Analysis of Genetic Susceptibility to Type Ii Diabetes in Mice

ANALYSIS OF GENETIC SUSCEPTIBILITY TO TYPE II DIABETES IN MICE

SOHA N. YAZBEK

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy

Dissertation advisor: Dr. Joseph H. Nadeau

Department of Genetics

CASE WESTERN RESERVE UNIVERSITY

August 2010

CASE WESTERN RESREVER UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Soha N. Yazbek ______candidate for the PhD degree*

Mark Adams (signed)______(chair of the committee)

Joseph Nadeau ______

Colleen Croniger ______

Mitchell Drumm ______

May 4, 2010

(date) ______

*We also certify that written approval has been obtained for any proprietary material contained therein.

Dedication

I dedicate this work to the soul of my late grandmother, Mountaha Harb, who died two years short of seeing what I have become. I am sure she is watching over me and I hope she is proud.

Table of Contents

Dedication ...... 3

TABLE OF CONTENTS...... 4

List of tables ...... 7

List of figures ...... 8

Acknowledgement ...... 9

Abstract ...... 11

1 CHAPTER 1: BACKGROUND AND SIGNIFICANCE ...... 13

1.1 Type II diabetes: An introduction ...... 14

1.2 Overview of Insulin Action on Glucose Homeostasis ...... 16 1.2.1 Insulin ...... 16 1.2.2 Insulin Secretion ...... 19 1.2.3 Insulin Signaling ...... 21 1.2.4 Glucose homeostasis ...... 22

1.3 Genetics of Type 2 diabetes ...... 25 1.3.1 Genetic studies in humans ...... 26 1.3.2 Missing Heritability ...... 30 1.3.3 Genetic studies in mouse models ...... 32 1. Spontaneous mutant mouse models of T2D ...... 32 2. Single gene Knock-out and transgenic models of T2D ...... 33 3. Inbred strains (mouse models of polygenic T2D) ...... 36

1.4 C57BL/6J and A/J Inbred Strains: Alternative model ...... 38 1.4.1 Advantage of B6 and A/J inbred strains ...... 38 1.4.2 B6-chrA CSS development and advantage ...... 40 1.4.3 Choosing CSS-6 ...... 45

1.6 Summary and Research Aims ...... 46

2 CHAPTER 2: INCREASED MITOCHONDRIAL OXIDATIVE PHOSPHORYLATION IN THE LIVER IS ASSOCIATED WITH OBESITY AND INSULIN RESISTANCE ...... 49

2.1 Abstract ...... 50

2.2 Introduction ...... 52

2.3 Materials and Methods ...... 54 2.3.1 Mice...... 54 2.3.2 RNA Isolation...... 54 2.3.3 Microarray hybridization...... 55 2.3.4 Microarray data analysis...... 55 2.3.5 Glucose tolerance test (GTT) and insulin secretion ...... 55 2.3.6 Insulin tolerance test (ITT)...... 56 2.3.7 Statistics...... 56

2.4 Results ...... 57 2.4.1 Localization of Obrq2 to a 30 Mb interval on chromosome 6 ...... 57 2.4.2 6C1 mice are insulin resistant relative to 6C2 ...... 59 2.4.3 Global gene expression analysis ...... 61 2.4.4 Genes involved in oxidative phosphorylation are over-expressed in the liver of 6C1 ...... 63

2.5 Discussion ...... 67

3 CHAPTER 3: SOLUTE RECEPTOR SLC35B4 OVER-EXPRESSION LINKED TO DIET-INDUCED OBESITY AND INSULIN RESISTANCE ...... 74

3.1 Abstract ...... 75

3.2 Introduction ...... 77

3.3 Materials and Methods ...... 80 3.3.1 Husbandry ...... 80 3.3.2 Generation of subcongenic and subsubcongenic strains ...... 80 3.3.3 Sequence Analysis...... 81 3.3.4 Fasting insulin, fasting glucose and HOMA-IR ...... 81 3.3.5 Glucose Tolerance Test...... 82 3.3.6 Tissue Collection and RNA extraction ...... 82 3.3.7 Real-time quantitative PCR...... 82 3.3.8 Hyperinsulinemic-Euglycemic Clamp...... 82 3.3.9 Determination of glucose uptake in peripheral tissues...... 84 3.3.10 Statistics...... 85

3.4 Results ...... 85 3.4.1 Obrq2a regulates body weight and IR ...... 85 3.4.2 Inheritance pattern of Obrq2a1 ...... 96 3.4.3 Obrq2a1 is associated with decreased glucose tolerance ...... 98 3.4.4 Increased hepatic glucose production disrupts glucose homeostasis in Obrq2a1 ...... 100 3.4.5 Candidate gene analysis of the Obrq2a1 interval ...... 103

3.5 Discussion ...... 106

4 CHAPTER 4: PATERNAL GENOTYPE DETERMINES BODY WEIGHT AND FOOD INTAKE FOR MULTIPLE GENERATIONS ...... 113

4.1 Abstract ...... 114

4.2 Introduction ...... 115

4.3 Materials and Methods ...... 120 4.3.1 Husbandry ...... 120 4.3.2 Genotyping...... 120 4.3.3 Food Intake ...... 121 4.3.4 Statistics...... 121

4.4 Results ...... 122 4.4.1 Parental effect of Obrq2a on body weight ...... 122 4.4.2 Inheritance of the Obrq2a parental effect is sequence independen ...... 126 4.4.3 Parental effect of Obrq2a is transgenerational ...... 128 4.4.4 Obrq2a regulates body weight when inherited through the paternal lineage ...... 130 4.4.5 Differences in food intake associated with paternal inheritance of obesity resistance ...... 135

4.5 Discussion ...... 136

5 CHAPTER 5: SUMMARY AND FUTURE DIRECTIONS ...... 142

5.1 Summary ...... 143 5.1.1 OXPHOS and Insulin Resistance ...... 145 5.1.2 Fractional Genetics and Discovery of Slc35b4 ...... 147 5.1.3 Transgenerational Effects on Obesity ...... 149

5.2 Future Directions ...... 151 5.2.1 Do expression differences in OXPHOS have a functional effect and where do they map to within the interval? ...... 151 5.2.2 Does the insulin resistant phenotype develop prior to the OXPHOS alterations and is it dependant on diet? ...... 153 5.2.3 What is the function of Slc35b4 on glucose production in HepG2 cells? ...... 155 5.2.4 What is the phenotype of Slc35b4 knockout mouse? ...... 156 5.2.5 What is the causative sequence variation in the parental effect on obesity? ...... 156 5.2.6 What is the underlying genetic basis of the identified QTLs? ...... 157

5.3 Conclusion ...... 158

6 CHAPTER 6: REFERENCES ...... 161

List of tables

CHAPTER 2

TABLE 2.1 PATHWAYS THAT ARE DIFFERENTIALLY EXPRESSED BETWEEN 6C1 AND 6C2 ...... 64

CHAPTER 3

TABLE 3.1 OBRQ2A METABOLIC PROPERTIES...... 90 TABLE 3.2 6C2D SUBCONGENIC PANEL ...... 91 TABLE 3.3 METABOLIC PHENOTYPE OF RECIPROCAL CROSSES...... 97 TABLE 3.4 QUANTITAVE RT-PCR ANALYSIS OF OBRQ2A1 CANDIDATE GENES ...... 105 TABLE 3.5 QTL EFFECT SIZE ...... 108

List of figures

CHAPTER 1

FIGURE 1.1 METABOLISM REGULATION BY INSULIN...... 18 FIGURE 1.2 GLUCOSE SENSING IN PANCREATIC BETA CELLS...... 20 FIGURE 1.3 INSULIN SIGNALING CASCADE...... 24 FIGURE 1.4 CONSOMIC STRAIN DEVELOPMENT...... 41 FIGURE 1.5 DETECTION OF QTL USING THE CONSOMIC STRAINS...... 44

CHAPTER 2

FIGURE 2.1 MAP OF CHROMOSOME 6 CONGENIC STRAINS...... 58 FIGURE 2.2 GLUCOSE AND INSULIN TOLERANCE TEST ...... 60 FIGURE 2.3 UPREGULATED GENES IN THE OXIDATIVE PHOSPHORYLATION PATHWAY...... 65 FIGURE 2.4 EXPRESSION IN ELECTRON TRANSPORT CHAIN COMPLEXES ...... 66

CHAPTER 3

FIGURE 3.1 SCHEMATIC OF SUB-CONGENIC PANEL DERIVED FROM STRAIN 6C2 AND 6C2D...... 88 FIGURE 3.2 SCHEMATIC OF SUB-CONGENIC PANEL DERIVED FROM STRAIN 6C2D- BODY WEIGHT...... 92 FIGURE 3.3 SCHEMATIC OF SUB-CONGENIC PANEL DERIVED FROM STRAIN 6C2D- GLUCOSE...... 93 FIGURE 3.4 SCHEMATIC OF SUB-CONGENIC PANEL DERIVED FROM STRAIN 6C2D- INSULIN...... 94 FIGURE 3.5 SCHEMATIC OF SUB-CONGENIC PANEL DERIVED FROM STRAIN 6C2D- HOMA-IR...... 95 FIGURE 3.6 GLUCOSE TOLERANCE TEST (GTT) ...... 99 FIGURE 3.7 HYPERINSULINEMIC – EUGLYCEMIC CLAMP...... 102

CHAPTER 4

FIGURE 4.1 PARENTAL EFFECT IN F1-BODY WEIGHT ...... 123 FIGURE 4.2 PARENTAL EFFECT IN F1-GLUCOSE ...... 124 FIGURE 4.3 PARENTAL EFFECT IN F1- INSULIN ...... 125 FIGURE 4.4 NON-GENOMIC PARENTAL EFFECT – BODYWEIGHT ...... 127 FIGURE 4.5 PARENTAL EFFECT IN F3- BODY WEIGHT ...... 129 FIGURE 4.6 PATERNAL EFFECT –BODY WEIGHT ...... 132 FIGURE 4.7 GRAND-PATERNAL EFFECT –BODY WEIGHT ...... 133 FIGURE 4.8 SCHEMATIC OF BACKCROSS- BODY WEIGHT ...... 134

Acknowledgement

My interest in the genetics of metabolic disease led me to the laboratory of

Dr. Joseph Nadeau in the spring of 2006. Dr. Nadeau has helped me to become an independent scientist capable of making my own scientific decisions. He taught me how to ask critical questions and pursue answers, how to be the toughest critic for my own work, and how to accept failure and always find a way to continue through with good research. For that, I am eternally grateful. My success would have not been achieved if it were not for all previous and current members of the Nadeau lab that always provided the best constructive criticism and helped improve my work. Particularly, I acknowledge the help of Dr. David

Sinasac in starting up my own project. I am also grateful for the guidance, mentorship and collaboration provided by Dr. David Buchner that led to the completion of my work and the synthesis of good science. I am also very thankful for the helpful discussions and technical assistance of Dr. Jason Heaney. Mostly

I am thankful for the friendship of Stephanie, Jennifer and Jason. I am also indebted to Kevin Jimenez and Lonnie Thomas for their wonderful care of our mouse colony. My thesis committee, Dr. Mark Adams, Dr. Colleen Croniger, and

Dr. Mitchell Drumm provided endless guidance and support for this work. I specifically acknowledge Dr. Colleen’s availability for help and advice at anytime throughout my 5 years in the program.

I could never have been able to survive leaving my family behind and moving thousands of miles away to pursue my Ph.D if it were not for the support

9 of the amazing Lebanese and Syrian community in Cleveland. I thank the Shaia family, Talal and Maise, Rouba and Bassam, Wael, Diana and Charles, Johny and lara, and Jihad for their support and friendship. Mostly I am glad to have had

Charbel and Marcelle Abou Diwan in my life in Cleveland and I am forever grateful for all the support Charbel gave me, as any brother would. I also consider myself blessed to have had a sister like Ghunwa and her husband Sam who shared all my laughter and tears, and who were there for me in all my needs for the past 5 years and have become closer than family.

I must also acknowledge my friends and family in Lebanon who extended their love and support to me over the miles. Particularly, my grandmother, my uncles Bahaa and Fawzi, their wives Dana and Nadine, both my aunts and all my cousins. I thank my amazing girl friends Barbara, Amira, Rebecca, Nadine,

Jossette, Maria and specially Maya and my sister Reine. I am also very thankful for the love and prayers of my aunty Bernadette and my in laws.

Last but not least, I acknowledge the support and love of my two brothers

Joe and Jad. I also am forever in debt to my parents Nabil and Zeina, who raised me to become the best I can , who were always there for emotional support and encouragement and who sacrificed their own pleasure to pay my way through my degree. Finally, I will always be grateful for the love of my husband Tony who endured a 5-year long-distance relationship and was always pleasant and supportive to the extreme.

Analysis of Genetic Susceptibility to Type II Diabetes in Mice

SOHA N. YAZBEK

Abstract

The prevalence of type 2 diabetes (T2D) is increasing as obesity increases worldwide and insulin resistance (IR) remains the most important factor in its development. The resulting hyperglycemia leads to complications including retinopathy, stroke and amputations. Life style changes and treatments have not been successful in elevating the progression of IR into diabetes nor preventing co-morbidities with cardiovascular involvement. Despite compelling evidence that susceptibility to obesity and T2D is highly heritable and considerable progress with gene identification, most susceptibility genes continue to elude discovery.

We used mouse models of diet-induced metabolic disease to facilitate gene discovery and better characterize the underlying genetic architecture of T2D.

Analysis of the C57BL/6JA/J panel of Chromosome substitution strains (CSS) identified 8 chromosomes with at least 1 QTL affecting glucose homeostasis.

First, we analyzed global gene expression patterns in 6C1 and 6C2 congenic strains of CSS-6 defining Obrq2, a QTL for body weight, and measures of IR.

Pathway analysis of global gene expression patterns in liver identified expression

11 level differences between 6C1 and 6C2 in pathways related to mitochondrial oxidative phosphorylation (OxPhos). The OxPhos expression differences were subtle but evident in each complex of the electron transport chain. This data suggests the importance of hepatic mitochondrial function in the development of obesity and insulin resistance. Next, we developed two consecutive subcongenic panels for Obrq2. Through genetic and phenotypic analysis of congenic, subcongenic and subsubcongenic strains, we uncovered a complex genetic architecture for metabolic traits associated with T2D. Multiple closely linked QTLs demonstrated strong effects with considerable phenotypic heterogeneity.

Analysis of one of them, Obrq2a1, identified the solute receptor Slc35b4 as a potential regulator of obesity and insulin resistance. Slc35b4 mRNA expression level differences in liver were associated with the phenotype. Finally, we provided first evidence for a non-imprinting transgenrational paternal effect on body weight and food intake using crosses between obesity resistant congenic strain 6C2d and obesity sensitive strain B6. The phenotype was transmitted through the paternal lineage for at least 3 generations, but was lost if passed through the female lineages.

1 Chapter 1: Background and Significance

1.1 Type II diabetes: An introduction

Type II diabetes (T2D) is the most common endocrine disorder worldwide and a leading health problem particularly throughout the developed world. The disorder has risen in prevalence dramatically in the past two generations reaching epidemic proportions (Frayling 2007). More than a 150 million individuals worldwide are affected with T2D, a number that is thought to double in the next two generation mounting up to 300 million by the year 2025 (Wild, Roglic et al. 2004; Moore and Florez 2008). Moreover, since the year 2000 it is thought that one in every three Americans born will develop T2D affecting 20 million (7% of the population) Americans thus far (Stolerman and Florez 2009). This has employed a huge economical burden on societies. For example, the cost of managing the disease is 174 million dollars/year in the United States alone

(American Diabetes Association statistics, http://www.diabetes.org/diabetes- basics/diabetes-statistics.jsp, last accessed March 2010).

T2D is diagnosed in patients with a fasting plasma level of glucose of at least 126 mg/dl. More importantly, T2D and associated hyperglycemia is the leading cause of new cases of blindness, renal failure and limb amputation. In addition, T2D is associated with increased risk of cardiovascular disease and mortality (Lazar and Saltiel 2006). The resulting hyperglycemia is currently controlled using therapies such as lifestyle modifications, sulfonylureas, or metformin, which dramatically reduce morbidity. However, these therapies

14 frequently fail to achieve optimal glycemic levels, which is particularly important to minimize the risk of microvascular complications (Nathan, Buse et al. 2009).

Insulin resistance (IR) remains the primary factor involved in the development of diabetes and still one of the key avenues for T2D treatment

(Mokdad, Ford et al. 2003). Patients with a fasting glucose level ranging between

90-125 mg/dl accompanied with elevated fasting insulin levels are said to be insulin resistant. IR is defined as the decreased ability of insulin to control blood glucose at homeostatic levels (70-90mg/dl). Thus, this translates to a decreased ability to promote glucose uptake into muscle and fat cells and increase hepatic glycogen production as well as the decrease of hepatic glucose production (see overview of insulin action –section 1.2) (Abdul-Ghani, Tripathy et al. 2006; Trout,

Homko et al. 2007). T2D develops when the increased demand for insulin brought about by IR is accompanied by a failure in the beta islets of the pancreas to secrete enough insulin to compensate (Keller, Choi et al. 2008).

IR occurs in around 25% of the worldwide population and it currently effects 57 million individuals in the united states (40% of which will go on to develop diabetes) (Altuntas, Bilir et al. 2005) (American Diabetes Association statistics, http://www.diabetes.org/diabetes-basics/diabetes-statistics.jsp, Last accessed March 2010). Thus, IR is often referred to as a state of “pre-diabetes” and associated metabolic abnormalities could develop long before one reaches a state of full blown diabetes. Understanding more about the pathophysiology of insulin resistance would allow the identification of diabetic risk factors and enable

15 the development of a more effective preventive measure and interventions as well as effective treatments.

Furthermore, the number of obesity and overweight individuals worlwide has reached over 2 billion giving rise to an epidemic increase in associated health problems with increased morbidity and mortality (Qatanani and Lazar

2007). Although the etiology is unclear, obese individuals have an increased risk for developing insulin resistance. Central obesity in particular is highly associated with IR which occurs in approximately half of the obese subjects (Nestel 2004). A better understanding of obesity and obesity –related insulin resistance would help in efforts to correct the metabolic outcomes leading to T2D.

1.2 Overview of Insulin Action on Glucose Homeostasis

1.2.1 Insulin

Insulin was first discovered in 1921 by Banting and Best (Mitrovic,

Pantelinac et al. 2006), since then efforts to decipher its role as a major regulator of intracellular metabolism has been a primary goal of researchers. Insulin is a potent hormone secreted by the β cells of the pancreatic island of Langerhans in response to an increase in blood levels of both glucose and amino acids after food intake (Pessin and Saltiel 2000). Insulin then exerts its effect on peripheral tissues such as muscle, fat and liver, as well as the pancreas. The hormone has

16 both anabolic and catabolic functions and is involved in nutrient and energy metabolism. Both these functions are well coordinated to control glucose homeostasis by promoting peripheral tissue glucose uptake and favoring glycogen, protein and lipid synthesis. In addition, insulin influences the distribution of energy substrates and the regulation of macronutrient balance by affecting vascular physiology and neurobiological control of food intake along with substrate oxidation (Tremblay, Boule et al. 2005) (Figure 1.1).

Figure ‎1.1 Metabolism regulation by insulin.

Insulin binds to the receptor to stimulate the uptake of glucose from the blood stream and converting it to glycogen. Insulin stimulates the uptake of amino acids and fatty acid and synthesis of lipid and protein while inhibiting their break down and release into the circulation. (FFA:free fatty acids). (Saltiel and Kahn;2003)

1.2.2 Insulin Secretion

To initiate insulin secretion, glucose has to enter the β-cell via glucose transporter 2 (GLUT2) and be extensively metabolized to generate an increase in the ATP/ADP ratio (Coore and Randle 1964; Rolland, Winderickx et al. 2001)

(Figure 1.2). The ratio increase would lead to the depolarization of the cell

+ membrane by closing the K ATP channels (Ashcroft, Harrison et al. 1984; McGarry and Dobbins 1999). As a result of this depolarization the cell membrane becomes permeable to Ca2+ which enters via the L-type channels. This in turn leads to the increase in cytoplasmic free Ca2+ concentration ([Ca2+ ]i) and thus the exocytosis of insulin (Hoenig and Sharp 1986). The glucose stimulation of

+ insulin secretion is known as the “triggering pathway” and is K ATP channel dependant and generates an initial spike in insulin levels in the blood (Straub and

Sharp 2002). The essentiality of this pathway is evident by the fact that glucose- induced insulin secretion is impaired in all experiments inhibiting the rise of

[Ca2+ ]i and increased in experiments that increase [Ca2+ ]i (Henquin 2000). In addition to the triggering pathway, there is substantial evidence of the ability of glucose to further amplify the secretion of insulin independent from the potassium channels, in a mechanism known as the amplification pathway. The amplification pathway does not raise [Ca2+]c but rather augments the action of triggering

Ca2+ and releases another dose of insulin into the blood stream (Gembal, Gilon et al. 1992; Sato, Aizawa et al. 1992; Henquin 2009).

Figure ‎1.2 Glucose sensing in pancreatic beta cells.

Glucose is transported by the GLUT-2 transporter and phosphorylated. Metabolism-dependent increases in the ATP:ADP ratio inhibit ATP-sensitive K+-channels, which causes membrane depolarization (Δψ). This activates voltage-gated Ca2+-channels, and the rise in intracellular Ca2+ which then triggers fusion of insulin storage vesicles with the plasma membrane. Insulin is shown as solid black squares. (Winderickx et al. 2001)

1.2.3 Insulin Signaling

Once insulin is released into the circulation, it mediates its action through a cell surface insulin receptor inducing a complex signaling network responsible for the downstream physiological effects. The insulin receptor (Insr) is expressed in almost all cell types with a high expression in muscle, liver and fat (Brunetti,

Manfioletti et al. 2001). The insulin receptor is a heterotamer consisting of two extra cellular alpha subunits linked by disulphide bonds to two intracellular beta subunits with a tyrosine kinase activity. The binding of insulin to Insr initiates a series of transphosphorylation reactions between the two β-subunits at specific tyrosine residues leading mainly to the phosphorylation of insulin receptor substrates 1 thru 4 (IRS 1-4), Gab-1, p60dok, Cbl, APS and isoforms of Shc10.

Phosphorylation include those on tyrosine residues with activating function and those on serine residues with inhibitory functions providing negative feedback to insulin signaling (Saltiel and Kahn 2001).

The downstream effects of Insr and IRS phosphorylation activates two major kinase pathways, PI3 kinase and MAP kinase pathway (Biddinger and

Kahn 2006). PI3 kinase activates the AKT/PKB downstream signaling pathway.

AKT is responsible for insulin stimulated glucose uptake, translocation of glucose transport receptor 4 (GLUT4) on the cell surface of muscle and adipocytes

(Okada, Kawano et al. 1994; Shimizu and Shimazu 1994; Terauchi, Tsuji et al.

1999), activation of glycogen synthase (Cross, Alessi et al. 1995) and altering of

FOXO transcription factors among others (Nakae, Barr et al. 2000). The end

21 result of the AKT pathway leads to alterations in the glucose and protein metabolism (Lin and Lawrence 1997), and modulations in gene expression. PI3 kinase also activates the PKC pathway resulting in insulin action on lipid synthesis. As for the effect of insulin on the activation of the MAP kinase pathway, it plays a role in mediating insulin’s effect on growth and cell proliferation (Skolnik, Batzer et al. 1993) (Figure 1-3).

1.2.4 Glucose homeostasis

Plasma glucose remains in a narrow range between 70 and 180 mg/dl in normal individuals during fluctuating periods of feeding and fasting. This tight control is a result of the balance between glucose absorption from the intestine, production by the liver and uptake and metabolism by peripheral tissues. Insulin increases glucose uptake in muscle and fat, and inhibits hepatic glucose production, thus serving as the primary regulator of blood glucose concent.

Insulin sensitive peripheral tissues, muscle and fat, have abundant levels of insulin responsive transporter GLUT4 in the cytoplasm that continuously cycle from intracellular stores to the plasma membrane. Insulin increases glucose transport into fat and muscle cells by stimulating the translocation of the transporter GLUT4 from intracellular sites to the plasma membrane. Insulin increases the rate of GLUT4-vesicle exocytosis, and decreases the rate of internalization (Pessin, Thurmond et al. 1999). An octameric exocyst complex including Sec3, Sec5, Sec6, Sec8, Sec10, Sec15, Exo70 and Exo84 is

22 responsible for the decking of GLUT4 vesicles on cell surface of plasma membrane particularly at lipid rafts (Inoue, Chang et al. 2003).

In the liver, insulin controls production and release of glucose by blocking gluconeogenesis (production of glucose from other sources) and glycogenolysis

(breaking down of glycogen to produce glucose). Insulin mediates its action by regulating the expression of genes encoding hepatic enzymes of gluconeogenesis particularly phosphoenolpyruvate carboxylase and glycolysis.

The hormone also decreases transcription of the genes encoding fructose-1,6- bisphosphatase and glucose-6-phosphatase, and increases transcription of glycolytic enzymes such as glucokinase and pyruvate kinase to stimulate glucose entry into the glycolytic pathway. Finally, insulin stimulates the formation of glycogen by phosphorylating enzymes such as glycogen synthase (reviewed in

(Saltiel and Kahn 2001). Of note, the flux of access glucose into the liver causes a small portion of the fructose-6-phosphate produced to enter the hexoamine biosynthetic pathway in the presence of glutamine. The end product of this pathway is UDP-N- acetylglucoseamine, essential building blocks of the glycosyl side chains of glycoproteins and glycolipids. The latter are forms of post- translation modification of significant functional proteins including transcription factors and signaling proteins (Buse 2006).

Figure ‎1.3 Insulin signaling cascade.

The binding of insulin to its receptor signals the phosphorelation of the insulin receptor subtrates and Shc, which in turn activates the PI-3 kinase and the MAP kinase pathways to induce insulin action on glucose, protein, lipids and cell growth. (Biddinger and Kahn, 2006)

1.3 Genetics of Type 2 diabetes

The heritability of T2D is one of the most established among the common diseases. Although the rapid increase in the prevalence and strong association with obesity of T2D indicates an environmental contribution, estimates of heritability (~0.55) in twin, family and population studies demonstrate that the majority of risk is due to genetic factors (Groop and Tuomi 1997; Stolerman and

Florez 2009). Measures of IR, including fasting insulin and glucose levels, demonstrate comparable levels of heritability (~0.44) (Panhuysen, Cupples et al.

2003).

Despite the fact that monozygotic twin pairs studied lived apart and had different body weights when diagnosed with T2D they showed almost complete concordance, and even if the other twin did not have full blown diabetes, the

“unaffected” twin showed metabolic abnormalities. Furthermore, the concordance in monozygotic twins ranges from 0.2-0.9, significantly higher than that of dizygotic twin ranging from 0.1-0.4. This suggests that sharing more DNA sequence with an effected individual increases the risk of T2D (Barnett, Eff et al.

1981; Newman, Selby et al. 1987; Ghosh and Schork 1996). Familial clustering of T2D further supports the existence of a strong genetic susceptibility, in addition to strong correlation with the degree of population admixture (Kim, Sen et al. 2001). Moreover, the prevalence of T2D varies among population groups and even those that share common environmental backgrounds. Finally,

25 additional evidence to the strong genetic influence on the risk of T2D comes from risk of child and sibling. The child of a parent with T2D has an increased risk of

40 % of having the disease a much higher risk when compared to the 7% risk of the general population. This risk increases to 70% if both parents are effected indicating that it is due to sharing more genetics and not only due to the shared environment. Sibling of an individual with T2D also has an increased relative risk of 3 compared to the general population (Moore and Florez 2008).

Given the strong genetic contribution, the genetic risk factors have been subject to intense research in humans and mouse models despite the difficulty attributed to gene environment interaction contributing to the disease. This complexity has previously earned diabetes the term “a geneticist’s nightmare” by

Neel in 1976, up until recently when the development of new technology has vastly increased our knowledge about the genetics. However, even today the genetic factors contributing to T2D are not fully understood.

1.3.1 Genetic studies in humans

Human genetic studies of T2D initially focused on two approaches: linkage analysis or candidate gene association. In the former families or pairs of siblings are identified and genetic markers spanning the whole genome are genotype in all study participants. The objective of the study is to determine if there is a linkage (an increase allele sharing) at particular marker among affected

26 individuals compared to unaffected siblings or family members. In the latter, genes that are suspected to play a role in T2D susceptibility (or genes in regions previously detected by linkage analysis) are typed for markers in a selected population of diabetic individuals or non-diabetic control group. The aim is to identify a polymorphism (SNP or haplotype) that is significantly associated with

T2D or quantitative diabetes traits such as insulin or glucose levels.

Linkage analysis was mainly successful in detecting rare genetic variants with strong effects, mainly genes involved in monogenic forms of diabetes and not T2D. These include genes involved in the onset of MODY (maturity-onset diabetes in the young). This syndrome accounts for about 1-2 % of diabetes in general and is described as a unique type of non-insulin dependent diabetes in thin young adults (Ledermann 1995). Following identification from linkage analysis, candidate association studies concentrated attention on totally 6 identified MODY genes (HNF4A, GCK, TCF1, PDX1,TCF2, and NEUROD1) which account for over 80% of MODY cases (Vaxillaire and Froguel 2006). In cases of T2D linkage analysis reported only a few possible causative genetic variants in CAPN10, ENPP1, HNF4A, and ACDC (Sladek, Rocheleau et al.

2007).

In parallel, candidate gene approaches succeeded in identifying several genetic variants associated with T2D and the association was reproducible. One of the variants identified was the P12A polymorphism in PPARG. This gene encodes peroxisome proliferator activator receptor gamma and was first considered as a candidate because of its role in anti-diabetic medication

(thiozolidinediones) (Altshuler, Hirschhorn et al. 2000). Another variant identified was a missense substitution of glutamic acid with lysine at codon 23 in KCNJ11, a gene encoding the islet ATP-sensitive inward rectifier potassium channel 11.

KCNJ11 works to regulate beta cell depolarization and insulin secretion.

Individuals carrying the risk allele have an increased impairment of insulin secretion (Aguilar-Bryan and Bryan 1999; Florez, Burtt et al. 2004).

Following results from linkage analysis, transcription factor 7 like-2

(TCF7L2) gene was identified as having a relatively strong effect on T2D. This finding was replicated in multiple populations and fine mapping implicated a particular T-allele at SNP marker rs7903164 for increased risk (Grant,

Thorleifsson et al. 2006; Zeggini and McCarthy 2007). Other variants associating with T2D have also been detected using candidate gene approach and these include mutation in the Wolfram syndrome gene (WFS1) and common variants that were previously associated with MODY, particularly variants in TCF2

(Bonnycastle, Willer et al. 2006; Sandhu, Weedon et al. 2007).

Although both these approaches produced results, the bulk of the knowledge of variants causing T2D stemmed from candidate approaches, which depended on previous knowledge of molecular or biological mechanisms and involvement. A more global search was required throughout the human genome.

Genome Wide Association Studies (GWAS) came as an unbiased solution were association between variants in whole genome were screened in selected study and control population. GWAS was feasible due to the advances in technology

28 that tendered the availability of million SNPs and haplotypes as well as the platforms and computational methods of analysis of large data sets.

Successive GWAS have both validated known genes and revealed new associations of loci with T2D. The first GWAS published by Saldek et al. reconfirmed the association at TCF7L2 and identified two new associations with

SLC30A8 and HHEX (Sladek, Rocheleau et al. 2007). Shortly thereafter three studies published together confirmed the previously reported associations of T2D with TCF7L2, KCNJ11, HHEX, SLC30A8 and PPARG. These three studies published by the Diabetic Genetic Initiative, the Wellcome Trust Case Control

Consortium, and the FUSION identified three novel association loci CDKAL1,

IGF2BP2, and CDKN2A/B (Saxena, Voight et al. 2007; Scott, Mohlke et al. 2007;

Zeggini, Weedon et al. 2007). Additional GWAS identified a range of loci associated with T2D including JFAZF1, THADA, ADAMTS9, NOTCH2, ADAM30,

KCNQ1, and KVLQT1 (Unoki, Takahashi et al. 2008; Yasuda, Miyake et al. 2008;

Zeggini, Scott et al. 2008).

In addition to GWAS that looked at T2D as a categorical trait, loci associated with quantitative glycemic traits have been identified. Common gene variants of GCK were associated with fasting glucose using quantitative GWAS, mutations in this gene were previously known to cause MODY (Weedon, Clark et al. 2006). Other variants also associated with impaired fasting glucose levels and insulin secretion were G6PC2 and MTNR1B variants (Bouatia-Naji, Rocheleau et al. 2008; Bouatia-Naji, Bonnefond et al. 2009). This indicates the need to identify

29 variants while looking at metabolic measurements of T2D as a quantitative variable.

On a final note, of all the identified variants, very few common genetic variants contribute to IR development. The only two examples of variants that have been identified and replicated are FTO (Jacobsson, Klovins et al. 2008) and

PPARG (Altshuler, Hirschhorn et al. 2000) and they explain a small amount of the heritability of IR (<1.5%) (Sabatti, Service et al. 2009). Almost all of the newly identified T2D loci are associated with insulin secretion defects.

1.3.2 Missing Heritability

Although the rapid increase in the prevalence of type II diabetes indicates an environmental contribution, estimates of heritability (~0.55) in twin, family and population studies demonstrate that the majority of type II diabetes risk is due to genetic factors (Groop and Tuomi 1997; Stolerman and Florez 2009). Measures of IR, including fasting insulin and glucose levels, demonstrate comparable levels of heritability (Panhuysen, Cupples et al. 2003). However, our knowledge of the genetic component of type II diabetes and IR in humans largely derived from candidate gene and GWAS, as well as linkage analysis (Frayling 2007) was still inadequate. Despite extensive effort, the genes identified account for only 6 % of type II diabetes heritability (Manolio, Collins et al. 2009).

A number of factors contribute to the difficulty in identifying genes that underlie complex traits such as T2D and IR. Linkage analysis is dependent on family pedigrees and thus have small sample sizes and difficulties detecting less common and week variants. Prior knowledge is necessary for candidate gene association approaches and this limits ability to detect novel biological components. As for GWAS lowering the P-value to ensure true positive results is a must given the number of statistical tests done to study all the genes on the whole genome. This would also allow for the missing of variants that are less common or with small effect sizes (Florez 2008; Moore and Florez 2008;

Manolio, Collins et al. 2009). Furthermore, given the strong association between obesity and IR, it might be necessary for bodyweight (BW) to be taken into account to search for the missing IR genes. This is not the case in most association studies. For example, Sladek et. al only studied cases with a BMI <

30 kgm-2 (Sladek, Rocheleau et al. 2007) . This is prompted by the inability to ascertain otherwise if the function of the polymorphism is via BW or a direct effect on IR. Finally, despite these limitations, 20 SNPs have been associated with type II diabetes, but it is not yet known if these are causative variants or are in linkage disequilibrium with the causal SNP. The limited ability of association studies to prove causation is a large impediment towards translating genetic studies into clinical benefits (Stolerman and Florez 2009).

1.3.3 Genetic studies in mouse models

Model organisms have many advantages that facilitate identification of genes that regulate T2D and IR. These include the ability to control environmental, conduct defined crosses, functionally evaluate candidate genes in vivo, and undertake rigorous mechanistic studies. Given this advantage many investigators have used mouse models in the study of complex traits and T2D in particular. Some of the mouse models include, naturally occurring diabetes mutants and genetically engineered knock-out or transgenic mice. These models are simple in that they usually involve a known mutation in one gene. In contrast, inbred strains are another type of mouse models that provide a more usefully tool to model and study the common complex polygenic form of T2D taking advantage of the naturally occurring variation in metabolic traits between them.

1. Spontaneous mutant mouse models of T2D

Among the most widely used models for T2D are the ob/ob and the db/db mouse models. These mice have either a mutation in the leptin gene (Lepob) or in the leptin receptor gene (Lepdb ), respectively (Zhang, Proenca et al. 1994;

Chung, Power-Kehoe et al. 1996). Ob having a nonsense mutation in Lep, are hyperinsulinemic by 2 wks of age and develop severe obesity at 3-4 wks of age followed by mild hyperglycemia by 8 wks and thus present a suitable model to study T2D and its progression from IR (Bates, Kulkarni et al. 2005). db mice on the other hand have a 106 nucleotide insertion that disrupts normal splicing and produces premature stop codon in one form of the leptin receptor (Chen, Charlat

32 et al. 1996). In the db hyperinsulinemia manifests at around 3-4 wks and is accompanied with IR and obesity (Coleman 1982) making it as well a good model to study obesity related diabetes and insulin resistance.

2. Single gene Knock-out and transgenic models of T2D

Since the mid-1990’s transgenic and knockout mice have become powerful tools to study the role of genes coding for individual proteins related to insulin-glucose homeostatic pathway in the development of diabetes. These provided an opportunity to study in details and in a simpler way the molecular mechanism behind a genetically complex disease like type II diabetes. Tissue- specific knockout and transgenics have also become recently popular in diabetic research in an attempt to overcome early lethality phenotypes, to asses aspects of the gene function in different tissues, and to the detect the ability of one tissue to induce defects or rescue defects in other normal tissue, cross-talk or compensation.

Insulin plays the major role in T2D and thus the initial studies were directed toward creating transgenic and knockout mouse models of diabetes focusing on the Insulin receptor (Insr) and followed by models of the insulin signaling pathway. Most of tissues studied for targeted mutations included skeletal muscle, liver, adipose and noncanonical tissues like pancreatic islets and brain. Below is a summary of the phenotype of the models available in the

33 literature and the important inferences towards the molecular mechanism of T2D learned from them.

In contrast to patients with mutations in Insr who show mild diabetes but severe growth retardation, mice models with global Insr knockout develop hyperglycemia and hyperketoacidosis shortly after birth and die with n few days from ketotic diabetes (Taylor 1992; Accili, Drago et al. 1996). Conditional knockouts in β-cells caused a reduction in cell mass and a decrease in insulin secretion with resulting hyperglycemia and diabetes in 25% of the animals. This implicated the role of insulin in β-cell development (Otani, Kulkarni et al. 2004).

Muscle specific Insr knockout and dominant negative over expression caused no weight gain but induce an increase in fat cell mass that seem to have compensated for muscle insulin resistance and thus glucose and insulin levels remained almost normal in the blood (Moller, Chang et al. 1996; Bruning, Michael et al. 1998). Knockout in neurons increased sensitivity to diet induced obesity with mild insulin resistance and Hyperinsulinemia (Bruning, Gautam et al. 2000).

On the other hand, fat disruption of Insr was protected from obesity and glucose intolerance (Bluher, Michael et al. 2002) indication the importance of brain and fat cell in the action of insulin on obesity. Finally a liver specific knockout an insulin resistant phenotype with glucose tolerance (Michael, Kulkarni et al. 2000).

Mice carrying null mutation in all different Insulin receptor substrates (IRS) identified in mice were generated and each had a distinct phenotype. Globally lacking IRS-1 showed mild hyperinsulinemia without any development of diabetes despite islet cells’ secretory defect. This could be attributed to

34 compensation by IRS-2 (Hennige, Ozcan et al. 2005). The KO of IRS2 causes mild to severe diabetes induced by severe IR and the inability of the β-islets to compensate (Kubota, Tobe et al. 2000). KO in IRS3 and 4 showed no effect on glucose homeostasis (Fantin, Wang et al. 2000; Bjornholm, He et al. 2002).

Single gene models in many downstream target of the insulin signaling cascade also aided in the uncovering of many of the molecular mechanisms of glucose homeostasis. For example, targeted disruption of AKT -2 expressed in high levels in insulin responsive tissues cause hyperglycemia and

Hyperinsulinemia and thus was identified as a predominant mediator of insulin signaling (Cho, Mu et al. 2001). Mice with glucokinase deletions, glucose sensor in the liver, die perinatally due to severe hyperglycemia while those for Glut4 only exhibit growth retardation and decreased longevity with cardiac hypertrophy. This data indicates an important role of glucokinase in glucose homeostasis and perhaps a compensatory role of other glucose transporters (Grupe, Hultgren et al. 1995; Katz, Stenbit et al. 1995). Its note worthy to state that specific knockouts of Glut4 in muscle or adipocytes develop severe insulin resistance and glucose intolerance implying the importance of the transporters in these two tissues (Zisman, Peroni et al. 2000; Abel, Peroni et al. 2001).

PPAR-Ɣ 2 isoform knockout showed impaired insulin sensitivity and reduced fat mass. Furthermore, null mutants for PGC-1 a coactivator of PPAR-Ɣ were resistant to diet-induced obesity (Zhang, Proenca et al. 1994; Yoon, Xu et al. 2003).Tissue specific modulation of either of these proteins showed contrasting variations of metabolic traits associated with T2D.

3. Inbred strains (mouse models of polygenic T2D)

Although the single gene mouse models provided good insight into the molecular mechanism of the insulin signaling cascade and its involvement in the development of obesity and type II diabetes, inbred mouse strains provide a better recourse for genetic study of the complexity and multifactorial nature of

T2D. The obesity, insulin resistance, and diabetes of these strains is due to a large number of genes and in some cases due to gene-environment interaction.

Each inbred strain has a unique combination of susceptibility or resistance loci and the phenotypic variation among inbred strains is due to the composite effct of all these loci. One can use many genetic methods in inbred strains leading to the discovery of causative genes.

The simplest way to study the genetics of T2D and diabetes-related traits in mouse inbred strains that differ in the traits of interest is gene mapping by correlating phenotypes with genotype at markers. The most common method is intercross or backcross. Once two inbred strains with phenotypic difference have been chosen, the mice are crossed to generate F1 mice. The F1 mice are then either intercrossed or backcrossed to one of the parental strains to generate F2 mice. Once the mice are collected the phenotype is tested for linkage with any of the molecular markers. Statistical methods are used to generate a LOD score that measures the likelihood that a gene affecting the trait resides in a particular region. Intercross provides better information on additivety and dominance while backcross has a higher power to detect dominant alleles (Darvasi 1998). In both cases, hundreds and even thousands of F2 mice need to be generated to test

36 the whole genome at a decent resolution. To overcome this problem some researchers have continued the intercross for 10 – 16 generations. This technique identified fine mapped QTLs for glucose and insulin levels in F16 generation, but is a slow process aimed at increasing location accuracy (Ehrich,

Hrbek et al. 2005).

Other approaches have been used for inbred strains to map QTLs. For example, recombinant inbred strains, which are generated by crossing two inbred strains followed by twenty generation of interbreeding have been used to overcome the inability of generating biological replicates in F2s. Several recombinant congenic strains have been created for C57Bl/6 and A/J and can be used to study the genetics of T2D susceptibility (Fortin, Diez et al. 2001).

Chromosome substitution strains have also been developed to study the genetics of complex traits like obesity and T2D and offer multiple advantages (discussed below).

Over a hundred QTLs for T2D and T2D-related metabolic traits (insulin and glucose levels) spanning the whole mouse genome have been mapped using inbred crosses but very few causative genes have been identified under the LOD peak scores. Furthermore, it is evident few of the chromosomes have relatively more hits than others. Many QTLs have been found on chromosome number 2 along with multiple adipocity QTLs. On the other hand, although many body weight loci have been mapped on chromosome 7, only one diabetes- related loci was mapped to the same chromosome indicating that these two phenotypes could be coupled or separable (Rankinen, Zuberi et al. 2006). Other

37 hot spots were found on chromosome 9, 11, 12, and 19. The inbred strains used in diabetes research to detect these QTL are too numerous to be detailed in here but I will detail two particular inbred strain relevant to the purpose of the study

C57BL/6J (B6) and A/J (see below), the rest are reviewed in (Clee and Attie

2007).

1.4 C57BL/6J and A/J Inbred Strains: Alternative model

1.4.1 Advantage of B6 and A/J inbred strains

The B6 and A/J inbred mouse strains are ideal models for type 2 diabetes studies because the strains differ in their susceptibility to high-fat, diet induced obesity and insulin resistance. Relative to A/J several studies have provided evidence that B6 can be considered “diabetes-susceptible”, keeping in mind that it has both diabetes susceptible and diabetes resistance alleles. A series of studies from Surwit et al. showed then when put on a high-fat, high simple carbohydrate diet B6 become obese and show a 50% increase in fasting plasma glucose and about a 10 fold increased in fasting plasma insulin. This is in contrast to the “diabetes-resistant” strain A/J whose plasma glucose remained the same and showed only a 2 fold increase in insulin levels (Surwit, Kuhn et al.

1988). B6 was also found to be the least glucose tolerant strain compared to many others including DBA/2 and 129X1 (Kaku, Fiedorek et al. 1988; Goren,

Kulkarni et al. 2004). On the other hand A/J has among the lowest glucose levels in inbred strains. All in all, compared to B6, A/J is resistant to diet-induced

38 obesity, insulin resistance and glucose tolerance (Rebuffe-Scrive, Surwit et al.

1993; Surwit, Feinglos et al. 1995). Furthermore, in vivo perfusion experiments showed that islets from B6 fed a high-fat, simple-carbohydrate diet had significantly impaired secretion of insulin in response to high dosage of glucose compared to A/J (Lee, Opara et al. 1995; Wencel, Smothers et al. 1995). Its note worthy to mention that, even the most resistant strain A/J was found to harbor alleles associating with glucose intolerance in crosses with the Akita mouse and

SMXA (Kobayashi, Io et al. 2006; Takeshita, Moritani et al. 2006).

On account of the differential susceptibility to diabetes B6 and A/J are useful models to study the genetics of T2D related traits. Inaddition, these two strains are inexpensive and readily available at Jackson Laboratory

(www.jax.org). Many microsattelite markers and single nucleotide polymorphisms

(SNPs) have been identified in these strains and are a useful mapping tool.

Moreover, bacterial artificial chromosome (BAC) libraries and the existence of many knockout and transgenic models on the B6 background help in pinpointing the causative gene or genes discovered in mapping. Finally, the B6-chrA chromosome substitution strain (CSS) is an important recourse to study the genetics in these two inbred mouse strains (discussed below).

1.4.2 B6-chrA CSS development and advantage

The B6-ChrA CSSs are the first complete panel of mammalian CSSs developed and it consists of 22 strains in which an individual chromosome from the donor strain A/J replaces the equivalent chromosome on B6,the recipient strain, background. The construction of the CSS starts with the intercross between A/J and B6, the generated F1 is then backcrossed to B6. After each backcross generation, mice with a non-recombinant A/J chromosome are selected by genotyping and backcrossed. This step was repeated for atleast ten generations. After ten generation, mice which inherit the same recombinant choromosome are selected by genotyping and intercrossed to homozygous the

A/J chromosome (Nadeau, Singer et al. 2000; Singer, Hill et al. 2004) (Figure

1.4).

Figure ‎1.4 Consomic strain development.

CSSs constitute a novel paradigm for studying complex traits such as T2D because they make detecting QTLs simpler (Figure 1.5). The CSS panel is screened for a particular phenotype, the results are compared to the recipient strain. If there is a difference between the CSS and B6 (in this case), it implies at least one gene on that chromosome is affecting the phenotype. If there is no difference between the phenotype of the CSS and the phenotype of the parental strain, then we were unable to detect a gene affecting the phenotype on that chromosome. Thus, there is no need for genotyping to detect QTLs (Singer et al.,

2004).

CSSs offer many statistical advantages as well. The whole genome is not segregating and this eliminates noise effects from background QTLs. They also dramatically reduce the multiple testing penalty and improve the ability to detect epistasis to identify susceptibility alleles (Shao, Burrage et al. 2008), while retaining the many other advantages of genetic studies in model organisms.

Congenic strains spanning the entire chromosome or even small region of the chromosome share these advantages with CSSs and can be generated following only four generations of breeding when derived from CSSs, rather than 10 generations using conventional breeding methodologies (Nadeau, Singer et al.

2000). Furthermore, each individual CSS and congenic is a fixed inbred strain and a renewable resource. After an individual QTL has been detected phenotypic studies can be repeated and broadened to define a better phenotype.

The CSS also differs from segregating crosses such as intercrosses or backcrosses in the ability to identify parental effects (Singer, Hill et al. 2004). The

CSS has the ability to study an individual loci for parental effect since parents are both on a similar homogeneous background and differ only by the allele at the location being tested. Parental strain with the unique feature are compared to heterosomic F1 hybrid without the complicating effects of other segregating QTLs on a heterogeneous background. The ability to produce large numbers of isogenic F1 that only differ at the isolated location in the parental strain gives us more power to detect parental effects that could be masked by heterogenous background in segregating crosses

Our laboratory and others have previously used mouse chromosome substitution strains (CSS) and congenic strains derived from them successfully to identify quantitative trait loci (QTLs) that regulate complex traits (Singer, Hill et al.

2004; Hill, Lander et al. 2006; Shao, Burrage et al. 2008; Gelegen, Pjetri et al.

2009; Leussis, Frayne et al. 2009; Soha N. Yazbek 2010).

Figure 1.5 Detection of QTL using the consomic strains.

1.4.3 Choosing CSS-6

Analysis of the C57BL/6JA/J panel of CSSs in the Nadeau Lab identified 16 chromosomes with at least 1 QTL affecting diet-induced obesity, and 8 affecting glucose homeostasis (Singer, Hill et al. 2004; Shao, Burrage et al. 2008). B6.A6 or CSS-6 was associated with 22% reduction in body weight corresponding to about a 75% of the difference between the parental strains A/J and B6. The search of the genetic basis of obesity resistance in the lab started with the A/J genome and then focused on a single chromosome, chromosome 6. A congenic panel of 20 strains spanning Css-6 was then developed in the lab (Buchner,

Burrage et al. 2008). The use of the congenic strains derived from one of the obesity-resistant strains, C57BL/6J-Chr 6A/J, identified 4 QTLs regulating body weight including Obrq2 (obesity resistance QTL-2) (Shao, Burrage et al. 2008).

Further analysis showed that the Obrq2B6 allele, leads to impaired glucose homeostasis, insulin resistance, increased adiposity, has an effect equivalent to over 50% of the body weight difference between the B6 and A/J parental strains

(Buchner, Burrage et al. 2008; Buchner, Yazbek et al. submitted). Thus, this

QTL was chosen for subsequent analysis to help elucidate the molecular basis of the genotype-phenotype correlations among the components that all lead to an increased susceptibility to type II diabetes, and increase our knowledge of the genes interacting with environment to predispose to T2D.

1.6 Summary and Research Aims

Type II diabetes has increased in prevalence reaching epidemic degrees in the United States and worldwide. This has become a major health problem and an economic burden on societies. Insulin resistance and obesity play a major role in the susceptibility and development of the condition. Current treatment has decreased morbidity rates, but has failed to inhibit the rise in prevalence of diabetes and the rise in insulin resistance and its progression to type II diabetes. Studies have shown evidence of a strong genetic component in the development of obesity, insulin resistance and the resulting disorder type II diabetes. Unfortunately, all genetic studies have failed to explain more than 6 % of the heritability and to clearly associate genes with the complex form of the metabolic condition. It is important to uncover more about the genetic control of type II diabetes and about the genes leading to predisposing conditions like obesity and insulin resistance that increase susceptibility to the condition. This would lead to the development of better interventions and treatments, as well as the ability to identify high risk individuals for better monitoring and prevention measures.

To achieve this goal, we took advantage of a novel detection paradigm available consisting of the chromosome substitution strain. The screen of multiple complex traits in the C57BL/6J-Chr A/J panel identified numerous large-effect

QTLs and observed that epistasis was very common at the level of the whole genome and at the level of a particular chromosome. The use of the congenic

46 strains derived from one of the obesity-resistant strains, C57BL/6J-Chr 6A/J, identified 4 QTLs regulating body weight including Obrq2 (obesity resistance

QTL-2, and insulin resistant QTL). Thus, we were interested in understanding the genetic architecture, the molecular mechanisms and genetic control underlying

Obrq2, an obesity and insulin resistance QTL.

The application of mRNA profiling to understand the molecular basis of insulin resistance has proven informative (Buechler and Schaffler 2007). The global nature of these techniques provides an unbiased approach to identify pathways that contribute to disease pathology. In chapter 2, we used molecular markers to further define the regions of B6- and A/J-derived sequence in strains defining Obrq2. We also performed, glucose tolerance test (GTT) and insulin tolerance test to define the nature of the defect in glucose homeostasis. Finally, we utilized multi-tissue global gene expression patterns to compare the two mouse strains with different susceptibilities to diet-induced obesity and insulin resistance that define the Obrq2 interval.

To further investigate the genetic architecture underlying the susceptibility to T2D in this particular QTL, in chapter 3 we generated and screened a subcongenic and a subsubcongenic panel of Obrq2 and Obrq2a (one of the

QTLs identified by the subcongenic panel) respectively. The genetic architecture in both panels were analyzed by estimating the effect sizes of all identified QTLs and the breakdown of the multiple phenotypes tested (body weight, insulin, glucose, and HOMA). In addition, we followed up one sub-QTL Obrq2a1 identified from the subsubcongenic panel. The three candidate genes in the

47 region were analyzed for sequence variation and expression differences to identify the causative gene associating with the phenotype.

Finally, growing evidence from model organisms implicates a range of nutritional, hormonal, xenobiotic and behavioral cues acting in one generation

(F0) with consequence in subsequent generations (first F1, second F2, etc.), after the initial exposure to the stressor is no longer present. However, current genetic approaches have focused on the affected individual’s genome to find variations linked to a phenotype, thereby excluding epigenetic factors despite considerable evidence suggesting their importance. As mentioned above, testing for parental effects was made easier with the use of the CSSs and derived congenics. In chapter 4, we tested the parental effect of Obrq2a on body weight, insulin levels, and glucose levels through a series of crosses and backcrosses.

The identified effect was then traced through male and female lineages for multiple generations. We also analyzed the mechanism behind the parental effect on weight gain by testing the effect on food intake.

2 Chapter 2: Increased mitochondrial oxidative phosphorylation in the liver is associated with obesity and insulin resistance

The following work was done in collaboration with many. Dr. Lindsay Burrage derived the congenic panel for CSS-6, screened for body weight differences and collected tissue from 6C2 and 6C1 for the microarray experiments. The candidate did the screening for fasting insulin, fasting glucose and HOMA for the original congenic panel leading to the identification of Obrq2 as a metabolic syndrome QTL. The candidate also performed the insulin resistance phenotyping (GTT-glucose, GTT-insulin, and ITT). Dr. David Buchner analyzed the microarray data. Conclusions and manuscript preparation was a collaborative effort between the candidate and Dr. David Buchner.

Authors: David A. Buchner*, Soha Yazbek*, Lindsay C. Burrage, and Joseph H. Nadeau

*These authors contributed equally to the work

Reference: Manuscript Submitted (Journal: obesity)

2.1 Abstract

Obesity is the result of excess energy intake relative to expenditure, however little is known about why some individuals are more prone to weight gain than others. Inbred strains of mice also vary in their susceptibility to obesity and therefore represent a valuable model to study the genetics and physiology of weight gain and its comorbidities such as type 2 diabetes. C57BL/6J mice are susceptible to obesity and insulin resistance when fed an obesogenic diet, whereas A/J mice are resistant despite increased caloric intake. Analysis of B6- and A/J-derived chromosome substitution strains and congenic strains revealed a complex genetic and physiological basis for this phenotype. To improve our understanding of the molecular mechanisms underlying susceptibility to metabolic disease we analyzed global gene expression patterns in 6C1 and 6C2 congenic strains. 6C1 is susceptible whereas 6C2 is resistant to diet-induced obesity. In addition, we demonstrate that 6C1 is insulin resistant relative to 6C2 due to peripheral tissue insulin sensitivity and increased hepatic gluconeogenesis, rather than decreased pancreatic insulin secretion. Pathway analysis of global gene expression patterns in muscle, adipose, and liver identified expression level differences between 6C1 and 6C2 in pathways related

50 to basal transcription factors, endocytosis, and mitochondrial oxidative phosphorylation (OxPhos). The OxPhos expression differences were modest but evident in each complex of the electron transport chain. This data suggests the importance of hepatic mitochondrial function in the development of obesity and insulin resistance.

2.2 Introduction

As the prevalence of obesity increases worldwide, so does the prevalence of comorbidities such as type 2 diabetes (T2D), cardiovascular disease, and cancer. More than 150 million people worldwide are affected with T2D, whose complications including blindness, renal failure, and coronary disease. T2D

-cells in the pancreas no longer secrete sufficient amounts of insulin to overcome peripheral tissue insulin resistance and maintain euglycemia.

The resulting hyperglycemia is currently controlled by lifestyle modifications or medications such as sulfonylureas or metformin which dramatically reduce morbidity. However, these therapies frequently fail to achieve optimal glycemic levels, which is particularly important to minimize the risk of microvascular complications (Nathan, Buse et al. 2009).

Insulin resistance is a key factor in the pathophysiological development of

T2D and remains the best indicator of future development of T2D among individuals with a family history of the disease (Petersen and Shulman 2006).

Insulin resistance occurs when target tissues such as skeletal muscle and adipose fail to properly respond to normal insulin levels, thereby requiring increasing amounts of insulin to maintain normal glucose uptake. The factors that lead to insulin resistance remain controversial, although an overabundance of adipokines, inflammatory mediators, and lipid deposits in non-adipose tissues are likely important (Muoio and Newgard 2008). Among the controversies surrounding the etiology of insulin resistance is the question of whether

52 mitochondrial dysfunction has a causal or compensatory role in the disease

(Turner and Heilbronn 2008). Although mitochondrial dysfunction in skeletal muscle has long been the primary focus of T2D-related mitochondrial research, recent studies have demonstrated the importance of hepatic mitochondrial function and even called into question whether insulin resistance is a result of decreased or increased mitochondrial activity (Pospisilik, Knauf et al. 2007;

Koves, Ussher et al. 2008). Answering these questions may have important therapeutic implications for the treatment of T2D, as several well characterized compounds exist that modulate mitochondrial function (Liu, Shen et al. 2009).

The application of mRNA profiling to understand the molecular basis of insulin resistance has proven informative (Buechler and Schaffler 2007). The global nature of these techniques provides an unbiased approach to identify pathways that contribute to disease pathology. For example, to investigate the role of mitochondria in the development of insulin resistance. In this report, we utilized multi-tissue global gene expression patterns to compare two mouse strains with different susceptibilities to diet-induced obesity and insulin resistance. Our results suggest the importance of hepatic mitochondrial function in the development of insulin resistance.

2.3 Materials and Methods

2.3.1 Mice. Mice were housed in ventilated racks, had access to food and water ad libitum, and were maintained at 21oC on a 12 hour light/12 hour dark cycle.

Strain 6C1 is susceptible to diet-induce obesity and 6C2 is resistant to diet- induced obesity. These strains were generated as previously described

(Buchner, Burrage et al. 2008). All mice for these studies were obtained from breeding colonies at CWRU that were fed LabDiet 5010 chow (PMI Nutrition

International, St. Louis, MO, USA). Five-week old male mice were placed on a high-fat simple carbohydrate diet (HFSC, D12331, Research Diets, New

Brunswick, NJ, USA) as previously described (Buchner, Burrage et al. 2008).

The Institutional Animal Care and Use Committee approved all procedures.

2.3.2 RNA Isolation. Liver, skeletal muscle, and gonadal fat pads were isolated following 28 days and 100 days on the HFSC diet. For each strain and time point, 4 or 5 mice from separate cages were randomly selected for analysis

(excluding the heaviest and leanest mouse from each strain and time point).

Prior to dissection, mice were fasted for 16 hours overnight and euthanized with cervical dislocation. Liver and skeletal muscle were stored in RNAlater (Ambion,

Austin, TX, USA). RNA isolations were performed using the RNeasy mini kit

(Qiagen, Valencia, CA, USA). Liver RNA was isolated using the standard protocol and skeletal muscle RNA was isolated using the fibrous tissues protocol.

Gonadal fat pads were snap-frozen in liquid nitrogen and stored at -80ºC. RNA

54 isolations were performed using the RNeasy lipid tissue kit (Qiagen) following the standard protocol.

2.3.3 Microarray hybridization. GeneChip Mouse Genome 430 2.0 arrays

(Affymetrix, Santa Clara, CA, USA) containing 45,000 probe sets corresponding to over 39,000 transcripts and variants were used for microarray analysis. Four biological replicates were analyzed for each tissue and time point except 100 day muscle for which 5 samples were analyzed for each strain. Hybridization procedures were carried out at the Gene Expression and Genotyping Core of the

Case Comprehensive Cancer Center in accordance with Affymetrix protocols for single round amplifications.

2.3.4 Microarray data analysis. The VAMPIRE web based microarray analysis suite was used under default conditions to analyze gene expression (Hsiao,

Ideker et al. 2005). The significance threshold was set at a Bonferroni corrected value of p < 0.05. Gene Set Enrichment Analysis was performed under default conditions using the curated gene sets (Version 2.5, April 2008 release)

(Subramanian, Tamayo et al. 2005). Pathways were considered significantly different between the 6C1 and 6C2 congenic strains if the false discovery rate was < 0.2 and the familywise-error rate was < 0.2.

2.3.5 Glucose tolerance test (GTT) and insulin secretion. Following 100 days on the

HFSC diet, mice were fasted overnight for 16 - 18 hrs. Blood samples (200 µl) were collected via the retro-orbital sinus at baseline (time 0) and following an intra-peritoneal injection of dextrose dissolved in water (2g/kg body weight) after

15, 30, 60 and 120 minutes respectively. Each time point represents a different cohort of 8-10 mice, except for the 0 and 120 minute time points which were collected from the same mice. Glucose was measured from the retro-orbital sinus using a hand-held glucometer (OneTouch Ultra, Life Scan Inc., Milpitas,

CA, USA). Whole blood was then centrifuged and plasma was stored at -80°C.

Insulin levels were determined using an ultrasensitive mouse ELISA kit

(Mercodia, Uppsala, Sweden).

2.3.6 Insulin tolerance test (ITT). Mice were fasted 4 - 6 hrs and then given an intra-peritoneal injection of recombinant human regular insulin (0.75 u/kg body weight) (Novolin R; NovoNordisk Inc., Bagsvaerd, Denmark). Glucose was then measured using a hand-held glucometer (OneTouch Ultra, Life Scan Inc.) from a tail blood sample at time 0, and 30 and 60 minutes post-injection.

2.3.7 Statistics. Measurements are presented as means ± standard error.

Comparisons were done using an unpaired student’s T-test assuming equal variance.

2.4 Results

2.4.1 Localization of Obrq2 to a 30 Mb interval on chromosome 6

The obesity-susceptible congenic strain 6C1 and the obesity-resistant congenic strain 6C2 define the adiposity QTL Obrq2 (Buchner, Burrage et al.

2008). Obrq2 was previously mapped to a 40.9 Mb interval on chromosome 6 between the microsatellite markers D6Mit138 and D6Mit223. Strains 6C1 and

6C2 have now been genotyped with additional polymorphic markers to further define the regions of B6- and A/J-derived sequence in these strains (Figure 2.1).

Analysis of this genotype information narrowed the Obrq2 candidate interval to

30.3 Mb between the SNP markers rs13478633 and rs30218447 and eliminated

88 genes from the candidate interval.

Figure ‎2.1 Map of chromosome 6 congenic strains.

Analysis of strains 6C1 and 6C2 define Obrq2, a 30.3 Mb diet-induced obesity-resistance QTL. IR, Insulin resistant. IS, Insulin sensitive.

2.4.2 6C1 mice are insulin resistant relative to 6C2

Strain 6C1 mice fed the HFSC diet for 100 days demonstrated a statistically significant ~ 25% increase in fasting plasma glucose levels relative to

6C2 (Buchner, Burrage et al. 2008) (Figure 2.2A). Fasting hyperglycemia can result from reduced pancreatic insulin secretion, increased hepatic gluconeogenesis, or both. The mild but significant increase in fasting insulin levels in strain 6C1 relative to 6C2 (Buchner, Burrage et al. 2008) (Figure. 2.2 B) suggests that the hyperglycemia in 6C1 is due to a failure of the liver to respond to insulin and downregulate gluconeogenesis, rather than a defect in insulin secretion. To confirm this hypothesis, a glucose tolerance test (GTT) was performed. Identical glucose loads given to 6C1 and 6C2 mice led to significantly increased levels of both insulin and glucose in strain 6C1 (Figure.

2.2 A, B), suggesting that a defect in insulin secretion does not account for the hyperglycemia of strain 6C1. In addition, the ability to clear glucose in response to an intraperitoneal bolus injection of insulin (0.75U/kg body weight) was also dramatically decreased in 6C1 compared to 6C2 (Figure. 2.2 C), confirming the decreased whole body sensitivity to insulin. Collectively, these results suggest that mice carrying the Obrq2B6 allele exhibit insulin resistance due to impaired peripheral tissue responsiveness to insulin including increased hepatic gluconeogenesis.

Figure ‎2.2 Glucose and Insulin Tolerance Test

Hyperglycemia in 6C1 due to a decrease in insulin sensitivity and not a reduction in insulin secretion. (A) Increased fasting glucose levels in 6C1 suggest an inability to shutdown hepatic glucose production. The significant increase in glucose levels 30, 60 and 120 minutes after an intraperitoneal injection of glucose (2g/kg body weight) demonstrates the inability of 6C1 to clear glucose as efficiently as 6C2. (B) 6C1 secretes more insulin than 6C2 in response to the same glucose load injected during the GTT. (C) Compared to 6C2, 6C1 has a decreased ability to clear glucose from the blood stream in response to a bolus intraperitoneal injection of insulin at a rate of 0.75U/kg of body weight. * p<0.05, **p<0.01, ***p<0.0001

2.4.3 Global gene expression analysis

To examine the molecular basis of obesity and insulin resistance associated with Obrq2, gene expression patterns were examined in liver, white adipose, and skeletal muscle of strains 6C1 and 6C2. Gene expression was measured with microarray hybridization following both 28 and 100 days on the

HFSC diet and analyzed using the VAMPIRE analysis package (Hsiao, Ideker et al. 2005). A total of 568 probes corresponding to 459 unique genes were differentially expressed between the two strains. Eight of these genes are located within the Obrq2 interval and therefore represent potential cis-acting eQTLs and candidate genes: ankyrin repeat and SOCS box-containing 15

(Asb15), carboxypeptidase A2, pancreatic (Cpa2), RIKEN cDNA D830026I12 gene, homeodomain interacting protein kinase 2 (Hipk2), leiomodin 2 (Lmod2),

RNA binding motif protein 28 (Rbm28), smoothened homolog (Smo), and tissue factor pathway inhibitor 2 (Tfpi2). These cis-eQTL genes were differentially expressed in only a single tissue at a single time point, except Asb15 which was differentially expressed in both 28 and 100 day skeletal muscle and D830026I12 which was differentially expressed in 28 and 100 day WAT.

The 451 differentially expressed genes that lie outside of the Obrq2 interval represent trans- eQTLs downstream of the causal genetic variants underlying Obrq2. Several genes with known functions related to obesity and insulin resistance were identified among these trans-eQTLs including insulin-like growth factor binding protein 2 (Igfbp2), uncoupling protein 1 (Ucp1), and peroxisome proliferative activated receptor, gamma, coactivator 1 alpha

(Ppargc1a or Pgc-1. Igfbp2 was over-expressed in adipose of the obesity- resistant strain 6C2 following 100 days on the HFSC diet. Over-expression of

Igfbp2 has been previously shown to protect against the development of obesity and insulin-resistance by inhibiting adipogenesis (Wheatcroft, Kearney et al.

2007; Hedbacker, Birsoy et al. 2010). Expression of Ucp1 was increased

1,250% in muscle of the lean strain 6C2 following 100 days on the HFHS diet.

Since Ucp1 is expressed in brown adipose tissue, the expression in muscle suggests the presence of more intermuscular brown fat cells in 6C2 mice. The additional brown adipose tissue would lead to a greater percentage of nutrients being used for heat generation rather than energy production as has been previously observed in obesity-resistant 129S6 mice (Almind, Manieri et al.

2007). This increase in uncoupling between mitochondrial respiration and thermogenesis is consistent with the lean phenotype of 6C2 despite similar food intake levels (Buchner, Burrage et al. 2008). Pgc- is a key mediator of hepatic metabolism (Li, Monks et al. 2007) and was over-expressed in the liver of the obese strain 6C1 following 100 days on the HFSC diet. The potential link between Pgc-1 and Obrq2 will be expanded upon in the discussion section.

Expression level variation in each of these genes may contribute to the obesity and insulin resistance associated with Obrq2. However, given that 459 genes are differentially expressed between 6C1 and 6C2, we turned to pathway analysis for additional molecular insight.

2.4.4 Genes involved in oxidative phosphorylation are over-expressed in the liver of 6C1

In addition to examining the effect of Obrq2 on the expression levels of individual genes, the expression level of genes in pathways was also studied.

Gene Set Enrichment Analysis (GSEA) is an algorithm that seeks to identify subtle (<2 fold) coordinated gene expression differences that are consistently identified among a priori sets of genes that are grouped based on similar functions or expression patterns (Subramanian, Tamayo et al. 2005). Three pathways were identified as differentially expressed (FDR < 0.2 and p < 0.2) between 6C1 and 6C2 (Table 2.1). The basal transcription factors pathway was increased in 6C1 muscle following 100 days on the HFHS diet. This pathway was derived from KEGG (Kanehisa, Araki et al. 2008) with 15 of the 29 genes overexpressed in strain 6C1. This suggests that overall transcription levels were elevated is strain 6C1 which can occur during muscle regeneration (Newlands,

Levitt et al. 1998). The Ndk Dynemin pathway was increased in 6C2 liver following 28 days on the HFHS diet. This pathway was contributed to GSEA by

BioCarta with 9 of the 19 genes overexpressed in strain 6C2. This pathway includes genes involved in endocytosis. Interestingly, Dynamin regulates endocytosis of the insulin receptor and therefore modulates insulin signaling

(Ceresa, Kao et al. 1998). The third pathway identified was the oxidative phosphorylation (OxPhos) pathway which was elevated in 6C1 liver following 100 days on the HFHS diet. The OxPhos pathway consists of 76 manually curated

63 genes (Mootha, Lindgren et al. 2003), of which 56 were overexpressed in 6C1

(Figure. 2.3). The increase in gene expression levels was consistent in all 5 electron transport chain complexes (Figure. 2.4).

Table 2‎ .1 Pathways that are differentially expressed between 6C1 and 6C2.

Figure 2.3 Upregulated Genes in the oxidative phosphorylation pathway.

Genes in the OXPHOS pathway are upregulated in 6C1 relative to 6C2. Each column represents one sample (chip) and each row represents one gene. The range of colors corresponds to the range of expression values (red = high, blue = low). Genes contributing to the core enrichment are shown in red.

Figure ‎2.4 Expression in Electron Transport Chain Complexes

Gene expression is upregulated in the liver of strain 6C1 relative to 6C2 following 100 days on the HFSC diet in each electron transport chain complex. Genes were classified according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa, Araki et al. 2008).

2.5 Discussion

We have refined the Obrq2 genetic interval to a 30 Mb region on mouse chromosome 6 and extended the phenotypic analysis of this strain to demonstrate that strain 6C1 is insulin resistant as well as obese relative to strain

6C2. The insulin resistance of strain 6C1 is due to a defect in peripheral tissue insulin sensitivity coupled with increased hepatic gluconeogenesis, rather than a defect in pancreatic insulin secretion. To further understand the molecular basis of the obesity and insulin resistance, we examined the global gene expression patterns associated with Obrq2 using microarray hybridization.

Pathway analysis tools were used to study the gene expression patterns which revealed that insulin resistance and increased hepatic gluconeogenesis were associated with a broad increase in the expression of genes involved in liver mitochondrial OxPhos. The upregulation of OxPhos gene expression in strain 6C1 relative to 6C2 was subtle but widespread, comprising 56 of 76

OxPhos genes that spanned each complex of the electron transport chain. The relationship between insulin resistance and OxPhos has been widely studied, although the focus has largely been on skeletal muscle, in part due to the ease of accessibility (Abdul-Ghani and DeFronzo 2008). In skeletal muscle, insulin resistance is associated with a decrease in OxPhos gene expression (Mootha,

Lindgren et al. 2003). However, whether impaired mitochondrial function is a cause or consequence of insulin resistance remains controversial.

Mild tissue-specific defects in either liver or skeletal muscle OxPhos due to apoptosis inducing factor (Aif) deficiency protects mice from obesity and insulin resistance, suggesting that the decrease in OxPhos activity in skeletal muscle is a consequence rather than a cause of insulin resistance (Pospisilik, Knauf et al.

2007). Additional evidence that decreased liver OxPhos activity can prevent insulin resistance comes from studies of gene expression patterns in human livers between obese, normoglycemic individuals and obese, insulin resistant individuals. These comparisons identified a decrease in OxPhos-related gene expression levels in the obese, normoglycemic individuals relative to their insulin resistant counterparts (Misu, Takamura et al. 2007; Takamura, Misu et al. 2008).

Gene expression analysis of the strains defining Obrq2 identified a similar expression pattern, with decreased OxPhos-related gene expression levels in the liver associated with protection from obesity and insulin resistance. Metabolite measurements in mice with a mild OxPhos reduction suggest a shift towards anaerobic glucose metabolism, which is considerably less efficient than aerobic glucose metabolism and may contribute to the reduction in adiposity (Pospisilik,

Knauf et al. 2007).

The OxPhos gene expression differences associated with Obrq2 were only observed in the liver, with no changes in the OxPhos pathway detected in adipose or skeletal muscle. Decreased OxPhos activity in muscle due to insulin resistance is detectable using pathway analysis of gene expression patterns

(Mootha, Lindgren et al. 2003). Therefore, the lack of change in OxPhos gene expression levels in muscle between 6C1 and 6C2 suggests that the increase in

68 liver OxPhos expression precedes any decrease in skeletal muscle. This further supports the notion that skeletal muscle mitochondrial dysfunction is a consequence of insulin resistance. The implication of these findings shifts the paradigm for the prevention of T2D from therapeutics to increase mitochondrial

OxPhos to those that can mildly reduce it. This may have immediate clinical relevance as a number of compounds exist that inhibit OxPhos (Liu, Shen et al.

2009).

Pgc-1 is a key transcriptional regulator of both mitochondrial oxidative metabolism and hepatic gluconeogenesis (Yoon, Puigserver et al. 2001; Finck and Kelly 2006). Expression levels of Pgc-1were 2.1-fold higher in strain 6C1 relative to strain 6C2 (p< 10-8) and may therefore provide a link between the increase in both OxPhos gene expression levels and hepatic gluconeogenesis in strain 6C1. Pgc-1 is a transcription coactivator that controls these metabolic processes by upregulating expression of a series of genes including Pepck,

G6pc, Hnf4a, Foxo1, NRF-1, NRF-2, among others (Finck and Kelly 2006).

However, none of these genes are significantly upregulated in strain 6C1 relative to 6C2. In fact, Hnf4a is downregulated in strain 6C1, suggesting that Pgc-1 may be acting through a different pathway. Humans with T2D and fasting hyperglycemia also lack overexpression of hepatic Pepck and G6pc, which indicates that this alternate pathway may have significant clinical implications

(Samuel, Beddow et al. 2009). One promising candidate in the Pgc-1 pathway is Lipin1, which is overexpressed in the liver of strain 6C1 relative to 6C2 following 28 days on the HFHS diet. Although this precedes the hepatic

69 upregulation of Pgc-1 gene expression, Lipin1 has been shown to regulate

OxPhos gene expression in a Pgc-1 dependent manner (Finck, Gropler et al.

2006). Further studies will be required to determine the key downstream effectors of Pgc-1 signaling or demonstrate that the link between OxPhos gene expression and hepatic gluconeogenesis in Obrq2 is Pgc-1-independent.

The differential expression of liver OxPhos genes occurred following 100 days on the HFHS diet. This change is preceded by expression differences in the liver between 6C1 and 6C2 in genes in the NDK Dynamin pathway. The

Dynamin complex is involved in both clathrin-dependent and clathrin- independent endocytosis (Doherty and McMahon 2009). Dynamin is a GTPase that directly interacts with NDK, which regulates dynamin function by serving as a guanine nucleotide exchange factor (GEF) (Krishnan, Rikhy et al. 2001).

Dynamin mediates the endocytosis of several key mediators of adiposity and insulin signaling including GLUT4 in adipose and muscle (Kao, Ceresa et al.

1998; Antonescu, Diaz et al. 2008) and MC4R in hypothalamus (Shinyama,

Masuzaki et al. 2003). Given that expression levels in the NDK dynamin pathway differed between 6C1 and 6C2 in the liver, of particular interest is the role of dynamin in regulating endocytosis of the insulin receptor. Overexpression of a dominant negative allele of dynamin in a hepatoma cell line disrupted endocytosis of the insulin receptor (Ceresa, Kao et al. 1998). This disrupted the phosphorylation and activation of ERK1 and ERK2 although phosphorylation of

Akt remained unaffected. ERK1/2 are localized in the mitochondria of many different cell types where they interact with with voltage-dependent anion channel

1 (Vdac) and histones H2A and H4, and causes a subtle but widespread increase in mitochondrial gene expression levels (Galli, Jahn et al. 2009). It is therefore possible that alterations in the ERK1/ERK2 signaling pathway provide a direct link between the variation in the NDK Dynamin pathway detected in liver following 28 days on the HFHS diet and the increased mitochondrial OxPhos gene expression levels seen in 6C1 liver following 100 days on the HFHS diet.

In addition to understanding the molecular physiology of Obrq2, identifying the genetic basis also promises to shed light on the etiology of obesity and insulin resistance. Among the 8 cis-eQTLs discovered by gene expression profiling, Asb15, Smo, and D830026I12 expression differences were identified following just 4 weeks on the HFHS diet and therefore represent the most likely variants causing the Obrq2 phenotype, rather than secondary to the development of obesity and insulin resistance. Smo is a key mediator of the hedgehog signaling pathway which regulates cell growth and differentiation during development (Rohatgi, Milenkovic et al. 2009). Smo and other components of the hedgehog signaling pathway are also expressed in adipose tissue, and inhibition of Smo has been shown to increase adipogenesis (Suh, Gao et al.

2006). While a statistically significant difference in Smo expression between 6C1 and 6C2 was only observed in liver, the expression levels in adipose tissue were decreased 41% (p < 10-5) and 32% (p<10-5) in strain 6C1 relative to 6C2 following both 28 days and 100 days on the HFHS diet, respectively (genome wide level of significance was p <10-7). This decrease in adipose expression in the obese strain 6C1 is consistent with the hypothesized anti-adipogenic role of

Smo. Smo was also recently identified in a genome-wide screen as a key determinant of brown versus white adipocyte differentiation in drosophila

(Pospisilik, Schramek et al. 2010).

Considerably less is known about the functions of Asb15 and D830026I12.

Asb15 is a target of the β-adrenergic signaling pathway and is thought to regulate protein synthesis and myotube differentiation (McDaneld and Spurlock 2008).

Clenbuterol, a beta-2 adrenergic receptor agonist, has been shown to decrease

Asb15 transcript levels in the muscle (McDaneld, Hancock et al. 2004) as well as decrease adiposity and improve insulin sensitivity (Pan, Hancock et al. 2001).

Interestingly, Clenbuterol has also been shown to increase the expression level of Pgc-1(Miura, Kai et al. 2008). Therefore, if the effects of Clenbuterol are in part mediated through transcriptional regulation of Asb15, then the lower levels of muscle Asb15 expression in strain 6C2 relative to 6C1 may contribute to the decreased adiposity and improved insulin sensitivity of strain 6C2.

D830026I12 corresponds to a cDNA identified in neonate heart and is part of the unigene cluster Mm.136046. It is not known whether D830026I12 encodes a protein or represents a noncoding RNA. However, portions of the D830026I12 transcript demonstrate high levels of evolutionary conservation among mammals.

Although the function of most large non-coding RNAs is not known, the finding that many large non-coding RNAs are highly evolutionarily conserved suggests that they have important biological functions (Guttman, Amit et al. 2009). Further studies of the signaling pathways described above as well as identifying the

72 genetic basis of Obrq2 will continue to provide new insights into the molecular and genetic basis of obesity and diabetes.

3 Chapter 3: Solute receptor Slc35b4 over-expression linked to diet-induced obesity and insulin resistance

For the following work the sub-congenic panels were derived in the lab by Dr. Lindsay Burrage and Dr. David Buchner and tested for differences in body weight, then mice were provided to the candidate for further experiments. All experiments were designed, performed and analyzed by the candidate except for the glucose clamps. The clamps were done at the Mouse Metabolic Phenotyping Core at Case Western Reserve under the supervision of Dr. Colleen Croniger. Dr. Croniger, Dr. Buchner and Dr. Jason Heaney also provided helpful discussions concerning the results. Jonathan Geisinger, an undergraduate assistant, helped with the qRT-PCR. The manuscript was prepared and written by the candidate with substantial contribution, edits and critique by Dr. Buchner.

Authors: Soha N. Yazbek, David A. Buchner, Jonathan Geisinger, Lindsay C. Burrage, Haifeng Shao, Colleen Croniger and Joseph H. Nadeau

3.1 Abstract

Despite the high heritability of obesity and type 2 diabetes, most susceptibility genes have not been identified. We used mouse models of diet-induced metabolic disease to facilitate gene discovery and better characterize the underlying genetic architecture. We applied a novel approach to gene discovery in mice, beginning with a survey of diet-induced obesity and insulin resistance related traits in chromosome substitution strains followed by genetic and phenotypic analysis of congenic, subcongenic and subsubcongenic strains.

Finally, functional evaluation of candidate genes was undertaken. Multiple closely linked loci on mouse chromosome 6 affect susceptibility to obesity and insulin resistance. These QTLs demonstrate strong effects with considerable phenotypic heterogeneity. Analysis of the strains defining the Obrq2a1 QTL on chromosome

6 identified the solute receptor Slc35b4 as a potential regulator of obesity and insulin resistance. Hepatic over-expression of Slc35b4 was associated with a diminished hepatic response to insulin, as indicated by a failure to shut down de novo hepatic glucose production. Solute receptor Slc35b4 mRNA expression level differences in liver are associated with susceptibility to diet-induced obesity and insulin resistance. We propose that this effect is mediated by altering the cytoplasmic to golgi transport of UDP-xylose and UDP-N-acetylglucosamine, thereby altering the nature of post-translational protein modifications in liver.

This work implicates a new gene in controlling susceptibility to obesity and insulin resistance, and demonstrates the considerable efficiency and power of this gene discovery platform.

3.2 Introduction

Type 2 diabetes currently affects 170 million individuals worldwide, a number that is expected to double over the next two decades (Wild, Roglic et al.

2004). Type 2 diabetes arises when pancreatic insulin secretion becomes insufficient to elicit the necessary physiological response to insulin in target tissues. The insulin resistance (IR) leads to impaired glucose uptake in muscle and fat as well as increased hepatic glucose production (Muoio and Newgard

2008). The resulting hyperglycemia leads to complications including retinopathy, stroke and amputations. Because current strategies often fail to adequately treat these and other comorbidities, disease risk remains high (Nathan, Buse et al.

2009). As a result, the need is urgent to identify alternative intervention modalities.

Identification of susceptibility genes could define new protein and pathway targets for interventions. Heritability estimates (~0.55) suggest that genetic factors account for the majority of the risk of becoming insulin resistant and developing type 2 diabetes (Stolerman and Florez 2009). Linkage analysis, candidate gene association studies, and genome-wide association studies

(GWAS) have all been undertaken to identify the genetic variants that influence susceptibility (Frayling 2007). These studies identified rare inactivating mutations in genes such as peroxisome proliferator-activated receptor gamma (PPARG) and the insulin receptor (INSR), as well as common variants in transcription factor 7-like 2 (TCF7L2), solute carrier family 30 (zinc transporter), member 8

(SLC30A8), among others (Kadowaki, Bevins et al. 1988; Barroso, Gurnell et al.

1999; Scott, Mohlke et al. 2007; Sladek, Rocheleau et al. 2007). To date, a total of 19 SNPs have been reproducibly associated with type 2 diabetes in humans

(Lyssenko and Groop 2009). However, the cumulative effects of these susceptibility alleles accounts for only 6% of heritability, and the nature of missing heritability remains to be determined (Manolio, Collins et al. 2009). For example, whether these are causative variants or are in linkage disequilibrium with the causal variant has not been resolved in most cases. The difficulty in proving causation is a large impediment towards translating genetic studies into clinical benefits (Lyssenko and Groop 2009).

Several factors contribute to the difficulty in identifying genes that underlie complex traits such as type 2 diabetes and IR. Linkage analysis depends on pedigrees that tend to have small sample sizes and thus limited statistical power to identify variants with small phenotypic effects. Candidate gene and genome- wide association studies utilize larger sample sizes. However, candidate gene studies are limited by the need for prior biological knowledge, whereas GWAS must overcome stringent multiple testing penalties and other population and statistical considerations. In addition, non-additive effects are notoriously difficult to detect (Phillips 2008). Model organisms have many advantages that facilitate identification of genes that regulate type 2 diabetes and IR. These include the ability to control environmental, conduct defined crosses, functionally evaluate candidate genes in vivo, and undertake rigorous mechanistic studies.

Chromosome substitution strains (CSSs), which are a novel paradigm for

78 studying complex traits, dramatically reduce the multiple testing penalty and improve the ability to detect epistasis to identify susceptibility alleles (Shao,

Burrage et al. 2008), while retaining the many other advantages of genetic studies in model organisms. Congenic strains share these advantages with

CSSs and can be generated following only four generations of breeding when derived from CSSs, rather than 10 generations using conventional breeding methodologies (Nadeau, Singer et al. 2000).

Analysis of the C57BL/6JA/J panel of CSSs identified 16 chromosomes with at least 1 QTL affecting diet-induced obesity, and 8 affecting glucose homeostasis (Singer, Hill et al. 2004; Shao, Burrage et al. 2008). The use of congenic strains derived from one of the obesity-resistant strains, C57BL/6J-Chr

6A/J, identified 4 QTLs regulating body weight including Obrq2 (obesity resistance

QTL-2) (Shao, Burrage et al. 2008). The Obrq2B6 allele, which leads to impaired glucose homeostasis and increased adiposity, has an effect equivalent to over

50% of the body weight difference between the B6 and A/J parental strains

(Buchner, Burrage et al. 2008; Buchner, Yazbek et al. submitted). In this report, we present genetic and physiological analysis of congenic, subcongenic and subsubcongenic strains derived from C57BL/6J-Chr 6A/J. We found a complex genetic architecture among the congenic panels, with multiple QTLs found at each level of genetic resolution with a remarkable variety of metabolic phenotypes. In addition, we identified variation in hepatic Slc35b4 expression levels as a likely contributor to the obesity and IR phenotype associated with

Obrq2.

3.3 Materials and Methods

3.3.1 Husbandry. B6 and congenic mice were obtained from breeding colonies at

Case Western Reserve University. All mice were housed in ventilated racks at

21°C in a 12hr light-dark cycle and weaned at 3 weeks of age. Mice were fed

Labdiet 5010 chow (PMI Nutrition International, Richmond, OH, USA) until they were 5 weeks of age, at which time male mice were fed a HFSC diet (D12331;

Research Diets, Inc, New Brunswick, NJ, USA) for 16 weeks. Mice had access to food and water ad libitum. All animal study protocols were approved by the

Institutional Animal Care and Use Committee of Case Western Reserve

University.

3.3.2 Generation of subcongenic and subsubcongenic strains. A panel of subcongenic strains was derived from strain 6C2 and a panel of subsubcongenic strains was derived from strain 6C2d (Fig. 1). A similar breeding strategy was used to generate each panel. 6C2 and 6C2d mice were each initially crossed to

B6 mice. The resulting (6C2 x B6) F1 and (6C2d x B6) F1 mice were backcrossed to B6. N2F1 offspring were genotyped for polymorphic markers

(Table S1) to identify mice carrying a recombinant chromosome. The recombinant N2F1 mice were again backcrossed to B6 and the offspring that were heterozygous for the recombinant chromosome were intercrossed to homozygose the A/J-derived segment. Once homozygosed, brother-sister mating was used to maintain the congenic strains.

3.3.3 Sequence Analysis. Evolutionary conserved noncoding sequence blocks were identified using the phastcon program that is available as part of the UCSC genome browser (Siepel, Bejerano et al. 2005). SNPs between A/J and B6 within the Obrq2a interval were obtained from the Ensembl genome browser and compared to the phastcon sequence to determine if the SNPs were located within a conserved sequence block. TranscriptSNPView (Cunningham, Rios et al. 2006), which is incorporated in the Ensembl genome browser, was used to search for sequence variants between B6 and A/J within the coding region of

Slc35b4, Sec8, and Lrguk. Exons for which complete sequence was not available from both B6 and A/J were analyzed by direct sequencing of B6 and

A/J genomic DNA.

3.3.4 Fasting insulin, fasting glucose and HOMA-IR. Following 16 weeks on the

HFSC diet, 20-30 male mice per strain were fasted overnight (16-18 hrs). The mice were then anesthetized using isoflurane and blood was collected from the retro-orbital sinus using a heparin coated capillary tube (Iris Sample Processing,

Westwood, MA, USA) into an EDTA coated microtainer (Becton Dickinson,

Franklin Lakes, NJ, USA). Glucose was measured from whole blood using a handheld glucometer (OneTouch Ultra, Lifespan, Milpitas, CA, USA). Blood was then centrifuged and plasma was separated and stored at -80°C until use. Insulin was measured using a mouse ultra–sensitive ELISA kit (Mercodia, Uppsala,

Sweden). Homeostasis model assessment (HOMA) of insulin resistance was calculated using glucose and insulin measurements obtained using the formula :

[fasting glucose (mmol/l)) x (fasting insulin (µU/ml)]/22.5 (Plank, Blaha et al.

2006).

3.3.5 Glucose Tolerance Test. Eight mice per strain were fasted with free access to water overnight (16-18 hrs). Glucose was measured from tail blood with a hand- held glucometer (OneTouch Ultra, Lifespan, Milpitas, CA, USA) at time 0 and 15,

30, 60 and 120 minutes after an intra-peritoneal injection of dextrose dissolved in water (2g/kg body weight).

3.3.6 Tissue Collection and RNA extraction. Six mice per strain were euthanized with a CO₂ overdose. Liver, pancreas, epididymal-fat pad and hind-limb muscle tissue were collected and immediately placed in RNAlater (Ambion, Austin, TX,

USA). RNA was extracted using an RNeasy mini-kit with RNAse-free DNAse treatment (Qiagen, Valencia, CA, USA).

3.3.7 Real-time quantitative PCR. cDNA was synthesized from 2 µg of total RNA using the SuperScript III kit (Invitrogen, Carlsbad, CA, USA). Taqman assays

00480588, 00486020, and 01166707 were used respectively for Slc35b4, Sec8 and Lrguk. QPCR was performed as previously described (Hill-Baskin,

Markiewski et al. 2009) with a Chromo4 Cycler (MJ Research, Waltham, MA,

USA). Gene expression levels were calculated relative to the 18S rRNA control

(Taqman assay 004319413E) using the 2- calculation.

3.3.8 Hyperinsulinemic-Euglycemic Clamp. Glucose clamp procedures were performed in awake, pre-catheterized, unrestrained, unanesthetized mice as described (Ayala, Bracy et al. 2006). Chronic catheterization of mice was

82 performed five days prior to the clamp procedure. Mice were anesthetized by inhalation of an isoflurane and oxygen mixture, and the left carotid artery and the right jugular vein were catheterized for blood sampling and intravenous infusion during the clamp, respectively. All surgeries were done in the Mouse Metabolic and Phenotyping Center at Case Western Reserve University. Mice were fasted for 5 hours prior to the hyperinsulinemic-euglycemic clamp procedure. A continuous infusion of [6,6- 2H] glucose was administered (0.3 mg · kg–1·min–1) during the 90 min (-90min to 0 min) basal period to estimate basal glucose turnover and continued for the duration of the experiment (0min to 120 min).

Blood samples (30l) for determination of plasma [6,6- 2H]glucose enrichment were obtained at -30 , -20, -10 and at 100, 110, and 120 min. A continuous infusion of insulin (4 mU· kg–1·min–1) was initiated at 0 min to acutely increase and then maintain high, but physiological, plasma insulin levels. Red blood cells

(3l/min) were also infused to maintain a stable hematocrit over the duration of the experiment. A variable infusion of 50% dextrose was also initiated at 0 min and adjusted periodically to maintain euglycemia at the basal level and during the hyperinsulinemic phase of the study. During euglycemic conditions, the rate of glucose disappearance (Rd) is equal to the rate of glucose appearance (Ra). The whole-body glucose turnover (Ra) was then determined under clamped insulin- stimulated conditions at 100, 110, and 120 min, as previously described (Ayala,

Bracy et al. 2006; Kang, Sebastian et al. 2007). The rate of plasma glucose turnover was calculated using the following equation: Ra = (ENRinf ⁄ENRpl -1) x

F, where ENRinf is the isotopic enrichment of the infusate, ENRpl is the isotopic

83 enrichment of plasma and F is the rate of the isotope infusion. The glucose infusion rate (GIR) adjusted for body weight was used as a measure of insulin sensitivity. Hepatic glucose production (EGP) during the clamp was calculated as the difference between Ra and GIR. Isotope enrichment was determined by

GC–Mass spectrometry under electron impact ionization, ions of mass-to-charge ratios (m⁄ z) 319 to 323 were monitored (Kang, Sebastian et al. 2007) .

3.3.9 Determination of glucose uptake in peripheral tissues. At 120 min, 50 µCi of

3 3 2-deoxy-D-[1, 2- H] glucose ([ H]2 DG) was administered as an intravenous bolus and blood samples were collected every 5 min for 25 min to determine the radioactive content. At 25 min after the bolus, mice were euthanized by cervical dislocation and gastrocnemius, superficial vastus lateralis, soleus muscles, epididymal fat, liver and brain were removed and snap frozen in liquid nitrogen.

Tissue samples were processed as previously described (Halseth, Bracy et al.

1999). Briefly, tissue samples were weighed, homogenized in 0.5% perchloric acid, and centrifuged at 2,300 g for 5 min, and the supernatants were neutralized with KOH. One aliquot of homogenate was counted without further treatment to

3 3 yield total counts of [ H]2 DG and 2-deoxy-D-[1, 2- H]glucose phosphate

3 ([ H]2DG-P). A second aliquot of homogenate was treated with barium hydroxide

3 (0.3 N) and zinc sulfate (0.3 N) to remove [ H]2DG-P, and then counted to yield

3 3 [ H]2DG radioactivity. The tissue radioactivity of [ H]2DG-P was calculated by the

3 3 3 difference of total counts ([ H]2DG and [ H]2DG-P) and the[ H]2DG count alone).

3.3.10 Statistics. Data are presented as mean ± SEM. Measurements were compared using an unpaired student’s T-test with Welch’s correction. Analysis of body weight, insulin level, glucose level and HOMA in the congenic panels was based on the sequential analysis method as previously described (Shao, Burrage et al. 2008; Millward, Burrage et al. 2009). Briefly, each congenic strain was compared to the most genetically similar congenic strain with phenotypic differences (after Bonferoni correction for multiple hypothesis testing) attributed to sequence variation within the regions of DNA sequence that differ between the two strains.

3.4 Results

3.4.1 Obrq2a regulates body weight and IR

Obrq2 is defined by the obese congenic strain 6C1 and the obesity- resistant congenic strain 6C2, and is located in a 30.3 Mb interval between markers rs13478633 and rs30218447 (Figure 3.1; (Buchner, Yazbek et al. submitted),chapter 2). For higher resolution mapping of the genetic basis for

Obrq2, a panel of five subcongenic strains was generated that together span the entire Obrq2 interval (Figure 3.1A,B). Analysis of body weight of male mice from each subcongenic strain after 100 days on a high-fat, simple carbohydrate

(HFSC) diet identified 4 QTLs that regulate diet-induced obesity (Figure. 3.1B).

The A/J-derived alleles of Obrq2a, Obrq2c, and Obrq2d prevented obesity

85 whereas the A/J-derived allele of Obrq2b promoted obesity. Obrq2d overlaps both Obrq2a and Obrq2c; potentially indicating a common genetic basis and that

Obrq2d may not represent a distinct QTL. Obrq2A/J accounts for a 7.17g reduction in body weight, whereas the four sub-QTLs identified account for reductions of 6.04g (Obrq2aA/J), 5.93g (Obrq2cA/J), and 3.44g (Obrq2dA/J), and a

5.35g increase in body weight (Obrq2bA/J).

Figure ‎3.1 Schematic of sub-congenic panel derived from strain 6C2 and 6C2d.

For each strain the region derived from the A/J genome is shown in dark grey, B6 in white and undetermined region representing the ambiguity of the crossover point is shown in light grey. Selected genetic markers with their location in Mb are drawn above the map. For the purpose of illustration of the QTLs the recombination break point was estimated in the middle of the undetermined region. Data is presented as means ± SEM and is found to the right of the panel map. * indicates statistical significance after bonferronni correction for the number of strains tested. (A) Drawn to scale the location of Obrq2a on CSS-6 (B) Strain 6C2d defines the Obrq2a QTL that regulates body weight. (C) Summary of traits associated with each QTL is presented below QTL name.

To delineate the limits of the A/J-derived congenic segment in strain 6C2d that defines Obrq2a, additional SNPs were genotyped. These SNPs narrowed the Obrq2a interval to 3.2 Mb between SNP markers rs29927775 and rs30221945, thereby reducing the number of candidate genes from 216 to 19.

To characterize the phenotype of the 6C2d subcongenic strain that defines

Obrq2a, levels of fasting insulin and glucose were measured and HOMA-IR was calculated (Table 3.1). Strain 6C2d had significantly reduced fasting insulin, glucose and HOMA-IR relative to strain B6. Thus, Obrq2a regulates diet-induce obesity and measures of insulin resistance.

To further localize Obrq2a, we generated a panel comprised of 8 subsubcongenic strains derived from 6C2d that spanned the Obrq2a interval

(Figure 3.1 C). Five-week old males of each strain were placed on the HFSC diet for 100 days, at which time body weight was measured and insulin resistance assessed (Tables 3.2 and Figures 3.3, 3.4, 3.5, 3.6). Six QTLs were detected within the Obrq2a interval that each controlled distinct combinations of traits, often uncoupling the link between obesity and IR (summary Figure. 3.2 C).

Table ‎3.1 Obrq2a metabolic properties.

Strain Body Weight (g) Glucose (mg/dl) Insulin (µg/l) HOMA-IR

44.33 ± 0.56 185.3 ± 12.5 1.06 ± 0.2 13.6 ± 2.9 B6 (n=101) (n=19) (n=19) (n=19)

38.29 ± 1.09 142 ± 5.6 0.36 ± 0.05 2.0 ± 0.4 6C2d (n=32) (n=19) (n=18) (n=18)

Data presented as mean ± SEM. All comparisons were statically significant (P<0.005) after Bonferroni correction for multiple testing (number of strains tested).

Table ‎3.2 6C2d subcongenic panel

Data presented as mean ± SEM. Shaded cells represent means and P-values of statistically significant comparisons after Bonferroni correction for multiple testing (number of strains tested). Parental strains are in bold.

Figure ‎3.2 Schematic of sub-congenic panel derived from strain 6C2d- Body Weight.

For each strain the region derived from the A/J genome is shown in dark grey, B6 in white and undetermined region representing the ambiguity of the crossover point is shown in light grey. Selected genetic markers with their location in Mb are drawn above the map. For the purpose of illustration of the QTLs the recombination break point was estimated in the middle of the undetermined region. Data is presented as means ± SEM and is found to the right of the panel map. * indicates statistical significance after Bonferroni correction for the number of strains tested. The means are compared sequentially in order of the strains drawn. Each strain is compared to the one below it. A statistical P-value indicates the presence of a QTL spanning the sequence interval different between the strains.

Figure ‎3.3 Schematic of sub-congenic panel derived from strain 6C2d- Glucose.

For each strain the region derived from the A/J genome is shown in dark grey, B6 in white and undetermined region representing the ambiguity of the crossover point is shown in light grey. Selected genetic markers with their location in Mb are drawn above the map. For the purpose of illustration of the QTLs the recombination break point was estimated in the middle of the undetermined region. Data is presented as means ± SEM and is found to the right of the panel map. * indicates statistical significance after Bonferroni correction for the number of strains tested. The means are compared sequentially in order of the strains drawn. Each strain is compared to the one below it. A statistical P-value indicates the presence of a QTL spanning the sequence interval different between the strains.

Figure ‎3.4 Schematic of sub-congenic panel derived from strain 6C2d- Insulin.

For each strain the region derived from the A/J genome is shown in dark grey, B6 in white and undetermined region representing the ambiguity of the crossover point is shown in light grey. Selected genetic markers with their location in Mb are drawn above the map. For the purpose of illustration of the QTLs the recombination break point was estimated in the middle of the undetermined region. Data is presented as means ± SEM and is found to the right of the panel map. * indicates statistical significance after Bonferroni correction for the number of strains tested. The means are compared sequentially in order of the strains drawn. Each strain is compared to the one below it. A statistical P-value indicates the presence of a QTL spanning the sequence interval different between the strains.

Figure ‎3.5 Schematic of sub-congenic panel derived from strain 6C2d- HOMA-IR.

For each strain the region derived from the A/J genome is shown in dark grey, B6 in white and undetermined region representing the ambiguity of the crossover point is shown in light grey. Selected genetic markers with their location in Mb are drawn above the map. For the purpose of illustration of the QTLs the recombination break point was estimated in the middle of the undetermined region. Data is presented as means ± SEM and is found to the right of the panel map. * indicates statistical significance after Bonferroni correction for the number of strains tested. The means are compared sequentially in order of the strains drawn. Each strain is compared to the one below it. A statistical P-value indicates the presence of a QTL spanning the sequence interval different between the strains.

3.4.2 Inheritance pattern of Obrq2a1

We focused on Obrq2a1, which regulates body weight and fasting glucose levels, because it contained only 3 candidate genes, the fewest number among the QTLs identified. To determine the inheritance pattern of Obrq2a1 and test for parental effects, body weight and measures of IR were compared between male offspring fed the HFSC diet from reciprocal crosses between 6C2d-2 and 6C2d-3 mice. No significant differences were found between the male (6C2d-2 x 6C2d-

3)F1 and (6C2d-3 x 6C2d-2)F1 offspring in body weight, fasting insulin levels, fasting glucose levels or HOMA-IR. These data demonstrate that parental genotype did not impact offspring phenotype. Therefore, the pooled F1 data were compared to the parental strains 6C2d-2 and 6C2d-3. Body weight, fasting insulin, fasting glucose and HOMA did not differ between the pooled F1s and

6C2d-3, but were significantly lower than 6C2d-2 for all four measurements

(Table 3.3). Therefore, the phenotypic effects of the Obrq2a1A/J allele in 6C2d-3 were dominant relative to the B6-derived allele in 6C2d-2.

Table ‎3.3 Metabolic phenotype of reciprocal crosses.

F:female.M:male. *indicates statistical significance compared to 6C2d-2. ¥ indicates statistical significance compared to 6C2d-3. Comparing (6C2d-2x6C2d-3)F1 to (6C2d-2x6C2d-3) F1 showed no statistical significance. Pooled data was used for parental comparisons.

3.4.3 Obrq2a1 is associated with decreased glucose tolerance

The increase in fasting glucose levels associated with Obrq2a1B6 in 6C2d-

2 suggests a disruption of glucose homeostasis. To characterize the dynamics of glucose metabolism regulated by Obrq2a1 using a more sensitive method than measuring fasting glucose levels, a glucose tolerance test (GTT) was performed on strains 6C2d-2 and 6C2d-3. Strain 6C2d-3 showed a decreased fasting glucose level and a significant reduction in plasma glucose levels at all time- points following a bolus intraperitoneal injection of glucose (Figure. 3.6A). The reduction in glucose levels in 6C2d-3 corresponds to an approximately 25% decline throughout the tolerance curve (Figure 3.6B). Thus, strain Obrq2a1B6

(6C2d-2) exhibited enhanced tolerance to glucose relative to Obrq2a1A/J (6C2d-

3).

Figure ‎3.6 Glucose tolerance test (GTT)

(A) and Area under the curve (AUC) representing the summed GTT (B). 6C2d-3 shows a significant decrease in fasting glucose levels and an enhanced glucose tolerance compared to 6C2d-2. (A) GTT was performed on overnight fasted mice (n=8) and glucose levels following an i.p. injection of glucose was graphed. (B) AUC was calculated and the sum of the integration values of consecutive linear segments of the glucose curve (0-15, 15-30, 30-60, 60-120) was plotted. *p<0.05, **p<0.01, ***p<0.001.

3.4.4 Increased hepatic glucose production disrupts glucose homeostasis in Obrq2a1

To identify the cause of the disruption of glucose homeostasis associated with Obrq2a1, a hyperinsulinemic-euglycemic clamp was performed on strains

6C2d-2 and 6C2d-3 to test insulin sensitivity and tissue-specific glucose uptake.

Strains 6C2d-2 and 6C2d-3 were maintained at equivalent and physiologically normal plasma glucose levels throughout the experiment (Figure. 3.7A). 6C2d-3 required an increased glucose infusion rate (GIR) relative to 6C2d-2 to maintain normoglycemia under hyperinsulinemic conditions (Figure. 3.7B). Therefore,

Obrq2a1A/J in 6C2d-3 is associated with increased insulin sensitivity. Glucose clearance rate and glucose uptake in peripheral tissue did not differ between the two strains (Figure. 3.7C, E, F). The difference in GIR is therefore due to a decrease in endogenous glucose production and not to differences in glucose uptake (Figure. 3.7D). Thus, the relative improvement in the ability to shut down de novo gluconeogenesis demonstrates that Obrq2a1A/J enhances hepatic sensitivity to insulin.

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Plasma Glucose Glucose Infusion Rate A B

c Glucose Clearance D Glucose Production

Glucose Uptake E F Glucose Uptake

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Figure ‎3.7 Hyperinsulinemic – euglycemic clamp.

6C2d-3 is insulin resistant relative to 6C2d-2 as demonstrated by an impaired ability to shut down endogenous glucose production in response to insulin despite no difference in glucose clearance rates. (A) Plasma glucose levels measured throughout the clamp experiment. (B) Glucose infusion rate needed to maintain normoglycemia is plotted through time. (C) Glucose clearance rate Rd is measured at the beginning and end of the experiment. (D) Endogenous glucose production is calculated for both strains. (E,F) Muscle, brain and white adipose tissue (WAT) levels of radiolabeled glucose was compared between the two strains at the end of the experiment to reflect glucose uptake. Mean ± SEM is plotted. *p<0.05.

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3.4.5 Candidate gene analysis of the Obrq2a1 interval

To evaluate candidate genes within Obrq2a1, DNA sequence and gene expression levels were examined for the 3 candidate genes located within the

QTL interval (Ensembl version 56). Exocyst complex 4 (Exoc4, alias Sec8) encodes a protein that is part of the octameric exocyst complex (Saito, Shibasaki et al. 2008). Decreased expression of Sec8 blocks insulin-stimulated glucose uptake (Inoue, Chiang et al. 2006) by inhibiting Glut4 docking at the plasma membrane (Lyons, Peck et al. 2009). The second gene is leucine-rich repeat and guanylate kinase domain containing gene (Lrguk), a gene that has no known function. The third gene is solute receptor 35b4 (Slc35b4), which is localized in the golgi membrane where it facilitates transport of UDP-xylose and UDP-N- acetylglucosamine into the golgi (Ashikov, Routier et al. 2005). There are no polymorphisms between B6 and A/J within the coding or untranslated regions of

Exoc4, Lrguk and Slc35b4, or in any of the evolutionarily conserved non-coding sequence blocks within the Obrq2a1 interval (Ensembl v56 and data not shown).

In addition to sequence analysis, the relative mRNA expression levels were evaluated for each of the 3 candidate genes in physiologically relevant tissues (liver, muscle, pancreas, and white adipose) (Table 3.4). Lrguk mRNA was not detected in the pancreas and was found at low levels in the other tissues, consistent with evidence that Lrguk is primarily expressed in testes (Wu,

Orozco et al. 2009). Moreover, expression of both Sec8 and Lrguk did not differ in any of the tissues analyzed between the two strains that define Obrq2a1.

Given the role of Sec8 in glucose uptake, these data are consistent with the

103 observation that glucose uptake did not differ between strains 6C2d-2 and 6C2d-

3 (Figure. 3.7E, F). Interestingly, Slc35b4 expression in the liver was 1.5-fold higher in strain 6C2d-2 relative to strain 6C2d-3 (p < 0.05), but did not differ in the other tissues examined. Thus, Slc35b4 is the only gene within the QTL interval with an expression level difference among the tissues surveyed. We therefore propose that altered hepatic expression of Slc35b4 leads to the increased hepatic insulin resistance associated with Obrq2a.

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Table ‎3.4 Quantitave RT-PCR analysis of Obrq2a1 candidate genes

N=5-6/group. * Indicates statistical significance after bonferroni correction for number of tissues tested and the number of genes (12 multiple tests). Values presented as mean ± SEM. N.D not detected .

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3.5 Discussion

This work addressed two important issues in genetic studies of susceptibility to complex traits and diseases. The first involves genetic architecture, namely the number of genes, their mode of inheritance, and the nature of their interactions. The second issue involves establishing the identity of the susceptibility genes. We report the first use of CSSs and congenic, subcongenic and subsubcongenic strains to address both issues.

Analysis of CSSs revealed a genetic architecture for complex traits that is dominated by multiple genes with large and non-additive phenotypic effects

(Shao, Burrage et al. 2008). The structure of the congenic panels prevented testing for non-additive interactions because most QTLs were not studied independently, but rather in combination with other QTLs. However, analysis of newly developed subcongenic and subsubcongenic strains identified multiple

QTLs with a pattern of large effects similar to that identified in the CSSs. Body weight was measured at each level of genetic resolution; therefore the average effect of each QTL on body weight relative to the size of the QTL interval and number of candidate genes was analyzed (Table 3.5). The total genetic variation between B6 and A/J mice is associated with an 11.09g difference in body weight between the two strains when fed the HFSC diet. The CSS strains account for an average difference of 8.36g (75% of parental difference), despite only containing 4.4% of the A/J genome and 1,485 candidate genes. The congenic, subcongenic, and subsubcongenic strains account for body weight differences of

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6.37g (57% of parental difference), 5.77g (52% of parental difference), and 4.28g

(39% of parental difference) despite only containing 1%, 0.5%, and 0.03% of the

A/J genome and including 342, 135 and 4 candidate genes respectively. Thus, by starting with a CSS survey and then analyzing congenic, subcongenic and subsubcongenic strains, we were able to assess genetic and phenotypic variation over a 2,000-fold level of resolution, from the entirety of the A/J genome to QTLs identified using subsubcongenic strains that average just 800 kb. The results indicate that as higher resolution mapping reduced the size of the QTL intervals and the number of candidate genes, the effect on body weight did not decrease proportionally (Table 3.5).

Furthermore, many of these QTLs were detected only in a specific genomic context. For example, Obrq2a3 and Obrq2a4 are adjacent QTLs that have counterbalancing effects on fasting insulin levels. These two QTLs were only detected because of the recombination breakpoint in strain 6C2d-5, suggesting that the number of QTLs identified will be highly dependent on the number of congenic strains analyzed. Therefore, the 10-12 obesity or insulin resistance QTLs identified on mouse chromosome 6 are likely a considerable underestimate of the genetic variation contributing to the phenotype. The large number of tightly linked QTLs with opposing effects highlights the genetic complexity of complex traits and likely contributes to the difficulty in identifying susceptibility genes in humans.

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Table ‎3.5 QTL effect size

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Obrq2a1 was one of the QTLs identified that regulates body weight and fasting glucose levels. Through analysis of sequential congenic panels, this QTL was mapped to a 1.1 Mb interval containing 3 genes: Sec8, Lrguk, and Slc35b4.

Sec8 was a strong functional candidate based on its role in Glut4 docking and glucose uptake as a member of the exocyst complex (Inoue, Chang et al. 2003).

However, similar levels of glucose uptake in muscle and adipose tissue between strains 6C2d-2 and 6C2d-3 suggests that genetic variation in Sec8 does not to contribute to the IR phenotype associated with Obrq2a1. Absence of DNA sequence or gene expression level differences in Sec8 between strains 6C2d-2 and 6C2d-3 are consistent with this finding. Similarly, no sequence or expression level differences were found for Lrguk. By contrast, a significant 1.5- fold increase in hepatic expression levels was detected in strain 6C2d-2 relative to strain 6C2d-3, although there are also no sequence differences in Slc35b4 between B6 and A/J. Collectively, the functional studies and candidate gene analysis implicate Slc35b4 in the regulation of body weight, hepatic gluconeogenesis and IR.

Interestingly, a SNP in the human SLC35B4 gene (rs1619682) was associated with waist circumference in the Framingham Heart Study (Fox,

Heard-Costa et al. 2007). The human SLC35B4 gene is located on chromosome

7q33 where QTLs have been reported that regulate body mass index (BMI), metabolic syndrome, lipid profiles, fasting glucose, pro-insulin levels, and fat stores (Arya, Blangero et al. 2002; Feitosa, Borecki et al. 2002; Tang, Miller et al.

2003; Saunders, Chiodini et al. 2007; Laramie, Wilk et al. 2008). Other nearby

109 candidate genes includes LEPTIN and SEC8, and our data indicate that

SLC35B4 should be considered a strong candidate gene as well. Of note, the interval spanning 7q21.1 -7q34 appears to contain at least two genes influencing obesity and obesity related traits (Li, Li et al. 2003). Further studies are needed to determine whether additional QTLs are present at this locus in humans and to establish the causal variant underlying these QTLs.

Identifying the genetic factors affecting obesity and diabetes susceptibility can open a window into the underlying pathophysiology that provides new insight into the molecular basis of disease (Altshuler, Daly et al. 2008). Towards this end, we found that Slc35b4 hepatic expression was increased ~50% in strain

6C2d-2 relative to strain 6C2d-3 (p< 0.05). Liver was also found to be insulin resistant in the euglycemic-hyperinsulinemic clamp experiments, as evidenced by the inability to respond to insulin and shut down glucose production.

Together, this suggests that a liver autonomous defect is responsible for the hepatic insulin resistance. Slc35b4 encodes a protein that transports UDP- xylose and UDP-N-acetylglucosamine from the cytosol into the golgi (Ashikov,

Routier et al. 2005). Therefore, over-expression of Slc35b4 may alter the cellular localization of its cargo nucleotide sugars.

UDP-xylose is required for glycosaminoglycan (GAG) biosynthesis on the core protein of proteoglycan sugar chains (Moriarity, Hurt et al. 2002). Proteins that are post-translationally modified with a preoteoglycan function in a variety of cellular and physiological activities including differentiation, signaling, adhesion, cell division, and wound repair (Wang, Julenius et al. 2007). UDP-xylose is also

110 part of a trisaccharide found on the epidermal growth factor repeats of proteins, such as Notch and the coagulation Factors VII and IX (Hase, Kawabata et al.

1988; Moloney, Shair et al. 2000). Data from mutant chinese hamster ovary cells that contain cytoplasmic, but not golgi UDP-xylose synthesis demonstrated the in vivo ability of cytoplasmic UDP-xylose to be incorporated into protein xylosylation in the golgi (Bakker, Oka et al. 2009). This suggests a potential role for Slc35b4, which is the only known cytoplasmic to golgi transporter of UDP-xylose, in the biosynthesis of GAGs and protein xylosylation.

Slc35b4 is also a transporter for UDP-N-acetylglucosamine, which is the major end product of the hexosamine biosynthesis pathway (HBP). UDP-N- acetylglucosamine then serves as both a substrate for O-linked glycosylation (O-

GlcNAC) and as a negative feedback inhibitor of the HBP pathway (Buse 2006).

Alterations in hepatic HBP flux lead to endoplasmic reticulum stress, lipid accumulation, and inflammation which could all contribute to development of obesity and IR (Sage, Walter et al. 2010). Phosphoinositide-dependent regulation of insulin signaling through O-GlcNAC modification of Akt and IRS1 represents a molecular mechanism linking the HBP pathway to IR (Yang,

Ongusaha et al. 2008). In addition, the HBP pathway stimulates hepatic gluconeogenesis through an insulin-independent O-GlcNAC modification of

CREB regulated transcription coactivator 2 (CRTC2) (Dentin, Hedrick et al.

2008). Although these individual proteins are likely important to pathogenesis, over 600 proteins are modified with the addition of an O-GlcNAC moiety and may therefore contribute to the phenotype (Love and Hanover 2005).

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We hypothesize that hepatic over-expression of Slc35b4 alters transport and therefore bioavailability of UDP-xylose and UDP-N-acetylglucosamine.

Importantly, even a modest 20% increase in O-GlcNAC transferase levels leads to increased insulin resistance suggesting that levels of UDP-N- acetylglucosamine levels are tightly regulated (McClain, Lubas et al. 2002).

Therefore, the 1.5-fold increase in hepatic Slc35b4 mRNA levels is predicted to alter the post-translational modification of many proteins. Further work will be required to determine the effect of hepatic Slc35b4 overexpression on glycosylation and xylosylation patterns, and which of the affected proteins mediate the proposed effect on body weight and IR.

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4 Chapter 4: Paternal genotype determines body weight and food intake for multiple generations

The work described in the following chapter was based on preliminary data provided for the interval by Dr. David Buchner. The subsequent experimental design of the crosses were a collaborative effort between candidate and Dr.Buchner. Dr. Buchner performed the F1 and F3 crosses and food intake analysis. The candidate performed F2 crosses in all parental direction and all backcrosses. The candidate also measured glucose and insulin in the F1 cross, and performed all data analysis and statistics. Undergraduate assistants Jonathan Phan and Mike Morgan helped with genotyping of the mice. The manuscript was prepared and written by candidate and critiqued by Dr. Buchner.

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Authors: Soha N. Yazbek, Joseph H. Nadeau and David A. Buchner

4.1 Abstract

Obesity prevalence has increased worldwide along with co-morbidities such as cancer, type II diabetes and heart disease. Current treatments have failed in inhibiting the rise in obesity and adverse consequences. Despite the evidence of a significant genetic contribution to obesity development (40-70%), less than three percent of the heritability can be explained with the current knowledge of monogenic abnormalities and common sequence variations. Epigenetic inheritance is rarely tested in human studies and is difficult to assess because of the confounded environmental variation and learning behaviors. Evidence from mammals implicates genetic and environmental cues in one generation showing effect in subsequent generations. In this report, we provided the first evidence for a non-genomic transgenrational paternal effect on body weight and food intake.

We used obesity resistant congenic strain 6C2d having the Obrq2aA/J allele on an otherwise C57BL6/J background to test for parental and transgenerational effect.

Crosses between 6C2d and obesity sensitive strain B6 showed that having a paternal or a grand-paternal mouse with the A/J allele at the Obrq2a interval conferred an obesity resistance ability on the high-fat, simple-carbohydrate diet in

B6, an otherwise obesity sensitive strain. The obesity resistant phenotype was transmitted through the paternal lineage to the F3 generation, but was diluted and lost if passed through one or more female lineages. The effect was due to decreased food intake per gram of body weight.

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4.2 Introduction

The prevalence of obesity is increasing worldwide, driving a rise in the incidence of co-morbidities including type 2 diabetes, cardiovascular disease and cancer. The increased disease burden has an adverse impact on public health and imposes a tremendous economic burden (Wang, Beydoun et al. 2008).

Therapeutic interventions involving life style modification, medication, or surgery have been ineffective in decreasing the prevalence of obesity. As epidemiological evidence suggests that genetic factors determine 40-70% of inter-individual variation in body weight, uncovering the genetic basis of obesity may lead to improved treatment strategies through the use of predictive markers or risk and targeted therapeutic design (Maes, Neale et al. 1997; Haworth, Butcher et al.

2008).

Towards this end, rare cases of extreme obesity have been identified due to mutations in genes including leptin, leptin receptor, and the melanocortin 4 receptor (MC4R) as well as multi-gene copy number variants (Bochukova, Huang et al.; Ranadive and Vaisse 2008; Sha, Yang et al. 2009; Walters, Jacquemont et al. 2010). In addition, genome wide association studies (GWAS) identified common variants that influence body weight in fat mass and obesity-associated

(FTO), MC4R, among others (Frayling, Timpson et al. 2007; Loos, Lindgren et al.

2008; Walley, Asher et al. 2009). However, the genetic variation identified thus far collectively accounts for only a small portion (<3%) of the overall heritability of body weight (Sabatti, Service et al. 2009).

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Missing heritability may be due to several factors (Manolio, Collins et al.

2009). Candidate gene studies have been used to find obesity susceptibility genes, but these studies depend on prior biological knowledge and few obese individuals have causative variants in genes within the known body weight homeostatic system, with the exception of MC4R (Ranadive and Vaisse 2008;

Bogardus 2009). Additionally, genes with small effect sizes are difficult to detect because of the statistical power required and rare genetic variants can elude discovery in the absence of complete genomic sequence (Maher 2008). Copy number variation could also contribute to the missing heritability, as this possibility has not yet been thoroughly evaluated using comprehensive techniques (Yang, Khoury et al. 2005; Bogardus 2009; Sabatti, Service et al.

2009).

Current genetic approaches have focused on the affected individual’s genome to find variations linked to a phenotype, thereby excluding epigenetic factors despite considerable evidence suggesting their importance. Epigenetic inheritance is defined as inheriting a molecule responsible for a phenotypic effect to one or more generations without inheriting the initial variation in the genetic sequence or the exposure to the environmental trigger (Youngson and Whitelaw

2008). The mechanisms underlying epigenetic inheritance include DNA or histone modification (i.e. methylation or acytelation) or through the inheritance of parental RNA molecules. A common example of epigenetic inheritance is imprinting, which describes when allelic expression differences are due to the

116 parent of origin. Parental effects due to imprinting are dependent on the allele being inherited from a parent of a specific sex.

Growing evidence from model organisms implicates a range of nutritional, hormonal, xenobiotic and behavioral cues acting in one generation (F0) with consequence in subsequent generations (first F1, second F2, etc.), after the initial exposure to the stressor is no longer present (Gluckman, Hanson et al.

2007). For example, male or female rats exposed to dexamethasone have F1 and F2 male offspring with reduced birth weight and metabolic syndrome phenotype (Drake, Walker et al. 2005). In addition, supplementing the mother’s diet with foods rich in methyl groups silenced the agouti gene in female dams, resulting in leaner offspring. However, there was no evidence of transgenerational epigenetic inheritance of diet-induced hypermethylation at Avy and as such the decrease in obesity was independent of the silencing of agouti gene in lean offspring. Conversely, female offspring from obese mothers not fed a methyl supplemented diet grew more obese with each passing generation irrespective of the methylation status of the inherited transposon as long as Avy passed through the maternal lineage. The Avy mutation presented in this case a susceptibility to obesity and its effect was exaggerated as it passes through the female lineage irrespective of the inheritance of its methylated state (Waterland,

Travisano et al. 2008).

Human studies, mainly in the form of epidemiological observations, provide evidence for epigenetic inheritance as well. Studies in Pima Indians show that offspring of diabetic fathers weighed less than the offspring of nondiabetic

117 fathers and the lower birth weight predicted diabetes in the offspring if paternal but not maternal diabetes is present (Lindsay, Dabelea et al. 2000). Moreover,

GWAS often lack parental genotype information and therefore treat the paternal and maternal alleles as interchangeable. However, two recent GWAS testing for parental effects found novel genomic associations controlling susceptibility to cancer, and type 1 and 2 diabetes (Kong, Steinthorsdottir et al. 2009; Wallace,

Smyth et al. 2010). When parent-of-origin effects on obesity, in particular, were tested multiple chromosomal locations with potential imprinting effects were implicated (Lindsay, Kobes et al. 2002; Gorlova, Amos et al. 2003; Dong, Li et al.

2005). Therefore, unconventional searches for epigenetic effects would help in understanding the biology, and discover more genes for better disease modeling and design of therapeutic intervention. In addition, this might help in individual risk estimation and diagnosis.

Our laboratory and others have previously used mouse chromosome substitution strains (CSS) and congenic strains derived from them to identify quantitative trait loci (QTLs) that regulate complex traits (Singer, Hill et al. 2004;

Hill, Lander et al. 2006; Shao, Burrage et al. 2008; Gelegen, Pjetri et al. 2009;

Leussis, Frayne et al. 2009). The CSS paradigm differs from segregating crosses such as intercrosses or backcrosses in a number of ways including the ability to identify parental effects (Singer, Hill et al. 2004). The CSS has the ability to study an individual loci for parental effect since parents are both on a similar homogeneous background and differ only by the allele at the location being tested. Parental strain with the unique feature are compared to heterosomic F1

118 hybrid without the complicating effects of other segregating QTLs on a heterogeneous background. The ability to produce large numbers of isogenic F1 that only differ at the isolated location in the parental strain gives us more power to detect parental effects that could be masked by heterogenous background in segregating crosses.

Obrq2a is a body weight and insulin resistance QTL that was mapped to a

3.2 Mb interval between SNP markers rs29927775 and rs30221945 using the

CSS mapping strategy (Soha N. Yazbek 2010). 6C2d is an obesity-resistant, insulin sensitive congenic strain that defines Obrq2a. In this report, we show that strain 6C2d has reduced food intake relative to the obese control stain C57BL/6J

(B6), and that this effect and the reduction in body weight is inherited for multiple generations through the paternal lineage, independent of inheriting the causative genetic variant.

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4.3 Materials and Methods

4.3.1 Husbandry. B6 and 6C2d mice were obtained from our breeding colonies at

Case Western Reserve University. Mice were housed in ventilated racks at 21°C in a 12hr light-dark cycle. The mice were weaned at 3 weeks of age and fed

Labdiet 5010 chow (PMI Nutrition International, Richmond, OH, USA) unless otherwise noted. For the high-fat diet experiments, mice were fed a high-fat, simple-carbohydrate (HFSC) diet (D12331; Research Diets, Inc, New Brunswick,

NJ, USA) for 100 days, beginning at 5 weeks of age. The HFSC diet derives 58% of its kilocalories from saturated fat from soybean and coconut oil, 25.5% from simple carbohydrate in the form of sucrose and maltodextrin, and 16.4% from casein protein. Mice had access to food and water ad libitum. All animal study protocols were approved by the Institutional Animal Care and Use Committee of

Case Western Reserve University.

4.3.2 Genotyping. Mice were genotyped for the B6- and A/J-derived alleles of

Obrq2a using polymorphic SNP markers at the proximal (rs30468068) and distal

(rs30320124) ends of the QTL interval. PCR primers used to amplify the rs30468068 SNP were: 5’ GAAGG GACCT TCTGA GCAAA TA 3’ and 5’

GTGTG GACAT GTATG TCTGT GC 3’. PCR primers used to amplify the rs30320124 SNP were: 5’ TGGGG TGATT TTTGT TGTTG 3’ and 5’ CCAGG

GGACA TTTTC TGTTG 3’. Both PCR products were digested with the restriction enzyme Rsa1 (New England Biolabs, MA, USA) and analyzed with agarose gel electrophoresis.

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4.3.3 Food Intake. Thirty-five day old mice were acclimated to the HFSC diet for 2 days. On the third day, mice were placed in a clean cage and the amount of food was weighed. After 24 hours, food was again weighed to determine daily food intake and the mice were weighed to calculate food intake per gram body weight.

Mice were housed 2-3 per cage to avoid the stress induced by individual housing. Therefore, each data point shown represents an average value per cage.

4.3.4 Statistics. Data were compared using one way ANOVA with a Bonferroni correction for multiple testing. Corrected p-values are shown.

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4.4 Results

4.4.1 Parental effect of Obrq2a on body weight

To test for a parental effect on body weight due to Obrq2a, body weight was analyzed in F1 offspring of reciprocal crosses between the obesity-sensitive strain B6 and the obesity-resistant strain 6C2d. The 6C2d congenic strain carries the A/J allele at the Obrq2a interval on an otherwise B6 background and shows a

20% reduction in body weight (BW) compared to B6 (Soha N. Yazbek 2010).

Five-week-old (B6 x 6C2d)F1 and (6C2d x B6)F1 male mice were fed the HFSC diet for 100 days. At the end of this study, (B6 x 6C2d)F1 mice weighed 5.58 g less than the isogenic (6C2d x B6)F1 mice (p< 0.001, Figure 4.1). This 20% reduction in body weight shows that the effect of Obrq2a depends on the parent- of-origin. Moreover, the body weight of the (B6 x 6C2d)F1 mice was not significantly different from the obesity-resistant 6C2d parent strain, whereas the

(6C2d x B6)F1 mice were not significantly different from obese B6 mice. This is consistent with parental effects accounting for much of the effect of Obrq2a on body weight.

In contrast, no differences were detected in levels of fasting insulin and fasting glucose in the reciprocal F1 hybrids. Levels of insulin were similar to parental strain 6C2d, suggesting that the Obrq2aA/J allele has dominant affects whereas glucose levels where similar to parental strain B6 suggesting that the

Obrq2aB6 allele is dominant (p>0.05, Figure 4.2, 4.3). Thus, these three metabolically related traits may each have a different genetic basis seeing that

122 the parental effect specifically regulates body weight and not measures of insulin resistance.

Figure ‎4.1 Parental Effect in F1-Body Weight

Each dot graphed represents the value of one mouse. N > 30/group. A One-way ANOVA was used with correction for all pair wise comparisons. Only P value compared to control B6 is shown. F1 mice derived from a male B6 are similar to control B6 whereas F1 mice derived from a male 6C2d are leaner than B6. The data indicates a differential response of isogenic mice based on the direction of the cross.

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Figure ‎4.2 Parental Effect in F1-Glucose

Each dot graphed represents the value of one mouse. N > 15/group. A One-way ANOVA was used with correction for all pair wise comparisons. Only P value compared to control B6 is shown. F1 mice derived from a male B6 are similar to control B6 and F1 mice derived from a male 6C2d. The data indicates a no differential response of isogenic mice based on the direction of the cross. No detectable parental effect on Glucose and the B6 allele is dominant.

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Figure ‎4.3 Parental Effect in F1- Insulin

Each dot graphed represents the value of one mouse. N > 15/group. A One-way ANOVA was used with correction for all pair wise comparisons. Only P value compared to control B6 is shown. F1 mice derived from a male B6 are similar to control 6C2d and F1 mice derived from a male 6C2d. The data indicates a no differential response of isogenic mice based on the direction of the cross. No detectable parental effect on Insulin and the A/J allele is dominant.

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4.4.2 Inheritance of the Obrq2a parental effect is sequence independent

The reduction in body weight of (B6 x 6C2d)F1 mice relative to (6C2d x

B6)F1 mice may be due to parental genotype independent of inheriting the

Obrq2a allele, or to a parent-specific inheritance of the Obrq2a allele (i.e. imprinting). To test these possibilities, (B6 x 6C2d)F1 or (6C2d x B6)F1 mice were intercrossed to generate offspring with the following Obrq2a alleles in a mendelian 1:2:1 expected ratio: Obrq2aB6/Obrq2aB6, Obrq2aB6/Obrq2aA/J, and

Obrq2aA/J/Obrq2aA/J. For mice of each of these genotypes, the genome outside of the Obrq2a interval was entirely B6-derived. Therefore, the Obrq2aB6/Obrq2aB6 mice are genetically identical to B6 mice despite having parents of a different genotype. Likewise, the Obrq2aA/J/Obrq2aA/J are genetically identical to 6C2d mice despite differences in parental genotype. The Obrq2aA/J/Obrq2aA/J mice are hereby referred to as F2-6C2d and the Obrq2aB6/Obrq2aB6 mice as F2-B6. F2-

6C2d, F2-B6 and their respective control mice were fed the HFSC diet for 100 days and the resulting body weight was analyzed.

The body weight of each genotype class did not differ between the (B6 x

6C2d)F1 or (6C2d x B6)F1 intercrosses or between F2-B6 mice housed in the same cage as F2-6C2d mice or housed without F2-6C2d mice. Therefore, data from each of experiment were pooled. Following 100 days on the HFSC diet, F2-

B6 mice had a 6.26g reduction in body weight relative to control B6 mice

(p<0.001), which translates to a similar 20% lower body weight. The F2-B6 did not differ with respect to body weight from the control 6C2d or the F2-6C2d mice

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(p>0.05) (Figure. 4.4). This data demonstrates that the parental effect on body weight does not depend on inheriting of the causal sequence variant.

Figure ‎4.4 Non-genomic Parental Effect – Bodyweight

Each dot graphed represents the value of one mouse. N > 20/group. A One-way ANOVA was used with correction for all pair wise comparisons. Only P value compared to control B6 is shown. The data shown are for control B6 and 6C2d and B6 and 6C2d mice derived from an F1 intercross. The latter mice are labeled F2-B6 and F2-6C2d respectively and are isogenic to B6 and 6C2d from conventional cross. F2-B6 are significantly leaner than isogenic B6 control and similar to F2-6C2d and 6C2d control. Data indicates that the parental effect is not dependant on inheriting the parental sequence (i.e. not an imprinting effect).

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4.4.3 Parental effect of Obrq2a is transgenerational

Having generated F2-B6 mice that are obesity-resistant, despite being isogenic with obesity-susceptible B6 mice that are offspring of conventional brother-sister matings, we sought to test whether this epigenetic phenomenon was transmissible to the “F3” generation. To test this, F2-B6 mice were intercrossed and the body weight of the resulting F3-B6 offspring was analyzed following 100 days on the HFSC diet. Body weight of the F3-B6 mice did not differ from the F2-B6 (p > 0.05) but was significantly lower than control B6 mice

(p<0.001) (Figure. 4.5). Therefore, the Obrq2aA/J allele demonstrates a transgenerational effect by reducing body weight in the F3 generation despite the fact that neither the F2 nor the F3 generation inherited the Obrq2aA/J allele.

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Figure ‎4.5 Parental Effect in F3- Body Weight

Each dot graphed represents the value of one mouse. N > 20/group. A One-way ANOVA was used with correction for all pair wise comparisons. Only P value compared to control B6 is shown. The data shown are for control B6, F2-B6 and F3-B6 which are isogenic to control B6 but are derived from F2-B6 intercross. F3-B6 are significantly leaner than isogenic B6 control and similar to F2-B6 and 6C2d control. Data indicates that the parental effect is not dependant on inheriting the parental sequence (i.e. not an imprinting effect) and is transgenerational.

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4.4.4 Obrq2a regulates body weight when inherited through the paternal lineage

To assess whether the transgenerational epigenetic effect of Obrq2a was inherited through the maternal or paternal lineage, (6C2d x B6)F1 male and female mice were separately backcrossed to control B6 mice. The resulting B6 offspring (those mice that did not inherit the Obrq2a A/J allele) were then fed the

HFSC diet for 100 days. B6 offspring from the male (6C2d x B6)F1 x control female B6 cross were significantly leaner than isogenic control B6 mice

(p<0.001) and B6 offspring from the female (6C2d x B6)F1 x control male B6 cross (p<0.001). The latter were similarly obese when compared to control B6

(Figure. 4.6). Therefore, having one copy of the Obrq2aA/J allele in the male parent was necessary and sufficient to confer resistance to obesity in B6 mice, an otherwise obesity-sensitive strain.

We also backcrossed the reciprocal (B6 x 6C2d)F1 male and female to control B6. Interestingly, B6 offspring of both paternal and maternal F1 showed a resistance to obesity when compared to B6 from the conventional B6xB6 intercross (P<0.05, P<0.01 respectively) (Figure 4.7). The presence of the

Obrq2aA/J allele in the grand-paternal mouse was sufficient to induce obesity resistance (to a much lesser extent evident by comparison of B6 from maternal

(B6 x 6C2d)F1 to control B6). This is despite the fact that the epigenetic mark/product was passed through the female lineage for one generation (F0 through F2 on the right section of Figure. 4.8). Of note, this weak grand paternal effect was inconsistent and was not apparent when looking at male (6C2d x

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B6)F1 . (6C2d x B6)F1 were obese when the A/J allele passed through one female generation despite the fact that the grand-parental genotype was

6C2d/6C2d (P0 through F2 on the left side of Figure. 4.8). Furthermore, from the reciprocal cross female (6C2d x B6)F1 x male control B6, it is evident that when the A/J allele was passed through the female lineage for two generations the epigenetic effect is lost and B6 mice become obese again (Figure. 4.8). The data suggests that there is a weak grand-paternal effect on obesity is sometimes able to surpass one generation through the female lineage. However, as evident from intercrossing F2-B6 mice, the paternal effect is persistent to F3-B6 as long as it goes through the male lineage.

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Figure ‎4.6 Paternal Effect –Body Weight

Each dot graphed represents the value of one mouse. N > 15/group. A One-way ANOVA was used with correction for all pair wise comparisons. Only P value compared to control B6 is shown. The data shown are for control B6 and 6C2d and B6 mice derived from an F1 backcrossed to B6 males and females. The latter mice are labeled F2-B6 and are isogenic to each other and B6 from conventional cross. F2-B6 from a paternal F1 are significantly leaner than isogenic B6 control and F2-B6 from maternal F1. Data indicates that the parental effect is being passed through the male lineage for having when A/J allele in the male parent is necessary and sufficient to lower body weight. Note that Maternal and Paternal F1 in this case are derived from 6C2d female crossed to a B6 male.

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Figure ‎4.7 Grand-Paternal Effect –Body Weight

Each dot graphed represents the value of one mouse. N > 15/group. A One-way ANOVA was used with correction for all pair wise comparisons. Only P value compared to control B6 is shown. The data shown are for control B6 and 6C2d and B6 mice derived from an F1 backcrossed to B6 males and females. The latter mice are labeled F2-B6 and are isogenic to each other and B6 from conventional cross. F2-B6 from a paternal F1 are significantly leaner than isogenic B6 control and F2-B6 from maternal F1. Data indicates that the parental effect is being passed through the male lineage for having when A/J allele in the male parent is necessary and sufficient to lower body weight. In this cross, F2-B6 from a maternal F1 is similar to F2-B6 from the paternal F1 and also significantly leaner than isogenic B6 control. Maternal F1 in this case are derived from B6 female crossed to a 6C2d male. Data indicates the presence of the A/J allele in the grand-parent male mouse is sufficient to lower body weight in the F2 generation but to a lesser extent.

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Figure ‎4.8 Schematic of Backcross- Body Weight

Male phenotype after a 100 days on the HFSC diet is traced through 4 generations in all directions of the crosses. Data shows that the resistance to obesity initiated by the A/J sequence variation in 6C2d is maintained if passed through the male lineage and is diluted and gone when passed through one or more generations of female mice.

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4.4.5 Differences in food intake associated with paternal inheritance of obesity resistance

To begin addressing the mechanism behind differences in obesity resistance between obese control B6 and lean isogenic F2-B6 generated from the F1 backcross, we tested for differences in food intake. F2-B6 mice generated from male and female (B6 x 6C2d)F1 backcrossed to male and female control

B6. Food intake for F2-B6 and isogenic ctrl-B6 was measured over a period of 24 hours. When calculated per gram of body weight F2-B6 from male (B6 x 6C2d)F1 backcross consumed an average of 0.144g of food per gram of body weight. The intake was significantly lower than ctrl-B6 with a consumption of 0.178g (p<0.05).

Interestingly, although the body weight effect was weak for F2-B6 from maternal

(B6 x 6C2d)F1 backcross, they consumed 0.148g/g of body weight over a period of 24h similar to F2-B6 from the paternal cross. The data indicate that food consumption is decreased in lean B6 mice with obrq2aA/J in either the paternal or grand-paternal mice compared to isogenic control B6 from conventional intercross.

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4.5 Discussion

This report demonstrated the first evidence of non-genomic transgenerational paternal inheritance of diet-induced obesity. The sequence variation between 6C2d and B6 parental cross is the only variable in the designed experiments and is considered as an initial genetic insult. Thus, genetic sequence variation in Obrq2a is mediating the phenotypic effect. The use of

6C2d, a CSS-6-derived congenic strain, has narrowed down the causative allele to one or more of the 19 candidate genes in the variable interval and excluded any background influence. Using the CSS-2 Heidi et al. had also found evidence of grand-parental genotype influencing ethanol preference (Lesscher, Kas et al.

2009). The difficulty in attaining multi-generation pedigrees and controlling for environmental exposure in humans make most reports of parental and transgenerational effects not definitive. CSSs and derived congenics have in these cases provided definitive evidence implicating a finite number of genes and provided a model for extensive mechanistic studies. The body weight phenotype of an additional congenic strain derived from CSS-6 was also found to be determined by non-genomic parental effects (Buchner D.A., personal communication). This implies that this phenomenon is not restricted to one region. CSSs and congenics are helpful means to systematically and thoroughly survey the genome for parental effects.

The (B6 x 6C2d)F1 and (6C2d x B6)F1 mice showed a differential response to the HFSC diet depending on the parental genotype indicating that a

136 parental effect contributes to the obesity-resistance phenotype of 6C2d. A (6C2d x B6)F1 backcross to B6 revealed that only F2-B6 offspring of the paternal F1, and not maternal F1, was obesity-resistant. Rance et al. found evidence of a paternally imprinted QTL on mouse chromosome 8 influencing body mass in adult F2 (Rance, Fustin et al. 2005). Paternally derived alleles in humans also influence body weight mainly through imprinting epigenetic inheritance. For example, paternally derived imprinting defects in patients with Prader-Willi syndrome have been shown, and are attributed to failure to erase grand-maternal imprint during spermatogenesis of the patients’ fathers (Buiting, Gross et al.

2003). Paternal transmission of class I variable tandem repeat (VNTR) polymorphisms within the promoter of the insulin gene increased the risk of childhood obesity in children of European and North African descent. This class I

VNTR was associated with variation in expression of insulin and insulin-like growth factor hormone (Le Stunff, Fallin et al. 2001). The majority of paternal effects described are cases of epigenetic imprinting. However, our F2-B6 lacking the paternal A/J allele showed an ability to resist increase in body weight when compared to isogenic ctrl-B6 and were able to transmit the phenotype to a third generation of B6 mice from F2-B6 intercross. The latter is evidence of a non- sequence dependant transgenerational inheritance of the phenotype and thus is not a case of paternal imprinting. Although further investigation into the specific mechanism behind this inheritance is warranted, this prompts explanations outside the well documented imprinting paternal effect.

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The paternal genome undergoes a major transformation during spermatogenesis. This includes the replacement of histone nucleosomes with protamines – proteins responsible for the tight packaging of DNA structures and is crucial for normal sperm function (Wykes and Krawetz 2003). Hammoud et al. found that around 4% of the haploid genome in sperm retains histone nucleosome. Furthermore, the retained nucleosomes were modified and enriched in the sperm genome at genes involved in embryo development, imprinted gene clusters and microRNAs (miRNAs) among others. The modifications were specific in pattern and correlated with regulation and function of the genes. For example, paternally expressed non-coding RNAs and genes were correlated with the retention of histones with H3K4me3 (Hammoud, Nix et al. 2009). The retention of these marks at specific genes and their ability to modulate expression of underlying gene present an explanation of a transgenerational non-genomic inheritance of paternally derived phenotypes, such as the one seen in our mouse model. Interestingly, of the 19 genes in our region, 8 that have human homologues were identified by Hammoud at al. to be present around the retained nucleosomes with specific modifications. These genes include aldo keto reductase 1(AKR1b1), plexin 4 (Plxn4a), solute receptor

35b4 (Slc35b4), exocyst complex protein 4 (Exoc4), coiled-coil-helix-coiled-coil- helix domain containing 3 (CHCHD3), 2,3-bisphosphoglycerate mutase (BPGM), leucine-rich repeats and guanylate kinase domain (LRGUK), caldesmon

1 (CALD1). AKR1b1 is a broad-specificity aldehyde reductase. It catalyzes the reduction of several endogenously generated aldehydes including glucose and

138 plays a role in development of secondary diabetic complications including heart disease (Baba, Barski et al. 2009). Moreover, a previous report in our lab has localized several QTLs within the Obrq2a interval, two of which (Obrq2a5 and

Obrq2a6) have an effect on body weight, but not measures of insulin resistance, similar to the paternal effect of Obrq2a (Soha N. Yazbek 2010). Obrq2a6 contains several aldo keto reductases that represent good candidates for mediating the paternal effect on body weight. Analysis of the subcongenic panel for paternal effects would allow us to further narrow down the number of potential candidate genes to evaluate.

Another possible explanation for the observed epigenetic inheritance is the activity of RNAs in sperm. RNA mediated epigenetic inheritance through both the maternal and paternal lineages have been demonstrated in mice. The exposure of fertilized eggs to Kit specific miRNA was enough to induce a heritable effect on tail color and abnormally levels of Kit RNA was detected in sperm providing an explanation of the paternal inheritance (Rassoulzadegan,

Grandjean et al. 2006). The presence of RNA in the form of both miRNA and small non-coding RNA has been well documented in human sperm (Krawetz

2005; Girard, Sachidanandam et al. 2006). The inheritance of RNA from paternal

6C2d male may play a role in post-fertilization reprogramming leading to the persistence of the epigenetic mark through male lineage we saw through to the

F3-B6. This continued inheritance through the male generation is passed without exposure of the intervening generations to the initial insult similar to the effect of maternal F0 exposure of rats to vinclozoin that transmits susceptibility to

139 developmental abnormalities through four unexposed male generations (Anway,

Leathers et al. 2006). Of note, our (B6 x 6C2d)F1 female backcross to control B6 male mice detected a weak grand-paternal effect going through one female generation to induce the resistance to obesity in the F2-B6 males from the maternal F1 (Figure. 4.8). This same effect, however, was not detected when we tested the (6C2d x B6)F1 males on the diet. The produced F1 from maternal

6C2d remained obese despite the fact that they had a grand-paternal 6C2d

(Figure. 4.8). This suggests that the epigenetic mark is diluted as it passes through the female lineage, but persists when passed through the male lineage.

Finally, given the fact that the father is housed with his offspring until week three when they are weaned, an effect of paternal behavior is also possible.

6C2d sequence variation in fathers could be influencing paternal behavioral changes, which could then influence the obesity phenotype of offspring. One similar example described includes attention during suckling, whichdetermined stress induced responses in adult rat F1 generation. This effect was also found in the F2 generation and correlated with changes in pattern of DNA methylation

(Francis, Diorio et al. 1999). The next generation inheritance was thus dependant on the behavior in turn inducing a heritable epigenetic mark. (6C2d x B6)F1 male mice are obese and have been housed with B6 male fathers, yet they are still able to produce male F2-B6 with the obesity resistant phenotype. Accordingly, the initial behavioral change is likely to be paralleled by epigenetic changes inherited through generations. F2-B6 mice from a paternal and maternal (B6 x

6C2d)F1 backcross to control B6 mice weighed less and ate less food/gram of

140 bodyweight compared to control B6 mice. The eating habit could be the behavior that is heritable from male fathers to their sons either directly through behavioral influence or through an indirect induction of an epigenetic mark or product.

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5 Chapter 5: Summary and Future Directions

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5.1 Summary

The prevalence of metabolic disease such as type 2 diabetes (T2D) is increasing as obesity increases worldwide. More than a 150 million individuals suffer from T2D, a number predicted to double in the next two decades. Insulin resistance (IR) remains the most important factor in the development of type 2 diabetes. IR, a decreased responsiveness to insulin, leads to impaired glucose uptake in muscle and fat as well as diminished suppression of hepatic glucose production. T2D arises when pancreatic insulin secretion is insufficient to overcome peripheral insulin resistance. The resulting hyperglycemia leads to complications including retinopathy, stroke and amputations. Life style changes and treatments such as sulfonylureas and metformin have had limited success in elevating the progression of IR into diabetes nor preventing co-morbidities with cardiovascular involvement (Nathan, Buse et al. 2009).

Despite compelling evidence that susceptibility to obesity and T2D is highly heritable and considerable progress with gene identification, most susceptibility genes continue to elude discovery. Linkage analysis along with candidate and genome wide association studies lead to the bulk of knowledge of the genetics of the latter disorders. However, the identified genetic variants account for 3% to 6% of obesity and of T2D heritability respectively. The missing heritably is largely due to decreased statistical power to detect genes with small effects or less common genetic variant and the limited ability to identify the causative variant and study the function (Manolio, Collins et al. 2009; Stolerman

143 and Florez 2009). This has been a large impediment towards translating genetic studies to clinical benefits. The study of model organisms in T2D overcomes some of the problems with better control of environment, genetic uniformity, and ability to detect causative genes and study their function (Clee and Attie 2007).

Using animal models to understand the genetics of T2D susceptibility would lead to the development of better interventions and treatments, as well as the ability to identify high risk individuals for better monitoring and prevention measures.

To achieve this goal, we took advantage of a novel detection paradigm available consisting of the chromosome substitution strain. The Nadeau lab, in collaboration with Eric Lander’s Lab at broad institute, created the first complete panel of CSSs in mammals to study genetically complex multifactorial disease.

The CSS panel screen identified 16 chromosomes involved in diet-induced obesity and 8 involved in glucose homeostasis. The screen of over 90 traits in the CSS panel revealed an architecture for complex traits that consists of many genetic variants with unexpectedly strong, non-additive effects (Shao, Burrage et al. 2008). I have used CSS-6 and its derived congenic panel to screen for obesity and IR QTLs. We identified 4 body weight QTLs, one of which (Obrq2) was associated with impaired glucose tolerance and insulin sensitivity as well. Thus, I was interested in understanding the genetic architecture, the molecular mechanisms and genetic control underlying Obrq2 an obesity and insulin resistance QTL.

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5.1.1 OXPHOS and Insulin Resistance

Insulin resistance is a key factor in the pathophysiological development of

T2D. The factors that lead to insulin resistance remain controversial. Among the controversies surrounding the etiology is the question of whether mitochondrial dysfunction has a causal or compensatory role in the disease (Turner and

Heilbronn 2008). Mitochondria, which generate ATP through oxidative respiration, play a key role in energy metabolism. Decreased skeletal muscle mitochondrial function has long been associated with the development of obesity and insulin resistance (Lowell and Shulman 2005). In contrast, a decrease in either liver or skeletal muscle mitochondrial function due to disruption of the gene encoding apoptosis inducing factor prevented obesity and insulin-resistance

(Pospisilik, Knauf et al. 2007).

It remains puzzling how both increases and decreases of mitochondrial respiration can be associated with obesity and IR. Although mitochondrial dysfunction in skeletal muscle has long been the primary focus of T2D-related mitochondrial research, recent studies have demonstrated the importance of hepatic mitochondrial function and even called into question whether insulin resistance is a result of decreased or increased mitochondrial activity in the liver

(Pospisilik, Knauf et al. 2007; Koves, Ussher et al. 2008). Answering these questions may have important therapeutic implications for the treatment of T2D, as several well characterized compounds exist that modulate mitochondrial function (Liu, Shen et al. 2009).

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Pathway analysis tools were used to study the gene expression patterns which revealed that insulin resistance and increased hepatic gluconeogenesis were associated with a broad increase in the expression of genes involved in liver mitochondrial OxPhos. Similar to evidence in both human and mice, these comparisons identified a decrease in Oxphos gene levels in the liver associated with protection from obesity and IR. The upregulation of OxPhos gene expression in strain 6C1 relative to 6C2 was subtle but widespread, comprising

56 of 76 OxPhos genes that spanned each complex of the electron transport chain.

The OxPhos gene expression differences associated with Obrq2 were only observed in the liver, with no changes in the OxPhos pathway detected in adipose or skeletal muscle. Therefore, the lack of change in OxPhos gene expression levels in muscle between 6C1 and 6C2 suggests that the increase in liver OxPhos expression precedes any decrease in skeletal muscle.

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Furthermore, Pgc-1 is a key transcriptional regulator of both mitochondrial oxidative metabolism and hepatic gluconeogenesis (Yoon,

Puigserver et al. 2001; Finck and Kelly 2006). Expression levels of Pgc-1were

2.1-fold higher in strain 6C1 relative to strain 6C2 and may therefore provide a link between the increase in both OxPhos gene expression levels and hepatic gluconeogenesis in strain 6C1.

Finally, the differential expression of liver OxPhos genes occurred following 100 days on the HFHS diet. This change is preceded by expression differences in the liver between 6C1 and 6C2 in genes in the NDK Dynamin pathway. Dynamin mediates the endocytosis of several key mediators of adiposity and insulin signaling including GLUT4 in adipose and muscle (Kao,

Ceresa et al. 1998; Antonescu, Diaz et al. 2008) and MC4R in hypothalamus

(Shinyama, Masuzaki et al. 2003).

5.1.2 Fractional Genetics and Discovery of Slc35b4

The screen of two consequetive sub-congenic panels spanning Obrq2 for measures of obesity, glucose tolerance and insulin sensitivity continued to successively locate various QTL intervals within Obrq2, with phenotypic heterogeneity at each loci. Thus, by starting with a CSS survey and then analyzing congenic, subcongenic and subsubcongenic strains, we were able to assess genetic and phenotypic variation over a 2,000-fold level of resolution, from the entirety of the A/J genome to QTLs identified using subsubcongenic

147 strains that average just 800 kb. Furthermore, many of these QTLs were detected only in a specific genomic context. The large number of tightly linked

QTLs with opposing effects even at this small region highlights the genetic complexity of complex traits and likely contributes to the difficulty in identifying susceptibility genes in humans.

Each QTL provided an opportunity for further study. We chose Obrq2a1 one of the sub-QTLs within Obrq2a because we found compelling evidence of multiple linkages of obesity related phenotypes to the human region on 7q33 with genetic synteny. Obrq2a1 was one of the QTLs identified that regulates body weight and fasting glucose levels.

Through analysis of sequential congenic panels, this QTL was mapped to a 1.1 Mb interval containing 3 genes: Sec8, Lrguk, and Slc35b4. Sec8 was a strong functional candidate based on its role in Glut4 docking and glucose uptake as a member of the exocyst complex (Inoue, Chang et al. 2003). However, similar levels of glucose uptake in muscle and adipose tissue between strains

6C2d-2 and 6C2d-3 suggests that genetic variation in Sec8 does not to contribute to the IR phenotype associated with Obrq2a1.

We found that only Slc35b4 hepatic expression was increased ~50% in strain 6C2d-2 relative to strain 6C2d-3 among all tissues and genes tested. Liver was also found to be insulin resistant in the euglycemic-hyperinsulinemic clamp experiments, as evidenced by the inability to respond to insulin and shut down glucose production. Collectively, the functional studies and candidate gene

148 analysis implicate Slc35b4 in the regulation of body weight, hepatic gluconeogenesis and IR.

Slc35b4 encodes a protein that transports UDP-xylose and UDP-N- acetylglucosamine from the cytosol into the golgi (Ashikov, Routier et al. 2005).

Therefore, over-expression of Slc35b4 may alter the cellular localization of its cargo nucleotide sugars and therefore bioavailability of UDP-xylose and UDP-N- acetylglucosamine. We propose that the change in bioavailability mediate the solute receptor’s action on obesity and IR by altering post-translation protein modification.

5.1.3 Transgenerational Effects on Obesity

Genetic Studies of diabetes, obesity and insulin resistance have mainly focused on linking individual genotype variation, and environmental exposure to phenotype and disease. However, emerging evidence from animal models show that factors acting in one generation (F0) affect phenotype of these diseases in subsequent generations (F1,F2 and beyond). Such non-genomic inheritance

(effect without inheriting a variation in the gene sequence) has been seen in humans as well mainly in the example of maternal exposure during pregnancy that shows an effect that persist after birth. This new way of looking at inheritance is not well understood and studied, and would be an additional explanation for not finding functional genes using conventional genetic techniques.

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We aimed at testing the presence of parental effect on obesity in Obrq2a interval. Obrq2a is one of the identified obesity-resistance QTL that we have mapped to a 3.0Mb interval on chromosome 6. Congenic strain 6C2d has the

Obrq2a A/J allele on the B6 background.

First, we crossed B6 female mice (obesity sensitive) to 6C2d (obesity resistant) male mice. We put the generated F1 male mice on HFSC diet and they remain lean (i.e obesity resistant) compared to the F1 male mice from the reciprocal cross. This showed that there is a specific response whether the allele is inherited from the male lineage or the female lineage. Thus, parental effect accounts for part of the phenotype in Obrq2a. Interestingly, the result is not a classic case of parental imprinting. The B6 mice generated from the intercross of two F1 mice were lean and obesity resistant which is significantly different then isogenic B6 generated from a conventional B6 x B6 cross. The above data shows that there is a parental effect and it is being transmitted without the inheritance of the gene sequence.

Next, we crossed a B6 female mice to a heterozygote F1 male (having one allele from B6 and one for A/J at the obrq2a interval ) and genotyped the resulting offspring for B6 male mice. These B6 mice were significantly leaner compared to genetically identical male B6 mice generated from a normal cross between two B6 parents. Interestingly, the B6 generated from the reciprocal cross are obese. That means that only the presence of the A/J allele in the paternal F1 was able to induce the obesity resistant in the B6 mice. This data suggests that the parental effect is being transmitted through the paternal

150 lineage. More intriguingly, these lean B6 mice, which did not inherit the

Obrq2aA/J, are still able to transmit the resistance to obesity to the next generation.

Preliminary data showed that the resistance to obesity is mediated by decreased food intake. B6 mice generated from B6 x F1 cross eat less than their genetically identical B6 mice from a conventional B6 x B6 cross. Paternal effects are usually mediated through genetic imprinting where inheriting the gene is necessary. This study is evidence of the first detected non-sequence dependant paternal effect on obesity.

5.2 Future Directions

5.2.1 Do expression differences in OXPHOS have a functional effect and where do they map to within the interval?

In chapter 2, analysis of the genome-wide mRNA expression profiles of strains 6C1 and 6C2 using the computational pathway analysis tool Gene Set

Enrichment Analysis (GSEA) identified that genes involved in OXPHOS were overexpressed in the liver of the obese strain 6C1 following 100 days on the

HFSC diet. This effect was not statistically significant in skeletal muscle or white adipose at either 28 or 100 days on the HFHS diet, or in the liver following 28 days on the diet.

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To assess whether the expression data correlates with a mitochondrial phenotype, we propose to test the functional effect of increased OXPHOS gene expression following 100 days on the HFSC diet, in isolated liver mitochondria as well as other tissues to assess cross-talk. Mitochondria can also be isolated and characterized from liver, skeletal muscle, and brown adipose tissue.

Mitochondrial oxidative function can be determined by measuring oxygen consumption polarographically. In this method, substrate oxidation is coupled to utilization of ADP to synthesize ATP. Then one can examine various substrates that use different mitochondrial transporters for uptake and different dehydrogenases which provide reducing equivalents at different sites in the

Electron Transport Chain (ETC) and allow the dissection of the various components of the ETC in oxidation. These studies will directly localize defects within the ETC that are predicted from studies of intact mitochondria.

The availability of a sub-congenic panel for Obrq2 allows us to further localize the phenotype to a smaller region. If mitochondrial function is altered in

Obrq2, then the same technique could be used to test for altered function in isolated liver mitochondria from each of the subcongenic strains. Narrowing down the interval to a smaller region would allow us to analyze the sequence, expression and functionality of candidate genes to determine the causative variation. This approach is similar to the screen done in chapter 3 using body weight and insulin resistant measures as a phenotype.

Genes of interest within the Obrq2b interval include the aldo-keto reductase family members, which have NADPH oxidoreductase activity. Among

152 these family members, Akr1b3 is the ortholog of human AKR1B1 (Aldose reductase). The gene is expressed in the liver and is in the NRF2 pathway, a key for mitochondrial biogenesis (Nishinaka and Yabe-Nishimura 2005). There are no coding sequence changes in Akr1b3 or any of the other genes in the Obrq2 interval suggesting that the causative variant may alter gene expression or mRNA splicing rather than protein function.

Testing the mitochondrial function in liver, muscle, fat and brain in the obese insulin resistant strain 6C1 and the lean insulin sensitive strain 6C2 after

100 days on the HFSC diet is currently a work in progress in collaboration with

Dr. Charles Hoppel. Preliminary data shows a correlation between expression and phenotype but much more work still needs to be done.

5.2.2 Does the insulin resistant phenotype develop prior to the OXPHOS alterations and is it dependant on diet?

In skeletal muscle, insulin resistance is associated with a decrease in

OxPhos gene expression (Mootha, Lindgren et al. 2003). In addition, evidence that decreased liver OxPhos activity can prevent insulin resistance comes from studies of gene expression patterns in human livers between obese, normoglycemic individuals and obese, insulin resistant individuals. These comparisons identified a decrease in OxPhos-related gene expression levels in the obese, normoglycemic individuals relative to their insulin resistant counterparts (Misu, Takamura et al. 2007; Takamura, Misu et al. 2008).

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However, whether impaired mitochondrial function is a cause or consequence of insulin resistance remains controversial.

We have identified a difference in OXPHOS expression in liver between strains 6C1 and 6C2 following 100 days on the HFHS diet. At this time insulin resistance has already developed in 6C1 and if the expression difference is correlated with OXPHOS function then mitochondrial dysfunction are present as well. To understand the progression of these two associated phenotypes, we propose to screen 6C1 for fasting insulin and fasting glucose to estimate its insulin resistant phenotype at different time points starting at 35 days of age before the mice are put on the diet. Once a time point is established were insulin resistance has not yet developed, mitochondrial OXPHOS will be assayed to determine if there dysfunction is potential causative or compensatory compared to IR.

Furthermore, because the diet was held constant, it cannot account for the mitochondrial differences between these strains. Therefore the phenotype is due to genetics alone or to a gene-diet interaction. To determine the effect of diet, we propose to measure mitochondrial function as described above after 100 days on either the HFSC or LFLS diet. Mitochondrial function will also be assayed at 35 days of age, prior to the time when mice are switched to the HFHS or LFLS diets to determine if differences in mitochondrial function are a cause or consequence of the diet regimens. The OXPHOS function could also be tested at birth to determine if it is an innate genetic defect.

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5.2.3 What is the function of Slc35b4 on glucose production in HepG2 cells?

In chapter 3, we propose that Slc35b4 over-expression is inducing an increase in the solute receptor protein on the golgi apparatus membrane. This in turn is increasing the uptake of UDP-N-acytelglucoseamine, changing its concentration in the cytoplasm and effecting post translation modification of O- glycosylated proteins. Of these proteins, many may be relevant to the obesity and insulin resistance phenotype of Obrq2a1, including PI3-Kinase, UDP-glucose pyrophosphorylase, glycogen synthase, GAPDH and insulin receptor substrates

1 and 2 all with known O-Glycosylation sites. Any one of these proteins could be involved in altering hepatic glucose production.

To begin addressing mechanism using hepatic cell culture lines (HepG2), we propose the knockdown of Slc35b4 RNA expression with siRNA and over- expressing it using readily available human cDNA. Following the confirmation of the ability to alter protein levels using gene manipulation, one could use a labeled water or labeled glutamine in the cell culture medium to measure the direct effect of the gene expression manipulation on glucose production in these cell lines.

We could also test global levels of glycosylation and that of candidate proteins using western blots and mass spectrophotometer. These studies would tell us if slc35b4 works, in human cell lines, to directly affect glucose production in liver and explain the insulin resistance phenotype. It will also begin to shed light on the mechanism through which it acts.

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This experiment is currently a work in progress.

5.2.4 What is the phenotype of Slc35b4 knockout mouse?

Although inbred strains are a superior model to study the genetics of complex traits, once a gene has been implicated it is necessary to revert back to single gene mouse model to uncover the molecular mechanisms underlying its function. Transgenic mice have been successfully used in diabetes research to uncover the role of many genes in the insulin signaling cascade. For example, conditional knockouts in insulin receptor β-cells caused a reduction in cell mass and a decrease in insulin secretion with resulting hyperglycemia and diabetes in

25% of the animals. This implicated the role of insulin in β-cell development

(Otani, Kulkarni et al. 2004). Furthermore, knockout of the different insulin receptor substrates identified IRS2 as the major player in the downstream signaling of insulin. This knowledge gained from knockouts or conditional transgenics would help in understanding the molecular role of Slc35b4 and help in the development of therapeutic interventions. ES cell lines for Slc35b4 knockout are available on the B6 background.

5.2.5 What is the causative sequence variation in the parental effect on obesity?

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The parental effect identified in chapter 4 was a transgenerational effect on body weight transmitted through the male lineage. The effect was specific to body weight and did not affect insulin or glucose levels alterations underlying the

Obrq2a interval. This data suggested the uncoupling of the genetic basis of the traits of Obrq2a. Analysis of the subcongenic strains that span the length of the

Obrq2a interval further supported this hypothesis. We were able to identify at least six QTLs in the interval, each of which was specific for a combination of the traits assayed (body weight, insulin, glucose, and HOMA). Interestingly, two of these QTLs Obrq2a5 and Obrq2a6 were detected as body weight QTLs and affected no other trait.

Given that the parental effect is strictly on body weight, screening the congenics defining Obrq2a5 and Obrq2a6 will allow us to narrow down the causative genes to < 5 and to an interval ranging between 400-800 kb only.

Mechanistic studies will now be most informative if they are performed after the region has been narrowed. Some of these studies include testing for methylation differences and retention of protamines. The narrowed interval can now also be tested for coding for small RNAs that can be transmitted by sperm cells.

5.2.6 What is the underlying genetic basis of the identified QTLs?

Each identified QTL on CSS-6 and within Obrq2 and Obrq2a presents an opportunity for further analysis similar to the one followed in this doctoral study.

The generation and analysis of subcongenic strains is the first step towards gene

157 identification. Subcongenic strains which span the candidate intervals of QTLs will further localize the QTLs and definitively narrow the list of candidate genes.

Once a series of overlapping,homozygous subcongenic strains are constructed, they can be screened on the high-fat diet to test each strain for a series of metabolic traits. Better phenotyping studies should then be done and will be most informative after the region has been narrowed (e.g.subsubcongenic) like in the case of Obrq2a1 described in chapter 3. Otherwise, if phenotyping differences are detected in a congenic or subcongenic strain relative to C57BL/6J or a control strain, these differences may or may not be due to the same QTL that confers the phenotype. Thus, a small candidate interval will increase the likelihood that the additional trait differences discovered (including expression differences involving genes) are the result of the same QTL. Genes within the candidate region can then be analyzed for the presence of polymorphisms in

C57BL/6J vs. A/J genomic sequence. Finally, parental effect must be evaluated because of prior evidence we showed in chapter 4 that the difference in the genetics of the parents may contribute to the differences observed between the surveyed strains.

5.3 Conclusion

The goal of my thesis research was to understand the genetic architecture and complexity underlying the genetic susceptibility to diet induced type II diabetes in CSS-6. I chose to achieve this goal by screening developed congenic, subcongenic, and subsubcongenic strains for genetic susceptibility to

158 differences in body weight and measurements of insulin resistance (levels of glucose, insulin and HOMA-IR). I also performed molecular and phenotypic analysis on particular intervals and assessed the involvement of parental effect in the underlying genetic cause.

The initial screen of 20 congenic strains spanning the whole chromosome led to the identification of 4 obesity resistant QTLs, one of which Obrq2 proved to be a QTL for glucose homeostasis as well. I performed a whole genome test for expression using microarray analysis. I concluded that susceptibility to insulin resistance, obesity and diabetes was associated with an increased expression in liver OXPHOS, in agreement with data observed in the literature in mouse and human studies.

The screen of subcongenic panel spanning Obrq2, identified an additional four obesity resistant QTLs underlying the interval. I then selected Obrq2a for further analysis. Testing Obrq2a for more phenotypes revealed that it also represents a QTL for glucose tolerance and insulin resistance. Another subsubcongenic panel spanning Obrq2a was developed. The screen of the panel identified at least 6 closely linked QTLs, each with a unique combination of the phenotypic traits assayed in the panel. Analysis of the strains defining the

Obrq2a1 QTL recognized the solute receptor Slc35b4 as a potential regulator of obesity and insulin resistance. This work implicated a new gene in controlling susceptibility to obesity and insulin resistance, and demonstrated the considerable efficiency and power of this gene discovery platform, as well as the underlying complexity of multifactorial traits.

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Finally, I tested for the parental genotype effect on the obesity and insulin resistant phenotype of Obrq2a. The data showed evidence of a paternal effect on obesity being inherited independent of the inheritance of the initial sequence variation, transmitted only through the male lineage and for atleast 3 generations.

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