Using RNA-Seq Technology to Explore the Impact of Growth Hormone on Angiogenesis and Other Cellular Pathways in Subcutaneous and Epididymal Adipose Tissue from Wild

Home , Amphiregulin, Betacellulin, Growth hormone 1

Using RNA-seq Technology to Explore the Impact of Growth Hormone on Angiogenesis and Other Cellular Pathways in Subcutaneous and Epididymal Adipose Tissue from Wild

Type and Bovine Growth Hormone Transgenic Mice

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

Silvana Duran Ortiz

August 2014

This thesis titled

Using RNA-seq Technology to Explore the Impact of Growth Hormone on Angiogenesis

and Other Cellular Pathways in Subcutaneous and Epididymal Adipose Tissue from

Wild Type and Bovine Growth Hormone Transgenic Mice

SILVANA DURAN ORTIZ

has been approved for

the School of Applied Health Sciences and Wellness

and the College of Health Sciences and Professions by

Darlene E. Berryman

Professor of Applied Health Sciences and Wellness

Randy Leite

Dean, College of Health Sciences and Professions 3

Abstract

DURAN ORTIZ, SILVANA, M.S., August 2014, Food and Nutrition Sciences

Using RNA-seq Technology to Explore the Impact of Growth Hormone on Angiogenesis and Other Cellular Pathways in Subcutaneous and Epididymal Adipose Tissue from

Wild Type and Bovine Growth Hormone Transgenic Mice

Director of Thesis: Darlene E. Berryman

Several studies suggest that inappropriate adipose tissue (AT) vascularization and angiogenesis may contribute to the negative effects of obesity by altering several AT characteristics, such as immune cell infiltration and fibrosis. Growth hormone (GH) has an impact on AT plasticity. That is, disruption of GH action increases AT mass while increased GH action, such as in bovine GH (bGH) mice, decreases AT mass in a depot- specific manner. GH has been implicated in angiogenesis in several tissues; however, to date, no one has investigated the effect of GH on angiogenesis in AT. For this thesis,

RNA-seq technology was used to compare the expression levels of angiogenic factors in inguinal (subcutaneous) and epididymal (intra-abdominal) AT depots of male bGH mice to wild type (WT) controls. Because RNA-seq technology also allows one to organize a large amount of data into biologically meaningful groups, this work also classified the significantly altered genes into the predominant cellular processes, networks and pathways that are altered in these animals. The results showed that the inguinal depot of bGH mice have a significant down-regulation of several angiogenic factors including leptin, vascular endothelial growth factor A, vascular endothelial growth factor B and metalloproteinase inhibitor 4 when compared to WT mice. Regarding the more global 4

RNA-seq analysis, the inguinal AT depot of bGH mice had many more significantly altered genes, suggesting a greater impact in this depot. The biological process and pathway analysis of the epididymal AT depot suggested that GH may down-regulate processes related with basic metabolism and organelle organization and biogenesis. In the inguinal AT, the up-regulated genes were related with immune cell activation, especially genes related with T cells. Overall, it was concluded that GH may have a negative impact on the angiogenesis status of the inguinal AT depot of bGH mice, an effect that is depot- dependent. Moreover, GH may decrease the expression levels of genes related with adipocyte differentiation, and increase the expression level of genes associated with T cell activation and regulation. The pathway and process analyses need additional experimental follow-up but provide additional avenues for exploration.

Dedication

For Marcela and Sergio, for their unconditional love and support, you mean the world to me, and having you in my life is my biggest treasure. For Johnnatan, for his patient and

kindness, I love you, and for the record…“all the time.”

Acknowledgments

This work was supported by the Student Research Award, and the interdisciplinary Award of the College of Health Sciences and Professions, and by the

National Institutes of Health grant AG031736. This work was also supported by the

Ohio’s Eminent Scholar Program and by the Diabetes Institute at Ohio University.

This thesis would not have been possible without the help of all the members of the Kopchick lab. Special thanks to Dr. Adam Jara for teaching me RNA-seq, and to Dr.

Ed. List for editing my abstracts, and providing samples for this work. Also, I would like to thank Nicole Brooks, Lara Householder, Kevin Funk, Ellen Lubbers, Johnathan

Young, Ross Comisford, and Amrita Basu for their advice with the experiments.

Additionally, I would like to thank Dr. Cheryl Howe for taking the time to be on my committee and for her helpful comments, and to Dr. Mark Berryman for teaching me immunohistochemistry.

Next, I want to say special thanks to Dr. John Kopchick. Thank you for all your advice, you have been a tremendous leader and role model for me; your teaching, enthusiasm, passion and corrections have been an invaluable contribution to this work.

Finally, I want to extend my highest gratitude to my wonderful mentor Dr.

Darlene Berryman. Your love for science and teaching, and your work ethic makes you the great role model that you are. You have been the kindest and most stimulating advisor that anyone could ever have. I wouldn’t have been able to get here without your help. It has been a pleasure and honor to work under your “wing.” 7

Table of Contents

Page

Abstract ...... 3

Dedication ...... 5

Acknowledgments...... 6

List of Tables ...... 11

List of Figures ...... 13

Chapter 1: Introduction ...... 15

Statement of Problem ...... 18

Research Questions ...... 19

Significance ...... 20

Limitations/Delimitations ...... 21

Definition of Terms ...... 22

Chapter 2: Literature Review ...... 26

Adipose Tissue (AT) ...... 26

Types of AT depots...... 28

AT depot differences...... 30

AT as an endocrine organ...... 31

Angiogenesis ...... 33

AT vasculature...... 35

AT angiogenesis...... 36

Molecules that regulate angiogenesis...... 38 8

Obesity and AT angiogenesis...... 44

AT depot differences in angiogenesis...... 45

Angiogenesis control as a therapeutic approach for obesity...... 46

Growth Hormone (GH) ...... 48

GH receptor (GHR) activation...... 49

Regulation of GH expression and physiological actions...... 51

GH action on AT...... 53

bGH mouse model for the study of excess of GH...... 54

AT specific depot differences in response to GH...... 56

Ribonucleic Acid (RNA) ...... 57

Methods to analyze RNA...... 59

RNA-seq experiments...... 61

Preparation of cDNA for RNA-seq...... 61

Next generation sequencing (NGS)...... 63

RNA-seq analysis workflow...... 64

Adipose tissue papers involving RNA-seq analysis...... 67

Conclusion ...... 71

Chapter 3: Methodology ...... 72

Animals ...... 72

Adipose Tissue Depots ...... 73

Total RNA and Messenger RNA (mRNA) Isolation ...... 73

cDNA Library Preparation and RNA Sequencing ...... 74 9

RNA-Seq Analysis ...... 74

Statistical Analysis ...... 77

Chapter 4: Results ...... 78

Gene Expression of Angiogenic Molecules ...... 78

Angiogenic genes not analyzed statistically...... 79

Angiogenic genes that were statistically evaluated...... 83

RNA-seq Data Analysis ...... 88

Differentially expressed genes...... 88

GO analysis for biological processes...... 89

Biological networks obtained with IPA tool...... 92

Biological pathway analysis made with the IPA tool...... 97

Most significantly up- and down-regulated genes...... 102

Chapter 5: Discussion ...... 105

AT Angiogenesis ...... 106

RNA-seq Analysis of Biological Pathways and Processes ...... 111

Network analysis and insulin growth factor binding protein 3...... 111

Significantly altered biological processes and pathways in the epididymal AT depot

of bGH mice...... 112

Significantly altered biological processes and pathways in the inguinal AT depot of

bGH mice...... 115

Limitations and Future Directions ...... 116

Conclusion ...... 120 10

References ...... 122

Appendix A: RNA Isolation ...... 157

Appendix B: mRNA from Total RNA Isolation Protocol ...... 160

Appendix C: Galaxy Workflow ...... 162

Appendix D: Angiogenic Molecules to be Tested for Gene Expression ...... 163

Appendix E: Genes of the Significantly Altered Networks in the Epididymal and Inguinal

AT Depots in bGH Mice ...... 165

List of Tables

Page

Table 1: List of Adipokines, Their Differential Expression Between AT Depots and

Their Main Roles...... 32

Table 2: Molecules Known to Alter Angiogenesis and Their Main Functions……...... 39

Table 3: Types of RNAs and Their Functions…………………………………………...59

Table 4: Commonly Used Techniques to Measure Gene Expression Levels...... 60

Table 5: Commonly Used NGS Methods...... 64

Table 6: Published Papers That Describe RNA-Seq Experiments on AT...... 68

Table 7: Angiogenic Genes of the Epididymal AT Depot That Did Not Express Enough

Transcripts for Statistical Analysis……...…………….………………………………....80

Table 8: Angiogenic Genes of the Inguinal AT Depot That Did Not Express Enough

Transcripts for Statistical Analysis………….…………………………………….…...... 81

Table 9: Statistical Analysis of Available Angiogenic Genes of the Epididymal AT

Depot…………………...... 85

Table 10: Statistical Analysis of Available Angiogenic Genes of the Inguinal AT

Depot.…………………………………………………………………………………….86

Table 11: Significant Biological Processes Associated With Down-Regulated Genes in

Epididymal AT Depot of bGH Mice...... 90

Table 12: Significant Biological Processes Associated With Up-Regulated Genes in bGH Inguinal AT Depot……….………………………………………………...…..…..91 12

Table 13: Significant Biological Processes Associated With the Down-Regulated Genes in the Inguinal AT Depot of bGH Mice…………………………………………….…....92

Table 14: Altered Pathways of the Epididymal AT Depot…………………….…….…..99

Table 15: Altered Pathways of the Inguinal AT Depot…………………………..…….101

Table 16: Ten Most Up-Regulated Genes in the Epididymal AT Depot of bGH Mice..103

Table 17: Ten Most Down-Regulated Genes in the Epididymal AT Depot of bGH

Mice…………………………………………………………………………………….103

Table 18: Ten Most Up-Regulated Genes in the Inguinal AT Depot of bGH Mice…...104

Table 19: Ten Most Down-Regulated Genes in the Inguinal AT Depot of bGH Mice..104

Table 20: Angiogenic Molecules for Gene Expression………………………………..163

Table 21: Genes of the “Cellular Function and Maintenance, Lipid Metabolism, Small

Molecule Biochemistry Network” of the Epididymal Depot……………………….....165

Table 22: Genes of the “Lipid Metabolism, Molecular Transport, Small Molecule

Biochemistry Network” of the Inguinal Depot………………………...……………...166

Table23: Genes of the “Carbohydrate Metabolism, Lipid Metabolism, Molecular

Transport Network” of the Inguinal Depot……………………………………………167

Table 24: Genes of the “Endocrine System Disorders, Gastrointestinal Disease, Hepatic

System Disease Network” of the Inguinal Depot……………………………..………168 13

List of Figures

Page

Figure 1: Adipose tissue depots in mice…………………………………………………29

Figure 2: Steps involved in angiogenesis………………………………………………..34

Figure 3: Angiogenesis regulation in adipose tissue…………………………………….38

Figure 4: Vascular endothelial growth factors (VEGFs)………………………………...44

Figure 5: GH signaling pathways………………………………………………………..50

Figure 6: Regulation of GH secretion and GH main targets…………………………….52

Figure 7: Mouse models with normal and high growth hormone (GH) action………….56

Figure 8: cDNA library preparation before sequencing…………………………………62

Figure 9: RNA-seq analysis workflow…………………………………………………..67

Figure 10: RNA-seq data analysis workflow…………………………………………....76

Figure 11: Angiogenic genes that were evaluated with RNA-seq technology………….79

Figure 12: Venn diagram of the angiogenic genes that were not detected in AT……….83

Figure 13: Heat map of the genes that were statistically tested in only one AT depot….87

Figure 14: Heat map of the 13 genes that were sufficiently expressed in both AT depots……………………………………………………………………………………87

Figure 15: Total number of significantly altered genes in inguinal and epididymal AT depots……………………………………………………………………………………89

Figure 16: The “cellular function and maintenance, lipid metabolism, small molecule biochemistry network”………………………………………………………………….93 14

Figure 17: The “lipid metabolism, molecular transport, small molecule biochemistry network”…………………………………………………………………………………95

Figure 18: The “carbohydrate metabolism, lipid metabolism, molecular transport network”………………………………………………………………………………....96

Figure 19: The “endocrine system disorders, gastrointestinal disease, hepatic system disease network”………………………………………………………………………...97

Figure 20: The ten most altered biological pathways in the epididymal AT depot of bGH mice…………………………………………………………………………………….100

Figure 21: The ten most altered biological pathways in the inguinal AT depot of bGH mice…………………………………………………………………………………….102

Chapter 1: Introduction

Historically, adipose tissue (AT) was thought to perform a passive role in whole body homeostasis based on its well known ability to store excessive energy in times of caloric excess and release energy in times of caloric restriction or starvation (Peinado,

Pardo, de la Rosa, & Malagón, 2012; Richard & Stephens, 2011). In recent years, AT has become appreciated as a very complex tissue comprised of various cell types, an extracellular matrix and an intricate vasculature (Berryman et al., 2011; Poulos,

Hausman, & Hausman, 2010). AT is also now recognized as an active endocrine organ that secretes peptides, called adipokines, which enable AT to interact with cells in other organs. Furthermore, AT is the only tissue in the body that can undergo dramatic expansion (obesity) or reduction (lipodystrophy). Both severe adiposity and extreme leanness can lead to metabolic dysfunctions such as insulin resistance, type 2 diabetes, hypertension and vascular diseases.

AT expansion is intimately linked with appropriate vascularization and angiogenesis (or the formation of blood vessels using preexisting vasculature)

(Christiaens & Lijnen, 2010). That is, remodeling of AT vasculature is required for AT to expand (Kurki, Shi, Martonen, Finckenberg, & Mervaala, 2012). Blood vessels help tissue expansion by providing the necessary nutrients, oxygen, growth factors and cytokines that help the growth and survival of adipocytes (Cao, 2010). In addition, AT plays an active role in angiogenesis, producing proangiogenic factors such as leptin, vascular endothelial growth factor-A (VEGF-A) and fibroblast growth factor (FGF) that support vessel growth (Cao, 2010; Mannerås-Holm & Krook, 2012). Thus, AT plasticity 16 requires angiogenesis to occur, which at the same time is regulated by the expression of angiogenic factors expressed by AT in a paracrine or autocrine manner.

There are other endocrine signals that also affect AT plasticity, one of which is

Growth Hormone (GH) (Garten, Schuster, & Kiess, 2012). GH is a 191 amino acid (aa) hormone secreted by the cells of the anterior pituitary gland. GH induces the secretion of a potent growth factor termed Insulin Growth Factor-1 (IGF-1) by the liver and other cell types. Together, these hormones form what is called the GH/IGF-1 axis (Kopchick &

Andry, 2000). GH has a major impact on AT. GH has a well documented effect of promoting lipolysis and preventing lipogenesis (Vernon, 1996). Thus, lack of or decreased GH signaling in humans and mice increases AT mass whereas increased GH signaling decreases AT mass (Berryman et al., 2011). GH also regulates the secretion of several adipokines, which in turn may affect the body homeostasis (Vijayakumar,

Novosyadlyy, Wu, Yakar, & LeRoith, 2010). Further, GH has an impact on AT plasticity since it is involved in adipocyte differentiation and preadipocyte proliferation; however, whether GH does this directly or indirectly (via GH-induced up-regulation of IGF-1) is heavily debated (Garten et al., 2012).

Bovine GH transgenic (bGH) mice, the focus of this thesis, have high serum levels of GH and IGF-1 and are well established to have increased body mass and impairments in glucose homeostasis, at least at young ages (Balbis, Dellacha, Calandra,

Bartke, & Turyn, 1992). They also have reduced AT and have other alterations in AT physiology such as altered adipokine production (Berryman et al., 2004), increased 17 extracellular matrix (Householder, 2013), and abnormal immune cell infiltration

(Harshman, 2012).

GH appears to play a role in angiogenesis in most tissues throughout the body although, to date, no one has addressed whether it does in AT. Endothelial cells, which line the blood vessels, express receptors for both GH and IGF-1 and, not surprisingly, several studies have demonstrated a relationship between GH/IGF-1 and angiogenesis

(Corbacho, Martinez De La Escalera, & Clapp, 2002; Smith et al., 1997). Although GH and IGF-1 are generally considered promoters of angiogenesis, the actions of GH on blood vessels appear to differ among tissues. For example, reduced GH action appears to protect mice from retinal neovascularization, but GH excess does not promote retinal neovascularization (Smith et al., 1997). In contrast, in the cerebral cortex of rats, increased GH levels promote blood vessel development (Corbacho et al., 2002). Thus, it is important to evaluate GH’s impact on angiogenesis tissue by tissue.

Since GH impacts angiogenesis in other tissues and since angiogenic capacity impacts AT expansion, it is possible that GH influences vascularization in AT, although this has not yet been explored. Furthermore, when there is insufficient blood supply in the process of AT expansion, hypoxia may occur (Hosogai et al., 2007). Hypoxia in AT results in macrophage infiltration and inflammation, situations related to insulin resistance (Mannerås-Holm & Krook, 2012). Understanding the angiogenic process in relation to GH action in AT and how this influences tissue expansion/reduction might help to understand obesity and its related conditions. 18

Statement of Problem

Great efforts are being made to control obesity in the United States. Obesity is related to comorbidities that can lead to impaired quality of life or even death (Mannerås-

Holm & Krook, 2012) and has recently been labeled a “disease” by the American

Medical Association (AMA, 2013). Obesity, or excess adipose tissue (AT), remains a growing problem in the U.S. population. In fact, it is predicted that 51% of the U.S. population will be obese by 2030 (Finkelstein et al., 2012). Currently, more than one- third of American adults are obese, and the latest estimates for U.S. medical costs for obesity-related problems is approximately $147 billion (Centers for Disease Control and

Prevention [CDC], 2012). Thus, there is an important need to understand the ramifications of AT expansion in order to control obesity and its associated complications.

AT is a very complex endocrine tissue with an intricate vasculature (Berryman et al., 2011). Discrete depots of AT are functionally, compositionally and metabolically distinct (Ronti, Lupattelli, & Mannarino, 2006). For AT to be able to expand, angiogenesis must occur (Christiaens & Lijnen, 2010); thus, it is likely that both expansions and reductions in AT mass involve changes in AT vasculature. Furthermore,

AT expansion and regression is strongly influenced by two hormones that are thought to influence angiogenesis in other tissues; these hormones are GH and IGF-1(Garten et al.,

2012). Endothelial cells in blood vessels express receptors for both GH and IGF-1.

However, GH actions on blood vessels appear to differ among tissues (Corbacho et al.,

2002) and among AT depots, requiring one to evaluate GH’s role in angiogenesis in a 19 tissue-specific and depot-specific manner. To date, no one has explored the relationship between GH and AT angiogenesis. Finally, the positive relationship between tissue hypoxia and angiogenesis is well documented. AT inflammation may involve macrophage infiltration and fibrosis, which in turn might result in AT hypoxia (Spencer et al., 2011). Preliminary data from our laboratory show increased fibrosis, an unfavorable adipokine profile, and altered immune cell infiltration preferentially in the subcutaneous versus intra-abdominal AT in bGH mice versus littermate controls

(Harshman, 2012; Householder, 2013). Thus, it is likely that there may be a relationship between the fibrosis seen in bGH mice and the angiogenesis status found in AT and that this relationship may differ in particular AT depots. This relationship was explored in this thesis.

Research Questions

To study the influence of GH in AT vasculature, bGH mice, which are genetically engineered to produce excess GH, and littermate controls of the C57Bl/6J background strain were used. AT tissue samples from male bGH mice and wild type (WT) controls were examined and compared using RNA-sequencing or RNA-seq technology. RNA-seq is a potent methodology that allows one to examine the whole transcriptome of a tissue

(Trapnell et al., 2012); therefore, even though the main objective of this thesis was to examine the angiogenic status of AT at the ribonucleic acid (RNA) level, we took advantage of RNA-seq to determine other biological processes and pathways that also may be altered in the AT of bGH mice. It was hypothesized that bGH mice would express higher rates of angiogeneic genes due to the excessive levels of GH and IGF-1 20 and that this expression level would be higher in the subcutaneous depot, as this depot seems to be the most altered with extremes in GH and IGF-1 signaling. It was also hypothesized that bGH mice would have an increase in expression of genes and pathways related to lipid metabolism, immune cell biology, and fibrosis/extracellular matrix remodeling based on previous research in our laboratory (Harshman, 2012; Householder,

2013). The research questions to address these hypotheses were:

1. Using RNA-seq technology, what are the RNA expression levels of known

angiogenic genes in the inguinal (subcutaneous) and epididymal (intra-

abdominal) AT depots of bGH and WT male mice?

2. Using RNA-seq technology, what are the predominant cellular pathways and

genes that are altered in the inguinal (subcutaneous) and epididymal (intra-

abdominal) AT depots when comparing bGH and WT male mice?

Significance

There is evidence that GH influences angiogenesis in tissues throughout the body, but the influence of GH on that process are not consistent and are tissue dependent. To date, no one has explored the relationship between GH and AT vasculature. AT structure and function are altered in both obesity and GH excess; thus, it is imperative to clarify the role of GH on AT structure and plasticity since modulation of AT vasculature could have a significant impact on the treatment and prevention of the obesity epidemic. Thus, the purpose of this thesis was to investigate the effect of GH on AT angiogenesis of bGH versus WT mice. Our aim was to elucidate the impact of GH action not only on angiogenesis in subcutaneous and intra-abdominal AT depots, but also reveal possible 21 pathways impacted by GH on AT. Confirmation of the hypotheses provides insight into the direct and indirect role of high GH action on AT vasculature in an AT depot specific manner. Since intra-abdominal AT is considered “bad” whereas subcutaneous AT is thought to be “good” for health, a better understanding of AT and its complexity may have a positive impact on the understanding of the etiology of obesity. In turn, this may provide new avenues for possible therapeutic treatments.

Limitations/Delimitations

A list of limitations and delimitations include the following:

1. Results may not be directly extrapolated to humans due to differences between

mice and humans in depot location and cellular composition.

2. When comparing results between bGH and wild type mice, chronological age

must be taken into account in the interpretation of results. That is, bGH animals

age prematurely (Kucia et al., 2013).

3. In RNA-seq methods, fragmentation of complementary deoxyribonucleic acid

(cDNA) may introduce a bias towards identification of transcripts from the 3′

end. A problem seen in the library construction using RNA-seq are the

amounts of cDNA shorts reads; these fragments could be a reflection of high

amounts of RNA or could also be polymerase chain reaction (PCR) artifacts.

This issue can be problematic for the analysis of RNA expression levels.

4. RNA-seq data organization can be challenging due to the complexity of the

dataset, the bioinformatics tool required to analyze the data, and the large

amount of information. 22

5. There is not a specific guideline to follow for RNA-seq analysis. Since it is a

new technology, many software tools can be used to analyze the data, and

several of these use different mathematical and statistical tests to process the

data; thus, it is possible to get different results depending on the software used.

6. Besides the different software that can be used to analyze RNA-seq data, there

can also be differences in the methodology (different platforms with different

specifications and sequencing protocols) that could generate conflicting results

when comparing the results with other experiments or different sequencing

methods.

7. Due to the high sensitivity of RNA-seq and the artifacts of the sequencing, it is

recommended to confirm the expression levels of the specific genes of interests

with other methodologies, such a real time-PCR (RT-PCR).

Definition of Terms

Adipokine. An adipose tissue secreted protein, most of which are hormones.

Adipose tissue. Connective tissue that stores fuel and is comprised of several cell types including pre-adipocytes, adipocytes, immune cells and endothelial cells as well as a connective matrix, and neural and vascular tissue (Berryman et al., 2011; Poulos et al.,

2010).

Adipose tissue depots. Distinct deposits of fat that can be localized under the skin, intra-abdominal, or interscapular.

Angiogenesis. The formation of blood vessels using the preexisting vasculature. 23

Biological pathway. Subgroup of nodes and interactions in a network with a specific energy flow (Tkačik & Bialek, 2009).

Biological network. Dynamic system of nodes of interacting nodes that can evolve with time in the species (Tkačik & Bialek, 2009).

Biological process. Group of molecular events with a specific beginning and end; they are not as specific as pathways, because they do not describe specific interactions or dynamics between molecules (Gene Ontology Consortium, 2014).

Bovine growth hormone transgenic (bGH) mice. Mice that express a bovine

GH transgene; thus, these mice have excess circulating GH.

cDNA. The complementary DNA of a mRNA fragment.

cDNA library. The collection of cDNAs of a cell or tissue.

cDNA short reads. cDNA pieces that are made from the fragmented mRNA during the cDNA library preparation.

Cufflinks. On-line tool formed by three programs: Cufflink, Cuffmerge, Cuffdiff.

This on-line tool allows one to calculate gene expression and differences in gene expression between samples.

CummeRbund. R package that helps with the visualization of the RNA-seq data.

Galaxy project. Open source web-based platform that contains the tools necessary for RNA-seq data analysis.

Growth hormone (GH). An anterior pituitary derived polypeptide hormone, which is a member of the class I cytokine hormone family. 24

Insulin-like growth factor 1 (IGF-1). A growth factor or hormone secreted by peripheral tissues (liver, bone, muscle, heart, kidney, and fat tissue) in response to GH as well as other stimuli.

Insulin resistance. Inability of the tissues to respond to insulin, resulting in impaired glucose uptake of the tissues.

Next generation sequencing (NGS). Sequencing methodologies that came after

Sanger sequencing.

Mouse genome mm10. Mouse genome sequenced by the Genome Reference

Consortium. Version released on December of 2011 by the University of California,

Santa Cruz.

mRNA. Acronym for messenger RNA, a type of RNA that carries the genomic information.

Network node. A term used to describe a constitutive component or connecting component of the network for RNA-seq data (Tkačik & Bialek, 2009); in this thesis, a network node is a gene.

PCR. Acronym for polymerase chain reaction, a molecular biology technique used to amplify small copies of DNA (Deepak et al., 2007).

RT-PCR. Acronym for real time PCR, is quantitative method that allows to detect and quantify the PCR amplification (Deepak et al., 2007).

R. Free software for statistical analysis and graphics.

RNA sequencing (RNA-seq). A technology that uses next generation sequencing

(NGS) approaches to sequence the transcriptome of a cell/tissue. 25

Transcriptome. Proportion of the genetic code that is transcribed into RNA molecules.

Top Hat. An on-line tool to map the RNA-seq reads to the mouse reference genome.

Chapter 2: Literature Review

Obesity is a growing problem in the U.S. population. In fact, by 2030, a 51% increase in obesity rates is expected. Currently, more than one-third of American adults are obese, and the latest estimates for U.S. medical costs for obesity-related problems is around $147 billion (CDC, 2012). Obesity is a risk factor for cardiovascular diseases, metabolic syndrome, heart disease, diabetes and some cancers as well as other conditions.

In this literature review, we will discuss the main tissue responsible for obesity or adipose tissue (AT), the importance of angiogenesis in AT expansion, and the role of growth hormone (GH) on obesity and angiogenesis. Further, the methodology used to study the transcriptome will be introduced.

Adipose Tissue (AT)

Obesity is due to excessive AT expansion. An increase in the world wide obesity epidemic has intensified the interest in better understanding this tissue. There are three types of AT: bone marrow AT, brown AT, and white AT (Casteilla, 2008). The focus of this thesis is white AT; thus, this literature review will highlight the unique features of this tissue. The “AT” abbreviation describes only white AT.

The primary function of AT is to store energy. That is, when excess calories are consumed, AT can store that extra energy in the form of triglycerides, which can eventually lead to AT expansion or obesity. AT’s capability to store energy is evolutionarily adaptive, as it allows an organism to survive in times of energy deprivation. When energy is needed, the stored triglycerides are hydrolyzed and converted into free fatty acids by a process called lipolysis; free fatty acids can then be 27 used by other organs as fuel. Excessive lipolysis results in AT regression (Feng, Zhang,

& Xu, 2013) through loss of the stored triglyceride. However, AT also has other functions, such as thermoregulation, mechanical protection, and a role in inflammatory processes and glucose homeostasis (Trayhurn & Beattie, 2001).

Although previously perceived as a simple storage tissue, AT is much more complex. One level of AT complexity is its distribution. That is, AT is distributed throughout the body with distinct localization called AT depots. These depots are metabolically distinct and are divided mainly in two groups: intra-abdominal and subcutaneous AT depots (Berryman et al., 2011), which will be more fully described later in this literature review. A second level of AT complexity is its structure and cellular composition. AT is an enormous organ supported by connective tissue; it has significant vascular and nerve innervation and is comprised of various cell types including adipocytes, preadipocytes, fibroblasts, endothelial cells and immune cells (Berryman et al., 2011; Cinti, 2012). White adipocytes contain one large cytoplasmic lipid droplet that occupies about 90% of the cell, and a nucleus that is situated to the side of the cell (Cinti,

2012). Even though adipocytes are not the major cell type in AT, they account for about

90% of the tissue volume (Lee, Wu, & Fried, 2013). A third level of complexity comes with the relatively recent understanding that AT is an endocrine organ. That is, in 1994, with the discovery of leptin (a hormone secreted by AT) (Zhang et al., 1994), AT was first recognized as an endocrine organ, capable of secreting hormones, cytokines, and chemokines that are important for energy and whole body homeostasis. Peptides that are secreted by AT are called adipokines. Distinct AT depots have different gene expression 28 patterns of adipokines, adipokine secretion, and adipogenic profile (Fain, Madan, Hiler,

Cheema, & Bahouth, 2004). Finally, and further adding to its complexity, AT is plastic, meaning that it is able to expand and regress throughout adult life. Furthermore, AT respond to environmental stimuli. For example, cells may infiltrate the tissue, the structure can be modified with alterations in the extracellular matrix, and adipocyte volume can fluctuate, depending on energy status. All of these variables change in a manner that is dependent on the depot location as will be discussed in more detail later in this review of literature. Overall, AT is a uniquely complex organ.

Types of AT depots. In mice, AT depots can be divided into groups depending on the definition used. Typically, AT depots are divided in two groups, including intra- abdominal and subcutaneous (SubQ) depots. However, intra-abdominal AT can be further divided in visceral (VISC) and non-VISC AT (Berryman et al., 2011). While both the VISC and non-VISC AT depots are located in the intra-abdominal region surrounding visceral organs, they are differentiated based on drainage of blood flow. That is, true

VISC depots drain into the portal vein whereas non-VISC depots drain into systemic circulation. The SubQ AT depots are located under the skin (Berryman et al., 2011; Cinti,

2012). In mice, there are two main groups of SubQ AT depots: the anterior and posterior subcutaneous depot. The anterior depot is found in the upper dorsal area at the level of the scapulae and is often referred to as the subscapular depot. This depot can be further subdivided into the interscapular, subscapular, axillary, and cervical AT depots. The posterior SubQ AT depot is located in the lower ventral part of the body. It is composed of three AT depots: dorso-lumbar, inguinal, and gluteal (Cinti, 2012); the inguinal depot 29 is the most common subcutaneous depot studied in mice because of its size and the ability to easily distinguish it from other SubQ depots.

There are four intra-abdominal depots that are called VISC and non-VISC AT

(see Figure 1). The non-VISC AT depots include the perirenal (AT surrounding the kidneys), retroperitoneal (AT behind the kidneys), and perigonadal (epididymal AT next to the testes in males and periovarian AT next to the ovaries in females) AT depots. The only true VISC AT depot in mice is the mesenteric AT, which lines the small intestines

(Cinti, 2005; Tran & Kahn, 2010).

Figure 1. Adipose tissue depots in mice. Shown are (i) Anterior white AT, (ii) Posterior white AT, (iii) VISC Mesenteric AT depot, (iv) Non-VISC retroperitoneal AT depot, (v) Non-VISC perirenal AT depot, and (vi) non-VISC perigonadal AT depot. From “Transplantation of Adipose Tissue and Stem Cells: Role in Metabolism and Disease,” by T. T. Tran and C. R. Kahn, 2010, Nature Review Endocrinology, 6, p. 20. Copyright 2010 by Nature Publishing Group. Reprinted with permission.

AT depot differences. AT depots are sometimes considered “mini-organs” as they significantly vary from depot to depot (Lee et al., 2013). Depots have differences in their cellular composition, microvasculature, metabolic characteristics, extracellular matrix, and adipokine profile (Lee et al., 2013). Furthermore, some researchers believe that intra-abdominal AT depots are more significantly responsible for the comorbidities seen in obesity (Wajchenberg, 2000). Even adipocyte size varies among depots. For example, rat epididymal (non-VISC) adipocytes are larger than inguinal SubQ adipocytes

(Deveaud, Beauvoit, Salin, Schaeffer, & Rigoulet, 2004). Adipocyte size is important since larger adipocytes have increased basal lipolysis and triglyceride synthesis; as a result, they are also less sensitive to insulin and catecholamines (Ibrahim, 2010; Lee et al., 2010). Also, in humans, omental AT depot (VISC AT) seems to have a lower response to the antilipolytic effects of insulin in vivo (Meek & Jensen, 1999). Thus, VISC

AT appears to lead to a more substantial FFA pool for hepatic uptake, which ultimately affects hepatic function. Interestingly, hepatic insulin resistance can be reversed by removing VISC fat (Barzilai et al., 1999), further suggesting a role specifically for VISC

AT in metabolic dysfunction.

The cellular composition of AT depots can also contribute to and help maintain depot differences. For example, preadipocyte differences exist among AT depots. In human AT cultures, it has been shown that there are mainly two types of preadipocytes that differ in their replication and differentiation capabilities (Tchkonia et al., 2004). That is, SubQ AT and mesenteric VISC ATs depots are mainly formed by the rapidly- replicating and differentiating preadipocyte type, while omental VISC AT has more of 31 the slowly replicating, differentiated type. This difference in preadipocyte composition could partially explain the number and size of adipocytes in various AT depots. There are many other examples of inherent cell differences among the depots. For this reason, it is important to investigate AT depots as if they are independent organs, because they are likely to have different characteristics and responses towards the same treatments.

AT as an endocrine organ. AT is now recognized as an important regulator of whole body homeostasis capable to secrete hormones that act in an autocrine/paracrine/endocrine manner (Zhang et al., 1994). These hormones are called adipokines (Mohamed-Ali, Pinkney, & Coppack, 1998). Adipocytes are not the only cells in AT that secrete adipokines; other cells such as endothelial, preadipocytes, and immune cells can also secrete select adipokines (Ouchi, Parker, Lugus, & Walsh, 2011). Leptin and adiponectin are two of the best studied adipokines and are secreted exclusively by adipocytes in AT. In contrast, other adipokines, such as those related to angiogenesis

(vascular endothelial growth factor [VEGF] and angiopoetin-2) and inflammation (tumor necrosis factor [TNFα], interleukin -6 [IL-6], and monocyte chemotactic protein-1[MCP-

1]), are secreted partially by the nonadipocyte cells present in AT (stromal vascular fraction) (Raucci et al., 2013). Depot differences in the expression levels of some adipokines and their receptors have been reported. Importantly, VISC AT has been shown to express higher levels of proinflammatory cytokines, which in turn can have an effect on AT inflammation, fibrosis, and insulin resistance (Berg & Scherer, 2005). A summary of some of the adipokines that are expressed in AT, their depot-specific expression level, and their biological role is provided in Table 1. 32

Table 1

List of Adipokines, Their Differential Expression Between AT Depots and Their Main Roles

Differential Gene expression Role

Adipose hormones/cytokines/chemokines Leptin om < sc Energy / immunity Adiponectin om ≤ sc Energy / anti-inflammatory RBP4 om > sc ? Angiotensinogen om > sc Blood pressure Angiotensin II TNF-α om > sc Proinflammatory IL-6 om > sc Proinflammatory IL-8 om > sc Proinflammatory IP-10 om ≤ sc Anti-inflammatory MCP-1 om > sc Chemotactic Macrophage inflammatory protein 1-α om > sc Proinflammatory Plasminogen activator inhibitor-1 om > sc Coagulation RANTES om > sc Proinflammatory GM-CSF om > sc Proinflammatory Resistin om > sc Proinflammatory Visfatin om ≈ sc Insulin signaling? Omentin om » sc Insulin signaling? TSP-1 om > sc Angiogenesis

Growth Factors VEGF om > sc Angiogenesis HGF om > sc Angiogenesis Fibroblast growth factors-1 and -9 om > sc ?

Extracellular matrix VIM om < sc ? SPARC (osteonectin) om < sc ? ANXA5 om < sc ? Note. Omental AT depot is referred to as “om” and subcutaneous as “sc.” Adapted from “Depot-Specific Biology of Adipose Tissues: Links to Fat Distribution and Metabolic Risk,” by M.-J. Lee & S. K. Fried, 2010, Adipose Tissue in Health and Disease, p. 292. Copyright 2010 by Wiley-VCH Verlag GmbH & Co. KGaA. Reprinted with permission. 33

Angiogenesis

Blood vessels are formed by endothelial cells, a basement membrane, and pericytes (Clapp, Thebault, Jeziorski, & Martínez De La Escalera, 2009). Blood vessel formation is crucial for the development of all tissues, including AT. In adult organs, new vasculature is formed due to sprouting of pre-existing vasculature; this process is called angiogenesis and is summarized in Figure 2 (Groothuis, 2005). Angiogenesis is seen in normal tissue development and growth but also in pathological processes like cancer.

Angiogenesis starts when hypoxia is present in the tissue, which up-regulates nitric oxide (NO) (Clapp et al., 2009). The hypoxia indicates a need for new blood vessels to restore the oxygen and nutrient delivery to the tissue. In the presence of NO, endothelial cells express proteases and proangiogenic factors, including the main proangiogenic factor VEGF-A. Collectively, this results in an enzymatic degradation of the capillary basement membrane, and endothelial cell proliferation takes place (Clapp et al., 2009). Next, a specialized endothelial cell called a tip cell, which has a high concentration of VEGF receptors and does not have a lumen nor does it proliferate, is localized at the tip of the capillary sprout. There, this specialized, elongated cell directs the sprouting of the endothelial cells through modulation of the extracellular matrix

(ECM) through the proangiogenic factors (VEGF-A). Endothelial cells follow the tip cell causing the sprout to grow. Vacuoles then develop, forming the lumen within the endothelial cells. When two or more tip cells fuse together, a continuous lumen is created that allows blood to flow. Adequate blood flow will bring oxygen to the tissue, down regulating the expression of VEGF-A and other proangiogenic factors. Finally, 34 antiangiogenic factors are expressed, and the pericytes and ECM stabilize the newly formed capillary (Adair, 2010). In general, the basic steps of angiogenesis are: enzymatic destabilization of the blood vessel, sprouting, branching between two or more sprouts, and stabilization of the new blood capillary (Clapp et al., 2009).

Figure 2. Steps involved in angiogenesis. a) Tissue hypoxia induces the production of nitric oxide (NO) and the expression of vascular endothelial growth factor (VEGF) and other proangiogenic factors. b) The-proangiogenic factors induce endothelial cell proliferation, and interact with proteases inducing the degradation of the capillary basement membrane, and deposition of new ECM that helps endothelial cells to migrate. c) Endothelial cells then migrate and proliferate to form tubules. The endothelial “tip cell”, located in the tip of the capillary sprout ai ded by proangiogenic molecules and chemoattractants, guide the sprouting of the new blood vessel through alterations to ECM. Two tip cells eventually meet and fuse forming a continuous lumen between the two new blood vessels. d) Maturation of the newly formed vessel is accompanied by increased expression of antiangiogenic factors, released as a result of the increased oxygen concentration within the tissue, and the proteolysis that took place in the angiogenic process. Adapted from “Molecular Regulation of Angiogenesis and Lymphangiogenesis,” by R. H. Adams and K. Alitalo, 2007, Nature Reviews Molecular Cell Biology, 8(6), p. 466. Copyright 2007 by Nature Publishing Group. Reprinted with permission.

AT vasculature. Blood capillaries are an important part of AT. Blood vessels are responsible for the delivery and release of nutrients, oxygen, lipids and adipokines. Even though little is known about AT vasculature, differences in vascularity can be found among and within AT depots. For example, SubQ AT tissue from lean subjects shows fewer capillaries than omental VISC AT (Gealekman et al., 2011). In addition, the epididymal AT depot of mice has a region that contains higher vascularity than the rest of the depot (Cho et al., 2007). Thus, differences in vasculature among depots are another factor that contributes to the complexity of this tissue.

Expansion and reduction of AT mass is dependent on AT vasculature. Insufficient vasculature leads to deficient circulation and hypoxia within AT (Tang et al., 2008).

Since some adipocyte progenitor cells arise from the mural cells that reside in the vasculature of adipose tissue (Dani & Billon, 2012), a short supply of blood vessels also results in reduction of adipocyte progenitors (Tang et al., 2008). Even though it is clear that the formation of the vasculature is very important for AT expansion, little is known about the origin of the endothelial cells required for the process of neovascularization.

Some studies have shown that endothelial progenitor cells (EPCs) are derived from the cells of the bone marrow that infiltrate into AT (Du et al., 2008; Tomiyama, 2008).

However, more studies are warranted for any definitive conclusion about their origin.

AT vasculature also has an important role in immunity and inflammation. For example, monocytes (macrophage precursors) use AT microcirculation to reach their targets; therefore, changes in vasculature can modulate immune cell migration (Curat et al., 2004; Heilbronn & Campbell, 2008). Furthermore, macrophages secrete pro- 36 inflammatory cytokines, which in turn act as proangiogenic factors (Rodrigues,

Matafome, & Seiça, 2013).

Finally, the lymphatic circulation has an important role in AT homeostasis, being important for the metabolism, storage and transport of lipids, as well as transport of immune cells. The lymphatic capillaries differ from blood capillaries in their gene expression and structure; they are formed by a tight network of lymphatic endothelial cells, are not surrounded by pericytes, and have a minimal basement membrane that is not continuous (Albrecht & Christofori, 2011). Their special structure allows them to have an open flow from and to the interstitial space and the endothelial cells, allowing for movement of particles as big as 1 micrometer (Baluk et al., 2007; Swartz, 2001). It is important to clarify that even though AT does not have lymphatic capillaries, AT is surrounded by lymph vessel and nodes. The physical proximity of these lymphoid structures may suggest an interaction between the AT vasculature and the lymph nodes/vessels (Pond, 2005).

AT angiogenesis. It is known that AT angiogenesis is necessary for AT expansion or obesity. It also has been suggested adipogenesis and angiogenesis are related processes that need each other to progress (Christiaens & Lijnen, 2010; Rupnick et al., 2002). As well as in other tissues, the main molecule in AT that is necessary for angiogenesis to occur is VEGF. Similarly, ECM remodeling must occur by the action of proteases, mainly plasminogen and metalloproteinases (Folkman, 2006).

It is important to note that when expansion of AT occurs, not only does angiogenesis increase but also levels of hypoxia and hypoxia inducible factor-1 alpha 37

(HIF-1a) increase that, in turn, lead to an increase in fibrosis, macrophage infiltration, and inflammatory molecules, such as IL-6, TNFα and monocyte chemotactic protein-1

(Halberg et al., 2009). Cytokines and adipokines present in AT can also modify AT vasculature by changing endothelial permeability and dysfunction. For example, leptin may increase NO production and up-regulate VEGF (Talavera-Adame et al., 2008),

TNFα (Chudek & Wiecek, 2006), and HIF-1a, which collectively elevates immune cell infiltration and induces AT fibrosis (Halberg et al., 2009). As a result, all of these molecules promote AT vasculature to grow. On the other hand, adiponectin (produced by the adipocytes in AT) action decreases endothelial cell migration and VEGF; IL-6 is secreted by the immune cells and decreases NO production in endothelial cells (Chudek

& Wiecek, 2006). Both of these hormones have a negative effect on blood vessel grow, so these molecules can have a positive or negative effect on the vasculature. Figure 3 shows some of the cytokines/adipokines that affect AT vasculature and their origin.

Figure 3. Angiogenesis regulation in adipose tissue. Preadipocytes and adipocytes, stromal cells, and inflammatory cells present in AT express angiogenic factors that contribute to the vascular remodeling of the tissue. The angiogenic factors expressed by each of these cell types are shown in the figure. Red arrows indicate antiangiogenic factors, while green arrows represent proangiogenic factors. The blue arrow indicates components of the matrix metalloproteinase system, which are proangiogenic aiding to remodel the ECM. From “Adipose Tissue Angiogenesis in Obesity,” by A. Y. Lemoine, S. Ledoux, and E. Larger, 2013, Thrombosis and Haemostasis, 110(4), p. 664. Copyright 2013 by Schattauer GmbH. Reprinted with permission.

Molecules that regulate angiogenesis. Since one of the main objectives of this thesis is to evaluate the angiogenic status of AT, it is important to introduce the molecules that modulate angiogenesis. Some of the adipokines related with angiogenesis are described in Table 2. Note that there are many anti- and proangiogenic factors, although they vary in potency and expression patterns. The entire list of molecules linked to angiogenesis that will be assessed in this thesis are summarized in Appendix D. 39

Table 2

Molecules Known to Alter Angiogenesis and Their Main Functions

Pro/Anti Molecule Function angiogenic Reference

Amphiregulin (Areg) Cell growth modulator Proangiogenic (Shoyab, Plowman, McDonald, Bradley, & Todaro, 1989) Betacellulin (Btc) Epidermal growth factor Proangiogenic (Yokoyama et al., 1995) Epidermal growth factor (Egf) Growth factor Proangiogenic (Stoscheck & King, 1986) Endoglin (Eng) Regulator of endothelial Proangiogenic (Koleva et al., 2006) cell adhesion and growth c-fos induced growth factor (also VEGF-D) (Figf) Growth factor Proangiogenic (Roskoski, 2007) Fibroblast growth factor 1 (Fgf1) Growth factor Proangiogenic (Cuevas et al., 1991) Fibroblast growth factor 2 (Fgf2) Growth factor Proangiogenic (Cuevas et al., 1991) Hepatocyte growth factor (Hgf) Growth factor Proangiogenic (Santos et al., 2013) Insulin-like growth factor 1 (Igf1) Growth factor Proangiogenic (Belardi, Gallagher, Novosyadlyy, & LeRoith, 2013) Transforming growth factor, beta 1 (Tgfb1) Anti-apoptogenic/Growth Proangiogenic (Samad, Pandey, & factor Loskutoff, 1998) Vascular endothelial growth factor (Vegfa) Growth factor Proangiogenic (Roskoski, 2007) Vascular endothelial growth factor (Vegfb) Growth factor Proangiogenic (Roskoski, 2007) Vascular endothelial growth factor (Vegfc) Growth factor Proangiogenic (Roskoski, 2007)

Table 2: Continued

Pro/Anti Molecule Function angiogenic Reference

Interleukin 1 beta (IL1b) Anti-inflammatory. Antiangiogenic (Trayhurn & Wood, 2004) Interleukin 6 (IL6) Immune cells activator Antiangiogenic (Vona-Davis & Rose, 2009) Interleukin 17A (IL17a) Interconnects myeloid and Antiangiogenic (Moran et al., 2011) lymphoid host defense Colony stimulating factor 3 (Csf3) Production and functional Proangiogenic (Bordbar, Malekzadeh, activation of neutrophils Ardekani, Doroudchi, & Ghaderi, 2012) Chemokine (C-C motif) ligand 2 (Ccl2) Chemotactic Proangiogenic (Kolak et al., 2012) Chemokine (C-C motif) ligand 3 (Ccl3) Chemotactic Proangiogenic (Kolak et al., 2012) Chemokine (C-X-C motif) ligand 1 (Cxcl1) Chemotactic Proangiogenic (Wagner et al., 2012) Chemokine (C-X-C motif) ligand 2 (Cxcl2) Chemotactic Proangiogenic (Rouault et al., 2013) Fas ligand (Fasl) Apoptotic Proangiogenic (Ledoux et al., 2008) Tumor necrosis factor (Tnf) Apoptotic Proangiogenic (Trayhurn & Wood, 2004) Tissue inhibitor of metalloproteinase 1 (Timp1) Inhibition of active MMPs Antiangiogenic (Wagner et al., 2012) Tissue inhibitor of metalloprotease-2 (Timp2) Inhibition of active MMPs Antiangiogenic (van Hinsbergh & Koolwijk, 2008) Tissue inhibitor of metalloprotease-3 (Timp3) Inhibition of active MMPs Antiangiogenic (van Hinsbergh & Koolwijk, 2008) Tissue inhibitor of metalloprotease-4 (Timp4) Inhibition of active MMPs Antiangiogenic (van Hinsbergh & Koolwijk, 2008)

Table 2: Continued

Pro/Anti Molecule Function angiogenic Reference

Angiogenin (Ang) Angiogenesis Proangiogenic (Cao, 2007) Angiopoietin-2 (Angpt2) Survival factor in stressed Proangiogenic (Clapp et al., 2009) endothelial cells Endothelin 1 (Edn1) Vasoconstrictor Proangiogenic (Kurki et al., 2012) Hypoxia inducible factor 1, alpha subunit (Hif1a) Mediators of the cellular Proangiogenic (Roskoski, 2007) oxygen-signaling pathway Leptin (Lep) Satiety Proangiogenic (Vona-Davis & Rose, 2009) Plasminogen (Plg) Conversion of plasminogen Proangiogenic (Cao, 2007) to plasmin

VEGFs are among the most potent of angiogenic proteins. These molecules are members of the platelet-derived growth factor (PDGF)/VEGF growth factor cysteine- knot (eight cysteine residues) superfamily of hormones. This family of hormones includes placental growth factor (PLGF), VEGF-B, VEGF-C, and VEGF-D and the most well known, VEGF-A. VEGFs actions are mediated by receptors that are part of the receptors tyrosine kinases (RTKs) family: VEGFR-1, VEGFR-2 and VEGFR-3 (Holmes

& Zachary, 2005). The mouse VEGF-A gene is located in the chromosome 17 and consists of 8 exons and 7 introns (Shima et al., 1996). The VEGF-A gene generates the main VEGF-A protein, which has123 amino acids (aa) (Ng, Rohan, Sunday, Demello, &

D'Amore, 2001); however, through alternative precursor messenger RNA (mRNA) splicing, 9 other protein isoforms of varying lengths (146, 190, 214, 69, 113, 137, 324, ,

368, and 392 amino acids) have been identified (National Center of Biotechnology

Information [NCBI], 2014). VEGF-A encodes a glycosylated mitogen homodimer that specifically acts on endothelial cells augmenting vascular permeability, angiogenesis, lymphangiogenesis, vasculogenesis, endothelial cell growth, and cell migration, while inhibiting apoptosis (Neufeld, Cohen, Gengrinovitch, & Poltorak, 1999).

VEGFs are expressed by adipocytes and the stromal vascular fraction of AT.

VEGF-A is expressed in AT, with omental AT expressing higher levels compared to

SubQ AT (Zhang et al., 1997). VEGF-B is also expressed by adipocytes and activates plasminogen helping with ECM degradation. VEGF-C and VEGF- D increase lymphangiogenesis (Lemoine, Ledoux, & Larger, 2013). Even though adipose tissue expresses VEGFR-1 and VEGFR-2, only blocking VEGFR-2 decreases vascularity and 43

AT mass (Sorensen et al., 2009). Furthermore, and as explained in subsequent sections, over-expression of VEGF-A during expansion of AT increases insulin sensitivity, promotes “browning” of white AT (the change between white AT to brown AT) and has an anti-inflammatory effect due to a chemotaxic activity towards M2 macrophages (Elias,

Franckhauser, & Bosch, 2013). Figure 4 summarizes the types of VEGFs, their receptors, and their main functions.

Figure 4. Vascular endothelial growth factors (VEGF’s), its receptors and physiological actions. Each of the VEGF-R’s have specific ligands and are expressed in specific cells. VEGF-R1 is expressed on macrophages, monocytes, and the vascular endothelium. VEGF-R1 binds to VEGF-A,-B, and platelet-derived growth factor (PLGF), increasing vasculogenesis, angiogenesis, and monocyte and macrophage migration to AT tissue. Furthermore, VEGF-R1 activates VEGF-R2. VEGF-R2 is expressed on both vascular and lymphatic endothelium and is activated by VEGF-A and-E. VEGF-R2 activation increases vasculogenesis and angiogenesis. VEGF-R3 is expressed on lymphatic endothelium and blood vessels (not shown), and it binds to VEGF-C and -D, stimulating lymphanogenesis and angiogenesis. From “Adipose Tissue Angiogenesis in Obesity,” by A. Y. Lemoine, S. Ledoux, and E. Larger, 2013, Thrombosis and Haemostasis, 110(4), p. 664. Copyright 2013 by Schattauer GmbH. Reprinted with permission.

Obesity and AT angiogenesis. Obesity is characterized by AT hypertrophy

(increase in adipocyte size) and hyperplasia (increase in adipocyte number) (Jo et al.,

2009), resulting in a net increase in AT mass. AT hypertrophy, specifically in intra- abdominal AT, is partially responsible for AT endocrine dysfunction (Adamczak &

Wiecek, 2013), which in turn drives endothelial dysfunction, fibrosis, dyslipidemia, 45 hyperglycemia, insulin resistance, and atherosclerosis progression (Zahorska-

Markiewicz, 2006).

It is thought that expansion of AT can occur in a “healthy” or in an “unhealthy” manner (Rutkowski, Davis, & Scherer, 2009). One of the factors that determines the health of AT expansion is the angiogeneic state of the tissue. If AT increases its mass with proper angiogenesis and vascularization, fibrosis and immune cell infiltration may decrease, resulting in a “healthy” expansion of AT. On the contrary, if there are not enough capillaries to supply proper oxygen and nutrients to the growing tissue, immune cell infiltration and fibrosis may occur, leading to “unhealthy” AT expansion (El-Ftesi,

Longaker, & Gurtner, 2009; Rutkowski et al., 2009; Spencer et al., 2011).

Different factors can influence the angiogeneic capacity of AT. For example, increasing age can diminish the expression levels of proangiogenic factors and increase insulin resistance in AT from leptin receptor deficient mice (db/db mice) (El-Ftesi et al.,

2009). AT localization can also have a great influence in the AT angiogenic capacity, discussed below.

AT depot differences in angiogenesis. Few studies have assessed depot differences in angiogenesis with most of these studies focused on obese mouse lines. In general, murine obesity increases the expression levels of proangiogenic factors when compared with lean mice. Specifically, SubQ and gonadal AT depots from mice with defective leptin (ob/ob mice) and mice on a high-fat diet (HFD) have greater blood vessel volume and blood content than genetically normal mice fed a standard fat diet.

Furthermore, SubQ AT from these obese mice express more proangiogenic factors than 46 gonadal intra-abdominal AT (Voros, 2005). Also, obese mice on a HFD show increased expression of proangiogenic factors in intra-abdominal fat depots than lean mice (Kurki et al., 2012; Voros, 2005).

Several clinical studies have also assessed depot differences in angiogenesis.

SubQ AT in humans, as in mice, has greater mRNA expression levels of proangiogenic factors, but the opposite tendency regarding AT vasculature. In humans, abdominal SubQ

AT has higher angiogenic capacity than omental VISC AT, but this capacity is reduced with morbid obesity and insulin resistance (Gealekman et al., 2011). In general, these studies show that both human and mouse studies show an increased blood vessel size and density in SubQ AT than in some of the intra-abdominal AT depots. Also, obesity correlates with higher expression of proangiogenic factors. A discrepancy between human and mouse studies is the vascular density found in obese subjects. That is, while human data show an increase in blood vessel density that does not correlate with obesity and insulin resistance, obese mice present higher blood vessel volume than lean mice.

Angiogenesis control as a therapeutic approach for obesity. Angiogenesis is necessary for AT expansion (Tang et al., 2008). Thus, it is not surprising that blocking angiogenesis may be a therapeutic target to reduce or prevent obesity. To that end, it has been shown that inhibition of AT angiogenesis leads to inhibition of white AT expansion and obesity in mice (White, Acton, & Considine, 2012). Angiogenic inhibitors, such as angiostatin (amino-terminal fragment of plasminogen), endostatin (cleaved product of the carboxyl-terminal domain of collagen XVIII) and TNP-470 (antibiotic described below), prevent or reduce obesity in ob/ob mice and mice treated with HFD (Rupnick et al., 2002; 47

White et al., 2012). TNP-470 is an antibiotic isolated from the fungus Aspergillus fumigatus fresenius, which binds irreversibly and inactivates methionine animopeptidase-

2 (MetAP2), causing endothelial cell cycle arrest. TNP-470 not only decreases body weight in mice fed a HFD, it also acts in the brain to diminish food intake and increase energy expenditure (White et al., 2012). Moreover, mice increase their body weight when treatment with TNP-470 is stopped. Mice treated several times for obesity with this compound do not show drug resistance. This feature makes researchers very optimistic about the potential use of this drug for obesity (Cao, 2010).

A new procedure directed at endothelial cells in AT has been developed and has been tested in obese mice, rats and monkeys. This treatment consists of a ligand-directed peptidomimetic or adipotide, a molecule that destroys the vasculature by targeting the endothelium of AT (Barnhart et al., 2011; Kim, Woods, & Seeley, 2010; Kolonin, Saha,

Chan, Pasqualini, & Arap, 2004). The adipotide consists of a chimeric peptide that targets specifically AT and a proapoptotic peptide. Adipotide targets the membrane-associated protein called prohibitin found in endothelial cells. This protein regulates cell survival and growth through the interaction with the mitochondria and cell cycle proteins

(Kolonin et al., 2004). The adipotide induces apoptosis of AT blood vessels and results in decreased body weight, endothelial cell proliferation, and energy intake. It also improves insulin sensitivity, energy expenditure, and lipid metabolism (Barnhart et al., 2011; Kim et al., 2010; Kolonin et al., 2004). While antiangiogenic agents appear to be beneficial for obesity treatment, angiogenesis is required for the “healthy” expansion of AT. Lack of

AT vasculature can lead to lipotoxicity due to ectopic fat accumulation (McQuaid et al., 48

2011). For instance, while AT-specific VEGF knockout mice showed reduced vascularity and AT mass but are insulin resistant and have increased inflammation (Sung et al.,

2013), ablation of VEGF-A in AT in ob/ob mice show impaired vascularity, reduction of

AT mass, and increased insulin sensitivity. Taken together, these results indicate that proangiogenic activity is beneficial in the case of AT expansion while antiangiogenic activity is favorable when treating pre-existing obesity (Sun et al., 2012).

Growth Hormone (GH)

GH, as the name implies, is a hormone intimately associated with growth. As mentioned above, this hormone, also called somatotropin, plays a key role in the plasticity and homeostasis of AT. In addition to its action on AT, GH also has other effects in the human body, including nutrient metabolism.

GH is member of the somatotropin/prolactin family of hormones, which includes

GH and the placental lactogens and prolactin-related proteins. The members of this family have a similar tertiary structure, and their functions may overlap; in fact, it is thought that this family evolved from a common ancestral gene (Gahete et al., 2009). The

GH gene cluster in humans is located on the long arm of chromosome 17, and it is comprised of 5 genes that apparently are derived from gene duplication. These genes include: growth hormone 1 (GH-1), chorionic somatomammotropin pseudogene (CSL), chorionic somatomammotropin gene (CS1), growth hormone 2 (GH-2), and chorionic somatomammotropin hormone 2 (CS2). GH-1 is expressed by the somatotrophs in the anterior pituitary, the function of CSL is unknown, and CS1, GH-2 and CS2 are expressed in the placenta. In mouse, the GH-1 (called GH thoughout this thesis) gene is 49 the most abundant and results in a propeptide protein that is 216 aa with a signal peptide of 26 aa. This signal peptide is cleaved during the process of secretion, leading to a 191 aa product with 2 disulfide bridges (Rougeon, 1979) and resulting in a final molecular weight of 22 kDa (Baumann, 2009). Similarly, bovine GH is composed by 217 aa, and its signal peptide also has 26 aa (Heidari, Azari, Hasani, Khanahmadi, & Zerehdaran, 2012).

Thus, GH genes in various species are highly conserved.

GH receptor (GHR) activation. GH is secreted by the anterior pituitary and travels in the bloodstream bound to GH binding protein (GHBP) (Blüher, Kratzsch, &

Kiess, 2005). GH exerts its activity upon binding to the GHR, which is a 620 aa cytokine class 1 receptor located in the plasma membrane as a dimer (Brooks & Waters, 2010).

Each GHR monomer has two domains: the extracellular domain and the transmembrane domain. Each GHR monomer binds constitutively to one Janus tyrosine Kinase-2 (JAK2)

(Waters & Brooks, 2011). The extracellular domain of the dimer has two sites for GH binding: “site 1” is on one monomer with higher affinity for GH binding and “site 2” is on the other monomer with lower affinity GH binding (Birzniece, Sata, & Ho, 2008). A

GH monomer, containing two GHR binding sites, will interact with one monomer of the

GHR dimer. When GH binds to both sites in GHR, the receptor undergoes a conformational change in which one of the monomers rotates 25 degrees and is elevated from the cell membrane by 8 angstrom respective to the other receptor (Brooks &

Waters, 2010). The conformational change permits each intracellular JAK2 to interact with each other, allowing tyrosine phosphorylation to occur (Brooks & Waters, 2010). 50

Phosphorylation of JAK2 is the first step for the intracellular signaling cascade induced by GH binding to its receptor.

GHR activates several pathways including the Janus Kinase-Signal Transducer and Activator of Transcription (JAK-STATs), mitogen-activated protein kinases

(MAPK), and phosphatase trioxide PI3-kinase pathway (see Figure 5). GHR mediates signal transduction to ultimately activate transcription factors and modulate gene expression of a variety of genes (Birzniece et al., 2008).

Figure 5. GH signaling pathways. GH binding to the GHR dimer producing a conformational change of the GHR dimer. This in turn activates the Janus Kinase - Signal Transducer and Activator of Transcription (JAK-STATs) pathway, the mitogen- activated protein kinases (MAPK), and the phosphatase trioxide PI3-kinase pathway (PI3K). Activation of any of these pathways leads to transcription factor activation, which in turn modulate the expression of genes. 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. 519. Copyright 2010 by Nature Publishing Group. Reprinted with permission.

Regulation of GH expression and physiological actions. GH is secreted by somatotrophs, the most abundant cell type of the anterior pituitary gland. GH’s main regulators of secretion are somatostatin or somatotropin release-inhibiting factor (SRIF) and growth hormone release hormone (GHRH), both of which are secreted by the hypothalamus from the periventricular and arcuate nuclei, respectively (see Figure 6).

GHRH stimulates and somatostatin inhibits GH secretion (Goodman, 2009). Even though the two main regulators of GH secretion are GHRH and somatostatin, other hormones including ghrelin, leptin, and insulin growth factor-1 (IGF-1) help to control GH release.

Figure 6. Regulation of GH secretion and GH’s main targets. GH secretion by the anterior pituitary is modulated mainly by two hormones produced in the hypothalamus: somatostatin and GH Released Hormone (GHRH). Somatostatin decreases GH release, while GHRH stimulates GH release. Other factors such as stress, sleep, and exercise can also increase the production of GH via GHRH. GH is secreted in a pulsatile manner into the circulation reaching its main target tissues (liver, muscle, adipose, and bone). At these target tissues, GH binds to the GHR dimer, causing the activation of a signaling cascade that ultimately causes the expression of insulin-like growth factor 1 (IGF-1) among other genes. IGF-1 can then inhibit GHRH and GH release. From “Growth Hormone (GH) and the Cardiovascular System: Studies in Bovine GH Transgenic and Inducible, Cardiac- specific GH Receptor Gene Disrupted Mice,” by A. Jara, OhioLink. Copyright 2014 by A. Jara. Reprinted with permission.

After the hormone is secreted into the bloodstream, it binds to the GHR dimer on target tissues, activating a signaling cascade, which ultimately is responsible for GH’s actions on tissues. The outcomes of GH-induced intracellular signaling are diverse. GH regulates linear postnatal growth, and is also involved in lipid, carbohydrate, nitrogen, 53 and mineral metabolism. GH also possesses diabetogenic or anti-insulin action. GHR is found on almost all cells/tissue with its main targets being liver, AT, bone, and muscle.

Because GH signaling results in significant increase of IGF-1 expression and secretion,

GH exerts its biological effects either directly or indirectly via IGF-1 (Kopchick &

Andry, 2000). Most of the circulating IGF-1 is released by the liver, but other tissues also contribute to the IGF-1 levels in circulation or have paracrine/autocrine IGF-1 effects.

Overall, GH and IGF-1 promote nitrogen retention in most tissues, and when energy is scarce, GH promotes the increased use of lipids instead of consumption of carbohydrates and protein to obtain the required energy (Moller & Jorgensen, 2009). Thus, GH in AT promotes lipolysis and reduces AT mass, while in muscle GH and IGF-1 act as an anabolic molecule, increasing protein synthesis and decreasing protein breakdown

(Mauras & Haymond, 2005). Thus, GH exerts both anabolic and catabolic actions depending on the tissue that it is affecting.

GH action on AT. GH has profound effects on AT. GH dramatically alters AT mass. For example, bGH mice and acromegalic patients with elevated circulating GH levels show a decreased fat mass when compared to subjects with normal GH levels

(Berryman et al., 2004; O'Sullivan, Kelly, Hoffman, Freund, & Ho, 1994). On the other hand, dwarf patients with Laron syndrome and GHR knockout mice with decreased GH signaling are obese and insulin sensitive (Guevara-Aguirre et al., 2011; List et al., 2010).

It appears that the main mechanisms responsible for the reduction of AT mass in the presence of GH is an increase in the adipocyte lipolysis and a decrease in the triglycerides via inhibition of lipoprotein lipase activity (Bluher, Kratzsch, & Kiess, 54

2005) as well as other lipogenic enzymes (Flint, Binart, Kopchick, & Kelly, 2003). GH also appears to influence preadipocyte differentiation, although the impact varies according to the model system used (Bluher et al., 2005). Regarding adipokine secretion, high GH levels result in a decrease in leptin levels and adiponectin levels (Silha et al.,

2003), while low GH action increases both leptin and adiponectin levels (Joaquin et al.,

2008).

bGH mouse model for the study of excess of GH. Excess GH action in humans can lead to gigantism and acromegaly (Christiansen et al., 1990). To study these conditions using a mouse model system as well as a means to better understand the physiological actions of GH, various GH transgenic mice have been created. In this thesis, I used a transgenic mouse model for the study of GH excess, called bovine GH or bGH mouse. Details of this mouse line are presented in this section.

bGH mice produce endogenous bGH prior to birth. bGH mice are produced by injecting bGH DNA into the embryonic male pronucleus (Berryman et al., 2004). It is important to note that since bovine GH has a high homology with mouse GH and can interact with mouse GH receptor, excessive bovine GH is functional in mice, resulting in a giant transgenic mouse.

bGH mice have a phenotype that is similar to acromegalic patients (Katznelson,

2009). These phenotypic characteristics includes increased GH and IGF-1 plasma levels

(Dominici, Cifone, Bartke, & Turyn, 1999) and decreased lifespan (Orme, McNally,

Cartwright, & Belchetz, 1998b). High levels of GH and IGF-1 levels have been implicated in contributing to other conditions including increasing the risk of select 55 cancers (Orme et al., 1998b), renal diseases (Yang et al., 1993), heart failure (Munoz et al., 2014), and retinal dysfunction (Martin, List, Kopchick, Sauve, & Harvey, 2011).

Furthermore, when compared to wild type (WT) animals, bGH mice present high GHR and GHBP expression levels (Sotelo et al., 1995), as well as increased body size, lean mass, and decreased body fat (Palmer et al., 2009). Since GH is a diabetogenic molecule, relatively young bGH mice present alterations in their glucose and lipid metabolism, including decreased insulin sensitivity, but normal insulin plasma levels (Balbis et al.,

1992). High total cholesterol, HDL, and LDL levels, and decreased VLDL level have also been reported (Olsson et al., 2005). In conclusion, bGH mice are a good system to study the effects of GH excess in vivo, because the phenotypic characteristics of these mice resemble the phenotype of humans with acromegaly. Figure 7 illustrates the mouse models used in this thesis and their main phenotypic differences.

Figure 7. Mouse lines with normal and high growth hormone (GH) action. WT mice are normal mice, with normal GH levels, body composition and lifespan. bGH mice have been genetically engineered to produce high GH levels and thus have high GH action. They have decreased adiposity and lifespan. Adapted from: “The GH/IGF-1 Axis in Ageing and Longevity,” by R. K. Junnila, E. O. List, D. E. Berryman, J. W. Murrey, and J. J. Kopchick, 2013, Nature Review Endocrinology, 9(6), p. 371. Copyright 2013 by Nature Publishing Group. Reprinted with permission.

AT specific depot differences in response to GH. AT depots do not respond equally to GH action. Specifically, the SubQ depot appears to be the AT depot with the greatest response to GH (Berryman et al., 2010). For example, mice with impaired GH signaling due to GHR gene disruption show a preferential accumulation of SubQ AT

(Berryman et al., 2010). Likewise, 6-month-old bGH mice have the most dramatic reduction in the SubQ depot (Ding, Sackmann-Sala, & Kopchick, 2013). Depot differences besides mass also exist. For example, all AT depots in bGH mice have 57 increased collagen content and decreased adipocyte size when compared to WT controls

(Householder, 2013); however, the SubQ AT depot has more prominent collagen deposition than other AT depots (Householder, 2013). Regarding AT immune cell infiltration, bGH mice show an increase in leukocytes in the mesenteric AT depot and a decrease in the epididymal depot (Harshman, 2012). This same study shows macrophage cell infiltration, especially M2 macrophages, is higher in the SubQ and mesenteric AT depots of bGH mice. Overall, the depot differences in bGH mice require investigators to examine multiple depots.

Ribonucleic Acid (RNA)

RNA is a macromolecule composed of nucleic acids found in all living cells and is one of the first polymers that transmits information within the cells (Dworkin,

Lazcano, & Miller, 2003). During the 1950s and the 1960s, RNA was seen only as molecules that help with the protein synthesis process. The RNA that is responsible for carrying the DNA information is called messenger RNA or mRNA (Brenner, Jacob, &

Meselson, 1961), and the RNA that works in conjunction with the ribosomes to translate the mRNA into proteins is called transfer RNA or tRNA (Hoagland, Stephenson, Scott,

Hecht, & Zamecnik, 1958). Another RNA that helps aid with the protein synthesis process is ribosomal RNA (rRNA), which is a component of the ribosomes (Scherrer,

2003). In 1961, the Nobel Laureate Francis Crick conceived the “central dogma of molecular biology”; this dogma states that the genetic information of cells flows in one direction from DNA to RNA, and from RNA to protein (Dworkin et al., 2003). In 1975,

Howard Martin Temin, who also received the Nobel Prize, discovered reverse 58 transcriptase (Mizutani & Temin, 1975). This modified the “central dogma of molecular biology” by showing that RNA could be converted back into DNA. Thus, the flow from

DNA to RNA was not unidirectional. During these earlier years, RNA was characterized as molecules that only aid with the translation of the DNA information into proteins, while the proteins were seen as the molecules that carried out all the functions and catalyzed cellular processes (Vogel, Thelander, Wagner, Eckstein, & Hadlington, 2010).

The idea that RNA only acted in protein synthesis started to change in the late 1970s and early 1980s. At this time, RNA was discovered to have a catalytic function (Guerrier-

Takada, Gardiner, Marsh, Pace, & Altman, 1983), and other findings, such as precursor mRNA splicing and alternative splicing (Berget, Moore, & Sharp, 1977) and the discovery of different types of RNAs (small RNAs, micro RNAs, etc.) showed a further active role of these molecules in the cell (Vogel et al., 2010). Table 3 summarizes some of the important types of RNA present in cells and their functions.

The proportion of the genetic code that is transcribed into various RNA molecules is called the transcriptome (Adams, 2008). A transcriptome is more complex than the genome (or the entire DNA sequence of an organism) because there can be many transcripts from the same DNA. Also, the transcriptome changes depending on many factors. The study of the transcriptome can give information about specific processes that are happening in a single cell or in a specific tissue and can alter depending on specific stimuli or manipulations.

Table 3

Types of RNAs and Their Functions

Type of RNA Function Reference

Messenger RNA (mRNA) Carries the genomic (Lodish H, 2000) information to produce proteins. Transfer RNA (tRNA) Link mRNA with the (Lodish H, 2000) aminoacids in the translation process. Ribosomal RNA (rRNA) Part of the structure of the (Lodish H, 2000) ribosome. Small nuclear RNA (snRNA) Aids with the splicing events (Clancy, 2008) during transcription.

Small nucleolar RNA (snoRNA) Methylation of RNAs. (Clancy, 2008)

Methods to analyze RNA. There are many methods to assess RNA levels in a cell. Table 4 gives a basic overview of four commonly used techniques to measure and compare gene expression between experimental samples and the advantages and disadvantages of each. These methods include northern blot, real time RT-PCR, microarrays, and RNA sequencing (RNA-seq). It is important to clarify that RNA-seq is used in this thesis; thus, a more thorough description of this method is provided in the subsequent section. 60

Table 4

Commonly Used Techniques to Measure Gene Expression Levels

Method Basis of methodology Advantages Disadvantages References

Northern RNA hybridized to a Measures relative amount of Difficult to optimize and (Alwine, Kemp, & blotting labeled probe. mRNA and splicing variants standardize the hybridization Stark, 1977) conditions for each specific (Reue, 1998) probe. Time consuming. Real Time Simultaneous Fast and sensitive technique. High cost of the materials. (Bohm-Hofstatter, RT-PCR amplification and Requires low starting RNA Risk of false positives due to Tschernutter, & quantification of a material. RNA contamination, pipetting Kunert, 2010; specific DNA fragment, variation, and inaccurate Higuchi, assuming a specific PCR assumption of amplification Dollinger, Walsh, amplification efficiency. efficiencies. & Griffith, 1992) Microarray Hybridization of a RNA Highly sensitive. It is possible to Genome of the experimental (Mooney et al., to a very large set of test several genes or the entire organism must be known to be 2013) oligonucleotide probes, genome at the same time, as well as able to predesign the probes. which are attached to a compare biological samples Cross-hybridization may solid support. simultaneously. occur. Difficult analysis of the data. Even higher cost. RNA-seq Second generation Measures all the RNA being Difficult data analysis. There (Costa, Angelini, sequencing technology. expressed at a specific time in a aren't established tools or De Feis, & biological sample. It is not parameters to analyze the data. Ciccodicola, 2010) necessary to know genes of interest Bias due to multireads and beforehand. It can detect new spliced junctions. Large classes of RNA, SNPs and amounts of data. Highest cost. alternative splice variants.

RNA-seq experiments. As previously mentioned, RNA-seq is a methodology used to investigate the transcriptome of a cell or a tissue (Trapnell et al., 2012). Even though there are several techniques used to measure and compare gene expression, RNA- seq is becoming the preferred technique for gene expression measurement because it can give information simultaneously of all the RNA present in a cell or tissue of interest

(Auer & Doerge, 2010). This section and the subsequent sections will explain the general workflow that any RNA-seq experiment must follow.

Preparation of cDNA for RNA-seq. The first step of an RNA-seq experiment is the cDNA library preparation of the sample or samples of interest (in our case AT from wild-type and bGH mice). The cDNA library refers to the complementary DNA (cDNA) of a specific group of RNAs. The first step for cDNA library preparation is to isolate the total RNA or messenger RNA (mRNA) from the preserved tissue (Chen & Duan, 2011).

This can be done by either using kits that allow the direct isolation of mRNA, or by isolating the total RNA first, followed by mRNA extraction from the total RNA. For this step, it is important to take into account that RNA is vulnerable to RNAses as these enzymes are usually present in the environment and even the tissue itself. Thus, it is essential to extract the RNA in surfaces and with materials that have been previously treated with enzymes that degrade RNAses (Gayral et al., 2011).

The extracted mRNA is then fragmented to sizes that range from 35 nucleotides to several hundred, depending on the sample type and sequencer machine (Martin &

Wang, 2011). Reverse transcription is made to the fragmented mRNA to obtain cDNA.

Samples are then immobilized and ligated to adaptors, which will help with the 62 sequencing process. Finally, cDNA is amplified through PCR to obtain the cDNA libraries (Metzker, 2010). Figure 8 summarizes the cDNA library preparation process.

Figure 8. cDNA library preparation before sequencing. mRNA is isolated from total RNA, then the mRNA samples are fragmented into pieces of ~100 or 200 bp. cDNA whole transcriptome library preparation involves cDNA synthesis and purification of the fragmented mRNA, followed by the ligation of the cDNA fragments to adaptors. The final steps of the cDNA library are the amplification of the cDNAs through PCR and the purification and quality checking of the cDNA fragments. From “A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq,” by L. Wang, Y. Si, L. K. Dedow, Y. Shao, P. Liu, & T. P. Brutnell, 2011, PLoS One, 6(10):e26426. Copyright 2011 by Elsevier. Reprinted with permission.

As shown in Figure 8, it is important to evaluate concentration and integrity of the mRNA and cDNA, as well as smear analysis after the mRNA fragmentation to ensure appropriate sample preparation and management (Martin & Wang, 2011). After the cDNA library is prepared, the RNA-seq experiment can be performed

Next generation sequencing (NGS). RNA-seq is a methodology that uses the

NGS technology to determine the nucleotide position of a RNA molecule (Grada &

Weinbrecht, 2013). First generation sequencing technology was the Sanger method or the chain-termination method introduced in 1977 (Sanger, Nicklen, & Coulson, 1977). All sequencing methodologies that came after Sanger sequencing are called “NGS methods”

(Pareek, Smoczynski, & Tretyn, 2011). In 2005, Life Sciences presented the first NGS method, which was later validated with the sequencing of Micoplasma genitalia genome, showing a 99.9% sequencing accuracy (Margulies et al., 2005). NGS methods are being constantly developed due to the demand for sequencing methods that are faster and cheaper than the first generation sequencing technology. NGS allows one to sequence different nucleotide fragments at the same time (Grada & Weinbrecht, 2013). NGS is used for DNA sequencing or RNA-seq (Pareek et al., 2011). RNA-seq helps to analyze the connections between the RNA content of a specific sample by comparing all the RNA molecules present in different tissues or cells; in other words, RNA-seq allows for transcriptome analysis (Grada & Weinbrecht, 2013).

Many companies are developing new NGS methods (Glenn, 2011). The particular method used in this thesis is Ion-Semiconductor Sequencing. Table 5 provides a quick comparison of this and other NGS methods. 64

Table 5

Commonly Used NGS Methods

Method Basis of methodology Company Reference

Ion Changes in pH of the Life (Liu et al., 2012; Semiconductor solution due to positively Technologies Merriman & Sequencing charged hydrogen ions Rothberg, 2012) released by the addition of the proper nucleotide to the growing cDNA template that is being sequenced. Sequencing by Alternative of labeled Life (Liu et al., 2012; Oligonucleotide probes that bind to the Technologies Valouev et al., Ligation and growing cDNA template 2008) Detection that is being sequenced. (SOLiD) Cyclic reversible Binding of a Illumina (Bentley et al., termination complementary 2008; Liu et al., fluorescent modified 2014) nucleotides to the strand that is being sequenced.

Pyrosequencing The emission of light that Roche (Liu et al., 2012; is ultimately activated by Margulies et al., the PPi released by the 2005) addition of the proper nucleotide to the growing cDNA template that is being sequenced.

RNA-seq analysis workflow. In this section, I will describe in a very simple manner the workflow for RNA-seq analysis when comparing the transcriptome profiles of two biological samples. Because of the large amount of information, RNA-seq data require significant mathematical and statistical algorithms for analysis that fortunately are 65 available in several software programs. The intention of this section is not to explain the mathematical bases of the programs, but rather to explain the steps that must be followed to analyze data from a RNA-seq experiment.

The RNA-seq technique will ultimately produce “Fastq” text files of the data generated. These files will provide four pieces of information for each unique cDNA sequence including: two pieces provide identifier information for each read, a third provides the DNA sequence, and a fourth provides a Phred quality score for each base in the read. The quality score for each base is defined by the Qphred = ‐10 log10(p), where p is the probability of a base being labeled wrong. The Qphred can have values between ‐

5 to 40. A Qphred of 20 means that there is only 1% probability that the base is incorrect

(Cock, Fields, Goto, Heuer, & Rice, 2010).

The next step with the analysis and manipulation of the data is to trim the reads.

Usually base pairs that are in the 3’ and 5’ends of the reads are poorly sequenced, hence adding noise to the sequences. Thus, it is also important to trim the adaptors from the short reads, so only adaptor-free-sequences are manipulated in the downstream processing of the data. After the trimming process, additional features such as GC content

(~40%), Phred quality score, and fractions of duplicate reads (30-50%) must be evaluated

(Blankenberg et al., 2010). Because the data set is usually very large, graphs summarizing the quality of the data can be generated (in our case we used the program

FastQC).

After the trimming and quality checks of the data, the short reads of cDNA are combined and mapped. To actually characterize the mRNA present in the tissue, the short 66 reads must be combined to generate mRNA transcripts. The combined short reads are known as contiguous sequences or “contigs.” The gene sequences of the contigs are then mapped or aligned to the gene annotations present on a reference genome that is usually available in FASTA format (Wit et al., 2012). Mapping the contigs against the reference genome will generate a Sequence/Alignment Map or SAM file. SAM files are then used to analyze the number of contigs that align to each reference sequence, the single nucleotide polymorphisms, as well as the contigs that did not mapped to the reference genome (Trapnell et al., 2012; Wit et al., 2012).

The data can now be quantified. The magnitude of gene expression is related to the number of contigs. Usually, RNA-seq measures transcript abundance in a unit called

Fragments Per Kilobase of exon per Million fragments mapped (FPKM) (Trapnell et al.,

2012; Wit et al., 2012). Finally, the count data and the mapped data for each individual sample are combined into one sample that usually represents the groups that are being evaluated or compared. Thus, the transcriptome list of genes and variants for each group of samples is generated (Trapnell et al., 2012). Figure 9 summarizes and simplifies the steps needed to analyze RNA-seq data. 67

Figure 9. RNA-seq analysis workflow. After RNA-seq, the raw data is released in Fastq format. Short read sequences or “contigs” are then mapped against the reference genome. The mapping process will allow one to analyze the contigs that aligned to the reference genome. The number of reads that aligned to the reference genome for each individual sample is obtained. The count of the reads that aligned gives a measure of gene expression using FPKM units. Finally, FPKM for each gene is compared between experimental samples to calculate differences in gene expression.

Adipose tissue papers involving RNA-seq analysis. Only a handful of studies have performed RNA-seq analysis in adipose tissue. Table 6 summarizes the studies published as of May 2014. This summary provides important information related to depot differences or in methodology relevant to using this technique in AT. As can be seen, this technology has not been used frequently with this tissue. 68

Table 6

Published Papers That Describe RNA-Seq Experiments on AT

Species Samples Main conclusions Reference

Mus AT-derived stem cells Subcutaneous AT derived stem cells showed a slow collagenolysis (Tokunaga et al., musculus Inguinal and epididymal mediated by membrane-bound collagenases, while the same cells 2014) from Sca1 transgenic isolated from visceral AT had a rapid collagenolysis, partly mediated mice versus controls by secreted collagenase. Mus Inguinal AT from Mice have impaired subcutaneous AT development and express (Li et al., 2014) musculus animals deficient in lipin1 alternatively spliced isoform lipin1α, which is required for splicing factor SRSF10 initial adipocyte differentiation. versus controls Homo Adipocytes/monocytes Adipocytes and monocytes may be sources of lipopolysaccharide- (Liu et al., 2014) sapiens from healthy patients regulated intergenic noncoding RNA. treated with low dose polysaccharides Suidae Omental AT of obese Omental AT of obese pigs showed 37% increase in the total (Toedebusch et scrofa versus lean pigs transcript number, as well as an markedly increase in transcripts al., 2014) related with development, cellular function/maintenance, and connective tissue development and function and a decrease in transcripts associated with RNA post-translational modification, lipid metabolism, and small molecule biochemistry.

Table 6: Continued

Species Samples Main conclusions Reference

Bos taurus Intramuscular, 110 genes were differentially expressed in the intramuscular, (Sheng et al., subcutaneous and subcutaneous and perirenal AT depots. The differentially expressed 2014) perirenal AT genes were related to an increase in the following functions: cellular processes, biological regulation, and metabolic processes.

Rattus Epididymal and liver More differentially expressed genes in liver than in AT; differentially (Pashaj et al., norvegicus from obese fa/fa rats expressed genes showed a significant enrichment of functions related 2013) treated with a lipid- to immune response, stress response, lipid metabolism, and lowering bioactive food carboxylic acid metabolic processes. compound

Homo Gluteal subcutaneous To detect expressed genes and alternative spliced variants in AT, (Liu et al., 2013) sapiens AT ∼100 to 150 million reads are needed. Differential expression and differential alternative splicing need ∼300 M reads to detect 80% of the events. Sus scrofa Subcutaneous AT Visceral AT showed a functional enrichment of the following (Wang et al., domesticus depots (hypodermal pathways: fatty acid metabolism and the inflammatory response. 2013) layer of backfat) visceral AT depots (greater omentum and mesenteric)

Table 6: Continued

Species Samples Main conclusions Reference

Mus Liver and AT RNA-DNA differences (RDD) were investigated in this paper. While (Lagarrigue et musculus in liver 293 RDD were found, in AT 1667 RDD were described. A al., 2013) small number of apparent RDD sites overlapped between both tissues, indicating a high degree of tissue specificity for RDD.

Homo Subcutaneous AT and 62 genes shown significantly different expression between AT and (Jaager, Islam, sapiens fibroblasts stromal stem fibroblasts stromal stem cells. These differentially expressed genes Zajac, cells could be grouped into 6 categories: cell cycle, cytoskeleton, Linnarsson, & extracellular matrix and adhesion, biosynthesis, signal transduction Neuman, 2012) and development.

Conclusion

AT expansion is intimately related with appropriate vascularization and angiogenesis. GH has an impact on AT plasticity with disruption of GH action increasing

AT mass while increasing GH action decreases AT mass in a depot-specific manner. GH has been implicated in angiogenesis in several tissues; however, to date, no one has investigated the effect of GH on angiogenesis in AT. RNA-seq is a technology used to investigate the entire transciptome of a cell or tissue. Given its sensitivity and the large amount of information that this technique can offer, RNA-seq is a good option to analyze the expression levels of AT angiogenic factors in bGH mice compared to WT mice and also provides useful information about other potential pathways impacted.

Chapter 3: Methodology

The overall objective of this work was to investigate the effect of GH on angiogenesis in the AT of bGH mice. To accomplish this, the expression levels of angiogenic factors were compared in the subcutaneous and intra-abdominal AT depots from WT and bGH mice. RNA levels were examined using RNA-seq technology. In addition to evaluating the genes involved in angiogenesis simultaneously, this method provides the advantage of evaluating the entire transcriptome, in order to determine other key pathways and networks altered in the AT of bGH mice.

Animals

This study used mice that are of a C57BL/6J background and that were bred and maintained at Edison Biotechnology Institute at Ohio University. The bGH mice were generated by a metallothionein transcription regulatory element for the bGH complementary DNA, which was injected into the pronucleus of C57BL/6J mouse embryos as previously described (Berryman et al., 2004). Mouse genotypes were determined by polymerase chain reaction (PCR) using tail samples. Six bGH and 6 WT male mice at 6 months of age were used. Mice were fed with a standard laboratory rodent chow (ProLab RMH 3000) and provided food and water ad libitum. One to four mice were housed per cage in a room with temperature- and humidity-control as well as with controlled light cycles (14 hours light/10 hours dark). All procedures were approved by the Ohio University Institutional Animal Care and Utilization Committee. 73

Adipose Tissue Depots

Two white AT depots were studied: inguinal (subcutaneous) and epididymal

(intra-abdominal), which were dissected from each mouse. After a 12-hour fast, mice were anesthetized by placing them in a chamber with CO2. The unconscious mouse was bled via the orbital sinus and sacrificed by cervical dislocation. AT samples were dissected and weighed. For RNA isolation, tissue was flash frozen in liquid nitrogen and stored at -80 ºC until further processing.

Total RNA and Messenger RNA (mRNA) Isolation

Total RNA was isolated from AT depots with Trizol Reagent from Invitrogen, following the manufacturer’s protocol with minor changes. Briefly, 1 ml of Trizol reagent was homogenized with 80 to 100 mg of frozen AT. Samples were then centrifuged at

17,000 × g for 5 minutes at 4 °C. Using a syringe, most of the lipid layer located on the top of the solution was removed without disturbing the Trizol. 200 µL of choloroform was added to the sample and mixed, followed by centrifugation at max speed ~17,000 x g for 15 minutes at 4 ˚C. The clear aqueous phase was transferred to a new tube, and 0.5 mL of 100% isopropanol was then added to the aqueous phase. Samples were centrifuged at 12,000 × g for 10 minutes at 4 °C, then the supernatant was removed leaving only the

RNA pellet. The RNA pellet was washed three times as follows: 1 mL of ice cold 75% ethanol was added to the RNA pellet and mixed, then the sample was centrifuged at

17,000 × g for 15 minutes at 4 °C. Finally, the pellet was air-dried and resuspended using

50 μL of RNAase free water. 74

The quantity and quality of total RNA was measured with the NanoDrop ND-

2000 (Thermo Scientific). The RNA used for subsequent experiments had a 260/280 and a 260/230 ratio ≥ 1.8 to ensure purity and quality of the RNA. Subsequently, mRNA was isolated from the total RNA AT samples with the Dynabeads mRNA Purification Kit

(Invitrogen), according to the manufacturer’s protocol. The quantity and quality of the isolated mRNA was determined by the Agilent 2100 Bioanalyzer from Agilent

Technologies at the Genomics Facility (Ohio University). The mRNA used for RNA-seq had an integrity number or RIN number ≥ 7. cDNA Library Preparation and RNA Sequencing

The cDNA library was produced using the Ion Total RNA-Seq Kit (Life

Technologies) following the manufacturer’s protocol. Briefly, 20ng of mRNA was fragmented and ligated to identifier adaptors. Reverse transcription was performed to the samples to obtain cDNA, which in turn was amplified through PCR to obtain cDNA libraries. cDNA libraries were then sequenced using a single end methodology in an Ion

Torrent Personal Genome Machine at the Genomics Facility (Ohio University).

RNA-Seq Analysis

RNA-seq analysis was conducted using the mm10 mouse database as reference genome. This version was released in July 2012 by the Genome Reference Consortium; this database contains 22 chromosomes, 23,148 coding genes and 94,647 gene transcripts

(Ensembl, 2012). RNA-seq analysis was also performed using the on-line tool Galaxy, which contains all the software and tools needed to analyze the RNA-seq data. Reads were trimmed for the adaptors sequences and quality checked. Reads were then mapped 75 independently using Top Hat version 1.5.0, against the mouse genome mm10. Gene

Expression was calculated by running Cufflinks version 2.1.0 on the alignments from

Top Hat. Only reads with a Phred score ≥ 20, were used in the analysis, p-values with

False Discovery Rate (FDR) correction were used to determine significance.

In more detail, the open source web-based platform Galaxy Project was used. In a single site, Galaxy combines the bioinformatics tools needed to analyze the data, including Top Hat and Cufflinks (Blankenberg & Hillman-Jackson, 2014). Top Hat maps the reads to the mouse reference genome. Cufflinks is formed by three programs: the first one is Cufflink, which gathers the transcripts generated by Top Hat; the second program is Cuffmerge, which merges the assemblies and calculates gene expression; finally, the third program is Cuffdiff, which clusters the transcripts into biological meaningful sets and finds differences in gene expression (Trapnell et al., 2012). Figure 10 illustrates the analysis workflow used in Galaxy project.

Figure 10. RNA-seq data analysis workflow. After the mRNA has been sequenced, the RNA-seq data will be released in a FASTQ format that needs to be quality checked and processed before further analysis. After FASTQ processing, data are run through two software programs in the Galaxy platform: Top Hat and Cufflinks. Top Hat maps the reads generated by the RNA-seq to a reference genome. The Cufflinks program is further divided into three sequential programs: 1. Cufflinks, which groups the mapped transcripts generated by Top Hat, 2. Cuffmerge, which merges the assemblies and calculates the expression of the genes, and 3. Cuffdiff, which calculates differences in gene expression between experimental groups. CummeRbund is an R package that helps with the visualization and overall analysis of the data.

The R program with the CummeRbund 2.0 package (Goff, Trapnell, & Kelley,

2012) was used to visualize the homogeneity of the sample’s expression and other quality parameters of the dataset. Appendix D shows the angiogenic molecules that were tested for gene expression and that were picked after a thorough search of the scientific literature. 77

The bioinformatic on-line tool “GOstat” (http://gostat.wehi.edu.au/cgi- bin/goStat.pl) was used as a gene ontology (GO) analysis tool to determine the significantly altered biological processes of each of the two AT depots of bGH mice. To perform the GO analysis, the significantly altered genes of the inguinal and epididymal

AT depots were divided into two groups: the up- and the down-regulated genes. One GO analysis was made for each of the four groups (up-regulated genes of the epididymal depot, down-regulated genes of the epididymal depot, up-regulated genes of the inguinal depot, and down-regulated genes of the inguinal depot). Ingenuity Pathway Analysis

(IPA) tool from QIAGEN was used to identify the relevant canonical pathways and networks that were altered in AT depots between WT and bGH mice.

Statistical Analysis

Gene expression is expressed in FPKM units. Cuffdiff first sums the FPKM for transcripts sharing the same gene. Statistical significance is calculated using a t-test, calculated by a log (fold_change) distribution that divides the FPKM distributions of condition 1 (bGH) by that of condition 2 (WT) and then applies log to the division result.

The GOstat toll uses the Fisher exact test to calculate the probability that the observed genes are significantly associated with a GO terms that are involved with biological processes. For Gostat, p < 0.1 was considered significant. The IPA analysis also uses a

Fisher exact test to calculate pathways that were significant. A cutoff of 1.5 fold change was used for analysis with p < 0.05 considered statistically significant.

Chapter 4: Results

The present study had two purposes: a) to evaluate the expression levels of angiogenic molecules of two AT depots, epididymal and inguinal, in mice that have high levels of circulating bovine GH (bGH) and IGF-1 as compared to control mice; and b) to elucidate the main biological pathways, networks, and genes that are altered in the epididymal and inguinal AT depots of bGH mice as compared to control mice. RNA-seq technology using AT depots from 6-month-old male bGH mice and littermate controls was used for this study.

Gene Expression of Angiogenic Molecules

Gene expression of 43 angiogenic molecules (entire list of genes in Appendix D) was evaluated. In the next two sections, the results obtained by the RNA-seq technology regarding the angiogenic molecules are described. Briefly, the angiogenic genes evaluated in this work can be subdivided in two groups (see Figure 11):

1. genes whose corresponding RNA levels were too small to perform

statistical analysis, and

2. genes whose corresponding RNA levels were that sufficient in order to be

statistically evaluated.

Tested 43 Angiogenic Genes in the Inguinal and Epididymal AT Depots of bGH and WT Mice

Genes not statistically Genes statistically tested tested because of no or low because of sufficient expression expression

1 gene only in the 26 Genes 13 Genes inguinal depot, and 3 in both AT in both AT depots genes only in the epididymal depot

Figure 11. Angiogenic genes that were evaluated with RNA-seq technology. While 26 genes were not statistically tested because there was low or no expression in both AT depots, 13 genes had meaningful expression in both AT depots and 4 genes had sufficient expression in at least one depot.

Angiogenic genes not analyzed statistically. The number of genes that were not able to be analyzed could be important as it indicates that these genes are not significantly expressed in AT. For the epididymal AT depot, the Cuffdiff program was not able to perform statistical analysis on 27 of the 43 angiogenic molecules (see Table

7). In the inguinal AT depot, the program was not able to run a statistical test on 29 of the total of angiogenic molecules that were tested (see Table 8). By default, the Cuffdiff program does not run a statistical test in gene expression when there are insufficient reads or data for a specific gene to calculate expression levels. Specifically, the program requires at least 10 alignments in a locus to perform statistical analysis. Thus, these results indicate that there were not sufficient RNA transcripts for more than half of the 80 angiogenic molecules in both AT depots. Of note, 26 of these genes were shared by both

AT depots. Four genes were detected in only one of the AT depots, demonstrating that different depots have distinct gene expression profiles. Specifically, fibroblast growth factor 2 (Fgf2) was uniquely expressed in the epididymal AT depot, and fibroblast growth factor 1 (Fgf1), hypoxia inducible factor 1, alpha subunit (Hif1), and prolactin receptor (Prlr) were expressed by the inguinal AT depot (see Figure 12).

Table 7

Angiogenic Genes of the Epididymal AT Depot That Did Not Express Enough Transcripts for Statistical Analysis

WT epi bGH epi Log2 Gene value value (fold change)

Amphiregulin (Areg) 0 0 0 Angiopoietin-2 (Angpt2) 1.968 0.993 -0.986 Betacellulin (Btc) 2.535 1.695 -0.581 Chemokine (C-C motif) ligand 2 (Ccl2) 1.836 1.149 -0.677 Chemokine (C-C motif) ligand 3 (Ccl3) 0 1.932 - Chemokine (C-X-C motif) ligand 1 (Cxcl1) 0.202 0.623 1.622 Chemokine (C-X-C motif) ligand 2 (Cxcl2) 0.148 0 - Collagen, type VIII, alpha 1 (Col8a1) 1.505 1.506 0.001 Colony stimulating factor 3 (Csf3) 0.934 0 - Colony stimulating factor 3 receptor (Csf3r) 0.944 1.451 0.620 Endothelin 1 (Edn1) 2.190 0.684 -1.679 Epidermal growth factor (Egf) 3.121 0.497 -2.650 Fas ligand (also Tnfsf6) (Fasl) 0.462 0 - Fibroblast growth factor 2 (Fgf2) 9.492 10.299 0.118 Follistatin (fst) 1.484 1.986 0.421

Table 7: Continued

WT epi bGH epi Log2 Gene value value (fold change)

Hepatocyte growth factor (Hgf) 0.814 1.929 1.245 Insulin-like growth factor 1 receptor (Igf1r) 4.118 2.538 -0.698 Plasminogen (Plg) 0.059 0.071 0.263 Tumor necrosis factor (Tnf) 0.447 0 - Vascular endothelial growth factor (Vegfc) 2.961 5.505 0.895 FMS-like tyrosine kinase 1 (Flt1) 3.821 2.946 -0.375 FMS-like tyrosine kinase 4 (Flt4) 2.041 1.589 -0.361 Anaplastic lymphoma kinase (Alk) 0.026 0.032 0.263 Interleukin 1 beta (Il1b) 0 0 0 Interleukin 17A (Il17a) 0.156 0 - Interleukin 6 (Il6) 0 0 0 Tissue inhibitor of metalloproteinase 1 (Timp1) 0.456 3.232 2.825 Note. Genes that were not significantly expressed in both AT depots are noted by an underline in Table 7 and 8.

Table 8

Angiogenic Genes of the Inguinal AT Depot That Did Not Express Sufficient Transcripts for Statistical Analysis

WT bGH inguinal inguinal Log2 Gene value value (fold change)

Amphiregulin (Areg) 0 0.745 - Angiopoietin-2 (Angpt2) 0.770 2.541 1.723 Betacellulin (Btc) 3.378 0.278 -3.602 Chemokine (C-C motif) ligand 2 (Ccl2) 5.705 0 - Chemokine (C-C motif) ligand 3 (Ccl3) 0 0 0 Chemokine (C-X-C motif) ligand 1 (Cxcl1) 0 0 0 Chemokine (C-X-C motif) ligand 2 (Cxcl2) 0 0 0

Table 8: Continued

WT bGH inguinal inguinal Log2 Gene value value (fold change)

Collagen, type VIII, alpha 1 (Col8a1) 0.602 0.596 -0.014 Colony stimulating factor 3 (Csf3) 0 0 0 Colony stimulating factor 3 receptor (Csf3r) 0.993 2.386 1.264 Endothelin 1 (Edn1) 1.752 0.451 -1.959 Epidermal growth factor (Egf) 0.818 0.326 -1.326 Fas ligand (also Tnfsf6) (Fasl) 0.497 1.293 1.379 Fibroblast growth factor 1 (Fgf1) 3.670 1.694 -1.115 Follistatin (fst) 0.709 1.281 0.854 Hepatocyte growth factor (Hgf) 0.849 0.119 -2.829 Hypoxia inducible factor 1, alpha subunit (Hif1a) 9.219 7.905 -0.222 Insulin-like growth factor 1 receptor (Igf1r) 1.515 4.542 1.583 Plasminogen (Plg) 0 0.129 - Prolactin receptor (Prlr) 2.134 4.259 0.997 Tumor necrosis factor (Tnf) 0.592 3.812 2.687 Vascular endothelial growth factor (Vegfc) 1.351 0.557 -1.279 FMS-like tyrosine kinase 1 (Flt1) 3.788 2.836 -0.418 FMS-like tyrosine kinase 4 (Flt4) 1.596 2.375 0.574 Anaplastic lymphoma kinase (Alk) 0.306 0.318 0.057 Interleukin 1 beta (Il1b) 0.557 3.680 2.725 Interleukin 17A (Il17a) 0 0 0 Interleukin 6 (Il6) 0 0 0 Tissue inhibitor of metalloproteinase 1 (Timp1) 1.324 0.727 -0.864 Note. Genes that were not significantly expressed in both AT depots are noted by an underline in Table 7 and 8.

Figure 12. Venn diagram of the angiogenic genes that were not detected in AT. In total, the epididymal AT depot had 27 genes that were not expressed at sufficient levels for subsequent analysis, and the inguinal AT depot had 29 of these genes. While expression of most genes (26) were shared by both epididymal and inguinal AT depots, one gene was expressed by the epididymal AT depot, and 3 genes were o expressed by the inguinal AT depot.

Angiogenic genes that were statistically evaluated. The Cuffdiff program was able to perform a statistical analysis on 16 of the 43 angiogenic molecules in the epididymal depot (see Table 9), and 14 of the 43 angiogenic genes in the inguinal depot

(see Table 10). Thus, these genes gave rise to sufficient RNA transcripts to compare gene expression levels between biological samples. From these, 4 genes were evaluated in either inguinal or epididymal fat, but not both; however, none of these genes were expressed at statistically significant levels between genotypes. As noted above, this included 1 gene, Fgf2, by the inguinal AT depot and the 3 genes, Fgf1, Hif1, and Prlr, by the epididymal AT depot. A heat map illustrating the depot-specific analysis is provided in Figure 13.

Importantly, 13 genes were statistically tested in both AT depots. No significant differences in gene expression within the epididymal depot between genotypes were 84 found. However, in the inguinal depot, significant differences in expression of 4 genes were found. That is, RNA levels of leptin (Lep), vascular endothelial growth factor A

(Vegfa), vascular endothelial growth factor B (Vegfb), and tissue inhibitor of metalloproteinase 4 (Timp4) were significantly higher in the WT inguinal depot than in the bGH inguinal depot. A heat map showing the statistically tested gene expression pattern is provided in Figure 14. 85

Table 9

Statistical Analysis of Available Angiogenic Genes of the Epididymal AT Depot

WT epi bGH epi Log2 Gene value value (fold change) p-value

Angiogenin, Ribonuclease family 4 (Ang,Rnase4) 62.679 71.992 0.200 0.685 c-fos induced growth factor (also VEGF-D) (Figf) 14.783 20.716 0.487 0.366 Endoglin (Eng) 26.503 24.035 -0.141 0.811 Fibroblast growth factor 1 (Fgf1) 18.647 20.703 0.151 0.772 Hypoxia inducible factor 1, alpha subunit (Hif1a) 8.675 8.395 -0.047 0.937 Insulin-like growth factor 1 (Igf1) 140.616 337.117 1.261 0.069 Leptin (Lep) 422.336 343.335 -0.299 0.691 Platelet/endothelial cell adhesion molecule 1 (Pecam1) 35.029 27.867 -0.330 0.480 Prolactin receptor (Prlr) 8.245 11.885 0.528 0.532 Transforming growth factor, beta 1 (Tgfb1) 12.893 17.052 0.403 0.525 Vascular endothelial growth factor (Vegfa) 23.527 14.708 -0.678 0.143 Vascular endothelial growth factor (Vegfb) 95.446 116.441 0.287 0.566 Kinase insert domain protein receptor (Kdr) 10.294 8.241 -0.321 0.548 Tissue inhibitor of metalloprotease-2 (Timp2) 114.265 120.617 0.078 0.897 Tissue inhibitor of metalloprotease-3 (Timp3) 28.997 20.048 -0.532 0.241 Tissue inhibitor of metalloprotease-4 (Timp4) 568.615 317.997 -0.838 0.073

Table 10

Statistical Analysis of Available Angiogenic Genes of the Inguinal AT Depot

WT bGH inguinal inguinal Log2 Gene value 1 value 2 (fold change) p-value

Angiogenin, Ribonuclease family 4 (Ang,Rnase4) 33.303 22.221 -0.584 0.346 c-fos induced growth factor (also VEGF-D) (Figf) 11.532 2.283 -2.336 0.070 Endoglin (Eng) 16.395 12.916 -0.34 0.592 Fibroblast growth factor 2 (Fgf2) 15.173 1.504 -3.334 0.353 Insulin-like growth factor 1 (Igf1) 85.520 71.977 -0.249 0.816 Leptin (Lep) 311.579 69.634 -2.162 0.002 Platelet/endothelial cell adhesion molecule 1 (Pecam1) 31.026 48.319 0.639 0.172 Transforming growth factor, beta 1 (Tgfb1) 16.461 25.239 0.617 0.294 Vascular endothelial growth factor (Vegfa) 31.263 10.104 -1.630 0.002 Vascular endothelial growth factor (Vegfb) 73.907 11.144 -2.730 0.001 Kinase insert domain protein receptor (Kdr) 12.585 10.543 -0.255 0.681 Tissue inhibitor of metalloprotease-2 (Timp2) 44.232 43.565 -0.022 0.973 Tissue inhibitor of metalloprotease-3 (Timp3) 25.089 15.921 -0.656 0.196 Tissue inhibitor of metalloprotease-4 (Timp4) 264.278 92.162 -1.520 0.003 87

Figure 13. Heat map of the genes that were statistically tested in only one AT depot. While Fgf2 was statistically tested only in the inguinal depot, Fgf1, Hif1a, and Prlr were statistically tested only in the epididymal AT depot. None of the genes were significantly altered.

Figure 14. Heat map of the 13 genes that were sufficiently expressed in both AT depots. Lep, Vegfa, Vegfb, and Timp4 were significantly down-regulated in the inguinal AT depot of bGH mice as indicated by the red outline.

RNA-seq Data Analysis

RNA-seq is a powerful technique that measures the expression levels of all of the genes present in a biological sample at a given time point. Therefore, RNA-seq not only can answer specific questions of specific biological processes, such as angiogenesis noted above, but also can be used as an exploratory tool to further elucidate new research questions or biological hypotheses. With this objective, RNA-seq data can be analyzed with software and on-line tools that organize the massive amount of data into significantly altered biological pathways, networks and functions that are differentially modulated within the tissue or cell of interest. Specifically, two on-line tools were used to analyze the RNA-seq data, named Ingenuity Pathway Analysis (IPA) and the Gene

Ontology (GO) analysis. While the IPA software is able to recognize the significantly modulated biological pathways, networks and genes, the GO analysis recognized the significantly altered biological processes and the genes involved in those functions. Thus, both provide meaningful data. As will be explained in more detail below, biological processes, pathways and networks are defined differently by these on-line tools and provide overlapping and unique information.

Differentially expressed genes. Comparing bGH and WT mice, the inguinal AT depot had more significantly altered genes expressed than the epididymal AT depot (see

Figure 15), suggesting that GH has a greater impact on the inguinal depot than on the epididymal AT depot. Furthermore, since both depots had more down-regulated genes than up-regulated genes, it appears that GH has more of a negative impact on gene expression levels in intra-abdominal and subcutaneous AT. 89

Figure 15. Total number of significantly altered genes in inguinal and epididymal AT depots.

GO analysis for biological processes. For the GO analysis, we divided the significantly altered genes of each AT depot into two groups: the genes that were up- regulated and the genes that were down-regulated. These groups were then further analyzed with the GO analysis tool. While the GO analysis showed no significant results for the up-regulated group of genes within the epididymal AT depot, there were a variety of significantly altered processes associated with the down-regulated genes. The processes with the down-regulated genes were generally related to immune cells and carboxylic acid metabolic processes. Table 11 shows the 10 most significantly altered biological processes of the epididymal AT depot.

Table 11

Significant Biological Processes Associated With Down-Regulated Genes in Epididymal AT Depot of bGH Mice

Number Go number Biological process of genes p-value

GO:0033209 Tumor necrosis factor 2 0.0104 GO:0019752 Carboxylic acid metabolic process 7 0.0137 GO:0007010 Cytoskeleton organization and biogenesis 7 0.0137 GO:0006082 Organic acid metabolic process 7 0.0137 GO:0001818 Negative regulation of cytokine production 2 0.0175 GO:0019221 Cytokine and chemokine mediated signaling 3 0.0175 pathway GO:0022407 Regulation of cell-cell adhesion 2 0.0175 GO:0006996 Organelle organization and biogenesis 10 0.0175 GO:0050869 Negative regulation of B cell activation 2 0.021 GO:0006692 Prostanoid metabolic process 2 0.0228

As might be expected based on the total number of genes significantly altered (see

Figure 15 above), there were many more highly significant changes in biological processes in the inguinal depot. The GO analysis for the up-regulated genes of the inguinal AT depot showed that the 10 most significantly altered biological processes were related with the immune system biology, more specifically with lymphocyte and leukocyte activation, T cell activation, antigen receptor activation, and development of the immune system (see Table 12). On the other hand, the GO analysis for the down- regulated genes of the inguinal AT depot showed that the 10 most significantly altered biological processes were related with lipid metabolism and carboxylic acid metabolic 91 processes. Table 13 shows the 10 most significant biological processes associated with the down-regulated genes of the inguinal AT depot.

Table 12

Significant Biological Processes Associated With Up-Regulated Genes in bGH Inguinal AT Depot

Number Go number Biological process of genes p-value

GO:0002376 Immune system process 45 3.04E-24 GO:0045321 Leukocyte activation 23 1.30E-11 GO:0006955 Immune response 27 1.30E-11 GO:0001775 Cell activation 23 4.37E-11 GO:0046649 Leukocyte activation and lymphocyte 21 1.14E-10 activation GO:0042110 T cell activation 15 3.13E-08 GO:0007242 Intracellular signaling cascade 38 3.69E-08 GO:0050851 Antigen receptor-mediated signaling pathway 8 4.14E-07 GO:0002520 Immune system development 19 5.34E-07 GO:0048534 Hemopoietic or lymphoid organ development 18 8.18E-07

Table 13

Significant Biological Processes Associated With the Down-Regulated Genes in the Inguinal AT Depot of bGH Mice

Number Go number Biological process of genes p-value

GO:0006629 Lipid metabolic process 45 1.39E-19 GO:0044255 Cellular lipid metabolic process 41 1.89E-19 GO:0019752 Carboxylic acid metabolic process 32 7.24E-12 GO:0006082 Organic acid metabolic process 32 7.24E-12 GO:0032787 Monocarboxylic acid metabolic process 23 1.61E-08 GO:0006066 Alcohol metabolic process 19 4.00E-07 GO:0006631 Fatty acid metabolic process 18 7.34E-07 GO:0006091 Generation of precursor metabolites and 24 9.47E-06 energy GO:0046486 Glycerolipid metabolic process 7 5.07E-05 GO:0006662 Glycerol ether metabolic process 6 6.25E-05

Biological networks obtained with IPA tool. With the on-line tool IPA, we generated the main biological networks in each of the AT depots studied in the bGH mice. It is important to clarify that a biological network is not the same as a biological pathway in these on-line tools. That is, a network is a system of nodes that are interacting with each other; the nodes are represented by focus molecules. Also, since a pathway refers to a subset of nodes and interactions (Tkačik & Bialek, 2009), a network is the starting point to elucidate biological pathways.

The IPA tool showed only one biological network in the epididymal AT depot, termed “cellular function and maintenance, lipid metabolism, small molecule biochemistry” network. This network includes 20 genes, and 6 of these genes were 93 significantly altered in the epididymal AT depot of bGH mice. Specifically, 3 genes were down-regulated and 3 genes were up-regulated. Figure 16 illustrates this network and the partial relationship among the genes. Appendix E shows the full name of genes that are part of this network.

Figure 16. The “cellular function and maintenance, lipid metabolism, small molecule biochemistry network.” Solid lines/arrows indicate a direct relationship, while a dashed arrow indicates an indirect relationship among molecules. This network is the only one shown by the IPA tool for the epididymal AT depot of bGH mice. It contains 20 molecules, 3 of the genes are significantly down-regulated (denoted in green), and 3 of the genes are significantly up-regulated (denoted in red). The saturation of the green and red colors reflects the degree of fold change between WT and bGH mice.

The significantly altered genes found in the inguinal AT depot of bGH animals showed 3 main networks, including: a) “lipid metabolism, molecular transport, small 94 molecule biochemistry”; b) “carbohydrate metabolism, lipid metabolism, molecular transport”; and, c) “endocrine system disorders, gastrointestinal disease, hepatic system disease.” The first network has 35 focus molecules. From these molecules, 21 were significantly altered in the inguinal depot with 2 up-regulated and 19 down-regulated.

The second network consisted of 34 focus molecules with 18 down-regulated and 1 up- regulated genes. Finally, the last network had 34 focus genes. From these genes, 18 genes were significantly altered with 14 down-regulated and 4 up-regulated. Figures 17, 18 and

19 show significantly altered networks in the inguinal depot of bGH mice. Appendix E shows the full name of genes that are part of these networks.

Figure 17. The “lipid metabolism, molecular transport, small molecule biochemistry network.” Solid lines/arrows indicate a direct relationship, while a dashed arrow indicates an indirect relationship among molecules. This network was shown significant by the IPA tool for the inguinal AT depot of bGH mice. It contains 35 focus molecules. From these molecules, 21 were significantly altered in the inguinal depot, 2 up-regulated (denoted in red) and 19 down-regulated regulated (denoted in green). The saturation of the green and red colors indicates the degree of fold change between WT and bGH mice.

Figure 18. The “carbohydrate metabolism, lipid metabolism, molecular transport network.” Solid lines/arrows indicate a direct relationship, while a dashed arrow indicates an indirect relationship among molecules. This network was shown to be significant for the inguinal AT depot of bGH mice. It contains 34 focus molecules, 18 down-regulated (denoted in green) and 1 up-regulated genes (denoted in red). The saturation of green and red colors indicates the degree of fold change between WT and bGH mice.

Figure 19. The “endocrine system disorders, gastrointestinal disease, hepatic system disease network.” Solid lines/arrows indicate a direct relationship, while a dashed arrow indicates an indirect relationship among molecules. This network was shown to be significant by the IPA tool for the inguinal AT depot of bGH mice. It contains 34 focus genes. From these genes, 18 genes were significantly altered, 14 down-regulated (denoted in green) and 4 up-regulated (denoted in red). The green and red colors have different saturation which indicated the degree of fold change between WT and bGH mice.

Biological pathway analysis made with the IPA tool. After performing analyses of significantly altered biological processes, the data was then evaluated in terms of biological pathways. It is important to clarify that a biological process may involve several pathways; thus, the biological process examination is a more broad exploration 98 than the pathway analysis. It is also important to note that the data for up- and down- regulated genes were lumped together for this analysis unlike the process analyses described above.

The pathway results obtained with the IPA on-line tool showed a similar trend to the biological processes obtained by the GO analysis. The altered genes of the epididymal

AT depot were significantly linked with several different pathways related with immune cell biology, basic metabolism and lipid metabolism (see Table 14 and Figure 20). The pathway analysis of the inguinal AT depot of bGH mice showed that the 10 most significantly altered pathways in this depot were related with immune cell biology, most importantly with mechanisms regulating T cell action. This suggests that increasing GH levels alter the immune cell infiltration, and more specifically, the T cell action in the inguinal AT depot (see Table 15 and Figure 21).

Table 14

Altered Pathways of the Epididymal AT Depot

Ratio (significantly changed genes within a pathway altered in bGH mice/ total number of Canonical pathway p-value molecules in the pathway)

Agranulocyte adhesion and diapedesis 3.13E-03 0.043 (4/92) Calcium signaling 4.23E-03 0.040 (4/100) ILK signaling 5.93E-03 0.036 (4/100) Mechanisms of viral exit from host cells 1.36E-02 0.074 (2/27) Methylglyoxal degradation I 1.98E-02 0.333 (1/3) Hepatic fibrosis / hepatic stellate cell 2.49E-02 0.032 (3/95) activation Tight junction signaling 2.77E-02 0.030 (3/99) Prostanoid biosynthesis 3.92E-02 0.167 (1/6) TGF-β signaling 4.48E-02 0.039 (2/51) Oleate biosynthesis II (animals) 4.56E-02 0.140 (1/7)

100

Figure 20.The ten most altered biological pathways in the epididymal AT depot of bGH mice. Blue bars represent the –log (p-value) or the significance of each altered pathway. The orange line represents the ratio of the significantly altered genes in each of the pathways / total number of molecules in the pathway.

101

Table 15

Altered Pathways of the Inguinal AT Depot

Ratio (significantly changed genes within a pathway altered in bGH mice/total number of Canonical pathway p-value molecules in the pathway)

Glutatione redox reactions I 3.99E-05 0.545 (6/11) iCOS-iCOSL signaling in T helper cells 4.68E-05 0.224 (15/67) B cell development 5.32E-05 0.438 (7/16) Triacylglycerol degradation 5.32E-05 0.438 (7/16) PKCθ signaling in T lymphocytes 9.52E-05 0.211 (15/71) Primary immunodeficiency signaling 2.09E-04 0.320 (8/25) T cell receptor signaling 2.99E-04 0.210 (13/62) CTLA4 signaling in cytotoxic T 3.44E-04 0.218 (12/55) lymphocytes CD28 signaling in T helper cells 3.84E-04 0.188 (15/80) OX40 signaling pathway 4.98E-04 0.286 (8/28)

102

Figure 21. The ten most altered biological pathways in the inguinal AT depot of bGH mice. Green bars represent the –log (p-value) or the significance of each altered pathway. The orange line represents the ratio of the significantly altered genes in each of the pathways / total number of molecules in the pathway.

Most significantly up- and down-regulated genes. To determine the genes that are the most affected by the excess of GH, we identified the 10 most up and down- regulated genes in the epididymal and inguinal AT depots of bGH mice. Tables 16 and 17 correspond to the most significantly up- and down-regulated genes of the epididymal depot of bGH mice, respectively; Tables 18 and 19 describe the 10 most significantly up- and down-regulated genes of the inguinal depot of bGH mice, respectively. 103

Table 16

Ten Most Up-Regulated Genes in the Epididymal AT Depot of bGH Mice

Log2 Gene up-regulated (fold_change) p-value

CASP2 and RIPK1 domain containing adaptor with death 4.691 0.014 domain (Cradd) Serum amyloid A 3 (Saa3) 2.912 0.001 Growth arrest specific 5, microRNA 5117 (Gas5,Mir5117) 2.785 0.001 Aldo-keto reductase family 1, member C-like (Akr1cl) 2.493 0.001 Solute carrier family 38, member 3 (Slc38a3) 2.193 0.012 Hydroxyacylglutathione hydrolase-like (Haghl) 2.115 0.029 Spondin 1 (Spon1) 2.085 0.012 Chemokine (C-C motif) ligand 8 (Ccl8) 2.085 0.006 Insulin-like growth factor binding protein 2 (Igfbp2) 2.066 0.029 Osteoglycin (Ogn) 1.981 0.017

Table 17

Ten Most Down-Regulated Genes in the Epididymal AT Depot of bGH Mice

Log2 Gene down-regulated (fold_change) p-value

Prostaglandin-endoperoxide synthase 2 (Ptgs2) -6.697 0.018 CUB and zona pellucida-like domains 1 (Cuzd1) -6.466 0.004 CCR4-NOT transcription complex, subunit 3 (Cnot3) -5.016 0.001 Glutathione S-transferase, mu 7 (Gstm7) -4.646 0.001 Glycine decarboxylase (Gldc) -4.584 0.025 Prominin 1 (Prom1) -4.020 0.001 Cytochrome b-561 (Cyb561) -4.014 0.001 Keratin 18 (Krt18) -3.806 0.023 ATP-binding cassette, sub-family G, member 2 (Abcg2) -3.618 0.001 CD52 antigen (Cd52) -3.596 0.001

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Table 18

Ten Most Up-Regulated Genes in the Inguinal AT Depot of bGH Mice

Log2 Gene up-regulated (fold_change) p-value

Small nucleolar RNA, C/D box 22 (Snord22) 6.171 0.001 Predicted gene 7104 (Gm7104) 5.256 0.007 Ribosomal protein S6 kinase, polypeptide 4 (Rps6ka4) 4.142 0.001 Glycosylation dependent cell adhesion molecule 1 (Glycam1) 3.546 0.001 Death associated protein-like 1 (Dapl1) 3.394 0.040 Bridging integrator 2 (Bin2) 3.270 0.001 Dedicator of cyto-kinesis 2 (Dock2) 3.247 0.001 Homeodomain interacting protein kinase 3 (Hipk3) 3.215 0.013 TRAF3 interacting protein 3 (Traf3ip3) 3.141 0.020 Potassium voltage-gated channel, shaker-related subfamily, 3.120 0.001 beta member 2 (Kcnab2)

Table 19

Ten Most Down-Regulated Genes in the Inguinal AT Depot of bGH Mice

Log2 Gene down-regulated (fold_change) p-value

Chondroitin polymerizing factor 2 (Chpf2) -5.523 0.001 Secreted frizzled-related sequence protein 5 (Sfrp5) -4.446 0.018 Homeobox C8 (Hoxc8) -3.951 0.003 Corneodesmosin (Cdsn) -3.754 0.001 Epithelial membrane protein 1 (Emp1) -3.661 0.001 Neuronatin (Nnat) -3.611 0.001 Twist basic helix-loop-helix transcription factor 1 (Twist1) -3.397 0.020 Apolipoprotein L 6 (Apol6) -3.366 0.001 Phosphoenolpyruvate carboxykinase 1, cytosolic (Pck1) -3.291 0.001 Prostate transmembrane protein, androgen induced 1 (Pmepa1) -3.212 0.001

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Chapter 5: Discussion

bGH mice allow for the in vivo study of the effect of high circulating GH and

IGF-1 levels in different tissues and organs. AT is a main target of GH, reducing AT mass (O'Sullivan et al., 1994), increasing AT fibrosis (Householder, 2013), and modulating AT immune cell infiltration (Harshman, 2012). Furthermore, GH’s action on

AT are depot dependent (Berryman et al., 2010). Angiogenesis is a process that is involved in AT plasticity; that is, expansion of AT requires proper angiogenesis

(Christiaens & Lijnen, 2010). GH impacts angiogenesis in a tissue dependent manner

(Corbacho et al., 2002; Smith et al., 1997) in that GH can be proangiogenic or antiangiogenic depending on the tissue.

The purpose of this thesis was to investigate the specific effect of GH on the expression levels of angiogenic genes in the inguinal and epididymal AT depots, as well as to elucidate the general biological processes and pathways modulated in these AT depots. The hypothesis for this study was that GH would act as a proangiogenic molecule. Thus, we expected that the gene expression of proangiogenic genes would be increased, especially in the inguinal AT depot. The results of this thesis show that GH negatively modulates the expression of key genes that are required for angiogenesis and only in the inguinal depot. Thus, our hypothesis proved incorrect. We also hypothesized that the main biological processes and pathways altered in the AT depots would be related with lipid metabolism, immune cell action and fibrosis. This hypothesis proved fairly accurate, with GH altering biological processes involved in immune cell action, basic metabolism and lipid metabolism. Overall, I conclude that the inguinal AT depot is 106 the most impacted by GH and that GH may have a negative impact on AT angiogenesis yet a positive influence on AT immune cell infiltration.

AT Angiogenesis

AT blood vessels are responsible for the distribution and delivery of oxygen, nutrients, hormones, adipokines and some types of cells within the tissue. Therefore, the creation of new blood vessels from pre-existing vasculature (angiogenesis) is a requirement for AT expansion. It is known that inhibition of angiogenesis can decreased

AT mass (White et al., 2012). Furthermore, inhibition of AT angiogenesis can have beneficial or detrimental health outcomes, depending on the obesity status of the animal

(El-Ftesi, 2009; Rutkowski et al., 2009). That is, inhibition of AT angiogenesis improves insulin sensitivity in obese animals (Sun et al., 2012) but decreases insulin sensitivity in lean mice that are becoming obese (Sung et al., 2013). There are hormonal signals that impact AT expansion and regression; GH is one of such hormonal signal (O'Sullivan et al., 1994).

Using bGH mice and the inguinal and epididymal AT depots, our results show that GH does not significantly alter RNA levels of angiogenic factors in the epididymal

AT depot but does in the inguinal depot. This may be expected as studies in mouse lines with high and low GH action have also shown that the epididymal depot seems to be minimally impacted in these mice whereas the subcutaneous depot is more affected

(Berryman et al., 2004). For example, the epididymal depot has adipocytes similar in size as control mice, and the mass of this depot is proportional to the size of the mice in most

GH- modified mouse lines (Berryman et al., 2011). In contrast, the inguinal AT depot in 107 bGH mice is most routinely altered compared to controls. For example, the inguinal depot has greater AT fibrosis in bGH mice than other depots (Householder, 2013).

Consistent with these findings, a study that used obese mice injected with varying concentrations of GH reported that the epididymal depot was the least impacted whereas the inguinal depot was the most impacted by the treatment (List et al., 2009). It is also important to appreciate that there is no comparable epididymal fat pad in humans; thus, the clinical significance of this fat pad is questionable.

On the other hand, in the inguinal AT depot, several genes important for angiogenesis were significantly decreased, suggesting a decrease in angiogenesis within this tissue. These genes include leptin (Lep), vascular endothelial growth factor A

(Vegfa), vascular endothelial growth factor B (Vegfb), and metalloproteinase inhibitor 4

(Timp4). Lep is the gene that encodes the adipokine and satiety molecule, leptin. It has been reported that bGH mice have decreased levels of leptin in blood when compared to littermate controls (Berryman et al., 2004). Importantly, leptin is reported to be more highly expressed by subcutaneous AT than by other depots (Hantschel, Wagener,

Neuschl, Schmitt, & Brockmann, 2012); thus, the decrease in circulating leptin in bGH mice fits with a decrease in leptin expression within inguinal AT. Interestingly, endothelial cells present in AT of humans and mice express leptin receptors (Bornstein et al., 2000; Bouloumie, Drexler, Lafontan, & Busse, 1998; Sierra-Honigmann et al., 1998), and leptin appears to be a proangiogenic factor at least in some tissues, increasing retinal neovascularization and increasing Vegf and Fgf2 gene expression (Cao, Brakenhielm,

Wahlestedt, Thyberg, & Cao, 2001; Eri Suganami, 2004). Thus, our results are similar to 108 previous studies with respect to AT and leptin levels and suggest that the decreased leptin levels seen in bGH mice may contribute to the down-regulation of Vegf in the inguinal

AT depot of these mice.

Vegfa and Vegfb are also significantly down-regulated only in the inguinal depot.

These genes encode two very potent proangiogenic molecules and highly specific mitogens for endothelial cells called vascular endothelial growth factor A and vascular endothelial growth factor B (Hoeben et al., 2004). VEGFs are not only important for AT angiogenesis, but also for AT vascular permeability (Hausman & Richardson, 2004), thus it may influence the transit of molecules to and from the AT tissue. Furthermore, it appears that VEGFs not only act as angiogenic molecules but also have a role in lipid metabolism. For example, it has been suggested that VEGF-A promotes browning of white AT (Sun et al., 2012). Moreover, VEGF-B is also involved in fatty acid transport and insulin sensitivity (Hagberg CE, 2010). The crucial role of VEGFs in AT has been demonstrated with genetically engineered mice. For example, mice with no VEGF expression (due to gene disruption of VEGF) fed a high fat diet results in reduced AT mass and insulin resistance, and increased inflammation (Sung et al., 2013). Likewise, ablation of VEGF in exclusively AT of obese ob/ob mice produces reduction of AT mass and an increased insulin sensitivity (Sun et al., 2012). Although there are no studies that specifically assessed the effects of GH on the expression levels of Vegf in AT, Vegf expression has been shown to be greater in the subcutaneous depots as compared to the intra-abdominal depots in studies from cattle, mice and humans (Miyazawa-Hoshimoto et al., 2005; Schlich et al., 2013; Yamada, Kawakami, & Nakanishi, 2010). Thus, the depot- 109 specific trend is consistent in both WT and bGH mice. Results in the current study are also consistent with previous findings that demonstrate that Vegf expression is positively correlated with AT mass (Kurki et al., 2012; Voros, 2005). In this study, WT mice expressed more Vegfa and Vegfb than bGH mice, and the difference in gene expression was only significant for the inguinal AT depot. Because these molecules are so critical for angiogenesis and since both were modulated in a similar manner, it is likely that angiogenesis or the vasculature was different between genotypes in this depot as well.

The final gene significantly down-regulated in the inguinal AT depot in bGH mice was Timp4. TIMP4 is a tissue inhibitor of metalloproteinases. Unlike other metalloproteinases, Timp4 appears to have a restricted gene expression with its highest mRNA expression in heart, ovary and brain (Rahkonen, Koskivirta, Oksjoki,

Jokinen, & Vuorio, 2002). It is important to clarify that this molecule is not well studied, but it appears that its expression in mice is slightly decreased in the gonadal depot when compared to the subcutaneous depot (Maquoi, Munaut, Colige, Collen, &

Lijnen, 2002). TIMP4 inhibits the proteolytic action of several metalloproteinases, thus, decreasing the extracellular matrix degradation. Specifically, TIMP4 action appear to directly inhibit the matrix metalloproteinase 2, which is a type IV collagenase (Bigg et al., 2001). In general, TIMPs act as antiangiogenic molecules because a major step of angiogenesis is proper extracellular matrix remodeling, allowing the endothelial cells to mobilize and establish new blood vessels (Adams &

Alitalo, 2007; Melendez-Zajgla, Del Pozo, Ceballos, & Maldonado, 2008). The results of this thesis are in line with other findings that state that bGH mice have an 110 increased fibrosis or ECM remodeling specially in the inguinal AT depot

(Householder, 2013). One of the main characteristics of bGH mice is its lean phenotype (Palmer et al., 2009), which is accompanied with insulin insensitivity

(Balbis et al., 1992). Low expression levels of Timp4 could help to increase AT fibrosis and therefore may lead to an “unhealthy” AT remodeling. Interestingly, obese mice fed high fat diets also express lower levels of Timp4 when compared to mice fed with standard diet (Maquoi et al., 2002). The results obtained by this thesis, in conjunction with the studies mentioned above, suggest that the low expression level of Timp4 in the inguinal AT depot may have a role in the “unhealthy” AT remodeling seen in bGH mice characterized by increased fibrosis (Householder, 2013), alterations in the immune cell infiltration (Harshman, 2012), and probably decreased angiogenesis.

Four genes from the 43 genes tested were significantly altered in one depot of bGH mice. However, these results might be very significant since Vegfa and Vegfb, in particular, are key molecules that are required for angiogenesis to occur (Clapp et al.,

2009). Collectively, these results suggest that GH may have a negative impact on AT angiogenesis and that this impact is depot dependent. The lower expression of proangiogenic molecules in bGH mice is congruent with other studies showing that less

AT mass is usually associated with less expression of proangiogenic factors (Kurki et al.,

2012; Voros, 2005). These results may also contribute to the explanation why bGH mice have a lean but unhealthy phenotype; that is, bGH mice, despite being lean, have shorter lifespans and are less insulin sensitive than WT mice (Orme, McNally, Cartwright, & 111

Belchetz, 1998a). Recall that decreased AT angiogenesis may be harmful in the case of

AT expansion (Sun et al., 2012). Since GH may be acting as an antiangiogenic molecule in AT since birth, then angiogenesis would be expected to be decreased in AT of bGH mice, leading to AT hypoxia and inhibiting AT expansion. Therefore, other unhealthy characteristics of AT in bGH mice, namely increased AT fibrosis and immune cell infiltration, could be a consequence of the decreased angiogenesis.

RNA-seq Analysis of Biological Pathways and Processes

The RNA-seq analysis again pointed to the specific importance of the inguinal fat pad with respect to GH. That is, the epididymal AT depot of bGH mice had 79 genes that were significantly altered while the inguinal AT depot showed 759 genes that were significantly changed. These results support the hypothesis that the epididymal AT depot is less responsive to GH action than the inguinal AT depot.

As outlined in the research questions, we attempted to group the significantly altered genes of the bGH mice into biological process and pathways, as will be discussed below. However, in the results of this thesis, biological networks are also reported. Even though this discussion will be centered on the biological processes and pathways altered in the AT depots of bGH mice, a brief discussion will address one interesting feature of the altered networks in both depots. Regardless, it is important to consider that these analyses are exploratory and warrant additional studies to draw significant conclusions.

Network analysis and insulin growth factor binding protein 3. The network analyses showed a gene that was up-regulated in both AT depots in bGH mice and that is related with the GH/insulin growth factor-1 (GH/IGF-1) axis. This protein was insulin 112 growth factor binding protein 3 (IGFBP3) (Vottero, Guzzetti, & Loche, 2013). IGF-1 is a mitogen and antiapoptotic molecule that enhances growth and can bind to the IGF-1 receptor as well as the insulin receptor. Therefore, IGF-1 can emulate insulin actions by improving carbohydrate and lipid utilization in muscle, liver and AT (Martin & Baxter,

2011). IGFBP3 is very abundant in the rat and human serum; in rat, this protein is expressed in several tissues including AT. Moreover, in this animal model its RNA expression is highest in kidney, stomach, placenta, uterus, and liver when compared to other tissues (Albiston & Herington, 1992). In humans, tissue culture experiments using

AT explants have shown that IGFBP3 protein is expressed in both subcutaneous and visceral AT depots (Rittig et al., 2012). IGBP3 binds IGF-1 and increases its half-life in the blood (Firth & Baxter, 2002). Further, IGFBP3, along with the other IGFBPs, are thought to reduce IGF-1 action by inhibiting its binding with the receptor (Firth &

Baxter, 2002). Furthermore, studies have shown that both increased and decreased

IGFBP3 results in insulin resistance (Kim, 2013). That is, both IGFBP3 transgenic mice and IGFBP3 knockout mice fed with HFD are insulin resistant (Kim, 2013). Moreover, the results of this thesis are in line with other studies that show that GH increases the expression of IGFBP3, and that increased in IGBP3 may contribute to the insulin resistance seen in bGH mice.

Significantly altered biological processes and pathways in the epididymal AT depot of bGH mice. The 28 genes that were significantly up-regulated in the epididymal

AT depot of bGH mice did not reveal any significantly altered biological processes by the

GO analysis for this depot. The 51 genes that were significantly down-regulated in this 113

AT depot did reveal significantly altered biological processes, which could be mainly grouped as basic metabolism, organelle organization/biogenesis, and cytokine and chemokine action. These biological processes might be somewhat expected based on the understanding of GH’s impact on AT. For example, the processes related to basic metabolism, such as carboxylic acid and organic acid metabolic processes, had altered genes that are known to be involved in lipolysis or fatty acid metabolism, such as 24- dehydrocholesterol reductase (Glcd), hydroxyprostaglandin dehydrogenase 15 (Hpgd), prostaglandin-endoperoxide synthase 2 (Ptgs2), and fatty acid desaturase 2 (Fads2)

(Chilliard, 1993; Doris, Vernon, Houslay, & Kilgour, 1994; El Hafidi et al., 2004).

Likewise, altered pathways related to organelle organization/biogenesis may relate to the impact that GH has on AT adipogenesis and preadipocyte differentiation. It is thought that one of the main steps in adipogenesis is the differentiation of adipocyte precursors to adipocytes. Genes related to cell adhesion and cell shape have a great importance in preadipocyte differentiation (Ali, Hochfeld, Myburgh, & Pepper, 2013; Yang et al.,

2013). Thus, down-regulation of genes related with organelle organization and biogenesis would reduce the need to reorganize and produce cell organelles in preadipocytes, facilitating the inhibition of adipogenesis by GH. Of note, the down-regulated genes of the epididymal AT depot in bGH mice that were related to the cytokine and chemokine process where mainly linked to tumor necrosis factor alpha (TNF-a) regulation. These results are congruent with what is seen in the literature. For example, TNF-a levels are transiently decreased either by exercising mice with elevations in GH (Kizaki et al.,

2011) or by GH supplementation of cell cultures of macrophages and visceral adipocytes 114

(Kizaki et al., 2011; Kubota et al., 2008). Furthermore, experiments made on female GH transgenic mice that express porcine GH have decreased TNF-a in the subcutaneous and intra-abdominal AT depots (Chen et al., 2001). More recently, Kumar and colleagues have shown that GH in macrophage cell culture may decrease the expression of TNF-a via reduction of NF-kB activity (Kumar, Chitra, Lu, Sobhanaditya, & Menon, 2014).

These results suggest that GH may down-regulate inflammatory processes that are mediated by TNF-a.

The results of the pathway analysis for the epididymal AT depot show the same trend as the GO analysis in that significantly altered pathways were down-regulated with no significantly up-regulated pathways. Interestingly, pathways related with immune cell- related inflammation and fibrosis appear to be down regulated. The pathway analysis results, together with the biological processes results, suggest that GH down-regulates the mobilization of monocytes and lymphocytes into the epididymal depot of bGH mice, thus decreasing the expression of cytokines and chemokines within this AT depot. These data are consistent with previous data regarding macrophage and lymphocyte infiltration in bGH mice from our laboratory (Harshman, 2012). In this previous study, the epididymal depot contained a significantly lower percentage of lymphocytes and activated macrophages than the same depot in WT mice. Moreover, the percentage of the M2 macrophages, which are the anti-inflammatory macrophages, was significantly higher in the epididymal AT depot of bGH mice. While the pathway and process results are intriguing, it is important to remember that the changes in the epididymal depot were 115 minimal in comparison to the subcutaneous depot and that these changes would need to be confirmed using other experimental methods.

Significantly altered biological processes and pathways in the inguinal AT depot of bGH mice. The GO analysis revealed that the down-regulated processes of the inguinal AT depot were significantly related with lipid metabolism, specifically with lipogenesis. Lipogenesis is the production of triglycerides by using glucose, alcohol, and amino acids. Glucose is eventually converted to glycerol, and the alcohol and amino acids are transformed into acetyl coenzyme A, which can be converted into fatty acids.

Finally, both glycerol and fatty acids are used to form triglycerides (Insel, Ross,

McMahon, & Bernstein, 2011). The current study shows a down regulation of the biological processes related to glycerol metabolism, alcohol metabolism, fatty acid metabolism, and amino acid metabolism (carboxylic and organic acid metabolism). These results might be expected because it is known that GH decreases AT mass by increasing lipolysis and decreasing lipogenesis (Niemelä, Miettinen, Sarkanen, & Ashammakh,

2008).

The GO analysis for the up-regulated biological processes of the inguinal AT depot of bGH mice show that all of the 10 most significantly altered processes are related to immune cell biology, especially with leukocyte and lymphocyte activation and development. Again, the pathway analysis for the inguinal depot of bGH mice support the findings of the GO analysis and further elucidate that GH upregulates immune genes specifically related with T helper lymphocyte activation. These findings are also congruent with previous results obtained by Harshman and Swaminathan in which it 116 appears that GH may increase the percentage of T helper cells in the inguinal AT depot of bGH mice and decrease T helper cells in mice with no GH action, respectively

(Harshman, 2012; Swaminathan, 2008). It is known that cells from the adaptive immune system, such as T cells and B cells, can be residents of healthy peripheral tissues, including AT (Bouloumie, Casteilla, & Lafontan, 2008; Sathaliyawala et al., 2013).

Further, AT proximity to lymphoid organs may contribute to the infiltration of T and B lymphocytes into the AT (Bouloumie et al., 2008), and inguinal AT has significant lymph nodes. In 2008, Kintscher and colleagues reported that T cells accumulate in the perigonadal AT of mice on HFD even before macrophages and that the accumulation of these T lymphocytes occurs simultaneously to the onset of insulin resistance (Kintscher et al., 2008). Therefore, T lymphocytes may initiate the proinflammatory and unhealthy phenotype in AT. Consistent with this hypothesis, other studies in bGH mice have shown that around the 6 months of age these mice start having alterations in blood pressure (Jara et al., 2014) and in fat mass when compared to WT controls (Berryman et al., 2004).

Thus, it is possible that the proinflammatory phenotype of bGH mice begins also around the 6 months of age with the T lymphocyte infiltration the tissue.

Limitations and Future Directions

This thesis evaluated the effect of GH on AT angiogenesis at the mRNA level as well as described the main biological processes and pathways that are altered in the epididymal and inguinal AT depots of bGH mice. Future studies are needed to elucidate the effect of GH on AT vasculature at the protein level, as well as the mechanisms underlying the possible angiogenic changes in the AT of bGH mice. In addition, the 117 exploratory approach of utilizing the RNA-seq to identify processes and pathways that seem significantly altered in the AT of bGH mice provide many avenues for exploration.

The following issues related to methodologies and research questions might be pertinent for future study.

1. In this thesis, RNA-seq methodology was used. Because this methodology is a

very new methodology, there is not a specific guideline to perform RNA-seq

analysis. Thus, software tools to analyze the data may lead to different results

(Park, Tokheim, Shen, & Xing, 2013). Furthermore, RNA-seq experiments

may have technical and biological variations (Bhargava, Head, Ordoukhanian,

Mercola, & Subramaniam, 2014; Guo et al., 2008). Because RNA-seq is a

very expensive methodology, it was performed using a very low quantity of

biological replicates and no experimental replicates. Therefore, it may be

important to confirm gene expression of the angiogenic genes that were

significantly altered with a different methodology, such as real-time PCR.

2. This study only analyzed the effect of GH on the mRNA expression of genes

related to angiogenesis. Studies have shown that even though mRNA

expression can be a good predictor of protein expression, in some cases and

depending of the molecule, mRNA gene expression may fail to predict protein

levels (Guo et al., 2008). Thus, it is important to analyze whether there is a

change at the protein level in these angiogeneic genes in bGH mice. It would

also be important to assess whether the angiogenic changes are translated into

different vasculature between depots. One methodology that could give a 118

visual representation of the vasculature in the AT is immunohistochemistry

against the endothelial cell marker CD31. Studies have shown that even

though mRNA expression can be a good predictor of protein expression, in

some cases and depending of the molecule, mRNA gene expression may fail

to predict protein levels. For example, the mRNA variation is reported to

explain the protein variation in a range of 1.19 to 19.62% depending on the

gene in human monocytes (Guo et al., 2008).

3. This thesis compared gene expression levels of two AT depots between

genotypes (bGH vs WT mice). Nevertheless, RNA-seq also allows the

comparison of gene expression levels between AT depots in the same

genotype. This comparison could be relevant since it is known that each depot

has specific gene expression profile (Hishikawa et al., 2005). This would

require that the data be analyzed in a different manner, but a depot comparison

could elucidate the impact of GH in the specific AT depot.

4. The biological process and pathway analysis for the inguinal AT depot

showed that GH may up-regulate genes related with immune cell infiltration

and activation, especially genes related with T cell action. Since other studies

also confirmed that GH may alter the T cell populations in AT, it is important

to further analyze this trend by evaluating the type and quantity of T cells that

are present in the tissue. Specifically, it would be relevant to further elucidate

the subtypes of T cells since not all the T regulatory or T helper cells have the

same function (Cipolletta, 2014; Fabbrini et al., 2013; Nishimura et al., 2009; 119

Schipper et al., 2012). Not only is pertinent to evaluate the T cell populations

existing in the AT tissue, but it is equally important to evaluate the activity of

the T cells as presence does not always indicate that the cells are active

(Overed-Sayer, Mosedale, Goodall, & Grainger, 2009). While T cell

populations can be evaluating using fluorescence-activated cell sorting

(FACS), T cell activity could be evaluated through proliferative assays for T

cell function.

5. For the epididymal AT depot, this study showed that the majority of down-

regulated genes in this depot were related with organelle organization and

biogenesis, which presumably may be involved in adipocyte differentiation

and adipogenesis (Ali et al., 2013). Because the literature has controversial

findings regarding the effects of GH on adipogenesis (X. L. Chen et al., 2001;

Niemelä et al., 2008), it would be interesting to perform real time PCR and

confirm gene expression of the significant down-regulated genes related with

this function, as well as evaluate preadipocyte and adipocyte content in the

inguinal and epididymal AT depots through FACS.

6. For this study, we decided to use 6-month-old bGH mice because at that age

mice start to show a significant change in AT mass with respect to WT mice

(Berryman et al., 2004). It may be interesting to evaluate gene expression

profiles at later time points in life since a more severe phenotype comes with

advancing age (Jara et al., 2014; Palmer et al., 2009). Thus, it is possible that

unhealthy phenotypic changes in AT are more evident at later time points. 120

Conclusion

The study of AT has gained importance due to the worldwide increase in obesity and its comorbidities. GH is one of the endocrine signals that can alter AT mass. bGH mice are transgenic mice with high GH levels in the blood and are characterized as lean but “unhealthy.” One of the factors that may influence this phenotype is the AT angiogenesis status of the tissue. In this study, RNA levels that helped to evaluate the GH effect on AT angiogenesis in bGH mice were examined. To accomplish this aim, RNA- seq technology was used; this technology gives the gene expression of virtually all the genes present in the tissue. Thus, this study took full advantage of the RNA-seq to group the significantly altered genes into biological processes and pathways that were significantly altered in the AT of bGH mice to provide avenues for future study.

The results of this study show that in the inguinal AT depot GH may have a negative effect on AT angiogenesis, because bGH mice showed a down-regulation of key molecules of the angiogenic process, such as Vegfa and Vegfb. Furthermore, the RNA- seq results confirmed that GH may have a lower impact on the epididymal AT depot when compared to the inguinal depot. Also, the majority of the significantly altered genes in the epididymal AT depot were down-regulated and related to the organelle organization and biogenesis process; the results observed in this depot may indicate a possible negative effect of GH on adipocyte differentiation. The significantly altered biological process and pathways in the inguinal depot show that GH may up-regulate genes associated with immune cell infiltration and activation, especially genes related with T cell activation. While these results require additional confirmation, they provide 121 many opportunities to explore additional mechanisms for how decreased AT mass could become detrimental to overall health.

122

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Appendix A: RNA Isolation

Materials needed:

 Tissue (80 -100mg  Syringes and needles (~22.5G) recommended, lower quantities  Tweezers are ok, but result in lower yield)  Dry ice/ liquid nitrogen  Eppendorf tubes (3x the number  Chloroform (@RT) of samples) (Autoclaved,  Isopropanol (isopropyl alcohol RNAse/DNAse-free) @RT)  P200 pipette  Ice cold ethanol (put in -20˚C to  P200 filter tips cool before use)  Centrifuge  Molecular grade water (at RT)  Trizol reagent (@4˚C)  NaCl (will be diluted to 5mM,  Small probe homogenizer and can make 20mM stock) (at RT) tips, Precellys stuff  RNAse Zap or RNAse Away

Notes:  Be careful of contamination. RNAse Zap gloves, work surface, homogenizer tips, tweezers, and pipettes before use!  Trizol reagent (phenol) may not be put down sink, and must be properly disposed of with phenol waste  Always label tubes with the, sample number, tissue/depot, “RNA”, and date of isolation.

Procedure:

1. Add 200 µl Trizol reagent to 80-100 mg frozen tissue 2. Homogenize fat, lung, brain with small probe homogenizer until there are no pieces left (Do not let tissue get too warm while homogenizing) and other tissues with Precellys 3. Add an additional 800 µl Trizol to bring total volume to 1 ml and vortex 4. Spin at max speed (~17g) for 5 minutes at 4˚C a. Use syringe to remove Trisol layer (bottom, pink layer. Clear lipid layer will be on top) and put into new tube*Can be stored at this point at -80˚C 5. Add 200 µl chloroform and vortex 6. Spin at max speed (~17g) for 15 minutes at 4˚C 7. There will be three layers: clear aqueous layer with RNA on top, whitish DNA and protein layer in the middle, and the pink phenol layer at the bottom (and maybe a pellet of cellular debris at the very bottom). Remove aqueous phase (top, clear layer) with a P200 pipette and put into new tube. Leave a small amount of 158

the aqueous layer on top of the protein/DNA layer to prevent touching/disturbing the protein/DNA layer and causing contamination. 8. Add 500 µl isopropanol and vortex 9. Keep at room temperature for about 10 minutes (5 minutes is ok) 10. Spin at max for 10 minutes at 4˚C, pellet will form. It is helpful to arrange all tubes with the hinge in or out so the pellet location is known. 11. Pour out supernatant a. *At this point, you can proceed in two ways. Option 1 will give better yield but lower quality, while option 2 will yield better quality but lower quantity.

b. Option 1

12. Add 1ml 75% ice cold ethanol and vortex 13. Spin for 15 minutes at max speed (~17g) at 4˚C 14. Carefully pour out ethanol (pellet could be loose) to avoid loss, do not pour directly into waste. Pour into a second Eppendorf tube so if the pellet slides out, it can be recovered. 15. *Repeat steps 13-15 three times 16. Blot on paper towels sprayed with RNAse Zap and dry on side or upside-down on paper towel for at room temperature for at least 15 minutes 17. Resuspend in 25 µl molecular grade water and pipette up and down several times to resuspend pellet

a. Option 2

18. Add 1ml 75% ice cold ethanol and vortex 19. *Can be stored at this point at -20˚C 20. Spin for 15 minutes at max speed (~17g) at 4˚C 21. Carefully pour out ethanol (pellet could be loose) to avoid loss, do not pour directly into waste. Pour into a second Eppendorf tube so if the pellet slides out, it can be recovered. 22. Blot on paper towels sprayed with RNAse Zap and dry on side or upside-down on paper towel for at room temperature for at least 15 minutes 23. Add NaCl in molecular grade water to a final concentration of 5mM (For example, add 20 µl water then 5ul 20mM NaCl) 24. Add 3x the total volume of ice cold 100% ethanol (for example for 25ul, add 75ul ethanol) 25. Freeze in -80˚C overnight (or freeze in liquid nitrogen and thaw) 26. Spin at max speed (~17g) for 15 minutes at 4˚C 27. Carefully remove supernatant 28. Add 1 ml ice cold 75% ethanol 29. *Repeat steps 20-22 three times 159

30. Carefully remove supernatant and blot onto sterile Whatman paper to get out all droplets of ethanol 31. Let dry for 15 minutes on side it upside down at room temp 32. Add 25 µl molecular grade water and pipette up and down several times to resuspend pellet *Can now be stored in -80˚C and/or concentration and quality measured by nanodrop

160

Appendix B: mRNA from Total RNA Isolation Protocol

Materials

 Work RNase free and wear gloves.  Ice  Heater 65 C  micro centrifuge tubes  Magnet  Rotator

Preparation of RNA

1. Adjust the volume of your up-to 75 μg total RNA to 100 μl with distilled DEPC- treated water. Omit this step if only a small 2. Heat to 65°C for 2 minutes to disrupt secondary structures. Place on ice.

Preparation of Dynabeads®

1. Transfer 200 μl (1 mg) of well resuspended Dynabeads® to a microcentrifuge tube. 2. Place the tube on the magnet for 30 seconds, or until all Dynabeads® have migrated to the tube wall. 3. Pipette off the supernatant, remove the tube from the magnet. 4. Add 100 μl Binding Buffer to calibrate the beads. 5. Put the tube back on the magnet and remove the supernatant.

6. Remove the tube from the magnet. 7. Add 100 μl Binding Buffer to the Dynabeads® . Optimal hybridization conditions are obtained in Binding Buffer added in a 1:1 ratio relative to sample volume. If your total RNA is more dilute than 75 μg/100 μl, then simply add an equal volume ofBinding Buffer to the Dynabeads® .

Isolation of mRNA

1. Add the total RNA to the Dynabeads® /Binding Buffer suspension. Mix thoroughly. 2. Rotate on a roller or mixer for 3–5 minutes at room temperature to allow mRNA to anneal to the oligo (dT) on the beads. 3. Place the tube on the magnet until solution is clear. Remove the supernatant. 161

4. Remove the tube from the magnet and wash the mRNA-bead complex twice with 200 μl Washing Buffer B. Remove all the supernatant between each washing step with the help of the magnet – this is important when working with small volumes. 5. If elution is required, add desired amount (10–20 μl, or down to 5 μl) of 10 mM Tris-HCl, pH 7.5. 6. Heat to 65–80 °C for 2 minutes and place the tube immediately on the magnet. Transfer the eluted mRNA to a new RNase-free tube. Note: This protocol can be scaled up or down to adjust mRNA yield. Increase or decrease the quantities of the kit reagents proportionally with any changes in total RNA starting sample. Optimization may be needed.

162

Appendix C: Galaxy Workflow

Go to the galaxy website: https://usegalaxy.org/

Note: The parameter mentioned here are the parameter that need to be changed in the Galaxy website and are not pre-defined. 1. Up-load the raw data that is received from the NGS machine, and the reference genome mouse mm10. 2. NGS: QC and manipulation  FASTQ groomer (Galaxy requires that everything go into Sanger format to be used. If you know your data is in sanger format, select Sanger Ilumina 1.8+) 3. NGS: QC and manipulation  Clip adapter sequences (enter and clip each one of your adapter sequences, one by one). 4. NGS: QC and manipulation  FastQC:Read QC, to read and manipulate the data, it converted) generates a HTML report, gives quality report. 5. NGS: QC and manipulation  FASTQ Trimmer (5’end tend to be bad quality, so specify how many bases you want to remove in each end, do it in absolute values, we also trimmed the adaptors), in our case we will trimmed 15 bp based on FastQC quality checked. 6. NGS: QC and manipulation  FASTQ Quality Trimmer. Input: Window size: 3, Maximum number of bases to exclude from the window during aggregation: 1, and Quality Score: 20. 7. Re-check quality of the data set with NGS: QC and manipulation  FastQC:Read QC, the average read length should be between 150 to 300 bp. 8. If the average size of reads is not between 150 and 300 bp trim the sequences with NGS: QC and manipulation  Trim sequences, first base to keep: 1, last base to keep: 300. 9. NGS: RNA-seq Tophat for Illumina, Use a built in reference genome or own from your history: Use a genome from history and select the pre-uploaded mouse mm10 genome. Is this library mate-paired?: single-end, Use Own Junctions: yes, Minimum intron length that may be found during coverage search: dpends on yout FastQC analysis quality checking, usually 25. 10. NGS: RNA-seq Cufflinks. Use Reference Annotation: Use reference annotation, select mouse mm10. Perform Bias Correction: Yes, Use multi-read correct: Yes, Use effective length correction: Yes. 11. NGS: RNA-seq Cuffmerge. 12. NGS: RNA-seq Cufflinks. Use Reference Annotation: Use reference annotation, select mouse mm10. Use multi-read correct: Yes, Perform Bias Correction: Yes, Reference sequence data: cached, Include Read Group Datasets: Yes.

163

Appendix D: Angiogenic Molecules to be Tested for Gene Expression

Table 20

Angiogenic Molecules for Gene Expression

Proangiogenesis

Amphiregulin (Areg) Follistatin (fst) Angiogenin (Ang) Growth Hormone Receptor (GHR) Angiopoietin-2 (Angpt2) Hepatocyte growth factor (Hgf) Betacellulin (Btc) Hypoxia inducible factor 1, alpha subunit (Hif1a) c-fos induced growth factor (also VEGF-D) Insulin-like growth factor 1 (Igf1) (Figf) Chemokine (C-C motif) ligand 2 (Ccl2) Insulin-like growth factor 1 receptor (Igf1r) Chemokine (C-C motif) ligand 3 (Ccl3) Leptin (Lep) Chemokine (C-X-C motif) ligand 1 (Cxcl1) Plasminogen (Plg) Chemokine (C-X-C motif) ligand 2 (Cxcl2) Platelet/endothelial cell adhesion molecule 1 (Pecam1) Collagen, type VIII, alpha 1 (Col8a) Prolactin (Prl) Colony stimulating factor 3 (Csf3) Transforming growth factor, beta 1 (Tgfb1) Colony stimulating factor 3 receptor (Csf3r) Tumor necrosis factor (Tnf) Endoglin (Eng) Vascular endothelial growth factor (Vegfa) Endothelin 1 (Edn1) Vascular endothelial growth factor (Vegfb) Epidermal growth factor (Egf) Vascular endothelial growth factor (Vegfc) Fas ligand (also Tnfsf6) (Fasl) Vascular endothelial growth factor receptor 1 (Vegfr-1) Fibroblast growth factor 1 (Fgf1) Vascular endothelial growth factor receptor 2 (Vegfr-2) Fibroblast growth factor 2 (Fgf2) Vascular endothelial growth factor receptor 3 (Vegfr-3)

164

Table 20: Continued

Antiangiogenesis

Anaplastic lymphoma kinase (Alk) Tissue inhibitor of metalloprotease-2 (Timp2) Interleukin 1 beta (Il1b) Tissue inhibitor of metalloprotease-3 (Timp3) Interleukin 17A (Il17a) Tissue inhibitor of metalloprotease-4 (Timp4) Interleukin 6 (Il6) Tissue inhibitor of metalloproteinase 1 (Timp1)

165

Appendix E: Genes of the Significantly Altered Networks in the Epididymal and

Inguinal AT Depots in bGH Mice

Table 21

Genes of the “Cellular Function and Maintenance, Lipid Metabolism, Small Molecule Biochemistry Network” of the Epididymal Depot

Genes

ATP citrate lyase (ACLY) Adiponectin, C1Q and collagen domain containing (ADIPOQ) Cyclin-dependent kinase inhibitor 1A (P21) (CDKN1A) ELOVL family member 6, elongation of long chain fatty acids (yeast) (ELOVL6) Fatty acid binding protein 4, adipocyte (FABP4) Fatty acid synthase (FASN) Histone deacetylase 6 (HDAC6) Insulin-like growth factor binding protein 3 (IGFBP3) Keratin 18 (KRT18) Keratin 8 (KRT8) Leptin (LEP) Leptin receptor (LEPR) Matrix metallopeptidase 7 (MMP7) Nuclear receptor subfamily 1, group H, member 2 (NR1H2) Nuclear receptor subfamily 4, group A, member 1 (NR4A1) Peroxisome proliferator activated receptor gamma (PPARG) Peroxisome proliferative activated receptor, gamma, coactivator 1 alpha (PPARGC1A) Serum amyloid A 3 (Saa3) Sterol regulatory element binding transcription factor 1 (SREBF1) Transformation related protein 53 (TP53) 166

Table 22

Genes of the “Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry Network” of the Inguinal Depot

Genes

Acetyl-Coenzyme A carboxylase alpha (ACACA) Hexose-6-phosphate dehydrogenase (glucose 1-dehydrogenase) (H6PD) Acyl-coa synthetase long-chain family member 1 (ACSL1) Isocitrate dehydrogenase 1 (NADP+), soluble (IDH1) Alcohol dehydrogenase 1 (class I) (ADH1C) Insulin-like growth factor binding protein 3 (IGFBP3) Adiponectin, C1Q and collagen domain containing (ADIPOQ) Interleukin 1 beta (IL1B) Adrenergic receptor, beta 3 (ADRB3) Leptin (LEP) Angiopoietin-like 4 (ANGPTL4) Lectin, galactose binding, soluble 3 (LGALS3) ADP-ribosylation factor related protein 1 (ARFRP1) Lipase, hormone sensitive (LIPE) Cholecystokinin B receptor (CCKBR) Lipoprotein lipase (LPL) Chemokine (C-C motif) ligand 5 (CCL5) Poly (ADP-ribose) polymerase family, member 2 (PARP2) CD36 antigen (CD36) Phosphoenolpyruvate carboxykinase 1, cytosolic (PCK1) CCAAT/enhancer binding protein (C/EBP), alpha (CEBPA) Perilipin 1 (PLIN1) Cannabinoid receptor 1 (brain) (CNR1) Patatin-like phospholipase domain containing 2 (PNPLA2) Diacylglycerol O-acyltransferase 1 (DGAT1) Peroxisome proliferator activated receptor alpha (PPARA) Delta-like 1 homolog (Drosophila) (DLK1) Retinol binding protein 4, plasma (RBP4) Eukaryotic translation initiation factor 4E binding protein 1 Retinoid X receptor alpha (RXRA) (EIF4EBP1) Ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) Glyceronephosphate O-acyltransferase (GNPAT) Thyroid hormone responsive (THRSP) Glycerol-3-phosphate acyltransferase, mitochondrial (GPAM) 167

Table 23

Genes of the “Carbohydrate Metabolism, Lipid Metabolism, Molecular Transport Network” of the Inguinal Depot

Genes

Acyl-coa thioesterase 11 (ACOT11) Leptin receptor (LEPR) Adiponectin, C1Q and collagen domain containing (ADIPOQ) Lectin, galactose binding, soluble 12 (LGALS12) Aquaporin 7 (AQP7) Lectin, galactose binding, soluble 3 (LGALS3) CD44 antigen (CD44) Lipase, hormone sensitive (LIPE) Cysteine dioxygenase 1, cytosolic (CDO1) Macrophage migration inhibitory factor (MIF) CCAAT/enhancer binding protein (C/EBP), alpha (CEBPA) Nuclear receptor subfamily 1, group H, member 4 (NR1H4) CCAAT/enhancer binding protein (C/EBP), beta (CEBPB) Phocomelic (PC) Complement factor D (adipsin) (CFD) Phosphodiesterase 3B, cgmp-inhibited (PDE3B) Cytochrome P450, family 19, subfamily a, polypeptide 1 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (CYP19A1) (PFKFB3) Diacylglycerol O-acyltransferase 2 (DGAT2) Perilipin 2 (PLIN2) Eukaryotic translation initiation factor 4E binding protein 1 Peroxisome proliferator activated receptor gamma (PPARG) (EIF4EBP1) Fibroblast growth factor 21 (FGF21) Peroxisome proliferative activated receptor, gamma, coactivator 1 alpha (PPARGC1A) Forkhead box O1 (FOXO1) Protein kinase, camp dependent regulatory, type II beta (PRKAR2B) Glycerol-3-phosphate dehydrogenase 1 (soluble) (GPD1) Retinoic acid receptor responder (tazarotene induced) 2 (RARRES2) Hexokinase 2 (HK2) Signal transducer and activator of transcription 5A (STAT5A) Insulin receptor substrate 1 (IRS1) Uncoupling protein 1 (mitochondrial, proton carrier) (UCP1) Jun proto-oncogene (JUN) Vascular endothelial growth factor A (VEGFA) Leptin (LEP) 168

Table 24

Genes of the “Endocrine System Disorders, Gastrointestinal Disease, Hepatic System Disease Network” of the Inguinal Depot

Genes

Acetyl-Coenzyme A carboxylase alpha (ACACA) Lipase, hormone sensitive (LIPE) Adiponectin, C1Q and collagen domain containing Lipin 1 (LPIN1) (ADIPOQ) Arrestin, beta 1 (ARRB1) Lipoprotein lipase (LPL) CCAAT/enhancer binding protein (C/EBP), alpha (CEBPA) Methyl-cpg binding domain protein 1 (MBD1) Chemokine-like receptor 1 (CMKLR1) Metallothionein 1E (MT1E) Carnitine palmitoyltransferase 2 (CPT2) Nuclear receptor subfamily 4, group A, member 1 (NR4A1) Dystroglycan 1 (DAG1) Peroxisome proliferator activated receptor alpha (PPARA) Diacylglycerol O-acyltransferase 1 (DGAT1) Peroxisome proliferator activated receptor gamma (PPARG) Fatty acid binding protein 4, adipocyte (FABP4) Resistin (RETN) Fatty acid synthase (FASN) Ribosomal protein S6 kinase polypeptide 1 (RPS6KA1) Four and a half LIM domains 1 (FHL1) Sarcoglycan, beta (dystrophin-associated glycoprotein) (SGCB) Insulin-like growth factor I receptor (IGF1R) Sarcoglycan, beta (dystrophin-associated glycoprotein) (SGCD) Interleukin 16 (IL16) Sarcoglycan, epsilon (SGCE) Insulin receptor (INSR) Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) Lymphocyte cytosolic protein 1 (LCP1) Sarcospan (SSPN) Leptin (LEP) Surfeit gene 1 (SURF1) Leptin receptor (LEPR) Tumor necrosis factor (TNF) Lectin, galactose binding, soluble 1 (LGALS1)

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