genetic characterization of the st. kitts-origin ......wfu wake forest university wfupc wake forest...

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GENETIC CHARACTERIZATION OF THE ST. KITTS-ORIGIN VERVET MONKEY (CHLOROCEBUS AETHIOPS SSP.) AS A MODEL OF POLYGENIC OBESITY

BY

STANTON BRADLEY GRAY, D.V.M.

A Dissertation Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

Molecular Pathology

August 2009

Winston Salem, North Carolina

Approved By:

Janice D. Wagner, D.V.M., Ph.D., Advisor _________________________________

Examining Committee:

Carl D. Langefeld, Ph.D., Chairman _________________________________

Timothy D. Howard, Ph.D. _________________________________

Jay R. Kaplan, Ph.D. _________________________________

Lawrence L. Rudel, Ph.D. _________________________________

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ACKNOWLEDGEMENTS

I wish to thank my advisor, Dr. Jan Wagner, as well as Dr. Tim Howard, who

acted as my unofficial co-advisor. Their contribution, both together and individually, to

my Ph.D. program and overall academic experience at Wake Forest University has been

invaluable. I also thank Dr. Carl Langefeld, Chairman of my Final Examining Committee,

Dr. Larry Rudel, and Dr. Jay Kaplan for their service on my committee. I greatly

appreciate all committee members for their support, tremendous expertise in their

respective fields, and engaging scientific discussions and inquiries during our meetings.

Technical support and expertise with association analyses was provided by Ms.

Julie Ziegler from Public Health Genomics. Technical assistance and expertise with DNA

sequencing and other protocols was provided by Mr. Abdoulaye Diallo from the Center

for Human Genomics. I am grateful for their essential contributions and for the

invariably positive interactions I had with them.

Collaboration with Drs. Rich Broughton and Jim Thompson at the University of

Oklahoma, Department of Zoology is very much appreciated. Dr. Thompson was

responsible for fostering my early intellectual development and curiosity in genetics

research over 15 years ago. I am honored and fortunate to count him among my friends

and colleagues.

My gratitude is also extended for other more “behind-the-scenes”

collaborations, without which this work would not have been possible, with Drs. Kylie

Kavanagh, Lynn Fairbanks, Nelson Friemer, and Matthew Jorgensen.

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This work was funded, in part, by the Wake Forest University School of Medicine

Venture Fund, the Skorich Diabetes Research Fund, the Monty Blackmon Diabetes

Research Fund (Dr. Wagner), T32 RR07009 from NIH/NCRR (Dr. Gray), and an Animal

and Biological Materials Resources grant from the National Center for Research

Resources (P40 RR 019963 to Dr. Lynn Fairbanks at UCLA). Support of this project also

came from the Center for Public Health Genomics (Dr. Langefeld) at WFUSM.

Finally, I want to thank Dr. Shauna Gray for her love, friendship, and support. I

also want to thank my family for their unwavering love and belief in me. My daughter

and “Sunshine”, Ms. Hannah Stafford, deserves special mention; watching and helping

her grow up over the past thirteen and a half years has always reminded me what was

most important in life, and therefore helped me find proper perspective when aspects

of my academic program were difficult. I would also like to specifically thank my father,

Mr. Gordon Gray, for his particularly vocal and persistent encouragement of my

endeavor for a Ph.D. and research career.

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TABLE OF CONTENTS

Page LIST OF TABLES AND FIGURES…………………………………………………………………………… iv LIST OF ABBREVIATIONS…………………………………………………………………………………… vii ABSTRACT………………………………………………………………………………………………………… ix Chapter I. INTRODUCTION………………………………………………………………………………….. 1

II. COMPARATIVE ANALYSES OF SINGLE NUCLEOTIDE

POLYMORPHISMS IN THE TNF PROMOTER REGION

PROVIDE FURTHER VALIDATION FOR THE VERVET

MONKEY MODEL OF OBESITY……………………………………………………………… 32

III. PHYLOGENETIC AND TAXONOMIC IMPLICATIONS OF

TNF PROMOTER SEQUENCE COMPARISONS BETWEEN

ST. KITTS- AND AFRICAN-ORIGIN CHLOROCEBUS SUBSPECIES…………….. 61

IV. TNF SINGLE NUCLEOTIDE POLYMORPHISMS ARE

ASSOCIATED WITH BMI, WAIST CIRCUMFERENCE, AND

SERUM CHOLESTEROL LEVELS IN A PEDIGREED NONHUMAN

PRIMATE MODEL (CHLOROCEBUS AETHIOPS SSP.)……………………………… 79

V. CLINICOPATHOLOGIC CHARACTERIZATION OF

NATURALLY OCCURRING DIABETES MELLITUS IN

VERVET MONKEYS………………………………………………………………………………. 105

VI. OVERALL DISCUSSION AND CONCLUSIONS……………………………............... 126

BIBLIOGRAPHY……………………………………………………………………………………………….. 138 SCHOLASTIC VITA……………………………………………………………………………………………. 157

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LIST OF TABLES AND FIGURES

TABLES Page CHAPTER I Table 1. Selected inflammatory and metabolic regulators within adipose tissue and their putative interactions………………………………………. 6 Table 2. Summary of associations between TNF gene SNPs and various obesity-related phenotypic traits in humans………………………………… 23 CHAPTER II Table 1. Polymorphisms in the human ortholog St. Kitts-origin vervet TNF gene and flanking sequence…………………………………………………………… 41 Table 2. Haplotypes and frequencies of the human ortholog St. Kitts-origin vervet TNF gene and flanking sequence……………………………………. 43 Table 3. Distance matrices and percentage of DNA sequence identity between humans, chimps, vervets, and rhesus…………………………………… 44 Table 4. Results of Kolmogorov-Smirnov tests for non-uniform distribution………………………………………………………………………………….. 46 CHAPTER III Table 1. Presumed fixed nucleotide differences between Chlorocebus subspecies……………………………………………………………………………………. 70 CHAPTER IV Table 1. Heritability estimates (h2) for obesity-related phenotypes of VRC vervets………………………………………………………………………………. 85 Table 2. Polymorphisms in the human ortholog St. Kitts-origin vervet TNF gene and flanking sequence…………………………………………………………… 88 Table 3. Haplotypes and frequencies of the human ortholog St. Kitts-origin vervet TNF gene and flanking sequence……………………………………. 91

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TABLES Page CHAPTER V Table 1. Clinical Characteristics……………………………………………………………………….. 133 FIGURES CHAPTER II Figure 1. LD structure of the St. Kitts-origin vervet TNF locus…………………………… 42 Figure 2. Multi-species sequence comparison of TNF 5’ promoter region………………………………………………………………………………………………. 48 Figure 3. Phylogenetic shadowing and clustering of SNPs in the TNF promoter region in five primate species……………………………………………… 50 CHAPTER III Figure 1. Map of Africa showing approximate ranges for Chlorocebus subspecies……………………………………………………………………………………. 64 Figure 2. Multi-species and -subspecies sequence comparison of the TNF promoter region………………………………………………………….. 69 Figure 3. TNF promoter-based phylogentic trees…………………………………………….. 71 CHAPTER IV Figure 1. LD structure of the St. Kitts-origin vervet TNF locus………………………….. 89 Figure 2. Association of SNPs in the TNF gene in the St. Kitts-origin vervet monkey of the VRC…………………………………………………………. 92 Figure 3. Sequence comparison of TNF 5’ promoter region between humans and St. Kitts-origin vervet…………………………………………………….. 94

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FIGURES Page CHAPTER V Figure 1. Glucose and insulin responses during intravenous glucose tolerance tests in diabetic and nondiabetic monkeys………………………….. 114 Figure 2. Pancreatic Histology and Immunohistochemistry………………………………. 118 Figure 3. Comparison of amylin amino acid sequences…………………………………….. 119 Figure 4. Pedigree Analysis………………………………………………………………………………. 121

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LIST OF ABBREVIATIONS

AIDS Acquired Immunodeficiency Syndrome BMI body mass index BUN blood urea nitrogen C/EBP CCAAT-enhancer-binding proteins cDNA complimentary deoxyribonucleic acid CRE cyclic-AMP response element CRP C-reactive protein CVD cardiovascular disease D.M. distance matrix dbSNP Single Nucleotide Polymorphism database dNTP deoxynucleotide triphosphate ELISA ennzyme-linked immunosorbent assay ER endoplasmic reticulum ERK extracellular signal-regulated kinase FA fatty acid FABP fatty acid binding protein FAO fatty acid oxidation FAT fatty acid translocase FATP fatty acid transport protein FFA free fatty acid FG fasting glucose GLUT glucose transporter H&E hematoxylin and eosin HbA1c hemoglobin A1c HCT hematocrit HDL-C high density lipoprotein-cholesterol HOMA homeostasis model assessment HPLC high performance liquid chromatography IAPP islet amyloid polypeptide ICAM inter-cellular adhesion molecule IFN interferon IL interleukin IR insulin resistance IRS insulin receptor substrate JNK c-Jun N-terminal kinase KDa kiloDalton KO knockout LD linkage disequilibrium LDL-C low density lipoprotein-cholesterol LPL lipoprotein lipase MAF minor allele frequency

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MAPK mitogen-activated protein kinase MCP monocyte chemoattractant protein MHC major histocompatibility complex MODY maturity-onset diabetes of the young mRNA messenger ribonucleic acid mTNF membrane-bound tumor necrosis factor mTNFR membrane-bound tumor necrosis factor receptor

NF- B nuclear factor kappa-light-chain-enhancer of activated B cells NFAT nuclear factor of activated T-cells NHP non-human primate OWM Old World monkey PAI plasminogen activator inhibitor PCR polymer chain reaction PPAR peroxisome proliferator-activated receptor Ra receptor antagonist SIV simian immunodeficiency virus SK St. Kitts SNP single nucleotide polymorphism SOC suppresor of cytokine signalling sTNF soluble tumor necrosis factor sTNFR soluble tumor necrosis factor receptor SVF stromovascular fraction T2D type-2 diabetes TACE tumor necrosis factor-alpha converting enzyme TF transcription factor TG triglyceride TNF tumor necrosis factor TNF tumor necrosis factor gene symbol TNFR tumor necrosis factor receptor TNFR-DD tumor necrosis factor receptor-death domain TPC total plasma cholesterol TSS transcription start site UCLA University of California – Los Angeles UCSC University of California – Santa Cruz VCAM vascular cell adhesion molecule VRC Vervet Research Colony VTR variable tandem repeat W:H waist-to-hip ratio WAT white adipose tissue WBCC white blood cell count WC waist circumference WFU Wake Forest University WFUPC Wake Forest University Primate Center

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ABSTRACT

Tumor necrosis factor (TNF) is a cytokine with critical roles in inflammation, and

in mediating an intersection of chronic, low-grade inflammation and concurrent

metabolic dysregulation associated with obesity and its comorbidities. As part of an

ongoing initiative to further characterize the St. Kitts-origin vervet monkey as a

nonhuman primate (NHP) of polygenic obesity, the human ortholog vervet TNF gene

and flanking regions were sequenced and genotyped from 265 monkeys in a closed,

pedigreed colony. This revealed a total of 11 single nucleotide polymorphisms (SNPs),

and one 4 base pair insertion/deletion polymorphism. Many of these polymorphisms

were in strong linkage disequilibrium, and were contained within one haplotype block,

comprising five haplotypes.

Using sequences from humans, chimpanzees, vervets, baboons, and rhesus

macaques, phylogenetic shadowing of the TNF promoter region revealed that vervet

and other NHP SNPs were clustered non-randomly and non-uniformly (p<0.0001)

around conserved transcription factor binding sites. Thus, the distribution of SNPs in

vervets and other NHPs is shown to be analogous to that of humans.

The taxonomy of the St. Kitts vervet and the African Green monkey is identical

and designated Chlorocebus aethiops sabaeus, although it has long been regarded as

tentative. Phylogenetic analyses were perfomed using TNF putative promoter region

nucleotide sequence, among the St. Kitts-origin vervet, and four other African-origin

subspecies. Results show the St. Kitts vervet has significant sequence differences from

the African Green monkey. This is the strongest evidence to date challenging the

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current taxonomic classification, and suggests the sabaeus subspecies designation for

the St. Kitts vervet should be reconsidered.

Association analyses were performed between polymorphisms of the vervet

TNF orthologous gene and previously defined obesity-related phenotypes. Positive,

significant associations were observed between a subset of SNPs (all within a single

haplotype) and body mass index, waist circumference (p < 0.05), total plasma

cholesterol, and HDL-cholestrerol (p < 0.01). The former three associations have been

reported in humans and provide further model validation, whereas the latter has been

reported as an inverse association in humans. The novel association with HDL-

cholesterol warrants further study, but may provide insight into the newly emerging

dual inflammatory and anti-inflammatory role of HDL-cholesterol.

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CHAPTER I

INTRODUCTION

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Obesity and overweight epidemic

Currently, more than 30% of U.S. adults are obese, with another 35%

overweight; in addition, incidence of overweight children and adolescents is now over

15% (NHANES 2007). Compared to 30 years ago, these figures have approximately

doubled in adults and quadrupled in children and adolescents (NHANES 2007). The

simplest and most generally utilized surrogate measure for adiposity is body mass index

or BMI (weight / height2). The number of people classified as obese or overweight,

defined by BMI over 30 or 25 kg/m2 respectively, has reached epidemic proportions in

the U.S. and around the world (IOTF 2007), with commensurate social, medical, and

public health costs (Wyatt, Winters et al. 2006). Obesity, and overweight to a lesser

degree, have consistently been associated with increased morbidity and mortality linked

to cardiovascular disease (CVD), hypertension, type 2 diabetes (T2D), dyslipidemia,

metabolic syndrome (a set of CVD risk factors including hypertension, dyslipidemia,

central obesity and insulin resistance), airway inflammation, fatty liver disease,

gallstones, osteoarthritis, sleep apnea, and certain forms of cancer (NHLBI 2000; Eckel,

Barouch et al. 2002; Klein, Burke et al. 2004; Wellen and Hotamisligil 2005), with relative

risk of these comorbidities being positively correlated with BMI (Wyatt, Winters et al.

2006). In addition, visceral fat, or central adiposity as defined by waist circumference

over 40 inches in males and over 35 inches in females, is a risk factor for these diseases

even independent of a BMI over 30 or 25 kg/m2 (NHANES 2007).

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Genetics and phenotypic expression of obesity

The genetic and molecular mechanisms underlying adipose tissue distribution,

maintenance, excess and possible pathologic sequelae are complex and far from

completely understood. Adding to this complexity are important non-genetic

components permitting and modulating phenotypic expression of obesity, obesity-

related traits, overweight, and comorbidities such as: age, developmental programming

(in utero and post-natal), sex, hormonal status, lifestyles, nutrition, physical activity

level, risk behavior, socioeconomic status, and stress (Shively and Clarkson 1988;

Roseboom, van der Meulen et al. 2001; Hill, Wyatt et al. 2003; Roche, Phillips et al.

2005; Mutch and Clement 2006; Pasquali, Vicennati et al. 2006; Schneider, Tompkins et

al. 2006; Taylor and Poston 2007). Thus, obesity, overweight, and the possible resulting

comorbidities, have very heterogeneous phenotypic expression resulting from an

interaction between a wide array of non-genetic components and genetic susceptibility

factors. This is supported by numerous epidemiological studies in large, diverse human

populations [reviewed in (Sorensen 1995)]. According to several studies, 30 to 80% of

variation in body weight may be determined by genetic factors in humans [reviewed in

(Mutch and Clement 2006)] and similar heritability values have been shown in obesity-

related parameters in vervets (Kavanagh, Fairbanks et al. 2007) (Table 1, Chapter 4) and

other NHPs (Comuzzie, Cole et al. 2003). In discussing the genetic contributions to the

development of obesity, it is important to contrast monogenic and polygenic forms.

Monogenic forms of obesity involve single mutations, are very rare, very severe, and

usually involve early (childhood) onset. Polygenic forms of obesity are the more

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common manifestation and involve the previously described interaction of numerous

genetic variants with various age, sex, and environmental risk factors. In polygenic

obesity, a key conceptual point is that each susceptibility gene variant alone is thought

to have a minor, modulating effect on the obesity phenotype or disease risk.

Inflammatory cascade of obesity

Until relatively recently, adipose tissue was regarded only as a reservoir for

triglycerides. That perspective began to change when mRNA expression of the

inflammatory cytokine TNF- was discovered in adipose tissue of rodent models of

obesity (Hotamisligil, Shargill et al. 1993). Adipose tissue was then established as an

endocrine organ with the subsequent discovery of leptin (Zhang, Proenca et al. 1994),

which is a hormone-like cytokine secreted mostly by adipocytes (thus termed

‘adipokine’) acting on neurons in the hypothalamus regulating food intake and energy

homeostasis, but also upregulating phagocytosis and pro-inflammatory cytokines via

other cell targets (Loffreda, Yang et al. 1998; Wellen and Hotamisligil 2005). The role of

adipose tissue has expanded as additional secretory products with various physiologic

roles continue to be discovered such that it is now recognized to be an integral

endocrine organ. Fatty acids (FAs) are released during times of negative energy balance

and are the most abundant secretory product of adipocytes. The set of more recently

discovered secretory products of adipocytes, which includes leptin, TNF- , adiponectin,

and numerous other adipokines and cytokines, act in an autocrine, paracrine, or

endocrine manner to control various, often overlapping or integrated, metabolic

functions such as: angiogenesis, appetite, blood pressure, hemostasis, immunity,

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inflammation and acute-phase response, insulin sensitivity, lipid metabolism, and

overall systemic energy homeostasis [reviewed in (Trayhurn and Wood 2004; Esteve,

Ricart et al. 2005; Wellen and Hotamisligil 2005; Hotamisligil 2006; Cawthorn and Sethi

2008)]. Over 50 adipokines and cytokines produced by WAT have currently been

identified. Adipokines act in concert with traditional cytokines produced by other cells

of adipose tissue, called the stromovascular fraction (SVF). The SVF comprises

preadipocytes, endothelial cells, smooth muscle cells, fibroblasts, leukocytes, and

resident and infiltrated macrophages (Ross, Erickson et al. 2002; Weisberg, McCann et

al. 2003; Xu, Barnes et al. 2003; Curat, Miranville et al. 2004; Fain, Madan et al. 2004).

Coincident, and overlapping, with the expanding metabolic role of adipose tissue

is the notion that obesity is characterized by a state of chronic low-grade inflammation

(Yudkin, Stehouwer et al. 1999; Das 2001; Festa, D'Agostino et al. 2001; Engstrom,

Hedblad et al. 2003; Trayhurn 2005; Wellen and Hotamisligil 2005; Cawthorn and Sethi

2008), which seems to originate in, and be mediated by the cytokines, adipokines and

other factors secreted from adipose tissue. This view originated with observations that

increased circulating levels of several inflammatory markers are elevated in obesity

(Hotamisligil, Shargill et al. 1993; Hu, Liang et al. 1996; Visser, Bouter et al. 1999; Das

2001; Bullo, Garcia-Lorda et al. 2003; Trayhurn and Wood 2004)(Table 1). Subsequent

elucidation of the cellular sources and interactions of these factors and, most recently,

the finding that macrophages infiltrate adipose tissue in obesity (Weisberg, McCann et

al. 2003; Xu, Barnes et al. 2003) further support this idea.

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Table 1. Selected inflammatory and metabolic regulators within adipose tissue and their putative interactions. CRP, C-reactive protein; FAO, fatty acid oxidation; IL, interleukin; MCP, monocyte chemotactic protein; PAI, plasminogen activator inhibitor; TNF, tumor necrosis factor; GLUT, glucose transporter; Ra, receptor antagonist; ICAM, intracellular adhesion molecule; VCAM, vascular cell adhesion molecule; PPAR, peroxisome

proliferators ctivated receptor; IFN, interferon; WBCC, white blood cell (leukocyte) count [leptin (Maffei, Fei et al. 1995; Siegrist-Kaiser, Pauli et al. 1997; Bullo, Garcia-Lorda et al. 2003; Seufert 2004); CRP (Yudkin, Stehouwer et al. 1999; Pannacciulli, Cantatore et al. 2001) *CRP elevated in both overweight and obese states (Visser, Bouter et al. 1999); TNF-α (Hotamisligil, Shargill et al. 1993; Friedman and Halaas 1998; Moller 2000; Minokoshi, Kim et al. 2002; La Cava and Matarese 2004); adiponectin (Berg, Combs et al. 2001; Chandran, Phillips et al. 2003; Engeli, Feldpausch et al. 2003; Yamauchi, Kamon et al. 2003; Kumada, Kihara et al. 2004); review (Trayhurn and Wood 2004; Fantuzzi 2005; Juge-Aubry, Henrichot et al. 2005; Wellen and Hotamisligil 2005)]

Factors involved in the initiating event have not yet been definitively established,

although local adipose tissue hypoxia in obesity, due to adipocyte hyperplasia and/or

hypertrophism, is the most widely cited hypothesis (Neels and Olefsky 2006), and

convincing evidence has been put forth [e.g. (Hosogai, Fukuhara et al. 2007)]. Further,

whether the inflammatory cascade occurs early or later in obesity has not been clarified;

Factors Association Metabolic effects or

associations Putative interactions

Adiponectin in obesity inflammation, insulin

resistance, atherogenesis; FAO

TNF- , IL-6, ICAM-1, VCAM-1, resistin

IL-10, IL-1 Ra

TNF- in obesity inflammation, IR; FAO

adiponectin, GLUT4,

PPAR

CRP, IL-6, leptin, resistin, MCP-1, PAI-1

Leptin in obesity satiety, FAO, immune function,

inflammation, phagocytosis, TH1

response; T-cell apoptosis

WBCC, CRP, IL-6,

leptin, TNF , IFN- ,

IFN- , IL-2; insulin

CRP in obesity* inflammation, insulin

resistance, atherogenesis

TNF- , IL-6, leptin, PAI-1, ICAM-1, VCAM-

1, E-selectin

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this is particularly true for humans as little to no data are available from obese or

overweight children. Regardless of the uncertainties that remain, there is ample

evidence that inflammation may be causal in the development of insulin resistance,

hyperlipidemia and other components of the metabolic syndrome (Trayhurn and Wood

2004), thus suggesting a pathogenic link between obesity and T2D and CVD.

Table I summarizes three key adipokines (adiponectin, leptin, and TNF ) in

adipose tisse which coordinate, mediate, and perhaps initiate its integrated metabolic,

immune, and inflammatory functions (Hotamisligil 2006). C-reactive protein is also

listed as it is the most commonly used surrogate marker for systemic inflammation.

More detailed descriptions of each of these follow. Since TNF- , and specifically genetic

variation which potentially modulates its effect, is the focus of this thesis project, its role

is described in much greater detail.

Leptin is a hormone-like adipokine produced mainly by adipocytes in amounts

proportional to total body adiposity (Friedman and Halaas 1998). Its primary function is

regulation of long-term satiety and energy expenditure through receptors in several

hypothalamic nuclei (Fei, Okano et al. 1997) such that genetic defects in either the

leptin or leptin receptor gene result in morbid obesity, as shown in mouse models and

humans [reviewed in (Friedman and Halaas 1998)]. Leptin receptors are distributed

throughout multiple peripheral tissues and organs as well, modulating diverse

physiological functions such as reproduction (Chehab, Mounzih et al. 1997),

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angiogenesis (Sierra-Honigmann, Nath et al. 1998), hematopoiesis (Cioffi, Shafer et al.

1996), regulation of bone mass (Takeda, Elefteriou et al. 2002), stimulation of lipolysis in

WAT (Siegrist-Kaiser, Pauli et al. 1997), and regulation of cytokine (Maedler, Sergeev et

al. 2004) and insulin (Seufert 2004) gene expression in the pancreas. A large body of

evidence has accumulated pertaining to the latter effects of leptin on pancreatic -cells

[reviewed in (Seufert 2004)]. Taken together, all known effects of leptin on pancreatic

-cells act to exert long-term (i.e. not generally interfering with short-term stimulatory

actions of nutrients, such as glucose, and hormones) inhibition of insulin secretion and

expression, which adapts the amount of insulin secretion to the amount of adiposity.

Conversely, insulin stimulates both leptin biosynthesis and secretion from WAT, thus

establishing a classic endocrine negative feedback loop, or sometimes termed the

“adipo-insular axis” (Seufert 2004). Evidence to support this concept comes mainly

from animal model and in vitro studies, but is also supported in human studies [e.g.

(Guldstrand, Ahren et al. 2003)]. Table 1 summarizes the metabolic effects and

correlations of leptin with other serum factors.

Adiponectin is an adipokine discovered almost simultaneously by four groups

and therefore has various designations: adipocyte complement-related protein of 30

kDa (Acrp30) (Scherer, Williams et al. 1995), the most abundant gene transcript 1

(apM1) (Maeda, Okubo et al. 1996), gelatin-binding protein of 28kDa (GBP28) (Nakano,

Tobe et al. 1996), or ADIPOQ (Hu, Liang et al. 1996). It is produced exclusively by the

adipocyte fraction of WAT, and circulates at relatively high serum levels (e.g. in mg/ml

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range versus ng/ml for leptin) in lean animals and humans (Fain, Madan et al. 2004;

Fantuzzi 2005). Plasma levels are inversely correlated with BMI and body fat, as well as

fasting proinsulin, TNF- expression, and the insulin response, even after correction for

adiposity (Kern, Di Gregorio et al. 2003; Pellme, Smith et al. 2003). The relevance of

adiponectin was initially recognized by decreased glucose levels after administration to

obese and non-obese diabetic rodent models (Berg, Combs et al. 2001), as well as by

improved insulin resistance of diabetic rodents fed a high fat diet (Yamauchi, Kamon et

al. 2001). Direct evidence of its role in mediating insulin sensitivity was shown through

adiponectin gene knockout (Chandran, Phillips et al. 2003) and overexpression

(Yamauchi, Kamon et al. 2003) mouse studies. In addition to its pivotal role as an insulin

sensitizing factor, it also appears to have significant anti-inflammatory properties, which

are mediated through reduced secretion and expression of TNF- (Masaki, Chiba et al.

2004), reduced secretion of IL-6, and induction of anti-inflammatory cytokines IL-10 and

IL-1Ra (Kumada, Kihara et al. 2004; Wolf, Wolf et al. 2004; Wulster-Radcliffe, Ajuwon et

al. 2004) (Table 1). Inhibition of nuclear factor B (NF- B) by adiponectin may be the

more central molecular mechanism explaining some or all of these effects (Wulster-

Radcliffe, Ajuwon et al. 2004). Further, adiponectin reduces induction of the endothelial

adhesion molecules ICAM-1 and VCAM-1 (Table 1) via either TNF- or resistin (Ouchi,

Kihara et al. 1999; Kawanami, Maemura et al. 2004), thus having anti-atherogenic

properties (Engeli, Feldpausch et al. 2003), and possibly curtailing WAT inflammation by

interfering with local macrophage infiltration.

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C-reactive protein (CRP) was initially discovered as a substance in the serum of

patients with pneumococcal pneumonia, and named for its precipitation when

combined with polysaccaride C of Streptococcus pneumoniae (Tillett and Francis 1930).

It was subsequently shown to be synthesized in the liver, and went on to be

characterized as one of many proteins, termed “acute phase proteins”, induced in

response to a variety of acute infections and inflammatory states (Clyne and Olshaker

1999). Its utility as a biomarker of inflammation stems from the fact that it is normally

present in trace amounts in serum, but increases rapidly and significantly in response to

inflammatory conditions, as it is stimulated by various cytokines, particularly IL-6, IL-1,

and TNF- (Table 1). Further, CRP levels stay elevated until the inflammation has been

resolved, at which time levels rapidly decline with an elimination half-life estimated at

4-9 hours (Clyne and Olshaker 1999). The functional properties of CRP include: binding

of plasma lipoproteins (Pepys, Rowe et al. 1985), complement activation (Volanakis

1982), modulation of phagocytic activity (Kolb-Bachofen 1991), and induction of tissue

factor (Cermak, Key et al. 1993), although exact in vivo function is not known and how

much CRP plays a causative role in inflammation is subject to debate (Yudkin,

Stehouwer et al. 1999; Pannacciulli, Cantatore et al. 2001). Elevated CRP levels have

been shown to be associated with excess adiposity (Visser, Bouter et al. 1999; Yudkin,

Stehouwer et al. 1999; Pannacciulli, Cantatore et al. 2001), insulin resistance (Yudkin,

Stehouwer et al. 1999; Pannacciulli, Cantatore et al. 2001), risk of T2D (Pradhan,

Manson et al. 2001; Nakanishi, Yamane et al. 2003), and CVD and severity of

atherosclerosis and CVD (Berk, Weintraub et al. 1990; Thompson, Kienast et al. 1995; de

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Maat, Pietersma et al. 1996; Paul, Ko et al. 2004), as well as being predictive for

cardiovascular events or CVD-related mortality (Kuller, Tracy et al. 1996; Mendall, Patel

et al. 1996; Ridker, Cushman et al. 1997).

Role of TNF-α in adipocyte biology, and obesity-related inflammation

Testament to the importance of this area of research, there have been

numerous excellent reviews of this subject [e.g. (Esteve, Ricart et al. 2005; Juge-Aubry,

Henrichot et al. 2005; Wellen and Hotamisligil 2005; Hotamisligil 2006; Cawthorn and

Sethi 2008)]. One review in particular by Cawthorn and Sethi (2008), who were involved

in much of the primary research reported therein, is exceptionally useful for its specific

review of TNF- and its role in adipocyte biology and merits special mention as it greatly

facilitated writing this particular thesis section.

TNF- is a cytokine originally named for its ability to cause necrosis of tumors

when induced with bacterial endotoxin. It has subsequently been shown to be not only

the principal mediator of the acute inflammatory and febrile response, but also a multi-

functional cytokine that can regulate many cellular and biological processes such as

immune function, cell differentiation, proliferation, apoptosis, and energy metabolism

and homeostasis (Juge-Aubry, Henrichot et al. 2005; Cawthorn and Sethi 2008). More

recently, it has been recognized as a key initiator and regulator of several cytokines,

adipokines, and acute-phase proteins involved in adiposity-related inflammation

(Coppack 2001; Trayhurn and Wood 2004; Cawthorn and Sethi 2008) (Table 1). TNF-

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expression in WAT was initially demonstrated in obese rodent models, and found to be

markedly increased while downregulating glucose transporter-4 (GLUT4) in adipose

tissue (Hotamisligil, Shargill et al. 1993). Since then, it has been extensively examined in

relation to insulin action, and multiple effects have been described (Moller 2000;

Coppack 2001; Hotamisligil 2003; Juge-Aubry, Henrichot et al. 2005; Wellen and

Hotamisligil 2005), including attenuated insulin receptor signaling via decreased tyrosine

kinase activity (Hotamisligil, Peraldi et al. 1996), and general promotion of insulin

resistance through TNF- gene knockout (Uysal, Wiesbrock et al. 1997) and

overexpression (Xu, Hirosumi et al. 2002) mouse studies.

As previously mentioned, the factors involved in the initiation of obesity-related

inflammation have not been established. Likewise, the initial factor(s) responsible for

inducing TNF- is not clear. This is complicated by the fact that regulation of TNF gene

transcription is cell type-specific (Cawthorn and Sethi 2008). However, questions of

timing of TNF- and the inflammatory cascade of obesity in general, have focused on

identifying the intial trigger(s) that stimulate recruitment and activation of macrophages

into WAT since macrophages are thought to be largely responsible for obesity-related

TNF- secretion (Cawthorn and Sethi 2008). One previously mentioned hypothesis that

has some support is that expanding adipose tissue associated with obesity leads to

adipocyte cell death, and this may induce chemoattractant signals that recruit

monocytes (Neels and Olefsky 2006). Both rodent and human studies support this

hypthesis, which demonstrate increased numbers of apoptotic and necrotic adipocytes

13

in both WAT and brown adipose tissue, respectively (Nisoli, Briscini et al. 1997; Cinti,

Mitchell et al. 2005; Hosogai, Fukuhara et al. 2007; Strissel, Stancheva et al. 2007). One

study in particular shows that the majority of macrophages in WAT of obese mice and

humans are localized to dead adipocytes (Cinti, Mitchell et al. 2005). An alternative

hypothesis, which is not mutually exclusive with the former, relates to immune function

and nutritional status being closely linked (Hotamisligil 2006). It has been suggested

that dietary FAs can alter cytokine production (Kelley 2001) and may play a major role in

initiating obesity-related inflammation, specifically, the ratio of pro-inflammatory n-6 or

anti-inflammatory marine-derived n-3 FAs (Cawthorn and Sethi 2008). Like many other

cytokines, levels of TNF-α are nutritionally regulated but, can be enhanced by

hyperinsulinemia alone (McTernan, Harte et al. 2002). But, again, which of these

conditions comes first is currently unclear. As a final note on timing of obesity-related

inflammation, it is important to keep in mind that in vivo cytokine actions result from a

complex interplay of both anti- and pro-inflammatory cytokines. The cellular milieu of

coordinate anti-inflammatory cytokines, such as adiponectin and IL-10, is likely to play a

role in modulating the intial inciting factors or events triggering TNF- , and obesity-

related inflammation in general.

TNF- is synthesized as a 26-kDa transmembrane monomer (mTNF- ) (Kriegler,

Perez et al. 1988). Subsequent proteolytic cleavage by TNF- converting enzyme (TACE)

yields an additional 17-kDa circulating soluble TNF- molecule (sTNF- ) (Black, Rauch et

al. 1997). Both sTNF- and mTNF- mediate metabolic and inflammatory processes

14

(Perez, Albert et al. 1990; Xu, Sethi et al. 1999). Levels of both have been shown to be

increased in the WAT of obese, diabetic rodent models (Hotamisligil, Shargill et al. 1993;

Xu, Uysal et al. 2002). However, due to the fact that circulating levels of sTNF- are

much lower than in the catabolic diseases, this initially caused debate as to whether

sTNF-α played a significant role inducing states of insulin resistance (Cawthorn and Sethi

2008). Subsequent improvement in assays have helped to show that circulating sTNF-

are well correlated to BMI (Zahorska-Markiewicz, Janowska et al. 2000), and systemic

insulin resistance in mice and rat studies (Togashi, Ura et al. 2002; Weisberg, McCann et

al. 2003; Serino, Menghini et al. 2007).

The molecular mechanisms of TNF- action in adipocytes are mediated through

two distinct cell surface receptors: tumor necrosis factor alpha receptor 1 (TNFR1) and

TNFR2 (Cawthorn and Sethi 2008). TNFR1 is a 55-kDa peptide in humans and 60-kDa in

rodents; TNFR2 is 75-kDa in humans and 80-kDa in rodents. TNFR structure has not

been defined in any NHP at the time of this writing. Both TNFR1 and TNFR2 are

ubiquitously expressed transmembrane glycoproteins that trimerize upon ligand

binding, and like TNF- , both can be proteolytically cleaved to release soluble forms

(sTNFR) (Cawthorn and Sethi 2008), which may be involved in neutralization and

excretion of TNF- thereby modulating TNF- activity (Gatanaga, Hwang et al. 1990).

Both TNFRs are nutritionally regulated as well (Zahorska-Markiewicz, Janowska et al.

2000), and have been reported to increase in both obese and non-obese adults with

pro atherogenic lipid profiles (Hotamisligil, Arner et al. 1997; Mohamed-Ali, Goodrick et

15

al. 1999). TNFR1 and TNFR2 contain highly homologous extracellular domains, but their

intracellular domains have no sequence homology and thus activate separate and

distinct signalling pathways (MacEwan 2002). The vast majority of the obesity-related

and adipocyte biology TNF- effects have been shown to be mediated through the

trimeric TNFR1 death domain (TNFR1-DD) (Csehi, Mathieu et al. 2005; Cawthorn, Heyd

et al. 2007). Specifically, TNFR1-DD can activate cell survival and pro-inflammatory

signalling pathways that lead to the activation of nuclear factor-kappa B (NF- B) and of

mitogen-activated protein kinase (MAPK) cascades, such as those involving extracellular

signal-related protein kinase (ERK), p38 MAPK, and c-Jun N-terminal kinase (JNK) (Jain,

Phelps et al. 1999; Ryden, Dicker et al. 2002; Chae and Kwak 2003; Suzawa, Takada et al.

2003; Kim, Kim et al. 2005).

A major role for TNF- in cellular metabolism was evident upon its discovery as

the agent responsible for cancer cachexia (Kriegler, Perez et al. 1988). Its metabolic role

was further defined by in vitro studies showing it affected glucose homeostasis in

adipocytes (Stephens and Pekala 1991), promoted lipoloysis in cultured adipocytes

(Kawakami, Murase et al. 1987), and inhibited adipocyte differentiation and lipogenesis

(Beutler, Milsark et al. 1985; Torti, Dieckmann et al. 1985). The landmark discovery

implicating TNF-α in T2D was made by Hotamisligil et al (1993) who showed that TNF-

is elevated in adipose tissue from obese diabetic rodents, and is, in fact, a mediator of

obesity-related insulin resistance and T2D. This role was verified with TNF KO mice

(Uysal, Wiesbrock et al. 1997) which had insulin sensitivity restored. Thus, TNF-

16

production appears to contribute to the pathogenesis of rodent obesity-induced insulin

resistance. Evidence that this is true for humans had been less well established before

the aforementioned assay difficulties correlating circulating sTNF- with obesity were

resolved. These issues made it specifically difficult to ascertain whether TNF- activity,

from both mTNF- and sTNF- , was completely neutralized by various anti-TNF

treatments (Cawthorn and Sethi 2008). More recent reports utilizing improved

technologies demonstrate that more chronic administration of anti-TNF- antibodies

improves insulin sensitivity in both lean (Kiortsis, Mavridis et al. 2005; Huvers, Popa et

al. 2007; Tam, Tomlinson et al. 2007) and obese patients (Hermann, Hebert et al. 2004).

Adipocytes of WAT secrete a significant portion of sTNF- . However, the

stromovascular fraction (SVF), which contains preadipocytes, endothelial cells, smooth

muscle cells, fibroblasts, leukocytes, and macrophages, has been shown to produce

substantially more TNF- than adipocytes (Ross, Erickson et al. 2002; Weisberg, McCann

et al. 2003; Xu, Barnes et al. 2003; Fain, Madan et al. 2004). In particular, adipose tissue

macrophages (ATMs) infiltrating into WAT in obese states are thought to be responsible

for a large proportion of the TNF- elevations in obesity (Weisberg, McCann et al. 2003;

Xu, Barnes et al. 2003; Coenen, Gruen et al. 2007; Lumeng, Bodzin et al. 2007) although

their relative contribution has not been precisely established. A recent rodent model

study illustrates this uncertainty by showing that, although macrophage-derived TNF-

can impair insulin sensitivity in TNF KO mice, the absence of TNF- in macrophages

17

alone does not protect otherwise wild-type mice from obesity-related insulin resistance

(De Taeye, Novitskaya et al. 2007).

Role of TNFα in insulin resistance and dyslipidemia. Adipose tissue is a key regulator of

systemic carbohydrate metabolism. As a prime example, mice with tissue-specific

GLUT4 KO in adipose tissue develop systemic glucose intolerance and hyperinsulinemia

(Abel, Peroni et al. 2001). Subsequent studies have clearly established the ability of

TNF- specifically to induce insulin resistance in adipocytes (Ruan and Lodish 2003)

through both transcriptional, and more direct cellular signalling mechanisms via TNFR1-

DD (Csehi, Mathieu et al. 2005). Several of these interrelated effectors, mechanisms,

and cellular pathways are summarized below:

1) Insulin receptor, insulin receptor substrate-1 (IRS-1), and GLUT4, which are all

required for insulin-stimulated glucose uptake in adipocytes, have been shown to be

suppressed at the transcriptional level by TNF-α (Stephens, Lee et al. 1997; Ruan,

Hacohen et al. 2002; Ruan, Miles et al. 2002).

2) Peroxisome proliferator-activated receptor gamma (PPAR ), a transcription factor

(TF) of central importance, is targeted by TNF- in adipocytes (Miles, Romeo et al.

1997; Peraldi, Xu et al. 1997). TNF- suppresses transcriptional activity of PPAR via

serine phosphorylation of its N-terminal domain, mediated by JNK and extracellular

signal-regulated kinase (ERK1/2) (Hu, Kim et al. 1996; Adams, Reginato et al. 1997).

Post-transcriptional regulation is also thought to occur by suppression of PPAR

18

mRNA by TNF- , although this may simply occur due to the fact that PPAR

expression is upregulated by its own activity (Cawthorn and Sethi 2008).

3) NF- B, which is thought of as the classic TNF- -induced TF, also mediates inhibition

of PPAR transcription (Suzawa, Takada et al. 2003).

4) CCAAT/enhancer binding protein (C/EBPα), another adipogenic TF and PPAR target

gene, is also suppressed by TNF- at the mRNA level (Jain, Phelps et al. 1999).

5) The promoter region of the GLUT4 gene contains response elements for both PPAR

and C/EBPα (Jain, Phelps et al. 1999), which are two of the likely mechanisms by

which TNF- downregulates GLUT4. But, whether this is true for all genes

downregulated in insulin resistant states is not currently clear (Cawthorn and Sethi

2008).

6) Another MAPK which may contribute to PPAR and GLUT4 suppression by TNF- is

mitogen activated protein kinase kinase kinase kinase 4 (MAPK4) which activates the

JNK signalling pathway in adipocytes (Tang, Guilherme et al. 2006). However, its

specific role and mechanism are currently not known (Cawthorn and Sethi 2008).

7) The transcriptional events required for TNF- -induced insulin resistance may also

involve the transcriptional activity of NF- B itself, and hence NF- B target gene

expression. But, this has yet to be fully demonstrated in adipocytes. Despite this,

there is ample evidence to support the requirement for the upstream signals that

lead to NF- B activation in TNF- -induced insulin resistance in vitro and in vivo

(Yuan, Konstantopoulos et al. 2001; Ruan, Hacohen et al. 2002; Arkan, Hevener et al.

2005; Cai, Yuan et al. 2005). Several potential NF- B target genes that are

19

upregulated by TNF- in adipocytes have been implicated in adipocyte insulin

resistance. Suppressor of cytokine signalling-3 (SOC-3) is one particularly relevant

NF- B target gene which inhibits insulin receptor-dependent IRS-1 tyrosine

phosphorylation and thereby suppresses insulin-stimulated glucose uptake

(Emanuelli, Peraldi et al. 2001; Peraldi, Filloux et al. 2001; Ueki, Kondo et al. 2004).

8) Serine phosphorylation of IRS-1 (Hotamisligil, Peraldi et al. 1996; Peraldi, Hotamisligil

et al. 1996; Zick 2005) is one of the most well studied non-transcriptional

mechanisms of compromising insulin receptor/IRS-1 signalling. TNF-α is thought to

effect this outcome via direct signalling events that mediate shared signal

transduction between TNFR1-induced pathways and proximal insulin signals

(Engelman, Berg et al. 2000).

9) Alterations in membrane lipid composition can be caused by TNF-α action. These

can lead to insulin resistance through several means, including activating serine

kinase signalling cascades, and altered membrane fluidity which impairs receptor

internalization, cyling and function [reviewed in (Cawthorn and Sethi 2008)].

10) Endoplasmic reticulum (ER) stress (Ozcan, Cao et al. 2004; Nakatani, Kaneto et al.

2005; Ozawa, Miyazaki et al. 2005; Ozcan, Yilmaz et al. 2006), oxidative stress and

mitochondrial dysfunction (Furukawa, Fujita et al. 2004; Lin, Berg et al. 2005; Lowell

and Shulman 2005; Houstis, Rosen et al. 2006) have been linked to obesity-related

inflammation, insulin resistance, and T2D [reviewed in (Hotamisligil 2006)]. All of

these pathways can be induced by TNF- , and have been linked to activation of the

JNK [e.g. (Urano, Wang et al. 2000)] or NF- B [e.g. (Hu, Han et al. 2006)] pathways.

20

It has been reported that production of reactive oxygen species (ROS) contributes to

both TNF- -induced NF- B activation (Gloire, Legrand-Poels et al. 2006) and TNF- -

induced insulin resistance (Houstis, Rosen et al. 2006). In addition, oxidative stress is

suggested to promote increased TNF- production under adverse metabolic

conditions such as hyperglycemia (Esposito, Nappo et al. 2002). TNF- in adipose

tissue upregulates expression of many genes that encode proteins involved in

responses to ER stress or oxidative stress (Ruan, Hacohen et al. 2002), and

downregulates components of the electron transport chain (Dahlman, Forsgren et

al. 2006) likely inducing mitochondrial dysfunction. This idea is further supported by

evidence showing TNF- inhibiting fatty acid oxidation (FAO) in differentiated

human adipocytes (Dahlman, Forsgren et al. 2006).

11) The principal insulin-sensitizing adipokines are adiponectin and leptin (Sethi and

Vidal-Puig 2007). Adiponectin promotes insulin sensitivity and favorable serum lipid

profiles through AMPK activation and increased FAO (Yamauchi, Kamon et al. 2002;

Yamauchi, Kamon et al. 2003). TNF- suppresses adiponectin, which is one of

several overlapping mechanisms by which TNF- effects systemic insulin resistance

and dyslipidemia (Kappes and Loffler 2000; Fasshauer, Klein et al. 2002; Ruan,

Hacohen et al. 2002). This mechanism of adiponectin suppression is thought to be

mediated, at least in part, by suppression of PPAR (Matsuzawa 2005), C/EBPb (Kita,

Yamasaki et al. 2005), and/or through activation of JNK (Hirosumi, Tuncman et al.

2002; Kim, Kim et al. 2005). How TNF- regulates leptin is not as well understood.

TNF- actually promotes leptin release from adipocytes (Kirchgessner, Uysal et al.

21

1997; Finck and Johnson 2000). In the absence of TNF- , obesity-related

hyperleptinemia is significantly reduced; however, the mechanism is unclear as the

transcriptional upregulation is context-dependent (Grunfeld, Zhao et al. 1996;

Kirchgessner, Uysal et al. 1997; Trujillo, Lee et al. 2006).

12) Despite the realization over the past 15 years that adipocytes play larger metabolic

and endocrine roles, their major functions continue to be the ability to store and

release surplus energy through lipogenesis and lipolysis/thermogenesis respectively.

These processes are regulated by numerous extracellular stimuli, such as insulin,

cortisol, catecholamines, growth hormone, testosterone, free fatty acids (FFA), and

cytokines [reviewed in (Ramsay 1996)]. Lipid metabolism in adipocytes can be

directly affected by TNF- via inhibition of FFA uptake and lipogenesis, as well as

stimulation of FFA release via lipolysis thereby contributing to the development of

dyslipidemia and the metabolic complications thereof (Cawthorn and Sethi 2008).

Conversion of lipids into triacylglycerol is dependent on the availability of substrates,

namely glucose, and the regulation of key enzymatic pathways. This conversion is

affected by TNF-α by the induction of adipocyte insulin resistance and resultant

impaired glucose uptake into adipocytes. In addition, TNF- downregulates

expression of FA transport protein (FATP) and translocase (FAT) in adipose tissue

(Memon, Feingold et al. 1998), as well as the FA binding protein FABP4/aP2

(Cawthorn and Sethi 2008), although the exact mechanisms have not been

definitively established. FFA uptake into adipocytes is also affected by lipoprotein

lipase (LPL) (Ramsay 1996), and TNF- downregulates LPL expression in both murine

22

adipose tissue and in 3T3-L1 adipocytes (Ruan, Miles et al. 2002; Ruan, Pownall et al.

2003), likely via TNFR1 (Lopez-Soriano, Llovera et al. 1997). However, the effects of

TNF- on LPL in human adipose are not clear at this time (Kern 1988; Fried and

Zechner 1989). TNF- also suppresses transcription (likely through PPAR ) and

mRNA expression of many proteins involved in gluconeogenesis, and de novo FA

synthesis and esterification (Cawthorn and Sethi 2008), which leads to impaired TG

storage in adipose tissue. This can contribute to hyperlipidemia, along with the

potential ability of TNF- to stimulate lipolysis which can occur in the absence of

insulin (Cawthorn and Sethi 2008). Thus, it would appear that TNF-α does not simply

antagonize insulin’s anti-lipolytic actions. In addition, extracellular glucose is

required for TNF- -mediated adipocyte lipolysis (Green, Rumberger et al. 2004),

which suggests that ATP is required. These mechanisms have yet to be fully

elucidated.

Association studies in humans

Numerous polymorphisms have been identified in cytokine, adipokine, and

inflammatory biomarker candidate genes for the putative inflammatory cascade of

obesity. Those shown to be statistically associated with obesity-related phenotypic

traits or disease risk in various human populations are mostly single nucleotide

polymorphisms (SNPs), primarily located in the enhancer/promoter regions, although a

significant number can also be found within the intronic and 3’/5’ untranslated (UTR)

regions. Very few genetic polymorphisms in the coding (exon) regions resulting in non-

23

synonymous amino acid changes have been observed. Of all such association studies,

those examining the TNF- (TNF) gene variants are among the most abundant in the

literature and are the focus of this dissertation. Table 2 summarizes selected studies

which have demonstrated significant phenotypic associations with certain genotypes or

haplotypes (sets of statistically associated alleles within a gene or on a single

chromatid). In addition, whole genome scans have shown linkage of the region near the

adiponectin gene with T2D in French and Japanese populations (Vionnet, Hani et al.

2000; Mori, Otabe et al. 2002) and with fasting insulin levels in European Americans

(Kissebah, Sonnenberg et al. 2000).

Table 2. Summary of associations between TNF gene SNPs and various obesity-related phenotypic traits in humans.

BP, blood pressure; FFA, free fatty acids; FG, fasting glucose; HDL, high density lipoprotein; LDL, low density lipoprotein; TG, triglycerides; WC, waist circumference; W:H, waist-to-hip ratio; (Fernandez-Real, Gutierrez et al. 1997; Wilson, Symons et al. 1997; Hoffstedt, Eriksson et al. 2000; Skoog, Eriksson et al. 2001; Fernandez-Real and Ricart 2003; Wybranska, Malczewska-Malec et al. 2003; Corbalan, Marti et al. 2004; Markovic, O'Reilly et al. 2004; Um, Kang et al. 2004); review (Fernandez-Real 2006)]

As previously discussed, obesity is paradigmatic of phenotypes that are the

resolution of complex gene-gene interactions, as well as interactions of genotype with

environment, developmental age, and sex (Scriver 2001). Association studies attempt

to further elucidate genetic bases for susceptibility to such complex disease phenotypes.

To best interpret such studies and put them in the proper context, it is important to

Gene symbol

Gene name

Traits or disease risk associated with certain genotypes or haplotypes

Size (kb)

TNF TNF-alpha

obesity, BMI, FG, FFA, insulin resistance, TG, WC, W:H, CRP

leptin, TNF- 2.8

24

recognize the way these traits likely evolved. Such phenotypes almost certainly

conferred significant selective advantage throughout a very large span of evolutionary

history, but have now become detrimental with the vastly different environments and

selective pressures, or lack thereof, humans now experience compared to the hunter-

gatherer, or even pre-industrial, lifestyle. As a prime example, genetic predisposition to

obesity is presumably the result of evolution in environments in which food was not

easily attainable, thus favoring selection for metabolically efficient phenotypes

involving, among other things, the ability to store transient caloric excess as triglycerides

within adipocytes (Neel 1962). In environments of caloric abundance and much less

need for caloric expenditure, such as those experienced in industrialized countries,

chronic metabolic overload and thus obesity may ensue. Local and systemic

inflammatory responses to obesity are characteristic to mammalian species from

rodents and felines to primates (Miller, Bartges et al. 1998; Dandona, Aljada et al. 2004;

Pickup 2004). Why obesity elicits an inflammatory response, which can clearly be

detrimental to health, is an open question but related to: 1) chronically overweight or

obese states and their associated disorders were likely to have been rare throughout

most of the evolutionary history of animals in their reproductive years, and therefore

rarely subject to negative selective pressures and; 2) metabolic and immune pathways

have evolved to be closely linked and interdependent such that many hormones,

cytokines, adipokines, transcription factors, and bioactive lipids can function in both

metabolic and immune roles (Wellen and Hotamisligil 2005) (Table 1). A model of

overlapping metabolic and inflammatory signaling and sensing pathways in adipocytes

25

or macrophages has been put forth by Wellen and Hotamisligil (2005). This model

describes cellular homeostasis as striking a balance between metabolism and

inflammation, which becomes challenging in conditions of chronic overnutrition since

the processes required for response to nutrients and nutrient utilization, such as

mitochondrial oxidative metabolism and increasing protein synthesis in the endoplasmic

reticulum, can also induce the inflammatory response. To what degree this model is

supported by subsequent mechanistic studies is of secondary importance to the

rationale put forth here. The relevant points to the present study are: 1) the molecular

and genetic mechanisms of adipose-related inflammation also involve metabolic and

immune systems, which are among the most basic requirements across the animal

kingdom and therefore highly conserved (Wellen and Hotamisligil 2005), and likely

entail numerous genes which each have small permissive or modulatory effects on

phenotype and; 2) in any effort to investigate the genetic bases for susceptibility to

obesity, the environment (including age and sex) is a critical determinant of phenotypic

expression, and subsequently, of what genes will be identified as contributing to

phenotype (Scriver 2001).

Limitations of human studies

Controlling for the multitude of environmental risk factors has been a major

confounder of efforts to elucidate the role and relative contribution of variation in

candidate genes in human studies. Although there is little to no debate regarding the

essential role gene-gene and gene-environment interactions play in the development of

26

obesity, these aspects are rarely addressed in association studies due to the complex

experimental designs and data processing they would entail (Mutch and Clement 2006).

In addition, converging lines of evidence from epidemiological and animal model studies

indicate that interactions between genes and embryonic, fetal, and early postnatal

environments (termed ‘epigenetics’ or ‘developmental programming’) also contribute

[reviewed in (Taylor and Poston 2007)], adding another dimension of complexity and

difficulty in environmental control for human studies. Further, longitudinal studies in

humans, particularly those starting in childhood and/or spanning a significant portion of

lifetime, are logistically impossible or highly improbable. Other shortfalls often include

lack of statistical power to detect modest effect, lack of control over type-1 error rate,

population stratification and overinterpretation of marginal data (Mutch and Clement

2006).

Limitations of existing obesity and diabetic models

Research directed at further elucidating the genetic and molecular mechanisms

of obesity and disentangling the age- and environment-related factors through more

relevant animal models has been assigned as one of the highest priorities in the U.S.

(Eckel, Barouch et al. 2002; USHHS 2007). Rodents currently represent the most

common animal model of obesity and its comorbidities, and have greatly advanced

knowledge in the field. However, there are major physiological and developmental

differences between rodents and humans that are reflective of their evolutionary

divergence approximately 80 million years ago (Eizirik, Murphy et al. 2001). In contrast,

27

the separation of humans and other great apes from Old World Monkeys (OWM)

occurred about 25 million years ago (Page and Goodman 2001), making them a much

more genetically appropriate model for studying human obesity (West and York 1998;

Wagner, Kavanagh et al. 2006). One important example of this is the similarity of

reproductive physiology (e.g. menstruation, cycling, and menopause) between OWM

and humans, which is starkly different in rodents. Sex hormones and estrogen in

particular, have been shown to affect obesity and insulin resistance as well as risk of T2D

and CVD in humans and OWM (Salpeter, Walsh et al. 2006; Schneider, Tompkins et al.

2006; Wagner, Kavanagh et al. 2006). Furthermore, estrogen and/or progestin

(synthetic progesterone) levels and/or administration have been positively correlated

with leptin (Hong, Yoo et al. 2007), negatively correlated with TNF- (Hong, Yoo et al.

2007) and negatively associated with adiponectin (Im, Lee et al. 2006) in humans. The

development of obesity in OWM is similar to humans in that it can develop without

experimental manipulation and while on a standard diet (Kemnitz 1984; Comuzzie, Cole

et al. 2003; Wagner, Kavanagh et al. 2006), and obesity is a risk factor for the

development of diabetes in those species as it is in humans (Wagner, Kavanagh et al.

2006). T2D develops naturally in several species of OWM, and they exhibit similar

clinical and pathological changes in the pancreas as those observed in humans (Wagner,

Kavanagh et al. 2006). The Wagner lab has extensive experience with the

characterization of obesity, diabetes and CVD in OWM [e.g. (Wagner, Bagdade et al.

1996; Wagner, Carlson et al. 1996; Wagner, Cline et al. 2001; Wagner, Kavanagh et al.

2006)]. However, one limitation has been insufficient numbers of spontaneously obese

28

or diabetic monkeys, thus necessitating the use of diet-induced obesity or chemically-

induced diabetes to study interactions and various aspects of these diseases. Another

limitation has been lack of pedigree information, which limits analysis of genetic and age

components of disease, and does not allow prospective assessment of pre-diabetic

conditions.

The Vervet Research Colony (VRC)

The Wake Forest University Primate Center VRC was founded in 1975 at UCLA

with 57 founders (29 females, 28 males) wild-caught from St. Kitts, West Indies. The

current population includes approximately 450 descendants with 24 of the original

matrilines now in their 5th to 8th generation. Animals are currently housed in 8

indoor/outdoor corrals and, prior to 2009, fed a commercial primate chow daily,

supplemented by fresh fruits and vegetables. All animals have ad libitum access to food

and opportunities to exercise. Current colony management practices have been

designed to reflect the natural social composition of vervet monkeys in the wild. All

animals are reared by their mothers in indoor/outdoor enclosures. Infants and juveniles

remain in the natal group with their mothers and female kin. Males are removed at

adolescence and transferred to other groups, and adult males are rotated between

groups at 3-4 year intervals to mimic the natural processes of emigration and

immigration. These policies were originally intended to promote normal development

and provide opportunities for animals to express age and sex-appropriate species-

typical behavior. They have produced a population of animals that is particularly

29

valuable in two respects: 1) matrilineal grouping of females and movement of males

between groups has produced a large, complex, inter-related pedigree that is ideal for

behavioral and medical genetics research and; 2) maintaining animals in stable

matrilineal groups allows the study of naturally occurring individual differences in

physiology, behavior, reproduction, and disease in a developmental and social context

that is within the normal range for this species. Paternity has been verified for the

entire pedigree using microsatellite markers (Newman, Fairbanks et al. 2002). The

multigenerational pedigree of animals with known mothers and fathers at the VRC

allows use of variance components techniques to estimate the genetic contributions to

different traits and to identify animals at risk for specific disorders.

Working in collaboration with Lynn Fairbanks at UCLA, a high prevalence of

spontaneous obesity, gestational diabetes and T2D in the VRC population has been

observed over the years. Also, maternal BMI can predict the BMI of offspring at 4-5

years of age (Fairbanks & Wagner, unpublished data). Thus, given that Old World

nonhuman primates (NHP) are particularly important animal models for the study of

obesity and related inflammation given their close phylogenetic relationship to humans,

similar reproductive and endocrine physiology to humans, and their potential to exhibit

spontaneous obesity, insulin resistance, T2D and CVD, the practical and more proximate

rationale of this study was to further characterize the St. Kitts-origin vervet monkey

(Chlorocebus aethiops ssp.), both phenotypically and genetically, to refine it as a model

of obesity and its comorbidities. The longer term goal of this thesis project was to more

30

fully elucidate the cellular, molecular and genetic mechanisms involved in the initiation

and regulation of adipose-related inflammation. A better understanding of the role of

adipose-induced inflammation will likely be a crucial step in developing effective

preventative and therapeutic strategies to address obesity and its inflammation-related

comorbidities.

The overarching hypothesis of this project is that defining genetic associations

between genetic polymorphisms in the orthologous vervet monkey TNF gene and

previously defined obesity related phenotypes will yield valuable insight into the

pathophysiologic mechanisms and genetic contributions underlying obesity and its

inflammatory-related comorbidities in humans. Specific aims are:

1) Identify and characterize the common genetic variation (SNPs +/-

microsatellites) in the orthologous TNF vervet gene using DNA sequencing and

linkage disequilibrium structure.

A. Inherent in this aim is comparing vervet TNF gene sequences to that of

humans and other relevant NHPs using bioinformatics. Such comparisons are

used to assess percentage of sequence identity, regions of conservation,

ancestral alleles, and phylogeny.

2) Assess any genetic associations between TNF genetic polymorphisms and the

following, previously characterized obesity-related phenotypic traits by

Kavanagh et. al (2007): body mass index (BMI), waist circumference (WC), total

31

plasma cholesterol (TPC), triglycerides (TG), and high density lipoprotein

cholesterol (HDL-C).

3) Provide a basis for future studies involving longitudinal measures of obesity-

related traits and cytokine levels, as well as other potential phenotypic

measures including gene expression, metabolomic, and proteomic profiles, in

order to further dissect genotypic and age-related contributions to disease

using the vervet monkey model.

32

CHAPTER II

COMPARATIVE ANALYSES OF SINGLE NUCLEOTIDE POLYMORPHISMS IN THE TNF

PROMOTER REGION PROVIDE FURTHER VALIDATION FOR THE VERVET MONKEY

MODEL OF OBESITY

Stanton B. Gray, Timothy D. Howard, Carl D. Langefeld, Gregory A. Hawkins,

Abdoulaye F. Diallo, Janice D. Wagner

The following manuscript has been conditionally accepted for publication in the journal

Comparative Medicine. Stylistic variations are due to the requirements of the journal.

Dr. S.B. Gray prepared the manuscript and was primarily responsible for performing and

directing the experiments, as well as data analysis and interpretation. Technical

assistance was provided by Mr. A.F. Diallo. Analytical assistance was provided by Drs.

T.D. Howard and G.A. Hawkins. Statistical analysis assistance was provided by Dr. C.D.

Langefeld. Drs. T.D. Howard and J.D. Wagner acted in an advisory capacity. All co-

authors provided editorial input.

33

Abstract

Tumor necrosis factor (TNF) is a cytokine which plays critical roles in inflammation, the

innate immune response, and a variety of other physiological and pathophysiological

processes. It has also recently been shown to mediate an intersection of chronic, low-

grade inflammation and concurrent metabolic dysregulation associated with obesity and

its comorbidities. As part of an ongoing initiative to further characterize the St. Kitts-

origin vervet monkey as an animal model of obesity and inflammation, we sequenced

and genotyped the human ortholog vervet TNF gene and ~1 kb of the flanking 3’ and 5’

regions from 265 monkeys in a closed, pedigreed colony. This revealed a total of 10

single nucleotide polymorphisms (SNPs), and one 4 bp insertion/deletion, with minor

allele frequencies of 0.08-0.39. Many of these polymorphisms were in strong or

complete linkage disequilibrium with one another, and were contained within one

haplotype block, comprising five haplotypes with frequencies of 0.075-0.298. Using

sequences from humans, chimpanzees, vervets, baboons, and rhesus macaques,

phylogenetic shadowing of the TNF promoter region revealed that vervet SNPs, like the

SNPs in related species, were clustered non-randomly and non-uniformly (p<0.0001)

around conserved TF binding sites. These data, combined with previously defined

heritable phenotypes, permit future association analyses in this nonhuman primate

model which have great potential to help dissect the genetic and non-genetic

contributions to complex diseases like obesity. More broadly, the sequence data and

comparative analyses reported herein facilitates study of the evolution of regulatory

sequences of inflammatory and immune-related genes.

34

Introduction

Obesity is a worldwide epidemic, and a major risk factor for several chronic diseases

including type 2 diabetes (T2D) and cardiovascular disease (CVD) (Wyatt, Winters et al.

2006). Chronic, low-grade, systemic inflammation is associated with obesity and also

plays a major role in the pathogenesis of T2D and CVD (Trayhurn and Wood 2004).

Excessive adipose tissue, as seen in overweight and obese states, is a significant source

of circulating inflammatory cytokines which represents a potential pathogenic

mechanism linking obesity to these comorbidities. Many cytokine regulators of the

putative inflammatory cascade of obesity have been identified. Of these, TNF is among

the most well studied. TNF is a cytokine secreted by adipocytes and other tissues which

plays critical roles in lymphocyte biology, immune responses, and promoting

inflammation (Juge-Aubry, Henrichot et al. 2005); it also increases insulin resistance (IR)

by downregulating glucose transporter 4 (GLUT4) in adipocytes (Hotamisligil, Shargill et

al. 1993), serine phosphorylation of IRS-1 (Hotamisligil, Peraldi et al. 1996), as well as

several other mechanisms. Serum TNF levels are correlated with obesity and serum

levels of C-reactive protein (CRP), leptin and other inflammatory cytokines (Coppack

2001; Trayhurn and Wood 2004).

Exempting some rare monogenic forms, obesity, T2D, and CVD are classic examples of

complex, polygenic diseases. As such, expression of these disease phenotypes requires

a combination of genetic susceptibility at multiple gene loci and certain environmental

risk factors such as lifestyle habits, socio-economic status, and diet (Shively and Clarkson

1988; Roseboom, van der Meulen et al. 2001; Hill, Wyatt et al. 2003; Mutch and

35

Clement 2006; Pasquali, Vicennati et al. 2006; Taylor and Poston 2007). In addition,

there are significant gender- (Salpeter, Walsh et al. 2006; Schneider, Tompkins et al.

2006) and age-related components to progression and severity of disease expression.

Fully delineating these components and their relative contributions has been

problematic in human studies (Mutch and Clement 2006). Animal models, of course,

provide essential research avenues to control or specifically manipulate environmental

factors, thereby yielding more powerful studies through increased homogeneity of

conditions, as well as enable the study of disease progression throughout the lifetime of

the animal. Old world nonhuman primates (NHPs) are particularly important animal

models for the study of obesity and inflammation given their close phylogenetic

relationship to humans, comparable fat distribution (Bergman, Kim et al. 2007), similar

reproductive and endocrine physiology to humans, and their potential to exhibit

spontaneous obesity, IR, T2D and CVD (Wagner, Bagdade et al. 1996; Wagner, Carlson et

al. 1996; West and York 1998; Wagner, Cline et al. 2001; Wagner, Kavanagh et al. 2006;

Kavanagh, Fairbanks et al. 2007; Kavanagh, Jones et al. 2007).

Despite obesity and its comorbidities being polygenic diseases, detailed analysis of each

gene implicated is needed to better understand its role in the disease cascade, including

gene-gene and gene-environment interactions, and to ultimately expand the range of

diagnostic and therapeutic targets. As a prime example, certain risk alleles in humans at

single nucleotide polymorphisms (SNPs), primarily within the regulatory

promoter/enhancer regions, of the TNF gene have various significant associations with

obesity and related traits in humans including: CVD or T2D disease risk, IR, lipid profiles,

36

and serum and/or expression levels of adiponectin, CRP, leptin and TNF levels

(Fernandez-Real 2006). Despite the multitude of genetic association studies, those

demonstrating functional SNPs affecting TNF secretion [e.g. (Skoog, Eriksson et al.

2001)], or in vitro transcription factor (TF) binding (Kroeger, Carville et al. 1997; Wilson,

Symons et al. 1997; Knight, Udalova et al. 1999; Udalova, Richardson et al. 2000) are

fewer. A major reason for the difficulty in identifying functional SNPs in TNF, or

anywhere within the major histocompatibility complex (MHC), is the strong degree of

linkage disequilibrium (LD), or non-random association of two or more alleles, within

this region (Shiina, Ota et al. 2006), yielding extended haplotypes (Ceppellini, Siniscalco

et al. 1955).

As part of an ongoing initiative to further genetically and phenotypically characterize the

St. Kitts-origin vervet monkey (Chlorocebus aethiops sabaeus) as a model of obesity,

insulin resistance, and type 2 diabetes (Kavanagh, Fairbanks et al. 2007; Kavanagh, Jones

et al. 2007), we sequenced and genotyped the TNF gene and its promoter region in 265

monkeys from the pedigreed Vervet Research Colony (VRC), and performed

comparative analyses with human and other NHPs commonly used in biomedical

research. In addition, we tested our prediction that SNPs in the human ortholog vervet

TNF promoter region would be distributed non-randomly around conserved TF binding

sites, as shown in humans and other NHPs (Leung, McKenzie et al. 2000; Baena,

Mootnick et al. 2007).

Materials and Methods

37

Animals. All methods for the care and use of nonhuman primates conformed to U.S.

Department of Agriculture and Public Health Service standards, and all experimental

protocols were approved by the Chancellor’s Animal Research Committee at UCLA and

the Institutional Animal Care and Use Committee for the Department of Veterans Affairs

Greater Los Angeles Healthcare System. Two hundred and sixty five vervets

(Chlorocebus aethiops sabaeus) from the VRC were used in this study. The VRC was

founded in 1975 at UCLA from 57 vervets (28 males, 29 females) wild caught from 1975-

1983 in St. Kitts, West Indies, as described in Fairbanks et al. (2004). The current

population includes approximately 450 descendants with 24 of the original matrilines in

their 5th to 8th generation, and now resides at the Wake Forest University Primate

Center (WFUPC). All vervets were fed a commercial primate laboratory chow which is

relatively low fat and high fiber (Laboratory Diet 5038; Purina, St. Louis, MO) ad libitum

supplemented with fresh fruits and vegetables.

PCR and Sequencing. Genomic DNA was isolated from peripheral blood samples using

standard protocols previously described (Jasinska, Service et al. 2007). For sequencing,

PCR primers were generated with the PrimerSelect software (Laser Gene software,

DNASTAR, Madison, WI.) using a consensus TNF gene sequence created from human,

chimpanzee (Pan troglodytes) and rhesus monkey (Macaca mulatta) found in the NCBI

gene database (www.ncbi.nlm.nih.gov; uc003nui.1, NM_001045511, NM_001047149

respectively). Primer consensus sequences were created using the editing feature in

Sequencher v4.6 (Gene Codes Corp., Ann Arbor, MI), and were designed to amplify the

entire TNF gene plus ~1 kb 3’ and ~1 kb 5’ flanking regions. PCR conditions were as

http://www.ncbi.nlm.nih.gov/

38

follows: initial denaturation at 95oC for 10 min, followed by 30 cycles of 96oC for 1 min,

58-60oC for 1 min and 72oC for 60-90 sec with final elongation for 10 min at 72oC in a 20-

µl reaction mixture. PCR products were purified (QuickStep, Edge Biosystems Inc.,

Gaithersburg, MD) to remove dNTPs and excess primers that interfere with sequencing

reactions. Purified PCR products were sequenced using dye-terminator chemistry

(BigDye, ABI, Foster City, CA). Dye-terminator reactions were performed in 5-10 µl

reactions in 96-well plates. The dye-terminator kit was diluted fourfold using a 2.5X

dilution buffer (50 mM Tris, 10 mM MgCl2 pH 9.0). Sequencing reactions were purified

by ethanol precipitation prior to loading on an ABI3730XL DNA Analyzer using the POP7

sequencing polymer.

Sequence Analyses. Sequences from the 265 VRC vervets were aligned and genotyped

using Sequencher v4.6 (Gene Codes Corp., Ann Arbor, MI). LD structure and

allele/haplotype frequencies were determined with Haploview, version 4.0 (Barrett, Fry

et al. 2005). Sequence alignment scores were generated using ClustalW, version 1.7

(Thompson, Higgins et al. 1994). This vervet sequence (EU626004), and all other

sequences, except baboon, reported herein are in the NCBI gene database (accession

numbers noted above); baboon TNF promoter sequence has been published elsewhere

(Haudek, Natmessnig et al. 1998; Leung, McKenzie et al. 2000; Baena, Mootnick et al.

2007). Transcription factor binding sites for NFAT, ETS, 3, CRE, and SP1 (Goldfeld,

Doyle et al. 1990; Tsai, Jain et al. 1996; Tsai, Yie et al. 1996; Falvo, Uglialoro et al. 2000;

Tsai, Falvo et al. 2000; Tsytsykova and Goldfeld 2002; Barthel, Tsytsykova et al. 2003),

and 1 and 2 (Goldfeld, Doyle et al. 1990) were crosschecked with ORegAnno

39

(Montgomery, Griffith et al. 2006) as implemented in the UCSC genome browser

(www.genome.ucsc.edu/). Conserved regions were identified and illustrated by

phylogenetic shadowing (Boffelli et al. 2003), using the web-based program eShadow

[http://eshadow.dcode.org/; (Ovcharenko, Boffelli et al. 2004)]. Only validated (i.e.

demonstrated experimentally) human SNPs (dbSNP build 128) are reported in the

promoter sequence comparison; of those, only those with 5% minor allele frequency

(MAF) or greater were used to compare to the eShadow output. SNP locations were

compiled from dbSNP (Build 128, www.ncbi.nlm.nih.gov) for humans, and reports in the

literature for chimpanzee (Baena, Mootnick et al. 2007), and rhesus macaque (Singh and

Schmidtke 2005); TNF SNPs for the baboon were not available at the time of this writing.

Statistical Analyses. Komolgorov-Smirnov tests whether the empirical distribution of a

sample statistically deviates from a specified theoretic distribution. Here the

Komolgorov-Smirnov test was computed using Analyse-It® for Microsoft Excel version

2.12 (Analyse-it Software, Ltd. http://www.analyse-it.com/; 2008) to test whether the

distribution of the p-values deviated from a uniform distribution; under the null

hypothesis the p-values of our test follow a uniform distribution. Thus, the distribution

of SNPs p-values along a 50-base pair sliding window for each species (human, chimp,

vervet, and rhesus) plus the consensus distribution of all four was tested for non-

uniformity.

http://www.genome.ucsc.edu/


http://www.analyse-it.com/

40

Results

Analysis of ~1 kb 5’ upstream, ~0.9 kb 3’ downstream, all four exons, and all intronic

regions of the human ortholog vervet TNF gene in 265 vervet monkeys revealed a total

of 12 polymorphisms: 11 SNPs and one 4 bp insertion/deletion, with MAF ranging from

0.08 to 0.39 (Table 1). None of the loci corresponding to the SNPs found in vervets have

been reported to be polymorphic in human populations (dbSNP build 128). The +611

(TGAA)4/5 insertion/deletion, located in intron 1, is a (TGAA)6 in humans and is

apparently fixed; chimpanzees also contain (TGAA)6. The rhesus monkey sequence is

reported as (TGAA)5, but it is unknown if this sequence is polymorphic in rhesus or any

other NHP species. The entire human ortholog vervet TNF gene was contained within a

single haplotype block (Figure 1), as determined by the confidence interval algorithm

(Gabriel, Schaffner et al. 2002) implemented within Haploview 4.0 (Barrett, Fry et al.

2005). The C/T+2681 SNP located at the farthest 3’ UTR was the only SNP not included

with this block (Figure 1). Many of the polymorphisms were found to be in strong or

complete LD with one another (Figure 1), as is common within and between genes and

intergenic regions of the human MHC (Ceppellini, Siniscalco et al. 1955; Shiina, Ota et al.

2006). Five haplotypes were contained in this block, with frequencies ranging from

0.075 to 0.298 (Table 2). No similarities were detected upon comparing TNF LD

structures of vervet and human (www.hapmap.org).

Table 3 compares sequence identities for the 1176 bp TNF promoter region between

human and chimpanzee, St. Kitts-origin vervet, Hamadrayas baboon, and rhesus

macaque, which are 99.7%, 94.4%, 94.6%, and 94.5%, respectively. Identities for the

41

Table 1. Polymorphisms in the human ortholog St. Kitts-origin vervet TNF gene and

flanking sequence

*Polymorphism names denote nucleotide variants flanking gene position relative to

human transcription start site. Repeat polymorphisms denoted by repeated sequence

in parentheses and repeat number variants subscripted and separated by a slash (e.g.

(tgaa)4/5).

Polymorphism* MAF Minor allele

A-809G 0.309 G

A-756G 0.311 A

G-602T 0.210 G

C-352T 0.393 C

A-322G 0.312 A

C+426G 0.209 C

+611(tgaa) 4 / 5

0.078 (tgaa) 4

G+1285T 0.387 T

C+2133T 0.309 T

A+2362G 0.316 A

A+2405G 0.318 A

C+2681T 0.077 C

42

Figure 1.

Figure 1. LD structure of the St. Kitts-origin vervet TNF locus. The single haplotype block

(outlined triangle) was determined with the confidence interval algorithm (Gabriel et al.

2002) of HaploView. Numbers indicate pairwise r2 values X100. Darker blocks denote

higher correlation and black squares with no numbers represent complete LD (r2 = 1).

Relative positions of polymorphisms are indicated on diagram of the human ortholog St.

Kitts-origin vervet TNF gene and flanking sequence. Colored regions denote

promoter/enhancer region (green), untranslated regions (blue), exons (red blocks), and

introns (red lines).

5’

3’

43

Table 2. Haplotypes and frequencies of the human ortholog St. Kitts-origin vervet TNF

gene and flanking sequence

Haplotype Freq

G A T C A G (tgaa)5 T T A A 0.298

A G G T G G (tgaa)5 G C G G 0.208

A G T T G C (tgaa)5 G C G G 0.205

A G T T G G (tgaa)5 G C G G 0.183

A G T C G G (tgaa)4 T C G G 0.075

44

1000Macaca mulatta

98.51.891000Chlorocebus aethiops

94.07.5594.207.131000Pan troglodytes

94.07.5594.207.131000.001000Homo sapien

% IDtr

D.M.% IDtr

D.M.% IDtr

D.M.% IDtr

D.M.

Macaca mulattaChlorocebus aethiopsPan troglodytesHomo sapienA

1000Macaca mulatta

98.51.891000Chlorocebus aethiops

94.07.5594.207.131000Pan troglodytes

94.07.5594.207.131000.001000Homo sapien

% IDtr

D.M.% IDtr

D.M.% IDtr

D.M.% IDtr

D.M.

Macaca mulattaChlorocebus aethiopsPan troglodytesHomo sapienA

100

97.7

92.3

%IDpr

100

98.4

93.1

% IDtot

0

2.12

7.97

D.M.

Macaca mulatta

100

92.9

%IDpr

100

%IDpr

Macaca mulatta

1000Chlorocebus aethiops

93.37.711000Homo sapien

%IDtot

D.M.%IDtot

D.M.

Chlorocebus aethiopsHomo sapienB

100

97.7

92.3

%IDpr

100

98.4

93.1

% IDtot

0

2.12

7.97

D.M.

Macaca mulatta

100

92.9

%IDpr

100

%IDpr

Macaca mulatta

1000Chlorocebus aethiops

93.37.711000Homo sapien

%IDtot

D.M.%IDtot

D.M.

Chlorocebus aethiopsHomo sapienB

Table 3. Distance matrices (D.M.) and percentage of DNA sequence identity (% ID)

between humans, chimps, vervets, and rhesus (A), or between humans, vervets and

rhesus (B). Subscripts for % ID refer to cDNA transcript only (tr), total TNFA gene (tot),

or only the enhancer/promoter region (pr).

45

entire ~4 kb TNF gene including 5’ and 3’ flanking regions are >99%, 93.3%, and 93.1%,

respectively. The entire TNF sequence was not available for baboon at the time of this

writing.

Figure 2 illustrates a sequence comparison of the TNF promoter region in the five

primate species. The transcription binding sites NFAT, ETS, 3, CRE, and SP1,

confirmed to be essential for TNF gene regulation (Goldfeld, McCaffrey et al. 1993; Tsai,

Jain et al. 1996; Tsai, Yie et al. 1996; Falvo, Uglialoro et al. 2000; Tsai, Falvo et al. 2000;

Tsytsykova and Goldfeld 2002; Barthel, Tsytsykova et al. 2003)are noted, as well as 1

and 2, which bind NF- B proteins, but are not essential to TNF gene regulation

(Goldfeld, Doyle et al. 1990). There is complete conservation of those sites essential for

gene regulation among the species examined here. The vervet, rhesus, and baboon

contain an A instead of a G in 2 (position -206), but 1 is completely conserved in all

species examined. SNP locations for human, chimpanzee, rhesus, and the St. Kitts-origin

vervet relative to the human transcription start site are noted (Figure 2). All fall outside

major blocks of conserved regions known to be critical for TNF gene regulation (Figure

3A), In addition, there is non-random clustering of SNPs among the four species

examined at sites shown to contain alleles in humans significantly associated with risk

for a variety of inflammatory- or innate immunity-related diseases (Figure 3B) (i.e. -308,

-857, -863) (Fernandez-Real 2006).

The statistical significance of these SNP clusters was assessed with the Kolomogorov-

Smirnov test. The null hypothesis, that the distribution of SNPs in the 1176 nucleotide

46

Table 4. Results of Kolmogorov-Smirnov tests for non-uniform distribution.

sequence source D p

H. sapien 0.31 < 0.0001

P. troglodytes 0.47 < 0.0001

C. aethiops sabaeus 0.46 < 0.0001

M. mulatta 0.41 < 0.0001

Consensus 0.19 < 0.0001

47

Figure 2. Sequence comparison of TNF 5’ promoter region between humans (H sapien),

chimpanzee (P troglodytes), St. Kitts-origin vervet (C a sabaeus), rhesus monkey (M

mulatta), and baboon (P hamadrayas) with SNPs underlined and bolded with adjacent

nucleotide position for the species in which they have been reported. Nucleotide

positions are relative to the human TSS. The single letter code is used to identify SNPs :

M=A/C, R=A/G, W=A/T, S=C/G, Y=C/T, K=G/T. TF binding sites are underlined with

names above the sequence. NFAT (nuclear factor of activated T cells), Ets, 3, CRE

(cyclic AMP response element), and SP-1 are critical for TNF gene regulation (Goldfeld et

al. 1993; Tsai et al. 1996a; Tsai et al. 1996b; Falvo et al. 2000; Tsai et al. 2000;

Tsytsykova and Goldfeld 2002; Barthel et al. 2003); 1 and 2 are reported to bind NF-

B proteins and are not essential for gene regulation (Goldfeld et al. 1990; Leung et al.

2000).

48

Figure 2.

-1153 -1072 -1055

H sapien GGGAGCAAGAGCTGTGGGGAGAACAAAAGGATAA-GGGCTCAGAGAGCTTCAGGGATATGTGATGGACTCACCAGGTGAGGCYGCCAGACTGCTGCAGGG

P troglodytes ..................................-...............................................C.................

C a sabaeus ..................................A.....................C.........................C.................

M mulatta ..................................G.....................C.......A.......T.........C.................

P hamadrayas ..................................G........G............C.........................C.................

-1054 -1030 -955

H sapien GAAGCAAAGGAGAAGCTGAGAAGAYGAAGGAAAAGTCAGGGTCTGGAGGGGCGGGGGTCAGGGAGCTCCTGGGAGATATGGCCACATGTAGCGGCTCTGA

P troglodytes ........................AΔΔΔ........................................................................

C a sabaeus ........................CA...................A........................................A....T........

M mulatta ........................CA...................A........................................A....T........

P hamadrayas ...................T....CA...................A........................................A....T........

-954 -862 -856

H sapien GGAATGGGTTACAGGAGACCTCTGGGGAGATGTGACCACAGCAATGGGTAGGAGAATGTCCAGGGCTATGGAAGTCGAGTATGGGGACCCCCMCTTAAYG

P troglodytes ............................................................................................C.....C.

C a sabaeus ............................................A...........A..............GG..T................C.---GCC

M mulatta ............................................A..........................GG..T................C..--GCC

P hamadrayas ............................................AA.........................GG..T................CT---GCC

-854 -805 -781 -758

H sapien AAGACAGGGCCATGTAGAGGGCCCCAGGGAGTGAAAGAGCCTCCAGGACYTCCAGGTATGGAATACAGGGGACRTTTAAGAAGATATGGCCACACAMTGG

P troglodytes .................................................C....R..................G......................C...

C a sabaeus ........................T....................R...C..T....................G......................T.R.

M mulatta ........................T......Y.................C..T................K...G......................T...

P hamadrayas ........................T........................C..T....................G......................T...

-754 -655

H sapien GGCCCTGAGAAGTGAGAGCTTCATGAAAAAAATCAGGGACCCCAGAGTTCCTTGGAAGCCAAGACTGAAACCAGCATTATGAGTCTCCGGGTCAGAATGA

P troglodytes .......................................................................................T............

C a sabaeus ..................T...........................................................G......C..........G...

M mulatta ..................T...........................................................G......C..R.......G...

P hamadrayas ..................T...........................................................G......C..........G...

-654 -645 1 -571-569 -556

H sapien AAGAAGAAGRCCTGCCCCAGTGGGGTCTGTGAATTCCCGGGGGTGATTTCACTCCCC-GGGGCTGTCCCAGGCTTGTCCCTGCTMCMCCCACCCAGCCTT

P troglodytes .........G...............................................-..........................A.C.............

C a sabaeus .........G..............................T...........K....-..........................A.C.............

M mulatta .........G..........G...................T................G..........................A.C....T.M......

P hamadrayas .........G..............................T................-..........................A.C.............

-555 -510 -458

H sapien TCCTGAGGCCTCAAGCCTGCCACCAAGCCCCCAGCTCCTTCTCCCSGCAGGGACCCAAACACAGGCCTCAGGACTCAACACAGCTTTTC-CC-TCCAACC

P troglodytes .............................................C..................................Y........-..-.......

C a sabaeus C.........CTG........C.......................CCA...A.....................................T..C.......

M mulatta C.........CTG........C.......................CCA...A.....R.YG............................T..C.......

P hamadrayas C.........CTG........C.......................CCA...A........G..A.........................T..C.......

-457 -375 -358

H sapien CCGTTTTCTCTCCCTCAAGGACTCAGCTTTCTGAAGCCCCTCCCAGTTCTAGTTCTATCTTTTTCCTGCATCCTGTCTGGAARTTAGAAGGAAACAGACC

P troglodytes ..................................................................................G.................

C a sabaeus ..A........A...................................................................A..A.C...G...........

M mulatta ..A........AY.....................................................................A.C...G...........

P hamadrayas ..A........A......................................................................A.C...G...........

-357 -307 -258

H sapien ACAGACCTGGTCCCCAAAAGAAATGGAGGCAATAGGTTTTGAGGGGCATGRGGACGGGGTTCAGCCTCCAGGGTCCTACACACAAATCAGTCAGTGGCCC

P troglodytes ..........................R.......................G.................................................

C a sabaeus .....Y..........G..................RC...........G.A.C.....A....A.......A....C.......................

M mulatta ................G......Y............C...........G.A.......A....A.......A....C...............R.W.....

P hamadrayas ................G...................C...........G.A.......A....A.......A....C.......................

-257 -243 -237 2 -158

H sapien AGAAGACCCCCCTCRGAATCRGAGCAGGGAGGATGGGGAGTGTGAGGGGTATCCTTGATGCTTGTGTGTCCCCAACTTTCCAAATCCCCGCCCCCGCGAT

P troglodytes ..............G.....G...............................................................................

C a sabaeus ..............G.....G.........T.G.......A......A................G...................................

M mulatta ..............G.....G.........T.G.......A......A................G...................................

P hamadrayas ..............G.....G.........T.G.......A......A................G...................................

-157 NFAT/Ets CRE NFAT5/ 3 ETS NFAT/5 -58

H sapien GGAGAAGAAACCGAGACAGAAGGTGCAGGGCCCACTACCGCTTCCTCCAGATGAGCTCATGGGTTTCTCCACCAAGGAAGTTTTCCGCTGGTTGAATGAT

P troglodytes ....................................................................................................

C a sabaeus ...........A...G....................................................................................

M mulatta ...........A........................................................................................

P hamadrayas ...........A........................................................................................

-57 SP1 +1 +11 +24

H sapien TCTTTCCCCGCCCTCCTCTCGCCCCAGGGACATATAAAGGCAGTTGTTGGCACACCCAGCCAGCAGAYGCTCCCTCAGCAA

P troglodytes ................................................T..................C.............

C a sabaeus ..............---....................C..........T..................C.............

M mulatta ..A...........---....................C..........T..................C.............

P hamadrayas ..A...........---....................C..........T..................C.............

49

Figure 3. (A) Phylogenetic shadowing of the TNF promoter region in five primate

species. Multiple sequence alignment of TNF, including the promoter region, for

humans, chimpanzees, St. Kitts-origin vervet, rhesus monkey, and baboon was analyzed

with eShadow (Ovcharenko et al. 2004). Top x-axis is nucleotide position relative to

human TSS. The relative positions of polymorphisms in 4 of the 5 species studied here

are indicated along the bottom of the x-axis (H=human, C=chimpanzee, G=SK vervet,

R=rhesus monkey); no SNP information for the baboon was available. Percentage of

nucleotide variation (inverse of conservation) along a 50-nucleotide (nt) sliding window

is on the y-axis, and darker shading of peaks represents regions of higher conservation.

(B) Clustering of SNPs in NHPs. Plot of the number of SNPs in each of 4 primate species

(human, chimpanzee, vervet monkey, rhesus macaque) which cluster along a sliding 50-

nt window of the TNF promoter/enhancer region (Y-axis). SNPs in each of the 4 species

is designated by color (human=blue; chimp=red; vervet=green; rhesus=purple). X-axis is

nucleotide position relative to human TSS.

50

Figure 3.

A

B

51

sequence 5’ to the TSS does not differ from uniformity, was rejected (p < 0.0001) in each

species as well as the consensus sequence (Table 4).

Discussion

We report a total of five SNPs within the 5’ UTR, promoter/enhancer, and flanking

regions, with another six SNPs plus one insertion/deletion polymorphism in the intronic

and 3’ UTR and flanking regions of the human ortholog St. Kitts-origin vervet TNF gene

(Table 1, Figure 1). All polymorphisms except one (C/T+2681) form a single haplotype

block (Figure 1), which contains five haplotypes (Table 2) within the VRC population. To

our knowledge, this represents the most extensive report of SNPs, haplotypes, and LD

for any NHP of the TNF gene and flanking regions, as well as the first report of complete

TNF gene sequence data for the vervet monkey, or any other Chlorocebus species.

Old World nonhuman primates (NHP) are extremely valuable animal models for the

study of complex, polygenic diseases like obesity and its comorbidities (Comuzzie, Cole

et al. 2003; VandeBerg and Williams-Blangero 2003; Wagner, Kavanagh et al. 2006).

Particularly when pedigree and genomic structure is known, they offer great power to

dissect genetic from non-genetic components of disease through increased

homogeneity of conditions [e.g. (Freimer, Service et al. 2007; Vinson, Mahaney et al.

2008)], as well as enable study of disease progression throughout the lifetime of the

animal, thereby also controlling for age and some epigenetic effects [e.g. (de Vries,

Holmes et al. 2007)]. In addition, comparative sequence analyses utilizing NHPs are an

essential component of research directed at identifying functional regions within the

52

human genome (Villinger, Brar et al. 1995; Haudek, Natmessnig et al. 1998; Leung,

McKenzie et al. 2000; Boffelli, McAuliffe et al. 2003; Cooper and Sidow 2003) as well as

providing insight into the evolutionary history of transcriptional regulation (Goldfeld,

Leung et al. 2000; Shiina, Ota et al. 2006; Baena, Mootnick et al. 2007).

We have demonstrated that vervet TNF promoter SNPs are non-randomly and non-

uniformly distributed outside TF binding sites and other conserved regions. This is

consistent with differential rates of accumulation of mutations along the TNF promoter

region, also shown in previous published reports (Leung, McKenzie et al. 2000; Baena,

Leung et al. 2002; Higasa and Hayashi 2006; Baena, Mootnick et al. 2007). Those

regions not affecting TNF transcription will presumably be under neutral selection, while

there will be high selective pressure to preserve those regions that are essential to TF

binding. An unresolved question is whether SNPs around TF binding sites can modulate

transcription (or are in LD with other MHC alleles causing such effects), and if certain

alleles could impart greater survivability for an individual according to the specific

immunologic and/or metabolic challenges of that individual’s environment. The

concept that SNPs have accumulated independently in NHPs, and confer analogous

disease phenotypes and risk, has been proposed by others working with NHPs as models

of neuropsychiatric disorders (Lesch, Meyer et al. 1997; Bennett, Lesch et al. 2002;

Miller, Bendor et al. 2004; Bailey, Breidenthal et al. 2007). Here we provide the first

suggestion to our knowledge that SNPs in the promoter/enhancer region of TNF could

have accumulated in NHPs and humans experiencing similar selective pressures over

evolutionary time for modulating immune and/or inflammatory responses. The

53

demonstration that SNPs in humans, chimpanzees, vervets, and rhesus monkeys all

cluster together in two regions on the TNF promoter/enhancer region is particularly

interesting (Figure 3B). What makes this finding even more curious is that these regions

are within 50 nucleotides of three important human SNPs ( -308 and -857/-863) which

have been significantly associated with numerous obesity- and diabetes-related

phenotypes (Fernandez-Real 2006). It is possible that SNPs in these particular regions

modulate transcription in important but subtle ways and have thus accumulated in

these regions through convergent evolution in these 4 primate species (human,

chimpanzee, vervet, rhesus).

Research directed at further elucidating the genetic and molecular mechanisms of

obesity and disentangling the age- and environment-related factors through more


(Eckel, Barouch et al. 2002). The information presented here represents the second

phase of our effort to further genetically characterize the St. Kitts-origin vervet as a

model of obesity and type 2 diabetes, both of which involve chronic, low-grade

inflammation and concurrent metabolic dysregulation mediated by TNF (Esteve, Ricart

et al. 2005; Hotamisligil 2006; Cawthorn and Sethi 2008). This adds to prior

characterization of obesity-related phenotypic measures, as well as demonstration of

these traits being heritable (Kavanagh, Fairbanks et al. 2007; Kavanagh, Jones et al.

2007). The vervet monkey has long been one of the most important NHP models of

biomedical research, with a PubMed citation record over the past 10 years close to that

of rhesus macaque and greater than any other NHP (Freimer, Dewar et al. 2007).

54

However, gene sequence and genome information for the vervet has only recently

begun to emerge (Jasinska, Service et al. 2007). This report involves one of the largest

numbers of NHPs sequenced for TNF or any other individual candidate gene, which has

provided power to uncover both common and relatively rare SNPs/haplotypes, as well

as show that the distribution of these polymorphisms is consistent with the regions of

conservation or neutral selection shown in humans and other NHPs. Baena et al (2007)

demonstrated differential Sp1 TF binding and TNF transcription between certain species

of Asian apes and all other Old World apes and monkeys, based on nucleotide

differences within that TF binding site. However, no such intraspecies comparisons of

TNF TF binding in NHPs have been attempted to our knowledge. If significant genetic

associations exist between obesity-related phenotypes in the vervet and the SNPs and

/or haplotpyes presented here, then an important next step would be to evaluate those

variable nucleotide sequences for differential TF binding.

The broader value of these analyses addresses the intersection of metabolism and

innate immunity/inflammation, which are among the most fundamental requirements

for an organism’s survival. As such, these two processes show a high degree of

evolutionary conservation, are highly integrated, and are essentially one central

homeostatic mechanism (Hotamisligil 2006) , a greater understanding of which is

paramount given the current worldwide epidemic of obesity and its comorbidities

(Wyatt, Winters et al. 2006).

In summary, our report on sequence for the human ortholog St. Kitts-origin vervet TNF

gene, as well as polymorphisms and haplotype/LD structure, provides both proximate

55

and long-term value into research directed at further elucidating the pathogenic

mechanisms of obesity-related inflammation and metabolic dysregulation. Association

studies are currently underway using these SNP and haplotype data, as well as

previously defined obesity-related traits. Once any such associations have been

identified, this will allow targeted efforts towards understanding the functional

relevance of associated polymorphisms through TF binding assays, for example. This

approach, in a pedigreed NHP model, offers great potential to dissect genetic from

environmental components of polygenic, complex diseases such as obesity and its

comorbidities. In addition, the sequence data facilitates future comparative analyses,

and research into the evolution of regulatory gene sequences of complex disease.

Acknowledgements This work was funded, in part, by the Wake Forest University

School of Medicine Venture Fund, the Skorich Diabetes Research Fund, the Monty

Blackmon Diabetes Research Fund (JDW), T32 RR07009 from NIH/NCRR (SBG), and an

Animal and Biological Materials Resources grant from the National Center for Research

Resources (P40 RR 019963 to Dr. Lynn Fairbanks at UCLA). Support of this project from

the Center for Public Health Genomics (CDL) at WFUSM is greatly appreciated. The

authors would also like to thank Drs. Lynn Fairbanks (UCLA), Nelson Friemer (UCLA),

Matthew Jorgensen (WFUSM), Kylie Kavanagh (WFUSM), and Larry Rudel (WFUSM) for

their input and collaboration. This work constitutes partial fulfillment of Ph.D.

requirements for Dr. Gray.

56

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61

CHAPTER III

PHYLOGENETIC AND TAXONOMIC IMPLICATIONS OF TNF PROMOTER SEQUENCE COMPARISONS BETWEEN ST. KITTS- AND AFRICAN-ORIGIN CHLOROCEBUS SUBSPECIES Stanton B Gray, Richard E Broughton, Carl D Langefeld, , James N Thompson jr, Gregory

A Hawkins, Abdoulaye F Diallo, Janice D Wagner, Timothy D Howard

The following manuscript will be submitted for publication to the American Journal of

Primatology. Stylistic variations are due to the requirements of the journal. Dr. S.B.

Gray prepared the manuscript and was primarily responsible for performing and

directing the experiments, as well as data analysis and interpretation. Technical

assistance was provided by Mr. A.F. Diallo and Dr. R.E. Broughton. Analytical assistance

was provided by Dr. R.E. Broughton. Drs. T.D. Howard and J.D. Wagner acted in an

advisory capacity. All co-authors provided editorial input.

62

Introduction

The vervet monkey (also called the green monkey) is an Old World Monkey (OWM)

model for a variety of complex human diseases and behaviors such as diabetes,

cardiovascular disease, AIDS, substance abuse, attention deficit disorder, alcoholism,

reproduction, and tissue regeneration. European ships brought small numbers of

African vervets to the Caribbean islands of Barbados, St. Kitts, and Nevis as early as the

16th century and continued through the 18th century (McGuire 1974; Denham 1987).

Since then, vervet population numbers on these islands have increased quickly and

sustainably, with vervets now considered a pest species in some areas. Subsequently,

vervets from the Caribbean islands, and St. Kitts (SK) in particular, have become the

main source of vervet monkeys used in biomedical research.

Characteristics of the SK vervet which contribute to its being a biomedical model of

increasing importance are: 1) abundance; 2) free from the zoonotic Cercopithecene

Herpes Virus-1 (Herpes B; 3) relatively small and temperamentally tractable compared

to macaques; 4) no evidence of other confounding pathogens such as filovirus, SIV,

STLV, yellow fever, malaria, or schistomiasis; 5) evolutionarily as close, or closer, to

humans as the baboon and macaque species and; 6) relatively easily bred in captivity

(National Research Council (U.S.) and Institute for Laboratory Animal Research (U.S.)

2003). Further, the first draft of a linkage map for the vervet has recently been

completed (Jasinska, Service et al. 2007), which will enable genetic mapping of complex

63

traits, and it is as an approved target OWM species for genome sequencing

(www.genome.gov).

The current taxonomic classification of the SK vervet is Chlorocebus aethiops sabaeus,

which is based on sequence comparisons of a 389 nucleotide fragment of the

mitochondrial 12S rRNA gene between SK vervet (isolated from the feces of a single

monkey) and four other African Chlorocebus subspecies: C. a. aethiops, C. a.

pygerythrus, C. a. sabaeus and, C. a. tantalus (van der Kuyl and Dekker 1996) (Figure 1).

However, this taxonomic classification has long been regarded as tentative (Denham

1987; Wang 2006) stemming from: 1) historical evidence indicating that the most likely

sources of SK vervets along the west coast of Africa contained other subspecies in

addition to sabaeus (Handler, Lange et al. 1978; Denham 1987; Wang 2006) (Figure 1)

and, 2) the documentation of all four of the Chlorocebus subspecies producing viable

hybrid offspring along the edges of their geographic ranges (Groves 2001). More

recently, in his doctoral dissertation, Wang (2006) repeated the comparison made by

van der Kuyl and Dekker (1996), but used multiple vervets of SK-origin instead of a

single monkey and found that a purportedly fixed nucleotide difference is actually a

single nucleotide polymorphism (SNP). While not conclusive, this finding diminishes the

strength of van der Kuyl and Dekker’s data that originally led them to suggest that SK

vervets are solely descendants from Chlorocebus aethiops sabaeus.

64

C. a. sabaeus C. a. tantalus

C. a.aethiops

C. a. pygerythrus

Figure 1. Map of Africa showing approximate ranges for Chlorocebus subspecies. Adapted from van der Kuyl and Dekker (1996).

65

As part of our ongoing initiative to further characterize the vervet monkey as a model of

obesity and its comorbidities (Kavanagh et al. 2007a; Kavanagh et al. 2007b), we

sequenced and genotyped the tumor necrosis factor (TNF) promoter region in 265

monkeys of SK-origin from the pedigreed Vervet Research Colony (VRC) at the Wake

Forest University Primate Center (WFUPC) in Winston-Salem, NC. Previous comparative

analyses have shown that fixed nucleotide differences and polymorphisms in the TNF

promoter region have great value as markers of primate phylogeny and speciation

(Goldfeld, Leung et al. 2000; Baena, Leung et al. 2002; Baena, Mootnick et al. 2007). We

have used this region to perform phylogenetic analyses on the SK-origin vervet, the

African-origin vervet, and three other Chlorocebus subspecies of African origin. Here we

report the implications these analyses have for the uncertain taxonomic and

phylogenetic classification of the SK vervet.

Methods

Animals. Two hundred and sixty five vervets from the VRC were used in this study. The

VRC was founded in 1975 at UCLA from 57 vervets (28 males, 29 females) wild caught

from 1975-1983 in St. Kitts, West Indies, as described in Fairbanks et al (2004). There

have been no introductions or outbreeding since that time, and matings have been

carefully controlled to avoid inbreeding. The current population includes approximately

450 descendants with 24 of the original matrilines now in their 5th-8th generation, and

now resides at the Wake Forest University Primate Center (WFUPC).

66

Animal tissues. DNA was isolated from stored frozen aorta from four different

individual Chlorocebus aethiops pygerythrus monkeys and four different individual C. a.

aethiops monkeys. All of these monkeys were of African origin. DNA from SK vervets

was isolated from peripheral blood.

PCR and Sequencing. Genomic DNA was isolated from peripheral blood samples or

frozen aorta using standard protocols previously described (Jasinska, Service et al.

2007). For sequencing, PCR primers were generated with the PrimerSelect software

(Laser Gene software, DNASTAR, Madison, WI.) using a consensus TNF gene sequence

created from human, chimpanzee (Pan troglodytes) and rhesus monkey (Macaca

mulatta) found in the NCBI gene database (www.ncbi.nlm.nih.gov; uc003nui.1,

NM_001045511, NM_001047149 respectively). Sequences were obtained in both the

forward and reverese directions from -1152 to +24 with the ABI3730XL DNA Analyzer

using the POP7 sequencing polymer. The sequences of the primers used are described

in Supplementary Table 1.

Sequence Analyses. Sequences from the 265 VRC vervets were aligned and genotyped

using Sequencher v4.7 (Gene Codes Corp., Ann Arbor, MI). Sequence alignment scores

were generated using ClustalW, version 1.7 (Thompson, Higgins et al. 1994). This vervet

sequence (EU626004), human, chimpanzee, and rhesus macaque reported herein are in

the NCBI gene database (accession numbers noted above); baboon (Haudek,

Natmessnig et al. 1998; Leung, McKenzie et al. 2000; Baena, Mootnick et al. 2007) and

African Chlorocebus subspecies (Baena, Mootnick et al. 2007) TNF promoter sequences

have been reported elsewhere. Phylogenetic analyses employed maximum parsimony


67

using PAUP (version 4.10b) (Swofford 2002). One hundred heuristic searches were

performed with tree bisection-reconnection (TBR) branch swapping and all characters

weighted equally. The human sequence was designated as the outgroup. SNPs were

converted to the allele with dominant population frequency for purposes of

phylogenetic analysis. Phylogenetic trees were formatted for publication using MrEnt

(version 2.0) (Zuccon and Zuccon 2008).

Results

Multiple sequence alignment illustrates that the SK-origin vervet shares two SNPs with

C. a. tantalus, and one with the remaining three subspecies (Figure 2). The number of

presumed fixed nucleotide differences is shown in Table 1. Of particular relevance is the

observation that there are nine presumed fixed differences between the SK vervet and

the African subspecies of the same taxonomic classification (C. a. sabaeus), compared to

seven between the SK vervet and C. a. tantalus, and only one between the SK vervet

and both C. a. aethiops and C. a. pygerythrus.

Phylogenetic relationships between the SK-origin vervet and the three African-origin

Chlorocebus spp are illustrated in Figure 3. African C. a. sabaeus and C. a. tantalus form

one clade, while C. a. pygerythrus and the SK-origin vervet form another clade. C. a

aethiops forms the third clade within the Chlorocebus subspecies examined. There are

68

Table 1. Presumed fixed nucleotide differences between Chlorocebus subspecies.

AFR C a sabaeus

C a aethiops

C a pygerythrus

C a tantalus

SK C a sabaeus 9 1 1 7 AFR C a sabaeus 8 9 11 C a aethiops 2 6 C a pygerythrus 8

69

Figure 2. Multiple sequence comparison of the TNF promoter region. Nucleotides are

numbered from -1152 to +24 relative to human transcription start site. Periods (.)

denote identical nucleotide to the human reference sequence. SNP codes are: R = A/G;

Y = C/T; M = A/C; W = A/T; S = C/G; K = G/T. SK denotes St. Kitts-origin. AFR denotes

African-origin.

70

-1153 Homo sapien GGGAGCAAGAGCTGTGGGGAGAACAAAAGGATAA-GGGCTCAGAGAGCTTCAGGGATATGTGATGGACTCACCAGGTGAGGCCGCCAGACTGCTGCAGGG

C sabaeus SK ..................................A.....................C...........................................

C sabaeus AFR ..............................G...A.....................C...........................................

C aethiops ..................................A.....................C...........................................

C pygerythrus ..................................A.....................C...........................................

C tantalus ..................................A.....................C...........................................

Homo sapien GAAGCAAAGGAGAAGCTGAGAAGATGAAGGAAAAGTCAGGGTCTGGAGGGGCGGGGGTCAGGGAGCTCCTGGGAGATATGGCCACATGTAGCGGCTCTGA

C sabaeus SK ........................CA...................A........................................A....T........

C sabaeus AFR ........................CA........A..........A........................................A....T........

C aethiops ........................CA...................A........................................A....T........

C pygerythrus ........................CA...................A........................................A....T........

C tantalus ........................CA...................A........................................A....T........

Homo sapien GGAATGGGTTACAGGAGACCTCTGGGGAGATGTGACCACAGCAATGGGTAGGAGAATGTCCAGGGCTATGGAAGTCGAGTATGGGGACCCCCCCTTAACG

C sabaeus SK ............................................A...........A..............GG..T..................---G.C

C sabaeus AFR ................C...........................A...........A..............GG..T..................---G.C

C aethiops ...................................M........A...........A..............GG..T..................---G.C

C pygerythrus ............................................A...........A..........W...GG..T.................Y---G.C

C tantalus ............................................A...........A..............GG..Y..................---G.C

Homo sapien AAGACAGGGCCATGTAGAGGGCCCCAGGGAGTGAAAGAGCCTCCAGGACCTCCAGGTATGGAATACAGGGGACGTTTAAGAAGATATGGCCACACACTGG

C sabaeus SK ........................T....................R......T...........................................T.R.

C sabaeus AFR ........................T....................A......T....................C......................T...

C aethiops ........................T....................M......T...........................................T.R.

C pygerythrus ........................T....................A......T....................A......................T...

C tantalus R.......................T....................A......T...........................................T.R.

Homo sapien GGCCCTGAGAAGTGAGAGCTTCATGAAAAAAATCAGGGACCCCAGAGTTCCTTGGAAGCCAAGACTGAAACCAGCATTATGAGTCTCCGGGTCAGAATGA

C sabaeus SK ..................T...........................................................G......C..........G...

C sabaeus AFR ..................T.........................C.................................G......C..C.......G...

C aethiops ..................T...........................................................G......C..........G...

C pygerythrus ..................T...........................................................G......C..........G...

C tantalus ..................T.........................A.................................G......C..C.....A.G...

Homo sapien AAGAAGAAGGCCTGCCCCAGTGGGGTCTGTGAATTCCCGGGGGTGATTTCACTCCCCGGGGCTGTCCCAGGCTTGTCCCTGCTACCCCCACCCAGCCTTT

C sabaeus SK ........................................T...........K..............................................C

C sabaeus AFR ........................................K..........................................................C

C aethiops ........................................T..........................................................C

C pygerythrus ........................................T..........................................................C

C tantalus ..A..A.G................................T..........................................................C

Homo sapien CCTGAGGCCTCAAGCCTGCCACCAAGCCCCCAGCTCCTTCTCCCCGCAGGGA-CCCAAACACAGGCCTCAGGACTCAACACAGCTTTTC--CCTCCAACC

C sabaeus SK .........CTG........C........................-..A...A....................................TC.........

C sabaeus AFR .........CTG........C........................-..A...A....................................TC.........

C aethiops .........CTG........C........................-..A...A....................................TC.........

C pygerythrus .........CTG........C........................-..A...A.............................R......TC.........

C tantalus .........CTG........C........................-..A...A....................................TC.........

Homo sapien CCGTTTTCTCTCCCTCAAGGACTCAGCTTTCTGAAGCCCCTCCCAGTTCTAGTTCTATCTTTTTCCTGCATCCTGTCTGGAAGTTAGAAGGAAACAGACC

C sabaeus SK ..A........A...................................................................A..A.C...G...........

C sabaeus AFR ..A........A...................................................................A..A.C...G...........

C aethiops ..A........R...................................................................A..A.C...G...........

C pygerythrus ..A........A...................................................................A..A.C...G...........

C tantalus ..A........A...................................................................A..A.C...G...........

Homo sapien ACAGACCTGGTCCCCAAAAGAAATGGAGGCAATAGGTTTTGAGGGGCATGAGGACGGGGTTCAGCCTCCAGGGTCCTACACACAAATCAGTCAGTGGCCC

C sabaeus SK .....Y..........G..................RC...........G...C.....A....A.......A....C.......................

C sabaeus AFR ................G..................RC...........G...C.....A....A.......A....CG......................

C aethiops ................G...................C...........G...C.Y...A....A.......A....C.......................

C pygerythrus .....Y..........G...........K.......C...........G...C.....A....A.......A....C.......................

C tantalus .....Y..........G...................C...........G...C.....A....A.......A....C.......................

Homo sapien AGAAGACCCCCCTCGGAATCGGAGCAGGGAGGATGGGGAGTGTGAGGGGTATCCTTGATGCTTGTGTGTCCCCAACTTTCCAAATCCCCGCCCCCGCGAT

C sabaeus SK ..............................T.G.......A......A................G...................................

C sabaeus AFR ..........T...................T.G.......A......A................G...................................

C aethiops ..............................T.G.......A......A................G...................................

C pygerythrus ..............................T.G.......A......A................G...................................

C tantalus ..............................T.G.......A......A................G...................................

Homo sapien GGAGAAGAAACCGAGACAGAAGGTGCAGGGCCCACTACCGCTTCCTCCAGATGAGCTCATGGGTTTCTCCACCAAGGAAGTTTTCCGCTGGTTGAATGAT

C sabaeus SK ............A...G...................................................................................

C sabaeus AFR ............A...G...................................................................................

C aethiops ............A...G...................................................................................

C pygerythrus ............A...G...................................................................................

C tantalus ............A...G...................................................................................

+24

Homo sapien TCTTTCCCCGCCCTCCTCTCGCCCCAGGGACATATAAAGGCAGTTGTTGGCACACCCAGCCAGCAGACGCTCCCTCAGCAA

C sabaeus SK ..........--.-........................C.........T................................

C sabaeus AFR ..........--.-........................C.........T................................

C aethiops ..........--.-........................C.........T................................

C pygerythrus ..........--.-........................C.........T................................

C tantalus ..........--.-........................C.........T................................

Figure 2. Multiple sequence comparison of the TNF promoter region.

71

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C. aeth

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s p

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C. aeth

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©MrEnt

Figure 3. TNF promoter-based phylogentic trees.

72

insufficient numbers of informative nucleotide differences to resolve the relationships

between the African sabaeus-tantalus clade, the St. Kitts sabaeus-African pygerythrus

clade, and the African aethiops clade. However, the placement of African C. a. sabaeus

with C. a. tantalus instead of the SK-origin vervet in one clade is striking, and counter to

expectation given the identical taxonomic classification.

Discussion

The vervet monkey has been one of the most important non-human primate (NHP)

models for biomedical research, with a PubMed citation record over the past 10 years

close to that of rhesus macaque and much greater than any other NHP

(www.genome.gov). The utility as a replacement to the rhesus macaque is being

increasingly recognized and is a major contributor to the dramatic recent increases in

the use of vervets.

The extensive use of rhesus macaques in biomedical research has highlighted very

important differences related to disease phenotypes between Chinese- and Indian-

origin populations of rhesus monkeys (Macaca fascicularis). These differences relate to

immunological phenotypes of the T-cells in response to infection with

immunodeficiency virus, which can severely confound research data if such regions of

origin are not considered. Simple genetic screening tests now exist to differentiate

73

between Chinese- and Indian-origin rhesus (Smith and McDonough 2005). Similarly,

there is evidence that significant differences exist in disease phenotypes between SK-

origin and African-origin vervets. Examples are serum lipid profiles and propensity for

atherosclerotic lesions in response to a high fat diet (Rudel, Reynolds et al. 1981). Thus,

as it pertains to potential impact on research outcomes, investigation into phylogenetic

relationships within and between Chlorocebus subspecies has relevant biomedical

importance.

The sequence comparisons reported here indicate that there is an urgent need for more

extensive phylogenetic analyses of the SK, SK-origin, African, and African-origin vervets,

particularly those used in biomedical research. We have shown that African

Chlorocebus aethiops sabaeus is more closely related to Chlorocebus aethiops tantalus

than it is to St. Kitts Chlorocebus aethiops sabaeus, which is most closely related to

Chlorocebus aethiops pygerythrus. These results provide strong evidence that the

current taxonomic classification of the Chlorocebus subspecies needs to be revised.

In summary, prior to this report there have been multiple lines of evidence to question

the current taxonomy, which calls the vervets of St. Kitts and the vervets of Africa

identical subspecies: 1) the orginal European ships carrying the vervet monkeys from

Africa ported in various locations within different subspecies’ ranges (Figure 1), 2) the

74

ability and propensity for subspecies to viably hybridize and, 3) the molecular data from

Wang (2006). Our phylogenetic analyses strongly suggest that the SK vervet and the

African vervet are not the same subspecies, but do not provide sufficient resolution of

nucleotide differences to assign a new taxonomic classification to the SK vervet. Only

one fixed nucleotide difference was seen between the SK vervet and both C. a.

pygerythrus and C. a. aethiops, while the latter two subspecies only had two (Table 1).

Thus, there is only minimal resolution for separating the SK sabaeus-African pygerythrus

clade from the aethiops clade. However, we report the strongest data thus far

challenging the validity of the current taxonomic classification. We suggest that the

data reported herein, together with the aforementioned lines of evidence, provides

ample evidence to question whether or not the sabaeus subspecies designation for the

St. Kitts vervet should continue to be used. Our sequence data shows the SK vervet

sharing one or two SNPs with each of the African subspecies examined, and that it is

most closely related to C. a. pygerythrus, with C. a. aethiops being a close second.

Potential limitations of this study include the fact that the VRC was founded from 57

wild-caught SK vervets. However, we have compared the entire orthologous TNF gene

plus 5’ and 3’ flanking regions in 24 presumably unrelated vervets taken directly from SK

to those of the VRC and found complete sequence identity; in addition, SK-origin VRC

vervets shared all SNPs found in vervets taken directly from the island of SK

(unpublished data). This suggests that the VRC experienced no significant loss of genetic

75

variability subsequent to the founding event. Another factor to be considered is that

the analyses reported herein rely on sequence data for African-origin C. a. sabaeus (n=4)

and C. a. tantalus (n=6) from a report by Baena et al. (2007), although there is no

perceived reason to believe this is a significant limitation to this study. It is also possible

that TNF could be subject to selective pressures given its role in metabolism and innate

immunity. Future phylogenetic analyses to further address the questions posed here

should likely include nucleotide sequence that is under neutral selective pressure, which

should allow greater amounts of genetic variation to accumulate over time and thus

presumably provide greater resolution of phylogenetic differences.

It is important to note that, regardless of any eventual revisions to the taxonomy of the

SK vervet, or whether further gene sequence or genome-wide SNP data support the

phylogenetic relationships reported here, the SK vervet appears to be a distinct

subspecies with distinct and reproducible phenotypes. These phenotypes, both normal

and associated with modeled disease states, have been observed to be markedly

different from vervet monkeys of African origin. Thus, we believe that further genetic

and phenotypic characterization of Chlorocebus monkeys used in biomedical research is

urgently needed, and that reporting the origin of all Chlorocebus monkeys in scientific

publications will greatly facilitate that process.

76

Acknowledgements: This work was funded, in part, by the Wake Forest University


Blackmon Diabetes Research Fund (JDW), T32 RR07009 from NIH/NCRR (SBG), an


Resources (P40 RR 019963 to Dr. Lynn Fairbanks at UCLA), and the Center for Public

Health Genomics (CDL). The authors would also like to thank Drs. Lynn Fairbanks

(UCLA), Nelson Friemer (UCLA), Matthew Jorgensen (WFUSM), Kylie Kavanagh

(WFUSM), and Larry Rudel (WFUSM) for their input and collaboration. This work

constitutes partial fulfillment of Ph.D. requirements for Dr. Gray.

77

References

Baena A, Leung JY, Sullivan AD, Landires I, Vasquez-Luna N, Quinones-Berrocal J, Fraser PA, Uko GP, Delgado JC, Clavijo OP et al. 2002. TNF-alpha promoter single nucleotide polymorphisms are markers of human ancestry. Genes Immun 3(8):482-7.

Baena A, Mootnick AR, Falvo JV, Tsytskova AV, Ligeiro F, Diop OM, Brieva C, Gagneux P, O'Brien SJ, Ryder OA et al. 2007. Primate TNF promoters reveal markers of phylogeny and evolution of innate immunity. PLoS ONE 2(7):e621.

Denham WW. 1987. West Indian green monkeys : problems in historical biogeography. Basel ; New York: Karger. 78 p. p.

Fairbanks LA, Newman TK, Bailey JN, Jorgensen MJ, Breidenthal SE, Ophoff RA, Comuzzie AG, Martin LJ, Rogers J. 2004. Genetic contributions to social impulsivity and aggressiveness in vervet monkeys. Biol Psychiatry 55(6):642-7.

Goldfeld AE, Leung JY, Sawyer SA, Hartl DL. 2000. Post-genomics and the neutral theory: variation and conservation in the tumor necrosis factor-alpha promoter. Gene 261(1):19-25.

Groves CP. 2001. Primate taxonomy. Washington, DC: Smithsonian Institution Press. viii, 350 p. p.

Handler JS, Lange FW, Riordan RV. 1978. Plantation slavery in Barbados : an archaeological and historical investigation. Cambridge: Harvard University Press. xiii, 368 p. p.

Haudek SB, Natmessnig BE, Redl H, Schlag G, Giroir BP. 1998. Genetic sequences and transcriptional regulation of the TNFA promoter: comparison of human and baboon. Immunogenetics 48(3):202-7.

Jasinska AJ, Service S, Levinson M, Slaten E, Lee O, Sobel E, Fairbanks LA, Bailey JN, Jorgensen MJ, Breidenthal SE et al. 2007. A genetic linkage map of the vervet monkey (Chlorocebus aethiops sabaeus). Mamm Genome 18(5):347-60.

Leung JY, McKenzie FE, Uglialoro AM, Flores-Villanueva PO, Sorkin BC, Yunis EJ, Hartl DL, Goldfeld AE. 2000. Identification of phylogenetic footprints in primate tumor necrosis factor-alpha promoters. Proc Natl Acad Sci U S A 97(12):6614-8.

McGuire MT. 1974. The St. Kitts vervet (Cercopithecus aethiops). J Med Primatol 3(5):285-97.

National Research Council (U.S.), Institute for Laboratory Animal Research (U.S.). 2003. International perspectives : the future of nonhuman primate resources : proceedings of the workshop held April 17-19, 2002. Washington, D.C.: National Academies Press. xii, 249 p. p.

Rudel LL, Reynolds JA, Bullock BC. 1981. Nutritional effects on blood lipid and HDL cholesterol concentrations in two subspecies of African green monkeys (Cercopithecus aethiops). J Lipid Res 22(2):278-86.

Smith DG, McDonough J. 2005. Mitochondrial DNA variation in Chinese and Indian rhesus macaques (Macaca mulatta). Am J Primatol 65(1):1-25.

Swofford DL. 2002. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. Sunderland,

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Massachusetts.: Sinauer Associates,. Thompson JD, Higgins DG, Gibson TJ. 1994. CLUSTAL W: improving the sensitivity of

progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22(22):4673-80.

van der Kuyl AC, Dekker JT. 1996. St. Kitts green monkeys originate from West Africa: Genetic evidence from feces. American Journal of Primatology 40(4):361-364.

Wang Y. 2006. Molecular polymorphisms for phylogeny, pedigree and population structure studies [Thesis (Ph D )]: School of Biological Sciences, Faculty of Science, University of Sydney, 2006. xvii, 232 leaves p.

www.genome.gov. Zuccon A, Zuccon D. 2008. MrEnt v.2. Program distributed by authors.

http://www.genome.gov/

79

CHAPTER IV

TNF SINGLE NUCLEOTIDE POLYMORPHISMS ARE ASSOCIATED WITH BMI, WAIST CIRCUMFERENCE, AND SERUM CHOLESTEROL LEVELS IN A PEDIGREED NONHUMAN

PRIMATE MODEL (CHLOROCEBUS AETHIOPS SSP.)

Stanton B Gray, Carl D Langefeld, Julie T Ziegler, Gregory A Hawkins, Janice D Wagner, Timothy D Howard

The following manuscript will be submitted for publication to the journal BMC

Genomics. Stylistic variations are due to the requirements of the journal. Dr. S.B. Gray

prepared the manuscript and was primarily responsible for performing and directing the

experiments, as well as data analysis and interpretation. Technical assistance was

provided by Ms. J.T. Ziegler and Mr. A.F. Diallo. Analytical assistance was provided by

Drs. C.D. Langefeld and T.D. Howard, and Ms. J.T. Ziegler. Drs. T.D. Howard and J.D.

Wagner acted in an advisory capacity. All co-authors provided editorial input.

80

Introduction

The number of people classified as obese or overweight, defined by a BMI over 30 or 25

kg/m2 respectively, has reached epidemic proportions in the U.S. and around the world

(IOTF 2007), with commensurate social, medical, and public health costs (Wyatt,

Winters et al. 2006). Currently, more than 30% of U.S. adults are obese, with another

35% overweight; in addition, incidence of overweight children and adolescents is now

over 15% (NHANES 2007). Compared to 30 years ago, these figures have approximately

doubled in adults and quadrupled in children and adolescents (NHANES 2007). To put

the magnitude of this epidemic into global perspective, approximately 1 billion adults

are overweight worldwide, with 300 million of those clinically obese according to World

Health Organization estimates (Brundtland 2002). Obesity, and overweight to a lesser

degree, have consistently been associated with increased morbidity and mortality linked

to cardiovascular disease (CVD), hypertension, insulin resistance (IR), type 2 diabetes

(T2D), dyslipidemia, metabolic syndrome (a set of CVD risk factors defined as at least 3

of the following: hypertension, dyslipidemia, central obesity and IR), airway

inflammation, fatty liver disease, gallstones, osteoarthritis, sleep apnea, and certain

forms of cancer (NHLBI 2000; Eckel, Barouch et al. 2002; Klein, Burke et al. 2004; Wellen

and Hotamisligil 2005), with relative risk of these comorbidities being positively

correlated with BMI (Wyatt, Winters et al. 2006).

Over the past 15 years, two critically important elements of the pathogenesis of obesity

and many of its comorbidities have been realized: 1) obesity is associated with chronic,

81

systemic, low-grade, inflammation (Wellen and Hotamisligil 2005), and 2) adipose tissue

is not simply a reservoir for triglycerides, but is an integral endocrine organ, playing a

central role in secretion and mediation of inflammatory cytokines (Hotamisligil, Shargill

et al. 1993; Zhang, Proenca et al. 1994). Chronic, low-grade inflammation associated

with obesity has been shown to be causal in many of its comorbidities (Esteve, Ricart et

al. 2005; Wellen and Hotamisligil 2005; Hotamisligil 2006). For the purposes of this

study, and the nonhuman primate (NHP) model we are developing, we will focus the

discussion on CVD, T2D, and elements of metabolic syndrome in regards to obesity

comorbidities.

Obesity, excluding rare monogenic and syndromic forms, is paradigmatic of complex,

polygenic disease phenotypes that are the resolution of complex gene-gene

interactions, as well as interactions of genotype with environment (Scriver 2001). Thus,

the genetic basis is far from completely understood, but is clearly extensive, with new

candidate genes being discovered at a pace concordant with the rapidly developing

technology behind genome-wide association studies (GWAS) (Li and Loos 2008; Norris,

Langefeld et al. 2009). Adding to this complexity, many environmental, and other non-

genetic components, have been shown to modulate phenotypic expression such as age,

developmental programming (in utero and post-natal), gender, hormonal status and

cycling, lifestyles, nutrition, physical activity level, risk behavior, socioeconomic status,

and stress (Shively and Clarkson 1988; Roseboom, van der Meulen et al. 2001; Hill,

82

Wyatt et al. 2003; Roche, Phillips et al. 2005; Mutch and Clement 2006; Pasquali,

Vicennati et al. 2006; Schneider, Tompkins et al. 2006; Taylor and Poston 2007).

Tumor necrosis factor (TNF) is an obesity candidate gene of central importance. It

encodes a cytokine secreted by adipocytes and other tissues that plays critical roles in

lymphocyte biology, immune responses, and promoting inflammation (Cawthorn and

Sethi 2008). It has been shown to be a principal initiator and mediator of obesity-

related inflammation, with TNF serum levels correlated with obesity and serum levels of

other pro-inflammatory cytokines such as IL-6, leptin, resistin, MCP-1, and PAI-1 (Juge-

Aubry, Henrichot et al. 2005). It also promotes IR by downregulating glucose

transporter 4 (GLUT4) in adipocytes (Hotamisligil, Shargill et al. 1993), serine

phosphorylation of IRS-1 (Hotamisligil, Peraldi et al. 1996), as well as several other

mechanisms. Single nucleotide polymorphisms (SNPs) in the promoter region of TNF

have been extensively characterized in humans, and reports abound in the literature

indicating associations with an array of infectious, immune-mediated, and

inflammatory-related disease phenotypes, including obesity, CVD, T2D, IR, dyslipidemia,

waist circumference, and hypertension [reviewed in (Fernandez-Real 2006)]. However,

controlling for the multitude of environmental risk factors has been a major confounder

of efforts to elucidate the role and relative contribution of variation in TNF and other

candidate genes in human studies (Risch 2000; Botstein and Risch 2003) due to the

complex experimental designs and large sample size they would entail (Mutch and

Clement 2006). Another major difficulty in identifying functional SNPs in TNF, or

83

anywhere within the major histocompatibility complex (MHC), is the strong degree of

linkage disequilibrium (LD) within this region (Shiina, Ota et al. 2006), yielding extended

haplotypes (Ceppellini, Siniscalco et al. 1955).

Research directed at further elucidating the genetic and molecular mechanisms of

obesity and disentangling the age- and environment-related factors through more


(Eckel, Barouch et al. 2002; USHHS 2007). Animal models, in general, have the distinct

advantage of allowing a large degree of control over environmental factors; in addition,

measures can be taken over the lifetime of the animal. Pedigreed colonies of Old World

monkeys (OWM) are particularly important NHP animal models for the study of obesity

and inflammation given their close phylogenetic relationship to humans, comparable fat

distribution (Bergman, Kim et al. 2007; Kavanagh, Fairbanks et al. 2007), similar

reproductive and endocrine physiology to humans, and their potential to exhibit

spontaneous obesity, IR, T2D and CVD (Wagner, Bagdade et al. 1996; Wagner, Carlson et

al. 1996; West and York 1998; Wagner, Cline et al. 2001; Wagner, Kavanagh et al. 2006;

Kavanagh, Fairbanks et al. 2007; Kavanagh, Jones et al. 2007). As part of an ongoing

initiative to further genetically and phenotypically characterize the St. Kitts-origin vervet

monkey (Chlorocebus aethiops ssp.) as a model of obesity, insulin resistance, and type 2

diabetes (Kavanagh, Fairbanks et al. 2007; Kavanagh, Jones et al. 2007), we sequenced

the orthologous TNF gene and its promoter region in 265 monkeys from the pedigreed

Vervet Research Colony (VRC) at the Wake Forest University Primate Center (WFUPC),

84

and performed association analyses between each SNP and each previously defined,

obesity-related trait previously reported to be significantly heritable in this colony (Table

1) (Kavanagh, Fairbanks et al. 2007).

Materials and Methods

Animals. All methods for the care and use of nonhuman primates conformed to U.S.

Department of Agriculture and Public Health Service standards, and all experimental

protocols were approved by the Chancellor’s Animal Research Committee at UCLA and

the Institutional Animal Care and Use Committee for the Department of Veterans Affairs

Greater Los Angeles Healthcare System. Two hundred and sixty five St. Kitts-origin

vervets (Chlorocebus aethiops ssp.) from the VRC were used in this study. The VRC was

founded in 1975 at UCLA from 57 vervets (28 males, 29 females) wild caught from 1975-

1983 in St. Kitts, West Indies, as described in Fairbanks et al. (2004). The current

population includes approximately 450 descendants with 24 of the original matrilines in

their 5th to 8th generation, and now resides at the Wake Forest University Primate

Center (WFUPC) in Winston-Salem, NC, USA. All vervets were fed a commercial primate

laboratory chow which is relatively low fat and high fiber (Laboratory Diet 5038; Purina,

St. Louis, MO) ad libitum supplemented with fresh fruits and vegetables, and were pen-

housed with equal access to exercise.

PCR and Sequencing. Genomic DNA was isolated from peripheral blood samples using

standard protocols previously described (Jasinska, Service et al. 2007). For sequencing,

PCR primers were generated with the PrimerSelect software (Laser Gene software,

85

Table 1. Heritability estimates (h2) for obesity-related phenotypes of VRC vervets

(n=295). Taken from Kavanagh et al. (2007).

Trait h2 (S.E.) P

BMI 0.44 (0.15) 0.0001 WC 0.37 (0.16) 0.02 TPC 0.37 (0.13) 0.0006 TG 0.33 (0.13) 0.0001

HDL-C 0.29 (0.13) 0.002 GHb% 0.18 (0.10) 0.03 Insulin 0.21 (0.11) 0.02

Glucose 0.07 (0.11) 0.26 HOMA-IR 0.02 (0.10) 0.36

86

DNASTAR, Madison, WI.) using a consensus TNF gene sequence created from human,

chimpanzee (Pan troglodytes) and rhesus monkey (Macaca mulatta) from in the NCBI

gene database (www.ncbi.nlm.nih.gov; uc003nui.1, NM_001045511, NM_001047149

respectively). Primer consensus sequences were created using the editing feature in

Sequencher v. 4.8 (Gene Codes Corp., Ann Arbor, MI), and were designed to amplify the

entire TNF gene plus ~1 kb 5’ and ~1 kb 3’ flanking regions. PCR conditions were as

follows: initial denaturation at 95oC for 10 min, followed by 30 cycles of 96oC for 1 min,

58-60oC for 1 min and 72oC for 60-90 sec with final elongation for 10 min at 72oC in a 20-

µl reaction mixture. PCR products were purified (QuickStep, Edge Biosystems Inc.,

Gaithersburg, MD) to remove dNTPs and excess primers that interfere with sequencing

reactions. Purified PCR products were sequenced using dye-terminator chemistry

(BigDye, ABI, Foster City, CA). Sequencing reactions were purified by ethanol

precipitation prior to loading on an ABI3730XL DNA Analyzer using the POP7 sequencing

polymer. The sequences of the primers used are described in Supplementary Table 1.

Sequence Analyses. Sequences from the 265 VRC vervets were aligned and the

genotypes were determined with Sequencher v. 4.8 (Gene Codes Corp., Ann Arbor, MI).

LD structure, allele and haplotype frequencies, and a 2 goodness-of-fit test for

deviations from Hardy-Weinberg equilibrium were determined with Haploview, version

4.0 (Barrett, Fry et al. 2005). Human SNPs were compiled from dbSNP (Build 128,

www.ncbi.nlm.nih.gov) for comparison to orthologous vervet SNPs.

Association Analyses. Association between individual polymorphisms and each

quantitative phenotype was tested using a variance component model as implemented



87

in software SOLAR [(Sequential Linkage Analysis Routines) v. 2.1.2 (Almasy and Blangero

1998)]. For each vervet TNF polymorphism, we used maximum likelihood methods to

estimate and test the possible effects of each genotype on each of 5 phenotypes: BMI,

waist circumference (WC), triglycerides (TG), total plasma cholesterol (TPC), and high

density lipoprotein-cholesterol (HDL-C). To best approximate the distributional

assumptions of the test (i.e., conditional normality and homogeneity of variance), BMI,

TG, and WC values were natural log- and TPC were square root-transformed to better

approximate normal distribution. After removing pregnant females, models for lipid

measures, TG, TPC, and HDL-C, were adjusted for age, gender, and BMI; BMI and WC

models were adjusted for age and gender. Each trait was tested against each SNP under

4 models of inheritance: the two degree of freedom genotypic test and three a priori

genetic models (i.e., additive, dominant, and recessive).

Results

Analysis of ~1 kb 5’ upstream, ~0.9 kb 3’ downstream, all four exons, and all intronic

regions of the vervet TNF gene in 265 vervet monkeys revealed a total of 12

polymorphisms: 11 SNPs and one 4 bp insertion/deletion, with MAF ranging from 0.077

to 0.393 (Table 2). Five SNPs were located in the 5’ putative promoter region, two in

intron 1, one in intron 3, and four in the 3’ UTR (Figure 1). None of the loci which are

polymorphic in vervets have been reported to be polymorphic in human populations

(dbSNP build 129). The +611 (TGAA)4/5 insertion/deletion, located in intron 1, is not

88

Table 2. Polymorphisms in the human ortholog St. Kitts-origin vervet TNF gene and

flanking sequence.

Polymorphism MAF Minor allele

A-809G 0.309 G A-756G 0.311 A G-602T 0.210 G C-352T 0.393 C A-322G 0.312 A C+426G 0.209 C

+611(tgaa) 4 / 5

0.078 (tgaa) 4

G+1285T 0.387 T C+2133T 0.309 T A+2362G 0.316 A A+2405G 0.318 A C+2681T 0.077 C

89

Figure 1. LD structure of the St. Kitts-origin vervet TNF locus. The single haplotype block

(outlined triangle) was determined with the confidence interval algorithm (Gabriel,

Schaffner et al. 2002) of HaploView. Numbers indicate pairwise r2 values multiplied by

100. Darker blocks denote higher correlation and black squares with no numbers

represent complete LD (r2 = 1). Polymorphism names denote nucleotide variants

flanking gene position relative to human transcription start site. Repeat polymorphisms

denoted by repeated sequence in parentheses and repeat number variants subscripted

and separated by a slash (e.g. (tgaa )4/5).

90

reported to be polymorphic and is (TGAA)6 in humans. The entire vervet TNF gene was

contained within a single haplotype block (Figure 1), as determined by the confidence

interval algorithm (Gabriel, Schaffner et al. 2002) implemented within Haploview 4.0

(Barrett, Fry et al. 2005). The C/T+2681 SNP, located at the farthest 3’ portion examined,

was the only SNP not included with this block (Figure 1). Many of the polymorphisms

were in strong or complete LD with one another (Figure 1), as is common within and

between genes and intergenic regions of the primate, including human, MHC

(Ceppellini, Siniscalco et al. 1955; Shiina, Ota et al. 2006). Five haplotypes were

identified in this block, with frequencies ranging from 0.075 to 0.298 (Table 3).

Multiple SNPs in the vervet TNF gene were associated with the obesity-related

phenotypes, BMI, WC, TG, TPC, and HDL-C (Figure 2). Consistent with the LD pattern

and block structure, significant phenotypic associations among several SNPs and

phenotypes were observed (Figure 1, Table 3). All or nearly all of the SNPs (-806, -756, -

352, -322, +1285, +2133, +2362, and +2405) are associated with BMI, WC, TPC, and HDL-

C (Figure 1), and are contained within the same haplotype block (Figure 1).

Interestingly, the minor alleles for each of these form the H1 haplotype and are the risk

alleles for BMI, WC, and TPC; these alleles are not found on other haplotypes in these

vervets (Table 3). They were also associated with higher levels of HDL-C, which would

not traditionally be considered “risk” since HDL-C is involved in reverse cholesterol

transport (Lewis and Rader 2005), and is considered anti-inflammatory (Ley, Laudanna

et al. 2007) and atheroprotective (Castelli, Garrison et al. 1986). Similarly, the set of

91

Table 3. Haplotypes and frequencies of the human ortholog St. Kitts-origin vervet TNF

gene and flanking sequence.

Haplotype name

Haplotype Freq

H1 G A T C A G (tgaa)5 T T A A 0.298

H2 A G G T G G (tgaa)5 G C G G 0.208

H3 A G T T G C (tgaa)5 G C G G 0.205

H4 A G T T G G (tgaa)5 G C G G 0.183

H5 A G T C G G (tgaa)4 T C G G 0.075

92

Figure 2. Association of SNPs in the TNF gene in the St. Kitts-origin vervet monkey of

the VRC. Categorical x-axis lists SNPs. Y-axis is the negative log of the p values for

association. Dashed lines indicate statistical significance with one asterisk (*) denoting

p<0.05, and two asterisks (**) denoting p<0.01.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

2.80

3.00

-80

9 A

/G

-75

6 G

/A

-60

2 T/G

-35

2 T/C

-32

2 G

/A

+42

6 G

/C

+61

1 5

/4

+12

85

G/T

+21

33

C/T

+23

62

G/A

+24

05

G/A

+26

81

T/C

BMI WC TPC TG HDL-C

**

*

93

SNPs with no significant phenotypic associations were not in LD (had r2 values 0.02-0.16)

with these SNPs (Figure 1). No significant associations were observed between any SNP

and TG levels.

Figure 3 illustrates a sequence comparison of the TNF promoter region, and the

respective SNPs, between the St. Kitts-origin vervet and human. The transcription

binding sites NFAT, ETS, 3, CRE, and SP1, confirmed to be essential for TNF gene

regulation (Goldfeld, McCaffrey et al. 1993; Tsai, Jain et al. 1996; Tsai, Yie et al. 1996;

Falvo, Uglialoro et al. 2000; Tsai, Falvo et al. 2000; Tsytsykova and Goldfeld 2002;

Barthel, Tsytsykova et al. 2003) are noted, as well as the sites for 1 and 2, which bind

NF- B proteins, but are not essential to TNF gene regulation (Goldfeld, Doyle et al.

1990). There is complete conservation of those sites essential for gene regulation

between vervet and human, and also among several other apes, and Old and New

World monkeys as reported elsewhere (Leung, McKenzie et al. 2000). The vervet,

rhesus, and baboon (Leung, McKenzie et al. 2000) contain an A instead of a G in 2

(position -206), but 1 is completely conserved between vervet and human, as well as

the other primate species just mentioned (Leung, McKenzie et al. 2000).

94

-1153 -1072 -1055

H sapien GGGAGCAAGAGCTGTGGGGAGAACAAAAGGATAA-GGGCTCAGAGAGCTTCAGGGATATGTGATGGACTCACCAGGTGAGGCYGCCAGACTGCTGCAGGG

C aethiops ..................................A.....................C.........................C.................

-1054 -1030 -955

H sapien GAAGCAAAGGAGAAGCTGAGAAGAYGAAGGAAAAGTCAGGGTCTGGAGGGGCGGGGGTCAGGGAGCTCCTGGGAGATATGGCCACATGTAGCGGCTCTGA

C aethiops ........................CA...................A........................................A....T........

-954 -862 -856

H sapien GGAATGGGTTACAGGAGACCTCTGGGGAGATGTGACCACAGCAATGGGTAGGAGAATGTCCAGGGCTATGGAAGTCGAGTATGGGGACCCCCMCTTAAYG

C aethiops ............................................A...........A..............GG..T................C.---GCC

-854 -805 -781 -758

H sapien AAGACAGGGCCATGTAGAGGGCCCCAGGGAGTGAAAGAGCCTCCAGGACYTCCAGGTATGGAATACAGGGGACRTTTAAGAAGATATGGCCACACAMTGG

C aethiops ........................T....................R...C..T....................G......................T.R.

-754 -655

H sapien GGCCCTGAGAAGTGAGAGCTTCATGAAAAAAATCAGGGACCCCAGAGTTCCTTGGAAGCCAAGACTGAAACCAGCATTATGAGTCTCCGGGTCAGAATGA

C aethiops ..................T...........................................................G......C..........G...

-654 -645 1 -571-569 -556

H sapien AAGAAGAAGRCCTGCCCCAGTGGGGTCTGTGAATTCCCGGGGGTGATTTCACTCCCC-GGGGCTGTCCCAGGCTTGTCCCTGCTMCMCCCACCCAGCCTT

C aethiops .........G..............................T...........K....-..........................A.C.............

-555 -510 -458

H sapien TCCTGAGGCCTCAAGCCTGCCACCAAGCCCCCAGCTCCTTCTCCCSGCAGGGACCCAAACACAGGCCTCAGGACTCAACACAGCTTTTC-CC-TCCAACC

C aethiops C.........CTG........C.......................CCA...A.....................................T..C.......

-457 -375 -358

H sapien CCGTTTTCTCTCCCTCAAGGACTCAGCTTTCTGAAGCCCCTCCCAGTTCTAGTTCTATCTTTTTCCTGCATCCTGTCTGGAARTTAGAAGGAAACAGACC

C aethiops ..A........A...................................................................A..A.C...G...........

-357 -307 -258

H sapien ACAGACCTGGTCCCCAAAAGAAATGGAGGCAATAGGTTTTGAGGGGCATGRGGACGGGGTTCAGCCTCCAGGGTCCTACACACAAATCAGTCAGTGGCCC

C aethiops .....Y..........G..................RC...........G.A.C.....A....A.......A....C.......................

-257 -243 -237 2 -158

H sapien AGAAGACCCCCCTCRGAATCRGAGCAGGGAGGATGGGGAGTGTGAGGGGTATCCTTGATGCTTGTGTGTCCCCAACTTTCCAAATCCCCGCCCCCGCGAT

C aethiops ..............G.....G.........T.G.......A......A................G...................................

-157 NFAT/Ets CRE NFAT5/ 3 ETS NFAT/5 -58

H sapien GGAGAAGAAACCGAGACAGAAGGTGCAGGGCCCACTACCGCTTCCTCCAGATGAGCTCATGGGTTTCTCCACCAAGGAAGTTTTCCGCTGGTTGAATGAT

C aethiops ...........A...G....................................................................................

-57 SP1 +1 +11 +24

H sapien TCTTTCCCCGCCCTCCTCTCGCCCCAGGGACATATAAAGGCAGTTGTTGGCACACCCAGCCAGCAGAYGCTCCCTCAGCAA

C aethiops ..............---....................C..........T..................C.............

Figure 3. Sequence comparison of TNF 5’ promoter region between humans (H sapien)

and St. Kitts-origin vervet (C aethiops ssp.). SNPs are underlined and bolded with

adjacent nucleotide position for human SNPs. Nucleotide positions are relative to the

human TSS. The single letter code is used to identify SNPs : M=A/C, R=A/G, W=A/T,

S=C/G, Y=C/T, K=G/T. Transcription factor binding sites are underlined with names

above the sequence. NFAT (nuclear factor of activated T cells), Ets, 3, CRE (cyclic AMP

response element), and SP-1 are critical for TNF gene regulation (Goldfeld, McCaffrey et

al. 1993; Tsai, Jain et al. 1996; Tsai, Yie et al. 1996; Falvo, Uglialoro et al. 2000; Tsai,

Falvo et al. 2000; Tsytsykova and Goldfeld 2002; Barthel, Tsytsykova et al. 2003); 1 and

2 are reported to bind NF- B proteins and are not essential for gene regulation

(Goldfeld, Doyle et al. 1990; Leung, McKenzie et al. 2000).

95

Discussion

For an initial genetic characterization of the St. Kitts-origin vervet of the VRC as a model

of obesity and metabolic syndrome, we performed sequencing and genotyping of the

orthologous vervet TNF gene in 265 of the originally characterized 295 vervets

(Kavanagh et al. 2007). We report a total of five SNPs within the 5’ UTR and putative

promoter region, and another six SNPs, plus one insertion/deletion polymorphism, in

the introns, 3’ UTR, and 3’ flanking regions (Table 2, Figures 1 and 3). All

polymorphisms, except one (C/T+2681), were contained within a single haplotype block

(Figure 1), which contains five haplotypes (Table 3) within the VRC population. All

polymorphisms fall outside major conserved regions of transcription factor binding,

known to be critical for TNF gene regulation (Figure 3). In addition, there is non-random

clustering of SNPs between these vervets and humans (Figure 3), and also within Pan

troglodytes and Macaca mulatta at sites shown to contain alleles in humans significantly

associated with risk for a variety of inflammatory- or innate immunity-related diseases,

including obesity- and diabetes-related phenotypes (i.e. -308, -857, -863) (Fernandez-

Real 2006). Thus, we have previously reported that we believe the distribution of TNF

promoter SNPs in this vervet monkey model can be considered analogous to human.

The concept that polymorphisms have accumulated independently in NHPs, and confer

analogous disease phenotypes and risk, has been demonstrated in the St. Kitts-origin

VRC vervet (Bailey, Breidenthal et al. 2007) and the rhesus macaque (Lesch, Meyer et al.

1997; Bennett, Lesch et al. 2002; Miller, Bendor et al. 2004) as models for

96

neurobehavioral disorders, and also in baboons modeling hypertension (Zheng,

Kammerer et al. 2009). Three of these four previous reports of analogous NHP

polymorphisms and associated phenotypes represent functional exonic polymorphisms,

thus more closely modeling a monogenic, Mendelian disorder (Miller, Bendor et al.

2004; Bailey, Breidenthal et al. 2007; Zheng, Kammerer et al. 2009). One of these four is

within the promoter region of the serotonin transporter gene (Lesch, Meyer et al. 1997),

but is a 21-bp insertion/deletion polymorphism with much greater potential to exert

differential transcriptional activity than a SNP. We have suggested that the SNP

distribution in the vervet TNF promoter/enhancer region is analogous to the distribution

of SNPs in the human TNF promoter/enhancer region, and that this constitutes the first

report, to our knowledge, of SNP characterization in a NHP to model applicable to a

polygenic (additive) model of inheritance for a complex, quantitative trait.

Summarizing the phenotypic and genetic characterization of the St. Kitts-origin vervet

model of obesity prior to this report, it has been shown to display obesity-related

phenotypes with significant heritabilities (Table 1) (Kavanagh, Fairbanks et al. 2007;

Kavanagh, Jones et al. 2007), as well as SNP distribution along the TNF promoter is

analogous to humans. The question that follows from these findings is whether these

vervet TNF SNPs show associations with the obesity-related phenotypes. We have

demonstrated that a subset of SNPs in strong LD (-809, -756, -352, -322, +1285, +2133,

+2362, and +2405) within the putative promoter, introns, 3’ UTR, and 3’ flanking regions

all have significant associations with BMI, WC, TPC, and HDL-C (Figure 2). The minor

97

alleles of this subset of SNPs (Table 2) are found exclusively on haplotype H1 (Table 3),

and are associated with increased BMI, WC, TPC, and HDL-C, which obviated the need to

perform an additional haplotypic association analysis. It is not unexpected that this

pattern of genetic associations is observed given the LD pattern and that all “risk” alleles

are located on a single haplotype, H1. An important question is whether there is one

“functional” SNP which is responsible for the phenotypic differences, possibly by

modulating transcription factor binding, and the other SNPs show phenotypic

association simply due to strong LD with the functional SNP. At the other end of the

spectrum of possibilities is that this set of SNPs, inherited together in a haplotype block,

act together to modulate the phenotype, possibly through a combination of differential

transcription factor binding and intronic splicing. The data presented here seems to

suggest that if one SNP is responsible for most or all of the phenotypic differences, that

-322 is the most likely candidate given that it displays the greatest number of, and most

highly significant, associations (Figure2). This idea is particularly intriguing since -322 is

near SNP -308 in humans, -331 in chimpanzees, and -334 in rhesus macaques . There is

an abundance of reports in the literature of significant associations between the -308

SNP in humans and various inflammatory- and immune-related disease phenotypes,

including BMI, WC, and TPC (Fernandez-Real 2006).

The same set of alleles located on haplotype H1 associated with ‘risk’ phenotypes BMI,

WC, and TPC, was also associated with increased values of HDL-C, representing a novel

association not previously seen in human studies. HDL-C levels have traditionally been

98

regarded as negative risk factors for CVD based on its relatively well-characterized role

in reverse cholesterol transport (Lewis and Rader 2005), its inhibition of adhesion

molecules ICAM-1 and VCAM-1 (Ley, Laudanna et al. 2007), as well as epidemiological

studies (Castelli, Garrison et al. 1986; Singh, Shishehbor et al. 2007). Thus, ‘risk’ has

been linked to low levels of HDL-C, and human studies have shown lower values to be

associated with certain risk alleles within the TNF promoter (Dalziel, Gosby et al. 2002;

Parra-Rojas, Ruiz-Madrigal et al. 2006). The positive associations between higher levels

of HDL-C and haplotype H1 in these VRC vervets is therefore quite intriguing. Factors

contributing to this result could include one or more of the following: 1) Chlorocebus

aethiops ssp. have been shown to have high HDL-C relative to humans and other NHPs

(Rudel, Reynolds et al. 1981), 2) Rudel et al. (1981) further demonstrated a high degree

of correlation between TPC and HDL-C in these vervets and this is also reflected in this

VRC subset (r2 = 0.75, Kavanagh, unpublished data) and, 3) soy has been shown to

increase HDL-C in cynomolgus macaques (Greaves, Wilson et al. 2000) and in a human

meta-analysis (Zhan and Ho 2005). However, the fact that HDL-C levels are positively

associated with haplotype H1 in the present study may be providing some small glimpse

into a changing paradigm related to HDL-C. That is, HDLs are extremely diverse in

structure and function, and mounting evidence suggests that the notion of higher levels

always conferring, and being indicative of, protection from CVD and inflammatory-

related disease states could be overly simplistic. Specifically, the cardioprotective effect

of HDL-C is increasingly thought to be due to the apolipoprotein apoA-I and apoA-II

content rather than simply HDL-C levels (Watts, Barrett et al. 2008). For example, a

99

major post-hoc analysis recently indicated that very high HDL-C levels (>70 mg/dL) in

humans conferred a two-fold increase in risk of CVD (van der Steeg, Holme et al. 2008).

In conclusion, we have provided additional validation of the St. Kitts-origin vervet model

of obesity and metabolic syndrome by demonstrating analogous SNP distribution and

phenotypic associations with the orthologous TNF gene. Given the complex, polygenic

nature of obesity and its comorbidities, development of a NHP model of obesity and its

inflammatory-related comorbidities is of paramount importance to address some of the

limitations of genetic studies in humans. In addition, with metabolism, inflammation,

immune function, and dyslipidemia being inextricably linked, evolutionarily conserved

physiologic processes (Esteve, Ricart et al. 2005; Hotamisligil 2006), we believe a NHP

model offers insight into the ancestral state of human risk alleles that cannot be realized

in non-primate models (Di Rienzo and Hudson 2005).

Acknowledgements This work was funded, in part, by the Wake Forest University


Blackmon Diabetes Research Fund (JDW), T32 RR07009 from NIH/NCRR (SBG), an


Resources (P40 RR 019963 to Dr. Lynn Fairbanks at UCLA), and the Center for Public

Health Genomics (CDL, JTZ) at WFUSM. The authors would also like to thank Drs. Lynn

Fairbanks (UCLA), Nelson Friemer (UCLA), Matthew Jorgensen (WFUSM), Kylie Kavanagh

(WFUSM), Larry Rudel (WFUSM), Jay Kaplan (WFUSM), Yongmei Liu (WFUSM) and Don

100

Bowden (WFUSM) for their input and collaboration. This work constitutes partial

fulfillment of Ph.D. requirements for Dr. Gray.

101

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CHAPTER V

CLINICOPATHOLOGIC CHARACTERIZATION OF NATURALLY OCCURRING DIABETES MELLITUS IN VERVET MONKEYS

Jennifer A. Cann, Kylie Kavanagh, Sunish Mohanan, Matthew J. Jorgensen, Stanton B. Gray, Timothy D. Howard, Lynn A. Fairbanks, Gregory A. Hawkins, Janice D. Wagner

The following manuscript will be submitted for publication to the journal Veterinary

Pathology. Stylistic variations are due to the requirements of the journal. Dr. J.A. Cann

prepared the manuscript which is an expansion of the following: Gray SB, Cann JA,

Trybus JA, Kavanagh K, Wagner JD. Characterization of clinical pathology and pancreatic

islet pathology in captive, spontaneously diabetic vervet monkeys. J of Am Assoc of Lab

Anim Sci 2007, 46(4), abstr P25. Dr. S.B. Gray prepared this abstract and poster

presentation and was directly involved in gene sequencing and interpretation.

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ABSTRACT

Diabetes mellitus (DM) is a group of chronic metabolic diseases characterized by

hyperglycemia due to defects in insulin production, secretion, or action in target tissues.

Numerous subtypes of DM have been identified including the polygenic forms, Type 1

and Type 2. Animal models have played an important role in elucidating the

pathophysiology of Type 1 and Type 2 DM. In contrast to the polygenic forms, some

monogenic forms display normal body weight and insulin production, and islet

amyloidosis is not seen. We describe 4 cases of naturally occurring diabetes mellitus in

vervet monkeys (Chlorocebus aethiops sabaeus), three of which have presentation and

inheritance patterns resembling an atypical monogenic form of diabetes.

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INTRODUCTION

Diabetes mellitus (DM) is a group of chronic metabolic diseases characterized by

hyperglycemia due to defects in insulin production, secretion, or action in target tissues.

Numerous subtypes of DM have been identified including Type 1, Type 2, and Maturity

Onset Diabetes of the Young (MODY). Animal models have played an important role in

elucidating the pathophysiology of Type 1 and Type 2 DM, however, monogenic forms

of diabetes have not previously been characterized in NHPs.

Type 1 DM is a polygenic autoimmune disease in which pancreatic beta cells are

targeted and destroyed.(Pinkse, Tysma et al. 2005) It presents more commonly in

children and adolescents than adults, however adult-onset Type 1 DM does occur.

Treatment generally consists of exogenous insulin administration, although pancreatic

islet cell transplantation is being investigated experimentally.(Rood, Buhler et al.

2006),(Gaur 2004) Spontaneous Type 1 DM occurs at a low frequency in

macaques.(Wagner, Cline et al. 2001) Other useful animal models of Type 1 DM include

the NOD mouse and streptozotocin-treated animals.(von Herrath and Nepom

2009),(Thomas, Contreras et al. 2001; Contreras, Smyth et al. 2004),(Gaur, Nepom et al.

2001) Conversely, Type 2 DM more commonly develops in adults, but prevalence in

children is increasing as childhood obesity incidence grows. This form of DM accounts

for >90% of overall diabetes prevalence, with U.S. and worldwide incidence increasing at

epidemic rates.(Zimmet, Alberti et al. 2001) This is a complex, polygenic form of the

disease in which persistent insulin resistance in target tissues (e.g. liver, muscle, fat)

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results in a compensatory increase in pancreatic insulin production in attempts to

maintain euglycemia. Insulin secretion is accompanied by co-production of islet amyloid

polypeptide, also known as amylin. Amylin has been shown to induce beta cell

apoptosis, (Huang, Lin et al. 2007) and the accumulation of islet amyloid with

concurrent islet cell loss is a hallmark of Type 2 DM. Of its 37 constituent amino acids,

the amyloidogenic properties of the amylin protein are thought to result from a lack of

proline within amino acid residues 20-29.(Westermark, Engstrom et al. 1990) This lack

of proline within the amyloidogenic region has been demonstrated in people,

macaques, dogs, and cats with naturally occurring Type 2 DM.(Westermark, Engstrom et

al. 1990; O'Brien, Wagner et al. 1996) Conversely, rodent species have proline residues

within this region, and amyloid deposition does not occur unless the human IAPP gene is

inserted experimentally.(D'Alessio, Verchere et al. 1994) In macaques, the close

phylogenetic relationship to humans, their similar reproductive and endocrine

physiology to humans, and their potential to exhibit spontaneous obesity and insulin

resistance, make them a valuable animal model of Type 2 DM.(O'Brien, Wagner et al.

1996; Wagner, Carlson et al. 1996; Wagner, Kavanagh et al. 2006)

MODY is a heterogeneous group of disorders which comprises about 5% of the clinical

cases of diabetes in the U.S. and the world.(Fajans, Bell et al. 2001) There are at least six

types, each associated with monogenic mutations in different autosomal genes: HNF4A

(hepatic nuclear factor 4 alpha; MODY 1), GCK (glucokinase; MODY 2), HNF1A (MODY 3),

IPF1 (insulin promoter factor 1; MODY 4), HNF1B (MODY 5), and NEUROD1 (neurogenic

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differentiation 1; MODY 6). Of these, all except glucokinase (GCK, MODY 2) are

transcription factors which regulate expression of insulin and other proteins involved in

glucose metabolism and transport within the pancreatic beta cell.(Fajans, Bell et al.

2001) The result of these mutations is defective glucose sensing or insulin signaling

resulting in impaired insulin secretion in the face of normal or near-normal insulin

production and islet morphology.

We describe 4 cases of naturally occurring diabetes mellitus in vervet monkeys

(Chlorocebus aethiops sabaeus), three of which have clinicopathologic and inheritance

similarities to a monogenic form of diabetes, perhaps similar to MODY.

MATERIALS AND METHODS

Animals

All four vervets were part of the multigenerational pedigreed Vervet Research Colony

(VRC), currently located at Wake Forest University Primate Center. The VRC was

founded in 1975 at UCLA from founder animals (28 males, 29 females) wild caught from

1975-1983 in St. Kitts, West Indies, as described in McGuire (1974)(McGuire 1974) and

Fairbanks et al (2004).(Fairbanks, Newman et al. 2004) The current population consists

of ~500 descendants with 24 of the original matrilines now in their 2nd to 8th generation.

All animals were handled in accord with the Guide for the Care and Use of Laboratory

Animals, the Institutional Animal Care and Use Committee at Wake Forest University

Health Sciences, and the Animal Research Committee at UCLA. Diet consisted of a

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commercial primate laboratory chow relatively low in fat and high in fiber, as well as

fresh fruits, vegetables and water ad libitum.

Clinical Pathology

Animals were fasted overnight and sedated with ketamine hydrochloride (10-15mg/kg

IM) (Ketaset, Fort Dodge Animal Health, Fort Dodge, IA) prior to examination and

sample collection. Circulating glucose, insulin, HbA1c, and triglyceride concentrations

were determined as previously reported.(Kavanagh, Fairbanks et al. 2007) Briefly,

glucose was measured in whole blood by glucometer (One Touch Ultra Glucometer;

Lifescan, Inc., Milpitas, CA) or in plasma by the glucose oxidase method (Roche, Basel,

Switzerland). Samples were analyzed for insulin and glycation of hemoglobin A1c

(HbA1c) using a commercial ELISA kit (Mercodia, Uppsala, Sweden) and via HPLC (Primus

PDQ, Primus Diagnostics, Kansas City, MO), respectively. (Kavanagh 2007) Triglycerides

were measured enzymatically. Body condition was assessed on routine physical

examination by an experienced primate veterinarian.

Histopathology and Immunohistochemistry

5um sections of formalin-fixed paraffin-embedded pancreata from each animal were

stained with hematoxylin & eosin (H&E) and Congo Red. For immunohistochemistry

5um sections were incubated with guinea pig anti-insulin polyclonal antibody

(prediluted; Biomeda, Foster City, CA), rabbit anti-glucagon polyclonal antibody

(prediluted; BioGenex, San Ramon, CA), or rabbit anti-amylin polyclonal antibody (1:75;

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BioGenex, San Ramon, CA) for 75 minutes at 35°C. Pancreas from a diabetic cynomolgus

macaque (Macaca fascicularis) was used as positive control tissue and the negative

controls were incubated with non-immune sera instead of primary antibody for 75

minutes at 35°C. Primary antibodies were localized with appropriate biotinylated

secondary antibodies, streptavidin-alkaline phosphatase (Biogenex, San Ramon, CA) and

Vector Red (Vector Labs, Burlingame, CA) substrate. Sections were counterstained with

Mayer’s Hematoxylin and examined by light microscopy by a board-certified veterinary

pathologist (JAC).

Amylin PCR and Amino Acid Sequencing

Genomic DNA was isolated from peripheral blood samples using standard protocols. For

DNA sequencing, PCR primers were generated with the PrimerSelect software (Laser

Gene software, DNASTAR, Madison, WI.) using a consensus amylin (IAPP) gene

sequence created from human, chimpanzee (Pan troglodytes) and rhesus monkey

(Macaca mulatta) found in the NCBI gene database (www.ncbi.nlm.nih.gov;

NC_000012.10, NC_006479.2, NC_007868.1 respectively). Primer consensus sequences

were created using the editing feature in Sequencher v4.6 (Gene Codes Corp., Ann

Arbor, MI), and were designed to amplify the IAPP gene encompassing the

amyloidogenic region. PCR conditions were as follows: initial denaturation at 95oC for

10 min, followed by 30 cycles of 96oC for 1 min, 58oC for 1 min and 72oC for 60 sec with

final elongation for 10 min at 72oC in a 20-µl reaction mixture. PCR products were

purified (QuickStep, Edge Biosystems Inc., Gaithersburg, MD) to remove dNTPs and


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excess primers that interfere with sequencing reactions. Purified PCR products were

sequenced using dye-terminator chemistry (BigDye, ABI, Foster City, CA). Dye-

terminator reactions were performed in 5-10 µl reactions in 96-well plates. The dye-

terminator kit was diluted fourfold using a 2.5X dilution buffer (50 mM Tris, 10 mM

MgCl2 pH 9.0). Sequencing reactions were purified by ethanol precipitation prior to

loading on an ABI3730XL DNA Analyzer using the POP7 sequencing polymer. Nucleotide

gene sequence was evaluated using Sequencher v4.6, and the proper protein coding

reading frame, and hence amino acid sequence, was determined through sequence

comparison to human, chimp, and rhesus monkey.

RESULTS

Clinical Findings

Routine clinical examination of animals within the VRC between 2001 and 2008 revealed

a number of vervets with fasting hyperglycemia (Kavanagh et al. 2007). Four adult

female vervets with mild to moderate fasting hyperglycemia are presented here.

Monkey 982 was mildly overweight, and 978 and 1005 were in average body condition.

Monkey 1988-014 had a previous history of weight gain followed by gradual weight loss

in response to dietary management. Age, body weight, fasting blood glucose

concentrations, HbA1c, and plasma insulin and triglyceride concentrations are shown in

Table 1. Age-matched means for vervets and values from a cynomolgus macaque with

Type 2 DM are shown for comparison. Serum insulin and glucose tolerance test results

for animals 978 and 982 are shown in Figure 1; values from a non-diabetic vervet (979)

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Table 1. Clinical Characteristics

NS = not sampled

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Figure 1. Glucose (top) and insulin (bottom) responses during intravenous glucose

tolerance tests in diabetic (982, 978) and nondiabetic (979) monkeys

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are shown for comparison.

Monkeys 978 and 1005 had a 6-month and 2-month history of DM, respectively, and

were managed with parenteral NPH insulin (Novolin 70/30; NovoNordisk, Bagsvaerd,

Denmark) at a dose adapted from diabetic cynomolgus macaques (Wagner et al. 1996).

Both animals were found to be very sensitive to parenteral insulin as evidenced by rapid

rebounding hypoglycemia. Both animals succumbed to hypoglycemic complications

within four months of initiation of insulin therapy. Animal 982 had a 3-year history of

mild to moderate fasting hyperglycemia (72-119 mg/dL) and did not require insulin

therapy. Final clinical examination revealed a grade II-III heart murmur, hypothermia

(91.8°F), hyperglycemia (301mg/dL), severe anemia (HCT 7.5%), azotemia (BUN 70

mg/dL), and hyperkalemia (3.1 mEq/L); humane euthanasia was elected. Animal 1988-

014 had a 6-year history of intermittent fasting hyperglycemia (75-165 mg/dL), and also

did not receive insulin therapy. This monkey was euthanized and sections of pancreas

were fixed in10% neutral buffered formalin and submitted to WFUPC for histologic

evaluation.

Pathologic Findings

All animals underwent complete diagnostic necropsies with gross and histologic

evaluations of all major organs, except 1988-014 in which case only formalin-fixed

pancreas was examined. No gross lesions were noted in the pancreata or in any other

organs involved in glucose metabolism (liver, adrenal glands, pituitary gland). Pancreatic

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histology was normal in monkeys 978, 1005, and 1988-014. In monkey 982, multifocal,

mild to moderate, segmental amyloidosis was seen on H&E and confirmed with Congo

Red staining (Figure 2). In all four cases, immunohistochemistry for insulin, glucagon,

and amylin revealed diffuse, moderate to intense, cytoplasmic staining within 60-90% of

pancreatic islet cells (Figure 2). In monkey 982, insulin staining was reduced in islets

containing amyloid. For comparison, pancreas from a Type 2 DM cynomolgus macaque

(Macaca fascicularis) was examined and marked diffuse effacement of pancreatic islets

by pale eosinophilic, relatively acellular, hyaline material which stained pale orange-pink

with Congo Red (amyloid) was seen. Immunohistochemistry for insulin revealed intense

cytoplasmic staining in <10% of islet cells; and moderate to intense cytoplasmic staining

for glucagon was seen in 30-90% of islet cells. Anti-amylin staining was diffuse and

intense in nearly all islets (Figure 2).

Amylin Sequencing

Hypothesizing that the lack of amyloid accumulation within pancreatic islets of diabetic

vervets may be due to the presence of a proline within the amyloidogenic region of the

amylin protein, we sequenced the amylin gene (IAPP) and extrapolated the amino acid

sequence for vervet amylin. Results are shown in Figure 3. Similar to humans,

macaques, dogs, and cats, (Westermark, Engstrom et al. 1990; O'Brien, Wagner et al.

1996) no proline residues are predicted in the amyloidogenic region.

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Figure 2. All images 200X magnification; bars = 100um

Panels A-O: Pancreatic histology and immunohistochemistry of three vervet monkeys

suspected to have MODY: 978, 1988-014, 1005. H&E: normal morphology of pancreatic

islets and surrounding exocrine acini. Congo Red: normal; no staining within islets

Insulin, glucagon, and amylin immunohistochemistry: normal; diffuse, moderate to

intense, cytoplasmic staining within 60-90% of pancreatic islet cells.

Panels P-T: Pancreatic histology and immunohistochemistry of vervet monkey

suspected to have Type 2 DM. H&E: upper islet is normal; in the lower islet the

preexisting endocrine cells are replaced by pale eosinophilic acellular hyaline material

(amyloid). Congo Red: pale pink-orange staining in areas expanded amyloid

Insulin immunohistochemistry: reduced insulin staining in islets containing amyloid.

Glucagon, and amylin immunohistochemistry: normal

Panels U-Y: Pancreatic histology and immunohistochemistry of confirmed Type 2 DM in

a cynomolgus macaque (Macaca fascicularis). H&E: Marked diffuse effacement of

pancreatic islets by pale eosinophilic, relatively acellular, hyaline material (amyloid);

adipocyte infiltration throughout the exocrine pancreas. Congo Red: pale pink-orange

staining in areas expanded amyloid. Insulin immunohistochemistry: reduced insulin

staining (<10%) in islets containing amyloid. Glucagon and amylin

immunohistochemistry: normal

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Figure 2. Pancreatic Histology and Immunohistochemistry

H&E Congo Red Insulin Glucagon Amylin

978 1988-014 1005

982

Type 2 DM control (M. fascicularis)

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Figure 3. Comparison of amylin amino acid sequences

1 8 18 29 37

Human KCNTATCATQRLANFLVHSSNNFGAILSSTNVGSNTY

Macaque -----------------R------T----------D—

Vervet -----------------R------T-----D------

Cat ----------------IR----L------P-------

Dog -----------------RT---L------P-------

Rat -----------------R----L-PV-PP--------

Mouse -----------------R----L-PV-PP--------

Figure 3. Amino acid sequence comparison of amylin between various species. Vervet

sequence determined by extrapolation from nucleotide sequence, whereas others were

by both nucleotide and direct amino acid sequencing reported previously in the

literature. Human, macaque, vervet, dog, and cat sequences reveal a lack of proline

within the amyloidogenic region (yellow).

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Pedigree Analysis

Given that the MODY form of DM in humans is passed as an autosomal dominant trait,

we evaluated blood glucose concentrations of relatives of animal 1988-014. This animal

had five offspring, from three different fathers, and all offspring had elevated fasting

blood glucose concentrations (Figure 4). In addition, a maternal half-sibling of 1988-014,

1989-017, also had elevated fasting blood glucose concentrations. These pedigree data

are consistent with either an autosomal dominant or mitochondrial inheritance pattern.

DISCUSSION

Clinical diagnosis of DM is not difficult as blood glucose concentrations are routinely

measured and hyperglycemia is easily identified. However, differentiating between the

different types of DM can be challenging. Described here, the clinicopathologic

presentation of monkey 982 was similar to cynomolgus macaques with Type 2 DM

where overweight body condition, persistent hyperglycemia, islet amyloidosis, and

reduced insulin staining are common features. This is in contrast to animals 978, 1005,

and 1988-014, all of whom presented in a manner more closely resembling MODY in

humans. With MODY, obesity, islet amyloidosis, and reduced insulin production are not

primary features, and were not found in these three cases. In addition, with MODY,

insulin sensitivity in target tissues is maintained, and at least two of these animals were

found to be extremely sensitive to exogenous insulin therapy. Thus, from a clinical

perspective these findings indicate that it is important to determine the type of DM in

monkeys before initiating therapy. In human medicine, MODY is generally considered

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Figure 4. Pedigree Analysis

Figure 4. A familial pattern of increased blood glucose concentrations of relatives of

animal 1988-014 are depicted by solid symbols. This animal had five offspring, from

three different fathers, and all offspring had elevated fasting blood glucose

concentrations. A maternal half-sibling of 1988-014, 1989-017, also had elevated fasting

blood glucose concentrations. These pedigree data are consistent with either an

autosomal dominant or mitochondrial inheritance pattern.

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easier to manage than the polygenic Type 1 and Type 2 forms of DM; oral hypoglycemic

agents are generally sufficient, although parenteral insulin therapy is sometimes

required.(Fajans, Bell et al. 2001) It is also important to note that while in our cases the

animals were adults at the time of diagnosis, and in spite of its name, MODY can present

clinically in both adults and adolescents.(Fajans, Bell et al. 2001). It is not the intention

of this report to suggest the atypical presentation of diabetes is similar to MODY.

However, there are apparent similarities in some cliniopathologic features and

inheritance patterns.

Prior to presentation of monkey 982, we suspected that the vervet amylin gene might

contain proline residues within its amyloidogenic region, thus preventing amyloid

accumulation. However, extrapolation of the amino acid sequence revealed that a

proline residue is not predicted in this region (amino acids 20-29), which suggests that

vervets are indeed susceptible to the development of islet amyloidosis, as we later saw

in monkey 982. In addition, a recent publication describes three different types of

amyloidosis in an aged male vervet, including islet amyloidosis.(Nakamura, Okabayashi

et al. 2008)

Interestingly, pedigree analysis of animal 1988-014 revealed that all five of this animal’s

offspring were hyperglycemic. These cases may represent a less common, but also

monogenic and highly heritable form of DM, referred to as “mitochondrial diabetes”

due to its association with mutations in mitochondrial DNA.(Maassen, LM et al. 2004)

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The hallmark of mitochondrial inheritance is that all offspring of affected mothers are

affected, whereas no offspring from affected fathers are affected since fathers

contribute little to no mitochondria to their offspring. In mitochondrial diabetes, gradual

aging-associated beta cell dysfunction is the main mechanism by which glucose

intolerance develops. In our atypical cases, insulin production as assessed

immunohistochemically was within normal limits, making this form less likely. In

humans, definitive diagnosis of MODY is made via genetic testing of target genes,

including HNF4A (MODY 1), GCK (MODY 2), HNF1A (MODY 3), IPF1 (MODY 4), HNF1B

(MODY 5), and NEUROD1 (MODY 6).(Fajans, Bell et al. 2001). It is possible that the

current linkage map for the vervet monkey (Jasinska, Service et al. 2007) could aid in

identifying a candidate region responsible for this apparently monogenic atypical form

of diabetes.

Acknowledgments This work was funded, in part, by the Wake Forest University School

of Medicine Venture Fund, the Skorich Diabetes Research Fund, the Monty Blackmon

Diabetes Research Fund (JDW), T32 RR07009 from NIH/NCRR (SBG), and an Animal and

Biological Materials Resources grant from the National Center for Research Resources

(P40RR019963: original PI, Lynn Fairbanks, UCLA; current PI, Jay Kaplan, Wake Forest).

The authors would also like to thank Drs. Nelson Friemer (UCLA) and Jay Kaplan

(WFUSM) for their input and collaboration and Abdoulaye Diallo for technical support.

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induced diabetes with stable allogeneic islet function in a preclinical model of type 1 diabetes. Diabetes 50: 1227-1236, 2001

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CHAPTER VI

OVERALL DISCUSSION AND CONCLUSIONS

127

The purpose of this study was to further genetically define the St. Kitts-origin

vervet monkey (Chlorocebus aethiops ssp.) as a model for polygenic obesity and its

inflammation-related comorbidities, specifically T2D. As an initial step towards that

ongoing objective, the gene for TNF-alpha was chosen to sequence and analyze for

genetic variants within the VRC population. Utilizing bioinformatic analyses, that

sequence was used to determine percentage identity among the St. Kitts-origin vervet

monkey, human, and other NHPs used in biomedical research, providing an initial

baseline validation. Further, sequence information was utilized to perform phylogenetic

analyses using the TNF putative promoter region to provide greater insight into the

uncertain taxonomic classification of the St. Kitts vervet. Polymorphisms within TNF and

its flanking regions were similarly compared, in regards to placement and spacing,

among the St. Kitts-origin vervet, human, and other NHPs using the NCBI database and

NHP literature reports. Lastly, leveraging the pedigree information of the VRC to match

genotype information with previously defined obesity-related phenotypes, association

analyses were performed to determine if any particular alleles, genotypes, or

haplotypes were associated with, and presumably conferred risk for, obesity and

obesity-related traits in the St. Kitts-origin vervet monkey. Concurrent with the central

thesis project involving genetic characterization were continued efforts to

phenotypically characterize the clinopathologic presentation of naturally-occurring

diabetes in the St. Kitts-origin vervet. The results of these investigations into the

diabetic disease phenotype have significant implications on the genetic association

results presented here.

128

Initial validation consisted of sequencing 24 St. Kitts vervet monkeys presumably

unrelated to St. Kitts-origin vervets of the VRC. This was done as proof of principle to

demonstrate that the St. Kitts-origin vervets of the VRC were not genetically dissimiliar

in sequence or variation from vervets taken directly from the island of St. Kitts. We

showed that sequence between these 24 and the 265 of the VRC had exact sequence

indentity for the orthologous TNF gene and its flanking regions, and that the genetic

polymorphisms were essentially identical. The only discrepancy was that an additional

rare SNP (MAF 0.078) at +2681 (Chapter 2, Table 1) was discovered upon sequencing

the 265 monkeys of the VRC. This was not unexpected given that the power to detect

genetic polymorphisms with MAF of 0.05 or greater was 91.47% in 24 unrelated

monkeys (using the equation P = 1-(1-fq)2N where P = power to detect polymorphism; fq

= the lowest frequency of detection desired; 2N = number of chromosomes screened,

where N = number of DNA samples). This finding suggests that genetic variation has

been maintained within the VRC despite the current gene pool being derived from 57

founders (28 males, 29 females) wild caught from St. Kitts from 1975-1983. One

practical ramification of this finding is that the unrelated 24 have been validated as a

SNP screen for the VRC thereby saving considerable expense and time for any future

genotyping of TNF or sequencing/genotyping of other candidate genes within the VRC.

Additional proof-of-priniciple validation came from comparing orthologous St.

Kitts-origin vervet TNF gene sequence with human, and other NHPs used in biomedical

research. Sequence identity of TNF between vervet and human is 93-94%, nearly

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identical to rhesus macaque compared to human; further, vervet and rhesus share 98-

99% identity (Chapter 2, Tables 3A &B). Distance matrices (D.M.), which weigh

nucleotide position in each codon of the reading frame, are estimates of evolutionary

divergence with lower scores indicating more recent divergence, and thus closer

phylogenetic relationships. Percentages of sequence identities and distance matrices

among St. Kitts-origin vervet, rhesus macaque, chimpanzee, and human appear to agree

with published phylogenetic relationships based on intronic regions of the serum

albumin gene (Page & Goodman, 2001).

The third aspect of the initial characterization of TNF which falls under the

category of proof-of-principle involves analysis of the LD structure, which demonstrated

that 11 of the 12 polymorphisms along the entire orthologous vervet TNF gene, were

contained within a single haplotype block (Chapter 2, Figure 1). Many of the

polymorphisms within this block were found to be in strong or complete LD with one

another which is consistent with expectations, as many genes within the primate MHC

region have been shown to be in strong LD (Shiina, Ota et al. 2006), often yielding

extended haplotypes (Ceppellini, Siniscalco et al. 1955).

A total of five SNPs within the 5’ UTR and putative promoter region, with

another six SNPs plus one insertion/deletion polymorphism in the intronic and 3’ UTR of

the orthologous St. Kitts-origin vervet TNF gene and flanking regions is reported here

(Chapter 2, Figure 1). This is the most extensive report of SNPs, haplotypes, and LD for

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any NHP of the TNF gene and flanking regions, as well as the first report of complete

TNF gene sequence for any Chlorocebus species. Not only are these orthologous vervet

TNF putative promoter SNPs shown to be non-randomly and non-uniformly distributed

outside TF binding sites and other conserved regions, but this is also true for reported

SNPs in the same region of human, chimpanzee, and rhesus macaque (Chapter 2, Table

4). In addition, the consensus of all SNPs from all four of these species demonstrates

the same non-uniform, non-random distribution. The fact that SNPs in all four species

cluster together in two regions on the TNF promoter/enhancer region is particularly

interesting (Chapter 2, Figure 3B). That is, SNP clusters are within 50 nucleotides of

three important human SNPs ( -308 and -857/-863) which have been significantly

associated with numerous inflammatory- and immune-related phenotypes, including

BMI, WC, and TPC (Chapter 1, Table 2) (Fernandez-Real 2006). Thus, the distribution of

SNPs along the orthologous vervet TNF putative promoter can be described as

analogous to that of human. This observation hints that polymorphisms in these

specific regions across species could be important in modulating transcription of TNF,

with presumed subsequent phenotypic effect, upon which natural selection may act. A

logical question to follow from such an observation is whether NHP polymorphisms in

these clustered regions similarly associate with NHP obesity-related phenotypes (or

indeed any of the numerous immune- and inflammation-related phenotypes) as has

been demonstrated in humans.

131

To put the results and conclusions of the vervet association analyses in

perspective, it is useful to first briefly summarize the results, assumptions and

limitations of selected human TNF association studies at the -308 promoter locus. In

vitro results from Wilson et al. (1997) and Kroeger et al. (1997) have shown that the -

308 A allele increases transcriptional activation of the TNF gene. Although there have

been a number of inconsistencies, the majority of in vivo human association studies

have reported an association between the -308 A allele and obesity and related traits

(Sookoian, Gonzalez et al. 2005). These inconsistencies are presumed to be a function

of the inherent limitations of human studies. Namely, 1) there is a large degree of

uncontrolled environmental variability affecting the phenotype, 2) genetic and

phenotypic associations are partly a function of correlation between traits and, 3)

genetic associations can vary by the ethnicity and/or admixture of the study population

(Fernandez and Shiver 2004; Enoch, Shen et al. 2006).

Selected association reports between the -308 A allele and the following obesity-

related phenotypes are summarized here: BMI (Hoffstedt, Eriksson et al. 2000;

Corbalan, Marti et al. 2004), WC (Corbalan, Marti et al. 2004), impaired glucose

tolerance (Kubaszek, Pihlajamaki et al. 2003), insulin resistance (Dalziel, Gosby et al.

2002; Wybranska, Malczewska-Malec et al. 2003), TG (Wybranska, Malczewska-Malec et

al. 2003), and decreased HDL-C (Dalziel, Gosby et al. 2002). Interestingly, all of these

reports found associations within groups classified as obese and/or T2D. Examples of

reports which failed to show associations between -308 A and diabetic or obesity

132

phenotypes involved non-obese or non-diabetic control groups (Hamann, Mantzoros et

al. 1995; Walston, Seibert et al. 1999). The most relevant examples to the present

discussion are: 1) Dalziel et al (2002) showed that an association between HOMA-IR and

BMI/WC existed in all obese, non-diabetic subjects, but a negative correlation between

HDL-C and HOMA-IR was only seen with certain genotypes (-308 GA or AA); 2) increased

dietary intake of polyunsaturated fatty acids (PUFAs) by subjects with T2D was positively

associated with increased HDL-C in carriers of the -238 A and -308 G alleles, but an

inverse association was seen in carriers of the -238 GG genotype and the -308 A allele

(Fontaine-Bisson, Wolever et al. 2007) and; 3) carriers of the -308 A allele with T2D

showed an attenuated response to rosiglitazone, a PPAR agonist (Liu, Lin et al. 2008).

Taken together, the results of these human association studies show the -308 A allele to

be associated with obesity-related phenotypes, pre-diabetic states, and unfavorable

lipid profiles within individuals classified as either obese or T2D. Thus, it is appropriately

termed the ‘risk’ allele; however, the context of that risk is a state of presumed chronic

inflammation as seen in obesity and T2D.

Association analyses of the VRC vervets revealed that a subset of SNPs in strong

LD within the 5’ UTR, putative promoter, introns, 3’ UTR, and flanking regions all have

significant positive associations with BMI, WC, TPC, and HDL-C (Chapter 4, Figure 2).

The minor alleles of this subset are associated with increased BMI, WC, TPC, and HDL-C,

and are found exclusively on haplotype H1 which represents about 30% of the sample

population (Chapter 4, Table 3). No significant associations were found between TG

133

and any of the polymorphisms tested. Thus, genetic associations have been

demonstrated that are similar to humans with BMI, WC, and TPC.

Although a lack of association should not be weighted comparably to positive

associations for making interpretations, one possibility for why TG had no significant

associations could be that the range of values for TG was not sufficiently wide in this

sample population to statistically detect partitioning by genotype. This could be due to

one or more of the following: 1) TG values tend to be lower for Chlorocebus species

relative to humans and other NHPs (Rudel, Reynolds et al. 1981), 2) animals were fed a

soy-based chow diet at the time of blood collection and soy has been shown to lower

human TG values in large epidemiologic studies (Zhan and Ho 2005) and, 3) TNFα has

been shown to stimulate both lipolysis and hepatic FA synthesis in chow-fed animals

whereas TNFα only induces FA synthesis in sucrose-fed animals (Feingold, Adi et al.

1990; Esteve, Ricart et al. 2005). Thus, although a proportion of these VRC vervets have

previously been shown to display obesity, insulin resistance, and associated lipid profiles

(Kavanagh, Fairbanks et al. 2007; Kavanagh, Jones et al. 2007), the number of individuals

displaying obesity-related inflammation or insulin resistance sufficient to induce TG

elevations is thought to be nominal. It is expected that dietary manipulation involving a

composition more closely resembling that of a typical Western diet would increase the

phenotypic expression of obesity and its associated dyslipidemic profiles.

The same set of alleles located on haplotype H1 associated with ‘risk’

phenotypes BMI, WC, and TPC, was also associated with increased values of HDL-C,

134

representing a novel association not previously seen in human studies. HDL-C levels

have traditionally been regarded as negative risk factors for CVD based on its relatively

well-characterized role in reverse cholesterol transport (Lewis and Rader 2005), its

inhibition of adhesion molecules ICAM-1 and VCAM-1 (Ley, Laudanna et al. 2007), as

well as epidemiological studies (Castelli, Garrison et al. 1986; Singh, Shishehbor et al.

2007). Thus, ‘risk’ has been linked to low levels of HDL-C, and human studies have

shown lower values to be associated with certain risk alleles within the TNF promoter

(Dalziel, Gosby et al. 2002; Parra-Rojas, Ruiz-Madrigal et al. 2006). The positive

associations between higher levels of HDL-C and haplotype H1 in these VRC vervets is

therefore quite intriguing. Factors contributing to this result could include one or more

of the following: 1) Chlorocebus aethiops ssp. have been shown to have high HDL-C

relative to humans and other NHPs (Rudel, Reynolds et al. 1981), 2) Rudel et al. (1981)

further demonstrated a high degree of correlation between TPC and HDL-C in these

vervets and this is also reflected in this VRC subset (r2 = 0.75, Kavanagh, unpublished

data) and, 3) soy has been shown to increase HDL-C in cynomolgus macaques (Greaves,

Wilson et al. 2000) and in a human meta-analysis (Zhan and Ho 2005). However, the

fact that HDL-C levels are positively associated with haplotype H1 in the present study

may be providing some small glimpse into a changing paradigm related to HDL-C. That

is, HDLs are extremely diverse in structure and function, and mounting evidence

suggests that the notion of higher levels always conferring, and being indicative of,

protection from CVD and inflammatory-related disease states could be overly simplistic.

Specifically, the cardioprotective effect of HDL-C is increasingly thought to be due to the

135

apolipoprotein apoA-I and apoA-II content rather than simply HDL-C levels (Watts,

Barrett et al. 2008). For example, a major post-hoc analysis recently indicated that very

high HDL-C cholesterol levels (>70 mg/dL) in humans conferred a two-fold increase in

risk of CVD (van der Steeg, Holme et al. 2008).

In an effort to explore the possibility that the positive associations could be due

to the large correlation between TPC and HDL-C, we ran additional association analyses

with the estimated values for non-HDL-C, or all apoB-containing cholesterol such as

VLDL and LDL. Although a slight trend was seen with the same set of ‘risk’ alleles

located on haplotype H1, the results failed to yield any highly significant associations

(data not shown).

The data presented here cannot explain the association of H1 with HDL-C.

However, subsequent studies may provide the unique opportunity to explore this

further. As of 2009, the VRC was switched from a soy-based chow diet to a diet

designed to reflect the composition of a typical American diet (TAD). It is expected that

consumption of this diet will result in a much greater proportion of the population

exhibiting obesity, high WC, insulin resistance, and associated dyslipidemic profiles. The

next likely steps to address the novel HDL-C association would be to measure

phenotypic traits again at the end of 2009, after the vervets have acclimated to the diet

and have been eating it consistently for at least 6 months. In addition to remeasuring

BMI, WC, TPC, TG, and HDL-C, it would be useful to remeasure GHb% and insulin

136

resistance, as well as a reliable proxy measure of inflammation such as CRP. Association

analyses on these more recent measures may provide greater insight into the dynamics

of inflammation, TPC, and HDL-C in the VRC population. Based on previous

investigations into the HDL-C response in vervets (Rudel, Reynolds et al. 1981; Rudel,

Nelson et al. 1984), it would be expected that increasing amounts of dietary cholesterol

would eventually shift the correlation between TPC and HDL-C from a positive to a

negative relationship. How these lipid profile dynamics associate or not with the H1

‘risk’ haplotype reported here may provide some insight into the newly inflammatory

role of HDL-C.

Additional future studies will certainly include additional clinicopathologic

characterization of all present and future diabetic vervets of the VRC to better delineate

those vervets with a more typical T2D presentation, and those with the more atypical

insulin sensitive presentation with little to no islet amyloid deposition. Such atypical

presentations could be due to a vastly different pathogenesis, perhaps independent of

inflammatory pathways, as well as vastly different modes of inheritance, perhaps

dominant or maternal (Chapter 5, Figure 4). These individuals have enormous potential

to confound genetic association studies which attempt to dissect environmental and

genetic components of polygenic, inflammatory-related obesity and its comorbidities.

Another potential confounder to association studies is the possibility of

population stratification. The phylogenetic analyses presented here provide relatively

137

strong evidence to suggest that the St. Kitts-origin vervet should not bear the same

subspecies designation as the African-origin Chlorocebus aethiops sabaeus. However, it

raises some concern that the St. Kitts vervet may be stratified by introgression of more

than one subspecies founding that island population. Current projects at separate

institutions involve genome sequencing as well as genome-wide SNP markers in the

vervet monkey (www.genome.gov), which will provide essential tools to explore

additional investigations into phylogeny and population stratification.

In summary, the data presented here demonstrates: 1) SNP distribution along

the putative TNF promoter in St. Kitts-origin vervets analogous to that of human as well

as other NHPs, 2) evidence to discard the current taxonomic classification of St. Kitts

vervets which designates them as identical to African Chlorocebus aethiops sabaeus, 3)

genetic associations between TNF SNPs and BMI, WC, and TPC which are analogous to

that shown in human literature, and 4) novel association between the same set of ‘risk’

SNPs and HDL-C.

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SCHOLASTIC VITA

STANTON BRADLEY GRAY

BORN: September 22, 1968, Oklahoma City, Oklahoma UNDERGRADUATE University of Oklahoma STUDY: Norman, Oklahoma B.S., Zoology, 1991 GRADUATE STUDY: Oklahoma State University College of Veterinary Medicine

151

Stillwater, Oklahoma D.V.M., 2001 Wake Forest University Winston Salem, North Carolina Ph.D., 2009 SCHOLASTIC AND PROFESSIONAL EXPERIENCE Residency, Comparative Medicine, SUNY at Buffalo, 2004-2005 Post-doctoral Fellowship, Comparative Medicine, Wake Forest University Health

Sciences, Winston Salem, NC, 2005-2008

genetic characterization of the st. kitts-origin ......wfu wake forest university wfupc wake forest...

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