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Investigation of the atherogenic potential of different human macrophage phenotypes Chu, Eugene 2014

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INVESTIGATION OF THE ATHEROGENIC POTENTIAL OF DIFFERENT HUMAN MACROPHAGE PHENOTYPES  by  Eugene Chu  B.Sc., Simon Fraser University, 2007  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  Doctor of Philosophy  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Pathology and Laboratory Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)   March 2014  ? Eugene Chu, 2014  ii Abstract Atherosclerosis is the underlying cause of ischemic heart disease and strokes which accounts for the majority of cardiovascular deaths.  Macrophages are one of the key cells in atherosclerotic lesions and are responsible the accumulation of lipids as well as the destabilization of plaques through the expression of proteases.  Macrophages display a number of phenotypes depending on their specific environment which may affect atherosclerosis in different ways.  This thesis describes the study of macrophages derived from primary human peripheral blood monocytes that were cultured and induced towards different phenotypes with combinations of interferon ? (IFN?) and tumour necrosis factor ? (TNF?), interleukin(IL)-4 and -13, or IL-10. The macrophage phenotypes were assessed for their protease and protease inhibitor profile with the use of a microarray and were also evaluated for their ability to maintain cholesterol homeostasis.   Macrophages treated with IFN?/TNF? demonstrated a trend for increased protease activation.  The gene SPINT2 was found to be up-regulated by IL-4/13 treatment when compared with all other macrophage phenotypes and may serve as a marker of alternative macrophage activation.   Both IFN? and TNF? were found to decrease the amount of cholesterol accumulated when macrophages were incubated with oxidized low density lipoprotein, which may be explained by the concurrent down regulation of the macrophage scavenger receptor and CD36.  IFN? was also found to inhibit the peroxisome proliferator-activated receptor (PPAR)?-mediated upregulation of CD36  iii protein by rosiglitazone without modulating PPAR? protein levels.  Additionally, IL-4/13 treatment was found to increase the rate of apolipoprotein AI-mediated cholesterol efflux, yet cause a decrease in ABCA1 protein levels.   The cell line U937 was then evaluated as a model of primary human macrophages to study the regulation of CD36 by IFN?.  Although IFN? treated U937 cells showed a reduction of mature CD36 protein, they  did not show an inhibition of PPAR? activity.  Further studies validating the targets identified in the microarray may unveil novel proteases involved in atherosclerosis.  These findings also provide insight into how different macrophage phenotypes may handle cholesterol in atherosclerotic conditions.    iv Preface Chapter 2:  Reinhild Kappelhoff from Dr. Chris Overall's laboratory at UBC performed the microarray.  Eugene Chu was responsible for the isolation of the RNA and all downstream analyses.  Chapter 3: Much of the work presented in chapter 3 pertaining to cholesterol accumulation was published in the following manuscript: Chu EM, Tai DC, Beer JL, Hill JS. Macrophage heterogeneity and cholesterol homeostasis: classically-activated macrophages are associated with reduced cholesterol accumulation following treatment with oxidized LDL.  Biochim Biophys Acta. 2013 Feb;1831(2):378-86. doi: 10.1016/j.bbalip.2012.10.009.   Eugene Chu and John Hill were responsible for the conception and design of the study.  Eugene Chu wrote the manuscript which was edited by John Hill.  Daven Tai was responsible for performing some of the cholesterol accumulation assays and assisting with the flow cytometry.  Jennifer Beer collected cells and operated the flow cytometer.  Chapter 4:  Jennifer Beer and Gabriel Dorighello  assisted with the flow cytometry experiments.  Sarah Kam performed Western blots on MG-132 treated macrophages.  All other work was performed by Eugene Chu.  v Table of contents Abstract .................................................................................................................... ii Preface ..................................................................................................................... iv Table of contents ..................................................................................................... v List of tables ............................................................................................................ xi List of figures ......................................................................................................... xii List of abbreviations ............................................................................................. xiv Acknowledgements ............................................................................................ xviii Dedication ............................................................................................................. xix Chapter 1: Introduction ........................................................................................... 1 1.1 The global health problem of heart disease ................................................. 1 1.2 Pathogenesis of atherosclerosis .................................................................. 1 1.3 Innate immunity in atherosclerosis .............................................................. 2 1.4 Adaptive immunity in atherosclerosis .......................................................... 4 1.5 Role of macrophages in atherosclerosis ...................................................... 7 1.6 Macrophage lipoprotein uptake by scavenger receptors ............................. 7 1.6.1 Macrophage scavenger receptor ............................................................. 8 1.6.2 CD36 ...................................................................................................... 10 1.6.3 Other scavenger receptors involved in lipoprotein uptake...................... 14 1.7 Overview of cholesterol trafficking derived from LDL ................................. 17 1.8 Macrophage cholesterol efflux in atherosclerosis ...................................... 20  vi 1.8.1 Aqueous diffusion-mediated cholesterol efflux ....................................... 21 1.8.2 SR-BI -mediated cholesterol efflux ......................................................... 22 1.8.3 ABCA1-mediated cholesterol efflux ....................................................... 24 1.8.4 ABCG1-mediated cholesterol efflux ....................................................... 27 1.9 Macrophage heterogeneity ........................................................................ 28 1.9.1 Induction of classically-activated macrophages ..................................... 31 1.9.2 Classically-activated macrophages and cholesterol homeostasis .......... 35 1.9.3 Induction of alternatively-activated macrophages .................................. 36 1.9.4 Alternatively-activated macrophages and cholesterol homeostasis ....... 39 1.9.5 Distribution, abundance and localization of macrophage phenotypes in the atherosclerotic plaque ................................................................................. 40 1.10 Macrophage-derived proteases in atherosclerosis .................................... 42 1.11 Macrophage heterogeneity and protease expression ................................ 47 1.12 Overall research objectives ....................................................................... 49 Chapter 2: Characterization of the protease and protease inhibitor profile of different macrophage sub-phenotypes ................................................................ 52 2.1 Background and rationale .......................................................................... 52 2.2 Specific aims and hypotheses ................................................................... 53 2.3 Materials and methods .............................................................................. 54 2.3.1 Cell culture ............................................................................................. 54 2.3.2 CLIP/CHIP microarray ............................................................................ 54 2.3.3 Statistical analysis of microarray data .................................................... 56 2.3.4 Microarray data mining ........................................................................... 56  vii 2.4 Results ....................................................................................................... 57 2.4.1 Determining the protease and protease inhibitor profile of different macrophage phenotypes .................................................................................. 57 2.4.2 Identification of proteases and protease inhibitors differentially regulated by macrophage phenotypes.............................................................................. 58 2.4.3 Hierarchical clustering and characterization of differentially expressed genes  ..............................................................................................................  61 2.4.4 Discussion .............................................................................................. 69 Chapter 3: Macrophage heterogeneity and cholesterol homeostasis: M1 macrophages are associated with reduced cholesterol accumulation following treatment with oxidized LDL while M2a macrophages have increased rates of cholesterol efflux. .................................................................................................. 80 3.1 Background and rationale .......................................................................... 80 3.2 Specific aims and hypotheses ................................................................... 81 3.3 Materials and methods .............................................................................. 83 3.3.1 Cell culture ............................................................................................. 83 3.3.2 Isolation and oxidation of low density lipoprotein and high density lipoprotein ......................................................................................................... 83 3.3.3 oxLDL cellular association ..................................................................... 85 3.3.4 Cellular cholesterol accumulation .......................................................... 85 3.3.5 Cholesterol efflux ................................................................................... 86 3.3.6 RNA extraction and analysis .................................................................. 87 3.3.7 Protein extraction and Western blot analysis ......................................... 87  viii 3.3.8 Flow cytometry ....................................................................................... 88 3.3.9 Statistical analysis .................................................................................. 89 3.4 Results ....................................................................................................... 90 3.4.1 Macrophages treated with IFN?/TNF? associate with less oxLDL and have reduced CD36 and MSR1 expression. ..................................................... 90 3.4.2 Macrophages treated with IFN?/TNF? accumulate less total cholesterol when treated with oxLDL and have reduced CD36 and MSR1 expression. ..... 93 3.4.3 IL-4/13 treatment increases PPAR? expression levels........................... 97 3.4.4 IFN? attenuates the upregulation of CD36 by rosiglitazone ................... 99 3.4.5 IL-4/13 treatment increases ApoA-mediated cholesterol efflux. ........... 101 3.4.6 IL-4/13 treatment reduces the expression of LXR? target proteins. ..... 103 3.5 Discussion ............................................................................................... 105 Chapter 4: Evaluation of the monocyte cell line U937 as a model to study the regulation of CD36 expression by IFN? treatment. ........................................... 114 4.1 Background and rationale ........................................................................ 114 4.2 Specific aims and hypotheses ................................................................. 117 4.3 Materials and methods ............................................................................ 117 4.3.1 Cell culture ........................................................................................... 117 4.3.2 Protein extraction and Western blot analysis ....................................... 118 4.3.3 Flow cytometry ..................................................................................... 118 4.3.4 Proteasomal and lysosomal degradation studies. ................................ 118 4.3.5 Statistical analysis ................................................................................ 119 4.4 Results ..................................................................................................... 120  ix 4.4.1 Treatment of U937 macrophages with IFN? decreases cell surface CD36 protein expression but does not attenuate rosiglitazone-mediated increases in cell surface CD36. .......................................................................................... 120 4.4.2 Treatment of U937 macrophages with IFN? decreases the 90-105 kDa CD36 protein band. ......................................................................................... 122 4.4.3 Treatment of U937 macrophages with IFN? does not decrease PPAR? protein expression. ......................................................................................... 124 4.4.4 IFN? does not induce CD36 degradation by the lysosomal or proteasomal pathways in U937 cells. ............................................................. 125 4.4.5 IFN? does not induce CD36 degradation by the proteasomal pathway in U937 cells. ...................................................................................................... 127 4.5 Discussion ............................................................................................... 129 Chapter 5: Conclusions and future directions .................................................. 134 5.1 Conclusions ............................................................................................. 134 5.2 Limitations and future directions .............................................................. 136 References ........................................................................................................... 141 Appendices .......................................................................................................... 197 Appendix A Additional material pertaining to methodology ................................ 197 A.1 Verification of LDL oxidation ................................................................ 197 A.2 Taqman gene expression assays used ................................................ 197 A.3 Immunohistochemical staining for macrophage phenotype markers ... 201 Appendix B Supplemental data .......................................................................... 202 B.1 Complete protease and protease inhibitor profile ................................. 202  x B.2 Cell surface CD36 protein does not increase beyond 24 hours of rosigltiazone treatment ................................................................................... 218   xi List of tables Table 1-1: Summary of macrophage phenotypes .................................................... 30 Table 1-2: Macrophage-derived proteases and protease inhibitors in atherosclerosis ................................................................................................................................. 46 Table 2-1:  Number of proteases and protease inhibitors with a greater than or equal to 1.5-fold change compared with the untreated control. ......................................... 58 Table 2-2:  Proteases and protease inhibitors that differentially expressed upon cytokine treatment. ................................................................................................... 61 Table 2-3: List of known pathways, functions and associations with disease for differentially expressed genes. ................................................................................ 69   xii List of figures Figure 1-1: Cholesterol trafficking of exogenous cholesterol ................................... 19 Figure 1-2: Classical activation of macrophages ..................................................... 34 Figure 1-3: IL-4 and IL-13 signaling through STAT6 and IL-10 signaling through STAT3. ..................................................................................................................... 38 Figure 2-1:  Expression heatmap image of all proteases and protease inhibitors. ... 57 Figure 2-2: Hierarchical clustering of differentially expressed genes. ...................... 62 Figure 2-3:  Comparison of the relative expression levels of differentially expressed genes in their respective cluster............................................................................... 63 Figure 3-1: Macrophages treated with IFN?/TNF? associate with less oxLDL and have reduced CD36 and MSR1 expression: ............................................................ 92 Figure 3-2: Macrophages treated with IFN?/TNF? accumulate less total cholesterol when treated with oxLDL and have reduced expression of CD36 and MSR1. ......... 94 Figure 3-3: Both IFN? and TNF? treatments reduce cholesterol accumulation and the expression of CD36 and MSR1: ......................................................................... 96 Figure 3-4:  IL-4/13 treatment increases PPAR? expression levels: ........................ 98 Figure 3-5:  IFN? attenuates the upregulation of CD36 by rosiglitazone: ............... 100 Figure 3-6:  IFN? treatment of MDMs attenuates PPAR? activity. ......................... 101 Figure 3-7:  IL-4/13 tre.atment increases ApoA-mediated cholesterol efflux. ......... 102 Figure 3-8: IL-4/13 treatment reduces the expression of LXR? target proteins...... 104 Figure 4-1:  Treatment of U937 macrophages with IFN? decreases cell surface CD36 protein expression but does not attenuate rosiglitazone-mediated increases in cell surface CD36. .................................................................................................. 121  xiii Figure 4-2:  Treatment of U937 macrophages with IFN? decreases the 90-105 kDa CD36 protein band. ................................................................................................ 123 Figure 4-3:  Treatment of U937 macrophages with IFN? does not decrease PPAR? protein expression. ................................................................................................. 124 Figure 4-4:  IFN? does not induce CD36 degradation by the lysosomal pathway in U937 cells. ............................................................................................................. 126 Figure 4-5:  IFN? does not induce CD36 degradation by the proteasomal pathway in U937 cells. ............................................................................................................. 128   xiv List of abbreviations  15d-PGJ2  15-deoxy ?12,14 prostaglandin J2 ABC   ATP-binding cassette ACAT   Acetyl-coenzyme A acetyltransferase acLDL   Acetylated low density lipoprotein ACTB   Beta actin ADAM   A disintegrin and metalloproteinase domain-containing protein AP-1    Activator protein 1 APC   Antigen presenting cell ApoAI   Apolipoprotein AI ARG   Arginase BSA   Bovine serum albumin CD   Clusters of differentiation CE   Cholesteryl ester CETP   Cholesteryl ester transfer protein CRP   C-reactive protein CXCL4  Chemokine ligand DAVID  Database for annotation, visualization and integrated discovery DEM    Diethylmaleate DiI 1,1 ? -dioctadecyl-3,3,3 ? ,3 ? -tetramethylindocarbocyanine perchlorate ECM   Extracellular matrix EE   Early endosome ER   Endoplasmic reticulum ERC   Endocytic recycling compartment FC   Free cholesterol FGA   Fibrinogen GAS    ?-activated sequence GM-CSF  Granulocyte macrophage colony stimulating factor HAI-2   Hepatocyte growth factor activator inhibitor-2 HETE   Hydroxy-5Z,8Z,11Z,13E-eicosatetraenoic acid  xv HODE   Hydroxyoctadecadienoic acid IC   Immune complex ICAM-1  Intercellular adhesion molecule-1 IFNGR   IFN? Receptor IFN?   Interferon ? IKK    I?B kinase IL   Interleukin IL-10R1   IL-10 receptor 1 IL-13R?1   IL-13 receptor ?1 IL-4R?   IL-4 receptor ? iNOS   Inducible nitric oxide synthase IRF    Interferon regulator factor ISRE    IFN-stimulated response element JAK    Janus kinase KEGG   Kyoto encyclopedia of genes and genomes LAL   Lysosomal acid lipase LBP   LPS binding protein LCAT   Lecithin:cholesterol acyltransferase LDL   Low density lipoprotein LDLR   Low density lipoprotein receptor LE   Late endosome LOX-1   Lectin-like oxidized low density lipoprotein receptor-1 LPS   Lipopolysaccharide LXR   Liver X receptor MARCO  Macrophage receptor with collagenous structure M-CSF  Macrophage colony stimulating factor MDM   Monocyte-derived macrophage mmLDL  Minimally modified low density lipoprotein MMP   Matrix metalloproteinase MSR   Macrophage scavenger receptor MyD88   Myeloid differentiation primary response gene 88  xvi NBD   Nucleotide binding domain nCEH   Neutral cholesterol ester hydrolase NF?B    nuclear factor kappa-light-chain-enhancer-of-activated B cells NIK    NF?B-inducing kinase nLDL   Native LDL NPC   Niemann-Pick, type C Nrf2   NF-E2 related factor 2 NZW    New Zealand White OMIM   Online Mendelian inheritance in man ORO   Oil Red O oxLDL   oxidized low density lipoprotein oxPL   oxidized phospholipid PC   Phosphatidylcholine PLG   Plasminogen PLTP   Phospholipid transfer protein PMA   Phorbol-12-myristate-13-acetate PPAR?  Peroxisome proliferator-activated receptor ? PPIB   Peptidylprolyl isomerase B PRR   Pattern recognition receptor RCT   Reverse cholesterol transport RIPA   Radio-immunoprecipitation assay  RIP1    Receptor-interacting kinase 1 RSG   Rosiglitazone RXR   Retinoid X receptor SAA   Serum amyloid A SAM   Significance analysis of microarrays SFN    Sulforaphane SH    Src homology domain SNP   Single nucleotide polymorphism SPHK1  Sphingosine kinase 1 SR-BI   Scavenger receptor BI  xvii SRCL   Scavenger receptor with C-type lectin SSC   Saline-sodium citrate STAT    Signal transducers and activators of transcription TBST   Tris-buffered saline and tween-20 TGF?   Transforming growth factor ? Th   T helper TIMP   Tissue inhibitor of metalloproteinases TIR    Toll interleukin-1 receptor TIRAP  TIR domain-containing adaptor protein, also known as Mal, MyD88-adaptor-like TLR   Toll-like receptor TNF-RI   TNF-receptor I TNF?   Tumour necrosis factor ? tPA   Tissue plasminogen activator TRADD   TNF receptor-associated death domain TRAF2   TNF receptor-associated factor 2 TRAM   TRIF-related adaptor molecule Treg   T regulatory TRIF    TIR domain-containing adaptor inducing IFN? TYK    Tyrosine kinase TZD   Thiazolidinedione ULS   Universal linkage system uPA   Urokinase-type plasminogen activator UPR   Unfolded protein response VCAM-1  Vascular cell adhesion molecule-1    xviii Acknowledgements I would like to thank all of the staff, students and faculty at St. Paul's hospital who have helped and encouraged me throughout this journey.  I would like to extend my gratitude to all of my colleagues in the laboratory.  In particular, I would like to thank Daven Tai for his support, encouragement and friendship.    I am especially grateful to my supervisor Dr. John Hill who has mentored me and fostered my pursuit for knowledge.  I am thankful for all that he has taught me about science and am lucky to have had the opportunity to work with him.   I would also like to thank all of the members of my committee including Ken Harder, Haydn Pritchard, Laura Sly, and Cheryl Wellington for their helpful insight.  This thesis would not have been possible without your guidance.   Special thanks are owed to my mom, dad, and two wonderful sisters for all of their unconditional love and unwavering support.  I am eternally grateful for everything that you have done.  Finally, I would like to thank Gwen Do for her patience, love, and support.       xix Dedication       To my family and friends,  who have always supported and believed in me. 1 Chapter 1:     Introduction 1.1 The global health problem of heart disease  Cardiovascular disease and more specifically ischemic heart disease continues to reign as the leading cause of death worldwide1,2.  It is responsible for approximately 12.8% of all worldwide deaths, followed closely by stroke and cerebrovascular disease1.  The problem is especially prevalent in high-income countries such as Canada and the United States and the problem is expected to worsen over the next 20 years3.  1.2 Pathogenesis of atherosclerosis  Atherosclerosis is a chronic inflammatory disease that is the major underlying cause for ischemic heart disease.  It is characterized by the formation of plaques, which contain lipid, immune cells, extracellular matrix, and debris, in the innermost layer of medium and large-sized arteries known as the intima, causing the hardening and thickening of these arteries.  Although several different theories exist to explain exactly how atherosclerosis develops, it is generally accepted that inflammation and the accumulation of cholesterol are fundamental to this process.  Under conditions of hypercholesterolemia, high levels of small, cholesterol-containing lipoproteins such as low density lipoprotein (LDL) are able to diffuse through the endothelial layer and enter and accumulate in the intima4.  LDL can be enzymatically modified or oxidized by either endothelial cells or by reactive oxygen or nitrogen species which are commonly found in inflamed environments forming oxidized LDL (oxLDL)5.  OxLDL itself is pro-inflammatory and can cause the  2 activation of endothelial cells, stimulating the expression of cellular adhesion molecules which allow circulating immune cells such as monocytes to bind to the artery wall and extravasate into the intima6?8.  Once in the intima, monocytes differentiate into macrophages and engulf oxLDL through scavenger receptors causing an eventual accumulation of cholesterol9.  Normally, cells are able to sense internal levels of cholesterol and down regulate endogenous cholesterol production as well as the expression of receptors responsible for the uptake of LDL such as the LDL receptor (LDLR)10.  However, unlike LDL, the uptake of oxLDL is mediated through scavenger receptors which are not regulated by internal cholesterol levels11.  In fact, exposure of oxLDL can induce the expression of scavenger receptors to further increase oxLDL uptake12.  As a result, macrophages may accumulate excessive amounts of cholesterol and become lipid engorged foam cells.  These foam cells constitute a large proportion of the core of advanced atherosclerotic lesions and secrete specific cytokines that act on surrounding cells propagating a pro-inflammatory response recruiting more immune cells to the area13,14.  As the intimal lesion grows, it can invade the lumen of the artery and restrict blood flow or even rupture causing the release of thrombolytic components that can cause myocardial infarcts and strokes (reviewed in 15,16).  1.3 Innate immunity in atherosclerosis  The innate immune system is responsible for recognizing molecular signatures typically present on bacterial or viral agents and mounting a response to remove them (Reviewed in 17,18).  Professional innate immune cells include  3 macrophages, dendritic cells and mast cells which possess germline-encoded pattern recognition receptors (PRRs) that are responsible for recognizing these molecular signatures.  These receptors can be separated into two categories: endocytic and signaling receptors.  Endocytic receptors such as scavenger receptors and opsonic receptors bind to and endocytose lipoproteins, apoptotic cell fragments and pathogens so that they can be cleared and processed for antigen presentation. The ability to endocytose cholesterol containing lipoproteins is especially relevant to atherosclerosis development as cholesterol has been identified to be sufficient to induce atherosclerosis19.  The combined efforts of the macrophage scavenger receptor (MSR) and CD36 have been implicated as being responsible for the majority of oxLDL taken up by macrophages in vitro20. Furthermore, the uptake of oxLDL through scavenger receptors has been shown to not be regulated by internal levels of cholesterol which may lead to excessive accumulation of cholesterol and formation of foam cells11.  Binding of ligands to signaling receptors such as toll like receptors (TLRs) on the other hand does not cause their endocytic uptake but instead initiates a signaling cascade that invokes acute inflammation and influx of immune cells by increasing the production of pro-inflammatory cytokines as well as increasing the expression of adhesion molecules on endothelial cells; this can result in the recruitment of additional immune cells to the area and perpetuates the inflammatory process 21,22.  Although inflammation is essential to combating foreign invaders, it can be harmful if  4 chronically elevated as it is during atherosclerosis.  Increased levels of inflammatory markers such as C-reactive protein (CRP) is associated with atherosclerosis23.    Ten different human and twelve different murine TLRs have been characterized, however most studies have centered on TLR2 and TLR4.  Both are highly expressed in healthy and atherosclerotic arteries24,25.  TLR4 has been shown to display increased expression during conditions of atherosclerosis in either ApoE-/- mice or human coronary artery sections26.  Furthermore, when either TLR4 or TLR2 is stimulated in the atherosclerosis prone mouse models ApoE-/- or Ldlr-/- respectively, atherosclerosis severity increased 27,28.  More recently, several TLR-binding antigens have been identified in the atherosclerotic plaque including minimally modified LDL (mmLDL) and oxidized phospolipids (oxPLs) found on oxLDL29?31.  1.4 Adaptive immunity in atherosclerosis  As opposed to the molecular patterns recognized by germline encoded scavenger receptors of the innate immune system, the adaptive immune system has the ability to acquire the specificity for new target antigens through processing and antigen presentation. This ability is dependent on the work of professional antigen presenting cells (APCs) such as macrophages and dendritic cells which process and present antigen to T cells.  Both CD4+ and CD8+ T cells are found in human atherosclerotic lesions and can be found in an activated state, demonstrating the involvement of the adaptive immune response13,32.  Furthermore, ApoE-/- mice crossed with immunodeficient Scid/Scid mice showed a 73% reduction in lesion size  5 compared with immunocompetent ApoE-/- mice strongly implicating that the immune system contributes to atherosclerosis.33.    Several studies have provided additional evidence that CD4+ T cells play a pro-atherogenic role.  In vivo murine studies that  use either depleting CD4 antibodies or C57BL/6 mice that are deficient for CD4 have reduced atherosclerosis when fed an atherogenic diet34. Conversely,  when CD4+ T cells were transferred from ApoE-/- mice to ApoE-/-/Scid/Scid mice, atherosclerotic lesion size in the aortic root increased by 164%33.  Activated CD4+ T cells can differentiate into a number of subtypes depending on the cytokine environment that they are exposed to.  Early  models divided effector T cells into two subtypes known as T helper (Th) 1 and Th2 which corresponds to a stimulation by the cytokines interferon gamma (IFN?) and interleukin-4 (IL-4) respectively (reviewed in 35).  Early models proposed that atherosclerosis was a consequence of an imbalance between these two responses.  This model is now believed to be over simplistic as other subtypes have been identified such as Th17 and T regulatory cells (Tregs)36,37.  The pro-inflammatory Th17 response for example was recently discovered in 2007 and is characterized by IL-17 producing cells which can inhibit the Th1 response36,38.  This cross-inhibition of responses is not unique - IFN? has been shown to inhibit Th2 cell proliferation while conversely, IL-4 has been shown to inhibit Th1 responses39?41.    In general, the pro-inflammatory Th1 response has been shown to be pro-atherogenic.  Mice that produce a predominantly Th1 response such as C57BL/6 mice, are more prone to developing atherosclerosis when compared with BALB/c  6 mice which favour a Th2 response42,43.  In addition, several Th1 cytokines such as IFN? and TNF? have also been shown to increase atherosclerosis in vivo.  For example, intraperitoneal injections of exogenous IFN? into ApoE-/- mice for 30 days increased atherosclerotic lesion area in the ascending aorta by two-fold44.  On the contrary, when ApoE-/- mice were crossed with IFN? receptor (Ifn?R)-/- deficient mice, a 59% reduction in lesion lipid content was observed, strongly implicating that IFN? contributes to atherosclerosis development45.  Tnf?-/- mice expressing a dysfunctional apoE variant, ApoE*3Leiden, showed fewer advanced lesions and contained less necrosis and more apoptosis46.     Since Th2 responses have been shown to inhibit the pro-atherogenic Th1 responses, it has been postulated to be anti-atherogenic, however not all evidence is in agreement.  When the transcription factor signal transducers and activators of transcription (STAT) 6 which is critical for Th2 responses, is knocked out of the Th2 prone BALB/c mice, a Th1 response is mounted and the mice develop larger atherosclerotic lesions47.  Meanwhile, lethally irradiated Ldlr-/- mice transplanted with bone marrow from  Il-4-/- mice showed a 69% reduction in lesion size in the aortic arch when compared with mice transplanted with bone marrow from Il-4+/+ mice48.  To add further complexity, intraperitoneal injection of exogenous IL-4 was found to have no effect on lesion size in ApoE-/- mice fed either normal or saturated fat diets49.      7 1.5 Role of macrophages in atherosclerosis  Macrophages have been shown to be important cells in atherosclerosis development.  Indeed, ApoE-/- mice that are deficient in macrophage colony-stimulating factor (M-CSF), which is essential for normal monocyte to macrophage differentiation showed significantly decreased lesion area50,51.   First and foremost is their involvement in the formation of lipid-laden "foam cells" which forms the basis of the development of the fatty streak in early atherosclerosis52 .  The delicate balance between cholesterol uptake into the cell and efflux to acceptors for removal has been the focus of a large field of research.  Secondly, macrophages may respond to their immediate microenvironment by displaying different phenotypes which may perpetuate a pro-inflammatory environment or aid in its resolution (Reviewed in 53).  Thirdly, macrophages are responsible for the clearance of apoptotic cells and debris (Reviewed in 54,55).  Defective clearance and apoptosis of macrophages contribute to the formation of the necrotic core, leading to further inflammation56,57.  Finally, macrophages may affect the stability of the lesion by secreting matrix metalloproteinases (MMPs) which can degrade a variety of extracellular matrix (ECM) proteins leading to a thinner fibrous cap 58(Reviewed in 59).    1.6 Macrophage lipoprotein uptake by scavenger receptors  The term macrophage scavenger receptor was first coined in 1979 when Brown and Goldstein noticed that the rate of acetylated LDL (acLDL) uptake was much higher than that of native LDL60.  This acLDL binding site was called the macrophage scavenger receptor and gave rise to an entire field of research.   8 Scavenger receptors are a family of receptors that bind to a wide variety of molecules that contain molecular signatures which are associated with apoptotic cells, specific phopholipids and bacterial components.  Numerous scavenger receptors have been identified and are divided into several classes, although most of this introduction will be focused on MSR type I/II and CD36 since these have been strongly linked with atherosclerosis development.  In fact, between 70-90% of all macrophage uptake of oxLDL uptake can be attributed to the MSR type I and II and Class B scavenger receptor CD36 in vitro20.  1.6.1 Macrophage scavenger receptor  The macrophage scavenger receptor encompasses three isoforms, type I, II and III, and belongs to the Class A scavenger receptors.  All class A scavenger receptors share a homotrimeric structure and collagen-like domain on the extracellular side.  This family also includes macrophage receptor with collagenous structure (MARCO), scavenger receptor Class A, member 5 (SCARA5), and scavenger receptor with C-type lectin (SRCL) (Reviewed in 61).  The MSR receptors are encoded by the macrophage scavenger receptor (MSR1) gene and were the first scavenger receptors identified in this family62,63.  The MSR type I protein contains 451 amino acids divided into several domains including an N-terminal cytoplasmic domain, a transmembrane domain, an extracellular ?-helical coiled-coil domain, a collagenous domain and finally a cysteine-rich C-terminal domain64.  MSR type II contains 358 amino acids and possesses a similar structure but does not possess the cysteine-rich C-terminus64. The first two MSR isoforms (type I and II) are  9 expressed primarily on macrophages but can also be found on endothelial cells as well as smooth muscle cells65?68.  All Class A members are transmembrane receptors and all except for MSR type III are found on the plasma membrane69.    MSR type I and MSR type II are strongly induced upon monocyte to macrophage differentiation and are also induced upon treatment with the bacterial component lipopolysaccharide (LPS)70.  MSR type I/MSR type II mRNA expression can also be induced in RAW264.7 murine macrophages or C57BL/6 peritoneal macrophages upon treatment with components seen in the atherosclerotic plaque such as oxLDL71,72.  Likewise, there is also evidence that MSRs can be regulated by cytokines as it has been shown that IFN? can reduce MSR1 mRNA while TNF? can reduce both MSR1 mRNA and protein in human macrophages73,74.  Both MSR type I and MSR type II have been shown to be able to bind to both acLDL and oxLDL but not to native LDL60,75.  AcLDL binding to MSR type I has been shown to initiate either endocytosis through clathrin-coated pits or macropinocytosis into early endosomes76?78. Msr1 deficient macrophages from ApoE-/- mice showed an 80% decrease in acLDL uptake in vitro79.  The in vivo findings were much less clear as clearance of 125I-labeled acLDL in Msr1 deficient mice was not affected, suggesting that other scavenger receptors were compensating for the absence of MSR79.  In early atherosclerosis studies, the knockout of MSR1 has been shown to decrease lesion area in ApoE-/- and Ldlr-/- models79?81.  Not all studies are in agreement, as a more recent study has shown that ApoE-/- male mice deficient for Msr1 showed a 40% increase in aortic sinus lesion area after 8 weeks on a Western diet 82.  Furthermore, it is not clear that MSRs play a pro-atherogenic role since  10 overexpression of the Msr1 gene in Ldlr-/- mice reduced atherosclerosis lesion in both the aortic arch and the aortic root83.    The importance of MSR in humans has been shown in binding assays performed in vitro with primary human monocyte-derived macrophages (MDMs) which demonstrate that MSRs could account for up to 80% of acLDL uptake84.  Analysis of human atherosclerotic lesions show that MSR is localized in macrophage-rich areas but is absent in the normal vessel wall making MSRs a potential therapeutic target 64,84.  Recently, a synthetic antagonist was selected using a phage display with MSR type I specificity  facilitating powerful options  in the development of targeted imaging strategies or therapeutic treatments85.  1.6.2 CD36  CD36 belongs to the Class B scavenger receptors which also include LIMPII and the two SR-B isoforms SR-BI and SR-BII.  These receptors are characterized by the presence of multiple transmembrane regions where both the C-terminal and N-terminal regions are found on the cytoplasmic side. The extracellular side contains a heavily glycosylated hairpin-like structure that is capable of binding to a multitude of ligands86,87.    CD36 is a 88kDa glycoprotein which is widely expressed in a number of tissues and cells including monocytes and macrophages, platelets, microvascular endothelial cells, retinal pigment epithelium as well as adipose tissue and is believed to play a multitude of roles in the body88?92.  CD36 was first identified as the thrombospondin membrane receptor in 1987 but has since been acknowledged to  11 bind to a much wider range of ligands93.  For example, CD36 is also known as a fatty acid transporter for long chain fatty acids and is thought to be important for regulating rates of fatty acid oxidation in the heart and skeletal muscle, lipid storage in adipose tissue and fatty acid uptake by enterocytes94?98.  In addition, like other scavenger receptors, CD36 has also been implicated in protecting the host against bacterial and fungal infections, as well as the clearance of apoptotic cells99?104.  However, perhaps more relevant to the topic of atherosclerosis is the ability of CD36 to bind to and endocytose oxLDL105.  After CD36 binds with oxLDL the complex is internalized into early endosomes through either macropinocytosis or through actin-dependent endocytosis76,106,107.     Studies investigating the role of CD36 in atherosclerosis through in vivo knockout experiments have mostly been in consensus with one another.  Several investigators have tested the effect of knocking out Cd36 in ApoE-/- mice and have found a decrease in atherosclerosis108?110.  Cd36 knockout experiments have been performed in other mouse models with similar results.  For example, bone marrow transplant from Ldlr-/ -/ Cd36-/- mice into ApoE-/- mice resulted in decreased atherosclerosis when compared with those transplanted with bone marrow from Ldlr-/- mice suggesting that CD36 plays a pro-atherogenic role111.  Although knockout of both Msr and Cd36 has been shown to decrease atherosclerosis in ApoE-/- mice compared to wild type mice, there is evidence to suggest that no additional protection is conferred to Cd36-/- mice when Msr1 is knocked out as well109.  In vitro studies conducted on macrophages derived from a population of CD36-deficient Japanese patients show an impaired ability to bind to oxLDL when  12 compared with normal subjects112.  Despite the decrease in oxLDL binding, CD36 deficiency does not necessarily equate to a lower risk for atherosclerosis as these subjects show increased total serum cholesterol and triglycerides, mild hypertension, and increased fasting plasma glucose levels - all of which may predispose them to atherosclerosis113?115.  A study involving 40 CD36 deficient patients found that the morbidity of coronary artery disease was significantly higher in CD36 deficient patients than the general population116.  Furthermore, the frequency of CD36 deficiency was three times higher than in coronary artery disease patients than in healthy subjects suggesting that CD36 may have atheroprotective properties in humans116.    CD36 expression has been shown to be regulated primarily by the transcription factor peroxisome proliferator-activated receptor (PPAR)?117.  PPAR? is a nuclear receptor that is known to regulate many genes involved in lipid metabolism.  Although initially recognized for its role in adipogenesis, PPAR? has been shown to be highly expressed in macrophages and is critical for regulating lipid metabolism (Reviewed in 118?120).  It has been shown to cause increased expression of proteins related to cholesterol influx such as CD36, but also those responsible for cholesterol efflux such as ATP-binding cassette (ABC)G1 as well as ABCA1 indirectly through liver X receptor (LXR)?121.  PPAR? ligands such as 15-deoxy ?12,14 prostaglandin J2 (15d-PGJ2) and thiazolidinediones (TZDs) such as rosiglitazone, pioglitazone, and troglitazone have been shown to cause the upregulation of CD36 mRNA and protein122,123.   Ex vivo studies taking RNA from human carotid atherosclerotic lesions show that CD36 mRNA in rosiglitazone treated  13 patients was higher than patients treated with a placebo124.  Additional studies have confirmed these findings in macrophages derived from cell lines such as THP-1 as well as primary hMDMs122,125,126.   The importance of PPAR? in the regulation of CD36 can also be demonstrated by the reduction of CD36 expression in Ppar? deficient mouse macrophages which consequently resulted in a 50% reduction in 125I-oxLDL uptake and degradation127,128.  Interestingly, in addition to acting as a ligand, oxLDL has been shown to increase the expression of CD36 - even stimulating its own uptake129.  This increase in expression has been attributed to at least two major oxidized lipid components 9-hydroxyoctadecadienoic acid (HODE) and 13-HODE which act as PPAR? ligands125.  The cytokine IL-4 can also induce PPAR? activation by up regulating 12/15-lipoxygenase, increasing the abundance of the PPAR? ligands 13-HODE and 15-hydroxy-5Z,8Z,11Z,13E-eicosatetraenoic acid (15-HETE)130.  IL-4 is also capable of up regulating CD36 protein expression in 12/15-lipoxygenase-deficient murine macrophages demonstrating that IL-4 may be affecting CD36 protein expression through multiple pathways130.     Studies have implicated the Protein Kinase C pathway as a potential explanation to tie many of the above observations together.  PMA and oxLDL are potent PKC agonists, and they along with other agonists such as diacylglycerol and ingenol are capable of upregulating Cd36 mRNA in RAW264.7 cells131?133. Conversely, the PKC inhibitors calphostin C has been shown to reduce CD36 protein levels, and also block the induction of CD36 expression by oxLDL and IL-4 in RAW264.7 murine macrophages and in hMDMs132.  Although not completely  14 understood, PKC activation is thought to be an upstream event leading to the phosphorylation and subsequent activation of PPAR?134,135.  Although PPAR? is unquestionably important to CD36 regulation, it alone is unable to account for all of CD36 expression.  Macrophage-specific knockout of Ppar? in C57BL/6 mice show significantly lower levels of Cd36 mRNA but it is not completely abolished136.   One other transcription factor, NF-E2 related factor 2 (Nrf2) has been shown to regulate CD36 expression in response to treatment with oxLDL, and importantly this pathway appears to be independent of PPAR?137,138.  Ppar?-deficient RAW264.7 macrophages were shown to be unresponsive to the PPAR? agonists rosiglitazone but did increase CD36 mRNA and protein upon treatment with the Nrf2 activators sulforaphane (SFN) and diethylmaleate (DEM)139.    1.6.3 Other scavenger receptors involved in lipoprotein uptake  Other scavenger receptors have shown the ability to bind to oxLDL and possibly contribute to atherosclerosis.  The Class A scavenger receptors MARCO and SRCL have been shown to bind to acLDL and oxLDL, respectively; however their role in atherosclerosis remains unclear140,141.  The third splice variant of MSR, MSR type III, was discovered in 1998 and was found to be expressed in both THP-1 and primary human MDMs69.  Unlike MSR type I and II, MSR type III is restricted to the endoplasmic reticulum (ER) and is unable to perform endocytic functions69.  MSR type III is structurally similar to MSR type I and MSR type II but contains only four cysteine residues on the C-proximal end as opposed to six that are found on MSR type I69.    15  Despite being expressed in the ER, MSR type III may affect the uptake of lipoproteins indirectly.  It has been proposed to play a regulatory role for the two other isoforms.  Since alternative splicing is required to generate this isoform, this reduces the amount of transcript available for the creation of MSR type I and MSR type II.  Furthermore, MSR type III may also reduce the binding and uptake ability of other isoforms as CHO cells transfected with both MSR type III and MSR type II showed a 60% inhibition of uptake compared with CHO cells transfected with MSR type II and a control plasmid69.  The expression of MSR type III in the cholesterol-fed New Zealand White (NZW) rabbit model increases with lesion progression and is suggested to be up regulated as a response to protect the cell from taking up excess lipid142.  The Class E scavenger receptor lectin-like oxidized LDL receptor-1 (LOX-1) is a 50kDa transmembrane glycoprotein that is capable of binding to oxLDL.  LOX-1 has four domains: a cytoplasmic N-terminal domain, transmembrane domain, extracellular neck domain and a C-type lectin-like domain at the C-terminal end143.  It has been shown to be expressed on a wide variety of cell types including endothelial cells, macrophages, vascular smooth muscle cells and platelets144?146.  Like other scavenger receptors, LOX-1 is able to bind to bacterial components as well as apoptotic cells which hints at a host defense role in the innate immune response147,148.  LOX-1 is also able to bind to oxLDL and to a lesser extent acLDL but is unable to bind to nLDL which makes it an interesting receptor with regards to foam cell formation149?151.  Although murine Lox-1-/- macrophages do not show a difference in 125I-oxLDLuptake or degradation compared with wild type  16 macrophages, it is possible that its contribution is masked by the large amount of LDL taken up through CD36 and MSR type I/II152.  In support of this idea, LOX-1 has been shown to contribute to oxLDL uptake since up regulation of LOX-1 through treatment with lysophosphatidylcholine increased oxLDL uptake in wild type but not Lox-1 deficient macrophages152?154.  Studies have proposed that LOX-1 may play several roles in atherosclerosis development.  LOX-1 is up regulated under conditions of hypercholesterolemia and LOX-1 activation through binding of oxLDL induces numerous atherogenic responses including the production of free radicals; increased expression of endothelial cell attachment molecules such as E- and P-selectin, intercellular adhesion molecule (ICAM)-1; vascular cell adhesion molecule (VCAM)-1; the chemotactic protein MCP-1; as well as pro-inflammatory cytokines mediated through the nuclear factor kappa-light-chain-enhancer-of-activated B cells (NF?B) pathway155?159.  LOX-1 expression itself can be regulated by a wide variety of pro-inflammatory stimuli including phorbol-12-myristate-13-acetate (PMA), TNF?, oxLDL and IL-1? and IL-1? suggesting that LOX-1 expression can be autoregulated149,150,153,160?162.  The pro-atherogenic effects observed during in vitro studies are mirrored in in vivo studies. Mice overexpressing bovine LOX-1 crossed with ApoE-/- mice show a 10-fold increase in lesion area compared with ApoE-/- mice163.  On the other hand, Lox-1 deficient mice crossed with Ldlr-/- mice show reduced atherosclerotic lesion area in addition to decreased expression of NF?B and increased IL-10 expression in aortic specimens164.     17 1.7 Overview of cholesterol trafficking derived from LDL  Once scavenger receptors internalize oxLDL, the nascent vesicle can fuse with early endosomes165.  Once in the early endosome, vesicles can be sorted to the endocytic recycling compartment (ERC) to return lipids and receptors back to the plasma membrane166,167 (Cholesterol trafficking reviewed in 168?170).  Early endosomes that do not get recycled transition into low pH late endosomes or fuse with Golgi-derived vesicles to form lysosomes. These vesicles contain hydrolytic enzymes such as lysosomal acid lipase which facilitate the hydrolysis of cholesteryl esters found within the oxLDL particle171.  Free cholesterol generated by lysosomal acid lipase can be shuttled from late endosomes or lysosomes to other cellular compartments with the help of the two proteins, Niemann-Pick, type C-1 (NPC1) and NPC2 which work in conjunction with one another165.   The importance of these two proteins has been demonstrated by the fact that deficiencies in either protein leads to an accumulation of unesterified cholesterol in late endosomes and lysosomes172.  NPC2 is believed to transfer unesterified cholesterol generated from lysosomal acid lipase or intraendosomal membranes to NPC1 which in turn shuttles the cholesterol to either the Golgi, ER, or the plasma membrane173?175.  Cholesterol from endosomes destined for the ER can follow at least two different pathways; direct transport to the ER or indirectly by a Golgi to plasma membrane dependent pathway.  When cells are treated with cyclodextrin to deplete plasma membrane-derived cholesterol, esterification of LDL-derived cholesterol is inhibited by ~70% which suggests that the majority of cholesterol from endosomes destined to the ER is dependent on the plasma membrane176.  Once in the plasma membrane,  18 cholesterol may reach the ER by vesicular transport through the Golgi or endosomes, or it is also possible to use a non-vesicular transport method with the use of shuttling proteins177,178.  Cholesterol in the ER can be shuttled back to the plasma membrane through a non-vesicular method179,180.  Excess free cholesterol can be toxic causing an unfolded protein response so the ER-localized enzyme acetyl-Coenzyme A acetyltransferase (ACAT) can re-esterify cholesterol181,182.  This leads to the formation of cytosolic lipid droplets containing esterified cholesterol, a hallmark of foam cell formation181.  This process can be reversed by the enzyme neutral cholesterol ester hydrolase (nCEH) which hydrolyzes esterified cholesterol forming what is known as the cholesterol ester cycle.  The balance between free and esterified cholesterol in the cell is critical - although excess free cholesterol can be toxic, esterified cholesterol is unavailable for removal by cholesterol efflux183,184.  An overview of the cholesterol trafficking pathways is shown in Figure 1.  19  Figure 1-1: Cholesterol trafficking of exogenous cholesterol Exogenous oxLDL is taken into the cell through receptor-mediated endocytosis.  These vesicles fuse with early endosomes (EE) which can then be trafficked to either the endocytic recycling compartment (ERC) or the late endosome (LE).  Lysosomal acid lipase (LAL) in the LE hydrolyzes the cholesteryl esters which can be shuttled to the endoplasmic reticulum (ER) or the Golgi with the help of the proteins Niemann-Pick type-C (NPC1) and NPC2.  Cholesterol in the Golgi can be delivered to the plasma membrane where it is available for cholesterol efflux.  Cholesterol in the plasma membrane can also be shuttled to and from the ER through non-vesicular methods.  Once in the ER, free cholesterol (FC) can be esterified by acetyl-Coenzyme A acetyltransferase (ACAT) to form lipid droplets.  This process can be reversed by neutral cholesterol ester hydrolase (nCEH) which hydrolyzes the cholesteryl esters (CE).  20 1.8 Macrophage cholesterol efflux in atherosclerosis  The removal of cholesterol from macrophages to circulating cholesterol acceptors is only one part of the overall reverse cholesterol transport (RCT) system which outlines a pathway that transports excess cholesterol from peripheral cells to the liver for removal by incorporation into bile and excretion (Reviewed in 185).  The system begins with the production of lipid-poor apolipoprotein A-I (ApoAI) in the liver and intestines which associate with peripheral tissues containing the membrane protein ABCA1.  ABCA1 is capable of mediating the efflux of phospholipids and cholesterol to ApoAI resulting in the formation of a nascent HDL particle186,187.  Next, nascent HDL particles can obtain more cholesterol through interactions with other efflux mediators such as ATP-binding cassette G1 (ABCG1) and scavenger receptor BI (SR-BI).  The enzyme lecithin:cholesterol acyltransferase (LCAT) esterifies free cholesterol  which can then be transferred to other lipoproteins such as LDL through its interaction with cholesteryl ester transfer protein (CETP) or taken up by the liver through SR-BI188,189.    Foam cell formation is believed to occur when cells acquire cholesterol at a faster rate than they are able to eliminate it through cholesterol efflux.  Macrophage cholesterol efflux can be mediated through several mechanisms including aqueous diffusion, SR-BI, or through the ABC family members such as ABCA1 and ABCG1.   In vitro studies show that aqueous diffusion appears to be the most predominant method of cholesterol efflux in murine peritoneal macrophages when unloaded accounting for approximately 80% of all efflux190.  However, under cholesterol loaded conditions, the contribution of both ABCA1 and aqueous diffusion become  21 roughly equal at 35%, followed by ABCG1 at 21% and lastly SR-BI at 9%190.  Injection of cholesterol-loaded ABCA1 or ABCG1 knockdown macrophages led to a decrease in RCT compared to control macrophages which demonstrates the major contribution of these two proteins to the removal of cholesterol191.   This effect was not seen in Sr-bI deficient macrophages suggesting that macrophage SR-BI does not contribute to murine RCT in vivo191.  The relative contribution of these pathways appears to be dependent on the species as similar experiments conducted in THP-1 cells as well as hMDMs show that cellular cholesterol efflux to HDL is independent of ABCG1 based on ABCG1 knockdown studies192.    1.8.1 Aqueous diffusion-mediated cholesterol efflux  Free cholesterol in the plasma membrane can diffuse into the surrounding aqueous phase which can then be taken up by colliding acceptors such as HDL193.  This is a passive process and accounts for bidirectional movement of cholesterol to and from the plasma membrane. Aqueous diffusion rates can be increased by the presence of unsaturated phospholipids or decreased by increased sphingomyelin content194.  The rate of aqueous diffusion is dependent on several factors: 1) the concentration of cholesterol in the donor cell and acceptor , 2) the concentration and composition of the acceptor to allow uptake of the desorbed cholesterol, and 3) the rate of desorption of cholesterol from the host cell194?196.  The cholesterol concentration is important to mediate a net transfer of cholesterol from donor to acceptor since it is dependent on moving down the concentration gradient193.  The concentration of the acceptor is also important to increase the rate of the collision  22 with desorbed cholesterol195.  Acceptor size is also important as smaller acceptors are able to diffuse much closer to the plasma membrane and accept desorbed cholesterol197.  Although aqueous diffusion is an often overlooked mechanism of cholesterol efflux, it accounts for a large percentage and in some cases the majority of total cholesterol efflux in macrophages190.  1.8.2 SR-BI-mediated cholesterol efflux  SR-BI is a Class B scavenger receptor that is capable of binding to traditional scavenger receptor ligands such as apoptotic cells and advanced glycation end products198,199.  Also, like several other scavenger receptors, SR-BI is capable of binding to several lipoproteins including VLDL, LDL as well as HDL200,201.  However, SR-BI is unique in the sense that it plays a crucial role in removing circulating cholesteryl esters from lipoproteins to the liver in the RCT pathway.  SR-BI is expressed on hepatic cells and is able to bind to HDL and selectively take up cholesteryl esters without the endocytic uptake of the lipoprotein202.  Mice with liver-specific overexpression of SR-BI showed increased RCT in vivo when injected with cholesterol loaded macrophages while Sr-bI deficient mice had increased circulating cholesterol levels and lower amounts excreted in the feces202?206.    SR-BI has also been shown to facilitate passive bidirectional cholesterol efflux to HDL, APOE, as well as phospholipid-rich acceptors192,207?210.  The presence of phospholipid appears to be important for efflux as lipid-free ApoAI cannot induce cholesterol efflux through SR-BI even though it is capable of binding to it211.  The importance of phospholipid has been validated by other studies which show  23 increased cholesterol efflux to HDL or serum when enriched with phosphatidylcholine (PC) or conversely decreased efflux when HDL PC is depleted by phopholipase-A2 209,212.  The mechanism by which SR-BI conducts cholesterol efflux is not completely understood, however it is believed to decrease PC-cholesterol interactions which leads to enhanced cholesterol desorption from the membrane increasing the rate of aqueous diffusion213.  Several studies have shown that SR-BI can be protective against the development of atherosclerosis in vivo.  Young ApoE-/-/Sr-bI -/- mice show accelerated atherosclerosis and all mice died by 8 weeks of age214,215.  Unlike most models of atherosclerosis, ApoE-/-/Sr-bI -/- double knockout mice exhibit similar characteristics as those seen in humans such as myocardial infarctions and cardiac dysfunction215.  Ldlr-/- mice with attenuated Sr-bI expression show increased atherosclerosis and those overexpressing Sr-bI had decreased atherosclerosis216?218. Some studies have suggested that the anti-atherogenic effects of SR-BI are liver-specific and that the macrophage-mediated SR-BI contribution to cholesterol efflux appears to be low in vitro and non-existent in vivo190,191.  However, there is evidence to support that myeloid-specific SR-BI may protect against atherosclerosis, as bone marrow transplant from myeloid-specific Sr-bI-/- mice into ApoE-/- or Ldlr-/- mice caused increased atherosclerosis compared to Sr-bI+/+ mice218,219.  A missense variant of SR-BI was recently described in humans where a proline at position 297 was replaced with a serine220.  These patients display increased HDL circulating levels, however MDMs obtained from these patients had a decreased ability to efflux cholesterol220.  There was no evidence suggesting that SR-BI deficiency had an  24 effect on atherosclerosis as no differences were observed in the carotid intima-media thickness of these patients220.  1.8.3 ABCA1-mediated cholesterol efflux  A rare inherited dyslipidemia disorder characterized by very low levels of circulating HDL levels, yellow or orange enlarged tonsils and premature atherosclerosis development was first described in 1961221.  The disease was named Tangier disease for the location where the first case was identified.  Fibroblasts from these patients had severely decreased rates of cholesterol mobilization to ApoAI and had decreased levels of cellular binding sites to ApoAI which suggested that the mutation encoded for a receptor for ApoAI222?224.  It wasn't until 1999 when several groups identified that the mutation was linked to a gene encoding the ABC transporter ABCA1224?227.  ABCA1 is a member of the ATP-binding cassette family of membrane transporters which currently totals 49 different members228.  This family of proteins transport a wide variety of substances including peptides, amino acids, cholesterol and phospholipids across the plasma membrane in an ATP-dependent fashion229?231.  ABCA1 is a large integral membrane protein consisting of 2261 amino acids which is divided into two halves of six transmembrane helices, with two large extracellular domains and a cytoplasmic hydrophobic regulatory domain separating the two halves232.  All ABC transporter family members possess a cytoplasmic nucleotide binding domain (NBD) with conserved protein motifs at each half known as Walker A and Walker B as well as a unique sequence that spans between them  25 that defines the family233.  ABCA1 can be found in most tissues but is highly expressed in the liver, placenta, small intestine, lung, and tissue macrophages234.  Within the cell, ABCA1 can be found on the plasma membrane as well as intracellular compartments such as the Golgi235?237.  In order to reconcile the phenotype observed in Tangier disease patients, investigators studied the possible role ABCA1 in HDL biogenesis and ApoAI-dependent cholesterol efflux.  ABCA1 has been shown to promote lipidation of lipid-free or lipid-poor ApoAI to form nascent HDL and is highly expressed in the liver238?243.  In addition to ApoAI, the apolipoproteins ApoAII, ApoAIV, ApoCI-III, and ApoE are also suitable as acceptors244.  Acceptor binding requirements appear to be linked to the presence of amphipathic ?-helical repeats found on all of the aforementioned apolipoproteins245.  Several molecules containing this amphipathic ?-helix such as serum amyloid A (SAA), phospholipid transfer protein (PLTP) or even synthetic 18-mer amphipathic ?-helix sequences are capable of binding to ABCA1246?248.    Evidence that the cholesterol used by ABCA1 for efflux is derived from the same pool utilized by ACAT suggests that ABCA1 may prevent the formation of cytosolic lipid droplets249.  The atheroprotective properties of ABCA1 is perhaps best exemplified by its role in Tangier disease where ABCA1 deficiency leads to an increased risk for cardiovascular disease250. However, the role of ABCA1 in atherosclerosis has also been studied in vivo with mixed results.  Overexpression of human ABCA1 in C57BL/6 mice decreased plasma cholesterol and simultaneously raised HDL cholesterol, resulting in decreased atherosclerosis compared to mice  26 with normal levels of ABCA1251.  Likewise, ABCA1 overexpression in Ldlr-/- and ApoE-/- mice was found to lead to decreased atherosclerosis239,252.  On the other hand, some studies have found that ABCA1 has either no effect or can even increase atherosclerosis.  For example, one study found that when C57BL/6 mice overexpressing human ABCA1 were crossed with ApoE-/- mice, there was a surprising increase in aortic lesion area251.  To further complicate matters, the effect of ABCA1 on atherosclerosis may be dependent on the tissue in which it is expressed.  Although hepatic ABCA1-deficiency in ApoE-/- mice resulted in increased atherosclerosis, no effect was seen when the deficiency was macrophage-specific suggesting that liver-derived ABCA1 plays a separate role from macrophages239,253.  It has been suggested that this role in HDL biogenesis as hepatic, but not extrahepatic ABCA1 expression was required to maintain levels of circulating mature HDL254.    ABCA1 expression can be regulated by nuclear receptor LXR ? and/or ? which form obligate heterodimer with retinoid X receptor (RXR).  These heterodimers bind to the LXR response elements on the ABCA1 gene which can then be induced upon binding of LXR or RXR ligands such as 22(R)-hydroxycholesterol and 9-cis-retinoic acid, respectively255.  These ligands induce ABCA1 transcription 8- and 9-fold respectively when used individually, and seem to work synergistically causing a 37-fold induction when used in combination255.  LXR exists in two different isoforms, ? and ?, and both are capable of activating transcription of ABCA1; however, they differ in their expression profile: LXR? is predominantly expressed in the liver, small intestine, and macrophages  while LXR?  27 is more ubiquitous256,257.  Interestingly, LXR? has been shown to induce its own expression in human macrophages as the LXR? gene promoter contains an LXR response element258. The transcription factor PPAR? has also been shown to regulate ABCA1 expression indirectly by inducing LXR? expression121,259.  1.8.4 ABCG1-mediated cholesterol efflux  Like ABCA1, ABCG1 belongs to the ABC transporter family and shares a similar but distinct function.  Unlike ABCA1 which is a whole transporter, ABCG1 is a half-transporter that requires the formation of a homodimer in order to function in macrophages.  Each half contains a NBD as well as an extracellular domain260.  ABCG1 can be found in numerous tissues such as the brain, kidney, spleen, lung, and intestine and is found in many cell types including macrophages261.  It is localized to the plasma membrane as well as intracellular compartments such as the Golgi and ERC262.  Like ABCA1, ABCG1 can also be induced by LXR and RXR263.  Although both ABCA1 and ABCG1 belong to the same family and are capable of promoting cholesterol efflux they differ in significant ways.  For example, in contrast to ABCA1, ABCG1 prefers larger lipidated HDL particles which is not dependent on apolipoprotein binding261,264?266.  This difference in acceptor may suggest that the two proteins work sequentially to remove cholesterol from peripheral cells: ABCA1 transfers phospholipids and cholesterol to lipid-poor ApoAI which creates nascent HDL particles which then in turn act as acceptors for ABCG1 to further remove cholesterol267.    28  In vivo studies investigating the role of ABCG1 in atherosclerosis show varied results: bone marrow transfer from Abcg1-/- mice to Ldlr-/- has been shown to cause non-significant changes, increased, or even decreased atherosclerosis268?271.  Studies that examine the effects of knocking out both Abca1 and Abcg1 suggest that the absence of ABCG1 may exacerbate the effects of lacking ABCA1 since Ldlr-/- mice transplanted with bone marrow from Abca1-/-/Abcg1-/- mice had significantly greater atherosclerosis than those only lacking Abca1272.  1.9 Macrophage heterogeneity  Like T cells, macrophages have been shown to exhibit different phenotypes depending on the surrounding signals of their microenvironment.  Mirroring the Th1/Th2 nomenclature of Th cells, macrophages can be classified as either classically-activated (also known as M1) or alternatively-activated (also known as M2).  Like Th1 and Th2 cells, IFN? induces classical-activation of macrophages while IL-4 and IL-13 have been shown to induce an alternative activation273,274.  As macrophages became better characterized, it was apparent that this classification system was far too simplified.  In actuality, macrophages have been shown to exhibit numerous characteristics along a continuous spectrum of phenotypes depending on the specific stimulation in the surrounding environment.  Despite this challenge, it remains useful to categorize macrophages to allow scientists to refer to accepted phenotypes.  This general classification system has since been expanded to include other sub-phenotypes of M1 and M2 activation as well as other unique phenotypes. Classically-activated macrophages have been divided into M1a and M1b depending  29 on if they were induced with an interferon or through TLRs, respectively275,276.  Likewise, alternatively-activated macrophages have been broken down into the sub-phenotypes M2a, M2b, and M2c.  M2a macrophages can be induced by treatment with the cytokines IL-4 and IL-13, M2b macrophages can be induced with immune complexes in combination with IL-1? or LPS while M2c macrophages can be induced with IL-10 or transforming growth factor (TGF) ?273,277?279.  To complicate matters further, two additional macrophage phenotypes have been recently characterized: Mox and M4 macrophages.  Mox macrophages have been shown to be induced by the oxidized phospholipid 1-palmitoyl-2arachidonoyl-sn-glycero-3-phosphorylcholine while M4 macrophages are induced with CXC chemokine ligand (CXCL)4280,281.  A summary of the various macrophage phenotypes, their known inducers and distinctive characteristics is provided in Table 1-1.   Macrophage phenotype is believed to be very plastic as in vitro evidence has demonstrated that macrophages can change from one phenotype to another by changing the cytokine environment, and that this change can occur several times as the environment changes282?284.  The majority of this thesis is focused on the phenotypes that are most well studied in the context of atherosclerosis, M1a, M2a, and M2c.         30 Table 1-1: Summary of human macrophage phenotypes   List of characterized human macrophage subphenotypes with known inducers.  A list of cytokines, chemokines, cell surface markers and other defining characteristics of each phenotype are provided. *IL-12 is only produced in M1a macrophages, not M1b. Immune complex (IC). Phenotype Inducer Cytokines produced Chemokines produced Other markers Surface markers References M1a/M1b IFNs + TNF?/ TLR ligands TNF ligand superfamily, member 2 and 10, IL-1?, IL-6, IL-12*, IL-15, IL-18, IL-23, TRAIL CCL2, CCL5, CCL15, CCL19, CCL20, CXCL1, CXCL2, CXCL3, CXCL9, CXCL10, CXCL11 Nitric oxide (NO) CD68, CD86, MHC-II 275,276,285?287 M2a IL-4, IL-13 IL-10high, IL-12low, IL-23low, IL-1RA, TGF? CCL13, CCL18, CCL22, CCL23, CCL24  Mannose receptor (MR),  decoy IL-1-RII.     273,288,289 M2b IC + TLR ligand IL-10high, IL-12low, IL-23low, TNF?, IL-1?, IL-6, TNF superfamily 14 CCL1  Sphingosine kinase 1 (SPHK1) -  290?293 M2c IL-10, TGF? IL-10high, IL-12low, IL-23low, IL-1RA, TGF? CCL16, CCL18, CXCL13   CD163  291,294,295 Mox oxPAPC IL-10 and decreased TNF? Decreased CCL2 Increased HO-1, iNOS,    VEGF 280,296 M4 CXCL4 TRAIL, TNF?, IL-6 CCL18, CCL22   CD45, CD14, CD86, CD163low 281  31 1.9.1 Induction of classically-activated macrophages  Classically activated macrophages were first described in 1969 when mice treated with bacteria displayed enhanced anti-microbial activity297.  Since then, they have been characterized by the expression of high levels of pro-inflammatory cytokines, reactive oxygen and nitrogen species - all of which are critical for mounting a response against bacterial infections298?300.  Macrophages can be stimulated through TLRs to induce classically-activated M1b macrophages.  LPS, a component of the outer membrane of Gram-negative bacteria has been identified as a potent inducer of classical activation by stimulating TLR4301.  This stimulation is dependent on the interaction of several proteins including LPS binding protein (LBP), CD14, MD-2 and TLR4302?304.  The pathway begins when LPS binds to soluble LBP which then facilitates the association of LPS and membrane-bound CD14304.  CD14 in turn transfers the LPS to the TLR4/MD-2 complex which initiates the signaling cascade conducted through cytosolic domains of the complex known as Toll-interleukin-1 receptor (TIR) domains303.   These domains can then bind to several adaptor proteins including myeloid differentiation primary response gene 88 (MyD88); TIR domain-containing adaptor protein, also known as Mal, MyD88-adaptor-like (TIRAP); TIR domain-containing adaptor inducing IFN? (TRIF); and TRIF-related adaptor molecule (TRAM) (Reviewed in 305).  Two known LPS signaling pathways exist, the MyD88-dependent pathway and the MyD88-independent pathway.  Both pathways ultimately activate pro-inflammatory transcription factors such as NF?B, activator protein (AP)-1, interferon regulatory  32 factor (IRF)3 and IRF5 causing the production of the pro-inflammatory cytokines tumor necrosis factor ? (TNF?), IFN?, and IL-12306?315.  The second pathway to induce classical activation is through interferons which generate M1a macrophages.  As previously mentioned, TLR4 stimulation can induce IFN? production.  IFN? can act on the macrophage itself through the interferon ?/? receptor to activate the transcription factor IRF5 which has been associated with M1 macrophage polarization316.  IRF5 binds to promoters containing the IFN-stimulated response element (ISRE) sequence, inducing the expression of the pro-inflammatory cytokines TNF?, MCP-1, IFN?, IL-12, IL-23 and suppressing the production of the M2 associated cytokine IL-10312,316?318.    The type II interferon, IFN?, is also capable of stimulating classical activation through STAT1. As summarized in Figure 1-2, IFN? exerts its effects through the IFN? receptor which is a tetrameric complex composed of two chains of IFN? receptor (IFNGR) 1 and two chains of IFNGR2 which are constitutively  associated with the Janus kinases (JAK) 1 and JAK2 respectively319?322.  Binding of IFN? homodimers and receptor oligomerization causes JAK2 autophosphorylation which can then cause the phosphorylation of JAK1 as well as Y440 on the IFNGR1 chain, providing a docking site for STAT1 at its Src homology 2 (SH2) domain323?325.  STAT1 in turn is phosphorylated by JAK2 at Y701 causing dimerization with another STAT1 molecule324.  STAT1 dimers translocate to the nucleus, initiating the expression of primary responsive genes regulated by ?-activated sequences (GAS) and to a lesser extent ISRE sequences, inducing classical activation326,327.  One of the primary responsive genes is the transcription factor IRF1 which can then  33 modulate secondary responsive genes by binding to the promoter of genes containing ISRE elements such as STAT1, perpetuating the signaling pathway328,329.    M1 macrophage polarization towards an M1a phenotype by treatment with IFN? can be enhanced by subsequent treatment with TNF?330,331.  TNF? signals as a trimer primarily through the TNF-Receptor I (TNF-RI) 332.  TNF? binding induces trimerization of TNF-RI and the recruitment of several adaptor proteins such as TNF receptor-associated death domain (TRADD) to its cytoplasmic death domains. TRADD in turn recruits TNF receptor-associated factor 2 (TRAF2), and receptor-interacting kinase 1 (RIP1)333,334.  TRAF2 interaction with the NF?B-inducing kinase (NIK) causes the phosphorylation of the I?B kinases (IKK) IKK? and IKK? which in turn phosphorylate the inhibitory protein I?B, leading to its degradation and allowing NF?B activation335.    The expression of several IFN? target genes such as IRF1, IL-6 and iNOS can be synergistically enhanced by treatment with TNF?328,336?338. The reason for this may be due to the presence of STAT1 and IRF-1 binding sites as well as an NF?B site in the promoter of these genes.  There is also evidence that NF?B and IRF1 can physically associate with one another and facilitate the interaction with other transcription factors339,340.    34  Figure 1-2: Classical activation of macrophages IFN? homodimers bind to the tetrameric IFN? receptor composed of 2 chains of IFN? receptor (IFNGR) 1 and 2 chains of IFNGR2.  Janus kinase (JAK) 2 which is associated with IFNGR2 autophosphorylates and also phosphorylates both IFNGR1 and JAK1 which is constitutively associated with IFNGR1.  STAT1 docks with IFNGR1 and is phosphorylated by JAK2 promoting dimerization with another STAT1 molecule.  STAT1dimers translocate to the nucleus and induces the expression of primary response genes with ?-activated sequences (GAS).  One primary response gene known as the interferon response factor (IRF) 1 is a transcription factor that can induce the expression of secondary response genes which includes STAT1 (A).  TNF? trimers bind to TNF-RI inducing trimerization of TNF-RI.  Cytosolic death domains of TNF-RI associate with TNF receptor-associated death domain (TRADD) and receptor-interacting kinase 1 (RIP1) which recruits TNF receptor-associated factor 2 (TRAF2).  TRAF2 can interact with NF?B-inducing kinase (NIK) which induces the activation of I?B kinase (IKK) stimulating the phosphorylation and degradation of the inhibitory I?B protein, allowing NF?B to translocate to the nucleus and begin transcription at ?B elements (B). Some genes such as inducible nitric oxide synthase (iNOS) contain both a GAS and ?B sequence, and are synergistically up-regulated by both IFN? and TNF?.  35 1.9.2 Classically-activated macrophages and cholesterol homeostasis  Classically activated macrophages can promote foam cell formation through multiple mechanisms.  IFN? has been demonstrated to induce foam cell formation in THP-1 cells and increase the rate of oxLDL uptake in either THP-1 or primary hMDMs 341,342.  Inhibition of STAT1 by transfection with  a STAT1-specific DNA oligomer inhibited THP-1 foam cell formation, possibly through the observed decrease in CD36 expression343.  This result was mirrored in murine bone marrow-derived macrophages deficient for Stat1 when compared with macrophages from wild type mice providing additional evidence of the pro-atherogenic role of classically activated macrophages343.  It is important to note that some of these observations appear to be model-specific.  Some studies conducted in primary hMDMs demonstrate that IFN?-treatment can actually result in a decrease in CD36 expression and a reduced amount of accumulated cholesterol12,73.  IFN? signaling through STAT1 has also been demonstrated to reduce the expression of ABCA1 and consequently, the ability of both murine peritoneal macrophages and THP-1 macrophages to efflux cholesterol to ApoAI344?346.  The IFN?-mediated reduction in cholesterol efflux may also be the consequence of multiple other factors.  For example, IFN? has also been shown to reduce the expression of the potential efflux acceptor APOE in human MDMs, and may even decrease the size of the pool of cholesterol available for efflux by skewing the distribution of cholesterol to favour cholesteryl esters by increasing the expression of ACAT347,348.  36  The effect of TNF? on cholesterol homeostasis is still controversial.  It has been observed that TNF? treatment of the murine macrophage cell line J774A.1 reduced the expression of MSR type I as well as neutral lipid accumulation after treatment with oxLDL for 5 hours349.  A similar effect was observed in THP-1 and primary hMDMs treated with TNF? in the presence of aggregated LDL or VLDL350.  Despite in vitro evidence suggesting that TNF? treatment decreases foam cell formation, it has also been observed that the absence of the TNF receptor TNF-p55 led to decreased MSR expression as well as decreased murine macrophage oxLDL uptake in vitro and smaller atherosclerotic lesions in vivo351.  The role of TNF? in cholesterol efflux is also controversial as it has been observed to both increase and decrease ABCA1 expression in different studies352?354.  1.9.3 Induction of alternatively-activated macrophages  Alternatively-activated macrophages were first described in 1992, when treatment of murine peritoneal macrophages with IL-4 induced a phenotype that was distinct from those treated with IFN?273.  These macrophages had increased cell levels of the macrophage mannose receptor and reduced TNF? mRNA production273.  It has been observed that these macrophages are important for combating parasitic infections, and may play a role in wound healing355,356.  M2a macrophages can be induced by treating with IL-4 which binds to the IL-4 receptor ? (IL-4R?) which then forms a complex with the common ?-chain357.  These two chains associate with JAK1 and JAK3 respectively, which phosphorylate one another as well as Y575, Y603 and Y631 on the IL-4R? chain which are  37 important for STAT6 SH2 domain binding358,359.  As shown in Figure 1-3, activated JAK1 phosphorylates STAT6 at Y641 which favours the formation of STAT6 homodimers360,361.  STAT6 can then translocate into the nucleus and induce the expression of target genes such as IL-4, 12/15-lipoxygenase, and IL-4R?362?364.  IL-13 is also able to induce alternative activation through phosphorylation and activation of STAT6.  IL-13 also makes use of the IL-4R? chain but instead of the common ?-chain, is dependent on the recruitment of the IL-13 receptor ?1 (IL-13R?1) chain365.  As with IL-4 signaling, IL-13 binding causes IL-4R? and IL-13R?1 to associate with tyrosine kinases, but here it is with JAK2 and tyrosine kinase (TYK) 2 which are also capable of transphosphorylation.  Both kinases are required to induce the phosphorylation and dimerization of STAT6365.  Macrophages can also adopt a M2c phenotype upon exposure to the cytokine IL-10.  Although considered an alternatively-activated phenotype, it is distinct from macrophages stimulated by IL-4 and IL-13.  IL-10 signaling is conducted through the IL-10 receptor - a tetrameric complex composed of two  IL-10R1 chains and two IL-10R2 chains366,367.  JAK1 and TYK2 are associated with IL-10R1 and IL-10R2 respectively and mediate phosphorylation of each other as well as Y446 and Y496 on IL-10R1 which serves as a docking site for STAT3 binding368,369.  STAT3 phosphorylation at Y705 by JAK1 allows homodimerization by reciprocal binding of the STAT3 SH2 domain with Y705 on the two STAT3 molecules which can then initiate transcription upon translocation into the nucleus370.   38  Figure 1-3: IL-4 and IL-13 signaling through STAT6 and IL-10 signaling through STAT3. Treatment with IL-4 causes the recruitment of the common ?-chain to the IL-4 receptor ? (IL-4R?).  The associated kinases JAK1 and JAK3 phosphorylate each other as well as the IL-4R? chain, providing a docking site for STAT6 to bind (A).  Likewise, upon IL-13 treatment, the IL-13R?1 chain is recruited to IL-4R? and TYK2 and JAK1 phosphorylate each other as well as the IL-4R? chain (B).  In both cases, STAT6 binds to the IL-4R? and is phosphorylated by JAK1, allowing homodimerization and translocation to the nucleus where is can induce transcription of target genes.  Treatment of macrophages with IL-10 causes the formation of the tetrameric IL-10 receptor composed of 2 chains of IL-10 receptor 1 (IL-10R1) and 2 chains of IL-10R2 which are associated with JAK1 and TYK2 respectively.  Transphosphorylation of the kinases and the IL-10R1 chains allows docking of STAT3 which is phosphorylated, dimerizes and translocates into the nucleus to begin the activation of transcription of target genes (C).    39 1.9.4 Alternatively-activated macrophages and cholesterol homeostasis  Treatment of macrophages with IL-4 or IL-13 has been shown to induce the expression of CD36 which is believed to be responsible for the corresponding increase in acLDL binding and cholesterol esterification observed in J774A.1 macrophages and murine peritoneal macrophages for IL-4 and IL-13 treatment respectively130,132,371,372.  IL-13 also induces the expression of ABCA1 and ABCG1 and increases HDL-mediated efflux suggesting a possible anti-atherogenic role372.  However, murine IL-4 knockout models on ApoE-/- or Ldlr-/- background show either no change or even decreased atherosclerosis49,373,374.  Exogenous administration of IL-13 did not affect lesion size in ApoE-/- mice but did reduce macrophage content and increased collagen content, suggesting a role in plaque stabilization372.    Interpretation of these results must be performed with caution as studies conducted on primary human MDMs demonstrate that IL-4 does not affect the expression of CD36 or MSR but does decrease LOX-1375.  Unlike murine macrophages, IL-4 treatment resulted in a reduction in cholesterol accumulation when incubated with oxLDL compared with resting macrophages375.  In addition, IL-4 caused a decrease in ABCA1, APOE, and LXR? expression and a subsequent decrease in HDL and ApoAI-mediated cholesterol efflux375.  Unlike IL-4, IL-13 can induce CD36 expression in primary human MDMs which may be explained by the fact that they signal through different receptor complexes376?379.  IL-10 has been shown to increase the expression of MSR and CD36 in murine bone marrow-derived and RAW264.7 macrophages which increases acLDL uptake380,381.  These same studies also concluded  that IL-10 increases the  40 expression of ABCA1 and resulting ApoAI-mediated cholesterol efflux380,381.  Studies employing THP-1 or primary human MDMs as a cell model also demonstrate that IL-10 increases ABCA1 and ABCG1 expression as well as cholesterol efflux382,383.  However, these studies are still at odds with one another with regards to the effect of IL-10 on oxLDL uptake and cholesterol accumulation382?384.     1.9.5 Distribution, abundance and localization of macrophage phenotypes in the atherosclerotic plaque  Different macrophage sub-phenotypes have been identified in both human and mouse atherosclerotic lesions.  Bouhlel and colleagues showed the presence of alternatively activated macrophages in human carotid lesions in 2007124.  They demonstrated the presence of both classically and alternatively-activated macrophages by staining for CD68 as a macrophage marker and looking for colocalization with MCP-1 as a M1 marker and the mannose receptor (MR) for M2a.  They discovered that alternatively-activated macrophages were found in distinct regions of the plaque and that they did not colocalize with lipid as determined by Oil Red O (ORO) staining124.   Another study looking at M2 macrophage distribution in atherosclerosis found that MR+ macrophages were located in the shoulder region and periphery while MR- macrophages were localized to the lipid core375.  It is important to note that although these markers are used to define different phenotypes, their expression is not mutually exclusive and categorization is dependent on relative differences amongst phenotypes282.  This is perhaps best demonstrated by the most recent and comprehensive study by St?ger et al. which  41 arrived at a different conclusion.  In this study, macrophage phenotypes were defined with multiple markers, including iNOS, CD86 and HLA-DP/Q/R for M1 and MR, CD163 and dectin-1 for M2385.  M1 macrophages were found more abundantly than M2 macrophages in the shoulder regions.  No differences were seen in the fibrous cap and M2 macrophages were predominant in the perivascular adventitial tissue386.  Although it is difficult to explain the difference in results at the shoulder region, it is possible that the discrepancy in the shoulder region may be due to how M1 and M2 macrophages were defined.  The presence or absence of MR was used to define M1 and M2 macrophages in earlier studies, however MR as a marker alone may not be sufficient.  In agreement with the previous studies, St?ger et al. observed that foam cell macrophages indeed do express low levels of MR, however, they also maintained high levels of the M2 marker dectin-1386.  Although these studies only looked at M2a macrophages, other alternatively activated sub-phenotypes have been identified in the plaque as well.  CD163 has been used as a marker to distinguish M2c from M2a macrophages and CD163+ macrophages are found in the outer layers of the plaque with little association with foam cells282,387.    It is also important to note that different macrophage phenotypes are dominant in the atherosclerotic plaque at different stages of progression and regression.  Most evidence suggests that the early lesion is dominated by M2 macrophages but later succumbs to an M1 profile as atherosclerosis progresses to later stages.  In human endarterectomy specimens with advanced plaques, IFN? mRNA was detected in 7 of 10 specimens while IL-4 mRNA was only detected in 1388.  This is supported in murine in vivo studies where the early plaque in ApoE-/-  42 mice contains high levels of IL-4 with undetectable levels of IFN? while lesions from older mice shows a reversal of this trend with IFN? becoming dominant and IL-4 decreasing to lower levels389.  The high level of IL-4 at early stages of atherosclerosis may be important for atherosclerosis development as KO of IL-4 in C57BL/6 mice reduces the size of the fatty streak when compared with WT mice390.  The proportion of different macrophage phenotypes was recently described in the late stages of atherosclerosis in Ldlr-/- mice fed an atherogenic diet for 30 weeks.  39% were identified to be M1, 22% were M2 and 34% were identified to be Mox280.    Interestingly, reverting the late plaque from M1 dominance to M2 may be required for atherosclerosis development and regression.  In one mouse model of regression, plaque containing arterial segments from ApoE-/- mice were transplanted into either wild type or ApoE-/- mice391.  The wild type mouse recipients demonstrated a decrease in both plaque size and foam cell content which was accompanied by a decrease in the M1 marker MCP-1 mRNA when compared with ApoE-/- mice391.  Another mouse model of plaque regression using the reversa mouse which is conditionally Ldlr-deficient in addition to being limited to only producing apoB100, demonstrated that when regression is triggered, mRNA levels of the M2 markers ARGI, FIZZ-1, CD163, and MR all increased in plaque macrophages392.  1.10 Macrophage-derived proteases in atherosclerosis Macrophages play several key roles in the development of atherosclerosis including influencing cell migration and proliferation, clearing apoptotic cells and  43 debris, modulating the plaque environment by the secretion of various inflammatory mediators, and influencing the vulnerability of the lesion to rupture.  Macrophage-derived proteases and protease inhibitors are responsible for several of these functions.  For example, exogenous addition of the serine protease urokinase-type plasminogen activator (uPA) can directly stimulate human smooth muscle cell migration in vitro393.  Overexpression of uPA or MMP-9 increases cell migration and neointimal formation in vivo following balloon catheter carotid injury in rats while uPA KO inhibits it394?397.  Metalloproteases have also been shown to cleave precursors of inflammatory mediators such as IL-1? and TNF? to their biologically active forms while other proteases such as plasmin are able to induce apoptosis398?400.   However, perhaps the most notorious function of proteases in atherosclerosis is their involvement with plaque stability.  Atherosclerotic lesions can remain asymptomatic for decades, slowly increasing in size and reducing the cross sectional area of the artery.  Many of these lesions remain silent until they rupture and cause potentially fatal heart attacks and strokes.  Plaques that are deemed vulnerable to rupture contain large lipid cores, an abundance of foam cells, a thin fibrous cap and an extensively degraded extracellular matrix (ECM)401,402.  Early characterization of the fibrous cap revealed the presence of macrophages whose density was inversely proportional to the strength and stability of the cap403.  It was later discovered that macrophages were responsible for the secretion of a number of ECM-degrading enzymes such as matrix metalloproteinases (MMPs)404.  Although several other protease classes exist including threonine, and aspartate proteases,  44 the three classes that are predominantly implicated in contributing to plaque vulnerability are metalloproteinases, serine and cysteine proteases. Metalloproteinases are proteases that are dependent on metal binding for its catalytic activity like the metzincin superfamily of endopeptidases which are dependent on zinc binding for their activity.  Several families belonging to this superfamily have been shown to be up regulated in atherosclerosis including MMPs, adamalysins, and pappalysins.  Some of the metalloproteinases known to be localized to macrophage-rich areas of the atherosclerotic plaque include MMP-1, -2,-3, -7, -8, -9, -11, -12, -13, -14 and -16, the adamlysins a disintegrin and metalloproteinase domain-containing protein (ADAM)9, ADAM15 and ADAM17405?417.  The importance of MMPs in plaque vulnerability relates to their ability to cleave several ECM substrates associated with plaque stability including collagen, gelatin, fibronectin, and laminin418?422.  Indeed, several MMPs including MMP-8 and -9 are associated with plaque instability in human endarterectomy samples423.  Furthermore, MMP-1, -3, and -8 colocalize with cleaved type I collagen fragments in human lesions which normally provide plaque stability407,413.  Human MMP activity is regulated by four different tissue inhibitors of metalloproteinases (TIMPs) including TIMP1 through 4 although only TIMP1-3 have been identified within the atherosclerotic plaque406,424,425.  TIMP-1 has a low affinity for a couple of MMPs involved in plaque instability including MMP-3, -9 and -14 while TIMP-2, -3, and -4 have much broader specificities426?428.  A list of some of the macrophage-derived proteases and protease inhibitors found within the atherosclerotic plaque is summarized in Table 1-2.  45  Table 1-2: Macrophage-derived proteases and protease inhibitors in atherosclerosis Known macrophage-derived proteases and protease inhibitors as well as their known substrates are listed. Matrix metalloproteinase (MMP), a disintigrin and metalloproteinase domain-containing protein (ADAM), tissue plasminogen activator (tPA), urokinase-type plasminogen activator (uPA).  Protease type Protease Role in atherosclerosis Reference MMPs MMP-1 Degradation of type I, II, III, VII, VIII, X, and XI collagen, gelatin, fibronectin 418,429?435 MMP-2 Degradation of gelatin,  proteoglycans and type I, IV, V, and VII collagen 436?439 MMP-3 Degradation of elastin, fibronectin, proteoglycans, gelatin, type II, III, IV, V, IX, X, and XI collagen 440?444 MMP-7 Degradation of elastin, fibronectin, and proteoglycans, gelatin, type IV collagen, laminin 438,445,446 MMP-8 Degradation of type I, II, III, X and XI collagen 447,448 MMP-9 Degradation of gelatin,  elastin, laminin, proteoglycans and type I, III, IV, V, and XI collagen 436,438,449?451 MMP-11  Type IV collagen, fibronectin, laminin 452 MMP-12 Degradation of type IV and V collagen, fibronectin, gelatin, laminin and elastin 431,450,453,454 MMP-13 Degradation of type I, II, III, IV, IX, X and XIV collagen, gelatin, fibronectin 455?457 MMP-14 Degradation of elastin, type I, II, and III collagen, gelatin, fibronectin, laminin, and proMMP-2 433,458,459 MMP-16 Degradation of gelatin, fibronectin, laminin and proMMP-2 460,461 Adamalysins ADAM9 Integrin binding and cell adhesion  Release of membrane bound TNF? 399,462 ADAM15 Integrin binding and cell adhesion Platelet activation 463,464 ADAM17 Release of membrane bound TNF? and VCAM-1 399,465 Serine proteases tPA Activation of plasminogen 466,467 uPA Activation of plasminogen 467,468 Neutrophil elastase Activation of MMP-2, -3, and -9  469?471         46     Protease type Protease Role in atherosclerosis Reference Cysteine proteases Cathepsin B Degradation of type II, IX, and XI collagen  472 Cathepsin F Degradation of apoB-100  473 Cathepsin K Degradation of type I collagen, ApoB-100  473,474 Cathepsin L Degradation of type II, IX, and XI collagen and elastin  472,475 Cathepsin S Degradation of elastin, apoB-100  473,475,476 Protease Inhibitors TIMP-1 Inhibition of MMP-3, 9 and weak inhibition of MMP-14  426?428 TIMP-2 Inhibition of most MMPs  477?479 TIMP-3 Inhibition of most MMPs 461,480,481 PAI-I Inhibition of uPA and tPA 482?484 ?-1 proteinase inhibitor Inhibition of neutrophil elastase 485 Cystatin C Inhibition of cathepsins 486?488   Serine proteases such as uPA and tissue plasminogen activator (tPA) have been shown to colocalize with foam cells in human carotid atherosclerotic plaque samples482,489.  uPA and tPA convert the zymogen plasminogen into its active form of plasmin which is capable of cleaving fibrin and fibrinogen466?468.  In addition to plasminogen, both tPA and uPA may also activate other proteases such as MMP-2 and MMP-9490,491.  Yet another macrophage-derived serine protease found in human atherosclerotic plaques, neutrophil elastase, can promote plaque vulnerability by cleaving inactive forms of MMPs such as MMP-2, MMP-3, MMP-9 into their active form as well as inactivating TIMP-1427,469?471,492.  Interestingly, neutrophil elastase may be regulated in a positive feedback loop as MMP-9 can inactivate the inhibitor of neutrophil elastase, ?-1 proteinase inhibitor493.  Several cysteine protease cathepsins have also been shown to associate with atherosclerotic lesions.  Although most cathepsins function optimally in acidic  47 environments and are predominantly found intracellularly in the lysosome, many cathepsins including cathepsin B, L, S and K have been found extracellularly475,494.  One explanation for their translocation comes from observations that treatment of the monocyte cell line U937 with oxLDL leads to lysosomal destabilization and the leakage of cathepsin B and L into the cytosol495.  Their ability to function extracellularly may be aided by the expression of vacuole-type H+-ATPase in the plasma membrane which creates an acidic extracellular environment475.  Cathepsin B, F, K, L, and S have all been found to localize to macrophage infiltrated regions of human atherosclerotic lesions and may increase plaque vulnerability by degrading laminin, fibronectin, elastin and collagen473,494?499.  Furthermore, the expression of cathepsin K, L and S is up-regulated in atherosclerosis while levels of the cathepsin inhibitor cystatin C is severely reduced486,494.    1.11 Macrophage heterogeneity and protease expression  Macrophages are responsible for the secretion of a number of proteases found in the atherosclerotic plaque.  As a result of the dramatic differences in functional abilities and behaviour, it is inviting to speculate that different macrophage phenotypes will display unique protease expression profiles.  Although no comprehensive studies covering all proteases have been conducted, several specific sets of proteases have been examined.  Due to the localization of M1 macrophages to vulnerable shoulder regions of the plaque, they are highly suspected to facilitate plaque rupture.  This is supported by the fact that blocking IFN? in murine ApoE-/- by injecting a soluble mutant IFN?  48 receptor caused an increase in plaque stability demonstrated by increased collagen expression and decreased macrophage content in the plaque500.  Furthermore, mRNA expression of MMP-9 and -13  was significantly reduced, suggesting that IFN? contributes to plaque vulnerability500.  IFN? has also been shown to increase MMP-1 mRNA and protein levels in U937 cells as well as human MDMs501.  IFN? treatment also induces the expression of a number of cysteine proteases in various models including cathepsin B, D, H, L, and S while reducing the expression of the cysteine protease inhibitor cystatin C494,502?506.  TNF? has also been shown to increase the expression of cathepsin B and L in murine RAW264.7 macrophages507.  IFN? and TNF? have also been shown to induce the serine protease uPA in both the monocytic cell line U937 as well as primary human MDMs508.  The cumulative evidence supports the idea that both IFN? and TNF? induce protease expression and suggests that they may increase plaque vulnerability.  Conversely, M2 macrophages are suspected to antagonize M1 responses and promote plaque stability.  A greater proportion of M2 macrophages were found in stable plaques of carotid endarterectomy samples when compared with M1 macrophages509.  In addition, the Th2 cytokines IL-4 and IL-10, which are known to induce M2 polarization in vitro, have all been shown to increase type VI collagen production in THP-1 cells510.  This evidence does suggest a regulation of plaque stability, however, the effects of these cytokines on the expression of individual proteases is still poorly studied with conflicting results depending on the model used.  For example, IL-4 inhibits MMP-1 production in freshly isolated human monocytes but induces its expression when these monocytes are differentiated into  49 macrophages with GM-CSF511.  IL-4 has also been shown to increase some proteases such as MMP-12, cathepsin L and cathepsin S expression in murine bone marrow derived cells yet decrease the expression of others such as cathepsin D secretion in human monocytes512?514.   Interestingly, IL-10 expression was found to be higher in the plaques of patients with unstable compared with stable angina515.  This observation does not necessarily imply that IL-10 causes plaque instability but does show a correlation.  When the effect of IL-10 is tested in vitro, studies have demonstrated that IL-10 increases TIMP-1 expression and decrease MMP-9 activity in human monocytes and macrophages516?519.  In vivo studies also support that IL-10 functions to increase plaque stability; double knockout of IL-10 and ApoE increased MMP-9 activity, systemic markers of coagulation, and susceptibility to a thrombotic response upon injection of thrombin when compared with ApoE-/- control mice520.  In addition, knockout of leukocyte-derived IL-10 not only increased atherosclerosis in Lldr-/- mice, but also significantly reduced collagen content suggesting that IL-10 is important for plaque stability521.  1.12 Overall research objectives Macrophages have been confirmed to be involved a variety of key processes during atherosclerosis development and their importance to the disease is undeniable.  Macrophages have also been shown to exhibit a number of different phenotypes depending on their specific microenvironment and the importance of acknowledging these phenotypes when considering the function and involvement of macrophages in disease is becoming increasingly accepted.  It is becoming far less  50 common to group all macrophages under a single label since different macrophage phenotypes have been shown to have vastly different responses to the same situation.  Perhaps the best example is the enhanced ability of classical activated M1 macrophages to clear bacteria during infections as discussed earlier. The effect of different macrophage phenotypes is no different in the context of atherosclerosis; certain macrophage phenotypes may be more or less protective at certain times, locations and for specific functions during atherosclerosis.  This idea led to the overall goal of this thesis to investigate how different macrophage phenotypes may affect the development of atherosclerosis.  More specifically, a complete protease and protease inhibitor profile of different phenotypes was assessed as was the ability to maintain cholesterol homeostasis through cholesterol accumulation in response to oxLDL and efflux cholesterol in the presence of ApoAI or HDL.  Finally, I investigate the potential of the cell line U937 to study functional differences observed in primary human MDMs.   I hypothesize that M1 macrophages will display a far more atherogenic profile when compared with M2 macrophages with increased expression of proteases and decreased ability to handle cholesterol upon treatment with oxLDL. Alternatively, I hypothesize that M2 macrophages will show a more atheroprotective profile with increased expression of protease inhibitors and increased cholesterol handling capabilities.  Finally, I hypothesize that the cell line U937 will act as a suitable model to study atherogenic processes observed in primary MDMs.  In the following chapters, I present the findings of my investigation and discuss their implications on the field of atherosclerosis in the context of the current  51 literature.  I address the limitations of my study as well as propose future studies to further the understanding of the field.  52 Chapter 2:     Characterization of the protease and protease inhibitor profile of different macrophage sub-phenotypes  2.1 Background and rationale Proteases have been shown to contribute to atherosclerosis development and many have displayed location specific expression.  For example, many proteases including tPA, neutrophil elastase, cathepsin K, cathepsin S, and MMP-1, -2, and -9 are abundantly expressed at the shoulder region in human atherosclerosis which is most vulnerable to rupture405,406,471,494.  Alternatively, MMP-14 is more abundantly expressed in macrophages near the lipid core while others such as MMP-11 are only found during late stages of atherosclerosis412,522.  The physical and temporal nature of this localization suggests that proteases are induced under specific conditions.  Knowing that many proteases are produced by macrophages and that different macrophage phenotypes localize to different areas of the plaque raises the possibility that different macrophage phenotypes may exhibit unique protease expression profiles.  Interestingly, like many proteases implicated in plaque vulnerability, M1 macrophages dominate in the shoulder region in human lesions suggesting that these macrophages may be responsible for protease production386. As reviewed in the previous chapter, although there are studies that look at the effect of specific treatments on the expression of a small number of proteases in macrophages, wide gaps in knowledge remain, especially with primary human MDMs.  There are currently no studies that comprehensively investigate the protease and protease inhibitor expression profile of different macrophage phenotypes.  Such a study  53 has the potential to discover novel proteases that are expressed by different macrophage phenotypes which may serve as functional markers.  Knowledge of the protease and protease inhibitor profile may also provide valuable insight into which protease pathways are expressed by different macrophage phenotypes and may provide insight regarding their overall functional effect on atherosclerosis.  In this chapter, human MDMs were polarized to different phenotypes and a preliminary profile was assessed by comparing the mRNA expression of every human protease and protease inhibitor known to date with the use of a CLIP-CHIP microarray.    2.2 Specific aims and hypotheses The specific aims for this chapter are as follows: 1. To generate a preliminary protease and protease inhibitor expression profile of human MDMs polarized to resting, M1, M2a, and M2c phenotypes. 2. To determine which proteases or protease inhibitors are differentially expressed amongst different macrophage phenotypes. 3. To determine if specific protease pathways are invoked by cytokine treatment and to investigate if these pathways may affect atherosclerosis development.  As inflammatory stimulation has been shown to both reduce collagen production and increase the expression of several proteases, I hypothesized that not only will each macrophage phenotype display a unique protease and protease inhibitor profile, but  54 also that M1 macrophages will express higher levels of proteases known to contribute to plaque vulnerability and atherosclerosis development when compared with M2a and M2c macrophages501,504,505,510,523,524.  This hypothesis is further supported by the known colocalization of M1 macrophages to shoulder regions of the atherosclerotic plaque and the expression of several proteases implicated in plaque vulnerability405,406,471,494.  2.3 Materials and methods 2.3.1 Cell culture CD14+ human peripheral blood monocytes (AllCells) were cultured in RPMI 1640 complete media (Hyclone) containing 10% FBS (Hyclone), 2% sodium bicarbonate, 1% sodium pyruvate, and 1% penicillin-streptomycin (Invitrogen).   Cells were plated at a density of 106/ml in 6-well CellBind plates (Corning).  Recombinant human M-CSF (R&D Systems) was added for the initial 4 days of culture at a concentration of 10 ng/ml.  A media change was performed at day 4 of culture in the absence of M-CSF.  Macrophages were treated with specific cytokines (R&D Systems) for 72 hours starting at day 8 of culture including human IFN? and TNF? (IFN?/TNF?, 10 ng/ml each) for M1 polarization, IL-4 and IL-13 (IL-4/13, 10 ng/ml each) for M2a, IL-10 (20 ng/ml) for M2c, or an untreated control containing complete media with no additional cytokines (resting).      2.3.2 CLIP/CHIP microarray Total RNA was isolated from polarized macrophages using an RNeasy mini kit (Qiagen).  RNA quality and concentration was assessed using a NanoDrop 8000 (Thermo Scientific).  A human CLIP-CHIP microarray developed by Dr. Christopher  55 Overall was used to evaluate the expression of all human proteases, non-proteolytic homologs and protease inhibitors525.  Briefly, 1ug of RNA was reverse transcribed using the MessageAMP II aRNA amplification kit (Invitrogen) followed by second strand cDNA synthesis to create what is termed amplified RNA (aRNA).  The aRNA of the samples was then labeled with the universal linkage system (ULS, Kreatech) using ULS linked with the fluorescent dye Cy3.  Briefly, aRNA was incubated with Cy3-ULS for 15 minutes at 85?C and snap cooled on ice.  Free ULS was removed by using a KREApure spin column.   A human universal control aRNA sample was also labeled with Cy5 for normalization.   The CLIP-CHIP contains oligonucleotides printed at 30 ?M to aminosilane-coated slides and cross-linked via UV light.  The oligonucleotides were hydrated with a prehybridization buffer saline- sodium citrate (SSC, 0.75M sodium chloride, 0.075M sodium citrate, 0.1% SDS, 0.2% BSA) for 45 minutes at 48?C and denatured for 15-30 seconds in 100% isopropanol.  2 ?g of the sample and control aRNA were then added to the CLIP-CHIP in 2x hybridization buffer (0.3M sodium chloride, 0.03M sodium citrate, 50% formamide, 1% SDS), coverslipped with a 22x60 mm LifterSlip and allowed to hybridize for 18 hours at 42?C in a Corning microarray hybridization chamber (Corning).  The CLIP-CHIP was then washed with decreasing concentrations of SSC (1xSSC with 0.2% SDS, followed by 0.1xSSC with 0.2%SDS, and finally 0.1xSSC) at 42?C and dried.  The CLIP-CHIP was then scanned at 550 nm and 650 nm for Cy3 and Cy5 respectively on a 428 Array Scanner (MWG Biotech) and the images were saved for analysis.  Each image was overlaid with a grid containing the information about the identity of each spot and used as the input information for the image analysis software  56 Imagene (BioDiscovery).  The raw intensity of each spot was recorded and the web-based CARMAweb application was used to apply normexp background correction, print-tip loess within-array normalization, and quantile between-array normalization526?528.  Both M-value (M-value= log2 (Cy5)-log2(Cy3)) and A-values (A-value= 0.5 (log2(Cy5)+log2(Cy3)) were calculated and provided as a text file.  These values were inputted into the multiexperiment viewer v4.8.1 (MeV) from TM4.org for statistical analysis.   2.3.3 Statistical analysis of microarray data A-values from a panel of negative control spots were used as a cutoff to determine which genes were expressed in the experimental samples.  Microarray M-values were analyzed by significance analysis of microarrays (SAM) performed in a two class, unpaired manner setting the delta value such that the Q-value was ?0.05.     2.3.4 Microarray data mining Hierarchical cluster analysis was performed on differentially expressed genes using the Pearson correlation metric and average linkage clustering with a distance threshold of 0.75 using MeV software.  This list was also submitted to the database for annotation, visualization and integrated discovery (DAVID) in order to discern patterns in function529,530.  Information from NCBI Entrez gene and online Mendelian inheritance in man (OMIM) was used to determine known associations with human disease while Biocarta, Kyoto encyclopedia of genes and genomes (KEGG), and UniProt was used to determine involvement in known functional pathways.  57 2.4 Results 2.4.1 Determining the protease and protease inhibitor profile of different macrophage phenotypes A CLIP-CHIP microarray was used to create a complete protease and protease inhibitor mRNA profile of primary human MDMs that were polarized with IL-4/13, IFN?/TNF?, IL-10 or left untreated.  The expression of each target gene was compared against a panel of negative controls with random sequences, empty spots and buffers to determine a cutoff defining which genes could be detected as expressed.  A total of 370 of the 579 proteases and 79 of the 161 protease inhibitors were detected in at least one of the four macrophage phenotypes that were tested (Figure 2-1).    Figure 2-1:  Expression heatmap image of all proteases and protease inhibitors.   The colour represents the expression level when compared with a human universal control.  The numerical scale represents the M-value as defined by the equation: M-value= log2 (Cy5)-log2(Cy3).  Red indicates low expression in the experimental sample while green represents high expression relative to the universal control.   58 A complete list of these proteases and protease inhibitors and their expression relative to the untreated control can be found in Appendix B.1.  The results can be summarized and visualized by the number of targets that had a change in expression greater than or equal to 1.5-fold as shown in Table 2-1.  IFN?/TNF? treatment resulted in the greatest number of genes with altered expression.  More specifically, this treatment induced the greatest number of proteases up regulated compared with down regulated and also induced the fewest number of protease inhibitors.  Conversely, IL-4/13 treatment induced the most protease inhibitors and down regulated the fewest.  Finally, IL-10 treatment resulted in a nearly even ratio of both proteases and inhibitors up regulated compared with down regulated.  Table 2-1:  Number of proteases and protease inhibitors with a greater than or equal to 1.5-fold change compared with the untreated control. Treatment Proteases Protease inhibitors  Increased expression Decreased expression Increased expression Decreased expression IL-4/13 23 16 10 3 IFN?/TNF? 34 20 7 6 IL-10 16 13 8 6  2.4.2 Identification of proteases and protease inhibitors differentially regulated by macrophage phenotypes In order to determine which proteases and protease inhibitors were induced or repressed by treatment with different cytokine treatments, a SAM analysis was performed in a pairwise fashion using all possible permutations while the false discovery rate was controlled by selecting genes with a Q-value of ?0.05.  The results of each cytokine treatment compared with the untreated control are in agreement with the general summary and indicate that IFN?/TNF? favours protease activation while IL-4/13  59 and IL-10 displayed a more even and balanced profile based on the number of proteases and proteases inhibitors up and down regulated (Table 2-2).  Of all of the genes identified in Table 2-2, only one gene was identified to be differentially expressed upon cytokine treatment when compared with each of the other conditions.  This gene, SPINT2, is a protease inhibitor that is up regulated by IL-4/13 treatment.                        60 Table 2-2:  Proteases and protease inhibitors that were differentially expressed upon cytokine treatment.   Protease and protease inhibitor classes are abbreviated as follows:  CP, cysteine protease; M, metalloproteinase; PI, protease inhibitor; SP, serine protease; TP, threonine protease.  Treatment Description Gene Name Class Change in expression (A)/(B) Q-value Cytokine treatments compared with untreated  IL-4/13 (A) vs. Untreated (B) aminopeptidase N ANPEP M 2.28 0.0 chymotrypsin B CTRB1 SP 2.34 0.0 placental bikunin SPINT2 PI 5.16 0.0 protease inhibitor 6/CAP SERPINB6 PI 1.77 0.0 IFN?/TNF? (A) vs. Untreated (B) leucyl aminopeptidase LAP3 M 2.77 0.0 aminopeptidase-like 1 NPEPL1 M 1.95 0.0 complement factor B BF SP 2.08 0.0 proteasome catalytic subunit 2i PSMB10 TP 2.08 0.0 serine carboxypeptidase 1 RISC SP 2.14 0.0 glutaminyl cyclase 2 QPCT2 M 2.85 0.0 proteasome catalytic subunit 1i PSMB9 TP 5.03 0.0 histidine-rich glycoprotein HRG PI 2.63 0.0 IL-10 (A) vs. Untreated (B) ADAM8 ADAM8 M 2.77 0.0 macrophage elastase MMP12 M 2.18 0.0 cathepsin H CTSH CP 0.47 0.0 Archeometzincin 2 AMZ2 M 0.55 0.0 NAIP BIRC1 PI 1.83 0.0 Phosphatidylethanolamine binding protein PEBP1 PI 0.49 0.0 Cytokine treatments compared with other cytokine treatments  IL-4/13 (A) vs. IFN?/TNF? (B) glutaminyl cyclase QPCT M 4.46 0.0 placental bikunin SPINT2 PI 6.23 0.0 IL-4/13 (A) vs. IL-10 (B) UCR1 UQCRC1 M 1.81 0.0 chymotrypsin B CTRB1 SP 2.76 0.0 Phosphatidylethanolamine binding protein PEBP1 PI 2.36 0.0 placental bikunin SPINT2 PI 3.44 0.0 IFN?/TNF? (A) vs. IL-10 (B) u-plasminogen activator PLAU SP 0.33 0.0 Vitamin K epoxide reductase complex, subunit 1 VKORC1 SP 0.48 0.0 glutaminyl cyclase 2 QPCTL M 3.85 0.0 proteasome catalytic subunit 1i PSMB9 TP 6.28 0.0 tissue inhibitor of metalloprotease-1 TIMP1 PI 0.38 0.0 a2-HS-glycoprotein/fetuinA AHSG PI 0.39 0.0 C1 inhibitor SERPING1 PI 3.38 0.0  61 2.4.3 Hierarchical clustering and characterization of differentially expressed genes Genes that were differentially expressed were subjected to hierarchical clustering using the Pearson correlation metric, which identified 5 different gene clusters corresponding to similarities in expression level changes upon cytokine treatment (Figure 2-2).  A summary of the each cluster as a ratio of the expression in the sample compared to the universal control is shown in Figure 2-3.  Genes in cluster 1 were observed to be induced by IL-10 treatment, while all genes in cluster 2 had lower expression upon treatment with IFN?/TNF?.  Cluster 3 encompassed genes with higher expression upon IL-4/13 treatment, cluster 4 showed genes with genes with a trend for decreased expression upon treatment with IL-4/13 and IL-10 while cluster 5 showed increased expression upon IFN?/TNF? treatment.  Hierarchical clustering was also conducted on the samples and revealed most similarity between the untreated control and IL-4/13 treatment followed by IL-10, and lastly IFN?/TNF? (Figure 2-2).  Interestingly, this clustering also revealed that the experimental sample from donor 2067 treated with IL-4/13 did not cluster with the other IL-4/13 treated samples.   62  Figure 2-2: Hierarchical clustering of differentially expressed genes.   The numerical scale represents the M-value as defined by the equation: M-value= log2 (Cy5)-log2(Cy3).  Red indicates low expression in the experimental sample while green represents high expression relative to the universal control. C1 through C5 represent different clusters of genes.  63  Figure 2-3:  Comparison of the relative expression levels of differentially expressed genes in their respective cluster.  (A) Genes associated with cluster 1, (B) cluster 2, (C), cluster 3, (D), cluster 4, and (E) cluster 5. Reults are expressed as the relative ratios of Cy3/Cy5.  Data was analyzed by SAM analysis.  Error bars represent SD. * q<0.05.  64 A list of differentially expressed genes was submitted to the Database for Annotation, Visualization and Integrated Discovery (DAVID) which utilizes data from several databases including NCBI Entrez gene, Biocarta, Kyoto encyclopedia of genes and genomes (KEGG), OMIM, and Uniprot in order to discover shared properties in the submitted gene list such as function and involvement in disease529,530.  Genes with known pathway functions were identified using Biocarta, KEGG, and OMIM databases and are presented in Table 2-3.  Several of the genes were found to be involved in either the complement or coagulation cascades including BF, HRG, PEBP1, PLAU, SERPING1, SPINT2, and VKORC1 or the immunoproteasome such as PSMB9 and PSMB10 respectively.  Many of these genes were from cluster 5 and were positively upregulated by IFN?/TNF?.  Other common functions included degradation of lysosomal proteins, ECM remodeling, and metabolism of arginine, proline and glutathione.  Knowing some of the basic functions of the differentially expressed genes, the list was then compared against NCBI Entrez gene, OMIM and UniProt to find associations with human disease.  Genes from all clusters excluding cluster 4 including ADAM8, AHSG, MMP12, PSMB9, PSMB10 and VKORC1 were found to be involved in atherosclerosis or coronary artery disease (found bolded in Table 2-3).  Several genes were also found to be associated with disease processes closely related to atherosclerosis such as QPCT and TIMP1 which were involved in arterial hypertension and restenosis, respectively.  Several of the genes including PLAU, TIMP1, PEBP1, QPCT, UQCRC1, BF, PSMB9, and PSMB10 were implicated in other pathological processes such as Alzheimer's disease.  QPCT protein for example, has been shown to facilitate amyloid beta aggregation while plasmin activity modulation through the protein  65 product of PLAU, uPA has been demonstrated to induce amyloid beta degradation531,532.  Diabetes was another commonly associated pathology that had ties with PLAU, AHSG, CTSH, PSMB9, and  PSMB10 (Table 2-3).                        66 Table 2-3: List of known pathways, functions and associations with disease for differentially expressed genes.  Associations with atherosclerosis are bolded.  Protease and protease inhibitor classes are abbreviated as follows:  CP, cysteine protease; M, metalloproteinase; PI, protease inhibitor; SP, serine protease; TP, threonine protease.  Gene Name Class Cluster Known pathways and functions identified through KEGG and Biocarta Associated human diseases identified by  NCBI Entrez gene, OMIM and UniProt ADAM8 M 1 ECM remodeling, cell migration, processing of membrane bound molecules Abdominal aortic aneurysm, aneurysm, bladder cancer, breast cancer, chronic obstructive pulmonary disease/COPD, coronary artery disease, coronary artery luminal dimensions, lung cancer, lung function, nasopharyngeal cancer, rheumatoid arthritis, subarachnoid hemorrhage. MMP12 M 1 ECM remodeling, degradation of elastin Aortic aneurysm, arthritis, atherosclerosis, metastasis BIRC1 PI 1 NOD-like receptor signaling pathway, inhibition of apoptosis Spinal muscular atrophy PLAU SP 1 Complement cascade, fibrinolysis pathway, platelet amyloid precursor protein pathway Alzheimer's disease; Abeta load; Abeta42 concentration, asthma atopy, bladder cancer, bone density, bone density; osteoporosis, bronchopulmonary dysplasia, colorectal cancer, insulin; diabetes, type 1,late-onset Alzheimer's disease, mitral valve prolapse, nephrolithiasis, oral cancer, prostate cancer, rheumatoid arthritis, urolithiasis. SERPINB6 PI 2 Maintaining lysosome integrity Deafness VKORC1 SP 2 Vitamin K processing, coagulation Acenocoumarol response, anticoagulant complications, aortic dissection atherosclerosis, coronary stroke, bleeding complications, ischemic heart disease, over anticoagulation, phenprocoumon requirements, protein C protein S, warfarin sensitivity and therapy. TIMP1 PI 2 ECM remodeling, inhibition of matrix metalloproteinases Abdominal aortic aneurysm, Alzheimer's Disease, aneurysm, arthritis, asthma, brain aneurysm, Crohn's disease ulcerative colitis, H. pylori infection stomach cancer, intracranial aneurysms, myopia, rectal cancer, restenosis, sclerosis, systemic. AHSG PI 2 Endocytosis, brain development and the formation of bone tissue Alzheimer's Disease, carotid atherosclerosis, bone density, bone size, diabetes, type 2,endometriosis, fetuinA, insulin, lipolysis, obesity, osteoporosis, phosphate serum levels.  67 Gene Name Class Cluster Known pathways and functions identified through KEGG and Biocarta Associated human diseases identified by  NCBI Entrez gene, OMIM and UniProt ANPEP M 3 Dendritic cells in regulating TH1 and TH2 Development, SARS coronavirus protease, glutathione metabolism, renin-angiotensin system Attention deficit disorder, conduct disorder, oppositional defiant disorder, coeliac disease, SARS. CTRB1 SP 3  Protein digestion and absorption, pancreatic secretion Unknown SPINT2 PI 3 Coagulation and fibrinolysis pathways, inhibition of hepatocyte growth factor, and cathepsin B Congenital diarrhea PEBP1 PI 3 Coagulation pathway, signal transduction through IL1R Prostate cancer, Alzheimer's disease QPCT M 3 Biosynthesis of pyroglutamyl peptides Alzheimer's Disease, arterial hypertension, bone density. UQCRC1 M 3 Electron transport reaction in mitochondria, cardiac muscle contraction. Alzheimer's disease, Huntington's disease, Parkinson's disease CTSH CP 4 Degradation of lysosomal proteins Type I diabetes mellitus, Batten disease, prostate tumours. AMZ2 M 4 Aminopeptidase activity against angiotensin III Unknown LAP3 M 5 Arginine, proline and glutathione metabolism Unknown NPEPL1 M 5 Removal of N-terminal amino acids Unknown BF SP 5 Complement and coagulation cascades Alzheimer's disease; Parkinson's disease; insulin; lung function; depression; longevity, type 2 diabetes, macular degeneration, systemic lupus erythematosus. PSMB10 TP 5 Formation of immunoproteasome Acute coronary syndrome, Alzheimer's disease; juvenile arthritis, ankylosing spondylitis, dermatitis, atopic, diabetes, type 1,Graves disease, hepatitis B, interferon response, juvenile arthritis, malaria; hypoglycemia; hyperparasitemia, multiple sclerosis; IgA nephropathy, psoriasis, rheumatoid arthritis, spondyloarthropathies. RISC SP 5 Degradation of lysosomal proteins Unknown QPCTL M 5 Biosynthesis of pyroglutamyl peptides Unknown  68 Gene Name Class Cluster Known pathways and functions identified through KEGG and Biocarta Associated human diseases identified by  NCBI Entrez gene, OMIM and UniProt PSMB9 TP 5 Formation of immunoproteasome Acute coronary syndrome, Alzheimer's disease; juvenile arthritis, ankylosing spondylitis, dermatitis, atopic, diabetes, type 1,Graves disease, hepatitis B, interferon response, juvenile arthritis, malaria; hypoglycemia; hyperparasitemia, multiple sclerosis; IgA nephropathy, psoriasis, rheumatoid arthritis, spondyloarthropathies. HRG PI 5 Inhibition of fibrinolysis and coagulation Thrombophilia. SERPING1 PI 5 Inhibition of the complement cascade, intrinsic prothrombin activation pathway Type II hereditary angio-oedema.  69 2.4.4 Discussion Proteases affect a number of processes throughout all stages of atherosclerosis, beginning with the recruitment of cells to the area by influencing cell migration, and cleaving precursors such as TNF? to create an inflammatory environment393,533.  Proteases have also been shown to be involved in a number of processes that are known to affect atherosclerosis progression such as apoptosis and the unfolded protein response (UPR)400,534,535.  Finally, proteases are involved in affecting the outcome of late stage atherosclerosis by determining plaque vulnerability and promoting plaque rupture404,423.  Much like specific macrophage phenotypes, many macrophage-derived proteases have been shown to be present and localized to specific areas of the atherosclerotic plaques.  I hypothesized that different macrophage phenotypes would exhibit distinct protease expression profiles and that M1 macrophages would express more proteases when compared with M2a or M2c.   In this chapter, the protease and protease inhibitor RNA profile of primary hMDMs treated with IL-4/13, IFN?/TNF?, IL-10 or no additional cytokines (untreated control) was examined with a CLIP-CHIP microarray.  About 60% (449/740) of all known human proteases and inhibitors were found at detectable levels in at least one of the four treatments.  Based on the number of genes with a fold change greater than 1.5-fold, we observe that IFN?/TNF? induced the most number of proteases as well as the fewest protease inhibitors, suggesting that it favours a protease dominant profile.  We also observed that IL-4/13 and IL-10 both induced slightly more proteases than they down regulated.  Interestingly, IL-4/13 stimulated  70 the expression of the greatest number of protease inhibitors followed closely by IL-10.  These observations are consistent with studies that suggest that IL-4, IL-13 and IL-10 promote collagen production while IFN? inhibits collagen production and promotes its destruction510,523.  Of all of the genes examined in the array, only one was found to be differentially expressed by one cytokine treatment when compared with all other treatments.  This gene, SPINT2, was up regulated by IL-4/13 treatment which is in agreement with literature that suggests that SPINT2 is regulated by STAT6 activation536.  The SPINT2 gene encodes the protein hepatocyte growth factor activator inhibitor (HAI)-2, a serine protease inhibitor that despite the single target mentioned in its name, has a broad specificity 537?539.  Although no studies have examined the effect of modulation of SPINT2 expression on atherosclerosis, there is evidence that it can act as an anti-inflammatory agent by reducing LPS-induced expression of TNF? and IL-1?540,541.  Furthermore, it has been shown to reduce the expression of uPA which has been shown to induce atherosclerosis development and MMP activity542,543.  Due to its unique expression profile it may be possible to use HAI-2 expression as a M2a macrophage marker although follow up experiments must be conducted to verify its expression profile.  Furthermore, the fact that there is a lack of knowledge of this target with respect to atherosclerosis makes it a compelling target to pursue further. Hierarchical sample clustering revealed that IFN?/TNF? treated macrophages displayed a profile that is the most distinct from all of the other treatment conditions with the greatest similarity observed between untreated and IL-4/13.  One  71 observation of note is that one sample, the one from donor 2067 treated with IL-4/13 did not cluster with the other IL-4/13 samples.  It is possible that since the hierarchical cluster was conducted on all genes that were observed to be differentially expressed amongst any treatment conditions, this list includes data from other samples where either no differences was observed or there was significant variation in the results obtained which could skew the clustering.  It is also possible this donor responded differently to IL-4/13 treatment.  Hierarchical gene clustering and functional analysis by DAVID was also performed on the differentially expressed genes to discover which protease pathways were affected by each cytokine treatment which could in turn have an effect on atherosclerosis.  The coagulation and fibrinolysis cascades are interrelated pathways which appeared most frequently amongst the differentially expressed genes.  In fact, clusters 1, 2, 3, and 5 all contained genes from these pathways.  The overall effect of each cytokine treatment on coagulation and fibrinolysis is mixed as genes corresponding to both pathways were both induced and down regulated.  For example, IFN?/TNF? treatment resulted in an up regulation of HRG which can act as both an anti-coagulant as well as an anti-fibrinolytic544?546.  Similarly, IL-4/13 treatment induced SPINT2 expression which can inhibit proteins involved in both pathways including factor Xa, kallikrein, and plasmin547,548.   When comparing the results of this analysis with the literature, there are both consistencies and inconsistencies as well as areas that remain unstudied.  In agreement with our findings, VKORC1 mRNA has been shown to be down regulated by IFN? treatment in mice while SPINT2 mRNA has been shown to be regulated by  72 STAT6536,549.  Furthermore, although no experimental evidence could be found supporting that HRG is induced by IFN?/TNF? treatment, its promoter does have a predicted NF?B binding site.  Likewise, no evidence was found to suggest that PEBP1 is up regulated by IL-4/13, however, much like IL-4, PEBP1 protein itself can inhibit TNF? signaling through NF?B550.  One discrepancy with the literature exists which predicts that uPA, the protein product of PLAU, is down regulated in human monocytes by IL-10 treatment while the results of the CLIP-CHIP suggest the opposite551.  As my study examines mRNA levels while their study examined cell surface protein, it is possible that there is a discrepancy between mRNA and protein levels as a result of post-transcriptional or post-translational regulation.  It is possible for instance that although IL-10 increases PLAU mRNA, that modifications prevent translation or uPA protein may be degraded at an increased rate.  Although many of the differentially expressed genes in the coagulation and fibrinolysis pathways were not associated with atherosclerosis based on NCBI Entrez gene, UniProt and OMIM databases, there is evidence to support a role of fibrinolysis in the development of atherosclerosis.  Fibrin and fibrin degradation products for example have been found to be positively correlated with plaque development in human atherosclerotic aortas552,553.  Correspondingly, the expression of uPA is increased in human atherosclerotic lesions and colocalizes with intimal macrophages at the rim of the necrotic core489.   Results from direct experimentation investigating the effect of modulating the fibrinolysis system on atherosclerosis indicate a complex situation.  Increasing fibrinolysis activity by overexpression of uPA protein in macrophages of ApoE-/- mice results in intraplaque  73 hemorrhage, an increase intima size as well as increased MMP activity554,555.  Counter intuitively, inhibiting fibrinolysis by double knockout of the gene encoding plasminogen (Plg) and ApoE also results in accelerated lesion formation suggesting that plasminogen may have protective effects as well556.  An investigation of the effect of the coagulation cascade on atherosclerosis by performing a double knockout of the gene encoding the fibrinogen alpha chain (Fga) and ApoE in mice resulted in little difference in lesion size, suggesting that coagulation may not play a significant role in atherosclerosis development557.  Due to the controversial nature of the role of fibrinolysis in atherosclerosis and the fact that cytokine treatments did not induce a uniform pro- or anti-fibrinolytic profile, it is difficult to speculate if specific cytokine treatments or the genes that were found to be differentially expressed could affect atherosclerosis development.  However, genes such as SPINT2 and HRG which are differentially expressed, involved in fibrinolysis but not established to play a role in atherosclerosis, may be potential targets to investigate further.   Several genes involved in the complement system were also differentially expressed including PLAU, BF, and SERPING1.  The complement system is closely intertwined with the coagulation and fibrinolysis pathways and the expression of genes from one pathway can influence another.  For example, both plasmin and thrombin protein can cleave inactive components of the complement system to generate their active forms while complement proteins such as mannan-binding lectin serine protease 2 can induce coagulation558,559.  Like the fibrinolysis pathway, the complement system has also been shown to be positively correlated with atherosclerosis development although whether or not it actively contributes to its  74 progression remains unknown560.  Treatment of ApoE-/- mice with the protein product of SERPING1 known as C1 inhibitor, has been shown to decrease neointima formation after wire injury561.  Double knockout of Bf and Ldlr in mice on a high fat diet shows a decrease in aortic root lesion size while triple knockout of ApoE, Ldlr, and Bf results in no difference562,563.  Similar to the fibrinolytic system, the genes found to be differentially expressed in the complement system do not show a cytokine-specific trend for either activation or inhibition as both the complement component BF and the inhibitor SERPING1 were up regulated by IFN?/TNF? treatment.  This regulation is supported by evidence in the literature where SERPING1 can be induced by IFN? treatment in human monocytes and that BF mRNA can be induced by IFN? and synergistically enhanced by addition of TNF?564?566.  It is also possible that since the RNA was isolated from macrophages exposed to cytokines for a 48 hour period that even if the complement pathway was being induced, that a compensatory response had already been mounted.  It will be necessary to examine the protease profile at different lengths of cytokine treatment to determine how expression is affected by time.  It is also possible that protein levels or the protease activity levels do not correspond with what is seen at the mRNA level, and further validation would be required. The genes PSMB9 and PSMB10 encode proteasome subunit ?1i and ?2i respectively; two components of the immunoproteasome and were both up-regulated upon treatment with IFN?/TNF?.  The immunoproteasome is an alternate form of the standard proteasome which replaces the ?-type subunits ?1, ?2, and ?5 with ?1i, ?2i, and ?5i and in agreement with our observation has been found to be  75 stimulated upon stimulation with IFN? 567?569.  Experimental evidence links the expression of the immunoproteasome with atherosclerosis as it is localized to the shoulder region of human carotid plaques and its expression is correlated with inflammatory cell infiltration and IFN? levels570.  In addition, the immunoproteasome has been shown to be important for several potentially pro-atherogenic responses including regulating IFN?-mediated apoptosis of lesion-derived cells by degrading the apoptosis inhibitor MCL-1 as well as propagating the inflammatory response by processing the precursor of NF?B571,572.  Despite the implied pro-atherogenic properties of the immunoproteasome, there are no studies that directly investigate its involvement in atherosclerosis and remain a potentially interesting area of research.    Another common function of the genes that were differentially expressed was extracellular matrix remodeling.  The CLIP-CHIP revealed that MMP12 and ADAM8 were up regulated in response to IL-10 treatment while TIMP1 was down regulated in response to IFN?/TNF?.  The observations pertaining to MMP12 and ADAM8 are novel when compared with the literature, as no studies have investigated the effect of IL-10 on MMP12 or ADAM8 gene regulation in macrophages, although one study did examine the effect of IL-10 on the expression of the protein product of MMP12 known as macrophage elastase in human glioma cell lines which concluded that it had no effect573.  TNF? and IL-4 have been shown to up regulate macrophage elastase expression which is in the same direction of the response observed in the CLIP-CHIP although not reaching statistical significance, while Mmp12 mRNA is inhibited by IFN? in murine BMDM512,574.  Information pertaining to ADAM8 is even more limited, with no information regarding the effect of IL-10 or IFN? on expression,  76 while evidence that IL-4/13 and TNF? may induce ADAM8 expression exists only in non-macrophage cells575?577.  Of the three ECM remodeling genes, TIMP1 which encodes the protein metalloproteinase inhibitor 1 is perhaps the best studied.  Although no studies have investigated the effect of combined treatment with both IFN? and TNF? on human MDMs, the effects of each have been studied individually.  Both IFN? and TNF? were found not to affect the amount of secreted metalloproteinase inhibitor 1 in human alveolar macrophages, which was confirmed in U937 cells for IFN? and human MDMs for TNF? respectively578?580.  To explain this, it is possible that the combined treatment of IFN?/TNF? is necessary to observe a decrease in expression.  Furthermore, the aforementioned studies examined the amounts of secreted metalloproteinase inhibitor 1 while the CLIP-CHIP only examines mRNA expression levels making the studies difficult to directly compare.  IL-10 treatment has been shown to increase the expression of metalloproteinase inhibitor 1 in human alveolar macrophages which may be dependent on STAT3517,581.  All three ECM remodeling targets have been shown to be present during atherosclerosis.  In one study, macrophage elastase was shown to be absent in normal human carotid endarterectomy samples but is present in atherosclerotic tissue, while another demonstrated that its expression is especially enhanced in samples that had undergone rupture, suggesting a role in plaque stabilization405,582.  In agreement with this observation, murine double knockout of Mmp12 and ApoE show a protection against transmedial elastin degradation583.  In vivo knockout and  77 transgenic studies investigating the potential role of macrophage elastase in atherosclerosis show that it either has no effect or a pro-atherogenic role583?586.   ADAM8 protein has also been shown to be up regulated under conditions of aortic or carotid human atherosclerosis when compared with normal arteries and a single nucleotide polymorphism (SNP) of ADAM8 has been found to increase risk for MI587,588.  Although no studies have investigated the effect of overexpression or knockout of ADAM8 on atherosclerosis, it does have potentially pro-atherogenic capabilities such as the ability to cleave fibronectin and cleave TNF? precursors, warranting further investigation589,590.  Metalloproteinase inhibitor 1 expression is also increased in human atherosclerosis samples compared to control arteries591.  Although its ability to maintain plaque stability is universally accepted, its role in preventing or contributing to atherosclerosis remains controversial as in vivo studies have shown TIMP1 protein to  induce, prevent or even have no effect on atherosclerosis development592?596.  QPCT and QPCTL are genes encoding two proteases involved in the formation of pyroglutamyl peptides were up regulated by IL-4/13 and IFN?/TNF? treatment and belong to cluster 3 and 5 respectively.  Both QPCT and QPCTL have been found to be induced by TNF? in vitro which is likely due to the presence of a NF?B binding site located in its promoter region597.  Although there is no experimental evidence to support their up regulation by IL-4/13, there is a predicted STAT6 binding site suggesting that this may be a possible.  These enzymes protect CCL2 from degradation by modifying the N-terminal to a pyroglutamyl residue which  78 in the context of atherosclerosis can increase monocyte infiltration597,598.  One study has shown that inhibition of both of these enzymes does indeed reduce monocyte infiltration attenuated atherosclerosis development in ApoE3*Leiden mice598.    The remaining proteases that have not been discussed including SERPINB6, RISC, CTSH, CTRB1, LAP3, NPEPL1, ANPEP, AMZ2, UQRC1, AHSG, and BIRC1 are mainly involved in the degradation of lysosomal, cytosolic, or extracellular proteins and are associated with general metabolism with a wide range of functions.  It is beyond the scope of this chapter to investigate every differentially expressed protease and protease inhibitor, however several major pathways have been discussed and several targets of interest for further study have been identified.  Although some of the proteases have established roles in atherosclerosis development, most of the proteases identified either show only correlations but no information regarding causation of atherosclerosis leaving room for investigation.   It would be prudent to discuss proteases and inhibitors known to be regulated by cytokine treatment that were not identified to be differentially expressed by this microarray.  For example, although it has been observed that many of the cysteine proteases including cathepsin B, D, L and S are up regulated while the protease inhibitor cystatin C is down regulated in response to IFN? treatment in several models, they were not deemed to be differentially expressed503?506,524,599.  Interestingly, the raw data from the CLIP-CHIP array indicates that every one of these genes displayed an expression profile in the IFN?/TNF? treatment that matched what is observed in the literature albeit with Q-values that exceeded 0.05, suggesting that the inability to detect these differences may be due to limited power  79 of our pilot study.  It is also possible that since the majority of studies that examined the regulation of proteases by cytokine treatment were conducted in murine cells or human derived cell lines, that inconsistencies in results may be due to model-specific differences.  Likewise, a multitude of experimental treatment times and concentrations were used in the literature, making direct comparisons difficult.   To conclude, we have generated a protease and protease inhibitor gene expression profile for primary human MDMs treated with IL-4/13, IFN?/TNF?, IL-10 or left untreated.  Through this process, increased expression of SPINT2 was identified as a possible protease marker for M2a macrophages.  In addition, several shared functional pathways invoked or inhibited by cytokine treatment including the fibrinolysis, coagulation, complement, immunoproteasome, ECM remodeling, and pyroglutamyl peptide generation were identified and their possible association with atherosclerosis was discussed.  Additional studies must be pursued to validate the targets identified by this study on both the mRNA and protein level, and treatments should be expanded to include multiple time points to elucidate the details of the temporal regulation of proteases and protease inhibitors with different cytokine treatments.    80  Chapter 3:     Macrophage heterogeneity and cholesterol homeostasis: M1 macrophages are associated with reduced cholesterol accumulation following treatment with oxidized LDL while M2a macrophages have increased rates of cholesterol efflux.  3.1  Background and rationale In the previous chapter, the potential role of different macrophage phenotypes to contribute to atherosclerosis was investigated by assessing their protease expression profile.  The current chapter builds on the underlying theme of macrophage phenotypes in atherosclerosis by investigating the fundamental ability to maintain cholesterol homeostasis.  Foam cell formation is a result of an imbalance between the amount of cholesterol accumulated by a macrophage either through endogenous production or exogenous uptake, and the amount that it is able to remove by efflux to surrounding cholesterol acceptors.  As discussed in the introduction, scavenger receptor-mediated, and more specifically CD36 and MSR1-mediated uptake of modified forms of LDL are critical pathways implicated in foam cell formation600?602.  Alternatively, cholesterol efflux is conducted passively through either diffusion or SR-BI, and actively through ABC transporters such as ABCA1 and ABCG1. Few studies make direct comparisons of the atherogenic potential amongst multiple different macrophage phenotypes and many gaps in knowledge exist regarding the effect of IL-4/13 and IL-10 treatment on cholesterol homeostasis.   81 Furthermore, no studies have examined the effect of TNF? either as an individual treatment or in combination with IFN? in primary human MDMs.  It is also important to highlight that primary human MDMs were used as the model in this study as this is arguably the most physiologically relevant in vitro model.  It is also worth noting that several species and model-specific differences have been observed in the literature.  Studies in human THP-1 and murine BMDM for example suggest that IFN? and signaling through STAT1 is necessary for foam cell formation whereas studies in human MDMs suggest that IFN? may actually prevent it73,603,604.  Likewise, IL-4 treatment of murine peritoneal macrophages has been shown to induce CD36 expression but not in primary human MDMs130,132,375.  With these discrepancies in mind, the ability of different primary human MDM phenotypes to maintain cholesterol homeostasis was compared and the mechanisms governing these differences were investigated.    3.2 Specific aims and hypotheses The specific aims for this chapter were as follows: 1. To determine the propensity of different primary human MDM phenotypes to associate with and accumulate cholesterol when treated with oxLDL. 2. To determine the propensity of different primary human MDM phenotypes loaded with oxLDL to efflux cholesterol to the cholesterol acceptors ApoAI and HDL.  82 3. To elucidate the mechanisms governing how cytokine treatment may affect any functional differences observed amongst macrophage phenotypes.  As discussed in chapter 1, IFN? can reduce MSR1 mRNA and both CD36 mRNA and CD36 protein expression in human MDMs12,73.  Additionally, IFN? has also been shown to reduce the ability of both murine and THP-1 cells to efflux cholesterol to ApoAI344?346.  TNF? has been shown to reduce the amount of neutral lipid accumulated in oxLDL treated J774A.1 macrophages in vitro although its role in the regulation of cholesterol efflux remains controversial352?354.  Based on this evidence, I hypothesized that primary human MDMs treated with IFN?/TNF? would also show decreased cholesterol accumulation and reduced ability to efflux cholesterol.  IL-4 and IL-13 signal through the transcription factor STAT6 which facilitates PPAR? signaling605.  PPAR? in turn has been shown to regulate the expression of the scavenger CD36 which mediates oxLDL uptake130,605.  In addition to its effect on CD36, PPAR? has also been observed to affect the expression of ABCA1 indirectly through the induction of LXR? 121.  These observations lead to my second hypothesis that IL-4/13 treated macrophages will display both increased cholesterol accumulation upon exposure to oxLDL, and also increased rates of cholesterol efflux through ABCA1. Evidence in the literature also indicates that IL-10 has been shown to up regulate MSR1 and ABCA1 protein expression, albeit in murine models or human- 83 based cell lines380,382,384.  Several studies in other models also show that IL-10 can increase cholesterol accumulation and efflux making it plausible that the same effect is present in human MDMs as well. If differences in oxLDL cellular association or cholesterol accumulation are seen, I hypothesize that this will be a result of a modulation in the expression of the two scavenger receptors CD36 and MSR1 which are primarily attributed to the binding and uptake of oxLDL.  Likewise, if differences in the ability to efflux cholesterol to either ApoAI or HDL are observed amongst the different macrophage phenotypes, I hypothesize that this is a result of changes in the expression of ABCA1 and ABCG1 respectively.  3.3 Materials and methods 3.3.1 Cell culture CD14+ human peripheral blood monocytes (AllCells) were cultured in 6 or 12-well plates depending on the assay as described in chapter 2 (Section 2.3.1).  In experiments where rosiglitazone (RSG) was used, RSG was added to wells at a final concentration of 5 uM on day 9 for 48 hours.  3.3.2 Isolation and oxidation of low density lipoprotein and high density lipoprotein Pooled normal human plasma using K3EDTA as an anticoagulant (Innovative Research) was adjusted to a density of 1.019 g/ml using sodium bromide (Sigma Aldrich) and spun at 281,000 x g using a L8-55M Ultracentrifuge (Beckman Coulter)  84 for 24 hours at 8?C.  The top layer containing chylomicrons and VLDL was removed and the density of the remaining solution was adjusted to 1.063 g/ml using sodium bromide and spun again at 281,000 x g for 24 hours at 8?C.  The top LDL layer was collected and the remaining solution was readjusted to 1.21g/ml using sodium bromide and spun again at 281,000 x g for 24 hours at 8?C.  The top fraction was retained as the HDL fraction.  Both LDL and HDL were dialyzed using 7K MWCO Slide-A-Lyzer Dialysis Cassettes (Thermo Scientific) in 4 L of 1 x PBS for 48 hours.  PBS was changed twice per day. Protein concentration was determined using a bicinchoninic acid protein assay (Thermo Scientific).  Copper oxidation of LDL was achieved with 5 ?M copper sulfate per 200 ?g/ml of LDL for a period of 18 hours at 37?C.  3-morpholinosydnonimine, hydrochloride (SIN-1) oxidation of LDL was performed by incubating LDL with 1mM SIN-1 per 1mg of LDL at 37?C overnight.  Oxidation was verified by determining the electrophoretic mobility using a Paragon Lipo Kit (Beckman) and by measuring the fluorescence of Schiff base imine products at excitation/emission 360 nm/430 nm on a Safire fluorescence plate reader (Tecan)606.  Representative gels can be found in the Appendix A.1.  After oxidation, both copper and SIN-1 oxidized LDL were dialyzed in 4L of 1xPBS for 48 hours, with a fresh change twice a day.  Copper oxidized LDL was used for all assays except for oxLDL cellular association where SIN-1 oxLDL was used concurrently.     85 3.3.3 oxLDL cellular association Oxidized LDL (oxLDL) was labeled with the lipophilic dye 1,1 ? -dioctadecyl-3,3,3 ? ,3 ? -tetramethylindocarbocyanine perchlorate (DiI, Sigma) based on the protocol developed by Stephan and Yurachek607 by adding 300 ?g of DiI dissolved in DMSO per 1 mg of LDL and incubating at 37?C for 18 hours followed by filtering with a 0.22 ?m filter (Millipore).  DiI-labeled oxidized LDL from here on in will be referred to as DiI-oxLDL. Polarized macrophages in 12-well CellBind plates (Corning) were washed twice with sterile 1 x PBS and subsequently treated with serum-free RPMI 1640 containing 10 ?g/ml of DiI-oxLDL for 4 hours.  Cytokine treatments were maintained during the loading period.  The wells were washed twice with 1xPBS and the cells were lysed with 100 ?l of RIPA buffer.  The wells were scraped with a cell scraper and collected in a 1.5 ml microcentrifuge tube prior to reading with a Safire fluorescent plate reader (Tecan) at excitation wavelength 520 nm and emission at 580 nm.  Fluorescence was compared with a DiI-oxLDL standard curve and normalized by cellular protein levels.  3.3.4 Cellular cholesterol accumulation Polarized macrophages in 12-well CellBind plates were washed twice with plain RPMI 1640 and loaded in serum-free RPMI 1640 containing 50 ?g/ml of oxLDL on day 10 for 24 hours.  Cytokines were maintained during the entire loading period.  After the loading period, macrophages were washed twice with 1xPBS and lysed with 100 ?l of RIPA buffer.  The wells were scraped with a cell scraper and collected  86 in 1.5 ml microcentrifuge tubes.  Endogenous peroxides in the cellular lysates were removed by treating with 1U of bovine catalase and incubating at 37?C for 15 minutes.  Total cholesterol was then determined by using the Amplex Red Kit (Invitrogen) and measured using a Safire fluorescent plate reader (Tecan) at the excitation wavelength 545 nm and emission at 590 nm.  Cellular cholesterol was normalized by cellular protein levels.  3.3.5 Cholesterol efflux Radioactive labeling of oxLDL was performed by drying 3H-cholesterol at a concentration of 1?Ci per ml of media in a glass tube, and washing with three times the volume of anhydrous ethanol.  This was then added to a solution of oxLDL at 1 mg/ml in RPMI 1640 and allowed to incubate at 37?C overnight.  The labeled oxLDL was used to load the polarized macrophages in 12-well plates at a final concentration of 50 ?g/ml on day 10 for 24 hours.  After 24 hours of loading, polarized macrophages were washed twice with efflux media - RPMI 1640 containing 0.2% bovine serum albumin (BSA).  Cells were allowed to equilibrate in efflux media for 2 hours.  The media was aspirated and replaced with either efflux media, efflux media containing 10 ?g/ml of ApoAI (generously provided by Dr. Gordon Francis) or efflux media containing 25 ?g/ml of HDL to incubate for 24 hours at 37?C.  After 24 hours, the media was collected, the cells were washed twice with 1xPBS and lysed with radioimmunoprecipitation assay (RIPA) buffer (150mM NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris pH 8.0) and collected.  The media was centrifuged at 10,000xg for 10 minutes and the  87 supernatant was collected.  Radioactivity in the media and lysate was counted with a Beckman LS 6500 liquid scintillation counter (Beckman).  The % efflux was calculated as the radioactive counts in the media as a fraction of the total radioactivity in both the media and lysate.  3.3.6  RNA extraction and analysis Total RNA was isolated using an RNeasy mini kit (Qiagen).  RNA quality and concentration was assessed using the NanoDrop 8000 (Thermo Scientific).  Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted using a one-step method using the qScript One-step Fast MGB qRT-PCR Kit, ROX (Quantas Biosciences) combined with Taqman gene expression assays (Applied Biosystems) containing the probe and primers for the targets of interest.  qRT-PCR was conducted on an ABI 7900 and mRNA levels were normalized to peptidylprolyl isomerase B (PPIB) and beta actin (ACTB).  A list of the Taqman gene expression assays used in this study is provided in Appendix A.2.  3.3.7 Protein extraction and Western blot analysis Polarized macrophages in 6-well plates were washed twice with 1 x PBS and lysed with RIPA buffer containing protease and phosphatase inhibitors (Pierce) on ice.  Wells were scraped with a cell scraper and collected into 1.5 ml microcentrifuge tubes.  Lysates were left on ice for 30 minutes, vortexing briefly every 10 minutes.  Lysates were then spun at 4?C at 20,000 x g for 10 minutes and the supernatant was collected and stored at -80?C.  88 Western blots were performed by first adding 2 x laemmli buffer to 15 ?g of total cellular protein which were then boiled at 90?C for 10 minutes.  Samples were vortexed, briefly spun down and added to lanes of a 4-15% gradient polyacrylamide gel (Biorad Laboratories).  Gels were run at 100V for 1.5 hours and a wet transfer was performed to a PVDF membrane (Millipore) overnight at 35V, in a cold room set to 4?C.  After transfer was completed, membranes were blocked for 1 hour at room temperature with SuperBlock (Pierce).  Membranes were incubated with primary antibodies followed by 5 x 5-minute washes in 1xTris buffered saline with Tween-20 (TBST).  Primary antibodies include mouse anti-?-actin (Sigma), mouse anti-MSR1 (Cosmo Bio) rabbit anti-PPAR? (Santa Cruz Biotechnology), mouse anti-ABCA1 (generous gift from Dr. Michael Hayden), and goat anti-ApoE (Millipore).  Membranes were then incubated with the either HRP-conjugated mouse or rabbit TrueBlot (eBioscience) or HRP-conjugated anti-goat (Abcam) where appropriate followed by another set of 5 x 5-minute wash.  Protein-antibody interactions were visualized using Super Signal West Femto enhanced chemiluminescence reagent (Pierce) on a Chemigenius (Syngene).  Densitometric analysis of protein bands was performed using ImageJ Software.  3.3.8 Flow cytometry Polarized macrophages in 24-well plates were washed with plain RPMI 1640 and subsequently incubated with 1 ml of Accutase (Innovative Cell Technologies) at 37?C for 15 minutes. Adherent cells were removed by pipetting up and down.  Accutase was neutralized with an equal volume of complete RPMI 1640 and  89 transferred to 5 ml polystyrene tubes.  Cells were spun down at 2000 RPM at 4 ?C for 5 minutes and washed 1 ml of PBS +0.5% BSA.  Samples were incubated with either phycoerythrin (PE)-conjugated, mouse anti-CD36 antibody, (BD Pharmingen) or a PE-conjugated mouse IgM ? isotype control (BD Pharmingen) for 30 minutes on ice, in the dark.  Cells were washed twice with PBS + 0.5% BSA and analyzed on a Beckman Coulter EpicsXL-MCL flow cytometer using Coulter System II Software v3.0.   3.3.9 Statistical analysis Statistical differences among groups for functional assays including oxLDL cellular association cholesterol accumulation, and ORO staining which tested our hypothesis that differences among our macrophage sub-phenotypes would exist were analyzed using a one-way analysis of variance followed with a Tukey?s post-hoc test.   Mechanistic studies investigating changes in mRNA and protein expression levels were expressed as values relative to the untreated control.  These values were log transformed in order to obtain symmetrical ratios and a one sample t-test was performed on each treatment condition to evaluate if their means were significantly different from the untreated control.  In scenarios where two different treatment conditions were compared with one another, a Student?s t-test was performed.  In experiments investigating the effect of rosiglitazone on downstream mRNA expression, a paired t-test against the untreated control was performed.  All  90 statistical tests were performed using GraphPad Prism 5.04.  Values of p<0.05 were considered significant.  3.4 Results 3.4.1 Macrophages treated with IFN?/TNF? associate with less oxLDL and have reduced CD36 and MSR1 expression. To evaluate the ability of different macrophage sub-phenotypes to associate with oxLDL, macrophages were treated with IL-4/13, IFN?/TNF?, IL-10 or left untreated and subsequently exposed to DiI-labeled copper oxidized LDL for 4 hours at 37?C.  A one-way analysis of variance showed that very significant differences in oxLDL cellular association were detected among our macrophage sub-phenotypes (p=0.003).  Post-hoc analyses using Tukey?s multiple comparisons test revealed an 86% decrease in copper oxidized LDL cellular association in our IFN?/TNF? treated macrophages compared with untreated controls (p<0.05, Figure 3-1A).  A similar trend was observed with SIN-1 oxidized LDL with a 50% reduction in cellular association in the IFN?/TNF? treatment although this did not reach statistical significance (Figure 3-1B).  This decrease led to the investigation of the expression of two of the primary scavenger receptors involved in oxLDL metabolism, CD36 and MSR1.  There was a modest 14% decrease in MSR1 mRNA in macrophages treated with IL-10 (p=0.02, Figure 3-1C), and a 30% reduction in MSR1 protein for both IL-4/13 and IL-10 treated macrophages (p=0.03 and p=0.03 respectively, Figure 3-1D).  IFN?/TNF? treated macrophages also had a 57% decrease in MSR1 protein as determined by Western blotting (p=0.0004, Figure 3-1D).  CD36 mRNA was reduced  91 by 51% and 49% in IL-4/13 and IFN?/TNF? treated macrophages, respectively (p=0.006, p=0.003 Figure 3-1F).  A corresponding 46% decrease in cell surface CD36 protein expression was observed for IFN?/TNF? treated macrophages when evaluated by flow cytometry (p=0.002, Figure 3-1G).         92  Figure 3-1: Macrophages treated with IFN?/TNF? associate with less oxLDL and have reduced CD36 and MSR1 expression:  Association of DiI-labelled oxLDL with different macrophage sub-phenotypes was measured after loading with 10 ?g/ml of (A) Cu2+ oxidized (n=4) or (B) SIN-1 oxidized LDL for 4 hours (n=4).  (C) mRNA levels of MSR1 and (F) CD36 was measured in different macrophage sub-phenotypes by RT-PCR and normalized by PPIB (n=4 each) . (D) Protein expression of MSR1 was measured by Western blot and normalized to ?-actin (n=10). (E) A representative blot is shown. (G) Cell surface protein expression of CD36 was measured by flow cytometry (n=4). (H) A representative flow cytometry histogram is shown.  Results are expressed as means ?SD of at least 3 different donors. Cellular association data was analyzed by one-way ANOVA followed by Tukey?s post-hoc test compared with untreated. mRNA and protein expression data was analyzed by a one-sample t-test.  *p<0.05, **p<0.01, ***p<0.001.  93 3.4.2 Macrophages treated with IFN?/TNF? accumulate less total cholesterol when treated with oxLDL and have reduced CD36 and MSR1 expression.  To determine if differences in oxLDL cellular association were related to changes in the concentration of cellular cholesterol over a longer incubation time, we measured the amount of cholesterol accumulated by different macrophage sub-phenotypes over a 24-hour time period following treatment with 50 ?g/ml of copper oxidized LDL. Loading with oxLDL in the untreated condition led to a doubling of total cholesterol accumulated when compared with unloaded macrophages (Figure 3-2A).  Significant differences were detected among the macrophage sub-phenotypes (p<0.0001).  Post-hoc analysis revealed that IFN?/TNF? treatment led to a 30% reduction in accumulated cholesterol compared to loaded, untreated controls (p<0.01) while IL-10 treatment caused a modest but significant 1.2-fold increase (p<0.05, Figure 3-2A).  CD36 and MSR1 mRNA and protein were analyzed once again to address whether changes in scavenger receptor expression were responsible for the observed differences using different cholesterol loading conditions.  No changes were seen in MSR1 mRNA (Figure 3-2B), but IL-4/13 and IFN?/TNF? caused a 21% and 54% reduction in MSR1 protein, respectively, as assessed by Western blot (p=0.007, p=0.0001, Figure 3-2D).  CD36 mRNA and cell surface protein expression were reduced by 50% and 81% in IFN?/TNF? treated macrophages respectively (p=0.01, Figure 3-2E and p<0.0001, Figure 3-2F), corresponding to a reduction in cholesterol accumulation.    94  Figure 3-2: Macrophages treated with IFN?/TNF? accumulate less total cholesterol when treated with oxLDL and have reduced expression of CD36 and MSR1. (A) Total cholesterol of different macrophage sub-phenotypes was measured after loading with 50 ?g/ml of Cu2+ oxidized LDL for 24hours (n=5 for unloaded, n=7 for all other treatments).  (B) mRNA levels of MSR1 and (E) CD36 was measured in different macrophage sub-phenotypes by RT-PCR and normalized to PPIB (n=4) . (C) Protein expression of MSR1 was measured by Western blot and normalized by ?-actin (n=10). (D) A representative blot is shown. (F) Cell surface protein expression of CD36 was measured by flow cytometry (n=4). (G) A representative flow cytometry histogram is shown.  Results are expressed as means ?SD of at least 3 different donors.  Accumulation data was analyzed by one-way ANOVA followed by Tukey?s post-hoc test compared with untreated. mRNA and protein data was analyzed by one-sample t-test. *p<0.05. **p<0.01, ***p<0.001.   95 Cholesterol accumulation was also measured in macrophages treated with either IFN? or TNF? individually in order to determine if both cytokines were capable of affecting cholesterol homeostasis.  Both IFN? and TNF? decreased total cholesterol accumulated to a similar degree (33%, p=0.012, and 28%, p=0.027, respectively, Figure 3-3A).  TNF? caused a reduction in MSR1 mRNA compared with both untreated (69%, p=0.0006) as well as IFN?/TNF? (71%, p<0.001, Figure 3-3B).  IFN? and TNF? also caused a 30% and 68% reduction in MSR1 protein levels, respectively (p=0.003, p=0.03, Figure 3-3B, C).  Combined IFN?/TNF? treatment resulted in a further 34% decrease in MSR1 protein compared with IFN? alone (p<0.05, Figure 3-3C).  Both IFN? and TNF? caused a reduction in CD36 mRNA levels (27%, p<0.05 and 40%, p<0.05, respectively, Figure 3-3E) while treatment with IFN?/TNF? resulted in a further 39% decrease compared with IFN? alone (p<0.05, Figure 3-3E).  IFN? and TNF? also caused a corresponding reduction in cell surface CD36 protein levels (35%, p<0.05, and 44%, p=0.006 respectively, Figure 3-3F).  Interestingly, the combined IFN?/TNF? treatment resulted in a further reduction of cell surface CD36 protein compared with either IFN? or TNF? alone (64% and 58%, respectively, p<0.001 for both, Figure 3-3F).  96  Figure 3-3: Both IFN? and TNF? treatments reduce cholesterol accumulation and the expression of CD36 and MSR1: (A)  Macrophages were polarized with individual cytokines and subsequently loading with 50 ?g/ml of oxLDL for 24 hours and total cholesterol levels were measured.  (n=5). (B) mRNA levels of MSR1 and (E) CD36 were measured by qRT-PCR and normalized to PPIB and ACTB (n=3,4). (C) Protein levels of MSR1 were determined by Western blotting and shown with (D) a representative blot (n=5). (F) Cell surface CD36 protein expression was determined by flow cytometry, shown with (G) a representative histogram (n=6).  Results are expressed as means ? SD of at least 3 different donors. Cholesterol accumulation was analyzed by a paired t-test compared with untreated. mRNA and protein data was analyzed with a one-sample t-test to evaluate a change from the untreated. *p<0.05, **p<0.01, ***p<0.001. Differences in mRNA and protein between IFN?/TNF? and either IFN? or TNF? was evaluated by a student's t-test. #p<0.05, ###p<0.001.  97  3.4.3 IL-4/13 treatment increases PPAR? expression levels The transcription factor PPAR? has been directly linked to modulating CD36 expression, and thus its expression in response to cytokine treatment was assesed130.  Both mRNA and protein levels were determined in cytokine treated macrophages that were either unloaded or loaded with 50 ?g/ml of oxLDL. IL-4/13 treatment increased PPAR? mRNA expression 1.9-fold in unloaded macrophages (p=0.001) and 2.5-fold under loaded conditions (p=0.001, Figure 3-4A, B).  IL-4/13 treatment also increased PPAR? protein levels 1.3-fold in unloaded macrophages (p=0.04, Figure 3-4C) and 1.6-fold under loaded conditions (p=0.04, Figure 3-4D).  Conversely, IFN?/TNF? treatment decreased PPAR? mRNA by 42% in unloaded macrophages (p=0.02, Figure 3-4A) and 55% under loaded conditions (p=0.0006, Figure 3-4).  Despite the decrease in PPAR? mRNA with IFN?/TNF? treatment, only a minor 16% decrease in PPAR? protein was observed in unloaded macrophages (p=0.005, Figure 3-4C) no significant decrease was observed under loaded conditions (Figure 3-4D).  When treated with IFN? or TNF? individually, a 56% and 49% reduction in PPAR? mRNA was observed under loaded conditions (p=0.01, p=0.04, Figure 3-4B) although no differences in PPAR? protein were observed.   98  Figure 3-4:  IL-4/13 treatment increases PPAR? expression levels: mRNA levels of PPAR? were measured with qRT-PCR and normalized to PPIB mRNA in polarized macrophages that were either (A) unloaded or (B) loaded with 50 ?g/ml of oxLDL (n=6).  PPAR? protein levels were measured by Western blotting in (C) unloaded and (D) loaded macrophages (n=8).  Representative Western blots for (E) unloaded and (F) loaded macrophages are shown.  Results are expressed as means ? SD of at least 3 different donors.   mRNA and protein data was analyzed with a one-sample t-test. *p<0.05, **p<0.01, ***p<0.001.     99 3.4.4 IFN? attenuates the upregulation of CD36 by rosiglitazone Despite the lack of change in PPAR? protein under loaded conditions, it was still possible that IFN? was affecting cell surface CD36 protein expression through a PPAR??dependent pathway.  To test this, macrophages were treated with IFN?, TNF?, both IFN? and TNF? or left untreated and subsequently exposed to the PPAR? specific agonist rosiglitazone to evaluate its ability to stimulate cell surface CD36 protein expression.  Rosiglitazone caused a 1.5-fold increase in cell surface CD36 protein expression in untreated macrophages, however treatment with either IFN? in combination with TNF? or IFN? alone inhibited this increase by 42% and 37%, respectively (p=0.002 and p= 0.036, Figure 3-5A).  Interestingly, although TNF? causes a reduction in CD36 expression, addition of rosiglitazone is capable of up regulating its expression 1.4-fold suggesting that the reduction in cell surface CD36 by IFN? is PPAR?-dependent while TNF? is unable to attenuate PPAR?-mediated signaling.  In order to confirm that PPAR? activity was being altered by IFN?, the fold change in mRNA expression of two PPAR? target genes, CD36 and FABP4 was evaluated upon the addition of rosiglitazone.  IFN? attenuated the response of CD36 mRNA to rosiglitazone by 42% compared to untreated macrophages (p=0.01, Figure 3-6A).  IFN? also caused a 57% reduction in the fold induction of FABP4 although this difference did not reach statistical significance (Figure 3-6B).   100  Figure 3-5:  IFN? attenuates the upregulation of CD36 by rosiglitazone: Macrophages were polarized with specific cytokine treatments and subsequently treated with 5 ?M rosiglitzone for 24 hours prior to loading with 50 ?g/ml of oxLDL.  (A) Cell surface CD36 protein expression was determined by flow cytometry and analyzed with paired t-test compared with untreated. (n=5). (B) Representative flow cytometry histograms are shown.  Results are expressed as means ? SD of at least 3 different donors.   *p<0.05, **p<0.01.  101   Figure 3-6:  IFN? treatment of MDMs attenuates PPAR? activity.   Macrophages were polarized with specific cytokine treatments and subsequently treated with 5 ?M rosiglitazone for 24 hours prior to loading with 50 ?g/ml of oxLDL.  (A) Fold change in CD36 (n=3) and (B) FABP4 mRNA (n=4) upon rosiglitazone treatment were measured by qRT-PCR and normalized to PPIB and ?-actin.  Results are expressed as means ? SD of at least 3 different donors.   *p<0.05, paired t-test compared with untreated.  3.4.5 IL-4/13 treatment increases ApoA-mediated cholesterol efflux.   The uptake and accumulation of oxLDL is only one aspect of cholesterol homeostasis. The ability of different macrophage phenotypes loaded with oxLDL labeled with 3H-cholesterol to efflux cholesterol to either ApoAI or HDL was also examined.  A baseline level of cholesterol efflux was generated by incubating MDMs in efflux media in the absence of exogenously added cholesterol acceptors. A trend for increased cholesterol efflux was seen in IL-4/13 treated MDMs but no statistically significant results were obtained (Figure 3-7A).  This baseline level was subtracted from the efflux obtained from MDMs incubated with ApoAI or HDL to generate the ApoAI-mediated and HDL-mediated rates of efflux respectively.  IL-4/13 treatment resulted in a 2.5-fold increase in ApoAI-mediated cholesterol efflux (p<0.05, Figure 3-7B).  Although IFN?/TNF? treatment resulted in a trend for decreased ApoAI- and  102 HDL-mediated cholesterol efflux, this difference did not reach statistical significance (Figure 3-7B, C).    Figure 3-7:  IL-4/13 tre.atment increases ApoA-mediated cholesterol efflux.   Primary hMDMs were treated with either IL-4/13, IFN?/TNF?, IL-10 or left untreated and loaded with 50?g/mL of  3H-labeled oxLDL for 48 hours.  The cells were then washed and allowed to equilibriate in efflux media for 2 hours prior to the incubation with (A) fresh efflux media for baseline comparison (n=5), (B) 10 ?g/mL of ApoA (n=5) or (C) 25 ?g/ml of HDL for 24 hours (n=7). After 24 hours, the percent efflux radioactivity was determined by the ratio of the radioactivity in the media compared to the total radioactivity in the media and cell lysate. Results are expressed as means ? SD of at least 3 different donors. Data was analyzed by one-way ANOVA. *p<0.05.    103 3.4.6 IL-4/13 treatment reduces the expression of LXR? target proteins. As a significant difference in the rate of cholesterol efflux was observed in IL-4/13 treated MDMs, the mechanism behind this observation was investigated.  The membrane transporter ABCA1 is primarily implicated in the efflux of cholesterol to ApoAI, and thus we decided to determine its expression level in the different macrophage phenotypes. In addition, the apolipoprotein ApoE, which is also capable of accepting cholesterol from ABCA1 and is endogenously produced by macrophages, was measured.  Finally, the transcription factor LXR? which controls the expression of both ABCA1 and ApoE was measured.  Although no differences in ABCA1 mRNA were observed amongst the different macrophage phenotypes, IL-4/13 treatment resulted in a 46% reduction in ABCA1 protein expression (p<0.05 Figure 3-8D, respectively).  IL-4/13 treatment also resulted in a minor 16% decrease in ApoE mRNA (p<0.05. Figure 3-8B) and 23% decrease in ApoE protein (p<0.01 Figure 3-8E).  IFN?/TNF? treatment resulted in no statistically significant changes in the mRNA levels of any of these targets, although a 32% reduction in ApoE protein was observed (p<0.01, Figure 3-8E).   104  Figure 3-8: IL-4/13 treatment reduces the expression of LXR? target proteins.   Primary hMDMs were treated with either IL-4/13, IFN?/TNF?, IL-10 and loaded with 50 ?g/mL of oxLDL.  mRNA expression of (A) ABCA1, (B) ApoE and (C) LXR? was measured by RT-PCR(n=4 for IFN?/TNF? treatment and n=6 for all others).  Expression is normalized for PPIB and ACTB.  (D) ABCA1 and (E) ApoE protein expression was evaluated by Western blot.  Representative blots for (F) ABCA1 and (G) ApoE are shown.  Results are expressed as means ? SD of at least 3 different donors. Data was analyzed by one-sample t-test. *p<0.05, **p<0.01.   105 3.5 Discussion Macrophage heterogeneity has been observed to exist in several human pathologies.  For example, classically activated macrophages exposed to IFN? and microbial products such as LPS during bacterial infection are critical for efficient immune responses while alternatively activated macrophages have been shown to be important in the resolution of  inflammation608,609.  Both macrophage sub-phenotypes have been identified in human atherosclerosis and have been found to localize to unique areas of the plaque suggesting that they are involved in different processes124,280,610.  Because of these differences in function, I sought to compare the ability of established macrophage sub-phenotypes to maintain cholesterol homeostasis. The data in this chapter demonstrates that human MDMs polarized towards a pro-inflammatory M1 phenotype with either IFN? or TNF? associate with less copper oxLDL than untreated macrophages or macrophages polarized with either IL-4/13 or IL-10.   SIN-1 oxLDL was also used during the oxLDL cellular association studies to support the copper oxLDL data.  SIN-1 oxLDL is not as extensively degraded as those subjected to copper but still recognizable by CD36611.  The results from the SIN-1 oxLDL cellular association assay shows a similar trend as the copper assay of decreased oxLDL cellular association in the IFN?/TNF? treated MDMs which bordered on but did not reach statistical significance.  It is possible that variation in the results may have arisen from donor-specific differences as well as from experimental error as a result of the low amount of SIN-1 oxLDL taken up when compared to its copper oxLDL equivalent.    106 IFN?/TNF? treatment was also found to result in a decrease in cholesterol accumulation in response to oxLDL.  I hypothesized that this may have been due to a decrease in the expression of CD36 and/or MSR1 so both mRNA and protein levels for these receptors were measured.  The results indicate that treatment with IFN? and TNF? was indeed reducing MSR1 protein levels and both mRNA and protein levels of CD36.  When comparing these results with the limited number of other studies employing primary human MDMs to compare different macrophage sub-phenotypes, the results are very similar.  Van Tits et al. used GM-CSF and M-CSF in a different model of macrophage polarization and also found that pro-inflammatory macrophages accumulate less cholesterol612.  There are no previous studies investigating the effects of TNF? on cholesterol accumulation upon exposure to oxLDL in primary human MDMs.  Studies investigating the effect of IFN? on macrophage cholesterol metabolism have revealed inconsistent results which appear to be model dependent.  Reports using the human monocyte cell line THP-1 indicate that IFN? and the transcription factor that it signals through, STAT1, are responsible for increased foam cell formation and CD36 protein expression603,604. By contrast, similar studies performed using primary human MDMs are in agreement with my own12,73,613.  Geng and Hansson, for example, found that IFN? caused a reduction in acetylated LDL cellular association and cholesterol accumulation73.  Likewise, Nakagawa et al. found that IFN? caused a decrease in CD36 protein and mRNA expression in primary human MDMs12.  A recent publication by Oh et al. using human MDMs from diabetic patients  treated with IFN? and LPS showed reduced cholesterol accumulation when compared with alternatively activated  107 macrophages613.  These differences reinforce the need to exercise caution when comparing results obtained from different macrophage cell models.  I investigated the transcription factor PPAR?, a known regulator of CD36 expression as a possible mechanism by which IFN? and TNF? were exerting their effects.  Although IFN? and TNF? did not cause significant reductions in PPAR? protein under loaded conditions, it was still possible that IFN? and TNF? were reducing CD36 expression in a PPAR?-dependent manner by regulating its activity.  I tested this possibility by treating macrophages with the PPAR?-specific agonist rosiglitazone and looked for changes in CD36 expression.  I found that rosiglitazone was unable to up regulate CD36 expression when macrophages were treated with IFN?, but this inhibition was not present in untreated macrophages or macrophages treated with TNF? suggesting that IFN? and TNF? may regulate CD36 through independent mechanisms.  The results suggest that IFN? down-regulates CD36 mRNA and protein expression primarily by preventing PPAR? activation as opposed to reducing PPAR? protein expression.  It is possible that IFN? signaling through STAT1 causes a sequestering of the coactivator proteins CREB binding protein (CBP) and p300 which are in limiting amounts in the cell614?617.  Since PPAR? and STAT1 are in competition for cellular CBP and p300, this would prevent PPAR? activation by rosiglitazone.  MSR1 protein expression may be being regulated in a similar manner.  MSR1 transcription is positively regulated by AP-1/ets ? two transcription factors which can be inhibited by IFN? treatment618,619.  This inhibition is dependent on STAT1 binding to CBP and can be relieved by overexpressing CBP618.    108 I also observed that despite the fact that IL-4/IL-13 treatment caused an increase in PPAR? protein, there was no corresponding increase in the PPAR? downstream target CD36.  In contrast to expectations, IL-4/IL-13 treatment caused a decrease in CD36 mRNA during the oxLDL cellular association assay.  This observation supports the notion demonstrated with IFN? treatment that merely measuring the protein levels of a transcription factor alone may not provide enough information to make predictions about transcription factor activity.  While the mechanism behind this phenomenon is not entirely clear, it is possible that IL-4/IL-13 signaling through STAT6 is influencing downstream targets that prevent the activation of PPAR? while simultaneously increasing its expression.  Further investigation will be required to understand the mechanism in more detail.  The results show that TNF? is also capable of causing a decrease in both CD36 and MSR1 protein which has been confirmed by several in vitro studies in different models74,126,139,349.  MSR1 mRNA expression appears to be regulated both transcriptionally and post-transcriptionally and likely involves the phosphatidylinositol 3-kinase/Rac1/PAK/JNK and phosphatidylinositol 3-kinase/Rac1/PAK/p38 pathways74,349.  Boyer et al. also found that CD36 protein and mRNA expression is down regulated by TNF? in primary human MDMs, but in contrast to the results of this chapter reported that TNF? treatment was sufficient to prevent rosiglitazone-mediated PPAR? activation126.  The reason for this discrepancy could be due to the shorter duration of treatment with the cytokine (5-30 minutes) in the study by Boyer et al., whereas this study was focused on prolonged chronic TNF? treatment over a 72 hour period.  109 Due to the established roles of CD36 and MSR1 in foam cell formation, I believe that their reduced expression is at least in part responsible for the decrease in oxLDL cellular association and cholesterol accumulation in IFN? treated macrophages.  This can be exemplified by the fact that blocking CD36 with specific antibodies decreases oxLDL binding, association and cholesterol accumulation in multiple cell models105,620?622.  MSR1 has also been demonstrated to play a prominent role in oxLDL uptake as Msr1 deficient murine peritoneal macrophages demonstrate reduced  oxLDL binding and uptake20.  While the results indicate that a combined treatment of IFN? and TNF? cannot reduce cholesterol accumulation beyond what is seen from either cytokine alone, I have demonstrated that they can work additively to further reduce CD36 expression.  It is unclear why a further reduction in cholesterol accumulation in the IFN?/TNF? combination treatment was not observed although it is possible that other scavenger receptors capable of binding oxLDL, such as LOX-1, are up regulated to compensate for the decrease in CD36 expression.   In addition the cholesterol influx, the ability of different macrophage phenotypes to efflux cholesterol was also examined.  The results showed that IFN?/TNF? treated macrophages exhibited a trend for decreased ApoAI and HDL-mediated cholesterol efflux that did not reach statistical significance.  The lack of statistical significance might be attributed to the possibility that IFN? and TNF? may have opposing effects.  There are several lines of evidence that suggest that IFN? can decrease the rate of both HDL and ApoAI-mediated cholesterol efflux in murine and THP-1 cells by reducing ABCA1 protein and mRNA expression345,346.  On the  110 other hand, some studies  have demonstrated that TNF? can increase the expression of ABCA1 and cholesterol efflux rates in THP-1 and murine cells352,354.  Further studies will be necessary to evaluate the effect of each cytokine individually in primary human MDMs to confirm whether they are acting in opposition to one another.  In addition to ABCA1, both IFN? and TNF? have been reported to decrease the mRNA and protein expression of ApoE in fully differentiated macrophages348,623.  In agreement with these findings, my results show that a combined treatment of IFN?/TNF? treatment results in a decreased expression of ApoE mRNA and protein and a trend for decreased ABCA1 protein possibly through an LXR related mechanism.  IFN? has recently been shown to inhibit LXR activity and thus the transcription of its downstream target genes such as ABCA1 which appears to be mediated by STAT1 sequestering of coactivating factors such as CBP and p300624.  The inhibition of IFN? signaling on LXR activation is reciprocal as activation of LXR can inhibit the expression of IFN? target genes624.  Furthermore, this inhibition can be reduced by overexpressing CBP/p300 providing further support for the notion that transcriptional regulation may be dependent on the availability of cofactors in addition to the mere abundance of transcription factors624.  There is evidence to support that a similar phenomenon is taking place between LXR and TNF? as reciprocal inhibition has also been demonstrated625,626.  The results also demonstrate that although IL-4/13 treatment caused an increase in ApoAI-mediated cholesterol efflux as hypothesized, it also resulted in a surprising decrease in the amount of ABCA1 protein expressed.  IL-4/13 also resulted in a trend for reduced LXR? mRNA as well as a decrease in ApoE protein  111 and mRNA.  To explain the IL-4/13-mediated increase in ApoAI-specific efflux but decrease in ABCA1 protein, it is possible that the observed efflux results are a reflection of the size of the pool of cholesterol available for efflux.  To explain, IL-4 has been shown to induce the expression and activity of PPAR?130.  PPAR? activation through rosiglitazone has been shown to increase nCEH mRNA levels, reduce ACAT mRNA levels, and also reduce cholesteryl ester accumulation in THP-1 macrophages627.  Cholesteryl ester levels are also found to be decreased in rosiglitazone treated murine peritoneal macrophages628. With less cholesteryl esters formed in PPAR? activated macrophages, there may be more free cholesterol available for efflux which may dictate the amount of cholesterol efflux possible.  If this is indeed what is occurring, even if IL-4/13 treated macrophages have decreased protein levels of ABCA1, more cholesterol efflux may be observed if the cells are able to reach an equilibrium.  Alternatively, IFN? has been shown to regulate cholesterol distribution in the opposite direction.  IFN? treatment of murine peritoneal macrophages increases the mRNA levels of ACAT as well as its activity resulting in a greater proportion of cholesterol found as cholesteryl esters347.  Time course studies must be performed in order to determine the rate of cholesterol efflux prior to reaching or nearing equilibrium in order to determine if differences in ApoAI-mediated cholesterol efflux are a result of ABCA1 expression or cholesterol distribution.  Additional studies will also be necessary to measure ACAT and nCEH activity as well as the amount and ratio of free and cholesteryl esters.  Only one other study by Chinetti-Gbaguidi et al. has looked at the effect of IL-4 treatment on the ability of primary human MDMs to efflux cholesterol and the  112 expression of related proteins375.  While our ABCA1 and ApoE data is consistent, and our trend for decreased LXR? expression is in line with their findings, the increased ability to efflux cholesterol to ApoAI was not.  Furthermore, contrary to the previously mentioned studies, Chinetti-Gbaguidi et al.  demonstrated that IL-4 treatment caused an increase in ACAT mRNA expression, and a resulting increase in cholesteryl ester formation.  This discrepancy may be a result of a difference in the method of cholesterol loading in the two studies since this study employed copper oxidized LDL while the study by Chinetti-Gbaguidi et al. used acLDL375.  Unlike acLDL, oxLDL loading can inactivate lysosomal acid lipase preventing the degradation of cholesteryl esters to free cholesterol in lysosomes which ultimately reduces the size of the pool of cholesterol available for efflux629,630.  Interestingly, the gene encoding for lysosomal acid lipase has been found to contain a PPAR? binding site, making it possible that IL-4 treatment of macrophages increases the amount of free cholesterol and thus the amount available for cholesterol efflux631.  Furthermore, even if IL-4 treatment was reducing ACAT activity as found by Chinetti-Gbaguidi et al., this effect may only be relevant upon acLDL loading.  A comparison of the effect of ACAT activity on acLDL or oxLDL loaded macrophages has shown that ACAT inhibition only alters the ratio of free to cholesteryl esters in  acLDL treated macrophages632.  Further study will be needed to compare the effects of the loading methods, assess the activity of the transcription factors PPAR? and LXR? as well as the expression and activity of lysosomal acid lipase. Despite the results presented in this chapter, it is not possible to make wide generalizations about the net atherogenic potential of the different macrophage  113 phenotypes examined.  Although IFN? and TNF? have been associated with reduced CD36 expression and cholesterol accumulation in this study, the results have also shown that IFN? and TNF? show a trend for decreased cholesterol efflux.  IFN? has also been shown to perform several potentially atherogenic functions such as inhibiting collagen synthesis while both IFN? and TNF? can increase the expression of several MMPs that could potentially lead to plaque destabilization345,523,579.  Furthermore, reduced CD36 protein expression itself may not be athero-protective.  While macrophages from CD36-deficient humans do show reduced oxLDL binding, patients are also more likely to exhibit hypertriglyceridemia, impaired glucose metabolism, and have mild hypertension113.  Likewise, although IL-4/13 treated macrophages showed increased ApoAI-mediated efflux, this may simply be a result of the distribution of cholesterol in the cell upon treatment with oxLDL.   In conclusion, the propensity of different macrophage sub-phenotypes to uptake, accumulate, and efflux cholesterol when exposed to oxLDL has been assessed.  Specifically, changes in expression of CD36 and MSR1 were identified as likely reasons for the differences in cholesterol accumulation and postulate that the distribution of the ratio of free to cholesteryl esters may be affecting the ability of macrophage phenotypes to perform cholesterol efflux.  Additional studies will be required to fully describe the underlying mechanisms behind how each cytokine treatment affects the expression of targets involved in cholesterol homeostasis with particular attention to the involvement of coactivating factors.   114 Chapter 4:     Evaluation of the monocyte cell line U937 as a model to study the regulation of CD36 expression by IFN? treatment.  4.1 Background and rationale  The choice of model and species used to examine functional abilities and mechanistic pathways in human pathology is extremely important.  As previously discussed, functional pathways that may exist in one model may not necessarily be intact in another.  Ideally, it is best to use the most physiologically relevant model to investigate functional differences and mechanistic pathways which in the case of in vitro studies often entails the use of primary cells.  With that said, immortalized cell lines offer several advantages over primary cells that warrant consideration.  First, immortalized cell lines are continuously dividing which makes them far more economical and practical to use.  This also enables the user to increase the scale of experiments in assays where certain targets are at low concentrations without fear of wasting precious resources.  Proliferating cells also expedite the rate that experiments can take place as experiments are not limited by the availability of donors.  Second, immortalized cell lines are often much easier to culture and resistant to adverse culture conditions such as the addition of oxLDL.  Finally, immortalized cell lines often provide  more reproducible results as they are genetically identical and are not subject to donor-specific differences.  The benefits of immortalized cell lines make them ideal models to embark on preliminary investigations.  Indeed, monocytic cell lines and more specifically, THP-1 and U937 cells are commonly used to investigate pathways in atherosclerosis.   115 However, THP-1 and U937 cells have been shown to display some behaviour that differs from primary cells and translations of results from cell lines must be conducted with care.  For example, THP-1 accumulate more cholesteryl esters in response to acLDL than their primary MDM counterparts633.  Other studies have indicated that an induction of STAT1 in primary human MDMs through IFN? decreases CD36 expression12,73,634.  This is unlike THP-1 cells where inhibition of STAT1 with decoy sequences is required to obtain the same effect343.  One study investigated the difference between THP-1 and primary cells using common differentiation protocols for the two models.  The transcriptome of phorbol 12-myristate 13-acetate (PMA) differentiated THP-1 cells and granulocyte macrophage colony-stimulating factor (GM-CSF) treated MDMs were compared and of the 75 induced genes for THP-1 and 104 induced genes for MDMs respectively, only 17 were common to the two models leading the authors to caution that the two models are very different635.  With regards to CD36 expression, cell lines including THP-1 and U937 have been shown to display a much lower level of cell surface CD36 expression than primary cells636.  In addition to differences between cell lines and primary derived models, differences between the THP-1 and U937 have also been observed.  For example, IFN? stimulation of macrophages causes the production of the anti-oxidant 7,8-dihydroneopterin that can inhibit the cytotoxicity of oxLDL in human MDMs and U937 cells but not THP-1 cells637. The amount of CD36 expressed and the CD36 synthesis rate of U937 is much greater than THP-1s which may make it more like primary derived cells638.  It is for all of the above reasons that THP-1 cells are unlikely to be a suitable model.  116  Despite the aforementioned difference, there is still value in pursuing cell lines as a model.  Ultimately, if a cell line is used, it is the responsibility of the investigator to determine if the cell line is indeed an appropriate substitute for the pathway or function under investigation and must be verified in other more physiologically relevant models.  In the previous chapter, IFN? was found to decrease CD36 protein expression as well as to inhibit the PPAR?-specific up regulation of CD36 without decreasing PPAR? protein.  I planned to investigate this phenomenon with the use of cell lines but needed to first evaluate the potential of the immortalized monocyte cell line U937 as a model to investigate the study the regulation of PPAR? by IFN?.   One of the possible methods of IFN? mediated reductions in CD36 protein levels is by increasing its rate of degradation.  As mentioned in chapter 2, IFN? has been demonstrated to induce an alternate form of the proteasome known as the immunoproteasome567?569.  The immunoproteasome cleaves polyubiquinated proteins into 3-15 amino acid oligomers more efficiently than the standard proteasome which can then be presented on MHC class I molecules to be recognized by CD8+ T cells639?642.  In addition, IFN? has been shown to increase the expression of several proteases that reside in the lysosome such as cathepsin B, D, L and S503?505,524,599.  In this chapter, U937 cells were evaluated as possible a model to study the regulation of CD36 protein expression levels by IFN?.  Increased rates of degradation by either the lysosome or proteasome were investigated as a possible mechanism of regulation.    117 4.2 Specific aims and hypotheses The specific aims for this chapter are as follows: 1. To determine if the cell line U937 show reduced CD36 protein expression upon treatment with IFN?. 2. To determine if IFN?-treatment of U937 cells inhibits the rosiglitazone-mediated increase in CD36 protein expression without affecting PPAR? levels as observed with primary MDMs. 3. To investigate if IFN? increases the rate of CD36 protein degradation through the proteasome or lysosome.  As mentioned earlier in the rationale, U937 cells have been shown to display some similarities in CD36 profile and response to IFN? as primary cells, especially when compared with THP-1 cells637,638.  As a result, I hypothesize that the results of specific aims 1 and 2 will demonstrate that U937 cells respond to IFN? in a similar manner as primary cells.   I also hypothesize that since IFN? has been shown to up regulate several proteasome and lysosome-based proteases that IFN? treatment will induces CD36 protein degradation.  4.3 Materials and methods 4.3.1 Cell culture THP-1 and U937 cells (AllCells) were cultured in RPMI 1640 complete media (Hyclone) containing 10% FBS (Hyclone), 2% sodium bicarbonate, 1% sodium pyruvate, and 1% penicillin-streptomycin (Invitrogen).   Cells were plated at a density  118 of 5x105/ml in 12-well CellBind plates (Corning) with PMA at a final concentration of 100 ng/ml and allowed to incubate at 37?C, 5% CO2 for 48 hours.  PMA stimulation has been shown to induce a macrophage-like phenotype in U937s643. After 48 hours (Day 2), cells were washed twice with plain RPMI 1640 and replaced with complete RPMI 1640.   After an additional 72 hours (Day 5), the media was aspirated and replaced with fresh complete RPMI 1640.  On day 6, the media was aspirated and replaced with media containing IFN? (10 ng/ml), or replaced with complete RPMI1640 as an untreated control.  For the applicable studies, rosiglitazone was added at a final concentration of 5 ?m for 24 hours while maintaining the cytokine treatment.  4.3.2 Protein extraction and Western blot analysis The methods for this section are the same as those reported in section 3.3.7.  4.3.3 Flow cytometry The methods for this section are the same as those reported in section 3.3.3.  4.3.4 Proteasomal and lysosomal degradation studies. U937-derived macrophages differentiated with PMA were treated on day 6 with either the lysosomotropic agent chloroquine (CQ, Sigma) for 2 hours, the proteasomal inhibitor Z-Leu-Leu-Leu-al (MG-132, Selleck) for 2 hours, or the inhibitor of the deubiquitinating enzyme ubiquitin specific peptidase (USP)14, (IU1, Sigma) for 6 hours.  CQ has been shown to successfully inhibit lysosomal protein  119 degradation in a several cell macrophage models644?648. Similarly, MG-132 has been shown to inhibit while IU1 enhances proteasomal activity640,649?651. Macrophages were then cultured in the presence or absence of IFN? for 48 hours and subsequently harvested for protein.   4.3.5 Statistical analysis Studies investigating protein expression levels including flow cytometry and Western blotting were expressed as values relative to the untreated control.  These values were log transformed in order to obtain symmetrical ratios and a one sample t-test was performed on each treatment condition to evaluate if their means were significantly different from the untreated control.  Experiments investigating the effect of rosiglitazone on the fold change in expression used a paired student's t-test against the untreated control.  The experiments investigating the effect of proteasome and lysosomal protease inhibitors were evaluated through two different statistical tests.  A one sample t-test was used to determine if any of the concentrations used was having an effect compared with untreated.  Comparisons of the fold change between untreated and IFN? treated were made using a paired student's t-test.  All statistical tests were performed using GraphPad Prism 5.04.  Values of p<0.05 were considered significant.      120 4.4 Results 4.4.1 Treatment of U937 macrophages with IFN? decreases cell surface CD36 protein expression but does not attenuate rosiglitazone-mediated increases in cell surface CD36. U937 cells were investigated to assess if IFN? treatment could both reduce cell surface CD36 protein expression as well as inhibit the up regulation of CD36 expression by rosiglitazone as observed with primary human MDMs.  U937 showed a 21% decrease in cell surface CD36 protein expression upon treatment with IFN?, similar to hMDMs (p<0.05, Figure 4-1A).  IFN? treatment resulted in a minor inhibition of cell surface CD36 protein expression upon treatment with the PPAR? agonist rosiglitazone (Figure 4-1B).  121  Figure 4-1:  Treatment of U937 macrophages with IFN? decreases cell surface CD36 protein expression but does not attenuate rosiglitazone-mediated increases in cell surface CD36. (A) Cell surface CD36 protein expression of U937 macrophages (n=3) and hMDMs (n=5) treated with interferon ? or left untreated was measured by flow cytometry.   (B) Fold change in cell surface CD36 protein upon rosiglitazone treatment was measured by flow cytometry (n=3 for U937, n=5 for hMDM)).  (C) Representative flow cytometry histograms for U937 and D) hMDMs. Results are expressed as means ? SD of at least 3 different donors for hMDMs. CD36 cell surface protein expression data was analyzed with a one-sample t-test while fold change in CD36 was analyzed with a paired student?s t-test vs. untreated.   122 4.4.2 Treatment of U937 macrophages with IFN? decreases the 90-105 kDa CD36 protein band. Although the cell surface expression of CD36 did decrease upon IFN? treatment, this does not represent the all of the CD36 protein in the cell.  For this reason, total CD36 protein expression was evaluated by Western blot.  CD36 is known to be present in two major states, as a 74 kDa precursor prior to glycosylation in the Golgi and a mature 90-105 kDa glycosylated form638.  Both forms of CD36 were detected.  Treatment with IFN? resulted in a 27% decrease in the 90-105kDa CD36 protein (p<0.05, Figure 4-2A) although no differences were observed in the 74kDa band (Figure 4-2B).  IFN? treatment also resulted in trend for a 32% inhibition of the up regulation of the 74kDa CD36 precursor by rosiglitazone although this difference was not statistically significant (Figure 4-2D).     123  Figure 4-2:  Treatment of U937 macrophages with IFN? decreases the 90-105 kDa CD36 protein band.    (A) The 90-105 kDa CD36 band and (B) 74 kDa CD36 band of U937 macrophages treated with IFN? or left untreated was measured by Western blot (n=7).   (C) Fold change in 90-105 kDa band and (D) 74 kDa band upon rosiglitazone treatment was measured by Western blot (n=4).  (E) A representative Western blot is shown. Results are expressed as means ? SD. CD36 protein expression data was analyzed with a one-sample t-test while fold change in CD36 was analyzed with a paired student?s t-test vs untreated. *p<0.05   124 4.4.3 Treatment of U937 macrophages with IFN? does not decrease PPAR? protein expression. U937 cells treated with IFN? were tested to determine if cytokine treatment would have no effect on PPAR? protein levels as observed with primary human MDMs.  The results of the Western blot demonstrates that IFN? treatment does not result in a statistically significant decrease in PPAR? (Figure 4-3A).  Figure 4-3:  Treatment of U937 macrophages with IFN? does not decrease PPAR? protein expression.   (A) Total PPAR? protein expression of U937 macrophages (n=5) and hMDMs (n=8) treated with IFN? or left untreated measured by Western blot. (B) A representative Western blot is shown. Results are expressed as means ? SD of at least 3 different donors for hMDMs.  Data was analyzed with a one-sample t-test.     125 4.4.4 IFN? does not induce CD36 degradation by the lysosomal or proteasomal pathways in U937 cells.   As IFN? has been shown to decrease the level of total CD36 protein expression, degradation through the lysosomal and proteasomal pathways were examined as possible mechanisms.  To test the involvement of the lysosome, PMA-treated U937 cells were incubated in the presence or absence of the lysomotropic agent CQ to inhibit lysosome-mediated degradation at various concentrations for 2 hours prior to treatment with IFN? or left untreated.  The expression of total CD36 protein was evaluated by Western blot and the fold change in expression upon treatment with CQ compared with no CQ treatment was recorded.  If IFN? was in fact increasing the rate of CD36 protein degradation through the lysosome, incubation with CQ should increase the relative amount of CD36 protein observed in the IFN? treated macrophages compared to the untreated which was not observed.  A one sample t-test reveals that CQ increased the amount of the mature 90-105 kDa CD36 band at 100 ?M 1.9-fold (p<0.05, Figure 4-4A) and 1.7-fold at 200 ?M (p<0.01, Figure 4-4A) in the untreated condition but not the IFN? treatment.  Comparisons between untreated and IFN? revealed that CQ treatment resulted in a 1.7-fold greater preservation of the mature form of CD36 at 200 ?M (p<0.05, Figure 4-4A).  CQ also resulted in a modest 1.2, 1.4, and 1.3-fold increase in amount of the immature 74 kDa band in the untreated condition (p<0.05 for all, Figure 4-4B) while no changes were observed in the IFN? treatment.  Comparisons between untreated and IFN? revealed that CQ treatment resulted in a 1.3-fold greater preservation of the 74 kDa CD36 band at 200 ?M (p<0.05, Figure 4-4B).    126  Figure 4-4:  IFN? does not induce CD36 degradation by the lysosomal pathway in U937 cells.   PMA stimulated U937 cells were treated with increasing concentrations of the lysosomotropic agent chloroquine to inhibit lysosomal proteases for 2 hours prior to treatment with IFN? or left untreated.  (A) The 90-105 kDa CD36 band and (B) 74 kDa band was measured by Western blot (N=3). Representative Western blots are shown for (C) untreated and (D) IFN? treated macrophages. Results are expressed as means ? SD. Evaluation of the effect of CQ concentration within untreated and IFN? treated samples data was analyzed by one-sample t-test.  An asterisk above the line refers to a comparison of the untreated in the absence of CQ with the indicated concentration while an asterisk below the line refers to a comparison of the IFN? condition in the absence of CQ with the indicated concentration.  *p<0.05, **p<0.01.  Comparisons between untreated and IFN? were conducted with a paired student's t-test. #p<0.05, ##p<0.01      127 4.4.5 IFN? does not induce CD36 degradation by the proteasomal pathway in U937 cells. A similar experiment was conducted to test the possibility that IFN? was increasing rates of CD36 protein degradation through the proteasome.  The proteasome inhibitor MG-132 or the inhibitor of the deubiquitinating enzyme ubiquitin specific peptidase (USP)14 known as IU1 were used at a various concentrations to inhibit or enhance proteasome activity respectively.  Inhibiting proteasome activity by treatment of U937 cells with MG-132 resulted in an overall trend for increased total CD36 protein in both the 90-105 kDa and 74 kDa forms, but no statistically significant differences were observed (Figure 4-5A).  Furthermore, no differences were observed when comparing untreated cells with IFN? treated U937s.  When U937 cells were treated with IU1 to induce proteasomal activity, a 17% decrease in the mature form of CD36 protein was observed in the untreated control treatment at the 100 ?M concentrations (p<0.05, Figure 4-5C). A 26% decrease in the 74 kDa form of CD36 was seen at the 50 ?M concentration in the untreated control (p<0.05, Figure 4-5D). Again, no differences were observed between the untreated control and IFN? treated macrophages at any given concentration.  128  Figure 4-5:  IFN? does not induce CD36 degradation by the proteasomal pathway in U937 cells.    (A) The 90-105 kDa CD36 band and (B) 74 kDa band was measured in PMA stimulated U937 cells were treated with the proteasome inhibitor MG-132 (n=3). (C) The 90-105 kDa band and (D) 74 kDa CD36 band was also measured in U937 cells treated with the inhibitor of deubiquinating enzyme USP14 associated with the proteasome (IU1, n=3) by Western blot. Representative Western blots are shown for (E) MG-132 and (F) IU1. Results are expressed as means ? SD.  Evaluation of the effect of MG-132 and IU1 concentration within untreated and IFN? treated samples data was analyzed by one-sample t-test.  An asterisk above the line indicates a comparison of the untreated in the absence of MG-132 or IU1 with the indicated concentration while an asterisk below the line refers to a comparison of the IFN? condition.  *p<0.05.  Comparisons between untreated and IFN? were conducted with a paired student's t-test.   129 4.5 Discussion Cell lines have proven to be popular models to investigate human pathologies and for good reason.  They offer a flexible, reproducible and rapid method to examine functional pathways with a relatively low cost.  Unfortunately, they are further from being physiologically relevant as primary cells or in vivo models, resulting in findings that are not always directly transferable to a therapeutic treatment.  Because of the many benefits associated with using cell lines, it is desirable to employ cell lines for initial study of functions and pathways that is later verified in a more physiological model.  For this reason, in this chapter, I have chosen to evaluate the cell line U937 as a model to investigate the regulation of CD36 protein expression by treatment with IFN?. The data in this chapter has demonstrated that U937 cells show a decrease in cell surface CD36 protein expression but not an inhibition of rosiglitazone-mediated induction of cell surface CD36 upon IFN? treatment as was observed with primary human MDMs.  Despite this observation, it was possible that this negative result may have been due to an insufficient amount of time for rosiglitazone to be exerting an effect.  A preliminary time course of rosiglitazone treatment reveals that this may not be the case.  Increased treatment time with rosiglitazone did not correlate with increased cell surface CD36 expression (APPENDIX B.2).   It was possible that IFN? was decreasing CD36 protein expression from a non-cell surface pool of CD36, possibly as an immature intracellular form that would not be observable through flow cytometry.  In order to test for this possibility, total CD36 protein expression was evaluated by Western blot.  CD36 has been  130 demonstrated to exist in at least one immature 74 kDa form prior to glycosylation in the Golgi and subsequent expression on the cell surface as a 90-105 kDa protein638,652.  The Western blot results demonstrate that both forms are indeed present.  As hypothesized, IFN? treatment resulted in a decrease in the 90-105 kDa form of CD36 as detected by flow cytometry.  There was no difference observed between the treatments with respect to the amount of the immature form of CD36 suggesting that the IFN?-mediated reduction in CD36 may be transient.  Transient inductions are often observed; stimulation of murine RAW264.7 macrophages with IFN? and LPS induces the production of TNF? protein that peaks at 32 hours and drops off sharply at 48 hours despite constant IFN? exposure653.  Likewise, stimulation of U937 cells with IFN? has been shown to induce temporary increases in IL-1?, IL-1? and TNF? mRNA654.  It is also possible that the reduction in the mature but not immature form of CD36 in the IFN? treatment may not be a result of transcriptional regulation but rather a post-translational event, perhaps through protein degradation or impaired glycosylation.  These explanations are less likely as will be discussed later, IFN? did not appear to increase the degradation of CD36 while impaired glycosylation might lead to an increase in the immature form which was not observed. Conversely, IFN? caused a trend for inhibition of CD36 induction by rosiglitazone in the 74 kDa form but not the 90-105 kDa form suggesting involvement at the transcriptional level, although this did not reach statistical significance.  This may also be a reflection of the specific timing used in the experiment where the initial inhibition of the 74 kDa form may be reflected in a  131 reduction of the 90-105 kDa form at a later time which could be verified by a time course experiment.  The reduction in amount of only mature form yet inhibition of only the immature form upon rosiglitazone treatment could also be explained if IFN? was capable of both inhibiting PPAR? signaling by rosiglitazone as well as reducing mature CD36 protein levels.  Although the data shows a trend for IFN?-mediated inhibition of PPAR? activity and no change in PPAR? protein, it is not enough to support the hypothesis that U937 would act as a suitable model without further study. In addition to the aforementioned deviances from my observations in primary cells, U937 cells did not show as robust of an increase in mature CD36 in response to rosiglitazone as primary cells do.  It is likely that the difference in cell model may affect the responsiveness to rosiglitazone.  As an example, in a study investigating the effect of the endogenous ligands of PPAR?, 9- and 13-HODE, it was observed that both were able to induce the expression of vascular endothelial growth factor in THP-1 cells but not U937 cells655.  Furthermore, in a study investigating the effect of thiazolidinediones in different cells types,  it was observed that the PPAR? agonist troglitazone acts as a partial agonist in HEK 293T cells but as a full agonist in 3T3L1 cells showing cell-specific responses656. Future studies should examine the effects of multiple PPAR? agonists to ensure maximal activation.  The underlying reason for this difference in responsiveness could be caused by differences in PPAR? protein levels.  While the protein levels of PPAR? were evaluated in both U937 and primary human MDMS, it is difficult to make direct comparisons without analyzing both samples on the same blot due to differences in exposures between membranes.  It  132 is also possible that different models vary in the abundance or the availability of cofactors that could either limit the induction or inhibition of the expression of PPAR? target genes.  Although no comparisons have been conducted in various monocyte cell lines, breast cancer cell lines have demonstrated line-specific expression of certain cofactors657.  Finally, differences observed between cell lines and primary cells may also be a result of differences in gene methylation.  An investigation of both THP-1 and U937 cells showed significant hypermethylation when compared with primary human monocytes which may result in the silencing of several genes658.  Further investigation examining the relative PPAR? protein and activity levels between U937 and primary cells will need to be examined as will the expression of coactivating factors and binding to PPAR? in order to elucidate the mechanism behind observed differences.  It was also hypothesized that IFN? treatment would cause an increase in CD36 degradation by both the lysosomal and proteasomal pathways but this did not appear to occur in U937 cells.  If IFN? was inducing an increase in CD36 degradation, inhibition of the lysosome or proteasome would be expected to provide a larger increase in the amount of CD36 observed compared with no inhibition in the IFN? treated cells.  When cells were treated with CQ, the opposite effect was seen with a greater increase observed in the untreated compared with IFN? treated cells suggesting that IFN? may in fact decrease lysosomal degradation of CD36.  As endocytosis of plasma membrane derived CD36 is the major pathway for lysosomal degradation, then the reduction in cell surface CD36 protein expression by IFN? may indeed reduce the amount degraded by the lysosome as reflected in the results645.   133 No significant inhibition of CD36 degradation was observed in MG-132 treated cells making interpretation difficult and further optimization of the experiment must be completed to draw more meaningful conclusions.  Alternatively, IU1 successfully enhanced CD36 degradation supporting earlier studies observing that CD36 can be ubiquitinated and degraded through the proteasome but no differences were observed between untreated and IFN? treated cells650.    To conclude, U937 cells have been shown to display several functional similarities in response to IFN? when compared with human MDMs.  They display a reduced cell surface expression level of CD36 and did not significantly decrease PPAR? protein.  However, compelling evidence to suggest that IFN? treatment of U937 could attenuate PPAR? activity was not provided making U937 cells an unsuitable model in the absence of further experimental optimization.  Furthermore, IFN? did not increase the rate of CD36 degradation through the proteasomal or lysosomal pathways.    134 Chapter 5:     Conclusions and future directions 5.1 Conclusions It has become increasingly clear that not all macrophages behave identically when presented with the same situation and there is a need to be cognizant of different macrophage phenotypes when considering function.  The work presented in this thesis evaluated several functional differences amongst distinct macrophage phenotypes in the context of atherosclerosis including the expression of proteases and proteases inhibitors and the ability to maintain cholesterol homeostasis. The work presented in chapter 2 assessed and compared the complete protease and protease inhibitor profile of primary human MDMs treated with IFN?/TNF?, IL-4/13, IL-10 or left untreated with the use of the CLIP-CHIP microarray.  Differentially expressed proteases and inhibitors were identified using a SAM analysis which revealed that as hypothesized, IFN?/TNF? treated macrophages displayed an overall trend for protease activation.  One protease inhibitor SPINT2 was found to be significantly up regulated upon treatment with IL-4/13 when compared with all other treatments.  All differentially expressed genes were organized by hierarchical clustering and the data mined for similarities in functional pathways and known associations with disease.  Several protease pathways including coagulation/fibrinolysis, complement, immunoproteasome and ECM remodeling were found to be commonly clustered amongst the different phenotypes.   The polarized macrophages were then tested for their ability to maintain cholesterol homeostasis when challenged with oxLDL.  Both IFN? and TNF? were  135 found to be capable of reducing both oxLDL association and total cholesterol accumulation in response to oxLDL which is likely mediated through a reduction of the scavenger receptors MSR1 and CD36.  Having shown an IFN?-mediated reduction of CD36, the mechanism of regulation was then pursued.  IFN? was also found to inhibit the up regulation of CD36 through PPAR? without reducing PPAR? protein levels.  The effect of cytokine treatment on cholesterol efflux was also examined and IL-4/13 treatment was found to increase ApoAI-mediated cholesterol efflux despite causing a reduction in ABCA1 protein.  With functional differences in cholesterol homeostasis revealed, the viability of employing the cell line U937 to act as a model to study the effect of IFN? on CD36 protein expression was investigated.  Although IFN? resulted in a reduction of mature CD36, untreated U937 cells showed minimal response to the PPAR? agonist rosiglitazone, making U937 unsuitable to study this particular pathway.  The proteasome and lysosome were investigated as potential mechanisms of reduced CD36 protein expression upon IFN? treatment.  Treatment of IFN? treated U937 cells with a lysosome inhibitor did not cause a greater increase in CD36 protein expression compared with untreated, suggesting that IFN? does not increase lysosomal degradation of CD36.  Likewise, no differences in the change in CD36 protein were observed between untreated and IFN? treated macrophages incubated in the presence the proteasomal inhibitor MG-132.     136 5.2 Limitations and future directions The results from chapter 2 are derived from the results of a microarray which, while an extremely powerful tool to compare different phenotypes, contains inherent limitations.  Microarrays are challenged by the effects of multiple comparisons where due to the large datasets tested, are prone to false positives (reviewed in 659).  It is thus necessary to evaluate the targets identified by RT-PCR which is more sensitive and specific to ensure that the targets of interest are indeed regulated by cytokine treatment660.  Once the mRNA of the differentially expressed genes are verified, the expression can then be checked by Western blotting to check for regulation at the protein level.   With regards to specific targets and pathways identified in the study, SPINT2 was found to be significantly up-regulated in response to IL-4/13 when compared with all other macrophage phenotypes.  After validation of the RNA by RT-PCR and confirmation of increased protein expression of its protein product HAI-2 by Western blotting in vitro, HAI-2 may serve as an additional marker of M2a activation.  Further validation of HAI-2 as a marker could be performed by immmunohistochemical evaluation of human and murine coronary arteries and assessing for colocalization of other M2a markers such as the mannose receptor.   In addition to the potential utility as a M2a macrophage marker, it is possible that HAI-2 may have an effect on atherosclerosis which currently remains unstudied.  As previously discussed, HAI-2 has demonstrated anti-inflammatory properties in response to PMA or TNF? through its interaction with CD44543.  The cell surface receptor CD44 has been shown to directly negatively mediate NF?B activation  137 through an association with TLR2661.  Inflammatory stimulation with PMA causes a clustering of CD44, preventing its ability to associate with TLR2 and attenuate inflammation542.  This clustering can be suppressed by HAI-2 receptor binding to CD44542.  Interestingly, TLR2 stimulation results in increased atherosclerosis in either ApoE-/- or Ldlr-/- mice making HAI-2 a potential therapeutic target27,28.   Future studies could be conducted to test the effect of HAI-2 overexpression and knockout on atherosclerosis development in vivo.  As TLR2 signaling and induction of the NF?B downstream target have been shown to be atherogenic, I would predict that overexpression of HAI-2 will result in an attenuation of TLR2 and NF?B signaling and show a resultant decrease in altherosclerosis28,662,663.  One pathway of interest identified by the CLIP-CHIP is the immunoproteasome which has been shown to be induced upon IFN? or TNF? treatment.  It is associated with several pro-atherogenic responses including increasing apoptosis and activation of NF?B but remains unstudied in the context of atherosclreosis567?569,571,572.  Specific inhibitors of the immunoproteasome such as immunoproteasome-specific inhibitor (IPSI)-001 and immunoproteasome mice have been making it relatively easy to investigate its effects on atherosclerosis664,665.  I predict that despite the pro-atherogenic properties previously mentioned, complete inhibition of or elimination of the immunoproteasome would accelerate atherosclerosis development.  The immunoproteasome has been shown to be critical for the regulation of inflammation as human patients with a homozygous missense mutation in the immunoproteasome subunit PSMB8 suffer from auto-inflammatory responses and lipodystrophy666.  These patients also had defective  138 proteasome function leading to an accumulation of ubiquitin-coupled proteins666.  This was verified by Seifert et al. who discovered that the immunoproteasome is required for the clearance of protein aggregates and the prevention of oxidative stress induced by IFN?667.  The results of the cholesterol homeostasis studies revealed an interesting regulation of PPAR? activity by IFN? treatment.  Based on the results obtained, I hypothesized that IFN? was capable of inhibiting PPAR? activity independently of regulating PPAR? protein levels by means of sequestering coactivating factors which serves as an interesting potential future direction.  Although I was able to demonstrate through Western blot that PPAR? protien levels were not reduced by IFN? or TNF? treatment, more quantitative measurements such as protein mass spectrometry will provide additional confidence.  Future studies investigating the effect of IFN? treatment on the relative binding of CBP and p300 to either STAT1 or PPAR? in the presence and absence of rosiglitazone through immunoprecipitation experiments will yield interesting results about cytoine-mediated transcriptional regulation.  As the coactivating factors CBP and p300 are required for transcriptional activity, and are in limiting amounts, I predict that IFN? will cause STAT1 activation and the subsequent binding to the coactivating factors preventing their association PPAR?614?617.  Furthermore, if transcriptional regulation by coactivator sequestering was taking place, macrophages transfected with a PPAR? reporter construct would also show decreased PPAR? activity with prior treatment with IFN? and a restoration of this activity upon overexpression of CBP and p300.  Studies have already demonstrated a reciprocal inhibition of activation between STAT1 and other  139 transcription factors such as LXR? which can be diminished by overexpression of CBP and p300, providing additional evidence to support this mechanism of transcriptional regulation624. Although effort was made to use a physiologically relevant model to study the atherogenic potential of different macrophage phenotypes, it is still an isolated system devoid of the interactions with other cells, and numerous factors.  Caution must be exercised when interpreting these results with respect to their role in atherosclerosis.  Ideally, the results obtained from this study would be repeatable in primary macrophages from another model such that in vivo studies could be conducted.   As discussed, although U937 and murine cells do not seem to show all of the same characteristics as primary human MDMs, other animal models can be investigated such as hamster which has shown to display several similarities to human atherosclerosis over mice including CETP activity, heptatic apoB-100 and intestinal apoB-48 production, as well as the majority of LDL cholesterol uptake through the LDL receptor (Reviewed in 668,669).   To conclude, the studies outlined in this thesis have characterized different macrophage phenotypes with respect to their protease and protease inhibitor profile and identifies several pathways such as the immunoproteasome which is a potential target of interest in the modulation of atherosclerosis.  Follow up studies may confirm the regulation of novel proteases that have not been previously reported to be modulated by cytokine treatment.  The results also improve our understanding of how different human MDM-derived macrophage phenotypes may respond to exposure to oxLDL in the plaque through differential expression of CD36, MSR1,  140 and ABCA1.  Finally, this study also provides evidence of IFN?-mediated inhibition of PPAR? activity independent of PPAR? protein level and that U937 cells may not be a suitable model to study to study this pathway.  141 References 1.  World Health Organization. WHO | The top 10 causes of death. 2011. 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A.2 Taqman gene expression assays used Target Assay ID RefSeq Assay Location Amplicon Length Approximated Amplicon Context Sequence ACTB Hs01060665_g1 NM_001101.3 208 63 ttcccc tccatcgtgg ggcgccccag gcaccagggc gtgatggtgg gcatgggtca gaaggattcc t ABCA1 Hs01059118_m1  NM_005502.3  564 61 cggctgagct acccacccta tgaacaacat gaatgccatt ttccaaataa agccatgccc tctg  198 Target Assay ID RefSeq Assay Location Amplicon Length Approximated Amplicon Context Sequence ApoE Hs00171168_m1 NM_000041.2  318 108 actgtctg agcaggtgca ggaggagctg ctcagctccc aggtcaccca ggaactgagg gcgctgatgg acgagaccat gaaggagttg aaggcctaca aatcggaact ggag CD36 Hs00354519_m1 NM_000072.3 695 83 cagcagcaac attcaagtta agcaaagagg tccttatacg tacagagttc gttttctagc caaggaaaat gtaacccagg acgctgagg NM_001001547.2 656 83 cagcagcaa cattcaagtt aagcaaagag gtccttatac gtacagagtt cgttttctag ccaaggaaaa tgtaacccag gacgctgagg NM_001001548.2 695 83 cagcagcaac attcaagtta agcaaagagg tccttatacg tacagagttc gttttctagc caaggaaaat gtaacccagg acgctgagg NM_001127443.1 401 83 cagc agcaacattc aagttaagca aagaggtcct tatacgtaca gagttcgttt tctagccaag gaaaatgtaa cccaggacgc tgagg NM_001127444.1 576 83 cagcagcaa cattcaagtt aagcaaagag gtccttatac gtacagagtt cgttttctag ccaaggaaaa tgtaacccag gacgctgagg FABP4 Hs01086177_m1 NM_001442.2 315 96 att tccttcatac tgggccagga atttgacgaa gtcactgcag atgacaggaa agtcaagagc accataacct tagatggggg tgtcctggta catgtgcaga aatgggatgg aa LXR? Hs00172885_m1 NM_001130101.2  1058 78 caa ggatttcagt tataaccggg aagactttgc caaagcaggg ctgcaagtgg aattcatcaa ccccatcttc gagttctc NM_001130102.2  1047 78 atca ccttcctcaa ggatttcagt tataaccggg aagactttgc caaagcaggg ctgcaagtgg aattcatcaa ccccatc   199 Target Assay ID RefSeq Assay Location Amplicon Length Approximated Amplicon Context Sequence   NM_001251934.1  1186 78 tcatt gctatcagca tcttctctgc agaccggccc aacgtgcagg accagctcca ggtagagagg ctgcagcaca catatg NM_001251935.1  1227 78 cagg accagctcca ggtagagagg ctgcagcaca catatgtgga agccctgcat gcctacgtct ccatccacca tccccat NM_005693.3  1238 78 cca ggtagagagg ctgcagcaca catatgtgga agccctgcat gcctacgtct ccatccacca tccccatgac cgactgat MSR1 Hs00234007_m1 NM_002445.3 418 63 gcagttctc atccctctca ttggaatagt ggcagctcaa ctcctgaagt gggaaacgaa gaattgctca gtta NM_138715.2 418 63 gcagttctc atccctctca ttggaatagt ggcagctcaa ctcctgaagt gggaaacgaa gaattgctca gtta NM_138716.2 418 63 gcagttctc atccctctca ttggaatagt ggcagctcaa ctcctgaagt gggaaacgaa gaattgctca gtta PPAR? Hs01115513_m1 NM_005037.5 711 90 c agtactgtcg gtttcagaaa tgccttgcag tggggatgtc tcataatgcc atcaggtttg  ggcggatgcc acaggccgag aaggagaagc tgttggcgga ga NM_015869.4 713 90 cagtactgt cggtttcaga aatgccttgc agtggggatg tctcataatg ccatcaggtt tgggcggatg ccacaggccg agaaggagaa gctgttggcg gaga NM_138711.3 812 90 cagtactgtc ggtttcagaa atgccttgca gtggggatgt ctcataatgc catcaggttt gggcggatgc cacaggccga gaaggagaag ctgttggcgg aga NM_138712.3 785 90 cagtact gtcggtttca gaaatgcctt gcagtgggga tgtctcataa tgccatcagg tttgggcgga tgccacaggc cgagaaggag aagctgttgg cggaga   200 Target Assay ID RefSeq Assay Location Amplicon Length Approximated Amplicon Context Sequence PPIB Hs01018503_m1 NM_000942.4 419 96 ttcggaaag actgttccaa aaacagtgga taattttgtg gccttagcta caggagagaa aggatttggc tacaaaaaca gcaaattcca tcgtgtaatc aaggacttca tgatcc                      201 A.3 Immunohistochemical staining for macrophage phenotype markers  Appendix A.3: Immunohistochemical staining of human macrophages.   (A) IHC staining of primary human MDMs incubated with IL-4/13, IFN?/TNF?, IL-10 or left untreated for 48 hours. Cells were stained with antibodies specific for (B) CD68, (C) CCR7 and (D) MR and quantified as the percentage of cellular area that stained positive for each marker. (n=1).  202 Appendix B  Supplemental data B.1 Complete protease and protease inhibitor profile Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated Aspartyl proteases           pepsin C PGC NM_002630 0.565 1.498 0.748 beta-secretase 1 BACE NM_012104 1.067 0.921 0.937 cathepsin D CTSD NM_001909 0.791 1.405 0.893 cathepsin E CTSE NM_001910 1.012 0.965 1.357 napsin A NAP1 NM_004851 0.918 0.891 1.115 Nuclear recept. interacting prot. 2 NRIP2 NM_031474 1.145 0.671 0.858 Nuclear recept. interacting prot. 3 NRIP3 NM_020645 1.158 2.084 1.055 presenilin 1 PSEN1 NM_007318 1.092 1.250 1.145 presenilin 2 PSEN2 NM_000447 0.919 1.198 1.293 presenilin homolog 2 PSH2 XM_091623 0.705 0.828 0.927 presenilin homolog 3/SPP PSH3 BC008959 0.751 0.663 0.954 presenilin homolog 4/SPPL2B PSH4 AB040965 0.858 0.987 0.867 presenilin homolog 5 PSH5 NM_032802 0.989 1.001 0.751 GCDFP15 PIP NM_002652 0.927 1.122 0.942             Cysteine proteases           cathepsin B CTSB NM_001908 0.883 1.574 0.942 cathepsin C CTSC NM_001814 2.472 0.728 1.539 cathepsin F CTSF NM_003793 0.963 0.849 0.845 cathepsin H CTSH NM_004390 0.392 0.838 0.473 cathepsin K CTSK NM_000396 0.696 0.685 1.060 cathepsin L CTSL NM_001912 0.598 1.225 2.045 cathepsin O CTSO NM_001334 1.204 1.041 0.946 cathepsin S CTSS AK024855 1.613 1.535 0.797 cathepsin W CTSW NM_001335 1.361 1.080 0.794 cathepsin Z CTSZ NM_001336 0.411 1.415 0.824 TINAG related protein LCN7 NM_022164 1.215 1.208 1.234 bleomycin hydrolase BLMH NM_000386 0.965 0.931 0.974 calpain 1 CAPN1 NM_005186 1.009 0.980 1.157  203 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated calpain 5 CAPN5 NM_004055 1.160 1.161 1.208 calpain 6 CAPN6 NM_014289 0.957 0.951 1.313 calpain 7 CAPN7 NM_014296 0.928 1.100 0.982 calpain 10 CAPN10 NM_023087 0.884 0.948 1.101 calpain 11 CAPN11 NM_007058 0.804 0.888 0.899 ubiquitin C-terminal hydrolase 1 UCHL1 NM_004181 0.670 0.623 1.056 ubiquitin C-term. hydrolase BAP1 BAP1 NM_004656 1.569 1.185 1.367 ubiquitin C-terminal hydrolase 5 UCHL5 NM_015984 2.232 1.975 2.400 cylindromatosis protein CYLD1 NM_015247 0.994 1.396 1.143 legumain LGMN BC008004 0.519 0.533 1.027 hGPI8 PIGK AF022913 0.875 0.908 1.172 caspase-1 CASP1 NM_033292 0.807 2.035 1.102 caspase-2 CASP2 NM_032982 0.983 0.997 1.024 caspase-3 CASP3 NM_004346 1.547 1.064 1.090 caspase-9 CASP9 NM_001229 1.599 1.618 1.436 caspase-10 CASP10 NM_032977 0.903 1.093 1.090 homologue ICEY ICEYH XM_062003 1.021 1.198 1.079 casper CFLAR Y14039 1.067 1.410 0.915 caspase-14-like CASP14L AF098666 1.055 1.192 0.955 pyroglutamyl peptidase I PGPEP1 NM_017712 0.852 1.229 1.251 USP1 USP1 NM_003368 1.080 1.019 0.950 USP4 USP4 NM_003363 1.041 1.120 1.187 USP5 USP5 NM_003481 0.889 0.678 0.675 USP7 USP7 NM_003470 1.264 0.983 1.103 USP9X USP9X NM_004652 0.999 0.964 1.012 USP10 USP10 AL162049 1.421 1.390 1.349 USP11 USP11 NM_004651 1.018 1.157 1.146 USP12 USP12 AF022789 1.280 1.513 1.395 USP13 USP13 NM_003940 0.966 1.006 1.000 USP14 USP14 NM_005151 0.987 0.792 1.381 USP15 USP15 NM_006313 1.253 1.290 0.993 USP16 USP16 NM_006447 0.898 0.958 0.772 USP18 USP18 NM_017414 1.545 1.281 1.391 USP20 USP20 NM_006676 0.897 0.874 0.962  204 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated USP21 USP21 NM_016572 0.889 1.465 0.966 USP22 USP22 AB028986 0.882 0.968 0.830 USP25 USP25 AF170562 1.184 0.916 1.271 USP27 USP27 AW851065 0.935 0.891 0.885 USP28 USP28 NM_020886 1.460 1.646 1.456 USP30 USP30 NM_032663 1.186 1.136 1.323 VDU1 USP33 AB029020 1.382 1.457 1.430 USP35 USP35 AB037793 0.970 1.053 0.914 USP37 USP37 AB046814 1.072 0.927 0.859 SAD1 USP39 NM_006590 1.039 1.085 0.911 USP40 USP40 NM_018218 0.948 0.878 0.853 USP40 USP40 XM_291008 0.770 1.078 1.111 USP41 USP41 XM_036729 0.963 0.886 0.749 USP47 USP47 AK027362 0.765 0.840 0.753 USP48 USP48 NM_018391 1.064 1.287 1.190 USP49 USP49 NM_018561 0.934 1.155 1.225 USP52 USP52 NM_014871 1.701 1.708 1.729 USP54 USP54 NM_152586 0.874 1.033 0.842 gamma-glutamyl hydrolase GGH NM_003878 1.088 1.021 1.126 Gln-fructose-6-P transamidase 2 GFPT2 NM_005110 1.285 1.191 1.110 indian hedgehog protein IHH L38517 0.891 0.987 1.138 desert hedgehog protein DHH NM_021044 0.992 1.201 1.269 sentrin/SUMO protease 3 SENP3 NM_015670 0.912 0.960 0.929 sentrin/SUMO protease 8 SENP8 AY008293 0.986 1.550 0.896 separase ESPL1 NM_012291 0.671 0.637 0.760 autophagin-2 AUTL2 NM_052936 0.860 0.924 1.125 autophagin-4 AUTL4 NM_032885 0.832 0.942 0.850 DJ-1 DJ-1 NM_007262 0.841 0.784 0.709 Cezanne CEZANNE NM_020205 1.197 1.248 1.078 TNFa-induced protein 3/A20 TNFAIP3 NM_006290 1.408 1.944 1.757 Hin-2 HSHIN2 NM_024810 1.192 1.181 1.180 Hin-4 HSHIN4 XM_166659 1.019 0.802 0.848  205 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated Hin-5 HSHIN5 XM_054098 1.019 0.970 1.049 Hin-6 HSHIN6 XM_066765 0.997 0.905 1.127 Hin-7 HSHIN7 BI829009 0.758 1.074 0.861 Otubain-1 OTUB1 NM_017670 0.627 0.700 0.739 CGI-77 CGI77 AL137441 1.009 1.077 1.292 HetF-like HETFL NM_015281 1.118 0.879 0.700 josephin-1 JSPH1 NM_014876 0.901 0.780 0.904 josephin-2 SBBI54 NM_138334 0.817 0.763 1.057 nuclear distribution-oligopeptidase NDE1 NM_017668 0.820 0.940 0.914             Protease inhibitors           serine PI Kazal type 5-like 2 SPINK5L2 NM_001001325 0.926 1.195 0.936 esophagus cancer-rel. Prot. 2 ECG2 NM_032566 0.753 1.066 0.762 serine PI Kazal type 5-like 3 SPINK5L3 XM_376433 0.705 0.670 0.817 SPARC related mod. Ca binding 1 SMOC AJ249900 1.207 1.330 0.967 "sparc/osteonectin, testican-2" SPOCK2 NM_014767 1.127 1.206 0.782 "sparc/osteonectin, testican-3" SPOCK3 NM_016950 0.747 1.314 1.084 osteonectin SPARC NM_003118 0.926 0.541 0.582 "kazal, EF-hand and Ig protein" FSTL5 NM_020116 0.910 1.439 0.748 HGF activator inhibitor 1 SPINT1 NM_003710 1.697 1.407 0.810 placental bikunin SPINT2 NM_021102 5.158 0.828 1.498 amyloid-b precursor protein APP NM_000484 1.812 1.405 1.129 amyloid-b precursor-like prot. 2 APLP2 NM_001642 1.303 0.904 1.394 papilin PAPLN BC042057 0.867 1.093 0.893 WFIKKNRP-related protein WFIKKNRP NM_175575 1.434 0.897 0.914 "collagen, type VI, alpha 3" COL6A3 NM_004369 0.897 1.210 2.070 a1-antitrypsin/a1-PI SERPINA1 AF113676 1.368 3.020 1.741        206 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated a1-antitrypsin member 2 SERPINA2 NM_006220 0.898 0.974 0.848 corticosteroid-binding glob. SERPINA6 NM_001756 1.327 0.790 0.753 a1-antitrypsin member 12 SERPINA12 NM_173850 1.162 0.884 0.846 protease inhibitor 2 SERPINB1 NM_030666 1.565 1.125 1.546 protease inhibitor 6/CAP SERPINB6 AK057138 1.770 0.596 1.219 protease inhibitor 9/CAP3 SERPINB9 BC002538 1.049 0.586 1.313 maspin SERPINB5 NM_002639 1.073 0.859 0.924 megsin SERPINB7 NM_003784 0.983 1.124 0.979 protease inhibitor 8/CAP2 SERPINB8 NM_002640 0.842 0.943 0.978 yukopin SERPINB12 AF411191 0.646 1.038 1.079 heparin cofactor II SERPIND1 NM_000185 1.087 0.996 1.044 C1 inhibitor SERPING1 NM_000062 2.108 8.098 2.399 colligin/CBP1 SERPINH1 NM_004353 0.813 0.721 1.139 von Willebrand factor VWF NM_000552 0.927 1.254 1.272 mucin type 5A/C MUC5AC AJ001403 0.936 1.226 1.100 mucin type 5B MUC5B U06711 0.915 1.193 1.175 crossveinless 2 CVL2 (BMPER) XM_166592 1.098 1.087 1.014 tectorin a TECTA NM_005422 0.838 1.992 0.948 antileukoproteinase SLPI NM_003064 0.981 0.697 0.960 WAP four-disulfide core 2 WFDC2 NM_006103 1.153 0.887 0.912 WAP four-disulfide core 11 WFDC11 NM_147197 0.933 1.134 0.961 WAP four-disulfide core 12 WFDC12 NM_080869 0.933 0.771 1.072 WAP four-disulfide core-like 1 WFDCL1 XM_086637 1.647 1.783 0.954 secretogranin V/7B2 SGNE1 NM_003020 0.917 0.993 1.248  207 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated cystatin A CSTA NM_005213 0.886 0.729 0.658 cystatin SN CST1 NM_001898 0.931 0.970 1.158 cystatin SA CST2 NM_001322 1.181 1.058 1.223 cystatin C CST3 NM_000099 0.993 0.743 1.196 cystatin D CST5 NM_001900 1.250 0.817 0.932 testatin CST9L NM_080610 1.213 0.894 0.718 cystatin L1 CSTL1 AK056477 1.045 0.975 0.787 cystatin E/M CST6 NM_001323 0.849 1.191 1.039 cystatin B CSTB NM_000100 0.843 0.976 1.790 a2-HS-glycoprotein/fetuinA AHSG NM_001622 0.770 0.446 1.145 histidine-rich glycoprotein HRG NM_000412 1.069 2.627 0.852 kininogen KNG NM_000893 1.525 1.452 0.828 calpastatin CAST NM_001750 1.673 0.967 1.541 MHC II invariant gamma chain CD74 NM_004355 1.190 1.891 1.070 IGF binding protein 2 IGFBP2 NM_000597 0.724 1.034 0.811 IGF binding protein 3 IGFBP3 M35878 1.174 0.967 0.933 insulin-like growth factor binding protein 5 IGFBP5 NM_000599 1.080 0.877 0.773 nidogen NID1 NM_002508 1.337 1.145 1.201 NAIP BIRC1 NM_004536 1.286 1.417 1.826 cIAP2 BIRC3 AF070674 1.758 1.425 0.559 XIAP BIRC4 NM_001167 0.950 0.993 1.110 survivin BIRC5 NM_001168 0.823 0.839 0.747 apollon BIRC6 NM_016252 0.777 0.769 0.831 ML-IAP BIRC7 NM_022161 0.985 1.225 1.019 ILP2 BIRC8 NM_033341 0.803 1.314 0.922 tissue inhibitor of metalloprotease-1 TIMP1 NM_003254 0.990 0.430 1.147 tissue inhibitor of metalloprotease-2 TIMP2 NM_003255 0.856 1.079 1.285 tissue inhibitor of metalloprotease-3 TIMP3 NM_000362 1.401 0.794 1.227 tissue inhibitor of metalloprotease-4 TIMP4 NM_003256 1.063 0.647 0.636 tissue inhibitor of metalloprotease-4 TIMP4 NM_003256 0.901 1.161 1.128 tissue inhibitor of metalloprotease-4 TIMP4 NM_003256 0.852 1.125 1.094  208 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated a-2-macroglobulin A2M NM_000014 1.005 0.714 0.609 human pregnanzy-zone prot. PZP NM_002864 1.021 1.631 1.047 a-2-macroglobulin-like A2ML AK057908 0.683 0.968 1.191 a-2-macroglobulin-family VIP VIP NM_003381 1.264 1.152 1.099 histatin 3 HTN3 NM_000200 0.130 2.020 1.070 retinoic acid receptor responder RARRES1 NM_002888 2.220 1.431 2.704 proSAAS PCSK1N NM_013271 0.623 0.882 0.893 phosphatidylethanolamine binding protein PEBP1 NM_002567 1.157 0.723 0.490             Metalloproteases           aminopeptidase A ENPEP NM_001977 0.887 0.941 1.117 aminopeptidase B RNPEP NM_020216 0.927 0.913 0.986 aminopeptidase MAMS AMPEP NM_022350 0.675 1.186 0.790 aminopeptidase N ANPEP NM_001150 2.276 1.066 0.878 leukotriene A4 hydrolase LTA4H NM_000895 0.379 0.668 1.202 cytosol alanyl aminopeptidase NPEPPS NM_006310 0.866 0.942 0.938 leucyl-cystinyl aminopeptidase LNPEP NM_005575 1.587 1.238 1.237 aminopeptidase B-like 1 RNPEPL1 NM_018226 0.924 0.690 0.767 aminopeptidase Q AQPEP AK075131 0.947 0.721 0.726 angiotensin-converting enzyme 2 ACE2 NM_021804 1.166 1.121 1.242 thimet oligopeptidase THOP1 NM_003249 1.099 0.942 0.732 neurolysin NLN AB033052 0.523 0.670 0.543 Archeometzincin 1 AMZ1 NM_133463 1.418 1.340 0.853 Archeometzincin 2 AMZ2 NM_016627 0.715 0.807 0.549 collagenase 1 MMP1 NM_002421 1.052 1.002 0.703 gelatinase A MMP2 NM_004530 0.643 0.697 0.753 matrilysin MMP7 NM_002423 1.367 2.150 2.294 collagenase 2 MMP8 NM_002424 1.286 0.740 1.092 gelatinase B MMP9 NM_004994 1.376 1.824 1.365 gelatinase B MMP9 NM_004994 1.730 1.328 0.865 gelatinase B MMP9 NM_004994 1.867 1.477 1.131  209 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated stromelysin 3 MMP11 NM_005940 1.372 1.171 1.449 macrophage elastase MMP12 NM_002426 1.311 1.295 2.183 collagenase 3 MMP13 NM_002427 0.950 0.937 1.064 MT1-MMP MMP14 NM_004995 0.685 1.352 0.804 MT2-MMP MMP15 NM_002428 0.842 0.629 0.959 MT3-MMP MMP16 NM_005941 1.062 1.211 1.083 MT5-MMP MMP24 NM_006690 1.342 0.993 1.411 MT6-MMP MMP25 NM_022468 0.857 1.638 0.982 MT6-MMP MMP25 NM_022468 1.134 1.626 1.138 matrilysin-2 MMP26 NM_021801 0.876 1.016 0.853 epilysin MMP28 NM_032950 0.828 1.159 0.892 meprin alpha subunit MEP1A NM_005588 1.131 0.961 1.048 meprin beta subunit MEP1B NM_005925 0.829 0.744 1.091 procollagen C-proteinase BMP1 NM_006129 0.744 0.838 0.894 mammalian tolloid-like 1 protein TLL1 AF282732 1.059 1.281 1.162 Ovastacin OVCN BI061462 0.794 0.738 0.803 DECYSIN ADAMDEC1 NM_014479 0.597 1.594 1.026 ADAM2/Fertilin-b ADAM2 NM_001464 1.074 1.082 0.965 ADAM8 ADAM8 NM_001109 0.983 1.329 2.765 ADAM10 ADAM10 NM_001110 0.816 0.701 1.020 ADAM11 ADAM11 NM_002390 0.995 0.822 0.862 ADAM12 ADAM12 NM_003474 1.556 1.161 1.499 ADAM15 ADAM15 NM_003815 1.326 1.046 1.439 ADAM19 ADAM19 NM_033274 1.210 1.055 1.566 ADAM20 ADAM20 NM_003814 1.263 1.377 1.104 ADAM22 ADAM22 NM_021723 0.786 1.006 0.880 ADAM28 ADAM28 NM_014265 1.501 1.414 1.596 ADAM29 ADAM29 NM_014269 1.198 0.960 1.523        210 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated ADAM 30 ADAM30 NM_021794 0.893 1.054 1.215 ADAM 33 ADAM33 AL117415 0.902 1.415 1.121 ADAMTS6 ADAMTS6 NM_014273 1.055 0.879 0.951 ADAMTS7 ADAMTS7 NM_014272 0.978 1.037 0.944 ADAMTS8 ADAMTS8 NM_007037 1.176 1.389 0.909 ADAMTS9 ADAMTS9 AB037733 1.146 1.393 1.905 ADAMTS9 ADAMTS9 AB037733 1.110 0.844 1.268 ADAMTS10 ADAMTS10 AF163762 1.119 1.053 1.186 ADAMTS12 ADAMTS12 NM_030955 1.251 1.444 1.437 ADAMTS13 ADAMTS13 AB069698 1.172 1.063 0.961 ADAMTS14 ADAMTS14 NM_080722 1.034 0.721 1.144 ADAMTS15 ADAMTS15 NM_139055 0.875 0.743 1.008 ADAMTS16 ADAMTS16 NM_139056 0.793 0.608 0.985 ADAMTS17 ADAMTS17 NM_139057 0.780 0.856 0.738 neprilysin-2 MMEL2 NM_033467 0.898 0.829 0.985 endothelin-converting enzyme 2 ECE2 NM_014693 0.977 0.801 0.983 DINE peptidase ECEL1 NM_004826 0.830 1.083 0.932 Kell blood-group protein KEL NM_000420 0.837 0.734 1.073 PHEX endopeptidase PHEX NM_000444 1.077 1.011 1.454 carboxypeptidase A1 CPA1 NM_001868 1.066 1.252 0.792 carboxypeptidase A2 CPA2 NM_001869 0.979 0.891 0.750 carboxypeptidase A4 CPA4 NM_016352 1.030 0.975 0.916 carboxypeptidase A5 CPA5 AF384667 0.982 1.530 0.984 carboxypeptidase A6 CPA6 NM_020361 0.934 1.155 0.973 carboxypeptidase B CPB1 NM_001871 0.721 0.716 1.225 carboxypeptidase O CPO NM_173077 0.703 0.969 0.620 carboxypeptidase E CPE NM_001873 1.090 0.783 0.710  211 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated carboxypeptidase N CPN NM_001308 1.131 1.330 0.791 carboxypeptidase M CPM AF368463 1.784 1.318 0.886 carboxypeptidase D CPD NM_001304 1.218 1.648 1.423 carboxypeptidase Z CPZ NM_003652 0.929 1.053 1.179 carboxypeptidase X1 CPX1 NM_019609 0.927 0.887 1.197 carboxypeptidase X2 CPX2 XM_058409 0.733 1.019 1.027 adipocyte-enhancer binding prot. 1 AEBP1 NM_001129 1.189 1.230 0.933 insulysin IDE NM_004969 1.129 1.473 1.511 mitochondrial processing peptidase beta-subunit PMPCB NM_004279 0.719 0.897 1.016 nardilysin NRD1 NM_002525 1.126 1.244 1.364 pitrilysin metalloproteinase 1 PITRM1 NM_014968 1.240 1.308 1.044 mitochondrial processing protease INPP5E D50913 1.054 0.798 0.762 UCR1 UQCRC1 NM_003365 1.252 0.864 0.691 UCR2 UQCRC2 NM_003366 1.148 0.821 0.935 leucyl aminopeptidase LAP3 NM_015907 1.658 2.769 1.423 aminopeptidase-like 1 NPEPL1 NM_024663 0.755 1.953 0.757 aspartyl aminopeptidase DNPEP NM_012100 1.018 1.282 0.814 membrane dipeptidase DPEP1 NM_004413 1.019 0.730 0.587 membrane dipeptidase 2 DPEP2 NM_022355 1.136 0.645 0.751 membrane dipeptidase 3 DPEP3 NM_022357 1.024 1.378 1.390 glu-carboxypeptidase-like 1 CPGL AK024471 0.979 1.142 1.286 glu-carboxypeptidase-like 2 CPGL2 NM_032649 1.271 1.258 1.306 O-sialoglycoprotein endopeptidase OSGEP NM_017807 1.204 0.940 1.008 O-sialoglycoprotein endopeptidase 2 OSGEP2 AK027836 1.044 1.375 1.379 methionyl aminopeptidase I METAP1 D42084 0.868 1.043 0.899 methionyl aminopeptidase II METAP2 NM_006838 1.293 1.223 1.305  212 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated methionyl aminopeptidase-like 1 METAPL1 XM_171022 1.196 0.940 0.963 X-prolyl aminopeptidase 2 XPNPEP2 NM_003399 0.891 0.918 0.831 X-Pro dipeptidase PEPD NM_000285 1.271 0.887 0.746 aminopeptidase P1 XPNPEP1 BC007579 1.094 1.104 1.008 aminopeptidase P homologue PEPP NM_022098 1.392 1.124 1.700 proliferation-association protein 1 PA2G4 NM_006191 1.003 0.794 0.865 suppressor of Ty 16 homolog SUPT16H NM_007192 1.157 0.959 1.361 glutamate carboxypeptidase II FOLH1 NM_004476 1.238 0.887 0.874 NAALADASE II NAALAD2 NM_005467 0.964 0.992 1.085 NAALADASE III NAALAD3 XM_173084 1.007 1.371 0.916 plasma Glu-carboxypeptidase PGCP NM_006102 1.209 1.323 1.266 Ojeda peptidase OJP AB058718 0.738 1.094 0.747 transferrin receptor protein (transferrin receptor) TFRC NM_003234 1.283 1.038 1.339 transferrin receptor 2 protein (transferrin receptor 2) TFR2 NM_003227 1.032 0.873 1.220 glutaminyl cyclase QPCT NM_012413 8.439 1.892 0.757 glutaminyl cyclase 2 QPCT2 NM_017659 0.998 2.847 0.740 dihydroorotase CAD NM_004341 1.189 1.413 0.946 dihydropyrimidinase DPYS NM_001385 0.821 0.916 0.757 dihydropyrimidinase-related prot. 1 CRMP1 NM_001313 1.241 1.286 0.769 dihydropyrimidinase-related prot. 2 DPYSL2 U97105 0.988 0.977 0.809 dihydropyrimidinase-related prot. 3 DPYSL3 NM_001387 1.175 0.874 0.922 i-AAA protease YME1L1 NM_014263 0.923 1.243 1.029 paraplegin SPG7 NM_003119 1.531 0.786 0.614 Afg3-like protein 2 AFG3L2 NM_006796 1.047 0.983 0.640 pappalysin-1 PAPPA U28727 1.102 0.921 0.877  213 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated procol. III N-endopeptidase PCOLN3 NM_002768 1.006 0.852 0.963 FACE-1/ZMPSTE24 FACE1 NM_005857 1.120 1.044 1.199 metalloprotease related protein 1 MPRP-1 NM_145243 0.918 0.911 1.019 dipeptidyl-peptidase III DPP3 NM_005700 0.954 0.812 0.938 S2P protease MBTPS2 NM_015884 0.959 0.965 0.815 JAB1 COPS5 NM_006837 0.724 0.745 0.953 COPS6 COPS6 NM_006833 0.689 1.006 1.214 AMSH AMSH NM_006463 0.942 0.939 0.844 C6.1A C6.1A NM_024332 0.893 1.104 0.950 jammin-like protease 1 JAMML1 AB067502 0.802 0.987 1.139 jammin-like protease 2 JAMML2 NM_032868 0.941 1.246 0.935 PSMD7 PSMD7 NM_002811 1.110 1.140 1.219 PRPF8 PRPF8 NM_006445 1.273 0.866 0.995 eukar. translation initiation F3S3 EIF3S3 NM_003756 1.003 0.797 0.665 eukar. translation initiation F3S5 EIF3S5 NM_003754 1.203 1.146 1.007 eukar. translation initiation F3S5B EIF3S5B XM_062387 0.849 1.125 1.309 IFP38 IFP38 NM_031943 0.717 0.822 0.814 IFP38-like IFP38L XM_290345 1.001 1.154 0.968 aspartoacylase-2 ASPA/ACY-2 NM_000049 0.589 1.129 1.465 aspartoacylase-3 ACY-3 BC008689 1.152 1.063 0.545             Serine proteases           kallikrein hK3 KLK3 NM_001648 0.901 0.930 0.779 kallikrein hK4 KLK4 NM_004917 1.053 1.261 1.131 kallikrein hK5 KLK5 NM_012427 1.030 0.942 1.034 kallikrein hK6 KLK6 NM_002774 0.819 1.209 0.885 kallikrein hK8 KLK8 NM_007196 0.764 0.537 0.715 kallikrein hK9 KLK9 NM_012315 0.928 0.843 1.030 kallikrein hK10 KLK10 NM_002776 0.886 1.151 1.166 kallikrein hK11 KLK11 NM_006853 1.122 0.727 0.545 kallikrein hK12 KLK12 NM_019598 0.869 0.734 1.075  214 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated kallikrein hK14 KLK14 NM_022046 0.491 0.470 0.982 kallikrein hK15 KLK15 NM_017509 1.152 0.902 1.014 coagulation factor VIIa F7 NM_000131 1.242 1.112 1.068 coagulation factor Xa F10 NM_000504 0.781 0.560 1.030 coagulation factor XIa F11 NM_000128 1.188 0.868 0.882 coagulation factor XIIa F12 NM_000505 1.164 0.862 1.090 protein C PROC NM_000312 0.881 1.048 0.913 hepatocyte growth factor activator HGFAC NM_001528 0.949 1.046 1.094 hyaluronan-binding ser-protease HABP2 NM_004132 1.149 1.091 1.108 tryptase alpha/beta 1 TPS/TPSB1 NM_003294 0.797 0.699 0.780 tryptase gamma 1 TPSG1 NM_012467 0.766 0.923 0.867 marapsin MPN AK055576 0.875 1.490 0.954 brain serine proteinase 2 PRSS22 NM_022119 0.949 0.597 0.893 tryptase delta 1 TPSD1 NM_012217 0.793 0.612 0.954 prostasin PRSS8 NM_002773 0.735 0.952 0.952 prostasin-like 1 PSTL1 NM_173502 0.765 0.822 0.832 prostasin-like 2 PSTL2 NM_024006 0.883 0.531 1.115 testis serine protease 5 TESSP5 BN000137 0.871 1.030 0.993 chymase CMA1 NM_001836 1.014 0.823 1.058 cathepsin G CTSG NM_001911 0.734 0.651 1.022 granzyme M GZMM NM_005317 1.383 1.344 1.200 proteinase 3 PRTN3 NM_002777 0.722 1.057 0.739 azurocidin AZU1 NM_001700 1.066 1.248 0.764 enteropeptidase-like PRSS7L BQ638967 1.044 1.274 1.162 hepsin HPN NM_002151 1.246 1.515 1.007 HAT-like 1 HATL1 BN000133 1.235 0.977 1.215 HAT-like 5 HATL5 XM_068002 0.756 1.013 0.684 corin PRSC NM_006587 0.889 1.087 0.916 matriptase MTSP1 NM_021978 0.850 0.930 0.792 transmembrane Ser-protease 4 TMPRSS4 NM_019894 0.990 1.262 1.126 matriptase-2 TMPRSS6 AL022314 1.215 1.379 1.120 matriptase-3 TMPRSS7 BN000125 0.724 1.887 1.412        215 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated membrane-type mosaic Ser-prot. MSPL NM_032046 1.005 1.264 0.983 chymotrypsin B CTRB1 NM_001906 2.339 0.716 0.847 pancreatic elastase II (IIA) ELA2A NM_033440 0.605 1.283 0.854 pancreatic endopeptidase E (A) ELA3A NM_005747 0.748 0.729 1.069 pancreatic endopeptidase E (B) ELA3B NM_007352 1.234 0.730 1.441 complement component 2 C2 NM_000063 0.920 1.516 1.282 complement factor B BF NM_001710 1.183 2.082 1.205 complement C1r-homolog C1RL NM_016546 0.832 1.143 0.849 complement component C1ra C1R AK024951 1.232 3.397 1.169 complement factor D DF NM_001928 0.762 0.506 0.979 complement factor D-like DF2 XM_115647 0.928 0.917 0.816 complement factor I IF NM_000204 1.401 0.818 1.247 MASP1/3 MASP1/3 D17525 0.836 1.233 1.279 MASP2 MASP2 NM_006610 1.113 1.062 0.987 u-plasminogen activator PLAU NM_002658 0.768 0.661 2.014 t-plasminogen activator PLAT NM_000930 1.044 1.127 0.958 acrosin ACR NM_001097 0.800 0.779 0.976 haptoglobin-1 HP AK055872 0.991 0.888 0.795 haptoglobin-related protein HPR NM_020995 1.300 0.848 0.944 HTRA2 HTRA2 NM_013247 1.283 1.332 1.579 HTRA4 HTRA4 NM_153692 0.647 0.892 0.592 similar to Arabidopsis Ser-prot. SASP BC016840 1.148 1.176 1.128 site-1 protease MBTPS1 NM_003791 0.853 1.185 1.030 proprotein convertase 9 PCSK9 NM_174936 0.973 1.143 0.948 proprotein convertase 1 PCSK1 NM_000439 1.491 1.540 0.926 furin PCSK3 NM_002569 1.085 0.973 1.230 proprotein convertase 4 PCSK4 AK057235 0.649 1.386 0.646 PACE4 proprotein convertase PCSK6 NM_002570 0.942 0.726 0.926  216 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated tripeptidyl-peptidase II TPP2 NM_003291 1.175 1.274 1.430 prolyl oligopeptidase PREP NM_002726 1.025 1.093 1.039 prolyl-oligopeptidase 2 PREP2 AB007896 1.661 1.122 1.172 dipeptidyl-peptidase 4 DPP4 NM_001935 1.755 1.050 1.448 dipeptidyl-peptidase 6 DPP6 NM_001936 1.082 1.070 1.053 dipeptidyl-peptidase 8 DPP8 NM_017743 0.980 0.966 0.996 dipeptidyl-peptidase 9 DPP9 BC000970 0.956 1.125 0.981 acylaminoacyl-peptidase APEH NM_001640 1.247 0.851 0.871 CGI-67-like protease-1 CGI-67L1 NM_031213 0.862 0.641 0.865 CGI-67-like protease-2 CGI-67L2 AL390079 1.303 1.154 1.374 BEM46-like 2 BEM46L2 BC014049 1.301 0.874 1.094 lysosomal carboxypeptidase A PPGB NM_000308 0.799 1.051 0.922 vitellogenic carboxypeptidase-L. CPVL NM_031311 0.807 1.009 0.924 serine carboxypeptidase 1 RISC NM_021626 0.861 2.135 1.049 endopeptidase Clp CLPP NM_006012 1.018 1.241 1.010 PIM2 endopeptidase PIM2 NM_006875 1.294 1.344 1.072 signalase 18 kDa component SPC18 NM_014300 0.977 0.846 0.831 signalase 21 kDa component SPC21 NM_033280 1.037 0.747 1.124 lysosomal Pro-X carboxypeptidase PRCP NM_005040 1.143 1.097 1.091 dipeptidyl-peptidase II DPP7 NM_013379 0.820 0.599 0.802 tripeptidyl-peptidase I CLN2 NM_000391 0.910 1.019 1.131 rhomboid-like protein 1 RHBDL NM_003961 1.073 1.222 1.334 rhomboid-like protein 4 RHBDL4 NM_138328 0.945 1.085 0.734 rhomboid-like protein 5 RHBDL5 NM_032276 0.837 1.173 0.849 rhomboid-like protein 6 RHBDL6 NM_024599 0.626 2.020 0.988 Presenilins associated rhomboid like PARL NM_018622 1.587 1.472 1.020 tumor rejection antigen (gp96) TRA1 NM_003299 1.405 0.513 1.126  217 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated "heat shock 90kDa protein 1, alpha" HSPCA AK056446 1.295 1.052 1.155 "heat shock 90kDa protein 1, beta" HSPCB NM_007355 0.997 0.719 1.093 heat shock protein 75 TRAP1 NM_016292 1.224 0.947 0.928             Threonine proteases           proteasome catalytic subunit 1 PSMB6 BF698890 0.923 0.955 0.613 proteasome catalytic subunit 3 PSMB5 NM_002797 1.059 0.997 1.078 proteasome catalytic subunit 1i PSMB9 NM_002800 0.995 5.031 0.801 proteasome catalytic subunit 2i PSMB10 NM_002801 0.977 2.081 1.099 proteasome catalytic subunit 3i PSMB8 NM_004159 0.941 1.758 0.911 proteasome beta 1 subunit PSMB1 AK023290 0.982 0.950 0.934 proteasome beta 2 subunit PSMB2 NM_002794 1.115 0.971 1.076 proteasome beta 2 subunit PSMB2 BC000268 1.332 0.995 0.954 proteasome beta 3 subunit PSMB3 NM_002795 0.675 0.837 0.920 proteasome beta 4 subunit PSMB4 NM_002796 1.436 0.989 0.899 proteasome alpha 2 subunit PSMA2 NM_002787 0.953 1.049 0.958 proteasome alpha 3 subunit PSMA3 NM_002788 1.162 1.126 1.020 proteasome alpha 4 subunit PSMA4 AK055714 1.407 1.402 0.733 proteasome alpha 5 subunit PSMA5 NM_002790 1.430 1.344 0.691 proteasome alpha 6 subunit PSMA6 X59417 1.001 1.199 0.911 proteasome alpha 7 subunit PSMA7 NM_002792 1.226 0.550 0.974 glycosylasparaginase-2 ASRGL1 NM_025080 0.954 1.011 0.885 glycosylasparaginase-3 AGA3 NM_017714 1.325 0.955 1.136 gamma-glutamyltransferase 1 GGT1 NM_013421 1.185 1.154 1.344  218 Description Gene name Refseq Fold Change -  4/13 vs Untreated Fold change - IFN?/TNF? vs Untreated Fold change - IL-10 vs Untreated gamma-glutamyltransferase-like 3 GGTL3 NM_052830 0.818 1.003 1.305 gamma-glutamyltransferase 2 GGT2 M30474 0.848 1.019 0.739 gamma-glutamyltransferase m-3 GGTL4 NM_080839 0.805 0.740 1.055  B.2 Cell surface CD36 protein does not increase beyond 24 hours of rosigltiazone treatment  Appendx B.2:  Cell surface CD36 protein expression does not increase beyond 24 hours of rosiglitazone treatment. PMA stimulated U937 cells were treated with rosiglitazone at a concentration of 5 ?M for various amounts of time.  Cell surface CD36 protein expression was measured by flow cytometry (n=1).     

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