging

Subcutaneous WAT and interscapular BAT depots used for IHC were dissected after cardiac perfusion. A detailed protocol for tissue staining and imaging can be found in Appendix D. Briefly, small slices of tissue from each depot were dissected (efforts were made to avoid vasculature) and fixed in 4% PFA. The fixed tissue was then permeabilized in 0.5% PBS-T, washed with PBS, and blocked in a solution of 4%

BSA/TBS. Following blocking, samples were stained with primary antibodies for CD31 and UCP1, as well as secondary fluorescent secondary antibodies. All antibodies used for

IHC are listed in Table 4. Samples were then incubated with a green neutral lipid stain

(HCS LipidTOX™, Invitrogen™, Molecular Probes™), placed on slides with DAPI

(Invitrogen™, Molecular Probes™) and VECTASHIELD® mounting media (Vector

Laboratories). Images were obtained using a Nikon® Eclipse Ti-E inverted confocal microscope at 40x magnification.

Table 4

Antibodies for Immunohistochemistry

Catalog Source number Antibody Species Dilution

Abcam, Cambridge, ab150160 Alexa Fluor® Goat, anti-rat 1:500 Massachusetts 594

BD Biosciences, San 550274 CD31 Rat 1:200 Jose, California

GeneTex, Irvine, GTX112784 UCP1 Rabbit 1:1000 California

Thermo Fisher SA5-10041 DyLight® Donkey, anti- 1:1000 Scientific, Waltham, 650 rabbit Massachusetts

Protein Isolation and Bradford Assay

Protein used for Western blotting was isolated from interscapular BAT. After dissection of depots, small pieces were taken for use in a Seahorse assay, and the remaining tissue was used for protein isolation. Briefly, approximately 40-50 mg of BAT was homogenized with a Fisherbrand® Disposable Pestle Grinder System in 1x radioimmunoprecipitation assay buffer (RIPA) buffer (Cell Signaling Technology®) with phenylmethylsulfonyl fluoride (PMSF). Samples were centrifuged and the protein layer extracted with a syringe. A detailed protocol for protein isolation can be found in

Appendix E.

For quantification purposes, a Bradford assay was performed using isolated mitochondria from cohort 3 and isolated protein from cohort 5. A protocol for Bradford 74

assay can be found in Appendix F. Briefly, bovine serum albumin (BSA) standards and sample dilutions were prepared. 10 μL of standards and sample were then pipetted into a

96 well plate in triplicate. Bradford reagent (Bio-Rad®) was diluted to 1x and 200 uL was added into each well. The plate was incubated and read by a SpectraMax® 250 microplate reader (Molecular Devices) at 595 nm absorbance.

Western Blot

A detailed Western blot protocol is available in Appendix G and solution recipes are listed in Appendix H. For the SDS-PAGE, 12% acrylamide-bis resolving gels were prepared with a 4% stacking gel. Each sample was diluted to a concentration of 1-2 μg/ml with Laemmli Buffer. A Spectra™ Multicolor Broad Range protein ladder (Thermo

Fisher Scientific) and 40 μg of each sample were loaded into gels and run at 35 milliamps per gel. PVDF membranes were cut to size and equilibrated prior to transfer. After electrophoresis, transfer “sandwiches” consisting of the gel, membranes, and filter paper, were prepared in 1x transfer buffer and run at 100 volts for 1 hour. Membranes were then cut in half at a molecular weight between that of the protein of interest and reference protein, and blocked in 5% nonfat dry milk/TBS-T. Each half of the membrane was then incubated in its respective primary antibody, UCP1 (GeneTex, GTX112784) or β-tubulin

(Cell Signaling Technology®, #2182), diluted 1:1000 in 5% BSA/TBS-T. After primary incubation, membranes were stained with fluorescent secondary antibody (Thermo Fisher

Scientific, SA5-10041), diluted 1:1000 in TBS-T. After washing, membranes were scanned with an External Laser Molecular Imager (BioRad®) at 50 µm resolution and imaged using a Pharos FX Plus Molecular Imager (BioRad®). Images were captured 75

using Quantity One 1-D analysis software (BioRad®). UCP1 protein bands from images were quantified using ImageJ software from the NIH and normalized to β-tubulin.

Seahorse Assay

Small pieces (~4 mg) of subcutaneous WAT and interscapular BAT were taken and weighed at the time of dissection for use in the whole tissue Seahorse assay. A detailed protocol is available in Appendix I. Briefly, tissue slices were placed in whole tissue wash buffer following dissection and transferred to an XF 24 Islet Capture

Microplate (Seahorse Bioscience, Billerica, MA, USA). Screens were placed on top of the tissue and 450 μL of whole tissue assay solution was added to each well. Plates were equilibrated in a non-CO2 incubator for 10-15 minutes during machine calibration. The sample-containing plates were then placed into a Seahorse XFe24 Extracellular Flux

Analyzer (Seahorse Bioscience), which recorded basal OCR and extracellular acidification rate (ECAR). OCR values were normalized to tissue weight.

Statistical Analysis

Statistical analysis for RNA-Seq was performed as previously described (Duran-

Ortiz, 2014). Briefly, the program Cuffdiff was used to group transcripts and sum

Fragments Per Kilobase of exon per Million fragments mapped (FPKM); t-tests were then performed to determine significance. A cutoff of 1.5 fold change was used and statistical significance was found at p < 0.05.

Remaining statistical analyses were performed using R version 3.3.3 and RStudio version 1.0.136. Results are shown as mean ± SEM. Normality and homogeneity of variance were tested using Shapiro-Wilk and Levene’s tests, respectively. Independent t- 76

tests were used to evaluate genotype differences in body weight, body composition, tissue weight and UCP1 expression in cohorts 2 and 3. Age and genotype differences for body weight, body composition, tissue weight, and OCR for cohort 5 were assessed using two- way ANOVAs, followed by Tukey’s post hoc tests. A Welch’s one-way ANOVA was used to compare age and genotype differences in UCP1 expression for cohort 5, due to unequal variances. Statistical significance was found at p < 0.05.

Chapter 4: Results

This study examined the impact of excess GH action on the expression of brown and beige fat markers in adipose tissue using five cohorts of male bGH and WT littermate control mice. Body weight and body composition measurements were taken the day before sample collection and tissue weights were recorded prior to processing. A preexisting RNA-Seq dataset was used to investigate genotype and depot differences in

RNA expression levels of beiging-associated genes from subcutaneous and epididymal

WAT. Expression of Ucp1 RNA was also confirmed through qPCR in subcutaneous and epididymal depots, with interscapular BAT serving as a positive control. Western blot analysis using isolated mitochondria was performed to measure UCP1 protein from four

WAT depots (subcutaneous, epididymal, mesenteric, and retroperitoneal) and BAT.

Immunohistochemistry and confocal imaging were then used to enable visual inspection of UCP1 in histological samples of subcutaneous WAT and BAT. Because UCP1 was undetectable at both the RNA and protein levels in almost all WAT samples tested, BAT depots from young (4-month-old) and old (11-month-old) mice were used for UCP1 protein quantification via Western blot, as well as measurements of whole tissue oxygen consumption rate (OCR).

Cohort 1

RNA expression. RNA sequencing involves the quantification of virtually all transcripts in a sample at a given point in time, allowing many genes to be examined at once. For this study, an existing RNA-Seq dataset was used to investigate the expression of beiging-associated genes from the subcutaneous and epididymal depots of 6-month-old 78

bGH and WT littermate control mice. The literature was scanned to select genes of interest and 169 were chosen based on their identification as positive or negative regulators of WAT beiging (see Appendix A). Of these, 63 were significantly altered between genotype and depot. A summary of significant genes is shown in Figure 11.

RNA expression values for genes that were found to be significantly different between genotype or depot are shown in Tables 5-8; note that all genes shown are positively associated with beiging except for those marked with an asterisk, which are known to be negatively associated with beiging. Genotype comparisons revealed 37 genes in subcutaneous WAT and 9 genes in epididymal WAT that were significantly different between bGH mice and WT controls. All 37 genes in subcutaneous WAT were significantly downregulated in bGH animals relative to WT controls and were all positively associated with beiging. Six of the 9 epididymal genes were significantly downregulated in bGH animals, including 2 negative and 4 positive regulators. When comparing between depots, 6 genes were significantly different in WT mice, compared to

49 in bGH. All 6 genes in WT mice were positive beiging regulators, 2 of which were significantly upregulated in the subcutaneous depot relative to epididymal. Only 1 negative beiging regulator was identified between depots in the bGH mice, and its expression was significantly higher in the subcutaneous depot. The remaining 48 genes were positive beiging regulators, 47 of which were significantly higher in subcutaneous

WAT. 79

Figure 11. Significantly altered genes in the subcutaneous and epididymal depots of bGH and WT mice.

Table 5

Genotype Comparison of Significantly Altered Genes in Subcutaneous WAT

Gene WT SubQ bGH SubQ q value

Acaa2 98.23 38.41 1.50E-03

Acads 50.75 19.75 0.01

Acadvl 66.11 33.04 0.03

Adrb3 118.85 34.25 8.24E-04

Angpt14 202.15 31.82 8.24E-04

Aqp7 46.57 9.25 8.24E-04

Arl4a 30.31 6.41 8.24E-04

Bckdha 88.09 29.73 1.50E-03

Carhsp1 63.88 16.78 8.24E-04

Cebpa 376.91 96.66 8.24E-04

Chchdh10 41.84 12.26 0.02

Chpt1 385.60 65.25 8.24E-04

Cox8b 296.67 46.83 3.13E-03

Cpt2 52.18 16.32 8.24E-04

Crat 63.89 19.26 8.24E-04

Decr1 39.26 14.04 6.13E-03

Eif4epb1 170.08 41.92 8.24E-04

Fam13a 30.90 11.50 3.13E-03 81

Table 5 (continued)

Gene WT SubQ bGH SubQ q value

Fam195a 39.05 8.87 0.03

Fbxo21 38.81 7.28 8.24E-04

Idh3a 53.27 13.60 8.24E-04

Ldhb 51.66 8.78 4.50E-03

Lipe 544.24 73.44 8.24E-04

Nr1d1 67.87 13.77 4.50E-03

Oplah 20.54 4.91 8.24E-04

Pck1 716.28 73.18 8.24E-04

Pdk4 19.85 4.42 3.61E-03

Pex19 33.38 14.68 0.01

Pnpla2 387.87 55.29 8.24E-04

Prkaca 60.96 24.29 4.91E-03

Prkar2b 66.84 17.63 8.24E-04

Slc1a5 282.25 85.38 8.24E-04

Sod2 18.26 5.92 5.76E-03

Tef 28.97 13.88 0.03

Tspan18 10.91 3.74 0.04

Vegfa 31.26 10.10 2.10E-03

Table 5 (continued)

Gene WT SubQ bGH SubQ q value

Zfp703 17.81 4.96 2.10E-03

Table 6

Genotype Comparison of Significantly Altered Genes in Epididymal WAT.

Gene WT Epi bGH Epi q value

Adora1 12.95 4.71 9.02E-03

Aqp7 46.14 25.03 0.03

Fbxo21 37.64 18.58 0.02

Hacl1 10.32 20.82 0.04

Notch1* 13.80 7.93 0.05

Nr1h3* 41.55 73.19 0.05

Pck1 523.06 203.87 0.04

Rgs7 2.49 8.66 0.03

Tle3* 22.81 12.44 0.04

* Indicates genes that are negatively associated with beiging.

Table 7

Depot Comparison of Significantly Altered Genes in WT Mice.

Gene WT SubQ WT Epi q value

Cox 8b 296.67 33.26 8.24E-04

Cpt1 12.43 26.01 3.13E-03

Pdk2 16.57 42.15 1.50E-03

Retsat 23.77 54.03 0.01

Tspan18 10.91 4.74 0.03

Uqcr10 313.89 730.55 0.03

Table 8

Depot Comparison of Significantly Altered Genes in bGH Mice.

Gene bGH SubQ bGH Epi q value

Acaa2 38.41 127.81 8.24E-04

Acads 19.75 121.68 8.24E-04

Acadvl 33.04 118.01 8.24E-04

Acc 77.62 35.66 0.03

Adrb3 34.25 204.08 8.24E-04

Angpt14 31.82 168.18 8.24E-04

Aqp7 9.25 25.03 0.02

Arl4a 6.41 23.01 1.50E-03

Bckdha 29.73 139.60 8.24E-04

Carhsp1 16.78 63.76 8.24E-04

Cebpa 96.66 547.64 8.24E-04

Chchdh10 12.26 34.95 0.04

Chpt1 65.25 599.69 8.24E-04

Coasy 10.55 40.60 1.50E-03

Coq9 14.15 36.55 0.05

Cpt2 16.32 49.00 8.24E-04

Crat 19.26 52.41 3.61E-03

Cyc1 66.42 173.94 8.24E-04 86

Table 8 (continued)

Gene bGH SubQ bGH Epi q value

Decr1 14.04 29.73 0.03

Eif4epb1 41.92 318.82 8.24E-04

Fam13a 11.50 32.22 2.10E-03

Fam195a 8.87 51.63 0.01

Fbxo21 7.28 18.58 0.02

Gpr120 3.12 20.74 0.05

Hacl1 8.71 20.82 0.04

Idh3a 13.60 29.10 0.03

Ifnar2 26.67 69.34 0.04

Ldhb 8.78 62.97 2.62E-03

Lipe 73.44 783.93 8.24E-04

Mtor 4.33 10.66 0.01

Nr1d1 13.77 99.40 8.24E-04

Nr1h3* 25.78 73.19 3.13E-03

Oplah 4.91 34.72 8.24E-04

Pck1 73.18 203.87 6.13E-03

Pdk2 8.79 36.99 8.24E-04

Per3 2.30 10.16 4.07E-03

* Indicates genes that are negatively associated with beiging.

Table 8 (continued)

Gene bGH SubQ bGH Epi q value

Pex19 14.68 40.87 8.24E-04

Pnpla2 55.29 418.17 8.24E-04

Pparg 41.85 103.12 4.91E-03

Prkaca 24.29 82.08 8.24E-04

Prkar2b 17.63 90.35 8.24E-04

Retsat 11.81 42.84 4.91E-03

Rgs7 1.57 8.66 0.04

Slc1a5 85.38 426.44 8.24E-04

Slc25a20 6.66 23.30 0.02

Sod2 5.92 19.70 4.50E-03

Tef 13.88 27.60 0.04

Uqcr10 314.42 1061.89 8.24E-04

Zfp703 4.96 17.88 1.50E-03

Cohort 2

Body weight and body composition. Comparisons of average body weight and body composition of 7-month-old WT and bGH mice are shown in Figure 12. Body composition data are depicted in grams (see Figure 12b) as well as a percentage of total body weight (see Figure 12c). As expected, bGH mice had significantly greater total body weight compared to WT controls. Absolute fat mass was significantly decreased and lean and fluid weights were significantly increased in bGH animals relative to WT.

When normalized to body weight, percentage of fat mass was reduced threefold in bGH mice, although there was no significant difference in either percentage of lean or percentage of fluid mass. 89

Figure 12. Average body weight and body composition comparisons of 7-month-old bGH and WT mice. A. Body weight. bGH mice weighed significantly more than WT animals. B. Absolute body composition. Fat mass was significantly decreased and lean and fluid weights were significantly increased in bGH animals compared to WT littermates. C. Body composition relative to total body weight. Percentage of fat was significantly decreased in bGH animals. There were no significant genotype differences in percentage of lean or percentage of fluid. Data are expressed as mean ± SEM. WT n = 10 and bGH n = 9. Independent samples t-tests were performed to compare genotypes. * p < 0.05, ** p < 0.01, *** p < 0.00. 90

Adipose tissue weight. Absolute adipose tissue depot weights and depots as a percentage of total body weight are shown in Figures 13a and 13b, respectively. Absolute tissue weights for subcutaneous and epididymal depots were significantly decreased in bGH mice compared to WT, an effect that became more pronounced when expressed as a percentage of total body weight. Absolute BAT mass was not significantly different between genotypes but when normalized to body weight was significantly reduced in the bGH mice relative to WT controls.

Figure 13. Adipose tissue depot weights for male bGH and WT mice at 7 months of age. A. Absolute adipose tissue depot weights. Subcutaneous and epididymal depots from bGH mice weighed significantly less than WT controls. There was no significant genotype effect on absolute BAT weight. B. Adipose tissue depot weights as a percentage of total body weight. All depots from bGH mice weighed significantly less than WT controls. Data are expressed as mean ± SEM. WT n = 10 and bGH n = 9. Independent samples t-tests were performed to compare genotypes. * p < 0.05, ** p < 0.01, *** p < 0.001.

RNA expression. We used qPCR to measure Ucp1 mRNA expression in the subcutaneous, epididymal, and BAT depots of 7-month-old bGH and WT mice. Relative

Ucp1 mRNA expression from BAT is shown in Figure 14. Expression data for the subcutaneous and epididymal depots are not shown because Ucp1 was undetectable in these fat pads. There was no significant genotype effect on relative expression of Ucp1 in

BAT.

1.5

WT bGH

Expression 0.5 Relative UCP1 mRNA UCP1 mRNA Relative

0 BAT

Figure 14. Ucp1 mRNA expression in the BAT depots of 7-month-old male bGH and WT mice. There was no significant difference in UCP1 mRNA expression between the two genotypes. Data are expressed as mean ± SEM. WT n = 10 and bGH n = 9. Independent samples t-tests were performed to compare genotypes.

Cohort 3

Body weight and body composition. Average body weight and body composition of 6-month-old bGH and WT mice are shown in Figure 15. Body composition data are presented in grams (see Figure 15b) and as a percentage of total 92

body weight (see Figure 15c). As with Cohort 2, bGH mice in this cohort weighed significantly more than WT controls. Absolute and relative fat weights were significantly decreased in bGH animals while absolute lean and fluid weights were significantly increased compared to littermate controls. There was no significant genotype effect on percentage of lean and percentage of fluid mass.

Figure 15. Average body weight and body composition comparisons of male bGH and WT mice at 6 months of age. A. Average body weight. bGH mice weighed significantly more than WT controls. B. Comparison of absolute body composition. Fat mass was significantly decreased and lean and fluid weights were significantly increased in bGH animals compared to WT. C. Body composition relative to total body weight. Percentage of fat mass was significantly decreased in bGH animals. There was no significant genotype effect on percentage of lean or percentage of fluid. Data are expressed as mean ± SEM. WT n = 3 and bGH n = 3. Independent samples t-tests were performed to compare genotypes. * p < 0.05, ** p < 0.01, *** p < 0.001. 94

UCP1 protein expression. Western blots for UCP1 were performed using isolated mitochondria from the subcutaneous, epididymal, mesenteric, retroperitoneal, and BAT depots of 6-month-old WT and bGH mice. With this cohort of animals, isolated mitochondria were chosen for use in Western blots as an alternative to isolated protein from whole tissue because they reduced nonspecific binding of the UCP1 antibody and produced cleaner blots in WAT. Results were quantified and normalized to β-tubulin protein. Blots and relative UCP1 protein expression are shown in Figures 16a and 16b, respectively. UCP1 was undetectable in WAT depots, except for the mesenteric depot of one WT animal. Therefore, only bands from BAT were quantified. There was no significant genotype difference in relative UCP1 protein expression in BAT. 95

Figure 16. UCP1 protein expression from isolated mitochondria in the WAT and BAT depots of 6-month-old bGH and WT mice. A. Western blot analysis of UCP1. β-Tubulin was used as a loading control. Brown adipose tissue (BAT), retroperitoneal WAT (R), mesenteric WAT (Mes), epididymal WAT (Epi), and subcutaneous WAT (SubQ) are shown. B. Comparison of UCP1 protein content relative to β-Tubulin in BAT mitochondria. Data are expressed as mean ± SEM. WT n = 3 and bGH n = 3. Independent samples t-tests were performed to compare genotypes. 96

Cohort 4

UCP1 protein expression. Immunohistochemistry and confocal imaging were also used to visualize UCP1 protein in fixed slices of subcutaneous WAT and BAT.

Figure 17 shows a representative image of stained subcutaneous and BAT samples from one bGH and one WT mouse at 12 months of age. Three primary antibodies were used to probe for the nucleus (DAPI), whole lipid (LipidTOX), and UCP1. Each image shows the same section of tissue scanned with lasers at different wavelengths. DAPI and LipidTOX staining can be seen in all samples, and although UCP1 is clearly visible in the BAT depots of both animals, it is absent in subcutaneous WAT. 97

Figure 17. Immunohistochemical analysis of subcutaneous and BAT depots of bGH and WT mice at 12 months of age. Images were taken at 40x magnification. UCP1 staining is apparent in the BAT depots of both mice but is not visible in subcutaneous WAT. WT n = 1 and bGH n = 1. 98

Cohort 5

Because UCP1 RNA and protein were undetectable in the WAT depots of bGH and WT mice, BAT from mice at 4- and 11- months of age were included in this study.

Because bGH mice have reduced longevity, we wanted to determine if and age and genotype differences would impact the UCP1 content of BAT or the basal oxygen consumption rate of subcutaneous WAT and BAT.

Body weight and body composition. Average body weight and body composition of 4- and 11-month-old bGH and WT mice are presented in Figure 18. Body composition data are shown in grams (see Figure 18b) and as a percentage of total body weight (see Figure 18c). bGH mice weighed significantly more than WT controls at both ages and WT mice at 11 months of age weighed significantly more than 4-month-old WT animals. Absolute and percentage of fat mass were significantly decreased in 11-month- old bGH mice compared to WT littermates. In addition, WT mice at 11 months of age had significantly greater absolute and percentage of fat mass compared to 4-month-old

WT mice. Absolute lean mass was significantly greater in bGH animals relative to WT at both ages; however, when normalized to body weight, only a significant age effect was observed, with 11-month-old WT animals having decreased percentage of lean mass compared to those at 4 months of age. Significant age effects were also observed for absolute lean and fluid, with lean mass significantly increased in 11-month-old bGH mice compared to 4-month-old bGH animals and fluid mass significantly greater in 11-month- old WT mice compared to 4-month-old WT controls. As expected, there was no significant effect of genotype or age on percentage of fluid mass. 99

Figure 18. Average body weight and body composition comparisons of male bGH and WT mice at 4 and 11 months of age. A. Average body weight. bGH mice weighed significantly more than WT animals at both ages. 11-month-old WT mice weighed significantly more than WT animals at 4 months of age. B. Absolute body composition. bGH animals at 11 months of age have 100

significantly reduced fat mass compared to WT controls. 11-month-old WT mice have significantly greater fat mass than those at 4 months. bGH mice have significantly increased lean mass relative to WT controls at both ages and significantly greater fluid mass at 4 months compared to WT mice. bGH mice at 11 months of age have significantly greater lean mass than 4-month-old bGH animals and 11-month-old WT mice have significantly greater fluid mass than those at 4 months of age. C. Body composition relative to total body weight. bGH animals have significantly reduced fat mass at 11 months of age compared to WT controls. 11-month-old WT mice have significantly greater fat mass and significantly less lean mass than WT animals at 4 months of age. Data are expressed as mean ± SEM. 4-month WT n = 5, 4-month bGH n = 4, 11-month WT n = 4, and 11-month bGH n = 4. Two-way ANOVAs were used to evaluate the effects of genotype and age, followed by Tukey’s post hoc tests. 101

Adipose tissue weight. Absolute subcutaneous and BAT depot weights and depots as a percentage of total body weight are shown in Figures 19a and 19b, respectively. Absolute and relative tissue weights for subcutaneous WAT were significantly decreased in bGH mice at 11 months of age relative to WT controls and significantly increased in 11-month-old WT mice relative to 4-month-old WT animals.

Absolute and percentage of BAT mass were significantly decreased in 11-month-old bGH mice compared to WT littermates. There was no significant effect of genotype on tissue weight in the 4-month-old mice. 102

Figure 19. Adipose tissue depot weights for male bGH and WT mice at 4 and 11 months of age. A. Absolute adipose tissue depot weights. Subcutaneous and BAT tissue weights were significantly reduced in bGH mice at 11 months of age compared to WT animals. 11-month-old WT mice had significantly increased subcutaneous tissue weight compared to 4-month-old WT animals. B. Adipose tissue depot weights relative to total body weight. Percentage of tissue weight is significantly decreased in both depots of bGH mice at 11 months of age compared to WT controls. 11-month-old WT mice have significantly increased percentage of tissue weight in subcutaneous WAT compared to 4-month-old WT animals. Data are expressed as mean ± SEM. 4-month WT n = 5, 4- month bGH n = 4, 11-month WT n = 4, and 11-month bGH n = 4. Two-way ANOVAs were used to evaluate the effects of genotype and age, followed by Tukey’s post hoc tests. * p < 0.05, ** p < 0.01, *** p < 0.001. 103

UCP1 protein expression. Western blot analysis was performed using isolated protein from whole tissue from the subcutaneous WAT and BAT depots of 4- and 11- month-old bGH and WT mice. Blots and relative UCP1 protein expression are shown in

Figures 20a and 20b, respectively. There was no significant effect of age or genotype on the relative abundance of UCP1 in the BAT depots of these mice.

Figure 20. UCP1 protein expression in the BAT depots of male bGH and WT mice at 4 and 11 months of age. A. Western blot analysis of UCP1 from isolated BAT protein. β- Tubulin was used as a loading control. B. Comparison of UCP1 protein content relative to β-Tubulin in whole protein isolated from BAT. There was no significant difference in relative UCP1 protein expression between genotype or age. Data are expressed as mean ± SEM. 4-month WT n = 5, 4-month bGH n = 4, 11-month WT n = 4, and 11-month bGH n = 3. Two-way ANOVAs were used to evaluate the effects of genotype and age, followed by Tukey’s post hoc tests. 104

Oxygen consumption rate. A Seahorse XFe24 Extracellular Flux Analyzer was used to measure the basal oxygen consumption rate (OCR) of whole tissue subcutaneous

WAT and interscapular BAT samples from 4- and 11-month-old bGH and WT mice.

Results are shown in Figure 21. OCR in the subcutaneous depot of 4-month-old bGH animals was significantly lower than age-matched littermate controls. Conversely, subcutaneous OCR in 11-month-old bGH mice was significantly higher than in WT controls. There was also a significant effect of age, with 11-month-old WT mice having significantly decreased OCR compared to WT animals at 4 months. Conversely, 11- month-old bGH mice had significantly increased OCR compared to bGH animals at 4 months of age. No significant effect of genotype or age was observed in the BAT depots of these mice.

105

Figure 21. Comparison of oxygen consumption rate in adipose depots of male bGH and WT mice at 4 and 11 months of age. 4-month-old bGH mice had significantly lower OCR in the subcutaneous depot than WT controls while 11-month-old bGH mice had significantly higher OCR in the subcutaneous depot compared to WT littermates. WT mice at 11 months of age had significantly lower subcutaneous OCR compared WT animals at 4 months of age. In contrast, bGH mice at 11 months of age had significantly higher subcutaneous OCR than bGH animals at 4 months. No significant genotype or age differences in OCR of BAT depots were observed. Data are expressed as mean ± SEM. 4 month WT n = 5, 4 month bGH n = 4, 11 month WT n = 4, and 11 month bGH n = 3. Two-way ANOVAs were used to evaluate the effects of genotype and age, followed by Tukey’s post hoc tests. * p < 0.05, ** p < 0.01, *** p < 0.001. 106

Chapter 5: Discussion

The purpose of this study was to investigate the impact of GH on WAT beiging and BAT using a GH transgenic mouse line. Major findings indicate that GH overexpression does not directly promote WAT beiging under basal conditions, but may impart a greater propensity for beiging in the epididymal WAT depot. Additionally, it appears that GH may negatively regulate the depot weight and UCP1 content of BAT.

These results describe the potential for a novel depot-specific role of GH in WAT and

BAT.

Body Weight and Body Composition

GH has profound and well-documented effects on body composition, including increased body weight and decreased adiposity. In the current study, bGH mice exhibited similar patterns of body composition to those reported previously (Berryman et al., 2004;

Palmer et al., 2009). That is, bGH animals had significantly greater total body weight and absolute lean mass than WT controls at all ages (4, 6, 7, and 11 months), as well as significantly decreased absolute and percentage of fat mass than WT littermates at most ages (6, 7, and 11 months). No significant genotype effect was observed for absolute or percentage of fat mass in 4-month-old animals, consistent with previous findings that male bGH mice at younger ages have significantly greater absolute and percentage of fat mass relative to WT controls, a trend which begins to reverse around 4 to 5 months of age

(Palmer et al., 2009). As expected, absolute fluid mass, but not percentage of fluid, was significantly greater in bGH mice compared to WT controls at most ages (4, 6, and 7 months) (Palmer et al., 2009). 107

We also investigated the impact of age on body composition using young (4- month-old) and old (11-month-old) bGH and WT animals. In bGH mice, there was no significant effect of age on body weight, fat, or fluid. This is consistent with previous reports that these measurements remain relatively stable throughout life in bGH mice, and age-related differences in body composition between genotype are mainly due to increased fat mass in the WT animals over time (Palmer et al., 2009). The only significant effect of age in bGH mice was increased absolute lean weight at 11 months, consistent with findings that lean mass is gained at earlier time points with a plateau around 6 months of age (Palmer et al., 2009). In contrast, WT mice demonstrated significant alterations in body composition with age, including increased body weight and fat mass and significantly decreased lean mass at 11 months compared to 4 months of age. These patterns of body composition changes are common features of aging and have been previously described in WT mice (Ding, Berryman, & Kopchick, 2011; Houtkooper et al., 2011; Palmer et al., 2009). Overall, body weight and body composition data from this study agree with previous findings and underscore the importance of considering age when conducting studies in these animals.

Adipose Tissue Weights

Consistent with the trends in total fat mass, decreases in WAT depot weights have been reported for bGH mice relative to WT controls (Berryman et al., 2004; Palmer et al.,

2009). In the present study, bGH animals at 7 and 11 months of age had significantly decreased subcutaneous WAT mass compared to littermates, and 7-month-old bGH mice had significantly less epididymal WAT compared to WT controls. The reduction in 108

epididymal fat of 7-month-old bGH animals was more pronounced than in subcutaneous

WAT, supporting previous observations that GH does impact WAT depots uniformly

(Benencia et al., 2015; Bengtsson et al., 1993; Berryman et al., 2004). No significant genotype differences in subcutaneous WAT mass were observed for mice at 4 months of age, as would be expected based on their total body fat. There was no significant effect of age on subcutaneous WAT mass in bGH mice, however, absolute and relative subcutaneous depot weights were significantly increased in WT mice at 11 months of age relative to those at 4 months. This age-dependent increase in subcutaneous WAT mirrors the increase in total fat mass of 11-month-old WT mice.

Because our data revealed little to no direct impact of GH on WAT beiging, we also incorporated BAT depots into this study, as the effect of GH on BAT has not been thoroughly elucidated. Several mouse lines with altered GH action have been used to assess this relationship and provide insight into how GH may impact BAT mass. A study by Li et al. (2003) used bGH mice, as well as two dwarf mouse lines, one with reduced

GH action due to expression of a growth hormone antagonist (GHA) and one with absent

GH action (GHR-/-), to investigate the impact of GH on interscapular BAT mass. Both the GHA and GHR-/- animals had significantly increased BAT mass compared to WT littermates, while no significant difference in BAT depot weight was observed between bGH mice and WT controls. Additionally, a study using Ames dwarf mice, which are deficient in pituitary GH, reported significant increases in BAT mass of the dwarf animals compared to WT controls (Darcy et al., 2016). In contrast, a study using bGH mice reported significantly increased BAT mass in the giant animals compared to 109

littermate controls (Olsson et al., 2005). Data from the current study support the concept of negative BAT regulation by GH, with significantly greater percentage of BAT mass in

7 and 11-month-old bGH mice compared to WT controls. Although not significant, BAT mass in the 4-month-old bGH animals also trended toward a decrease relative to WT controls.

Beiging-Associated RNA Expression

WAT beiging is a complex physiological process that requires the interaction of several transcriptional regulators and cofactors to repress white adipocyte gene expression while inducing beige or brown adipocyte gene expression. A number of molecules have been found to act directly on these transcriptional regulators and their cofactors to induce or suppress the beiging process. Thus, studies of WAT beiging greatly benefit from technologies like RNA-Seq, as the expression levels of multiple transcripts can be measured simultaneously.

In the current study, RNA-Seq analysis revealed several significantly altered genes in the subcutaneous and epididymal WAT depots of bGH and WT littermate control mice. RNA expression levels of 169 positive and negative beiging markers were examined between genotype and depot. First, the expression levels of Ucp1 and 3 core transcriptional regulators, Pparg (PPARγ), Prdm16 (PRDM16), and Ppargc1a (PGC-1α), were investigated. Previous studies led us to hypothesize that beiging-associated gene expression would be greater in the subcutaneous depot relative to epididymal and higher in bGH mice compared to WT controls (Hioki et al., 2004; Rosenwald et al., 2013;

Swaminathan, 2008). However, we found that Ucp1 RNA was detectable only at low 110

levels in the epididymal depot of bGH mice and was not expressed in any other WT or bGH samples. The absence of Ucp1 expression in most WAT samples was not entirely surprising, as previous studies have reported similar results in these depots under basal conditions (Hioki et al., 2004; Kalinovich, de Jong, Cannon, & Nedergaard, 2017).

However, detectable Ucp1 RNA in the epididymal depot of bGH mice was unexpected, given that this depot is reported to be fairly resistant to beiging (Rosen & Spiegelman,

2014). In fact, one study using a WT strain of mice that are prone to beiging reported that expression of Ucp1 was undetectable in epididymal WAT, despite being present in the subcutaneous depot (Wu et al., 2012). This suggests that the epididymal depot of bGH mice may have an inherent capacity for beiging that is not found in this fat pad in other genotypes. Consistent with expression of Ucp1, core transcriptional regulators Prdm16 and Ppargc1a were expressed at low levels in all samples and exhibited no significant genotype or depot differences. Contrary to our hypothesis, Pparg expression was significantly upregulated in the epididymal depot of bGH mice relative to bGH subcutaneous WAT, although no genotype differences were observed. This was unexpected, considering that the subcutaneous depot is generally the most susceptible to

WAT beiging while epididymal is the least (Seale et al., 2011). However, it is important to consider that Pparg plays a variety of roles in WAT apart from beiging, such as regulation of adipogenesis and inflammation (Ahmadian et al., 2013). Furthermore,

Pparg cofactors Ebf2 and Sirt1, which are indispensable for its role in WAT beiging, were not significantly different between genotype or depot. 111

Between-genotype comparisons of subcutaneous WAT revealed 37 genes that were significantly different in the subcutaneous depots of bGH and WT mice.

Importantly, expression levels of all 37 transcripts were significantly downregulated in bGH mice compared to WT, and all were positive regulators of beiging. This suggests that, upon environmental or chemical stimulation, the subcutaneous depot of bGH mice may be less prone to beiging than that of WT animals. Again, we expected to see increased beiging marker expression in bGH mice relative to WT based on what has been reported in the literature. However, decreased expression of these genes in the bGH animals could imply a depot-specific effect of GH on WAT beiging. When comparing between genotype in the epididymal depot, only 9 transcripts were significantly different,

3 of which were negative regulators of beiging. These include Notch1, which acts to inhibit the expression of PRDM16 and PGC-1α (Bi et al., 2014), Nr1h3, which displaces

PGC-1α at its binding site (Wang et al., 2008), and Tle, which disrupts the interaction of

PPARγ and PRDM16 (Villanueva et al., 2013). Six of the 9 significant genes in epididymal WAT were downregulated in the bGH mice compared to WT, including

Notch1 and Tle. This downregulation negative beiging regulators could imply that the epididymal depot in bGH mice has similar or increased capacity for beiging relative to

WT controls.

Prior studies have indicated that, like many other characteristics of WAT, beiging exhibits depot-specific differences, occurring more readily in the subcutaneous depot compared to epididymal (Rosen & Spiegelman, 2014; Rosenwald et al., 2013; Wang et al., 2013). Therefore, we anticipated that beiging marker expression would be greater in 112

subcutaneous WAT compared to epididymal. Few differences were observed when comparing beiging gene expression between depot in WT control mice. Only 6 genes were significantly different between the fat pads, 4 of which were upregulated in the epididymal WAT. This was somewhat surprising, as all 6 transcripts were positive beiging regulators and we expected to see greater differences between depots in both genotypes. However, under basal conditions, this upregulation of positive beiging genes in the epididymal depot is not unexpected and more pronounced depot differences may be observed upon stimulation. The greatest differences in gene expression were observed when comparing between depot in bGH mice. Of the 49 transcripts that were found to be significantly different between depot, 48 were upregulated in epididymal WAT compared to the subcutaneous depot. Of these 48, only 1 gene, Nr1h3, was a negative beiging regulator. Again, this is surprising when considering the respective propensities of epididymal and subcutaneous depots to undergo beiging.

Although we hypothesized that subcutaneous WAT from bGH mice would exhibit the greatest levels of beiging gene expression, between-genotype and between-depot comparisons revealed that positive beiging regulators were downregulated in the subcutaneous WAT of bGH mice relative to WT controls and upregulated in epididymal

WAT of bGH animals compared to the subcutaneous depot. One explanation for this is that the visceral fat pad has been shown to be more susceptible to the lipolytic action of

GH (Chaves et al., 2013). Therefore, it is entirely possible that the epididymal depots of bGH mice experience higher rates of lipolysis than subcutaneous and ultimately have higher levels of circulating FFAs with which to activate UCP1. Additionally, the larger 113

size of the bGH animals creates a smaller surface area-to-volume ratio compared to WT mice, which could result in a reduced need for thermogenesis to regulate body temperature and therefore, less WAT beiging. Taken together, these data suggest a potential depot-specific role for GH in WAT thermogenesis. However, further research will be necessary to fully elucidate these interactions.

Ucp1 RNA Expression

To confirm the absence of Ucp1 in WAT depots, qPCR was used to measure

Ucp1 mRNA expression levels in subcutaneous and epididymal WAT and interscapular

BAT of 7-month-old bGH and WT littermate control mice. Ucp1 mRNA was undetectable in the WAT depots of both bGH and WT control mice. Based on previous studies showing detectable Ucp1 RNA expression in WAT under basal conditions, we expected to see quantifiable levels of Ucp1 in our samples as well (Fisher et al., 2012;

Kim et al., 2015; Wu et al., 2012). However, differences in genetic background and housing temperatures may have contributed to these disparities. Additionally, we selected two housekeeping genes for normalization, tested multiple primers for Ucp1, and used a strict threshold cutoff of 34 cycles for gene detection. Thus, differences in experimental design could also account for conflicting results under basal conditions. In agreement with our findings, lack of Ucp1 expression in WAT depots from WT mice has been reported previously (Hioki et al., 2004; Kalinovich, de Jong, Cannon, & Nedergaard,

2017; Waldén, Hansen, Timmons, Cannon, & Nedergaard, 2012).

Although Ucp1 expression appeared to be decreased in the BAT depot of bGH mice, the difference was not significant. However, the trend toward decreased Ucp1 114

expression in bGH BAT is consistent with the significant reduction in percentage of BAT mass observed in these animals. Studies using transgenic mouse lines with altered GH support this finding. Specifically, Ucp1 RNA expression was found to be significantly higher in the BAT depot of Ames dwarf, GHA, and GHR-/- mice, and significantly decreased in the BAT of bGH animals (Darcy et al., 2016; Li et al., 2003). A separate study investigating Ucp1 expression in BAT of bGH and WT animals found no significant differences between genotype, consistent with our data. Additionally, no significant changes in Ucp1 expression in BAT of WT mice were observed with varying doses of GH treatment (Hioki et al., 2004). However, increases in Ucp1 mRNA expression were seen in the interscapular BAT of genetically obese mice upon treatment with GH, suggesting a potential acute effect of GH on Ucp1 expression in states of obesity (Hioki et al., 2004).

UCP1 Protein Expression

UCP1 protein expression was measured using Western blot analysis and visualized with immunohistochemistry and confocal imaging. Both isolated mitochondria and isolated protein from whole WAT and BAT depots were used for Western blot analysis. Mitochondria were isolated from four WAT depots (subcutaneous, epididymal, mesenteric, and retroperitoneal) and interscapular BAT from bGH and WT mice at 6 months of age. Due to the absence of Ucp1 mRNA in subcutaneous and epididymal

WAT, mitochondria from mesenteric and retroperitoneal WAT were included to confirm that UCP1 protein was undetectable in all commonly studied WAT depots. Additionally, because few studies have investigated the impact of GH on UCP1 content in BAT, 115

protein was isolated from the BAT depots of 4- and 11-month-old bGH and WT animals and used for Western blotting. Two different ages were used to account for genotype differences in longevity and because BAT activity is known to decline with age (Graja &

Schulz, 2015). UCP1 was undetectable in all WAT samples, except the mesenteric depot of one WT mouse. This was unexpected, given the absence of Ucp1 mRNA and UCP1 protein in other WAT samples. However, slight variations in environment, especially temperature, or other factors such as increased physical activity, could account for this anomaly. Although UCP1 expression in isolated protein from BAT depots appeared to be decreased in the older mice relative to those at 4 months, and in bGH animals compared to WT, the differences were not statistically significant. Additionally, no significant genotype differences in UCP1 expression were observed in isolated mitochondria from

BAT. These findings align with the previous RNA expression data and confirm that

UCP1 is not present in the WAT of these mice under basal conditions.

To visualize the distribution of UCP1 protein in whole tissue, slices of subcutaneous WAT and BAT were taken from bGH and WT mice at 12 months of age, stained with antibodies, and imaged using a confocal microscope. Stains for nuclei and whole lipid were visible in all tissues, however, UCP1 was undetectable in subcutaneous

WAT. As expected, UCP1 was visible in both BAT depots, confirming our Western blot results. Although we hypothesized that GH may directly impact UCP1 expression in

WAT, the absence of UCP1+ adipocytes is not surprising, as previous studies report induction of UCP1 expression in beige adipocytes only under certain environmental or chemical conditions (Harms & Seale, 2013). While GH may not directly stimulate 116

beiging, further study is necessary to understand how it may act on other beiging regulators to influence this process under stimulated conditions.

Oxygen Consumption Rate

Oxygen consumption rate was measured using slices of subcutaneous WAT and interscapular BAT in 4- and 11-month-old bGH and WT mice. A significant effect of genotype was observed in the subcutaneous depot, with 4-month-old bGH animals exhibiting significantly lower OCR than WT littermates. This trend reversed in the subcutaneous WAT of 11-month old mice, with bGH mice having significantly higher

OCR than WT controls. Additionally, significant age effects were seen in the subcutaneous depot of both genotypes. OCR in subcutaneous WAT was significantly decreased in 11-month-old WT mice compared to those at 4 months of age and significantly increased in the 11-month-old bGH animals compared to 4-month-old bGH mice. The observed decrease in subcutaneous OCR of older WT mice was not surprising, as both increased fat accumulation and decreased mitochondrial mass and function have been reported to occur with aging (Palmer & Kirkland, 2016; Payne & Chinnery, 2015).

The most intriguing aspect of these data was the significant increase in OCR in the subcutaneous depot of bGH mice at 11 months of age compared to those at 4 months, as increased mitochondrial activity with age would not be expected based on what has been reported in the literature (Payne & Chinnery, 2015). Additionally, no significant differences were observed between genotype or age in any of the BAT depots, suggesting a potential depot-specific role of GH in mitochondrial activity with age. The free radical theory of aging, which suggests that mitochondrial production of reactive oxygen species 117

drives the aging process, is highly debated in the scientific community, but may provide some basis for the decreased longevity observed in the bGH animals. That is, if mitochondrial activity is increased in other WAT depots and tissues in bGH mice, the resulting free radical production could lead to the rapid aging and decreased longevity reported in these animals. However, many more studies in several different tissues would be needed to provide a solid foundation for this hypothesis.

Future Directions

This study is one of the few to examine the effect of GH on WAT beiging and the response of BAT to GH overexpression. As such, these data are limited in their ability to provide adequate understanding of GH’s role in these processes. Therefore, further research is needed to elucidate the interactions between GH and beiging regulators, as well as GH and BAT. Below are suggestions for future projects to help expand upon the data presented in this thesis.

1. Treatment of WT mice with GH and subsequent measurement of UCP1 could

be used to account for potential differences in BAT activity and WAT beiging

caused by the smaller surface area-to-volume ratio of bGH animals relative to

WT.

2. The current study investigated the effect of GH overexpression on WAT

beiging only in a basal state (i.e., in the absence of beiging stimulation). The

use of environmental or chemical stimulators, such as cold exposure or β-

adrenergic agonists, would help confirm the role of GH in susceptibility to

WAT beiging in various depots. 118

3. To provide a more complete picture of the effect of GH action on BAT and

WAT beiging, other mouse models of GH action could be incorporated into

these studies. For example, those with reduced (GHA) or absent GH action

(GHR-/-) may reveal further depot-specific effects of GH on these processes.

4. Future experiments to measure OCR should be performed or validated using

isolated mitochondria rather than whole tissue, as it is more precise (specific

amounts of mitochondria can be loaded in each well) helping to reduce

variability. Additionally, the use of a mitochondrial stress test via drug

injections would be beneficial to investigate various aspects of mitochondrial

function in different WAT depots.

5. Due to the depot-specific differences exhibited by WAT and the depot-

dependent effects of GH, multiple WAT depots should be utilized in these

studies, including the four discussed in this thesis (subcutaneous, epididymal,

mesenteric, and retroperitoneal).

Conclusions

The increasing prevalence of obesity and obesity-related comorbidities has created a need for therapeutic agents to more effectively treat these conditions. Targeting mitochondrial uncoupling as a tool for weight loss resulted in the development of drugs to treat obesity, but ubiquitous insertion of chemical uncouplers made them unsafe. Thus, the focus of thermogenesis research has shifted to search for novel therapies to activate naturally occurring UCP1 in adipose tissues. This study was one of the first to examine the impact of GH overexpression on BAT and WAT beiging using GH transgenic mice 119

and WT littermate controls. Genes positively associated with the beiging process were significantly upregulated in the epididymal WAT depot of bGH mice. Conversely, bGH mice tended to have reduced BAT mass and UCP1 expression compared to WT littermates at several ages. These results demonstrate a potential depot-specific relationship between GH and activation of thermogenesis, which could advise its potential future use as a therapy for obesity and diabetes. 120

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Appendix A: Beiging-Associated Molecules for RNA-Seq Analysis

Table 9

Beiging Molecules for Gene Expression Regulation of Beiging Molecule (Positive/Negative) Reference

Placenta expressed transcript 1 opposite strand (Christian, 2015); + Pletos1 (2310014F07Rik) (Rosell et al., 2014)

Protein DEPP + (Rosell et al., 2014) (8430408G22Rik)

Uncharacterized protein LOC77123 + (Rosell et al., 2014) (9130214F15Rik)

Androgen dependent TFPI regulating protein + (Rosell et al., 2014) Adrtp (9530008L14Rik)

155

Table 9 (continued)

Acetyl-CoA acetyltransferase 2 (Nakamura, Sato, Shiimura, Miura, & + (Acaa2) Kojima, 2013); (Rosell et al., 2014)

Acyl-CoA dehydrogenase (short chain) (Nakamura, Sato, Shiimura, Miura, & + (Acads) Kojima, 2013); (Rosell et al., 2014)

Acyl-CoA dehydrogenase (long chain) (Nakamura, Sato, Shiimura, Miura, & + (Acadl) Kojima, 2013); (Rosell et al., 2014)

Acetyl-CoA carboxylase alpha + (Rosell et al., 2014) (Acc)

Acyl-CoA thioesterase 11 (Christian, 2015); + (Acot11) (Rosell et al., 2014)

Acyl-CoA thioesterase 4 + (Rosell et al., 2014) (Acot4)

Acyl-CoA synthetase family member 2 + (Rosell et al., 2014) (Acsf2)

156

Table 9 (continued)

Adenylate cyclase 3 + (Rosell et al., 2014) (Adcy3)

Adenosine A1 Receptor + (Rosell et al., 2014) (Adora1)

ADP-ribosylhydrolase like 1 + (Rosell et al., 2014) (Adprhl1)

β-adrenergic receptor + (Beranger et al., 2013) (Adrb3)

Expressed sequence AI317395 + (Rosell et al., 2014) (AI317395)

A kinase anchor protein 7 + (Rosell et al., 2014) (Akap7)

Angiopoietin-like 3 + (Rosell et al., 2014) (Angpt13)

157

Table 9 (continued)

Angiopoietin-like 4 + (Rosell et al., 2014) (Angpt14)

Aquaporin 7 + (Rosell et al., 2014) (Aqp7)

ADP-ribosylation factor-like 4a + (Rosell et al., 2014) (Arl4a)

Branched chain keto acid dehydrogenase E1, alpha polypeptide + (Rosell et al., 2014) (Bckdha)

Bone morphogenetic protein 7 (Bmp7) + (Boon et al., 2013)

Bone morphogenetic protein 8b + (Whittle et al., 2012) (Bmp8b)

Complement component 8, gamma polypeptide + (Rosell et al., 2014) (C8g)

158

Table 9 (continued)

AarF domain containing kinase 3 + (Rosell et al., 2014) (Cabc1)

Calreticulin 3 + (Rosell et al., 2014) (Calr3)

Calcium regulated heat stable protein 1 + (Rosell et al., 2014) (Carhsp1)

Cyclin O + (Rosell et al., 2014) (Ccno)

Cyclin-dependent kinase-like 3 + (Rosell et al., 2014) (Cdkl3)

C/EBPα + (Vernochet et al., 2009) (Cebpa)

Choline phosphotransferase 1 + (Rosell et al., 2014) (Chpt1)

159

Table 9 (continued)

Cell death-inducing DFFA-like effector a + (Petrovic et al., 2010) (Cidea)

Cell death-inducing DFFA-like effector b + (Rosell et al., 2014) (Cideb)

CoA synthase + (Rosell et al., 2014) (Coasy)

Coenzyme Q3 methyltransferase + (Rosell et al., 2014) (Coq3)

Coenzyme Q5 + (Rosell et al., 2014) (Coq5)

Coenzyme Q9 + (Rosell et al., 2014) (Coq9)

Cytochrome c oxidase subunit VIIa + (Rosell et al., 2014) (Cox7a)

160

Table 9 (continued)

Cytochrome c oxidase subunit VIIIb + (Rosell et al., 2014) (Cox8b)

Cytochrome c oxidase subunit + (Vegiopoulos et al., 2010) (Cox2)

Cytochrome c oxidase subunit VIIa polypeptide 1 + (Rosell et al., 2014) (Cox7a)

Carboxypeptidase N, polypeptide 2 + (Rosell et al., 2014) (Cpn2)

Carnitine palmitoyltransferase 1 + (Fernández-Galilea et al., 2015) (Cpt)

Carnitine palmitoyltransferase 2 + (Rosell et al., 2014) (Cpt2)

Carnitine acetyltransferase + (Rosell et al., 2014) (Crat)

161

Table 9 (continued)

C-Terminal Binding Protein 1 + (Vernochet et al., 2009) (Ctbp1)

C-Terminal Binding Protein 2 + (Vernochet et al., 2009) (Ctbp2)

Cytochrome C-1 + (Rosell et al., 2014) (Cyc1)

Cytochrome P450, family 2, subfamily b, polypeptide 10 + (Rosell et al., 2014) (Cyp2b10)

2,4-Dienoyl CoA Reductase 1 + (Rosell et al., 2014) (Decr1)

Type 2 iodothyronine deiodinase + (Rosell et al., 2014) (Dio2)

Early B-Cell Factor 2 + (Rajakumari et al., 2013) (Ebf2)

162

Table 9 (continued) Eukaryotic translation initiation factor 4E-binding protein 1 + (Tsukiyama-Kohara et al., 2001) (Eif4epb1)

Elongation of very long chain fatty acids protein 3 + (Boström et al., 2012) (Elovl3)

(Rosell et al., 2014) Estrogen-related receptor alpha + (Esrra)

(Rosell et al., 2014) Estrogen-related receptor gamma + (Esrrg)

(Rosell et al., 2014) Electron-transferring-flavoprotein dehydrogenase + (Etfdh)

Fatty acid-binding protein 3 Nakamura, Sato, Shiimura, Miura, & Kojima, + (Fabp3) 2013; (Rosell et al., 2014)

Fatty acid-binding protein 4 + (Rosell et al., 2014) (Fabp4)

163

Table 9 (continued)

Family with sequence similarity 13, member A + (Rosell et al., 2014) (Fam13a)

Family with sequence similarity 15, member A + (Rosell et al., 2014) (Fam15a)

Family with sequence similarity 195, member A + (Rosell et al., 2014) (Fam195a)

Family with sequence similarity 69, member B + (Rosell et al., 2014) (Fam69b)

Family with sequence similarity 82, member b + (Rosell et al., 2014) (Fam82b)

Fructose-1,6-bisphosphatase 2 + (Rosell et al., 2014) (Fbp2)

F-box protein 21 + (Rosell et al., 2014) (Fbxo21)

164

Table 9 (continued)

Fibroblast growth factor 21 + (Fisher et al., 2012) (Fgf21)

Fructosamine 3 kinase + (Rosell et al., 2014) (Fn3k)

Irisin + (Boström et al., 2012) (Fndc5)

Forkhead box protein C2 + (Cederberg et al., 2001) (Foxc2)

GTP cyclohydrolase I feedback regulatory protein + (Rosell et al., 2014) (Gchfr)

G-protein subunit alpha-13 + (Rosell et al., 2014) (Gna13)

Guanine nucleotide-binding protein G(O) subunit alpha + (Rosell et al., 2014) (Gnao1)

165

Table 9 (continued)

G protein-coupled receptor 4 + (Rosell et al., 2014) (Gpr120)

G protein-coupled receptor 135 + (Rosell et al., 2014) (Gpr135)

G protein-coupled receptor 146 + (Rosell et al., 2014) (Gpr146)

G protein-coupled receptor 4 + (Rosell et al., 2014) (Gpr4)

Glycerol kinase + (Rosell et al., 2014) (Gyk)

2-hydroxyl-coA-lyase 1 + (Rosell et al., 2014) (Halc1)

Orexin + (Sellayah, Bharaj, & Sikder, 2011) (Hcrt)

166

Table 9 (continued)

Homeobox protein Hox-C9 + (Petrovic et al., 2010) (Hoxc9)

Isocitrate dehydrogenase 3 (NAD+) alpha + (Rosell et al., 2014) (Idh3a)

Interferon (alpha, beta and omega) receptor 2 + (Rosell et al., 2014) (Ifnar2)

Immunoglobin superfamily, member 21 + (Rosell et al., 2014) (Igsf21)

Interleukin 15 receptor, alpha + (Rosell et al., 2014) (Il15ra)

Inhibitor of CDK, cyclin A1 interacting protein 1 + (Rosell et al., 2014) (Inca1)

Kruppel-like factor 11 + (Rosell et al., 2014) (Klf11)

167

Table 9 (continued)

Kininogen 1 + (Rosell et al., 2014) (Kng1)

Keratin 79, type II + (Rosell et al., 2014) (Krt79)

Lactate dehydrogenase b + (Rosell et al., 2014) (Ldhb)

Leucine zipper-EF-hand containing transmembrane protein 1 + (Rosell et al., 2014) (Letm1)

LETM1 domain containing 1 + (Rosell et al., 2014) (Letmd1)

LIM homeobox 8 + (Petrovic et al., 2010) (Lhx8)

Hormone sensitive lipase + (Rosell et al., 2014) (Lipe)

168

Table 9 (continued)

Mitogen-activated protein kinase kinase 5 + (Rosell et al., 2014) (Map2k5)

Malic Enzyme 3, NADP(+)-Dependent, Mitochondrial + (Rosell et al., 2014) (Me3)

Mesenchyme homeobox + (Petrovic et al., 2010) (Meox)

Mammalian target of rapamycin + (Rosell et al., 2014) (Mtor)

Transcriptional intermediary factor-2 TIF2 + (Picard et al., 2002) (Ncoa2)

Nucleolar protein 3 + (Rosell et al., 2014) (Nol3)

Notch 1 transmembrane receptor - (Bi et al., 2014) (Notch1)

169

Table 9 (continued)

Atrial natriuretic peptide + (Bordicchia et al., 2012) (Nppa)

Ventricular natriuretic peptide + (Bordicchia et al., 2012) (Nppb)

Nuclear receptor subfamily 1, group D, member 1 + (Rosell et al., 2014) (Nr1d1)

Nuclear receptor subfamily 1, group d, member 2 + (Rosell et al., 2014) (Nr1d2)

Nuclear receptor subfamily 1, group H, member 3 - (Wang et al., 2008) (Nr1h3)

Neuregulin 4 + (Rosell et al., 2014) (Nrg4)

5-Oxoprolinase + (Rosell et al., 2014) (Oplah)

170

Table 9 (continued)

Otopetrin-1 (Boström et al., 2012); + (Otop1) (Rosell et al., 2014)

Pantothenate kinase 1 + (Rosell et al., 2014) (Pank1)

Phosphoenolpyruvate carboxykinase 1 + (Rosell et al., 2014) (Pck1)

Phosphodiesterase 8A + (Rosell et al., 2014) (Pde8a)

Pyruvate dehydrogenase (lipoamide) beta + (Rosell et al., 2014) (Pdhb)

Pyruvate dehydrogenase complex, component x + (Rosell et al., 2014) (Pdhx)

Pyruvate Dehydrogenase Kinase, Isozyme 2 + (Rosell et al., 2014) (Pdk2)

171

Table 9 (continued)

Pyruvate Dehydrogenase Kinase, Isozyme 4 + (Rosell et al., 2014) (Pdk4)

Period Circadian Clock 3 + (Rosell et al., 2014) (Per3)

Peroxisomal Biogenesis Factor 19 + (Rosell et al., 2014) (Pex19)

Peroxisomal biogenesis factor 3 + (Rosell et al., 2014) (Pex3)

T-plasminogen activator + (Rosell et al., 2014) (Plat)

Perilipin 5 + (Rosell et al., 2014) (Plin5)

Peptidase M20 domain containing 1 + (Rosell et al., 2014) (Pm20d1)

172

Table 9 (continued)

Patatin-like phospholipase domain containing 2 + (Rosell et al., 2014) (Pnpla2)

Polymerase (DNA directed) nu + (Rosell et al., 2014) (Poln)

Peroxisome proliferator-activated receptor alpha + (Rosell et al., 2014) (Ppara)

Peroxisome proliferator-activated receptor gamma + (Petrovic et al., 2010) (Pparg)

Peroxisome proliferator-activated receptor gamma, coactivator 1 alpha + (Puigserver et al., 1998) (Ppargc1a) Peroxisome proliferator-activated receptor gamma, coactivator 1 beta + (Rosell et al., 2014) (Ppargc1b)

PR domain containing 16 + (Seale et al., 2007) (Prdm16)

173

Table 9 (continued)

Protein kinase, CAMP-dependent, catalytic, alpha + (Rosell et al., 2014) (Prkaca)

Protein kinase, CAMP-dependent, regulatory, type II, beta + (Rosell et al., 2014) (Prkar2b)

Retinoblastoma 1 - (Scimè et al., 2005) (Rb1)

Retinoblastoma-like 1 - (Scimè et al., 2005) (Rbl1)

Recombination signal binding protein for immunoglobulin kappa J region - (Bi et al., 2014) (Rbpj)

Retinol saturase + (Rosell et al., 2014) (Retsat)

Regulator Of G-Protein Signaling 7 + (Rosell et al., 2014) (Rgs7)

174

Table 9 (continued)

Rab interacting lysosomal protein + (Rosell et al., 2014) (Rilp)

S100 Calcium Binding Protein B + (Rosell et al., 2014) (S100b)

Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 12 + (Rosell et al., 2014) (Serpina12)

Sirtuin 1 + (Qiang et al., 2012) (Sirt1)

Sirtuin 3 + (Rosell et al., 2014) (Sirt3)

Solute carrier family 1 member 5 + (Rosell et al., 2014) (Slc1a5)

Solute carrier family 25 member 20 + (Rosell et al., 2014) (Slc25a20)

175

Table 9 (continued)

Solute carrier family 25 member 33 + (Rosell et al., 2014) (Slc25a33)

Solute carrier family 25 member 34 + (Rosell et al., 2014) (Slc25a34)

Solute carrier family 25, member 42 + (Rosell et al., 2014) (Slc25a42)

Solute carrier family 27 (fatty acid transporter), member 2 + (Rosell et al., 2014) (Slc27a2) Solute carrier family 4 (sodium bicarbonate cotransporter), member 4 + (Rosell et al., 2014) (Slc4a4)

SET and MYND domain containing 4 + (Rosell et al., 2014) (Smyd4)

Superoxide dismutase 2 + (Rosell et al., 2014) (Sod2)

176

Table 9 (continued)

Sex determining region Y-box 6 + (Rosell et al., 2014) (Sox6)

Steroid receptor coactivator-1 (Src1) + (Picard et al., 2002)

T-box protein 15 (Gburcik, Cawthorn, Nedergaard, Timmons, + (Tbx15) & Cannon, 2012)

Thyrotroph embryonic factor + (Rosell et al., 2014) (Tef)

Mitochondrial transcription factor A - (Vernochet et al., 2012) (Tfam)

Transcription factor B2, mitochondrial + (Rosell et al., 2014) (Tfb2m)

Transcription factor Dp-2 (E2F dimerization partner 2) + (Rosell et al., 2014) (Tfdp2)

177

Table 9 (continued)

Thyroid receptor A + (Harms & Seale, 2013) (Thra)

Transducin-like enhancer protein 3 - (Villanueva et al., 2013) (Tle3)

Transmembrane protein 37 + (Rosell et al., 2014) (Tmem37)

TRPV4 - (Ye et al., 2012) (Trpv4)

Tetraspanin 18 + (Rosell et al., 2014) (Tspan18)

Tubulin, alpha 8 + (Rosell et al., 2014) (Tuba8)

Uncoupling protein 1 + (Rosenwald & Wolfrum, 2014) (UCP1)

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Table 9 (continued) Ubiquinol-cytochrome c reductase, complex III subunit X + (Rosell et al., 2014) (Uqcr10) Ubiquinol-cytochrome c reductase, rieske iron- sulfur polypeptide 1 + (Rosell et al., 2014) (Uqcrfs1)

Vascular endothelial growth factor + (Elias et al., 2012) (Vegfa)

Zinc finger protein 703 + (Rosell et al., 2014) (Zfp703)

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Appendix B. qPCR Plate Design

The qPCR plate design used in this thesis allowed for seven samples to be run per plate. All samples were loaded in triplicate for the gene of interest and two housekeeping genes. In addition, three separate controls were used, including a no-reverse transcriptase control (RTC), an interplate control (IPC) and a no-template control (NTC).

1 2 3 4 5 6 7 8 9 10 11 12 RTC RTC RTC A UCP1 UCP1 UCP1 RPS3 RPS3 RPS3 B2M B2M B2M UCP1 RPS3 B2M RTC RTC RTC B UCP1 UCP1 UCP1 RPS3 RPS3 RPS3 B2M B2M B2M UCP1 RPS3 B2M RTC RTC RTC C UCP1 UCP1 UCP1 RPS3 RPS3 RPS3 B2M B2M B2M UCP1 RPS3 B2M RTC RTC RTC D UCP1 UCP1 UCP1 RPS3 RPS3 RPS3 B2M B2M B2M UCP1 RPS3 B2M RTC RTC RTC E UCP1 UCP1 UCP1 RPS3 RPS3 RPS3 B2M B2M B2M UCP1 RPS3 B2M RTC RTC RTC F UCP1 UCP1 UCP1 RPS3 RPS3 RPS3 B2M B2M B2M UCP1 RPS3 B2M RTC RTC RTC G UCP1 UCP1 UCP1 RPS3 RPS3 RPS3 B2M B2M B2M UCP1 RPS3 B2M

H IPC IPC NTC NTC IPC IPC NTC NTC IPC IPC NTC NTC

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Appendix C: Mitochondrial Isolation

Mitochondrial Isolation from Adipose Tissue

*Day before assay charge drill for homogenization, label 3 sets of 50 mL conical tubes and 1 set of 1.5 mL Eppendorf tubes®.

*Samples should be kept on ice at all times to prevent protein degradation.

Solution recipes *Do not make solutions until the morning of the assay a. 1X Mitochondrial Isolation Buffer (MIB) (500 mL)  11.98 g sucrose, 19.13 g mannitol  0.60 g HEPES  0.19 g EGTA  Add 2.5 g fatty acid free (FAF)-BSA (Sigma-Aldrich®, 9048- 46-8)  pH to 7.2 b. 2X Mitochondrial Assay Solution (MAS) (100 mL)  4.79 g sucrose  8.02 g mannitol  0.272 g KH2PO4  0.095 g MgCl2  0.095 g HEPES  0.076 g EGTA  0.1 g FAF-BSA  pH to 7.2

1. Set all centrifuges at 4°C 2. Dissect fat pads & place in ~5 mL MIB on ice in a 50 mL conical 3. Mince tissue in a petri dish on ice 4. Transfer to glass homogenizer tube and add MIB to 40 mL 5. After homogenization, centrifuge for 10 min at 8400 rpm 6. Discard hard packed fat layer and supernatant (may need to wipe fat off walls of tube) 7. Resuspend pellet in 1 mL MIB and transfer to a new conical 8. Dilute solution to 40 mL with MIB and centrifuge for 10 min at 2600 rpm 9. Transfer supernatant to clean tube and centrifuge for 10 min at 8400 rpm 10. OPTIONAL: Resuspend pellet in 5 mL MAS and centrifuge for 10 min at 8400 rpm (if pellet is large) 181

11. Resuspend pellet in 1 mL MAS or 0.5 mL if pellet if small and transfer to 1.5 mL Eppendorf tube® 12. Perform Bradford and store samples at -80°C

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Appendix D: Immunohistochemistry

Whole mount Staining in Adipose Tissue

*Make sure tissue is submerged in solution throughout the experiment

Dissection & fixation of tissue 1. Dissection of adipose tissue (0.5 x 0.5 cm size or smaller)  Consider using dissecting scope, try to cut thin equal size slices 2. Fixation with 4% PFA for 1 hour at room temperature (RT)  8% Paraformaldehyde (formaldehyde) aqueous solution (Electron Microscopy Sciences, 157-8), dilute to 4 %  Place tissue samples in a 96 well plate with ~200 μL of PFA in each well

Permeabilization 1. In the 96 well plate, permeabilize tissues with 0.5% phosphate buffered saline with Tween 20 (PBS-T) 3x for 10 min each (~200 μL/well) 2. Wash tissue with filtered PBS 3x for 10 min each (~200 μL/well)

Block & primary/ secondary incubation 1. Block tissue with 4% bovine serum albumin (BSA) in filtered PBS for 1 hour at RT 2. Incubate with primary antibody solution Antibody solutions for 24 hours or overnight at 4°C (~200 μL/well) 3. Next day, take out the antibody solution and collect it (if reusing) 4. Wash with filtered PBS 3x for 10 min each 5. Incubate with appropriate conjugated secondary antibody for 1 hour (~200 uL/well)  If using a fluorescent secondary, cover the plate with tinfoil to protect from light 6. Wash with filtered PBS 3x for 10 min each 7. Incubate with HCS LipidTOX™ Green Neutral Lipid Stain, diluted 1:500 in ddH20 (Invitrogen™, Molecular Probes™, H34475) for 30 min at RT 8. Place tissue on slide in ring of Silly Putty and cover with 1 drop of VECTASHIELD® mounting media (Vector Laboratories, H-1300) 9. Add 8 uL DAPI, diluted 1:100 in PBS (Invitrogen™, Molecular Probes™, D1306) 10. Place coverslip over Silly Putty a. Make sure to squeeze out air bubbles b. Use clear nail polish to seal the cover slip to the slide 11. Observe under a confocal fluorescent microscope (inverted type microscope needed due to whole mount tissue) 183

Appendix E: Protein Isolation

Protein Isolation from Adipose Tissue

Materials  Tissue  Labeled Eppendorf tubes® (4x the number of samples) (Autoclaved)  Pipettes and tips  Centrifuge  Fisherbrand® Disposable Pestle System (Thermo Fisher Scientific, 03-392-103)  Syringes and needles (21 G 1½ ) (2x the number of samples)  RIPA buffer (Cell Signaling Technology®, #9806)  PMSF Stock (100x) 1mL o Dissolve 17.42mg PMSF in 1 mL isopropanol

*Protein will denature when warmed, so you must be careful to keep samples cold as you work, and work quickly.

Procedure 1. Prepare the necessary volume (300 μL x number of samples, plus a little extra to account for pipetting error) of RIPA buffer mix (RIPA + PMSF) 2. Cut frozen tissue and transfer into a 1.5ml Eppendorf tube® (keep on ice) 3. Add 200 μL RIPA buffer mix to the tissue 4. Homogenize until there are no large pieces of tissue left 5. Add an additional 100 μL of RIPA buffer mix to sample (to a total volume of 300 μL) 6. Use syringe to mix solution and shear DNA by going up and down several times. 7. Spin for 15 min at max speed (~17,000 x g) at 4˚C 8. There will be three layers, white lipid on top, protein solution in the middle, and some cellular debris and DNA at the bottom. Use a syringe to remove the middle layer and make 3 aliquots. One small one (~15 μL) for Bradford assay, and two larger ones for further use.

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Appendix F: Bradford Assay

Materials  Isolated protein samples  BSA standards  Eppendorf Tubes®  Pipettes and tips  96 well plate  Bradford Reagent (5x) (Bio-Rad®, 500-0006)  ddH2O  Spectrophotometer

Procedure 1) Prepare Bradford reagent 1x stock. Bio-Rad Bradford Reagent is supplied as 5x. Dilute to 1x with ddH2O. 2) Make BSA standards in same buffer used for samples (or if you are diluting samples with H2O you can make BSA standards with ddH2O). a. Stock 1μg/μL b. 0.50 μg/μL c. 0.25 μg/μL d. 0.125 μg/μL e. 0.0625 μg/μL f. 0.03125 μg/μL g. Blank (buffer or ddH2O)

3) If you have no idea about the range of the concentration of your sample, make 1:10, 1:50, and 1:100 dilutions. *For WAT and BAT, 1:10 and 1:25 dilutions are usually sufficient 4) Add 10 μL of sample dilution or standard (in triplicate) to 200 μL 1x Bradford Reagent, gently mix. 5) Incubate for 5 minutes at RT (no longer than 1 hour). Read plate at 595nm absorbance.

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Appendix G: Western Blotting Protocol

Step 1: SDS-PAGE

1) Decide on the percentage of resolving gel necessary to put your protein of interest around the middle of the gel. See Appendix D for protein gel recipes. *Remember to position gels into running cassette such that the small plates are facing inward* 2) Calculate ul of each sample needed to load between 20-30ug. *Depending on the protein, this amount may need to be increased* 3) Boil diluted samples in Laemmli Buffer for 5 min (use heating block or boiling water bath). 4) Load gel, including protein ladder. *Remember to mix samples thoroughly before loading* 5) Run @ 35 mAmp/gel for ~45 min – 1.5 hr (this depends on gel%; a higher % means longer running time). *Run until dye front reaches the bottom* 6) Remove stacking gel and any remaining dye front before continuing to transfer. 7) Cut the top left corner from the resolving gel so that you can keep the gel oriented throughout the experiment.

Step2: Protein Transfer

1) Prepare 1L of 1x transfer buffer 2) Soak gels in cold transfer buffer for 20 min (the gels will change size due to the methanol in the buffer) 3) Cut membrane and filter paper (2 filters per gel) to 9.5cm wide x 5.5cm tall (assuming mini gels). 4) Submerge PDVF membranes in ice cold 100% methanol for 2 min. -Rinse in ddH2O for 2 min -Equilibrate in transfer buffer for 5 minutes 5) Submerge sponges and filter papers in transfer buffer just prior to assembling the transfer sandwich. 6) To minimize the chance of introducing air bubbles between the membrane and gel, assemble the sandwich under transfer buffer. 7) Do this by placing the black side of the cassette (- end) in the buffer, apply one sponge on top and gently push air bubbles out of the sponge, lay one filter paper on the center of the sponge, then lay the gel (noting the orientation) on top of the filter paper, next place the membrane on top of the gel, use a serological pipette to roll any air bubbles out of the membrane-gel interface, next place the remaining filter paper on top of the

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membrane, then place the remaining sponge to complete the sandwich. Slide the latch to secure the transfer sandwich. *The order of your transfer sandwich should be: Black (- end) Sponge Filter paper Gel Membrane Filter paper Sponge Clear (+ end) 8) Place the sandwich in the transfer apparatus ensuring that the latch is on the top and the black end is oriented towards the black end of the apparatus. 9) Insert an ice pack into the running tank. 10) Fill the running tank with transfer buffer. 11) Place the running tank into a bucket filled with ice and run at 100V for 1hr at room temperature. *Note, a longer transfer can be performed if this does not work*. *20V overnight, under gentle stirring at 4C*. 12) Gauge the level of protein transfer by the amount of rainbow protein ladder that has been transferred to the membrane. 13) Transfer membrane immediately to 10mL TBS used for rinsing in next step.

Step 3. Optional staining of membrane and gel to verify transfer

Membrane Stain with Ponceau Red 1) If the membrane is dry, wet it in 100% methanol for 2min prior to staining. 2) Cover membrane with Ponceau red, and agitate for 1 minute. 3) Rinse membrane with H2O until contrast is achieved. 4) Wash membrane with 0.1N NaOH to remove stain.

Gel Stain with Commassie Blue 1) Turn on eStain® 2.0 Protein Staining System. 2) Remove staining pads from wrapper and place anode pad (white) on the graphite anode, followed by the gel, and the cathode pad (blue) on top. 3) Close the top of the machine and gently push down until you hear a click. 4) Set the timer for 7 min of staining. 5) De-stain for 2 hours or overnight in ddH2O.

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Step 4. Membrane Blocking and Primary Antibody

1) Wash membrane in 10mL TBS for 2 min under gentle agitation. 2) Block membrane in 10mL of Blocking Buffer for 1 hr. 3) Wash membrane in 10mL TBS-T for 5 min under gentle agitation. 4) Add primary antibody at optimal dilution in 5%BSA/TBS-T. *Typically this is 1:1000 for Cell Signaling, but check manufacturer. 5) Incubate overnight at 4C, sealed with parafilm with gentle agitation.

Step 5. Secondary Antibody

*Secondary antibodies (CyDye Antibodies) are light sensitive. The remaining steps should be performed in a room with minimal light and incubation trays should be wrapped in Parafilm and tinfoil to avoid light exposure.

1) Rinse membrane once in an excess of TBS-T. 2) Wash membrane six times in an excess of TBS-T for 5 min under gentle agitation. 3) Add secondary antibody at optimal dilution in TBS-T. *For Cy5/Cy3 GE Antibodies this is 1:2500. 4) Incubate for 1 hour at room temperature, sealed with Parafilm and protected from light. 5) Rinse membrane once in an excess of TBS-T. 6) Wash membrane three times in in an excess of TBS-T for 5 min under gentle agitation. 7) Keep membrane in 10mL TBS-T.

Step 6. Imaging

1) Scan using the PharosFX Plus. 2) Ensure that external laser is turned on. 3) Set channel to proper fluorophore (Cy5, Cy3, DyLight, etc.). 4) Place membranes on screen using a small drop of water to ensure no air is introduced between membrane and screen. *Place membrane with proteins facing up, the machine scans from above. 5) Capture a low resolution scan (200um) to ensure correct membrane orientation. 6) Capture high resolution (50um) final scans. 7) Be sure to export jpeg files at 100% quality and store both the scan and jpegs on an external drive.

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Step 7. Analysis Using ImageJ

*This step is only necessary if you wish to quantify your results.

Appendix H: Western Blot Recipes

1) Laemmli Buffer (2x) 10mL

4mL 10% (w/v) SDS* 2mL Glycerol 0.5mL 0.1% (w/v) Bromophenol Blue 2.5mL 0.5M Tris-HCl, pH6.8 * Dilute to 10mL ddH2O

**Prior to use add betamercaptoethanol (B-ME) 1:20 (9.5mL Laemmli 2x + 500uL B-ME)

*10% SDS: 10g SDS diluted to 100mL with ddH2O, may need heating *0.5M Tris-HCl: 39.4g Tris-HCl diluted to 500mL with ddH20, adjust pH 6.8.

2) TBS (10x) 1L

24.2g Tris Base 80g NaCl To 1L ddH2O

Adjust pH to 7.6 Dilute 1:10 for working solution.

3) TBS-T 100mL

10mL 10x TBS 90mL ddH2O 0.1mL Tween-20

4) Stock Transfer Buffer (10x) 2L

288g Glycine 60.4g Tris Base To 2L ddH2O 189

5) Working Transfer Buffer (1x) 1L

100mL Methanol 100mL 10x Stock Transfer Buffer To 1L ddH2O

**For proteins > 80kDa, you may need to include 0.1% SDS for better transfer.

6) Blocking Buffer 150mL

15mL 10x TBS 7.5g Nonfat Dry Milk 0.15mL Tween-20 To 150mL ddH2O

**Filter this solution for best results. **If doing phosphoprotein Western blots, it is best to use BSA instead of nonfat dry milk. Caseins in the milk are phosphoproteins and may cause a high background reaction.

7) Primary Antibody Dilution Buffer

Dilute antibody to recommend range using TBS-T.

If you are not experiencing high background, include 0.5% BSA in dilution buffer. This will produce a stronger signal.

8) Seconday Antibody Dilution Buffer

Dilute secondary antibody in TBS-T.

If you are experiencing high background, use blocking buffer as a diluent instead. This, however, may lead to a less specific signal.

9) Electrophoresis Running Buffer (5x)

72g Glycine 15g Tris Base 5g SDS To 1L ddH2O

Dilute 1:5 for working solution. 190

10) Upper Stacking Gel Tris (0.5M) 1L

60.6g Tris Base 1g SDS To 1L ddH2O

Adjust pH to 6.8.

11) Lower Resolving Gel Tris (1.5M) 1L

181.8g Tris Base 1g SDS To 1L ddH2O

Adjust pH to 8.8.

Protein Gel Recipes

10% Acryl-Bis Resolving Gel (10mL/gel) [15-100kDa]

2 gels 4 gels 6 gels 8 gels 40% Acrylamide:Bis 5mL 10mL 15mL 20mL Lower Tris (pH 8.8, SDS) 5mL 10mL 15mL 20mL ddH2O 10mL 20mL 30mL 40mL 10% APS* 143uL 286uL 429uL 572uL TEMED** 28.5uL 57uL 85.5uL 114uL

4% Acryl-Bis Stacking Gel (1mL/gel)

6 gels 8 gels 10 gels 12 gels 40% Acrylamide:Bis 0.625mL 0.83mL 1.04mL 1.25mL Upper Tris (pH 6.8, SDS) 1mL 1.34mL 1.67mL 2mL ddH2O 4.25mL 5.66mL 7.08mL 8.5mL 10% APS* 50uL 66.6uL 83.3uL 100uL TEMED** 10uL 13.34uL 16.67uL 20uL

***Prepare all gel solutions on ice***

Always prepare enough mixture for 2 gels more than what you plan to run. Keep the excess in a conical tube to determine when the gels have polymerized.

*APS is a powder, prepare fresh (10mg/100uL ddH2O, 10%) just before using. 191

**ADD APS AND TEMED JUST PRIOR TO POURING GELS. These initiate the polymerization.

Cover resolving gels with isopropanol after pouring to level the top.

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Appendix I: Seahorse Assay

Whole Tissue Seahorse Assay

Solutions a. Whole tissue wash buffer (20 mL total)  20 mL Seahorse XF Assay Medium  0.119 g HEPES  0.09 g glucose  pH to 7.4 b. Whole tissue assay solution (20 mL total)  20 mL Seahorse XF Assay Medium  0.09 g glucose  200 μL sodium pyruvate  pH to 7.4

1. Dissect tissue, weigh, and place in whole tissue wash buffer 2. 4 mg slices of tissue should be cut and placed in wells of plate (see plate setup below) *Leave at least 3 wells blank for negative controls **Avoid using corner wells to minimize “edging effect” of the Seahorse machine 3. Add screen on top of tissue and 450 μL of whole tissue assay solution on top of screen 4. Once loaded with tissue and assay solution, put plate in non-CO2 incubator (at least 10 mins) 5. Load drugs in cartridge and put plate in Seahorse to calibrate (20 mins) * Start this step before plate is loaded with tissue 6. Remove calibration plate and place islet plate in Seahorse to begin run

Sample 1 Sample 3 Sample 4 Sample 5 Sample 6 X

X Sample 2 Sample 3 Sample 4 Sample 5 Sample 6

X Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

X X Sample 1 Sample 2 Sample 6 X

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