DISTINCTIVE CHARACTERISTICS OF INSULIN GLUCOSE METABOLISM IN INTRAUTERINE GROWTH RESTRICTED AND IMPAIRED GLUCOSE TOLERANCE NONHUMAN PRIMATES

DISTINCTIVE CHARACTERISTICS OF INSULINGLUCOSE METABOLISM IN INTRAUTERINE GROWTH RESTRICTED AND IMPAIRED GLUCOSE TOLERANCE NONHUMAN PRIMATES TAN YONG CHEE (B.Sc.(Hons.), NTU) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF SCIENCE DEPARTMENT OF OBSTETRICS AND GYNAECOLOGY, YONG LOO LIN SCHOOL OF MEDICINE NATIONAL UNIVERSITY OF SINGAPORE 2012 DECLARATION I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. __________________ Tan Yong Chee 27 September 2012 II | P a g e ACKNOWLEDGEMENTS First, I would like to extend my deepest gratitude appreciation to my main supervisor, A/P Chong Yap Seng, and co-supervisor, Dr Keefe Chng, for accepting me as his student, giving invaluable advice and guidance during my candidature. I sincerely appreciate your guidance and support towards the completion of this thesis. Many thanks to NHP facility members Louiza Chan, Grace Lim, Angelynn Soo, Ang Qiu Rong, Natalie Hah, Ryan Maniquiz, Carine Lim and Angela Chew for their help in animal husbandry, veterinary procedures and tissue collection. Also, special thanks go to the project coordinator Carnette C. Pulma, and platform administrator Dorothy Chen, for helping me with the administrative work on the various projects. Last but not least, I am indebted to all the DevOS staff and fellow students for your support which has helped me during my candidature. III | P a g e TABLE OF CONTENTS ACKNOWLEDGEMENTS ...............................................................................................III TABLE OF CONTENTS .................................................................................................. IV SUMMARY……… ......................................................................................................... VII LIST OF TABLES ............................................................................................................ IX LIST OF FIGURES ............................................................................................................X ABBREVIATIONS ......................................................................................................... XII CHAPTER 1 INTRODUCTION ....................................................................................1 1.1 Intrauterine growth restriction ...................................................................................1 1.1.1 Implications of IUGR .....................................................................................1 1.2 Type 2 diabetes mellitus ............................................................................................2 1.2.1 Diagnosis of T2DM ........................................................................................3 1.2.2 Progression of T2DM .....................................................................................4 1.3 Insulin-glucose signaling pathway ............................................................................4 1.3.1 Overview of insulin action through IRS/PI3K/AKT pathway ........................5 1.3.2 Abnormal gene regulation of insulin-glucose signaling pathway in T2DM...7 1.3.3 Linking IUGR and T2DM ..............................................................................8 1.4 Gaps in current research of IUGR and T2DM ........................................................10 1.5 Nonhuman primate: a better animal model of IUGR and T2DM ...........................10 1.6 Hypotheses and objectives ......................................................................................13 CHAPTER 2 MATERIALS AND METHODS ............................................................15 2.1 Cynomolgus macaque nutrition-mediated IUGR model .........................................15 2.2 Adult cynomolgus macaque prediabetic model ......................................................16 2.3 IVGTT, blood test and physical measurement ........................................................17 2.4 Muscle biopsy..........................................................................................................18 2.5 Oligonucleotide primers design and production .....................................................19 2.6 Total RNA extraction ..............................................................................................19 2.7 DNase I digestion ....................................................................................................23 2.8 RNA purification .....................................................................................................23 2.9 RNA Quantification ..............................................................................................24 IV | P a g e 2.10 RNA integrity assay ..............................................................................................24 2.11 First strand cDNA synthesis ..................................................................................25 2.12 Real time PCR .......................................................................................................26 2.13 Gel extraction and sequencing ..............................................................................26 2.14 Real time PCR data analysis .................................................................................27 2.15 Statistical analysis .................................................................................................28 CHAPTER 3 RESULTS ...............................................................................................29 3.1 Primer efficiency and specificity .............................................................................29 3.2 Cynomolgus macaque nutrition-restricted IUGR model.........................................33 3.2.1 Morphometric analysis: juvenile macaques from 0 to 9 months ..................33 3.2.2 IVGTT analysis: juvenile macaques at 12 months .......................................35 3.2.3 Physical and biochemical properties analysis: juvenile macaques at 15 months……… ........................................................................................................37 3.2.4 Metabolic gene expression analysis: Juvenile macaques at 15 months ........37 3.2.5 Association of biochemical parameters with metabolic gene expression level: juvenile macaques at 15 months ..................................................................39 3.2.6 Physical and biochemical properties analysis: juvenile macaques at 24 months (9 months after diet treatment) ..................................................................40 3.2.7 Metabolic gene expression analysis: juvenile macaques at 24 months ........42 3.2.8 Association of biochemical parameters with metabolic gene expression level: juvenile macaques at 24 months ..................................................................46 3.3 Adult cynomolgus macaque prediabetic model ......................................................46 3.3.1 Morphometric analysis: adult macaques .......................................................46 3.3.2 Biochemical analysis: adult macaques..........................................................46 3.3.3 Metabolic gene expression analysis: adult macaques ...................................52 3.3.4 Association of biochemical parameters with metabolic gene expression Level: adult macaques……………........................................................................54 CHAPTER 4 DISCUSSION .........................................................................................57 4.1 Primer validated for all gene expression studies in cynomologus macaque ...........57 4.2 Nutrition-mediated IUGR macaque were born lighter and experienced ‘catch-up growth’.... ......................................................................................................57 V|Page 4.3 Higher glucose clearance rate, total cholesterol and triglycerides observed in IUGR juvenile macaques at 15 months .........................................................................59 4.4 Accelerated insulin-glucose signaling observed in IUGR juvenile macaques ........59 4.5 Faster deterioration of insulin-glucose signaling in IUGR juvenile macaques compared to control juvenile macaques exposed to an high fat diet .............................60 4.6 Adult cynomoglus macaque IGT model established and validated ........................62 4.7 Deterioration of insulin-glucose signaling observed in IGT macaque ....................64 4.8 Similar gene expression of AKT1, AKT2 and IRS1 between IUGR juvenile macaques and adult IGT macaques - the transition point from insulin sensitive to insulin resistance.... ..................................................................................................66 4.9 Strengths and limitations of these studies ...............................................................68 CHAPTER 5 CONCLUSIONS.....................................................................................70 BIBLIOGRAPHY ..............................................................................................................72 VI | P a g e SUMMARY Proposed by Hales & Barker, the thrifty phenotype hypothesis explains how a change in fetal environment leading to fetal growth retardation, causes a permanent alteration in development of metabolic organs and their functions. These adaptions are necessary in order to survive and grow in a poor nutritional environment, but may cause metabolic diseases in later life if postnatal life is paradoxically characterized by having improved nutrition and catch-up growth. Although there are many studies in animal models showing associations between intrauterine growth restriction (IUGR) and the development of type 2 diabetes mellitus (T2DM), most studies have been done on the rodent model, which does not display similar reproductive physiology and disease progression as humans. Also, there is a lack of skeletal muscle tissues analysis of IUGR subjects in this field of research. A better animal model, such as the nonhuman primate model, is needed to reflect how the development of IUGR may later lead to T2DM in humans. Therefore, the aims of this thesis are to explore distinctive characteristics of insulin-glucose metabolism at the gene expression level, physical and biochemical characteristics in nutrition-mediated IUGR and impaired glucose tolerance (IGT) cynomolgus macaques. Studies on IUGR and IGT cynomolgus macaques were done concurrently. After a 35% high fat diet treatment, IGT macaques were heavier in weight, higher in body mass index, lower glucose clearance rate, hyperinsulinemia, and showed greater insulin resistance as compared to control macaques. Gene expression analysis from real time polymerase chain reaction showed a 1.25-1.4 fold increase in AKT1, AKT2 and MSTN, and 2.3-2.8 VII | P a g e fold decrease in IRS1 and SLC2A4RG in IGT macaques, indicating less responsive insulin-glucose signaling. IUGR macaques were weighed 10% lighter at birth and experienced a ‘catch-up growth’ in the first 3 months, before having similar growth patterns as control macaques till 9 months. Higher glucose clearance rates, total cholesterol and triglycerides level were observed in IUGR macaques at 15 months. Furthermore, gene expression analysis showed a 3-6 fold decrease in AKT1, AKT2 and MSTN, and 1.9-7.7 fold increase in PIK3R1, IRS1 and SLC2A4RG, indicating accelerated glucose-insulin signaling. Subjected to high fat diet treatment, IUGR macaques exhibited similar characteristic as IGT macaques, having up regulated AKT1, MEF2A and GSK3b, and down regulated IRS1 compared to IUGR macaques undergoing a standard diet. Whereas, control macaques with high fat diet showed 1.8-4.7 fold increase in SLC2A4, HK2, IRS1 and SLC2A4RG, and 4 fold decrease in MSTN, indicating elevated insulin-glucose signaling. All these conclude that the insulin glucose metabolism in IUGR subjects were accelerated at the beginning and thus developed symptoms of IGT faster than normal subjects after being fed with a high fat diet. VIII | P a g e LIST OF TABLES Table 1: Summary of studies using IUGR rodent model and exhibit changes in organs and gene involving in insulin-glucose metabolism. ...........................................................11 Table 2: List of gene, its function and the primer designed for this study ........................20 Table 3: Details of gentleMAC Dissociator setting for samples homogenization.………23 Table 4: Details of real time PCR setting for different gene of interest ............................26 Table 5: Sequence homology of PCR products against human and rhesus macaque sequences derived from multiple sequence alignment using ClustalW2 ...........................31 Table 6: Noenates’ morphometric at birth .........................................................................33 Table 7: Infant macaques’ morphometric at 3 months old ................................................33 Table 8: Juvenile macaques’ morphometric at 6 months old ............................................34 Table 9: Juvenile macaques’ morphometric at 9 months old ……… ...............................34 Table 10: Juvenile macaques’ morphometric and IVGTT k-value at 12 months old........37 Table 11: Juvenile macaques’ morphometric and biochemical parameters at 15 months old……………………………………………………………………………………….. 38 Table 12: Relative quantification of IUGR juvenile macaques gene expression against control juvenile macaques..................................................................................................39 Table 13: Juvenile macaques’ morphometric and biochemical parameters at 24 months old, 9 months after diet treatment ......................................................................................44 Table 14: Relative quantification of C-H, I-S and I-H juvenile macaques gene expression against C-C-S juvenile macaques as reference group ……… ...........................................49 Table 15 Adult macaques’ morphometric before and after diet treatment ........................52 Table 16: Adult macaques’ biochemical parameters before and after diet treatment........53 Table 17: Relative quantification of IGT macaques gene expression against NGT macaques ……… ...............................................................................................................54 Table 18: A direct comparison in the insulin-glucose biochemical data and the gene expression data between IUGR high fat juvenile macaques and IGT adult macaques .....67 IX | P a g e LIST OF FIGURES Figure 1: Progression of T2DM, highlighting the key metabolic syndrome in the genesis of the disease……… ............................................................................................................5 Figure 2: : Part of the insulin-glucose signaling system, focusing on IRS/PI3K/AKT pathway…………………………………………………………………………………… 6 Figure 3: Causes of IUGR, which have the impact on metabolic sites and develop T2DM in the later stage of life.…………........................................................................................9 Figure 4: Progression of T2DM from lean to obese with IGT, hyperinsulinemia, and T2DM in cynomolgus monkeys.........................................................................................13 Figure 5: Muscle biopsy of cynomolgus macaques indicating the position of thigh pelvis joint and the site of muscle to be taken ..............................................................................18 Figure 6: Standard curve of FOXO1 Real time PCR for primer efficiency calculation ....29 Figure 7: PCR efficiency of all the primers used in the experiments ................................30 Figure 8: 1.5% Agarose gel electrophoresis of PCR products...........................................31 Figure 9: Dissociation curve analysis of PCR products.....................................................32 Figure 10: Trend of juvenile macaques’ weight and weight gains from 0 to 9 months ....35 Figure 11: Trend of juvenile macaques’ CRL and CRL gains from 0 to 9 months ...........36 Figure 12: Trend of juvenile macaques’ BMI and BMI changes from 0 to 9 months .......36 Figure 13: Relative quantification of IUGR juvenile macaques gene expression against control juvenile macaques..................................................................................................40 Figure 14: 15 months juveniles macaque scatterplots and linear trendline .......................41 Figure 15: Graphic repersentation of Juvenile macaques’ morphometric and biochemical parameters at 24 months old, 9 months after diet treatment ..............................................45 Figure 16: : Graphic representation of relative quantification of SLC2A4, IRS2, MEF2A, HK2, MSTN, PIK3R1, INSR, GCK, PKM2, GYS1, AKT1 and AKT2 in C-H, I-S and IH juvenile macaques against C-S juvenile macaques as reference group ……………. ...47 Figure 17: : Graphic representation of relative quantification of PIKC3a, PIK2Cb, PDPK1, GSK3b, FOXO1, IRS1 and SLC2A4RG in C-H, I-S and I-H juvenile macaques, against C-S juvenile macaques as reference group ………… ...........................................48 Figure 18: 24 months juveniles macaque scatterplots and linear trendline .......................51 Figure 19: Graphic representation of relative quantification of IGT macaques gene expression against NGT macaques ....................................................................................55 Figure 20: Adult macaques scatterplots and linear trendline .............................................56 X|Page Figure 21: Photos of adult macaques involved in prediabetes study .................................62 Figure 22: A schematic diagram of the hypothesis on accelerated insulin-glucose signaling and early development of metabolic disease in IUGR subject...........................68 XI | P a g e ABBREVIATIONS T2DM WHO OGTT IGT IRS INSR PI3K PDPK GSK3b GYS FOXO1 GCK HK PKM SLC2A4 MEF2A SLC2A4RG IUGR LBW SGA HC/AC PEPCK G6Pase PGC-1α BACT GAPDH RPL13a GD PCR IVGTT BMI HOMA-IR QUICKI ANOVA SD RIN HDL LDL Type 2 diabetes mellitus World Health Organization Oral glucose tolerance test Impaired glucose tolerance Insulin receptor substrate Insulin receptor Phosphatidylinositol 3-kinase phosphatidylinositol 3-kinase dependent kinases Glycogen synthase kinase 3 beta Glycogen synthase Forkhead box O1 Glucokinase Hexokinase Pyruvate kinase Glucose transporter 4 Myocyte Enhancer Factor 2A Glucose transporter 4 regulatory gene Intrauterine growth restriction Low birth weight Small for gestational age Head-to-abdominal circumference ratio Phosphoenolpyruvate carboxykinase Glucose-6-phosphatase Peroxisome proliferator-activated receptor-γ coactivator-1α Beta actin Glyceraldehyde-3-phosphate dehydrogenase Ribosomal protein large subunit 13a Gestational day Polymerase chain reaction Intravenous glucose tolerance test Body mass index Homeostasis model assessment of insulin resistance Quantitative insulin sensitivity check index Analysis of variance Standard deviation Ribonucleic acid integrity number High density lipoprotein Low density lipoprotein XII | P a g e DNA RNA mRNA cDNA RQ rcf CRL CHL Deoxyribonucleic acid Ribonucleic acid Messenger ribonucleic acid Complementary deoxyribonucleic acid Relative quantification Relative centrifugal force Crown-rump length Crown-heel length XIII | P a g e CHAPTER 1 INTRODUCTION 1.1 Intrauterine growth restriction Intrauterine growth restriction (IUGR) is a condition of poor fetal growth in utero, due to a maternal restricted environment in which a fetus is unable to achieve its maximum growth potential before it is born (Monk and Moore, 2004). Currently, the common markers used for detecting IUGR babies are the birth weight and the infant size relative to the population growth curves. Defined by World Health Organization (WHO), birth weight below 2500g and head/abdominal circumference below 10th percentile of the birth population are considered as ‘low birth weight (LBW)’ and ‘small for gestational age (SGA)’ respectively. A term baby born with LBW and SGA can be diagnosed as having IUGR (Harkness and Mari, 2004). IUGR can be subdivided into symmetric and asymmetric fetal growth. Head-to-abdominal circumference ratios (HC/AC) have been used at classifying fetuses into various subtypes based on head proportionality; overall smaller HC and AC with ratio close to normal baby morphometric (symmetrical) or those with relative head sparing (asymmetrical) (Harkness and Mari, 2004). Although the terms ‘IUGR’, ‘LBW’ and ‘SGA’ have generally similar classification with one another, they are not interchangeable: not all LBW or SGA babies are IUGR (Tan and Yeo, 2005). 1.1.1 Implications of IUGR IUGR is one possible cause of adverse health effects in the later stages of an individual. Many epidemiological studies in human have uncovered associations between restricted growth in utero and the susceptibility to developing insulin resistance and/or impaired glucose tolerance (IGT) due to LBW, leading to the acquisition of chronic diseases such 1|Page as Type 2 diabetes mellitus (T2DM) and hyperlipidaemia in the later stages of life (Phillips et al, 1994; Eriksson et al, 2003; Gesina et al, 2004). These observations were explained using the “thrifty phenotype” hypothesis proposed by Hales & Barker, which states that fetal growth retardation causes a change in fetal environment, leading to a permanent alteration in development of metabolic organs and their functions, serving to protect key organs, especially the brain. Such fetal “programming” is necessary in order to survive and grow in a poor nutritional environment. However this may lead to metabolic diseases after having improved nutrition and catch-up growth later in life (Hales and Barker, 1992). 1.2 Type 2 diabetes mellitus T2DM is one of the most common chronic diseases in the world. Until 2011, more than 300 million people worldwide have been diagnosed with diabetes, of which 90% are classified as T2DM (WHO, 2001). This number is projected to double both by population size and mortality rates by the year 2030 (Wild et al, 2004). In Singapore, diabetes is the fifth most common medical condition diagnosed affecting more than 400,000 adults from 18 to 65 years old. This number is about 11.3% of Singapore’s population. It is also one of the top 6 killer diseases in Singapore that have accounted for 1,700 to 3,500 deaths per annum from 2008 to 2010 (Ministry of Health, Singapore, 2011). Acquisition of T2DM is mainly due to lifestyle factors, and obesity is one of the factors strongly associated with T2DM (Weir and Leahy 1994). In Singapore, the prevalence of obesity in 2010 was 10.8%. This is almost double the figure in 1998, where the prevalence was only 6.0% (Ministry of Health, Singapore, 2011), indicating a strong trend of growing numbers of T2DM patients in the future. High dietary fat diet, defined as diet with more than 30% of 2|Page calories derived from fat (Surwit et al, 1988), is one of the lifestyle factors that cause obesity. Foods which are high in fat content are commonly found in deep fried food, cream cakes, cookies, processed meat and canned food. Such diets are viewed as unhealthy as they increase the amount of fatty acids available for oxidation in skeletal muscle, resulting in excess energy available and causing energy imbalance in the body (Bray & Popkin, 1998). To deal with such problem, the body covert those excess energy to adipose tissues and distribute around organs and inside abdominal cavity for storage, causing obesity when excess body fats are accumulated. There are many long term complications that are associated with T2DM, and they are categorized into two groups: microvascular diseases (such as nephropathy, retinopathy and neuropathy) and macrovascular diseases (such as peripheral vascular disease, stroke, ischemic and coronary heart disease) (Betteridge, 1996; Coutinho et al, 1999; Sarwar et al, 2010; Boussageon et al, 2011) All these complications may lead to increased mortality, making T2DM a metabolic disease that cannot be neglected. 1.2.1 Diagnosis of T2DM Singapore uses the same guidelines as WHO recommendations. T2DM can be diagnosed if any of the 3 following observations is presented: 1. Fasting plasma glucose more than 7.0mmol/L 2. Casual plasma glucose more than 11.1mmol/L 3. 2 hours plasma glucose during 75g oral glucose tolerance test (OGTT) more than 11.1mmol/L (Goh et al, 2011) 3|Page 1.2.2 Progression of T2DM T2DM is a progressive disease that develops over the years with different stages: from normal glucose tolerance to an intermediate stage of IGT called prediabetes, and lastly aggravated to T2DM (Edelstein et al, 1997). T2DM is linked with a significant period of prediabetes characterized by increased basal insulin secretion, decreased insulin sensitivity and presence of insulin resistance (figure 1) (Cefalu WT, 2000; Barr et al, 2007). Studies have showed that during this period of time, patients suffered a gradual drop in the insulin secretory capacity of pancreatic islet β-cell, causing IGT (Buchanaan 2003; Weyer et al.1999, 2001). Using WHO and Singapore diagnostic criteria, IGT is diagnosed if fasting plasma glucose is between 6.1 to 7.0mmol/L or 2 hours plasma glucose during 75g OGTT between 7.8 to 11.1mmol/L (Goh et al, 2011). As the disease progresses, the islet function deteriorate to the point whereby it is unable to compensate fully for the degree of insulin resistance, clinically overt T2DM develops. (Buchanaan, 2003; Weyer et al.1999, 2001). Increased risk of hypertension, dyslipidemia, arteriosclerotic vascular disease and cardiovascular pathology were observed due to complications of abnormal glucose homeostasis (Cefalu WT, 2000; Barr et al, 2007). 1.3 Insulin-glucose signaling pathway The insulin-glucose signaling system regulates the storage and usage of energy (primarily glucose), as well as the growth and development of tissue. Insulin plays a major role in blood glucose regulation as it promotes cellular glucose uptake, glycogen synthesis in skeletal muscle and liver, and inhibits gluconeogenesis in the liver (DeFronzo and 4|Page Ferrannini, 2001). It works in tandem with the glucose glycolysis pathway, utilizing this energy source to promote growth and development of tissue (DeFronzo and Ferrannini, 2001). Figure 1: Progression of T2DM, highlighting the key metabolic syndrome in the genesis of the disease. The shaded area signifies the presence of the metabolic syndrome. Adopted from Cefalu WT, 2000. 1.3.1 Overview of insulin action through IRS/PI3K/AKT pathway The overview of the insulin-glucose signaling pathway for glucose metabolism is shown in figure 2. At the start of the pathway, insulin binds to a cell surface receptor that belongs to a sub-family of growth factor receptor tyrosine kinases: Insulin receptor (INSR). INSR propagates the signal to insulin receptor substrate (IRS) by phosphorylation and then phosphatidylinositol 3-kinase (PI3K). PI3K activates a PI3Kdependent kinases, PDPK1 (Alessi et al, 1997) which in turn phosphorylates and activates additional serine/threonine kinases, mainly AKT1 and AKT2 (Burgering and 5|Page Coffer, 1995). AKT phosphorylates glycogen synthase kinase 3 beta (GSK3b) (Cross et al, 1995), which removes the inhibition of glycogen synthase (GYS). Such action allows glycogenesis to proceed, converting excess glucose to glycogen for storage. AKT also phosphorylates and inhibits FOXO transcription factor Forkhead box O1 (FOXO1) (Brunet et al, 1999), leading to stimulation of glycolysis and gene expression for enzymes involving glycolysis, such as glucokinase (GCK), hexokinase (HK) and pyruvate kinase (PKM). In addition, there is evidence of AKT activation involved in stimulation of glucose transport via aiding the translocation of glucose transporter 4 (SLC2A4) (Kohn et al, 1996). On the other hand, the expression of SLC2A4 is regulated by both Myocyte Enhancer Factor 2A (MEF2A) and glucose transporter 4 regulatory gene (SLC2A4RG). Both interact with each other to control the amount of SLC2A4 available for translocation to the cell membrane (Mora and Pessin, 2000; Sparling et al, 2008). Figure 2: Part of the insulin-glucose signaling system, focusing on IRS/PI3K/AKT pathway. 6|Page 1.3.2 Abnormal gene regulation of insulin-glucose signaling pathway in T2DM There is evidence linking changes in expression profile of insulin-glucose gene with T2DM. Mice with IRS1 and IRS2 knockout exhibit insulin resistance and subsequently develop diabetes (Tamemoto et al, 1994; Araki et al, 1994). Reduced activation of PI3K due to decreased IRS1 signaling was observed in insulin resistant ob/ob mice and these observations were similar to streptozotocin induced diabetes rats (Folli et al, 1993). In addition, deletion of PI3K catalytic subunits alpha, beta, and regulatory subunit 1 in mice displayed IGT and hyperinsulinemia as compared to the control group (Brachmann et al, 2004). Because of reduced IRS/PI3K activation, a decrease in PDPK1 activation was also seen in muscle tissue of human subjects suffering from T2DM (Kim et al, 1999). Another human muscle study showed that up regulated GSK3b activity was observed, which causes increased phosphorylation of GYS1 and deactivates glycogen synthase. The rate of glycogen synthesis was assessed after 75g OGTT and 3 hours hyperinsulinemic euglycemic clamps, whereby diabetes subjects had a slower rate of glycogen synthesis compared to normal subjects (Nikoulina et al, 2000). AKT phosphorylation was observed to be impaired in an in vitro insulin resistance muscle cell culture system, but this observation was not made in a muscle biopsy specimen (Ueki et al, 1998). Impaired activity and dysregulation of glycolytic enzymes HK, GCK and PKM were detected in patients with T2DM (Vestergaard et al, 1995; Njølstad et al, 2001; Wang et al 2002; Beale et al, 2004). Lastly, SLC2A4 and its gene regulation were affected in T2DM subjects. Impaired translocation of SLC2A4 to the cell membrane surface was reported in a study looking at insulin resistant human podocytes (Lennon et al, 2009). SLC2A4 knockout mice developed severe insulin-resistant diabetes with high blood glucose 7|Page (Stenbit et al, 1997; Joost et al, 2002). Reduced MEF2A and/or SLC2A4RG decreases interaction at DNA binding site of the SLC2A4 gene and was discovered in diabetic mice (Mora and Pessin, 2000; Sparling et al, 2008). All the above indicates that any abnormal changes in this signaling pathway will result in insulin resistance, IGT and T2DM. 1.3.3 Linking IUGR and T2DM Although there is significant evidence linking IUGR with the development of diabetes as each individual grows, the molecular mechanisms underlying the association between IUGR and the development of diabetes are not very well understood. Hence, there is an urgent need to understand the pathogenesis of T2DM caused by IUGR, in order to determine effective treatment and management of the disease. In order to investigate the molecular mechanisms by which IUGR leads to eventual development of T2DM, different animal models, mostly rodent models, have been developed and are widely used for such studies. There are four methods of generating IUGR animals: Bilateral uterine artery ligation, maternal low protein diet, maternal caloric restriction and over-exposure of the maternal glucocorticoid. Table 1 presents a meta-analysis of rodent models used for IUGR induction using the four methods mentioned, and their findings on the immediate and future impact on offspring. All, except the Vuguin et al study, showed significant lower birth in IUGR pups. Using birth weight as the main factor for successful IUGR induction, maternal caloric restriction appears as the best method out of the four mentioned. Islet and β-cell mass were smaller as compared to control pups, with decreased insulin secretion (Arantes et al, 2002; Styrud et al, 2005; Inoue et al, 2009). As for organs development, gene expression of key gluconeogenesis enzymes, phosphoenolpyruvate carboxykinase (PEPCK), glucose-6-phosphatase (G6Pase) and 8|Page peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) were up regulated in the liver of IUGR subjects (Nyirenda et al, 1998; Vuguin et al, 2004; Buhl et al, 2007; Liu et al, 2009). This phenomenon was explained by the failure to inhibit gluconeogenesis via AKT signaling pathway (Vuguin et al, 2004). Skeletal muscle research by Thamotharan et al discovered SLC2A4 expression level and protein were decreased in IUGR subjects (Thamotharan et al, 2004). As IUGR subjects grew up, they developed fasting hyperglycemia, hyperinsulinemia and IGT at the early stage of life, and then subsequently developed T2DM. The above mentioned data is summarized in figure 3. Figure 3: Causes of IUGR, which have the impact on metabolic sites and develop T2DM in the later stage of life. Modification from Martin-Gronert and Ozanne, 2007. 9|Page 1.4 Gaps in current research of IUGR and T2DM Although there are many studies in animal models showing associations between IUGR and the development of T2DM, most studies were done on the rodent model. Rodent models have obvious advantages such as ease of maintenance, short gestation periods, short lifespan, and most importantly lower cost, which makes longitudinal studies using large number of animals attractive. However a major limitation is that rodent model of diabetes does not demonstrate the similarities for pathophysiological conditions observed in humans with T2DM (Cefalu WT, 2006). In addition, most of the studies were focused on pancreas islet, β-cell and liver conditions in IUGR subjects, but only a handful of studies focused on skeletal muscle tissues of IUGR subjects. Being the main site of insulin-dependent glucose disposal, any abnormal condition or dysregulated gene detected at this area is an indication of the development of IGT and subsequently T2DM (Cline et al, 1999). Therefore, studies on this tissue are necessary to give a more comprehensive overview on the impact of growth restriction on the metabolic organs and the molecular pathogenesis of T2DM. Lastly, rodents have polytocous pregnancies and give birth to litters of offspring. Natural IUGR may arise from such pregnancies and will decrease the reliability of the study. Human pregnancies are generally monotocous. All these reasons suggest that there is a need for a better animal model reflecting the disease conditions in humans. 1.5 Nonhuman primate: a better animal model of IUGR and T2DM There are many nonhuman primate models of diabetes in various existing studies. Old world nonhuman primates have reported natural cases of T2DM, with the disease starting 10 | P a g e Reference Strain Induction method % weight lighter in IUGR 15% Tissue analyzed Observations in IUGR subjects Islet and βcell Mild fasting hyperglycemia and hyperinsulinemia observed. Became glucose intolerance, insulin-resistant and having 50% lesser in β-cells mass after 7 weeks Basal hepatic glucose production was significantly higher in IUGR. PEPCK and G6Pase expression level was higher in IUGR β-cell mass and insulin content were reduced by 35–40% in IUGR. No difference in glucose tolerant between 2 groups initially, but IUGR were glucose intolerant after 3 month PEPCK and GR expression level was higher in IUGR. Fasting hyperglycemia, reactive hyperglycemia and hyperinsulinemia observed Fasting hyperglycemia and glucose intolerance observed. PEPCK and IGFBP-1 expression level was higher. No difference in IGF-I and GR expression level IUGR have decrease in islet mass and insulin secretion. PDX-1 protein and mRNA levels were reduced in IUGR Fasting hyperglycemia observed in IUGR. G6Pase, PEPCK,PGC-1α expression level was higher in IUGR No difference in SLC2A4 expression level in adipose tissue. Decrease SLC2A4 expression level in skeletal muscle was observed Simmons et al., Spraque2001 Dawley Bilateral uterine artery ligation Vuguin et al., 2004 SpraqueDawley Bilateral uterine artery ligation No difference Liver Styrud et al., 2005 SpraqueDawley Bilateral uterine artery ligation 10% Islet and βcell Nyirenda et al., Wistar 1998 Dexamethasone administration 10% Liver Buhl et al., 2007 SpraqueDawley Dexamethasone administration 13% Liver Arantes et al., 2002 Liu et al., 2009 Wistar 8% Islet and βcell Liver Thamotharan et al., 2004 SpraqueDawley Low protein diet (6%) Low protein diet (8%) Caloric restriction (50%) Wistar 10% 25% Skeletal muscle & white adipose tissue Islet and βcell Caloric 19% 75% decrease in β-cell mass and 60% decrease in islet restriction density observed. Fasting hyperglycemia and glucose (30% ) intolerance observed in IUGR. Table 1: Summary of studies using IUGR rodent model and exhibit changes in organs and gene involving in insulin-glucose metabolism. Inoue et al., 2009 C57BL6J 11 | P a g e from glucose tolerance and insulin resistance with compensatory hyperinsulinemia, followed by IGT with declining glucose clearance, reported in k-value derived from intravenous glucose tolerance test (IVGTT), and lastly continued deterioration of insulinglucose prior to signs of hyperglycemia and diabetes (figure 4) (Hansen and Bodkin, 1986; Bodkin, 2000; Wagner et al 2001; Tigno et al, 2004). T2DM prevalence increases in nonhuman primates with age and obesity (Bodkin, 2000). As the progressive history of the disease and the response to dietary management are closely similar to humans, therein lies the major advantage of disease detection as compared to rodent model whereby the prediabetic phase is often undetectable. Another advantage over the rodent model is the development of atherosclerosis in nonhuman primate models and increased risk of cardiovascular disease as T2DM progresses (Clarkson 1998), where these observations are not present in the rodent model. The nonhuman primate genome is genetically similar to the human genome, with the examples of the rhesus macaque (Macaca mulatta) and cynomolgus macaques (Macaca fascicularis) having 93% and 91% homology with humans respectively (Gibbs et al, 2007). Nonhuman primates are a better model for IUGR studies, as evidence shows similar reproduction physiology and in utero development of the fetus, especially endocrine development, compared to humans (Tarantal and Hendrickx, 1988). Furthermore, primates have monotocous pregnancies. The gestational period is shorter (154-180 days) compared to humans, though comparatively longer than rodent. However the high maintenance costs of nonhuman primate models may make them less attractive for longitudinal studies, explaining on the lack of longitudinal IUGR studies in nonhuman primate. Regardless, a longitudinal study on this area is highly beneficial, as any developments made through this nonhuman 12 | P a g e primate study, enables a controlled study of the development of IUGR and later leading to T2DM in humans, and has potential for further studies in disease prevention. Figure 4: Progression of T2DM from lean to obese with IGT, hyperinsulinemia (HI), and T2DM in cynomolgus monkeys. The proposed association with cardiovascular disease (Vascular Dz) is projected. Adopted from Bodkin N.L, 2000. 1.6 Hypotheses and objectives The proposed hypotheses in this thesis are: 1. Metabolic gene expression levels, physical and biochemical characteristics are different in IUGR offspring displaying abnormal catch-up growth, as compared to normal offspring at the early juvenile stage of life 2. Metabolic gene expression levels of genes involved in insulin and glucose metabolism, physical and biochemical characteristics are different between normal and IGT adult macaques 13 | P a g e 3. IUGR macaques with high fat diet develop IGT earlier than normal macaques, with the progression similar to the adult IGT macaques model From the hypotheses, the objectives derived are: 1. To morphologically characterize nutrition-mediated IUGR infant cynomolgus macaques for the first 9 months of their life. 2. To investigate the gene expression levels of genes involved in insulin and glucose metabolism, physical and biochemical characteristics, before and after high fat diet treatment in nutrition-mediated IUGR model. 3. To establish an adult nonhuman primate IGT model using cynomolgus macaques 4. To investigate the metabolic gene expression levels of genes involved in insulin and glucose metabolism, physical and biochemical characteristics in the adult IGT macaques model 5. To explore any similarity in gene expression, physical and biochemical characteristics between IUGR macaques with high fat diet and the adult IGT macaques model. 14 | P a g e CHAPTER 2 MATERIALS AND METHODS 2.1 Cynomolgus macaque nutrition-mediated IUGR model Nutrition-mediated IUGR macaque model was set up by Chng et al (unpublished work) prior to my candidature. The study is as follows: sexually mature male and female cynomolgus macaques were group housed for natural breeding. Female macaques were routinely scanned by ultrasound (GE Logiq S6, GE Healthcare) every three weeks to facilitate pregnancy detection. Once pregnancy was confirmed, macaques were randomly assigned to either the control or IUGR group. Control dams were given 100% standard lab diet (Laboratory Fiber-Plus Monkey Diet 5049, Lab Diet) throughout the pregnancy, whereas IUGR dams were given 35% fewer in amount compared to the control dams from Gestational day (GD) 32 to GD 70, and then 30% fewer in amount from GD 71 to the end of pregnancy. All food intake were monitored throughout pregnancy and pregnant dams were scanned every month from GD 30 to GD 125 to monitor fetal viability and growth in utero. All neonates were delivered naturally and their birth weights and morphometrics were measured at birth. The growth of macaques derived from the control and IUGR group were monitored throughout the study. Weight and morphometrics were measured every three months staring from birth. IVGTT and blood test were done at 12 months of age. All were given 100% standard lab diet, until 15 months where diet treatment was started, and macaques were further divided randomly into four groups: Control-Standard diet (C-S), ControlHigh fat diet (C-H), IUGR-Standard diet (I-S), IUGR-High fat diet (I-H). Standard diet groups continued to receive standard lab diet (Laboratory Fiber-Plus Monkey Diet 5049, 15 | P a g e Lab Diet) comprising 26% protein, 14% fat and 60% carbohydrate. However, high fat diet groups were given 35% high fat diet (Obesity induced primate diet, Altromin) comprising 18% protein, 35% fat and 47% carbohydrate. Weight, physical measurement, IVGTT and blood tests and muscle biopsies were carried out at 15 months (before diet treatment) and 24 months (9 months after diet treatment) All animal procedures were approved and conducted in compliance with standards of Agri-Food & Veterinary Authority of Singapore, and guidelines established by the Institutional Animal Care and Use Committee of Singapore Health Services, under protocol IACUC #2009/SHS/445. Animal husbandry and veterinary procedures were done with the assistance of research veterinarian and technicians. The studies in this thesis started when most of the juvenile macaques were at 18 months old. 2.2 Adult cynomolgus macaque IGT model 14 male macaques were randomized into two groups, NGT (Normal glucose tolerance) and IGT (Impaired glucose tolerance). NGT group was given the standard lab diet (Laboratory Fiber-Plus Monkey Diet 5049, Lab Diet), while the IGT group was given high fat diet (Obesity induced primate diet, Altromin). Muscle biopsy, IVGTT, blood test and morphometric measurements were scheduled 6 months after diet treatment. All animal procedures were approved and conducted in compliance with standards of AgriFood & Veterinary Authority of Singapore, and guidelines established by the Institutional Animal Care and Use Committee of Singapore Health Services, under protocol IACUC #2008/SHS/418. 16 | P a g e 2.3 IVGTT, blood test and physical measurement Each subject was fasted overnight for at least 16 hours prior to an IVGTT. On the day of the procedure, the subject was sedated with 10mg/kg ketamine hydrochloride (Parnell) intramuscularly. After the subject was anesthetized, it was weighed and transferred to procedure table. A total of 3 ml of blood was drawn and the tubes were centrifuged at 3,000 relative centrifugal force (rcf) for 10 minutes at room temperature. The blood serum was transferred into new tubes and they were sent to NUS referral laboratory for glucose, insulin and lipid panel analysis. IVGTT was performed after blood taking. Fasting glucose (t = 0 minute) was measured using a handheld glucometer (Medisense Optium Xceed, Abbott Singapore), before injecting 750mg/kg dextrose mixed with an equal volume of saline (0.9% sodium chloride) intravenously over 3 minutes. Glucose was measured at 9 different time points (t = 1, 5, 7, 10, 15, 20, 30, 40, 60 minutes) after dextrose injection. One final measurement at t = 90 minutes was taken to ensure that blood glucose had returned to normal levels, additional measurements were taken every 10 minutes if blood glucose was still above the normal range. Subject’s weight, crown-rump length (CRL) and crown-heel length (CHL) were taken prior to transport back to the cage. The k-value from the IVGTT data was calculated using the formula proposed by Dreval and Ametov, 2007. Body mass index (BMI) for adult macaques were calculated using the formula: or . With the fasting glucose and insulin obtained, homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using formula written by Matthew et al, 1985, and 17 | P a g e Quantitative insulin sensitivity check index (QUICKI) was calculated using formula written by Katz et al, 2000. All values were recorded in the subject file and kept for further analysis. 2.4 Muscle biopsy Subject was fasted overnight at least 16 hours prior to the procedure. On the day of the biopsy, the subject was sedated with 10mg/kg ketamine hydrochloride (Parnell) intramuscularly. After the subject was anesthetized, it was weighed and transferred to a procedure table. The hair at the right lateral thigh was shaved to expose the skin. Using the thigh-pelvis joint as a reference point, appropriately 3cm to the left of greater trochanter of femur was marked for site of biopsy (figure 5). The area was disinfected using hexodane and septanol (ICM Pharma) followed by punching the area using a sterile 6mm biopsy punch (Stiefel). Immediately, the tissue extracted was trimmed and weighed, ~ 3cm thigh-pelvis joint Figure 5: Muscle biopsy of cynomolgus macaques indicating the position of thigh-pelvis joint and the site of muscle to be taken. 18 | P a g e and the muscle tissue was transferred into a cryovial, which was snap frozen in liquid nitrogen. At the same time, the excision area was sutured and cleaned, followed by administration of subcutaneous analgesic and antibiotic (1.4mg/kg Carpofen and 75mg/kg Betamox respectively). The subject was returned to the cage and placed under observation for a few days. All tissues were stored at -80oC until further processing 2.5 Oligonucleotide primers design and production Oligonucleotide primers were designed using the web-based program NCBI primer BLAST (www.ncbi.nlm.nih.gov/tools/primer-blast) and primer3 (frodo.wi.mit.edu). As cynomolgus macaque genome was not yet available at that point of time, human and rhesus macaque genome were used for the primer design and sequence alignment was done to confirm that flanking regions were conserved. Desired primers were ordered from Sigma and resuspended in 100ul of Milli-Q water (Merck Millipore). Working primer solutions were prepared by diluting the stock to 2uM with Milli-Q water. All primers were stored at -20oC. The primers used are listed in table 2. 2.6 Total RNA extraction 1ml of TRIzol (Invitrogen) was added into each muscle tissue sample, followed by samples homogenization using gentleMAC Dissociator and M-tube (Miltenyi Biotec) with the following manufacturer settings shown in table 3. After homogenization, the tubes were centrifuged at 3,000 rcf for 5 minutes at 4oC, transferring the supernatant to a new 2ml microtubes and discarding the pellet. The microtubes were incubated at room temperature (25oC to 30oC) for 5 minutes, before adding 200ml chloroform (Sigma) to each tube, then vortexing them for 15 seconds and incubating for another 3 minutes 19 | P a g e Gene code Gene name SLC2A4 Glucose transporter 4 INSR Insulin receptor GCK Glucokinase IRS2 Insulin receptor substrate 2 MEF2A Myocyte enhancer factor 2A PKM2 Pyruvate kinase muscle GYS1 Glycogen synthase 1 HK2 Hexokinase 2 AKT1 v-akt murine thymoma viral oncogene homolog 1 Function Transportation of glucose across cell membrane insulin-activated receptor tyrosine kinase phosphorylation of glucose to glucose-6phosphate signal transducer of insulin-glucose metabolism transcription factor for cellular and growth differentiation dephosphorylation of phosphoenolpyruvate to pyruvate Synthesis of glycogen from glucose phosphorylation glucose to glucose 6phosphate protein serine/threonine kinase Primer Sequence (5’ to 3’) Product size (bp) Forward: AGCCTCATGGGCCTGGCCAA Reverse: CCCAGCACCTGGGCGATCAG 203 Forward: AGGGCTGAAGCTGCCCTCGA Reverse: AGATGGCCTAGGGTCCTCGGC 247 Forward: ACTCCATCCCCGAGGACGCC Reverse: TCTCGCAGAAGCCCCACGACA 238 Forward: CGAGGGCTGCGCAAGAGGAC Reverse: GTCGTCTGCCCCCAGGTTGC 249 Forward: AGAGGGTGCGACAGCCCAGA Reverse: GCTGGCTGCCAAAGATGGGGA 234 Forward: CGCCCATTACCAGCGACCCC Reverse: GCCTCGGGCCTTGCCAACAT 299 Forward: TGGCTGATCGAGGGAGGCCC Reverse: CGGGCACGACACAGGCAGAG 266 Forward: CCCCTGCCAGCAGAACAGCC Reverse: GCATTGCTGCCCGTGCCAAC 240 Forward: TGAAGCTGCTGGGCAAGGGC Reverse: GAGGCGGTCGTGGGTCTGGA 212 20 | P a g e AKT2 v-akt murine thymoma viral oncogene homolog 2 MSTN Myostatin PIK3Ca PIK3Cb Phosphoinositide-3kinase, catalytic, alpha polypeptide Phosphoinositide-3kinase, catalytic, beta polypeptide PIK3R1 Phosphoinositide-3kinase, regulatory subunit 1 PDPK1 3-phosphoinositide dependent protein kinase1 GSK3b Glycogen synthase kinase 3 beta FOXO1 Forkhead box O1 protein serine/threonine kinase Muscle growth differentiation factor protein serine/threonine kinase protein serine/threonine kinase transmembrane receptor protein tyrosine kinase adaptor 3-phosphoinositidedependent protein serine/threonine kinase phosphorylation and inactivation of enzyme glycogen synthase transcription factor for gluconeogenesis and glycogenolysis processes Forward: AGTGGCGGTCAGCAAGGCAC Reverse: AAAGCACAGGCGGTCGTGGG 271 Forward: GCGATGGCTCTTTGGAAGATGACG Reverse: ACCAGTGCCTGGGTTCATGTCA 215 Forward: AGCCAGAGGTTTGGCCTGCT Reverse: CCACAGTGGCCTTTTTGCAGAGG 300 Forward: TGGGGATGACCTGGACCGAGC Reverse: ACTGGCGGAACCGGCCAAAC 284 Forward: TCGCCTCCCACACCAAAGCC Reverse: TGCCAGGTTGCTGGAGCTCTG 231 Forward: AACCTGCACCAGCAGACGCC Reverse: GGGTTTCCGCCAGCCTGCTT 296 Forward: GCCAAACAGACGCTCCCTGTGA Reverse: AGCCAACACACAGCCAGCAGA 300 Forward: TGACAGCAACAGCTCGGCGG Reverse: TCTTGGCAGCTCGGCTTCGG 215 21 | P a g e signal transducer of Forward: CCCAGTGGCCGAAAGGGCAG IRS1 insulin-glucose Reverse: AGCTGGTCCCGGAAGGGACG metabolism Regulation of Glucose transporter 4 Forward: TCTCCGTCCACCCCGTCACC SLC2A4RG SLC2A4 gene and regulatory gene Reverse: TGCTCAGGCTCTGCCTGCCT glucose transporter 4 Synthesis of glycerate 1,3-bisphosphate to Glyceraldehyde-3glyceraldehyde 3Forward: GGTCGTATTGGGCGCCTGGT GAPDH phosphate dehydrogenase phosphate Reverse: TACTCAGCGCCAGCATCGCC (Housekeeping gene for this thesis) Cell cytoskeleton Forward: GTACCCCATCGAGCACGGCA BACT Beta actin (Housekeeping gene Reverse: CCAGTGGTACGGCCAGAGGC for this thesis) Component of ribosomes for protein Forward: TGGTCGTACGCTGCGAAGGC RPL13A Ribosomal protein L13a systhesis Reverse: GGCGGTGGGATGCCGTCAAA (Housekeeping gene for this thesis) Table 2: List of gene, its function and the primer designed for this study Insulin receptor substrate 1 217 203 248 246 226 22 | P a g e Step no Speed Direction Duration 1 4000 rpm clockwise 10 seconds 2 3700 rpm anticlockwise 8 seconds 3 2300 rpm clockwise 10 seconds 4 3400 rpm clockwise 7 seconds 5 2600 rpm anticlockwise 10 seconds 6 3400 rpm clockwise 10 seconds Table 3: Details of gentleMAC Dissociator setting for samples homogenization. at room temperature. The microtubes were further centrifuged at 12,000 rcf for 15 minutes at 4oC. After centrifugation, 3 phases were visible in the microtubes: aqueous upper phase containing RNA, white interphase containing DNA and red lower phase containing protein. The aqueous phase was carefully removed and transferred into a new 2ml microtube. Total RNA was precipitated by adding 525ul of Isopropanol (Sigma) to each microtube and inverting them several times before incubating them for 15 minutes at room temperature. Next, the microtubes were centrifuged at 12,000 rcf for 10 minutes at 4oC, then removing the supernatant and collect the RNA pellet. The RNA pellets were resuspended in 87.5ul of molecular grade water (First Base Pte Ltd). 2.7 DNase I digestion DNase I was prepared by mixing 2.5ul of DNase I stock solution (Qiagen) with 10ul of buffer RDD (Qiagen) for each reaction. The 12.5ul working DNase I was added to 87.5ul of RNA and incubated at room temperature for 20 minutes. Lastly, the digestion was stopped by incubating the solution at 70oC for 10 minutes. 2.8 RNA purification RNA purification was done using Qiagen RNeasy Mini Kit (Qiagen) according to the manufacturer’s protocol. 23 | P a g e With the 100ul DNase I digested RNA solution, 350ul of Buffer RLT was added, followed by adding 250ul of 100% ethanol (Sigma). The entire volume was transferred to a RNeasy spin column and centrifuged at 12,000 rcf at room temperature for 1 minute. The flow through was discarded, 500ul Buffer RPE was added to the spin column and centrifuged at 12,000 rcf at room temperature for 1 minute. The previous step was repeated with a longer centrifugation time of 5 minutes. Subsequently, the column was placed in a new 1.5ml microtube, 30ul of DEPC water was added to the spin column and incubated at room temperature for 2 minutes. The spin column was centrifuged at 12,000 rcf for 5 minute at room temperature. Finally, the column was discarded and the microtube containing purified RNA was kept at -80oC for storage. 2.9 RNA quantification All RNA was quantified using a Nanodrop ND-8000 spectrophotometer (Thermo Fisher Scientific). 1ul of the RNA was pipetted onto the detector and the absorbance at 230, 260 and 280nm was read. All the values from the readings were used to determine the purity and quantity of RNA in the sample. 2.10 RNA integrity assay The integrity of total RNA extracted was analyzed using Agilent 2100 Bioanalyzer and Agilent RNA 6000 Nano Kit (Agilent Technologies) according to the manufacturer’s protocol. RNA 6000 Nano dye concentrate was placed on the work bench to equilibrate to room temperature for 30 minutes. Next, 550ul of RNA 6000 Nano gel matrix was pipetted into a spin filter and centrifuged at 1,500 rcf for 10 minutes at room temperature. 65ul of the filtered gel was aliquoted into a new 1.5ml microtube and 1ul of RNA 6000 24 | P a g e Pico dye concentrate was added. The mixture was vortexed for 10 seconds, followed by centrifuging at 13,000 rcf for 10 minutes at room temperature. The gel-dye mix was added to RNA 6000 nano chip and the chip was primed using the chip priming station, before adding 1ul of heat denatured (70oC for 2 minutes) RNA samples and RNA 6000 Nano ladder, both mixed with 5ul of RNA 6000 nano maker, into the primed chip. The chip was vortexed at 2400 rpm for 1 minutes using IKA vortexer (IKA laboratory technology). Lastly the chip was inserted into Agilent 2100 bioanalyzer and the setup was run according to the default program setting. RNA with RNA integrity number (RIN) of more than 5.0 was deemed acceptable and suitable for gene quantification using real time Polymerase Chain Reaction (PCR) as recommended by the manufacturer protocol. 2.11 First strand cDNA synthesis cDNA was synthesized using Applied Biosystems High-capacity cDNA Reverse Transcription Kits (Applied Biosystems) according to the manufacturer’s protocol. 1ug of total RNA was added to a reaction mix containing 5.8ul of 10x RT buffer, 100mM dNTP, 10x RT random primers and 50 U/ul MultiScribe Reverse Transcriptase. The solution was adjusted to 20ul with molecular grade water, giving a final working solution of 1 ug RNA, 1x RT buffer and random primers, 4mM dNTP and 25U reverse transcriptase. The reactions were incubated at 25oC for 10 minutes, then 37oC for 120 minutes and lastly 85oC for 5 minutes. All cDNA was stored at -20oC 2.12 Real time PCR Real time PCR was performed using Power SYBR Green PCR Master Mix (Applied Biosystems). 20ng of cDNA was added to a reaction mix containing 12ul of 2x PCR 25 | P a g e master mix and 2uM of both forward and reverse primers. The solution was adjusted to 20ul with molecular grade water, giving a final working solution of 20ng cDNA, 1x PCR master mix, 100nM forward and reverse primers. Reactions were pipetted on a 384-well plate and the plate was inserted into 7900HT Fast Real-Time PCR System (Applied Biosystems) with the following setting in SDS 2.3 shown in table 4. Gene of interest SLC2A4, INSR, GCK, IRS2, MEF2A,PKM2, GYS1, HK2, AKT1, AKT2, MSTN, PIK3Ca, PIK3Cb, PIK3R1, PDPK1, GSK3b, FOXO1, GAPDH, BACT, RPL13a PCR Process Temperature Taq Polymerase 95oC activation Denaturation 95oC Annealing/ extension 58oC Duration Cycle 10 minutes 1 15 seconds 40 60 seconds Taq Polymerase 95oC 10 minutes activation SLC2A4RG, IRS1 Denaturation 95oC 15 seconds Annealing/ 58oC 60 seconds extension Table 4: Details of real time PCR setting for different gene of interest. 1 50 Dissociation curve analysis was done using the default setting in SDS v2.3, ramping 60oC to 95oC at 0.2% increment speed over 15 minutes. The PCR products were run on a 1.5% agarose gel and stained with ethidium bromide. 2.13 Gel extraction and sequencing Selected PCR products (INSR, SLC2A4, IRS1, IRS2, PKM2, GCK, GYS1, MEF2A, GAPDH) were extracted from agarose gel and purified using BMIAquick Gel Extraction Kit (Qiagen) according to the manufacturer’s protocol. Gel slice with DNA fragment was trimmed to 200mg and transferred into a 2ml microtube containing 600ul of Buffer QG. The microtube was incubated at 50oC for 10 minutes with vortexing every 2 minutes during the incubation. Next, 100ul of isopropanol was added to the microtube and the 26 | P a g e whole volume was transferred to the BMIAquick column before centrifuging at 13,000 rcf for 1 minute at room temperature. The flow-through was discarded and 0.5ml of Buffer QG was added to the column, followed by centrifuging at 13,000 rcf for 1 minute at room temperature. Once again, the flow-through was discarded. After that, 0.75ml of Buffer PE was added to the column and centrifuged at 13,000 rcf for 1 minute at room temperature. Additional 1 minute of centrifugation at 17,900 rcf was done after the flowthrough was discarded. The column was placed into a clean 1.5ml microtube and 50ul of Buffer EB was added to the center of the column. The column was allowed to stand for 1 minute, before centrifuging at 13,000 rcf for 1 minute at room temperature to elute the DNA fragment. Purified PCR products were sent to First Base Pte Ltd, Singapore, for sequencing. Sequences received were analyzed and aligned against Human and Rhesus macaque genome using ClustalW2. Also, sequences were aligned against cynomolgus macaque genome using web-based program NCBI BLAST. 2.14 Real time PCR analysis SDS v2.3 software was used to determine the Ct value of all reactions. Ct value of all genes had been normalized against the geometric mean of three housekeeping genes, GAPDH, BACT and RPL13a, to generate the Δct value for all genes expression for each macaque. Group Δct was calculated by taking the average Δct of each individual macaque gene expression. Using the comparative CT method, relative quantification (RQ, 2-ΔΔct) of each gene expression level was determined using the NGT group as a reference group. 27 | P a g e 2.15 Statistical analysis All statistical analysis was carried out using SPSS 19.0 software package (IBM). Independent student’s t-tests, one-way analysis of variance (ANOVA) and Pearson product-moment correlation coefficient were used for parametric data analysis and. Mann–Whitney U test, Kruskal-Wallis one-way ANOVA and Spearman's rank correlation coefficient were used on non-parametric data analysis. All descriptive statistic will be presented in mean (standard deviation, SD), except for data that were highly skewed, in which median (minimum-maximum) will be reported instead. All probability values were 2-tailed, and p value less than or equal to 0.05 was considered statistically significant for all tests. 28 | P a g e CHAPTER 3 RESULTS 3.1 Primer efficiency and specificity Primer efficiency tests were done by performing real time PCR with four DNA templates of different concentration (1.0, 0.1, 0.01, 0.001 ng). Figure 6 shows the standard curve of FOXO1 PCR. Using the formula: , primer for FOXO1 has 100% efficiency. Calculation of all primer efficiency displayed at least 90% efficiency in all reactions (figure 7). Therefore the comparative CT method can be deployed to calculate ΔΔct, RQ and fold change of the expression of genes of interest. Figure 6: Standard curve of FOXO1 Real time PCR for primer efficiency calculation. 29 | P a g e Figure 7: PCR efficiency of all the primers used in the experiments. All primers designed were tested and optimized on adult cynomolgus macaque’s muscle cDNA. Figure 8 shows an agarose gel electrophoresis of the PCR products using primers listed in table 2. All primers yielded only one product of the intended size. Dissociation curve analysis supported the agarose gel electrophoresis result by showing one peak for all reactions (figure 9). Sequencing results for selected PCR products confirmed the specificity of the primers, as ClustalW2 showed at least 95% and 98% homology against human and rhesus macaque sequences respectively (table 5). With the cynomolgus macaque genome available recently, primers and PCR sequences were aligned against them using web-based program NCBI BLAST. The BLAST results showed the primer sequence have at least 90% homology with cynomolgus macaque genome, and the PCR sequences have at least 95% homology with the expected value not more than 0.0001. 30 | P a g e Figure 8: 1.5% Agarose gel electrophoresis of PCR products. Top from left: 100bp marker, SLC2A4, INSR,AKT1,AKT2,MEF2A,GYS1,MSTN,PIK3Ca, PIK3Cb, PIK2R1, PDPK1, GSK3b. Bottom from left: 100bp marker, FOXO1,HK2, GCK, PKM2, SLC2A4RG, IRS1, GAPDH, BACT, RPL13A. % homology against % homology against Human Rhesus macaque INSR 97% 99% SLC2A4 95% 99% IRS1 98% 98% IRS2 96% 98% PKM2 98% 99% GCK 97% 99% GYS1 95% 99% MEF2A 98% 99% GAPDH 96% 99% Table 5: Sequence homology of PCR products against human and rhesus macaque sequences derived from multiple sequence alignment using ClustalW2 Gene 31 | P a g e Figure 9: Dissociation curve analysis of PCR products. A: SLC2A4, B: AKT2, C: GAPDH, D: HK2 32 | P a g e 3.2 Cynomolgus macaque nutrition-restricted IUGR model 3.2.1 Morphometric analysis: juvenile macaques from 0 to 9 months Table 6 to 9 summarizes the juvenile macaques’ morphometric at 0 month, 3 months, 6 months and 9 months treatment. Mann-Whitney U test was used for statistical analysis to access any differences between control and IUGR cohort. Macaques that were born less than 154 days of gestational period were not included in this study as they were considered premature neonates. From the results, the IUGR cohort were 10% lighter than the control cohort at birth and this difference in birth weight was significant (Z = -1.981, p = 0.048). After 3 months, the two groups had similar weights (±1%) and this observation persisted throughout till they were nine months old. For CRL and BMI, there were no significant differences observed from birth to 9 months old. Physical Control cohort IUGR cohort Mann–Whitney U test morphometric (n = 24) (n = 12) (2-tailed) at birth U Z p (0 month) Weight (g) 323 (39.0) 291 (43.2) 86 -1.981 0.048 CRL (cm) 14.0 (1.49) 14.1 (1.67) 141 -0.103 0.918 BMI (kg/m2) 16.9 (2.91) 15.1 (3.85) 114 -1.007 0.132 Table 6: Neonates’ morphometric at birth. Values presented are in the format of mean (SD). Highlighted row indicates p ≤ 0.05. Physical Control cohort IUGR cohort Mann–Whitney U test morphometric (n = 24) (n = 12) (2-tailed) at 3 months U Z p old Weight (g) 653 (101.8) 655 (61.2) 133 -0.352 0.724 CRL (cm) 20.1 (1.95) 19.3 (1.62) 115 -0.976 0.329 BMI (kg/m2) 16.3 (3.16) 18.0 (3.55) 112 -1.074 0.283 Table 7: infant macaques’ morphometric at 3 months old. Values presented are in the format of mean (SD). 33 | P a g e Physical Control cohort IUGR cohort morphometric (n = 24) (n = 12) at 6 months old Weight (g) 976 (113) 965 (104) CRL (cm) 23.2 (1.77) 23.7 (2.13) BMI (kg/m2) 18.2 (2.74) 17.1 (2.69) Table 8: Juvenile macaques’ morphometric at 6 months format of mean (SD). Physical Control IUGR cohort morphometric cohort (n = 12) at 9 months (n = 24) old Weight (g) 1178 (101) 1165 (113) CRL (cm) 25.8 (1.76) 25.6 (2.13) BMI (kg/m2) 17.9 (2.19) 18.1 (3.27) Table 9: Juvenile macaques’ morphometric at 9 months format of mean (SD). Mann–Whitney U test (2-tailed) U Z p 143 -0.034 144 -1.008 114 -1.007 old. Values presented 0.973 0.313 0.314 are in the Mann–Whitney U test (2-tailed) U Z p 115 -0.195 114 -0.234 108 -0.467 old. Values presented 0.846 0.815 0.640 are in the The differences in weight, CRL and BMI between 0 to 3 months, 3 to 6 months and 6 to 9 months were calculated to determine if there were any significant fluctuations during those three months period. Again, Mann-Whitney U test was used for such assessment. It was noticed that IUGR cohort gained more weight from 0 to 3 months as compared to control cohort. However, this result was not significant (p > 0.05). Weight gained from 3 to 6 months and 6 to 9 months were similar for both cohorts (figure 10). For the length of the body (CRL), IUGR cohort grew slower from 0 to 3 month, but the growth accelerated during 3 to 6 months as compared to control cohort. All these observations were not significant (p > 0.05). Growth in body length was similar for both cohorts from 6 to 9 months (figure 11). Interestingly, IUGR cohort had a 19% increase in BMI from 0 to 3 months. This increase was significant as compared to the change in BMI for control cohort, in which there was 34 | P a g e a slightly decrease in BMI from 0 to 3 months (Z = -2.05, p = 0.040). Fluctuations in BMI continued from 3 to 6 months and 6 to 9 months (figure 12), but such fluctuations observed were not significant (p > 0.05). Eventually, BMI stabilized at the 9 month old point whereby both cohorts had similar BMI. 3.2.2 IVGTT analysis: juvenile macaques at 12 months 16 control juvenile macaques and 12 IUGR juvenile macaques had undergone IVGTT and the rate of glucose clearance (k-value) was calculated. Mann-Whitney U test was used to determine any significant differences. It was found that IUGR juvenile macaques were 19% higher in the rate of glucose clearance as compared to the control cohort, however this difference observed was not significant (Z = -1.811, p = 0.070). There were no differences in their weight, CRL and IQ for both cohorts at 12 months old (table 10). Figure 10: Trend of juvenile macaques’ weight and weight gains from 0 to 9 months. Error bars denote SD. Numbers in blue and red show the average weight of control juvenile macaques and IUGR juvenile macaques respectively. Numbers in bracket show the weight gain between 2 periods. * indicates p < 0.05 35 | P a g e Figure 11: Trend of juvenile macaques’ CRL and CRL gains from 0 to 9 months. Error bars denote SD. Numbers in blue and red show the average weight of control juvenile macaques and IUGR juvenile macaques respectively. Numbers in bracket show the CRL gain between 2 periods. Figure 12: Trend of juvenile macaques’ BMI and BMI changes from 0 to 9 months. Error bars denote SD. Numbers in blue and red show the average weight of control juvenile macaques and IUGR juvenile macaques respectively. Numbers in bracket show the BMI gain/loss between 2 periods. * indicates p < 0.05 36 | P a g e Parameters at 12 months old Control cohort (n = 16) IUGR cohort (n = 12) Mann–Whitney U test (2-tailed) U Z p 81 -0.697 0.486 69 -1.262 0.207 92 -0.186 0.853 Weight (kg) 1.31 (0.13) 1.26 (0.17) CRL (cm) 27.3 (1.00) 26.5 (1.81) BMI (kg/m2) 17.6 (1.91) 17.9 (1.84) IVGTT 4.17 (2.21) 4.95 (1.38) 57 -1.811 0.070 k-value Table 10: Juvenile macaques’ morphometric and IVGTT k-value at 12 months old. Values presented are in the format of mean (SD). 3.2.3 Physical and biochemical properties analysis: juvenile macaques at 15 months For 15 months analysis, only the data of 6 control juvenile macaques and 2 IUGR juvenile macaques were valid and analyzed. As the distribution of this set of data is highly skewed, median and range were reported instead. Mann–Whitney U test was deployed to access any differences between the two groups. From the results shown in table 11, IUGR juvenile macaques had a higher glucose clearance, total cholesterol and triglycerides. The differences observed were significant (p < 0.05). There were no significant differences in weight, CRL, BMI, fasting glucose and insulin, HDL, LDL, insulin resistance and sensitively indexes (p > 0.05). 3.2.4 Metabolic gene expression analysis: juvenile macaques at 15 months RNA extracted from 8 muscle tissues achieved RIN of 6.2 to 7.8, confirming their suitability for cDNA synthesis and real time PCR analysis. During the calculation of the gene expression analysis shown in table 12 and figure 13, normalization against the geometric mean of three housekeeping genes, GAPDH, BACT and RPL13a, were done and relative quantification of each gene expression level was calculated using control group as reference group. Mann–Whitney U test was used to check any significant differences in the regulation observed. 37 | P a g e parameters at 15 months old Control cohort (n = 6) IUGR cohort (n = 2) Mann–Whitney U test (2-tailed) U Z p 4.0 -0.67 0.505 6.0 0.00 1.000 3.0 -1.00 0.317 Weight (Kg) 1.30 (1.13-1.42) 1.41 (1.26-1.56) CRL (cm) 26.4 (25.0-27.3) 26.7 (24.8-28.5) BMI (kg/m2) 18.9 (17.3-19.8) 19.8 (19.2-20.4) IVGTT 3.46 (0.73-4.43) 6.01 (5.73-6.29) 0.0 -2.00 0.046 k-value Fasting glucose 3.00 (2.10-5.70) 2.60 (2.40-2.80) 3.0 -1.03 0.306 (mmol/L) Fasting insulin 8.45 (6.80-48.3) 7.75 (7.70-7.80) 4.0 -0.67 0.505 (mU/L) Total cholesterol 2.77 (2.09-3.51) 4.52 (3.72-5.31) 0.0 -2.00 0.046 (mmol/L) Triglycerides 0.40 (0.38-0.53) 0.90 (0.57-1.22) 0.0 -2.01 0.044 (mmol/L) HDL (mmol/L) 1.08 (0.69-1.91) 1.28 (0.26-2.29) 6.0 0.00 1.000 LDL (mmol/L) 1.32 (1.04-2.12) 1.59 (0.70-2.47) 6.0 0.00 1.000 HOMA-IR 1.15 (0.66-12.2) 0.90 (0.83-0.96) 2.0 -1.33 0.182 QUICKI 0.37 (0.27-0.41) 0.39 (0.39-0.40) 2.0 -1.33 0.182 Table 11: Juvenile macaques’ morphometric and biochemical parameters at 15 months old. Values presented are in the format of median (min-max). Highlighted rows indicate p < 0.05 From the results, 11 genes were down regulated in IUGR juvenile macaques, with 6 genes having more than 2 to 6 fold decrease in expression level. However, only AKT2, which was decreased about 6 fold in expression level in IUGR, was found to be significant (U = 0.0, Z = -2.0, p = 0.046). 7 genes were found to be up regulated with a magnitude of 1.4 to 7.6 fold. Out of the 7 genes, 3 genes were found to be significantly up regulated: PIK3R1 (1.9 fold increase, U = 0.0, Z = -2.0, p = 0.046), IRS1 (5.4 fold increase, U = 0.0, Z = -2.0, p = 0.046) and SLC2A4RG (7.6 fold increase, U = 0.0, Z = 2.0, p = 0.046). INSR and HK2 were considered not regulated with less than 10% changes. 38 | P a g e Gene Relative quantification of IUGR group (control group as reference) Magnitude Direction Mann–Whitney U test (2-tailed) U Z p SLC2A4 3.73 x Down regulated 3.0 INSR 1.08 x No Change 5.0 GCK 1.49 x Down regulated 5.0 IRS2 2.68 x Up regulated 3.0 MEF2A 3.33 x Down regulated 1.0 PKM2 1.17 x Down regulated 6.0 GYS1 5.31 x Down regulated 2.0 HK2 1.08 x No Change 5.0 AKT1 3.05 x Down regulated 1.0 AKT2 5.96 x Down regulated 0.0 MSTN 6.01 x Down regulated 1.0 PIK3Ca 1.47 x Up regulated 0.0 PIK3Cb 1.44 x Up regulated 3.0 PIK3R1 1.90 x Up regulated 4.0 PDPK1 1.66 x Down regulated 3.0 GSK3b 2.05 x Up regulated 4.0 FOXO1 1.73 x Down regulated 3.0 IRS1 5.42 x Up regulated 0.0 SLC2A4RG 7.63 x Up regulated 0.0 Table 12: Relative quantification of IUGR juvenile macaques gene control juvenile macaques. Highlighted rows indicate p < 0.05 -1.00 0.317 -0.33 0.739 -0.33 0.739 -1.00 0.317 -1.67 0.096 0.00 1.000 -1.33 0.182 -0.33 0.739 -1.00 0.317 -2.00 0.046 -1.67 0.096 -1.00 0.317 -1.33 0.182 -2.00 0.046 -1.00 0.317 -0.67 0.505 -1.00 0.317 -2.00 0.046 -2.00 0.046 expression against 3.2.5 Association of biochemical parameters with metabolic gene expression level: juvenile macaques at 15 months Spearman's rank correlation coefficient was used to look for association between biochemical parameters, morphometrics and muscle gene expression levels in 15 months juvenile macaques. 3 genes had significant correlations with IVGTT k-value: MSTN had a negative correlation (ρ = -0.810, p = 0.015) (figure 14A) and IRS1 and SLC2A4RG had positive correlations (ρ = 0.881 and 0.762 respectively, p < 0.05) (figure 14B and 14C). AKT1 was positively associated with fasting glucose (ρ = 0.732, p = 0.039) (figure 14D). No significant associations were observed for fasting insulin, lipid panel tests, insulin resistance and sensitively indexes. (p > 0.05) 39 | P a g e Figure 13: Graphic representation of relative quantification of IUGR juvenile macaques gene expression against control juvenile macaques. Error bars denote range. * indicates p < 0.05. 3.2.6 Physical and biochemical properties analysis: juvenile macaques at 24 months (9 months after diet treatment) At the time of data collection, 25 juvenile macaques had undergone diet treatment that consisted of standard lab diet or high fat obesity diet. Out of the 25, 9 control and 9 IUGR macaques had reached 9 months of diet treatment, on which measurements and muscle biopsies were taken. 40 | P a g e Figure 14: 15 months juveniles macaque scatterplots and linear trendline. A: MSTN expression level against k-value, B: IRS1 expression level against k-value, C: SLC2A4RG expression level against k-value, D: AKT1 expression level against fasting glucose. Strenght of correlationship, ρ, and the p value are stated on the top right of the plot. For the 18 juvenile macaques, the grouping and the number of subjects were as follows: 1. Control & standard diet (C-S): n = 3 2. Control & high fat diet (C-H): n = 6 3. IUGR & standard diet (I-S): n = 3 4. IUGR & high fat diet (I-H): n = 6 As the distribution of this set of data is highly skewed, median and range were reported instead, Kruskal-Wallis one-way ANOVA was used to check on any differences among the 4 groups, followed by 4 Mann–Whitney U tests to access any difference between 1) 41 | P a g e C-S and C-H, 2) C-S and I-S, 3) C-H and I-H, 4) I-S and I-H. Differences between C-H and I-S & C-S and I-H were not looked into, as their results were not meaningful for the study. Kruskal-Wallis analysis showed total cholesterol and HDL having significant differences among the 4 groups (p < 0.05) (table 13). Further analysis using multiple Mann–Whitney U tests indicated significant differences only between I-S and I-H & between C-S and CH for both total cholesterol and HDL (U = 0.0, Z = -2.32, p =0.020) (figure 15A), with the high fat diet group having more cholesterol and HDL than standard diet groups . In addition, I-S were significantly shorter in CRL than C-S (U = 0.0, Z = -1.96, p =0.050) (figure 17C), and I-S were significantly lighter in weight as compared to I-H (U = 1.0, Z = -2.07, p =0.039) (figure 15B). There were no significant differences for BMI, IVGTT k-value, triglyceride, LDL, fasting glucose and insulin, as well as insulin resistance and sensitively indexes among all 4 groups (p > 0.05). 3.2.7 Metabolic gene expression analysis: juvenile macaques at 24 months RNA extracted from all 18 muscle tissues achieved RIN of 5.4 to 8.2, which were acceptable for cDNA synthesis and real time PCR analysis. During the calculation of the gene expression analysis shown in table 14 and figure 16, normalization against the geometric mean of three housekeeping genes, GAPDH, BACT and RPL13a, were done and RQ of each gene expression level was calculated using C-S group as a reference group (RQ = 1.0) . Kruskal-Wallis one-way ANOVA was used to check on any differences among 4 groups in gene expression level, followed by 4 Mann–Whitney U tests to access any differences between 1) C-S and C-H, 2) C-S and I-S, 3) C-H and I-H, 42 | P a g e 4) I-S and I-H. Differences between C-H and I-S & C-S and I-H in gene expression were not investigated, as their results were not meaningful for this study. Kruskal-Wallis test showed no significant differences among the 4 groups in the expression levels of all 19 genes (table 14). However, multiple Mann-Whitney U comparison found some groups having significant differential expression levels in 6 genes (p < 0.05, table 14 & figure 16A), and listed as follows: 1. SLC2A4: C-S had 2.1 fold decrease compared to C-H (Z = -2.32, p = 0.020) 2. IRS2: C-H had 2.5 fold decrease compared to I-H (Z = -2.08, p = 0.037) 3. MEF2A: C-S had 1.5 fold decrease compared to I-S (Z = -1.96, p = 0.050) 4. HK2: C-S had 1.8 fold increase compared to C-H (Z = -2.32, p = 0.020) 5. MSTN: C-S had 3.9 fold decrease compared to C-H (Z = -2.07, p = 0.039) 6. C-S had 4.9 fold decrease compared to I-S (Z = -1.96, p = 0.050) 7. PIK3R1: C-S had 2.1 fold increase compared to I-S (Z = -1.96, p = 0.050) No significant differences was observed for the other 13 genes in multiple comparison tests (p > 0.05, figure 16B & 17) 43 | P a g e Parameters at 24 months old C-S (n = 3) C-H (n = 6) I-S (n = 3) I-H (n = 6) Kruskal-Wallis p value Weight (kg) 2.08 (1.65-2.42) 1.88 (1.58-2.38) 1.39 (1.24-1.71) 1.96 (1.71-2.11) 0.126 CRL (cm) 31.0 (29.3-32.3) 31.6 (29.0-33.8) 28.4 (26.8-28.8) 30.8 (27.0-32.0) 0.076 BMI (kg/m2) 19.9 (19.2-25.2) 19.3 (16.6-22.5) 19.3 (14.9-21.1) 21.6 (17.2-23.4) 0.409 5.39 (5.09-6.13) 5.50 (2.32-7.32) 5.49 (4.82-5.84) 5.23 (3.01-7.67) 0.825 2.40 (2.20-3.70) 3.20 (2.30-4.20) 2.60 (2.10-3.40) 2.70 (1.90-4.10) 0.706 26.5 (14.8-28.5) 19.2 (4.60-84.1) 15.7 (4.10-50.2) 40.1 (16.6-66.2) 0.402 2.85 (2.36-2.95) 4.79 (3.94-5.88) 2.94 (1.86-2.97) 4.90 (3.25-7.50) 0.010 0.37 (0.20-0.42) 0.39 (0.17-0.82) 0.31 (0.30-0.34) 0.39 (0.17-0.51) 0.835 HDL (mmol/L) 1.37 (1.29-1.60) 2.30 (1.94-3.00) 1.16 (1.15-1.28) 2.06 (1.56-2.81) 0.007 LDL (mmol/L) 1.32 (1.04-2.12) 2.13 (0.66-3.33) 1.52 (0.56-1.67) 2.23 (0.70-5.33) 0.337 HOMA-IR 3.04 (1.45-4.36) 2.60 (0.63-13.8) 1.47 (0.47-7.59) 5.94 (1.42-7.80) 0.691 QUICKI 0.32 (0.31-0.36) 0.33 (0.27-0.42) 0.36 (0.29-0.44) 0.30 (0.29-0.36) 0.679 IVGTT k-value Fasting glucose (mmol/L) Fasting insulin (mU/L) Total cholesterol (mmol/L) Triglycerides (mmol/L) Table 13: Juvenile macaques’ morphometric and biochemical parameters at 24 months old, 9 months after diet treatment. Values presented are in the format of median (min-max). Highlighted rows indicate p < 0.05 44 | P a g e Figure 15: Graphic repersentation of Juvenile macaques’ morphometric and biochemical parameters at 24 months old, 9 months after diet treatment. Error bars denote range. * beside parameter at x-axis indicates p ≤ 0.05 by Kruskal-Wallis test. * on top between 2 bar indicates p ≤ 0.05 by Mann–Whitney U test between 2 groups. 45 | P a g e 3.2.8 Association of biochemical parameters with metabolic gene expression level: juvenile macaques at 24 months Spearman's rank correlation coefficient was used to look for association between biochemical morphometrics and muscle gene expression level in 24 months juvenile macaques. 2 significant associations were observed: IVGTT k-value was negatively correlated with MEF2A (ρ = -0.569, p = 0.014) (figure 18A) and fasting glucose was negatively correlated with PKM2 (ρ = -0.529, p = 0.024) (figure 18B). No significant associations was observed for fasting insulin, lipid panel tests, insulin resistance and sensitively indexes (p > 0.05). 3.3 Adult cynomolgus macaque IGT model 3.3.1 Morphometric analysis: adult macaques Table 15 shows the summary for the macaques’ morphometrics, before and after diet treatment. Mann-Whitney U test was used for statistical analysis. Before diet treatment, all 14 adults have an average weight of 6.95 ± 0.95kg and an average BMI of 13.62 ± 1.06kg/m2. After six month high fat diet, IGT macaques had a significant 64% weight gain as compared to the weight before high fat treatment (Z = -3.130, p = 0.002). Also, IGT macaques were 54% higher on BMI as compared to the BMI before high fat treatment and this difference is significant (Z = -3.134, p = 0.002). No significant difference was observed in CHL between the two groups (Z = -1.670, p = 0.095). 3.3.2 Biochemical analysis: adult macaques Table 16 illustrates the macaques’ biochemical parameters before and after diet treatment. Mann-Whitney U test was used for statistical analysis. From the analysis, IVGTT k-value, 46 | P a g e Figure 16: Graphic representation of relative quantification of SLC2A4, IRS2, MEF2A, HK2, MSTN, PIK3R1, INSR, GCK, PKM2, GYS1, AKT1 and AKT2 in C-H, I-S and IH juvenile macaques against C-S juvenile macaques as reference group, with significant differences in A. Error bars denote range. * on top between 2 bar indicates p ≤ 0.05 by Mann–Whitney U test between 2 groups 47 | P a g e Figure 17: Graphic representation of relative quantification of PIKC3a, PIK2Cb, PDPK1, GSK3b, FOXO1, IRS1 and SLC2A4RG in C-H, I-S and I-H juvenile macaques, against C-S juvenile macaques as reference group. Error bars denote range. 48 | P a g e Gene SLC2A4 INSR GCK IRS2 MEF2A PKM2 GYS1 HK2 Group C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H RQ 1.000 2.127 1.581 2.384 1.000 1.436 1.168 1.236 1.000 0.752 0.410 0.637 1.000 1.588 1.255 3.891 1.000 1.004 0.674 0.839 1.000 0.968 1.542 1.628 1.000 1.714 1.037 1.659 1.000 1.828 1.674 2.745 Kruskal-Wallis p value 0.072 0.660 0.345 0.074 0.267 0.445 0.110 0.070 Multiple Mann–Whitney U tests Group 1 C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S Group 2 C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H Z p value -2.32 -1.09 -0.80 -1.29 -1.29 -0.22 -0.32 -0.26 -0.52 -1.53 -0.48 -1.29 -1.03 -0.22 -2.08 -1.81 -0.26 -1.97 -0.64 -0.78 -0.26 -0.66 -1.28 -0.52 -1.81 -0.22 -0.32 -1.55 -2.32 -1.09 -1.44 -1.03 0.020 0.275 0.423 0.197 0.197 0.827 0.749 0.796 0.606 0.127 0.631 0.197 0.302 0.827 0.037 0.071 0.796 0.050 0.522 0.439 0.796 0.513 0.200 0.606 0.071 0.827 0.749 0.121 0.020 0.275 0.150 0.302 49 | P a g e Gene AKT1 AKT2 MSTN PIK3Ca PIK3Cb PIK3R1 PDPK1 GSK3b Group C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H C-S C-H I-S I-H RQ 1.000 1.143 0.976 1.175 1.000 0.993 0.985 1.061 1.000 0.255 0.204 0.178 1.000 1.157 0.941 0.892 1.000 1.180 1.313 1.010 1.000 1.193 2.089 0.987 1.000 1.392 1.318 1.229 1.000 1.047 0.715 0.967 Kruskal-Wallis p value 0.611 0.933 0.076 0.631 0.824 0.273 0.563 0.562 Multiple Mann–Whitney U tests Group 1 C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S C-S C-S C-H I-S Group 2 C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H C-H I-S I-H I-H Z p value -0.78 -0.22 0.00 -1.03 -0.26 -0.22 -0.64 0.00 -2.07 -1.97 -0.32 -0.27 -0.52 -0.22 -1.44 -0.52 -0.26 -0.22 -0.64 -1.03 -0.78 -1.96 -0.48 -1.55 -1.03 -1.09 -0.80 -0.26 -0.52 -1.09 -0.32 -1.03 0.439 0.827 1.000 0.302 0.796 0.827 0.522 1.000 0.039 0.050 0.749 0.796 0.606 0.827 0.150 0.606 0.796 0.827 0.522 0.302 0.439 0.050 0.631 0.121 0.302 0.275 0.423 0.796 0.606 0.275 0.749 0.302 50 | P a g e Gene Group RQ Kruskal-Wallis p value Multiple Mann–Whitney U tests Group Group Z p value 1 2 C-S 1.000 C-S C-H 0.00 1.000 C-H 1.185 C-S I-S -1.53 0.127 FOXO1 0.838 I-S 1.259 C-H I-H 0.00 1.000 I-H 1.008 I-S I-H -0.52 0.606 C-S 1.000 C-S C-H -1.29 0.197 C-H 3.046 C-S I-S -1.53 0.127 IRS1 0.116 I-S 6.767 C-H I-H -1.44 0.150 I-H 4.708 I-S I-H 0.00 1.000 C-S 1.000 C-S C-H -1.29 0.197 C-H 4.660 C-S I-S -1.09 0.275 SLC2A4 0.431 RG I-S 9.345 C-H I-H -0.64 0.522 I-H 10.909 I-S I-H -0.26 0.796 Table 14: Relative quantification of C-H, I-S and I-H juvenile macaques gene expression against C-C-S juvenile macaques as reference group. Kruskal-Wallis test was deployed for comparison across 4 groups, followed by 4 set of Mann-Whitney U test between 2 groups. Highlighted rows indicate p ≤ 0.05. Figure 18: 24 months juveniles macaques scatterplots and linear trendline. A: MEF2A expression level against k-value, B: PKM2 expression level against fasting glucose. Strenght of correlationship, ρ, and the p value are stated on the top right of the plot. 51 | P a g e Physical morphometric Before treatment After treatment Mann–Whitney U test (2-tailed) U Z p NGT IGT NGT IGT (n = 7) (n = 7) (n = 7) (n = 7) 6.80 7.12 6.73 11.72 Weight (kg) 0.0 -3.130 (0.95) (1.09) (0.75) (1.15) 71.6 71.8 71.1 74.1 CHL (cm) 11.5 -1.670 (1.85) (2.02) (3.23) (2.57) 13.3 13.8 13.3 21.3 BMI (kg/m2) 0.0 -3.134 (1.12) (1.36) (0.79) (1.26) Table 15: Adult macaques’ morphometric before and after diet treatment. presented are in the format of mean (SD). Highlighted rows indicate p < 0.05. 0.002 0.095 0.002 Values fasting insulin, HOMA-IR and QUICKI exhibited significant differences between NGT and IGT macaques (p < 0.05). IGT macaques had a 22% decrease in IVGTT k-value (Z = -2.108, p = 0.035), 5.2 times increase in fasting insulin value (t = Z = -3.130, p = 0.001), 5.8 times increase in insulin resistance index HOMA-IR (Z = -3.130, p = 0.002) and a 23% decrease in insulin sensitivity index QUICKI (Z = -3.090, p = 0.002). No significant differences were observed in the fasting glucose and the lipid panel tests between both groups (p > 0.05). 3.3.3 Metabolic gene expression analysis: adult macaques All RNA extracted from muscle tissue achieved RNA integrity number (RIN) of 5.5 to 8.0, stating that they were suitable for cDNA synthesis and real time PCR analysis. During the calculation of the gene expression analysis, normalization against the geometric mean of three housekeeping genes, GAPDH, BACT and RPL13a, were done and RQ of each gene expression level was calculated using NGT group as a reference group. Table 17 shows the magnitude and direction of muscle gene regulation in IGT macaques. Any changes less than 10% (< 1.10 fold) were not considered as the observed value can 52 | P a g e Physical morphometric IVGTT k-value Fasting glucose (mmol/L) Fasting insulin (mU/L) Total cholesterol (mmol/L) Triglycerides (mmol/L) HDL (mmol/L) LDL (mmol/L) Before treatment After treatment NGT (n = 7) 3.95 (0.86) IGT (n = 7) 4.03 (0.92) NGT (n = 7) 3.78 (0.57) IGT (n = 7) 2.91 (0.82) 2.89 (0.61) 2.93 (0.63) 2.96 (0.75) 18.5 (8.3) 20.5 (8.3) 2.75 (0.90) 2.69 (1.18) Mann–Whitney U test (2-tailed) U Z p 8.0 -2.108 0.035 3.47 (0.59) 13 -1.474 0.140 18.3 (7.6) 94.1 (37.7) 0.0 -3.130 0.001 2.50 (1.08) 3.36 (0.83) 6.0 -1.807 0.071 0.73 0.69 0.71 0.80 21 -0.449 0.654 (0.43) (0.41) (0.42) (0.36) 1.32 1.26 1.67 1.71 9.0 -1.873 0.061 (1.02) (0.98) (0.91) (0.29) 0.80 0.80 0.74 1.28 9.0 -1.873 0.061 (0.42) (0.42) (0.36) (0.58) 2.46 2.60 2.54 14.61 HOMA-IR 0.0 -3.130 0.002 (0.64) (0.70) (1.25) (6.64) 0.34 0.34 0.35 0.27 QUICKI 0.0 -3.090 0.002 (0.017) (0.016) (0.049) (0.020) Table 16: Adult macaques’ biochemical parameters before and after diet treatment. Values presented are in the format of mean (SD). Highlighted rows indicate p < 0.05. be due to chance and experimental error. Mann-Whitney U test was used to access any significant differences in the regulation observed. Figure 19 illustrates the regulation of muscle gene expression level in a bar graph. It was observed that 4 genes were down regulated with a magnitude of 1.1 fold to 2.8 fold, whereas 13 genes were up regulated with similar magnitude. Out of these 13 up regulated genes, AKT1 and AKT2 expression level were significantly up regulated in IGT macaques by 1.36 fold and 1.29 fold respectively (p < 0.05). Also IRS1 expression level was significantly down regulated in IGT macaque by 2.29 fold (Z = -1.981, p = 0.048). GCK and IRS2 were not regulated and there were no significant differences in the 53 | P a g e remaining 14 gene expression level, although some genes like SLC2A4RG exhibited a larger decrease in expression level in IGT macaques (2.79 fold, p > 0.05). Gene Relative quantification of IGT Mann–Whitney U test (NGT as reference) (2-tailed) Magnitude Direction U Z p SLC2A4 1.12 x Down regulated 20.0 -0.575 0.562 INSR 1.22 x Up regulated 14.0 -1.342 0.180 GCK 1.08 x No Change 24.0 -0.064 0.949 IRS2 1.01 x No Change 24.0 -0.064 0.949 MEF2A 1.33 x Up regulated 14.0 -1.342 0.180 PKM2 1.25 x Up regulated 17.0 -0.958 0.338 GYS1 1.17 x Up regulated 16.0 -1.086 0.277 HK2 1.14 x Up regulated 24.0 -0.064 0.949 AKT1 1.36 x Up regulated 8.0 -2.108 0.035 AKT2 1.29 x Up regulated 6.0 -2.364 0.018 MSTN 1.40 x Up regulated 10.0 -1.853 0.064 PIK3Ca 1.15 x Up regulated 17.0 -0.958 0.338 PIK3Cb 1.22 x Up regulated 16.0 -1.086 0.227 PIK3R1 1.51 x Up regulated 14.0 -1.342 0.180 PDPK1 1.26 x Down regulated 13.0 -1.469 0.142 GSK3b 1.39 x Up regulated 12.0 -1.597 0.110 FOXO1 1.66 x Up regulated 10.0 -1.853 0.064 IRS1 2.29 x Down regulated 9.0 -1.981 0.048 SLC2A4RG 2.79 x Down regulated 15.0 -1.214 0.225 Table 17: Relative quantification of IGT macaques gene expression against NGT macaques. Highlighted rows indicate p < 0.05 3.3.4 Association of biochemical parameters with metabolic gene expression level: adult macaques Using Spearman's rank correlation coefficient to check for association between biochemical morphometrics and muscle gene expression level, there were a number of significant associations observed. IVGTT k-value was negatively correlated with AKT2 expression level (ρ = -0.564, p = 0.036, figure 20A). Next, Fasting glucose value was negatively correlated with IRS2 expression level (ρ = -0.557, p = 0.039, figure 20B). Fasting insulin value was positively associated with AKT1 expression level (ρ = 0.617, 54 | P a g e Figure 19: Graphic representation of relative quantification of IGT macaques gene expression against NGT macaques. Error bars denote SD. * indicates p < 0.05, ** indicates p < 0.01 p = 0.019, figure 20C) and MSTN expression level (ρ = 0.600, p = 0.023, figure 20D), whereas the same parameter is negatively correlated with SLC2A4RG expression level (ρ = -0.641, p = 0.014, figure 20E). Interestingly, insulin resistance index HOMA-IR shared similar relationships with AKT1, MSTN and SLC2A4RG as what fasting insulin had, with slight difference in the strength of association (ρ = 0.596, ρ = 0.643 and ρ = -0.647 55 | P a g e respectively, p < 0.05, figure 20F-20H). There were no significant associations observed with the lipid panel tests and insulin sensitivity index QUICKI against any gene expression levels (p > 0.05) Figure 20: Adult macaques scatterplots and linear trendline. A: AKT2 expression level against k-value, B: IRS2 expression level against k-value, C: AKT1 expression level against fasting insulin, D: MSTN expression level against fasting insulin, E: SLC2A4RG expression level against fasting insulin, F: AKT1 expression level against HOMA-IR, G: MSTN expression level against HOMA-IR, H: SLC2A4RG expression level against HOMA-IR. Strength of correlationship, ρ, and the p value are stated on the top right of the plot. 56 | P a g e CHAPTER 4 DISCUSSIONS 4.1 Primer validated for all gene expression studies in cynomologus macaque One of the core objectives of these studies is to look at metabolic gene expression levels in cynomolgus macaque. Due to a lack of cynomolgus macaque’s sequence information in any genetic sequence database at the point of work, in house primer design based on human and rhesus macaque sequences were necessary. As human, rhesus and cynomolgus macaques share more than 90% identity in sequence, designing primers that flank the conserved region of the gene of interest in both human and rhesus macaque would have a higher chance of success when using them to amplify cynomolgus macaque genes. All sets of primers yielded a single product of the expected sizes. Furthermore, the sequence of PCR products indicated at least 95% homology with the source of the genetic sequences used to design primer. In addition to the recent release of cynomolgus macaque genome, BLAST results of primers and PCR products displayed at least 90% and 95% homology respectively, confirming that all the primers were amplifying the intended genes of interest and their locus. Hence they were validated to be used in real time PCR for gene expression studies. All primers had an efficiency of at least 90%, fitting the criteria of deploying comparative CT method to normalize gene of interest with housekeeping genes to obtain Δct, then comparing the control group to obtain ΔΔct, and lastly using 2-ΔΔct to calculate the fold change (in RQ). 4.2 Nutrition-mediated IUGR macaque were born lighter and experienced ‘catch-up growth’ Our first objective for this study is to investigate any differences between normal and 57 | P a g e nutrition-mediated IUGR infant macaque in their weight and body length for their first 9 months of life. After birth, it was found that IUGR neonates were 10% lighter than control neonates and this finding was significant by Mann–Whitney U test. Our observations were consistent with the literature presented on growth restricted neonates having LBW in animal models, as well as in human. On average, the BMI of IUGR neonates was 10% lower, but this was not significant by Mann–Whitney U test. Despite lower birth weight, IUGR neonates had similar body length as compared to the control neonates. Although the definition of IUGR is associated with LBW and SGA, there were rodent models in which no significant differences in the body size of IUGR pups were observed as compared to the normal pups (Schwartz et al, 1998; Simmons et al, 2001; Coupe et al, 2009). Nevertheless, LBW is still the main criteria for human IUGR offspring, which all validated IUGR animal models have reflected, and we have achieved this criteria too. Hence we can conclude that our nonhuman primate IUGR model is valid. Physical morphometric measures for both control and IUGR cohort at 3 months to 9 months were similar throughout. However an analysis on the changes of these physical morphometrics in a 3 month period indicated a significant change in BMI in IUGR cohort from birth to 3 months old as compared to control cohort. This was attributed to the larger weight gain in IUGR infant macaques than the control infant macaques, but that observation was not significant by Mann–Whitney U test. From this analysis, it we demonstrated that IUGR infant macaques experienced “catch-up growth” during the first 3 months of their life. This phenomenon was also observed in other IUGR rodent models with the hypothesis that IUGR subjects accelerated in growth to match with their peers, but at a disadvantage in later life with early onset of metabolic disease (Coupe et al, 2009; 58 | P a g e Shahkhalili et al 2010). The changes in weight, CRL and BMI were almost similar for both cohorts from 3 to 6 months and 6 to 9 months, stating that both cohorts were growing at the similar rate. 4.3 Higher glucose clearance rate, total cholesterol and triglycerides observed in IUGR juvenile macaques at 15 months Biochemical parameters of both cohorts showed that IUGR macaques had a higher glucose clearance rate, total cholesterol and triglycerides at 15 months old. All these findings were significant by Mann–Whitney U test (p < 0.05). From this, we hypothesized that the observed differences could be the fetal programming in utero. As IUGR infants need to “catch-up” with their normal peer, their overall metabolism increases in order to gain weight and build. Hence glucose uptake is increased, which explains the fast glucose clearance rate from the blood. Also, lipid metabolism is accelerated to create an alternate energy source should glucose not be available (a condition which was likely to happen in utero during nutrition restriction of mothers). This survival response increases the production and absorption of fats, which causes an increase in plasma lipid levels in IUGR juveniles macaques, 90% increase in total cholesterol and triglycerides and 20% increase in HDL and LDL cholesterol. 4.4 Accelerated insulin-glucose signaling observed in IUGR juvenile macaques We are interested to find any differences in gene expression level between both groups at 15 months. Real time PCR analysis and Mann–Whitney U test showed AKT2 was significantly down regulated , and PIK3R1, IRS1 and SLC2A4RG were significantly up regulated in muscle tissue of IUGR juvenile macaques (p < 0.05). These observations 59 | P a g e were opposite of the gene expression in IGT adult macaques earlier in section 4.2, suggesting that insulin and glucose signaling pathways were accelerated. Even though the following differences were not significant by Mann–Whitney U test, IUGR juvenile macaques had a lower HOMA-IR and higher QUICKI as control group (p > 0.05), suggesting that they were more insulin sensitive. Also, myostatin expression level was decreased in IUGR juvenile macaques, suggesting that muscle differentiation, growth and associated metabolism were encouraged. All the results derived from gene expression analysis indicated that cellular insulin and glucose metabolism in muscle tissue was elevated, which support the phenotype observed in the biochemical results of IUGR juvenile macaques. High glucose clearance and insulin sensitivity persisted in 24 months old IUGR juvenile macaques when comparing C-S against I-S. Also, the regulations of AKT, IRS, SLC2A4RG, PIK3R1 and MSTN in muscle tissues were of the same pattern as the observations made at the 15 months analysis, but only IRS2, PIK3R1 and MSTN were significant (p < 0.05). These observations hint that fast insulin and glucose metabolism continues for the past nine months. 4.5 Faster deterioration of insulin-glucose signaling in IUGR juvenile macaques compared to control juvenile macaques exposed to a high fat diet We are interested to investigate any changes in biochemical and physical morphometrics, as well as expression levels of 19 metabolic genes, after 9 months of diet treatment. Comparing C-S against C-H and I-S against I-H, macaques in high fat diet groups had increased plasma lipid levels with total cholesterol and HDL having significance (p < 60 | P a g e 0.05). Looking at the changes between these two pairs, IUGR juvenile macaques had significant weight gain after high fat diet treatment (p < 0.05), whereas control juvenile macaques had a similar weight before and after high fat diet treatment. Interestingly, control juvenile macaques had slightly better insulin sensitivity after high fat diet, based on IVGTT, HOMA-IR, QUICKI and gene expression data, whereas IUGR juvenile macaques were more insulin resistant after high fat diet. However, these data were not statistically significant at this point of time. These seem to suggest that insulin metabolism might be greatly altered in IUGR juvenile macaques and leads towards IGT after a 9 months high fat diet challenge. On the other hand, the insulin and glucose metabolism of control juvenile macaques under high fat diet started to accelerate similar to the observations made in IUGR macaques at 15 months. Viewing the high fat diet as an environmental stimulus of an unhealthy lifestyle, we theorized that IUGR subjects are programmed to accelerate overall metabolism in order to adapt to a nutrient-deprived environment, but this developmental trade-off that they have made and the exposure to an unhealthy lifestyle predisposes them to metabolic disease at an earlier stage of life. This could be due to the prolonged stimulation of the insulin-glucose pathway and when the ability of the pathway to adapt to the stimulus is exceeded, this leads to pathology and disease. Gene expression data showed significant up regulation of HK2, IRS2, and SLC2A4, and down regulation of MSTN in C-H as compared to C-S (p < 0.05). These observations are similar to 15 months juvenile macaques with elevated insulin and glucose metabolism in relation to mRNA expression level mentioned in section 4.6. There were no significant differences in all gene expression data between I-S and I-H (p > 0.05). 61 | P a g e 4.6 Adult cynomolgus macaque IGT model established and validated An objective in this study is to establish an adult nonhuman primate diabetic model using cynomolgus macaques. From our study, macaques fed with 35% high fat diet for six months significantly gained about 5kg (64%) in weight as compared to those continued on standard lab diet (p < 0.05). IGT macaques were physically larger than normal macaques in the NGT group, with little or no change in their length from head to heel (figure 21). Thus, IGT macaques gained about 8kg/m2 in their BMI, to an average of 21.3kg/m2, 54% increase as compared to NGT macaques which had an average BMI of 13.3kg/m2. This indicated that the high fat diet used was effective in generating obese macaques. To test for diabetes, insulin resistance and sensitivity, IVGTT and measurement for fasting glucose and insulin were deployed. Using the cut-off proposed by Amatuzio et al, Figure 21: Photos of adult macaques involved in prediabetes study. Left: macaque in IGT group. Right: macaque in NGT group 62 | P a g e 1953 for human, a mean k-value of more than 3 is considered normal glucose tolerance; a mean k-value between 2.0 to 3.0 is considered IGT; a mean k-value between 1.5 to 2.0 is considered mild diabetes, and a mean k-value less than 1.5 is considered severe diabetes. Results of IVGTT showed that IGT macaques had a k-value of less than 3.0, demonstrating macaques having impaired glucose tolerance. The control group displayed normal glucose clearance rate with k-value of more than 3.0. Elevated fasting blood glucose was observed in IGT macaques but this was not significant and the values were within the normal range of less than 6.0 mmol/L. This was expected as normal glycemic control is maintained prior to the progression to T2DM, from normal to IGT state. In addition, fasting blood insulin in IGT macaques were significantly raised by 5 fold as compared to control group. Also using fasting glucose and insulin values, insulin indexes HOMA-IR and QUICKI were calculated. These 2 indexes had shown high correlation and reliability with the gold standard hyperinsulinemic euglycemic clamp (Yokoyama et al, 2004), whereby the latter is complicated and time consuming to perform. From the calculations, IGT macaques were over 7.0 in HOMA-IR and under 0.30 for QUICKI, revealing signs of insulin resistance and insensitivity in IGT macaques. All these signs are similar to macaque models by Cefalu WT, 2000 and Bodkin, 2000, indicating early T2DM progression. Such observations validated our nonhuman primate IGT model. Lipid panel tests showed IGT macaques on average had a slight increase in total cholesterol, triglycerides, HDL and LDL, due to higher fat intake. However such increments observed as compared to control group were not significant and still within the normal range, confirming that the animals were not suffering from hyperlipidemia, a 63 | P a g e condition which may complicate our study on metabolic gene expression level in an IGT subject. 4.7 Deterioration of insulin-glucose signaling observed in IGT macaque The next objective is to characterize mRNA expression of genes involved in insulin and glucose metabolism and check for any differences between normal and IGT macaques. IRS1 expression level was decreased by 2.3 fold in muscle of IGT macaques and it was significant by Mann–Whitney U test (p < 0.05). In addition, IRS2 expression level was shown to have significant negative correlation with fasting blood glucose (p < 0.05). These observations are consistent with other studies on IRS expression level in insulin resistant cell lines and diabetic models. As IRS regulate the insulin signaling pathway at the beginning by relaying insulin signals from the receptor to intracellular effectors, a reduction in IRS will slow down the downstream signaling pathway, which leads to a reduced sensitivity in response to glucose regulation. In response to insulin signaling down regulation after IRS, insulin receptor production was observed to increase to compensate for the reduced sensitivity (a negative feedback loop in muscle insulin signaling pathway). This was shown by an up regulation of INSR by 1.2 fold in muscle tissue of IGT macaques, but unfortunately such differences were not significant by Mann–Whitney U test (p > 0.05), probably due to a small sample size. On the other hand, AKT1 and AKT2 were found to be up regulated in muscle tissue of IGT macaques. As the role of AKT and phosphorylated AKT are well established in glucose transport and downstream signaling of insulin-glucose pathway, we hypothesized that by up regulating AKT1 and AKT2, more kinases are available to be activated via 64 | P a g e phosphorylated, and bring the insulin signaling back to normal level, after the pathway has slowed down because of reduced IRS1. Also, more kinases are available trying to compensate the reduced activity of AKT1 and AKT2 due to reduced PDPK1 activation. Furthermore, AKT1 expression level was positively associated with fasting blood insulin and HOMA-IR (p < 0.05), reinforcing our hypothesis on up regulation of AKT in insulin resistant tissue. Next, we observed a decrease in SLC2A4RG expression level in muscle tissue of IGT macaques. Although this was not significant by Mann–Whitney U test (p > 0.05), associations between fasting blood insulin and HOMA-IR were significant by Pearson correlation coefficient (p < 0.05). Such correlation translates to a lower SLC2A4RG expression level when fasting blood insulin and HOMA-IR increases. These observations are similar to other studies on glucose transporter 4 and its regulatory gene, whereby a decrease in SLC2A4RG will decrease SLC2A4 expression, hence leading to lower glucose transporter 4 proteins on the membrane to facilitate glucose uptake. Expression levels of SLC2A4 in muscle tissue of IGT macaques were slightly reduced, but this was not significant by Mann–Whitney U test (p > 0.05), probably due to a small sample size. Such reduction will lead to a decreased insulin sensitivity and increased insulin resistance Lastly, myostatin expression was found to be increased in muscle tissues of IGT macaques. This finding was not significant by Mann–Whitney U test (p > 0.05), but associations between fasting blood insulin and HOMA-IR were significant by Pearson correlation coefficient (p < 0.05). This result is similar to a study done by Hittel at el, 2009, whereby increased secretion and expression of myostatin was found in muscle tissue of obese subjects with higher BMI and HOMA-IR than the lean subjects. As a 65 | P a g e negative regulator of muscle differentiation and growth belonging to a member of the TGF beta protein family, myostatin was predicted to have involvement in insulin and glucose metabolism after studies showed hypermuscular myostatin null mice had reduced fat mass and were spared from dietary-induced insulin resistance after diet treatment (Zhao et al, 2005; Feldman et al, 2006). To date, the cellular mechanism of myostatin regulating insulin and glucose metabolism has not established. However we speculated that the reduced insulin- glucose signaling causes myostatin expression level to be up regulated. This condition is similar to that of starving muscle whereby little glucose is available and the oxidative catabolism of lipid is reduced or incomplete when such alternative energy source is used (Pender at el, 2005). To summarize, down regulation of IRS1, SLC2A4RG and up regulation of MSTN, AKT1, AKT2 were seen in IGT adult macaques. The expression profiles of these five genes were found to be altered as they are slowly progressing toward IGT. 4.8 Similar gene expression of AKT1, AKT2 and IRS1 between IUGR juvenile macaques and adult IGT macaques - the transition point from insulin sensitive to insulin resistance A direct comparison in the insulin-glucose biochemical data and the gene expression data between IUGR high fat (I-H) juvenile macaques and IGT adult macaques (table 18) shows that I-H juvenile macaques are better in glucose clearance and sensitivity, and have a faster insulin-glucose signaling as compare to IGT adult macaques. However taking a closer look at AKT1, MEF2A, GSK3b and IRS1 expression levels, we observed a similar pattern as what was observed in IGT adult macaques, but with a smaller 66 | P a g e magnitude. This observation may indicate a transition point from being insulin sensitive to developing insulin resistance. With all these observations, figure 22 depicts the hypotheses proposed in this chapter: IUGR subjects programmed to have insulin-glucose signaling accelerated in order to achieve an elevated growth rate to match its normal peers, but the animals deteriorate earlier with unhealthy diet due to exhaustion of the body’s ability to adapt. Parameter IUGR high fat (I-H) IGT juvenile macaques adult macaques IVGTT k-value 5.23 2.91 Fasting glucose (mmol/L) 2.70 3.47 Fasting insulin (mU/L) 40.1 94.1 HOMA-IR 5.94 14.61 QUICKI 0.30 0.27 Gene RQ SLC2A4 1.506 0.891 INSR 1.058 1.223 GCK 1.554 1.081 IRS2 3.087 1.010 MEF2A 1.254 1.334 PKM2 1.056 1.257 GYS1 1.600 1.167 HK2 1.647 1.142 AKT1 1.204 1.361 AKT2 1.077 1.290 MSTN 0.900 1.404 PIK3Ca 1.077 1.146 PIK3Cb 0.948 1.218 PIK3R1 0.769 1.506 PDPK1 0.932 0.796 GSK3b 1.352 1.386 FOXO1 0.801 1.659 IRS1 0.696 0.436 SLC2A4RG 1.167 0.359 Table 18: A direct comparison in the insulin-glucose biochemical data and the gene expression data between IUGR high fat juvenile macaques and IGT adult macaques 67 | P a g e Figure 22: A schematic diagram of the hypothesis on accelerated insulin-glucose signaling and early development of metabolic disease in IUGR subject. Blue region represents hyper-sensitive signaling, white region represents normal sensitivity, red region represents slow signaling and insulin resistance. Red line shows the trend of an IUGR offspring and blue line shows the trend of a normal offspring 4.9 Strengths and limitations of these studies Although there have been many studies on the effect of IUGR on metabolic disease in later life, these were not done on a nonhuman primate model. The strength of using a nonhuman primate model is that they share many similarities in reproductive physiology and disease progression with humans, In addition, interventional studies and sequential tissue samples collection are not feasible in human, but are possible in nonhuman primate. The findings in this thesis can be useful in translational research and screening of earlyonset T2DM in teenager and young adult, especially on those who were classified as IUGR after birth. Further research on AKT1, MEF2A, GSK3b and IRS1 can be done as they are potential molecular markers for diagnosing the transition point from insulin 68 | P a g e sensitive to insulin resistance, whereby blood tests and OGTT cannot pick up the differences. This thesis does have its limitations. Due to logistic reasons, diet treatment is only possible for six months for adult macaques. It will be better if the high fat diet treatment for adult IGT macaques can last for nine months, such that the comparison between IUGR high fat juvenile macaques and IGT adult macaques will be more controlled and relevant. Also muscle biopsy and diet treatment of juvenile macaques at any time point before 15 months is not possible, as the work of this thesis was started when most of the juvenile macaques were reaching 18 months. Although most of the data were continuous variables, some of them exhibited a non-normal distribution and many of them had different variances (Levene's test p < 0.05), in which the assumption for parametric statistics is not valid. Hence parametric tests, such as student t-test and ANOVA, cannot be used for this thesis and statistical analysis were resorted to a more robust but less powerful non-parametric tests Mann–Whitney U test and Kruskal-Wallis test. As this is still an ongoing study, some of the analyses in this thesis are preliminary. Further works such as protein expression studies and epigenetic of the metabolic genes are required to validate our proposed hypotheses. 69 | P a g e CHAPTER 5 CONCLUSION This thesis has established an IGT adult cynomolgus macaque model with higher BMI, elevated fasting insulin, increased insulin resistance and reduced glucose clearance which are consistent with other nonhuman primate diabetic models. IGT macaques displayed differential gene expression levels of down regulated IRS1, AKT1, AKT2 and SLC2A4RG, and up regulated MSTN. These are indicators of abnormal regulation of such genes in T2DM subjects. This thesis also argues in favor of the nutrient-mediated IUGR cynomolgus macaque model set up by us, in which IUGR neonates are lighter at birth and experience a catchup growth in the first 3 months of life, similar with other IUGR animal models. IUGR and control cohorts subsequently have similar growth patterns until 15 months. The IUGR group had accelerated insulin glucose metabolism and faster glucose clearance rate, with higher cholesterol and triglycerides as compared to the control. With a 9 months high fat diet treatment in place, IUGR juvenile macaques showed signs of deterioration in insulin glucose metabolism, with gene expression leading towards the profile observed in IGT adult macaques. On the other hand, control juvenile macaques on a high fat diet displayed up regulation of SLC2A4 and HK2, inferring faster glucose uptake and glycolysis. All these observations fit our hypothesis on distinctive features in metabolic gene expression levels, physical and biochemical characteristics between normal and IGT macaques, and between normal and IUGR offspring, with the refinement written as follows: IUGR subjects are programmed in utero to have accelerated insulin-glucose signaling in order to achieve an elevated growth rate to match its normal peers, but are more prone to deterioration at an earlier stage of life when exposed to unhealthy diet due 70 | P a g e to exhaustion in insulin-glucose signaling cascade after the body has adapted to the accelerated pathway. 71 | P a g e BIBLIOGRAPHY Alessi, D. R., Deak, M., Casamayor, A., Caudwell, F. B., Morrice, N., Norman, D. G., Gaffney, P., et al. 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Biochemical and Biophysical Research Communications, 337(1), 248-255. 81 | P a g e [...]... tissue Islet and βcell Caloric 19% 75% decrease in β-cell mass and 60% decrease in islet restriction density observed Fasting hyperglycemia and glucose (30% ) intolerance observed in IUGR Table 1: Summary of studies using IUGR rodent model and exhibit changes in organs and gene involving in insulin- glucose metabolism Inoue et al., 2009 C57BL6J 11 | P a g e from glucose tolerance and insulin resistance... hyperinsulinemia, followed by IGT with declining glucose clearance, reported in k-value derived from intravenous glucose tolerance test (IVGTT), and lastly continued deterioration of insulinglucose prior to signs of hyperglycemia and diabetes (figure 4) (Hansen and Bodkin, 1986; Bodkin, 2000; Wagner et al 2001; Tigno et al, 2004) T2DM prevalence increases in nonhuman primates with age and obesity (Bodkin,... levels of genes involved in insulin and glucose metabolism, physical and biochemical characteristics, before and after high fat diet treatment in nutrition-mediated IUGR model 3 To establish an adult nonhuman primate IGT model using cynomolgus macaques 4 To investigate the metabolic gene expression levels of genes involved in insulin and glucose metabolism, physical and biochemical characteristics in the... βcell Mild fasting hyperglycemia and hyperinsulinemia observed Became glucose intolerance, insulin- resistant and having 50% lesser in β-cells mass after 7 weeks Basal hepatic glucose production was significantly higher in IUGR PEPCK and G6Pase expression level was higher in IUGR β-cell mass and insulin content were reduced by 35–40% in IUGR No difference in glucose tolerant between 2 groups initially,... but IUGR were glucose intolerant after 3 month PEPCK and GR expression level was higher in IUGR Fasting hyperglycemia, reactive hyperglycemia and hyperinsulinemia observed Fasting hyperglycemia and glucose intolerance observed PEPCK and IGFBP-1 expression level was higher No difference in IGF-I and GR expression level IUGR have decrease in islet mass and insulin secretion PDX-1 protein and mRNA levels... the storage and usage of energy (primarily glucose) , as well as the growth and development of tissue Insulin plays a major role in blood glucose regulation as it promotes cellular glucose uptake, glycogen synthesis in skeletal muscle and liver, and inhibits gluconeogenesis in the liver (DeFronzo and 4|Page Ferrannini, 2001) It works in tandem with the glucose glycolysis pathway, utilizing this energy... metabolism is shown in figure 2 At the start of the pathway, insulin binds to a cell surface receptor that belongs to a sub-family of growth factor receptor tyrosine kinases: Insulin receptor (INSR) INSR propagates the signal to insulin receptor substrate (IRS) by phosphorylation and then phosphatidylinositol 3-kinase (PI3K) PI3K activates a PI3Kdependent kinases, PDPK1 (Alessi et al, 1997) which in turn phosphorylates... There is evidence linking changes in expression profile of insulin- glucose gene with T2DM Mice with IRS1 and IRS2 knockout exhibit insulin resistance and subsequently develop diabetes (Tamemoto et al, 1994; Araki et al, 1994) Reduced activation of PI3K due to decreased IRS1 signaling was observed in insulin resistant ob/ob mice and these observations were similar to streptozotocin induced diabetes rats... Bodkin N.L, 2000 1.6 Hypotheses and objectives The proposed hypotheses in this thesis are: 1 Metabolic gene expression levels, physical and biochemical characteristics are different in IUGR offspring displaying abnormal catch-up growth, as compared to normal offspring at the early juvenile stage of life 2 Metabolic gene expression levels of genes involved in insulin and glucose metabolism, physical and. .. SD RIN HDL LDL Type 2 diabetes mellitus World Health Organization Oral glucose tolerance test Impaired glucose tolerance Insulin receptor substrate Insulin receptor Phosphatidylinositol 3-kinase phosphatidylinositol 3-kinase dependent kinases Glycogen synthase kinase 3 beta Glycogen synthase Forkhead box O1 Glucokinase Hexokinase Pyruvate kinase Glucose transporter 4 Myocyte Enhancer Factor 2A Glucose ... Fasting hyperglycemia and glucose (30% ) intolerance observed in IUGR Table 1: Summary of studies using IUGR rodent model and exhibit changes in organs and gene involving in insulin -glucose metabolism. .. SLC2A4RG, and fold decrease in MSTN, indicating elevated insulin -glucose signaling All these conclude that the insulin glucose metabolism in IUGR subjects were accelerated at the beginning and thus... protein serine/threonine kinase protein serine/threonine kinase transmembrane receptor protein tyrosine kinase adaptor 3-phosphoinositidedependent protein serine/threonine kinase phosphorylation and