Cholesterol Efflux and Impact Gene Expression in MDFC, Liver and Intestine

The Pennsylvania State University

The Graduate School

Department Veterinary and Biomedical Sciences

REGULATION OF REVERSE CHOLESTEROL TRANSPORT BY MICRORNAs

AND IMPACT OF DIET AND XENOBIOTICS

A Dissertation in

Molecular Medicine

Josephine Akyeamah Garban

 2015 Josephine Garban

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

December 2015

The dissertation of Josephine Garban was reviewed and approved* by the following:

John P. Vanden Heuvel Professor of Veterinary and Biomedical Sciences Dissertation Advisor Chair of Committee

Sandeep K. Prabhu Professor of Immunology and Molecular Toxicology

Pamela A. Hankey-Giblin Professor of Immunology

Wendy Hanna-Rose Associate Professor Biochemistry and Molecular Biology

Adam B. Glick Associate Professor Head of the Molecular Medicine Graduate Program

*Signatures are on file in the Graduate School

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ABSTRACT

Coronary heart disease, specifically atherosclerosis, is the major cause of death in the US and most of the developed world. Factors that promote atherosclerosis typically involve perturbations of cholesterol and lipid homeostasis as well as a sustained chronic inflammation, ultimately leading to the formation of atherosclerotic plaques. Hence it is important to develop and improve interventions that slow the development of arterial lipid plaques. An endogenous physiological process that contributes to the reduction of cholesterol accumulation and reduces the progression of atherosclerosis and cardiovascular disease (CVD) risk is the Reverse Cholesterol Transport (RCT) pathway.

RCT involves the removal of excess cholesterol from atherosclerotic plaques to be transported back to the liver and excreted as bile acids through the intestines. Numerous endogenous and exogenous factors regulate the RCT pathway. In the in vitro and ex vivo studies presented herein, human macrophages, liver and intestinal cell lines were used as a model to study the effects of regulators of cholesterol uptake, transport and efflux.

The nuclear receptor Farnesoid X Receptor (FXR) is a bile acid sensor that has gained considerable interest as a therapeutic target for the treatment of CVD. Agonists of

FXR increase cholesterol efflux and impact gene expression in MDFC, liver and intestine. The expression of genes involved in cholesterol, triglyceride (TG), and bile acid production, as well as the expression of microRNAs (miRs) are affected by FXR.

MicroRNAs are non-coding transcripts that regulate the expression of key metabolic genes, although their role in FXR-dependent cholesterol efflux was not known. In parallel studies we explored the miRs whose levels were affected by the FXR ligand GW4064 in

THP-1 macrophage derived foam cell (MDFC) the effects of various miRs on cholesterol efflux was examined. Of the several miRs that were regulated by GW4064 and in turn influenced cholesterol efflux in vitro, miR-708 was chosen for further study.

Whether the induction of miR-708 by GW4064 was causally related to enhanced cholesterol efflux was examined in several in vitro studies. Following miR-708 transfection and subsequent treatment with oxidized LDL (oxLDL) and GW4064 in

MDFC, there was a significant decrease in nuclear factor kappa B (NFκB) and interleukin

1 beta (Il-1β) gene expression, concomitant with an increase in cholesterol efflux. Ectopic expression of miR-708 repressed CD38 mRNA and protein levels in the in vitro models listed above. CD38 is a multifunctional enzyme that utilizes NAD+ in the synthesis of cyclic ADP-ribose and is involved in immune function and calcium signaling. Targeted knockdown of CD38 augmented expression of the metabolism regulator gene, Sirtuin-1

(Sirt-1) and enhanced cholesterol efflux. Our results suggest miR-708 affects cholesterol efflux, at least in part via its regulation of CD38, and is a beneficial microRNA in regards to its role in atherosclerosis and the reverse cholesterol transport pathway.

Diets rich in omega-3 polyunsaturated fatty acids (PUFAs), such as docosahexanoic acid (DHA), are protective against CVD and increase cholesterol efflux and RCT. Multiple targets for DHA exist including nuclear receptors Peroxisome

Proliferator-Activated Receptors (PPARs) and FXR, which in turn affect cholesterol homeostasis via altered gene and miR expression. Serum samples from subjects participating in a postprandial feeding study were investigated for effects on cholesterol efflux and microRNA expression. In foam cells treated with postprandial serum samples ex vivo, the DHA-supplemented diet increased cholesterol efflux. The expression of mir-

30, -33 and-181 was increased in serum of those individuals receiving the DHA-enriched diet, relative to their serum prior to intervention. In THP-1 MDFC, DHA increased the expression of mir-30 and mir-708, without affecting miR-33, -144 and -181. These observations stress the complexities of how PUFAs modulate cellular cholesterol homeostasis in vitro and ex vivo. Also, these studies identified certain miRs as potential biomarkers for PUFA activity and CVD risk

The model systems developed were used to explore the potential effects of xenobiotic exposure on the RCT pathway. Some cross-sectional epidemiology studies have suggested an association between perfluorooctanoic acid (PFOA) and perfluoroctane sulphonate (PFOS) exposure with increased serum cholesterol levels.

However, whether this linkage is causal is unclear, in part due to the lack of mechanistic understanding of this response and the absence of laboratory animal concordance. Since cholesterol homeostasis is dependent on multiple tissues, the present studies examined the effects of PFOA and PFOS on cholesterol transport and gene expression in model systems described above. Both PFOA and PFOS increased cholesterol efflux from THP-1

MDFC, Huh-7 and Caco-2 cells with no observable differenced in cholesterol uptake.

Several genes involved in cholesterol homeostasis were examined in THP1, Huh7 and

Caco-2. In THP1 cells, PFOS increased liver X receptor α (LXRα) mRNA while Very

Low Density Lipoprotein Receptor (VLDLR) mRNA was increased by both PFOA and

PFOS in Huh7 cells. There were no changes in mRNAs involved in cholesterol homeostasis in Caco-2 cells. Overall, these studies show that PFOA and PFOS have effects on cholesterol efflux but do not affect the uptake of cholesterol nor do they cause

vi changes in the expression of cholesterol homeostatic genes consistent with the observed epidemiology associations, in these in vitro models systems.

Taken together, the model systems and approaches developed herein were utilized to study the effects of several endogenous and exogenous factors on the RCT pathway.

Of particular note is the FXR and DHA regulated miR-708, which exhibits anti- atherogenic and anti-inflammatory effects and may be beneficial in the prevention and/or regression of coronary heart disease.

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TABLE OF CONTENTS List of Figures…………………………………………………………………………..ix List of Tables…………………………………………………………………………..xi List of Abbreviations……………………………………………………………………xii Acknowledgements……………………………………………………………………..xiii

Chapter 1 Introduction ...... 1

Epidemiology of Atherosclerosis ...... 1 Development of Atherosclerosis ...... 2 Reverse Cholesterol Transport ...... 6 MicroRNA Regulation of RCT ...... 12 Omega 3 Fatty Acids and Cholesterol efflux ...... 15 Perfluorinated fatty acids (PFASs) and Cholesterol Metabolism ...... 17 HYPOTHESIS OF STUDIES ...... 21 References ...... 23

Chapter 2 Dietary regulation of cholesterol efflux and microRNA expression ...... 29

Abstract ...... 29 Introduction ...... 30 Materials and Methods ...... 32 Results ...... 36 Discussion ...... 43 References ...... 47

Chapter 3 Role of microRNA miR-708 on Reverse Cholesterol Transport in macrophages, hepatocytes and intestinal cell models ...... 49

Abstract ...... 49 Introduction ...... 50 Materials and Methods ...... 52 Results ...... 56 Discussion ...... 75 Acknowledgements ...... 80 References ...... 81

Chapter 4 Effects of perfluorooctanoic acid (PFOA) and perfluorooctane sulphonic acid (PFOS) on Reverse Cholesterol Transport and Gene Expression in THP-1, Huh-7 and Caco-2 cells ...... 83

Abstract ...... 83 Introduction ...... 84 Materials and Methods ...... 87 Results ...... 91 References ...... 107

Chapter 5 Factors involved in Reverse Cholesterol Transport Regulation: Findings in Serum, Macrophages, Liver and Intestinal Models ...... 111

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References ...... 123 Appendix A Screening of microRNAs that affect cholesterol efflux ...... 125 Appendix B Gene expression and microRNA expression determined by high- density microarray ...... 135 Appendix C Altered gene expression in foam cells by miR-574 ...... 150

LIST OF FIGURES

Figure 1-1. Percentage breakdown of deaths attributable to cardiovascular disease (United States: 2010). From (3) ...... 2

Figure 1-2. Development of Atherosclerosis and Plaque Formation ...... 4

Figure 1-3. FXR regulation of cholesterol and lipid homeostasis ...... 5

Figure 1-4. Cholesterol Uptake, Transport and Efflux Mechanisms ...... 8

Figure 1-5. 3-dodecanoyl-NBD Cholesterol Structure ...... 11

Figure 1-6. MicroRNA Regulation of Gene Expression ...... 12

Figure 1-7. Metabolism of omega-3essential fatty acids ...... 17

Figure 2-1: Sera of subjects consuming diets rich in DHA and effects on cholesterol efflux ...... 37

Figure 2-2: High Oleic Canola oil and DHA consumption affects microRNA expression in Serum ...... 38

Figure 2-3: DHA effects cholesterol efflux ...... 39

Figure 3-1: FXR affects cholesterol efflux ...... 56

Figure 3- 2: Optimization of conditions necessary for microRNA transfection and screening ...... 58

Figure 3-3: Transfection of miR-708 and treatment with GW4064 increases Cholesterol Efflux ...... 60

Figure 3-4: Transfection of miR-708 and treatment with GW4064 affects CD38 gene expression...... 62

Figure 3-5: Attenuation of CD38 protein levels in THP-1 macrophages, Huh-7 cells and HCT-116 cells following has-miR-708 transfection ...... 64

Figure 3-6: Knockdown of CD38 using siRNA and treatment with GW4064 increases Cholesterol Efflux in Macrophage Derived Foam Cells (MDFC’s), Huh-7 cells and HCT-116 cells...... 65

Figure 3-7: Decreased expression of NFκB following miR-708-5p transfection ...... 68

Figure 3-8: Decreased expression of IL-1β following miR-708-5p transfection ...... 71

Figure 3-9: miR-708-5p and siRNA knockdown of CD38 augments SIRT1 gene expression...... 74

Figure 4-1. Evaluation of Reverse Cholesterol Transport and Cholesterol uptake in THP- 1, HUH-7 and Caco-2 Cell Models...... 92

Figure 4-2. Evaluation Cholesterol uptake in THP-1, HUH-7 and Caco-2 Cell Models ...... 92

Figure 4-3. Messenger RNA for cholesterol metabolism genes in THP-1 cells treated with PFOA or PFOS ...... 94

Figure 4-4. Messenger RNA for cholesterol metabolism genes in Huh-7 cells treated with PFOA or PFOS ...... 95

Figure 4-5. Messenger RNA for cholesterol metabolism genes in Caco-2 cells treated with PFOA or PFOS...... 96

Figure 4-6. Transactivation of nuclear receptors by PFOA and PFOS...... 98

LIST OF TABLES

Table 4-1. Oligonucleotides used in quantitative real time PCR ...... 90

Table 4-2. Genes that were significantly affected by at least one dose of PFOA or PFOS (2-fold and p<0.05 relative to DMSO control) ...... 100

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LIST OF ABBREVIATIONS

ABCA1 ATP- binding cassette transporters ACAT Acetyl-CoA acetyltransferase 1 APOA1 Apo lipoprotein A-1 APOE Apolipoprotein E ABCB11 Bile salt efflux pump BSA Bovine Serum Albumin C Cholesterol CEs Cholesterol esters CD36/ 38 Cluster of differentiation 36 and 38 CVD Cardiovascular Disease DHA Docosahexaenoic Acid DMSO Dimethyl Sulfoxide FBS Fetal Bovine Serum FXR Farnesoid X Receptor HDL High Density Lipoprotein IL Interleukin LCAT Lecithin—cholesterol acyltransferase LDL Low Density Lipoprotein LXR Liver X Receptor MTP Mitochondrial Transfer Protein miR MicroRNA PUFA Polyunsaturated Fatty Acid RCT Reverse Cholesterol Transport siRNA Small Interfering RNA SCD1 Stearoyl-Coenzyme A desaturase 1 SHP Small heterodimer partner 1 TG Triglycerides VLDL Very Low Density Lipoprotein

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ACKNOWLEDGEMENTS

I would like to thank my parents, Mr. and Mrs. Garban for their enduring love and support. My wonderful sister Philipa Garban for her constant encouragement, my brothers, Emmanuel, Joseph and William and nephews, Tristan and Maurice who have all been instrumental in my graduate pursuits over the years. I am indebted to all the many friends that I have made here at Penn State, thank you all for your support and encouragement. I would like to thank my advisor Dr. Vanden Heuvel for his guidance and assistantship throughout my graduate career. Thank you for allowing me into your laboratory group and for granting me the independence to purse all my scientific interest.

It has been a great experience learning and training under your supervision.

To my thesis committee members, Dr. Pamela Hankey, Dr. Prabhu Sandeep and

Dr. Wendy Hanna-Rose, thank you all very much for your kindness, guidance and fruitful scientific input throughout my years here at Penn State. I am very grateful for all the advice and assistance towards my thesis research. I am very thankful to Dr. Jerry

Thompson for his supervision and training during my time in the lab. I learned a great deal of techniques and how to apply my skills as a scientist. Lastly I would like to thank

Sloan foundation for their financial support and mentoring.

Chapter 1

Introduction

Epidemiology of Atherosclerosis

Cardiovascular disease remains the leading cause of death in the developed world.

According to the American Heart Association, approximately every 25 seconds there is a coronary incident and every minute someone dies of a heart attack in the U.S alone. It is estimated that 195,000 Americans suffer silent heart attacks and 1out of every 6 deaths in the United States in the year 2006 resulted from coronary heart disease (1). In 2010 alone approximately 785,000 Americans had coronary attack, and approximately 470,000 had a recurrent attack (1). Similar statistics are presented for stroke, obesity and metabolic related diseases, emphasizing the role of cardiovascular diseases in the resounding death statistics nationwide. Atherosclerosis still remains the major cause of morbidity and mortality in the United States, with coronary heart disease and stroke being its two most common expressions (Fig. 1-1) (2). The underlying pathological process is arterial wall thickening due to the formation of atherosclerotic plaque, which is frequently complicated by thrombus, thereby giving rise to the possibility of acute coronary syndrome or stroke. Numerous risks factors promote and progress atherosclerosis including age, genetic disposition, chronic inflammation, hypertension, hyperlipidemia,

2 diabetes mellitus, family history of premature atherosclerosis, smoking as well as infectious pathogens that stimulate Low density lipoprotein (LDL) oxidation.

Figure 1-1. Percentage breakdown of deaths attributable to cardiovascular disease (United States: 2010). From (3)

Development of Atherosclerosis

Oxidization of LDL in circulation and within vessel walls is an important step in the development of atherosclerosis as it in turn promotes the recruitment of monocytes and differentiated macrophages to sites of inflammation. This results in unregulated oxLDL uptake through interactions with scavenger receptors into the vessel wall.

Consequential to the macrophage uptake of oxLDL is the formation of Macrophage- derived Foam Cells (MDFC), a characteristic and an initial hallmark of atherosclerosis

(Fig. 1-2). Foam cell formation establishes a chronic inflammatory insult that leads to the production of growth factors, cytokines, vascular-smooth-muscle cells proliferation and development of plaques. Complications from plaque rupture and thrombosis ultimately results in myocardial infarction and stroke (Ross, 1999)

3 Cholesterol regulation is important in every stage of atherosclerosis development and progression, and many other diseases resulting from agitations in lipid homeostasis, including metabolic syndrome and obesity. Hence it is necessary to define existing mechanisms responsible for maintaining cholesterol homeostasis. Sterol levels are maintained at a homeostatic balance by cells through transcriptional regulation of sterol regulatory element-binding proteins (SREBPs)(4). Under low cholesterol levels,

SREBPs are transported from the endoplasmic reticulum to the Golgi and are proteolytically processed. Active peptides produced translocate into the nucleus and induce expression of target genes such as the 3-hydroxy-3-methylglutaryl coenzyme A reductase (Hmgcr) and low-density lipoprotein receptor (Ldlr). Under high cholesterol levels, SREBP is unable to exit the ER, and as a consequence the transcription of target genes is reduced (5).

Cholesterol homeostasis which encompasses uptake, transport and efflux is vital to the regression of atherosclerosis. Removal of excess cellular cholesterol to HDL initiates Reverse Cholesterol Transport (RCT) in all cell tissues type. Therefore identification of mechanisms that regulate cholesterol balance at the macrophage, hepatic and intestinal level is necessary for a comprehensive assessment of the reverse cholesterol transport pathway. Cholesterol delivery to the liver, as well as to other cholesterol-using organs, can be mediated through multiple mechanisms, including nuclear receptors, noncoding RNA -microRNAs and Xenobiotic agents such as

Perfluorooctanoic acid (PFOA, ‘C8’) and perfluoroctane sulphonate (PFOS).

Figure 1-2. Development of Atherosclerosis and Plaque Formation

Adapted from (6)

The nuclear bile acid receptor Farnesoid X Receptor (FXR) is important for bile acid and lipid homeostasis through regulation of metabolic gene expression in the liver and intestine. FXR activation modulates the transcriptional activity of different proteins involved lipogenesis and cholesterol homeostasis (7). Disturbance of FXR in transgenic mice have been associated with various metabolic disorders including hypercholesterolemia, cholesterol gallstone disease, fatty liver, and type 2 diabetes (8).

Activation of FXR in diabetic obese mice improved metabolic outcomes by reducing serum glucose and lipid levels (9). Transintestinal cholesterol excretion (TICE) a process that directly transports plasma derived cholesterol through the intestinal wall is a pathway that has recently gained much interest (10). The flux of cholesterol excreted via TICE accounts for approximately 33% of the fecal sterol excretion in untreated C57Bl/6J mice, whereas biliary cholesterol accounts for about 25% emphasizing the importance of TICE, at least in mice, in overall cholesterol efflux (11).

Figure 1-3. FXR regulation of cholesterol and lipid homeostasis

From Vanden Heuvel John, Nuclear Receptor Resource. Apolipoprotein CIII

(ApoCIII), Bile acid exporter pump (BSEP), Cytochrome 7 (CYP7), Cytochrome 3A4

(CYP34A), Sodium-taurocholate cotransporting polypeptide (NTCP), Multidrug resistance-associated protein 2 (MRP2), stearoyl-CoA desaturase 1(SCD-1), small heterodimer partner l (SHP-1), Sterol regulatory element-binding proteins (SREBPs) and

Phospholipid Transfer Protein (PLTP).

The potential to alter RCT via pharmacological and dietary means, makes cholesterol removal a viable avenue the treatment of atherosclerosis. MicroRNA

6 networks regulate the response of macrophages towards inflammatory and atherogenic stimuli and may be important in the progression or regression of atherosclerosis. There is an extensive interest in the mechanisms governing miR entry into exosomes, delivery, targeting, and recognition machinery. In the liver for instance, miR-122 a highly abundant liver expressed microRNA when antagonized in mice fed a high fat diet improves liver steatosis through reduction of triglyceride levels and increased fatty acid

β-oxidation (12).

Reverse Cholesterol Transport

Reverse cholesterol transport (RCT) is a complex multi-step pathway central to the removal of excess cholesterol from atherosclerotic plaques to be excreted as bile acids. RCT process involves 1) the transport of excess free cholesterol from peripheral cells, tissues and deposits, 2) the transfer of free cholesterol to cholesterol accepting lipoproteins 3) the esterification of free cholesterol via LCAT reactions 4) the hydrolysis of triglycerides via the activities of hepatic lipases and the functional activity of LDL and chylomicron remnant receptors in the liver (13). RCT was first introduced by Glomset in

1968 (14) and interest in this pathway for potentially regression atherosclerosis has continued. Glomset proposed that the imbalance of cholesterol accumulation, removal and the subsequent inflammation following monocyte recruitment results in atherosclerotic lesions and reduced integrity of arterial intima. Following this initial hypothesis Miller & Miller (15) suggested the inverse relation of High Density

Lipoprotein (HDL) content and rising cholesterol accumulation as associated with

7 cardiovascular disease risk. Their research supported increasing HDL levels as a means to promote the clearance of amassed cholesterol from the arterial wall. RCT is generally conceptualized as a macrophage-specific process which is partially true since the process is predominantly regulated in macrophage-derived foam cells in atherosclerotic lesions.

However, other peripheral cell types modulate cholesterol efflux. The biological process of RCT occurs and can be measured predominately in all peripheral tissues, and in essence the levels of cholesterol uptake, transport and excretion should be tracked and evaluated from the macrophages to the liver and the final removal in intestinal cells.

Cellular cholesterol efflux is facilitated by HDL along with the major HDL apoprotein component, apolipoprotein A-I(16). Higher levels of HDL protect against the development of atherosclerosis and are associated with decreased cardiovascular risk.

HDLs also maintain antioxidant and anti-inflammatory properties and support the regression of the chronic inflammatory state of atherosclerosis, specifically by inhibiting monocyte transmigration in response to oxidized LDL (17). Other in vitro studies also indicate that endothelial cell adhesion proteins and chemokines such as monocyte chemotactic protein-1 (MCP-1), vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1), and E-selectin are all suppressed by higher

HDL levels aiding in the maintenance of arterial intima integrity. In order to prevent toxic cholesterol overload in peripheral cells, the reverse cholesterol transport pathway directs excess cholesterol through HDL acceptors to the liver and intestines for elimination thereby maintaining cholesterol homeostasis. HDL precursors are acceptors for un-esterified cholesterol and phospholipids transferred by the ATP-binding cassette

8 transporter A1 (ABCA1) on peripheral cells, giving rise to discoidal lipoproteins containing apoA-I. Numerous other cellular lipid transporters, receptors, acceptors and

HDL intermediates participate in this transport of peripheral cholesterol.

Figure 1-4. Cholesterol Uptake, Transport and Efflux Mechanisms

From (18). Cholesterol (C), Cholesterol esters (CEs), ATP- binding cassette transporters (ABCA1), (ABCG5), (ABCG8), Apo lipoprotein A-1 (APOA1),

Apolipoprotein E (APOE), bile salt efflux pump (ABCB11), Mitochondrial Transfer

Protein (MTP), Cluster of differentiation 36 (CD36), Acetyl-CoA acetyltransferase 1

(ACAT), Lecithin—cholesterol acyltransferase (LCAT)High Density Lipoprotein (HDL),

Liver-x-receptor (LXR), High Density Lipoprotein (HDL), Very Low Density

Lipoprotein (VLDL)

Lipid-poor HDL particles are typically produced from various tissue sources. For instance during lipolysis, hepatocytes, enterocytes, chylomicrons, and very low density

9 lipoproteins (VLDL) generate HDL particles. Similarly, HDL can be generated from

(alpha)-HDL by cholesteryl ester transfer protein (CETP), phospholipid transfer protein

(PLTP), or hepatic lipase (HL) (19). In the last step of RCT, HDL is typically removed from circulation by scavenger receptor BI (SR-BI), apoE receptors in a process that mediates the selective uptake of cholesteryl esters into hepatocytes without internalizing

HDL (20). Another important process to the removal of excess cholesterol is through the

ATP-Binding Cassatte Transporter (ABAC1) and the apolipoprotein A-1 (APOA-1) cholesterol acceptor. Apolipoprotein-mediated cholesterol efflux is effective in removing cholesterol from intracellular pools of macrophage-derived foam cells (21).

Various drugs and dietary supplementation strategies have been developed to reduce the risk of atherosclerosis development and progression. Statin drugs, which reduce intracellular cholesterol synthesis, have become the most common intervention for risk reduction (22). However, emerging therapeutic approaches are focusing on cholesterol efflux as a means to reduce the progression of the disease. Cholesterol efflux from peripheral tissues and cells such as MDFC in atherosclerotic plaque is an initial and critical step in RCT. Enhancing cholesterol efflux is a promising mechanistic approach for drug therapies aimed at treating or preventing atherosclerosis and cardiovascular diseases. The ultimate outcome of RCT is to bring peripheral cholesterol to the liver for excretion as bile acids, thus lowering the peripheral lipid burden and contributing to atherosclerosis regression (23). There are many genes involved in the transport of free cholesterol from the MDFC. The major players including ATP-binding cassette (ABC) transporters (ABCA and G), enzymes that regulate cholesterol and cholesterol ester

10 concentration such as ACAT1, NCEH and HMGCR and the transcription factors that regulate expression of these proteins (PPARs, FXRs, LXRs, SREBP1c) (24). Members of the ABC transporter family facilitate the efflux of a wide variety of molecules across the plasma membrane and are of critical importance in cholesterol homeostasis. The two major membrane transporters in macrophages, ABCA1 and ABCG1, unidirectionally export intracellular free cholesterol to extracellular acceptors, such as apoA-I or HDL.

The concentration of the ABCA1 transporter in the plasma membrane determines the rates of phospholipid and free cholesterol efflux and nascent HDL particle formation. The primary acceptor for cellular cholesterol efflux via the ABCA1 pathway includes cholesterol-deficient and phospholipid-depleted apoA-I complexes (25). The binding of apoA-I to ABCA1 prevents its intracellular degradation and increases the level of

ABCA1 transporter in the plasma membrane. The ABCA1 transporter and apoA-I facilitates the mobilization of cholesterol from the late endocytic compartment to the cell membrane (26). The regulation of the ABCAI/HDL pathway is complex and influenced by both genetic factors and posttranscriptional mechanisms, including regulation via microRNAs and other xenobiotic agents. Since atherosclerosis is deemed a chronic inflammatory state of cholesterol accumulation, current efforts aimed at finding mechanisms that reduce and slow the progression of the disease rather than factors of causation seem most effective.

Conventional radioisotope-labeled cholesterol has been used in measuring efflux efficiency, but provides severe limitations for high-throughput screening. Hence an alternative method using a fluorescent cholesterol mimic is needed. 3-NBD Cholesterol is

11 fluorescently-tagged cholesterol with the hydrophilic NBD fluorophore attached to the hydrophilic end of cholesterol, separated by a 12-carbon spacer. This design allows the cholesterol to properly orient in membrane bilayers while the fluorescent tag is presented outside of the bilayer. Fluorescently-tagged lipids are now being used to study their interactions with proteins, their utilization by cells and liposomes, and for the development of assays for lipid metabolism. (23) has developed a reproducible high- throughput fluorescence assay aimed at examining macrophage-dependent cholesterol efflux. This assay allows for the analysis of the effects of various microRNAs and toxic agents on the process which then can be verified in more complex in vivo model systems.

Figure 1-5. 3-dodecanoyl-NBD Cholesterol Structure

12 MicroRNA Regulation of RCT

Noncoding RNAs (ncRNA) include microRNA and represent a class of RNA molecules that although they do not code for proteins, do have effects on cell function.

Emerging data implicates many ncRNAs play in several physiological and pathological conditions such as cancer and cardiovascular diseases, including atherosclerosis.

MicroRNAs are 21–22-nucleotide, non-coding small RNAs produced within the genomic loci of cells. A majority of microRNA genes are intergenic or intronic (27); however, a small number of microRNA transcripts are derived from protein-coding mRNAs. The expression of microRNAs themselves is often subject to regulation by transcriptional factors in response to cellular stimuli. The primary transcripts of microRNAs (pri-miRs), which are similar to mRNAs, are processed by Drosha to form 70–80 nt precursor microRNAs (pre-miRs). The pre-miRs are then exported to the cytoplasm and processed by Dicer into mature microRNAs. The mature microRNA, together with Dicer and

Argonaute proteins, forms the microRNA induced silencing complex (miRISC) (28).

Guided by the microRNA through base pairing, the miRISC binds to the target genes, usually in the 3’ UTR, thereby repressing translation and/or inducing degradation of target gene mRNAs.

Figure 1-6. MicroRNA Regulation of Gene Expression

Adapted from (29)

Several miRs are regulators of lipid metabolism genes, including miR-122, miR-

33, and miR-106b (30). Various strategies are being developed to modulate miRNA activity for diagnostic and therapeutic purposes; for instance, the use of anti-miR-122 in human preclinical studies raises the possibility for other miRs as viable targets in future

(31). miR-122 is highly expressed in the liver, and it is estimated to account for approximately 70% of all liver miRNA. Specific inhibition of miR-122 by antisense oligonucleotides (ASO) in mice resulted in increased hepatic fatty-acid oxidation and a reduced cholesterol synthesis (12). This miR has become a strong candidate as a therapeutic target in the treatment of hypercholesterolemia in humans. However the mechanism by which miR-122 regulates lipid metabolism remains undetermined.

Similarly, miR-106b regulates neurological functions and affects the pathogenesis of

Alzheimer’s disease through posttranscriptional repression of ABCA1 transporter.

Specifically, Neuro2a cells transfected with miR-106b decreased ABCA1 expression and

14 impaired cellular cholesterol efflux in neuronal cells resulting in increased levels of secreted amyloid-β (32)

There is tremendous therapeutic potential for the treatment of cardiovascular diseases, by either overexpression or inhibition of miRNAs. In macrophages stimulated with oxLDL/IFN-γ in vitro, and in lesional macrophages, loss of miR-155 reduced the expression of the chemokine CCL2, which promotes the recruitment of monocytes to atherosclerotic plaques (33). It was also determined that miR-155 directly repressed expression of BCL6, a transcription factor that attenuates proinflammatory NF-κB signaling. Lastly, miR-33 has been identified as a key regulator of cholesterol homeostasis in mammals and a potential therapeutic target for atherosclerosis. As a regulator of lipid metabolism it decreases ABCA1 expression, and also affects cholesterol and HDL generation. It functions via a negative feedback loop triggered by the cholesterol content of cells (34).

For studies aimed at examining systemic effects on atherosclerosis in vivo, experiments with genetically engineered animals are more appropriate; however, for those intended for isolating specific effects on macrophage physiology and role of microRNA’s such as those described herein, in vitro experiments are advantageous.

Macrophages in the vessel wall have many roles that affect the development of atherosclerosis. Probing the role of microRNA activity on gene expression levels may be critical for understanding the processes that facilitate arterial cholesterol efflux.

15 Omega 3 Fatty Acids and Cholesterol efflux

A challenging factor in cardiovascular disease research is the identification and quantification of reliable biomarkers in readily accessible plasma and serum samples. miRNA’s in plasma are often shielded and protected from degradation by storage in protein or lipid vesicles or through association with other protein and liproprotein complexes such as HDL (35). The stability of circulating miRNAs has generated an interest in their use as potential tools in the diagnosis and prognosis of patients with cardiovascular disease. Quantitative methods such as real-time PCR and microarrays are useful in detecting miRNA levels in plasma and serum. miR-1 levels are significantly higher in plasma or acute myocardial infarction (AMI) patients placing it as an importer marker for AMI diagnosis (36) . Likewise miR423-5p (37) is most strongly related to the clinical diagnosis of Heart Failure while miR-499-5p exhibited > 80-fold increase in geriatric patients with acute non-ST elevation myocardial infarction (NSTEMI) (38).

Epidemiological and controlled interventional studies have indicated the beneficial effects of long-chain omega-3 fatty acids in the form of docosahexaenoic acid

(DHA), eicosapentaenoic acid (EPA) from fish and fish oils. Omega-3 fatty acids are polyunsaturated fatty acids (PUFAs) with a double bond carbon bonds at the third carbon atom from the end of the carbon chain (39). The three types of omega-3 fatty acids involved in human physiology are α-linolenic acid (ALA) found mainly in plant oils, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) both commonly found in marine oils. Dietary sources of DHA can be found in cold-water, fatty fish, including

16 salmon and tuna. Evidence suggest an approximately 50% reduction in heart attacks with

200 mg per day of DHA from fish. Supplementation of healthy human volunteers with fish oil-derived n−3 PUFA results in decreased monocyte infiltration and decreased production of pro-inflammatory cytokines. Fish oil feeding ameliorates the development of inflammation due to coronary heart disease thereby supporting an anti-inflammatory role of n−3 PUFA in fish oils. Dietary ALA decreases circulating cholesterol, favorably increases cholesterol efflux and decreases cholesterol accumulation in foam cells (40).

Walnuts, a rich source of ALA, significantly decreased total and low-density lipoprotein cholesterol in normo- and hypercholesterolemic individuals; its bioactive molecules significantly improve cholesterol efflux in MDFC (41). Emphasizing the role of bioactive components of fish oils and dietary regulation could potentially have on cardiovascular disease risk.

The concentrations of other monounsaturated fatty acids (MUFAs) typically contained in the Mediterranean diet have also been deemed cardioprotective in multiple human studies. Monounsaturated fat consumption has been associated with decreased low-density lipoprotein (LDL) cholesterol, and possibly increased high-density lipoprotein (HDL) cholesterol. There is increasing evidence on the beneficial use of canola oil on circulating lipids and consumption of canola oil affects various physiological factors of heart disease. Canola oil was initially bred naturally from rapeseed in the early 1970s by Keith Downey and Baldur R. Stefanssonat the University of Manitoba, Canada and maintains a much different nutritional profile due to the reduction in erucic acid. Compared to diets rich in saturated fatty acids, diets high in

17 canola oil are shown to reduce total cholesterol and low density lipoprotein levels (42).

Addressing the importance of circulating miRNA’s following dietary interventions rich in

Omega 3’s proves to be a viable avenue in determining diagnostic and therapeutic measures for the treatment of cardiovascular diseases specifically atherosclerosis.

Figure 1-7. Metabolism of omega-3essential fatty acids

From (43)

Perfluorinated fatty acids (PFASs) and Cholesterol Metabolism

Poly- and per-fluorinated alkyl substances (PFASs), specifically

Perfluorooctanoic acid (PFOA, ‘C8’) and perfluoroctane sulphonate (PFOS) are environmentally stable compounds with industrial and consumer uses. PFOA and PFOS are synthetic perfluorinated carboxylic acid useful as surfactants in the emulsion

18 polymerization of fluoropolymers and more known for its use in the production of

Teflon, non-stick surfaces on cookware and waterproof, breathable membranes for clothing (44). The Environmental Protection Agency (EPA) has yet to provide steps for

PFOA and PFOS reduction as scientific uncertainties suggests no unreasonable risks.

Epidemiological studies however have alluded to a rising concern on the chronic use of industrial chemical agents and their effects on cholesterols and lipid metabolism but mechanistic mice studies contradict such reports. A yearlong human study, the C8 Health

Project from 2005-2006 evaluated serum PFOA/PFOS (C8) and total cholesterol among

46,294 West Virginia residents over the age of 18, who lived, worked, or went to school in a C8 contaminated drinking-water district. Data showed C8 cohort had lower rates of high cholesterol than the general public. The risk for high total cholesterol (>240 mg/dL) measured via odds ratios (ORs) in logistic regression models showed sequential OR increases with PFOA quartile, in comparison to the lowest quartile (OR = 1.00), that were each significantly elevated (OR = 1.21, 1.33, and 1.40, respectively), but age, sex, and body mass index were stronger correlates (45).

Some cross-sectional human studies suggest that increased exposure to PFOA and

PFOS can lead to metabolic disruptions and more specifically cholesterol imbalance.

Toxicity due to the bioaccumulation of PFOA and PFOS is of great concern to both human and wildlife as these agents are chemically and biologically inert and persist in the host(46) . The National Health and Nutrition Examination Survey (NHANES) showed detectable levels of PFOS and PFOA in serum samples in all Americans at median concentrations of 30 ng/mL (PFOS) and 5 ng/mL (PFOA) (47). Nonetheless, these observations are varying across other epidemiological studies and do not show a clear

19 exposure-response relationship. In addition, there is a disparity with in vivo and in vitro mice studies that support decreased dyslipidemia and indicate patterns consistent with increased cholesterol transport and efflux. Gene expression profiles show genes associated with fatty acid oxidation, lipid transport and bile acid synthesis (48–51) and activation of the nuclear receptor peroxisome proliferator-activated receptor-alpha

(PPARα) in a pathway that affects metabolism (52) following PFOA/PFOS exposure, hence evidence in mice contradicts accumulated epidemiological data. Most studies in populations and workers with exposure to PFOA/PFOS show no consistency of the elevated high cholesterol (53–55) and also depict questionable dose relationship(56)

Similarly confounding factors such as obesity, age and genetics (56,57) all imply a confusion about the risk of PFASs and CVD.

Evidence also suggests a sex differential regulation of PFOA which may correlate with some epidemiological studies. A study by Vanden Heuvel et al. (58) on the elimination, tissue distribution, and metabolism perfluorooctanoic acid (PFOA) in male and female rats over a 28 day single dose (9.4 μmol/kg, 4 mg/kg) showed a sex difference in urinary elimination of PFOA. Data showed rapid elimination of PFOA in urine samples of female mice with 91% of the dose being excreted in the first 24 hr and roughly 6% in male rats during the same period of time. Whole body elimination in male mice was at a half-life of 15 days compared to less than a day in female mice.

Investigators hypothesized that increased urinary elimination may in part be due to increased metabolism to a PFOA-glucuronide or sulfate ester even though no evidence supports an increased lipid hybrid. However in male rats it was suggested that the

20 persistence of PFOA was due to a more rapid formation of a PFOA-containing lipid. Data also revealed that PFOA was primarily distributed in liver and plasma of male rats and in plasma and kidneys of female rats emphasizing gender differential storage and accumulation of PFAS. PFOA and PFOS regulation of cholesterol homeostasis is largely elusive as little evidence exists for the causal link between PFASs and increased cholesterol levels or CVD. More importantly since PFASs do not increase cholesterol synthesis (59), delineating the mechanisms of how fluorochemicals affect cholesterol metabolism with in vitro models of cholesterol transport and homeostasis will be most effective in finding mechanistic plausibility that may lead to significant public health benefits.

HYPOTHESIS OF STUDIES

Reverse Cholesterol Transport is an existing endogenous pathway that reduces atherosclerosis, mainly through the reduction of total cholesterol (TC) and low density lipoprotein (LDL-C). Both environmental and endogenous factors regulate RCT; however modification of other CVD risk factors has not been well defined following nutritional supplementation and microRNA (miR) regulation. Clinical studies by our group indicate that diets rich in walnuts significantly reduce TG, TC and LDL-C in vivo and ex vivo and promote RCT. Similarly, various groups have shown a role of miRs in regulating cholesterol efflux in vitro. Interestingly a recent study suggests the miR-708 regulated gene CD38, is necessary for the development of obesity and metabolic syndrome, implicating miR-708 and CD38 in CVD risk.

HYPOTHESES I: Dietary supplementation with fish oil would affect ex vivo cholesterol efflux

HYPOTHESES II: Dietary supplementation would regulate circulating microRNA expression

HYPOTHESIS III: MicroRNAs would regulate Reverse Cholesterol Transport in vitro

HYPOTHESES IV: miR-708-5p would affect Reverse Cholesterol Transport in macrophage derived foam cells, hepatocytes and intestinal cells. It was further hypothesized that this change, if it existed, was through CD38 expression and activity.

22 HYPOTHESES V: Based on observed increases in cholesterol efflux following miR-708 transfection, miR-708 will affect foam cell formation through NFKB and IL-1b expression

23 References

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Chapter 2

Dietary regulation of cholesterol efflux and microRNA expression

Abstract

Although much is known about the cardio-protective properties of omega-3 polyunsaturated fatty acids such as docosahexanoic acid (DHA), the role of micro-RNAs

(mIR) in this response has not been extensively studied. Here we investigate the effects of oleic and docosahexaenoic acid consumption on cholesterol efflux and mIR expression in a non-random subset of the Canola Oil Multi-center Intervention Trial (COMIT) participants. COMIT employed a randomized, double-blind, five-period, cross-over trial design. Three of the treatment oil diets; 1) Baseline Control; 2) High oleic canola oil; and

3) DHA-enriched high oleic canola oil were selected for analysis. Cholesterol efflux was increased following macrophage derived foam cell incubation with human sera.

Effectively, Canola-DHA significantly increased cholesterol efflux when compared to canola oil and baseline control values. The three selected diets affected the expression of miRs regulated by DHA and involved in CVD risk, specifically Canola- DHA significantly improved the expression of miR-30, miR-33 and miR-181a when compared to post-prandial baseline serum levels, while no significant differences were observed in the expression of miR-144 and miR-708 following either dietary intervention in the ex vivo experiment. Interestingly DHA induced the expression of miR-708 and miR-30 in

30 human THP-1 macrophages in vitro and increased Reverse Cholesterol Transport. The stability of miRs creates the possibility of a non-invasive method of biomarker assessment in serum and as useful tools in defining disease states, particularly that of coronary heart disease.

Introduction

Canola oil is a plant oil comprising of low amounts of saturated fatty acids (SFA), high levels of monounsaturated fatty acids (MUFA), and intermediate levels of polyunsaturated fatty acids (PUFA) more specifically alpha-linolenic acid (ALA), an omega 3 fatty acid. MUFA maintain similar plasma cholesterol lowering abilities as

PUFA, making the cardio benefit properties of canola oil even more ideal. The ALA component of canola oil increases the cardio-protective benefits of canola oil. ALA can be converted in vivo to docosahexaenoic acid (DHA). ALA has been shown to decrease circulating cholesterol, positively increase cholesterol efflux and reduce cholesterol accumulation in foam cells(40). Walnuts which mainly contain ALA significantly decrease total and low-density lipoprotein (LDL) cholesterol in normo- and hypercholesterolemic individuals; its bioactive molecules significantly improve cholesterol efflux in Macrophage Derived Foam Cells (MDFC) (60). Supplementation with omega-3 PUFAs from fish oils specifically DHA has received widespread attention as a determinant for cardiovascular disease risk. In vitro studies in animal models show that consumption of fish oils reduce inflammation, improve arterial integrity and

31 endothelial function. DHA in particular has been shown to regulate triglyceride levels. A

4 weeks study supplementation of DHA significantly reduced TAG levels in normolipidemic human subjects by 22% (61). Evidence also suggests that DHA increased both

LDL and HDL particle size in hyperlipidemic and healthy human subjects (62).

Similarly, dietary canola oil containing omega-3 polyunsaturated fatty acids (PUFAs) maintain cardiovascular benefits by improving endothelial function and lowering LDL- cholesterol, thus a combined intervention of high oleic canola oil and DHA supplementation could potentially enhance and improve heart health.

MicroRNA’s are a class of non-coding regulatory RNAs, meaning they typically do not code for proteins but are however involved in the posttranscriptional regulation of target gene expression. MiRs are about 21–22-nucleotide sequences in length and are produced within the genomic loci of cells. A majority of microRNA genes are intergenic or intronic (27). MiRs are transcribed in the nucleus by RNA polymerase II as long primary miRNAs (pri-miRNAs) and are later micro-processed through a Drosha/Pasha complex in the nucleus and Dicer in the cytoplasm. Following the pri-miRNA processing, the mature miRNA (25–21 nt) is incorporated into the RNA-induced silencing complex

(RISC) and binds preferentially to the 3′ untranslated region of the mRNA target genes(28). This results in target mRNA being transcriptionally repressed, degraded or silenced, thereby ensuring no protein translation. Numerous circulating miRs have been proposed as sensitive and useful markers for cardiovascular and cancers diagnosis. A limiting factor to this line of investigation has been the identification and quantification of reliable biomarkers in readily accessible plasma and serum samples.

32 Numerous studies reveal a role of miRs in regulating cholesterol and lipid metabolism. Mir-33a/b were identified as regulators of sterol-regulatory element binding factor 2 (Srebf2) and Srebf1 genes and as direct targets of the ABCA1 transporter, which is one of the primary cholesterol transporters involved in Reverse Cholesterol Transport

(RCT) (63). Overexpression of miR-144 and miR-106 are known to regulate ABCA1 expression as well at the posttranscriptional level in hepatocytes, macrophages, and neuronal cells thereby impairing RCT (64)(32). MiR-30c mainly reduces lipid synthesis

(65) and more recently miR-181a was shown to increase genes involved in β-oxidation and decrease genes involved in lipid synthesis thereby inhibiting lipid accumulation. It was also reported that systemic delivery of miR-181, inhibits the activity of the pro- inflammatory gene NFKB, preventing vascular inflammation and atherosclerosis in ApoE deficient mice (66). Thus, when considering biomarkers of omega-3 fatty acids effects,

MicroRNA’s are ideal candidates for evaluating dietary effectiveness. Understanding the role of micro-RNA expression and the effects of omega-3 fatty acid consumption could serve as a feasible diagnostic for Cardiovascular Disease (CVD) risk.

Materials and Methods

Chemicals

Docosahexaenoic acid (DHA), Human oxLDL, GW4064, and 8-Br cAMP were purchased from Sigma-Aldrich (St. Louis, MO). Fetal Bovine Serum was purchased from

Gemini Bio-Products (West Sacramento, CA). MicroRNA primers were purchased from

Quanta Biosciences (Gaithersburg, MD, US) and MicroRNA mimics were provided by

33 Jungsun Kim, PhD (Mayo Clinic). Lipofectamine and Block-it Oligonucleotides were purchased from Invitrogen (Grand Island, NY). ApoA-I, HDL and oxLDL were purchased from Biotechnical Technologies Inc (Stouhgton, MA) and 3-NBD Cholesterol from Calbiochem (La Jolla, CA).

Preparation of BSA conjugated DHA

Docosahexaenoic Acid (DHA) was conjugated to fatty acid free Bovine Serum

Albumin (BSA) based on method described by (67). Briefly, stock concentration of 0.5

M DHA was dissolved in ethanol and dried under argon. KOH at a stock of 0.15 M was added and incubated at 70 °C for 1 h. Following incubation, filter-sterilized BSA (2 mM) in PBS was added to DHA and the vehicle control for a final fatty acid concentration of 8 mM, pH was adjusted to 7.2 to 7.4 and stored at –20 °C until use.

Cell Line and Cell Culture

THP-1 Human Monocytes

THP-1 (Homo sapiens, monocyte) cell line was obtained from the American Type

Culture Collection (ATCC; Rockville, MD) and were grown in RPMI 1640 with 10% heat inactivated FBS, 50μM 2-ME, 1mM sodium pyruvate, and antibiotics. To differentiate THP-1 monocytes to macrophages, cells were incubated in growth medium minus 2-ME with addition of 100nM PMA for 48 h.

High Throughput Screen Cholesterol Efflux Assay

THP-1 monocytes were plated in 96-well plates at a density of 1 × 105 cells/well and differentiated into macrophages by addition of 100 nM PMA for 48 h. After differentiation, cells were washed twice with PBS and cultured in the growth medium

34 overnight. To induce foam cell formation and equally label intracellular cholesterol pool, cells were loaded with 50 μg/mL oxLDL and 1ug/ml of 3-NBD cholesterol for 24hr.

After overnight treatment cells were washed twice with PBS and incubated human sera

(10%, v:v) to induce cholesterol efflux for 1h in ex vivo experiments. In In vitro experiment cells were incubated with HDL or Apoa-1 cholesterol acceptors overnight.

Following, 100 μL of the medium in each well was collected and transferred to a new 96- well plate by multi-channel pipette. Residual liquid in each well was removed by tapping.

Cells were treated with 100 μL lysis buffer (5 mM Tris HCl + 0.1% sodium dodecyl sulfate) and homogenized on an orbital shaker for 10 min. Fluorescence from the PBS fraction and the cell lysate fraction were measured at excitation and emission wavelengths of 485 and 535 nm, respectively, relative to the values of standard curves.

MicroRNA Transfection for Cholesterol Efflux

Thp-1 macrophages were transfected with 20μM synthetic microRNA (hsa-miR-

181, hsa-miR-30c, hsa-miR-708 and miR-33) in 20μL of Lipofectamine transfection reagent for 3-6 hours in a 96 well plate at a density of 1x 105cells/well. The volume was brought up to 100 μL with growth media for 24 hr. 10 μM GW4064 treatments were added to the cells and incubated for 18 to 20 hr. The media/treatment was removed and cholesterol efflux was examined through our high-throughput screening (HTS) reverse cholesterol transport assay as described previously (23).

RNA extraction, reverse transcription, real time PCR

RNA was isolated from serum samples and Thp-1 cells using a kit from

AMRESCO LLC (Solon, OH), a simple spin-column based method used for siRNAs and microRNAs in serum samples. qScript microRNA cDNA Synthesis Kit (Quanta

35 Biosciences Inc, Gaithersburg, MD , US) was used to convert miRNAs into cDNA starting from total RNA. Individual microRNAs are quantitated in real-time qRT-PCR amplification reactions using the PerfeCTa microRNA Assays along with the PerfeCTa

Universal PCR Primer and the PerfeCTa SYBR Green SuperMix family of products.

Trial design

A multicenter, double blind, randomized crossover controlled feeding trial was conducted at the Richardson Centre for Functional Foods and Nutraceuticals (RCFFN)

(University of Manitoba), the institute of Nutrition and Functional Foods (INAF) (Laval

University), and the Department of Nutritional Sciences, Pennsylvania State University

(PSU), as previously reported. Briefly the trial included 5 treatment periods of 4 weeks, separated by 2 to 4-week washout intervals (Figure1). The date range for participant recruitment to last follow–up was September 20, 2010 to Dec 13, 2011 at the RCFFN,

November 1, 2010 to December 22, 2011 at INAF and November 16 2010 to April 12

2012 at PSU.

Ethics Statement

Institutional ethics boards within the participating universities reviewed and approved the trial protocol. Written informed consent was obtained from participants as prescribed by institutional research ethics boards. The trial was registered with clinicaltrials.gov (NCT01351012). The registration of the clinical trial with clinicaltrials.gov (March 14, 2011) was delayed due to staff turnover and did not occur until after the enrolment of participants had begun at the RCFFN center (September 20th,

36 2010). The authors confirm that all ongoing and related trials for this intervention are registered.

Selection of subset samples

Three of the COMIT diets, the baseline no treatment, high oleic canola oil, with the highest n-9 MUFA content, and a high oleic canola oil with DHA blend, with the highest long chain n-3 PUFA were selected for cholesterol efflux evaluation. There were a total of 780 serum samples available however a subset of 90 samples was selected for micro-RNA quantification, as this was an experimental analysis and the impact of dietary fatty acid modification on microRNA expression in human serum relatively unclear.

Results

Diet affects ex vivo cholesterol efflux

To investigate the role of ex vivo DHA supplementation on RCT, Thp-1 macrophages were induced into foam cells by addition of oxLDL and cells intracellularly labeled with 3-NBD cholesterol for 24hr. Cholesterol efflux was then examined following incubation with human sera (10%, v:v). Results show that dietary supplementation with Canola-DHA significantly improves cholesterol efflux in MDFCs when compared to Canola oil alone or baseline control. DHA- supplemented diet increased cholesterol efflux thereby promoting RCT and effectively reducing inflammation. The effects of DHA on increased ex vivo cholesterol efflux was used to

37 examine the effects of diets on miRs as a diagnostic tool for usefulness of dietary intervention.

Figure 2-1: Sera of subjects consuming diets rich in DHA and effects on cholesterol efflux

Ex Vivo Efflux 1.15 *

)

n 1.10

E 1.05

e t 1.00

t 0.95

R 0.90

( 0.85 Baseline DHA+CO CO

Foam cells were induced in Thp-1 macrophages with OxLDL (50ug/ml) and 3NDB

Cholesterol (10ug/ml) for 24hr. Following the induction of foam cells, human sera (10%, v:v) from Baseline, DHA+CO and CO were applied as treatment to induce cholesterol efflux for 1h on a sample set of patients with metabolic syndrome. Cholesterol efflux was examined as described in the materials and methods. Asterisks denote significant difference in efflux from the baseline (pre-diet) ex vivo efflux (matched t-test)

Diet regulates microRNA expression

Five microRNAs, miR-181c-5p, miR-30c-5p, miR-33, miR-144 and miR-708 known for their roles in cholesterol metabolism and regulated by DHA were selected for quantification. MiRs were quantified in a total of 90 serum samples in three of the

38 treatment oil diets; 1) Baseline Control; 2) High oleic canola oil; and 3) DHA-enriched high oleic canola oil. Results show that intake of high oleic canola oil and canola DHA significantly increases the expression of key circulating microRNAs (Fig 2) miR-181a, miR-30 and interestingly miR-33 in serum when compared to baseline treatment. There were no significant differences however observed in the expression of the other metabolic micro-RNAs, miR-708 or miR-144 among all groups. These data suggest the impact of fish oils on circulating micro-RNA amount and a role for micro-RNAs as biomarkers in interventions aimed for dietary assessments and diagnostics.

Figure 2-2: High Oleic Canola oil and DHA consumption affects microRNA expression in Serum

)

e MicroRNA

e 25 *

a *

o 20

v *

t 15

l *

( 10 *

a 5

c 0

e e e e e

O O O O O

n n n n n

i i i i i

C C C C C

l l l l l

+ + + + +

e e e e e

s A s A s A s A s A

a a a a a

H H H H H

B B B B B

D D D D D

miR-30 miR-33 miR-708 miR-144 miR-181

MiRs were quantified in serum treatment samples as described in Materials and Methods using qRT-PCR and the amount was expressed relative to the control gene RNU6. Bars represent mean ± SEM (n=30) per each treatment group. Statistical significance was

39 determined by One-Way ANOVA followed by Tukeys Mulitcomparison test within each group, Asterisk denote significance (p<0.05)

DHA promotes Reverse Cholesterol Transport

To examine the role of DHA on cholesterol efflux, Thp-1 monocytes differentiated into macrophages were treated with 100μM conjugated-DHA, cAMP and

BSA control for 24 h. Cholesterol efflux was then examined to HDL, Apoa-1 and no acceptor conditions. Figure 2-3 (A) DHA significantly increased cholesterol efflux to

HDL when compared to either Apoa-1 or nonspecific transport and was comparable in significance to the positive control cAMP control, but significantly higher than the vehicle BSA control, suggesting a direct role of DHA on RCT regulation.

Figure 2-3: DHA effects cholesterol efflux

A. 1.50

) l Control

r t cAMP n *

x o 1.25 *

DHA

1.00

t R 0.75

( 0.50 HDL ApoA-1 None

Foam cells were induced in Thp-1 macrophages with OxLDL (50ug/ml) and 3NDB

Cholesterol (10ug/ml) for 24h. Following 24h incubation cells were treated with DHA

40 (100uM), cAMP (100uM) or BSA (100uM) for 24hr. Cholesterol efflux was determined as described in Materials and Methods and the relative efflux for each treatment was expressed relative to a cAMP positive control cells and BSA negative control. The data presented are mean ± SEM values of triplicate wells. The results are representative of 3 independent experiments. Statistical significance was determined by One-Way ANOVA followed by Tukeys Mulitcomparison test within each group, Asterisk denote significance (p<0.05).

DHA affects in vitro microRNA expression

To examine macrophage expression of these miRs as a correlation to cardiovascular disease and inflammation, Thp-1 monocytes were differentiated into macrophages and treated with conjugated- DHA or BSA control. Following the 24hr incubation, RNA was isolated and microRNA expression quantified using RT-PCR. The microRNAs were selected for quantification based on previous data associating to cardiovascular disease risk, more specifically atherosclerosis and lipid metabolism regulation. MiR-181a in particular was targeted because previous data indicates its induced by DHA and oleic acid (68) along with miR-30c in caco-2 intestinal cells and various studies suggest the roles of miR-33, miR-144 and miR-708 in RCT. Our results show a significant induction of miR-30 and miR-708 following DHA treatment when compared to control BSA (Figure 2-4). No comparable differences were observed in miR-181, miR-33 or miR-144 expression in macrophages following DHA treatment.

These data however emphasize the influence of Fatty acids on microRNA expression and the opportunity of miRs to serve as biomarkers for the evaluation of dietary effectiveness.

41 Figure 2-4: Quantification of microRNA in THP-1 Macrophages

Thp-1 monocytes were differentiated in macrophages by addition of PMA (100nM) for

48h. Following differentiation macrophages were treated with DHA (100uM) or BSA for

24h. MicroRNA expression was determined as described in Materials and Methods and the relative amount of each gene was expressed relative to the control gene RNU6. Bars represent mean ± SEM (n=3) replicates. Statistical significance was determined by One-

Way ANOVA followed by Tukeys Mulitcomparison test within each group, Asterisk denote significance (p<0.05).

MicroRNAs regulate cholesterol efflux

To examine the role of microRNAs in regulating cholesterol efflux, miR-181a-5p, miR-181c-5p, miR-30c-5p, miR-33 and miR-708 were transfected in Thp-1 human monocytes differentiated into macrophages. Following foam cell induction with oxLDL

42 and labeling of intracellular cholesterol pool with 3-NBD for 24hrs, the FXR-agonist

GW4064 was added to the cells for additional 18hr. Cells were incubated with apoA-I or

HDL cholesterol acceptors to induce efflux and florescence emissions measured and analyzed. MiR-181-1-5p, miR-181c-5p and miR-708-5p significantly increased cholesterol efflux when compared to the control miR alone (Figure 2-5). Even though not statistically significant, there was a slight decrease in cholesterol efflux following miR-33 transfection and subsequent treatment with GW4064, however no observable differences were seen in the event of miR-30c-5p in foam cells.

Figure 2-5: MicroRNAs Regulate Reverse Cholesterol Transport

Cholesterol Efflux 3 * * *

f 2

a l 1

p p p p

5 5 5 5

- - -

c 8

0 0

3 7

- -

r r

i i

m m

Thp-1 monocytes were differentiated into macrophages with PMA (100nM) for 48h.

Cells were transfected with synthetic microRNAs (20uM) and control scramble sequence

(20uM) for 24hrs. Foam cells were induced with OxLDL (50ug/ml) and 3NDB

43 Cholesterol (10ug/ml) for 24hr. Following 24hr incubation cells were treated with

GW4064 (10uM) for an addition 18h. Cholesterol efflux was determined as described in

Materials and Methods and the relative efflux for each treatment was expressed relative to control cells (control miR, DMSO treatment). Bars represent mean ± SEM (n=4, shown is representative experiment repeated at least 2 times). Statistical significance was determined by One-Way ANOVA followed by Tukeys Mulitcomparison test. Asterisk denote significance (p<0.05).

Discussion

The COMIT study, is a first of its kind to use a large body of research participants to compare and explore the effects of multiple fatty acid classes (69). The trial’s approach to recruitment and retention of participants sustained the duration of the study and maintained satisfactory statistical power to resolve small differences in outcome measures leading to numerous other studies for evaluating the effects fatty acids and oils on heart health. Preliminary findings in the trial indicates that consumption of a blend of high–oleic acid canola oil and DHA oil improves HDL and triglyceride profiles (70).

Canola oil differs from other oils due to increased concentration of the omega-3 fatty acid

ALA. ALA can be reduced to DHA, which is a more nutritionally valuable. Fish oil, particularly DHA consumption is highly athero-protective with its bioactive component suggested as a determinant for cardiovascular disease risk. The rise in combined dietary supplementation of fish oils invites the validation of methods that support its efficacy for

44 disease risk. Identification of potential biomarkers of association can serve as novel diagnostic markers in the treatment of CVD.

MicroRNA’s are well known differential regulators of gene expression and potential biological and physiological modulators of diseases especially cardiovascular diseases. MiRs have been shown to regulate lipid homeostasis and affect excess cholesterol removal from MDFCs. MiRs transcriptionally target specific genes involved in cholesterol homeostasis and direct their abilities to function properly. The easy accessibility and stability of circulating microRNAs in bodily fluids specifically serum make them ideal noninvasive biomarkers for coronary heart disease. For instance miR-21 expression is dysregulated following high fat diet and exposure to polyunsaturated fatty acids typically leads to a decrease in colon and brain cancer in the presence of carcinogens (71). Levels of microRNAs in serum are stable, reproducible and consistent among groups and deregulated expression of a specific miRNA can be associated with a heart disease as indicated here by miR-33,-155, -758 and 30c (33,64,65,72) . MiR-1, miR-133, miR-499, and miR-208 are also shown to be particularly up-regulated following cases of myocardial infarctions and are highly expressed in heart tissues and less in circulation under normal/healthy circumstances (73). A biomarker status obtained from an association will have important clinical outcomes.

Here we investigate the importance of dietary interventions and the role of microRNA’s as useful biomarkers. Our data reveals that, diet indeed does affects ex vivo cholesterol removal and modulates the expression of circulating microRNAs in serum.

We quantified the expression of selected miRs in human serum following a nutritional intervention and evaluated the direct role of DHA on ex vivo and in vitro microRNA

45 expression. Results showed that 1) Diet affected reverse cholesterol transport, 2) Diet regulated microRNA expression and 3) miRs regulated cholesterol efflux. In the three diets selected for ex vivo RCT, Canola-DHA effectively increased cholesterol efflux in foam cells when compared to high oleic canola oil and baseline control, emphasizing the role of functional HDL in cholesterol mobilisation. These findings are particularly important as they imply that there are circulating bioactive molecules from the diet that affect MDFC function. Intake of Canola- DHA also significantly increased the expression of selected miRs involved in cholesterol and lipid metabolism. Circulating micro-RNAs, miR-30, miR-181a and miR-33 were significantly up-regulated when quantified in serum following Canola-DHA consumption, indicating a role of DHA influence on miR expression. Interesting enough, DHA alone in vitro increased cholesterol efflux to HDL promoting RCT. DHA treatment in foam cells also increased the expression of other key metabolic miRs, such as miR-30 and miR-708, highlighting the regulatory role of omega-3s on circulating micro-RNA expression.

Using a controlled feeding human intervention trial, we showed the effects of diets on microRNAs as a diagnostic tool for usefulness of dietary intervention. These observations support a role of fish oils and miRs in regulating metabolic syndrome and

CVD. The correlation of miRs and DHA-Canola oil can serve as a potential diagnostic marker in the treatment of heart and metabolic syndrome diseases. It is likely that the beneficial effects of plant oils such as canola oil and omega-3 PUFAs, particularly DHA may be regulated in part through miRNAs and vice-versa. The relationship behind miRs and DHA effects on CVD need further investigation and the molecular mechanisms of regulation clearly defined. Understanding the mechanisms behind the molecular

46 pathways that influence n-3 PUFAs and micro-RNA regulation will assist in the treatment of coronary heart disease with microRNAs serving as potential diagnostic and monitoring biomarkers of disease.

47 References

1. Zhang J, Kris-Etherton PM, Thompson JT, Hannon DB, Gillies PJ, Vanden Heuvel JP. Alpha-linolenic acid increases cholesterol efflux in macrophage-derived foam cells by decreasing stearoyl CoA desaturase 1 expression: Evidence for a farnesoid-X-receptor mechanism of action. J Nutr Biochem. 2012;23(4):400–9. 2. Zhang J, Grieger JA, Kris-Etherton PM, Thompson JT, Gillies PJ, Fleming JA, et al. Walnut oil increases cholesterol efflux through inhibition of stearoyl CoA desaturase 1 in THP-1 macrophage-derived foam cells. Nutrition & Metabolism. 2011. p. 61. 3. Buckley R, Shewring B, Turner R, Yaqoob P, Minihane AM. Circulating triacylglycerol and apoE levels in response to EPA and docosahexaenoic acid supplementation in adult human subjects. The British journal of nutrition. 2004. 4. Mori TA, Burke V, Puddey IB, Watts GF, O’Neal DN, Best JD, et al. Purified eicosapentaenoic and docosahexaenoic acids have differential effects on serum lipids and lipoproteins, LDL particle size, glucose, and insulin in mildly hypedipidemic men. Am J Clin Nutr. 2000;71(5):1085–94. 5. Großhans H, Filipowicz W. Proteomics Joins the Search for MicroRNA Targets. Cell. 2008. p. 560–2. 6. Friedman RC, Farh KKH, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19(1):92–105. 7. Gerin I, Clerbaux LA, Haumont O, Lanthier N, Das AK, Burant CF, et al. Expression of miR-33 from an SREBP2 intron inhibits cholesterol export and fatty acid oxidation. J Biol Chem. 2010;285(44):33652–61. 8. Ramirez CM, Dávalos A, Goedeke L, Salerno AG, Warrier N, Cirera-Salinas D, et al. MicroRNA-758 regulates cholesterol efflux through posttranscriptional repression of ATP-binding cassette transporter A1. Arterioscler Thromb Vasc Biol. 2011;31(11):2707–14. 9. Kim J, Yoon H, Ramírez CM, Lee SM, Hoe HS, Fernández-Hernando C, et al. MiR-106b impairs cholesterol efflux and increases Aβ levels by repressing ABCA1 expression. Exp Neurol. 2012;235(2):476–83. 10. Soh J, Iqbal J, Queiroz J, Fernandez-Hernando C, Hussain MM. MicroRNA-30c reduces hyperlipidemia and atherosclerosis in mice by decreasing lipid synthesis and lipoprotein secretion. Nat Med [Internet]. 2013;19(7):892–900. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23749231 11. Sun X, Sit A, Feinberg MW. Role of miR-181 family in regulating vascular inflammation and immunity. Trends in Cardiovascular Medicine. 2014. p. 105–12. 12. Calder PC, Bond JA, Harvey DJ, Gordon S, Newsholme EA. Uptake and incorporation of saturated and unsaturated fatty acids into macrophage lipids and their effect upon macrophage adhesion and phagocytosis. Biochem J. 1990;269(3):807–14. 13. Zhang J, Cai S, Peterson BR, Kris-Etherton PM, Heuvel JP Vanden. Development of a cell-based, high-throughput screening assay for cholesterol efflux using a fluorescent mimic of cholesterol. Assay Drug Dev Technol. 2011;9(2):136–46.

48 14. Gil-Zamorano J, Martin R, Daimiel L, Richardson K, Giordano E, Nicod N, et al. Docosahexaenoic Acid Modulates the Enterocyte Caco-2 Cell Expression of MicroRNAs Involved in Lipid Metabolism. J Nutr. 2014;575–85. 15. Senanayake VK, Pu S, Jenkins DA et al. 2014. Plasma fatty acid changes following consumption of dietary oils containing n-3, n-6, and n-9 fatty acids at different proportions: preliminary findings of the Canola Oil Multicenter Intervention Trial (COMIT). 16. Jones PJ, Senanayake VK, Pu S, Jenkins DJ, Connelly PW, Lamarche B, Couture P, Charest A, Baril-Gravel L, West SG, Liu X, Fleming JA, McCrea CE K-EP. DHA-enriched high-oleic acid canola oil improves lipid profile and lowers predicted cardiovascular disease risk in the canola oil multicenter randomized controlled trial. Am J Clin Nutr [Internet]. 2014; Available from: http://www.ncbi.nlm.nih.gov/pubmed/24829493 17. Palmer JD, Soule BP, Simone BA, Zaorsky NG, Jin L, Simone NL. MicroRNA expression altered by diet: Can food be medicinal? Ageing Research Reviews. 2014; 18. Horie T, Baba O, Kuwabara Y, Chujo Y, Watanabe S, Kinoshita M, et al. MicroRNA-33 deficiency reduces the progression of atherosclerotic plaque in ApoE-/- mice. J Am Heart Assoc. 2012;1(6). 19. Nazari-Jahantigh M, Wei Y, Noels H, Akhtar S, Zhou Z, Koenen RR, et al. MicroRNA-155 promotes atherosclerosis by repressing Bcl6 in macrophages. J Clin Invest. 2012;122(11):4190–202. 20. Salic K, De Windt LJ. MicroRNAs as biomarkers for myocardial infarction. Curr Atheroscler Rep. 2012;14(3):193–200.

Chapter 3

Role of microRNA miR-708 on Reverse Cholesterol Transport in

macrophages, hepatocytes and intestinal cell models

Abstract

Certain microRNAs (miRs) are regulators of cholesterol homeostasis in mammalian systems and are potential therapeutic targets for the treatment of atherosclerosis. These miRs regulate expression of important lipid metabolism genes such as ATP Binding Cassette (ABC) transporters, specifically ABCA1, which in turn affects cholesterol levels and HDL generation. The nuclear receptor Farnesoid X

Receptor (FXR) functions as a bile acid sensor that impacts the expression of genes involved in cholesterol, triglyceride (TG), and bile acid production. Several miRs, including miR-708, are regulated by the FXR ligand GW4064 and also affect cholesterol efflux in Macrophage Derived Foam Cells (MDFC). Hence, the objective of these studies was to examine the role of miR-708 in FXR-dependent cholesterol efflux by examining its role on cholesterol transport in MDFC (THP-1), liver (Huh-7) and intestinal (Hct-116) model systems. We show that ectopic expression of miR-708 through transfection represses CD38 gene and protein levels and attenuates expression of the pro- inflammatory genes NFκB and Il-1β in all cell type models. Similarly targeted siRNA

50 knockdown of CD38 augments Sirt-1 transcripts levels, in a pathway that reduces foam cell formation and enhances cholesterol efflux. Our results suggest the possibility of miR-

708 and its regulation of CD38 as a potential beneficial approach for the treatment of atherosclerosis via the enhancement of the reverse cholesterol transport pathway.

Introduction

Atherosclerosis is a major cause of morbidity and mortality in the United States, with coronary heart disease and stroke being its two most common expressions(2).

(MDFC) in the atherosclerotic plaque is the initial and critical step in reverse cholesterol transport (RCT). The final outcome of RCT is the transport of peripheral cholesterol to the liver for excretion as bile acids. This results in the lowering of peripheral lipid content and promotes the regression of atherosclerosis (13). Numerous factors can affect RCT due to the contribution of myriad of genes in the transport of free cholesterol from the

MDFC. The major players are ATP-binding cassette (ABC) transporters (ABCA and G), enzymes that regulate cholesterol and cholesterol ester concentration (i.e. ACAT1,

NCEH and HMGCR) and the transcription factors that regulate expression of these

51 proteins (PPARs, FXRs, LXRs) (75). Pro-inflammatory factors such as nuclear factor kappa b (NFκB) and interleukin-1 beta (IL-1β) are considered atherogenic by impairing

RCT and cholesterol efflux (76). The regulation of the RCT pathway is complex and influenced by both genetic factors and posttranscriptional mechanisms, including regulation via MicroRNAs that can affect expression of specific target genes. Delineating the mechanisms of such regulation can lead to effective tools in the maintenance of cholesterol homeostasis and the regression of atherosclerosis.

Several miRNAs are regulators of lipid metabolism genes, including miR-122, miR-33, and miR-106b (30).The lesser studied miR-708 has been proposed to be an effector in cancer biology (77) however its role in lipid metabolism is largely unknown.

This mIR resides in the intron of ODZ4, a member of the highly conserved teneurin family of developmental regulators (78). Overexpression of miR-708 inhibited cell proliferation and invasion and induced apoptosis in the human GBM cell lines A172 and

T98G (79). Restoration of miR-708 functionality in metastatic triple negative breast cancer prevented metastasis in patients (77). Potential targets of mir-708 include the

NAD deacetylase CD38(80) and the inflammatory factor NFκB (81) , thus understanding its role in lipid metabolism can provide viable therapies for enhancing RCT.

CD38 antigen is a type II transmembrane glycoprotein with a molecular weight of

45-kDa spanning a short N-terminal cytoplasmic domain and a long C-terminal extracellular domain (82). It is expressed in multiple cell types such as thymocytes, activated T cells, and terminally differentiated B cells (plasma cells), monocytes, macrophages and some epithelial cells. The CD38 acts as a multifunctional enzyme and typically uses nicotinamide adenine dinucleotide (NAD) as a substrate to generate second

52 messengers functioning in cell adhesion, signal transduction and calcium signaling.

Inhibition of CD38 with flavonoids results in higher intracellular NAD+ levels and decreases global acetylation (83). CD38 knockout mice are resistant to high-fat diet– induced obesity and other aspects of metabolic disease through a Sirt-1 dependent pathway (84). Interestingly, multiple ABC transporter genes involved in cholesterol transport are highly expressed in normal primitive CD34+CD38− hematopoietic cells and active cholesterol metabolism and efflux in normal CD34+CD38- cells have been observed(85). Here we show the increase in FXR dependent cholesterol efflux in multiple cell-types is mediated by regulation of hsa-miR-708-5p and concomitant suppression of

CD38. Probing the role of miR activity on CD38 transcript levels may be critical for understanding the processes that facilitate arterial cholesterol uptake, transport and efflux.

Materials and Methods

Chemicals

Human LDL, TO901317, GW4064, and 8-Br cAMP were purchased from Sigma-

Aldrich (St. Louis, MO). MicroRNA mimics were provided by Jungsun Kim, PhD

(Mayo Clinic) and CD38- siRNA were purchased from Santa Cruz Biotechnology Inc.

(Santa Cruz, CA). FBS was purchased from Gemini Bio-Products (West Sacramento,

CA). Lipofectamine and Block-it Oligonucleotides were purchased from Invitrogen

(Grand Island, NY). ApoA-I, HDL and oxLDL were purchased from Biotechnical

Technologies Inc (Stouhgton, MA) and 3-NBD Cholesterol from Calbiochem (La Jolla,

CA).

53 Cell Lines and Cell Culture

THP-1 Human Monocytes

THP-1 (Homo sapiens, monocyte) cell line was obtained from the American Type

Huh-7 Hepatocytes

Huh-7 hepatocytes were obtained from the American Type Culture Collection

(ATCC; Rockville, MD). Cells were cultured in HG-DMEM containing 10% heat inactivated FBS, 1mmol/L sodium pyruvate, 100μmol/L non-essential amino acid, and

100 U/mL penicillin and 100 μg/mL streptomycin.

Hct-116 Intestinal Cells

Hct-116 cells were obtained from the American Type Culture Collection (ATCC;

Rockville, MD). Cells were cultured in McCoy's 5a Medium Modified containing 10% heat inactivated FBS, 1mmol/L sodium pyruvate, 100μmol/L non-essential amino acid, and 100 μ/mL penicillin and 100 μg/mL streptomycin.

HTS Cholesterol Efflux Assay

THP-1 cells were plated in 96-well plates at a density of 1 × 105 cells/well and differentiated to macrophages by addition of 100 nM PMA for 48 h. Subsequently, cells were washed twice with PBS and cultured in the growth medium overnight. Foam cell formation was induced with 50 μg/ml of OxLDL and the cholesterol pool labeled by

54 addition of 3-NBD cholesterol at 10 μg/ml. After overnight treatment with 10 μM

GW4064 (16 to 18 h), cells were washed twice with PBS and incubated with 40 μg/mL apoA-I or 100 μg/mL HDL in PBS containing 0.2% bovine serum albumin (BSA) to induce efflux. After 1 h, 100 μL of the medium in each well was collected and transferred to a new Fluotrac clear bottom black 96-well plate. Residual liquid in each well was removed by tapping. Cells were treated with 100 μL lysis buffer (what is in this?) and homogenized on an orbital shaker for 10 min. Fluorescence from the PBS fraction and the cell lysate fraction were measured at excitation and emission wavelengths of 485 and

535 nm, respectively.

Huh-7 and Caco-2 cells were plated in 96-well plates at a density of 1 × 105 cells/well and labeled with 3-NBD cholesterol at 10 μg/ml. After overnight treatment with 10 μM GW4064 cholesterol efflux was measured as described above.

MicroRNA Transfection for HTS Cholesterol Efflux

Cells were transfected with 20 mM synthetic microRNA hsa-miR-708-5p in 20μL of Lipofectamine transfection reagent for 3-6 hours in a 96 well plate at a density of 1x

105cells/well. The volume was brought up to 100 μL with growth media for 24 hr. 10 μM

GW4064 treatments were added to the cells and incubated for 18 to 20 hr and the media/treatment was removed and cholesterol efflux was examined through our high- throughput screening (HTS) reverse cholesterol efflux assay as described previously

(Zhang et al. 2011).

MicroRNA Transfection for Gene Expression

THP-1, Huh-7 and Hct-116 Cells were plated in 12 well plates at 12 × 104 cells

/well and transfected with 100 nM synthetic hsa-miR-708-5p or Control-miR microRNA

55 using Lipofectamine transfection reagent for 3-6 hr. Cells were lysed and harvested using a kit from AMRESCO LLC (Solon, OH). High capacity cDNA Archive kit (Applied

Biosystems; Foster City, CA) was used for reverse transcription. cDNA was amplified by

SYBR Green PCR Master Mix (Applied Biosystems; Foster City, CA) and detected by

ABI 7300 Sequence Detection System (Applied Biosystems; Foster City, CA).

RNA extraction, reverse transcription, real time PCR

Cells were lysed and harvested using miTotal RNA mini according to the manufacturer’s instructions (Viogene BioTek Group, Taipei city, Taiwan). High capacity cDNA Archive kit (Applied Biosystems; Foster City, CA) was used for reverse transcription. Twenty ng/μl of cDNA was amplified by SYBR Green PCR Master Mix

(Applied Biosystems; Foster City, CA) and detected by ABI 7000 Sequence Detection

System (Applied Biosystems; Foster City, CA). Primer sequences are listed in Table 1.

Western Blot

THP-1-derived macrophages, Huh -7 hepatocytes and HCT-116 intestinal cell lines were seeded in 6 well plates at a density of 3× 105/plate. Cells were transfected with 100 nM synthetic hsa-miR-708-5p, Control-miR microRNA, CD38- siRNA or

Control - siRNA using Lipofectamine transfection reagent for 3-6 hr and treated with and without 10 uM GW4064 for 24 h. Following treatment, cells were treated with RIPA lysis buffer plus 1 mM EDTA. Lysates were centrifuged at 16,000 g for 20 minutes at 4

ºC to remove nuclear and mitochondria fractions. Protein concentration of supernatant was measured by Bio-RAD DC protein assay kit (Bio-RAD Laboratories; Hercules, CA).

Total soluble protein was separated on a 12% SDS-PAGE gel and transferred to a PVDF membrane (Immobilon P; Millipore, Bedford MA). The membrane was blocked by 5%

56 BSA TBS + 0.2% Tween 20 (TBS+) at room temperature for 1h. The membrane was incubated with primary antibody (anti-CD38 1:1000; anti-Cox iv 1:2000) overnight.

After incubation with primary antibody, the blots were washed three times with TBS+ and incubated with horseradish peroxidase-linked secondary anti-goat antibodies and anti-rabbit antibodies (1:2000) at room temperature for 1 h. Blots were visualized by

Bio-RAD Molecular Imager, ChemiDoc XRS (Bio-RAD Laboratories; Hercules, CA).

Statistical analyses

One-Way ANOVA followed by Tukeys Mulitcomparison test. Within each group, bars with different letters are significantly different (p<0.05). All data analyses were performed and plotted by Prism 5.01 (GraphPad Software, Inc., San Diego, CA)

Results

FXR regulates Reverse Cholesterol Transport (RCT)

THP-1 human monocytes differentiated into macrophages were induced to form foam cells using oxLDL. Cholesterol efflux was quantified using 3-NBD to label the intracellular cholesterol pool. Cells were incubated with Apoa-I or HDL cholesterol acceptors to induce efflux and florescence emissions measured and analyzed as indicated in materials and methods.

Figure 3-1: FXR affects cholesterol efflux

MDFC Cholesterol Efflux (3NBD)

100 None

o ApoA1

p 75

e HDL

o 50

f f 25

0 cAMP LXR FXR BSA Treatment

Discovery of miRs that affect cholesterol efflux

Since macrophages are notoriously difficult to transfect, our next objective was to optimize conditions for transfection of THP-1 cells in 96-well plate format and evaluate its compatibility with the HTS-RCT assay. THP-1 cells were transfected and efficiency examined using BLOCK-iT™ Fluorescent Oligonucleotides (Invitrogen). This is a fluorescein-labeled dsRNA oligomer designed for use in RNAi analysis to facilitate assessment and optimization of cationic lipid-mediated delivery or electroporation of dsRNA oligonucleotides into mammalian cells. In Figures (3-2), in the absence of a lipid mediated transfection reagent, low fluorescence emissions are observed. Lipofectamine- induced transfection surpassed all other reagents and was used as the primary reagent in all following experiments. A library of 102 synthetic microRNA acquired from Jungsun

Kim (Mayo Clinic) was screened and evaluated for their roles in cholesterol efflux. MiRs were transfected in differentiated THP-1 cells and cholesterol efflux examined to either an ApoA-1 or HDL acceptor (Appendix A).

58 Figure 3- 2: Optimization of conditions necessary for microRNA transfection and screening

(A) Differentiated and undifferentiated THP-1 cells transfected with 100nM fluorescence-tagged single strand oligonucleotides to examine transfection efficiency in a time-dependent manner for compatibility with RCT assay. Fluorescence transfection uptake in using lipofectamine reagent, (B) Thp-1 cells, (C) Huh-7 cells and (D) Hct-116 cells. THP-1 cells were plated and differentiated in 96-well plates at a density of 2 ×

105/well. Wells were transfected with 20 μM synthetic microRNA in 20 μL of

Lipofectamine transfection reagent for 3 hr. The well volume was brought up to 100 μl with growth media for 24 hr. GW4064 was then added to the cells and incubated

59 overnight. After the media/treatment was removed, cholesterol efflux was examined as described previously (Zhang et al. 2011). (Appendix I)

miR-708-5p and treatment with GW4064 affects cholesterol efflux in Macrophage

Derived Foam Cells (MDFC’s), Huh-7 cells and HCT-116 cells

Although miR-708 is highly conserved across species and has been implicated in the suppression of metastasis (77), its role in cholesterol homeostasis and specifically cholesterol efflux remains unknown. Transfection of hsa-miR-708-5p affected cholesterol transport in macrophage derived foam cells, hepatocytes and intestinal cells, but to varying degrees (Figure 3-3). In MDFC (Panel A), the efflux of cholesterol to HDL was only slightly increased by GW4064 alone. However, in the presence of miR-708, this

FXR ligand significantly affected HDL dependent cholesterol transport. The effects were more pronounced for apoA1-dependent transport where GW4064 or miR-708 alone affected transport and the combination was more effective. Nonspecific transport of cholesterol transport (to BSA) was increased by GW4064 alone. The liver (Huh-7, Panel

B) and colon (HCT-116, Panel C) were not as responsive to either intervention or to the various carrier proteins. In Huh7 cells, the combination of GW4064 and miR-708 increased efflux of cholesterol to HDL. In the HCT-116 cells, transfection with miR-708 increased efflux to apoA1. Taken together, this shows cell-type dependent regulation of cholesterol transport to carrier proteins by both FXR and miR-708.

60 Figure 3-3: Transfection of miR-708 and treatment with GW4064 increases Cholesterol Efflux

A. MDFC B. Huh-7

1.40 1.30 a 1.35 a ) HDL ) 1.25 b a HDL

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Thp-1 monocytes were plated and differentiated into macrophages with PMA (100nM) for 48h along with Huh-7 and Hct-116 cells in 96 well plates for 24h. Cells were transfected with synthetic microRNAs (20uM) and control scramble sequence (20uM) for

24hrs. Foam cells were induced with OxLDL (50ug/ml) and 3NDB Cholesterol

(10ug/ml) for 24hr. Cholesterol efflux was determined as described in Materials and

Methods and the percent efflux for each treatment was expressed relative to control cells

(control miR, DMSO treatment) in A) MDFC B) HUH-7 and C) HCT-116 Cells. Bars represent mean ± SEM (n=4, shown is representative experiment repeated at least 3 times). Statistical significance was determined by One-Way ANOVA followed by

61 Tukeys Mulitcomparison test. Within each group, bars with different letters are significantly different (p<0.05) miR-708-5p inhibits CD38 transcript

To examine the role of miR-708 on gene regulation of CD38, quantitative real time PCR (qrtPCR) was used to quantify CD38 gene expression in all cell type models following miR-708-5p transfection and to truly confirm CD38 as the target, siRNA was used against CD38 expression in all cell types. Figure 3-4: In MDFC (A) there was a significant decrease in CD38 gene expression following miR-708 transfection and combination treatment with GW4064 when compared to the control miR alone indicating a direct role of miR-708 in the regulation of CD38. B) Transfection with CD38-siRNA significantly reduced CD38 transcript levels, and a combined intervention with GW4064 was even more pronounced. (C &D) In Thp-1 macrophages only, miR-708 was equally as effective in significantly reducing CD38 expression when compared to control miR with combined GW4064 treatment being most effective and similar results were observed following siRNA reduction of CD38 transcription.( E &F) Combined intervention of miR-708 and treatment with GW4064 significantly reduced CD38 expression and similarly following siRNA against CD38. Transfection of miR-708 effectively decreased

CD38 gene expression in Hct-116 cells (G&H) when compared to control miR alone.

Small differences were observed in combination with GW4064 treatment. CD38-siRNA alone significantly repressed CD38 gene expression when compared to the control siRNA but effects was equally as pronounced following GW4064 intervention. Taken together these data demonstrate miR-708 as a true regulatory target of CD38mNRA expression.

62 Figure 3-4: Transfection of miR-708 and treatment with GW4064 affects CD38 gene expression

A. MDFC B. MDFC

n 1.5 n 4

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G. HCT-116 H. HCT-116

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63 Thp-1 monocytes were seeded in 6 well plates and differentiated into macrophages with

PMA (100nM) for 48h. Foam cells were induced in macrophages with OxLDL (50μg/ml) and Huh-7 and Hct-116 cells seeded in 6 well plates as well for 24h. Cells were transfected with synthetic microRNAs (20μM) and control scramble sequence (20μM) for 24hrs or CD38-siRNA (20μM) and control siRNA (20μM). Following 24h incubation cells were treated with GW4064 (10μM) or control DMSO for an addition 18h.Gene expression was determined as described in Materials and Methods and the relative amount of each gene. CD38 was expressed in (A&B) MDFC, (C&D) Thp-1

Macrophages, (E&F) Huh-7 Cells, (G&H) Hct-116 cells relative to the housekeeping gene 18S. Bars represent mean ± SEM (n=3) triplicates. Statistical significance was determined by One-Way ANOVA followed by Tukeys Mulitcomparison test. Within each group, bars with different letters are significantly different (p<0.05)

miR-708-5p attenuates CD38 protein expression

Consistent with the gene expression data is the significant suppression of CD38 protein levels following transfection of miR-708-5p (100nM) and treatment with

GW4064 (10uM). Figure 4-5: (A) In Thp-1 macrophages alone, CD38 protein levels were repressed following miR-708 transfection and treatment with GW4064 when compared to control miR alone and confirmatory experiments with CD38-siR indicated similar results. In (B) Huh-7 cells similar differences were observed as in seen in the macrophages with a reduction in CD38 protein expression when compared to control miR and siR. (C) Hct-116 cells, however, miR-708 alone completely repressed CD38 protein

64 levels even when compared to siRNA against CD38 and the overall loading control Cox- iv gene.

Figure 3-5: Attenuation of CD38 protein levels in THP-1 macrophages, Huh-7 cells and HCT-116 cells following has-miR-708 transfection

A. B. C. THP-1 Macrophages HUH-7 Cells HCT-116 Cells

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R 0.0 0.0 0.0 8 0 W iR W iR W R W 7 s i G G m G s G R 8 8 R n R n R miR 708 Con miR Con siR miR 708 Con miR Con siR i 0 3 i i o i CD38 siR CD38 siR m 7 D s o m s C 8 C C n miR 709 GW Con siR GW miR 709 GW Con siR GW iR 3 n o CD38 siR GW Con miR GW CD38 siR GW Con miR GW m D o C C C

Attenuation of CD38 protein levels in A) Thp-1 macrophages, B) Huh-7 cells and C)

HCT-116 cells following has-miR-708 and CD38-siRNA transfection. Western blot analysis of cell lysates following transfection with 100nM of hsa-miR-708 or control- miR microRNA and treatment with 10uM GW4064 or DMSO for 24 h were performed with antibodies against CD38. Blotts were quantified using ImageJ (Version 1.49).

65 Suppression of CD38 increases cholesterol efflux to HDL acceptor

To investigate the role of CD38 deficiency in the regulation of cholesterol efflux, thp-1 macrophages, macrophage derived foam cells, huh-7 hepatocytes and hct-116 intestinal cells were transfected with CD38- siRNA (50nM) along with GW4064 (10uM) treatment and cholesterol efflux examined to HDL, Apoa-1 or No cholesterol acceptors as indicated in the methods. Figure 3-6: (A) In MDFC cholesterol efflux was significantly increased to HDL in the presence of CD38-siRNA with treatment of

GW4064 affecting cholesterol transport. Once again the (B) liver and (C) Colon were not as responsive as the foam cells; however there was still an increase in efflux to the HDL acceptor when compared to control-siRNA. Slight differences were observed with addition of GW4064 following siRNA transfection in both cell types, still emphasizing the differential regulation in all cell types and acceptors.

Figure 3-6: Knockdown of CD38 using siRNA and treatment with GW4064 increases Cholesterol Efflux in Macrophage Derived Foam Cells (MDFC’s), Huh-7 cells and HCT-116 cells.

66 Thp-1 monocytes were plated and differentiated into macrophages with PMA (100nM) for 48h along with Huh-7 and Hct-116 cells in 96 well plates for 24h. Cells were transfected with CD38 siRNA (20uM) and control siRNA (20uM) for 24hrs. Foam cells were induced with OxLDL (50ug/ml) and 3NDB Cholesterol (10ug/ml) for 24hr.

Following 24hr incubation cells were treated with GW4064 (10μM) for an addition 18h.

Cholesterol efflux was determined as described in Materials and Methods and the percent efflux for each treatment was expressed relative to control cells (control siR, DMSO treatment). Bars represent mean ± SEM (n=4, shown is representative experiment repeated at least 3 times). Statistical significance was determined by One-Way ANOVA followed by Tukeys Mulitcomparison test. Within each group, bars with different letters are significantly different (p<0.05) miR-708-5p regulates inflammation through NFKB

Following the suppression of CD38 expression in macrophages, foam cell, hepatocytes and intestinal cells, we sought to evaluate the anti-inflammatory role of miR-

708 on foam cell formation. Figure 3-7: In MDFC (A&B), miR-708 in combination with GW4064 significantly reduced the expression of NFκB at consistent levels; however no differences were observed following CD38-siRNA transfection or combination intervention with GW4064. (C) In Thp-1 cells, NFκB was significantly reduced following miR-708 alone and addition of GW4064 when compared to control miR. A slight decrease was also seen when transfected with D) CD38-siRNA but was not significant. (E&F) In Huh-7 cells, NFκB remained consistently suppressed when transfected with miR-708 and GW4064 treatment when compared to control-miR alone.

It was however not responsive to CD38-siRNA transfection. There were no observable

67 differences in NFκB expression following either miR-708 or Cd38-siRNA in Hct-116 cells (G&H).

Figure 3-7: Decreased expression of NFκB following miR-708-5p transfection

A. MDFC B. MDFC 1.5 n 1.5

i a i

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G. H. HCT-116 HCT-116 1.5 n 1.5

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69 Thp-1 monocytes were seeded in 6 well plates and differentiated into macrophages with

ANOVA followed by Tukeys Mulitcomparison test. Within each group, bars with different letters are significantly different (p<0.05)

miR-708-5p affects cytokine IL-1 β expression

Consistent with NFκB regulation, miR-708 affects IL-1β cytokine expression all cell types Figure 3-8: miR-708 significantly reduced the expression of IL-1β in (A)

MDFCs with the addition of GW4064 being the most responsive. B) No differences were observed however when transfected with CD38-siR. In Thp-1(C &D) macrophages as well, IL-1β was significantly down-regulated following combination intervention with miR-708 and GW4064 when compared to control miR, but no effects observed following

CD38-siRNA as seen in MDFCs. In Huh-7 cells (E&F), miR-708 with GW4064 significantly repressed IL-1β expression but had no varying effects when transfected with

CD38-siRNA. There was a significant reduction in IL-1β expression in Hct-116 cells

70 when transfected with (G) miR-708 but like all previous cell types (H) CD38-siRNA was not responsive.

71 Figure 3-8: Decreased expression of IL-1β following miR-708-5p transfection

72 Thp-1 monocytes were seeded in 6 well plates and differentiated into macrophages with

ANOVA followed by Tukey’s Multicomparison test. Within each group, bars with different letters are significantly different (p<0.05) miR-708-5p affects SIRT1 gene expression

To investigate the role of miR-708 on SIRT-1 expression, THP-1 macrophages, macrophage derived foam cells, Huh-7 hepatocytes and Hct-116 intestinal cells were transfected with miR-708-5p (100nM) and treated with GW4064 (10uM) for 24hrs and

Sirt-1 transcript levels examined. Sirt-1 expression was significantly increased following miR-708 transfection in Figure 3-9: (A) MDFC when compared to control-miR; however treatment with GW4064 had minimal effects. (B) Following CD38-siRNA transfection however, both siR NAand addition of GW4064 significantly increased Sirt-1 gene expression. In Thp-1 macrophages, (C&D) there was no significant differences in

Sirt-1 expression following miR-708 but significant differences were observed when transfected with CD38-siRNA. CD38 effectively increased Sirt-1 expression when compared to Control siR even though GW4064 had no effects. Huh-7 cells (E&F) had no

73 differences in Sirt-1 expression in neither miR-708 nor CD38-siRNA transfections.

Likewise in Hct-116 cells (G&H) there were no effects on CD38 transcription following miR-708 transfection, but significant a increase was seen following CD38-siRNA and treatment with GW4064.

74 Figure 3-9: miR-708-5p and siRNA knockdown of CD38 augments SIRT1 gene expression

75 Thp-1 monocytes were seeded in 6 well plates and differentiated into macrophages with

PMA (100nM) for 48h. Foam cells were induced in macrophages with OxLDL (50μg/ml) and Huh-7 and Hct-116 cells seeded in 6 well plates as well for 24h. Cells were transfected with synthetic microRNAs (20μM) and control scramble sequence (20μM) for 24hrs or CD38-siRNA (20μM) and control siRNA (20μM). Following 24h incubation cells were treated with GW4064 (10μM) or control DMSO for an addition 18h.Gene expression was determined as described in Materials and Methods and the relative amount SIRT-1 gene was expressed in (A&B) MDFC, (C&D) Thp-1 Macrophages,

(E&F) Huh-7 Cells, (G&H) Hct-116 cells relative to the housekeeping gene 18S. Bars represent mean ± SEM (n=3) triplicates. Statistical significance was determined by One-

Way ANOVA followed by Tukey’s Multicomparison test. Within each group, bars with different letters are significantly different (p<0.05)

Discussion

Nuclear Receptors (NRs) are intracellular proteins that interact with chemicals or dietary bioactives and regulate gene expression. Several microRNAs have been shown to affect cholesterol efflux through the action of NRs, by altering gene expression related to cholesterol storage and transport(86). Our current study represents an important addition to the understanding of lipid metabolism and more specifically the uptake, transport and removal of excess cholesterol via FXR and miR-708. Mir-708-5p has been investigated for its role in tumor physiology(87) and airway hyper-responsiveness (88); however its regulation of cholesterol homeostasis was unknown. In this study, we show that in the

76 presence of miR-708, the FXR ligand GW4064 significantly affects HDL dependent cholesterol transport and increases cholesterol efflux specifically in macrophage derived foam cells and to a lesser extent in hepatocytes. The effects of miR-708 on cholesterol transport to carrier proteins appeared to be cell type dependent. In intestinal cells for instance, transfection with miR-708 alone increased efflux to apoA-1indicating a differential regulation by this particular miR. Following improved changes in cholesterol transport and efflux we sought to evaluate the anti-atherogenic role of miR-708 and its regulation of the Reverse Cholesterol Transport pathway (RCT).

Here we characterized miR-708 in Thp-1 macrophages, macrophage derived foam cells, Huh-7 hepatocytes and Hct-116 intestinal cells and evaluated its effects on CD38 gene, the inflammatory marker NFκB and cytokine Il-1β, which were all reduced during in vitro foam cell formation, leading to enhanced cholesterol transport and efflux. We show that ectopic expression of miR-708 through transfection represses CD38 transcript and protein levels. Recent studies show direct binding of miR-708-5p to CD38 3’UTR and the repression of CD38 gene expression in human airway smooth muscles (ASM) cells (80). The Specific inhibition of CD38 expression, in all cell types was established through transfection with miR-708-5p mimic and a control scrambled sequence followed by the addition of the ligand GW4064. Exogenous expression of mir-708-5p and targeted siRNA repression significantly reduced CD38 gene expression in all cell types indicating a direct regulation of CD38 by miR-708-5p (Figure 2). CD38 is primarily known for its enzymatic hydrolysis of NAD in mammalian cells and its regulation of intracellular

NAD. Tissue levels of NAD in CD38 deficient mice are 10- to 20-fold higher than in wild-type animals (88). Overexpression of CD38 has been shown to decrease cellular

77 NAD levels and alter expression of proteins involved in antioxidant and energy metabolism (89). CD38 essentially decreases cofactor accessibility to the NAD- dependent acetylase Sirt-1 (83). Sirt-1 transgenic mice on a high-fat diet display lower lipid-induced inflammation, higher glucose tolerance and are protected from hepatic steatosis (90). We show that targeted siRNA knockdown of CD38 increases Sirt-1 transcript levels which may have added benefits in the protection of metabolic disturbances such as cholesterol accumulation observed in coronary heart disease. These findings suggests in part that the regulation and inhibition CD38 by miR-708 may lead to

Sirt1 activation due to NAD+ availability thereby resulting in beneficial effects on metabolism. The increase in Sirt1 a known inhibitor of inflammation gene NFKB activation and regulator of IL-1b may be an effector in the miR-708 regulation of CD38 and RCT pathway. These observations strongly implicate the CD38 gene in the RCT pathway and as a potential novel pharmacological target to modulate atherosclerosis.

Following CD38 reduction in all cell type models, we addressed the anti- inflammatory role of miR-708 as previously demonstrated by others(91) and its potential effects on RCT. Inhibition of NFκB activation has been shown to be athero-protective and through reduced foam cell formation resulting from decreased inflammation. We investigated the role of NFκB on foam cell formation following miR-708-5p transfection and subsequent inhibition of CD38, a downstream NFKB target gene. Results showed a decrease in NFκB gene expression corroborating previous epigenetic gene regulation of

NFκB by miR-708-5p(81). Nfkb1 transcript levels were significantly repressed following miR-708 transfection in all cell type models with a similar trend following CD38 siRNA transfection. We observed a significant decrease in Il-1β expression following miR-708

78 transfection while CD38 siRNA transfection remained unresponsive. The silencing of

CD38 by miR-708 involves suppression of pro-inflammatory NFκB and Il-1β expression, which are likely to have a significant impact on foam cell formation, reduction of inflammation and improved cholesterol transport and efflux. However, the cell-type differences as well as discordance between effects of miR-708 and CD38 knockdown suggest multiple targets for this miR.

Overall our in vitro results supported our hypothesis that miR-708 promotes cholesterol efflux in macrophages, hepatocytes and intestinal cells through the reduction of CD38. Supporting evidence indicated that the reduction of CD38 augmented the expression of Sirt1 thereby ensuring availability of NAD toSirt1 and enhancing metabolism as suggested by others (83). Also we established an inflammatory role by showing that miR-708 promotes cholesterol efflux by reducing foam cell formation due through reduced inflammation in an NFκB and Il-1β mechanism. We show that transfection of miR-708 and treatment with the FXR-ligand GW4064, elicits an in vitro

RCT response in a pathway that may directly involve CD38 through an increase in Sirt-1 activity and a suppression of NFκB activity; ultimately leading to a reduction in foam cell formation and inflammation. Even though the in vitro evidence of increased cholesterol efflux following miR-708 is clear. The role of CD38 still remains somewhat inconclusive as its direct targets and effects on cholesterol metabolism is still unclear. The likelihood that a different mechanism of action apart from the CD38 pathway exists for miR-708 is very probable due to the possibility of multiple targets existing for this miR. Gene expression (Appendix I) in macrophages, foam cells, hepatocytes and intestinal cells indicate a differential regulation of expression patterns across multiple target genes

79 involved in lipid metabolism and storage that may be responsible for observed in vitro response to cholesterol efflux. Additional studies are needed and required to address the major connection between our genes of interest and increased cholesterol efflux. Our findings suggest the possibility of miR-708 and its regulation of CD38 as a potential therapeutic strategy for the treatment of atherosclerosis via the reverse cholesterol transport pathway.

80 Acknowledgements

We thank Dr. Jungsun Kim of Mayo Clinic for providing us with the microRNA library. The study was supported by the department of Veterinary Sciences, Pennsylvania

State University.

81 References

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82 16. Lambert G, Amar MJA, Guo G, Brewer HB, Gonzalez FJ, Sinal CJ. The farnesoid X-receptor is an essential regulator of cholesterol homeostasis. J Biol Chem. 2003;278(4):2563–70. 17. Saini S, Yamamura S, Majid S, Shahryari V, Hirata H, Tanaka Y, et al. MicroRNA-708 induces apoptosis and suppresses tumorigenicity in renal cancer cells. Cancer Res. 2011;71(19):6208–19. 18. Aksoy P, White TA, Thompson M, Chini EN. Regulation of intracellular levels of NAD: A novel role for CD38. Biochem Biophys Res Commun. 2006;345(4):1386–92. 19. Hu Y, Wang H, Wang Q, Deng H. Overexpression of CD38 decreases cellular NAD levels and alters the expression of proteins involved in energy metabolism and antioxidant defense. J Proteome Res. 2014;13(2):786–95. 20. Pfluger PT, Herranz D, Velasco-Miguel S, Serrano M, Tschöp MH. Sirt1 protects against high-fat diet-induced metabolic damage. Proc Natl Acad Sci U S A. 2008;105(28):9793–8. 21. Jude J a, Dileepan M, Panettieri R a, Walseth TF, Kannan MS. Altered CD38/Cyclic ADP-Ribose Signaling Contributes to the Asthmatic Phenotype. J Allergy. 2012;:289468.

Chapter 4 Effects of perfluorooctanoic acid (PFOA) and perfluorooctane sulphonic acid (PFOS) on Reverse Cholesterol Transport and Gene Expression in THP-1, Huh-7 and Caco-2 cells

Abstract

Some cross-sectional epidemiology studies have suggested an association between perfluoro-octanoic acid (PFOA) and perfluoroctane sulphonate (PFOS) exposure with increased serum cholesterol levels. However, whether this linkage is causal is unclear, in part due to the lack of mechanistic understanding of this response and the absence of laboratory animal concordance. Since cholesterol homeostasis is dependent on multiple tissues, the present studies examined the effects of PFOA and PFOS on cholesterol transport and gene expression in model systems for macrophage-derived foam cells (MDFC, THP-1), liver (Huh7) and intestine (Caco-2). Both PFOA and PFOS increased HDL- and ApoA1-dependent cholesterol efflux from MDFC, Huh-7 and Caco-

2 cells; In THP1 cells PFOA and PFOS were equally potent (EC50, 0.2-0.3 µM) whereas in Huh7 and Caco-2 cells PFOS was more potent and efficacious. There were no observable differenced in cholesterol uptake following PFOA and PFOS exposure in these cell lines. Several genes involved in cholesterol homeostasis were examined in

THP1, Huh7 and Caco-2. In THP1 cells, PFOS increased liver X receptor α (LXRα) mRNA while Very Low Density Lipoprotein Receptor (VLDLR) mRNA was increased by both PFOA and PFOS in Huh7 cells. There were no changes mRNAs involved in

84 cholesterol homeostasis in Caco-2 cells. In transactivation assays, PFOA and PFOS activate the nuclear receptors PPARα and PPARɣ with slight induction of constitutive androstane receptor (CAR) activity and no effect on thyroid hormone receptor alpha

(TRα) or Pregnane X receptor (PXR). Gene expression microarray analysis in THP-1

MDFC shows that both PFOA and PFOS differentially affect messenger RNAs, with very little evidence for influencing cholesterol homeostasis. Taken together, these studies show that PFOA and PFOS have effects on cholesterol efflux but do not affect the uptake of cholesterol nor do they cause changes in the expression of cholesterol homeostatic genes consistent with the observed epidemiology associations, in these in vitro models systems.

Introduction

Poly- and per-fluorinated alkyl substances (PFASs) have many industrial applications, due primarily to their chemical stability and surfactant properties.

Perfluorooctanoic acid (PFOA) and perfluoroctane sulphonate (PFOS) are found in over

200 industrial and consumer applications ranging from water-, soil-, and stain-resistant coatings for fabrics, oil-resistant coatings for paper products, hydraulic fluids, fire- fighting foams and floor polishes (Renner 2001). Due to the chemical, thermal and biological inertness of PFASs, there is concern for bioaccumulation and potential to cause toxicity in humans as well as wildlife. Detectable levels of both PFOA and PFOS are found in human, fish, bird, and marine mammal samples (Houde et al. 2006).

Analysis of serum samples from the National Health and Nutrition Examination Survey

85 (NHANES) showed that PFOS and PFOA were detectable in all Americans with median concentrations in serum of 30 ng/mL (PFOS) and 5 ng/mL (PFOA) (47).

No definite association has been established between exposure to PFOS and

PFOA and adverse health effects in several occupational studies (47). The C8 Health

Project provided an opportunity for examining a very large population of U.S. residents

(69,030 persons) who lived near a chemical production facility that released perfluorooctanoic acid (PFOA) (92,45). This cross-sectional study was conducted to evaluate PFOA/PFOS (perfluorooctanesulfonate) dose and to search for possible health impacts of these chemicals in an exposed population. Among this cohort, there is relatively consistent evidence of modest positive associations with cholesterol, although the magnitude of the cholesterol effect is inconsistent across different exposure levels

(93). Increased serum cholesterol is associated with risk of cardiovascular disease;

However, this study did not examine the prevalence of heart disease and the mechanistic relationship of these findings to human health remains tenuous (56). Serum estradiol levels were increased in workers exposed to high PFOA and serum cholesterol and triglyceride levels positively linked to PFOA exposure (53), but a later study indicated body mass index of participants was a confounding factor (94,95) . Other potential disease outcomes that have been associated with PFOA/PFOS exposure in cross-sectional studies include ulcerative colitis (96) , rheumatoid arthritis (97), cardiovascular disease

(98) and immunotoxicity (99).

There is inconsistent results of several of the disease risks mentioned above across epidemiologic studies, including hypercholesterolemia, and interpretation of causality is often difficult (56,93). In these instances, examination of mechanistic studies

86 in animal or in vitro model systems is helpful. PFOA and PFOS consistently decrease cholesterol levels in serum of animal models (100–104). In addition, in several comprehensive gene expression studies, PFOA affects genes involved in cholesterol homeostasis in animal models consistent with decreased cholesterol. It is well established that the major target for the biological effects of PFOA and PFOS is Peroxisome

Proliferator-Activated Receptor (PPAR)(50,105–111) . Alternative targets for PFOA and PFOS exist and include PPAR, pregnane-X-receptor (PXR) and constitutive androstane receptor (CAR) (107,112,113). However, activation of these nuclear receptors cannot explain the effects observed in the epidemiological studies.

In a recent study, 290 individuals exposed to elevated concentrations of PFOA in drinking water had higher levels of expression of genes involved in cholesterol metabolism in their blood (114). These authors suggest that PFOA/PFOS create a

“hypercholesterolemic” environment through affecting gene expression in liver as well as extrahepatic tissues (57). Cholesterol can be made de novo or absorbed from the diet and once in the body, it is then subjected to various modes of transport, storage and metabolism. Reverse cholesterol transport (RCT) is a process by which cholesterol is removed from peripheral cells and transported to the liver for catabolism and excretion

(115). Since PFASs do not increase cholesterol synthesis (59), in the present study we explored whether PFOA or PFOS could directly affect RCT and hence cholesterol homeostasis using in vitro model systems (23).

87 Materials and Methods

Chemicals

GW4064 was purchased from Sigma-Aldrich (St. Louis, MO). FBS was purchased from Gemini Bio-Products (West Sacramento, CA). ApoA-I, HDL and oxLDL were purchased from Biotechnical Technologies Inc. (Stoughton, MA) and 3-NBD

Cholesterol from Calbiochem (La Jolla, CA).

Cell Lines and Cell Culture

THP-1 (Homo sapiens, monocyte) cell line was obtained from the American Type

American Type Culture Collection (ATCC; Rockville, MD). Cells were cultured in HG-

DMEM containing 10% heat inactivated FBS, 1mmol/L sodium pyruvate, 100μmol/L non-essential amino acid, and 100 U/mL penicillin and 100 μg/mL streptomycin.

HTS Cholesterol Efflux Assay

Cholesterol transport was examined essentially as described previously (Zhang et al. 2011, Zhang et al. 2012, Zhang et al. 2011). THP-1 cells were plated in 96-well plates at a density of 1 × 105 cells/well and differentiated to macrophages by addition of 100 nM

PMA for 48 h. Huh-7 and Caco-2 cells were plated in 96-well plates at a density of 1 ×

105 cells/well. Subsequently, cells were washed twice with PBS and cultured in the

88 growth medium overnight. Foam cell formation was induced 50 μg/ml of OxLDL and the cholesterol pool labeled by addition of 3-NBD cholesterol at 10 μg/ml. After overnight treatment with 0, 0.1, 0.3, 1, 3 and 10 μM PFOA or PFOS (16 to 18 h), cells were washed twice with PBS and incubated with 40 μg/mL apoA-I or 100 μg/mL HDL in PBS containing 0.2% bovine serum albumin (BSA) to induce efflux. After 1 h, 100 μL of the medium in each well was collected and transferred to a new Fluotrac clear bottom black

96-well plate by multi-channel pipette. Residual liquid in each well was removed by tapping. Cells were treated with 100 μL lysis buffer and homogenized on an orbital shaker for 10 min. Fluorescence from the PBS fraction and the cell lysate fraction were measured at excitation and emission wavelengths of 485 and 535 nm, respectively.

Percent efflux was calculated as (Relative Fluorescent Units (RFU) PBS fraction)/(RFU cell lysate + RFU PBS fraction). For estimation of cholesterol uptake, 3-NBD was added and cells treated with PFOA and PFOS as stated above. Following washing with PBS, the cells were lysed and fluorescence measured.

Transcription Factor Profiling

Human PPARα, PPARβ/δ, PPARγ, LXRβ, RXRα, TRα, PXR and CAR3 reporter assay systems will be purchased from INDIGO Biosciences, Inc. (State College, PA).

Assays will be performed according to the manufacturer’s instructions (Gillies et al.

2012).

Gene Expression Microarray

THP-1 cells were treated with PFOA, PFOS (5, 25, 50 μM) or control (DMSO,

0.1% v/v) for 16 hours. RNA was extracted using Qiagen RNeasy and quality assessed by

RNA Nano Chips on the Agilent Bioanalyzer. Each sample was labeled using the

89 Affymetrix IVT Express Kit according to the manufacturer’s protocol. The GeneChip

Human Genome U133A 2.0 (Affymterix), representing 14,500 well-characterized genes, was hybridized with the labeled RNA using GeneChip Hybridization Wash and Stain Kit

(#702232) in the Affymetrix GeneChip Hybridization Oven 640, according to the manufacturer’s instructions. Following hybridization the arrays were washed and stained using the Affymetrix GeneChip Fluidics Station 450 according to the manufacturer’s protocol and scanned using the GeneChip Scanner 3000 7G. The scanned image file

(DAT) and the intensity data (CEL) were imported into ArrayStar (DNASTAR, Inc.,

Madison WI). The Robust Multi-array Average (RMA) was used to normalize the data.

The slides were grouped based on treatment and Student t-test with asymptotic p-value and Benjamini-Hochberg multiple corrections was performed comparing each treatment versus DMSO. At a p-value of 0.05 and a 1.5-fold change, a total of 251 entities were significantly regulated by PFOA, 297 were affected by PFOS using this criterion. These groups of genes were examined by hierarchical clustering using complete linkage analysis of the normalized data (JMP 7.0, SAS Institute, Cary, NC). Gene Ontology and pathway analysis was performed using Ingenuity Pathway Analysis (Qiagen, Redwood

City, CA).

RNA extraction, reverse transcription, real time PCR

Human macrophages cell line THP-1, hepatoma cell line Huh-7 and intestinal cell line Caco-2 were treated with 0, 0.1, 0.3, 1, 3 and 10 mM PFOA or PFOS. Total RNA was isolated by Qiagen RNeasy Mini Kit (Qiagen, Valencia, CA) according to the manufacturer’s instructions. The total RNA was reverse transcribed using the ABI High

Capacity cDNA archive kit (Applied Biosystems, Foster City, CA). Standard curves were

90 made using serial dilutions from pooled cDNA samples. Real-time polymerase chain reaction (PCR) was performed with the use of the SYBR Green PCR Master Mix

(Applied Biosystems) according to the manufacturer’s protocol and amplified on the ABI

Prism 7000 Sequence Detection System. Primer sequences are listed in Table 1.

Statistical analyses

One-way ANOVA, followed by Dunnett’s post-hoc test, was used to test the difference between treatments (P < .05). The values were expressed as mean ± SEM. All data analyses were performed by JMP 7.0 (SAS Institute, Cary, NC) and data plotted by

Prism 5.01 (GraphPad Software, Inc., San Diego, CA).

Table 4-1. Oligonucleotides used in quantitative real time PCR Forward (5’3’) Reverse (5’3’) ABCA1 TGTGCAGATCATAGCCAAAAGC AGCCGCCATACCTAAACTCATT APOA-1 CCCAGTTGTCAAGGAGCTTT TGGATGTGCTCAAAGACAGC ACAT AGATGCAGCGAAGAGGCTCAA TACAGCAGCGTCAGCAAATGC CD36 ACAGATGCAGCCTCATTTCCA CTGCAATACCTGGCTTTTCTCA FXR TGCGGTTGAAGCTATGTTCCTT TGCCCAGACGGAAGTTTCTTAT LPL CCGCCGACCAAAGAAGAGAT TAGCCACGGACTCTGCTACT nCEH CAGCTCCTCCCAAAGACCTACA TGCCATCGTCTCTGAGGACAT SCD1 GAGTACCGCTGGCACATCAA GGCCATGCAATCAATGAAGA SHP GGACCTCTTCTTTCGCCCTATC AAGAAGGCCAGCGATGTCAAC LXRa GGAGGTACAACCCTGGGAGT AGCAATGAGCAAGGCAAACT VLDLR GTATGTAACCAGGAGCAGGAC ACAGTCACACTCGTAGCCTAT ATP- binding cassette transporter-1 (ABCA1), Apo lipoprotein A-1 (APOA1), Farnesoid x-Receptor (FXR), Small heterodimer partner 1(SHP-1), Acetyl-CoA acetyltransferase 1 (ACAT), Stearoyl-Coenzyme A desaturase 1 (SCD1), Very low density lipoprotein receptor (VLDLR), Cluster of differentiation 36 (CD36), Neutral cholesterol ester hydrolase 1 (nCEH), Liver-x-receptor (LXR), Lipoprotein Lipase (LPL).

91 Results

PFOA and PFOS increase cholesterol efflux in THP-1, Huh-7 cells and Caco-2 cells without affecting uptake.

Human macrophage (THP-1)-derived foam cells, the hepatoma cell line Huh-7 and the intestinal cell line Caco-2 were treated with 0, 0.1, 0.3, 1, 3 and 10 μM PFOA or

PFOS for 16 hr and cholesterol uptake and efflux to ApoA1 HDL, and nonspecific-carrier

(no carrier, BSA) was examined (Figure 4-1). In THP-1 macrophages, both PFOA (Panel

A) and PFOS (Panel B) increased cholesterol efflux to HDL acceptor at 3 μM and 10 μM with an EC50 0.4 and 0.2 µM, respectively. The extent of efflux (20% increase) was similar to that seen with the prototypical inducers TO901317 and GW 4064 (data not shown). There was no significant increase in efflux of the cholesterol mimic to either aopA1 or BSA. Cholesterol efflux in Huh-7 cells did not exhibit a consistent dose- response relationship and was increased using HDL as a carrier by PFOA at 0.3 µM

(Panel C) and PFOS at 0.1, 0.3 and 3 µM (Panel D). In Caco-2 cells, PFOA did not affect cholesterol efflux to any carrier (Panel E) whereas PFOS increased export to both apoA1 and HDL (EC50 0.04 and 0.01 µM).

The amount of 3-NBD present in the cellular lysate, prior to addition of a carrier molecule, was used to estimate the uptake of cholesterol. As shown in (Figure 4-2), regardless of the cell line examined, there was no significant effect of PFOA or PFOS on cholesterol uptake. There were modest trends for increasing cellular 3-NBD, in particular in the THP-1 and Huh-7 cells treated with PFOS, but the variability and magnitude of effect precluded reaching statistical significance.

92 Figure 4-1. Evaluation of Reverse Cholesterol Transport and Cholesterol uptake in THP-1, HUH-7 and Caco-2 Cell Models.

THP-1 Huh-7 Caco-2 A. PFOA C. PFOA E. PFOA

x 1.4 1.4 x 1.4

l ApoA1 HDL None ApoA1 HDL None l ApoA1 HDL None

E 1.3 1.3 E 1.3

l * * l

r 1.2 1.2 r 1.2

l 1.1 1.1 * l 1.1

1.0 1.0 1.0

t 0.9 0.9 t 0.9

R 0.8 0.8 R 0.8

1 3 0 0

1 3 0

1 3 0 0

1 3 0

1 3 0 0

1 3 0

1 3

. .

1 Dose (mM) Dose (mM) Dose (mM)

B. PFOS D. PFOS F. PFOS

x 1.4 1.4 x 1.4

ApoA1 HDL None ApoA1 HDL None l

f f ApoA1 HDL None

E 1.3 E 1.3

1.3

* * l

o * *

r r * 1.2 1.2 r * *

e e * *

t t 1.2 e *

s s * * *

e e *

1.1 1.1 e

o o 1.1

1.0 1.0

e 1.0

0.9 0.9 i

l l 0.9

R 0.8 0.8 e

0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0

. .

. . 0.8

0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0

. .

1 Dose (mM) Dose (mM) 1 Dose (mM)

Cholesterol efflux was determined as described in Materials and Methods and the percent efflux for each treatment was expressed relative to DMSO. Bars represent mean ±

SEM (n=4). Shown is representative experiment repeated at least 3 times. Statistical significance was determined by One-Way ANOVA followed by Dunnett’s

Mulitcomparison test. Within each group, asterisks denote significantly different than vehicle control (p<0.05).

Figure 4-2. Evaluation Cholesterol uptake in THP-1, HUH-7 and Caco-2 Cell Models

A. PFOA B. PFOS

e e

k 4 k 4

a THP-1 Huh-7 Caco-2 a THP-1 Huh-7 Caco-2

t t

p p

U U

l 3 l 3

o o

r r

e e

t t

s s

e 2 e 2

l l

o o

h h

C C

e 1 e 1

v v

i i

t t

a a

l l

e e

R 0 R 0

0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0

1 3 1 3 1 3 1 3 1 3 1 3

......

1 1 1 1 1 1 Dose (mM) Dose (mM)

Cholesterol uptake was determined as described in Materials and Methods and the

RFU in the cellular lysate for each treatment was expressed relative to DMSO. Bars represent mean ± SEM (n=4). Shown is representative experiment repeated at least 3 times. Statistical significance was determined by One-Way ANOVA followed by

Dunnett’s Mulitcomparison test. Within each group, asterisks denote significantly different than vehicle control (p<0.05).

PFOA and PFOS affect Cholesterol Metabolism Genes in THP-1, Huh-7 cells and

Caco-2 cells

THP-1, Huh-7 and Caco-2 were treated with PFOA or PFOS and genes involved in cholesterol efflux examined. As shown in Figure 3, THP-1 cells PFOA lacked a significant effect on mRNA expression of genes involved in cholesterol storage (ACAT, nCEH), membrane transport (ABCA1, CD36, VLDLR, apoA1), lipogenesis (LPL,

SCD1) or transcriptional control (LXRα, FXR, SHP1). However the ABCA1 mRNA was reduced and LXR mRNA was induced in a dose-dependent manner following PFOS exposure. In Huh-7 cells (Figure 4) there were no significant effects of PFOA on cholesterol metabolism transcripts with the exception of the 10μM dose significantly

94 induced the VLDL receptor mRNA. PFOS increased ABCA1 and VLDLR mRNA while it decreased FXR, SHP1, CEH mRNA; all these effects were observed at the highest concentration studied (10μM). Surprisingly, even though PFOS affected cholesterol efflux in Caco-2 cells, there were no significant alterations in mRNA for these cholesterol metabolism genes by either PFOA or PFOS (Figure 5). Taken together, this data indicates that neither PFOA nor PFOS dramatically affect genes involved in cholesterol transport but there are differences between the PFASs in terms of magnitude of effects and cell- type specificity.

Figure 4-3. Messenger RNA for cholesterol metabolism genes in THP-1 cells treated with PFOA or PFOS

A 1 T 6

P A D H

R D R L

D E

B H C C L

X X P

A F S A S V C C L L

S 4

E PFOA R 3

E 2

A 1

R ND 0

0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0

......

1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dose (mM)

A 1 T 6

L *

P A D H

R D R L

D E

B H C C L

X X P

A F S A S V C C L L

I *

S 6

E PFOS

X 4

T 2

R * * * ND 0

0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0

......

1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dose (mM)

95 Quantitation of mRNA was determined as described in Materials and Methods and relative expression for each treatment and gene was expressed relative to vehicle control. Bars represent mean ± SEM (n=4). Statistical significance was determined by

One-Way ANOVA followed by Dunnett’s Mulitcomparison test. For each gene, asterisks within the legend denote significantly different dose-dependent effect while those above the bar represent significantly different than vehicle control (p<0.05). Abbreviations:

ATP- binding cassette transporter-1 (ABCA1), Apo lipoprotein A-1 (APOA1), Farnesoid x-Receptor (FXR), Small heterodimer partner 1 (SHP-1), Acetyl-CoA acetyltransferase 1

(ACAT), Stearoyl-Coenzyme A desaturase 1 (SCD1), Very low density lipoprotein receptor (VLDLR), Cluster of differentiation 36 (CD36), Neutral cholesterol ester hydrolase 1 (nCEH), Liver-x-receptor (LXR), Lipoprotein Lipase (LPL), not detected

(ND).

Figure 4-4. Messenger RNA for cholesterol metabolism genes in Huh-7 cells treated with PFOA or PFOS

A 1 T 6

P A D

o R D R L

B p H C C L

X X P

A A F S A S V C L L

E 2 PFOA *

I 1

E ND R 0

0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0

......

1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dose (mM)

* *

A 1 T 6

* L

P A

o R D R L

B p H C L

X X P

A A F S A V C L L

N *

E 2 PFOS

X *

V I 1 *

A * *

E ND R 0

0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0 0 1 3 1 3 0

......

1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dose (mM)

See legend to Figure 4-3 for details.

Figure 4-5. Messenger RNA for cholesterol metabolism genes in Caco-2 cells treated with PFOA or PFOS.

PFOA

15y PPARA* PPARG*

i RXRA PXR

v i PPARB LXRB CAR3* t * TRA

10e *

L 5

e * *

i *

t *

a * **

R 0

0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0

......

1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 Dose (mM)

PFOS

15y PPARA* PPARG*

i RXRA PXR*

v i PPARB LXRB CAR3* t TRA*

A * 10e

i *

u * * L 5

e *

i t * * * * *

R 0

0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0

......

1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 Dose (mM)

See legend to Figure 4-3 for details.

Nuclear Receptor transactivation by PFOA and PFOS

Several studies have shown that PFOA and PFOS are agonists for the PPARs with

PPARα being the most sensitive to these PFASs {Rosen 2010; VandenHeuvel 2006}. In these experiments, other human nuclear receptors that may influence cholesterol metabolism were also examined, including liver X receptor β (LXRβ), retinoic X receptor

α (RXRα), thyroid hormone receptor α (TRα), pregnane X receptor (PXR) and constitutive androstane receptor variant 3 (CAR3)(Figure 6). As expected, PFOA activated PPARα (EC50 45 μM, 15-fold peak) and PPARɣ (EC50 31 μM, 2.5-fold peak) with significant activation first seen at 0.4 μM for PPARα and 11 μM for PPARɣ. PFOA also affected CAR3 activity but the maximal activity was only 1.7 fold (4 μM). PFOS

98 increased PPARα activity (EC50 150 μM, 20-fold peak) and PPARɣ (EC50 25 μM, 4.5- fold peak). Minimal, but statistically significant, increases in transactivation were seen with PFOS for TRα, PXR and CAR3 (maximal increases of 1.4-, 1.7- and 2-fold, respectively).

Figure 4-6. Transactivation of nuclear receptors by PFOA and PFOS. PFOA

15y PPARA* PPARG*

i RXRA PXR

v i PPARB LXRB CAR3* t * TRA

10e *

L 5

e * *

i *

t *

a * **

R 0

0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0

......

1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 Dose (mM)

PFOS

15y PPARA* PPARG*

i RXRA PXR*

v i PPARB LXRB CAR3* t TRA*

A * 10e

i *

u * * L 5

e *

i t * * * * *

R 0

0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0 0 4 1 4 1 3 0

......

1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0 1 3 0

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 Dose (mM)

Nuclear receptor activity was determined as described in Materials and Methods

(performed and analyzed by Vanden Heuvel lab) and the percent luciferase activity for each treatment was expressed relative to DMSO. Bars represent mean ± SEM (n=3).

Shown is representative experiment repeated twice. Statistical significance was determined by One-Way ANOVA followed by Dunnett’s Mulitcomparison test. Within each group, asterisks denote significantly different than vehicle control (p<0.05).

Comprehensive analysis of gene expression in THP-1 cells.

99 THP-1 derived foam cells were treated with PFOA and PFOS at four concentrations (0, 5, 25 and 50 μM) or control (BSA) for 24 h and gene expression was examined by microarray. As was observed in the gene expression studies described above, neither PFAS caused a dramatic alteration in mRNA levels. When significance was determined using the criteria of 2-fold change and p<0.05 relative to the vehicle control for any dose of PFOA, there were only 9 genes affected (Table 1). Using this same benchmark, PFOS altered the expression of 21 genes. None of the transcripts affected by the PFASs is known to affect cholesterol uptake, export or metabolism. In order to perform gene ontology and pathway analysis, the criteria for significance was set at a 1.5-fold threshold. There were 247 genes and 219 genes regulated by at least one dose of PFOA or PFOS, respectively, as listed in Supplemental Table 1 and 2. Several genes known to be involved in cholesterol homeostasis were increased by PFOA including ABCA2 (1.5-fold at 25 μM), ABDC1 (1.8, 25 μM), LDLR (1.9, 25 μM) and

TGFβ1 (1.8, 5 μM) while ABCG1 (-1.7, 25) and C/EBPɣ (-1.6, 5 μM) mRNA were decreased. PFOS had less of an impact on affecting mRNAs encoding for cholesterol homeostatic genes with only HSD11B1 (-1.6 fold at 25 μM) and SCP2 (-1.6, 25 μM) being significantly regulated. The pattern of altered gene expression where dissimilar between PFOA and PFOS with only 12 genes contained in both lists (Supplemental Table

3) and none of which associated with lipid metabolism. Gene ontology and pathway analysis were performed (Ingenuity Pathway Analysis) on the list of 1.5-fold altered transcripts (Supplemental Table 4). Of note were the pathways associated with Cellular

Growth and Proliferation (37 genes, predicted increased activation) and Cell Death and

Survival (32 genes, predicted decreased activation) with all three doses of PFOA. With

Table 4-2. Genes that were significantly affected by at least one dose of PFOA or PFOS (2-fold and p<0.05 relative to DMSO control)

100 PFOS treatment, Cell Death and Survival (10 genes, predicted increased activation) and

Free Radical Scavenging (8 genes, predicted increased activation) pathways were

enriched. Few genes associated with PPARα or ɣ activation were seen with either PFAS,

with the possible exception of ABCA2 and TGFβ1 in the case of PFOA. Thus,

comprehensive analysis of gene expression shows little evidence of altered cholesterol

metabolism in the THP-1 derived foam cell, despite the increased efflux of cholesterol to

HDL. However, genes associated with cell proliferation, death and survivals as well as

increased generation of reactive oxygen species were altered.

101 PFO PFO Gene PFOA A 25 A 50 Probe Set ID Symbol Gene Title 5 μM μM μM CLPT cleft lip and palate associated transmembrane 11746128_x_at M1 protein 1 2.27 1.43 1.70 integrin, alpha 5 (fibronectin receptor, alpha 11715702_at ITGA5 polypeptide) 2.03 1.32 1.58 11745835_a_at SF3B1 splicing factor 3b, subunit 1, 155kDa 0.84 0.63 0.47 11723759_at ZFR zinc finger RNA binding protein 0.80 0.56 0.43 11728232_a_at CLDN1 claudin 1 0.79 0.48 0.58 MAD2 11748136_a_at L1 MAD2 mitotic arrest deficient-like 1 (yeast) 0.73 0.49 0.53 11744030_a_at MMP7 matrix metallopeptidase 7 (matrilysin, uterine) 0.67 0.41 1.05 11727789_a_at USP1 ubiquitin specific peptidase 1 0.60 0.45 0.57 vacuolar protein sorting 35 homolog (S. 11743391_a_at VPS35 cerevisiae) 0.51 0.49 0.66

PFOS PFOS Gene PFOS 25 50 Probe Set ID Symbol Gene Title 5 μM μM μM PLXN 11752559_a_at B2 plexin B2 0.81 0.71 2.22 SRRM 11758214_s_at 2 serine/arginine repetitive matrix 2 1.11 2.01 1.56 11752199_a_at APP amyloid beta (A4) precursor protein 0.64 0.43 1.08 11720837_a_at HIRA histone cell cycle regulator 1.59 2.01 0.99 11742211_x_at APP amyloid beta (A4) precursor protein 0.61 0.40 0.93 MMP1 matrix metallopeptidase 14 (membrane- 11725989_x_at 4 inserted) 1.24 2.02 0.90 11747666_a_at WARS tryptophanyl-tRNA synthetase 0.64 0.64 0.89 signal recognition particle receptor (docking 11758316_s_at SRPR protein) 0.58 0.49 0.89 N-deacetylase/N-sulfotransferase (heparan 11719777_at NDST1 glucosaminyl) 1 1.47 2.24 0.86 11728874_a_at LIN9 lin-9 homolog (C. elegans) 0.64 0.74 0.83 PRPF1 11716736_at 9 pre-mRNA processing factor 19 1.37 2.21 0.81 SH3GL 11762470_x_at 3 SH3-domain GRB2-like 3 1.05 0.45 0.75 LAMP 11749875_a_at 1 lysosomal-associated membrane protein 1 1.38 2.60 0.75

102

v-ets avian erythroblastosis virus E26 11722301_a_at ETS1 oncogene homolog 1 1.32 2.13 0.65 HNRN 11725971_a_at PF heterogeneous nuclear ribonucleoprotein F 0.98 1.04 0.50 11760212_at SPAG9 sperm associated antigen 9 1.10 1.15 0.49 ZNF42 11753531_s_at 8 zinc finger protein 428 1.27 1.98 0.48 11749256_a_at RRP1 ribosomal RNA processing 1 0.84 1.12 0.47 11716034_a_at BST2 bone marrow stromal cell antigen 2 1.26 1.60 0.47 11753491_s_at ERP29 endoplasmic reticulum protein 29 1.10 1.30 0.42 11754085_a_at PMVK phosphomevalonate kinase 1.08 1.19 0.38 Discussion

The relationship between PFOA/PFOS and cardiovascular disease (CVD) is of

concern because of their ubiquitous presence and the reported association with important

risk factors, including hypertension as well as higher serum cholesterol, uric acid and

homocysteine levels (96). However, studies directly examining the relationship between

PFASs and coronary artery disease have not shown consistent associations (96). The

increased serum cholesterol levels in cross-sectional studies, in particular the C8 Health

Project, are the CVD risk factor that has received the most attention. Of note is the fact

that there is no consistency of the elevated high cholesterol prevalence with the high

PFOA/PFOS doses among several studies of populations with occupational and/or

environmental exposures to these chemicals (53,55,116) and the dose-response

relationship has been questioned (56). The small change in blood cholesterol levels in the

C8 Health Study, the potential for confounding factors such as obesity and age, and

questions of biological plausibility (56,57) have all lead to confusion about the risk posed

by these PFASs and CVD. In the present studies, we examined in vitro models of

cholesterol transport and homeostasis, with hopes of examining direct effects of PFOA

and PFOS on cholesterol levels and adding important mechanistic plausibility.

103 The ability of HDL to stimulate efflux of cholesterol from peripheral tissues, transport in the plasma, uptake in the liver and excretion into the bile is termed reverse cholesterol transport (RCT) (117). The specific process involving efflux of cholesterol from macrophage foam cells in the artery wall has been termed macrophage RCT and is central to the anti-atherogenic properties of HDL. There are many genes involved in the transport of free cholesterol from the cell. The major players are the ABC transporters (A and G), the enzymes that regulate cholesterol and cholesterol ester concentration (i.e.

ACAT1, NCEH and HMGCR) and the transcription factors that regulate their expression

(PPARs, LXRs, SREBP1c) (118). Both PFOA and PFOS can affect the efflux of cholesterol from an in vitro model of MDFC (23) . The extent of efflux is similar to what we observed with alpha linolenic acid (ALA) (40,41), a dietary fatty acid with beneficial effects on CVD risk factors. Increased efflux is associated with activation of nuclear receptors such as PPARɣ, LXR or FXR(118). We have shown that ALA increases cholesterol efflux via activation of FXR and decreased Scd-1 activity via a SHP-1 dependent pathway (40,119). The present study shows that the PFASs do not affect a similar pathway since there was no evidence of FXR activation (Figure 6) or regulation of Scd-1 or SHP-1 expression (Figure 3). Thus, PPAR activation, in particular PPARɣ, is a possible explanation for the observed effects on 3-NBD export; however, the altered gene expression pattern is not entirely consistent with activation of this nuclear receptor.

PFOA and PFOS have been consistently shown to decrease cholesterol levels in serum of animal models (100,101,103,104,120) . In addition, in several comprehensive gene expression studies, PFOA affects genes involved in cholesterol homeostasis in animal models consistent with decreased cholesterol (48–51). The vast majority of data is

104 obtained in liver and not from extrahepatic sites such as peripheral lymphocytes and macrophages. In mouse liver, altered gene expression in response to both PFOA and

PFOS is associated with PPARα activation with genes such as apoA1, ACAT1 and Scd-1

(50) being effected. In human hepatocytes, PFOA and PFOS increased expression of

ABCA1 and SHP1 (107). Comparing the pattern of expression in the Huh-7 cells to these previous studies show that PFOA was less efficacious than expected, perhaps due to the low concentrations employed herein. PFOS was more comparable with effects seen with

ABCA1, Scd-1 and SHP-1 in Huh-7 cells. Comparing the expression of cholesterol metabolism genes in mouse liver (121) to that of Fletcher et al. in human blood of individuals exposed to PFOA and PFOS, shows a high degree of contradiction. For example, in mouse liver Abca1 mRNA is dramatically increased and Acat1 mRNA decreased whereas in human blood the opposite trends are observed. Whether this is due to species or tissue differences is unclear. In the present study, Abca1 mRNA was decreased in THP-1 MDFC and increased in Huh-7 cells by PFOS, consistent with both

Rosen (121) and Fletcher (114). PFOA exposure increased ABCG1, NPC1 and LXRβ mRNA while PFOS exposure increased NCEH1 and LXRα mRNA in blood (114). In

THP-1 human monocytes, PFOA did not significantly affect any transcripts measured whereas PFOS increased LXRα with no effect on NCEH1 (Figure 3). Taken together this data shows that there are both species as well as tissue specificity in the response to

PFOA and PFOS. Also, the differences between in vivo exposure to PFASs in humans

(114) and direct application to human-derived lymphocytes suggest that the previously reported effects that result in a “hypercholesterolemic environment” may be indirect or result of a confounding factor, as previously suggested (57).

105 The pattern of altered gene expression seen in THP-1 derived foam cells showed distinct patterns between PFOA and PFOS, with only 12 genes being shared of the 466 genes significantly regulated (2.6%). This was unexpected based on the similarity in the nuclear receptor activation profile (Figure 6), although differences were noted in tissue responsiveness (Figure 3-5). There exists a disconcordance between the gene expression response with the sensitive effects of PFOA/PFOS on cholesterol efflux. The reason for this observation is not known, but apparently the PFASs are able to affect efflux independent of changes in the mRNA levels. Nongenomic responses seen with

PFASs include mitochondrial dysfunction (122) , gap junction control (123) and altered membrane permeability (123). However, these effects were seen at much higher doses and their connection to cholesterol export is not evident. PFOA and PFOS affect expression of microRNAs (mIRs) in humans exposed these compounds (124) in in mice dosed with PFOA (124). There are several miRs that affect cholesterol efflux, in particular mir-33 (reviewed in (125)), but whether the known PFASs-regulated miRs

(miR-26b and miR-28-5p, 32-5p, 122-5p, 192-5p, miR-199−3p) are responsible for the effects observed herein, has not been explored. Withstanding the lack of genes involved in cholesterol homeostasis being affected, both PFOA and PFOS result in transcript level that may be indicative of a untoward response. Reported immunomodulation in experimental animals exposed to PFOA and PFOS include altered inflammatory responses, production of cytokines and other proteins, reduced lymphoid organ weights, and altered antibody synthesis (126). PFOA and PFOS decreased LPS-induced NF-κB activation and hence cytokine expression in THP-1 cells (127). Interestingly, the mechanism of the decreased inflammatory response between PFOA and PFOS was

106 dissimilar with only PFOA being PPARα dependent (127). In the lipid loaded THP-1 derived foam cells, the anti-inflammatory response was not seen. In fact, in the case of

PFOS there is evidence for increased generation of reactive oxygen species (increased

AKT2,BAX,ETS1,IL24 mRNA). PFOA’s pattern of gene expression is consistent with increased proliferation and decreased apoptosis, which may have implications for carcinogenesis. Taken together, the altered gene expression as well as enhanced cholesterol efflux does not support the increased circulating cholesterol seen in cross- sectional epidemiological studies, nor an enhancement in CVD risk. However, the macrophage-derived foam cell, previously not examined as a potential target for PFASs, does exhibit responsiveness to PFOA and PFOS exposure.

107 References

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111

Chapter 5

Factors involved in Reverse Cholesterol Transport Regulation: Findings in

Serum, Macrophages, Liver and Intestinal Models

Conclusions

RCT is a multi-step regulated process that involves: 1) transport of excess free cholesterol from peripheral cells, tissues and deposits; 2) the transfer of free cholesterol to cholesterol-accepting lipoproteins; 3) the esterification of free cholesterol via LCAT reactions; and 4) the hydrolysis of triglycerides via the activities of hepatic lipases and the functional activity of LDL and chylomicron remnant receptors in the liver.

Perturbations of macrophage physiology have remained the central focus and perspective of RCT investigation. However, the complicated nature of cholesterol efflux befits an examination of angles that support the process of cholesterol uptake, transport and excretion in order to provide a comprehensive view of the RCT pathway. A recent study by Altemus et al., 2014 (1) showed the effects of ABCG5/G8 deficiency and LXR agonist-induction of RCT from macrophages to feces in vivo. Treatment with a LXR agonist increased liver ABCG5/G8 expression and increased macrophage-derived cholesterol in feces of ABCG5/G8+/+ mice. However, the agonist alone had no effect on fecal cholesterol excretion in ABCG5/G8−/− mice indicating other potential intestinal factors of control. Similarly, the role of biliary sterol secretion in reverse cholesterol transport has been largely understudied and the correlation of macrophage to fecal sterol

112 excretion and intestinal cholesterol absorption is still unclear. It is possible that the intestine could promote a direct transport of HDL-bound cholesterol and a few studies have suggested a possibility of intestines excreting cholesterol from plasma to the intestinal lumen and feces. These observations emphasize the importance of studies in cholesterol homeostasis incorporating the processes of uptake, transport and final excretion as bile acids.

The fish oil fatty acid DHA is suggested as a determinant for cardiovascular disease risk and data in mice studies reveal that fish oil consumption is highly beneficial in the reduction of inflammation and overall improvement of arterial and endothelial function. Our findings support a role of DHA on increased cholesterol efflux following the induction of foam cells in Thp-1 cells. Treatment of macrophage-derived foam cells with human sera (10%, v:v) as cholesterol acceptors, induced cholesterol efflux after one

1 hour, promoting RCT. These findings are important as they imply that there are circulating bioactive molecules from the diet that affect MDFC function. Following these observations, we sought to address the role of microRNAs in dietary supplementation and correlating the amount of circulating microRNA’s with dietary intervention. In our clinical assessment of the diagnostic values of microRNA’s, we utilized the Canola Oil

Multi-center Intervention Trial (COMIT). The trial employed a randomized, double- blind, five-period, cross-over trial design and evaluated the effects of five diets that provided oils/oil blends differing in fatty acid content on CVD risk factors including plasma lipids in individuals with abdominal obesity. We selected three of the diets 1)

Baseline Control; 2) High oleic canola oil; and 3) DHA-enriched high oleic canola oil,

113 for examination and primarily interested in the fish oil blend, due to previous data linking the beneficial effects of DHA on cardiovascular disease and RCT. Here we quantified the expression of selected miRs in human serum samples following a dietary

Canola oil and DHA intervention. We evaluated the direct role of DHA in affecting ex vivo and in vitro microRNA expression and showed that 1) Diet affected reverse cholesterol transport, 2) Diet regulated microRNA expression and 3) miRs regulated cholesterol efflux. Here diet specifically Canola-DHA supplementation increased cholesterol efflux in human serum through HDL cholesterol acceptance. We showed that several of our selected miRs including miR-33, miR-181 and miR-30c were all regulated following Canola-DHA supplementation in diets indicating a regulatory role of omega-3s on circulating microRNA expression. Interestingly our data also revealed that DHA alone in vitro promotes reverse cholesterol transport in macrophage derived foam cells and also regulates the expression of other key miRs such as miR-30 and miR-708, the mechanistic study of chapter three. Using a controlled feeding human intervention trial, we showed the effect of microRNAs on dietary oleic acid consumption and the effects of diets on microRNAs as a diagnostic tool for usefulness of dietary intervention in serum.

Delineating the mechanisms that modulate and enhance the RCT process has several implications for practice. For instance the role of microRNA’s in fine tuning the

RCT process opens both therapeutic and diagnostic avenues for the treatment of atherosclerosis and other metabolic disturbances as observed in Chapter Two of our studies. The potential diagnostic value of circulating microRNAs is evident as reflected in our findings. Diets influence and affect circulating microRNA expression and could

114 serve as potential biomarkers for cardiovascular disease risk. The easy accessibility and stability of circulating microRNAs in bodily fluids specifically serum make them ideal noninvasive biomarkers for coronary heart disease. For instance miR-21 expression is dysregulated following high fat diet and exposure to polyunsaturated fatty acids typically leads to a decrease in colon and brain cancer in the presence of carcinogens (2). Levels of microRNAs in serum are stable, reproducible and consistent among groups and deregulated expression of a specific miRNA can be associated with a heart disease as shown with miR-33, -155, -758 and 30c (3–6) . A biomarker status obtained from an association will have important clinical outcomes in the treatment of cardiovascular diseases. Some limitations however exist for microRNA quantification mainly involving logistics problems that challenge assay sensitivity and specificity. Quantification of circulating miRs remains difficult due to the small amount extracted from blood, serum or other body fluids. An important limiting factor is the use of an appropriate control microRNA or gene; our studies employed a control-miR, one that was expected to be unaltered by treatment, as a reference. Much less is known about “housekeeping” microRNAs than their messenger RNA counterparts. The method we utilized is consistent with several references and the choice of reference miR was suggested by the supplier of our microRNA extraction and detection reagents (Quanta Biosciences, personal communication).

The causal relationship between lipoprotein and cholesterol metabolism disorders, elevated serum cholesterol levels and the development of coronary heart disease is well documented clinically and experimentally. Statin and cholesterol lowering drugs along with efforts to stabilize the lipid content of atherosclerotic plaques are some of the only

115 effective methods for treating coronary heart disease. According to the National

Cholesterol Education Program (NCEP) dietary modification, exercise and weight control are also necessary in the treatment of cholesterol overload (7). The NCEP suggest that a reduction in total cholesterol by 1 percent may decrease a person's risk of developing coronary heart disease by 2 percent and a much more intensive intervention for dyslipidemia is recommended for patients at higher risk which include dietary supplementation (8). These recommendations emphasize the importance of nutritional supplements. Certainly, there is increasing evidence that omega-3 PUFAs may be an important dietary factor in preventing atherosclerosis. However, it is unclear the mechanism by which fatty acids such as DHA affect RCT. Whether DHA dependent microRNA regulation is causally related to enhanced efflux has not been delineated fully.

Future efforts can be made towards evaluating other derivatives and metabolites of dietary DHA, such as 4-oxo-DHA and 4-OH-DHA and their roles on cholesterol efflux and miR expression. Previous research in our laboratory indicates that 4-OH-DHA is a more potent PPARγ ligand than is the parent compound (Vanden Heuvel, unpublished results). Although it is believed that PPARγ activation is important for DHA’s anti- inflammatory and perhaps its anti-cancer properties, whether this mechanism is involved in cholesterol efflux is unclear. The ability of the ALA to increase cholesterol efflux is partially dependent on FXR activation (Zhang et al., 2011); again, whether this is true of

DHA has not been determined. Similarly, the anti-inflammatory and pro-resolving benefits of DHA through the bioactivities of Resolvins and Protectins may be important in the regulation of RCT and microRNA expression. Future studies aimed at

116 understanding the resolution phase of RCT can test the role of these important facilitators.

The basis of our studies have been to focus on endogenous and exogenous factors that regulate and affect RCT and in Chapter 3 specifically we examined the direct role of microRNA’s in the regulation of cholesterol efflux. Our initial hypothesis stated that miR-708 promotes cholesterol efflux in macrophages, hepatocytes and intestinal cells through the reduction of CD38, which augments the expression of Sirt1, increasing availability of NAD to Sirt1 and enhancing metabolism. Furthermore, miR-708 promotes cholesterol efflux by reducing foam cell formation due to a decrease in inflammation through Nfkb1 and IL-1b. We examined this hypothesis by suppression of CD38 expression as well as inhibiting Sirt1 activity. The CD38 gene is located near the major metabolic syndrome marker D4S403 found on chromosome 4 and has since been associated with metabolic disturbances such as dyslipidemia, obesity and insulin insensitivity (9). The primary role of this ectoenzyme has been the catalysis and conversion of nicotinamide adenine dinucleotide (NAD+) into nicotinamide, adenosine diphosphate–ribose (ADPR), and cyclic ADPR. Extensive work on the role of CD38 has been done in T cell and B-cell regulation with limited studies on its role on metabolism and cholesterol regulation. The deleterious role of CD38 in high fat diet-induced obesity has been investigated previously (10). CD38 is as a regulator of Sirt1 through altering levels of available intracellular and extracellular NAD and the presence of CD38 in different intracellular compartments may have a crucial role on the regulation of NAD functions in specific organelles. Sirt1 is a major regulator of glucose and fat metabolism

117 and protects animals from high fat diet (HFD)-induced metabolic syndrome, liver steatosis, and obesity. Sirt1 activation suppresses SREBP1 expression and attenuates inflammation through NFKB. Sirt1 regulates the transcriptional activity of NFKB through a physically interaction with the RelA/p65 subunit of NFKB and inhibiting its transcription by deacetylating RelA/p65 subunit (11)

NAM, a known Sirt-1 inhibitor showed no differences in cholesterol efflux

(unpublished data) so it is likely that the mechanism of miR-708 is independent of direct

Sirt-1 activity. MiR-708 may in fact be regulating multiple other genes responsible for observed increases in RCT. Of particular interest was the Apoa-5 gene which is responsible for lipoprotein lipase catalysis of triglycerides into cholesterol via VLDL and

LDL conversion. Apoa5 was increased in macrophages, hepatocytes and intestinal cells following miR-708 transfection, but was significantly reduced in macrophage-derived foam cells following transfection and oxLDL stimulation, indicating a differential cell type specific modulation following microRNA transfection. Similar differences were observed in SCD1, ACAT-1 and NCEH expression with significances in macrophages and foam cells in particular. Differences in efficacy were only observed in all cell-types by CD38, making the ectoenzyme particularly interesting and warranting further examination.

CD38 has multifunctional roles in cell adhesion, signal transduction and calcium signaling and is widely expressed in cells and tissues, thus complete knockout is unfavorable to the host genome. There exist potential immunologic dysfunction along

118 with susceptibility to lethal bacterial infection and the ability to fight bacterial infection

(12). To date not much has been reported about viable CD38 inhibitors, thus making miR-708 derived therapies particularly important. NAD analogs (arabiono-NAD), nicotinamide derivatives (nicotinamide and nicotinic acid) and reducing agents (such as dithiothreitol) are potential CD38 inhibitors, although their clinical efficacy is unclear

(13). Studies in cardio-myocytes show that the CD38 inhibitor 2,2'- dihydroxyazobenzene (DAB) compound protects against cardiac dysfunction-induced by angiotensin II (14). Interestingly, it is quite possible that factors the inhibit CD38 may inhibit Sirt1 activity as both genes maintain similar catalytic properties and consume and degrade NAD.

The reduction in foam cell formation and a decrease in inflammation is a possible beneficial mechanism in the miR-708 regulation of cholesterol efflux. The overriding consensus and hallmark in the development of atherosclerosis is the accumulation of lipid-laden macrophages and the chronic state of inflammation. Atherosclerotic lesions that express a variety of NFκB-dependent genes are crucial to the progression of atherosclerosis. Evidence in literature indicates that, under specific inhibition of NFKB activation, ABCA1-mediated cholesterol efflux prevails over CD36-mediated lipid influx

(15) and here our studies confirm and support these conclusions. Following miR-708 transfection and subsequent treatment with oxLDL and the FXR-agonist GW4064, we observed a significant decrease in Nfκb1 and Il-1β gene expression, along with an increase in cholesterol efflux, the net result of these changes suggesting a reduction in foam-cell formation and overall decrease in inflammation. Our conclusions back

119 evidence provided by (16) on the epigenetic silencing of miR-708 enhancing NFKB signaling.

In general, our in vitro results supported our hypothesis that miR-708 promotes cholesterol efflux in macrophages, hepatocytes and intestinal cells through the reduction of CD38. Supporting evidence indicated that the reduction of CD38 augmented the expression of Sirt1. Since sirtuins use and degrade NAD in enzymatic reactions, a decrease in CD38 would lead to Sirt1 activation through an increase in NAD+ levels, resulting in beneficial effects on metabolic syndrome. Also we established an inflammatory role by showing that miR-708 promotes cholesterol efflux by reducing foam cell formation due to attenuating inflammation through Nfkb1 and IL-1b. However further studies are needed and required to address the major connection between our genes of interest and increased cholesterol efflux. The role of CD38 remains inconclusive as its direct targets and effects on cholesterol metabolism are unclear. The in vitro evidence of increased cholesterol efflux following miR-708 is clear, however. Future experiments can explore the role of Nicotinamide ribose on cholesterol efflux to correlate the increases in efflux as a factor of increase NAD via a CD38 reduction. This particular experiment will clarify the role of NAD and increased expression of Sirt-1 activity in

Reverse Cholesterol Transport. Similarly furthers studies can explore the role of NAD using flow cytometry to examine CD38 positive cells in a physiologically relevant animal model of atherosclerosis such as ApoE deficient mice primary macrophages. Both in vivo and in vitro cholesterol efflux can be examined under these models following miR-708 transfection and delivery.

120

Anti-miR technology can also be explored in the investigation of the exact role of miR-708 in all cell type models. Anti-miR Inhibitors are chemically modified, single stranded nucleic acids designed to specifically bind to and inhibit endogenous microRNA

(miRNA) molecules. Since microRNAs are relatively short single-stranded oligonucleotides, synthesis of anti-miR inhibitors is easily achieved and can be introduced into cells using transfection parameters similar to those used for siRNAs, and enable detailed study of miRNA biological effects. Anti-miR miRNA Inhibitors allow for miRNA functional analysis by down-regulation of miRNA activity, through miRNA target site identification and validation as well as effects on other cellular process. Anti- miR technology has been used to validate effectiveness of miR-33 in vitro and in vivo whereby antagonism promotes RCT (17). Commercially available anti-miRs against hsa- miR-708 can be used to examine reverse cholesterol transport and similar gene and protein experiments repeated for further validation. Additionally, a limiting factor in microRNA studies especially for therapeutic purposes will likely include in vivo targeted delivery or miRs or anti-miR.

Our in vitro studies have shown that PFOA and PFOS do not negatively alter cholesterol homeostasis by directly affecting the foam cell, hepatocyte or intestinal epithelium. However, this in vitro data, plus the information gathered by several other laboratory animal data, brings into question the causative relationship between serum cholesterol and PFOA and PFOS exposure. Since the cause of the altered cholesterol transport in the absence of representative alteration in gene expression is currently

121 unclear. One potential mechanism that has not been explored is the possibility that PFOA and PFOS’s effects on cholesterol homeostasis are due to secondary events where one tissue alters the function of another. PFOA and PFOS may affect microRNA expression in the liver which may then have secondary effects on macrophage-derived foam cells.

Previous studies in mice have shown PFOA affects expression of miR-28-5p, 32-5p, 122-

5p, 192-5p, and 26b-5p (18,19). In workers at a fluorocarbon factory, circulating miR-

26b and miR-199a-3p were correlated with serum concentration of PFOA. There has been no analysis on whether the altered expression of these miRs by PFOA and PFOS is related to any biological outcome. The effects of PFOA and PFOS on microRNA expression can be examined in a model system for human liver, Huh-7 cells. The

PFOA/PFOS-responsive miRs can then be examined for effects on gene expression and cholesterol efflux in Huh-7 cells as well as THP-1 derived foam cells. Since miRs are stable in the circulation and have become important biomarkers of exposure to a variety of xenobiotics and nutrients. It will be important to link the altered levels of these biomarkers to the ultimate biological endpoint of disease risk.

In summary the hypotheses discussed here in support the role of microRNA’s in regulating Reverse cholesterol transport, an important process in the regression of coronary heart disease. The research proposed and answered the hypotheses that microRNA’s would be beneficial to atherosclerotic regression through inhibition of foam cell formation and attenuation of inflammation. We effectively show the improvement of cholesterol efflux through microRNA, specifically miR-708 transfection and the suppression of inflammatory genes. Multiple approaches have shown that this effect may

122 be mediated by down-regulation of the ectoenzyme CD38 in foam cells in an FXR dependent pathway. Similarly we hypothesized the regulation of miRs by dietary interventions and effectively showed the improvement of circulating miR-181a in support of the usefulness of nutritional supplementation. Thus identification of mechanisms, drugs and bioactive compounds that contribute to the regression of atherosclerosis remains ultimately important.

123 References

1. Altemus JB, Patel SB, Sehayek E. Liver-specific induction of Abcg5 and Abcg8 stimulates reverse cholesterol transport in response to ezetimibe treatment. Metabolism: Clinical and Experimental. 2014; 2. Palmer JD, Soule BP, Simone BA, Zaorsky NG, Jin L, Simone NL. MicroRNA expression altered by diet: Can food be medicinal? Ageing Research Reviews. 2014; 3. Horie T, Baba O, Kuwabara Y, Chujo Y, Watanabe S, Kinoshita M, et al. MicroRNA-33 deficiency reduces the progression of atherosclerotic plaque in ApoE-/- mice. J Am Heart Assoc. 2012;1(6). 4. Ramirez CM, Dávalos A, Goedeke L, Salerno AG, Warrier N, Cirera-Salinas D, et al. MicroRNA-758 regulates cholesterol efflux through posttranscriptional repression of ATP-binding cassette transporter A1. Arterioscler Thromb Vasc Biol. 2011;31(11):2707–14. 5. Soh J, Iqbal J, Queiroz J, Fernandez-Hernando C, Hussain MM. MicroRNA-30c reduces hyperlipidemia and atherosclerosis in mice by decreasing lipid synthesis and lipoprotein secretion. Nat Med [Internet]. 2013;19(7):892–900. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23749231 6. Nazari-Jahantigh M, Wei Y, Noels H, Akhtar S, Zhou Z, Koenen RR, et al. MicroRNA-155 promotes atherosclerosis by repressing Bcl6 in macrophages. J Clin Invest. 2012;122(11):4190–202. 7. National Cholesterol Education Program (NCEP) Expert Panel. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) [Internet]. Archives of Internal Medicine. 2002. Available from: https://www.nhlbi.nih.gov/guidelines/cholesterol/atp3_rpt.htm 8. Ansell BJ, Watson KE, Fogelman AM. An evidence-based assessment of the NCEP Adult Treatment Panel II guidelines. National Cholesterol Education Program. JAMA. 1999;282(0098-7484 (Print)):2051–7. 9. Cai G1, Cole SA, Freeland-Graves JH, MacCluer JW, Blangero J CA. Principal Component for Metabolic Syndrome Risk Maps to Chromosome 4p in Mexican Americans: The San Antonio Family Heart Study. Hum Biol. 76(5):651. 10. Aksoy P, White TA, Thompson M, Chini EN. Regulation of intracellular levels of NAD: A novel role for CD38. Biochem Biophys Res Commun. 2006;345(4):1386–92. 11. Yeung F, Hoberg JE, Ramsey CS, Keller MD, Jones DR, Frye RA, et al. Modulation of NF-kappaB-dependent transcription and cell survival by the SIRT1 deacetylase. EMBO J. 2004;23(12):2369–80. 12. Partida-Sánchez S, Randall TD, Lund FE. Innate immunity is regulated by CD38, an ecto-enzyme with ADP-ribosyl cyclase activity. Microbes and Infection. 2003. p. 49–58. 13. Beers KW, Chini EN, Dousa TP. All-trans-retinoic acid stimulates synthesis of cyclic ADP-ribose in renal LLC-PK1 cells. J Clin Invest [Internet].

124 1995;95(5):2385–90. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt =Citation&list_uids=7537765 14. Higashida H, Zhang J, Hashii M, Shintaku M, Higashida C, Takeda Y. Angiotensin II stimulates cyclic ADP-ribose formation in neonatal rat cardiac myocytes. Biochem J. 2000;352 Pt 1:197–202. 15. Ferreira V, van Dijk KW, Groen AK, Vos RM, van der Kaa J, Gijbels MJJ, et al. Macrophage-specific inhibition of NF-κB activation reduces foam-cell formation. Atherosclerosis. 2007;192(2):283–90. 16. Baer C, Oakes CC, Ruppert AS, Claus R, Kim-Wanner SZ, Mertens D, Zenz T, Stilgenbauer S, Byrd JC PC. Epigenetic silencing of miR-708 enhances NF-κB signaling in chronic lymphocytic leukemia. Int J cancer [Internet]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25704289 17. Rayner KJ, Sheedy FJ, Esau CC, Hussain FN, Temel RE, Parathath S, et al. Antagonism of miR-33 in mice promotes reverse cholesterol transport and regression of atherosclerosis. J Clin Invest. 2011;121(7):2921–31. 18. Yan S, Wang J, Zhang W, Dai J. Circulating microRNA profiles altered in mice after 28d exposure to perfluorooctanoic acid. Toxicol Lett. 2014;224(1):24–31. 19. Vecitis CD, Park H, Cheng J, Mader BT, Hoffmann MR. Treatment technologies for aqueous perfluorooctanesulfonate (PFOS) and perfluorooctanoate (PFOA). Front Environ Sci Eng China. 2009;3(2):129–51.

125

Appendix A

Screening of microRNAs that affect cholesterol efflux

Materials and Methods

Chemicals

Human LDL, TO901317, GW4064, and 8-Br cAMP were purchased from Sigma-

Aldrich (St. Louis, MO). MicroRNA mimics were provided by Jungsun Kim, PhD (Mayo

Clinic) and CD38- siRNA were purchased from Santa Cruz Biotechnology Inc. (Santa Cruz,

CA). FBS was purchased from Gemini Bio-Products (West Sacramento, CA). Lipofectamine and Block-it Oligonucleotides were purchased from Invitrogen (Grand Island, NY). ApoA-I,

HDL and oxLDL were purchased from Biotechnical Technologies Inc (Stouhgton, MA) and 3-

NBD Cholesterol from Calbiochem (La Jolla, CA).

THP-1 Human Monocytes

THP-1 (Homo sapiens, monocyte) cell line was obtained from the American Type

MicroRNA Transfection for HTS Cholesterol Efflux

126

Cells were transfected with 20 mM synthetic microRNA in 20μL of Lipofectamine transfection reagent for 3-6 hours in a 96 well plate at a density of 1x 105cells/well. The volume was brought up to 100 μL with growth media for 24 hr. The media/treatment was removed and cholesterol efflux was examined through our high-throughput screening (HTS) reverse cholesterol efflux assay as described previously (Zhang et al. 2011).

Statistical Analysis

The efflux data was normalized to the average percent efflux for each acceptor molecule

(HDL, Apo A-1 and none). One way ANOVA and Ott’s analysis of mean (AOM) and Dunnett’s t-test were performed in JMP (SAS, Cary, NC).

Table 1. Relative cholesterol efflux in the presence of transfected miRs miR HDL Apo-A1 None Control-mir 1.00 ± 0.034 1.00 ± 0.021 1.000 ± 0.023 mir-100-5p 0.988 ± 0.031 1.679 ± 0.044 1.646 ± 0.284 mir-101 0.805 ± 0.163 1.266 ± 0.224 mir-103-3p 1.048 ± 0.042 1.826 ± 0.014 1.559 ± 0.283 mir-106 1.035 ± 0.008 0.951 ± 0.012 mir-107-3p 1.345 ± 0.025 1.308 ± 0.036 1.115 ± 0.264 mir-124-3p 1.086 ± 0.008 1.557 ± 0.012 1.536 ± 0.295 mir-125a 0.684 ± 0.135 1.483 ± 0.036 1.724 ± 0.155 mir-125b 0.992 ± 0.127 1.407 ± 0.023 1.546 ± 0.254 mir-126-3p 1.154 ± 0.215 1.445 ± 0.037 1.388 ± 0.301 mir-127-3p 1.165 ± 0.007 1.400 ± 0.027 1.455 ± 0.307 mir-128-3p 1.205 ± 0.090 1.468 ± 0.045 1.396 ± 0.309 mir-129-5p 0.828 ± 0.054 1.275 ± 0.024 1.548 ± 0.158 mir-130a 1.007 ± 0.085 1.555 ± 0.226 1.520 ± 0.258 mir-130b-3p 1.313 ± 0.009 1.567 ± 0.260 0.713 ± 0.067 mir-135a-5p 1.176 ± 0.009 0.934 ± 0.194 0.816 ± 0.030 mir-135b-5p 1.428 ± 0.084 0.565 ± 0.045 0.655 ± 0.126 mir-136-3p 1.285 ± 0.109 0.735 ± 0.118 0.709 ± 0.099 mir-136-5p 1.200 ± 0.232 0.859 ± 0.087 0.700 ± 0.143 mir-138-5p 1.272 ± 0.047 1.218 ± 0.258 0.679 ± 0.042 mir-139-5p 1.227 ± 0.020 0.542 ± 0.078 0.730 ± 0.025 mir-140-3p 1.113 ± 0.048 0.772 ± 0.077 1.321 ± 0.436 mir-142-3p 1.110 ± 0.053 0.656 ± 0.054 1.241 ± 0.399

127 miR HDL Apo-A1 None mir-143 1.133 ± 0.005 0.509 ± 0.086 1.026 ± 0.262 mir-145a-5p 1.290 ± 0.112 0.779 ± 0.051 1.011 ± 0.325 mir-146b-5p 0.997 ± 0.076 0.604 ± 0.081 1.655 ± 0.519 mir-148b 1.036 ± 0.037 0.874 ± 0.108 1.482 ± 0.515 mir-149-5p 0.935 ± 0.059 2.107 ± 0.128 1.994 ± 0.407 mir-150 0.970 ± 0.039 2.192 ± 0.074 1.954 ± 0.412 mir-152-3p 0.822 ± 0.130 2.271 ± 0.082 2.109 ± 0.396 mir-154-3p 1.067 ± 0.065 2.104 ± 0.090 1.919 ± 0.458 mir-17-5p 1.016 ± 0.032 2.209 ± 0.222 2.166 ± 0.533 mir-181-1-5p 0.896 ± 0.034 2.241 ± 0.068 2.068 ± 0.416 mir-181c-5p 0.953 ± 0.051 2.246 ± 0.136 2.141 ± 0.494 mir-183-5p 0.914 ± 0.011 2.006 ± 0.124 1.784 ± 0.300 mir-185-5p 0.886 ± 0.023 1.904 ± 0.017 1.825 ± 0.307 mir-186 0.876 ± 0.029 1.971 ± 0.152 1.769 ± 0.277 mir-18a-5p 0.765 ± 0.027 1.322 ± 0.037 mir-194-5p 0.999 ± 0.020 2.052 ± 0.064 1.690 ± 0.312 mir-195-5p 0.968 ± 0.009 2.314 ± 0.124 1.749 ± 0.317 mir-19a 0.958 ± 0.027 2.551 ± 0.092 1.978 ± 0.416 mir-19b-3p 1.047 ± 0.029 1.029 ± 0.116 1.083 ± 0.072 mir-1a-3p 0.692 ± 0.104 1.741 ± 0.064 1.848 ± 0.201 mir-204-5p 1.332 ± 0.043 0.942 ± 0.054 0.885 ± 0.159 mir-20a 0.936 ± 0.060 0.866 ± 0.056 1.124 ± 0.041 mir-20b 0.894 ± 0.049 1.069 ± 0.169 1.167 ± 0.051 mir-21-5p 0.938 ± 0.011 1.084 ± 0.015 mir-218-5p 1.462 ± 0.078 1.423 ± 0.074 mir-219-5p 1.010 ± 0.007 2.219 ± 0.187 1.622 ± 0.291 mir-21a-5p 1.442 ± 0.326 1.403 ± 0.123 mir-22-3p 0.959 ± 0.024 0.726 ± 0.125 1.011 ± 0.053 mir-221-3p 0.951 ± 0.171 0.747 ± 0.137 1.083 ± 0.124 mir-221-5p 1.250 ± 0.043 0.700 ± 0.110 0.811 ± 0.094 mir-23a-3p 0.982 ± 0.020 1.367 ± 0.054 1.184 ± 0.082 mir-23b-3p 0.904 ± 0.032 1.026 ± 0.106 0.980 ± 0.073 mir-24-3p 1.084 ± 0.063 1.225 ± 0.087 1.035 ± 0.115 mir-25-3p 1.008 ± 0.022 0.988 ± 0.030 mir-26a-5p 1.070 ± 0.027 0.903 ± 0.037 mir-26b-5p 0.994 ± 0.017 1.007 ± 0.023 mir-27a 0.432 ± 0.004 1.808 ± 0.227 1.872 ± 0.055 mir-27b 0.481 ± 0.013 2.065 ± 0.142 1.795 ± 0.084 mir-28a-5p 0.708 ± 0.095 1.824 ± 0.352 1.739 ± 0.187 mir-29a-3p 0.408 ± 0.009 1.716 ± 0.089 2.267 ± 0.345 mir-29b-3p 0.403 ± 0.014 1.226 ± 0.019 1.652 ± 0.082 mir-29c-3p 0.352 ± 0.086 1.140 ± 0.036 1.666 ± 0.120

128 miR HDL Apo-A1 None mir-300-3p 0.484 ± 0.005 1.787 ± 0.151 1.700 ± 0.054 mir-30c-5p 0.426 ± 0.026 1.062 ± 0.134 1.474 ± 0.141 mir-328-3p 0.507 ± 0.007 1.063 ± 0.098 1.389 ± 0.130 mir-33 0.728 ± 0.185 1.372 ± 0.255 mir-338-3p 0.712 ± 0.016 1.352 ± 0.095 1.217 ± 0.081 mir-340-5p 0.519 ± 0.025 1.043 ± 0.043 1.415 ± 0.113 mir-342-5p 0.437 ± 0.007 0.823 ± 0.004 1.353 ± 0.187 mir-34a-5p 0.527 ± 0.053 0.763 ± 0.030 1.253 ± 0.181 mir-34b-5p 0.506 ± 0.023 1.678 ± 0.032 mir-34c-5p 0.771 ± 0.048 1.604 ± 0.042 1.617 ± 0.141 mir-365-3p 0.959 ± 0.095 1.533 ± 0.231 1.523 ± 0.225 mir-378a-3p 0.944 ± 0.154 1.534 ± 0.162 1.540 ± 0.232 mir-378a-5p 1.247 ± 0.014 2.294 ± 0.105 1.591 ± 0.418 mir-381-3p 1.111 ± 0.038 1.548 ± 0.042 1.431 ± 0.262 mir-384-3p 1.372 ± 0.105 2.110 ± 0.277 1.253 ± 0.383 mir-39-3p 0.827 ± 0.047 1.236 ± 0.064 mir-409-3p 1.195 ± 0.075 2.346 ± 0.143 1.552 ± 0.371 mir-410-3p 0.624 ± 0.039 1.326 ± 0.081 1.755 ± 0.163 mir-433-3p 1.269 ± 0.020 0.629 ± 0.028 mir-451a- 1.302 ± 0.033 1.286 ± 0.016 1.065 ± 0.216 mir-485-3p 1.354 ± 0.040 1.709 ± 0.418 1.159 ± 0.290 mir-485-5p 1.213 ± 0.030 1.689 ± 0.072 1.310 ± 0.271 mir-487b-3p 1.324 ± 0.058 1.655 ± 0.101 1.451 ± 0.407 mir-497-5p 1.284 ± 0.065 1.300 ± 0.079 1.138 ± 0.245 mir-505-3p 0.507 ± 0.092 1.226 ± 0.036 1.940 ± 0.140 mir-574-3p 0.445 ± 0.062 2.547 ± 0.109 2.128 ± 0.174 mir-708-5p 0.806 ± 0.045 2.380 ± 0.146 1.757 ± 0.225 mir-758 0.951 ± 0.033 1.044 ± 0.068 1.565 ± 0.241 mir-7a 0.795 ± 0.061 1.280 ± 0.084 mir-7a-5p 0.962 ± 0.171 1.632 ± 0.043 1.638 ± 0.283 mir-7b-5p 1.319 ± 0.016 1.604 ± 0.067 1.416 ± 0.384 mir-874 0.551 ± 0.010 1.306 ± 0.027 2.027 ± 0.188 mir-9 0.651 ± 0.085 1.478 ± 0.118 mir-9-3p 0.743 ± 0.075 1.270 ± 0.118 1.801 ± 0.213 mir-93-3p 0.590 ± 0.035 1.326 ± 0.073 1.678 ± 0.072 mir-93-5p 1.044 ± 0.034 1.103 ± 0.065 1.499 ± 0.258 mir-96 0.584 ± 0.076 1.559 ± 0.008 1.646 ± 0.059 mir-98-5p 0.964 ± 0.078 1.425 ± 0.078 1.442 ± 0.258 mir-99a-5p 0.832 ± 0.009 1.426 ± 0.097 1.292 ± 0.088 mir-99b-5p 0.519 ± 0.091 1.126 ± 0.043 1.588 ± 0.067 mir218-5p 0.906 ± 0.017 1.129 ± 0.024

129

Figure 1. Cholesterol efflux with HDL as acceptor; Analysis of means (AOM).

Table 1. miRs that significantly affected cholesterol efflux to HDL (AOM) Lower Group Upper Limit Level Group N Limit Mean Limit Exceeded mir-107-3p 3 0.676126 1.34536 1.182448 Upper mir-128-3p 3 0.676126 1.205338 1.182448 Upper mir-130b-3p 3 0.676126 1.313648 1.182448 Upper mir-135b-5p 3 0.676126 1.428984 1.182448 Upper mir-136-3p 3 0.676126 1.285703 1.182448 Upper mir-136-5p 3 0.676126 1.20007 1.182448 Upper mir-138-5p 3 0.676126 1.27291 1.182448 Upper mir-139-5p 3 0.676126 1.227035 1.182448 Upper mir-145a-5p 3 0.676126 1.290927 1.182448 Upper mir-204-5p 3 0.676126 1.332543 1.182448 Upper mir-221-5p 3 0.676126 1.250897 1.182448 Upper mir-378a-5p 3 0.676126 1.247264 1.182448 Upper mir-384-3p 3 0.676126 1.372536 1.182448 Upper mir-409-3p 3 0.676126 1.195439 1.182448 Upper mir-433-3p 3 0.676126 1.26919 1.182448 Upper mir-451a- 3 0.676126 1.302271 1.182448 Upper mir-485-3p 3 0.676126 1.354499 1.182448 Upper mir-485-5p 3 0.676126 1.213895 1.182448 Upper mir-487b-3p 3 0.676126 1.324005 1.182448 Upper mir-497-5p 3 0.676126 1.284752 1.182448 Upper mir-7b-5p 3 0.676126 1.319585 1.182448 Upper mir-27a 3 0.676126 0.432196 1.182448 Lower mir-27b 3 0.676126 0.481552 1.182448 Lower mir-29a-3p 3 0.676126 0.408211 1.182448 Lower mir-29b-3p 3 0.676126 0.403179 1.182448 Lower mir-29c-3p 3 0.676126 0.352637 1.182448 Lower mir-300-3p 3 0.676126 0.48436 1.182448 Lower mir-30c-5p 3 0.676126 0.426878 1.182448 Lower mir-328-3p 3 0.676126 0.507825 1.182448 Lower

130

Lower Group Upper Limit Level Group N Limit Mean Limit Exceeded mir-340-5p 3 0.676126 0.51945 1.182448 Lower mir-342-5p 3 0.676126 0.437445 1.182448 Lower mir-34a-5p 3 0.676126 0.52775 1.182448 Lower mir-34b-5p 3 0.676126 0.506561 1.182448 Lower mir-410-3p 3 0.676126 0.624646 1.182448 Lower mir-505-3p 3 0.676126 0.507184 1.182448 Lower mir-574-3p 3 0.676126 0.44596 1.182448 Lower mir-874 3 0.676126 0.551785 1.182448 Lower mir-9 3 0.676126 0.651768 1.182448 Lower mir-93-3p 3 0.676126 0.590571 1.182448 Lower mir-96 3 0.676126 0.584551 1.182448 Lower mir-99b-5p 3 0.676126 0.519796 1.182448 Lower

Table 2. miRs that significantly affected cholesterol efflux to HDL (Dunnett’s)

Abs(Dif)- Level LSD p-Value mir-300-3p 0.176 0.0001 mir-27b 0.179 0.0001 mir-574-3p 0.214 0.0001 mir-342-5p 0.223 0.0001 mir-27a 0.228 0.0001 mir-30c-5p 0.233 0.0001 mir-29a-3p 0.252 0.0001 mir-29b-3p 0.257 0.0001 mir-29c-3p 0.308 0.0001 mir-328-3p 0.152 0.0002 mir-505-3p 0.153 0.0002 mir-34b-5p 0.154 0.0002 mir-99b-5p 0.14 0.0004 mir-340-5p 0.141 0.0004 mir-34a-5p 0.133 0.0005 mir-874 0.108 0.0013 mir-135b-5p 0.089 0.0027 mir-96 0.076 0.0045 mir-93-3p 0.07 0.0056 mir-410-3p 0.036 0.0173 mir-384-3p 0.033 0.019 mir-485-3p 0.015 0.0328 mir-9 0.008 0.0393 mir-107-3p 0.006 0.0427

131

Figure 2. Cholesterol efflux with Apo-A1 as acceptor; Analysis of means (AOM).

Table 3. miRs that significantly affected cholesterol efflux to Apo-A1 (AOM) Lower Group Upper Limit Level Group N Limit Mean Limit Exceeded mir-149-5p 3 1.003951 2.107992 1.895438 Upper mir-150 3 1.003951 2.192483 1.895438 Upper mir-152-3p 3 1.003951 2.27162 1.895438 Upper mir-154-3p 3 1.003951 2.104898 1.895438 Upper mir-17-5p 3 1.003951 2.209922 1.895438 Upper mir-181-1-5p 3 1.003951 2.241215 1.895438 Upper mir-181c-5p 3 1.003951 2.246759 1.895438 Upper mir-183-5p 3 1.003951 2.006253 1.895438 Upper mir-185-5p 3 1.003951 1.904064 1.895438 Upper mir-186 3 1.003951 1.971362 1.895438 Upper mir-194-5p 3 1.003951 2.052705 1.895438 Upper mir-195-5p 3 1.003951 2.314226 1.895438 Upper mir-19a 3 1.003951 2.551255 1.895438 Upper mir-219-5p 3 1.003951 2.219626 1.895438 Upper mir-27b 3 1.003951 2.065144 1.895438 Upper mir-378a-5p 3 1.003951 2.294206 1.895438 Upper mir-384-3p 3 1.003951 2.110103 1.895438 Upper mir-409-3p 3 1.003951 2.346589 1.895438 Upper mir-574-3p 3 1.003951 2.547848 1.895438 Upper mir-708-5p 3 1.003951 2.380235 1.895438 Upper Control-mir 3 1.003951 1 1.895438 Lower mir-135a-5p 3 1.003951 0.934347 1.895438 Lower mir-135b-5p 3 1.003951 0.56582 1.895438 Lower mir-136-3p 3 1.003951 0.735287 1.895438 Lower mir-136-5p 3 1.003951 0.859202 1.895438 Lower mir-139-5p 3 1.003951 0.542672 1.895438 Lower mir-140-3p 3 1.003951 0.772753 1.895438 Lower mir-142-3p 3 1.003951 0.656606 1.895438 Lower mir-143 3 1.003951 0.509696 1.895438 Lower

132

Lower Group Upper Limit Level Group N Limit Mean Limit Exceeded mir-145a-5p 3 1.003951 0.779837 1.895438 Lower mir-146b-5p 3 1.003951 0.60447 1.895438 Lower mir-148b 3 1.003951 0.874536 1.895438 Lower mir-204-5p 3 1.003951 0.942582 1.895438 Lower mir-20a 3 1.003951 0.86632 1.895438 Lower mir-22-3p 3 1.003951 0.72699 1.895438 Lower mir-221-3p 3 1.003951 0.747282 1.895438 Lower mir-221-5p 3 1.003951 0.700019 1.895438 Lower mir-342-5p 3 1.003951 0.823959 1.895438 Lower mir-34a-5p 3 1.003951 0.76373 1.895438 Lower

Table 4. miRs that significantly affected cholesterol efflux to ApoA1 (Dunnett’s)

Abs(Dif)- Level LSD p-Value mir-19a 0.952 0.0001 mir-574-3p 0.949 0.0001 mir-708-5p 0.781 0.0001 mir-409-3p 0.748 0.0001 mir-195-5p 0.715 0.0001 mir-378a-5p 0.695 0.0001 mir-152-3p 0.673 0.0001 mir-181c-5p 0.648 0.0001 mir-181-1-5p 0.642 0.0001 mir-219-5p 0.621 0.0001 mir-17-5p 0.611 0.0001 mir-150 0.594 0.0001 mir-384-3p 0.511 0.0001 mir-149-5p 0.509 0.0001 mir-154-3p 0.506 0.0001 mir-27b 0.466 0.0001 mir-194-5p 0.454 0.0001 mir-183-5p 0.407 0.0001 mir-186 0.372 0.0001 mir-185-5p 0.305 0.0001 mir-103-3p 0.228 0.0006 mir-28a-5p 0.225 0.0007 mir-27a 0.21 0.0009 mir-300-3p 0.189 0.0015 mir-1a-3p 0.143 0.0039 mir-29a-3p 0.118 0.0064 mir-485-3p 0.111 0.0073

133 mir-485-5p 0.09 0.0108 mir-100-5p 0.08 0.0129 mir-487b-3p 0.056 0.0197 mir-7a-5p 0.033 0.0293 mir-34c-5p 0.005 0.0459 mir-7b-5p 0.005 0.046

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Gene expression Profile of miR-708 transfection in Macrophages, Foam Cells,

Hepatocytes and Intestinal Cells

A. THP1 Macrophage MIRCtrl DMSO 5 MIR708 DMSO N MIRCtrl GW

O I a a a S 4 MIR708 GW

S a a E a a a R 3 a P b a X ab

E 2 b a V b a a I a a b a a b a b a a T b a b a A 1 b b L b b b b bb b b b b b E b b b bb N.D R b 0

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Appendix B

Gene expression and microRNA expression determined by high-density microarray

Methods

Cell Lines and Cell Culture

THP-1 Human Monocytes THP-1 (Homo sapiens, monocyte) cell line was obtained from the American Type Culture Collection (ATCC; Rockville, MD) and were grown in RPMI 1640 with 10% heat inactivated FBS, 50μM 2-ME, 1mM sodium pyruvate, and antibiotics. To differentiate THP-1 monocytes to macrophages, cells were incubated in growth medium minus 2-ME with addition of 100nM PMA for 48 h. Cells were treated with 10 uM GW4064 for 16 hr.

Messenger and micro RNA extraction and microarray For microRNA quantitation, cells were lysed and harvested using miTotal RNA mini according to the manufacturer’s instructions (Viogene BioTek Group, Taipei city, Taiwan). Messenger RNA was extracted using Qiagen RNeasy and quality assessed by RNA Nano Chips on the Agilent Bioanalyzer. Each sample was labeled using the Affymetrix IVT Express Kit according to the manufacturer’s protocol. For miR detection, the GeneChip® miRNA 2.0 Array (Affymetrix, Santa Clara, CA) was utilized. The GeneChip Human Genome U133A 2.0 (Affymterix), representing 14,500 well-characterized genes was used to examine mRNAs, The slides were hybridized with the labeled RNA using GeneChip Hybridization Wash and Stain Kit (#702232) in the Affymetrix GeneChip Hybridization Oven 640, according to the manufacturer’s instructions. Following hybridization the arrays were washed and stained using the Affymetrix GeneChip Fluidics Station 450 according to the manufacturer’s protocol and scanned using the GeneChip Scanner 3000 7G. The scanned image file (DAT) and the intensity data (CEL) were imported into ArrayStar (DNASTAR, Inc., Madison WI). The Robust Multi-array Average (RMA) was used to normalize the data. The slides were grouped based on treatment and Student t-test with asymptotic p-value and Benjamini-Hochberg multiple corrections was performed comparing GW versus DMSO. The GW4064 treatment resulted in 76 miRs (Table 1 and Figure 1) and 123 mRNAs (Table 2 and Figure 2) that were significantly affected (criteria for miRs was set at p<0.05, 1.5-fold change while for mRNA a 2-fold change was used). The significantly affected transcripts were analyzed using Ingenuity Pathway Analysis (IPA; Qiagen, Redwood City, CA) and matched mIR/mRNA determined (Table 3, Figure 3).

136

Results Table 1. MicroRNAs significantly regulated by GW4064 in THP-1 macrophages GW4064 Probe Set ID p [Ctrl] [GW] (FC) kshv-miR-K12-3_st 0.036 -1.164 0.821 3.959 tae-miR1125_st 0.015 -0.784 0.810 3.017 mmu-miR-1224_st 0.015 -0.779 0.782 2.950 bta-miR-2887_st 0.035 -0.613 0.817 2.693 eca-miR-489_st 0.011 -0.587 0.842 2.693 hsa-miR-4530_st 0.012 -0.507 0.834 2.534 hsa-miR-4485_st 0.006 -0.628 0.694 2.500 mcmv-miR-M87-1_st 0.006 -0.519 0.800 2.495 cfa-miR-489_st 0.013 -0.582 0.727 2.479 hsa-miR-4507_st 0.035 -0.357 0.899 2.388 bta-miR-2316_st 0.024 -0.472 0.772 2.369 ppt-miR1066_st 0.006 -0.488 0.738 2.339 hsa-miR-4449_st 0.012 -0.704 0.498 2.301 bmo-miR-13a-star_st 0.037 -0.460 0.740 2.298 hsa-miR-4656_st 0.026 -0.731 0.448 2.265 cre-miR1144b_st 0.029 -0.505 0.664 2.249 rno-miR-1224_st 0.003 -0.541 0.616 2.229 bta-miR-2309_st 0.017 -0.519 0.600 2.171 bta-miR-2487_st 0.017 -0.450 0.663 2.163 ACA7B_s_st 0.047 -0.581 0.531 2.161 tca-miR-3049-5p_st 0.007 -0.505 0.577 2.116 eca-miR-197_st 0.047 -0.302 0.768 2.099 mmu-miR-5105_st 0.046 -0.417 0.648 2.091 hp_hsa-mir-4449_st 0.031 -0.558 0.490 2.068 gga-miR-1584_st 0.026 -0.314 0.732 2.066 ptr-miR-1275_st 0.043 -0.395 0.652 2.065 rno-miR-378-star_st 0.029 -0.611 0.431 2.059 U38A_x_st 0.029 -0.528 0.457 1.979 tgu-miR-2974_st 0.022 -0.319 0.629 1.930 bta-miR-2428_st 0.015 -0.378 0.543 1.893 hsa-miR-3656_st 0.034 -0.341 0.566 1.875 ppy-miR-564_st 0.021 -0.353 0.541 1.858 hsa-miR-1275_st 0.030 -0.218 0.663 1.843 hsa-miR-2861_st 0.033 -0.396 0.479 1.834 hsa-miR-4463_st 0.042 -0.311 0.529 1.790 hp_hsa-mir-4485_st 0.025 -0.312 0.479 1.731 dps-miR-289_st 0.027 -0.455 0.335 1.729 mgU2-25-61_st 0.015 -0.305 0.465 1.706 dya-miR-289_st 0.030 -0.337 0.428 1.700

137 osa-miR1858a_st 0.038 -0.335 0.414 1.681 ssc-miR-1285_st 0.015 -0.358 0.384 1.672 gga-miR-1686_st 0.005 -0.295 0.405 1.624 bta-miR-763_st 0.033 -0.313 0.311 1.540 ame-miR-3477_st 0.031 -0.313 0.307 1.537 hsa-miR-557_st 0.014 -0.305 0.315 1.537 ENSG00000239005_st 0.014 -0.279 0.339 1.535 AFFX-M27830_M_st 0.012 -0.355 0.259 1.531 gga-miR-1723_st 0.041 -0.201 0.399 1.515 cel-miR-4923b_st 0.003 -0.289 0.307 1.512 pma-miR-30a_st 0.015 0.217 -0.387 0.658 oan-miR-1419e-star_st 0.043 0.424 -0.213 0.643 hsa-miR-26b_st 0.029 0.232 -0.406 0.643 tgu-miR-30b-5p_st 0.036 0.291 -0.388 0.625 oan-miR-30a_st 0.023 0.221 -0.536 0.592 bta-miR-30a-5p_st 0.018 0.343 -0.420 0.589 pma-miR-199a-star_st 0.002 0.407 -0.366 0.585 mmu-miR-29b_st 0.028 0.456 -0.327 0.581 aly-miR165b_st 0.013 0.373 -0.436 0.571 mmu-miR-3107_st 0.047 0.374 -0.456 0.562 hsa-miR-17-star_st 0.046 0.452 -0.383 0.561 ssc-miR-30e-5p_st 0.039 0.439 -0.416 0.553 bta-miR-29b_st 0.017 0.486 -0.583 0.477 pma-miR-30b_st 0.033 0.352 -0.755 0.464 fru-miR-125a_st 0.039 0.652 -0.481 0.456 oar-miR-654-5p_st 0.004 0.630 -0.518 0.451 osa-miR2102-5p_st 0.031 0.617 -0.638 0.419 rno-miR-188_st 0.021 0.424 -0.870 0.408 xtr-miR-30e_st 0.030 0.374 -0.959 0.397 hsa-miR-181d_st 0.037 0.321 -1.060 0.384 hsa-miR-92a-1-star_st 0.031 0.729 -0.746 0.360 ssc-miR-183_st 0.015 0.810 -0.897 0.306 ppy-miR-193a-3p_st 0.016 0.757 -1.183 0.260 gga-miR-193a_st 0.014 0.715 -1.291 0.249 ssc-miR-193a-3p_st 0.027 1.007 -1.298 0.202 hsa-miR-193a-3p_st 0.019 1.022 -1.308 0.199 dre-miR-193a_st 0.032 0.567 -1.860 0.186

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Figure 3. Network of MicroRNAs

139

Table 2. Messenger RNAs significantly regulated by GW4064 in THP-1 macrophages

Probe Set ID Gene Entrez Gene Title GW4064 Symbol Gene (FC) 11737127_a_at ITGA1 3672 integrin, alpha 1 3.779 11716384_at CCL2 6347 chemokine (C-C motif) ligand 2 3.691 11736524_at DCSTAM 81501 dendrocyte expressed seven 3.191 P transmembrane protein 11730484_at PTGER3 5733 prostaglandin E receptor 3 (subtype EP3) 3.009 11753365_a_at DCSTAM 81501 dendrocyte expressed seven 2.987 P transmembrane protein 11729769_a_at CD38 952 CD38 molecule 2.906 11735112_at PTGER3 5733 prostaglandin E receptor 3 (subtype EP3) 2.893 11754026_a_at IL8 3576 interleukin 8 2.850 11763226_x_at IL8 3576 interleukin 8 2.821 11739781_a_at TGM2 7052 transglutaminase 2 (C polypeptide, 2.805 protein-glutamine-gamma- glutamyltransferase) 11725198_at IL1A 3552 interleukin 1, alpha 2.720 11735111_a_at PTGER3 5733 prostaglandin E receptor 3 (subtype EP3) 2.656 11718399_s_at TGM2 7052 transglutaminase 2 (C polypeptide, 2.479 protein-glutamine-gamma- glutamyltransferase) 11753632_x_at ATF3 467 activating transcription factor 3 2.467 11720051_at SPOCK1 6695 sparc/osteonectin, cwcv and kazal-like 2.463 domains proteoglycan (testican) 1 11726966_a_at CXCR5 643 chemokine (C-X-C motif) receptor 5 2.436 11747104_s_at CYP1B1 1545 cytochrome P450, family 1, subfamily B, 2.395 polypeptide 1 11737312_at CCL1 6346 chemokine (C-C motif) ligand 1 2.312 11755955_a_at FAP 2191 fibroblast activation protein, alpha 2.267 11752844_x_at PTGER3 5733 prostaglandin E receptor 3 (subtype EP3) 2.244 11730353_a_at CYP26B1 56603 cytochrome P450, family 26, subfamily 2.222 B, polypeptide 1 11730907_a_at ABCB1 5243 ATP-binding cassette, sub-family B 2.220 (MDR/TAP), member 1 11758664_s_at RHOBTB 22836 Rho-related BTB domain containing 3 2.217 3 11719344_a_at ATF3 467 activating transcription factor 3 2.206 11755219_a_at THBD 7056 thrombomodulin 2.199 11720301_a_at SLA 6503 Src-like-adaptor 2.170 11720302_a_at SLA 6503 Src-like-adaptor 2.166 11720300_a_at SLA 6503 Src-like-adaptor 2.158 11716455_at RHOBTB 22836 Rho-related BTB domain containing 3 2.113 3

140

Probe Set ID Gene Entrez Gene Title GW4064 Symbol Gene (FC) 11729847_a_at CCL7 6354 chemokine (C-C motif) ligand 7 2.105 11763594_a_at HS3ST3B 9953 heparan sulfate (glucosamine) 3-O- 2.104 1 sulfotransferase 3B1 11754337_s_at NDRG1 10397 N-myc downstream regulated 1 2.094 11746087_a_at CD84 8832 CD84 molecule 2.088 11717215_a_at ATP9A 10079 ATPase, class II, type 9A 2.077 11716457_x_at RHOBTB 22836 Rho-related BTB domain containing 3 2.074 3 11731584_a_at FCRLA 84824 Fc receptor-like A 2.070 11724993_at FAM101 359845 family with sequence similarity 101, 2.067 B member B 11736029_a_at ITGA6 3655 integrin, alpha 6 2.063 11729344_at BPI 671 bactericidal/permeability-increasing 2.059 protein 11741984_a_at PTGER3 5733 prostaglandin E receptor 3 (subtype EP3) 2.044 11724994_at FAM101 359845 family with sequence similarity 101, 2.039 B member B 11720409_s_at CYP1B1 1545 cytochrome P450, family 1, subfamily B, 2.039 polypeptide 1 11757415_s_at SLC5A3 6526 solute carrier family 5 (sodium/myo- 2.036 inositol cotransporter), member 3 11759048_at ABCB1 5243 ATP-binding cassette, sub-family B 2.028 (MDR/TAP), member 1 11728477_at CXCL3 2921 chemokine (C-X-C motif) ligand 3 2.012 11753631_a_at ATF3 467 activating transcription factor 3 2.004 11745165_a_at TRAF3IP 80342 TRAF3 interacting protein 3 0.500 3 11734412_at ZNF280A 129025 zinc finger protein 280A 0.498 11733767_a_at LILRA2 11027 leukocyte immunoglobulin-like receptor, 0.498 subfamily A (with TM domain), member 2 11719788_s_at TUBB2A 7280 tubulin, beta 2A class IIa 0.497 11756803_a_at MS4A14 84689 membrane-spanning 4-domains, 0.494 subfamily A, member 14 11753667_s_at ITM2C 81618 integral membrane protein 2C 0.493 11715465_at IGFBP4 3487 insulin-like growth factor binding protein 0.492 4 11716014_a_at SERPINF 5176 serpin peptidase inhibitor, clade F (alpha- 0.492 1 2 antiplasmin, pigment epithelium derived factor), member 1 11731366_a_at TSPAN32 10077 tetraspanin 32 0.491 11763426_a_at TRAF3IP 80342 TRAF3 interacting protein 3 0.491 3

141

Probe Set ID Gene Entrez Gene Title GW4064 Symbol Gene (FC) 11743677_at P2RY8 286530 purinergic receptor P2Y, G-protein 0.491 coupled, 8 11728515_a_at SP140 11262 SP140 nuclear body protein 0.489 11763718_x_at CDKN1C 1028 cyclin-dependent kinase inhibitor 1C 0.488 (p57, Kip2) 11738990_x_at GANAB 23193 glucosidase, alpha; neutral AB 0.487 11751282_x_at MIR4647 1006161 microRNA 4647 /// solute carrier family 0.484 /// 24 /// 35, member B2 SLC35B2 347734 11740213_a_at TBL1X 6907 transducin (beta)-like 1X-linked 0.483 11756975_s_at TBX2 6909 T-box 2 0.480 11716408_a_at MET 4233 met proto-oncogene (hepatocyte growth 0.478 factor receptor) 11721983_a_at PID1 55022 phosphotyrosine interaction domain 0.477 containing 1 11733768_x_at LILRA2 11027 leukocyte immunoglobulin-like receptor, 0.477 subfamily A (with TM domain), member 2 11744636_a_at OPRL1 4987 opiate receptor-like 1 0.477 11717742_a_at GPRC5C 55890 G protein-coupled receptor, family C, 0.477 group 5, member C 11740873_x_at MS4A7 58475 membrane-spanning 4-domains, 0.476 subfamily A, member 7 11740872_a_at MS4A7 58475 membrane-spanning 4-domains, 0.474 subfamily A, member 7 11719222_at SPINK1 6690 serine peptidase inhibitor, Kazal type 1 0.473 11726201_a_at OAS2 4939 2'-5'-oligoadenylate synthetase 2, 0.473 69/71kDa 11731148_at TLR7 51284 toll-like receptor 7 0.472 11762107_at CAST 831 calpastatin 0.472 11741908_a_at CACNB4 785 calcium channel, voltage-dependent, beta 0.471 4 subunit 11719886_a_at IFI44 10561 interferon-induced protein 44 0.470 11754198_at LINC003 79686 long intergenic non-protein coding RNA 0.466 41 341 11717678_at AVPI1 60370 arginine vasopressin-induced 1 0.464 11717575_at PPP1R3C 5507 protein phosphatase 1, regulatory subunit 0.464 3C 11739657_a_at LTB 4050 lymphotoxin beta (TNF superfamily, 0.464 member 3) 11735317_a_at CACNB4 785 calcium channel, voltage-dependent, beta 0.463 4 subunit 11722598_s_at CXCR7 57007 chemokine (C-X-C motif) receptor 7 0.462

142

Probe Set ID Gene Entrez Gene Title GW4064 Symbol Gene (FC) 11716036_x_at BST2 684 bone marrow stromal cell antigen 2 0.458 11722855_at PDGFD 80310 platelet derived growth factor D 0.458 11719375_x_at METTL7 25840 methyltransferase like 7A 0.457 A 11741483_a_at CACNB4 785 calcium channel, voltage-dependent, beta 0.455 4 subunit 11733521_at NTSR1 4923 neurotensin receptor 1 (high affinity) 0.454 11757404_x_at ID3 3399 inhibitor of DNA binding 3, dominant 0.453 negative helix-loop-helix protein 11716195_a_at ID1 3397 inhibitor of DNA binding 1, dominant 0.452 negative helix-loop-helix protein 11718664_a_at MIR4647 1006161 microRNA 4647 /// solute carrier family 0.449 /// 24 /// 35, member B2 SLC35B2 347734 11716035_at BST2 684 bone marrow stromal cell antigen 2 0.447 11753661_a_at ID1 3397 inhibitor of DNA binding 1, dominant 0.447 negative helix-loop-helix protein 11716091_x_at PRG2 5553 proteoglycan 2, bone marrow (natural 0.446 killer cell activator, eosinophil granule major basic protein) 11763447_x_at YME1L1 10730 YME1-like 1 (S. cerevisiae) 0.443 11716196_x_at ID1 3397 inhibitor of DNA binding 1, dominant 0.443 negative helix-loop-helix protein 11728232_a_at CLDN1 9076 claudin 1 0.440 11754008_x_at ID3 3399 inhibitor of DNA binding 3, dominant 0.433 negative helix-loop-helix protein 11717132_s_at AHNAK2 113146 AHNAK nucleoprotein 2 0.431 11726060_at LRRC4 64101 leucine rich repeat containing 4 0.422 11731101_at MFI2 4241 antigen p97 (melanoma associated) 0.420 identified by monoclonal antibodies 133.2 and 96.5 11730477_at RNF125 54941 ring finger protein 125, E3 ubiquitin 0.420 protein ligase 11722088_s_at NR1D2 9975 nuclear receptor subfamily 1, group D, 0.417 member 2 11723048_at CX3CR1 1524 chemokine (C-X3-C motif) receptor 1 0.414 11731181_a_at EPSTI1 94240 epithelial stromal interaction 1 (breast) 0.414 11716034_a_at BST2 684 bone marrow stromal cell antigen 2 0.412 11724983_at PCDH7 5099 protocadherin 7 0.390 11756806_a_at ISG20 3669 interferon stimulated exonuclease gene 0.388 20kDa 11756027_a_at MYOZ2 51778 myozenin 2 0.388 11718140_a_at EMP3 2014 epithelial membrane protein 3 0.381

143

Probe Set ID Gene Entrez Gene Title GW4064 Symbol Gene (FC) 11716974_a_at PDK4 5166 pyruvate dehydrogenase kinase, isozyme 0.379 4 11760212_at SPAG9 9043 sperm associated antigen 9 0.376 11716666_a_at ID3 3399 inhibitor of DNA binding 3, dominant 0.374 negative helix-loop-helix protein 11733323_a_at TRDN 10345 triadin 0.364 11729887_at PTPLA 9200 protein tyrosine phosphatase-like (proline 0.356 instead of catalytic arginine), member A 11716203_a_at MGP 4256 matrix Gla protein 0.354 11733913_at HS3ST1 9957 heparan sulfate (glucosamine) 3-O- 0.353 sulfotransferase 1 11761648_at ACSL6 /// 1005055 acyl-CoA synthetase long-chain family 0.351 LOC1005 72 /// member 6 /// uncharacterized 05572 23305 LOC100505572 11761790_x_at YME1L1 10730 YME1-like 1 (S. cerevisiae) 0.349 11737237_a_at EOMES 8320 eomesodermin 0.347 11717661_a_at PPP1R16 26051 protein phosphatase 1, regulatory subunit 0.336 B 16B 11740235_at RAB27B 5874 RAB27B, member RAS oncogene family 0.313 11761649_x_at ACSL6 23305 acyl-CoA synthetase long-chain family 0.312 member 6 11719538_at BBOX1 8424 butyrobetaine (gamma), 2-oxoglutarate 0.264 dioxygenase (gamma-butyrobetaine hydroxylase) 1

144

Figure 4. Messenger RNA network regulated by GW4064

145

Table 3. Matched miRs and mRNAs affected by GW4064 treatment in THP-1 macrophages Fold Fold Chang Confiden Symbo Chang ID Symbol e Source ce ID l e Moderate hsa-miR-193a- miR-193a-3p (and other miRNAs TargetScan (predicte SLC5 3p_st w/seed ACUGGCC) -5.028 Human d) 11757415_s_at A3 2.036 High hsa-miR- miR-181a-5p (and other miRNAs TargetScan (predicte CYP26 181d_st w/seed ACAUUCA) -2.603 Human d) 11730353_a_at B1 2.222 High hsa-miR- miR-181a-5p (and other miRNAs TargetScan (predicte 181d_st w/seed ACAUUCA) -2.603 Human d) 11725198_at IL1A 2.720 High hsa-miR- miR-181a-5p (and other miRNAs TargetScan (predicte 181d_st w/seed ACAUUCA) -2.603 Human d) 11736029_a_at ITGA6 2.063 High hsa-miR- miR-181a-5p (and other miRNAs TargetScan (predicte 181d_st w/seed ACAUUCA) -2.603 Human d) 11720301_a_at SLA 2.170 Moderate rno-miR- miR-188-5p (and other miRNAs TargetScan (predicte PTGE 188_st w/seed AUCCCUU) -2.453 Human d) 11730484_at R3 3.009 High mmu-miR- miR-486-5p (and other miRNAs TargetScan (predicte CXCR 3107_st w/seed CCUGUAC) -1.778 Human d) 11726966_a_at 5 2.436 High hsa-miR- miR-26a-5p (and other miRNAs TargetScan (predicte ATP9 26b_st w/seed UCAAGUA) -1.556 Human d) 11717215_a_at A 2.077 High hsa-miR- miR-26a-5p (and other miRNAs TargetScan (predicte 26b_st w/seed UCAAGUA) -1.556 Human d) 11736029_a_at ITGA6 2.063 hsa-miR- miR-507 (and other miRNAs TargetScan Moderate CDKN 557_st w/seed UUUGCAC) 1.537 Human (predicte 11763718_x_at 1C -2.049

146

d) Moderate hsa-miR- miR-507 (and other miRNAs TargetScan (predicte 557_st w/seed UUUGCAC) 1.537 Human d) 11716974_a_at PDK4 -2.638 Moderate hsa-miR- miR-507 (and other miRNAs TargetScan (predicte PTPL 557_st w/seed UUUGCAC) 1.537 Human d) 11729887_at A -2.810 Moderate hsa-miR- miR-507 (and other miRNAs TargetScan (predicte 557_st w/seed UUUGCAC) 1.537 Human d) 11728515_a_at SP140 -2.043 Moderate hsa-miR- miR-4463 (miRNAs w/seed TargetScan (predicte 4463_st AGACUGG) 1.790 Human d) 11717678_at AVPI1 -2.153 High hsa-miR- miR-4463 (miRNAs w/seed TargetScan (predicte CLDN 4463_st AGACUGG) 1.790 Human d) 11728232_a_at 1 -2.271 High hsa-miR- miR-4463 (miRNAs w/seed TargetScan (predicte 4463_st AGACUGG) 1.790 Human d) 11726201_a_at OAS2 -2.116 Moderate hsa-miR- miR-4463 (miRNAs w/seed TargetScan (predicte SPAG 4463_st AGACUGG) 1.790 Human d) 11760212_at 9 -2.657 Moderate hsa-miR- miR-2861 (and other miRNAs TargetScan (predicte GPRC 2861_st w/seed GGGCCUG) 1.834 Human d) 11717742_a_at 5C -2.098 Moderate hsa-miR- miR-2861 (and other miRNAs TargetScan (predicte NTSR 2861_st w/seed GGGCCUG) 1.834 Human d) 11733521_at 1 -2.202 Moderate hsa-miR- miR-2861 (and other miRNAs TargetScan (predicte 2861_st w/seed GGGCCUG) 1.834 Human d) 11743677_at P2RY8 -2.038 hsa-miR- miR-2861 (and other miRNAs TargetScan High 2861_st w/seed GGGCCUG) 1.834 Human (predicte 11716974_a_at PDK4 -2.638

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d) Moderate hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte CX3C 1275_st w/seed UGGGGGA) 1.843 Human d) 11723048_at R1 -2.413 High hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte GANA 1275_st w/seed UGGGGGA) 1.843 Human d) 11738990_x_at B -2.053 Moderate hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte HS3ST 1275_st w/seed UGGGGGA) 1.843 Human d) 11733913_at 1 -2.835 Moderate hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte 1275_st w/seed UGGGGGA) 1.843 Human d) 11716196_x_at ID1 -2.259 Moderate hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte NTSR 1275_st w/seed UGGGGGA) 1.843 Human d) 11733521_at 1 -2.202 Moderate hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte 1275_st w/seed UGGGGGA) 1.843 Human d) 11726201_a_at OAS2 -2.116 Moderate hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte PPP1R 1275_st w/seed UGGGGGA) 1.843 Human d) 11717575_at 3C -2.156 Moderate hsa-miR- miR-1275 (and other miRNAs TargetScan (predicte SPAG 1275_st w/seed UGGGGGA) 1.843 Human d) 11760212_at 9 -2.657 High hsa-miR- miR-3656 (and other miRNAs TargetScan (predicte 3656_st w/seed GCGGGUG) 1.875 Human d) 11731101_at MFI2 -2.381 High hsa-miR- miR-4656 (and other miRNAs TargetScan (predicte 4656_st w/seed GGGCUGA) 2.265 Human d) 11753667_s_at ITM2C -2.028 hsa-miR- miR-4656 (and other miRNAs TargetScan Moderate 4656_st w/seed GGGCUGA) 2.265 Human (predicte 11726201_a_at OAS2 -2.116

148

d) Moderate hsa-miR- miR-4656 (and other miRNAs TargetScan (predicte OPRL 4656_st w/seed GGGCUGA) 2.265 Human d) 11744636_a_at 1 -2.097 Moderate hsa-miR- miR-4656 (and other miRNAs TargetScan (predicte PPP1R 4656_st w/seed GGGCUGA) 2.265 Human d) 11717661_a_at 16B -2.973 Moderate hsa-miR- miR-3940-5p (and other miRNAs TargetScan (predicte PPP1R 4507_st w/seed UGGGUUG) 2.388 Human d) 11717575_at 3C -2.156 Moderate hsa-miR- miR-3940-5p (and other miRNAs TargetScan (predicte 4507_st w/seed UGGGUUG) 2.388 Human d) 11733323_a_at TRDN -2.746 Moderate hsa-miR- miR-4530 (miRNAs w/seed TargetScan (predicte CACN 4530_st CCAGCAG) 2.534 Human d) 11741483_a_at B4 -2.199 Moderate hsa-miR- miR-4530 (miRNAs w/seed TargetScan (predicte PCDH 4530_st CCAGCAG) 2.534 Human d) 11724983_at 7 -2.566 High hsa-miR- miR-4530 (miRNAs w/seed TargetScan (predicte 4530_st CCAGCAG) 2.534 Human d) 11756975_s_at TBX2 -2.083 Moderate hsa-miR- miR-4530 (miRNAs w/seed TargetScan (predicte 4530_st CCAGCAG) 2.534 Human d) 11731148_at TLR7 -2.117 Moderate mmu-miR- miR-1224-5p (and other miRNAs TargetScan (predicte 1224_st w/seed UGAGGAC) 2.950 Human d) 11753667_s_at ITM2C -2.028 High mmu-miR- miR-1224-5p (and other miRNAs TargetScan (predicte TUBB 1224_st w/seed UGAGGAC) 2.950 Human d) 11719788_s_at 2A -2.013

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Figure 5. Network of matched miRs and mRNA targets

150

Appendix C

Altered gene expression in foam cells by miR-574

METHODS

Cell Lines and Cell Culture

THP-1 Human Monocytes and transfection

THP-1 (Homo sapiens, monocyte) cell line was obtained from the American Type

574 in 20μL of Lipofectamine transfection reagent for 3-6 hours in a 96 well plate at a density of

1x 105cells/well. The volume was brought up to 100 μL with growth media for 24 hr. 10 μM

GW4064 treatments were added to the cells and incubated for 18 to 20 hr.

Messenger and micro RNA extraction and microarray

Messenger RNA was extracted using Qiagen RNeasy and quality assessed by RNA Nano

Chips on the Agilent Bioanalyzer. Each sample was labeled using the Affymetrix IVT Express

Kit according to the manufacturer’s protocol. The GeneChip Human Genome U133A 2.0

(Affymterix), representing 14,500 well-characterized genes was used to examine mRNAs, The slides were hybridized with the labeled RNA using GeneChip Hybridization Wash and Stain Kit

(#702232) in the Affymetrix GeneChip Hybridization Oven 640, according to the manufacturer’s instructions. Following hybridization the arrays were washed and stained using the Affymetrix

151

GeneChip Fluidics Station 450 according to the manufacturer’s protocol and scanned using the

GeneChip Scanner 3000 7G. The scanned image file (DAT) and the intensity data (CEL) were imported into ArrayStar (DNASTAR, Inc., Madison WI). The Robust Multi-array Average

(RMA) was used to normalize the data. The slides were grouped based on treatment and Student t-test with asymptotic p-value and Benjamini-Hochberg multiple corrections was performed comparing GW versus GW with miR-574 transfection. The miR-574 transfection resulted in 85 miRs (Table 1) that were significantly affected (criteria for miRs was set at p<0.05, 2.0 -fold change. The significantly affected transcripts were analyzed using Ingenuity Pathway Analysis

(IPA; Qiagen, Redwood City, CA). The majority of the genes regulated were involved in inflammation (Figure 1) and the top upstream regulators were related to interferon and mitogen- activated protein kinase (MAPK) signaling (Table 2).

Results Table 1. MicroRNAs significantly regulated by miR-574 in THP-1 foam cells

Gene Entrez Probe Set ID Symbol Gene Gene Title miR574 11715670_a_at IFITM1 8519 interferon induced transmembrane protein 1 16.280 interferon-induced protein with 11756820_a_at IFIT1 3434 tetratricopeptide repeats 1 15.419 radical S-adenosyl methionine domain 11739216_x_at RSAD2 91543 containing 2 13.170 11732467_x_at CXCL11 6373 chemokine (C-X-C motif) ligand 11 12.096 11715671_x_at IFITM1 8519 interferon induced transmembrane protein 1 11.951 11720298_at CXCL10 3627 chemokine (C-X-C motif) ligand 10 11.915 11749245_a_at CXCL11 6373 chemokine (C-X-C motif) ligand 11 9.525 myxovirus (influenza virus) resistance 1, 11716167_a_at MX1 4599 interferon-inducible protein p78 (mouse) 9.515 11718986_a_at IFI6 2537 interferon, alpha-inducible protein 6 8.649 11731181_a_at EPSTI1 94240 epithelial stromal interaction 1 (breast) 7.713 11755587_a_at TRIM22 10346 tripartite motif containing 22 6.958 11732466_a_at CXCL11 6373 chemokine (C-X-C motif) ligand 11 6.202

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Gene Entrez Probe Set ID Symbol Gene Gene Title miR574 cytidine monophosphate (UMP-CMP) 11723105_at CMPK2 129607 kinase 2, mitochondrial 6.170 11757480_x_at IFI27 3429 interferon, alpha-inducible protein 27 5.939 11726770_x_at XAF1 54739 XIAP associated factor 1 5.786 11719886_a_at IFI44 10561 interferon-induced protein 44 5.486 11726201_a_at OAS2 4939 2'-5'-oligoadenylate synthetase 2, 69/71kDa 5.122 11723234_at IFI44L 10964 interferon-induced protein 44-like 5.022 interferon-induced protein with 11721874_at IFIT2 3433 tetratricopeptide repeats 2 4.947 11715419_at LY6E 4061 lymphocyte antigen 6 complex, locus E 4.800 11748003_a_at IFI44 10561 interferon-induced protein 44 4.785 11741202_x_at XAF1 54739 XIAP associated factor 1 4.485 11723235_a_at IFI44L 10964 interferon-induced protein 44-like 4.242 11716895_s_at ISG15 9636 ISG15 ubiquitin-like modifier 4.054 11724256_s_at OAS1 4938 2'-5'-oligoadenylate synthetase 1, 40/46kDa 3.886 11746088_a_at IFI44 10561 interferon-induced protein 44 3.875 11722370_a_at TRIM22 10346 tripartite motif containing 22 3.852 11726769_a_at XAF1 54739 XIAP associated factor 1 3.792 11728039_s_at CCL8 6355 chemokine (C-C motif) ligand 8 3.774 11719588_a_at OAS1 4938 2'-5'-oligoadenylate synthetase 1, 40/46kDa 3.639 11722369_x_at TRIM22 10346 tripartite motif containing 22 3.562 11728038_at CCL8 6355 chemokine (C-C motif) ligand 8 3.537 HECT and RLD domain containing E3 11723128_a_at HERC6 55008 ubiquitin protein ligase family member 6 3.367 radical S-adenosyl methionine domain 11739217_a_at RSAD2 91543 containing 2 3.290 11723698_a_at OAS3 4940 2'-5'-oligoadenylate synthetase 3, 100kDa 3.228 11726771_a_at XAF1 54739 XIAP associated factor 1 3.176 interferon-induced protein with 11731407_x_at IFIT3 3437 tetratricopeptide repeats 3 3.131 11723699_s_at OAS3 4940 2'-5'-oligoadenylate synthetase 3, 100kDa 2.956 poly (ADP-ribose) polymerase family, 11744434_a_at PARP9 83666 member 9 2.781 11720209_at IRF9 10379 interferon regulatory factor 9 2.670 myxovirus (influenza virus) resistance 2 11726479_a_at MX2 4600 (mouse) 2.655 interferon stimulated exonuclease gene 11756806_a_at ISG20 3669 20kDa 2.611 11722368_a_at TRIM22 10346 tripartite motif containing 22 2.564 11733149_a_at DDX58 23586 DEAD (Asp-Glu-Ala-Asp) box polypeptide 2.552

153

Gene Entrez Probe Set ID Symbol Gene Gene Title miR574 58 sialic acid binding Ig-like lectin 1, 11755646_a_at SIGLEC1 6614 sialoadhesin 2.540 11720208_a_at IRF9 10379 interferon regulatory factor 9 2.513 HECT and RLD domain containing E3 11755374_a_at HERC5 51191 ubiquitin protein ligase 5 2.508 tumor necrosis factor (ligand) superfamily, 11743730_at TNFSF10 8743 member 10 2.487 tumor necrosis factor (ligand) superfamily, 11747952_x_at TNFSF10 8743 member 10 2.477 11722371_x_at TRIM22 10346 tripartite motif containing 22 2.468 11719492_s_at IFI35 3430 interferon-induced protein 35 2.458 11723236_at IFI44L 10964 interferon-induced protein 44-like 2.447 DEAD (Asp-Glu-Ala-Asp) box polypeptide 11755819_a_at DDX58 23586 58 2.446 guanylate binding protein 1, interferon- 11752931_x_at GBP1 2633 inducible 2.415 11736136_s_at OAS2 4939 2'-5'-oligoadenylate synthetase 2, 69/71kDa 2.388 11718916_a_at IRF7 3665 interferon regulatory factor 7 2.352 11716587_at AXL 558 AXL receptor tyrosine kinase 2.302 signal transducer and activator of 11726690_a_at STAT1 6772 transcription 1, 91kDa 2.284 11736135_at OAS2 4939 2'-5'-oligoadenylate synthetase 2, 69/71kDa 2.272 guanylate binding protein 1, interferon- 11726329_x_at GBP1 2633 inducible 2.270 11730458_at AIM2 9447 absent in melanoma 2 2.270 tumor necrosis factor (ligand) superfamily, 11743731_a_at TNFSF10 8743 member 10 2.247 guanylate binding protein 1, interferon- 11752930_a_at GBP1 2633 inducible 2.241 11724255_a_at OAS1 4938 2'-5'-oligoadenylate synthetase 1, 40/46kDa 2.219 11719491_a_at IFI35 3430 interferon-induced protein 35 2.219 coagulation factor III (thromboplastin, 11721290_a_at F3 2152 tissue factor) 2.202 11715239_x_at IFITM3 10410 interferon induced transmembrane protein 3 2.183 11728176_a_at BMP8A 353500 bone morphogenetic protein 8a 2.180 lectin, galactoside-binding, soluble, 3 11715370_s_at LGALS3BP 3959 binding protein 2.177 DEAD (Asp-Glu-Ala-Asp) box polypeptide 11756330_a_at DDX58 23586 58 2.164 11740891_a_at OAS1 4938 2'-5'-oligoadenylate synthetase 1, 40/46kDa 2.142

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Gene Entrez Probe Set ID Symbol Gene Gene Title miR574 11716036_x_at BST2 684 bone marrow stromal cell antigen 2 2.135 11715893_s_at IFI27 3429 interferon, alpha-inducible protein 27 2.116 11736134_a_at OAS2 4939 2'-5'-oligoadenylate synthetase 2, 69/71kDa 2.104 11718231_x_at HLA-F 3134 major histocompatibility complex, class I, F 2.079 11718204_a_at REC8 9985 REC8 homolog (yeast) 2.053 11716035_at BST2 684 bone marrow stromal cell antigen 2 2.050 11736739_at CLEC5A 23601 C-type lectin domain family 5, member A 2.047 coagulation factor III (thromboplastin, 11721291_a_at F3 2152 tissue factor) 2.038 11746156_x_at IRF9 10379 interferon regulatory factor 9 2.031 11716034_a_at BST2 684 bone marrow stromal cell antigen 2 2.027 osteoclast stimulatory transmembrane 11740498_at OCSTAMP 128506 protein 2.008 signal transducer and activator of 11726689_a_at STAT1 6772 transcription 1, 91kDa 2.006 SAM domain, SH3 domain and nuclear 11735710_s_at SAMSN1 64092 localization signals 1 0.472 11753823_a_at S100A8 6279 S100 calcium binding protein A8 0.413

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Figure 6. Network of genes affected by miR-574 transfection into THP-1 derived foam cells

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Table 2. Top upstream regulators of genes significantly affected by miR-574 transfection. Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset BST2,CXCL11,DDX58,GBP1,HE RC5,IFI27,IFI35,IFI44,IFI6,IFIT1, IFIT2,IFIT3,IFITM1,IFITM3,IRF7 ,IRF9,ISG15,ISG20,LGALS3BP, MX2,OAS1,OAS2,OAS3,STAT1, MAPK1 kinase Inhibited -5.072 8.59E-36 TNFSF10,TRIM22 CXCL11,DDX58,GBP1,HERC6,I FI27,IFI44,IFI44L,IFI6,IFIT3,IRF 7,IRF9,ISG20,MX1,MX2,OAS1,O AS2,OAS3,RSAD2,TNFSF10,TRI IL1RN cytokine Inhibited -4.461 6.75E-34 M22 AIM2,BST2,CCL8,CXCL10,CXC L11,F3,IFI27,IFI35,IFIT1,IFIT2,IF chemical - kinase IT3,IRF7,ISG15,ISG20,MX1,STA SB203580 inhibitor Inhibited -3.954 1.87E-19 T1,TNFSF10 CMPK2,CXCL10,DDX58,EPSTI1 ,HERC6,IFI35,IFI44,IFIT2,IFIT3,I RF7,IRF9,ISG15,LGALS3BP,OA TRIM24 transcription regulator Inhibited -3.819 6.89E-25 S1,STAT1 CXCL10,DDX58,IFI44,IFIT2,IFIT g-protein coupled 3,IRF7,ISG15,ISG20,OAS1,OAS2 ACKR2 receptor Inhibited -3.606 1.17E-26 ,OAS3,RSAD2,STAT1 CXCL10,DDX58,IFI44,IFIT1,IFIT 2,IFIT3,IRF7,ISG15,ISG20,MX1, SOCS1 other Inhibited -3.401 1.68E-19 OAS1,OAS2,STAT1 CMPK2,CXCL10,DDX58,HERC6 g-protein coupled ,IFI35,IFIT2,IRF7,ISG20,RSAD2, PTGER4 receptor Inhibited -3.317 4.27E-15 TNFSF10,XAF1 dexamethasone chemical drug Inhibited -3.215 1.03E-09 CMPK2,CXCL10,F3,GBP1,IFIT1,

157

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset IFIT3,IFITM1,IFITM3,IRF7,IRF9, ISG20,LGALS3BP,LY6E,MX1,S1 00A8,SAMSN1,STAT1,TNFSF10 CMPK2,DDX58,GBP1,HLA- F,LY6E,PARP9,STAT1,TRIM22, NKX2-3 transcription regulator Inhibited -3.000 5.16E-10 XAF1 CXCL10,CXCL11,GBP1,IRF7,IR IRF4 transcription regulator Inhibited -2.969 1.16E-11 F9,ISG15,OAS1,STAT1,TNFSF10 CXCL10,IFI6,IFITM3,IRF7,IRF9, USP18 peptidase Inhibited -2.942 1.55E-20 ISG15,MX1,OAS1,TNFSF10 CXCL10,IFIT1,IFIT2,ISG20,MX1 SOCS3 phosphatase Inhibited -2.613 1.88E-10 ,OAS1,OAS2 IFI6,IFIT2,IFITM1,OAS1,OAS2, GAPDH enzyme Inhibited -2.611 4.31E-13 OAS3,STAT1 CXCL10,DDX58,IFI6,IFIT1,IFIT2 2-aminopurine chemical reagent Inhibited -2.606 2.51E-14 ,ISG15,OAS1 CXCL10,CXCL11,F3,IFIT2,S100 IL10 cytokine Inhibited -2.598 7.27E-06 A8,STAT1,TNFSF10 CXCL10,IFIT2,IFIT3,IRF7,OAS2, Irgm1 other Inhibited -2.449 3.33E-10 RSAD2 CXCL10,F3,IFI6,IFIT1,IFITM1,IF ITM3,IRF9,ISG15,MX1,MX2,OA KRAS enzyme Inhibited -2.447 4.18E-12 S1,STAT1 CXCL10,GBP1,IFIT1,IRF7,TNFS TAB1 enzyme Inhibited -2.433 2.11E-11 F10,XAF1 epigallocatechin AXL,CXCL10,F3,ISG15,STAT1, -gallate chemical drug Inhibited -2.324 6.25E-06 TNFSF10 fontolizumab biologic drug Inhibited -2.236 1.89E-10 CCL8,IFI35,ISG15,MX1,RSAD2 CLEC5A,CXCL10,OAS2,OAS3,S mir-21 microrna Inhibited -2.236 1.04E-06 IGLEC1,STAT1

158

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset ISG15 4.054 other Inhibited -2.222 6.51E-13 DDX58,IFI6,IFITM3,MX1,OAS1 CXCL10,IFIT3,IRF7,ISG15,OAS1 DNASE2 enzyme Inhibited -2.213 3.40E-13 ,OAS3,RSAD2 BST2,CMPK2,HERC6,IFI27,IFI3 5,IFI44L,IFI6,IFITM1,ISG15,LGA LS3BP,OAS1,OAS2,OAS3,STAT CNOT7 transcription regulator Inhibited -2.213 8.75E-31 1 CXCL10,GBP1,IFI35,IRF7,ISG15 IRF2 transcription regulator Inhibited -2.144 4.75E-11 ,OAS1,TNFSF10 BST2,CXCL10,GBP1,IFI35,IFIT1 ,IFITM1,IRF9,STAT1,TNFSF10, CD3 complex Inhibited -2.142 5.88E-07 XAF1 CCL8,CXCL10,HLA- F,IFI27,IFIT1,MX1,OAS2,S100A fluticasone chemical drug Inhibited -2.132 8.62E-11 8,STAT1 IL10RA transmembrane receptor Inhibited -2.000 3.66E-03 CLEC5A,IRF7,RSAD2,STAT1 PRDM1 transcription regulator Inhibited -2.000 1.08E-03 AIM2,RSAD2,S100A8,TNFSF10 BST2,CCL8,CMPK2,CXCL10,CX CL11,DDX58,GBP1,HERC5,HER C6,IFI27,IFI35,IFI44,IFI44L,IFI6,I FIT1,IFIT2,IFIT3,IFITM1,IFITM3 ,IRF7,IRF9,ISG15,ISG20,LGALS 3BP,LY6E,MX1,MX2,OAS1,OAS 2,OAS3,PARP9,REC8,RSAD2,SA IFNA2 cytokine Activated 5.919 9.45E-67 MSN1,STAT1,TNFSF10,XAF1 AIM2,BST2,CCL8,CLEC5A,CMP K2,CXCL10,CXCL11,DDX58,F3, GBP1,HERC6,HLA- F,IFI27,IFI35,IFI44,IFI44L,IFI6,IF IFNG cytokine Activated 5.650 2.73E-41 IT1,IFIT2,IFIT3,IFITM1,IFITM3,I

159

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset RF7,IRF9,ISG15,ISG20,LGALS3 BP,LY6E,MX1,MX2,OAS1,OAS2 ,OAS3,PARP9,RSAD2,S100A8,S TAT1,TNFSF10,TRIM22 BST2,CXCL10,CXCL11,DDX58, GBP1,HERC5,HERC6,IFI27,IFI35 ,IFI44,IFI44L,IFI6,IFIT1,IFIT2,IFI T3,IFITM1,IFITM3,IRF9,ISG15,I SG20,LGALS3BP,MX1,OAS1,O AS2,OAS3,RSAD2,STAT1,TRIM IFNL1 cytokine Activated 5.219 6.31E-60 22 BST2,CMPK2,CXCL10,CXCL11, DDX58,EPSTI1,HERC5,HERC6,I FI35,IFI44,IFI44L,IFI6,IFIT1,IFIT 3,IFITM1,IRF7,IRF9,ISG15,LY6E ,MX2,OAS1,OAS2,OAS3,REC8,R PRL cytokine Activated 5.138 2.54E-40 SAD2,STAT1,XAF1 AIM2,BST2,CXCL10,CXCL11,D DX58,F3,GBP1,IFI27,IFI35,IFI6,I FIT1,IFIT2,IFIT3,IFITM1,IFITM3 ,IRF7,IRF9,ISG15,ISG20,MX1,M X2,OAS1,OAS2,RSAD2,STAT1, Interferon alpha group Activated 5.111 2.73E-37 TNFSF10,TRIM22 CCL8,CLEC5A,CMPK2,CXCL10, CXCL11,DDX58,F3,GBP1,HERC 5,IFI27,IFI35,IFI44,IFI44L,IFI6,IF IT1,IFIT2,IFIT3,IFITM1,IRF7,IRF 9,ISG15,ISG20,LGALS3BP,MX1, lipopolysacchari MX2,OAS1,OAS2,OAS3,RSAD2, de chemical drug Activated 5.064 3.25E-30 S100A8,SAMSN1,STAT1,TNFSF

160

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset 10,TRIM22,XAF1 CCL8,CMPK2,CXCL10,DDX58, GBP1,HERC5,IFI35,IFI44,IFI44L, IFI6,IFIT1,IFIT2,IFIT3,IFITM1,IF ITM3,IRF7,IRF9,ISG15,ISG20,M X1,MX2,OAS1,OAS2,OAS3,RSA D2,S100A8,STAT1,TNFSF10,TRI IRF7 2.352 transcription regulator Activated 5.058 1.33E-53 M22,XAF1 AXL,BST2,CCL8,CXCL10,CXCL 11,DDX58,GBP1,HLA- F,IFI27,IFIT1,IFIT2,IFIT3,IRF7,I RF9,ISG15,ISG20,LY6E,MX1,OA S1,OAS2,OAS3,RSAD2,STAT1,T poly rI:rC-RNA chemical reagent Activated 4.793 1.39E-29 NFSF10 BST2,CXCL10,CXCL11,DDX58, HERC5,IFI27,IFI35,IFI44,IFI6,IFI T1,IFIT2,IFIT3,IFITM1,IRF7,IRF 9,ISG15,MX1,MX2,OAS1,OAS2, IFN Beta group Activated 4.782 6.72E-42 RSAD2,STAT1,TNFSF10,XAF1 BST2,CMPK2,CXCL10,CXCL11, DDX58,HERC5,IFI27,IFI6,IFIT1,I FIT2,IFIT3,IFITM1,IRF7,IRF9,IS G15,ISG20,MX1,OAS1,OAS2,RS IFNB1 cytokine Activated 4.504 2.09E-32 AD2,STAT1,TNFSF10,XAF1 CMPK2,CXCL10,DDX58,GBP1, HLA- F,IFI27,IFI44,IFI6,IFIT1,IFIT2,IFI T3,IFITM3,IRF7,ISG15,ISG20,O IRF3 transcription regulator Activated 4.365 5.04E-31 AS1,OAS2,OAS3,RSAD2,STAT1 IRF1 transcription regulator Activated 4.170 1.21E-29 CXCL10,CXCL11,IFI35,IFI44L,I

161

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset FIT1,IFIT2,IFIT3,IFITM1,IFITM3 ,IRF7,IRF9,ISG15,MX1,OAS1,O AS2,RSAD2,STAT1,TNFSF10,TR IM22 CMPK2,CXCL10,CXCL11,DDX5 8,IFI44,IFIT1,IFIT2,IFIT3,IFITM3 ,IRF7,ISG15,ISG20,OAS1,OAS2, IRF5 transcription regulator Activated 4.031 4.14E-32 RSAD2,STAT1,TNFSF10 BST2,IFI27,IFI35,IFI44,IFI6,IFIT 1,IFIT3,IFITM1,IRF7,ISG15,MX1 ,MX2,OAS1,OAS2,RSAD2,STAT oblimersen biologic drug Activated 4.000 1.43E-31 1 AXL,CMPK2,CXCL10,CXCL11, GBP1,HERC6,IFI27,IFI35,IFI6,IF IT1,IFIT2,IFIT3,IFITM1,IFITM3,I RF7,IRF9,ISG15,LY6E,MX1,OAS 2,RSAD2,STAT1,TNFSF10,TRIM STAT1 2.284 transcription regulator Activated 3.994 4.02E-34 22 CXCL10,F3,IFI27,IFI44,IFI6,IFIT bromodeoxyurid 1,IFIT2,IFIT3,IFITM1,IRF7,ISG1 ine chemical drug Activated 3.812 4.08E-29 5,MX1,OAS1,STAT1,TNFSF10 CXCL10,F3,IFI27,IFI44,IFI6,IFIT 1,IFIT2,IFIT3,IFITM1,IRF7,ISG1 stallimycin biologic drug Activated 3.812 2.44E-30 5,MX1,OAS1,STAT1,TNFSF10 CXCL10,DDX58,IFI35,IFIT2,IFIT 3,IFITM3,IRF7,IRF9,ISG20,OAS1 ,OAS2,RSAD2,STAT1,TNFSF10, Ifnar group Activated 3.805 6.71E-26 XAF1 CCL8,CXCL10,CXCL11,IFI27,IFI IFNA1/IFNA13 cytokine Activated 3.786 5.61E-29 6,IFIT1,IFIT2,IFITM1,ISG15,MX

162

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset 1,OAS1,OAS2,RSAD2,SIGLEC1, STAT1 BST2,CLEC5A,CXCL10,CXCL11 ,DDX58,F3,GBP1,HERC5,HLA- F,IFI27,IFI6,IFIT1,IFIT3,IFITM1,I RF7,ISG15,MX1,OAS1,OAS2,OA TNF cytokine Activated 3.743 5.93E-15 S3,S100A8,STAT1,TNFSF10 DDX58,IFI27,IFI35,IFI6,IFIT1,IFI TM1,ISG15,ISG20,LGALS3BP,O EIF2AK2 kinase Activated 3.689 1.65E-23 AS1,OAS3,PARP9,REC8,STAT1 CMPK2,CXCL10,DDX58,IFIT1,I FIT2,IFIT3,IFITM3,IRF7,ISG15,I SG20,OAS1,OAS2,RSAD2,STAT MAVS other Activated 3.672 1.85E-26 1 CXCL10,IFI35,IFI6,IFIT1,IFIT2,I FIT3,IRF9,LY6E,OAS1,OAS2,OA S3,PARP9,S100A8,STAT1,TRIM TGM2 enzyme Activated 3.499 6.45E-21 22,XAF1 CXCL10,CXCL11,DDX58,IFIT1,I FITM1,IFITM3,IRF7,ISG15,ISG2 0,MX1,OAS2,OAS3,RSAD2,S100 Ifn group Activated 3.403 1.45E-25 A8,STAT1,TRIM22 CMPK2,CXCL10,CXCL11,DDX5 8,IFIT1,IFIT2,IFIT3,IRF7,ISG15,I TICAM1 other Activated 3.373 7.27E-17 SG20,RSAD2,TNFSF10 AIM2,CXCL10,IFI27,IFI35,IFIT2, IFITM1,IRF7,ISG15,MX1,OAS1, decitabine chemical drug Activated 3.297 2.67E-09 STAT1 CXCL10,GBP1,IFI27,IFI35,IFI6,I STAT2 transcription regulator Activated 3.218 2.59E-35 FIT1,IFIT2,IFIT3,IFITM1,IRF7,IR

163

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset F9,ISG15,MX1,OAS1,OAS2,RSA D2,TNFSF10 DDX58,HERC5,IFI44,IFI44L,IFIT 3,IFITM3,ISG15,ISG20,OAS2,OA PAF1 other Activated 3.162 2.67E-18 S3 CMPK2,CXCL10,CXCL11,F3,GB P1,HERC5,IFIT1,IFIT3,IRF7,ISG 15,ISG20,MX1,OAS2,RSAD2,S10 IL1B cytokine Activated 3.076 3.97E-13 0A8,STAT1,TNFSF10 CMPK2,CXCL10,IFIT2,IFIT3,IRF DOCK8 other Activated 3.000 1.67E-14 7,ISG15,ISG20,RSAD2,STAT1 CMPK2,CXCL10,IFIT2,IFIT3,I RF7,ISG15,ISG20,RSAD2,STAT SASH1 other Activated 3 2.19E-14 1 CMPK2,CXCL10,IFIT2,IFIT3,IRF NFATC2 transcription regulator Activated 3.000 1.40E-11 7,ISG15,ISG20,RSAD2,STAT1 - CMPK2,CXCL10,IFIT2,IFIT3,IRF SAMSN1 2.116 other Activated 3.000 2.23E-13 7,ISG15,ISG20,RSAD2,STAT1 IFI35,IFITM1,IRF7,ISG20,MX1,O imiquimod chemical drug Activated 2.993 4.15E-14 AS1,RSAD2,STAT1,TNFSF10 DDX58,HERC5,IFI27,IFI44L,IFIT ribavirin chemical drug Activated 2.985 3.85E-17 3,IFITM1,IRF7,ISG15,MX1 CXCL10,DDX58,IFI27,IFI35,IFI4 4,IFIT1,IFIT2,IFIT3,IRF7,ISG15,I DDX58 2.552 enzyme Activated 2.968 1.04E-26 SG20,OAS1,RSAD2,STAT1 HLA- F,IFI27,IFI6,IFIT1,IFITM1,IRF9,I TNFSF10 2.487 cytokine Activated 2.954 1.63E-13 SG15,STAT1,TNFSF10 AXL,CXCL10,IFIT2,IFIT3,IRF7, IFN alpha/beta group Activated 2.929 6.71E-14 LY6E,RSAD2,STAT1,TNFSF10

164

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset CCL8,CXCL10,CXCL11,F3,HER NFkB C5,HLA- (complex) complex Activated 2.919 2.74E-07 F,IRF7,ISG15,RSAD2,TNFSF10 CXCL10,DDX58,F3,IFI27,IFI35,I FI44,IFI44L,IFI6,IFIT1,IFIT2,IFIT 3,IFITM1,IRF9,ISG15,LGALS3B P,LY6E,OAS1,OAS2,OAS3,PAR chemical - endogenous P9,REC8,S100A8,STAT1,TNFSF tretinoin mammalian Activated 2.820 1.42E-19 10,TRIM22,XAF1 IFI44,IFIT1,IFIT2,IFIT3,IRF7,ISG PARP9 2.781 enzyme Activated 2.804 4.47E-18 15,OAS2,STAT1 CCL8,CXCL10,CXCL11,F3,GBP P38 MAPK group Activated 2.783 5.08E-07 1,IRF7,STAT1,TNFSF10 AXL,CMPK2,CXCL10,IFI44,IFIT 2,IFIT3,IRF7,ISG15,OAS1,OAS2, IFNAR1 transmembrane receptor Activated 2.738 5.16E-20 OAS3,RSAD2,STAT1 CCL8,CMPK2,CXCL10,F3,IFIT2, IFIT3,IFITM3,IRF7,ISG15,ISG20, TLR4 transmembrane receptor Activated 2.725 1.15E-15 MX1,RSAD2,STAT1,TNFSF10 EPSTI1,IFI27,IFI44,IFI44L,IFIT1, MSC transcription regulator Activated 2.646 2.57E-11 IRF7,XAF1 salmonella minnesota R595 lipopolysacchari chemical - endogenous AXL,CCL8,CMPK2,CXCL10,IFI des non-mammalian Activated 2.607 5.56E-09 T2,ISG15,RSAD2 CMPK2,CXCL10,DDX58,HERC6 ,IFI35,IFIT2,IRF7,ISG20,RSAD2, APP other Activated 2.594 1.13E-07 S100A8,TNFSF10,XAF1 AIM2,F3,GBP1,HLA- SMARCA4 transcription regulator Activated 2.588 2.45E-06 F,IFI27,IFIT1,IFITM1,IFITM3,TN

165

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset FSF10 CCL8,CXCL10,CXCL11,IFIT1,IF IFNA4 cytokine Activated 2.583 3.54E-14 IT2,ISG15,MX1 5-O-mycolyl- beta-araf-(1- >2)-5-O- mycolyl-alpha- araf-(1->1')- chemical - endogenous CCL8,CXCL10,CXCL11,IFIT1,IF glycerol non-mammalian Activated 2.449 2.24E-08 IT2,IFIT3 CCL8,CXCL10,IFIT2,IRF9,ISG15 MAP2K6 kinase Activated 2.436 1.55E-10 ,STAT1,TNFSF10 CMPK2,CXCL10,IFIT2,ISG15,IS Map3k7 kinase Activated 2.433 2.16E-09 G20,RSAD2 CCL8,CXCL10,CXCL11,IFIT1,IS IFNA8 cytokine Activated 2.428 2.85E-13 G15,MX1 CXCL10,CXCL11,F3,GBP1,HLA- FAS transmembrane receptor Activated 2.425 8.09E-07 F,IFIT1,RSAD2,TNFSF10 CCL8,CXCL10,IRF9,ISG15,STA MAP2K3 kinase Activated 2.425 6.29E-09 T1,TNFSF10 CXCL10,GBP1,IFI44L,STAT1,T Ifn gamma complex Activated 2.418 2.57E-08 NFSF10,XAF1 CCL8,CXCL10,CXCL11,IFIT1,IS IFNA10 cytokine Activated 2.412 1.72E-13 G15,MX1 CCL8,CXCL10,CXCL11,IFIT1,IS IFNA5 cytokine Activated 2.412 1.72E-13 G15,MX1 CCL8,CXCL10,CXCL11,IFIT1,IS IFNA7 cytokine Activated 2.412 1.72E-13 G15,MX1 CCL8,CXCL10,CXCL11,IFIT1,IS IFNA14 cytokine Activated 2.412 1.72E-13 G15,MX1 IFNA6 cytokine Activated 2.412 1.72E-13 CCL8,CXCL10,CXCL11,IFIT1,I

166

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset SG15,MX1 CCL8,CXCL10,CXCL11,IFIT1,IS IFNA16 cytokine Activated 2.412 2.85E-13 G15,MX1 CCL8,CXCL10,CXCL11,IFIT1,IS IFNA21 cytokine Activated 2.409 1.72E-13 G15,MX1 Immunoglobuli CCL8,CXCL10,CXCL11,F3,RSA n complex Activated 2.401 8.46E-06 D2,S100A8 AIM2,CMPK2,CXCL10,CXCL11, DDX58,HERC5,IFI44,IFI44L,IFI6 ,IFIT1,IFIT2,IFIT3,IRF7,ISG15,IS G20,MX1,MX2,OAS1,RSAD2,S1 TLR3 transmembrane receptor Activated 2.398 2.02E-31 00A8,STAT1,TNFSF10 F3,GBP1,HERC5,IFI35,IRF7,IRF 9,ISG15,MX1,OAS1,STAT1,TNF TP53 transcription regulator Activated 2.384 2.92E-06 SF10,TRIM22,XAF1 CXCL10,GBP1,IFI27,IFIT2,IFIT3 ,IFITM1,IRF7,ISG15,MX1,OAS2, IRF9 2.670 transcription regulator Activated 2.384 4.93E-25 STAT1,TNFSF10 CLEC5A,CXCL10,OAS1,OAS2,O cigarette smoke chemical toxicant Activated 2.366 4.18E-06 AS3,STAT1 BST2,CXCL10,CXCL11,MX1,ST IL27 cytokine Activated 2.297 2.25E-07 AT1,TNFSF10 JAK1 kinase Activated 2.236 1.94E-08 HLA-F,IFIT2,IRF9,MX1,STAT1 AXL,F3,HERC5,IFI44,LGALS3B SYVN1 transporter Activated 2.236 8.50E-06 P IFIT2,IRF7,ISG20,OAS2,TNFSF1 TNK1 kinase Activated 2.236 1.89E-10 0 HLA- F,IFI35,IFITM1,IRF9,MX1,STAT aldesleukin biologic drug Activated 2.236 5.83E-10 1

167

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset CXCL10,F3,GBP1,HLA- F,IFI35,IRF7,IRF9,ISG20,MX1,O OSM cytokine Activated 2.225 3.12E-11 AS1,S100A8,STAT1,TRIM22 g-protein coupled CXCL10,IFI44,IRF7,ISG15,STAT FZD9 receptor Activated 2.219 1.89E-10 1 CXCL10,IFIT2,ISG15,ISG20,STA STAT4 transcription regulator Activated 2.219 8.95E-05 T1 deferoxamine chemical drug Activated 2.219 2.19E-05 F3,IFI6,ISG20,MX2,RSAD2 F3,IRF7,LGALS3BP,STAT1,TNF doxorubicin chemical drug Activated 2.219 9.82E-04 SF10 E. coli B4 CLEC5A,CXCL10,DDX58,F3,IFI lipopolysacchari 35,IFIT3,IFITM3,PARP9,RSAD2, de chemical toxicant Activated 2.214 7.49E-12 SAMSN1,TNFSF10 DDX58,IFI27,IFI6,IFIT1,IFIT2,IFI BRCA1 transcription regulator Activated 2.214 8.17E-13 T3,IFITM1,IRF7,MX1,STAT1 CXCL10,CXCL11,OAS1,OAS2,O ORMDL3 other Activated 2.200 3.95E-11 AS3 CXCL10,IRF7,ISG15,OAS1,OAS IFIH1 enzyme Activated 2.190 6.01E-09 2 CXCL10,IFIT2,IFIT3,IRF7,ISG15 TLR9 transmembrane receptor Activated 2.178 2.03E-09 ,RSAD2,S100A8,TNFSF10 CMPK2,CXCL10,CXCL11,IFIT1, TBK1 kinase Activated 2.174 8.46E-14 IFIT2,IRF7,ISG15,ISG20,RSAD2 Salmonella enterica serotype abortus equi lipopolysacchari de chemical toxicant Activated 2.000 4.26E-04 F3,GBP1,IFIT2,ISG15 SMARCB1 transcription regulator Activated 2.000 3.90E-04 IFITM1,MX1,OAS1,OAS3

168

Fold Predicted Upstream Chan Activation Activatio p-value of Regulator ge Molecule Type State n z-score overlap Target molecules in dataset DNMT3B enzyme Activated 2.000 4.53E-05 IFI27,IFIT1,IFITM1,STAT1 cocaine chemical drug Activated 2.000 3.47E-04 IFIT1,MX1,OAS1,STAT1

169

VITA

Josephine Garban

EDUCATION

Pennsylvania State University (2011- 2015)-Present  PhD. Candidate Molecular Medicine  Jack Vanden Heuvel PhD, The Pennsylvania State University, Professor of Toxicology, (2011-Present) Thesis: Regulation of Cholesterol Efflux by MicroRNAs and impact of Diet and Xenobiotics Temple University (2007-2011)  Bachelors of Science, with Distinction, Neuroscience

HONORS AND AWARDS

 Alfred P. Sloan Fellow (2013-Present)  Pennsylvania State University College of Agricultural Sciences Entrepreneurship and Innovation Award (2015)  College of Agricultural Sciences Travel Award (2015) PUBLICATIONS

 Garban J. A., and Vanden Heuvel J., (In preparation). The microRNA miR-708 Affects FXR-Dependent Regulation of Cholesterol Efflux in Macrophages, Hepatocytes and Intestinal Cells  Garban J. A., Lui X., Kris-Etherton P., and Vanden Heuvel J., (In preparation) Use of microRNAs as biomarkers for efficacy of dietary supplementation  Vanden Heuvel, J.P., Garban, J.A., Hannon, D.H., and Toyokawa, K. (In preparation). Effects of PFOA and PFOS on cholesterol efflux and gene expression in THP-1, Huh-7 and Caco-2 cells.  X. Liu1, P. J. H. Jones2, S. G. West1,3, J. Vanden Heuvel4, J. Garban4, B. Lamarche5, , D. J. A. Jenkins6, Philip W. Connelly7, P. Couture5, J. A. Fleming1, P. M. Kris-Etherton., (In Preparation) Vegetable Oils with Different Unsaturated Fatty Acid Profiles Increase Serum Mediated Cholesterol Efflux from THP-1 Macrophages: The Canola Oil Multicenter Intervention Trial