Amyloid Formation and Metabolism in Induced Pluripotent Stem Cell Derived Neural Models

Amyloid formation and metabolism in induced pluripotent stem cell derived neural models

A thesis submitted to the University of Manchester for the degree of

Doctor of Philosophy (PhD)

In the Faculty of Biology, Medicine and Health

Helen A Rowland

2018

School of Biological Sciences

Table of contents

Table of contents ...... 2 List of figures ...... 7 List of tables ...... 9 List of abbreviations ...... 10 Abstract ...... 13 Declaration Statement ...... 14 Copyright Statement ...... 14 Acknowledgements ...... 15 Chapter 1: Introduction ...... 16 1.1 Overview of dementia and Alzheimer’s disease ...... 16 1.1.1 Proposed hypotheses of AD ...... 17 1.2 Hallmarks of AD...... 18 1.2.1 APP processing ...... 18 1.2.2 Aβ toxicity ...... 24 1.2.3 Tau pathology ...... 24 1.3 Familial and sporadic AD and genetic risk factors ...... 26 1.3.1 Familial AD (fAD) ...... 26 1.3.2 Sporadic AD (sAD) ...... 27 1.3.3 Apolipoprotein E ...... 27 1.3.4 Genome wide association studies (GWAS) identified AD risk factors ...... 28 1.4 Clearance mechanisms of Aβ ...... 29 1.4.1 Interstitial fluid (ISF) drainage pathway ...... 29 1.4.2 Blood brain barrier transport ...... 29 1.4.3 Phagocytosis and autophagy ...... 30 1.4.4 Proteolytic degradation ...... 31 1.5 Deriving iPSC-neurons ...... 35 1.6 Modelling AD with iPSCs ...... 38 1.6.1 iPSC-derived fAD models ...... 38 1.6.2 iPSC-derived sAD models ...... 43 1.6.3 Astrocytes ...... 44 1.7 Environmental risk factors in AD ...... 44 1.7.1 Ageing ...... 45

1.7.2 Mitochondrial dysfunction and oxidative stress ...... 45 1.7.3 Hypoxia ...... 46 1.7.4 Inflammation ...... 48 1.8 Thesis objectives ...... 51 1.8.1. What pathways are involved in the degradation of Aβ by iPSC-derived neurons? ...... 52 1.8.2 How does hypoxia alter Aβ production and degradation in iPSC-derived neurons? ...... 52 1.8.3 What role do iPSC-astrocytes play in the production and degradation of Aβ? .... 52 1.8.4 How do hypoxic or activated astrocytes affect Aβ production and degradation in iPSC-derived neurons? ...... 53 Chapter 2: Methods ...... 54 2.1 Cell culture ...... 54 2.1.1 Immortalised cell line culture ...... 54 2.1.2 iPSC lines ...... 54 2.1.3 iPSC maintenance ...... 56 2.1.4 Neuronal induction ...... 56 2.1.5 Expansion and differentiation of induced cells ...... 58 2.1.6 iPSC-astrocyte differentiation ...... 60 2.1.7 Primary astrocyte culture ...... 62 2.1.8 Inducing hypoxic conditions...... 62 2.1.9 Inducing astrocytic inflammatory response...... 62 2.1.10 Treatment of iPSC-neurons with astrocyte conditioned media (ACM) ...... 62 2.2 Sample preparation ...... 63 2.2.1 Media preparation ...... 63 2.2.2 Lysate preparation ...... 63 2.2.3 Bicinchoninic acid (BCA) assay ...... 63 2.3 Degradation and production of amyloid ...... 64 2.3.1 Amyloid-β preparation ...... 64 2.3.3 Measurement of Aβ ...... 64 2.3.4 FAβB (FAM-Aβ-Biotin) degradation assay ...... 65 2.4 Immunostaining ...... 65 2.4.1 Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) ...... 65 2.4.2 Immunoblotting ...... 66 2.4.3 Immunofluorescence microscopy ...... 66

2.4.4 Microscopes ...... 67 2.5 Polymerase chain reaction (PCR) ...... 70 2.5.1 APOE genotyping ...... 70 2.5.2 RT qPCR ...... 71 2.6 Membrane potential assay ...... 73 2.7 Statistics ...... 73 Chapter 3: What pathways are involved in the degradation of Aβ by iPSC-derived neurons? ...... 74 3.1 Introduction ...... 74 3.1.1 Characterisation of iPSC-neuron models ...... 74 3.1.2 iPSC-neuron models of AD ...... 74 3.1.3 Proteolytic degradation of Aβ ...... 75 3.1.4 Aims...... 76 3.2 Results ...... 77 3.2.1 Characterisation of OX1-19 iPSC neuronal differentiation ...... 77 3.2.2 APP processing in iPSC-neurons over time ...... 83 3.2.3 Characterisation of AD (APOE4/4) iPSC neuronal differentiation ...... 86 3.2.4 Characterisation of control (SBAD-02) iPSC neuronal differentiation ...... 86 3.2.5 Optimisation of the Aβ degradation assay ...... 90 3.2.6 Aβ degradation in neuronal models ...... 95 3.3 Discussion ...... 99 3.3.1 Characterisation of iPSC-neuron differentiation ...... 99 3.3.2 APP processing in iPSC-neurons ...... 100 3.3.3 Establishment and optimisation of an Aβ degradation assay...... 101 3.3.4 IDE is the major Aβ degrading protease in iPSC-neurons ...... 102 3.4 Chapter summary...... 103 Chapter 4: How does hypoxia alter Aβ production and degradation in iPSC-derived neurons? ...... 104 4.1 Introduction ...... 104 4.1.1 Hypoxia alters APP processing and Aβ levels ...... 104 4.1.2 Hypoxia alters Aβ degradation and clearance ...... 105 4.1.3 Hypoxia and APOE ...... 106 4.1.4 Aims...... 107 4.2 Results ...... 108

4.2.1 APP processing is altered in hypoxia ...... 108 4.2.2 Aβ degradation and IDE levels are decreased in hypoxia ...... 112 4.2.3 APP expression is increased in an AD cell line exposed to hypoxia ...... 115 4.2.4 IDE levels are decreased in an AD cell line exposed to hypoxia ...... 115 4.3 Discussion ...... 122 4.3.1 Inducing Hypoxia in iPSC-neurons ...... 122 4.3.2 APP expression and sAPPα are altered in iPSC-neurons exposed to hypoxia .... 122 4.3.3 Hypoxia decreases Aβ Degradation and IDE expression in iPSC-neurons exposed to hypoxia ...... 123 4.3.4 Conclusions and Future Work ...... 124 4.4 Chapter summary...... 125 Chapter 5: What role do iPSC-astrocytes play in the production and degradation of Aβ? . 126 5.1 Introduction ...... 126 5.1.1 Production of Aβ by astrocytes ...... 126 5.1.2 Clearance of Aβ by astrocytes ...... 126 5.1.3 Diversity of astrocytes ...... 127 5.1.4 Robust differentiation of stem-cell derived astrocytes ...... 128 5.1.5 Astrocyte models of AD ...... 129 5.1.6 Aims...... 130 5.2 Results ...... 131 5.2.1 Differentiation and characterisation of iPSC-astrocytes ...... 131 5.2.2 APP processing in iPSC-astrocytes and primary astrocytes ...... 137 5.2.3 Degradation of Aβ in iPSC-astrocytes and primary astrocytes ...... 140 5.2.4 The effect of iPSC-astrocyte conditioned media on Aβ in neurons ...... 143 5.3 Discussion ...... 146 5.3.1 Differentiation of iPSC-astrocytes ...... 146 5.3.2 APP processing is different between iPSC-astrocytes and iPSC-neurons ...... 147 5.3.3 Aβ is degraded by iPSC-astrocytes and primary astrocytes similar to iPSC-neurons ...... 148 5.3.4 The effect of astrocytes on neuron function and Aβ levels ...... 149 5.3.5 Conclusions and future work ...... 149 5.4 Chapter summary...... 150 Chapter 6: How do hypoxic or activated astrocytes affect Aβ production and degradation in iPSC-derived neurons? ...... 151 6.1 Introduction ...... 151

6.1.1 Astrocyte activation ...... 151 6.1.2 Astrocytes and hypoxia ...... 152 6.1.3 Aims...... 153 6.2 Results ...... 154 6.2.1 Astrocytes are activated with microglial secreted factors ...... 154 6.2.2 Activated astrocytes do not affect iPSC-neuron membrane potential, Aβ production or degradation ...... 159 6.2.3 Hypoxic response can be induced in astrocytes ...... 164 6.2.4 Hypoxic astrocyte media alters neuronal Aβ levels ...... 169 6.3 Discussion ...... 175 6.3.1 Human astrocytes can be activated by microglial secreted factors ...... 175 6.3.2 Activated astrocytes did not alter APP processing or Aβ degradation ...... 175 6.3.3 Activated astrocytes do not alter neuronal activity ...... 176 6.3.4 Hypoxic astrocytes demonstrate changes in APP processing ...... 177 6.3.5 Hypoxic astrocytes alter neuronal Aβ42 levels ...... 178 6.3.6 Conclusions and Future work ...... 181 6.4 Chapter Summary ...... 182 Chapter 7: Discussion ...... 183 7.1 The generation of neurons and astrocytes from iPSCs to model AD...... 183 7.2 Aβ degradation and the role of IDE ...... 185 7.2.1 Aβ degradation ...... 185 7.2.2 The role of IDE ...... 186 7.3 AD risk factors ...... 188 7.3.1 APOE ...... 188 7.3.2 Inflammation ...... 189 7.3.3 Hypoxia ...... 189 7.4 Concluding remarks ...... 190 References ...... 192

Word count: 47,982

List of figures

Figure 1.1: APP isoforms ...... 20 Figure 1.2: APP processing ...... 23 Figure 1.3: Sites in Aβ cleaved by NEP and IDE ...... 31 Figure 1.4: Cell-stages during neural differentiation of iPSCs to neurons and astrocytes .... 37

Figure 2.1: Representative neural rosette formation in OX1-19 iPSCs at day 16...... 59 Figure 2.2: Differentiation and maturation of iPSC-astrocytes over time...... 61

Figure 3.1: Confirmation of pluripotency in iPSCs ...... 78 Figure 3.2: Timeline of successful neural induction of iPSCs ...... 79 Figure 3.3: Characterisation of neural rosettes ...... 80 Figure 3.4: Characterisation of iPSC-derived neurons ...... 82 Figure 3.5: APP and sAPPα expression in iPSC-derived neurons over time ...... 84 Figure 3.6: Characterisation of Aβ levels in conditioned media from iPSC-neurons over time ...... 85 Figure 3.7: Characterisation of AD (APOE4/4) iPSC-neurons ...... 87 Figure 3.8: Characterisation of AD Aβ levels in iPSC-neurons over time ...... 88 Figure 3.9: Characterisation of control (SBAD-02) iPSCs and neurons ...... 89 Figure 3.10: Schematic of the FAβB degradation assay ...... 92 Figure 3.11: Optimisation of the FAβB degradation assay...... 93 Figure 3.12: Recombinant NEP and IDE can degrade FAβB...... 94 Figure 3.13: SH-SY5Y and NB7 cells degrade the FAβB substrate...... 96 Figure 3.14: iPSC-derived neurons degrade FAβB...... 97 Figure 3.15: IDE expression in iPSC-neurons over time ...... 98

Figure 4.1: GLUT1 expression is increased in iPSC-derived neurons exposed to hypoxia .. 109 Figure 4.2: APP processing is altered in iPSC-neurons exposed to hypoxia ...... 110 Figure 4.3: Aβ40 is decreased in iPSC-derived neurons exposed to hypoxia...... 111 Figure 4.4: Aβ degradation is reduced in the lysates of iPSC-derived neurons exposed to hypoxia ...... 113 Figure 4.5: IDE expression is decreased in the lysates of iPSC-neurons exposed to hypoxia ...... 114 Figure 4.6: APOE genotype of OX1-19 iPSC line is ε3 /ε3 ...... 116 Figure 4.7: GLUT1 expression is increased in AD iPSC-derived neurons exposed to hypoxia ...... 117 Figure 4.8: APP processing is altered in AD iPSC-neurons exposed to hypoxia ...... 118 Figure 4.9: Aβ levels are decreased in AD iPSC-neurons exposed to hypoxia ...... 119 Figure 4.10: IDE expression is decreased in the lysates of iPSC-neurons exposed to hypoxia ...... 120

Figure 5.1: Optimisation of Astrocyte differentiation protocols ...... 133 Figure 5.2: Characterisation of iPSC-astrocytes and primary human astrocytes ...... 134 Figure 5.3: GLAST and ALDH1L1 expression in iPSC-neurons, iPSC-astrocytes and primary human astrocytes ...... 136 Figure 5.4: APP processing in iPSC-neurons, iPSC-astrocytes and primary human astrocytes ...... 138 Figure 5.5: Characterisation and comparison of Aβ levels in iPSC-neurons, iPSC-astrocytes and primary human astrocytes...... 139 Figure 5.6: Aβ degradation in iPSC and primary astrocytes ...... 141 Figure 5.7: Characterisation of IDE expression in iPSC-neurons, iPSC-astrocytes, and primary human astrocytes...... 142 Figure 5.8: Membrane potential is unchanged in neurons cultured in ACM ...... 144 Figure 5.9: Aβ levels in iPSC-neurons are unchanged following culture in astrocyte conditioned media...... 145

Figure 6.1: Activation of human astrocytes by microglial secreted factors ...... 155 Figure 6.2: APP processing is unchanged in activated astrocytes ...... 156 Figure 6.3: Aβ40 is decreased in activated astrocytes ...... 157 Figure 6.4: Aβ degradation and IDE expression is unchanged in activated astrocytes ...... 158 Figure 6.5: Activated astrocyte medium does not affect neuronal membrane potential .. 160 Figure 6.6: APP expression is unaltered in neurons treated with the conditioned media of activated astrocytes...... 161 Figure 6.7: Amyloid levels are unaltered in neurons treated with the conditioned media of activated astrocytes ...... 162 Figure 6.8: Aβ degradation and IDE expression is unaltered in neurons treated with the conditioned media of activated astrocytes ...... 163 Figure 6.9: Hypoxic response in astrocytes ...... 165 Figure 6.10: APP processing in hypoxic astrocytes ...... 166 Figure 6.11: Aβ40 and Aβ42 are reduced in astrocytes exposed to hypoxia ...... 167 Figure 6.12: Aβ degradation and IDE expression in hypoxic astrocytes ...... 167 Figure 6.13: Membrane potential of neurons cultured in the conditioned media of hypoxic astrocytes...... 170 Figure 6.14: APP expression in neurons treated with the conditioned media of hypoxic astrocytes ...... 171 Figure 6.15: Amyloid levels in neurons treated with the conditioned media of hypoxic astrocytes ...... 172 Figure 6.16: Aβ degradation and IDE expression in neurons treated with the conditioned media of hypoxic astrocytes ...... 173

List of tables

Table 1.1: iPSC models from patients with familial AD ...... 40 Table 1.2: iPSC models from patients with sporadic AD ...... 42 Table 1.3: AD associated inflammatory molecules secreted by microglia and astrocytes ... 49

Table 2.1: iPSC lines for experimental use ...... 55 Table 2.2: Media used in neuronal induction and differentiation of iPSCs...... 57 Table 2.3: List of primary and secondary antibodies used in western blots...... 68 Table 2.4: List of antibodies used in immunofluorescence microscopy...... 69 Table 2.5: Primers used in RT-qPCR ...... 72

Table 4.1: Key changes in control (OX1-19) and AD (APOE4/4) iPSC-derived neurons exposed to hypoxia...... 121

Table 6.1: Key changes in activated and hypoxic astrocytes, and their effect on iPSC- neurons ...... 174

List of abbreviations

α2-M α2-macroglobulin Aβ Amyloid beta ACE Angiotensin converting enzyme ACM Astrocyte conditioned media ACT α1-antichymotrypsin AD Alzheimer’s disease ADAM A disintegrin and metalloprotease AEP Asparagine endopeptidase AICD Amyloid intracellular domain ALDH1L1 10-formyltetrahydrofolate dehydrogenase AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic APC Astrocyte progenitor cell APH-1 Anterior pharynx-defective 1 APOE Apolipoprotein E APLP Amyloid precursor like protein APP Amyloid precursor protein APS Ammonium persulphate AQP4 Aquaporin 4 BACE1 Beta-site APP cleaving enzyme 1 BBB Blood brain barrier BCA Bicinchonic acid Bcl-2 B-cell lymphoma 2 BFCN Basal forebrain cholinergic neuron BMP Bone morphogenetic protein BSA Bovine serum albumin BSE Bovine spongiform encephalopathy C1q Complement component 1q C3 Complement component 3 cAMP Cyclic adenosine monophosphate CBD Corticobasal degeneration CJD Creutzfeldt-Jakob disease CNS Central nervous system CSF Cerebrospinal fluid CTF C terminal fragment DAPI 4’,6-diamidino-2-phenylindole DMEM Dulbecco’s modified Eagle’s medium DMSO Dimethyl sulfoxide DS Down syndrome DTT dithiothreitol EB Embryoid body ECE Endothelin converting enzyme ECL Enhanced chemiluminescence ER Endoplasmic reticulum ETC Electron transport chain FAβB FAM-Aβ-Biotin

10 fAD Familial Alzheimer’s disease FBS Foetal bovine serum FGF2 Fetal growth factor-2 FOXG1 Forkhead box protein G1 FTD Frontal temporal dementia FTDP-17 Frontotemporal dementia with parkinsonism-17 GABA Gamma-aminobutyric acid GAPDH Glyceraldehyde-3-Phosphate Dehydrogenase GFAP Glial fibrillary acidic protein GLAST/EAAT1 Glutamate aspartate transporter GLT-1/EAAT2 Glutamate transporter 1 GLUT1 Glucose transporter 1 GWAS Genome wide association study HFIP 1,1,1,3,3,3-hexafluoropropanol-2-ol HIF1α Hypoxia inducible factor alpha HMGA1a High mobility group A protein 1a HRP Horseradish peroxidase IDE Insulin degrading enzyme IL1α Interleukin 1 alpha IM Induction media iPSC Induced pluripotent stem cell ISF Interstitial fluid kDa Kilodalton KO Knock-Out KPI Kunitz protease inhibitor LC3 Microtubule-associated protein 1A/1B-light chain 3 LDL Low density lipoprotein LPS Lipopolysaccharides LRP1 (LDL) receptor-related protein 1 MAP2 Microtubule associated protein-2 MAPK Mitogen-activated protein kinases MARCO Macrophage receptor with collagenous structure MMP Matrix metalloprotease MSD MesoScale Discovery mTOR Mammalian target of rapamycin NANOG Nanog Homeobox NEP Neprilysin NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells NFT Neurofibrillary Tau tangles NMDA N-methyl-D-aspartate NMM Neural maintenance media NPC Neural progenitor cell NSC Neural stem cell NVU Neurovascular unit OCT4 Octamer-binding transcription factor 4 OS Oxidative stress PAX6 Paired box protein PBS Phosphate buffered saline

PBST Phosphate buffered saline with Tween-20 PCR Polymerase chain reaction PFA Paraformaldehyde PI3K Phosphatidylinositol-3-kinase PLO Poly-L-ornithine PSD-95 Post-synaptic density-95 PSEN Presenilin PSP Progressive super nuclear palsy PVDF Polyvinylidene difluoride RA Retinoic acid RAGE Receptor for advanced glycation end products RIPA Radioimmunoprecipitation assay ROS Reactive oxygen species RPL13A 60S ribosomal protein L13a RPMI Roswell park memorial institute 1640 medium RT-qPCR Real time quantitative polymerase chain reaction sAD Sporadic Alzheimer’s disease sAPPα/β Soluble APP alpha/beta SATB2 Special AT-rich sequence-binding protein 2 SDS-PAGE Sodium dodecyl sulphate polyacrylamide electrophoresis SHH Sonic hedgehog SMAD SMA/MAD homology SOX2 Sex determining region-Y-box-2 SREBP Sterol regulatory element-binding protein SSEA4 Stage-specific embryonic antigen-4 STAT3 Signal transducer and activator of transcription 3 SUMO-1 Small ubiquitin-related modifier 1 SYN Synaptophysin S100β S100 calcium-binding protein beta SVZ Subventricular zone β-TUB Beta-tubulin III T2D Type II diabetes mellitus TACE TNF-α converting enzyme/ADAM17 TBR1 T-box brain 1 TEMED tetramethylethylenediamine TGFβ transforming growth factor beta TRA160 Podocalyxin TLR Toll-like receptor TNFα Tumour necrosis factor alpha TREM-2 Triggering receptor expressed on myeloid cells 2 VEGF Vascular endothelial growth factor VGLUT1 Vesicular glutamate transporter 1 VZ Ventricular zone WNT Wingless/integrated

Abstract

Dementia currently affects over 46.8 million people, with Alzheimer’s disease (AD) accounting for 50-75% of all cases. There are no disease modifying treatments and the aetiology that drives disease is still relatively unknown. Amyloid-β (Aβ) is hypothesised to be a main proponent behind AD pathogenesis, where Aβ aggregation and deposition results in neuronal cell death, and eventually brain atrophy, cognitive decline, and death. Support for this amyloid hypothesis is demonstrated by inherited, familial AD (fAD) cases, where mutations result in increased production of Aβ, and/or the more aggregate prone Aβ42. However, the majority of cases are sporadic (sAD) and there is evidence to suggest that impaired Aβ degradation rather than increased production results in Aβ deposition. iPSCs offer the opportunity to model human disease from affected patients. Currently most studies utilising iPSC-derived neural models have focussed on neurons, and on patients affected by fAD. These studies have demonstrated increased Aβ levels, whereas studies that have used iPSC-neurons to model sAD do not always demonstrate increased Aβ production. This suggests that degradation of Aβ is impaired, but proteolytic degradation of Aβ has not been investigated in these cells. Furthermore, there may be other cell types involved, whose roles have not been as well characterised, or other environmental factors that are initiating disease onset in sAD. The data in this thesis shows that iPSC-neurons and iPSC-astrocytes can be generated, and a way in which proteolytic Aβ degradation can be measured using fluorescently tagged Aβ. Insulin-degrading enzyme (IDE) was shown to be the largest contributor to Aβ degradation in both iPSC-neurons and astrocytes. Hypoxia is increased in the brain in ageing and in AD, and this environmental risk factor was applied to control and sAD iPSC-neurons. The data suggested not only increased Aβ production was occurring in response to hypoxia, but that Aβ degradation and IDE expression and localisation were also affected. The data in this thesis also demonstrated that primary human astrocytes can be made activated and hypoxic. Astrocytes exposed to hypoxia demonstrated similar effects in the levels and degradation of Aβ compared to the iPSC-neurons. Furthermore when neurons were cultured in the conditioned media collected from hypoxic astrocytes, iPSC-neurons exhibited higher levels of Aβ42. Altogether the data presented in this thesis has shown that environmental cell stressors such as hypoxia are relevant to disease pathogenesis in these cells, and that not only is Aβ production affected, but Aβ degradation is also altered. This understanding of how environmental risk factors and different cell types can contribute to sAD pathology, will help better model the aetiology of disease, and ultimately lead to improved therapeutic targets.

Declaration Statement

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

Copyright Statement i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and she has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24420), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The University’s policy on Presentation of Theses

For my family, always

Acknowledgements

I would like to thank my supervisors Prof. Nigel Hooper and Dr. Katherine Kellett for allowing me this excellent and interesting opportunity, and providing me all the support I needed during this project. I would also like to thank our funding bodies, the MRC and the Dr. Donald Dean fund in dementia research for allowing me to carry out this research.

It has been an absolute pleasure to be in the Hooper Lab, and I am very grateful for all the assistance, advice and support from all its members past and present. Thank you Alys Jones Beth Noble, Ben Allsop, David Hicks, Geoff Potjewyd, Heledd Jarosz-Griffiths, James Quinn, Kate Fisher, Kate Kellett, Michael Haycox, Nicola Corbett, Sam Moxon, and Sreemoti Banerjee.

I would especially like to thank Kate F, Kate K, and Nicola, who have been so kind and helped me with so many technical aspects, particularly with experimental help with PCR, Mesoscale and membrane potential assays. I don’t want to thank them for encouraging McDonald’s Fridays though, but I will miss them.

I would also really like to thank the stem cell crew; Alys Jones, Nicola Corbett, Kate Fisher, Kate Kellett, Wenjun Zhang, Laura Castro-Aldrete, Sam Moxon, Geoff Potjewyd, Ben Allsop, and Beth Noble, for advice, cell-feeding, and sharing the tribulations and jubilations of stem cell culture. Thank you Alys for getting me started with stem cells in the lab.

Thank you also to the SPB lab, for their advice and support, and in particular Sara for your help with the PCR and APOE genotyping. Thank you Sarah, for sharing great advice, and general good chat. I am also grateful to Prof. Hugh Piggins as well for his general academic advice. I would also like to thank everyone on the AV Hill 2nd floor for their help and support whether in the lab or the brew space.

To my parents, two of the toughest individuals I have ever met, thank you for being so truly supportive and caring in all my endeavours. You have both given me so many opportunities in life, and I am so grateful. Thank you for continually listening to me describe the ups and downs of the last three years, and thanks to time-zones, at literally all times of the night!

I am greatly indebted to you all, thank you.

Chapter 1: Introduction

1.1 Overview of dementia and Alzheimer’s disease

Dementia, recognised by several symptoms of cognitive decline, is a commonly seen syndrome of neurodegeneration. Dementia affects approximately 46.8 million people worldwide in 2015, and this is expected to rise to 131.5 million by 2050. Currently, this costs the UK an estimated £26 billion including all associated costs from diagnosis to care (Prince et al. 2015). Dementia affects not only patients, but impacts the family and friends who care for those living with dementia. Ageing is the largest risk factor for dementia, and with an ageing population this financial and personal cost is also expected to rise. Dementia itself can be caused by a range of different diseases including dementia with Lewy bodies, vascular dementia, frontotemporal dementia and Alzheimer’s disease (AD). A range of other diseases or conditions such as Parkinson’s, Huntington’s and Creutzfeldt-Jacob disease can also result in dementia during later stages. For all forms of dementia there are no disease modifying treatments, and available treatments can only provide some improvement to the quality of life of the patient. Therefore there is great need for better understanding of the disease, and the development of effective therapies.

AD remains the most common cause of dementia accounting for 50-75% of all cases (Karantzoulis and Galvin 2011). Alois Alzheimer first described both the clinical and pathological features of the disease over 110 years ago in a 50-year-old patient (Alzheimer et al. 1995). AD is characterised typically by cognitive impairment with symptoms such as memory loss, behavioural changes including psychosis, and disorientation, and after a period of disease progression, problems with language and mobility. AD is a proteopathy, and disease progression shows atrophy, with loss of the neurons in the hippocampus, cortices, and middle temporal gyrus (Ray and Zhang 2010). The consistent presence of extracellular deposits of amyloid-beta (Aβ) which aggregate to form plaques, and neurofibrillary tangles built up of hyperphosphorylated tau, in AD patients are hallmarks of the disease, and their presence is one of the main hypotheses behind development of this disease.

1.1.1 Proposed hypotheses of AD

The amyloid cascade hypothesis

Perhaps most of the research in AD has been based on the amyloid cascade hypothesis. The amyloid cascade hypothesis was proposed by Hardy and Higgins (1992) stating that A is the main proponent behind AD. This then results in the build-up of Aβ plaques and the formation of NFTs. This has been supported by genetic forms of AD such as mutations in APP or PSEN that result in increased A. However, the increased production of Aβ does not recapitulate all aspects of AD. ‘Anti-amyloid’ therapies designed to reduce Aβ load have been relatively unsuccessful (Castello et al. 2014), and has highlighted the shortcomings of the Aβ hypothesis. This has prompted repeated review of the amyloid hypothesis, as well as the proposal of many of the hypotheses described below (Hardy and Selkoe 2002;Selkoe and Hardy 2016).

Cholinergic hypothesis

Described briefly are some of the other hypotheses proposed to be behind the causes of AD. Basal forebrain cholinergic neurons (BFCNs) are severely affected in AD, and serve as projection neurons to the cortex, hippocampus and amygdala (Ballinger et al. 2016). This has led to the hypothesis that cholinergic dysfunction leads to neuronal loss and cognitive decline. Currently only cholinesterase and N-methyl-D-aspartate (NMDA) receptor inhibitors are approved for symptomatic treatment of AD (Yiannopoulou and Papageorgiou 2013). However, loss of cholinergic enzymes are associated with late stage AD and not early AD, and these treatments only provide some relief of symptoms, and do not prevent disease progression (Terry and Buccafusco 2003).

Mitochondrial hypothesis

A ‘mitochondrial cascade hypothesis’ was proposed in 2004 (Swerdlow and Khan 2004). In AD, altered mitochondrial morphology and deficiencies in electron transport chain (ETC) have been described (Swerdlow et al. 2014). A recent study demonstrated oxidative stress and altered mitochondrial protein expression in the absence of Aβ and tau pathology (Birnbaum et al. 2018). Changes in mitochondrial bioenergetics and resulting dysfunction increases reactive oxygen species (ROS) production, which can lead to increased inflammation and also appears to increase generation of Aβ (Leuner et al. 2012).

Inflammation hypothesis

Inflammation has been proposed as a hypothesis of AD, although initially considered downstream of the amyloid hypothesis. In individuals who are cognitively normal but have shown high levels of pathology including amyloid plaques and NFTs post mortem, normal to low levels of inflammatory cytokines have been found (Perez-Nievas et al. 2013;Perez-Nievas and Serrano-Pozo 2018). Neurons are not the only cell type affected in AD. Astrogliosis and reactive microglia have also been identified surrounding amyloid plaques. These cells show increased production of inflammatory cytokines, leading to increased production of neurotoxic substances (Zotova et al. 2010).

1.2 Hallmarks of AD

1.2.1 APP processing

Aβ is generated from the amyloid precursor protein (APP) gene found on chromosome 21. The exact function of APP remains to be elucidated, but it is currently thought to be a cell adhesion molecule involved in neurodevelopment in neuronal network organisation (Sosa et al. 2017). APP is part of a family of proteins that also include APP like protein 1 (APLP1) and APP like protein 2 (APLP2). This family of proteins is conserved across invertebrates. In rodent models, studies of APP KO, or even APLP KO in mice have shown limited effects. APP KO mice show changes in long-term potentiation, spatial learning and memory, and axonal transport, supporting other studies that APP has a role in neuronal cell migration (Nalivaeva and Turner 2013). These KO studies show relatively mild phenotypes, suggesting that other members of the APP family can compensate for loss of APP expression. However KO of APLP2 in combination with APP, APLP1 or both have been shown to be embryonic lethal in mice. In both rats and mice APP can be detected as early as gastrulation also indicating this protein may have an important role in development (van der Kant and Goldstein, 2015).

Eight isoforms of APP are generated through alternative splicing. The three most common are the 695, 751 and the 770 isoforms, termed according to their amino acid length. APP751 and APP770 both contain a kunitz protease inhibitor (KPI) domain (Zhang et al. 2011). Expression of the 695 isoform is generally localised to the central nervous system (CNS), whereas the other two are expressed ubiquitously (O'Brien and Wong 2011). In neurons, APP695 is the predominant isoform (deSilva et al. 1997). A diagram of different APP isoforms is demonstrated in Fig 1.1 APP is also found in mature (N- and O- glycosylated) and immature

(N- glycosylated) forms. APP is synthesised in the ER (immature) and transported to the Golgi apparatus where it subjected to O-glycosylation, and then transported via secretory vesicles to the cell surface (Saito et al. 2011). It is then sequentially cleaved in a non-amyloidogenic or amyloidogenic pathway by a series of secretases, outlined in Fig 1.2.

Figure 1.1: APP isoforms

A schematic of the APP family of proteins and key isoforms. The E1 and E2 domains are conserved. The three most common APP isoforms are APP770, APP751, and APP695. APP770, APP751 and APLP2 contain the KPI domain. Both APP770 and APLP2 also contain an OX2 domain. Adapted from (Nalivaeva and Turner 2013).

In the non-amyloidogenic pathway, APP is first cleaved by α-secretase. This is carried out by members of ‘a disintegrin and metalloprotease’ (ADAM) family of membrane-bound proteases. In particular, ADAM9, ADAM10 and ADAM17 have demonstrated the capacity to cleave APP. In neurons, ADAM10 appears to predominantly cleave APP to generate the extracellular soluble APPα (sAPPα) and the membrane bound C-terminal fragment CTFα (C83) (Jorissen et al. 2010;Lichtenthaler 2011). sAPPα is considered to be neuroprotective (Goodman and Mattson 1994;Furukawa and Mattson 1998). Studies utilising secreted sAPPα have demonstrated a reduction in neuronal oxidative metabolic and excitotoxicity (Plummer et al. 2016). Generally, sAPPα has also been shown to increase neurite outgrowth and cell proliferation and increase spatial memory, and memory consolidation (Corbett and Hooper, 2018). sAPPα may also have an important role in development during synaptogenesis. Imbalances in sAPPα levels at critical times in development are associated with fragile X syndrome and autism (Pasciuto et al. 2015; Ray et al. 2011).

In contrast, in the amyloidogenic processing of APP, the membrane-bound aspartic-protease β-secretase instead initially cleaves APP. This is carried out by β-APP cleaving enzyme 1 (BACE1) in the brain (Vassar et al. 1999). BACE1 cleaves APP at a more carboxy-terminal position compared to α-secretase cleavage, and this gives rise to soluble APPβ (sAPPβ) and the membrane bound CTFβ (C99). In both amyloidogenic and non-amyloidogenic pathways, the C-terminal intramembrane site is then cleaved by another membrane-bound aspartic- protease; γ-secretase. γ-secretase cleavage in the non-amyloidogenic pathway generates AICD and p3, whereas in the amyloidogenic pathway γ-secretase is responsible for the generation of AICD and Aβ. AICD is generally considered prone to degradation. However some studies have indicated that AICD can be translocated to the nucleus to form a complex with Fe65 and Tip60 (Cao and Sudhof, 2001). It has since been established that AICD is involved in transcriptional regulatory functions including on proteins involved in cell death regulation, and in Aβ degradation and clearance pathways. For example AICD can regulate the function of NEP, LRP1, and CLU whose functions are described later (Pardossi-Piquard and Checler, 2012). It has also been shown that binding of AICD and Fe65 promotes amyloidogenic processing of APP (Borquez and Gonzalez-Billault, 2012). However, only 10% of APP processing is normally amyloidogenic (Nalivaeva and Turner 2013).

γ-secretase is made up of several subunits including PSEN1 or PSEN2, APH-1, nicastrin and presenilin enhancer 2, which can influence the generation of different sizes of Aβ (Salminen

21 et al. 2017). Depending on how APP is cleaved different size Aβ fragments are generated. As shown in Fig 1.2, prior to the generation of AICD and Aβ, C99 undergoes ε-cleavage on the cytosolic end of the transmembrane domain. This leaves behind cytosolic AICD, and a membrane bound Aβ peptide (Sanders, 2016). γ-secretase reduces the Aβ peptide, by removing approximately 3 residues at a time through a stepwise tripeptide cleavage process of γ-secretase. This normally occurs via Aβ49 → Aβ46 → Aβ43 → Aβ40 or Aβ48 → Aβ45 → Aβ42. Relatively recently it has been demonstrated that this is through the S1’, S2’ and S3’ sites that are located after the cleavage sites. Changes to these sites, prevents γ-secretase from reducing the Aβ peptide size before it is released into the media (Bolduc et al. 2016). While Aβ40 is the most predominant, the generation of Aβ42 is more prone to aggregation and is considered to be the most deleterious due to the extra amino acids, which make the protein more hydrophobic than Aβ40 (Barage and Sonawane 2015).

Figure 1.2: APP processing

Non-amyloidogenic pathway: APP is first cleaved by α-secretase producing sAPPα (possibly possessing a neuroprotective role) and C83. C83 is subsequently cleaved by ϒ-secretase to produce p3 and AICD. Amyloidogenic pathway: APP is instead first cleaved by β-secretase, producing sAPPβ and C99. C99 is subsequently cleaved by γ-secretase to produce AICD, and Aβ. In AD, Aβ is commonly found in aggregates of the extracellular space that make up the amyloid plaques (Vardy et al. 2005).

1.2.2 Aβ toxicity

Aβ can lead to downstream effects such as increased inflammation and hyperphosphorylation of tau. The effects of Aβ are wide-ranging and have been well reviewed (Yankner and Lu 2009). The toxicity of Aβ also depends on the equilibrium of intra- and extracellular Aβ, in addition to monomeric and oligomeric forms. Oligomers of Aβ form fibrils with their β-sheet structure, which, via interaction with other proteins, start to form plaques. Specifically, Aβ42 peptides have an ‘unstructured’ N-terminus where its β-strands form parallel to the fibril axis unlike Aβ40 peptides. This packing structure may alter solubility, and the impact on Aβ clearance mechanisms (Ahmed et al. 2010). Aβ, particularly Aβ42, with its propensity to aggregate, can form plaques, which are thought to have numerous toxic effects.

The toxic effects of Aβ include cell membrane permeability, which is affected through detergent effects or through Aβ-binding complexes resulting in membrane pores (Bharadwaj et al. 2018). Aβ42 inserts into the lysosomal membrane causing destabilisation of the lysosome and loss of function (Liu et al. 2010). Calcium permeable pores are created, and Aβ increases cytoplasmic Ca2+ levels (Supnet and Bezprozvanny 2010). This can lead to mitochondrial dysfunction and the increase in ROS. Aβ oligomers in the cell membrane can directly cause increased ROS though lipid peroxidation (Butterfield et al. 2013). Aβ also appears to depress synaptic activity and reduce α-amino-3-hydroxy-5-methyl-4- isoxazolepropionic (AMPA) and NMDA receptor density (Priller et al. 2006;Hsieh et al. 2006;Snyder et al. 2005). This shows that Aβ toxicity occurs through several routes, however increased ROS, for example, can lead to increased Aβ, demonstrating a positive feedback loop.

1.2.3 Tau pathology

The presence of pathological tau protein has been much more closely related to clinical disease severity than amyloid. The tau gene MAPT is encoded on chromosome 17 and is alternatively spliced at exons 2, 3, and 10 to generate six isoforms of tau. These isoforms differ in the number of N-terminal repeats (0N, 1N & 2N) expressed, and the number of microtubule binding regions (3R & 4R) (Buee et al. 2000). Tau plays an important role in microtubule assembly and maintaining neuronal cytoskeletal stability particularly within the axon.

Tau has also been shown to have a role in synaptic function, whereby in disease not only is microtubule stabilisation affected, but glutamatergic receptors and mitochondrial function are also impacted (Pooler et al. 2014). In AD, due to changes such as its concentration, or its protein structure, tau is unable to complete its function and becomes misfolded. This results in incorrect aggregation of the protein, particularly with fibrillary structures (Kolarova et al. 2012). Tau is generally regulated by its phosphorylation status, whereby when it is post- translationally modified to a hyperphosphorylated state and is responsible for abnormal accumulation including the generation of paired helical filaments (PHFs) and the formation of NFTs in disease. Hyper-phosphorylation of tau can be due to dysfunctional activity of several kinases and phosphatases (Noble et al. 2013) including glycogen synthase kinase-3 (GSK-3), which also has a role in the regulation of APP cleavage (Phiel et al. 2003).

In AD tau pathology is clinically classified by Braak staging and the appearance of NFTs in 6 stages progressing from the entorhinal regions (stages I-II), to the limbic regions (stages III- IV) and to the neocortex (V-VI) (Braak et al. 2006). Tau pathology is not found in AD alone, while tau mutations do not directly cause AD, they are linked with other neurodegenerative diseases such as frontotemporal dementia with parkinsonism-17 (FTDP-17), Pick’s disease, progressive super nuclear palsy (PSP) and corticobasal degeneration (CBD) (Goedert and Spillantini 2000). Tau pathology can be exacerbated by neuroinflammation (Shi et al. 2017;Bemiller et al. 2017). In AD models increased phosphorylation of tau has been demonstrated to be dependent on the presence of A (Zheng et al. 2002;Tokutake et al. 2012;Gao et al. 2013;Kametani and Hasegawa 2018).

1.3 Familial and sporadic AD and genetic risk factors

1.3.1 Familial AD (fAD)

Less than 5% of patients with AD have genetically inherited mutations. These are due to autosomal dominant mutations encoding either APP or presenilins (PSEN1 or PSEN2) that result in early-onset AD. This means that age of onset is below the age of 65 and typically in the 40-50s. Over 50 mutations in APP have been identified, including 32 APP missense mutations that account for 10-15% of individuals affected by fAD (http://www.molgen.ua.ac.be/ADMutations) (Bekris et al. 2011). The most common APP mutation, APPV717I (London), was the first identified and causes an increase the ratio of Aβ42:40 (Goate et al. 1991;Suzuki et al. 1994). Other APP mutations have also been associated with increased total Aβ such as the APP KM670/671NL (Swedish mutation) (Citron et al. 1992). This Swedish mutation has been used in the Tg2576 mice model for in vivo studies of AD.

Mutations in PSEN are the most common cause of fAD. Nearly 220 mutations have been identified in PSEN1 (http://www.molgen.ua.ac.be/ADMutations). The most severe PSEN1 mutations cause disease onset as young as 30. PSEN1 is located on chromosome 14 and, as mentioned previously, is a sub-unit of γ-secretase acting as the catalytic core (Bekris et al. 2011). Over 15 mutations have also been identified in PSEN2, a PSEN1 homologue that can also be the catalytic sub-unit of γ-secretase. As PSEN2 is less efficient at producing Aβ, mutations in PSEN2 generally have a later onset than in PSEN1. Mutations in either PSEN1 or PSEN2 are associated with an increase in the ratio of Aβ42:40 due to changes in ε‐cleavage and carboxypeptidase-like activity (De Strooper et al. 2012;Chavez-Gutierrez et al. 2012). Specifically, mutations in PSEN1 can result in changes in the catalytic core and the binding to the Aβ peptide. Decreases in the efficiency of the tripeptide cleavage process of γ-secretase through modification or changes in the stability of the substrate binding pockets can result in longer Aβ peptides and consequently an increase in the ratio of Aβ42:40 (Bolduc et al. 2016). Different mutations can affect the tripeptide cleavage of Aβ42 to Aβ38, or Aβ43 to Aβ40, and therefore alternately affect whether more Aβ42 or less Aβ40 is generated (Li et al. 2016).

1.3.2 Sporadic AD (sAD)

Termed sporadic AD (sAD), in most cases of AD the aetiology remains unknown. In sAD this is typically late onset, meaning that it occurs over the age of 65. Unlike in fAD, where disease onset is directly caused by increased production of Aβ, or the increase in the more aggregation prone Aβ42, sAD is also linked with impaired Aβ clearance (Wildsmith et al. 2013). Disease onset may be due to a combination of genetic and environmental factors. The numerous factors involved, in particular the sheer number of genetic risk factors highlights the complexity of AD. The environmental risk factors for disease onset are discussed later, but there are several genetic risk factors that increase the risk of developing AD by increasing Aβ production and/or impairing Aβ clearance that are described below.

1.3.3 Apolipoprotein E

The most common genetic risk factor for sAD is Apolipoprotein E (APOE). APOE is produced predominantly in the liver, followed by the brain. In the brain, APOE is expressed primarily by glial cells, although neuronal cells also express APOE to a lesser extent. APOE is located on chromosome 19 and has three alleles ε2, ε3 and ε4. ε3 is the most commonly expressed isoform (around 77% of the population) compared to ε2 (8%) and ε4 (15%) (Kanekiyo et al. 2014). ε4 may account for just a small percentage of the population, but is present in about 40% of people with AD. The presence of one ε4 allele increases your risk of developing AD approximately 4-fold, and possessing both ε4 alleles increases risk approximately 12-fold (Ulrich et al. 2017). This allele is also a risk factor for cardiovascular diseases including atherosclerosis. These three alleles differ by a single amino acid at positions 112 and 158. ε2= Cys112, Cys158; ε3= Cys112, Arg158 and ε4= Arg112, Arg158 (Zannis et al. 1982). At position 112 ε4 has an Arg/Cys change which is thought to affect movement of Arg114, and the ionisation of His140 (Frieden and Garai 2012). This results in a difference of charge between ε3 and ε4 creating structural differences (Yu et al. 2014).

The efficiency of APOE binding is important for a number of its functions. APOE ε4 is linked with increased deposition of Aβ (Kok et al. 2009;Reiman et al. 2009). APOE predominantly binds to low-density lipoprotein (LDL) receptors, essential for the maintenance of cholesterol homeostasis. Low-density lipoprotein receptor related protein 1 (LRP1) is a major APOE receptor, where the APOE ε4 isoform has a greater affinity for binding with LRP1 compared to the other isoforms. Aβ also binds to LRP1 in part of a clearance pathway, where APOE ε4 can inhibit Aβ clearance by competitively binding to LRP1 (Verghese et al. 2013). Impaired

Aβ clearance as a result of APOE ε4 has also been demonstrated in iPSC-neuron models (Lin et al. 2018). Reduced cholesterol levels mean that Aβ is delivered more rapidly to lysosomes. APOE was not only shown to regulate cholesterol levels, but was also shown to facilitate the recycling of Rab7, which recruits late endosomes and lysosomes (Lee et al. 2012). This finding has also been supported by Li et al. (2012) who found that Aβ is endocytosed in neurons and trafficked to lysosomes more rapidly with the over expression of Rab5 and 7. Some Aβ is also trafficked through Rab11-positive recycling vesicles. If this pathway is inhibited, then cellular Aβ rapidly accumulates. This uptake of Aβ through lysosomal trafficking was also shown to be accelerated by APOE and efficiency also in an isoform dependent manner where ε4 is least efficient. Recently it has also been shown that in iPSC-microglia like cells expressing APOE ε4 the uptake of Aβ42 was less efficient and there was an increase in inflammatory response (Lin et al. 2018). APOE has also been shown to affect proteolytic degradation of Aβ, and is described in 1.4.4 Additionally studies have demonstrated that APOE can stimulate APP transcription in an isoform dependent manner, whereby the ε4 allele increased APP transcription the most (Huang et al. 2017). This is further supported by a recent study using isogenic controls demonstrating APOE ε4/ε4 increased secretion of Aβ42 in neurons (Lin et al. 2018).

1.3.4 Genome wide association studies (GWAS) identified AD risk factors

Aside from APOE, GWAS has identified many different genes that are associated with increased risk of AD. These have been recently reviewed by Rowland et al. (2018) and are outlined below. For example several genes that result in altered APP trafficking have been identified. These genes include PICALM, BIN1 and CD2AP, which traffic APP through clathrin mediated endocytosis (Tan et al. 2013). Increased expression of APP is not localised to APP mutations alone, but has also been identified in variants of CLU (Zandl-Lang et al. 2018). Risk factor genes have also been associated with secretase activity. ADAM10 mutations can decrease α-secretase activity and PICALM has also been shown to affect γ-secretase activity increasing the ratio of Aβ42:40 (Suh et al. 2013;Zhao et al. 2015). Risk factor genes are not only associated with increased Aβ production, and several have also been identified to alter Aβ clearance. As mentioned previously CLU and PICALM can mediate cholesterol uptake and LRP1 function, respectively. Furthermore, by example ABCA7 and TREM2 are genes identified to be a risk factor that has a role in the phagocytic clearance Aβ (Fu et al. 2016;Zhao et al. 2018). Discussed below, NEP and IDE are important proteases involved in Aβ

28 degradation. While not especially prevalent, polymorphisms have also been identified in these genes that increase the risk of AD (Helisalmi et al. 2004;Vepsalainen et al. 2007).

1.4 Clearance mechanisms of Aβ

While fAD may be associated with increased production of Aβ, impaired Aβ clearance may be more important in sAD (Wildsmith et al. 2013). In a study of the CNS of cognitively normal and AD participants no difference in the production of either Aβ40 or Aβ42 was observed. However, in the AD group there was an impairment of Aβ clearance. This supports the idea that in sAD the degradation of Aβ may be more relevant to disease pathology than production of Aβ (Mawuenyega et al. 2010). The clearance of Aβ occurs in several different ways: the interstitial fluid (ISF) drainage pathway, transcytosis across the blood brain barrier (BBB), phagocytosis and autophagy, and proteolytic degradation. All of these pathways have been implicated with late onset AD and are described in more detail below (Arbel-Ornath et al. 2013).

1.4.1 Interstitial fluid (ISF) drainage pathway

Clearance of Aβ can occur from the ISF and the cerebrospinal fluid (CSF). ISF proteins enter into the CSF through ISF bulkflow and into the parenchymal vasculature (Iliff et al. 2012). Despite this being hypothesised in 1985, this drainage pathway is still not fully understood (Rennels et al. 1985). However Aβ clearance via the ISF drainage pathways may be more prevalent than first thought (Tarasoff-Conway et al. 2015). Failure of perivascular drainage results in deposition of insoluble Aβ in arterial walls that prevents the removal of soluble Aβ, and failure of Aβ clearance may be due to the aging and stiffening of arteries and the build- up of fibrillar Aβ (Weller et al. 2008).

1.4.2 Blood brain barrier transport

Although the relative contribution of different Aβ clearance systems is unknown, it is thought that a large proportion of Aβ clearance is through transport across the BBB (Tarasoff-Conway et al. 2015). Several proteins are involved such as receptor for advanced glycation end- products (RAGE) and LRP1, a major Aβ chaperone. In AD, RAGE levels appear to be increased allowing the re-entry of Aβ into the brain (Sweeney et al. 2018). Additionally, mutations in LRP1 are associated with late onset AD (Lambert et al. 1998). Several other proteins bind to

Aβ primarily in the CSF that act as chaperones and facilitate Aβ clearance. These include APOE, clusterin, α1-antichymotrypsin (ACT), and α2-macroglobulin (α2-M) which are proposed to alter the formation of Aβ fibrils and mediate clearance via LRP1 (Ries and Sastre 2016) (although there is now evidence, that APOE and Aβ compete for clearance via LRP1) (Verghese et al. 2013). Clearance of Aβ42 is far slower by LRP1 than Aβ40 (Bell et al. 2007).

1.4.3 Phagocytosis and autophagy

Microglia can remove Aβ via phagocytosis. Microglia can take up Aβ via class A and B scavenger receptors. Aβ then undergoes endosomal/lysosomal degradation that is primarily carried out by cathepsin D in human microglia (Nakanishi 2003). Microglia become activated in response to Aβ, particularly fibrillar Aβ (Bamberger et al. 2003;Pan et al. 2011). Microglia undergo cell senescence with ageing and this is exacerbated by the presence of Aβ (Flanary et al. 2007). Uptake of Aβ by microglia is beneficial, but the production of inflammatory cytokines in the process may also have harmful effects.

The most prevalent form of autophagy, macroautophagy, involves the formation of a phagophore encompassing cellular waste targeted for removal in a double membrane bound vesicle called the autophagosome. This is transported and fuses with the lysosomal membrane, where it is subsequently degraded. Autophagy has an important role in reducing Aβ burden in neuronal and glial cells (Nixon 2007;Yang et al. 2014). Astrocytes surrounding Aβ plaques show increased expression for LC3, a marker of autophagy (Pomilio et al. 2016). In several neurodegenerative diseases autophagic vacuoles (AVs) accumulate (Funderburk et al. 2010). It has even been demonstrated that dysfunctional autophagy results in upregulated Aβ through increased γ-secretase activity by upregulating γ-secretase subunits (Cai et al. 2015).

Lipid rafts are assemblies in membranes, high in amounts of cholesterol and sphingolipids. APP and β- and γ-secretases are associated with lipid raft domains, and facilitate amyloidogenic processing and can promote Aβ oligomerisation (Rushworth and Hooper 2011). High cholesterol is associated with higher amounts of intracellular Aβ in AD mice (Shie et al. 2003). Lipid rafts are considered a primary method in which Aβ undergoes endocytosis (Lai and McLaurin 2010). Aβ uptake can also occur through chaperone proteins described above but it has been proposed that Aβ can also enter the cell through passive diffusion (Kandimalla et al. 2009).

Aβ degradation can also occur by the ubiquitin-proteosome system (UPS). The UPS is generally localised to the cytosol and degrades proteins by tagging them with a polyubiquitin chain. In a three-step process the ubiquitinated protein is degraded by the 26S proteasome. The UPS system is able to degrade Aβ, and inhibition of the proteasome has demonstrated a decrease in Aβ degradation (Salon et al. 2003). Studies have also shown that Aβ inhibits the activity of the 26S proteasome (Hong et al. 2014).

1.4.4 Proteolytic degradation

There is evidence that the majority of Aβ production actually occurs within the cell as the activity of β- and γ-secretase requires the intracellular acidic environment to be enzymatically active. Aβ aggregation is also accelerated in an acidic environment, making intracellular degradation of Aβ an important function (Leissring 2014). Many proteases that have been identified to degrade Aβ are localised both intracellularly and extracellularly.

Figure 1.3: Sites in Aβ cleaved by NEP and IDE

There are several cleavage sites at which NEP (blue) and IDE (grey) cleave in the Aβ sequence. (Adapted from Eckman and Eckman (2005))

The majority of Aβ degrading enzymes are metalloproteases and include angiotensin converting enzyme (ACE), endothelin converting enzyme (ECE), insulin degrading enzyme (IDE), matrix metallo protease (MMP) (including MMP2, MMP9 and MMP14), and neprilysin (NEP). NEP, ECE, and IDE are considered endogenous regulators of Aβ (Saido and Leissring 2012). ACE may also be an endogenous regulator, but this has been determined through mutations in ACE as a risk factor for sAD, rather than through ACE inhibition studies (Bertram et al. 2007;Hemming et al. 2007). MMP is not considered a strong endogenous degrader of Aβ, but MMPs make up a family of different proteases that have great capacity to degrade monomeric and fibrillar Aβ (Saido and Leissring 2012). NEP and IDE significantly contribute to degradation of Aβ (Shirotani et al. 2001;Qiu et al. 1998;Iwata et al. 2001;Farris et al. 2003). As shown in Fig 1.3, NEP and IDE both cleave Aβ at several different sites. Both NEP and IDE expression is altered in AD (Eckman and Eckman 2005).

NEP

The most investigated and characterised Aβ degrading protease is NEP. NEP is a zinc metallo endopeptidase with a HEXXH zinc-binding motif, first identified, and predominantly found, in the kidney (Rawlings and Barrett 2013). NEP has several substrates, including the enkephalin family of neuropeptides, substance P, and is even suggested to have some role in the immune system and in skin ageing and damage. In vivo it was identified as having the principal substrates of enkephalin, atrial natriuretic peptide, tachykinins, bradykinins, endothelins, adrenomedullins, and glucogon. However of most significance here, the Aβ peptide is also an endogenous substrate (Nalivaeva et al. 2012). NEP is thought to be the most effective Aβ degrading protease (Shirotani et al. 2001).

NEP shares similar primary structure to ECE, another Aβ degrading protease. NEP has a homologue NEP2; however their cellular localisation is quite different (Whyteside and Turner 2008;Baranello et al. 2015). NEP is expressed in many tissues, and in the brain it is most abundant in the nigrostriatal pathway and basal ganglia, but NEP is also found in the hippocampus and cerebral cortex (Carson and Turner 2002). NEP has been identified in the extracellular space, Golgi and ER. NEP is primarily localised to the plasma membrane with its active site facing the extracellular space (Rawlings and Barrett 2013;Saido and Leissring 2012). In neurons, NEP is localised at the axon and synapses and only expressed in certain neuronal cell types (cholinergic neurons do not express NEP) (Fukami et al. 2002b). NEP

32 expression has been demonstrated to be regulated by AICD, which binds to its promoter (Belyaev et al. 2009). This is isoform specific, where APP695 but not APP751 increases NEP upregulation (Belyaev et al. 2010).

NEP levels are reduced in the AD brain (Wang et al. 2005), reduced with age and are associated with the accumulation of Aβ plaques (Wood et al., 2007;Apelt et al. 2003). Deletion of NEP in mice resulted in an increase in Aβ levels (Farris et al. 2007). A couple of studies have also showed that polymorphisms in NEP affect the risk of developing sAD (Wang et al. 2016). Saito et al. found that somatostatins reduced Aβ levels in the brain through activation of NEP (Saito et al. 2005). NEP is also upregulated in astrocytes that surround Aβ plaques (Apelt et al. 2003).

IDE

Insulin Degrading Enzyme (IDE) is an 110kDa zinc metalloprotease belonging to the inverzincin family with a HXXEH zinc binding motif (Hooper 1994). IDE preferentially binds to and degrades insulin as its name aptly suggests, but IDE also breaks down naturietic peptide, glucagon, and Aβ, first identified by Kurochkin and Goto (1994). Notably, these substrates have different primary structures, but each have a similar secondary structure with the formation of β sheets, which IDE can target. IDE is primarily found in the cytoplasm, but is expressed in organelles including peroxisomes and mitochondria, as well as in the extracellular space (Saido and Leissring 2012). Further understanding of where IDE predominantly degrades Aβ is required, as this may be intracellularly, on the cell surface, or following secretion of IDE. IDE mediated degradation may be dependent on the cell type (Hersh 2006).

To determine the role of IDE in extracellular Aβ degradation, several studies have looked into the secretion of IDE in neurons or neuron-like cells (Vekrellis et al. 2000;Bulloj et al. 2008;Bulloj et al. 2010) and also in astrocytes (Dorfman et al. 2010;Son et al. 2015;Son et al. 2016). So far, several mechanisms have been proposed including an unconventional pathway and through autophagy dependent secretion. Studies have also shown that this may partially be through exosome release (Bulloj et al. 2010;Tamboli et al. 2010;Zhao et al. 2009). It is possible that the mechanism by which this occurs is different depending on the cell type, and it has also been suggested that IDE is not secreted at all (Song et al. 2018).

IDE has been implicated in sAD. There was a two-fold increase in plaques containing IDE in sAD brains compared to fAD (Dorfman et al. 2010). IDE was reduced in the hippocampus of late onset AD patients carrying the APOE ε4 allele (Cook et al. 2003). As APOE status affects insulin metabolism, it was considered a likely candidate to affect IDE activity as well. Changes in IDE levels have subsequently been demonstrated to occur through activation of NMDA receptors in hippocampal neurons (Du et al. 2009). Degradation of Aβ by IDE has been compared in astrocytes with and without expression of Abca1 resulting in less lipidated forms of APOE. In the Abca1 KO astrocytes, degradation by both NEP and IDE was reduced (Jiang et al. 2008). There have also been a few studies demonstrating that mutations in IDE are associated with sAD (Vepsalainen et al. 2007;Perez et al. 2000;Zou et al. 2010).

Insulin is closely regulated by IDE and thus may provide a link as to how Type 2 diabetes Mellitus (T2DM) and Metabolic Syndrome (MetS) increase the risk of developing AD, and the association between sAD and impaired Aβ degradation (Wildsmith et al. 2013). In a study with mice that did not produce insulin, IDE expression was significantly reduced (Jolivalt et al. 2010). IDE preferentially binds to insulin over Aβ, and again supports the idea that the effects of T2DM or MetS may prevent breakdown of Aβ, where in the early stages, insulin levels are high and cause competitive inhibition (Schilling 2016;Zhao et al. 2004). IDE is affected by changes in glucose levels, which not only affects insulin levels, but also inhibits IDE activity through s-nitrosylation (Schilling 2016;Akhtar et al. 2016).

1.5 Deriving iPSC-neurons

Much research into AD has been carried out using animal models. However, on their own, animals do not show formation of Aβ plaques and NFTs. Transgenic rodent models have been widely used, often with overexpression of APP to demonstrate some aspects of AD pathology (Drummond and Wisniewski, 2017). Yet this recapitulates fAD and not sAD that accounts for over 95% of cases. Understanding the more subtle alterations in sAD may therefore be better understood in human cell models.

The ability to generate induced pluripotent stem cells (iPSCs) from human fibroblasts using just four transcription factors (KLF4, c-MYC, OCT4, and SOX2) has revolutionised research capabilities (Takahashi et al. 2007). For AD research, this means that human cell types such as neurons, which were previously inaccessible for disease modelling, can now be generated from patients with AD. There exist several different protocols for the generation of different neuronal types.

Specific neuronal sub-types are derived from neural progenitors that arise from different regions of the neural tube that are temporally and spatially coordinated during embryogenesis. The anterior-posterior axes are patterned by gradients of morphogens including fibroblast growth factors (FGFs), wingless/integrated (WNTs), and retinoic acid (RA), whereas the dorsal ventral axes is patterned by gradients of WNTs, bone morphogenetic proteins (BMPs) and sonic hedgehog (SHH) (Tao and Zhang 2016). Following BMP inhibition leading to a neural stem cell stage, the forebrain expresses low quantities of WNT, comparatively, the midbrain expresses more WNT and the eventual spinal cord is exposed to high quantities of WNT and RA. Neurons from forebrain progenitors with low quantities of SHH will ultimately determine neurons of a cortical and glutamatergic fate. By contrast, neurons with SHH signalling will differentiate to forebrain gamma-aminobutyric acid (GABA)ergic/cholinergic neurons. Neurons that are exposed to medium WNT, then low WNT and SHH differentiate to dopaminergic neurons. This is reflected in current protocols and the neurotrophic factors they use (Shi et al. 2012a;Ma et al. 2011;Crompton et al. 2013).

While the neuronal sub-type will be disease dependent, another important consideration is the method in which they are differentiated. There are protocols that can directly convert fibroblasts to neurons without undergoing an iPSC stage. These direct conversion methods are advantageous in the speed in which neurons can be generated and maintain an ‘ageing signature’ of the patient they have been derived from, however they have not been as well

35 characterised and are not efficient (Hu et al. 2015;Mertens et al. 2015;Ulrich et al. 2017). Other protocols have generated neurons from iPSCs but used notch inhibitors to enable cells to speed up differentiation and prevent further proliferation (Borghese et al. 2010). While this has the benefit of purifying the cell population by lowering the number of progenitors leading to different cell types, notch, as a γ-secretase substrate, can cause alterations in APP processing which could be problematic when using these cells to investigate AD.

A way in which cortical neurons can be derived is by dual SMAD inhibition. This method causes rapid differentiation via a neuroectoderm route, blocking non-neural differentiation. Noggin has been identified as an inhibitor of BMP and so prevents mesoderm induction. Another SMAD inhibitor, SB431542, prevents phosphorylation of ALK4, 5, and 7 and thus inhibits the transforming growth factor-β (TGFβ) pathway and stops cells adopting an endodermal fate. The combination of Noggin and SB431542 has been identified as sufficient to differentiate iPSCs to neurons (Chambers et al. 2009). Several different cell types are formed during stages of iPSC differentiation to cortical neurons and astrocytes. Labels of these stem-cell types, e.g. progenitor and precursor cells, are often used interchangeably (Ray and Gage 2006). Shown in Fig 1.4 are the different cell types generated during differentiation via dual SMAD inhibition, and markers that can be used to identify them. Despite differences in classification of stem-cell progenitors and methods to generate iPSC- derived neurons, NPCs and cholinergic and cortical iPSC-neurons have been used to demonstrate pathological features of AD.

Figure 1.4: Cell-stages during neural differentiation of iPSCs to neurons and astrocytes

The differentiation of iPSCs to neurons and astrocytes with key markers that distinguish each cell type. Markers shown are used in this thesis. Cell illustrations from Mortifolio. Abbreviations: iPSC; induced pluripotent stem cell, NSC; neural stem cell, NPC; neural progenitor cell, APC; astrocyte progenitor cell

1.6 Modelling AD with iPSCs

The importance of using human cells to model AD has been demonstrated by inserting human NPCs in a transgenic AD mouse model to act as a natural 3D environment. This study showed that there is increased neuronal maturation, tau phosphorylation, and neuronal loss, which was only observed in the grafted human neurons and not in grafted mouse neurons (Espuny-Camacho et al. 2017). As iPSCs offer a new method to model human disease, progress in the field has been reviewed extensively. Recent reviews have discussed in detail what has been learned from fAD and sAD models (Arber et al. 2017;Ghaffari et al. 2018;Rowland et al. 2018). Even since the recent time of publication of these reviews, the number of studies using iPSC-derived models to investigate AD has increased substantially. The key pathological findings of iPSCs used to model AD are discussed below.

1.6.1 iPSC-derived fAD models

Most studies that have utilised iPSCs to model AD have been derived from patients with fAD, Down syndrome (DS) or genetically manipulated to KO or overexpress AD-related proteins. There are several studies that have used iPSCs taken from patients with mutations in APP, including APP E693Δ , APP V717L (London), APPV717F (Indiana), and APP KM670/671NL (Swedish) (Kondo et al. 2013;Moore et al. 2015;Muratore et al. 2014;Woodruff et al. 2016). These studies have differentiated the iPSCs into NPCs or neurons and have consistently demonstrated an increase in Aβ, as well as an increase in the ratio of Aβ42:40 and increased p-tau. iPSCs from individuals with DS have also been differentiated into neurons (Chang et al. 2015;Ovchinnikov et al. 2018;Moore et al. 2015;Shi et al. 2012b). This additional copy of APP has also demonstrated similar phenotypes with increased Aβ, Aβ42:40 ratio and p-tau that is consistent with iPSC-neurons used with APP duplication (Israel et al. 2012;Moore et al. 2015;Raja et al. 2016). It is interesting to note, that while neurons predominantly express APP, APP-related dysfunction is not isolated to neurons. Recently a study utilising astrocytes has demonstrated that astrocytes with APP-KO or APPSwe have lower levels of cholesterol, lipoprotein endocytosis, and Aβ uptake (Fong et al. 2018).

Currently more studies have utilised mutations in PSEN1 to study AD using iPSC cortical or cholinergic neurons. These have included mutations most commonly in PSEN1-A246E (Armijo et al. 2017;Mahairaki et al. 2014;Raja et al. 2016;Sproul et al. 2014;Yang et al. 2017;Yagi et al. 2011;Duan et al. 2014) but also PSEN1 ΔE9 (Woodruff et al. 2013;Woodruff et al. 2016), PSEN2-N141I (Moreno et al. 2018;Ortiz-Virumbrales et al. 2017;Yagi et al. 2011),

PSEN1 ΔI4, PSEN1 Y115C (Moore et al. 2015) PSEN1 M136I (Raja et al. 2016), PSEN1 M146L (Sproul et al. 2014), and PSEN1-S169del (Yang et al. 2017). One study has also overexpressed PSEN1, PSEN1–P117L, PSEN1-G378E and PSEN1-D385A to study disease in iPSC-neurons (Honda et al. 2016). As expected all of these studies have observed the increase in Aβ42:40, if not increases in Aβ compared to control. Many of these studies have demonstrated that a reduction in Aβ can be achieved with the application of β or γ-secretases. In addition to synaptic alterations and impaired endocytosis, increases in total tau and phosphorylation of tau have been observed at several sites including Thr231, Thr181, Thr212, Thr205, Ser202 and Ser396 in neurons derived from fAD patients with APP mutations (Yagi et al. 2011;Shi et al. 2012b;Moore et al. 2015;Woodruff et al. 2013;Raja et al. 2016;Ochalek et al. 2017) (Reviewed Rowland et al., 2018). iPSC-neurons derived from patients with a PSEN1 mutation have not consistently indicated changes in tau phosphorylation (Moore et al. 2015;Ochalek et al. 2017). One study also suggests that the role of tau in AD is independent of Aβ pathology as increased tau phosphorylation in neurons derived from a patients with DS was unchanged when the APP gene dosage was corrected (Ovchinnikov et al. 2018).

Each of these studies looking at mutations or overexpression of genes related to fAD have observed slightly different effects such as in changes to Aβ, tau, response to stress, and how disease phenotype can be modified. A summary of these findings is described in Table 1.1. Previously studies have utilised several different disease and control lines, in order to define these changes. There appears more recently to be a move toward generating isogenic lines that may help further elucidate disease specific differences causing AD pathology (Ortiz- Virumbrales et al. 2017;Moreno et al. 2018).

Table 1.1: iPSC models from patients with familial AD

iPSC-derived Cell fAD Line Phenotype(s) Disease APOE Additional culture conditions Type(s) genotype and experimental results (Armijo et al. 2017) NPCs, Neurons PSEN1-A246E, sAD (PSEN1-A246E): * ↑Aβ42:40 ↑Aβ42 ↑Aβ induced toxicity (Duan et al. 2014) BFCNs PSEN1-A246E, sAD ↑Aβ42:40 ε3/ε4 Ionomycin, L-glutamate, γ- secretase inhibition: ↓Aβ40 =↑Excitotoxicity

(Chang et al. 2015) Neurons DS ↑A40 ↑A42, ↑t-tau Rescued with γ-secretase ↑p-tau inhibition

(Fong et al. 2018) Astrocytes APP-KO, APPSwe ↓ cholesterol, ↑ SREBP, ↓ Rescued with β-secretase lipoprotein endocytosis, ↓ Aβ inhibition uptake (Honda et al. 2016) hESC and iPSC-derived Overexpressing wild-type ↑Aβ42:40 ↑Aβ43/Aβ40 Neurons PSEN1, PSEN1–P117L, PSEN1- Synaptic alterations in mutant G378E or PSEN1-D385A PSEN (Israel et al. 2012) Neurons APP duplication, sAD ↑Aβ40, ↑ p-tau, ↑GSK3β ε3/ε3 + Human astrocytes (Lonza) = ↑ Very large early endosomes (Kondo et al. 2013) Neurons, Astrocytes APP E693Δ, APP V717L, sAD ↑ER stress, ↑OS, ↑Aβ oligomers Rescued by DHA

(Mahairaki et al. 2014) Neurons PSEN1-A246E ↑Aβ42:40 Rescued with γ-secretase ↑sAPPβ inhibition

(Moreno et al. 2018) BFCNs PSEN2-N141I ↑Aβ42:40 ε3/ε4, ε3/ε3 Insulin treatment (with CRISPR/Cas9-corrected =↓Aβ42:40 ↑Ca2+ flux control) (Moore et al. 2015) Neurons APP V717I, APP duplication, ↑Aβ42:40 , *↑ p-tau, *↑t-tau Rescued with γ-secretase PSEN1 ΔI4, PSEN1 Y115C, DS inhibition

Table 1.1: iPSC models from patients with familial AD (continued)

iPSC-derived Cell fAD Line Phenotype(s) Disease APOE Additional culture conditions Type(s) genotype and experimental results (Muratore et al. 2014) Forebrain neurons APP V717I ↑A42:40 ↑p-tau Rescued with γ-secretase inhibition (Ortiz-Virumbrales et al. BFCNs PSEN2-N141I ↑Aβ42:40 ε3/ε4, ε3/ε3 2017) (with CRISPR/Cas9-corrected control) (Ovchinnikov et al. 2018) Neurons DS ↑Aβ40,↑Aβ42, ↑Aβ42:40

(Raja et al. 2016) Neurons APP duplication ↑Aβ40,↑Aβ42, ↑Aβ42:40, γ-secretase inhibition, PSEN1 M136I ↑BACE, ↑p-tau Rescued with β-secretase PSEN1 A264E inhibition

(Shi et al. 2012b) Neurons DS ↑Aβ, ↑p-tau Rescued with γ-secretase inhibition (Sproul et al. 2014) NPCs PSEN1 A246E ↑Aβ40,↑Aβ42,↑Aβ42:40 ↑p-tau PSEN1 M146L (Woodruff et al. 2013) NPCs/Neurons PSEN1 ΔE9 ↑Aβ42:40 γ-secretase inhibition = ↑Aβ42:40 of wild-type neurons (Woodruff et al. 2016) Neurons PSEN1ΔE9 ↑Aβ42:40 Rescued with β-secretase APPV717F, APPswe inhibition = except in APPswe neurons (Yagi et al. 2011) Neurons PSEN1 A246E, PSEN2 N141I impair endocytosis and transcytosis of APP and lipoproteins

(Yang et al. 2017) iPSC-NPCs PSEN1-S169del, PSEN1-A246E, ↑Aβ42,↑Aβ42:40

(Updated and adapted from Arber et al., 2017 and Rowland et al. 2018) Abbreviations: BFCNs: Basal forebrain cholinergic neurons, DS: Down syndrome, ER: Endoplasmic reticulum, NPC: neural progenitor cells, OS: oxidative stress, ROS: reactive oxygen species. SREBP: sterol regulatory element-binding protein *Phenotypes not observed in all lines used in study

Table 1.2: iPSC models from patients with sporadic AD sAD Line iPSC-derived Phenotype(s) Disease APOE Additional Culture Environmental or Experimental Results cell type(s) Genotype Conditions Genetic Risk Factor (Balez et al. 2016) Neurons ↑Aβ42 H2O2, NO ↑neurite retraction, apoptosis, hyper-excitable Ca2+ signalling (Birnbaum et al. 2018) Neurons (iN) ↑ROS, ↑ DNA Damage ε3/ε3, ε3/ε4

(Chen et al. 2018) Neurons 3D neuro-spheroid Neuronal dysfunction similar to AD brain tissue (Duan et al. 2014) BFCNs ↑Aβ42:40 ε3/ε4 Ionomycin, L- ↑Excitotoxicity glutamate (Hossini et al. 2015) Neurons ↑GSK3β Rescued with γ-secretase inhibition = ↓p-tau (Israel et al. 2012) Neurons *↑Aβ40, ↑ p-tau, ↑GSK3β ε3/ε3 + Human astrocytes ↑ Very large early endosomes (Lonza) (Jones et al. 2017) Astrocytes Altered S100β, EAAT1, GS ε4/ε4 and inflammatory mediators expression and localisation (Kondo et al. 2013) Neurons, *↑ER stress, ↑OS, ↑Aβ + Astrocytes of same ↑ROS ↑Aβ Oligomers Astrocytes oligomers iPSC line (Lee et al. 2016) Neurons 3D neuro-spheroid (Lin et al. 2018) Neurons ↓ Aβ uptake, ↑Aβ42 ε4/ε4, ε3/ε3 Organoids ↑p-tau Astrocyte Microglia-like (Ochalek et al. 2017) Neurons ↑Aβ42:40, ↑APP, ↑GSK3β, Aβ oligomers, H2O2 ↑Sensitivity to OS ↑p-tau (Young et al. 2015) Neurons SORL1

(Zollo et al. 2017) NSCs ↓SORL1 ε4/ε4

Abbreviations: BFCNs: Basal forebrain cholinergic neurons, ER: Endoplasmic reticulum, NSC: neural stem cells, OS: oxidative stress, ROS: reactive oxygen species. *Phenotypes not observed in all sAD lines in study Table from Rowland et al. (2018)

1.6.2 iPSC-derived sAD models

Far fewer studies have investigated AD-related changes in iPSC models of sAD. As has been demonstrated in fAD studies, increased Aβ production, and the ratio of Aβ42:40, GSK3β and p-tau has been observed in sAD iPSC-neurons (Balez et al. 2016;Birnbaum et al. 2018;Chen et al. 2018;Duan et al. 2014;Hossini et al. 2015;Israel et al. 2012;Kondo et al. 2013;Lin et al. 2018;Ochalek et al. 2017). Studies using sAD iPSC-neurons have specifically indicated an increase in total tau and phosphorylation of tau at Thr231 Thr205, Thr181, Thr403, Ser202, Ser400 and Ser404 compared to controls (Israel et al. 2012;Hossini et al. 2015;Ochalek et al. 2017). In iPSC-neurons derived from sAD patients have also shown increased activation of GSK3β (Israel et al.;Ochalek et al. 2017). This tau-kinase has been demonstrated to cause abnormal tau phosphorylation in AD. Despite observing changes in tau and GSK3β phosphorylation in both fAD and sAD models, mutations in tau are associated with FTD, and are not found in AD. Additionally tau is alternatively spliced to produce six isoforms where splicing of tau is regulated differently during development. In the adult brain 4-repeat tau is more commonly observed and more prone to aggregation in disease. iPSC-neurons typically express 3-repeat tau, and require extended culture for between 150-365 days before 4- repeat tau is observed (Sposito et al. 2015).

A few studies have also found that sAD iPSC-neurons exhibit increased sensitivity to stress (Birnbaum et al. 2018;Duan et al. 2014;Kondo et al. 2013;Ochalek et al. 2017). In two studies, sAD iPSC-neurons were compared against fAD iPSC-neurons and it was noted that not all sAD lines exhibited changes in Aβ or p-tau (Israel et al. 2012;Kondo et al. 2013). These differences and inconsistencies in the AD-related changes observed in sAD iPSC-models are likely reflective of the genetic and environmental risk factors sAD patients have been exposed to before disease onset. A summary of the findings of each study utilising sAD iPSC-models is described in Table 1.2. Such studies have prompted research to see how iPSC-neurons from sAD lines respond to cell stressors. When stressors that generated oxidative stress or Aβ oligomers were applied, the iPSC-neurons from sAD cell lines demonstrated increased sensitivity compared to those from control cell lines (Balez et al. 2016;Duan et al. 2014;Ochalek et al. 2017). However, a greater understanding of the mechanisms behind this increased sensitivity still needs to be investigated. Furthermore, in all of these studies, changes in Aβ production have been investigated, but Aβ degradation has not yet been investigated.

The large number of genetic risk factors identified in sAD suggests that disease onset may be due to different mechanisms. As discussed, fewer studies have utilised iPSCs derived from sAD patients, and there is a strong need for high numbers of lines to model sAD to uncover these mechanisms and understand the inconsistencies observed between these cell lines (Israel et al. 2012). A couple studies have indicated that approximately 50% of variation in iPSC lines is due to genetic variation (Carcamo-Orive et al. 2017;Kilpinen et al. 2017). Genetic stratification may be essential to draw associations of cell lines with specific disease phenotypes to further understand sAD and disease onset. Stratification of cell lines by for instance polygenic risk score such as through GWAS may also help to more accurately model sAD (Rowland et al. 2018).

1.6.3 Astrocytes

As described above, most studies investigating AD have used iPSC-neurons; this is understandable given AD results in neuronal loss. However, astrocytes are just, if not more, abundant than neurons in the cortex (Vasile et al. 2017). Astrocytes have the capacity to both clear and produce Aβ and are therefore relatively underappreciated for their role in AD. Only a few studies have used iPSC-astrocytes to demonstrate AD related changes. This includes the generation of astrocytes with PSEN1 ΔE9, and PSEN1 M146L mutations (Oksanen et al. 2017;Jones et al. 2017). Changes included increased Aβ42, calcium dysregulation, morphology alterations, and increased levels of oxidative stress. Changes in astrocyte morphology have also been observed in sAD iPSC-astrocytes with an APOE ε4/ε4 genotype, with alterations in gene transcription observed in isogenic APOE ε3/ε3 and ε4/ε4 controls (Jones et al. 2017;Lin et al. 2018). APOE ε4/ε4 astrocytes have also demonstrated impaired Aβ uptake and resulting decreased Aβ degradation and clearance (Lin et al. 2018;Zhao et al. 2017).

1.7 Environmental risk factors in AD

As reviewed in Rowland et al. (2018) in sAD, genetic risk factors are not mutations driving disease as observed in fAD with mutations in APP or PSEN1. As previously described, not all sAD iPSC models have recapitulated the pathology observed in fAD iPSC models such as an increase in Aβ (Israel et al. 2012;Kondo et al. 2013). There could be many reasons for this; iPSC derived neurons may not accurately model neurons of the AD brain, but it may also be that there are other environmental risk factors that initiate or drive disease onset. Many of

44 the environmental risk factors described below are based on the work presented in Rowland et al. (2018).

1.7.1 Ageing

Ageing, as the largest risk factor for the majority of cases for AD, is still not completely understood and is an obviously complex, multi-faceted process by which cells are irreversibly lost. There are several theories proposed as to why this happens which include accumulation of somatic DNA damage, telomere shortening, and the free radical theory behind ageing (Tosato et al. 2007).

Importantly, iPSC derived models do not recapitulate the epigenetic changes that occur in ageing which are lost during reprogramming. While direct conversion methods of iPSC reprogramming do not lose all of these epigenetic changes, the application of cell stressors may not only mimic changes that occur in ageing, but cause changes that have been observed in and may be a cause of AD. Klotho is considered an ‘anti-ageing’ protein where deficiency can increase ageing. Klotho has been shown to protect neurons from oxidative stress (Zeldich et al. 2014) and may have a role in Aβ deposition through regulation of autophagy. Another protein associated with ageing, progerin, is present in Hutchinson-Gilford Progeria syndrome. iPSC-derived smooth muscle cells from patients with in Hutchinson-Gilford Progeria syndrome display premature ageing (Liu et al. 2011).

In ageing an increase in hypoxic environment, inflammation, and oxidative stress are all observed. Ageing results in reduced vascularisation leading to hypoxia (Valli et al. 2015). A greater inflammatory response is seen due to an increase in the number of activated microglia with ageing (Norden and Godbout 2013). Ageing also triggers an increase in reactive astrocyte genes that were not found in mice lacking IL-1α, TNF and C1q expression. Therefore, astrocytes are not just activated by microglia in response to injury or disease, but also as a result of ageing (Clarke et al. 2018). It has also been shown that changes in astrocyte expression patterns with age, predict age more accurately than neurons (Soreq et al. 2017).

1.7.2 Mitochondrial dysfunction and oxidative stress

While the Aβ cascade hypothesis is perhaps the most accepted cause of AD, there have still been other hypotheses proposed with credible evidence over what drives AD pathology. As

45 discussed in section 1.1.1 mitochondrial dysfunction is an example which is associated with changes in morphology and ROS production.

Changes in APP expression and APP processing which result in an increase of Aβ are associated with mitochondrial dysfunction due to alterations in mitochondrial membrane proteins (reviewed Swerdlow et al. 2014). Mitochondrial dysfunction, leading to oxidative stress, has been demonstrated in iPSC-derived neuron models of Parkinson’s disease and frontotemporal dementia with mutations in PARK2, PINK1, and tau (Esteras et al. 2017;Chung et al. 2016;Imaizumi et al. 2012). fAD iPSC-derived neurons containing a PSEN1 mutation demonstrated decreased mitophagy and autophagy which are due to changes in PINK1 and PARK2 (Martin-Maestro et al. 2017). Mitochondrial dysfunction could therefore initiate disease onset through oxidative stress.

It has been established that oxidative stress may play a role in AD, contributing to the ‘free radical hypothesis of ageing’ (Simoncini et al. 2015), and indeed may play a role in several other neurodegenerative diseases. Oxidative stress is damaging to cells because of its ability to impair cellular function through several mechanisms, including dysregulation of calcium signalling, and the creation of toxic ROS (Barnham et al. 2004). In AD brains markers of DNA oxidative damage, protein oxidation, and lipid peroxidation are increased while there is a decline in anti-oxidant function (Marcus et al. 1998;Omar et al. 1999)(Reviewed Zhao and Zhao 2013). This decline is often localised to synapses and has been correlated with disease severity (Ansari and Scheff 2010). Zhao and Zhao (2013) also suggested that oxidative stress can appear as an intermediate between an ageing and a disease state. Using iPSC models, fAD iPSC-astrocytes showed higher levels of oxidative stress (Oksanen et al. 2017). Neurons derived from iPSCs of both fAD and sAD patients were more susceptible to H2O2 treatment than control (Oksanen et al. 2017;Kondo et al. 2013). iPSC-neurons from sAD patients show increased ROS sensitivity due to mitochondrial stress (Ochalek et al. 2017;Birnbaum et al. 2018). However, whether oxidative stress, possibly due to mitochondrial dysfunction, can drive the onset of AD remains to be addressed.

1.7.3 Hypoxia

Hypoxia has been proposed as a mechanism that can contribute to AD pathology. In the brain, hypoxic conditions increase with age (Roffe 2002) but there are also extreme forms such as stroke, ischaemic injury, cardiac arrest, and neurovascular disease. All of these conditions have been linked directly with neurodegeneration (Roberts et al. 1997). Currently

46 no sAD iPSC models have explored the effects of hypoxia. iPSC based studies have used hypoxia to maintain iPSC pluripotency, as well as for differentiation to a neural fate (Yoshida et al. 2009;Santilli et al. 2010;Mung et al. 2016).

Several studies have shown that APPSWE and PS1A246E transgenic mice that underwent hypoxic treatment experienced increased Aβ deposition, and hypoxic cell models have also shown increased Aβ levels (Sun et al. 2006;Li et al. 2009;Zhang et al. 2007). More recently it has also been shown in the mouse model that HIF1α regulates γ-secretase activity with decreased expression of DNA methyltransferase 3b resulting in upregulation of activity (Liu et al. 2016). In neuroblastoma cells, BACE1 expression was also increased, due to increased ROS and HIF-1α expression (Guglielmotto et al. 2009;Tamagno et al. 2012).

Hypoxia also impairs Aβ degradation and clearance due to changes in the expression of various Aβ proteases. Under hypoxic conditions, both ECE-1 and NEP activity and mRNA are downregulated, while in contrast, IDE mRNA is upregulated by ischaemia (Fisk et al. 2007;Nalivaeva et al. 2004;Hiltunen et al. 2009). Hypoxia also increased Aβ levels through impaired autophagic function (Cho et al. 2015).

The effects of hypoxia are primarily linked to changes in Aβ, but hypoxia has also been shown to cause increased tau phosphorylation in several rodent studies. From gene ontology-based microarray analysis, intermittent hypoxia increased p-tau similar to ageing through the MAPK, PI3K-AKt and glutamatergic synapse pathways (Yagishita et al. 2017). This increase in phosphorylated tau was also associated with an increase in GSK-3β, CDK5 and APH1 (Zhang et al. 2018;Gao et al. 2013). Several of these studies have utilised APP/PSEN1 double transgenic mice, where Gao et al. (2013) demonstrated an increase in tau phosphorylation in transgenic mice exposed to hypoxia that was not seen in the control mice. The authors proposed that tau phosphorylation was amyloid dependent.

Hypoxia is also known to induce an inflammatory response in neurons, astrocytes and microglia (Mukandala et al. 2016) linking it to other AD risk factors. The induction of pro- inflammatory cytokine release occurs through hypoxia-activated astrocytes and microglia via HIF1α binding to hypoxic responsive elements (HRE) (Zhang et al. 2006). Hypoxic treatment of astrocytes leads to increased intracellular acidosis (Bondarenko and Chesler 2001) and induces NF-κB activation in astrocytes which is a transcription factor for many of these inflammatory genes (Stanimirovic et al. 2001). Hypoxia also increases expression of agiopoeitin 2 (Ang-2) and MMP-2 which are inflammatory proteins, and, as demonstrated in

47 endothelial cells, hypoxia mediates an inflammatory response through upregulation of p38 and MAPK (Sanchez et al. 2012).

1.7.4 Inflammation

In AD, neuroinflammation was originally considered to be a result of the build-up of Aβ plaques and NFTs. There is now increasing evidence to suggest that inflammation contributes to the onset of disease (Heneka et al. 2015). While polymorphisms in inflammatory cytokines have not been directly linked with sAD (Bertram et al. 2007) the complexity of inflammatory mechanisms has made it difficult to determine whether neuroinflammation is harmful or protective.

Microglia and astrocytes are the two main mediators of inflammatory response in the brain. Microglia probably play the most important role in neuroinflammation and release a wide range of inflammatory factors. Generally the release of cytokines and other factors are neurotoxic through a multitude of effects including synaptic remodelling, activation of the inflammasome and caspases, and release of ROS resulting in cell death. In both microglia and astrocytes, Aβ causes activation and an inflammatory response (Tan et al. 1999) Activation of astrocytes limits plaque growth and amyloid load (Kraft et al. 2013). In an APP/PSEN1 mouse model reactive astrocytes surrounded Aβ plaques and engulfed dystrophic neurites. This is thought to be neuroprotective by removing dysfunctional synapses and also reducing the inflammatory stimulus produced by neurons (Gomez-Arboledas et al. 2018). Interestingly activated astrocytes increase production of Aβ (Zhao et al. 2011).

Table 1.3: AD associated inflammatory molecules secreted by microglia and astrocytes (Updated from Tuppo and Arias 2005)

Microglia Astrocytes Complement proteins Complement proteins Complement inhibitors Complement inhibitors Aβ Aβ Cytokines and chemokines Cytokines and chemokines IL-1 IL-1 TNF-α TNF-α IL-6 IL-6 IL-8 IL-8 MIP-1 S100 Reactive oxygen species Reactive oxygen species MHC II COX-2

Astrocyte activation

Less is known about astrocyte activation, but outlined in Table 1.3 is a comparison of known inflammatory mediators released by both microglia and astrocytes (Tuppo and Arias 2005). Microglia can activate astrocytes, and conversely astrocytes can also attenuate microglial response (Kim et al. 2010). Ageing itself can also activate astrocytes, and despite the various ways astrocytes become activated, the effect can be neurotoxic or neuroprotective. For example, astrocytes have also been activated with toll-like receptors (TLRs) resulting in ROS production that was neurotoxic in mouse co-cultures (Ma et al. 2013). Microglia activated with lipopolysaccharides (LPS) secreted three factors TNFα, IL1-β and C1q which was sufficient to activate astrocytes (Liddelow et al. 2017). Activated astrocytes were shown to be neurotoxic in several neuronal cell types. However, these secreted factors may have multi-functional effects, as C1q has also been shown to enhance Aβ uptake by astrocytes (Iram et al. 2016). Prevention of microglial induced activation of astrocytes (via glucagon-like peptide-1 receptor) prevented dopaminergic neuron loss in a Parkinson’s disease (PD) mouse model (Yun et al. 2018). It has recently been proposed that there are two classifications of activated astrocytes, A1 and A2. A1 is thought to be an inflammatory state and neurotoxic, whereas A2 is an ischaemic state and neuroprotective (Liddelow and Barres 2017).

Aβ also results in a decrease in HIF-1. When HIF1α levels are maintained, there is reduced astrocytes activation (Schubert et al. 2009). This suggests that hypoxia in astrocytes may be neuroprotective. In a kidney cell study, STAT3 was activated by hypoxia and interacted with HIF1α (Jung et al. 2005). Studies have shown that STAT3 has an important role in regulating astrocyte activation/reactive gliosis. Without STAT3 signalling, astrocytes produce increased amounts of ROS and decreased glutathione resulting in reduced metabolic and proliferative rates (Sarafian et al. 2010). Specifically STAT3 regulates astrocyte process formation and expression of TSP-1, which mediates astrocyte recovery of neuronal synapses (Tyzack et al. 2014). These data suggest that STAT3 leads to neuroprotective activation of astrocytes. Supporting this, EphB1 is upregulated in injured motor neurons and also activates astrocytes via STAT3. EphB1 signalling is disrupted in amyotrophic lateral sclerosis (ALS) iPSC-astrocytes (SOD1 mutation), suggesting that astrocytes fail to be neuroprotective in disease (Tyzack et al. 2017).

Several genetic risk factors including TREM2, CR1, CD33 and MS4A are associated with impaired Aβ clearance and inflammatory gene activation in AD (Krasemann et al. 2017;Lin et al. 2018). Exploring these genes in iPSC-microglia is still in its infancy, as these iPSC derived

50 cells may have similar transcriptomic profiles to human microglia, but are still only described as microglial-like (Abud et al. 2017;Muffat et al. 2016;Haenseler et al. 2017;Pandya et al. 2017). The role of TREM2 has been investigated using iPSC-neurons in an FTD cell line. In the absence of TREM2, iPS microglia-like cells still retained their phagocytic functions, which suggests that the effects are subtle, and may still require external environmental factors to initiate disease onset (Brownjohn et al. 2018). In other iPSC models, H2O2 and NO have been used in iPSC-neurons to investigate inflammation. In these models viability was reduced as a result of H2O2 and NO and this was exacerbated in fAD and sAD lines (Balez et al. 2016;Ochalek et al. 2017). Therefore, while increased inflammation may be associated with genetic risk factors, application of stressors to stimulate an inflammatory response may be a\n important factor in the study of sAD.

1.8 Thesis objectives

iPSCs offer the capacity to investigate disease in previously inaccessible human cell types. As discussed above, iPSCs differentiated into neurons from fAD patients have demonstrated increased Aβ levels and/or Aβ42:40 ratio in support of the amyloid hypothesis of AD. In contrast to fAD, it has been proposed in sAD that impairment of Aβ degradation and clearance may result in its increased deposition. The majority of patients with AD are sporadic and this is a currently under investigated aspect in AD-iPSC research. Increases in Aβ levels have been described in iPSC-neurons from some sAD cell lines, but not in all.

Therefore, it is hypothesised that there may be other risk factors, both genetic and environmental, that may alter not just Aβ production but also impair Aβ degradation and cause AD. Furthermore, many of the risk factors identified to cause sAD do not exclusively affect neurons but also affect other cell types. The majority of research utilising iPSCs have used iPSC-neurons; only recently have more studies started to address how astrocytes may contribute to AD pathology. Therefore the main aims of this thesis are to investigate the degradation of Aβ in iPSC-neurons and astrocytes and understand how this may be modified by AD relevant risk factors. This has been carried out via the following specific questions:

1.8.1. What pathways are involved in the degradation of Aβ by iPSC-derived neurons?

In fAD, increased production of Aβ results in Aβ deposition in the brain. However, the majority of AD cases are sporadic, where Aβ deposition may instead be due to impaired degradation and clearance. Therefore this chapter examines Aβ degradation in iPSC-derived neurons by known Aβ-degrading enzymes, IDE and NEP, and addresses the following key questions:

 Can neurons be generated and differentiated from iPSCs?  Can the proteolytic degradation of Aβ be assessed in iPSC-neurons?  What proteases are involved in the degradation of Aβ by iPSC-neurons?

1.8.2 How does hypoxia alter Aβ production and degradation in iPSC-derived neurons?

Hypoxia is a risk factor for AD and other neurodegenerative diseases and can occur as a result of ageing. Impaired clearance of Aβ is associated with ageing and late-onset AD. This chapter therefore investigates the hypothesis that hypoxia causes alterations in Aβ levels due to changes in Aβ degradation, and determines whether these effects are exacerbated in an AD cell line. The chapter focusses on the following key questions:

 Does hypoxia cause changes in the production and degradation of Aβ?  How does hypoxia affect the degradation of Aβ?  Are these effects exacerbated in an AD cell line with an APOE ε4/ε4 genotype?

1.8.3 What role do iPSC-astrocytes play in the production and degradation of Aβ?

Astrocytes may have a larger contribution to the enzymatic degradation of Aβ than neurons. The production and degradation of Aβ by iPSC-astrocytes has not been fully characterised. This chapter investigates whether iPSC-derived and primary astrocytes degrade Aβ and can modulate Aβ levels in cultured neurons by investigating the following key questions:

 Can iPSCs be differentiated into astrocytes?  How does APP processing and Aβ production and degradation compare between iPSC-astrocytes and primary astrocytes?

 Does astrocyte conditioned media (ACM) from iPSC-astrocytes affect neuronal Aβ production and degradation?

1.8.4 How do hypoxic or activated astrocytes affect Aβ production and degradation in iPSC-derived neurons?

This final chapter investigates the hypothesis that the application of hypoxia and microglia- secreted cytokines on astrocytes may alter Aβ production and/or Aβ degradation in astrocytes and that ACM taken from these cells and added to neurons, may cause changes to neuronal Aβ production and degradation. In this chapter the following questions are addressed:

 Does hypoxia or microglial-secreted inflammatory cytokines cause astrocyte activation?  Do hypoxic and activated astrocytes exhibit changes in APP processing including production and degradation of Aβ?  Does astrocyte conditioned media (ACM) from hypoxic and activated astrocytes cause changes in neuronal APP processing, including Aβ production and Aβ degradation?

Chapter 2: Methods

2.1 Cell culture

o All cell lines were maintained at 37 C with 5% CO2 unless otherwise stated. All cell lines were cultured under sterile conditions and routinely checked for mycoplasma contamination.

2.1.1 Immortalised cell line culture

SH-SY5Y and NB7 human neuroblastoma cells were provided by the Hooper Laboratory (University of Manchester, UK). SH-SY5Y cells were cultured in DMEM (Lonza), and NB7 cells in RPMI (Sigma). Cultures were supplemented with 10% FBS (Life Tech). Cells were routinely passaged at 85% confluence from T75 flasks using PBS (without metals) to remove cells from the flask and centrifuged at 3000xg for 3 minutes to recover the cells. Cells were resuspended in fresh media and split into either 6 well plates or T75 flasks.

2.1.2 iPSC lines

A range of iPSC lines were used for experimental work and are listed in the Table 2.1. ‘OX1- 19’ iPSCs, which have previously been characterised (van Wilgenburg et al. 2013), were provided by Sally Cowley (University of Oxford, UK). A sporadic AD line termed ‘APOE4/4’ and another healthy control line termed ‘SBAD-02’ were StemBANCC lines provided by Zam Cader (University of Oxford, UK). Both APOE4/4 and SBAD-02 cell lines are karyotypically normal. The SBAD-02 line has been previously characterised (Baud et al. 2017). The individual with sporadic AD of which the ‘APOE4/4’ line was derived was diagnosed with posterior cortical atrophy. This is rare in AD, and cognitive dysfunction would include visual deficits such as difficulties recognising faces, and problems with numeracy and literacy (Crutch et al. 2017).

Table 2.1: iPSC lines for experimental use

Cell Line Abbreviation Patient Karyotype APOE Disease Reprogramming sex/age Abnormalities genotype Status method OX1-19 OX1-19 M/36 Minor SNP ε3/ε3 Control Integrating, change on Chr. retrovirus 9 relative to parent fibroblasts. May affect LINGO2. Non-integrating, SBAD3-05-18A SBAD-02 F/31 None Detected Control sendai virus NHDF-Ad-Der- Fibroblast, Lonza (cat: CC-2511)

SFC042-03-02 APOE4/4 F None Detected ε4/ε4 SF02, AD Non-integrating, 04A patient sendai virus (Posterio r cortical atrophy)

2.1.3 iPSC maintenance

All iPSC lines were cultured in mTeSR (STEMCELL Tech) where media was changed daily. Cells were cultured on 6 well plates coated with Matrigel (Corning) or Geltrex (ThermoFisher) diluted in KO-DMEM (Life Tech), according to the manufacturer’s instruction. At 80% confluency cells were passaged using EDTA (Versene, 0.02%) (Lonza). At the time of plating cells, ROCK inhibitor (10µM) (Abcam) was added.

2.1.4 Neuronal induction

Differentiation of human iPSCs to cortical neurons used the protocol developed by Shi et al. (2012a). iPSCs were split onto Matrigel or Geltrex coated 12 well plates and once cells had reached 100% confluency they were switched to neural induction media containing SB431542 and noggin/dorsomorphin, which induces dual SMAD inhibition (media composition shown in Table 2.2). Induction media was replaced daily and cells were cultured for 10-11 days.

Table 2.2: Media used in neuronal induction and differentiation of iPSCs.

N2 Media B27 Media Neural Maintenance Induction Media Media (NMM) Sterile filtered (0.22µm pore filter) DMEM F12 glutamax Neurobasal media N2 Media NMM media 1X N2 1X B27 B27 Media SB431542 (10µM)

L-glutamine (1mM) L-glutamine (1mM) 1:1 ratio Noggin (500ng/ml) or dorsomorphin (1µM) Penicillin/ streptomycin Penicillin/ streptomycin (50 U/ml, 50mg/ml) (50 U/ml, 50mg/ml)

2-mercaptoethanol (100µM)

Insulin (5µg/ml)

Non-essential amino acids (100µM)

DMEM F12 glutamax media, neurobasal media, penicillin/streptomycin, L-glutamine, 2- mercaptoethanol, N2, B27 (Life Tech). Insulin (Sigma). SB431542, dorsomorphin, noggin (R&D Systems)

2.1.5 Expansion and differentiation of induced cells

After neural induction, cells were rinsed once in 1ml of dispase (STEMCELL Tech) and a 2X2 grid was scratched onto the well using the side of a sterile needle tip. Cells were then incubated in 0.5ml dispase at 37oC for 5 minutes. The dispase was removed and cells gently removed from the well in DMEM/F12. Cells were carefully transferred to a bijou tube, with minimal titration to maintain large cell ‘clumps’ of around 100-300 cells, gently resuspended in 10ml DMEM/F12 and then left to settle for a maximum of 10 minutes or centrifuged for 3 minutes at 350xg. The supernatant was removed but cells were left covered in media to prevent disturbance of the cell pellet before resuspension in 2ml of induction media. Cells were plated into one well of a laminin (20µg/ml) (Sigma) coated 6 well plate.

Media was replaced the following day with NMM and cells maintained in culture for a further 4-6 days with media change every other day. Between days 12-17 of neuronal induction, neural rosettes formed (Fig 2.1). FGF2 (20ng/ml) was then added for 2-4 days to help promote a neural fate. Cells were passaged 1:3 into laminin-coated plates using dispase; this dispase step follows the same procedure as above, but without scoring a 2x2 grid. Media was replaced the following day and cells maintained in culture for a further 4-6 days in NMM with media change every other day. Cells were then passaged again (1:3) using dispase onto laminin-coated plates and maintained in NMM as described previously.

Figure 2.1: Representative neural rosette formation in OX1-19 iPSCs at day 16.

FGF2 has been added. Arrows indicate examples of rosettes. Scale bar represents 200µm.

If the culture appeared to be contaminated with non-neuronal cell types, a neural rosette selection was performed using STEMdiff Rosette Selection Reagent (STEMCELL Tech). Cells were incubated in STEMdiff for 1 hour at 37oC, and the rosettes were gently lifted from the surface of the culture dish using DMEM/F12. The rosettes were then collected by centrifugation at 300xg for 5 minutes, washed gently in a further 10ml of DMEM/F12, and again recovered by centrifugation. Rosettes were then resuspended in NMM and plated in a 1:1 well ratio in laminin-coated 6-well plates.

Between days 27-31 after neuronal induction, neural progenitor cells were passaged using Accutase (Life Tech). Cells were incubated in Accutase for 5 minutes at 37oC, resuspended with NMM into single cell suspension and plated onto laminin coated 6 well plates with approximately 250000 cells per well. Cells were then cultured in NMM, with a media change every other day. A second and final Accutase step was also taken before day 40 to enable cells to be plated at the required density for further experiments. Plates used for the final Accutase step were coated in poly-L-ornithine (PLO) (Sigma) and laminin. If required, cells were frozen in NMM with 10% dimethyl sulfoxide (DMSO) and 20ng/ml FGF2 and stored in liquid nitrogen prior to the second Accutase stage. Cells were revived at day 40 (±2 days). All cells used were matured until day 80.

2.1.6 iPSC-astrocyte differentiation

For astrocyte differentiation, cells were cultured as before until the rosette stage or from revived NPCs (between days 25 and 40) and followed the STEMCELL Tech astrocyte differentiation protocol. They were then transferred from NMM to the same volume of STEMdiff™ Astrocyte Differentiation medium (STEMCELL Tech, #08450) for 3 weeks. Cells were passaged as needed using Accutase onto laminin coated plates. Media was changed every 3-4 days or as needed.

Astrocyte precursors were then transferred to STEMdiff™ Astrocyte Maturation Medium (STEMCELL Tech, #08550). Cells were cultured in maturation medium up until day 100-125. The changes in morphology between rosette, astrocyte differentiation, and maturation are indicated in Fig 2.2. Cells were passaged when confluent and also to remove contamination of neurons. After 100 days post astrocyte differentiation and maturation, media was changed and collected every 4 days.

Figure 2.2: Differentiation and maturation of iPSC-astrocytes over time.

OX1-19 and SBAD-02 NPCs were differentiated into astrocytes. Images were collected at day 25, 40, 100, and 125 at 10x magnification on an EVOS light microscope. Between days 25 and 40, NPCs were cultured in astrocyte differentiation media and frequently passaged, as needed, for 3 weeks. Astrocytes were then cultured in astrocyte maturation media up until day 100 to day 125. Between days 100 and 125, astrocytes show different morphology to NPCs with increasing branching complexity. Scale bar indicates 400µm.

2.1.7 Primary astrocyte culture

Primary human astrocytes isolated from the cerebral cortex were purchased from ScienCell (#1800). Primary astrocytes were cultured according to the ScienCell protocol in complete Astrocyte Medium (#1801) which was changed every 3-4 days. Primary astrocytes were passaged with trypsin (ScienCell), with trypsin neutralising solution (ScienCell) added after a 3-5 minute incubation, and collected by centrifugation for 5 minutes at 300xg. Cells were plated into T75 flasks and 6 well plates previously coated with an aqueous solution of poly- L-lysine 0.1mg/ml (Sigma) diluted 1:4 in PBS. Primary astrocytes were used between passages 2-3.

2.1.8 Inducing hypoxic conditions

Day 80 neurons and primary astrocytes were cultured in 6 well plates and transferred to a hypoxic incubator (ThermoFisher) held at 2.5% oxygen levels. The same volume of media on normoxic and hypoxic cells was changed at the same time every other day in neuronal, and every 3 days in primary astrocyte, cultures. Cells were kept in hypoxic conditions for 7 days. 24 hours prior to the experimental endpoint, cell media was removed completely and 2ml of OptiMEM were placed on cells in normoxic and hypoxic conditions. Media was collected, cells were pelleted, and lysates prepared as described in section 2.2.

2.1.9 Inducing astrocytic inflammatory response

Generation of ‘A1’ type/activated astrocytes was induced using a previously published protocol (Liddelow et al., 2017). Il-1α (3ng/ml, R&D Systems), TNFα (30ng/ml, Abcam) and C1q (400ng/ml, Sigma) were added to 10ml of OptiMEM (ThermoFisher) and cultured on primary astrocytes in T75 flasks for 24 hours. Media was then removed and collected, and primary astrocytes were washed once with PBS. 10ml OptiMEM, not containing cytokines, was then replaced on the primary astrocytes for a further 24 hours. Media was collected, cells were pelleted, and lysates prepared as described in section 2.2.

2.1.10 Treatment of iPSC-neurons with astrocyte conditioned media (ACM)

Day 80 neurons were treated with the conditioned media of primary and iPSC-astrocytes. iPSC-astrocytes were cultured in Astrocyte Maturation Media until day 100. Between days 100-125 the media was collected every 4 days and immediately frozen at -80oC. Astrocyte Maturation Media conditioned over 3 days was collected from hypoxic primary astrocytes at

62 day 6. Astrocyte media (OptiMEM) conditioned over 24 hours was collected from activated astrocytes, after the 24 hour activation treatment period (section 2.2.8). All ACMs were centrifuged to remove cells and cellular debris. Neurons were then treated in 1:1 with NMM and ACM for 48 hours. Cells were pelleted, media collected and lysates prepared as described in section 2.2.

2.2 Sample preparation

2.2.1 Media preparation

Culture media was removed and 10ml of OptiMEM (ThermoFisher) added to T75 flasks, or 2ml to each well of a 6 well plate, for 24 hours. For concentrated conditioned media, the conditioned media was removed from the cells and centrifuged at 2000xg for 3 minutes to remove cell debris. The supernatant was then transferred to a 3000kDa cut-off Vivaspin column and centrifuged at approximately 4500xg at 4oC to concentrate to ~300µl.

2.2.2 Lysate preparation

Cells cultured in T75 flasks were washed once with PBS, scraped into 5ml of PBS, and collected by centrifugation at 2000xg for 5 minutes. The supernatant was then removed and 1ml of RIPA buffer (150mM sodium chloride, 1% (v/v) Nonidet-P40, 0.5% (w/v) sodium deoxycholate, 50mM Tris, EDTA-free protease inhibitor cocktail (11873580001, Sigma) pH 8.0) added for 25 minutes on ice. For cells cultured in 6 well plates, cells were scraped into 1ml of PBS and once the supernatant was removed, 50µl of RIPA buffer was added. The solution was then centrifuged at 14000xg for 10 minutes at 4oC and the clarified lysate recovered by taking 85% of the supernatant. Cells collected for use in the FAβB degradation assay were prepared as above, using a modified RIPA buffer (without sodium deoxycholate or protease or phosphatase inhibitors).

2.2.3 Bicinchoninic acid (BCA) assay

Protein concentration was calculated by taking 1-3µl of cell lysate and media collections made up to 10µl with dH2O, and loading them into a 96 well plate in duplicate. A standard curve was made by loading of 2-10µg/µl of 1mg/ml bovine serum albumin (BSA) also in duplicate. 200μl of BCA assay solution (ThermoFisher) containing 4% (w/v) CuSO4 in a 50:1 ratio solution was added to each well and incubated for 20 minutes at 37oC. Absorbance was then measured at 562nm on an Elx800 plate reader (BioTek).

2.3 Degradation and production of amyloid

2.3.1 Amyloid-β preparation

Preparations of oligomer and monomer Aβ were provided by Ben Allsop. Aβ1-42 (Anaspec; AS-24224 was reconstituted in 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) at 1mg/ml and left at room temperature for 3 hours. Aβ was aliquoted and evaporated using a SPD131DDA vacuum concentrator (Savant). A vacuum was applied at slowest ramp speed (ramp 1) for 90 minutes whilst samples were under centrifugation. Peptide films were then stored at -80oC prior to use.

For use in SDS-PAGE, DMSO was added to the Aβ peptide films to give a 1mM concentration. To keep Aβ monomeric, the preparation was kept at 4oC for 16-20 hours and further diluted in Ham’s F-12 (Sigma) before use. Oligomeric Aβ was generated by diluting in Ham’s F-12 and keeping the solution at room temperature for 16-20 hours. To remove fibrils, oligomeric Aβ was centrifuged at 14000xg for 20 minutes and the supernatant collected.

Human Aβ ((1-40)-Lys(LC-biotin)-NH2, FAM-labeled), (FAM-Aβ-Biotin, FAβB) (Anaspec; AS-

o 61962-01) was reconstituted in 1% NH4OH, aliquoted, and stored at -80 C. Aliquots were further diluted in water to the appropriate concentrations immediately before use.

2.3.3 Measurement of Aβ

Measurement of Aβ levels was carried out in the unconcentrated media of cells in a multiplex immunoassay by Mesoscale Discovery (MSD) (K15200E). Cells were previously cultured in the same (2ml) of OptiMEM. The assay was carried out according to manufacturer’s protocol, with samples containing 50mM HEPES (pH 7.5). Aβ38, Aβ40, and Aβ42 were measured with the 6E10 antibody. Standards were diluted in OptiMEM with HEPES. Samples were kept on a plate shaker and covered with a plate seal at room temperature during incubations. The MSD Workbench 4.0 software (MSD) was used to analyse Aβ levels.

2.3.4 FAβB (FAM-Aβ-Biotin) degradation assay

Assessment of Aβ degradation in cell lysates/concentrated conditioned media was optimised and established based on a previously published fluorescence polarisation assay (Leissring et al. 2003). Samples containing 20µg total protein (as calculated by BCA assay) were loaded onto black clear bottom 96 well plates. Samples were kept on ice, contained no protease or phosphatase inhibitors, and freeze thaw cycles were matched for all samples. FAβB (Anaspec; AS-61962-01) was then added to give a final concentration of 500nM per well. DMEM was used to make a final volume of 100µl. Starting fluorescence was measured (excitation: 485nm, emission: 508nm) on a Synergy HT plate reader (Biotek). The plate was then incubated for 4 hours at 37oC. The plate fluorescence was then measured again for confirmation of baseline. 2µl of magnetic Dynabeads (MyOne Streptavidin T1, ThermoFisher) were added to each well and the plate placed on a shaker for 30 minutes. The plate was then transferred to a magnetic platform (ThermoFisher) and the magnetic beads pulled to one side for 5 minutes. 95µl of the supernatant was transferred to new wells and the final fluorescence value was measured. Degradation was then calculated by taking the final fluorescence value over the starting fluorescence value. Background fluorescence from a blank well containing FAβB was then deducted and values were then normalised to a control.

For control experiments demonstrating the FAβB substrate can be degraded, proteases at the following concentrations and amounts were used: 1ng/µl trypsin (Sigma), 25ng NEP (Bio- Techne), or 25ng IDE (Bio-Techne). To inhibit proteolytic activity the following inhibitors were added at the final concentrations: 1mM 1,10-phenanthroline (Sigma), 100µM phosphoramidon (Tocris Bioscience), 10µM 6bK (Tocris Biosciences), 10µM ML345 (Sigma), 100µM insulin (Sigma). Inhibitors and proteases were added, given a gentle shake, and incubated for 30 minutes at 37oC prior to the addition of FAβB.

2.4 Immunostaining

2.4.1 Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE)

Lysates or media samples were made up to equal protein concentrations using additional RIPA buffer or PBS respectively, with sample buffer added (1.6 mM Tris, pH 6.8, 2.2 % (w/v) sodium dodecyl sulphate (SDS), 1.6 % (w/v) dithiothreitol (DTT), 11 % (v/v) glycerol, bromophenol blue). Samples were heated to 95oC for 5 minutes, and 30µg of sample was then loaded onto polyacrylamide gels. 10% polyacrylamide resolving gels and 3%

65 polyacrylamide stacking gel to load samples were made containing 1M Tris pH 8.8, dH20, 10% SDS, 16% ammonium persulphate (APS) and tetramethylethylenediamine (TEMED). Where the FAβB and control amyloidgenic proteins were resolved, a precast 1.5mm thick 10-20% acrylamide gradient Tris-tricine gel (Biorad) was used. Samples were loaded along with a molecular weight marker. Protein separation was then conducted in a Tris-glycine-SDS buffer or Tris-tricine-SDS buffer (Biorad) for approximately 1 hour at 45mA.

2.4.2 Immunoblotting

Proteins separated by molecular weight on the SDS gel were transferred to polyvinylidene difluoride (PVDF) membrane (Biorad) in a buffer containing (150 mM glycine, 20mM Tris, 20% (v/v) methanol, pH 8.4). The proteins were transferred to the membrane for 75 minutes at 120V in a box containing an ice block. Once complete, the membrane was left to block in 5% skimmed milk (Sigma) diluted in PBST for 2 hours at room temperature. The membrane was then rinsed in PBS with 0.01% Tween-20 (PBST) and placed into primary antibody diluted in PBST with 3% BSA overnight at 4oC. Primary antibodies are listed in Table 2.3. The membrane was then washed three times for 10 minutes in PBST before incubation in secondary antibody diluted in PBST with 3% BSA, as listed in Table 2.3, and left at room temperature for 90 minutes. The membrane was then washed another two times for 10 minutes in PBST before a final wash in PBS for 10 minutes. Enhanced chemiluminescence (ECL) solution (ThermoFisher) was added and the membrane was then imaged using a Syngene Gbox X4 (Syngene). Analysis of membrane densitometry was carried out on ImageJ.

2.4.3 Immunofluorescence microscopy

Cells were cultured on coverslips for the desired time before being fixed in 4% paraformaldehyde (PFA) for 20 minutes. Coverslips were then rinsed with PBS three times and placed into 10% donkey serum (Abcam) overnight at 4oC. 50µl of primary antibody was placed on top of coverslips as listed in Table 2.4 contained in a darkened humidified chamber and left overnight at 4oC. Coverslips were washed 3 times with PBS for 10 minutes before incubation with 50µl of secondary antibody (listed in Table 2.4) for two hours in the humidified chamber at room temperature. Coverslips were washed once with PBS for 10 minutes, once in PBST for 10 minutes, and rinsed in dH2O before being left to dry overnight in the dark. Coverslips were then inverted onto 25µl of prolong gold containing 4’,6- diamidino-2-phenylindole (DAPI) (358nm absorbance), (ThermoFisher) on a slide and left to

66 dry in the dark. Coverslips were then imaged at the corresponding wavelength of the secondary antibody (488, 594, or 358nm, see Table 2.4).

2.4.4 Microscopes

Images were captured on an EVOS FL cell imaging system (Invitrogen). For comparison and of marker expression between cells, samples were identically processed and images captured under the same fluorescence channel exposure time. Negative controls with/without primary and secondary antibody were used to avoid measurement of background fluorescence. Channel separation and composition was carried out on Image-J.

Table 2.3: List of primary and secondary antibodies used in western blots.

Antigen/Antibody Antigen raised in Source Dilution β-Actin (AC-15) Mouse Sigma-Aldrich A5441 1:5000 Aβ (6E10) Mouse BioLegend SIG-39320 1:10000 APP (22C11) Mouse Millipore MAB348 1:2500 GLUT1 Mouse Abcam ab40084 1:5000 IDE Rabit Abcam ab32216 1:500 sAPPα (6E10) Mouse Millipore NE1003 1:4000

Secondary IgG HRP Anti-Mouse Sigma-Aldrich A9044 1:4000 IgG HRP Anti-Rabbit Sigma-Aldrich A16096 1:4000

Table 2.4: List of antibodies used in immunofluorescence microscopy.

Antigen Antigen Source Dilution Localisation Role raised in β-TUB Rabbit Abcam 1:500 Cytoplasm NPC marker ab18207 FOXG1 Mouse Abcam 1:500 Nucleus NSC marker ab18259 GFAP Chicken Abcam 1:500 Cytoplasm Glial/NSC marker ab4674 MAP2 Mouse Abcam 1:500 Cytoplasm Neuronal marker Ab92434 NANOG Rabbit Abcam 1:500 Nucleus Pluripotency ab109884 marker OCT4 Rabbit Abcam 1:500 Nucleus Pluripotency ab109884 marker PAX6 Mouse Covance 1:500 Nucleus NSC marker PRB-278P SATB2 Mouse Abcam 1:500 Nucleus Neuronal Marker ab51502 SOX2 Rabbit Abcam 1:500 Nucleus Pluripotency ab109884 marker SSEA4 Mouse Abcam 1:250 Cytoplasm Pluripotency ab109884 marker SYN Rabbit Abcam 1:250 Nucleus Neuronal marker ab68851 S100β Rabbit Abcam 1:500 Cytoplasm/ Astrocyte marker ab52642 Nucleus TBR1 Rabbit Abcam 1:500 Nucleus Neuronal marker ab31940 TRA160 Mouse Abcam 1:500 Cytoplasm Pluripotency ab109884 marker VGLUT1 Rabbit Sysy 135303 1:500 Cytoplasm/ Neuronal marker Membrane Secondary

Alexa Fluor Anti-Mouse Life Tech 1:500 488nm 21202

Alexa Fluor Anti-Rabbit Life Tech 1:500 594nm A21207

Alexa Fluor Anti-Chicken Life Tech 1:500 647nm A21449

2.5 Polymerase chain reaction (PCR)

2.5.1 APOE genotyping

APOE genotyping of iPSC lines OX1-19 was done by assessing the Rs7412 and RS429358 SNPs using the primers APOE (forward) 5’-CTCTGGCTCATCCCCATCTC-3’ and (reverse) 5’- GCCAGGGAGCCCACAGT-3’.

Genomic DNA was extracted from OX1-9 iPSCs cultured in 96 well plates. 50µL of lysis buffer containing 10mM Tris pH 7.5, 10mM EDTA, 10mM NaCl, 0.5% sarkosyl, and 40µg/ml proteinase K was added to each well, sealed tightly with parafilm and placed in an incubator at 58oC overnight. DNA was then precipitated with 100µl of cold 95% ethanol with 75mM NaCl for 2 hours. The plate was centrifuged for 5 minutes at 1000rpm and the supernatant removed. Wells were washed with 150µl of 70% ethanol three times. The plate was then left to evaporate residual ethanol at room temperature. Genomic DNA was then resuspended from each well in 30µl of water and placed on a shaker for 2 hours. DNA was quantified by Nanodrop (ThermoFisher).

50ng of genomic DNA was combined with 10pM/µl primers and 2X Primestar® premix

(Takara-Bio) with dH2O to a volume of 25µl. The samples were then placed into a thermal cycler (ThermoFisher) and amplified under the following conditions: 15 minutes at 97oC, then 35 cycles of 40s at 95oC, 30s at 64oC, and 30s at 72oC, before a final 5 minutes extension at 72oC.

The amplified product was then resolved on an agarose gel. The gel was made up from 2% agarose (Sigma) in Tris-acetate-EDTA (TAE) buffer, containing ethidium bromide (1:10,000). TAE buffer was used to cover the gel, and the amplified product was combined with 4µl DNA gel loading dye (NEB), loaded into the gel with a 100bp ladder. The gel was then resolved at 100V for 1 hour and then viewed on a transilluminator and the correct sized product was isolated manually.

The DNA was purified from the TBE agarose gel using the PureLink® Quick Gel Extraction Kit (Invitrogen) according to the manufacturer’s protocol. The DNA was prepared for sequencing using the standard protocol of BigDye® Direct Sanger Sequencing kit (ThermoFisher). Sequencing was carried out by the DNA Sequencing Facilities at the University of Manchester on an ABI 3730 DNA analyser. Chromatograms at rs429358, and rs7412 were visually inspected using Sequencher 4.8 (Gene Codes).

2.5.2 RT qPCR mRNA from cell pellets was isolated using the Rneasy Mini Kit (Qiagen) according to the manufacturer’s protocol. RNA was eluted in 30µl, which was centrifuged through the collection column twice. RNA concentration was then measured by Nanodrop (ThermoFisher) and 1µg of mRNA was converted to cDNA using the iScript™ cDNA Synthesis kit (BioRad) according to the manufacturer’s protocol. cDNA was combined with 2X FastStart Essential Green Master Mix (Roche) and primers, which are listed in Table 2.5, at a final concentration of 1pM/µl. The volume was made up to 20µl with dH20. A plate seal was used to cover samples and plates were briefly centrifuged. qPCR was run on the QuantStudio 3 (Applied Biosystems) Real-Time PCR system using the following settings:

Samples were held for 2 minutes at 50oC and then at 95oC for 10 minutes. During the PCR stage samples were cycled from 95oC for 15s and 60oC for 1min for 35 cycles. The melt curve stage kept samples at 95oC for a further 15s before holding at 60oC for 60s.

The standard curve method was used to analyse RT-qPCR. The quantitative value of a sample was interpolated from the standard curve and corrected against control GAPDH (or Rpl13a in hypoxia experiments). The experimental control value was set to a value of 1, and experimental conditions are shown as a normalised fold difference.

Table 2.5: Primers used in RT-qPCR Gene Forward Reverse (Sequence 5’3’) ALDH1L1 TCACAGAAGTCTAACCTGCC AGTGACGGGTGATAGATGAT C3 AAAAGGGGCGCAACAAGTTC GATGCCTTCCGGGTTCTCAA GAPDH CCTGTTCGACAGTCAGCCG CGACCAAATCCGTTGACTCC GFAP CACCACGATGTTCCTCTTGA GTCCCAGACCTTCCTCTTGA GLAST (EAAT-1) ACCCCAAGCATTCTGTGC TTCCGAAATAGAGCCTCGAC MAP2 TTCGTTGTGTCGTGTTCTCA AACCGAGGAAGCATTGATTG NANOG ATGGAGGAGGGAAGAGGAGA GATTTGTGGGGCCTGAAGAAA OCT4 GGTTCTCGATACTGGTTCGC GTGGAGGAAGCTGACAACCAA RPL13A TACGCTGTGAAGGCATCAAC GGGAGGGGTTGGTATTCATC SOX2 ACTTTTGTCGGAGACGGAGA GTTCATGTGCGCGTAACTGT SYN TGACGAGGAGTAGTCCCCAA CGAGGTCGAGTTCGAGTACC

2.6 Membrane potential assay

Membrane potential was measured using the FLIPR assay according to the manufacturer’s protocol (Molecular Devices). iPSC-neurons were differentiated in 96 well black clear bottom plate until the appropriate age. iPSC-neurons were incubated in the RED indicator dye for 30

o minutes in 5% CO2 at 37 C as per the manufacturer’s protocol. Plates were then placed into the FlexStation™ instrument measuring fluorescence (excitation: 530nm, emission:565nm), and 60mM KCl was added 19 seconds into the plate recording and fluorescence measured for a total of 90 seconds. KCl was added in 10-60µl/s depending on cell confluence of the plate to avoid dislodging cells. Settings were the same for experimental comparisons. Increasing fluorescence indicates cell depolarisation.

2.7 Statistics

Statistical significance was set at p<0.05. Statistical tests were carried out using SPSS (IBM, v23). Cell populations were assumed to have a normal distribution and parametric tests were used. Comparisons of two groups were made using an unpaired t-test. Levene’s test for equality of variances was applied, and where appropriate Welch’s correction was used to account for unequal variance. For experiments comparing iPSC-neurons, using multiple differentiations of iPSC-neurons or iPSC-astrocytes (inductions), data was normalised to each induction and the control value set to 100; therefore no error bar is shown. For experiments comparing astrocytes, data was normalised to the control. For comparisons of data sets above two samples, one-way ANOVA with Tukey’s multiple comparisons post-hoc test was used. Data are shown as mean ± standard error (SEM) unless otherwise indicated.

Chapter 3: What pathways are involved in the degradation of Aβ by iPSC-derived neurons?

3.1 Introduction

3.1.1 Characterisation of iPSC-neuron models

In AD, hippocampal and cortical neurons are primarily affected by Aβ accumulation (Arber et al. 2017). Therefore the generation of glutamatergic cortical neurons is of interest. iPSC technology offers the opportunity to generate human neurons. Primarily protocols to differentiate iPSC-neurons have attempted to mimic neural development in vivo. The majority of these neuronal differentiation protocols use either dual SMAD inhibition or embryoid body (EB) formation. Dual SMAD inhibition is typically used in 2D cultures to reduce the variability of neuronal differentiation produced in EBs (Tao and Zhang 2016). Therefore in this work neurons have been differentiated from iPSCs according to the protocol by Shi et al., (2012a).

With many protocols producing different neuronal populations it remains of high importance to demonstrate that neurons are representative of cortical layers, and are functionally active. In development, neural progenitors from the ventricular (VZ) and subventricular zones (SVZ) form the six cortical layers. The cortex is formed in reverse order from layer VI up to layer I. While layers are defined, markers of particular neurons making up a cortical layer may overlap (Molyneaux et al. 2007). It is therefore essential to use multiple markers to observe cortical and functional development. It must still be considered that even with the successful differentiation of cortical neurons, these neurons will not be of the same age as neurons affected by neurodegenerative disease; this must be taken into account when using these cell models.

3.1.2 iPSC-neuron models of AD

Cholinergic and cortical neurons have been used to model various aspects of AD. Most studies have predominantly focused on fAD and the outcomes of these studies have been reviewed (Arber et al. 2017). iPSC-neuron models used cell lines which have APP mutations or duplications, and mutations in PSEN1. Output measurements have observed increases in Aβ or in the ratio of Aβ42:40 in the AD models compared to the controls (Kondo et al. 2013;Moore et al. 2015;Sproul et al. 2014). The key findings from these fAD iPSC studies are summarised in Table 1.1.

In familial forms of AD, increased production of Aβ or an increase in the ratio of Aβ42 to Aβ40 is linked with Aβ deposit accumulation. However, in the majority of AD patients, which are sporadic cases, there is evidence that impaired clearance of Aβ is a more significant contributing factor to AD pathogenesis than increased Aβ production (Wang et al. 2006a;Wildsmith et al. 2013).

There have been far fewer studies utilising sAD iPSC-models and these have recently been reviewed (Rowland et al. 2018). The key findings from these studies have also been presented (Table 1.2). Unlike fAD studies, not all sAD patients have demonstrated pathological phenotypes such as increased Aβ (Israel et al. 2012;Kondo et al. 2013), and these studies have primarily focused on characterising the increase in Aβ or Aβ42:40 ratio, and as yet, no iPSC-neuron studies have characterised proteolytic Aβ degradation.

3.1.3 Proteolytic degradation of Aβ

A number of metallopeptidases including neprilysin (NEP), insulin degrading enzyme (IDE), endothelin-converting enzyme (ECE), and angiotensin-converting enzyme (ACE) can all degrade Aβ. NEP and IDE appear to be the two largest contributors to Aβ degradation (Iwata et al. 2001;Farris et al. 2003;Hellstrom-Lindahl et al. 2008), and the expression of both appears to be altered in AD (Eckman and Eckman 2005). There was a two-fold increase in Aβ plaques containing IDE in sAD brains compared to fAD (Dorfman et al. 2010). NEP was not found in plaques of fAD brains, and this supports the idea that impaired Aβ degradation by NEP and IDE results in sAD (Dorfman et al. 2010). Furthermore polymorphisms in both NEP and IDE have been implicated in sAD (Wang et al. 2016;Fu et al. 2009;Helisalmi et al. 2004;Vepsalainen et al. 2007). NEP and IDE are both expressed in neurons. NEP is predominately expressed in the neuronal plasma membrane at pre- and post-synaptic terminals. IDE, however, is mostly in the cytosol, although also found in organelles and at the plasma membrane (reviewed Carson and Turner 2002).

Interestingly, a recent study investigated APP expression during cortical differentiation and identified that APP is differentially regulated over time (Bergstrom et al. 2016). APP processing may in turn regulate NEP and IDE levels. NEP expression is increased with increased AICD. APP695 upregulates nuclear AICD and NEP levels (Belyaev et al. 2010). Whereas in contrast, APP isoforms 751 and 770 upregulated IDE (Nalivaeva et al. 2016).

3.1.4 Aims

In familial forms of AD, increased production of Aβ results in Aβ deposition in the brain. However, the majority of AD cases are sporadic, where Aβ deposition may instead be due to impaired degradation and clearance. Most iPSC-neuron based models of AD have focused on Aβ production and not Aβ degradation. Therefore the role of Aβ degradation in iPSC-neurons will be addressed with the following specific questions:

3.2 Results

3.2.1 Characterisation of OX1-19 iPSC neuronal differentiation

Differentiation of neurons from iPSCs was carried out according to the protocol by Shi et al. (2012a). The timeline for the differentiation process is shown (Fig 3.1A). OX1-19 iPSCs were demonstrated to be positive for pluripotent markers OCT4, SSEA4, NANOG, SOX2 and TRA- 160 by immunofluorescence microscopy (Fig 3.1B). Expression of pluripotent markers NANOG, SOX2 and OCT4 was quantified; these were increased 65, 80 and 50-fold, respectively, in iPSCs relative to iPSC-neurons at day 80 by qPCR (Fig 3.1C). Positive staining for pluripotent markers was confirmed before every neural induction. iPSCs underwent neural induction for 10-11 days. During this process, between day 5 and day 7 there was a rapid decrease in expression of the pluripotent marker OCT4, and a rapid increase in the NSC marker PAX6 (Fig 3.2).

NSCs and NPCs were expanded and differentiated between days 15 and 40 according to the neuronal differentiation time line (Fig 3.1A). During this expansion phase, neural rosettes were formed, which were demonstrated to be positive for the NSC/NPC markers PAX6, FOXG1 and SOX2 as identified by immunofluorescence microscopy (Fig 3.3). Neural rosettes were also positive for the neuronal markers β-TUB and MAP2 in cells that had migrated from the rosette epicentre, and showed positive staining for the cortical layer marker TBR1 and limited positive staining for the upper-cortical layer marker SATB2 (Fig 3.3). Further differentiation and maturation of iPSC-neurons to day 80 demonstrated changes in neuronal morphology, which included smaller cell bodies and extended processes and the formation of large networks (Fig 3.4). Neurons matured until day 80 also showed more reactivity for the neuronal markers β-TUB and MAP2 and the cortical markers TBR1 and SATB2. By day 80, iPSC-neurons were also positive for the pre-synaptic marker VGLUT1 (Fig 3.4A). The membrane potential of iPSC-neurons was assessed; neurons from day 60 onwards depolarised in response to KCI stimulation (Fig 3.4B), with a significant increase in the size of the response between day 60 and day 90. A change in membrane potential in response to KCl stimulation was not significantly different between day 80 and day 90 (Fig 3.4C). At day 80 iPSC-neurons also showed increased expression of MAP2 and synaptophysin by qPCR compared to day 50 neurons (0.85 and 0.7 fold, respectively) but no significant difference between day 80 and day 100 neurons (Fig 3.4D&E).

Figure 3.1: Confirmation of pluripotency in iPSCs

(A) Schematic overview of differentiation timeline of neurons from iPSCs starting at day 0. OX1-19 iPSCs were plated on coverslips or 6-well plates and cultured until confluent. (B) Quantification by qPCR of iPSC markers NANOG, SOX2 and OCT4 expression was corrected against GAPDH. Cells on coverslips were washed and then fixed and immunostained for OCT4, SSEA4, SOX2, NANOG, TRA1- 160 and nuclei were stained with DAPI. Images were obtained at 20x magnification on an EVOS FL microscope. Representative images of OX1-19 iPSCs. iPSCs were positive for pluripotent markers OCT4, SSEA4, SOX2, NANOG and TRA1-160. Scale bar indicates 200μm. (C) Cells in 6 well plates were pelleted, and the mRNA extracted. Quantification of NANOG, SOX2 and OCT4 expression by qPCR in iPSCs relative to day 80 iPSC-neurons. Expression of pluripotent markers is strongly expressed in iPSCs relative to neurons. Data shown as mean ± SEM, n=3 inductions.

Figure 3.2: Timeline of successful neural induction of iPSCs

OX1-19 iPSCs were plated on coverslips and underwent neural induction. Between day 3 to day 11 coverslips were taken every two days, washed and fixed to demonstrate the time course of neural induction. Coverslips were immunostained for the pluripotent marker OCT4, the neural stem cell marker PAX6 and nuclei were stained with DAPI. Images were obtained at 20x magnification on an EVOS FL microscope. Representative images demonstrate that between approximately day 5 and day 7 OCT4 expression was rapidly reduced and by day 9, was no longer expressed. From day 7, PAX6 expression rapidly increased to be strongly expressed at the end of the induction period at day 11. Scale bar represents 200μm. Cells were prepared and imaged by Alys Jones.

Figure 3.3: Characterisation of neural rosettes

OX1-19 iPSCs were differentiated to NPCs and cultured on coverslips. Between day 25-40 when neural rosettes were seen to form, cells were washed, fixed, and immunostained for NPC markers PAX6, FOXG1, SOX2, GFAP, and for neuronal markers β-TUB, MAP2, TBR1, SATB2. Nuclei were stained with DAPI. Images were obtained at 20x magnification on an EVOS FL microscope. Representative images of NPCs at neural rosette stage; cells are PAX6, SOX2, FOXG1 and GFAP positive. NPCs were positive for β-TUB and showed MAP2 staining in cells that have migrated from the rosette epicentre. NPCs were positive for cortical layer marker TBR1 but did not show strong staining for SATB2. Scale bar indicates 200µm

Figure 3.4: Characterisation of iPSC-derived neurons

Figure 3.4: Characterisation of iPSC-derived neurons (continued)

OX1-19 iPSCs were differentiated to neurons. (A) iPSC-neurons were cultured on coverslips until day 80, when they were washed, fixed, and immunostained for β-TUB, MAP2, VGLUT1, TBR1, SATB2 and nuclei were stained with DAPI. Images were obtained at 20x magnification (A1&A3) and 40x magnification (A2) on an EVOS FL microscope. Representative images demonstrate day 80 neurons were positive for neuronal markers MAP2, β-TUB, pre-synaptic marker VGLUT1, and cortical layer markers TBR1 and SATB2. Scale bar represents 200µm (A1&A3) and 400µm (A2). (B&C) iPSC-neurons were cultured in 96-well plates until day 60, 80 or 90. Neuronal membrane potential was measured using the FLIPR® Membrane Potential Assay Kit and quantified by calculating maximum RFU – minimum RFU. (B) Representative traces of neuronal membrane potential in neurons at day 60 (light grey), day 80 (black), and day 90 (grey). OX1-19 timeline membrane potential data was collected by Nicola Corbett. (C) Quantification of the membrane potential of neurons demonstrated a significant increase in membrane potential at day 90 compared to day 60. (D&E) iPSCs were cultured in 6-well plates until day 0, 50, 80 or 100 when cells were collected, and the mRNA extracted. Quantification of neuronal markers MAP2 and synaptophysin expression by qPCR was corrected against GAPDH. (D) Quantification of MAP2 expression in iPSC-neurons over time relative to iPSCs (day 0). (E) Quantification of synaptophysin expression in iPSC-neurons over time relative to iPSCs (day 0). Data shown as mean ± SEM, n=3. *, p<0.05, ** p<0.005 *** P<0.0005, **** P<0.0001 using one-way ANOVA, Tukey’s multiple comparisons test.

3.2.2 APP processing in iPSC-neurons over time

APP processing in iPSC-neurons was assessed between day 60 and day 100 from the same induction as determined by western blot analysis (Fig 3.5). iPSC-neurons expressed all three isoforms of APP, APP695, APP751 and APP770. At day 60 the distribution of mature 751/770, mature 695 and immature 751/770 and immature 695 was roughly equal. At day 80 and 90, iPSC-neurons predominantly expressed the immature 695 isoform. By day 100, this APP isoform distribution had altered to give a more equal distribution of isoforms as seen in the less mature day 60 neurons (Fig 3.5A). The overall quantification of all APP isoforms expressed demonstrated no significant difference in the total amount of APP in the iPSC- neurons from the same induction (Fig 3.5B).

APP underwent α-secretase processing in iPSC-neurons to produce sAPPα. The sAPPα produced was formed from both the APP695 and from the longer isoforms (Fig 3.5C). sAPPα was significantly increased by 50% at day 80 and 90, compared to day 60, and this increase appeared to be predominantly due to the cleavage of the APP695 isoform (Fig 3.5D).

Aβ levels were assessed in neurons from separate neuronal inductions at different time points. Aβ levels were variable in iPSC-neurons, and there was no time-dependent effect (Fig 3.6).

Figure 3.5: APP and sAPPα expression in iPSC-derived neurons over time

OX1-19 iPSCs were differentiated to neurons and cultured until day 60, 80, 90 and 100. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-neurons were harvested and lysates prepared. Samples were then immunoblotted using antibodies for APP and actin in lysates, and sAPPα in conditioned media. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from time points were normalised, as a percentage, to day 60. (A) Representative western blot of APP expression in iPSC-neurons over time. (B) Quantification of total APP expression over time was not significantly different. (C) Representative western blot of sAPPα, in iPSC-neurons, over time. (D) Quantification of sAPPα was significantly increased at day 80 and 90 compared to day 60. Data shown as mean ± SEM, n=1 induction with three technical repeats *, p<0.05 using one-way ANOVA, Tukey’s multiple comparisons test.

Figure 3.6: Characterisation of Aβ levels in conditioned media from iPSC-neurons over time

OX1-19 iPSCs were differentiated to neurons and cultured to day 60, 70, 80, 90, and 100. Conditioned media was collected and Aβ38, Aβ40 and Aβ42 levels were measured using MSD multiplex immunoassay. (A) Aβ38 (B) Aβ40 (C) Aβ42 levels and (D) Aβ40:42 ratio over time. Each point represents the results from a single neuronal induction (n=1-5), taking an average from a minimum of 3 technical replicates.

3.2.3 Characterisation of AD (APOE4/4) iPSC neuronal differentiation

An AD (APOE4/4) iPSC line was also used to generate functional iPSC-neurons. AD iPSCs were shown to be positive for pluripotent markers OCT4 and SSEA4 (Fig 3.7A). AD iPSCs were differentiated to neurons using the Shi et al. protocol (2012a). NPCs generated were positive for the NPC and neuronal markers FOXG1 and MAP2, respectively (Fig 3.7B). At day 100, AD iPSC-neurons were positive for the synaptic marker synaptophysin by immunofluorescence microscopy (Fig 3.7C). The membrane potential of AD iPSC-neurons was assessed to demonstrate that neurons at day 80 depolarised in response to KCI stimulation (Fig 3.7).

Aβ levels were assessed in AD iPSC-neurons from separate neuronal inductions at different time points. Aβ levels were variable in iPSC-neurons as observed in the OX1-19 iPSC-neurons, and there was no difference in levels of any of the forms of Aβ over time (Fig 3.8). There was no obvious difference in Aβ levels between the control (OX1-19) and AD (APOE4/4) cell lines.

3.2.4 Characterisation of control (SBAD-02) iPSC neuronal differentiation

An additional iPSC control line. SBAD-02 also underwent neuronal differentiation. SBAD-02 iPSCs were positive for the pluripotent markers OCT4 and SSEA4 (Fig 3.9A). SBAD-02 NPCs were positive for the NPC and neuronal markers FOXG1 and MAP2, respectively (Fig 3.9B). By day 80, SBAD-02 iPSC-neurons were positive for the pre-synaptic marker, VGLUT1 (Fig 3.9C). The membrane potential of SBAD-02 iPSC-neurons was assessed to demonstrate that neurons from day 60 onwards depolarise in response to KCI stimulation (Fig 3.9D).

Figure 3.7: Characterisation of AD (APOE4/4) iPSC-neurons

AD (APOE4/4) iPSCs were differentiated to neurons. iPSCs, NPCs and iPSC-neurons were cultured on coverslips washed and fixed at day 0 (A), approximately day 35 (B), and day 100 (C). Cells were immunostained for OCT4, SSEA4, MAP2, FOXG1, SYN and nuclei were stained with DAPI. Images were obtained at 20x magnification. Scale bar represents 200µm. (A) iPSCs were positive for pluripotent markers. (B) NPCs were positive for neuronal markers. (C) iPSC-neurons were positive for neuronal and synaptic markers. Immunofluorescence microscopy data was collected by Alys Jones. Neuronal membrane potential was measured using the FLIPR® Membrane Potential Assay Kit. (D) Representative traces of neuronal membrane potential in neurons at day 60 (light grey), and day 80 (black). AD (APOE4/4) membrane potential data was collected by Nicola Corbett.

Figure 3.8: Characterisation of AD Aβ levels in iPSC-neurons over time

AD (APOE4/4) iPSCs were differentiated to neurons and cultured to day 60, 70, 80, 90, and 100. Conditioned media was collected, and Aβ38, Aβ40 and Aβ42 levels were measured using MSD multiplex immunoassay. (A) Aβ38, (B) Aβ40, (C) Aβ42 levels and (D) Aβ40:42 ratio over time. Each point represents the results from a single neuronal induction (n=2-4), taking an average from a minimum of 3 technical replicates.

Figure 3.9: Characterisation of control (SBAD-02) iPSCs and neurons

Control (SBAD-02) iPSCs were differentiated to neurons. iPSCs, NPCs and iPSC-neurons were cultured on coverslips washed and fixed at day 0 (A), approximately day 35 (B), and day 100 (C). Cells were immunostained for OCT4, SSEA4, MAP2, FOXG1, VGLUT1 and nuclei were stained with DAPI. Images were obtained at 20x magnification. Scale bar represents 200µm. (A) iPSCs were positive for pluripotent markers. (B) NPCs were positive for neuronal markers. (C) iPSC-neurons were positive for neuronal and synaptic markers. Immunofluorescence microscopy data at iPSC and NPC (A&B) was collected by Alys Jones. Neuronal membrane potential was measured using the FLIPR® Membrane Potential Assay Kit. (D) Representative traces of neuronal membrane potential in neurons at day 60 (light grey), and day 80 (black).

3.2.5 Optimisation of the Aβ degradation assay

In order to assess Aβ degradation in iPSC-neural models, a cell-based Aβ degradation assay was adapted from a fluorescence polarisation assay (Leissring et al. 2003). The assay was established by adding the FAM-Aβ(40)-Biotin (FAβB) substrate to a 96-well plate containing cell lysates. Proteases in the lysate may cleave the FAβB, and, after an incubation period, streptavidin-coated dynabeads were added. These dynabeads bind to biotin, and the FAβB not cleaved can be magnetically pulled down. The supernatant containing the fluorescent cleaved Aβ fragment can then be separated and measured. The more degradation that has occurred, the higher the fluorescence value. A schematic demonstrating this process is shown in Fig 3.10.

It was demonstrated that 400nM of FAβB was sufficient to detect fluorescence (Fig 3.11A). When this amount of FAβB substrate was added, 2µl of streptavidin-coated dynabeads were necessary to pull-down the majority of uncleaved substrate before the effect of adding increasing volumes of dynabeads began to plateau (Fig 3.11B). It was demonstrated that the FAβB substrate itself when added was primarily monomeric, and that oligomeric species were not present compared to oligomeric Aβ control. Due to the addition of FAM and biotin tags, the monomeric FAβB has a higher molecular weight than untagged Aβ (Fig 3.11C). To demonstrate that this FAβB substrate can be degraded, and this effect can be measured by differences in protein concentration, trypsin was added. Trypsin degraded the FAβB substrate with increasing concentration until a plateau was reached (Fig 3.11D). To identify how much cell lysate was necessary to degrade the Aβ substrate and avoid excess proteases, increasing concentrations of protein from a neuroblastoma lysate were added. Aβ degradation plateaued at 75µg of protein; therefore 20µg of protein was used in future assays (Fig 3.11E).

Recombinant NEP and IDE are both able to degrade the FAβB substrate (Fig 3.12). In order to investigate which protease was degrading the FAβB in cell lysates, selective inhibitors were used. It was demonstrated that NEP mediated degradation of Aβ could be inhibited with 1, 10-phenanthroline (1mM) (a general metalloprotease inhibitor) and phosphoramidon (100µM), which is a NEP (and ECE) selective inhibitor. Both these inhibitors reduced Aβ degradation by recombinant NEP by 85% (Fig 3.12A). Conversely, IDE mediated degradation of Aβ could not be inhibited by 100µM phosphoramidon, but could be inhibited completely by 1, 10-phenanthroline (1mM) (Fig 3.12B). IDE degradation activity could also be inhibited by the specific IDE inhibitors 6bK (10µM), ML345 (10µM)

90 and insulin (100µM) (with a decrease of 60, 92 and 100% respectively). These inhibitors did not alter NEP mediated Aβ degradation (Fig 3.12A).

Legend:

(FAM tag, ) (Aβ, ) (Biotin tag, ) (Protease, ) (Dynabeads, )

Figure 3.10: Schematic of the FAβB degradation assay

In a well of a 96-well plate with cell lysates containing proteases (A), the fluorescently tagged Aβ- Biotin (FAβB) is added (B). These proteases cleave the Aβ substrate leaving a fluorescently cleaved portion (C). Any substrate that has not been cleaved will retain its biotin tag. When streptavidin coated dynabeads are added, these will bind to the biotin and be separated from the rest of the supernatant. The fluorescently tagged cleaved Aβ can then be transferred to another well and fluorescence measured (D). The higher the fluorescence value, the more FAβB substrate has been degraded.

Figure 3.11: Optimisation of the FAβB degradation assay.

The FAβB degradation assay was optimised by adjusting concentrations of the assay components; the substrate FAβB, and streptavidin-coated dynabeads. The assay was also optimised to assess degradation of Aβ using trypsin and then adjusting protein concentration of a cell lysate. (A) RFU against the addition of increasing quantities of the FAβB substrate. 400nM was detectable. (B) RFU against the addition of increasing quantities of dynabeads with 400nM FAβB. 2μl dynabeads solution was able to pull down most of the uncleaved FAβB. (C) Characterisation FAβB. Monomeric Aβ migrated on SDS-PAGE at approximately 4kDa as expected. Due to the additional tags, the FAβB, was detected at 5kDa and was monomeric. Control Aβ preparations and blot were performed by Ben Allsop. (D) Validation of FAβB Degradation Assay. Trypsin was able to degrade the FAβB substrate. Increasing concentration of trypsin is proportional to FAβB cleavage until a plateau was reached. (E) An optimal protein concentration from lysates was also assessed. The optimal amount determined was 20µg.

Figure 3.12: Recombinant NEP and IDE can degrade FAβB.

Recombinant NEP or IDE were pre-incubated with NEP inhibitor phosphoramidon, IDE inhibitors 6bK, ML345, insulin, and the general metalloprotease inhibitor 1, 10-phenanthroline for 30 minutes before the addition of FAβB and a further incubation of 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Results from rNEP and rIDE were normalised, as a percentage, to their respective controls without inhibitors. (A) rNEP degraded the FAβB substrate and its activity was significantly inhibited by the NEP inhibitor phosphoramidon, and metalloprotease inhibitor 1, 10- phenanthroline. (B) rIDE degraded the FAβB substrate and its activity was significantly inhibited by the IDE inhibitors 6bK, ML345, insulin and by the metalloprotease inhibitor 1, 10-phenanthroline. Data shown as mean ± SEM, n=3. ** p<0.005, **** P<0.0001 using one-way ANOVA, Tukey’s multiple comparisons test.

3.2.6 Aβ degradation in neuronal models

The Aβ degradation assay using cell lysates was initially validated using the neuroblastoma cell lines SH-SY5Y and NB7. Aβ degradation was detected in lysates from both SH-SY5Y (Fig 3.13A) and NB7 cells (Fig 3.13B). In both cell types, degradation activity was inhibited by the IDE inhibitor 6bk, and the general metalloprotease inhibitor 1, 10-phenanthroline by an average of 75%. However, Aβ degradation by SH-SY5Y and NB7 cells was not inhibited by the NEP specific inhibitor phosphoramidon.

Lysates prepared from iPSC-neurons were also able to degrade the FAβB substrate and this degradation was inhibited by the general metalloprotease inhibitor 1, 10-phenanthroline. Several IDE specific inhibitors (6bK, ML345 and insulin) all inhibited Aβ degradation in the iPSC-neuron lysate also by an average of 75% (Fig 3.14A). The NEP inhibitor phosphoramidon did not inhibit degradation of the FAβB by iPSC-neurons (Fig 3.14A). Degradation of the FAβB substrate was also assessed in the concentrated conditioned media of iPSC-neurons (Fig 3.14B). The concentrated conditioned media was also able to degrade Aβ, but the addition of NEP, IDE and metalloprotease inhibitors had no effect on Aβ degradation in the concentrated conditioned media.

To demonstrate IDE is expression detected in iPSC-neurons this was observed by western blot in iPSC-neurons from one induction over time (Fig 3.15A). IDE expression decreased over time, with 50% less IDE at day 100 compared to day 60. The decrease in IDE expression from day 60 to day 90 and 100 approached significance (p=0.08, p=0.051 respectively) (Fig 3.15B).

Figure 3.13: SH-SY5Y and NB7 cells degrade the FAβB substrate.

SH-SY5Y and NB7 neuroblastoma cells were harvested and lysates prepared. NEP inhibitor phosphoramidon (blue), IDE inhibitor 6bK (green), and general metalloprotease inhibitor, 1, 10- phenanthroline (blue-green) were pre-incubated with the lysates for 30 minutes pre-incubation before FAβB was added to lysates for further 4 hour incubation. The cleaved FAβB was separated and the fluorescence value measured. Results from the SH-SY5Y and NB7 samples were normalised, as a percentage, to their respective controls without inhibitors. SH-SY5Y (A) and NB7 (B) cell lysates were able to degrade FAβB. Degradation was not inhibited by the addition of the NEP inhibitor phosphoramidon. The IDE inhibitor 6bK and the metalloprotease inhibitor 1, 10-phenanthroline significantly inhibited Aβ degradation. Data shown as mean ± SEM, n=3 **** P<0.0001, using one-way ANOVA, Tukey’s multiple comparisons test.

Figure 3.14: iPSC-derived neurons degrade FAβB.

OX1-19 iPSC-neurons were differentiated until day 80. For the final 24 hours media of iPSC-neurons was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-neurons were harvested and lysates prepared. NEP inhibitor phosphoramidon (blue), IDE inhibitors 6bK, ML345, insulin (green) and general metalloprotease inhibitor 1,10-phenanthroline (blue-green) were pre-incubated with the lysates for 30 minutes before FAβB was added for a further 4 hour incubation. The cleaved FAβB was separated and the fluorescence value measured. Results from the iPSC-neuron samples were normalised, as a percentage, to their respective controls without inhibitors. (A) iPSC- neuron lysates degraded the FAβB substrate. IDE inhibitors inhibited Aβ degradation where as phosphoramidon the NEP inhibitor did not. (B) 20µg of concentrated conditioned media of iPSC- neurons degraded the FAβB substrate. Aβ degradation in the media was not altered by the addition of inhibitors. Data shown as mean ± SEM, n=3. **** P<0.0001, using one-way ANOVA, Tukey’s multiple comparisons test.

Figure 3.15: IDE expression in iPSC-neurons over time

OX1-19 iPSCs were differentiated to neurons and cultured until day 60, 80, 90 or 100. iPSC-neurons were harvested and lysates prepared. Samples were then immunoblotted using antibodies for IDE and actin in lysates. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from time points were normalised, as a percentage, to day 60. (A) Representative western blot of IDE expression in iPSC-neurons over time. (B) Quantification of total IDE expression over time was not significantly different. Data shown as mean ± SEM, n=1 induction with 3 technical replicates *, p<0.05 using one-way ANOVA, Tukey’s multiple comparisons test.

3.3 Discussion

In this chapter, the data demonstrate neurons can be differentiated from different iPSC lines, and that iPSC-neurons are positive for cortical and synaptic markers and are electrophysiologically active by day 80. The data also shows that Aβ degradation in cell lysates and conditioned media of iPSC-neurons can be assessed, and that IDE is the major Aβ degrading protease in iPSC-neurons.

3.3.1 Characterisation of iPSC-neuron differentiation

Differentiation of iPSC-neurons according to Shi et al. mimics cortical development, and required differentiation and maturation of neurons up to day 80 to demonstrate sophisticated spontaneous activity (Shi et al. 2012a). The data presented here agrees that 80 days is necessary for the differentiation to functional neurons.

The culturing and maintenance of pluripotency in these iPSCs is essential prior to neuronal induction for successful and pure culture of neurons. The data shows that in three separate cell lines OX1-19, APOE4/4, and SBAD-02 iPSCs all were positive for pluripotent markers (OCT4 and SSEA4) (Fig 3.1, 3.7A, and 3.9A respectively). More extensive characterisation in the OX1-19 line demonstrated quantification of the pluripotency markers OCT4, NANOG and SOX2 by qPCR, which were all expressed at higher levels relative to day 80 neurons.

During neural induction there was a rapid decrease in pluripotent marker expression and increase in NSC/NPC markers. This was clearly evidenced by the immunofluorescence microscopy data showing a ‘switch’ between day 5 and 7 whereby iPSCs undergoing neural induction stop expressing the pluripotent marker OCT4 and start expressing NSC/NPC marker PAX6 (Fig. 3.2). Between days 25-40 neural rosettes formed; this mimics a sagittal view of the formation of the neural tube (Wilson and Stice 2006). The presence of FOXG1, as an early NSC marker was found in all three cell lines (Fig 3.3, 3.7B, and 3.9B). In OX1-19s further characterisation demonstrated positive staining for other early NSC/NPC markers PAX6, SOX2 and GFAP. It was noticeable that that GFAP and SOX2 expression was highest in the epicentre of rosettes, reflecting the continued proliferative capacity (Elkabetz et al. 2008). The neuronal marker MAP2 was also present at the neural rosette stage in all cell lines (Fig 3.3, 3.7B, and 3.9B). In the OX1-19 NPCs there was stronger MAP2 reactivity in cells that had migrated further from the rosette epicentre (Fig 3.3). This likely further represents increasing neuronal maturity from the proliferative population at the rosette epicentre. At day 40 in the OX1-19 iPSC-neurons there was positive TBR1 staining. However, limited reactivity for

SATB2, an upper cortical layer marker, relative to TBR1 suggests cells had not yet reached a maturity where they reflect all layers of the cortex given that the cortex develops from layers VI to I (Molyneaux et al. 2007).

By day 80 iPSC-neurons showed widespread reactivity for neuronal markers β-TUB and MAP2 in all cell lines (Fig 3.4A, 3.7C, and 3.9C). OX1-19 neurons were also used to demonstrate positive staining for TBR1 and SATB2, markers for the upper and lower cortical layers (Fig 3.4A). These data suggest that cortical neurons have successfully been derived. In each of the OX1-19, APOE4/4 or SBAD-02 cell lines, there was positive staining for pre-synaptic (VGLUT1) and post-synaptic markers (SYN) (Fig 3.4A, 3.7C, and 3.9C respectively). The presence of synaptic proteins should indicate that neurons will exhibit functional properties. To ensure that neurons were functional by day 80, membrane potential was measured. It was observed that OX1-19, SBAD-02 and APOE4/4 cell lines exhibited some difference in membrane potential between days 60 and 80 (Fig 3.4B, 3.7D, 3.9D). Quantification of membrane potential at different ages in OX1-19 iPSC-neurons indicated that there was a significant increase in the change of membrane potential in response to KCl between day 60 and day 90 (Fig 3.4C). To further quantify differences in marker maturity over age MAP2 and synaptophysin were assessed in iPSC-neurons over time. The data showed a significant increase in expression of both neuronal markers between day 50 and 80 indicating this is an important time for achieving greater neuronal maturity. However, with neither MAP2 nor synaptophysin was there a significant difference in expression between day 80 and 100 (Fig 3.4D&E). Therefore in this differentiation process, with these cell lines, day 80 was selected as the time point for use of ‘mature’ neurons. This timeline for differentiation matches what has been demonstrated by the original protocol with similar expression of neuronal markers and functional output (Shi et al. 2012a). Positive staining for neuronal markers and demonstrating that iPSC-neurons are electrophysiologically active has been confirmed in studies utilising iPSC-derived cortical neurons investigating AD-related changes (Israel et al. 2012;Kondo et al. 2013;Moore et al. 2015).

3.3.2 APP processing in iPSC-neurons

Understanding how differentiation and maturation of iPSC-neurons affects APP expression and processing is important for subsequent objectives investigating Aβ formation and degradation. In an induction of OX1-19, APP expression was investigated at 60, 80, 90 and 100 days. The data demonstrated that overall APP expression at these time points did not significantly differ (Fig 3.5B). However, there appear to be changes in isoform expression at

100 day 80 and 90 compared to day 60 and 100. At days 80 and 90 iPSC-neurons appeared to express the APP695 isoform only with very little APP751/770. In comparison, at day 60 and 100 neurons expressed both isoforms (Fig 3.5A). This change in isoform expression is supported by recent work that shows APP expression increases in iPSC-neurons from day 45 onwards (Bergstrom et al. 2016). It also appears, that in the time points assessed by the authors, days 60 and 105 appear to express more APP bands than at day 75 and 90 (although the APP antibody the authors used was 6E10 and not 22C11 as has been used here)(Bergstrom et al. 2016). The prevalence of more APP695 at day 80 may be useful for study of AD in iPSC-neurons as this isoform is associated with increased generation of Aβ (Belyaev et al. 2010).

From day 60 onwards the data demonstrated that APP undergoes α-secretase processing. Levels of sAPPα increased significantly in day 80 and 90 iPSC-neurons compared to day 60 (Fig 3.5D). It appeared that at day 80 and 90 there was more sAPPα produced from the APP695 isoform (Fig 3.5C). These data are also supported by findings that sAPPα expression peaked at day 75 (Bergstrom et al. 2016).

Aβ levels in iPSC-neurons across several inductions were variable (Fig 3.6). However it appears that Aβ38, Aβ40, and Aβ42 were lowest at day 60 and 90, which may reflect the finding that APP695 expression was lower at those time points (Fig 3.5A) and therefore less Aβ would be generated (Belyaev et al. 2010). However there was such large variation between inductions that Aβ levels from a single induction over time would need to be analysed to confirm this. Comparison of several inductions is needed to normalise Aβ levels in future experiments.

3.3.3 Establishment and optimisation of an Aβ degradation assay

The method to assess Aβ degradation in iPSC-neurons was adapted from a fluorescence polarisation assay (Leissring et al. 2003) and is outlined in Fig 3.10. Upon determining that the FAβB added was monomeric, could be detected and that uncleaved FAβB could be separated from the cleavage product, increasing concentrations of trypsin demonstrated increasing FAβB degradation (Fig 3.11A-D). Therefore, these data demonstrated that the degradation of FAβB could be measured and used for further experimental investigations. The data also demonstrated that recombinant NEP and IDE both degraded the FAβB substrate in this assay (Fig 3.12A&B). The capacity of both NEP and IDE to degrade Aβ was also inhibited by a metalloprotease inhibitor 1, 10-phenanthroline. NEP degradation activity was selectively inhibited by phosphoramidon, which inhibits both NEP and ECE (Warner et

101 al. 1992;Shirotani et al. 2001). IDE was selectively inhibited by 6bK, ML345 and insulin. These IDE inhibitors have been shown to cause inhibition of IDE in different ways. 6bK binds close to the catalytic zinc ion (Maianti et al. 2014). ML345 instead targets a specific cysteine residue (Cys819) (Bannister et al. 2010). Insulin was used as a competitive inhibitor as IDE has far greater affinity to insulin compared to amyloid (Km = 85nM vs 25µM, respectively) (Malito et al. 2008).

3.3.4 IDE is the major Aβ degrading protease in iPSC-neurons

Validation and optimisation of the assay was performed in lysates of SH-SY5Y and NB7 neuroblastoma cells (Fig 3.13A&B). The NEP inhibitor phosphoramidon had no effect on Aβ degradation by the lysates of either neuroblastoma cell line. The IDE inhibitor 6bK and the metalloprotease inhibitor 1, 10-phenanthroline both caused significant decrease in Aβ degradation. This, therefore, indicated that IDE was mostly responsible for the Aβ degradation in these cell lines.

The role of IDE in Aβ degradation was then assessed in iPSC-neurons. Lysates from iPSC- neurons degraded the FAβB substrate (Fig 3.14A). Degradation by the iPSC-neurons was also not inhibited by phosphoramidon. However, Aβ degradation was inhibited by the general metalloprotease inhibitor 1, 10-phenanthroline, and more specifically, by the IDE inhibitors 6bK, ML345 and insulin. Therefore, it was concluded that IDE must be the major Aβ degrading protease in the lysate of iPSC-neurons. As discussed in section 3.3.3, 6bK, ML345, and insulin each inhibit IDE in different ways. This was therefore taken as additional confirmation that the degradation was due to IDE. Interestingly, the degradation of the FAβB substrate by concentrated conditioned media from iPSC-neurons could also be measured (Fig 3.14B). However, the addition of inhibitors did not affect the Aβ degradation capacity of the media. It was therefore concluded that Aβ degradation in the media was not occurring as a result of metalloprotease activity.

NEP and IDE are the proteases that make the largest contribution to Aβ degradation (Iwata et al. 2001;Farris et al. 2003;Hellstrom-Lindahl et al. 2008). The expression of both of these proteases has been shown to be modified in AD (Eckman and Eckman 2005). Lack of NEP expression in the mouse brain increased Aβ levels by between 1.5-2 fold (Iwata et al. 2001), whereas lack of IDE expression has only been shown to increase Aβ levels from 1.2-1.6 fold (Farris et al. 2003;Miller et al. 2003). In AD brains NEP mRNA is decreased (Yasojima et al. 2001;Wang et al. 2010). By contrast, no change in IDE activity has been observed, but IDE mRNA expression has been shown to increase in AD (Wang et al. 2010). It has also been

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3 shown that IDE has a larger catalytic cavity (~15,700 Å ) but lower Aβ binding affinity (Km

3) 25µM), whereas NEP has a smaller cavity (~5,100 Å , but greater Aβ binding affinity (Km 11µM) (Malito et al. 2008). The capacity of each of these proteases to degrade the FAβB substrate could have been affected by the addition of the FAM and biotin tags. However, the recombinant proteases were both able to degrade the substrate. Therefore, differences in cavity size and binding affinity should not play a significant role in capacity to degrade the FAβB substrate. Another consideration is the expression of APP695 is associated with increased NEP expression, while increased APP751 and 770 is associated with IDE upregulation (Belyaev et al. 2010). Neurons at day 80 expressed predominantly APP695, and therefore more NEP expression might have been expected. However, NEP did not appear to be involved in the degradation of Aβ. The localisation of the two proteases may therefore play an important role. On a cellular level, NEP appears predominantly based in the plasma membrane, whereas IDE is more cytosolic (Carson and Turner 2002). IDE is found in more organelle than NEP which is predominantly membrane-bound and there may simply be higher levels of IDE than NEP in iPSC-neurons. This remains to be determined. IDE was expressed in iPSC-neurons (Fig 3.15). IDE expression in an induction of iPSC-neurons showed a general decrease in expression over time that approached significance when day 60 and day 100 were compared (p=0.051). Neuronal expression of IDE over development has not yet been investigated. This may be interesting to investigate further in more inductions to understand if there is a relationship between IDE levels and APP processing, and how IDE expression is modified in healthy ageing. However, in the context of this FAβB assay, it appeared that NEP has little effect in Aβ degradation in lysates of iPSC-neurons, and IDE is the major Aβ degrading protease.

3.4 Chapter summary

In summary, this chapter demonstrates that functional iPSC-derived cortical neurons can be generated, that produce and process APP. An Aβ degradation assay has been established and optimised for use in the lysate and concentrated conditioned media of iPSC-neurons. The data demonstrated that IDE has an important role in Aβ degradation in the lysate of iPSC- neurons. The establishment of this assay means that not just the formation of Aβ, but its metabolism can be assessed when environmental cell stressors such as hypoxia are applied.

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Chapter 4: How does hypoxia alter Aβ production and degradation in iPSC-derived neurons?

4.1 Introduction

Hypoxia is the most common cause of cell damage in the brain (Zhou et al. 2013). It has also been proposed as a mechanism contributing to AD pathology. Chronic cases (causing reduced oxygen levels) of hypoxia, such as stroke, ischaemic injury, cardiac arrest and neurovascular disease, have all been identified as risk factors for the development of AD (Liu and Le 2014). Hypoxic conditions in the brain are also increased with age adding to sAD risk (Roffe 2002). The effects of hypoxia are not only linked to several AD risk factors, but also directly initiate or exacerbate AD pathology.

4.1.1 Hypoxia alters APP processing and Aβ levels

In post-mortem brain tissue following mild or severe ischaemia, APP levels were increased (Pluta et al. 1998). An increase in APP was also seen in rodent ischemic studies (Stephenson et al. 1992). In NB7 cells, APP expression was increased in hypoxia, however in SH-SY5Y neuroblastoma cells, a small decrease in APP was observed (Fisk et al. 2007;Webster et al. 2002). A more recent study has suggested that hypoxia-induced down regulation of DNA methyltransferase 3b results in the upregulation of APP (Liu et al. 2016). Whether APP levels alter due to hypoxia is still therefore unclear.

The generation of sAPPα through the non-amyloidogenic processing of APP is decreased in hypoxia. Studies utilising SH-SY5Y cells have demonstrated decreased expression of both sAPPα, and the α-secretases, ADAM10 and ADAM17 (TACE) (Webster et al. 2002;Marshall et al. 2006). Changes in mRNA levels of both ADAM10 and TACE have varied between studies in cell and rodent models of hypoxia, suggesting a time-dependent and regional effect of hypoxia (Marshall et al. 2006;Auerbach and Vinters 2006;Rybnikova et al. 2012). sAPPα also inhibits activity of BACE1, therefore a decrease in sAPPα as a result of hypoxia will increase amyloidogenic processing of APP (Peters-Libeu et al. 2015). Several studies, both in cells and in transgenic mice, that underwent hypoxic treatment resulted in increased Aβ deposition due to increased BACE1 expression and activity (Sun et al. 2006;Li et al. 2009;Zhang et al. 2007;Guglielmotto et al. 2009). This up-regulation in BACE1 has been explained through two mechanisms including ROS release from mitochondria and the activation of HIF-1α (Guglielmotto et al. 2009;Tamagno et al. 2012). HIF-1α binds to a hypoxia responsive

104 element on the BACE1 promotor facilitating its transcription (Majd et al. 2017;Sun et al. 2006;Zhang et al. 2007)(reviewed by Salminen et al., 2017).

An increase in γ-secretase activity has also been observed in response to hypoxia, which would induce Aβ deposition in AD (Villa et al. 2014;Kocki et al. 2015). Increased levels of γ- secretase may occur through several mechanisms including downregulation of DNA methyltransferase 3b or by Hif-1α acting as a regulatory subunit for γ-secretase (Villa et al. 2014;Liu et al. 2016). The γ-secretase complex is comprised of PSEN1 or PSEN2, APH-1, nicastrin and presenilin enhancer 2, and changes in sub-unit expression are thought to increase γ-secretase activity. Hypoxia also increases expression of APH-1a, (Wang et al. 2006b;Li et al. 2009), and regulates PSEN2 (Salminen et al. 2017), both of which are components of the γ-secretase complex. The PSEN2 gene aberrant splicing isoform PS2V, which does not contain exon 5, has been observed in, and can be used as, a sporadic AD marker (Manabe et al. 2007). Hypoxic stress in vitro resulted in aberrant splicing of PSEN2 to PS2V due to high mobility group A protein 1a (HMGA1a). Hypoxia increases expression of HMGA1a, of which an increase has also been observed in sAD brain tissue (Higashide et al. 2004;Manabe et al. 2007).

4.1.2 Hypoxia alters Aβ degradation and clearance

Hypoxia is known to affect not just the generation of Aβ, but also its degradation and clearance. Ischemia and hypoxia have been linked with an increase in autophagy. In a rat ischemic model autophagosomes, autolysosomes, LC3-II, and Bcl-2 (proteins used as markers of autophagy) were increased in neurons (Wen et al. 2008;Puyal et al. 2009;Carloni et al. 2008). Targeting autophagy is a potential stroke treatment to avoid cell death and reduce lesion size (Puyal et al. 2009). With hypoxia-induced increased autophagy, autophagic vacuoles have been shown to accumulate abnormally. These vacuoles were enriched with β and γ-secretase and resulted in an increase in Aβ levels, particularly elevation of Aβ42 (Li et al. 2009). AD is associated with impaired transport of autophagic vacuoles, which can be induced by hypoxia through activation of the AMPK-mTOR pathway (Liu et al. 2015). Autophagy is also induced by SUMO-1, which has been identified to increase the stability of HIF-1α through its sumoylation (Bae et al. 2004;Shao et al. 2004). Hypoxia increases expression of SUMO-1, which has been shown to increase BACE1 and Aβ accumulation (Yun et al. 2013). This is likely through a role where SUMO-1 increases autophagic vacuole accumulation, and through lack of clearance, increases Aβ levels (Cho et al. 2015).

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Hypoxia induced changes in gene regulation have also been associated with various Aβ- degrading proteases. Under hypoxic conditions, both ECE-1 and NEP activity and mRNA are downregulated (Fisk et al. 2007;Nalivaeva et al. 2004). Whereas in ischaemic conditions, IDE appears to be upregulated (Hiltunen et al. 2009). In another study, IDE was differentially expressed at different time points after induction of ischemia in a mouse model, which also showed that the TRIF pathway modified IDE expression (Famakin et al. 2014).

4.1.3 Hypoxia and APOE

APOE is the largest genetic risk factor for developing sAD, but disease onset may be initiated by another environmental factor. APOE protects astrocytes from hypoxia-induced apoptosis (Zhou et al. 2013). Astrocytes are generally considered more resistant to the damaging effects of hypoxia, as well as secrete more APOE than neurons. After ischaemic insult in a rat brain model, APOE production was increased (Hayashi et al. 2006). In a cell model and in vivo mouse model, the application of APOE isoforms prior to insult, did not modify neuronal viability. Differences in the outcome of these studies may in part be due to different cell types, animal models, and APOE preparation (Lendon et al. 2000). It will therefore be of interest to see how APOE isoforms may modify the effects of hypoxia in a human iPSC-neuron model. Interest in the effects of hypoxia and APOE on AD pathology, are also piqued by the role APOE and hypoxia have in Aβ degradation and clearance. APOE has a large role in Aβ clearance, and data suggests that hypoxia also affects not just the generation of Aβ, but also its degradation and clearance. Additionally studies have demonstrated that APOE can stimulate APP transcription in an isoform dependent manner, whereby the ε4 allele increased APP transcription the most (Huang et al. 2017). Therefore the data suggest that hypoxia may exacerbate Aβ production in an AD line with an APOE ε4/ε4 genotype leading to an increase in Aβ generation. This provides a mechanism to explain how a genetic risk factor that is no guarantee of disease onset is then more susceptible to environmental risk factors such as hypoxia.

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4.1.4 Aims

Inducing hypoxia in iPSC/iPSC-neurons has primarily been used to maintain and differentiate iPSCs to a neural fate (Yoshida et al. 2009;Santilli et al. 2010;Mung et al. 2016). There is limited understanding of how hypoxia may contribute to AD pathology in iPSC-derived neurons (reviewed by Rowland et al., 2018). As discussed in this introduction research using animal primary neurons or neuron-like cell lines have shown strong evidence that hypoxia increases production of Aβ. As hypoxia is a risk factor for sAD, it may also exacerbate pathology in individuals genetically predisposed to AD. Additionally, given sAD is linked with decreased Aβ clearance, and hypoxia has been linked to changes in Aβ degrading enzymes, the degradation and clearance of Aβ may also be affected by hypoxia.

The aim of this chapter was, therefore, to investigate how hypoxia may contribute to Aβ production and degradation in iPSC-derived neurons and how it may be exacerbated in AD by investigating the genetic risk factor such as APOE ε4/ε4. This has been investigated through addressing the following specific questions:

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4.2 Results

Hypoxia was induced in day 80 iPSC-derived neurons by incubation in 2.5% O2 for 7 days. Under hypoxic conditions metabolism by oxidative phosphorylation is reduced and anaerobic glycolysis increased, including an increase in glucose uptake. This is primarily facilitated by glucose-transporter-1 (GLUT1). GLUT1 was therefore used as a downstream marker to indicate the successful induction of hypoxia (Zhang et al. 1999;Fisk et al. 2007). In all experiments GLUT1 was increased by about 250% when exposed to hypoxia (Fig 4.1A&B).

4.2.1 APP processing is altered in hypoxia

Previous studies in neuroblastoma cell lines have indicated that APP expression is variable in response to hypoxia (Fisk et al. 2007;Webster et al. 2002). In the iPSC-neurons APP expression was highly variable between technical repeats (Fig 4.2A) and between neuronal inductions (data not shown). There was no difference in APP expression between normoxic and hypoxic conditions. In contrast sAPPα levels, as detected in the media by western blot were significantly decreased in hypoxic samples by 50% (Fig 4.2B).

Aβ levels in the conditioned media were assessed under hypoxic conditions. Aβ40 was significantly decreased by 50% with hypoxia (Fig 4.3B). There was no significant decrease in Aβ38 or Aβ42 (Fig 4.3A&C). Analysis of the Aβ40:42 ratio between normoxic and hypoxic samples showed that despite the significant decrease in the presence of Aβ40 under hypoxia, there is no significant change in the ratio of Aβ40 to Aβ42 (Fig 4.3D).

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Figure 4.1: GLUT1 expression is increased in iPSC-derived neurons exposed to hypoxia

OX1-19 iPSC-neurons were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. iPSC-neurons were harvested and lysates prepared. Samples were then immunoblotted using antibodies for GLUT1 and actin. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Representative western blots of GLUT1 expression in iPSC-derived neurons exposed to hypoxia. (B) Quantification of GLUT1 shows a significant increase under hypoxic conditions. Data shown as mean ± SEM, n= 5 inductions ** p <0.05 using an unpaired t-test.

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Figure 4.2: APP processing is altered in iPSC-neurons exposed to hypoxia

OX1-19 iPSC-neurons were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-neurons were harvested and lysates prepared. Samples were then immunoblotted using antibodies for APP and actin in lysates, and sAPPα in conditioned media. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Representative western blot of APP expression in iPSC-neurons exposed to hypoxia. (B) Quantification of APP expression is variable between biological and technical repeats and not significantly different between normoxic and hypoxic conditions. (C) Representative blot of sAPPα, in iPSC-neurons exposed to hypoxia. (D) Quantification of sAPPα showed a significant decrease under hypoxic conditions. Data shown as mean ± SEM, n=3 inductions. * p<0.05 using an unpaired t-test.

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Figure 4.3: Aβ40 is decreased in iPSC-derived neurons exposed to hypoxia.

OX1-19 iPSC-neurons were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, and Aβ38, Aβ40, and Aβ42 levels were measured using MSD multiplex immunoassay. Aβ levels in iPSC-derived neurons exposed to hypoxia were normalised as a percentage to normoxic conditions. (A) Aβ38, (B) Aβ40 and (C) Aβ42 levels. Aβ40 levels are significantly decreased in hypoxia. (D) Ratio of Aβ40:42 is not changed. Data shown as mean ± SEM, n=4 inductions. * p<0.05 using an unpaired t-test.

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4.2.2 Aβ degradation and IDE levels are decreased in hypoxia

Assessment of Aβ degradation in lysates of hypoxic and normoxic iPSC-neurons demonstrated a significant decrease of 10% in hypoxic samples (Fig 4.4A). These data suggest that not only does hypoxia affect the generation of Aβ, but it also impacts the degradation of Aβ. Assessment of Aβ degradation in the concentrated conditioned media of hypoxic and normoxic iPSC-neurons demonstrated an increase in Aβ degradation in the hypoxic samples although this was not significant due to variability between samples (Fig 4.4B).

Having established the importance of IDE in the degradation of Aβ in iPSC-derived neurons (Fig 3.14), and given that Aβ degradation was decreased in the iPSC-neurons under hypoxia, the expression of IDE in iPSC-neurons exposed to hypoxia was examined. IDE, as determined by western blot, was significantly decreased by 50% in iPSC-neurons exposed to hypoxia (Fig 4.5A&B). The data also showed IDE expression increased in the concentrated conditioned media from iPSC-neurons exposed to hypoxia, although this was variable and therefore not significant (Fig 4.5C&D). Secretion of actin into the conditioned media was determined by western blot as a measure of cell membrane integrity. Low amounts of actin were detected in the media and there was no difference between normoxic and hypoxic conditions suggesting cell integrity was maintained (Fig 4.5C).

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Figure 4.4: Aβ degradation is reduced in the lysates of iPSC-derived neurons exposed to hypoxia

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Figure 4.5: IDE expression is decreased in the lysates of iPSC-neurons exposed to hypoxia

OX1-19 iPSC-neurons were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-neurons were harvested and lysates prepared. Samples were then immunoblotted using antibodies for IDE and actin in lysates and conditioned media. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Representative western blot of IDE expression in the lysate of iPSC-neurons exposed to hypoxia. (B) Quantification of IDE showed a significant decrease under hypoxic conditions. (C) Representative blot of IDE in the media of iPSC- neurons exposed to hypoxia. (D) Quantification of IDE in the media under hypoxic conditions. Data shown as mean ± SEM, n=4 inductions. * p<0.05 using an unpaired t-test.

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4.2.3 APP expression is increased in an AD cell line exposed to hypoxia

To determine whether hypoxia may indeed act as an environmental factor that can initiate or exacerbate disease onset or progression, a cell line from a patient with confirmed AD and an APOE ε4/ε4 genotype was used. For comparison, the APOE genotype was assessed in iPSCs from the control OX1-19 line. From sequencing data the OX1-19 line was identified to have an APOE genotype of ε3/ε3 (Fig 4.6).

The effect of hypoxia was confirmed by a significant 150% increase in GLUT1 expression (Fig 4.7 A&B). The AD line in hypoxia showed a significant increase in APP expression, by 7%, compared to normoxic conditions (Fig 4.8A&B). sAPPα decreased in 2 of the 3 inductions but was not significantly decreased overall (Fig 4.8C&D). Aβ levels in the AD cell line showed a significant decrease in Aβ38 (Fig 4.9A), Aβ40 (Fig 4.9B), and Aβ42 (Fig 4.9C). Although no significant change in the ratio of Aβ40:42 was observed (Fig 4.9D).

4.2.4 IDE levels are decreased in an AD cell line exposed to hypoxia

The degradation capacity of Aβ in lysates from AD iPSC-neurons decreased but did not reach statistical significance (p=0.11) (Fig 4.10A). In the concentrated conditioned media from AD iPSC-neurons, Aβ degradation capacity was also unaltered (Fig 4.10B). IDE expression in lysates from the AD line exposed to hypoxia was significantly decreased by 50% compared to normoxic conditions (Fig 4.10C&D). IDE expression increased in the concentrated conditioned media from AD iPSC-neurons exposed to hypoxia, as had been observed in control iPSC-neurons (Fig 4.5C&D); however this was not significant (Fig 4.10E&F). A summary comparing these findings between control and AD iPSC-neurons is described in Table 4.1.

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Figure 4.6: APOE genotype of OX1-19 iPSC line is ε3 /ε3

OX1-19 iPSCs were pelleted and gDNA was extracted. The DNA was amplified for APOE, purified, and sequenced. The two SNPs were identified and the APOE polymorphisms were T at rs429358, and C at rs7412. As listed in the table, the APOE genotype of OX1-19 cell line is therefore ε3 /ε3.

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Figure 4.7: GLUT1 expression is increased in AD iPSC-derived neurons exposed to hypoxia

AD iPSC-neurons with APOE ε4/ε4 genotype were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. iPSC-neurons were harvested and lysates prepared. Samples were then immunoblotted using antibodies for GLUT1 and actin. Semi- quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Representative western blots of GLUT1 expression in iPSC-derived neurons exposed to hypoxia. (B) Quantification of GLUT1 shows a significant increase under hypoxic conditions. Data shown as mean ± SEM, n= 3 inductions * p <0.05 using an unpaired t-test

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Figure 4.8: APP processing is altered in AD iPSC-neurons exposed to hypoxia

AD iPSC-neurons with APOE ε4/ε4 genotype were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-neurons were harvested and lysates prepared. Samples were then immunoblotted using antibodies for APP and actin in lysates, and sAPPα in conditioned media. Semi- quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Representative western blot of APP expression in iPSC-neurons exposed to hypoxia. (B) Quantification of APP expression showed a significant increase under hypoxic conditions. (C) Representative blot of sAPPα, exposed to hypoxia. (D) Quantification of sAPPα under hypoxic conditions. Data shown as mean ± SEM, n=3 inductions. * p<0.05 using an unpaired t-test.

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Figure 4.9: Aβ levels are decreased in AD iPSC-neurons exposed to hypoxia

AD iPSC-neurons with APOE4/4 genotype were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, and Aβ38, Aβ40, and Aβ42 levels were measured using MSD multiplex immunoassay. Aβ levels in iPSC- derived neurons exposed to hypoxia were normalised, as a percentage, to normoxic conditions. (A) Aβ38, (B) Aβ40, and (C) Aβ42 levels were significantly decreased in hypoxia. (D) Ratio of Aβ40:42 is not changed. Data shown as mean ± SEM, n=4 inductions. * p<0.05, **, p<0.005, ***, p<0.0005 using an unpaired t-test.

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Figure 4.10: IDE expression is decreased in the lysates of iPSC-neurons exposed to hypoxia

AD iPSC-neurons with APOE4/4 genotype were differentiated until day 80 and exposed to 2.5% O2 for 7 days. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-neurons were harvested and lysates prepared. FAβB was added to lysates or concentrated conditioned media and incubated for 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Samples were also immunoblotted using antibodies for IDE and actin in lysates and in conditioned media. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Degradation of Aβ in lysates of iPSC- neurons under hypoxic conditions. (B) Degradation of Aβ by media of iPSC-neurons under hypoxic conditions. C) Representative blot of IDE in the lysate of iPSC-neurons exposed to hypoxia. (D) Quantification of IDE in the lysate showed a significant decrease under hypoxic conditions. (E) Representative blot of IDE in the media of iPSC-neurons exposed to hypoxia. (F) Quantification of IDE in the media under hypoxic conditions. Data shown as mean ± SEM, n=3 inductions. ** p<0.01 using an unpaired t-test.

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Table 4.1: Key changes in control (OX1-19) and AD (APOE4/4) iPSC-derived neurons exposed to hypoxia.

Control (OX1-19) AD (APOE4/4) GLUT1 expression ↑↑ ↑↑ APP expression ↑ ↑↑ sAPPα ↓↓ ↓ Aβ38 ↓ ↓↓ Aβ40 ↓↓ ↓↓ Aβ42 ↓ ↓↓ Aβ40:42 - ↓ Aβ degradation capacity lysate ↓↓ ↓ Aβ degradation capacity media ↑ ↑ IDE expression ↓↓ ↓↓ IDE levels in media ↑ ↑

Key: -, no change, ↓, indicates trend, ↓↓ indicates statistically significant change

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4.3 Discussion

Overall these data demonstrate that hypoxia in iPSC-derived neurons results in decreased levels of sAPPα, decreased Aβ degradation capacity, and decreased expression of IDE. These findings were exacerbated in an AD line, where APP expression and Aβ levels were more severely affected by hypoxia.

4.3.1 Inducing Hypoxia in iPSC-neurons

In the human brain, oxygen levels are typically between 1-5% (Zhu et al. 2012). In traditional cell models 20% oxygen levels are typically termed normoxic. While this may not be physiologically as relevant, dropping oxygen levels to 2.5% in cell models has demonstrated similar effects to those observed in the hypoxic brain; which makes such models useful for study of hypoxia (Zhang and Le 2010). The large increase in GLUT1 expression consistently across all inductions, in both control and AD cell lines (Fig 4.1), demonstrates that hypoxia can easily and simply be induced in iPSC-derived neurons. While GLUT1 was consistently increased, there was significant variation in GLUT1 expression levels between inductions. HIF-1 is also directly linked to the hypoxic response, with a binding site on the GLUT1 promoter (Chen et al. 2001). HIF-1α is a key marker of hypoxic response, but was not detected at quantifiable levels in iPSC-neurons. This has previously been observed in neurons, and therefore GLUT1 was used (Fisk et al. 2007).

4.3.2 APP expression and sAPPα are altered in iPSC-neurons exposed to hypoxia

APP expression was increased in iPSC-neurons as a result of hypoxia. Although this increase was not significant in control cells due to variation between samples (Fig 4.2A&B), there was a significant increase in the AD line (Fig 4.8A&B). This is in agreement with previous studies showing that APP expression is increased in response to hypoxia (Pluta et al. 1998;Stephenson et al. 1992) and that APP expression in response to hypoxia can vary between cell lines (Fisk et al. 2007;Webster et al. 2002). Previous studies have indicated that contradictory differences in their findings may be due to both the cell model and the duration of hypoxia, and the time at which the study took place post hypoxia (Lendon et al. 2000). As discussed earlier, APOE production is increased in the ischaemic rat brain (Hayashi et al. 2006).

The general increase in APP levels may be due to an increase in protein expression, or due to a decrease in APP processing. In the control iPSC-neurons exposed to hypoxia levels of sAPPα were decreased (Fig 4.2C&D). In AD iPSC-neurons a decrease in sAPPα levels was also

122 observed, but was not significant due to variability in one of the samples (Fig 4.8C&D). This is supported by previous studies showing that hypoxia decreases protein levels of sAPPα, and ADAM10, and TACE in human neuroblastoma cells (SH-SY5Y)(Marshall et al. 2006).

Some studies have identified a reciprocal relationship between amyloidogenic and non- amyloidogenic processing (Harris et al. 2009). Therefore the data suggest that if there is a decrease in non-amyloidogenic processing an increase in amyloidogenic processing should be observed. Particularly as previous research has shown an increase in amyloidogenic processing through an increase in BACE1 and γ-secretase activity (Majd et al. 2017;Sun et al. 2006;Zhang et al. 2007;Villa et al. 2014;Liu et al. 2016;Wang et al. 2006b;Li et al. 2009). However Aβ40 in the conditioned media of control iPSC-neurons and Aβ38, Aβ40, and Aβ42 in AD iPSC-neurons were significantly decreased under hypoxic conditions (Fig 4.3 & 4.9). Therefore this data does not confirm previous findings that Aβ levels are increased. It is important to note that while the levels of Aβ are decreased in these data, it does not mean that an increase in Aβ production has not occurred, but possibly a more important change in Aβ degradation has affected total Aβ levels.

4.3.3 Hypoxia decreases Aβ Degradation and IDE expression in iPSC-neurons exposed to hypoxia

Investigating the impact of hypoxia on the degradation of the added Aβ substrate in cell lysates from iPSC-neurons showed a significant decrease in Aβ degradation capacity in control iPSC-neurons (Fig 4.4). This decrease detected by the assay is small, and this was not significant in the AD cell line which had a smaller n number (Fig 4.10). Given that previous data find an increase in Aβ production with hypoxia (Sun et al. 2006;Li et al. 2009;Zhang et al. 2007;Guglielmotto et al. 2009), it is possible that with more Aβ present, less of the FAβB is degraded due to competitive inhibition, if for example levels of Aβ degrading proteases produced by the neurons remain unchanged. However, this is not consistent with decreased Aβ levels due to hypoxic conditions in iPSC-neurons, suggesting that changes in proteolysis of Aβ may occur as a result of hypoxia (Fig 4.3).

In both control and AD iPSC-neurons, IDE protein levels were decreased under hypoxic conditions (Fig 4.5). This is contrary to previous findings that IDE mRNA is upregulated in hypoxia (Hiltunen et al. 2009). This may demonstrate why a reduction in Aβ degradation in the lysates of hypoxic neurons was observed. As the importance of IDE in the degradation of Aβ in neuronal lysates has previously been established in chapter 3, a decrease in IDE expression will significantly contribute to the decrease in Aβ degradation in hypoxia.

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Interestingly, the concentrated conditioned media from both hypoxic control and AD iPSC- neurons showed a trend toward increased Aβ degradation (Fig 4.4B & 4.10B). This finding was not significant likely due to a large error margin that has been consistently observed in Aβ degradation experiments conducted using conditioned media. Yet this increased trend was consistent in later work with astrocytes exposed to hypoxia (Fig 6.12B). This increase in Aβ degradation in conditioned media may be explained by a similar trend of increased IDE expression in conditioned media in hypoxic conditions in both control (Fig 4.5C&D) and AD iPSC-neurons (Fig 4.10E&F). The decrease of IDE in lysate and increase in conditioned media in hypoxic conditions may be due to a variety of causes. The first hypothesis explored was simply that hypoxia was causing an increase in loss of cell integrity, resulting in increased levels of IDE leaking into the media. However, while actin is barely detectable in the media, actin levels appeared unchanged between normoxic and hypoxic conditions, suggesting that loss of cell integrity is not responsible for increased extracellular IDE levels. Although previous research has disagreed whether IDE is secreted (section 1.4.4) several studies have concluded that low oxygen levels increase exosome release, and these vesicles contain proteolytically active IDE (Bulloj et al. 2010). Regardless, an increase in the secretion of IDE could also explain the decreased Aβ observed in hypoxia.

4.3.4 Conclusions and Future Work

These data show that hypoxia increases non-amyloidogenic processing of APP and that amyloid degradation is decreased in the iPSC-neurons. Decreased Aβ degradation in the lysate may be due to decreased IDE expression in lysates and increased IDE in the media. Therefore hypoxia alters not just Aβ production, but also Aβ degradation. It is therefore possible that IDE secretion is compensatory in response to hypoxia. Exploring these effects over an extended time would not only elucidate differences between acute and chronic hypoxia, but also determine whether compensatory mechanisms such as IDE secretion are sustained. In the cases of sAD where impaired clearance and degradation of Aβ may lead to disease onset, it will be interesting to pursue different time points of hypoxic treatment to see how both Aβ degradation and production are affected; this may also help address discrepancies in previous work investigating the role of hypoxia in AD. In particular, investigating changes in IDE mRNA levels in comparison to protein levels may unravel how this protease may be affected long-term and will be relevant to the investigation of sAD. It has yet to be determined if IDE mRNA levels are increased in iPSC-neurons following hypoxia. Additionally, further work into understanding the secretion of IDE, and understanding exosome release may also offer IDE as a potential therapeutic target against increases in Aβ

124 production. It would also be interesting to understand how IDE, given the importance of IDE in insulin degradation, may affect other risk factors for AD such as type-2 diabetes mellitus.

The fact that hypoxia increases amyloidogenic processing of APP in an AD line with APOE ε4/ε4 genotype may help explain how disease onset is initiated in sAD. In this chapter the AD APOE4/4 line is compared to the OX1-19 line which is demonstrated to have an APOE ε3/ε3 genotype (Fig 4.6). To further investigate the effect of APOE in hypoxia, isogenic APOE cell lines should be generated to allow better understanding of the role of APOE in hypoxia. Without isogenic controls, there are too many genetic and environmental variables between cell lines (Reviewed in Rowland et al., 2018).

4.4 Chapter summary

In summary, this chapter addressed whether hypoxia changes production and degradation of Aβ, how hypoxia affected Aβ, and if these effects were exacerbated in an AD cell line with the APOE ε4/ε4 genotype. The data demonstrated that a hypoxic response can easily be generated in iPSC-derived neurons, and that this response affects the production and degradation of Aβ. Non-amyloidogenic processing was reduced, as was the Aβ degradation capacity of the neurons. The effect of hypoxia on Aβ levels and degradation capacity may be due to IDE. IDE expression was decreased in lysates, but increased in conditioned media as a result of hypoxia possibly due to increased IDE secretion. The effect of hypoxia on an AD line showed a significant increase in APP expression and a significant decrease in all Aβ isoforms compared to the control line. Overall, the data presented here demonstrate an important role for Aβ degradation and the activity of the Aβ degrading protease IDE under hypoxic conditions, indicating how disease onset may be initiated or exacerbated by hypoxia.

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Chapter 5: What role do iPSC-astrocytes play in the production and degradation of Aβ?

5.1 Introduction

Astrocytes play a key role in homeostasis of the brain. Several recent reviews have highlighted the importance of astrocytes in AD, but also discussed how little they have been studied in terms of AD pathology (Crompton et al. 2017;Verkhratsky et al. 2017). In animal models, there is a vast body of evidence supporting the role of astrocyte dysfunction in AD (Acosta et al. 2017). However, the differences between astrocytes in animals compared to humans are striking, indicating the need for better human models to study the role of astrocytes in disease.

5.1.1 Production of Aβ by astrocytes

Previously, Aβ production was thought to occur primarily in neurons. However, in a technique able to assess Aβ and sAPPα from single cells, a small percentage of iPSC- astrocytes secreted high levels of Aβ (Liao et al. 2016). APP is expressed by astrocytes in both non-reactive and reactive forms although APP isoform expression varies between neurons and astrocytes. While neurons predominantly express APP695, the KPI-containing isoforms APP751 and APP770, are more commonly expressed in astrocytes, where increased expression of APP has been observed in response to brain injury (Guo et al. 2012;deSilva et al. 1997).

BACE1 is thought to be primarily expressed by neurons, and localised to endosomes and the Golgi. However, it has been shown that astrocytes also express BACE1 at sufficient levels to produce their own Aβ (Roßner et al. 2005;Frost and Li 2017). Under stress conditions, and particularly the activation of astrocytes, BACE1 expression is increased (Hartlage-Rubsamen et al. 2003;Zhao et al. 2011). Similarly the PS1 sub-unit of γ-secretase is also increased in activated astrocytes and under inflammatory conditions (Ren et al. 1999). However, γ- secretase activity is not directly correlated with increased γ-secretase sub-unit expression (Frost and Li 2017). Whether this increase in PS1 gives increased γ-secretase activity in astrocytes, remains to be determined.

5.1.2 Clearance of Aβ by astrocytes

Clearance of Aβ by astrocytes occurs in several ways and has been well reviewed (Ries and Sastre 2016). In an AD model, 5xFAD mice, astrocytes demonstrated a reduced capacity to

126 clear Aβ plaques compared to control mice (Simonovitch et al. 2016). Astrocytes produce several chaperone proteins such APOE, clusterin, ACT and α2-M, and several cell surface receptors such as LRP-1, RAGE, and TREM-2 that allow for receptor mediated endocytosis (reviewed in Ries and Sastre 2016). APOE is primarily secreted by astrocytes (Cudaback et al. 2015) and many studies have demonstrated that impaired clearance of Aβ is associated to the wide-ranging functions of APOE. For example, autophagy has been shown to be impaired in mouse astrocytes expressing APOE ε4 compared to astrocytes expressing APOE ε3. When rapamycin an inducer of autophagy was applied in APOE ε4 astrocytes, an increase in Aβ plaque degradation was observed, indicating an important role for autophagic disposal of Aβ (Simonovitch et al. 2016). Research into the role autophagy plays in AD pathology has been predominantly carried out in microglia, and while astrocytes are not as efficient in Aβ internalisation as microglia, they have been shown to play a role in the autophagic response to Aβ. This is evidenced by an increase in LC3 expression, a marker of autophagy, in astrocytes surrounding Aβ plaques (Pomilio et al. 2016).

Astrocytes also degrade Aβ through the major degrading proteases including NEP, IDE, ECE and MMPs. Changes in expression of these proteases may differ in astrocytes compared to neurons in response to environmental stressors. For example, in hypoxic conditions NEP mRNA was decreased in neurons, but increased in astrocytes (Fisk et al. 2007). NEP expression in astrocytes is also upregulated in response to human Aβ (Pihlaja et al. 2011). Aβ induces increased secretion of IDE, but does not alter its mRNA levels (Son et al. 2016). Aβ also upregulates MMP-2 and MMP-9 expression in astrocytes surrounding Aβ plaques (Yin et al. 2006).

5.1.3 Diversity of astrocytes

Once considered a homogenous cell population, astrocytes display morphological and physiological differences that led to the identification of distinct astrocyte populations with distinct purposes based upon their location (Ben Haim and Rowitch 2017). More generally, they are divided into two main forms, fibrous (which are generally located in the white matter) and protoplasmic (which are found in the grey matter, and make up part of the neurovascular unit (NVU)) (Molofsky et al. 2012;Cai et al. 2017). Astrocytes have also been divided into 9 classes, based on GFAP and S100β staining that are all found in the same regions of the brain. These include not just protoplasmic and fibrous astrocytes, but also tanycytes, ‘radial’ cells, Bergmann, velate, marginal, perivascular, and ependymal glia. Within these sub-types there is a lot of heterogeneity in expression of transmitter receptors

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2+ such as AMPA, NMDA, GABAA GLAST/GLT-1 Gap junction networks, and Ca . Therefore despite establishing at least 9 different classes based on morphology, this classification of astrocytes does not yet account for differences in physiology that exists, sometimes between morphologically indistinguishable astrocytes (Matyash and Kettenmann 2010). Investigating the signaling pathways involved in astrocyte differentiation may, therefore, help to identify new specific markers of astrocyte identity (Malik et al. 2014).

For a long time it has been assumed that the ratio of astrocytes to neurons in the brain is 10:1 (von Bartheld et al. 2016). More recent studies found that the ratio is smaller, and greatly dependent on the brain region. In the cerebral cortex the ratio of neurons to astrocytes is between 1:1 and 1:4. This is still a striking difference compared to rats where the ratio is 0.4 Vasile et al. (2017). In fact the largest genetic differences between the transcriptome profile between rodent and human brains are in astrocytes (Zhang et al. 2016). While this is not specific evidence of increased cortical function there are several different sub-types of astrocytes that are not found in rodents, demonstrating human astrocytes are not just physically larger cells, but more structurally diverse. Increased astrocyte complexity, which includes faster propagation of Ca2+, may allow higher cognitive function in humans (Oberheim et al. 2009). It has been shown that mice hosting human astrocytes outperformed wild type mice in several memory based tests (Han et al. 2013;Verkhratsky et al. 2017). Ultimately this highlights the limitations of exploring astrocyte dysfunction in AD using animal models and the necessity for human models to better understand and target disease pathology.

5.1.4 Robust differentiation of stem-cell derived astrocytes

Differentiation of astrocytes, predominantly from NPCs still requires robust and well-defined protocols with better characterisation to address the problem that astrocytes are not a homogenous cellular population, and their diversity both regionally and with regard to their expression of genes and functions, is not well defined (Hall et al. 2003;Matyash and Kettenmann 2010;Ben Haim and Rowitch 2017). Generation of astrocytes has been carried out according to several different protocols (Krencik and Zhang 2011;Roybon et al. 2013;Shaltouki et al. 2013;Zhang et al. 2016). These protocols have all demonstrated the production of astrocytes positive for at least some of the well-known astrocyte markers including GFAP, S100β, AQP4, ALDH1L1, Vimentin, GLT-1, and GLAST. Many of the protocols differentiating astrocytes from the NPC stage for a further 35-80 days, arguably cannot necessarily be considered mature based upon the markers they express, and further

128 characterisation is required (Roybon et al. 2013). Comparably, maximum expression of ‘mature’ astrocyte markers is 6-12 months into human development (Zhang et al. 2016). As with neurons, astrocytes can also be directly converted from fibroblasts to ‘iAstrocytes’, where in approximately 12 days they show positive staining for GFAP and S100β although with relatively low efficiency (Caiazzo et al. 2015). Going forward the differentiation of astrocytes will need to recapitulate all aspects of mature astrocytes, and address the time- limitations of the differentiation process that are perhaps more pronounced than in neurons. Additionally, while there are methods to differentiate different neuronal types (e.g. cortical glutamatergic, forebrain cholinergic, and dopaminergic (Shi et al., 2012b;Bissonnette et al. 2011;Kriks et al. 2011) this is not the case for astrocytes, where identification and maintenance of different sub-types is still lacking, although strategies are being developed to generate regional-specific astrocytes by mimicking regional patterning (Tyzack et al. 2016).

5.1.5 Astrocyte models of AD

While there are several protocols that can be used to derive astrocytes from iPSCs, there are still a limited number of studies that have used iPSC-astrocytes to model AD. (This has been recently summarised by Rowland et al. (2018)). fAD has been modelled in astrocytes with PSEN1 mutations. Studies have used PSEN ΔE9 and PSEN1 M146L mutations to demonstrate increased Aβ1-42 production, Ca2+ dysregulation, alterations in metabolism as a result of oxidative stress, and morphological changes compared to a control line. Interestingly, the authors found that the astrocytes only expressed slightly lower levels of APP and BACE compared to neurons from the same lines (Oksanen et al. 2017;Jones et al. 2017). fAD has also been modelled in astrocytes with an APP mutation; in this study the fAD line demonstrated increased Aβ, ROS and ER stress (Kondo et al. 2013). The genetically defined sAD risk factor APOE ε4 has also been explored in iPSC-astrocytes, demonstrating that APOE ε4/ε4 is distinctly less lipidated, and that there are morphological differences, increased expression of synaptic proteins, and a decreased capacity to clear extracellular Aβ (Jones et al. 2017;Zhao et al. 2017;Lin et al. 2018). There have not yet been studies that have used astrocytes from an AD patient without an identified genetic risk factor.

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5.1.6 Aims

The role of human astrocytes in AD pathology has not been as well defined as that of neurons. As astrocytes have a larger role in Aβ clearance, they may have a different contribution to the proteolytic degradation of Aβ than neurons. AD relevant cell stressors such as hypoxia may affect astrocyte Aβ production and capacity to degrade Aβ differently, and cause further changes to neuronal production and degradation of Aβ. Before this can be investigated characterisation of APP processing and Aβ production and degradation in astrocyte and neurons was carried out. iPSC-astrocytes have been compared to commercially available primary foetal astrocytes (ScienCell).

This has been investigated by addressing the following specific questions:

 Can iPSCs be differentiated into astrocytes?  How does APP processing and Aβ production and degradation compare between iPSC-astrocytes and primary astrocytes?  Does astrocyte conditioned media (ACM) from iPSC-astrocytes affect neuronal Aβ production and degradation?

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5.2 Results

5.2.1 Differentiation and characterisation of iPSC-astrocytes

Two strategies to generate iPSC-astrocytes were tested. Both protocols generate astrocytes from NPCs; the NPCs used were generated as for neuronal differentiation (Shi et al. 2012a). NPCs were then seeded into different astrocyte media for comparison to the commercially available differentiation and maturation media from STEMCELL Tech (#08540 and #08550), and the astrocyte medium from ThermoFisher Scientific comprising DMEM Glutamax media supplemented with 10% FBS and N2 (1%) (A1261301).

At day 60, preliminary immunofluorescence microscopy demonstrated that GFAP staining in the astrocyte/astrocyte progenitor cells (APCs) was far greater in those grown in STEMCELL tech media compared to those cultured in 10% FBS and N2. GFAP was by comparison not detectable in neurons at day 80. In contrast, where neurons strongly expressed β-Tub and MAP2, APC cultures in STEMCELL tech media showed mild β-TUB staining and very limited MAP2 expression. APCs cultured in 10% FBS and N2 however, expressed more MAP2 (Fig 5.1A). Based on these findings, the protocol utilising the media from STEMCELL tech was chosen. An outline of this adapted protocol and how it compares to neuronal differentiation is shown (Fig 5.1B).

In addition to iPSC-astrocytes, commercially available ScienCell human foetal astrocytes (primary astrocytes) were also used. Expression of astrocytic markers, GFAP and S100β, were compared between iPSC-astrocytes and primary astrocytes over time (Fig 5.2). Both iPSC- derived and primary astrocytes were positive for GFAP expression, and no obvious difference in expression between the two sources of astrocytes was observed. Between day 60 to 100 astrocytic morphology increased in the iPSC-astrocytes to become similar to that of the primary astrocytes. Another astrocytic marker, S100β, was also used, and this appeared more strongly expressed in iPSC-astrocytes compared to the expression in the primary astrocytes. To confirm that neurons had not been generated, or strongly contaminated the astrocyte populations, cells were checked for neuronal markers MAP2 and β-TUB. An increase in both markers was detected over time, however by day 100 the iPSC-astrocytes and primary astrocytes showed similar expression of these markers (Fig 5.2).

Glutamate homeostasis is an important function of astrocytes; therefore glutamate- aspartate transporter (GLAST) is an indicator of functional astrocytes. GLAST was most strongly expressed in the primary astrocytes, but there was also an approximately 3 fold

131 increase in iPSC-astrocytes compared to iPSC-neurons (Fig 5.3A). The astrocyte marker ALDH1L1 was also increased in iPSC-astrocytes and primary astrocytes compared to neurons, but this was not significant, due to strong variation between inductions and technical repeats (Fig 5.3B).

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Figure 5.1: Optimisation of Astrocyte differentiation protocols

OX1-19 iPSC-neural progenitor cells were expanded and differentiated and then underwent astrocyte differentiation cultured in either STEMCELL Tech media or in DMEM Glutamax media with 10% FBS + N2 until day 60. Cells were washed then fixed on coverslips, and immunostained for GFAP, β-TUB, MAP2 and DAPI (shown in blue). Images were obtained at 20x magnification on an EVOS FL microscope. (A) Representative images of astrocytes and neuron expression of GFAP (green) β-TUB (red) and MAP2 (green). Expression of GFAP was compared between astrocytes cultured in two different astrocyte differentiation mediums. Astrocytes cultured in STEMCELL Tech media exhibited stronger GFAP staining compared to astrocytes differentiated in 10% FBS + N2. GFAP signalling was not present in neurons. Astrocytes in STEMCELL Tech media show limited MAP2 staining, but astrocytes grown in 10% FBS and N2 are more MAP2 positive. Neurons show strong MAP2 and β-TUB staining. Scale bars indicate 100µm. n=1 induction. (B) Timeline of astrocyte differentiation protocol as compared to neuronal differentiation.

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Figure 5.2: Characterisation of iPSC-astrocytes and primary human astrocytes

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Figure 5.2: Characterisation of iPSC-astrocytes and primary human astrocytes (continued)

OX1-19 and SBAD-02 iPSC-astrocytes were differentiated and cultured on coverslips until day 60, 80, 100 where iPSC-astrocytes and primary human astrocytes were washed and fixed, and immunostained for GFAP, S100β, β-TUB, MAP2 and nuclei were stained with DAPI (shown in blue). Images were obtained at 20x magnification on an EVOS FL microscope. Representative images of iPSC- astrocyte and primary astrocyte expression of GFAP (green), S100β (red), MAP2 (green), and β-TUB (red). Astrocytic morphology and expression of GFAP and S100β increases in iPSC- astrocytes over time, where S100β is increased compared to primary astrocytes. Both astrocyte types express β-TUB but limited MAP2 expression. n=5 inductions. Scale bars indicate 200µm.

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Figure 5.3: GLAST and ALDH1L1 expression in iPSC-neurons, iPSC-astrocytes and primary human astrocytes

OX1-19 and SBAD-02 iPSC-neurons were differentiated until day 80 and iPSC-astrocytes differentiated until day 120, where cells were pelleted and the mRNA extracted. Quantification of astrocyte markers GLAST and ALDH1L1 expression by qPCR was corrected against GAPDH. (A) Quantification of GLAST expression in iPSC-astrocytes and primary astrocytes relative to iPSC-neurons. GLAST expression is increased in astrocytes. (B) ALDH1L1 expression in iPSC-astrocytes and primary astrocytes relative to iPSC-neurons. ALDH1L1 expression was variable between biological and technical repeats in astrocytes. Data shown as mean ± SEM, n=3 inductions *, p<0.05 using one-way ANOVA, Tukey’s multiple comparisons test.

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5.2.2 APP processing in iPSC-astrocytes and primary astrocytes iPSC-derived and primary astrocytes expressed all three isoforms of APP, APP695, 751 and 770 (Fig 5.4A). This is in contrast to the iPSC-derived neurons which express predominantly APP695. APP levels appeared to be lower in iPSC and primary astrocytes compared to iPSC- neurons, but this was not significant (Fig 5.4A&C). Notably primary astrocytes expressed a double band for actin (Fig 5.4A).

APP underwent α-secretase processing in both iPSC and primary astrocytes as evidenced by the presence of sAPPα (Fig 5.4B). Primary astrocytes had a greater level of sAPPα (albeit not significant) than iPSC-astrocytes. Both iPSC and primary astrocytes produced exclusively sAPPα from the longer APP isoforms, APP751 and APP770, compared to the iPSC-neurons which predominantly produce sAPPα from the APP695 isoform, although there is also some sAPPα produced from the longer APP isoforms. The amount of sAPPα in the iPSC-astrocytes was significantly lower than that from iPSC-neurons (Fig 5.4B&D).

Aβ40 and Aβ42 levels were measured in iPSC and primary astrocytes and compared to day 80 iPSC-neurons (Fig 5.5B&C) (Aβ38 was below detection in astrocytes (Fig 5.5A)). There was no significant difference between iPSC and primary astrocytes levels of Aβ40 (Fig 5.5B) and Aβ42 (Fig 5.5C). Average levels of Aβ40 and Aβ42 in iPSC-neurons were 770pg/ml and 100pg/ml, respectively. iPSC-astrocytes had average levels of 100pg/ml (Aβ40) and 10pg/ml (Aβ42). Primary astrocytes by comparison had average levels of 60pg/ml (Aβ40) and 10pg/ml (Aβ42). Average Aβ38 levels in iPSC-neurons were 250pg/ml but Aβ38 was below the detectable range in iPSC and primary astrocytes (Fig 5.5A). The ratio of Aβ40:42 appeared to be slightly decreased in the primary astrocytes compared to iPSC-neurons, although this was not significantly different (p=0.0539) (Fig 5.5D). These data demonstrate there was significantly lower Aβ levels in iPSC and primary astrocytes compared to iPSC-neurons.

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Figure 5.4: APP processing in iPSC-neurons, iPSC-astrocytes and primary human astrocytes

OX1-19 and SBAD-02 iPSC-neurons were differentiated until day 80 and iPSC-astrocytes differentiated until day 120. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. Cells were harvested and lysates prepared. Samples were then immunoblotted using antibodies for APP and actin in lysates, and sAPPα in conditioned media. Semi- quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from astrocyte samples were normalised, as a percentage, to iPSC-neurons. (A) Representative western blot of APP expression in iPSC-neurons, iPSC-astrocytes and primary astrocytes. (B) Representative western blot of sAPPα in iPSC-neurons, iPSC-astrocytes and primary astrocytes. (C) Quantification of APP expression between cell types is not significantly different. (D) Quantification of sAPPα between cell types. iPSC-astrocytes have significantly less sAPPα than iPSC-neurons . Data shown as mean ± SEM, n=3 inductions *, p<0.05 using one-way ANOVA, Tukey’s multiple comparisons test.

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Figure 5.5: Characterisation and comparison of Aβ levels in iPSC-neurons, iPSC-astrocytes and primary human astrocytes.

OX1-19 and SBAD-02 iPSC-neurons were differentiated until day 80 and iPSC-astrocytes differentiated until day 120. For the final 24 hours media was changed to OptiMEM, conditioned media was collected, and Aβ38, Aβ40, and Aβ42 levels were measured using MSD multiplex immunoassay. (A) Aβ38 levels are not detected in astrocytes. (B) Aβ40 levels are significantly higher in iPSC-neurons compared to iPSC and primary astrocytes. There is no significant difference between astrocyte types. (C) Aβ42 levels are significantly higher in iPSC-neurons compared to iPSC and primary astrocytes. There is no significant difference between astrocyte types. (D) The ratio of Aβ40:42 is unchanged between iPSC- neurons and iPSC-astrocytes. There is a decrease in the Aβ40:42 ratio in primary astrocytes which is approaching significance p=0.0539. Data shown as mean ± SEM, n=3 inductions. * p<0.05, ** p<0.005 using one-way ANOVA, Tukey’s multiple comparisons test.

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5.2.3 Degradation of Aβ in iPSC-astrocytes and primary astrocytes

Aβ degradation was compared in iPSC-astrocytes and primary astrocytes. The data demonstrates that lysates of both iPSC-astrocytes (Fig 5.6A) and primary astrocytes (Fig 5.6B) degraded the substrate FAβB. The degradative capacity of both types of astrocytes was not inhibited by the NEP inhibitor phosphoramidon, but was inhibited by the IDE inhibitor 6bK, and the general metalloprotease inhibitor 1, 10-phenanthroline. In the concentrated media of iPSC-astrocytes (Fig 5.6C) and primary astrocytes (Fig 5.6D) the FAβB substrate was also degraded. The addition of inhibitors did not cause any significant change to the degradative capacity of the media in either cell type. IDE expression in iPSC-neurons and iPSC and primary astrocytes was compared by western blot; there were no significant differences in IDE expression (Fig 5.7A&B).

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Figure 5.6: Aβ degradation in iPSC and primary astrocytes

OX1-19 and SBAD-02 iPSC-astrocytes were differentiated until day 120. For the final 24 hours media of iPSC-astrocytes and primary astrocytes was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-astrocytes and primary astrocytes were harvested and lysates prepared. The NEP inhibitor phosphoramidon, IDE inhibitor 6bK, and general metalloprotease inhibitor 1, 10-phenanthroline were added 30 minutes before FAβB was added to lysates or concentrated media and incubated for 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Results from the astrocyte samples were normalised, as a percentage, to control conditions without inhibitors. (A) Lysates from iPSC-astrocytes degrades the FAβB substrate. IDE inhibitors inhibit Aβ degradation where as phosphoramidon, the NEP inhibitor, does not. (B) FAβB substrate is also degraded by the concentrated media of iPSC- astrocytes. Degradation was variable and not inhibited. (C) Lysates from primary astrocytes degrades the FAβB substrate. IDE inhibitors inhibit Aβ degradation where as phosphoramidon, the NEP inhibitor, does not. (D) FAβB substrate is also degraded by the concentrated media of iPSC- astrocytes. Degradation was variable and not inhibited. Data shown as mean ± SEM, n=3 inductions. * p<0.05, ** p<0.005, *** P<0.0005, using one- way ANOVA, Tukey’s multiple comparisons test.

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Figure 5.7: Characterisation of IDE expression in iPSC-neurons, iPSC-astrocytes, and primary human astrocytes.

OX1-19 and SBAD-02 iPSC-neurons were differentiated until day 80 and iPSC-astrocytes differentiated until day 120. For the final 24 hours media was changed to OptiMEM. iPSC-neurons, iPSC-astrocytes, and primary astrocytes were harvested and lysates prepared. Samples were then immunoblotted using antibodies for IDE and actin in lysates. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from astrocyte samples were normalised, as a percentage, to iPSC-neurons. (A) Representative western blot of IDE expression in iPSC-neurons, iPSC-astrocytes and primary astrocytes. (B) Quantification of IDE expression between cell types is not significantly different. Data shown as mean ± SEM, n = 3 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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5.2.4 The effect of iPSC-astrocyte conditioned media on Aβ in neurons

Day 80 iPSC-neurons were cultured in either 1:1 neural maintenance media (NMM) and Astrocyte Maturation Media or a ratio of 1:1 NMM and ACM for 48 hours as previously described in the methods (2.1.10). To determine whether astrocyte secretions do indeed support neuronal function the membrane potential of the neurons was assessed, but was not affected by the addition of ACM (Fig 5.8A&B).

The effect of culturing neurons in ACM on Aβ levels was then determined (Fig 5.9A-C). The Aβ levels in the iPSC-neurons, iPSC-neurons cultured in ACM, and the ACM only were measured. There was no difference between the Aβ produced by neurons cultured with ACM, compared to neurons cultured without ACM, once the Aβ levels of the ACM were accounted for (Fig 5.9A-C). There was also no significant difference in the Aβ degradation capacity between lysates of iPSC-neurons and iPSC-neurons cultured in ACM (Fig 5.9D).

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Figure 5.8: Membrane potential is unchanged in neurons cultured in ACM

OX1-19 and SBAD-02 iPSC-astrocytes were differentiated until day 80 and media was collected every 3 days until day 120. Day 80 OX1-19 and SBAD-02 neurons were cultured with NMM and ACM (1:1 ratio) for 48 hours. Neuronal membrane potential was measured using the FLIPR® Membrane Potential Assay Kit and quantified by calculating maximum RFU – minimum RFU. (A) Representative traces of neuronal membrane potential in neurons (black) and neurons cultured with ACM (grey). (B) Quantification of the membrane potential of neurons cultured in normal NMM (black) and neurons supplemented with ACM (grey). Data shown as mean ± SEM, n=5 astrocyte inductions, Unpaired t- test.

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Figure 5.9: Aβ levels in iPSC-neurons are unchanged following culture in astrocyte conditioned media.

OX1-19 and SBAD-02 iPSC-astrocytes were differentiated until day 80 and media was collected every 3 days until day 120. Day 80 OX1-19 and SBAD-02 neurons were cultured with NMM and ACM (1:1 ratio) for 48 hours. Conditioned media was collected, and Aβ38, Aβ40, and Aβ42 levels were measured using MSD multiplex immunoassay. iPSC-neurons cultured with/without ACM were harvested and lysates prepared. FAβB was added to lysates and incubated for 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Results from the neurons cultured with ACM were normalised, as a percentage, to neurons cultured without ACM and are presented with Aβ levels of the ACM added (shown in grey). (A) Aβ38 (B) Aβ40 (C) Aβ42 levels were not altered by culturing neurons in ACM compared to control neuronal Aβ levels once the Aβ levels produced by astrocytes in the ACM were accounted for. (D) There was no significant difference in Aβ degradation between control neurons and neurons cultured in ACM. Data shown as mean ± SEM, n=3 astrocyte inductions, unpaired t-test.

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5.3 Discussion

In this chapter, these data demonstrate that iPSC- astrocytes can be generated and process APP and Aβ similarly to primary human astrocytes (ScienCell). IDE in iPSC-astrocytes and primary astrocytes is primarily responsible for the degradation of Aβ in the lysate, similarly to neurons. iPSC-astrocyte conditioned media does not appear to alter the functionality of iPSC-neurons nor the amount of Aβ produced or degraded by neurons.

5.3.1 Differentiation of iPSC-astrocytes

Two commercially available media/protocols were tested for the ability to generate astrocytes from NPCs. Media made up according to ThermoFisher contained FBS, which as an undefined factor may cause increased variation in reproducibility. Media from STEMCELL tech does not contain FBS but the media composition is not available. Immunofluorescence microscopy showed that astrocyte progenitor cells (day 60) cultured in STEMCELL tech media expressed greater staining for the astrocyte marker GFAP than those cultured in ThermoFisher media (Fig 5.1). Likewise, the astrocytes grown in STEMCELL tech media did not express as much of the neuronal marker MAP2. Based on these results the STEMCELL tech media was determined the best method to derive astrocytes.

At day 60, astrocyte morphology was limited with few processes visible. There also appeared to be strong contamination with other cell types, especially neurons. To reduce neuronal contaminants, more frequent passaging was used to enrich for astrocytes. By day 80 neuronal number was reduced and a higher proportion of GFAP positive cells were visible (Fig 5.2). Astrocyte-like morphology increased noticeably by a minimum of day 100. This included more defined processes, and classic ‘star-like’ shape. Primary and iPSC-astrocytes both expressed similar amounts of GFAP and limited expression of the neuronal marker, MAP2. It was anticipated that β-TUB was a specific neuronal marker, however as it positively stained both astrocyte types, it was concluded that this marker may not be neuronal specific. iPSC-astrocytes appeared to exhibit more heterogeneous morphology compared to primary astrocytes. This is partially supported by the differences in positive S100β staining within the cell population (Fig 5.2). Interestingly, compared to the primary astrocytes, the iPSC- astrocytes expressed more S100β. S100β is a classic astrocytic marker; it is identified as a neurotrophic factor and associated with astrocyte proliferation (Marshak 1990;Selinfreund et al. 1991). iPSC-astrocytes were continually expanded up to 120 days; this may explain the larger quantities of S100β expressed in the iPSC-astrocytes compared to the primary

146 astrocytes (although these too, were proliferative). It may also suggest that the iPSC- astrocytes are a more heterogeneous population, which would appear to support previous protocols (Roybon et al. 2013) that find the iPSC-astrocyte populations to be more varied. Expression of GLAST was quantified as a pseudo-measure of functionality. If astrocytes are expressing the glutamate-aspartate transporter protein, then they should be able to carry out normal astrocytic function such as glutamate uptake, necessary for their role in maintaining homeostasis of neurotransmitters. There was a significant increase in GLAST mRNA in primary astrocytes compared to iPSC-neurons, but no significant difference between iPSC-astrocytes and iPSC-neurons, or iPSC-astrocytes and primary astrocytes (Fig 5.3A). This would suggest that iPSC-astrocytes are not as functionally mature as primary astrocytes, but given the variation seen in marker expression (such as GFAP, S100β, and ALDH1L1) this may be representative of the differences in astrocyte populations. A recent pilot study has identified large diversity between different primary and iPSC-astroglia models (Lundin et al. 2018). Further work is needed to better assess the functional capabilities of iPSC-astrocytes at this 100-125 day age point.

5.3.2 APP processing is different between iPSC-astrocytes and iPSC-neurons iPSC-astrocytes and primary astrocytes both expressed APP , but at a lower level than iPSC- neurons. Both iPSC and primary astrocytes expressed more of the longer APP isoforms, APP751 and APP770 than the iPSC-neurons; iPSC-neurons primarily expressed the neuronal APP695 isoform (Fig 5.4A). These data support previous findings of APP expression in astrocytes in the brain, suggesting that astrocytes play a role in Aβ production through APP processing (Guo et al. 2012;deSilva et al. 1997). It is also interesting to note, that despite efforts to load the same amount of protein between conditions, there was consistently different expression of actin in primary astrocytes (Fig 5.4A). A different housekeeping protein that is more similar between neurons and astrocytes should be used in future studies to demonstrate that equal loading has occurred.

APP processing by α-secretase occurs in both iPSC-and primary astrocytes with the production of sAPPα from APP751/770 (Fig 5.4B). In contrast, as iPSC-neurons predominantly express the 695 APP isoform, they produce more of the sAPPα 695 isoform (Fig 5.4B). Overall, iPSC-astrocytes produced significantly less sAPPα than iPSC-neurons, but there was no difference in sAPPα levels between iPSC and primary astrocytes.

Both iPSC and primary astrocytes produced detectable levels of Aβ40 and Aβ42 and there was no significant difference in Aβ levels. Aβ levels in iPSC-astrocytes and primary astrocytes

147 were, however, significantly lower than iPSC-neurons (Fig 5.5). This agrees with previous findings that astrocytes do not produce large amounts of Aβ compared to neurons (Frost and Li 2017). Given there was a large difference in Aβ levels between neurons and astrocytes, and no significant difference in Aβ levels between iPSC and primary astrocytes (which are previously purified by the supplier) this also provides evidence that the iPSC-astrocytes did not have contaminant neurons in the population. Taken together this is further evidence that iPSC-astrocytes can be successfully generated and isolated and used as a relevant model in AD.

5.3.3 Aβ is degraded by iPSC-astrocytes and primary astrocytes similar to iPSC- neurons

Aβ degradation capacity was assessed in primary and iPSC-astrocytes. Previously in iPSC- neurons, IDE was identified to be the largest contributor to the degradation of the Aβ (Fig 3.14). The data demonstrated that Aβ degradation is inhibited in the cell lysates of both astrocyte types when both the general metalloprotease inhibitor 1, 10-phenanthroline and the specific IDE inhibitor 6bK was added (Fig 5.6). Interestingly when these inhibitors were added, the degradation was not inhibited to the same extent as previously observed in iPSC- neurons. IDE expression was therefore assessed in iPSC-neurons, iPSC-astrocytes and primary astrocytes, however there was no significant difference in IDE expression (Fig 5.7). IDE expression varies between neurons and astrocytes in sAD and fAD cases (Dorfman et al. 2010), but the data did not find evidence of differences in IDE expression in control lines. This difference in Aβ degradation inhibition would suggest other non-metallo proteases may be contributing to Aβ degradation in astrocytes. In a mouse model NEP is almost exclusively expressed in neurons but not astrocytes, but as there are such large differences between human and rodent astrocytes, this may not be an accurate model of human astrocyte Aβ degrading enzyme localisation (Fukami et al. 2002a;Oberheim et al. 2009). Also demonstrated in mouse primary cultures, NEP expression is altered in hypoxia differently between astrocytes and neurons, with astrocytic NEP expression increasing in hypoxia (Fisk et al. 2007). Although no mention of comparison of baseline NEP levels between neurons and astrocytes was made, astrocytes, particularly when activated, may have a higher contribution to Aβ degradation by NEP upregulation (Pihlaja et al. 2011). However in the Aβ degradation assay, the NEP inhibitor phosphoramidon did not cause any significant decrease in Aβ degradation and therefore may not contribute to Aβ degradation as previously proposed (Iwata et al. 2001;Saito et al. 2003). As has been previously observed in neurons, in both iSPC- and primary astrocytes Aβ degradation in the concentrated media was variable

148 and degradation was not inhibited by any of the inhibitors used (Fig 5.6 B&D). As degradation of Aβ was observed, this also suggests some other protease is involved.

5.3.4 The effect of astrocytes on neuron function and Aβ levels

To determine whether iPSC-astrocytes secrete factors that may be protective or toxic to neurons, membrane potential was assessed in neurons cultured in ACM over 48 hours. No change in neuronal function as assessed by membrane potential was observed (Fig 5.8). iPSC- neurons generated via the Shi et al. protocol (2012) using duel SMAD inhibition will generate some astrocytes (Bergstrom et al. 2016). This is likely due to the importance of astrocytes in neuronal function particularly with synaptic maintenance (Chung et al. 2015). It is not surprising no adverse effects were found, as astrocytes control synapse formation in neurons through secreted factors (Christopherson et al. 2005;Kucukdereli et al. 2011). However, astrocyte secreted factors are not necessarily secreted by iPSC-astrocytes at this stage of differentiation or not secreted from astrocytes cultured in isolation. Secreted factors in the ACM also did not have any impact on Aβ levels of iPSC-neurons. Aβ in neurons cultured in ACM was at a similar level to the additive Aβ produced by the two cultures in isolation (Fig 5.9). The time point of 48 hours was selected as this is the normal length of time neurons would be cultured in media before it was replaced and with limited astrocyte media, stress by lack of nutrients may have been induced. Although it has been established that astrocytes themselves have no adverse effects on neuronal function or Aβ levels, further studies could investigate the effects of ACM on neurons over a longer time period.

5.3.5 Conclusions and future work

These data demonstrate that iPSC-astrocytes can successfully be generated in approximately 100 days. These cells exhibit similar characteristics to primary human astrocytes, but while primary astrocytes can be cultured in a shorter time-frame they cannot be obtained from AD patients and pose ethical issues surrounding the acquisition of astrocytes from foetal donors. Astrocytes from foetal donors are between 140-168 days old, which may also help explain why only small differences between these astrocyte types were observed. Both astrocyte types however, may not be considered mature when compared to astrocytes in patients with AD (Roybon et al. 2013). While further characterisation of astrocyte sub-types is necessary for the field, the differentiation and generation of different astrocyte types will be useful for future studies, in order to elucidate their functions in the CNS. Identification of differences between the derivation and acquisition of iPSC or primary human astrocyte models will be

149 essential in choosing the appropriate models to answer biological questions (Lundin et al. 2018).

Despite conflicting previous data about the expression and localisation of Aβ degrading enzymes between neurons and astrocytes, the data here shows that both iPSC and primary astrocytes degrade Aβ similarly and express a similar IDE profile. Future work may seek to further characterise the contribution of different Aβ degrading enzymes in addition to the relative contribution of proteolytic degradation between cell types such as astrocytes and neurons. This may be useful in identifying future therapeutic targets against Aβ deposition in AD.

As astrocytes do not produce as much Aβ, when neurons are incubated in ACM Aβ levels were additive. Neuronal membrane potential was also not affected by ACM. This may be a feature of the exposure time, but it also suggests that astrocytes may need to be in contact with neurons, for astrocytes to secrete factors that may alter neuronal functions. There are foreseeable challenges in identifying the contribution of each cell type in a co-culture model. However co-culture of astrocytes and neurons better model the in vivo environment in AD and could be used to determine if astrocytes remain supportive or become neurotoxic in disease pathology. The potential neurotoxic role of astrocytes is addressed in the next chapter.

5.4 Chapter summary

In summary, this chapter lays the groundwork for future studies, demonstrating that iPSC- astrocytes can be generated, expressing characteristic astrocyte markers although they appear to be a heterogeneous population when compared to primary human astrocytes. iPSC-astrocytes process APP like the primary astrocytes, and both cell types degrade Aβ, but do not have the same levels of Aβ as found in neurons. The conditioned media from iPSC- astrocytes did not have any adverse effect on neuronal function and do not change neuronal Aβ levels. This data suggest that iPSC-astrocytes can be used as a viable cell model for future studies.

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Chapter 6: How do hypoxic or activated astrocytes affect Aβ production and degradation in iPSC-derived neurons?

6.1 Introduction

As discussed in chapter 5, astrocytes have a role in the formation and clearance of Aβ in AD, and yet studies detailing the characterisation and modelling of human astrocytes are still limited. Studies using iPSC-astrocytes to model AD have only begun to appear in the last couple of years (Oksanen et al. 2017;Jones et al. 2017;Zhao et al. 2017;Lin et al. 2018). Astrocytes have a huge variety of functions, particularly to neurons including maintaining glutamate and metabolic homeostasis, as well modulating synaptic function. In AD, astrocytes have demonstrated several dysfunctional changes including changes in Ca2+ signalling, glutamate excitotoxicity, synaptic plasticity, Aβ production, degradation and clearance.

6.1.1 Astrocyte activation

At post-mortem, individuals who are cognitively normal, but have high levels of amyloid plaques and NFTs in the brain, also had normal to low levels of inflammatory cytokines, suggesting an important role for inflammation in AD (Perez-Nievas et al. 2013;Perez-Nievas and Serrano-Pozo 2018). Like microglia, astrocytes are also involved in immune responses. Astrocyte activation, known as reactive astrocytes or astrogliosis occurs as a result to a variety of different types of stimuli associated with injury and disease. Activated astrocytes have been termed A1 or A2 following from the M1 and M2 classification in macrophages/microglia. It has also been proposed that astrocytes may exist in a heterogeneous population continuum, where A1 astrocytes display an inflammatory response, as opposed to A2 astrocytes which result from ischemia. It has also been proposed that A1 is neurotoxic, whereas A2 is neuroprotective (Liddelow and Barres 2017) (Zamanian et al. 2012). Astrocytes become activated in AD (Acosta et al. 2017) and this increase in reactive astrocytes has been correlated to plaque burden (Simpson et al. 2010). The effects of activated astrocytes in AD have been proposed to be both neurotoxic and neuroprotective.

Microglia have been shown to activate astrocytes and studies have shown this is both neuroprotective and neurotoxic. Following traumatic mouse brain injury, microglia first became activated via pro-inflammatory cytokines before enhancing astrocytic activation.

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Suppression of activation occurred by blocking TNF-α, IL-6 and IL-1β function, and it was further demonstrated that P2Y1 receptors were downregulated in astrocytes by microglia.

In mice with P2Y1 KO, there was earlier onset reactive astrogliosis and this reduced injury core size suggesting activation is protective (Shinozaki et al. 2017). However, these neuroprotective findings are in contrast to another recent study that has demonstrated that microglia have neurotoxic effects. Microglia activated with LPS secreted three factors with again TNFα, and IL1-β but also C1q which was sufficient to activate astrocytes (Liddelow et al. 2017). These astrocytes were shown to be neurotoxic to human dopaminergic neurons, and astrocytes lost several of their functions including their ability to phagocytose and clear Aβ. A1 astrocytes were upregulated in the hippocampus and cortex of an AD patient (Liddelow et al. 2017). Activated astrocytes may have important roles in Aβ formation, degradation and clearance. IDE expression increased in reactive astrocytes, particularly near plaques in an AD mouse model (Leal et al. 2006). Pro-inflammatory cytokines TNFα and IFNγ stimulation on astrocytes have demonstrated an increase in APP and BACE1 expression resulting in increased Aβ40 (Zhao et al. 2011).

6.1.2 Astrocytes and hypoxia

Key pathways involved with hypoxia that influence cell survival include NF-κB, p53, cAMP, c- jun and HIF1α (Vangeison and Rempe 2009). As discussed in chapter 4, HIF1α has an important role in neurons in several of the processes that lead to amyloidgenic processing and increased Aβ production, and decreased Aβ clearance (Villa et al. 2014;Zhang et al. 2007). HIF1 has also been demonstrated to have a neuroprotective role in astrocytes by regulating erythropoetin and VEGF. These two factors have a protective role in promoting angiogenesis, and protecting against oxidative stress. Interestingly the Aβ degrading protease MMP-9 has been shown to increase stroke severity, and yet several days after ischaemic induction, MMP-9 activates VEGF (Asahi et al. 2000;Zhao et al. 2006;Vangeison and Rempe 2009) Hypoxia also increases astrocyte release of adenosine which regulates excitatory synaptic transmissions, by preventing presynaptic transmitter release; this is viewed as a protective mechanism as it prevents glutamate release (Martin et al. 2007). However, other work has described this release of adenosine as resulting in glutamate dysfunction, where hypoxia suppresses glutamate uptake in a manner independent of Aβ treatment (Boycott et al. 2007).

The effect of hypoxia on the production and degradation of Aβ in astrocytes has not been as well investigated as in neurons. This is understandable as astrocytes produce little Aβ,

152 however it has been demonstrated that activated astrocytes contribute to Aβ production (Zhao et al. 2011), so further elucidation of the effect of hypoxia on astrocytic function in AD is needed. Increased astrocytic production of Aβ has been demonstrated in one study in response to hypoxia where PSEN1, a sub-unit of γ-secretase was also increased (Smith et al. 2004). Ischaemia has also been shown to upregulate BACE1 expression in astrocytes (Hartlage-Rubsamen et al. 2003).

6.1.3 Aims

As discussed above and in chapter 5, astrocytes may have a large and underappreciated role in AD pathology that needs further investigation. The effects of astrocytes on neuronal functions appear to be wide-ranging and particularly depend on the environment that astrocytes have been exposed to. Both inflammation and hypoxia in astrocytes, which share some parallels in their effects, may be either neurotoxic or neuroprotective. Again, these effects may vary regionally and temporally, but some of these mechanisms of action do not require direct contact between neurons and astrocytes in order to observe a toxic effect (Allaman et al. 2010). In the previous chapter, the data demonstrated that astrocytes only produce small amounts of Aβ compared to neurons, but proteolytic degradation occurs by similar means. Under control conditions, iPSC-astrocytes do not alter neuronal Aβ levels; however astrocytes may respond differently to neurons under AD-relevant stressors, and furthermore may affect neuronal Aβ levels. Therefore the aim of this chapter was to investigate whether secreted factors from activated astrocytes or hypoxic astrocytes affect neuronal function. This was done by addressing the following specific questions:

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6.2 Results

6.2.1 Astrocytes are activated with microglial secreted factors

Liddelow et al. (2017) demonstrated in a mouse model that just three factors secreted by microglia IL1α, TNFα, and C1q, are sufficient to activate astrocytes. By applying the equivalent human factors to primary astrocytes for 24 hours morphology was noticeably different between control and ‘activated’ astrocytes, with processes of activated astrocytes becoming elongated and more spindle-like (Fig 6.1A). After 24 hours, media was replaced without the microglial secreted factors for a further 24 hours. After 48 hours, changes in morphology were still apparent (Fig 6.1A). qPCR data demonstrated that after 24 hours C3 expression was increased, and by 48 hours this was increased over 1000-fold compared to control at 48 hours (Fig 6.1B). This was taken as confirmation that the astrocytes had been activated. To demonstrate that this pathway was independent of hypoxia, GLUT1 expression was measured and was unaltered in activated astrocytes (Fig 6.1C).

Following astrocyte activation, APP levels and APP processing were investigated. There was large variation between replicates in APP expression; however APP expression was not significantly altered in activated astrocytes (Fig 6.2A&B). sAPPα levels were also not significantly altered in activated astrocytes (Fig 6.2C&D). Despite no change in APP processing in activated astrocytes, levels of Aβ40 were decreased in activated astrocytes compared to controls (Fig 6.3A). Aβ42 levels were also decreased in activated astrocytes although this did not reach the significance threshold (p=0.059) (Fig 6.3B). The ratio of Aβ40:42 was unchanged (Fig 6.3C). Aβ38 could not be detected in these astrocytes.

There was no significant difference in Aβ degradation in the lysates between control and activated astrocytes (Fig 6.4A). Degradation of Aβ in the concentrated conditioned media of activated astrocytes appeared decreased, however, this was not significantly different (p=0.089) (Fig 6.4B). No significant difference was identified in IDE expression between control and activated astrocytes (Fig 6.4C&D).

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Figure 6.1: Activation of human astrocytes by microglial secreted factors

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Astrocytes were imaged at 24 and 48 hours. Cells were then harvested, pelleted and the mRNA was extracted before quantification of activation marker C3 by qPCR. At 48 hours lysates were also prepared and immunoblotted using antibodies for GLUT1 and actin. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the activated samples were normalised, as a percentage, to control conditions. (A) Brightfield images obtained at 10x magnification on an EVOS microscope. Morphology of astrocytes both 24 and 48 hours post treatment have elongated and thinner processes. Scale bar represents 200µm. (B) C3 expression measured by qPCR, is elevated at 24 and 48 hours after treatment. One-way ANOVA with Tukey’s post-hoc test, **, p>0.005 ***, p>0.0005. (C) Representative blot of GLUT1 expression in activated primary astrocytes. (D) Quantification of GLUT1 expression in activated and control astrocytes was not significantly different. Data shown as mean ± SEM, n= 3, unpaired t-test.

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Figure 6.2: APP processing is unchanged in activated astrocytes

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. Astrocytes were harvested and lysates prepared. Samples were collected and immunoblotted using antibodies for APP and actin in lysates, and sAPPα in conditioned media. Semi- quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the activated samples were normalised, as a percentage, to control conditions. (A) Representative western blot of APP expression in activated astrocytes. (B) Quantification of APP expression showed no significant difference with activation. (C) Representative blot of sAPPα in activated astrocytes. (D) Quantification of sAPPα showed no significant difference with activation. Data shown as mean ± SEM, n=6, unpaired t-test.

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Figure 6.3: Aβ40 is decreased in activated astrocytes

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, and Aβ38, Aβ40 and Aβ42 levels were measured using MSD multiplex immunoassay. Aβ levels in activated astrocytes were normalised, as a percentage, to control conditions. Aβ38 was below the threshold levels of detection. (A) Aβ40 levels were decreased in activated astrocytes. (B) Aβ42 levels were also decreased in activated astrocytes, however this did not reach significance (p=0.059). (C) The ratio of Aβ40 to Aβ42 was unchanged between control and activated astrocytes. Data shown as mean ± SEM, n=4. **, p<0.005, using an unpaired t-test.

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Figure 6.4: Aβ degradation and IDE expression is unchanged in activated astrocytes

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. Astrocytes were harvested and lysates prepared. FAβB was added to lysates or concentrated conditioned media and incubated for 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Samples were also immunoblotted using antibodies for IDE and actin in lysates. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the activated samples were normalised, as a percentage, to control conditions. (A) Aβ degradation capacity in the lysates between control and activated astrocytes was unchanged. (B) A trend decrease in Aβ degradation capacity in the concentrated media of activated astrocytes was observed, however this was not significant (p=0.089). (C) Representative blot of IDE expression in activated astrocytes. (D) IDE expression in control and activated astrocytes was unchanged. Data shown as mean ± SEM, n=6, unpaired t-test.

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6.2.2 Activated astrocytes do not affect iPSC-neuron membrane potential, Aβ production or degradation

The effect of the secretome of activated astrocytes on iPSC-neurons was investigated. Membrane potential as a marker of neuronal health and function was assessed. iPSC- neurons were cultured in untreated (control) and activated ACM at a ratio of 1:1 with NMM. Two other controls were also used including unconditioned astrocyte media and activated ACM that had been heated to 60oC for 30 minutes. A large variation in neuronal membrane potential, particularly in astrocyte media that had been heat activated, was observed (Fig 6.5A&B). No overall difference in membrane potential was found between neurons cultured in either control ACMs or activated ACM.

Activated ACM had no effect on APP expression in iPSC-neurons (Fig 6.6A&B). To assess neuronal Aβ levels, the contribution of astrocyte Aβ under each condition was measured and deducted from the neuronal Aβ levels in each condition. While astrocytic Aβ levels were significantly different in control and activated astrocytes (Fig 6.3) there was no change in Aβ levels or the Aβ40:42 ratio between neurons treated with control ACMs or activated ACM (Fig 6.7A-D).

Aβ degradation capacity was assessed in lysates of iPSC-neurons cultured in activated ACM (Fig 6.8A). No difference was observed between the controls and treated conditions. Expression of IDE was also unaltered under the same parameters (Fig 6.8B&C). Activated astrocyte conditioned media has therefore not demonstrated any effects on APP processing, or on the production or degradation of Aβ under these conditions.

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Figure 6.5: Activated astrocyte medium does not affect neuronal membrane potential

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Conditioned media from the final 24 hours was collected and cultured 1:1 with NMM on day 80 control (OX1-19, SBAD-02) neurons for 48 hours. Neurons were exposed to unconditioned astrocyte media, control ACM, activated ACM and activated ACM that has been heat-inactivated. Neuronal membrane potential was measured using the FLIPR® Membrane Potential Assay Kit and quantified by calculating maximum RFU – minimum RFU. (A) Representative traces of neuronal membrane potential of control (black) and activated (green) ACM treated neurons. (B) Quantification of membrane potential in all conditions. Membrane potential was variable and no significant difference was found. Data shown as mean ± SEM, n=4 inductions, one- way ANOVA, Tukey’s multiple comparisons test.

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Figure 6.6: APP expression is unaltered in neurons treated with the conditioned media of activated astrocytes.

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Conditioned media from the final 24 hours was collected and cultured 1:1 with NMM on day 80 control (OX1-19, SBAD-02) neurons for 48 hours. Neurons were exposed to unconditioned astrocyte media, control ACM, activated ACM and activated ACM that has been heat-inactivated. iPSC-neurons were harvested and lysates prepared. Samples were collected and immunoblotted using antibodies for APP and actin. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from samples were normalised, as a percentage, to neurons exposed to activated control conditions. (A) Representative western blot of APP expression in iPSC-neurons exposed to activated ACM. (B) Quantification of APP expression showed no significant difference in iPSC-neurons exposed to activated ACM. Data shown as mean ± SEM, n=4 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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Figure 6.7: Amyloid levels are unaltered in neurons treated with the conditioned media of activated astrocytes

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Conditioned media from the final 24 hours was collected and cultured 1:1 with NMM on day 80 control (OX1-19, SBAD-02) neurons for 48 hours. Neurons were exposed to unconditioned astrocyte media, control ACM, activated ACM and activated ACM that has been heat-inactivated. Conditioned media was collected, and Aβ38, Aβ40 and Aβ42 levels were measured using MSD multiplex immunoassay. Aβ levels in samples were normalised, as a percentage, to neurons exposed to activated control conditions. Astrocytic Aβ (less than 5%) was deducted from each condition. No difference was observed in (A) Aβ38, (B) Aβ40, (C) Aβ42 or (D) the ratio of Aβ40:42. Data shown as mean ± SEM, n=4 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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Figure 6.8: Aβ degradation and IDE expression is unaltered in neurons treated with the conditioned media of activated astrocytes

Primary astrocytes were treated with microglial secreted factors IL1-β, TNFα and C1q for 24 hours. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. iPSC-neurons were harvested and lysates prepared. Samples were also immunoblotted using antibodies for IDE and actin in lysates. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. FAβB was added to lysates and incubated for 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Results from samples were normalised, as a percentage, to neurons exposed to activated control conditions. (A) Aβ degradation capacity was unchanged in iPSC-neurons exposed to activated ACM. (B) Representative western blot of IDE expression in iPSC-neurons exposed to activated ACM. (C) Quantification of IDE expression showed no significant difference in iPSC-neurons exposed to activated ACM. Data shown as mean ± SEM, n=4 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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6.2.3 Hypoxic response can be induced in astrocytes

To determine the hypoxic response in primary astrocytes, astrocytes were subjected to 2.5%

O2 for 7 days. GLUT1 was increased by 200% in astrocytes following hypoxia (Fig 6.9A&B); it was also notable that GLUT1 expression was far more variable in astrocytes than was previously observed in neurons (Fig 4.1). To test for astrocyte activation as a result of hypoxia, qPCR of C3 in hypoxic astrocytes was performed. C3 could not be detected by qPCR at quantifiable levels between normoxic and hypoxic conditions and is demonstrated by RT- PCR compared to a positive control from activated astrocytes (Fig 6.9C).

APP expression in astrocytes was not affected by hypoxia (Fig 6.10A&B). However astrocytes exposed to hypoxia demonstrated a decrease in sAPPα levels by about 30% (Fig 6.10C&D). Under hypoxic conditions, both Aβ40 (Fig 6.11A) and Aβ42 (Fig 6.11B) were significantly decreased in astrocytes by 50%. A decrease in the ratio of Aβ40:42 was observed in hypoxic astrocytes suggesting they are generating more Aβ42 relative to Aβ40 (Fig 6.11C). This change in ratio was not observed in activated astrocytes, or in hypoxic neurons (both control and AD lines). As was previously observed in astrocytes, Aβ38 levels were below the threshold level of detection.

Aβ degradation in primary astrocytes exposed to hypoxia was not significantly changed, although a slight decrease was observed (Fig 6.12A). There was also no significant difference in Aβ degradation between normoxic and hypoxic astrocytes in the concentrated conditioned media; however as was previously observed in iPSC-neurons, degradation of Aβ was increased under hypoxic conditions (Fig 6.12B). IDE expression in hypoxic astrocytes was significantly decreased by 55%, as was observed in neurons (Fig 6.12C&D). Hypoxic astrocytes also demonstrated a significant increase of 70% in IDE expression in the conditioned media (Fig 6.12E&F).

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Figure 6.9: Hypoxic response in astrocytes

Primary human astrocytes were cultured for 7 days in 2.5% O2. For the final 24 hours media was changed to OptiMEM. Astrocytes were harvested and lysates prepared. Samples were then immunoblotted using antibodies for GLUT1 and actin. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. Astrocytes were also pelleted and mRNA extracted. Quantification of astrocyte activation marker C3 expression by qPCR was corrected against Rpl13a. Amplification product of C3 expression by qPCR was analysed on a 2% agarose gel containing ethidium bromide. Expected PCR product is at 224bp. (A) Representative blot of GLUT1 in normoxic and hypoxic astrocytes. (B) Quantification of GLUT1 shows a significant increase under hypoxic conditions. (C) qPCR of C3 in normoxic and hypoxic conditions did not amplify sufficiently to quantify. Gel demonstrates presence of C3 amplification in the positive control, but limited expression and no obvious difference between normoxic and hypoxic astrocytes. Data shown as mean ± SEM, RT-PCR n=3, WB n=8, ** p <0.05 using an unpaired t-test.

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Figure 6.10: APP processing in hypoxic astrocytes

Primary human astrocytes were cultured for 7 days in 2.5% O2. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. Astrocytes were harvested and lysates prepared. Samples were collected and immunoblotted using antibodies for APP and actin in lysates, and sAPPα in conditioned media. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Representative western blot of APP expression in astrocytes exposed to hypoxia. (B) Quantification of APP expression showed no significant difference under hypoxic conditions. (C) Representative blot of sAPPα in astrocytes exposed to hypoxia. (D) Quantification of sAPPα showed a significant decrease under hypoxic conditions. Data shown as mean ± SEM, n=6 (lysate), n=3 (media), unpaired t-test.

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Figure 6.11: Aβ40 and Aβ42 are reduced in astrocytes exposed to hypoxia

Primary human astrocytes were cultured for 7 days in 2.5% O2. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, and Aβ38, Aβ40 and Aβ42 levels were measured using MSD multiplex immunoassay. Aβ levels in hypoxic astrocytes were normalised, as a percentage, to normoxic conditions. Aβ38 was below the threshold level of detection. (A) Aβ40 (B) Aβ42 and (C) the ratio of Aβ40 to Aβ42 were significantly decreased in hypoxic astrocytes. Data shown as mean ± SEM, n=4. **, p<0.005, ***, p<0.0005, using an unpaired t-test.

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Figure 6.12: Aβ degradation and IDE expression in hypoxic astrocytes

Primary human astrocytes were cultured for 7 days in 2.5% O2. For the final 24 hours media was changed to OptiMEM. Conditioned media was collected, pooled, and concentrated. Astrocytes were harvested and lysates prepared. FAβB was added to lysates or concentrated conditioned media and incubated for 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Samples were also immunoblotted using antibodies for IDE and actin in lysates. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from the hypoxic samples were normalised, as a percentage, to normoxic conditions. (A) Degradation of Aβ in astrocyte lysates is decreased under hypoxia, however this was not significant (p=0.062). (B) Degradation of Aβ in astrocytes in the conditioned media is unchanged under hypoxia. (C) Representative blot of IDE expression in the lysate of astrocytes exposed to hypoxia. (D) Quantification of IDE expression in the lysate showed a significant decrease under hypoxic conditions. (E) Representative blot of IDE expression in the conditioned media of astrocytes exposed to hypoxia. (F) Quantification of IDE expression in the conditioned media showed a significant increase under hypoxic conditions. Data shown as mean ± SEM, n=6 (lysate), n=3 (media), unpaired t-test.

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6.2.4 Hypoxic astrocyte media alters neuronal Aβ levels

Having established here that astrocytes are activated and respond to hypoxia through separate pathways/mechanisms, and that the effect on Aβ metabolism is different, the effect of the secretome of hypoxic astrocytes on neuronal function was investigated. iPSC-neurons were cultured in normoxic and hypoxic ACM at a ratio of 1:1 with NMM. Two other controls were also used including unconditioned astrocyte media and hypoxic ACM that had been heated to 60oC for 30 minutes. There was no difference in membrane potential between neurons supplemented with media from normoxic or hypoxic astrocytes, as demonstrated by a representative trace (Fig 6.13A). When quantified, membrane potential was not significantly different between normoxic and hypoxic ACM, or hypoxic heat-inactivated ACM, or in unconditioned astrocyte media (Fig 6.13B).

No change in APP expression of iPSC-neurons cultured in hypoxic ACM was observed (Fig 6.14B). A small but not significant increase in APP expression was observed in some neuron inductions that had been cultured with either normoxic or hypoxic ACM as demonstrated by a representative APP blot (Fig 6.14A). Neurons cultured in normoxic ACM tended to show lower Aβ levels than neurons not exposed to ACM. Aβ38 (Fig 6.15A) or Aβ40 levels (Fig 6.15B) of neurons cultured in normoxic ACM were not significantly reduced compared to hypoxic ACM, heat-inactivated hypoxic ACM, or media that had not been previously cultured with astrocytes in. However neurons cultured in normoxic ACM showed lower Aβ42 levels that were significantly reduced compared to astrocyte free media and heat inactivated hypoxic ACM (Fig 6.15C). The Aβ40:42 ratio was highest in neurons with normoxic ACM, but this was not significantly different compared to hypoxic ACM (Fig 6.15D).

Despite a change in Aβ levels, Aβ degradation capacity of neuronal lysates was not affected by hypoxic ACM (Fig 6.16A). No change in neuronal IDE expression in hypoxic ACM was found (Fig 6.16B&C). A summary of the key findings from this results section is described in Table 6.1

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Figure 6.13: Membrane potential of neurons cultured in the conditioned media of hypoxic astrocytes.

Primary human astrocytes were cultured for 7 days in 2.5% O2. Conditioned media prior to the final 24 hours was collected and cultured 1:1 with NMM on day 80 control (OX1-19, SBAD-02) neurons for 48 hours. Neurons were exposed to unconditioned astrocyte media, normoxic ACM, hypoxic ACM and hypoxic ACM that had been heat-inactivated. Neuronal membrane potential was measured using the FLIPR® Membrane Potential Assay Kit and quantified by calculating maximum RFU – minimum RFU. (A) Representative traces of neuronal membrane potential of normoxic (black) and hypoxic (blue) ACM treated neurons. (B) Quantification of membrane potential in all conditions. Membrane potential was variable and no significant difference was found. Data shown as mean ± SEM, n=4 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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Figure 6.14: APP expression in neurons treated with the conditioned media of hypoxic astrocytes

Primary human astrocytes were cultured for 7 days in 2.5% O2. Conditioned media prior to the final 24 hours was collected and cultured 1:1 with NMM on day 80 control (OX1-19, SBAD-02) neurons for 48 hours. Neurons were exposed to unconditioned astrocyte media, normoxic ACM, hypoxic ACM and hypoxic ACM that had been heat-inactivated. iPSC-neurons were harvested and lysates prepared. Samples were collected and immunoblotted using antibodies for APP and actin. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. Results from samples were normalised, as a percentage, to neurons exposed to normoxic ACM. (A) Representative western blot of APP expression in iPSC-neurons exposed to hypoxic ACM. (B) Quantification of APP expression showed no significant difference in iPSC-neurons exposed to hypoxic ACM. Data shown as mean ± SEM, n=4 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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Figure 6.15: Amyloid levels in neurons treated with the conditioned media of hypoxic astrocytes

Primary human astrocytes were cultured for 7 days in 2.5% O2. Conditioned media prior to the final 24 hours was collected and cultured 1:1 with NMM on day 80 control (OX1-19, SBAD-02) neurons for 48 hours. Neurons were exposed to unconditioned astrocyte media normoxic ACM, hypoxic ACM and hypoxic ACM that has been heat-inactivated. Conditioned media was collected, and Aβ38, Aβ40, and Aβ42 levels were measured using MSD multiplex immunoassay. Aβ levels in samples were normalised, as a percentage, to neurons exposed to hypoxic ACM. Astrocytic Aβ (less than 10%) was deducted from each condition. No difference was observed in (A) Aβ38, (B) Aβ40 or (D) the ratio of Aβ40:42. (C) Aβ42 levels were significantly decreased in neurons treated with normoxic ACM compared to neurons treated with astrocyte free media and neurons treated with heat-inactivated ACM. Data shown as mean ± SEM, n=4 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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Figure 6.16: Aβ degradation and IDE expression in neurons treated with the conditioned media of hypoxic astrocytes

Primary human astrocytes were cultured for 7 days in 2.5% O2. Conditioned media prior to the final 24 hours was collected and cultured 1:1 with NMM on day 80 control (OX1-19, SBAD-02) neurons for 48 hours. Neurons were exposed to unconditioned astrocyte media, normoxic ACM, hypoxic ACM and hypoxic ACM that has been heat-inactivated. iPSC-neurons were harvested and lysates prepared. Samples were also immunoblotted using antibodies for IDE and actin in lysates. Semi-quantitative densitometry of the membrane was then performed to quantify relative protein levels. FAβB was added to lysates and incubated for 4 hours. The cleaved FAβB was separated and the fluorescence value measured. Results from samples were normalised, as a percentage, to neurons exposed to hypoxic ACM. (A) Aβ degradation capacity was unchanged in iPSC-neurons exposed to hypoxic ACM. (B) Representative western blot of IDE expression in iPSC-neurons exposed to hypoxic ACM. (C) Quantification of IDE expression showed no significant difference in iPSC-neurons exposed to hypoxic ACM. Data shown as mean ± SEM, n=4 inductions, one-way ANOVA, Tukey’s multiple comparisons test.

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Table 6.1: Key changes in activated and hypoxic astrocytes, and their effect on iPSC- neurons (Effect on Astrocytes) Activation Hypoxia Altered morphology Yes No GLUT1 - ↑↑ C3 ↑↑ - APP expression ↓ - sAPPα - ↓↓ IDE expression - ↓↓ IDE levels in media NA ↑↑ Aβ degradation capacity lysate - ↓ Aβ degradation capacity media ↓ ↑ Aβ40 ↓↓ ↓↓ Aβ42 ↓ ↓↓ Aβ40:42 - ↓↓ (Effect on Neurons) Activated Astrocyte Hypoxic Astrocyte Media Media Membrane potential - - Aβ degradation capacity lysate - - Aβ levels - Aβ42↑ APP expression - - IDE expression - -

Key: -, no change, ↓, indicates trend, ↓↓ indicates statistically significant change

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6.3 Discussion

The data in this chapter demonstrate that astrocytes can be activated by microglial secreted factors, and made hypoxic, and these effects occur through independent mechanisms. Activated astrocytes do not show changes in APP processing, or Aβ degradation capacity, but do show decreased Aβ40 levels compared to control astrocytes. When activated ACM was applied to neurons there appeared to be no neurotoxic effect or changes in the formation or metabolism of Aβ. Hypoxic astrocytes responded similarly to hypoxic neurons, and showed decreased sAPPα and IDE expression compared to normoxic controls. Aβ degradation capacity was reduced although this was not significant, and total Aβ levels were significantly reduced. Application of hypoxic ACM to neurons did not alter Aβ degradation or IDE expression. However, normoxic ACM significantly reduced Aβ42 levels in neurons and this effect was lost with hypoxic ACM.

6.3.1 Human astrocytes can be activated by microglial secreted factors

Liddelow et al. (2017) demonstrated that mouse astrocytes could be activated by just three microglial secreted factors (IL-1β, TNFα, and C1q) and that they maintain this state without further stimulation for at least 7 days. Given the aims to assess how other cell types, such as astrocytes, contribute to AD pathology, the activation of astrocytes by microglia is relevant. C3 upregulation was the strongest specific marker for A1 reactive astrocytes and was upregulated in astrocytes in the hippocampus and cortex of AD patients (Liddelow et al. 2017). Here, the data demonstrate that human microglial secreted factors are able to activate human astrocytes, which has not been demonstrated previously. Relatively speaking these astrocytes are only between 20-24 weeks old and, therefore, arguably not the mature astrocytes found in an AD brain. Nonetheless changes in morphology and an increase in C3 mRNA were observed (Fig 6.1), suggesting that these astrocytes are of sufficient maturity and functional capacity to increase an inflammatory phenotype. This finding is important in modelling AD, given there are many differences between human astrocytes and those from other species, particularly rodents (Oberheim et al. 2009)(see chapter 5).

6.3.2 Activated astrocytes did not alter APP processing or Aβ degradation

Changes in APP processing and Aβ degradation were assessed in activated astrocytes, but no changes were observed. APP expression and sAPPα levels were not affected (Fig 6.2). Therefore, it was not expected that any changes in Aβ levels would be detected. However Aβ40 levels were significantly reduced in activated astrocytes and Aβ42 was also reduced,

175 although not significantly (p=0.059) (Fig 6.3A&B). These results do not support previous findings that have indicated that APP and Aβ production was increased in mouse reactive astrocytes following activation via cytokines (Zhao et al. 2011).

Aβ degradation in the lysates of activated astrocytes was unchanged (Fig 6.4A) but a decrease in degradation of Aβ in the concentrated conditioned media was observed that approached significance (p=0.089) (Fig 6.4B). There was, however, no change in expression of IDE (6.4C&D). This result is also unexpected as reactive astrocytes are found surrounding Aβ plaques, and IDE expression in mouse reactive astrocytes surrounding Aβ plaques is upregulated (Leal et al. 2006;Kraft et al. 2013;Liddelow et al. 2017). This may suggest that astrocytes made reactive by microglial-secreted factors have different effects on IDE expression to those which are activated from an increase in Aβ.

6.3.3 Activated astrocytes do not alter neuronal activity

Liddelow et al. (2017) demonstrated that their A1 mouse astrocyte media was neurotoxic to mouse retinal ganglion cells (RGCs), oligodendrocytes and human dopaminergic neurons. When the activated ACM was applied to the human cortical neurons, no significant difference in membrane potential was observed suggesting that the media was not severely neurotoxic to the cortical neurons (Fig 6.5A&B). Activated ACM in these studies was not concentrated as described in the previous study (Liddelow et al., 2017), but applied at a 1:1 concentration with NMM. To address the differences in the media preparation protocols, the total protein levels were assessed. Media collected from primary astrocytes and prepared according to Liddelow et al. was concentrated, but had approximately half the total protein amount of the 1:1 ACM to NMM used in these studies. It was considered that the 1:1 ratio was more representative of all astrocyte secretions in the brain. ACM from activated astrocytes did not cause toxicity and this may be due to a lower concentration of a higher molecular weight unidentified toxic protein that would have been concentrated in the Liddelow et al., (2017) media preparation. It may also be that cortical neurons are more resistant to stress than dopaminergic neurons. It has previously been demonstrated that dopaminergic neurons are more sensitive to stress, such as oxidative stress, than cortical neurons (Wang and Michaelis 2010).

Although these cortical neurons did not exhibit a neurotoxic response to activated ACM as assessed by membrane potential, there may still have been adverse effects on APP processing and Aβ production and degradation that may contribute to disease pathology. However, iPSC-neurons showed no change in APP expression (Fig 6.6), Aβ levels (Fig 6.7), Aβ

176 degradation capacity (Fig 6.8A), or IDE expression (Fig 6.8B&C) when treated with activated ACM. Therefore, toxicity or adverse effects may only be occurring in small amounts, or mediated by other astrocyte secretions (Liddelow et al. 2017). Modifications to the experimental conditions may better assess these effects in the future and are discussed later in this section. However, these data shows which has not previously been demonstrated, that reactive astrocytes do not secrete factors that influence neuronal APP, the generation of Aβ, or its metabolism.

6.3.4 Hypoxic astrocytes demonstrate changes in APP processing

Hypoxia results in upregulation of GLUT1 in astrocytes, indicating a hypoxic response as seen in neurons (Fig 6.9A&B). Having confirmed that astrocytes were exhibiting a hypoxic response, it was asked whether this also caused A1 astrocyte activation. The mRNA of the A1 marker C3 could not be quantified in the normoxic and hypoxic astrocytes by qPCR as expression was too low to accurately compare, suggesting that hypoxic astrocytes are not A1 activated. RT-PCR further demonstrated very little amplification of C3 DNA product in normoxic or hypoxic astrocytes compared to the A1 astrocytes used as a positive control (Fig 6.9C). These data therefore confirm that astrocytes respond to hypoxia, and that this response is independent to activation of astrocytes through inflammatory mediators secreted by microglia. Further investigation is needed to determine if hypoxic treatment results in the ischaemic-activated ‘A2’ astrocytes, such as upregulation of genes associated with A2 status, e.g. Emp1 (Liddelow et al. 2017;Clarke et al. 2018).

Hypoxic astrocytes were assessed to see if they exhibited changes in APP processing, Aβ production and degradation. The results showed many of the same changes that had been observed in neurons (chapter 4). APP expression was not significantly altered (Fig 6.10A&B), but sAPPα levels were reduced in hypoxic astrocytes (Fig 6.10C&D). α-secretase activity in response to hypoxia has not been previously explored in astrocytes. Studies have been predominantly based in neurons and demonstrated a decrease in α-secretase activity in response to hypoxia (Webster et al. 2002;Marshall et al. 2006). The data presented here demonstrate a decrease in sAPPα production in hypoxic astrocytes and indicate that this effect is not specific to neurons only. This decrease in sAPPα may be due to reduced α- secretase activity.

Aβ levels in astrocytes were also reduced under hypoxia (Fig 6.11). A previous study in rat cortical astrocytes has shown an increase in Aβ levels in hypoxia and an increase in PSEN1 levels suggesting an increase in γ-secretase activity (Smith et al. 2004). The majority of

177 studies have assessed changes in Aβ levels and deposition under hypoxia in neuronal cells, and have also shown an increase in Aβ and an increase in γ-secretase activity (Salminen et al. 2017). Although, in contrast to the literature, these findings (Fig 6.11) show Aβ levels are decreased, this is in agreement to what was observed in neurons (Fig 4.3), and suggests a common mechanism in response to hypoxia regardless of cell type.

Aβ degradation in lysates taken from hypoxic astrocytes was also reduced although this was not significant. This experiment had a reduced n number which may account for why significance was not obtained. Aβ degradation in the media was also increased, although not significantly, sharing a similar trend to that observed in hypoxic neurons. The data also demonstrate that astrocytes have decreased IDE expression in hypoxic conditions, which has also been observed in neurons (Fig 6.12 and 4.5 respectively). It has been shown that NEP is downregulated in both neurons and astrocytes in hypoxia (Fisk et al. 2007). As IDE is reduced in both neurons and astrocytes, this suggests a common mechanism. Therefore astrocytes also contribute to increased amyloidogenic processing of APP and decreased Aβ degradation, and this is likely through the same mechanisms as described in chapter 4.

6.3.5 Hypoxic astrocytes alter neuronal Aβ42 levels

The effects of hypoxic ACM were then assessed on control iPSC-neurons. The data demonstrated that hypoxic ACM had no effect on neuronal membrane potential (Fig 6.13) suggesting that this does not alter neuronal function and is therefore not neurotoxic. Hypoxic ACM also demonstrated no effect on APP expression (Fig 6.14). While there was no significant difference between the different conditions, there did appear to be some small increases in APP expression when neurons were cultured with ACM.

The data demonstrated neurons cultured in ACM reduced Aβ42 levels (Fig 6.15). However Aβ42 levels of neurons cultured in hypoxic ACM were not significantly reduced as compared to neurons not cultured with any ACM, or with ACM that had been heat-inactivated (Fig 6.15C). This suggests that under these circumstances, astrocyte secreted factors in the media reduce Aβ levels and that this effect is lost with hypoxia.

Degradation capacity of neurons treated in hypoxic ACM was also assessed and demonstrated that hypoxia does not affect Aβ degradation capacity (Fig 6.16A). This suggests that hypoxic astrocytes do not secrete any factors that are either taken up by neurons or directly alter neuronal Aβ degrading proteases. To support this observation, hypoxic ACM did not affect neuronal expression of IDE. This finding indicates that the

178 increase in Aβ of neurons cultured in hypoxic ACM relative to neurons cultured in normoxic ACM is not related to the decrease of IDE in the lysate and increase of IDE in the media that has been demonstrated as a result of hypoxia (Fig 4.5). The higher Aβ levels in hypoxic ACM compared to normoxic ACM (Fig 6.15) could occur through two potential ways: hypoxic astrocytes could increase neuronal Aβ production, or they could reduce Aβ degradation in these conditions via a mechanism not measured in these studies.

Aβ levels of both astrocytes and neurons cultured alone were reduced in hypoxia, which may have been in part due to decreased IDE expression in the lysate and increased IDE in the conditioned media. Increased Aβ degrading proteases secreted into the media by astrocytes would explain why Aβ levels were lower in neurons cultured with ACM. Despite an increase in IDE in the hypoxic ACM (Fig 6.12E&F), the increase in neuronal Aβ levels is contrary to this hypothesis. This therefore suggests that hypoxic ACM could be doing the following:

 Decreasing neuronal secretion of other Aβ degrading proteases  Increasing Aβ production by neurons.

While other proteases may be involved, Aβ degrading protease secretion appears unlikely to be a large contributing factor as there were not changes in IDE expression in the lysate of neurons treated with and without normoxic or hypoxic ACM (Fig 6.16B&C) that have previously correlated with increased IDE levels in the media (Fig 6.12E&F). This would mean that IDE or other proteases are not affected unless by direct stimulation of hypoxia, whereas changes in production of Aβ are modified by astrocyte secreted factors exposed to hypoxia. If IDE levels remained constant between neurons cultured in normoxic and hypoxic ACM, then hypoxic ACM must be increasing neuronal Aβ levels relative to normoxic ACM.

The fact that Aβ levels were highest in neuronal media that had not been conditioned with astrocytes or ACM that had been heat inactivated suggests that these astrocytes have an important role in either preventing production of Aβ, or degrading extracellular Aβ by their secreted factors. Therefore exposing astrocytes to hypoxia appears to mitigate this capacity, resulting in higher Aβ levels compared to normoxic conditions. Under stress conditions astrocytes also increase BACE1 expression (Roßner et al. 2005;Hartlage-Rubsamen et al. 2003;Ren et al. 1999). Future studies could examine whether hypoxic ACM stimulates increased secretase activity without altering IDE as a compensatory mechanism, which may cause the neuronal increase in Aβ when cultured in hypoxic ACM compared to normoxic ACM.

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While astrocyte contribution to Aβ levels was deducted from the neuronal Aβ in this experiment, the data are not necessarily in keeping with findings that astrocytes do not affect neuronal Aβ levels under control conditions (Fig 5.9). Therefore several caveats need to be addressed. The hypoxic ACM incubated on neurons was collected over 48 hours rather than 24 hours, and was Astrocyte Maturation Media rather than OptiMEM. These experiments also differed in their use of primary and iPSC-astrocytes. These differences were not expected to cause large changes particularly between astrocyte types as they are relatively similar in gene and protein expression profiles (chapter 5). However because of these variables, this experiment (Fig 6.15) would not necessarily be a fair comparison to the experiment previously conducted (Fig 5.9).

Differences in treatment and the collection method of hypoxic and activated astrocytes may have resulted in different base levels of Aβ. The Aβ levels after 72 hours (hypoxic) compared to 24 hours (activated) showed that the hypoxic astrocytes had 7 times as much Aβ present. Although Aβ levels in astrocytes were still very low compared to neurons, there may have been large differences in the concentration of other proteins in the ACM. As changes in APP processing and Aβ degradation in astrocytes and neurons exposed to hypoxia are relatively small, and given that previous studies have indicated a role for reactive astrocytes in modified APP processing and Aβ levels, future experiments using activated astrocytes should use conditioned media collected over a longer time point. This may enhance any effects that may be observed, which may help uncover the mechanisms behind the changes that have been identified. Furthermore, these effects may not just be concentration dependent but also time dependent. Therefore it may be useful for future studies to assess any changes over different lengths of time.

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6.3.6 Conclusions and Future work

These results demonstrate that human primary astrocytes can be activated by microglial secreted factors, which, to my knowledge has not been previously demonstrated. However, activated astrocytes, did not exhibit changes in Aβ degradation, nor increases in APP or Aβ, as previous studies have found using cytokine stimulation of astrocytes in mice (Zhao et al. 2011). Activated astrocytes did not exhibit neurotoxic effects on cortical neurons or any other changes in neuronal APP, Aβ production or degradation. Further work has been described to optimise activation exposure time and to enable greater assessment of smaller changes that might be occurring. Future studies should also compare other forms of activation in these cell models, to see if the effects observed are specific to the type of astrocyte activation.

The data have also demonstrated that astrocytes have a hypoxic response that is independent of the activation resulting from the addition of microglial secreted factors. Hypoxic astrocytes have decreased sAPPα, Aβ, IDE and a trend toward decreased Aβ degradation capacity. These data demonstrate that the effects of hypoxia are not isolated to neurons (chapter 4), but also alter astrocytes. The conditioned media of astrocytes exposed to hypoxia did not appear to alter neuronal viability, but there is some evidence that neurons have higher Aβ levels cultured with hypoxic ACM compared to normoxic ACM. Aβ42 levels in neurons cultured in normoxic ACM were significantly lower than in neurons cultured in Astrocyte Maturation Media alone or cultured in heat-inactivated ACM. Without observing alterations in neuronal IDE or Aβ degradation capacity, these data suggest that hypoxic ACM increases Aβ levels by reducing other means to degrade Aβ or stimulating increased Aβ production. Future work should identify whether hypoxic ACM is sufficient to alter neuronal secretase activity. This could describe how an AD risk factor may increase Aβ production as well as also having a role in decreased Aβ degradation and could be used to show that while there are compensatory mechanisms such as IDE release from the direct effects of hypoxia, there may be longer term indirect effects. Neurons and astrocytes were directly cultured under hypoxic conditions for 7 days as opposed to the 48 hours that neurons were cultured with hypoxic ACM; therefore these effects may be temporally regulated. Understanding a timeline of these effects will also be able to identify whether hypoxia is ultimately neuroprotective or neurotoxic, or most likely, both. This could lead to identifying therapeutic targets that are time-dependent to mediate the many effects observed.

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6.4 Chapter Summary

In summary, this chapter addressed whether hypoxia and microglial secreted inflammatory cytokines cause astrocyte activation and if this altered APP processing including Aβ production and degradation. The effect of activated and hypoxic astrocyte conditioned media on neuronal APP processing and Aβ degradation and production was also investigated. The data showed that human primary astrocytes can be made hypoxic and activated through separate pathways. Hypoxic astrocytes caused decreases in sAPPα, and Aβ levels, whereas activated astrocytes showed decreased Aβ levels. Activated astrocyte conditioned media had no effect on neurons, but hypoxic astrocyte conditioned media altered Aβ42 levels. These data indicate that hypoxic astrocyte media causes changes in Aβ production in neurons, but not Aβ degradation, and suggests that hypoxia may cause adverse effects that are relevant to AD.

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Chapter 7: Discussion iPSC-derived neurons and astrocytes can model human cell complexity more accurately than immortalised cell lines or animal models of AD. Yet the relatively recent body of work investigating AD using iPSCs (predominantly neurons) from sporadic AD do not always demonstrate disease pathology, particularly they do not always show increased Aβ levels (Israel et al. 2012;Kondo et al. 2013). This has prompted the need for other considerations in disease modelling, such as the role of impaired Aβ degradation in sporadic AD, genetic and environmental risk factors, and other non-neuronal cell types. This thesis has addressed these considerations by investigating the proteolytic degradation of Aβ by iPSC-neurons and iPSC-astrocytes and the effect of risk factors including APOE, hypoxia, and inflammation in both human neurons and astrocytes. The data has demonstrated an important role of IDE that is affected by hypoxia, and an interaction between neurons and hypoxic astrocytes. Discussed here are the implications for this work in the context of current understanding of AD, and the remaining questions that need to be addressed.

7.1 The generation of neurons and astrocytes from iPSCs to model AD

In this thesis, neurons and astrocytes have been differentiated from iPSCs. The generation of neural models differentiated from iPSCs from sAD patients means that the unknown aetiology can be investigated in relevant human cell types. As the reprogramming stage of iPSC generation loses much of the ageing signature of these cells (Miller et al. 2013), and as ageing is the largest risk factor for AD, the characterisation of neurons during the maturation process was essential to demonstrate that these iPSC-derived cells can be used to model AD. The data demonstrates the successful generation of functional cortical neurons that produce Aβ. A curious finding showed APP isoform expression changes during maturation over the course of approximately 20 days. This finding was similar to what had been observed in a similar experiment demonstrating different isoforms of APP over the same time period (Bergstrom et al. 2016). While these data both support a role for APP in neuronal development, the data presented in this thesis supports previous work that increased APP695 leads to the generation of increased Aβ (Belyaev et al. 2010). At day 80, the time point chosen for these experiments, APP695 is predominantly expressed in iPSC-neurons which matches what is observed in neurons in the brain (Wertkin et al. 1993). It would be interesting to know if this cycle observed in differential APP695 and APP751/770 expression is continued over longer lengths of time and if there is a point at which no further changes

183 in isoform expression occur. Understanding the APP isoforms at particular time points, and the corresponding generation of Aβ, may make comparisons of differences between iPSC- neuron studies easier to account for and understand why so much variation in Aβ levels have been observed.

The data presented in chapter 5 also demonstrated successful generation of astrocytes from iPSCs with functional capacity. These cells required a lengthier differentiation process compared to neurons, as most protocols rely on the generation of NSCs from neuroepithelial cells that predominantly generate neurons until gliogenesis occurs. Only after gliogenesis has occurred can differentiation of glial progenitors be promoted (Tao and Zhang 2016). Additionally, comparatively to neurons, differentiation of astrocytes is not well defined, and there are a limited number of protocols that differentiate specific sub-types of astrocytes (Li et al. 2018;Krencik et al. 2011). Despite these short-comings, the data presented shows that neurons and astrocytes derived from iPSCs are physiologically distinct, have different APP isoform expression profiles, and contribute differently to Aβ levels. Although iPSC-neurons and astrocytes produce different isoforms of APP and have different levels of Aβ, the data has demonstrated that both of these cell types degrade Aβ. Astrocytes have been described to have multiple pathways in which they are involved in the clearance of Aβ (Ries and Sastre 2016). However iPSC-neurons and iPSC-astrocytes expressed similar quantities of IDE, suggesting the same proteolytic capabilities to degrade Aβ. iPSC-neurons and iPSC-astrocytes exposed to hypoxia both demonstrated a decrease in non- amyloidogenic APP processing. APP expression appeared to increase slightly in the control iPSC-neurons, and this was significantly increased in AD iPSC-neurons. In contrast astrocytes exposed to hypoxia did not show any difference in APP expression. Both neurons and astrocytes showed decreased Aβ levels in hypoxia that corresponded with decreased IDE in the lysate and increased IDE in the concentrated conditioned media. Therefore the data has shown that cell stressors may affect the generation of Aβ differently, but similarly alter Aβ degradation in both neurons and astrocytes.

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7.2 Aβ degradation and the role of IDE

7.2.1 Aβ degradation

In this work, a method to assess Aβ degradation has been developed. Most cases of AD are sporadic, and it is thought that where fAD is associated with increased production of Aβ, sAD is associated with impaired Aβ degradation (Wildsmith et al. 2013). Therefore the development of this assay to assess Aβ degradation in cell lysates and concentrated conditioned media provided great scope to investigate Aβ degradation in iPS cell types and model sAD. This assay demonstrated an important role for IDE, and that hypoxia reduced neuronal capacity to degrade Aβ.

Further optimisation of this assay to determine total amounts of FAβB degraded would be useful to make stronger comparisons between control and AD cell lines. Currently the assay does not use a standard curve, and therefore comparisons made are relative within each experiment. This provides some limitations for comparison, as cells must be prepared at the same time, to avoid differences in freeze/thaw cycles and length of time in storage, due to the lack of protease inhibitors included. Quantification of the Aβ degraded would be useful to further understand the balance between Aβ production and degradation. While measurement of Aβ degradation in cell lysates was performed in these studies, measurement in intact cells would be beneficial. However despite efforts to achieve this it was not possible due to the sensitivity of the assay, suggesting that Aβ degradation by membrane-bound proteases may not be abundant and occurring at detectable levels. This concurs with a previous assay from the Leissring lab based on the work that first established this fluorescence polarisation assay (Leissring et al. 2003). Here the authors showed that only intact HeLa cells transfected with IDE were able to show degradation of Aβ (Zhao et al. 2009).

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7.2.2 The role of IDE

IDE and NEP are considered the two main Aβ degrading proteases in the brain (Shirotani et al. 2001;Qiu et al. 1998;Iwata et al. 2001;Farris et al. 2003). In chapter 3, the data indicated that IDE, and not NEP, was the major protease responsible for degrading the FAβB substrate in iPSC-neurons. This was subsequently demonstrated in iPSC-astrocytes in chapter 5. This result was partially unexpected as NEP has been shown to be more effective at degrading Aβ than IDE (Shirotani et al. 2001). However, a previous study has demonstrated that IDE principally degrades Aβ in a rat primary brain culture and in pheochromocytoma (PC12) cells (Vekrellis et al. 2000). Interestingly, the authors also found it was membrane-associated IDE rather than cytosolic IDE that degraded the Aβ. This suggests that because NEP is also membrane bound, NEP proteolytic function has not necessarily been altered by the process of collecting cell lysates to measure Aβ degradation. This then, does not account for why so little Aβ degradation due to NEP was observed, therefore the proteolytic activity of membrane-bound NEP should still be assessed.

Localisation of IDE may vary between cell types, and it has been suggested that IDE may be secreted, particularly as a result of differentiation (in PC12 cells) (Vekrellis et al. 2000) or because astrocytes and microglia secrete more IDE (Dorfman et al. 2010). A comparison of IDE in the lysates of iPSC-neurons and astrocytes demonstrated that both cell types express similar amounts. In brain tissue and N2a cells, localisation of IDE in the detergent resistant membrane fraction was considered essential for the structural integrity of IDE to retain its capacity for insulin proteolysis (Bulloj et al. 2008). IDE was then shown to be associated with the plasma membrane in N2a cells and predominantly sorted into exosomes (Bulloj et al. 2010). Secretion of IDE via exosomes has also been shown in BV-2 and mouse microglial models (Tamboli et al. 2010). However, in mouse primary astrocytes and human astrocytoma cells IDE secretion has been shown to be mediated by an autophagy-based unconventional secretory pathway (Son et al. 2016), which has also been previously demonstrated in HeLa and murine hepatocyte cells (Bulloj et al. 2010). Secretion of IDE may be triggered by specific factors. For example statins such as lovastatin and simvastatin, have been shown to increase IDE secretion, possibly through autophagy-based secretion (Tamboli et al. 2010;Son et al. 2015). In contrast, it has been shown that IDE is not secreted and the effects of statins causes reduced membrane integrity possibly from altered cholesterol levels (Song et al. 2018).

The association of cholesterol levels with AD and the potential role that IDE plays in Aβ degradation therefore indicates a need to further understand IDE secretion to determine

186 whether this is important in AD. In chapters 4 and 6 both iPSC-neurons and primary astrocytes exposed to hypoxia showed decreased IDE in the cell lysate, and increased IDE in the media. Using actin as a marker of cell integrity, the results showed that the increase of IDE in the media did not correlate with any change in actin levels in the media. These findings disagree with Song et al., (2018) that IDE is only released into the media as a result of reduced cell membrane integrity, and support previous studies suggesting a secretion pathway is involved (Bulloj et al. 2010;Son et al. 2016). Hypoxia increases exosome secretion leading to an increase in the secretion of IDE (Zhao et al. 2009;Bulloj et al. 2010) but this is yet to be confirmed in differentiated neurons.

Whether secretion of IDE, induced by hypoxia, is a protective mechanism is not known. In ageing and AD, in the hippocampus, IDE expression and activity is inversely proportional to AD pathology (Cook et al. 2003;Miners et al. 2008). However, IDE levels in the cortex have been shown to be increased in ageing and AD (Caccamo et al. 2005;Wang et al. 2010). On a cellular level, Perez et al., (2000) reported reduced cytosolic IDE, whereas IDE has also been shown to be reduced in the membrane fraction instead (Zhao et al. 2007). These inconsistencies may be brain region dependent, but they also may vary as a response to the aetiology in each AD study. The data presented in this thesis uses cortical neurons and primary foetal human astrocytes to understand the role of hypoxia in Aβ production and degradation. When exposed to hypoxia, cytosolic IDE decreased and extracellular IDE increased in both neurons and astrocytes, suggesting similarity in the response despite the different cell types. This finding, however, shows how a reduction of IDE in hypoxia mimics the reduction of IDE seen in AD. In iPSC-neurons and iPSC-astrocytes far more Aβ was detected in the conditioned media than in lysates, suggesting this may be a beneficial response to reduce Aβ levels. Furthermore, IDE has been found localised to Aβ plaques (Dorfman et al. 2010). However, if IDE mRNA levels have been shown to be both up and downregulated in hypoxia based on the length of time post induction of hypoxia (Hiltunen et al. 2009;Famakin et al. 2014), localisation out of the cytosol may also become detrimental. Questions remain over whether changes in IDE localisation and levels may be impacted by insulin signalling dysfunction in T2DM, another associated risk-factor for AD. These findings raise several interesting questions, highlighting the need to further understand IDE secretion and its localisation, particularly over longer time periods, in order to understand the role of IDE in impaired Aβ degradation and allow the generation of better models of sAD.

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7.3 AD risk factors

7.3.1 APOE

While APOE has been attributed to increased Aβ production, neurons derived from the AD line with an APOE ε4/ε4 genotype did not show an obvious increase in Aβ levels compared to neurons from the control OX1-19 line (with an APOE ε3/ε3 genotype) as shown in chapter 3. Given neurons from most fAD lines show an increase in Aβ levels compared to control lines, this demonstrates that a sAD line, even with a well-known genetic risk factor does not necessarily demonstrate pathological features such as increased Aβ in culture. This could also stand as further evidence for a more important role of impaired Aβ degradation in sAD despite APOE being associated with both impaired degradation and increased Aβ production (Jiang et al. 2008;Huang et al. 2017). It also demonstrates that even APOE ε4 as the largest genetic risk factor for AD, may still require environmental stress to initiate disease onset.

The results in chapter 4 showed, however, that the effects of hypoxia on a cell line were variable. Due to time constraints there were fewer neuronal inductions of the AD cell line, and this may account for why there was no significant difference in capacity to degrade Aβ despite similar trends to the OX1-19 control line. Under hypoxia, increased APP expression was observed in neurons from the AD line, which was not observed in neurons from the OX1- 19 line, which could suggest that the generation of Aβ is increased in the AD line under hypoxic conditions. Hypoxia has been previously shown to increase APOE production (Hayashi et al. 2006), and APOE has been shown to stimulate APP transcription in an allele dependent manner whereby APOE ε4 is more efficient (Huang et al. 2017). This may account for the increase in APP in the AD line with APOE ε4 genotype. Astrocytes predominantly produce APOE (Boyles et al. 1985), therefore, it would be interesting to see if astrocytes are affected more severely. This may also be particularly relevant as APOE ε4 has also been shown to reduce hippocampal IDE (Cook et al. 2003). However the Aβ degradation capacity in the AD line exposed to hypoxia was not significantly different to the normoxic AD line (again likely due to the smaller n number). It is important to note that future studies will need to be confirmed using isogenic controls, which may also further elucidate the role of APOE, and stands to demonstrate how genetic and environmental risk factors including hypoxia, exacerbate or initiate disease.

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7.3.2 Inflammation

The effects of activated astrocytes were also investigated in chapter 6 modelling inflammation as an environmental risk factor in AD. The successful generation of ‘A1’ activated astrocytes by three microglial secreted factors has not, to my knowledge, previously been demonstrated in a human model. These activated primary human astrocytes did not, however, demonstrate any changes in APP processing, or Aβ degradation, although curiously showed decreased levels of Aβ. This finding disagrees with previous work that showed that activated astrocytes increased Aβ production and could therefore be a contributing source of Aβ in AD. Due to the nature in which astrocytes were activated, the exposure time was only 48 hours, compared to the 7 days that astrocytes (and neurons) were exposed to hypoxia. Given the wealth of conflicting data on hypoxia using different exposure times, this raises the question over whether activated astrocytes need to be exposed to the microglial secreted factors for longer. The time point originally chosen for this experiment was based on how long the added factors would remain active in cell culture. These findings highlight the complexity of inflammation, and the different ways in which astrocytes may become activated, and respond when activated. This work supports the idea for a continuum of activation states (proposed by Liddelow and Barres 2017) that may have different effects on cells and therefore play different roles in neurodegenerative disease.

7.3.3 Hypoxia

Hypoxia has not previously been used as a cell stressor in iPSC models in the context of AD, despite hypoxia being increased in ageing and AD risk factors such as stroke and other neurovascular diseases (Roffe 2002;Roberts et al. 1997). The work shown here has already been discussed in terms of the role of IDE, but hypoxia has been demonstrated to have an effect on both the production and degradation of Aβ in iPSC-neurons and primary human astrocytes. This is important for its potential to cause sAD, as both Aβ production and degradation are affected and may lead to disease pathology. In the AD cell line, Aβ degradation did not appear to be affected more than the control cell line in response to hypoxia, but the significant increase in APP, could indicate that more Aβ is produced. Total Aβ levels were also significantly reduced, indicating a potential role for IDE that may be compensatory during this length of hypoxic exposure.

As previously discussed, astrocytes and neurons show some similarities in response to hypoxia, therefore the interaction of hypoxic astrocyte secretions on neurons is interesting

189 to explore in order to determine whether astrocytes contribute to neuronal dysfunction. The data showed that the addition of ACM from primary astrocytes to neurons beneficially reduced Aβ42 levels. However, hypoxic ACM did not significantly reduce Aβ42 levels. There were no changes in Aβ degradation, suggesting that overall hypoxia will lead to higher levels of Aβ42 in the brain. This may be due to astrocyte secreted factors that impair neuronal degradation of Aβ, or stimulate neuronal production of Aβ. Previous studies have demonstrated that ACM is protective to neurons exposed to hypoxia (Yan et al. 2013). These data now demonstrate that ACM from hypoxic astrocytes can be detrimental to neurons by increasing Aβ42 levels. Further studies may elucidate a greater role in the interaction of activated or hypoxic astrocytes and neurons by use of co-culture models, but establishing the relative contribution of either neurons or astrocytes to Aβ production and degradation in co-culture is not a simple undertaking.

7.4 Concluding remarks

AD is a devastating disease that affects an increasing number of the population, and the aetiology that drives disease for the majority of individuals affected is still unknown. iPSC models are one of the best tools available to study human disease, and offer the opportunity to directly model sporadic AD from affected patients. Most studies utilising iPSCs thus far have focused on fAD and the production of Aβ, and the proteolytic degradation of Aβ has not been investigated. The data presented in this thesis demonstrate that Aβ degradation in iPSC-neurons and astrocytes can be investigated and that the metalloprotease IDE is the largest contributor to Aβ degradation in these cells using this assay.

This work has created physiologically relevant models to show that environmental stressors do affect Aβ levels, and that specifically hypoxia, affects both Aβ production and Aβ degradation. This has raised interesting questions as to the role of IDE release as a compensatory and beneficial mechanism or a dysfunctional response over a long exposure period.

While the different cell stressors of hypoxia and astrocytic activation have had different effects on the generation and degradation of Aβ, the hypoxic response between neurons and astrocytes is similar. This demonstrates that there are many similarities between these two cell types that make up the majority of cells found in AD affected areas of the brain. Overall the data presented in this thesis describes how AD environmental cell stressors can affect Aβ levels in human neurons and astrocytes and may contribute to sAD disease pathology.

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Ultimately applying these cell stressors to more sAD and control lines that do not on their own show increased Aβ levels, and assessing changes such as Aβ production and degradation will better model the aetiology of the disease and help to determine potential primary mechanisms for disease onset causing sAD.

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