Evaluating the Potential Therapeutic Role of Angiotensin Converting Enzyme Inhibitors and Angiotensin Receptor

Blockers for Alzheimer’s Disease using a Drosophila Model

by

Sarah Gomes

A thesis submitted in conformity with the requirements for the degree of Master of Science

Institute of Medical Science University of Toronto

© Copyright by Sarah Gomes 2017

ii

Evaluating the Potential Therapeutic Role of Angiotensin

Converting Enzyme Inhibitors and Angiotensin Receptor Blockers

for Alzheimer’s Disease using a Drosophila Model

Sarah Gomes

Master of Science

Institute of Medical Science University of Toronto

2017

Abstract

Presenilins (PS) play a role in familial Alzheimer’s disease (AD) and Notch signalling. In a

genetic screen looking for modifiers of APP but not Notch, we identified Drosophila orthologs

of Angiotensin Converting Enzyme (ACE). Interestingly, ACE polymorphisms are associated

with AD and Apo-E, the best characterized risk factor for late-onset AD. Moreover, ACE

inhibitors (ACE-I) delayed the onset of cognitive impairment and neurodegeneration in mice and

humans. However, it remains unclear why ACE-I are beneficial in AD. Here, we explore the link

between PS and ACE in a Drosophila model using genetics and pharmacology. We found that

ACE disruption does not affect Notch related phenotypes. Moreover, we found that ACE-I and

Angiotensin Receptor Blockers are beneficial in an AD Drosophila model. Since inhibition of

ACE has no detrimental effects on Notch and modulates AD related phenotypes, it could provide

an important therapeutic target for AD.

iii

Acknowledgments

I would like to begin by thanking my supervisor, Gabrielle Boulianne, for her support and

guidance. She has not only taught me how to think like a scientist, but also how to create a work-

life balance. She has been tremendously proactive and supportive of my professional goals and

always instilled confidence in my abilities, especially when graduate school tested me. Without

her leadership, this thesis would not have been possible and I am deeply grateful to her.

Thank you to my committee members Lucy Osborne, Howard Mount, and Freda Miller, for

providing valuable scientific insight to my project and for challenging me, it has made me

become a better scientist.

I would also like to thank past and present Boulianne lab members, Mike Garroni, Irene Trinh,

Dragan Gligorov, Kostas Iliadi, Nataly Iliadi, Oxana Gluscencova, Greg Chernomas, Alex Lee,

Dave Knight, Sili Liu, and Attey Rostami for all of their help and friendship. Everyone in the lab

has helped me in some way, from intellectual or technical support, to coffee breaks on long days

in the lab. I am so grateful to have had the opportunity to work with and learn from all of you.

I am forever indebted to my family for their unconditional love and support. My parents,

siblings, grandparents, aunts, uncles, and cousins have all brought me so much joy throughout

my life and always encouraged me to pursue my dreams. To my mom, thank you for always

supporting my love of science from when we would study high school biology together, to your

continued encouragement today; you are my unwavering inspiration and I am so grateful to you.

To my dad, thank you for always supporting my decisions and for helping me realize my goals

by being my loudest cheerer (and for reading this thesis!). Thank you to my siblings, Katelyn and

John, and to my friends, for reminding me not to take life too seriously and have some fun. I

would also like to give a big thank you to Raed Makhlouf, your endless love and encouragement

gave me strength when graduate school pushed my limits.

Finally, I would like to dedicate this thesis in loving memory of my Avó, Filomena Gomes

(03/05/1931 – 12/24/2016).

iv

Statement of Contributions

The author, Sarah Gomes, completed all work presented in this thesis.

v

Table of Contents

Acknowledgments .......................................................................................................................... iii

Statement of Contributions ............................................................................................................ iv

Table of Contents ............................................................................................................................ v

List of Tables ................................................................................................................................. ix

List of Figures ................................................................................................................................. x

List of Abbreviations .................................................................................................................... xii

Chapter 1 ......................................................................................................................................... 1

1 Introduction ................................................................................................................................ 1

1.1 Alzheimer’s Disease ........................................................................................................... 1

1.1.1 Incidence and Clinical Presentation ........................................................................ 1

1.1.2 AD genetics ............................................................................................................. 3

1.1.2.1 APP Mutations .......................................................................................... 3

1.1.2.2 PS1 Mutations ........................................................................................... 6

1.1.2.3 PS2 Mutations ........................................................................................... 6

1.1.2.4 Risk Factor Genes ..................................................................................... 6

1.1.3 Cellular & Molecular Mechanisms ......................................................................... 7

1.1.3.1 Amyloid Beta and Tau .............................................................................. 7

1.1.3.1.1 Non-amyloidogenic pathway ........................................................................... 9

1.1.3.1.2 Amyloidogenic pathway ................................................................................ 10

1.1.3.1.3 The γ-Secretase Complex .............................................................................. 12

1.1.4 Current AD Standard of Care ................................................................................ 15

1.2 The Link Between ACE and AD ...................................................................................... 16

1.2.1 The Use of ACE-I and ARB in Humans ............................................................... 20

vi

1.2.2 In vitro Analysis of ACE and Aβ .......................................................................... 25

1.2.3 In vivo Analysis of ACE and AD Models ............................................................. 25

1.3 Drosophila as a model organism ...................................................................................... 30

1.3.1 Drosophila Life Cycle .......................................................................................... 30

1.3.2 Drosophila development ....................................................................................... 32

1.3.3 Drosophila genetics and genomics ....................................................................... 35

1.3.3.1 Drosophila ACE like factors .................................................................. 35

1.3.4 Genetic tools ......................................................................................................... 37

1.3.5 Drosophila models of AD ..................................................................................... 40

1.3.6 Drug testing using Drosophila .............................................................................. 41

1.3.6.1 The Genetic Interaction between ACE, PS, and APP ............................ 42

1.4 Rationale ........................................................................................................................... 44

Chapter 2 ....................................................................................................................................... 45

2 Objective and Hypotheses ........................................................................................................ 45

2.1 Main Objective .................................................................................................................. 45

2.2 Specific Hypotheses .......................................................................................................... 45

2.2.1 Test whether ACE and ATR disruption will modify phenotypes associated with the Notch signalling pathway ........................................................................ 45

2.2.2 Test how ACE-I and ARB administration affects AD-related phenotypes .......... 45

Chapter 3 ....................................................................................................................................... 48

3 Materials and Methods ............................................................................................................. 48

3.1 Drosophila Genetics ......................................................................................................... 48

3.1.1 Notch Genetics ...................................................................................................... 48

3.1.2 APP Genetics ........................................................................................................ 48

3.2 Drugs ................................................................................................................................. 50

3.3 Egg Laying Assay ............................................................................................................. 50

vii

3.4 Wing Mounting ................................................................................................................. 50

3.5 Microscopy ........................................................................................................................ 50

3.6 ELISA Analysis ................................................................................................................ 51

3.7 Statistics ............................................................................................................................ 51

Chapter 4 ....................................................................................................................................... 52

4 Results ...................................................................................................................................... 52

4.1 The absence of ACE-like factors does not disrupt γ-secretase dependent Notch signalling in Drosophila .................................................................................................... 52

4.1.1 Genetic Interaction ................................................................................................ 52

4.1.1.1 Is Captopril Effective in Drosophila? .................................................... 59

4.1.2 Pharmacological Intervention ............................................................................... 62

4.2 ACE-I and ARBs modify AD-related degeneration at the level of C99 and Aβ42 in a Drosophila AD model ....................................................................................................... 64

4.2.1 Phenotype characterization of AD-related Transgenes ......................................... 64

4.2.2 Are ACE-I or ARBs toxic under non-disease conditions? ................................... 72

4.2.3 Captopril and losartan modify rough eye phenotypes in Drosophila expressing UAS-C99V717I and UAS-Aβ42 ................................................................................. 75

Chapter 5 ....................................................................................................................................... 88

5 Discussion ................................................................................................................................ 88

5.1 Overview of key findings .................................................................................................. 88

5.2 Drosophila homologs of the renin-angiotensin system .................................................... 88

5.2.1 Drosophila ACE and ATR interaction with Notch signalling .............................. 89

5.2.2 Future Directions ................................................................................................... 92

5.3 The renin-angiotensin system and AD .............................................................................. 93

5.3.1 Future Directions ................................................................................................... 98

5.4 Conclusions ....................................................................................................................... 99

References ................................................................................................................................... 101

viii

Copyright Acknowledgments ..................................................................................................... 119

ix

List of Tables

Table 1.1 Summary Cognitive Outcomes following ACE-I/ARB use in Humans ....................... 24

Table 1.2 Summary of in vivo (mouse model) data on the effects of ACE-I administration on

AD-related phenotypes ................................................................................................................. 28

Table 4.1 Description of Acer and Ance mutants .......................................................................... 54

Table 4.2 Description of Transgenes Used ................................................................................... 66

Table 4.3 Summary of Pharmacological and Genetic Data on AD Related Phenotypes .............. 87

x

List of Figures

Figure 1.1 APP exon map ............................................................................................................... 5

Figure 1.2 Non-amyloidogenic pathway versus amyloidogenic pathway .................................... 11

Figure 1.3 γ-secretase complex components ................................................................................. 14

Figure 1.4 The renin-angiotensin system and it’s physiological role in blood pressure ............... 17

Figure 1.5 Drosophila life cycle ................................................................................................... 31

Figure 1.6 Schematic of the fly’s nervous system ........................................................................ 34

Figure 1.7 The Gal4-UAS expression system ............................................................................... 39

Figure 1.8 Acer and Ance-5 identified as modifiers of Psn and C99 in a genetic screen using

Drosophila .................................................................................................................................... 43

Figure 2.1 Proposed mechanism of ACE/AD interaction ............................................................. 47

Figure 3.1 Generation of GFP and AD-related transgenic flies .................................................... 49

Figure 4.1 Schematic of Acer and Ance genomic locus. ............................................................... 53

Figure 4.2 Drosophila ACE-like factors do not genetically interact with Dl ............................... 56

Figure 4.3 Drosophila ACE like factors do not genetically interact with the Notch receptor ...... 58

Figure 4.4 Captopril inhibits egg laying in Drosophila ................................................................ 61

Figure 4.5 ACE inhibitors and Angiotensin receptor blockers do not modify Dl or Notch

phenotypes .................................................................................................................................... 63

Figure 4.6 GFP expression profiles of young adult flies .............................................................. 68

Figure 4.7 GFP expression profiles of old adult flies ................................................................... 69

Figure 4.8 GFP Expression Profiles of AD Transgenes Over Time ............................................. 71

xi

Figure 4.9 Effects of captopril and losartan on wild-type flies ..................................................... 73

Figure 4.10 Effects of captopril and losartan on UAS-lacZ flies .................................................. 74

Figure 4.11 Effect of Captopril or Losartan Administration to flies expressing UAS-C99WT ...... 76

Figure 4.12 Effect of Acer knockdown on the Aβ peptide profile of flies expressing UAS-C99WT

....................................................................................................................................................... 78

Figure 4.13 Effect of Captopril or Losartan Administration to flies expressing UAS-C99V717I ... 80

Figure 4.14 Effect of Acer knockdown on the Aβ peptide profile of flies expressing UAS-

C99V717I ......................................................................................................................................... 82

Figure 4.15 Effect of Captopril or Losartan Administration to flies expressing UAS-Aβ42 .......... 84

Figure 4.16 Effect of Acer knockdown on the Aβ peptide profile of flies expressing UAS-Aβ42 . 86

xii

List of Abbreviations

Aβ Amyloid Beta

Aβ40 Amyloid Beta, 40 amino acid peptide

Aβ42 Amyloid Beta, 42 amino acid peptide

Aβ43 Amyloid Beta, 43 amino acid peptide

ABCA7 ATP Binding Cassette Subfamily A Member 7

ACE Angiotensin Converting Enzyme

ACE-I Angiotensin converting enzyme inhibitors

ACE2 Angiotensin converting enzyme 2

Acer Angiotensin converting enzyme related

Acer2 precise excision (wild type) Acer line, used as control

AcerΔ164 Imprecise excision Acer mutant line (chromosomal Acer deletion)

AcerΔ168 Imprecise excision Acer mutant line (chromosomal Acer deletion)

AcerK07704 Acer P-element mutant line

acn-1 C. elegans homolog of human ACE

AD Alzheimer’s Disease

ADL Activities of Daily Living

AICD Amyloid intracellular domain

Ance Drosophila Angiotensin Converting enzyme

Ance-2 Drosophila Angiotensin Converting enzyme-2

xiii

Ance-3 Drosophila Angiotensin Converting enzyme-3

Ance-4 Drosophila Angiotensin Converting enzyme-4

Ance-5 Drosophila Angiotensin Converting enzyme-5

AnceMB09828 Ance Minos-element mutant line

Ance34Eb2 point mutant Ance line

AnceMI05748 Ance Minos-element mutant line

APH1 Anterior pharynx defective 1

APOE Aplioprotein E

APP Amyloid Precursor Protein

ARB Angiotensin II receptor blockers

AT-1/ATR Angiotensin II receptor type 1

ATRAP Drosophila Angiotensin II receptor type 1 associated protein

BBB Blood brain barrier

BGDP Berkeley Genome Disruption Project

BIN1 Box-dependent-interacting protein 1

C83 Amyloid Precursor Protein cleaved by α-secretase, 83 amino acids

C99 Amyloid Precursor protein cleaved by γ-secretase, 99 amino acids

C99J6/C99WT Wild-type form of C99

C99V717I London mutant form of C99

CAA Cerebral amyloid angiopathy

xiv

CD2AP CD2 associated protein

CD33 Siglec-3

CDT clock drawing test

CI Cholinesterase Inhibitors

CLU Clusterin

CNS Central nervous system

CR1 Complement receptor 1

CSF Cerebral spinal fluid

CTCF corrected total cellular fluorescence

CyO Curly mutation (Drosophila second chromosome balancer)

DD 287 base pair homozygous deletion at intron 16 of the ACE gene

dAPPl Drosophila amyloid precursor protein like

Df(1)N-8 Chromosomal Notch deletion line

Dl Delta

dTau Drosophila Tau

ELISA Enzyme linked immunosorbent assay

EOAD Early Onset Alzheimer’s Disease

FAD Familial Alzheimer’s Disease

gACE Germinal ACE

Gal4 Encodes yeast transcription activator protein GAL4

xv

GFP Green fluorescence protein

GMR Glass multimer reported

GWAS Genome Wide Association Studies

HPLC High performance liquid chromatography

I/D 287 base pair insertion/ deletion at intron 16 of the ACE gene

II 287 base pair homozygous insertion at intron 16 of the ACE gene

ICV Intracerebroventricular

LOAD Late Onset Alzheimer’s Disease

LPS Lipopolysaccharide

mCD8-GFP Fusion protein (extracellular and transmembrane domains of mouse CD8

antigen fused to GFP

MCI Mild cognitive impairment

MMSE Mini Mental State Examination

MS4A6A Membrane Spanning 4 Domains A6A

MS4A6E Membrane Spanning 4 Domains A6E

NCT Nicastrin

NFT Neurofibrillary tangles

NGA Negative geotaxis assay

OMIM Online Mendelian Inheritance in Man

P3 P3 peptide (24-26 residue)

xvi

PBS Phosphate buffered saline solution

PEN2 Presenilin enhancer 2

PICALM Phosphatidylinositol binding clathrin assembly protein

PS Presenilin

PS1 Presenilin-1

PS2 Presenilin-2

psn Drosophila presenilin

RAS Renin-angiotensin system

RIPA Radioimmuniprecipitation assay buffer

RNAi RNA interference

RT Room temperature

sACE Somatic ACE

sAPPα Soluble APP α

sAPPβ Soluble APP β

SAD Sporadic Alzheimer’s Disease

SDS-PAGE Sodium dodecyl sulphate polyacrylamide gel electrophoresis

SNP Single nucleotide polymorphism

SOG Subesophogael gangli

SORL1 Sortilin Related Receptor 1

TM2 Drosophila third chromosome balancer with Ubx mutation

xvii

TREM2 Triggering Receptor Expressing on Myeloid Cells 2

Twi24B Myocardin related transcription factor

UAS Upstream Activating Sequence

w1118 white 1118 line (wild type)

1

Chapter 1

1 Introduction

1.1 Alzheimer’s Disease

1.1.1 Incidence and Clinical Presentation

Alzheimer’s Disease (AD) is a growing problem within the Western world, with approximately

5.2 million Americans and 500,000 Canadians living with AD. Of these 5.2 million Americans

living with AD, 5 million are over the age of 65 whereas 200,000 are under the age of 65. With a

large aging population in North America, it is projected that these numbers will triple by 2050

with an estimate of 13.8 million Americans living with AD. This rise in AD incidence not only

has large implications for patients, but also for caregivers and the health care system itself. In the

United States, it is estimated that the cost of AD will increase from $214 billion in 2014 to $1.2

trillion in 2050 (“Latest Alzheimer’s Facts and Figures,” 2013).

AD is a neurodegenerative disease that gives rise to progressive memory loss and cognitive

decline (reviewed in Holtzman, Morris, & Goate, 2011). Alois Alzheimer was the first to

describe the disease in 1906, when a 51-year-old woman presented with cognitive deficits and

post-mortem neurofibrillary changes within her cerebral cortex (reviewed in Holtzman et al.,

2011). According to Alzheimer’s original case report, she presented with memory loss,

confusion, paranoia, disruptive behaviour, disorientation, hallucinations, an inability to acquire

new knowledge, and language deficits. In a translated version of the case report, Alzheimer

states:

“Soon, a rapidly worsening memory weakness was noticeable; she could no longer

negotiate her way around her dwelling; dragged objects back and forth and hid them; and

at times she believed she was about to be murdered and started yelling loudly…. Her

ability to retain information is impaired to the profoundest degree. When shown objects,

she mostly labels them correctly; but soon thereafter she has forgotten everything again.”

(translated by Strassnig & Ganguli, 2005)

2

Since this first clinical description of AD, the disease has been widely studied and more clearly

defined. Currently, there are three classified clinical stages of AD: mild/early, moderate/middle,

and severe/late. In the mild/early stage of the disease patients begin to experience the hallmark of

the disease, memory loss. Common signs of disease can include losing sense of direction, mood

and personality changes, compromised judgment, trouble with money, timelines, and normal

daily activities. In the moderate stages of disease, clinical symptoms worsen with increased

memory loss and confusion, shortened attention span, difficulty recognizing loved ones,

language problems, inability to acquire new knowledge, anxiety, wandering, hallucinations,

paranoia, delusions, loss of impulse control (often undressing at inappropriate times or

inappropriate language), and motor problems. Here, patients begin to experience an inability to

appropriately respond to their surroundings. In the severe/late stage of AD, patients are unable to

recognize loved ones, unable to communicate, and depend on others for basic care. Patients also

exhibit other symptoms such as seizures, difficulty swallowing, and a lack of bladder and bowel

control. In the late stage of the disease, patients often lose a sense of self and their personality

may be severely altered. Current clinical diagnosis of the disease is often done using the

guidelines set out in the The Diagnostic and Statistical Manual of Mental Disorders 5th edition

(DSM-5) (American Psychiatric Association, 2013) or the National Institute on Aging

Alzheimer’s Association (NIA-AA) criteria (McKhann et al., 2011). However, there can be

heterogeneity in the clinical presentation of AD, depending on age, genetic factors, and

comorbidities such as cerebrovascular disease (Lam et al., 2013; Jellinger et al., 2004).

In addition to defining the clinical presentation of AD, Alois Alzheimer was also the first to

provide insight into the neuropathology of AD. Following the death of his patient, brain sections

revealed atherosclerotic changes to brain vessels, neurofibrils throughout the ganglionic cells,

and cellular atrophy (translated by Strassnig & Ganguli, 2005). Since this initial description of

AD, the neuropathology associated with the disease has been more clearly defined both

macroscopically and microscopically. Macroscopically, there is a symmetric pattern of brain

atrophy, particularly in the medial temporal lobes, which includes the hippocampus and

surrounding regions (Serrano-Pozo, Frosch, Masliah, & Hyman, 2011). This atrophy results in ex

vacuo dilation of the lateral ventricles in the temporal horns (Serrano-Pozo et al., 2011). In

3

contrast, at the microscopic level, there are two main hallmarks: amyloid beta (Aβ) plaques and

neurofibrillary tangles (NFT; also known as tau tangles).

Despite the diagnostic criteria and prototypical clinical and pathological presentation of disease,

AD can be sub-classified into four forms: (1) early onset, (2) late onset, (3) familial or (4)

sporadic. Early onset AD (EOAD) refers to individuals with disease onset prior to the age of 65

years, whereas late onset AD (LOAD) typically starts after 65 years of age, with mid 70’s being

a common age of onset. Familial AD (FAD) is typically characterized in families that have

multiple affected individuals across generations due to mutations with have been identified.

Sporadic AD (SAD) however, is typically characterized by genetic risk factors rather than

causative mutations.

1.1.2 AD genetics

It was first proposed in 1992 that Aβ formation and aggregation initiates AD pathogenesis and

disease progression (Hardy & Higgins, 1992). It is argued that Aβ plaque formation leads to

hyper-phosphorylated and aggregated tau, and ensuing neurodegeneration and clinical

presentation of AD. This hypothesis is based on the findings that specific mutations within

amyloid precursor protein (APP) lead to autosomal dominant, aggressive forms of AD (Goate et

al., 1991). EOAD and FAD often follow an autosomal dominant Mendelian pattern of

inheritance, with mutations in APP, presenilin-1 (PS1), and presenilin-2 (PS2) being the most

common (reviewed by Tanzi, 2012). In general, mutations within APP, PS1, and PS2 are thought

to increase the amount of neurotoxic Aβ formed within the brain (Tanzi, 2012). Approximately

200 causative mutations within these three genes have been identified as causative for AD with

extremely high penetrance (Tanzi, 2012).

1.1.2.1 APP Mutations

Mutations within APP alter APP processing leading to a higher ratio of more toxic forms of Aβ

(i.e. Aβ42) relative to other Aβ isoforms (Kang et al., 1987). There have been over 32 identified

mutations within APP, accounting for approximately 10% - 15% of EOAD cases (Campion et

al., 1999). The age of onset of AD associated with these specific mutations is typically between

40 to 50 years of age, however cases of 65 years of age or older have also been reported. The

4

majority of APP mutations are missense mutations present in the secretase cleavage sites,

especially the APP transmembrane domain encoded by exons 16 and 17 (Figure 1.1), which

contain the Aβ peptide sequence (Tanzi, 2012). Examples of APP mutations include the Swedish

mutation (APPSWEDISH, APPK670N, and APPM671L) and the London mutation (APPLON, and

APPV717I), which lead to increased Aβ production and development of EOAD (Goate et al.,

1991; Tanzi, 2012).

5

Figure 1.1 APP exon map

Aβ domain is encoded by exon 16/17. Examples of APP mutations resulting in early onset AD include the Swedish mutation (missense mutation of 2 amino acids KM - NL) and the London mutation (missense mutation of 1 amino acid V - I)

6

1.1.2.2 PS1 Mutations

PS1 is located on chromosome 14q and acts as the catalytic component of the γ-secretase

complex. Currently, there are 185 known PS1 mutations, accounting for 30-70% of cases of

EOAD, making them the most common within the EOAD population (Cruts & Van

Broeckhoven, 1998; Campion et al., 1999; Rogaeva, Tandon, & St George-Hyslop, 2001; Lleó et

al., 2002; Janssen et al., 2003). Age of onset is typically 40 to 50 years of age, however cases as

early as 30 years of age and as late as 60 have been reported (Bird, 1993). PS1 mutations have

been reported as having 100% penetrance by age 65, and are typically associated with rapid

disease progression over 6-7 years, seizures, myoclonus, and language deficits (Fox et al., 1997;

Menéndez, 2004). Similar to APP mutations, mutations within PS1 exhibit preferential Aβ42

processing resulting in an increased Aβ42: Aβ40 ratio.

1.1.2.3 PS2 Mutations

PS2 is located on chromosome 1q and also gives rise to the active site of the γ-secretase

complex. Compared to mutations in APP and PS1, mutations within PS2 are the least common

within the EOAD population, accounting for less than 5% of cases (Bird et al., 1988; Finckh et

al., 2000; Marcon et al., 2004; Jayadev et al., 2010). Age of onset for PS2 mutations is less

defined than APP or PS1 mutations, ranging from 40 to 80 years. Disease duration is typically 11

years, with 90% penetrance (Jayadev et al., 2010). Mutations within PS2 result in variable forms

of AD, with variable disease severity.

1.1.2.4 Risk Factor Genes

The LOAD associated genes show much more complexity in terms of inheritance patterns and

disease progression than the EOAD genes. Following genome wide association studies (GWAS),

and meta-analyses by the AlzGene database, variations in ten genes have been strongly

associated with AD (Bertram et al., 2007; Scarpini, 2014). These genes include APOE, BIN1,

CD33, CLU, CR1, PICALM, ABCA7, MS4A6A, MS4A4E, and CD2AP (Bertram et al., 2007;

Scarpini, 2014). These genes have been proposed to contribute to some aspect of AD

pathogenesis, including Aβ-associated events, tau-phosphorylation, synaptic transmission,

7

immune function, and cholesterol metabolism (Scarpini, 2014). APOE, CLU, ABCA7, PICALM,

BIN1, CD2AP, CD33, and SORL1 have been proposed to affect Aβ processes such as

production, aggregation, and clearance (Scarpini, 2014). PICALM, BIN1, CD33, CD2AP, SORL1

have been implicated in synaptic transmission and function (Scarpini, 2014). BIN1 and PICALM

are associated with tau pathology, including microtubule stability, tau phosphorylation, and NFT

formation (Scarpini, 2014). SORL1 variants have also been confirmed as associated with AD risk

following GWAS (Lambert et al., 2013; Scarpini, 2014). SORL1 is thought to be associated with

Aβ and synaptic function (Scarpini, 2014). Recently, heterozygous loss-of-function in TREM2

has been associated with increased LOAD risk (Guerreiro et al., 2013; Jonsson et al., 2013). It is

hypothesized that TREM2 associated LOAD is due to dysfunctional inflammatory pathways

(Neumann & Daly, 2013).

Among these risk factor genes, APOE is arguably the most recognized gene associated with

LOAD risk. More specifically, it is thought that APOE polymorphisms are responsible for

approximately 50% of LOAD susceptibility (Blacker et al., 1997). The APOE ε4 allele in

particular is associated with increased risk of AD development (Blacker et al., 1997). The APOE

ε4 allele has been shown to have a gene-dose dependent effect on increased AD risk and severity

of disease (Corder et al., 1993). In reference to AD pathologies, it is thought that APOE ε4

impedes Aβ clearance from the brain while promoting Aβ deposition (Huang & Mucke, 2012).

Additionally, the APOE ε4 allele is thought to have toxic effects independent of Aβ in an AD

context (Huang & Mucke, 2012). According to the APOE proteolysis hypothesis, in times of

stress, neuronal APOE is cleaved with the intent of neuronal repair. In the case of the ε4 allele,

APOE cleavage leads to the formation of neurotoxic peptide fragments (Huang 2012).

1.1.3 Cellular & Molecular Mechanisms

1.1.3.1 Amyloid Beta and Tau

Glenner and Wong (1984) first identified and characterized Aβ peptides as the key component

making up the amyloid plaques described in AD. Glenner and Wong (1984) identified novel

amyloid peptides through isolation and HPLC analysis of the amyloid plaques in post-mortem

AD brains. Further characterization of the amyloid peptide revealed its small size (4.2kDa)

8

through SDS-PAGE and a novel 24 residue sequence (Glenner & Wong, 1984). Following the

identification of this novel AD peptide, Glenner and Wong (1984) continued to further

characterize the Aβ peptide in the context of Down’s Syndrome. They found that the amyloid

plaques in Down’s Syndrome are homologous to those seen in AD (Glenner & Wong, 1984).

Since trisomy 21 is responsible for Down’s Syndrome, it was this Aβ link between Down’s

Syndrome and AD that led researchers to investigate the role of chromosome 21 in AD, leading

to the identification of APP as the precursor protein to Aβ (Goldgaber, et al., 1987; Robakis et

al., 1987; Tanzi et al., 1987).

Since these initial discoveries, the structure and function of APP have been extensively

characterized. APP is a type 1 integral membrane protein, which gives rise to multiple isoforms

that are found in both neuronal and non-neuronal tissues (Dries & Yu, 2008). Within neurons,

the most common isoform is 695 residues (Kang & Müller-Hill, 1990). Beyond it’s role in AD,

APP has been shown to play a role in numerous processes including axonal transport,

transcription control, cell adhesion, apoptosis, cell proliferation, differentiation, neurite

outgrowth, and synaptogenesis (Dawkins & Small, 2014; Dries & Yu, 2008). Although the

precise mechanisms regulating APP functions have yet to be fully elucidated, several studies

have shown that proteolytic cleavage of APP can have a pronounced effect on its role in

development and AD.

In addition to the formation of Aβ plaques, NFTs are also a pathological hallmark of AD. Tau is

present in both neurons and glia, and under non-disease conditions tau binds tubulin, stabilizing

microtubules. In AD however, tau becomes hyper-phosphorylated, resulting in tau dissociation

from microtubules and subsequent aggregate formation (Kosik et al., 1986). Unlike Aβ, tau

aggregates form intracellularly within neuronal cell bodies. Similar to Aβ plaque formation, tau

aggregates also result in neuronal dysfunction and disease progression. The formation of these

NFTs results in microtubule destabilization and subsequent disruption of axonal transport and

axon degeneration (reviewed by Perl, 2010). Aβ plaques and NFTs are associated with neuronal

and synaptic loss, brain atrophy, regional hypometabolism, altered brain activation, network

dysfunction, inflammation, and oxidative stress (reviewed by Holtzman, 2011). Furthermore,

substantial loss of neuronal network connections in AD was shown by Masliah et al (1989), who

9

found a 50% loss of synaptic density in the parietal, temporal, and midfrontal cortices in port-

mortem AD brains compared to normal controls. Additionally, Aβ plaques have been associated

with immune cell recruitment and activation (reviewed by Serrano-Pozzo et al., 2011). More

specifically, proliferation of astrocytes and mircoglia occurs, contributing to a substantial

inflammatory response that has been shown to increase brain injury load (reviewed by Serrano-

Pozzo et al., 2011). In fact, neuroinflammation involving the innate immune system is now

thought to significantly contribute to AD development and progression (reviewed by Heppner et

al., 2015). It is thought that the presence Aβ aggregates results in a chronic inflammatory

response, with continued production of cytokines and chemokines, leading to microglia

impairment, neurodegeneration, and neuronal loss (reviewed by Heppner et al., 2015). It is also

hypothesized that chronic neuroinflammation can contribute to tau pathology (Heppner et al.,

2015). As AD progresses from milder to more severe stages, NFT formation increases along with

neuronal dysfunction, inflammation, cell death, and extensive brain atrophy (Serrano-Pozzo et

al., 2011).

In the case of AD, APP undergoes amyloidogenic cleavage by β-secretase and γ-secretase,

producing the Aβ peptides associated with AD pathology. However, APP can be cleaved by

either the non-amyloidogenic or amyloidogenic pathway, depending on which secretase initiates

cleavage.

1.1.3.1.1 Non-amyloidogenic pathway

In the case of the non-amyloidogenic pathway, α-secretase initiates the first proteolytic cleavage

of APP to produce soluble APPα (sAPPα) and a C-terminal fragment, C83 (Figure 1.2 A).

sAPPα is released into the extracellular space and C83 remains anchored in the membrane.

sAPPα is thought to be neuroprotective and has been implicated in synaptogenesis, neurite

outgrowth, and neuronal survival (Chow et al., 2010). γ-secretase then cleaves C83 within the

lipid bilayer of the membrane, releasing P3 into the extracellular space and APP-intracellular-

domain (AICD) into the intracellular space (Figure 1.2 A). AICD has been implicated in nuclear

signalling, transcriptional regulation, and axon transport (Chow et al., 2010). P3 function

however, has not been clearly defined (Chow et al., 2010).

10

1.1.3.1.2 Amyloidogenic pathway

As the name suggests, the amyloidogenic pathway gives rise to amyloid peptides and therefore is

the key APP pathway related to AD. In the amyloidogenic pathway, β-secretase is the first

enzyme to cleave APP, releasing soluble APPβ (sAPPβ) into the extra-cellular space, and leaving

the C-terminal fragment, C99, in the membrane (Figure 1.2 B). Unlike sAPPα, sAPPβ lacks

neuroprotective effects and has been suggested to function in synapse pruning during

development and stem-cell differentiation for glia, however these functions are poorly

understood (Chow et al., 2010). Following β-secretase cleavage of APP, γ-secretase cleaves C99

within the lipid bilayer of the membrane, similar to the non-amyloidogenic pathway releasing

AICD into the intracellular space (Figure 1.2 B). However, in the amyloidogenic pathway, an Aβ

peptide is released into extracellular space. It is this sequential cleavage of APP by β-secretase

and γ-secretase that yields an Aβ peptide.

The most commonly generated Aβ peptide is Aβ40 (40 amino acids in length), however Aβ

peptide length can range from 38 to 43 amino acids long. The shorter forms of Aβ, including

Aβ40, were originally considered non-toxic and not thought to aggregate (Burdick et al., 1992),

but have since been shown to form tetramers (Bernstein et al., 2009). Generally, longer Aβ

peptides, particularly Aβ42, are more prone to self aggregate, forming Aβ fibrils and oligomers,

and are more concentrated in the amyloid plaques seen in AD (Burdick et al., 1992; reviewed by

Serrano-Pozzo et al., 2011). In a normal brain, approximately 90% of Aβ peptides are the shorter

forms of Aβ40, whereas 5-10% are the longer forms of Aβ42 (Dries & Yu, 2008). In a brain

affected by AD, the relative percentage of Aβ42 increases. Therefore, the Aβ42: Aβ40 ratio is often

used as a diagnostic tool for disease prognosis and progression (Hansson et al., 2007; Lewczuk

et al., 2015). Aβ also aggregates in cerebral blood vessel walls, referred to cerebrovascular

plaques or cerebral amyloid angiopathy (CAA) in approximately 90% of AD patients (reviewed

by Kehoe & Wilcock, 2007; Holtzman, 2011). In severe cases, Aβ accumulation can result in

weakening of the vessel walls and subsequent haemorrhages (Serrano-Pozzo et al., 2011).

11

Figure 1.2 Non-amyloidogenic pathway versus amyloidogenic pathway

(A) Non-amyloidogenic pathway, APP is sequentially cleaved by α-sectretase and γ-secretase producing sAPPα and C83, and P3 and AICD, respectively. (B) Amyloidogenic pathway, APP is sequentially cleaved by β-secretase and γ-secretase producing sAPPβ and C99, and Aβ and AICD, respectively. The amyloidogenic pathway can give rise to the neurotoxic Aβ peptides capable of aggregating, forming the Aβ plaques seen in AD.

12

1.1.3.1.3 The γ-Secretase Complex

The γ-secretase complex is the final enzyme involved in Aβ formation, releasing the Aβ peptide

into the extracellular space. However, the C-terminal fragment of APP is not the only γ-secretase

substrate. The γ-secretase complex has many diverse targets and plays roles in numerous cellular

processes that regulate transcriptional activity and regulation, signal transduction, regulation of

cell proliferation, cell adhesion and cell survival (Parks & Curtis, 2007). Moreover, γ-secretase

has over 25 substrates, including the Notch receptor and the Notch ligands Delta and

Serrate/Jagged (Parks & Curtis, 2007). The vast majority of γ-secretase substrates are type 1

transmembrane proteins that have previously been cleaved, such as C99 (Parks & Curtis, 2007).

γ-secretase cleavage occurs at two distinct sites, the ε- or S3 cleavage, and the γ- or S4 cleavage.

ε-cleavage occurs at or near the junction of the transmembrane domain and the intracellular

domain, releasing the substrate’s intracellular domain. γ-cleavage however, occurs in the middle

of the substrate transmembrane domain and amino terminal of the ε-cleavage site, releasing the

ectodomain portion into the extracellular space (Parks & Curtis, 2007)

The γ-secretase complex is composed of four components necessary for catalytic function: PS1

(Sherrington et al., 1995) or PS2 (Rogaev et al., 1995), nicastrin (NCT; Yu et al., 2000), anterior

pharynx defective 1 (APH1; Francis et al., 2002), and presenilin enhancer 2 (PEN2; Francis et

al., 2002) (Figure 1.3). PS is responsible for the catalytic activity of γ-secretase, cleaving type 1

transmembrane proteins (Wolfe et al., 1999). PS1 and PS2 were first identified following genetic

studies linking AD to PS mutations on chromosome 1 (Levy-Lahad et al., 1995; Rogaev et al.,

1995; Sherrington et al., 1995) and chromosome 14 (Sherrington et al., 1995). Currently, there is

debate over the precise structure of PS, however there is agreement that it has a transmembrane

barrel-like structure with a central aqueous space (St George-Hyslop & Fraser, 2012) (Figure 2).

The catalytic aspartic acid residues responsible for γ-secretase aspartyl protease activity are

located at the 6th and 7th transmembrane domains (Wolfe et al., 1999).

The remaining three components of the γ-secretase complex were discovered following Notch

based C. elegans genetic screens and are required for PS function, however they do not possess

13

proteolytic activity on their own. NCT is a large transmembrane glycoprotein, encompassing a

large N-terminal extracellular domain and a shorter C-terminal intracellular portion (Yu et al.,

2000). NCT has been shown to interact specifically and directly with PS, including PS mutants

(Yu et al., 2000). Following co-immunoprecipitation experiments of C83 and C99 with NCT, it

was suggested that NCT functions as a substrate receptor for the γ-secretase complex (Dries &

Yu, 2008; Yu et al., 2000). APH1 is a seven-pass transmembrane protein that associates with

both NCT and PS (Lee et al., 2002; Goutte et al., 2002). APH1 is thought to function in PS

processing, in addition to stabilization and trafficking of the mature γ-secretase complex (Dries

& Yu, 2008). Finally, PEN2 is the regulator of PS activity and enhances γ-secretase activity

(Fraering et al., 2004; Luo et al., 2003; Prokop et al., 2004).

14

Figure 1.3 γ-secretase complex components

(A) PEN2 (red), PS (blue), NCT (green), and APH1 (purple). PEN2 is responsible for PS endoproteolysis and γ-secretase enhancement. PS contains the active aspartyl protease of the complex (indicated by the stars at TMD 6 and 7) responsible for substrate cleavage. NCT is responsible for substrate recognition and acts as the complex’s substrate receptor. APH1 is required for complex stabilization and trafficking of the mature complex (B). All four components of the γ-secretase complex are responsible for function and activity of the complex.

15

The γ-secretase complex is assembled in a step-wise manner, beginning with APH1 and

immature NCT (LaVoie & Selkoe, 2003; Shirotani et al., 2004) or PS association within the

endoplasmic reticulum of the cell (Fraering et al., 2004; Luo et al., 2003; Prokop et al., 2004;

Steiner et al., 2002). Subsequently, APH1, immature NCT, and PS associate with PEN2,

resulting in processing of PS into its N- and C-terminal fragments, and transfer to the cis-Golgi

(Lee et al., 2002). The final step in producing an active γ-secretase complex is maturation of the

immature NCT in the trans-Golgi (Prokop et al., 2004). Following this assembly process, an

active form of the γ-secretase complex is produced, cycling between the endoplasmic reticulum,

the Golgi, and the plasma membrane (Dries & Yu, 2008).

1.1.4 Current AD Standard of Care

According to the National Institute on Aging at the National Institute of Health (U.S. Department

of Health and Human Services, 2016), there are no therapeutics that cure AD. Current standard

of care for AD patients include cholinesterase inhibitors (CI) and memantine to treat cognitive

symptoms such as memory loss, with short-term benefits (U.S. Department of Health and Human

Services, 2016). CI are prescribed to treat mild – moderate AD and can delay disease progression

and control behavioural symptoms (U.S. Department of Health and Human Services, 2016). CI

treatment is based on the cholinergic hypothesis of AD that states deficiencies in cholinergic

transmission are related to cognitive decline (Knowles, 2006). CI increases acetylcholine

neurotransmission (Knowles, 2006). Memory improvements with CI are modest, with delayed

worsening of symptoms for 6 – 12 months in about half of prescribed patients (reviewed by

Ballard, 2010).

In moderate to severe stages of AD, memantine, an NMDA receptor antagonist, is prescribed to

delay disease progression and allow patients prolonged independent daily functions (U.S.

Department of Health and Human Services, 2016). Patients on memantine show cognitive

improvements over 6 months (reviewed by Ballard, 2010). Memantine regulates glutamate

activity and is prescribed to treat the cognitive symptoms of AD such as memory and learning

deficits (Scarpini, 2014; U.S. Department of Health and Human Services, 2016). When used in

combination with CI, moderate to severe AD patients on memantine experience temporary

improvements in cognition (Lopez et al., 2009). Memantine does not prevent disease progression

16

and eventual symptomatic decline (Scarpini, 2014; U.S. Department of Health and Human

Services, 2016; Ballard, 2010).

1.2 The Link Between ACE and AD

In humans, ACE is a zinc metalloprotease of the renin-angiotensin system (RAS) located on

chromosome 17q23 and is 21kB in length (Coates, 2003). Two isoforms of human ACE exist,

the germinal, sperm specific, ACE (gACE) and the classic somatic ACE (sACE). Both ACE

isoforms are encoded by the same gene, but arise from different promoters, making sACE larger

(1306 residues) than gACE (732 residues) (Coates, 2003). sACE is a translated tandem

duplication of gACE. Therefore, sACE and gACE have distinct active site characteristics, with

sACE containing two catalytically active sites whereas gACE only possesses one (Coates, 2003).

Furthermore, gACE and sACE have different physiological roles. gACE is thought to play an

important role in the ability of sperm to successfully fertilize the oocyte (Hagaman et al., 1998),

whereas sACE (from this point forward referred to as simply ACE) functions in blood pressure

homeostasis through RAS. More specifically, in the classical view of RAS, ACE is responsible

for cleavage of angiotensin I to angiotensin II, which then binds the angiotensin II receptor type

1 (AT1 or ATR) (Coates, 2003). Following this sequence of events, the following physiological

events are initiated to increase blood pressure: increased heart hypertrophy and fibrosis,

increased vessel vasoconstriction and inflammation, increased sympathetic nervous system

activity, and increased sodium reabsorption, aldosterone effects, and vasoconstriction in the

kidneys (Figure 1.4). Interestingly, expressed sequence tag analysis of ACE has revealed strong

expression in the thymus, brain, skeletal muscle, prostate, and kidney and DNA array analysis

has identified ACE mRNA as being ubiquitously expressed (Coates, 2003).

17

Angiotensin

Angiotensin I

Angiotensin II

AT-1 Receptor

ACE Inhibitor

Angiotensin Receptor Blocker

Renin

ACE

Vessels: ñVasoconstriction ñInflammation

Heart: ñHypertrophy

ñFibrosis

CNS: ñSympathetic Activity

Kidneys: ñSodium reabsorption ñAldosterone effects ñVasoconstriction

Figure 1.4 The renin-angiotensin system and it’s physiological role in blood pressure

Angiotensin is cleaved by renin to angiotensin I, which is cleaved by ACE to angtiotensin II, which binds the AT-1 receptor, promoting downstream physiological effects, thereby increasing blood pressure. ACE-I act at the level of ACE, inhibiting ACE cleavage of angiotensin I to angiotensin II, thereby preventing increases in blood pressure. ARBs act at the level of angiotensin II binding the AT-1 reception, inhibiting angiotensin II binding the ATR, thereby preventing increases in blood pressure. ACE-I and ARBs are commonly prescribed to hypertensive patients to lower blood pressure.

18

Beyond the classic physiological role of ACE in relation to blood pressure, it is thought that each

organ system has it’s own locally acting RAS. Brain RAS functions are largely unrelated to

circulatory RAS. Downstream peptides of the AT1 receptor in brain RAS have been implicated

in many different neurobiological functions, including thirst (Wright & Harding, 2013), neuronal

regeneration and injury (Thöne-Reineke et al., 2006) and learning and memory (von Bohlen et

al., 2006).

Hypertension has also been implicated in AD (reviewed by Skoog & Gustafson, 2006). Multiple

studies have shown that increases in either systolic or diastolic blood pressure occurs decades

before AD onset (Skoog et al., 1996; Launer et al., 2000; Kivipelto et al., 2001; Qiu et al., 2003;

Wu et al., 2003; Luchsinger et al., 2005), and that increases in blood pressure are associated with

a worsening cognitive decline in AD (Bellew et al., 2004). Hypertensive patients also showed

decreased brain weight and increased plaque and NFT formation (Petrovitch et al., 2000; Sparks

et al., 1995; Beach et al., 2007). Changes in the arterial system have been associated with

cognitive impairment in AD (Hanon et al., 2005) and increased Aβ load (Hughes et al., 2014). In

humans, several studies have shown an association between hypertension or ischemia and Aβ

accumulation (Toledo et al., 2012; Langbuam et al., 2012; Rodrigue et al., 2013). Furthermore,

multiple animal studies have demonstrated that ischemia can lead to Aβ accumulation (Hall et

al., 1995, Bennett et al., 2000, Kalaria et al., 1993) and can influence PS expression

(Pennypacker et al., 1999; Tanimukai et al., 1998).

It is hypothesized that hypertension is implicated in AD through cerebrovascular disease,

particularly white matter lesions, changes in cerebral blood flow, and blood brain barrier

deterioration (Skoog & Gustafson, 2006; Nation et al., 2012). White matter lesions are highly

associated with AD and hypertension, and correlate with AD severity (Petrovitch et al., 2005;

Ble et al., 2006). Cerebral blood flow has also been shown to decrease in both AD and

hypertension, disrupting brain metabolism (Girouard et al., 2006). Finally, both hypertension and

AD have implications in blood brain barrier (BBB) deterioration (Johansson, 1984; Nag, 1984;

Shah & Mooradiah, 1997; Wisniewski et al., 1982; Elovaara et al., 1985; Blennow et al., 1990;

Skoog et al., 1998). Given these associations between hypertension and AD, it is not surprising

that ACE has been associated with aging and AD.

19

ACE was first suggested to play a role in longevity following a genetic association study in

centenarians. Schächter et al (1994) studied the association between longevity and characterized

candidate genes related to cardiovascular disease (i.e. APOE and ACE). The APOE ε4 allele has

been implicated in ischemic heart disease and the ACE deletion polymorphism is a risk factor for

myocardial infarction. The ACE insertion/deletion polymorphism (ID) is a 287 base pair

insertion/deletion at intron 16 of the ACE gene (Alvarez et al., 1999). The deletion

polymorphism is associated with higher ACE plasma levels, whereas the ACE insertion

polymorphism is associated with lower ACE plasma. Interestingly, the ACE genotype

distribution was shifted towards the homozygous deletion (DD) genotype in centenarians;

suggesting increases in ACE plasma are related to longevity in humans (Schächter et al., 1994).

Despite the DD genotype increasing ACE levels, it appeared to have a protective effect for

longevity. The authors suggest that one of ACE’s alternate biological roles may be in longevity,

such that an increase in brain ACE may provide an adaptive response to brain aging, proposed

through cleavage of neuropeptides or through immune modulation in the CNS (Schächter et al.,

1994).

Following this suspected link between brain aging and ACE polymorphisms, it was hypothesized

that ACE may be implicated in AD, and that the DD genotype may have a protective effect

(Kehoe et al., 1999). Furthermore, Kehoe et al (1999) hypothesized that the deletion allele may

protect against AD development, and the insertion allele may be associated with an increased

AD risk. They found an increase in genetic distribution in the ID and II genotypes in AD

patients, independent of APOE (Kehoe et al., 1999). Since these initial studies, there have been

numerous findings suggesting that there is a link between RAS and AD. There have been

numerous genetic polymorphism studies similar to that conducted by Kehoe et al (1999) in

multiple populations, showing similar links between the different ACE alleles and AD risk

(Alvarez et al., 1999; Belbin et al., 2013; Edwards et al., 2009; Kauwe et al., 2014; Miners et al.,

2009). Given this apparent link between AD and RAS, the effects of RAS targeting drugs on the

incidence and outcomes of AD were studied. More specifically, given the link between different

ACE polymorphisms and AD development, researchers began to investigate whether there was a

link between the use of ACE inhibitors (ACE-I) or other anti-hypertensive drugs, such as AT-1

receptor blockers (ARB), and AD.

20

1.2.1 The Use of ACE-I and ARB in Humans

Several studies have suggested that ACE-I and ARB might be useful in AD (summarized in

Table 1.1). ACE-I and ARBs have been associated with a decreased incidence in AD (Ohrui et

al., 2004, Davies et al., 2011, Qiu et al., 2013, Yasar et al., 2013) and delayed cognitive decline

in patients with mild-moderate AD (Ohrui et al., 2004, Hajjar et al., 2005, Soto et al., 2013, de

Oliveira et al., 2014, O’Camoimh et al., 2014). Ohrui et al (2004) was one of the first groups to

show an association between lower AD incidence and ACE-I. They studied hypertensive patients

prescribed anti-hypertensive drugs (i.e. ACE-I, beta-blockers, diuretics, and calcium channel

blockers) over a ten-year period and found that brain penetrating ACE-I were associated with a

lower AD incidence compared to other anti-hypertensive drugs, including non-brain penetrating

ACE-I. Another study published in 2004 by the same group tested the effects of ACE-I on

cognitive impairment. Treatment with brain penetrating ACE-I slowed cognitive decline in AD

patients with mild to moderate symptoms, measured by the Mini Mental State Examination

(MMSE), compared to other anti-hypertensive drugs (Ohrui et al., 2004). In both studies, there

were no differences in blood-pressure levels between anti-hypertensive drug used, despite

changes in cognitive outcomes (Ohrui et al., 2004). Consequently, the cognitive benefits seen in

the brain-penetrating ACE-I group are suggested to be independent of blood pressure.

Since 2004, numerous studies have investigated the apparent link between ACE-I and cognitive

decline in the elderly and in AD. Similar to the findings of Ohrui et al (2004), ACE-I protected

against cognitive decline in an elderly population with mild cognitive impairment (MCI)

following a multivariate logistic regression analysis for predictors of memory decline (Rozzini et

al., 2006). Here, beta-blockers and calcium channel inhibitors did not affect cognitive decline,

again suggesting that ACE-I are more associated with a lower risk in cognitive decline (Rozzini

et al., 2006). In a 2005 longitudinal analysis of medical records by Hajjar et al, ACE-I, ARBs,

and beta-blockers showed a decreased risk of cognitive decline in an elderly population

compared to other anti-hypertensive drugs. This 2005 study reported novel findings that ARBs

improved cognitive scores measured by the MMSE and clock drawing test (CDT), suggesting

that ARBs may lead to improved cognitive outcomes in an elderly population (Hajjar et al.,

2005). This is further supported by findings obtained by Davies et al (2011), who suggested that

21

ARBs might be superior to ACE-I in an AD context. More specifically, in a case-control study,

patients on ARBs showed a 53% decrease in AD incidence compared to other anti-hypertensive

drugs, whereas ACE-I showed a 24% decrease compared to other anti-hypertensive drugs.

Replicating the findings of Davies et al (2011), ACE-I, ARBs, and diuretics were associated with

a 40 – 50% decreased risk in AD development in a community dwelling elderly population

following a longitudinal post-hoc analysis of a randomized controlled trail (Yasar et al., 2013).

In this study however, ACE-I and ARBs were only associated with a decreased AD risk in

participants with normal baseline cognition (Yasar et al., 2013). Similarly, Soto and colleagues

(2013) found that both continual and intermittent use of ACE-I significantly slowed cognitive

decline in mild to moderate AD patients over a 4-year period measured by the MMSE.

Interestingly, cognitive decline ratings were not significantly different until 2 years after

continuous or intermittent treatment, suggesting that long term treatment may be necessary to

observe cognitive outcomes (Soto et al., 2013). This was supported by a study conducted by

O’Caoimh et al (2014), which looked at AD progression in mild to moderate presenting patients

on different ACE-I. Data used for this study was collected from a previous multi-center, blind

randomized trial studying Doxycycline and Rifampin for AD treatment. At the 12-month follow

up period, patients on centrally acting ACE-I had a 25% reduction in cognitive decline according

to the activities of daily living (ADL) scale and a 20% reduction in decline according to the

Quick MCI screen. It is of note that there were no differences in blood pressure among the two

ACE-I groups and therefore the reduction in cognitive decline seen with centrally acting ACE-I

is again thought to be independent of blood pressure (O’Caoimh et al., 2014). De Olivera et al

(2014) found that ACE-I slowed cognitive decline in AD patients with ACE haplotypes that

increase ACE plasma levels and enzyme activity (rs1800764 and rs4291). These polymorphisms

have previously been reported as risk factors for AD, independent of APOE status or gender

(Kehoe et al., 2004). De Oliveria et al (2014) reported that AD patients with the haplotype

rs1800764: rs4291 responded beneficially to ACE-I treatment, significantly slowing cognitive

decline.

Despite the reported positive effects associated with angiotensin targeting drugs and AD, there

are inconsistencies in the literature. Jochemsen et al (2014) found an association between low

ACE serum levels and low Aβ cerebral spinal fluid (CSF) levels. Since low CSF Aβ levels are

22

correlated with high cerebral Aβ, it was hypothesized that decreases in ACE result in more

cerebral Aβ deposition. Therefore, the association between low ACE and low CSF Aβ suggests

that ACE is involved in Aβ degradation (Jochemsen et al., 2014). The authors also reported that

low ACE levels are also associated with low CSF tau. The same group also reported an

association between high ACE activity and lower rates of brain atrophy in AD patients

(Jochemsen et al., 2015). These studies suggest that the use of ACE-I would be detrimental in an

AD context. Kehoe et al (2013) found that ACE-I exposure was related to an increased mortality

rate in a clinical prognostic cohort study of AD patients on anti-hypertensive medications. ACE-I

were associated with a higher mortality rate than any other anti-hypertensive drug (Kehoe et al.,

2013). However, it should be noted that APOE status in this study was not identified. ARBs were

found to have an opposite effect, with an 18% decrease in mortality rate and 16% decrease in

hospitalization likelihood compared to those on ACE-I, suggesting that ARBs may be more

beneficial in AD (Kehoe et al., 2013. However, the authors discuss that more work needs to be

done to elucidate these findings, such as a randomized clinical trail.

Interestingly, the beneficial effects of ACE-I appear to be APOE specific. In the absence of the

APOE ε4 allele, patients on either central or peripheral acting ACE-I experienced a lower

incidence of AD (Qiu et al., 2013). However, this association is no longer present in patients

with the APOE ε4 allele (Qiu et al., 2013). In a follow up cross sectional analysis by the same

group, an association between the APOE ε4 allele and ACE-I use was reported (Qiu et al., 2014).

In this study, it was found that in the presence of the APOE ε4 allele, ACE-I increased the risk of

AD development (Qiu et al., 2014). These studies suggest the presence of an interaction between

ACE-I and APOE and that there may be specific genetic links underlying the successful use of

ACE-I in AD. Furthermore, the association between ACE-I and specific APOE allele could help

explain the inconsistencies reported in the literature.

Interestingly, several studies have compared the differences between ACE-I and ARB in an AD

context. In a large brain autopsy study by Hajjar et al (2012), it was shown that patients

prescribed ARBs had less AD related pathology (i.e. neuritic plaque and neurofibrillary tangle

densities) compared to other anti-hypertensive treatments, including ACE-I, in both non-AD and

AD patients. Therefore, this study suggests that ARB use is associated with less Aβ

23

accumulation (Hajjar et al., 2012). Nation and colleagues (2016) demonstrated similar findings,

showing that ARB use is associated with higher CSF Aβ levels, suggesting that ARBs are linked

to Aβ clearance. Nation et al (2016) also demonstrated that ARBs were associated with a

decrease in cognitive decline. Furthermore, ARBs have also been associated with a lower AD

incidence and rate of cognitive decline than other anti-hypertensive treatments, including ACE-I

(Davies et al., 2011; Li et al., 2010).

Altogether these studies suggest that ACE-I, and especially ARBs, delay cognitive decline and

reduce AD incidence. These findings have been replicated numerous times by many different

groups, and multiple studies have suggested that positive effects seen are independent of blood

pressure. Despite these rather consistent findings, negative effects of ACE-I have also been

reported. The human studies on ACE-I and ARBs in AD has inspired researchers to further

explore this link utilizing in vitro and in vivo model systems.

24

Table 1.1 Summary Cognitive Outcomes following ACE-I/ARB use in Humans

Aut

hors

St

udy

Type

O

utco

mes

Ohr

ui e

t al.,

200

4 Pr

ospe

ctiv

e ra

ndom

ized

par

alle

l gro

up c

ohor

t tria

l Sl

owed

cog

nitiv

e de

clin

e in

pat

ient

s with

mild

– m

oder

ate

AD

inde

pend

ent o

f BP

Haj

jar e

t al.,

200

5 Lo

ngitu

dina

l R

educ

tion

in c

ogni

tive

impa

irmen

t in

AD

(AC

E-I,

AR

B, a

nd

beta

blo

cker

s)

Dav

ies e

t al.,

201

1 C

ase

cont

rol

53%

dec

reas

ed A

D in

cide

nce

(AR

B)

24%

dec

reas

ed A

D in

cide

nce

(AC

E-I)

Qiu

et a

l., 2

013/

Q

iu e

t al.,

201

4 C

ross

sect

iona

l stu

dy (f

rom

long

itudi

nal d

ata)

D

ecre

ased

AD

inci

denc

e in

abs

ence

of A

POE ε4

alle

le

Incr

ease

d A

D in

cide

nce

in p

rese

nce

of A

POE ε4

alle

le

Soto

et a

l., 2

013

Pros

pect

ive

coho

rt st

udy

Slow

ed c

ogni

tive

decl

ine

in m

ild –

mod

erat

e AD

pat

ient

s ov

er a

4-y

ear p

erio

d

Yasa

r et a

l., 2

013

Seco

ndar

y lo

ngitu

dina

l 40

-50%

dec

reas

e in

AD

risk

in p

artic

ipan

ts w

ith n

orm

al

base

line

cogn

ition

(AC

E-I a

nd A

RB

)

Keh

oe e

t al.,

201

3 Pr

ogno

stic

coh

ort s

tudy

In

crea

se in

mor

talit

y ra

te

No

chan

ge in

hos

pita

lizat

ion

de O

livei

ra e

t al.,

20

14

Inve

stig

atio

nal

Slow

ed c

ogni

tive

decl

ine

in A

D p

atie

nts w

ith th

e rs

1800

764:

rs42

91 A

CE

hapl

otyp

e

O’C

amoi

mh

et a

l.,

2014

Fo

llow

-up

of a

blin

d ra

ndom

ized

con

trol t

rial

25%

dec

reas

e in

cog

nitiv

e de

clin

e in

mild

– m

oder

ate A

D

Haj

jar e

t al.,

201

2 O

bser

vatio

nal –

bra

in a

utop

sy se

ries

Few

er Aβ

depo

sits

and

AD

rela

ted

path

olog

y (A

RB

)

Nat

ion

et a

l., 2

016

Cro

ss se

ctio

nal a

nd lo

ngitu

dina

l ana

lyse

s H

ighe

r Aβ

CSF

leve

ls/ b

rain

Aβ

clea

ranc

e (A

RB

) D

ecre

ases

AD

inci

denc

e an

d co

gniti

ve d

eclin

e (A

RB

)

Li e

t al.,

201

0 Pr

ospe

ctiv

e co

hort

stud

y Lo

wer

inci

denc

e an

d pr

ogre

ssio

n of

AD

(AR

B)

25

1.2.2 In vitro Analysis of ACE and Aβ

In vitro analyses have reported that ACE degrades Aβ peptides and prevents peptide aggregation.

Multiple groups have shown that purified ACE inhibits Aβ aggregation, and the addition of an

ACE-I induces an aggregation phenotype (Hemming & Selkoe, 2005; Kehoe et al., 1999; Liu et

al., 2014; Zou et al., 2007). These data support the ACE polymorphism data, suggesting that an

increase in ACE activity is protective against AD. Purified human ACE was shown to convert

toxic Aβ42 to the less toxic Aβ40, decreasing the Aβ42: Aβ40 ratio in media, in addition to

degrading both Aβ42 and Aβ40 (Kehoe et al., 1999). Similarly, Hemming et al (2005) reported

that neuroblastoma cloned ACE degrades both Aβ40 and Aβ42, promoting Aβ clearance in APP-

expressing cellular media. The use of an ACE-I on APP-expressing cellular media containing

ACE induces Aβ accumulation (Hemming et al., 2005). In support of these findings, another

group also found that ACE and ACE2 converted neurotoxic forms of Aβ (e.g. Aβ43 and Aβ42) to

less toxic forms (e.g. Aβ40) in mouse brain lysates (Zou et al., 2007). More recently, Liu et al

(2014) reported that the use of an ACE-I impedes the conversion of longer Aβ peptides to shorter

forms by ACE in mouse brain lysates. These in vitro studies show that ACE may be involved in

Aβ degradation, conversion, and clearance, suggesting that ACE-I would be detrimental in AD.

However, the current literature suggests that ACE-I and ARBs are beneficial in AD cognitive

outcomes and therefore the beneficial effects seen in AD patients following ACE-I or ARB

administration remains unclear.

1.2.3 In vivo Analysis of ACE and AD Models

In vivo studies have aimed to gain an understanding of why ACE-I and ARBs may be beneficial

in AD, despite ACE’s Aβ degradation and clearance abilities observed in vitro. In vivo studies

researching the link between AD and ACE have been inconsistent. Some studies have reported

that ACE-I are detrimental in AD models, while others report beneficial effects. Zou et al (2007)

reported that ACE-I treatment in an AD transgenic mouse model containing the human APP

Swedish mutation (Tg2576) promotes Aβ42 deposition. Long-term ACE-I treatment of 17-

month-old mice (administered at 6 months and treated for 11 months) resulted in a >2.5 fold

increase in neocortex and hippocampal Aβ42 deposits but unchanged Aβ40 (Zou et al., 2007).

26

These data supports the notion that ACE activity is related to a reduction in Aβ42 aggregation, as

suggested by the in vitro studies. When investigating the effects of ACE-I or ARBs in mouse

models with either high or low Aβ burdens, no differences in Aβ peptide levels or plaque

deposits were detected, suggesting that these drugs have little to no effect on plaque deposition

(Hemming et al., 2007). It should be noted that brain ACE activity in this particular study

showed limited inhibition at the administered concentrations, with the highest concentration

showing only a 28% decrease in brain ACE activity (Hemming et al., 2007). To further

investigate ACE’s apparent role in Aβ clearance, Bernstein et al (2014) tested the effects of ACE

overexpression in myelomonocytes of an APP Swedish mutant mouse model of AD. These

AD/ACE overexpression mice showed reductions in soluble and insoluble forms of brain Aβ42

and improved behavioural outcomes, such that 11 – 12 month old AD/ACE overexpression mice

were cognitively equivalent to wild type mice. It is interesting to note that heterozygous ACE

overexpression mice performed cognitively equivalent to both homozygous ACE overexpression

and wild type mice, suggesting that the heterozygous state of ACE overexpression is sufficient in

ameliorating AD related phenotypes. ACE overexpression in myelomonocytes also decreased

overall cerebral inflammation in the AD mice, suggesting a link between ACE and the

neuroinflammatory response in AD (Bernstein et al., 2014). The authors also tested the effects of

ACE-I on Aβ in these mice and found that ACE-I administration increased Aβ peptide levels.

Cognitive outcomes following ACE-I treatment in this mouse model were not tested. Altogether,

these data support the hypothesis that ACE leads to a reduction in Aβ deposition, but does not

explain why ACE-I appear to improve cognition in AD patients. In fact, these results

contradicted the findings that ACE-I improved cognitive outcomes in AD.

Despite the discussed studies showing detrimental effects of RAS intervention in AD mouse

models, there is also substantial evidence supporting the hypothesis that ACE-I and ARBs are

beneficial in AD. In a 2009 study, ACE-I treatment of a lipopolysaccharide (LPS) mouse model

of AD ameliorated cognitive decline (El Sayed, Kassem, & Heikal, 2009). LPS injected mice

treated with ACE-I performed significantly better on Y-maze and open field tasks than untreated

LPS mice. Furthermore, working and short-term memory impairments were reversed with brain-

penetrating ACE-I administration in mice injected with Aβ25-35, compared to non-brain-

penetrating ACE-I (Yamada et al., 2010). In fact, non-brain-penetrating ACE-I did not improve

27

cognitive impairments (Yamada et al., 2010). This study reduced brain ACE activity to a greater

extent with ACE-I than Hemming et al (2007). Following 10 days of treatment, brain penetrating

ACE-I reduced brain ACE activity to less than 50%, whereas non-brain penetrating ACE-I only

reduced brain ACE activity to 70-80%. This suggests that brain ACE activity must be

significantly reduced to see cognitive effects in AD models. ACE-I prevention of cognitive

decline has also been shown to occur without affecting Aβ plaque deposition (Dong et al., 2011).

Dong et al (2011) demonstrated that acute administration of brain penetrating ACE-I improves

cognitive performance in two AD mouse models (Aβ1-40 ICV and APPSWEDISH /PS2 mutant

transgenic models), despite unchanging Aβ levels. This result may be due to the acute treatment

of the ACE-I (24 days), beginning in young mice (3 months old), compared to other studies,

where treatment begins in older adults (6 – 12 months old) and for longer periods of time (2 – 6

month treatment). In a study conducted by Abdalla et al (2013), AD mouse models were

administered brain-penetrating ACE-I at 12 months of age, when Aβ plaque formation begins,

for 6 months. Mice treated for 6 months showed significantly less Aβ deposits in the

hippocampus compared to untreated mice (Abdalla et al., 2013). ACE-I treatment also

significantly decreased β-secretase activity (by 56%), γ-secretase activity (by 53%), sAPPβ, and

AICD production, suggesting that ACE-I may play a direct role in Aβ production (Abdalla et al.,

2013). Microarray analysis of hippocampal genes following ACE-I treatment showed

significantly modified gene expression (Abdalla et al., 2013). Gene expression profiles of 18 –

month old AD mouse models with a high Aβ load showed significant changes in hippocampal

gene expression compared to 12 – month old AD mouse models with low Aβ load. ACE-I

treatment resulted in a 53% increase in genes normally down regulated with a high Aβ load. In

depth analysis of these up-regulated genes following ACE-I treatment revealed genes that

function in neuronal regeneration and cognition (Abdalla et al., 2013). Table 1.2 summarizes the

in vivo (mouse model) data on the effects of ACE-I administration on AD-related phenotypes.

28

Table 1.2 Summary of in vivo (mouse model) data on the effects of ACE-I administration

on AD-related phenotypes A

utho

rs

Mou

se M

odel

U

sed

Trea

tmen

t E

ffec

ts o

n Aβ

Eff

ects

on

Cog

nitio

n

Zou

et a

l 20

07

Tg25

76

(APP

SWED

ISH)

• C

apto

pril

• Lo

ng te

rm (1

1mos

tre

atm

ent s

tarti

ng a

t 6m

os o

ld)

Incr

ease

d Aβ

depo

sitio

n

N/A

Ber

nste

in e

t al

201

4 A

PPSW

EDIS

H/P

S1Δ

E9

• R

amip

ril

• A

cute

(28

or 6

0 da

y tre

atm

ent s

tarti

ng a

t 12

mos

old

)

Incr

ease

d Aβ

depo

sitio

n

N/A

El S

ayed

et

al 2

009

LPS

• 

Perin

dopr

il

• A

cute

(7 d

ays)

N

/A

Impr

oved

cog

nitiv

e pe

rfor

man

ce (o

pen

field

and

y-

maz

e te

sts)

Yam

ada

et

al 2

010

Aβ 2

5-35

ICV

inje

cted

• 

Perin

dopr

il • 

Acu

te (5

day

s)

N/A

Im

prov

ed c

ogni

tive

perf

orm

ance

(obj

ect

reco

gniti

on ta

sks)

Don

g et

al

2011

Aβ 1

-40 I

CV

inje

cted

an

d A

PPSW

EDIS

H/P

S2

mut

ant T

g

• Pe

rindo

pril

• IC

V: a

cute

(5 d

ays)

• 

Tg: a

cute

(24

days

st

artin

g at

3m

os o

ld)

No

effe

ct o

n Aβ 4

2 or A

β 40 i

n Tg

mod

el

Impr

oved

cog

nitiv

e pe

rfor

man

ce (Y

-maz

e te

sts)

for

both

ICV

and

Tg

mod

els

Abd

alla

et

al 2

013

APP

SWED

ISH T

g • 

Cap

topr

il or

Ena

lopr

il • 

Long

term

(6m

os

star

ting

at 1

2mos

old

) D

ecre

ased

Aβ

depo

sitio

n

N/A

29

The inconsistencies between in vivo studies suggest that ACE-I may both increase Aβ load and

slow AD progression and cognitive decline. Generally, the transgenic mouse models (expressing

APP mutations favouring Aβ42 accumulation) showed increased Aβ accumulation following long

term ACE-I administration (Bernstein et al., 2014; Zou et al., 2007), but better cognitive

performance (AbdAlla et al., 2013) (Table 1.2). These studies varied in initial age of ACE-I

treatment, stage of AD when ACE-I were given, and durations of treatment. These changes in

ACE-I treatment plans may be responsible for the contrasting evidence presented. These studies

suggest that the beneficial effects associated with ACE-I observed may be due to other

mechanisms, independent of Aβ, perhaps through inflammatory response.

The effects of ARBs on in vivo models of AD have not been as extensively studied as those of

ACE-I. In a 2014 study, Ongali and colleagues tested the preventative and therapeutic effects of

losartan, a common ARB, in APPSWEDISH transgenic mice. To differentiate between preventative

and therapeutic action, mice were given losartan at different ages. In a prevention context, ARB

treatment began at 2 months of age and lasted until 12 months. For therapeutic action, ARB

treatment began at 15 months of age and lasted until 18 months. ARB treatment protected against

cognitive decline, measured through the Morris Water Maze, without influencing soluble Aβ

levels, when given in a prevention manner (Ongali et al., 2014). ARB treatment of the aged

group improved performance on memory but not learning tasks, again independent of changes in

Aβ production (Ongali et al., 2014). Similarly, ARB treatment of an AD rat model ameliorated

spatial memory deficits seen in AD models, measured through an eight-arm radial maze task

(Shindo et al., 2012). In vivo studies have shown that ARB treatment does not alter Aβ levels

(Ongali et al., 2014) or aggregation (Ferrington et al., 2011), but improves cognitive function in

rodent models (Shindo et al., 2012; Singh et al., 2013; Takeda et al., 2009; Tsukuda et al., 2009).

In vitro studies of ARBs are more limited, however a high-throughput screen of anti-

hypertensive drugs that inhibit Aβ oligomerization, identified an ARB that was shown to prevent

Aβ40 and Aβ42 oligomerization (Zhao et al., 2009).

30

1.3 Drosophila as a model organism

Drosophila melanogaster, or the fruit fly, has been used in biological research for over 100

years. In 1908, the fly was used to develop the theory of Mendelian heredity. In the 1950s, the

fly was used to research gene structure and define introns and exons. By the 1970s, researchers

showed that the fly could also be used to model complex behaviours such as conditioned

learn