Medicine, Faculty of
Medicine, Department of
DSpace
UBCV
Ortega, Carleena Angelica S.
Master of Science - MSc
University of British Columbia
Atherosclerosis, which manifests clinically as ischemic cardiovascular disease (CVD), is the leading cause of global mortality. From a lipid standpoint, CVD risk has traditionally been assessed by measuring low-density lipoprotein (LDL) cholesterol in the fasted state. Recently, both the measurement of the complete non-high-density lipoprotein (non-HDL) cholesterol fraction and the lipids in the nonfasting state have gained traction as better indicators of CVD risk and prevention. In addition, while macrophages in atherosclerotic lesions have been the focus of most plaque cholesterol metabolism studies, growing evidence suggests that smooth muscle cells (SMCs) are the key cell type driving initial lesion development and the main cell type contributing to cholesterol-overloaded “foam cells” in these lesions. Further evidence suggests that macrophages and SMCs may influence each other’s tendency to become foam cells. The current study’s aims are to compare the effects of LDL and non-HDL from both fasting and nonfasting plasma on the lipid loading of SMCs and macrophages, and the effects of SMC-macrophage coculture on the tendency of each cell type to become foam cells in vitro. We hypothesized that the nonfasting lipoproteins, the non-HDL fractions, and coculture with macrophages will promote more SMC foam cells compared with the fasting lipoproteins, LDL fractions, and monoculture. Our results show that lipoprotein loading of human SMCs have a trend of higher cholesterol accumulation with fasting and non-HDL fractions via cholesterol mass quantification with mass spectrometry. However, flow cytometry analysis demonstrated a trend of more BODIPY-high SMCs with the nonfasting and non-HDL fractions whether in monoculture or direct co-culture with macrophages. Coculture demonstrated a trend of more BODIPY-high SMCs in the presence of the non-fasting non-HDL compared with the monocultured SMCs. Monocultured macrophage demonstrated a trend of higher cholesterol accumulation in the presence of fasting lipoproteins and non-HDL fraction compared with the non-fasting lipoproteins and the LDL fraction. These preliminary findings indicate that the atherogenicity of the non-fasting and non-HDL fractions in vivo may be apparent in in vitro cell culture studies via the increased foam cell formation of SMCs and macrophages when incubated with these fractions.
THE INFLUENCE OF DIFFERENT PLASMA LIPOPROTEIN FRACTIONS AND MACROPHAGES ON SMOOTH MUSCLE CELL FOAM CELL FORMATION by Carleena Angelica S. Ortega B.S., The University of Waterloo, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2021 © Carleena Angelica S. Ortega, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The Influence of Different Plasma Lipoprotein Fractions and Macrophages on Smooth Muscle Cell Foam Cell Formation submitted by Carleena Angelica S. Ortega in partial fulfillment of the requirements for the degree of Master of Science in Experimental Medicine Examining Committee: Gordon Francis, Department of Medicine Supervisor Pascal Bernatchez, Department of Anesthesiology, Pharmacology and Therapeutics Supervisory Committee Member Edward Pryzdial, Department of Pathology and Laboratory Medicine Additional Examiner Additional Supervisory Committee Members: John Boyd, Department of Medicine Supervisory Committee Member Mari DeMarco, Department of Pathology and Laboratory Medicine Supervisory Committee Member iii Abstract Atherosclerosis, which manifests clinically as ischemic cardiovascular disease (CVD), is the leading cause of global mortality. From a lipid standpoint, CVD risk has traditionally been assessed by measuring low-density lipoprotein (LDL) cholesterol in the fasted state. Recently, both the measurement of the complete non-high-density lipoprotein (non-HDL) cholesterol fraction and the lipids in the nonfasting state have gained traction as better indicators of CVD risk and prevention. In addition, while macrophages in atherosclerotic lesions have been the focus of most plaque cholesterol metabolism studies, growing evidence suggests that smooth muscle cells (SMCs) are the key cell type driving initial lesion development and the main cell type contributing to cholesterol-overloaded “foam cells” in these lesions. Further evidence suggests that macrophages and SMCs may influence each other’s tendency to become foam cells. The current study’s aims are to compare the effects of LDL and non-HDL from both fasting and nonfasting plasma on the lipid loading of SMCs and macrophages, and the effects of SMC-macrophage coculture on the tendency of each cell type to become foam cells in vitro. We hypothesized that the nonfasting lipoproteins, the non-HDL fractions, and coculture with macrophages will promote more SMC foam cells compared with the fasting lipoproteins, LDL fractions, and monoculture. Our results show that lipoprotein loading of human SMCs have a trend of higher cholesterol accumulation with fasting and non-HDL fractions via cholesterol mass quantification with mass spectrometry. However, flow cytometry analysis demonstrated a trend of more BODIPY-high SMCs with the nonfasting and non-HDL fractions whether in monoculture or direct co-culture with macrophages. Coculture demonstrated a trend of more BODIPY-high SMCs in the presence of the non-fasting non-HDL compared with the monocultured SMCs. Monocultured macrophage iv demonstrated a trend of higher cholesterol accumulation in the presence of fasting lipoproteins and non-HDL fraction compared with the non-fasting lipoproteins and the LDL fraction. These preliminary findings indicate that the atherogenicity of the non-fasting and non-HDL fractions in vivo may be apparent in in vitro cell culture studies via the increased foam cell formation of SMCs and macrophages when incubated with these fractions. v Lay Summary Atherosclerosis, which can lead to heart attack and stroke, is the accumulation of cholesterol-rich cells in arteries. Smooth muscle cells (SMCs), which are found in the middle layer of arteries, and immune cells called macrophages can take up cholesterol-carrying lipoproteins retained in arteries and become cholesterol-laden cells in atherosclerosis. Recent clinical studies have indicated that the nonfasting plasma and the non-high-density lipoprotein cholesterol (non-HDL-C), which includes all the atherogenic cholesterol provide better risk predictions than the previous standards of fasting and low-density lipoprotein cholesterol (LDL-C), respectively. Most studies of cellular uptake and metabolism of cholesterol similarly use fasting LDL as the cholesterol loading vehicle. We explore the effects of the nonfasting and non-HDL fractions, and interactions between SMCs and macrophages in cell culture studies. Preliminary findings indicate that the clinical observations may be observed in the cellular level as the nonfasting non-HDL fraction generally promoted the most cellular cholesterol accumulation. vi Preface For Chapter 1, Section 1.3 contains a previously published review article written by Dr. Sima Allahverdian, Carleena Ortega, and Dr. Gordon Francis. Dr. Allahverdian performed the literature review and drafted the sections on smooth muscle cell-matrix interactions, edited the article, and designed the figures. Carleena Ortega performed the literature review and drafted the sections on proteoglycan-lipoprotein interactions, edited the article, and designed and generated the figures. Dr. Francis edited the article and designed the figures. Allahverdian S, Ortega C, Francis GA. Smooth muscle cell-proteoglycan-lipoprotein interactions as drivers of atherosclerosis. 2020. Section 1.5 contains a previously published review article written by Dr. Colin Pryma, Carleena Ortega, Dr. Joshua Dubland, and Dr. Gordon Francis. Dr. Pryma drafted the original article under Dr. Francis’ supervision. Carleena Ortega performed the literature review and drafted the section on SMC-macrophage interactions, drafted and assisted with editing the article, and designed and generated all figures. Dr. Dubland drafted and edited the article and designed the figures. Dr. Francis edited the article. Pryma CS, Ortega C, Dubland JA, Francis GA. Pathways of smooth muscle foam cell formation in atherosclerosis. Curr Opin Lipidol. 2019;30(2):117-124. For Chapter 2, Carleena Ortega performed the lipid loading, Lowry protein assay, Amplex Red cholesterol assay, Oil Red O staining, confocal imaging, macrophage isolation, and ELISA based on established protocols, and optimized the direct co-culture, indirect co-culture, macrophage detachment, peripheral blood mononuclear cell cryopreservation, and flow cytometry methods. vii Dr. Dubland established the confocal imaging method and developed the LC/MS/MS assay and performed the cholesterol quantification. Dr. Ying Wang assisted with the flow cytometry and established the transwell protocol that the current study adapted. Basak Sahin assisted with building the flow cytometry panel, assisted with the fluorescence-activated cell sorting (FACS) protocol, and performed the FACS acquisition. Dr. Sima Allahverdian guided the optimization of the direct co-culture, flow cytometry, and FACS, and the Oil Red O staining. Teddy Chan assisted with the direct co-culture optimizing, and mouse smooth muscle cell isolation. Beth Whalen guided the building of the flow cytometry panel. Amrit Samra guided the Oil Red O staining and performed the blood collections required for lipoprotein and macrophage isolation with the approval by the UBC-PHC Research Ethics Board (H07-02720). The HLI animal facility processed the mice aorta for smooth muscle cell isolation under the approval of the UBC Animal Care Committee (A21-0019). For Chapter 3, the experimental design, data acquisition, result analysis, text and figures in Chapter 3 are from Carleena Ortega’s main project. 90% of the work in this chapter was performed by Carleena Ortega. Dr. Joshua Dubland developed and adapted the LC/MS/MS assay for cholesterol and cholesteryl ester analysis and performed the analysis and quantification using the mass spectrometer. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Abbreviations ................................................................................................................. xiv Acknowledgements .................................................................................................................... xvi Dedication ................................................................................................................................. xviii CHAPTER 1: Introduction .......................................................................................................... 1 1.1 Cardiovascular Disease ...................................................................................................... 1 1.1.1 Canadian and Global Burden of Cardiovascular Disease........................................... 1 1.1.2 Cardiovascular Risk Assessment .................................................................................. 2 1.2 Atherosclerosis .................................................................................................................... 3 1.2.1 Artery Wall Architecture .............................................................................................. 3 1.2.2 Diffuse Intimal Thickening ........................................................................................... 4 1.2.3 Theories of Atherogenesis ............................................................................................ 5 1.2.4 Advanced Atherosclerotic Lesions and Thrombosis..................................................... 9 1.2.5 Atherosclerosis in Mice and Differences with Human Atherogenesis ....................... 10 1.3 Lipoprotein Contribution to Atherosclerosis ................................................................ 11 1.3.1 Structure and Types of Lipoproteins .......................................................................... 12 ix 1.3.2 Predictive Value of LDL-C versus Non-HDL-C for Cardiovascular Risk and Benefits of Treatment ................................................................................... 15 1.3.3 Comparison of Fasting and Non-fasting Lipoproteins ............................................... 18 1.3.4 Lipoprotein – Proteoglycan Interactions in the Arterial Wall ................................... 20 1.4 Smooth Muscle Cell–Macrophage Interactions in Atherosclerosis ............................. 25 1.4.1 Architecture/Location of Smooth Muscle Cells and Macrophages in Developing Atherosclerosis Lesions ........................................................................... 25 1.4.2 Influence of Smooth Muscle Cells on Monocytes and Macrophages in Relation to Foam Cell Formation and Other Cell Functions ................................. 27 1.4.3 Influence of Monocytes/Macrophages on Smooth Muscle Cells in Relation to Smooth Muscle Cell Foam Cell Formation and Other Cell Functions ............................................................................................................. 29 1.4.4 Role of Various Macrophage Polarities in Atherosclerosis ....................................... 31 1.5 Coculture Techniques in Atherosclerosis ....................................................................... 32 1.5.1 Direct Coculture ......................................................................................................... 32 1.5.2 Indirect Coculture ...................................................................................................... 33 1.6 Questions; Hypotheses; and Specific Aims .................................................................... 35 CHAPTER 2: Materials and Methods ...................................................................................... 37 2.1 Materials ........................................................................................................................... 37 2.2 Methods ............................................................................................................................. 38 2.2.1 Preparation of Lipoproteins ....................................................................................... 38 2.2.2 Cells Used in the Study ............................................................................................... 38 2.2.3 Isolation of Mouse Aortic Smooth Muscle Cells ........................................................ 39 x 2.2.4 ELISA of Mouse Aortic Cells to Identify Smooth Muscle Cells.................................. 39 2.2.5 Isolation and Differentiation of Human Peripheral Blood Mononuclear Cells ...................................................................................................... 40 2.2.6 Cell Culture ................................................................................................................ 40 2.2.7 Cell Monoculture ........................................................................................................ 41 2.2.8 Direct Coculture ......................................................................................................... 41 2.2.9 Indirect Coculture ...................................................................................................... 41 2.2.10 Lipid Loading of Smooth Muscle Cells and Macrophages ...................................... 42 2.2.11 Confocal Imaging ..................................................................................................... 42 2.2.12 Flow Cytometry ........................................................................................................ 42 2.2.13 Fluorescence-activated Cell Sorting (FACS) of Smooth Muscle Cells and Macrophage Foam Cells ............................................................................ 43 2.2.14 LC/MS/MS Analysis of Total Cholesterol, Free Cholesterol, and Cholesteryl Esters ....................................................................................................... 43 2.2.15 Amplex Red Cholesterol Assay ................................................................................. 45 2.2.16 Lowry Protein Assay................................................................................................. 45 2.2.17 Oil Red O Staining of Foam Cells ............................................................................ 45 2.2.18 Statistical Analysis.................................................................................................... 45 CHAPTER 3: Results ................................................................................................................. 47 3.1 Quantifying the Cholesterol Content of Human Smooth Muscle Cells Loaded with Nonfasting LDL and non-HDL using Mass Spectrometry ................................................................................................................... 47 xi 3.2 Measuring the Lipid Human Smooth Muscle Cell Monocultures with Fasting or Nonfasting LDL and non-HDL using BODIPY ................................ 49 3.3 Loading of Human Macrophage Monocultures with LDL or Non-HDL from Fasting and Nonfasting Plasma .................................................................. 52 3.4 Optimizing the Detachment of Primary Human Macrophages for Cell Culture Studies ........................................................................................................ 54 3.5 Lipoprotein Loading of Smooth Muscle Cells in Direct Contact with Macrophages with Fasting and Nonfasting LDL and Non-HDL ............................... 55 CHAPTER 4: Discussion ............................................................................................................ 59 4.1 Discussion .......................................................................................................................... 59 4.2 Future directions .............................................................................................................. 63 4.3 Concluding Remarks ....................................................................................................... 65 References .................................................................................................................................... 66 xii List of Tables Table 1.1 The lipoproteins are characterized by size, density, lipid content, and major apolipoproteins. ..................................................................................................................... 12 Table 1.2 The various apolipoproteins.......................................................................................... 15 Table 1.3 Proteoglycans in the arterial wall .................................................................................. 24 xiii List of Figures Figure 1.1 The structure of a normal arterial wall .......................................................................... 4 Figure 1.2 The diffuse intimal thickening (DIT) ............................................................................ 5 Figure 1.3 Pathogenesis of early atherosclerosis ............................................................................ 8 Figure 1.4 The stable and vulnerable plaque in atherosclerosis ..................................................... 9 Figure 1.5 Atherogenesis in humans and mice ............................................................................. 11 Figure 1.6 The structure and types of lipoproteins ....................................................................... 13 Figure 1.7 Residual cardiovascular risk from LDL-C lowering treatment ................................... 16 Figure 1.8 Proteoglycans in atherogenesis.................................................................................... 22 Figure 1.9 The delivery and retention of ApoB-containing lipoproteins in the arterial wall ...... 23 Figure 1.10 Early atherogenesis in mouse models of atherosclerosis and humans ...................... 26 Figure 1.11 SMC direct coculture with macrophages increases cholesterol accumulation .......... 31 Figure 1.12 SMC-macrophage coculture methods in atherosclerosis studies .............................. 33 Figure 1.13 Pathogenesis of early atherosclerosis ........................................................................ 34 Figure 3.1 Cholesterol quantification of monocultured human SMCs via mass spectrometry .... 49 Figure 3.2 Flow cytometry estimation of BODIPY-high monocultured human SMCs ............... 51 Figure 3.3 Cholesterol quantification of monocultured human macrophages via mass spectrometry .......................................................................................................................... 53 Figure 3.4 Flow cytometry estimation of BODIPY-high human SMCs in direct contact with macrophages ......................................................................................................................... 57 Figure 3.5 Flow cytometry comparison of BODIPY-high human SMCs between monoculture and direct contact with macrophages .................................................................................... 58 xiv List of Abbreviations ag aggregated Apo apolipoprotein ApoA apolipoprotein A ApoB apolipoprotein B ApoC apolipoprotein C ApoE apolipoprotein E BODIPY 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene CHD coronary heart disease Chol cholesterol Cholesterol-d7 deuterated cholesterol CI confidence interval CS chondroitin sulfate CVD cardiovascular disease DIT diffuse intimal thickening DMEM Dulbecco’s modified Eagle’s medium DS dermatan sulfate ELISA enzyme-linked immunosorbent assay FAFA fatty acid free albumin FBS fetal bovine serum FHS Framingham Heart Study GAG glycosaminoglycan HASMC human aortic smooth muscle cells HDL high-density lipoprotein HDL-C high-density lipoprotein cholesterol HMDM human monocyte-derived macrophages HPLC high performance liquid chromatography HR hazard ratio HS heparin sulfate ICAM-1 intercellular adhesion molecule 1 IDL intermediate-density lipoproteins IFNγ interferon γ IL interleukin LCAT lecithin cholesterol acyltransferase LDL low-density lipoproteins LDL-C low-density lipoprotein cholesterol LDLR low-density lipoprotein receptor xv LOX-1 lectin-like oxidized low-density lipoprotein receptor-1 LPDS lipoprotein deficient serum LPS lipopolysaccharide LRP low-density lipoprotein receptor-related protein M-CSF macrophage-colony stimulating factor MASMC mouse aortic smooth muscle cell MCP-1 monocyte chemotactic protein-1 MMP matrix metalloproteinase Non-HDL non-high-density lipoprotein Non-HDL-C Non-high-density lipoprotein cholesterol OR odds ratio PBMC peripheral blood mononuclear cells PBS phosphate-buffered saline PBSFC phosphate-buffered saline with FBS and CaCl2 PCSK9 proprotein convertase subtilisin/kexin type 9 PDGF platelet-derived growth factors PFA paraformaldehyde PIT pathologic intimal thickening PL phospholipid pNPP p-Nitrophenyl phosphate PS penicillin/streptomycin RPMI Roswell Park Memorial Institute SEM standard error of mean SM22 Smooth muscle 22 SMA smooth muscle actin SMC smooth muscle cell SMMHC smooth muscle myosin heavy chain SR-AI scavenger receptor-AI SR-BI scavenger receptor-BI TC total cholesterol TG triglyceride TGF-β transforming growth factor- β TNFα tumor necrosis factor α VCAM-1 vascular cell adhesion molecule-1 VLDL very-low-density lipoproteins VSMC vascular smooth muscle cell α-SMA α-smooth muscle actin xvi Acknowledgements I acknowledge and appreciate my supervisor, Dr. Gordon Francis, for giving me the opportunity to be a part of his team and for his guidance and support during my growth as a graduate student. I also appreciate and thank my supervisory committee members, Dr. Pascal Bernatchez, Dr. John Boyd, and Dr. Mari DeMarco for their guidance and support throughout my training. I would also like to extend my gratitude to my External Examiner, Dr. Edward Pryzdial, for his involvement in the final stages of my training program. I am extremely fortunate to have worked with our laboratory members and centre staff and I am grateful for their friendship, support, and assistance. I offer my enduring gratitude to Teddy Chan for his exceptional technical assistance, training, and limitless patience. I would also like to thank Dr. Sima Allahverdian for her guidance, and technical assistance with immunohistochemical techniques and flow cytometry. I also extend my gratitude to Dr. Joshua Dubland for his mentorship and mass spectrometry cholesterol mass determination. Thank you also to Dr. Ying Wang for her guidance and technical support with flow cytometry. To my other laboratory members, Katrina Besler, Eric Xiang, Dr. Valentin Blanchard, Gurpreet Chopra, and Jordan Penner, thank you for your kindness, support, and helpful insights. Thank you to the Molecular Phenotyping Core Laboratory (MPCL) group members, Beth Whalen and Basak Sahin, Dr. Aaron Barlow from the Cellular Imaging and Biophysics core, and Amrit Samra from Histology for their excellent technical support and Yolanda Yang for her statistics help. I am also grateful for the HLI technicians, staff, and students for their guidance and friendship especially Ivan Leversage. I also extend my gratitude to the blood donors for my xvii research study. I am grateful for my family and friends for inspiring me and providing their enduring support and understanding during my studies. A special thanks to the funding agencies that supported this research project: CIHR Project Grant PJT-148883 and Heart and Stroke Foundation of Canada Grant-In-Aid to Dr. Gordon Francis. xviii Dedication I dedicate this to my siblings: JC, Nathan, Via, Rufa, and Carlo, for the beautiful dreams we are achieving together; to my mother, Marianne, for your love, and encouragement; to my partner, Mark Anthony, for your strength, love, and support; and my father, Carlos. I would also like to thank my aunts, nephews and nieces for the love and inspiration, and those who forged the path for me. This dream would not have been possible without you. 1 CHAPTER 1: Introduction 1.1 Cardiovascular Disease 1.1.1 Canadian and Global Burden of Cardiovascular Disease By the middle of the 20th century, widespread use of antibiotics and improved sanitation markedly decreased deaths due to infections1. Consequently, ischemic cardiovascular disease (CVD) replaced infectious diseases as the leading cause of mortality1. Despite extensive knowledge of the causal risk factors for atherosclerosis, the primary cause of ischemic CVD, its manifestations as coronary heart disease (CHD) and stroke remain the leading cause of death globally2,3. In 2015, CVD accounted for 37% of premature deaths from noncommunicable diseases3. The following year, it led to almost a third of global deaths and approximately 85% of cardiovascular-related deaths were due to heart attack and stroke3. In North America, an individual is lost to CVD every 36 seconds in America4 while heart attack and stroke together are the second most common cause of death in Canada5. CVD also comes with a heavy economic burden. The global estimated economic cost of CVD is US$863 billion in 2010 and is estimated to increase to US$1 trillion by the year 20306. In North America, it costs Americans $219 billion annually4 and accounted for Canada’s most expensive annual direct health care cost ($11.7 billion) in 20087. CVD disproportionately affects developing countries, which accounts for 75% of cases, as interventions in high-income countries have driven a downward trend of CVD mortality rates3. However, low-, middle-, and high-income countries are experiencing an increasing trend in the absolute number of mortalities from CVD3 and this trend is predicted to continue with the growing and aging population8,9. Therefore, preventing CVD has become more crucial. Two targets of the World Health Organization’s Global Action Plan for the Prevention and Control of 2 Noncommunicable Diseases aim to counteract these alarming trends: 1) drug therapy and counselling for preventing heart attacks and strokes accessed by at least half of qualified individuals; and 2) a 25% relative risk reduction of premature mortality from chronic diseases (including CVDs)10. 1.1.2 Cardiovascular Risk Assessment The Framingham Heart Study (FHS), along with the Seven Countries Study and other prospective studies, were pivotal in shifting the paradigm of CVD prevention11-14. “Risk factor” was coined in the FHS’ early years as investigators identified elevated lipids, high blood pressure, smoking, obesity, diabetes, and physical inactivity as major predictors of CVD development1,12. The FHS and other epidemiological studies have indicated a powerful relationship between serum total cholesterol and premature coronary heart disease risk and mortality13,15. Further technological advancements and improved techniques allowed for separating the serum total cholesterol into separate lipoprotein fractions, highlighting the risk associated with cholesterol from two fractions: the low-density lipoprotein cholesterol (LDL-C) and the high-density lipoprotein cholesterol (HDL-C)16,17. HDL-C was deemed the strongest but inverse biochemical predictor for CVD events18-20. Multiple prospective studies have corroborated that a 1 mg/dL increment in HDL-C was associated with a 2-3% decrement in CHD risk and 3.7-4.7% decrement in CVD mortality14. This made raising HDL-C a desirable cardioprotective therapy18. However, successful therapeutic agents targeting HDL-C have been elusive1,21. LDL-C, which normally accounts for the largest fraction of cholesterol in the serum, also gained traction as a strong indicator of CVD22,23. Subsequent studies correlating LDL-C with 3 heart attacks24, and the effective diminishing of adverse CVD events and improved survival with LDL-C reduction therapies25-27 further supported the link between LDL-C and CVD risk. Despite the effectiveness of lowering LDL-C as a therapeutic endpoint, a significant residual risk of 60-70% was observed in patients even with aggressive statin therapy28,29. Initially overlooked, other atherogenic lipoproteins, apolipoproteins, and triglycerides became prospective targets for better risk assessment30,31. Since lipoproteins are a key driver for atherosclerosis, it was not surprising that the apolipoprotein B/apolipoprotein A-I (ApoB/ApoA-I) ratio, which captures the ratio of the atherogenic lipoproteins over the non-atherogenic lipoproteins, stood out as the strongest indicator of cardiovascular risk31-34. The INTERHEART and INTERSTROKE studies determined that the risk factors identified by the FHS, and additional factors such as diet, exercise level, alcohol consumption, and psychosocial factors, account for at least 90% of the population attributable risk for both heart attack and stroke; the ApoB/ApoA1 ratio was the strongest risk factor for heart attack and a major one for stroke35,36. 1.2 Atherosclerosis Atherosclerosis is a chronic, inflammatory disease of the arteries37. It presents as plaque development in sites with low sheer stress, adjacent to branches, and along curvatures38. Atherosclerotic CVD accounts for 85% of CVD incidents and death3. 1.2.1 Artery Wall Architecture The arterial wall has three main layers: the intima, media, and adventitia (Figure 1.1)39,40. The intima is a monolayer of endothelial cells that prevent circulating cells from adhering to the artery wall and regulate the infiltration of blood components such as lipoproteins into the subendothelium39. The media is a matrix of elastic fibers, collagen, and proteoglycans surrounding phenotypically contractile smooth muscle cells (SMCs), which control the arterial 4 diameter39,41,42. The adventitia is the outermost layer composed mostly of collagen and fibroblasts that provide structural support42. In healthy arteries, the lumen is intact and free from stenosis, while atherosclerotic arteries are distinguished by plaque formation that narrows the arterial lumen, promotes thrombocyte formation, and decreases blood flow42. 1.2.2 Diffuse Intimal Thickening The SMCs in various regions of the aorta have different embryonic origins that can account for their heterogeneous behaviour and contribution to CVD43,44. Atherosclerosis-susceptible arteries, in contrast to atherosclerosis-resistant arteries such as the descending thoracic aorta and abdominal organ arteries, increases in thickness in the process known as diffuse intimal thickening (DIT) (Figure 1.2)19,38,40,44,45. DIT develops in utero and is considered by the Council of the American Heart Association as a normal pre-atherosclerotic process since it is present in all humans by two years of age41,45-47. Atherosclerosis-susceptible arteries Figure 1.1 The structure of a normal arterial wall. The human intima with endothelial cells and smooth muscle cells (SMCs) is separated from the medial layer of SMCs surrounded by a complex matrix. The adventitia, the outermost layer, has mast cells and fibroblasts in a matrix of collagen. The layers are delineated by an elastic lamina. Adapted from 172 with permission from Springer Nature, copyright © 2011. 5 including the aortic arch, abdominal aorta, coronary arteries, and carotid arteries have DIT, and the thickness of DIT in children correlates positively with family history of CVD40,45-47. DIT has an organized structure of elastin and synthetic SMCs that, in contrast to the contractile SMCs in the medial layer, have lost their contractile properties, and gained a synthetic phenotype of increased proliferation and proteoglycan secretion (Section 1.4.2)41,47. These proteoglycans in DIT provide the soil for the deposition of lipoproteins45,46. 1.2.3 Theories of Atherogenesis Endothelial cells are a selective barrier that regulate the passage of molecules into the subendothelium48,49. Injury, activation, or denudation of endothelial cells caused by hyperlipidemia, mechanical stress, and other injurious agents can increase permeability, or expose the more adherent subendothelium to circulating cells and molecules50. The response-to-injury hypothesis by Ross et al. states that the accumulation of lipoproteins and infiltration of macrophages in the intima of early atherosclerosis is a consequence of endothelial dysfunction51-53. However, the much earlier accumulation of lipoproteins in DIT than the Figure 1.2 The diffuse intimal thickening (DIT). The a) cross sectional and b) longitudinal images of diffuse intimal thickening of the right coronary artery in a 25-year-old male showing a thickened intima (I) compared to the medial layer (M). Elastic van Gieson stain. Scale bar for 500 μm. Adapted from 40 with permission from Springer Nature, copyright © 2002. 6 infiltration of macrophages54, and the presence of an intact endothelium in fatty streaks55,56, do not support this hypothesis but rather favour the response-to-retention hypothesis. The response-to-retention hypothesis proposes that early atherogenesis is initiated by the entrapment of infiltrating atherogenic lipoproteins in the subendothelium, which elicits their modification, and uptake by cells40,45,57. The majority of lipoproteins can enter the intima through endothelial cell transcytosis, which is the entry of lipoproteins from the luminal side of the cell in a vesicle and its release onto the opposite side (Figure 1.3A)48,49,57,58. The infiltrating lipoproteins can then become trapped by interacting with the matrix proteoglycans generated in the DIT (Figure 1.3B)51,59,60. Retained lipoproteins can undergo modifications such as oxidation and aggregation, which converts them to scavenger receptor ligands, and therefore increases their atherogenic and inflammatory capabilities61. Oxidized LDL also stimulates endothelial cells to express adhesion molecules (vascular cell adhesion molecule-1 (VCAM-1)) and macrophage-colony stimulating factor (M-CSF)62,63. VCAM-1 initiates monocyte ‘rolling’ on endothelial cells and invasion of the intima (Figure 1.3C); M-CSF promotes monocyte proliferation and differentiation into macrophages62,63. The surrounding SMCs and recruited macrophages also secrete monocyte chemotactic protein-1 (MCP-1), a potent chemoattractant that induces further infiltration of circulating monocytes into the intima45,63. Native LDL is primarily taken up through the feedback regulated LDL receptor pathway but modified LDL, which is not recognized by the LDL receptor, becomes a ligand for the unregulated scavenger receptors61. Hence, the modification of lipoproteins is fundamental for the transformation of SMCs and macrophages into lipid-laden cells or “foam cells”63,64. Foam cell accumulation is a hallmark of the fatty streak, but lipids can also localize extracellularly in the intima along with collagen, elastin, and proteoglycans (Figure 1.3D)57,65. The continued presence of elevated lipoproteins 7 further increases foam cell formation and eventually foam cell necrosis—the tipping point for plaque progression (Figure 1.3E)65. Dead foam cells release their lipids and enzymes into the extracellular compartment; the lipids give rise to the lipid rich necrotic core while the enzymes such as the matrix metalloproteinases (MMPs) and proteases destabilize the plaque and augment the inflammation (further discussed in Section 1.4)65,66. The cytokines and growth factors secreted by macrophages and T cells also induce further SMC migration and proliferation in the intima62. The synthetic SMCs between the lumen and the necrotic core form a fibrous cap, which increases plaque integrity and prevents the necrotic core from being exposed to the lumen62. 8 Figure 1.3 Pathogenesis of early atherosclerosis. A) The diffuse intimal thickening (DIT), which has synthetic vascular smooth muscle cells (VSMCs), is between the endothelium and the medial VSMCs and is present in atherosclerosis-prone sites. B) Lipoproteins (LPs) that enter the arterial wall interact with proteoglycans in the DIT and are retained. C) The retained LPs are modified (i.e., oxidation, aggregation, or enzymatically) and circulating monocytes are recruited into the proteoglycan-rich DIT by chemoattractants. The medial layer enriched with contractile VSMCs, however, is not enriched with proteoglycans and lipoprotein entrapment. D) Monocytes and VSMCs take up modified lipoproteins and become foam cells while some VSMCs form the fibrous cap. E) An advanced plaque with increased LP accumulation, inflammation, necrotic core enlargement, fibrous cap thinning. Adapted from 57 with permission from Wolters Kluwer Health, Inc., copyright © 2007. 9 1.2.4 Advanced Atherosclerotic Lesions and Thrombosis Advanced atherosclerotic lesions may feature a necrotic core, inflammation, neovascularisation, and calcification62. Stable plaques have a small necrotic core and thick fibrous cap (Figure 1.4)62. Stable plaques, however, can still be detrimental if they grow and significantly obstruct blood flow67 or if the chronic inflammation in atherosclerosis drive them towards eruption and thrombosis62. On the other hand, unstable or vulnerable plaques have an enlarged necrotic core and a thinning fibrous cap that can promote rupture (Figure 1.4). The plaque shoulders are particularly vulnerable sites of rupture due to their inflammatory nature and density of foam cells62,65. Monocyte infiltration, differentiation, and protease secretion that degrade the extracellular matrix and T cell secretion of IFN-γ that prevents SMC matrix production contribute to the inflammation of the plaque shoulders62,65. Rupture can lead to both nonfatal and fatal thrombosis; the former can result in heart attack or stroke and the latter can lead to sudden cardiac or cerebrovascular death62,63. Figure 1.4 The stable and vulnerable plaque in atherosclerosis. The stable fibrous plaque has a thick fibrous cap of SMCs in a matrix of collagen and elastin. The fibrous cap is a barrier that prevents the plaque from rupture and the necrotic core from leaking into the lumen (left). In contrast, the vulnerable plaque, which is characterized by a thinned fibrous cap and an enlarged necrotic core, is at risk for eruption (right). 10 1.2.5 Atherosclerosis in Mice and Differences with Human Atherogenesis The inaccessibility of human tissues and the inability to study the disease process directly in humans make mouse models of atherosclerosis an extremely valuable alternative to study the disease. Mouse models of atherosclerosis have been instrumental in advancing atherosclerosis research since their first employment in the 1970’s68. Mice are naturally atherosclerosis resistant and can only develop atherosclerosis after being made extremely hyperlipidemic through mutations (e.g., ApoE or LDL receptor deficiency) and provision of a high fat diet68,69. There are key distinctions between the early atherogenesis in humans versus mice (Figure 1.5). Humans have preatherosclerotic DIT, while mice arteries do not develop DIT68,69. The artery wall structure alone therefore presents a major difference that distinguishes atherosclerosis between the species even before atherosclerosis is initiated. The presence of the SMC-rich DIT enables SMCs’ pivotal role in human atherogenesis as described in the response-to-retention hypothesis (Section 1.2.3) whereas murine atherosclerosis is believed to initiate with endothelial injury caused by severe hyperlipidemia, and subsequent recruitment of monocytes as proposed in the response-to-injury hypothesis (Section 1.2.3)68,70. In addition, the induced atherosclerosis in mouse models is a massively accelerated version of human atherosclerosis, developing over weeks as opposed to decades in humans68. Interestingly, despite these differences, the origin of foam cells in both species are dominated by the same cell type. Our laboratory showed that most of the human foam cells are of SMC origin, and not macrophages, which is supported by the presence of DIT and the inability of SMCs to effectively release cholesterol due to low expression of the membrane lipid transporter ATP-binding cassette transporter A1 supported this observation71. Despite the huge role that macrophages play in driving murine atherosclerosis and 11 their abundance in the mouse plaque, most of the foam cells were of SMC origin and only 30% were of macrophage origin even after only 6 weeks of high fat feeding70. 1.3 Lipoprotein Contribution to Atherosclerosis Lipoprotein fractions were initially separated by ultracentrifugation of plasma by Gofman in 195524. Soon after, they were linked with myocardial infarction; today they are widely known simplistically as “good cholesterol” or “bad cholesterol” based on whether the lipoproteins protect against or promote CVD, repectively24. Figure 1.5 Atherogenesis in humans and mice. Species differences such as the presence of diffuse intimal thickening (DIT) in humans contribute to the different mechanisms of atherogenesis in humans and mice. Adapted from 70 with permission from Wolters Kluwer Health, Inc., copyright © 2019. 12 1.3.1 Structure and Types of Lipoproteins Cholesterol and triglycerides are lipids essential for cellular function and organism survival72. Cholesterol is a vital element of all animal cell membranes, and a precursor of hormones, while triglycerides are the primary source of energy and storage in animals73. These hydrophobic lipids can only circulate in the plasma when associated with proteins and the more soluble phospholipids72,73. The lipoproteins that become trapped in the arterial wall during atherosclerosis are intended for the transportation of these lipids24,72. Plasma lipoproteins can be categorized by their size, hydrated density, electrophoretic mobility, cholesterol and triglyceride content, apolipoproteins, and other protein content into major classes: chylomicrons, very-low-density lipoproteins (VLDL), intermediate-density lipoproteins (IDL), low-density lipoproteins (LDL), and high-density lipoproteins (HDL) (Table 1.1 and Figure 1.6B)72,73. Table 1.1 The lipoproteins are characterized by size, density, lipid content, and major apolipoproteins. Cholesterol (Chol); triglyceride (TG); phospholipid (PL); apolipoprotein (apo). Adapted from 72 with permission under a Creative Commons license and 73. Lipoprotein particle Size (nm) Density (g/dL) Content (%) Apolipoproteins Chol TG PL Apo Chylomicrons 75-1200 <0.930 3 90 5 9 AI, AII, B48, CI, CII, CIII, E Chylomicron remnants 30-80 0.930-1.006 - - - - B48, E Very-low-density lipoprotein (VLDL) 30–80 0.930–1.006 10 70 10 10 B100, CI, CII, CIII, E Intermediate-density lipoprotein (IDL) 25–35 1.006–1.019 – – – – B100, CIII, E Low-density lipoprotein (LDL) 18–25 1.019–1.063 26 10 15 25 B100 High-density lipoprotein (HDL) 5–12 1.063–1.210 20 5 25 50 AI, AII, C, E 13 Lipoproteins are composed of a hydrophobic core of cholesteryl esters and triglycerides that is enveloped by a hydrophilic shell of apolipoproteins, cholesterol, and phospholipids (Figure 1.6A)72. All these lipoproteins, except for HDL, are atherogenic and collectively known as the non-HDL fraction74. These atherogenic lipoproteins each contain one molecule of apolipoprotein B (apoB); hence, they are also known as apoB-containing lipoproteins72. As such, the non-HDL and apoB-containing lipoproteins are synonymous73. Figure 1.6 The structure and types of lipoproteins. A) Lipoproteins have a hydrophobic core that often contain cholesteryl ester and triglycerides and an outer core of phospholipids, and apolipoprotein and free cholesterol. B) The non-HDL fraction includes chylomicrons and its remnants, very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL) while high-density lipoproteins (HDL) are the only anti-atherogenic lipoproteins. Adapted from 72 with permission under a Creative Commons license. 14 Apolipoproteins are fundamental in lipoprotein metabolism as they: a) direct the formation of lipoproteins, b) maintain the lipoprotein structure, c) serve as cofactors for enzymes involved with lipid metabolism, and d) are the ligands for lipoprotein receptors72,75. Apolipoprotein nomenclature uses alphabetic indicators and numbers for the types and subtypes, respectively75. Apolipoprotein A (ApoA) has 2 subtypes (I, II) associated with lipoproteins72,75. ApoA-I is synthesized both intestinally and hepatically, while apoA- II is synthesized solely in the liver72. ApoAs are mostly associated with structural support, interaction with lipoprotein receptors, and enzyme activation. Other apolipoprotein features are as indicated in Table 1.272,76. There are two isoforms of apoB in humans, apoB100, and apoB4875. ApoB100 is expressed in the liver and each hepatic-originating lipoprotein (VLDL), and its metabolized forms (IDL and LDL) has one molecule of apoB10072,75. ApoB48 is a truncated version of apoB100 synthesized in the intestine and present in chylomicrons and chylomicron remnants in a 1 molecule:1 particle ratio72,75. ApoBs are core structural proteins that act as receptor ligands in the retention of lipoproteins via interaction with glycosaminoglycans in the arterial wall (Section 1.3.4), and the metabolism of lipoproteins via apoB100 recognition by the LDL receptor (LDLR) that leads to the uptake and hepatic breakdown of most LDL (Table 1.2)75. Apolipoprotein C (apoC) is primarily synthesized hepatically and has three subtypes (C-I, II, III) associated with atherogenic lipoproteins such as IDL, VLDL, and chylomicrons72,75. They are the ligands for lipoprotein interactions with cellular receptors and enzymes (Table 1.2)72,75,76. Apolipoprotein E (ApoE) is associated with HDL and triglyceride-rich lipoproteins such as VLDL, chylomicrons, and chylomicron remnants72,75,77. ApoE contributes to the receptor-mediated uptake of lipoproteins as it is a ligand for the LDLR and apoE receptor (Table 1.2) 72,75. 15 1.3.2 Predictive Value of LDL-C versus Non-HDL-C for Cardiovascular Risk and Benefits of Treatment LDL was first identified as a risk factor for coronary heart disease in the early 1950’s24. The standard lipid profile for predicting cardiovascular risk in the clinical setting includes the total cholesterol (TC), LDL-C, HDL-C, and triglycerides78. TC and HDL-C are used in the Framingham Risk Score calculation while LDL-C (as mentioned in Section 1.1.2) has been the primary target of lipid-lowering treatment for reducing atherosclerotic CVD risk78. ~90% of the atherogenic particles (apoB) and 70% of the atherogenic cholesterol in the plasma are in LDL79,80. Meta-analysis studies showed that decreasing plasma LDL-C with statin treatment has Table 1.2 The various apolipoproteins. The apolipoproteins are associated with different lipoproteins and functions. High-density lipoproteins (HDL); lecithin cholesterol acyltransferase (LCAT); very-low-density lipoprotein (VLDL); intermediate-density lipoprotein (IDL); low-density lipoprotein (LDL). Adapted from 67 with permission from Elsevier, copyright © 1998. 16 consistently proved to lower major CVD events by 21% and CHD mortality by 19% per 1 mM/L decrease in LDL-C, with a further reduction of both with more intensive LDL-C lowering treatments81,82. Despite the remarkable results from lipid-lowering treatment in several large outcome studies, CVD remains highly prevalent even in treated patients (Figure 1.7)28,83. The residual risk, which may stem from the complexity of the disease and various comorbidities in the patient, may be reflected better through other risk parameters. Atherogenesis is primarily driven by the retention of lipoproteins, thus, targeting the entire atherogenic fraction instead of just LDL-C may prove beneficial. Figure 1.7 Residual cardiovascular risk from LDL-C lowering treatment. Large prospective studies of optimal statin therapy in a broad range of cohorts demonstrating the presence of residual risk. Scandinavian Simvastatin Survival Study (4S); Cholesterol and Recurrent Events (CARE); Long-term Intervention with Pravastatin in Ischaemic Disease (LIPID); Heart Protection Study (HPS); Prospective Study of Pravastatin in the Elderly at Risk (PROSPER); Anglo-Scandinavian Cardiac Outcomes Trial–Lipid Lowering Arm (ASCOT-LLA); Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT); Atorvastatin Study for Prevention of Coronary Heart Disease Endpoints in Non- Insulin-dependent Diabetes Mellitus (ASPEN); West of Scotland Coronary Prevention Study (WOSCOPS); Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS); Collaborative Atorvastatin Diabetes Study (CARDS); Treating to New Targets (TNT). Adapted from 28 with permission from Springer Nature, copyright © 2011. 17 The ApoB/ApoA-I is the strongest risk factor for CVD compared with non-biochemical risk factors such as smoking habits, hypertension, and physical inactivity, and captures all apoB-containing atherogenic lipoproteins35. However, apolipoprotein measurements incur additional costs in the clinical setting78. The non-HDL-C, which quantifies the cholesterol in all atherogenic lipoproteins, can be calculated from the standard lipid profile by subtracting the HDL-C from the total cholesterol measurements78. Furthermore, several studies have indicated that the non-HDL-C is a better indicator of risk than the LDL-C in various populations. In a population with type 2 diabetes, which is a common comorbidity in CVD, the non-HDL-C was better than LDL-C in predicting CVD risk for both men and women84. The hazard ratio for the highest tertiles of the LDL and non-HDL-C were 1.71 (1.17–2.48) and 2.23 (1.41–3.43) for men, and 1.61 (1.19–2.17) and 1.80 (1.32–2.46) for women, respectively84. In another study, non-HDL-C was able to predict the severity of coronary atherosclerosis in patients who have traditional risk factors for CVD better than LDL-C85. The Gensini Score system combines the degree and location of the luminal stenosis into a score system to quantify the severity of coronary atherosclerosis85. After adjusting for traditional risk factors such as age, sex, BMI, family history of CVD, metabolic comorbidities, and smoking habits, the odds ratio (OR) of non-HDL-C (OR=1.326, 95% confidence interval (CI) 1.165-1.508) for predicting a high Gensini Score was moderately better than LDL-C (OR=1.286, 95% CI 1.130-1.463), TC/HDL-C ratio (OR=1.232, 95% CI 1.123-1.351), and triglyceride (TG)/HDL-C (OR=1.045, 95% CI 0.980-1.114)85. In patients with a recent acute myocardial infarction, rising non-HDL-C levels were associated with higher risk for another adverse CVD event but rising LDL-C levels were not86. Non-HDL-C was also more predictive than LDL-C of CVD mortality in both men and women; a 30 mg/dL increase in LDL-C and non-HDL-C led to an increase in CVD by 15% and 19% in men, and 8% and 11% in 18 women, respectively87. Another meta-analysis comparing non-HDL-C, LDL-C, and apoB in cardiovascular risk determination during statin therapy indicated significantly more powerful hazard ratio reduction per 1 mM of non-HDL-C decrement compared to either LDL-C or apoB reduction30. These combined data demonstrating non-HDL-C as a better indicator of first and subsequent CVD events, CVD severity, and primary therapeutic target has led the Canadian Cardiovascular Society Guidelines and other societies worldwide to recommend routine measurement and targeting of non-HDL-C as an alternative to LDL-C since 201288. 1.3.3 Comparison of Fasting and Non-fasting Lipoproteins Lipid profiling for cardiovascular risk assessment has traditionally been performed following an overnight fast. However, this practice was belief-driven and based on a) the speculated substantial change in lipid components between the fasted and non-fasted states; b) the observation that triglyceride levels vary according to the fasting state and may affect LDL-C calculations; and c) unfamiliarity with the alternative (nonfasting) that raises uncertainties in cut-offs of normal ranges78. The substantial triglyceride level increase after a fat tolerance test (consumption of 1 gram fat/ kilogram of body weight) that led to the notion that the non-fasted state may substantially alter plasma lipid profiles was shown to be unobservable after regular food intake, in which much less fat is consumed relative to the fat tolerance test78. In addition, a cross-sectional study on the Copenhagen population exploring the effects of normal food consumption on the plasma lipoproteins and lipid levels observed minimal and, in most cases, clinically insignificant changes: +0.3mM/L triglycerides, -0.2 mM/L total cholesterol, -0.2 mM/L LDL-C, and -0.1 mM/L HDL-C89. Thus, the LDL-C can still be measured directly or with the Friedewald equation (LDL-C (mM/L)= TC – HDL-C – TG/2.2) from the nonfasting plasma. Moreover, the European 19 Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine released cut-offs for flagging abnormal concentrations from nonfasting plasma90. The fasted state initiates after an 8-hour fast but most humans spend more time in the non-fasted state with daily food consumption over a 24-hour period78. In addition to the scientific evidence supporting nonfasting plasma collections, nonfasting plasma collection is more convenient and safer for patients (e.g., avoid early blood draws to maintain fasting, and prevent hypoglycemia in diabetics and elderly), and requires no additional cost in adoption as the new paradigm78. Nonfasting triglyceride levels were associated with risk of stroke, ischemic heart disease, myocardial infarction, and death in the general population91,92. Moreover, comparing the highest and lowest tertiles for nonfasting TC, HDL-C, LDL-C, non-HDL-C, triglyceride, apoA-I, apoB, apoB/apoA-I, and TC/non-HDL-C predicted between 1.7 to 2.4 times increased risk for cardiovascular events89. These suggest that the nonfasting plasma can predict CVD risk, which is further corroborated by the following studies comparing the nonfasting plasma with the fasting plasma. Triglyceride levels from both fasting and nonfasting plasma adjusted for traditional risk factors were correlated with cardiovascular incidents such as heart attack, stroke, coronary revascularization, and cardiovascular death in a prospective study on American women93. Only the nonfasting triglyceride maintained the correlation when adjusted for additional factors93. An increase of 55 mg/dL to 150 mg/dL in triglyceride level was associated with a hazard ratio (HR) [95% CI] of 1.11 [0.92-1.34] for fasting triglycerides and 1.67[1.18-2.45] for nonfasting triglyceride93. Furthermore, the risk correlation of both the nonfasting triglyceride and nonfasting cholesterol with myocardial infarction at high levels were preserved; the nonfasting cholesterol was the best indicator for women, and the nonfasting triglyceride was the best indicator for men and exclusively associated with cardiovascular death94. The association of nonfasting lipid levels 20 with major coronary events were comparable to the fasting lipid levels95. The evidence-based studies supporting nonfasting plasma for CVD risk assessment, in addition to disproving or resolving the bases of the fasting plasma, paved the way for adapting nonfasting as the standard timing for lipid determination. Since the Danish Society of Clinical Biochemistry’s first endorsement of nonfasting lipid profiles in 2009, other international associations, societies and guidelines including Canada’s have followed78. 1.3.4 Lipoprotein – Proteoglycan Interactions in the Arterial Wall Proteoglycans are involved in various processes in atherogenesis such as lipid accumulation, inflammation, and thrombosis96. This section expands on the involvement of proteoglycans in lipid accumulation during atherogenesis. Proteoglycans are a diverse group of complex macromolecules composed of one or more glycosaminoglycans (GAGs) covalently attached to a protein core96. GAGs are negatively charged polysaccharides of different lengths made up of a disaccharide repeating unit96. The disaccharide subunits give rise to various types of GAGs, three of which are found in the arterial wall: heparin sulfate (HS), chondroitin sulfate (CS), or dermatan sulfate (DS)96. Various combinations of these GAGs and their attachment to a protein core give rise to several proteoglycans local to the arterial wall including biglycan, decorin, versican, and perlecan (Figure 1.8A)97. Biglycan, which is enriched in the DIT, the plaque core, and fibrous cap, can have a combination of two CS or DS GAG chains attached to its protein core54,96. Decorin is in similar sites as biglycan and is also structurally similar to biglycan but with only one CS or DS side chain54,96. Biglycan and decorin are widely associated with the retention of lipoproteins in the arterial wall as they are present in high concentrations in intimal areas with the highest deposition of lipoproteins54. Versican, which can only have CS GAGs, is found in most regions 21 of the arterial wall, and provides the matrix for the migration and growth of SMCs96. Perlecan is an HS proteoglycan found in the basement membrane between endothelial cells and SMCs97. It is abundant in mice and is involved in the artery wall permeability and lipoprotein retention in this species; its role in human arterial walls is not as clear (Table 1.3)97. The type, length, and degree of sulfation of the proteoglycan GAG side chains influence their interaction with lipoproteins as most lipoproteins have a higher affinity for CS and DS GAGs compared with the other GAG chains, and with longer and more sulfated side chains97. Lipoproteins up to 70 nm, which are all lipoproteins excluding chylomicrons and large VLDLs (Table 1.2), can permeate an intact endothelium, passing through the vessel wall into the intima freely60,98. When the rate of influx exceeds the ability of artery wall cells to clear lipoproteins, such as in hyperlipidemia and lesion susceptible sites, more lipoproteins are retained in the arterial wall64,98. The carotid intima of normolipidemic and hyperlipidemic rabbit retained both LDL and chylomicron remnants; the former retained equivalent amounts of both lipoproteins while the latter had increased chylomicron remnant retention and decreased LDL retention (Figure 1.9I)98. The investigators also demonstrated that lipoprotein delivery was similarly efficient although chylomicron remnants were selectively retained (Figure 1.9II and III)98. Lipoprotein entrapment occurs via an electrostatic bond between their positively charged apoB and the negatively charged GAGs in proteoglycans (Figure 1.8B)60. The interactions of proteoglycans with LDL or its apolipoprotein, apoB, has been intensively explored in lipoprotein retention studies that there are eight known sites that can potentially bind proteoglycans60. Site B is the primary site of proteoglycan-binding that coincides with the LDLR binding site99. ApoB48 lacks site B but can still bind with lipoproteins via an exposed binding site, site B-lb, that is not 22 functional in apoB10060. Site A is another proteoglycan binding site that is only exposed in LDL particles post modification by secretory phospholipase A2 into a smaller and denser particle, suggesting that a smaller particle results in a better exposure of the binding sites100,101. Site A and B work together in these denser LDL particles, which may account for the higher affinity of these smaller LDL particles for biglycan and decorin than the native LDL60. ApoE, which is in VLDL and IDL, was found to have a strong affinity for HS proteoglycans such as perlecan, which bridges its uptake by cells via the LDLR and LDL receptor-related protein (LRP)102. Other lipoprotein characteristics may also influence lipoprotein binding to proteoglycans. The affinity of apoB100-containing lipoproteins to biglycan was found to correlate with the ApoC-III content of the lipoproteins (LDL<IDL<VLDL)103. The presence of apoE also enhanced the binding of β-VLDL to HS proteoglycans104. These may result from conformational change that makes the apolipoproteins more accessible to proteoglycan binding. Sialic acid, a negatively Figure 1.8 Proteoglycans in atherogenesis. A) The structure of proteoglycans found in the extracellular matrix. B) The interaction between negatively charged proteoglycan glycosaminoglycan (GAG) chain and positively charged apoB in lipoproteins. Adapted from 60 with permission from Elsevier, copyright © 2012. 23 charged sugar residue found in apolipoproteins, also influences their interaction with proteoglycans as desialylated LDL uptake by cells was much higher than native LDL105-107. Besides the availability and affinity of apolipoproteins to proteoglycans, the interaction between lipoproteins and proteoglycans are also influenced by the action of other modifying enzymes such as lipases and sphingomyelinase that promote lipoprotein aggregation and consequently their binding with proteoglycans64. The marked affinity of accumulated versican, biglycan, and decorin in atherosclerotic plaques for LDL, which are mostly modified (i.e., oxidized, and aggregated) in this stage, support the observation that modifications of LDL increase interactions with proteoglycans59. Figure 1.9 The delivery and retention of ApoB-containing lipoproteins in the arterial wall. I) Lipoproteins are strongly retained in hyperlipidemic rabbit carotid aorta (bottom) than in the normolipidemic counterpart (top). II) ApoB100-containing lipoproteins (red) and apoB48-containing lipoproteins both enter the arterial wall in similar amounts (top 80x; bottom 40x magnification). III) However, apoB48 containing lipoproteins are retained more readily (top 80x; bottom 40x magnification). Red (LDL), Yellow (Remnant). Adapted from 98 with permission from Wolters Kluwer Health, Inc., copyright © 2003. 24 Collectively, the interaction of lipoproteins with proteoglycans is influenced by the properties of both parties: the inherent properties of proteoglycans such as their GAG types, length, and degree of sulfation; and the sialic acid content, apolipoprotein conformation, presence of enhancing apolipoproteins and number of modifications that activate more binding sites in the lipoproteins. Proteoglycan GAG Location Role in atherogenesis and vascular homeostasis Versican CS • Throughout the arterial wall • Enriched in areas with many SMCs (e.g., DIT, fibrous cap, and the edge of lipid cores) • Provides a matrix that prepares SMC for proliferation and migration Biglycan CS, DS • Abundant in DIT, plaque core, and fibrous cap • Mediation of lipoprotein retention Decorin CS, DS • Adventitia of normal and atherosclerotic arteries • Similar location as biglycan [e.g., deep (outer) intima in early atherosclerosis] but to a lesser extent • Enriched in the intima of atherosclerosis-resistant arteries • May contribute to plaque stability Perlecan HS • Basement membrane between endothelial cells and intimal SMCs (abundant in mice but not humans) • In mice: permeability barrier and lipoprotein retention • In humans: unclear Table 1.3 Proteoglycans in the arterial wall. The various proteoglycans found in the arterial wall, their primary GAG chains, location, and function in the arterial wall. Adapted from 97 with permission under a Creative Commons license. . 25 The various forms of LDL (native, acetylated, oxidized, or aggregated) have been used in in vitro studies of cellular lipid accumulation or foam cell formation of both SMCs and macrophages70,71,108-111. These studies demonstrate that lipoprotein modifications also increase cellular uptake, in addition to increasing proteoglycan interactions. SMC monocultures with native or acetylated LDL slightly increase their cholesterol content compared with non-loaded SMCs112. Enzymatically modified LDL induced the most SMC foam cells compared with no loading, native LDL, acetylated LDL, and oxidized LDL113. Macrophage cholesterol accumulation is also stimulated better by modified LDL than native LDL61,114,115. These modifications eventually lead to lipoprotein aggregation, which is the most common and most potent inducer of foam cells115-118. 1.4 Smooth Muscle Cell–Macrophage Interactions in Atherosclerosis SMCs and macrophages are the two main cell types in human and mouse atherosclerotic lesions, and the two major sources of lesion foam cells70,71. Evidence suggests these two cell types likely influence foam cell development by the other cell type, but the mechanisms of this interaction are not yet understood. The second aim of my research is to explore the importance of SMC-macrophage interactions in promoting foam cell development. 1.4.1 Architecture/Location of Smooth Muscle Cells and Macrophages in Developing Atherosclerosis Lesions The previous section (Section 1.2.5) elaborated that humans and mice have quite different arterial wall structures, which directly influences the initial stages of atherosclerosis (Figure 1.10). These differences impact the site of initial lipid deposition and interactions between artery wall macrophages and SMCs in foam cell formation. In murine models, initial lipoprotein deposition is around macrophages in the thin intimal space which is void of SMCs (Figure 26 1.10C)45. In humans, lipoproteins initially deposit in the deep intimal space around SMCs accumulated during the DIT stage, with little or no presence of macrophages (Figure 1.10D)45. Thus, the ensuing fatty streak, where the initial foam cells accumulate, is situated in the superficial intima in the mouse artery wall (Figure 1.10F), while it starts in the deeper intima in humans (Figure 1.10G)45,119. In the later stages of atherosclerosis, macrophages secrete growth factors such as platelet-derived growth factors (PDGF) and transforming growth factor (TGF)-β1 that induce or inhibit SMC proliferation in both species, respectively120,121. Moreover, macrophage and T cells are important for SMC migration, proliferation, and extracellular matrix production for the fibrous cap formation62. Macrophages, however, can turn and contribute to fibrous cap thinning and plaque instability in later stages of atherosclerosis by activating the Fas apoptotic pathway in SMCs, decreasing their synthesis of collagen, and presumably stimulate the accumulation of matrix metalloproteinase-2 (MMP-2) and MMP-9 on the plaque shoulders122. Figure 1.10 Early atherogenesis in mouse models of atherosclerosis and humans. The species differences in arterial wall morphology and disease vulnerability lead to key differences in disease progression. A, C, and F show the progression of early atherogenesis in mice while B, D, E, and G demonstrate the events in early human atherogenesis. Adapted from 45 with permission from Oxford University Press, copyright © 2008. 27 1.4.2 Influence of Smooth Muscle Cells on Monocytes and Macrophages in Relation to Foam Cell Formation and Other Cell Functions Although SMCs are the primary cell type in all stages of human atherosclerosis and most foam cells in atherosclerosis are SMC-derived, the literature on SMC cellular interactions and the mechanism of SMC foam cell formation is limited47. Macrophages, which are more studied in foam cell formation, take up lipoproteins via their scavenger receptor (SR)-AI, SR-BI, CD36, lectin-like oxidized LDL receptor-1 (LOX-1), LDL receptor, other receptors, and phagocytosis114,123-126. The mechanism of SMC foam cell formation is less studied than macrophages, but similar scavenger receptors and scavenger receptor independent pathways have been implicated108,112,113,117,118,127. Enzymatically modified LDL uptake via micropinocytosis113 and phagocytosis of lipoprotein-proteoglycan complexes112 are some of the scavenger receptor-independent pathways for SMC foam cell formation. The SMCs in a normal medial layer are stable, highly differentiated, quiescent, and express SMC markers such as α-smooth muscle actin (α-SMA) and smooth muscle myosin heavy chain (SMMHC)47. These contractile SMCs are responsible for the primary function of SMCs in the normal artery – blood flow control47. SMCs can alter their phenotype in response to environmental influences such as growth factors/inhibitors, cellular interactions with other cells or the matrix, and lipoprotein uptake. This is observed in the intimal SMCs in the developing atherosclerotic plaque as they transform into a synthetic phenotype that is more proliferative, less contractile, more proteoglycan-secreting, and with reduced or even loss of α-SMA and SMMHC markers128. Some SMCs develop a phenotype in between: SMCs in the fibrous cap are more synthetic but retain their SMC markers47 while SMC foam cells are also more synthetic and phagocytic but express markers for macrophages such as CD68 and Macrophage-271,127. This 28 capacity of SMCs to wear various “masks” led to the old and erroneous assumption that macrophages were the primary foam cell contributors and the current gap in knowledge on the activities of SMCs and the mechanism of their foam cell formation41. A few studies have explored the influence of SMC on macrophages. A study exploring the influence of SMCs on the phenotype of recruited macrophages identified TGF-β1 as an important modulator of macrophage phenotype and observed that macrophages exposed to the conditioned media of SMCs increased interleukin-6 (IL-6) and IL-12a129. Direct contact between SMCs and monocytes increased the concentration of MMP-1, IL-1β, tumor necrosis factor α (TNFα), and IL-6 compared to their monocultures; culturing the SMCs with macrophages in place of monocytes further augmented the observed increase130. Another experiment on SMCs and monocytes demonstrated that in addition to IL-1β, TNFα, and IL-6, direct coculture also increases MMP-2, and MMP-9 mRNA131. These molecules can promote autocrine monocyte differentiation, augment the inflammatory environment, and contribute to plaque erosion131. Incubation of human peripheral blood mononuclear cells (PBMCs) with human SMCs increased the CD36 expression of the PBMCs in both direct and indirect coculture132. In contrast, SMC-secreted proprotein convertase subtilisin/kexin type-9 (PCSK9) reduced the LDL receptor expression, and LDL and β-VLDL of macrophages in indirect coculture133. The in vitro studies reviewed in this section may offer some insight but given the complexity of the disease and heterogeneity of both cell types, there is more to explore on SMC modulation of macrophage phenotypes. The same is true for the SMC effects on macrophage foam cell formation, and this is what we seek to address in the current study. 29 1.4.3 Influence of Monocytes/Macrophages on Smooth Muscle Cells in Relation to Smooth Muscle Cell Foam Cell Formation and Other Cell Functions Macrophages, which are the most abundant type of leukocytes in atherosclerosis, can influence the progression of disease in many ways134. In this section I have summarized previous in vitro studies of macrophage effects on SMCs. Macrophages have been shown to both stimulate and inhibit the proliferation of SMCs. Macrophage secretion of PDGF accelerates the growth of SMCs, which may contribute to the proliferation of SMCs in atherosclerosis135. Macrophages can also have an opposite effect as SMC cultures exposed to the secretory factors of macrophages had reduced proliferation136,137. SMCs can undergo changes in response to environmental stimuli and adapt various phenotypes47. The lipoprotein loaded SMCs adapt a foamy appearance, macrophage marker expression and higher phagocytic activity when cultured with macrophages in the presence of acetylated LDL127,138. In addition, macrophages induce SMCs to adapt a procalcific phenotype139,140. Macrophages were found to decrease SMC 1α(I) procollagen expression, which may contribute to the loss of the extracellular matrix in the developing lipid core136. Moreover, the abundance of MMP-1, which digests collagens, in vulnerable plaques is linked with the weakening of the fibrous cap; in vitro experiments identified that indirect contact between macrophages and SMCs increases MMP-1 synthesis in both cell types and is further amplified by direct contact130. The presence of both cells in in vivo plaque shoulders may explain the abundance of MMP-1 and the resulting vulnerability from the extracellular matrix remodelling by MMP-1130. 30 The direct contact between SMCs and macrophages induces SMC apoptosis112. Moreover, the monocytes differentiated via the M-CSF synthesized by SMCs and endothelial cells were found to cause SMC apoptotic cell death when the cells are in direct contact and in the presence of M-CSF141. Further studies by the same group identified that direct contact via the cell binding mediated by Mac-1 and Intercellular Adhesion Molecule-1 (ICAM-1) on macrophages and SMCs was required to activate the Fas apoptotic pathway, respectively142. These studies provide evidence that macrophages influence SMC proliferation and death, which may partially explain the loss of SMCs in progressing atherosclerosis136. There are limited studies on the effects of macrophages on SMCs’ propensity to form foam cells. Weinert et al. observed that direct contact was required for macrophages to induce SMC lipid accumulation in the presence of acetylated LDL138. In contrast, Wang et al. demonstrated that direct contact was not necessary for macrophages to increase SMC foam cells70. The indirect coculture of mouse SMCs with macrophages in a collagen gel and in the presence of aggregated LDL increased SMC foam cell formation compared with SMC monocultures in the presence or absence of aggregated LDL70. An elegant study by Vijayagopal et al. found that both direct and indirect coculture methods increase the cholesterol content of SMCs when cultured with macrophages and lipoprotein-proteoglycan complexes extracted from human lesions, but direct contact was required for a multifold enhancement of cholesterol content (- insert) compared with SMCs cultured physically separated from macrophages (+ insert) or in monoculture (SMC) (Figure 1.11)112. The duality of macrophage influences on SMC proliferation, phenotype, activity, and propensity to form foam cells may reflect the complexity of atherosclerosis and the various polarities that macrophages can adapt. 31 1.4.4 Role of Various Macrophage Polarities in Atherosclerosis The circulating monocytes recruited into the artery wall are exposed to cytokines such as M-CSF that promote their proliferation and differentiation into macrophages (M “naught”, M0)62. These macrophages can further adopt polarities between the spectrum of pro-inflammatory (M1) and anti-inflammatory (M2) akin to the T helper cell classification143. In vitro, M1 macrophages can be generated via exposure of M0 macrophages to interferon γ (IFNγ), TNFα and/or lipopolysaccharide (LPS). In vivo, approximately 40% of the macrophages are M1 macrophages and are located on the shoulders of unstable plaques from patients undergoing cardiovascular remodelling143,144. M2 macrophages, on the other hand, are generated in vitro in the presence of IL-4 and IL-13; in vivo, they make up 20% of the macrophages and are present in stable plaques and areas of neovascularization143,144. M2 macrophages can be further divided into M2a, M2b, and M2c macrophages145. M2a and M2b are induced via IL-4 and Il-13 exposure and are involved in immune and Th2 response regulation145. The M2c sub phenotype is activated by IL-Figure 1.11 SMC direct coculture with macrophages increases cholesterol accumulation. Macrophages induce cholesterol ester accumulation of SMCs in direct coculture (-insert) more effectively than in indirect culture (+ insert) as measured by the cholesteryl[14C] oleate formed in cells. Adapted from 112 with permission from Wolters Kluwer Health, Inc., copyright © 1996. 32 10, TGF-β, or glucocorticoid exposure and modulates immune system suppression and tissue remodeling145. A few studies have observed the effect of lipid loading on macrophage polarity, however there is a paucity of studies on how macrophage polarities influence their propensity to accumulate cholesterol or interact with SMCs. Studies do not usually refer to the macrophage phenotypes and simply indicate that the macrophages were exposed to activating molecules during culture. LPS-activated macrophages, which may be akin to M1 macrophages, had enhanced cholesteryl ester accumulation compared with unstimulated macrophages146. Direct coculture of SMCs with LPS-treated macrophages increased TNFα and MMP-9 expression in both cell types compared to their monocultures; in addition, the increase in SMC expression was higher when cocultured with LPS-treated macrophages than non-activated macrophage131. Further studies are required to explore this area of study, in addition to the phenotypes’ influences on other cells in atherosclerosis, which the present research study is working towards. 1.5 Coculture Techniques in Atherosclerosis In vitro coculture studies better resemble the arterial wall in vivo compared to monoculture studies. In this section, we will discuss the various coculture methods of SMCs and macrophages employed in atherosclerosis studies. 1.5.1 Direct Coculture The direct coculture method allows physical contact between the SMCs and macrophages (Figure 1.12a)147. This may mirror the later stages of human atherosclerosis where the macrophages have infiltrated deep in the intima to allow paracrine signalling with SMCs, and earlier stages of mouse atherosclerosis where macrophages and SMCs appear to be comingling within even a few weeks of high fat feeding (Figure 1.13m-o)117. Various studies in Section 1.4 33 have implemented this coculture method to observe the cell-cell communication between SMCs and macrophages. 1.5.2 Indirect Coculture The indirect coculture method prevents the physical contact between the SMCs and macrophages via the use of transwell inserts (Figure 1.12b.ii) or transferring the conditioned media of one cell type to the culture medium of the other cell type (Figure 1.12b.i)147. Transwell inserts have pores large enough to allow secretory factors but not the cells to pass through, while the conditioned media employs the exposure of one cell type to the medium collected from the other cell type117,147. This may reflect the earlier stages of human atherosclerosis where the macrophages are in the shallow intima and distant from SMCs; thus, limiting their Figure 1.12 SMC-macrophage coculture methods in atherosclerosis studies. Coculture studies can either be a) directly cultured or b) indirectly cocultured. Indirect coculture can be performed via the i) transfer of the condition medium of one cell type to another or via ii) employing a transwell insert that physically separates the cell types. Adapted from 117 with permission from Wolters Kluwer Health, Inc., copyright © 2019. 34 communication to endocrine signaling (Figure 1.13d-f)117. Some studies stated in the previous section (Section 1.4) utilized this coculture method to explore the cell-cell communication between SMCs and macrophages. Figure 1.13 Pathogenesis of early atherosclerosis. In DIT, the vessel is devoid of lipids (b) and macrophages (c). The lipids then infiltrate into the deep intima (e) while macrophages are merely on the superficial intima (f) in fatty infiltration. As the disease progresses macrophages (i, l, o, r) infiltrate deeper and co-localize with the accumulating lipoproteins (h, k, n, q). Stains used were elastic Van Gieson for elastic fibers, Sudan IV for lipids, and CD68 for macrophages. Diffuse intimal thickening (DIT); smooth muscle cells (SMC); pathologic intimal thickening (PIT); arrows the internal elastic lamina. Scale bar for 100 μm. Adapted from 54 with permission from Wolters Kluwer Health, Inc., copyright © 2007. 35 1.6 Questions; Hypotheses; and Specific Aims In summary, atherosclerosis is a global health problem that requires further research exploration. SMCs are the primary cells in atherosclerosis plaque, and our lab has observed that they contribute the largest fraction of cholesterol-overloaded foam cells in both human and murine atherosclerosis. However, their influences on other cells in atherosclerosis and their mechanism of foam cell formation are still unknown. Similarly, nonfasting lipid levels and the non-HDL-C fraction are known to predict risk for CVD better than fasting lipids and LDL-C, but most in vitro studies still employ LDL from fasting plasma as the main lipid-loading vehicle, creating a disconnect between the clinical observation and laboratory research. Lastly, macrophages are the predominant leukocytes in atherosclerosis, but their heterogeneity requires further attention, particularly on the role of different macrophage phenotypes (M0, M1, M2) on other cells and their propensity to form foam cells. Our proposal seeks to examine the role of the whole atherogenic lipoprotein fraction (non-HDL) from the nonfasting plasma in loading cultured cells with cholesterol, and macrophages of different phenotypes, on the formation and phenotype of SMC foam cells, as well as SMC influence on macrophage foam cell formation, in vitro. The questions, specific aims and hypotheses of this study are: Question 1: What is the key fraction of cholesterol-containing particles in blood that drives the formation of foam cells in macrophages and SMCs? Aim: To test the ability of the non-HDL cholesterol fraction in both fasting and nonfasting plasma to promote SMC and macrophage foam cell formation relative to LDL. Hypotheses: The non-HDL fraction will promote in vitro foam cell formation more readily than LDL. The nonfasting aggregated non-HDL fraction will provide the most foam cell formation. 36 Question 2: How do macrophages in different inflammatory states affect the production of cholesterol-overloaded SMCs, and vice versa, using different cell coculture methods? Aim: To determine the effect of co-incubation with macrophages in different states of polarization (M0 – naïve, M1– pro-inflammatory, M2 – anti-inflammatory) on the formation and phenotype of SMCs, and vice versa, in direct and indirect contact. Hypotheses: Foam cell formation and phenotype of SMCs will be critically impacted by co-incubation with macrophages of varying polarity. SMCs will influence the phenotypes and foam cell formation of macrophages. 37 CHAPTER 2: Materials and Methods 2.1 Materials The cell culture media used, Dulbecco’s modified Eagle’s medium (DMEM) and Roswell Park Memorial Institute (RPMI), were purchased from HyClone (Logan, Utah). Fetal bovine serum (FBS) and penicillin/streptomycin (PS) antibiotic were from Gibco. Lipoprotein deficient serum (LPDS) was prepared by density ultracentrifugation and dialysis of FBS in 150 mM NaCl, 50 mM Tris-HCl and 0.3M EDTA buffer at pH 7.4 to remove lipoproteins. CaCl2 was from Capitol Scientific (Austin, Texas). Smooth Muscle Actin (SMA) rabbit polyclonal antibody (ab66133) was purchased from Abcam (Cambridge, United Kingdom). Hoechst 33342 (H3570) was from Invitrogen. 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) 493/503 (D3922), Trypan blue solution, and the Amplex Red cholesterol assay kit (A12216) were from Thermo Fisher (Waltham, Massachusetts). Normal goat serum was from Life Technologies. Mouse BD Fc Block, Alexa Fluor 700 rat anti-mouse CD45 (560510) and the corresponding IgG control, Alexa Fluor 700 rat anti-mouse CD45 (557964), were from BD Biosciences. APC anti-human CD45 (368512) and the corresponding IgG control, APC Mouse IgG1, kappa Isotype Ctrl FC (400122) were from BioLegend. Transgelin/Smooth muscle 22 antibody (SM22) (60213-1-IgG) was from Proteintech (Rosemont, Illinois). Collagenase, elastase, soybean trypsin inhibitor, SIGMAFAST p-Nitrophenyl phosphate (pNPP), goat anti-rabbit peroxidase and gelatin were from Sigma Aldrich (St. Louis, MO). Bovine fatty acid free albumin (FAFA), Accutase and Fluoromount Aqueous Mounting medium were bought from Sigma Aldrich. Paraformaldehyde (PFA) was from VWR (Radnor, Pennsylvania). 38 The mass spectrometry reagents and chemicals: methanol, isopropanol, hexanes, and chloroform were high performance liquid chromatography (HPLC) grade and bought from Thermo Fisher; anhydrous ethanol was purchased from GreenField Specialty Alcohols Inc (Brampton, Ontario); potassium hydroxide was from EM Science (Darmstadt, Germany); and the cholesterol-d7 (deuterated cholesterol) was purchased from Avanti Polar Lipids, Inc. (Alabaster, Alabama). The Oil Red O staining kit was provided by the Histology department at The Centre for Heart Lung Innovation. Tissue cultured plates and flasks were bought from Corning. 2.2 Methods 2.2.1 Preparation of Lipoproteins Lipoproteins were isolated from the plasma donated by fasted or non-fasted normolipidemic donors via density gradient ultracentrifugation148. The cholesterol of the lipoproteins was measured via mass spectrometry (Section 2.2.14) and the protein quantified by Lowry protein assay (Section 2.2.16). The lipoproteins were vortexed for 60 seconds to make aggregated lipoproteins149 at 500 μg cholesterol/mL in the respective culture medium of the cells. 2.2.2 Cells Used in the Study Primary human aortic smooth muscle cells (HASMCs) (ATCC F13145) and RAW 264.7 monocytes/macrophages (ATCC TIB-71) were purchased from the American Type Culture Collection (Manassas, VA). Mouse aortic smooth muscle cells (MASMCs) were isolated from the abdominal aorta of 3-month-old C57BL/6J mice (Section 2.2.3). Human monocyte-derived macrophages (HMDMs) were isolated from the peripheral blood mononuclear cells (PBMCs) of healthy donors and differentiated into macrophages (Section 2.2.5). HASMCs, RAW 264.7 cells, and MASMCs were grown in DMEM with 10% FBS and 1% PS while HMDMs were cultured 39 in RPMI with 10% FBS and 1% PS. The SMCs and RAW264.7 cells were used for experiments between passages 7 and 14, and 6 and 16, respectively. 2.2.3 Isolation of Mouse Aortic Smooth Muscle Cells Our laboratory was using MASMCs previously isolated from BALB/c mice; however, this mouse model is atherosclerosis resistant. Culturing SMCs from C57Bl6/J mice was more appropriate for our studies since it is the background strain used for apoE-/- and LDLR-/- mice that are widely used in atherosclerosis studies150. C57Bl6/J aorta cells from adult mice were not commercially available and were isolated within the laboratory. 3-month-old C57BL6/J mice on chow diet were sacrificed and the abdominal aorta isolated. The aorta was opened longitudinally, and the epithelium was scraped off prior to mincing and digestion in a solution of 0.1% collagenase, 0.05% elastase, trypsin inhibitor (0.375 mg/mL), and FAFA (2 mg/mL) for an hour. The disassociated cells were washed with PBS and grown in DMEM with 20% FBS until confluent151. The cells were trypsinized and individual cells were seeded in DMEM with 10% FBS onto gelatin coated 96-well plates. Media was refreshed every couple of days and cell growth monitored. After a month, wells with confluent cells were split into two plates and grown until confluent. Cells in the second plate were tested on ELISA for SMC markers including SMA and SM22 and compared with rat smooth muscle cells and human skin fibroblasts as positive and negative controls, respectively (Section 2.2.4). Cells with high expression of both SMA and SM22 were considered SMCs. Corresponding cells in the first plate were expanded and frozen for future experiments. 2.2.4 ELISA of Mouse Aortic Cells to Identify Smooth Muscle Cells Confluent cells were washed with phosphate-buffered saline (PBS) with 1% FBS and 0.25 mM CaCl2 (PBSFC) and incubated with 200 μL of SM22(1:1000) or SMA (1:500) for 2 hours. 40 Cells were washed thrice with PBSFC and incubated with 200 μL of peroxidase (1:5000 for SM22 or 1:4000 for SMA) for an hour at 37℃. The wells were washed again three times prior to incubating with 200 μL of p-Nitrophenyl phosphate (pPNP), which reacts with phosphatases to form a water-soluble yellow substrate with an absorption at 405 nm, for 30 minutes at room temperature and in the dark according to company protocol152. The plate was read at 405 nm at room temperature with the Tecan Safire II plate reader. Cells with high absorbance readings for SM22 and SMA expression relative to the rat SMC positive controls were identified as SMCs. 2.2.5 Isolation and Differentiation of Human Peripheral Blood Mononuclear Cells Peripheral blood mononuclear cells (PBMCs) were isolated from the plasma of healthy donors and differentiated into macrophages (HMDMs)153. The collection followed ethical approval guidelines of the University of British Columbia Research Ethics Board. PBMCs were separated by density gradient centrifugation with Lymphoprep (StemCell Technologies, Vancouver, Canada). The cells were either frozen for future use or used fresh by seeding immediately in a 12-well plate. The cells were plated at 2.5  106 cells/mL in serum-free RPMI for 45 minutes. The medium was then changed to complete medium (RPMI medium containing 10% FBS and 1% PS) and the cells are at confluence. After 24 hours, the medium was refreshed with complete medium containing 10 ng/mL of recombinant human M-CSF (StemCell Technologies, Vancouver, Canada). After another 4 days, the medium was refreshed again with complete medium containing 10 ng/mL M-CSF. The next day, the cells were washed and used for experiments. 2.2.6 Cell Culture Frozen vials of the HASMCs, MASMCs, or RAW264.7 cells were thawed, and re-established in 75cm2 flasks with 10 mL of DMEM supplemented with 10% FBS and 1% PS and 41 incubated in humidified 95% air and 5% CO2 at 37℃ as a monolayer of cells. The cells were sub-cultured near confluence using Accutase, a proprietary cell detachment solution of proteolytic and collagenolytic enzymes154. 2.2.7 Cell Monoculture To assess results using single cell types, SMCs and macrophages were plated separately on 12-well plates. 1  105 HASMCs or 5  104 MASMCs per well were grown in DMEM with 10% FBS and 1% PS until approximately 70% confluent. RAW 264.7 cells were grown at 2.5  104 cells per well or PBMCs were also grown in monoculture from fresh or frozen cells according to Section 2.2.6. 2.2.8 Direct Coculture Differentiated human macrophages or RAW264.7 cell were washed once with PBS with 1 mg/mL FAFA and twice with PBS before seeding HASMCs or MASMCs directly on top. The coculture was grown 10% FBS overnight, incubated with 10% LPDS for 24 hours, and then treated with the various lipoprotein fraction on the following day according to Section 2.2.10. Monocultures of both SMCs and macrophages were run in parallel with the direct coculture for some experiments. 2.2.9 Indirect Coculture HASMCs or MASMCs grown to approximately 70% confluent in a transwell insert were washed once each with PBS with 1 mg/mL FAFA and PBS only and transferred to wells containing differentiated macrophages or RAW264.7 that were washed with PBS with 1 mg/mL FAFA and twice with PBS prior to the transfer. The cells were treated with 10% LPDS for 24 hours prior to incubation with the various lipoprotein fractions according to Section 2.2.10. 42 2.2.10 Lipid Loading of Smooth Muscle Cells and Macrophages Cells in monoculture or coculture were washed once each with PBS with 1 mg/mL FAFA and PBS alone. Cells were then incubated in 10% LPDS in their respective medium for 24 hours to upregulate LDL receptors. The cells were washed once each with PBS containing 1 mg/mL FAFA then with PBS alone and then incubated with ±95 mg/mL of aggregated fasting LDL, fasting non-HDL, nonfasting LDL, or nonfasting non-HDL and 2 mg/mL FAFA for 24 hours. The cells were washed once with PBS with 1 mg/mL FAFA and twice with PBS and analyzed immediately by flow cytometry (Section 2.2.12), or confocal imaging (Section 2.2.11), or frozen at -80℃ for future analysis with mass spectrometry (Section 2.2.14). All cell culture experiments were performed in humidified incubators with 5% CO2 at 37℃. 2.2.11 Confocal Imaging SMCs or macrophages were grown in 24-well plates with glass cover slips and treated as stated in Section 2.2.10 and then washed once with PBS with 1 mg/mL FAFA and twice with PBS alone prior to fixing with 3% PFA. The cells were washed again twice with PBS and incubated with 10 μg/mL of BODIPY 493/503 for 30 minutes. The cells were washed with PBS quickly first and for 5 minutes twice then stained with Hoechst 33342 (1:5000) for 10 minutes. The cells were washed twice with PBS and dried in the dark for 45 minutes before mounting and imaging with a Zeiss LSM 880 using a 63x/1.4 oil immersion objective. Fluorescence intensity analysis of the images was performed with the ZEN software. 2.2.12 Flow Cytometry Cells were detached by incubating with Accutase at 37℃ for 45 minutes for human cells and 15 minutes for mouse cells. The cells were washed by adding PBS with 1 mg/mL FAFA to the detached cells in the Accutase solution and pelleting the cells before staining with the 43 eFluor450 Fixable Viability dye (1:1000) for 20 minutes on ice. The cells were blocked: a mouse Fc Block (1:100) for 20 minutes was used on mouse cells while 3% goat serum for 30 minutes was used to block human cells. The cells were then incubated with CD45 and BODIPY 493/503 (1:2000) for 20 minutes on ice. The Alexa Fluor 700 rat anti-mouse CD45 (1:100) and the APC mouse anti-human CD45 (5μL) were used with their corresponding IgG controls. After staining, the cells were washed again and ran on a Beckman Coulter Gallios Flow Cytometer. Data files from the flow cytometer were analyzed with Kaluza and gates were placed according to appropriate controls. The blocking step, CD45 and IgG staining were only performed for direct coculture experiments. 2.2.13 Fluorescence-activated Cell Sorting (FACS) of Smooth Muscle Cells and Macrophage Foam Cells Cells sorted on a Beckman Coulter MoFlo Astrios EQ High Speed cell sorter were prepared similar to Section 2.2.12 with the addition of the steps or modifications in this section. The direct cocultured SMCs and macrophages were separated via CD45 staining since all macrophages are CD45+ and all SMCs are CD45-, and the BODIPY-high cells were isolated as foam cells. The separated cells were collected into 1 mL microcentrifuge tubes with 2% FAFA in PBS. The sorted samples were pelleted at 500x g for 5 minutes and the supernatant aspirated before storing at -80℃ for future mass spectrometry analysis (Section 2.2.14). The data files were analyzed with the Kaluza program and gates were placed accordingly. The sorting was performed by Basak Sahin, MSc. 2.2.14 LC/MS/MS Analysis of Total Cholesterol, Free Cholesterol, and Cholesteryl Esters The cells were thawed at room temperature and incubated with 1 mL of hexanes: isopropanol (3:2) for 20 minutes at room temperature155. The solvent extracts were transferred to 44 75x100 mm glass test tubes. Another 1 mL of the solvent was added to the wells and quickly transferred to the test tubes. The extracts were then evaporated under a stream of air flow and a heating block then 2 mL of ethanol and 100 μL of 5 μg/mL cholesterol-d7 were added and quickly vortexed. 1 mL of each solution was transferred to 75x100 mm screw capped test tubes for total cholesterol analysis. The cholesteryl esters in the test tubes for total cholesterol analysis were hydrolyzed by adding 1 mL of 0.625 N ethanoic potassium hydroxide (KOH), vortexing, and heating in an 80℃-water bath for 30 minutes. The solvent was then evaporated under a stream of air and a heating block. The Folch extraction method156 was followed by adding 1 mL of deionized water and 4 mL of chloroform: methanol (2:1) to the screw capped tubes for total cholesterol analysis. The screw capped test tubes were quickly vortexed, equilibrated at room temperate for 10 minutes, and had the aqueous layer aspirated. The organic layer was washed by repeating the vortex, equilibration, and aspiration steps. The solvent in both the screw capped test tubes for total cholesterol and in the glass test tubes for free cholesterol analysis were evaporated under a stream of air and a heating block. The tubes were then reconstituted with 300 μL of methanol, quickly vortexed, and 200 μL was transferred to 96-well plates for LC/MS/MS analysis. The cholesterol content in the samples was analyzed with a Shimadzu Prominence HPLC system coupled to an ABSCIEX 5500 triple quadrupole mass spectrometer. Cholesterol and cholesterol-d7, were detected similar to a previously reported method157. The total and free cholesterol were quantified by determining the peak area ratio of cholesterol to cholesterol-d7 and a calibration curve was generated from the extracted standards spiked with cholesterol-d7. The measured total cholesterol and free cholesterol concentrations were normalized to the total protein in cells measured by the Lowry assay (Section 2.2.16) and reported as nM cholesterol/mg protein, or the cell numbers measured by FACS (Section 2.2.13) and reported as 45 nM cholesterol/cell. The cholesteryl ester mass was quantified by subtracting the free cholesterol from total cholesterol. Lipoprotein total cholesterol was quantified similarly but were diluted prior to extraction with hexanes: isopropanol. The cholesterol analyses were performed by Dr. Joshua Dubland. 2.2.15 Amplex Red Cholesterol Assay The Amplex Red assay was employed as an alternative to mass spectrometry. The standard kit protocol from the company was followed148. 2.2.16 Lowry Protein Assay The total protein content of cells and lipoproteins were quantified using the Lowry protein assay as previously described158 using the Tecan Safire II plate reader. 2.2.17 Oil Red O Staining of Foam Cells The working Oil Red O solution was made fresh by mixing the stock solution of 0.25% to 0.5% Oil Red O in isopropyl alcohol with distilled water (3:2). The cells grown in coverslips were washed once each with PBS with 1% FAFA and PBS only then fixed with 1% PFA for 10 minutes. The cells were rinsed with distilled water and then in 60% isopropyl alcohol prior to staining with Oil Red O for 20 minutes, washed with 60% isopropyl alcohol to remove excess and non-specific staining and then washed with distilled water. The nuclei were then counterstained quickly in hematoxylin, rinsed with distilled water, dipped in the 1.5% Na2HCO3 base, and rinsed again with distilled water. The coverslips were then dried for at least 30 minutes and mounted on glass slides. 2.2.18 Statistical Analysis The mass spectrometry data were analyzed with the ABSCIEX Analyst software version 1.6. Results were analyzed using GraphPad Prism version 9.1.2 and are presented as the mean ± 46 standard error of mean (SEM). The normality of the data sets was determined by the Shapiro-Wilk normality test (alpha=0.05) and by visual inspection of the Q-Q plots. Normally distributed data was analyzed using parametric tests, either the one-way ANOVA with the Bonferroni post hoc comparison or the paired t test. Data not normally distributed was inspected using non-parametric tests: the Friedman test with the Dunn’s multiple comparison, Wilcoxon matched pairs sign rank test, or multiple Wilcoxon test with Bonferroni-Dunn comparison. A p-value <0.05 was considered significant in all cases. 47 CHAPTER 3: Results Smooth muscle cells (SMCs) and macrophages accumulate cholesterol in the presence of modified lipoproteins and transform into foam cells in the artery intima123. In vitro studies of cholesterol accumulation by these cells have routinely been performed using the fasting plasma LDL (i.e., native, oxidized or aggregated) fraction, since fasting has been the routine state for determining lipid levels clinically and because LDL cholesterol (LDL-C) has been the traditional key plasma lipid fraction of interest in clinical trials of cholesterol-lowering medications. Given the recent studies indicating the increased ability of non-HDL cholesterol (non-HDL-C) to predict risk for coronary heart disease, stroke and mortality compared to LDL-C30,84,87, and also the increased ability of nonfasting lipids to predict risk for cardiovascular events and mortality better than fasting lipid levels78,93-95, we compared the effects of isolated LDL-C and non-HDL-C fractions from either fasting or nonfasting plasma on lipid loading of cultured SMCs and macrophages. Loading cells in vitro with lipoprotein-derived cholesterol is commonly performed based on the protein content of lipoproteins. Since we are comparing the ability of various lipoproteins with different cholesterol levels to deliver cholesterol to cultured cells, we opted instead to incubate cells with lipoproteins normalized to their cholesterol content, as has been previously performed112. The lipoproteins were added in the aggregated form as it is the most common form of lipoproteins in the artery wall115. 3.1 Quantifying the Cholesterol Content of Human Smooth Muscle Cells Loaded with Nonfasting LDL and non-HDL using Mass Spectrometry Mass spectrometry is the gold standard for quantifying cellular cholesterol content and has been employed by previous laboratory members159. During these studies, however, we 48 initially had limited access to and expertise in mass spectrometry. We tested the Amplex Red cholesterol assay, which is one of many commercial cholesterol assay kits available, as an alternative. This alternative quantification was not comparable to mass spectrometry during our tests, and we made the decision to quantify lipoprotein cholesterol using mass spectrometry. We speculated that the improved CVD risk prediction of nonfasting plasma in vivo would be manifested at the cellular level as higher cellular cholesterol accumulation in cells incubated with nonfasting plasma lipoproteins compared with cells incubated with fasting plasma lipoproteins. Cholesterol quantification of monocultured SMCs, however, demonstrated a trend of higher total cholesterol and cholesteryl ester content following loading with aggregated LDL (ag LDL) and aggregated non-HDL (ag non-HDL) derived from fasting plasma compared with their respective nonfasting counterparts. There were no significant differences between the comparisons using *p<0.05 (Figure 3.1) Based on studies indicating the superiority of the non-HDL cholesterol fraction, which in addition to LDL, includes all the atherogenic cholesterol, as a predictor and target for lowering CVD risk than LDL30,84,87, we hypothesized that in vitro loading of cells with the non-HDL fraction would result in higher cellular cholesterol accumulation compared with LDL. Our in vitro culture demonstrated a trend of higher total cholesterol and cholesteryl ester accumulation from ag non-HDL than ag LDL in monocultured SMCs. However, there were no significant differences between the comparisons using *p<0.05 (Figure 3.1). Overall, mass spectrometry analysis of the monocultured SMCs’ cholesterol content indicated that the highest total cholesterol accumulation is from the fasting ag non-HDL (p<0.001), followed by similar accumulation from the fasting ag LDL and nonfasting ag non-HDL (p<0.05), and then the nonfasting ag LDL (p>0.05). The trend is similar for SMC 49 cholesteryl ester accumulation but only the fasting ag non-HDL is significantly higher than the control (p<0.01) (Figure 3.1). 3.2 Measuring the Lipid Human Smooth Muscle Cell Monocultures with Fasting or Nonfasting LDL and non-HDL using BODIPY We employed flow cytometry as another method to observe SMC lipid accumulation from various lipoprotein loading vehicles and confirm the mass spectrometry trends observed. BODIPY 493/503 is a fluorescent neutral lipid dye that our laboratory utilizes for estimating intracellular lipid accumulation. BODIPY 493/503 can stain free fatty acids, triglycerides, unesterified cholesterol, and esterified cholesterol160. Our early experiments estimated cholesterol accumulation in cells by BODIPY 493/503 staining and imaging with confocal Figure 3.1 Cholesterol quantification of monocultured human SMCs via mass spectrometry. Human SMCs were treated as described in Sections 2.2.7 and 2.2.10. The total cholesterol (left) (n=5) and cholesteryl ester (right) (n=4) content of SMCs incubated with 95 μg/mL aggregated LDL (ag LDL) or aggregated non-HDL (ag non-HDL) from either fasting or nonfasting plasma were measured using mass spectrometry (Section 2.2.14). Results are presented as mean ± SEM. Solid lines indicate comparisons with non-loaded control; dashed lines compare fasting versus nonfasting LDL or non-HDL; dotted lines compare the ag LDL and ag non-HDL from either fasted or non-fasted plasma. ns=p>0.05; *p<0.05; **p<0.01, ***p<0.001. 50 microscopy, but we did not find the results to be reproducible due to high background staining and inconsistent cellular cholesterol content between replicates. Our laboratory has previously utilized BODIPY 493/503 for flow cytometry quantification of foam cells in both human and mouse tissues. Hence, we moved forward with flow cytometry, and our laboratory’s protocols were adopted for cell culture using a panel of a viability dye to separate live and dead cells; CD45 to distinguish macrophages (CD45+) from SMCs (CD45-); and BODIPY 493/503 to quantify the cellular lipids as previously performed70. The non-loaded control was used to position the gate for BODIPY-high cells. Foam cell formation, or BODIPY-high cell count, was expressed as a per cent of BODIPY-high cells out of total cell number per cell type. The flow cytometry analyses, together with the mass spectrometry measurements make up the data presented in this chapter. Higher SMC cholesterol accumulation with fasting plasma compared to nonfasting plasma lipoproteins measured by mass spectrometry (Figure 3.1) suggested we should observe more BODIPY-high cells following fasting plasma lipoprotein loading compared with nonfasting plasma lipoprotein from flow cytometry analysis of similarly cultured and treated cells. Our observations, however, showed a trend of more BODIPY-high SMCs from incubation with the nonfasting than fasting plasma lipoproteins. A significant difference (*p<0.05) between the fasting and nonfasting ag LDL fractions was observed but there was no significant difference (p>0.05) between the fasting and nonfasting ag non-HDL fractions (Figure 3.2B). Similar to the trend in comparing the ag LDL and ag non-HDL observed by mass spectrometry, the fasting plasma samples showed more BODIPY-high SMCs with the ag non-HDL fraction than the ag LDL fraction. The trend is reversed in the nonfasting lipoproteins as 51 there were more BODIPY-high cells with the ag LDL fraction. There were no significant differences between the comparisons using *p<0.05 ((Figure 3.2B). Figure 3.2 Flow cytometry estimation of BODIPY-high monocultured human SMCs. A) Scatter plot of various lipoprotein loading of SMC monocultures showing the threshold for BODIPY-high SMCs set from the control. B) Comparison of BODIPY-high populations of human SMC monocultures. For both A and B, human SMCs were treated with 95 μg/mL of their respective aggregated lipoprotein fractions for 24 hours and quantified for BODIPY-high cells via flow cytometry as described in Section 2.2.12. The scatter plot is representative of the trends. BODIPY-high estimations include six replicates and are presented as mean ± SEM. Solid lines indicate comparisons with control; dashed lines compare fasting versus nonfasting LDL or non-HDL; dotted lines compare the LDL and non-HDL from either fasted or non-fasted plasma. ns=p>0.05; *p<0.05; **p<0.01, ***p<0.001. 52 Overall, the flow cytometry analysis of monocultured SMCs point to the nonfasting ag LDL as the promoter of the most SMC foam cells. The trend is nonfasting ag LDL (***p<0.001) > nonfasting ag non-HDL (**p<0.01) > fasting ag non-HDL (*p<0.05) > fasting ag LDL (p>0.05) (Figure 3.2B). This trend is opposite from the mass spectrometry results as loading with the fasting plasma was higher than the nonfasting counterpart, the nonfasting ag LDL promoted the least cholesterol accumulation, and the fasting ag non-HDL promoted the most cholesterol accumulation (Figure 3.1). The BODIPY-low and BODIPY-high cell clusters of the monocultured SMCs analyzed with flow cytometry are not clearly separated, thus, the gating may consequently influence the results (Figure 3.2A). Where there is disagreement between the results from mass spectrometry and flow cytometry, mass spectrometry may more closely show the actual trends since it is a more robust technique. In addition, the variation in trends between the methods may be due to the inability of monoculture studies to reflect the in vivo phenomenon, which may be clarified by coculture studies. 3.3 Loading of Human Macrophage Monocultures with LDL or Non-HDL from Fasting and Nonfasting Plasma Macrophages are phagocytic cells that exhibit higher expression of scavenger receptors than SMCs123 and accumulate at least three times as much cholesterol (Figure 3.1 compared with (Figure 3.3). Comparing the total cholesterol and cholesteryl ester content of macrophages cultured with aggregated LDL (ag LDL) and aggregated non-HDL (ag non-HDL) from both fasting and nonfasting plasma shows a general trend of higher cholesterol accumulation with the fasting 53 plasma lipoproteins, although neither difference was significant, compared with the nonfasting plasma lipoproteins (Figure 3.3). A general trend of macrophages accumulating more total cholesterol and cholesteryl ester when incubated with the ag non-HDL compared to ag LDL fraction from either the fasting or nonfasting plasma was observed, although there were no significant differences between the comparisons using *p<0.05 (Figure 3.3). Comparing all the loading parameters to a non-loaded control demonstrated that macrophages cultured with the nonfasting ag non-HDL fraction promoted the most cholesterol accumulation (*p<0.5). The fasting ag LDL, fasting ag non-HDL, and nonfasting ag LDL also promoted cholesterol accumulation, however, none were significant (Figure 3.3). The 050010001500Cholesteryl Esters (nmol chol/mg protein)ControlFasting ag LDL Non-fastingag LDLNon-fasting ag non-HDLFasting ag non-HDLFigure 3.3 Cholesterol quantification of monocultured human macrophages via mass spectrometry. Human macrophages were treated with 95 μg/mL of either aggregated LDL (ag LDL) or aggregated non-HDL (ag non-HDL) from fasting or nonfasting plasma for 24 hours and their total cholesterol mass (left) (n=3) measured with mass spectrometry and cholesteryl ester content calculated (right) (n=2) (Section 2.2.14). Results are presented as mean ± SEM. Solid lines indicate comparisons with control; dashed lines compare fasting versus nonfasting LDL or non-HDL; dotted lines compare the LDL and non-HDL from either fasted or non-fasted plasma. ns=p>0.05; *p<0.05. 54 lipoprotein ranking for increased macrophage cholesterol accumulation is fasting ag non-HDL > nonfasting ag non-HDL ~ fasting ag LDL > nonfasting ag LDL. Although the nonfasting ag non-HDL was only the third highest, the other samples had larger standard error of means, while the nonfasting ag non-HDL had tighter data, which may have contributed to the significant difference. More replicates are required to observe whether these trends are consistent and reveal statistically significant differences. Flow cytometry analysis similar to the SMC experiments may provide insights on the results. 3.4 Optimizing the Detachment of Primary Human Macrophages for Cell Culture Studies The human macrophages used in the current studies were isolated from the peripheral blood mononuclear cells of healthy donors and are the same monocytes that infiltrate the intima and differentiate into macrophages. In vitro, these macrophages differentiated from fresh monocytes are challenging to work with due to their sensitivity and strong adhesion to cell culture surfaces; thus, detaching the cells with a high recovery and preserved viability required considerable time spent on optimizing methods. Our laboratory has previously used trypsin as a general cell detachment solution, which did not work well for detaching macrophages. We tested other detaching agents including Accutase, cold 5 mM EDTA, and cold 2 mM EDTA161. Among these, only Accutase and trypsin detached most of the macrophages. The viability of cells using trypan blue was estimated from the detachment of cells in monoculture, direct coculture, and indirect coculture experiments using Accutase and trypsin. Accutase provided a higher viability (>90%) and more cells detached from the transwells used in indirect coculture, which was the most challenging surface to remove cells from. In addition, Accutase provided better cell viability during longer incubations required for detaching human macrophages. With these results, we 55 optimized the parameters for mouse and human cell detachment at 10 and 45 minutes with Accutase at 37℃, respectively. We then analyzed the successfully detached cells with flow cytometry as described in Section 2.2.12. The macrophages’ lipid accumulation was lower than the expected values based on previous experiments and their phagocytic nature, hence the data was not included. Further troubleshooting identified reagent and methodology issues such as inadequate lipoprotein aggregation, non-specific binding of some dyes utilized, and insufficient macrophage maturation due to reagent issues but there was insufficient time to include experiments for the present report. Future analyses with the optimized method will be required to quantify macrophage lipid accumulation. 3.5 Lipoprotein Loading of Smooth Muscle Cells in Direct Contact with Macrophages with Fasting and Nonfasting LDL and Non-HDL Cocultures are a better model of the in vivo environment and are more patho-physiologically relevant than monocultures. Based on this, we believe that the results obtained using mass spectrometry and flow cytometry estimates of cellular cholesterol accumulation of SMCs and macrophages from coculture studies are more relevant than results from monocultures. We therefore decided to move ahead with the planned SMC-macrophage coculture studies prior to reaching a solid conclusion from the monocultures. SMCs and macrophages have been shown to influence each other’s behaviour and fate in atherosclerosis. A few studies have observed the influence of SMCs on macrophage foam cell formation132,133 and of macrophages on SMCs70,112,138. The novelty of the current study is employing more physiologically relevant lipoproteins as the lipid loading vehicles in addition to comparing the various lipoprotein fractions’ capacity to induce lipid accumulation in the cultured 56 cells. The present study also plans to perform both direct and indirect coculture methods using SMCs and macrophages; monocultures of SMCs and macrophages were run in parallel to direct cocultures of SMCs and macrophages as controls. The cells were analyzed with flow cytometry, fluorescence activated cell sorting (FACS), and mass spectrometry. However, only the flow cytometry analysis was included in the analysis as the FACS data had insufficient sorted cells in some samples, and the resulting cholesterol quantification from mass spectrometry was therefore incomplete. The indirect coculture method utilizing transwell inserts has only been performed during optimizing and only with a few lipoprotein fractions for preliminary studies to date (data not shown). SMCs in direct coculture with macrophages had a trend towards more BODIPY-high SMCs formed with the nonfasting plasma lipoproteins, although not significantly different than with fasting plasma lipoproteins (Figure 3.4). This trend was similar to monocultured SMCs (Figure 3.2B). Comparing the number of BODIPY-high SMCs grown in direct coculture showed a trend indicating incubation with ag non-HDL resulted in more cholesterol loading than with ag LDL from either fasted plasma (Figure 3.4). There were no significant differences between the comparisons using *p<0.05 and based on the current number of replicates performed. Overall, a significantly higher increase in BODIPY-high SMCs was observed in the presence of nonfasting lipoproteins. The trend is nonfasting ag non-HDL (***p<0.001) > nonfasting ag LDL (*p<0.05) > fasting ag non-HDL (p>0.05) > fasting ag LDL (p>0.05) (Figure 3.4). Longer incubation with lipoproteins may provide a significant degree of difference in the results of the various lipoprotein loading conditions. 57 Interestingly, comparing the number of BODIPY-high cells between the SMC monocultures and direct coculture with macrophages showed that the number of BODIPY-high cells from the fasting ag LDL and fasting ag non-HDL did not change and only the nonfasting lipoproteins changed the BODIPY-high population: the nonfasting ag LDL decreased while the nonfasting ag non-HDL fraction increased with coculture. There were no significant differences between the comparisons using *p<0.05 (Figure 3.5). Figure 3.4 Flow cytometry estimation of BODIPY-high human SMCs in direct contact with macrophages. Human SMCs were cultured in direct contact with macrophages (Section 2.2.8) and treated with 95 μg/mL of either aggregated (ag) LDL or ag non-HDL from fasting or nonfasting plasma for 24 hours (Section 2.2.10). Results are from five replicates and presented as mean ± SEM. Solid lines indicate comparisons with control; dashed lines compare fasting versus nonfasting LDL or non-HDL; dotted lines compare the LDL and non-HDL from either fasted or non-fasted plasma. ns=p>0.05; *p<0.05; ***p<0.001. 58 Other aims of this study are to coculture SMCs and macrophage indirectly via a transwell insert, to use other macrophage polarities (pro-inflammatory and anti-inflammatory) in both direct and indirect coculture experiments, and to observe their influences on SMC foam cell formation and vice versa. We also aimed to perform these experiments utilizing murine cell lines, though the emphasis has been studies using human cells. However, these were not included in the current report as we are currently in the process of generating the polarized cells in vitro. Although the indirect cocultures have been optimized, time constraints from experimental delays and laboratory closure due to the pandemic prevented the performance of the experiments in time for this report. 0102030% BODIPY-high cells MonocultureDirect Co-cultureControlFasting ag LDL Non-fastingag LDLNon-fasting ag non-HDLFasting ag non-HDLFigure 3.5 Flow cytometry comparison of BODIPY-high human SMCs between monoculture and direct contact with macrophages. Human SMCs were grown in monocultures (circles) (Section 2.2.7) or cultured in direct contact with macrophages (squares) (Section 2.2.8) and treated with 95 μg/mL of either aggregated (ag) LDL or ag non-HDL from fasting or nonfasting plasma for 24 hours (Section 2.2.10). Results are from five replicates and presented as mean ± SEM. 59 CHAPTER 4: Discussion 4.1 Discussion Our laboratory previously reported that SMCs are the major contributors of foam cells in both murine70 and human71 atherosclerosis. The present study aims to expand these findings by exploring the influence of various lipoprotein fractions and macrophages on the propensity of SMCs to form foam cells. The key preliminary findings of this study that require additional testing to confirm statistical relevance are that: 1) the nonfasting ag non-HDL fraction promotes the most cholesterol accumulation in human macrophages, 2) human SMCs accumulate the most cholesterol in the presence of fasting ag non-HDL but form the most foam cells with the nonfasting ag LDL in monoculture and with nonfasting ag non-HDL coculture when cocultured with macrophages. The nonfasting plasma89,90 and the non-HDL fraction84,85,162,163 have been indicated as better risk predictors and therapeutic targets in in vivo studies of ischemic cardiovascular diseases (CVD) than the fasting plasma and the LDL fraction, respectively. In vitro studies with these lipoprotein fractions have been scarce and we believe that the present study is the first of its kind in contrasting the fasting and nonfasting lipoproteins, and the LDL and non-HDL fractions in cell culture studies of foam cell formation. We hypothesized that the increased CVD risk predicted by the nonfasting and the non-HDL fractions in vivo may be evident in vitro at the cellular level as a higher capacity to induce cellular cholesterol accumulation in both SMCs and macrophages. We observed monocultured SMC cholesterol accumulation via mass spectrometry and flow cytometry. Quantifying cellular cholesterol content by mass spectrometry demonstrated a trend that cholesterol accumulation of monocultured SMCs is increased more with fasting 60 plasma lipoproteins than nonfasting, with ag non-HDL loading more than ag LDL loading, although both comparisons were not of significant difference (Figure 3.1). The highest cholesterol accumulation was in cells incubated with the fasting ag non-HDL fraction (***p<0.001 for total cholesterol and **p<0.01 for cholesteryl ester) compared with the non-loaded control (Figure 3.1). The flow cytometry analysis indicated a trend of more BODIPY-high cells, or foam cell, formation with the nonfasting plasma lipoproteins than the fasting plasma lipoproteins; it is inconclusive so far whether the trend points to ag non-HDL or the ag LDL as the better nonfasting lipid loading vehicle (Figure 3.2B). The nonfasting ag LDL loading promoted the most BODIPY-high cells compared to the non-loaded control (p<0.001) (Figure 3.2B). In these studies, a “foam cell” has a higher level of cholesterol accumulation measured either via mass spectrometry or flow cytometry of BODIPY-stained cells relative to a cell incubated in the absence of lipoproteins. The difference in trends between mass spectrometry and flow cytometry may partly be due to the definition of a foam cell in the current study. The degree of foaminess or lipid accumulation in cells via flow cytometry varies with the amount of lipid content in the control cells not exposed to lipoproteins. Cells with higher lipid content or foaminess compared to the baseline control are considered the BODIPY-high population. However, the BODIPY-low and BODIPY-high populations do not form separate clusters and are not easily distinguishable (Figure 3.2A). On the other hand, the mass spectrometry analysis in the current study quantifies the cholesterol in the entire isolated cell samples and the cholesterol content of the cells exposed to lipoproteins are compared to a control cell group that was not exposed to lipoproteins. Our laboratory has observed a clearer cluster separation of foam cells from explanted human and murine aortas. However, the 24-hour lipoprotein exposure in vitro compared to at least weeks in murine models and years in human aortas may be an insufficient 61 incubation period to clearly distinguish foam cells from non-foam cells as SMCs by nature are difficult to convert into foam cells. IL-1β can increase cholesteryl ester accumulation via increased LDL164 and decrease VLDL uptake via the VLDL receptor pathway165 while IFN-γ increases CD36 expression166. The pathways enhanced or diminished by the cytokines present likely influence individual cells in culture to have a broad spectrum of ability to accumulate cholesterol and thereby form foam cells in this time frame in vitro. The direct coculture with naïve or M0 macrophages pointed to the nonfasting ag non-HDL as the promoter of the most BODIPY-high SMCs (***p<0.001) followed by the nonfasting ag LDL (*p<0.05) (Figure 3.4). The rest of the comparisons were not of significant difference but contrasting between the lipoproteins of the fasting and nonfasting states showed a trend of increased BODIPY-high populations with the nonfasting plasma lipoproteins (Figure 3.4). The comparison between the ag LDL and ag non-HDL fractions indicated a trend of more BODIPY-high cells with ag non-HDL fraction (Figure 3.4). The agreement of the cocultured cells, but not the monocultured cells, with our original hypothesis may indicate that the complexity of foam cell formation in vivo is better reflected in vitro through coculture studies, as the interactions between SMCs and macrophages are likely a major contributing factor to this phenomenon in vivo. Direct coculture with macrophages was shown to increase SMC cholesteryl ester content when loaded with a proteoglycan-lipoprotein complex112. However, our results show that only the nonfasting ag non-HDL fraction increase SMC BODIPY-high cells when in direct coculture but even then, the difference is not of significance (Figure 3.5). This may be due to the less robust flow cytometry methods, or the short duration of incubations in these experiments, and 62 may be clarified upon performing the mass spectrometry analysis of 24-hour samples and/or lengthening the period of incubation with the various lipoproteins. The monocultured macrophages analyzed with mass spectrometry accumulate the most cholesterol in the presence of the nonfasting ag non-HDL fraction (*p<0.05) (Figure 3.3), similar to the flow cytometry analysis of cocultured SMCs. Macrophages are phagocytic cells that can take up modified lipoproteins more efficiently than SMCs, which is why macrophages accumulate much more cholesterol (at least 3 times more) than SMCs under the same incubation conditions in the current study and may also contribute to why macrophages support our hypothesis even in monocultures. Although the results show much higher cholesterol accumulation upon loading, the differences are not significant (except for the nonfasting ag non-HDL) (Figure 3.3). This may be due to the small number of replicates and the wide distribution of the replicate values. Performing more replicates with smaller deviations may exhibit the expected significant differences. Both the cocultured SMCs and monocultured macrophages agree with our hypothesis that the nonfasting ag non-HDL fraction promotes the most foam cell formation. This may be consistent with the multiple in vivo interactions that are also present in the in vitro culture. The enhanced retention of remnant lipoproteins such as VLDL, IDL, and chylomicron remnants in the arterial wall98,167 may also occur in vitro. As discussed in Section 1.3.4, the remnant lipoproteins are selectively retained via their stronger interactions with proteoglycans that correlates with their apoC-III content103 and is enhanced by the presence of apoE104. The extracellular matrix produced by the cells, especially the proteoglycan matrix produced by SMCs cultured in vitro168, may promote the same proteoglycan-lipoprotein interactions that entrap the lipoprotein loading particles in vivo. In addition, the remnant lipoproteins are more concentrated 63 in nonfasting plasma as the remnant cholesterol increases from 30% to 50% of the entire atherogenic fraction postprandially80,169. The presence of the lipoprotein-retaining matrix combined with the higher concentration of lipoproteins present an optimal condition for lipoprotein retention64,98 and its consequent modification and uptake by cells. Both macrophages and SMCs express VLDL receptors and can take up remnant lipoproteins via this pathway108,123,170. LDL requires further modifications (e.g., oxidation) to be recognized by the same receptors that readily take up the remnant lipoproteins such as CD36 and SR-BI123. In addition, both macrophages and SMCs also take up lipoproteins via scavenger-receptor independent pathways (e.g., phagocytosis, micropinocytosis, and macropinocytosis)108, which may lead to higher lipoprotein uptake and consequently higher cholesterol accumulation with high lipoprotein concentrations. The network of lipoprotein uptake pathways in vivo may not be fully captured in the current in vitro studies due to the absence of other mediators from other cell types or phenotypes but may be adequate to demonstrate some of the “big picture” observations. The increased atherogenicity of the nonfasting fraction compared with its fasting counterpart was observable likely due to the increased concentration and recognition of the remnant lipoprotein fraction from the nonfasting plasma. This entire atherogenic fraction is accounted for in the non-HDL fraction but missed in the LDL fraction alone. Therefore, the nonfasting non-HDL fraction captures the entire atherogenic fraction and promotes the most cellular cholesterol accumulation compared with the fasting LDL, fasting non-HDL, and nonfasting LDL fractions. 4.2 Future directions Some of the flow cytometry and mass spectrometry analysis presented in the study require further experimental replicates to confirm the accuracy and consistency of the findings. The laboratory will continue further experiments required to answer the research questions such as 64 exploring the effects of the pro-inflammatory and anti-inflammatory macrophages on SMCs foam cell formation and vice versa, and the indirect coculture studies using transwell inserts. We are also considering lengthening the lipoprotein incubation period up to 72 hours as previously performed70 to observe whether this would allow SMC foam cells to accumulate enough cholesterol to form a more discreet cluster of foam cells as assessed by flow cytometry. During this study, considerable time was spent attempting to optimize cell culture and coculture methods. Coculture studies of macrophages and SMCs usually seed the SMCs on the bottom well and the macrophages directly on top for direct coculture or with the macrophages on the transwell insert for indirect coculture. We initially followed this plan. However, due to difficulties in detaching macrophages from the transwell membrane and our plan to polarize macrophage, we adjusted this plan to seed the macrophages on the lower plastic dish surface and SMCs on the transwell membrane for indirect coculture experiments or seeded on top of macrophages for the direct coculture studies to avoid detaching the macrophages during the polarization stage. Our initial tests indicated that this provides a good recovery and viability of both cell types, and we plan on performing future coculture experiments this way. We initially estimated foam cell formation with BODIPY staining via confocal microscopy, but the method was too subjective for our analysis and the differences between the loading conditions were difficult to interpret. Switching to flow cytometry partially addressed these issues, despite the varying gating positions used to define foam cells. Some replicates were excluded as we experienced fluctuations in BODIPY readings, which we suspect is due to potential instability of the BODIPY dye. We have sought other methods for foam cell quantification such as testing other fluorescent lipid dyes (e.g., LipidTOX) as used previously171. Moving forward, we will be performing the flow cytometry, FACS, and mass spectrometry of 65 the cells stained with LipidTOX instead. We will also continue with the planned sorting of foam cells and RNA analysis on the cells cultured with various lipoproteins, coculture conditions, and macrophage polarities. We predict this will identify important cytokines and receptors directing and influencing the foam cell formation and hence guide us in designing future experiments for elucidating the mechanism of foam cell formation in SMCs. 4.3 Concluding Remarks The present study findings indicate that the in vivo observations of non-HDL and the nonfasting plasma as better indicators of risk for CVD may be reflected at the cellular level in vitro via the propensity of SMCs and macrophages, the main cellular players in atherosclerotic lesion cholesterol metabolism, to form foam cells. In vitro experiments on foam cell in atherosclerosis studies may adapt the non-HDL fraction to better reflect the capacity of the entire atherogenic lipoprotein fraction to promote foam cell formation. Coculture studies, which have an additional layer of complexity, may better reflect the in vivo conditions compared with monocultures, as observed by the increase in foam cells of SMCs in cocultures with macrophages and incubated with the nonfasting non-HDL fraction compared with its monoculture counterpart. A better grasp on the influence of various factors on SMC foam cell formation in atherosclerosis can provide novel insights and targets to reduce the burden of atherosclerosis globally. 66 References 1. O'Donnell CJ, Elosua R. Cardiovascular risk factors. insights from framingham heart study. Revista Española de Cardiología (English Edition). 2008;61(3):299-310. 2. Naghavi M, Abajobir AA, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, et al. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: A systematic analysis for the global burden of disease study 2016. The Lancet. 2017;390(10100):1151-1210. 3. World Health Organization. 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Experimental Medicine
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Francis, Gordon A.
The influence of different plasma lipoprotein fractions and macrophages on smooth muscle cell foam cell formation
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