Adipose Tissue Inflammation, Hepatic Fat Accumulation and

Thesis for doctoral degree (Ph.D.)2008

Adipose Tissue Inflammation,Hepatic Fat Accumulation and

Insulin Resistance

Maria Kolak

Thesis for doctoral degree (Ph.D.) 2008

Maria K

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From Department of Medicine, Solna

Atherosclerosis Research Unit Karolinska Institutet, Stockholm, Sweden

Adipose Tissue Inflammation, Hepatic Fat Accumulation and

Insulin Resistance

Maria Kolak

Stockholm 2008

All previously published papers were reproduced with permission from the publisher.

Published by Karolinska Institutet.

© Maria Kolak, 2008 ISBN 978-91-7409-224-0

2008

Gårdsvägen 4, 169 70 Solna

Printed by

ABSTRACT Insulin resistance is the central feature of a variety of metabolic disorders, such as type

2 diabetes and the metabolic syndrome. The incidence of these disorders, often together with obesity, is currently increasing throughout the world. Insulin resistance occurs together with obesity, but also when adipose tissue is absent, indicating an important role of normal adipose tissue function for the maintenance of insulin sensitivity. Also, an accumulation of fat in the liver is observed together with both the excess and the absence of adipose tissue and insulin resistance. The interplay between the metabolic disturbances in adipose tissue and accumulation of fat in the liver which may underlie the development of insulin resistance is not well understood. Therefore, the aim of this thesis is to improve our knowledge of the molecular pathways underlying metabolic disturbances in adipose tissue and hepatic fat accumulation as well as the interplay between them in order to better understand the development of insulin resistance and related disorders in man.

Twenty type 2 diabetes patients were treated with either rosiglitazone (an insulin sensitizer, an agonist of the transcription factor PPAR , which is predominantly expressed in adipose tissue) or metformin (an insulin sensitizer believed to act mainly in the liver). Rosiglitazone decreased liver fat content in these patients, while metformin did not. Biopsies from subcutaneous adipose tissue were taken before and after 16 weeks of treatment. Expression of 50 genes previously shown to be altered by TZDs in experimental models was measured. We found that rosiglitazone, but not metformin treatment, increased expression of genes involved in fatty acid synthesis and storage and in glucose uptake and decreased some macrophage and inflammation markers. This suggests that together with an improvement in insulin sensitivity and a decrease in liver fat content, rosiglitazone increases lipid storage and decreases inflammation within subcutaneous adipose tissue.

In order to investigate changes in the liver when it becomes fatty, we analyzed gene expression in liver biopsies from 24 subjects with histologically determined liver fat content ranging from normal (n=8) to non-alcoholic hepatic steatosis (n=16). We found that expression of genes involved in fatty acid storage and inflammation was grater in livers of subjects with high liver fat content, indicating an alteration of hepatic gene expression towards an adipogenic and inflammatory profile upon excess fat accumulation.

Next, we investigated whether adipose tissue is inflamed in subjects with increased liver fat content independently of the degree of obesity. Twenty obese, but otherwise healthy women with a wide range of liver fat contents were recruited and divided in normal liver fat (n=10) and high liver fat (n=10) groups. Biopsies from subcutaneous adipose tissue were analyzed by quantitative PCR, immunohistochemistry and lipidomic analysis. Expression of inflammatory markers was higher in the adipose tissue of women with high liver fat. Immunohistochemical analysis revealed increased macrophage infiltration in adipose tissue of women with high liver fat. These macrophages were mostly found surrounding dead adipocytes. Lipidomic analysis showed higher concentrations of long-chain TGs and some sphingomyelins and ceramides in the adipose tissue of women with high liver fat.

In order to investigate the increase in ceramides in the inflamed adipose tissue of women with high liver fat content, we characterized ceramide metabolism within adipose tissue. Gene expression analysis suggested that the increase in ceramide might be explained by an increase in sphingomyelin hydrolysis by sphingomyelinases, especially by SMPD3, rather than by de novo synthesis of ceramide. Sphingomyelinases were expressed by both adipocytes and macrophages, but strongest expression was found in and around blood vessels within adipose tissue.

In summary, the work contained in this thesis demonstrates the importance of inflammation in adipose tissue and liver and the degree of hepatic fat accumulation, but not the degree of obesity per se, in the development of insulin resistance. Sphingomyelin-mediated ceramide production, which shares some features with the process of atherosclerosis, offers a link between the accumulation of fat in the liver and the development of inflammation in adipose tissue.

LIST OF PUBLICATIONS I. Maria Kolak, Hannele Yki-Järvinen, Katja Kannisto, Mirja Tiikkainen, Anders

Hamsten, Per Eriksson, Rachel M. Fisher. Effects of chronic rosiglitazone therapy on gene expression in human adipose tissue in vivo in patients with type 2 diabetes. The Journal of Clinical Endocrinology & Metabolism,2007, 92:720-724

II. Jukka Westerbacka, Maria Kolak, Tuula Kiviluoto, Perttu Arkkila, Jukka Sirén, Anders Hamsten, Rachel M. Fisher and Hannele Yki-Järvinen. Genes involved in fatty acid partitioning and binding, lipolysis, monocyte/macrophage recruitment, and inflammation are overexpressed in the human fatty liver of insulin-resistant subjects. Diabetes, 2007, 56:2759-2765

III. Maria Kolak, Jukka Westerbacka, Vidya R. Velagapudi, Dick Wågsäter, Laxman Yetukuri, Janne Makkonen, Aila Rissanen, Anna-Maija Häkkinen, Monica Lindell, Robert Bergholm, Anders Hamsten, Per Eriksson, Rachel M. Fisher, Matej Oreši , and Hannele Yki-Järvinen. Adipose tissue inflammation and increased ceramide content characterize subjects with high liver fat content independent of obesity. Diabetes, 2007, 56:1960-1968

IV. Maria Kolak, Jukka Westerbacka, Anders Hamsten, Scott A. Summers, Anders Franco-Cereceda, Jan Liska, Matej Oreši , Hannele Yki-Järvinen, Per Eriksson, Rachel M Fisher. Characterization of altered ceramide metabolism in inflamed adipose tissue. Manuscript.

TABLE OF CONTENTS

1 INTRODUCTION .............................................................................. 1

1.1 Adipose tissue..................................................................................................................1 1.1.1 Structure and function of adipose tissue .................................................................1 1.1.2 Adipose tissue and obesity......................................................................................2 1.1.3 Adipokines..............................................................................................................3

1.2 Insulin resistance ............................................................................................................5 1.2.1 Insulin signaling......................................................................................................5 1.2.2 Insulin resistance and metabolic syndrome.............................................................7 1.2.3 Insulin resistance in adipose tissue, muscle and liver .............................................8

1.3 Inflammation ................................................................................................................10 1.3.1 Inflammation in adipose tissue .............................................................................10

1.4 Diabetes .........................................................................................................................12 1.4.1 Treatment of type 2 diabetes.................................................................................13

1.5 Fatty liver ......................................................................................................................14

1.6 Ceramide metabolism ..................................................................................................15

2 HYPOTHESIS AND AIMS.............................................................. 19

2.1 Hypothesis .....................................................................................................................19

2.2 Aims...............................................................................................................................19

3 MATERIALS AND METHODS ....................................................... 20

3.1 Subjects .........................................................................................................................20 3.1.1 Type 2 diabetes patients (Paper I).........................................................................20 3.1.2 Patients undergoing laparoscopic gastric bypass surgery (Papers II and IV)........21 3.1.3 Overweight or obese women (Papers III and IV) .................................................21 3.1.4 Patients undergoing heart-valve surgery (Paper IV) .............................................21

3.2 Measurements of liver fat content...............................................................................22 3.2.1 Estimation of liver fat by histopathology (Paper II) .............................................22 3.2.2 Non-invasive measurements of liver fat content (Paper III) .................................22

3.3 Measurements of body composition............................................................................23 3.3.1 Measurements of subcutaneous and abdominal fat areas......................................23 3.3.2 Measurements of total body fat.............................................................................23

3.4 Total RNA and cDNA preparation (Papers I-IV) .....................................................23

3.5 Selection of genes for quantification (Paper I)...........................................................24

3.6 Quantification of gene expression (Papers I-IV)........................................................24

3.7 Housekeeping genes......................................................................................................25

3.8 Immunohistochemistry (Papers III and IV) ..............................................................26

3.9 Measuring adipocyte cross-sectional area and total area of the sections (Paper III) .....................................................................................................................28

3.10 Lipidomic analysis (Papers III and IV) ......................................................................29

3.11 Statistical analysis ........................................................................................................29

4 RESULTS .......................................................................................31

4.1 Effects of rosiglitazone on adipose tissue gene expression (Paper I)........................31

4.2 Gene expression in fatty liver (Paper II) ....................................................................33

4.3 Gene expression and lipid composition in adipose tissue in obese women with varying amounts of liver fat (Paper III).............................................................35

4.4 Characterization of sphingolipid metabolism in relation to adipose tissue inflammation (Paper IV)..............................................................................................37

5 DISCUSSION..................................................................................41

5.1 Effects of rosiglitazone on gene expression in human adipose tissue.......................41

5.2 Gene expression in human fatty liver .........................................................................45

5.3 Inflammation in adipose tissue and fatty liver...........................................................48

5.4 Altered ceramide metabolism in inflamed adipose tissue .........................................51

6 GENERAL DISCUSSION ...............................................................56

7 GENERAL CONCLUSIONS ...........................................................59

8 ACKNOWLEDGEMENTS...............................................................61

9 REFERENCES................................................................................64

LIST OF ABBREVIATIONS 11 HSD1 Hydroxysteroid (11 ) dehydrogenase 1 ACSL Acyl-CoA synthetase AFLD Alcoholic fatty liver disease Akt/PKB Akt/protein kinase B AMPK AMP-activated protein kinase ASAH N-acylsphingosine amidohydrolase (acid ceramidase) ATGL Adipose triglyceride lipase BMI Body mass index CCL Chemokine (C-C motif) ligand CGT UDP glycosyltransferase CIDEC Cell death-inducing DFFA-like activator C CLS Crown-like structure COL1A1 Procollagen, alpha-1 type 1 CPT Carnitine palmitoyltransferase DEGS Degenerative spermatocyte homolog 1, lipid desaturase FABP Fatty acid binding protein FASN Fatty acid synthase FATP Fatty acid transport protein FN1 Fibronectin 1 FOXO1 Forkhead box O1 FVT 3-ketodihydrosphingosine reductase GLUT Glucose transporter GSK3 Glycogen synthase kinase 3 HDL High-density lipoprotein HSL Hormone-sensitive lipase IHCL Intra hepatocellular lipid IFN Interferon IL6 Interleukin 6 IRS Insulin receptor substrate KO Knock-out LIRKO Liver-specific insulin receptor KO LASS LAG1 homolog, ceramide synthase LDL Low-density lipoprotein LPL Lipoprotein lipase MCP-1 Monocyte chemoattractant protein 1 MRI Magnetic resonance imaging NAFLD Non-alcoholic fatty liver disease NASH Non-alcoholic steatohepatitis NEFA Non-esterified fatty acid PAI-1 Plasminogen activator inhibitor-1 PDE3B Phosphodiesterase 3B

PDK1 3-phosphoinositide-dependent protein kinase 1 PEPCK Phosphoenolpyruvate carboxykinase PI3K Phosphotydylinositol 3 kinase PIP2 Phosphatidylinositol-3,4,5-diphosphate PIP3 Phosphatidylinositol-3,4,5-triphosphate PKC Protein kinase C PPAR Peroxisome proliferator-activated receptor RPLP0 Ribosomal protein large P0 RT-PCR Reverse transcriptase PCR RXR Retinoic X receptor S1P Sphingosine-1-phosphate SCD Stearoyl-CoA desaturase SEM Standard error of the mean SGMS Sphingomyelin synthase SGPL Sphingosine-1-phosphate lyase SM Sphingomyelin SMPD Sphingomyelin phosphodiesterase SPHK Sphingosine kinase SPT Serine palmitoyltransferase TBP TATA-box binding protein TG Triacylglycerol TNF Tumor necrosis factor TZD Thiazolidinedione UGCG UDP-glucose ceramide glucosyltransferase UPLC Ultra performance liquid chromatography WHO World Health Organization VLDL Very low-density lipoprotein

Adipose tissue inflammation, hepatic fat accumulation and insulin resistance 1

1 Introduction

1.1 Adipose tissue

Adipose tissue has a high capacity for storing excess energy in the body. But besides being a simple storage place, adipose tissue is also a complex and active metabolic organ that secretes cytokines with important endocrine functions (1). Since these metabolic functions of adipose tissue became known in the mid 80’s, the field of adipose tissue research has evolved enormously, improving our knowledge of the causes and consequences of various metabolic disorders such as insulin resistance, type 2 diabetes and cardiovascular diseases. This thesis is another piece in the puzzle to improve our understanding of the role of adipose tissue in these disorders.

1.1.1 Structure and function of adipose tissue

There are two types of adipose tissue in mammals: white and brown adipose tissue. The main function of brown adipose tissue is generation of body heat. Rodents and some hibernating mammals have it throughout life, but in humans it only exists in the fetus and neonates.

White adipose tissue in humans is located either under the skin (subcutaneous fat) or around inner organs (visceral fat). A normal-weight adult has between 15-30% adipose tissue in the body, depending on gender and age (2). The main function of adipose tissue is energy storage, but it also cushions and insulates the body and provides important endocrine functions.

Excess energy in the body is mainly stored in lipid droplets in adipocytes as triacylglycerols (TGs). After a meal, non-esterified fatty acids

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(NEFAs) are released from chylomicron-TG and very low-density lipoprotein (VLDL) by lipoprotein lipase (LPL), transported into adipocytes and esterified into TGs. When the needs for energy increase, the stored TGs are hydrolyzed by adipose triglyceride lipase (ATGL) and hormone sensitive lipase (HSL) (3) and released into the circulation.

Besides adipocytes, which are the main building blocks, adipose tissue contains connective tissue matrix, nerve tissue, preadipocytes and stromovascular cells, including endothelial cells, fibroblasts, smooth muscle cells and immune cells, which together form a complex entity (1).

Adipose tissue is composed of around 87% lipids, compared to the whole body, which contains up to 30% fat (2). The fatty acid composition of adipose tissue reflects the composition of dietary fatty acids (4). Several studies have investigated the composition of dietary fatty acids and its association with metabolic disorders. For example, a high intake of saturated and trans-unsaturated fatty acids and a low intake of fish oils containing -3 fatty acids has been associated with an increased risk of cardiovascular diseases (5). The composition of other lipids in adipose tissue and their association with insulin resistance is largely unknown.

1.1.2 Adipose tissue and obesity

According to the World Health Organization (WHO), there were 1.6 billion overweight and 400 million obese adults around the world in 2005 (6). The prevalence of obesity and related metabolic disorders exceeds 50% in the adult population of western countries (7). Obesity is a consequence of increased consumption of energy-dense foods, high in fat and sugars, as well as a sedentary life style. Obesity increases the risk of a range of severe disorders, such as type 2 diabetes, cardiovascular diseases, asthma and some forms of cancer (6). Obesity negatively influences life expectancy. For severely obese young males, the expected length of life was found to be up to 22% shorter than that of a matched lean person (8).

When adipose tissue mass changes, there are alterations in both adipocyte cell size and cell number (9). At the beginning of weight gain, cells become larger until the so called critical mass is achieved. If more fat


is to be stored, more cells differentiate from preadipocytes to become mature adipocytes (10).

Maintenance of normal amounts of adipose tissue is important since both too much and too little fat can have severe metabolic consequences. Thus, insulin resistance is associated with obesity, but also with a deficiency of adipose tissue, i.e. lipodystrophy, in both humans and animals (11; 12). The A-ZIP/F-1 mouse is an animal model of lipodystrophy that completely lacks adipose tissue. These mice develop insulin resistance and have high levels of fat in both liver and skeletal muscle (13). In humans, lipodystrophy is a rare disorder. It is often seen in HIV-infected patients as a side effect of antiretroviral drugs (12). Patients with lipodystrophy may develop the same metabolic disorders as those with excess fat, namely insulin resistance. Obesity as well as lipodystrophy may lead to fat accumulation in non-adipose tissues such as liver and muscle and in this way increase insulin resistance in these tissues (14). The signal or, alternatively, the absence of signal that causes insulin resistance from either too much or too little fat is not known. There are several possible candidates, one of them being high levels of circulating NEFAs (15).

1.1.3 Adipokines

Adipokines are cytokines secreted by adipose tissue. Many of the known adipokines, including adiponectin and resistin, have only recently been described and characterized. These adipokines have been shown to have important endocrine functions (16).

Adiponectin is expressed exclusively by adipose tissue (17). Concentrations of adiponectin in plasma were found to be low in obese subjects (18) and in patients with type 2 diabetes (19). Low concentrations were also associated with increased liver fat (20) and increased risk of cardiovascular events (21). Adiponectin is believed to act through adiponectin receptors, which are expressed in liver and muscle, by increasing -oxidation of fatty acids in both liver and muscle, increasing muscle glucose uptake and decreasing hepatic glucose production (22).

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Resistin got its name from “resistance to insulin”. In mice, it has opposite effects to those of adiponectin. A high circulating level of resistin has been seen in obese and diabetic mice, where it is believed to act in the liver, increasing hepatic insulin resistance (23). Whereas resistin is expressed by adipocytes in mice (24), it is mostly expressed by macrophages in humans (25), which might explain the controversies around the involvement of resistin in metabolic diseases in humans. Plasma resistin was found to be correlated with insulin resistance, but not with body mass index (BMI) (18). However, in another study, no correlations were found between resistin expression and insulin resistance using only human adipocytes (26).

Leptin is an adipokine involved in the regulation of appetite. Circulating concentrations of leptin vary with the degree of obesity in both humans and rodents (27). Both leptin-deficient mice (ob/ob mice) and leptin receptor-deficient mice (db/db) develop severe obesity and have been used as models of obesity in numerous studies. Leptin treatment of ob/ob mice reverses obesity, but this treatment was not successful in obese humans (28).

Chemokine (C-C motif) ligand 2 (CCL2), also known as monocyte chemoattractant protein 1 (MCP-1), is important for recruitment of macrophages to the site of inflammation. Adipose tissue expression of CCL2 increases in obesity (29). Transgenic overexpression of CCL2 in adipose tissue in mice increased insulin resistance, macrophage accumulation in adipose tissue and liver fat content. Conversely, a major decrease in high-fat diet-induced insulin resistance was found in CCL2 knock out (KO) mice (30).

Interleukin 6 (IL6) is a proinflammatory cytokine secreted by T-cells and macrophages, but also by adipocytes, myocytes and endothelial cells, as part of the acute phase response. About one third of the total circulating IL6 originates from adipose tissue (31). An increase in IL6 concentration has been associated with obesity and insulin resistance (32).

TNF is expressed by both adipocytes and macrophages. In contrast to IL6, TNF is not secreted into the circulation by subcutaneous adipose tissue (31). TNF inhibits insulin-stimulated glucose uptake in 3T3-L1 adipocytes (33), possibly by increasing levels of ceramides (34).


Adipose tissue secretes a large number of other cytokines and chemokines, such as plasminogen activator inhibitor 1 (PAI-1), visfatin, vaspin, omentin and retinol-binding protein 4, whose expression levels correlate with obesity and/or metabolic disorders (16; 35).

1.2 Insulin resistance

1.2.1 Insulin signaling

Insulin is secreted by -cells in the pancreas in response to elevated glucose concentrations in blood. The main function of insulin in regulation of glucose metabolism is to inhibit hepatic glucose production and to stimulate glucose uptake. The first step in insulin action is insulin binding to the insulin receptor, which is highly expressed on the surface of insulin sensitive cells, such as adipocytes, myocytes and hepatocytes. The receptor consists of two extracellular -subunits that bind insulin and two transmembrane -subunits with intracellular protein tyrosine kinase activity. Upon insulin binding, the subunits phosphorylate each other at several tyrosine sites.

The activated insulin receptor phosphorylates tyrosine residues of one of six insulin receptor substrates (IRS 1-6) (36) or other functionally similar proteins (e.g. Gab1 (37) and Cbl (38)). Phosphorylated IRS-1 protein recruits phosphatidylinositol 3 kinase (PI3K) to the plasma membrane where it catalyses conversion of phosphatidylinositol-3,4,5-diphosphate (PIP2) to phosphatidylinositol-3,4,5-triphosphate (PIP3). PIP3 in turn recruits both 3-phosphoinositide-dependent protein kinase 1 (PDK1) and Akt/protein kinase B (Akt/PKB) to the cell membrane, where PDK1 activates Akt/PKB. Akt/PKB is central in mediating most of insulin’s downstream effects (39). It activates translocation of vesicles containing glucose transporter 4 (GLUT4) to the cell membrane, possibly via activation of Akt substrate 160 kDa (AS160) (40). GLUT4 is the main transporter of glucose involved in insulin stimulated glucose uptake. Akt/PKB also inactivates glycogen synthase kinase 3 (GSK3), which when activated inhibits glycogen synthase (41). This results in an increase in glycogen

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synthesis and provides a way of storing excess glucose in the cell. The third pathway in which Akt/PKB is involved is downregulation of gluconeogenesis by phosphorylation and inactivation of the forkhead box O1 (FOXO1) transcription factor, which in liver activates phosphoenolpyruvate carboxykinase (PEPCK) and other gluconeogenic genes (42). Akt/PKB is also involved in the upregulation of protein synthesis by inactivation of the repressor of mRNA synthesis (43).

Insulin

Insulin receptor Glucose

-cell

Insulin sensitive cell

AutophosphoylationP P

IRS-1

PI3KAS160

GSK3FOXO1

Cell growth anddifferentiation

GLUT4

Insulin


-cell


AutophosphoylationPP PP

IRS-1IRS-1

PI3K

Akt/PKB

AS160AS160

GLUT4

GSK3FOXO1

TSC1/2

Protein synthesis

Gluconeogenesis

Glycogen synthesis

TranslocationGlucose uptake

GLUT4GLUT4

Acetyl-CoA + NADPH

PDE3B

Lipolysis

Acyl-CoA

FASN

Lipogenesis

Insulin


-cell



IRS-1IRS-1

PI3KPI3KAS160AS160

GSK3FOXO1

Cell growth anddifferentiation

GLUT4GLUT4

Insulin


-cell



IRS-1IRS-1

PI3K

Akt/PKB

AS160AS160

GLUT4

GSK3FOXO1

TSC1/2

Protein synthesis

Gluconeogenesis

Glycogen synthesis

TranslocationGlucose uptake

GLUT4GLUT4

Acetyl-CoA + NADPH

PDE3B

Lipolysis

Acyl-CoA

FASN

Lipogenesis

Figure 1. Insulin secretion by -cells in the pancreas is stimulated by elevated glucose concentrations. Insulin binds to the insulin receptor and regulates several important metabolic pathways, such as those involved in glucose production and uptake, lipogenesis, lipolysis, protein synthesis and cell growth and differentiation (modified from Avram et al (44) and Taniguchi et al (39)).

In adipose tissue, insulin stimulates de novo lipogenesis by increasing transcription of fatty acid synthase (FASN) (45). Simultaneously, insulin decreases lipolysis of stored TGs by activation of cAMP phosphodiesterase 3B (PDE3B), which decreases cAMP levels and in that way blunts


activation of HSL. A summary of insulin signaling pathways is presented in Figure 1.

1.2.2 Insulin resistance and metabolic syndrome

Insulin resistance is an inability of insulin sensitive tissues to respond properly to normal concentrations of insulin. This inability leads to increased concentrations of blood glucose.

In 1988, Reaven proposed that insulin resistance is a central component of a syndrome he named Syndrome X, which consists of a group of metabolic anomalies that together increase the risk of type 2 diabetes and cardiovascular diseases (46). Today this syndrome is more commonly known as the metabolic syndrome. There are several alternative definitions of the metabolic syndrome, most of which include insulin resistance, central obesity, dyslipidemia (increased TG and low HDL cholesterol concentrations) and elevated blood pressure. Other factors such as cigarette smoking (47) and a sedentary lifestyle (48) also contribute to the development of the metabolic syndrome.

Insulin resistance, possibly caused by an increase in circulating NEFA concentrations, is believed to be the most important underlying cause for the development of the metabolic syndrome (49). Obesity, especially central obesity due to excess visceral adipose tissue, is very common in patients with the metabolic syndrome. Visceral fat has higher lipolytic activity and is less sensitive to the antilipolytic action of insulin than subcutaneous adipose tissue (50). Therefore, an increase in visceral fat may lead to a proportionally greater release of NEFAs, which are released directly to the liver via the portal vein (51), without passing the peripheral tissues. Surgical removal of visceral fat has resulted in an improved metabolic profile in both humans and animal models (52; 53).

There are two theories as to the mechanisms by which elevated concentrations of NEFAs may induce insulin resistance. Back in 1963, Randle et al postulated that an increase in NEFA leads to an inhibition of glycolysis and thereby to an increase in intracellular glucose concentrations and a decrease in glucose uptake (54). This theory was challenged several

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times by findings of reduced glucose transport, but not glucose oxidation, which led to an alternative hypothesis about mechanisms for NEFA-induced insulin resistance being proposed by Shulman (55). Glucose transport decreases when intracellular fatty acid metabolites, such as fatty acyl-CoA, diacylglycerol and ceramides, activate protein kinase C (PKC ), which phosphorylates IRS-1 on serine/threonine sites thereby reducing downstream effects of insulin, including glucose uptake (Figure 2).

Insulin

Insulin receptor

PI3K

GLUT4

Insulin

Insulin receptorGlucose

IRS-1

PI3K

Glucose uptake

X XX

NEFAPKC

Ser/Thr phosphorylation

Tyrosine phosphorylation X

Fatty acyl CoADiacylglycerolCeramide

Insulin

Insulin receptor

PI3KPI3K

GLUT4GLUT4

Insulin

Insulin receptorGlucose

IRS-1

PI3K

Glucose uptake

X XX

NEFAPKC

Ser/Thr phosphorylation

Tyrosine phosphorylation X

Fatty acyl CoADiacylglycerolCeramide

Figure 2. Mechanism of NEFA-induced insulin resistance (modified from Shulman et al (55)).

1.2.3 Insulin resistance in adipose tissue, muscle and liver

Insulin resistance affects diverse pathways of insulin signaling in different tissues. Insulin stimulated glucose uptake is primarily affected in skeletal muscle. This is very important since skeletal muscle is responsible for a large portion of the total glucose uptake. In adipose tissue, insulin resistance results in an impaired antilipolytic action of insulin, which results in increased NEFA release. In liver, insulin resistance results in impaired suppression of hepatic glucose production.


Adipose tissue lipolysis has been shown to be the pathway most sensitive to insulin action compared with other insulin regulated pathways (51). When the amount of NEFAs is greater than needed, as after a meal, NEFAs are esterified and stored as TGs. Large amounts of fat can in this way be stored as lipid droplets in adipocytes. Other cells can store only limited amounts of fat. If concentrations of NEFAs are high in the plasma, transport of fat into cells increases throughout the body, including cells with low capacity for fat storage, such as hepatocytes, myocytes, pancreatic and kidney cells (56). Fatty acid overload in tissues other than adipose tissue can lead to cell and tissue death, a phenomenon called lipotoxicity (56). Lipotoxicity is involved in the pathogenesis of insulin resistance, type 2 diabetes and cardiovascular diseases.

In muscle, increased intramyocellular fat accumulation has been associated with insulin resistance (55). An increase in muscle TG content results in a downregulation of insulin signaling pathways and reduced glucose uptake by impairment of activation of Akt1 and protein kinase C (57). Akt1 activation can also be reduced by an increase in ceramides (58). Defects in mitochondrial oxidative phosphorylation have been found in insulin resistant and type 2 diabetes patients (59) who display increased muscle fat content. Like adipose tissue, skeletal muscle can also produce and release cytokines (60), but the concentrations of these have not been associated with cardiovascular risk factors (14).

Hepatic insulin resistance is characterized by an impaired ability of insulin to suppress hepatic glucose production. Fat accumulation in the liver is associated with hepatic insulin resistance (61). In rats on a high fat diet, the activation of IRS1 and IRS2 in the liver is impaired, leading to decreased glycogen synthase activity and increased gluconeogenesis (62). Fatless A-ZIP/F1 mice have increased fat accumulation in both muscle and liver, resulting in defects in PI3K activity and severe insulin resistance. Transplantation of white adipose tissue normalizes both liver and muscle fat content as well as insulin signaling action in these mice (11; 13). Liver-specific insulin receptor knockout (LIRKO) mice develop severe glucose intolerance due to failure of insulin to induce hepatic glucose uptake and suppress hepatic glucose production (63). LIRKO-mice also develop a pro-

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atherogenic lipoprotein profile (64), showing that hepatic insulin resistance alone is sufficient to induce dyslipidemia and atherosclerosis. Also in humans, fatty liver is associated with a failure of insulin to reduce gluconeogenesis and serum NEFAs, independently of obesity (65).

1.3 Inflammation

The classical definition of inflammation involves the response of the body to diverse injuries. It is characterized by movement of inflammatory cells to the site of inflammation.

There are two types of inflammation: acute and chronic. Although there are similarities, most of the features of acute and chronic inflammation are different (66). Acute inflammation is typical in early phases of the inflammatory response and it involves neutrophils as cellular effectors. Chronic inflammation takes place over longer time and involves mononuclear phagocytes (e.g. monocytes, macrophages and lymphocytes). Chronic inflammation has recently been associated with several metabolic disorders. Activation of low-grade chronic inflammation appears to be a common factor for obesity, type 2 diabetes and cardiovascular diseases (67).

1.3.1 Inflammation in adipose tissue

Adipose tissue is inflamed in obese subjects compared with lean subjects (68; 69). The inflammation is characterized by macrophage infiltration and an increase in the expression of macrophage markers, such as CD68. There are positive correlations between BMI, adipocyte size and the number of macrophages (70). Weight-loss, hypocaloric diet and exercise reduce inflammation in adipose tissue and plasma (71-73). While inflamed adipose tissue is infiltrated with macrophages, almost no macrophage infiltration was seen in skeletal muscle (73; 74).

When adipose tissue mass increases, the pattern of adipokine secretion is altered. Adipose tissue production of pro-inflammatory cytokines such as CCL2, IL6 and TNF increases (75), while the production of anti-inflammatory adiponectin decreases (17). However, adipocytes are not the only cells responsible for the release of adipokines from adipose tissue (76).


The increase in inflammatory cytokines could in part be due to increased macrophage infiltration of adipose tissue (68; 69).

When macrophage infiltration of adipose tissue increases, a larger proportion of the adipokines produced in adipose tissue are derived from macrophages. These cytokines cause adipocytes to produce even more (or, in the case of adiponectin, less) adipokines, which eventually may lead to insulin resistance (7; 68; 69). Macrophages and adipocytes have similar features and overlapping biological functions (7). Charriere et al showed that 3T3-L1 preadipocytes have phagocytic ability and can be converted into macrophages (77). The implication of this transdifferentiation is not understood. Several studies have shown that the majority of macrophages in rodent adipose tissue were recruited from other sources. In a study with irradiated mice that received a bone marrow transplantation, 85% of the macrophages contained in adipose tissue were shown to be bone marrow derived (68). Lumeng et al showed that 83% of macrophages in adipose tissue were recruited and that the rest were resident before weight gain on a high fat diet (78). The recruited macrophages have a pro-inflammatory phenotype of M1 or “classically activated” macrophages, while the resident macrophages have an M2 phenotype or “alternatively activated” macrophages (78; 79). Resident M2 macrophages are involved in tissue repair and remodeling and they have different phenotypes in different tissues, for example Kupffer cells in the liver and Langerhans cells in the skin (80). Increased adiposity leads to an increase in M1 macrophage infiltration, and the protective effects of the M2 phenotype are no longer dominant (79). Insulin-sensitizing agents can alter the macrophage phenotype towards the anti-inflammatory M2 type in multiple tissues (81; 82).

It is clear that macrophages infiltrate adipose tissue, but it is less clear why they do this. Several studies have shown that most of the macrophages are found around certain adipocytes, forming so called crown-like structures (CLSs). Cinti et al showed that macrophages surrounded necrotic adipocytes that lack perilipin (83). They postulated that adipocyte death triggers macrophage infiltration of adipose tissue. The signals for this infiltration are unknown. Possibly the lipids released during adipocyte death are oxidized

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and provide a signal for macrophage recruitment, as in atherosclerosis. Dead adipocytes, which together with macrophages and other blood elements form lesions, have been seen in adipose tissue of obese children (84). An increase in interstitial fibrosis associated with an infiltration of inflammatory cells was observed in subcutaneous adipose tissue of obese individuals (85).

The cause of adipocyte death is unknown, but Cinti et al proposed that hypertrophic adipocytes die due to various types of stress, such as hypoxia, increases in TNF , reactive oxygen species and NEFAs (83).

There is a positive correlation between adipocyte size and adipocyte death (86). Larger adipocytes express more proinflammatory cytokines compared to smaller adipocytes (87; 88). Adipocyte size correlate with the number of macrophages and crown-like structures within both subcutaneous and visceral adipose tissue (68; 89). Visceral fat contains more macrophages and crown-like structures than subcutaneous fat in both mice and humans (68; 89; 90), but adipocytes are generally smaller in visceral fat (89), suggesting that adipocyte size plays a role in determining macrophage infiltration only within, rather than between depots.

1.4 Diabetes

Diabetes mellitus is a condition of constant high glucose concentrations in the blood (hyperglycemia). There are two major forms of diabetes: type 1 and type 2. Type 1 diabetes is an autoimmune disorder characterized by -cell destruction. Type 2 diabetes is a more common form and accounts for 85-90% of all cases of diabetes. There are around 300 000 type 2 diabetes patients in Sweden (constituting 3% of the total population). The risk of developing type 2 diabetes increases with age, overweight and obesity.

The characteristics of type 2 diabetes include reduced insulin sensitivity, manifested as a decrease in muscle glucose uptake and an increase in hepatic glucose production, which leads to hyperglycemia. Type 2 diabetes predisposes to an increased risk of cardiovascular diseases. Most patients with type 2 diabetes die from cardiovascular events (91).


1.4.1 Treatment of type 2 diabetes

The first line of treatment of type 2 diabetes includes lifestyle intervention. Both weight loss (92) and exercise (93) have been shown to improve glycemic control. However, most patients will eventually need drug therapy (94). A combination of several treatments is used when glucose concentrations cannot be controlled by a single drug. These combinations may also include adjunct treatment with exogenous insulin.

Metformin and sulfonylureas are common first-line drugs. Sulfonylureas increase the pancreatic secretion of insulin. They are preferentially used for the treatment of non-obese patients, since they may result in initial weight-gain. Metformin belongs to the biguanide class, which originate from the plant French lilac (Galega officinalis), known for centuries to have positive effects on patients with diabetes (95). Despite its known therapeutic benefits, the actual mode of action of metformin is not fully understood. However, it is believed that it acts by activating AMP-activated protein kinase (AMPK) in liver cells, leading to increased fatty acid oxidation and glucose uptake by cells and decreased lipogenesis and hepatic glucose production (96).

Thiazolidinediones (TZDs), another class of anti-diabetic drugs, are synthetic agonists for peroxisome proliferator activated receptor gamma (PPAR ). PPARs are a family of nuclear receptors that regulate gene expression in response to ligand binding. PPARs build a heterodimer with the retinoic X receptor (RXR), which then binds to PPAR responsive elements in the promoter region of target genes. In humans, there are 3 different PPARs: , (or ) and . PPAR is mostly expressed in liver, heart and skeletal muscle, where it regulates expression of genes involved in fatty acid metabolism, while PPAR is ubiquitously expressed (97). There are two isoforms of PPAR with alternative transcription start sites and alternative splicing (98). PPAR 1 is predominantly expressed in adipose tissue and the large intestine, with much lower expression in liver, muscle and kidney. PPAR 2 is mostly expressed in adipose tissue, where it is believed to regulate lipid homeostasis and adipocyte differentiation (97). PPAR 2 KO mice show a reduction in white adipose tissue (99). While

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PPAR KO is lethal, specific adipose tissue PPAR KO results in less adipose tissue, but bigger and more insulin resistant adipocytes (100). These mice also develop fatty liver and hepatic insulin resistance, but do not develop insulin resistance in muscle. Natural ligands for PPAR include different NEFAs and eicosanoids (101).

TZDs are pure PPAR ligands, without affinity for other PPARs. There are several forms of TZDs used as approved drugs, such as rosiglitazone and pioglitazone. The beneficial effect of TZDs on insulin sensitivity appears to be due to enhanced insulin action in both the liver and peripheral tissues (102). However, it is unclear whether this is due to direct effects of PPAR activation in liver and muscle, or whether it is an indirect effect, i.e. a consequence of the actions of PPAR in adipose tissue.

1.5 Fatty liver

Liver fat content is normally below 5% (103). Under certain circumstances, excess fat may accumulate in the liver. Both high alcohol consumption and insulin resistance are associated with the development of fatty liver disease. Despite differences in etiology and different mechanisms leading to fat accumulation in the liver (104), alcoholic fatty liver disease (AFLD) and non-alcoholic fatty liver disease (NAFLD) are histologically indistinguishable (105). Hepatic steatosis is reversible, but if it persists, it can lead to inflammation in the liver (steatohepatitis) and further to cirrhosis (replacement of liver tissue by fibrous scar tissue) and end-stage liver disease.

NAFLD is becoming the most common chronic liver disease in the Western world (106) The prevalence of NAFLD is estimated to be approximately one third of the adult population in western countries (107-109), while 2-3% suffer from non-alcoholic steatohepatitis (NASH) (103). The prevalence of NAFLD increases linearly as a function of obesity (61) and is found in three quarters of type 2 diabetes patients (110-112). In numerous studies NAFLD has been associated with obesity (especially visceral obesity), insulin resistance, type 2 diabetes and dyslipidemia (increased plasma concentrations of TG and LDL cholesterol and decreased


HDL cholesterol concentrations) (113-115). Improvement of insulin sensitivity and weight loss decrease liver fat content (116-118).

The exact mechanisms underlying the development of NAFLD are under investigation. High fat diet is associated with NAFLD in both humans and in animal models (119; 120). An interaction between adipose tissue metabolism and the development of a fatty liver is suggested by findings that plasma concentrations of adipokines, such as leptin and adiponectin, together with NEFA concentrations, all released predominantly from adipose tissue, were associated with the development of NAFLD (121; 122). Animal models of obesity, insulin resistance and also lipodystrophy develop fatty liver (11; 123), indicating the importance of dysfunctional adipose tissue for liver fat accumulation. A deficiency of CCR2, the receptor for CCL2, protect mice from a diet-induced increase in liver fat content and adipose tissue inflammation (124).

Fatty liver is insulin resistant, as described in section 1.2.3. Although fatty liver is often seen in obese individuals, the strong correlation between insulin resistance and liver fat content is independent of obesity (65; 125), Fatty liver overproduces VLDL (126), fibrinogen, C-reactive protein (127) and PAI-1 (128), all of which have been associated with cardiovascular diseases and can affect other organs and tissues (14).

Studies in human liver are limited, since liver biopsies can not be routinely taken due to the risk of complications (103; 129). To improve treatment of NAFLD, a better understanding of the mechanisms behind fat accumulation in NAFLD through investigation of pathways leading to insulin resistance and fat accumulation in human liver is needed.

1.6 Ceramide metabolism

Ceramides are members of a large family of lipids, so called sphingolipids. Sphingolipids, together with phospholipids and cholesterol are the common lipids found in cell membranes. Similar to phospholipids, sphingolipids consist of a polar head and two non-polar fatty acid tails, although sphingolipids contain a backbone derived from serine rather than from glycerol. There are several hundreds of naturally occurring

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sphingolipids, that differ from each other depending on which polar head they have and which fatty acid is incorporated into them (Figure 3).

CO

NH

CH

CHHO

Polar head

Fatty acid

Sphingosine

Figure 3. General structure of sphingolipids. The polar head can be hydrogen (ceramide), phosphocholine (sphingomyelin) or carbohydrate (glycosphingolipids). The fatty acid chain length varies.

In a cell, sphingolipids function as structural components, as cell receptors or as second messengers. In cell membranes and particularly in myelin, sphingomyelin has a structural role, but it also participates in cell signaling, forming, together with cholesterol, the lipid rafts where numerous proteins involved in cellular signaling and trafficking are assembled (130). The carbohydrates within glycosphingolipids are involved in cell surface recognition, for example they are blood group antigens. Ceramides and other sphingolipid-derivates, such as sphingosine-1-phosphate and ceramide-1-phosphate have been shown to be involved in several key cellular biological pathways, including the stress response and apoptosis (34; 131; 132).

Biosynthesis of sphingolipids by the de novo synthesis pathway starts with the condensation of palmitoyl-CoA with serine (Figure 4). Sphingolipids are degraded via irreversible cleavage of sphingosine-1-phosphate (S1P) and sphinganine-1-phosphate by S1P lyase (133).

Disturbances in sphingolipid metabolism have been associated with several rare human diseases, such as Fabry, Krabbe and Niemann-Pick diseases (134). These conditions all present with an abnormal accumulation of sphingolipids in various tissues and are therefore called sphingolipidoses. For example, deficiency of functional acid sphingomyelinase (SMPD1) results in Niemann-Pick disease, characterized by an accumulation of sphingomyelin in different organs, such as liver and spleen, causing cell death and eventually organ failure.


Figure 4. Schematic overview of sphingolipid metabolism. SPT: serine palmitoyl transferase; FVT: 3-ketodihydrosphingosine reductase; LASS: LAG1 homolog (ceramide synthase); DEGS: dihydroceramide desaturase; SGMS: sphingomyelin synthase; SMPD: sphingomyelin phosphodiesterase (sphingomyelinase); ASAH: acid ceramidase; CERK: ceramide kinase; CGT: ceramide glycosyl transferase; UGCG: UDP-glucose ceramide glucosyltransferase; SPHK: sphingosine kinase; SGPL: sphingosine-1-phosphate lyase.

Ceramide produced by the hydrolysis of sphingomyelin has been

implicated in diseases associated with increased oxidative stress, such as cancer, diabetes and inflammation (135). The hydrolysis of sphingomyelin is activated by a variety of signals, including TNF , interferon (IFN ), but

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also by oxidative species, radiation and heat (34). The importance of sphingolipid metabolism in metabolic disorders was suggested by findings of increased ceramide and sphingomyelin levels in insulin resistant liver and skeletal muscle in rats (136; 137) and in skeletal muscle of obese, insulin resistant subjects (138; 139). Ceramides have been shown to decrease insulin action by several pathways, including blocking Akt/PKB activation (140) and activation of the transcription factors Jun and NF B (141), which regulate the expression of several genes important for insulin sensitivity, e.g. down-regulation of GLUT4.

A large body of evidence has implicated ceramide in apoptosis and as a tumor suppressing agent (142). Ceramide may act by inducing mitochondrial dysfunction, producing reactive oxygen species and activating the pro-apoptotic protein Bax (34).


2 Hypothesis and Aims

2.1 Hypothesis

The hypothesis for this thesis is that metabolic disturbances in adipose tissue and accumulation of fat in the liver are closely related and that they may underlie the development of insulin resistance and related disorders in man. Inflammation within both tissues may be of particular importance in this context.

2.2 Aims

The general aim of this thesis is to improve our understanding of the molecular pathways underlying disturbed adipose tissue metabolism and hepatic fat accumulation and the interplay between them.

The specific aims are:

To study the effects of chronic treatment with therapeutic doses of rosiglitazone on gene expression in subcutaneous adipose tissue in type 2 diabetes patients and to compare them with effects of metformin.

To characterize human liver with respect to mRNA expression of genes involved in insulin resistance, inflammation and fat deposition and their relation to liver fat content.

To investigate whether adipose tissue is more inflamed in individuals with high compared to normal liver fat content and to investigate possible causes of any difference in inflammation.

To characterize ceramide metabolism within adipose tissue and its relation to adipose tissue inflammation.

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3 Materials and Methods

3.1 Subjects

3.1.1 Type 2 diabetes patients (Paper I)

Twenty type 2 diabetes patients (13 women and 7 men) were treated for 16 weeks with either rosiglitazone (8mg/day) or metformin (2g/day). Treatment was assigned by randomization using a method which minimizes the differences in age, sex, BMI, HbA1c and duration of diabetes between groups. This method may result in an unequal number of patients in the groups. In this case, there were 9 patients in the rosiglitazone group and 11 patients in the metformin group. Pre-treatment patient characteristics are shown in Table 1.

Blood samples and needle aspiration biopsies from subcutaneous adipose tissue were taken before and after 16 weeks of treatment. The biopsies were immediately frozen in liquid nitrogen and kept at -80 C.

Table 1. Baseline characteristics of the subjects in Paper I. Rosiglitazone Metformin n 9 11 M/F 3/6 4/7 Age (years) 50 ± 3 46 ± 4 BMI (kg/m2) 30.6 ± 0.9 30.6 ± 1 Waist to hip ratio 0.97 ± 0.02 0.98 ±0.02 Serum insulin (mU/l) 12.3 ± 1.4 13.9 ± 2.2 Serum cholesterol (mmol/l) 4.7 ± 0.4 5.0 ± 0.2 Serum TGs (mmol/l) 1.40 (1.27,1.73) 1.96 (1.29, 4.14) Serum LDL cholesterol (mmol/l) 2.95 ± 0.32 2.93 ± 0.18 Serum HDL cholesterol (mmol/l) 1.12 ± 0.06 1.13 ± 0.07

Data are means SEM, except for serum TGs, shown as median (25th and 75th percentiles)


3.1.2 Patients undergoing laparoscopic gastric bypass surgery (Papers II and IV)

In Paper II, blood samples and liver biopsies (both needle and wedge) were taken after an overnight fast from 24 patients undergoing laparoscopic gastric bypass surgery or from patients referred to a gastroenterologist due to elevated liver function tests. All subjects were recruited at Helsinki University Central Hospital. The subjects were classified into groups with and without NAFL, based on histological assessment (>10% liver fat, (103)). RNA was isolated and cDNA synthesized from all biopsies as described below.

In Paper IV, subcutaneous and intra-abdominal biopsies were taken at surgery from 8 patients (2 women, 6 men, age 45±2 years, BMI 52.6±2.0 kg/m2) undergoing laparoscopic gastric bypass surgery at Helsinki University Central Hospital. The biopsies were immediately frozen in liquid nitrogen and kept at -80°C prior to RNA extraction, described below.

3.1.3 Overweight or obese women (Papers III and IV)

The subjects are described in detail in Paper III. Briefly, twenty overweight/obese and physically inactive, but otherwise healthy women participated in this study. After an overnight fast, blood samples and surgical subcutaneous adipose tissue biopsies were taken. A part of each biopsy was frozen for mRNA and lipidomic analyses. The other part was fixed in formalin and embedded in paraffin and used for immunohistochemical studies. Liver fat content, subcutaneous and abdominal fat areas and whole body composition were determined as described below. Participants were divided according to their median liver fat content into normal and high liver fat groups.

3.1.4 Patients undergoing heart-valve surgery (Paper IV)

Biopsies from subcutaneous (n=30), mediastinal (the visceral depot within the thoracic cavity, n=30) and intra-abdominal (n=23) adipose tissue were obtained from patients undergoing heart-valve surgery at Karolinska

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University Hospital. These patients were free from coronary artery diseases. Part of each adipose tissue biopsy was fixed in RNA-later for future RNA extraction. Another piece was fixed in 4% zinc formaldehyde and then embedded in paraffin for immunohistochemistry. Blood samples were taken after an overnight fast.

3.2 Measurements of liver fat content

3.2.1 Estimation of liver fat by histopathology (Paper II)

Liver fat content was estimated as the percentage of fat-laden hepatocytes in a liver section observed under light microscopy. All biopsies were analyzed by the same person.

3.2.2 Non-invasive measurements of liver fat content (Paper III)

Magnetic resonance proton spectroscopy was used to determine liver fat content. Estimates of liver fat content obtained by this non-invasive method have been shown to correlate to histopathological measurements of liver fat content (143).

Figure 5. An example of proton magnetic resonance spectra. Spectrum (a) shows a liver with low fat content, (b) a liver with moderate fat content, and spectrum (c) shows a liver with very high liver fat content. Figure modified from Thomas et al (113).

A voxel localized outside any vascular structure was chosen from a magnetic resonance image of the right lobe of the liver. The spectra were recorded and the chemical shift was measured relative to water. Figure 5

1 %10 %

Liver fat content

75 %


shows an example of results from measurements of liver fat content, where both water resonance and resonance of (CH2)n or intra hepatocellular lipid (IHCL) are shown. The water content is constant and used as a reference to which IHCL is related.

3.3 Measurements of body composition

3.3.1 Measurements of subcutaneous and abdominal fat areas

Magnetic resonance imaging (MRI) was used to determine areas of subcutaneous and abdominal fat. The MRI scan resulted in a series of cross-sectional images of the abdominal region, where fat appears white due to its lower water content. The images were analyzed by image analysis software, which measures the number of pixels with high intensity (“light” pixels) within an encircled area. This value corresponds to the amount of fat in the given area.

3.3.2 Measurements of total body fat

Bioelectrical impedance was used for the determination of whole body composition (% of body fat). Bioelectrical impedance is calculated from measurements of resistance of the body to a small electrical current. A body with high fat content will have a higher impedance, since fat is a poorer conductor than lean mass.

3.4 Total RNA and cDNA preparation (Papers I-IV)

From biopsies taken in Finland (Papers I-IV), total RNA was isolated using the RNA-STAT60 reagent. RNA was purified using an RNeasy kit. RNA concentrations were measured by RiboGreen fluorescent nucleic acid staining. The quality of the RNA was analyzed by an Agilent Bioanalyzer. cDNA was synthesized using M-MLV reverse transcriptase and oligo (dT)12-18 primers.

From biopsies taken at Karolinska University Hospital (Paper IV), total RNA was isolated in Trizol using the Fastprep Homogenizer. After

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DNase treatment, RNA was purified using RNeasy mini kits. RNA concentrations were measured using a NanoDrop spectrophotometer. RNA was transcribed into cDNA using Superscript III reverse transcriptase and oligo (dT)12-18 primer.

3.5 Selection of genes for quantification (Paper I)

The selection of genes for analysis was performed by means of a literature search for genes whose expression had previously been shown to be altered by TZD treatment in different models of adipose tissue such as white adipose tissue of rodents or in cultured adipocytes. In addition, genes whose expression in adipose tissue is known to be related to insulin resistance or obesity were investigated. The selected genes (n=50) are shown in Table 2.

3.6 Quantification of gene expression (Papers I-IV)

Gene expression was measured by quantitative real-time PCR. This method is more sensitive than Northern blot, more quantitative than reverse transcriptase PCR (RT-PCR) and does not require the same degree of confirmation as do methods involving microarrays.

cDNA corresponding to 15 ng of purified RNA was mixed with TaqMan Universal PCR Master Mix and predeveloped gene-specific TaqMan primer and probe mixture (both from Applied Biosystems, but also some in-house assays) in a final volume of 15µl (Paper I) or 25 µl (Papers II-IV). In Paper I, plates were pipetted by a robot, which allowed higher pipetting precision and therefore smaller volumes. All samples were run in duplicate and the specificity of primers and probes were visualized as single bands on agarose gel. A serially diluted 5-point standard curve (7-point in Paper IV) was used on each plate. This was necessary since the efficiencies of PCR amplification for all of the analyzed genes were not similar enough to allow the Ct method to be used. The Ct method is a method in which a Ct-value is used directly in further analysis. The Ct-value is the cycle number at which the amplification curve crosses a given threshold. In


Figure 6, the relative efficiency plots for four genes related to the housekeeping gene RPLP0 are shown. When the Ct method is used, the difference between the efficiency of amplification for the gene of interest and the housekeeping gene (or the slope of the curves in Figure 6) should not be greater than 0.1, which is true only for 11 HSD1, but not for the other three genes in Figure 6. Therefore, the use of the Ct method was unsuitable in these studies and standard curves, which compensate for differences in efficiency of amplification, were used instead.

Figure 6. Relative efficiency of PCR amplification plot for four assays: CCL2 ( ), 11 HSD1 ( ), GLUT4 ( ) and FATP1 ( ). All assays are related to the housekeeping gene RPLP0. The Ct method could be used for the 11 HSD1 gene, but not for the other genes in this figure.

3.7 Housekeeping genes

A housekeeping gene is a gene whose expression should not be affected by any of the studied variables. Expression of this gene is used as an endogenous reference to which all other measured expression levels are related in order to compensate for differences in the amounts of cDNA loaded.

y = 0.25x + 0.66

y = 0.19x + 0.81

y = -0.08x + 2.37

y = -0.26x + 3.75

0

1

2

3

4

0 1 2 3log ng total RNA

Ct (

Ct(a

ssay

) – C

t(RPL

P0))

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Gene expression measured in each sample and expressed as a relative quantity was related to the relative quantity of two housekeeping genes, Ribosomal protein large P0 (RPLP0) and TATA-box binding protein (TBP). The variation between different samples was low for both genes, and the mean expression levels did not differ between groups in either study and were not changed by either of the treatments in Paper I.

The decision to use two housekeeping genes was made after reports that the use of only one gene is inappropriate in studies involving human tissues (144). The second reason was that it is preferable if the expression level of a housekeeping gene is comparable with that of the analyzed gene. RPLP0 is highly expressed while the expression of TBP is low in both human adipose tissue and liver. The combination of these two housekeeping genes thus covered a broad range of expression levels, enabling the housekeeping genes to be used for the whole study, regardless of which genes were analyzed.

3.8 Immunohistochemistry (Papers III and IV)

In Paper III, formalin fixed and paraffin embedded tissue blocks were sectioned in 4µm thick sections. Four consecutive sections from each study subject were mounted on a slide (Figure 7). Prior to staining, slides were deparaffinized with xylene, rehydrated with ethanol and water and then treated in a microwave in citrate buffer (10mM, pH 6.0). This step is necessary for removing methylene bridges, which are formed during tissue fixation. The methylene bridges crosslink proteins and in that way mask potential antigens in tissue. Sections were then incubated in primary and secondary antibodies as indicated in Figure 7, with a rinsing step in PBS buffer in between. This was followed by incubation with avidin-biotin peroxidase and visualization with 3.3´-diaminobenzidine tetrachloride, which resulted in a brown color (see Figure 7). Counterstaining, which brings out the background details such as cell membranes and nuclei, was performed with Harris hematoxylin. This staining resulted in a blue color.


Figure 7. Immunohistochemical staining of serial sections A-D in Paper III. One slide containing four consecutive sections was prepared for each study subject and stained for CD68 and perilipin.

In Paper IV, paraffin embedded tissue blocks were sectioned in 5 µm thick sections. The serial sections were deparaffinized in X-Tra solve and then boiled in TE-buffer (10mM Tris-Cl, 1mM EDTA, pH 7.5) at 98°C for 30 min to demask the epitopes. After washing in PBS, sections were incubated with normal serum (goat or horse, depending on in which animal the secondary antibody was made) for 30 min. Sections were again rinsed with PBS and than incubated with primary antibodies at 4°C over night. The following day, sections were washed in PBS buffer and incubated with secondary antibodies. Staining was visualized using avidin-biotin peroxidase complex (ABC) and 3.3´-diaminobenzidine tetrachloride (DAB). All sections were counterstained with Harris hematoxylin. Sections were dehydrated and mounted in non-aqueous mounting solution.

None Perilipin CD68 CD68

Mouse monoclonal isotypic control

Guinea pig polyclonal anti-perilipin

Mouse monoclonal anti-CD68 (1:600)

Mouse monoclonal anti-CD68 (1:200)

Biotynilated goat anti-mouse

Biotynilated goat anti-guinea pig



Brown Brown Brown

A B C DSlide Tissue sections

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Collagen staining was performed directly after deparaffinization by incubating sections in Sirius red for at least 1h to achieve equilibrium. Collagen staining was visualized by polar light microscopy.

3.9 Measuring adipocyte cross-sectional area and total area of the sections (Paper III)

Two sets of pictures were taken with a digital camera coupled to a light microscope for every slide: one with low resolution (magnification x 4) covering the whole section and one with high resolution (magnification x 40) covering a representative part of the section. Measurements of total section area were performed using the whole-section pictures. The area was calculated as the number of pixels in the encircled area, measured using GNU Image Manipulation Program 2.2 (GIMP 2.2). The area was encircled by hand excluding damaged cells at the edges of the section. The number of macrophages (stained brown in sections A and B) and crown-like structures in each section were counted by two independent observers and corrected for total section area. A crown-like structure was defined as a perilipin-free adipocyte (seen in section C) surrounded by at least three macrophages (seen in sections A or B). Adipocyte cross-sectional area was determined from high-resolution pictures by measuring the area of adipocytes which were intersected by a line randomly drawn across the picture of the section, until the count of measured cells reached 30. Each cell was encircled by hand and the number of pixels in the encircled area was measured using the GIMP program.

Adipocyte size was also measured by collagenase digestion, which allows separation of adipocytes from preadipocytes and stromovascular cells. Adipose tissue was incubated with collagenase for 30 min at 37 C. From this sample, the diameter of 100 adipocytes was determined using a light microscope.


3.10 Lipidomic analysis (Papers III and IV)

Lipidomics is the large-scale characterization of lipid species in a tissue. Ultra-performance liquid chromatography (UPLC) coupled to mass spectrometry was used to analyze the lipid content of adipose tissue, as described in detail in Paper III. Briefly, lipids were extracted from 20 mg of adipose tissue with chloroform: methanol (2:1) solvent and subsequently separated by centrifugation. Labeled standards were added to the lipid extract. The lipids were then run on UPLC to achieve a separation of the different lipid species. UPLC allows both smaller scale and faster analysis compared with traditional HPLC analysis.

After UPLC, separated lipid species were profiled by mass spectrometry. Lipids were first vaporized by passing through a very small, charged capillary to form aerosol droplets. When lipids leave the capillary, they become charged. The solvent in the droplets was vaporized with hot nitrogen gas. Remaining lipids were analyzed by mass spectrometer with mass-to-charge ratio (m/z) range 300-2000. The data obtained were analyzed using MZmine software (145).

3.11 Statistical analysis

The Statview (SAS Institute Inc, Cary, NC, USA) software (Papers I, III and IV) and SPSS (Paper II) were used to perform the statistical analysis. Statistical significance was assigned to a value of p 0.05. Data are presented as mean ± SEM.

mRNA expression levels were expressed in arbitrary units and normalized relative to the housekeeping genes RPLP0 and TBP to compensate for differences in cDNA loading. These two values were averaged. In Paper I, fold changes of gene expression after treatment (relative to baseline levels) were calculated and logarithmically transformed before analysis. Comparison of variables before and after treatment was performed with a paired t-test.

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In Paper II, differences in gene expression were compared by unpaired t-test, after logarithmical transformation of variables with a skewed distribution.

In Papers III and IV, gene expression and physical and biochemical characteristics of the groups were compared using non-parametric Man-Whitney test. All correlations were estimated using non-parametric Spearman’s rank correlation coefficient.


4 Results

4.1 Effects of rosiglitazone on adipose tissue gene expression (Paper I)

Previously published data showed that both rosiglitazone and metformin treatment resulted in decreased serum HbA1c, insulin and NEFA concentrations and increased hepatic insulin sensitivity. Body weight was reduced in the metformin group and unchanged in the rosiglitazone group. Rosiglitazone, but not metformin, resulted in decreased liver fat content and increased insulin stimulated glucose uptake. Gene expression of PPAR , adiponectin and LPL in subcutaneous adipose tissue was increased by rosiglitazone, but not by metformin. Also concentrations of serum adiponectin were increased in the rosiglitazone group (116).

In order to investigate effects of rosiglitazone on gene expression in subcutaneous adipose tissue, we selected 50 genes based on results from other studies, as shown in Table 2. Most of the genes chosen were previously analyzed by an array technique, and in some instances confirmed by real-time PCR.

Effects of rosiglitazone on expression of these genes were measured by real-time PCR and are presented in Table 2. In general, genes involved in fatty acid and TG synthesis (e.g. CD36, FASN and SCD), remodeling of adipose tissue (FN1 and COL1A1) and glucose uptake (GLUT4) were upregulated, while some inflammation and macrophage markers (IL6 and CCL3) were downregulated in adipose tissue of rosiglitazone treated patients. None of these genes were affected by metformin treatment.

Serum concentrations of resistin decreased after rosiglitazone treatment, but were unchanged by metformin. Serum concentrations of IL6 were not affected by either of the treatments, while serum concentrations of CCL3 were too low to be detected by the assay.

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Table 2. Genes selected for quantification of gene expression in human subcutaneous adipose tissue

Gene symbol Gene name Previously reported

effect of a TZD (ref) Effects of rosiglitazone in Paper I

11 HSD1 Hydroxysteroid (11- ) dehydrogenase 1 (146)b

ACADM Medium-chain acyl-CoA dehydrogenase (147)a, (148)d –

ACSL1 Acyl-CoA synthase 1 (149)a, (150)a, –(151)a, (152)d, –(153)d, –

ADAM8 Disintegrin and metalloproteinase domain 8 (69)a –

ADRB3 Beta3 adrenergic receptor – and (154)b, (155)b, (156)b –

BCKDHB Branched chain keto acid dehydrogenase E1 (150)a, –(147)a,b –

CA3 Carbonic anhydrase 3 (157)a – CAP1 Adenylyl cyclase-associated protein (154)b, (152)d –

CASP8 Caspase 8, apoptosis-related cysteine protease (154)b –

CCL2 Chemokine (C-C motif) ligand 2 –(69)a – CCL3 Chemokine (C-C motif) ligand 3 –(69)a

CD36 CD36 (147)a,b, (158)a, (150)a, (159)a, – and (154)b, (160)d, (148)d

CD68 CD68 (69)a –

CEBPA CCAAT/enhancer binding protein (C/EBP), alpha (154)b, (155)b –

CIDEC Cell death-inducing DFFA-like effector C (147)a,b, (161)a, –(154)b

COL1A1 Alpha-1 type 1 procollagen (147)a, (147)b CTNNB1 Catenin beta 1 –(147)a,b, (154)b – DF Adipsin (154)b, (155)b –

EMR1 Egf-like module containing, mucin-like, hormone receptor-like 1 (69)a –

FABP4 Fatty acid binding protein 4 (147)a,b, (150)a, (154)b, (155)b, (146)b, (160)d,

–(153)d, (148)d –

FAH Fumarylacetoacetate hydrolase (150)a, – (147)a, (147)b –

FASN Fatty acid synthase (150)a, –(154)b, –(155)b, (152)d, –(160)d, –(162)d

FATP1 Fatty acid transport protein 1 (149)a, (161)a, (150)a, (159)b, –(163)c,–(153)d

FATP4 Fatty acid transport protein 4 –(153)d – FN1 Fibronectin 1 – and (154)b FOSL1 FOS-like antigen 1 (147)a,b – G0S2 G0/G1 switch gene (154)b – GAS1 Growth arrest-specific 1 –(147)a, (147)b, –(154)b – GLUT1 Glucose transporter 1 (164)b, (165)b, –(153)d

GLUT4 Glucose transporter 4 (158)a, –(164)b, –(165)b, –(163)c, (166)d, –(153)d

GPD1 Glycerol-3-phosphate dehydrogenase 1 (155)b –

HSL Hormone-sensitive lipase –(154)b, (155)b, –(160)d –


Gene symbol Gene name Previously reported

effect of a TZD (ref) Effects of rosiglitazone in Paper I

IGF2 Insulin-like growth factor 2 (155)b – IL6 Interleukin 6 (153)d, –(162)d

ITGAM Integrin, alpha M, macrophage antigen polypeptide (69)a –

ITIH4 Inter-alpha (globulin) inhibitor H4 (147)a – LEP Leptin (146)b, (163)c, –(162)d –

LXR Nuclear receptor subfamily 1, group H, member 3 (155)b –

NR1D2 Nuclear receptor subfamily 1, group D, member 2 (154)b –

NR4A1 Nuclear receptor subfamily 4, group A, member 1 (154)b –

PC Pyruvate carboxylase (161)a, (150)a, (155)b –

PDGFB Platelet-derived growth factor beta polypeptide – and (154)b –

PMM1 Phosphomannomutase 1 (147)a,b –

PPARGC1Peroxisome proliferative activated receptor, gamma, coactivator 1, alpha

(153)d –

RETN Resistin (155)b, (162)d

SCD Stearoyl-CoA desaturase (150)a, (147)a,b, (158)a, (15)a, –(154)b, (155)b, (167)b, (168)d

SCD4 Stearoyl-CoA desaturase 4 – SLC25A1 Citrate transporter 1 (147)a – TNF Tumor necrosis factor alpha (158)a, –(162)d – UCP2 Uncoupling protein 2 –(154)b, (169)b, (163)c – References refer to previously published investigations of the effect of TZDs on gene expression in adipose tissue or adipocyte cell lines. In some studies, contradictory results were obtained by using two different methods. In such cases both results are indicated. Significantly (p<0.05) upregulated by a TZD; Significantly downregulated by a TZD; – Not

altered by a TZD. a White adipose tissue of mice or rats; b 3T3-L1 or similar cell lines; c Isolated human adipocytes; d Human adipose tissue.

4.2 Gene expression in fatty liver (Paper II)

In order to characterize the liver when it becomes fatty, we quantified the expression of genes known to be involved in the development of insulin resistance, inflammation of fat storage in livers of 24 subjects with a wide range of liver fat contents. The subjects were divided into two groups: with fatty liver (n=16), and without fatty liver (n=8), according to the cutoff of 10% of liver fat estimated by histological examination. Subjects with high liver fat had higher concentrations of fasting serum insulin, C-peptide and TGs and lower HDL cholesterol concentrations compared to subjects

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without fatty liver. Liver fat correlated positively with BMI, fasting serum insulin, C-peptide and serum TGs and negatively with HDL cholesterol. Serum adiponectin correlated negatively with liver fat, but not with BMI. Also, PAI-1 plasma concentrations correlated positively with liver fat, but not with BMI.

Table 3. Coefficients of correlation between relative hepatic gene expression and liver fat, alternatively liver fat adjusted for BMI. Relative gene expression levels between the subjects without fatty liver (NAFL-) and the subjects with fatty liver (NAFL+).

Gene Liver fat Liver fat adjusted for BMI

NAFL- NAFL+ Fold

difference

PAI1 0.32 0.05 2.12±0.90 1.85±0.28 0.9 PPAR 0.39 0.22 0.13±0.01 0.17±0.01 1.3 PPAR 2 0.64*** 0.34 0.05±0.01 0.15±0.02* 2.8 PGC1 -0.19 -0.19 5.60±1.37 3.29±0.56(*) 0.6 PPAR 0.06 -0.05 4.58±0.97 4.31±0.52 0.9 PPAR 0.29 0.1 0.18±0.03 0.24±0.03 1.3 ADIPOR1 -0.08 -0.14 1.23±0.10 1.29±0.13 1.1 ADIPOR2 0.05 0.03 3.01±0.53 3.03±0.46 1.0 HMGCS2 -0.07 -0.17 6.36±0.71 5.96±0.44 0.9 IGFBP1 -0.11 -0.28 4.88±1.38 6.76±2.50 1.4 CD68 0.14 0.17 0.17±0.03 0.20±0.02 1.1 CCL2 0.61** 0.45* 0.32±0.05 0.57±0.05** 1.8 CCL3 0.42* 0.36 0.03±0.01 0.04±0.01 1.6 SCD1 0.18 0.22 0.44±0.13 0.48±0.09 1.1 ACSL4 0.53** 0.43* 0.11±0.05 0.31±0.07(*) 2.8 CPT1A 0.24 0.11 2.69±0.53 3.39±0.44 1.3 ACACA 0.27 0.2 1.39±0.31 1.89±0.18 1.4 FATP5 -0.16 -0.22 8.57±0.87 7.81±0.70 0.9 FABP4 0.83**** 0.76**** 0.002±0.001 0.009±0.001** 3.9 FABP5 0.74*** 0.62** 0.016±0.003 0.039±0.008* 2.5 LPL 0.73*** 0.66*** 0.002±0.001 0.006±0.002 3.6

Data are means SEM. mRNA concentration of selected genes in the liver in NAFL- subjects and NAFL+ subjects. (*) p<0.10; * p<0.05; ** p<0.01; *** p<0.001; **** p<0.0001 for correlation between mRNA expression and liver fat or liver fat adjusted for BMI or for NAFL- versus NAFL+.


Liver fat content correlated significantly with hepatic expression of PPAR 2, CCL2, CCL3, ACSL4, FABP4, FABP5, and LPL (Table 3). Correlations between liver fat and CCL2, ACSL4, FABP4, FABP5, and LPL expression remained statistically significant even after adjustment for BMI. Subjects with fatty livers had significantly increased hepatic expression of PPAR 2, CCL2, FABP4, FABP5, and LPL compared with subjects without fatty liver (Table 3).

4.3 Gene expression and lipid composition in adipose tissue in obese women with varying amounts of liver fat (Paper III)

The women were divided, based on their median liver fat, into two groups: normal liver fat content (n=10, liver fat 1-3.5%) and high liver fat content (n=10, liver fat 6-34%). The groups were matched for age, BMI and body fat distribution. The high liver fat group had higher fasting serum insulin and lower HDL cholesterol concentrations.

Expression of macrophage and inflammation markers CD68, CCL2 and CCL3 and of PAI-1 in subcutaneous adipose tissue was higher in the high liver fat group. Expression of adiponectin and PPAR was lower. Expression of CD68 correlated with liver fat, but not with BMI (Figure 8).

Figure 8. CD68 expression in adipose tissue of 20 obese women correlates with liver fat content (left panel), but not with BMI (right panel).

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Mean adipocyte cell size measured by collagenase digestion correlated significantly with the mean adipocyte cross-sectional area, but was not different between the groups for either of the methods. There was a significant difference in the distribution of adipocyte cross-sectional area (Figure 9B). Women with high liver fat had more larger adipocytes, but this could not be confirmed by measurements of cell size after collagenase digestion (Figure 9A).

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Figure 9. Distribution of adipocyte diameter (A) measured after collagenase digestion of adipose tissue and adipocyte cross sectional area (B) measured in sectioned adipose tissue in women with normal ( ) and high ( ) liver fat content. The distributions tended to differ between the normal and high liver fat groups in A (p=0.14). The distributions differed significantly in B (p=0.000009).

Macrophages were found in immunohistochemical sections of adipose tissue of 19 women. The reproducibility of macrophage number counts performed by two observers was high (correlation between all results: r2=0.95, p<0.0001). The number of macrophages correlated with CD68 mRNA expression in adipose tissue. Crown-like structures were found in


sections from 13 women. The number of crown-like structures correlated with border-line significance with CD68 mRNA expression (r=0.44, p=0.057).

Lipidomic analysis of adipose tissue revealed differences in the lipid profiles between the groups. The group with high liver fat had more long-chain TGs and some sphingomyelins and ceramides in adipose tissue compared with the normal liver fat group, while the concentrations of diacylglycerols did not differ between the groups.

4.4 Characterization of sphingolipid metabolism in relation to adipose tissue inflammation (Paper IV)

To investigate the basis for the differences in adipose tissue ceramide/sphingomyelin content between the groups with different degrees of inflammation in their subcutaneous adipose tissue described in Paper III, expression levels of selected genes involved in ceramide synthesis and metabolism were quantified and compared between the groups (Table 4).

Genes involved in de novo ceramide synthesis (SPTLC1, SPTLC2, DEGS1, LASS1, LASS4 and LASS6) and genes involved in ceramide degradation (SGMS1, SGMS2 and UGCG) were not differentially expressed (for enzyme function, see Figure 4). However, ASAH1 was expressed at significantly higher levels in inflamed adipose tissue of the high liver fat group. Expression levels of three sphingomyelinases (SMPD1-3) were greater in the high as compared to the normal liver fat group, but expression of a fourth sphingomyelinase, SMPD4, did not differ between the groups.

Correlations between differentially expressed genes (SMPD1, SMPD2, SMPD3 and ASAH1) and concentrations of ceramides and sphingomyelin in adipose tissue of obese women revealed that SMPD3 correlated with 8 out of 9 ceramides and sphingolipids, while the other three genes showed no significant correlation to sphingolipid concentrations.

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Table 4. Relative gene expression levels of genes involved in ceramide and sphingomyelin metabolism in subcutaneous adipose tissue of women with normal and high liver fat content.

Gene Normal liver fat

n=10 High liver fat

n=10 P

SPTLC1 0.99±0.04 1.05±0.06 0.20 SPTLC2 0.97±0.07 1.00±0.05 0.39 DEGS1 0.99±0.05 1.07±0.05 0.11 LASS1 9.07±3.12 9.22±1.63 0.48 LASS4 1.08±0.10 1.07±0.16 0.49 LASS6 0.87±0.07 0.89±0.07 0.42 ASAH1 1.16±0.10 1.47±0.13 0.03 UGCG 0.91±0.08 0.96±0.06 0.34 SGMS1 1.01±0.05 1.10±0.06 0.12 SGMS2 2.09±0.27 2.57±0.32 0.13 SMPD1 1.51±0.10 1.85±0.10 0.01 SMPD2 1.13±0.06 1.26±0.06 0.08 SMPD3 1.33±0.13 1.76±0.18 0.05 SMPD4 1.57±0.09 1.52±0.09 0.35

Gene expression was normalized to housekeeping genes RPLP0 and TBP, which did not differ between the groups (for RPLP0: 1.94±0.26 vs. 2.11±0.17, NS; for TBP: 3.96±0.48 vs. 3.97±0.44, NS). Values are expressed as mean ± SEM.

To establish the location of sphingomyelinases SMPD1-3 in adipose tissue, immunohistochemical analysis of subcutaneous adipose tissue from the obese women was performed. SMPD1-3 proteins were expressed in both macrophages and adipocytes within adipose tissue. All three sphingomyelinases were also found in and around blood vessels (Figure 10).

Quantification of mRNA concentrations of SMPD1-3 and investigation of the distribution of SMPD1-3 proteins were performed in three different adipose tissue depots from patients undergoing heart-valve surgery: subcutaneous (n=30), mediastinal (n=30) and intra-abdominal (n=23) adipose tissue. Gene expression levels of the inflammatory markers CD68, CCL2 and IL6 were significantly higher in intra-abdominal compared to subcutaneous fat. SMPD3 mRNA was higher in both mediastinal and intra-abdominal compared to subcutaneous fat, while expression of SMPD2 was lower in intra-abdominal compared to subcutaneous fat. Expression of SMPD1 was similar across the three adipose tissue depots.


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Figure 10. Representative picture showing serial staining of a small blood vessel within subcutaneous adipose tissue for (A) SMPD1, (B) SMPD2 and (C) SMPD3. Positive staining is colored brown (arrows). Negative control (secondary antibody only) is shown in (D).

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Immunohistochemical analysis of subcutaneous adipose tissue located SMPD1 protein to extracellular regions surrounding blood vessels, while SMPD2 and SMPD3 were mostly found in the inner layer of larger blood vessels. Analysis of mediastinal and intra-abdominal adipose tissue demonstrated similar staining patterns.

In addition, the mRNA expression patterns of SMPD1-3 in subcutaneous and intra-abdominal adipose tissue were also determined in an independent group of 8 subjects undergoing laparoscopic gastric bypass surgery. Levels of SMPD3 mRNA were significantly greater in intra-abdominal than in subcutaneous adipose tissue, but no significant differences were observed for SMPD1 or SMPD2.


5 Discussion

5.1 Effects of rosiglitazone on gene expression in human adipose tissue

In order to investigate effects of therapeutic doses of rosiglitazone on gene expression in human adipose tissue, 20 patients were randomized to receive either rosiglitazone or metformin. Expression of 50 genes was measured in adipose tissue biopsies taken before and after 16 weeks of treatment. Rosiglitazone, but not metformin, increased expression of genes involved in fatty acid synthesis and storage and decreased macrophage and inflammation related genes.

Both metformin and rosiglitazone are insulin sensitizers, but with different sites of action, metformin acting in liver and rosiglitazone probably in adipose tissue. Metformin was chosen as the control treatment in order to investigate changes in adipose tissue which were not a consequence of improvement in insulin sensitivity itself.

Rosiglitazone increased expression of genes involved in fatty acid uptake, metabolism and TG synthesis. The most upregulated gene of all analyzed genes was stearoyl-CoA desaturase (SCD). This gene was previously found to be upregulated by a TZD in both human and rodent adipose tissue, but was downregulated in the 3T3-L1 cell line. This discrepancy suggests either different regulation of the SCD gene or different action of a TZD in 3T3-L1 cells compared with adipose tissue. SCD catalyses the synthesis of monounsaturated fatty acids, primarily oleate (18:1) and palmitoleate (16:1). SCD is important for adipose tissue growth since mice lacking SCD are resistant to diet-induced obesity (170). Inhibition of SCD expression in mice was associated with reduced body adiposity and improved insulin sensitivity (171). Increased adipose tissue SCD expression may increase TG storage within adipose tissue instead of in

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other tissues, which may explain the relative weight gain in patients treated with rosiglitazone compared with metformin. There are 4 isoforms of SCD in mouse (SCD1-4) with great homology, but only two isoforms in humans, namely SCD, very similar to all rodent SCDs, and SCD5, an isoform found only in primates (172). The expression of SCD5 (called SCD4 at the time of publication of Paper I) was not changed by rosiglitazone treatment.

CD36, also known as fatty acid translocase, is a membrane-bound protein involved in fatty acid transport. CD36 deficient mice have markedly reduced fatty acid uptake in heart, skeletal muscle and adipose tissue (173). Transgenic mice over-expressing CD36 have decreased circulating TG and fatty acid concentrations (174). FASN catalyses the synthesis of palmitate, which than can be esterified into TGs and stored in adipose tissue (175). The increases in SCD, CD36 and FASN expression may reflect rosiglitazone-induced increase in TG storage within adipose tissue, which would prevent ectopic fat storage and lipotoxicity.

In contrast to upregulation of genes involved in TG storage in adipose tissue, FATP1 was significantly downregulated by rosiglitazone. FATP1 facilitates the uptake of long chain fatty acids and was proposed to play a similar role for fatty acids as GLUT4 plays for glucose, namely that in response to insulin it increases fatty acid uptake into adipose tissue and muscle (176). FATP1 KO mice are protected from diet-induced obesity (176). Since rosiglitazone improved insulin sensitivity and increased expression of FATP1 in mouse adipose tissue (149; 161), but not in our human study, the question arises of whether functions of human and mouse FATP1 are similar or not and how the rosiglitazone-induced decrease in FATP1 expression in human adipose tissue is beneficial in terms of improvement of insulin sensitivity.

In order to store more fat, adipose tissue will eventually need more adipocytes, which requires the remodeling of extracellular matrix (177). Both fibronectin and collagen are extracellular matrix proteins and the observed increase in the expression of genes coding for these proteins (FN1 and COL1A1) is therefore expected. The dependency of adipose tissue growth on collagen synthesis has previously been demonstrated (178). The remodeling of adipose tissue may also involve the replacement of larger


adipocytes with smaller, more insulin sensitive cells through apoptosis. Cell death-inducing DFFA-like effector C (CIDEC), also known as fat specific protein 27, is found in lipid droplets within adipocytes (179) and has been shown to have apoptosis-inducing activity (180). The apoptosis-inducing activity of CIDEC may also be involved in the removal of macrophages (see below). Bodles et al showed that another TZD, pioglitazone, induces macrophage apoptosis in human adipose tissue (181). Adipose tissue expression of another gene involved in apoptosis, caspase 8, was not detectable.

Insulin resistance is associated with low-grade inflammation and macrophage accumulation in adipose tissue (68; 69). Several studies have shown association between obesity and inflammation in adipose tissue (68; 69; 83) and weight loss has been associated with a decrease of inflammation in adipose tissue (90; 182). No weight loss was observed in the rosiglitazone-treated patients in Paper I, despite an improvement in peripheral insulin sensitivity. There was instead a decrease in the expression of CCL3 and IL6, which are expressed by macrophages (183). This suggests that inflammation in adipose tissue is related to insulin resistance, independently of obesity. Metformin did not alter the expression of any macrophage or inflammation marker in adipose tissue. Serum concentrations of IL6 were not affected by rosiglitazone treatment, probably because only one third of circulating IL6 originates from adipose tissue (31). Expression of another adipokine, resistin, was also decreased by rosiglitazone both in adipose tissue and in serum. In mice, resistin is expressed by adipocytes (24), but in human it is mostly expressed by macrophages (25). Thus, a decrease in inflammation seen as a reduction in macrophage number would explain the rosiglitazone-induced decrease in resistin expression.

While GLUT4 is rate-limiting for insulin-stimulated glucose uptake (184), GLUT1 mediates non-insulin dependent, basal glucose uptake (185). The increase in adipose tissue GLUT4 expression is important in the improvement of insulin sensitivity. Different regulation of GLUT1 and GLUT4 by rosiglitazone has previously been described in denervated rat muscle (186), 3T3-L1 adipocytes (164) and rat adipose tissue (165).

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Expression levels of GLUT1 are much lower compared with those of GLUT4 and a decrease in GLUT1 expression may be caused by a compensatory regulation in response to the increased levels of GLUT4.

Expression of 11 HSD1 was decreased by rosiglitazone in adipose tissue. 11 HSD1 catalyses the conversion of inactive cortisone to active cortisol in adipose tissue (187) and reductions in 11ßHSD1 expression have been associated with an improved metabolic profile (188). While 11ßHSD1 expression was undetectable in any cells present in human blood, levels increased markedly during differentiation of monocytes into macrophages (189). This suggests that the decrease in 11 HSD1 could in part be due to a reduced presence of macrophages in adipose tissue and might be another feature of the anti-inflammatory effect of rosiglitazone in adipose tissue.

Although most of the genes chosen for analysis were shown to be affected by rosiglitazone in previous studies (references in Table 2), expression of only 13 out of 50 genes was altered by rosiglitazone in Paper I. A small sample size could explain why changes in expression for some of the genes did not reach statistical significance, since there were only 9 patients in the rosiglitazone group. Expression of the genes found to be affected by rosiglitazone in several other studies, such as upregulation of fatty acid binding protein 4 (FABP4) and uncoupling protein 2 (UCP2), were not significantly affected in our patients, although their expression tended to show the same upregulation as in experimental models. The power of our study was probably not enough to detect small changes in gene expression. Other genes, for example CEBPA, showed no tendency towards a TZD-induced downregulation as in experimental models. There may be several explanations for these discrepancies. Firstly, differences between species are important to remember. Although humans and rodents share many features, the differences between them become more apparent as we zoom in from the whole organism to the level of single molecules, such as mRNA and proteins. Animal and cell models are very important for our understanding of the various processes in an organism, but they can never simulate a human body. Secondly, the doses of rosiglitazone used in experimental models were up to 100 times higher than those used for treatment of humans. At high concentrations, rosiglitazone has been shown


to act independently of PPAR in macrophages (190). He et al showed that TZD treatment improved liver insulin resistance in mice with targeted deletion of PPAR in adipose tissue (100), which would imply that TZD can act without adipose tissue PPAR . The dose used in that study was 3mg/(kg*day), which is about 30 times higher than the therapeutic dose used in Paper I.

Since completion of the study in Paper I, several gene expression studies on TZD effects in human adipose tissue have been published (for example (148; 152; 162; 191)). Effects of TZDs observed in these studies were in agreement with the results in Paper I. Both Bogacka et al and Boden et al concluded that TZDs increase expression of genes involved in adipocyte lipid storage pathways in adipose tissue of type 2 diabetes patients (148; 152). In other studies, an increase in expression of GLUT4 (191) and a decrease in resistin expression (162) were observed. Similar to the results in Paper I, many of the genes previously shown to be TZD-responsive genes in experimental models were not affected by a TZD in human adipose tissue also in these human studies.

5.2 Gene expression in human fatty liver

We quantified the expression of genes involved in the development of insulin resistance, inflammation and fat storage in livers of 24 subjects with varying degrees of liver fat, ranging from normal to non-alcoholic steatosis. We found that liver fat content correlates with mRNA concentrations of four genes involved in fatty acid transport and synthesis (FABP4, FABP5, LPL and ACSL4), chemokine genes CCL2 and CCL3, and the PPAR 2 gene, a key regulator of adipocyte differentiation. Of these genes, PPAR 2, FABP4 and LPL are normally expressed mostly in adipose tissue and only at low levels in liver. The results indicate that the fatty liver undergoes a transformation towards an adipogenic and inflammatory profile.

Expression studies of human non-alcoholic fatty liver prior to our study were very limited, mainly due to complications associated with taking liver biopsies. Younossi et al. analyzed liver biopsies from morbidly obese patients with NAFLD and found a decreased expression of enzymes

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involved in defense against oxidative stress and an increase in some fibrogenic and apoptotic genes (192). In another study by the same group, livers from obese NASH patients, when compared to healthy controls, showed increased expression of genes involved in lipid metabolism, insulin resistance and extracellular matrix remodeling (193). Microarray data comparing livers from 9 patients with NAFLD and 9 lean controls revealed an increase in mitochondrial and three inflammatory genes (194).

CCL2 and CCL3 are leukocyte-recruiting cytokines whose increased expression has been observed together with inflammation in multiple tissues. High levels of CCL2 were found in the plasma of NASH patients and the presence of CCL2 in steatotic liver was shown by immunohistochemistry (195). Since patients in our study did not have any histological signs of inflammation in their livers, increased expression of these chemokines may be part of an early pro-inflammatory profile of NAFLD, before irreversible inflammation and NASH occurs. Resident macrophages in the liver, so called Kupffer cells, may be a source for the increased expression of these chemokines (196). The lack of increased expression of CD68 in NAFLD may suggest that recruitment of new macrophages into liver is not a feature of the early pro-inflammatory profile of NAFLD.

There are 9 known forms of human fatty acid binding proteins, with similar structure and function but different tissue distribution. Their main function is the uptake and intracellular transport of fatty acids. We found that liver expression of both FABP4 and FABP5 correlated to liver fat content. FABP4 was believed to be expressed exclusively by adipose tissue (197), but its expression was also found in macrophages (198). FABP5 is expressed in multiple tissues (199). As in our study, FABPs were found to be increased in the fatty livers of ob/ob mice, when compared to lean controls (200). Mice lacking FABP4 and FABP5 are protected from diet induced obesity, insulin resistance, diabetes and fatty liver (201).

LPL hydrolyzes TGs from circulating lipoproteins and releases NEFAs so that they can be taken up by peripheral tissues, mainly adipose tissue and skeletal muscle. Expression of LPL in liver is normally very low (202). Liver X receptor agonists and TNF have both been shown to


increase liver LPL expression (203; 204). Overexpression of LPL in mouse liver resulted in hepatic steatosis and hepatic insulin resistance due to an impaired ability of insulin to suppress gluconeogenesis. This inability is possibly a consequence of an increased intracellular accumulation of fatty acyl-CoA, DAG and ceramide (205).

Acyl-CoA synthase (ACSL) catalyzes activation of fatty acids by acyl-CoA formation. There are 5 mammal ACSLs with different substrate specificity and tissue distribution. ACSL4 acts preferentially on arachidonic acid (206). We found expression of ACSL4 to be increased in fatty liver. Increased ACSL4 has been observed in association with hepatic tumor cell growth (207). An increased ACSL4 expression could lead to increased activation of fatty acids and subsequent TG synthesis, leading to an increased storage of TGs in hepatocytes.

PPAR has an essential role in adipocyte differentiation (208). In liver, increased PPAR expression has been associated with the development of hepatic steatosis in several animal models of obesity and diabetes (209-212). The PPAR 2 isoform, usually expressed by adipose tissue, was found to be overexpressed in steatotic liver in ob/ob mice (213). Overexpression of PPAR 2 induced steatosis in a hepatic cell line by upregulating adipogenic genes and thereby enhancing hepatic lipogenesis. (211). We observed no differences in total PPAR expression, indicating that the PPAR 2 isoform may have a key function in the development of human fatty liver. Similar to our results in human fatty liver, induction of fatty liver in mouse by overexpression of liver PPAR 1 increased the mRNA expression of genes involved in adipogenesis and lipid metabolism, resulting in the transformation of hepatocytes into more adipocyte-like, lipid droplet containing cells (212; 214).

The natural ligands of PPAR are fatty acids. An increased intake of dietary fat can lead to the development of fatty liver in both humans and in animal models (119; 120). A high fat diet is associated with an increase in liver PPAR expression (120). Therefore it is possible that increased liver PPAR 2 expression was a consequence of increased fat intake in the NAFLD patients.

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Induction of SCD1 has been shown to precede the induction of other lipogenic genes in liver following a high-fat diet in mice (215). We did not see any alterations in the expression of SCD in human fatty liver. Moreover, no alteration was observed in mRNA expression levels of the rate limiting enzyme for mitochondrial -oxidation, CPT1A, suggesting that the mitochondrial fatty acid oxidation may not be affected in the early stages of the development of fatty liver.

The correlation between liver fat content and mRNA expression levels of genes involved in fatty acid transport and synthesis, and of CCL2 remained significant after adjustment for BMI, indicating that obesity per se is not sufficient for the development of fatty liver. Having BMI-matched groups, rather than solely adjusting for BMI, as was done in our study, would provide a more powerful tool for investigating the role of obesity in the development of fatty liver.

A subset of ten samples from this study (5 with low and 5 with high liver fat content) was used in analysis of gene expression by Affymetrix GeneChips (216). This unbiased approach resulted in similar findings to those in Paper II, namely that genes involved in lipid metabolism and inflammation were increased in fatty liver.

In summary, this study showed that an increase in liver fat content correlated to an increase in PPAR 2 gene expression. An increase of PPAR 2, possibly as a consequence of a higher fat intake in NAFLD patients, may lead to stimulation of fatty acid storage in hepatocytes, by promoting import (LPL), intracellular transport (FABPs) and activation of FAs (ACSL), which together may promote hepatic TG synthesis and storage. The increase in hepatic fat content may lead to a pro-inflammatory profile characterized by increased CCL2 and CCL3 expression, but not by increased macrophage infiltration.

5.3 Inflammation in adipose tissue and fatty liver

It is well established that obesity is associated with low-grade inflammation in adipose tissue (68) as well as with insulin resistance (69), but it has not been investigated whether insulin resistance is associated with


inflammation in adipose tissue regardless of obesity. In order to investigate this, 20 apparently healthy, obese women with different degrees of liver fat and insulin resistance, but the same degree of obesity, were recruited. Adipose tissue of women with high liver fat was characterized by inflammation and increased levels of ceramides compared with adipose tissue of women with normal liver fat content.

Although not only BMI, but also body composition and amounts of intra-abdominal and subcutaneous adipose tissue were similar between the groups, women with high liver fat content had higher fasting insulin and lower HDL cholesterol concentrations. These features together with an increase in liver fat have been associated with insulin resistance in previous studies (65), suggesting that women in the high liver fat group were more insulin resistant than women with normal liver fat.

Adipose tissue expression of macrophage markers CD68, CCL2 and CCL3 was higher in the high liver fat group, which indicates a higher macrophage presence and thereby a higher degree of inflammation in adipose tissue of women with high liver fat. Macrophage infiltration of adipose tissue has previously been observed in both obese rodents and humans, but was only associated with increased adiposity (68; 69).

Since the expression of macrophage markers was higher in the high liver fat group, immunohistochemical staining of adipose tissue sections was performed in order to visualize macrophages and to characterize and quantify macrophage accumulation. Macrophages were previously seen around necrotic adipocytes, forming so called crown-like structures (CLS) (68; 69). Perilipin is a protein associated with lipid droplets in adipocytes. Cinti et al used perilipin staining as a marker of viable adipocytes (83). Absence of perilipin staining together with electron microscope data was assumed to indicate necrotic-like adipocyte death. We defined a CLS as a perilipin-free adipocyte surrounded by at least three macrophages. Although there was a difference in adipose tissue CD68, CCL2 and CCL3 expression between the groups, there were no significant differences in the number of macrophages or the number of CLSs. Whether the disparity between the results of macrophage counting and the expression of macrophage markers is a reflection of the same number of macrophages with altered expression

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patterns, an indication of an insufficiency in the morphometric method used or a power issue due to small sample size remains unclear.

Fat cell size did not differ between the groups, regardless of the method used for measurement of cell size. Mean cell size appears to correlate with obesity (10), but, according to Paper III, not with the degree of inflammation. This contradicts previous findings where the fraction of CD68+ cells in human adipose tissue correlated with mean adipocyte area (68). The distributions of adipocyte cross-sectional area were significantly different between the groups (Figure 9B) and showed more large adipocytes in the high liver fat group. Adipocyte size measured after collagenase digestion did not show the same difference between distributions (Figure 9A) possibly because collagenase digestion destroys large cells (217).

Lower circulating adiponectin concentrations have previously been associated with obesity and insulin resistance (18) and with high liver fat content in humans (20). In Paper III, there was a decrease in adipose tissue adiponectin expression in women with high liver fat. Since adiponectin is mostly expressed by adipocytes, it would be reasonable to expect that there is a difference in circulating adiponectin concentrations between the groups. However, such differences could not be confirmed by measurements of circulating adiponectin.

PPAR expression was lower in adipose tissue in women with high liver fat compared with women with normal liver fat. PPAR is necessary for the survival of mature adipocytes (218). Low levels of PPAR may cause adipocyte death, which can aggravate inflammation in adipose tissue (218). PAI-1 is an inhibitor of fibrinolysis. Increased adipose tissue levels of PAI-1 have been seen in obesity (219) and is a strong predictor of type 2 diabetes (220). Weight reduction reduced plasma PAI-1 (221). In Paper III, levels of adipose tissue PAI-1 mRNA were lower in women with normal liver fat, although the groups were matched for BMI. This suggests that PAI-1 levels are not dependent on obesity only, but also on the degree of inflammation in adipose tissue. Both low PPAR and high PAI-1, as seen in the women with high liver fat, have been associated with a decrease in preadipocyte differentiation (208; 222), which fits well with the idea that


having too few adipocytes predisposes to ectopic fat storage and insulin resistance (223).

Paper III is the first to use lipidomics in order to characterize human adipose tissue. Lipidomics revealed different lipid profiles between the groups. The high liver fat group had more TGs containing long-chain fatty acids, more sphingomyelins and more ceramides.

In humans, an increase in skeletal muscle ceramide content has been found to correlate with obesity (138) and with a decrease in insulin sensitivity in healthy men (224). Accordingly, an improvement of insulin resistance was associated with decreased muscle ceramide content in mice (225). Ceramides, as possible mediators, offer an interesting link between an increase in inflammation and insulin resistance.

Long-chain TGs, especially those with odd number of carbons, were over represented in adipose tissue in the high liver fat group. Odd-chain fatty acids have been found to be biological markers of dairy fat intake (226). An increase in long-chain fatty acid uptake has been observed in adipose tissue in obese and in diabetic rodents (227; 228) and in morbidly obese humans (229). Also, hepatic long-chain fatty acid uptake was increased in obese rats (104). Whether an increase in long-chain TG content of adipose tissue, as seen in the high liver fat group, is a consequence of increased long-chain fatty acid uptake is unclear. It also remains to be resolved as to whether a high liver fat content was a result of an increased long-chain fatty acid uptake in both adipose tissue and liver in our subjects. However, despite these restrictions, we can propose the hypothesis that the increased long-chain TG content in adipose tissue was more dependent on the degree of fat accumulation in the liver than on the degree of obesity in these subjects.

5.4 Altered ceramide metabolism in inflamed adipose tissue

We have investigated sphingolipid metabolism in human adipose tissue in order to better understand the increase in ceramide and sphingomyelin in adipose tissue of women with high liver fat content and

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inflammation in their adipose tissue (Paper III). Firstly, we investigated expression of genes involved in ceramide metabolism and found that hydrolysis of sphingomyelin rather than de novo ceramide synthesis may be responsible for the increase in adipose tissue ceramide concentrations. Secondly, we show that of all sphingomyelinases, only SMPD3 correlates significantly with concentrations of ceramide and sphingomyelin in adipose tissue. The expression of SMPD3 was higher in visceral than in subcutaneous fat. Moreover, immunohistochemical analysis showed that sphingomyelinases are expressed by both adipocytes and macrophages, but mostly in and around blood vessels. Our results indicate that alterations in sphingolipid metabolism resulting in increased ceramide concentrations may play an important role in the development of adipose tissue inflammation and may be a link between adipose tissue inflammation and hepatic fat accumulation.

In animal studies, de novo synthesis of ceramides has been implicated in the regulation of adipose tissue ceramide content (230; 231) and in mediation of glucose tolerance (232). In these studies, obese animals were compared with lean ones, while in our study the groups were matched for BMI, which could explain why we observed no differences in the expression of genes involved in de novo ceramide synthesis. Sphingomyelinases hydrolyze sphingomyelin to ceramide. Sphingomyelinases in adipose tissue increase when mice are fed a high fat diet (230; 231). However, our study is the first to investigate sphingomyelinases in human adipose tissue. There is one acid (SMPD1) and three neutral (SMPD2-4) human sphingomyelinases. A secretory form of acid sphingomyelinase has been found in the extracellular matrix in atherosclerotic lesions (233), where it may hydrolyze sphingomyelin on the surface of lipoproteins, promoting lipoprotein aggregation (234; 235) and lipoprotein uptake by macrophages and foam cell formation (236). The neutral sphingomyelinase SMPD2 has been shown to have phospholipase C rather than sphingomyelinase activity (237) and overexpression of it did not alter the cellular concentrations of ceramide (238), indicating that SMPD2 may have a minor roll in regulating ceramide generation by sphingomyelin hydrolysis. On the contrary, both SMPD3 and SMPD4 were shown to be


TNF responsive and to induce apoptosis through increased ceramide production (239-241). While SMPD4 is only recently described and poorly characterized (241), there are several studies implicating SMPD3 activation in states of increased oxidative stress, such as cancer and aging associated inflammation (242; 243). In our study, although the mRNA expression of both SMPD1 and SMPD3 was significantly higher in adipose tissue of women with high liver fat, only SMPD3 correlated significantly with ceramide and sphingomyelin concentrations in adipose tissue and had higher expression levels in adipose tissue with more inflammation (visceral fat). This indicates a particular importance of SMPD3 in the development of adipose tissue inflammation.

Immunohistochemical analysis showed that the strongest staining for sphingomyelinases was in association with blood vessels. The acid sphingomyelinase SMPD1 was located to extracellular regions surrounding blood vessels. Neutral SMPD2 and SMPD3 were also found around small vessels (capillaries), but in larger vessels they were mostly found in intima-media layers of the vessels. The role of sphingomyelinases in the vicinity of blood vessels is unclear. The uptake of whole lipoprotein particles has been implicated in mouse adipose tissue (244), and extracellular matrix within adipose tissue was recently implicated in the pathology of obesity-related adipose tissue inflammation (85). It remains to be established whether there is any lipoprotein uptake in human adipose tissue, whether sphingomyelinases found in and around adipose tissue blood vessels hydrolyze sphingomyelin from such lipoproteins and whether these lipoproteins aggregate. It is, however, tempting to speculate that sphingomyelinases act similarly within inflamed adipose tissue as in the atherosclerotic arterial wall and that a process similar to atherosclerosis, where lipoproteins enter the tissue, become modified (possibly by sphingomyelinases to produce ceramide), aggregate and are digested by recruited macrophages, underlies the development of adipose tissue inflammation. The similarity between adipose tissue inflammation and atherosclerosis has been emphasized previously (245), but not the involvement of sphingomyelinases, as shown in Figure 11.

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Monocyte

Endothelium Smooth muscle cells

Endothelium Adipocytes

Atherosclerosis “Adiposclerosis”

Dis

ease

dN

orm

al

SMPD

Fibrous tissue

Cell death

Proinflammatory cytokines

Necrotic core

Foam cells

Macrophage recruitment

Cytokine Lipoprotein Ceramide Adhesion molecule

SMPD

Monocyte

Arterial wall Adipose tissue

Figure 11. Similarities between mechanisms in atherosclerosis and inflammation within adipose tissue. Sphingomyelin on the surface of lipoproteins become modified within the tissue by sphingomyelinases, causing lipoprotein aggregation, their digestion by macrophages and foam cell formation, resulting in cell death, tissue remodeling and fibrous tissue (scar) formation.

The increased sphingomyelin content of adipose tissue found in the women with high liver fat content in Paper III could be explained by an increased delivery of sphingomyelin through receptor-mediated lipoprotein uptake. Increased liver fat content is associated with an increased liver production of proinflammatory cytokines (Paper II), which could in turn lead to an increased sphingomyelin synthesis in the liver and an increased content of sphingomyelin in lipoproteins secreted by the liver (246). The


increase in adipose tissue ceramide and sphingomyelin content offers a link between fatty liver and adipose tissue inflammation, as illustrated in Figure 12.

SM

SM

SMSM

SM SM

SMSM

SM and Cer

SM

SM

SMSM Cer

High fat diet

Fat contentSM synthesis

SMPD

Inflammation

Adipose tissueLiver

Figure 12. A possible link between inflammation in adipose tissue and hepatic fat accumulation. High fat diet may result in an increased sphingomyelin (SM) synthesis and secretion by the liver. This may lead to an increase in adipose tissue sphingomyelin and ceramide concentrations, possibly through receptor-mediated lipoprotein uptake, which in turn can aggravate the inflammation within adipose tissue by mechanisms illustrated in Figure 11.

Ceramide and other bioactive sphingolipids are involved in processes required for angiogenesis, such as apoptosis and smooth muscle cell migration (247; 248). Hypoxia and reduced vascular density are hallmarks of inflamed adipose tissue and they stimulate adipose tissue angiogenesis (249). Sphingomyelinase-mediated ceramide production in association with blood vessels may play a role in angiogenesis, leading to an increased vascular density and decreased hypoxia and inflammation in adipose tissue.

In summary, ceramide production by sphingomyelinases and by SMPD3 in particular, may contribute to the development of inflammation within adipose tissue and may link adipose tissue inflammation with an increase in hepatic fat content.

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6 General Discussion

Insulin resistance is a central feature of the metabolic syndrome, which strongly predisposes to cardiovascular diseases (250). Since almost every third death worldwide in 2002 was caused by a cardiovascular event, elucidation of the mechanisms underlying the development of insulin resistance is of paramount importance.

Insulin resistance has been associated with increased liver fat content (14) and with both an excess and an absence of adipose tissue (15). Adipose tissue is not only a storage place for excess lipids, but also an active endocrine organ which releases some very potent cytokines involved in the regulation of important metabolic pathways. The liver is central for many metabolic processes in the body, including regulation of glucose and lipid metabolism. All this implies that mechanisms within both adipose tissue and liver are likely to be important in insulin sensitivity.

This thesis addresses several issues connected with the mechanisms underlying insulin resistance. The first is an increase in adipose tissue inflammation. Previously, obesity has been associated with both a decrease in insulin sensitivity and an increase in adipose tissue inflammation. This thesis shows that the degree of insulin resistance may depend on the degree of inflammation in adipose tissue regardless of obesity. An improvement in insulin sensitivity by rosiglitazone was shown to decrease inflammation in adipose tissue without decreasing body weight.

Another important issue is that of hepatic fat accumulation. Increased hepatic fat content almost always occurs together with whole body insulin resistance and is associated with risk factors for cardiovascular and metabolic disturbances. We have shown that the gene expression profile of fatty liver is altered towards an adipogenic and inflammatory profile.

The third issue is the characterization of the inflammation within adipose tissue. It has previously been shown that inflammation is characterized by increased macrophage infiltration of adipose tissue (68;


69). These macrophages were mostly found around adipocytes believed to be necrotic (83). We could confirm that adipose tissue was infiltrated with macrophages, which mostly surrounded perilipin-free adipocytes. The expressions of some macrophage and inflammation markers within adipose tissue were associated with liver fat content, regardless of obesity.

The next issue is the signal for an increase in macrophage infiltration of adipose tissue. An increase in chemoattractant proteins, such as CCL2 and CCL3, is a possible explanation, but it raises the question as to the causes of their increased expression. Macrophages appear to surround necrotic cells, perhaps just doing their job by cleaning up cellular debris. Is necrotic adipocyte death the signal that triggers macrophage infiltration? Oxidation of lipids released into the extracellular matrix due to adipocyte death may attract macrophages, in the same way as in atherosclerosis. Also, an uptake of lipoproteins by adipose tissue and their modification and aggregation, possibly mediated by sphingomyelinases, may lead to an increase in macrophage infiltration of adipose tissue, similar to that in atherosclerosis (Figure 11).

The fifth issue is the cause of adipocyte death. Several causes of adipocyte death have been proposed, such as hypoxia due to too rapid growth of adipose tissue or other stress signals (83). There is a strong correlation between adipocyte size and adipocyte death (86). A signal which has been shown to induce apoptosis in cells is ceramide. An increase in ceramide production by sphingomyelinases, as we observed in women with high liver fat content, could contribute to inflammation by causing adipocyte death.

Finally, there is the question of finding a link between adipose tissue inflammation, increased hepatic fat content and whole body insulin resistance. An increased ceramide and sphingomyelin content of inflamed adipose tissue could be a consequence of both increased ceramide production within adipose tissue and import of liver-produced sphingomyelin, linking the increase in liver fat content to inflammation in adipose tissue (Figure 11 and Figure 12). Moreover, it is possible that there is a signal from inflamed adipose tissue that exacerbates fat accumulation and inflammation in the liver, closing the vicious cycle of insulin resistance.

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Revealing these signals is essential for a better understanding of the causes of insulin resistance, type-2 diabetes and cardiovascular diseases. Further investigation of sphingomyelin and ceramide metabolism within both adipose tissue and liver seems necessary in order to clarify these issues.

In summary, the work contained in this thesis demonstrates the importance of fat accumulation and inflammation within both adipose tissue and liver in the development of insulin resistance.


7 General Conclusions

Long-term rosiglitazone treatment of type 2 diabetes patients results in an increase in expression of genes involved in fatty acid uptake and storage and a decrease in some macrophage and inflammation markers.

Hepatic fat accumulation is associated with an increase in expression of genes involved in fatty acid metabolism and in inflammation, indicating a shift in the hepatic expression profile towards an atherogenic and inflammatory pattern.

Women with high liver fat content are more insulin resistant and have more inflammation in their adipose tissue compared with women with normal liver fat content, independent of the degree of obesity.

Inflamed adipose tissue in obese women with high liver fat contains more sphingolipids, ceramides and long-chain TGs than adipose tissue in comparably obese women with normal liver fat content.

Increased ceramide production within inflamed adipose tissue may be a result of increased sphingomyelinase, and in particular SMPD3, expression.

Sphingomyelinases are expressed mostly in and around blood vessels within adipose tissue, suggesting a role of adipose tissue vascular sphingomyelinases in adipose tissue inflammation, possibly similar to the atherosclerotic process in the arterial wall.

These studies imply that the degree of inflammation in adipose tissue

and liver and the degree of hepatic fat accumulation, but not the degree of obesity per se, are very important in the development of insulin resistance. A decrease in adipose tissue inflammation may improve whole body insulin

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sensitivity regardless of obesity. It is possible that sphingomyelin-mediated ceramide production is a link between the accumulation of fat in the liver and the development of inflammation in adipose tissue.


8 Acknowledgements

The work contained in this thesis was conducted at the Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet during 2004-2008. Over the years of my PhD journey, I had the privilege to interact with many people without whose help and support I would not be writing this. I would like to express my deepest gratitude towards:

Rachel Fisher, my main supervisor, for giving me the opportunity to become a PhD student. I am grateful for how you always find time to help me and encourage me, for your ability to bring me back on track and focus on important things. Thank you for being so understanding and making it possible for me to combine PhD studies with my family life.

Per Eriksson, my co-supervisor, for being such a great scientist and at the same time standing with both feet on the ground. Thank you for not closing your office door to shield yourself from my and everybody else’s numerous questions and requests.

Anders Hamsten, my co-supervisor, without your approval I would not be here in the first place and without your perseverance there would be no unit. Thank you for your time and your generosity.

Hannele Yki-Järvinen, my co-supervisor, for your great projects that I was allowed to take part in. Without these projects, my thesis would take considerably longer time to finish. I am greatly impressed by your extensive knowledge and your devotion to science.

Agneta Nordenskjöld, my mentor, for your sincere interest in my well-being.

My co-authors: Jukka Westerbacka for your ability to successfully combine clinical and scientific work; Anders Franco-Cereceda and Jan Liska for giving me the opportunity to witness the extraordinary world of open-heart surgery; Matej Oreši , Mirja Tiikkainen, Vidya Velagapudi, Laxman Yetukuri, Janne Makkonen, Tuula Kiviluoto, Perttu Arkkila,

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Jukka Siren, Aila Rissanen, Anna-Maija Häkkinen, Monica Lindell and Robert Bergholm, without your collaboration this thesis would not exist.

Senior scientist at the unit: Ewa Ehrenborg and Ferdinand van’t Hooft for many witty conversations during lunches and for constructive feedback during Wednesday meetings; Angela Silveira for being helpful and managing to organize the group meetings; Alejandro Bertorello for bringing the protein perspective to our group; John Öhrvik for teaching us statistics; Jacob Odeberg, Mai-Lis Hellénius, Johan Björkegren and Jesper Tegnér for your contribution to scientific discussions.

Present and past PhD students and post-docs at the unit: Josefin Skogsberg for bringing so much positive energy every time you enter the room; Katja Chernogubova for always having time to listen and support me and for caring for people around you; Sergey Krapivner for many helpful advises during these last months that helped me realize that there is light at the end of the tunnel; Petra Thulin for your cheerfulness and many friendly chats; Anna Aminoff for your ambition to make both CMM and whole Karolinska Institutet a better place to work at; Dick Wågsäter for teaching me immunohistochemistry and giving me some very tasty cake recipes; Kristina Eneling for being a great support during my Weight-watchers meetings (not that they helped me); Karin Stenström for being such an open person and for expressing what everyone else is thinking; Justo Sierra-Johnson, we all miss your jokes here; Maria Nastase Mannila for being energetic and humorous; Valentina Paloschi for bringing a piece of Italian warmth to our group; Maria Jesús Iglesias for being such a warmhearted person; Anders Mälarstig for many chats about everything; Kerstin Lundell, I always enjoy your company; Fei Chen for showing me that people raised on the opposite sides of the globe can have so much in common; Karl Gertow and Per Sjögren for generously sharing the same supervisor (you showed me the way); Sergej Popov, Vincent Fontaine, Malin Andersen, Sara Hägg, Jesper Lundström, Shohreh Maleki, Alexandra Bäcklund, Zongpei Chen, Alexander Kovacs, Massimiliano Ria, Helena Ledmyr, Katja Kannisto, Ann Samnegård, Tiina Skoog, Monsur Kazi and all others for contributing to a pleasant working atmosphere.


The technicians: Barbro Burt for helping everybody in the lab and for many lunches together; Karin Husman for always being helpful and for conversations that made the long hours in the lab go faster; Karin Danell-Toverud for your down-to-earth sense of humor; Fariba Foorogh for your kindness and help in the lab; Peri Noori, Therese Olsson and Birgitta Söderholm for making the lab a nice place to work at.

The administrative staff: Ami Björkholm for taking care of complicated and bamboozling administrative matters; Karolina Anner for being such a strong person; Camilla Berg for helping me with my application; Magnus Mossfeldt for making my computer work most of the time.

My friends and relatives all over the world. My parents Gordana and Mario Kolak for all the love and support

throughout my life and for unselfishly helping me all the time with my children, my house and everything else.

My sister Ivana Kolak for being a really, really good sister and her husband Sanjin Pejkovi for making my little sister happy.

My grandmother Dragica Patruci . Draga bako, hvala sto si me uvala kad sam bila mala i za svu životnu mudrost koju si me nau ila.

In memory of my late grandmother Ljubica Kolak who was looking forward to this thesis, but had to leave us before I finished it.

My family: Davor for enduring my temper during the last two decades and for all the love, support and help, Matea for reminding me that I have to work and not play computer games, Martin for reminding me of computer games all the time and Robert for being such a sunshine. I love you all.

Stockholm, October 2008

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