VASOPROTECTIVE FUNCTIONS OF HIGH-DENSITY LIPOPROTEINS ON THE CEREBROVASCULATURE AND THEIR RELEVANCE FOR ALZHEIMER’S DISEASE by EMILY BETH BUTTON B.Sc., The University of Waterloo, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Pathology and Laboratory Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2020 © Emily Beth Button, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Vasoprotective Functions of High-Density Lipoproteins on the Cerebrovasculature and their Relevance for Alzheimer's Disease submitted by Emily Button in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Pathology and Laboratory Medicine Examining Committee: Dr. Cheryl Wellington, Professor, Department of Pathology and Laboratory Medicine, UBC Supervisor Dr. Haakon Nygaard, Assistant Professor, Division of Neurology, UBC Supervisory Committee Member Dr. Lara Boyd, Professor, Department of Physical Therapy, UBC University Examiner Dr. Thalia Field, Associate Professor, Division of Neurology, UBC University Examiner Additional Supervisory Committee Members: Dr. Mari DeMarco, Clinical Associate Professor, Department of Pathology and Laboratory Medicine, UBC Supervisory Committee Member Dr. Shernaz Bamji, Professor, Department of Cellular & Physiological Sciences, School of Biomedical Engineering, UBC Supervisory Committee Member iii Abstract One in eleven Canadians over the age of 65 suffers from dementia, the most common form being Alzheimer’s disease (AD). AD has traditionally been characterized by the presence of amyloid beta (Aβ) plaques and neurofibrillary tangles and in more recent years also by neuroinflammation and cerebrovascular dysfunction. We hypothesize that circulating high-density lipoproteins (HDL) may protect against such cerebrovascular dysfunction and inflammation to reduce AD risk. HDL in healthy people is already known to have several vasoprotective functions on cells in peripheral arteries and higher levels of HDL cholesterol (HDL-C) or apolipoprotein A-I (apoA-I), its primary protein component, in blood are associated with reduced AD risk. Furthermore, studies in AD transgenic mouse models suggest that HDL protects against vascular Aβ deposition, memory deficits, and neuroinflammation. This thesis used mouse models and in vitro human cellular models to extend the current knowledge on the protective effects of HDL against AD. First, transgenic AD mice were employed to further investigate the role of HDL on cerebrovascular-specific pathologies in AD using a genetic approach and a pharmacological approach. Genetic loss of apoA-I in AD transgenic mice exacerbated vascular Aβ deposition, activation of cerebrovascular endothelial cell and vessel-associated astrocytes, global amyloid burden, neuroinflammation, and cognitive deficits. Pharmacological modulation of cholesterol metabolism exclusively outside of the brain resulted in reduced neuroinflammation, activation of cerebrovascular endothelial cells, and cognitive deficits. Next, 2-dimensional (2D) in vitro cell cultures of human brain-derived endothelial cells (EC) and 3D bioengineered human arteries were used to show that several of the known vasoprotective functions of HDL extend to brain cells and that HDL has novel protective functions against Aβ vascular deposition and Aβ-induced vascular inflammation in bioengineered tissues. Finally, efforts were made to develop high-throughput assays of these novel HDL functions in order to evaluate whether these functions are lost in people with AD or AD risk factors. iv In summary, this thesis suggests novel pathways by which circulating HDL can promote cerebrovascular health using in vivo and in vitro models and suggests that improving the levels of functional HDL may be a valuable therapeutic or preventative strategy for AD. v Lay Summary Alzheimer’s disease (AD) is a devastating illness affecting half a million Canadians. People with AD have clumps of amyloid beta proteins in their brain and most have problems with blood vessel function in the brain. This thesis aimed to learn how high-density lipoproteins (HDL), particles in the blood made of fats and proteins, may protect brain blood vessels in AD. HDL already have known beneficial effects on blood vessels outside of the brain and there are links between high levels of HDL and lower risk of AD. We used mouse models and test tube models of human blood vessels to show that HDL prevents harmful deposits of amyloid beta in the walls of brain blood vessels and protects brain blood vessels against inflammation. Overall, this thesis suggests that raising the levels of functional HDL through drugs or healthy lifestyle changes may protect against detrimental brain blood vessels changes in AD. vi Preface All work in this thesis was performed at the Djavad Mowafaghian Centre for Brain Health and the Centre for Disease Modeling at the University of British Columbia or the Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute in Vancouver, BC unless otherwise noted. All projects and methods were approved by the University of British Columbia Research Ethics Board. • Biosafety approval: B14-0136 (Factors affecting the risk of Alzheimer’s Disease), B15-0066 (Biosafety for AD and TBI) • Animal care approval: A14-0003 (HDL, vascular biology and Alzheimer’s Disease), A14-0057 (ABCA1 and apoE in Alzheimer’s Disease) • Human ethics approval: H14-03357 (Lipoprotein function in cerebrovascular health), H17-01776 (Lipoprotein function and cerebrovascular health in diabetes). • Radiation safety: R17-0017 (Open Source Radiation Permit) • Personal animal care, safety, and ethics training: Rodent Biology and Husbandry (00000151), Canadian Council on Animal Care (6909-14), Rodent Anaesthesia (00000088), Animal Ethics (6906-14), Biological Safety (2019-nL4uo), Chemical Safety (2014-5oB3y), Radiation Safety (2015-RS1f), Tri-Council Policy Statement 2 Course on Research Ethics (Date of issue 16 June, 2015) Details on the personal contributions and contribution of collaborators to the data in Chapter 2, 3, 4, and 5 are available in Appendix A. Portions of Chapter 1 have been previously published in two review articles: Boyce G, Button E, Soo S, Wellington C (2017) Pleiotropic vasoprotective functions of high-density lipoproteins (HDL). Journal of Biomedical Research. May 26. doi: 10.7555/JBR.31.20160103. This review was designed and prepared by all co-authors. I was responsible for approximately 40% of the writing and led the design and creation of the figure and table in the published review vii also appearing in Chapter 1 (Figure 1.2 and Table 1.1). I wrote sections focused on high-density lipoproteins (HDL) while Guilaine Boyce wrote sections on the cerebrovascular system, Sonja Soo wrote other minor sections, and Dr. Cheryl Wellington had major contributions to the concepts, structure, and revisions and wrote sections on Gaps and Opportunities. I wrote all published sections appearing within this thesis. Button EB, Robert J, Caffrey TM, Fan J, Zhao W, Wellington CL (2019) HDL from an Alzheimer’s disease perspective. Current Opinion in Lipidology. Jun;30(3): 224-234. All co-authors contributed to the concepts, structures, and content of this review. I wrote the sections on HDL and vascular resilience, epidemiological evidence linking HDL and Alzheimer’s disease (AD), HDL effects in AD animal models, considerations for evaluating HDL and an AD therapeutic, and considerations for HDL as an AD biomarker in addition to contributing to sections on the cerebrovasculature in AD in total comprising approximately 60% of the writing. I also led the design and creation of the table and figure in the published review that appear in Chapter 6 (Figure 6.1 and Table 6.1). Dr. Jerome Robert wrote the introduction and the section on the cerebrovasculature in AD, Dr. Tara Caffrey wrote the section on in vitro models, and Dr. Jianjia Fan and Wenchen Zhao wrote the paragraph on drugs targeting adenosine triphosphate binding cassette transporter A1 (ABCA1). Sections from the above publications make up approximately 25% of Chapter 1, approximately 70% of that being originally written by myself. The remaining 75% of Chapter 1 is my own writing expanding the published sections or entirely new sections. A version of Chapter 2 has been published in Button EB, Boyce GK, Wilkinson A, Stukas S, Hayat A, Fan J, Wadsworth BJ, Robert J, Martens KM, Wellington CL (2019) ApoA-I deficiency increases cortical amyloid deposition, cerebral amyloid angiopathy, cortical and hippocampal astrogliosis, and amyloid-association astrocyte reactivity in APP/PS1 mice. Alzheimer’s Research and Therapy. 11(4): 44. viii This study was originally designed by Arooj Hayat, Guilaine Boyce, Sophie Stukas, and Cheryl Wellington. I took over the re-design and led the study after all mouse tissues were collected. Specifically, I performed plasma lipid analysis, ribonucleic acid (RNA) isolation, enzyme linked immunosorbent assay (ELISA) measurements, image processing, imaging analysis, and half of the real-time quantitative polymerase chain reaction (RT-qPCR) analysis. I led all of the data analysis and interpretation to re-orient the direction of the study and prepare it for publication including writing the manuscript and generating figures. Contributions of co-authors: • Guilaine Boyce contributed the design to the original study, was responsible for the collection of experimental mouse tissues, and was responsible for generating the behavior data. • Anna Wilkinson was responsible for protein extraction, fixed brain sectioning and immunohistological staining including contributions to the developing of co-staining methods. • Dr. Sophie Stukas contributed to the design of the original study. • Arooj Hayat contributed to the design of the original study as well as breeding and tissue collection in a related pilot study. • Dr. Jianjia Fan contributed to RT-qPCR analysis. • Brennan Wadsworth made major contributions to the development of image analysis protocols. • Dr. Jerome Robert contributed to interpretation of the data. • Dr. Kris Martens was responsible for the breeding of mouse cohorts. • Dr. Cheryl Wellington led the design of the original study and had major contributions to the re-design of the study, data interpretation, and manuscript revisions. I designed the study in Chapter 3 along with Dr. Sophie Stukas and Dr. Cheryl Wellington. I led the treatments of the mice, tissue collection, data collection, data analysis and interpretation, writing, and figure generation. In terms of data collection, I was specifically responsible for ix analysis of plasma lipids, RNA extraction, RT-qPCR analysis, ELISA measurements, image processing, and image analysis. Contributions of others: • Dr. Sophie Stukas contributed to the design of the study and was responsible for the cytokine protein analysis. • Dr. Kris Martens was responsible for the behavior analysis. • Sabrina Wei and Harleen Cheema were responsible for the protein extraction. • Anna Wilkinson was responsible for fixed brain sectioning and most immunohistological staining. • Dr. Wai Hang Cheng was responsible for the Iba1 immunohistochemical staining. • Dr. Cheryl Wellington contributed to the design of the study and had major contributions to data interpretation. • Dr. Yuri Bukhtiyarov from Vitae Pharmaceuticals Inc. (Fort Washington, PA) supplied the VTP compound. • Joan Guo and Linghang Zhuang from Vitae Pharmaceuticals Inc. were responsible for the mass spectrometry analysis. A version of Chapter 4 has been published in Robert J*, Button EB*, Stukas S, Boyce GK, Gibbs E, Cowan CM, Gilmour M, Cheng WH, Soo SK, Yuen B, Bahrabadi A, Kang K, Kulic I, Francis G, Cashman N, Wellington CL (2017) High-density lipoproteins suppress Aβ-induced PBMC adhesion to human endothelial cells in bioengineered vessels and in monoculture. Molecular Neurodegeneration. Aug 22;12(1): 60. *co-first author. I contributed to the design of the study with Dr. Jerome Robert, Dr. Sophie Stukas, and Dr. Cheryl Wellington. Dr. Jerome Robert and I had equal efforts in collecting the majority of the data, specifically, I isolated HDL for the majority of experiments, performed approximately 40% of the experiments performed in monocultured endothelial cells, prepared and measured human brain homogenates by ELISA, and performed the analysis and interpretation of these experiments. I also x assisted in the writing of the manuscript and was responsible for the generation of approximately 50% of the figures and lead the overall design of figure layout. Co-author contributions: • Dr. Jerome Robert was co-first author on this manuscript and was responsible for the experiments in three dimensional (3D) bioengineered arteries and immunofluorescent staining experiments as well as approximately 50% of the monoculture experiments. Dr. Robert also undertook the majority of the manuscript writing in particular in the introduction and discussion sections and provided partial funding for the study. • Dr. Sophie Stukas contributed to the concepts of the manuscript and data interpretation as well as preliminary experiments in monoculture and HDL isolation. • Guilaine Boyce was responsible for the collection of blood for peripheral blood mononuclear cells (PBMC) isolation and assisted in PBMC isolation. • Dr. Ebrima Gibbs produced oligomeric and fibrillar amyloid beta (Aβ) from monomeric Aβ. • Dr. Catherine Cowan provided transmission electron microscopic images of monomeric, oligomeric, and fibrillar Aβ. • Megan Gilmour cultured 3D bioengineered arteries. • Dr. Wai Hang Cheng assisted in immunofluorescent staining and statistical analysis. • Sonja Soo, Brian Yuen, Arvin Bahrabadi, and Kevin Kang provided assistance in protein extraction, western blotting, immunofluorescence, and cell culture. • Dr. Iva Kulic provided insight on data analysis in particular regarding the combination of data across experiments. • Dr. Gordon Francis provided isolated HDL for initial experiments and data interpretations • Dr. Neil Cashman provided assistance in producing oligomeric and fibrillar Aβ as well as images of Aβ species. • Dr. Cheryl Wellington had major contributions to the design of the study, data interpretation, manuscript writing, and providing funding and equipment. xi Small portions of Chapter 4 (Figure 4.12 and Figure 4.13) were published in Robert J, Button EB, Yuen B, Gilmour M, Kang K, Bahrabadi A, Stukas S, Zhao W, Kulic I, Wellington CL (2017) Clearance of beta-amyloid is facilitated by apolipoprotein E and circulating high-density lipoproteins in bioengineered human vessels. Elife. Oct 10;6. pii: e29595. This study was designed and led by Dr. Jerome Robert who also performed the majority of the experiments. I assisted in data interpretation and was responsible for all of the ELISA measurements. All other co-authors contributed as described for Robert, Button et al. (2017) above. Portions of Chapter 5 were previously published in Button EB, Gilmour M, Cheema HK, Martin EM, Agbay A, Robert J, Wellington CL (2019) Vasoprotective functions of high-density lipoproteins relevant to Alzheimer’s disease are partially conserved in apolipoprotein B-depleted plasma. International Journal of Molecular Sciences. Jan 22;20(3). pii:E462. I led the study design, most experimental procedures, data analysis, interpretation, writing, and figure generation for this manuscript. Co-author contributions: • Meghan Gilmour and Andrew Agbay generated 3D bioengineered arteries and assisted in cell culture. • Harleen Cheema assisted in HDL isolation and apolipoprotein B-depletion of plasma as well as immunoblotting. • Emma Martin assisted in the quantification of monocytes adhered to 3D bioengineered arteries, immunoblotting, and cell culture. • Dr. Jerome Robert had major contributions to study design, data interpretation, and manuscript writing. Dr. Robert was also responsible for cholesterol efflux experiments and monocyte adhesion experiment in 3D bioengineered arteries. • Dr. Cheryl Wellington had major contributions to study design, data interpretation, and manuscript writing. xii Unpublished sections of Chapter 5 include the comparison of HDL and serum in 3D bioengineered artery assays, assessment of serum assay variation, and a proof-of-concept study with plasma obtained from a clinical laboratory to assess the feasibility of these assays in investigating differences in HDL function on the basis of disease. I designed these studies with Dr. Jerome Robert and Dr. Cheryl Wellington. I performed ELISA measurements for all experiments, isolated HDL from serum, performed the assays for all experiments except those comparing HDL and serum, performed data analysis and interpretation, generated figures, and wrote the Chapter text. Megan Gilmour and Andrew Agbay produced the 3D bioengineered arteries. Dr. Robert performed assays comparing serum with HDL and analyzed monocyte adhesion. Dr. Mari DeMarco and Amy Nguyen provided plasma for the clinical proof-of-concept study working at the clinical laboratories at St. Paul’s Hospital in Vancouver, BC. Drs. Robert and Wellington had major contributions to data interpretation. Sections of Chapter 6 were previously published in Button EB, Robert J, Caffrey TM, Fan J, Zhao W, Wellington CL (2019) HDL from an Alzheimer’s disease perspective. Current Opinion in Lipidology. Jun;30(3): 224-234. As discussed above, all co-authors contributed to the concepts, structures, and content of this review. Overall, approximately 10% of Chapter 6 appears in this published review, 30% is composed of expansions of the published sections, and 60% is unrelated to the published review. I wrote all sections, published and unpublished, in Chapter 6. xiii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... xiii List of Tables ............................................................................................................................. xxii List of Figures ........................................................................................................................... xxiii List of Abbreviations ............................................................................................................... xxvi Acknowledgements ................................................................................................................ xxxiii Dedication .................................................................................................................................xxxv Chapter 1: Introduction ................................................................................................................1 1.1 Summary ......................................................................................................................... 1 1.2 Alzheimer’s disease overview ........................................................................................ 1 1.2.1 Epidemiology and economic burden ...................................................................... 2 1.2.2 Familial versus sporadic AD ................................................................................... 3 1.3 Amyloid pathology ......................................................................................................... 4 1.4 Tau pathology ................................................................................................................. 5 1.5 Neurodegeneration .......................................................................................................... 7 1.6 Neuroinflammation ......................................................................................................... 7 1.7 Clinical criteria for diagnosing AD ................................................................................. 8 1.8 Blood-based biomarkers for AD ..................................................................................... 9 1.9 Risk factors for AD and dementia ................................................................................ 10 1.9.1 Non-modifiable risk factors .................................................................................. 10 1.9.2 Apolipoprotein E ................................................................................................... 11 1.9.2.1 Function of peripheral apoE .............................................................................. 11 1.9.2.2 Function of brain apoE ...................................................................................... 12 xiv 1.9.2.3 APOE as a risk factor for AD ........................................................................... 13 1.9.2.4 ApoE isoform effects on amyloid pathology .................................................... 14 1.9.2.5 ApoE isoform effects on tau pathology ............................................................ 14 1.9.2.6 ApoE isoform effects on blood-brain barrier function ..................................... 15 1.9.2.7 ApoE isoform effects on astrocytes .................................................................. 15 1.9.2.8 ApoE isoform effects on microglia ................................................................... 15 1.9.3 Modifiable risk factors .......................................................................................... 17 1.9.3.1 Physical activity ................................................................................................ 18 1.9.3.2 Diet .................................................................................................................... 19 1.9.3.3 Type 2 diabetes mellitus ................................................................................... 19 1.9.3.4 Hypertension ..................................................................................................... 20 1.9.3.5 Hypercholesterolemia ....................................................................................... 20 1.10 Therapeutics for AD ..................................................................................................... 21 1.10.1.1 Existing AD therapeutics .................................................................................. 21 1.10.1.2 AD therapeutics in ongoing clinical trials ........................................................ 21 1.11 The cerebrovascular system .......................................................................................... 22 1.11.1 The relationship between the cerebrovasculature and AD ................................... 23 1.11.2 Cerebrovascular clearance of Aβ from the brain .................................................. 24 1.11.3 Cerebral amyloid angiopathy ................................................................................ 25 1.11.4 Tau and the cerebrovasculature ............................................................................ 26 1.11.5 Cerebrovascular inflammation .............................................................................. 27 1.11.6 Vascular co-morbidities in AD ............................................................................. 29 1.12 In vitro models of the BBB ........................................................................................... 30 1.13 High-density lipoproteins (HDL) .................................................................................. 33 1.13.1 HDL protective associations with cardiovascular diseases .................................. 34 1.13.2 HDL function in health and disease ...................................................................... 35 1.13.2.1 HDL and cholesterol efflux capacity ................................................................ 35 1.13.2.2 HDL and inflammation ..................................................................................... 38 1.13.3 HDL composition in health and disease ............................................................... 39 1.14 The relationship between HDL and AD ....................................................................... 42 xv 1.14.1 Mixed genetic evidence on HDL and cardiovascular disease and AD risk. ......... 42 1.14.2 Epidemiological evidence for a protective effect of HDL on AD ........................ 43 1.14.3 Potential protective mechanisms of action of HDL against AD ........................... 44 1.14.4 Vasoprotective functions of HDL in AD animal models ...................................... 45 1.14.4.1 Mouse models of AD ........................................................................................ 45 1.14.4.2 Genetic alteration of HDL in AD animal models ............................................. 46 1.14.4.3 HDL-based therapeutics on AD-relevant outcomes in animal models ............. 47 1.15 Liver X receptor and its therapeutic potential for AD .................................................. 47 1.16 Summary, research hypothesis, and specific objectives ............................................... 51 1.16.1 Summary ............................................................................................................... 51 1.16.2 Research hypothesis .............................................................................................. 51 1.16.3 Specific objectives ................................................................................................ 52 Chapter 2: Amyloid pathology, neuroinflammation, cerebrovascular pathology, and cognition in apoA-I deficient, APP/PS1 mice ............................................................................53 2.1 Summary ....................................................................................................................... 53 2.2 Introduction ................................................................................................................... 54 2.3 Methods......................................................................................................................... 55 2.3.1 Animals ................................................................................................................. 55 2.3.2 Tissue collection ................................................................................................... 56 2.3.3 Plasma lipid measurements ................................................................................... 57 2.3.4 Histology ............................................................................................................... 57 2.3.5 Image analysis ....................................................................................................... 58 2.3.6 Protein extraction .................................................................................................. 59 2.3.7 Enzyme linked immunosorbent assay (ELISA) .................................................... 60 2.3.8 Isolation of RNA and real-time quantitative polymerase chain reaction (RT-qPCR) 60 2.3.9 Contextual and cued fear conditioning ................................................................. 61 2.3.9.1 Training ............................................................................................................. 61 2.3.9.2 Context testing .................................................................................................. 61 2.3.9.3 Cued testing ...................................................................................................... 61 xvi 2.3.10 Statistical analysis ................................................................................................. 61 2.4 Results ........................................................................................................................... 62 2.4.1 Loss of apoA-I significantly reduces plasma total cholesterol and HDL cholesterol concentrations ....................................................................................................................... 62 2.4.2 ApoA-I deficiency increases cortical parenchymal and vascular amyloid burden of APP/PS1 mice .................................................................................................................. 63 2.4.3 ApoA-I deficiency increases pro-inflammatory protein and mRNA levels in the cortices of APP/PS1 mice ..................................................................................................... 66 2.4.4 ApoA-I deficiency increases total ICAM-1 protein concentration in the brain, total ICAM-1 positive area in the hippocampus, and vascular ICAM-1 positive area in the cortex and hippocampus of APP/PS1 mice .......................................................................... 68 2.4.5 ApoA-I deficiency exacerbates Aβ-mediated increases in total and cerebrovascular GFAP levels in both cortex and hippocampus ........................................... 70 2.4.6 ApoA-I deficiency worsens fear memory deficits in APP/PS1 mice ................... 73 2.5 Discussion ..................................................................................................................... 75 2.6 Supplemental figures .................................................................................................... 80 Chapter 3: Amyloid pathology, neuroinflammation, cerebrovascular pathology, and cognition in APP/PS1 mice treated with a non-brain penetrant LXR agonist .......................84 3.1 Summary ....................................................................................................................... 84 3.2 Introduction ................................................................................................................... 84 3.3 Methods......................................................................................................................... 86 3.3.1 Animals ................................................................................................................. 86 3.3.2 Tissue collection ................................................................................................... 86 3.3.3 Plasma lipid measurements ................................................................................... 86 3.3.4 Histology ............................................................................................................... 86 3.3.5 Image analysis ....................................................................................................... 87 3.3.6 Protein extraction .................................................................................................. 87 3.3.7 Enzyme-linked immunosorbent assay (ELISA) ................................................... 87 3.3.8 Ribonucleic acid (RNA) isolation and real-time quantitative polymerase chain reaction (RT-qPCR) .............................................................................................................. 87 xvii 3.3.9 Contextual and cued fear conditioning ................................................................. 87 3.3.10 Measurement of plasma and brain drug concentration ......................................... 87 3.3.11 Statistical analysis ................................................................................................. 88 3.4 Results ........................................................................................................................... 88 3.4.1 VTP does not enter the brain in aged WT or APP/PS1 mice ................................ 88 3.4.2 VTP induces Abca1 messenger RNA (mRNA) expression in the small intestine but not in the brain ................................................................................................................ 89 3.4.3 Soluble Aβ42 concentrations tend to be reduced in APP/PS1 mice treated with the non-brain penetrant LXR agonist VTP in pilot study ........................................................... 90 3.4.4 VTP induces Abca1 messenger RNA (mRNA) expression in the small intestine but not in the brain ................................................................................................................ 91 3.4.5 VTP does not significantly alter Aβ or tau pathologies ........................................ 94 3.4.6 VTP treatment reduces some markers of neuroinflammation .............................. 95 3.4.7 VTP does not alter the number of cortical or hippocampal Iba1+ microglia ....... 97 3.4.8 VTP reduces total and vascular ICAM-1 expression in the hippocampus ........... 97 3.4.9 VTP does not alter total levels of GFAP+ astrocytes in the brain but may subtly reduce cerebrovascular astrogliosis .................................................................................... 100 3.4.10 VTP rescues deficits in cued fear memory in APP/PS1 mice ............................ 103 3.5 Discussion ................................................................................................................... 105 3.6 Supplemental Figures .................................................................................................. 111 Chapter 4: Investigation of high-density lipoprotein functions relevant to Alzheimer’s disease using brain-derived endothelial cells and 3-dimensional bioengineered arteries. ..113 4.1 Summary ..................................................................................................................... 113 4.2 Introduction ................................................................................................................. 113 4.3 Methods....................................................................................................................... 115 4.3.1 Fabrication of tissue engineered vessels ............................................................. 115 4.3.2 Characterization of engineered vascular tissue ................................................... 116 4.3.3 Preparation of HDL and PBMCs ........................................................................ 116 4.3.4 Preparation of Aβ peptides ................................................................................. 117 4.3.5 Monocyte adhesion in engineered vessels .......................................................... 117 xviii 4.3.6 Static monotypic cell culture .............................................................................. 118 4.3.7 Monolayer PBMC adhesion assay ...................................................................... 118 4.3.8 Ab oligomerization/fibrilization and electron microscopy confirmation ........... 119 4.3.9 Measurement of intracellular NO ....................................................................... 119 4.3.10 Cell surface biotinylation .................................................................................... 120 4.3.11 Monolayer Ab association, binding and uptake ................................................. 120 4.3.12 Molecular Biology .............................................................................................. 121 4.3.13 Immunoblot ......................................................................................................... 121 4.3.14 Beta-sheet formation assay ................................................................................. 122 4.3.15 Human brain protein extraction and ELISA ....................................................... 122 4.3.16 Vascular Aβ accumulation and clearance in bioengineered vessels ................... 123 4.3.17 Statistical analysis ............................................................................................... 123 4.4 Results ......................................................................................................................... 124 4.4.1 Bioengineering dynamic, human 3D vessels ...................................................... 124 4.4.2 HDL suppresses Ab-mediated monocyte adhesion to ECs in 3D dynamic engineered human vessels ................................................................................................... 125 4.4.3 HDL attenuates Ab-induced PBMC adhesion in monotypic ECs ...................... 126 4.4.4 HDL reduces the fibrillization rate of Ab42 and Ab40 in cell-free conditions .. 128 4.4.5 Aβ structure does not affect HDL’s ability to reduce PBMC adhesion to hCMEC/D3 ......................................................................................................................... 130 4.4.6 The ability of HDL to suppresses Aβ-induced PBMC adhesion to hCMEC/D3 is independent of NO production and Anx1 ........................................................................... 131 4.4.7 HDL-mediated suppression of Ab-induced PBMC adhesion to hCMEC/D3 is independent of miR-233 ..................................................................................................... 133 4.4.8 HDL-mediated suppression of Ab-induced PBMC adhesion to hCMEC/D3 is independent of ICAM-1 and VCAM-1 ............................................................................... 134 4.4.9 Suppressing Ab uptake into hCMEC/D3 blocks PBMC adherence ................... 138 4.4.10 SR-BI is necessary for HDL to suppress Ab-induced PBMC adhesion ............. 140 4.4.11 Aβ accumulates in the walls of 3D bioengineered vessels ................................. 142 xix 4.4.12 HDL suppresses the accumulation of Aβ in the vessel wall of bioengineered arteries and tends to increase Aβ transport into the circulation .......................................... 144 4.5 Discussion ................................................................................................................... 144 4.6 Supplemental Figures .................................................................................................. 148 Chapter 5: Development of assays for the evaluation of brain-relevant high-density lipoprotein functions in human blood specimens. ...................................................................152 5.1 Summary ..................................................................................................................... 152 5.2 Introduction ................................................................................................................. 152 5.3 Methods....................................................................................................................... 154 5.3.1 Isolation and comparison of HDL isolated by ultracentrifugation and apoB-depleted plasma ................................................................................................................... 154 5.3.1.1 Blood collection .............................................................................................. 154 5.3.1.2 HDL and apoB-depleted plasma isolation ...................................................... 155 5.3.1.3 Fabrication of 3D bioengineered vessels ........................................................ 156 5.3.1.4 Amyloid beta clearance, accumulation, and monocyte adhesion assays using engineered vessels ........................................................................................................... 156 5.3.1.5 Enzyme linked immunosorbent assay (ELISA) .............................................. 156 5.3.1.6 Static monotypic cell culture .......................................................................... 156 5.3.1.7 Intracellular NO production ............................................................................ 156 5.3.1.8 Preparation of Aβ monomers .......................................................................... 157 5.3.1.9 PBMC adhesion assays ................................................................................... 157 5.3.1.10 Cell-free assay of Aβ fibrillization ................................................................. 157 5.3.1.11 Characterization of HDL preparations ............................................................ 157 5.3.1.12 Gel electrophoresis .......................................................................................... 157 5.3.2 Comparison of HDL isolated by ultracentrifugation and serum in 3D bioengineered artery functional assays ............................................................................... 158 5.3.3 Evaluation of variability in Aβ accumulation assay in 3D bioengineered arteries with serum from healthy volunteers ................................................................................... 158 5.3.4 Proof-of-concept assays of plasma from T2DM subjects ................................... 158 5.3.4.1 Plasma collection ............................................................................................ 158 xx 5.3.4.2 Plasma defibrination ....................................................................................... 159 5.3.4.3 Aβ accumulation in 3D bioengineered arteries ............................................... 159 5.3.5 Statistical analysis ............................................................................................... 159 5.4 Results ......................................................................................................................... 160 5.4.1 ApoB-depleted plasma recapitulated the ability of HDL isolated by ultracentrifugation to reduce Ab accumulation in the walls of bioengineered vessels. ...... 160 5.4.2 ApoB-depleted plasma does not retain the ability of HDL to reduce Aβ-induced monocyte binding to the endothelium ................................................................................ 161 5.4.3 ApoB-depleted plasma is functionally equivalent to HDL with respect to delaying Aβ42 fibrillization ............................................................................................................... 163 5.4.4 ApoB-depleted plasma does not induce NO in human brain-derived ECs ......... 164 5.4.5 Non-HDL plasma components in apoB-depleted plasma interfere with some anti-inflammatory activities of purified HDL ............................................................................ 165 5.4.6 Human serum recapitulates the ability of HDL to suppress Aβ accumulation in 3D bioengineered arteries but not Aβ-induced monocyte adhesion ......................................... 167 5.4.7 Aβ accumulation assays in 3D bioengineered arteries with human serum have sufficiently low variation to allow for the analysis of clinical biospecimens ..................... 170 5.4.8 Plasma from individuals with T2DM does not differ from those without T2DM in its ability to suppress Aβ accumulation in 3D bioengineered arteries ................................ 172 5.5 Discussion ................................................................................................................... 175 5.6 Supplementary Figures and Tables ............................................................................. 182 Chapter 6: Discussion and concluding remarks .....................................................................186 6.1 Summary and significance of findings ....................................................................... 186 6.2 Limitations of these studies ........................................................................................ 189 6.2.1 In vivo mouse model ........................................................................................... 189 6.2.2 In vitro model of the BBB .................................................................................. 190 6.2.3 Mechanistic data on AD-relevant HDL functions .............................................. 191 6.2.4 Simplified view of inflammation in AD ............................................................. 193 6.3 Future directions ......................................................................................................... 194 xxi 6.3.1 Evaluation of HDL structure and subclasses important for AD-relevant vasoprotective functions ..................................................................................................... 194 6.3.2 HDL-based therapeutics in development for AD ............................................... 196 6.3.2.1 HDL-based therapeutics in development of the treatment of acute coronary syndrome and atherosclerosis ......................................................................................... 198 6.3.2.2 Lipid modifying therapeutics for dementia prevention or treatment .............. 199 6.3.2.3 HDL as a potential treatment for the adverse effects of therapeutics on the cerebrovasculature .......................................................................................................... 201 6.3.2.4 Considerations for the repurposing of HDL-based therapeutics for AD ........ 201 6.3.3 Lifestyle modifications affecting HDL levels and function ............................... 203 6.3.4 HDL as a biomarker for cerebrovascular dysfunction ........................................ 205 6.4 Conclusions ................................................................................................................. 208 References ...................................................................................................................................210 Appendices ..................................................................................................................................289 Appendix A ............................................................................................................................. 289 xxii List of Tables Table 1.1 HDL compositional and functional heterogeneity in disease. ...................................... 37 Table 1.2 Summary of studies treating AD model mice with GW3965 investigating amyloid, neuroinflammation, and memory deficits. .................................................................................... 49 Table 1.3 Summary of studies treating AD model mice with TO901317 investigating amyloid, neuroinflammation, and memory deficits. .................................................................................... 50 Table 3.1 VTP brain penetrance in 12-month old APP/PS1 mice and wildtype littermates. ....... 89 Table 3.2 VTP brain penetrance in 12-month old APP/PS1 mice and wildtype littermates. ....... 92 Table 3.3 VTP and GW brain penetrance in 9-month old APP/PS1 mice and wildtype littermates...................................................................................................................................................... 111 Table 4.1 Demographic data, Aβ40 and Aβ42, and adhesion molecule quantification in AD patient and NCI control brains. ................................................................................................... 135 Table 5.1 Donor demographics, HDL-C concentration, and apolipoprotein concentrations in serum and isolated HDL. ............................................................................................................ 168 Table 5.2 Donor demographics for the analysis of assay variation using 25% serum in 3D bioengineered vessels. ................................................................................................................. 170 Table 5.3 Coefficients of variation for Aβ accumulation assay in 3D bioengineered vessels with 25% human serum. ...................................................................................................................... 171 Table 5.4 Characteristics of patient cohort. ................................................................................ 173 Table 6.1 HDL-based therapeutics under investigation in clinical trials. ................................... 196 Table 0.1 Specific contributions of candidate and collaborators in Chapter 2. .......................... 289 Table 0.2 Specific contributions of candidate and collaborators in Chapter 3. .......................... 289 Table 0.3 Specific contributions of candidate and collaborators in Chapter 4. .......................... 290 Table 0.4 Specific contributions of candidate and collaborators in Chapter 5. .......................... 292 xxiii List of Figures Figure 1.1 Aβ clearance via the cerebrovasculature. .................................................................... 25 Figure 1.2 HDL composition and function ................................................................................... 33 Figure 2.1 APP/PS1 survival rates. ............................................................................................... 56 Figure 2.2 Plasma lipid and brain apolipoprotein concentrations in apoA-I deficient APP/PS1 mice. .............................................................................................................................................. 63 Figure 2.3 Amyloid plaques and vascular Aβ deposition in apoA-I deficient APP/PS1 mice. .... 64 Figure 2.4 Pro-inflammatory protein and mRNA markers in apoA-I deficient APP/PS1 mice. .. 67 Figure 2.5 Total and endothelial associated ICAM-1 in apoA-I deficient APP/PS1 mice. .......... 69 Figure 2.6 Reactivity of astrocytes associated with the cerebrovasculature in apoA-I deficient APP/PS1 mice. .............................................................................................................................. 71 Figure 2.7 Astrocyte reactivity to Aβ plaques and vascular Aβ deposits in apoA-I deficient APP/PS1 mice. .............................................................................................................................. 73 Figure 2.8 Cued and contextual fear memory in apoA-I deficient APP/PS1 mice. ...................... 74 Figure 2.9 Analysis strategy for histology. ................................................................................... 80 Figure 2.10 Morphological discrimination of vascular and parenchymal amyloid. ..................... 81 Figure 2.11 Total vascular area in apoA-I deficient APP/PS1 mice. ............................................ 82 Figure 2.12 GFAP staining area in the hypothalamus in apoA-I deficient APP/PS1 mice. ......... 83 Figure 3.1 Intestinal and brain Abca1 mRNA expression in VTP and GW treated APP/PS1 mice in pilot study. ................................................................................................................................ 89 Figure 3.2 Brain Aβ peptide concentrations in VTP and GW treated APP/PS1 mice in pilot study. ............................................................................................................................................. 90 Figure 3.3 Lipoprotein metabolism in VTP and GW treated APP/PS1 mice. .............................. 93 Figure 3.4 Amyloid and tau pathology in VTP and GW treated APP/PS1 mice. ......................... 94 Figure 3.5 Biochemical markers of neuroinflammation in VTP and GW treated APP/PS1 mice........................................................................................................................................................ 96 Figure 3.6 Iba+ microglia numbers in VTP and GW treated APP/PS1 mice. .............................. 97 Figure 3.7 Endothelial and parenchymal ICAM-1-positive staining area in VTP and GW treated APP/PS1 mice. ............................................................................................................................ 100 xxiv Figure 3.8 Total and vessel associated GFAP levels in VTP and GW treated APP/PS1 mice. . 102 Figure 3.9 Cued and contextual fear conditioning in VTP and GW treated APP/PS1 mice. ..... 104 Figure 3.10 Body weight comparison by housing facility. ......................................................... 112 Figure 3.11 Cortical and hippocampal vascular area in VTP and GW treated APP/PS1 mice. . 112 Figure 4.1 Bioengineering dynamic human 3-dimensional vessels. ........................................... 125 Figure 4.2 HDL suppresses Aβ-induced monocyte adhesion to 3D bioengineered vessels. ...... 126 Figure 4.3 Aβ40 and Aβ42 induce PBMC adhesion to ECs, which is suppressed by HDL. ..... 127 Figure 4.4 HDL delays beta-sheet formation and attenuates Aβ-induced PBMC adherence independent of Aβ structure. ....................................................................................................... 129 Figure 4.5 HDL suppression of Aβ-induced inflammation is independent of eNOS and S1P. .. 132 Figure 4.6 HDL does not signal through miR-223 to reduce Aβ-induced inflammation in hCMEC/D3. ................................................................................................................................ 134 Figure 4.7 Adhesion molecules are enhanced by TNFα but not Aβ. .......................................... 136 Figure 4.8 Aβ does not activate phosphorylation of multifunctional serine/threonine protein kinases. ........................................................................................................................................ 137 Figure 4.9 HDL reduces Aβ association, binding and uptake to hCMEC/D3 whereas blocking Aβ binding or uptake reduces PBMC adhesion to hCMEC/D3. ...................................................... 139 Figure 4.10 HDL suppression of Aβ-induced inflammation requires SR-BI. ............................ 141 Figure 4.11 HDL suppresses Aβ-induced monocyte adhesion in engineered vessels via SR-BI...................................................................................................................................................... 142 Figure 4.12 Aβ40 and Aβ42 accumulation within and transport through bipartite vessels. ....... 143 Figure 4.13 HDL facilitates Aβ transport and reduces accumulation in bioengineered bipartite vessels. ........................................................................................................................................ 144 Figure 4.14 Aβ induces dose-dependent PBMC adhesion to hCMEC/D3 and HDL attenuates the PBMC adhesion in a dose-dependent manner. ........................................................................... 148 Figure 4.15 HDL suppression of Aβ-induced inflammation is independent of eNOS and S1P in HUVEC. ...................................................................................................................................... 149 Figure 4.16 Cortical ICAM-1 expression is increased in AD. .................................................... 150 Figure 4.17 HDL treatment does not alter LRP1 or RAGE protein levels in hCMEC/D3. ........ 151 Figure 5.1 HDL isolation and depletion of apoB-containing lipoproteins from human plasma. 155 xxv Figure 5.2 Aβ accumulation in bioengineered vessels treated with HDL isolated by ultracentrifugation or apoB-depleted plasma. ............................................................................. 161 Figure 5.3 Monocyte binding to EC treated with HDL isolated by ultracentrifugation or apoB-depleted plasma. .......................................................................................................................... 162 Figure 5.4 Anti-fibrillary effects of HDL isolated by ultracentrifugation and apoB-depleted plasma. ........................................................................................................................................ 164 Figure 5.5 NO production in human brain-derived ECs treated with HDL isolated by ultracentrifugation or apoB-depleted plasma. ............................................................................. 165 Figure 5.6 Effect of non-HDL plasma components and PEG on PBMC adhesion and NO production assays. ....................................................................................................................... 166 Figure 5.7 Clots on 3D bioengineered vessels treated with plasma and serum. ......................... 167 Figure 5.8 Aβ accumulation in 3D bioengineered vessels treated with HDL and serum from young healthy donors. ................................................................................................................. 169 Figure 5.9 Intra- and inter-donor and experimental variability of the Aβ accumulation assay in 3D bioengineered vessels with unfractionated serum. ................................................................ 171 Figure 5.10 Power calculation for comparison of subject groups for the ability of 25% serum to reduce Aβ accumulation in 3D bioengineered vessels. .............................................................. 172 Figure 5.11 Aβ accumulation in 3D bioengineered vessels treated with plasma from individuals with T2DM and age and sex-matched controls .......................................................................... 174 Figure 5.12 Composition and cholesterol efflux function of HDL and apoB-depleted plasma. 183 Figure 5.13 HDL-C and apolipoprotein concentrations in HDL isolated by ultracentrifugation and unfractionated serum. ........................................................................................................... 183 Figure 5.14 Raw data for comparison of HDL and serum in Aβ accumulation and monocyte adhesion assays 3D bioengineered vessels. ................................................................................ 184 Figure 5.15 Raw data for intra- and inter-donor and experimental variability of the Aβ accumulation assay in 3D bioengineered vessels with unfractionated serum. ........................... 184 Figure 5.16 HDL-C and apolipoprotein concentrations in treatments for 3D bioengineered vessels. ........................................................................................................................................ 185 Figure 6.1 Vasoprotective functions of HDL relevant for Alzheimer’s disease. ........................ 208 xxvi List of Abbreviations 2D two dimensional 3D three dimensional ATN amyloid/tau/neurodegeneration framework α-SMA alpha smooth muscle actin Aβ amyloid beta AAV adeno-associated virus ABC Aβ plaque score (A), Braak NFT score (B), CERAD staging for neuritic plaques ABCA1 ATP binding cassette transporter A1 ABCA7 ATP binding cassette transporter A7 ABCG1 ATP binding cassette transporter G1 AD Alzheimer’s disease ADAS-Cog13 AD Assessment Scale-Cognitive Subscale ADCS-ADL-MCI AD Cooperative Study-Activity of Daily Living Inventory (MCI version) ADNI Alzheimer’s Disease Neuroimaging Initiative AIBL Australian Imaging Biomarkers and Lifestyle Study of Ageing ALS amyotrophic lateral sclerosis ANOVA analysis of variation Anx1 annexin A1 Apo apolipoprotein APP amyloid precursor protein APP/PS1 transgenic mice with the Swedish APP mutation and exon 9 deletion in PS1 ARIA amyloid-related imaging abnormalities ARIC Atherosclerosis Risk in Communities ASL-MRI arterial spin labelling MRI ATP adenosine triphosphate BACE-1 beta-secretase 1 BBB blood-brain barrier xxvii BCA bicinchoninic acid BLT-1 block lipid transport-1 BSA bovine serum albumin CAA cerebral amyloid angiopathy CAARI CAA-related inflammation CAD coronary artery disease CAIDE Cardiovascular Risk Factors Aging and Dementia CCCDTD4 Canadian Consensus Conference on the Diagnosis and Treatment of Dementia CD31 cluster of differentiation 31 cDNA complementary DNA CDR-SB Clinical Dementia Rating-Sum of Boxes CEC cholesterol efflux capacity CERAD Consortium to Establish a Registry for AD CETP cholesterol ester transfer protein CHD coronary heart disease Clu clusterin/apoJ CNS central nervous system CR1 complement receptor 1 CSF1F colony stimulating factor 1 receptor CVD cardiovascular disease CSF cerebrospinal fluid CTFβ carboxy-terminal fragment beta DAB 3,3’Diaminobenzidine DAF-2-DA 4,5-diaminofluorescein diacetate DAPI 4’,6-diamidino-2-phenylindole DMEM Dulbecco’s Modified Eagle Medium DMSO dimethyl sulfoxide DNA deoxyribonucleic acid EC endothelial cell xxviii ECL enhanced chemiluminescence EDTA ethylenediaminetetraacetic acid ELISA enzyme linked immunosorbent assay eNOS endothelial nitric oxide synthase EOAD early-onset Alzheimer’s disease ETV2 ETS variant 2 FBS fetal bovine serum FDG-PET fluorodeoxyglucose positron emission tomography FINGER Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability FITC fluorescein isothiocyanate FTLD frontotemporal lobar degeneration GAPDH glyceraldehyde 3-phosphate dehydrogenase GFAP glial fibrillary acidic protein GW GW3965, classical LXR agonist GWAS genome-wide association studies HbA1c glycated hemoglobin hCMEC/D3 human cerebral microvascular endothelial cell line HDL high-density lipoprotein HDL-C HDL cholesterol HEM hemizygous HEPES 4-(2-hydroxyethyl)-piperazineethanesulfonic acid HIF-1α hypoxia-inducible factor alpha HFIP hexafluoroisopropanol HMG-CoA 3-hydroxy-3-methyl-glutaryl-CoA HR hazard ratio HSPG heparin sulphate proteoglycans HUVEC human umbilical vein endothelial cell Iba1 ionized calcium binding adaptor molecule 1 ICAM-1 intercellular adhesion molecule 1 xxix IL-1β interleukin 1 beta iPSC induced pluripotent stem cells JNK jun amino-terminal kinase KO knockout LCAT lecithin cholesterol acyltransferase LDL low-density lipoprotein LDLR LDL receptor L-NAME L–NG-nitroarginine methyl ester LOAD late-onset Alzheimer’s disease LRP-1 LDL receptor related protein LXR liver X receptor MAPK mitogen activated protein kinase MAPT microtubule associated protein tau MAPT French Multidomain Alzheimer Prevention Trial MCI mild cognitive impairment miRNA micro RNA MMSE mini-mental state examination MRI magnetic resonance imaging mRNA messenger RNA MS multiple sclerosis NCI non-cognitively impaired NIA-AA National Institute on Aging and Alzheimer’s Association NFκB nuclear factor kappa beta NfL neurofilament light chain NGS normal goat serum NFT neurofibrillary tangle NMDA N-methyl-D-aspartate NO nitric oxide NOS nitric oxide synthase PBMC peripheral blood mononuclear cell xxx PBS phosphate buffered saline PCL polycaprolactone PDGFRβ platelet derived growth factor beta PEG polyethylene glycol PEG-P apoB-depleted plasma isolated by PEG precipitation PEG-UC HDL isolated by a combination of PEG precipitation and ultracentrifugation PGA polyglycolic-acid PI3K phosphoinositide 3-kinase PIB-PET Pittsburgh compound B PET PICALM phosphatidylinositol binding clathrin assembly protein PET positron emission tomography PFA paraformaldehyde PLA polylactate PON-1 paraoxonase 1 PPARγ peroxisome proliferator activating receptor gamma PREDIMED Primary Prevention of Cardiovascular Disease with a Mediterranean Diet PreDIVA Dutch Prevention of Dementia by Intensive Vascular Care PrP prion protein PSEN1 presenilin 1 PSEN2 presenilin 2 p-tau phosphorylated tau PVDF polyvinylidene difluoride RAGE receptor for advanced glycated endproducts RAP receptor associated protein RCT randomized controlled trial rHDL reconstituted HDL RIPA radioimmunoprecipitation assay RNA ribonucleic acids ROI region of interest RPMI Roswell Park Memorial Institute (medium) xxxi RR relative risk RT-qPCR real-time quantitative polymerase chain reaction RXR retinoid X receptor S1P sphingosine-1-phosphate SAA serum amyloid A SAPK stress activated protein kinase SD standard deviation SDS sodium dodecyl sulphate SDS-PAGE SDS-polyacrylamide gel electrophoresis Ser serine SMC smooth muscle cell sMRI structural magnetic resonance imaging SORL1 sortilin related receptor 1 SREBP-1c sterol regulatory element binding protein-1c SR-B1 scavenger receptor class B type I T2DM Type 2 diabetes mellitus TBS tris buffered saline TBS-X TBS with triton X-100 TEM transmission electron microscopy THP-1 human monocytic cell line Thr threonine tRNA transfer RNA TREM2 triggering receptor expressed on myeloid cells 2 Tyr tyrosine UC-HDL HDL isolated by ultracentrifugation UC-P apoB-depleted plasma produced by ultracentrifugation VCAM-1 vascular cell adhesion molecule VEGF vascular endothelial growth factor VLDL very low-density lipoprotein VTP VTP-38443, non-brain penetrant LXR agonist xxxii WHO World Health Organization WT wildtype X-34 1,4-Bis(3-carboxy-4-hydroxyphenylethenyl)benzene ZO-1 zona occludens 1 xxxiii Acknowledgements First and foremost, thank you to my supervisor Dr. Cheryl Wellington. I am very grateful that five years ago she saw a scientist within me and gave me the best support and mentorship to become that scientist. Her guidance in building skills of scientific thinking, her ability to challenge me to grow and to support me through the growing pains, her help in identifying my strengths, and her encouragement for me to own my work have made a world of difference throughout my studies. The Wellington lab has been a special place to conduct by PhD studies largely because of all of its fantastic people with their scientific input, technical support, and friendship. I would like to thank the current and existing members for all their support, Dr. Jerome Robert, Dr. Sophie Stukas, Dr. Jianjia Fan, Dr. Tara Caffrey, Dr. Asma Bashir, Dr. Wai Hang Cheng, Guilaine Boyce, Andrew Agbay, Anna Wilkinson, Carlos Barron, Jasmine Gill, Jennifer Cooper, Wenchen Zhao, Dr. Iva Kulic, Megan Gilmour, Sonja Soo, Dr. Kris Martens, Dr. Dhananjay Namjoshi, and Jeniffer Chan and the many co-op students over the years. Thank you also to the volunteers, co-op students, and junior graduate students that I had the opportunity to mentor, Sabrina Wei, Harleen Cheema, Emma Martin, and Elyn Rowe. Their technical support was invaluable and each of them helped me to grow as a mentor. Another special thank you to my co-supervisor in everything but name, Dr. Jerome Robert. He provided critical technical and scientific mentorship to get me started as a doctoral student and has continued to enhance my work through his support and feedback. Thank you also to both Pathology graduate student advisors that have existed during my studies, Drs. Haydn Pritchard and Dana Devine. In particular thank you to Dr. Haydn Pritchard who was instrumental in my decision to study at UBC. My PhD project would not have been possible without the support and guidance of my supervisory committee who always made time for our meetings despite their busy schedules. Thank you to Drs. Shernaz Bamji, Mari DeMarco, Angela Devlin, and Haakon Nygaard for your time, your advice, and your feedback. xxxiv I was very fortunate to receive funding for the first four years of my PhD, in particular I would like to acknowledge the Canadian Institutes of Health Research for the Doctoral Award I received that not only provided me with three years of salary funding but also with travel funding for conferences. The opportunity to present my work, build an international scientific network, and travel the world greatly enriched my PhD studies and was incredibly valuable to me personally. I would also thank and acknowledge critical people in my academic journey that lead me to begin a career in research in the first place. First, Dr. Robin Duncan at the University of Waterloo who provided me with my first taste of research over two years of volunteering, working, and learning in her lab. Even earlier, I would like to thank my high school Biology and Chemistry teacher Mr. Campbell who was the first person who taught me how to think like a scientist. Critical to my success has been my family and friend network. My parents, who have always been my most avid supporters, my friends both in Vancouver and elsewhere, who have provided well-needed balance to my life, and my husband, who has been the most supportive partner and greatest friend through this journey. xxxv Dedication To my parents, whose hard work opened the doors for me to pursue this PhD and whose unending encouragement helped to push me through and complete it. 1 Chapter 1: Introduction 1.1 Summary Alzheimer’s Disease International estimates there to be over 50 million people around the world living with dementia in 2019 [1,2]. The most common form of dementia in people over the age of 65 is Alzheimer’s disease (AD), a disease characterized by amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFT), and neurodegeneration [3]. In addition to these pathological hallmarks, cerebrovascular dysfunction has emerged as another leading contributor to AD [4]. Cerebral amyloid angiopathy (CAA), infarcts, vascular degeneration, or other forms of cerebrovascular dysfunction exist in 60-90% of AD autopsy cases [5] and changes to blood-brain barrier permeability have been observed in living people even at early stages of cognitive impairment [6]. The cerebrovasculature also provides two major routes of Aβ clearance from the brain [7]. Cerebral vessels are therefore an attractive target for future AD therapeutics or preventative strategies. High-density lipoproteins (HDL) have well-established vasoprotective functions on peripheral vessels [8] and therefore may also protect against cerebrovascular dysfunction in AD. Human epidemiological studies and studies in mouse models also support a protective role for HDL against AD however the mechanisms behind this protective effect are poorly understood. In this thesis, mouse models and human in vitro models are used to define AD-relevant, cerebrovascular protective functions of HDL and efforts are made to develop in vitro assays of these functions capable of evaluating HDL from clinical populations. Together, work in this thesis provides a rationale for the exploration of HDL-based therapies for AD and HDL-based functional assays as biomarkers for cerebrovascular health. 1.2 Alzheimer’s disease overview Alzheimer’s disease is named for Alois Alzheimer, a German physician and researcher who documented one of the first cases. Dr. Alzheimer first examined the 51-year old August Deter in 1901 and noted reduced comprehension and memory, aphasia, disorientation, unpredictable behavior, paranoia, auditory hallucinations, and psychosocial impairments [9]. Upon autopsy, 2 Alzheimer found plaques, neurofibrillary tangles, and arteriosclerotic changes in this patient, which he reported in a lecture in 1906 and published in 1907 [10]. This case was followed by several other similar published reports and in 1910 dementia with plaques and tangles was first referred to as “Alzheimer’s disease” in a psychiatric textbook [9]. Arguably equally important yet less known in the characterization of AD is Dr. Oskar Fischer [11]. Dr. Fischer was the first to describe neuritic plaques and published his findings of this feature in 12 cases of senile dementia in 1907 [12], the same year as Dr. Alzheimer’s initial publication. Dr. Fischer continued his neuropathological study of dementia and published his findings of neuritic plaques and neurofibrillary tangles in 191 [13]0 and 1912 [14]. However, his contributions were forgotten for many decades and remain undervalued compared to that of Dr. Alzheimer due to political and anti-Semitic tensions in the Austro-Hungarian Empire where he performed his research [11]. Tragically, Dr. Fischer was forced out of his research institution during this tumultuous time and later imprisoned at a concentration camp until his death in 1942 [11]. 1.2.1 Epidemiology and economic burden The most recent statistics on the global prevalence of AD and dementia from 2016 found that 43.8 million people suffer from dementia worldwide [15]. A systematic review and meta-analysis of 119 studies on dementia prevalence by Fiest et al. found an overall annual prevalence of dementia in people over 60 years of age of 30.4/1,000 people ranging from 11.7/1000 people in Asia to 103.6/1,000 in North America. Similarly, global annual incidence in people over 60 years of age overall was reported as 34.1/1,000 people but ranged from 11.5 in Nigeria to 97.8 in the USA. These regional differences, although not significant, are interesting and may be explained by differences in screening, reporting, life expectancy, competing risks, or dementia risk factor exposure [16]. In Canada, over 500,000 people are reported to have dementia with an increasing prevalence with age and a higher prevalence in females at each age. Specifically, 1.9% of males and 2.8% of females between the ages of 65 and 74 are affected by dementia while 28.7% of males and 37.1% of females over the age of 85 are affected. The direct and indirect costs of dementia in Canada is estimated at up to $33 billion annually [17]. 3 Changes in prevalence and incidence over time are of great interest to allow for prediction of disease burden in the future. The Global Disease Burden study on dementia evaluated the change in global prevalence from 1990 to 2016 and reported an increase from 20.2 million people worldwide to 43.8 million people. While this represents a 177% increase in total prevalence, the age adjusted increase was only 1.7%, indicating that most of the increase in dementia prevalence is due to a growing and aging population. They also found region specific differences including a reduced prevalence in high income countries [15]. Other have similarly seen decreases in dementia prevalence or incidence. The Framingham Study reported a decline in dementia incidence over their three decade study and suggested that better control of vascular risk factors may explain some of this shift [18]. The Cognitive Function and Ageing Study in England and Wales reported a 20% decrease in dementia incidence over two decades that was driven by reduced incidence in males [19]. 1.2.2 Familial versus sporadic AD People with AD are subcategorized into those diagnosed at ages below 65, called early-onset AD (EOAD), and over 65, called late-onset AD (LOAD). It is estimated that approximately 3-5% of AD cases are EOAD [20,21]. Although it has historically been assumed that EOAD cases are primarily due to autosomal dominant inheritance of mutated genes in amyloid-related proteins, more recent research suggests that only around 10% of EOAD cases are inherited this way [22,23]. Of the small proportion of EOAD patients with an autosomal dominant inheritance pattern, only 12% had known mutations in amyloid related proteins [22]. This suggests that 90% of EOAD cases are likely to have some form of genetic causality due to autosomal recessive mutations, genes with variable heterozygous penetrance, a mixture of genetic risk factors, or not yet discovered mutations [23]. In addition to the well-known, autosomal dominant mutations in APP, PSEN1, and PSEN2, other mutations in MAPT, SORL1, TREM2, and several genes in the endolysosomal pathway have also been found to be associated with EOAD [24]. Several gene associations, such as TREM2, APOE, and SORL1, are common to EOAD and LOAD [24,25]. Although heritability of EOAD is estimated at over 90%, heritability of LOAD is estimated to be between 45% and 81% [23]. 4 EOAD and LOAD are not only different based on their onset and heritability but also based on pathophysiology and clinical presentation. Non-memory impairment, specifically apraxia and visuospatial dysfunction may be higher in EOAD patients [26]. Although total neuropsychiatric index scores have been reported to be similar between EOAD and LOAD cases [27], others have reported more psychosocial problems in EOAD patients [25]. In terms of pathophysiology, EOAD patients show greater tau, NFT, and neuritic plaque burden, greater posterior hypometabolism, and reduced hippocampal volume loss [25]. Due to the reduced age of onset of EOAD, subjects exhibit less co-morbidities [25] including fewer vascular risk factors [28]. 1.3 Amyloid pathology Aβ plaques are one of the neuropathological characteristics of AD first observed by Dr. Alzheimer [10] and the “amyloid hypothesis”, which posits that Aβ aggregates cause neurodegeneration and memory loss in AD, is a leading theory for the pathobiology of AD [29]. Aβ peptides are formed when the membrane-bound amyloid precursor protein (APP) is cleaved first by β-secretase (BACE-1) to release a soluble APP fragment and a β-carboxyl terminal fragment (CTFβ). The CTFβ is then cleaved by γ-secretase to produce an APP intracellular domain and Aβ monomers. Cleavage of APP by BACE-1 and γ-secretases occurs predominantly in endosomes where the pH environment is optimal for BACE-1 function and γ-secretases are abundant relative to their expression at the plasma membrane [30–32]. The major components of γ-secretase are presenilin 1 and presenilin 2, whose encoding genes PSEN1 and PSEN2 are mutated in some EOAD cases [30]. Aβ is released as peptides ranging from 37 to 49 residues, depending on the final cleavage site by γ-secretase, although Aβ40 and Aβ42 are the most commonly found species in amyloid plaques [33]. Monomeric Aβ, in particular Aβ42, is prone to aggregation and forms oligomers then protofibrils then fibrils, which aggregate into plaques [33]. However, it is the oligomeric species of Aβ that is thought to be the most toxic to neurons [34]. The physiological functions of APP and Aβ are not fully understood, however, recent research has suggested that Aβ may function as an anti-microbial agent that traps infectious agents in the brain to be neutralized [35] and APP may function in regulating synaptic transmission by binding a γ-aminobutyric acid neurotransmitter receptor [36]. 5 Importantly, Aβ plaques and vascular amyloid deposition are also commonly observed in the brains of people without cognitive impairment. For example, 23% of healthy 70-year olds in a Swedish cohort had elevated cerebrospinal fluid (CSF) Aβ42 levels [37] and 33% of healthy controls in the Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) had amyloid pathology observed with Pittsburgh compound B (PIB) positron emission tomography (PET) [38]. The presence of these pathologies in some cognitively normal people may occur because there is a delay between the onset of amyloid pathology and cognitive impairment. Indeed, the proportions of cognitively normal people with positive amyloid PET scans in the AIBL study reflected the proportions of people with cognitive impairment 15 to 20 years later [38]. Similarly, a recent study found the duration of amyloid positivity correlates better with cognitive decline than amyloid positivity on its own, suggesting a cumulative effect of amyloid on cognition [39]. Alternatively, some individuals may have more resilience to the presence of amyloid in their brains. Factors that contribute to this resilience are of great interest to the field and may include genetics, cognitive reserve, or sociodemographic characteristics [40]. 1.4 Tau pathology The other major pathological hallmark of AD first observed by Dr. Alzheimer was NFTs [10], later found to be composed of hyperphosphorylated tau protein. Tau is most studied for its function in microtubule assembly and stability where it maintains neuronal projections to allow for transmission of signals along the axon to the synapse and reception of synaptic signals in post-synaptic dendrites [41]. Phosphorylation of tau reduces its binding to microtubules and results in self-assembly of tau into filaments that then form tangles [41]. It is not completely clear which structural forms of tau are the most toxic [41]. Pathological tau has also been shown to bind nucleoporins in neurons resulting in impaired nuclear import and export [42]. Tau is known to spread between neurons in a pattern throughout the brain that has been classically used for Braak staging of AD [43] and has been compared to prion propagation. Braak staging refers to the histological classification of abnormal tau neuropathology into stages that correspond to severity of cognitive deficits. Briefly, the distribution of tau tangles progresses from isolation in the transentorhinal layer to additional involvement of entorhinal cortex then finally to 6 involvement of the entire isocortext [44]. In human brains, tau seeds capable of misfolding monomeric tau are found not only in regions containing NFT but also in subsequent Braak staging regions without NFT. That these seeds are found mainly in the synaptic fraction of regions without NFT suggests they are transmitted synaptically [45]. Interestingly, when tau-containing brain homogenates are injected into the hippocampi of mice, tau first propagates to the hippocampus contralateral to the injection then spreads to synaptically connected regions and fails to spread to regions without synaptic connections, even when those regions are in close proximity [46]. How tau is spread between functionally connected neurons is less clear, although it has been demonstrated that neural activity is required [47] and some evidence suggests that exosomes may be responsible. Tau has been found in exosomes in the CSF [48] and blood [49] of AD patients in higher amounts than in controls even up to 10 years before diagnosis [49]. The feasibility of exosomal transmission of tau has been demonstrated in hippocampal slices and microfluidic systems [50] however this mechanism remains controversial. PET imaging of tau pathology has allowed for great advances in the understanding of tau pathology progression and its relationship with cognitive function. From these studies it has been observed that tau pathology may in fact be a better predictor of cognitive decline than amyloid pathology or structural changes by magnetic resonance imaging (MRI). For example, in a retrospective longitudinal analysis of a cohort of 152 subjects aged 55 to 91 years old, only tau predicted decline in episodic memory and executive function [51]. Furthermore, the temporality of AD pathologies have been evaluated using PET imaging in cognitively normal people where it has been shown that first Aβ levels rise and then elevated tau levels follow, and that people with the greatest changes in tau were most likely to progress to mild cognitive impairment (MCI) [52]. Pathological tau is not unique to AD. Mutations in microtubule associated protein tau (MAPT), the gene encoding tau, are associated with frontotemporal lobar degeneration (FTLD), a group of disorders often affecting personality, behaviour, executive function and language, and with Parkinson’s disease [41]. Chronic traumatic encephalopathy is a neurodegenerative tauopathy pathologically characterized by aggregates of phosphorylated tau in neurons and astrocytes surrounding small blood vessels in the depths of cortical sulci [53]. 7 1.5 Neurodegeneration Neurodegeneration is another pathological hallmark of AD that is obvious at autopsy based on gross atrophy of the brain. Ante-mortem, neurodegeneration can now be observed using MRI and suspected based on the result of fluid biomarkers. Early and progressive hippocampal atrophy is a key marker of AD on MRI, although it is not specific to AD [54]. Other areas of atrophy in AD include the neocortex, which reflects NFT pathology development, and subcortical structures [55]. CSF total-tau is used as a fluid biomarker for neurodegeneration in AD [37] and ongoing research is investigating additional biomarkers including CSF and plasma neurofilament light chain (NfL). NfL is an axon cytoskeletal protein released from damaged neurons [56]. Longitudinal measurements show that high baseline or change in plasma or serum NfL levels associate with reductions in cortical thickness, hippocampal volume, brain glucose metabolism measured with fluorodeoxyglucose (FDG)-PET [57,58], and cognitive function [57–59] as well as increases to amyloid PET burden [57]. In people with familial AD, plasma NfL levels begin to increase 16.2 years before diagnosis and the rate of change can predict conversion to dementia [59]. Elevated plasma NfL levels are not specific to AD and are also observed in other neurodegenerative conditions including frontotemporal dementia, amyotrophic lateral sclerosis (ALS), Huntington’s disease, and multiple sclerosis (MS) [60], as well as after neurotrauma [61]. 1.6 Neuroinflammation In recent years, neuroinflammation has emerged as another key component of AD pathophysiology. About 50% of genetic AD risk loci are related to microglia and innate immunity including genes for adenosine triphosphate (ATP)-binding cassette transporter A7 (ABCA7) and TREM2, which are expressed on microglia and myeloid cells, and complement receptor 1 (CR1) [62–64]. In vivo imaging studies show increased microglia in AD subjects in the temporal lobe and these increases correlate with amyloid load and inversely correlate with mini-mental state examination (MMSE) scores [65]. The role of microglia in AD is complex. In the early stages of AD, microglia may promote Aβ seeding in an attempt to trap infectious agents [35,62]. Mid-stage microglia may be more protective as they promote compaction of amyloid plaques to limit their toxicity to surrounding neurons. Late-stage microglia help to remove dead neurons but also 8 contribute to further neuronal damage as they activate tau kinases, produce reactive oxygen species and nitric oxide (NO), and promote astrocyte activation [62]. The switch from a homeostatic to a “neurodegenerative” or “disease associated” state is coupled with several transcriptional changes, including upregulation of APOE and TREM2 and downregulation of TGFβ, and is commonly observed in mouse models of AD, ALS, and MS and in human AD brains [66,67]. Indeed, several recent studies have found that depleting microglia with an inhibitor for colony stimulating factor 1 receptor (CSF1R) in AD mouse models can improve pathology. Administration of CSF1R inhibitor in 2-month old 5xFAD mice reduced neuritic plaque load and cognitive impairments [68] while treating 10-month old 5xFAD mice, 15-month old 3xTg-AD, or 6-month old APP/PS1 mice also improved memory but had no effect on amyloid load [69–71]. Similarly, CSF1R blockade in 8-week old P301S mice, expressing a mutant form of human MAPT, reduced tau pathology, neurodegeneration, and motor deficits [72] while microglia ablation in 6-month old P301S mice expressing human APOE-ε4, the greatest genetic risk factor for LOAD, suppressed neurodegeneration and had subtle effects on tau pathology [73]. A key role for microglia in AD pathogenesis is further supported by a recent analysis of cell-specific regulatory regions. As most AD GWAS hits correspond to noncoding regions, researchers defined enhancer-promoter regions specific to neurons, astrocytes, oligodendrocytes, and microglia in healthy brains then searched for AD risk genes in those regions. Variants associated with AD were mostly found in microglial enhancers suggesting the detrimental effects of these variants occurs through the actions of microglia [74]. 1.7 Clinical criteria for diagnosing AD In 2011, a working group from the National Institute on Aging and Alzheimer’s Association (NIA-AA) published updated criteria for diagnosing AD and MCI as well as a research framework for classifying preclinical AD and, in 2012, updated neuropathological criteria for AD [54,75–78]. These guidelines have been widely adopted, including by Canada at the 4th Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (CCCDTD4) [79]. According to the 2011 NIA-AA guidelines, the criteria for AD dementia is defined by cognitive or behavioural symptoms interfering with functional abilities, a decline in these functions from 9 previous observations, no alternative disorder existing to explain the symptoms, and diagnosis of cognitive impairment both from a patient history and an objective cognitive assessment. The cognitive assessment must include at least two of the following impairments: cannot acquire and remember new information, impaired reasoning and handling of complex tasks, poor judgement, impaired visuospatial abilities, impaired language abilities, or changes in personality or behaviour [54]. The core clinical criteria for MCI are much the same as those for AD, however, the impairment is in only one or more cognitive domains and does not interfere with the individual’s functional abilities. Tests for episodic memory are used to identify MCI patients likely to progress to dementia [80]. The presence of autosomal dominant AD mutations as well as imaging and fluid biomarkers increases the certainty of the AD or MCI diagnosis. Low CSF Aβ42, elevated CSF total tau or phosphorylated-tau (p-tau), elevated amyloid PET, decreased FDG-PET, or atrophy in particular regions on structural MRI (sMRI) were recommended as biomarkers in the 2011 guidelines [54,76]. The NIA-AA guidelines in 2011 recognized the importance of the preclinical stage of AD and the utility of biomarkers in identifying subjects in this stage, however, the working group determined that the existing evidence was only sufficient to develop a research framework, and not clinical criteria, for preclinical AD [77]. The most recent NIA-AA guidelines for preclinical AD, the ATN Framework, were proposed in 2018 [3]. In this framework, people are grouped according to their status in amyloid (A), tau (T), and neurodegeneration or neuronal injury (N) categories. Amyloid biomarkers are reduced CSF Aβ42, reduced CSF Aβ42/40 ratio, or increased amyloid PET. Positivity for tau is indicated by elevated CSF p-tau or tau PET. Reduction in FDG-PET, atrophy on sMRI, and increased CSF total tau constitute positivity for neuronal injury. Subjects are then classified as A-T-N- if they have no positive biomarkers, A+T+N+ if they have positive biomarkers in each category, or any other combination in between depending on their biomarker status. 1.8 Blood-based biomarkers for AD Although the importance of imaging and CSF biomarkers were recognized in the 2011 and 2018 NIA-AA clinical criteria and research frameworks for AD [3,75] and CSF biomarkers are entering 10 clinical practice for use in supporting diagnoses [81], these biomarkers remain expensive and invasive. Fortunately, less-invasive blood-based biomarkers have also seen substantial developments in recent years. Examples of the more promising candidates include Aβ42/Aβ40 ratio [82], total tau [83], p181-tau [84], NfL [59], BACE-1 [85], and multi-protein panels [56,86]. Measurement of plasma Aβ42/Aβ40 in particular has made great strides towards clinical use with the mass spectrometry-based device from C2N Diagnostics receiving FDA breakthrough designation in 2019 [87,88] and several other platforms following close behind. Other plasma Aβ42/Aβ40 measurement methods include the electrochemiluminescence-based Elecsys system by Roche [89] already in use for CSF, sandwich enzyme linked immunosorbent assays (ELISA) such as the ABtest system by Araclon Biotech [90], ultrasensitive paramagnetic ELISA platforms such as the Simoa (Single molecule array) system by Quanterix [82,91], immunomagnetic ELISAs [92], and assays that combine immunoprecipitation with mass spectrometry assays such as the Shimadzu assay [93,94]. These assays have had considerable success in predicting amyloid positivity by PIB PET imaging in patients, even in people without cognitive impairment [95], and especially when combined with APOE genotype data [89]. The use of plasma Aβ assays rather than amyloid PET in screening for clinical trials would also come with significant cost and time saving benefits [89]. 1.9 Risk factors for AD and dementia 1.9.1 Non-modifiable risk factors Age is the single greatest risk factor for AD. Prevalence of AD increases dramatically with increasing age, although, importantly, AD is not a normal part of aging [15,21]. Sex is another non-modifiable risk factor for AD with an enormous burden. Women have twice the lifetime risk of AD and make up 2/3 of AD cases [15,21]. Although it was initially assumed that women’s longer life expectancy explained their greater AD risk, there is actually little difference in AD incidence at any given age [96], suggesting that other factors are at play. Other potential explanations for the increased risk for women are hormonal effects, lower education levels, more sleep disorders, and survival bias due to the increased risk among men for cardiovascular disease (CVD) [97]. Many studies have focused on the potentially protective effect of estrogen including a recent report that demonstrated an inverse association between duration of reproductive status 11 (from menarche to menopause) and dementia risk as well as an increased risk for women with a late menarche, early menopause, or a hysterectomy [98]. Genetics makes up another large share of the non-modifiable risk for AD, the heritability of LOAD is estimated to be 45-81% and EOAD up to 90% [23]. A small portion of the heritability of AD is attributed to rare autosomal dominant mutations in amyloid processing genes [99]. Mutations in APP, encoding the precursor to Aβ, as well as in PSEN1 and PSEN2, encoding components of the γ-secretase complex that cleaves APP into Aβ [30,34], have long been known to be associated with EOAD [100–102]. Genome-wide association studies (GWAS) have provided the most recent wave of discovery for the genetic contribution to AD. Most genes identified in GWAS have a relatively small effect on AD risk [99], with the exception of the recently discovered TREM2 risk alleles [103,104]. Importantly, a majority of these risk loci are in genes that regulate cholesterol and lipid metabolism, immune, endocytosis, APP metabolism, and tau binding protein pathways [63,64]. APOE is the greatest genetic risk factor for AD as it is relatively common and can exert a 2- to 33-fold increase in AD risk for people homozygous for the APOE-ε4 allele with greater AD risk for women compared to men [105,106]. 1.9.2 Apolipoprotein E ApoE is the major apolipoprotein forming HDL-like particles in the brain [107,108] and is found on 5-10% of HDL in the plasma [109–111] and the majority of particles in the CSF [112]. The source of peripheral apoE is primarily the liver while CNS apoE is produce mainly by astrocytes and microglia [107,108]. Observations in subjects after a liver transplant have suggested that these pools of apoE are separate from each other, as plasma apoE was found to phenoconvert to that of the liver transplant donor but CSF apoE remained the same [113]. 1.9.2.1 Function of peripheral apoE ApoE is well understood to be a key regulator of peripheral lipid metabolism as it facilitates the uptake of lipoproteins by the low-density lipoprotein receptor (LDLR) and low-density lipoprotein receptor-related protein 1 (LRP1) [114]. Knockout of apoE in mice results in hypercholesterolemia 12 due to the inability to clear triglyceride-rich lipoproteins and these mice develop atherosclerosis, particularly when on a high-fat diet [115,116]. ApoE-containing HDL make up a small but important subclass of HDL formed through secretion of apoE from the liver after uptake, recycling of triglyceride-rich lipoproteins, and from apoE secreted from macrophages [114]. It is estimated that apoE-containing HDL accounts for approximately 10% of total HDL based on percent measurements by cholesterol mass [111] and by the percent of apoA-I mass [109,110] found on HDL particles containing apoE. ApoE-HDL also makes up close to 10% of HDL synthesis [109]. Recent focus on apoE-containing HDL has shown that after secretion into circulation, it quickly expands then is rapidly cleared [109] in a manner that can be affected by dietary fat consumption [117]. This rapid expansion and clearance suggests that apoE-HDL may contribute to reverse cholesterol transport, a role that may in part explain epidemiological reports of protective associations between apoE-HDL levels and coronary heart disease (CHD) risk [109,118,119]. ApoE-containing HDL has been shown to be cardioprotective through its actions reducing arterial stiffness, modifying extracellular matrix gene expression [120], and displacing hepatic lipase from heparin sulfate proteoglycans to allow for lipoprotein triglyceride hydrolysis [121]. Levels of plasma apoE-HDL are lower in adolescent males with T2DM compared to lean controls [122], in normolipidemic females compared to males [121], and during the postprandial state compared to during fasting in normolipidemic individuals [121]. Interestingly, evidence for a protective effect of plasma apoE-HDL on brain health is also emerging. In the Gingko Evaluation of Memory study, higher levels of apoE on plasma HDL was associated with reduced amyloid burden on PET scans [123] and higher levels of apoA-I on HDL particles containing apoE was associated with higher scores on a modified MMSE [124]. 1.9.2.2 Function of brain apoE In contrast to the effects of apoE deficiency on peripheral lipids and vascular health, the effects within the mouse brain are subtle and not consistent across studies. Although some have observed no effect of apoE deficiency on spatial learning with Morris Water Maze or on synaptic density or plasticity [125,126], others have observed specific alterations to hippocampal synaptic plasticity [127] and deficits in spatial learning by Morris Water Maze [128–130]. Importantly, the studies 13 by Anderson et al. finding no effect were performed both in male and female mice of similar ages to the other studies and the genotype of the mice was confirmed on the basis of significantly elevated plasma cholesterol levels [125,126]. However, different transgenic lines were used across studies, which may lead to different phenotypes. The function of apoE-containing lipoproteins in the brain is similar to the periphery in that they are responsible for transporting cholesterol and phospholipids however there are many other functions related to inflammation, amyloid pathology, tau pathology, and the cerebrovascular system, to be discussed below, all of which are affected by apoE isoform. 1.9.2.3 APOE as a risk factor for AD In humans, apoE is mainly found in three isoforms, although other rare variants exist [131,132]. These three isoforms differ in amino acids at two residues with Cys112 and Cys158 corresponding to apoE2, Cys112 and Arg158 corresponding to apoE3, and Arg112 and Arg158 corresponding to apoE4 [133]. The three most common APOE gene variants have varying associations with AD risk and age of onset where APOE-ε2 is protective, APOE-ε3 neutral, and APOE-ε4 detrimental. The effect of APOE-ε4 on AD risk ranges from approximately a 2 to 4-fold increase in risk for people carrying one allele and a 2 to 33-fold increase for people with two alleles [106]. The large range in APOE-ε4 risk effects depends in part on ethnic group as demonstrated in a meta-analysis where Japanese people were most affected and Hispanics least affected by the risk allele [106]. Age of onset also shows a gene-dose dependent effect of APOE-ε4 by approximately 10 years per allele, declining from an average age of onset of 84 years old with no APOE-ε4 alleles to 76 years old for APOE-ε4 heterozygotes and 68 years old for APOE-ε4 homozygotes [105]. The prevalence of one copy of the APOE-ε4 allele in the people without AD is approximately 15-30% and two copies less than 5% depending on the region studied, while the rates for two copies in AD cases is approximately 10-15% [106]. Overall, the APOE-ε4 allele is the greatest genetic risk factor for late-onset AD [134] and the risk of cognitive decline associated with APOE-ε4 only increases when combined with other factors including vascular risk factors [135] and female sex [136]. 14 1.9.2.4 ApoE isoform effects on amyloid pathology Given the substantial effect of APOE-ε4 on AD risk, many researchers have explored how apoE4 affects amyloid pathology as a possible explanation for the increased disease risk. A meta-analysis has shown that APOE-ε4 carriers have a 2 to 3-fold increase in amyloid pathology as observed with PET imaging [137] while other studies have observed greater amyloid plaque density and CAA in human APOE-ε4 carriers post-mortem [138]. Studies in mice have aimed to understand the mechanisms behind the increase in amyloid burden and have mainly converged on differences in the clearance of Aβ via the blood-brain barrier (BBB). Castellano et al. found that the concentration of Aβ in the interstitial fluid of transgenic mouse brains was higher in those expressing human APOE-ε3/ε4 or APOE-ε4/ε4 compared to APOE-ε4 noncarriers and the clearance of Aβ across the BBB was reduced [139]. Work in mice by the Zlokovic group demonstrated that apoE facilitates Aβ clearance across the BBB using specific receptors and that the preferred receptor and speed of transport via those receptors is dependent on apoE isoform. Specifically, exogenously administered radiolabelled apoE4 redirects Aβ to the slower VLDLR receptor while apoE2 and apoE3 more rapidly transport Aβ across the BBB using both VLDLR and LRP1 [140]. ApoE can also affect the proteolytic cleavage of LRP1 and LDLR transporters at the endothelial cell (EC) surface induced by Aβ [141]. ApoE2 and apoE3 suppress this Aβ-induced ectodomain shedding, thereby improving Aβ clearance, while apoE4 has no such beneficial effect [142]. A mouse study using cell-specific, inducible expression of human apoE isoforms suggests that the deleterious effect of astrocytic apoE4 on amyloid pathology is most pronounced during the Aβ seeding stage [143]. 1.9.2.5 ApoE isoform effects on tau pathology The effect of apoE isoform on tau pathology has been understudied compared to its effect on amyloid however a few studies suggest a detrimental effect of apoE4. Among humans with frontotemporal dementia, a tauopathy, those who carry an APOE-ε4 allele show worse behavioural symptoms [144]. A relatively recent study crossed P301S mice expressing a human mutant form of tau with human APOE knock-in mice and observed worse neurodegeneration and more neuroinflammation in the APOE-ε4 knock-in mice compared to APOE-ε3 knock-in controls [145]. Building upon this work, this research group next depleted microglia in the same human mutant 15 tau expressing APOE-ε4 knock-in mice and found that these mice were protected against neurodegeneration [73]. Surprisingly, they observed that mice lacking microglia had increased astrocytic and neuronal apoE concentrations, yet improved tau pathology and neurodegeneration. Together this suggests that the cellular source of apoE or cell-specific apoE signaling affects the pathological consequences of apoE in tau mice. 1.9.2.6 ApoE isoform effects on blood-brain barrier function Human APOE-ε4 carriers with AD show more BBB breakdown than noncarriers [146] including a higher CSF/plasma albumin quotient and greater levels of pericyte degeneration [147]. Studies in mice suggest that apoE-isoform dependent BBB breakdown is caused by pro-inflammatory cyclophilin A-matrix metalloproteinase 9 signaling in pericytes [148]. Studies with isogenic human EC produced from iPSC show more pro-inflammatory and pro-thrombotic signaling in APOE-ε4 cells even if these cells do not secret apoE [149]. Interestingly, APOE-ε4 and isogenic APOE-ε3 iPSC derived BBB-EC show no differences [150]. 1.9.2.7 ApoE isoform effects on astrocytes As astrocytes are the primary producer of apoE in the brain, it is of great interest to determine how astrocytes expressing various apoE isoforms may differ. Studies in APOE-ε4 knock-in mice [151] and human iPSC derived astrocytes expressing APOE-ε4 [152] both suggest that APOE-ε4 astrocytes have reduced phagocytic activity. Lipid metabolism is also disrupted in APOE-ε4 iPSCs, specifically, APOE-ε4 astrocytes show increased cholesterol accumulation [152] and secrete smaller, less lipidated particles [153] than APOE-ε3 astrocytes. Interestingly, these APOE-ε4 iPSC derived astrocytes did not support the growth and synaptogenesis of co-cultured iPSC neurons as well as APOE-ε3 iPSC derived astrocytes [153]. 1.9.2.8 ApoE isoform effects on microglia Although astrocytes are the primary producers of apoE in the brain in a healthy, resting state, microglia have been shown to have increased apoE expression during aging and neurodegenerative or inflammatory conditions in mice [154,155]. It has been long been reported that Aβ-expressing apoE knockout mice have reduced amyloid burden compared to apoE expressing mice [156–158], 16 however, it has more recently been shown that amyloid plaques in apoE deficient mice are larger, less compact, and surrounded by more dystrophic neurons [159,160], suggesting instead that apoE may protect against amyloid. The ability of apoE to compact amyloid plaques is dependent on TREM2. Mice lacking TREM2 or expressing a mutated form show reduced microglial activation, more amyloid deposition during the amyloid seeding phase, less microglial coverage of plaques, and less apoE deposition in the plaques [159]. Similarly, human AD patients with TREM2 mutations exhibit less clustering of microglia around plaques, particularly in those with TREM2 mutations which are associated with increased AD risk [159]. TREM2-dependent apoE signaling has been shown to be a key regulator of the switch of microglia from a homeostatic state to a neurodegenerative state in aging mice and mouse models of neurodegenerative diseases [66]. Studies on apoE isoform effects in microglia suggest that APOE-ε4 microglia are also dysfunctional. Human APOE-ε4 knock-in AD transgenic mice have larger amyloid plaques [161,162], reminiscent of the increased plaque size in apoE knockout mice described above, as well as reduced microglia clustering around plaques and reduced TREM2 expression by microglia compared to APOE-ε3 knock-in mice [162]. In vitro, murine microglia expressing human APOE-ε4 have a reduced ability to phagocytose Aβ compared to microglia expressing APOE-ε3 [163] and iPSC microglia from APOE-ε4 donors were found to have markedly altered phagocytic activity, inflammatory function, cholesterol metabolism, Aβ production, and tau phosphorylation compared to those from APOE-ε3 donors or APOE-ε3 isogenic cells [150,152,164]. Interestingly, iPSC microglia from people with familial AD mutations showed little difference from wildtype controls, suggesting APOE is a particularly important genetic factor in microglia [164]. Finally, as discussed above, a recent study depleted microglia from APOE-ε4-knock-in mice expressing human mutant tau [73]. They found that the neurodegeneration observed in mice with tau and APOE-ε4 does not occur in the absence of microglia, suggesting a detrimental function specifically of apoE4 produced by microglia or of apoE4 signalling in microglia. 17 1.9.3 Modifiable risk factors Although non-modifiable risk factors, namely age, sex, and genetics, undeniably have a large contribution to AD risk, increasing evidence in recent years suggests that dementia can potentially be prevented through addressing modifiable risk factors. According to a 2017 report commissioned by The Lancet, 35% of dementia risk is potentially modifiable [165]. This report evaluated the population attributable fraction for various modifiable risk factors, specifically education as an early-life risk factor, midlife hearing loss, hypertension and obesity as mid-life risk factors, and smoking, depression, physical inactivity, social isolation, and diabetes as late-life risk factors [165]. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) study was a double-blind randomized controlled trial (RCT) of 1,260 individuals aged 60 to 77 and was the first RCT evaluating a multi-modal lifestyle intervention for cognitive decline. The two-year intervention consisted of diet, exercise, cognitive training, and vascular risk factor monitoring while the control group received general health advice. In this landmark study, the intervention group showed improvements in executive function, processing speed, and neuropsychological test battery score and reduced the risk of cognitive decline [166] even in APOE-ε4 carriers [167]. Although both the French Multidomain Alzheimer Prevention Trial (MAPT) and Dutch Prevention of Dementia by Intensive Vascular Care trial (PreDIVA) found no benefits of multidomain lifestyle interventions in their RCTs overall, subgroup and post-hoc analyses were more promising. The MAPT trial showed benefits of the lifestyle intervention to parts of the MMSE and improvements to individuals at increased risk of dementia [168] while the PreDIVA trial found that lifestyle interventions reduced dementia incidence for non-AD dementia and reduced dementia risk in people with baseline untreated hypertension [169,170]. Several trials are ongoing testing the successful FINGER trial multi-domain intervention in other regions [170]. In May 2019, the World Health Organization (WHO) formally recognized the importance of modifiable risk factor reduction for dementia prevention in a report recommending preventative interventions and outlining the strength of each intervention based on existing research. The WHO guidelines strongly recommend tobacco cessation, physical activity, a balanced diet, management 18 of hypertension, and management of type 2 diabetes mellitus (T2DM). Recommendations of conditional strength were given for management of alcohol abuse, cognitive interventions, weight management, and management of dyslipidemia [171]. Select modifiable risk factors with a vascular component are discussed below. 1.9.3.1 Physical activity Physical activity interventions were not only a component of the seminal, multi-domain FINGER study but have also been extensively evaluated as a single intervention for the prevention of cognitive decline. A meta-analysis of 15 prospective studies found that all levels of physical activity consistently protected against cognitive decline, with an overall hazard ratio (HR) of 0.62 for high physical activity and HR of 0.65 for low to moderate levels [172]. A recent 44-year longitudinal Swedish birth cohort study of 800 women aged 38 to 54 found that physical activity was associated with an HR of 0.43 for mixed dementia and 0.47 for dementia with cerebrovascular disease, however, no risk reduction was found specifically for AD [173]. Data from RCTs studying older adults (generally over 65 years of age) are less consistent. Although several meta-analyses of RCTs including cognitively normal subjects or subjects with MCI or dementia have found a beneficial effect of aerobic exercise on cognitive function [174–177], a Cochrane systematic review found no benefit. This Cochrane review analyzed 12 RCTs including cognitively normal subjects at least 55 years of age and found that aerobic exercise exerted no benefit on any cognitive domain regardless of the type of control group used or the benefits to cardiovascular fitness acquired during the trial [178]. Although the trials included in the Cochrane review were shorter in duration than most in the previously mentioned meta-analyses, a recent, longer RCT similarly found no effect of exercise on cognition in healthy adults. This multi-centre US-based RCT of 1,635 sedentary adults between ages 70 and 89 studied the effects of a 24-month moderate-intensity intervention and found no improvements to any cognitive outcomes compared to a health education intervention, except in those over 80 years old and those with the lowest baseline physical performance [179]. Differences in the cognitive assessments used, age of study participants, aerobic exercise intensity, duration, and frequency, and the type of control group used may account for some of these inconsistencies. Overall, while observational 19 studies suggest that physical activity during mid-life protects against cognitive impairment, RCTs do not consistently show a benefit of aerobic exercise on the cognitive function of older adults. 1.9.3.2 Diet Research on diet and dementia has mainly focused on the potential benefits of the Mediterranean diet. For example, in a 6.5-year RCT of 522 people with high vascular risk aged 75 on average, those on the Mediterranean diet supplemented with nuts or extra virgin olive oil performed better on the MMSE and clock drawing test than those on the low-fat control diet [180]. An observational study on a younger population of 70 people aged 30 to 60 similarly found benefits to the Mediterranean diet, those with higher reported adherence to the diet had improved FDG-PET and PIB-PET both at baseline and three years later [181]. A cross-sectional study also in 30- to 60-year old subjects found that greater Mediterranean diet adherence was associated with greater cortical thickness on MRI [182]. 1.9.3.3 Type 2 diabetes mellitus Although T2DM is not entirely a modifiable risk factor, its association with dementia risk is strong and management of T2DM has been recommended to prevent dementia [171]. A meta-analysis of 17 studies with a total of 1,747,777 participants found that the relative risk (RR) for AD for people with diabetes is 1.52 [183]. In particular, people with untreated diabetes [184] and diabetics who are also APOE-ε4 carriers [185] are at an increased risk of dementia. Brain imaging abnormalities have been well-documented in subjects with T2DM including cortical [182,186] and hippocampal [187] atrophy and deep white matter lesions [188] on MRI. T2DM also has specific effects on the BBB, for example, increased BBB permeability that was also correlated with biomarkers of endothelialisation in the CSF [189]. Others have observed increased BBB permeability as well as pericyte degeneration, basement membrane thickening, and capillary density changes in in vitro and in vivo models of diabetes or hyperglycemia, which may result from oxidative stress and inflammation [190]. 20 1.9.3.4 Hypertension The relationship between blood pressure and AD risk is complex with different associations occurring at different ages, indications of risks of both hypertension and hypotension, and mixed results in clinical trials. Several recent, large population studies have made great advances in understanding the relationship. For example, the Atherosclerosis Risk in Communities (ARIC) has suggested that mid-life, but not late-life hypertension is associated with more cognitive decline two decades later [191]. Furthermore, they found that mid-life hypertension followed by late-life hypotension actually increases the risk of dementia (HR 1.62) at a rate comparable to sustained hypertension during mid-life through to late-life (HR 1.49) [192]. The 1946 British birth cohort study (Insight 46) found that hypertension even earlier in life can have damaging effects on the brain. Specifically, high systolic and diastolic blood pressure at 36 and 53 years of age as well as an increase in blood pressure from 43 to 53 years of age in this cohort was associated with increased white matter hyperintensity volume at 69 to 71 years of age. Similarly, higher diastolic blood pressure at 43 years of age and increases from 36 to 43 years of age was associated with smaller whole brain volume at late-life follow-up while increases in systolic blood pressure from 36 to 43 years of age was associated with smaller hippocampal volume at follow-up [193]. It is possible that dementia risk could be reduced in hypertensive individuals if blood pressure is successfully lowered, although few studies attempting this preventative strategy have been published, most trials fail to find any benefits, and the benefits may depend on the age at which hypertension is treated and the severity of the hypertension at baseline [194–196]. 1.9.3.5 Hypercholesterolemia The effect of blood cholesterol concentration on AD risk similarly seems to depend on age. Systematic reviews and meta-analyses suggest that mid-life total cholesterol concentrations positively correlate with AD risk but late-life total cholesterol has no such association or even an inverse association [197–199]. For example, serum total cholesterol concentration measured at 75 years of age had no association with incident AD risk in original cohort of the Framingham study [200] while positive associations were observed between mid-life cholesterol concentrations in both the ARIC study at follow-up 20 to 25 years later [201] and in the Three-City Study at follow-up 13 years later [202]. On the other hand, mid-life cholesterol measured in the Prospective 21 Population Study of Women in Sweden was not associated with AD 32 years later [203]. The effect of statins on AD risk is similarly unclear. Statins are drugs that inhibit a key enzyme in cholesterol synthesis and are widely administered to people with hypercholesterolemia. Although prospective studies consistently find that statin use reduces dementia and AD risk [204–206], RCTs find no such effect [204,207]. Mendelian randomization studies have attempted to discern whether the associations between hypercholesterolemia and AD are causal or a result of cofounding variables by studying the effects of genetically inherited polymorphisms that alter plasma cholesterol concentrations. Several such studies have found that genetic predisposition to high serum total cholesterol is not related to AD risk [165,208,209], however, this does not rule out an effect of hypercholesterolemia arising from non-genetic causes. High plasma triglyceride and low-density lipoprotein cholesterol (LDL-C) concentrations and low HDL-C concentrations are also found to be associated with increased AD risk in some studies, although the relationship is even less clear [197,202,210]. The association between plasma HDL-C and AD risk is more thoroughly discussed in section 1.14.2. 1.10 Therapeutics for AD 1.10.1.1 Existing AD therapeutics There are currently no disease altering AD therapeutics, only drugs for easing AD symptoms. The two classes of symptomatic drugs currently available include cholinesterase inhibitors (donepezil, rivastigmine, and galantamine) and an N-methyl-D-aspartate (NMDA) receptor antagonist (memantine). Meta-analyses suggest that donepezil can improve cognitive dysfunction in AD [211] and while memantine can also be beneficial in moderate-severe AD, its effects are subtle [212], inferior to donepezil [211], and best when in combination with donepezil [213]. 1.10.1.2 AD therapeutics in ongoing clinical trials As amyloid is found in all AD subjects and the “amyloid hypothesis” suggests that Aβ accumulation is responsible for neurodegeneration and memory loss in AD, most therapeutics in development for AD target Aβ [214]. Amyloid antibodies, such as aducanumab, have progressed further in clinical trials than any other AD therapeutic. Although promising phase I trial results for aducanumab [215] were followed by a phase III trial termination due to futility [216], a recent re- 22 analysis of the phase III trials has revived excitement for this drug [217]. The re-analysis accounted for a mid-study protocol amendment that altered the dosing of many APOE-ε4 carrying participants who were originally on lower doses due to concerns regarding amyloid related imaging abnormalities (ARIA). Once these additional participants on the high dose of aducanumab were included, the larger EMERGE trial met its primary endpoint of slowed decline on the Clinical Dementia Rating-Sum of Boxes (CDR-SB). However, data from this trial has not yet been peer-reviewed and the re-analysis of the smaller aducanumab phase III trial ENGAGE did not replicate the positive findings of EMERGE, resulting in continued uncertainty as to the future of aducanumab [218]. Several BACE-inhibitors, drugs which block the cleavage of APP by β-secretase into Aβ precursors, have failed in clinical trials. Atabecestat was terminated in phase II/III due to liver toxicity [219], umibecestat was terminated in phase II/III due to a worsening in cognitive function in treated subjects [220] and verubecestat [221], LY3202626 (NCT02791191), and Atabecestat (NCT02406027) were all terminated in phase II or II/III due to futility. Elenbecestat is the last remaining BACE-inhibitor in development with a phase III clinical trial currently recruiting (NCT02956486). Other researchers have turned their attention away from amyloid-based therapies to instead attempt to target tau or inflammation. A phase II trial of BIIB092 for early AD has completed recruitment and is now ongoing (NCT03352557) while a phase II trial of ABBV-8E12 for early AD is currently enrolling by invitation (NCT03712787). In terms of neuroinflammation, a recent trial of naproxen, a non-steroidal anti-inflammatory drug, failed to slow cognitive decline and had dangerous intestinal and cardiovascular side effects, cooling enthusiasm for further research in this area [222]. 1.11 The cerebrovascular system Despite comprising only 2% of total body mass, the brain consumes approximately 20% of total cardiac output [223]. The high metabolic activity of the neural network and lack of glucose stores in the central nervous system (CNS) means that the brain must be highly vascularized to enable oxygen and glucose influx, maintain ion balance, and remove neurotoxic waste products [224]. 23 The BBB has a specialized anatomy compared to peripheral vessels to regulate the transport of waste products and nutrients into and out of the brain. Tight junctions between endothelial cells (ECs) create a selective barrier between the blood and the brain parenchyma through which transport is regulated by specialized proteins on ECs. ECs of the cerebrovasculature are surrounded by mural cells, either pericytes in capillaries or vascular smooth muscle cells in arteries and arterioles, and astrocytic end-feet [225–227]. 1.11.1 The relationship between the cerebrovasculature and AD The importance of the vasculature to dementia is underscored by the fact that most dementia cases exhibit vascular pathologies [228]. Early, anecdotal observations by Hassler et al. found arteriole and precapillary deformities to be very common in the brains of people with dementia, less common in aged people without dementia, and not present in people younger than 68 years old [229]. Subsequent systematic and quantitative analysis of post-mortem AD and healthy brains built upon these initial observations and found reduced vascular density [230,231], increased vessel tortuosity [231], and more vessel remnants lacking a lumen and EC coverage [146,232,233] in the brains of people with AD. More recently, the US-based National Alzheimer’s Coordinating Center studied nearly 6,000 brain autopsies with various neurodegenerative diseases and found that AD subjects had a greater burden of cerebrovascular pathologies including macro- and micro-infarcts, atherosclerosis, arteriosclerosis, and CAA than people with other types of neurodegenerative diseases [228]. Similarly, the Religious Orders Study and Rush Memory and Aging Project studied over 1,100 post-mortem brains and found that the severity of atherosclerosis or arteriosclerosis and presence of macro- and micro-infarcts was correlated with AD risk [234]. Technological advancements in imaging techniques and biomarker measurements have provided further evidence of cerebrovascular dysfunction in AD in living people. An elegant analysis of plasma and CSF biomarkers along with 7,700 brain images obtained through the Alzheimer’s Disease Neuroimaging Initiative (ADNI) tracked the progression of AD and suggested that cerebrovascular dysfunction, specifically reduced cerebral blood flow measured with arterial spin labelling magnetic resonance imaging (ASL-MRI) and reduced glucose metabolism measured with FDG-PET, is an early temporal event in the development of AD [235]. Many other groups 24 have also observed cerebrovascular dysfunction in living AD subjects with imaging techniques. Transcranial doppler techniques have shown that reduced cerebral blood flow predicts dementia risk and cognitive decline [236] and microbleeds observed on MRI have been associated with AD risk [237,238]. Pulse wave velocity tests have shown that greater arterial stiffness is associated with greater Aβ burden observed with PET imaging, lower brain volume in certain brains regions observed by MRI, and higher white matter hyperintensity burden [239]. Furthermore, dynamic contrast-enhanced MRI has shown that BBB breakdown occurs in the hippocampus in an age-dependent manner, worsens with MCI [240], and occurs even in the early stages of cognitive impairment independent of Aβ and/or tau biomarker changes [6]. Taken together, vascular pathology, including micro- and macro-infarcts, small vessel disease, white matter abnormalities, arteriosclerosis and CAA, can be the major pathology in 10-20% of dementia cases and are associated with slower perceptual speed and poorer episodic memory [5,241–244]. MRI sequences that can evaluate disrupted cerebral blood flow and cerebral small vessel disease were recently proposed as potential vascular biomarkers to be added into the 2018 ATN Research Framework for preclinical AD in recognition of their importance in identifying vascular dysfunction in the early stages of AD pathogenesis [4]. 1.11.2 Cerebrovascular clearance of Aβ from the brain The cerebrovasculature plays a pivotal role in removing Aβ from the brain. This can occur through active transport across ECs of the BBB or via vascular drainage in mid- and large-sized arteries [245]. Direct transcytosis through the BBB involves the LRP1, LRP2, VLDLR, p-glycoprotein and phosphatidylinositol binding clathrin assembly protein (PICALM) [246–249] (Figure 1.1). Lipoproteins affect direct BBB clearance of Aβ by acting as receptor chaperones whereby apoE2 and apoE3 direct Aβ clearance through the faster LRP1 route whereas apoE4 prefers the slower VLDLR and apoJ assists in transport via LRP2. Aβ is also drained through the paravascular and intramural space of the brain artery wall toward the cervical lymph nodes [7]. The space between the pia mater and glia limitans forms the paravascular space where Aβ transport is facilitated by astroglial aquaporin 4 and diffusion, whereas for intramural drainage, soluble Aβ enters the basement membrane of the capillaries and is drained along the basement membrane surrounding 25 smooth muscle cells [7,250,251]. Modification in vascular compliance has been proposed as a cause of impairment leading to vascular Aβ accumulation known as CAA [252]. Figure 1.1 Aβ clearance via the cerebrovasculature. In addition to clearance of Aβ by intracellular and extracellular degradation, Aβ can be cleared via the cerebrovasculature. Direct Aβ transport across the BBB at the capillaries occurs via receptors such as LRP1, LRP2, and VLDLR. LRP1 receptor-mediated endocytosis of Aβ is assisted by PICALM. Aβ also drains to the lymph system via perivascular drainage pathways whereby Aβ enters the capillaries then drains through the paravascular or Virchow-Robin space with the assistance of AQP4 or drains through the intramural pathway between layers of smooth muscle cells of arteries. Aβ: amyloid beta, AQP4: aquaporin 4, LRP1: low-density lipoprotein receptor-related protein 1, VLDLR: very low-density lipoprotein receptor, PICALM: phosphatidylinositol binding clathrin assembly protein, P-gyp: P-glycoprotein, apo: apolipoprotein. 1.11.3 Cerebral amyloid angiopathy The deposition of Aβ in vessels, known as CAA, has many similarities to Aβ deposition in neuritic plaques including their similar genetic and sporadic origins. Mutations in APP, in particular in or near the Aβ coding regions, increased APP gene dosage, and mutations in PSEN1 and PSEN2 are all related to familial CAA while APOE is the greatest risk factor for sporadic CAA [253]. Unlike parenchymal Aβ deposits, which are predominantly composed of Aβ42, Αβ40 dominates in vascular amyloid deposits [253,254]. 26 Sporadic CAA is found in two forms. In Type I CAA both arterioles and capillaries are affected while in Type II CAA capillaries are unaffected [255] although autopsy studies suggest that capillary CAA presence and severity is a better predictor of neuritic plaque and other AD pathologies and dementia [256,257]. Autopsy studies find a high prevalence of CAA both in normal aging and in dementia. For example, in the Religious Orders study, 85% of their aged cohort (average age 87 years old, N=404) had CAA pathology and CAA was found in 94% of cases with dementia [258]. CAA is diagnosed based on the Boston criteria. The original Boston criteria relied on post-mortem or biopsy diagnoses however the updated criteria now includes the diagnosis of probable or possible CAA based on MRI or CT findings. Specifically, the presence of multiple hemorrhages (in lobar, cortical, or subcortical regions or a single lobar, cortical, or subcortical hemorrhage with cortical superficial siderosis) found using neuroimaging in people at least 55 years of age with no other cause of hemorrhage constitutes probable CAA. Possible CAA uses the same criteria but with the presence of a single hemorrhage [259]. Although the presence of hemorrhages is used to assist in the diagnosis of CAA, ischemic lesions are also observed [260] and include white matter hyperintensities, structural dysfunction observed using diffusion tensor imaging MRI, and cerebral microinfarcts [253]. 1.11.4 Tau and the cerebrovasculature In addition to Aβ, it is also important to consider the interaction of the cerebrovascular system with tau. Researchers have long observed NFT and p-tau in the perivascular regions of immunostained post-mortem AD brains [261–263]. This perivascular tau pathology correlates with the severity of vascular Aβ pathology [264] and vessels afflicted with CAA are more likely to show perivascular tau pathology than non-CAA vessels [265]. More recent work systematically quantified perivascular tau pathology and BBB impairment in post-mortem brains at each Braak stage of tau deposition and revealed that deposition of perivascular tau and vascular degeneration, specifically loss of α-smooth muscle actin (α-SMA) expression, occurs in early Braak stages and can be independent of CAA deposition [266]. While tau is typically an intracellular protein and the perivascular tau observed in the above studies is assumed to be intracellular, Castillo-Carranza et 27 al. recently demonstrated that extracellular tau oligomers can also deposit in perivascular spaces. They used a tau oligomer specific antibody for immunostaining on post-mortem brains and observed increased perivascular deposition of extracellular tau oligomers in AD brains and in Tg2576 AD model mice compared to non-AD humans and mice [267]. Studies using mice transgenic for human tau or injected with adeno-associated virus (AAV) tau constructs have shown that perivascular tau and cerebrovascular dysfunction can also occur in the absence of Aβ pathology. For example, increased BBB leakiness [268–270], abnormal blood vessel morphology [271], and perivascular astrocyte swelling [270] are observed in mice expressing human mutant tau. Also emerging from studies in mice is the role of the cerebrovasculature in clearing tau from the brain. Tau-specific BBB transporters have not yet been discovered and most tau clearance is assumed to occur via intracellular degradation [7,272]. However, it has been suggested that the cerebrovasculature may be involved in clearing soluble extracellular tau as peripheral administration of tau oligomer antibodies in transgenic human mutant tau mice reduced brain concentrations and increased serum concentrations of oligomeric tau [273]. Furthermore, extracellular human tau injected directly into the cerebral cortex of mice appears to be cleared via glymphatic pathways along perivascular spaces [274]. 1.11.5 Cerebrovascular inflammation Inflammation is another component of cerebrovascular dysfunction known to occur in AD. Microvessels isolated from the brains of people with AD have elevated concentrations of a number of pro-inflammatory markers compared to age-matched control brain microvessels including hypoxia-inducible factor α (HIF-1α), IL-8 [275], monocyte chemoattractant protein 1 (MCP-1), IL-1β [276], NOS activity [277], TGFβ [278] and phosphorylated p38 MAPK [279]. More recent unbiased gene expression profiling supports these earlier observations with gene ontology analysis suggesting that 2,865 genes are differentially regulated in microvessels from AD brains, most of which overlap in immune system, defense, chemotaxis, and inflammatory response pathways [280]. 28 Studies both in humans and in mice suggest that abnormal thrombosis and fibrinolysis contribute to cerebrovascular inflammation in AD brains. While the thrombosis and fibrinolysis are part of a process critical in preventing hemorrhage, the healthy balance of clot formation and degradation may be disrupted in disease resulting in prolonged fibrin deposition in tissues, which is problematic in the brain due to the inflammatory properties of fibrin [281]. Fibrin positive blood vessels [282] and parenchymal fibrin deposits [283,284] are observed more commonly in AD mouse and human brains than controls. Parenchymal fibrin deposits in the AD mouse brains are found in areas with BBB breakdown, increase with age, and correlate with Aβ deposition [283]. Reducing fibrinogen levels with genetic or pharmacological methods in AD mice results in improvements to AD pathologies including reduced BBB damage, neuroinflammation [283], CAA, and cognitive impairment [282]. Similarly, cerebrovascular thrombin levels are elevated in human and mouse AD brains [275,285] and treatment of AD mice with a thrombin inhibitor reduces HIF-1α, IL-6, MCP-1, and MMP2 expression on cerebral vessels [285]. The cerebrovascular system also provides a route for immune cell entrance into the brain. While peripheral immune cell infiltration is a major component of some neurodegenerative diseases, such as MS and stroke [286], the role of this process is less clear in AD. Although evidence for infiltrating immune cells in human AD brains is limited [287], extravasation of neutrophils [288], monocytes [289], and T cells [290] into the parenchyma of AD mouse brains have been demonstrated. Neutrophils can cause cerebrovascular dysfunction in AD mice as they adhere to capillaries resulting in stall blood flow [291] and as they extravasate to areas with Aβ then release pro-inflammatory signals [288]. Antibodies against neutrophils, neutrophil depletion, or blocking of neutrophil receptors can reverse these effects and results in reduced cognitive decline [288,291]. Live two-photon microscopy reveals patrolling Ly6Clo monocytes similarly are attracted to Aβ as they are observed to bind and crawl into the walls of Aβ-positive veins, take up Αβ, and return to the blood stream. Blocking monocyte extravasation in AD mice by Ly6Clo monocyte ablation worsens plaque load [289] while deficiency in the CC chemokine receptor 2 (CCR2) required for monocyte recruitment both increases both plaque load and worsens memory deficits [292], suggesting that monocyte infiltration may play a protective role against Aβ pathologies. 29 Perivascular macrophages likely play a similar role as depletion of these cells in AD mice leads to increased CAA while perivascular macrophage stimulation reduces CAA [293]. In order for leukocytes to enter the brain, endothelial cells of inflamed tissues express adhesion proteins on their cell surface that bind patrolling immune cells [294]. Interestingly, although it is unclear whether leukocyte extravasation occurs in AD, plasma concentrations of several of these proteins are increased in AD subjects [295–297]. Vascular cell adhesion molecule 1 (VCAM-1) plasma concentrations in AD subjects increases depending on AD severity, correlates with CDR-SB cognitive scores, and is associated with white matter changes [297]. VCAM-1 has also been tied to the detrimental outcomes on the brain in aging. In experiments where aged plasma was administered to young mice, the young mice suffered increased microglial reactivity and cognitive deficits similar to those of old mice. These detrimental effects were suppressed with an anti-VCAM-1 antibody, suggesting that leukocyte extravasation is harmful to the aging brain [298]. Changes to leukocyte infiltration in AD may also be related to the microbiome. A recent study in AD mice found that microbiome altering drug sodium oligomannate reduced neuroinflammation and cognitive deficits [299] and in an unpublished phase III clinical trial it was reported to improve cognition in AD patients [299]. Microbiome alterations have been reported in people with AD [300,301] but conclusions of the effectiveness of sodium oligomannate and its mechanisms of action are not possible until more data from the previous phase III trial is made available and until after the completion of the global phase III trial that begins in 2020. There is also skepticism among AD researchers of the connection between microbiome changes and inflammation in the treated mice as some reported microbiome alterations are in fact related to increased inflammation [302,303]. 1.11.6 Vascular co-morbidities in AD The important role of the vasculature in AD is further put into perspective when one considers that CVDs such as stroke and CHD, are associated with increased AD risk [243,304,305]. Associations between CVD and AD might be due to direct causal effects, as cerebral perfusion is often reduced in CVD, or due to shared genetic and environmental risk factors. Indeed, numerous genetic studies 30 have shown that individuals who carry at least one copy of the APOE-ε4 allele have an increased risk for both AD and CVD compared to those without the APOE-ε4 allele [306]. AD and CVD share many cardiometabolic and lifestyle risk factors, especially in middle-aged and elderly people, including age, sex, smoking, blood pressure, physical activity, blood lipids, and T2DM [182,191,307]. Several of these factors have been compiled into composite measures of general cardiovascular risk known as the Cardiovascular Risk Factors Aging and Dementia (CAIDE) risk score [308,309], which has recently been shown to correlate with executive function, visual perception and construction, APOE, white matter hyperintensities and CSF AD biomarkers including Aβ and tau in healthy adults [308]. CAIDE dementia risk scores above 11 points were also recently associated with a 2.10 increase in the odds ratio for cerebral infarcts up to 10 years later [309]. In the population-based Rotterdam Study, a cerebral small vessel disease sum score based on MRI was recently found to be associated with higher risk of stroke, dementia and death [310]. The Framingham cardiovascular risk profile score also predicts conversion from amnestic MCI to AD within 24 months [311]. 1.12 In vitro models of the BBB Given the importance of the cerebrovascular system to the pathogenesis of AD, developing human-based vascular models that retain both anatomical and physiological similarities to humans are highly desirable. A wealth of studies on the BBB have been performed using traditional two dimensional (2D) cell culture methods from primary human or animal brain ECs, immortalized human or animal brain ECs, and brain ECs produced from human or mouse pluripotent stem cells sometimes in co-culture with other BBB cell types [312–320], however it is increasingly recognized that cells behave very differently in three dimensional (3D) compared to 2D environments [321]. Breakthroughs in stem cell biology and tissue engineering have resulted in several innovative in vitro models of the human BBB with increased relevance for human physiology. The validity of each model is assessed by examining the hallmark properties of the BBB including the anatomical structure, microenvironment, barrier function, cell function and composition [322]. 31 Traditional trans-well systems offer highly reproducible models for permeability assays [323,324]. A recent example using a BBB trans-well system demonstrated that apoA-I and apoJ increase Aβ40 efflux through the endothelium [325]. While this system displays high trans-endothelial electrical resistance and functional efflux, it lacks the complex cell-cell and cell-matrix interactions of the cerebral vasculature. Advanced microfluidic devices have advantages over trans-well methods as they allow for cell-cell interactions and can be perfused. The first 3D neurovascular microfluidic model brought together primary murine neurons and glia cells with human cerebral ECs to study BBB transport [326]. To address concerns about the notable differences in drug transporters between murine cell types to human physiology, Kamm and colleagues have now populated microfluidic devices with human iPSC-derived ECs, primary human pericytes and astrocytes [327]. Maoz and colleagues have developed a distributed neurovascular unit organ-on-chip microfluidic system by linking a BBB chip to a brain chip composed of neurons and astrocytes [328]. This novel system successfully identified previously unknown metabolic coupling between the BBB and neurons but is unable to model the anatomical connections between cells of the neural vascular unit. Vatine et al. also used iPSC to create a microfluidic BBB with neural spheroids and brain EC separated by a porous barrier allowing for cell to cell interactions [329]. EC is this model stained positive for BBB markers and spontaneous neuronal activity was observed by calcium imaging. Interestingly, whole blood perfused through the lumen of this system was contained by the capillary wall and did not induce neuronal toxicity, demonstrating the formation of a physiological BBB. Cerebral organoid models provide further advancements to physiological relevance in that they are capable of forming distinct cortical layers [330] and can be partially vascularized using a variety of strategies. Ham et al. aimed to vascularize their embryonic stem cell (ESC) cerebral organoids using treatments of recombinant vascular endothelial growth factor (VEGF) [331]. VEGF-treated organoids had increased ANGPT and PECAM1 mRNA expression levels and increased cluster of differentiation 31 (CD31) staining with minimal changes to stem cell and neuroectoderm markers. At 32 days of age, CD31 and claudin-5 positive tubes were visible in the VEGF-treated organoids yet absent in untreated organoids. The addition of wnt7a, a component 32 of the wnt/βcatenin pathway crucial to BBB development [332], resulted in the presence of α-SMA-positive cells surrounding CD31-positive cells in 4-month old brain organoids. Song et al. employed an entirely different method in organoid vascularization, opting instead to create a hybrid iPSC-derived neuro-vascular organoid from the addition of a vascular spheroid to a neuronal spheroid [333]. These hybrid organoids were positive for CD31, zona occludens 1 (ZO-1), and VE-cadherin markers, which were further increased by the addition of mesenchymal stem cells (MSC) at the time of spheroid combination. A third strategy employed by Cakir et al involved engineering embryonic stem cells to express human ETS variant 2 (ETV2), a transcription factor involved in vascular development [334]. Their vascularized organoids with 5-20% ETV2 expressing cells featured lumens positive for more CD31, claudin-5, ZO-1, GFAP, and occludin than organoids without ETV2 expressing cells and showed significantly less necrosis and hypoxia. Although perfusion with dextran demonstrated the perfusibility of CD31 and claudin-5 positive lumens, only 8% of the lumens were perfused in total suggesting a lack of connectivity of the vascular network. A final strategy attempted by several groups was simply to co-culture various cell types of interest to form an organoid with a neuronal core and endothelial surface. Pham et al. coated 34-day old iPSC-derived cerebral organoids with iPSC-derived EC and observed vascularization of the outer layers of the organoid after 3-5 weeks in vitro or two weeks after implantation into a mouse [335]. Cho et al. instead used primary astrocytes, pericytes, and EC cultured simultaneously and observed self-organization of organoids with an astrocyte core and a surface of pericytes and EC after 48 h [336,337]. The surface of these organoids stained positive for claudin-5, occludin, ZO-1, and LRP-1 and expressed functional P-glycoprotein as demonstrated with successful rhodamine 123 transport into the spheroid interior. Since then, Nzou et al. co-cultured organoids with a vascularized surface containing a total of six cell types from primary or iPSC sources [338]. They began by culturing neurons, astrocytes, oligodendrocytes, and microglia into spheroids then added pericytes and EC after 48 h to produce vascularized organoids with surface expression of ZO-1, claudin-5, P-glycoprotein, GLUT-1, β-catenin, and VE-cadherin. While these recent advances are innovative and exciting, 3D organoids with a completely perfusible vascular network have yet to be developed. 33 1.13 High-density lipoproteins (HDL) As thoroughly described above, promoting vascular health is a key strategy in preventing dementia and may be a novel strategy in treating AD. Studying the effects of known vasoprotective agents on the brain may therefore lead to innovative therapeutic strategies for AD. One such vasoprotective agent is HDL. Like other mature lipoproteins, HDL consists of a core of hydrophobic lipids surrounded by a phospholipid and free cholesterol monolayer studded by proteins (Figure 1.2) [339]. A key protein found in most HDL particles is apoA-I, which makes up 70% of its protein content [340]. The major lipid classes found on HDL includes cholesterol and other steroids, phospholipids, cholesteryl esters, sphingolipids, and triglycerides [341]. Overall, HDL particles consist of approximately 85-95 distinct proteins [342] and hundreds of lipid subtypes [343], that together mediate diverse HDL functions including lipid metabolism, anti-oxidation, immune response, hemostasis, metal binding, and vitamin transport [343–345]. Figure 1.2 HDL composition and function HDL is the smallest and densest of the plasma lipoproteins and contains an estimated 85-95 distinct proteins, 200 lipid species and several other nonpolar cargo molecules. HDL components and subclass distribution can vary between individuals and is altered by diseased states. EC: endothelial cell; PL: phospholipid; S1P: sphingosine-1-phosphate; SM: sphingomyelin; NO: nitric oxide; p: phosphate group; eNOS: endothelial nitric oxide synthase; apo: 34 apolipoprotein; SAA: serum amyloid A; PON-1: paraoxonase 1; miRNA: micro RNA; LDL: low density lipoprotein; oxLDL: oxidized LDL [346]. (modified from Boyce et al. 2017 [347]) 1.13.1 HDL protective associations with cardiovascular diseases The association between low HDL cholesterol (HDL-C) concentration and elevated CVD risk was first suggested in the 1960s in the Framingham Heart Study [348]. Since then, a multitude of clinical studies have supported this relationship [349–351]. Although Mendelian randomization studies have demonstrated that HDL-C concentration per se has no causal relationship with CVD [352–354], the question remains as to whether HDL-C, a static measure of HDL’s cholesterol content, adequately reflects the beneficial functions of HDL on vascular health. In humans, genetic deficiency of APOA1 and ATP-binding cassette transporter 1 (ABCA1) lead to very low concentrations of plasma HDL-C that can be associated with increased risk of and accelerated onset of coronary artery disease (CAD). Similar outcomes are observed in some but not all cases of lecithin cholesterol acyltransferase (LCAT) deficiency [355]. However, in other cases the genetic alteration of HDL-C concentrations has more complex outcomes. For example, the apoA-I Milano variant was discovered in a family in Northern Italy and has a substitution resulting in the formation of homodimers of apoA-I and heterodimers with apoA-II. Carriers of apoA-I Milano have very low plasma HDL-C concentrations but similar levels of atherosclerosis and CAD as controls with normal HDL-C concentrations [356,357]. Conversely, carriers of mutations in SCARB1, the gene encoding scavenger receptor class B type I (SR-BI), have abnormally high HDL-C concentrations yet are at increased risk for CAD [358]. The predominant plasma lipoprotein in human plasma is LDL [359] while the major plasma lipoprotein in mice is HDL, making them generally resilient to CVD and advanced atherosclerosis compared to humans [360]. As a result, atherosclerosis studies in mice are overwhelmingly based on genetically modified models to allow the therapeutic effect of HDL on atherosclerosis to be studied [361]. For example, genetic deletion of either apoA-I, ABCA1, LCAT or SR-BI on the pro-atherosclerotic backgrounds apoE or LDLR knockout alters murine HDL-C concentrations, however, the effects on atherosclerosis vary with genetic background and animal diet [362]. Nonetheless, HDL-targeted therapies in murine and rabbit models of atherosclerosis appear to be 35 beneficial. For example, transgenic overexpression of apoA-I, gene transfer of human apoA-I, adenoviral transfer of apoA-I, and infusion with recombinant apoA-I or HDL can reduce or stabilize atherosclerotic plaques in apoE or LDLR knockout mice [363]. Similar improvements have been observed in rabbit models treated with HDL-based therapeutics. Aortic plaque area [364] and aortic inflammation [364,365] in rabbits with high-fat diet induced atherosclerosis are reduced when they are treated intravenously with purified plasma human apoA-I. Likewise, atherosclerotic progression in LDLR knockout or LDLR mutant rabbits was inhibited with intravenous apoA-I mimetic [366] or adenoviral apoA-I treatment [367]. While the protective effect of HDL against CAD has long been assumed to be related to HDL’s function in reverse cholesterol transport, other protective functions of HDL may also play a role. 1.13.2 HDL function in health and disease Circulating HDL is best known for its pivotal role in reverse cholesterol transport, the process by which HDL removes excess cholesterol from cells, the lipoprotein remodels, and the excess cholesterol is delivered to the liver for excretion [368]. However, only 1/3 of the identified 95 proteins on HDL [110] have roles in lipid metabolism [343,369] while others function in protease inhibition, complement regulation, hemostasis and inflammation [370]. Known vasoprotective functions of HDL include promoting endothelial nitric oxide synthase (eNOS) activity, reducing inflammation, and suppressing vascular adhesion molecule expression [347,371–374]. Several of these functions have been observed to change during disease. 1.13.2.1 HDL and cholesterol efflux capacity The cholesterol efflux capacity (CEC) of HDL is modified in CVD [375–379], metabolic syndrome [380], during acute inflammation [381], and, interestingly, in AD [382,383] (Table 1.1). In T2DM, the CEC of HDL is reduced in some [384,385], but not all [380], studies. How distinct CEC and HDL-C are as biomarkers of disease has been debated, with some studies observing a reduction in CEC independent of changes in HDL-C [375,376,378,381,386,387], while others find that CEC and HDL-C changes correlate [377,379,380]. Possible mechanisms to explain reduced CEC in most of these studies is increased HDL-associated serum amyloid A (SAA) [381] and reduced HDL-associated paraoxonase 1 (PON-1) [379] in inflammatory states, which will be 36 discussed below. Importantly, despite its obvious implication for reverse cholesterol transport, CEC is not the only known function of HDL. 37 Table 1.1 HDL compositional and functional heterogeneity in disease. Disease Proteome Lipidome Size Function Cardiovascular disease ↓/-- PON-1 [379,388,389] ↑ apoA-I, apoA-II, apoE [390] ↑ SAA [390–392] ↑S1P [393], SM [394] ↑/↓ PL [392,394,395] ↑ TG [392,394] ↑/↓ large HDL [376,396–398] ↓ small HDL [388] ↓ cholesterol efflux [378,379], eNOS phosphorylation [376,391,399] ↑ inflammatory activity [400] Inflammation ↓ PON-1 [381,401,402] ↑ SAA, apoA-II, complement C 3[401,402] ↑/↓ apoA-I [401,402] ↓S1P, SM[402,403] ↑ TG, FFA [403] ↓/-- cholesterol efflux [381,401,402] ↓ NO production [401,402] ↑ inflammatory activity, oxidative activity [404] Chronic kidney disease ↓/-- PON-1 [399,405] ↑ SAA, SDMA, apoC-II [399,406–408] ↓apoA-I, apoA-II [399,408] ↓ PL [408] ↑ TG [408] ↓ small HDL[409] ↓ NO production [406], cholesterol efflux [406,408], EC proliferation and migration [410] ↑ inflammatory activity [406,410], oxidative activity [406,407] Cirrhosis ↑/↓ PON-1 [411] ↓ apoA-I, apoA-II, apoC-II, apoC-III [412] ↑ SAA, apoE [412] ↑ PL[411] ↑ large HDL[412] ↓ cholesterol efflux [412], PON-1 activity [411] Aging -- PON-1 [413,414] ↓ apoE [413] ↑ SAA, complement C3 [413] ↑ SM[415] ↑ oxidative activity [413,414] ↑/-- cholesterol efflux [413,415] ↓ PON-1 activity [413,414] Arthritis ↓ PON-1 [416] ↑ SAA [416] ↑ SM, PL [417] ↑ inflammatory activity [418] Type 2 diabetes mellitus ↓ apoA-II, PON-1, apoE [122] ↑ SAA [419] ↑ TG [420] ↑/↓ S1P [421,422] --/↓ cholesterol efflux [380,384,385] ↓ eNOS activation [423,424] ↑ inflammatory activity [423,425–427] ↓ anti-oxidative [422,424,426,428] Alzheimer’s disease ↑ inflammatory activity [383] ↓ cholesterol efflux [382,383] Age-related Macular Degeneration ↑ SAA [429] ↑ anti-inflammatory activity [429] PON-1: paraoxonase 1; SAA: serum amyloid A; apo: apolipoprotein; S1P: sphingosine-1-phosphate; SM: sphingomyelin; PL: phospholipid; TG: triglyceride; eNOS: endothelial nitric oxide synthase; FFA: free fatty acid; NO: nitric oxide; SMDA: symmetrical dimethylarginine. 38 1.13.2.2 HDL and inflammation Several mechanisms by which HDL exerts anti-inflammatory effects on ECs have been described. These include pathways dependent on the HDL receptor SR-B1 [430–432] as well as through vascular sphingosine-1-phosphate (S1P) receptor 1 and 3 [433], which trigger a signaling cascade through the phosphoionositide 3-kinase (PI3K)/Akt pathway leading to phosphorylation of eNOS. The vasoprotective effects of nitric oxide (NO) produced by eNOS phosphorylation are well-established, and include vasodilation, reduced EC permeability, and inhibition of vascular adhesion molecule expression via cells downregulation of the pro-inflammatory nuclear factor kappa beta (NFκB) signaling pathway [434]. Although moderate amounts of NO produced by EC through HDL signaling are vasoprotective, high or sustained NO production from inducible NOS signalling or uncoupled eNOS signalling contribute to oxidative stress [435]. HDL-S1P action can also directly inhibit NFκB signaling to suppress adhesion molecule expression [433], reduce endothelial exocytosis [431], and maintain annexin-1 expression [430]. Additionally, HDL can indirectly increase eNOS activity via actions of the lipid transporter ATP-binding cassette G1 (ABCG1) to maintain proper membrane fluidity for eNOS function [436,437]. The HDL-associated protein PON-1 prevents lipid and LDL oxidation thereby protecting ECs from oxidative damage, pro-inflammatory signaling, and apoptosis [438,439]. Many disease states, particularly those with an inflammatory component, can affect HDL’s vasoprotective functions (Table 1.1). For example, HDL isolated from CVD patients exhibits reduced ability to phosphorylate eNOS [344,391,440] and has a distinct repertoire of immune cell trafficking proteins [391]. HDL isolated from children with chronic kidney disease exhibits reduced ability to protect against EC activation [406,410]. Acute inflammation, as in the case of periodontal therapy, can also alter the ability of HDL to induce eNOS phosphorylation [404]. In abdominal aortic aneurysm, a quantitative reduction in apoA-I-mediated vasoprotection may result from the decrease in circulating HDL3, a process which itself may partially be due to the sequestering of apoA-I at the site of inflammation in thrombotic aortic tissue [441,442]. Lack of anti-inflammatory function has also been consistently observed in HDL from people with T2DM. For example, HDL from people with T2DM is less effective in inhibiting tumour necrosis factor 39 alpha (TNFα)-induced EC activation [423,425,426] and cytokine release by macrophages [427]. These anti-inflammatory deficits may be due to reduced activation of eNOS signalling by T2DM HDL [423,424]. The anti-oxidative functions of HDL are similarly impaired in T2DM [422,424,426,428]. 1.13.3 HDL composition in health and disease HDL can be classified by a variety of schemes including apolipoprotein content, size, surface charge, and density (Figure 1.2) [340]. The distribution of HDL-C among different sizes has been observed to vary with exercise, CVD status, and lipid-lowering medications, however, the direction of the association between HDL subclass and disease outcomes has been controversial. Many recent studies have found that large HDL subclasses appear to be beneficial for cardiovascular health with stronger associations with disease than total HDL-C [376,396,397,443,444]. By contrast, other studies have observed that CAD patients have lower concentrations of small HDL (HDL3) [388] and elevated concentrations of large HDL (HDL2) [398]. Most studies investigating the effect of statins on HDL subclass find an increase in large HDL-C levels [445–451], although the number of HDL particles of any size may not change [452]. Similarly, investigations on the effect of statins on HDL subclass is not as clearly defined as their well-established ability to elevate HDL-C concentrations [453]. Statins have been shown to exert no effect [454] or increase the concentration of HDL2 while decreasing HDL3 concentration [447,455] although contrasting reports indicate an increase in HDL3 levels [456]. Fibrates increase the concentration of plasma HDL3 and decrease the concentration of HDL2 [453] whereas niacin has the opposite effect on HDL subclass promoting conversion to mature HDL2 particles [453]. Combination treatments of lipid lowering drugs result in no change in HDL subclass compared to monotherapy [455] or an additive effect and improved HDL functionality [457]. Variations in cohort, drug regimen and experimental techniques likely contribute to the varying observations on the effect of medication on HDL subclass distribution. More consistent is the change in HDL subclass distribution in people with T2DM where HDL is smaller overall [458], concentrations of HDL3 are higher [458,459], and concentrations of HDL2 are lower [420,458–460]. HDL size has also been shown to predict T2DM incidence in a cohort of women where small HDL had a positive and large HDL a negative association with incidence [461]. 40 The process by which various HDL subclasses form has also come under debate. Traditionally, it was understood that HDL is first secreted from the liver as small, lipid-poor, discoidal HDL and evolves into larger, more spherical forms over time as lipids are added to the particle. However, in a study monitoring HDL secretion with endogenous ectopic labelling, HDL appeared to be secreted from the liver in all of its unique sizes and remained in those size classes for several days before excretion [462,463]. Another measure of interest is the heterogeneity of the HDL proteome. The HDL proteome varies considerably between individuals based on disease, diet, age, and inflammatory status (Table 1.1). For example, PON-1 content or activity on HDL is reduced in patients with CVD [379,389], liver cirrhosis [411,412], acute inflammation [381,402,404], chronic kidney disease [399,405], rheumatoid arthritis [416] and in the elderly [413,414], and is elevated with exercise [464] or a diet rich in olive oil [465]. Conversely, the SAA content on HDL has been found to increase in chronic kidney disease [406,408], aging [413], acute inflammation [402,404], rheumatoid arthritis [416], T2DM [419], and cirrhosis [412]. Importantly, changes to the HDL proteome are sometimes observed in inflammatory or disease states without a change in total plasma HDL-C [404,413], again highlighting the importance of looking beyond HDL-C when considering lipoprotein function in the etiology of disease. Other alterations to the HDL proteome that have been observed in vascular and inflammatory pathologies include reduced or elevated apoA-I, apoA-II, apoC-II and apoE, and elevated complement C3 and apoC-III [390,399,402,404,408,412,413,466]. For example, apoE, apoA-II, and PON-1 levels on HDL are all reduced in young males with T2DM compared to nondiabetic youths [122]. The HDL proteome, and therefore also HDL function, is also subject to change by hypolipidemic agents such as statins, niacin and fibrates. Green et al. reported that CAD-associated changes in HDL3 apolipoprotein profile, including increased apoE and decreased levels of apoF, and phospholipid transfer protein, are reversed by combination therapy of atorvastatin and niacin [467]. Niacin is also shown in a separate study to exhibit a synergistic enhancement of apoA-I in concert with atorvastatin [457]. Fibrates increase plasma apoA-I and, to a greater extend, apoA-II 41 concentrations [468] . Gordon et al. [452] reported that rosuvastatin treatment dramatically increases the levels of α-1-antitrypsin in large HDL which in turn enhances HDL’s anti-inflammatory properties. Additionally, statins and fibrates can alter the activity of HDL-associated anti-oxidant proteins such as PON-1 to augment its vasoprotective function [469,470]. The HDL lipidome is another field of emerging interest. While most of the work thus far has investigated the lipidome of HDL from healthy subjects, it is becoming clear that changes to the proportions of HDL lipids in disease can have functional consequences. For example, the CEC, anti-oxidant, and anti-inflammatory activities of HDL are impaired with excess triglyceride, cholesterol ester, oxidized lipids, and sphingomyelin content [369]. A well-studied bioactive lipid on HDL is S1P, which is well known to be at least partially responsible for the anti-inflammatory actions of HDL. S1P on HDL can be reduced in CVD [393], T2DM [422] and acute inflammation [402] resulting in impaired signaling to eNOS. In another study in T2DM, S1P was instead found to be elevated on HDL possibly as a compensatory mechanism [421]. Many other changes to the HDL lipidome have been observed including changes to triglyceride, phospholipid, and sphingomyelin content (Table 1.1). Among other cargo carried on HDL are small non-coding ribonucleic acids (RNAs) including transfer RNA (tRNA)-derived RNA fragments, RNase P-derived RNA fragments, and microRNAs (miRNA) [471]. MiRNAs in particular have emerged as an exciting topic in lipid research for their potential as biomarkers and in therapeutic approaches. HDL has been found to be regulated by and to carry a number of miRNA that vary between individuals according to a number of factors including diet [472], weight loss [473], and CAD [389]. As with changes to the HDL proteome, changes to the miRNA profile of HDL can be observed even when there is no change in total plasma HDL-C [389,473] or apoA-I concentration [472]. Interestingly, HDL-carried miR-223, which can be altered with diet or weight loss [472,473], has even been found to be transferred to ECs [474] and to alter gene expression of intercellular adhesion molecule 1 (ICAM-1) in those cells [475]. 42 HDL also carries a number of other nonpolar molecules including fat-soluble vitamins, vitamin binding proteins, carotenoids, steroids and other hormones, potentially serving as a transporter for delivery to other tissues [471]. Polar metabolites have also been found on HDL, some of which correlate with insulin resistance [476]. An additional molecule of interest on HDL is symmetric dimethylarginine, a metabolite that is increased in children with chronic kidney disease and may be partially responsible for impaired vasoprotective actions of HDL in these patients [407]. A further layer of complexity to HDL heterogeneity includes modifications to its protein components including the addition of aldehydes such as acrolein [477], modifications of apoA-I by myeloperoxidase [478], or carbamylation of HDL-associated proteins [405]. Oxidation of HDL lipids are commonly observed including in people with T2DM [424,426,479]. HDL protein modifications are associated with CVD and compromise HDL functions including cholesterol efflux, anti-oxidant properties, and promotion of EC migration and proliferation [405,477,478]. 1.14 The relationship between HDL and AD 1.14.1 Mixed genetic evidence on HDL and cardiovascular disease and AD risk. Mendelian randomization aims to determine the causality of a modifiable risk factor on disease risk by measuring how disease risk changes based on randomly distributed genetic variants that affect the risk factor [405]. Although it is well-accepted that high plasma HDL-C concentrations associate with reduced heart disease mortality [480], Mendelian randomization questions the causality of this relationship. Several groups observe that genetic variants associated with HDL-C do not alter CHD, myocardial infarction, or carotid atherosclerosis risk [353,354,481], although one study found that an allele score based on all known genetic variants associated with HDL-C was significantly associated with CHD risk [353]. Two Mendelian randomization studies also suggest plasma HDL-C concentration is not causal for AD risk [208,209]. Importantly, these studies address only a causal link between disease risk and elevated HDL-C mediated by particular genes; they do not take into account the complex changes to HDL function and composition that can occur in disease and that can be superior predictors of disease risk [376,413,423,424,482–486]. Recently, two large GWAS for AD found lipoprotein metabolism and HDL particle gene sets to be significantly associated with AD risk. Genes in these sets encode HDL biogenesis 43 proteins and HDL protein components such as APOE, ABCA1, APOC1, APOM, APOA2, PON1, CLU, LCAT, CETP and APOA1 [63,64]. 1.14.2 Epidemiological evidence for a protective effect of HDL on AD Several cross-sectional and prospective cohort studies suggest that AD risk is attenuated by the concentration of HDL-C or apoA-I and that these measures also have established associations with lower CVD risk [487]. For example, cross-sectional studies in AD patients and healthy controls have found that serum apoA-I and HDL-C concentrations are significantly lower in AD patients [488,489], serum apoA-I concentrations are inversely correlated with MMSE scores [488,489], and a serum apoA-I cut-off level of 1.5 g/L can distinguish between healthy people and those with AD with 71% sensitivity and 69% specificity [488]. Montañola and colleagues reported that circulating apoA-I concentrations positively correlate with plasma Aβ40 among CAA patients, suggesting a potential role of HDL in removing Aβ from the brain [490]. This role in Aβ clearance is further supported by an inverse correlation of HDL-C concentration and brain amyloid burden as measured by PIB-PET imaging in elderly people with mild dementia [491]. Interestingly, higher HDL-C may also promote cognitive function in people without dementia as positive associations have been found between HDL-C concentration and working memory [492,493], MMSE scores [492], and verbal learning scores [493] in cognitively normal subjects. However, other cross-sectional studies have found no association between HDL-C concentration and cognitive function, for example, in a cohort of 1,100 elderly subjects in the Framingham study [200] and in a small cohort of nonagenarians in Spain [494]. Large prospective cohort studies aiming to determine if circulating HDL-C or apoA-I concentration can predict future AD or cognitive function have also been mixed. The Honolulu-Aging study followed 929 Japanese-American men and found that those in the highest quartile of plasma apoA-I at baseline had the lowest risk of dementia 16 years later [495]. Another prospective cohort study followed 1,130 elderly people in the Manhattan area for a median of 4 years and found that those with the highest baseline HDL-C concentration had a reduced risk of AD [210]. Furthermore, a recent report on the Baltimore Longitudinal Study of Aging found that those with higher baseline HDL-C concentrations were less likely to be cognitively impaired 20 years later 44 and were protected from reductions in entorhinal cortex and parahippocampal gyrus volume [496]. On the other hand, two reports on the Adult Changes in Thought study and two studies in cognitively normal elderly women found that baseline HDL-C concentrations were not related to the risk of AD or cognitive impairment upon follow-up [497–500]. Some have suggested that differences in age at baseline measurement and length of follow-up may explain these inconsistencies in the literature [495,498]. Indeed, the studies discussed above with follow-up time greater than 10 years found significant associations between HDL-C concentration and AD risk [495,496] while some others with less than 10 years of follow-up did not [498,500]. Furthermore, those measuring baseline HDL-C concentration when subjects were on average 50 to 70 years old all found significant associations with AD risk [489,493,495] whereas those with baseline measures when subjects were on average 70 years old or older did not [499,500]. It is therefore possible that HDL has its greatest influence on AD risk at mid-life. 1.14.3 Potential protective mechanisms of action of HDL against AD The mechanism(s) by which HDL may influence AD risk remain unknown and is a major challenge in the field. Many HDL associated proteins such as apoA-I, clusterin (apoJ), apoE, apoC-III, apoD and apoA-IV are present within the brain parenchyma, CSF and cerebrovascular intima of leptomeningeal arteries [112,501–503]. With the exception of apoE, the CSF concentrations of these proteins correlate moderately with their respective concentrations in plasma, suggesting transport or diffusion from the periphery to the brain. Although Fung and colleagues reported that HDL can be transported through human brain microvascular ECs in vitro via SR-BI [504], and it is known that CSF lipoproteins have a similar density to plasma HDL [505], there is currently no evidence that HDL can enter the brain as an intact lipoprotein particle in vivo. Furthermore, neither cholesterol nor apoE cross the BBB [113]. These observations suggest that HDL might indirectly influence brain health as a circulating factor that primarily acts from the cerebrovascular lumen and intima. This raises the possibility that blood may be a suitable and relatively non-invasive matrix to develop assays of HDL function(s) relevant to AD as better predictors of AD disease risk or progression than apoA-I or HDL-C concentration per se. 45 1.14.4 Vasoprotective functions of HDL in AD animal models 1.14.4.1 Mouse models of AD AD, as defined by the presence of Aβ plaques and neurofibrillary tangles in the brain resulting in cognitive and behavioural impairment, is a disease specific to humans [506]. Although mice naturally produce Aβ and tau, they do not accumulate plaques or tangles in their brains nor do they experience the dementia associated with AD [507]. Therefore, a number of genetically engineered mouse models have been developed in order to allow for AD research in an in vivo system. Mouse models of AD typically overexpress transgenes of mutated human amyloid processing proteins, specifically, APP, the precursor to Aβ, and PSEN1, a subunit of the γ-secretase complex that cleaves APP into Aβ [507–511]. Differences in the transgenes expressed, the specific mutations in the genes, the promoter used to drive expression of the transgene, the number of copies of the transgene expressed, and the wild type background the mice are bred onto all result in variation in the severity and onset of amyloid pathologies. Common AD mouse models include APP/PS1, which have the Swedish APP mutation (huAPP695) and a deletion in exon 9 of PS1 under the mouse prion promoter (PrP), Tg2576, which have the Swedish APP mutation under the hamster PrP, and PDAPP, which have the Indiana APP mutation (huAPP770) under the platelet derived growth factor beta (PDGFRB) promotor [511,512]. That all AD research in mice has relied on the expression of a small number of mutations is a major limitation of our current understanding of the disease, especially as the mutations used are rare and found only in people with the familial, early-onset form of AD and not the much more common late-onset form of AD [507,509,510,512]. AD in humans is a heterogeneous disease involving a complex interaction of genetics and lifestyle over a human life-span, factors which are not feasible to model in mice. Furthermore, the introduction of transgenes into mice has the potential to disrupt off-target genes unless it is introduced by gene targeting [508,512]. Such disruptions have been recently shown in transgenic rTg4510 tau mice [513] and compromise the validity of the observations made in this mouse model to human disease. Saito et al. aimed to avoid these issues with their APP knock-in mouse model that expresses the human APP gene at wildtype levels but maintains overexpression of Aβ in order to eliminate artifacts resulting from disruption of off-target genes and overproduction of APP and APP fragments [514]. An additional limitation 46 to the validity of AD mice is the lack of tau pathology arising in amyloid-based models [510] despite observations in humans suggesting that tau pathology is a better predictor of cognitive decline than amyloid pathology [52,515]. Mouse models with tau mutations alone or in combination with amyloid mutations have been created that result in the formation of tau tangles, however, these tau mutations are not found in humans with AD [510,511]. Nonetheless, mice are a key model organism for research and many discoveries on brain function and AD have origins in mouse studies. Recently, attempts have been made to increase the validity of AD mouse models by creating new mouse models combining various human mutations and gene isoforms [510]. Alternatively, efforts have been made to increase the variability of the genetic background of the mice by expressing the transgenes in wild mice rather than highly inbred mice as has been done in the past [512]. The development of non-human primate models injected with human AD brain homogenates [510] and transgenic minipig models [516] have been ongoing and some interest in studying spontaneous cognitive impairment in age canines exists [517]. Compared to mice, these animals have a more similar neuroanatomy to humans but cost, time, and ethical concerns remain important issues limiting their use [518]. 1.14.4.2 Genetic alteration of HDL in AD animal models Studies in AD transgenic mice have been used to explore the impact of altering plasma HDL concentration on AD-relevant pathological and behavioral outcomes. Genetic ablation of apoA-I robustly reduces plasma HDL-C concentration, worsens memory deficits and increases CAA in 12-month old APP/PS1 mice [519], without altering parenchymal Aβ plaque load [519,520]. Transgenic overexpression of apoA-I from its native promoter in 12-month old APP/PS1 mice results in the opposite phenotype, including attenuation of memory deficits, selective reduction of CAA, and reduced neuroinflammation [521]. On the other hand, a recent study observed improvements in total and vascular amyloid burden in apoA-I knockout mice crossed to the Tg2576 mice compared to Tg2576 mice with normal concentrations of plasma apoA-I [522]. Differences between the Aβ transgenic mouse lines used and the methodology for the analysis of amyloid burden may explain these contradictory results, as will be discussed more thoroughly in section 2.5. 47 1.14.4.3 HDL-based therapeutics on AD-relevant outcomes in animal models Although there are no HDL-based therapeutic strategies that have specifically been tested for AD in clinical trials, several preclinical studies have been performed in APP/PS1 or other AD model mice. For example, our group showed that raising plasma apoA-I concentration through intravenous administration of reconstituted pre-β HDL composed of human plasma-derived apoA-I and phospholipids acutely reduced soluble brain Aβ concentrations in APP/PS1 mice [523]. Another reconstituted HDL formulation based on apoE3 also improved AD pathology in SAMP8 mice by reducing Aβ deposition, microgliosis, and memory deficits when injected in the tail vein [524]. APP23 mice treated intravenously with recombinant apoA-I Milano similarly showed reduced microgliosis and Aβ deposition with a specific reduction in vascular Aβ [525]. Recently, APP23 mice treated intravenously with free apoJ or apoJ lipidated to form a reconstituted (rHDL) were shown to have reduced insoluble Aβ and CAA burden [526]. Finally, the orally administered apoA-I mimetic D-4F improves memory, Aβ deposition, microgliosis, astrogliosis, and other markers of inflammation in APPswe/PS1ΔE9 mice [527]. The effects of HDL on cerebrovascular health have also been studied in mice outside of the context of AD. For example, administration of D-4F after middle cerebral artery occlusion in mice reduced neuroinflammation and white matter damage while improving neurological functional outcomes [528]. Similarly, D-4F treated atherosclerotic mice lacking LDLR and fed a western diet show improved cognition and reduced brain arteriole inflammation as measured by arteriole association with macrophages, microglia, or inflammatory proteins [529]. 1.15 Liver X receptor and its therapeutic potential for AD Liver X receptors (LXR) are nuclear receptors and master regulators of cholesterol metabolism and inflammation [530,531]. LXRs are found in two isoforms, LXRα is highly expressed in adipose tissue, adrenal tissue, intestine, kidney, liver, lung and spleen while LXRβ is more ubiquitously expressed [532]. LXRs form heterodimers with retinoid X receptors (RXR) and when activated by oxysterols, their endogenous agonist, or synthetic agonists, they transcribe the genes in the bound LXR response element [533,534]. Oxysterols are cholesterol metabolites therefore their activation of LXR signalling acts as a feedback loop on cholesterol metabolism [533]. 48 LXR signalling pathways were first suggested to be protective against AD by two groups in 2003. These groups found that Aβ production or secretion was reduced when rat primary neuronal and non-neuronal cells [535] or mouse neuroblastoma cells [536] with the Swedish APP mutation were treated with natural or synthetic LXR agonists. Soon after, these results were translated into AD mouse models where treatment with the synthetic LXR agonists GW3965 or TO901317 reduced Aβ concentrations [537–539] and prevented memory deficits [537,538]. Since then many studies have treated various AD mouse models at various ages with LXR agonists and observed a number of beneficial effects (summarized in Table 1.2 and Table 1.3). While LXR agonist treatment consistently improves memory in AD model mice across studies [537,538,540–546], the effect on Aβ and amyloid load has been more controversial. Some studies found reductions to soluble Aβ [537–539,541,546] while others found no difference [542,543,545,547] and others found an increase in certain brain regions [540]. Similarly, LXR agonist treatment has been found to reduce amyloid plaque load in some studies [537,540–542,546] while no difference was found in others [543–545,547,548]. Interestingly, Donkin et al. reported that the beneficial effects of LXR agonist treatment on memory and amyloid load is dependent on the presence of ABCA1, a well-established LXR target gene [540]. ABCA1 is a cell lipid transporter functioning to maintain lipid homeostasis within cells by effluxing cholesterol and phospholipids across the cell membrane to apolipoprotein acceptors such as apoA-I, to form HDL, and apoE [549]. LXR agonists have also been shown consistently to reduce neuroinflammation in AD model mice [537,542,543,546,550]. 49 Table 1.2 Summary of studies treating AD model mice with GW3965 investigating amyloid, neuroinflammation, and memory deficits. Reference Dose (mg/kg/day) Route Age at treatment onset (mo) Duration AD model Sex Aβ and amyloid Neuroinflammation Memory Jiang (2008) [537] 33 (120*) Chow (oral gavage*) 12 4mo (6d*) Tg2576 -- ¯sol Αβ (hp) ¯plaques (hp) ¯CD45+ number, area ¯IL-6 mRNA ­contextual fear memory Donkin (2010) [540] 33 Chow 10 2mo APP/PS1 F ­sol Aβ (hp, cx) trend ¯ plaques (hp) -- ­novel object recognition, spatial memory Wesson (2011) [541] 33 Oral gavage 14-16 14d Tg2576 Both ¯sol and insol Aβ ¯plaques in ob, pcx ­odour habituation behaviour Skerrett (2015) [542] 50 Oral gavage 6 9d APP/PS1 M No change in Aβ ¯ plaque (hp) ¯Iba1 mRNA, area ¯CD45, IL-6, IL-1β mRNA ­contextual fear memory Sandoval-Hernández (2015) [543] 33 Oral gavage 12 12w 3xTg-AD Both No change in Aβ, plaques ¯GFAP area ­spatial memory Sandoval-Hernández (2016a) [544] 50 Oral gavage 12 6d 3xTg-AD F No change in plaques -- ­spatial memory Sandoval-Hernández (2016b) [550] 50 Oral gavage 24 6d 3xTg-AD -- ¯perivascular Aβ ¯GFAP area -- M: male, F: female, mo: month, w: week, d: day, hp: hippocampus, cx: cortex, pcx: piriform cortex, GFAP: glial fibrillary acidic protein, CD45: cluster of differentiation 45, IL-6: interleukin 6, IL-1β: interleukin 1 beta, Iba1: ionized calcium binding adaptor molecule 1. * Sub-study with oral gavage. -- data not reported. 50 Table 1.3 Summary of studies treating AD model mice with TO901317 investigating amyloid, neuroinflammation, and memory deficits. Reference Dose (mg/kg/day) Route Age at treatment onset (mo) Duration AD model Sex Aβ and amyloid Neuroinflammation Memory Koldamova (2005) [539] 50 Gastric gavage 2 6d APP23 -- ¯soluble Aβ -- -- Riddel (2007) [538] 50 Oral gavage 4 6-7d Tg2576 M ¯Aβ42 (hp), no change Aβ (cx) -- ­contextual fear memory Lefterov (2007) [551] 50 Gastric gavage 6 25d APP23 -- ¯ insoluble Aβ ¯IL-1β, IL-6, TNFα mRNA -- Vanmierlo (2011) [545] 30 Chow 21 6-9w APPSLxPS1mut M No change in Aβ, plaque load -- ­novel object recognition, spatial memory Jansen (2012) [548] 30 Chow 12 3mo AβPP-PS1 M No change in plaque -- -- Cui (2012) [546] 30 Oral gavage 6 30d APP/PS1 M ¯Aβ42, plaque load ¯GFAP, CD11b area ¯COX-2, iNOS, p65 levels ­spatial memory Stachel (2016) [547] 10 Subcutaneous 12 3w Tg2576 -- No change in soluble Aβ No change in locomotor activity M: male, mo: month, w: week, d: day, ISF: interstitial fluid, hp: hippocampus, cx: cortex, GFAP: glial fibrillary acidic protein, CD11b: cluster of differentiation 11b, COX-2: cyclooxygenase 2, iNOS: inducible nitric oxide synthase. -- data not reported. 51 Unfortunately, LXR agonists are unlikely to be a viable therapeutic option for treating human AD patients due to off-target effects resulting in hepatoxicity [552,553]. Clinical trials in human subjects have occurred testing bexarotene, an RXR agonist, for AD [554,555]. Some preliminary studies with bexarotene showed that treatment reduced amyloid burden and improved cognition in mouse models [556–560], although other rodent studies found no benefits [561–563]. Human clinical studies began with a promising case study showing improved memory and reduced CSF tau concentration in a single patient [554], however, a phase I clinical trial found no effect on Aβ metabolism and poor CNS penetrance [555]. 1.16 Summary, research hypothesis, and specific objectives 1.16.1 Summary AD is a devastating, neurodegenerative disease characterized neuropathologically by amyloid plaques and NFTs and clinically by a decline in memory and executive function. Vascular health is also tied to brain health and AD. Vascular risk factors make up many of the modifiable risk factors that together may be responsible for 35% of dementia cases. The cerebrovasculature system is crucial for proper brain functioning as it is responsible for providing oxygen and nutrients to energy-hungry neurons and clearing brain waste, including a portion of the Aβ produced in the brain. Dysfunction in this system is well documented in AD and may even precede amyloid pathologies and clinical symptoms. Given the frequent failure of amyloid and tau-based therapeutics in clinical trials for AD, a therapeutic strategy instead targeting the cerebrovasculature is attractive. HDL is well documented to exert beneficial functions on peripheral arteries in healthy people, for example by acting to promote endothelial migration, inducing endothelial nitric oxide production, preventing oxidation, and preventing inflammation. Previous work in mouse models and human epidemiological research suggests that HDL may also protect against AD. 1.16.2 Research hypothesis HDL protects against AD by promoting cerebrovascular health acting from the lumen of brain blood vessels. 52 1.16.3 Specific objectives 1. Investigate amyloid pathology, neuroinflammation, cerebrovascular inflammation, and cognitive deficits in an AD mouse model (APP/PS1) deficient for apoA-I. 2. Investigate amyloid pathology, neuroinflammation, cerebrovascular inflammation, and cognitive deficits in an AD mouse model (APP/PS1) treated with a non-brain penetrant LXR agonist to selectively act on peripheral lipid and lipoprotein metabolism. 3. Investigate the vasoprotective functions of human HDL against Aβ on brain-derived ECs and in 3D bioengineered arteries. 4. Develop assays of AD-relevant HDL vasoprotective functions to determine whether the protective functions of HDL against AD are lost in disease. 53 Chapter 2: Amyloid pathology, neuroinflammation, cerebrovascular pathology, and cognition in apoA-I deficient, APP/PS1 mice 2.1 Summary Alzheimer’s disease (AD) is defined by amyloid beta (Aβ) plaques and neurofibrillary tangles and characterized by neurodegeneration and memory loss. Most AD patients also have Aβ deposition in cerebral vessels known as cerebral amyloid angiopathy (CAA), microhemorrhages, and vascular co-morbidities, suggesting cerebrovascular dysfunction contributes to AD etiology. Promoting cerebrovascular resilience may therefore be a promising therapeutic or preventative strategy for AD. Plasma high-density lipoproteins (HDL) have several vasoprotective functions, are associated with reduced AD risk in some epidemiological studies, and improve pathology and memory in AD model mice. Here we investigate the interaction of HDL, amyloid, and inflammation on the cerebrovasculature in AD model mice by breeding APP/PS1 mice with apolipoprotein (apo)A-I-deficient mice and aging up to 12 months. We found that APP/PS1 mice without apoA-I had worse amyloid pathology and neuroinflammation both globally and specifically on cerebral vessels. Specifically, apoA-I-deficient mice had increased parenchymal amyloid in the cortex and increased total brain levels of intercellular adhesion molecule 1 (ICAM-1) and of the reactive astrocyte marker glial fibrillary acidic protein (GFAP). Additionally, apoA-I-deficient mice had significantly elevated cortical vascular amyloid as well as cortical and hippocampal ICAM-1 and GFAP specifically associated with endothelial cells (ECs). Loss of apoA-I increased the reactivity of astrocytes to parenchymal and vascular amyloid beta. We also confirmed previous observations of memory impairments in APP/PS1 mice lacking apoA-I with cued and contextual fear conditioning tests. In summary, apoA-I-containing HDL can reduce amyloid pathology, cerebrovascular inflammation, and astrocyte reactivity to parenchymal and vascular amyloid in Aβ expressing mice. 54 2.2 Introduction Cerebrovascular dysfunction is a common [5,225,234] and early event [6,235] in AD pathogenesis, therefore, methods to protect cerebrovascular health may present valuable therapeutic or preventative opportunities for AD. HDL is one such therapeutic target of interest as extensive research in the cardiovascular field has demonstrated that plasma HDL in healthy people has numerous vasoprotective functions. Furthermore, higher levels of plasma HDL-C, especially when measured at middle age, have been shown to be associated with reduced AD risk and memory impairments in later life [210,488–491,493,495,496] and GWAS studies find associations between HDL-related genes and AD risk [63,64]. Many researchers have turned to AD mouse models to show how plasma HDL may protect against AD pathologies by treating mice intravenously with HDL-based therapeutics. For example, intravenous administration of reconstituted apoA-HDL acutely reduces soluble brain Aβ concentrations [523], reconstituted apoJ-HDL reduces Aβ deposition and CAA burden [526], reconstituted apoE3-HDL reduces Aβ deposition, CAA, neuroinflammation, and memory deficits [524], and recombinant apoA-I Milano reduces parenchymal and vascular Aβ deposition, microgliosis, and memory deficits [525]. Similarly, oral administration of the apoA-I mimetic peptide D-4F reduces Aβ deposition and neuroinflammation [527]. Other researchers instead genetically altered the expression of endogenous murine apoA-I, the main protein component of HDL, to study the effect of plasma HDL in AD mice. Although apoA-I deficiency does not affect total amyloid burden in PDAPP [520] or APP/PS1 mice [156,519], a specific worsening of CAA has been observed [519]. Conversely, overexpression of apoA-I has been shown to reduce CAA but have no effect on total amyloid burden [521]. APP/PS1 mice overexpressing apoA-I also have also been shown to have reduced neuroinflammation and improved memory [521] while apoA-I deficient APP/PS1 mice have worsened memory deficits [519]. Although there is a wealth of evidence from the studies above suggesting that HDL can protect Aβ expressing mice from amyloid pathology and neuroinflammation, a recent study has found the 55 opposite. Contu et al. reported that apoA-I-deficient Tg2576 mice have less Aβ plaque load and CAA at 12 months of age compared to Tg2576 mice with normal apoA-I expression levels [522]. Therefore, the effect of apoA-I expression in Aβ expressing mice is not entirely clear. Furthermore, the specific effects of apoA-I on the cerebrovasculature in Αβ expressing mice have not been studied outside of CAA despite a wealth of research demonstrating vasoprotective functions of HDL in peripheral arteries. In the present study we aimed to fill these gaps in the research. We used APP/PS1 mice deficient or hemizygous for apoA-I to extend the previous in vivo studies, confirm a clear role for apoA-I on cortical CAA and on cortical and hippocampal inflammation, and report a novel interaction among apoA-I, astrogliosis and vascular and parenchymal amyloid. 2.3 Methods 2.3.1 Animals All procedures involving animals were approved by the Canadian Council of Animal Care and the University of British Columbia Committee on Animal Care. APP/PS1 mice (Jackson laboratories, B6.Cg-Tg(APPswe,PSEN1dE9)85Dbo/Mmjax, MMRRC stock no: 34832-JAX ) on a C57Bl/6 background co-express two transgenes from the murine prion promoter: a chimeric mouse/human amyloid precursor protein (APP) cDNA containing the Swedish (K670M/N671L) mutations, and the human presenilin-1 (PS1) gene deleted for exon 9 [564]. APP/PS1 mice were first bred with apoA-I deficient mice (Jackson Laboratories, B6.129P2-Apoa1tm1Unc/J, Stock no: 002055, also on a C57Bl/6 background) to produce an F1 generation hemizygous for both apoa1 and the APP/PS1 transgenes. These animals where then backcrossed to apoA-I knockouts to produce F2 male and female mice of four genotypes: APP/PS1 mice hemizygous (HEM) for apoa1 (APP/PS1 apoA-IHEM), APP/PS1 mice with complete apoa1 knockout (KO) (APP/PS1 apoA-IKO), nontransgenic littermates (wildtype, WT) hemizygous for apoa1 (WT apoA-IHEM), and nontransgenic littermates with apoa1 knockout (WT apoA-IKO). Although APP/PS1 mice on a pure C57Bl/6 background have an increased seizure risk, we selected this strain to maintain genetic homogeneity throughout this breeding strategy. Overall, 80% of the animals survived until the end of the experiment, with specific survival rates of 66% for APP/PS1 mice, 63% for APP/PS1 apoA-IHEM mice, and 74% for APP/PS1 apoA-IKO mice (Figure 2.1). 56 While this breeding strategy was successful in generating N=6-8 mice per group, insufficient animals survived to yield sex-specific cohorts. We therefore used mixed sexes, which is an acknowledged limitation of this study, yet tracking of individual animals throughout the study showed no clear sex bias throughout the results and justifies the use of pooled sexes. Figure 2.1 APP/PS1 survival rates. Survival of (a) female and (b) male mice of each genotype was monitored through aging to the experiment endpoint. Only animals with known genotype upon death or experiment endpoint are included. Exact p-values for pair-wise comparisons by log-rank test and p-values after Bonferroni correction for multiple comparisons are shown below the graphs. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermates for APP/PS1, APP/PS1: APP/PS1 transgenic. 2.3.2 Tissue collection Mice were fasted for 4 h prior to anesthetization by intraperitoneal injection of 20 mg/kg xylazine (Bayer) and 150 mg/kg ketamine (Bimeda-MTC). Blood was collected by cardiac puncture in ethylenediaminetetraacetic acid (EDTA)-containing syringes, centrifuged at 21,000 g for 10 min at 4°C, and the resulting plasma was stored at -80°C until use. Mice were then perfused for 6 min with ice-cold phosphate buffered saline (PBS) containing 2500 U/L heparin at 6 mL/min. Brains were excised and bisected in the sagittal plane. A 2x5 mm piece of parietal cerebral cortex was removed and stored separately for messenger ribonucleic acid (mRNA) analysis. The piece of brain for mRNA analysis and the remaining half-brain were snap-frozen on dry ice and stored at -80°C until use. The remaining half-brain was fixed in 4% paraformaldehyde (PFA) for 2 days at 4°C followed by storage in PBS containing 30% sucrose and 0.1% sodium azide at 4°C. 57 2.3.3 Plasma lipid measurements Plasma HDL-cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol concentrations were measured using Wako kits (Category no: 997-01301 for HDL-C, 993-00404 and 999-00504 for LDL-C, and 999-02601 for total cholesterol) as per the manufacturer’s directions adapted for 96-well microplates. Plasma specimens were randomized and the researcher performing the measurements was blinded to specimen genotype. Plasma lipids were analyzed in N=6-7 mice per genotype. 2.3.4 Histology Immunofluorescence triple co-staining was performed to visualize ECs (cluster of differentiation 31, CD31), Aβ plaques (1,4-Bis(3-carboxy-4-hydroxyphenylethenyl)benzene, X-34), and activated astrocytes (GFAP) as follows. Cryoprotected half brains were sectioned at 40 μm on a Leica CM3050 Research Cryostat and stored in PBS containing 0.1% sodium azide until staining. Three sections from each half-brain were selected from the anterior to posterior hippocampus at 200 μm intervals and mounted on Superfrost Plus slides. Antigen retrieval was performed with citrate buffer (10 mM citric acid, 0.05% Tween 20, pH 6.0) at 95°C for 5 min followed by washing with PBS, tissue permeabilization with 0.25% Triton X-100 in PBS for 30 min and blocking with 5% donkey serum and 1% bovine serum albumin (BSA) and 0.3% Triton X-100 in PBS for 60 min. Sections were incubated in primary antibodies for CD31 (Abcam, ab28364, 1:200 dilution), GFAP Alexa Fluor 488 (eBioscience, 53-9892-80 1:400 dilution), or ICAM-1 (R&D, AF796, 1:50 dilution) in 5% donkey serum, 1% BSA and 0.3% Triton X-100 in PBS overnight at 4°C. After washing with PBS, sections were incubated with secondary antibodies for 60 min at room temperature. Secondary antibodies were as follows: goat anti-rabbit Alexa Fluor 594 antibody (Life Tech, A11012, 1:600 dilution) to detect CD31 on its own and when co-stained with GFAP, goat anti-rabbit Alexa Fluor 647 antibody (Life Tech, A27040, 1:400 dilution) to detect CD31 co-stained with ICAM-1, and donkey anti-goat Alexa Fluor 594 antibody (Life Tech, A-11058, 1:400 dilution) to detect ICAM-1. Sections were then washed with PBS. Amyloid staining was subsequently performed with X-34 (Sigma-Aldrich, SML 1954, 2 μM in PBS with 0.5% Tween-20) for 20-30 min followed by washing with 40% ethanol in PBS then PBS alone. Coverslips were mounted onto slides in prolong antifade (Invitrogen, P36970) and slides were stored at 4 °C until 58 imaging with an Axio Scan.Z1 (Zeiss). Brain sections were randomized and the researcher performing the staining was blinded to mouse genotype. The cellular localization of ICAM-1 expression was visualized using an LSM 880 confocal laser scanning microscope (Zeiss). Sections incubated with secondary antibody only were used as negative controls to rule out unspecific antibody staining. Sections from wildtype mice stained with X-34 were used as negative controls to rule out unspecific X-34 staining. 2.3.5 Image analysis Image analysis was performed with ImageJ (NIH) by a researcher blinded to mouse genotype as illustrated in Figure 2.9. Exported images for each channel were converted to 8-bit black and white images. The threshold for positive and negative pixels was manually determined for X-34 and GFAP images using histogram-based segmentation then applied to all of the images of that channel. Due to regional variation in CD31 staining, auto-local thresholding using the Bernsen method was performed on CD31 images. Cortical and hippocampal regions were manually selected and saved as “regions of interest” (ROIs) for each section. Total CD31, X-34, and GFAP positive area was measured in the threshold segmented cortical and hippocampal ROIs and normalized to the total ROI area. Vascular GFAP was quantified as the GFAP positive area associated with CD31 in each region whereby a mask of the segmented CD31 image for a section was created then applied to the segmented GFAP image of the same section. The GFAP positive area within the CD31 mask was measured then normalized to the total CD31 positive area within the ROI. Plaque-associated GFAP was similarly quantified as the GFAP positive area associated with X-34 in each region whereby a mask of the segmented X-34 image for a section was created then applied to the segmented GFAP image of the same section. The GFAP positive area within the X-34 mask was measured then normalized to the total X-34 positive area within the ROI. Total and vascular ICAM-1 quantification was performed using the same techniques as described above to measure total and vascular GFAP. Parenchymal ICAM-1 positive area was calculated as the difference between total and vascular ICAM-1 positive area in the cortical and hippocampal regions. 59 CAA was quantified using methods similar to that published in Nature Protocols by Wilcock et al. in 2006 [565], which has been used by many others [521,566,567] including in the study by Lefterov et al. using apoA-I deficient APP/PS1 mice [519]. A mask containing areas of vascular amyloid was created by manually discriminating vascular amyloid from parenchymal plaques in segmented X-34 images based on morphology, as illustrated in Figure 2.10. Identification of vascular versus parenchymal amyloid was performed on inverted images to ease visualization. These vascular amyloid masks were then applied back onto segmented X-34 images and the percent positive area was measured and normalized to total area of the cortical ROI to give percent cortical CAA area. The X-34 positive area within the vascular amyloid mask was then used to create a new CAA mask. This CAA mask was then applied to a mask of vascular GFAP created as above. The vascular GFAP positive area within the CAA mask was measured and normalized to CAA area to give the percent area of CAA associated with vascular GFAP. The above analysis steps, excluding the drawing of cortical and hippocampal ROIs and vascular amyloid areas, were repeated for each section using an automated ImageJ macro. Figure 2.9 shows schematics illustrating the image analysis process. Analysis of vessel diameter and tortuosity was performed with Vesselucida 360 software (MBF Bioscience) using CD31 as the vessel marker. For amyloid staining, N=4-5 animals per genotype were used, for CD31, GFAP, and ICAM-1 staining, N=5-6 animals per genotype were used. 2.3.6 Protein extraction Protein from half-brains was serially extracted to produce soluble and insoluble protein fractions. Half-brains were first homogenized with a Tissuemite homogenizer for 20 s in carbonate buffer (10 mM carbonate, 50 mM sodium chloride, cOmplete protease inhibitor tablet, pH 11.5) then sonicated. Homogenates were cleared by centrifugation at 12,500 g for 45 min at 4°C. The resulting supernatant was neutralized with 1.5 volumes Tris (1 M Tris, pH 6.8) and labelled as the soluble fraction. The pellet was resuspended in 1 mL of guanidine hydrochloride (5 M guanidine hydrochloride in 1 M Tris, pH 8.0) by pipetting followed by rotation overnight at room temperature and labeled as the insoluble fraction. Soluble and insoluble extracts were stored at -80°C until use. Lysate protein concentrations were determined using the DC Protein assay kit (BioRad). 60 2.3.7 Enzyme linked immunosorbent assay (ELISA) Human Aβ40 (KHB-3482, ThermoFisher, 1:20, 1:2500), human Aβ42 (KHB-3442, ThermoFisher, 1:40, 1:2500), murine apoE (3752-1HP-2, ThermoFisher, 1:20, 1:200), and murine apoJ (ab199079, Abcam, 1:200, 1:200) concentrations were measured in carbonate soluble and guanidine hydrochloride insoluble half-brain lysates using commercial sandwich ELISA kits according to manufacturer’s instructions. Murine GFAP (NS830, Millipore, 1:400), intercellular adhesion molecule 1 (ICAM-1) (ab100688, Abcam, 1:2), vascular cell adhesion molecule (VCAM-1) (ab100750, Abcam, 1:2000), platelet derived growth factor receptor beta (PDGFRβ) (MBS919047, MyBioSource, 1:10), interleukin 1 beta (IL-1β) (K15245D, MesoScale Discovery, 1:2), and tau (K15121D, MesoScale Discovery, 1:50) concentrations were measured in carbonate soluble half-brain lysates using commercial sandwich or MesoScale Discovery ELISA kits according to the manufacturer’s instructions. Data points were interpolated from a standard curve using four parameter nonlinear regression curve fitting and normalized to total soluble protein concentration. Brain lysates were randomized and the researcher performing the ELISAs was blinded to mouse genotype. For apoE ELISA, N=6 mice per genotype were used. For Aβ ELISA, N=5-7 mice per genotype were used. For IL-1β, ICAM-1, VCAM-1, and PDGFRβ ELISA, N=5-19 mice per genotype were used. For GFAP ELISA, N=6-14 mice per genotype were used. 2.3.8 Isolation of RNA and real-time quantitative polymerase chain reaction (RT-qPCR) RNA was extracted from parietal cerebral cortices using Trizol (Invitrogen) and treated with DNaseI (Life Technologies) prior to complementary deoxyribonucleic acid (cDNA) synthesis. cDNA was generated using the Taqman Reverse Transcription Kit (ThermoFisher, N8080234). RT-qPCR was performed using the Light Cycler 96 Real-Time PCR System (Roche) and FastStart Essential DNA Green Master Mix (Roche). Expression of murine Il1b (Fwd: TTGACGGACCCCAAAAGA; Rev: CAGCTTCTCCACAGAGCCACA) was normalized to murine β-actin (Fwd: ACGGCCAGGTCATCACTATTG; Rev: CAAGAAGGAAGGCTGGAAAAG). RNA homogenates were randomized and the researcher performing the RT-qPCR was blinded to mouse genotype. For Apoe and Il1b mRNA analyses, N=7-12 mice per genotype were used. 61 2.3.9 Contextual and cued fear conditioning 2.3.9.1 Training During training, mice were placed in an illuminated compartment of a shuttle box (Med Associates Inc, St. Albans, VT) and allowed to habituate to the internal testing environment for 120 s. After the habituation period, an 80-dB auditory cue was played for 30 s. During the last 2 s of the auditory cue, a mild foot shock (0.3 mA) was administered. Shocked mice were left undisturbed in the testing chamber for an inter-trial interval of 60 s, after which mice were presented with a second identical tone, which was called the shock trial. After the second tone, shocked mice were again left undisturbed in the testing chamber for 60 s. 2.3.9.2 Context testing Mice were tested for their ability to remember the context in which they received the foot shock 24 h after training. Mice were placed in the same illuminated compartment and observed for the presence/absence of a freezing response over 5 min. During this time mice were not exposed to the tone or shock and the percent freezing time was measured using the ANY-Maze system (Stoelting Co., Wood Dale, IL). Contextual testing included N=7-23 mice per genotype. 2.3.9.3 Cued testing Mice were tested for their ability to remember the 80-dB auditory cue presented during the training session 1 h following context testing. Testing was conducted in an environment different from the training and context testing session (darkened compartment, false floor placed over the steel rods of the shuttle box, different cleaning solution). Mice were placed in the darkened compartment and allowed to habituate for 120 s, followed by the 80-dB auditory cue for 120 s. After the tone, mice were left undisturbed in the testing chamber for 60 s and percent freezing time was measured using the ANY-Maze system. Cued testing included N=7-23 mice per genotype. 2.3.10 Statistical analysis All statistical analyses, with the exception of fear memory analysis, were performed with GraphPad Prism 7, p-values <0.05 were considered statistically significant. For the analysis of all 62 non-amyloid data, statistical comparisons were made by two-way analysis of variation (ANOVA) considering APP/PS1 genotype as one factor and apoA-I genotype as a second factor (omnibus analysis) followed by Sidak’s multiple comparisons test if significant factor or interaction effects were observed. Analysis of Aβ concentration, amyloid plaque area, and CAA were performed with unpaired t-test or Mann Whitney test for parametric and non-parametric data respectively. Analysis of fear memory was performed with IBM SPSS Statistics Build 1.0.0.1347 using three-way ANOVA to analyze freezing time as the dependent variable and time, APP/PS1 genotype, and apoA-I genotype as fixed variables. Pairwise comparisons of estimated marginal means and standard error were performed and adjusted by Sidak’s multiple comparisons test, adjusted p-values <0.05 were considered statistically significant. 2.4 Results 2.4.1 Loss of apoA-I significantly reduces plasma total cholesterol and HDL cholesterol concentrations By omnibus two-way ANOVA, apoA-I deficiency significantly reduced plasma total cholesterol (p=0.013), HDL-C (p<0.0001), and LDL-C concentrations (p=0.048) (Figure 2.2a-c) relative to apoA-IHEM mice, as previously reported [115,568]. HDL-C was the cholesterol pool most affected by the complete loss of apoA-I with reductions from 48.3 mg/dL to 14.6 mg/dL (p=0.011) in wildtype (WT) apoA-IKO vs. WT apoA-IHEM mice and from 50.1 mg/dL to 8.3 mg/dL (p=0.003) in APP/PS1 apoA-IKO vs. APP/PS1 apoA-IHEM mice. Plasma cholesterol pools were not affected by APP/PS1 genotype. ApoA-I genotype did not affect soluble (Figure 2.2d) or insoluble (Figure 2.2e) brain apoE protein concentration or cortical Apoe mRNA expression (Figure 2.2f), although insoluble brain apoE protein concentrations were significantly higher in APP/PS1 mice compared to WT controls overall (p<0.0001 by omnibus two-way ANOVA) (Figure 2.2e). Soluble (Figure 2.2g) and insoluble (Figure 2.2h) apoJ protein concentrations in the brain were increased in APP/PS1 mice compared to WT controls overall (p=0.018 and p<0.0001 respectively by omnibus two-way ANOVA). Insoluble apoJ protein was also increased overall in apoA-IKO compared to apoA-IHEM (p=0.015 by omnibus two-way ANOVA). 63 Figure 2.2 Plasma lipid and brain apolipoprotein concentrations in apoA-I deficient APP/PS1 mice. Plasma levels of (a) total cholesterol, (b) HDL-C, and (c) LDL-C were measured with commercially available kits. ApoE protein in (d) soluble and (e) insoluble half brain homogenates was measured by ELISA. (f) Apoe mRNA expression was measured in the parietal cerebral cortex by RT-qPCR. ApoJ protein in (g) soluble and (h) insoluble half brain homogenates was measured by ELISA. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. Omnibus analyses of apoA-I genotype effects (across APP/PS1 genotype) and APP/PS1 genotype effects (across apoA-I genotype) by two-way ANOVA are displayed as exact p-values below graphs. Sidak’s multiple comparisons test results are displayed within graphs as *p<0.05, **p<0.01, and ***p<0.0001. For plasma lipid and brain protein analysis N=6-7 mice per genotype, for mRNA analysis N=7-17 mice per genotype were used. apo: apolipoprotein, TC: total cholesterol, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermate for APP/PS1, APP/PS1: APP/PS1 transgenic. 2.4.2 ApoA-I deficiency increases cortical parenchymal and vascular amyloid burden of APP/PS1 mice Histological examination of X-34-stained sections showed that apoA-I deficiency led to significantly greater amyloid burden in the cortex, an increase from a mean of 0.28% to 0.91% 64 (Figure 2.3a-b) (p=0.001 by unpaired t-test), but not in the hippocampus (Figure 2.3c-d) of APP/PS1 mice. Consistent with a previous study [519], we also observed significantly increased cortical CAA from a mean of 0.01% to 0.05% cortical area in APP/PS1 apoA-IKO compared to APP/PS1 apoA-IHEM mice (p=0.016 by Mann-Whitney test) (Figure 2.3e-f). CAA was not observed in the hippocampus of any animal. These observations suggest that apoA-I protects against Aβ deposition in the walls of cortical arteries, which may in turn reduce parenchymal amyloid deposition in the surrounding cortex. Figure 2.3 Amyloid plaques and vascular Aβ deposition in apoA-I deficient APP/PS1 mice. 65 Total (a) cortical and (c) hippocampal area occupied by amyloid deposits and (e) CAA area were evaluated by staining fixed cryosections with X-34. (b,d,f) Representative images are below graphs. (g) Soluble Aβ40, (h) soluble Aβ42, (i) insoluble Aβ40, and (j) insoluble Aβ42 protein concentrations were measured in half-brain homogenates by ELISA, values were normalized to total protein concentration in the homogenates. (k) Total tau and (l) phosphorylated tau proteins were measured by ELISA and used to calculate the (m) phosphorylated tau/total tau ratio, values were normalized to total protein concentration in the homogenates. All analytes were normalized to homogenate total protein concentration. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. Arrowheads in (f) indicate areas of CAA. Results of unpaired t-test (a) and Mann-Whitney test (e) are displayed within graphs as *p<0.05 and **p<0.01. Omnibus analyses of apoA-I genotype effects (across APP/PS1 genotype) and interaction effects by two-way ANOVA are displayed as exact p-values below graphs (k,l). Sidak’s multiple comparisons test results (l) are displayed within the graph as *p<0.05. For amyloid staining N=4-5 mice per genotype were used, for Aβ ELISA N=5-7 mice per genotype were used, for tau and p-tau ELISA N=6-19 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermate for APP/PS1, APP/PS1: APP/PS1 transgenic, CAA: cerebral amyloid angiopathy, Aβ: amyloid beta, p-tau: phosphorylated tau, X-34: 1,4-Bis(3-carboxy-4-hydroxyphenylethenyl)benzene, amyloid stain. Biochemical analysis of total soluble and insoluble Aβ40 and Aβ42 pools in half-brain homogenates revealed no differences by apoA-I genotype, although soluble Aβ40, insoluble Aβ40, and insoluble Aβ42 were all very slightly and non-significantly increased in APP/PS1 apoA-IKO vs. APP/PS1 apoA-IHEM mice (Figure 2.3g-j). The lack of a significant effect of apoA-I genotype on Aβ concentration, despite the significant increase in cortical amyloid deposition, may be due to a region-specific effect of apoA-I on amyloid that is not detectable in crude half-brain homogenates. Indeed, immunofluorescence analysis (described above) confirms that apoA-I genotype influences amyloid plaque burden in the cortex but not in the hippocampus. Although no significant differences in total tau or tau phosphorylated at Thr231 (p-tau) were observed by APP/PS1 genotype, total tau concentration was significantly higher (Figure 2.3k) and p-tau (Figure 2.3l) concentration tended to be higher in apoA-IKO mice compared to apoA-IHEM mice (p=0.041 and p=0.054, respectively, by omnibus two-way ANOVA). ApoA-I genotype significantly interacted with APP/PS1 genotype with respect to p-tau (p=0.023). Post-hoc Sidak’s multiple comparison tests revealed a nearly significant increase in total tau in APP/PS1 apoA-IKO vs. APP/PS1 apoA-IHEM mice (p=0.063) and a significant increase in p-tau from 1.09 ng/mg in APP/PS1 apoA-IHEM to 1.40 ng/mg in APP/PS1 apoA-IKO (p=0.026). Concentration of p-tau showed a trend towards a reduction in APP/PS1 apoA-IHEM vs. WT apoA-IHEM mice (p=0.099 by Sidak’s multiple comparisons test). The total tau:p-tau ratio was unaffected by loss of apoA-I (Figure 2.3m). 66 2.4.3 ApoA-I deficiency increases pro-inflammatory protein and mRNA levels in the cortices of APP/PS1 mice IL-1β protein concentrations in half-brain homogenates were elevated in APP/PS1 compared to WT mice from a mean of 0.36 pg/mg and 0.42 pg/mg in WT apoA-IHEM and apoA-IKO, respectively, to 0.79 pg/mg and 0.82 pg/mg in APP/PS1 apoA-IHEM and apoA-IKO, respectively, (p<0.0001 by omnibus two-way ANOVA). IL-1β protein concentrations were not significantly affected by apoA-I genotype (Figure 2.4a). However, cortical Il1b mRNA expression levels were significantly elevated overall in APP/PS1 compared to WT mice (p<0.0001 omnibus by two-way ANOVA) and in apoA-IKO vs. apoA-IHEM mice (p=0.0021 by omnibus two-way ANOVA) (Figure 2.4b), where the mean expression ratio was 4.85e-11 in WT apoA-IHEM, 1.08e-10 in WT apoA-IKO, 1.36e-10 in APP/PS1 apoA-IHEM, and 2.17e-10 in APP/PS1 apoA-IKO. The discrepancy between IL-1β protein and mRNA observations may be due to both the lower limit of sensitivity for the protein assay and regional selectivity. Brain protein and mRNA expression of tumour necrosis factor alpha (TNFα) and interleukin 6 (IL-6) were also measured but were all below limits of detection (data not shown). We next investigated markers of inflammation expressed specifically by cells of the blood-brain barrier. PDGFRβ is a pericyte marker that is elevated in some cases of neuroinflammation [569]. We observed a significant interaction effect of APP/PS1 and apoA-I genotypes (p=0.026 by omnibus two-way ANOVA) on PDGFRβ protein concentrations in the brain, where PDGFRβ protein tended to be elevated only in APP/PS1 apoA-IKO mice compared to APP/PS1 apoA-IHEM mice (p=0.065 by Sidak’s multiple comparisons test) (Figure 2.4c). Mean PDGFRβ concentrations were 5.03 ng/mg in WT apoA-IHEM, 4.56 ng/mg in WT apoA-IKO, 3.76 ng/mg in APP/PS1 apoA-IHEM, and 5.61 ng/mg in APP/PS1 apoA-IKO. To evaluate EC specific inflammation, we measured protein concentrations of VCAM-1 in the brain. VCAM-1 is expressed on the cell surface of ECs and assists in the adhesion of circulating immune cells to the vessel wall [373,570]. We found that VCAM-1 tended to be elevated in apoA-IKO mice compared to apoA-IHEM mice overall (p=0.076 by omnibus two-way ANOVA) independent of APP/PS1 genotype (Figure 2.4d). ICAM-1 is another EC adhesion molecule that functions similarly to VCAM-1 and 67 can also be expressed in other cells such as astrocytes and microglia [570–574]. We observed an overall effect of apoA-I genotype on brain ICAM-1 concentrations with increases in apoA-IKO mice (p=0.013 by omnibus two-way ANOVA), a specific increase in APP/PS1 apoA-IKO mice compared to APP/PS1 apoA-IHEM mice from a mean of 0.72 ng/mg to 1.25 ng/mg (p=0.023 by Sidak’s multiple comparisons test), and a trend towards a significant interaction effect between apoA-I and APP/PS1 genotypes (p=0.079 by omnibus two-way ANOVA) (Figure 2.4e). Finally, we observed a significant interaction effect between APP/PS1 and apoA-I genotypes on total GFAP protein concentrations in half-brain homogenates (p=0.007 by omnibus two-way ANOVA). GFAP was elevated both in APP/PS1 mice compared to WT controls overall (p<0.0001 by omnibus two-way ANOVA) and in APP/PS1 apoA-IKO compared to APP/PS1 apoA-IHEM mice (p=0.030 by post-hoc Sidak’s multiple comparisons test) (Figure 2.4f). Mean GFAP protein concentrations were 20.41 μg/mg in WT apoA-IHEM, 18.55 μg/mg in WT apoA-IKO, 25.75 μg/mg in APP/PS1 apoA-IHEM, and 30.59 μg/mg in APP/PS1 apoA-IKO. Together, these data suggest that loss of apoA-I can exacerbate neuroinflammation in APP/PS1 mice, particularly in cell types associated with the cerebrovasculature. Figure 2.4 Pro-inflammatory protein and mRNA markers in apoA-I deficient APP/PS1 mice. (a) IL-1β, (c) PDGFRβ, (d) VCAM-1, (e) ICAM-1, and (f) GFAP protein concentrations were measured by ELISA in soluble half-brain homogenates, values were normalized to total protein concentration in the homogenates. (b) Il1b mRNA expression was measured the cortex by RT-qPCR and normalized to β-actin expression. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. 68 Omnibus analyses of apoA-I genotype effects (across APP/PS1 genotype), APP/PS1 genotype effects (across apoA-I genotype), and interaction effects by two-way ANOVA are displayed as exact p-values below graphs. Sidak’s multiple comparisons test results are displayed within graphs as *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. For ELISA N=5-19 mice per genotype were used, for mRNA N=7-21 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermate for APP/PS1, APP/PS1: APP/PS1 transgenic, IL-1β: interleukin 1 beta, VCAM-1: vascular cell adhesion molecule 1, PDGFRβ: platelet derived growth factor receptor beta, ICAM-1: intercellular adhesion molecule 1, GFAP: glial fibrillary acidic protein. 2.4.4 ApoA-I deficiency increases total ICAM-1 protein concentration in the brain, total ICAM-1 positive area in the hippocampus, and vascular ICAM-1 positive area in the cortex and hippocampus of APP/PS1 mice Confocal imaging confirmed co-localization of ICAM-1 with CD31 (Figure 2.5a). Co-localizations of parenchymal ICAM-1 with GFAP was minimal, suggesting expression of ICAM-1 in the parenchyma is predominantly by other cell types (Figure 2.5b). The effect of apoA-I genotype on total brain ICAM-1-positive area was found to be region specific using immunofluorescence. Although we observed no significant differences in total cortical ICAM-1 staining by apoA-I genotype (Figure 2.5c,e), total ICAM-1-positive area was increased in APP/PS1 apoA-IKO compared to APP/PS1 apoA-IHEM in the hippocampus, from 2.34% of hippocampal area to 4.67% (p=0.001 by Sidak’s multiple comparisons test) (Figure 2.5g,i). Interestingly, vascular specific ICAM-1, measured as the ICAM-1 positive area associated with CD31, was significantly elevated in both cortical (Figure 2.5d,f) and hippocampal regions (Figure 2.5h,j) in APP/PS1 apoA-IKO compared to APP/PS1 apoA-IHEM. Cortical vascular ICAM-1 positive areas were 7.87% and 12.26% of CD31 positive area in APP/PS1 apoA-IHEM and APP/PS1 apoA-IKO, respectively (p=0.001 by Sidak’s multiple comparisons test) (Figure 2.5d,f) and hippocampal levels were 15.02% and 24.19% (p=0.001 by Sidak’s multiple comparisons test) (Figure 2.5h,j). 69 Figure 2.5 Total and endothelial associated ICAM-1 in apoA-I deficient APP/PS1 mice. Confocal microscopy was used to identify (a) co-localization of ICAM-1 with the EC marker CD31 and (b) evaluate the co-localization of some ICAM-1 in the brain parenchyma with GFAP-positive astrocytes. Total ICAM-1 staining area was visualized by immunofluorescence in (c,e) cortical and (g,i) hippocampal regions and positive staining area was normalized to total region area. Vascular-specific and parenchymal ICAM-1 expression was visualized using immunofluorescence and shows association of ICAM-1 with CD31 in (d,f) cortical and (h,j) hippocampal regions, where positive co-stained area was normalized to total CD31 positive area. Representative images for immunofluorescent data are below the graphs, green arrowheads indicate examples of vascular ICAM-1. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. Omnibus analyses of apoA-I genotype effects (across APP/PS1 genotype), APP/PS1 genotype effects (across apoA-I genotype), and interaction effects by two-way ANOVA are displayed as exact p-values below graphs. Sidak’s multiple comparisons test results are displayed within graphs as *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. 70 For ELISA N=6-19 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermate for APP/PS1, APP/PS1: APP/PS1 transgenic, ICAM-1: intercellular adhesion molecule 1, GFAP: glial fibrillary acidic protein, CD31: cluster of differentiation 31. 2.4.5 ApoA-I deficiency exacerbates Aβ-mediated increases in total and cerebrovascular GFAP levels in both cortex and hippocampus Immunofluorescent staining for GFAP confirmed the biochemical total protein data, with an additive effect of Aβ overexpression and apoA-I loss on GFAP staining area in the cortex (p=0.001 for APP/PS1 vs. WT omnibus two-way ANOVA analysis; p=0.001 for APP/PS1 apoA-IKO vs. WT apoA-IKO, and p=0.040 for APP/PS1 apoA-IKO vs. APP/PS1 apoA-IHEM by post-hoc Sidak’s multiple comparisons test) (Figure 2.6a,c) and a synergistic effect in the hippocampus (p=0.008 interaction effect by omnibus two-way ANOVA; p=0.004 for APP/PS1 apoA-IKO vs. WT apoA-IKO, and p=0.004 for APP/PS1 apoA-IKO vs. APP/PS1 apoA-IHEM by Sidak’s multiple comparisons test) (Figure 2.6e,g). Cortical GFAP positive area increased from 0.07% and 0.06% in WT apoA-IHEM and apoA-IKO, respectively, to 0.70% in APP/PS1 apoA-IHEM and 1.66% in APP/PS1 apoA-IKO. In the hippocampus, GFAP positive area was 2.27%, 1.77%, and 1.65% in WT apoA-IHEM, WT apoA-IKO, and APP/PS1 apoA-IHEM, respectively, and 4.28% in APP/PS1 apoA-IKO. 71 Figure 2.6 Reactivity of astrocytes associated with the cerebrovasculature in apoA-I deficient APP/PS1 mice. Total GFAP staining area was visualized by immunofluorescence in (a,c) cortical and (e,g) hippocampal regions and positive staining area was normalized to total region area. Vascular-specific GFAP expression was visualized using immunofluorescence and observing the association of GFAP with CD31 in (b,d) cortical and (f,h) hippocampal regions, positive co-stained area was normalized to total CD31 positive area. (Representative images for immunofluorescent data are below graphs. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. Yellow arrowheads indicate examples of areas of CD31-associated GFAP in the cortex (d) and hippocampus (h) that are quantified in (b) and (f), respectively. Omnibus analyses of apoA-I genotype effects (across APP/PS1 genotype), APP/PS1 genotype effects (across apoA-I genotype), and interaction effects by two-way ANOVA are displayed as exact p-values below graphs. Sidak’s multiple comparisons test results are displayed within graphs as *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. For GFAP total and vascular staining N=5-6 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermate for APP/PS1, APP/PS1: APP/PS1 transgenic, GFAP: glial fibrillary acidic protein, CD31: cluster of differentiation 31. We next used immunofluorescence to examine the association of CD31 and GFAP and observed a robust and statistically significant increase in the cerebrovascular area associated with GFAP in both the cortex and hippocampus of APP/PS1 apoA-IKO mice compared to APP/PS1 apoA-IHEM 72 mice (p=0.008 in cortex, p=0.007 in hippocampus by Sidak’s multiple comparisons test) (Figure 2.6b,d,f,h). In addition, significant elevations were observed overall in APP/PS1 compared to WT mice (p<0.0001 in the cortex by Sidak’s multiple comparisons test) and in apoA-IKO compared to apoA-IHEM mice (p=0.025 in cortex, p=0.049 in hippocampus by Sidak’s multiple comparisons test). Mean cortical vascular GFAP levels were elevated from 0.21% and 0.30% in WT apoA-IHEM and apoA-IKO, respectively, to 1.80% in APP/PS1 apoA-IHEM mice and 3.86% in APP/PS1 apoA-IKO. In the hippocampus, mean vascular GFAP levels increased from 6.86%, 6.20%, and 5.20% in WT apoA-IHEM, WT apoA-IKO, and APP/PS1 apoA-IHEM, respectively, to 10.86% in APP/PS1 apoA-IKO. Strikingly, the elevation in vascular GFAP in apoA-IKO mice was statistically more robust than the changes in total GFAP positive area in both the cortex and the hippocampus. The total vascular area as defined by CD31 staining, vessel thickness, and vessel tortuosity were all unchanged by apoA-I genotype in the cortex and the hippocampus (Figure 2.11a-h). A closer examination of APP/PS1 mice revealed significantly greater levels of cortical plaque-associated GFAP in APP/PS1 apoA-IKO compared to APP/PS1 apoA-IHEM, which increased from a mean of 0.02% to 0.51% of plaque area (p=0.030 by Mann-Whitney test) (Figure 2.7a-b), suggesting that apoA-I deficiency promotes astrocyte reactivity to Aβ plaques. Parenchymal plaque-associated GFAP was not significantly affected by apoA-I genotype in hippocampus (Figure 2.7c-d). We also investigated whether astrocyte reactivity was affected by CAA, and observed a significant elevation of GFAP area on CAA positive vessels from 0.17% of CAA area on average in APP/PS1 apoA-IHEM mice compared to 2.67% in APP/PS1 apoA-IKO controls (p=0.023 by unpaired t-test) (Figure 2.7e,g). Notably, this effect was larger in magnitude than the increases in either CAA levels or vascular GFAP expression alone in APP/PS1 apoA-IKO mice compared to APP/PS1 apoA-IHEM mice. Finally, we observed a trend toward increased CAA in the areas of vascular GFAP expression where CAA made up 0.04% of vascular GFAP area on average in APP/PS1 apoA-IHEM mice compared to 0.54% in APP/PS1 apoA-IKO controls (p=0.116 by unpaired t test) (Figure 2.7f-g). Taken together, our data support an interaction among Aβ, astrogliosis, and apoA-I. 73 Figure 2.7 Astrocyte reactivity to Aβ plaques and vascular Aβ deposits in apoA-I deficient APP/PS1 mice. GFAP associated with Aβ plaques in the (a) cortex and (c) hippocampus was visualized with immunofluorescence and normalized to total area of the region. The association of vascular-specific GFAP (GFAP colocalized with CD31) with CAA was visualized using immunofluorescence and normalized to (e) total cortical CAA area and (f) total vascular GFAP area in the cortex. Representative images are (b,d) beside or (g) below graphs. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. Yellow closed arrowheads ( ) indicate examples of areas of plaque-associated or CAA-associated GFAP, white open arrowheads ( ) indicate examples areas of plaque or CAA not associated with GFAP. Results of Mann-Whitney test (a) and unpaired t-test (e) are displayed within graphs as *p<0.05. For plaque and CAA area associated with GFAP N=5-6 mice per genotype were used, for CAA area associated with vascular GFAP N=4-5 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, APP/PS1: APP/PS1 transgenic, GFAP: glial fibrillary acidic protein, CD31: cluster of differentiation 31, X-34: 1,4-Bis(3-carboxy-4-hydroxyphenylethenyl)benzene , amyloid stain, CAA: cerebral amyloid angiopathy. 2.4.6 ApoA-I deficiency worsens fear memory deficits in APP/PS1 mice Others have observed that genetically altering apoA-I expression in APP/PS1 mice affects spatial learning and memory, with increased apoA-I improving performance and loss of apoA-I worsening performance in the Morris Water Maze [519,521]. We performed cued and contextual fear conditioning tests to evaluate fear memory and overall observed memory deficits in APP/PS1 mice compared to WT and a worsening of those deficits in APP/PS1 apoA-IKO mice (Figure 2.8a-b). For cued fear conditioning, a significant interaction between APP/PS1 and apoA-I genotype effects was observed (p=0.001 by three-way ANOVA) whereby APP/PS1 apoA-IHEM mice showed 1.8-fold more freezing behaviour than APP/PS1 apoA-IKO (p=0.026 by Sidak’s multiple comparisons test) yet for WT mice, apoA-IKO mice showed 1.4-fold more freezing behaviour than apoA-IHEM mice (p=0.009 by Sidak’s multiple comparisons test). Furthermore, WT apoA-IKO mice also 74 showed 2-fold more freezing behaviour than APP/PS1 apoA-IKO mice (p=0.002 by Sidak’s multiple comparisons test). Notably, non-significant differences in freezing behaviour were present between groups even before the presence of the auditory cue, suggesting that some of the differences in freezing in this test are not related to cued fear memory alone. Figure 2.8 Cued and contextual fear memory in apoA-I deficient APP/PS1 mice. (a) Cued and (b) contextual fear memory was measured using a foot shock as the stimulus, the foot shock box as the context, and a tone as the cue. During cued fear conditioning testing, the cue was played during min 3 and 4. Points and error bars represent estimated marginal means and ±standard error used to calculate pairwise comparisons for statistics. Results from three-way ANOVA are displayed as exact p-values below graphs. N=7-24 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermates for APP/PS1, APP/PS1: APP/PS1 transgenic. For the contextual fear conditioning test, WT mice froze 1.5-fold longer than APP/PS1 mice overall (p=0.019 by three-way ANOVA) and apoA-IHEM mice froze 1.6-fold longer than apoA-IKO mice overall (p=0.004 by three-way ANOVA). More specifically, WT apoA-IKO mice tended to perform better than APP/PS1 apoA-IKO (1.8-fold, p=0.065 by Sidak’s multiple comparisons test), WT apoA-IHEM performed 1.4-fold better than WT apoA-IKO (p=0.020 by Sidak’s multiple comparisons test), and APP/PS1 apoA-IHEM tended to perform better than APP/PS1 apoA-IKO (2-fold, p=0.057 by Sidak’s multiple comparisons test). Taken together, this data suggests that APP/PS1 mice have memory impairments and apoA-I-deficiency exacerbates these impairments. Significant trial effects were also observed in cued and contextual fear conditioning tests (p=0.001 and p=0.019, respectively, by three-way ANOVA) with the time spent freezing tending to increase over the testing period (Cued: p=0.0255 for min 1 vs. min 3, p=0.008 for min 1 vs. min 4, p=0.002 75 for min 1 vs. min 5 by Sidak’s multiple comparisons test. Context: p=0.055 for min 1 vs. min 3, p=0.019 for min 1 vs. min 5 by Sidak’s multiple comparisons test). 2.5 Discussion A better understanding of the vascular contributions to AD may reveal attractive therapeutic targets that act systemically and therefore may not necessarily need to cross the blood-brain barrier to have beneficial effects on the brain. Circulating factors such as HDL, which has multiple vasoprotective properties, could promote cerebrovascular health by acting on the luminal side of cerebral vessels. We therefore aimed to confirm and extend previous findings by us and others on the role of apoA-I on AD pathology in mice. In agreement with Lefterov et al. [519], here we observed that complete loss of apoA-I worsens cognitive deficits and increases CAA in APP/PS1 mice. We also show for the first time that eliminating apoA-I in APP/PS1 mice increases total cortical Aβ deposition and astrogliosis in both cortex and hippocampus. Additionally, we observed that loss of apoA-I increased several markers neuroinflammation and vascular inflammation within the brain. Specifically, brain levels of Il1b mRNA, ICAM-1 protein, PDGFRβ protein, and GFAP protein were elevated in the absence of apoA-I. Furthermore, apoA-IKO mice showed elevated levels of ICAM-1 on ECs and increased GFAP-positive astrocytes in direct contact with the cerebrovasculature in the cortex and hippocampus. While Lefterov et al. did not observe changes in amyloid pathology or neuroinflammation in apoA-IKO mice, other groups have increased apoA-I expression in APP/PS1 mice using transgenic overexpression of apoA-I or by injecting apoA-I mimetics and observed reduced cerebral amyloid and astrogliosis [521,523–525,527]. Finally, we found that astrocyte reactivity to both parenchymal and vascular amyloid was increased in apoA-I deficient mice. Importantly, the previous study by Lefterov et al. compared APP/PS1 apoA-IKO mice to APP/PS1 apoA-IWT mice whereas the current work compares APP/PS1 apoA-IKO mice to APP/PS1 apoA-IHEM mice. Although others have shown that there is a greater difference in plasma apoA-I concentrations between apoA-IKO and apoA-IHEM mice compared to apoA-IHEM and apoA-IWT mice [115], the differences in genotypes used in each of these studies may contribute to some of the differences in the findings. Nonetheless, our observations support the hypothesis that plasma HDL acts to protect cerebral vessels not only from Aβ deposition but also from Aβ-induced astrocyte reactivity and endothelial activation. 76 Most studies altering plasma HDL-C concentration in Aβ expressing mice either find no effect on parenchymal amyloid deposition or a protective effect of raised HDL as well as a protective effect of HDL on CAA [519,521,523–525,527]. Contrary to the current report and others, Contu et al. reported a reduction in both amyloid plaques and CAA in apoA-IKO mice [522]. Several methodological differences between previous studies, the current study, and the study by Contu et al. may explain these inconsistent observations. A notable difference between the studies discussed are the lines of Aβ expressing mice. The present study as well as those by Lefterov et al. and Lewis et al. utilized APP/PS1 mice at 12 months of age [519,521]. APP/PS1 mice overexpress the Swedish amyloid precursor protein (APP) (APPSwe) mutation, have a deletion in the gene for presenilin 1 (PS1), and have extensive amyloid and moderate CAA pathology at 12 months of age [575,576]. However, the 12-month old Tg2576 mice used by Contu et al. only overexpress APPSwe and exhibit relatively mild amyloid pathology [575,576]. As suggested by the study authors, the observed reduction in CAA and amyloid deposition in apoA-IKO mice by Contu et al. may be only a temporary effect and evaluating amyloid pathology in older mice may produce results more in line with the other studies. Additionally, the histological methods used to detect amyloid pathology vary between studies. For example, the current study and those by Lefterov et al. and Lewis et al used amyloid fibril stains to detect amyloid plaques and CAA while Contu et al. used antibodies for Aβ40 and Aβ42 [519,521]. Therefore, the Aβ detected by Contu et al. may include soluble or oligomeric forms that would not be detected in the current study and others. Interestingly, Contu et al. observed elevated brain apoJ levels in WT apoA-IKO mice compared to WT apoA-IWT mice. They suggest elevated brain apoJ may explain their observed reduction in CAA as loss of apoJ in Aβ expressing mice shifts amyloid deposition from parenchymal plaques to the vasculature [248]. However, brain apoJ concentrations in the present study were elevated in the insoluble fraction of brain homogenates of apoA-IKO mice compared to apoA-IHEM mice while CAA levels worsened. Therefore, changes to brain apoJ levels in APP/PS1 apoA-IKO mice are unlikely to explain changes to CAA burden. The mechanisms by which circulating HDL can reduce amyloid pathology and neuroinflammation are not completely understood but may include effects of apoA-I within the brain and effects of HDL/apoA-I from the periphery. ApoA-I has been shown to enter the brain parenchyma of mice 77 via the blood-CSF barrier at the choroid plexus [501]. ApoA-I that has crossed into the brain may thus have direct anti-inflammatory effects on astrocytes consistent with apoA-I’s anti-inflammatory effects on other cell types [434,577] or may reduce Ab-induced inflammation by inhibiting Aβ fibrillization as has been observed in vitro [578]. Alternatively, HDL may exert its effects on the brain indirectly through actions on the cerebrovasculature, where it would act from the lumen of cerebral vessels on cells of the neurovascular unit without crossing into the brain parenchyma. The latter explanation appears more plausible for several reasons. First, apoA-I concentrations observed within the brain are low compared to the concentrations circulating in the blood, ELISA and immunoblot levels of apoA-I in brain interstitial fluid are <0.005% of plasma levels [501]. Second, HDL has multiple vasoprotective properties including anti-inflammatory activity, modulation of vessel tone, and promotion of EC repair [579]. Finally, previous work genetically manipulating apoA-I expression levels in APP/PS1 mice found changes specifically in vascular amyloid deposition without any changes to parenchymal plaques suggesting that the cerebrovasculature is the most sensitive target of HDL in the brain [519,521]. As described above, we observed several vascular specific changes in APP/PS1 mice based on apoA-I genotype. Briefly, we found increased protein concentrations of PDGFRβ and ICAM-1 as well as ICAM-1 positive area on ECs and GFAP-positive astrocytes associated with the cerebrovasculature. Our observations of increased plaque-associated GFAP in apoA-IKO APP/PS1 mice suggests an interaction among Aβ, astrogliosis, and apoA-I. CAA-associated GFAP, but not vascular GFAP-associated CAA, was significantly elevated in apoA-IKO mice. Therefore, we speculate that the loss of apoA-I increases the reactivity of astrocytes to amyloid, as opposed to loss of apoA-I increasing astrogliosis first and then leading to increased amyloid deposition as a result. In other words, the anti-inflammatory effects of HDL in the brain are working specifically against Aβ. In support of this hypothesis, loss of apoA-I did not significantly affect any neuroinflammatory marker in WT mice, which do not accumulate Aβ. Others have similarly observed that the elimination of apoA-I is not sufficient to observe pathological vascular changes. For example, apoA-I deficiency alone does not cause atherosclerosis in mice even at 15 months of age on an atherosclerotic diet, whereas apoA-I deficiency can worsen the atherosclerotic pathology of mice lacking LDL receptor (LDLR) [580]. Interestingly, loss of apoA-I expression may indeed be 78 sufficient to result in hypothalamic astrogliosis [581], however, we did not observe any significant apoA-I genotype effects on GFAP expression in the hypothalamus in the current study (Figure 2.12a-b). If the primary mechanism of action of HDL on AD-relevant pathologies is through HDL’s peripheral activities, increasing circulating HDL may be an attractive therapeutic approach that may complement anti-amyloid approaches. Although HDL-based therapeutics have not yet been studied in humans for their potential benefits against cognitive decline, several studies have investigated whether treating vascular risk factors can affect dementia and AD risk. Potential HDL-based therapeutic approached for AD will be thoroughly discussed in section 6.3.2. Our study has several limitations. First, our breeding strategy was designed to maximize the production of mice with a total absence of apoA-I rather than produce control animals with wildtype levels of apoA-I. Nevertheless, our observation of a robust reduction in plasma HDL-C concentration between apoA-IHEM and apoA-IKO mice is consistent with previous literature showing that the difference in plasma HDL-C concentration is indeed larger in magnitude between apoA-IHEM and apoA-IKO mice compared to apoA-IHEM and apoA-IWT mice. Therefore, we posit that the apoA-IHEM and apoA-IKO cohorts in this study are sufficient to test the hypothesis that reducing plasma HDL-C concentration worsens cerebrovascular phenotypes in APP/PS1 mice. Further limitations include the use of mixed sexes and analysis at a single age. It is well known that in humans, females have a higher risk of AD than males and their disease progresses more quickly upon diagnosis [582]. Although we were unable to make statistical comparisons based on sex due to limitations in breeding and survival of animals of each genotype and sex, male and female mice are distinguished visually in all of our presented data and no clear sex bias is evident even though the sex of the mice was not balanced between groups. Aging is another major risk factor for AD that we did not extensively investigate as our study examines mice only at 12 months of age when amyloid pathology is well developed. A technical limitation in this study is our limited investigation of the interaction of vascular amyloid and astrogliosis by the method of immunofluorescence. While we were conservative in our investigation by only analyzing amyloid, CD31, and GFAP that were completely associated, it is possible that some association could arise 79 from the overlap of these markers in different planes due to the z-stack imaging technique used to visualize the brain sections. A second technical limitation is that biochemical analysis was only evaluated in crude half-brain homogenates, therefore our ability to detect region-specific changes was limited for biochemical assessments. Despite these limitations, this study makes significant progress in developing a strong rationale to test HDL as a therapeutic agent in AD that specifically targets the vasculature. This study also replicates the work by both Lewis et al. and Lefterov et al. showing that genetic manipulation of apoA-I can affect CAA and memory in APP/PS1 mice that has not otherwise been confirmed since the original publications in 2010. Our study also expands upon the role of apoA-I in cerebrovascular health by showing that loss of apoA-I increases Aβ within the brain and exacerbates the potentially pathological reactivity of astrocytes to vascular and parenchymal Aβ. Some groups have already begun to evaluate the potential benefits of apoA-I mimetics or reconstituted HDL in AD mice and have observed benefits with respect to amyloid pathology, CAA, and whole brain neuroinflammation [523,525,527,583], although not all studies found significant improvements or reported data for all of these pathologies. Our current study demonstrates that the investigation of vascular-specific pathologies, including CAA, cerebrovascular astrogliosis, and endothelial ICAM-1 expression, may be critical in future studies using HDL-based therapeutics to target AD pathology in general and vascular specific amyloid pathologies including CAA. 80 2.6 Supplemental figures Figure 2.9 Analysis strategy for histology. Images with bold, coloured outlines indicate steps where percent positive area was measured. The type of measurement and figure presenting data from each measured image is displayed in matching coloured text beside the colour-outlined images. Coloured arrows between images indicate steps where the pixels positive in both images were combined into a new image. A detailed description of each step of the analysis is found in section 2.3.5. 81 Figure 2.10 Morphological discrimination of vascular and parenchymal amyloid. Morphological discrimination of vascular amyloid from parenchymal amyloid was necessary to analyze CAA area. Examples of inverted 8-bit X-34 images are shown with (right) and without (left) vascular (teal boxes) and parenchymal (green circles) amyloid indicated. A mask of areas containing vascular amyloid was created for each X-34 image and analyzed as described in methods section 2.3.5 and Figure 2.9. X-34: 1,4-Bis(3-carboxy-4-hydroxyphenylethenyl)benzene (amyloid stain), CAA: cerebral amyloid angiopathy. 82 Figure 2.11 Total vascular area in apoA-I deficient APP/PS1 mice. Vascular area as defined by immunofluorescent staining of CD31 was visualized in the (a) cortex and (c) hippocampus. (b,d) Representative images are below graphs. Vessel thickness (e-f) and tortuosity (g-h) were measure using Vesselucida 360 software analysis of CD31 staining in the cortex and hippocampus. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. N=5-6 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermates for APP/PS1, APP/PS1: APP/PS1 transgenic , CD31: cluster of differentiation 31. 83 Figure 2.12 GFAP staining area in the hypothalamus in apoA-I deficient APP/PS1 mice. (a) GFAP staining area was visualized by immunofluorescence in the hypothalamus and positive staining area was normalized to total region area. (b) Representative images for immunofluorescent data are beside graphs. Points represent individual mice and bars represent mean values. Circles represent female mice and squares represent male mice. Scale bars represent 400 μm. N=5-6 mice per genotype were used. apoA-I: apolipoprotein A-I, HEM: hemizygous apoA-I genotype, KO: knockout apoA-I genotype, WT: wildtype littermates for APP/PS1, APP/PS1: APP/PS1 transgenic, GFAP: glial fibrillary acidic protein. 84 Chapter 3: Amyloid pathology, neuroinflammation, cerebrovascular pathology, and cognition in APP/PS1 mice treated with a non-brain penetrant LXR agonist 3.1 Summary The liver X receptor (LXR) is a master regulator for transcription of lipid and inflammatory pathways. Treatment of Alzheimer’s disease (AD) mouse models with LXR agonists can reduce brain Aβ concentrations, amyloid pathology, neuroinflammation, and memory deficits. It is known that the ATP-binding cassette transporter A1 (ABCA1) is important for these beneficial effects but the relative importance of ABCA1 within the brain and outside of the brain to these benefits is unknown. Brain ABCA1 is known to lipidate apolipoprotein (apo)E and promote amyloid beta (Aβ) degradation while peripheral ABCA1 lipidates peripheral apolipoproteins, including apoA-I as part of the rate-limiting step of high-density lipoprotein (HDL) biogenesis. We compared the effects of 7 days of treatment with a traditional, brain penetrant LXR agonist to a novel, non-brain penetrant LXR agonist on AD pathology in 12-month old female APP/PS1 mice. We found that neither LXR agonist significantly altered plasma lipid concentrations, brain amyloid beta (Aβ) concentrations, or brain amyloid burden. However, we observed that both LXR agonists, and in particular the non-brain penetrant agonist VTP-38443, significantly reduced neuroinflammation and VTP significantly reduced cerebrovascular inflammation and rescued cued fear memory deficits in APP/PS1 mice. These data suggest that therapeutics do not need to be designed to cross the blood-brain barrier (BBB) in order to have beneficial effects within the brain. 3.2 Introduction The human brain contains 20% of the body’s cholesterol, a higher proportion than any other organ [584]. Proper cholesterol metabolism within the brain is vital to form the cell membranes of neurons and glial cells and the myelin sheaths around axons that allow for efficient propagation of electrical signals in the brain [584]. ApoE is the main transporter of cholesterol in the brain [133] and is encoded by the APOE gene, the greatest genetic risk factor for AD [105]. Cholesterol in the blood can also affect brain health. Epidemiological studies in humans have found that high concentrations of total plasma cholesterol and low-density lipoprotein cholesterol (LDL-C) as well 85 as low concentrations of HDL cholesterol (HDL-C) can increase the risk of AD and cognitive impairment, at least in some studies [210,488,489,495,497,585]. Drugs that can modify cholesterol metabolism are therefore of great interest for AD researchers and LXR signalling pathways may provide targets for such drugs. LXR is a ubiquitously expressed nuclear transcription factor that modulates a number of genes including many related to lipid metabolism and inflammation [586]. LXR agonists were first used in Aβ expressing transgenic mice in 2005 where reductions in soluble Aβ40 and Aβ42 concentrations and increased ABCA1 expression levels in the brains of treated mice were observed [539]. Following this report, LXR agonists were shown to also reduce Aβ plaque deposition [537,540–542], neuroinflammation [537,541–543,550,551], and memory deficits [537,540–542,544] in various AD mouse models (summarized in Table 1.2 and Table 1.3). The importance of ABCA1 in mediating the amyloid and memory improvements by LXR agonist treatment was confirmed by Donkin et al. They administered GW3965 to APP/PS1 mice with and without ABCA1 and observed that ABCA1 was required for the LXR agonist to increase brain and cerebrospinal fluid (CSF) apoE levels, improve amyloid burden in the hippocampus and whole brain, and improve novel object recognition memory [540]. This work together with that showing increased apoE lipidation by LXR agonists [537] lead to the assumption that LXR agonists improve pathology in AD mice by increasing apoE lipidation by ABCA1, which then promotes Aβ degradation. However, ABCA1 is also expressed in peripheral tissues [549] and LXR agonist treatment can increase plasma HDL-C concentration through increased lipidation of apoA-I by peripheral ABCA1 [587], the rate-limiting step in HDL biogenesis [588–591]. Interestingly, evidence from some human epidemiological studies [210,488–491,493,495,496], GWAS studies [63,64], and preclinical studies in AD mouse models [519,521,524–527], including the study in Chapter 2 [592], suggests that circulating HDL may protect against AD. These observations raise the question of whether the beneficial effects of LXR agonists in AD mice are due to effects of LXR activation inside or outside of the brain. This is a crucial question as it will not only inform on whether targets related to peripheral LXR signaling may be of therapeutic benefit for AD but also whether drugs that do not cross the BBB are able to reduce 86 AD-relevant pathologies within the brain. To answer this question, we administered a non-brain penetrant LXR agonist, known as VTP-38443 (VTP), to 12-month old female APP/PS1 mice and compared its effects to GW3965 (GW), a brain penetrant LXR agonist [593]. 3.3 Methods 3.3.1 Animals All procedures involving animals were approved by the Canadian Council of Animal Care and the University of British Columbia Committee on Animal Care. Female APPswe/PENS1dE9 (APP/PS1) mice (line85) were maintained on a mixed C3H/H3J × C57Bl/6 background until 12 months of age at which time they were randomly assigned to control, GW3965 (GW), or VTP-38443 (VTP) diets for 7 days before sacrifice and tissue collection. GW, a brain-penetrant LXR agonist, and VTP, a non-brain penetrant LXR agonist, were dissolved in ethanol and compounded in chow diet at 120 mg/kg and 15 mg/kg, respectively (Envigo, 2920X Teklad global soy protein-free extruded rodent diet). Dosing for VTP was chosen to optimize target engagement and minimize drug penetrance into the brain and dosing of GW was defined as previously reported [540]. Importantly, animals for the pilot study were housed at a different animal facility than animals in the larger study and differences in baseline body weights were observed (Figure 3.10). 3.3.2 Tissue collection Procedures were performed as described in section 2.3.2. 3.3.3 Plasma lipid measurements Procedures were performed as described in section 2.3.3. 3.3.4 Histology Procedures were performed as described in section 2.3.4. In addition, ionized calcium binding adaptor molecule 1 (Iba1) immunohistochemistry was performed on fixed sections. Sections were washed in tris-buffered saline (TBS), treated with 0.3% H2O2, blocked with 3% normal goat serum (NGS) in TBS with 0.25% Triton X-100 (TBS-X) for 30 min, incubated with Iba1 antibody (Wako Chemicals, #019-19741, 1:1000) in TBS-X with 1% NGS overnight, washed, incubated with 87 biotinylated goat-anti-rabbit secondary antibody (Jackson Laboratories, 111-035-003, 1:1000) in TBS-X for 1 h, then developed with horseradish peroxidase (Vectastain Elite ABC kit, PK-6120, Vector laboratories) and 3,3’Diaminobenzidine (DAB) substrate. Stained sections were then mounted and dehydrated with alcohol at 50, 70, 90, then 95% concentrations, cleared with xylene before imaging with an Axio Scan.Z1 (Zeiss). 3.3.5 Image analysis Procedures were performed as described in section 2.3.5. Analysis of Iba1 positive microglia were similarly performed using ImageJ to count positively stained particles in the cortex and hippocampus and measuring regional area for normalization. Positively stained debris greater than 160 pixels was filtered out of the analysis. 3.3.6 Protein extraction Procedures were performed as described in section 2.3.6. 3.3.7 Enzyme-linked immunosorbent assay (ELISA) Procedures were performed as described in section 2.3.7. 3.3.8 Ribonucleic acid (RNA) isolation and real-time quantitative polymerase chain reaction (RT-qPCR) Procedures were performed as described in section 2.3.8. 3.3.9 Contextual and cued fear conditioning Procedures were performed as described in section 2.3.9. 3.3.10 Measurement of plasma and brain drug concentration GW and VTP concentrations were measured in plasma and half-brains using semi-quantitative, label-free mass spectrometry. Briefly, frozen plasma and half-brains were thawed on ice, lysed, fractionated, digested, separated by gas chromatography, then analyzed by mass spectrometry 88 using spectral counts and a standard curve of GW or VTP of known concentrations for quantification. 3.3.11 Statistical analysis All statistical analyses, with the exception of fear memory, were performed with GraphPad Prism 7, p-values <0.05 were considered statistically significant. Unless stated otherwise, statistical comparisons were made by two-way analysis of variation (ANOVA) considering APP/PS1 genotype as one factor and drug as a second factor followed by Sidak’s or Dunnett’s multiple comparisons test respectively if significant factor or interaction effect was observed. To analyze AD relevant pathologies, namely brain Aβ concentrations, amyloid plaque area, and cerebral amyloid angiopathy (CAA), APP/PS1 were analyzed separately of wild type by one-way ANOVA with Dunnett’s multiple comparisons test if a significant factor effect was observed. Analysis of fear memory was performed with IBM SPSS Statistics Build 1.0.0.1347 using three-way ANOVA to analyze freezing time as the dependent variable and time, APP/PS1 genotype, and apoA-I genotype as fixed variables. Pairwise comparisons of estimated marginal means and standard error were performed and adjusted by Sidak’s multiple comparisons test, adjusted p-values <0.05 were considered statistically significant. 3.4 Results 3.4.1 VTP does not enter the brain in aged WT or APP/PS1 mice The non-brain penetrant LXR agonist VTP was originally tested by the drug developer for brain penetrance in young C57Bl/6 mice, however, aged APP/PS1 mice are expected to exhibit BBB dysfunction with a potential increase in BBB permeability [594]. We first investigated brain penetrance of VTP 12-month old female APP/PS1 mice treated for 7 days with the drugs at 15 mg/kg in chow. The brain concentration of VTP was below the lower limit of quantification the assay and estimated to be 0.66 nM in WT mice and 0.76 nM in APP/PS1 mice (Table 3.1). Plasma concentrations of VTP were on average 42 nM and 53 nM in WT and APP/PS1 mice, respectively. The brain:plasma ratio of VTP was on average 0.017 and 0.013 in WT and APP/PS1 mice, respectively. The brain penetrance of GW3965 administered in chow (120 mg/kg) for 7 days was confirmed in a separate study in 9-month old mice (Table 3.3). 89 Table 3.1 VTP brain penetrance in 12-month old APP/PS1 mice and wildtype littermates. Genotype WT (n=4) APP/PS1 (n=5) Brain (nM) (mean ± SD) 0.66 ± 0.43 0.76 ± 0.59 Plasma (nM) (mean ± SD) 42 ± 20 53 ± 26 Brain:Plasma 0.017 ± 0.012 0.013 ± 0.0079 WT: wildtype. Lower limit of quantification = 2 nM. 3.4.2 VTP induces Abca1 messenger RNA (mRNA) expression in the small intestine but not in the brain We next investigated the drugs ability to induce expression of Abca1, an LXR target gene, in cortical and intestinal tissue. Both drugs significantly increased the expression of Abca1 mRNA in the small intestine, by 4.5-fold for GW and 5.8-fold for VTP overall (p=0.0165 and p=0.0043 for overall vehicle vs GW and VTP respectively by Dunnett’s multiple comparisons test) (Figure 3.1a). In the cortex, VTP did not affect Abca1 mRNA expression levels (p=0.7727 for overall vehicle vs VTP by Dunnett’s multiple comparisons test) while GW caused a non-significant 1.8-fold increase compared to vehicle control treated mice (p=0.0929 for overall vehicle vs GW by Dunnett’s multiple comparisons test) (Figure 3.1b). The utility of the LXR agonist VTP to study the peripheral-specific effects of LXR signaling in AD mice was therefore confirmed. Figure 3.1 Intestinal and brain Abca1 mRNA expression in VTP and GW treated APP/PS1 mice in pilot study. Abca1 mRNA expression levels were measured by RT-qPCR in the (a) small intestine and (b) cortex of 12-month old female APP/PS1 mice or WT treated with GW (120 mg/kg in chow) or VTP (15 mg/kg in chow) for 7 days. Points represent individual mice and bars represent mean. Results from omnibus two-way ANOVA of drug effect across genotype and Dunnett’s multiple comparisons test for drug effects across genotypes (in brackets) are displayed as exact p-values below graphs. Results from Dunnett’s multiple comparisons test within each genotype are displayed within graphs as *p<0.05 and **p<0.01. WT: wildtype, GW: GW3965 treated group, VTP: VTP treated group, Abca1: ATP-binding cassette transporter A1 gene, RT-qPCR: real-time quantitative polymerase chain reaction. 90 3.4.3 Soluble Aβ42 concentrations tend to be reduced in APP/PS1 mice treated with the non-brain penetrant LXR agonist VTP in pilot study Having confirmed that VTP is not brain penetrant in aged APP/PS1 mice and it elevates peripheral Abca1 mRNA without significantly affecting brain Abca1 mRNA expression levels, we next aimed utilize this pilot cohort of 12-month old female APP/PS1 mice to determine whether VTP shows any indication of improving AD pathologies. Although changes to brain Aβ concentrations are not consistently observed across LXR agonist studies (Table 1.2, Table 1.3), we chose to begin with evaluation of Aβ changes for our pilot study as it is a relatively high-throughput analysis. To our surprise, we observed a significant reduction in soluble and insoluble Aβ42 brain concentrations with VTP treatment in this pilot study of n=4 mice per group and therefore proceeded to a larger cohort to further probe differences to other AD pathologies. Specifically, the non-brain penetrant LXR agonist VTP significantly reduced soluble brain Αβ42 from by 1.8-fold (p=0.0322 for APP/PS1 vehicle vs VTP by Dunnett’s multiple comparisons test) (Figure 3.2b) and insoluble brain Aβ42 by 1.4-fold (p=0.0252 for APP/PS1 vehicle vs VTP by Dunnett’s multiple comparisons test) (Figure 3.2d). However, GW only resulted in a non-significant 1.5-fold reduction in soluble brain Aβ42 concentration (p=0.1032 for APP/PS1 vehicle vs GW by Dunnett’s multiple comparisons test) and did not significantly reduce insoluble Aβ42 concentration (p=0.5908 for APP/PS1 vehicle vs GW by Dunnett’s multiple comparisons test). Brain Aβ40 concentration was not significantly altered with drug treatment although there was a non-significant 1.8-fold reduction in soluble brain Aβ40 in VTP treated mice compared to vehicle control (p=0.0534 for APP/PS1 vehicle vs VTP by Dunnett’s multiple comparisons test) (Figure 3.2a, c). Figure 3.2 Brain Aβ peptide concentrations in VTP and GW treated APP/PS1 mice in pilot study. (a) Soluble Aβ40, (b) soluble Aβ42, (c) insoluble Aβ40, and (d) insoluble Aβ42 concentrations were measured in half-brain homogenates by ELISA. Points represent individual mice and bars represent mean. Results from one-way ANOVA are displayed as exact p-values below graphs. Results from Dunnett’s multiple comparisons test are 91 displayed within graphs as *p<0.05. WT: wildtype, GW: GW3965 treated group, VTP: VTP treated group, Aβ: amyloid beta. As VTP was confirmed in this pilot study to be non-brain penetrant, significantly upregulated peripheral Abca1 mRNA expression levels, and significantly reduced soluble and insoluble Aβ42 concentrations, a study with a larger number of mice was initiated to further probe the changes to amyloid, inflammation, and memory with classical and non-brain penetrant LXR agonists. Although we had originally intended to use our pilot study to perform power calculations, a significant difference was already found in soluble and insoluble brain Aβ42 concentrations with the pilot sample size thereby eliminating the utility of a power analysis for this outcome. Therefore, we instead aimed to develop a cohort with n=6-10 mice per group, as has been used in similar studies, [538,540,542,543], to investigate other neuroinflammatory and cognitive changes. 3.4.4 VTP induces Abca1 messenger RNA (mRNA) expression in the small intestine but not in the brain A larger cohort of 12-month old female APP/PS1 mice was created and the lack of BBB penetrance of VTP was confirmed in a subset of animals (Table 3.2). The effect of the drugs on Abca1 mRNA induction in peripheral tissues and the brain (Figure 3.3a-b) was also confirmed. Both drugs significantly increased the expression of Abca1 mRNA in the small intestine, overall by 4.5-fold for GW and 4.3-fold for VTP (p<0.0001 and p=0.0001 for overall vehicle vs. VTP and GW respectively by Dunnett’s multiple comparisons test), (Figure 3.3a) whereas only the brain penetrant agonist GW increased Abca1 mRNA expression in the cortex by 1.6-fold (p=0.0006 for overall vehicle vs. GW, p=0.2186 for overall vehicle vs. VTP by Dunnett’s multiple comparisons test) (Figure 3.3b). APP/PS1 genotype had no effect on Abca1 mRNA expression levels in the intestine or cortex. Despite the successful target engagement, neither agonist had any significant effect on plasma apoA-I, apoE total cholesterol, HDL-C, or LDL-C concentrations (Figure 3.3c-f, i). Brain apolipoproteins were also generally unaffected by either agonist. There was no drug effect on insoluble apoA-I or soluble apoE concentrations in the brain although there was an overall drug effect on insoluble apoE in the brain (p=0.0207 for overall drug effect by two-way ANOVA) driven by a 1.2-fold increase in the GW treated mice (p=0.0275 for overall vehicle vs. GW by Dunnett’s multiple comparisons test) Figure 3.3h) and in soluble apoA-I (p=0.0113 for 92 overall drug effect by two-way ANOVA) driven by a 1.6-fold reduction in the VTP treated mice (p=0.0115 for overall vehicle vs. VTP by Dunnett’s multiple comparisons test, p=0.0286 APP/PS1 vehicle vs. VTP) (Figure 3.3j). APP/PS1 genotype did affect both apoE and apoA-I in the brain. Soluble apoE, insoluble apoE and soluble apoA-I protein concentrations were significantly elevated on average by 1.2-, 2.6-, and 1.5-fold (p=0.0026, p<0.0001, and p=0.0047 for genotype effect by two-way ANOVA omnibus analysis) (Figure 3.3g-h,j) while insoluble apoA-I showed a nearly significant 1.2-fold reduction in (p=0.0534 for genotype effect by two-way ANOVA omnibus analysis) (Figure 3.3k) in the brains of transgenic APP/PS1 mice compared to non-transgenic controls. Table 3.2 VTP brain penetrance in 12-month old APP/PS1 mice and wildtype littermates. Genotype WT (n=2) APP/PS1 (n=2) Brain (nM) (mean ± SD) 0.999 by Sidak’s multiple comparisons test ). GW-treated mice also demonstrated increased freezing during the third min of testing, a 1.7-fold increase over vehicle animals of both genotypes although the effect was only significant in WT mice (p=0.023, p=0.164 for WT vehicle vs. GW and APP/PS1 vehicle vs GW, respectively, by Sidak’s multiple comparisons test). Overall, these data suggest that 7 days of treatment with VTP can rescue the fear memory deficits in 12-month old female APP/PS1 mice and GW can improve the fear memory of WT and APP/PS1 mice, although the effect is only significant in the WT. In terms of the significant trial effect observed, the time spent freezing 104 increased across all groups up to min 3 then decreased for the last 2 min of testing (p=0.048 min 1 vs. 2, p<0.001 min 1 vs. 3, p<0.001 min 1 vs. 4, p<0.001 min 1 vs. 5, p<0.001 min 2 vs. 3, p<0.001 min 2 vs. 4, p<0.001 min 3 vs. 5 by Sidak’s multiple comparisons test) suggesting an initial fear response to the cue exposure then acclimation. Figure 3.9 Cued and contextual fear conditioning in VTP and GW treated APP/PS1 mice. (a) Cued and (b) contextual fear memory was measured using a foot shock as the stimulus, the foot shock box as the context, and a tone as the cue. During cued fear conditioning testing, the cue was played during min 3 and 4. Points and error bars represent estimated marginal means and ±standard error used to calculate pairwise comparisons for statistics. Results from three-way ANOVA are displayed as exact p-values below graphs. At min indicated: *p<0.05 vs. APP/PS1 vehicle, #p<0.05 vs. WT vehicle, †p<0.05 vs. WT VTP by Sidak’s multiple comparisons test. WT: wildtype, GW: GW3965 treated group, VTP: VTP treated group. Significant drug, genotype, and time effects were also observed in the contextual fear conditioning test (p=0.015, p=0.010, p=0.046, respectively, by three-way ANOVA) (Figure 3.9b). Overall, time spent freezing by WT mice was 1.3-fold longer than APP/PS1 mice. However, a significant difference between WT and APP/PS1 mice was only observed in the GW-treated group (1.7-fold, p=0.001 by Sidak’s multiple comparisons test) suggesting a lack of sensitivity of this test to detect cognitive deficits in this cohort. Across genotypes, GW-treated mice spent 1.4-fold as much time freezing as vehicle control mice and 1.3-fold as much time as VTP-treated mice (p=0.022 and p=0.048, respectively, by Sidak’s multiple comparisons test). However, a significant drug and genotype interaction effect was also observed (p=0.035 by three-way ANOVA) whereby GW treatment only improved the performance of WT mice (1.6-fold, p=0.006 by Sidak’s multiple 105 comparisons test). Minute-by-minute analysis revealed a significant increase in time spent freezing by GW-treated WT mice compared to vehicle control during minute 2 of testing (2.2-fold, p=0.026 by Sidak’s multiple comparisons test). Post-hoc testing did not reveal any differences between specific minutes of testing although an overall reduction in time spent freezing over the testing period was observed. Together, these fear conditioning tests suggest that GW may improve the memories of WT mice while VTP can rescue some memory function in APP/PS1 mice. 3.5 Discussion It is well established that synthetic LXR agonists can have beneficial effects on amyloid pathology, neuroinflammation, and memory deficits in AD model mice [537,541–544,550] and these benefits are at least in part dependent on the lipid transporter ABCA1 [540]. However, the relative importance of ABCA1 upregulation in the brain compared to upregulation in peripheral tissues has not yet been evaluated. To address this question, we used a pharmacological approach with a non-brain penetrant LXR agonist that selectively upregulates peripheral ABCA1 in 12-month old female APP/PS1 mice and observed improved neuroinflammation and cued fear memory. Despite the benefits of LXR agonist treatment for AD model mice in this study and others, the utility of LXR agonists as therapeutics for human AD patients is limited due to the detrimental off-target effects, namely hepatic steatosis and hypertriglyceridemia [597]. Although no LXR agonists have been tested in clinical trials, a retinoid X receptor (RXR) agonist known as bexarotene made it as far as a phase II clinical trial. RXR forms a heterodimer with LXR and together they are nuclear transcription factors for the LXR response element [586]. The failure of bexarotene in clinical trials to reduce brain amyloid, except in APOE-ε4 noncarriers, and the detrimental rise in serum triglycerides associated with treatment has further diminished the enthusiasm of researchers for LXR or RXR based AD therapeutics [598]. Instead of suggesting that non-brain penetrant LXR agonists are a valuable therapeutic target, this study proposes more generally that therapeutics that do not cross the BBB can still have beneficial effects on AD-relevant outcomes in the brain. This is of interest as the need to cross the BBB has proven to be an obstacle for drug development [599]. The failure rate of AD drugs in development was 99.6% between 2002 and 2012 [600], suggesting 106 that alternative strategies for drug development are crucial, including pursuing drugs that do not need to cross the BBB. Another important point of consideration is our use of a pharmacological approach to study the importance of peripheral LXR agonist signaling rather than a genetic approach. We preferred a pharmacological approach for this study as ABCA1flox-/- mice, including those on an AD background, are difficult to breed due to perinatal lethality of ABCA1flox-/- pups [601–603] and poor fertility of ABCA1flox-/- females due at least in part to placental malformation [601]. Furthermore, a pharmacological approach provides proof-of-concept for the translation into a pharmaceutical approach in human AD subjects. After confirming the lack of brain penetrance of VTP in APP/PS1 mice, we proceeded to perform a pilot study to evaluate the ability of VTP to induce peripheral and brain Abca1 expression and probe whether it exerts any benefits against AD pathologies in 12-month old female APP/PS1 mice. We observed a significant reduction to soluble and insoluble brain Aβ42 with VTP treatment even with the relatively small sample size of the pilot cohort and decided to proceed with the same treatment regimen in a larger cohort aiming for n=6-10 mice per group to more closely reflect the sample size used by similar studies [538,540,542,543]. However, the reduction in soluble brain Aβ concentrations was not replicated in this larger study of 12-month old female APP/PS1 mice. One possible reason for this discrepancy is that the animals were bred and housed at different animal facilities and maintained on different diets until the experimental diet was administered due to centralization of animal care facilities at UBC during the course of these studies. It has recently become clear that the microbiome of research animals can significantly alter their response to experimental procedures and that different housing environments and diets can result in changes to the microbiome [604]. While specific measurements of the gut bacteria of the mice at each facility was not possible, we did observe a significant difference in body weight between the 12-month old female mice housed at each facility (Figure 3.10). Specifically, APP/PS1 mice for the larger study were significantly heavier at baseline compared to APP/PS1 mice in the pilot study (p=0.012 by Sidak’s multiple comparisons test). Furthermore, APP/PS1 mice for the larger study were significantly heavier than WT mice (p=0.012 by Sidak’s multiple comparisons test), whereas 107 there was no significant difference in body weight by genotype in the pilot study (p=0.24 by Sidak’s multiple comparisons test). These body weight differences suggest that mice at each facility may have other metabolic differences that could contribute to differences in AD pathology and response to LXR agonists, in particular as LXR agonists act on cholesterol metabolism and the mice were treated with agonists compounded in chow. That GW failed to improve amyloid pathology in this study was consistent with other previous studies (Table 1.2). While some have observed that GW treatment can reduce Aβ concentrations or amyloid burden in AD model mice, others have not observed such improvements. For example, Jiang et al. reported reduced Aβ40 and Aβ42 as well as reduced plaque number and area in Tg2576 mice treated with GW at 120 mg/kg in chow [537]. However, Donkin et al. similarly treated APP/PS1 mice with GW and observed an increase in soluble Aβ40 and Aβ42 concentrations in the cortex and only a trend towards reduced plaque area [540]. Furthermore, Sandoval-Hernandez et al. saw no differences in Aβ concentration or plaque burden in 3xTg-AD mice treated with GW by oral gavage at an equivalent dosage [543,544]. A key difference between the present study and previous work is the specific AD mouse model used. It is known that the genetic background of AD model mice and the specific transgenes present can alter the severity and timing of amyloid pathology. While Tg2576, APP/PS1, and 3xTg-AD mice all carry the Swedish APP mutation, the onset of amyloid plaques in these mice are 7 to 10-months old, 6 to 7-months old, and 6-months old, respectively [576]. Additionally, differences in the sex of animals, route of administration, drug dosage, treatment duration, age at treatment, protein and staining analysis techniques used, and brain regions analyzed may all contribute to inconsistencies in the observed effects of GW treatment on brain Aβ concentrations and amyloid pathology. Although no benefits to amyloid or Aβ concentrations with LXR agonist treatment were observed in the 12-month old female APP/PS1 mice in this study, we did find reduced neuroinflammation in these mice. Specifically, we found reduced brain IL-1β protein and mRNA, brain PDGFRβ protein, hippocampal ICAM-1-positive area and also a trend towards a reduced vessel-associated astrogliosis in VTP-treated mice. Reduced neuroinflammation by LXR agonist treatment has been consistent across the literature when reported (Table 1.2), in line with our observations. For 108 example, others have observed reduced microglial number [537,550], IL-6 expression [537,542,551], IL-1β expression [542,551], and reduced astrogliosis [543,550]. Contrary to others, we did not observe any changes to GFAP expression or microglia counts, possibly due to differences in the AD mouse models used, age at treatment initiation, or route of drug administration, as discussed above. Although it is known that LXR agonists such as GW can reduce neuroinflammation in AD model mice, it has been assumed that this occurs through direct inhibition of inflammatory gene transcription in glial cells [537,605]. However, VTP is non-brain penetrant and therefore cannot directly alter the transcriptional profiles of microglia or astrocytes. Instead, the effects of VTP on neuroinflammation must occur via peripheral mechanisms, for example, by limiting the infiltration of circulating immune cells into the brain. IL-1β in the brain is produced by many different cell types including microglia, astrocytes, endothelial cells, and infiltrating immune cells. IL-1β is also an inducer of peripheral immune cell recruitment into the brain [606]. Taken together, this suggests that Aβ in the brains of APP/PS1 mice increases production of IL-1β by glial cells and recruitment of peripheral immune cells into the brain, and these infiltrated immune cells in turn also produce IL-1β. When APP/PS1 mice are treated with the non-brain penetrant LXR agonist VTP, peripheral immune cell infiltration may then be reduced, therefore reducing the total number of cells producing IL-1β in the brain. VTP may reduce immune cell trafficking into the brains of APP/PS1 mice by several mechanisms. First, we found that the levels of endothelial ICAM-1 in the hippocampus were significantly reduced in APP/PS1 mice treated with VTP. Adhesion molecules, such as ICAM-1 and VCAM-1, are expressed by activated endothelial cells and promote the adhesion and subsequent transmigration of immune cells through the endothelial cell barrier into the surrounding inflamed tissue [570]. Indeed, it has previously been shown that LXR agonists can reduce VCAM-1 expression on endothelial cells in vitro after tumour necrosis factor alpha (TNFα) stimulation [607]. Second, VTP may act directly on circulating immune cells to reduce their propensity to infiltrate into the brain as LXR agonists have previously been shown to directly reduce inflammatory gene expression in circulating immune cells [608,609]. Thirdly, VTP may limit the 109 adhesion of circulating immune cells to activated endothelial cells through its effects on lipoprotein metabolism. Although no significant changes to total cholesterol, HDL-C, or LDL-C were observed with VTP treatment, there was a robust and significant increase in Abca1 expression in the intestine. It is therefore possible that the lipidation status of HDL and hence its anti-inflammatory functions may be improved with VTP treatment. Future studies could further test this immune cell trafficking hypothesis by measuring inflammatory gene expression in circulating immune cells and using markers specific for resident or infiltrating immune cells such as TMEM [610] and CD45 [611,612] for histological analysis of the brain. In addition to the effects of VTP on neuroinflammation, we observed improved memory in VTP-treated APP/PS1 mice. VTP rescued the deficits of APP/PS1 in recognizing a conditioned stimulus (auditory cue) during the first minute of cue presentation. As VTP did not enter the brain or affect amyloid pathology, the cognitive benefits observed in this study are more likely a result of reductions to neuroinflammation conferred peripherally, potentially due to its effects on endothelial ICAM-1 expression in the hippocampus. In support of this, endothelial cell VCAM-1 expression was recently shown to be a key mediator of the detrimental effects of aged mouse plasma on the brains of young mice. Specifically, peripheral administration of an antibody against VCAM-1 prevented deficits in Barnes maze, novel object recognition, and contextual fear conditioning tests in young mice treated with aged mouse plasma [298]. The ability of LXR agonists to improve cognition in AD mice has been consistently reported across a variety of tests including contextual and cued fear conditioning [537,538,542], Morris Water Maze [540,543,544,546], and Novel Object Recognition [540,545]. However, we observed only a trend towards improved memory in GW-treated APP/PS1 mice, although GW-treated WT mice showed significant improvements. Although several other groups have reported improvements in fear conditioning tests with GW treatment, these studies treated mice at younger ages [537,542], used different AD models [537], used only male mice [542], or administered GW by oral gavage [537,542]. The use of oral gavage, rather than treatment in chow, can be expected to reduce variability in drug consumption and therefore reduce variability in cognitive outcomes. Although the number of mice per group used in this study to evaluate memory is similar to some other studies 110 where LXR agonist benefits were demonstrated [538,540,542,543], the group size was smaller than that used in other studies [537,545,546]. The potential increase in response variation due to drug administration in chow as well as the large number of comparisons being made (vehicle, VTP, and GW diets; WT and APP/PS1 genotypes) may have reduced the statistical power of this study and the ability to detect memory improvements with GW treatment. Interestingly, we only observed deficits in cued fear memory in the APP/PS1 mice in this study and no deficits in contextual fear memory. A similar deficit in cued but not contextual fear memory was observed in 12- to 14-month old male APP/PS1 mice by Knafo et al [613]. Conversely, Gu et al. observed deficits in contextual fear memory but not cued memory in 7-month old male APP/PS1 mice [614], suggesting cued and contextual fear memory deficits may depend on age. Indeed, LeClair et al. observed contextual memory deficits in 8-month old male and female APP/PS1 mice but not in 5- or 10-month old mice [561]. Deficits in both of these tests may converge at 10.5-months of age, as demonstrated by Kim et al. who observed memory impairments in 10.5-month old male APP/PS1 mice [615]. Other memory tests can also detect cognitive deficits in 12-month old APP/PS1 mice however these tests often require several days to administer compared to the two-day protocol used for cued and contextual fear conditioning. As the mice were only treated with the LXR agonists for seven days, the four- to five-day protocols required to test memory with Morris Water Maze [540,546] or Barnes maze [616] were not appropriate. Nonetheless, VTP conferred memory benefits in 12-month old female APP/PS1 mice in this study without entering the brain or affecting amyloid pathology. Although we did not observe any changes to plasma HDL-C concentrations with either LXR agonist, changes to specific HDL subclasses or to HDL composition may have occurred, including the concentration of apoE-containing HDL. HDL biogenesis has been shown to be more complex than originally understood with secretion of HDL into the circulation as particles of all sizes, rather than as lipid-poor particles that are progressively lipidated to become larger [462,463]. ApoE-containing HDL may be secreted directly from the liver [463] however the effect of LXR agonists on this process is unknown as well as the effect on the concentration of large, lipid-rich apoE-containing HDL. ApoE-containing HDL are of particular interest as they promote reverse 111 cholesterol transport [109] and they are associated with reduced CHD risk [109,118], reduced amyloid burden [123], and improved MMSE scores [124]. Recent work also suggests that apoE-containing HDL have an enhanced ability to prevent vascular Aβ deposition in 3D bioengineered artery models [617]. The present study has several limitations. First, we chose to administer LXR agonists in chow ad libitum, which may result in a greater variation in drug uptake between mice compared to oral gavage. We were also limited in this study to investigating only 12-month old mice. Amyloid pathology is fully established in 12-month old APP/PS1 mice therefore it is possible that in this study the LXR agonists were administered too late and for too short a duration to have any effect on amyloid. This study was also limited to analysis of only female mice. It is known that male and female APP/PS1 mice develop amyloid pathology at different rates [618,619] therefore these results may not translate across sex. However, the majority of people with AD are women [96,620] therefore a study with a female specific population of mice has its own merits. Technical limitations of this study include the immunofluorescence method used to assess vascular specific inflammation, as was discussed in section 2.5. Finally, as already discussed, LXR agonists are not suitable for translation to human subjects due to off-target effects resulting in hypertriglyceridemia and hepatic steatosis [552,553,621]. Therefore, the development of alternative therapeutics targeting peripheral ABCA1 will be necessary to test whether the neuroinflammatory and cognitive benefits observed in this study translate to humans. In summary, we show here that a non-brain penetrant LXR agonist can reduce neuroinflammation, cerebrovascular inflammation, and memory deficits in 12-month old APP/PS1 mice. The benefits to neuroinflammation and cognition are independent of improvements to amyloid pathology and plasma lipid concentrations. These findings support the development of AD therapeutics targeting ABCA1 in peripheral tissues and suggest that crossing the BBB is not necessary for therapeutics to reduce neuroinflammation and improve cognition. 3.6 Supplemental Figures Table 3.3 VTP and GW brain penetrance in 9-month old APP/PS1 mice and wildtype littermates. Genotype WT APP/PS1 112 Drug VTP (n=4) GW (n=4) VTP (n=4) GW (n=4) Brain (nM) (mean ± SD) 3.0 ± 1.1 197 ± 49*** 2.3 ± 0.12 178 ± 73*** Plasma (nM) (mean ± SD) 63 ± 39 180 ± 50** 33 ± 19 190 ± 42*** Brain:Plasma (mean ± SD) 0.069 ± 0.044 1.1 ± 0.17**** 0.086 ± 0.034 0.90 ± 0.22**** **p<0.01, ***p<0.001, ****p<0.0001 VTP vs. GW3965 by Sidak’s multiple comparisons test after one-way ANOVA. WT: wildtype. Lower limit of quantification = 2 nM. Figure 3.10 Body weight comparison by housing facility. Body weights at baseline for all WT and APP/PS1 12-month old female mice. Experimental mice for the pilot study were bred and housed a different facility (Facility A) than experimental mice for the full-scale study (Facility B). WT: wildtype. Points represent individual mice and bars represent mean. Results from two-way ANOVA are displayed as exact p-values below the graph. Results from Sidak’s multiple comparisons test are displayed within the graph as *p<0.05. Figure 3.11 Cortical and hippocampal vascular area in VTP and GW treated APP/PS1 mice. Vascular area as defined by CD31-positive endothelial cells was measured in the (a) cortex and (b) hippocampus. Points represent individual mice and bars represent mean. Scale bars represent 200 μm. WT: wildtype, GW: GW3965 treated group, VTP: VTP treated group, CD31: cluster of differentiation 31. 113 Chapter 4: Investigation of high-density lipoprotein functions relevant to Alzheimer’s disease using brain-derived endothelial cells and 3-dimensional bioengineered arteries. 4.1 Summary High-density lipoproteins (HDL) are known to exert beneficial effect on endothelial cells (ECs) of peripheral vessels including anti-inflammatory effects, inducing nitric oxide (NO) signaling, anti-apoptotic effects, and promotion of cell proliferation and migration. HDL has also been shown to protect against cerebral amyloid angiopathy (CAA) and cerebral vessel inflammation in mouse models of Alzheimer’s disease (AD). However, whether the beneficial effects of HDL on peripheral vessels extends to cerebral vessels or whether additional protective functions against AD exist in a human-based system is unknown. We used 3-dimensional (3D) bioengineered vessels composed entirely of human cells grown on a scaffold in a bioreactor facilitating perfusion to assess this question. Using this model, we find that HDL, isolated from human serum or plasma, reduces amyloid beta (Aβ)-induced inflammation and prevents Aβ accumulation in the vessel wall. We also use 2D cell cultures of brain-derived EC cultures to further probe the mechanism of HDL’s anti-inflammatory effect. We discovered that although HDL can induce EC NO signalling and delays Aβ fibrillization, neither of these actions are necessary for HDL to prevent Aβ-induced EC activation. Instead HDL acts through scavenger receptor class B type 1 (SR-BI) to prevent EC uptake of Aβ in order to prevent Aβ-induced inflammation. We then confirmed the SR-BI dependence of this function in our 3D bioengineered artery model. Together, this works suggests four ways that HDL can act from the lumen of cerebral vessels to protect from dysfunction both related and unrelated to Aβ, namely suppressing Aβ-induced inflammation, inducing endothelial NO production, delaying Aβ fibrillization, and preventing Aβ vascular accumulation. 4.2 Introduction Evidence from studies in humans [63,64,210,488–491,493,495,496] and mice [519,521,524–527], including the study in Chapter 2 [592], suggests that systemically circulating HDL may benefit the brain. However, the mechanisms by which it may do so are unknown. HDL is well established to possess several potent vasoprotective functions in peripheral vascular ECs including reducing 114 inflammation, increasing vascular tone through promoting endothelial NO synthase (eNOS) activity, and suppressing vascular adhesion molecule expression [8]. Whether these functions translate to cells of cerebral vessels and whether other cerebral vessel specific HDL functions exist is poorly understood. Although the studies described in Chapters 2 and 3 have contributed toward understanding how HDL and lipid metabolism may impact AD pathophysiology, their translational value is tempered by differences in the distribution of circulating lipoproteins between mice and humans. The protein composition of lipoproteins is similar between mice and humans but the relative abundance of various lipoproteins differs [360]. In mice, circulating lipids are mainly carried by HDL, leading to a high HDL:LDL ratio, while in humans they are mainly carried by LDL, leading to a low HDL:LDL ratio [360]. The HDL:LDL ratio is, in part, governed by the activity of cholesterol ester transfer protein (CETP), which mice do not naturally express [622]. Furthermore, the murine and human APOE genes are substantially different [623]. As APOE-ε4 is the most established risk gene for sporadic AD, extensive efforts have been made to develop targeted replacement or transgenic mice expressing each human apoE isoform, which have made major contributions to our understanding of amyloid and tau pathology and AD-relevant neurodegeneration [143,145,624–628]. Yet, compared to humans, these models may still under-report cerebrovascular compromise in AD, as circulating HDL concentrations in mice are naturally high. Researchers have responded to these limitations by developing advanced culture techniques of human cells into 3D models of the blood-brain barrier (BBB), as thoroughly described in section 1.12. Trans-well systems [323–325], microfluidic devices [326,328,329], and cerebral organoids [330,331,335–338] have advanced our understanding of BBB transport and permeability and cell-cell interactions at the BBB. However, these models still are limited in that none of them simultaneously incorporate cell-cell interactions, ongoing perfusion, and the ability to manipulate both the cerebral and luminal environments. We therefore developed a 3D, perfusible, scaffold-directed bioengineered artery model made up of primary human cells and cultured in a bioreactor enabling a dynamic pulsatile flow through the 115 lumen of the vessel. Manipulation of the media circulating through the lumen of vessels and in the outer chamber housing the vessels allows for the study of factors on both the “blood” and the “brain” side of the BBB. We used this model and 2D cultures of brain-derived ECs to investigate potential Aβ-related vasoprotective functions of HDL. 4.3 Methods 4.3.1 Fabrication of tissue engineered vessels Scaffold-directed human engineered vessels were generated under flow bioreactor conditions as described [629]. Briefly, non-woven polyglycolic-acid meshes (PGA; BMS) were coated with polycaprolactone (PCL, Purac) and polylactic acid (PLA, Purac) by dipping into a 1.75% (w/w/w) solution of PCL/PLA/tetrahydrofuran (Sigma Aldrich). A tubular shape (length 10 mm and inner diameter 2 mm) was obtained by heat welding before external coating with a 10% (w/w) PCL/tetrahydrofuran solution. After sterilization with 70% ethanol for 30 min followed by three washes with phosphate buffered saline (PBS), scaffolds were pre-incubated in advanced Dulbecco’s Modified Eagle Medium (DMEM) overnight before cell seeding. Human umbilical cord smooth muscle cells were isolated as described [629] and 2×106 cells/cm2 were seeded in the inner surface of the vascular scaffold using fibrinogen (10 mg/mL clottable protein, Sigma Aldrich) and thrombin (10 mU) as cell carriers [630]. After a short static incubation period of 3 days, vascular constructs were exposed to dynamic conditioning in a flow bioreactor, where the flow of nutrient medium (Advanced DMEM (Invitrogen) with 10% FBS; 0.05% Penicillin/Streptomycin, 1% L-glutamine and 1.5 mM L-ascorbic acid) was directed through the inner lumen of the bioreactor circulation loop at 6 mL/min. After 14 days of flow conditioning, vascular grafts were endothelialized with HUVECs (1.5×106 cells/cm2) seeded into the lumen followed by cultivation under static conditions for 5 days in complete endothelial basal medium (EBM; Lonza) supplemented with 10% fetal bovine serum (FBS) and growth supplements to form complete endothelial growth media (EGM-2). After the static phase, vascular grafts were placed back in the bioreactor for 14 additional days with increasing medium flow (4 to 6 mL/min) in complete EGM with 10% FBS. 116 4.3.2 Characterization of engineered vascular tissue For histological characterization, engineered vessels were fixed in formalin (Fisher), dehydrated through a graded ethanol series using a Sakura Tissue Processor (Sakura), embedded in paraffin and sectioned at 7 µm thickness. Sections were deparaffinized, rehydrated through a graded ethanol series and stained using haematoxylin & eosin (SigmaAldrich) following the manufacturer’s instructions. For immunofluorescence analyses, tissues were cryopreserved in Cryomatrix (ThermoFisher) and sectioned at 20 µm thickness using a cryotome (Leica). Immunostaining was performed as described [629] using antibodies specific for the EC marker CD31 (clone JC/70A Biolegend, 1:50) and α-smooth muscle actin (clone 1A4 SigmaAldrich, 1:200). Sections were counter-stained with 4’,6-diamidino-2-phenylindole (DAPI) and imaged with an inverted microscope (Carl Zeiss). Endothelial barrier integrity was analyzed by injecting Evans blue (Sigma Aldrich) at a final concentration of 0.5% in the circulation loop of the bioreactor for 10 min followed by continuous PBS washing for 20 min. Vessels were cut open longitudinally and en face preparations were analyzed macroscopically with photo documentation. 4.3.3 Preparation of HDL and PBMCs All experiments were conducted under an approved clinical protocol (UBC Clinical Ethics Research Board H14-03357). Upon receipt of written informed consent, 100 mL of fasted blood was collected from normolipidemic healthy donors into vacutainer tubes. Plasma HDL (1.063-1.21 g/mL) was isolated by sequential potassium bromide gradient ultracentrifugation as described [631]. The purity of the HDL preparations was verified by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Coomassie blue staining to ensure no LDL or albumin contamination. Total HDL protein concentrations were measured with the Pierce BCA protein assay (ThermoFisher). Eight independent donors were used across the experiments, six isolated in-house and two commercially obtained (LeeBioscience). Human-derived, lipid free apoA-I was a kind gift from CSL-Behring. Immortalized human THP1 monocytes (ATCC) were cultured in Roswell Park Memorial Institute (RPMI) media containing 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine and 0.1% b-mercaptoethanol. Primary human PBMC were isolated from healthy donors by centrifugation on a continuous density 117 gradient (LymphoprepTM, Stemcell) following the manufacturer’s instructions. Freshly isolated PBMC were fluorescently labeled with 10 µM of Cell-Tracker Red for 30 min (Invitrogen) following the manufacturer’s recommendations. 4.3.4 Preparation of Aβ peptides Recombinant Ab40 and Ab42 peptides (California Peptide) were dissolved in hexafluoroisopropanol (HFIP). The HFIP was removed by evaporation overnight and stocks were stored at -20oC. On the day of the assay, soluble monomers were prepared by reconstituting the peptide film in dimethyl sulfoxide (DMSO) to 5 mM, diluted further to 100 µM in RPMI without FBS. 100 µl of Ab solution was injected in the tissue chamber containing 900 µl of DMEM (Gibco) without FBS to the desired concentration using a syringe under flow conditions. For fibrils, after reconstitution in RPMI Ab40 and Ab42 were incubated at 370C for 48 h. Fibrilization was confirmed by dot blot with fibril antibody (OC AB2286 EMD Millipore 1:1000, not shown). For luminal recovery, 100 µl circulating medium was collected at the indicated time. 4.3.5 Monocyte adhesion in engineered vessels Vascular grafts were perfused with complete EGM-2 with 2% FBS. 1 µM Ab42 or Ab40 monomers were injected directly into the graft chamber to mimic Ab originating from the brain (antelumen) side of the vessel. At time points ranging from 2 to 72 h, THP1 cells were fluorescently labeled with Cell-Tracker Red as described above, injected in the graft circulation at a concentration of 1x106 cells/mL and maintained under flow conditions for 3 h. For HDL experiments, vascular grafts were perfused with luminal HDL (200 µg/mL total protein) for 2 h before injecting Ab in the antelumen side for 8 h. Tissues were longitudinally cut open, washed extensively with PBS and fixed with 4% paraformaldehyde (PFA). After 20 min, tissues were washed three times with PBS and mounted in Prolong Gold antifade reagent with DAPI. For each independently seeded tissue, adherent monocytes and monocytes undergoing diapedesis were counted in three random squares of 1.23 mm2 using a z-stack covering the whole tissue thickness with a SP8 confocal microscope (Leica), averaged and expressed as percent of vehicle normalized to 100% for Figure 4.2d and f or percent of Ab normalized to 100% for Figure 4.2e and g, and Figure 4.11a and b. 118 4.3.6 Static monotypic cell culture Human cerebral microvascular EC line (hCMEC/D3) (Fisher; passage 27-35), and human umbilical vein EC (HUVEC) (passage 4-7, isolated as described [629]) cells were cultured using complete EGM-2 with 2% FBS, ECs were cultured in a humidified incubator at 37 °C at 5% carbon dioxide. For mechanistic experiments, ECs were treated with SR-BI blocking antibody (NB440-113 Novus, 1:500), the SR-BI inhibitor block lipid transport-1 (BLT-1, SigmaAldrich, 10 µM), the eNOS inhibitor L–NG-nitroarginine methyl ester (L-NAME, SigmaAldrich, 1 mM), the receptor associated protein (RAP, Oxford biome, 1 µM), RAGE blocking antibody (176902, R&D Systems, 1:50), heparin (10 mU), heparinase (SigmaAldrich, 0.2 mM) or the cluster of differentiation 36 (CD36) blocking antibody (JC63.1, ABCAM, 1:500) for 1 h before HDL priming. For miRNA experiments, cells were transiently transfected using Lipofectamine 2000 (Life Technology) 2 h before Ab stimulation with the miR-223 mimetic (Life Technology, 4464066 (MC12301), 100 nM) or miR-223 inhibitor (Life Technology, 4464084 (MH12301), 100 nM) in EBM-2 containing 0.2% bovine serum albumin (BSA). 4.3.7 Monolayer PBMC adhesion assay ECs were seeded at 1x105 cells/well in 24-well plates and cultured until confluent for 2 to 3 days. On the day of the assay, ECs were primed for 2 h with BSA (100 μg/mL) as vehicle control or HDL (100 μg/mL total protein) before stimulation with 1 ng/mL of TNFα (Preprotech) or various concentrations of monomeric Ab40 and Ab42 (0.001-1µM, California Peptide), prepared as described below. After 3 h, 5x105 Cell-Tracker Red-labeled PBMC per well were added to ECs for an additional 3 h before washing three times with PBS to remove non-adherent PBMC. Cells were then fixed with 4% PFA for 15 min before three additional PBS washes and DAPI counterstaining. For each independent experiment, adherent monocytes were counted in five random squares of 7.84 mm2 using a fluorescent inverted microscope (Zeiss), averaged and expressed as percent of vehicle normalized to 100%. DAPI counterstaining was used to ensure EC coverage. 119 4.3.8 Ab oligomerization/fibrilization and electron microscopy confirmation Recombinant Ab40 and Ab42 peptides (California Peptide) were dissolved in ice-cold hexafluoroisopropanol (HFIP). The HFIP was removed by evaporation overnight. To prepare soluble monomers, the peptide film was reconstituted in DMSO to 5 mM, diluted further to 100 µM in DMEM and used immediately. Oligomers were prepared by diluting the 5 mM DMSO peptide solution in phenol red-free F12 medium (Life Technologies) to a final concentration of 100 µM and incubating for 24 h at 4 °C. Fibrils were prepared by diluting the 5 mM peptide solution in 0.1 μM of HCl to a final concentration of 100 µM and incubating for 24 h at 37 °C. Ab monomer, oligomer and fibril preparations were then either used to stimulate hCMEC/D3 monolayers, as given, or were analyzed by transmission electron microscopy (TEM) and dot blot. For TEM, 0.5 μL of 100 μM Aβ preparation was diluted in 2 μL filtered distilled water, spotted onto formvar-coated 200-mesh nickel grids (EM Sciences) and allowed to dry. Grids were then negatively stained with 0.5% aqueous uranyl acetate for 30 s and viewed on a FEI Tecnai G2 Spirit Transmission Electron Microscope. For dot blot, aliquots of Ab (0.1 µM) were added to polyvinylidene difluoride (PVDF) membrane, which were dried and blocked in 3% skimmed milk PBS with 0.5% Tween-20 (PBST). After 1 h, blots were incubated with b-amyloid 1-16 antibody (6E10 Biolegend 1:500), amyloid A11 oligomeric antibody (AB9234 Millipore, 1:1000) or amyloid fibrils OC antibody (AB2286 Millipore, 1:1000) in blocking buffer for 16 h, washed extensively in PBST and incubated with anti-mouse or anti-rabbit (1:1000) secondary antibody in blocking buffer. After 1 h, blots were washed as above and developed using enhanced ECL and a ChemiDoc MP imager. 4.3.9 Measurement of intracellular NO ECs were seeded at 5x105 cells/well in 6-well plates and cultured for 2 days until confluent in complete EBM-2. ECs were serum-depleted in EBM-2 containing 0.2% FBS 16 h before the assay. On the day of the assay, ECs were incubated with HDL (100 μg/mL total protein) in serum-depleted EBM-2 medium containing 1 µM of 4,5-diaminofluorescein diacetate (DAF2-DA, Caymanchem) at 37 °C. After 6 h, ECs were washed with PBS, trypsinized, and triazolofluorescein fluorescence was measured (excitation wavelength of 485 nm, emission 538 nm), using an Infinite M200Pro plate reader (Tecan). In addition to DAF2-DA measurement, the phosphorylation of 120 eNOS at Ser1177 was compared to total eNOS by immunoblotting (below) in cell lysates harvested 15 min after HDL treatment. 4.3.10 Cell surface biotinylation ECs were seeded at 5x105 cells/well in 6-well plates and cultured for 2 days until confluent. On the day of the assay, ECs were treated with HDL, Ab or TNFa as above. After stimulation, EC monolayers were washed twice with ice cold PBS (pH 8), cooled on ice for 15 min and biotinylated with 250 µg/mL EZ-linkTM-sulfo-NHS-biotin (ThermoFisher Scientific) in PBS (pH 8) at 4 °C. After 1 h the reaction was stopped with a 5 min incubation in DMEM with 10% FBS. After two additional PBS washes, ECs were lysed in radioimmunoprecipitation assay (RIPA) buffer. Following quantification of protein concentration using a bicinchoninic acid (BCA) assay (ThermoFisher Scientific), at least 100 µg of protein was incubated with streptavidin-conjugated sepharose beads (Pierce) at 4 °C overnight. Beads were washed three times with RIPA buffer and the recovered proteins were resolved on an SDS-PAGE. 4.3.11 Monolayer Ab association, binding and uptake ECs were seeded at 1x105 cells/well in 24-well plates and cultured until confluent for 2 to 3 days. On the day of the assay, ECs were primed for 2 h with 100 µg/mL HDL before stimulating with 0.1 µM of Ab40 and Ab42 monomers at 37 °C for total association, or at 4 °C for cell surface binding. After 3 h, hCMEC/D3 were washed three times with PBS and lysed in RIPA buffer containing 10 mM Tris pH 7.4, 150 mM NaCl, 1.0% NP-40, 1.0% sodium deoxycholate, 0.1% SDS and cOmplete protease inhibitor with ethylenediaminetetraacetic acid (EDTA) (Roche). Ab40 (KHB3442, Life Tech) and Ab42 (KHB3482, Life Tech) were quantified using commercial enzyme immunosorbent assay (ELISA)s and normalized to total protein concentration. For Ab uptake, hCMEC/D3 were seeded at 1x105 cells/well in 24-well plates and cultured to confluence for 2 to 3 days. On the day of the assay ECs were primed for 2 h with 1 mg/mL of HDL before stimulating with 1 µM monomeric fluorescein isothiocyanate (FITC)-Ab40 and FITC-Ab42 (Bachem) prepared as described above. After 3 h at 37 °C, hCMEC/D3 were washed three times with PBS and fixed in 4 % PFA for 20 min. After a Tris-HCl and two PBS washes, hCMEC/D3 were mounted in Prolong antifade reagent. 121 4.3.12 Molecular Biology For messenger RNA (mRNA), cells were lysed in Trizol (Invitrogen) and RNA was extracted and treated with DNase I (Invitrogen) according to the manufacturer’s protocol. Complementary deoxyribonucleic acid (cDNA) was generated using oligo-dT primers and Taqman reverse transcription reagents (Applied Biosystems). Real-time quantitative polymerase chain reaction (RT-qPCR) was done using FastStart Universal SYBR Green Master reagent (Roche) on a Light Cycler 96 system (Roche) to quantify gene expression relative to vehicle using specific primer against intercellular adhesion molecule 1 (ICAM-1) (fwd : ATGGCAACGACTCCTTCTCG ; rev : CGCCGGAAAGCTGTAGATGG) and vascular cell adhesion molecule 1 (VCAM-1) (fwd: TGTTTGCAGCTTCTCAAGCTTTT ; rev: GATGTGGTCCCCTCATTCGT) and normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (fwd: CCTGCACCACCAACTGCTTA ; rev : CATGAGTCCTTCCACGATACCA). MiRNA was isolated using an miRNeasy mini kit (Qiagen) following the manufacturer’s instructions, respectively. Reverse transcription was performed using 2 µg of total RNA with specific miRNA primers (Life Technology). MiR223 and U6 were quantified using specific TaqMan probes (Life Technology). 4.3.13 Immunoblot ECs were lysed in RIPA buffer containing either cOmplete protease inhibitor or Phosphostop (Roche) and quantified using a BCA assay. Equal amounts of total protein were separated by SDS-PAGE followed by electrophoretic transfer to PVDF membranes (Millipore). After blocking membranes for 1 h with 5% skim milk powder in PBST, or 5% BSA in tris buffered saline with 0.5% Tween-20 (TBST) for phosphoproteins, ICAM-1, (EP1442Y Abcam, 1:1000), VCAM-1 (EPR5047 Novus, 1:1000), phosphorylated (p)-eNOS (ser1177 Cellsignalling, 1:1000), eNOS (M221 ABCAM, 1:1000), annexin A1 (Anx1) (D5V2T, Cellsignalling, 1:1000), p-Akt (Ser473 D9E, Cellsignalling, 1:2000), Akt (9272, Cellsignalling, 1:1000), p-nuclear factor kappa beta (p-NF-kB) p65 (Ser536 93H1, Cellsignalling, 1:1000), NF-kB p65 (D14E12, Cellsignalling, 1:1000), p-stress activated protein kinase/jun amino-terminal kinase (p-SAPK/JNK) (Thr183/Tyr185 G9, Cellsignalling, 1:1000), JNK (2C6, Cellsignalling, 1:1000), p-p42/44 mitogen activated protein 122 kinase (MAPK) (Erk1/2) (Thr202/Tyr204 D13.14.4E, Cellsignalling, 1:1000), p42/44 MAPK (Erk1/2) (137F5, Cellsignalling, 1:1000), p-Stat3 (Tyr705, Cellsignalling 1:1000), Stat3 (124H6, Cellsignalling, 1:1000) and GAPDH (MAB374 1:10000, Millipore) were immunodetected by incubating for 16 h in primary antibody in blocking buffers. Membranes were washed extensively with PBST or TBST and incubated with anti-mouse or anti-rabbit (1:1000-10’000, Jackson ImmunoResearch) secondary antibody in blocking buffer. After 1 h, membranes were washed as above and developed using enhanced chemiluminescence (ECL, Amersham) and a ChemiDoc MP imager (Biorad). Densitometric images were captured with ImageJ and band intensity normalized to GAPDH as a loading control. 4.3.14 Beta-sheet formation assay Cell-free Thioflavin-T fibrillization assays were performed on an Infinite M2000 Pro plate reader (Tecan) as described (Truran 2015). Briefly, 10 µM monomeric Ab40 or Ab42 were incubated in a buffer consisting of 20 mM of Thioflavin-T in 150 mM NaCl and 5 µM of 4-(2-hydroxyethyl)-piperazineethanesulfonic acid (HEPES) at pH 7.4, with and without 1 mg/mL of HDL, at 37 °C with 20 s of orbital shaking (3 mm amplitude) every 5 min in a black 96-well plate. Formation of fibrillar β-amyloid pleated sheets over time was monitored by excitation at 440 nm and measuring emission intensity at 490 nm every 5 min up to 12 h in total. 4.3.15 Human brain protein extraction and ELISA Frozen brain tissues (cortex Brodmann area 9 and cerebellum) were provided by the Harvard Brain Tissue Resource Center under an approved UBC protocol (C04-0595) and extracted with eight volumes of ice-cold carbonate buffer (100 mM Na2CO3, 50 mM NaCl, pH 11.5) containing cOmplete protease inhibitor (Roche Applied Science) by manual homogenization with a tissue probe. After incubating on ice for 10 min, lysates were clarified by centrifugation at 16,600 g for 45 min at 4 °C. The supernatant was removed and neutralized by adding 1.5-volumes of 1 M Tris-HCl pH 6.8 to give a final pH of approximately 7.4. Brain tissues from all mice were extracted in an identical manner, and fractions were aliquoted and immediately frozen at -80 °C until analysis. Protein concentrations were determined using the Lowry Protein Assay (Biorad). ICAM-1 (ab174445, Abcam), VCAM-1 (ab187393, Abcam), Ab40 (KHB3442, Life Tech) and Ab42 123 (KHB3482, Life Tech) in carbonate extracts were quantified using commercial ELISAs. ELISA data were normalized to total protein concentration of the extract. For immunofluorescent staining, brains were sectioned at 20 µm, rehydrated in PBS and blocked in 4% PFA. After 20 min, sections were washed once with Tris-HCl (0.5 mM, pH 8), twice with PBS, and blocked in blocking buffer (5% goat serum and 1% BSA). After 60 min, sections were incubated at 4 °C with antibodies against CD31 (WM59 Biolegend, 1:50), ICAM-1 (EP1442Y Abcam 1:200), and VCAM-1 (EPR5047 Abcam 1:200) in blocking buffer overnight. Sections were washed three times in PBS and incubated at room temperature with Alexa-488 anti-mouse and Alexa-594 anti-rabbit fluorescently labeled secondary antibodies (LifeTechnologies, 1:600). After 45 min and three additional PBS washes, sections were mounted in Prolong Gold antifade reagent with DAPI (LifeTechnologies) and imaged on an inverted fluorescent microscope (Zeiss) 4.3.16 Vascular Aβ accumulation and clearance in bioengineered vessels Aβ42 monomers were injected within the tissue chamber (the “brain side”) to a final concentration of 1 μM. Immediately afterward, the bioreactor media was substituted with complete EGM2 media (2% FBS) containing HDL (200 μg/mL total protein) isolated by ultracentrifugation. Luminal medium was collected from the circulation chamber (the “blood side”) from 5, 15, 30, 60, 120, and 240 after treatment, and bioengineered tissues were collected and lysed in RIPA buffer (10 mM Tris pH 7.4, 150 mM NaCl, 1.0% NP-40, 1.0% sodium deoxycholate, 0.1% SDS and cOmplete protease inhibitor with EDTA (Roche, Switzerland)) at 24 h. Aβ42 (KHB3482, ThermoFisher) was quantified in circulating media and tissue lysates using commercial ELISA and tissue concentrations were normalized to total protein content measured by BCA protein assay kit (ThermoFisher). 4.3.17 Statistical analysis All statistical analyses were performed using SPSS and p-values <0.05 were considered significant. Data were obtained from at least three independent experiments and are presented as mean ± SD if not indicated otherwise. Data were first log transformed and analyzed by two-way analysis of variation (ANOVA) with a blocking factor (experiment) with direct comparison when comparing two treatments or Dunnett’s or Bonferroni multi-comparison tests. After statistical 124 calculations, vehicle data were normalized to 100% and represented as a dashed line in the graph, and tested conditions were expressed and graphed as percentage of vehicle if not otherwise stated. 4.4 Results 4.4.1 Bioengineering dynamic, human 3D vessels To mimic the complexity of native cell-cell and/or cell-matrix interactions observed in native human vessels, we used innovative tissue engineering technology to generate in vitro 3D human vessels composed of primary HUVEC and primary human smooth muscle cells (SMC) to maximize translational relevance of our studies relative to in vitro studies that solely use traditional static cell culture models. Our system uses a scaffold-directed dynamic, semi-pulsatile flow bioreactor system, on which primary human cells are sequentially seeded into 2 mm diameter biodegradable PGA/PCL composite matrices. After a short static incubation, vascular constructs are exposed to dynamic flow in a bioreactor, where nutrient medium is directed through the lumen of the bioreactor circulation loop to mimic native blood flow. We previously demonstrated that these scaffold-directed engineered vessels are useful for studies of endothelial activation in 3D culture [629]. A schematic of the bioreactor system is presented in Figure 4.1a. Under our flow bioreactor conditions, haematoxylin and eosin staining confirmed a dense tissue formation composed of cells with extracellular matrix on the luminal side of the scaffold (Figure 4.1b-d). Immunofluorescent staining confirmed a SMC phenotype of cells in the inner layers and an EC monolayer on the luminal side (Figure 4.1c,d). Integrity of the endothelial barrier was functionally assessed by injecting Evans blue dye into the bioreactor circulation loop, which was excluded after EC seeding, demonstrating a functionally tight endothelial barrier (Figure 4.1e). 125 Figure 4.1 Bioengineering dynamic human 3-dimensional vessels. (a) Schematic representation of bioengineered tissue. (b) Histological structure of engineered tissue using hematoxylin-eosin staining to reveal a dense tissue formation composed of cells and extracellular matrix in engineered vessels. (c) α-smooth muscle actin (α-SMA) confirmed the smooth muscle phenotype of cells in the inner layers and (d) CD31 confirmed an endothelial monolayer. Scale bar represents 200 μm. (e) Evans blue staining confirmed a tight endothelium. (f-g) 1 µM of Ab40 or (h-i) Ab42 monomers were injected within the tissue chamber (abluminal). 4.4.2 HDL suppresses Ab-mediated monocyte adhesion to ECs in 3D dynamic engineered human vessels Having established a 3D human vascular model, we injected 1 µM Ab40 or Ab42 directly into the graft chamber to mimic Ab coming from the brain anteluminal side, followed by injection of human THP-1 monocytes into the lumen chamber to mimic circulating monocytes. Both Ab40 and Ab42 led to THP1 adhesion to EC in the engineered vessels, with the most robust response appearing 8 h after Ab injection (Figure 4.2a-d). We then tested the ability of HDL, isolated from healthy human donors using KBr density gradient ultracentrifugation, to suppress Ab-induced monocyte adhesion to ECs in engineered vessels by circulating either media alone or 200 µg/mL of HDL for 2 h prior to injecting 1 µM Ab into the graft chamber. In this engineered vascular model, HDL robustly suppressed monocyte adhesion to endothelium by both Ab40 (5 fold, p=0.0286) and Ab42 (10 fold, p=0.0016) (Figure 4.2e-h). 126 Figure 4.2 HDL suppresses Aβ-induced monocyte adhesion to 3D bioengineered vessels. (a-b) 1 μM of Aβ40 or (c-d) Aβ42 monomers were injected within the tissue chamber (abluminal). Fluorescently labeled human monocytes (THP1, white) were circulated in the lumen of the engineered vessels and analyzed using confocal microscopy over time. (e-g) HDL (200 μg/mL total protein) were circulated through the lumen of the grafts for 2 h prior injection of 1 μM of (e-f) Aβ40 or (g-h) Aβ42 monomers within the tissue chamber (abluminal) for 8 h prior to circulating fluorescent THP1 in the lumen. Graphs represent means ± SD of adhered monocytes relative to Aβ treated tissues from at least three individual grafts. *p<0.05, **p<0.01. 4.4.3 HDL attenuates Ab-induced PBMC adhesion in monotypic ECs To define the mechanisms by which HDL attenuates Ab-induced monocyte binding to ECs, we used static monolayer EC cultures and PBMC isolated by continuous density gradient centrifugation from healthy human donors. We first confirmed that Ab induces PBMC adhesion in monotypic HUVEC cultures and that HDL attenuates this activity (Figure 4.3a,b). As Ab originates within the brain and the primary pathways by which Ab is cleared from the brain involve cerebral vessels [7], we also showed that the human brain microvascular EC line hCMEC/D3, a commonly used cell line for in vitro studies of the BBB [632], also exhibit increased PBMC 127 adhesion upon Ab treatment and that this too is attenuated by HDL (Figure 4.3,d). As HUVEC and hCMEC/D3 give nearly identical results, we focused on hCMEC/D3 cells for all subsequent mechanistic experiments. Figure 4.3 Aβ40 and Aβ42 induce PBMC adhesion to ECs, which is suppressed by HDL. HUVEC (a-b) or hCMEC/D3 (c-d) were primed with 100 μg/mL HDL (total protein concentration) and stimulated with 0.1 μM (a, c) Aβ40 (light grey) or (b, d) Aβ42 (dark grey) monomers for 3 h. Fluorescently labelled PBMC were allowed to adhere to stimulated cells for 3 h followed by washing, fixation, imaging, and counting. hCMEC/D3 were primed with either 100 μg/mL of HDL (total protein concentration) or 50 or 100 μg/mL lipid-free human apoA-I for 2 h followed by stimulation with 0.1 μM (e) Aβ40 (light grey) or 0.1 μM Aβ42 (dark grey) monomers for 3 h. Cells were washed, imaged and counted as above. Graphs represent mean ± SD of adhered PBMC relative to vehicle control from at least three independent trials where * p<0.05, **p<0.01, ***p<0.001 versus vehicle, § p<0.05, §§ p<0.01 versus Aβ. Our first question was to determine if lipid-free apoA-I was functionally equivalent to mature HDL in its ability to suppress Ab-mediated PBMC binding to hCMEC/d3. Our results clearly showed that pre-incubation of hCMEC/D3 with lipid-free apoA-I (50 and 100 µg/mL) did not alter Ab-induced PBMC adhesion (Figure 4.3e,f), demonstrating that mature HDL particles are required to attenuate Ab-induced PBMC adhesion to brain-derived ECs. To establish the experimental conditions to investigate how HDL suppresses Ab-mediated PBMC adhesion to hCMEC/D3, we performed PMBC adhesion assays in hCMEC/D3 after a 3 h exposure to increasing concentrations human Ab40 and Ab42 monomers. Compared to baseline, Ab40 128 concentrations of 0.01, 0.1 and 1.0 µM led to significant 175%, 169% and 179% increases in PMBC adhesion (p=0.033, 0.002 and 0.019 respectively, Figure 4.14a), The same concentrations of Ab42 also led to significant 184%, 161% and 223% increases in PMBC adhesion (p=0.019, 0.0406 and 0.0421, respectively) relative to baseline (Figure 4.14b). Having demonstrated that 0.1 µM of monomeric Ab40 or Ab42 is sufficient to activate hCMEC/D3, we then tested whether pretreating hCMEC/D3 for 2 h with 0 to 400 µg/mL HDL could attenuate Ab-induced PBMC adhesion. Significant suppression of PBMC adhesion was observed at concentrations of HDL from 100 µg/mL and above. At 100 µg/mL HDL there was an 80% and 60% reduction of adhered PBMCs in cells induced by 0.1 µM Ab40 (p=0.017) or 0.1 µM Ab42 (p=0.018) respectively (Figure 4.14c,d). 4.4.4 HDL reduces the fibrillization rate of Ab42 and Ab40 in cell-free conditions As Ab has been reported to bind HDL-like particles in both cerebrospinal fluid (CSF) and blood [633], and apoA-I has been reported to affect Ab aggregation [519,634], we reasoned that one mechanism by which HDL may attenuate PBMC adhesion to hCMEC/D3 could be by affecting Ab structure to prevent the formation of toxic higher order species [635]. A cell-free Thioflavin-T reporter assay was used to determine whether HDL affects the fibrillization kinetics of Ab40 or Ab42 over a 12 h period, using 10 µM Ab and 10 mg/mL HDL (ratio: 1:1), which is the same Ab:HDL ratio used for our cellular PBMC adhesion assays and allows detection of fluorescence signal using a plate reader. Under these conditions, in the absence of HDL, the onset of Ab fibrillization requires at least 3 h for Ab42 and at least 10 h for Ab40. Notably, in the presence of HDL, we observed delayed onset of fibrillization for both Ab42 and Ab40, and a slower rate of Ab42 fibrillization (Figure 4.4a). As HDL can suppress PBMC adhesion to hCMEC/D3 induced by both Ab40 and Ab42 within 3 h of Ab addition, we conclude that suppression of Ab fibrillization cannot be the only mechanism by which HDL functions to protect ECs from the detrimental effects of Ab. 129 Figure 4.4 HDL delays beta-sheet formation and attenuates Aβ-induced PBMC adherence independent of Aβ structure. (a) Representative graph ± SD of technical triplicates of three individual experiments where 10 μM Aβ40 or Aβ42 with or without 10 mg/mL HDL were incubated with 20 µM of Thio-T in 150 mM NaCl and 5 µM of HEPES (pH 7.4) for 12 h at 37 °C. Formation of β-amyloid pleated sheets was monitored every 5 min at excitation 440 nm and emission 490 nm. (b-c) Aβ structures were confirmed using dot blot with antibodies against oligomers (A11) or fibrils (OC) and TEM. (d-e) hCMEC/D3 were stimulated for 3 h with 0.1 μM monomeric (m-Aβ, solid bar), oligomeric (o-Aβ, striped bar) or fibrillar-aggregated (f-Aβ, cross-hatched bar) (d) Aβ40 or (e) Aβ42 in the presence or absence of HDL. (f-g) hCMEC/D3 were pre-incubated for 2 h with HDL (100 μg/mL total protein) prior to Aβ addition (pre, solid bar), co-incubated with HDL and Aβ (co, striped bar), or post-incubated by adding HDL 1 h following Aβ stimulation (post, double striped bar). Graphs represent means ± SD of adhered PBMC relative to vehicle treated cells from at least five independent trials where. *p<0.05, **p<0.01, ***p<0.001 * p<0.05, **p<0.01, ***p<0.001 versus vehicle, § p<0.05, §§ p<0.01, §§§p<0.001 versus Ab. 130 4.4.5 Aβ structure does not affect HDL’s ability to reduce PBMC adhesion to hCMEC/D3 Several experiments were then performed to further test whether Ab structure affects HDL’s anti-inflammatory activity on hCMEC/D3. We varied the structural species of the Ab preparation added to hCMEC/D3, reasoning that if HDL acts primarily through maintaining Ab in a soluble monomeric state, the protective effect of HDL should be diminished if ECs are stimulated with preformed Ab oligomers or fibrils. Soluble monomeric and oligomeric, and insoluble fibrillar Ab, were prepared as described [636] and structures were confirmed using a dot blot assay as well as electron microscopy (Figure 4.4b,c). Interestingly, monomeric and oligomeric Ab40 and Ab42 preparations induced comparably robust PBMC adherence, whereas the insoluble fibrillary form failed to significantly increase PBMC adhesion. Importantly, the ability of HDL to suppress PMBC adhesion was unaffected by input of either Ab monomers or oligomers (Figure 4.4d-e). These results suggest that delayed Ab fibrillization may not be the major pathway by which HDL suppresses PBMC adhesion to ECs under our experimental conditions as HDL maintained its protective effect when ECs are treated with pre-formed oligomers. To confirm this, we also varied the timing of HDL and Ab addition to hCMEC/D3 such that, in addition to a 2 h HDL pre-incubation as above, HDL was added at the same time as Ab (co-incubation) or 1 h after Ab addition (post-incubation). We reasoned that if HDL acts primarily through maintaining Ab solubility, pre-incubation and co-incubation designs should give equivalent and maximal suppression of Ab-induced PBMC adhesion as, in both scenarios, Ab is always in the presence of HDL and aggregation could be delayed. In contrast, as the post-incubation scenario would allow Ab aggregation to begin prior to addition of HDL, the protective effect of HDL should be reduced if added after the Ab stimulus if Ab structure is the key driver of PBMC adhesion to ECs. We observed that PMBC adhesion was significantly and similarly reduced regardless of when HDL was added to hCMEC/D3 relative to Ab40 or Ab42, providing additional support that HDL’s ability to attenuate Ab-mediated PBMC adhesion to hCMEC/D3 is independent of input Ab structure (Figure 4.4f,g). These two lines of evidence suggest that, although HDL can affect Ab structure in cell-free conditions, these effects are neither rapid nor robust enough to fully explain how HDL suppresses Ab-mediated PBMC adhesion to hCMEC/D3. We next evaluated signalling pathways implicated in HDL’s anti-inflammatory effects on ECs. 131 4.4.6 The ability of HDL to suppresses Aβ-induced PBMC adhesion to hCMEC/D3 is independent of NO production and Anx1 Stimulation of NO synthesis by HDL is reported to reduce arterial EC activation [482]. We therefore tested whether HDL induces NO production in hCMEC/D3 using the cell permeable DAF2-DA, which reacts with NO to produce highly fluorescent triazolofluorescein. Compared to baseline conditions, HDL-treated hCMEC/D3 showed a significant 130% increase (p=0.01) in NO production (Figure 4.5a), as well as significantly elevated p-eNOS on serine 1177 (161%, p=0.047) (Figure 4.5b,c). These results confirm that HDL can induce NO production in hCMEC/D3, as observed in ECs derived from other origins. 132 Figure 4.5 HDL suppression of Aβ-induced inflammation is independent of eNOS and S1P. (a) Intracellular NO production was measured by treating hCMEC/D3 with HDL (100 μg/mL total protein) in the presence of 1 μM DAF-2 for 6 h. Fluorescence was measured at 485 nm. (b) Phosphorylation of eNOS was measured by treating hCMEC/D3 with HDL (100 μg/mL total protein) for 15 min before immunoblotting for phosphorylated eNOS (p-eNOS) or total eNOS. Representative immunoblots are shown in (c). hCMEC/D3 were pretreated for 1 h with the eNOS inhibitor L-NAME (d-f) or the S1P1 and S1P3 inhibitor VPC23019 (g-i) followed by HDL (100 μg/mL total protein) for 2 h. Cells were then stimulated with 0.1 μM Aβ40 monomers (d,g) Aβ42 monomers, (e,h) or 1 ng/mL of TNFa (f,i) for 3 h before testing PBMC adherence. Graphs represent means ± SD of adhered PBMC relative to vehicle treated cells for at least five independent trials. *p<0.05, **p<0.01, ***p<0.001 * p<0.05, **p<0.01, ***p<0.001 versus vehicle, § p<0.05, §§ p<0.01, §§§p<0.001 versus Ab or TNFa. To test whether NO generation is required for HDL to suppress Ab-mediated PBMC adhesion to hCMEC/D3, we treated cells with the eNOS inhibitor L-NAME, followed by the addition of HDL, and finally stimulation with either Ab40, Ab42 or TNFa. We observed that blocking eNOS with L-NAME had no effect on the ability of HDL to suppress either Ab- or TNFa-mediated PBMC 133 adhesion to hCMEC/D3 (Figure 4.5d-f). Additional experiments confirmed the potency of L-NAME to block eNOS in HDL-stimulated HUVEC (Figure 4.15a), yet this also had no effect on the ability of HDL to suppress either Ab- or TNFa- mediated PBMC binding in HUVEC (Figure 4.15b-d). We further ruled out a role for the NO pathway in hCMEC/D3 by measuring the effect of HDL in the presence of VPC23019, an antagonist of sphingosine-1 phosphate 1 (S1P1) and S1P3 receptors required for eNOS activity [637] , and again found no effect on the ability of HDL to suppress either Ab- or TNFa-mediated PBMC adhesion to hCMEC/D3 (Figure 4.5g-i) or HUVEC (Figure 4.15e-h). Taken together, these results provide strong support that the eNOS pathway is not involved in the ability of HDL to suppress PBMC adhesion to Ab- or TNFa-activated hCMEC/d3 and HUVEC. In contrast to a recent report suggesting that HDL up-regulates Anx1, which affects HDL’s ability to suppress ECs activation [430], we observed no significant change in Anx1 protein expression in hCMEC/D3 after either HDL or Ab treatment (Figure 4.15i). 4.4.7 HDL-mediated suppression of Ab-induced PBMC adhesion to hCMEC/D3 is independent of miR-233 In addition to numerous proteins and lipid species, HDL particles carry miRNAs that can correlate with vascular disease risk [474]. One of the most abundant miRNAs in HDL is miR-233, which has recently been shown to affect gene expression in human coronary artery ECs including suppressing ICAM-1 expression [475]. Three lines of evidence suggest that HDL-mediated suppression of Ab-mediated PBMC binding to hCMEC/D3 is independent of miR-233. First, direct transfection of a miR-223 mimetic did not prevent Ab40 or TNFa-mediated PBMC adhesion to hCMEC/D3, although a statistically significant reduction with Ab42 was observed (Figure 4.6a-c). Second, priming hCMEC/D3 with HDL in the presence of a specific miR-223 inhibitor did not diminish the ability of HDL to suppress PMBC adhesion by Ab40, Ab42 or TNFa (Figure 4.6d-f). Third, in the 5 h period where hCMEC/D3 were exposed to HDL (2 h pretreatment then 3 h with Ab), no significant transfer of miR-223 was detected (Figure 4.6g), consistent with a previous report that after 6 h, miR-223 was not transferred from HDL to several types of ECs including HUVEC [638]. These results suggest that, although the miR-233 mimetic can suppress 134 Aβ42-mediated PBMC binding to hCMEC/D3 when added directly, miR-233 is unlikely to underlie the ability of HDL to suppress PBMC adhesion to hCMEC/D3 triggered by Ab40, Ab42 or TNFa. Figure 4.6 HDL does not signal through miR-223 to reduce Aβ-induced inflammation in hCMEC/D3. (a-c) hCMEC/D3 were pretreated with 100 µg/mL of HDL as described in Figure 4.2 with or without 10 nM of miR-223 mimetic nucleotides or (d-f) in the absence or presence of a specific miR223 inhibitor for 2 h before stimulation with (a,d) Ab40, (b,e) Ab42 or (c,f) TNFa before testing PBMC adherence to ECs. (g) Intracellular levels of mature miR-223 in hCMEC/D3 were quantified by real-time PCR and normalized to U6 after a 5 h treatment with 100 µg/mL of HDL. Graphs represent means ± SD of adhered PBMC relative to vehicle treated cells for at least five independent trials. *p<0.05, **p<0.01, ***p<0.001 * p<0.05, **p<0.01, ***p<0.001 versus vehicle, § p<0.05, §§ p<0.01, §§§p<0.001 versus Ab or TNFa. 4.4.8 HDL-mediated suppression of Ab-induced PBMC adhesion to hCMEC/D3 is independent of ICAM-1 and VCAM-1 ICAM-1 and VCAM-1 are canonical markers of EC activation that mediate PBMC adhesion by classical inflammatory stimuli [639]. Several studies have reported elevated microvascular 135 inflammatory markers in the AD brain [640,641]. Using ELISA, we confirmed that protein concentration of ICAM-1, but not VCAM-1, are increased in the cortex of AD patients compared to either cerebellum from the same individual or the cortex from non-cognitively impaired (NCI) control subjects (Table 4.1). Increased ICAM-1 expression was further confirmed using immunofluorescence revealing both vascular and glial ICAM-1 reactivity whereas VCAM-1 remained unchanged (Figure 4.16a-b). As expected, soluble Ab40 and Ab42 concentrations were elevated in the cortex of AD patients compared to their corresponding cerebellum or to the cortex of NCI subjects (Table 4.1). Table 4.1 Demographic data, Aβ40 and Aβ42, and adhesion molecule quantification in AD patient and NCI control brains. Group (n) NCI (n=5) AD (n=5) Brain region Cortex Cortex Cerebellum Age (y) (mean ± SD) 62 ± 3.8 67 ± 9.7 Sex (%male) 100 40 Braak stage N/A Braak IV Aβ40 (ng/mg) 0.87 ± 0.36 1.4 ± 0.22* 0.79 ± 0.13 Aβ42 (ng/mg) 0.02 ± 0.03 1.6 ± 0.91** 0.13 ± 0.2 ICAM-1 (ng/mg) 4.0 ± 1.8 24 ± 15** 4.1 ± 1.2 VCAM-1 (ng/mg) 23 ± 8.2 25 ± 8.8 23 ± 2.2 *p<0.05, **p<0.01 AD cortex vs. NCI cortex. Because ICAM-1 is a classical marker of EC activation and HDL can attenuate PBMC adherence by reducing ICAM-1 expression in aortic derived ECs [642], we also evaluated the roles of ICAM-1 and VCAM-1 in hCMEC/D3 activation. Using Western blotting, we observed that pretreatment of monotypic hCMEC/D3 cultures with HDL significantly suppressed TNFa-mediated induction of ICAM-1 (from 1343% to 920%, p=0.041) and VCAM-1 (from 2838% to 1928% p=0.012), relative to untreated cells (Figure 4.7a-c), results that are consistent with HDL’s anti-inflammatory effects on peripherally derived ECs. In contrast, Ab treatment did not affect either mRNA expression levels or total protein concentrations of VCAM-1 and ICAM-1 in hCMEC/D3 (Figure 4.7d-m). Although Ab40 and Ab42 treatment had no effect on the amount of total cellular ICAM-1 protein, biotinylation assays revealed significantly increased cell surface ICAM-1 levels, 136 but not VCAM-1 levels, in response to 0.1 µM of Ab40 (p=0.009) and a strong trend in response to Ab42 (p=0.06) (Figure 4.7h-j). Unexpectedly, however, we observed that cell surface ICAM-1 levels were not reduced by a 2 h exposure to HDL (Figure 4.7k-m), showing that cell surface ICAM-1 levels do not correlate with PMBC adhesion in Ab-activated hCMEC/D3. Figure 4.7 Adhesion molecules are enhanced by TNFα but not Aβ. (a-c) hCMEC/D3 were primed with HDL (100 μg/mL total protein) for 2 h followed by stimulation with TNFα (1 ng/mL) for 3 h. Cell lysates were prepared in RIPA and protein levels of (a) ICAM-1 and (b) VCAM-1 were measured by denaturing immunoblotting (c). (d-g) hCMEC/D3 were stimulated with monomeric (d,f) Ab40 or (e,g) Ab42 at the 137 indicated concentrations and (d,e) ICAM1 and (f,g) VCAM1 mRNA expression levels were measured by RT-qPCR. (h-j) Following Ab stimulation, cell surface proteins were biotinylated and isolated by immunoprecipitation. Cell surface and total (h) ICAM-1 and (i) VCAM-1 levels were measured by denaturing immunoblotting (j). (k-m) hCMEC/D3 were pretreated with 100 µg/mL of HDL for 2 h followed by stimulation with Ab. After 3 h total and cell surface ICAM-1 expression were measured as above. Graphs represent means ± SD from at least three independent trials. ***p<0.001 versus vehicle, § p<0.05, versus Ab or TNFa. We next analyzed the effects of Ab on the phosphorylation of multifunctional serine/threonine protein kinases that are involved in EC activation, survival, apoptosis, proliferation and migration. TNFa activated NFkB p-P65 (p=0.002), SAPK/JNK (p=0.005), MAPK/ERK (p=0.01), but not Akt or STAT2 pathways, whereas Ab did not alter any of these pathways in hCMEC/D3 (Figure 4.8). Taken together, these results suggest that Ab does not activate any of classical inflammatory pathways known to increase PBMC adhesion through ICAM-1 or VCAM-1. Figure 4.8 Aβ does not activate phosphorylation of multifunctional serine/threonine protein kinases. hCMEC/D3 were stimulated with 0.1 mM of monomeric Ab or 1 ng/mL of TNFa for 15 min before lysing cells in RIPA containing phosphostop. Phosphorylation of (a-b) p65, (c) STAT3, (d) Akt, (e) SAPK/JNK and (f) p42/44 MAPK were analyzed by immunoblotting and compared to respective total p65, STAT3, Akt, SAPK/JNK and p42/44 138 MAPK respectively. (b) Nuclear translocation of p65 was analyzed by immunofluorescence 15 min after Ab stimulation. Graphs represent means ± SD relative to vehicle treated cells in four trials. *p<0.05, **p<0.01. 4.4.9 Suppressing Ab uptake into hCMEC/D3 blocks PBMC adherence As our results show that Ab activates hCMEC/D3 through a mechanism that is independent of canonical EC activation intracellular signalling pathways, we hypothesised that binding or uptake of Ab to ECs might influence PBMC adhesion. To test this hypothesis, we first used temperature modulation experiments to investigate how HDL affects the interactions of Ab with hCMEC/D3 and observed that HDL pretreatment significantly reduced total association measured at 37 °C (Figure 4.9a,b), cell surface binding measured at 4 °C measured by ELISA (Figure 4.9c,d) and intracellular uptake of fluorescently labeled Ab40 and Aβ42 (Figure 4.9e). Second, we tested whether blocking RAGE and LRP1, known receptors that modulate Ab binding and uptake in ECs [643], might reduce PBMC adhesion. When RAGE is blocked with a specific antibody, there is no longer a statistically significant increase in PBMC adhesion to hCMEC/D3 with Aβ40 or Aβ42 stimulation although TNFα-induced PBMC adhesion remains significantly increased (Figure 4.9f-h). Notably, a non-significant increase in PBMC adhesion is still observed in hCMEC/D3 stimulated with Aβ42 when RAGE is blocked (Figure 4.9g) Similarly, blocking LRP1 with RAP abolished Ab40 and Ab42 but not TNFa effects (Figure 4.9i-k). Finally, as heparin sulphate proteoglycans (HSPG) are also involved in binding and uptake of Ab in neurons [644], we then saturated or removed HSPG by treating hCMEC/D3 with heparin or heparinase III, respectively. Both treatments abolished the ability of Ab40 and Ab42 to induce PBMC binding whereas TNFa induced-PBMC adhesion remained significant (Figure 4.9l-n). Together, these observations show that, unlike TNFa, Ab induces PBMC adhesion to ECs through a pathway that requires interactions with or internalization through Ab receptors on the EC surface. We also observed that HDL does not decrease the expression of RAGE or LRP1 (Figure 4.17a-c), suggesting that down-regulation of these receptors cannot explain the protective effect of HDL on Ab-induced PBMC binding to hCMEC/D3. 139 Figure 4.9 HDL reduces Aβ association, binding and uptake to hCMEC/D3 whereas blocking Aβ binding or uptake reduces PBMC adhesion to hCMEC/D3. (a-d) hCMEC/d3 were pretreated with 100 µg/mL of HDL and 0.1 µM (a,c) Ab40 or (b,d) Ab42 monomers as described in Figure 4.2 at either 37 °C (association a,b) or at 4 °C (binding c,d). Cells were lysed in RIPA buffer and Ab measured using commercial ELISA. (e) hCMEC/D3 were pretreated with HDL (1 mg/mL) for 2 h before stimulating with 1 mM of fluorescently labelled Aβ40 or Aβ42 monomers. Scale bar represents 10 μm. (f-n) hCMEC/D3 were pretreated with (f-h) RAGE blocking antibody, (i-k) RAP or (l-n) heparin or heparinase III 1 h 140 before stimulation with Aβ40 or Aβ42 monomers or TNFa for 3h. Graphs represent means ± SD relative to vehicle treated cells for at least three independent trials. *p<0.05, **p<0.01 versus vehicle. 4.4.10 SR-BI is necessary for HDL to suppress Ab-induced PBMC adhesion SR-BI is the principal HDL receptor on ECs and activates several HDL signalling pathways in addition to mediating selective cholesterol uptake upon HDL docking [645]. We observed that inhibiting SR-BI binding with a specific blocking antibody abolished the ability of HDL to suppress both Ab- and TNFa-mediated PBMC adhesion to hCMEC/D3 (Figure 4.10a-c). Selectivity to SR-BI was confirmed by demonstrating that blocking scavenger receptor CD36 with a specific antibody had no effect on the ability of HDL to suppress either Ab- or TNFa-mediated PBMC adhesion to hCMEC/D3 (Figure 4.10d-f). Intriguingly, inhibiting SR-BI-mediated selective cholesterol uptake with BLT1 did not affect the ability of HDL to suppress either Ab- or TNFa-mediated PBMC adhesion to hCMEC/D3 (Figure 4.10g-i). We further observed that inhibiting SR-BI with a specific blocking antibody abolished the ability of HDL to suppress Ab uptake in hCMEC/D3 (Figure 4.10j). These results demonstrate that HDL requires SR-BI-mediated signalling to inhibit Ab uptake and subsequently suppress PBMC adhesion to hCMEC/D3 in response to Ab or TNFa in a manner independent of lipid uptake. 141 Figure 4.10 HDL suppression of Aβ-induced inflammation requires SR-BI. hCMEC/D3 were pretreated for 1 h with (a-c) SR-BI, (d-f) CD36 blocking antibodies, or (g-i) BLT1 followed by HDL (100 μg/mL total protein) for 2 h. Cells were then stimulated with 0.1 μM monomeric (a,d,g) Aβ40, (b,e,h) Aβ42, or (c,f,i) 1 ng/mL of TNFa for 3 h before evaluating PBMC adherence. (j) hCMEC/D3 were pretreated for 1 h with SR-BI blocking antibody followed by HDL (1 mg/mL) for 2 h before stimulating with 1 mM of fluorescently 142 labelled Aβ40 or Aβ42 monomers. Scale bar represents 10 μm. Graphs represent means ± SD of adhered PBMC relative to vehicle treated cells from at least four independent trials. *p<0.05, **p<0.01, ***p<0.001 * p<0.05, **p<0.01, ***p<0.001 versus vehicle, § p<0.05 versus Ab or TNFa. Having used monotypic static cell cultures to demonstrate that HDL suppresses Ab-induced PBMC adhesion to ECs through a mechanism that requires SR-BI and is independent of ICAM-1 expression, we confirmed that this mechanism also explains the results in our 3D bioengineered vessels. We observed that inhibiting SR-BI with a specific antibody circulated through the vessel lumen abolished the ability of HDL to suppress Ab-mediated monocyte adhesion to the vascular endothelium (Figure 4.11a-b) and that ICAM-1 expression was not affected by Aβ40 or Aβ42 treatment (Figure 4.11c-d). Figure 4.11 HDL suppresses Aβ-induced monocyte adhesion in engineered vessels via SR-BI. SR-BI blocking antibody was circulated through the lumen of engineered vessels prepared using HUVEC for 1 h prior treatment with 200 µg/mL of HDL for 2 h followed by injection of 1 µM of monomeric (a) Ab40 or (b) Ab42 within the tissue chamber (abluminal) for 8 h prior to injecting circulating fluorescent THP1 into the lumen. (c-d) ICAM-1 protein was measured in tissue lysates prepared in RIPA buffer by commercial ELISA. Graphs represent means ± SD of adhered monocytes relative to Ab treated tissues from at least four individual grafts. * and # p<0.05. 4.4.11 Aβ accumulates in the walls of 3D bioengineered vessels In addition to the effect of Aβ on vascular inflammation, we were also interested in using these 3D bioengineered vessels to model CAA, the pathological accumulation of Aβ in the walls of mainly large penetrating arteries of the brain, as up to 90% of AD patients have CAA [5]. To do this we injected recombinant Aβ40 or Aβ42 monomers (1 μM) into the anteluminal chamber of the bioengineered vessels, as was done to evaluate Aβ-induced monocyte adhesion above. Vessels were harvested after 24 h and either homogenized in RIPA buffer of fixed for histology. We observed a dose-dependent increase in Aβ40 and Aβ42 concentrations in the RIPA soluble fraction 143 as measured by ELISA with increasing concentrations of Aβ added (Figure 4.12a-b). Staining for Aβ peptides with 6E10 and amyloid deposits with Thio-S also revealed a dose-dependent increase in Aβ and amyloid deposition in the vessel wall (Figure 4.12c-d). Vascular Aβ concentration and amyloid deposition were also monitored over time. We observed that Aβ40 and Aβ42 accumulate in the vessel wall in as short as 2 h then the levels remain relatively stable up to at least 8 h and 48 h respectively (Figure 4.12g-h). Finally, we measured the clearance of Aβ across the vessel wall and into the circulation by ELISA and observed that Aβ40 and Aβ42 was transported at significantly lower levels through fully formed bioengineered vessels compared to transport across scaffolds alone (Figure 4.12i-j). Figure 4.12 Aβ40 and Aβ42 accumulation within and transport through bipartite vessels. Ab40 and Ab42 monomers (0, 0.1, 1.0 and 10 µM) were injected into the tissue chamber (antelumen) and incubated for 48 h under flow conditions (a-f). Ab deposition within bioengineered vessels was measured using (a-b) ELISA, (c-d) immunostaining with the anti-Ab antibody 6E10 Ab, (e-f) and Thioflavin-S staining. To determine the kinetics of CAA formation, (g) Ab40 and (h) Ab42 monomers (1 µM) were injected into the tissue chamber and incubated for 144 the indicated times before measuring Ab tissue concentrations by ELISA. Ab transport was measured after injecting (i) Ab40 and (j) Ab42 monomers (1 µM) into the anteluminal chamber and sampling media from the circulation (luminal) chamber at the indicated times. Graphs represent mean ± SEM for at least four independent tissues ** p=0.01 and *** p=0.001. Bars represent 50 µm, L: lumen, S: scaffold. 4.4.12 HDL suppresses the accumulation of Aβ in the vessel wall of bioengineered arteries and tends to increase Aβ transport into the circulation It has previously been shown by us and others that HDL can protect against CAA in mouse models of amyloidosis [519,521,526,592]. We therefore used our 3D bioengineered vessel model of CAA to evaluate whether circulating HDL can have any beneficial effects in a human based system. We circulated HDL (200 μg/mL total protein) through the lumen of bioengineered vessels treated with Aβ40 or Aβ42 (1 μM) for 24 h. There was a trend towards increased Aβ40 and Aβ42 cleared to the circulation in HDL treated vessels (Figure 4.13a-b) and a significant, 4-fold reduction in Aβ42 accumulation in the walls of vessels treated with HDL (Figure 4.13d). HDL treatment had no statistically significant effect on Aβ40 concentration in the vessel wall although a non-significant decrease was observed (Figure 4.13c). Figure 4.13 HDL facilitates Aβ transport and reduces accumulation in bioengineered bipartite vessels. Ab40 and Ab42 monomers (1 µM) were injected into the tissue chamber in the absence or presence of 200 µg/ml of circulating HDL. (a-b) Transported and (c-d) accumulated Aβ was evaluated by ELISA. Graphs represent mean ± SEM for at least four independent tissues. *, § and # p=0.05, ## and ** p=0.01 and *** p=0.001. 4.5 Discussion Far from being an inert vascular lining, ECs form a metabolically active and specialized interface between blood and underlying tissues. In response to an inflammatory stimulus, ECs undergo activation, which is classically defined by increased interaction with blood leukocytes. EC 145 dysfunction plays a central role in peripheral vascular disease, stroke, heart disease, type 2 diabetes, insulin resistance, chronic kidney failure, tumour growth, metastasis, venous thrombosis, and severe viral infectious diseases [646]. The richly vascularized human brain contains ECs that form the nearly impenetrable BBB, which regulates the flux of substances in and out of the CNS. The contribution of cerebrovascular dysfunction in dementia and cognitive decline is well acknowledged [647,648], yet pathways that mediate cerebrovascular EC dysfunction and protection are not well understood. Using classical PMBC adhesion assays, we demonstrate that Ab can act as an inflammatory stimulus to increase PBMC adhesion to brain microvascular ECs and that this is suppressed by HDL. Our results show that the ability of HDL to attenuate Ab-induced PBMC adhesion to ECs is independent of Ab structure, NO production, miR-223 and, unexpectedly, of ICAM-1 and VCAM-1 expression. Rather, HDL requires SR-BI to suppress Ab-mediated PBMC adhesion to ECs, which can also be attenuated by inhibiting internalization of Ab by blocking RAGE or LRP1, or by inhibiting Ab-HSPG interactions. We also show that HDL can also prevent Ab fibrillization, suggesting that HDL may also help to maintain Ab solubility as it transits across the BBB. However, as this function of HDL is relatively slow, it is unlikely to account for HDL’s ability to suppress Ab-mediated PBMC adhesion to ECs. Our observations are consistent with a previous study that demonstrated that deficiency of SR-BI increases cerebrovascular amyloid deposition in the J20 mouse model of AD [649]. As ICAM-1 concentration is clearly elevated in AD cortex, and previous studies have reported induced adhesion molecule expression after Ab stimulation [650], we were surprised to observe that Ab had negligible effects on adhesion molecule expression and kinase activation under our experimental conditions, whereas TNFa robustly induces ICAM-1 and VCAM-1 expression as well as NFkB, MAPK/ERK, and SAPK/JNK signalling. The cellular processes triggered by internalized Ab that promote PBMC adhesion remain to be determined. 146 In cultured aortic ECs, HDL reduces adhesion molecule expression and subsequent monocyte binding via a mechanism involving SR-BI signalling through eNOS, which requires the S1P receptor [482]. Our observations that HDL’s ability to suppress Ab- and TNFa-mediated PBMC adhesion to hCMEC/D3 is independent of NO production and S1P activity suggests that brain ECs may have pathways distinct from ECs derived from arterial sources, at least under the culture conditions used here. Reduced eNOS expression in the AD brain has been hypothesized to increase Ab deposition and promote production of reactive oxygen species production, which reduces vasomotor regulation of penetrating arterioles [651,652]. HDL clearly induces NO production in hCMEC/D3 and HUVEC, and although NO production is not required to protect hCMEC/D3 from Aβ- or TNFα-mediated PBMC adhesion per se, HDL-stimulated NO production may provide beneficial effects on other cerebrovascular EC functions. A wealth of studies have explored in vitro associations of Ab with lipoprotein components, primarily focusing on APOE as genetic variants as APOE has established effects on Ab metabolism in AD [653]. As Ab is a hydrophobic peptide, it is not surprising that it associates with lipoprotein particles and also to amphipathic apolipoproteins. Elucidating the structure-function relationships of Ab with lipoproteins remains an active area of research. While Ab might associate with plasma lipoproteins [654], the effect of HDL on Ab structure remains poorly understood. Interestingly, HDL constituents on their own have been reported to both interact with Ab and diminish its toxic effects. Specifically, small liposomes (<50 µm) accelerate Ab40 fibrillization and the amounts of amorphous aggregates become larger as liposomes increase (>50 µm) [655]. In contrast, nanoliposomes (100 nm) prevent Ab42 fibrillization and reduce Ab42-induced EC dysfunction [656]. In addition, lipid-free apoA-I induces Ab aggregation and generation of amorphous complexes, both of which reduce amyloid toxicity [634]. Our results clearly show that mature HDL delays Ab fibrillization in a cell-independent assay with substantially slower kinetics than HDL’s ability to attenuate Ab-induced PBMC adhesion to ECs. As HDL also reduces Ab-induced PBMC adhesion to ECs independent of monomeric vs. oligomeric input structures, we believe that HDL’s ability to delay Ab fibrillization is a distinct function compared to its ability to reduce Ab-mediated inflammation, which relies exclusively to a cellular process involving SR-BI. 147 As traditional static monotypic cell cultures do not reproduce the physiological complexity of the native vascular bed, we also used a 3D model of the human vasculature in our studies. These engineered vessels mimic a native vessel with a luminal monolayer of human ECs surrounded by several layers of human SMCs, through which human mononuclear cells can be circulated under flow conditions. This model has been previously used to study atherosclerosis in peripheral arteries [629]. Here, we injected Ab on the anteluminal side to mimic brain-produced Ab and circulated HDL and monocytes through the lumen. We confirmed that Ab induces monocyte adhesion to ECs under native-like flow conditions in 3D bioengineered human vessels, which can be suppressed by HDL through a mechanism that requires SR-BI and that is independent of changes in ICAM-1 levels. Using this 3D model of the human vasculature we also found the HDL circulating through the lumen of vessels can prevent vascular Αβ deposition in a model of CAA, a condition affecting up to 90% of people with AD that can lead to microbleeds [5]. We have since shown that this HDL function is independent of SR-BI, unlike the anti-inflammatory function of HDL against Aβ described above, and is enhanced in HDL containing apoE compared to apoE-deficient HDL [617]. Our observation of reduced CAA with HDL treatment is in line with several studies using animal models with altered plasma HDL concentrations by genetic methods or peripheral administration of HDL-based therapeutics. Specifically, we and others have found that APP/PS1 mice lacking apoA-I have significantly increased CAA [519,592] while APP/PS1 mice overexpressing apoA-I and AD mice treated intravenously with apoA-I Milano or rHDL have reduced CAA [521,525,526]. Confirmation of the anti-CAA effects of HDL in human-based in vitro studies suggests that HDL-based therapeutics may be a valuable tool in prevent or treating CAA in human subjects. Numerous studies show that HDL has several properties that are associated with improved vascular function [657]. While some lines of evidence also suggest a beneficial role for HDL in protecting from cognitive decline, the nature of this association remains largely unknown. Whereas lipid-free apoA-I can be transported into the CNS [501], there is thus far no evidence that mature HDL might 148 cross the BBB, supporting the view that HDL might act predominately from the blood compartment and would therefore primarily affect ECs. Our data suggest at least four mechanisms by which HDL could have beneficial effects on cerebral vessels. First, HDL prevents Ab-induced PBMC adhesion to brain microvascular ECs through a mechanism that requires SR-BI and suppresses Ab uptake. The cellular pathways by which internalized Ab promotes PBMC adhesion to ECs remains to be defined and appears to be distinct from those induced by classical inflammatory stimuli. Second, similar to its effects in peripheral EC, HDL induces NO secretion in hCMEC/D3. Although we ruled out a role for eNOS in Ab-induced PBMC adhesion, HDL-stimulated NO production may still help to attenuate vasomotor dysfunction as observed in the aging brain [652]. Third, although HDL can protect hCMEC/D3 from PBMC adhesion independent of Ab structure, the ability of HDL to maintain Ab in a soluble state may facilitate efficient Ab clearance out of the brain. Finally, HDL can prevent Aβ accumulation in the vessel wall and facilitate Aβ transport through the vessel wall into the circulation. 4.6 Supplemental Figures Figure 4.14 Aβ induces dose-dependent PBMC adhesion to hCMEC/D3 and HDL attenuates the PBMC adhesion in a dose-dependent manner. In all conditions, hCMEC/D3 were stimulated with 0 to 1 μM Aβ40 (light grey) or Aβ42 (dark grey) monomers for 3 h. Fluorescently labelled PBMC were allowed to adhere to (a) Aβ40- or (b) Aβ42- stimulated cells for 3 h. Cells were washed, fixed, and imaged to count adhered PBMC. hCMEC/D3 were primed with increasing doses (25 to 400 μg/mL 149 total protein) of HDL for 2 h and stimulated with 0.1 μM (c) Aβ40 (light grey) or (d) Aβ42 (dark grey) for 3 h. Fluorescently labelled PBMC were allowed to adhere to stimulated cells for 3 h followed by washing, fixation, imaging, and counting. Graphs represent mean ± SD of adhered PBMC relative to vehicle control from at least three independent trials where * p<0.05, **p<0.01, ***p<0.001 versus vehicle, § p<0.05, §§ p<0.01 versus Ab. Figure 4.15 HDL suppression of Aβ-induced inflammation is independent of eNOS and S1P in HUVEC. (a) L-NAME and (e) VPC23019 potency was tested by measuring intracellular NO production in HUVEC after incubating with HDL (100 μg/mL total protein) and DAF-2-DA (1μM) for 6 h. Fluorescence was measured at 485 nm. (b-d, f-h) In all conditions, HUVEC or hCMEC/D3 were stimulated with 0.1 μM monomeric Aβ40 or Aβ42 or of TNFa (1 ng/mL) for 3 h prior to measuring PBMC adherence. HUVEC were pretreated for 1 h with (b-d) the eNOS inhibitor L-NAME or (f-h) the S1P1 and S1P3 receptor inhibitor VPC23019 followed by HDL (100 μg/mL total protein) for 2 h. (i) hCMEC/D3 were pretreated with 100 µg/mL of HDL for 2 h before simulating with Aβ40 or Aβ42. Total cellular expression of Anx1 was analyzed by immunoblotting and compared to GAPDH. Graphs represent means ± SD from at least three independent trials. * p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 versus vehicle, § p<0.05, §§ p<0.01, §§§p<0.001, §§§§ p<0.0001 versus Ab or TNFa. 150 Figure 4.16 Cortical ICAM-1 expression is increased in AD. Cryopreserved cortex and cerebellum of AD or NCI patients were cut at 20 µm. After PFA fixation sections were washed with PBS and stained against (a) ICAM-I or (b) VCAM-1 with CD31 as a vascular marker and imaged using an inverted fluorescent microscope. Arrow demonstrates colocalization of ICAM-1 and CD31. Bar represents 50 µm. 151 Figure 4.17 HDL treatment does not alter LRP1 or RAGE protein levels in hCMEC/D3. hCMEC/D3 were treated with HDL (0 to 400 µg/mL) for 5 h before lysis in RIPA. (a) LRP1 and (b) RAGE protein levels were quantified by immunoblotting (c). Graphs represent means ± SD relative to vehicle treated cells in three trials. 152 Chapter 5: Development of assays for the evaluation of brain-relevant high-density lipoprotein functions in human blood specimens. 5.1 Summary In the Chapter 4 we showed that high-density lipoproteins (HDL) maintain their vasoprotective functions on brain-derived endothelial (EC) cells and discovered novel vasoprotective functions of HDL relevant for Alzheimer’s disease (AD) using 3-dimensinal (3D) bioengineered arteries. Specifically, HDL isolated from the blood of young healthy human donors by density gradient ultracentrifugation suppressed the accumulation of amyloid beta (Aβ) in the walls of these bioengineered vessels and prevented Aβ-induced vascular inflammation. It has previously been shown that HDL can lose its beneficial anti-inflammatory and cholesterol efflux functions in aging, cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM) and AD thus whether the novel brain-relevant functions of HDL described above are also altered is of interest. We therefore aimed to develop high-throughput assays capable of measuring HDL functions from small volumes of human plasma. We began by comparing assays using HDL isolated by ultracentrifugation to assays using apolipoprotein (apo)B-depleted plasma and found that while apoB-depleted plasma is sufficient to study some brain-relevant HDL functions, it does not suppress Aβ-induced EC activation. We next aimed to further simplify our Aβ vascular accumulation assay by comparing HDL isolated by ultracentrifugation to unfractionated serum. We found that serum more effectively and more consistently reduced Aβ accumulation. Finally, we assessed the feasibility of using this assay to detect differences in HDL functions in clinical studies using plasma from middle-aged adults with T2DM. No differences were detected in Aβ accumulation between vessels treated with T2DM plasma and plasma from age and sex-matched controls. 5.2 Introduction There are several lines of evidence suggesting that HDL can have protective functions on cerebral vessels that may contribute to reduced risk of AD. In Chapter 2, we showed that apoA-I-deficient APP/PS1 mice have increased amyloid burden, CAA burden, neuroinflammation, cerebrovascular inflammation, and cognitive deficits [592]. Although the studies in Chapter 3 did not directly investigate HDL as a therapeutic target, we did find that a drug targeting peripheral lipid 153 metabolism, a non-brain penetrant liver X receptor (LXR) agonist, was able to improve neuroinflammation, cerebrovascular inflammation, and cognitive deficits in APP/PS1 mice. Both of these studies are in line with previous work demonstrating a beneficial function of HDL in AD model mice [519,521,524–527] and beneficial effects of treatment with other LXR agonists [537,540–544,550,551]. We have also translated these finding to a human in vitro system. In Chapter 4 we described several cerebral vasoprotective functions of human HDL relevant to AD using a blood-brain barrier (BBB) cell line (hCMEC/D3) and a novel in vitro 3D perfusible bioengineered cerebral artery model composed of primary human EC and smooth muscle cells (SMC). We reported that HDL from young healthy human donors has at least four vasoprotective functions on brain vessels that are relevant to AD, including reducing Aβ vascular accumulation and increasing transport through the vasculature, inhibiting Aβ-induced EC activation, delaying Aβ fibrillization, and inducing nitric oxide (NO) production [578,658]. Importantly, the beneficial functions of HDL observed in healthy individuals are not always maintained during aging and disease [413,482,485,486]. For example, HDL from people with T2DM and AD lacks anti-inflammatory abilities and does not effectively efflux cholesterol compared to HDL from metabolically healthy and cognitively normal people [382,383,424,426]. Changes to HDL functions in disease are often independent of HDL-C [375,376,378,381,386,387,404,406] therefore alterations in AD-relevant HDL functions may explain the lack of association between HDL-C and AD risk in Mendelian randomization studies [208,209] and some epidemiological studies [659–661]. However, clinical biospecimens are typically available in much smaller volumes than the ~3 ml of plasma or serum required for traditional ultracentrifugation purification of HDL that was used to discover these novel functions. Fortunately, other methods of HDL “isolation” exist that may overcome this barrier, such as the use of polyethylene glycol (PEG) to deplete non-HDL lipoproteins from plasma. The resulting apoB-depleted plasma has been well validated for use in clinical assays of cholesterol efflux capacity, the best-established HDL functional assay [485,486,662]. While apoB-depleted plasma is not “HDL” as many other plasma components remain in the preparation, its isolation process is more cost-effective and higher-throughput than ultracentrifugation and requires much less starting material. Consequently, the benefits and weaknesses of apoB-depleted plasma must be weighed 154 against those of HDL isolated by ultracentrifugation when developing novel assays of HDL function that could be developed for clinical use. The first section of this study was therefore designed to evaluate how the pre-analytic factors of ultracentrifugation and PEG precipitation affect assay performance of HDL cerebrovascular functions relevant to AD, namely increasing Ab vascular clearance, anti-inflammatory activity against Aβ, delay of Aβ fibrillization, and promotion of NO secretion. We then tested whether the Aβ accumulation assay could be further simplified with the use of unfractionated serum and evaluated the inter- and intra- experimental and donor variability of the serum-based assay. Finally, we compared plasma from healthy subjects and subjects with T2DM with our Aβ accumulation assay in 3D bioengineered arteries to assess the feasibility of this assay to detect HDL functional differences on the basis of disease in clinical studies. 5.3 Methods 5.3.1 Isolation and comparison of HDL isolated by ultracentrifugation and apoB-depleted plasma 5.3.1.1 Blood collection All experiments were conducted under an approved protocol (UBC Clinical Ethics Research Board H14-03357). Nonfasting blood was obtained from healthy, normolipidemic male or female volunteers aged 20 to 35 years in K2-EDTA coated plasma vacutainer tubes (BD Biosciences, San Jose, CA) by venipuncture after receipt of informed consent. Plasma was isolated from whole blood by centrifugation at 1500 g for 10 min. Additional plasma was obtained from the Network Centre for Applied Development (netCAD) through the Canadian Blood Services under an approved protocol (Canadian Blood Services REB 2016.015, nc0027). Plasma was either immediately processed to isolate lipoproteins or aliquoted and stored at -20 °C until use. Whole blood for PBMC isolation was collected from healthy volunteers as described above and PBMC were by isolated by centrifugation on a continuous density gradient (Lymphoprep, StemCell, Vancouver, BC) following the manufacturer’s directions. 155 5.3.1.2 HDL and apoB-depleted plasma isolation Except where indicated otherwise, HDL was isolated from plasma by two step sequential potassium bromide (KBr) gradient density ultracentrifugation previously described (section 4.3.3) [578] (Figure 5.1a) and apoB-depleted plasma was produced by polyethylene glycol (PEG) precipitation as previously described [663] (Figure 5.1b). HDL and apoB-depleted plasma were dialyzed in dialysis buffer (150 mM NaCl, 0.3 mM EDTA, pH 7.4) for 2 days at 4°C, with the buffer refreshed four times. Figure 5.1 HDL isolation and depletion of apoB-containing lipoproteins from human plasma. HDL or apoB-depleted plasma was prepared from young healthy donor plasma by (a) sequential density gradient ultracentrifugation (HDL) using KBr, (b) polyethylene glycol (PEG-P) precipitation of non-HDL lipoproteins, (c) a combination of the two methods (PEG-UC), or (d) a single UC spin (UC-P). KBr: potassium bromide, LDL: low-density lipoprotein, VLDL: very low-density lipoprotein, HDL: high-density lipoprotein. Two additional isolation methods were used for a subset of experiments. First, PEG precipitation was used to remove apoB-containing lipoproteins after which the density of the supernatant was adjusted to 1.21 g/mL and subjected to a single ultracentrifugation step to remove other plasma proteins from the HDL fraction. The upper fraction containing HDL was collected and dialyzed as 156 above (Figure 5.1c). Second, a single step ultracentrifugation was performed by adjusting plasma density to 1.063 g/mL with KBr and centrifuging at 160,000 g for 16 h at 14 °C. The upper fraction containing non-HDL lipoproteins was removed and the bottom fraction containing HDL and other plasma proteins was collected and dialyzed as above (Figure 5.1d). 5.3.1.3 Fabrication of 3D bioengineered vessels Procedures were performed as described in section 4.3.1. 5.3.1.4 Amyloid beta clearance, accumulation, and monocyte adhesion assays using engineered vessels Procedures were performed as described in section 4.3.16 and 4.3.5 with HDL (100 μg/mL total protein) isolated by ultracentrifugation or an equivalent amount of apoB-depleted plasma based on cholesterol concentration, both from the same plasma donor. 5.3.1.5 Enzyme linked immunosorbent assay (ELISA) ApoA-I concentrations were quantified by ELISA (Mabtech, Cincinnati, OH, 3710-1HP-2) following the manufacturer’s directions with a 50,000-fold dilution in the supplied dilution buffer. ApoE concentrations were quantified using an in-house sandwich ELISA [664]. ELISA data for apoA-I and apoE were normalized to HDL-C concentrations for HDL and apoB-depleted plasma preparations. 5.3.1.6 Static monotypic cell culture Procedures for culture of hCMEC/D3 and THP1 monocytes were performed as described in section 4.3.6. 5.3.1.7 Intracellular NO production Procedures were performed as described in section 4.3.9 with HDL (50 μg/mL total protein) isolated by ultracentrifugation or an equivalent amount of apoB-depleted plasma based on cholesterol concentration, both from the same plasma donor. 157 5.3.1.8 Preparation of Aβ monomers Procedures were performed as described in section 4.3.4. 5.3.1.9 PBMC adhesion assays Procedures were performed as described in section 4.3.7 with HDL (50 μg/mL total protein) isolated by ultracentrifugation or an equivalent amount of apoB-depleted plasma based on cholesterol concentration, both from the same plasma donor. 5.3.1.10 Cell-free assay of Aβ fibrillization Procedures were performed as described in section 4.3.14 with or without HDL (1 mg/mL total protein) isolated by ultracentrifugation or an equivalent amount of apoB-depleted plasma based on HDL-C concentration, both from the same donor. Formation of fibrillar β-amyloid over time was monitored by excitation at 440 nm and measuring emission intensity at 490 nm every 5 min up to 12 h in total. 5.3.1.11 Characterization of HDL preparations Protein concentration of HDL was quantified using the Pierce bicinchoninic acid (BCA) Protein Assay Kit (ThermoFisher, Waltham, MA) following the manufacturer’s directions. HDL-C, phospholipid, free and esterified cholesterol, and triglycerides were quantified using commercially available kits (Wako Diagnostics) following the manufacturer’s directions. 5.3.1.12 Gel electrophoresis HDL, apoB-depleted plasma, and total plasma were analyzed using native and sodium dodecyl sulphate (SDS) polyacrylamide gel electrophoresis. HDL (10 μg of total protein) and an equivalent amount of apoB-depleted plasma based on HDL-C concentration, both from the same plasma donor, were separated by electrophoresis, using 6% acrylamide for native and 10% for denaturing, respectively. To visualize all proteins, gels were washed in water, stained with Coomassie R-250 (0.1% Coomassie R-250, 40% methanol, 10% acetic acid) overnight, de-stained in a solution of 10% methanol and 7.5% acetic acid in water for at least 2 h, and imaged using a ChemiDoc MP imager (BioRad). 158 5.3.2 Comparison of HDL isolated by ultracentrifugation and serum in 3D bioengineered artery functional assays Blood was collected as in section 5.3.1.1 from five young healthy donors and HDL isolated by density gradient ultracentrifugation as in section 4.3.3. Aβ accumulation and monocyte adhesion assays were performed in 3D bioengineered vessels as in sections 4.3.16 and 4.3.5 with 100, 200, or 400 μg/mL HDL (total protein concentration) and 10, 20, or 40% serum diluted in cell culture media. 5.3.3 Evaluation of variability in Aβ accumulation assay in 3D bioengineered arteries with serum from healthy volunteers Blood was collected as in section 5.3.1.1 from four young healthy donors. Aβ accumulation and monocyte adhesion assays were performed in 3D bioengineered vessels as in sections 4.3.16 and 4.3.5 with 25% serum diluted in cell culture media. 5.3.4 Proof-of-concept assays of plasma from T2DM subjects 5.3.4.1 Plasma collection Human EDTA plasma was obtained from the clinical laboratory at St. Paul’s Hospital using the following inclusion criteria: • Specimens with a request for glycated hemoglobin measurement (HbA1c%), as part of routine clinical care; • Specimens with sufficient plasma volume (at least 1 mL) remaining after all physician ordered tests have been performed. • Specimens with HbA1c >7% (for inclusion in the T2DM group) and HbA1c <6% (for inclusion in the control group). EDTA whole blood plasma specimens were collected via standard collection procedures for HbA1c, as part of routine care, and stored at 4°C prior to inclusion in this study. For inclusion in the study, specimens were deidentified by researchers in the clinical laboratory, centrifuged and the plasma decanted. Coded plasma specimens were frozen at -80°C until use in the described assays. 159 5.3.4.2 Plasma defibrination Plasma was defibrinated overnight to remove clotting factors. One μU/mL thrombin and 1:100 CaCl2 (20% w/v in distilled water) were added to the plasma then the plasma was rotated overnight at 4°C. The resulting clot was precipitated by centrifugation at 14,000 rpm for 10 min at 4°C. 5.3.4.3 Aβ accumulation in 3D bioengineered arteries Aβ accumulation assays were performed in 3D bioengineered vessels as in section 4.3.16 with 20% defibrinated plasma diluted in media. 5.3.5 Statistical analysis Statistical analyses were performed with GraphPad Prism 7 software and p<0.05 was considered significant. For the comparison of HDL and apoB-depleted plasma, data were obtained from at least three independent experiments with three independent donors. Raw data, or log-transformed data in the case of the NO production assay due to non-normality, were analyzed by one-way analysis of variation (ANOVA) with Tukey’s multiple comparison test or two-way ANOVA in the case of Aβ42 recovery into the circulation of engineered vessels. Analysis of Aβ fibrillization in the cell free thioflavin T assay was performed after fitting to a Boltzman sigmoidal curve using GraphPad Prism 7 software then extracting t50 and curve maximum from the model. For the comparison of HDL and serum, assessment of the assay variation, and assessment of Aβ accumulation in the proof-of-concept study, data were analyzed either by one-way ANOVA or by two-way ANOVA considering only the effect of treatment as a factor due to variation across experiments, in both cases followed by Tukey’s or Dunnett’s multiple comparisons test. Results from analysis by two-way ANOVA are displayed on graphs where the raw data was normalized to percent fold change from vehicle control. Data are represented as scatter plots overlaid with calculated means ±SD using error bars. Spearman correlations were used to assess the relationship between vascular Aβ and age or %HbA1c in the proof-of-concept study. 160 5.4 Results 5.4.1 ApoB-depleted plasma recapitulated the ability of HDL isolated by ultracentrifugation to reduce Ab accumulation in the walls of bioengineered vessels. CAA is the pathological accumulation of Aβ in the smooth muscle layers of large arteries and arterioles within the brain and is observed in 75-98% of AD cases [665]. As discussed in Chapter 4, we developed an in vitro, 3D model of CAA using primary human EC and SMC cells cultured under native-like flow conditions [578,658], and showed that when HDL isolated by ultracentrifugation from the blood of young healthy donors is circulated through the lumen of the arteries, the accumulation of Aβ42 in the arterial wall is prevented [658]. In the present study, we used this 3D human CAA model to assess whether apoB-depleted plasma produced by PEG precipitation reproduces the protective effects of purified HDL related to Aβ42 deposition and transport. After normalizing to cholesterol concentrations, HDL and apoB-depleted plasma were circulated at 10.5 mg/dL HDL-C through bioengineered arteries. This dose mirrors that previously used [578,658] and corresponds to 20% and 25% of normal plasma HDL-C concentrations for healthy adult females and males respectively [666]. We observed that HDL isolated by ultracentrifugation and apoB-depleted plasma were equally effective in reducing soluble Aβ42 concentration in vascular tissue after 24 h; from 61.17 ng/mg in vehicle control treated tissues to 17.49 ng/mg and 27.69 ng/mg for HDL and apoB-depleted plasma treated tissues, respectively (p=0.009 for vehicle vs. HDL, p=0.036 for vehicle vs. apoB-depleted plasma by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.2a), albeit with higher variability observed with apoB-depleted plasma. We also measured Aβ42 transported through the vessel wall into the circulation over the first 4 h of treatment and at 24 h. Over the first 4 h (Figure 5.2b) and after 24 h (Figure 5.2c) HDL, but not apoB-depleted plasma, significantly promoted Aβ42 transport into the circulation compared to Aβ42 alone (p=0.020 for 4 h, p=0.005 for 24 h for vehicle vs. HDL by two-way ANOVA and Sidak’s multiple comparisons test and one-way ANOVA and Tukey’s multiple comparisons test, respectively). Aβ42 transport was also significantly different between tissues treated with HDL and those treated with apoB-depleted plasma over the first 4 h and after 24 h (p=0.0006 for 4 h, p=0.010 for 24 h for HDL vs. apoB-depleted plasma by two-way ANOVA and Sidak’s multiple comparisons test and one-way ANOVA and Tukey’s multiple comparisons test, respectively). Importantly, Aβ42 concentrations in ultracentrifuge-isolated HDL and apoB- 161 depleted plasma measured by ELISA were below the analytical sensitivity provided by the manufacturer (<10 pg/mL), therefore, the differences in Aβ42 measured in the circulation media cannot be attributed to Aβ42 bound to HDL. However, it is possible that non-HDL plasma components, such as albumin or immunoglobulins, or residual PEG solution present in the apoB-depleted plasma could mask the Aβ42 epitope used by the ELISA. Therefore, the lack of observed effect of apoB-depleted plasma on Aβ42 transport through the vessel wall may be due to a technical limitation. Figure 5.2 Aβ accumulation in bioengineered vessels treated with HDL isolated by ultracentrifugation or apoB-depleted plasma. 3D bioengineered human vessels were subjected to abluminal Aβ42 treatment with or without luminal HDL or apoB-depleted plasma treatment. Aβ42 concentrations were measured in (a) soluble tissue homogenates after 24 h and in circulating media (b) over the first 4 h and (c) after 24 h by ELISA. Scatter plots represent independent experiments with mean ± standard deviation. *p<0.05, **p<0.01 by one-way ANOVA with Tukey’s multiple comparisons test. Aβ42: amyloid beta 42, UC-HDL: HDL isolation by sequential density gradient ultracentrifugation, PEG-P: apoB-depleted plasma by polyethylene glycol precipitation. 5.4.2 ApoB-depleted plasma does not retain the ability of HDL to reduce Aβ-induced monocyte binding to the endothelium AD is associated with cerebrovascular inflammation and Aβ has been reported by several independent groups to activate ECs [578,650,651,656]. In Chapter 4, we demonstrated that HDL isolated by ultracentrifugation from the plasma of young healthy donors reduced Aβ-induced monocyte binding to ECs [578]. To asses this anti-inflammatory function of HDL in apoB-depleted plasma, we performed monocyte-binding assays where HDL or apoB-depleted plasma were circulated for 21 h through 3D bioengineered arteries that were at the same time treated abluminally with Aβ42 after which fluorescently labelled monocytes were added to the circulation for 3 h. Quantification of adhered monocytes to the vessel lumen demonstrated that circulating ultracentrifugation-isolated HDL reduced endothelial activation from a mean of 16.57 adhered 162 monocytes to 5.68 cells but that circulation of apoB-depleted plasma did not significantly affect monocyte adhesion with a mean of 12.64 adhered cells observed (p=0.0107 for vehicle vs. HDL, p=0.435 for vehicle vs. apoB-depleted plasma, p=0.101 for HDL vs. apoB-depleted plasma by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.3a). Figure 5.3 Monocyte binding to EC treated with HDL isolated by ultracentrifugation or apoB-depleted plasma. 3D bioengineered human vessels were treated with abluminal Aβ42 (1 μM) and luminal HDL isolated by ultracentrifugation (200 μg/mL total protein, 10.5 mg/dL HDL-C) or apoB-depleted plasma (10.5 mg/dL HDL-C) for 21 h before fluorescently labelled THP-1 monocytes were added to the circulating media for 3 h. (a) The monocytes adhered to the lumen of the bioengineered vessels were visualized with fluorescent microscopy and counted. Brian-derived ECs (hCMEC/D3) in monoculture were pretreated with HDL isolated by ultracentrifugation (100 μg/mL total protein, 5 mg/dL HDL-C) or apoB-depleted plasma (5 mg/dL HDL-C) for 2 h then stimulated for 3 h with (b) Aβ42 (0.1 μM) or (c) TNFα (1 ng/mL) before the addition of fluorescently labelled PBMC for 3 h. The PBMC adhered to the ECs were visualized with fluorescent microscopy and counted. Scatter plots represent independent experiments with mean ± standard deviation. * p < 0.05, ** p < 0.01, or exact p-value by one-way ANOVA with Tukey’s multiple comparisons test. UC-HDL: high-density lipoproteins isolated by sequential density gradient ultracentrifugation; PEG-P: apoB-depleted plasma by polyethylene glycol precipitation; TNFα: tumour necrosis factor alpha; PBMC: peripheral blood mononuclear cell; Aβ42: amyloid beta 42. In Chapter 4, we reported that HDL suppresses Aβ-induced PBMC binding to brain-derived EC in a mechanism distinct from tumour necrosis factor alpha (TNFα)-induced PBMC adhesion [578]. To further investigate whether the lack of response observed with apoB-depleted plasma is unique to Aβ or extends to all inflammatory stimuli, we measured PBMC adhesion to the well-characterized BBB EC model hCMEC/D3 cultured in regular tissue culture plates. First, we confirmed that apoB depleted plasma did not suppress Aβ42-induced endothelial activation in this 2D model. We observed that pretreatment of hCMEC/D3 for 2 h with HDL isolated by ultracentrifugation reduced the mean number of adhered PBMC to Aβ42 stimulated hCMEC/D3 from 8.6 to 3.04 PBMC while pretreatment with apoB-depleted plasma did not significantly change PBMC adhesion (p=0.037 for vehicle vs. Αβ42 alone, p=0.006 for Aβ42 alone vs. Aβ42 with HDL, p=0.794 for Aβ42 alone vs. Aβ42 with apoB-depleted plasma, p=0.050 for Aβ42 with HDL vs. Aβ42 with apoB-depleted plasma by one-way ANOVA and Tukey’s multiple 163 comparisons test) (Figure 5.3b). On the other hand, both preparations significantly suppressed PBMC adhesion to hCMEC/D3 stimulated with the classical inflammatory stimulus TNFα from a mean of 14.39 to 6.65 and 7.40 adhered PBMC for HDL and apoB-depleted plasma, respectively (p=0.001 for vehicle vs. TNFα, p=0.007 for TNFα alone vs. TNFα with HDL, and p=0.017 for TNFα alone vs. TNFα with apoB-depleted plasma by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.3c). 5.4.3 ApoB-depleted plasma is functionally equivalent to HDL with respect to delaying Aβ42 fibrillization In Chapter 4, we showed that HDL isolated by ultracentrifugation from healthy human donors can delay Aβ42 fibrillization in a cell-free Thioflavin T assay [578]. We therefore used this assay to compare HDL isolated by ultracentrifugation against apoB-depleted plasma produced by PEG precipitation and found that both preparations delayed the onset of Aβ42 fibrillization to an equivalent extent (Figure 5.4a). Fluorescence curves were fitted to a Boltzman sigmoidal curve to determine the time to half-maximal fluorescence (t50) and maximal fluorescence. The t50 was significantly delayed with the addition of either HDL or apoB-depleted plasma from 3.85 h to 9.22 h and 7.84 h, respectively (p=0.001 for vehicle vs. HDL, p=0.004 for vehicle vs. apoB-depleted plasma by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.4b). However, we note that the curve fitting approach used here may require future optimization due to the distinct shape of the apoB-depleted plasma curve. Although the data for apoB-depleted plasma with Aβ42 fit the Boltzman sigmoidal curve well (R2 = 0.94), the sharp increase in fibrillization observed with vehicle and HDL is not evident with apoB-depleted plasma, and the maximal fluorescence observed in conditions with HDL isolated by ultracentrifugation is significantly higher compared to Aβ42 alone and apoB-depleted plasma (50895 nm vs. 30090 nm and 29324 nm respectively, p=0.044 for vehicle vs. HDL, p=0.038 for HDL vs. apoB-depleted plasma by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.4c). 164 Figure 5.4 Anti-fibrillary effects of HDL isolated by ultracentrifugation and apoB-depleted plasma. (a) The formation of β-amyloid pleated sheets from Aβ42 monomers (10 μM) was measured in a cell-free assay using thioflavin T over 12 h with and without HDL isolated by ultracentrifugation (1 mg/ml protein, 50 mg/dL HDL-C) or apoB-depleted plasma (50 mg/dL HDL-C). Data were fit to a Boltzman curve to determine the (b) time to half-maximal fluorescence and (c) maximal fluorescence. Scatter plots represent independent experiments with mean ± standard deviation. Time course data is represented with a point for mean ± standard deviation. * p < 0.05, ** p < 0.01, *** p < 0.001, or exact p-value by one-way ANOVA with Tukey’s multiple comparisons test. RFU: relative fluorescence units; UC-HDL: high-density lipoproteins isolated by sequential density gradient ultracentrifugation; PEG-P: apoB-depleted plasma by polyethylene glycol precipitation. 5.4.4 ApoB-depleted plasma does not induce NO in human brain-derived ECs A key vasoprotective function of HDL is the ability to induce NO production in ECs [482]. In Chapter 4, we showed that HDL isolated by ultracentrifugation maintains this function in brain-microvascular ECs and that the mechanism by which HDL induces NO production in these cells is distinct from other anti-inflammatory mechanisms [578]. Here we compared the ability of HDL isolated by ultracentrifugation and apoB-depleted plasma to induce NO production in hCMEC/D3. Treatment with HDL for 4 h increased intracellular NO levels from a log fluorescence intensity of 2.14 nm to 2.57 nm (p=0.028 for vehicle vs. HDL by one-way ANOVA and Tukey’s multiple comparisons test), however, the intracellular levels of NO in cells treated with apoB-depleted plasma were unchanged (p=0.994 for vehicle vs. apoB-depleted plasma, p=0.027 for HDL vs. apoB-depleted plasma by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.5). 165 Figure 5.5 NO production in human brain-derived ECs treated with HDL isolated by ultracentrifugation or apoB-depleted plasma. Intracellular NO production in hCMEC/D3 was measured using the fluorescent indicator DAF-2-DA after 4 h of HDL treatment (100 μg/mL total protein, 5 mg/dL HDL-C) or apoB-depleted plasma treatment (5 mg/dL HDL-C). Scatter plots represent independent experiments with mean ± standard deviation. * p < 0.05 by one-way ANOVA with Tukey’s multiple comparisons test. RFU: relative fluorescence units; UC-HDL: high-density lipoproteins isolated by sequential density gradient ultracentrifugation; PEG-P: apoB-depleted plasma by polyethylene glycol precipitation; DAF-2 DA: 4,5-diaminofluorescein diacetate. 5.4.5 Non-HDL plasma components in apoB-depleted plasma interfere with some anti-inflammatory activities of purified HDL Two hypotheses were tested to explain why apoB-depleted plasma fails to suppress Aβ-induced monocyte binding and induce endothelial NO production. First, residual PEG polymer can remain in apoB-depleted plasma, even after extensive dialysis, and may interfere with specific HDL functions due a reduction in the hydration shell surrounding the HDL particles [663]. Second, apoB-depleted plasma contains abundant non-HDL plasma proteins that may interfere with certain HDL functions. Two additional plasma processing protocols were used to test these hypotheses. First, we tested whether PEG interferes with HDL functions by first using PEG precipitation to deplete apoB-containing lipoproteins followed by ultracentrifugation to remove non-HDL plasma proteins, thereby producing pure HDL that has been exposed to PEG polymers (Figure 5.1c). To test whether non-HDL plasma components interfere with specific HDL activities we used a single ultracentrifugation step to produce an isolate with a similar composition to PEG precipitated plasma but with no exposure to PEG (Figure 5.1d). HDL isolated by ultracentrifugation and HDL isolated by the combination of PEG precipitation and ultracentrifugation suppressed Aβ-induced PBMC adhesion from 5.48 average cell counts to 1.93 and 2.89 average cell counts respectively (p=0.003 for vehicle vs. HDL from ultracentrifugation, p=0.037 for vehicle vs. HDL from combination method vs. Aβ alone by one- 166 way ANOVA and Tukey’s multiple comparisons test), whereas neither preparation of apoB-depleted plasma had any significant effect on PBMC adhesion (p=0.996 for vehicle vs. apoB-depleted plasma from PEG precipitation, p=0.238 for vehicle vs. apoB-depleted plasma from ultracentrifugation by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.6a). Similarly, HDL isolated by ultracentrifugation and by the combination method increased NO-production from a log fluorescence intensity of 3.64 nm to 4.92 nm and 4.15 nm respectively (p=0.047 for vehicle vs. HDL from ultracentrifugation, p=0.006 for vehicle vs. HDL from combination method vs. vehicle by one-way ANOVA and Tukey’s multiple comparisons test) while neither preparation of apoB-depleted plasma significantly altered NO production (p>0.999 for vehicle vs. apoB-depleted plasma from PEG precipitation, p=0.658 for vehicle vs. apoB-depleted plasma from ultracentrifugation vs. vehicle by one-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.6b). These observations support the hypothesis that non-HDL plasma components present in apoB-depleted plasma, rather than exposure of HDL particles to PEG, are responsible for the functional differences in the brain-relevant HDL activities evaluated in this study. Figure 5.6 Effect of non-HDL plasma components and PEG on PBMC adhesion and NO production assays. (a) Adhesion of fluorescently labelled PBMC to hCMEC/D3 was measured using fluorescent microscopy on cells that were pretreated with HDL isolated by ultracentrifugation (UC-HDL, 100 μg/mL total protein, 5 mg/dL HDL-C), apoB-depleted plasma isolated by PEG precipitation (PEG-P, 5 mg/dL HDL-C), apoB-depleted plasma isolated by PEG precipitation followed by ultracentrifugation (PEG-UC, 5 mg/dL HDL-C) or apoB-depleted plasma isolated by a single ultracentrifugation step (UC-P, 5 mg/dL HDL-C) for 2 h then stimulated with Aβ42 (0.1 μM) for 3 h, (b) NO production in unstimulated hCMEC/D3 was measured using the DAF-2-DA and treatment with UC-HDL, PEG-P, PEG-UC, or UC-P treatment (5 mg/dL HDL-C) for 4 h. Scatter plots represent independent experiments with mean ± standard deviation. * p < 0.05, ** p < 0.01, *** p < 0.001, or exact p-value by one-way ANOVA with Tukey’s multiple comparisons test. RFU: relative fluorescence units; PBMC: peripheral blood mononuclear cell; Aβ42: amyloid beta 42; DAF-2 DA: 4,5-diaminofluorescein diacetate. 167 5.4.6 Human serum recapitulates the ability of HDL to suppress Aβ accumulation in 3D bioengineered arteries but not Aβ-induced monocyte adhesion We next aimed to further simplify our functional assays in 3D bioengineered arteries through the use of unfractionated plasma or serum rather than HDL or apoB-depleted plasma. As plasma was found to often clot when circulated through the bioengineered vessels during pilot tests whereas circulating serum did not (Figure 5.7), serum was the matrix of choice for comparison to HDL isolated by ultracentrifugation. For the comparison of serum and HDL, blood was collected from five young healthy donors (Table 5.1) and HDL was isolated by density gradient ultracentrifugation from serum. Figure 5.7 Clots on 3D bioengineered vessels treated with plasma and serum. 3D bioengineered vessels were treated for 24 h with circulating (a) 25% human plasma or (b) 25% human serum diluted in media. Vessels were removed from the bioreactor after 24 h and photographed. 168 Table 5.1 Donor demographics, HDL-C concentration, and apolipoprotein concentrations in serum and isolated HDL. Donor Age (y) Sex Serum HDL-C (mg/dL) Serum apoE (mg/dL) Serum apoA-I (mg/mL) HDL-C in isolated HDL (mg/dL) ApoE in isolated HDL (mg/dL) ApoA-I in isolated HDL (mg/mL) 1 26 F 63 8.7 2.6 641 8.4 15.5 2 35 M 29 9.7 1.5 331 6.5 11.3 3 27 M 39 10.4 2.3 221 5.0 12.8 4 22 F 50 12.7 1.6 336 12.0 10.2 5 35 F 48 16.1 1.6 N/A N/A N/A mean 31 -- 45 10.4 1.2 382 8.0 12.4 N/A: not available. 169 Recombinant Aβ42 (1μM) was injected into the tissue chamber of 3D bioengineered arteries immediately after HDL (100, 200, or 400 μg/mL total protein) or serum (10, 20, or 40%) was added to the circulating media. After 21 h, fluorescently labelled THP1 monocytes were also added to the circulating media. Tissues were harvested for measurement of Aβ42 in the vessel wall and THP1 monocyte adhesion to the luminal wall 3 h later. Inter-experimental variation was relatively high in these experiments, likely due to batch-to-batch differences in the vessels and in Aβ reconstitution (raw data is available in Figure 5.14). Therefore, data is presented both fold change from vehicle control and statistics were computed on raw data by two-way ANOVA considering only treatment as a factor as was done in Chapter 4. All treatments except 400 μg/mL HDL significantly or nearly significantly reduced Aβ accumulation compared to vehicle control (p=0.051 for vehicle vs. 100 μg/mL HDL, p=0.059 for vehicle vs. 200 μg/mL HDL, p=0.013 for vehicle vs. 10% serum, p=0.0001 for vehicle vs. 20% serum, p=0.0001 for vehicle vs. 40% serum by two-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.8a). Interestingly, unfractionated serum suppressed Aβ accumulation to a greater extent than HDL at several concentrations. Figure 5.8 Aβ accumulation in 3D bioengineered vessels treated with HDL and serum from young healthy donors. HDL isolated by density gradient ultracentrifugation and unfractionated serum from young healthy donors were compared for their ability to suppress (a) the accumulation of recombinant Aβ42 in the walls of 3D bioengineered arteries and (b) monocyte adhesion in response to Aβ42 treatment. Scatter plots represent individual vessels where each donor is represented by a different shape. Bars represent mean and error bars represent ± standard deviation. *p<0.05, ***p<0.001, or exact p-values for comparisons to vehicle control and #p<0.05 for comparisons between HDL vs. serum treated vessels by two-way ANOVA on raw data followed by Tukey’s multiple comparisons test with blocking of the experiment factor test. HDL: high-density lipoprotein, Aβ42: amyloid beta, FC: fold change. In contrast to the effects on Aβ accumulation, only HDL was able to suppress Aβ-induced monocyte adhesion. Circulating 100, 200, and 400 μg/mL HDL all reduced monocyte adhesion 170 compared to vehicle control (p=0.014 for vehicle vs. 100 μg/mL HDL, p=0.0008 for vehicle vs. 200 μg/mL HDL, p=0.0007 for vehicle vs. 400 μg/mL HDL by two-way ANOVA and Tukey’s multiple comparisons test) (Figure 5.8) while unfractionated serum had no effect. That unfractionated serum did not suppress Aβ-induced monocyte adhesion to 3D bioengineered vessels was not surprising as previous experiments had shown that apoB-depleted plasma was not functional in this assay most likely due to interference by abundant non-HDL proteins present in plasma (Figure 5.6). 5.4.7 Aβ accumulation assays in 3D bioengineered arteries with human serum have sufficiently low variation to allow for the analysis of clinical biospecimens We next evaluated the inter- and intra-donor and experimental variability of the serum based Aβ accumulation assay in order to determine whether it would be feasible for the assessment of clinical specimens. Serum from four young healthy donors (Table 5.2) was collected and circulated at 25% in media through the lumen of two to three vessels immediately before Aβ42 (1 μM) was injected into the anteluminal chamber. Table 5.2 Donor demographics for the analysis of assay variation using 25% serum in 3D bioengineered vessels. Donor Age (y) Sex Serum HDL-C (mg/dL) Serum apoE (mg/dL) Serum apoA-I (mg/mL) 1 26 F 47 10.5 4.7 2 22 F 47 11.1 2.7 3 35 M 29 16.1 1.5 4 28 M 38 13.8 3.5 Mean 28 N/A 161 12.9 3.1 N/A: not applicable. Vessels were harvested 24 h later and soluble Aβ in the vessel wall was measured by ELISA . This experiment was repeated three separate times with independently prepared vessels over three consecutive weeks. Reductions in Aβ accumulation by serum was relatively similar within and between donors but not across experimental replicates (Figure 5.9). In experiment 1 and 3, serum from each donor significantly reduced Aβ accumulation (p=0.0031, p=0.0003, p=0.0003, p=0.0004 for vehicle vs. donor 1, 2, 3, and 4, respectively, in experiment 1 and p<0.0001, p=0.0003, p<0.0001, p=0.0002 for vehicle vs. donor 1, 2, 3, and 4, respectively, in experiment 3 all by one-way ANOVA and Dunnett’s multiple comparisons test) (Figure 5.9a,c) whereas in 171 experiment 2 there were only non-significant reductions (p=0.0667, p=0.2299, p=0.0837, p=0.1276 for vehicle vs. donor 1, 2, 3, and 4, respectively, by one-way ANOVA and Dunnett’s multiple comparisons test) (Figure 5.9b). Figure 5.9 Intra- and inter-donor and experimental variability of the Aβ accumulation assay in 3D bioengineered vessels with unfractionated serum. Aβ42 accumulated in the walls of 3D bioengineered arteries after 24 h of treatment with Aβ42 in the abluminal chamber and 25% human serum in the circulating media in (a) experiment 1, (b) experiment 2, (c) experiment 3, and (d) all experiments pooled together. Points represent individual vessels in (a), (b), and (c) and represent the mean of the vessels from the same donor and same experiment in (d). Donors are represented by different shapes while experimental replicates are represented by different colours (circle=donor 1, square=donor 2, triangle=donor 3, diamond=donor 4, open shape=experiment 1, grey shape=experiment 2, black shape=experiment 3). Bars represent mean and error bars represent ± standard deviation. p<0.05, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 or exact p-values for comparisons to vehicle control by one-way ANOVA followed by Dunnett’s multiple comparisons test in (a-c) and by (d) two-way ANOVA followed by Dunnett’s multiple comparisons test. HDL: high-density lipoprotein, Aβ42: amyloid beta, FC: fold change. Sizeable variation between experimental replicates was again observed (raw data in Figure 5.15) therefore pooled raw data were statistically analyzed by two-way ANOVA considering only the treatment as a factor and displayed as fold change from vehicle control. Serum from all donors significantly reduced Aβ accumulation to levels 32% to 44% of vehicle control (p=0.0210, p=0.0409, p=0.0090, p=0.0371 for vehicle vs. donor 1, 2, 3, and 4, respectively, by two-way ANOVA and Dunnett’s multiple comparisons test) (Figure 5.9d). Coefficients of variation for the suppression of Aβ accumulation by serum expressed as fold change from vehicle control are displayed in Table 5.3 and confirm that experimental variation is high and assay variation is lower. Power calculations based on the fold change from vehicle control data suggest that n=20 subjects per group would be required to detect a 28% effect size between groups with 90% power (Figure 5.10). Table 5.3 Coefficients of variation for Aβ accumulation assay in 3D bioengineered vessels with 25% human serum. Experimental variation Donor variation %CV (calculated from fold change data) Intra Intra 21 172 Intra Inter 22 Inter Intra 28 Inter Inter 27 CV: coefficient of variation, intra: variation within the experimental set or single donor, inter: variation between experimental sets or donors. Figure 5.10 Power calculation for comparison of subject groups for the ability of 25% serum to reduce Aβ accumulation in 3D bioengineered vessels. The number of subjects required to detect various effects sizes between groups with 80% and 90% power. n: sample size. 5.4.8 Plasma from individuals with T2DM does not differ from those without T2DM in its ability to suppress Aβ accumulation in 3D bioengineered arteries Having established that unfractionated serum from healthy human donors can suppress Aβ accumulation in the walls of 3D bioengineered arteries in a manner similar to HDL isolated by ultracentrifugation with moderate consistency, we next performed a proof-of-concept study to assess the utility of this assay in measuring differences in anti-CAA functions in clinical populations. T2DM is a major risk factor for AD [183] and HDL from people with T2DM is known to have impaired anti-inflammatory, anti-oxidant, and cholesterol efflux capacity [384,424,426,667]. We therefore chose to use a cohort of people with and without T2DM for this proof-of-concept study (Table 5.4). A cohort of 25 subjects per group was established as n=20 was suggested by power calculations to provide 90% power to detect a 28% effect size between groups. Plasma specimens were first defibrinated overnight with thrombin and CaCl2 to limit the formation of clots during the assay. Defibrinated plasma was then circulated through the lumen of the bioengineered vessels at 20% concentration in media immediately before 1 μM monomeric Aβ42 was added to the tissue chamber. Unfortunately, the 3D bioengineered vessels failed for eight control and eight T2DM plasma specimens during the course of the experiments, mainly due 173 to spontaneous vessel chamber opening likely due to clot formation, resulting in a final sample size of n=17 per group. Table 5.4 Characteristics of patient cohort. Control T2DM p-value N 25 25 N/A HbA1c (%, mean ± SD) 5.3 ± 0.3 8.3 ± 1.3 <0.0001* Age (y, mean ± SD) 55.7 ± 3.2 55.6 ± 3.1 0.9297# Sex (% female) 40 46 0.4752§ Failed during experiment† (n) 8 8 >0.9999§ N/A: not applicable * by Mann-Whitney test, # by unpaired t-test, § by Fisher’s exact test. †Failure due to spontaneous opening of tissue chamber (n=6 control, n=7 T2DM), leakage from tissue chamber (n=1 T2DM), or clotting of circulating media preventing flow through the bioreactor (n=2 control). Measurement of vascular Aβ42 accumulation after 24 h revealed no significant differences between vessels treated with 20% plasma from individuals with T2DM and plasma from age and sex-matched controls. Plasma from both T2DM subjects and age and sex-matched controls failed to reduce vascular Aβ accumulation overall. Specifically, accumulated Aβ42 levels were 128% and 122% of vehicle control treated vessels for control and T2DM groups, respectively (p=0.9893 for vehicle vs. age and sex-matched control, p=0.7017 for vehicle vs. T2DM by two-way ANOVA and Dunnett’s multiple comparisons test) (Figure 5.11a). Select vessels in each experimental batch were treated with 20% serum from a young healthy donor as a positive control (age 26, female). Notably, serum from this young healthy donor caused only a non-significant reduction in vascular Aβ42 accumulation to 75% of that in vehicle control treated vessels (p=0.8566 for vehicle vs. young health control by two-way ANOVA and Dunnett’s multiple comparisons test). In contrast, previously described experiments with 20% serum from four young healthy donors resulted in a significant reduction to levels only 29% of that in vehicle control treated vessels (Figure 5.8a) and experiments with 25% serum resulted in reductions to 32% to 44% of vehicle control across five young healthy donors (Figure 5.9d). 174 Figure 5.11 Aβ accumulation in 3D bioengineered vessels treated with plasma from individuals with T2DM and age and sex-matched controls Plasma from T2DM patients, plasma from age and sex-matched controls, and serum from a young healthy control was circulated through 3D bioengineered vessels at a concentration of 20% in media immediately before 1 μM Aβ42 monomers were injected into the tissue chamber. Vessels were harvested 24 h later in RIPA buffer. (a) Soluble Aβ42 was measured in treated vessels and levels were compared to vehicle control treated vessels. (b) Aβ accumulation in T2DM and control plasma treated vessels was analyzed in female and male plasma treated vessels independently. (c) Direct analysis of vascular Aβ accumulation in vessels treated with age and sex-matched pairs of plasma. Correlation between vascular Aβ accumulation and (d) age or (e) %HbA1c. Points represent individual vessels treated with plasma or serum. Bars represent mean. Statistics by Spearman correlation in (e). Further analysis revealed no significant differences in vascular Aβ accumulation between groups when subdivided based on sex (p=0.3455 for sex effect, p=0.7409 for T2DM effect, p=0.7795 for interaction effect by two-way ANOVA omnibus analysis) (Figure 5.11b) or between pairs of age and sex-matched plasma-treated vessels (p=0.3817 for T2DM vs. age and sex-matched control by paired t test) (Figure 5.11c). No significant correlations were observed between levels of vascular Aβ accumulation and age in control or T2DM subjects (Figure 5.11d). A significant negative correlation was found between vascular Aβ levels and %HbA1c in the control group (r=-0.6916, p=0.0027 by Spearman’s correlation) although no significant correlation was observed in the T2DM group (r=-0.2789, p=0.2763 by Spearman’s correlation) (Figure 5.11e). The significant correlation between vascular Aβ and %HbA1c in the control group persisted in female subjects when the group was subdivided by sex (r=-0.9856, p=0.0056 by Spearman’s correlation). 175 5.5 Discussion It is well-known that HDL composition varies according to the method by which this heterogeneous lipoprotein subclass is isolated from plasma or serum [663,668,669]. It is also increasingly clear that HDL functions, for example cholesterol efflux capacity, are often a better predictor of coronary artery disease (CAD) risk than plasma HDL-C concentration [485,486,662]. Although Mendelian randomization studies showed that high circulating HDL-C concentration does not causally contribute to reduced CAD risk [353], HDL may still have cerebrovascular functions that lower AD risk. Aging, T2DM, hypertension, and hypercholesterolemia are established AD risk factors and all of these conditions have been associated with reduced HDL function [347]. Indeed, HDL isolated from AD patients displays a reduced cholesterol efflux capacity and a reduced ability to suppress TNFα-induced ICAM-1 expression in EC compared to HDL isolated from cognitively healthy individuals [383]. Whether HDL has additional functions that are more specific to cerebrovascular health in AD and whether these functions are altered in disease are now important questions. In Chapter 4, we established several novel functions of HDL in 2D cultures of brain-derived ECs and in a 3D bioengineered artery model. Importantly, it is increasingly recognized that in vitro experimental data obtained using traditional 2D cell culture methods may be of limited translational relevance [670]. As our 3D bioengineered vascular model maintains anatomical and the physiological complexity of a native vessel, it may be a better model to investigate HDL functions under more physiological conditions. However, our 3D models require substantial specimen volume. Therefore, this study was designed to optimize HDL preparation method to prepare for studies using clinical biospecimens from patients with known AD risk factors such as age and cardiometabolic diseases. Density gradient ultracentrifugation is the method by which lipoprotein subclasses were first discovered in human plasma, and remains a key method in their isolation today including in several clinical studies [424,482]. However, this method has several established drawbacks including loss of some HDL proteins, such as apoE and apoA-I, due to high shear forces [668]. Indeed, we observed that apoE concentrations were significantly lower in HDL isolated by ultracentrifugation compared to plasma, apoB-depleted plasma, unfractionated plasma (Figure 5.12c), and unfractionated serum (Figure 5.13c) when normalized to HDL-C content. Ultracentrifugation also 176 requires a substantial volume of starting material, is relatively costly, and is low throughput. A higher-throughput method of HDL isolation uses the neutral polymer PEG to precipitate apoB-containing, non-HDL lipoproteins from plasma. ApoB-depletion using PEG is attractive for clinical studies as it is cost effective, high-throughput and requires minimal specimen volume. ApoB-depleted plasma has been used previously in clinical studies to evaluate cholesterol efflux capacity and demonstrates a similar maximal efflux capacity compared to HDL isolated by ultracentrifugation [671], which we confirmed here (Figure 5.12f). Nevertheless, it is important to note that apoB-depleted plasma is a heterogeneous preparation that retains proteins such as albumin and globulins as well as other factors that may affect certain functional assays. Furthermore, Davidson et al. recently evaluated the use of PEG precipitation in comparison to other precipitation reagents and found that while PEG precipitation does not drastically alter the phospholipid or cholesterol content of particles isolated in the HDL size range compared to its profile in native serum, a finding that we also confirmed (Figure 5.12a,d), the distribution of apoA-I and apoE as well as the cholesterol efflux capacity of specific HDL fractions was altered. Caution was therefore suggested in using PEG precipitation in HDL functional assays [663]. Here we show that sequential ultracentrifugation is superior to PEG precipitation as a method to isolate HDL particles for certain assays of cerebrovascular functions relevant to AD. Where specimen volume may be too small for ultracentrifugation, apoB-depleted plasma functioned equally as well as ultracentrifuge-isolated HDL with respect to assays of vascular Aβ accumulation in 3D bioengineered arteries. As CAA cannot yet be evaluated in living humans, this HDL assay may provide an unprecedented proxy of human CAA risk in vivo. Using apoB-depleted plasma instead of HDL isolated by ultracentrifugation may streamline efforts to evaluate the anti-CAA effects of HDL in human specimens. We also demonstrated here that TNFα-induced EC inflammation is reduced by apoB-depleted plasma similarly to HDL isolated by ultracentrifugation, which is of interest for investigators studying cardiovascular disease. However, apoB-depleted plasma is not suitable for other AD-relevant assays, namely promoting Aβ transport through the wall of 3D bioengineered arteries, suppressing Aβ-induced EC activation in 3D bioengineered arteries or 2D cultures of brain-derived ECs, or inducing NO production in brain-derived EC cultures. 177 The discrepancy between ultracentrifuge-isolated HDL and apoB-depleted plasma in these specific functions cannot be attributed to residual PEG in the latter preparation. This is because combining PEG precipitation with a single ultracentrifugation step to remove non-HDL proteins produces a preparation with similar functions to the traditional ultracentrifugation method. Instead, the functional differences in PEG precipitated plasma may result from remaining non-HDL plasma proteins that potentially interfere with certain HDL functions. That PEG precipitated plasma and plasma subjected to a single ultracentrifugation to remove apoB-containing lipoproteins perform similarly in the assays tested here supports the interference hypothesis. In the case of assays of vascular Aβ transport, it is also possible that the presence of extensive plasma proteins in the apoB-depleted plasma circulating in the luminal media may mask the Aβ42 epitope detected by the ELISA antibody during measurements of the luminal media. Another important consideration in isolating HDL for functional assays is the effect of the isolation method on the subpopulation of HDL containing apoE. ApoE-containing HDL are of particular interest because of its key role in reverse cholesterol transport [109], protective associations against CHD risk [118,119], effects on extracellular matrix gene expression [120], negative associations with amyloid burden [123], positive association with MMSE [124], and its ability to prevent Aβ accumulation in 3D human bioengineered arteries [617]. The high shear forces used to isolate HDL by ultracentrifugation can result in a loss of HDL-apoE [668,669,672], a finding that we confirm in this study. Unfortunately, methods of plasma apoB-depletion may also result in loss of apoE-containing HDL. PEG does not specifically precipitate apoB-containing lipoproteins but instead selectively reduces the solubility of large lipoproteins. As apoE-containing HDL are mainly large and lipid-rich [122], some of this population may also be depleted by PEG. Indeed, Davidson et al. found that although the total levels of apoE-containing HDL were not altered by PEG-precipitation, the distribution of apoE-containing HDL shifted such that less large apoE-containing particles were found in the resulting supernatant [663]. Therefore, alternative methods of HDL purification may need to be employed when functions related to apoE-containing HDL are of interest. Using immunoprecipitation to deplete apoB-containing lipoproteins rather than PEG may be one suitable alternative. 178 Nevertheless, we determined in the present study that apoB-depleted plasma acts similarly to HDL for the assay of vascular Aβ accumulation and next sought to further simplify this assay by evaluating the applicability of unfractionated serum. Surprisingly, we found that serum was more effective and consistent than ultracentrifuge-isolated HDL in this assay at several treatment concentrations. We initially suspected that the reduced efficacy of the isolated HDL could be due to treatments with a lower concentration of HDL than vessels treated with serum. However, calculation of the HDL-C treatment concentration suggests that this is not the case. In fact, HDL-C and apoA-I concentrations used to treat vessels were significantly higher in the HDL-treated vessels than the serum treated vessels at many treatment doses, including those for which serum performed significantly better in the Aβ accumulation assay (Figure 5.16a,b). Conversely, the concentrations of apoE in the treatments were significantly lower in all HDL treated vessels compared to those treated with 20% or 40% serum (Figure 5.16c). Although a proportion of the apoE in serum is found on non-HDL lipoproteins, apoE is also present on HDL and it may be lost due to shear forces during ultracentrifugation [668,669,672]. As discussed above, while only 5-10% of HDL contains apoE, it has previously been suggested that apoE-containing HDL have unique properties [118,120,122] and we have recently shown that apoE-containing HDL can more effectively suppress Aβ accumulation in 3D bioengineered arteries than apoE-deficient HDL [617]. Taken together, this set of experiments suggests that not only is it possible to use serum in our vascular Aβ accumulation assay, but it is also easier, more consistent, and more effective than using HDL isolated by ultracentrifugation. Unfortunately, but expectedly, unfractionated serum was found to be ineffective in the assay of Aβ-induced monocyte adhesion in 3D bioengineered vessels, likely due to interference by non-HDL proteins as was observed with apoB-depleted plasma. Our systematic evaluation of the intra- and inter-donor and experimental variability of this assay of Aβ accumulation with unfractionated serum revealed that while serum from young healthy donors suppressed Aβ accumulation similarly across donors, the experimental variability for this assay is high. However, the fold change effect of serum compared to vehicle control across experiments was relatively reliable, suggesting that the biological effect of serum against 179 Aβ accumulation is consistent but the absolute concentration of Aβ accumulating in these vessels is variable between batches. Potential reasons for this batch-to-batch variation include variation in the vessels themselves or in the preparation of the recombinant Aβ used to treat them. Nevertheless, these experiments assessing assay variability suggest that differences in Aβ accumulation of 28% could be detected between groups with 90% power using a sample size of 20 in assays using 25% plasma (Figure 5.10). Less thorough tests with 20% plasma suggest a sample size of 20 gives 90% power to detect a 52% difference between groups. We therefore moved forward with a proof-of-concept study to evaluate differences in the ability of plasma from people with T2DM to suppress Aβ accumulation in these 3D bioengineered arteries. Plasma was obtained from a hospital clinical laboratory after HbA1c was measured on whole blood. In total n=25 T2DM and n=25 age and sex-matched control plasma specimens were assayed over four sets of experiments and Aβ accumulation in the 3D bioengineered arteries were evaluated. Notably, Aβ accumulation in vessels treated with young healthy control serum from a single donor was inconsistent and levels were only mildly reduced to 75±52% of levels in vehicle control vessels. This mild effect is in contrast to a reduction to levels 29±14% of vehicle control in previous experiments with 20% young healthy serum and levels 43±12% of vehicle control in experiments with 25% young healthy serum. Aβ accumulation was also variable in the vessels treated with plasma from T2DM patients and age and sex-matched controls and no reductions compared to vehicle control were observed. Increased variation in the clinical specimens is to be expected due to the heterogeneity likely to exist in a hospital-based population as well as the use of defibrinated plasma rather than serum. The reasons for the increased variation in vessels treated with control serum from a young healthy donor are less clear. Variation in the absolute concentration of Aβ accumulating in the walls of vehicle control-treated vessels was also high compared to earlier tests suggesting a loss of vessel consistency over time. The increase in assay variation and reduction in sample size due to loss of specimens during the experiment resulted in a reduction in the expected power to detect any differences between groups. Power calculations using the mean and standard deviation from the age and sex-matched control group suggest that our sample size of 17 has 90% power to detect only a 127% difference between 180 groups. If instead power calculations are performed using the control group data excluding one outlying data point, the sample size of 17 has 90% power to detect a 50% difference between groups. In either case, the variability of this assay precludes the detection of subtle differences between groups. Therefore, it is possible that plasma from T2DM subjects may indeed have an impaired ability to suppress vascular Aβ accumulation, but the impairment may be too mild to be detected by this assay. Furthermore, several confounding variables may exist in our patient cohorts which may conceal any functional differences between groups. For example, duration of T2DM, obesity, and lipid-modifying drugs may all impact the effect of plasma on vascular Aβ accumulation however these factors were not corrected for in the analysis as such clinical information was not available. Given the potential importance of apoE-containing HDL in the suppression of vascular Aβ accumulation [617], the fasting state and APOE genotype of the subjects are also important factors to be considered in future studies. However, it is also possible that T2DM, as defined by HbA1c >7%, does not alter the effect of plasma on vascular Aβ accumulation in this assay compared to age and sex-matched control plasma. Future studies should investigate whether this lack of effect persists for isolated HDL and whether any differences exist in the ability of HDL to suppress Aβ-induced monocyte adhesion to the vessel wall. Given the known differences to the anti-inflammatory function of HDL from subjects with T2DM, differences to Aβ-specific anti-inflammatory functions in T2DM are plausible. The need to use isolated HDL rather than apoB-depleted plasma or unfractionated plasma in assays of Aβ-induced vascular inflammation prevented the completion of this investigation with the available patient cohort due to limitations in plasma volume availability. There are several limitations to this study. First, a relatively small number of functional tests assayed in this study were selected for their potential relevance to AD or cerebrovascular health. Furthermore, HDL was only assessed at a single concentration for comparisons to apoB-depleted plasma, which corresponds to the most frequent concentration used in studies evaluating HDL functions in vitro [383,404,424,482,673,674], therefore presenting the possibility of effect saturations that may mask subtle functional differences by isolation method. Similarly, we evaluated only two common HDL isolation methods. Other HDL isolation methods such as 181 immunoaffinity chromatography or fast protein liquid chromatography were not pursued here as these methods do not provide sufficient HDL for the cell-based functional assays in this study. Another limitation is that we chose to normalize preparations to HDL-C concentrations. HDL and apoB-depleted plasma preparations could alternatively have been normalized to apoA-I concentrations, however, this was avoided due to potential loss of apoA-I from HDL particles during ultracentrifugation [668]. HDL particle number is another method of normalization, which could be used in the future. Although normalization to the initial plasma volume could have been used, potential variances in product loss between isolation methods was a concern. Furthermore, future mass-spectrometry studies will be needed to define the compositional differences between HDL preparations and understand their association to each of the AD-relevant HDL functions tested here. While the 3D vessels used for these assays represent a leap forward in bioengineering the vasculature, there are still many improvements that could be made to further their use as a platform for functional assays of HDL or other serum fractions. First, these vessels are produced by low-throughput methods, generally in batches of 16. As it was found that batch-to-batch variation is large, a higher-throughput system would greatly improve our ability to perform functional assays on large numbers of clinical specimens. Second, the production of these vessels is highly technical and subject to failure for a number of reasons, further lowering the throughput and consistency of results across batches. Thirdly, large volumes of serum or HDL are required for assays with these vessels. Treating these vessels with 20% serum circulating in media requires approximately 1 mL of serum, a volume that is not always available from clinical populations. Finally, the high variation of the assay precludes the detection of small differences between groups without an unfeasibly large sample size. For example, using data from the proof-of-concept study above, a cohort of 163 per group would be required to detect a 25% difference in Aβ accumulation compared to young healthy control subjects and at least 89 per group compared to the middle-aged control group. For these reasons it will be important to consider other platforms for performing our novel HDL functional assays. Many potential platforms are emerging for this purpose including those where up to 96 vessels can be created on a single plate [675] and microfluidic systems [327,328,676]. 182 In summary, we report here that apoB-depleted plasma can be used to evaluate select HDL functions relevant to cerebrovascular health, but ultracentrifugation is the preferred method to isolate HDL that exhibits at least four distinct AD-relevant vasoprotective functions. We also further developed the Aβ accumulation assay towards clinical use by evaluating unfractionated serum as a replacement for HDL. From this we determined that unfractionated serum is effective in the Aβ accumulation assay in 3D bioengineered vessels, the effect is consistent across young healthy donors, the fold change of the effect is relatively consistent across experiments, and a samples size of n=20 per group would give 90% power to detect a 28% difference between subjects groups in fold-change from vehicle control. We then assayed plasma from T2DM plasma in this system in a proof-of-concept experiment and observed no differences in vascular Aβ accumulation compared to vessels treated with age and sex-matched control plasma. While this lack of difference on the basis of %HbA1c may be true, various confounding variables in the cohort were unexplored and high assay variation in these experiments limited the power to detect any small differences between groups. 5.6 Supplementary Figures and Tables 183 Figure 5.12 Composition and cholesterol efflux function of HDL and apoB-depleted plasma. The concentrations of (a) HDL-C, (b) apoA-I and (c) apoE, (d) phospholipid, and (e) cholesterol ester were measured in unfractionated plasma, HDL isolated from plasma by ultracentrifugation, and apoB-depleted plasma produced by PEG precipitation and normalized to HDL-C concentration. (f) Cholesterol efflux capacity was measured using RAW 264.7 macrophages loaded with [3]H-cholesterol. (g) Total protein was observed in plasma, LDL, HDL, and apoB-depleted plasma using SDS-PAGE denaturing gels stained with Coomassie Brilliant Blue. Scatter plots represent independent experiments with mean ± standard deviation. *p<0.05, **p<0.01, ***p<0.001, or exact p-values by one-way ANOVA with Tukey’s multiple comparisons test. MW: molecular weight, d: diameter, kDa: kilodalton, LDL: low-density lipoprotein, HDL: high-density lipoproteins isolated by sequential density gradient ultracentrifugation, PEG-P: apoB-depleted plasma by polyethylene glycol precipitation, A.U.: arbitrary units. Figure 5.13 HDL-C and apolipoprotein concentrations in HDL isolated by ultracentrifugation and unfractionated serum. 184 The concentration of HDL-C was measured in HDL isolated by ultracentrifugation and in unfractionated serum with a commercial kit. (b) ApoA-I and (c) apoE concentrations in HDL and serum were measured by ELISA then normalized to HDL-C concentration. Donors are represented by different shapes. Bars represent mean and error bars represent ± standard deviation. **p<0.01 and ***p<0.001 by unpaired t test. HDL-C: high-density lipoprotein cholesterol, apoA-I: apolipoprotein A-I, apoE: apolipoprotein E, A.U.: arbitrary unit. Figure 5.14 Raw data for comparison of HDL and serum in Aβ accumulation and monocyte adhesion assays 3D bioengineered vessels. HDL isolated by density gradient ultracentrifugation and unfractionated serum from young healthy donors were compared for their ability to suppress (a) the accumulation of recombinant Aβ42 in the walls of 3D bioengineered arteries and (b) monocyte adhesion in response to Aβ42 treatment. Scatter plots represent individual vessels where each donor is represented by a different shape. Bars represent mean and error bars represent ± standard deviation. *p<0.05 and **p<0.01 by one-way ANOVA on raw data followed by Tukey’s multiple comparisons test with blocking of the experiment factor test. HDL: high-density lipoprotein, Aβ42: amyloid beta 42. Figure 5.15 Raw data for intra- and inter-donor and experimental variability of the Aβ accumulation assay in 3D bioengineered vessels with unfractionated serum. Aβ42 accumulated in the walls of 3D bioengineered arteries after 24 h of treatment with Aβ42 in the abluminal chamber and 25% human serum in the circulating media in all experiments pooled together. Points represent the mean of the vessels from the same donor and same experiment in. Experimental replicates are represented by different colours (open shape=experiment 1, grey shape=experiment 2, black shape=experiment 3). Bars represent mean and error bars represent ± standard deviation. Aβ42: amyloid beta 42. 185 Figure 5.16 HDL-C and apolipoprotein concentrations in treatments for 3D bioengineered vessels. (a) The concentration of HDL-C, (b) apoA-I and (c) apoE in the final HDL and serum treatments circulating through 3D bioengineered vessels. Donors are represented by different shapes. Bars represent mean and error bars represent ± standard deviation. *p<0.05, **p<0.01, ****p<0.0001 compared to 10% serum treated vessels, ##p<0.01, ###p<0.001, ####p<0.0001 compared to 20% serum treated vessels, and §§p<0.01, §§§p<0.001, §§§§p<0.0001 compared to 40% serum treated vessels all by one-way ANOVA followed by Sidak’s multiple comparisons test. HDL-C: high-density lipoprotein cholesterol, apoA-I: apolipoprotein A-I, apoE: apolipoprotein E. 186 Chapter 6: Discussion and concluding remarks 6.1 Summary and significance of findings AD is a devastating disease for afflicted individuals and their loved ones and has an enormous financial cost to the economy [17,21]. Unfortunately due to population aging, the burden of AD is only expected to grow if disease altering therapies or effective preventative strategies do not emerge [21]. Cerebrovascular dysfunction plays an early and prominent role in AD pathology [4,235] yet AD therapeutic strategies targeting the blood-brain barrier (BBB) remain relatively unexplored. High-density lipoproteins (HDL) have known protective effects against AD pathology in mouse models [519,521,523,525] and in human epidemiological studies [210,488–493,495,496] and HDL in healthy people is well-established to act on peripheral vessels to promote vascular health [368,371–374]. However, the potential vasoprotective functions of HDL specifically in the brain have not yet been fully studied. The objective of this thesis was therefore to fill this informational gap by investigating the cerebrovascular specific effects of HDL in AD mouse models and AD-relevant vasoprotective HDL functions in in vitro models. In Chapter 2, apoA-I deficient APP/PS1 mice were compared to apolipoprotein (apo)A-I expressing APP/PS1 mice to investigate the effect of HDL specifically on the BBB. It has previously been shown that lack of apoA-I exacerbates CAA pathology [519] and intravenous treatment of reconstituted HDL or overexpression of apoA-I can reduce vascular amyloid burden and neuroinflammation [521,525,526]. This study first confirmed the previous CAA findings and further found a worsening of total amyloid burden in the cortex. Chapter 2 also showed for the first time that apoA-I deficiency in APP/PS1 mice worsens total neuroinflammation and cerebrovascular inflammation measured as endothelial cell (EC) inflammation and vessel-associated astrogliosis. Remarkably, the effect of apoA-I deficiency was greater for vascular-specific pathologies than global pathologies. Specifically, vascular-specific amyloid was increased by 4.8-fold while total amyloid was increased only by 3.2-fold. Although the effect of apoA-I deficiency on vascular-specific and total astrogliosis was similar, 2.1-fold and 2.4-fold, respectively, a statistically significant effect was only observed for vascular-specific astrogliosis (p=0.0245 for apoA-I genotype effect on vascular-specific astrogliosis, p=0.0929 for apoA-I 187 genotype effect on total astrogliosis by two-way ANOVA). We also confirmed that apoA-I deficiency can exacerbate cognitive impairment in APP/PS1 mice using cued and contextual fear conditioning tests. Taken together, data in Chapter 2 suggest the loss of plasma HDL in AD model mice has detrimental effects on AD pathologies and these effects are strongest on the vasculature. Studies in Chapter 3 built upon the findings in Chapter 2 and aimed to reverse AD pathologies using a non-brain penetrant liver X receptor (LXR) agonist specifically modulating peripheral lipoprotein metabolism. LXR agonists have previously been shown to reduce amyloid pathology, neuroinflammation, and memory deficits [537,539,542–546,677] and at least some of these effects are dependent on ATP-binding cassette transporter A1 (ABCA1) [540]. A non-brain penetrant LXR agonist (VTP) was compared to a brain-penetrant LXR agonist (GW) to determine whether the beneficial effects in AD model mice are due to increased expression ABCA1 in the brain or in the periphery. In the brain, ABCA1 expressed by microglia and astrocytes and lipidates primarily apoE while in the periphery, ABCA1 expressed by the liver and intestine also lipidates apoA-I as the rate-limiting step in HDL biogenesis [588–591]. Although we observed the expected induction of Abca1 in the periphery with both drugs and in the brain only with GW, the drug treatment concentration or duration was only sufficient to result in relatively mild effects on AD pathology and no differences on total or vascular amyloid burden. However, VTP treatment still had several beneficial effects in APP/PS1 mice. Specifically, VTP treatment significantly reduced global neuroinflammation as shown by Il1b mRNA expression in the cortex and reduced brain IL-1β protein concentration. VTP also significantly reduced cerebrovascular specific inflammation in the hippocampus as evaluated by CD31-associated ICAM-1 immunofluorescence. Finally, VTP treatment rescued deficits to cued fear memory in APP/PS1 mice. In summary, activation of ABCA1 exclusively outside of the brain may have protective effects against neuroinflammation and cognitive impairment in AD model mice. This work suggests that therapeutics do not necessarily have to cross the BBB to have effects within the brain and targeting HDL-related pathways may have some benefits in AD. Chapter 4 aimed to investigate whether known HDL vasoprotective functions are maintained in a human in vitro system and whether HDL has any Aβ-related vasoprotective effects. The novel 188 functions of HDL investigated in Chapter 4 built directly upon those studied in Chapter 2 and 3, namely the reduction of vascular Aβ accumulation and Aβ-related vascular inflammation. Human-derived HDL was found to prevent Aβ-induced monocyte adhesion to the wall of 3D bioengineered vessels and prevent the accumulation of Aβ in the vessel wall. In 2D cultures of brain-derived EC, HDL was found to inhibit both tumour necrosis factor alpha (TNFα) and Aβ-induced monocyte adhesion and promote endothelial NO production. HDL was also found to delay Aβ fibrillization in a cell-free assay. Importantly, it was established that many of these AD-relevant HDL functions are independent from each other. While the anti-inflammatory effects of HDL have previously been shown to act through NO-production pathways, the suppression of Aβ-induced monocyte adhesion to brain-derived ECs was found to be NO-independent. Furthermore, the ability of HDL to delay Aβ fibrillization was unrelated to its anti-inflammatory effects against Aβ. Instead, HDL was found to suppress Aβ-induced monocyte adhesion both in 2D and 3D models via SR-BI binding then suppression of Aβ uptake into ECs. In total, four distinct, AD-relevant, vasoprotective functions of healthy human HDL were established in Chapter 4. Chapter 5 aimed to develop assays of the novel human HDL functions described in Chapter 4 with the ultimate goal of investigating whether these protective functions are lost in disease, as has been shown previously with other HDL functions [404,413,424,426,482]. First, HDL isolated from human plasma by ultracentrifugation and apoB-depleted plasma was prepared by polyethylene glycol (PEG) precipitation and these two isolates were compared for their utility in assays for the functions described in Chapter 4. ApoB-depleted plasma was chosen as the high-throughput method for comparison as it has previously been used to establish deficiencies in HDL’s cholesterol efflux capacity in people with atherosclerosis and to predict incident cardiovascular events in several landmark studies [485,486,662]. Comparison of the methods found that apoB-depleted plasma is sufficient to study the anti-CAA and TNFα-specific anti-inflammatory functions of HDL, but non-HDL proteins interfere with assays of Aβ-induced monocyte adhesion and endothelial NO production. Next, attempts to further simplify functional assays in 3D bioengineered arteries were conducted by comparing HDL isolated by ultracentrifugation to unfractionated serum. Serum was, unsurprisingly, not effective in reducing Aβ-induced inflammation however it consistently and robustly reduced vascular Aβ accumulation. Assessment 189 of the inter- and intra-donor and experimental variation in the serum assays of vascular Aβ accumulation suggested that comparing functions of serum from people with and without disease would be feasible. Finally, a proof-of-concept assessment of the anti-CAA capabilities of HDL in serum from T2DM and non-diabetic subjects was performed. No difference was observed in the effect on vascular Aβ accumulation between vessels treated with plasma from T2DM subjects and age and sex-matched control subjects, although assay variation was high in these experiments resulting in limitations in the power to detect small differences between groups. 6.2 Limitations of these studies 6.2.1 In vivo mouse model Chapters 2 and 3 used transgenic mice expressing human Aβ and manipulated their lipoprotein metabolism either through genetic or pharmacological methods to study the effects of HDL on AD-related cerebrovascular dysfunction. The use of only a single animal model, APP/PS1 mice, at a single age, 12-months old, is a limitation of this thesis. APP/PS1 mice have human transgenes for APP with the Swedish mutation and PSEN1 with an exon 9 deletion. By 3-months old, amyloid pathology begins to develop and by 6 months CAA development begins [576]. Many other transgenic AD mouse models exist with different mutations, promoters, and genetic backgrounds, resulting in variation in onset and severity of AD pathology [511,576]. While the study in Chapter 2 found a detrimental effect of apoA-I deficiency in 12-month old APP/PS1 mice, the same outcomes may not occur in other AD transgenic models or in APP/PS1 mice at a different age. Notably, a recent study by Contu et al. evaluated amyloid and CAA pathology in 12 to 13-month old Tg2576 mice deficient for apoA-I and found improvements to these pathologies compared to mice with normal apoA-I expression levels [522]. Tg2576 mice only begin to develop amyloid at 12 to 13-months of age and have mild CAA compared to 12-month old APP/PS1 mice [576]. The differences in outcomes between the study by Contu et al. and the study in Chapter 2 of this thesis therefore suggest that apoA-I deficiency may have different effects in AD mice at different stages of disease progression. In Chapter 3, APP/PS1 mice were treated with LXR agonists only in 12-months old mice with well-established amyloid pathology. Given the importance of mid-life vascular risk factors to late-life AD risk [201,678,679], a preventative rather than therapeutic LXR agonist treatment strategy may have more effectively reduced AD pathologies. Understanding the 190 dynamics of the protective effects of HDL during AD progression will be critical for the development of HDL-based therapeutics. The use of mice with APP and PSEN1 transgenes to model AD is on its own a limitation to the studies in Chapter 2 and 3, as thoroughly discussed in section 1.14.4.1. Off-target effects are possible with the introduction of transgenes into the mouse genome, as has been recently shown in rTg4510 tau mice [508,512,513]. Knock-in models, such as that developed by Saito et al., attempt to overcome this barrier and should be considered for use in future studies to confirm the findings of Chapters 2 and 3 [514]. APP/PS1 mice also have a genetic basis of disease that is only representative of a miniscule proportion of humans with AD. While 45-81% of late-onset LOAD risk may be heritable, the vast majority of autosomal dominantly inherited AD cases are EOAD [22,23,99], which make up only approximately 5% of total AD cases [20]. Furthermore, only about 10% of EOAD subjects show a pattern of autosomal dominant inheritance and only 12% of those possess mutations in known amyloid processing genes such as APP or PS1 [22,23]. Instead, genetic risk factors, such as APOE, make up a large portion of the genetic component of LOAD risk [23]. Importantly, murine apoE only exists as one isoform that functions most similarly to the human apoE3 [623] however around 40% of AD subjects carry an APOE-ε4 allele [106] and APOE-ε4 is the greatest genetic risk factor for AD [105]. Therefore, the use of mice lacking human APOE limits to translation of these study findings to human disease. Lipoprotein metabolism in mice also differs from that in humans in that mice have a lipoprotein profile with a higher HDL:LDL ratio while humans have a higher LDL:HDL ratio [360]. Finally, mice are not subject to the majority of modifiable risk factors, like smoking or poor diet, that may contribute to 35% of AD risk in humans [165]. Because of these numerous limitations in the translation of the in vivo studies in Chapters 2 and 3 to humans, an entirely human-based in vitro system was used in Chapters 4 and 5. 6.2.2 In vitro model of the BBB While the entirely human-based in vitro model used in Chapters 4 and 5 overcomes many of the limitations of the mouse studies Chapters 2 and 3 and has many advantages over other existing vascular models, this model still has its own limitations. First, studies in this thesis used 191 bioengineered arteries composed of only ECs and SMCs collected from umbilical cords. The bipartite vessels created may be a useful representation of the leptomeningeal arteries of the brain, yet they lack the astrocytes present on most of the cerebrovascular system [680]. Furthermore, cerebral vessels are not isolated from the rest of the brain, instead they intricately interact with neurons in order to precisely modulate cerebral blood flow to meet the metabolic needs of the brain. Tripartite [658] and quadripartite bioengineered arteries containing astrocytes and neurons, respectively, have been developed however validation of these models was not yet completed by the time the studies in this thesis were performed. The inflexible scaffold used to form the structure of bioengineered vessels is another important limitation to these studies. Although it was possible to study endothelial NO signaling induced by circulating HDL through these bioengineered arteries [658], it was not possible to evaluate changes to vessel tone due to the stiffness of the scaffold. Changes to cerebral blood flow are frequently observed in AD patients and can occur before the onset of amyloid and other pathologies [235]. Therefore, it will be important for future bioengineered artery models to allow for vasodilation and constriction. Finally, limitations to the scalability, specimen volumes needed, and reproducibility of the bioengineered vessels, as thoroughly described in Section 5.5, highlight the need for continued model improvements in order to investigate the potential benefits of HDL against cerebrovascular dysfunction AD. 6.2.3 Mechanistic data on AD-relevant HDL functions This thesis is limited in its exploration of the mechanisms behind the improvements to Aβ-induced cerebrovascular dysfunction by HDL. Studies on mechanism in Chapters 2 and 3 were difficult as the extensive time needed to produce mice with abundant AD pathological changes (12 months) limited the analysis to that which could be performed on already harvested tissues. Nonetheless, some observations in Chapter 2 begin to suggest that HDL protects against vascular Aβ accumulation and Aβ-induced vascular inflammation through distinct mechanisms. We initially hypothesized that the elevation in astrogliosis in apoA-I deficient APP/PS1 mice was due to the increase in total and vascular amyloid deposition. However, GFAP-positive percent areas surrounding parenchymal and vascular amyloid deposits were elevated in the apoA-I deficient mice even after normalization to the percent amyloid-positive area. Therefore, HDL may suppress 192 astrogliosis both by reducing the presence of activating amyloid deposits and by reduce the reactivity of astrocytes to these amyloid deposits more directly. Further studies in Chapters 4 and 5 using in vitro models supports the presence of multiple distinct mechanisms of action for the protective effects of HDL against Aβ. Here we observed that HDL was capable of inducing NO production in brain-derived EC cultures and delaying the fibrillization of Aβ monomers into more toxic species in a cell-free assay. However, neither of these protective functions were required for HDL to suppress Aβ-induced monocyte adhesion to brain-derived ECs. Instead, HDL was found to suppress Aβ-induced vascular inflammation in 2D cell cultures and 3D bioengineered arteries in an SR-BI-dependent mechanism resulting in the blocking of Aβ uptake into ECs or the removal of Aβ from ECs. Studies in Chapter 5 further suggest that the effects of HDL on vascular Aβ accumulation and Aβ-induced EC activation are distinct. We observed that apoB-depleted plasma was functional in assays of Aβ accumulation in 3D bioengineered vessels but did not maintain the anti-inflammatory functions of HDL against Aβ, most likely due to the presence of abundant non-HDL proteins in the isolated apoB-depleted plasma. That the presence of abundant non-HDL proteins inhibits to ability of HDL to suppress Aβ-induced inflammation but not vascular Aβ deposition suggests that these HDL functions are mechanistically distinct. Additional studies related to this thesis work have since confirmed that the anti-CAA function of HDL is distinct from the suppression of Aβ-induced monocyte adhesion and works instead in an SR-BI-independent manner to alter collagen-I binding of Aβ [617]. Yet, several gaps in our understanding of the mechanisms of these novel HDL functions still exist. While we understand that HDL prevents to uptake of Aβ into ECs, we do not understand how Aβ acts in these cells to increase monocyte adhesion to the cell surface. Studies in Chapter 4 showed that unlike the classical pro-inflammatory stimulus TNFα, Aβ40 and Aβ42 monomers do not increase the expression of intercellular adhesin molecule (ICAM-1), vascular cell adhesion molecule (VCAM-1), or nuclear factor kappa beta (NFκB) signalling pathway proteins in EC cultures. Other potential mechanisms for exploration in future studies include alterations to glycocalyx composition, which may result in a stickier surface for immune cell binding. The 193 studies in this thesis are also limited in their understanding of the specific HDL components responsible for its protective effects against Aβ, as will be thoroughly explored in Section 6.3.1. 6.2.4 Simplified view of inflammation in AD The current understanding on the role of inflammation in AD suggests a complex progression of inflammatory changes occur in the brain throughout disease development, as discussed in Section 1.6. The studies in this thesis instead view neuroinflammation through a relatively simple lens and interpret the anti-inflammatory effects of HDL against Aβ as beneficial outcomes. Furthermore, the tools used to evaluate inflammation in this thesis were simple. In Chapters 2 and 3, GFAP protein concentration and positive staining area were used to evaluate astrogliosis however, recent studies have shown that the inflammatory state of astrocytes is more complex. Astrocytes have been shown to have different responses to different stimuli, for example to stroke versus lipopolysaccharide administration. At least two reactive phenotypes, A1 and A2, exist and their genomic expression profiles vary drastically from each other and from quiescent astrocytes, although both express GFAP [681]. Furthermore, the development of neurotoxic A1 astrocytes has been suggested to be a consequence of microglial activation [682], which was not evaluated in Chapter 2 and only minimally investigated in Chapter 3. In Chapters 4 and 5 we focused our studies of inflammation on the adhesion of THP1 monocytes or primary peripheral blood mononuclear cells to ECs in a simplified model of immune cell infiltration into the brain. While we interpreted the ability of HDL to suppress this monocyte adhesion during Aβ treatments as a protective effect, it could also be argued that immune cell infiltration into the brain may assist microglia in the degradation of Aβ and therefore play a protective role in AD [683]. Additionally, the studies in this thesis did not thoroughly investigate the microglial responses to changes in peripheral HDL-C concentration or LXR activation. As discussed in section 1.6, microglia are emerging as a key player in AD pathogenesis. Many AD risk genes are related to microglia or exert their functions only in microglia [62–64,74]. Depletion of microglia in amyloid 194 or tau mouse models reduces neurodegeneration and memory impairments and can improve amyloid or tau pathologies depending on the age at which the microglia are depleted [68–73]. Microglia are especially important to consider when studying lipoproteins in AD as they produce apoE during neurodegeneration and neuroinflammation [154,155] and have been shown to have many apoE isoform-dependent effects. Specifically, APOE-ε4 targeted replacement mice have reduced microglia clustering around plaques [162] and iPSC-derived APOE-ε4 microglia have reduced phagocytic functions, increased inflammatory activity, and altered cholesterol metabolism [150,152,164]. ApoE is also a key transcriptional regulator of the switch of microglia from a homeostatic state to a neurodegenerative state in animal models of aging, multiple sclerosis, amyotrophic lateral sclerosis, and AD and in human AD brains [66,67]. Thus, evaluation of microglial activation in response to peripheral changes in HDL-C concentration or LXR activation will be an important aspect of future studies. 6.3 Future directions 6.3.1 Evaluation of HDL structure and subclasses important for AD-relevant vasoprotective functions Although up to 95 different proteins and 200 lipid species have been identified on HDL particles, not all HDL particles host all of these proteins and lipids [343,369]. Instead, subclasses of HDL particles with varying compositions exist [110,684]. An active area of HDL research is investigating the functions of these HDL subspecies. Lessons learned from CVD drug trials suggests that simply raising HDL-C concentration is not always an effective therapeutic strategy, drugs may instead be more effective if they raise levels of the specific HDL particles with vasoprotective functions [685]. Two such subspecies under investigation are HDL containing sphingosine-1-phosphate (S1P) and HDL containing apoE. The phospholipid metabolite S1P and its protein chaperone apoM, found on only 5% of HDL particles [686], signal through S1P receptors on ECs to phosphorylate endothelial NO synthase [372], induce EC adherence junctions [687,688], and suppress endothelial ICAM-1 expression [433]. S1P-HDL concentration has been investigated as a biomarker due to these protective functions and it has been found that S1P content on HDL is reduced in T2DM [422] and concentrations inversely correlate with ischemic heart disease occurrence [393]. Similarly, HDL-containing apoE, about 5-10% of the total HDL 195 population [117], has specific functions relating to cholesterol efflux [109] and arterial stiffness [120]. ApoE-HDL concentrations are reduced in youth with T2DM [122] and low concentrations are associated with CHD risk [118]. Which HDL components are responsible for the beneficial functions investigated in this thesis is an area of ongoing and future investigation. In Chapter 2, apoA-I containing HDL was found to protect against total and vascular-specific amyloid pathologies, neuroinflammation, and cerebrovascular inflammation in APP/PS1 mice. However, this study did not investigate which particular proteins or lipids on apoA-I-containing HDL may contribute to its beneficial functions. Other studies administrating reconstituted HDL (rHDL) in AD model mice do provide some evidence for specific proteins with protective functions. For example, intravenous treatment with an rHDL of apoA-I and phosphatidylcholine acutely reduced soluble Aβ concentrations in the brains of APP/PS1 mice [523], intravenous lipid-free apoA-I Milano reduced brain Aβ concentrations and amyloid burden as well as neuroinflammation in APP23 mice [525], intravenous rHDL composed of apoJ, DMPC, and free cholesterol reduced insoluble Aβ concentrations and vascular Aβ levels in APP23 mice [526], and intravenous rHDL composed of apoE3 and DMPC reduced Aβ deposition, neuroinflammation, and memory deficits in SAMP8 mice [524,583]. Importantly, the composition of these rHDL may be modified in vivo therefore the specific functional components responsible for the beneficial effects are still not entirely clear. In Chapters 4 and 5, it was shown that HDL within the density range of 1.063 to 1.21 g/mL can protect against Aβ accumulation and Aβ-induced vascular inflammation in 3D bioengineered arteries. However, investigation of the specific HDL subspecies within this density range responsible for these beneficial effects was not extensively pursued beyond experiments demonstrating that lipid-free apoA-I on its own is not sufficient to protect 2D cultures of brain-derived ECs from Aβ-induced monocyte adhesion (Figure 4.3e-f). More recent studies on the protective effect of HDL against CAA in 3D bioengineered arteries have begun to suggest that apoE-containing HDL is the most important subspecies for this function [617]. Whether APOE genotype affects this particular HDL function is therefore an important area of future research given that APOE-ε4 is the greatest genetic risk factor for AD [105,106]. 196 6.3.2 HDL-based therapeutics in development for AD Work in this thesis and by others shows that HDL protects against vascular Aβ both in mouse models [519,521,525,592] and 3D bioengineered artery models [578,658,689]. As CAA is found in 60-90% of people with AD and likely increases the risk of cerebral hemorrhage [5], HDL is therefore an attractive candidate agent to prevent CAA. HDL may also reduce AD-related neuroinflammation based on its observed protective effects against neuroinflammation in AD mouse models and 3D bioengineered arteries in this thesis and in other studies [519,521,524,525,527,578,689]. Finally, an emerging area of HDL-based therapeutics is its role as a carrier for drugs and micro ribonucleic acids (miRNAs) as brain penetrance is a major barrier in AD drug discovery [690]. Although whole HDL particles are unlikely to cross the BBB, reconstituted, HDL-like particles that can be loaded with drugs or cell-targeting molecules are of interest for brain researchers. For example, a reconstituted HDL composed of apoE and an Aβ-target drug was found to enter the brains of AD mice where it acted to reduce amyloidosis and improve memory [583]. Several direct and indirect HDL-based therapeutics have already been developed with the goal of treating cardiovascular diseases therefore allowing for efficient drug repurposing for AD. Existing HDL-based therapeutics are described below in Section 6.3.2.1 and summarized in Table 6.1. Table 6.1 HDL-based therapeutics under investigation in clinical trials. Indication HDL-targeting approach Drug type Drug name Study population Safety Efficacy Ref Cardiovascular disease Direct Recombinant apoA-I CER-001 acute coronary syndrome no issues no improvement to atherosclerosis [691–693] ApoA-I mimetic D-4F coronary heart disease no issues improved anti-inflammatory activity of HDL [694,695] L-4F coronary heart disease no issues no improvement to HDL function [696] Reconstituted HDL CSL-112 acute coronary syndrome no issues may improve cholesterol efflux function of HDL [697] Autologous administration acute coronary syndrome no issues tended to reduce atherosclerosis [698] Indirect ApoA-I transcription inducer RVX-208 atherosclerosis elevated liver transaminase concentrations no improvement to atherosclerosis [699,700] 197 LCAT recombinant protein ACP-501 stable atherosclerotic cardiovascular disease no issues improved HDL metabolism [701] Niacin Niacin cardiovascular disease events flushing reduced CVD events, may be independent of HDL [702–704] CETP inhibitors Dalcetrapib acute coronary syndrome no issues no effect on cardiovascular events [705] Evacetrapib high-risk vascular disease no issues no effect on cardiovascular events [706] Torcetrapib high-risk for coronary events increased mortality and morbidity increased risk of cardiovascular events [707] Anacetrapib atherosclerotic vascular disease no issues reduced major coronary events [708] Dementia Indirect Statins various dementia possible short-term memory impairment improvements in prospective trials, no improvements in RCT [204–207] Niacin Niacin dementia flushing protective effects in retrospective studies [709,710] ABCA1 modulators Bexarotene dementia no issues raised CSF apoE, no improvements to cognitive function [555] 198 6.3.2.1 HDL-based therapeutics in development of the treatment of acute coronary syndrome and atherosclerosis HDL as thus far been developed as a therapeutic agent for acute coronary syndrome or stable coronary heart disease, and existing clinical trials reveal much about the safety and efficacy of various formulations. For example, CER-001 and MDCO-216 are recombinant apoA-I proteins that were well tolerated in acute coronary syndrome patients but did not improve atherosclerosis in phase II clinical trials [691–693]. ApoA-I mimetics in clinical trials include D-4F and L-4F for the treatment of coronary heart disease patients. D-4F was well tolerated and while there were issues with oral bioavailability, improvements to the anti-inflammatory abilities of HDL have been observed [694,695]. L-4F was similarly well tolerated however HDL functions were unaffected [696]. CSL-112 is the latest formulation of a plasma-derived, reconstituted pre-β HDL currently in phase III clinical trials for acute coronary syndrome (NCT03473223). This agent has so far been well tolerated and shows promise for its ability to increase cholesterol efflux capacity [697]. Autologous administration of pre-β HDL has also been investigated where patient-derived HDL is delipidated then administered to the same subject. This therapeutic strategy tended to reduce atherosclerosis in acute coronary syndrome patients in early trials and was well tolerated [698]. A phase III trial testing the ability of this method to reduce atherosclerosis in subjects with familial hypercholesterolemia is underway (NCT03135184). Several agents aiming to increase plasma HDL-C concentration through indirect mechanisms have also been developed. For example, RVX-208 is a small molecule that increases apoA-I transcription but had no efficacy against atherosclerosis and concerning safety data, specifically a dose-dependent increase in liver transaminase concentration was observed during treatment [699,700]. ACP-501 is a lecithin-cholesterol acyltransferase (LCAT) recombinant protein that is well tolerated in people with stable coronary heart disease [701] and is undergoing a phase II trial evaluating its effects on apoB metabolism in cardiovascular disease patients (NCT03773172). Niacin has modest effects on HDL-C concentration through incompletely understood mechanisms and has been extensively studied in CVD populations. Early trials suggested that niacin treatment could reduce cardiovascular events and atherosclerosis [702], however, two large randomized 199 controlled trials (RCT) investigating the effectiveness of niacin to prevent major cardiovascular events in high-risk patients were terminated due to lack of efficacy [703,704]. Cholesterol ester transfer protein (CETP) inhibitors have had an uncertain path for the prevention of cardiovascular events with the early termination of several trials due to insufficient efficacy and safety issues [705–707]. However, the most recent phase III trial of a potent CETP inhibitor known as anacetrapib had no adverse effects and appeared to effectively reduce major coronary events [708]. CETP inhibitors may especially be of interest for repurposing as AD-based therapeutics as CETP and AD have previously been linked through the observed associations with certain CETP polymorphisms and AD risk and memory decline, particularly in APOE-ε4 carriers [711–713]. While the efficacy of these direct and indirect HDL-based therapeutics for the treatment of CVD is still unclear, the encouraging positive safety data for several clinically tested agents offers promising candidates to consider repurposing for AD. 6.3.2.2 Lipid modifying therapeutics for dementia prevention or treatment In addition, lipid modifying approaches not directly targeting HDL-C have been investigated for their possible benefits against dementia. Statins are drugs that inhibit 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase to block cholesterol synthesis and reduce LDL, which subtly increases the HDL:LDL ratio [714]. The consensus from several meta-analyses is that statin use lowers dementia and AD risk in prospective trials [204–206] but statins were not effective in any of the large RCTs conducted [204,207]. Importantly, it has also been reported that statins can cause short-term, reversible cognitive impairment in some people although these observations are not conclusive [715]. Furthermore, it is unclear whether the benefits of statins can be attributed to increases in plasma HDL-C as HDL-C concentrations are generally only elevated by 5-10% by statins and a meta-analysis examining the effect of statin use on cardiovascular event risk found that they do not improve the risk of those with low baseline plasma HDL-C concentration [716]. Several studies have investigated how statins affect specific HDL subspecies and have mainly found an increase in cholesterol on large HDL [445–451] although others have observed no effect the number of small, medium, or large HDL particles [452]. Interestingly, several studies have measured reduced apoE concentrations on HDL with statin treatment [451,717,718], a finding that 200 runs contrary to the negative association observed between apoE-HDL concentration and CHD risk [118,119]. Several observational studies have begun to probe the potential benefits of dietary niacin, a compound with complex effects on HDL-C and CVD risk as discussed in section 6.3.2.1, on cognitive function. Early studies suggest that higher niacin intake during young adulthood can improve some measures of cognitive function 25 years later [709] and older adults with higher intakes of niacin have reduced risk of AD and cognitive decline over 6 years of follow-up [710]. However, these studies are limited by their dependence on dietary questionnaires to evaluate niacin intake. An emerging indirect target for HDL-based AD therapeutics is ABCA1. ABCA1 effluxes cholesterol and phospholipids onto lipid-free or lipid-poor apolipoproteins and plays a pivotal role in nascent HDL biogenesis [588–591]. ABCA1 also regulates CNS apoE concentration and function [719,720]. ABCA1 is transcriptionally regulated by the nuclear receptors LXR, peroxisome proliferator activating receptor gamma (PPARγ) and retinoid-X-receptor (RXR) [586,721]. Direct LXR and RXR agonists stimulate ABCA1 activity, enhance cholesterol efflux, increase plasma HDL-C concentration [722–725], increase CNS apoE lipidation and improve cognitive function in AD animal models (reviewed in [586]). In Chapter 3, upregulation of ABCA1 outside of the brain with a non-brain penetrant LXR agonist was found to have subtle, beneficial effects on neuroinflammation, cerebrovascular inflammation, and cognition in APP/PS1 mice. The first ABCA1-targetting compounds to reach clinical trials is an RXR agonist known as bexarotene. Unfortunately, bexarotene had poor bioavailability in a phase I clinical trial in normal subjects although it did successfully raise the concentration of apoE in CSF [555]. Significant hepatotoxic and systemic side effects have hampered clinical development of direct LXR/RXR agonists. The major liability of current direct LXR agonists is induction of hypertriglyceridemia and liver steatosis due to activation of hepatic sterol regulatory element binding protein-1c (SREBP-1c), fatty acid synthase (FAS), and stearoyl CoA desaturase-1 (SCD-1) [552,553,621]. New ABCA1 modulators that do not act as direct LXR agonists have recently been identified. These compounds 201 enhance CNS apoE production and lipidation but have minimal effects on SREBP-1c expression in liver cells, suggesting they may avoid the hepatotoxicity of conventional LXR agonists [726]. 6.3.2.3 HDL as a potential treatment for the adverse effects of therapeutics on the cerebrovasculature In addition to the potential of HDL as an AD therapeutic, HDL may be a valuable tool to prevent or treat the cerebrovascular adverse effects of other AD therapies. Anti-Aβ monoclonal antibodies are one of the major drug types currently in phase II and III clinical trials for AD [727]. The success of these drugs is still to be determined through ongoing trials, but it has become clear that ARIAs [215,728] on magnetic resonance imaging (MRI), which indicate cerebrovascular edema, are a potential adverse effect. ARIA in anti-Aβ monoclonal antibody trials is thought result from the vascular accumulation of Aβ42 that is solubilized from plaques by the therapeutic antibodies and cleared from the brain via periarterial pathways [729]. The work in this thesis showing that apoA-I reduces vascular Aβ deposition in mouse models and in vitro in 3D bioengineered human arteries [592,658,689] suggests that HDL may be a valuable companion therapeutic to administer in conjunction with anti-amyloid therapies to prevent or treat ARIA. Our findings also suggest that HDL may be a valuable tool in the treatment of CAA-related inflammation (CAARI). CAARI is a form of CAA presenting as an MRI abnormality similar to ARIA but with an acute onset and the presence of inflammation and amyloid deposition together [730]. This thesis demonstrates that lack of apoA-I exacerbates astrocyte reactivity and endothelial activation in response to vascular Aβ in an AD mouse model [592] and circulating HDL can suppress Aβ-induced monocyte adhesion in 3D bioengineered arteries [578], therefore, HDL-based therapeutics may have a unique ability to protect against CAARI by suppressing the immunoreactivity of cells to CAA. 6.3.2.4 Considerations for the repurposing of HDL-based therapeutics for AD The failure of several large clinical trials evaluating anti-amyloid antibodies in AD patients has highlighted the importance of finding an effective therapeutic window for treating AD. Many have suggested that certain drugs have failed in clinical trial because they have only been tested in subjects in which irreversible neurodegeneration has already occurred [214,731–734]. Timing of HDL-based therapeutics is therefore an important concern. The studies in this thesis suggest that 202 HDL may be a valuable therapeutic tool for treating the vascular components of AD therefore the fact that cerebrovascular dysfunction is most likely an early even in AD must be considered. Several large studies have highlighted the association of vascular risk factors at mid-life with dementia risk in late life [201,678,679] and BBB permeability, brain microbleeds, deficits to cerebrovascular reactivity, reductions in resting CBF, and increased cerebrovascular resistance have all been observed in imaging studies to occur early in AD [4]. For example, a multi-modal imaging study of over 7,700 brain images found that dysfunction in cerebral blood flow measured with MRI occurred before amyloid deposition, glucose hypometabolism, structural changes, or neuronal dysfunction [235]. Studies in this thesis provide some evidence for the optimal timing of HDL-based therapeutics and lay the groundwork for future studies to further probe this question. In Chapter 2, it was observed that a life-time deficiency in apoA-I exacerbated total and vascular-specific amyloid pathology and neuroinflammation in APP/PS1 mice. Life-time apoA-I deficiency [519] and overexpression [521] in APP/PS1 mice has also been previously shown by others to alter CAA burden, memory deficits, and neuroinflammation. It will be important to understand whether changes in apoA-I expression have the same effect when amyloid pathology is already established. Chapter 3 aimed to answer this question by administering a lipid-metabolism targeting drug to 12-month old APP/PS1 mice with well-established amyloid pathology. That the drug did not effectively improve amyloid pathology or neuroinflammation may suggest that modifying lipid metabolism cannot reverse AD-related changes to the brain although it is also possible that the dosage or duration of the drug treatment was not sufficient, especially as plasma HDL-C concentrations were not changed with drug treatment. Indeed, other studies have successfully improved amyloid and CAA burden, neuroinflammation, and memory deficits with intravenous HDL-based therapeutics administered to mice with well-established amyloid pathology [523–526]. Future studies could also use mice with conditional gene modifications to apoA-I (Cre-inducible knockout or overexpression) allowing for the evaluation of HDL modification at various points in the progression of AD. Further studies on the relative benefits of a preventative versus treatment strategy for HDL could also be performed using the in vitro models discussed in this thesis. Preliminary work with these models in Chapter 4 found that HDL effectively suppresses Aβ- 203 induced monocyte adhesion to 2D cultures of brain-derived ECs both when added 2 h before Aβ and when added 1 h after Aβ, suggesting HDL may be able to protect against some Aβ-induced vascular inflammation after the onset of amyloid pathology. Work in Chapter 4 and 5 only evaluated the effect of HDL on CAA deposition when HDL was circulated through 3D bioengineered arteries concomitantly with abluminal Aβ treatment. However, more recent studies found that HDL circulated for 24 h before Aβ treatment, even when followed by an HDL wash-out, was even more effective in reducing both soluble and insoluble Aβ concentrations in the vascular wall of 3D bioengineered arteries, supporting a primarily protective effect of HDL [617]. Given the importance of vascular risk factors at mid-life, the occurrence of vascular dysfunction early in AD progression, and the evidence for a primarily preventative role of HDL in the studies in this thesis, HDL-based therapeutics for AD may therefore be more effective when administered before clinical disease onset and perhaps most effective when administered as a preventative measure. 6.3.3 Lifestyle modifications affecting HDL levels and function Given the difficulty in administering therapeutics early enough in the progression of AD to be effective, lifestyle modifications to improve HDL-C concentration or function, particularly during mid-life, are an attractive alternative. It is estimated that up to a third of AD cases are related to modifiable risk factors, many of which are vascular related [165], and large lifestyle modification trials have shown considerable improvements to cognitive function [166,167]. Considering this evidence, the WHO recently recommended a set of lifestyle modifications for dementia prevention including physical activity, a balanced diet, smoking cessation and management of T2DM [171]. Indeed, all of these recommendations have also all been extensively studied for their effects on HDL-C concentration and HDL function [735]. Exercise was first shown to raise plasma HDL-C concentration in 1979 when middle-aged male subjects in a 4-month aerobic exercise program experienced an increase in their HDL-C concentration from 1.27 mM to 1.41 mM [736]. A meta-analysis of 10 different exercise interventions over six different cohorts found that medium HDL particles decrease and large HDL 204 particles increase with regular exercise although the increase in total HDL-C was non-significant overall [737]. Functionally, aerobic exercise has sometimes [738] but not always [739–741] been found to improve cholesterol efflux capacity and some evidence exists for improvements to anti-inflammatory and anti-oxidative activity [739–741]. Physical activity can also improve HDL function in metabolically unhealthy people. Aerobic exercise has been found to raise the levels of large sized HDL in overweight and obese subjects [742] and improve some HDL functions, including anti-oxidant, anti-inflammatory, and cholesterol efflux functions, in people with obesity, metabolic syndrome, or cardiac heart failure [464,743–745]. Combination of a 3-week aerobic exercise program with a high-fiber and low-fat diet improved the anti-inflammatory activity of HDL in middle-aged obese men, despite reductions to overall HDL-C concentration [746]. Dietary changes on their own can also improve HDL functions both in short-term and long-term studies. In healthy adults without cardiovascular risk factors, HDL was found to be more pro-inflammatory 6 h after a meal rich in saturated fats and more anti-inflammatory 6 h after a meal rich in polyunsaturated fats compared to HDL from fasted individuals [747]. The Primary Prevention of Cardiovascular Disease with a Mediterranean Diet (PREDIMED) study evaluated HDL functions after adherence to a Mediterranean diet enriched with virgin olive oil or mixed nuts in a 1-year RCT. They observed increases to HDL’s cholesterol efflux capacity for both diets compared to the low-fat control diet and improvements to anti-oxidative and vasodilatory functions with the diet enriched with olive oil [748]. The PREDIMED diet has also been tested for its potential benefits for cognition in 50 to 80-year old people with high vascular risk. After 6.5 years, those on either Mediterranean diet had improved MMSE and clock drawing scores compared to people on the low-fat control diet [180]. The detrimental effect of smoking on HDL-C concentration has been shown in many studies, although the effect on HDL function is less clear. Specifically, current smokers have been shown to have reduced plasma HDL-C concentrations [749], lower concentrations of large HDL and higher concentrations of small HDL [750] even in young people (mean age 24) who have smoked for a maximum of three years [751]. Functionally, HDL has reduced anti-oxidant activity 205 immediately after smoking in non-smokers [752] and in young smokers [751] and in vitro exposure of HDL to cigarette smoke can reduce its cholesterol efflux capacity [753]. Finally, HDL from people with T2DM is well established to have impairments in its vasoprotective functions, including reduced anti-inflammatory, anti-oxidant, and cholesterol efflux activities [384,424–426]. Fortunately, management of prediabetes appears to be able to reverse some of these negative outcomes on HDL. Lifestyle modifications focusing on nutrition, exercise, and weight loss can increase the concentration of plasma HDL-C in people with prediabetes according to a recent meta-analysis [754] and cholesterol efflux capacity improvements are observed in prediabetic subjects on a high-intensity, 6-month exercise program [738]. Furthermore, treatment of diabetes with niacin has been shown to improve HDL’s NO producing, vasodilatory, and anti-oxidant capabilities [424]. On the other hand, others have shown that aerobic exercise provides no benefits to HDL cholesterol efflux capacity or anti-oxidant activity in adults who have already progressed to full-blown T2DM [755]. That all of these examples of conditions altering HDL functions coincide with lifestyle modifications recommended to prevent dementia suggests that improving HDL functions may be a component of a healthy lifestyle protecting against cognitive decline. Lifestyle modification to reduce vascular risk factors for the prevention of dementia presents many advantages over pharmaceutical strategies. For example, lifestyle modifications are more cost-effective, they can be recommended without the requirement of a disease diagnosis, they are less likely to produce negative side-effects, and they can provide benefits for conditions beyond dementia. Future studies should investigate how these lifestyle modifications may alter the specific AD-relevant HDL functions discussed in this thesis. These assays could then theoretically be adopted as monitoring tools for the success of a lifestyle modification program in improving vascular health to prevent dementia. 6.3.4 HDL as a biomarker for cerebrovascular dysfunction In recent decades the success of imaging, CSF, and blood-based biomarkers for AD has been staggering. However, late-onset AD is a heterogenous disease with a number of subtypes therefore 206 identification of biomarkers that can stratify patients into these subtypes will be critical in order to more effectively treat the specific pathologies involved [756]. Biomarkers that allow for such precision medicine have greatly improved the effectiveness of therapies for cancer and CVD, however, the AD field lags in comparison [56]. HDL-based measurements, as discussed in this thesis, could potentially fill this need by acting as predictive biomarkers for AD subjects with vascular pathologies that may benefit from vascular specific therapeutic interventions. Importantly, plasma or serum HDL-C measurements are unlikely to have such predictive power as these assays simply measure the concentration of cholesterol residing on HDL and do not capture the full complexity of HDL. Indeed, apoA-I and other HDL-associated proteins were not found to be hits for AD pathology in a recent nontargeted proteomic analysis study aiming to develop a multi-protein panel as an AD biomarker [86] and Mendelian Randomization studies have ruled out HDL-C as a causal factor in AD [208,209]. One aspect of HDL not captured by HDL-C measurements is the functionality of HDL particles. Although HDL are known to have vasoprotective function in young healthy people, HDL isolated from people with CHD, T2DM, chronic kidney disease, or from elderly people can lack these functions disease [413,482,485,486]. In fact, measurement of the cholesterol efflux capacity of HDL has been demonstrated in several landmark studies to predict incident CHD, incident cardiovascular disease events, or likelihood of CAD more effectively than HDL-C [485,486,662]. Measurements of HDL-C are also oversimplified at they treat HDL as a homogenous population of particles whereas in reality, numerous subspecies of HDL exist with varying compositions, functions, and relationships with disease [343]. HDL-containing apoE are one such subspecies shown to have predictive power for CHD [118,119], an important role in reverse cholesterol transport [109], and a unique ability to remodel extracellular matrix [120]. ApoE-containing HDL concentration has also been shown to be reduced in youth with T2DM and obesity [122] but can increase in obese individuals after weight loss surgery to levels similar to lean individuals [757]. This HDL subpopulation is of particular interest as a biomarker of cerebrovascular function as recent studies have found that it has a unique ability to prevent Aβ accumulation in the wall of 3D human bioengineered arteries [617]. Furthermore, the concentration of apoE on plasma HDL has 207 been shown to be negatively associated with amyloid burden on PET scans [123] whereas the concentration of apoA-I on apoE-containing HDL particles was positively associated with MMSE scores [124]. Therefore, when investigating the utility of HDL-based measurements as predictive biomarkers, it will be important to look beyond HDL-C measurements and to also consider HDL functional measurements and measurement of HDL subspecies. An important step in the development of HDL-based predictive biomarkers for vascular dysfunction in AD is to assess how the function of an individual’s HDL measured with in vitro functional assays or concentrations of relevant HDL subspecies correlate to their cerebrovascular pathologies as measured by the presence of CAA, microinfarcts, or white matter hyperintensities observed post-mortem or with imaging techniques. If such HDL-based measurements can accurately predict the presence of cerebrovascular pathologies in AD patients, they might be used as a less invasive method to stratify AD patients into subpopulations with and without vascular pathologies and to identify specific patients who may benefit from vascular treatments [56]. It may also be interesting to determine if HDL-related measures can predict which patients are at risk of developing ARIA in response to anti-Aβ immunotherapies. ARIA likely results from the deposition of Aβ in the cerebrovasculature as it is solubilized from plaques and cleared through the perivascular pathway [729], therefore, biomarkers that predict ARIA risk or that could monitor ARIA progression and resolution are important to develop. A major obstacle in effectively treating AD is identifying those that will suffer from the disease early enough in disease development before unrepairable neurodegeneration occurs. Prognostic biomarkers that can predict a patient’s progression into AD earlier than existing biomarkers could be a solution [758]. As vascular dysfunction occurs early in AD [4,6,235,678], biomarkers that indicate the presence of cerebrovascular dysfunction, such as HDL-based measurements, have considerable potential in predicting cognitive decline. It will therefore be important to evaluate longitudinal changes to HDL-based biomarkers in cognitively normal people in conjunction with other AD biomarkers to determine if HDL-based measurements could improve prognostic precision for AD’s vascular components. 208 Current assays of HDL function relevant to AD discussed in this thesis include cell-free assays against Aβ fibrillization, 2D cell-based assays of NO production and suppression of TNFα or Aβ-induced monocyte adhesion in brain derived ECs, or 3D cell-based assays of suppression of Aβ vascular deposition and Aβ-induced monocyte adhesion in human bioengineered arteries [578,658]. Improved scalability and reliability of these assays and the pursuit of new assays of HDL composition or function relevant to AD is expected to facilitate the development of HDL-based biomarkers in AD. 6.4 Conclusions Studies in mouse models and human-based in vitro models in this thesis suggest several ways by which HDL may act on cells of the cerebrovasculature to prevent dysfunction related to Aβ in AD, as summarized in Figure 6.1. These observations help to explain the protective associations between plasma HDL-C concentration and AD in some human epidemiological and mouse studies and lay the groundwork for future HDL-based therapeutics, preventative strategies, or biomarkers related to cerebrovascular health in AD. Figure 6.1 Vasoprotective functions of HDL relevant for Alzheimer’s disease. HDL has been shown to have at least four distinct functions that could protect against AD. (i) HDL suppresses the pathological accumulation of Aβ in cerebral vessels known as CAA. (ii) HDL suppresses vascular inflammation induced by Aβ or pro-inflammatory cytokines and global neuroinflammation in AD. (iii) HDL stimulates the production of nitric oxide from brain ECs. (iv) HDL delays the fibrillization of Aβ. Although large, spherical HDL is 209 unlikely to cross the BBB, apoA-I can gain access to the brain via the blood-CSF barrier at the choroid plexus, at least in mouse models. HDL-like particles in the brain are mainly apoE-based. ApoE is found in three isoforms in humans; apoE2, apoE3, and apoE4. APOE-ε4 is the major genetic risk factor for late-onset AD and apoE4 has several detrimental functions including delaying Aβ transport out of the brain, promoting BBB breakdown, and increasing inflammation. ApoE is also found in the CSF along with apoA-I. AD: Alzheimer’s disease, Aβ: amyloid beta, HDL: high-density lipoprotein, apo: apolipoprotein, CSF: cerebrospinal fluid, CAA: cerebral amyloid angiopathy, EC: endothelial cell, LRP-1: low-density lipoprotein receptor-related protein 1, VLDLR: very low-density lipoprotein receptor. 210 References 1. Alzheimer’s Disease International. World Alzheimer Report 2019: Attitudes to dementia. London, UK; 2019. 2. Prince M, Wilmo A, Guerchet M, Ali G-C, Wu Y-T, Prina M, et al. World Alzheimer Report 2015: The Global Impact of Dementia. London, UK; 2015. 3. Jack CRRR, Bennett DAAA, Blennow K, Carrillo MCCC, Dunn B, Haeberlein SBB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018 Apr;14(4):535–62. 4. Sweeney MD, Montagne A, Sagare AP, Nation DA, Schneider LS, Chui HC, et al. Vascular dysfunction—The disregarded partner of Alzheimer’s disease. Alzheimer’s Dement. 2019;15(1):158–67. 5. Attems J, Jellinger KA. The overlap between vascular disease and Alzheimer’s disease – lessons from pathology. BMC Med. 2014;12(1):206. 6. Nation D, Sweeney MD, Montagne A, Sagare AP, D’Orazio L, Pachicano M, et al. Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med. 2019;25(2):270–6. 7. Tarasoff-Conway JM, Carare RO, Osorio RS, Glodzik L, Butler T, Fieremans E, et al. Clearance systems in the brain-implications for Alzheimer disease. Nat Rev Neurol. 2015;11(8):457–70. 8. Riwanto M, Landmesser U. High density lipoproteins and endothelial functions : mechanistic insights and alterations in cardiovascular disease. J Lipid Res. 2013;54(12):3227–43. 9. Maurer K, Volk S, Gerbaldo H. Auguste D and Alzheimer’s disease. Lancet. 1997;349(9073):1546–9. 10. Alzheimer A. Über einen eigenartigen schweren Erkrankungsprozeß der Hirnrinde. Neurol Cent. 1906;23:1129–36. 11. Goedert M. Oskar Fischer and the study of dementia. Brain. 2009;132(4):1102–11. 211 12. Fischer O. Miliare Nekrosen mit drusigen Wucherungen der Neurofibrillen, eine regelmässige Veränderung der Hirnrinde bei seniler Demenz. Monatsschr Psychiat Neurol. 1907;22:361–72. 13. Fischer O. Die presbyophrene Demenz, deren anatomische Grundlage und klinische Abgrenzung. Z ges Neurol Psychiat. 1910;3:371–471. 14. Fischer O. Ein weiterer Beitrag zur Klinik und Pathologie der presbyophrenen Demenz. Z ges Neruol Psychiat. 1912;12:99–135. 15. Nichols E, Szoeke CEI, Vollset SE, Abbasi N, Abd-Allah F, Abdela J, et al. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(1):88–106. 16. Fiest KM, Roberts JI, Maxwell CJ, Hogan DB, Smith EE, Frolkis A, et al. The Prevalence and Incidence of Dementia Due to Alzheimer’s Disease: a Systematic Review and Meta-Analysis. Can J Neurol Sci. 2016;43(S1):S51–82. 17. Chambers L, Bancej C, McDowell I. Prevalence and monetary costs of dementia in Canada. Toronto, ON; 2016. 18. Satizabal C, Beiser A, Chouraki V, Chêne G, Dufouil C, Seshadri S. Incidence of Dementia over Three Decades in the Framingham Heart Study. N Engl J Med. 2016;374(6):523–32. 19. Matthews FE, Robinson L, Jagger C, Barnes LE, Arthur A, Brayne C, et al. A two decade dementia incidence comparison from the Cognitive Function and Ageing Studies I and II. Nat Commun. 2016;7:11398. 20. Zhu XC, Tan L, Wang HF, Jiang T, Cao L, Wang C, et al. Rate of early onset Alzheimer’s disease: a systematic review and meta-analysis. Ann Transl Med. 2015;3(3):38. 21. Alzheimer’s Association. 2019 Alzheimer’s Disease Facts and Figures. Alzheimers Dement. 2019;15(3):321–87. 22. Jarmolowicz AI, Chen HY, Panegyres PK. The patterns of inheritance in early-onset 212 dementia: Alzheimer’s disease and frontotemporal dementia. Am J Alzheimers Dis Other Demen. 2015;30(3):299–306. 23. Wingo TS, Lah JJ, Levey AI, Cutler DJ. Autosomal recessive causes likely in early-onset Alzheimer disease. Arch Neurol. 2012;69(1):59–64. 24. Kunkle BW, Vardarajan BN, Naj AC, Whitehead PL, Rolati S, Slifer S, et al. Early-onset Alzheimer disease and candidate risk genes involved in endolysosomal transport. JAMA Neurol. 2017;74(9):1113–22. 25. Mendez M. Early-onset Alzheimer Disease and Its Variants. Contin (Minneap Minn). 2016;25(1):34–51. 26. Koedam ELGE, Lauffer V, Van Der Vlies AE, Van Der Flier WM, Scheltens P, Pijnenburg YAL. Early-versus late-onset Alzheimer’s disease: More than age alone. J Alzheimer’s Dis. 2010;19(4):1401–8. 27. Baillon S, Gasper A, Wilson-Morkeh F, Pritchard M, Jesu A, Velayudhan L. Prevalence and Severity of Neuropsychiatric Symptoms in Early- Versus Late-Onset Alzheimer’s Disease. Am J Alzheimers Dis Other Demen. 2019;34(7–8):433–8. 28. Chen Y, Sillaire AR, Dallongeville J, Skrobala E, Wallon D, Dubois B, et al. Low Prevalence and Clinical Effect of Vascular Risk Factors in Early-Onset Alzheimer’s Disease. J Alzheimer’s Dis. 2017;60(3):1045–54. 29. Selkoe D, Hardy J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med. 2016;8(6):595–608. 30. Yuksel M, Tacal O. Trafficking and proteolytic processing of amyloid precursor protein and secretases in Alzheimer’s disease development: An up-to-date review. Eur J Pharmacol. 2019;856:172415. 31. Zhang X, Song W. The role of APP and BACE1 trafficking in APP processing and amyloid-β generation. Alzheimer’s Res Ther. 2013;5(5):46. 32. Haass C, Kaether C, Thinakaran G, Sisodia S. Trafficking and proteolytic processing of APP. Cold Spring Harb Perspect Med. 2012;2(5):1–25. 213 33. Chen GF, Xu TH, Yan Y, Zhou YR, Jiang Y, Melcher K, et al. Amyloid beta: Structure, biology and structure-based therapeutic development. Acta Pharmacol Sin. 2017;38(9):1205–35. 34. Chen X-Q, Mobley WC. Alzheimer Disease Pathogenesis: Insights From Molecular and Cellular Biology Studies of Oligomeric Aβ and Tau Species. Front Neurosci. 2019;13:659. 35. Moir RD, Lathe R, Tanzi RE. The antimicrobial protection hypothesis of Alzheimer’s disease. Alzheimer’s Dement. 2018;14(12):1602–14. 36. Rice HC, De Malmazet D, Schreurs A, Frere S, Van Molle I, Volkov AN, et al. Secreted amyloid-β precursor protein functions as a GABABR1a ligand to modulate synaptic transmission. Science (80- ). 2019;363(6423). 37. Kern S, Zetterberg H, Kern J, Zettergren A, Waern M, Höglund K, et al. Prevalence of preclinical Alzheimer disease: Comparison of current classification systems. Neurology. 2018;90(19):e1682–91. 38. Rowe CC, Ellis KA, Rimajova M, Bourgeat P, Pike KE, Jones G, et al. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging. 2010;31(8):1275–83. 39. Koscik RL, Betthauser TJ, Jonaitis EM, Allison SL, Clark LR, Hermann BP, et al. Amyloid duration is associated with preclinical cognitive decline and tau PET. Alzheimer’s Dement Diagnosis, Assess Dis Monit. 2020;12:e12007. 40. Arenaza-Urquijo EM, Vemuri P. Resistance vs resilience to Alzheimer disease: Clarifying terminology for preclinical studies. Neurology. 2018;90(15):695–703. 41. Naseri NN, Wang H, Guo J, Sharma M, Luo W. The complexity of tau in Alzheimer’s disease. Neurosci Lett. 2019;705:183–94. 42. Eftekharzadeh B, Daigle JG, Kapinos LE, Coyne A, Schiantarelli J, Carlomagno Y, et al. Tau Protein Disrupts Nucleocytoplasmic Transport in Alzheimer’s Disease. Neuron. 2018;99(5):925-940.e7. 214 43. Neddens J, Temmel M, Flunkert S, Kerschbaumer B, Hoeller C, Loeffler T, et al. Phosphorylation of different tau sites during progression of Alzheimer’s disease. Acta Neuropathol Commun. 2018;6(1):52. 44. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–59. 45. DeVos SL, Corjuc BT, Oakley DH, Nobuhara CK, Bannon RN, Chase A, et al. Synaptic tau seeding precedes tau pathology in human Alzheimer’s disease brain. Front Neurosci. 2018;12:267. 46. Ahmed Z, Cooper J, Murray TK, Garn K, McNaughton E, Clarke H, et al. A novel in vivo model of tau propagation with rapid and progressive neurofibrillary tangle pathology: The pattern of spread is determined by connectivity, not proximity. Acta Neuropathol. 2014;127(5):667–83. 47. Yamada K, Holth JK, Liao F, Stewart FR, Mahan TE, Jiang H, et al. Neuronal activity regulates extracellular tau in vivo. J Exp Med. 2014;211(3):387–93. 48. Saman S, Kim WH, Raya M, Visnick Y, Miro S, Saman S, et al. Exosome-associated tau is secreted in tauopathy models and is selectively phosphorylated in cerebrospinal fluid in early Alzheimer disease. J Biol Chem. 2012;287(6):3842–9. 49. Fiandaca MS, Kapogiannis D, Mapstone M, Boxer A, Eitan E, Schwartz JB, et al. Identification of preclinical Alzheimer’s disease by a profile of pathogenic proteins in neurally derived blood exosomes: A case-control study. Alzheimer’s Dement. 2015;11(6):600-607.e1. 50. Wang Y, Balaji V, Kaniyappan S, Krüger L, Irsen S, Tepper K, et al. The release and trans-synaptic transmission of Tau via exosomes. Mol Neurodegener. 2017;12(1):5. 51. Aschenbrenner AJ, Gordon BA, Benzinger TLS, Morris JC, Hassenstab JJ. Influence of tau PET, amyloid PET, and hippocampal volume on cognition in Alzheimer disease. Neurology. 2018;91(9):e859–66. 52. Hanseeuw BJ, Betensky RA, Jacobs HILL, Schultz AP, Sepulcre J, Becker JA, et al. Association of Amyloid and Tau with Cognition in Preclinical Alzheimer Disease: A 215 Longitudinal Study. JAMA Neurol. 2019;76(8):915–24. 53. McKee AC, Cairns NJ, Dickson DW, Folkerth RD, Dirk Keene C, Litvan I, et al. The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathol. 2016;131(1):75–86. 54. McKhann G, Knopman D, Chertkow H, Hyman B, Jack CJ, Kawas C, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7(3):263–9. 55. Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, et al. Brain atrophy in Alzheimer’s Disease and aging. Ageing Res Rev. 2016;30:25–48. 56. Hampel H, O’Bryant SE, Molinuevo JL, Zetterberg H, Masters CL, Lista S, et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol. 2018;14(11):639–52. 57. Mielke MM, Syrjanen JA, Blennow K, Zetterberg H, Vemuri P, Skoog I, et al. Plasma and CSF neurofilament light: Relation to longitudinal neuroimaging and cognitive measures. Neurology. 2019;93(3):e252-260. 58. Mattsson N, Cullen NC, Andreasson U, Zetterberg H, Blennow K. Association between Longitudinal Plasma Neurofilament Light and Neurodegeneration in Patients with Alzheimer Disease. JAMA Neurol. 2019;76(7):791–9. 59. Preische O, Schultz SA, Apel A, Kuhle J, Kaeser SA, Barro C, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med. 2019;25(2):277–83. 60. Bridel C, Van Wieringen WN, Zetterberg H, Tijms BM, Teunissen CE, Alvarez-Cermeño JC, et al. Diagnostic Value of Cerebrospinal Fluid Neurofilament Light Protein in Neurology: A Systematic Review and Meta-analysis. JAMA Neurol. 2019;76(9):1035–48. 61. Shahim P, Zetterberg H, Tegner Y, Blennow K. Serum neurofilament light as a biomarker for mild traumatic brain injury in contact sports. Neurology. 2017;88(19):1788–94. 216 62. Shi Y, Holtzman DM. Interplay between innate immunity and Alzheimer disease: APOE and TREM2 in the spotlight. Nat Rev Immunol. 2018;18(12):759–72. 63. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019;51(3):404–13. 64. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet. 2019;51(3):414–30. 65. Edison P, Archer HA, Gerhard A, Hinz R, Pavese N, Turkheimer FE, et al. Microglia, amyloid, and cognition in Alzheimer’s disease: An [11C](R)PK11195-PET and [11C]PIB-PET study. Neurobiol Dis. 2008;32(3):412–9. 66. Krasemann S, Madore C, Cialic R, Baufeld C, Calcagno N, El Fatimy R, et al. The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases. Immunity. 2017;47(3):566-581.e9. 67. Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell. 2017;169(7):1276-1290.e17. 68. Sosna J, Philipp S, Albay RI, Reyes-Ruiz JM, Baglietto-Vargas D, LaFerla FM, et al. Early long-term administration of the CSF1R inhibitor PLX3397 ablates microglia and reduces accumulation of intraneuronal amyloid, neuritic plaque deposition and pre-fibrillar oligomers in 5XFAD mouse model of Alzheimer’s disease. Mol Neurodegener. 2018;13(1):1–11. 69. Spangenberg EE, Lee RJ, Najafi AR, Rice RA, Elmore MRP, Blurton-Jones M, et al. Eliminating microglia in Alzheimer’s mice prevents neuronal loss without modulating amyloid-β pathology. Brain. 2016;139(4):1265–81. 70. Dagher NN, Najafi AR, Kayala KMN, Elmore MRP, White TE, Medeiros R, et al. Colony-stimulating factor 1 receptor inhibition prevents microglial plaque association and improves cognition in 3xTg-AD mice. J Neuroinflammation. 2015;12:139. 217 71. Olmos-Alonso A, Schetters STT, Sri S, Askew K, Mancuso R, Vargas-Caballero M, et al. Pharmacological targeting of CSF1R inhibits microglial proliferation and prevents the progression of Alzheimer’s-like pathology. Brain. 2016;139(3):891–907. 72. Mancuso R, Fryatt G, Cleal M, Obst J, Pipi E, Monzón-Sandoval J, et al. CSF1R inhibitor JNJ-40346527 attenuates microglial proliferation and neurodegeneration in P301S mice. Brain. 2019;142(10):3243–64. 73. Shi Y, Manis M, Long J, Wang K, Sullivan PM, Serrano JR, et al. Microglia drive APOE-dependent neurodegeneration in a tauopathy mouse model. J Exp Med. 2019;216(11):2546–61. 74. Nott A, Holtman I, Coufal N, Schlacketzki J, Yu M, Hu R, et al. Brain cell type-specific enhancer-promoter interactome maps and disease risk association. Science (80- ). 2019;336(6469):1134–9. 75. Jack CR, Albert MS, Knopman DS, McKhann GM, Sperling RA, Carrillo MC, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7(3):257–62. 76. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):270–9. 77. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7(3):280–92. 78. Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement. 2013;8(1):1–13. 79. Gauthier S, Patterson C, Chertkow H, Gordon M, Herrmann N, Rockwood K, et al. 4th 218 Canadian Consensus Conference on the Diagnosis and Treatment of Dementia. Can J Neurol Sci. 2012;39(6 Suppl 5):S1-8. 80. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7(3):270–9. 81. Hansson O, Lehmann S, Otto M, Zetterberg H, Lewczuk P. Advantages and disadvantages of the use of the CSF Amyloid β (Aβ) 42/40 ratio in the diagnosis of Alzheimer’s Disease. Alzheimer’s Res Ther. 2019;11(34):1–15. 82. Verberk IMW, Slot RE, Verfaillie SCJ, Heijst H, Prins ND, van Berckel BNM, et al. Plasma Amyloid as Prescreener for the Earliest Alzheimer Pathological Changes. Ann Neurol. 2018;84(5):648–58. 83. Mattsson N, Zetterberg H, Insel PS, Baker D, Hehir CAT, Hanlon D, et al. Plasma tau in Alzheimer disease. Neurology. 2016;87(17):1827–35. 84. Mielke MM, Hagen CE, Xu J, Chai X, Vemuri P, Val J, et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimer’s Dement. 2019;14(8):989–97. 85. Shen Y, Wang H, Sun Q, Yao H, Keegan AP, Mullan M, et al. Increased Plasma Beta-Secretase 1 May Predict Conversion to Alzheimer’s Disease Dementia in Individuals With Mild Cognitive Impairment. Biol Psychiatry. 2018;83(5):447–55. 86. Ashton NJ, Nevado-Holgado AJ, Barber IS, Lynham S, Gupta V, Chatterjee P, et al. A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Sci Adv. 2019;5(2):eaaau7220. 87. Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93(17):e1647–59. 88. West T. C2N Diagnostics Receives Breakthrough Device Designation from U.S. FDA for Blood Test to Screen for Alzheimer’s Disease Risk [Internet]. C2N Diagnostics. 2019. 219 Available from: https://www.c2ndiagnostics.com/press/press/2019/1/24/c2n-diagnostics-receives-breakthrough-device-designation-from-us-fda-for-blood-test-to-screen-for-alzheimers-disease-risk 89. Palmqvist S, Janelidze S, Stomrud E, Zetterberg H, Karl J, Zink K, et al. Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease-Related β-Amyloid Status. JAMA Neurol. 2019;76(9):1060–9. 90. Fandos N, Pérez-Grijalba V, Pesini P, Olmos S, Bossa M, Villemagne VL, et al. Plasma amyloid β 42/40 ratios as biomarkers for amyloid β cerebral deposition in cognitively normal individuals. Alzheimer’s Dement Diagnosis, Assess Dis Monit. 2017;8:179–87. 91. Janelidze S, Stomrud E, Palmqvist S, Zetterberg H, Van Westen D, Jeromin A, et al. Plasma β-amyloid in Alzheimer’s disease and vascular disease. Sci Rep. 2016;6:26801. 92. Tzen KY, Yang SY, Chen TF, Cheng TW, Horng HE, Wen HP, et al. Plasma Aβ but not tau is related to brain PiB retention in early Alzheimer’s disease. ACS Chem Neurosci. 2014;5(9):830–6. 93. Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimer’s Dement. 2017;13(8):841–9. 94. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V, et al. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature. 2018;554(7691):249–54. 95. Vergallo A, Mégret L, Lista S, Cavedo E, Zetterberg H, Blennow K, et al. Plasma amyloid β 40/42 ratio predicts cerebral amyloidosis in cognitively normal individuals at risk for Alzheimer’s disease. Alzheimer’s Dement. 2019;15(6):764–75. 96. Chêne G, Beiser A, Au R, Preis SR, Wolf PA, Dufouil C, et al. Gender and incidence of dementia in the Framingham Heart Study from mid-adult life. Alzheimer’s Dement. 2015;11(3):310–20. 97. Ferretti MT, Iulita MF, Cavedo E, Chiesa PA, Dimech AS, Chadha AS, et al. Sex differences in Alzheimer disease — The gateway to precision medicine. Nat Rev Neurol. 220 2018;14(8):457–69. 98. Gilsanz P, Lee C, Corrada MM, Kawas CH, Quesenberry CP, Whitmer RA. Reproductive period and risk of dementia in a diverse cohort of health care members. Neurology. 2019;92(17):e2005–14. 99. Gaiteri C, Mostafavi S, Honey CJ, De Jager PL, Bennett DA. Genetic variants in Alzheimer disease — molecular and brain network approaches. Nat Publ Gr. 2016;12(7):413–27. 100. Goate A. Segregation of a missense mutation in the amyloid β-protein precursor gene with familial Alzheimer’s disease. Nature. 1991;349(6311):704–6. 101. Levy-lahad AE, Wasco W, Poorkaj P, Romano DM, Pettingell WH, Yu C, et al. Candidate gene for the chromosome 1 familial Alzheimer’s disease locus. Science (80- ). 1995;269(973–976). 102. Rogaev E, Sherrington R, Rogaeva E, Levesque G, Ikeda M, Liang Y, et al. Familial Alzheimer’s disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer’s disease type 3 gene. Nature. 1995;376(6543):775–8. 103. Jonsson T, Stefansson H, Steinberg S, Jonsdottir I, Jonsson P V., Snaedal J, et al. Variant of TREM2 associated with the risk of AD. N Engl J Med. 2013;368(2):107–16. 104. Guerreiro R, Ph D, Wojtas A, Bras J, Carrasquillo M, Rogaeva E, et al. TREM2 variants in AD. N Engl J Med. 2013;368(2):117–27. 105. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW, et al. Gene Dose of Apolipoprotein-E Type-4 Allele and the Risk of Alzheimers-Disease in Late-Onset Families. Science (80- ). 1993;261(5123):921–3. 106. Farrer L, Cupples L, Haines J, Hyman B, Kukull W, Mayeux R, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA. 1997;278(16):1349–56. 107. Mahley RW. Apolipoprotein E: cholesterol transport protein with expanding role in cell 221 biology. Science (80- ). 1988;240(4852):622–30. 108. Liao F, Yoon H, Kim J. Apolipoprotein e metabolism and functions in brain and its role in Alzheimer’s disease. Curr Opin Lipidol. 2017;28(1):60–7. 109. Morton AM, Koch M, Mendivil CO, Furtado JD, Tjønneland A, Overvad K, et al. Apolipoproteins E and CIII interact to regulate HDL metabolism and coronary heart disease risk. JCI Insight. 2018;3(4):pii: 98045. 110. Furtado JD, Yamamoto R, Melchior JT, Andraski AB, Gamez-guerrero M, Mulcahy P, et al. Distinct Proteomic Signatures in 16 HDL (High-Density Lipoprotein) Subspecies. Arter Thromb Vasc Biol. 2018;38(12):2827–42. 111. Chiba H, Akizawa K, Fujisawa S, Osaka-Nakamori T, Iwasaki N, Suzuki H, et al. A rapid and simple quantification of human apolipoprotein E-rich high-density lipoproteins in serum. Biochem Med Metab Biol. 1992;47(1):31–7. 112. Koch S, Donarski N, Goetze K, Kreckel M, Stuerenburg HJ, Buhmann C, et al. Characterization of four lipoprotein classes in human cerebrospinal fluid. J Lipid Res. 2001;42(7):1143–51. 113. Linton MF, Gish R, Hubl ST, Bütler E, Esquivel C, Bry WI, et al. Phenotypes of apolipoprotein B and apolipoprotein E after liver transplantation. J Clin Invest. 1991;88(1):270–81. 114. Phillips MMC. Apolipoprotein E isoforms and lipoprotein metabolism. IUBMB Life. 2014;66(9):616–23. 115. Plump AS, Azrolan N, Odaka H, Wu L, Jiang X, Tall A, et al. ApoA-I knockout mice : characterization of HDL metabolism in homozygotes and identification of a post-RNA mechanism of apoA-l up-regulation in heterozygotes. J Lipid Res. 1997;38(5):1033–47. 116. Zhang SH, Reddick RL, Piedrahita JA, Maeda N. Spontaneous hypercholesterolemia and arterial lesions in mice lacking apolipoprotein E. Science (80- ). 1992;258(5081):468–71. 117. Morton AM, Furtado JD, Mendivil CO, Sacks FM. Dietary unsaturated fat increases HDL metabolic pathways involving apoE favorable to reverse cholesterol transport. JCI Insight. 222 2019;4(7):pii: 124620. 118. Qi Y, Liu J, Wang W, Wang M, Zhao F, Sun J, et al. Apolipoprotein E-containing high-density lipoprotein (HDL) modifies the impact of cholesterol-overloaded HDL on incident coronary heart disease risk: A community-based cohort study. J Clin Lipidol. 2018;12(1):89-98.e2. 119. Sacks FM, Alaupovic P, Moye L a., Cole TG, Sussex B, Stampfer MJ, et al. VLDL, Apolipoproteins B, CIII, and E, and Risk of Recurrent Coronary Events in the Cholesterol and Recurrent Events (CARE) Trial. Circulation. 2000;102(16):1886–92. 120. Kothapalli D, Liu SL, Bae YH, Monslow J, Xu T, Hawthorne EA, et al. Cardiovascular Protection by ApoE and ApoE-HDL Linked to Suppression of ECM Gene Expression and Arterial Stiffening. Cell Rep. 2012;2(5):1259–71. 121. Young EK, Chatterjee C, Sparks DL. HDL-ApoE Content Regulates the Displacement of Hepatic Lipase from Cell Surface Proteoglycans. Am J Pathol. 2009;175(1):448–57. 122. Gordon SM, Davidson WS, Urbina EM, Dolan LM, Heink A, Zang H, et al. The effects of type 2 diabetes on lipoprotein composition and arterial stiffness in male youth. Diabetes. 2013;62(8):2958–67. 123. Koch M, DeKosky ST, Fitzpatrick AL, Furtado JD, Lopez OL, Kuller LH, et al. Apolipoproteins and Alzheimer’s pathophysiology. Alzheimer’s Dement Diagnosis, Assess Dis Monit. 2018;10:545–53. 124. Koch M, DeKosky S, Goodman M, Sun J, Furtado J, Fitzpatrick AL, et al. High-density lipoprotein and its apolipoprotein-defined subspecies and risk of dementia. J Lipid Res. 2020;61(3):445–54. 125. Anderson R, Barnes J, Bliss T, Cain D, Cambon K, Davies HA, et al. Behavioural, physiological and morphological analysis of a line of apolipoprotein E knockout mouse. Neuroscience. 1998;85(1):93–110. 126. Anderson R, Higgins GA. Absence of central cholinergic deficits in ApoE knockout mice. Psychopharmacology (Berl). 1997;132(2):135–44. 223 127. Krugers H, Mulder M, Korf J, Havekes L, de Kloet ER, Joëls M. Altered synaptic plasticity in hippocampal CA1 area of apolipoprotein E deficient mice. Neuroreport. 1997;8(11):2505–10. 128. Oitzl M, Mulder M, Lucassen P, Havekes L, Grootensdorst J, de Kloet E. Severe learning deficits in apolipoprotein E-knockout mice in a water maze task. Brain Res. 1997;752(1–2):189–96. 129. Gordon I, Grauer E, Genis I, Sehayek E, Michaelson DM. Memory deficits and cholinergic impairments in apolipoprotein E-deficient mice. Neurosci Lett. 1995;199(1):1–4. 130. Masliah E, Samuel W, Veinbergs I, Mallory M, Mante M, Saitoh T. Neurodegeneration and cognitive impairment in apoE-deficient mice is ameliorated by infusion of recombinant apoE. Brain Res. 1997;751(2):307–14. 131. Belloy ME, Napolioni V, Greicius MD. A Quarter Century of APOE and Alzheimer’s Disease: Progress to Date and the Path Forward. Neuron. 2019;101(5):820–38. 132. Laws SM, Hone E, Gandy S, Martins RN. Expanding the association between the APOE gene and the risk of Alzheimer’s disease: Possible roles for APOE promoter polymorphisms and alterations in APOE transcription. J Neurochem. 2003;84(6):1215–36. 133. Yamazaki Y, Zhao N, Caulfield TR, Liu C-C, Bu G. Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat Rev Neurol. 2019;15(9):501–18. 134. Naj AC, Jun G, Reitz C, Kunkle BW, Perry W, Park YS, et al. Effects of multiple genetic loci on age at onset in late-onset Alzheimer disease: a genome-wide association study. JAMA Neurol. 2014;71(11):1394–404. 135. Bangen K, Beiser A, Delano-Wood L, Nation D, Lamar M, Libon D, et al. APOE genotype modifies the relationship between midlife vascular risk factors and later cognitive decline. J Pediatr. 2013;22(8):1361–9. 136. Neu SC, Pa J, Kukull W, Beekly D, Kuzma A, Gangadharan P, et al. Apolipoprotein E Genotype and Sex Risk Factors for Alzheimer Disease: A Meta-analysis. JAMA Neurol. 2017;74(10):1178–89. 224 137. Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, Scheltens P, Verhey FRJ, et al. Prevalence of Cerebral Amyloid Pathology in Persons Without Dementia A Meta-analysis. JAMA. 2015;313(19):1924–38. 138. Caselli RJ, Walker D, Sue L, Sabbagh M, Beach T. Amyloid load in nondemented brains correlates with APOE e4. Neurosci Lett. 2010;473(3):168–71. 139. Castellano JM, Kim J, Stewart FR, Jiang H, DeMattos RB, Patterson BW, et al. Human apoE isoforms differentially regulate brain amyloid-β peptide clearance. Sci Transl Med. 2011;3(89):89ra57. 140. Deane R, Sagare A, Hamm K, Parisi M, Lane S, Finn MB, et al. apoE isoform-specific disruption of amyloid beta peptide clearance from mouse brain. J Clin Invest. 2008;118(12):4002–13. 141. Shackleton B, Crawford F, Bachmeier C. Inhibition of ADAM10 promotes the clearance of Aβ across the BBB by reducing LRP1 ectodomain shedding. Fluids Barriers CNS. 2016;13(1):14. 142. Bachmeier C, Shackleton B, Ojo J, Paris D, Mullan M, Crawford F. Apolipoprotein E Isoform-Specific Effects on Lipoprotein Receptor Processing. Neuromolecular Med. 2014;16(4):686–96. 143. Liu CC, Zhao N, Fu Y, Wang N, Linares C, Tsai CW, et al. ApoE4 Accelerates Early Seeding of Amyloid Pathology. Neuron. 2017;96(5):1024-1032.e3. 144. Engelborghs S, Dermaut B, Mari P, Symons A, Vloeberghs E, Maertens K, et al. Dose dependent effect of APOE epsilon4 on behavioral symptoms in frontal lobe dementia. Neurobiol Aging. 2006;27(2):285–92. 145. Shi Y, Yamada K, Liddelow SA, Smith ST, Zhao L, Luo W, et al. ApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy. Nature. 2017;549(7673):523–7. 146. Zipser BD, Johanson CE, Gonzalez L, Berzin TM, Tavares R, Hulette CM, et al. Microvascular injury and blood–brain barrier leakage in Alzheimer’s disease. Neurobiol Aging. 2007;28(7):977–86. 225 147. Halliday MR, Rege S V, Ma Q, Zhao Z, Miller C a, Winkler E a, et al. Accelerated pericyte degeneration and blood-brain barrier breakdown in apolipoprotein E4 carriers with Alzheimer’s disease. J Cereb Blood Flow Metab. 2015;36(1):216–27. 148. Bell RD, Winkler E a., Singh I, Sagare AP, Deane R, Wu Z, et al. Apolipoprotein E controls cerebrovascular integrity via cyclophilin A. Nature. 2012;485(7399):512–6. 149. Rieker C, Migliavacca E, Vaucher A, Mayer FC, Baud G, Marquis J, et al. Apolipoprotein E4 Expression Causes Gain of Toxic Function in Isogenic Human Induced Pluripotent Stem Cell-Derived Endothelial Cells. Atheroscler Thromb Vasc Biol. 2019;39(9):e195-207. 150. TCW J, Liang SA, Qian L, Pipalia NH, Chao MJ, Shi Y, et al. Cholesterol and matrisome pathways dysregulated in human APOE ε4 glia. bioRxiv. 2019; 151. Chung W, Verghese PB, Chakraborty C, Joung J, Hyman BT, Ulrich JD, et al. Novel allele-dependent role for APOE in controlling the rate of synapse pruning by astrocytes. Proc Natl Acad Sci U S A. 2016;113(36):10186–91. 152. Lin YT, Seo J, Gao F, Ko T, Yankner BAB, Tsai LHL, et al. APOE4 Causes Widespread Molecular and Cellular Alterations Associated with Alzheimer’s Disease Phenotypes in Human iPSC-Derived Brain Cell Types. Neuron. 2018;98(6):1141-1154.e7. 153. Zhao J, Davis MD, Martens YA, Shinohara M, Graff-radford NR, Younkin SG, et al. APOE ε4/ε4 diminishes neurotrophic function of human iPSC-derived astrocytes. Hum Mol Genet. 2017;26(14):2690–700. 154. Holtman IR, Raj DD, Miller JA, Schaafsma W, Yin Z, Brouwer N, et al. Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis. Acta Neuropathol Commun. 2015;23(3):31. 155. Yin Z, Raj D, Saiepour N, Dam D Van, Brouwer N, Holtman IR, et al. Immune hyperreactivity of Aβ plaque-associated microglia in Alzheimer’s disease. Neurobiol Aging. 2017;55:115–22. 156. Fitz NF, Tapias V, Cronican AA, Castranio EL, Saleem M, Carter AY, et al. Opposing effects of Apoe/Apoa1 double deletion on amyloid-β pathology and cognitive 226 performance in APP mice. Brain. 2015;138(12):3699–715. 157. Bales KR, Verina T, Dodel R, Du Y, Altstiel L, Bender M, et al. Lack of apolipoprotein E dramatically reduces amyloid beta-peptide deposition. Nat Genet. 1997;17(3):263–4. 158. Kim J, Jiang H, Park S, Eltorai AEM, Stewart FR, Yoon H, et al. Haploinsufficiency of human APOE reduces amyloid deposition in a mouse model of amyloid-β amyloidosis. J Neurosci. 2011;31(49):18007–12. 159. Parhizkar S, Arzberger T, Brendel M, Kleinberger G, Deussing M, Focke C, et al. Loss of TREM2 function increases amyloid seeding but reduces plaque-associated ApoE. Nat Neurosci. 2019;22(2):191–204. 160. Ulrich JD, Ulland TK, Mahan TE, Nyström S, Nilsson KP, Song WM, et al. ApoE facilitates the microglial response to amyloid plaque pathology. J Exp Med. 2018;215(4):1047–58. 161. Rodriguez GA, Tai LM, Ladu MJ, Rebeck GW. Human APOE4 increases microglia reactivity at Aβ plaques in a mouse model of Aβ deposition. J Neuroinflammation. 2014;11:111. 162. Stephen TL, Cacciottolo M, Balu D, Morgan TE, Ladu MJ, Finch CE, et al. APOE genotype and sex affect microglial interactions with plaques in Alzheimer’s disease mice. Acta Neuropathol Commun. 2019;7(1):82. 163. Muth C, Hartmann A, Sepulveda-falla D, Glatzel M, Krasemann S. Phagocytosis of Apoptotic Cells Is Specifically Upregulated in ApoE4 Expressing Microglia in vitro. Front Cell Neurosci. 2019;13:1–15. 164. Konttinen H, Cabral-da-Silva M, Ohtonen S, Wojciechowski S, Shakiryzanova A, Caligola S, et al. PSEN1ΔE9, APPswe, and APOE4 confer disparate phenotypes in human iPSC-derived microglia. Stem Cell Reports. 2019;13(4):669–83. 165. Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, et al. Dementia prevention, intervention, and care. Lancet. 2017;390(10113):2673–734. 166. Ngandu T, Lehtisalo J, Solomon A, Levälahti E, Ahtiluoto S, Antikainen R, et al. A 2 year 227 multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet. 2015;385(9984):2255–63. 167. Solomon A, Turunen H, Ngandu T, Peltonen M, Levälahti E, Helisalmi S, et al. Effect of the apolipoprotein e genotype on cognitive change during a multidomain lifestyle intervention a subgroup analysis of a randomized clinical trial. JAMA Neurol. 2018;75(4):462–70. 168. Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol. 2017;16(5):377–89. 169. Moll van Charante EP, Richard E, Eurelings LS, van Dalen J, Ligthart SA, van Bussel EF, et al. Effectiveness of a 6-year multidomain vascular care intervention to prevent dementia (preDIVA): a cluster-randomised controlled trial. Lancet. 2016;388(10046):797–805. 170. Kivipelto M, Mangialasche F, Ngandu T. Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol. 2018;14(11):653–66. 171. World_Health_Organization. Risk reduction of cognitive decline and dementia: WHO guidelines. Geneva; 2019. 172. Sofi F, Valecchi D, Bacci D, Abbate R, Gensini G, Casini A, et al. Physical activity and risk of cognitive decline: a meta-analysis of prospective studies. J Intern Med. 2011;269(1):107–17. 173. Najar J, Östling S, Gudmundsson P, Sundh V, Johansson L, Kern S, et al. Cognitive and physical activity and dementia: A 44-year longitudinal population study of women. Neurology. 2019;92(12):e1322–30. 174. Heyn P, Abreu BC, Ottenbacher KJ. The effects of exercise training on elderly persons with cognitive impairment and dementia: A meta-analysis. Arch Phys Med Rehabil. 2004;85(10):1694–704. 175. Gates N, Sachdev P, Fiatarone Singh M, Valenzuela M. Cognitive and memory training in 228 adults at risk of dementia: a systematic review. BMC Geriatr. 2011;11:55. 176. Zheng G, Xia R, Zhou W, Tao J, Chen L. Aerobic exercise ameliorates cognitive function in older adults with mild cognitive impairment: A systematic review and meta-analysis of randomised controlled trials. Br J Sports Med. 2016;50(23):1443–50. 177. Smith PJ, Blumenthal JA, Hoffman BM, Cooper H, Strauman TA, Welsh-Bohmer K, et al. Aerobic exercise and neurocognitive performance: A meta-analytic review of randomized controlled trials. Psychosom Med. 2010;72(3):239–52. 178. Young J, Angevaren M, Rusted J, Tabet N. Aerobic exercise to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev. 2015;22(4):CD005381. 179. Sink KM, Espeland MA, Castro CM, Church T, Cohen R, Dodson JA, et al. Effect of a 24-month physical activity intervention vs health education on cognitive outcomes in sedentary older adults: The LIFE randomized trial. JAMA. 2015;314(8):781–90. 180. Martínez-Lapiscina EH, Clavero P, Toledo E, Estruch R, Salas-Salvadó J, San Julián B, et al. Mediterranean diet improves cognition: The PREDIMED-NAVARRA randomised trial. J Neurol Neurosurg Psychiatry. 2013;84(12):1318–25. 181. Berti V, Walters M, Sterling J, Quinn CG, Logue M, Andrews R, et al. Mediterranean diet and 3-year Alzheimer brain biomarker changes in middle-aged adults. Neurology. 2018;90(20):E1789–98. 182. Mosconi L, Walters M, Sterling J, Quinn C, McHugh P, Andrews RE, et al. Lifestyle and vascular risk effects on MRI-based biomarkers of Alzheimer’s disease: a cross-sectional study of middle-aged adults from the broader New York City area. BMJ Open. 2018;8(3):e019362. 183. Zhang J, Chen C, Hua S, Liao H, Wang M, Xiong Y, et al. An updated meta-analysis of cohort studies: Diabetes and risk of Alzheimer’s disease. Diabetes Res Clin Pract. 2017;124:41–7. 184. McIntosh EC, Nation DA. Importance of treatment status in links between type 2 diabetes and Alzheimer’s disease. Diabetes Care. 2019;42(5):972–9. 229 185. Tang Y, Li YM, Zhang M, Chen YQ, Sun Q. ε3/4 genotype of the apolipoprotein E is associated with higher risk of Alzheimer’s disease in patients with type 2 diabetes mellitus. Gene. 2019;703:65–70. 186. Schmidt R, Launer L, Nilsson L-G, Pajak A, Sans S, Berger K, et al. Magnetic resonance imaging of the brain in diabetes. Diabetes. 2004;53(3):687–92. 187. Den Heijer T, Vermeer SE, Van Dijk EJ, Prins ND, Koudstaal PJ, Hofman A, et al. Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI. Diabetologia. 2003;46(12):1604–10. 188. Van Harten B, Oosterman JM, Potter Van Loon BJ, Scheltens P, Weinstein HC. Brain lesions on MRI in elderly patients with type 2 diabetes mellitus. Eur Neurol. 2007;57(2):70–4. 189. Janelidze S, Hertze J, Nägga K, Nilsson K. Increased blood-brain barrier permeability is associated with dementia and diabetes but not amyloid pathology or APOE genotype. Neurobiol Aging. 2017;51:104–12. 190. Bogush M, Heldt NA, Persidsky Y. Blood Brain Barrier Injury in Diabetes : Unrecognized Effects on Brain and Cognition. J Neuroimmune Pharmacol. 2017;12(4):593–601. 191. Gottesman RF, Albert MS, Alonso A, Coker LH, Coresh J, Davis SM, et al. Associations Between Midlife Vascular Risk Factors and 25-Year Incident Dementia in the Atherosclerosis Risk in Communities (ARIC) Cohort. JAMA Neurol. 2017;74(10):1246–54. 192. Walker KA, Sharrett AR, Wu A, Schneider ALC, Albert M, Lutsey PL, et al. Association of Midlife to Late-Life Blood Pressure Patterns With Incident Dementia. JAMA. 2019;322(6):535. 193. Lane C, Barnes J, Nicholas J, Sudre C, Cash D, Parker T, et al. Associations between blood pressure across adulthood and late-life brain structure and pathology in the neuroscience substudy of the 1946 British birth cohort (Insight 46): an epidemiological study. Lancet Neurol. 2019;4422(19):30228–5. 194. Peters R, Warwick J, Anstey KJ, Anderson CS. Blood pressure and dementia: What the 230 SPRINT-MIND trial adds and what we still need to know. Neurology. 2019;92(21):1017–8. 195. Walker KA, Power MC, Gottesman RF. Defining the Relationship Between Hypertension, Cognitive Decline, and Dementia: a Review. Curr Hypertens Rep. 2017;19(3):24. 196. McGuinness B, Todd S, Passmore P, Bullock R. Blood pressure lowering in patients without prior cerebrovascular disease for prevention of cognitive impairment and dementia. Cochrane Database Syst Rev. 2009;7(4):CD004034. 197. Reitz C. Dyslipidemia and dementia: Current epidemiology, genetic evidence, and mechanisms behind the associations. J Alzheimer’s Dis. 2012;30(2):127–45. 198. Anstey KJ, Ashby-Mitchell K, Peters R. Updating the Evidence on the Association between Serum Cholesterol and Risk of Late-Life Dementia: Review and Meta-Analysis. J Alzheimers Dis. 2017;56(1):215–28. 199. Meng XF, Yu JT, Wang HF, Tan MS, Wang C, Tan CC, et al. Midlife vascular risk factors and the risk of Alzheimer’s disease: a systematic review and meta-analysis. J Alzheimers Dis. 2014;42(4):1295–310. 200. Tan Z, Seshadri S, Beiser A, Wilson P, Kiel D, Tocco M, et al. Plasma total cholesterol level as a risk factor for Alzheimer disease: the Framingham Study. Arch Intern Med. 2003;163(9):1053–7. 201. Knopman DS, Gottesman RF, Sharrett AR, Tapia AL, DavisThomas S, Windham BG, et al. Midlife vascular risk factors and midlife cognitive status in relation to prevalence of mild cognitive impairment and dementia in later life: The Atherosclerosis Risk in Communities Study. Alzheimer’s Dement. 2018;14(11):1406–15. 202. Schilling S, Tzourio C, Soumare A, Kaffashian S, Dartigues J, Ancelin M, et al. Differential associations of plasma lipids with incident dementia and dementia subtypes in the 3C Study : A longitudinal , population- based prospective cohort study. PLoS Med. 2017;14(3):e1002265. 203. Mielke MM, Zandi PP, Shao H, Waern M, Östling S, Guo X, et al. The 32-year relationship between cholesterol and dementia from midlife to late life. Neurology. 231 2010;75(21):1888–95. 204. Larsson SC, Markus HS. Does Treating Vascular Risk Factors Prevent Dementia and Alzheimer’s Disease? A Systematic Review and Meta-Analysis. J Alzheimers Dis. 2018;64(2):657–68. 205. Zhang X, Wen J, Zhang Z. Statins use and risk of dementia: A dose-response meta analysis. Medicine (Baltimore). 2018;97(30):e11304. 206. Swiger KJ, Manalac RJ, Blumenthal RS, Blaha MJ, Martin SS. Statins and Cognition: A Systematic Review and Meta-analysis of Short- and Long-term Cognitive Effects. Mayo Clin Proc. 2013;88(11):1213–21. 207. McGuinness B, Craig D, Bullock R, Passmore P. Statins for the prevention of dementia. Cochrane Database Syst Rev. 2016;(1):CD003160. 208. Proitsi P, Lupton MK, Velayudhan L, Newhouse S, Fogh I, Tsolaki M, et al. Genetic Predisposition to Increased Blood Cholesterol and Triglyceride Lipid Levels and Risk of Alzheimer Disease: A Mendelian Randomization Analysis. PLoS Med. 2014;11(9):e1001713. 209. Østergaard SD, Mukherjee S, Sharp SJ, Proitsi P, Lotta LA, Day F, et al. Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study. PLoS Med. 2015;12(6):e1001841. 210. Reitz C, Tang M-X, Schupf N, Manly JJ, Mayeux R, Luchsinger JA. Association of Higher Levels of High-Density Lipoprotein Cholesterol in Elderly Individuals and Lower Risk of Late-Onset Alzheimer Disease. Arch Neurol. 2010;67(12):1491–7. 211. Cui C-C, Sun Y, Wang X-Y, Zhang Y, Xing Y. The effect of anti-dementia drugs on Alzheimer disease-induced cognitive impairment. Med. 2019;98(27):e16091. 212. Matsunaga S, Kishi T, Iwata N. Memantine monotherapy for Alzheimer’s Disease:A systematic review and meta-analysis. PLoS One. 2015;10(4):e0123289. 213. Matsunaga S, Kishi T, Nomura I, Sakuma K, Okuya M, Ikuta T, et al. The efficacy and safety of memantine for the treatment of Alzheimer’s disease. Expert Opin Drug Saf. 232 2018;17(10):1053–61. 214. Panza F, Lozupone M, Logroscino G, Imbimbo BP. A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease. Nat Rev Neurol. 2019;15(2):73–88. 215. Sevigny J, Chiao P, Bussière T, Weinreb PH, Williams L, Maier M, et al. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature. 2016;537(7618):50–6. 216. Selkoe D. Alzheimer disease and aducanumab: adjusting our approach. Nat Rev Neurol. 2019;15(7):365–6. 217. Sandrock A, Budd Haeberlein S. Financial Results and Business Update: Detailed Aducanumab Update [Internet]. 2019. Available from: https://investors.biogen.com/static-files/5a31a1e3-4fbb-4165-921a-f0ccb1d64b65 218. Howard R, Liu KY. Questions EMERGE as Biogen claims aducanumab turnaround. Nat Rev Neurol. 2020;16(2):63–4. 219. Janssen. Update on Janssen’s BACE Inhibitor Program [Internet]. Titusville; 2018. Available from: https://www.janssen.com/update-janssens-bace-inhibitor-program 220. Novartis. Novartis, Amgen and Banner Alzheimer’s Institute discontinue clinical program with BACE inhibitor CNP520 for Alzheimer’s prevention [Internet]. Basel; 2019. Available from: https://www.novartis.com/news/media-releases/novartis-amgen-and-banner-alzheimers-institute-discontinue-clinical-program-bace-inhibitor-cnp520-alzheimers-prevention 221. Egan M, Kost J, Voss T, Mukai Y, Aisen P, Cummings J, et al. Randomized Trial of Verubecestat for Prodromal Alzheimer’s Disease. N Engl J Med. 2019;380(15):1408–20. 222. Meyer PF, Tremblay-Mercier J, Leoutsakos J, Madjar C, Lafaille-Maignan MÉ, Savard M, et al. INTREPAD: A randomized trial of naproxen to slow progress of presymptomatic Alzheimer disease. Neurology. 2019;92(18):e2070–80. 223. Davison A. Basic Neurochemistry: Molecular, Cellular, and Medical Aspects. J Neurol Neurosurg Psychiatry. 1989;52(8):1021. 224. Zlokovic B V. The blood-brain barrier in health and chronic neurodegenerative disorders. 233 Neuron. 2008;57(2):178–201. 225. Sweeney MD, Sagare AP, Zlokovic B V. Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol. 2018;14(3):133–50. 226. Zenaro E, Piacentino G, Constantin G. The blood-brain barrier in Alzheimer’s disease. Neurobiol Dis. 2017;107:41–56. 227. Nelson AR, Sweeney MD, Sagare AP, Zlokovic B V. Neurovascular dysfunction and neurodegeneration in dementia and Alzheimer’s disease. Biochim Biophys Acta. 2016;1862(5):887–900. 228. Toledo JB, Arnold SE, Raible K, Brettschneider J, Xie SX, Grossman M, et al. Contribution of cerebrovascular disease in autopsy confirmed neurodegenerative disease cases in the National Alzheimer’s Coordinating Centre. Brain. 2013;136(9):2697–706. 229. Hassler O. Vascular Changes in Senile Brains. Acta Neurol Scand. 1965;5(1):40–53. 230. Bell MA, Ball MJ. Morphometric Comparison of Hippocampal Microvasculature in Ageing and Demented People: Diameters and Densities. Acta Neuropathol. 1981;53(4):299–318. 231. Fischer V, Siddiqi A, Yusufaly Y. Altered angioarchitecture in selected areas of brains with Alzheimer’s disease. Acta Neuropathol. 1990;79(6):672–9. 232. Kalaria R, Hedera P. Differential degeneration of the cerebral microvasculature in Alzheimer’s disease. Neuroreport. 1995;6(3):477–80. 233. Challa VR, Thore CR, Moody DM, Anstrom JA, Brown WR. Increase of white matter string vessels in Alzheimer’s disease. J Alzheimers Dis. 2004;6(4):379–83. 234. Arvanitakis Z, Capuano AW, Leurgans SE, Bennett DA, Schneider JA. Relation of cerebral vessel disease to Alzheimer’s disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol. 2016;15(9):934–43. 235. Iturria-Medina Y, Sotero R, Toussaint P, Mateos-Pérez J, Evans A, Alzheimer’s Disease Neuroimaging Initiative. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nat Commun. 2016;7:11934. 234 236. Ruitenberg A, Heijer T Den, Bakker SLM, Swieten JC Van, Koudstaal PJ, Hofman A, et al. Cerebral Hypoperfusion and Clinical Onset of Dementia : The Rotterdam Study. Ann Neurol. 2005;57(6):789–94. 237. Akoudad S, Wolters FJ, Viswanathan A, de Bruijn RF, van der Lugt A, Hofman A, et al. Association of Cerebral Microbleeds With Cognitive Decline and Dementia. JAMA Neurol. 2016;73(8):934–43. 238. Shams S, Martola J, Granberg T, Li X, Shams M, Fereshtehnejad S, et al. Cerebral microbleeds: different prevalence, topography, and risk factors depending on dementia diagnosis—the Karolinska Imaging Dementia Study. AJNR Am J Neuroradiol. 2015;36(4):661–6. 239. Hughes T, Wagenknecht L, Craft S, Mintz A, Hiess G, Palta P, et al. Arterial stiffness and dementia pathology: Atherosclerosis Risk in Communities (ARIC)-PET Study. Neurology. 2018;90(14):e1248–56. 240. Montagne A, Pa J, Zlokovic B. Vascular plasticity and cognition during normal aging and dementia. JAMA Neurol. 2015;72(5):495–6. 241. Kapasi A, Schneider JA. Vascular contributions to cognitive impairment , clinical Alzheimer ’ s disease , and dementia in older persons. Biochim Biophys Acta. 2016;1862(5):878–86. 242. Venkat P, Chopp M, Chen J. Models and mechanisms of vascular dementia. Exp Neurol. 2015;272:97–108. 243. Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology. 2007;69(24):2197–204. 244. Schneider J, Bennett D. Where Vascular meets Neurodegenerative Disease. Stroke. 2010;41(10):S144-6. 245. Stukas S, Robert J, Wellington C. High-density lipoproteins and cerebrovascular integrity in Alzheimer’s disease. Cell Metab. 2014;19(4):574–91. 235 246. Storck SE, Hartz AMS, Bernard J, Wolf A, Kachlmeier A, Mahringer A, et al. The concerted amyloid-beta clearance of LRP1 and ABCB1/P-gp across the blood-brain barrier is linked by PICALM. Brain Behav Immun. 2018;73:21–33. 247. Zhao Z, Sagare AP, Ma Q, Halliday MR, Kong P, Kisler K, et al. Central role for PICALM in amyloid-β blood-brain barrier transcytosis and clearance. Nat Neurosci. 2015;18(7):978–87. 248. Wojtas AM, Kang SS, Olley BM, Gatherer M, Shinohara M, Lozano P, et al. Loss of clusterin shifts amyloid deposition to the cerebrovasculature via disruption of perivascular drainage pathways. Proc Natl Acad Sci U S A. 2017;113(33):E6962–71. 249. Nelson AR, Sagare AP, Zlokovic B V. Role of clusterin in the brain vascular clearance of amyloid-β. Proc Natl Acad Sci U S A. 2017;114(33):8681–2. 250. Hawkes CA, Jayakody N, Johnston DA, Bechmann I, Carare RO. Failure of perivascular drainage of β-amyloid in cerebral amyloid angiopathy. Brain Pathol. 2014;24(4):396–403. 251. Diem AK, Sharp MMG, Gatherer M, Bressloff NW, Carare RO, Richardson G. Arterial pulsations cannot drive intramural periarterial drainage: Significance for Aβ drainage. Front Neurosci. 2017;11:475. 252. Zlokovic B V, Yamada S, Holtzman D, Ghiso J, Frangione B. Clearance of amyloid beta-peptide from brain: transport or metabolism? Nat Med. 2000;6(7):718–9. 253. Greenberg S, Bacskai B, Hernandez-Guillamon M, Pruzin J, Sperling R, van Veluw S. Cerebral amyloid angiopathy and Alzheimer disease - one peptide, two pathways. Nat Rev Neurol. 2020;16(1):30–42. 254. Miller DL, Papayannopoulos IA, Styles J, Bobin SA, Lin YY, Biemann K, et al. Peptide Compositions of the Cerebrovascular and Senile Plaque Core Amyloid Deposits of Alzheimer′s Disease. Arch Biochem Biophys. 1993;301(1):41–52. 255. Thal DR, Ghebremedhin E, Rüb U, Yamaguchi H, Del Tredici K, Braak H. Two types of sporadic cerebral amyloid angiopathy. J Neuropathol Exp Neurol. 2002;61(3):282–93. 256. Attems J, Jellinger KA. Only cerebral capillary amyloid angiopathy correlates with 236 Alzheimer pathology - A pilot study. Acta Neuropathol. 2004;107(2):83–90. 257. Mäkelä M, Paetau A, Polvikoski T, Myllykangas L, Tanskanen M. Capillary Amyloid-β Protein Deposition in a Population-Based Study (Vantaa 85+). J Alzheimer’s Dis. 2015;49(1):149–57. 258. Arvanitakis Z, Leurgans SE, Wang Z, Wilson R, Bennett D, Schneider J. Cerebral amyloid angiopathy pathology and cognitive domains in older persons. Ann Neurol. 2011;69(2):320–7. 259. Greenberg SM, Charidimou A. Diagnosis of cerebral amyloid angiopathy evolution of the Boston criteria. Stroke. 2018;49(2):491–7. 260. Ellis R, Olichney J, Thal L, Mirra S, Morris J, Beekly D, et al. Cerebral amyloid angiopathy in the brains of patients with Alzheimers disease: The CERAD experience, part XV. J Am Geriatr Soc. 1996;45(6):1592–6. 261. Delacourte A, Defossez A, Persuy P, Peers MC. Observation of morphological relationships between angiopathic blood vessels and degenerative neurites in Alzheimer’s disease. Virchows Arch A Pathol Anat Histopathol. 1987;411(3):199–204. 262. Peers M, Lenders M, Défossez A, Delacourte A, Mazzuca M. Cortical angiopathy in Alzheimer’s disease: the formation of dystrophic perivascular neurites is related to the exudation of amyloid fibrils from the pathological vessels. Virchows Arch A Pathol Anat Histopathol. 1988;414(1):15–20. 263. Vidal R, Calero M, Piccardo P, Farlow MR, Unverzagt FW, Méndez E, et al. Senile dementia associated with amyloid protein angiopathy and tau perivascular pathology but not neuritic plaques in patients homozygous for the APOE-ε4 allele. Acta Neuropathol. 2000;100(1):1–12. 264. Oshima K, Uchikado H, Dickson DW. Perivascular Neuritic Dystrophy Associated with Cerebral Amyloid Angiopathy in Alzheimer’s Disease. Int J Clin Exp Pathol. 2008;1(5):403–8. 265. Williams S, Chalmers K, Wilcock GK, Love S. Relationship of neurofibrillary pathology to cerebral amyloid angiopathy in Alzheimer’s disease. Neuropathol Appl Neurobiol. 237 2005;31(4):414–21. 266. Merlini M, Wanner D, Nitsch RM. Tau pathology‑dependent remodelling of cerebral arteries precedes Alzheimer’s disease‑related microvascular cerebral amyloid angiopathy. Acta Neuropathol. 2016;131(5):737–52. 267. Castillo-Carranza DL, Nilson AN, Van Skike CE, Jahrling JB, Patel K, Garach P, et al. Cerebral Microvascular Accumulation of Tau Oligomers in Alzheimer’s Disease and Related Tauopathies. Aging Dis. 2017;8(3):257–66. 268. Forman MS, Lal D, Zhang B, Dabir D V, Swanson E, Lee VM, et al. Transgenic mouse model of tau pathology in astrocytes leading to nervous system degeneration. J Neurosci. 2005;25(14):3539–50. 269. Blair LJ, Frauen HD, Zhang B, Nordhues BA, Bijan S, Lin Y, et al. Tau depletion prevents progressive blood-brain barrier damage in a mouse model of tauopathy. Acta Neuropathol Commun. 2015;3:8. 270. Jaworski T, Lechat B, Demedts D, Gielis L, Devijver H, Borghgraef P, et al. Dendritic degeneration, neurovascular defects, and inflammation precede neuronal loss in a mouse model for tau-mediated neurodegeneration. Am J Pathol. 2011;179(4):2001–15. 271. Bennett RE, Robbins AB, Hu M, Cao X, Betensky RA, Clark T, et al. Tau induces blood vessel abnormalities and angiogenesis-related gene expression in P301L transgenic mice and human Alzheimer’s disease. Proc Natl Acad Sci U S A. 2018;115(6):1289–98. 272. Xin S, Tan L, Cao X, Yu J, Tan L. Clearance of Amyloid Beta and Tau in Alzheimer’s Disease: from Mechanisms to Therapy. Neurotox Res. 2018;34(3):733–48. 273. Castillo-Carranza DL, Sengupta U, Guerrero-Munoz MJ, Lasagna-Reeves CA, Gerson JE, Singh G, et al. Passive Immunization with Tau Oligomer Monoclonal Antibody Reverses Tauopathy Phenotypes without Affecting Hyperphosphorylated Neurofibrillary Tangles. J Neurosci. 2014;34(12):4260–72. 274. Iliff JJ, Chen MJ, Plog BA, Zeppenfeld DM, Soltero X, Yang L, et al. Impairment of Glymphatic Pathway Function Promotes Tau Pathology after Traumatic Brain Injury. J Neurosci. 2014;34(49):16180–93. 238 275. Grammas P, Samany PG, Thirumangalakudi L. Thrombin and inflammatory proteins are elevated in Alzheimer’s disease microvessels: Implications for disease pathogenesis. J Alzheimer’s Dis. 2006;9(1):51–8. 276. Grammas P, Ovase R. Inflammatory factors are elevated in brain microvessels in Alzheimer’s disease. Neurobiol Aging. 2001;22(6):837–42. 277. Dorheim M, Tracey W, Pollock J, Grammas P. Nitric oxide synthase activity is elevated in brain microvessels in Alzheimer’s disease. Biochem Biophys Res Commun. 1994;205(1):659–65. 278. Grammas P, Ovase R. Cerebrovascular transforming growth factor-β contributes to inflammation in the Alzheimer’s disease brain. Am J Pathol. 2002;160(5):1583–7. 279. Sanchez A, Tripathy D, Yin X, Desobry K, Martinez J, Riley J, et al. p38 MAPK: A mediator of hypoxia-induced cerebrovascular inflammation. J Alzheimer’s Dis. 2012;32(3):587–97. 280. Wang S, Qaisar U, Yin X, Grammas P. Gene expression profiling in Alzheimer’s disease brain microvessels. J Alzheimer’s Dis. 2012;31(1):193–205. 281. Davalos D, Akassoglou K. Fibrinogen as a key regulator of inflammation in disease. Semin Immunopathol. 2012;34(1):43–62. 282. Cortes-Canteli M, Paul J, Norris EH, Bronstein R, Ahn HJ, Zamolodchikov D, et al. Fibrinogen and β-Amyloid Association Alters Thrombosis and Fibrinolysis: A Possible Contributing Factor to Alzheimer’s Disease. Neuron. 2010;66(5):695–709. 283. Paul J, Strickland S, Melchor JP. Fibrin deposition accelerates neurovascular damage and neuroinflammation in mouse models of Alzheimer’s disease. J Exp Med. 2007;204(8):1999–2008. 284. Ryu JK, McLarnon JG. A leaky blood-brain barrier, fibrinogen infiltration and microglial reactivity in inflamed Alzheimer’s disease brain. J Cell Mol Med. 2009;13(9 A):2911–25. 285. Tripathy D, Sanchez A, Yin X, Luo J, Martinez J, Grammas P. Thrombin, a mediator of cerebrovascular inflammation in AD and hypoxia. Front Aging Neurosci. 2013;5:19. 239 286. Lopes Pinheiro MA, Kooij G, Mizee MR, Kamermans A, Enzmann G, Lyck R, et al. Immune cell trafficking across the barriers of the central nervous system in multiple sclerosis and stroke. Biochim Biophys Acta. 2016;1862(3):461–71. 287. Haneka M, Carson M, El Khoury J, Landreth G, Brosseron F, Feinstein D, et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 2015;14(4):388–405. 288. Zenaro E, Pietronigro E, Bianca V Della, Piacentino G, Marongiu L, Budui S, et al. Neutrophils promote Alzheimer’s disease-like pathology and cognitive decline via LFA-1 integrin. Nat Med. 2015;21(8):880–6. 289. Michaud JP, Bellavance MA, Préfontaine P, Rivest S. Real-time in vivo imaging reveals the ability of monocytes to clear vascular amyloid beta. Cell Rep. 2013;5(3):646–53. 290. Ferretti MT, Merlini M, Späni C, Gericke C, Schweizer N, Enzmann G, et al. T-cell brain infiltration and immature antigen-presenting cells in transgenic models of Alzheimer’s disease-like cerebral amyloidosis. Brain Behav Immun. 2016;54:211–25. 291. Cruz Hernández JC, Bracko O, Kersbergen CJ, Muse V, Haft-Javaherian M, Berg M, et al. Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in Alzheimer’s disease mouse models. Nat Neurosci. 2019;22(3):413–20. 292. Naert G, Rivest S. CC chemokine receptor 2 deficiency aggravates cognitive impairments and amyloid pathology in a transgenic mouse model of Alzheimer’s disease. J Neurosci. 2011;31(16):6208–20. 293. Hawkes CA, McLaurin J. Selective targeting of perivascular macrophages for clearance of β-amyloid in cerebral amyloid angiopathy. Proc Natl Acad Sci U S A. 2009;106(4):1261–6. 294. Hordijk PL. Recent insights into endothelial control of leukocyte extravasation. Cell Mol Life Sci. 2016;73(8):1591–608. 295. Nielsen HM, Londos E, Minthon L, Janciauskiene SM. Soluble adhesion molecules and angiotensin-converting enzyme in dementia. Neurobiol Dis. 2007;26(1):27–35. 240 296. Zuliani G, Cavalieri M, Galvani M, Passaro A, Munari MR, Bosi C, et al. Markers of endothelial dysfunction in older subjects with late onset Alzheimer’s disease or vascular dementia. J Neurol Sci. 2008;272(1–2):164–70. 297. Huang CW, Tsai MH, Chen NC, Chen WH, Lu YT, Lui CC, et al. Clinical significance of circulating vascular cell adhesion molecule-1 to white matter disintegrity in Alzheimer’s dementia. Thromb Haemost. 2015;114(6):1230–40. 298. Yousef H, Czupalla CJ, Lee D, Chen MB, Burke AN, Zera KA, et al. Aged blood impairs hippocampal neural precursor activity and activates microglia via brain endothelial cell VCAM1. Nat Med. 2019;25(6):988–1000. 299. Wang X, Sun G, Feng T, Zhang J, Huang X, Wang T, et al. Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer’s disease progression. Cell Res. 2019;29(10):787–803. 300. Vogt NM, Romano KA, Darst BF, Engelman CD, Johnson SC, Carlsson CM, et al. The gut microbiota-derived metabolite trimethylamine N-oxide is elevated in Alzheimer’s disease. Alzheimer’s Res Ther. 2018;10(1):124. 301. Zhuang ZQ, Shen LL, Li WW, Fu X, Zeng F, Gui L, et al. Gut Microbiota is Altered in Patients with Alzheimer’s Disease. J Alzheimer’s Dis. 2018;63(4):1337–46. 302. Shugart J. China Approves Seaweed Sugar as First New Alzheimer’s Drug in 17 Years. Alzforum [Internet]. 2019 Nov 7; Available from: https://www.alzforum.org/news/research-news/china-approves-seaweed-sugar-first-new-alzheimers-drug-17-years 303. Servick K, Normile D. Alzheimer’s experts greet China’s surprise approval of a drug for brain disease with hope and caution. Science [Internet]. 2019 Nov 5; Available from: https://www.sciencemag.org/news/2019/11/alzheimer-s-experts-greet-china-s-surprise-approval-drug-brain-disease-hope-and-caution 304. de Bruijn RF, Ikram MA. Cardiovascular risk factors and future risk of Alzheimer’s disease. BMC Med. 2014;12:130. 241 305. Jellinger KA, Attems J. Neuropathological approaches to cerebral aging and neuroplasticity. Dialogues Clin Neurosci. 2013;15(1):29–43. 306. Huang Y, Mahley RW. Apolipoprotein E: Structure and function in lipid metabolism, neurobiology, and Alzheimer’s diseases. Neurobiol Dis. 2014;72(Part A):3–12. 307. Norton S, Matthews FE, Barnes DE, Yaff K, Brayne C. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol. 2014;13(8):788–94. 308. Ecay-Torres M, Estanga A, Tainta M, Izagirre A, Garcia-Sebastian M, Villanua J, et al. Increased CAIDE dementia risk, cognition, CSF biomarkers, and vascular burden in healthy adults. Neurology. 2018;91(3):217–26. 309. Hooshmand B, Polvikoski T, Kivipelto M, Tanskanen M, Myllykangas L, Paetau A, et al. CAIDE Dementia Risk Score, Alzheimer and cerebrovascular pathology: a population-based autopsy study. J Intern Med. 2018;283(6):597–603. 310. Yilmaz P, Ikram M, Niessen W, Ikram M, Vernooij M. Practical Small Vessel Disease Score Relates to Stroke, Dementia, and Death. Stroke. 2018;49(12):2857–65. 311. Viticchi G, Falsetti L, Buratti L, Sajeva G, Luzzi S, Bartolini M, et al. Framingham Risk Score and the Risk of Progression from Mild Cognitive Impairment to Dementia. J Alzheimers Dis. 2017;59(1):67–75. 312. Jamieson JJ, Searson PC, Gerecht S. Engineering the human blood-brain barrier in vitro. J Biol Eng. 2017;11:37. 313. Mrsulja B, Mrsulja B, Fujimoto T, Klatzo I, Spatz M. Isolation of brain capillaries: a simplified technique. Brain Res. 1976;110(2):361–5. 314. Weksler B, Romero IA, Couraud P. The hCMEC/D3 cell line as a model of the human blood brain barrier. Fluids Barriers CNS. 2013;10(1):16. 315. Lippmann ES, Al-Ahmad A, Azarin SM, Palecek SP, Shusta E V. A retinoic acid-enhanced, multicellular human blood-brain barrier model derived from stem cell sources. Sci Rep. 2014;4:4160. 242 316. Sano Y, Shimizu F, Abe M, Maeda T, Kashiwamura Y, Ohtsuki S, et al. Establishment of a new conditionally immortalized human brain microvascular endothelial cell line retaining an in vivo blood-brain barrier function. J Cell Physiol. 2010;225(2):519–28. 317. Cecchelli R, Aday S, Sevin E, Almeida C, Culot M, Dehouck L, et al. A Stable and Reproducible Human Blood-Brain Barrier Model Derived from Hematopoietic Stem Cells. PLoS One. 2014;9(6):e99733. 318. Eigenmann DE, Xue G, Kim KS, Moses A V, Hamburger M, Oufir M. Comparative study of four immortalized human brain capillary endothelial cell lines, hCMEC/D3, hBMEC, TY10, and BB19, and optimization of culture conditions, for an in vitro blood-brain barrier model for drug permeability studies. Fluids Barriers CNS. 2013;10(1):33. 319. Bernas MJ, Cardoso FL, Daley SK, Weinand ME, Campos AR, Ferreira AJG, et al. Establishment of primary cultures of human brain microvascular endothelial cells to provide an in vitro cellular model of the blood-brain barrier. Nat Protoc. 2010;5(7):1265–72. 320. Thomsen LB, Burkhart A, Moos T. A Triple Culture Model of the Blood-Brain Barrier Using Porcine Brain Endothelial cells, Astrocytes and Pericytes. PLoS One. 2015;10(8):e0134765. 321. Duval K, Grover H, Han LH, Mou Y, Pegoraro AF, Fredberg J, et al. Modeling physiological events in 2D vs. 3D cell culture. Physiology. 2017;32(4):266–77. 322. DeStefano JG, Jamieson JJ, Linville RM, Searson PC. Benchmarking in vitro tissue-engineered blood–brain barrier models. Fluids Barriers CNS. 2018;15(1):32. 323. Man S, Ubogu EE, Williams KA, Tucky B, Callahan MK, Ransohoff RM. Human brain microvascular endothelial cells and umbilical vein endothelial cells differentially facilitate leukocyte recruitment and utilize chemokines for T cell migration. Clin Dev Immunol. 2008;2008:384982. 324. Hatherell K, Couraud PO, Romero IA, Weksler B, Pilkington GJ. Development of a three-dimensional, all-human in vitro model of the blood-brain barrier using mono-, co-, and tri-cultivation Transwell models. J Neurosci Methods. 2011;199(2):223–9. 243 325. Merino-Zamorano C, Fernández-de-Retana S, Montañola A, Batlle A, Saint-Pol J, Mysiorek C, et al. Modulation of Amyloid-β1-40 Transport by ApoA1 and ApoJ Across an in vitro Model of the Blood-Brain Barrier. J Am Geriatr Soc. 2016;53(2):677–91. 326. Adriani G, Ma D, Pavesi A, Kamm RD, Goh EL. A 3D neurovascular microfluidic model consisting of neurons, astrocytes and cerebral endothelial cells as a blood-brain barrier. Lab Chip. 2017;17(3):448–59. 327. Campisi M, Shin Y, Osaki T, Hajal C, Chiono V, Kamm RD. 3D self-organized microvascular model of the human blood-brain barrier with endothelial cells, pericytes and astrocytes. Biomaterials. 2018;180:117–29. 328. Maoz BM, Herland A, Fitzgerald EA, Grevesse T, Vidoudez C, Pacheco AR, et al. A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells. Nat Biotechnol. 2018;36(9):865–77. 329. Vatine GD, Barrile R, Workman MJ, Sances S, Barriga BK, Rahnama M, et al. Human iPSC-Derived Blood-Brain Barrier Chips Enable Disease Modeling and Personalized Medicine Applications. Cell Stem Cell. 2019;24(6):995-1005.e6. 330. Lancaster MA, Knoblich JA. Organogenesisin a dish: Modeling development and disease using organoid technologies. Science (80- ). 2014;345(6194):1247125. 331. Ham O, Jin YB, Kim J, Lee MO. Blood vessel formation in cerebral organoids formed from human embryonic stem cells. Biochem Biophys Res Commun. 2020;521(1):84–90. 332. Wang Y, Cho C, Williams J, Smallwood PM, Zhang C, Junge HJ, et al. Interplay of the Norrin and Wnt7a/Wnt7b signaling systems in blood–brain barrier and blood–retina barrier development and maintenance. Proc Natl Acad Sci U S A. 2018;115(50):E11827–36. 333. Song L, Yuan X, Jones Z, Griffin K, Zhou Y, Ma T, et al. Assembly of Human Stem Cell-Derived Cortical Spheroids and Vascular Spheroids to Model 3-D Brain-like Tissues. Sci Rep. 2019;9(1):5977. 334. Cakir B, Xiang Y, Tanaka Y, Kural MH, Parent M, Kang Y-J, et al. Engineering of human brain organoids with a functional vascular-like system. Nat Methods. 2019;16(11):1169– 244 75. 335. Pham M, Pollock K, Rose M, Cary W, Stewart H, Zhou P, et al. Generation of human vascularized brain organoids. Neuroreport. 2018;29(7):588–93. 336. Cho CF, Wolfe JM, Fadzen CM, Calligaris D, Hornburg K, Chiocca EA, et al. Blood-brain-barrier spheroids as an in vitro screening platform for brain-penetrating agents. Nat Commun. 2017;8:1–14. 337. Bergmann S, Lawler SE, Qu Y, Fadzen CM, Wolfe JM, Regan MS, et al. Blood–brain-barrier organoids for investigating the permeability of CNS therapeutics. Nat Protoc. 2018;13(12):2827–43. 338. Nzou G, Wicks RT, Wicks EE, Seale SA, Sane CH, Chen A, et al. Human cortex spheroid with a functional blood brain barrier for high-throughput neurotoxicity screening and disease modeling. Sci Rep. 2018;8(1):7414. 339. Rye K-A. High density lipoprotein structure, function, and metabolism: a new Thematic Series. J Lipid Res. 2013;54(8):2031–3. 340. Murphy AJ. High Density Lipoprotein : Assembly , Structure , Cargo , and Functions. ISRN Physiol. 2013;2013:1–20. 341. Kontush A, Lhomme M. Lipidomics of Plasma High-Density Lipoprotein: Insights into Anti-Atherogenic Function. J Glycomics Lipidomics. 2015;5(3):1–4. 342. Davidson WS. HDL Proteome Watch [Internet]. 2015. Available from: https://homepages.uc.edu/~davidswm/HDLproteome.html 343. Shah AS, Tan L, Long JL, Davidson WS. Proteomic diversity of high density lipoproteins: our emerging understanding of its importance in lipid transport and beyond. J Lipid Res. 2013;54(10):2575–85. 344. Lüscher TF, Landmesser U, Von Eckardstein A, Fogelman AM. High-density lipoprotein: Vascular protective effects, dysfunction, and potential as therapeutic target. Circ Res. 2014;114(1):171–82. 345. Riwanto M, Rohrer L, Roschitzki B, Besler C, Mocharla P, Mueller M, et al. Altered 245 Activation of Endothelial Anti- and Proapoptotic Pathways by High-Density Lipoprotein from Patients with Coronary Artery Disease: Role of High-Density Lipoprotein-Proteome Remodeling. Circulation. 2013;127(8):891–904. 346. Button EB, Robert J, Caffrey TM, Fan J, Zhao W, Wellington CL. HDL from an Alzheimer’s disease perspective. Curr Opin Lipidol. 2019;30(3):224–34. 347. Boyce G, Button EB, Soo S, Wellington C. The pleiotropic vasoprotective functions of high density lipoproteins (HDL). J Biomed Res. 2017; 348. Kannel WB, Dawber TR, Friedman GD, Glennon WE, McNamara PM. Risk Factors in Coronary Heart Disease: An Evaluation of Several Serum Lipids as Predictors of Coronary Heart Disease: The Framingham Study. Ann Intern Med. 1964;61:888–99. 349. Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR. High density lipoprotein as a protective factor against coronary heart disease. Am J Med. 1977;62(5):707–14. 350. Gordon DJ, Probstfield JL, Garrison RJ, Neaton JD, Castelli WP, Knoke JD, et al. High-Density Lipoprotein Cholesterol and Cardiovascular Disease Four Prospective American Studies. Circulation. 1989;79(1):8–15. 351. Rasmussen KL, Tybjaerg-Hansen A, Nordestgaard BG, Frikke-Schmidt R. Data on plasma levels of apolipoprotein E, correlations with lipids and lipoproteins stratified by APOE genotype, and risk of ischemic heart disease. Data Br. 2016;6:923–32. 352. Swerdlow DI, Kuchenbaecker KB, Shah S, Sofat R, Holmes M V, White J, et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. Int J Epidemiol. 2016;45(5):1600–16. 353. Holmes M V., Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP, et al. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J. 2015;36(9):539–50. 354. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: A mendelian randomisation study. Lancet. 2012;380(9841):572–80. 246 355. Weissglas-Volkov D, Pajukanta P. Genetic causes of high and low serum HDL-cholesterol. J Lipid Res. 2010;51(8):2032–57. 356. Franceschini G, Sirtori CR, Capurso A, Weisgraber K, Mahley R. A-I(Milano) apoprotein. Decreased high density lipoprotein cholesterol levels with significant lipoprotein modifications and without clinical atherosclerosis in an Italian family. J Clin Invest. 1980;66(5):892–900. 357. Weisgraber KH, Rall SC, Bersot TP, Mahley RW, Franceschini G, Sirtori CR. Apolipoprotein A-IMilano. Detection of normal A-I in affected subjects and evidence for a cysteine for arginine substitution in the variant A-I. J Biol Chem. 1983;258(4):2508–13. 358. Zanoni P, Khetarpal S, Larach D, Hancock-Cerutti W, Millar J, Cuchel M, et al. Rare variant in scavenger receptor BI raises HDL cholesterol and increases risk of coronary heart disease. Science (80- ). 2016;351(6278):1166–71. 359. Langsted A, Freiberg JJ, Nordestgaard BG. Fasting and nonfasting lipid levels influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Circulation. 2008;118(20):2047–56. 360. Gordon SSM, Li H, Zhu X, Shah AAS, Lu LJ, Davidson WS. A Comparison of the Mouse and Human Lipoproteome: Suitability of the Mouse Model for Studies of Human Lipoproteins. J Proteome Res. 2015;14(6):2686–95. 361. Getz G, Reardon C. Animal models of atherosclerosis. Arter Thromb Vasc Biol. 2012;32(5):1104–15. 362. von Eckardstein A, Kardassis D. High Density Lipoproteins. von Eckardstein A, Kardassis D, editors. Vol. 224. Springer, Cham; 2015. 527–565 p. 363. Feig JE, Hewing B, Smith JD, Hazen SL, Fisher EA. High-density lipoprotein and atherosclerosis regression: Evidence from preclinical and clinical studies. Circ Res. 2014;114(1):205–13. 364. Li J, Wang W, Han L, Feng M, Lu H, Yang L, et al. Human apolipoprotein A-I exerts a prophylactic effect on high-fat diet-induced atherosclerosis via inflammation inhibition in a rabbit model. Acta Biochim Biophys Sin (Shanghai). 2017;49(2):149–58. 247 365. Patel S, Di Bartolo BA, Nakhla S, Heather AK, Mitchell TW, Jessup W, et al. Anti-inflammatory effects of apolipoprotein A-I in the rabbit. Atherosclerosis. 2010;212(2):392–7. 366. Iwata A, Miura SI, Zhang B, Imaizumi S, Uehara Y, Shiomi M, et al. Antiatherogenic effects of newly developed apolipoprotein A-I mimetic peptide/phospholipid complexes against aortic plaque burden in Watanabe-heritable hyperlipidemic rabbits. Atherosclerosis. 2011;218(2):300–7. 367. Van Craeyveld E, Lievens J, Jacobs F, Feng Y, Snoeys J, De Geest B. Apolipoprotein A-I and lecithin: Cholesterol acyltransferase transfer induce cholesterol unloading in complex atherosclerotic lesions. Gene Ther. 2009;16(6):757–65. 368. Mineo C, Shaul PW. Novel biological functions of high-density lipoprotein cholesterol. Circ Res. 2012 Sep;111(8):1079–90. 369. Kontush A, Lhomme M, Chapman MJ. Unraveling the complexities of the HDL lipidome. J Lipid Res. 2013;54(11):2950–63. 370. Heinecke JW. The HDL proteome: a marker--and perhaps mediator--of coronary artery disease. J Lipid Res. 2009;50 Suppl:S167–71. 371. Yuhanna IS, Zhu Y, Cox BE, Hahner LD, Osborne-Lawrence S, Lu P, et al. High-density lipoprotein binding to scavenger receptor-BI activates endothelial nitric oxide synthase. Nat Med. 2001 Jul;7(7):853–7. 372. Nofer JR, van der Giet M, Tolle M, Wolinska I, von Wnuck Lipinski K, Baba HA, et al. HDL induces NO-dependent vasorelaxation via the lysophospholipid receptor S1P3. J Clin Invest. 2004;113(4):569–81. 373. Calabresi L, Franceschini G, Sirtori CR, De Palma A, Saresella M, Ferrante P, et al. Inhibition of VCAM-1 expression in endothelial cells by reconstituted high density lipoproteins. Biochem Biophys Res Commun. 1997;238(1):61–5. 374. Cockerill G, Rye K, Gamble J, Vadas M, Barter P. High-density lipoproteins inhibit cytokine-induced expression of endothelial cell adhesion molecules. Arter Thromb. 1995;15(11):1987–94. 248 375. Potočnjak I, Degoricija V, Trbušić M, Tereśak S, Radulović B, Pregartner G, et al. Metrics of High-Density Lipoprotein Function and Hospital Mortality in Acute Heart Failure Patients. PLoS One. 2016;11(6):e0157507. 376. Monette JS, Hutchins PM, Ronsein GE, Wimberger J, Irwin AD, Tang C, et al. Patients With Coronary Endothelial Dysfunction Have Impaired Cholesterol Efflux Capacity and Reduced HDL Particle Concentration. Circ Res. 2016;119(1):83–90. 377. Norimatsu K, Kuwano T, Miura S, Shimizu T, Shiga Y, Suematsu Y, et al. Significance of the percentage of cholesterol efflux capacity and total cholesterol efflux capacity in patients with or without coronary artery disease. Heart Vessels. 2017;32(1):30–8. 378. Ishikawa T, Ayaori M, Uto-Kondo H, Nakajima T, Mutoh M, Ikewaki K. High-density lipoprotein cholesterol efflux capacity as a relevant predictor of atherosclerotic coronary disease. Atherosclerosis. 2015;242(1):318–22. 379. Bounafaa A, Berrougui H, Ikhlef S, Essamadi A, Nasser B, Bennis A, et al. Alteration of HDL functionality and PON1 activities in acute coronary syndrome patients. Clin Biochem. 2014;47(18):318–25. 380. Annema W, Dikkers A, Freark De Boer J, Van Greevenbroek MMJ, Van Der Kallen CJH, Schalkwijk CG, et al. Impaired HDL cholesterol efflux in metabolic syndrome is unrelated to glucose tolerance status: the CODAM study. Sci Rep. 2016;6:27367. 381. Vaisar T, Tang C, Babenko I, Hutchins P, Wimberger J, Suffredini AF, et al. Inflammatory Remodeling of the HDL Proteome Impairs Cholesterol Efflux Capacity. J Lipid Res. 2015;56(8):1519–30. 382. Camponova P, Le Page A, Berrougui H, Lamoureux J, Pawelec G, Witkowski MJ, et al. Alteration of high-density lipoprotein functionality in Alzheimer’s disease patients. Can J Physiol Pharmacol. 2017;95(8):894–903. 383. Khalil A, Berrougui H, Pawelec G, Fulop T. Impairment of the ABCA1 and SR-BI-mediated cholesterol efflux pathways and HDL anti-inflammatory activity in Alzheimer’s disease. Mech Ageing Dev. 2012;133(1):20–9. 384. Apro J, Tietge UJF, Dikkers A, Parini P, Angelin B, Rudling M. Impaired Cholesterol 249 Efflux Capacity of High-Density Lipoprotein Isolated From Interstitial Fluid in Type 2 Diabetes Mellitus-Brief Report. Arter Thromb Vasc Biol. 2016;36(5):787–91. 385. Murakami H, Tanabe J, Tamasawa N, Matsumura K, Yamashita M, Matsuki K, et al. Reduction of paraoxonase-1 activity may contribute the qualitative impairment of HDL particles in patients with type 2 diabetes. Diabetes Res Clin Pract. 2013;99(1):30–8. 386. Agarwal R, Talwar P, Kushwaha S, Tripathi C, Kukreti R. Effect of apolipoprotein E (APO E) polymorphism on leptin in Alzheimer′s disease. Ann Indian Acad Neurol. 2015;18(3):320. 387. Rohatgi A. High-Density Lipoprotein Function Measurement in Human Studies: Focus on Cholesterol Efflux Capacity. Prog Cardiovasc Dis. 2015;58(1):32–40. 388. Bostan M, Uydu HA, Yildirmiş S, Malkoç M, Atak M, Demir A, et al. Pleiotropic effects of HDL subfractions and HDL-associated enzymes on protection against coronary artery disease. Acta Cardiol. 2015;70(3):333–40. 389. Niculescu LS, Simionescu N, Sanda GM, Carnuta MG, Stancu CS, Popescu AC, et al. MiR-486 and miR-92a identified in circulating HDL discriminate between stable and vulnerable coronary artery disease patients. PLoS One. 2015;10(10):1–13. 390. Krishnan S, Huang J, Lee H, Guerrero A, Berglund L, Anuurad E, et al. Combined High-Density Lipoprotein Proteomic and Glycomic Profiles in Patients at Risk for Coronary Artery Disease. J Proteome Res. 2015;14(12):5109–18. 391. Oberbach A, Adams V, Schlichting N, Heinrich M, Kullnick Y, Lehmann SS, et al. Proteome profiles of HDL particles of patients with chronic heart failure are associated with immune response and also include bacteria proteins. Clin Chim Acta. 2016;453:114–22. 392. Rached F, Lhomme M, Camont L, Gomes F, Dauteuille C, Robillard P, et al. Defective functionality of small, dense HDL3 subpopulations in ST segment elevation myocardial infarction: Relevance of enrichment in lysophosphatidylcholine, phosphatidic acid and serum amyloid A. Biochim. 2015;1851(9):1254–61. 393. Argraves KM, Sethi AA, Gazzolo PJ, Wilkerson BA, Remaley AT, Tybjaerg-Hansen A, 250 et al. S1P, dihydro-S1P and C24:1-ceramide levels in the HDL-containing fraction of serum inversely correlate with occurrence of ischemic heart disease. Lipids Health Dis. 2011;10(1):70. 394. Papathanasiou A, Kostara C, Cung MT, Seferiadis K, Elisaf M, Bairaktari E, et al. Analysis of the composition of plasma lipoproteins in patients with extensive coronary heart disease using 1H NMR spectroscopy. Hell J Cardiol. 2008;49(2):72–8. 395. Ozerova IN, Perova N V, Shchel’tsyna N V, Mamedov MN. Parameters of high-density lipoproteins in patients with arterial hypertension in combination with other components of metabolic syndrome. Bull Exp Biol Med. 2007;143(3):320–2. 396. Musunuru K, Orho-Melander M, Caulfield MP, Li S, Salameh WA, Reitz RE, et al. Ion mobility analysis of lipoprotein subfractions identifies three independent axes of cardiovascular risk. Arterioscler Thromb Vasc Biol. 2009;29(11):1975–80. 397. Xu R-X, Zhang Y, Ye P, Chen H, Li Y-F, Hua Q, et al. Analysis of Lipoprotein Subfractions in Chinese Han Patients with Stable Coronary Artery Disease. Heart Lung Circ. 2015;24(12):1203–10. 398. McGarrah RW, Craig DM, Haynes C, Dowdy ZE, Shah SH, Kraus WE. High-density lipoprotein subclass measurements improve mortality risk prediction, discrimination and reclassification in a cardiac catheterization cohort. Atherosclerosis. 2016;246:229–35. 399. Shao B, De Boer I, Tang C, Mayer PS, Zelnick L, Afkarian M, et al. A Cluster of Proteins Implicated in Kidney Disease Is Increased in High-Density Lipoprotein Isolated from Hemodialysis Subjects. J Proteome Res. 2015;14(7):2792–806. 400. Ross DJ, Hough G, Hama S, Aboulhosn J, Belperio JA, Saggar R, et al. Proinflammatory high-density lipoprotein results from oxidized lipid mediators in the pathogenesis of both idiopathic and associated types of pulmonary arterial hypertension. Pulm Circ. 2015;5(4):640–8. 401. O’Neill F, Charakida M, Topham E, Mcloughlin E, Patel N, Sutill E, et al. Anti-inflammatory treatment improves high-density lipoprotein function in rheumatoid arthritis. Heart. 2016;103(10):776–773. 251 402. Gomaraschi M, Ossoli A, Favari E, Adorni MP, Sinagra G, Cattin L, et al. Inflammation impairs eNOS activation by HDL in patients with acute coronary syndrome. Cardiovasc Res. 2013;100(1):36–43. 403. Pruzanski W, Stefanski E, de Beer FC, de Beer MC, Ravandi A, Kuksis A. Comparative analysis of lipid composition of normal and acute-phase high density lipoproteins. J Lipid Res. 2000;41(7):1035–47. 404. O’Neill F, Riwanto M, Charakida M, Colin S, Manz J, McLoughlin E, et al. Structural and functional changes in HDL with low grade and chronic inflammation. Int J Cardiol. 2015;188:111–6. 405. Sun JT, Yang K, Lu L, Zhu Z Bin, Zhu JZ, Ni JW, et al. Increased carbamylation level of HDL in end-stage renal disease: carbamylated-HDL attenuated endothelial cell function. Am J Physiol Ren Physiol. 2016;310(6):F511–7. 406. Shroff R, Speer T, Colin S, Charakida M, Zewinger S, Staels B, et al. HDL in Children with CKD Promotes Endothelial Dysfunction and an Abnormal Vascular Phenotype. J Am Soc Nephrol. 2014;25(11):2658–68. 407. Speer T, Rohrer L, Blyszczuk P, Shroff R, Kuschnerus K, Kränkel N, et al. Abnormal high-density lipoprotein induces endothelial dysfunction via activation of toll-like receptor-2. Immunity. 2013;38(4):754–68. 408. Holzer M, Birner-Gruenberger R, Stojakovic T, El-Gamal D, Binder V, Wadsack C, et al. Uremia Alters HDL Composition and Function. J Am Soc Nephrol. 2011;22(9):1631–41. 409. Honda H, Hirano T, Ueda M, Kojima S, Mashiba S, Hayase Y, et al. High-Density Lipoprotein Subfractions and Their Oxidized Subfraction Particles in Patients with Chronic Kidney Disease. J Atheroscler Thromb. 2016;23(1):81–94. 410. Kaseda R, Jabs K, Hunley TE, Jones D, Bian A, Allen RM, et al. Dysfunctional high-density lipoproteins in children with chronic kidney disease. Metabolism. 2014;64(2):263–73. 411. Marsillach J, Aragonès G, Mackness B, Mackness M, Rull A, Beltrán-Debón R, et al. Decreased paraoxonase-1 activity is associated with alterations of high-density lipoprotein 252 particles in chronic liver impairment. Lipids Health Dis. 2010;9:46. 412. Trieb M, Horvath A, Birner-Gruenberger R, Spindelboeck W, Stadlbauer V, Taschler U, et al. Liver disease alters high-density lipoprotein composition, metabolism and function. Biochim Biophys Acta. 2016;1861(7):630–8. 413. Holzer M, Trieb M, Konya V, Wadsack C, Heinemann A, Marsche G. Aging affects high-density lipoprotein composition and function. Biochim Biophys Acta. 2013;1831(9):1442–8. 414. Jaouad L, De Guise C, Berrougui H, Cloutier M, Isabelle M, Fulop T, et al. Age-related decrease in high-density lipoproteins antioxidant activity is due to an alteration in the PON1’s free sulfhydyl groups. Atherosclerosis. 2006;185(1):191–200. 415. Berrougui H, Isabelle M, Cloutier M, Grenier G, Khalil A. Age-related impairment of HDL-mediated cholesterol efflux. J Lipid Res. 2007;48(2):328–36. 416. Gomez Rosso L, Lhomme M, Merono T, Sorroche P, Catoggio L, Soriano E, et al. Altered lipidome and antioxidative activity of small, dense HDL in normolipidemic rheumatoid arthritis: Relevance of inflammation. Atherosclerosis. 2014;237(2):652–60. 417. Parra S, Castro A, Masana L. The pleiotropic role of HDL in autoimmune diseases. Clin Investig Arter. 2015;27(2):97–106. 418. McMahon M, Grossman J, FitzGerald J, Dahlin-Lee E, Wallace DJ, Thong BY, et al. Proinflammatory high-density lipoprotein as a biomarker for atherosclerosis in patients with systemic lupus erythematosus and rheumatoid arthritis. Arthritis Rheum. 2006;54(8):2541–9. 419. Griffiths K, Pazderska A, Ahmed M, Mcgowan A, Maxwell AP, McEneny J, et al. Type 2 Diabetes in Young Females Results in Increased Serum Amyloid A and Changes to Features of High Density Lipoproteins in Both HDL2 and HDL3. J Diabetes Res. 2017;2017:1314864. 420. Amor AJ, Catalan M, Pérez A, Herreras Z, Pinyol M, Sala-Vila A, et al. Nuclear magnetic resonance lipoprotein abnormalities in newly-diagnosed type 2 diabetes and their association with preclinical carotid atherosclerosis. Atherosclerosis. 2016;247:161–9. 253 421. Tong X, Lv P, Mathew A V, Liu D, Niu C, Wang Y, et al. The compensatory enrichment of sphingosine -1- phosphate harbored on glycated high-density lipoprotein restores endothelial protective function in type 2 diabetes mellitus. Cardiovasc Diabetol. 2014;13:82. 422. Brinck JW, Thomas A, Lauer E, Jornayvaz FR, Brulhart-Meynet M, Prost J, et al. Diabetes Mellitus Is Associated With Reduced High-Density Lipoprotein Sphingosine-1-Phosphate Content and Impaired High-Density Lipoprotein Cardiac Cell Protection. Arter Thromb Vasc Biol. 2016;36(5):817–24. 423. Vaisar T, Couzens E, Hwang A, Russell M, Barlow CE, DeFina LF, et al. Type 2 diabetes is associated with loss of HDL endothelium protective functions. PLoS One. 2018;13(3):1–16. 424. Sorrentino SA, Besler C, Rohrer L, Meyer M, Heinrich K, Bahlmann FH, et al. Endothelial-vasoprotective effects of high-density lipoprotein are impaired in patients with type 2 diabetes mellitus but are improved after extended-release niacin therapy. Circulation. 2010;121(1):110–22. 425. Ebtehaj S, Gruppen EG, Parvizi M, Tietge UJF, Dullaart RPF. The anti-inflammatory function of HDL is impaired in type 2 diabetes: Role of hyperglycemia, paraoxonase-1 and low grade inflammation. Cardiovasc Diabetol. 2017;16(1):132. 426. Morgantini C, Natali A, Boldrini B, Imaizumi S, Navab M, Fogelman AM, et al. Anti-inflammatory and antioxidant properties of HDLs are impaired in type 2 diabetes. Diabetes. 2011;60(10):2617–23. 427. Liu D, Ji L, Zhang D, Tong X, Pan B, Liu P, et al. Nonenzymatic glycation of high-density lipoprotein impairs its anti-inflammatory effects in innate immunity. Diabetes Metab Res Rev. 2012;28(2):186–95. 428. Perségol L, Vergès B, Foissac M, Gambert P, Duvillard L. Inability of HDL from type 2 diabetic patients to counteract the inhibitory effect of oxidised LDL on endothelium-dependent vasorelaxation. Diabetologia. 2006;49(6):1380–6. 429. Pertl L, Kern S, Weger M, Hausberger S, Trieb M, Gasser-Steiner V, et al. High-density 254 lipoprotein function in exudative age-related macular degeneration. PLoS One. 2016;11(5):e0154397. 430. Pan B, Kong J, Jin J, Kong J, He Y, Dong S, et al. A novel anti-inflammatory mechanism of high density lipoprotein through up-regulating annexin A1 in vascular endothelial cells. Biochim Biophys Acta. 2016 Jun;1861(6):501–12. 431. Cameron SJ, Morrell CN, Bao C, Swaim AF, Rodriguez A, Lowenstein CJ. A novel anti-inflammatory effect for high density lipoprotein. PLoS One. 2015;10(12):e0144372. 432. Fruhwürth S, Krieger S, Winter K, Rosner M, Mikula M, Weichhart T, et al. Inhibition of mTOR down-regulates scavenger receptor, class B, type i (SR-BI) expression, reduces endothelial cell migration and impairs nitric oxide production. Biochim Biophys Acta. 2014;1841(7):944–53. 433. Galvani S, Sanson M, Blaho VA, Swendeman SL, Obinata H, Conger H, et al. HDL-bound sphingosine 1-phosphate acts as a biased agonist for the endothelial cell receptor S1P1 to limit vascular inflammation. Sci Signal. 2015;8(389):ra79. 434. Kimura T, Tomura H, Mogi C, Kuwabara A, Damirin A, Ishizuka T, et al. Role of scavenger receptor class B type I and sphingosine 1-phosphate receptors in high density lipoprotein-induced inhibition of adhesion molecule expression in endothelial cells. J Biol Chem. 2006;281(49):37457–67. 435. Förstermann U, Xia N, Li H. Roles of vascular oxidative stress and nitric oxide in the pathogenesis of atherosclerosis. Circ Res. 2017;120(4):713–35. 436. Terasaka N, Yu S, Yvan-Charvet L, Wang N, Mzhavia N, Langlois R, et al. ABCG1 and HDL protect against endothelial dysfunction in mice fed a high-cholesterol diet. J Clin Invest. 2008;118(11):3701–13. 437. Uittenbogaard A, Shaul PW, Yuhanna IS, Blair A, Smart EJ. High Density Lipoprotein Prevents Oxidized Low Density Lipoprotein-induced Inhibition of Endothelial Nitric-oxide Synthase Localization and Activation in Caveolae. J Biol Chem. 2000;275(15):11278–83. 438. Mackness MI, Arrol S, Abbott C, Durrington PN. Protection of low-density lipoprotein 255 against oxidative modification by high-density lipoprotein associated paraoxonase. Atherosclerosis. 1993;104(1–2):129–35. 439. Garcia-Heredia A, Marsillach J, Rull A, Triguero I, Fort I, MacKness B, et al. Paraoxonase-1 inhibits oxidized low-density lipoprotein-induced metabolic alterations and apoptosis in endothelial cells: A nondirected metabolomic study. Mediators Inflamm. 2013;2013:156053. 440. Graler M, Keul P, Weske S, Reimann C, Jindrova H, Kleinbongard P, et al. Defects of High-Density Lipoproteins in Coronary Artery Disease Caused by Low Sphingosine-1-Phosphate Content. J Am Coll Cardiol. 2015;66(13):1470–85. 441. Burillo E, Lindholt JS, Molina-Sánchez P, Jorge I, Martinez-Pinna R, Blanco-Colio LM, et al. ApoA-I/HDL-C levels are inversely associated with abdominal aortic aneurysm progression. Thromb Haemost. 2015;113(6):1335–46. 442. Delbosc S, Diallo D, Dejouvencel T, Lamiral Z, Louedec L, Martin-Ventura JL, et al. Impaired high-density lipoprotein anti-oxidant capacity in human abdominal aortic aneurysm. Cardiovasc Res. 2013;100(2):307–15. 443. Phillips CM, Perry IJ. Lipoprotein particle subclass profiles among metabolically healthy and unhealthy obese and non-obese adults: Does size matter? Atherosclerosis. 2015;242(2):399–406. 444. Li J-J, Zhang Y, Li S, Cui C-J, Zhu C-G, Guo Y-L, et al. Large HDL Subfraction But Not HDL-C Is Closely Linked With Risk Factors, Coronary Severity and Outcomes in a Cohort of Nontreated Patients With Stable Coronary Artery Disease. Medicine (Baltimore). 2016;95(4):e2600. 445. Schaefer EJ, McNamara JR, Asztalos BF, Tayler T, Daly JA, Gleason JL, et al. Effects of atorvastatin versus other statins on fasting and postprandial C-reactive protein and lipoprotein-associated phospholipase A2 in patients with coronary heart disease versus control subjects. Am J Cardiol. 2005;95(9):1025–32. 446. Schaefer EJ, McNamara JR, Tayler T, Daly JA, Gleason JL, Seman LJ, et al. Comparisons of effects of statins (atorvastatin, fluvastatin, lovastatin, pravastatin, and simvastatin) on 256 fasting and postprandial lipoproteins in patients with coronary heart disease versus control subjects. Am J Cardiol. 2004;93(1):31–9. 447. Asztalos BF, Le Maulf F, Dallal GE, Stein E, Jones PH, Horvath K V., et al. Comparison of the Effects of High Doses of Rosuvastatin Versus Atorvastatin on the Subpopulations of High-Density Lipoproteins. Am J Cardiol. 2007;99(5):681–5. 448. Asztalos BF, Horvath K V., McNamara JR, Roheim PS, Rubinstein JJ, Schaefer EJ. Effects of atorvastatin on the HDL subpopulation profile of coronary heart disease patients. J Lipid Res. 2002;43(10):1701–7. 449. Asztalos BF, Horvath K V., McNamara JR, Roheim PS, Rubinstein JJ, Schaefer EJ. Comparing the effects of five different statins on the HDL subpopulation profiles of coronary heart disease patients. Atherosclerosis. 2002;164(2):361–9. 450. Harangi M, Seres I, Harangi J, Paragh G. Benefits and difficulties in measuring HDL subfractions and human paraoxonase-1 activity during statin treatment. Cardiovasc Drugs Ther. 2009;23(6):501–10. 451. Green PS, Vaisar T, Pennathur S, Kulstad JJ, Moore AB, Marcovina S, et al. Combined statin and niacin therapy remodels the high-density lipoprotein proteome. Circulation. 2008;118(12):1259–67. 452. Gordon SM, McKenzie B, Kemeh G, Sampson M, Perl S, Young NS, et al. Rosuvastatin Alters the Proteome of High Density Lipoproteins: Generation of Alpha-1-antitrypsin Enriched Particles with Anti-inflammatory Properties. Mol Cell Proteomics. 2015;14(12):3247–57. 453. von Eckardstein A, Kardassis D. High density lipoproteins: From biological understanding to clinical exploitation. In: Handbook of Experimental Pharmacology. 2015. p. 593–615. 454. Tian L, Chen Y, Li C, Zeng Z, Xu Y, Long S, et al. Statin treatment improves plasma lipid levels but not HDL subclass distribution in patients undergoing percutaneous coronary intervention. Lipids. 2013;48(2):127–37. 455. Berthold HK, Rizzo M, Spenrath N, Montalto G, Krone W, Gouni-Berthold I. Effects of lipid-lowering drugs on high-density lipoprotein subclasses in healthy men - A 257 randomized trial. PLoS One. 2014;9(3). 456. Harangi M, Mirdamadi HZ, Seres I, Sztanek F, Molnár M, Kassai A, et al. Atorvastatin effect on the distribution of high-density lipoprotein subfractions and human paraoxonase activity. Transl Res. 2009;153(4):190–8. 457. Ronsein GE, Hutchins PM, Isquith D, Vaisar T, Zhao XQ, Heinecke JW. Niacin Therapy Increases High-Density Lipoprotein Particles and Total Cholesterol Efflux Capacity but Not ABCA1-Specific Cholesterol Efflux in Statin-Treated Subjects. Arterioscler Thromb Vasc Biol. 2016;36(2):404–11. 458. Garvey WT, Kwon S, Zheng D, Shaughnessy S, Wallace P, Hutto A, et al. Effects of insulin resistance and type 2 diabetes on lipoprotein subclass particle size and concentration determined by nuclear magnetic resonance. Diabetes. 2003;52(2):453–62. 459. Passaro A, Vigna GB, Romani A, Sanz JM, Cavicchio C, Bonaccorsi G, et al. Distribution of Paraoxonase-1 (PON-1) and Lipoprotein Phospholipase A2 (Lp-PLA2) across Lipoprotein Subclasses in Subjects with Type 2 Diabetes. Oxid Med Cell Longev. 2018;2018:1752940. 460. Fukui T, Hirano T. High-density lipoprotein subspecies between patients with type 1 diabetes and type 2 diabetes without/with intensive insulin therapy. Endocr J. 2012;59(7):561–9. 461. Mora S, Otvos JD, Rosenson RS, Pradhan A, Buring JE, Ridker PM. Lipoprotein particle size and concentration by nuclear magnetic resonance and incident type 2 diabetes in women. Diabetes. 2010;59(5):1153–60. 462. Mendivil CO, Furtado J, Morton AM, Wang L, Sacks FM. Novel Pathways of Apolipoprotein A-I Metabolism in High-Density Lipoprotein of Different Sizes in Humans. Arterioscler Thromb Vasc Biol. 2016;36(1):156–65. 463. Singh SA, Andraski AB, Pieper B, Goh W, Mendivil CO, Sacks FM, et al. Multiple apolipoprotein kinetics measured in human HDL by high-resolution/accurate mass parallel reaction monitoring. J Lipid Res. 2016;57(4):714–28. 464. Sang H, Yao S, Zhang L, Li X, Yang N, Zhao J, et al. Walk-run training improves the 258 anti-inflammation properties of high-density lipoprotein in patients with metabolic syndrome. J Clin Endocrinol Metab. 2015;100(3):870–9. 465. Pedret A, Catalan U, Fernandez-Castillejo S, Farras M, Valls RM, Rubio L, et al. Impact of Virgin Olive Oil and Phenol-Enriched Virgin Olive Oils on the HDL Proteome in Hypercholesterolemic Subjects: A Double Blind, Randomized, Controlled, Cross-Over Clinical Trial (VOHF Study). PLoS One. 2015;10(6):e0129160. 466. Sánchez-Quesada JL, Vinagre I, De Juan-Franco E, Sánchez-Hernández J, Bonet-Marques R, Blanco-Vaca F, et al. Impact of the LDL subfraction phenotype on Lp-PLA2 distribution, LDL modification and HDL composition in type 2 diabetes. Cardiovasc Diabetol. 2013;12:112. 467. Green PS, Vaisar T, Subramaniam P, Kulstad JJ, Moore AB, Marcovina S, et al. Combined Statin and Niacin Therapy Remodels the HDL Proteome. 2010;46(2):220–31. 468. Yetukuri L, Huopaniemi I, Koivuniemi A, Maranghi M, Hiukka A, Nygren H, et al. High density lipoprotein structural changes and drug response in lipidomic profiles following the long-term fenofibrate therapy in the FIELD substudy. PLoS One. 2011;6(8):e23589. 469. Masana L, Cabré A, Heras M, Amigó N, Correig X, Martínez-Hervás S, et al. Remarkable quantitative and qualitative differences in HDL after niacin or fenofibrate therapy in type 2 diabetic patients. Atherosclerosis. 2015;238(2):213–9. 470. Miyamoto-Sasaki M, Yasuda T, Monguchi T, Nakajima H, Mori K, Toh R, et al. Pitavastatin increases HDL particles functionally preserved with cholesterol efflux capacity and antioxidative actions in dyslipidemic patients. J Atheroscler Thromb. 2013;20(9):708–16. 471. Vickers KC, Remaley AT. HDL and cholesterol: life after the divorce? J Lipid Res. 2014;55(1):4–12. 472. Desgagné V, Guay S-P, Guérin R, Corbin F, Couture P, Lamarche B, et al. Variations in HDL-carried miR-223 and miR-135a concentrations after consumption of dietary trans fat are associated with changes in blood lipid and inflammatory markers in healthy men - an exploratory study. Epigenetics. 2016;11(6):438–48. 259 473. Tabet F, Cuesta Torres LF, Ong KL, Shrestha S, Choteau SA, Barter PJ, et al. High-Density Lipoprotein-Associated miR-223 Is Altered after Diet-Induced Weight Loss in Overweight and Obese Males. PLoS One. 2016;11(3):e0151061. 474. Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, Remaley AT. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol. 2011;13(4):423–33. 475. Tabet F, Vickers KC, Cuesta Torres LF, Wiese CB, Shoucri BM, Lambert G, et al. HDL-transferred microRNA-223 regulates ICAM-1 expression in endothelial cells. Nat Commun. 2014;5:3292. 476. Hyötyläinen T, Mattila I, Wiedmer SK, Koivuniemi A, Taskinen M-R, Yki-Järvinen H, et al. Metabolomic analysis of polar metabolites in lipoprotein fractions identifies lipoprotein-specific metabolic profiles and their association with insulin resistance. Mol Biosyst. 2012;8(10):2559–65. 477. Chadwick AC, Holme RL, Chen Y, Thomas MJ, Sorci-Thomas MG, Silverstein RL, et al. Acrolein impairs the cholesterol transport functions of high density lipoproteins. PLoS One. 2015;10(4):1–15. 478. Fonslow BR, Stein BD, Webb KJ, Xu T, Choi J, Park S, et al. Digestion and depletion of abundant proteins improves proteomic coverage. Nat Methods. 2013;10(1):54–6. 479. Morgantini C, Meriwether D, Baldi S, Venturi E, Pinnola S, Wagner AC, et al. HDL lipid composition is profoundly altered in patients with type 2 diabetes and atherosclerotic vascular disease. Nutr Metab Cardiovasc Dis. 2014;24(6):594–9. 480. MacMahon S, Duffy S, Rodgers A, Tominaga S, Chambless L, De Backer G, et al. Blood cholesterol and vascular mortality by age, sex, and blood pressure: A meta-analysis of individual data from 61 prospective studies with 55 000 vascular deaths. Lancet. 2007;370(9602):1829–39. 481. Shah S, Casas JP, Drenos F, Whittaker J, Deanfield J, Swerdlow DI, et al. Causal relevance of blood lipid fractions in the development of carotid atherosclerosis mendelian randomization analysis. Circ Cardiovasc Genet. 2013;6(1):63–72. 260 482. Besler C, Heinrich K, Rohrer L, Doerries C, Riwanto M, Shih DM, et al. Mechanisms underlying adverse effects of HDL on eNOS-activating pathways in patients with coronary artery disease. J Clin Invest. 2011;121(7):2693–708. 483. Vaisar T, Pennathur S, Green PS, Gharib SA, Hoofnagle AN, Cheung MC, et al. Shotgun proteomics implicates protease inhibition and complement activation in the antiinflammatory properties of HDL. J Clin Invest. 2007;117(3):746–56. 484. Kopecky C, Genser B, Drechsler C, Krane V, Kaltenecker CC, Hengstschläger M, et al. Quantification of HDL Proteins, Cardiac Events, and Mortality in Patients with Type 2 Diabetes on Hemodialysis. Clin J Am Soc Nephrol. 2015;10(2):224–31. 485. Rohatgi A, Khera A V, Berry JD, Givens EG, Ayers CR, Wedin KE, et al. HDL Cholesterol Efflux Capacity and Incident Cardiovascular Events. N Engl J Med. 2014;371(25):2383–93. 486. Khera A V, Cuchel M, de la Llera Moya M, Rodrigues A, Burke M, Jafri K, et al. Cholesterol Efflux Capacity, High-Density Lipoprotein Function, and Atherosclerosis. N Engl J Med. 2011;364(2):127–35. 487. Zuliani G, Cavalieri M, Galvani M, Volpato S, Cherubini A, Bandinelli S, et al. Relationship Between Low Levels of High-Density Lipoprotein Cholesterol and Dementia in the Elderly . The InChianti Study. J Gerontol. 2010;65(5):559–64. 488. Merched A, Xia Y, Visvikis S, Serot JM, Siest G. Decreased high-density lipoprotein cholesterol and serum apolipoprotein AI concentrations are highly correlated with the severity of Alzheimer’s disease. Neurobiol Aging. 2000;21(1):27–30. 489. Shih Y, Tsai K, Lee C, Shiesh S, Chen W, Pai M, et al. Apolipoprotein C-III is an amyloid-β-binding protein and an early marker for Alzheimer’s disease. J Alzheimers Dis. 2014;41(3):855–65. 490. Montañola A, de Retana SF, López-Rueda A, Merino-Zamorano C, Penalba A, Fernández-Álvarez P, et al. ApoA1, ApoJ and ApoE Plasma Levels and Genotype Frequencies in Cerebral Amyloid Angiopathy. NeuroMolecular Med. 2016;18(1):99–108. 491. Reed B, Villeneuve S, Mack W, DeCarli C, Chui HC, Jagust W. Associations between 261 serum cholesterol levels and cerebral amyloidosis. JAMA Neurol. 2014;71(2):195–200. 492. Crichton GE, Elias MF, Davey A, Sullivan KJ, Robbins MA. Higher HDL cholesterol is associated with better cognitive function: the Maine-Syracuse study. J Int Neuropsychol Soc. 2014;20(10):961–70. 493. Bates KA, Sohrabi HR, Rainey-Smith SR, Weinborn M, Bucks RS, Rodrigues M, et al. Serum high-density lipoprotein is associated with better cognitive function in a cross-sectional study of aging women. Int J Neurosci. 2016;127(3):243–52. 494. Formiga F, Ferrer A, Chivite D, Pinto X, Cuerpo S, Pujol R. Serum high-density lipoprotein cholesterol levels, their relationship with baseline functional and cognitive status, and their utility in predicting mortality in nonagenarians. Geriatr Gerontol Int. 2011;11(3):358–64. 495. Saczynski JS, White L, Peila RL, Rodriguez BL, Launer LJ. The relation between apolipoprotein A-I and dementia: The Honolulu-Asia aging study. Am J Epidemiol. 2007;165(9):985–92. 496. Armstrong NM, An Y, Beason-Held L, Doshi J, Erus G, Ferrucci L, et al. Predictors of neurodegeneration differ between cognitively normal and subsequently impaired older adults. Neurobiol Aging. 2019;75:178–86. 497. Marcum ZA, Walker R, Bobb JF, Sin M, Gray SL, Bowen JD, et al. Serum Cholesterol and Incident Alzheimer’s Disease: Findings from the Adult Changes in Thought Study. J Am Geriatr Soc. 2018;66(12):2344–52. 498. Li G, Shofer J, Kukull W, Peskind E, Tsuang D, Breitner J, et al. Serum cholesterol and risk of Alzheimer disease A community-based cohort study. Neurology. 2005;65(7):1045-50. 499. Mielke MM, Xue Q, Zhou J, Chaves PHM, Fried LP, Carlson MC. Baseline serum cholesterol is selectively associated with motor speed and not rates of cognitive decline: the Women’s Health and Aging Study II. J Gerontol A Biol Sci Med Sci. 2008;63(6):619–24. 500. Yaffe K, Barrett-Connor E, Lin F, Grady D. Serum Lipoprotein Levels, Statin Use, and 262 Cognitive Function in Older Women. Arch Neurol. 2002;59(3):378–84. 501. Stukas S, Robert J, Lee M, Kulic I, Carr M, Tourigny K, et al. Intravenously Injected Human Apolipoprotein A-I Rapidly Enters the Central Nervous System via the Choroid Plexus. J Am Hear Assoc. 2014;3(6):e001156. 502. Borghini I, Barja F, Pometta D, James R. Characterization of subpopulations of lipoprotein particles isolated from human cerebrospinal fluid. Biochim Biophys Acta. 1995;1255(2):192–200. 503. Manousopoulou A, Gatherer M, Smith C, Nicoll JAR, Woelk CH, Johnson M, et al. Systems proteomic analysis reveals that Clusterin and Tissue Inhibitor of Metalloproteinases 3 increase in leptomeningeal arteries affected by cerebral amyloid angiopathy. Neuropathol Appl Neurobiol. 2017;43(6):492–504. 504. Fung KY, Wang C, Nyegaard S, Heit B, Fairn GD, Lee WL. SR-BI mediated transcytosis of HDL in brain microvascular endothelial cells is independent of caveolin, clathrin, and PDZK1. Front Physiol. 2017;8:841. 505. Ladu MJ, Reardon C, Van Eldik L, Fagan AM, Bu G, Holtzman D, et al. Lipoproteins in the central nervous system. Ann N Y Acad Sci. 2000;903:167–75. 506. Walker LC, Jucker M. The Exceptional Vulnerability of Humans to Alzheimer’s Disease. Trends Mol Med. 2017;23(6):534–45. 507. Reardon S. Frustrated Alzheimer’s researchers seek better lab mice. Nature. 2018;563(7733):611–2. 508. Elder G, Gama Sosa M, De Gasperi R. Transgenic mouse models of Alzheimer’s disease. Mt Sinai J Med. 2010;77(1):69–81. 509. Hall AM, Roberson ED. Mouse models of Alzheimer’s disease. Brain Res Bull. 2012;88(1):3–12. 510. King A. The search for better animal models of Alzheimer’s disease. Nature. 2018;559(7715):S13–5. 511. Jankowsky JL, Zheng H. Practical considerations for choosing a mouse model of 263 Alzheimer’s disease. Mol Neurodegener. 2017;12(1):89. 512. Onos KD, Sukoff SJ, Howell GR, Sasner M. Toward more predictive genetic mouse models of Alzheimer’s disease. Brain Res Bull. 2016;122:1–11. 513. Gamache J, Benzow K, Forster C, Kemper L, Hlynialuk C, Furrow E, et al. Factors other than hTau overexpression that contribute to tauopathy-like phenotype in rTg4510 mice. Nat Comm. 2019;10(1):2479. 514. Saito T, Matsuba Y, Mihira N, Takano J, Nilsson P, Itohara S, et al. Single App knock-in mouse models of Alzheimer’s disease. Nat Neurosci. 2014;17(5):661–3. 515. Soldan A, Pettigrew C, Cai Q, Wang M, Moghekar AR, Brien RJO, et al. Hypothetical Preclinical Alzheimer Disease Groups and Longitudinal Cognitive Change. JAMA Neurol. 2016;73(6):698–705. 516. Jakobsen JE, Johansen MG, Schmidt M, Liu Y, Li R, Callesen H, et al. Expression of the Alzheimer’s Disease Mutations AβPP695sw and PSEN1M146I in Double-Transgenic Göttingen Minipigs. J Alzheimer’s Dis. 2016;53(4):1617–30. 517. Head E. A canine model of human aging and Alzheimer’s disease. Biochim Biophys Acta - Mol Basis Dis. 2013;1832(9):1384–9. 518. Sorby-Adams AJ, Vink R, Turner RJ. Large animal models of stroke and traumatic brain injury as translational tools. Am J Physiol Regul Integr Comp Physiol. 2018;315(2):R165–90. 519. Lefterov I, Fitz NF, Cronican AA, Fogg A, Lefterov P, Kodali R, et al. Apolipoprotein A-I Deficiency Increases Cerebral Amyloid Angiopathy and Cognitive Deficits in APP/PS1DeltaE9 Mice. J Biol Chem. 2010;285(47):36945–57. 520. Fagan AM, Christopher E, Taylor JW, Parsadanian M, Spinner M, Watson M, et al. ApoAI Deficiency Results in Marked Reductions in Plasma Cholesterol But No Alterations in Amyloid-Beta Pathology in a Mouse Model of Alzheimer’s Disease-Like Cerebral Amyloidosis. Am J Pathol. 2004;165(4):1413–22. 521. Lewis TL, Cao D, Lu H, Mans RA, Su YR, Jungbauer L, et al. Overexpression of Human 264 Apolipoprotein A-I Preserves Cognitive Function and Attenuates Neuroinflammation and Cerebral Amyloid Angiopathy in a Mouse Model of Alzheimer. J Biol Chem. 2010;285(47):36958–68. 522. Contu L, Carare RO, Hawkes CCA. Knockout of apolipoprotein A-I decreases parenchymal and vascular β-amyloid pathology in the Tg2576 mouse model of Alzheimer’s disease. Neuropathol Appl Neurobiol. 2019;45(7):698–714. 523. Robert J, Stukas S, Button EB, Hang W, Lee M, Fan J, et al. Reconstituted high-density lipoproteins acutely reduce soluble brain Aβ levels in symptomatic APP/PS1 mice. Biochim Biophys Acta. 2015;1862(5):1027–36. 524. Song Q, Huang M, Yao L, Wang X, Gu X, Chen JJJ, et al. Lipoprotein-based nanoparticles rescue the memory loss of mice with alzheimer’s disease by accelerating the clearance of amyloid-beta. ACS Nano. 2014;8(3):2345–59. 525. Fernández-de Retana S, Montañola A, Marazuela P, De La Cuesta M, Batlle A, Fatar M, et al. Intravenous treatment with human recombinant ApoA-I Milano reduces beta amyloid cerebral deposition in the APP23-transgenic mouse model of Alzheimer’s disease. Neurobiol Aging. 2017;60:116–28. 526. De Retana SF, Marazuela P, Solé M, Colell G, Bonaterra A, Sánchez-Quesada JL, et al. Peripheral administration of human recombinant ApoJ/clusterin modulates brain beta-amyloid levels in APP23 mice. Alzheimer’s Res Ther. 2019;11(1):42. 527. Handattu SP, Garber DW, Monroe CE, van Groen T, Kadish I, Nayyar G, et al. Oral apolipoprotein A-I mimetic peptide improves cognitive function and reduces amyloid burden in a mouse model of Alzheimer’s disease. Neurobiol Dis. 2009;34(3):525–34. 528. Cui X, Chopp M, Zacharek A, Cui C, Yan T, Ning R, et al. D-4F Decreases White Matter Damage After Stroke in Mice. Stroke. 2016;47(1):214–20. 529. Buga GM, Frank JS, Mottino GA, Hendizadeh M, Hakhamian A, Tillisch JH, et al. D-4F decreases brain arteriole inflammation and improves cognitive performance in LDL receptor-null mice on a Western diet. J Lipid Res. 2006;47(10):2148–60. 530. Zelcer N, Tontonoz P. Liver X receptors as integrators of metabolic and inflammatory 265 signaling. J Clin Invest. 2006;116(3):607–14. 531. Pehkonen P, Welter-Stahl L, Diwo J, Dyynanen J, Wienecke-Baldacchino A, Heikkinen S, et al. Genome-wide landscape of liver X receptor chromatin binding and gene regulation in human macrophages. BMC Genomics. 2012;13(1):50. 532. Repa JJ, Mangelsdorf DJ. The role of orphan nuclear receptors in the regulation of cholesterol homeostasis. Annu Rev Cell Dev Biol. 2000;16:459–81. 533. Lehmann JM, Kliewer SA, Moore LB, Smith-Oliver TA, Oliver BB, Su J-L, et al. Activation of the nuclear receptor LXR by oxysterols defines a new hormone response pathway. J Biol Chem. 1997;272(6):3137–40. 534. Willy PJ, Umesono K, Ong ES, Evans RM, Heyman RA, Mangelsdorf DJ. LXR, a nuclear receptor that defines a distinct retinoid response pathway. Genes Dev. 1995;9(9):1033–45. 535. Koldamova RP, Lefterov IM, Ikonomovic MD, Skoko J, Lefterov PI, Isanski BA, et al. 22R-hydroxycholesterol and 9-cis-retinoic acid induce ATP-binding cassette transporter A1 expression and cholesterol efflux in brain cells and decrease amyloid beta secretion. J Biol Chem. 2003;278(15):13244–56. 536. Sun Y, Yao J, Kim TW, Tall AR. Expression of liver X receptor target genes decreases cellular amyloid beta peptide secretion. J Biol Chem. 2003;278(30):27688–94. 537. Jiang Q, Lee CYD, Mandrekar S, Wilkinson B, Cramer P, Zelcer N, et al. ApoE Promotes the Proteolytic Degradation of Aβ. Neuron. 2008;58(5):681–93. 538. Riddell DR, Zhou H, Comery TA, Kouranova E, Lo CF, Warwick HK, et al. The LXR agonist TO901317 selectively lowers hippocampal Aβ42 and improves memory in the Tg2576 mouse model of Alzheimer’s disease. Mol Cell Neurosci. 2007;34(4):621–8. 539. Koldamova RP, Lefterov IM, Staufenbiel M, Wolfe D, Huang S, Glorioso JC, et al. The liver X receptor ligand T0901317 decreases amyloid beta production in vitro and in a mouse model of Alzheimer’s disease. J Biol Chem. 2005;280(6):4079–88. 540. Donkin JJ, Stukas S, Hirsch-reinshagen V, Namjoshi D, Wilkinson A, May S, et al. ATP-binding cassette transporter A1 mediates the beneficial effects of the liver X receptor 266 agonist GW3965 on object recognition memory and amyloid burden in amyloid precursor protein/presenilin 1 mice. J Biol Chem. 2010;285(44):34144–54. 541. Wesson DW, Borkowski AH, Landreth GE, Nixon RA, Levy E, Wilson D. Sensory network dysfunction, behavioral impairments, and their reversibility in an Alzheimer’s β-amyloidosis mouse model. J Neurosci. 2011;31(44):15962–71. 542. Skerrett R, Pellegrino MP, Casali BT, Taraboanta L, Landreth GE. Combined Liver X Receptor/Peroxisome Proliferator-activated Receptor γ Agonist Treatment Reduces Amyloid β Levels and Improves Behavior in Amyloid Precursor Protein/Presenilin 1 Mice. J Biol Chem. 2015;290(35):21591–602. 543. Sandoval-Hernández AG, Buitrago L, Moreno H, Cardona-Gómez GP, Arboleda G. Role of Liver X Receptor in AD Pathophysiology. PLoS One. 2015;10(12):e0145467. 544. Sandoval-Hernández A, Hernandez H, Restrepo A, Muñoz JI, Bayon G, Fernandez A, et al. Liver X Receptor Agonist Modifies the DNA Methylation Profile of Synapse and Neurogenesis-Related Genes in the Triple Transgenic Mouse Model of Alzheimer’s Disease. J Mol Neurosci. 2016;58(2):243–53. 545. Vanmierlo T, Rutten K, Dederen J, Bloks VW, van Vark-van der Zee LC, Kuipers F, et al. Liver X receptor activation restores memory in aged AD mice without reducing amyloid. Neurobiol Aging. 2011;32(7):1262–72. 546. Cui W, Sun Y, Wang Z, Xu C, Peng Y, Li R. Liver X receptor activation attenuates inflammatory response and protects cholinergic neurons in APP/PS1 transgenic mice. Neuroscience. 2012;210:200–10. 547. Stachel SJ, Zerbinatti C, Rudd MT, Cosden M, Suon S, Nanda KK, et al. Identification and in Vivo Evaluation of Liver X Receptor β-Selective Agonists for the Potential Treatment of Alzheimer’s Disease. J Med Chem. 2016;59(7):3489–98. 548. Jansen D, Janssen CIF, Vanmierlo T, Dederen PJ, Rooij D Van, Mulder M, et al. Cholesterol and synaptic compensatory mechanisms in Alzheimer’s disease mice brain during aging. J Alzheimers Dis. 2012;31(4):813–26. 549. Koldamova R, Fitz NF, Lefterov I. ATP-binding cassette transporter A1: From 267 metabolism to neurodegeneration. Neurobiol Dis. 2014;72(Pt A):13–21. 550. Sandoval-hernández AG, Restrepo A, Cardona-gómez GP, Arboleda G. LXR activation protects hippocampal microvasculature in very old triple transgenic mouse model of Alzheimer’s disease. Neurosci Lett. 2016;621:15–21. 551. Lefterov I, Bookout A, Wang Z, Staufenbiel M, Mangelsdorf D, Koldamova R. Expression profiling in APP23 mouse brain: inhibition of Abeta amyloidosis and inflammation in response to LXR agonist treatment. Mol Neurodegener. 2007;2:20. 552. Chu K, Miyazaki M, Man WC, Ntambi JM. Stearoyl-Coenzyme A Desaturase 1 Deficiency Protects against Hypertriglyceridemia and Increases Plasma High-Density Lipoprotein Cholesterol Induced by Liver X Receptor Activation. Mol Cell Biol. 2006;26(18):6786–98. 553. Joseph SB, Laffitte BA, Patel PH, Watson MA, Matsukuma KE, Walczak R, et al. Direct and indirect mechanisms for regulation of fatty acid synthase gene expression by liver X receptors. J Biol Chem. 2002;277(13):11019–25. 554. Pierrot N, Lhommel R, Quenon L, Hanseeuw B, Dricot L, Sindic C, et al. Targretin improves cognitive and biological markers in a patient with Alzheimer’s disease. J Alzheimer’s Dis. 2015;49(2):271–6. 555. Ghosal K, Haag M, Verghese PB, West T, Veenstra T, Braunstein JB, et al. A randomized controlled study to evaluate the effect of bexarotene on amyloid-β and apolipoprotein E metabolism in healthy subjects. Alzheimer’s Dement. 2016;2(2):110–20. 556. Corona AW, Kodoma N, Casali BT, Landreth GE. ABCA1 is Necessary for Bexarotene-Mediated Clearance of Soluble Amyloid Beta from the Hippocampus of APP/PS1 Mice. J Neuroimmune Pharmacol. 2016;11(1):61–72. 557. Boehm-Cagan A, Michaelson DM. Reversal of apoE4-driven brain pathology and behavioral deficits by bexarotene. J Neurosci. 2014;34(21):7293–301. 558. Cramer PE, Cirrito JR, Wesson DW, Lee CYD, Colleen J, Zinn AE, et al. ApoE-Directed Therapeutics Rapidly Clear β-Amyloid and Reverse Deficits in AD Mouse Models. Science (80- ). 2012;335(6075):1503–6. 268 559. Yuan C, Guo X, Zhou Q, Du F, Jiang W, Zhou X, et al. OAB-14, a bexarotene derivative, improves Alzheimer’s disease-related pathologies and cognitive impairments by increasing β-amyloid clearance in APP/PS1 mice. Biochim Biophys Acta. 2019;1865(1):161–80. 560. Casali BT, Reed-Geaghan EG, Landreth GE. Nuclear receptor agonist-driven modification of inflammation and amyloid pathology enhances and sustains cognitive improvements in a mouse model of Alzheimer’s disease. J Neuroinflammation. 2018;15(1):43. 561. LaClair KD, Manaye KF, Lee DL, Allard JS, Savonenko A V, Troncoso JC, et al. Treatment with bexarotene, a compound that increases apolipoprotein-E, provides no cognitive benefit in mutant APP/PS1 mice. Mol Neurodegener. 2013;8:18. 562. Balducci C, Paladini A, Micotti E, Tolomeo D, La Vitola P, Grigoli E, et al. The Continuing Failure of Bexarotene in Alzheimer’s Disease Mice. J Alzheimer’s Dis. 2015;46(2):471–82. 563. O’Hare E, Jeggo R, Kim EM, Barbour B, Walczak JS, Palmer P, et al. Lack of support for bexarotene as a treatment for Alzheimer’s disease. Neuropharmacology. 2016;100:124–30. 564. Jankowsky JL, Fadale DJ, Anderson J, Xu GM, Gonzales V, Jenkins NA, et al. Mutant presenilins specifically elevate the levels of the 42 residue beta-amyloid peptide in vivo : evidence for augmentation of a 42-specific gamma secretase. Hum Mol Genet. 2004;13(2):159–70. 565. Wilcock DM, Gordon MN, Morgan D. Quantification of cerebral amyloid angiopathy and parenchymal amyloid plaques with Congo red histochemical stain. Nat Protoc. 2006;1(3):1591–5. 566. Xiang Y, Bu X Le, Liu YH, Zhu C, Shen LL, Jiao SS, et al. Physiological amyloid-beta clearance in the periphery and its therapeutic potential for Alzheimer’s disease. Acta Neuropathol. 2015;130(4):487–99. 567. Lane DS, Thakker DR, Weatherspoon MR, Harrison J, Shafer LL, Stewart GR, et al. Intracerebroventricular amyloid- antibodies reduce cerebral amyloid angiopathy and 269 associated micro-hemorrhages in aged Tg2576 mice. Proc Natl Acad Sci. 2009;106(11):4501–6. 568. Li H, Reddick RL, Maeda N. Lack of apoA-I is not associated with increased susceptibility to atherosclerosis in mice. Atheroscler Thromb Vasc Biol. 1993;13(12):1814-21. 569. Yang P, Manaenko A, Xu F, Miao L, Wang G, Hu X, et al. Role of PDGF-D and PDGFR-β in neuroinflammation in experimental ICH mice model. Exp Neurol. 2016;283(Pt A):157–64. 570. Schnoor M, Alcaide P, Voisin MB, Van Buul JD. Crossing the Vascular Wall: Common and Unique Mechanisms Exploited by Different Leukocyte Subsets during Extravasation. Mediators Inflamm. 2015;2015:946509. 571. Akiyama H, Kawamata T, Yamada T, Tooyama I, Ishii T, McGeer P. Expression of intercellular adhesion molecule (ICAM)-1 by a subset of astrocytes in Alzheimer disease and some other degenerative neurological disorders. Acta Neuropathol. 1993;85(6):628–34. 572. Verbeek MM, Otte-Höller I, Westphal JR, Wesseling P, Ruiter DJ, de Waal RM. Accumulation of intercellular adhesion molecule-1 in senile plaques in brain tissue of patients with Alzheimer’s disease. Am J Pathol. 1994;144(1):104–16. 573. Miguel-Hidalgo JJ, Nithuairisg S, Stockmeier C, Rajkowska G. Distribution of ICAM-1 immunoreactivity during aging in the human orbitofrontal cortex. Brain Behav Immun. 2007;21(1):100–11. 574. Bell MD, Perry VH. Adhesion molecule expression on murine cerebral endothelium following the injection of a proinflammagen or during acute neuronal degeneration. J Neurocytol. 1995;24(9):695–710. 575. Lee J, Han P. An update of animal models of Alzheimer disease with a reevaluation of plaque depositions. Exp Neurol. 2013;22(2):84–95. 576. Klohs J, Rudin M, Shimshek DR, Beckmann N. Imaging of cerebrovascular pathology in animal models of Alzheimer’s disease. Front Aging Neurosci. 2014;6:32. 270 577. Nardo D De, Labzin LI, Kono H, Seki R, Schmidt S V, Beyer M, et al. High-density lipoprotein mediates anti-inflammatory reprogramming of macrophages via the transcriptional regulator ATF3. Nat Immunol. 2014;15(2):341–9. 578. Robert J, Button EB, Stukas S, Boyce GK, Gibbs E, Cowan CM, et al. High-density lipoproteins suppress Aβ-induced PBMC adhesion to human endothelial cells in bioengineered vessels and in monoculture. Mol Neurodegener. 2017;12(1):60. 579. Rye K, Barter PJ. Cardioprotective functions of HDLs. J Lip. 2014;55(2):168-79. 580. Moore RE, Kawashiri M, Kitajima K, Secreto A, Millar JS, Pratico D, et al. Apolipoprotein A-I Deficiency Results in Markedly Increased Atherosclerosis in Mice Lacking the LDL Receptor. Atheroscler Thromb Vasc Biol. 2003;23(10):1914–20. 581. Götz A, Lehti M, Donelan E, Striese C, Cucuruz S, Sachs S, et al. Circulating HDL levels control hypothalamic astrogliosis via apoA-I. J Lipid Res. 2018;59(9):1649–59. 582. Mazure CM, Swendsen J. Sex differences in Alzheimer’s disease and other dementias. Lancet Neurol. 2016;15(5):451–2. 583. Song Q, Song H, Xu J, Huang J, Hu M, Gu X, et al. Biomimetic ApoE-Reconstituted High Density Lipoprotein Nanocarrier for Blood-Brain Barrier Penetration and Amyloid Beta-Targeting Drug Delivery. Mol Pharm. 2016;13(11):3976–87. 584. Zhang J, Liu Q. Cholesterol metabolism and homeostasis in the brain. Protein Cell. 2015;6(4):254–64. 585. Zhou F, Deng W, Ding D, Zhao Q, Liang X, Wang F, et al. High Low-Density Lipoprotein Cholesterol Inversely Relates to Dementia in Community-Dwelling Older Adults: The Shanghai Aging Study. Front Neurol. 2018;9:952. 586. Hong C, Tontonoz P. Liver X receptors in lipid metabolism: opportunities for drug discovery. Nat Rev Drug Discov. 2014;13(6):433–44. 587. Stukas S, May S, Wilkinson A, Chan J, Donkin J, Wellington CL. The LXR agonist GW3965 increases apoA-I protein levels in the central nervous system independent of ABCA1. Biochim Biophys Acta. 2012;1821(3):536–46. 271 588. Brooks-Wilson A, Marcil M, Clee SM, Zhang LH, Roomp K, van Dam M, et al. Mutations in ABC1 in Tangier disease and familial high-density lipoprotein deficiency. Nat Genet. 1999;22(4):336–45. 589. Rust S, Rosier M, Funke H, Real J, Amoura Z, Piette J, et al. Tangier disease is caused by mutations in the gene encoding ATP-binding cassette transporter 1. Nat Genet. 1999;22(4):352–5. 590. Bodzioch M, Orsó E, Klucken J, Langmann T, Böttcher A, Diederich W, et al. The gene encoding ATP-binding cassette transporter 1 is mutated in Tangier disease. Nat Genet. 1999;22(4):347–51. 591. Genest J, Schwertani A, Choi HY. Membrane microdomains and the regulation of HDL biogenesis. Curr Opin Lipidol. 2018;29(1):36–41. 592. Button EB, Boyce GK, Wilkinson A, Stukas S, Hayat A, Fan J, et al. ApoA-I deficiency increases cortical amyloid deposition, cerebral amyloid angiopathy, cortical and hippocampal astrogliosis, and amyloid-associated astrocyte reactivity in APP/PS1 mice. Alzheimers Res Ther. 2019;11(1):44. 593. Collins JL, Fivush AM, Watson MA, Galardi CM, Lewis MC, Moore LB, et al. Identification of a nonsteroidal liver X receptor agonist through parallel array synthesis of tertiary amines. J Med Chem. 2002;45(10):1963–6. 594. Hartz AM, Bauer B, Soldner EL, Wolf A, Boy S, Backhaus R, et al. Amyloid-beta contributes to blood-brain barrier leakage in transgenic human amyloid precursor protein mice and in humans with cerebral amyloid angiopathy. Stroke. 2012;43(2):514–23. 595. Davies CA, Loddick SA, Toulmond S, Paul Stroemer R, Hunt J, Rothwell NJ. The progression and topographic distribution of interleukin-1β expression after permanent middle cerebral artery occlusion in the rat. J Cereb Blood Flow Metab. 1999;19(1):87–98. 596. Ajami B, Bennett JL, Krieger C, McNagny KM, Rossi FMV. Infiltrating monocytes trigger EAE progression, but do not contribute to the resident microglia pool. Nat Neurosci. 2011;14(9):1142–50. 597. Fessler MB. The challenges and promise of targeting the Liver X Receptors for treatment 272 of inflammatory disease. Pharmacol Ther. 2018;181:1–12. 598. Cummings JL, Zhong K, Kinney JW, Heaney C, Moll-tudla J, Joshi A, et al. Double-blind, placebo-controlled, proof-of-concept trial of bexarotene Xin moderate Alzheimer’s disease. Alzheimers Res Ther. 2016;8:4. 599. Pardridge WM. Alzheimer’s disease drug development and the problem of the blood-brain barrier. Alzheimer’s Dement. 2009;5(5):427–32. 600. Cummings J. Lessons Learned from Alzheimer Disease: Clinical Trials with Negative Outcomes. Clin Transl Sci. 2018;11(2):147–52. 601. Christiansen-Weber TA, Voland JR, Wu Y, Ngo K, Roland BL, Nguyen S, et al. Functional loss of ABCA1 in mice causes severe placental malformation, aberrant lipid distribution, and kidney glomerulonephritis as well as high-density lipoprotein cholesterol deficiency. Am J Pathol. 2000;157(3):1017–29. 602. McNeish J, Aiello RJ, Guyot D, Turi T, Gabel C, Aldinger C, et al. High density lipoprotein deficiency and foam cell accumulation in mice with targeted disruption of ATp-binding cassette transporter-1. Proc Natl Acad Sci U S A. 2000;97(8):4245–50. 603. Koldamova R, Staufenbiel M, Lefterov I. Lack of ABCA1 considerably decreases brain ApoE level and increases amyloid deposition in APP23 mice. J Biol Chem. 2005;280(52):43224–35. 604. Franklin CL, Ericsson AC. Microbiota and reproducibility of rodent models. Lab Anim. 2017;22(4):114–22. 605. Zelcer N, Khanlou N, Clare R, Jiang Q, Reed-geaghan EG, Landreth GE, et al. Attenuation of neuroinflammation and Alzheimer’s disease pathology by liver x receptors. Proc Natl Acad Sci U S A. 2007;104(25):10601–6. 606. Shaftel SS, Griffin WST, O’Banion MK. The role of interleukin-1 in neuroinflammation and Alzheimer disease: an evolving perspective. J Neuroinflammation. 2008;5:7. 607. Spillmann F, Van Linthout S, Miteva K, Lorenz M, Stangl V, Schultheiss H, et al. LXR agonism improves TNF-α-induced endothelial dysfunction in the absence of its 273 cholesterol-modulating effects. Atherosclerosis. 2014;232(1):1–9. 608. Rebe C, Filomenko R, Raveneau M, Chevriaux A, Ishibashi M, Lagrost L, et al. Identification of biological markers of liver X receptor (LXR) activation at the cell surface of human monocytes. PLoS One. 2012;7(11):1–9. 609. Nunomura S, Okayama Y, Matsumoto K, Hashimoto N, Endo-Umeda K, Terui T, et al. Activation of LXRs using the synthetic agonist GW3965 represses the production of pro-inflammatory cytokines by murine mast cells. Allergol Int. 2015;64:S11–7. 610. Wolfe H, Minogue AM, Rooney S, Lynch MA. Infiltrating macrophages contribute to age-related neuroinflammation in C57/BL6 mice. Mech Ageing Dev. 2018;173:84–91. 611. Unger MS, Schernthaner P, Marschallinger J, Mrowetz H, Aigner L. Microglia prevent peripheral immune cell invasion and promote an anti-inflammatory environment in the brain of APP-PS1 transgenic mice. J Neuroinflammation. 2018;15(1):274. 612. Galatro T, Vainchtein I, Brouwer N, Boddeke E, Eggen B. Isolation of Microglia and Immune Infiltrates from Mouse and Primate Central Nervous System. Methods Mol Biol. 2017;1559:333–42. 613. Knafo S, Venero C, Merino-Serrais P, Fernaud-Espinosa I, Gonzalez-Soriano J, Ferrer I, et al. Morphological alterations to neurons of the amygdala and impaired fear conditioning in a transgenic mouse model of Alzheimer’s disease. J Pathol. 2009;219(1):41–51. 614. Gu XH, Xu LJ, Liu ZQ, Wei B, Yang YJ, Xu GG, et al. The flavonoid baicalein rescues synaptic plasticity and memory deficits in a mouse model of Alzheimer’s disease. Behav Brain Res. 2016;311:309–21. 615. Kim HY, Kim HV, Jo S, Lee CJ, Choi SY, Kim DJ, et al. EPPS rescues hippocampus-dependent cognitive deficits in APP/PS1 mice by disaggregation of amyloid-β oligomers and plaques. Nat Commun. 2015;6:8997. 616. Cheng WH, Martens KM, Bashir A, Cheung H, Stukas S, Gibbs E, et al. CHIMERA repetitive mild traumatic brain injury induces chronic behavioural and neuropathological phenotypes in wild-type and APP/PS1 mice. Alzheimer’s Res Ther. 2019;11(1):1–21. 274 617. Robert J, Button EB, Martin E, McAlary L, Gidden Z, Gilmour M, et al. Cerebrovascular Amyloid Angiopathy in Bioengineered Vessels is Reduced by High-Density Lipoprotein Particles Enriched in Apolipoprotein E. Mol Neurodegener. 2020;15(1):23. 618. Li X, Feng Y, Wu W, Zhao J, Fu C, Li Y, et al. Sex differences between APPswePS1dE9 mice in A-beta accumulation and pancreatic islet function during the development of Alzheimer’s disease. Lab Anim. 2016;50(4):275–85. 619. Wang J, Tanila H, Puoliva J, Kadish I, van Groen T. Gender differences in the amount and deposition of amyloidbeta in APPswe and PS1 double transgenic mice. Neurobiol Dis. 2003;14(3):318–27. 620. Nebel RA, Aggarwal NT, Barnes LL, Gallagher A, Goldstein JM, Kantarci K, et al. Understanding the impact of sex and gender in Alzheimer’s disease: A call to action. Alzheimer’s Dement. 2018;14(9):1171–83. 621. Bradley MN, Hong C, Chen M, Joseph SB, Wilpitz DC, Wang X, et al. Ligand activation of LXRβ reverses atherosclerosis and cellular cholesterol overload in mice lacking LXRα and apoE. J Clin Invest. 2007;117(8):2337–46. 622. Agellon LB, Walsh A, Hayek T, Moulin P, Jiang XC, Shelanski SA, et al. Reduced high density lipoprotein cholesterol in human cholesteryl ester transfer protein transgenic mice. J Biol Chem. 1991;266(17):10796–801. 623. Mahley RW, Weisgraber KH, Huang Y. Apolipoprotein E: Structure determines function, from atherosclerosis to Alzheimer’s disease to AIDS. J Lipid Res. 2009;50(SUPPL.):183–8. 624. Sullivan PM, Knouff C, Najib J, Reddick RL, Quarfordt SH. Targeted Replacement of the Mouse Apolipoprotein E Gene with the Common Human. J Biol Chem. 1997;272(29):17972–80. 625. Zhao N, Liu CC, Van Ingelgom AJ, Linares C, Kurti A, Knight JA, et al. APOE ε2 is associated with increased tau pathology in primary tauopathy. Nat Commun. 2018;9(1):4388. 626. Moser V, Pike CJ. Obesity Accelerates Alzheimer-Related Pathology in APOE4 but not 275 APOE3 Mice. eNeuro. 2017;4(3):pii:ENEURO.0077-17.2017. 627. Tai LM, Balu D, Avila-Munoz E, Abdullah L, Thomas R, Collins N, et al. EFAD transgenic mice as a human APOE relevant preclinical model of Alzheimer’s disease. J Lipid Res. 2017;58(9):1733–55. 628. Holtzman DM, Bales KR, Wu S, Bhat P, Parsadanian M, Fagan AM, et al. Expression of human apolipoprotein E reduces amyloid-beta deposition in a mouse model of Alzheimer’s disease. J Clin Invest. 1999;103(6):R15–21. 629. Robert J, Weber B, Frese L, Emmert MY, Schmidt D, Von Eckardstein A, et al. A three-dimensional engineered artery model for in vitro atherosclerosis research. PLoS One. 2013;8(11):e79821. 630. Hoerstrup SP, Cummings Mrcs I, Lachat M, Schoen FJ, Jenni R, Leschka S, et al. Functional growth in tissue-engineered living, vascular grafts: follow-up at 100 weeks in a large animal model. Circulation. 2006 Jul;114(1 Suppl):I159-66. 631. Robert J, Lehner M, Frank S, Perisa D, von Eckardstein A, Rohrer L. Interleukin 6 stimulates endothelial binding and transport of high-density lipoprotein through induction of endothelial lipase. Arterioscler Thromb Vasc Biol. 2013;33(12):2699–706. 632. Czupalla CJ, Liebner S, Devraj K. In vitro models of the blood-brain barrier. Methods Mol Biol. 2014 Jan;1135:415–37. 633. Koudinov AR, Berezov TT, Koudinova N V. The levels of soluble amyloid beta in different high density lipoprotein subfractions distinguish Alzheimer’s and normal aging cerebrospinal fluid: Implication for brain cholesterol pathology? Neurosci Lett. 2001;314(3):115–8. 634. Paula-Lima AC, Tricerri MA, Brito-Moreira J, Bomfim TR, Oliveira FF, Magdesian MH, et al. Human apolipoprotein A-I binds amyloid-beta and prevents Abeta-induced neurotoxicity. Int J Biochem Cell Biol. 2009;41(6):1361–70. 635. Lee SJC, Nam E, Lee HJ, Savelieff MG, Lim MH. Towards an understanding of amyloid-β oligomers: characterization, toxicity mechanisms, and inhibitors. Chem Soc Rev. 2017;46(2):310–23. 276 636. Stine WB, Jungbauer L, Yu C, LaDu MJ. Preparing synthetic Aβ in different aggregation states. Methods Mol Biol. 2011;670:13–32. 637. Tölle M, Klöckl L, Wiedon A, Zidek W, van der Giet M, Schuchardt M. Regulation of endothelial nitric oxide synthase activation in endothelial cells by S1P1 and S1P3. Biochem Biophys Res Commun. 2016;476(4):627–34. 638. Wagner J, Riwanto M, Besler C, Knau A, Fichtlscherer S, Röxe T, et al. Characterization of levels and cellular transfer of circulating lipoprotein-bound microRNAs. Arterioscler Thromb Vasc Biol. 2013;33(6):1392–400. 639. Lawson C, Wolf S. ICAM-1 signaling in endothelial cells. Pharmacol Rep. 2009;61(1):22–32. 640. Canobbio I, Abubaker AA, Visconte C, Torti M, Pula G. Role of amyloid peptides in vascular dysfunction and platelet dysregulation in Alzheimer’s disease. Front Cell Neurosci. 2015;9:65. 641. Tchalla AE, Wellenius GA, Sorond FA, Gagnon M, Iloputaife I, Travison TG, et al. Elevated Soluble Vascular Cell Adhesion Molecule-1 Is Associated With Cerebrovascular Resistance and Cognitive Function. J Gerontol A Biol Sci Med Sci. 2017;72(4):560–6. 642. Calabresi L, Gomaraschi M, Villa B, Omoboni L, Dmitrieff C, Franceschini G. Elevated soluble cellular adhesion molecules in subjects with low HDL-cholesterol. Arter Thromb Vasc Biol. 2002;22(4):656–61. 643. Qosa H, Abuasal BS, Romero IA, Weksler B, Couraud P-O, Keller JN, et al. Differences in amyloid-β clearance across mouse and human blood-brain barrier models: kinetic analysis and mechanistic modeling. Neuropharmacology. 2014;79:668–78. 644. Liu C-C, Zhao N, Yamaguchi Y, Cirrito JR, Kanekiyo T, Holtzman DM, et al. Neuronal heparan sulfates promote amyloid pathology by modulating brain amyloid-β clearance and aggregation in Alzheimer’s disease. Sci Transl Med. 2016;8(332):332ra44. 645. Rohrer L, Ohnsorg PM, Lehner M, Landolt F, Rinninger F, von Eckardstein A. High-density lipoprotein transport through aortic endothelial cells involves scavenger receptor BI and ATP-binding cassette transporter G1. Circ Res. 2009;104(10):1142–50. 277 646. Rajendran P, Rengarajan T, Thangavel J, Nishigaki Y, Sakthisekaran D, Sethi G, et al. The vascular endothelium and human diseases. Int J Biol Sci. 2013;9(10):1057–69. 647. Snyder HM, Corriveau RA, Craft S, Faber JE, Greenberg SM, Knopman D, et al. Vascular contributions to cognitive impairment and dementia including Alzheimer’s disease. Alzheimer’s Dement. 2015;11(6):710–7. 648. Raz L, Knoefel J, Bhaskar K. The neuropathology and cerebrovascular mechanisms of dementia. J Cereb Blood Flow Metab. 2016;36(1):172–86. 649. Thanopoulou K, Fragkouli A, Stylianopoulou F, Georgopoulos S. Scavenger receptor class B type I (SR-BI) regulates perivascular macrophages and modifies amyloid pathology in an Alzheimer mouse model. Proc Natl Acad Sci U S A. 2010;107(48):20816–21. 650. Giri R, Selvaraj S, Miller CA, Hofman F, Yan SD, Stern D, et al. Effect of endothelial cell polarity on β-amyloid-induced migration of monocytes across normal and AD endothelium. Am J Physiol Physiol. 2002;283(3):C895–904. 651. Lamoke F, Mazzone V, Persichini T, Maraschi A, Harris MB, Venema RC, et al. Amyloid β peptide-induced inhibition of endothelial nitric oxide production involves oxidative stress-mediated constitutive eNOS/HSP90 interaction and disruption of agonist-mediated Akt activation. J Neuroinflammation. 2015;12:84. 652. Katusic ZS, Austin SA. Endothelial nitric oxide: protector of a healthy mind. Eur Heart J. 2014;35(14):888–94. 653. Kanekiyo T, Xu H, Bu G. ApoE and Abeta in Alzheimer’s disease: accidental encounters or partners? Neuron. 2014;81(4):740–54. 654. Wilson LM, Pham CLL, Jenkins AJ, Wade JD, Hill AF, Perugini MA, et al. High density lipoproteins bind Abeta and apolipoprotein C-II amyloid fibrils. J Lipid Res. 2006;47(4):755–60. 655. Terakawa MS, Yagi H, Adachi M, Lee Y-H, Goto Y. Small liposomes accelerate the fibrillation of amyloid β (1-40). J Biol Chem. 2015;290(2):815–26. 278 656. Truran S, Weissig V, Madine J, Davies HA, Guzman-Villanueva D, Franco DA, et al. Nanoliposomes protect against human arteriole endothelial dysfunction induced by β-amyloid peptide. J Cereb Blood Flow Metab. 2016;36(2):405–12. 657. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, et al. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 2009;302(18):1993–2000. 658. Robert J, Button EB, Yuen B, Gilmour M, Kang K, Bahrabadi A, et al. Clearance of beta-amyloid is facilitated by apolipoprotein E and circulating high- density lipoproteins in bioengineered human vessels. Elife. 2017;6:pii: e29595. 659. Tortelli R, Lozupone M, Guerra V, Rosaria M, Imbimbo BP, Capozzo R, et al. Midlife Metabolic Profile and the Risk of Late-Life Cognitive Decline. J Alzheimer’s Dis. 2017;59(1):121–30. 660. Ma C, Yin Z, Zhu P, Luo J, Shi X, Gao X. Blood cholesterol in late-life and cognitive decline : a longitudinal study of the Chinese elderly. Mol Neurobiol. 2017;12(1):24. 661. Kim W, He Y, Phan K, Ahmed R, Rye K, Piguet O, et al. Altered High Density Lipoprotein Composition in Behavioral Variant Frontotemporal Dementia. Front Neurosci. 2018;12:847. 662. Saleheen D, Scott R, Javad S, Zhao W, Rodrigues A, Picataggi A, et al. Association of HDL cholesterol efflux capacity with incident coronary heart disease events: a prospective case-control study. Lancet Diabetes Endocrinol. 2015;3(7):507–13. 663. Davidson WS, Heink A, Sexmith H, Melchior JT, Gordon SM, Kuklenyik Z, et al. The effects of apolipoprotein B depletion on HDL subspecies composition and function. J Lipid Res. 2016;57(4):674–86. 664. Fan J, Zareyan S, Zhao W, Shimizu Y, Pfeifer TA, Wood W, et al. Identification of a Chrysanthemic Ester as an Apolipoprotein E Inducer in Astrocytes. PLoS One. 2016;11(9):e0162384. 665. Cupino TL, Zabel MK. Alzheimer’s Silent Partner: Cerebral Amyloid Angiopathy. Transl Stoke Res. 2014;5(3):330–7. 279 666. Genest J, McPherson R, Frohlich J, Anderson T, Campbell N, Carpentier A, et al. 2009 Canadian Cardiovascular Society/Canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult – 2009 recommendations. Can J Cardiol. 2009;25(10):567–79. 667. Perségol L, Vergès B, Foissac M, Gambert P, Duvillard L. Inability of HDL from abdominally obese subjects to counteract the inhibitory effect of oxidized LDL on vasorelaxation. J Lipid Res. 2007;48(6):1396–401. 668. Kunitake ST, Kane JP. Factors affecting the integrity of high density lipoproteins in the ultracentrifuge. J Lipid Res. 1982;23(6):936–40. 669. van’t hooft, F, Havel R. Metabolism of Apolipoprotein E in Plasma High Density Lipoproteins from Normal and Cholesterol-fed Rats. J Biol Chem. 1982;257(18):10996–1001. 670. Hutchinson L, Kirk R. High drug attrition rates—where are we going wrong? Nat Publ Gr. 2011;8(4):189–90. 671. Hafiane A, Genest J. HDL-Mediated Cellular Cholesterol Efflux Assay Method. Ann Clin Lab Sci. 2015;45(6):659–68. 672. Stahlman M, Davidsson P, Kanmert I, Rosengren B, Boren J, Fagerberg B, et al. Proteomics and lipids of lipoproteins isolated at low salt concentrations in D2O/ sucrose or in KBr. J Lipid Res. 2008;49(2):481–90. 673. Ortiz-Munoz G, Couret D, Lapergue B, Bruckert E, Meseguer E, Amarenco P, et al. Dysfunctional HDL in acute stroke. Atherosclerosis. 2016;253:75–80. 674. Charakida M, Besler C, Batuca JR, Sangle S, Marques S, Sousa M, et al. Vascular Abnormalities, paraoxonase activity, and dysfunctional HDL in primary antiphospholipid syndrome. JAMA. 2009;302(11):1210–7. 675. Wevers NR, Kasi DG, Gray T, Wilschut KJ, Smith B, Vught R Van, et al. A perfused human blood-brain barrier on-a-chip for high-throughput assessment of barrier function and antibody transport. Fluids Barriers CNS. 2018;15(1):23. 280 676. Faley SL, Neal EH, Wang JX, Bosworth AM, Weber CM, Balotin KM, et al. iPSC-Derived Brain Endothelium Exhibits Stable, Long-Term Barrier Function in Perfused Hydrogel Scaffolds. Stem Cell Reports. 2019;12(3):474–87. 677. Fitz NF, Cronican A, Pham T, Fogg A, Fauq AH, Chapman R, et al. Liver X receptor agonist treatment ameliorates amyloid pathology and memory deficits caused by high-fat diet in APP23 mice. J Neurosci. 2010;30(20):6862–72. 678. Gottesman RRF, Schneider AALC, Zhou Y, Coresh J, Green E, Gupta N, et al. Association Between Midlife Vascular Risk Factors and Estimated Brain Amyloid Deposition. JAMA. 2017;317(14):1443–50. 679. McGrath ER, Beiser AS, DeCarli C, Plourde KL, Vasan RS, Greenberg SM, et al. Blood pressure from mid-to late life and risk of incident dementia. Neurology. 2017;89(24):2447–54. 680. Daneman R, Prat A. The Blood Brain Barrier (BBB). Cold Spring Harb Perspect Biol. 2015;7(1):a020412. 681. Zamanian JL, Xu L, Foo LC, Nouri N, Zhou L, Giffard RG, et al. Genomic analysis of reactive astrogliosis. J Neurosci. 2012;32(18):6391–410. 682. Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L, et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature. 2017;541(7638):481–7. 683. Zuroff L, Daley D, Black KL, Koronyo M. Clearance of cerebral Aβ in Alzheimer’s disease: reassessing the role of microglia and monocytes. Cell Mol Life Sci. 2017;74(12):2167–201. 684. Gordon SM, Deng J, Tomann AB, Shah AS, Lu LJ, Davidson WS. Multi-dimensional co-separation analysis reveals protein-protein interactions defining plasma lipoprotein subspecies. Mol Cell Proteomics. 2013;12(11):3123–34. 685. Kingwell BA, Chapman MJ, Kontush A, Miller NE. HDL-targeted therapies: progress, failures and future. Nat Rev Drug Discov. 2014;13(6):445–64. 281 686. Christoffersen C, Nielsen LB, Axler O, Andersson A, Johnsen AH, Dahlbäck B. Isolation and characterization of human apolipoprotein M-containing lipoproteins. J Lipid Res. 2006;47(8):1833–43. 687. Christoffersen C, Obinata H, Kumaraswamy SB, Galvani S, Ahnstrom J, Sevvana M, et al. Endothelium-protective sphingosine-1-phosphate provided by HDL-associated apolipoprotein M. Proc Natl Acad Sci U S A. 2011;108(23):9613–8. 688. Argraves KM, Gazzolo PJ, Groh EM, Wilkerson BA, Matsuura BS, Twal WO, et al. High density lipoprotein-associated sphingosine 1-phosphate promotes endothelial barrier function. J Biol Chem. 2008;283(36):25074–81. 689. Button E, Gilmour M, Cheema H, Martin E, Agbay A, Wellington CL, et al. Vasoprotective Functions of High-Density Lipoproteins Relevant to Alzheimer’s Disease Are Partially Conserved in Apolipoprotein B-Depleted Plasma. Int J Mol Sci. 2019;20(3):462. 690. Banks WA. Drug delivery to the brain in Alzheimer’s disease: consideration of the blood-brain barrier. Adv Drug Deliv Rev. 2012;64(7):629–39. 691. Nicholls SJ, Andrews J, Kastelein JJP, Merkely B, Nissen SE, Ray KK, et al. Effect of serial infusions of CER-001, a pre-β High-density lipoprotein mimetic, on coronary atherosclerosis in patients following acute coronary syndromes in the CER-001 atherosclerosis regression acute coronary syndrome trial: A randomized clinical tria. JAMA Cardiol. 2018;3(9):815–22. 692. Tardif JC, Ballantyne CM, Barter P, Dasseux JL, Fayad ZA, Guertin MC, et al. Effects of the high-density lipoprotein mimetic agent CER-001 on coronary atherosclerosis in patients with acute coronary syndromes: A randomized trial. Eur Heart J. 2014;35(46):3277–86. 693. Nicholls SJ, Puri R, Ballantyne CM, Jukema JW, Kastelein JJP, Koenig W, et al. Effect of infusion of high-density lipoprotein mimetic containing recombinant apolipoprotein A-I Milano on coronary disease in patients with an acute coronary syndrome in the MILANO-PILOT trial: A randomized clinical trial. JAMA Cardiol. 2018;3(9):806–14. 282 694. Bloedon LT, Dunbar R, Duffy D, Pinell-Salles P, Norris R, DeGroot BJ, et al. Safety, pharmacokinetics, and pharmacodynamics of oral apoA-I mimetic peptide D-4F in high-risk cardiovascular patients. J Lipid Res. 2008;49(6):1344–52. 695. Dunbar RL, Movva R, Bloedon LAT, Duffy D, Norris RB, Navab M, et al. Oral Apolipoprotein A-I Mimetic D-4F Lowers HDL-Inflammatory Index in High-Risk Patients: A First-in-Human Multiple-Dose, Randomized Controlled Trial. Clin Transl Sci. 2017;10(6):455–69. 696. Watson CE, Weissbach N, Kjems L, Ayalasomayajula S, Zhang Y, Chang I, et al. Treatment of patients with cardiovascular disease with L-4F, an apo-A1 mimetic, did not improve select biomarkers of HDL function. J Lipid Res. 2011;52(2):361–73. 697. Gibson CM, Korjian S, Tricoci P, Daaboul Y, Yee M, Jain P, et al. Safety and Tolerability of CSL112, a Reconstituted, Infusible, Plasma-Derived Apolipoprotein A-I, after Acute Myocardial Infarction: The AEGIS-I Trial (ApoA-I Event Reducing in Ischemic Syndromes I). Circulation. 2016;134(24):1918–30. 698. Waksman R, Torguson R, Kent KM, Pichard AD, Suddath WO, Satler LF, et al. A First-in-Man, Randomized, Placebo-Controlled Study to Evaluate the Safety and Feasibility of Autologous Delipidated High-Density Lipoprotein Plasma Infusions in Patients With Acute Coronary Syndrome. J Am Coll Cardiol. 2010;55(24):2727–35. 699. Nicholls SJ, Gordon A, Johansson J, Wolski K, Ballantyne CM, Kastelein JJ, et al. Efficacy and safety of a novel oral inducer of apolipoprotein a-I synthesis in statin-treated patients with stable coronary artery disease a randomized controlled trial. J Am Coll Cardiol. 2011;57(9):1111–9. 700. Nicholls SJ, Puri R, Wolski K, Ballantyne CM, Barter PJ, Brewer HB, et al. Effect of the BET Protein Inhibitor, RVX-208, on Progression of Coronary Atherosclerosis: Results of the Phase 2b, Randomized, Double-Blind, Multicenter, ASSURE Trial. Am J Cardiovasc Drugs. 2016;16(1):55–65. 701. Shamburek RD, Bakker-Arkema R, Shamburek AM, Freeman LA, Amar MJ, Auerbach B, et al. Safety and Tolerability of ACP-501, a Recombinant Human Lecithin:Cholesterol 283 Acyltransferase, in a Phase 1 Single-Dose Escalation Study. Circ Res. 2016;118(1):73–82. 702. Lavigne PM, Karas RH. The current state of niacin in cardiovascular disease prevention: a systematic review and meta-regression. J Am Coll Cardiol. 2013;61(4):440–6. 703. Boden W, Probstfield J, Anderson T, Chaitman B, Desvignes-Nickens P, K K, et al. Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med. 2011;365(24):2255–67. 704. HPS2-THRIVE Collaborative Group. HPS2-THRIVE randomized placebo-controlled trial in 25 673 high-risk patients of ER niacin/laropiprant: trial design, pre-specified muscle and liver outcomes, and reasons for stopping study treatment. Eur Hear J. 2013;34(17):1279–91. 705. Schwartz GG, Olsson AG, Abt M, Ballantyne CM, Barter PJ, Brumm J, et al. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med. 2012;367(22):2089–99. 706. Lincoff AM, Nicholls SJ, Riesmeyer JS, Barter PJ, Brewer HB, Fox KAA, et al. Evacetrapib and Cardiovascular Outcomes in High-Risk Vascular Disease. N Engl J Med. 2017;376(20):1933–42. 707. Barter PJ, Caulfield M, Eriksson M, Grundy SM, Kastelein JJ, Komajda M, et al. Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med. 2007;357(21):2109–22. 708. Bowman L, Hopewell J, Chen F, Wallendszus K, Stevens W, Collins R, et al. Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease. N Engl J Med. 2017;377(13):1217–27. 709. Qin B, Xun P, Jr DRJ, Zhu N, Daviglus ML, Reis JP, et al. Intake of niacin, folate, vitamin B-6, and vitamin B-12 through young adulthood and cognitive function in midlife: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Am J Clin Nutr. 2017;106(4):1032–40. 710. Morris MC, Evans DA, Bienias JL, Scherr PA, Tangney CC, Hebert LE, et al. Dietary niacin and the risk of incident Alzheimer’s disease and of cognitive decline. J Neurol 284 Neurosurg Psychiatry. 2004;75(8):1093–9. 711. Sundermann EE, Wang C, Katz M, Zimmerman ME, Derby CA, Hall CB, et al. Cholesteryl ester transfer protein genotype modifies the effect of apolipoprotein ε4 on memory decline in older adults. Neurobiol Aging. 2016;41:200.e7-200.e12. 712. Lythgoe C, Perkes A, Peterson M, Schmutz C, Leary M, Ebbert MTW, et al. Population-based analysis of cholesteryl ester transfer protein identifies association between I405V and cognitive decline: The cache county study. Neurobiol Aging. 2015;36(1):547.e1-547.e3. 713. Chen J-J, Li Y-M, Zou W-Y, Fu J-L. Relationships Between CETP Genetic Polymorphisms and Alzheimer’s Disease Risk: A Meta-Analysis. DNA Cell Biol. 2014;33(11):807–15. 714. Sirtori CR. The pharmacology of statins. Pharmacol Res. 2014;88:3–11. 715. Schultz BG, Patten DK, Berlau DJ. The role of statins in both cognitive impairment and protection against dementia: A tale of two mechanisms. Transl Neurodegener. 2018;7(1):1–11. 716. Jafri H, Alsheikh-Ali A, Karas R. Meta-analysis: statin therapy does not alter the association between low levels of high-density lipoprotein cholesterol and increased cardiovascular risk. Ann Intern Med. 2010;153(12):800–8. 717. Bach-Ngohou K, Ouguerram K, Frénais R, Maugère P, Ripolles-Piquer B, Zaïr Y, et al. Influence of atorvastatin on apolipoprotein E and AI kinetics in patients with type 2 diabetes. J Pharmacol Exp Ther. 2005;315(1):363–9. 718. Le NA, Innis-Whitehouse W, Li X, Bakker-Arkema R, Black D, Brown WV. Lipid and apolipoprotein levels and distribution in patients with hypertriglyceridemia: Effect of triglyceride reductions with atorvastatin. Metabolism. 2000;49(2):167–77. 719. Hirsch-Reinshagen V, Zhou S, Burgess BL, Bernier L, McIsaac S a., Chan JY, et al. Deficiency of ABCA1 impairs apolipoprotein E metabolism in brain. J Biol Chem. 2004;279(39):41197–207. 285 720. Fan J, Stukas S, Wong C, Chan J, May S, DeValle N, et al. An ABCA1-independent pathway for recycling a poorly lipidated 8.1 nm apolipoprotein E particle from glia. J Lipid Res. 2011;52(9):1605–16. 721. Pérez E, Bourguet W, Gronemeyer H, De Lera AR. Modulation of RXR function through ligand design. Biochim Biophys Acta. 2012;1821(1):57–69. 722. Joseph SB, McKilligin E, Pei L, Watson MA, Collins AR, Laffitte BA, et al. Synthetic LXR ligand inhibits the development of atherosclerosis in mice. Proc Natl Acad Sci. 2002;99(11):7604–9. 723. Tangirala RK, Bischoff ED, Joseph SB, Wagner BL, Walczak R, Laffitte BA, et al. Identification of macrophage liver X receptors as inhibitors of atherosclerosis. Proc Natl Acad Sci. 2002;99(18):11896–901. 724. Wang N, Tall AR. Regulation and mechanisms of ATP-binding cassette transporter A1-mediated cellular cholesterol efflux. Arterioscler Thromb Vasc Biol. 2003;23(7):1178–84. 725. Brunham LR, Kruit JK, Pape TD, Parks JS, Kuipers F, Hayden MR. Tissue-specific induction of intestinal ABCA1 expression with a liver X receptor agonist raises plasma HDL cholesterol levels. Circ Res. 2006;99(7):672–4. 726. Fan J, Zhao RQ, Parro C, Zhao W, Chou H-Y, Robert J, et al. Small molecule inducers of ABCA1 and apoE that act through indirect activation of the LXR pathway. J Lipid Res. 2018;59(5):850–842. 727. Cummings J, Lee G, Ritter A, Zhong K. Alzheimer’s disease drug development pipeline: 2018. Alzheimers Dement. 2018;4:195–214. 728. Sperling R, Salloway S, Brooks DJ, Tampieri D, Barakos J, Fox NC, et al. Amyloid-related imaging abnormalities in patients with Alzheimer’s disease treated with bapineuzumab: A retrospective analysis. Lancet Neurol. 2012;11(3):241–9. 729. Banerjee G, Carare R, Cordonnier C, Greenberg SM, Schneider JA, Smith EE, et al. The increasing impact of cerebral amyloid angiopathy: Essential new insights for clinical practice. J Neurol Neurosurg Psychiatry. 2017;88(11):982–94. 286 730. Kirshner HS, Bradshaw M, Bradshaw M. The Inflammatory Form of Cerebral Amyloid Angiopathy or “Cerebral Amyloid Angiopathy-Related Inflammation” ( CAARI ). Curr Neurol Neurosci Rep. 2015;15(8):54. 731. Golde BTE, Dekosky ST, Galasko D. Alzheimer’s disease: The right drug, the right time. Science (80- ). 2018;362(6420):1250–2. 732. van Dyck CH. Anti-Amyloid-β Monoclonal Antibodies for Alzheimer’s Disease: Pitfalls and Promise. Biol Psychiatry. 2018;83(4):311–9. 733. Viña J, Sanz-Ros J. Alzheimer’s disease: Only prevention makes sense. Eur J Clin Invest. 2018;48(10):e13005. 734. Gauthier S, Albert M, Fox N, Goedert M, Kivipelto M, Mestre-Ferrandiz J, et al. Why has therapy development for dementia failed in the last two decades? Alzheimer’s Dement. 2016;12(1):60–4. 735. Rosenson RS, Brewer H, Ansell BJ, Barter P, Chapman MJ, Heinecke JW, et al. Dysfunctional HDL and atherosclerotic cardiovascular disease. Nat Rev Cardiol. 2016;13(1):48–60. 736. Huttunen JK, Lansimies E, Voutilainen E, Ehnholm C, Hietanen E, Penttila I, et al. Effect of moderate physical exercise on serum lipoproteins. Circulation. 1979;60(6):19203. 737. Sarzynski MA, Burton J, Rankinen T, Blair SN, Church TS, Després JP, et al. The effects of exercise on the lipoprotein subclass profile: A meta-analysis of 10 interventions. Atherosclerosis. 2015;243(2):364–72. 738. Sarzynski M, Ruiz-Ramie J, Barber J, Slentz C, Apolzan J, McGarrah R, et al. Effects of Increasing Exercise Intensity and Dose on Multiple Measures of HDL (High-Density Lipoprotein) Function. Arter Thromb Vasc Biol. 2018;38(4):943–52. 739. Woudberg NJ, Mendham AE, Katz AA, Goedecke JH, Lecour S. Exercise intervention alters HDL subclass distribution and function in obese women. Lipids Health Dis. 2018;17(1):232. 740. Pagonas N, Vlatsas S, Bauer F, Seibert F, Sasko B, Buschmann I, et al. The impact of 287 aerobic and isometric exercise on different measures of dysfunctional high-density lipoprotein in patients with hypertension. Eur J Prev Cardio. 2019;26(12):1301–9. 741. Ruiz-Ramie JJ, Barber JL, Sarzynski MA. Effects of exercise on HDL functionality. Curr Opin Lipidol. 2019;30(1):16–23. 742. Kraus W, Houmard J, Duscha B, Knetzger K, Wharton M, McCartney J, et al. Effects of the amount and intensity of exercise on plasma lipoproteins. N Engl J Med. 2002;347(19):1483–92. 743. Wesnigk J, Bruyndonckx L, Hoymans VY, De Guchtenaere A, Fischer T, Schuler G, et al. Impact of Lifestyle Intervention on HDL-Induced eNOS Activation and Cholesterol Efflux Capacity in Obese Adolescent. Cardiol Res Pract. 2016;2016:2820432. 744. Adams V, Besler C, Fischer T, Riwanto M, Noack F, Höllriegel R, et al. Exercise training in patients with chronic heart failure promotes restoration of high-density lipoprotein functional properties. Circ Res. 2013;113(12):1345–55. 745. Mathew A V., Li L, Byun J, Guo Y, Michailidis G, Jaiswal M, et al. Therapeutic lifestyle changes improve HDL function by inhibiting myeloperoxidase-mediated oxidation in patients with metabolic syndrome. Diabetes Care. 2018;41(11):2431–7. 746. Roberts C, Ng C, Hama S, Eliseo J, Barnard R. Effect of a short-term diet and exercise intervention on inflammatory/anti-inflammatory properties of HDL in overweight/obese men with cardiovascular risk factors. J Appl Physiol. 2006;101(6):1727–32. 747. Nicholls SJ, Lundman P, Harmer JA, Cutri B, Griffiths KA, Rye KA, et al. Consumption of Saturated Fat Impairs the Anti-Inflammatory Properties of High-Density Lipoproteins and Endothelial Function. J Am Coll Cardiol. 2006;48(4):715–20. 748. Hernáez Á, Castañer O, Elosua R, Pintó X, Estruch R, Salas-Salvadó J, et al. Mediterranean Diet Improves High-Density Lipoprotein Function in High-Cardiovascular-Risk Individuals. Circulation. 2017;135(7):633–43. 749. Gossett LK, Johnson HM, Piper ME, Fiore MC, Baker TB, Stein JH. Smoking intensity and lipoprotein abnormalities in active smokers. J Clin Lipidol. 2009;3(6):372–8. 288 750. Zhao X, Zhang HW, Zhang Y, Li S, Xu RX, Sun J, et al. Impact of Smoking Status on Lipoprotein Subfractions: Data from an Untreated Chinese Cohort. Biomed Environ Sci. 2017;30(4):235–43. 751. Park KH, Shin DG, Cho KH. Dysfunctional lipoproteins from young smokers exacerbate cellular senescence and atherogenesis with smaller particle size and severe oxidation and glycation. Toxicol Sci. 2014;140(1):16–25. 752. Shen SQ, Chang H, Wang ZX, Chen HY, Chen LF, Gao F, et al. The Acute Effects of Cigarette Smoking on the Functional State of High Density Lipoprotein. Am J Med Sci. 2018;356(4):374–81. 753. Ueyama K, Yokode M, Arai H, Nagano Y, Zhi-Xiang L, Cho M, et al. Cholesterol efflux effect of high density lipoprotein is impaired by whole cigarette smoke extracts through lipid peroxidation. Free Radic Biol Med. 1998;24(1):182–90. 754. Sheng Z, Cao J-Y, Pang Y-C, Xu H-C, Chen J-W, Yuan J-H, et al. Effects of Lifestyle Modification and Anti-diabetic Medicine on Prediabetes Progress: A Systematic Review and Meta-Analysis. Front Endocrinol (Lausanne). 2019;10:455. 755. Ribeiro I, Iborra R, NEves M, Lottenberg S, Charf A, Nunes V, et al. HDL Atheroprotection by Aerobic Exercise Training in Type 2 Diabetes Mellitus. Med Sci Sport Exerc. 2008;40(5):779–86. 756. Au R, Piers RJ, Lancashire L. Back to the future: Alzheimer’s disease heterogeneity revisited. Alzheimers Dement. 2015;1(3):368–70. 757. Davidson WS, Inge TH, Sexmith H, Heink A, Elder D, Hui DY, et al. Weight loss surgery in adolescents corrects high-density lipoprotein subspecies and their function. Int J Obes. 2016;41(1):83–9. 758. Blennow K, Zetterberg H. Biomarkers for Alzheimer’s disease : current status and prospects for the future. J Intern Med. 2018;284(6):643–63. 289 Appendices Appendix A Table 0.1 Specific contributions of candidate and collaborators in Chapter 2. Figure Contribution of collaborators Personal contributions General Intellectual: Assistance in experimental design and data interpretation. Technical: mouse breeding, harvesting of brains and plasma, protein homogenization, brain sectioning, immunofluorescent staining. Intellectual: Lead experimental design, all data analysis, statistical analysis, and data interpretation, development of immunofluorescent staining protocols, development of image analysis programs (ImageJ code writing), text and figure preparation. Technical: Fluorescent microscopy, ELISA, qRT-PCR, RNA isolation, image processing and analysis. 2.1 Recording of unexpected deaths in mouse colony records. Data analysis, statistics, and interpretation. 2.2 Plasma and brain collection, protein extraction/homogenization Lipid assays, ELISA, RNA isolation, qRT-PCR. Data analysis, statistics, and interpretation. 2.3 Brain collection, sectioning, staining, protein extraction/homogenization. Imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, ELISA, data analysis, statistics and data interpretation. 2.4 Brain collection, protein extraction/homogenization ELISA, RNA isolation, qRT-PCR, data analysis, statistics, and data interpretation. 2.5 Brain collection, protein extraction/homogenization, brain sectioning, staining, confocal microscopy. ELISA, development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, data, statistics, and data interpretation. 2.6 Brain collection, protein extraction/homogenization, brain sectioning and immunofluorescent staining. ELISA, development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, data analysis, statistics and interpretation. 2.7 Brain collection, sectioning, staining, assistance in development of immunofluorescent staining protocols. Development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, statistics and interpretation. 2.8 Training and testing of mice, collection of freezing time data. Data analysis, statistics, and interpretation. 2.9, 2.10, 2.11, 2.12 Brain collection, sectioning, staining, assistance in development of immunofluorescent staining protocols. Development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, statistics and data interpretation. Table 0.2 Specific contributions of candidate and collaborators in Chapter 3. Figure Contribution of collaborators Personal contributions General Intellectual: Assistance in experimental design and data interpretation. Intellectual: Lead experimental design, all data analysis, statistical analysis, and data interpretation, development of immunofluorescent staining 290 Technical: Some protein extraction/homogenization, brain sectioning, immunofluorescent staining, ELISA protocols, development of image analysis programs (ImageJ code writing), text and figure preparation. Technical: Administration of experimental diets, collection of tissues, ELISA, RNA isolation, qRT-PCR, fluorescent microscopy, image processing and analysis. 3.1 Brain collection, RNA isolation, qRT-PCR, data analysis, statistics, and interpretation. 3.2 Brain collection, protein homogenization, ELISA. Data analysis, statistics, and interpretation. 3.3 Protein extraction/homogenization. Brain and plasma collection, ELISA. Data analysis, statistics, and interpretation. 3.4 Protein extraction/homogenization, brain sectioning and immunofluorescent staining. Brain collection, development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, ELISA, statistics and interpretation 3.5 Protein extraction/homogenization. Brain collection, RNA isolation, qRT-PCR, ELISA, data analysis, statistics, and data interpretation. 3.6 Brain sectioning and immunostaining, imaging, image processing. Brain collection, development of image analysis programs (ImageJ code writing), image analysis, statistics and data interpretation. 3.7 Brain sectioning and immunofluorescent staining. Brain collection, development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, statistics and interpretation 3.8 Brain sectioning and immunofluorescent staining, protein extraction/homogenization. Brain collection, development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, ELISA, data analysis, statistics and interpretation. 3.9 Training and testing of mice, collection of freezing time data. Data analysis, statistics, and interpretation. 3.10 Measurement of mouse body weights, data analysis, statistics, and interpretation. 3.11 Brain sectioning and immunofluorescent staining. Brain collection, development of immunofluorescent staining protocols, imaging, image processing, development of image analysis programs (ImageJ code writing), image analysis, statistics and interpretation. Table Contribution of collaborators Personal contributions 3.1, 3.2, 3.3 Measurement of drug concentrations by mass spectrometry. Brain and plasma collection. Data analysis, statistics, and interpretation. Table 0.3 Specific contributions of candidate and collaborators in Chapter 4. Figure Contribution of collaborators Personal contributions General Intellectual: Partial experimental design, data analysis, statistics, data interpretation, text and figure preparation. Technical: culture of 3D bioengineered vessels, blood collection (phlebotomy), Intellectual: Partial experimental design, data analysis, statistics, data interpretation, text and figure preparation. 291 maintenance of cultured cell lines, treatment of 3D bioengineered vessels, measurement of monocyte adhesion in 3D bioengineered vessels, 50% performance of 2D adhesion experiments. Technical: 50% performance of 2D adhesion experiments, ELISA analysis of 3D bioengineered experiments, HDL and PBMC isolation. 4.1 Culture, collection, staining of 3D bioengineered vessels. Design of bioreactor schematic. 4.2 Culture, treatment, data collection from 3D bioengineered vessels, data analysis, statistics, interpretation. Assistance in data interpretation. 4.3 Performance of PBMC adhesion assays, data analysis, statistics, interpretation (4.3e-f). Performance of PBMC adhesion assays, data analysis, statistics, interpretation (4.3a-d). 4.4 Performance of Aβ fibrillization assay, data analysis, statistics, interpretation (4.4a). Analysis of Aβ structure of electron microscopy (4.4c). Performance of PBMC adhesion assays, data analysis, statistics, interpretation (4.4d-g). Analysis of Aβ structure by dot blot (4.4b). 4.5 Western blot analysis of eNOS phosphorylation (4.5b). Performance of PBMC adhesion assays with L-NAME treatment, data analysis, statistics, interpretation (4.5d-f). Performance of nitric oxide production assay (4.5a). Performance of PBMC adhesion assays with VPC treatment, data analysis, statistics, interpretation (4.5g-i). 4.6 Performance of PBMC assays, measurement of cellular miR223 content, data analysis, statistics, interpretation. Assistance in conception of experimental design, data interpretation. 4.7 Performance of biotinylation experiments, data analysis, statistics, and interpretation (4.7h-m). Performance of total ICAM-1 and VCAM-1 experiments for protein and mRNA (4.7a-g). 4.8 Performance of NFκB signalling experiments, data analysis, statistics, and interpretation. 4.9 Performance of cellular Aβ uptake and association experiments, PBMC adhesion assays, data analysis, statistics, and interpretation. Assistance in data interpretation. 4.10 Performance of cellular Aβ uptake and association experiments, some PBMC adhesion assays (4.10a-f), data analysis, statistics, and interpretation. Performance of some PBMC adhesion assays (4.10g-i), data analysis, statistics, interpretation. 4.11 Performance of experiments in 3D bioengineered vessels, data analysis, statistics, and interpretation. Measurement of ICAM-1 by ELISA, data analysis, statistics, and interpretation. 4.12 Performance of experiments in 3D bioengineered vessels, data analysis, statistics, and interpretation. Measurement of Aβ concentrations by ELISA, data analysis, statistics, and interpretation. 4.13 Performance of experiments in 3D bioengineered vessels, data analysis, statistics, and interpretation. Measurement of Aβ concentrations by ELISA, data analysis, statistics, and interpretation. 4.14 Performance of some PBMC adhesion assays, data analysis, statistics, interpretation (4.14c-d). Performance of some PBMC adhesion assays, data analysis, statistics, interpretation (4.14a-b). 4.15 Performance of some PBMC adhesion assays (4.15b-d), measurement of Annexin-1, data analysis, statistics, interpretation. Performance of some PBMC adhesion assays (4.15f-h), measurement of nitric oxide production (4.15a, e), data analysis, statistics, interpretation 292 4.16 Sectioning and immunofluorescent staining. 4.17 Measurement of LRP1 and RAGE protein by western blot, data analysis, statistics, and interpretation. Table Contribution of collaborators Personal contributions 4.1 Assistance in experimental design and data interpretation. ELISA measurements, data analysis, statistics, interpretation. Table 0.4 Specific contributions of candidate and collaborators in Chapter 5. Figure Contribution of collaborators Personal contributions General Intellectual: Assistance in experimental design and data interpretation. Technical: Blood collection, culturing of 3D bioengineered vessels, cholesterol efflux measurement, maintenance of cell cultures, some monocyte adhesion and Aβ accumulation assays in 3D bioengineered vessels, collection of plasma from clinical laboratory, some HDL isolation. Intellectual: Lead experimental design, data analysis, statistics, and interpretation, text and figure preparation. Technical: HDL and PBMC isolation, PBMC adhesion assays, ELISA, nitric oxide production assays, participant recruitment, some Aβ accumulation and monocyte adhesion assays in 3D bioengineered vessels. 5.2 Culture of 3D bioengineered vessels. HDL isolation, performance of Aβ accumulation and clearance assays in 3D bioengineered vessels, data analysis, statistics, interpretation. 5.3 Culture of 3D bioengineered vessels, imaging and analysis of monocyte adhesion (5.3a). HDL isolation, performance of PBMC adhesion assays (5.3b-c), data analysis, statistics, and interpretation. 5.4 Performance of Aβ fibrillization assay, HDL isolation, data analysis, statistics, and interpretation. 5.5 Performance of nitric oxide production assays, HDL isolation, data analysis, statistics, and interpretation. 5.6 Some HDL isolation. Performance of nitic oxide and PBMC adhesion assays, HDL isolation, data analysis, statistics, and interpretation. 5.7 Culture of 3D bioengineered vessels. Treatment of 3D bioengineered vessels. 5.8, 5.14, 5.16 Culture of 3D bioengineered vessels, treatment with HDL/serum, analysis of monocyte adhesion. HDL isolation, ELISA, data analysis, statistics, and interpretation. 5.9, 5.10, 5.15 Culture of 3D bioengineered vessels. Performance of Aβ accumulation assays (treatment, ELISA), data analysis, statistics, and interpretation. 5.11 Collection of plasma, measurement of HbA1c, culture of 3D bioengineered vessels. Performance of Aβ accumulation assays, data analysis, statistics, and interpretation. 5.12 Some HDL isolation. Some HDL isolation, SDS-PAGE, ELISA, data analysis, statistics, and interpretation. 5.13 HDL isolation, ELISA, data analysis, statistics, and interpretation. Table Contribution of collaborators Personal contributions 5.1, 5.2 Collection of blood. Participant recruitment, HDL isolation, ELISA, data analysis, statistics, and interpretation. 5.3 Culture of 3D bioengineered vessels. Performance of Aβ accumulation assays (treatment, ELISA), data analysis, statistics, and interpretation.