Science, Faculty of
Microbiology and Immunology, Department of
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UBCV
Baeva, Maria-Elizabeth
Master of Science - MSc
University of British Columbia
Previous studies have characterized common pathological hallmarks of Alzheimer’s disease such as amyloid accumulation and plaque consolidation in the brain. The difficulty in accessing the brain in vivo has remained a barrier to the diagnosis or measuring the progression of the disease. Studies have shown that, in addition to amyloid deposition, angiogenesis and increased blood-brain barrier permeability are present in the brain of Alzheimer’s patients and animal models. Our study investigated whether these pathologies could be recapitulated in a more accessible organ associated with the central nervous system: the eyes. Using the Tg2576 model of Alzheimer’s disease, a transgenic mouse model which expresses human amyloid, an increase in amyloid was seen in the eyes of these mice and it was determined that the amyloid coalesced primarily around retinal blood vessels. This link between amyloid and ocular vasculature prompted a pilot study using a proteome profiler and found increased proangiogenic factors in the eyes and brains of the Tg2576 mouse, 20 proteins of which were upregulated in both organs. Performing further analyses of functional pathways on these findings revealed several reoccurring proteins in several pathways across both organs, including chemokines and fibroblast growth factors. Additional evidence of angiogenic processes was determined via the increased expression of CD105, a marker of neoangiogenesis, in whole eye homogenates of Tg2576 mice. When the distribution of the increased vasculature was examined, while the Tg2576 retina did not have increased amounts of CD105⁺CD31⁺ microvessels, the choroid did have more CD31⁺ vessels. Importantly, expression of tight junction proteins zonula occludens-1 (ZO-1) and occludin was decreased in the Tg2576 retinal pigment epithelia and in whole eye homogenates, respectively. Along with evidence of an increased presence of albumin in whole eye homogenates, these results suggest that there is increased permeability in the Tg2576 eyes. Our findings provide new insights into concurrent pathologies exhibited in a commonly used Alzheimer’s mouse model, suggesting that the eyes may be used as a proxy for brain pathology.
CHARACTERIZING OCULAR PATHOLOGY IN AN ANIMAL MODEL OF ALZHEIMER’S DISEASE by Maria-Elizabeth Baeva B.Sc., The University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Microbiology and Immunology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2021 © Maria-Elizabeth Baeva, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Characterizing ocular pathology in an animal model of Alzheimer’s disease. submitted by Maria-Elizabeth Baeva in partial fulfillment of the requirements for the degree of Master of Science in Microbiology and Immunology Examining Committee: Dr. Wilfred A. Jefferies, Professor, Microbiology & Immunology, UBC Supervisor Dr. Jacqueline Quandt, Professor, Pathology, UBC Supervisory Committee Member Dr. Santa Ono, Professor, Ophthalmology and Visual Sciences, UBC Supervisory Committee Member Dr. Marc Horwitz, Professor, Microbiology & Immunology, UBC Additional Examiner iii Abstract Previous studies have characterized common pathological hallmarks of Alzheimer’s disease such as amyloid accumulation and plaque consolidation in the brain. The difficulty in accessing the brain in vivo has remained a barrier to the diagnosis or measuring the progression of the disease. Studies have shown that, in addition to amyloid deposition, angiogenesis and increased blood-brain barrier permeability are present in the brain of Alzheimer’s patients and animal models. Our study investigated whether these pathologies could be recapitulated in a more accessible organ associated with the central nervous system: the eyes. Using the Tg2576 model of Alzheimer’s disease, a transgenic mouse model which expresses human amyloid, an increase in amyloid was seen in the eyes of these mice and it was determined that the amyloid coalesced primarily around retinal blood vessels. This link between amyloid and ocular vasculature prompted a pilot study using a proteome profiler and found increased proangiogenic factors in the eyes and brains of the Tg2576 mouse, 20 proteins of which were upregulated in both organs. Performing further analyses of functional pathways on these findings revealed several reoccurring proteins in several pathways across both organs, including chemokines and fibroblast growth factors. Additional evidence of angiogenic processes was determined via the increased expression of CD105, a marker of neoangiogenesis, in whole eye homogenates of Tg2576 mice. When the distribution of the increased vasculature was examined, while the Tg2576 retina did not have increased amounts of CD105+CD31+ microvessels, the choroid did have more CD31+ vessels. Importantly, expression of tight junction proteins zonula occludens-1 (ZO-1) and occludin was decreased in the Tg2576 retinal pigment epithelia and in whole eye homogenates, respectively. Along with evidence of an increased presence of albumin in whole iv eye homogenates, these results suggest that there is increased permeability in the Tg2576 eyes. Our findings provide new insights into concurrent pathologies exhibited in a commonly used Alzheimer’s mouse model, suggesting that the eyes may be used as a proxy for brain pathology. v Lay Summary The ability to diagnose or monitor Alzheimer’s disease before the onset of cognitive symptoms, which researchers now think may represent a late-stage symptom of Alzheimer’s disease, has been hindered by the inaccessibility of the brain. If changes observed in the Alzheimer’s brain can be recapitulated in the eyes, an extension of the central nervous system, we may be able to use the eyes as a “window” into the brain. By using a genetically manipulated mouse model which develops Alzheimer’s-like symptoms, we found characteristics of Alzheimer’s disease in the eyes that are typically seen in the brain: amyloid plaques, blood vessel malformation, and leakiness of the physiological barriers protecting the eye from the rest of the body. If these findings are verified in live mice over time and eventually in humans, this would make diagnosing and measuring the progression of Alzheimer’s disease much simpler by utilizing basic eye tests. vi Preface The research presented in this thesis was conducted in the Michael Smith Laboratories at the University of British Columbia, Point Grey campus. Animal studies were conducted in the Michael Smith Laboratories, Animal Resource Unit, Center for Disease Modelling, and Center for Comparative Medicine at the University of British Columbia, Point Grey campus. Genotyping was performed by Takahide Murakami at the BRC Genotyping Facility at the University of British Columbia. Wax-it Histology Services, Inc. performed paraffin embedding and sectioning of fixed tissues. Pathology analysis of H&E stains was performed with the assistance of Dr. Ian Welch at the Center for Comparative Medicine. Dr. Wilfred A. Jefferies was the supervisory author on this thesis and he was collaboratively involved throughout the design and analysis of all projects presented here as well as the manuscript composition. For the research discussed in Chapters 2, 3, and 4, I was the lead investigator with guidance and revision thoughtfully provided by Dr. Jefferies and my committee members Drs. Santa J. Ono and Jacqueline Quandt. All animal work was performed in strict accordance with the recommendations from the Canadian Council for Animal Care. Protocols were approved by the Animal Care Committee (ACC) (A16-0192) of the University of British Columbia and UBC Biosafety Level 2: Cells and Pathogens (B17-0002). vii Table of Contents Abstract.........................................................................................................................................iii Lay Summary.................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents.........................................................................................................................vii List of Tables..................................................................................................................................x List of Figures................................................................................................................................xi List of Abbreviations..................................................................................................................xiii Acknowledgments.......................................................................................................................xvi Dedication..................................................................................................................................xviii Chapter 1: Introduction................................................................................................................1 1.1 Alzheimer’s disease overview..............................................................................................1 1.2 Alzheimer’s disease proposed etiologies.............................................................................4 1.2.1 Amyloid cascade hypothesis.........................................................................................4 1.2.2 Neurovascular theory of Alzheimer’s disease...............................................................6 1.3 Ocular pathology in Alzheimer’s disease.............................................................................9 1.4 Tg2576 as a model for Alzheimer’s disease......................................................................13 1.5 Ocular pathology in Tg2565 mice......................................................................................15 1.6 Hypothesis and Aims..........................................................................................................16 1.6.1 Aim 1...........................................................................................................................16 1.6.2 Aim 2...........................................................................................................................17 1.6.3 Aim 3...........................................................................................................................17 viii Chapter 2: Ocular amyloid deposits and general structural pathologies in Tg2576 mice....19 2.1 Introduction........................................................................................................................19 2.2 Materials and methods........................................................................................................20 2.3 Results................................................................................................................................25 2.3.1 Perivascular amyloid-beta deposits are abundant in the eyes of Tg2576 mice...........25 2.3.2 Congo Red does not stain amyloid plaques in the eyes of Tg2576 and control mice.30 2.3.3 The presence of amyloid in the eyes of Tg2576 mice does not lead to classical retinal pathologies associated with AD.............................................................................................31 2.4 Summary............................................................................................................................35 Chapter 3: Vascular pathology observed in Tg2576 eyes........................................................36 3.1 Introduction........................................................................................................................36 3.2 Materials and methods........................................................................................................37 3.3 Results................................................................................................................................39 3.3.1 Preliminary evidence of increased angiogenesis-associated proteins and markers of angiogenesis was observed in Tg2576 eyes. ........................................................................ 39 3.3.2 STRING analysis identifies chemokines and fibroblast growth factors as key processes associated with angiogenic proteins in the eyes and brains of a Tg2576 mouse...44 3.3.3 Protein atlas reveals chemokine and fibroblast growth factor hits identified in the eyes and brain of a Tg2576 mouse are primarily linked to innate immune cells..................58 3.3.4 The eyes of Tg2576 mice have increased vasculature in the choroid but not in the retina......................................................................................................................................61 3.4 Summary............................................................................................................................63 Chapter 4: Retinal-blood barrier in Tg2576 eyes.....................................................................65 ix 4.1 Introduction........................................................................................................................65 4.2 Materials and Methods.......................................................................................................66 4.3 Results................................................................................................................................68 4.3.1 There is negligible deterioration in the RPE of Tg2576 mice.....................................68 4.3.2 Decreased expression of tight junction proteins in the RPE of Tg2576 mice.............69 4.3.3 The retinal blood barrier (RBB) is more permeable in Tg2576 mice.........................70 4.4 Summary............................................................................................................................ 71 Chapter 5: Overall Discussion and Perspectives.......................................................................73 5.1 Summary and Discussion...................................................................................................73 5.1.1 Amyloid presence in Tg2576 eyes..............................................................................76 5.1.2 Vascular changes in Tg2576 eyes...............................................................................80 5.1.3 Retinal-blood barrier in Tg2576 eyes..........................................................................86 5.2 Limitations..........................................................................................................................87 5.3 Future Directions................................................................................................................88 5.4 Conclusion..........................................................................................................................90 References.....................................................................................................................................92 Appendix.....................................................................................................................................112 x List of Tables Table 1. Relative change in angiogenesis protein expression in the eyes and brain of a Tg2576 mouse.............................................................................................................................................43 Table 2. Normalized annotation for Proteome Profiler and STRING Databases.........................47 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses.........50 Table S1. Supplementary RNA-Seq Data comparing wild-type and Tg2576 eyes and brain....112 xi List of Figures Figure 1. Human amyloid precursor protein (huAPP) deposits are present in the brains and eyes of Tg2576 mice ..............................................................................................................................27 Figure 2. Retinas from Tg2565 mice have greater amounts of human amyloid-β (Aβ) compared to control mice ...............................................................................................................................28 Figure 3. Retinas from Tg2565 mice have greater amounts of mouse amyloid-β (Aβ) compared to control mice ...............................................................................................................................29 Figure 4. 6E10 and M3.2 antibodies target the same amyloid protein in retinal amyloid plaques of Tg2576 mice ..............................................................................................................................30 Figure 5. Congo Red stain does not identify amyloid plaques in the eyes of wild-type and Tg2576 mice ..................................................................................................................................31 Figure 6. Absence of gross pathologies but evidence of longitudinal retinal thinning in the eyes of Tg2576 mice ..............................................................................................................................33 Figure 7. Upregulation of angiogenesis-associated proteins in the eye and brains of a Tg2576 mouse .............................................................................................................................................41 Figure 8. CD105 protein is increased in the eyes of Tg2576 mice ...............................................43 Figure 9. Network summaries for angiogenesis-associated proteins in Tg2576 eyes and brain reveal shared chemokines and fibroblast growth factors in both organs .......................................48 Figure 10. Hits from Proteome Profiler and STRING Analysis linked to innate immune cells in Human Protein Atlas ......................................................................................................................60 Figure 11. The number of retinal vessels is comparable in the eyes of Tg2576 mice to wild-type mice ................................................................................................................................................62 xii Figure 12. Choroid from the eyes of Tg2576 mice has a greater number of vessels ....................63 Figure 13. Retinas from Tg2576 mice have decreased tight junction protein expression and negligible evidence of retinal pigmental epithelial (RPE) senescence ..........................................69 Figure 14. Albumin is significantly increased in Tg2576 eyes.....................................................71 xiii List of Abbreviations Aβ Amyloid-beta ACAID Anterior Chamber-Associated Immune Deviation AD Alzheimer’s disease AMD Age-related macular degeneration APP Amyloid precursor protein BBB Blood-brain barrier BCA Bicinchoninic acid CAA Cerebral amyloid angiopathy CHO Choroid CNS Central nervous system CSF Cerebrospinal fluid C83 83 amino acid c-terminus fragment of amyloid precursor protein C99 99 amino acid c-terminus fragment of amyloid precursor protein DS Down Syndrome DAB 3,3'-Diaminobenzidine DAPI 4′,6-diamidino-2-phenylindole DNA Deoxyribonucleic acid EDTA Ethylenediaminetetraacetic acid ELISA Enzyme-linked immunosorbent assay EOAD Early-onset Alzheimer’s disease EtOH Ethanol xiv FAD Familial Alzheimer’s disease FDA United States Food and Drug Administration Fgf2 Fibroblast growth factor 2 Fgf7 Fibroblast growth factor 7 GCL Ganglion cell layer GM-CSF Granulocyte-macrophage colony-stimulating factor H&E Hematoxylin and eosin Hbegf Heparin-binding EGF-like growth factor IF Immunofluorescence IHC Immunohistochemistry IL Interleukin INL Inner nuclear layer IPL Inner plexiform layer LOAD Late-onset Alzheimer’s disease MCI Mild cognitive impairment MRI Magnetic resonance imaging mRNA Messenger ribonucleic acid NFT Neurofibrillary tangles OCT Optical coherence tomography OCTA Optical coherence tomography angiography ONL Outer nuclear layer OPL Outer plexiform layer PBS Phosphate buffer solution xv PBST Phosphate buffer solution with 1% Tween PCR Polymerase chain reaction PET Positron emission tomography PR Photoreceptors PrP Hamster prion protein promoter RBB Retinal blood barrier RNA Ribonucleic acid RPE Retinal pigmental epithelium sAPP-α Soluble amyloid precursor protein fragment alpha sAPP-β Soluble amyloid precursor protein fragment beta SC Sclera SDS Sodium dodecyl sulfate SDS-PAGE Sodium dodecyl sulfate-polyacrylamide gel electrophoresis STRING Search Tool for the Retrieval of Interacting Genes/Proteins TBS Tris Buffered Saline TGF-β Transforming growth factor-beta Treg cell T regulatory cell Tris Tris(hydroxymethyl)aminomethane VEGF Vascular endothelial growth factor VVAD Visual variant Alzheimer’s disease WT Wild-type ZO-1 Zonula occludens-1 xvi Acknowledgments I would like to firstly acknowledge, recognize and appreciate that this research was conducted on the traditional and unceded land of the Coast Salish peoples–Sḵwx̱wú7mesh (Squamish), Stó:lō and Səl̓ílwətaʔ/Selilwitulh (Tsleil-Waututh) and xʷməθkʷəy̓əm (Musqueam) Nations. The most important people in my life that I must acknowledge are my parents, who have sacrificed everything so that I could have a great education and the chance to fulfill my dreams. They have taught me many important lessons and provided much-needed emotional and financial support. Despite not being scientists, they patiently listened to me debate, complain or argue about every scientific topic under the sun. They were with me for all the highs and lows and never once asked to get off the ride. Special acknowledgment to my brother, whose calm, cool, and collected ways have helped me focus and carry on during tough times. I hope that I can always be there for him as he has been for me. Thank you to Dr. Wilf Jefferies for taking a chance on a wild student whose ambitions and passions sometimes eclipsed her technical skills. I am grateful for your knowledge, passion for science, and your trust in me to work independently and thus learn and grow from my own mistakes. Of course, my lab members, which have included but are not limited to Cheryl Pfeiffer, Lonna Munro, KB Choi, Franz Fenninger, Chaahat S. B. Singh, Hitesh Arora, Sarah Dada, Jeff Han, Clara Xia, Pablo L. Finkel, Grace Wang, and Gillian Savage all helped me feel welcome, respected and technically prepared for benchwork science. My two scientific mentors, Drs. Galina Vorobeychik and Jacqueline Quandt, are role models that have given me so much advice over the years. They have both helped me xvii professionally and personally by gifting me with amazing opportunities for which I am forever grateful. I would like to thank UBC, in particular the members of the Department of Microbiology and Immunology, the Michael Smith Laboratories, the Life Science Center the Bioimaging Facility, and all the Animal Care facilities, for their technical support. Research is a collaborative effort and the staff and administrators all around campus have supported this research and without them, it would be impossible. My friends and graduate cohort who act as both professional and personal colleagues are some of the most intelligent, thoughtful, caring, supportive, interesting, and fun people I have ever met. I am always in awe at how well-rounded everyone is and how willing they are to go above and beyond to help or teach someone. Thank you to Deanna Pepin, Ginny Pichler, Gyles Ifill, Jessica Chorolovski, Laura Daniela Morales, Leah Rankine-Wilson, Linda Dumalo, Morgan Alford, Naomi Fettig, Vivian Huang, and Victoria Peters for being amazing friends and inspirations. There are so many other wonderful people to name so if we chatted at UBC, trust me that this section is dedicated to you. Finally, I acknowledge the mice that were sacrificed for this research. To work with animals is an honor and a privilege and I hope their sacrifice will be recognized as justified and necessary. xviii Dedication To my family, thank you Boris Vladimir Baev, Tatiana Nicholai Goubina, and Philip Boris Baev. 1 Chapter 1: Introduction 1.1 Alzheimer’s disease overview Alzheimer’s disease (AD) is the most common form of dementia and affects 10% of people over the age of 65 [1]. As of 2015, the incidence of AD worldwide was estimated to be approximately 10 million cases[2] and this number is expected to rise, mostly due to the increasing global age[3]. This neurological disease is characterized clinically by cognitive dysfunction in many domains, including memory, language, problem-solving, executive functioning, and visuospatial/visuoconstructional skills[4]. Non-cognitive symptoms include behavioral issues such as aggression[5, 6], depression[6, 7], and apathy[5, 6], and visual symptoms such as decreased visual acuity, changes in contrast sensitivity, visual agnosia, poor color discrimination, and decreased motion perception[8-12]. Both behavioral and visual symptoms will often precede cognitive dysfunction[5, 7, 11]. The pathological hallmarks of AD include brain atrophy, notably in the hippocampus, the presence of extracellular amyloid plaques in the cerebral cortex, and the presence of hyperphosphorylated tau filaments in the neurons[13]. Since the discovery of amyloid plaques and neurofibrillary tangles in the cortex of the first credited case of what would later be coined “Alzheimer’s disease” by Alois Alzheimer in 1907[14, 15], a finding which has since been revalidated with modern techniques[16], the accumulation and consolidation of these amyloid plaques remain the current prevalent theory for the pathogenesis of AD. While AD is often considered a singular disease, it may be more accurate to characterize it as a syndrome or group of diseases given the known differential causes of AD[17]. Late-onset Alzheimer’s disease (LOAD), where cognitive dysfunction and eventual dementia occur typically after the age of 65, is the “archetypal AD” that accounts for approximately 95% of all 2 AD cases[18]. The specific cause of LOAD remains unknown and controversial. Early-onset (EOAD) or familial Alzheimer’s disease (FAD) accounts for the other 1-5% of AD cases and is directly caused by genetic mutations in one of three genes: amyloid precursor protein (APP), presenilin 1, or presenilin 2 [18]. These mutations have become the basis for animal model studies of AD. Patients with FAD show clinical symptoms of dementia much earlier than LOAD, typically between 40-64 years old[19]. Another lesser-known “type” of AD occurs in people with Down Syndrome (DS). DS is characterized by the presence of a third copy of chromosome 21, which is where the APP gene is located[20]. The subsequent overexpression of APP and its eventual cleavage and consolidation of amyloid-beta fragments is theorized to cause an AD-like condition in people with DS. The prevalence of AD-like clinical features or dementia in people with DS is unclear and the findings vary with the age of the cohort being examined and the study itself [21]. The simplest and most often used method for diagnosing and measuring the progression of a patient with dementia is cognitive testing performed at a clinic to determine cognitive impairment [22-24]. However, because dementia may also be caused by other neurological conditions such as vascular dementia, dementia with Lewy bodies, or frontotemporal lobar degeneration, the gold standard of definitive AD diagnosis remains post-mortem analysis of brains for amyloid plaques[25, 26]. Since AD accounts for 60-75% of dementia cases[27], a diagnosis of probable AD is typically acceptable based on clinical findings and ruling out differential diagnoses. The lack of accessibility in vivo to the target organ remains one of the great barriers to an early diagnosis of AD. Other methods for diagnosing AD have been suggested[28-30], such as cerebrospinal fluid (CSF) analysis[31], positron emission tomography (PET) scans for radioactive isotopes binding to amyloid and tau[32-34], magnetic resonance 3 imaging (MRI)[35, 36] and peripheral blood tests[37, 38]. However, these alternative methods have some issues. The amount of amyloid and tau measured by any of these methods can vary widely between patients that develop AD[39] and the presence of these biomarkers individually are not specific or sensitive enough to predict who will develop AD[30, 40-42]. Additionally, the linear correlation between amyloid and/or tau burden and level of cognitive impairment remains controversial[33, 34, 39, 43]. Another issue is the accessibility of PET scans and MRIs due to the high costs associated with purchasing equipment, running the machines, and staffing technicians and operators [33, 44]. One of the results of the limited diagnostic capabilities is the absence of virtually any therapeutics that may reverse neurological damage, slow the progression of the disease or cure the disease, despite the billions of dollars already spent on AD research[45]. As of 2019, there are only five disease-modifying therapies approved by the FDA for the treatment of AD and no new drugs have been approved since 2003[46, 47]. Currently approved therapeutics for AD all target neurotransmitter function and primarily aim to slow down cognitive impairment[48]. However, in recent years, more immune targeting therapies have begun entering clinical trials, suggesting that the future of AD therapeutics may rest in stimulating the immune system into targeting the pathologies such as excess amyloid[49, 50]. Some of the cited reasons for the inability to discover a new treatment for AD include insufficient understanding of the pathophysiology of AD, inappropriate drug targets, improper dosing, and late initiation of disease-modifying therapies during the disease course[47]. Growing research is suggesting that the accumulation of amyloid and other pathological biomarkers occurs years before the onset of cognitive dysfunction[43, 51, 52]. Therefore, the drugs themselves may not be failing, but the disease is perhaps being treated too late as cognitive dysfunction is probably a late-stage 4 symptom of AD yet simultaneously the method most often used to recruit clinical trial participants[53]. Earlier and overlooked symptoms of AD, such as the visual symptoms described earlier[8-12], may warrant investigations for early diagnosis and measuring the progression of the disease. 1.2 Alzheimer’s disease proposed etiologies As previously mentioned, the presumed cause of AD has been the accumulation of extracellular plaques composed of amyloid in the brain. Amyloid plays a key role in AD, however, its relationship to cognitive dysfunction, levels of tau protein, and brain atrophy remains unclear[54-57]. Regardless of controversies, improper cleavage and accumulation of amyloid fragments as a precursor to other Alzheimer’s pathological hallmarks (hyperphosphorylation of tau, neurofibrillary tangles, atrophy) and clinical presentation of dementia remains currently the most accepted model for the pathogenesis of AD[29, 53, 58]. 1.2.1 Amyloid cascade hypothesis The amyloid cascade hypothesis was first described by Hardy and Higgins in 1999[58] and has since remained the most popular model for describing the pathogenesis of AD. The model initially described the deposition of amyloid-beta (Aβ), a peptide fragment of APP, in the brain as the precursor and causative agent of all other AD pathologies. Since then, this model has been expanded to include the improper cleavage of APP into pathological fragments of Aβ to explain why the presence of amyloid in healthy brains does not always cause dementia[39, 59]. To summarize the current model of the amyloid cascade hypothesis, APP is a 695 amino acid transmembrane glycoprotein expressed in cells of the central nervous system (CNS), presumably mostly neurons[60]. APP is cleaved by proteases from one of two pathways: α-secretase or β-secretase[61]. Cleavage via α-secretase is considered the “non-amyloidogenic 5 pathway” and results in soluble APP fragment alpha (sAPP-α) and an 83-amino acid c-terminus fragment (C83), which is subsequently cleaved via the γ-secretase into P3 fragment[62]. The role of APP and these soluble fragments under homeostasis is currently unknown. Alternatively, if APP is cleaved via β-secretases such as BACE1, a soluble APP fragment beta (sAPP-β) and a 99-amino acid c-terminus fragment (C99) are produced[63]. Subsequent cleavage products of C99 from γ-secretase include short peptides of varying lengths (38–43 amino acids) termed amyloid-beta (Aβ). The reason for this alternative cleavage is presumed to be mutations or errors either in the APP itself or in the presenilin portion, the catalytic subunit, of γ-secretase[64-69]. The two most common forms of Aβ are a 40 amino acid length Aβ (Aβ40) and a similar fragment with two hydrophobic amino acid residues on the C-terminus, amyloid-beta 42 (Aβ42)[70]. It is thought that Aβ42 is more prone to self-aggregation and subsequent oligomerization and fibrilization; thus, it is the “toxic” form of amyloid-beta that contributes to plaque formation and neuronal damage[71, 72]. One of the main criticisms surrounding the amyloid cascade hypothesis is the assumption that the formation of neurofibrillary tangles (NFTs) due to hyperphosphorylation of tau protein (the main component of microtubules) is a downstream effect of amyloid plaque formation. However, it is unclear how amyloid plaques induce this phenomenon and if these plaques are required for NFT formation[73]. Similar to amyloid plaques, NFTs are also found in the brain in the absence of dementia[74, 75]. Additionally, although it was originally thought that the insoluble fragments of Aβ are responsible for pathogenesis, there is growing evidence for the role of soluble Aβ in neurodegeneration[76-81]. Regardless of these discrepancies, this model remains the basis for most therapeutics tested in clinical trials. 6 1.2.2 Neurovascular theory of Alzheimer’s disease A proposed alternative explanation for the pathogenesis of AD which compliments the amyloid cascade hypothesis is the neurovascular theory of Alzheimer’s[82-84]. Unlike other theories which oppose amyloid accumulation being a causative factor of AD, the neurovascular theory expands the amyloid cascade hypothesis by proposing that dysfunctional vessels may be resulting in amyloid plaque formation, either by preventing drainage of excess amyloid and/or increasing the amount of amyloid arriving from outside the brain. Both events result in an abundance of amyloid consolidating into plaques in the CNS. This theory also proposes a dynamic and changing role of malformed blood vessels. Firstly, angiogenesis results in the excess sprouting of unstable blood vessels, aiding in the accumulation of amyloid. Secondly, since these blood vessels are not properly formed, they would be unable to function as normal thus additionally resulting in dysfunctional clearance of amyloid. Angiogenesis and malformed “string” vessels have been reported in high density in the grey matter of post-mortem AD brains[85]. A separate post-mortem study found a decrease in vessel density in the basal forebrain and hippocampus and the remaining blood vessels were described as “kinking and looping”[86]. The original histopathological report by Alois Alzheimer specifically notes a “proliferation of vessels” present in the cortex[14, 15], supporting the involvement of angiogenesis as a pathological hallmark and potential causative agent of amyloid accumulation. This theory may have further gained interest after early observations linked atherosclerosis to an increased risk of AD[87]. Further epidemiological evidence throughout the years demonstrated that various risk factors associated with increased vascular and cardiovascular diseases such as smoking, the presence of Apoε4 allele, diabetes, and high blood pressure, were also linked with increased risk for Alzheimer’s disease, often 7 synergistically and cumulatively[88-97]. Finally, although vascular dementia is thought to be a separate disease from Alzheimer’s, mostly due to the former’s known etiology of strokes and brain infarcts, the overlapping pathologies in post-mortem brains and risk factors have caused the debate to surface on whether one disease may mask or mimic the other more frequently than previously assumed[88, 97-99]. A major link between the neurovascular theory and AD is cerebral amyloid angiopathy (CAA), or the accumulation of amyloid in the vessels of the brain. CAA was found by Arvanitakis et al. to be present in 94% of dementia cases and was moderately correlated with various AD pathologies[100]. Other studies also show that the severity of CAA is associated with neuritic plaque density and infarcts in post-mortem AD brains[101, 102]. This is thought to occur due to impaired vessels being unable to drain soluble amyloid, thus contributing to amyloid build-up[103, 104], in addition to a decrease in cerebral blood flow[105]. However, CAA does occur in the absence of dementia and is also strongly correlated with increasing age in the absence of disease[106, 107]. Since CAA is not a specific hallmark of AD, the neurovascular theory can be supported through other observed cerebrovascular pathologies in AD patients including decreased cerebral blood flow specifically in the hippocampus[108, 109] and grey matter[110], hypervascularization in the cortex and hippocampus[111], increased blood-brain barrier (BBB) permeability in vivo[110, 112], collapsed and thinning microvascular network in post mortem brains[113] and decreased and dysfunctional tight junction protein expression post mortem[114]. This altered cerebrovascular architecture and function have been directly linked to amyloid deposition and plaque formation. One study found that 77%-97% of amyloid plaques were located near and around blood vessels (perivascular plaques)[115], a finding which has 8 been consistently seen across multiple studies[101, 107, 116-119]. These findings are recapitulated in a transgenic Alzheimer’s mouse model which overexpresses human amyloid, APP23, where amyloid deposits were found usually near truncated vessels[117]. When Thomas et al. added exogenous Aβ to ex vivo blood vessels, they found increased vasoconstriction through the production of reactive oxygen species by the endothelium[120]. Similar results were recapitulated by Niwa et al. in vivo in mice and interestingly, these effects were found to be caused exclusively by the soluble and supposedly “nonpathogenic” variation of amyloid, Aβ40[121]. Miyakawa et al. used electron microscopy to visualize amyloid fibrils localized to basement membranes of endothelial cells in almost every single plaque[122]. In addition to the strong links between vessel pathology and amyloid plaque formation, the neurovascular theory may also solve a question that remains unanswered with the amyloid cascade hypothesis: the link between brain atrophy and plaque formation. Cerebral atrophy can be attributed, at least partially, to dysfunctional blood vessels in the brain being unable to supply nutrients or remove waste. [123]. An important trait of amyloid-beta is its angiogenic properties in vitro and in vivo in various animal models[111, 124-126]. A potential positive feedback loop may be occurring wherein dysfunctional blood vessels allow for amyloid accumulation; the amyloid then induces angiogenesis, which results in the formation of new blood vessels unable to sufficiently drain the amyloid, thus there is more amyloid accumulating, and this cycle continues to increase both amyloid plaque load and neovascularization[127-129]. This interplay has important clinical implications in that targeting vascular dysfunction may be a key component of reducing amyloid plaque formation. What remains to be determined to further support the neurovascular model is 9 that changes in the vasculature are occurring before amyloid deposition, for which currently there is some preliminary evidence[130]. 1.3 Ocular pathology in Alzheimer’s disease. The last twenty years have seen numerous studies and reviews suggesting an involvement of the visual system in AD[131-133]. The visual system is linked to the CNS via ganglion cells, which convert light taken in by the photoreceptors in the retina into electrical signals. Histologically, the retina can be separated into ten distinct layers: ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptors, and finally the retinal pigmental epithelium (RPE). Ganglion cell axons transport the signal through the optic nerve to the lateral geniculate nucleus in the thalamus, wherein the signal is sent to the occipital lobe. Given the visual symptoms of AD, there has been growing support for the idea that pre-dementia AD diagnosis could occur based on ophthalmological symptoms and examinations. This is further supported by the link between AD and various eye conditions including glaucoma[134], diabetic retinopathy[135, 136], and age-related macular degeneration (AMD) [137, 138]. The risk of developing AD is significantly higher in patients with any of these ocular conditions[139]. Several pathologies link these eye conditions and AD, although a mechanistic explanation for these has not been elucidated, include optic nerve degeneration, a decreased number of retinal ganglion cells, changes in retinal vascular caliber, and macular thinning [134, 140, 141]. Incidence rates of glaucoma are higher in AD patients than in normal controls[142]. Interestingly, vitreous fluid from a small study of patients with glaucoma and diabetic retinopathy showed increased tau and decreased Aβ42[143]. AMD and AD are both characterized by dysfunctional complement systems and increased choroidal thinning[144-147]. 10 It should also be noted that some characteristics do not overlap. Glaucoma is associated with lower intracranial pressure whereas AD is characterized most often by higher intracranial pressure[148-151]. There is also mixed evidence on whether diabetic retinopathy is associated with cognitive impairment[135, 152, 153]. AD also appears not to increase the risk of AMD[154, 155]. Another caveat to these correlation studies was that age was a confounding variable and all these conditions occur frequently in the elderly population. Therefore, while the correlations can provide clues to the pathogenesis of AD, AD-specific ocular biomarkers are needed. Early evidence implicating the involvement of the eye in AD includes the consistent observation that the area of the brain most commonly associated with CAA is the occipital lobe[102]. The first evidence for ocular pathology was the presence of nerve degeneration in eight out of ten post-mortem AD patients[140]. Three out of the four analyzed AD retinas showed a reduction in the number of ganglion cells and the thinning of the nerve fiber layer[140]. Despite this initial study having a small sample size and the analysis being performed post-mortem, these results have since been recapitulated, with thinning of the GCL being a consistent finding in vivo across multiple studies[156, 157]. Since the initial 1986 study, two aspects of the visual system are particularly under investigation for hallmarks of AD pathogenesis: amyloid deposition in the retina and altered ocular vasculature. Regarding amyloid deposition in the retinas, in a post-mortem analysis of 8 definite AD patients with heterogeneous brain pathology, 5 suspected early cases of AD, and 5 non-AD age-matched controls, Koronyo-Hamaoui et al. detected amyloid plaques in the retinas of all the definite and suspected AD cases and an absence of amyloid plaques in the retinas of the control group[158]. They were also able to correlate retinal plaque burden to disease severity[158]. In addition to post-mortem histological examination of retinas, Koronyo et al. were able to demonstrate that AD patients had 11 a two-fold increase in amyloid plaques in the retina compared to healthy controls[159]. Important to note is that this retinal amyloid deposition seems to occur before cognitive dysfunction[159]. A frontier less explored is the source of this amyloid. In vivo and in vitro studies demonstrating the ability of retinal ganglion cells[160] and retinal pigmental epithelium cells[161, 162] to secrete APP tend to suggest that the eye is the source of amyloid for itself and potentially for the brain, but this does not rule out the possibility that exogenous sources of amyloid can contribute to deposition in both areas, as evidenced by the presence of plaques in both the brain and eyes of AD patients near blood vessels[158, 159, 163]. Given the analogous structures of the choroid plexus and blood-brain barrier of the CNS and the outer and inner retinal-blood barrier of the visual system, respectively, alterations in the microvasculature of the eye may be a window into what is happening in the brain. By comparing the eye examinations of patients with AD, patients with mild cognitive impairments (MCI), and healthy controls using optical coherence tomography angiography (OCTA), Querques et al. found that AD patients had decreased arterial dilation and this was inversely correlated with the presence of amyloid-beta in cerebrospinal fluid[157]. Yoon et al. focused specifically on the vasculature of the superficial capillary plexus, the vasculature between the inner plexiform and inner nuclear layer of the retina, and found that AD patients had decreased vessel density and perfusion density compared to both patients with MCI and healthy controls, suggesting vascular remodeling [156]. These findings were replicated in a similar study by Zhang et al. which correlated these changes with decreased cognitive functioning[164]. Jiang et al. found decreased macular blood flow rate in AD patients compared to MCI and healthy controls, and that this was not correlated to age, level of dementia, or thickness of GCL[165]. In one of the larger studies, which looked at demented, cerebrovascular disease-excluded AD patients compared to healthy 12 controls, Cheung et al. focused on the morphology of the vessels and found that patients with narrower venular caliber, decreased vessel fractal dimension and increased vessel tortuosity were at an increased likelihood to have AD[166] with findings being replicated a year later with an additional 100 AD patients[167]. A common cause of angiogenesis and neovascularization is inflammation[168]. Inflammation is also known to contribute to pathology in AD, with increased inflammatory proteins and activated glial and peripheral immune cells often found near amyloid plaques and lesions[169, 170]. In general, the eye and brain are considered immune-privileged sites. Peter Brian Medawar first demonstrated that, unlike other organs, skin grafts to the brain and eyes did not undergo rejection, meaning that the immune system did not attack the graft[171]. Later, it would be discovered that this is due to several factors that are shared in the eyes and brain: the physical barriers restricting access of the immune system to the organs[172, 173], the production of anti-inflammatory cytokines and proteins such as transforming growth factor-beta (TGF-β) and interleukin (IL)-10 by glial cells and neurons[174, 175] and the regulation of the immune system, mainly through the steady-state presence of T regulator (Treg) cells[176, 177]. Originally, it was thought that the lack of a lymphatic drainage system also contributed to immune-privilege, however, it has since been shown that both the eyes and the brain contain lymphatics[178, 179]. In addition to a retinal-blood barrier, the vitreous humour of the eye contains anti-inflammatory proteins such as TGF-β and α-melanocyte-stimulating hormone produced by many retinal cells, including the RPE and the main glial cell of the retina, Müller cells[177, 180-184]. RPE cells can also induce T cells to become T regulatory cells while Müller cells suppress pro-inflammatory T cells[185-187]. One of the most unique anti-inflammatory mechanisms of the eye is Anterior Chamber-Associated Immune Deviation (ACAID). During 13 ACAID, if an antigen is introduced to the eye in the absence of a danger signal, ocular antigen-presenting cells will take the antigen, migrate to the thymus and spleen, and induce the production of immunoregulatory and suppressor adaptive effectors cells which will then circulate throughout the body suppressing inflammation against the antigen[188]. To our knowledge, while the dysregulation of these systems has been investigated in AD brains[189-191], there have yet to be studies examining whether dysregulation of ocular-specific immune mechanisms is occurring in AD. Visual variant of Alzheimer’s disease (VVAD) is a rarer form of AD where the visual symptoms, including difficulties with near vision and writing, impaired color vision, and dyslexia, are more predominant and precede cognitive dysfunction[192]. This specific form of AD is thought to occur due to localized pathology, in the form of amyloid plaques and NFTs, in the parieto-occipital region of the cortex resulting typically in posterior cortical atrophy[192-194]. The main difference between the VVAD and other AD forms such as familial or late-onset appears to be the regional distribution of the pathology and atrophy and the dominance of the visual symptoms over behavioral or cognitive symptoms. In a study of 523 neurological dementia patients, Snowden et al. found that 24 (5%) patients presented with visual symptoms unrelated to the visuospatial disturbances found in the 78% of those patients with the cognitive variant of AD, thus suggesting that VVAD specifically accounts for a small subset of overall AD cases[195]. 1.4 Tg2576 as a model for Alzheimer’s disease Currently, AD is studied primarily in transgenic mouse models which either overexpress amyloid or tau and/or contain mutations in enzymes or proteins thought to be important in AD pathogenesis such as APP, presenilin, or APOε4[196, 197]. One of the most commonly used 14 animal models for AD is the Tg2576 mouse. The transgenic mouse was developed by Hsiao et al. by inducing overexpression of human amyloid precursor protein with the Swedish FAD mutation (K670N/M671L) under the promotion of the hamster prion protein promoter (PrP) in a C57BL/6xSJL mouse[198]. The Swedish mutation leads to increased cleavage by β‐secretase, thus increasing the production of Aβ. Although the brain has the highest expression of the full human APP, with a five-fold increased expression compared to mouse APP, this protein has also been found in other organs, such as the spleen, lungs, heart, skin, bone, muscle, pancreas, stomach, and large intestine[198, 199]. Although PrP is commonly expressed in the central nervous system[200], other cell types, such as immune cells, have also been shown to express PrP[201]. This mouse model is advantageous in that it recapitulates many of the characteristics of Alzheimer’s disease. Typically, the disease course in these mice involves cognitive deficits at around 6-10 months of age, followed by amyloid plaque deposition in cortical and limbic regions beginning at 9-13 months of age and increasing in an age-dependent fashion[198, 199, 202], microgliosis at 10-16 months old[203], and oxidative stress from 13-25 months old[204, 205]. There are sex differences that should be noted within this model[206]. Female Tg2576 mice show 3-times higher amyloid plaque deposition in the brains at 15-months of age[207] as well as increased deficits in some cognitive-behavioral tasks at 3 months, before obvious amyloid consolidation[208]. Male Tg2576 mice tend to display more aggression[209]. Therefore, depending on the research question, the sex of Tg2576 mice may be relevant. The Tg2576 mouse model mirrors cerebrovascular pathologies seen in AD patients and the neurovascular theory, including increased blood-brain barrier permeability (BBB) before amyloid plaque formation, deposition of Aβ in vessel walls, abnormal BBB tight junction 15 morphology, decreased BBB tight junction protein expression, increased expression of proangiogenic factors, and increased hypervascularization[111, 127, 210]. Although this mouse model captures most of the pathological and clinical hallmarks of Alzheimer’s disease, it remains controversial whether these mice have the neurofibrillary tangles and neuronal loss typical of AD, given that some studies have shown them in this model while others have not [211-213]. 1.5 Ocular pathology in Tg2565 mice Previous studies of the eyes in Tg2576 mice have primarily focused on neurodegeneration of the retina and amyloid deposition[214-217]. Several studies have shown that APP and Aβ expression primarily occurs in the GCL and inner nuclear layer[217, 218]. One notable exception is the Liu et al. study using the Tg2576 mice found amyloid deposits and retinal amyloid angiopathy prevalent throughout most of the retina[169]. Interestingly, the latter study found that vaccinations of the mice with various forms of amyloid (Aβ peptide, Aβ fibrils, or human APP) decreased amyloid deposits in Tg2576 retinas but increased microvascular-associated Aβ[219]. More importantly, these events were also seen in the brain, suggesting that amyloid vaccinations are an effective strategy for “solubilizing” and promoting the removal of toxic deposits and that this phenomenon can be seen in the eyes, similar to the studies previously mentioned investigating the effects of amyloid vaccinations in the brain[128]. Liu et al. also found that retinal thickness was decreased in Tg2576 mice compared to wild-type mice[169]. To explain what is causing retinal thinning, Williams et al. were able to use electron microscopy and immunofluorescence (IF) to demonstrate that while 14-month old Tg2576 mice did not have decreased density of retinal ganglion cells, the cells themselves had decreased integrity as a result of decreased mitochondrial integrity[218]. This pathology was also seen in the CA1 region 16 pyramidal neurons of the hippocampus in the same mice[218]. The composition of amyloid was examined by Alexandrov et al. and found that APP, Aβ40, and Aβ42 peptides are present in the retina of Tg2576 mice[216]. Comparatively, there are few studies specifically examining the vasculature of the eyes of Tg2576 mice. One of the few studies found that retinal vessel attenuation occurred at a similar rate in Tg2576 mice (25%) and their non-transgenic littermate controls (22%)[220]. With the current need for easily accessible biomarkers of AD, we hope that researching the eyes of this well-established AD mouse model will provide insight into not only the pathogenesis of the disease but also support future studies to use this method to diagnose, monitor progression, and perhaps develop therapeutics against this devastating illness. 1.6 Hypothesis and Aims We hypothesize that ocular pathologies such as amyloid deposition, formation of leaky blood vessels, and retinal-blood barrier permeability are hallmarks of the Tg2576 AD animal model. 1.6.1 Aim 1 Our first aim was to determine whether the eyes of our animal model, the Tg2576 mouse, could serve as an ancillary measurement of amyloid deposition as seen in the brain in classical AD pathology. We designed experiments described in Chapter 2, to test the hypotheses that amyloid deposits could be detected and localized in whole eye homogenates of Tg2576 mice and that structural changes in the eyes of Tg2576 mice would demonstrate classical AD pathology. 17 1.6.2 Aim 2 After implementing multiple techniques to detect, localize, and measure amyloid deposition in the eyes of Tg2576 mice, we demonstrated that the eyes were indeed a potential ancillary measure of amyloid accumulation. Our results showed that amyloid accumulation was independent of gross atrophy, but deterioration may be occurring as a slow and long process. Results from Chapter 2 found amyloid deposits primarily located around blood vessels in the retina. In Chapter 3 we continued our exploration of AD pathology in Tg2576 mice to uncover potential changes in the vascular tissue of the eyes. In alignment with the neurovascular theory of AD that describes early malformation and leaky blood vessels in the brains of AD patients and animal models, we hypothesized that the vascular hallmarks of AD would also be observed in the eyes of our mouse model. We tested our hypothesis to determine if angiogenic factors were upregulated and shared across the brain and the eyes. We also quantified and visualized ocular vasculature with experiments demonstrated in Chapter 3. 1.6.3 Aim 3 Our vascular investigation in Chapter 3 offered preliminary observations of increased angiogenic proteins in the eyes of our Tg2576 mouse compared to an age-matched littermate control, neovascularization as a likely site-specific process, and potential changes in choroid and retina responses within a proangiogenic environment. To further demonstrate that our animal model exhibits hallmark AD pathologies, we investigated potential effects on blood barrier permeability, which is typically significantly affected in AD patients. Physiological barriers in the brain and eyes share similar structural and functional roles. As such, we designed experiments in Chapter 4 to investigate blood-brain 18 barrier pathologies in the retinal blood barrier (RBB) of Tg2576. We hypothesized that the structural and functional integrity of the RBB would show comparable pathologies in our Tg2576 mouse to those observed in classical AD. 19 Chapter 2: Ocular amyloid deposits and general structural pathologies in Tg2576 mice. 2.1 Introduction Amyloid accumulation in the brain is a hallmark pathology observed in AD. Increased amyloid precursor protein (APP) production, dysfunctional cleavage, and/or decreased clearance can lead to excess soluble amyloid consolidating into plaques. Plaques are categorized as neuritic, diffuse, or perivascular based on their localization and characteristics such as amyloid subtype composition [221]. Perivascular plaques are commonly associated with cerebral amyloid angiopathy (CAA) given their localization around blood vessels. Perivascular plaques stain weakly with conventional congophilic or silver stain techniques, similar to diffuse plaques [222]. Perivascular plaques also have a similar structure and composition of amyloid subtype amyloid-beta (Aβ)42 like neuritic plaques[223-226]. While there has been a large amount of research invested into understanding amyloid composition in plaques, it is important to note that common fixation techniques can pose a challenge in determining true ratios of amyloid subtypes like Aβ40 to Aβ42 [223, 225, 227]. The mechanism driving the accumulation of Aβ in the brain is not clear and could be the result of a neuronally-derived accumulation from improper protein recycling[228] or amyloid being transported into the brain from exogenous sources[229]. To better understand amyloid deposition in the brain, we investigated a surrogate tissue in our animal model with known AD pathologies. In the Tg2576 mouse model, APP and Aβ have been found to accumulate in retinal capillaries, ganglion cell layer (GCL), inner nuclear layer (INL), lens, and cornea of the eye [169, 217]. In comparison to those studies, we used a different set of antibodies, 6E10 and M3.2, which bind to the N-terminus on the human and mouse amyloid, 20 respectively. As such, we hypothesized that the eyes of our Tg2576 mice would demonstrate amyloid accumulation and structural changes consistent with AD pathologies. With quantification and localization methods, we identified an increase in amyloid deposits and structural changes in the eyes of Tg2576 mice that further demonstrate the usefulness of this animal model for studying AD and deepen our understanding of ocular pathologies in AD. 2.2 Materials and methods Mice. The Tg2576 AD model mouse expresses the Swedish mutant (K670N/M671L) of the amyloid precursor protein under the control of the hamster prion protein promoter[198]. Mice were maintained on mixed C57Bl6/SJL background by mating heterozygous Tg2576 males to C57Bl6/SJL F1 females. Ten-fifteen-month-old Tg2576 and wild-type mice of both sexes were used. Wild-type littermates were used as controls. All mice were maintained in the Centre for Disease Modeling (Life Sciences Centre, Vancouver, British Columbia). Mice were fed standard lab chow and water ad libitum and kept under a 12-hour light/dark cycle. All protocols and procedures involving the care and use of animals in these studies were reviewed and approved by the UBC Animal Care Committee. Genotyping. Genotyping was performed at the Biomedical Research Centre Genotyping Facility at the University of British Columbia by Takahide Murakami. DNA was extracted from ear notches using a standard protocol at 55°C with the Proteinase K (Sigma-Aldrich, St. Louis, US). DNA was amplified by PCR using forward 5’-CAGGTTCTGGGTTGACAAATATC-3’ and reverse 5’-CTGTTTCTTCTTCAGCATCAC-3’ primers to identify the human APP gene in Tg2576 mice. The target sequence is 230 base pairs in size. Samples were compared to an internal control, which yielded a 324 base pair fragment and was confirmed using forward 5’-21 CTAGGCCACAGAATTGAAAGATCT-3’ and reverse 5’-GTAGGTGGAAATTCTAGCATCATC C-3’ primers. A standard protocol was used for PCR with a QuantStudio™ 5 Real-Time PCR System (Applied Biosystems, Massachusetts, US). Tissue harvesting. Tissues were harvested from mice that were weighed and injected with an appropriate amount of ketamine and xylazine (0.4 mL/10 g of body weight) to induce anesthesia[128]. After toe pinch was done to determine that mice were not conscious, mice abdomen and chest cavity were opened, and transcardial perfusion was performed using 1X phosphate buffer at a flow rate of 5mL/minute for 5 minutes[84], after which eyes and brains were dissected from the mouse. Tissues were then either stored in 2% SDS for protein analysis or a fixation buffer (4% paraformaldehyde for the brain and Hartman’s fixative for the eyes) for downstream processing. Tissue processing for Western Blot. Tissues were immersed in 2% SDS and Halt™ Protease Inhibitor Cocktail (100X) (Thermo Fisher Scientific, Massachusetts, US) to prevent proteolytic degradation. Tissues were gently homogenized used Dounce homogenizer. Samples were placed on ice and further disrupted with sonication using the Fisher Scientific Model 550 Sonic Dismembrator for 1 minute at 30 seconds on and 20 seconds off intervals. Brain samples were sonicated at an amplitude of 3 and ocular samples were sonicated at an amplitude of 2. Samples were centrifugated at 15,000 RPM for 10 minutes at 4°C and isolated supernatants were used for subsequent protein analyses. Tissue processing for immunofluorescence and histopathological staining. Eyes were placed in Hartmann’s fixative solution (MilliporeSigma, St. Louis, Missouri, United States) for 48 hours before being switched to a 70% EtOH solution. Brains were placed in 4% paraformaldehyde solution for 24 hours before being switched to a 70% EtOH solution. 22 Protein quantification. Protein quantification was performed with the Pierce™ BCA Protein Assay Kit (Rockford, Illinois, US) as per the manufacturer’s instructions. Absorbance was read on the Molecular Devices Kinetic Microplate Reader (San Jose, California, USA) using the SoftMax® Pro Software Version 3.1.2. Samples and standards absorbance were measured at 545 nm. A standard curve was plotted to determine protein concentration. Western Blot. A standard Western Blot protocol was used. 400 ug of eye sample and 100 ug of brain samples were loaded per well on a 10% sodium dodecyl sulfate-polyacrylamide gels (SDS), transferred onto nitrocellulose membranes, blocked with 5% skim milk and PBST solution, and probed with monoclonal mouse anti-β-Amyloid (BioLegend, San Diego, California, USA) and polyclonal rabbit anti-β actin (Abcam, Cambridge, UK). Alexa Fluor Secondary Antibodies (Thermo Fisher Scientific, Massachusetts, USA) were used to detect primary antibodies at 680 nm and 790 nm using a ChemiDoc Imaging System (Biorad, California, USA). Immunofluorescence. Fixed eyes were sent to Wax-it Histology Services, Inc. for paraffin embedding and tissue slicing at 5 microns. Antigen retrieval was performed using the method described by Paulsen et al. [230]. Slides were placed in a dish containing Tris-EDTA buffer in a water bath at 85 °C for 30 minutes. The staining dish was removed and left at room temperature to cool for 20 minutes. Sections were rinsed three times for 5 minutes each in 0.1 M PBS (pH 7.4). Sections were blocked in 2-3% skim milk in 0.1 M PBST (pH 7.4) for 60 minutes. Primary antibody solution consisting of antibodies and blocking milk was placed on exposed tissue on slides overnight at 4°C. Primary antibodies used were monoclonal mouse anti-human β-Amyloid, [1-16, 6E10] (BioLegend, San Diego, California, USA), monoclonal mouse Alexa Fluor® 488 anti-human β-Amyloid [1-16, 6E10] (BioLegend, San Diego, California, USA), 23 monoclonal mouse anti-mouse/rat β-Amyloid, [1-16, M3.2] (BioLegend, San Diego, California, USA) and monoclonal mouse biotin anti-mouse/rat β-Amyloid [1-16, M3.2] (San Diego, California, USA). After overnight incubation, sections were washed with 0.1 M PBST three times for 5 minutes each. The secondary antibody solution was placed on exposed tissue on slides at room temperate for 1 hour. Primary antibodies were detected using rabbit anti-mouse IgG (H+L) cross-adsorbed secondary antibody, Alexa Fluor 633 (Invitrogen, California, USA) and streptavidin-Alexa Fluor™ 568 conjugate (Invitrogen, California, USA). Sections were rinsed three times for 5 minutes in 0.1M PBST. Slides were then rinsed with PBS. Excess moisture was removed from slides and a 1:1 ratio of ProLong™ Gold Antifade Mountant (Thermo Fisher Scientific, Massachusetts, USA) and ProLong™ Gold Antifade Mountant with DAPI (Thermo Fisher Scientific, Massachusetts, USA) solution was placed on the tissue before the coverslip was placed on the slide. For each animal, two cross-sections of the eye were used, and 5-12 images were taken per eye. Fluorescence was acquired using the Olympus FV1000 Laser Scanning/Two-Photon Confocal Microscope. Parameters were set based on one of the Tg2576 slides and used throughout scanning for consistency when acquiring the remaining images. A negative control slide was used with only secondary antibody (no primary antibody) staining to control for non-specific binding. After the acquisition, images were analyzed using Volocity software version 5.2. For Figure 2, images were acquired at 10X magnification. Mean of sum 6E10 amyloid fluorescence was measured above 4.81 standard deviations from background and fluorescence was normalized to the area measured. The average of all the measurements for each animal was depicted on the graphs. For Figure 3, images were acquired at 20X magnification. Mean M3.2 amyloid fluorescence was measured above 3.5 (whole retina) and 6 (retinal layers) standard 24 deviations, objects below 4 um2 were excluded to remove background signal and fluorescence was normalized to the area (um2) measured. Since the animal sample size was small (2 animals per genotype), measurements from all images per genotype were pooled together. To ensure that the number of images taken per animal did not contribute to the measured fluorescence, Pearson’s correlation matrix was plotted. If it was determined that there was no correlation (p>0.05) between the number of images acquired and the fluorescence measured, image measurements could be pooled together for each genotype. For Figure 3, it was determined that the number of images taken per animal did not affect the fluorescence measured (r=-0.1160, p=0.8389). Outliers were removed using ROUT at Q=1%. Histopathological staining. Fixed tissues were sent to Wax-it Histology Services, Inc. for paraffin embedding and tissue slicing. Brains were sliced at 10 microns and eyes were sliced at 5 microns. Standard protocols for Hematoxylin and eosin (H&E) stain and Congo Red stain were performed by Wax-It Histology Services, Inc. H&E stain was used to determine gross pathology and structural changes and Congo Red staining was used to visualize amyloid plaques. Statistics. Statistical analysis was performed using GraphPad Prism 9.0.1. For Figure 1, an unpaired t-test was used. For Figures 2, 3, and 6, Mann Whitney U test was performed. For Figure 4, Pearson’s correlation was used to measure 6E10 and M3.2 antibody co-staining in retinal layers. “ns” denotes p>0.05, * denotes p<0.05, ** denotes p<0.01, *** denotes p<0.001 and **** denotes p<0.0001. 25 2.3 Results 2.3.1 Perivascular amyloid-beta deposits are abundant in the eyes of Tg2576 mice. To investigate AD pathologies in Tg2576 mice, Aβ accumulation was quantified in the eyes of 10-month old Tg2576 mice by Western Blot and in the eyes of 15-month old Tg2576 mice by immunofluorescence (IF) using the 6E10 antibody clone that binds the human APP, Aβ40, and Aβ42 at the N-terminus (amino acids 5-10) RHDSGY epitope. Significantly higher amounts of human APP were detected in Tg2576 brain and eyes compared to wild-type control littermates (i.e. littermates without the mutated APP gene) (Figure 1), which confirms that our Tg2576 model exhibits expected AD pathologies. Using IF, we observed increased Aβ in the retinas of Tg2576 mice compared to the control mice (Figure 2). Amyloid was also distributed predominantly perivascularly with significant quantities found in the ganglion, inner plexiform, and outer plexiform layers of the retina (Figure 2). To ensure that the increase in Aβ was not antibody-dependent, we also probed for Aβ with the M3.2 antibody clone, which is specific for mouse APP, Aβ40, and Aβ42 at the N-terminus (amino acids 10-15) FEVRHQ epitope. Human and mouse APP have a 96.62% protein sequence homology. Immunofluorescence with the M3.2 antibody clone detected mouse amyloid in the retinas of Tg2576 and control mice, however, Tg2576 retinas had 1.6-times the amount of amyloid-associated fluorescence compared to control mice (Figure 3). Similar to what we observed using the 6E10 antibody, the amyloid probed with the M3.2 clone was found mostly in proximity to blood vessels in the ganglion cell, inner plexiform, and outer plexiform layer (Figure 3). To determine whether the 6E10 and M3.2 antibodies could recognize the same amyloid plaques in the eyes, given that they target different epitopes, retinas from Tg2576 and control 26 mice were co-stained with both antibodies (Figure 4a). Nearly all of the cell layers in the retinas, except for the inner plexiform layer (IPL), showed moderate to high correlations between 6E10 and M3.2 fluorescence co-localization (Figure 4b). Examination of plaques in the OPL (Figure 4c) by voxel ratio, a three-dimensional measurement determined by pixel size and slice thickness, showed roughly equal amounts of 6E10 to M3.2 antibody-associated amyloid (Figure 4d). While examining amyloid staining in plaques of retinal layers, we noted that it was challenging to specifically distinguish whether the amyloid was present in the nuclear or plexiform layer. We observed that the layers overlapped slightly, with retinal vessels being present at their interface (Figure 2d). Our results support our hypothesis that the eyes can be used as an ancillary measurement for amyloid deposition and that our mouse model is a good representation of AD pathologies both in the brain and the eyes. 27 Figure 1. Human amyloid precursor protein (huAPP) deposits are present in the brains and eyes of Tg2576 mice. Western blot of human APP normalized to the internal control GAPDH in the whole eye and brain homogenates of 10-month old wild-type (n=3) and Tg2576 (n=3) mice. Each well (a) and point on the graph (b) represents one mouse. One of 5 representative experiments shown. Errors bars represent standard deviation. Statistics: unpaired t-test (*p<0.05). 28 29 Figure 2. Retinas from Tg2565 mice have greater amounts of human amyloid-β (Aβ) compared to control mice (previous page). Human Aβ was detected by immunofluorescence in the retinas and retinal layers of 15-month old wild-type (n=2) and Tg2576 (n=2) mice with the 6E10 antibody clone. (a) Representative immunofluorescence images indicate areas of 6E10 (yellow) and DAPI (control, blue) staining as well as the location of photoreceptor (PR), choroid (CHO), and sclera (SC) regions. Fluorescence was quantified across entire mouse retinas (b) and within (c) retinal ganglion cell layer (GCL), inner nuclear layer (INL), outer plexiform layer (OPL), inner plexiform layer (IPL), and outer nuclear layer (ONL). (d) Overlap of amyloid fluorescence in the plexiform and nuclear layers of Tg2576 mice. Errors bars represent standard deviation. Statistics: Mann-Whitney U test (*p<0.05). Figure 3. Retinas from Tg2565 mice have greater amounts of mouse amyloid-β (Aβ) compared to control mice. Mouse Aβ was detected by immunofluorescence in the retinas and retinal layers of 15-month old wild-type (n=2) and Tg2576 (n=2) mice with the M3.2 antibody clone. (a) Representative immunofluorescence images indicate areas of M3.2 (pink) and DAPI (control, blue) staining as well as the location of photoreceptor (PR) and choroid (CHO) regions. Fluorescence was quantified across entire mouse retinas (b) and within (c) retinal ganglion cell layer (GCL), inner nuclear layer (INL), outer plexiform layer (OPL), inner plexiform layer (IPL), and outer nuclear layer (ONL). Each point (b & c) represents an image. Errors bars represent standard deviation. Statistics: Mann Whitney U test (*p<0.05. **p<0.01, ***p<0.001, ****p<0.0001). 30 Figure 4. 6E10 and M3.2 antibodies target the same amyloid protein in retinal amyloid plaques of Tg2576 mice. Immunofluorescence was performed on the eyes of Tg2576 mice (n=7) that were probed with 6E10 (yellow) and M3.2 (pink) antibodies. (a) Representative immunofluorescence image indicating areas of M3.2 antibody (pink), 6E10 antibody (yellow), DAPI control (blue), and M3.2 and 6E10 overlap (orange) staining in proximity to the location of retinal ganglion cell layer (GCL), inner nuclear layer (INL), outer plexiform layer (OPL), inner plexiform layer (IPL), and outer nuclear layer (ONL). (c) 6E10 and M3.2 antibody co-staining in retinal layers (white arrow). (d) Pearson’s correlation and the voxel ratio measured 6E10 and M3.2 antibody co-staining in plaques (n=4) from the OPL. Errors bars represent standard deviation. Pearson’s correlation tests: <0.3= low correlation, 0.3-0.5= moderate correlation, >0.5= high correlation. 2.3.2 Congo Red does not stain amyloid plaques in the eyes of Tg2576 and control mice. Congo Red is a standard staining technique used to identify amyloid plaques in brain tissue. While perivascular and diffuse amyloid plaques in brain sections from Tg2576 mice stained with Congo Red, we did not detect plaques in eye sections from both Tg2576 and wild-type mice with Congo Red stain (Figure 5). While this amyloid detection method suggests the 31 lack of amyloid plaques in the eye sections of Tg2576 and wild-type mice, which is contradictory to our results with Western Blot and IF methods, Congo Red was likely not staining the Aβ targeted with the 6E10 and M3.2 antibodies that we found in the eyes of Tg2576 mice. Figure 5. Congo Red stain does not identify amyloid plaques in the eyes of wild-type and Tg2576 mice. Representative perivascular (*) and diffuse (#) amyloid plaques were seen in all brain sections from Tg2576 mice (a, n=7) and none in wild-type (b, n=3). No congo red staining was seen in any eye sections of wild-type (c, n= 25) or Tg2576 mice (d, n=30) across retinal layers. GCL= ganglion cell layer, IPL= inner plexiform layer, INL= inner nuclear layer, OPL= outer plexiform layer, ONL= outer nuclear layer, PR= photoreceptors, RPE= retinal pigmental epithelium, CHO= choroid, SC= sclera. 2.3.3 The presence of amyloid in the eyes of Tg2576 mice does not lead to classical retinal pathologies associated with AD. The link between AD pathologies such as atrophy and neuronal loss with amyloid deposition is controversial[231-233]. It has been shown that Aβ42, the cleavage product of β and γ-secretase from APP, is more prone to aggregation and can be more toxic to neuronal cells in 32 vitro [3]. Whether the presence of amyloid in the eyes leads to retinal AD pathologies such as neurodegeneration remains unclear. To better understand and identify the development of potential AD pathologies in the eyes of Tg2576 mice, we conducted classical H&E staining on eye sections from wild-type (n=11) and Tg2576 (n=11) mice ages 9 to 13 months old (Figure 6a). Structural pathologies that we investigated included gross deformities of the RPE layer, hyper or hypopigmentation (notably in areas such as the RPE or choroid), and disorganization of the different retinal layers. We also examined immune cell infiltration and the presence of drusen deposits. We did not observe any of the previously described pathologies. Since previous studies have noted retinal thinning in AD patients and Tg2576 mice[169], we examined eye sections from Tg2576 mice and controls from three age groups: 3, 6, and 10 to 13-months-old. After H&E staining, we observed no significant difference in thickness of the retinas, outer nuclear layer, or inner nuclear layers of Tg2576 eyes compared to wild-type mice for each age group, which would imply degeneration or death of neurons (Figures 6b & c). When we plotted retinal thinning over time, the data suggested that the Tg2576 mice have a faster thinning rate (-4.85±1.46 um/month) compared to wild-type mice (-1.59± 0.63 um/month) (Figure 6d). These preliminary results suggest early or subtle deterioration and changes to the eye in the Tg2576 mouse may be occurring in the absence of obvious overall degeneration or pathology. 33 Figure 6. Absence of gross pathologies but evidence of longitudinal retinal thinning in the eyes of Tg2576 mice. (a) Representative images of eyes from wild-type (n=14) and Tg2576 (n=12) mice euthanized at ~9-24 months of age were stained with classical H&E methods. (b) A new cohort of mice was selected to measure retinal (*), ONL (#) and INL (+) thickness using Aperio Image Scope ruler. 34 Figure 6. Absence of gross pathologies but evidence of longitudinal retinal thinning in the eyes of Tg2576 mice (continued). (c) Retinal, ONL and INL thickness of 3-month-old (wild-type=3, Tg2576=3), 6-months old (wild-type=3, Tg2576=3) and 10–13-month-old (wild-type=11, Tg2576=11) mice was measured. An average of 3 different measurements was used per layer per animal. Each point represents an animal. (d) The dotted line represents the linear regression line. INL= inner nuclear layer, ONL= outer nuclear layer. Error bars represent standard deviation. Statistics: Mann Whitney U test (*p=0.0416, ns p>0.05). 35 2.4 Summary The accumulation of amyloid is a hallmark pathology described for AD in patients and the Tg2576 mouse as a model of AD. Herein, we demonstrate the visualization of amyloid deposits in the eyes and brains of Tg2576 mice using a variety of techniques. We confirmed previous studies[169, 214-217] which saw increased amyloid deposits in the retinas of the Tg2576 mice using Western Blot and IF and using two different anti-amyloid antibodies, 6E10 and M3.2. Confocal microscopy allowed us to specifically localize amyloid accumulation in retinal cross-sections at the intersections of the nuclear and plexiform layers of the eye, as well as the GCL; these deposits were predominantly perivascular. Contrary to previous studies, neither neuritic, diffuse, or perivascular plaques were detected using Congo Red staining in our AD mouse model[169]. Atrophy and neurotoxicity are hallmarks of AD thought to be linked to amyloid deposits, yet after staining cross-sections of eyes from Tg2576 mice with classical H&E methods, we did not observe obvious deformities that would indicate any developing damage or expected AD pathology. This could suggest either that amyloid accumulation precedes necrosis and dysfunction and that this is a slow and long process, or that amyloid deposition occurs independently of degeneration and atrophy[233-235]. Overall, we provide support for previous observations of amyloid accumulation in the eyes of Tg2576 mice. We also provide additional clarifications on the localization of the amyloid, methods for detecting it ex vivo in the eyes and support for their use as ancillary tissues to the brain in investigating AD pathologies in the Tg2576 model. We have thus added to the knowledge of potential AD pathologies in the Tg2576 model. 36 Chapter 3: Vascular pathology observed in Tg2576 eyes 3.1 Introduction Blood vessels are responsible for distributing oxygen, nutrients, immune cells, and other important compounds throughout the body[236]. The eye in particular is heavily vascularized, with two major circulations providing nutrients to the retina: the choroidal vasculature, which provides nutrients to the outermost part of the retina, including photoreceptors, and the inner retinal vasculature, which provides nutrients to the rest of the retina. The inner vascular layer extends capillaries into the deep vascular plexus, intermediate vascular plexus, and superficial vascular plexus, which corresponds to the outer plexiform layer (OPL), inner plexiform layer (IPL), and ganglion cell layer (GCL), respectively[237]. Angiogenesis is the process in which new blood vessels grow out of existing blood vessels. This may be a developmental, pathological, or reparative process[238-240]. Ocular angiogenesis occurs in three locations: the retina, the choroid, and the cornea. The focus of our study is on whether the transgenic expression of APP influences the vascularization of the retina and choroid. We hypothesized that since increased angiogenesis occurs in the brains of AD patients and Tg2576 mice, hypervascularization would be seen in the eyes of Tg2576 mice. While previous studies have noted amyloid deposits in the vessels of Tg2576 eyes[169], to our knowledge, the only study examining vasculature changes of this transgenic AD model found that Tg2576 and littermate control mice had similar amounts of retinal vessel narrowing[220]. Using immunofluorescence (IF), Western blotting, and proteome profiling, we demonstrate herein preliminary evidence that demonstrates the eyes of a Tg2576 mouse have upregulated expression of angiogenesis-associated proteins in comparison to a wild-type littermate. 37 Additionally, we demonstrate that neovascularization appears to occur in the choroid and not in the retina. 3.2 Materials and methods Mice, genotyping, tissue harvesting, tissue processing for Western Blot, and protein quantification were performed as previously described in Section 2.2. Angiogenesis Proteome Profiler. The Proteome Profiler Mouse Angiogenesis Array Kit (R&D Systems, Minneapolis, US) was used to analyze angiogenic markers in a 10-month old female Tg2576 and her wild-type littermate. Previous studies have relied on the profiler's technical duplicates to validate findings even when sample sizes were low[241-243]. Tissue preparation and analysis were performed as per the manufacturer’s guidelines. After membranes were placed in an autoradiography film cassette with Chemi reagent Mix and X-ray film, membranes were left in the cassette for 24 hours before being run through an X-Ray film processor. After the X-ray film was developed, images of the film were uploaded, and mean pixel density was measured using ImageJ software. Mean pixel density was averaged between the duplicate spots, then normalized to the three positive control spots of each membrane. Finally, the relative difference was determined between the wild-type and the Tg2576 brain and eyes. Western Blot. A standard Western Blot protocol was performed as described in Section 2.2 using a 12% SD-PAGE gel. 400 ug of protein was loaded per well. Primary antibodies used included polyclonal mouse Endoglin/CD105 Antibody (R&D Systems, Minneapolis, Minnesota, USA) and polyclonal rabbit anti-β-actin (Abcam, Cambridge, UK). Alexa Fluor Secondary Antibodies (Thermo Fisher Scientific, Massachusetts, USA) were used to detect primary antibodies at 680 nm and 790 nm using a ChemiDoc Imaging System (Biorad, California, USA). 38 Immunofluorescence. The immunofluorescence protocol was performed on 10-month old mice as described in Section 2.2. The primary antibodies used were polyclonal rabbit anti-CD31 (Abcam, Cambridge, UK) and polyclonal mouse Endoglin/CD105 Antibody (R&D Systems, Minneapolis, Minnesota). Primary antibodies were detected using goat anti-rabbit IgG (H+L) cross-adsorbed secondary antibody, Alexa Fluor 488 (Invitrogen, California, USA), donkey anti-goat IgG (H+L) cross-adsorbed secondary antibody, Alexa Fluor 488 (Invitrogen, California, USA), and donkey anti-rabbit IgG (H+L) highly cross-adsorbed secondary antibody, Alexa Fluor 568 (Invitrogen, California, USA). For each animal, two cross-sections of the eye were used, and 5-12 images were taken per eye. Fluorescence was acquired using the Olympus FV1000 Laser Scanning/Two-Photon Confocal Microscope. Parameters were set based on one of the slides and used throughout scanning for consistency when acquiring the remaining images. A negative control slide was used with only secondary antibody staining to control for non-specific binding. After the acquisition, images were analyzed using Volocity software version 5.2. For Figure 11, images were acquired at 60X magnification. Double positive-stained retinal vessels (yellow) were counted per retinal layer. The total amount of vessels was divided by the area of the retina measured (um2). For Figure 12, images were acquired at 10X magnification. Sum CD31 fluorescence was measured, and fluorescence was normalized to the area of choroid measured (um2). Statistics. Statistical analysis was performed using GraphPad Prism 9.0.1. For Figure 7 and Table 1, the fold-difference cut-off was set to 2. For Figure 8, the Mann Whitney U test and Two-way ANOVA were performed. Outliers were removed using ROUT at Q=1%. For Figure 39 11 and 12, Mann Whitney U test was performed. “ns” denotes p>0.05, * denotes p<0.05, ** denotes p<0.01, *** denotes p<0.001 and **** denotes p<0.0001. 3.3 Results 3.3.1 Preliminary evidence of increased angiogenesis-associated proteins and markers of angiogenesis was observed in Tg2576 eyes. The brains of AD patients and models of AD have shown increase proangiogenic factors[244, 245]. It is unclear whether angiogenesis markers are also present in the eye tissue of AD models and whether the levels of these factors behave similarly to what has been observed in brain tissue. Herein, we demonstrate results from a proteome analysis of angiogenesis-associated proteins in the eyes and brain of a Tg2576 mouse and their littermate control. Using a Proteome Profiler Mouse Angiogenesis Array Kit (R&D Systems, Minneapolis, US), we found a total of 37 angiogenesis-related proteins in the eye of the Tg2576 mouse were detected and shown to be at least two-fold higher than the number of proteins detected in the wild-type mouse (Figure 7 & Table 1a). In the brain of the Tg2576 mouse, 22 angiogenesis-related proteins were also expressed at a level that was two-fold higher and 7 angiogenesis-related proteins were downregulated compared to their expression in the brain of a wild-type mouse (Figure 7 & Table 1b). The eyes and brain from the Tg2576 mouse share 20 angiogenesis-associated proteins that were upregulated at least 2-fold compared to their expression in the wild-type mouse (Table 1c). The protein with the highest expression in the eye of the Tg2576 mouse was GM-CSF— a cytokine that stimulates stem cells to undergo differentiation into granulocytes and monocytes[246] The protein with the greatest decreased expression in the brain was prolactin (Figure 7b). Prolactin was also decreased in the eyes but not significantly (Figure 7b). Prolactin 40 is a protein most commonly associated with promoting milk production but is also involved in regulating the immune system and energy metabolism[247]. We recognize that because we are comparing different organs, there are confounding factors such as the difference in weight, with half a brain weighing approximately 10 times heavier (0.3-0.5 g) than one whole eye (0.03-0.05g), and the different proportions of cell types and blood vessels. Therefore, it is difficult to directly compare the quantities of these proteins being produced. Rather, we can comment on relative expression levels from our analysis, which suggests ongoing angiogenic processes in the eyes and brains of our Tg2576 mice. Our Western Blots also suggest angiogenic processes occurring in the eye of our AD model. In whole eye homogenates of Tg2576 mice, we quantified nearly a 5-fold increase in CD105 expression compared to wild-type mice (Figure 8a &b). CD105/endoglin is a receptor exclusively present on angiogenic neovessels [111, 248]. Interestingly, when segregated based on sex, we observed a trend of female Tg2576 mice having the highest levels of CD105 protein, but this result was not statistically significant (Figure 8c). Overall, these results support previous studies which demonstrated increased CD105 expression in the brains of Tg2576 mice and AD patients[111]. 41 Figure 7. Upregulation of angiogenesis-associated proteins in the eye and brains of a Tg2576 mouse. Brains and eyes from one 10-month old Tg2576 mouse and an age-matched wild-type mouse were analyzed using the Proteome Profiler Mouse Angiogenesis Array Kit (R&D Systems, Minneapolis, US). A heat map (a) and a bar graph (b) from one experiment are shown for a set of classical angiogenesis markers. The dotted line represents a 2-fold significance cut-off. 42 a. Eye Rank Protein Fold-change Rank Protein Fold-change 1 GM-CSF 90.8 20 Angiogenin 5.0 2 Hepatopoietin A 29.7 21 CXCL16 4.4 3 IL-1a 25.1 22 MCP-1 3.8 4 Cyr61 24.5 23 IGFBP-1 3.7 5 DLL4 23.8 24 PDGF-AA 3.0 6 Amphiregulin 17.67 25 Ang-3 2.6 7 Proliferin 17.4 26 IGFBP-3 2.6 8 ADAMTS1 14.23 27 IL-10 2.6 9 PIGF-2 11.4 28 Serpin E1 2.5 10 MMP-9 9.9 29 FGF-2 2.4 11 CD105 9.7 30 CXCL4 2.3 12 PTX3 9.3 31 Leptin 2.3 13 CX3CL1 9.0 32 Ang-1 2.3 14 HB-EGF 6.0 33 PDGF-AB 2.3 15 PD-ECGF 5.7 34 CXCL10 2.2 16 FGF-7 5.6 35 Il-1b 2.2 17 MMP-8 5.5 36 DPPIV 2.1 18 CCL3 5.1 37 NOV 2.1 19 CXCL1 5.1 b. Brain Rank Protein Fold-change Rank Protein Fold-change 1 Ang-3 90. 3 20 PD-ECGF 2.5 2 PTX3 23.1 21 Serpin E1 2.3 3 Hepatopoietin A 20.9 22 CX3CL1 2.1 4 CCL3 11.2 23 NOV -2.1 5 FGF-7 7.5 24 MMP-3 -2.1 6 CXCL1 6.0 25 IGFBP-3 -2.6 7 Amphiregulin 7.3 26 MMP-8 -2.7 8 HB-EGF 5.3 27 Proliferin -6.4 9 Ang-1 5.0 28 EGF -11.0 10 CXCL10 4.9 29 Prolactin -73.9 11 Leptin 4.6 12 GM-CSF 4.5 13 ADAMTS1 4.1 14 IL-10 4.0 15 MCP-1 3.9 16 Serpin F1 3.3 17 Cyr61 3.2 18 DLL4 3.0 19 IGFBP-1 2.7 c. Upregulated at least 2-fold in eyes and brain: ADAMTS1 GM-CSF Leptin Amphiregulin HB-EGF MCP-1 43 c. Upregulated at least 2-fold in eyes and brain: Ang-3 Hepatopoietin A CCL3 Cyr61 IGFBP-1 PD-ECGF DLL4 IL-10 PTX3 FGF-7 CXCL10 Serpin E1 CX3CL1 CXCL1 Table 1. Relative change in angiogenesis protein expression in the eyes and brain of a Tg2576 mouse. The eyes (a) and brains (b) of a Tg2576 mouse and an age-matched wild-type mouse were analyzed using the Proteome Profiler Mouse Angiogenesis Array Kit (R&D Systems, Minneapolis, US). Italics designates negative fold-change (higher in wild-type than Tg2576). Only proteins with >2-fold change are shown. (c) 20 proteins are listed with at least 2-fold upregulated expression in both brains and eyes of the Tg2576 mouse. Figure 8. CD105 protein is increased in the eyes of Tg2576 mice. (a) Representative Western blot of CD105 and control housekeeping protein β-actin. (b, c) Graphs of pooled data (wild-type mice n=18, Tg2576 n=17) from 5 Western blot experiments (b). Each well (a) and point on the graphs (b, c) represents an animal. Error bars represent standard deviation. Statistical significance was determined by the Mann-Whitney U test (b, *p<0.05) and Two-Way ANOVA (c, ns p>0.05). 44 3.3.2 STRING analysis identifies chemokines and fibroblast growth factors as key processes associated with angiogenic proteins in the eyes and brains of a Tg2576 mouse. The pathologies that we have observed in the eyes and brains of our AD model could be the result of many underlying processes. To better understand the functional landscape associated with the 20 angiogenesis-associated proteins we have identified to be upregulated in both the eyes and brains of the Tg2576 mouse, we undertook a functional analysis with STRING software Version 11 (https://string-db.org/) [249]. To ensure conservative results from the analysis, we set a minimum required interaction score for medium confidence of 0.4. Since the STRING database uses different protein names than those used in the Proteome Profiler, we normalized annotation and consolidated the protein names (Table 2). Network summaries reveal top processes associated with the proteins identified in the eyes and brain from the experiment outlined in Chapter 3.3.1 (Figure 9). When we filtered for biological processes, molecular functions, local network clusters, and reactome pathways of the proteome profiler data and ranked the top ten hits based on false discovery rate, we discovered several similar pathways between the eye and brain samples of the Tg2576 mouse. The top five biological processes that were shared in both organs included “regulation of signaling receptor activity (GO:0010469”, “regulation of cell migration (GO:0030334)”, “regulation of cell population proliferation (GO: 0042127)”, “positive regulation of cell population proliferation (GO: 0008284)” and “positive regulation of cell migration (GO: 0030335)” (Table 3a,b). Several proteins appeared in all the top 10 processes, including Ccl2, Cxcl10, and hepatocyte growth factor (Hgf) (Table 3a & b). Ccl2 and Cxcl10 are chemokines and Hgf acts as a growth factor for various cell types and these proteins have both inflammatory and reparative functions[250-252]. Organ-specific proteins that appear in the top 10 processes are Cxcl1, Fgf7, 45 Hbegf, and Prl2c2 for the brain and Fgf2 and Il1b for eyes (Table 3a & b). Il1b and Cxcl1 are both involved in the inflammatory pathway: the former as a cytokine and the latter as a chemokine[253, 254]. Fgf7, Fgf2, and Prl2c2 are growth factors involved in cell proliferation[250, 255]. Heparin-binding EGF-like growth factor (Hbegf) is a mitogen and chemotactic protein[256]. When we filtered for molecular functions, 9 out of 10 hits were shared: “receptor-ligand activity (GO:0048018)”, “signaling receptor binding (GO:0005102)”, “molecular function regulator (GO:0098772)”, “cytokine activity (GO:0005125)”, “cytokine receptor binding (GO:0005126)”, “growth factor receptor binding (GO:0070851)”, “growth factor activity (GO:0008083)”, “protein binding (GO:0005515)” and “heparin-binding (GO:0008201)” (Table 3c & d). Ccl2, Csf2, Cxcl10, and Fgf2 appeared in nearly all these hits (Table 3c & d). Csf2 is another name for GM-CSF (Table 2). The local network cluster, which reveals functional mechanisms that a set of protein may be involved in, revealed 4 hits: “mixed including PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and EGFR tyrosine kinase inhibitor resistance (CL:19350)”, “mixed including PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and Interleukin-2 family signaling (CL:19351)”, “Chemokine receptors bind chemokines (CL:22509)” and “CXC chemokine conserved site and thymocyte migration (CL:22512)” (Table 3e & f). Hbegf appeared most frequently in network clusters in the brain (4 local network clusters) and Pdgfa and Pdgfb, which are platelet-derived fibroblast growth factors, appeared most frequently in the eyes (6 local network clusters) (Table 3e & f). Hbegf, Pdgfa, and Pdgfb all appear in clusters “mixed including PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and EGFR tyrosine kinase inhibitor resistance” and “mixed including PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and Interleukin-2 family signaling” (Table 3e & f). 46 When filtering for reactome pathway, our analysis also showed 4 hits shared between eyes and brains: “Chemokine receptors bind chemokines (MMU-380108)”, “RAF/MAP kinase cascade (MMU-5673001)”, “signal transduction (MMU-162582)” and “PI5P PP2A and IER3 Regulate PI3K/AKT Signaling (MMU-6811558)” (Table 3g & h). Fgf2, Fgf7, and Hbegf7 appear in all of the shared clusters except for “Chemokine receptors bind chemokines (MMU-380108)” in both brain and eye (Table 3g & h). In summary, there are several chemokines (Ccl2, Cxcl10, and Cxcl1) and fibroblast growth factors (Fgf2, Fgf7, and Hbegf) that are potentially angiogenesis-associated proteins of interest shared between the eyes and brains of the Tg2576 AD mouse model. Proteome Profiler STRING database Proteome Profiler STRING database Proteome Profiler STRING database ADAMTS1 Adamts1 GM-CSF Csf2 Osteopontin Spp1 Amphiregulin Areg HB-EGF Hbegf PD-ECGF Tymp Angiogenin Ang Hepatopoietin A Hgf PDGF-AA Pdgfa Ang-1 Angpt1 IGFBP-1 Igfbp1 PDGF-BB Pdgfb Ang-3 Ang5 IGFBP-2 Igfbp2 PTX3 Ptx3 Coagulation Factor III F3 IGFBP-3 Igfbp3 CXCL4 Pf4 CXCL16 Cxcl16 IL-1a Il1a PIGF-2 Pgf Cyr61 Cyr61 Il-1b Il1b Prolactin Prl DLL4 Dll4 IL-10 Il10 Proliferin Prl2c2 DPPIV Dpp4 CXCL10 Cxcl10 CXCL12 Cxcl12 EGF Egf CXCL1 Cxcl1 Serpin E1 Serpine1 CD105 Eng Leptin Lep Serpin F1 Serpinf1 Endostatin Col18a1 MCP-1 Ccl2 TSP-2 Thbs2 Endothelin-1 Edn1 MIP-1a Ccl3 TIMP-1 Timp1 FGF-1 Fgf1 MMP-3 Mmp3 TIMP-4 Timp4 47 Proteome Profiler STRING database Proteome Profiler STRING database Proteome Profiler STRING database FGF-2 Fgf2 MMP-8 Mmp8 VEGF Vegfa FGF-7 Fgf7 MMP-9 Mmp9 VEGF-B Vegfb CX3CL1 Cx3cl1 NOV Nov Table 2. Normalized annotation for Proteome Profiler and STRING Databases. 48 49 Figure 9. Network summaries for angiogenesis-associated proteins in Tg2576 eyes and brain reveal shared chemokines and fibroblast growth factors in both organs (previous page). Network summary results from STRING analysis of proteins upregulated ≥2-fold in the eyes (a) and brain (b) in a 10-month-old Tg2576 mouse compared to a wild-type littermate. Fold-change in protein expression was measured using Proteome Profiler Mouse Angiogenesis Array Kit (R&D Systems, Minneapolis, US). Analysis was performed using STRING software Version 11 and a minimum required interaction score was set to medium confidence of 0.4. Stars represent proteins of interest. 50 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are showna. Biological Process (Eye) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp regulation of cell migration X X X X X X X X X X X X X X X X X X X X X X X X X positive regulation of cell migration X X X X X X X X X X X X X X X X X X X X X X regulation of cell population proliferation X X X X X X X X X X X X X X X X X X X X X X X X X X X X X regulation of signaling receptor activity X X X X X X X X X X X X X X X X X X X X X X positive regulation of cell population proliferation X X X X X X X X X X X X X X X X X X X X X X X regulation of vasculature development X X X X X X X X X X X X X X X X X chemotaxis X X X X X X X X X X X X X X X X X X regulation of localization X X X X X X X X X X X X X X X X X X X X X X X X X X X X X regulation of angiogenesis X X X X X X X X X X X X X X X X locomotion X X X X X X X X X X X X X X X X X X X X X X Count 7 4 0 6 3 10 6 3 0 8 10 7 6 7 6 3 0 7 10 8 8 10 0 3 6 7 10 7 0 0 8 0 8 9 9 8 0 8 1 8 0 2 51 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are shown.b. Biological Processes (brain) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp regulation of signaling receptor activity X X X X X X X X X X X X X X X X X X X X X X regulation of cell migration X X X X X X X X X X X X X X X X X X X X X X X X X regulation of cell population proliferation X X X X X X X X X X X X X X X X X X X X X X X X X X X X X positive regulation of cell population proliferation X X X X X X X X X X X X X X X X X X X X X X X positive regulation of cell migration X X X X X X X X X X X X X X X X X X X X X X regulation of multicellular organismal process X X X X X X X X X X X X X X X X X X X X X X X regulation of cell communication X X X X X X X X X X X X X X X X X X X X X X X regulation of signaling X X X X X X X X X X X X X X X X X X X X X X X regulation of signal transduction X X X X X X X X X X X X X X X X X X X X X X regulation of localization X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Count 6 3 0 8 7 10 8 7 4 10 10 5 4 9 8 2 4 3 6 10 10 10 3 3 8 5 6 8 3 0 5 4 6 6 5 6 4 10 1 10 1 3 52 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are shown.c. Molecular Function (Eye) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp receptor ligand activity X X X X X X X X X X X X X X X X X X X X X signaling receptor binding X X X X X X X X X X X X X X X X X X X X X X X X X X X X molecular function regulator X X X X X X X X X X X X X X X X X X X X X X X cytokine activity X X X X X X X X X X X X X cytokine receptor binding X X X X X X X X X X X X X X growth factor receptor binding X X X X X X X X X X X X growth factor activity X X X X X X X X X X X protein binding X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X glycosaminoglycan binding X X X X X X X X X X X heparin-binding X X X X X X X X X X Count 2 4 0 4 7 8 6 8 0 7 8 8 6 4 2 2 0 4 9 8 8 5 1 2 7 7 7 4 0 0 1 0 6 6 8 7 0 5 1 3 0 1 53 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are shown.d. Molecular Function (Brain) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp receptor ligand activity X X X X X X X X X X X X X X X X X X X X X signaling receptor binding X X X X X X X X X X X X X X X X X X X X X X X X X X X X molecular function regulator X X X X X X X X X X X X X X X X X X X X X X X growth factor activity X X X X X X X X X X X cytokine receptor binding X X X X X X X X X X X X X X cytokine activity X X X X X X X X X X X X X heparin binding X X X X X X X X X X growth factor receptor binding X X X X X X X X X X X X chemokine activity X X X X X protein binding X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Count 1 3 0 4 7 8 7 8 1 8 8 7 6 3 2 2 0 3 8 7 7 5 1 2 7 7 7 4 0 0 1 0 6 6 7 7 0 5 1 3 0 1 54 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are shown.e. Local Network Cluster (Eye) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp mixed incl. PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and EGFR tyrosine kinase inhibitor resistance X X X X X X X X X mixed incl. PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and Interleukin-2 family signaling X X X X X X X X mixed incl. Downstream signaling of activated FGFR2 and PDGF/VEGF domain X X X X X X Chemokine receptors bind chemokines and CC chemokine conserved site X X X X X PDGF/VEGF domain and Tie signaling pathway X X X X Chemokine receptors bind chemokines X X X X X PDGF/VEGF domain and vascular endothelial growth factor-activated receptor activity X X X CXC chemokine conserved site and thymocyte migration X X X Platelet-derived growth factor N-terminal and positive regulation of monocyte extravasation X X Interleukin-1/18 and interleukin-1 receptor activity X X Count 0 0 0 4 0 0 0 2 0 3 3 2 2 0 0 0 0 0 3 3 2 1 0 0 0 1 1 0 0 0 0 0 6 6 3 5 0 0 0 0 0 0 55 f. Local Network Cluster (Brain) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp mixed incl. PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and EGFR tyrosine kinase inhibitor resistance X X X X X X X X X mixed incl. PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and Interleukin-2 family signaling X X X X X X X X Chemokine receptors bind chemokines X X X X X Prolactin receptor signaling X X ERBB2 Activates PTK6 Signaling X X Interleukin receptor SHC signaling and Signaling by ERBB2 X X X O-glycosylation of TSR domain-containing proteins and Peptidase M10A X X X CXC chemokine conserved site and thymocyte migration X X X CC chemokine conserved site and C-C chemokine receptor activity X X PGBD-like superfamily and Dissolution of Fibrin Clot X X Count 1 0 0 2 0 1 1 3 0 2 2 1 1 0 0 0 2 0 2 2 4 1 0 0 0 0 0 0 2 0 0 0 2 2 2 2 1 1 0 2 0 0 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are shown.56 g. Reactome (Eye) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp Chemokine receptors bind chemokines X X X X X RAF/MAP kinase cascade X X X X X X Signal Transduction X X X X X X X X X X X X X X PI5P PP2A and IER3 Regulate PI3K/AKT Signaling X X X X Signaling by Receptor Tyrosine Kinases X X X X X X FGFR2b ligand binding and activation X X Interleukin-1 processing X X Synthesis secretion and inactivation of Glucagon-like Peptide-1 (GLP-1) X X G alpha (i) signaling events X X X X X Phospholipase C-mediated cascade; FGFR2 X X Count 0 0 0 2 0 0 0 2 0 3 3 3 3 0 1 1 0 0 6 6 4 4 0 0 0 1 1 1 0 0 2 0 0 0 3 2 0 0 0 0 0 0 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are shown.57 h. Reactome (Brain) Adamts1 Ang Ang5 Angpt1 Areg Ccl2 Ccl3 Csf2 Cx3cl1 Cxcl1 Cxcl10 Cxcl13 Cxcl16 Cyr61 Dll4 Dpp4 Egf Eng Fgf2 Fgf7 Hbegf Hgf Igfbp1 Igfbp3 Il10 Il1a Il1b Lep Mmp3 Mmp8 Mmp9 Nov Pdgfa Pdgfb Pf4 Pgf Prl Prl2c2 Ptx3 Serpine1 Serpinf1 Tymp RAF/MAP kinase cascade X X X X X X PI5P PP2A and IER3 Regulate PI3K/AKT Signaling X X X X Chemokine receptors bind chemokines X X X X X ERBB2 Activates PTK6 Signaling X X SHC1 events in ERBB2 signaling X X PI3K events in ERBB2 signaling X X ERBB2 Regulates Cell Motility X X NOTCH3 Activation and Transmission of Signal to the Nucleus X X Downregulation of ERBB2 signaling X X Signal Transduction X X X X X X X X X X X X X X Count 0 0 0 2 0 0 0 2 0 2 2 2 2 0 2 0 6 0 3 3 8 3 0 0 0 0 0 0 0 0 1 0 0 0 2 1 0 0 0 0 0 0 Table 3. Proteome profiler proteins present in the top 10 hit results for STRING analyses. Filtered results for biological processes (Gene Ontology) (a, b), molecular functions (Gene Ontology) (c, d), local network cluster (STRING) (e, f), reactome pathways (g, h) of eyes (a, c, e, g) and brain (b, d, f, h) are shown.58 3.3.3 Protein atlas reveals chemokine and fibroblast growth factor hits identified in the eyes and brain of a Tg2576 mouse are primarily linked to innate immune cells. To examine potential sources of angiogenesis-associated proteins in the eyes and brain of the Tg2576 mouse, we ran a second analysis on the data described in Chapters 3.3.1 and 3.3.2 using the Human Protein Atlas database. We believe that if the cellular source of the shared proteins that we identify with the Human Protein Atlas is also shared across the eyes and brain, this should support our hypothesis that there is a link between vasculature changes in the eye and the brain in the Tg2576 mouse. Our Proteome Profiler and STRING analysis identified certain cytokines (GM-CSF/Csf2), chemokines (Ccl2, Cxcl10, and Cxcl1), and fibroblast growth factors (Fgf2, Fgf7, and Hbegf) as top hits in both eyes and brain of our Tg2576 mouse (Figure 9). With the Human Protein Atlas database (https://www.proteinatlas.org/), our data cross-referenced cell types in the single-cell type atlas (Figure 10a) and the blood atlas (Figure 10b). When we filtered for the seven proteins of interest (Csf2, Ccl2, Cxcl10, Cxcl1, Fgf2, Fgf7, and Hbegf), Csf2 was primarily linked to Natural killer (NK) cells. When we ran the data with the single-cell type atlas, Ccl2 and Cxcl1 were associated with a variety of epithelial cells, endocrine cells, fibroblasts, endothelial cells, melanocytes, granulocytes, and macrophages/monocytes, which are all important cells in innate immune defenses. Analysis with the blood atlas shows Ccl2 primarily linked to eosinophils and classical monocytes. Interestingly, the consensus data from the blood atlas associated Cxcl1 with neutrophils, but not macrophages (Figure 10b). Analysis with the blood atlas shows Cxcl10 is primarily linked to macrophages and monocytes (Figure 10b). Fibroblast growth factors (Fgf2, Fgf7, and Hbegf) were also associated with various epithelial cells, smooth muscle cells, and fibroblasts (Figure 10a). In the single-cell atlas, only Hbegf was 59 greatly expressed in blood and immune cells, notably granulocytes and macrophages (Figure 10b). In comparison to the chemokines and cytokines previously discussed, the blood atlas links expression of Hbegf predominantly to non-classical and intermediate monocytes and myeloid dendritic cells and very little in granulocytes (Figure 10b). Overall, our protein database analysis reveals predominant associations between cytokines, chemokines, and fibroblast growth factors and cells in the innate immune system. Interesting to note is that none of the proteins of interest to us (Csf2, Ccl2, Cxcl10, Cxcl1, Fgf2, Fgf7, and Hbegf) appear to be linked to neuronal or retinal cells under homeostatic conditions (Figure 10a), indicating that these proteins are likely strictly associate to the immune responses being induced in AD pathogenesis and/or perhaps the proteins are arriving from an inter-neuronal source. While further studies are needed to confirm these findings, our preliminary analysis of angiogenic proteins and their sources in both the eyes and brains point to potential mechanistic links in the changing vasculature of the Tg2576 AD model. 60 61 Figure 10. Hits from Proteome Profiler and STRING Analysis linked to innate immune cells in Human Protein Atlas (previous page). Heat maps of cells producing angiogenesis proteins from “The Human Protein Atlas”. Fold-change in protein expression in eyes was measured using Proteome Profiler Mouse Angiogenesis Array Kit (R&D Systems, Minneapolis, US). Data was taken from The Single Cell Type Atlas” (a) and “The Blood Atlas” (b) (http://www.proteinatlas.org) on January 25th, 2021. Proliferin/Prl2c2 is not shown since it is a mouse-specific protein. White box represents Normalized eXpression (NX) value above 1000 (a) and 10 (b). 3.3.4 The eyes of Tg2576 mice have increased vasculature in the choroid but not in the retina. Given the previously noted evidence of a proangiogenic environment and increased expression of CD105 in the eyes of Tg2576 mice, we attempted to validate these findings by exploring potential vascularization further with immunofluorescence (IF) in cross-sections of the posterior end of 10-month old mouse eyes. The retina was stained with antibodies for CD105 and CD31, with CD31 being a marker of blood vessels (Figure 11a). CD31+CD105+ vessels were counted in each layer of the retina (GCL, IPL, INL, OPL, and ONL) and the total number of vessels counted was divided by the total area of the layer examined. Although there appeared to be a slight increase in retinal microvessels in the eyes of Tg2576 mice, specifically in the ganglion cell layer and the inner and outer plexiform layers, these results were not statistically significant when compared to wild-type littermate controls (Figure 11c). Although we saw no change in the number of vessels in the retina, we next decided to analyze the choroid, the vasculature on the basal side of the retinal pigmental epithelia. When performing qualitative and quantitative analysis with sum fluorescence of choroid staining, Tg2576 eyes had significantly more CD31+ microvessels in the choroid as determined by quantitative analysis of sum fluorescence (Figure 12). In summary, our results show evidence of neovascularization occurring in the choroid rather than the retina of Tg2576 mice. This supports previous findings in the brain of these mice regarding angiogenesis being pathological hallmarks 62 of AD. Additionally, while angiogenesis is thought to be an uncontrolled process, it does appear to be site-specific within the eye. Figure 11. The number of retinal vessels is comparable in the eyes of Tg2576 mice to wild-type mice. 10-month old retinas of wild-type (n=3) and Tg2576 (n=4) were examined using immunofluorescent microscopy for CD31+CD105+ retinal vessels. (a) White circles indicate microvessels. (b, c) Each dot represents an animal. Mann-Whitney U tests between wild-type and Tg2576 for the total number of microvessels and within each layer and were not statistically significant. GCL= ganglion cell layer, IPL= inner plexiform layer, INL= inner nuclear layer, OPL= outer plexiform layer, ONL= outer nuclear layer, PR= photoreceptors. The experiment was completed once. Error bars represent standard deviation. Statistics: Mann-Whitney U test (ns p>0.05). 63 Figure 12. Choroid from the eyes of Tg2576 mice has a greater number of vessels. The eyes of 9-13-month-old wild-type (n=4) and Tg2576 (n=5) mice were examined using immunofluorescent microscopy for CD31+ choroidal vessels. Each dot on the graph represents an animal. White arrows indicate microvessels in the choroid. CHO= choroid, SC= sclera. The experiment was completed twice. Error bars represent standard deviation. Statistics: Mann-Whitney U test (*p<0.05). 3.4 Summary Our observations of ocular vasculature remodeling in the context of AD provide insight into pathology in the Tg2576 mouse model and strengthens support for the vascular theory of AD[82-84]. With our Proteome Profiler analysis, we identified an increase in angiogenic proteins in both the eyes and brain of a Tg2576 mouse compared to the age-matched wild-type littermate. The eyes from the Tg2576 mouse did show mostly increased expression of angiogenic proteins, 64 whereas the brain showed some angiogenesis-associated proteins that were decreased. Twenty proteins were upregulated in both the brain and the eyes and further STRING analysis examining biological processes, clustering, protein domains, and molecular function point to a shared mechanistic pathway affecting vascularization between the two organs. When we ran a Western Blot for CD105, a protein marker of angiogenic blood vessels, we observed an increase in the eyes of Tg2576 mice. When looking at potential sources of our proteins of interest (GM-CSF/Csf2, Ccl2, Cxcl10, Cxcl1, Fgf2, Fgf7, and Hbegf) on the Human Protein Atlas, we found that the majority of the proteins were linked with the innate immune system. To determine whether this angiogenic environment resulted in increased formation of blood vessels, the number of CD105+CD31+ retinal microvessels were counted in the eyes of wild-type and Tg2576 mice but were not found to be significantly different. However, we did observe an increase in CD31-associated vessels in the choroid of the eyes of Tg2576 mice. To our knowledge, this is the first time choroidal neovascularization has been suggested in this AD model. These results suggest that neovascularization may be a site-specific process in the eyes and there may be differences in how the choroid and retina respond to a proangiogenic environment. Further studies may be interested in investigating the vascular differences between these two areas in the Tg2576 AD model. 65 Chapter 4: Retinal-blood barrier in Tg2576 eyes 4.1 Introduction The two vasculatures of the retina, the choroidal vasculature and the inner retinal vasculature, that we explored in Chapter 3, both contain the retinal-blood barrier (RBB) which separates the eye from the rest of the body. The RBB is composed of two parts: the retinal pigmental epithelium (RPE) forms the outer RBB (oRBB) and the retinal vessels form the inner RBB (iRBB)[173]. One of the potential pathological markers of the neurovascular theory is the permeability of the blood-brain barrier (BBB), which is characterized by the dysfunctional distribution and decreased expression of tight junction proteins [257]. Physiological barrier permeability is linked to angiogenesis because proangiogenic cytokines disrupt tight junction protein expression and result in decreased physiological barrier integrity [172, 258-260]. Studies using advanced dynamic contrast-enhanced MRI found increased deterioration and BBB permeability in patients with AD compared to healthy controls[261, 262]. Decreased tight junction expression and increased permeability of the BBB have been demonstrated in several transgenic AD mouse models, including Tg2576, J20, and 3×Tg mice[129, 263, 264]. To our knowledge, the only AD mouse model that has investigated RBB pathologies is the 5xFAD mouse, which has decreased occludin and ZO-1, both tight junction proteins, expression in the RPE[265]. In vivo and in vitro studies show that amyloid deposits at the RPE cause deterioration and damage[265-267]. Since studies with Tg2576 mice and AD patients have previously demonstrated increased BBB permeability [111], we hypothesized that our Tg2576 mice will show similar pathology in the RBB. As such, we examined the structure of the RPE using IF and quantified tight junction protein expression with an ELISA. Finally, the functionality of the RBB was assessed by the 66 presence or absence of albumin in whole eye homogenates, which is otherwise not able to cross an intact RBB[268]. 4.2 Materials and Methods Mice, genotyping, tissue harvesting, tissue processing for Western Blot, and protein quantification were performed as previously described in Section 2.2. Immunofluorescence. A standard immunofluorescence protocol was performed on 15-month old mice as described in Section 2.2. ZO-1 was probed with a polyclonal rabbit anti-ZO-1 primary antibody (Thermo Fisher Scientific, Massachusetts, US). The primary antibody was detected using an Alexa Fluor 633nm rabbit anti-mouse IgG (H+L) cross-adsorbed secondary antibody (Invitrogen, California, US). For each animal, two cross-sections of the eye were used, and 5-12 images were taken per eye. Fluorescence was acquired using the Olympus FV1000 Laser Scanning/Two-Photon Confocal Microscope. Parameters were set based on one of the wild-type slides and used throughout scanning for consistency when acquiring the remaining images. A negative control slide was used with only secondary body staining to control for non-specific binding. After the acquisition, images were analyzed using Volocity software version 5.2. For Figure 13, images were acquired at 60X oil magnification. Nuclei were counted and normalized to the area of RPE measured (um2). Mean DAPI fluorescence in RPE was measured. ZO-1 fluorescence was measured by setting the intensity threshold to above 1000 and measuring the mean fluorescence. Since the animal sample size was small (2-3 animals per genotype), measurements from all images per genotype were pooled together. To ensure that the number of images taken per animal did not contribute to the measured fluorescence, Pearson’s correlation matrix was plotted. 67 If it was determined that there was no correlation (p>0.05) between the number of images acquired and the fluorescence measured, image measurements were pooled together for each genotype. For Figure 13, it was determined that the number of images taken per animal did not affect the number of nuclei counted (r=-0.7, p=0.2333), DAPI fluorescence (r=-0.2052, p=0.7333), or ZO-1 fluorescence (r=0.2, p=0.7833), thus image measurements were pooled per genotype. Outliers were removed using the ROUT statistical method with Q=1%. ELISA. Tissues were harvested and processed as previously described in Section 2.2 “Tissue processing for Western Blot”. We used the mouse Occludin ELISA Kit (MyBioSource, San Diego, California, USA) to quantify occludin protein expression in whole eye homogenates. 50 ug of whole eye homogenate protein supernatant was used per animal. The assay procedure was followed as per the manufacturer’s guidelines. Optical density was measured using the 365 Tecan M200 microplate reader and Magellan™ software. Western Blot. A standard Western Blot protocol was performed as described in Section 2.2 using a 12% SD-PAGE gel. 200 ug of protein was loaded per well. Albumin and the control, actin, were detected with primary antibodies: polyclonal goat anti-albumin (Bethyl Laboratories, Montgomery, TX) and polyclonal rabbit anti-β-actin (Abcam, Cambridge, UK). Alexa Fluor Secondary Antibodies (Thermo Fisher Scientific, Massachusetts, USA) were used for dual detection of primary antibodies at 680 nm and 790 nm using a ChemiDoc Imaging System (Biorad, California, USA). Statistics. Statistical analysis was performed using GraphPad Prism 9.0.1. For Figure 13b-d, the Mann-Whitney U test was performed. Outliers were removed using ROUT at Q=1%. For Figure 13e, an unpaired t-test was performed. For Figure 14, the Mann-Whitney U test was 68 performed. “ns” denotes p>0.05, * denotes p<0.05, ** denotes p<0.01, *** denotes p<0.001 and **** denotes p<0.0001. 4.3 Results 4.3.1 There is negligible deterioration in the RPE of Tg2576 mice To determine whether there is deterioration present in the RPE of our mouse AD model, we measured the amount of DAPI staining in the RPE based on a similar experiment conducted by Chen et al[269]. Given that DAPI stains DNA, a decrease in the DAPI fluorescence in RPE would suggest deterioration and/or senescence[269]. Neither the nuclei count (Figure 13b) nor the DAPI mean fluorescence (Figure 13c) were significantly different between wild-type and Tg2576 RPE, suggesting that the cells maintained their integrity and are not undergoing active apoptosis/necrosis. 69 Figure 13. Retinas from Tg2576 mice have decreased tight junction protein expression and negligible evidence of retinal pigmental epithelial (RPE) senescence. The RPE of 15-month-old wild-type (n=2) and Tg2576 (n=2) mice were examined using immunofluorescent (IF) microscopy for DAPI and ZO-1 fluorescence. Each point on the graphs represents an image (a-d). Statistics for IF: Mann Whitney U test (ns p>0.05, ****p<0.0001). ELISA was used to quantify occludin from whole eye homogenates of 11-month-old mice (wild-type=10, Tg2576=10). Each point on the graph represents an animal (e). Errors bars represent standard deviation. Statistics for ELISA: Unpaired t-test (*p<0.05). 4.3.2 Decreased expression of tight junction proteins in the RPE of Tg2576 mice. Two tight junction proteins present in both the iRBB and oRBB are occludin and ZO-1. Occludin is a transmembrane protein that anchors two neighboring cells together. ZO-1 is a cytoplasmic protein that anchors the transmembrane tight junction proteins to the actin skeleton. Both tight junction proteins are found in the iRBB and oRBB[173, 270]. A decrease in tight junction protein expression would result in increased RBB permeability[173, 271]. To confirm this pathology in our model, we quantified the mean fluorescence of clusters of ZO-1 proteins 70 and observed that ZO-1 proteins were significantly decreased in Tg2576 eyes compared to wild-type (Figure 13a, d). Next, we performed an ELISA to quantify occludin in whole eye homogenates and found that Tg2576 eyes had a mean 1.753±0.2165 ng/mL of occludin compared to 2.237±0.6396 ng/mL in wild-type eyes (Figure 13e). Both experiments showed a significant decrease in tight junction protein expression in the Tg2576 eyes. 4.3.3 The retinal blood barrier (RBB) is more permeable in Tg2576 mice. The structural changes that we observed in the RPE (Figure 13) suggested that there may also be impairments in the functionality of RBB in the eyes of Tg2576 mice. As such, we investigated RBB functionality by examining the structure’s permeability. Specifically, we measured the amounts of albumin in whole-eye homogenates since albumin is a large blood protein used as a marker of barrier permeability that normally cannot pass through endothelial cells bound together by tight junctions[268]. Western blots confirmed that Tg2576 eyes had 2-fold greater amounts of albumin in their eyes compared to wild-type mice (Figure 14). Since albumin is not supposed to pass a healthy RBB[268], our observations of a high quantity of albumin present in whole eye homogenates of Tg2576 mice suggest that the RPE is impaired and permeable to albumin. The structural changes we observed earlier (Figure 13) may be contributing to the increased permeability. 71 Figure 14. Albumin is significantly increased in Tg2576 eyes. Western blot of albumin in wild-type and Tg2576 10-month old whole eye homogenates. The positive control (+) was 200 ug of Tg2576 whole brain homogenate. Each well (a) and point on the graph (b) represents an animal. One of three representative experiments is shown. Error bars represent standard deviation. Statistics: Unpaired t-test (*p<0.05). 4.4 Summary Our goal with these experiments was to determine whether the Tg2576 mouse model demonstrated characteristics of increased RBB permeability similar to the BBB permeability observed in the brains of AD patients and AD mouse models. With immunofluorescence, we demonstrated that there is a negligible difference in the number of nuclei in the RPE between Tg2576 mice and controls. Since RBB permeability is determined in part by the presence of the tight junction proteins, ZO-1 and occludin, we measured their expression levels in RPE (ZO-1) and whole eye homogenates (occludin). Occludin expression in whole eye homogenates was significantly decreased in the Tg2576 mice compared to controls (Figure 13e). We also observed a significant decrease in ZO-1 clusters in the RPE of Tg2576 mice compared to wild-type 72 (Figure 13d), which suggests that overall, tight junction protein expression is decreased in the eyes of Tg2576 mice. The decrease in two key tight junction proteins that we observed in whole eye homogenates of Tg2576 mice suggests critical changes to the integrity of the RPE. These observations may also suggest impaired tight junction functionality and thus, increase permeability in the RBB. We observed increased albumin quantities present in whole eye homogenates of Tg2576 mice (Figure 14), which supports the notion that the RBB of these mice is more permeable since a healthy RBB is normally impermeable to albumin. Our results suggest there is a complex alteration in the integrity of the RBB in Tg2576 mice and warrant further investigation into whether similar RBB pathologies can be observed in vivo using techniques such as optical coherence tomography angiopathy[272]. Nonetheless, our observations add to the knowledge of pathologies associated with AD mouse models, which for Tg2576 mice, can now include RBB permeability as a pathology to note. 73 Chapter 5: Overall Discussion and Perspectives 5.1 Summary and Discussion The eyes are an extension of the CNS and are the most accessible organ in which to indirectly measure and gain insight into the brain if a link can be determined. Pathological hallmarks of the AD brain in patients and mouse models include amyloid deposition, increased neovascularization, and blood-brain barrier permeability. Our goal was to see whether these hallmarks are recapitulated in the eyes of the Tg2576 mouse model. The presence of the huAPP protein was noted in the whole eye homogenates of the Tg2576 mice (Figure 1). When examining the retina using immunofluorescence, both 6E10 (which detects human Aβ) and M3.2 (which detects mouse Aβ) antibodies were able to show an increased amyloid presence in the Tg2576 mice, particularly in the retinal layers with microvessels: GCL, IPL, and OPL (Figure 2-4). However, when the eyes of the Tg2576 animals were stained with Congo Red, no amyloid could be detected (Figure 5). To determine whether or not amyloid deposition results in damage, H&E-stained eyes were examined for gross pathologies (Figure 6). Although there was no evidence of obvious deformities, preliminary data on retinal layer thinning over time suggests that the overall retinal thinning rate is higher in Tg2576 mice (Figure 6). These results potentially point to an accessible entryway into seeing amyloid deposition via the eyes, however, further studies will need to be conducted to see changes in the amyloid deposition in the eyes in vivo over time. Despite the noted perivascular deposition of amyloid and studies suggesting that vascularization is altered in the brains and eyes of AD patients, the ocular vasculature in AD mouse models remains sparsely explored. Firstly, increased angiogenic proteins were seen in the eyes and brains of Tg2576 and her wild-type littermate when using a Proteome Profiler, which 74 acts as a preliminary indication of a proangiogenic milieu being present in the eye (Figure 7). Specifically, 20 angiogenic proteins were at least two-fold upregulated in both the eye and brain of the Tg2576 mouse (Table 1). The eye had a total of 37 proteins that were upregulated whereas the brain had 22 (Figure 7 & Table 1). STRING analysis was performed to see how the upregulated proteins were linked (Figure 9). It was found that there were multiple shared biological processes, molecular functions, local network clusters, and reactome pathways that appeared in the top 10 hits for both brain and eyes (Table 3). GM-CSF/Csf2, Ccl2, Cxcl10, Cxcl1, Fgf2, Fgf7, and Hbegf appeared in most of the analyses across both organs (Table 3). Using the Human Protein Atlas, it was determined that these cytokines, chemokines, and fibroblast growth factors are predominantly linked to innate immune cells (Figure 10). After demonstrating that the Tg2576 eyes and brain are angiogenesis-favoring environments, the protein expression of CD105, a protein found exclusively on neoangiogenic vessels, was examined. Western Blot analysis of whole eye homogenates showed that when several experiments are pooled together, Tg2576 mice had higher CD105 in the eyes (Figure 8). Interestingly, when sub-categorized based on sex, female Tg2576 mice trended towards higher amounts of CD105, but this was not statistically significant. With evidence of an environment that is prone to the formation of new blood vessels and the increase in a protein associated with neovascularization and angiogenesis, the vasculature of the retina and choroid were measured ex vivo in Tg2576 mice and their wild-type counterparts. In the retina, when microvessels were counted in each layer, there was no significant difference in the overall amount of microvessels nor between any of the retinal layers (Figure 11). However, when the choroid was examined, there was an increase in fluorescence associated with CD31+ blood vessels in the Tg2576 samples (Figure 12). After examining this mouse model 75 using a variety of techniques, our data support the idea that the Tg2576 ocular vasculature is likely altered. This not only supports the neurovascular theory of AD but may also lead to another marker that can be easily measured in vivo, however, these findings will first need to be further investigated in vivo. Related to the vasculature are the restrictive physiological barriers that are present in both the brain and eyes, BBB and RBB respectively. In the eyes, the two main RBBs are composed of the RPE (oRBB) and the vessels providing nutrients to the inner retina (iRBB). First, the RPE was examined for signs of deterioration. There were no differences in nuclei count or DAPI fluorescence, which suggests that the RPE cells are maintaining integrity (Figure 13). If some form of deterioration or senescence is occurring, these changes are likely either in the early stages or are subtle. When RPE was stained for ZO-1, a tight junction protein essential for maintaining RBB integrity, there were decreased clusters of ZO-1 in the RPE that could be seen qualitatively and quantitatively (Figure 13). Another tight junction protein, occludin, was quantitatively measured in whole eye homogenates using an ELISA, and it was also found to be decreased in Tg2576 mice (Figure 13). These results point to a structural change occurring in the RBB which may lead to increased permeability. To test whether there is indeed a change in function, whole eye homogenates were measured for albumin, a large protein found in the blood that cannot normally permeate across the BBB or RBB. Western Blot analysis showed that there was more albumin present in the Tg2576 eyes (Figure 14). The cumulative evidence of these experiments points to a dysfunction RBB in Tg2576 mice and therefore it is worth further investigating the differences between iRBB and oRBB and demonstrate permeability in vivo using well-characterized assays using reagents such as Evan’s Blue, horseradish peroxidase, or sodium fluorescence. 76 Overall, our findings give preliminary support ex vivo of hallmarks of AD including amyloid deposition, neovascularization, and physiological barrier permeability occurring in the eyes of the Tg2576 mouse. 5.1.1 Amyloid presence in Tg2576 eyes While previous studies have noted amyloid being present in the eyes of various AD mouse models, including Tg2576, APP/PS1, 3xTg and 5xFAD mice [158, 217, 273-275], our results show that amyloid detected by both 6E10 and M3.2 is primarily located around retinal microvessels specifically in the GCL, IPL, and OPL. While amyloid in the GCL is a consistent finding, studies have shown the presence of amyloid in the nuclear retinal layers as well [158, 169, 217, 218, 273, 275]. One of the reasons for this discrepancy may be that the microvessels tend to lie between the nuclear and plexiform layers and with tissue processing, ex vivo cross-sections of retinas may displace amyloid or vessels from their location in vivo. The increased detection of both human Aβ and mouse Aβ by 6E10 and M3.2 antibodies, respectively, in the eyes of Tg2576 mice point to a generalized increase in amyloid regardless of origin. This may warrant further investigations into a potential synergistic relationship between mAβ and huAβ. A study by Steffen et al. looked at amyloid deposits in APP/PS1 transgenic mice with endogenous mAPP expression (mAPP+/+) and those crossbred with mAPP knockout (mAPP0/0) mice and found that vascular deposits composed of huAβ were increased in mAPP+/+ mice but mAPP0/0 mice had more senile plaques and higher levels of aggregated hAβ[276]. A similar result was shown in another study[277], however, the effects of endogenous amyloid in transgenic mouse models appear to be model-specific[278]. To our knowledge, the effects of endogenous mouse amyloid and transgenic human amyloid co-expression on the accumulation of 77 amyloid, plaque formation, or other pathologies such as neurodegeneration have not been explored in Tg2576 mice. It is unclear from our experiments which cells are producing the amyloid. While it has been widely accepted that neurons are the primary producers of amyloid[60], in the Tg2576 mouse, the APP is under the promotion of hamster prion protein (PrP) promoter [198]. The prion protein and mRNA are found in central nervous system tissue (most notably in neurons of the cerebral cortex, cerebellum, hippocampus and caudate and astrocytes), but is also found in other tissues as well including lung, skin, adrenal gland, salivary gland and pancreas [279-282]. PrP RNA is also expressed by many immune cells, most notably granulocytes and activated T cells[201]. PrP has also been found in the plexiform layers and expressed in retinal nuclear layers of the eyes of sporadic Creutzfeldt-Jakob disease patients post-mortem[283, 284]. It should also be noted that while PrP is found primarily in astrocytes and neurons in humans, in mice it is produced in nearly equal amounts in endothelial cells as in astrocytes and slightly more than in neurons[285, 286]. These findings suggest that the source of ocular amyloid in this transgenic mouse model needs to be more thoroughly examined. One of the surprising findings was the immunofluorescence (IF) and Western Blot data revealing increased amyloid in Tg2576 eyes, but this finding not being recapitulated in the Congo Red staining. Congo Red stains are a widely used and accepted method for detecting amyloid in the brain. The preparation methods for these techniques are different and may influence the ability of an antibody to recognize and bind to its epitope. For example, formic acid is commonly used in antigen retrieval of amyloid for immunohistochemistry (IHC) and IF, however, formic acid has been shown to covalently modify amyloid[222, 287-289]. Quantifying retinal amyloid appears to be method-dependent: ELISA, Thioflavin T, and IHC/IF appear to be 78 the most reliable detection methods[217, 290]. While some studies have been able to detect retinal and other amyloid species using Congo Red, others have not[159, 169, 221, 291-294]. This may have to do with where the amyloid is located: intracellularly or extracellularly, the type of amyloid plaque: neuritic, diffuse, and perivascular, the species: Aβ40 or Aβ42, and the structure of the amyloid: aggregated, fibrillated, soluble, oligomerized, or spherical. It has been noted that Congo Red binds weakly or does not bind to early plaques[292, 293], diffuse plaques[221], and spherical amyloid[294]. Age may also be playing a role, as amyloid deposition increases as the Tg2576 mouse becomes older beginning at 6-10 months with plaques similar to those in human patients with AD appearing between 12-23 months[199]. However, given the large sample size of retinas between the ages of 9-13 months old that were used and the fact that not a single one was able to show amyloid using Congo Red, this suggests perhaps that Congo Red is not the best method for reliably or consistently measuring retinal amyloid. Another surprising finding was the lack of obvious neurotoxicity or pathology associated with the increase in amyloid accumulation. Liu et al. examined the eyes of 14-month old Tg2576 mice and found that Tg2576 retinas were on average 100 μm thinner than wild-type mice[169]. While preliminary evidence of an increased rate of thinning over time was seen, at our age of interest (~10 months old), there was no significant difference. One possible explanation is that obvious neurotoxic damage and toxicity may appear in this mouse model at a stage after the establishment of amyloid plaques in the brain, given that we used a younger cohort of mice compared to the previously mentioned study which measured retinal amyloid deposition and thinning at 14-months of age. Changes could be occurring before amyloid consolidation but may be too subtle to be seen with an H&E stain at that point. As previously mentioned, Williams et al. found that 14-month old Tg2576 mice did not have decreased density of RGCs but a 79 decreased mitochondrial integrity resulted in decrease integrity of the cells [218]. Amyloid accumulation before retinal pathology has been shown previously in a double transgenic AD mouse model, APPSWE/PS1ΔE9 [295]. It is also currently unknown whether amyloid inside or outside the neuron contributes to neuropathology[231]. On a cellular level, special focus should be devoted to potential damage in the RPE and Müller cells. The RPE forms the outer retinal barrier and therefore its integrity is critical for the protection of the eye. For example, neprilysin, a zinc metalloproteinase involved in amyloid breakdown and clearance, is downregulated in the brains of Tg2576 mice and patients with AD[296, 297]. A study examining 27-month old neprilysin-deficient mice found that the RPE of these mice was degenerative and full of vacuoles[162]. When we examined the H&E stains, we were unable to determine whether the eyes of our Tg2576 mice had more vacuoles in the RPE compared to wild-type controls, primarily due to the difficulty in differentiating between vacuoles or tissue processing artefacts (data not shown). Although we did not investigate Müller cells in detail, these cells are critical for retinal homeostasis. In addition to maintaining an immunosuppressive and anti-inflammatory environment in the eye, these macroglia are also involved in modulating neuronal activity, waste removal, retinal metabolism, maintaining the structure of the retina, and producing pro-inflammatory cytokines during infection[184, 187, 298, 299]. In post-mortem analyses of retinas, intracellular APP and amyloid were found in the Müller cells in both the AD and non-AD eyes[300]. Under hypoxic conditions, Müller cells produce pro-angiogenic factors such as VEGF and angiopoietin-like 4 to promote neovascularization[301-303]. Further studies will need to be done to determine if Müller cells contribute to AD pathology, are dysregulated as a result of AD pathology or aid in amyloid clearance and repairing the retina, or a combination of all three[304]. 80 These differences in how amyloid is measured lead to another area in which studies, particularly those in humans, are divided: some studies have argued that there is a link between the amount of amyloid in the brain and the amount in the eyes[159, 305, 306] while some argue that there is no link[217, 295]. The Brain-Eye Amyloid Memory Study (BEAM) is an ongoing clinical trial (NCT02524405) assessing this link and is set to be completed in December 2021. In addition to variable detection methods and which area of the eye is under investigation, one of the reasons for the inconsistency may be in the size and composition of the organs discussed: the eyes and brains are different in terms of the number of neurons and vessels and it is difficult to create a normalization correlative equation across both organs. 5.1.2 Vascular changes in Tg2576 eyes The neurovascular theory, which argues that pathophysiological changes in cerebral vasculature are involved in AD, not only explains the number of vascular pathologies and risk factors associated with AD but also supports the amyloid cascade hypothesis and can provide an explanation for the dual possibility of impaired export of amyloid and increased import of amyloid. The formation of leaky blood vessels not only allows for the increased accumulation of amyloid but could decrease efficiency in clearing the aggregated forms that consolidate after increased accumulation. Additionally, with amyloid itself being pro-angiogenic, this can result in a positive feedback loop wherein amyloid increases angiogenesis, thus increasing amyloid accumulation while decreasing clearance, which then further propagates the formation of new blood vessels to compensate, and the cycle continues. The advantage of following a neurovascular model of AD, especially in the context of developing therapeutics, is that it is not 81 necessary to determine an exact “starting” point, rather the hope is that by focusing on targeting the vasculature, this will halt accumulation and allow for drainage of excess amyloid. But for neovascularization to be occurring, the environment in which angiogenesis is occurring needs to be primed with pro-angiogenic factors, cytokines, chemokines, and enzymes. Disruption of homeostasis will subsequently disrupt the balance between anti- and pro-angiogenic factors, increasing the likelihood of pathological blood vessel formation. When a proteome profiling kit was used to compare Tg2576 tissues to a wild-type littermate, the most significantly increased pro-angiogenic protein in the eye was GM-CSF. Studies in humans have noted that GM-CSF is increased in the CSF and serum of patients with AD and vascular dementia[307]. This also points to the well-established link between increased inflammation and angiogenesis, with the latter being a common consequence of the former[308]. Importantly, since innate immune system dysregulation is a common feature across genetically diverse AD mouse strains and in human AD studies[309], this could support the extrapolation of findings of vascular changes in mouse models of AD to human AD cases. The decrease in prolactin in the brain (74-fold) and eyes (1.2-fold) in our proteome profiler also deserves mentioning, as prolactin has been shown to have neuroprotective effects on the hippocampus in animal models of epilepsy[310, 311]. Since our pilot study was performed on female mice, it would be worth investigating further whether how sex hormones affect AD pathology in this animal model. When the proteome profiler was analyzed using the STRING database, there were many shared functional pathways and recurring proteins noted between the eyes and the brain. Two groups of proteins that regularly appeared across most of the analyses were the chemokines (Ccl2, Cxcl10, and Cxcl1) and fibroblast growth factors (Fgf2, Fgf7, and Hbegf). Ccl2, Cxcl10, and Cxcl1 were upregulated 3.8-, 2.2- and 5.1-fold respectively in the eyes in the Proteome 82 Profiler experiment. By comparison, the respective proteins were upregulated 3.9-, 4.9- and 6.0-fold in the brain. A study examining CSF of AD patients found a correlation between increased Ccl2 and cognitive impairment and age[312], a finding which has been seen in other studies[313, 314] and with the use of plasma levels of Ccl2[315]. However, the exact mechanism behind the effects of Ccl2 on AD pathogenesis remains unclear. The study previously mentioned found that increased Cxcl10 in CSF of AD patients was also positively correlated cognitive impairment, however, with only in early and not late stages of dementia[312], and this is thought to be because binding of Cxcl10 to its receptor CXCR3 on microglia decreases its ability to effectively clear amyloid[316]. Interestingly, it is not clear whether Cxcl1 is upregulated or downregulated in AD patients’ CSF or plasma. However, peripheral monocytes isolated from AD patients showed high levels of Cxcl1 expression and have increased transendothelial migration in response to Aβ in vitro[317]. Fgf2, Fgf7, and Hbegf were upregulated 2.4-, 5.6- and 6.0-fold, respectively, in the eyes in the Proteome Profiler experiment. Fgf-7 and Hbegf were upregulated 7.5 and 5.3-fold in the brain. However, Fgf2 was downregulated 1.3-fold in the brain, although given the 2-fold cut-off, this result may not be significant. The role of fibroblast growth factors in AD pathogenesis is slightly less explored compared to the role of chemokines. Interestingly, despite being pro-angiogenic, Fgf2 has been investigated as a potential therapeutic agent for AD, as there have been several studies that demonstrate reduced cognitive impairment and amyloid pathology in AD mouse models using various methods to increase Fgf2 [318-321]. Some of the therapeutic effects of Fgf2 include increased survival and protection in endothelial cells via Akt phosphorylation [321] and activating neuronal debris clearing by microglia[322]. Less is known about Fgf7, although an in vitro study examining cells exposed to Aβ found that reducing Fgf7 mRNA, which was increased in the CSF of AD patients, using micro RNA ameliorated cell 83 viability, increased proliferation, decreased inflammation, and decreased apoptosis[323]. Neurons increase Hbegf expression in response to hypoxic conditions, however, little is known of its role in the specific context of AD[324]. However, a meta-analysis of genome-wide association study (GWAS) investigating multiethnic groups of AD patients found significant associations with single-nucleotide polymorphisms in 7 genes, one of them being Hbegf[325]. Additionally, Hbegf is a ligand of membrane-bound APP, and it is thought that this interaction promotes neurogenesis[326]. The results from the STRING analysis support recent findings regarding the involvement of innate immune cells in Alzheimer’s disease[327], however, given the similarities between various phagocytic cells and the difficulties distinguishing bone-marrow-derived macrophages from neural microglia based on cell surface markers[328], it is difficult to determine whether these cells are playing a degenerative or reparative role. However, under homeostasis, it is known that macrophages, microglia, and neutrophils are not only present in the eye and retina, but also play a crucial role in maintaining a regulatory/immunosuppressive microenvironment[180]. Inflammation, as determined by the presence of morphologically activated microglia and astrocytes, has been previously described in the eyes of Tg2576 mice[169]. In a triple transgenic AD mouse model, 3xTg-AD, not only are amyloid-beta plaques and tau tangles found in the retinal ganglion cell layer, but astrogliosis and microglia activation are also seen even before cognitive and behavioral dysfunction[273]. Amyloid is likely triggering a proinflammatory environment in the retina in these mice[329]. Although inflammation tends to be considered neurodegenerative, it remains to be seen whether inflammation may be beneficial for proper amyloid clearance and neuroregeneration[170, 330, 331]. Since our H&E stains did not show immune cell infiltration in the eyes, these factors may 84 be produced within the eye or are secreted by the immune cells into the blood and are being either actively taken up or passively diffusing past a dysfunctional RBB. Further studies will need to be conducted to investigate how blood-borne immune factors cross the RBB. The most upregulated protein in the eye as determined by proteome profiler was GM-CSF/Csf2 and I further elaborated this result by examining its appearances in the STRING analysis. GM-CSF/Csf2 appeared in the top 2 hits shared by both eyes and brain for local network clusters: “mixed including PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and EGFR tyrosine kinase inhibitor resistance” and “mixed including PI5P PP2A and IER3 Regulate PI3K/AKT Signaling and Interleukin-2 family signaling” and in the reactome pathway analysis, GM-CSF/Csf2 appeared in “RAF/MAP kinase cascade” for both eyes and brain. RAF/MAP kinase cascade has been implicated in AD through the activation of the signaling cascade by oxidative stress, leading to neuronal apoptosis and upregulation of beta and gamma secretases, all of which contribute to AD pathology[332]. The PI3K/AKT signaling cascade could equally be of interest given that the inhibition of this pathway has been shown to lead to hyperphosphorylation of tau protein[333]. In mouse neural progenitor cells, GM-CSF/Csf2 appears to play a role in protecting from apoptosis via activation of the PI3K/AKT pathway[334]. Thus, GM-CSF/Csf2 may be upregulated in the eyes as an attempt to repair and protect the neurons of the eyes. Future studies can examine how GM-CSF/Csf2 interacts with chemokines and fibroblast growth factors and how these interactions may be involved in signaling pathways of AD pathogenesis. One of the proteins that was upregulated 9.7-fold in the proteome profiler and 5-fold in the Western Blot of the eyes of Tg2576 mice was CD105, a protein marker for angiogenic vessels. While this was expected given previous studies demonstrating increased CD105 in the 85 brain of AD patients and mouse models[111], when sub-categorizing the mice based on sex, the female Tg2576 mice appeared to have had greater amounts, especially compared to wild-type males, although this result was a trend and not yet statistically significant. Sex differences have been noted in AD, with women typically having higher levels of cognitive impairment at a faster rate, and increased brain atrophy[335]. Differential levels of sex hormones such as estrogen and androgen could explain such differences, but to our knowledge, there are no studies directly examining sex and angiogenesis. However, genome and biochemical studies looking at endothelial cells have noted sex differences in response to stress and at different stages of life[336, 337]. In Tg2576 mice, female mice were found to have a worse reference memory compared to their male counterparts. Interestingly, although amyloid as measured by Aβ42 ELISA Kit and methoxy-X04 did not show any sex differences, the authors theorized that female mice might be more sensitive to the toxic effects of amyloid[206]. In contrast, a different study that examined Tg2576 mice at roughly the same age found that female mice had higher Aβ40 (as measured by ELISA) and three-fold more senile plaques in the hippocampus and neocortex as measured by Campbell-Switzer silver staining compared to males[207]. Regarding the differences seen between the increase in blood vessels in the choroid but not the retina, this could be explained by site-specific neovascularization. For example, a study examining the different parameters of choroidal and retinal blood vessels found that patients had thinner choroids and decreased retinal vessel length density[338]. Another factor to consider is that retinal and choroidal endothelial cells do display heterogeneity and this is further highlighted in their differential response to hypoxia[339]. More sophisticated vessel measurement techniques such as whole retinal mounts and OCTs could be used to further elaborate whether ocular vasculature changes are site-specific in the eyes of Tg2576 mice. Our study also did not 86 determine whether the mechanisms of ocular angiogenesis are the same as those that occur in the brain or whether retinal or choroidal neovascularization shows greater similarity to brain blood vessel proliferation. However, it is known that some basic similarities exist such as vascular endothelial growth factor (VEGF) being a critical regulator for angiogenesis in both organs[340, 341]. 5.1.3 Retinal-blood barrier in Tg2576 eyes BBB and RBB integrity are important in the context of disease given that these normally restrictive barriers tightly regulate the flow of solutes, immune cells, nutrients, cytokines, chemokines, and proteins: the balance of which is crucial for maintaining homeostasis. While it is recognized that BBB impairment is a common pathology of AD[111, 128, 342, 343], there are some contradictory studies in the various AD mouse models, such as those that have not found increased BBB permeability in APP-PS1[344, 345] and PS2APP[346] mice using various in vivo permeability assays. However, a recent study found that APPSWE/PS1ΔE9 mice had worsened retinal capillary degeneration and this was correlated with vascular Aβ deposition[163]. These discrepancies may be a result of the different methods used to detect BBB permeability and integrity, the stage of AD being tested, or the mouse model being examined. It is important to note that there may be differences between iRBB and oRBB barrier permeability under homeostasis or during disease, although we were unable to find any studies examining such differences. A study found that the oRBB/RPE of pigs had similar P-glycoprotein 1 and multidrug resistance proteins efflux pump systems like those in the endothelial BBB[347], which lends support to the use of the RBB as an analog of the BBB. Given the integral role of RPE integrity in maintaining the oRBB, there have been several in 87 vitro studies examining the effects of amyloid on the integrity of the RPE. One study found that when Aβ42 was added to ARPE-19 (commonly used RPE cell line) cells it caused oxidative stress, apoptosis, decreased expression of occludin, and increased permeability[348]. A similar study showed that when human fetal RPE cells were exposed to Aβ42, this resulted in increased senescence, breakdown of ZO-1 and occludin tight junction complexes, and increased secretion of IL-8 and matrix metalloproteinase-9, both pro-inflammatory proteins[349]. 5.2 Limitations One of the limitations for the extrapolation of this research to human AD is the use of transgenic mouse models as a representative of late-onset AD (LOAD), whereas familial/genetic AD accounts for only 1-5% of all AD cases. Therefore, any epigenetic, environmental, or pathogenic factors that may be involved in LOAD are not represented in these studies. Additionally, pathological markers such as amyloid deposition, angiogenesis, and RBB/BBB permeability are not specific to AD and/or may also be common in healthy individuals during aging and in other neurological diseases. The species of amyloid, the conformation of amyloid, how a tissue sample was processed and the method used to detect amyloid all may contribute to resulting amyloid expression in an experiment, as was demonstrated when comparing the IF to the Congo Red staining. Therefore, amyloid characterization and quantification need to be validated using several different techniques. Evidence of pathology was limited to gross deformities that can be visible with H&E stain, however, other methods for determining neuro- or retinal degeneration may demonstrate other deficiencies not seen with H&E stains. 88 The major limitation in the proteome profiler data is the sample size since one mouse was used per genotype. However, these mice were littermates, same-sex, same age, and were harvested at the same time and thus allow for at least preliminary comparisons on how an AD genotype may be influencing angiogenic protein expression. Quantifying protein expression via immunofluorescence may be influenced by subtle changes in focus, gain, offset, tissue processing, staining, and other factors that can alter the amount of fluorescence detected. A consistent analysis is also affected by these issues. Extra precautions were taken to ensure all samples were processed, stained, and analyzed at one time to minimize potential confounding influences. However, additional methods of quantifying protein expression, such as ELISAs, in specific areas of the eye could be used. Although RNA sequencing was performed on the eyes and brains of wild-type and Tg2576 mice (see Appendix), the analysis has not been completed and thus comparisons of the two data sets should remain a priority for future studies. Additionally, all the work in this thesis was done using cross-sectional data and ex vivo. Statistical power would have also been increased if more animal samples were used per experiment. In vivo functional studies such as measuring vasculature with OCT or Evan’s blue permeability assays would be useful in further validating the findings highlighted, including vascular changes and RBB permeability. 5.3 Future Directions There remain some important unanswered questions that are worth investigating further. For example, whether or not amyloid seen in the eye is being produced by ocular neurons or whether this amyloid is coming from an outside source. The noted changes in vasculature may be contributing to the import and/or export of amyloid and thus it is worth further investigating how 89 the role of vascularization plays in amyloid accumulation or clearance. When Liu et al. vaccinated mice against Aβ, they found an increase in retinal perivascular deposits, which they interpreted to mean that clearance was occurring[169]. Other studies which have noted perivascular amyloid deposition may suggest another interpretation: that sources from outside the eye might be, at least in part, contributing to amyloid accumulation. There is also an opportunity to investigate whether the processing of amyloid species in the eye is different for the brain, as some have suggested[217]. While the mechanistic links may be similar, subtle differences in terms of how APP is processed into Aβ may result in different frequencies, thus contributing to whether or not plaques become consolidated or how neurotoxic the amyloid may be. RNA sequencing of wild-type and Tg2576 eyes and brains was performed on both male and female mice but the results have yet to be analyzed. This data can be compared to the proteome profiler data and be further used to corroborate the findings and further the understanding of the link between the eyes and brain in AD. Primary differential results for the sequencing can be found in the Appendix. To measure changes in vasculature, OCT, OCT-angiography, or whole retinal mounts with vessel stains would demonstrate changes in vasculature such as branching, tortuosity, vessel thinning, and vessel length, which is more descriptive than cross-sections of retinas. RBB barrier permeability could also be further examined by determining the gene and protein expression changes of other tight junction proteins such as claudin-5 and by performing in vivo permeability assays using Evan’s blue, sodium fluorescein, or horseradish peroxidase. At this time, the evidence of pathological AD hallmarks in the eyes such as amyloid deposition, angiogenesis, and RBB permeability are independent of time as there is no 90 determination of which comes first and how they are related to each other. Further work will need to be done, most likely in animal models, to demonstrate whether increased amyloid in the eyes directly induces neovascularization and RBB permeability and this further promotes the accumulation of amyloid in the eyes. This will help determine whether the relationships between amyloid accumulation, angiogenesis, and RBB integrity are causal or correlational. If the pathological hallmarks typically seen in the brains of AD mice are sufficiently validated in the eyes of this mouse model and correlated with changes in the brain, therapeutic interventions may be attempted. For example, treatments aimed to reverse angiogenesis such as anti-VEGF inhibitors are available to target wet AMD, an eye condition characterized by choroidal neovascularization. Furthermore, given the accessibility of the eye compared to the brain, changes over time in amyloid accumulation, vasculature, and retinal blood barrier permeability could be measured across time to determine whether these techniques can be used to diagnose or measure the progression of the disease. 5.4 Conclusion In this thesis, preliminary evidence of the hallmarks of AD in the brain, such as amyloid deposition, angiogenesis, and physiological barrier permeability, were identified in the eyes of the Tg2576 mouse model. The amyloid present in the retina was located typically near blood vessels. A studying investigating angiogenesis found that both eyes and brains of the Tg2576 mice were likely angiogenesis-promoting environments. While there was no evidence of altered vasculature in the retina, increased vascularization in the choroid, and an increase in ocular CD105 expression support the notion of site-specific neovascularization, a novel finding in this animal model. Finally, the decreased integrity of the RBB was demonstrated with decreased expression of tight junction proteins and increased permeability of albumin into the eye. The 91 strength of this thesis has been in the use of immunofluorescence to visualize qualitative changes and the analysis of proteins and molecular functions of interest which can be the basis for further mechanistic studies. These findings support further validation of the eye as an accessible and informative extension of the AD CNS that may mimic pathology occurring in an area currently difficult to assess in vivo. 92 References 1. 2018 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 2018. 14(3): p. 367-429. 2. Prince, M., et al., World Alzheimer Report 2015: Global Impact of Dementia. 2015, Alzheimer’s Disease International: London. 3. Mayeux, R. and Y. Stern, Epidemiology of Alzheimer disease. Cold Spring Harbor perspectives in medicine, 2012. 2(8): p. a006239. 4. López, O.L. and S.T. DeKosky, Clinical symptoms in Alzheimer's disease, in Handbook of Clinical Neurology. 2008, Elsevier. p. 207-216. 5. Lyketsos, C.G., et al., Prevalence of Neuropsychiatric Symptoms in Dementia and Mild Cognitive ImpairmentResults From the Cardiovascular Health Study. JAMA, 2002. 288(12): p. 1475-1483. 6. Deardorff, W.J. and G.T. Grossberg, Chapter 2 - Behavioral and psychological symptoms in Alzheimer's dementia and vascular dementia, in Handbook of Clinical Neurology, V.I. Reus and D. Lindqvist, Editors. 2019, Elsevier. p. 5-32. 7. Masters, M.C., J.C. Morris, and C.M. Roe, “Noncognitive” symptoms of early Alzheimer disease. A longitudinal analysis, 2015. 84(6): p. 617-622. 8. Armstrong, R.A., Alzheimer's Disease and the Eye. Journal of Optometry, 2019. 12(3): p. 103-111. 9. Kiyosawa, M., et al., Alzheimer's Disease with Prominent Visual Symptoms: Clinical and Metabolic Evaluation. Ophthalmology, 1989. 96(7): p. 1077-1086. 10. Javaid, F.Z., et al., Visual and Ocular Manifestations of Alzheimer's Disease and Their Use as Biomarkers for Diagnosis and Progression. Frontiers in neurology, 2016. 7: p. 55-55. 11. Cerquera-Jaramillo, M.A., et al., Visual Features in Alzheimer's Disease: From Basic Mechanisms to Clinical Overview. Neural Plast, 2018. 2018: p. 2941783. 12. Lakshminarayanan, V., et al., Vision in dementia: contrast effects. Neurol Res, 1996. 18(1): p. 9-15. 13. Serrano-Pozo, A., et al., Neuropathological alterations in Alzheimer disease. Cold Spring Harbor perspectives in medicine, 2011. 1(1): p. a006189-a006189. 14. Stelzmann, R.A., H. Norman Schnitzlein, and F. Reed Murtagh, An english translation of alzheimer's 1907 paper, “über eine eigenartige erkankung der hirnrinde”. Clinical Anatomy, 1995. 8(6): p. 429-431. 15. Alzheimer, A., Über eine eigenartige Erkrankung der Hirnrinde. Allgemeine Zeitschrift fur Psychiatrie und Psychisch-gerichtliche Medizin, 1907. 64: p. 146-148. 16. Graeber, M.B., et al., Histopathology and APOE genotype of the first Alzheimer disease patient, Auguste D. Neurogenetics, 1998. 1(3): p. 223-228. 17. Khachaturian, Z.S. and A.S. Khachaturian, The Paradox of Research on Dementia-Alzheimer's Disease. J Prev Alzheimers Dis, 2016. 3(4): p. 189-191. 18. Reitz, C., C. Brayne, and R. Mayeux, Epidemiology of Alzheimer disease. Nature reviews. Neurology, 2011. 7(3): p. 137-152. 19. Moschetti, K., et al., Mortality and Productivity Losses From Alzheimer Disease Among US Adults Aged 40 to 64 Years, 1999 to 2010. Alzheimer disease and associated disorders, 2014. 29. 93 20. Patterson, D., et al., Mapping of the gene encoding the beta-amyloid precursor protein and its relationship to the Down syndrome region of chromosome 21. Proceedings of the National Academy of Sciences of the United States of America, 1988. 85(21): p. 8266-8270. 21. Head, E., et al., Alzheimer's Disease in Down Syndrome. European journal of neurodegenerative disease, 2012. 1(3): p. 353-364. 22. McKhann, G., et al., Clinical diagnosis of Alzheimer's disease. Report of the NINCDS‐ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease, 1984. 34(7): p. 939-939. 23. Dubois, B., et al., Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria. The Lancet Neurology, 2007. 6(8): p. 734-746. 24. Dubois, B., et al., Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2016. 12(3): p. 292-323. 25. Grandal Leiros, B., et al., Prevalence and concordance between the clinical and the post-mortem diagnosis of dementia in a psychogeriatric clinic. Neurología (English Edition), 2018. 33(1): p. 13-17. 26. The Brain and Alzheimer's Disease. 2015 [cited 2020 April 21]; Available from: https://ncrad.iu.edu/understanding_autopsy_reports.html. 27. Barker, W.W., et al., Relative Frequencies of Alzheimer Disease, Lewy Body, Vascular and Frontotemporal Dementia, and Hippocampal Sclerosis in the State of Florida Brain Bank. Alzheimer Disease & Associated Disorders, 2002. 16(4): p. 203-212. 28. Lewczuk, P., et al., Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry. The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry, 2018. 19(4): p. 244-328. 29. Weiner, M.W., et al., 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2015. 11(6): p. e1-e120. 30. Cummings, J., Alzheimer's disease diagnostic criteria: practical applications. Alzheimer's Research & Therapy, 2012. 4(4): p. 35. 31. Paterson, R.W., et al., Cerebrospinal fluid in the differential diagnosis of Alzheimer’s disease: clinical utility of an extended panel of biomarkers in a specialist cognitive clinic. Alzheimer's Research & Therapy, 2018. 10(1): p. 32. 32. Marcus, C., E. Mena, and R.M. Subramaniam, Brain PET in the diagnosis of Alzheimer's disease. Clinical nuclear medicine, 2014. 39(10): p. e413-e426. 33. Villemagne, V.L., et al., Amyloid imaging with (18)F-florbetaben in Alzheimer disease and other dementias. J Nucl Med, 2011. 52(8): p. 1210-7. 34. Suppiah, S., M.-A. Didier, and S. Vinjamuri, The Who, When, Why, and How of PET Amyloid Imaging in Management of Alzheimer's Disease-Review of Literature and Interesting Images. Diagnostics (Basel, Switzerland), 2019. 9(2): p. 65. 35. Lama, R.K., et al., Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features. Journal of healthcare engineering, 2017. 2017: p. 5485080-5485080. 94 36. Johnson, K.A., et al., Brain imaging in Alzheimer disease. Cold Spring Harbor perspectives in medicine, 2012. 2(4): p. a006213-a006213. 37. Dong, X., et al., Predictive Value of Routine Peripheral Blood Biomarkers in Alzheimer’s Disease. Frontiers in Aging Neuroscience, 2019. 11(332). 38. Zetterberg, H. and S.C. Burnham, Blood-based molecular biomarkers for Alzheimer’s disease. Molecular Brain, 2019. 12(1): p. 26. 39. Aizenstein, H.J., et al., Frequent amyloid deposition without significant cognitive impairment among the elderly. Archives of neurology, 2008. 65(11): p. 1509-1517. 40. Hertze, J., et al., Evaluation of CSF Biomarkers as Predictors of Alzheimer's Disease: A Clinical Follow-Up Study of 4.7 Years. Journal of Alzheimer's Disease, 2010. 21: p. 1119-1128. 41. Westman, E., J.S. Muehlboeck, and A. Simmons, Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. NeuroImage, 2012. 62(1): p. 229-238. 42. Biagioni, M.C. and J.E. Galvin, Using biomarkers to improve detection of Alzheimer's disease. Neurodegenerative disease management, 2011. 1(2): p. 127-139. 43. Price, J.L. and J.C. Morris, Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Annals of Neurology, 1999. 45(3): p. 358-368. 44. Wittenberg, R., et al., Economic impacts of introducing diagnostics for mild cognitive impairment Alzheimer's disease patients. Alzheimer's & dementia (New York, N. Y.), 2019. 5: p. 382-387. 45. Cummings, J., C. Reiber, and P. Kumar, The price of progress: Funding and financing Alzheimer's disease drug development. Alzheimer's & dementia (New York, N. Y.), 2018. 4: p. 330-343. 46. Cummings, J.L., T. Morstorf, and K. Zhong, Alzheimer's disease drug-development pipeline: few candidates, frequent failures. Alzheimer's research & therapy, 2014. 6(4): p. 37-37. 47. Yiannopoulou, K.G., et al., Reasons for Failed Trials of Disease-Modifying Treatments for Alzheimer Disease and Their Contribution in Recent Research. Biomedicines, 2019. 7(4): p. 97. 48. Arvanitakis, Z., R.C. Shah, and D.A. Bennett, Diagnosis and Management of Dementia: Review. JAMA, 2019. 322(16): p. 1589-1599. 49. Gao, L.B., et al., Alzheimer's Disease therapeutics: current and future therapies. Minerva Med, 2016. 107(2): p. 108-13. 50. Yiannopoulou, K.G. and S.G. Papageorgiou, Current and Future Treatments in Alzheimer Disease: An Update. J Cent Nerv Syst Dis, 2020. 12: p. 1179573520907397. 51. Dumurgier, J., et al., Alzheimer's Disease Biomarkers and Future Decline in Cognitive Normal Older Adults. Journal of Alzheimer's disease : JAD, 2017. 60(4): p. 1451-1459. 52. Beason-Held, L.L., et al., Changes in brain function occur years before the onset of cognitive impairment. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2013. 33(46): p. 18008-18014. 53. Jack, C.R., Jr., et al., Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. The Lancet. Neurology, 2010. 9(1): p. 119-128. 95 54. Sala-Llonch, R., et al., Inflammation, Amyloid, and Atrophy in The Aging Brain: Relationships with Longitudinal Changes in Cognition. Journal of Alzheimer's Disease, 2017. 58: p. 829-840. 55. Fan, L.-Y., et al., The Relation Between Brain Amyloid Deposition, Cortical Atrophy, and Plasma Biomarkers in Amnesic Mild Cognitive Impairment and Alzheimer's Disease. Frontiers in aging neuroscience, 2018. 10: p. 175-175. 56. Chételat, G., et al., Relationship between atrophy and β-amyloid deposition in Alzheimer disease. Annals of Neurology, 2010. 67(3): p. 317-324. 57. Hardy, J., The relationship between amyloid and tau. Journal of Molecular Neuroscience, 2003. 20(2): p. 203-206. 58. Hardy, J. and G. Higgins, Alzheimer's disease: the amyloid cascade hypothesis. Science, 1992. 256(5054): p. 184-185. 59. Sojkova, J., et al., Longitudinal Patterns of β-Amyloid Deposition in Nondemented Older Adults. Archives of Neurology, 2011. 68(5): p. 644-649. 60. Guo, Q., et al., Amyloid precursor protein revisited: neuron-specific expression and highly stable nature of soluble derivatives. The Journal of biological chemistry, 2012. 287(4): p. 2437-2445. 61. Parvathy, S., et al., Cleavage of Alzheimer's amyloid precursor protein by alpha-secretase occurs at the surface of neuronal cells. Biochemistry, 1999. 38(30): p. 9728-34. 62. Haass, C., et al., beta-Amyloid peptide and a 3-kDa fragment are derived by distinct cellular mechanisms. J Biol Chem, 1993. 268(5): p. 3021-4. 63. Cai, H., et al., BACE1 is the major beta-secretase for generation of Abeta peptides by neurons. Nat Neurosci, 2001. 4(3): p. 233-4. 64. Mullan, M., et al., A pathogenic mutation for probable Alzheimer's disease in the APP gene at the N-terminus of beta-amyloid. Nat Genet, 1992. 1(5): p. 345-7. 65. Cai, X.D., T.E. Golde, and S.G. Younkin, Release of excess amyloid beta protein from a mutant amyloid beta protein precursor. Science, 1993. 259(5094): p. 514-6. 66. Suzuki, N., et al., An increased percentage of long amyloid beta protein secreted by familial amyloid beta protein precursor (beta APP717) mutants. Science, 1994. 264(5163): p. 1336-40. 67. Kelleher, R.J., 3rd and J. Shen, Presenilin-1 mutations and Alzheimer's disease. Proceedings of the National Academy of Sciences of the United States of America, 2017. 114(4): p. 629-631. 68. Kumar-Singh, S., et al., Mean age-of-onset of familial alzheimer disease caused by presenilin mutations correlates with both increased Abeta42 and decreased Abeta40. Hum Mutat, 2006. 27(7): p. 686-95. 69. Jankowsky, J.L., 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): p. 159-70. 70. Bitan, G., et al., Amyloid beta -protein (Abeta) assembly: Abeta 40 and Abeta 42 oligomerize through distinct pathways. Proceedings of the National Academy of Sciences of the United States of America, 2003. 100(1): p. 330-335. 71. Burdick, D., et al., Assembly and aggregation properties of synthetic Alzheimer's A4/beta amyloid peptide analogs. J Biol Chem, 1992. 267(1): p. 546-54. 96 72. Iijima, K., et al., Aβ42 Mutants with Different Aggregation Profiles Induce Distinct Pathologies in Drosophila. PLOS ONE, 2008. 3(2): p. e1703. 73. Götz, J., et al., Amyloid-induced neurofibrillary tangle formation in Alzheimer's disease: insight from transgenic mouse and tissue-culture models. International Journal of Developmental Neuroscience, 2004. 22(7): p. 453-465. 74. Arriagada, P.V., K. Marzloff, and B.T. Hyman, Distribution of Alzheimer-type pathologic changes in nondemented elderly individuals matches the pattern in Alzheimer's disease. Neurology, 1992. 42(9): p. 1681-8. 75. Davis, D.G., et al., Alzheimer neuropathologic alterations in aged cognitively normal subjects. J Neuropathol Exp Neurol, 1999. 58(4): p. 376-88. 76. Ferreira, S.T., et al., Soluble amyloid-β oligomers as synaptotoxins leading to cognitive impairment in Alzheimer's disease. Frontiers in cellular neuroscience, 2015. 9: p. 191-191. 77. Haass, C. and D.J. Selkoe, Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid β-peptide. Nature Reviews Molecular Cell Biology, 2007. 8(2): p. 101-112. 78. Arbel-Ornath, M., et al., Soluble oligomeric amyloid-β induces calcium dyshomeostasis that precedes synapse loss in the living mouse brain. Molecular Neurodegeneration, 2017. 12(1): p. 27. 79. Hu, N.W., et al., Soluble amyloid-beta peptides potently disrupt hippocampal synaptic plasticity in the absence of cerebrovascular dysfunction in vivo. Brain, 2008. 131(Pt 9): p. 2414-24. 80. Lue, L.F., et al., Soluble amyloid beta peptide concentration as a predictor of synaptic change in Alzheimer's disease. Am J Pathol, 1999. 155(3): p. 853-62. 81. McLean, C.A., et al., Soluble pool of Abeta amyloid as a determinant of severity of neurodegeneration in Alzheimer's disease. Ann Neurol, 1999. 46(6): p. 860-6. 82. Zlokovic, B.V., Neurovascular mechanisms of Alzheimer's neurodegeneration. Trends in Neurosciences, 2005. 28(4): p. 202-208. 83. Zlokovic, B.V., Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders. Nature reviews. Neuroscience, 2011. 12(12): p. 723-738. 84. Jefferies, W.A., et al., Adjusting the compass: new insights into the role of angiogenesis in Alzheimer's disease. Alzheimer's research & therapy, 2013. 5(6): p. 64-64. 85. Hunter, J.M., et al., Morphological and pathological evolution of the brain microcirculation in aging and Alzheimer's disease. PloS one, 2012. 7(5): p. e36893-e36893. 86. Fischer, V.W., A. Siddiqi, and Y. Yusufaly, Altered angioarchitecture in selected areas of brains with Alzheimer's disease. Acta Neuropathol, 1990. 79(6): p. 672-9. 87. Lathe, R., A. Sapronova, and Y. Kotelevtsev, Atherosclerosis and Alzheimer--diseases with a common cause? Inflammation, oxysterols, vasculature. BMC geriatrics, 2014. 14: p. 36-36. 88. Torre, J.C.d.l., Alzheimer Disease as a Vascular Disorder. Stroke, 2002. 33(4): p. 1152-1162. 89. Lee, H.J., et al., Diabetes and Alzheimer's Disease: Mechanisms and Nutritional Aspects. Clinical nutrition research, 2018. 7(4): p. 229-240. 97 90. Rodrigo, E.G.-R., et al., Alterations in Glucose Metabolism on Cognition: A Possible Link Between Diabetes and Dementia. Current Pharmaceutical Design, 2016. 22(7): p. 812-818. 91. Vagelatos, N.T. and G.D. Eslick, Type 2 Diabetes as a Risk Factor for Alzheimer's Disease: The Confounders, Interactions, and Neuropathology Associated With This Relationship. Epidemiologic Reviews, 2013. 35(1): p. 152-160. 92. Ahtiluoto, S., et al., Diabetes, Alzheimer disease, and vascular dementia. A population-based neuropathologic study, 2010. 75(13): p. 1195-1202. 93. Irie, F., et al., Enhanced Risk for Alzheimer Disease in Persons With Type 2 Diabetes and APOE ε4: The Cardiovascular Health Study Cognition Study. Archives of Neurology, 2008. 65(1): p. 89-93. 94. Cataldo, J.K., J.J. Prochaska, and S.A. Glantz, Cigarette smoking is a risk factor for Alzheimer's Disease: an analysis controlling for tobacco industry affiliation. Journal of Alzheimer's disease : JAD, 2010. 19(2): p. 465-480. 95. Kim, J., J.M. Basak, and D.M. Holtzman, The role of apolipoprotein E in Alzheimer's disease. Neuron, 2009. 63(3): p. 287-303. 96. Skoog, I. and D. Gustafson, Update on hypertension and Alzheimer's disease. Neurological Research, 2006. 28(6): p. 605-611. 97. Attems, J. and K.A. Jellinger, The overlap between vascular disease and Alzheimer's disease--lessons from pathology. BMC medicine, 2014. 12: p. 206-206. 98. Kalaria, R.N. and C. Ballard, Overlap Between Pathology of Alzheimer Disease and Vascular Dementia. Alzheimer Disease & Associated Disorders, 1999. 13. 99. Ravona-Springer, R., M. Davidson, and S. Noy, Is the distinction between Alzheimer's disease and vascular dementia possible and relevant? Dialogues in clinical neuroscience, 2003. 5(1): p. 7-15. 100. Arvanitakis, Z., et al., Cerebral amyloid angiopathy pathology and cognitive domains in older persons. Annals of neurology, 2011. 69(2): p. 320-327. 101. Brenowitz, W.D., et al., Cerebral amyloid angiopathy and its co-occurrence with Alzheimer's disease and other cerebrovascular neuropathologic changes. Neurobiology of aging, 2015. 36(10): p. 2702-2708. 102. Attems, J., et al., Topographical distribution of cerebral amyloid angiopathy and its effect on cognitive decline are influenced by Alzheimer disease pathology. Journal of the Neurological Sciences, 2007. 257(1): p. 49-55. 103. Preston, S.D., et al., Capillary and arterial cerebral amyloid angiopathy in Alzheimer's disease: defining the perivascular route for the elimination of amyloid β from the human brain. Neuropathology and Applied Neurobiology, 2003. 29(2): p. 106-117. 104. Weller, R.O., et al., Cerebral amyloid angiopathy: amyloid beta accumulates in putative interstitial fluid drainage pathways in Alzheimer's disease. The American journal of pathology, 1998. 153(3): p. 725-733. 105. Thal, D.R., et al., Capillary cerebral amyloid angiopathy is associated with vessel occlusion and cerebral blood flow disturbances. Neurobiology of Aging, 2009. 30(12): p. 1936-1948. 106. Kövari, E., et al., The relationship between cerebral amyloid angiopathy and cortical microinfarcts in brain ageing and Alzheimer's disease. Neuropathology and applied neurobiology, 2013. 39(5): p. 498-509. 98 107. Vinters, H.V. and J.J. Gilbert, Cerebral amyloid angiopathy: incidence and complications in the aging brain. II. The distribution of amyloid vascular changes. Stroke, 1983. 14(6): p. 924-928. 108. Roher, A.E., et al., Cerebral blood flow in Alzheimer's disease. Vascular health and risk management, 2012. 8: p. 599-611. 109. Kogure, D., et al., Longitudinal evaluation of early Alzheimer's disease using brain perfusion SPECT. Journal of Nuclear Medicine, 2000. 41(7): p. 1155-1162. 110. van de Haar, H.J., et al., Neurovascular unit impairment in early Alzheimer's disease measured with magnetic resonance imaging. Neurobiology of Aging, 2016. 45: p. 190-196. 111. Biron, K.E., et al., Amyloid Triggers Extensive Cerebral Angiogenesis Causing Blood Brain Barrier Permeability and Hypervascularity in Alzheimer's Disease. PLOS ONE, 2011. 6(8): p. e23789. 112. Haar, H.J.v.d., et al., Blood-Brain Barrier Leakage in Patients with Early Alzheimer Disease. Radiology, 2016. 281(2): p. 527-535. 113. Kalaria, R.N. and P. Hedera, Differential degeneration of the cerebral microvasculature in Alzheimer's disease. NeuroReport, 1995. 6(3). 114. Yamazaki, Y., et al., Selective loss of cortical endothelial tight junction proteins during Alzheimer's disease progression. Brain : a journal of neurology, 2019. 142(4): p. 1077-1092. 115. Hansra, G.K., et al., The neuritic plaque in Alzheimer's disease: perivascular degeneration of neuronal processes. Neurobiology of Aging, 2019. 82: p. 88-101. 116. Mountjoy, C.Q., B.E. Tomlinson, and P.H. Gibson, Amyloid and senile plaques and cerebral blood vessels. A semi-quantitative investigation of a possible relationship. J Neurol Sci, 1982. 57(1): p. 89-103. 117. Meyer, E.P., et al., Altered morphology and 3D architecture of brain vasculature in a mouse model for Alzheimer's disease. Proceedings of the National Academy of Sciences, 2008. 105(9): p. 3587-3592. 118. Kawai, M., et al., The relationship of amyloid plaques to cerebral capillaries in Alzheimer's disease. The American journal of pathology, 1990. 137(6): p. 1435-1446. 119. Vinters, H.V., Cerebral amyloid angiopathy. A critical review. Stroke, 1987. 18(2): p. 311-324. 120. Thomas, T., et al., β-Amyloid-mediated vasoactivity and vascular endothelial damage. Nature, 1996. 380(6570): p. 168-171. 121. Niwa, K., G.A. Carlson, and C. Iadecola, Exogenous Aβ1–40 Reproduces Cerebrovascular Alterations Resulting from Amyloid Precursor Protein Overexpression in Mice. Journal of Cerebral Blood Flow & Metabolism, 2000. 20(12): p. 1659-1668. 122. Miyakawa, T., Vascular pathology in Alzheimer's disease. Psychogeriatrics, 2010. 10(1): p. 39-44. 123. Du, A.-T., et al., White matter lesions are associated with cortical atrophy more than entorhinal and hippocampal atrophy. Neurobiology of Aging, 2005. 26(4): p. 553-559. 124. Cantara, S., et al., Physiological levels of amyloid peptides stimulate the angiogenic response through FGF-2. The FASEB Journal, 2004. 18(15): p. 1943-1945. 125. Cameron, D.J., et al., Alzheimer’s-Related Peptide Amyloid-β Plays a Conserved Role in Angiogenesis. PLOS ONE, 2012. 7(7): p. e39598. 99 126. Zand, L., J.K. Ryu, and J.G. McLarnon, Induction of angiogenesis in the β-amyloid peptide-injected rat hippocampus. NeuroReport, 2005. 16(2). 127. Hawkes, C.A., et al., Perivascular drainage of solutes is impaired in the ageing mouse brain and in the presence of cerebral amyloid angiopathy. Acta Neuropathologica, 2011. 121(4): p. 431-443. 128. Biron, K.E., et al., Cessation of Neoangiogenesis in Alzheimer's Disease Follows Amyloid-beta Immunization. Scientific Reports, 2013. 3(1): p. 1354. 129. Dickstein, D.L., et al., Aβ peptide immunization restores blood-brain barrier integrity in Alzheimer disease. The FASEB Journal, 2006. 20(3): p. 426-433. 130. Binnewijzend, M.A.A., et al., Cerebral perfusion in the predementia stages of Alzheimer's disease. European radiology, 2016. 26(2): p. 506-514. 131. Kusne, Y., et al., Visual system manifestations of Alzheimer's disease. Acta Ophthalmologica, 2017. 95(8): p. e668-e676. 132. Lim, J.K.H., et al., The Eye As a Biomarker for Alzheimer's Disease. Frontiers in neuroscience, 2016. 10: p. 536-536. 133. Mirzaei, N., et al., Alzheimer’s Retinopathy: Seeing Disease in the Eyes. Frontiers in Neuroscience, 2020. 14(921). 134. Mancino, R., et al., Glaucoma and Alzheimer Disease: One Age-Related Neurodegenerative Disease of the Brain. Curr Neuropharmacol, 2018. 16(7): p. 971-977. 135. Exalto, L.G., et al., Severe diabetic retinal disease and dementia risk in type 2 diabetes. Journal of Alzheimer's disease : JAD, 2014. 42 Suppl 3(0 3): p. S109-S117. 136. Sundstrom, J.M., et al., Proteomic Analysis of Early Diabetic Retinopathy Reveals Mediators of Neurodegenerative Brain Diseases. Investigative Ophthalmology & Visual Science, 2018. 59(6): p. 2264-2274. 137. Ohno-Matsui, K., Parallel findings in age-related macular degeneration and Alzheimer’s disease. Progress in Retinal and Eye Research, 2011. 30(4): p. 217-238. 138. Kaarniranta, K., et al., Age-Related Macular Degeneration (AMD): Alzheimer's Disease in the Eye? Journal of Alzheimer's Disease, 2011. 24: p. 615-631. 139. Lee, C.S., et al., Associations between recent and established ophthalmic conditions and risk of Alzheimer's disease. Alzheimer's & Dementia, 2019. 15(1): p. 34-41. 140. Hinton, D.R., et al., Optic-Nerve Degeneration in Alzheimer's Disease. New England Journal of Medicine, 1986. 315(8): p. 485-487. 141. Alber, J., et al., Developing retinal biomarkers for the earliest stages of Alzheimer's disease: What we know, what we don't, and how to move forward. Alzheimer's & Dementia, 2020. 16(1): p. 229-243. 142. Lu, Y. and N. Wang, Alzheimer Disease: Intracranial Pressure and Glaucoma, in Intraocular and Intracranial Pressure Gradient in Glaucoma, N. Wang, Editor. 2019, Springer Singapore: Singapore. p. 307-309. 143. Yoneda, S., et al., Vitreous fluid levels of beta-amyloid((1-42)) and tau in patients with retinal diseases. Jpn J Ophthalmol, 2005. 49(2): p. 106-8. 144. Manjunath, V., et al., Analysis of choroidal thickness in age-related macular degeneration using spectral-domain optical coherence tomography. American journal of ophthalmology, 2011. 152(4): p. 663-668. 145. Cunha, J.P., et al., Choroidal thinning: Alzheimer's disease and aging. Alzheimer's & dementia (Amsterdam, Netherlands), 2017. 8: p. 11-17. 100 146. Geerlings, M.J., E.K. de Jong, and A.I. den Hollander, The complement system in age-related macular degeneration: A review of rare genetic variants and implications for personalized treatment. Molecular immunology, 2017. 84: p. 65-76. 147. Tenner, A.J., Complement-Mediated Events in Alzheimer’s Disease: Mechanisms and Potential Therapeutic Targets. The Journal of Immunology, 2020. 204(2): p. 306-315. 148. Wostyn, P., Can chronic increased intracranial pressure or exposure to repetitive intermittent intracranial pressure elevations raise your risk for Alzheimer's disease? Med Hypotheses, 2004. 62(6): p. 925-30. 149. Roy Chowdhury, U. and M.P. Fautsch, Intracranial Pressure and Its Relationship to Glaucoma: Current Understanding and Future Directions. Medical hypothesis, discovery & innovation ophthalmology journal, 2015. 4(3): p. 71-80. 150. McCulley, T.J., J.R. Chang, and W.J. Piluek, Intracranial Pressure and Glaucoma. Journal of Neuro-Ophthalmology, 2015. 35. 151. Silverberg, G., et al., Elevated cerebrospinal fluid pressure in patients with Alzheimer's disease. Cerebrospinal fluid research, 2006. 3: p. 7-7. 152. Crosby-Nwaobi, R.R., et al., The Relationship Between Diabetic Retinopathy and Cognitive Impairment. Diabetes Care, 2013. 36(10): p. 3177-3186. 153. Ding, J., et al., Retinal microvascular abnormalities and cognitive dysfunction: a systematic review. Br J Ophthalmol, 2008. 92(8): p. 1017-25. 154. Keenan, T.D.L., R. Goldacre, and M.J. Goldacre, Associations Between Age-Related Macular Degeneration, Alzheimer Disease, and Dementia: Record Linkage Study of Hospital Admissions. JAMA Ophthalmology, 2014. 132(1): p. 63-68. 155. Smilnak, G.J., et al., Comorbidity of age-related macular degeneration with Alzheimer's disease: A histopathologic case-control study. PLoS One, 2019. 14(9): p. e0223199. 156. Yoon, S.P., et al., Retinal Microvascular and Neurodegenerative Changes in Alzheimer’s Disease and Mild Cognitive Impairment Compared with Control Participants. Ophthalmology Retina, 2019. 3(6): p. 489-499. 157. Querques, G., et al., Functional and morphological changes of the retinal vessels in Alzheimer’s disease and mild cognitive impairment. Scientific Reports, 2019. 9(1): p. 63. 158. Koronyo-Hamaoui, M., et al., Identification of amyloid plaques in retinas from Alzheimer's patients and noninvasive in vivo optical imaging of retinal plaques in a mouse model. NeuroImage, 2011. 54: p. S204-S217. 159. Koronyo, Y., et al., Retinal amyloid pathology and proof-of-concept imaging trial in Alzheimer's disease. JCI insight, 2017. 2(16): p. e93621. 160. Morin, P.J., et al., Amyloid precursor protein is synthesized by retinal ganglion cells, rapidly transported to the optic nerve plasma membrane and nerve terminals, and metabolized. J Neurochem, 1993. 61(2): p. 464-73. 161. Seko, Y., et al., Induction of Vascular Endothelial Growth Factor After Application of Mechanical Stress to Retinal Pigment Epithelium of the Rat In Vitro. Investigative Ophthalmology & Visual Science, 1999. 40(13): p. 3287-3291. 162. Yoshida, T., et al., The potential role of amyloid beta in the pathogenesis of age-related macular degeneration. The Journal of clinical investigation, 2005. 115(10): p. 2793-2800. 163. Shi, H., et al., Identification of early pericyte loss and vascular amyloidosis in Alzheimer's disease retina. Acta Neuropathol, 2020. 139(5): p. 813-836. 101 164. Zhang, Y.S., et al., Parafoveal vessel loss and correlation between peripapillary vessel density and cognitive performance in amnestic mild cognitive impairment and early Alzheimer’s Disease on optical coherence tomography angiography. PLOS ONE, 2019. 14(4): p. e0214685. 165. Jiang, H., et al., Impaired retinal microcirculation in patients with Alzheimer's disease. PLoS One, 2018. 13(2): p. e0192154. 166. Cheung, C.Y.-l., et al., Microvascular network alterations in the retina of patients with Alzheimer's disease. Alzheimer's & Dementia, 2014. 10(2): p. 135-142. 167. Williams, M.A., et al., Retinal microvascular network attenuation in Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2015. 1(2): p. 229-235. 168. Adair TH, M.J., Angiogenesis. 2010, San Rafael, California: Morgan & Claypool Life Sciences. 169. Liu, B., et al., Amyloid-peptide vaccinations reduce {beta}-amyloid plaques but exacerbate vascular deposition and inflammation in the retina of Alzheimer's transgenic mice. The American journal of pathology, 2009. 175(5): p. 2099-2110. 170. Kinney, J.W., et al., Inflammation as a central mechanism in Alzheimer's disease. Alzheimer's & dementia (New York, N. Y.), 2018. 4: p. 575-590. 171. Medawar, P.B., Immunity to homologous grafted skin; the fate of skin homografts transplanted to the brain, to subcutaneous tissue, and to the anterior chamber of the eye. British journal of experimental pathology, 1948. 29(1): p. 58-69. 172. Engelhardt, B. and S. Liebner, Novel insights into the development and maintenance of the blood–brain barrier. Cell and Tissue Research, 2014. 355(3): p. 687-699. 173. Campbell, M. and P. Humphries, The blood-retina barrier: tight junctions and barrier modulation. Adv Exp Med Biol, 2012. 763: p. 70-84. 174. Vitkovic, L., S. Maeda, and E. Sternberg, Anti-inflammatory cytokines: expression and action in the brain. Neuroimmunomodulation, 2001. 9(6): p. 295-312. 175. Sochocka, M., B.S. Diniz, and J. Leszek, Inflammatory Response in the CNS: Friend or Foe? Molecular neurobiology, 2017. 54(10): p. 8071-8089. 176. Lowther, D.E. and D.A. Hafler, Regulatory T cells in the central nervous system. Immunological Reviews, 2012. 248(1): p. 156-169. 177. Keino, H., S. Horie, and S. Sugita, Immune Privilege and Eye-Derived T-Regulatory Cells. Journal of immunology research, 2018. 2018: p. 1679197-1679197. 178. Yucel, Y. and N. Gupta, Lymphatic drainage from the eye: A new target for therapy. Prog Brain Res, 2015. 220: p. 185-98. 179. Sun, B.L., et al., Lymphatic drainage system of the brain: A novel target for intervention of neurological diseases. Prog Neurobiol, 2018. 163-164: p. 118-143. 180. Murakami, Y., et al., Innate immune response in retinal homeostasis and inflammatory disorders. Progress in Retinal and Eye Research, 2020. 74: p. 100778. 181. Taylor, A.W., Ocular Immune Privilege and Transplantation. Frontiers in Immunology, 2016. 7(37). 182. Runkle, E.A. and D.A. Antonetti, The blood-retinal barrier: structure and functional significance. Methods Mol Biol, 2011. 686: p. 133-48. 183. Zhou, R. and R.R. Caspi, Ocular immune privilege. F1000 biology reports, 2010. 2: p. 3. 102 184. Eichler, W., et al., Hypoxia: modulation of endothelial cell proliferation by soluble factors released by retinal cells. Neuroreport, 2001. 12(18): p. 4103-8. 185. Kawazoe, Y., et al., Retinoic acid from retinal pigment epithelium induces T regulatory cells. Exp Eye Res, 2012. 94(1): p. 32-40. 186. Sugita, S., et al., Retinal pigment epithelium-derived CTLA-2alpha induces TGFbeta-producing T regulatory cells. J Immunol, 2008. 181(11): p. 7525-36. 187. Caspi, R.R., F.G. Roberge, and R.B. Nussenblatt, Organ-resident, nonlymphoid cells suppress proliferation of autoimmune T-helper lymphocytes. Science, 1987. 237(4818): p. 1029-32. 188. Vendomèle, J., Q. Khebizi, and S. Fisson, Cellular and Molecular Mechanisms of Anterior Chamber-Associated Immune Deviation (ACAID): What We Have Learned from Knockout Mice. Frontiers in immunology, 2017. 8: p. 1686-1686. 189. Schwartz, M. and A. Deczkowska, Neurological Disease as a Failure of Brain-Immune Crosstalk: The Multiple Faces of Neuroinflammation. Trends Immunol, 2016. 37(10): p. 668-679. 190. Su, F., F. Bai, and Z. Zhang, Inflammatory Cytokines and Alzheimer's Disease: A Review from the Perspective of Genetic Polymorphisms. Neuroscience bulletin, 2016. 32(5): p. 469-480. 191. Dansokho, C., et al., Regulatory T cells delay disease progression in Alzheimer-like pathology. Brain, 2016. 139(4): p. 1237-1251. 192. Kaeser, P.-F., J. Ghika, and F.-X. Borruat, Visual signs and symptoms in patients with the visual variant of Alzheimer disease. BMC Ophthalmology, 2015. 15(1): p. 65. 193. Levine, D.N., J.M. Lee, and C.M. Fisher, The visual variant of Alzheimer's disease: a clinicopathologic case study. Neurology, 1993. 43(2): p. 305-13. 194. Galton, C.J., et al., Atypical and typical presentations of Alzheimer's disease: a clinical, neuropsychological, neuroimaging and pathological study of 13 cases. Brain, 2000. 123 Pt 3: p. 484-98. 195. Snowden, J.S., et al., Cognitive Phenotypes in Alzheimer's Disease and Genetic Risk. Cortex, 2007. 43(7): p. 835-845. 196. Esquerda-Canals, G., et al., Mouse Models of Alzheimer's Disease. J Alzheimers Dis, 2017. 57(4): p. 1171-1183. 197. Tai, L.M., et al., Chapter One - Relevance of transgenic mouse models for Alzheimer's disease, in Progress in Molecular Biology and Translational Science, D.B. Teplow, Editor. 2021, Academic Press. p. 1-48. 198. Hsiao, K., et al., Correlative Memory Deficits, Aβ Elevation, and Amyloid Plaques in Transgenic Mice. Science, 1996. 274(5284): p. 99-103. 199. Kawarabayashi, T., et al., Age-dependent changes in brain, CSF, and plasma amyloid (beta) protein in the Tg2576 transgenic mouse model of Alzheimer's disease. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2001. 21(2): p. 372-381. 200. Race, R.E., et al., Neuron-specific expression of a hamster prion protein minigene in transgenic mice induces susceptibility to hamster scrapie agent. Neuron, 1995. 15(5): p. 1183-1191. 201. Isaacs, J.D., G.S. Jackson, and D.M. Altmann, The role of the cellular prion protein in the immune system. Clinical and experimental immunology, 2006. 146(1): p. 1-8. 103 202. Westerman, M.A., et al., The Relationship between Aβ and Memory in the Tg2576 Mouse Model of Alzheimer's Disease. The Journal of Neuroscience, 2002. 22(5): p. 1858-1867. 203. Frautschy, S.A., et al., Microglial response to amyloid plaques in APPsw transgenic mice. The American journal of pathology, 1998. 152(1): p. 307-317. 204. Smith, M.A., et al., Amyloid-β Deposition in Alzheimer Transgenic Mice Is Associated with Oxidative Stress. Journal of Neurochemistry, 1998. 70(5): p. 2212-2215. 205. Pappolla, M.A., et al., Evidence of oxidative stress and in vivo neurotoxicity of beta-amyloid in a transgenic mouse model of Alzheimer's disease: a chronic oxidative paradigm for testing antioxidant therapies in vivo. The American journal of pathology, 1998. 152(4): p. 871-877. 206. Schmid, S., et al., Cognitive decline in Tg2576 mice shows sex-specific differences and correlates with cerebral amyloid-beta. Behavioural Brain Research, 2019. 359: p. 408-417. 207. Callahan, M.J., et al., Augmented senile plaque load in aged female beta-amyloid precursor protein-transgenic mice. The American journal of pathology, 2001. 158(3): p. 1173-1177. 208. King, D.L., et al., Progressive and gender-dependent cognitive impairment in the APPSW transgenic mouse model for Alzheimer’s disease. Behavioural Brain Research, 1999. 103(2): p. 145-162. 209. Alexander, G., et al., Increased aggression in males in transgenic Tg2576 mouse model of Alzheimer's disease. Behavioural Brain Research, 2011. 216(1): p. 77-83. 210. Ujie, M., et al., Blood—Brain Barrier Permeability Precedes Senile Plaque Formation in an Alzheimer Disease Model. Microcirculation, 2003. 10(6): p. 463-470. 211. Irizarry, M.C., et al., APPsw Transgenic Mice Develop Age-related Aβ Deposits and Neuropil Abnormalities, but no Neuronal Loss in CA1. Journal of Neuropathology & Experimental Neurology, 1997. 56(9): p. 965-973. 212. Li, L., et al., A Comparative Study of Five Mouse Models of Alzheimer's Disease: Cell Cycle Events Reveal New Insights into Neurons at Risk for Death. International Journal of Alzheimer's Disease, 2011. 2011: p. 171464. 213. Tomidokoro, Y., et al., Brain Aβ amyloidosis in APPsw mice induces accumulation of presenilin-1 and tau. The Journal of Pathology, 2001. 194(4): p. 500-506. 214. Chiu, K., et al., Neurodegeneration of the retina in mouse models of Alzheimer's disease: what can we learn from the retina? Age (Dordrecht, Netherlands), 2012. 34(3): p. 633-649. 215. Parnell, M., et al., Ocular Manifestations of Alzheimer’s Disease in Animal Models. International Journal of Alzheimer's Disease, 2012. 2012: p. 786494. 216. Alexandrov, P.N., et al., Retinal amyloid peptides and complement factor H in transgenic models of Alzheimer's disease. Neuroreport, 2011. 22(12): p. 623-627. 217. Dutescu, R.M., et al., Amyloid precursor protein processing and retinal pathology in mouse models of Alzheimer’s disease. Graefe's Archive for Clinical and Experimental Ophthalmology, 2009. 247(9): p. 1213-1221. 218. Williams, P.A., et al., Retinal ganglion cell dendritic degeneration in a mouse model of Alzheimer's disease. Neurobiology of Aging, 2013. 34(7): p. 1799-1806. 219. Vagnucci, A.H. and W.W. Li, Alzheimer's disease and angiogenesis. The Lancet, 2003. 361(9357): p. 605-608. 104 220. Render, J.A., et al., Clinical and Microscopic Characterization of Ocular Findings in Tg2576 ßAPP-Transgenic Mice. Investigative Ophthalmology & Visual Science, 2003. 44(13): p. 3063-3063. 221. DeTure, M.A. and D.W. Dickson, The neuropathological diagnosis of Alzheimer’s disease. Molecular Neurodegeneration, 2019. 14(1): p. 32. 222. Ikeda, S., D. Allsop, and G.G. Glenner, Morphology and distribution of plaque and related deposits in the brains of Alzheimer's disease and control cases. An immunohistochemical study using amyloid beta-protein antibody. Lab Invest, 1989. 60(1): p. 113-22. 223. Gravina, S.A., et al., Amyloid β Protein (Aβ) in Alzheimeri's Disease Brain: BIOCHEMICAL AND IMMUNOCYTOCHEMICAL ANALYSIS WITH ANTIBODIES SPECIFIC FOR FORMS ENDING AT Aβ40 OR Aβ42(43). Journal of Biological Chemistry, 1995. 270(13): p. 7013-7016. 224. Okoye, M.I. and I. Watanabe, Ultrastructural features of cerebral amyloid angiopathy. Human Pathology, 1982. 13(12): p. 1127-1132. 225. Roher, A.E., et al., beta-Amyloid-(1-42) is a major component of cerebrovascular amyloid deposits: implications for the pathology of Alzheimer disease. Proceedings of the National Academy of Sciences of the United States of America, 1993. 90(22): p. 10836-10840. 226. Probst, A., P.U. Heitz, and J. Ulrich, Histochemical analysis of senile plaque amyloid and amyloid angiopathy. Virchows Arch A Pathol Anat Histol, 1980. 388(3): p. 327-34. 227. Revesz, T., et al., Sporadic and familial cerebral amyloid angiopathies. Brain Pathol, 2002. 12(3): p. 343-57. 228. Baranello, R.J., et al., Amyloid-beta protein clearance and degradation (ABCD) pathways and their role in Alzheimer's disease. Current Alzheimer research, 2015. 12(1): p. 32-46. 229. Puig, K.L. and C.K. Combs, Expression and function of APP and its metabolites outside the central nervous system. Experimental gerontology, 2013. 48(7): p. 608-611. 230. Paulsen, I.M.S., H. Dimke, and S. Frische, A single simple procedure for dewaxing, hydration and heat-induced epitope retrieval (HIER) for immunohistochemistry in formalin fixed paraffin-embedded tissue. European journal of histochemistry : EJH, 2015. 59(4): p. 2532-2532. 231. Mucke, L. and D.J. Selkoe, Neurotoxicity of amyloid β-protein: synaptic and network dysfunction. Cold Spring Harbor perspectives in medicine, 2012. 2(7): p. a006338-a006338. 232. Urbanc, B., et al., Neurotoxic effects of thioflavin S-positive amyloid deposits in transgenic mice and Alzheimer's disease. Proceedings of the National Academy of Sciences, 2002. 99(22): p. 13990-13995. 233. Jack, C.R., et al., Rates of β-amyloid accumulation are independent of hippocampal neurodegeneration. Neurology, 2014. 82(18): p. 1605-1612. 234. Jagust, W.J. and S.M. Landau, Temporal Dynamics of β-Amyloid Accumulation in Aging and Alzheimer Disease. Neurology, 2021. 96(9): p. e1347-e1357. 235. Broersen, K., F. Rousseau, and J. Schymkowitz, The culprit behind amyloid beta peptide related neurotoxicity in Alzheimer's disease: oligomer size or conformation? Alzheimer's Research & Therapy, 2010. 2(4): p. 12. 105 236. Pugsley, M.K. and R. Tabrizchi, The vascular system. An overview of structure and function. J Pharmacol Toxicol Methods, 2000. 44(2): p. 333-40. 237. Fruttiger, M., Development of the retinal vasculature. Angiogenesis, 2007. 10(2): p. 77-88. 238. Mancuso, M.R., F. Kuhnert, and C.J. Kuo, Developmental angiogenesis of the central nervous system. Lymphatic research and biology, 2008. 6(3-4): p. 173-180. 239. Chung, A.S. and N. Ferrara, Developmental and Pathological Angiogenesis. Annual Review of Cell and Developmental Biology, 2011. 27(1): p. 563-584. 240. Sapieha, P., Eyeing central neurons in vascular growth and reparative angiogenesis. Blood, 2012. 120(11): p. 2182-2194. 241. Garcez, P.P., et al., Zika virus impairs the development of blood vessels in a mouse model of congenital infection. Scientific reports, 2018. 8(1): p. 12774-12774. 242. Zhu, Y., et al., The TRPC5 channel regulates angiogenesis and promotes recovery from ischemic injury in mice. J Biol Chem, 2019. 294(1): p. 28-37. 243. Hu, L., et al., PLF-1 (Proliferin-1) Modulates Smooth Muscle Cell Proliferation and Development of Experimental Intimal Hyperplasia. Journal of the American Heart Association, 2019. 8(24): p. e005886-e005886. 244. Mahoney, E.R., et al., Brain expression of the vascular endothelial growth factor gene family in cognitive aging and alzheimer’s disease. Molecular Psychiatry, 2019. 245. Singh, C., C.G. Pfeifer, and W.A. Jefferies, Pathogenic Angiogenic Mechanisms in Alzheimer's Disease, in Physiologic and Pathologic Angiogenesis - Signaling Mechanisms and Targeted Therapy. 2017. 246. Becher, B., S. Tugues, and M. Greter, GM-CSF: From Growth Factor to Central Mediator of Tissue Inflammation. Immunity, 2016. 45(5): p. 963-973. 247. Lopez-Vicchi, F., et al., Metabolic functions of prolactin: Physiological and pathological aspects. Journal of Neuroendocrinology, 2020. 32(11): p. e12888. 248. Afshar Moghaddam, N., P. Mahsuni, and D. Taheri, Evaluation of Endoglin as an Angiogenesis Marker in Glioblastoma. Iranian journal of pathology, 2015. 10(2): p. 89-96. 249. Szklarczyk, D., et al., STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic acids research, 2019. 47(D1): p. D607-D613. 250. Consortium, T.U., UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 2020. 49(D1): p. D480-D489. 251. Gruol, D.L., Impact of Increased Astrocyte Expression of IL-6, CCL2 or CXCL10 in Transgenic Mice on Hippocampal Synaptic Function. Brain sciences, 2016. 6(2): p. 19. 252. Miyagi, H., et al., The role of hepatocyte growth factor in corneal wound healing. Experimental eye research, 2018. 166: p. 49-55. 253. Lee, C.-H., et al., Interleukin-1 beta transactivates epidermal growth factor receptor via the CXCL1-CXCR2 axis in oral cancer. Oncotarget, 2015. 6(36): p. 38866-38880. 254. Biondo, C., et al., The interleukin-1β/CXCL1/2/neutrophil axis mediates host protection against group B streptococcal infection. Infection and immunity, 2014. 82(11): p. 4508-4517. 106 255. Katoh, M., FGFR inhibitors: Effects on cancer cells, tumor microenvironment and whole-body homeostasis (Review). International journal of molecular medicine, 2016. 38(1): p. 3-15. 256. Raab, G. and M. Klagsbrun, Heparin-binding EGF-like growth factor. Biochim Biophys Acta, 1997. 1333(3): p. F179-99. 257. Zenaro, E., G. Piacentino, and G. Constantin, The blood-brain barrier in Alzheimer's disease. Neurobiology of disease, 2017. 107: p. 41-56. 258. Yang, Y., et al., Vascular tight junction disruption and angiogenesis in spontaneously hypertensive rat with neuroinflammatory white matter injury. Neurobiol Dis, 2018. 114: p. 95-110. 259. Wallez, Y. and P. Huber, Endothelial adherens and tight junctions in vascular homeostasis, inflammation and angiogenesis. Biochimica et Biophysica Acta (BBA) - Biomembranes, 2008. 1778(3): p. 794-809. 260. Ved, N., et al., Vascular endothelial growth factor-A(165)b ameliorates outer-retinal barrier and vascular dysfunction in the diabetic retina. Clinical science (London, England : 1979), 2017. 131(12): p. 1225-1243. 261. Montagne, A., et al., Blood-Brain Barrier Breakdown in the Aging Human Hippocampus. Neuron, 2015. 85(2): p. 296-302. 262. Nation, D.A., et al., Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nature Medicine, 2019. 25(2): p. 270-276. 263. Polis, B., et al., Norvaline Restores the BBB Integrity in a Mouse Model of Alzheimer's Disease. Int J Mol Sci, 2019. 20(18). 264. Skike, C.E.V., et al., Inhibition of mTOR protects the blood-brain barrier in models of Alzheimer’s disease and vascular cognitive impairment. American Journal of Physiology-Heart and Circulatory Physiology, 2018. 314(4): p. H693-H703. 265. Park, S.W., et al., Expression of Amyloid Beta 42 in RPE of Alzheimer Disease Mouse Model. Investigative Ophthalmology & Visual Science, 2012. 53(14): p. 5298-5298. 266. Liu, C., et al., Subretinal injection of amyloid-β peptide accelerates RPE cell senescence and retinal degeneration. Int J Mol Med, 2015. 35(1): p. 169-176. 267. Masuda, N., et al., Effects of concentration of amyloid β (Aβ) on viability of cultured retinal pigment epithelial cells. BMC ophthalmology, 2019. 19(1): p. 70-70. 268. Vinores, S.A., et al., Cellular mechanisms of blood-retinal barrier dysfunction in macular edema. Documenta Ophthalmologica, 1999. 97(3): p. 217. 269. Chen, M., et al., Retinal pigment epithelial cell multinucleation in the aging eye - a mechanism to repair damage and maintain homoeostasis. Aging cell, 2016. 15(3): p. 436-445. 270. Morcos, Y., et al., Immunolocalization of occludin and claudin-1 to tight junctions in intact CNS vessels of mammalian retina. J Neurocytol, 2001. 30(2): p. 107-23. 271. Naylor, A., et al., Tight Junctions of the Outer Blood Retina Barrier. International Journal of Molecular Sciences, 2020. 21(1): p. 211. 272. Alnawaiseh, M., et al., OCT angiography in the mouse: A novel evaluation method for vascular pathologies of the mouse retina. Exp Eye Res, 2016. 145: p. 417-423. 273. Grimaldi, A., et al., Inflammation, neurodegeneration and protein aggregation in the retina as ocular biomarkers for Alzheimer's disease in the 3xTg-AD mouse model. Cell death & disease, 2018. 9(6): p. 685-685. 107 274. Parthasarathy, R., et al., Reduction of amyloid-beta levels in mouse eye tissues by intra-vitreally delivered neprilysin. Exp Eye Res, 2015. 138: p. 134-44. 275. Ning, A., et al., Amyloid-beta deposits lead to retinal degeneration in a mouse model of Alzheimer disease. Investigative ophthalmology & visual science, 2008. 49(11): p. 5136-5143. 276. Steffen, J., et al., Expression of endogenous mouse APP modulates β-amyloid deposition in hAPP-transgenic mice. Acta Neuropathologica Communications, 2017. 5(1): p. 49. 277. Jankowsky, J.L., et al., Rodent Aβ Modulates the Solubility and Distribution of Amyloid Deposits in Transgenic Mice*. Journal of Biological Chemistry, 2007. 282(31): p. 22707-22720. 278. Mahler, J., et al., Endogenous murine Aβ increases amyloid deposition in APP23 but not in APPPS1 transgenic mice. Neurobiology of Aging, 2015. 36(7): p. 2241-2247. 279. Chesebro, B., et al., Identification of scrapie prion protein-specific mRNA in scrapie-infected and uninfected brain. Nature, 1985. 315(6017): p. 331-3. 280. Oesch, B., et al., A cellular gene encodes scrapie PrP 27-30 protein. Cell, 1985. 40(4): p. 735-46. 281. Fagerberg, L., et al., Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics, 2014. 13(2): p. 397-406. 282. Kercher, L., et al., Prion Protein Expression Differences in Microglia and Astroglia Influence Scrapie-Induced Neurodegeneration in the Retina and Brain of Transgenic Mice. Journal of Virology, 2007. 81(19): p. 10340-10351. 283. Orrù, C.D., et al., Prion Seeds Distribute throughout the Eyes of Sporadic Creutzfeldt-Jakob Disease Patients. mBio, 2018. 9(6): p. e02095-18. 284. Head, M.W., et al., Prion Protein Accumulation in Eyes of Patients with Sporadic and Variant Creutzfeldt-Jakob Disease. Investigative Ophthalmology & Visual Science, 2003. 44(1): p. 342-346. 285. Zhang, Y., et al., An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex. The Journal of Neuroscience, 2014. 34(36): p. 11929-11947. 286. Zhang, Y., et al., Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse. Neuron, 2016. 89(1): p. 37-53. 287. Klunk, W.E. and J.W. Pettegrew, Alzheimer's β-Amyloid Protein Is Covalently Modified when Dissolved in Formic Acid. Journal of Neurochemistry, 1990. 54(6): p. 2050-2056. 288. D'Andrea, M.R., et al., The use of formic acid to embellish amyloid plaque detection in Alzheimer's disease tissues misguides key observations. Neurosci Lett, 2003. 342(1-2): p. 114-8. 289. Roberts, B.R., et al., Biochemically-defined pools of amyloid-β in sporadic Alzheimer's disease: correlation with amyloid PET. Brain, 2017. 140(5): p. 1486-1498. 290. Hart, N.J., et al., Ocular indicators of Alzheimer's: exploring disease in the retina. Acta neuropathologica, 2016. 132(6): p. 767-787. 291. Yakupova, E.I., et al., Congo Red and amyloids: history and relationship. Bioscience reports, 2019. 39(1): p. BSR20181415. 108 292. Rozemuller, J.M., et al., A4 Protein in Alzheimer's Disease: Primary and Secondary Cellular Events in Extracellular Amyloid Deposition. Journal of Neuropathology & Experimental Neurology, 1989. 48(6): p. 674-691. 293. Selkoe, D.J., Alzheimer's Disease: Genes, Proteins, and Therapy. Physiological Reviews, 2001. 81(2): p. 741-766. 294. Anderson, D.H., et al., Characterization of beta amyloid assemblies in drusen: the deposits associated with aging and age-related macular degeneration. Exp Eye Res, 2004. 78(2): p. 243-56. 295. Chidlow, G., et al., Investigations into Retinal Pathology in the Early Stages of a Mouse Model of Alzheimer's Disease. J Alzheimers Dis, 2017. 56(2): p. 655-675. 296. Apelt, J., K. Ach, and R. Schliebs, Aging-related down-regulation of neprilysin, a putative beta-amyloid-degrading enzyme, in transgenic Tg2576 Alzheimer-like mouse brain is accompanied by an astroglial upregulation in the vicinity of beta-amyloid plaques. Neurosci Lett, 2003. 339(3): p. 183-6. 297. Iwata, N., et al., Metabolic regulation of brain Abeta by neprilysin. Science, 2001. 292(5521): p. 1550-2. 298. Reichenbach, A. and A. Bringmann, New functions of Müller cells. Glia, 2013. 61(5): p. 651-78. 299. Bringmann, A., et al., Müller cells in the healthy and diseased retina. Prog Retin Eye Res, 2006. 25(4): p. 397-424. 300. den Haan, J., et al., Amyloid-beta and phosphorylated tau in post-mortem Alzheimer’s disease retinas. Acta Neuropathologica Communications, 2018. 6(1): p. 147. 301. Le, Y.-Z., VEGF production and signaling in Müller glia are critical to modulating vascular function and neuronal integrity in diabetic retinopathy and hypoxic retinal vascular diseases. Vision Research, 2017. 139: p. 108-114. 302. Xin, X., et al., Hypoxic retinal Müller cells promote vascular permeability by HIF-1–dependent up-regulation of angiopoietin-like 4. Proceedings of the National Academy of Sciences, 2013. 110(36): p. E3425-E3434. 303. Qi, X., V.P. Sarthy, and J.S. Penn, VEGF Induction by Hypoxia in Muller cells. Investigative Ophthalmology & Visual Science, 2004. 45(13): p. 1887-1887. 304. Fernández-Albarral, J.A., et al., Retinal glial changes in Alzheimer's disease - A review. Journal of optometry, 2019. 12(3): p. 198-207. 305. Shah, T.M., et al., Beta-amyloid sequelae in the eye: a critical review on its diagnostic significance and clinical relevance in Alzheimer's disease. Mol Psychiatry, 2017. 22(3): p. 353-363. 306. Mei, X., et al., Retinal Levels of Amyloid Beta Correlate with Cerebral Levels of Amyloid Beta in Young APPswe/PS1dE9 Transgenic Mice before Onset of Alzheimer’s Disease. Behavioural Neurology, 2020. 2020: p. 1574816. 307. Tarkowski, E., et al., Local and systemic GM-CSF increase in Alzheimer's disease and vascular dementia. Acta Neurologica Scandinavica, 2001. 103(3): p. 166-174. 308. Granger DN, S.E., Angiogenesis, in Inflammation and the Microcirculation. 2010, Morgan & Claypool Life Sciences: San Rafael (CA). 309. Hyman, B. and R.E. Tanzi, Effects of Species-Specific Genetics on Alzheimer’s Mouse Models. Neuron, 2019. 101(3): p. 351-352. 109 310. Reyes-Mendoza, J. and T. Morales, Post-treatment with prolactin protects hippocampal CA1 neurons of the ovariectomized female rat against kainic acid-induced neurodegeneration. Neuroscience, 2016. 328: p. 58-68. 311. Tejadilla, D., M. Cerbón, and T. Morales, Prolactin reduces the damaging effects of excitotoxicity in the dorsal hippocampus of the female rat independently of ovarian hormones. Neuroscience, 2010. 169(3): p. 1178-85. 312. Galimberti, D., et al., Intrathecal chemokine synthesis in mild cognitive impairment and Alzheimer disease. Arch Neurol, 2006. 63(4): p. 538-43. 313. Westin, K., et al., CCL2 is associated with a faster rate of cognitive decline during early stages of Alzheimer's disease. PLoS One, 2012. 7(1): p. e30525. 314. Corrêa, J.D., et al., Chemokines in CSF of Alzheimer's disease patients. Arquivos de Neuro-Psiquiatria, 2011. 69: p. 455-459. 315. Lee, W.-J., et al., Plasma MCP-1 and Cognitive Decline in Patients with Alzheimer’s Disease and Mild Cognitive Impairment: A Two-year Follow-up Study. Scientific Reports, 2018. 8(1): p. 1280. 316. Krauthausen, M., et al., CXCR3 promotes plaque formation and behavioral deficits in an Alzheimer's disease model. The Journal of clinical investigation, 2015. 125(1): p. 365-378. 317. Zhang, K., et al., CXCL1 contributes to β-amyloid-induced transendothelial migration of monocytes in Alzheimer's disease. PloS one, 2013. 8(8): p. e72744-e72744. 318. Katsouri, L., et al., Systemic administration of fibroblast growth factor-2 (FGF2) reduces BACE1 expression and amyloid pathology in APP23 mice. Neurobiol Aging, 2015. 36(2): p. 821-31. 319. Chen, X., et al., Low and High Molecular Weight FGF-2 Have Differential Effects on Astrocyte Proliferation, but Are Both Protective Against Aβ-Induced Cytotoxicity. Frontiers in Molecular Neuroscience, 2020. 12(328). 320. Kiyota, T., et al., FGF2 gene transfer restores hippocampal functions in mouse models of Alzheimer's disease and has therapeutic implications for neurocognitive disorders. Proceedings of the National Academy of Sciences, 2011. 108(49): p. E1339-E1348. 321. Donnini, S., et al., FGF-2 overexpression opposes the beta amyloid toxic injuries to the vascular endothelium. Cell Death Differ, 2006. 13(7): p. 1088-96. 322. Noda, M., et al., FGF-2 released from degenerating neurons exerts microglial-induced neuroprotection via FGFR3-ERK signaling pathway. Journal of Neuroinflammation, 2014. 11(1): p. 76. 323. Chen, W., et al., MicroRNA-107 Ameliorates Damage in a Cell Model of Alzheimer’s Disease by Mediating the FGF7/FGFR2/PI3K/Akt Pathway. Journal of Molecular Neuroscience, 2020. 70(10): p. 1589-1597. 324. Jin, K., et al., Heparin-Binding Epidermal Growth Factor-Like Growth Factor: Hypoxia-Inducible Expression In Vitro and Stimulation of Neurogenesis In Vitro and In Vivo. The Journal of Neuroscience, 2002. 22(13): p. 5365-5373. 325. Jun, G.R., et al., Transethnic genome-wide scan identifies novel Alzheimer's disease loci. Alzheimers Dement, 2017. 13(7): p. 727-738. 326. da Rocha, J.F., et al., APP binds to the EGFR ligands HB-EGF and EGF, acting synergistically with EGF to promote ERK signaling and neuritogenesis. bioRxiv, 2020: p. 2020.06.12.149062. 110 327. Heneka, M.T., D.T. Golenbock, and E. Latz, Innate immunity in Alzheimer's disease. Nature Immunology, 2015. 16(3): p. 229-236. 328. Grassivaro, F., et al., Convergence between Microglia and Peripheral Macrophages Phenotype during Development and Neuroinflammation. The Journal of Neuroscience, 2020. 40(4): p. 784-795. 329. Ratnayaka, J.A., L.C. Serpell, and A.J. Lotery, Dementia of the eye: the role of amyloid beta in retinal degeneration. Eye (London, England), 2015. 29(8): p. 1013-1026. 330. Rogers, J., et al., Microglia and inflammatory mechanisms in the clearance of amyloid beta peptide. Glia, 2002. 40(2): p. 260-9. 331. Rivera-Escalera, F., et al., IL-1β-driven amyloid plaque clearance is associated with an expansion of transcriptionally reprogrammed microglia. Journal of Neuroinflammation, 2019. 16(1): p. 261. 332. Kim, E.K. and E.-J. Choi, Pathological roles of MAPK signaling pathways in human diseases. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 2010. 1802(4): p. 396-405. 333. Mercado-Gómez, O., et al., Inhibition of Wnt and PI3K Signaling Modulates GSK-3β Activity and Induces Morphological Changes in Cortical Neurons: Role of Tau Phosphorylation. Neurochemical Research, 2008. 33(8): p. 1599-1609. 334. Choi, J.K., et al., Granulocyte macrophage colony-stimulating factor shows anti-apoptotic activity via the PI3K-NF-κB-HIF-1α-survivin pathway in mouse neural progenitor cells. Mol Neurobiol, 2014. 49(2): p. 724-33. 335. Laws, K.R., K. Irvine, and T.M. Gale, Sex differences in Alzheimer's disease. Current Opinion in Psychiatry, 2018. 31(2): p. 133-139. 336. Lorenz, M., et al., Does cellular sex matter? Dimorphic transcriptional differences between female and male endothelial cells. Atherosclerosis, 2015. 240(1): p. 61-72. 337. Hartman, R.J.G., et al., Intrinsic transcriptomic sex differences in human endothelial cells at birth and in adults are associated with coronary artery disease targets. Scientific Reports, 2020. 10(1): p. 12367. 338. Li, Z., et al., Evaluation of retinal and choroidal changes in patients with Alzheimer’s type dementia using optical coherence tomography angiography. 2019. 339. Alizadeh, E., P. Mammadzada, and H. André, The Different Facades of Retinal and Choroidal Endothelial Cells in Response to Hypoxia. International Journal of Molecular Sciences, 2018. 19(12): p. 3846. 340. Rajappa, M., P. Saxena, and J. Kaur, Ocular angiogenesis: mechanisms and recent advances in therapy. Adv Clin Chem, 2010. 50: p. 103-21. 341. Plate, K.H., Mechanisms of angiogenesis in the brain. J Neuropathol Exp Neurol, 1999. 58(4): p. 313-20. 342. Marques, F., et al., Blood–brain-barriers in aging and in Alzheimer’s disease. Molecular Neurodegeneration, 2013. 8(1): p. 38. 343. Erickson, M.A. and W.A. Banks, Blood-brain barrier dysfunction as a cause and consequence of Alzheimer's disease. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 2013. 33(10): p. 1500-1513. 111 344. Poduslo, J.F., et al., Permeability of Proteins at the Blood–Brain Barrier in the Normal Adult Mouse and Double Transgenic Mouse Model of Alzheimer's Disease. Neurobiology of Disease, 2001. 8(4): p. 555-567. 345. Gustafsson, S., et al., Blood-brain barrier integrity in a mouse model of Alzheimer's disease with or without acute 3D6 immunotherapy. Neuropharmacology, 2018. 143: p. 1-9. 346. Bien-Ly, N., et al., Lack of Widespread BBB Disruption in Alzheimer's Disease Models: Focus on Therapeutic Antibodies. Neuron, 2015. 88(2): p. 289-297. 347. Steuer, H., et al., Functional Characterization and Comparison of the Outer Blood–Retina Barrier and the Blood–Brain Barrier. Investigative Ophthalmology & Visual Science, 2005. 46(3): p. 1047-1053. 348. Bruban, J., et al., Amyloid-β(1-42) alters structure and function of retinal pigmented epithelial cells. Aging Cell, 2009. 8(2): p. 162-177. 349. Cao, L., et al., Aβ-Induced Senescent Retinal Pigment Epithelial Cells Create a Proinflammatory Microenvironment in AMD. Investigative Ophthalmology & Visual Science, 2013. 54(5): p. 3738-3750. 112 Appendix Supplementary RNA-Seq Data comparing wild-type and Tg2576 eyes and brain Method: Brains and eyes from eleven-month old mice were harvested and RNA was extracted using PureLink™ RNA Mini Kit (Invitrogen, California, US) as per manufacturer’s instructions and standard protocols. After RNA extraction, samples were sent to BRC-Seq for quality check and RNA sequencing using Illumina NextSeq 500 machine (Ilumina, San Diego, US). The eyes and brains from a total 3 wild-type male (WT-M), 3 wild-type female (WT-F), 3 Tg2576 male (Tg-M) and 3 Tg2576 female (Tg-F) mice were used for sequencing. Description: Raw data of differential analyses between WT vs Tg brains (Table S1a), WT vs Tg eyes (Table S1b), WT-M vs WT-F brains (Table S1c), WT-M vs WT-F eyes (Table S1d), Tg-M vs Tg-F brains (Table S1e), Tg-M vs Tg-F eyes (Table S1f), WT-M vs Tg-M brains (Table S1g), WT-M vs Tg-M eyes (Table S1h), WT-F vs Tg-F brains (Table S1i), WT-F vs Tg-F eyes (Table S1j) File: Tg2576_Mice_Brain_Eye_RNA_Seq_Tables.csv.zip
Thesis/Dissertation
eng
Microbiology and Immunology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Characterizing ocular pathology in an animal model of Alzheimer’s disease
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