n-3 polyunsaturated fatty acids and …...safflower oil diet, or a fish oil diet containing n-3...
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N-3 Polyunsaturated Fatty Acids and Neuroinflammation in
Alzheimer’s Disease
by
Kathryn E. Hopperton
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Department of Nutritional Sciences
University of Toronto
© Copyright by Kathryn E. Hopperton 2017
ii
N-3 Polyunsaturated Fatty Acids and Neuroinflammation in a
Mouse Model of Alzheimer’s Disease
Kathryn E. Hopperton
Doctor of Philosophy
Department of Nutritional Sciences
University of Toronto
2017
Abstract
Neuroinflammation may factor in the etiology of Alzheimer’s Disease (AD). n-3 polyunsaturated
fatty acids (PUFA) and their bioactive lipid mediator derivatives have inflammation-modulating
properties. Epidemiological and animal data suggests n-3 PUFA may be protective in AD, but
whether this protection is conferred by modulating neuroinflammation is unknown.
To determine how integral neuroinflammation is to AD pathology, a systematic review was
conducted of studies comparing microglial markers in post-mortem human brain samples from
patients with AD and controls. The analysis of 114 studies presented in Chapter 2 showed that
markers of microglial activation are elevated in AD, suggesting that neuroinflammation is an
important feature of the disease.
A series of experiments were conducted to examine the effects of n-3 PUFA on
neuroinflammation in an AD model. Fat-1 transgenic mice, animals that endogenously
synthesize n-3 PUFA, and their wildtype littermates were fed either a n-3 PUFA deprived
safflower oil diet, or a fish oil diet containing n-3 PUFA. In Chapter 3, we examined the time-
course of neuroinflammation and its resolution following intracerebroventricular infusion of
amyloid-β 1-40. Wildtype mice fed the n-3 PUFA-deprived diet exhibited a greater increase in
microglia proliferation, more neuronal death, and alterations in microglia morphology consistent
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with activation, with no changes in the time-course of resolution. In Chapter 4, we show that fish
oil-fed mice have a greater astrocyte activation response to amyloid-β than either the safflower-
fed or fat-1 animals. Using a microarray in Chapter 5, we found that safflower oil-fed mice
exhibited greater enrichment of gene categories associated with inflammation than fish oil-fed
mice, independent of changes in levels of lipid mediators.
Together, the data in this thesis show that neuroinflammation is a common pathological feature
of AD that is modulated by brain n-3 PUFA. This does not seem to require detectable changes in
bioactive lipid mediators.
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Acknowledgements
Many wonderful people have helped me complete this PhD.
First and foremost, my supervisor, Richard Bazinet, has been an incredible mentor and friend
throughout my graduate studies. The passion for science and critical thinking skills he has taught
me through our hundreds of conversations will stay with me and inform my career decisions for
the rest of my life. I feel so lucky to have been part of his group, and can only hope to one day
become as good a mentor to others as he has been to me. I can never repay his kindness, so I can
only hope to pay it forward.
The Bazinet Lab is home to some of the brightest, kindest, people I know. Shoug Alashmali,
Chuck Chen, Raphaël Chouinard-Watkins, Anthony Domenichiello, Tony Fong, Vanessa
Giuliano, Kayla Hildebrand, Maha Irfan, Nick James, Alex Kitson, Scott Lacombe, Lauren Lin,
Lyyn Lin, Adam Metherel, Dana Mohammad, Sarah Orr and Marc-Olivier Trépanier, along with
our half-siblings Luke Johnson and Ingrid Santaren, and our honourary lab members Ashleigh
Wiggins and Julie Ennis, have taught and challenged me so much over the years, all the while
becoming real friends. Marc, Vanessa, Dana, Nick and Chuck in particular have provided so
much assistance and support, without which the experiments in this thesis would not have been
possible. I really treasure the time we have spent together and am so grateful for all that these
people have done to make my time in the BazLab so enriching and fun.
The Department of Nutritional Sciences is an incredibly friendly and collaborative environment.
The students and faculty in the Anderson Lab, the Comelli Lab, the Thompson Lab, the El
Sohemy Lab and the Hanley Lab in particular have been so friendly and helpful to me during my
degrees. I am grateful to our extraordinary administrators, Louisa Kung and Emiliana D’Souza,
our Chair, Mary L’Abbé, and all the volunteers on the NSGSA for setting such an open and
helpful tone in our department.
I have encountered some wonderful mentors during my PhD. Dr. Carol Greenwood and Dr.
Joanne McLaurin served on my committee, and have been endless sources of advice and
encouragement during my degree. I feel so fortunate to have had them in my corner. Dr.
Guylaine Ferland of the University of Montreal served as my external examiner, and her
comments improved this thesis. My Master’s advisor, Dr. Michael Archer, has continued to take
an interest in my progress despite retiring, and I am grateful both for his kindness and for the
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endless reference letters he has provided over the years. Beatrice Boucher was my teacher in
Nutritional Epidemiology and has since become a friend and mentor for me through our work on
the Alumni Association. Dr. Tony Hanley, Dr. Valerie Tarasuk and Dr. Harvey Anderson have
all provided mentorship and instruction through courses they have taught me in, and through
many discussions during my degree – I always feel so much richer for these conversations. Dr.
Fiona Wallace of the DNS alumni association has been a true mentor and friend during my
degree. I feel lucky to have gotten to know her and for her endless support. Dr. Elena Comelli
has been an important contributor to our lab meetings over the years, and I am grateful for the
interest she has taken in my work and my future plans. Dr. Sophie Layé and Dr. Agnès Nadjar
have been valuable collaborators during my PhD and have given me insight into the field of
neuroinflammation.
I am also grateful to have also received technical and experimental assistance from many
quarters. The staff at the Department of Comparative Medicine at the University of Toronto,
particularly Nancy Tomas, AJ Wang, and Tracy McCook provided exceptional assistance and
advice related the use of animals in my projects. Denis Reynaud and Michael Leadley of the
Analytical Facility for Bioactive Molecules of the Centre for the Study of Complex Childhood
Diseases at the Hospital for Sick Children performed LC/MS/MS analysis and answered
innumerable questions. The Microscopy Imaging Lab at the University of Toronto and staff
Battista Calvieri and Steven Doyle provided training and assistance with the confocal
microscopy. The Princess Margaret Genomics Centre performed the microarray and its analysis.
Fat-1 mice were provided as a generous gift from Dr. David Ma at the University of Guelph. Dr.
Amel Talbi of the Comelli Lab assisted with the qPCR analysis. Dr. Catharine Mielnik, Dr.
Laura Vecchio and Wendy Horsfall of Dr. Amy Ramsey’s and Dr. Ali Salaphour’s labs provided
technical advice and access to lab equipment for the immunohistochemistry experiments. Dr.
Ruslan Kubant of the Anderson lab was a wealth of information on many experimental problems
I faced during my degree. Tarek Ibrahim and Dr. Joanne McLaurin provided advice and
experimental assistance on the electron microscopy. Dr. Ignacio Arganda-Carreras of the
Universidad del Pais Vasco developed the Analyze Skeleton plugin used in Chapter 3 and gave
us permission to adapt his illustration to show how the method works in figure 3-6A.
My family and friends have been tireless supporters of me during my degree. My mom, Jan
Morrissey, and Dad, Peter Hopperton, have believed in me and cheered me on every step of the
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way – I can only hope to one day live up to being as great as they think I am! My brothers, Peter
and John, have also always been on my side and interested to hear what I am doing – Johnny
especially was helpful as my DCM insider and occasional emergency mouse helper! My friends
Kelly, Kaili, Leslie, Natalie, Kayla, and many others, have always been there to listen, make me
laugh and encourage me to reach high.
Lastly, my husband Robin Jones, to whom I dedicate this thesis, has listened, cheered, and loved
me every moment of this degree. I don’t think I would have started a graduate degree without his
encouragement, and I know I wouldn’t have finished one without him! His wisdom, patience,
humour, love and bottomless burrito budget made all the work and anxiety manageable. I’ll
always treasure the years we’ve had together while I’ve been a student, and I can’t wait to see
what the future holds for us and our growing family.
vii
Table of Contents
Abstract ........................................................................................................................................... ii
Table of Contents .......................................................................................................................... vii
List of Tables ............................................................................................................................... xiii
List of Figures ............................................................................................................................... xv
List of Appendices ...................................................................................................................... xvii
List of Abbreviations ................................................................................................................. xviii
Chapter 1: Introduction .............................................................................................................. 1
1.1 Alzheimer’ Disease ............................................................................................................. 2
1.1.1 Clinical characteristics ............................................................................................ 2
1.1.2 Neuropathology ....................................................................................................... 3
1.1.3 Treatments ............................................................................................................... 8
1.2 Polyunsaturated Fatty Acids ............................................................................................... 9
1.2.1 Requirements .......................................................................................................... 9
1.2.2 PUFA dietary sources ........................................................................................... 10
1.2.3 Effects on inflammation ........................................................................................ 12
1.3 n-3 PUFA and AD ............................................................................................................ 14
1.3.1 Animal models ...................................................................................................... 14
1.3.2 Epidemiology ........................................................................................................ 15
1.3.3 Clinical data .......................................................................................................... 17
1.3.4 Mechanisms .......................................................................................................... 19
1.4 Summary ........................................................................................................................... 20
1.5 Objectives and Hypotheses ............................................................................................... 21
1.5.1 Specific Objectives ............................................................................................... 21
1.5.2 Hypotheses ............................................................................................................ 21
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Chapter 2: Markers of microglia in post-mortem brain samples from patients with
Alzheimer’s Disease: a systematic review ............................................................................... 22
2.1 Abstract ............................................................................................................................. 23
2.2 Introduction ....................................................................................................................... 24
2.3 Methods ............................................................................................................................. 24
2.4 Results ............................................................................................................................... 28
2.4.1 Major histocompatibility complex (MHC) II ....................................................... 28
2.4.2 Ionized calcium-binding adaptor molecule 1 (Iba1) ............................................. 44
2.4.3 CD68 ..................................................................................................................... 52
2.4.4 CD11b ................................................................................................................... 60
2.4.5 CD45 ..................................................................................................................... 63
2.4.6 Ferritin ................................................................................................................... 67
2.4.7 CD33 ..................................................................................................................... 71
2.4.8 Triggering receptor expressed on myeloid cells 2 (TREM2) ............................... 74
2.4.9 CD11c ................................................................................................................... 78
2.4.10 IL-1α-expressing microglia ................................................................................... 81
2.4.11 Ricinus Communis Agglutinin 1 (RCA-1) ........................................................... 84
2.4.12 Translocator Protein (TSPO) ................................................................................ 87
2.4.13 CD163 ................................................................................................................... 89
2.4.14 Microglia identified by morphology ..................................................................... 91
2.4.15 Other ..................................................................................................................... 93
2.4.16 High throughput Techniques: Microarray and Proteomics ................................. 101
2.4.17 Non-quantitative comparisons ............................................................................ 107
2.5 Discussion ....................................................................................................................... 121
2.5.1 Limitations .......................................................................................................... 126
2.6 Conclusion ...................................................................................................................... 127
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Chapter 3: Brain n-3 polyunsaturated fatty acids modulate microglia cell number and
morphology in response to intracerebroventricular amyloid-β 1-40 in mice ......................... 128
3.1 Abstract ........................................................................................................................... 129
3.2 Introduction ..................................................................................................................... 130
3.3 Methods ........................................................................................................................... 131
3.3.1 Animals ............................................................................................................... 131
3.3.2 Diets .................................................................................................................... 132
3.3.3 Genotyping .......................................................................................................... 134
3.3.4 Gas Chromatography .......................................................................................... 134
3.3.5 Preparation of amyloid-β 1-40 and 40-1 injections ............................................ 134
3.3.6 Negative stain transmission electron microscopy ............................................... 135
3.3.7 Intracerebroventricular amyloid-β infusion surgery ........................................... 135
3.3.8 Sample preparation and immunohistochemistry ................................................. 136
3.3.9 Epi-fluorescence microscopy and cell counting ................................................. 137
3.3.10 Confocal microscopy and microglia morphology ............................................... 137
3.3.11 Statistical analysis ............................................................................................... 138
3.4 Results ............................................................................................................................. 138
3.4.1 Time course of microglia and astrocyte activation following icv amyloid-β 1-
40 or control peptide ........................................................................................... 138
3.4.2 Effect of brain fatty acid composition on time course of microglia and
astrocyte activation ............................................................................................. 142
3.4.3 Fluoro-Jade C Cell Counts .................................................................................. 144
3.4.4 Microglia Morphology ........................................................................................ 144
3.5 Discussion ....................................................................................................................... 153
3.6 Conclusions ..................................................................................................................... 157
Chapter 4: Dietary fish oil, and to a lesser extent the fat-1 transgene, increases astrocyte
activation in response to intracerebroventricular amyloid-β 1-40 ......................................... 158
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4.1 Abstract: .......................................................................................................................... 159
4.2 Introduction ..................................................................................................................... 160
4.3 Methods ........................................................................................................................... 161
4.3.1 Animals and diets ................................................................................................ 161
4.3.2 Intracerebroventricular infusion of amyloid-β 1-40 and sample preparation ..... 162
4.3.3 Fatty Acid Analysis ............................................................................................. 162
4.3.4 Immunohistochemistry ....................................................................................... 162
4.3.5 GFAP fluorescence intensity measurement ........................................................ 163
4.3.6 Astrocyte morphology ........................................................................................ 163
4.3.7 Statistical analysis ............................................................................................... 163
4.4 Results ............................................................................................................................. 164
4.5 Discussion ....................................................................................................................... 169
4.6 Conclusion ...................................................................................................................... 171
Chapter 5: Fish oil feeding attenuates neuroinflammatory gene expression without
concomitanht changes in brain eicosanoids and docosanoids in a mouse model of
Alzheimer’s Disease ............................................................................................................... 172
5.1 Abstract ........................................................................................................................... 173
5.2 Introduction ..................................................................................................................... 175
5.3 Methods ........................................................................................................................... 177
5.3.1 Animals and diets ................................................................................................ 177
5.3.2 Intracerebroventricular infusion of amyloid-β 1-40 or 40-1 ............................... 179
5.3.3 Collection of brains for RNA measurements ...................................................... 179
5.3.4 Collection of brains for fatty acid measurements ............................................... 179
5.3.5 Gas Chromatography .......................................................................................... 180
5.3.6 RNA extraction ................................................................................................... 180
5.3.7 Microarray analysis ............................................................................................. 180
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5.3.8 RT-qPCR ............................................................................................................. 181
5.3.9 Extraction and quantification of eicosanoids and docosanoids .......................... 181
5.3.10 Statistical analysis ............................................................................................... 182
5.4 Results ............................................................................................................................. 183
5.4.1 Group characteristics .......................................................................................... 183
5.4.2 Lipid mediator-associated genes ......................................................................... 191
5.4.3 Microarray Validation ......................................................................................... 191
5.4.4 Eicosanoids and Docosanoids ............................................................................. 195
5.5 Discussion ....................................................................................................................... 195
5.6 Conclusion ...................................................................................................................... 206
Chapter 6: General Discussion ............................................................................................... 207
6.1 Review of Findings and General Discussion .................................................................. 208
6.2 Strengths ......................................................................................................................... 211
6.3 Limitations and Future Directions .................................................................................. 212
6.4 Significance ..................................................................................................................... 214
6.5 Conclusions ..................................................................................................................... 216
References ................................................................................................................................... 217
7 Appendices .......................................................................................................................... 258
7.1 Appendix 1: Summary of microglial marker functions and expression ......................... 258
7.2 Appendix 2: Chapter 2: Full search for Embase – other database searches used similar
terms ................................................................................................................................ 260
7.3 Appendix 3: Chapter 5: Genes altered by Amyloid-β Infusion Shared Between
Genotype/diet Groups ..................................................................................................... 273
7.4 Appendix 4: Chapter 5: Genes altered by Amyloid-β Infusion Unique to Each
Genotype/diet Groups ..................................................................................................... 274
7.5 Appendix 5: Chapter 5: DAVID Version 6.7 Gene Ontology of Genes Changed in
Fat-1 amyloid-β-infused vs Fat-1 non-surgery ............................................................... 281
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7.6 Appendix 6: Chapter 5: DAVID Version 6.7 Gene Ontology of Genes Changed in
WTFO amyloid-β-infused vs WTFO non-surgery ......................................................... 282
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List of Tables
Table 1-1: Studies examining neuroinflammatory markers in AD molecules with n-3
interventions………………………………………………………………………...................…16
Table 2-1: Systematic review - MHC Class II in Alzheimer’s Disease……………..............…...30
Table 2-2: Systematic review - Iba1 in Alzheimer’s Disease…………………………................45
Table 2-3: Systematic review - CD68 in Alzheimer’s Disease………………………….............53
Table 2-4: Systematic review - CD11b in Alzheimer’s Disease………………………...............61
Table 2-5: Systematic review - CD45 in Alzheimer’s Disease………………………….............64
Table 2-6: Systematic review – Ferritin in Alzheimer’s Disease……………………............…..68
Table 2-7: Systematic review - CD33 in Alzheimer’s Disease………………………….............72
Table 2-8: Systematic review - TREM2 in Alzheimer’s Disease……………………..................75
Table 2-9: Systematic review - CD11c in Alzheimer’s Disease………………………................79
Table 2-10: Systematic review - IL-1α-expressing microglia in Alzheimer’s Disease….............82
Table 2-11: Systematic review - RCA-1 in Alzheimer’s Disease…………………….................85
Table 2-12: Systematic review – TSPO in Alzheimer’s Disease………………………..............88
Table 2-13: Systematic review - CD163 in Alzheimer’s Disease……………………….............90
Table 2-14: Systematic review - Microglia identified based on morphology in Alzheimer’s
Disease…………………………………………………………………………………...............92
Table 2-15: Systematic review - Other Markers in Alzheimer’s Disease……………..............…94
Table 2-16: Systematic review - High throughput studies in Alzheimer’s Disease……............102
Table 2-17: Non Quantitative Comparisons................................................................................108
Table 3-1: Fatty acid composition of 10% safflower oil and 2% fish oil, 8% safflower
oil diets………………………………………………………………………………….............133
Table 5-1: Fatty acid composition of 10% safflower and 8% safflower, 2% fish oil diets.........178
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Table 5-2: List of significantly enriched gene ontology categories in WTSO amyloid-β
1-40-infused compared to non-surgery mice…………………………………………..............188
Table 5-3: List of measured fatty acid derivatives that were not detected..................................196
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List of Figures
Figure 1-1: Synthesis of long chain PUFA ...................................................................................11
Figure 1-2: Bioactive lipid mediators derived from DHA, EPA and ARA...................................13
Figure 1-3: Model of appearance of biomarkers of AD over the life course……………….........18
Figure 2-1: Flow diagram of systematic search.............................................................................27
Figure 2-2: Summary of results of systematic search..................................................................122
Figure 3-1: Time-course of microglia and astrocyte proliferation……………………….......…140
Figure 3-2: Whole brain fatty acid composition..........................................................................143
Figure 3-3: Time-course of microglia activation following icv amyloid-β in the fat-1 and
wildtype mice...............................................................................................................................145
Figure 3-4: Time-course of astrocyte activation following icv amyloid-β in the fat-1 and wildtype
mice..............................................................................................................................................147
Figure 3-5: Neurodegeneration in the hippocampus....................................................................149
Figure 3-6: Microglia morphology..............................................................................................151
Figure 4-1. Hippocampal fatty acid composition……………......…………………….....……..165
Figure 4-2: Astrocyte response to intracerebroventricular infusion of amyloid-β 1-40 in fat-1
transgenic mice or their wildtype littermates fed diets containing 2% fish oil (WTFO) or a
safflower oil diet containing negligible quantities of n-3 PUFA (WTSO)..................................167
Figure 5-1: Hippocampus total and non-esterified acid composition, body weight and
temperature of amyloid-β 1-40 or control peptide-infused surgery mice, or of age-matched non-
surgery mice.................................................................................................................................184
Figure 5-2: Analysis of the microarray data................................................................................187
Figure 5-3: Genes driving enrichment of neuroinflammation-associated gene expression
categories in wildtype safflower oil-fed mice.............................................................................192
Figure 5-4: Genes involved in the synthesis of eicosanoids and docosanoids............................194
Figure 5-5: Validation of a subset of genes driving the enrichment of inflammation-associated
gene expression categories in the microarray..............................................................................199
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Figure 5-6: Hippocampal docosanoid and EPA eicosanoid concentrations………….......…….204
Figure 5-7: Hippocampal ARA eicosanoid concentrations………………………………….....205
xvii
List of Appendices
Appendix 1 – Chapter 2: Summary of microglial marker functions and expression...........258
Appendix 2 - Chapter 2: Full search for Embase – other database searches used similar
terms.....................................................................................................................................260
Appendix 3: Chapter 5: Genes altered by Amyloid-β Infusion Shared Between Genotype/diet
Groups………………………………………………………………………………….….273
Appendix 4: Chapter 5: Genes altered by Amyloid-β Infusion Unique to Each Genotype/diet
Groups……………………………………………………………………………………..274
Appendix 5: Chapter 5: DAVID Version 6.7 Gene Ontology of Genes Changed in Fat-1
amyloid-β-infused vs Fat-1 non-surgery…………………………………………………...281
Appendix 6: Chapter 5: DAVID Version 6.7 Gene Ontology of Genes Changed in WTFO
amyloid-β-infused vs WTFO non-surgery…………………………………………………282
xviii
List of Abbreviations
AD: Alzheimer’s Disease;
AI: Adequate Intake;
ALA: alpha-linolenic acid;
ANOVA: analysis of variance;
APOE: apolipoprotein E;
APP: amyloid precursor protein;
CA: cornu ammonis;
cPLA2: cytosolic phospholipase A2;
CSF: cerebrospinal fluid;
CD: cluster of differentiation;
COX: cyclooxygenase;
DG: dentate gyrus;
DHA: docosahexaenoic acid;
EPA: eicosapentaenoic acid;
FJC: fluoro Jade C;
GAPDH: glyceraldehyde-3 phosphate dehydrogenase;
GFAP: glial fibrillary acidic protein;
HEPE: hydroxyeicosapentaenoic acid;
HETE: hydroxyeicosatetraenoic acid;
HPC: high pathology control;
HLA: human leukocyte antigen;
iba1: ionized calcium-binding adapter molecule 1;
icv: intracerebroventricular;
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iPLA2: calcium independent phospholipase A2;
IFN: interferon;
IL: interleukin;
LC/MS/MS: liquid chromatography tandem mass spectrometry;
LPS: lipopolysaccharide;
LO: lipoxygenase;
MRI: magnetic resonance imaging;
MCP: monocyte chemoattractant protein;
MHC: major histocompatibility complex;
NMDA: N-methyl-D-aspartate;
NSAID: non-steroidal anti-inflammatory drug;
PBS: phosphate buffered saline;
PCR: polymerase chain reaction;
PET: positron emission tomography;
PGES: prostaglandin E synthase;
PPAR: peroxisome proliferator-activated receptor;
PUFA: polyunsaturated fatty acids;
qPCR: quantitative polymerase chain reaction;
RMA: robust multi-array average;
TNF: tumor necrosis factor;
TREM: triggering receptor expressed on myeloid cells;
WTSO: wildtype mice fed safflower;
WTFO: wildtype mice fed fish oil;
1
Chapter 1: Introduction
2
1.1 Alzheimer’ Disease
Alzheimer’s Disease (AD) is the most common form of dementia, affecting an estimated 564 000
Canadians2. It is characterized by a progressive cognitive decline, leading to death an average of
8 years after the onset of symptoms. AD is thought to have cost Canadians 10.4 billion dollars in
2016 in direct healthcare costs, or 33 billion when indirect costs, such as lost earning potential
for patients and caregivers, was included2. While the economic and social impacts of AD are
immense in 2017, they will reach still greater levels by 2031, when the prevalence of AD is
expected to reach 937 000 due to changing demographics2.
AD can be classified by the age of symptom onset and its apparent heritability. The bulk of AD
cases are late-onset, with symptoms beginning to appear after the age of 65. Early-onset AD
accounts for less than 5% of all AD cases, and symptoms typically begin to appear between the
ages of 30-653. While both genes and environmental factors likely play a role in the
development of early and late-onset AD, genetic factors are thought to dominate in the earlier
form, while environmental factors are thought to become more important in the late-onset
variety. About 60% of early-onset AD cases are linked with a family history, of which nearly
70% are associated with single gene mutations in amyloid precursor protein (APP), presenilin 1
or presenilin 2, that can follow an autosomal dominant inheritance pattern4, 5. These mutations
can occur in families, producing clusters of disease, or can arise sporadically. Genes
predisposing towards late-onset AD typically have a lower penetrance, such as the apolipoprotein
E (APOE) ε4 risk allele, which raises the risk of AD 3-126 fold, but does not guarantee an AD
diagnosis.
1.1.1 Clinical characteristics
AD is broadly characterized by cognitive and behavioral symptoms that a) interfere with the
performance of regular activities, b) worsen over time, and c) are not explained by any other
physical or psychiatric disorder (reviewed in 7). The most common cognitive deficits in AD are
amnestic, meaning they involve impairments in learning and remembering new information.
Other presentations include deficits in language use, spatial awareness, recognition of faces, and
executive dysfunction, such as declines in problem-solving and reasoning. This is usually
diagnosed on the basis of a detailed medical history from the patient and caregivers, and by
3
direct cognitive assessment, using techniques such as the Modified Mini-Mental State
Examination or the Montreal Cognitive Assessment8.
1.1.2 Neuropathology
Underlying the clinical characteristics of AD is neuronal death, leading to loss of volume in the
frontal cortex and temporal and parietal lobes, and atrophy of brain regions important to learning
and memory, such as the hippocampus and entorhinal cortex9. The hippocampus is the focus of
the experimental work in this thesis, both because of its functional importance, and because it is
a site sensitive to early neuronal loss and dysfunction in AD10. The two main histological
features of AD are the deposition of amyloid-β plaques and neurofibrillary tangles. Although
these features are ubiquitous in AD and can be visualized in vivo either directly, using positron
emission tomography (PET) or magnetic resonance imaging (MRI), or indirectly via blood or
cerebrospinal fluid (CSF) measures, this is not a part of regular clinical AD diagnosis11. This is
both because these measures currently have little treatment utility, and because the sensitivity
and specificity of many cognitive tests for AD diagnosis are thought to be over 80%8. A
definitive diagnosis of AD does, however, require histological confirmation of plaques and
tangles11.
1.1.2.1 Amyloid-β
Amyloid-β is a 25-43 amino acid peptide produced through sequential cleavage from APP, a
transmembrane protein found in neurons (reviewed in12). In the non-amyloidogenic cleavage
pathway, the ectodomain of APP is first cleaved by the enzyme α-secretase, releasing APPsα.
The intracellular domain is then cleaved by γ-secretase, producing two peptides: the amino-
terminal APP intracellular domain (AICD) and p3. AICD and APPsα may be neurotrophic,
promoting the formation of synapses and down-regulating apoptotic signaling 13. In the
amyloidogenic pathway, APP is first acted upon by β-secretase, which releases a shorter
extracellular component, APPβs. When γ-secretase acts on this longer remaining membrane-
associated component, it releases AICD and amyloid-β. γ-secretase can cleave APP at slightly
different sites, allowing it to produce amyloid-β peptides of varying lengths. The longer forms
of amyloid-β, 1-40 and 1-42 have traditionally been considered the most neurotoxic because they
oligomerize readily to form plaques, however soluble and shorter forms of amyloid-β also have
demonstrated neurotoxic effects14-16.
4
Amyloid-β is the neuropathological feature of AD that has received the most research attention.
The amyloid cascade hypothesis of AD suggests that impairment in the production or clearance
of amyloid-β causes accumulation of the more neurotoxic, longer forms of the peptide, resulting
in oligomerization into insoluble fibres and plaques that accumulate in and around neurons
(reviewed in 17). This leads to neuronal dysfunction, possibly including the formation of
neurofibrillary tangles, eventually leading to neuronal death. This is thought to produce the
atrophy of various brain regions seen in AD that underlie declines in cognitive function.
There is strong evidence for the amyloid hypothesis of AD. All known dominantly-inherited
mutations that cause early-onset AD are related to the production of amyloid-β. For example, the
Swedish mutation alters the binding site on APP to facilitate cleavage by β-secretase, which
shifts the processing of APP towards the amyloidogenic pathway18, 19. Presenilin proteins form
the catalytic domain of γ-secretase. Several mutations in presenilin 1 and presenilin 2 genes
have been identified in cases of familial AD, where they increase its production of the longer
forms of amyloid-β that form plaques 17, 20, 21. APP is located on chromosome 21, which is the
same chromosome that is duplicated in Down Syndrome. Patients with Down Syndrome exhibit
substantial accumulations of amyloid-β plaques and neurofibrillary tangles in their brains by the
age of 40, and over 50% are estimated to develop AD by 6022, 23. In animal models, insertion of
genes for familial AD mutations results in significant amyloid-β plaque accumulation and
cognitive impairment24, 25, while direct infusion of amyloid-β peptide also elicits neuronal death
and cognitive deficits26, 27. Together, this evidence strongly suggests that amyloid-β is involved
in the development of AD.
There is also evidence contradicting the amyloid hypothesis of AD. Post-mortem studies have
shown that even heavy loads of amyloid-β plaque can exist in cognitively normal subjects28. In
addition, the severity of cognitive decline does not appear to be directly proportional to plaque
load in patients with AD29. Perhaps most significantly, several large-scale, phase 3 clinical trials
aimed at targeting amyloid-β directly through monoclonal antibodies or by targeting its
production have failed to slow cognitive decline despite reducing plaque loads30-32, though
ongoing studies in patients with high plaque loads who are free of cognitive deficits will be a
more definitive test of this hypothesis33. This indicates that while amyloid-β may play an
important role in the development of AD, it is not the only factor at play.
5
1.1.2.2 Neurofibrillary tangles
Neurofibrillary tangles are intracellular aggregates of tau protein found in the neurons of patients
with AD. Under normal conditions, tau is involved in regulating the assembly and stabilization
of intracellular microtubule proteins34. The actions of tau are inhibited by phosphorylation to
regulate microtubule assembly. In AD, tau becomes hyperphosphorylated, causing it to clump
together into filaments, and destabilizing microtubule structures within the cell34. The cause for
hyperphosphorylation of tau is not completely understood, however downregulation of the
enzyme protein phosphatase 2A, which dephosphorylates tau, has been implicated35. Total tau
levels (phosphorylated and unphosphorylated) are elevated in the brains of patients with AD
relative to controls36.
The role of neurofibrillary tangles as a cause or consequence of AD is a matter of debate.
Neurofibrillary tangles form in neurons, and seem to precede neuronal death in brain regions that
are important for learning and memory, such as the entorhinal cortex, hippocampus and the
association areas of the cerebral cortex37, 38. The number of neurofibrillary tangles also appear to
correlate with cognitive decline better than density of plaques in AD, leading to its inclusion in
post-mortem diagnostic criteria39. Mutations in the tau gene may be a risk factor for AD, though
not in the familial autosomal dominant form as is the case with amyloid-β40. Animal models with
mutated human tau exhibit neuronal loss and cognitive impairment, which implicates tau as a
contributor to AD development41, 42. However these models do not develop amyloid-β plaques,
whereas models with increased plaques do develop neurofibrillary tangles, which suggests that
tangles are not the initiating factor in AD43. The first phase III trial of a drug targeting tau failed
to meet its primary end points in mild to moderate AD, though like with the research on amyloid-
β, trials in other patient populations and with other agents are ongoing44.
1.1.2.3 Neuroinflammation
Adapted from Hopperton et al. (2016) J. Neuroinflammation. 13(1):257
In addition to amyloid-β plaques and neurofibrillary tangles, neuroinflammation is increasingly
recognized as a hallmark of AD. Neuroinflammation, the inflammatory response that occurs in
the central nervous system, is distinct from peripheral inflammation in several ways. In the
periphery, inflammation is usually characterized acutely by the release of pathogen or damage
6
associated molecular patterns from the affected tissue, which causes an increase in the
recruitment and transmigration of neutrophils from the capillaries to the site of injury45.
Neutrophils then release a variety of pro-inflammatory cytokines, such as interleukin (IL)-1β,
lipid mediators such as prostaglandins and leukotrienes, and anti-microbial peptides that together
contribute to the canonical signs of inflammation, such as redness, swelling, pain and loss of
function46. Neutrophils also develop phagosomes, allowing them to phagocytose and clear
pathogens and cellular debris46. Neutrophils then undergo a lipid class switch, moving from the
production of pro-inflammatory to pro-resolving lipid mediators, such as resolvins, protectin and
maresin47. These mediators initiate the programmed death of the neutrophils and the recruitment
of blood macrophages, which clear the dead neutrophils and remaining pathogens, allowing the
tissue to return to homeostasis47.
In contrast, the brain is only rarely infiltrated by peripheral leukocytes, usually upon blood brain
barrier disruption, and instead relies on resident immune cells, the microglia and astrocytes, to
initiate and resolve the immune response. Under normal conditions, microglia exist in a resting
M0 phenotype, in which they survey the environment and perform supportive functions. Upon
stimulation by interferon (IFN)-γ, produced by either astrocytes or the microglia themselves in
response to recognition of pathogen or damage associated molecular patterns, microglia can
become activated to an M1 phenotype48. This phenotype is characterized by the production of
pro-inflammatory cytokines, chemokines, lipid mediators and reactive oxygen or nitrogen
species that recruit other microglia and target pathogens. Astrocytes can also become activated in
response to these pro-inflammatory mediators, releasing cytokines and reactive oxygen species
that contribute to the immune response49. In response to IL-4, microglia can switch to an M2
phenotype, characterized by the production of anti-inflammatory cytokines such as IL-10 and IL-
4, phagocytosis of pathogens and/or cellular debris and healing48.
In both the brain and the periphery, an effective immune response is essential for clearing
pathogens and initiating wound healing. However, if the immune response is excessive or
prolonged, either due to a failure to clear the initial insult or due to dysfunctional immune cells,
tissue damage can occur. In the brain, excessive pro-inflammatory cytokines and reactive
oxygen and nitrogen species produced in response to amyloid-β are thought to contribute to
neuronal dysfunction, and eventually death by apoptosis or necrosis49, 50. Neuronal death in turn
further stimulates the immune response through the release of damage associated molecular
7
patterns. This is one of the hypothesized mechanisms by which AD pathology leads to neuronal
death50.
In Chapter 2, we present the results of a systematic review of 114 studies showing that markers
of microglial activation are over-expressed in patients with AD relative to controls post-mortem,
providing convincing evidence that neuroinflammation is indeed a neuropathological feature of
this disease. Higher levels of other immune markers have also been reported in AD, such as
astrocytes, cytokines or complement51-55. Patients with AD have higher plasma levels of
cytokines, such as in IL-6 and IL-1β 56 than healthy controls, while in patients with mild to
moderate AD, elevation in serum TNF-α is associated with cognitive decline 57. PET studies
using ligands to the peripheral benzodiazepine binding receptor (such as [11C](R)-PK11195),
which is thought to label activated microglia, show higher binding in AD patients than controls
58, 59 which co-localizes with binding of [11C] Pittsburg Compound B, a marker of fibrillary
amyloid-β 60. Interestingly, scores on the mini mental state exam, a measure of cognitive
impairment where lower scores indicate greater impairment, are inversely correlated with
PK11195 binding, but not with uptake of PIB 60, suggesting an independent effect of
inflammation on cognitive decline. This is supported by studies associating genetic
polymorphisms in various inflammation-associated genes with AD risk, including
polymorphisms in Triggering Receptor Expressed On Myeloid Cell (TREM) 2, cluster of
differentiation (CD)33, IL-6, toll-like receptor (TLR)4 and IL-1 61-67.
Elevations in markers of neuroinflammation have also been identified in animal models of AD,
such as higher levels of IL-1β and chemokine CXCL motif (CXCL) 1 in the brains of TgCRND8
mice than their wildtype littermates 68 and increases in TNF-α, monocyte chemoattractant protein
(MCP)-1 and microglia in 3xTg mice compared to controls 69. In intracerebroventricular (icv)
infusion AD models, in which amyloid-β is injected into the brains of rodents either acutely or
chronically via a pump, there are elevations in brain cytokines, TNF-α and IL-1β 70, and glial
fibrillary acidic protein (GFAP) and CD68, markers of astrocytes and microglia, 71 relative to
controls. Treatments that decrease neuroinflammatory markers in animal models generally
improve behavioural scores and decrease AD pathology 72-75. Interestingly, immune activation
has been shown to increase the production of amyloid-β and the hyperphosphorylation of tau
proteins 76, and seems to precede the deposition of amyloid-β plaques 77, which supports the
hypothesis that inflammation is a causal factor in AD development (for review see: 78).
8
Use of non-steroidal anti-inflammatory drugs (NSAIDs), including both aspirin and non-aspirin
agents, was associated with a 28% reduced risk of AD in in a recent meta-analysis79. The
reduction in risk was an impressive 62% in the subgroup that used the drugs for at least 2 years.
In contrast to these positive epidemiological findings, randomized controlled trials in patients
with dementia show no benefit of NSAID treatment80. Only one trial has used NSAIDs for
primary prevention in a cohort of elderly patients with a family history of AD, and it also
reported no benefit relative to placebo, however this trial was halted with an average of 6 months
follow-up rather than the intended 5-7 years because of concerns over the cardiovascular risk
associated with celecoxib, one of the drugs used in the study81. Thus, it is unclear from the
literature whether anti-inflammatory therapies are protective in AD.
It should be noted that while neuroinflammation is a widely used term in the literature, there is
disagreement on its definition and what neurological diseases should be considered
neuroinflammatory. While many researchers consider AD a neuroinflammatory disease for the
reasons described above50, 82, 83, some view only diseases with an adaptive immune component
based on memory and specificity and mediated by T and B cells to be true neuroinflammatory
diseases84. By this definition, diseases such as AD, Parkinson’s Disease, or amyotrophic lateral
sclerosis would not be considered neuroinflammatory because the immune response in these
diseases is driven by innate immune cells, the microglia and astrocytes, rather than by invading T
and B lymphocytes. Proponents of this definition generally view the immune response in AD or
Parkinson’s Disease to be an innate response to neurodegeneration, rather than being a
contributing factor to disease development84. We chose to use the broader definition of
neuroinflammation in this thesis for simplicity and because as discussed above, the innate
immune response in the brain is distinct from that in the periphery and there is evidence that it
contributes to the development and progression of AD.
1.1.3 Treatments
There are currently no treatments that can prevent or cure AD. Existing treatments may slow the
rate of cognitive decline over a period of months to a few years, which can delay
institutionalization and improve quality of life. The currently approved drugs for AD in Canada
fall into two categories: cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor
antagonists. Cholinesterase inhibitors prevent the break-down of the neurotransmitter
9
acetylcholine, increasing the intensity and duration of its action. This maintains the function of
cholinergic neurons in the brain, which are particularly sensitive to AD pathology85. NMDA
receptor antagonists, such as memantine hydrochloride, prevent excitotoxicity from excessive
signaling by glutamate, which leaks from nerve terminals in moderate to advanced AD, delaying
cognitive deterioration86.
Because the existing approved therapies at best delay disease progression, it is clear that new
therapies are needed to prevent or treat AD. It is estimated that an intervention that could delay
onset by 5 years would decrease the prevalence of AD by over 40% over the next 35 years87,
which would have an enormous economic and social benefit.
1.2 Polyunsaturated Fatty Acids
Polyunsaturated fatty acids (PUFA) are acyl chains with more than one double bond. PUFA are
classified on the basis of the location of the first double bond from the methyl end, with PUFA
containing the first double bond 3 carbons away from the methyl group designated ω-3, omega-
3, or n-3, and PUFA with the first double bond 6 carbons away designated ω-6, omega-6 or n-6.
Mammals cannot directly synthesize n-3 or n-6 PUFA, though they are capable of producing
longer chain PUFA, such as the n-3 eicosapentaenoic (EPA, 20:5n-3) and docosahexaenoic
(DHA, 22:6n-3) acids or n-6 arachidonic acid (ARA, 20:4n-6), from shorter-chain precursors,
such as alpha linolenic acid (ALA, 18:3n-3) or linoleic acid (LA, 18:2n-6) via sequential
elongation and desaturation steps (Figure 1-1).
1.2.1 Requirements
Recommended Dietary Allowances have not been established for PUFA, though an adequate
intake (AI) exists for ALA and LA. In Canada, AIs for LA range from 4.4 grams per day in
infancy to 17 grams per day between the ages of 19 and 50 in men, and 12 grams per day for
women88. AIs for ALA range from 0.5 grams per day in infancy, to 1.6 grams per day in adult
males, and 1.1 grams per day in adult females. The Institute of Medicine states that up to 10% of
the AI for ALA can be EPA/DHA89. Analysis of the 2004 CCHS data suggests that intakes of
most Canadians meet or exceed these AIs90. Importantly however, AIs are based observations of
intake by groups of healthy people, and do not take into account markers of physiological
requirements or disease states.
10
Other countries and international bodies have made recommendations for EPA and DHA
(reviewed in 91). For example, the Australian and New Zealand Health Authorities recommend
160 and 90 mg per day of total long-chain n-3 PUFA for men and women respectively, while the
European Food Safety Authority recommends 250 mg/day of combined EPA and DHA for the
general adult population. The International Society for the Study of Fatty Acids and Lipids
recommends a higher level, 500 mg/ day combined EPA and DHA on the basis of cardiovascular
health.
1.2.2 PUFA dietary sources
The main sources of LA and ALA in North America are oils, such as corn and safflower for LA,
and soybean, canola or flax for ALA92. Long-chain PUFA are primarily consumed from animal
products. ARA is mostly consumed via meats, such as chicken, or beef93, while DHA and EPA
are most enriched in seafood, particularly fatty fish such as salmon, which contains 1000-2000
mg, or trout, which contains 660-740 mg per 75 gram serving94. Vegetarian sources of DHA and
EPA include fortified products, such as eggs, or algal oils.
The Canadian Health Measures Survey collected information on fish consumption from nearly
2000 Canadians from across the country. It reported that 73% consumed fewer than 1 serving of
oily fish per week, suggesting that most Canadians rely primarily on endogenous synthesis from
ALA to meet DHA and EPA requirements95. Whether endogenous synthesis rates are sufficient
to supply the body’s requirements for PUFA, particularly DHA, is a matter of debate. LA is
present in the diets of Canadians at 7-8 fold higher levels than ALA90. Because these fatty acids
are elongated and desaturated by the same enzymes (Figure 1-1), there is concern that this
competition may prevent the adequate synthesis of EPA and DHA. Stable isotope studies in
humans estimate that the rate of conversion of ALA to DHA is less than 1%96. This would
correspond to 11-16mg per day of DHA for men or women consuming n-3 PUFA only as ALA,
which is well below most international recommendations. It should be noted, however, that
synthesis rates of long chain PUFA, and their adequacy to meet requirements, are a matter of
debate96.
11
Figure 1-1: Synthesis of long chain PUFA
Reproduced from Alashmali S.M, Hopperton K.E and Bazinet R.P (2016) Current Opinion in
Lipidology: 7(1); 54-66.
12
1.2.3 Effects on inflammation
DHA, EPA and ARA are precursors to bioactive lipid mediators that are involved in regulating
inflammation. Molecules derived from DHA and EPA include the resolvins, protectin and
maresins (Figure 1-2 A and B). These molecules have both anti-inflammatory and pro-resolving
effects, meaning they both decrease the magnitude of the initial inflammatory response, and
actively bring the tissue back to homeostasis following inflammation (for review, see 97). ARA
is a precursor to a variety of pro-inflammatory lipid mediators, including prostaglandin E2,
leukotrienes and thromboxane (Figure 1-2C), that are involved in initiating and maintaining the
immune response98. Molecules derived from DHA are referred to as docosanoids, meaning that
they come from metabolism of a 22-carbon fatty acid, while molecules from EPA and ARA are
referred to as EPA-derived eicosanoids and ARA-derived eicosanoids respectively because they
are made from 20-carbon fatty acids.
n-3 PUFA may also directly exert anti-inflammatory effects without metabolism to lipid
mediators. Most studies that have examined inflammatory markers with an n-3 PUFA
intervention did not measure lipid mediators, so it is unclear whether changes in these mediators
were required to mediate the changes in inflammation, or whether the fatty acids exerted these
effects directly99-101. Cyclooxygenase (COX)-2 and lipoxygenase (LO) inhibition prevented the
initiation of resolution in a peritonitis model of systemic inflammation by decreasing infiltration
of phagocytes to clear infiltrating neutrophils and leukocytes, which suggests that the synthesis
of pro-resolving lipid mediators is necessary for resolution102. On the other hand, one study in
our lab administered either unesterified DHA or 17S-HpDHA, a precursor of protectin, into the
left ventricle of the brain via a pump over the course of 24 hours following injection of
lipopolysaccharide, a model of neuroinflammation. Both 17S-HpDHA and unesterified DHA
down-regulated the expression of pro-inflammatory cytokines to a similar extent, however only
17S-HpDHA increased levels of hippocampal protectin D1103. This suggests that unesterified
DHA may have anti-inflammatory properties independent of its conversion to lipid mediators,
however more research measuring other mediators and in other models is needed.
In part because of these immuno-modulatory effects, increasing the consumption of n-3 PUFA,
particularly DHA, has been suggested as a potential preventative treatment for AD.
13
Arachidonic Acid
HETEs
Lipoxin A4 Prostaglandins
Thromboxane
15-LO
12-LO
COX-2
TxS
15-LO 5-LO COX-2 PGES
Docosahexaenoic Acid
D-series
resolvins
Neuroprotectin D1
Maresins
15-LO 5-LO 12-LO
15-LO
A
B
Docosanoids
Anti-inflammatory and pro-resolving effects
ARA-derived Eicosanoids
Primarily pro-inflammatory effects
(except lipoxin A4)
Eicosapentaenoic Acid
E-series resolvins
HEPEs
Cytochrome P450
5-LO
EPA-derived Eicosanoids
Anti-inflammatory and pro-resolving effects
C
Cytochrome P450
5-LO
Major classes of lipid mediators and the enzymes involved in their synthesis from DHA, EPA
or ARA. Red italics indicates enzymes, black text indicates products. ARA: arachidonic acid,
COX: cyclooxygenase, DHA: docosahexaenoic acid, EPA: eicosapentaenoic acid, LO:
lipoxygenase, HEPE: hydroxyeicosapentaenoic acid; PGES: prostaglandin E synthase.
Figure 1-2: Bioactive lipid mediators derived from DHA, EPA and ARA
14
1.3 n-3 PUFA and AD
DHA is highly concentrated in the brain, making up approximately 10% of its total fatty acid
composition104. By comparison, EPA is present at nearly undetectable levels that may reflect
contamination by blood or blood vessels105. Lower brain DHA has been found in post-mortem
brain samples form subjects with AD relative to aged controls, particularly in the
hippocampus106, 107, even when differences in total fatty acid levels are corrected for108, which
suggests that these reductions may play a role in the disease. This has not been replicated for all
regions or lipid classes however 109, 110. Lower levels of pro-resolving lipid mediators have also
been reported in post-mortem brain samples of patients with AD relative to controls111, 112. This
is discussed in detail in the introduction to Chapter 5.
1.3.1 Animal models
Adapted in part from Hopperton et al. (2016) J. Neuroinflammation. 13(1):257
Animal models of AD widely demonstrate neuroprotective effects of fish oil or n-3 PUFA
feeding. In a 2012 meta-analysis in AD animal models, n-3 PUFA supplementation was found to
decrease the deposition of amyloid-β plaques, attenuate declines in memory, and reduce
hippocampal neurodegeneration113. Similar protective effects on cognition114-116 and amyloid-β
levels115 have been reported by studies published since 2012. All of these studies used either
DHA or a mixed intervention including DHA.
Six animal studies have measured an inflammatory outcome in an AD model following
interventions aimed at increasing brain n-3 PUFA. These are summarized in Table 1, (updated
from 117). Two studies fed rats eicosapentaenoic acid (EPA) for 4 weeks, and noted reductions in
hippocampal protein levels of IFN-γ and IL-1β, as well as increases in peroxisome proliferator-
activated receptor (PPAR)γ compared to control-fed animals 3 hours following icv infusion of
amyloid-β 1-40 118, 119. Another two studies used the same icv model but fed EPA 120 or DHA 121
for 27 days and identified dose-dependent reductions in hippocampal mRNA and protein for
CD11b, GFAP, IL-1β and TNF-α 7 days following icv infusion of amyloid-β relative to rats
consuming control chow. One study crossed triple transgenic (3xTg-AD) mice with fat-1 mice, a
transgenic animal expressing an n-3 desaturase gene that allows it to convert n-6 to n-3 fatty
acids, and detected lower levels of GFAP protein in the cortex of 3xTg-AD mice expressing the
15
fat-1 gene after 18 months 122. In contrast, Parrott et al. noted a deterioration in cognitive
functioning and an increase in hippocampal gene expression of TNF-α when TgCRND8 mice
were fed a whole food diet containing freeze-dried powdered fish, fruits and vegetables 123. As
the diet contained multiple interventions, it cannot be determined whether this increase in
inflammatory markers is attributable to the fish feeding 123.
Together, the animal data show that n-3 PUFA feeding decreases AD symptoms and pathology
in rodents, and that this may be associated with reductions in markers of neuroinflammation.
1.3.2 Epidemiology
A recent meta-analysis of epidemiological studies measuring consumption of either fish or DHA
showed a reduction in the risk of AD124. One serving of fish a week was associated with a 7%
risk reduction, while consuming 2 or 4 servings a week was associated with a 21% or 29%
reduction in risk respectively. Estimated dietary DHA intakes as low as 100mg of DHA per day
were associated with a 37% reduction in risk. The association also did not reach significance for
blood levels of DHA (Relative risk 0.85-1.01). There was no association between total dietary
PUFA, or EPA intake and AD, suggesting that DHA in particular may be protective.
16
Table 1-1: Studies examining neuroinflammatory markers in AD molecules with n-3 interventions
Author
(Year)
AD Model Species N-3 PUFA Treatment Timing of inflammation
measurement
Inflammatory Outcome
Minogue
(2007) 119
icv aβ 1-40
Rat 125 mg EPA / day vs MUFA x 4 weeks 3 hours post-surgery ⬇ IFN-γ, IL-1β protein
Lynch
(2007) 118
icv aβ 1-40
Rat 125 mg EPA / day vs MUFA x 4 weeks 3 hours post-surgery ⬇ IL-1β protein
Lebbadi
(2014) 122
3xTg-AD Mouse Fat-1 cross 12 or 20 months old ⬇GFAP
⬌iPLA2, cPLA2, protein
Parrott
(2015) 123
TgCRND8 Mouse Whole food diet containing salmon, fruits
and vegetables
2.46 mg DHA/gram
After 7 months feeding ⬆ TNF-α mRNA
Wen
(2016) 120
icv aβ 1-40
Rat 150 or 300 mg/kg/day EPA x 27 days 13 days post-surgery ⬇ CD11b, GFAP, TNF-α, IL-
1β mRNA and protein
Wen
(2016) 121
icv aβ 1-40
Rat 300 mg/kg/day DHA-PS or DHA PC x 27
days
27 days post-surgery ⬇ CD11b, GFAP, TNF-α, IL-
1β mRNA and protein
Aβ: amyloid-β; CD: cluster of differentiation; DHA: docosahexaenoic acid; EPA: eicosapentaenoic acid; GFAP: glial fibrillary acidic protein; icv:
intracerebroventricular; IFN: interferon; IL: interleukin; MUFA: monounsaturated fatty acid; PC: phosphatidylcholine; PS: phosphatidylserine; TNF:
tumor necrosis factor
17
1.3.3 Clinical data
A recent Cochrane review of n-3 PUFA supplementation in AD included 3 trials of 1750-2300
mg/ day of EPA+DHA for 6-19 months in mild to moderate AD125-127. It concluded that there
was no evidence of benefit for cognitive function, memory, activities of daily living, quality of
life or dementia severity128. Though null for its primary end-points, one of the trials included in
this review, the OmegAD study, identified a slower rate of cognitive decline over 6 and 12
months in patients with the mildest cognitive dysfunction (scoring over 27 on the Mini-Mental
State Exam)125. Similar results were seen in a study by Chiu et al., which was excluded from the
Cochrane review because it was less than 26 weeks (the Chiu trial is 24 weeks). Treatment with
1.8 grams of mixed DHA and EPA per day had no effect on cognition in the AD group, however
a decreased rate of cognitive decline was observed in patients with mild cognitive impairment129.
This suggests that n-3 PUFA may be more effective at preventing cognitive decline in milder
forms of dementia or prodromal AD, rather than treating established AD. The Cochrane review
excluded studies of patients without clinical dementia, and therefore could not address this point.
In support of this theory, a recent meta-analysis of 6 studies that examined the effect of a 400-
1800 mg per day of mixed DHA and EPA on cognitive decline in elderly patients identified a
protective effect on Mini-Mental State Exam scores130.
Depositions of amyloid-β, neurofibrillary tangles, synaptic-dysfunction and loss of brain volume
are all thought to begin appearing in cognitively normal or pre-clinical patients years or decades
before AD diagnosis (Figure 1-3)1, 11. If n-3 PUFA reduce the risk of AD as the epidemiological
data suggests, it is likely that they do so by interfering with the progression of these pathological
features over many years during these pre-clinical stages. Thus, studies in patients with
established AD may occur too late in the disease process for n-3 PUFA to have a discernable
benefit.
18
Figure 1-3: Model of appearance of biomarkers of AD over the life course
Taken from: Sperling R.A et al. (2011) J. Alzheimer’s and Dementia. 7(3): 280-2921
Neuropathological features of AD precede the appearance of clinical or pre-clinical AD
symptoms. From left:
i) Amyloid-β appearance measured in CSF or via PET imaging
ii) Synaptic function measured via glucose utilization (fluorodeoxyglucose PET) or by
functional magnetic resonance imaging (MRI)
iii) Tau or phosphor-tau in the CSF
iv) Changes in brain structure measured via MRI
v) Declines in cognitive function
vi) Clinical progression of AD
i
)
ii iii iv v vi
19
1.3.4 Mechanisms
As DHA is the main n-3 PUFA species in the brain, present at 250-300- fold higher levels than
EPA131 it is thought to be the main n-3 PUFA responsible for modulating neuroinflammation.
Within the brain, DHA is esterified in the sn-2 position of phospholipids, primarily phosphatidyl
serine and phosphatidyl ethanolamine104. Calcium independent phospholipase A2 (iPLA2) can
cleave DHA from the phospholipid, releasing it into the intracellular free pool. It can then act as
a precursor for docosanoids, such as D-series resolvins and neuroprotection D1 (NPD1) via
lipoxygenase (15-LO) or maresins via 12-lipoxygenase (12-LO) (reviewed in 97, 104). Levels of
brain NPD1 are lower in animal models of AD132, while lower levels of maresin 1112, resolvin
D2112 and NPD1111, 112 have been reported in post-mortem brain samples from human subjects,
suggesting that reductions in these molecules may contribute to AD. A protective role is
supported by studies showing that NPD1 and resolvin D1 promote amyloid-β phagocytosis while
decreasing inflammatory cytokine production in cultured microglia and peripheral mononuclear
cells 112, 132, 133.
In contrast to DHA, the n-6 PUFA, ARA, is the precursor to a variety of pro-inflammatory
eicosanoids. In response to insult or immune activation, cytosolic phospholipase A2 (cPLA2)
cleaves ARA from the membrane, allowing it to enter the free fatty acid pool. ARA can be
metabolized by COX-2 to produce prostaglandins and thromboxane, by cytochrome p450, 12-
LO or 15-LO to produce hydroxyeicosatetraenoic acids (HETEs), or by 5-LO to produce
leukotrienes104 (Figure 1-2). ARA can also be the precursor to a pro-resolving mediator through
metabolism by 15-LO, lipoxin A4. Higher levels of HETEs and PGE2 and lower levels of lipoxin
A4 have been reported in the brains of patients with AD112, 134-136, implicating changes in the
production of these molecules in disease etiology. DHA occupies the same position in the
phospholipid membrane as ARA, and concentrations of these molecules are somewhat inversely
correlated in the brain137. It is possible then, that in addition to direct anti-inflammatory and pro-
resolving effects of n-3 PUFA and their associated mediators, increasing brain levels of DHA
may also indirectly decrease neuroinflammation by displacing ARA, and thus lowering the
production of pro-inflammatory lipid mediators.
20
DHA may also be protective in AD via other mechanisms.
DHA is also a precursor to an ethanolamide referred to as synaptamide, a member of the
endocannabinoid family. Synaptamide promotes the growth of neurons, development of
synapses, and synaptic activity138. Brain concentrations of synaptamide are related to dietary
intake of DHA139, so increased synaptamide is a potential mechanism by which DHA
consumption could exert protective effects in AD, though no one has yet measured or tested
synaptamide in patients with AD or in an AD model.
DHA can also be protective in AD by decreasing levels of amyloid-β. DHA reduces the activity
of β- and γ-secretase enzymes, which shifts the cleavage of APP towards the non-amyloidogenic
pathway140. It also reduces production of the longer species amyloid-β that form plaques both in
vitro 141 and in vivo 142, and reduces the amount of amyloid-β plaques in transgenic models of
AD113. Resolvin D1 and neuroprotection D1 (NPD1), lipid mediators derived from DHA, also
increase the phagocytosis of amyloid-β in vitro112, 132, 133. Thus, DHA can influence AD
pathology by both decreasing amyloid-β production and by increasing its clearance.
DHA also has neuroprotective effects, and has been shown to prevent neuronal death in AD and
other disease models113, 117. It is possible that these are direct effects of DHA, direct effects of its
derivatives such as synaptamide or pro-resolving lipid mediators, or indirect effects via a
reduction in harmful immune activation, or via a reduction in cytotoxic amyloid-β.
1.4 Summary
In summary, inflammatory markers are elevated in AD, and interventions that decrease
inflammation also tend to be protective against neuronal death and cognitive decline. N-3 PUFA
and mediators derived from them are both anti-inflammatory and pro-resolving. These PUFA,
particularly DHA, are also protective in human epidemiological and animal studies, and possibly
in clinical trials of patients with mild-cognitive impairment. It is possible that DHA is protective
in AD via its immune-modulatory properties.
21
1.5 Objectives and Hypotheses
The overall goal of this thesis was to investigate the resolution of neuroinflammation as a
mechanism underlying the potential protective effects of n-3 PUFA, particularly DHA, in AD.
We hypothesize that neuroinflammation is an important pathological feature of AD, and that
increasing brain n-3 PUFA will decrease the neuroinflammatory response to amyloid-β.
1.5.1 Specific Objectives
1. To determine whether neuroinflammation (via markers of microglia) is a consistent
neuropathological feature of AD – Chapter 2
2. To determine whether and how changing brain n-3 PUFA modifies the
neuroinflammatory response to amyloid-β via:
a. Microglia – Chapter 3
b. Astrocytes – Chapters 3&4
c. Neuroinflammatory gene expression – Chapter 5
3. To determine whether changes in neuroinflammation associated with n-3 PUFA are
associated with changes in brain levels of bioactive lipid mediators - Chapter 5
1.5.2 Hypotheses
1) Microglial markers will be elevated in the brains of patients with AD relative to controls
2) Increasing brain n-3 PUFA will reduce the neuroinflammatory response to amyloid-β
3) Modulation in the neuroinflammatory response will be accompanied by increases in pro-
resolving lipid mediators, and decreases in pro-inflammatory lipid mediators
22
Chapter 2: Markers of microglia in post-mortem brain
samples from patients with Alzheimer’s Disease: a
systematic review
Kathryn E. Hopperton M.Sc, Dana Mohammad, Marc-Olivier Trépanier PhD, Vanessa Giuliano,
Richard P. Bazinet PhD
Paper accepted in Molecular Psychiatry
Contributions:
KEH designed the search with the assistance of MT, reviewed the articles returned by the search
for eligibility with the assistance DM, reviewed all data extraction, and wrote the paper. DM,
MT and VG assisted with the full text assessments, data extraction, and provided feedback on the
paper. RPB oversaw the project, and provided feedback on all steps.
23
2.1 Abstract
Background: Neuroinflammation is proposed as one of the mechanisms by which Alzheimer’s
Disease pathology, including amyloid-β plaques, leads to neuronal death and dysfunction.
Increases in the expression of markers of microglia, the main neuroinmmune cell, are widely
reported in brains from patients with Alzheimer’s Disease, however the literature has not yet
been systematically reviewed to determine whether this is a consistent pathological feature.
Methods: A systematic search was conducted in Medline, Embase and PsychInfo for articles
published up to February 23rd, 2017. Papers were included if they quantitatively compared
microglia markers in post-mortem brain samples from patients with Alzheimer’s Disease and
aged controls without neurological disease.
Results: One-hundred and thirteen relevant articles were identified. Consistent increases in
markers related to activation, such as major histocompatibility complex II (36/42 studies) and
cluster of differentiation 68 (17/20 studies), were identified relative to non-neurological aged
controls, whereas other common markers that stain both resting and activated microglia, such as
ionized calcium-binding adaptor molecule 1 (10/20 studies) and cluster of differentiation 11b
(2/5 studies) were not consistently elevated. Studies of ionized calcium-binding adaptor molecule
1 that used cell counts almost uniformly identified no difference relative to control, indicating
that increases in activation occurred without an expansion of the total number of microglia.
White matter and cerebellum appeared to be more resistant to these increases than other brain
regions. Nine studies were identified that included high pathology controls, patients who
remained free of dementia despite Alzheimer’s Disease pathology. The majority (5/9) of these
studies reported higher levels of microglial markers in Alzheimer’s Disease relative to controls,
suggesting that these increases are not solely a consequence of Alzheimer’s Disease pathology.
Conclusions: These results show that increased markers of microglia are a consistent feature of
Alzheimer’s Disease, though this seems to be driven primarily by increases in activation-
associated markers, as opposed to markers of all microglia.
24
2.2 Introduction
Elevations in neuroinflammatory markers are widely reported in Alzheimer’s Disease (AD) in
animal models68, 70, 143 and human subjects.28, 51, 53, 55 This has contributed to the development of
the neuroinflammatory hypothesis of AD, which suggests that aberrant activation of immune
cells may drive neuronal death and dysfunction in AD.78 This is supported by genome-wide
association studies that have identified polymorphisms in inflammation associated genes as risk
factors for the development of AD.144-146
Microglia are the resident immune cells of the brain, and are thought to be the main cells
responsible for initiating the immune response to AD pathology. Several of the inflammation-
associated genetic risk factors for AD, including human leukocyte antigen (HLA)-DRB1/B5144,
cluster of differentiation (CD)3365, triggering receptor expressed on myeloid cell (TREM) 264, 145
and phospholipase C γ2145 are highly expressed in microglia where they are involved in cell
function and activation. This suggests that aberrant microglial activation is a causal factor in the
development of AD, as opposed to a consequence of AD pathology. While it is commonly
accepted that there are increased microglia markers in the brains of patients with AD relative to
controls, no one has yet systematically synthesized the literature to see if this is supported by the
totality of the evidence. Here, we describe the results of a systematic review examining microglia
in post-mortem human brain samples from patients with AD and healthy controls. We find that
some markers associated with cell activation, such as major histocompatibility complex (MHC)II
and CD68, are consistently increased in the AD brain, but that studies using other common
microglial markers that stain both resting and activated cells, such as ionized calcium-binding
adaptor molecule (Iba)1 and CD11b, are heterogeneous and do not demonstrate a consistent
elevation. We further identify brain regions, such as the white matter and the cerebellum, that
appear to be more resistant to inflammation in AD.
2.3 Methods
The systematic search was conducted in MEDLINE, Embase and PsychINFO covering articles
published up to February 23rd, 2017. The search protocol was developed based on Preferred
Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and World Health
Organization (WHO) Review Protocol Template Guidelines where applicable for a systematic
25
review of descriptive (non-interventional) data. The search queried the following terms with
numerous synonyms and related words as both MeSH/Emtree terms (where applicable) and as
keywords (for title, abstract and keyword searches): Alzheimer’s Disease AND brain AND
inflammation. The additional term “AND post-mortem” with its synonyms and related words
was included for the Embase and PsychINFO searches. A full list of terms used for each search
can be found in Appendix 2.
Studies were included if they used human brain samples from patients with AD, included a
measure of inflammation, were conducted post-mortem, and included a comparison to a control
group without AD or a confounding neurological disease. This review initially set out to include
all inflammatory markers, including for astrocytes, complement, cytokines, lipid mediators and
other immune cells, however the title and abstract screening returned 744 eligible papers, making
it unfeasible to summarize all the evidence in a single paper. It was decided to proceed with a
review of microglia, as these are the main immune effector cells in the brain, and are the source
of many of the other inflammatory mediators measured in other studies. Microglia terms in the
initial search include the MeSH term: neuroglia and neurogenic inflammation, the Emtree terms:
neurogenic inflammation, glia and leukocyte antigen, and keywords: microglia, HLA, MHC,
CD11b, CD68, Iba1, OX-42 and CD45 along with their synonyms and alternate spellings (see
Appendix 2 of the Supplementary Materials for the full list of terms). In addition, any other
papers returned in the full search that used other markers identified by the study authors as being
specific to microglia or their activation were included. Studies were excluded if they were not in
English or not published in full in a peer-reviewed journal. Papers that measured markers
associated with an M1 or M2 phenotype, such as IL-1β, TNFα, IL4 or IL10, but that did not
localize these markers specifically to microglia, were not included.
Data from included studies were extracted by at least two independent reviewers (KEH and DM,
VG or MT), with a third reviewer employed in the case of a conflict. Extracted data included
origin of brain samples, number of subjects, sex, age, APOE genotype, histological confirmation
of AD status, Braak stage, control history of neurological or psychiatric disease, post-mortem
interval (length of time between death and retrieval of brain), brain regions examined,
medications at time of death, anti-inflammatory drug use at death, technique for measuring
microglia, marker of microglia used, and the results of the comparison between the AD subjects
26
and healthy controls. Where available, information on relationship between labelled microglia
and amyloid-β plaques or neurofibrillary tangles was also extracted. The terms “higher” or ↑ and
“lower” or ↓ are used in the text and tables referring to significantly higher or lower levels of the
microglial marker used in the study relative to the non-neurological aged control group. As the
type of outcome reporting was extremely heterogeneous, results were reported as higher, lower
or unchanged for AD relative to control as identified by the study authors. Meta-analysis or other
summary statistics were not used because of the large variability in assessment techniques and
brain regions examined between studies. Data were listed as ‘Not Reported’ if the relevant
information could not be found in the article, or in a previous article specifically referred to by
the authors in the methods section. In a few instances where information in the article was
unclear, the corresponding authors were contacted to provide clarification or additional detail.
27
Figure 2-1: Flow diagram of systematic search
28
2.4 Results
A total of 22 229 articles were screened, of which 757 met inclusion for full text review for
inflammatory markers, including 224 that examined microglia (Figure 1). One hundred and
thirteen papers that quantified microglia and compared the results statistically between control
and AD groups were fully extracted and are presented in the tables below (See Figure 2 for a
visual summary of the results). Fifty-three papers that made a non-quantitative comparison
between AD and control, often as a qualitative assessment of the intensity of
immunohistochemistry staining, were not fully extracted but are summarized under the “Non-
quantitative comparisons” heading.
The microglial markers analyzed in this review all serve distinct functions within the cell, and as
such, the interpretation of an up or down-regulation of expression will vary depending on the
marker used. The functions of all major markers included in this review, their association with
M1 or M2 polarization and their expression on other cell types besides microglia is shown in
Appendix 1. M1 and M2 polarization is increasingly regarded as an over-simplification of many
diverse functions of activated microglia, however it is used here to provide a reference for
whether a marker is associated with the pro-inflammatory, or phagocytic microglial phenotype.
As shown in the table, it is important to note that all the major markers in this review are also
expressed on other cell types, particularly on macrophages, so it is possible that other infiltrating
or perivascular immune cells may have contributed to the results.
2.4.1 Major histocompatibility complex (MHC) II
MHC II is expressed on the surface of antigen presenting cells and is responsible for antigen
recognition and the activation of the adaptive immune system. Within the brain, MHC II is
primarily expressed on microglia, where it is generally considered a marker of activated cells,
though it may have weaker expression in resting cells.147 Forty-three papers were identified that
quantitatively compared markers of MHC II between AD and control in post-mortem human
brains (Table 2-1). The majority (41/43) used immunohistochemistry for HLA-DR or for
multiple isoforms of HLA (HLA-DR-DQ-DP) quantified by cell counts, scoring or staining area,
29
while other techniques included gene expression by qPCR or protein quantification by Western
blot.
Thirty-seven papers reported higher MHC II in AD relative to control brains in at least one of the
measured brain regions28, 148-182, while 6 identified no difference between AD and control183-188,
and 5 identified no difference in at least one brain region.154, 165, 167, 174, 177 Increased MHC II
staining, counts or expression was noted in: the entorhinal cortex28, 151, 153, 168, 176, 180, frontal and
temporal gyri28, 152, 157, 165, 166, 172, 178, 189, hippocampus 153, 158, 160, 163, 165-171, 173, 175-177, 180 and
frontal151, 154, 158, 173, 174, 177, 179, 181, 182, 185, temporal149-151, 161, 163, 168, 176, 177, 181, 185 and occipital
cortices.158, 168, 174, 176, 177 HLA-DR was found to increase with AD plaque stage and clinical
dementia rating in the entorhinal cortex, hippocampus and occipital and temporal cortices. 176 180
HLA-DR stained microglia were reported to take on an activated morphology in AD152, 156, 178, 181,
182, indicating that AD pathology may stimulate the activation of microglia and upregulation of
MHC II. In contrast, seven papers identified no difference in MHCII between AD and control in
several regions including the hippocampus 154,188, frontal, temporal and parietal grey matter 185,
the temporal polar association cortex 187, and the subventricular zone of the lateral ventricle186.
Of five studies examining HLA-DR immunoreactivity in the cerebellum, four found no
differences between AD and control 158, 167, 177, 184, suggesting that regional differences may
explain some of the null findings. Overmyer et al. noted that women had higher HLA-DR
reactivity than men in AD, while the reverse was true in the controls185, suggesting that sex
differences in the AD and control groups could also contribute to between study variability.
HLA-DR reactivity increased in both AD and control patients over 75 in one study185, but
decreased in AD patients over 80 in another161, making the effect of age on HLA-DR expression
unclear. APOE genotype can also impact the results, with the ε4 risk allele increasing HLA-DR
positive cells or area in the frontal and temporal cortices181 and middle frontal gyrus189.
30
Table 2-1: MHC II
First Author
Brain
bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histological
ly
Confirmed
Braak
stage
C history of
neurological
or
psychiatric
disease
PMI
(h) Brain Region Technique Marker
Results (AD vs C
unless otherwise
specified)
Akiyama,
1990 NR
AD: 9
C: 6
AD: 77
C: 69 NR NR NR NR
No
neurological
disuse
All
within
2-12 h Temporal lobe IHC
HLA-
DR ⬆
Carpenter,
1993 NR
AD: 5
C: 5
AD 4/1
C: 5/0
AD:
75.4
C: 73.6 NR
Khachaturia
n NR
No history
of
neurological
or systemic
diseases
affecting the
brain
AD:
3.6
C: 2.4
Grey matter of
the middle
temporal
gyrus IHC
HLA-
DR
(LN3)
⬆ density of cells per
mm, staining area and
percent of area
Most cells of resting
morphology in Cs vs.
activated in AD (not
compared
statistically)
Dal Bianco,
2008 NR
AD: 9
C: 15
AD: 0/9
C: 13/2
AD: 81
C: 70 NR
Braak,
CERAD
AD:
IV: 2
V: 4
VI: 3
No
neurological
disease or
brain lesions NR
Cortical
areas of the
temporal lobe,
including
entorhinal
cortex,
hippocampus
and temporal
cortex
Immunoc
ytochemis
try MHCII ⬆ MHCII
31
Desai, 2009
Religiou
s Orders
Study
AD:8
C: 7
AD: 3/5
C: 4/3
AD:
85.2
C: 79.5 NR
NIA-
Reagan
Criteria
AD: III-
VI NR
AD:
5.4 C:
15.5
Hippocampus,
midfrontal
cortex, locus
ceruleus,
substantia
nigra pars
compacta IHC
HLA-
DR-DQ-
DP
(Cr3/43)
⬆ in the midfrontal
cortex, and locus
ceruleus
⬌ in density of HLA-
DR microglia in
hippocampus
Dickson,
1990 NR
AD:
15
C: 14
AD:
2/13
C: 3/11
AD:
76.6
C: 67.8
(as
young
as 6
months) NR
Khachaturia
n NR
Depressive
psychosis,
manic
depressive
psychosis
(n=2),
Parkinson’s
Diseae
(n=1)
AD:
6.4
C: 6.3
Midfrontal
cortex (BA9) IHC
HLA-
DR,
double
staining
with
Leu-M5
(CD11c)
⬆ Activated microglia (morphology) ⬆ proportion of HLA-1DR+ microglia in grey matter ⬌ in white matter Results not compared statistically
Dhawan,
2012
Universi
ty of
Washin
gton
ADRC NR NR NR NR NR NR NR NR Temporal lobe IHC
HLA-
DR
⬆ HLA-DR+ positive
microglia
Egensperger
, 1998
Institute
of
Neuropa
thology
of the
Universi
ty of
Munich
AD:
20
C: 5
AD:
5/15
C: NR
AD:
75.9
C: 71.6
APOE:
AD:
3/3: 7
3/4: 10
4/4: 3
C: NR
Braak,
CERAD NR
No
neurological
or
neuropathol
ogical
disorder NR
Frontal and
temporal
cortex IHC
HLA-
DR-DQ-
DP
(CR3/43
)
⬆ counts and area
- Plaque associated
microglia demonstrate
activated morphology
- Microglia number
correlates with
neuritic plaques and
NFT
32
Flanary,
2007 BSHRI
AD: 4
HPC:
3
C: 4
AD: 1/3
HPC:
2/1 C:
3/1
AD:
82.0
HPC:
87.7
C: 81.5 NR Yes NR NR
AD:
2.4
HPC:
3.1
C: 2.6
Superior
frontal and
temporal gyri IHC
HLA-
DR
⬆ dystrophic
microglia in AD vs C,
HPC vs. C and
AD+HPC vs. C
⬆ HPC vs AD (not
clear if statistically
significant)
Giulian,
1995 NR
AD: 6
C: 5 NR NR NR CERAD NR
No
neuropathol
ogical
disorder NR
Cerebellum,
hippocampus,
frontal,
occipital,
parietal
cortices, and
neocortical
white matter
IHC,
confocal
microscop
y
HLA-
DR
⬆ hippocampus,
frontal, occipital and
parietal cortices ⬌
cerebellum, white
matter
Gouw, 2008
NBB
and
Vrije
Universi
ty
Medical
Centre
AD:
11
C: 7
AD: 3/8
C: 3/4
AD:
82.6
C: 78.3 NR
Braak,
CERAD
AD: V
C: I
All had
white matter
hyperintensi
ties (small
vessel
disease),
other
neurological
diseases
excluded
AD:
6.1
C:
<24hr
Normal white
matter and
white matter
hyperintensiti
es in frontal,
parietal and
prefrontal
lobes IHC
HLA-
DR
⬆ overall than C
⬆ higher in white
matter
hyperintensities than
normal white matter
Halliday,
2000
Dementi
a clinics
at the
Repatria
tion
General
Hospital
Concord
and
Lidcom
be
Hospital
in
Sydney,
AD:
12 C:
10 NR
AD: 79
C: 77
APOE:
AD:
3/4: 1
C:
3/4: 1
CERAD,
NIA-
Reagan
Criteria NR NR
All
<45 h,
mean
19
Anterior
cingulate,
hippocampal,
inferior
temporal,
parahippocam
pal and
superior
frontal regions IHC
HLA-
DR
⬆ in AD vs C
⬌ AD patients taking
NSAIDS and AD
patients not taking
NSAIDS
33
Australi
a
Hensley,
1995 NR
AD: 3
C: 3
Whole
sample:
AD:
9/13 C:
4/3
Whole
sample:
AD:
78.1
C: 79.7 NR
Khachaturia
n, Mirra NR
No history
of
neurological
and/or
psychiatric
disorders
Whole
sampl
e:
AD:
4.6 C:
4.4
Cerebellum,
hippocampus,
inferior
parietal lobule IHC
HLA-
DR ⬆ in all regions
Hoozemans,
2005 NBB
Braak
stage
0: 5,
I-II:
16,
III-IV:
10,
V-VI:
9
Braak
stage
0: 3/2
I-II:
6/10
III-IV:
0/10
V-VI:
3/6
Braak
stage
0: 62
I-II: 83
III-IV:
89
V-VI:
76 NR Braak
0-VI
(not
divided
into C
and AD) NR
Braak
stage
0: 8
I-II:
7.5
III-IV:
6.5
V-VI:
5
Temporal
cortex IHC
HLA-
DR-DQ-
DP
(CR3/43
)
⬆ with increasing
Braak NFT or plaque
stage (p<0.05 for
trend), significant for
NFT group V-VI vs O
Hoozemans,
2011
Netherla
nds
Brain
Bank
AD:
19
C:19
AD:
3/16 C:
8/11
AD:
83.5
C: 76.8
APOE4:
AD: 12
C: 8 Braak
AD:
avg IV
C: avg I NR
AD:
5.1 C:
8.6
Mid-temporal
cortex IHC HLA
⬆
⬆ in AD patients
younger than 80
compared to those
older than 80
34
Imamura,
2001 NR
AD: 6
C: 6
AD: 2/4
C: 2/4
AD:
65.4
C: 62.8 NR Yes NR NR NR Temporal lobe IHC
HLA-
DR ⬆
Itagaki,
1988 NR
AD:
10
C: 5 NR NR NR Yes NR
No
neurological
complicatio
ns
All
within
2-12
Mixed:
hippocampus
and temporal
cortex
IHC –
semi-
quantitativ
e scoring
of staining
HLA-
DR,
LCA ⬆
Jantaratnota
i, 2010
Kinsme
n
Laborat
ory
Brain
Bank at
the
Universi
ty of
British
Columbi
a
AD
severe
: 9
AD
mild:
6 C: 9 NR
AD
severe:
74.2
AD
mild:
77.7
C: 83 NR
Braak, NIA-
Reagan
Criteria NR
No
neurological
disorders NR
Medial
temporal
cortex IHC
HLA-
DR ⬆
Kellner,
2009 NR
AD:
48
C: 48
AD:
19/29 C:
24/24
AD:
80.3
C: 77.5 NR
Braak,
CERAD
AD:
II-VI
(38>4)
C:
I-III (45
= 0) NR NR
Entorhinal,
frontal cortex,
temporal
cortex IHC
HLA-
DR ⬆
35
Korvatska,
2015
Universi
ty of
Washin
gton
Neuropa
thology
Core
Brain
Bank
AD
(norm
al
TRE
M2):
6
AD
(with
TRE
M2
R47H
varian
t): 7
C: 3 NR
Whole
sample:
84.9 NR CERAD
AD:
III: 1
V: 4
VI: 1
AD
R47H:
V: 6
VI: 1
C:
0: 1
I: 1
III: 1
NR, One C
CERAD
score A and
one B
Whole
sampl
e: 4.5
Frontal Lobe:
grey and
white matter
Hippocampus:
CA1, hilus,
parahippocam
pal gyrus and
white matter IHC MHCII
⬆ staining in
hippocampus CA1,
hilus,
parahippocampal
gyrus and white
matter ⬆
activated counts in
hippocampus white
matter
Lopes, 2008 BSHRI
AD:7
Young
C: 3
Aged
C: 7
HPC:
7
AD: 4/3
Young
C: 2/3
Aged C:
6/1
HPC:
5/2
AD:
80.3
Young
C: 36.3
Aged
C: 80.0
HPC:
83.4 NR Yes NR NR
AD:
2.3
Young
C: 2.8
Aged
C: 2.5
HPC:
2.90
Amygdala,
hippocampus,
superior
frontal gyrus,
superior,
middle, and
inferior
temporal gyri
IHC and
Morphom
etric
Analyses
HLA-
DR
Microglia counts
HLA-DR: ⬆ vs all
other groups
Dystrophic microglia
HLA-DR: ⬆ vs C, ⬌
vs HPC
Lue, 1996 NR
AD: 6
HPC:
6
C: 6
AD: 3/3
HPC:
5/1
C: 2/4
AD: 81
HPC: 78
C: 77 NR Markesbery NR
NR, Cs had
minimal AD
pathology or
sufficient
plaques or
tangles to
qualify for
AD
diagnosis
AD:
3.2
HPC:
3.2
C: 1.9
Entorhinal
cortex,
superior
frontal gyrus IHC
HLA-
DR
(LN3) ⬆ AD > HPC > C
36
Lue, 2001 BSHRI
AD:11
C:10
AD: 5/6
C: 4/6
AD:80.8
C: 80.5
APOE:
AD:
3/4: 4
4/4: 4
C:
3/4: 3
4/4: 1
Braak,
CERAD
AD: IV-
VI C:
I-III NR
AD:
2.6 C:
2.3
Hippocampus,
cerebellum,
superior
frontal gyrun IHC
HLA-
DR
(LN3)
⬆ in the hippocampus,
parahippocampal
gyrus and superior
frontal gyrus
⬌cerebellum
Matsuo,
1996 NR
AD: 8
C: 5 NR NR NR Yes NR
Neurologica
lly normal
All 2-
24
Angular,
entorhinal,
hippocampus,
occipitotempo
ral cortices IHC
HLA-
DR
⬆ HLA-DR (more
intense staining in
more severe AD
cases)
McGeer,
2000
Patholo
gy
Departm
ent of
the
Universi
ty
of
British
Columbi
a NR NR NR NR NR NR NR NR Hippocampus PCR
HLA-
DR ⬆
McGeer,
1988
Autopsy
Service
of the
Universi
ty of
British
Columbi
a
AD: 9
C: 7
AD: 5/4
C: NR
AD:
77.2
C: 73.4 NR NR NR
No
neurological
disorders
All >
3
days,
most
>10 h
Hippocampus,
substantia
nigra IHC
HLA-
DR ⬆
37
McGeer,
1987 NR
AD: 6
C: 5 NR
AD: 78,
C:73 NR NR NR
No
neurological
disease
All
within
2-12 Hippocampus IHC
HLA-
DR ⬆
Minett,
2016
Medical
Researc
h
Council
Cognitiv
e
Functio
n
andAgei
ng
Study -
six
centres
in UK
AD:
83
C: 130
AD:
30/53
C: 64/66
AD: 89
C: 84 NR CERAD NR NR NR
Middle frontal
gyrus IHC
HLA-
DR
⬌
Narayan,
2015
Neurolo
gical
Foundat
ion of
New
Zealand
Human
Brain
Bank
(Centre
for
Brain
Researc
h,
Universi
ty of
Aucklan
d)
AD:
14
C: 17
AD: 6/8
C: 10/7
AD:
74.1
C: 58.9 NR
Braak or
CERAD NR
Neurologica
lly normal,
four have
high plaque
load
AD:
16.7
C:
16.7
Inferior
temporal
gyrus IHC
HLA-
DP-DQ-
DR
⬆
HLA-DP, DQ, DR
positive cells correlate
with Iba1 positive
cells in C but not AD
38
Overmyer,
1999
Kuopio
Universi
ty
Hospital
AD:
73
C: 22
AD:
12/61,
C: 12/10
AD: 84
C: 78
APOE4
carriers:
AD: 31
C: 7
CERAD
Patients
with
possible AD
and vascular
dementia
included NR
NR - 55%
demonstrate
d plaque
and tangles,
32% enough
for
diagnosis of
possible AD
All
within
48
Grey and
white matter
of frontal,
temporal and
parietal
cortices IHC
HLA-
DR
⬌ with dementia
diagnosis (trend)
⬆ with CERAD in
grey matter
⬌ white matter
(counts and area)
⬌ with plaque
burden but ⬆
correlation with NFT
Parachikova
, 2007
Institute
for
Brain
Aging
and
Dementi
a Tissue
Reposit
ory, and
the
BSHRI
AD:
10
HPC:
10
C: 4
AD: 6/4
HPC:
4/6 C:
3/1
AD:
85.3
HPC:
86.6
C: 76.3 NR Braak
AD: IV-
V HPC:
1-V NR
AD:
2.6
HPC:
2.8
C: 3.0
Hippocampus
and prefrontal
cortex (gene
chip only)
Gene
chip,
Western,
IHC
GeneCh
ip:
MHCII
Western
: HLA-
DR-
DQ-DP
IHC:
CD4/43
GeneChip and
Western:
⬆ vs HPC+C (pooled)
IHC:
⬆ MHC II (not
quantified)
Pugliese,
2010
Neurolo
gical
Tissue
Bank
(Serveis
Cientific
o-
Tècnics)
,
Universi
tat de
Barcelo
na
AD: 7
C: 3
AD: 2/5
C: 1/2
AD:
84.0
C: 63.3 NR
Braak,
Newell
Criteria
AD:
II: 3
V: 1
VI: 3 NR
AD:
8.8
C: 5.1
Subventricular
zone of the
lateral
ventricle IHC
HLA-
DR
⬌ number of
microglia
⬆ Activated microglia
39
Rezaie,
2005
MRC
London
Neurode
generati
ve
Diseases
Brain
Bank
AD:
10
C: 10
AD: 4/6
C:7/3
AD:
79.3
C: 70.2
Not
reported CERAD NR
No history
of
neurological
disease or
neuropathol
ogy
AD:
20.9,
C:
43.2
Frontal blocks
included
agranular-
intermediate
frontal cortex
(BA 6/8),
cingulate
cortex (BA
24/32)
Occipital
blocks
included the
calcarine
sulcus (BA
17) and striate
cortex) IHC
HLA-
DR-DP-
DQ
(CR3/43
)
⬆ in frontal white
matter, occipital white
matter, plaque
associated frontal
grey matter, plaque
associated occipital
grey matter
⬌ in MHCII in
frontal grey matter, or
occipital grey matter.
Serrano-
Pozo, 2013
Massach
usetts
ADRC
Brain
Bank
AD:
40 C:
32
AD:
14/26
C: 13/19
AD:
81.3
C: 77.6
APOE4:
AD:
21/40
C: 5/27
NIA -
Reagan
Criteria NR
No clinical
history of
neurological
disorders
and did not
meet the
pathological
criteria
forany
neurodegen
erative
disease
AD:
18.0
C:
14.1
Temporal
polar
association
cortex (BA38)
IHC,
stereology
HLA-
DR-DQ-
DP
⬌ Total microglia
⬇Iba1+/MHC2-
microglia
⬆Iba1-/MHC2+
microglia
⬌Iba1+/MHC2+
microglia
⬆ Iba1+/MHC2+
microglia in APOE4+
⬌with microglia by
genotype
Shepherd,
2000
Collecte
d brains
from a
regional
brain
donor
program
for
neurode
generati
ve
diseases
in 1993
AD:10
C: 11 NR
AD:76
C: 71 NR CERAD
AD: V
or VI
No history
of
neurological
disease or
neuropathol
ogy
AD:
16 C:
21
Cortex and
hippocampus
(grey and
white matter) IHC
HLA-
DR
⬆ white and grey
matter
40
Szpak, 2001 NR
AD:
18 (7
had
Lewy
body
varian
t of
AD)
C: 6 NR
Whole
sample:
63-86
years
old NR CERAD NR
No
neuropathol
ogical
abnormality NR
Cortical layers
of limbic,
cingulate
cortex and
temporal
association
cortex IHC
CR 3/43
clone
HLA-
DP-DQ-
DR ⬆
Thal, 1998
Patholo
gical
Institute
of the
Universi
ty of
Leipzig
and
Universi
ty of
Frankfur
t
159
partici
pants
(68
non-
demen
ted, 24
and 19
in
GDS
scores
6 and
7) NR
Ages
46-93
(most
between
71 and
90) NR Braak
Whole
sample:
0: 23
I: 23
II: 42
III: 36
IV: 16
V: 13
VI: 6
No
confounding
neurological
diagnosis
All
within
12-72
Entorhinal
cortex,
hippocampus
(CA1, CA4),
occipital
region (BA
17), temporal
cortex IHC
HLA-
DR ⬆
Valente,
2012
Hospital
Clinic-
Universi
ty of
Barcelo
na
AD:7
AD
with
diabet
es: 7
C: 6
AD: 2/5
AD with
diabetes
: 5/2
C:3/3
AD:
83.9
AD with
Diabetes
: 73.0
C: 70.0 NR Braak
AD: VI
AD with
diabetes
: VI NR
AD:
8.9
AD
with
diabet
es:
11.5
C: 9.6 Hippocampus IHC HLA
⬌ AD vs C
⬆ AD + diabetes vs C
41
Van
Everbroeck,
2004 NR
AD:
10
C: 10 NR NR NR Braak
AD: at
least III-
IV
C: 0, Av
or A 1
Some had
protein
deposition
and some
had core
containing
plaques
(numbers
not given) NR
Cerebellum,
hippocampus
(CA1, CA4,
subiculum),
frontal,
temporal and
occipital
neocortices IHC
HLA-
DR
⬆ in grey matter and
hippocampus
⬌ in white matter
and cerebellum
Vehmas,
2003
Johns
Hopkins
Universi
ty
ADRC
and
Baltimo
re
Longitu
dinal
Study of
Aging
AD:9
HPC:
15
C:11
AD: 3/6
HPC:
10/5 C:
11/0
AD:
83.2
HPC:
86.3
C: 81.7 NR
Braak,
CERAD
AD: II-
V HPC:
I-IV C:
0-III
NR, free of
plaque NR
Mixed:
middle frontal
gyrus, middle
and superior
temporal
gyrus IHC
HLA-
DR
⬆ than C
⬌ high pathology C
Verwer,
2007 NBB
AD:
14
C: 7
AD:
4/10 C:
2/5
AD:
83.9
C: 79.0 NR Braak
AD:
IV: 3
V: 8
VI: 2
n/a: 1
C:
0: 3
I: 1
II: 1
III: 1
n/a: 1
No
neurological
causes of
death
AD:
4.6
C: 4.6 Neocortex IHC
HLA-
DR-DQ-
DP ⬌ p=0.08
42
Wilcock,
2015
Irvine
ADRC,
the
Marylan
d
Develop
mental
Disorder
s Brain
Bank
and the
Universi
ty of
Kentuck
y
Alzheim
er's
Disease
Center.
AD: 9
C: 9
IHC:
AD:6/2
C:3/6
qPCR +
Western
:
AD: 6/4
C: 12/4
IHC:
AD:
81.3, C:
81.6
qPCR +
Western
: AD: 80
C: 81.6 NR NR NR NR
IHC:
AD:
5.5, C:
3.3
qPCR
+West
ern:
AD:
6.8 C:
3.3 Frontal cortex
IHC for
HLA-DR
staining,
RT-qPCR
and
Western
for
expression
of M2 and
M1
markers
HLA-
DR
M1
markers:
IL1B,
IL6, IL-
12,
TNF-α
M2a
markers:
CH13L1
, IL1Ra,
IL-10,
MRc1,
M2b
markers:
CD86,
FCGR1
B M2c
markers:
TGFB
⬆ HLA-DR in AD vs
C
⬆ HLA-DR in AD vs.
AD+DS (in grey and
white matter)
- Pattern of increases
in both M1 and M2
markers: IL6, IL-12,
IL-10, CHI3L1,
TGFB1 in AD vs. C
Wojtera,
2012 NR
AD:4
C: 2 NR NR NR
NIA-
Reagan
Criteria NR NR NR
Mixed:
cerebellum,
cerebral
cortex IHC
HLA-
DR
⬌
⬌ in HLA-DR/CD68
ratio between AD and
C (activation)
43
Xiang, 2006
ADRC
of the
Mount
Sinai
School
of
Medicin
e
AD:
26
with
clinica
l
demen
tia
rating
0.5-5,
6
rated
5
(very
severe
)
C: 5
AD:
7/19 C:
0/5
AD:
88.7
C: 83.2 NR CERAD NR NR
AD:
4.2
C: 4.2
Entorhinal
cortex and
dorsal
hippocampus
(CA1
pyramidal cell
layer, DG
granule cell
layer and
upper
molecular
layer) IHC
HLA-
DR
Entorhinal cortex:
⬆ in grey and white
matter at CDR 5, in
grey matter only at
CDR 2
⬌ for CDR scores of
0.5 to 1 vs 0
Hippocampus :
⬆ in all regions for
CDR >2 vs 0, for
CA1 pyramidal layer
and upper molecular
layer for CDR 1 and
for the upper
molecular layer only
for CDR 0.5
- HLA-DR score
correlates with plaque
and tangle scores in
various regions
Table 2-1: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Alzheimer’s Disease Research Center (ADRC),
Apolipoprotein E (APOE), Average (Avg), Banner Sun Health Research Institute (BSHRI), Brodmann area (BA), Chitinase 3-like
(CHI3L), Cluster of differentiation (CD), Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Control (C), Cornu
ammonis (CA), Dentate gyrus (DG), Global deterioration scores (GDS), High pathology control (HPC), Human leukocyte antigen
(HLA), Hours (h), Immunohistochemistry (IHC), Interleukin (IL), Mannose receptor (MRc), Medical Research Council (MRC),
Neurofibrillary tangles (NFT), National Institute on Aging (NIA), Netherlands Brain Bank (NBB), Not reported (NR), Post-mortem
interval (PMI), Polymerase chain reaction based assays (PCR), Triggering receptor expressed on myeloid cells 2 (TREM2)
44
2.4.2 Ionized calcium-binding adaptor molecule 1 (Iba1)
Iba1 is a cytoplasmic protein expressed in monocyte lineage cells, and in the brain, is primarily
restricted to microglia.190 Although its expression is thought to increase with microglial
activation191, where it may be involved in membrane ruffling and phagocytosis192, it is
considered a marker of all microglia, rather than an activated subset.193 Twenty papers
quantitatively compared Iba1 between AD and control post-mortem human brains (Table 2-2).
Ten studies identified increases in Iba1 cell counts, staining intensity or expression in AD
compared to control samples in at least one brain region153, 194-201, while ten identified no
difference from controls165, 187, 202-209 and five reported lower Iba1 in AD than controls165, 187, 189,
208, 209 in at least one of the regions measured.
The results of studies measuring Iba1 are relatively heterogeneous. Higher Iba1 was noted in the
AD frontal cortex in four studies153, 196, 197, 201, but was the same as control in three others165, 203,
207 and lower in the white matter of the frontal lobe in one study.165 Iba1 positive cell density205
and gene expression207 was the same in AD and control in the temporal cortex in two studies, but
greater by Western or immunocytochemistry in three other studies.153, 194, 199 Similarly, while
Iba1 was higher in AD than control in the hippocampus in three studies153, 195, 200, it was
unchanged in five studies165, 202, 204, 206, 208, 209 and reduced in one study. 209 Iba1 was higher in the
entorhinal cortex153, 195 and inferior parietal cortex198 in AD compared to control, however this is
based off a limited number of studies. All but one of the ten studies that identified increases in
Iba1 in AD used expression based assays. The remaining positive study quantified plaque
associated microglia.153 In contrast, six of the ten null studies used cell counting, two of which
used stereology, the gold standard for quantifying cells without bias. Expression-based assays
like qPCR, Western blot, or intensity of immunohistochemistry staining indicate the amount of
Iba1 in a sample. An increase could reflect a change in cell numbers, cell size or function, as
Iba1 expression is thought to increase with microglial activation. In contrast, the studies using
cell counting for Iba1, which is expressed by all microglia191, 210, assess the absolute number of
microglia in the samples. The discrepancy between the studies using expression-based assays
and those that used cell counting suggests that Iba1 expression, and thus microglial activation,
increases in AD without affecting the absolute number of microglia.
45
Table 2-2: Iba1
First
Author Brain bank n Sex Age
AD
Geneti
c Risk
Factors
AD
Histologicall
y Confirmed
and criteria
Braak
stage
C history
of
neurolog
ical or
psychiatr
ic
disease
PMI
(h)
Brain
Region
Techniq
ue Marker
Direction of
results
Bachstett
er, 2015
University
of Kentucky
Alzheimer's
Disease
Center
AD:7
C: 9
AD:
4/3
C:
6/3
AD: 77
C: 86
AD:
N/A: 2
4/4: 1
3/4: 2
C:
3/4: 1 CERAD
AD:
~VI
C: ~II NR
AD:
4.2 C:
2.4
Hippocampu
s: CA1,
CA2/3,
CA4, DG,
subiculum
and adjacent
white
matter.
Morphology
assessed in
the CA1
only IHC Iba1
⬌Iba1 staining
or cell counts in
any hippocampal
area
⬌ in CA1 Iba1+
microglial
morphology
Griciuc,
2013
Massachuset
ts
ADRC
AD: 25
C: 15
AD:
7/18
C:
6/9
AD:
79.2
C: 79.9
APOE
carrier:
AD: 18
(8
homoz
ygous)
C: 5 (0
homoz
ygous)
NIA-Reagan
Institute
Criteria NR NR
AD: 17
C: 29
Frontal
cortex
IHC
(stereolo
gy),
Western Iba1
⬌ Iba1+
microglia (data
not shown)
⬌ Iba1 protein
(non-significant
increase)
46
Magistri,
2015 BSHRI
AD: 4
C: 4
AD:
1/4
C:
2/2
AD:
83.75
(not
exact,
one
age
just
listed
>90)
C: 83.5
AD: all
APOE
3/3 C:
APOE
2/3: 2
APOE
3/3: 1
NR: 1
NIA-Reagan
Criteria
AD:
V: 1
VI: 3
C:
I: 1
II: 3 NR
AD:
2.5 C:
2.5
Hippocampu
s
RNA seq
(gene
expressi
on) Iba1 ⬌
Nielsen,
2013 NBB
AD: 4
C: 5
AD:
1/3
C:
1/4
AD:
76.3
C: 77.6 NR Yes
AD:
III: 1
IV: 2
V: 1
C:
0: 1
I: 3
II: 1 NR
AD:
6.3
C: 6.1
Entorhinal
cortex,
hippocampu
s IHC Iba1 ⬆
Dal
Bianco,
2008 NR
AD: 9
C: 15
AD:
0/9
C:
13/2
AD: 81
C: 70 NR
Braak,
CERAD
AD:
IV: 2
V: 4
VI: 3
No
neurolog
ical
disease
or brain
lesions NR
Cortical
areas of the
temporal
lobe,
including
entorhinal
cortex,
hippocampu
s and
temporal
cortex
Immuno
cytoche
mistry AIF-1
⬆ AIF-1 near
plaque only
47
Davies,
2016
New South
Wales Brain
Bank
AD: 7
C: 5
AD:
3/4
C:
2/3
AD:
83.6
C: 83.0 NR
Braak,
CERAD,
National
Institute on
Aging-
Alzheimer's
Association
Guidelines
for
Neuropathol
ogical
Assessment
of AD
AD:
V: 3
VI: 4
C:
0: 4
I: 1
No co-
existing
patholog
y
AD:
12.3
C: 15.4
Cingulate
cortex,
inferior
temporal
cortex IHC Iba1
⬌ cell density
⬆ microglia with
dystrophic
morphology,
activated
morphology
(lower
ramification)
Satoh,
2015 NR
: AD: 7
C: 14
AD:
5/5
C:
6/5
AD: 70
C: 75 NR
Braak,
CERAD
AD:
VI: 10
4 died of
non-
neurolog
ical
causes, 3
with
Parkinso
n's, 4
ALS NR
Frontal
cortex
IHC,
qPCR Iba1
PCR: ⬆
IHC: ⬌
Tang,
2008
University
of Kentucky
Alzheimer's
Disease
Center
Autopsy
Program
AD: 10
C: 10
AD:
5/5
C:
6/4
AD:
82.1
C: 82.7 NR
NIA-Reagan
Criteria
AD:
VI: 6
C: I-
III,
(avg
1.6)
No
neuropat
hology
AD:
26.2 C:
6.2
Inferior
parietal
cortex Western Iba1 ⬆
Ekonom
ou, 2015
United
Kingdom
MRC
Cognitive
Function
and Ageing
Study
AD: 13
C: 15
14/28
(both
AD
and
C)
Whole
sample
: 84.8 NR Braak
Whole
sample
:
0-II: 12
III-IV:
11
V-VI:
5
No
neurolog
ical
disease
All
within
17.5 -
25.0
Hippocampu
s DG IHC Iba1
⬌ between AD
and C
⬆ in Braak stage
3-4 than 0-2 or 5-
6
48
Korvatsk
a, 2015
University
of
Washington
Neuropathol
ogy Core
Brain Bank
AD
(norma
l
TREM
2): 6
AD
(with
TREM
2
R47H
variant
): 7 C:
3 NR
Whole
sample
: 84.9 NR CERAD
AD:
III: 1
V: 4
VI: 1
AD
R47H:
V: 6
VI: 1
C:
0: 1
I: 1
III: 1
NR, One
C
CERAD
score A
and one
B
All avg
4.5
Frontal
Lobe: grey
and white
matter
Hippocampu
s: CA1,
hilus,
parahippoca
mpal gyrus
and white
matter IHC Iba1
⬇ staining in
hippocampus and
frontal lobe white
matter
⬌ staining in
hilus, CA1,
parahippocampal
gyrus or frontal
lobe grey matter
⬌ counts in
frontal lobe grey
matter or
activated counts
in hippocampus
white matter in
AD, though ⬇ in
R47H
Lastres-
Becker,
2014
Banco de
Tejidos de la
Fundacion
CIEN
AD: 4
C: 4 NR
AD:73
-90
C:78-
90 NR Braak
AD: II-
IV
No
neuropsy
chiatric
disease
or
neuropat
hology
All
within
5hr
Hippocampu
s
IHC,
immuno
blot Iba1 ⬆
Lee,
2016
OPTIMA
and
Newcastle
Brain Tissue
Resource
(NBTR)
AD: 12
C: 11
AD:
7/5
C:
6/5
AD:
73.1
C: 81.1 NR
Braak,
CERAD
AD: V-
VI
C: I-II NR
AD:
61.2
C: 41.5
prefrontal
(BA9) and
temporal
(BA22)
cortices PCR Iba1 ⬌
49
Lue,
2015 BSHRI
AD: 11
C: 11
HPC:
11
AD:
6/5
C:
7/4
HPC:
3/8
AD:
82.4
C: 85.4
HPC:
86.5
APOE
4
Carrier
s:
AD:
5/6
C: 1/10
HPC:
2/9
Braak,
CERAD
AD:
Avg
5.2 C:
Avg
2.8
HPC:
Avg
2.9 NR NR
Middle
temporal
cortices Western Iba1
⬆ than C and
HPC
Marlatt,
2014
Netherlands
Brain Bank
AD: 8
C: 8
AD:4
/4
C:4/4
AD:81
C: 80 NR Braak
AD:
Avg
4.8 C:
Avg
1.4 NR
All
within
5-7
Hippocampu
s (CA1/2,
CA3,
DG/SCZ,
Hilus) IHC Iba1
⬌ in cell number
or in morphology
Minett,
2016
Medical
Research
Council
Cognitive
Function
and
Ageing
Study - six
centres in
UK
AD: 83
C: 130
AD:
64/53
C:
51/66
AD: 89
C: 84 NR CERAD NR NR NR
Middle
frontal gyrus
(BA9) IHC Iba1
⬇ Iba1
No association
with cognition
(MMSE), positive
association with
AD pathology
(plaques, tangles)
Rangaraj
u, 2015
Emory
ADRC
Neuropathol
ogy Core,
Atlanta
AD: 10
C: 10
AD:
6/4
C:
6/4
AD:71.
5 C:
71.5
APOE:
AD: 8
with
APOE
4 (3
homoz
ygous)
C: 1
APOE
4 (0
homoz
ygous Yes
AD:
All VI
C: 0 NR NR
Frontal
cortex IHC Iba1
⬆ Iba1 staining
density, p=0.06
for staining
intensity
50
Rivera,
2005
KPBBB,
University
Medical
Center
Braak
stage
0-1:
12,
2-3: 12
4-5: 12
6: 9
Braa
k
stage
:
0-1:
6/6
2-3:
5/7
4-5:
3/9
6:
0/9
Braak
stage:
0-1:
74.4
2-3:
81.1
4-5:
82.1
6: 71.8
APOE
4/4:
Braak
stage
0-1: 1
2-3: 4
4-5: 5
6: 0
Braak, NIA-
Reagan
Criteria
AD: II-
VI
C: 0-I NR
All
<16 h
Anterior
frontal
cortex PCR
AIF1
IBA1 ⬆
Sanchez-
Mejias,
2016
Tissue bank
at Fundación
CIEN
Braak
stage
0: 8
II: 13
III-IV:
9
V-VI:
17
Braa
k
stage
0:
5/3
II:
7/13
III-
IV:
4/5
V-
VI:
7/11
Braak
stage
0: 19
II: 78
III-IV:
80
V-VI:
79 NR
Braak
Braak V-VI
clinically
classified as
AD, Braak
II age -
matched and
used as C
Braak
stage
0: 8
II: 13
III-IV:
9
V-VI:
17 NR
Braak
stage
0: 8
II: 7
III-IV:
6
V-VI:
8
Hippocampu
s CA1,
CA3,
parahippoca
mpal gyrus
IHC,
PCR Iba1
PCR:
⬌ Iba1
IHC:
⬇ in DG and CA3
⬌CA1 and
parahippocampal
gyrus
-More activated
morphology
More activated
morphology
Serrano-
Pozo,
2013
Massachuset
ts ADRC
Brain Bank
AD: 40
C: 32
AD:
14/26
C:
13/19
AD:
81.3 C:
77.6
APOE
4:
AD:
21/40
C: 5/27
NIA -
Reagan
Criteria NR
No
clinical
history
of
neurolog
ical
disorders
, no
neurodeg
enerative
disease
patholog
y
AD:
18.0 C:
14.1
Temporal
polar
association
cortex (BA
38)
IHC,
stereolog
y Iba1
⬌ Total
microglia
⬇IBA1+/MHC2-
microglia
⬆IBA1-/MHC2+
microglia
⬌IBA1+/MHC2
+ microglia
⬆ IBA1+/MHC2+
microglia in
APOE4+
⬌with microglia
by genotype
51
Walker,
2015 BSHRI
AD: 30
C: 41
Whol
e
samp
le:
AD:
49/48
C:
50/46
Whole
sample
:
AD:
82.2 C:
84.9
APOE
4/4
genoty
pes
exclud
ed
NIA-Reagan
Criteria NR NR
Whole
sample
:
AD:
3.6 C:
4
Temporal
cortex Western Iba1
⬆ for CD33
rs3865444 allele
A/C genotype
⬌ for C/C and
A/A genotypes
Table 2-2: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Alzheimer’s Disease Research Center (ADRC),
Apolipoprotein E (APOE), Average (Avg), Banner Sun Health Research Institute (BSHRI), Brodmann area (BA), Cluster of
differentiation (CD), Centro Investigación Enfermedades Neurológicas (CIEN), Consortium to Establish a Registry for Alzheimer’s
Disease (CERAD), Control (C), Cornu ammonis (CA), Dentate gyrus (DG), High pathology control (HPC), Hours (h), Ionized
calcium-binding adapter molecule 1 (Iba1), Immunohistochemistry (IHC), Kathleen Price Bryan Brain Bank (KPBBB), Medical
Research Council (MRC), National Institute on Aging (NIA), Netherlands Brain Bank (NBB), Newcastle Brain Tissue Resource
(NBTR), Not reported (NR), Oxford Project to Investigate Memory and aging (OPTIMA), Post-mortem interval (PMI), Polymerase
chain reaction based assays (PCR), Ribonucleic acid (RNA), Sequencing (Seq), Triggering receptor expressed on myeloid cells 2
(TREM2)
52
2.4.3 CD68
CD68 is a common marker for macrophage lineage cells, primarily localized to microglia within
the brain parenchyma, and perivascular macrophages in the cerebral blood vessels, and
occasionally, parenchyma211. Although there is some CD68 expression on resting microglia147, it
labels the lysosome and is therefore commonly considered a marker of activated phagocytic
microglia.193 Twenty-one studies were identified that compared CD68 between AD and control
post-mortem brain samples (Table 2-3). Twenty of these studies used immunohistochemistry to
visualize CD68 positive cells and measured cell counts or staining area, while one study
measured CD68 gene expression by qPCR. Seventeen identified an increase in CD68 expression,
staining or positive cell counts in AD relative to control samples in at least one region150, 151, 153,
161, 174, 189, 202, 209, 211-219, while four found no difference between AD and control184, 220-222, and
four reported no difference in at least one of the brain regions measured.153, 174, 202, 213
CD68 positive cell counts, staining area or gene expression were measured in the hippocampus
in eight studies: six identified higher levels in AD153, 202, 209, 211, 212, 219 and two identified no
differences.202, 220 CD68 was higher in AD than control in the frontal cortex in three studies151,
174, 215, though for white matter only in one of the studies.174 Elevations in CD68 were also
reported in the temporal cortex153, 161, the olfactory bulb213, the calcarine cortex202, 219, the
superior temporal sulcus216, the orbitofrontal cortex219, parahippocampal gyrus209 and temporal
association isocortex.217 No difference between AD and control was reported in the caudate
nucleus221, combined cerebellum and cerebral cortex184, mediodorsal nucleus of the thalamus221,
the middle frontal gyrus.189 CD68 immunoreactivity appears to increase with age in control
subjects, but decreases with age in patients with AD161. It also increases with APOE ε4
genotype189. Characteristics of AD and control groups could therefore contribute to between
study variability. On balance, CD68 appears to be increased in the brains of patients with AD,
though there is some variation between studies and brain regions.
53
Table 2-3: CD68
First
Author
Brain
bank n Sex Age
AD
Geneti
c Risk
Factor
s
AD
Histologicall
y Confirmed
and criteria
Braak
stage
C history of
neurological or
psychiatric
disease
PMI
(h) Brain Region
Techniq
ue Direction of results
Alvarez,
2015 NR
AD: 24
(for
cortex
and
CA1)
ADaβ-:
5
Control
: 24 (16
for
cortex
and
CA1) NR
AD:
70-86
ADaβ
-: 70-
76
Contr
ol:
70-86 NR
Braak,
CERAD
Adaβ- group
had no aβ
pathology AD: V-VI
No mental
disorder NR
Cerebellar
cortex and
hippocampus
white matter
molecular
layer,
Purkinje cell
layer, granule
cell layer,
white matter
core of the
folium,
central white
matter, layer
V of the
cortex and
CA1 IHC
⬌ between AD,
Adaβ-, and control
Arnold,
1998
Universi
ty of
Pennsylv
ania
Alzheim
er
Disease
Center
Core
AD: 10
C: 14
AD:
5/5
C: 6/8
AD:
81.8
C:
75.3 NR
Khachaturia
n NR
No major
psychiatric
illness, no
neuropathologic
abnormality
excep 1 patient
with lacunar
infarct, one with
small temporal
contusions
AD:
9.8
C:
11.4
Calcarine
cortex
(BA17),
enthorinal
cortex (BA
28)
hippocampus
CA1,
midfrontal
cortex (BA9
and 46)
orbitofrontal
cortex
(BA11),
subiculum IHC ⬆ in all regions
54
Arnold,
2000
AD and
FTD
from:
Universi
ty of
Pennsylv
ania’s
Alzheim
er
Disease
Center
Core
AD: 10
C: 10
AD:5/
5 C:
4/6
AD:
81.8
C:
76.1 NR Yes NR
No history of
major
neurological or
psychiatric
disorder, no
neuropathologic
abnormalities
relevant to mental
status
AD:
9.8
C:
11.6
Calcarine
cortex, frontal
lobe,
hippocampus IHC
⬆ (not compared
statistically)
Bachstette
r, 2015
Universi
ty of
Kentuck
y
Alzheim
er's
Disease
Center
AD:7
C: 9
AD:
4/3
C: 6/3
AD:
77
C: 86
AD:
N/A: 2
4/4: 1
3/4: 2
C:
3/4: 1 CERAD
AD:
Median VI
C: Median
II NR
AD:
4.2
C:
2.4
Hippocampus:
CA1, CA2/3,
CA4, DG,
subiculum
and adjacent
white matter.
Morphology
assessed in
the CA1 only IHC
⬆ CD68 staining in
subiculum, CA1,
DG, and mean of
hippocampal
regions
⬌ in CA2/3, CA4
or white matter
⬌ in CD68+
amoeboid in any
region except ⬆ DG
Dal
Bianco,
2008 NR
AD: 9
C: 15
AD:
0/9 C:
13/2
AD:
81
C: 70 NR
Braak,
CERAD
AD:
IV: 2
V: 4
VI: 3
No neurological
disease or brain
lesions NR
Cortical
areas of the
temporal lobe,
including
entorhinal
cortex,
hippocampus
and temporal
cortex
Immuno
cytoche
mistry
⬆ CD68 near
plaque only
DeLuca,
2015
Oxford
Brain
Bank
AD: 4
C: 8
AD:
3/1 C:
5/3
AD:
76.3
C:
63.0 NR NR
AD: V or
VI
No neurological
disease NR
Olfactory
bulb/tract IHC
⬆ in parenchyma
and meninges
⬌ perivascular
55
Doorn,
2014
NBB or
Departm
ent of
Patholog
y, Vrije
Universi
teit,
Universi
ty
Medical
Center in
Amsterd
am, The
Netherla
nds
AD: 8
C:11
AD:
3/5
C: 5/6
AD:
74.5
C: 84 NR Braak
NFT
AD: IV-VI
C: 0-III
Amyloid:
AD:
C: 7
B: 1 C:
0:4
A: 3
B: 3
C: 1
Without
neurological or
psychiatric
diseases
AD:
6.2
C:
5.9
Olfactory
bulb IHC
⬆ amoeboid
microglia
⬌ ramified
Falke,
2000
Universi
ty of
Pennsylv
ania
ARDC
AD: 12
C: 11
AD:
2/10
C: 7/4
AD:
79.4
C:
77.6 NR NR NR
No
neuropsychiatric
disease - 3
control subjects
had abnormality
at autopsy
(hemorrhageic
microinfarct,
bilateral
contusion,
adenocarcinoma
metastisis).
1 AD subject had
microinfarct, all
had aβ plaques
and NFT
AD:
10.9
C:
12.4
Caudate
Nucleus (6
AD, 7
Control),
mediodorsal
nucleus of the
thalamus (12
AD, 10
Control) IHC
⬌ in Caudate
Nucleus
⬌ in Mediodorsal
nucleaus of the
thalamus (p=0.06)
56
Fiala,
2002
UCLA
ADRC
Brain
Bank
AD: 8
C: 5 NR
AD:
77.6
C:
74.6 NR
Yes
One patient
with
vascular
dementia not
excluded NR
No
neuropathological
findings
All
5-6 h
Mix of areas
(different for
different
cases):
hippocampus,
frontal lobe
(mix of left
and right),
superior
temporal lobe IHC
⬆ CD68 staining in
AD than C
Hoozeman
s, 2005 NBB
Braak
stage
0: 5,
I-II: 16,
III-IV:
10,
V-VI:
9
Braak
stage
0: 3/2
I-II:
6/10
III-IV:
0/10
V-VI:
3/6
Braak
stage
0: 62
I-II:
83
III-
IV:
89
V-VI:
76 NR Braak
Subjects
vary from
0-VI (not
divided
into C and
AD) NR
Braa
k
stage
0: 8
I-II:
7.5
III-
IV:
6.5
V-
VI: 5
Temporal
cortex IHC
⬆ with increasing
Braak NFT or
plaque stage
(p<0.05 for trend),
significant for NFT
group V-VI vs 0
Hoozeman
s, 2011
Netherla
nds
Brain
Bank
AD: 19
C:19
AD:
3/16
C:
8/11
AD:
83.5,
C:
76.8
APOE
4:
AD:
12 C:
8 Braak
AD:
avg IV
C: avg I NR
AD:
5.1
C:
8.6
Mid-temporal
cortex IHC
⬆
⬆ in AD patients
younger than 80
compared to those
older than 80
Hultman,
2013
KPBBB,
Duke
Universi
ty,
North
Carolina
AD: 36
C: 22
AD:
13/23
C:
10/12
AD:
76.9
C:
79.1
APOE
4
Carrier
s:
AD:
14 C:
0
CERAD,
NIA-Reagan
Criteria
AD:
III: 11
IV: 3
V: 13
VI: 9
C:
I: 18
II: 3
III: 1
NR, some cases
and Cs had mild
to severe
atherosclerosis
AD:
9.2
C:
7.7
Frontal cortex
- perivascular IHC ⬆
57
Kellner,
2009 NR
AD: 48
C: 48
AD:
19/29
C:
24/24
AD:
80.3
C:
77.5 NR
Braak,
CERAD
AD:
II-VI
(38>4) C:
I-III (45 =
0) NR NR
Entorhinal,
frontal cortex,
temporal
cortex IHC ⬆
Lue, 2001 BSHRI
AD: 16
C: 21 NR NR NR NR NR NR
All
avg
3.1
Mixed: corpus
callosum,
superior and
middle frontal
gyri of the
right
hemisphere IHC ⬌
Minett,
2016
Medical
Research
Council
Cognitiv
e
Function
and
Ageing
Study -
six
centres
in UK
AD: 83
C: 130
AD:
64/53
C:
51/66
AD:
89
C: 84 NR CERAD NR NR NR
Middle frontal
gyrus (BA9) IHC
⬆
Negative
correlation with
cognition (MMSE),
positively with AD
pathology (plaques,
tangles)
Perez-
Nievas,
2013
Massach
usetts
General
Hospital,
Mayo
Clinic
and
Universi
ty of
Pittsburg
h ADRC
Brain
Banks
AD:15
LPC:
15
IPC: 12
HPC: 8 NR
AD:
87.2
LPC:
84.4
IPC:
89.8
HPC:
88.4 NR
Braak,
CERAD NR NR NR
Superior
temporal
sulcus IHC
⬆ in AD vs LPC,
IPC and HPC
⬌ in IPC or HPC
vs. C
58
Rezaie,
2005
MRC
London
Neurode
generati
ve
Diseases
Brain
Bank
AD: 10
C: 10
AD:
4/6
C:7/3
AD:
79.3
C:
70.2
Not
reporte
d CERAD NR
No history of
neurological
disease or
neuropathology
AD:
20.9,
C:
43.2
Frontal blocks
included
agranular-
intermediate
frontal cortex
(BA 6/8),
cingulate
cortex (BA
24/32)
Occipital
blocks
included the
calcarine
sulcus (BA
17) and striate
cortex) IHC
⬆ in frontal white
matter, occipital
white matter,
plaque associated
frontal grey matter,
plaque associated
occipital grey
matter
⬌ frontal grey
matter, or occipital
grey matter.
Sanchez-
Mejias,
2016
Tissue
bank at
Fundaci
ón CIEN
Braak
stage
0: 8
II: 13
III-IV:
9
V-VI:
17
Braak
stage
0: 5/3
II:
7/13
III-IV:
4/5
V-VI:
7/11
Braak
stage
0: 19
II: 78
III-
IV:
80
V-VI:
79 NR
Braak
Braak V-VI
clinically
classified as
AD, Braak II
age -
matched and
used as C
Braak
stage
0: 8
II: 13
III-IV: 9
V-VI: 17 NR
Braa
k
stage
0: 8
II: 7
III-
IV: 6
V-
VI: 8
Hippocampus
CA1, CA3,
parahippocam
pal gyrus PCR
⬆ with increasing
Braak stage Braak
stage V-VI had
clinical AD and
were compared to
stage II Cs
Serrano-
Pozo,
2011
Massach
usetts
ADRC
Brain
Bank
AD: 91
C: 15
AD:
33/58
C:
5/10
AD:
79.0
C:
79.9
E4
carrier
s
AD:
59/32
C:
4/11
NIA-Reagan
Criteria NR
NR, 10/15 had
some plaque
burden
AD:
13.9
C:
22.3
Temporal
association
isocortex (BA
38)
IHC,
stereolog
y
⬆ with increasing
disease stage and
NFT, no
correlation with
amyloid burden
59
Van
Everbroec
k, 2002 NR
AD: 21
C: 40 NR NR NR Yes NR
NR, 14 cases/Cs
suffered from
inflammatory
conditions NR
Mixed:
Cerebellum
(when
available),
frontal,
occipital and
temporal
cortices IHC ⬆
Wojtera,
2012 NR
AD:4
C: 2
AD:4
C: 2 NR NR
NIA-Reagan
Criteria NR NR NR
Mixed:
cerebellum,
cerebral
cortex IHC
⬌ microglia
number
⬌ cortex and
cerebellum
⬌ in HLA-
DR/CD68 ratio
between AD and
control (activation)
Table 2-3: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Alzheimer’s Disease Research Center (ADRC),
Apolipoprotein E (APOE), Average (Avg), Banner Sun Health Research Institute (BSHRI), Brodmann area (BA), Cluster of
differentiation (CD), Centro Investigación Enfermedades Neurológicas (CIEN), Consortium to Establish a Registry for Alzheimer’s
Disease (CERAD), Control (C), Cornu ammonis (CA), Dentate gyrus (DG), High pathology control (HPC), Human leukocyte antigen
(HLA), Hours (h), Immunohistochemistry (IHC), Intermediate pathology control (IPC), Kathleen Price Bryan Brain Bank (KPBBB),
Low pathology control (LPC), Medical Research Council (MRC), Neurofibrillary tangles (NFT), National Institute on Aging (NIA),
Netherlands Brain Bank (NBB), Not reported (NR), Post-mortem interval (PMI), Polymerase chain reaction based assays (PCR),
University of California Los Angeles (UCLA)
60
2.4.4 CD11b
CD11b forms part of complement receptor 3 that aids in the recognition and phagocytosis of
antigens, including amyloid-β223. Like Iba1, CD11b is expressed by both resting and activated
microglia, though it too is inducible with activation147. Six studies were identified that compared
CD11b between AD and control post-mortem human brain samples (Table 2-4). Two papers
reported an increase in CD11b148, 171, while 3 reported no differences between AD and
control.207, 209, 224
Like Iba1, the results of studies measuring CD11b in AD and control post-mortem human brain
samples are heterogeneous. Of two studies in the hippocampus, one identified an increase in
CD11b gene expression171, while the other identified no difference in expression between AD
and controls.209 Similarly, Akiyama et al reported an increase in CD11b positive cells in the
temporal lobe of AD cases207, while Lee and others found no difference in gene expression
relative to controls in the same region.207 Two studies that compared CD11b gene expression in
the prefrontal cortex identified no difference between AD and control.224 Interestingly, two of the
studies that identified increases in CD11b also measured MHCII, and both reported greater
increases in MHC II than CD11b in the AD brain.148, 171 Based on these studies, CD11b does not
appear to be consistently increased in the AD brain.
61
Table 2-4: CD11b
First
Author
Brain
bank n Sex Age
AD
Geneti
c Risk
Factors
AD
Histologicall
y Confirmed
and criteria
Braak
stage
C history of
neurological or
psychiatric
disease
PMI
(h) Brain Region
Techniq
ue
Direction of
results
Akiyama
, 1990 NR
AD: 9
C: 6
AD: 77
C: 69 NR NR NR NR
No neurological
disease
All
withi
n 2-
12 h Temporal lobe IHC
⬆, less
pronounced
increase than
HLA-DR
Lee,
2016
OPTIM
A and
NBTR
AD: 12
C: 11
AD:
7/5
C: 6/5
AD:
73.1
C:
81.1 NR
Braak,
CERAD
AD: V-VI
C: I-II NR
AD:
61.2
C:
41.5
prefrontal
(BA9) and
temporal
(BA22)
cortices PCR ⬌
McGeer,
2000
Patholog
y
Departm
ent of
the
Universit
y
of
British
Columbi
a NR NR NR NR NR NR NR NR Hippocampus PCR
⬆, less pronounce
increased than
HLA-DR
62
Sanchez-
Mejias,
2016
Tissue
bank at
Fundació
n CIEN
Braak
stage
0: 8
II: 13
III-IV:
9
V-VI:
17
Braak
stage
0: 5/3
II: 7/13
III-IV:
4/5
V-VI:
7/11
Braa
k
stage
0: 19
II: 78
III-
IV:
80
V-
VI:
79 NR
Braak V-VI
clinically
classified as
AD, Braak
II age -
matched and
used as C
Braak
stage
0: 8
II: 13
III-IV: 9
V-VI: 17 NR
Braa
k
stage
0: 8
II: 7
III-
IV: 6
V-
VI: 8
Hippocampus
CA1, CA3,
parahippocam
pal gyrus PCR
⬌ CD11b,
CD33, Iba1,
TREM2
⬆ CD45, CD68
for stages V-VI
IHC:
⬇ area V-VI DG
and CA3
More activated
morphology
Shan,
2012 NBB
7 per
Braak
stage
(49
total)
Braak
stage:
0: 4/3
1: 3/4
2: 3/4
3: 3/4
4: 3/4
5: 4/3
6: 3/4
Braa
k
stage:
0:
70.6
1:
80.3
2:
76.7
3: 85
4:
82.3
5:
74.3
6:
70.3 NR Yes
AD: 5-6
C 0-1
No history of
neurological
and/or psychiatric
disorders
Braa
k
stage:
0: 6.8
I: 6
II:
7.3
III: 6
IV:
5.1
V:
5.6
VI:
4.7
Prefrontal
cortex PCR
⬌with increasing
Braak stage
Braak stage V-VI
had clinical AD
and were
compared to stage
II Cs
Table 2-4: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Brodmann area (BA), Cluster of differentiation
(CD), Centro Investigación Enfermedades Neurológicas (CIEN), Consortium to Establish a Registry for Alzheimer’s Disease
(CERAD), Control (C), Cornu ammonis (CA), Harvard Brain Tissue Resource Center (HBTRC, Human leukocyte antigen (HLA),
Hours (h), Netherlands Brain Bank (NBB), Newcastle Brain Tissue Resource (NBTR), Not reported (NR), Oxford Project to
Investigate Memory and aging (OPTIMA), Post-mortem interval (PMI), Polymerase chain reaction based assays (PCR), Triggering
receptor expressed on myeloid cells 2 (TREM2)
63
2.4.5 CD45
CD45 is a cell surface antigen that is expressed on most hematopoietic lineage cells, where it is
involved in the regulation of numerous processes, including cell division and differentiation.
CD45 is expressed by both resting and activated microglia, but it does not appear to be inducible
with activation in humans.225 Six papers were identified that compared CD45 in AD and control
human brains (Table 2-5). Two used immunohistochemistry with cell counting, three measured
gene expression, and one used a combination of Western analysis and immunohistochemistry
with cell counting. Four studies reported higher CD45 in AD in at least one brain region148, 209,
226, 227, while two reported no difference relative to control204, 228 and one reported no difference
in at least one region.227
All three of the studies that used immunohistochemistry with cell counting reported an increase
in CD45 positive cells in AD patients in the temporal lobe148, midfrontal cortex226, frontal
cortex227, and hippocampus molecular and pyramidal layers227 respectively. In contrast, the
polymorphous layer of the hippocampus did not demonstrate increased CD45 positive cells.227
Studies using gene expression were more heterogeneous: Sanchez-Mejias et al detected higher
CD45 gene expression in the hippocampus and parahippocampal gyrus in AD209, while Magistri
and others identified no such increase in the hippocampus relative to controls204. No difference in
expression was also reported in the frontal cortex.228 The evidence suggests that there are more
CD45 positive cells in the brains of patients with AD, but that this is not necessarily
accompanied by increased CD45 gene expression.
64
Table 2-5: CD45
First
Author
Brain
bank n Sex Age
AD
Geneti
c Risk
Factors
AD
Histologicall
y Confirmed
and criteria
Braak
stage
C history of
neurological or
psychiatric
disease
PMI
(h) Brain Region
Techniq
ue
Direction of
results
Akiyama
, 1990 NR
AD: 9
C: 6
AD: 77
C: 69 NR NR NR NR
No neurological
disease
All
withi
n 2-
12 h Temporal lobe IHC
⬆, less
pronounced
increase than
HLA-DR
Colton,
2006 KPBBB
AD: 47
C: 29
AD:
23/24
C:
12/17
AD:
77.8
C:78.
3
APOE
4:
AD: 27
C: 5 Braak
AD: IV- V
C: I NR
AD:
6.8
C:
9.1 Frontal lobe PCR ⬌
Magistri,
2015 BSHRI
AD: 4
C: 4
AD:
1/4 C:
2/2
AD:
83.75
(not
exact,
one
age
just
listed
>90)
C:
83.5
AD: all
APOE
3/3 C:
APOE
2/3: 2
APOE
3/3: 1
NR: 1
NIA-Reagan
Criteria
AD:
V: 1
VI: 3 C:
I: 1
II: 3 NR
AD:
2.5
C:
2.5 Hippocampus
RNA seq
(gene
expressi
on) ⬌
65
Licastro,
1998
Tissue
bank of
the
ADRC
at the
Universit
y of
Californi
a San
Diego
AD: 18
C: 4
AD:
10/8 C:
2/2
AD:
80
C: 76
APOE:
AD:
3/4: 6
4/4: 2
C: NR
CERAD,
Khachaturia
n NR
No history or
histopathological
features of brain
disease
All
avg 6
Midfrontal
cortex IHC
⬆ both scattered
and clustered
cells
Masliah,
1991
ADRC
at the
Universit
y of
Californi
a
AD: 7
C: 5 NR
AD:
77
C: 73 NR Yes NR
Clinically and
histopathologicall
y free of
neurological
disease
AD:
5
C: 6
Frontal
cortex,
posterior
hippocampus
IHC,
Western
⬆ frontal cortex
hippocampus
molecular layer
and pyramidal
layer
⬌ hippocampus
stratus
polymorphous
Sanchez-
Mejias,
2016
Tissue
bank at
Fundació
n CIEN
Braak
stage
0: 8
II: 13
III-IV:
9
V-VI:
17
Braak
stage
0: 5/3
II: 7/13
III-IV:
4/5
V-VI:
7/11
Braa
k
stage
0: 19
II: 78
III-
IV:
80
V-
VI:
79 NR
Braak V-VI
clinically
classified as
AD, Braak
II age -
matched and
used as C
Braak
stage
0: 8
II: 13
III-IV: 9
V-VI: 17 NR
Braa
k
stage
0: 8
II: 7
III-
IV: 6
V-
VI: 8
Hippocampus
CA1, CA3,
parahippocam
pal gyrus PCR
⬆ with increasing
Braak stage
Braak stage V-VI
had clinical AD
and were
compared to
stage II Cs
Table 2-5: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Alzheimer’s Disease Research Center (ADRC),
Apolipoprotein E (APOE), Average (Avg), Banner Sun Health Research Institute (BSHRI), Centro Investigación Enfermedades
Neurológicas (CIEN), Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Control (C), Cornu ammonis (CA),
Human leukocyte antigen (HLA), Hours (h), Immunohistochemistry (IHC), Kathleen Price Bryan Brain Bank (KPBBB), National
66
Institute on Aging (NIA), Not reported (NR), Post-mortem interval (PMI), Polymerase chain reaction based assays (PCR),
Ribonucleic acid (RNA), Sequencing (Seq)
67
2.4.6 Ferritin
Iron is stored in the brain as heavy (H) or light (L) subunits. Neurons and other cells primarily
contain H ferritin, which is the less reactive form, whereas glial cells contain the L subunit,
which can be used to generate free radicals as part of the inflammatory response229, 230. Although
not specific to microglial cells, ferritin immunohistochemistry combined with morphological
identification can be used to visualize these cells in the central nervous system, and increases in
ferritin levels with inflammation are thought to be caused by increases in microglial number and
activation. Seven papers measured ferritin in post-mortem human brain samples from patients
with AD and controls (Table 2-6). Five of these papers identified greater levels of ferritin,
ferritin positive microglia or microglia associated plaques in AD in at least one of the brain
regions measured229, 231-234, while two found no difference in cell counts relative to control166, 184,
and one identified no change in ferritin associated plaques in one region and a decrease in
another233.
Three papers that measured ferritin positive cell counts or protein levels in the hippocampus
identified higher levels in AD 229, 232, 234, while one that combined counts from the hippocampus
with the amygdala, superior frontal gyrus, and the superior, middle and inferior temporal gyri
found no significant difference relative to control, though a non-significant increase was
noted166. Higher ferritin positive microglia was also reported in AD relative to control in the
amygdala, entorhinal cortex, frontal, occipital, parietal and temporal neocortices232. No
difference between AD and control was reported in one study that counted microglia over 12
slices of combined cerebellum and cerebral cortex184.
68
Table 2-6: Ferritin
First
Author
Brain
bank n Sex Age
AD
Geneti
c Risk
Factor
s
AD
Histological
ly
Confirmed
and criteria
Braa
k
stage
C history of
neurological or
psychiatric
disease
PMI
(h) Brain Region Technique Marker
Direction
of results
Kwiatek-
Majkusia
k, 2015
Mayo
Clinic
Florida
Brain
Bank
AD: 10
C: 20
AD:
6/4
C:
9/11
AD:
75.6
C: 72.6 NR Yes
AD:
5.3
No
neurodegenerati
ve disorders NR
Hippocampus
CA1, CA2,
CA4,
subiculum ELISA L-ferritin ⬆
DiPatre,
1997 NR
AD: 9
Age-
matche
d Cs: 9
Young
Cs: 8 NR
AD: 72
Age-
matche
d Cs:
73
Young
Cs: 38 NR CERAD NR
No
neuropathologic
al abnormality
NR but
did not
vary
betwee
n
groups
Entorhinal
cortex,
hippocampus
(CA1, CA2,
CA3, CA4,
DG, subiculum
separately) IHC Ferritin
⬆ in all
areas
Fukumoto
, 1996 NR
AD: 10
C: 26 NR NR NR
Khachaturia
n NR
NR, Cs had
neocortical
senile plaques NR
Occipital (BA
18), superior
frontal (BA 8
or 9) and
medial
temporal (BA
20, at the level
of the
entorhinal
cortex and IHC Ferritin
Plaques
associate
d with
microglia
:
⬆
uncored
plaques,
frontal
cortex
69
hippocampus)
neocortices
⬇ or ⬌
uncored
plaques
temporal,
occipital
cortices
⬇ or ⬌
cored
plaques
in frontal,
occipital
and
temporal
cortices
Lopes,
2008 BSHRI
AD:7
Young
Cs: 3
Aged
Cs: 7
HPC: 7
AD:
4/3
Youn
g Cs:
2/3
Aged
Cs:
6/1
HPC:
5/2
AD:
80.3
Young
Cs:
36.3
Aged
Cs:
80.0
HPC:
83.4 NR Yes NR NR
AD:
2.3
Young
Cs: 2.8
Aged
Cs: 2.5
HPC:
2.90
Amygdala,
hippocampus,
superior frontal
gyrus, superior,
middle, and
inferior
temporal gyri
IHC and
Morphometr
ic Analyses
Anti-
ferritin
Ferritin:
⬌
Dystrophi
c
microglia
⬆ vs all
other
groups
Mochizuk
i, 1996 NR
AD: 8
HPC: 4
AD:
5/3
HPC:
3/1
AD:
79.3
HPC:
73.3 NR Yes NR
No history of
neurological
and/or
psychiatric
disorders or AD
symptoms but
pathology meets
CERAD and
Khachaturian
criteria
AD:
5.6
C: 3.9
Amygdala,
entorhinal
cortex,
hippocampus
and frontal,
occipital,
parietal and
temporal
neocortices IHC Ferritin
⬆ total
microglia
⬌ in
percentag
e of
plaques
(diffuse
or non-
diffuse)
associate
d with
microglia
(p=0.055
and
0.052)
70
Ohgami,
1991 NR
AD: 13
HPC:
23 NR
AD:
59.8
HPC:
72.7
NR,
but all
AD
cases
used
were
early
onset
(<65) Khachaturia
n NR
NR, Cs had
senile plaques NR
Mixed: frontal,
insular,
occipital,
parietal and
temporal
cortices and
parahippocamp
al gyrus IHC
L-
ferritin
(microgli
a
positive
senile
plaques)
⬆
microglia
associate
d diffuse
plaques
⬌
microglia
l
associate
d
classical
or
compact
plaques
Wojtera,
2012
Not
reporte
d
AD:4
C: 2 NR NR
NR NIA-Reagan
Criteria NR Not reported NR
Mixed:
cerebellum,
cerebral cortex IHC
Ferritin
⬌
⬌
between
cortex
and
cerebellu
m
Table 2-6: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Apolipoprotein E (APOE), Brodmann area (BA),
Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Control (C), Cornu ammonis (CA Dentate gyrus (DG), High
pathology control (HPC), Hours (h), Immunohistochemistry (IHC, Not reported (NR), Post-mortem interval (PMI)
71
2.4.7 CD33
CD33 is a myeloid cell surface marker that is involved in the regulation of the innate immune
system. In microglia, CD33 seems to regulate phagocytosis of amyloid-β, with a reduction in
CD33 associated with increased phagocytosis.203 Two polymorphisms in the CD33 gene have
recently been identified that modulate AD risk235, 236, with the protective allele associated with
reduced CD3365, 199, 203 and increased phagocytosis.65 Four papers were identified that used
qPCR, Western or immunohistochemistry to compare levels of CD33 in the brains of patients
with AD to controls, three of which reported elevations in AD, while one reported no change
(Table 2-7).
Higher expression of CD33 genes or of CD33 immunolabelled cells was noted in three studies in
the superior and middle temporal gyri, frontal cortex, and temporal cortex of patients with AD
relative to controls.199, 203, 237 In contrast, Sanchez-Mejias et al reported no difference in CD33
gene expression between patients with AD and controls in the hippocampus cornu ammonis
(CA)1, CA3 and parahippocampal gyrus.209 CD33 expression was shown to correlate with pan-
microglial markers Iba1 or CD11b, supporting its microglial localization199, 203, 237. As CD33
increases when adjusting for Iba1 in most of studies, it is likely a marker of microglial function
or activity, and not of the number of microglia. Though few studies are currently available, the
evidence indicates that CD33 is likely increased in the brains of patients with AD.
72
Table 2-7: CD33
First
Author Brain bank n Sex Age
AD Genetic
Risk
Factors
AD
Histological
ly
Confirmed
and criteria
Braa
k
stage
C history
of
neurologic
al or
psychiatric
disease
PMI
(h) Brain Region Technique Marker
Direction of
results
Griciuc,
2013
Massachusetts
ADRC
AD:
25
C:
15
AD:
7/18
C:
6/9
AD:
79.2
C:
79.9
APOE
carrier: AD:
18 (8
homozygou
s) C: 5 (0
homozygou
s)
NIA-Reagan
Institute
Criteria NR NR
AD:
17 C:
29 Frontal cortex
IHC
(stereology
), PCR,
Western
CD33
⬆ protein,
mRNA,
CD33+
microglia
Malik,
2013
University of
Kentucky AD
Center
Neuropathology
Core
AD:
28
C:
27
AD:
12/1
6
C:
13/1
4 NR
CD33 SNPs
measures Yes NR NR NR
Superior/middl
e temporal gyri IHC, PCR
Ratio
CD33 to
mean of
microgli
al
referenc
e genes
⬆ gene
expression,
colocalizatio
n to
amoeboid
microglia
(non-
quantitative)
Sanche
z-
Mejias,
2016
Tissue bank at
Fundación CIEN
Braa
k
stage
0: 8
II:
13
III-
IV: 9
V-
VI:
17
Braa
k
stage
0:
5/3
II:
8/5
III-
IV:
4/5
V-
VI:
7/11
Braa
k
stage
0: 49
II:
78
III-
IV:
80
V-
VI:
79 NR
Braak
Braak V-VI
clinically
classified as
AD, Braak
II age -
matched and
used as
control
Braa
k
stage
0: 8
II:
13
III-
IV: 9
V-
VI:
17 NR
Braak
stage
0: 8
II: 7
III-IV:
6
V-VI:
8
Hippocampus
CA1, CA3,
parahippocamp
al gyrus PCR CD33 ⬌
73
Walker,
2015
Arizona Study
of Aging and
Neurodegenerati
ve Disorders via
BSHRI
AD:
97
C:
96
AD:
49/4
8
C:
50/4
6
AD:
82.2
C:
84.9
APOE4
excluded
Various
genotypes
of CD33
rs386544
risk allele
(C/C, C/A,
A/A) Yes NR NR
For
overall
sampl
e:
AD:
3.6
C: 4.0
Temporal
cortex
Western
blot
PCR CD33
⬆ RNA
⬌ Protein
- Positive
correlation
between
CD33 and
Iba1
-
Colocalizati
on with
HLA-DR
Table 2-7: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Alzheimer’s Disease Research Center (ADRC),
Apolipoprotein E (APOE), Banner Sun Health Research Institute (BSHRI), Centro Investigación Enfermedades Neurológicas (CIEN),
Cluster of differentiation (CD), Control (C), Cornu ammonis (CA), Hours (h), Human leukocyte antigen (HLA), Ionized calcium-
binding adapter molecule 1 (Iba1), Immunohistochemistry (IHC), National Institute on Aging (NIA), Not reported (NR), Post-mortem
interval (PMI), Polymerase chain reaction based assays (PCR), Ribonucleic acid (RNA), Single nucleotide polymorphism (SNP)
74
2.4.8 Triggering receptor expressed on myeloid cells 2 (TREM2)
TREM2 is a regulatory protein under the control of γ-secretase that controls TLR4 signalling. In
microglia, TREM2 seems to be important for microglial activation and phagocytosis of apoptotic
neurons.238 Polymorphisms in the R47H allele of the TREM2 gene have recently been identified
as a strong genetic risk factor for the development of AD64, though the actual impact of these
polymorphisms on microglial function remain unclear. Four papers were identified that
compared levels of TREM2 in the brains of patients with AD and controls (Table 2-8). Two
identified an increase of TREM2 in AD194, 239, while one reported no difference relative to
control209 and one reported opposing results for qPCR and Western analysis240.
Both papers that examined the temporal cortex identified an increase in TREM2, either for the
protein194, 239, staining intensity by IHC194 or gene expression.239 Roussos et al examined the
impact of R47H genotype on TREM2 mRNA and protein in the superior temporal gyrus240.
They also reported higher TREM2 gene expression in AD R47H carriers relative to controls and
no difference in TREM2 between AD non-carriers and control. Their protein measurements,
however, indicated decreased TREM2 in AD R47H carriers relative to controls with no
difference between AD non-carriers and control. The authors speculate that the discrepancy
between gene expression and protein quantity may be explained by increased immature TREM2
and increased degradation. In contrast, Sanchez-Mejias et al identified no difference in TREM2
gene expression relative to control in the hippocampus CA1, CA3 and the parahippocampal
gyrus. As with CD33, TREM2 expression is increased even when adjusting for Iba1239, so it can
be considered a marker of microglial function as opposed to number.
75
Table 2-8: TREM2
First
Author
Brain
bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histological
ly
Confirmed
and criteria
Braak
stage
C history of
neurological
or psychiatric
disease
PMI
(h) Brain Region
Techniq
ue Marker
Direction
of results
Lue,
2015 BSHRI
AD: 11
C: 11
Possible
AD: 11
AD:
6/5 C:
7/4
Possibl
e AD:
3/8
AD:
82.4
C:
85.4
Possibl
e AD:
86.5
APOE 4
Carriers:
AD: 5/6
C: 1/10
Possible
AD: 2/9
Braak,
CERAD
AD:
Avg
5.2 C:
Avg
2.8
Possibl
e AD:
Avg
2.9 NR NR
Middle
temporal
cortex
IHC,
Western
Iba1
TREM2
DAP12
(TYROB
P)
Western:
⬆
TREM2
and
DAP12
than C
and HPC
IHCs: ⬆
intensity
of
TREM2
76
Ma,
2016
Brain
Bank at
Mayo
Clinic,
Jacksonvil
le
AD: 33
C: 33
AD:
16/17
C:
15/18
AD:
72.7
C:
70.7
No risk
alleles for
APP,
PSEN or
TREM2
APOE4:
AD: 16/17
C: 15/ 18 Braak
AD:
>IV
C: <3
Most had
pathology
unrelated to
AD:
cerebrovascul
ar,
frontotempor
al dementia,
Lewy body
disease,
corticobasal
degeneration,
argyrophilic
grain disease,
multi-system
atrophy,
amyotrophic
lateral
sclerosis,
progressive
supranuclear
palsy NR
Temporal
cortex
PCR,
Western
Iba1
TREM2
⬆
TREM2
normaliz
ed to
Iba1
Rousso
s, 2014
Icahn
School of
Medicine
at Mount
Sinai and
the ADRC
Brain
Bank
AD
(genotype
difference
s): C/C:
16
C/T: 16
C: 16
AD:
C/C:
5/11
C/T:
5/11
C:
5/11
AD:
C/C:
83.0
C/T:
82.3
C:
82.6
APOE 4
and R47H
variant of
TREM2
significant
ly more
common
in cases
than Cs
Braak,
CERAD NR NR
AD:
C/C:
14.5
C/T:
13.9
C:
13.3
Superior
temporal
gyrus
PCR,
Western
TREM2
TYROBP
Gene
expressio
n:
⬆
TREM2
and
TYROBP
in AD
R47H
carriers
⬌ in AD
non
carriers
Protein
levels:
⬇
TREM2
in AD
R47H
carriers
77
⬌ AD
non
carriers
⬆
TYROBP
in non-
carriers
⬌ in
carriers
Sanche
z-
Mejias,
2016
Tissue
bank at
Fundación
CIEN
Braak
stage
0: 8
II: 13
III-IV: 9
V-VI: 17
Braak
stage
0: 5/3
II:
7/13
III-IV:
4/5
V-VI:
7/11
Braak
stage
0: 19
II: 78
III-IV:
80
V-VI:
79 NR
Braak V-VI
clinically
classified as
AD, Braak
II age -
matched
and used as
C
Braak
stage
0: 8
II: 13
III-IV:
9
V-VI:
17 NR
Braa
k
stage
0: 8
II: 7
III-
IV:
6
V-
VI:
8
Hippocampus
CA1, CA3,
parahippocam
pal gyrus PCR TREM2
⬌stage
V-VI vs
II
Braak
stage V-
VI had
clinical
AD and
were
compared
to stage
II Cs
Table 2-8: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Alzheimer’s Disease Research Center (ADRC),
Amyloid precursor protein (APP), Apolipoprotein E (APOE), Average (Avg), Banner Sun Health Research Institute (BSHRI), Centro
Investigación Enfermedades Neurológicas (CIEN), Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Control
(C), Cornu ammonis (CA), DNAX-activation protein 12 (DAP12), High pathology control (HPC), Hours (h), Ionized calcium-binding
adapter molecule 1 (Iba1), Immunohistochemistry (IHC), Not reported (NR), Post-mortem interval (PMI), Presenilin (PSEN),
Polymerase chain reaction based assays (PCR), Triggering receptor expressed on myeloid cells 2 (TREM2), TYRO protein tyrosine
kinase binding protein (TYROBP)
78
2.4.9 CD11c
CD11c is a transmembrane protein expressed on the surface of various immune cells, including
microglia, macrophages and neutrophils. Four studies were identified that compared CD11c in
AD and control post-mortem brains, all of which used immunohistochemistry and reported
higher staining scores or cell counts in AD in at least one brain region148, 241-243 (Table 2-9).
Three studies examined CD11c in the hippocampus. Two reported higher levels of staining
quantified by a semi quantitative scoring system in AD in the CA1 and subiculum.241, 243,
however Paulus et al quantified CD11c by unbiased cell counts and identified no difference in
CA1 or the granular layer of the dentate gyrus relative to control.242 Elevated CD11c was also
reported in the temporal lobe148, frontal cortex242, entorhinal cortex243 and superior temporal
gyrus.241 No difference between AD and control was detected in the frontal white matter.242
Based on the limited number of studies, it appears that CD11c is increased in brain of patients
with AD relative to controls, however this may be influenced by differences in methodology
between the studies.
79
Table 2-9: CD11c
First
author
Brain
bank n Sex Age
AD
Geneti
c Risk
Factor
s
AD
Histologicall
y Confirmed
Braa
k
stage
C history of
neurological or
psychiatric
disease
PM
I
(h)
Brain
Region
Techniqu
e
Marke
r
Direction of
results
Akiyama
, 1990 NR
AD: 9
C: 6
AD
: 77
C:
69 NR NR NR NR
No neurological
disease
2-
12
h
Temporal
lobe IHC
CD11
c
(Leu-
M5)
⬆ most
pronounced
difference
for HLA-
DR
Itoh,
1998
Yokufuka
i
Geriatric
Hospital
AD: 20
C: 20
Centenaria
n: 13 NR
AD: 80.9
C: 79.8
Centenaria
n: 101.5 NR CERAD NR
Other
neurological
diseases
excluded NR
Hippocampu
s (CA1 and
subiculum),
superior
temporal
gyrus IHC
CD11
c
(Ki-
M1P) ⬆
Paulus,
1993 NR
AD: 6
C: 6 NR
AD: 79.7
C: 67.9 NR
CERAD,
Khachaturia
n NR
No neurological
or
neuropathologic
al disorder
8-
48
CA1 sector
of the
hippocampu
s, granular
layer of the
DG,
fourth/fifth
frontal
neocortical
layers, IHC
CD11
c
(Ki-
M1P)
⬆ Frontal
cortex
⬌ White
matter and
hippocampu
s
80
frontal white
matter
Yamada,
2001 NR
AD: 5
C: 5 NR
AD: 92.8
C: 93.4 NR NR NR NR NR
Entorhinal
cortex,
hippocampu
s (CA1,
subiculum) IHC
CD11
c (Ki-
M1P) ⬆
Table 2-9: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Cluster of differentiation (CD), Consortium to
Establish a Registry for Alzheimer’s Disease (CERAD), Control (C), Cornu ammonis (CA), Dentate gyrus (DG), Human leukocyte
antigen (HLA), Hours (h), Immunohistochemistry (IHC), Not reported (NR), Post-mortem interval (PMI), Polymerase chain reaction
based assays (PCR)
81
2.4.10 IL-1α-expressing microglia
IL-1α is a proinflammatory cytokine that plays a central role in the induction of an immune
response. While IL-1α is not a microglia marker, three papers measuring it were included in this
review because they used immunohistochemistry and cell counting to examine IL-1α positive
microglia as an indication of activation (Table 2-10). All three papers are from the same research
group and reported higher IL-1α positive microglia in AD in at least one of the measured brain
areas, however two reported no difference relative to control in one region.
Elevated IL-1α positive microglia were reported in the parahippocampal cortex244, the
hippocampus245, and the frontal245, occipital245 and temporal lobes.245, 246 Within the temporal
cortex, layers III-VI were found to be enriched in IL-1α expressing microglia in AD, while layers
I-II were no different from controls.246 No difference between AD and control brains was
detected in the cerebellum.245 Based on the available evidence, IL-1α expressing microglia seem
to be increased in the brains of patients with AD.
82
Table 2-10: IL-1α-expressing microglia
First
Author
Brain
bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histologically
Confirmed
and criteria
Braak
stage
C history of
neurological
or psychiatric
disease
PMI
(h) Brain Region Technique Marker
Direction of
results
Sheng,
2001
NR
AD:
12
C: 9
AD:
4/8
C:
7/2
AD:
63-92
C: 50-
93 NR CERAD NR
No evidence
of
neurological
or psychiatric
disease
AD:
2-
13
C:
1-
15
Parahippocampal
cortex IHC, PCR
IL-1α
expressing
microglia
⬆ IL-1α positive
microglia
(activated
morphology)
Sheng,
1998
NR
AD:9
C: 4
AD:
8/1
C:
4/0
AD:65-
88 C:
61-83 NR CERAD NR
No clinical or
pathological
evidence of
neurological
disease
AD:
10
C: 5 Temporal lobe IHC
IL-1α
expressing
microglia
⬆ in cortical
layers III-VI
⬌ in cortical
layers I-II
- IL-1-α+
microglia
appeared enlarged
and highly
immunoreactive
in AD but smaller
and less
immunoreactive
in C
- In AD brains,
IL1-α+ microglia
distribution
correlated with
neuritic plaques
83
Sheng,
1995
NR
AD:
8
C: 6
AD:
6/2
C:
5/1
AD:
76.1
C: 63.5 NR CERAD NR
No evidence
of
neurological
disease, two
had
schizophrenia
AD:
9.3
C:
5.5
Cerebellum,
hippocampus,
frontal lobe,
occipital lobe,
temporal lobe IHC
IL-1α
expressing
microglia
⬆ Hippocampus,
frontal, occipital,
temporal, lobes
⬌ cerebellum
Table 2-10: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Consortium to Establish a Registry for
Alzheimer’s Disease (CERAD), Control (C), Hours (h), immunohistochemistry (IHC), Interleukin (IL), Not reported (NR), Post-
mortem interval (PMI), Polymerase chain reaction based assays (PCR)
84
2.4.11 Ricinus Communis Agglutinin 1 (RCA-1)
Microglia appear to be the only resident brain cells that express the lectin RCA-1.247 Two papers
were identified that used RCA-1 immunohistochemistry to compare microglia counts between
AD and control post-mortem human brain samples (Table 2-11). Both reported significantly
more RCA-1 labelled microglia in AD, in either the inferomedial temporal lobe248 and the
association cortex, periallocortex/allocortex and primary cortex respectively.249
85
Table 2-11: RCA-1
First
Author Brain bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histologically
Confirmed
and criteria
Braak
stage
C history of
neurological
or psychiatric
disease
PMI
(h) Brain Region Technique
Microglia
Marker
Direction
of results
Mackenzie,
1995
University
Hospital,
London,
Canada
AD:
11
C (no
sp): 14
C (dp
only):
12 C
(dp
and
np):
14 NR
AD:
76.7,
C (no
sp):
75.1
C (dp
only):
74.3
C (dp
and
np):
74.1 NR Yes NR
No history of
neurologic
disease or
systemic
condition
that could
affect
microglial
numbers NR
Anteromesial
temporal lobe IHC RCA-1
⬆ in AD
than all C
groups
⬆ in C
groups
with NP
than with
diffuse or
no plaque
Sheffield,
2000
AD:
University of
Iowa
Alzheimer's
Disease
Research
Center
C:
University of
Kansas
Medical
Center
Willed Body
Program
AD:
12
C: 4
AD:
6/6,
C:
2/2
AD:
79.4
C:
77.8 NR Khachaturian
AD:
all IV
C:
0: 3
I: 1
No history of
neurological
disease NR
Association
cortex,
periallocortex/
allocortex,
primary cortex IHC RCA-1 ⬆
86
Table 2-11: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Control (C), Diffuse plaque (dp), Hours (h),
Immunohistochemistry (IHC), Not reported (NR), Post-mortem interval (PMI), Ricinus communis agglutin-1 (RCA-1), Senile plaque
(sp)
87
2.4.12 Translocator Protein (TSPO)
TSPO, also known as the peripheral benzodiazepine receptor, is a mitochondrial protein
expressed on activated microglia in the brain. It is the ligand for (11)C-PK11195, which is
commonly used in positron emission tomography imaging of activated microglia in vivo. Two
papers returned in the systematic search compared in situ PK11195 binding or expression of
TSPO in control and AD post-mortem human brains samples (Table 2-12). Kravitz et al
identified higher [3H]-PK11195 binding in the entorhinal cortex, the subiculum, the striatum and
various areas of the hippocampus250. In contrast, Marutle et al identified no difference in
PK11195 binding in the hippocampus between control and AD brains, though they reported
higher levels in the frontal cortex251. Kravitz et al also measured TSPO mRNA and identified
higher expression in the hippocampus in AD relative to controls, but no difference in the
striatum.250 Because these two studies have somewhat opposing results for the brain region they
have in common, the hippocampus, it is unclear whether TSPO is increased in post-mortem brain
samples from patients with AD. PK11195 has a lower affinity for TSPO and a lower signal to
noise ratio than newer ligands, which may explain some of the discrepancy. A recent review of
PET imaging of microglia in AD in vivo found that of 5 papers using the PK11195 ligand, 3
identified increases in AD relative to controls while 2 identified no differences, whereas for the
second generation radioligands, 5/6 reported increases in AD in various brain regions.58
88
Table 2-12: TSPO
First
Author
Brain
bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histologically
Confirmed
and criteria
Braak
stage
C history of
neurological
or
psychiatric
disease
PMI
(h) Brain Region Technique Marker
Direction of
results
Marutle,
2013
Brain
Bank at
Karolinska
Instetuet
and the
NBB
AD:
11
C:
13 NR
AD:
75.2
C:
73.9 NR
CERAD,
NINCDS-
ADRDA
AD:
5-6
C: 1-
2 NR
AD:
15.9
C:
18.5
Frontal
Cortex,
hippocampus Binding assay PK11195
⬆Frontal cortex
⬌Hippocampus
Kravitz,
2013 NBB
AD:
23
C:
17
AD:
11/12
C:
9/8
AD:
79.7
C:
79.7
APOE
AD:
3/4: 12
4/4: 3
C:
3/4: 1 Braak
AD:
IV: 3
V: 17
VI: 3
C:
0: 5
I: 7
II: 4
ND:
1 NR
A:
5.7
C:
8.7
Entorhinal
cortex,
hippocampus,
subiculum,
striatum
Autoradiography,
PCR
PK11195
TSPO
Autoradiography:
⬆ PK11195 in all
regions
PCR:
⬆ TSPO in
hippocampus
⬌ TSPO
striatum
Table 2-12: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Apolipoprotein E (APOE), Consortium to
Establish a Registry for Alzheimer’s Disease (CERAD), Control (C), Hours (h), National Institute of Neurological and
Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA),
Netherlands Brain Bank (NBB), Not reported (NR), Post-mortem interval (PMI), Polymerase chain reaction based assays (PCR),
Translocator protein (TSPO)
89
2.4.13 CD163
CD163 is a scavenger receptor expressed on monocyte/macrophage lineage cells. Two papers
identified higher CD163 in AD than control post-mortem human brains (Table 2-13). Dal Bianco
et al identified a greater number of CD163 positive cells in the cortical areas of the temporal
lobe, including the hippocampus and entorhinal and temporal cortices.153 Like Dal Bianco et al,
Pey and others also found higher levels of CD163 staining in the hippocampus, as well as the
frontal and occipital cortices.252 Based on this small number of studies, CD163 seems to be
upregulated in AD relative to control post-mortem brains.
90
Table 2-13: CD163
First
Author Brain bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histologically
Confirmed and
criteria
Braak
stage
C history of
neurological
or psychiatric
disease
PMI
(h) Brain Region Technique
Direction of
results
Dal
Bianco,
2008 NR
AD:
9
C:
15
AD:
0/9 C:
13/2
AD:
81
C:
70 NR Braak, CERAD
AD:
IV: 2
V: 4
VI: 3
No
neurological
disease or
brain lesions NR
Cortical
areas of the
temporal lobe,
including
entorhinal cortex,
hippocampus and
temporal cortex Immunocytochemistry
⬆ CD163
Pey,
2014
Corsellis
Archival
Collection
AD:
31
C:
16
AD:
21/10
C: 8/8
AD:
76
C:
70 NR
Braak,
BrainNet
Europe
Consortium
Guidelines
AD: V
and VI
No
neurological
causes of
death NR
Hippocampus,
frontal cortex,
occipital cortex IHC ⬆
Table 2-13: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Consortium to Establish a Registry for
Alzheimer’s Disease (CERAD), Cluster of differentiation (CD), Control (C), Hours (h), Immunohistochemistry (IHC), Not reported
(NR), Post-mortem interval (PMI)
91
2.4.14 Microglia identified by morphology
Two studies used non-specific cell stains to visualize microglia, which were identified based on
morphology (Table 2-14). Shefer et al identified both a greater absolute number of glia, and a
greater number of microglia per volume in the subiculum of the archicortex in the hippocampal
fissure.253 This combination of relative and absolute counts provides evidence that the apparent
increase in microglia was not just a function of tissue shrinkage. In contrast, Pelvig et al also
identified microglia based on morphology and quantified them using stereology, but found no
difference in the total number of cells in the neocortex.254
92
Table 2-14: Microglia identified based on morphology
First
Author Brain bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histologically
Confirmed
Braak
stage
C history of
neurological or
psychiatric
disease
PMI
(h) Brain Region Technique Marker
Direction
of results
Pelvig,
2003
Nederlandse
Hersenbank,
Holland,
Johns
Hopkins
University
Hospital,
Baltimore,
USA and
from
departments
of pathology
in Denmark
AD: 14
C: 20
AD:
4/10
C:
6/14
AD:
81.1
C:
80.5 NR Yes NR
NR - non-
neurological
causes of death NR
Mixed:
cingulate
gyrus,
hippocampus,
insula, frontal,
medial,
occipital,
parietal and
temporal lobes,
and one or two
tiers of
mesencephalon
Stereology,
Cavalieri’s
principle
Identified
by
morphology
⬌ mean
total
number
of glial
cells in
neocortex
Shefer,
1977 NR
AD: 6
C: NR NR
AD:
67
C:77 NR NR NR
Neurological
illness not
examined,
psychologically
healthy NR
Subiculum of
the archicortex
in the
hippocampal
fissure Nissl
Identified
by
morphology
⬆ relative
number
of
microglia
per
volume
and
absolute
number
of glial
cells
Table 2-14: Where there are both young and older controls, values are reported for the older (age-matched controls). Alzheimer’s
Disease (AD), Control (C), Hours (h), Not reported (NR)
93
2.4.15 Other
Seventeen papers returned in the systematic search used markers other than those discussed in
previous sections (Table 2-15). Some of these, such as Ox-42 and GLUT-5, are known
microglial markers, while others were identified by the study authors as being generalizable
either to microglia or their activation. Most (16/17) reported changes in markers consistent with
increased numbers or activation of microglia in AD.
94
Table 2-15: Other markers
First
Author
Brain bank n Sex Age
AD
Genetic
Risk
Factors
AD
Histologi
cally
Confirme
d
Bra
ak
stag
e
C history
of
neurologic
al or
psychiatri
c disease
PMI
(h)
Brain
Region Technique Marker
Direction of
results
Akiyam
a, 1990
NR
AD:
9
C: 6
AD:
77
C:
69 NR NR NR NR
No
neurologic
al disease
All
withi
n 2-
12 h
Temporal
lobe IHC
CD11a,
CD64,
CD18
⬆ most
pronounced
difference
for HLA-
DR
Cimino,
2009
University
of
Washington
’s ADRC
AD:6
C: 6
AD:
2/4
C:
2/4
AD:
76.0
C:
77.2 NR NR NR NR
All
<8 h
Frontal
cortex and
Hippocamp
us IHC
DOCK2
(Co-
localized
with RCA-
1)
⬆ DOCK2+
cells in both
frontal
cortex and
hippocampu
s
⬆ DOCK2+
and RCA1+
co-labelled
cells in both
frontal
cortex and
hippocampu
s
95
Dal
Bianco,
2008
NR
AD:
9
C: 15
AD:
0/9
C:
13/2
AD:
81 C:
70 NR
Braak,
CERAD
AD:
IV:
2
V:
4
VI:
3
No
neurologic
al disease
or brain
lesions NR
Cortical
areas of the
temporal
lobe,
including
entorhinal
cortex,
hippocampu
s and
temporal
cortex
Immunocytoche
mistry
B2M,
GLUT, HC-
10, HMGB,
iNOS,
MHCII,
Siglec
⬌ B2M
⬆ HC-10
near plaque
only
⬆ Siglec
near plaque
only
⬆ iNOS
near plaque
only
⬆ HMGB
near plaque
only
⬆ GLUT
near plaque
only
Dhawan
, 2012
University
of
Washington
ADRC NR NR
NR -
"age
match
ed" NR NR NR NR NR
Temporal
lobe IHC
Protein
phosphotyro
sine
(Co-
localized
with MHC-
II)
⬆ microglia
with
phosphotyro
sine and
enzymes
involved in
tyrosine
phosphoryla
tion
Green,
2004
ADRC
Brain Bank
at
Massachuse
tts General
Hospital NR NR
AD:
73 C:
68 NR CERAD NR NR
AD:
12.0
C:
12.2
Mixed:
frontal,
parietal and
temporal
cortices
Peroxidase
activity, IHC,
immunoblot
Myeloperox
idase
(Colocalize
d with
HLA-DR)
⬆ MPO
reactivity in
AD
microglia
96
Kawagu
chi-
Niida,
2006
NR
AD:5
C:5
AD:
3/2
C:
2/3
AD:
81.4
C:
75.8 NR Yes NR NR NR
Parahippoca
mpal gyrus,
subiculum,
CA1 to
CA4
segments of
Sommer's
Sector, DG
and adjacent
white matter IHC
Protein
bound
carbonyl
crotonaldeh
yde
(Co-stained
for GLUT-
5)
⬆ protein
bound
carbonyl
crotonaldeh
yde positive
microglia
Lue,
2001
BSHRI
AD:1
1
C:10
AD:
5/6
C:
4/6
AD:80
.8 C:
80.5
APOE:
AD:
3/4: 4
4/4: 4
C:
3/4: 3
4/4: 1
Braak,
CERAD
AD:
IV-
VI
C:
I-III NR
AD:
2.6 C:
2.3
Hippocamp
us IHC
RAGE
(Colocalize
d with
HLA-DR)
⬆ RAGE+
microglia in
hippocampu
s (dentate
gyrus, CA,
subiculum)
Matsuo,
1996
NR
AD:
8
C: 5 NR NR NR Yes NR
Neurologi
cally
normal
All 2-
24
Angular,
entorhinal,
hippocampu
s,
occipitotem
poral
temporal
cortices IHC
CD43
(mainly
stains
microglia,
decreases
with
activation)
⬇
Minett,
2016
Medical
Research
Council
Cognitive
Function
and
Ageing
Study - six
centres in
UK
AD:
83
C:
130
AD:
64/5
3
C:
51/6
6
AD:
89
C: 84 NR CERAD NR NR NR
Middle
frontal
gyrus (BA9) IHC MSR-A
⬆ MSR-A
-Associated
negatively
with
cognition
(MMSE),
positively
with AD
pathology
(plaques,
tangles)
97
Pujol-
Gimene
z, 2014
Oxford
Projectto
Investigate
Memory
and Ageing
and the
HumanBrai
n Tissue
Biobank
“Biobanco
Navarrabio
med”
AD:
12
C: 12
AD:
5/7
C:
4/8
AD:
81
C: 75 NR CERAD
AD:
V
or
VI
C:
0-II
No history
of
neurologic
al disease
AD:
49 C:
39
Frontal
cortex (BA
10) Western OX-42 ⬆
Rangara
ju, 2015
Emory
ADRC
Neuropatho
logy Core,
Atlanta
AD:
10
C: 10
AD:
6/4
C:
6/4
AD:71
.5 C:
71.5
APOE:
AD: 8
with
APOE4
(3
homozyg
ous) C: 1
APOE4
(0
homozyg
ous Yes
AD:
All
VI
C: 0 NR NR
Frontal
cortex IHC Kv1.3 ⬆ Kv1.3
Ricciare
lli, 2004
Institute of
Pathology,
Case
Western
Reserve
University
and ADRC
at
the
University
of
Kentucky
AD:
6
C: 6
HPC:
6 NR
AD:
85 C:
65
HPCs:
74 NR CERAD NR
No history
of
neurologic
al disorder
AD:
7 C: 9
C
with
Plaqu
es: <3
Frontal
Cortex
PCR,
Immunoblot CD36
⬆ vs C, ⬌
vs HPC
98
Sanchez
-Mejias,
2016
Tissue bank
at
Fundación
CIEN
Braak
stage
0: 8
II: 13
III-
IV: 9
V-VI:
17
Braa
k
stag
e
0:
5/3
II:
7/13
III-
IV:
4/5
V-
VI:
7/11
Braak
stage
0: 19
II: 78
III-IV:
80
V-VI:
79 NR
Braak V-
VI
clinically
classified
as AD,
Braak II
age -
matched
and used
as C
Bra
ak
stag
e
0: 8
II:
13
III-
IV:
9
V-
VI:
17 NR
Braak
stage
0: 8
II: 7
III-
IV: 6
V-VI:
8
Hippocamp
us CA1,
CA3,
parahippoca
mpal gyrus IHC
P2ry12
⬇ area in
stage V-VI
DG and
CA3
⬌CA1 and
parahippoca
mpal gyrus
- More
activated
morphology
- Braak
stage V-VI
had AD and
were
compared to
Braak stage
II Cs
Satoh,
2015
NR
AD:
7
C: 14
AD:
5/5
C:
6/5
AD:
70 C:
75 NR
Braak,
CERAD
AD:
VI:
10
4 died of
non-
neurologic
al causes,
3 with
Parkinson'
s, 4 ALS NR
Frontal
cortex
IHC, PCR,
Western
TMEM119
(Co-
localised
with Iba1)
PCR: ⬆
IHC: ⬌
Western: ⬌
99
Strohme
yer,
2014
BSHRI NR NR NR NR Yes NR NR
All
<4 h
Limbic
cortex and
neocortex
(locus
ceruleus,
mid-frontal
gyrus,
superior
frontal
gyrus,
superior
parietal
lobule,
temporal
lobe, visual
cortex) IHC
C/EBPb
(Co-
localized
with HLA-
DR) ⬆
Verbeek
, 1995
NR
AD:
41
C: 13
AD:
14/2
4
C:
5/8
AD:
78.0
C:
71.5 NR Yes NR NR
AD:
2.1
C: 3.2
Grey matter
of:
cerebellum,
frontal
cortex,
hippocampu
s, and
parietal,
occipital
and
temporal
cortices IHC
25F9
(activated
microglia)
⬆ Activated
microglia
Walker,
2002
BSHRI
AD:
6-
9/regi
on
C: 5-
9/regi
on
Vari
es
by
regi
on
Varies
by
region
, for
overal
l
sampl
e:
AD:
81.9
C:
79.8 NR CERAD NR NR
Varie
s by
regio
n, for
overa
ll
sampl
e:
AD:
2.6
C: 2.4
Cerebellum,
hippocampu
s, inferior
and superior
temporal
gyri
microglia
isolation
(qualitative),
immunoblot
(quantitative),
immunohistoche
mistry
(qualitative) CD87
⬆
hippocampu
s, superior
and inferior
temporal
gyri
⬌
cerebellum
100
Table 2-15: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Arginase 1 (AG1), Alzheimer’s Disease Research
Center (ADRC), Apolipoprotein E (APOE), Banner Sun Health Research Institute (BSHRI), Brodmann area (BA), Cationic amino
acid transporter member 2 (CAT2), Cluster of differentiation (CD), Centro Investigación Enfermedades Neurológicas (CIEN),
Chitinase 3-like (CHI3L), Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Control (C), Cornu ammonis (CA),
Dedicator of cytokinesis 2 (DOCK-2), Dentate gyrus (DG), Glucose transporter (GLUT), High mobility group box 1 (HMGB1), High
pathology control (HPC), Human leukocyte antigen (HLA), Hours (h), Ionized calcium-binding adapter molecule 1 (Iba1),
Immunohistochemistry (IHC), inducible nitric oxide synthase (iNOS), Kathleen Price Bryan Brain Bank (KPBBB), Macrophage
scavenger receptor A (MSR-A), Mannose receptor (MRc), Myeloperoxidase (MPO), Not reported (NR), Post-mortem interval (PMI),
Polymerase chain reaction based assays (PCR), Receptor for advanced glycation endproducts (RAGE), Ricinus communis agglutin-1
(RCA-1), Sialic acid-binding immunoglobulin-type lectins (Siglec)
101
2.4.16 High throughput Techniques: Microarray and Proteomics
Microarrays and proteomic studies that specifically discussed microglia or their markers were
included in the interest of comprehensiveness, however they are presented separately (Table 2-
16) as there is significant risk that those presented here do not represent the balance of the
literature. Any microarray would include some microglial markers, such as HLA-DR, but studies
that did not identify significant differences between AD and control for these markers may be
less likely to have mentioned them by name in the title, abstract or article keywords, and would
therefore have been missed by the systematic search. Five studies used high throughput
techniques, either microarray146, 255-258 or proteomics259, to examine genes, proteins, or patterns
of gene expression associated with microglia in AD and control in various brain regions,
including the entorhinal cortex, hippocampus, post-central gyrus, superior frontal gyrus,
prefrontal cortex, cerebellum, dorsolateral prefrontal cortex, visual cortex, and precuneus. All
high throughput studies reported increases in AD relative to control in some of the microglial
markers measured.
102
Table 2-16: High throughput studies
First
Author Brain bank n
Sex
(m/f) Age
AD
Geneti
c Risk
Factor
s
AD
Histologically
Confirmed
and criteria
Braak
stage
C history of
neurological
or
psychiatric
disease
PMI
(h) Brain Region Technique Marker
Direction of results
(AD vs C)
Cribbs,
2012
National
Institute on
Aging
Alzheimer’s
Disease
Center
brain banks
located at the
University of
Califor-
nia, Irvine,
Sun Health
Research
Institute,
University of
Rochester,
Johns Hopkins
University,
University of
Maryland,
University of
Pennsylvania,
and the
University
of Southern
California
AD:
26
C:
33
AD:
11/15
C:
14/19
AD:
85.7
C:
84.2
APOE
4
Carrie
rs:
AD:
17 C:
4
CERAD,
NIA-Reagan
Criteria
AD:
I: 1
II: 1,
III: 3
IV: 6
VI: 12
C:
0: 5
I: 2
II: 12
III:6
IV: 3
No history
of
neurological
and/or
psychiatric
disorders,
no major
neuropathol
ogical
abnormalitie
s
AD:
4.4
C: 4.0
Entorhinal
cortex,
hippocampus,
post-central
gyrus,
superior
frontal gyrus
Microarray,
small subset
with PCR
Markers
of
microgli
al
activatio
n (Fc
receptor
s, MHC,
TLR)
⬌ For most genes
relative to age-
matched Cs: all
measured MHC I
genes, HLA-DPβ1,
HLA-DQα1, HLA-
DQβ1, HLA-
DRβ1,3,4 and 6, all
measured Fc fragment
genes, CD163, CD14,
TLR 10, TLR2,
TLR4, TLR8
⬆ HLA-DMα, HLA-
DMβ, HLA-DPα,
HLA-DRα, HLA-
DRβ5 (SFG), TLR 5 (
HC and SFG), TLR7
(SFG, not confirmed
by PCR), CD32
(SFG)
⬇ Toll interacting
protein (HC)
⬆ for most genes
relative to young Cs
103
Durrenber
ger, 2015
AD brains:
Tissue banks
within the
BNE network
(Barcelona,
Budapest,
Goettingen/M
annheim,
London
Imperial
College,
Munich and
Wurzburg) C
brains: Same
banks in
addition to
Human Brain
Tissue Bank
in Budapest
AD:
12
C: 6
AD:
7/5 C:
3/3
AD:
81.3
C:
60.3 NR Yes
AD: IV
and V
C: II NR
AD:
6.0
C: 8.7
Entorhinal
cortex
Microarray
was utilized,
RT-qPCR
validation of
11 genes
HLADP
A1,
HLA-
DR A,
HLA
DR B4,
TREM2
Plus
markers
of cells
of
myeloid
lineage:
Annexin
a1,
CD37,
CD74,
CPVL,
GRAM
D1C,
RFX4
v3,
TYROB
P
Durrenber
ger, 2015
Institute of
Neuropatholo
gy in
Barcelona and
Human Brain
Tissue Bank
in Budapest
AD:
12
C: 6
AD:
7/5
C: 3/3
AD:
81.3
C:
60.3 NR Yes, Braak
AD: IV-
V
C: <II
No
neurodegen
erative
disorder,
systemic
illness or
alcohol or
drug abuse
AD:
6.0
C: 8.7
Entorhinal
cortex
Microarray,
PCR
validation*
HLA-
DRA*,
HLA-
DPA,
HLA-
DRB4*,
TREM2
,
TYROB
P*,
CD74,
CPVL,
GRAM
D1C,
annexin
A1,
RFX4,
CD37 ⬆
104
and
TYROB
P
Li, 2015 NR
AD:
450
C:
212 NR NR NR NR NR NR NR
Super frontal
gyrus or
prefrontal
cortex
Meta-
analysis of
six gene
expression
studies
DOK3
(links
with
TYROB
P) ⬆
Podtelezni
kov, 2011 HBTRC
Vari
es
by
regi
on -
up
to
181
AD
and
125
C
Varies
by
region
Vari
es
by
regi
on
AD:
47-
100
C:
22-
106 NR Braak
AD:
>III NR
All
avg 18
Cerebellum,
dorsolateral
prefrontal
cortex (BA9)
visual cortex
(BA 17)
Microarray,
principal
component
analysis
Combin
ation of
microgli
al and
cytokine
genes ⬆
105
Seyfried,
2017
Baltimore
Longitudinal
Study of
Aging
(BLSA)
or the Emory
Alzheimer’s
Disease
Research
Center
(ADRC) Brain
Bank
AD:
20
(+8
vali
dati
on)
HP
C:
15
C:
15
(+8
vali
dati
on)
Whole
sampl
e:
AD:
16/20
HPC:
20/9
C:
21/7
AD:
>55
HPC
:
>71
C:
>57
Whole
sampl
e:
AD:
N/A: 1
2-3: 7
3-3:
21
3-4:
15
4-4: 3
HPC:
2-3: 2
3-3:
20
3-4: 7
C:
2-3:
11
3-3:
22
3-4: 1
4-4: 2
Braak,
CERAD
For
subset:
AD:
IV: 8
V: 8
VI: 24
HPC:
I: 0
II: 3
III: 8
IV: 16
VI: 2
C:
I: 5
II: 15
III: 4
IV: 4 NR
Dorsolateral
prefrontal
cortex (BA9)
Precuneus
(BA 7) Proteomics
Protein
network
enriched
with
microgli
a and
astrocyt
e
markers
⬆ relative to C
⬌ relative to HPC
Zhang,
2013
HBTRC,
validated with
brains from
National
Alzheimer's
Coordinating
Center Brain
Banks and the
Miami Brain
Bank
Mic
roar
ray:
AD:
376
C:
173
Vali
dati
on:
AD:
377
C:
359 NR NR
NR
(incre
ased
OR
for
AD
with
ε4
allele
confir
med in
overal
l
HBTR
C
sampl
e) Braak NR NR
All
avg
17.8
Microarray:
Cerebellum,
dorsolateral
prefrontal
cortex (BA9),
visual cortex
(BA17)
Validation:
temporal and
prefrontal
cortex
Microarray
with
analysis of
functional
categories
of gene
expression
Microgli
a gene
expressi
on
module,
TYROB
P ⬆
106
Table 2-16: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Alzheimer’s Disease (AD), Alzheimer’s Disease Research Center (ADRC),
Apolipoprotein E (APOE), Average (Avg), BrainNet Europe (BNE), Brodmann area (BA), Carboxypeptidase vitellogenic-like
(CPVL), Cluster of differentiation (CD), Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Control (C), GRAM
domain containing 1C (GRAMD1C), Harvard Brain Tissue Resource Center (HBTRC), High pathology control (HPC), Human
leukocyte antigen (HLA), Hours (h), National Institute on Aging (NIA), Not reported (NR), Post-mortem interval (PMI), Polymerase
chain reaction based assays (PCR), Regulatory factor X4 (RFX4), Superior frontal gyrus (SFG), Toll-like receptor (TLR), Triggering
receptor expressed on myeloid cells 2 (TREM2), TYRO protein tyrosine kinase binding protein (TYROBP)
107
2.4.17 Non-quantitative comparisons
Fifty-one papers that would otherwise have met the inclusion and exclusion criteria for this
review were not included in the full extraction because they were non-quantitative, though
results and key methodological details are presented in Table 2-17. Papers were considered non-
quantitative if quantitative data for the comparison between AD and control was not presented
and/or the methods did not indicate that quantification had occurred. These papers used
immunohistochemistry or immunohistochemistry with Western blot to measure microglia using
various markers, and commented qualitatively on the number of cells, amount of
staining/expression, or morphology. The most common brain region investigated by the non-
quantitative papers was the hippocampus (23/51 studies), however like in the quantitative papers
in the review, many other regions were also investigated, including the entorhinal cortex,
temporal cortex, occipital cortex, frontal cortex and anterior cingulate gyrus. Non-quantitative
studies used many of the same markers discussed previously, such as: HLA172, 260-276, Iba1210, 277,
CD68274, 278, ferritin272, 279-282, CD45274, 283-288, RCA-1270, 289 and various other markers or
combinations of markers, considered to related to microglial activation.290-307 All but two of
these papers230, 308, measuring CD68 and Iba1 respectively, reported elevations in microglial
markers in AD relative to control post-mortem brains. Thus, the null finding rate in the non-
qualitative papers is less than a third of the rate of quantitatively assessed markers like MHCII.
108
Table 2-17: Non-quantitative comparisons
Author, Year
Title Journal Brain Region Technique Marker Results
Akiyama, 1993 Microglia express the
type 2 plasminogen
activator inhibitor in the
brain of control subjects
and patients with
Alzheimer's disease
Neuroscience Letters Temporal gyrus,
angular gyrus,
hippocampus
(mixed)
IHC Plasminogen
activator
inhibitor-2
⬆ intensity of staining, microglia
had activated morphology
Akiyama, 1994 Expression of MRP14,
27E10, interferon-alpha
and leukocyte common
antigen by reactive
microglia in postmortem
human brain tissue
Journal of
Neuroimmunology
Hippocampus, mid
temporal gyrus
and angular
gyrus.
IHC MRP14 (calcium
binding protein),
CD45, Interferon-
alpha
↑CD45RB expression in
microglia in AD vs. control in the
cerebral cortex.
↑MRP-14 expression in microglia
in AD vs. control. In AD these
microglia appeared reactive in
shape and frequently formed
aggregates in the
↑interferon-alpha+ microglia in
AD vs. control in the cortex. In
AD microglia aggregates in senile
plaques were stained intensely.
109
Akiyama,
1994b
Expression of the
receptor for macrophage
colony stimulating factor
by brain microglia and
its upregulation in brains
of patients with
Alzheimer's disease and
amyotrophic lateral
sclerosis
Brain Research Hippocampus,
middle temporal
gyrus, precentral
gyrus
IHC CSF-1 ↑CSF-1 stained microglia in AD
vs. control, activated morphology
An, 2009 Expression and
localization of
lactotransferrin
messenger RNA in the
cortex of Alzheimer's
disease
Neuroscience Letters Temporal cortex
(cerebral cortex?)
IHC+in situ
hybridization
Lactoferritin (iron
binding protein)+
HLA-DR to
identify microglia
↑Lactoferritin mRNA in HLA-
DR+microglia in AD vs. control,
marker of activation
Arends, 2000 Microglia, amyloid and
dementia in Alzheimer
disease. A correlative
study
Neurobiology of
Aging
Middle frontal
gyrus (BA9)
IHC CD68 ⬌ correlation with dementia
rating, but all but least severe
patients had high density
⬆ Correlated with congophilic
amyloid deposits
⬌ correlation with Aβ or NFT
Bayer, 1999 Evidence for activation
of microglia in patients
with psychiatric illnesses
Neuroscience Letters Hippocampus and
frontal cortex
IHC HLA-DR ↑HLA-DR in AD vs. control
110
Bryan, 2008 Expression of CD74 is
increased in
neurofibrillary tangles in
Alzheimer's disease
Molecular
Neurodegeneration
Hippocampal
tissue
Immunocytochemistry CD74 ↑CD74 labelled microglia in AD
vs. control (marker of activation)
Carrano, 2011 Amyloid Beta induces
oxidative stress-
mediated blood-brain
barrier changes in
capillary amyloid
angiopathy
Antioxidants &
Redox Signaling
Occipital pole
cortex.
IHC NOX-2 +
morphology
↑
Christie, 1996 Expression of the
macrophage scavenger
receptor, a
multifunctional
lipoprotein receptor, in
microglia associated
with senile plaques in
Alzheimer's disease
American Journal of
Pathology
Hippocampal
formation and
adjacent temporal
neocortex
IHC LN3 (HLA-DR) ↑ number of activated microglia
(HLA-DR stained) in AD
(typically associated with
plaques) vs. control
Christie, 1996b Expression of the very
low-density lipoprotein
receptor (VLDL-r), an
apolipoprotein-E
receptor, in the central
nervous system and in
Alzheimer's disease
Journal of
Neuropathology &
Experimental
Neurology
Hippocampus and
adjacent temporal
lobe
IHC HLA-DR, ↑microglia of activated
morphology in AD cortex than in
control.
- In control there was uniform
staining of microglia throughout
depth of dentate gyrus, but in AD
there was more immunoreactivity
in the inner third layer.
111
– very low-density lipoprotein
receptor positive microglia
colocalize with Aβ in senile
plaques in AD.
Dickinson,
1988
Alzheimer's disease. A
double-labeling
immunohistochemical
study of senile plaques
American Journal of
Pathology
Hippocampus and
parahippocampal
gyrus
IHC RCA-1 ↑
Dickson, 1996 Glycation and microglial
reaction in lesions of
Alzheimer's disease
Neurobiology of
Aging
Hippocampus IHC HLA-DR ⬆
Drache, 1997 Bcl-xl-specific antibody
labels activated
microglia associated
with Alzheimer's disease
and other pathological
states
Journal of
Neuroscience
Research
Amygdala,
cerebellum,
hippocampus,
neocortex (BA 21,
22)
Western, IHC Bcl-xl
(homologue bcl-
2) in microglia
↓, lower microglia survival
Grundke-Iqbal Ferritin is a component
of the neuritic (senile)
plaque in Alzheimer
dementia
Acta
Neuropathologica
Hippocampus IHC Ferritin- positive
microglia
⬆
112
Guillemin,
2005
Indoleamine 2,3
dioxygenase and
quinolinic acid
immunoreactivity in
Alzheimer's disease
hippocampus
Neuropathology &
Applied
Neurobiology
Temporal lobe
(hippocampus,
amygdala,
fusiform cortex,
entorhinal cortex)
IHC Quinolinic acid,
indoleamine co-
labelled with
ferritin (thought
to mark activated
microglia)
⬆
Haga, 1989 Demonstration of
microglial cells in and
around senile (neuritic)
plaques in the Alzheimer
brain. An
immunohistochemical
study using a novel
monoclonal antibody
Acta
Neuropathologica
Not reported IHC and
immnoperoxidase
staining
AD11/8 (stains
microglia) and
peroxidase
staining for
microglia
↑
Jellinger, 1990 Brain iron and ferritin in
Parkinson's and
Alzheimer's diseases
Journal of Neural
Transmission -
Parkinsons Disease
& Dementia Section
Substantia nigra, IHC Ferritin ⬆, Numerous reactive microglia in
AD associated with plaques, not
in control
Liu, 2005 LPS receptor (CD14): A
receptor for phagocytosis
of Alzheimer's amyloid
peptide
Brain Hippocampus,
occipital cortex,
frontal cortex
IHC CD14+ microglia ⬆
113
Lopez-
Gonzalez,
2015
Neuroinflammatory
signals in Alzheimer
disease and APP/PS1
transgenic mice:
correlations with
plaques, tangles, and
oligomeric species.
Journal of
Neuropathology &
Experimental
Neurology.
74(4):319-44, 2015
Apr
Frontal cortex area
8
IHC Iba1 ⬆ number and size of microglia in
all stages of AD, more
hypertrophic and round microglia
at later stages
McGeer, 1989 Immune system response
in Alzheimer's disease
Canadian Journal of
Neurological
Sciences
Temporal cortex IHC HLA-DR ↑
Meadowcroft,
2015
Cortical iron regulation
and inflammatory
response in Alzheimer's
disease and
APPSWE/PS1ΔE9 mice:
a histological perspective
Frontiers in
Neuroscience. 9
(JUL) (no
pagination), 2015.
Article Number:
00255. Date of
Publication: 2015.
Entorhinal cortex IHC Iba1 ⬌ total staining, AD has more
clustering
Minnasch,
2003
Demonstration of
puromycin-sensitive
alanyl aminopeptidase in
Alzheimer disease brain
Legal Medicine Cortical and
hippocampal
tissue
IHC Puromycin-
sensitive alanyl
aminopeptidase
↑ Upregulation of PSA in AD
microglia.
Narayan, 2015 Increased acetyl and
total histone levels in
post-mortem Alzheimer's
disease brain
Neurobiology of
Disease
inferior temporal
gyrus
IHC HLA ⬆
114
Perlmutter,
1992
MHC class II-positive
microglia in human
brain: Association with
Alzheimer lesions
Journal of
Neuroscience
Research
neocortical,
hippocampus
IHC RCA, HLA, LN3 ⬆ activated morphology with
HLA and LN3, more clusters, less
uniform distribution than in
controls
R. Bowser and
S. Reilly
Expression of FAC1 in
activated microglia
during Alzheimer's
disease
Neuroscience Letters Midfrontal and
temporal cortex
IHC HLA-DR+FAC1
(DNA binding
protein, not
microglia
specific)
↑HLA-DR in AD vs. control
↑HLA-DR containing with FAC1
In AD vs. control (may indicate
activation)
Rogers, 1988 Expression of immune
system-associated
antigens by cells of the
human central nervous
system: relationship to
the pathology of
Alzheimer's disease
Neurobiology of
Aging
Cortical and
subcortical
structures
IHC HLA-DR ↑HLA-DR in gray matter in AD
vs. non AD elderly control .
⟷ in HLA-DR in white matter of
AD vs. non AD elderly control .
↑HLA-DR aggregates in AD vs.
control typically in layers II-V,
and appear most concentrated
around plaques. HLA-DR
clustering is also found in rare
plaques of healthy elderly
controls.
Rozemuller,
2000
Activated microglial
cells and complement
factors are unrelated to
cortical Lewy bodies
Acta
Neuropathologica
Anterior cingulate
gyrus
IHC HLA-1DR
(CR3/43, LN3),
CD68, RCA-1,
ferritin
⬆ Clustering around plaques,
activated
115
Ryu, 2008 A leaky blood-brain
barrier, fibrinogen
infiltration and
microglial reactivity in
inflamed Alzheimer's
disease brain
Journal of Cellular
and Molecular
Medicine
Entorhinal cortex IHC HLA-DR
(CR3/43)
⬆ activated microglia
Sasaki, 1997 Microglial activation in
early stages of amyloid
beta protein deposition
Acta
Neuropathologica
Isocortical area,
hippocampus,
cerrebellum
IHC LN3, LN1, LCA
CR3/43, KP1, Ki-
M1p,
2B11+PD7/26
⬆ microglia of activated
morphology
Scott, 1993 Inability to detect beta-
amyloid protein
precursor mRNA in
Alzheimer plaque-
associated microglia
Experimental
Neurology
Hippocampus,
superior and
middle temporal
gyri, visual cortex,
entorhinal cortex,
amygdala (mixed)
IHC LN3 ⬆ microglia with activated
morphology, associated with
amyloid deposits and tau
Streit, 2009 Dystrophic (senescent)
rather than activated
microglial cells are
associated with tau
pathology and likely
precede
neurodegeneration in
Alzheimer's disease
Acta
Neuropathologica
Temporal lobe IHC Iba-1 ↑degenerated microglia in AD vs.
Control
116
Su, 1997 Bax protein expression is
increased in Alzheimer's
brain: correlations with
DNA damage, Bcl-2
expression, and brain
pathology
Journal of
Neuropathology &
Experimental
Neurology
Hippocampal
formation
IHC Bax and HLA-
DR co-stain
(apoptotic
microglia)
↑ apoptotic microglia
Togo, 2000 Expression of CD40 in
the brain of Alzheimer's
disease and other
neurological diseases
Brain Research Hippocampus and
adjacent temporal
isocortex
IHC CD40 + HLA-DR ↑CD40 staining
Togo, 2002 Occurrence of T cells in
the brain of Alzheimer's
disease and other
neurological diseases
Journal of
Neuroimmunology
Hippocampus,
other cortical areas
IHC HLA-DR ⬆
Tooyama,
1990
Reactive microglia
express class I and class
II major
histocompatibility
complex antigens in
Alzheimer's disease
Brain Research Medial temporal
cortex including
the hippocampus
and
parahippocampal
gyrus
IHC HLA A, B C
(MHC I) HLA-
DR (MHCII),
LCA
⬆ MHC I, MHC II
117
van Dullin,
2013
Comparison of
Histological Techniques
to Visualize Iron in
Paraffin-embedded Brain
Tissue of Patients with
Alzheimer's Disease
Journal of
Histochemistry and
Cytochemistry
Frontal Cortex IHC (compared three
different iron IHC
methods to Ferritin
IHC)
Ferritin positive
microglia
(activation or
dysfunction)
↑ Iron-positive microglia labelling
in AD brains vs. control.
Wiendl, 2004 Expression of the
immune-tolerogenic
major histocompatibility
molecule HLA-G in
multiple sclerosis:
Implications for CNS
immunity
Brain Not reported IHC HLA-G ↑HLA-G in AD vs. control
(correlates with MHCII)
Wirths, 2013 Oligomeric
pyroglutamate amyloid-
beta is present in
microglia and a
subfraction of vessels in
patients with Alzheimer's
disease: implications for
immunotherapy
Journal of
Alzheimer's Disease
gyrus temporalis
superior
IHC 9D5 (truncated
amyloid) positive
microglia -->
phagocytic
microglia
↑
Wong, 2001 Advanced glycation
endproducts co-localize
with inducible nitric
oxide synthase in
Alzheimer's disease
Brain Research Temporal cortex
(BA 22)
IHC iNOS ⬆ Reactive microglia (expressing
iNOS and advanced glycation
end-products) in advanced (Braak
III-IV) but not early AD (I-II)
118
Wu, 2005 Apoptotic signals within
the basal forebrain
cholinergic neurons in
Alzheimer's disease
Experimental
Neurology
Nucleus basalis of
Meynert
IHC CD68 ↑
Xia, 1998 Immunohistochemical
study of the beta-
chemokine receptors
CCR3 and CCR5 and
their ligands in normal
and Alzheimer's disease
brains
American Journal of
Pathology
Yemporal cortex,
visual cortex,
caudate, putamen,
cerebellum
(mixed)
IHC CCR3, CCR5 on
microglia
⬆ chemokines CCR3 and CCr5
staining intensity on reactive
microglia
Yamada, 1994 Immunohistochemistry
using antibodies to
alpha-interferon and its
induced protein, MxA, in
Alzheimer's and
Parkinson's disease brain
tissues
Neuroscience Letters Parietal cortex IHC LCA, αIFN, MxA ⬆
Yamada, 1995 White matter microglia
produce membrane-type
matrix metalloprotease,
an activator of gelatinase
A, in human brain
tissues
Acta
Neuropathologica
Parietal lobe white
matter
IHC, in situ
hybridization
LCA, matrix
metalloprotease
↑ in IHC, ↔ in mRNA
119
Yamada, 1995 Microglial localization
of alpha-interferon
receptor in human brain
tissues
Neuroscience Letters Not reported IHC alpha-IFN
receptor
colocalized with
LCA
⬆
Yamada, 1995 Selective localization of
gelatinase A, an enzyme
degrading beta-amyloid
protein, in white matter
microglia and in
Schwann cells
Acta
Neuropathologica
Parietal white
matter
IHC LCA ⬆
Yamada, 1998 Possible roles of
transglutaminases in
Alzheimer's disease
Dementia &
Geriatric Cognitive
Disorders
Parietal lobe and
hippocampus
IHC, western blot LCA ⬆parietal cortex, not hippocampus
(control cases had staining in
hippocampus, AD had none)
Yamada, 1999 Melanotransferrin is
produced by senile
plaque-associated
reactive microglia in
Alzheimer's disease
Brain Research Not reported IHC, in situ
hybridization
Metallotransferrin ⬆ in reactive microglia
Yan, 1997 Amyloid-beta peptide-
receptor for advanced
glycation endproduct
interaction elicits
neuronal expression of
macrophage-colony
stimulating factor: a
Proceedings of the
National Academy
of Sciences of the
United States of
America
Temporal lobe IHC Macrophage
colony-
stimulating factor
colocalized with
CD68
↑
120
proinflammatory
pathway in Alzheimer
disease
Yoshiyama,
2000
Expression of invariant
chain and pro-cathepsin
L in Alzheimer's brain
Neuroscience Letters Hippocampal
Formation,
Entorhinal
cortex, and
parietal cortex
IHC HLA-DR, pro-
cathepsin L
MHCII invariant
chain
↑HLA-DR ↑Microglia stained
with pro-cathepsin L in AD vs.
control . ↑ Microglia stained with
MHC II invariant chain in AD vs.
control .
Zeineh, 2015 Activated iron-
containing microglia in
the human hippocampus
identified by magnetic
resonance imaging in
Alzheimer disease
Neurobiology of
Aging. 36 (9) (pp
2483-2500), 2015.
Date of Publication:
01 Sep 2015.
Subiculum IHC Iron containing
microglia
(CD163)
⬆
Table 2-17: Where there are both young and older controls, values are reported for the older (age-matched controls). Results are
expressed relative to control unless specified otherwise. Amyloid-β (Aβ), Alzheimer’s Disease (AD), Brodmann area (BA), Cluster of
differentiation (CD), Colony stimulating factor (CSF), Control (C), Cornu ammonis (CA), Dentate gyrus (DG), Human leulocyte
antigen (HLA), Ionized calcium-binding adapter molecule 1 (Iba1), Immunohistochemistry (IHC), Inducible nitric oxide synthase
(iNOS), Interferon (IFN), Leukocyte common antigen (LCA), Major histocompatibility complex (MHC), Neurofibrillary tangles
(NFT), Polymerase chain reaction based assays (PCR), Triggering receptor expressed on myeloid cells 2 (TREM2)
121
2.5 Discussion
Most (76/113) of the studies included in this review measured microglia using one of three
common markers: MHCII, CD68 and Iba1. While studies measuring MHCII or CD68
consistently identified increases in AD relative to control in most brain regions studied, ten of the
twenty studies that compared Iba1 identified no difference or a decrease relative to controls.
Importantly, 9/10 studies noting an increase in Iba1 in AD relative to controls used expression-
based quantification methods (qPCR, Western, fluorescence intensity), which indicate only the
amount of Iba1 in the sample, while most that identified no difference used cell counting,
including two studies that used stereological quantification. Iba1 is a pan-microglial marker
whose expression increases with microglial activation191, 210, so these results indicate that there
are increases in the expression of Iba1, but not the absolute number of microglia in AD. This,
along with the increases in MHCII and CD68, which are both markers of activated microglia,
suggest that the apparent increases in microglial markers in AD are attributable to increases in
activation rather than the absolute number of microglia. This is supported by findings with
CD11b which like Iba1, labels resting and activated microglia, that also had mixed null and
positive results, and by studies using other activation markers, such as ferritin, IL-1α, and CD33,
which were consistently increased in AD. However, there is still controversy surrounding what
markers are indicative of activation, as well as the type of activation they are associated with193,
so more research on the physiological significance of increases in these markers is needed.
122
Figure 2-2: Summary of results of systematic search.
Size of circle is proportional to the number of identified studies, while the colour and position on the
graph illustrates the percent of studies that identified an increase in AD in at least one brain region.
Cluster of differentiation (CD), Ionized calcium-binding adapter molecule 1 (Iba1), Interleukin (IL),
Major histocompatibility complex (MHC), Ricinus communis agglutin-1 (RCA-1), Translocator protein
(TSPO), Triggering receptor expressed on myeloid cells 2 (TREM2)
123
The nine studies identified by this review that compared microglial markers between AD and a
HPC group, cognitively intact subjects with AD neuropathology, shed light on whether increased
microglial activation is a cause or consequence of the disease. Five of these studies reported
higher levels of microglia markers in AD, three using HLA-DR 28, 166, 173, one using CD68216, and
one using both TREM2 and Iba1194. In contrast, three, using CD36, HLA-DR or proteomics,
reported no difference between AD and HPC despite increases relative to the regular control
group178, 259, 309, while one reported more HLA-DR positive microglia in the HPC group than in
AD.157 Though there appears to be increased microglia markers in AD relative to HPC subjects,
more studies using this reference group are needed to help elucidate the role of microglia in AD
pathogenesis. Polymorphisms in genes encoding microglial markers HLA-DR, CD33 and
TREM2 have been implicated as risk factors for late-onset AD144, 145, 236, which supports the
notion that increased microglial activation is a contributor to AD development, and not merely a
response to established AD pathology.
Some of the heterogeneity identified between studies may be attributed to differences in the brain
regions examined. More than half the studies that used tissue from the cerebellum identified no
difference between control and AD across a range of microglial markers158, 167, 177, 245, 310. This
lack of reactivity in the cerebellum has been remarked upon previously, leading the cerebellum
to be proposed as a reference region for PET imaging of TSPO.311 Similarly, half the studies
examining microglia in the white matter showed either no difference between AD and control158,
177, 185, 202, 227, 242, 271, or higher levels in control.165 Several studies report higher levels of HLA-
DR expressing microglia in the white matter than the grey matter of non-demented cases, which
suggests that microglia in this tissue may be constitutively activated182, 185, 275. The lack of
consistent differences in the white matter between patients with AD and controls in the studies in
our review could therefore indicate that AD pathology does not stimulate further increases in
microglial markers on top of their already activated state, whereas elevations in AD can be
observed in the grey matter, which does not demonstrate this constitutive elevation.
Heterogeneity can also exist within a brain region. For example, Bachstetter et al counted CD68
positive microglia in the hippocampus CA1, CA2/3, CA4 and the DG, and noted increases in the
CA1 and DG relative to controls, but no difference in CA2/3 or CA4.202 Similarly, Masliah et al
quantified microglia in different layers of the hippocampus and reported higher levels relative to
124
control in the molecular and pyramidal layer, but lower levels in the stratus polymorphous.227
Thus, studies that examined staining in the whole hippocampus could dilute potential differences
between AD and control by examining multiple regions simultaneously, which may help to
explain why nearly a third of studies in using hippocampal tissue reported no difference between
AD and control. The potential for heterogeneity of results between tissues is particularly
important for studies that performed their analyses in a mix of different tissues, rather than
examining each region separately.163, 222, 231, 254, 312
Only one of the identified studies reported on the use of anti-inflammatory drugs in their
subjects.149 Though this study did not identify significant differences in microglia counts in
users and non-users, NSAID treatment reduces activated glial cells in a mouse model of AD313,
and the use of NSAIDs is associated with less microglial activation in the brains of elderly
patients post-mortem314, which suggests that unreported NSAID use could be a potential source
of confounding. It is therefore feasible that unreported differences in medication use between AD
and controls within and between studies contributed to the variability in the results.
The genotypes of the subjects could also offer a source of heterogeneity. Microglial activation
appears to be affected by APOE genotype, with carriers of the ε4 risk allele exhibiting greater
activation in some181, 189 but not all studies.215 Only 29 of the studies included in this review
reported the APOE genotype of their subjects. Among those that reported genotype, there was
variability, with some studies excluding those with the ε4 allele201, 304, and others including a
majority of AD subjects carrying the risk allele.163, 195, 212
Differences in control group characteristics could also influence the results. Over half the
included studies did not report screening controls for neurological or neuropathological
abnormalities. In addition, only 11 studies reported excluding subjects with a history of
psychiatric disorders. As various neurological and psychiatric diseases have been associated
with neuroinflammation in some post-mortem studies 315-317, this lack of screening could
contribute heterogeneity to the results.
The severity of AD pathology in AD cases and controls also varies between studies. Braak stage
was the most widely reported pathological score, and is presented in the tables where available.
Most studies used AD cases with a Braak score of V-VI, but some used AD cases with scores as
125
low as I and II.151, 178, 186, 197, 200, 255 Similarly for controls, Braak stages less than III were most
commonly used, but some studies included controls with Braak stages IV or higher.255, 259 This
variation may be important, as some studies identified associations between Braak stage or
neurofibrillary tangles and microglia markers, including HLA-DR, Iba1 and CD68.150, 181, 185, 197,
217, 318 Few studies reported on the amount of plaque in the brains included in their cohorts, but
differences in plaque load between studies could also influence the results, as microglial markers
were found to correlate with plaque loads in several studies.180, 181, 185, 189, 318 These correlations
could be functionally important to the progression of AD, as post-mortem analyses from brains
of AD patients administered amyloid-β vaccines demonstrate positive correlations between Iba1
and CD68 and markers of neuronal loss and degeneration that are attenuated following
immunization and amyloid-β clearance319, when the number of activated CD68 positive, but not
total Iba1-positive, microglia are reduced318.
Very few studies reported using stereology to complete their cell counts187, 203, 216, 217, 254, which is
the gold standard for quantifying cells in a tissue without bias. In our review, Serrano-Pozo et al.
reported that the absolute number of microglia was the same in AD and control samples when
stereology was used, but higher in AD when non-stereology based counting was applied due to
an apparent concentration of cells caused by cortical atrophy187. The use of non-stereological
counting could therefore have introduced some bias into the results. Interestingly, the three
stereology studies that counted all microglia, identified by either Iba1 or morphology, reported
no differences in total microglial counts in AD relative to control samples187,254,203 while
increases were noted in the number of cells positive for activation-associated markers MHC II,
CD68 and CD33187, 203, 216, 217. This supports the suggestion that microglial activation is increased
in AD without an absolute increase in the number of microglia.
Technical differences could also contribute to variation between studies. For
immunohistochemistry, the most commonly used technique in this review, the method and
duration of tissue fixation320, 321, the thickness of the brain sections321, the use of antigen
retrieval322, the antibody selected210, 320 and variation in many steps in the immunohistochemistry
protocol itself can all influence the intensity of staining, which could affect the ability of studies
to pick up differences between AD and control subjects. Differences in the methods used for
measuring gene expression, or for quantifying protein by Western blot or ELISA could similarly
126
contribute to variability in the results. This technical information, with the exception of
technique used and post-mortem interval, was not extracted in the tables because the wide
variability in methods and reporting made it unfeasible, however it is an important potential
contributor to study heterogeneity.
2.5.1 Limitations
Though efforts were made to identify every published paper that compared microglia markers
between AD and control post-mortem human brain samples, it is possible that some papers have
been missed. The large number of papers returned in the initial search (>20 000) may have
increased the risk of misidentifying a paper. In addition, studies that measured a microglial
marker, but did not mention it in the title, abstract or keywords would have been missed by the
systematic search. Thus, studies that did not examine microglia as a primary objective are more
likely to have been omitted, which may introduce some bias into the results of this review.
Microglia interact with astrocytes to initiate and drive inflammation within the brain. Like with
microglia, markers of astrocytes are reported to be elevated in patients with AD relative to
controls323, 324, where they seem to colocalize with amyloid-β plaques.325, 326 While our review
focused only on microglial markers, it had initially set out to include other inflammatory markers
as well, including, astrocytes, cytokines, complement, lipid mediators and other immune cells.
Our title and abstract screening uncovered over 700 papers, nearly 297 for astrocytes alone, an
unfeasibly large number of papers which led us to focus on microglia for this review. Not
including astrocytes and other inflammatory mediators is a limitation of our review because it
results in an incomplete picture of neuroinflammation in AD.
Another important limitation of this review is that while it describes the results of the included
studies in the context of microglia, it should be noted that nearly all microglia markers are also
expressed by other myeloid cells such as infiltrating or perivascular macrophages, neutrophils, or
T cells (see Appendix 1 for a summary of marker expression in other cell types). While there are
generally few of these cells in the brain parenchyma under normal conditions, there may be
increased accumulation and infiltration in AD, which could lead to an overestimation of the
quantity of microglial markers in diseased relative to control brains.
127
2.6 Conclusion
Microglia markers are increased in various brain regions in patients with AD relative to controls.
The balance of the evidence suggests that this increase is more attributable to increased
activation than to an increase in absolute cell number, however more research measuring
microglial proliferation in post-mortem human brain samples would be useful for clarifying this
point.
128
Chapter 3: Brain n-3 polyunsaturated fatty acids modulate
microglia cell number and morphology in response to
intracerebroventricular amyloid-β 1-40 in mice
Kathryn E. Hopperton, Marc-Olivier Trépanier, Vanessa Giuliano, Richard P. Bazinet
This paper is published in:
Hopperton et al. (2016) J. Neuroinflammation. 13(1):257
Part of the introduction of this paper was moved to the general introduction section 3.3.1 to
avoid redundancy.
Contributions:
RPB and KEH designed the project while MT contributed to its development and direction. KEH
conducted the bulk of the experimental work and analysis with assistance from MT and VG. VG
assisted in the development of the microglia morphology measurement and validated the
immunohistochemistry counts. KEH wrote the manuscript and all authors read and approved the
manuscript prior to submission.
129
3.1 Abstract
Background: Neuroinflammation is a proposed mechanism by which Alzheimer’s Disease (AD)
pathology potentiates neuronal death and cognitive decline. Consumption of n-3 polyunsaturated
fatty acids (PUFA) is associated with a decreased risk of AD in human observational studies, and
exerts protective effects on cognition and pathology in animal models. These fatty acids and
molecules derived from them are known to have anti-inflammatory and pro-resolving properties,
presenting a potential mechanism for the protective effects.
Methods: Here, we explore this mechanism using fat-1 transgenic mice and their wildtype
littermates weaned onto either a fish oil diet, (high in n-3 PUFA), or a safflower diet (negligible
n-3 PUFA). The fat-1 mouse carries a transgene that enables it to convert n-6 to n-3 PUFA. At
12 weeks of age, mice underwent intracerebroventricular (icv) infusion of amyloid-β 1-40.
Brains were collected between 1 and 28 days post-icv and hippocampal microglia, astrocytes and
degenerating neurons were quantified by immunohistochemistry with epifluorescence
microscopy, while microglia morphology was assessed with confocal microscopy and skeleton
analysis.
Results: Fat-1 mice fed the safflower oil diet and wildtype mice fed the fish oil diet had higher
brain DHA in comparison to wildtype mice fed the safflower oil diet. Relative to wildtype mice
fed the safflower oil diet, fat-1 mice exhibited a lower peak in the number of labelled microglia,
wildtype mice fed fish oil had fewer degenerating neurons, and both exhibited alterations in
microglia morphology at 10 days post-surgery. There were no differences in astrocyte number at
any time point and no differences in the time course of microglia or astrocyte activation
following infusion of amyloid-β 1-40.
Conclusions: Increasing brain DHA, through either dietary or transgenic means, decreases some
elements of the inflammatory response to amyloid-β in a mouse model of AD. This supports the
hypothesis that n-3 PUFA may be protective against AD by modulating the immune response to
amyloid-β.
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3.2 Introduction
AD is characterized by neuronal loss, the deposition of amyloid-β plaques and the
hyperphosphorylation of intracellular tau proteins, leading to the formation of neurofibrillary
tangles. In addition to these features, neuroinflammation is increasingly recognized as a hallmark
of AD on the basis of elevations in inflammatory markers in the brain measured either post-
mortem51, 52, 271 or in vivo via PET ligands58, increases in inflammatory cytokines in the plasma56,
57, and the discovery of polymorphisms in inflammation-associated genes that are associated
with AD risk61-67. Increases in neuroinflammatory markers have also been reported in animal
models, and may precede the deposition of amyloid-β plaque, indicating that inflammation may
play a causal role in disease development76,77.
DHA, the main n-3 PUFA in the brain, may be protective in AD through several mechanisms
(for review see 104). DHA promotes neuronal development and synaptogenesis through its
conversion to synaptamide (docosahexaenoyl ethanolamide) 138, and also regulates levels of
brain-derived neurotrophic factor 327, which could protect against neuronal and synaptic loss in
AD. DHA and EPA are precursors to a class of bioactive lipid molecules, referred to as
specialized pro-resolving mediators, that have well-characterized anti-inflammatory and pro-
resolving properties (for review see 97). Studies in postmortem human brain samples detected
lower levels of specialized pro-resolving mediators, including maresin 1, NPD1, and resolvin D5
in the hippocampus 111 and entorhinal cortex 112 of patients with AD relative to controls,
suggesting that impairments in resolution of inflammation may be involved in this disease.
Fish or DHA consumption is associated in human observational studies with a decreased risk of
AD or dementia124. A recent meta-analysis of animal studies identified improvements in
amyloid-β plaque levels, cognition and neurodegeneration in AD models with n-3 PUFA
treatment 113. In contrast, human intervention studies in AD are generally null 128, however there
is some evidence of protection in more mild forms of the disease 328. As pathological features of
AD may develop over decades prior to the appearance of symptoms 329, the discrepancy between
the results of epidemiological studies, which are mostly primary prevention, and clinical trials in
patients with diagnosed AD may be explained by differences in the magnitude of pathology in
these populations, by existence of a critical window for effectiveness of a dietary intervention, or
by residual confounding. n-3 PUFA decrease markers of neuroinflammation in a variety of
131
disease models, including Parkinson’s disease, stroke, and traumatic brain injury. These
interventions also decrease inflammatory markers in the brain in mouse models of AD, such as
IFN-γ, CD68, GFAP, and TNF-α118, 119,120 ,121.
As markers of inflammation are produced dynamically in response to an insult, with an initial
increase in levels followed by resolution (a return to homeostasis), examining
neuroinflammatory markers over time can be useful to understand how inflammation and its
resolution are affected by n-3 PUFA. n-3 PUFA may affect neuroinflammation by decreasing the
peak in production of some markers but not others, or by shifting the time course of their
production in ways that are not captured by measurements at a single time point 117. As
inflammation in the brain is mainly controlled by a different set of cells than occur in the
periphery, the astrocytes and microglia, we set out first to characterize the time course of the
inflammatory response to amyloid-β 1-40 and then to see how this was affected by changing
brain levels of DHA through a dietary or transgenic approach. While we identified no effect of
changing brain DHA on astrocytes, we detected a lower peak in microglia activation in the
hippocampus of mice with elevated brain DHA, along with reductions in markers of
neurodegeneration and alterations in microglia morphology that may be indicative of a less
activated phenotype.
3.3 Methods
3.3.1 Animals
All animal procedures and husbandry were carried out in accordance with the Regulations of
Animals for Research Act in Ontario and the Guidelines of the Canadian Council on Animal
Care (2015/16 protocol #s 20011375 and 20011376). Mice were housed in the University of
Toronto Department of Comparative Medicine animal facility at a controlled temperature (21C)
and light cycle (14/10 light/dark), 1-4 per cage with ad libitum access to food and water.
In a first study, 10-week-old male C57BL/6 mice were obtained from Charles River Laboratories
(Saint Constant, Quebec, Canada) and were maintained on standard laboratory chow both during
a two-week acclimatization period prior to surgery, and following surgery until death.
132
Mice for the second study were obtained via breeding in house from male fat-1 mice provided as
a generous gift by Dr. David Ma (University of Guelph, ON, Canada). The fat-1 mouse carries a
fat-1 transgene from the roundworm Caenorhabditis elegans, enabling it to endogenously
convert n-6 to n-3 PUFA, and thus attain high tissue levels of n-3 PUFA on a deplete diet 330.
C57BL/6 dams were ordered from Charles River Laboratories at 5-6 weeks of age, and
maintained on the low n-3, 10% safflower oil (SO) diet for 2 weeks prior to breeding with fat-1
males. Dams were maintained on the SO diet throughout pregnancy and lactation to reduce
maternal transfer of n-3 PUFA. Fat-1 mice were weaned onto the SO diet, while the wildtype
(WT) offspring were weaned onto either the SO diet or a diet that contained 8% safflower oil and
2% fish oil (FO). Offspring were maintained on these diets until 12 weeks of age, at which point
they underwent icv surgery, and were returned to the same diets after surgery until death.
3.3.2 Diets
Animals were fed one of three experimental diets depending on the study: standard laboratory
chow (Teklad 2018, Envigo, Indianapolis, IN, USA) or one of two diets modified from the AIN-
93G rodent diet: the SO diet, which contains 10% safflower oil by weight (SO; D04092701;
Research Diets Inc., New Brunswick, NJ, USA), or the FO diet which contains 2% menhaden oil
and 8% safflower oil (FO; D04092702; Research Diets Inc.). Fatty acid composition of the FO
and SO diets was confirmed in triplicate on both fresh (sampled from a sealed box stored at 4ºC)
or week-old (sampled from hoppers in the animal facility after at least 1 week at room
temperature) pellets. The main fatty acid species of the two diets are shown in Table 3-1. As a
percent of fatty acids, the most abundant fatty acids in the SO diet are linoleic acid (18:2n-6,
70.7%), oleic acid (18:1n-9, 15.5%), palmitic acid (16:0, 8%) and steric acid (18:0, 2.9%). The
main fatty acid species of the FO diet are linoleic acid (59.9%), oleic acid (13.6%), palmitic acid
(10.3%), myristic acid (14:0, 2.7%), EPA (20:5n-3, 2.6%) and DHA (22:6n-3, 1.5%). Neither
diet contained >1% of any other fatty acid not listed in Table 3-1, and neither diet’s measured
composition differed from the manufacturer’s product specifications or what has been measured
in our lab previously 331. The fatty acid composition of the fresh and week-old diets also did not
differ.
133
Table 3-1: Fatty acid composition of 10% safflower oil and 2% fish oil, 8% safflower oil
diets
10% Safflower Oil
2% Fish Oil,
8% Safflower Oil
Fatty acid composition
14:0 n.d 2.7
16:0 8.0 10.3
16:1n-7 n.d 3.1
18:0 2.9 2.8
18:1n-9 15.5 13.6
18:1n-7 0.7 1.1
18:2n-6 70.7 59.9
18:3n-3 0.5 0.8
EPA n.d 2.6
DHA n.d 1.5
Fatty acid percent compositions are calculated as the percentage of the total identified fatty acids
and are means of triplicate analysis. Other fatty acids are present at levels <0.5% of total fatty
acids not shown. Not detected (n.d).
134
3.3.3 Genotyping
Genotyping was carried out using a method adapted from Orr et al 331. Tails of 2-3-week-old
mice were coated with EMLA analgesic cream (AstraZeneca, Mississauga, Canada), after which
2-3 mm of the tip of the tail was removed and the wound cauterized. Tails were digested
overnight in a cell lysis buffer (100mM Tris HCl pH 8.5, 5mM EDTA, 0.2% sodium dodecyl
sulfate, 200mM NaCl) with 0.8 mg/ml proteinase K. Tail debris was pelleted (20 minutes x
15700 rcf) and DNA was precipitated by eluting the supernatant into 1ml isopropanol. DNA was
pelleted (10 minutes x 15700 rcf) and the supernatant was removed to allow the pellet to dry.
The pellet was then resuspended in 1x Tris-EDTA buffer. One -1.5μl of DNA was used in a
polymerase chain reaction (PCR) with a commercial master mix (ThermoScientific, Waltham,
MA, USA) as per manufacturer’s instructions with the following PCR conditions: 2 minutes x
95°C, 30 cycles x (30 seconds 94°C, 30 seconds 55 °C, 1 minute 72 °C), followed by final
elongation step for 10 minutes at 72 °C. Resultant 250 base pair bands were visualized on a
1.5% agarose gel containing SYBR Safe DNA Gel Stain (Life Technologies, ThermoScientific,
Waltham, MA, USA) using a UV light box.
3.3.4 Gas Chromatography
A separate group of non-surgery mice were killed by CO2 asphyxiation at 12 weeks of age and
total lipids were extracted from whole brains using a method adapted from Folch et al 332. Total
fatty acids were measured and quantified as described by our lab previously 333.
3.3.5 Preparation of amyloid-β 1-40 and 40-1 injections
Amyloid-β 1-40 and a reverse peptide control, amyloid-β 40-1 were obtained from Bachem
Biochemicals (H-1194 and H-2972 respectively, Bachem Biochemicals, Bubendorf,
Switzerland). The lyophilized powder was diluted to 1 µg/µl in sterile 0.1M PBS and aggregated
at 37ºC for 96 hours to promote formation of oligomers, fibrils and fibres as described previously
70, 71, 334. Aggregation was confirmed by electron microscopy (Figure 3-1A) by identifying fibrils
100-500 nM long and smooth in appearance 335, 336. Treatment and control solutions were
aliquoted and stored at -20ºC until use.
135
3.3.6 Negative stain transmission electron microscopy
Electron microscopy was conducted according to published methods 337. Briefly, 1% piloform
coated copper grids (Canemco #G300HEX, Canada) were charged using a glow discharge
apparatus (Quorum Technologies, Laughton, East Sussex, United Kingdom) at 0.15 Torr x 15
seconds, 5 mA and 2 μl of 1μg/μl amyloid-β 1-40 in PBS was loaded for one minute, wicked to
remove large solvent droplets and allowed to air dry. Two μl of 1% phosphotungstic acid were
then loaded for 45 seconds to stain the grids, then wicked and allowed to air dry under an
incandescent light bulb. Grids were then loaded into a grid deck and visualized via a
transmission electron microscope (Hitachi H-7000 TEM, Japan) at 75 kV.
3.3.7 Intracerebroventricular amyloid-β infusion surgery
Surgeries were conducted as described previously 103. Mice were anesthetized by isoflurane
(induction 3%, maintenance 2%) and the top of head shaved. The head was secured via ear and
teeth bars in a stereotaxic setup with a digital reader (Stoelting, Wood Dale, IL, USA). The
analgesic Marcaine (Hospira Healthcare Corporation, Montreal, Québec, Canada) was injected at
1.5 mg/kg subcutaneously at the incision site. After 5 minutes, the skull was exposed and the
digital reader was calibrated to bregma. The head was gently raised or lowered to ensure the
skull was level (<0.1mm difference in height between bregma and lambda). A small hole was
then drilled -1.0 mm medial/lateral and -0.5 mm anterior/posterior to bregma and a 33-gauge
needle was lowered -2.4 mm dorsal/ventral into the left lateral ventricle. Five µl of amyloid-β 1-
40 or 40-1 were then infused at a rate of 1 µl per minute via a Quintessential Stereotaxic Injector
(Stoelting). The needle was kept in the ventricle for 25 minutes post-infusion to ensure treatment
diffusion in the cerebrospinal fluid before being slowly raised to prevent backflow. The accuracy
of this injection into the lateral ventricle was checked periodically by injection with Evan’s blue
dye. The hole in the skull was sealed with bone wax (Ethicon, Somerville, New Jersey, United
States) and the scalp sutured shut. Mice were monitored post-surgery until autonomous head
movement was recovered and were housed singly until death. Mice were euthanized at various
time points between 24 hours and 28 days post-surgery. Time points were selected based on
previous work in our lab that found that microglia and astrocyte activation following icv
lipopolysaccharide (LPS) began increasing after 24 hours (unpublished) and on preliminary
experiments described here.
136
3.3.8 Sample preparation and immunohistochemistry
Mice were anesthetized with 250 mg/kg intraperitoneal avertin and euthanized via transcardiac
perfusion with cold phosphate buffered saline (PBS) for 3 minutes, followed by 7 minutes of 4%
paraformaldehyde, infused at a rate of 4 ml/minute using a peristaltic pump (GE Healthcare,
Mississauga, ON, Canada). Brains were extracted and post-fixed for 24 hours in 4%
paraformaldehyde, followed by dehydration and storage in a 30% sucrose solution until
sectioning. Brains were frozen in Cryomatrix sectioning medium (ThermoScientific, Waltham,
MA, USA) and sliced into 40μM sections using a cryostat (Leica, CM 1510S, Concord, ON).
Slices were stored in 0.05% sodium azide until analysis.
For immunohistochemistry to visualize astrocytes and microglia, slices were washed three times
for 10 minutes each in PBS and quenched for 10 minutes in 0.5% sodium borohydride, followed
by another three PBS washes. Sections were blocked for two hours in a solution of 10% normal
goat serum, 0.75% bovine serum albumin and 0.1% triton-x in PBS, and incubated overnight in
antibody solution (2% normal goat serum, 0.01% triton-x in PBS), with rabbit anti-Iba1 (Wako
Chemicals, Richmond VA, USA) and mouse anti-GFAP (Antibodies Inc., Davis, CA, USA)
antibodies. Anti-Iba1 was diluted to a concentration of 1:2000 for epifluorescent microscopy and
1:1000 for confocal microscopy, while GFAP was diluted 1:1000 for epifluorescent microscopy
and 1:500 for confocal microscopy. Slices were washed three times in cold PBS, and then
incubated for one hour in antibody solution with 1:2000 goat anti-rabbit Alexa Fluor 568 and
1:2000 goat anti-mouse Alexa Fluor 488 (Life Technologies, Burlington, ON, Canada). Slices
were then washed three times in PBS and mounted onto glass microscope slides in Vectashield
Antifade Mounting Medium with DAPI (Vector Laboratories, Bulingame, CA, USA) and
coverslipped with #1 type micro cover glasses (VWR International, Mississauga, ON, Canada).
Fluoro Jade C (FJC, Millipore, Darmstadt, Germany) immunohistochemistry was used to
visualize degenerating neurons via a method adapted from the manufacturer’s specifications.
Whole brain coronal sections were washed three times in PBS, mounted onto poly L-lysine
coated slides (Sigma-Aldrich, Oakville, ON Canada) and allowed to dry overnight. Slides were
then placed in a staining rack, and moved sequentially through the following solutions: dH2O x 1
minute, 100% ethanol x 3 minutes, 70% ethanol x 1 minute, dH2O x 1 minute, 0.06% potassium
permanganate x 15 minutes on a shaker, dH2O x 1 minute, and then avoiding light: 0.001% FJC
137
+ 0.2% Hoescht stain in 0.1% acetic acid x 30 minutes followed by 3 x 1 minute washes in dH2O
prior to drying over-night in the dark.
3.3.9 Epi-fluorescence microscopy and cell counting
Astrocytes and microglia were counted in four regions of the hippocampus: CA1, CA2, CA3 and
the dentate gyrus (DG), while FJC-positive neurons were counted in the CA1 and DG regions
both ipsilateral and contralateral to the injection site. Cells were visualized (0.83mmx0.66mm
field of view) using epifluorescent microscopy. Iba1-labelled microglia and GFAP-labelled
astrocytes were counted using Nikon Elements software (NIS-Elements Basic Research, version
3.1) as described previously 338 with the 10x objective. Images were acquired using automated
exposure and the fluorescence intensity for each image was manually adjusted to fall within the
linear range. Three operations were applied to the images: 6x clean, 4x separate and smooth 0x.
Counting in Nikon Elements was performed by an experimenter (KEH) self-blinded (by
randomly assigning slices numbers 1, 2, 3…n prior to each immunohistochemistry run) to the
genotype/diet grouping and time point, and all images in the Iba1 channel were counted a second
time for validation by a second experimenter (VG), blinded by the same method, using ImageJ
software by manually thresholding the image and using the analyze particles plugin with a size
exclusion limit of 40μM2. FJC-positive neurons in the CA1 and DG regions of the hippocampus
were counted manually in three predetermined 150x150 μM boxes per image and scores were
validated in a subset of samples by a second blinded observer (DM).
3.3.10 Confocal microscopy and microglia morphology
As microglia are thought to take on an amoeboid morphology, characterized by fewer,
less complex branches and a larger cell body upon activation, brain sections at baseline and 10
days post-surgery (a peak point in microglia activation, Figure 3-3) were analyzed by confocal
microscopy and skeleton analysis to assess microglia morphology. Twenty μM z-stacks of CA1,
CA3 and DG in both the left and right hippocampus were acquired at 0.5 μM intervals using an
AxioObserverZ1 spinning disk confocal microscope (Zeiss, Oberkochen, Germany) at the 20x
objective. Microglia morphology was measured using a method adapted from Morrison et al.
339. As illustrated in Figure 3-6A, maximum intensity projections for the Iba1 channel of each
image were generated, binarized and skeletonized using the Skeletonize 2D/3D plugin in ImageJ,
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after which the Analyze Skeleton plugin (http://imagej.net/AnalyzeSkeleton) was applied with the
lowest intensity voxel prune cycle. This plugin analyzes the pixels of each skeletonized
microglia and categorizes them based on their relationship to one another, with pixels with only
one neighbouring pixel considered end points, pixels with two neighbours considered slabs (or in
this case a branch), and pixels with more than 2 neighbours considered junction points. The
average branch number (process end points per cell) and length per cell was recorded for each
image with a voxel size exclusion limit of 150 applied. The ratio of end points to junction points
was additionally calculated to give an indication of branching complexity.
3.3.11 Statistical analysis
Data are expressed as mean +/- standard error of the mean, normalized to control peptide-
injected animals (amyloid-β 40-1) in Figure 3-1, or to non-surgery animals of their treatment
group in Figures 3-3 and 3-4. Cell counts for each hippocampal region are the mean of the left
and right sides of 3 to 5 brains per treatment per time point for the work in the C57BL/6 mice,
and in 6-12 brain samples per treatment/genotype per time point in the work with fat-1 mice and
their wildtype littermates. One-way ANOVA with a Bonferroni post-test was applied to evaluate
differences by genotype/treatment group in brain fatty acid composition, FJC counts and
microglia morphology at 10 days post-icv, while a two-way ANOVA was used to examine main
and interactive effects of genotype/treatment groups and time, with a Bonferroni post-hoc test
applied where there was a significant interaction.
3.4 Results
3.4.1 Time course of microglia and astrocyte activation following icv amyloid-β
1-40 or control peptide
We first set out to identify time points at which to visualize the neuroinflammatory response to
amyloid-β 1-40 relative to a control peptide, amyloid-β 40-1. Increases in iba1-labelled microglia
were seen in the days following surgery in all four regions of the hippocampus measured relative
to control (Figure 3-1B, graph shown for mean of 4 fields), and counts were significantly
different from 24 hours at 15 days (CA2, CA3 and DG) and 21 days (CA1, CA2, CA3) post-icv.
Peak levels of microglia were approximately 50% greater in the animals injected with amyloid-β
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1-40 as opposed to the control peptide-injected animals at 15 days post-icv. In all regions, counts
were no longer different from 24 hours or the peak by 28 days following surgery.
Increases in GFAP-labelled astrocytes (Figure 3-1D, graph shown for mean of 4 fields) were
detected in the CA1, CA2 and CA3 regions, with a peak at 15 days post-surgery that was
significantly different from 24 hours. In all three regions where this pattern was detected, no
significant difference between the peak and its baseline was seen by 28 days post-surgery. No
significant differences were detected between any of the time points in the DG.
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Figure 3-1: Time-course of microglia and astrocyte proliferation.
A) TEM image of aggregated amyloid-β 1-40, length of 100-500 nM and smooth appearance
characteristic of fibres. B) Mean +/- SEM of Iba1-labelled microglia counts in the CA1, CA2,
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CA3 and DG of the hippocampus following intracerebroventricular infusion of amyloid-β 1-40,
normalized for the counts following infusion of control peptide (amyloid-β 40-1) C) Sample
images of the CA3 region of the hippocampus 15 days following intracerebroventricular infusion
of either amyloid-β 1-40 (right) or the control peptide amyloid-β 40-1 (left). D) Mean +/- SEM
of GFAP-labelled astrocyte counts in the CA1, CA2, CA3 and DG of the hippocampus following
intracerebroventricular infusion of amyloid-β 1-40, normalized for the counts following infusion
of control peptide. E) Samples images from the CA3 region of the hippocampus 15 days
following intracerebroventricular infusion of either amyloid-β 1-40 (right) or the control peptide
amyloid-β 40-1 (left). Top row of images in C and E are enhanced for publication, while the
bottom row of images are the same images in which a threshold was applied to show labelled
cells. Different letters denote significant differences (p<0.05) by one-way ANOVA followed by
Bonferroni post-test. Cornu ammonis (CA), Dentate gyrus (DG), glial fibrillary acidic protein
(GFAP), ionized calcium-binding adapter molecule 1 (iba1)
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3.4.2 Effect of brain fatty acid composition on time course of microglia and
astrocyte activation
The effect of brain n-3 PUFA composition on the neuroinflammatory response to amyloid-β was
assessed in fat-1 mice and their wildtype littermates weaned onto either a 10% safflower oil diet,
containing very low levels of n-3 PUFA, or a diet in which 2% of the safflower oil was replaced
with fish oil. A dietary and a transgenic approach to increasing brain n-3 PUFA was used to
account for potential confounding arising from either off-target effects of the transgene, in the
case of the fat-1 mice, or from changes in other elements of the diet in the case of the WTFO
mice, as adding 2% fish oil involves the removal of 2% safflower oil. Fat-1 and WTFO mice
had approximately 2-fold higher levels of brain DHA as a nanomol percent of fatty acids relative
to WTSO mice (Figure 3-2). They also had significantly lower levels of the n-6 PUFAs: ARA,
docosapentaenoic acid and docosatetraenoic acid. As a result, the fat-1 and WTFO mice had a 2-
3-fold lower ratio of brain n-6: n-3 PUFA than WTSO mice.
As earlier experiments showed that the injection of amyloid-β 1-40 is more potently
neuroinflammatory than the control peptide (Figure 3-1), microglia and astrocyte counts were
normalized to non-surgery animals for each diet/genotype group rather than to control peptide-
injected animals for each time point in an effort to reduce the number of animals required for this
study. Microglia activation of the mean of both left and right hippocampus regions CA1, CA2,
CA3 and DG peaked at 10 days post-surgery, and for CA2, CA3 and DG, were no longer
significantly different from baseline levels by 28 days (Figure 3-3 A-D). A two-way ANOVA
(genotype/diet x time) returned a significant main effect of time (p<0.001) in all four regions
with a significant interaction (p<0.05). Post-hoc analysis of the treatment effect within each time
point showed that fat-1 mice had a lower peak in iba1-labelled microglia number at 10 days post-
surgery than the WTSO mice, while WTFO mice were not different from either group at any
time in any region. Post-hoc analysis of the time effect showed that microglia counts for the
three groups were significantly higher than baseline levels at 10 days (CA1, CA2, CA3, and DG)
and 15 days (CA1 and DG) post-icv. Counts remained elevated from baseline at 28 days in the
CA1 and elevated compared to 3 days in the DG. When highest count rather than the average of
left and right regions was used for analysis, the significance of the interaction effect was lost in
CA1 and CA.
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Figure 3-2: Whole brain fatty acid composition
Bars represent mean (+/- SEM) of: wildtype mice fed safflower oil (WTSO), fat-1 transgenic mice fed safflower (fat-1) and wildtype
mice fed fish oil (WTFO). Bars illustrate nanomolar percent of all detected fatty acids in the brain. One-way ANOVA applied for each
fatty acid and, where significant, followed by a Bonferroni post-hoc test. Different letters denote significantly different means,
p<0.05. Alpha-linolenic acid (ALA), Linoleic Acid (LNA), Arachidonic acid (ARA), Eicosapentaenoic acid (EPA), Docosahexaenoic
acid (DHA
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When mean of GFAP-labelled astrocyte counts in the left and right hippocampus were analyzed,
no significant main effects of genotype/diet, time or interactions were found in CA1, CA2 or
DG, while a significant main effect of time was identified in CA3 (p<0.05). When the side with
the highest counts was used for analysis, a significant main effect of time was identified in the
CA1, CA2 and CA3 with no genotype/diet x time interaction. Highest levels of astrocyte counts
occurred at 10 and 15 days post-icv, with counts up to 50% above baseline levels.
3.4.3 Fluoro-Jade C Cell Counts
Degenerating neurons were visualized with FJC immunohistochemistry at 10 days post-
icv, the time point at which a difference between the genotype/treatment groups in microglia
counts was observed. No difference between the groups was detected in CA1, however, WTFO
mice had significantly fewer FJC-positive neurons in the DG compared to the WTSO mice,
while fat-1 mice were not different from either group (Figure 3-5).
3.4.4 Microglia Morphology
Microglia morphology was investigated to determine whether the differences in cell
counts between the diet/genotype groups identified at 10 days post-icv were related to microglia
activation. No significant differences between the groups were identified for the number of
microglia process endpoints per cell, used here and previously as an indicator of the number of
branches per cell 339, 340, in the CA1 or DG. Microglia of fat-1 mice had on average significantly
more endpoints per cell than the WTSO group, but not the WTFO group in CA3 (Figure 3-6 B,
C, D). Relative to WTSO mice, microglia process length was lower in fat-1 and WTFO mice in
CA1 and CA3, and in WTFO mice alone in DG (Figure 3-6 E, F, G). The number of process
endpoints per junction was calculated to give an indication of branching complexity, or the
number of new branches arising from branch splitting. Both fat-1 and WTFO mice had
significantly higher branching complexity following surgery than the WTSO mice in CA1, CA3
and DG (Figure 3-6 H, I, J).
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Figure 3-3: Time-course of microglia activation following icv amyloid-β in the fat-1 and
wildtype mice.
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A-D) Iba1-labelled microglia cell counts (+/- SEM) normalized to non-surgery counts in the
hippocampus regions CA1 (A), CA2 (B), CA3 (C) and dentate gyrus (D). E) Representative
images of the CA1 region of wildtype mice fed safflower oil (WTSO), fat-1 transgenic mice fed
safflower (fat-1) and wildtype mice fed fish oil (WTFO) prior to surgery (baseline, top two rows)
and at 10 days post intracerebroventricular infusion of amyloid-β peptide (bottom two rows).
Top rows for each time point are images enhanced for contrast and sharpness for publication,
bottom images are the same images in which a threshold was applied to show labelled cells.
Two-way ANOVA was applied, significant main effects and interactions are reported beneath
each graph. Different letters denote significantly different bars within a time point (e.g. 10 days
post-icv), while lines and * indicate overall differences between time points. * p<0.05, **p<0.01.
Cornu ammonis (CA), Dentate gyrus (DG), intracerebroventricular infusion (icv), ionized
calcium-binding adapter molecule 1 (iba1).
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Figure 3-4: Time-course of astrocyte activation following icv amyloid-β in the fat-1 and
wildtype mice
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A-D) GFAP-labelled astrocyte cell counts (+/- SEM) normalized to non-surgery counts in the
hippocampus regions CA1 (A), CA2 (B), CA3 (C) and dentate gyrus (D). E) Representative
images of the CA1 region of wildtype mice fed safflower oil (WTSO), fat-1 transgenic mice fed
safflower (fat-1) and wildtype mice fed fish oil (WTFO) at prior to surgery (baseline, top two
rows) and at 10 days post intracerebroventricular infusion of amyloid-β peptide (bottom two
rows). Top rows for each time point are images enhanced for contrast and sharpness for
publication, bottom images are the same images in which a threshold was applied to show
labelled cells. Two-way ANOVA applied, where significant main effects are reported beneath
each graph. Cornu ammonis (CA), Dentate gyrus (DG), Glial fibrillary acidic protein (GFAP).
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Figure 3-5: Neurodegeneration in the hippocampus
Fluoro-Jade C-positive cells (+/- SEM) in the CA1 (A) and DG (B) regions of the hippocampus.
Graphs represent counts (mean of 3 boxes per image) at 10 days post-icv amyloid-β 1-40.
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Representative images of the CA1 (C) and DG (D) of, from left to right: LPS-injected positive
control mice, wildtype mice fed safflower oil (WTSO), fat-1 transgenic mice fed safflower (fat-
1) and wildtype mice fed fish oil (WTFO) mice showing the FJC-positive cells (top) and Hoescht
staining (a non-discriminate DNA stain). Images are enhanced for contrast and sharpness for
publication. Different letters denote significant differences (p<0.05) as determined by one-way
ANOVA with Bonferroni post-hoc test. Cornu ammonis (CA), Dentate gyrus (DG), Wildtype
mice fed safflower oil (WTSO), fat-1 mice fed safflower oil (fat-1), Wildtype mice fed fish oil
(WTFO).
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Figure 3-6: Microglia morphology
A) Schematic illustrating the method for measuring microglia morphology with ImageJ analyze
skeleton (reproduced in part with permission). Confocal z-stacks are converted to maximum
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intensity projections, and then thresholded to create a binary image. Images were converted into
2D skeletons by the Skeletonize 2D/3Dplugin and pixels were analyzed by the Analyze Skeleton
plugin. Pixels with one neighbor (labelled in blue) are branch end points, pixels with two
neighbours (labelled in orange) are branches or slabs and pixels with three or more neighbours
(labelled in pink) are junctions. Average number of microglia process endpoints (an indicator of
the number of microglia processes) per cell (B-D), average process length per cell (E-G) and
process endpoints per junction, used here as an indicator of branching complexity (H-J) in the
CA1, CA3 and DG. All graphs represent values at 10 days post-intracerebroventricular infusion
of amyloid-β 1-40 normalized for non-surgery values, +/- SEM. Different letters denote
significantly different bars (p<0.05) as determined by one-way ANOVA with Bonferroni post-
test. Cornu ammonis (CA), Dentate gyrus (DG), Wildtype mice fed safflower oil (WTSO), fat-1
mice fed safflower oil (fat-1), Wildtype mice fed fish oil (WTFO).
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3.5 Discussion
Here we show that numbers of iba1-labelled microglia and GFAP-labelled astrocytes increase
following icv infusion of amyloid-β 1-40, peaking in C57BL/6 mice between 15 and 21 days
post-surgery, and in fat-1 and wildtype mice at 10 days post-surgery. The inflammatory response
resolves in most regions examined by 28 days post-surgery, with cell counts no longer
significantly different from baseline levels in most regions examined. There was no effect of the
2-fold increase in brain DHA in the fat-1 or WTFO mice compared to WTSO mice on astrocyte
counts in the four areas of the hippocampus measured, however a reduction in the peak in
microglia cell number at 10 days post-surgery was noted in fat-1, but not the WTFO mice,
compared to the WTSO mice. There was no effect of this change in n-3 PUFA on the time
course of microglia or astrocyte activation up to 28 days following surgery.
There are some indications that microglia in the hippocampi of fat-1 and WTFO mice take on a
less activated skeleton structure following icv amyloid-β than the WTSO mice. As microglia
become activated (switching from a surveillance and neurotrophic role to a phenotype in which
replication, migration, cytokine production and phagocytosis occur), they are thought to shift
from a ramified appearance, with numerous processes and a small cell body, to an amoeboid
phenotype, characterized by fewer, shorter processes and a larger cell body 50. Fat-1 mice had a
smaller reduction in process endpoints per cell in CA3 in response to icv amyloid-β, while both
fat-1 and WTFO mice retained more endpoints per junction, indicating more branch splitting. A
reduction in process endpoints with microglia activation has been measured using this same
method previously in both a stroke 339 and an AD 340 model. Surprisingly, branch length
increased in our model in response to icv amyloid-β, with greater increases in WTSO mice than
the other two groups, whereas branch length decreased with activation in the previous studies
using this method 339, 340. This unexpected finding could be explained by retractions of terminal
branches, leading pixels that were initially counted as multiple separate branches connected by
junction points to be counted as a single longer branch, which is supported by the reductions in
endpoints per junction seen in Figure 3-6 H, I and J. It should be noted that while skeleton
analysis has benefits of unbiased batch processing and branch quantification, it does not measure
the changes in soma size or branch thickness that may also occur with microglia activation or
dystrophia.
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While this study provides evidence for a potential mechanism that may underlie the
protective effects of n-3 PUFA in AD that have been observed in human observational studies
124, 130, animal models 113, and on cognitive decline some human clinical trials (in patients with
mild, but not moderate or advanced AD 328), there are limitations to the interpretation of the
results. The icv amyloid-β model was selected instead of a transgenic AD model as it allows for
the full dynamic response of microglia and astrocytes to amyloid-β, including an increase in
activation followed by resolution to baseline levels, to be measured. This would not be possible
with the sustained production of amyloid-β that occurs in transgenic models, the as glia would be
continually stimulated. This method is, however, limited in comparison with some transgenic
models in its applicability to AD in humans because it relies on an acute as opposed to chronic
exposure to amyloid-β and does not take into account the hyperphosphorylation of tau proteins.
Another limitation of this work is that increasing brain DHA via either a dietary or a
transgenic approach proportionately reduced brain n-6 PUFA, resulting in an altered n-6: n-3
ratio. It is possible, therefore, that some of the immune-modulatory effects reported here could
be attributed to the reduction in n-6 PUFA, rather than the increase in n-3 PUFA. This
proportional change in brain n-6 PUFA would arise in any intervention aimed at increasing brain
n-3 PUFA, so this confounder does not diminish the clinical or biological relevance of the
findings of this paper. To our knowledge, no one has yet investigated the effects of modulating
dietary n-6 PUFA on neuroinflammation in an AD model, though lowering dietary linoleic acid
has recently been shown to attenuate the increase in prostaglandin E2, a pro-inflammatory lipid
mediator derived from ARA, and the activity of cyclooxygenase (COX)-2, an enzyme involved
in its synthesis, in response to icv LPS 341.
A difficulty in testing for potential anti-neuroinflammatory effects of n-3 PUFA is that, in
addition to modulating inflammation, these fatty acids are also known to be neuroprotective,
decreasing the magnitude of neurological insult associated with a disease model. For example,
administration of DHA three hours after medial cerebral artery occlusion, a model of stroke,
decreases infiltration of microglia/macrophage lineage cells, but also decreases the volume of the
ischemic infarct 342. The reduction in microglia infiltration in these models could be explained
either by a direct effect of DHA treatment on microglia, or as a lower level response to a smaller
injury. The same concerns arise in transgenic models of AD, where DHA decreases the
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amyloidogenic processing of amyloid precursor protein 140, leading to decreased production of
plaque-forming amyloid-β both in vitro 141 and in vivo 142. The icv amyloid-β model used here
avoids some of this confounding by administering exogenous amyloid-β, however confounding
is not entirely removed because fewer FJC-positive neurons were detected in one region of the
hippocampus of WTFO mice compared to WTSO mice (Figure 3-5), indicating less neuronal
degeneration. It is possible then, that some of the differences in microglia cell number and
morphology reported here could be explained as a decreased response to a smaller neurological
insult, rather than a direct effect of brain PUFA on the activation of these cells. However,
because the fat-1 group had the smallest increase in microglia cell number in response to
amyloid-β but the WTFO group had the lowest level of FJC-positive neurons, these results do
not appear to be directly correlated.
While increasing brain DHA may attenuate the increase in microglia counts and
alterations in microglia morphology in response to icv amyloid-β, it is not known whether these
differences are of a sufficient magnitude to be functionally relevant, and if they are, whether they
would be beneficial in AD. While several groups have noted improvements in cognition and
neuronal death in association with reductions in inflammatory markers, including microglia
activation, in AD 343, 344 this is not consistent across studies. For instance, Michaud et al. noted
improvements in amyloid-β clearance and cognition in a transgenic model of AD in response to
an agonist of the TLR4 receptor, which activates microglia345. Chakrabarty et al. separately over-
expressed IL-6 346 and IFN-γ 347 in TgCRND8 mice and measured lower levels of amyloid-β
plaque deposition despite elevations in markers of astrocytes and microglia, suggesting that
increases in these cells may in fact be protective, at least at an early stage in the disease. In
contrast, n-3 PUFA, which have been shown here and in other studies to be anti-inflammatory in
AD models 118, 119, appear to exert protective effects on neuronal loss, amyloid burden and
cognition 113. Part of the discrepancy between these models may be related to the functional
effectiveness of microglia in AD. While an amoeboid phenotype is classically associated with
microglial activation, it is now known that amyloid-β contributes to microglial dysfunction,
including decreased phagocytic capacity, so that microglia in AD may be phenotypically, but not
functionally, activated 50, 348. Increasing activation of microglia may be beneficial acutely in AD
models by increasing clearance of amyloid-β; however, as microglia become dysfunctional due
to exposure to amyloid-β, the elevated numbers seen in human AD and animal models may
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instead contribute to neuronal death and dysfunction. As n-3 PUFA promote amyloid-β
phagocytosis by microglia and increase the expression of phagocytic markers 112, 349, they may
prevent this dysfunction in microglia activity, allowing microglia to exert beneficial effects in
AD.
In this study, differences in the time course of the astrocyte and microglia response to amyloid-β
were noted between the experiments conducted in C57BL/6 mice and the fat-1 strains. Counts
for both astrocytes and microglia peak later in the C57BL/6 mice, at 15 and 21 days post-icv
respectively, compared to at 10 days in the fat-1 study. In addition, there was a main effect of
time on astrocyte number in all four regions of the hippocampus measured in the study using
C57BL/6 mice, while in the experiment using the fat-1 mice, a main effect of time was evident in
CA3 but not CA1, CA2 or DG. Fat-1 mice have a C57BL/6 and C3H strain background, and
previous work in our lab found that fat-1 progeny are 76% genetically similar to C57Bl/6 mice
103. Therefore, genetic differences between the mice in the two studies could explain some of
these discrepancies. Another possibility is that dietary differences exerted an effect. The
C57BL/6 mice were maintained on a chow diet containing 6% alpha-linolenic acid (ALA), the
precursor to DHA, for the duration of the experiment while the fat-1 study used a safflower diet
containing <1% ALA. A diet containing 200mg/100g diet of ALA (2-2.5% of fatty acids on a
10% fat diet) is thought to be sufficient to maintain brain DHA levels 350, so these two studies
differ not only in the DHA content of the diets but also in the sufficiency of the ALA content to
maintain brain DHA levels.
Differences were also noted between the fat-1 and WTFO mice in the microglia response to
amyloid-β, despite these groups having similar brain levels of DHA. Fat-1 mice had significantly
lower numbers of iba1-labelled microglia 10 days post-surgery than the WTSO mice, while the
WTFO mice were not significantly different from either group. In contrast, WTFO, but not fat-1,
mice had significantly fewer degenerating neurons following icv amyloid-β than WTSO mice. In
previous work from our lab using fat-1 mice and the same diets, fat-1 mice exhibited enrichment
in brain unesterified DHA compared to wildtype mice fed a safflower diet, while C57BL/6 mice
fed the fish oil diet did not demonstrate this comparative enrichment relative to C57BL/6 mice
fed the safflower oil diet 103. In this study, the neuroinflammatory response to LPS was
attenuated in the fat-1 group, but not the fish oil group compared to safflower fed mice, and this
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was attributed to the higher levels of unesterified DHA in the fat-1 mice103. Unesterified fatty
acids are substrates for the synthesis of specialized pro-resolving lipid mediators 104, so it follows
that changes in the unesterified pool may be more functionally important than changes in the
whole brain. Fat-1 and WTFO mice also differ in the duration of exposure to n-3 PUFA and
DHA. As fat-1 mice produce n-3 PUFA endogenously, they are exposed to these fatty acids
throughout gestation and growth, while WTFO mice are exposed only after weaning. Thus,
differences between these groups of mice could also be attributed to early programming of the
inflammatory response due to exposure to n-3 PUFA during critical periods of development. It
should be noted, however, that the direction of effect was always the same for the fat-1 and
WTFO groups and these groups were never significantly different from one another. This
consistency suggests that the inflammation attenuating effects observed in the fat-1 and WTFO
mice is attributable to the common change in brain DHA that occurred in both groups, and not
due to residual confounding by diet or genotype.
3.6 Conclusions
Increasing brain n-3 PUFA, through transgenic, and to a lesser extent, through dietary means
decreased microglia responses to amyloid-β infusion in a mouse model of AD, though no effects
on astrocyte number, or the length of time for microglia activation to resolve to baseline levels
were evident. n-3 PUFA have been shown in many human observational and animal studies to
be protective against AD symptoms and pathology, and this study provides evidence that this
may occur through modulation of neuroinflammation, though further work is needed to test this
hypothesis directly.
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Chapter 4: Dietary fish oil, and to a lesser extent the fat-1
transgene, increases astrocyte activation in response to
intracerebroventricular amyloid-β 1-40
Kathryn E. Hopperton, Nicholas C.E James, Dana Mohammad, Maha Irfan and Richard
P. Bazinet
Paper currently submitted and under second revision
Contributions:
KEH wrote the paper and conducted all the work to acquire the brain images, which were then
analyzed by DM, NCEJ and MI. RPB oversaw the project. All authors reviewed and provided
feedback on the manuscript.
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4.1 Abstract:
Objectives: Increases in astrocytes and one of their main markers, glial fibrillary acidic protein
(GFAP) have been reported in the brains of patients with Alzheimer’s disease (AD). N-3
polyunsaturated fatty acids (PUFA) modulate neuroinflammation in animal models, however
their effect on astrocyte function is unclear. The objective of this work was to determine the
effect of brain n-3 PUFA composition on astrocyte activation in response to amyloid-β.
Methods: Fat-1 mice, transgenic animals that can convert n-6 to n-3 PUFA, and their wildtype
littermates were fed either a fish oil diet containing n-3 PUFA, or a safflower oil diet deprived of
n-3 PUFA. At 12 weeks of age, the mice underwent intracerebroventricular infusion of amyloid-
β 1-40. Brains were collected at baseline and 10 days post-surgery. GFAP expression and
astrocyte morphology in the hippocampus were assessed using immunohistochemistry with
various microscopy and image analysis techniques.
Results: GFAP increased in all groups in response to amyloid-β infusion, with a greater increase
in fish oil-fed mice than in either fat-1 the safflower oil fed mice. Astrocytes in this group were
more hypertrophic, suggesting increased activation. Both fat-1 and fish oil-fed mice had greater
increases in branch number and length in response to amyloid-β infusion than safflower-fed
animals.
Conclusion: Fish oil feeding, and to a lesser extent the fat-1 transgene, enhances the astrocyte
activation phenotype in response to amyloid-β 1-40. Astrocytes in mice fed fish oil appeared to
be more activated in response to amyloid-β than those in fat-1 mice despite similarities in levels
of hippocampal n-3 PUFA, which suggests that other fatty acids or dietary factors may
contribute to the enhanced astrocyte activation response.
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4.2 Introduction
Astrocytes fulfil a variety of supportive functions in the brain, including formation of the
blood-brain-barrier, the provision of nutrients, and response to injury. Astrogliosis is widely
reported in post-mortem brain samples from patients with Alzheimer’s Disease (AD) 323, 324
where astrocytes colocalize with amyloid-β plaques 325, 326. In response to amyloid-β, astrocytes
upregulate their production of proinflammatory genes and release reactive oxygen species 351.
This inflammation initiated by astrocytes, and by other neuroimmune cells such as microglia,
may be a mechanism underlying neuronal death and cognitive decline in AD 50, 323.
Consumption or high blood levels of n-3 polyunsaturated fatty acids (PUFA) is
associated with a reduced risk of AD 130. In addition, studies in animal models have shown that
feeding n-3 PUFA decreases neuroinflammation, neurodegeneration, amyloid-β plaque
deposition, and cognitive decline in AD models 99, 113. Astrocytes are commonly measured via
glial fibrillary acidic protein (GFAP), an intermediate filament protein. Previous studies on the
effect of n-3 PUFA on GFAP in neuroinflammation models have presented conflicting results,
with reports of increases 342, 352, decreases 122, 353 or no change 123, 354, 355 in mRNA or protein
expression with n-3 PUFA interventions. This heterogeneity in the literature raises the question
of what a change in GFAP expression means for astrocyte function in these models. As
astrocytes become reactive, they take on a hypertrophic phenotype, characterized by an increase
in cell size and the number and length of processes extending from the soma 355-358. An
examination of astrocyte morphology is therefore useful for elucidating the activation phenotype
of these cells in response to a neuroinflammatory insult and n-3 PUFA. Here, we use both a
dietary and a transgenic approach to increase brain n-3 PUFA content in an
intracerebroventricular amyloid-β model of neuroinflammation. We hypothesized that amyloid-β
would increase the activation of astrocytes in all groups, with an attenuated increase in animals
with higher brain n-3 PUFA. We instead saw an enhanced astrocyte response in the animals with
higher brain n-3 PUFA. Comparison of our dietary and transgenic groups suggests that some of
this enhanced astrocyte response may occur, at least in part, independent of increases in the
hippocampal n-3 PUFA content.
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4.3 Methods
The brain slices used here were obtained from a previous study published by our group examining
the effect of changing brain PUFA composition on the resolution of neuroinflammation caused by
intracerebroventricular infusion of amyloid-β peptide 143.
4.3.1 Animals and diets
All procedures were undertaken in accordance with the Guidelines of the Canadian Council on
Animal Care under the supervision of the University of Toronto Animal Care Committee (2015/16
protocol #20011376). Animals were housed under controlled conditions with ad libitum access to
food and water. Mice were fed one of two modified AIN-93G rodent diets. The safflower oil (SO;
D04092701; Research Diets Inc., New Brunswick, NJ, USA) diet contained 10% w/w safflower
oil, while the fish oil (FO; D04092702; Research Diets Inc.) diet contained 8% safflower oil and
2% menhaden oil. The SO diet contains 71% linoleic, 15.5% oleic, 8% palmitic, and 3% stearic
acids as a percent of fatty acids, while the FO diet contains 60% linoleic, 14% oleic, 10% palmitic,
3% myristic, 2.6% eicosapentaenoic acid (EPA), and 1.5% docosahexaenoic acid (DHA) as
described previously 143.
Male fat-1 breeders were obtained as a gift from Dr. David Ma (University of Guelph, ON,
Canada). Fat-1 mice are transgenic animals containing a n-3 desaturase enzyme, enabling them to
convert n-6 to n-3 PUFA. These animals can therefore attain high tissue levels of n-3 PUFA on a
deplete diet. They were bred with female C57BL/6 mice (Charles River Laboratories) that had
been acclimated on the SO diet for at least two weeks, to generate heterozygous fat-1 and wildtype
offspring. Male wildtype offspring were weaned onto either the SO or FO diets, while their male
fat-1 littermates were weaned onto the safflower diet only. An additional group of fat-1 mice fed
the fish oil diet was not used in this study because a previous analysis from our lab showed that
there was no difference in any brain phospholipid fatty acids between fat-1 mice fed fish oil and
fat-1 mice fed safflower oil, suggesting that these mice are at a plateau 331. This analysis also did
not identify any substantial differences between the wildtype fish oil fed mice and the fat-1s in any
of the phospholipid classes.
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4.3.2 Intracerebroventricular infusion of amyloid-β 1-40 and sample preparation
At 12 weeks of age, mice underwent a stereotaxic surgery in which 5μg of aggregated amyloid-β
1-40 peptide was injected into the left lateral ventricle as described in detail in our previous
publication 143. Preliminary experiments using a control peptide, amyloid-β 40-1, which contains
the same amino acids as the naturally occurring amyloid-β 1-40 in a reversed order, identified
minimal astrocyte response to the control peptide. At baseline (non-surgery) and at 10 days post-
intracerebroventricular (icv) infusion, a timepoint found to coincide with a peak of inflammation
following icv amyloid-β, mice were euthanized as described below and brains were collected for
analysis.
4.3.3 Fatty Acid Analysis
For fatty acid analysis, mice were euthanized by CO2 asphyxiation, after which hippocampi were
collected and rapidly frozen in liquid nitrogen. Mouse hippocampi were weighed and extracted
in chloroform: methanol: potassium chloride (2:1:0.8) with a known amount of 17:0 internal
standard (NuChek-Prep, Elysian, MN, USA) as described previously 359. Lipid extracts were
dried under nitrogen gas, reconstituted in hexane: boron trifluoride-methanol (0.1:1) and
methylated by heating for one hour at 110°C. Fatty acid methy esters were isolated by adding
doubled distilled water, and analysed on a Varian-430 flame gas chromatograph with a flame
ionization detector as previously described 333.
4.3.4 Immunohistochemistry
For immunohistochemistry, mice were euthanized by transcardiac perfusion of
paraformaldehyde. Brains were extracted and post-fixed for at least 24 hours in 4%
paraformaldehyde and stored at 4° Celsius in 30% sucrose. For sectioning, brains were blocked
and frozen in Cryomatrix sectioning medium (ThermoScientific, Waltham, MA, USA) and sliced
into 40 μM sections with a Leica CM 1510S cryostat (Concord, ON). Immunohistochemistry
was performed as described previously 143, with mouse anti-GFAP primary antibody (Antibodies
Inc., Davis, CA, USA) diluted 1:500, and 1:2000 goat anti-mouse AlexaFluor 488 secondary
antibody (Life Technologies, Burlington, ON, Canada).
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4.3.5 GFAP fluorescence intensity measurement
GFAP was visualized in the whole brain slice at 10x magnification using an AxioScan Z1 slide
scanner (Zeiss, Oberkochen, Germany), with laser power and exposure time optimized to a
positive control sample to ensure fluorescence detection was not saturated. Fluorescence
intensity was then quantified in the entire ipsilateral and contralateral hippocampus using Zen2
software (Zeiss, Oberkochen, Germany) and normalized to the minimal fluorescence intensity
for each image to remove variability in background staining.
4.3.6 Astrocyte morphology
Astrocytes were visualized in the cornu ammonis (CA) 1, CA3 and DG regions of both the
ipsilateral and contralateral hippocampus. Images were acquired at 20x magnification using an
AxioObserverZ1 spinning disk confocal microscope (Zeiss, Oberkochen, Germany) in 0.5 μM
intervals to form 20 μM z-stacks. Z-stacks were converted to maximal intensity projections and
binarized by manual thresholding in ImageJ (https://imagej.nih.gov/ij/). Total cell area and area
per cell were quantified using the Analyze Particles command with an inclusion minimum of 40
μm2. An increase in astrocyte size, or hypertrophy, has been shown to associate with astrocyte
activation in mouse models of neurological trauma 357 and AD 358. Sample confocal images with
the thresholding applied are shown in Figure 4-2A.
To evaluate astrocyte branch number and length, the closest two astrocytes to three a priori
selected x, y coordinates per image was manually traced using the Simple Neurite Tracer plugin
in ImageJ to ensure all processes were selected without overlap from adjacent cells. The Sholl
Analysis command was then applied to quantify branching complexity. Sholl analysis is widely
used to assess cell branching, particularly of microglia and astrocytes 339, 360, 361, by quantifying
the number of process branches at increasing distances from the soma, which are increase in
response to neurological injury, giving a marker of activation 356. Results are the mean of the
ipsilateral and contralateral hippocampi. A sample of the workflow is provided in Figure 4-2B.
4.3.7 Statistical analysis
For fatty acid analysis, results were analysed by 2-way ANOVA with Tukey’s multiple
comparison’s post-hoc test. For GFAP fluorescence intensity and astrocyte morphology, results
at 10 days post-icv were normalized to non-surgery values to allow for the comparison of the
164
response to amyloid-β between genotype/diet groups without confounding by baseline
differences. All groups were compared with a one-way ANOVA with Tukey’s post-hoc test.
Bars in the graphs represent mean and standard error in for 4-5 animals per group for fatty acid
analysis, and 6-10 animals per group for astrocyte morphology. A p<0.05 was considered
statistically significant. Immunohistochemistry analysis was completed by observers blind to the
genotype/diet or surgery group of the brain slices, and all morphology measurements were
validated by a second blinded observer.
4.4 Results
Fat-1 and WTFO mice were not different from one another in total hippocampal n-3 or n-6
PUFA, both having 64% higher levels total n-3 PUFA, and 26% lower levels of total n-6 PUFA
than the WTSO mice. Significant main effects of genotype/diet group were identified for oleate
(18:1n-9), linoleic acid (18:2n-6), 20:2n-6, 20:3n-6, arachidonic acid (ARA, 20:4n-6), 22:4n-6,
docosapentaenoic acid n-6 (22:5n-6), eicosapentaenoic acid (20:5n-3), and DHA (22:6n-3). A
significant main effect of surgery was identified for myristic acid (14:0). In the post-hoc tests,
both fat-1 and WTFO mice had significantly higher hippocampal DHA and oleate (18:1n-9) as a
nanomolar percentage of fatty acids than WTSO mice, and significantly lower 20:2n-6, 20:3n-6,
22:4n-6 and 22:5n-6, with no significant differences between them (Figure 4-1). Fat-1 mice had
significantly lower levels of ARA as a percentage of fatty acids than WTSO mice, while WTFO
mice were significantly lower than both WTSO and Fat-1 mice. Fat-1 mice also had a
significantly higher level of EPA than WTSO mice, with WTFO mice having significantly
higher levels than both the fat-1 and WTSO mice. The differences between fat-1 and WTFO
mice were small, 0.8% of fatty acids for ARA and 0.04% for EPA.
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Figure 4-1. Hippocampal fatty acid composition. All groups of mice were compared with a two-way ANOVA, followed by Tukey’s
post-hoc test, different letters denote significant differences between the groups. Bars represent mean with standard error of the mean
(SEM). # indicates significant main effect of surgery, * indicates significant main effect of genotype/diet without significant
differences in the post-hoc test. Different letters denote significant differences between genotype/diet groups following identification
of a significant main effect of genotype/diet. Arachidonic acid (ARA), docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA)
wildtype mice fed the fish oil diet (WTFO), wildtype mice fed the safflower oil diel (WTSO).
166
Intensity of GFAP staining increased at least 2-fold from non-surgery levels in all groups
following icv infusion of amyloid-β (Figure 4-2C). Staining intensity increased significantly
more in the WTFO mice than both the WTSO and fat-1 animals, with levels over 3-fold higher
than non-surgery values. This effect appears to be independent of brain total n-3 fatty acid
composition, as the fat-1 and WTFO mice both had over 1.6-fold higher levels of n-3 PUFA than
the WTSO mice and were not different from one another. Examination of astrocyte area
identified no differences between the genotype/diet groups in the CA1 or DG regions of the
hippocampus, as well as no change from non-surgery areas (Figure 4-2D, F). In the CA3,
average astrocyte area per cell increased by 50% in the WTFO group from non-surgery values,
resulting in a significantly greater response than in the fat-1 and WTSO mice (Figure 4-2E).
These effects may have been driven in part by differences in baseline values, as the WTFO mice
had non-significantly lower non-surgery values for GFAP fluorescence intensity and astrocyte
area per cell than the other groups (data not shown). Measurement of astrocyte morphology in
the CA3 was conducted to determine whether the increases in astrocyte area in the WTFO mice
could be attributed to increases in the length or complexity of astrocyte branches. A main effect
of genotype/diet was identified in the Sholl analysis (Figure 4-2G, p<0.0001). Post-hoc testing
revealed that both the fat-1 and WTFO mice had a greater increase in number of branches at
increasing distances from the soma in response to icv amyloid-β than the WTSO mice. WTFO
mice appeared to have a greater number of branches longer than 22μM than the other two
groups, which could explain the greater area per cell observed in Figure 4-2E. There was no
significant difference in the area under the curve for the Sholl analysis between groups (Figure 4-
2H), likely due to variability in the assay (p=0.12).
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Figure 4-2: Astrocyte response to intracerebroventricular infusion of amyloid-β 1-40 in fat-1 transgenic mice or their wildtype
littermates fed diets containing 2% fish oil (WTFO) or a safflower oil diet containing negligible quantities of n-3 PUFA
(WTSO)
168
A) Sample confocal images of astrocytes used for morphology assessment, B) Schematic illustrating Sholl analysis of astrocytes, C)
Intensity of glial fibrillary acidic protein staining in the whole hippocampus D-F) Average area per astrocyte in the CA1 (D), CA3 (E),
and DG (F) regions of the hippocampus, G) Sholl analysis, indicating numbers of branches present at increasing distances from the
soma, H) Sholl analysis area under the curve. Bars represent mean with standard error of the mean (SEM) of 5-10 animals per
genotype/diet group. Cornu ammonis (CA), glial fibrillary acidic protein (GFAP), wildtype mice fed the fish oil diet (WTFO),
wildtype mice fed the safflower oil diel (WTSO).
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4.5 Discussion
The astrocyte response to a neuroinflammatory insult is often measured through the expression
of mRNA or protein for GFAP 99. Increases in GFAP could indicate increases in the number of
astrocytes, an increase in astrocyte area, or an upregulation of GFAP expression with no change
in cell number or morphology, making it an unclear indicator of the astrocyte response. As our
group has previously shown that astrocytes do not proliferate substantially in response to
amyloid-β in the fat-1 mouse 143, and given the heterogeneity in the modulation of GFAP
expression by n-3 PUFA interventions in AD and other neuroinflammatory models 122, 123, 342, 352,
353, 355, we were interested in whether n-3 PUFA modulates the morphology of astrocytes in
response to amyloid-β. Despite the WTFO and fat-1 mice having similarly high levels of brain
total n-3 PUFA, we identified greater increases in GFAP staining intensity and average area per
astrocyte in response to icv amyloid-β in the WTFO mice than both the WTSO and fat-1 animals.
Both fat-1 and WTFO mice had greater increases in branch number and length in response to icv
amyloid-β than WTSO mice. We also noted a shift towards longer branches in the WTFO mice,
which may explain the increased area per cell in this group. This is in line with a previous study
in a mouse model of aging-related neuroinflammation, that identified an increase in astrocyte
process length in aged mice fed fish oil relative to control aged or young mice in the
hippocampus CA1 and CA3, and an attenuation of the age-related reduction in process length in
the DG 355. By identifying a different astrocyte response in WTFO and fat-1 mice, which
consumed the same n-3 PUFA deplete diet as the WTSO mice but attained similar levels of all
brain n-3 and n-6 PUFA as the WTFO mice, we can suggest that indirect effects, small fatty acid
differences, or other dietary components besides n-3 PUFA may contribute to modulating the
astrocyte response to an inflammatory insult. This provides an important consideration to the
interpretation of papers using fish oil as a dietary intervention for neuroprotection. This work is
novel in that it is the first to examine the effect of an n-3 PUFA intervention on astrocyte
morphology in an AD-related model, and to our knowledge, only the second to do so in a model
of neuroinflammation 355.
It is unclear what aspect of the diets mediated the differences in astrocyte response between the
fat-1 and WTFO groups. While both the SO and FO diets contained high levels of linoleic acid
170
as a percent of fatty acids, the SO diet contained 10% more. It is possible that this higher
exposure to n-6 PUFA may have stunted the astrocyte response, perhaps through conversion to
ARA-derived pro-inflammatory lipid mediators. This is supported by the fact that fat-1 mice had
significantly higher levels of brain ARA compared to WTFO mice, though these differences are
small (0.8% of fatty acids) and did not translate to differences in total brain n-6 PUFA.
Similarly, the fat-1 mice had lower levels of hippocampal EPA, which can give rise to pro-
resolving lipid mediators, than the WTFO mice, though like with ARA, this difference was
extremely small, so it is unclear whether it would have any physiological significance (<0.04%
of fatty acids). Other components of the diet may also have contributed to the differences
between groups. The FO diet contained 2% menhaden oil, which also contained 200 ppm of tert-
Butylhydroquinone, an anti-oxidant, which has been shown to modulate the astrocyte response to
MPTP in a Parkinson’s disease model 362. Fish oil contains iodine and vitamin E, which may
modulate neurological development and the neuroinflammatory response respectively 363, 364,
however the standard AIN-93G diet already contains adequate levels of both of these nutrients
for rodent growth, so these effects would have to occur at levels in excess of these requirements.
Fish oil also contains furan fatty acids at low concentrations, which are a group of fatty acids
characterized by the presence of a furan ring, which are synthesized by algae 365. Furan fatty
acids have anti-oxidant effects, and anti-inflammatory effects 366. A future study feeding fat-1
mice the fish oil diet would be useful for elucidating whether these other dietary components are
responsible for the observed differences in astrocyte response.
Whether an enhanced astrocyte response to amyloid-β 1-40 is beneficial or detrimental is
uncertain. In our previous study using this model, the WTFO mice had lower levels of neuronal
death than the WTSO mice, with the fat-1 mice being intermediate between the two 143. This
suggests that the enhanced astrocyte activation may be protective in this model. This is in
agreement with other studies that reported neuroprotection associated with increases in astrocyte
markers, such as better memory retention with higher hippocampal GFAP area in a model of
surgery-induced cognitive decline 367 and lower infarct size with higher levels of cortical GFAP
protein in an ischemia reperfusion model 352, 368, 369. However, other authors have reported
neuroprotection with a decrease in astrocytic markers, such as less axonal injury associated with
lower GFAP mRNA in a model of spinal cord injury 370 and worse Morris Water Maze
performance with higher hippocampal GFAP protein in an ischemia reperfusion model 353. More
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research is needed on the role of astrocytes in neuroinflammation and neurological diseases to
address this point.
4.6 Conclusion
In conclusion, astrocytes become activated in response to an amyloid-β 1-40 neuroinflammatory
insult. Dietary fish oil, and to a lesser extent, fat-1 transgene, enhances this response. This
suggests that other factors besides a change in total brain n-3 PUFA may contribute to
modulating neuroinflammation in studies using dietary fish oil as an intervention.
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Chapter 5: Fish oil feeding attenuates neuroinflammatory
gene expression without concomitanht changes in brain
eicosanoids and docosanoids in a mouse model of
Alzheimer’s Disease
Kathryn E. Hopperton, Marc-Olivier Trépanier, Nicholas C.E. James, Raphaël Chouinard-
Watkins and Richard P. Bazinet
Paper currently under review
Contributions
RPB and KEH conceived of the project while MT contributed to its development and direction.
KEH conducted the bulk of the experimental work and analysis with assistance from MT, R-CW
and NCEJ.
173
5.1 Abstract
Background: Neuroinflammation is a recognized hallmark of Alzheimer’s disease, along with
accumulation of amyloid-β plaques, neurofibrillary tangles and synaptic loss. n-3
polyunsaturated fatty acids (PUFA) and molecules derived from them, including
eicosapentaenoic acid-derived eicosanoids and docosahexaenoic acid-derived docosanoids, are
known to have both anti-inflammatory and pro-resolving properties, while human observational
data links consumption of these fatty acids to a decreased risk of Alzheimer’s disease. Few
studies have examined the neuroinflammation-modulating effects of n-3 PUFA feeding in an
Alzheimer’s disease-related model, and none have investigated whether these effects are
mediated by changes in brain eicosanoids and docosanoids. Here, we use both a fat-1 transgenic
mouse and a fish oil feeding model to study the impact of increasing tissue n-3 PUFA on
neuroinflammation and the production of pro-inflammatory and pro-resolving lipid mediators.
Methods: Fat-1 mice, transgenic animals that can convert n-6 to n-3 PUFA, and their wildtype
littermates were fed diets containing either fish oil (high n-3 PUFA) or safflower oil (negligible
n-3 PUFA) from weaning to 12 weeks. Animals then underwent intracerebroventricular infusion
of either amyloid-β 1-40 or a control peptide. Hippocampi were collected from non-surgery and
surgery animals 10 days after infusion. Microarray was used to measure enrichment of
inflammation-associated gene categories and expression of genes involved in the synthesis of
lipid mediators. Results were validated by real-time PCR in a separate cohort of animals.
Eicosanoids were measured via liquid chromatography tandem mass spectrometry.
Results: Fat-1 and wildtype mice fed fish oil had higher total hippocampal DHA than wildtype
mice fed the safflower oil diet. The safflower-fed mice, but not the fat-1 or fish oil-fed mice, had
significantly increased expression in gene ontology categories associated with inflammation in
response to amyloid-β infusion. These effects were independent of changes in the expression of
genes involved in the synthesis of eicosanoids or docosanoids in any group. Gene expression was
replicated upon validation in the wildtype safflower and fish oil-fed, but not the fat-1 mice.
Protectin, maresin and D and E series resolvins were not detected in any sample. There were no
major differences in levels of other eicosanoids or docosanoids between any of the groups in
response to amyloid-β infusion.
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Conclusions: Fish oil feeding decreases neuroinflammatory gene expression in response to
amyloid-β. Neither amyloid-β infusion or increasing brain DHA affects the brain concentrations
of specialized pro-resolving mediators in this model, or the concentrations of most other
eicosanoids and docosanoids.
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5.2 Introduction
Neuroinflammation is increasingly recognized as a hallmark of Alzheimer’s disease (AD).
Neuroinflammatory markers, such as glial cells, cytokines or complement, are elevated in animal
models of AD68, 70, 143 and in human subjects 28, 51, 53, 55. Studies using positron emission
tomography to measure microglia in vivo have demonstrated elevations in AD, with some
evidence for increases in mild-cognitive impairment as well (for review see 58). The
neuroinflammatory hypothesis of AD suggests that aberrant activation of immune cells in the
brain, perhaps in response to the deposition of amyloid-β plaques, contributes to neuronal loss
and dysfunction 78. This is supported by studies showing that patients with AD have greater
concentrations of microglia and astrocytes in the brain than cognitively intact controls with
similar levels of AD pathology 216. In animal models of AD, neuroinflammation appears to
precede plaque deposition 77, and treatments that decrease inflammation seem to decrease AD
pathology 72, 73, 75. This hypothesis is also supported by the fact that polymorphisms in a variety
of inflammation-associated genes have been implicated as risk factors for AD, including cluster
of differentiation (CD) 33 65, triggering receptor expressed on myeloid cell (TREM) 2 64,
interleukin (IL)-6 61, IL-1 66 and toll-like receptor (TLR) 4 62. Observational studies in NSAID
users show a decreased risk of AD development 371-373, which also supports this hypothesis,
though this has not been supported by clinical trials 80.
Animal studies report lower neuroinflammation with interventions aimed at increasing brain
docosahexaenoic or eicosapentaenoic acids (DHA and EPA), such as diets containing fish oil or
purified n-3 polyunsaturated fatty acids (PUFA), or direct injections of n-3 PUFA or their
derivatives (for review, see 99). n-3 PUFA may exert these effects either directly or indirectly
through conversion to other bioactive substances. DHA and EPA are precursors to a family of
molecules including resolvins, protectin and maresins, collectively referred to as specialized pro-
resolving mediators (Figure 1-2 A and B, for review, see 97). These molecules are known to
decrease the magnitude and duration of inflammation in various models in the periphery, and in
the brain in models of stroke 369, Parkinson’s Disease 374, surgery-induced cognitive decline 367
and traumatic brain injury 375. Levels of brain PD1 decrease with disease progression in the
3xTg mouse model of AD 132, while lower levels of maresin 1 112, resolvin D2 112 and NPD1 111,
112 have been reported in post-mortem brain samples from human patients with AD relative to
176
controls, suggesting that reductions in these molecules may contribute to AD. A protective role
for these molecules is supported by studies showing that NPD1 and resolvin D1 promote
amyloid-β phagocytosis while decreasing inflammatory cytokine production in cultured
microglia and peripheral mononuclear cells 112, 132, 133.
Human observational studies show that elevations in dietary fish or DHA consumption is
associated with a reduced risk of AD 124. This has not been supported by the balance of the
clinical data, though there is some evidence for benefit in patients with mild cognitive
impairment 328. Animal studies also widely show benefits of consumption of n-3 PUFA on AD
pathology, cognition and neuronal death (reviewed in 113). It is not known, however, whether
DHA is protective via its direct anti-inflammatory actions, metabolism to specialized pro-
resolving mediators, or via other mechanisms. Supplementation of n-3 PUFA has been shown to
increase resolvin D1 in macrophages from patients with AD or mild-cognitive impairment 376,
and to prevent the decline in plasma resolvin D1 in AD patients 377, which provides proof of
principle that n-3 PUFA consumption may be protective via conversion to mediators.
In contrast to DHA, the n-6 PUFA, arachidonic acid (ARA), is the precursor to a variety of pro-
inflammatory eicosanoids. In response to insult or immune activation, cytosolic phospholipase
A2 (cPLA2) cleaves ARA from the membrane, allowing it to enter the non-esterified fatty acid
pool. ARA can be metabolized by cyclooxygenase (COX)-2 to produce prostaglandins (PG) and
thromboxane, by cytochrome p450, 12-LO or 15-LO to produce hydroxyeicosatetraenoic acids
(HETE), or by 5-LO to produce leukotrienes 104 (Figure 1-2 C). ARA can also be the precursor to
a pro-resolving mediator through metabolism by 15-LO, lipoxin A4. Higher levels of HETE and
PGE2 and lower levels of lipoxin A4 have been reported in the brains of patients with AD 112, 134-
136, implicating changes in the production of these molecules in disease development. DHA
occupies the same position in the phospholipid membrane as ARA, and concentrations of these
molecules are somewhat inversely correlated in the brain 137. It is possible then, that in addition
to direct anti-inflammatory and pro-resolving effects of n-3 PUFA and their associated
mediators, increasing brain levels of DHA may also indirectly decrease neuroinflammation by
displacing ARA, thus lowering the production of pro-inflammatory lipid mediators.
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While several studies have shown that interventions that raise brain n-3 PUFA attenuate
neuroinflammation in AD models (reviewed in 99), no one has yet investigated whether these
anti-inflammatory actions are associated with changes in the brain content of pro-inflammatory
or pro-resolving lipid mediators. Here, we describe a series of experiments investigating the
hippocampal neuroinflammatory response to infusion of amyloid-β peptide in mice with high or
low brain n-3 PUFA. We identify suppression of inflammation-associated gene expression
networks in mice fed a fish oil diet, with no changes in the production of specialized pro-
resolving lipid mediators or most other eicosanoids and docosanoids, or of the genes involved in
their synthesis.
5.3 Methods
5.3.1 Animals and diets
All procedures were carried out in accordance with the guidelines of the Canadian Council on
Animal Care (protocol # 20011376). Mice were maintained under controlled light and
temperature conditions in the Department of Comparative Medicine animal facility at the
University of Toronto, with ad libitum access to food and water.
Fat-1 males were obtained as a gift from Dr. David Ma (University of Guelph, ON, Canada). Fat-
1 mice are transgenic animals with an n-3 desaturase gene from Caenorhabditis elegans,
enabling the conversion of n-6 to n-3 PUFA. Fat-1 males were bred with C57BL/6 females
(Charles River Laboratories, Saint Constant, Quebec, Canada) that were maintained on an n-3
deplete modified AIN-93G rodent diet (D04092701; Research Diets Inc., New Brunswick, NJ,
USA) containing 10% safflower oil by weight for at least two weeks prior to breeding. Offspring
were genotyped as described previously 143 and weaned at 3 weeks of age onto either the
safflower oil diet, or a fish oil diet (D04092702; Research Diets Inc.) in which 20% of the
safflower oil was replaced with menhaden oil. The fatty acid composition was confirmed in our
lab by gas chromatography. The most abundant fatty acids as a percent of total fatty acids in the
safflower oil diet were linoleic (18:2n-6, 71.9%), oleic (18:1n-9, 14.7%), palmitic (16:0, 7.2%),
and stearic (18:0, 2.5%). The most abundant fatty acids in the fish oil diet were linoleic (59.6%),
oleic (13.8%) palmitic (9.6%), palmitoleic (16:1n-7, 2.8%), stearic (2.6%), EPA (2.4%), and
DHA (1.1%). The full fatty acid composition of these diets is shown in Table 5-1
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Table 5-1: Fatty acid composition of 10% safflower and 8% safflower, 2% fish oil diets.
Fatty acids present at >1% shown. N.d: not detected.
Diet Fatty Acid Composition (nmol%)
10% Safflower Oil 8% Safflower, 2% Fish Oil
C14:0 0.16 2.14
C15:0 0.02 0.14
C16:0 7.20 9.58
C16:1n-7 0.11 2.80
C17:1n-7 0.02 0.34
C18:0 2.46 2.63
C18:1n-9 14.74 13.84
C18:1n-7 0.71 1.17
C18:2n-6 71.91 58.59
C20:0 0.31 0.27
C20:1n-9 0.16 0.36
C18:3n-3 0.20 0.43
C20:2n-6 0.06 0.26
C22:0 0.26 0.24
C20:4n-6 n.d 0.14
C22:2n-6 0.01 0.19
C20:5n-3 n.d 2.44
C24:1n-9 0.11 0.11
C22:5n-3 0.03 0.30
C22:6n3 0.03 1.05
179
5.3.2 Intracerebroventricular infusion of amyloid-β 1-40 or 40-1
At 12 weeks of age, mice underwent intracerebroventricular infusion (icv) of either amyloid-β 1-
40 (Bachem Biochemicals, Bubendorf, Switzerland) or a reverse peptide control, amyloid-β 40-1
(Bachem) as described previously 143. The two peptides were diluted to 1 μg/μl in sterile 0.1 M
phosphate buffered saline (PBS) and incubated at 37°C prior to use, which we previously found
to be sufficient for the amyloid-β 1-40 to aggregate into fibrils and fibres 143. Briefly, mice were
anesthetized, weighed, and immobilized in a stereotaxic setup with a digital reader (Stoelting,
Wood Dale IL, USA). The skull was exposed and a small hole was drilled -0.1 mm,
medial/lateral and -0.5 mm anterior/posterior to bregma. Five μl of amyloid-β 1-40 or the control
peptide was then injected at a depth of -2.4mm from the surface of the skull at a rate of 1
μl/minute with a Quintessential Stereotaxic Injector (Stoelting). Accuracy of the injection to the
left lateral ventricle was confirmed by periodic injection of Evan’s blue dye. At 10 days post-icv,
body weight and rectal temperature were measured, after which mice were euthanized as
described below. This 10 day time point was selected because it coincides with peak microglial
activation 143, and is therefore a time point where inflammation is likely to be detected.
5.3.3 Collection of brains for RNA measurements
For gene expression measurements, mice were euthanized by CO2 asphyxiation per institutional
protocols. Brains were rapidly harvested and the ipsilateral hippocampus dissected and flash
frozen with liquid nitrogen. Samples were stored at -80°C until further use.
5.3.4 Collection of brains for fatty acid measurements
For measurement of total fatty acid or docosanoid and eicosanoid concentrations, mice were
euthanized by high energy head-focused microwave fixation as described by our lab previously
359. Microwave fixation is the gold standard for studies measuring lipid mediators and other
docosanoids and eicosanoids because ischemia causes rapid alterations in the brain lipid profile
and induces the production of mediators such as PGE2, anandamide and PD1 359, producing
substantial artifacts. For microwave fixation, un-anesthetized mice were placed in a holder and
inserted into the vivostat (model S15P; Cober Electronics Inc., Norwalk, CT, USA). A
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microwave beam (0.5 kW, delivering 1925-1950 joules) was then aimed directly at the top of the
head, causing death in less than a second. Brains were then rapidly dissected, and the ipsilateral
and contralateral hippocampi stored at -80°C until further use.
5.3.5 Gas Chromatography
Fatty acids from contralateral hippocampi of mice euthanized via microwave fixation were
weighed and extracted in 2:1:0.8 chloroform: methanol: potassium chloride (0.88%) with a
known amount of 17:0 internal standard (NuChek-Prep, Elysian, MN, USA) as described
previously 333. Samples were then dried down under nitrogen gas, and methylated by heating at
100°C in 0.3:1 hexane: boron trifluoride-methanol. The fatty acid methyl esters in hexane were
removed by adding double distilled water, and analyzed via gas chromatography flame
ionization detection on a Varian-430 gas chromatograph (Varian, Lake Forest, CA, USA) as
previously described 378. Peaks were identified by comparison with authenticated standards
(NuChek Prep), while concentrations were determined by comparison with the 17:0 internal
standard peak. Data are expressed as nanomolar percent of fatty acids.
5.3.6 RNA extraction
Ipsilateral hippocampi from CO2-asphyxiated animals were homogenized from frozen in 150 μl
Trizol (ThermoFIsher Scientific, Waltham, MA, USA) with a Kimbel Kontes pestle
homogenizer (Fisher Scientific, Waltham, MA, USA). An additional 850 μl of Trizol was then
added and samples were mixed by shaking. RNA was then extracted per manufacturer’s
instructions. Extraction efficiency and the presence of contaminants was assessed with a
Nanodrop 1000 Spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). RNA
integrity was measured in all microarray samples using an Agilent 2100 Bioanalyzer (Agilent,
Santa Clara, CA, USA).
5.3.7 Microarray analysis
A microarray was conducted to identify patterns of inflammatory gene expression associated
with amyloid-β infusion and genotype/diet grouping. Extracted RNA was reverse transcribed
with a WT Expression Kit (ThermoFischer), then fragmented and labelled according to the
Affymetrix WT fragmentation and labelling protocol. cDNA was hybridized to an Affymetrix
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Mouse Gene 2.0 ST GeneChip (ThermoFischer) for 18 hours at 45°C and 60 RPM. Arrays were
then washed with a GeneChip Fluidics Station 450 (ThermoFischer) and scanned by an
Affymetrix GeneChip Scanner 7G (ThermoFischer). Hybridization controls were similar across
all arrays, indicating successful hybridization, while polyA spikes were present at recommended
levels and were similar across all arrays, indicating appropriate sample labelling. Data was then
imported into GeneSpring v13.1.1 (Agilent) for analysis. Data were normalized using robust
multi-array average (RMA) 16 summarization and quantile normalization, followed by median
centred normalization for each probe set. Data was filtered to remove probes with signals below
the 20th percentile of the distribution of intensities for all samples. The final list contained 27112
probe sets.
5.3.8 RT-qPCR
A subset of genes driving categorical enrichment in the microarray were measured in an
independent cohort of CO2-asphyxiated animals for validation. Extracted RNA was reverse
transcribed using a High Capacity cDNA Reverse Transcription Kit (ThermoFischer) per
manufacturer’s instructions. Gene expression was measured using TaqMan gene expression
assays (ThermoFischer) for murine major histocompatibility complex (MHC) II (H2-Ab1, assay
ID Mm00439216_m1), MHC I (H2-K1, Mm01612247_mH) and the low affinity
immunoglobulin gamma Fc region (Fcgr2b, Mm00438875_m1), along with TaqMan Gene
Expression 2X Master Mix (ThermoFischer) according to the manufacturer’s instructions. Each
10 μl reaction was run in triplicate in a 384-well optical plate on a 7900 HT Real-time PCR
machine (Applied Biosystems, Foster City, CA, USA) with an initial incubation at 95°C for 10
minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 60 seconds as described
previously 379. Results are expressed as fold change from control peptide-injected animals,
calculated by the ΔΔCt method normalized to Glyceraldehyde 3-phosphate dehydrogenase
(GAPDH, Mm99999915_g1).
5.3.9 Extraction and quantification of eicosanoids and docosanoids
Docosanoids and eicosanoids were extracted using a method adapted from Colas et al. 380. All
procedures were performed on ice, and once eicosanoids and docosanoids were extracted, all
procedures were performed in the dark to minimize formation of auto-oxidative products. A ten-
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point standard curve (0.01 to 10 ng) was performed by diluting stock external standards of lipid
metabolites (Cayman Chemicals Co., Ann Arbor, MI) in ethanol to enable quantification.
Contralateral hippocampi were weighed from frozen and homogenized in methanol (2x500 μl)
using a glass homogenizer. Fifty μl of internal standard mixture was added to each sample during
extraction. Homogenate was transferred to a 1.5 ml microcentrifuge tube and incubated for 45
minutes at -20°C to precipitate protein. Samples were then centrifuged (1200 g x 15 minutes)
and the supernatant moved to 15ml borosilicate glass tubes. SepPak C18 solid phase extraction
columns (Waters, Ireland) were loaded onto a Vac Elut SPS-24 manifold (Varian) and
conditioned by adding 1.5 ml methanol for ten minutes, then running through 12 ml methanol,
followed by 12 ml double distilled water, taking care not to allow the sorbent bed to dry. Nine ml
of double distilled water pH 3.5 was then added to the sample tubes and mixed by vortexing.
Acidified samples were then quickly loaded into the columns and run through slowly, followed
by 4 ml double distilled water and 10 ml hexane. Eicosanoids and docosanoids were eluted from
the column with 8 ml methyl formate. Samples were dried at 37°C under a stream of nitrogen
gas, and reconstituted in 70:30:0.02 water: acetonitrile: acetic acid and transferred to glass inserts
in amber vials for analysis by liquid chromatography tandem mass spectrometry (LC/MS/MS) as
described previously 381. The limit of detection was 0.01 ng, with values between 0.005 and 0.01
ng considered semi-quantitative. A fatty acid derivative was considered detected if it was
detected in at least half of the samples from at least one of the genotype/diet surgery groups.
5.3.10 Statistical analysis
Brain fatty acid composition, body weight, temperature, and eicosanoid/docosanoids were
compared between the genotype/diet and surgery groups using a two-way ANOVA with a
Tukey’s post-hoc test. Microarray data was analyzed in GeneSpring v13.1.1. Normalized
intensities were analyzed via one-way ANOVA with a Tukey’s post-hoc test to examine the
effect of amyloid-β infusion in each genotype/diet group. An unsupervised clustering was
performed on genes that varied in the one-way ANOVA using a Pearson-centred correlation as a
distance metric to build a hierarchical clustering heat map. The Venny online tool was used to
identify overlap and unique genes between each post-hoc list 382. The results of each post-hoc
test was divided into positive or negative fold change (fold change cut-off <1.5) and a Benjamini
and Yekutieli corrected hypergeometric test (p<0.3) was used to examine Gene Ontology (GO)
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functional category enrichment. GO categories were considered significant if they met the false
discovery rate cut-off and contained at least 2 probe sets per category. Similar GO results were
obtained when the samples were analyzed in the Database for Annotation, Visualization and
Integrated Discovery (DAVID) version 6.7, an online bioinformatics tool offered by the National
Institutes of Health 383-385. A two-way ANOVA was performed on normalized intensity values of
genes driving categorical enrichment to examine main and interactive effects of genotype/diet
and surgery groups. A p value <0.05 (raw or false discovery rate corrected depending on the
analysis) was considered significant.
5.4 Results
5.4.1 Group characteristics
There was a significant main effect of genotype/diet group on hippocampal total DHA and EPA
as measured by gas chromatography (p<0.01), with the fat-1 and wildtype mice fed the fish oil
diet (WTFO) having nearly double the levels as a molar percent of fatty acids than wildtype mice
fed the safflower oil diet (WTSO) (Figure 5-1A). There was no difference in the level of ARA
between any of the groups (Figure 5-1A). Quantification of non-esterified fatty acids by
LC/MS/MS showed similar results, with a significant main effect of genotype/diet group for free
DHA and EPA (p<0.05, Figure 5-1B), with the WTSO group having lower levels of EPA than
both the fat-1 and WTFO mice, and lower levels of DHA than the fat-1 mice. There were no
differences in the amount of non-esterified ARA between the groups. There was no main effect
of surgery group (amyloid-β 1-40, control peptide, or non-surgery) on any of the fatty acid
measures and no genotype/diet x surgery group interactions.
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Figure 5-1: Hippocampus total and non-esterified acid composition, body weight and
temperature of amyloid-β 1-40 or control peptide-infused surgery mice, or of age-matched
non-surgery mice.
A) Total docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and arachidonic acid
(ARA), measured by gas chromatography B) Free DHA, EPA and ARA measured by liquid
chromatography tandem mass spectrometry. Bars represent the mean +/- standard error of the
mean for n=3-4 samples for the total fatty acid measurements and 7-9 for the non-esterified fatty
acid measurements. Different letters denote significant differences by Tukey’s post-hoc test
following identification of a significant main effect of genotype/diet. There were no main effects
of surgery, and no genotype/diet x surgery interactions. Amyloid-β (Ab), Control (Ctrl), Non-
surgery (NS), Wildtype safflower oil-fed mice (WTSO), wildtype fish oil-fed mice (WTFO).
185
Microarray
Hierarchical clustering of genes found to be altered in a one-way ANOVA of the microarray data
shows that samples cluster together by their respective genotype/diet and surgery groupings,
indicating strong within group similarities in gene expression patterns (Figure 5-2A). The WTSO
group that received the amyloid-β 1-40 infusion exhibited a clustering of increased gene
expression (red) for genes related to inflammation, including MHC II (gene name
histocompatibility 2, class II antigen E beta or H2-Ab1), MHC I (gene name histocompatibility 2
k1, k region or H2-K1) and various components of immunoglobulin molecules (gene names
immunoglobulin kappa joining 5, immunoglobulinkappavariable1-135), Fc receptors (gene name
Fc receptor IgG low affinity IV (Fcgr2b), Fc receptor IgE high-affinity 1 gamma polypeptide
(Fcer1g)), cluster of differentiation markers (CD68, CD53, CD44) and genes related to cytokine
signaling (gene names tumor necrosis factor receptor-associated factor 1, IL-2 receptor gamma
chain, interferon regulatory factor 8 (Irf8)). These same genes appeared to be unchanged or
downregulated in the non-surgery and surgery groups for the fat-1 and WTFO animals.
Amyloid-β-injected and non-surgery animals within each genotype/diet group were compared in
post-hoc testing following the one-way ANOVA to identify genes modified in response to the
surgery in each group. After exclusion of un-indexed predicted genes, there were 58 differently
expressed probe sets between non-surgery and amyloid-β-infused animals in the fat-1 group, 94
in the WTFO group and 221 in the WTSO group. There was very little overlap of genes changed
by amyloid-β infusion between the genotype/diet groups, with only 5 genes shared by WTSO
and WTFO, 4 by WTSO and fat-1 and 1 by fat-1 and WTFO (Figure 5-2B, see Appendix 3 and 4
for full gene lists). None of the shared genes appeared to be functionally important for response
to amyloid-β (Figure 5-2C). GO analysis was applied to the list of genes altered by amyloid-β
infusion in each genotype/diet group to look for functional categories of gene expression altered
in response to the surgery. None of the genes in the WTFO or fat-1 mice clustered significantly
into functional categories after false discovery rate correction (Appendix 5 and 6). In contrast,
the WTSO mice exhibited enrichment in 54 functional categories (Table 5-2). The majority
(>70%) are directly related to immune system activation, such as antigen processing and
presentation of exogenous peptide antigen, immunoglobulin-mediated immune response, or
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phagocytosis, while most of the remaining categories are parent categories upstream of immune-
related categories, such as response to external stimulus or cell activation.
Increased expression of 12 genes drove this categorical enrichment in the WTSO group,
including many of the same genes highlighted in the hierarchical cluster analysis: MHC II
(Figure 5-3A, H2-Ab1), MHC I (Figure 5-3B, H2-K1), Fcgr2b (Figure 5-3C), Fcer1g (Figure 5-
3D), unc-93 homolog B1 (Figure 5-3E, UNC93b1), apolipoprotein B mRNA editing enzyme
(Figure 5-3F, Apobec3), complement component 1 q sub-compartment beta polypeptide (Figure
5-3G, C1qb), immunoglobulin kapa joining 1 (Figure 5-3H, Igkj1), cathepsin C (Figure 5-3I,
Ctsc), interferon regulatory factor 8 (Figure 5-3J, Irf8), moesin (Figure 5-3K, Msn), and
arachidonate 5-lipoxygenase activating protein (Figure 5-3L, Alox5ap). No p values for
individual genes remained significant after false discovery rate correction. Comparison of the
normalized expression of these genes between all the genotype/diet groups via uncorrected 2-
way ANOVA identified significant genotype/diet x surgery interactions for all but MHC I and
Igkj1, and post-hoc tests for all genes revealed higher expression in the WTSO amyloid-β-
infused mice than fat-1 or WTFO mice (Figure 5-3).
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Figure 5-2: Analysis of the microarray data
A) Hierarchical cluster of genes significantly increased in the one-way ANOVA (uncorrected
p<0.05), left panel represents whole gene list, while right panel is zoomed in on key regions of
clustering with labeled branches corresponding to individual samples, B) Venn diagram of genes
increased by amyloid-β infusion in each group, C) List of genes increased by surgery in more
than one genotype/diet group. Wildtype safflower oil-fed mice (WTSO), wildtype fish oil-fed
mice (WTFO), amyloid-β (Aβ). Figures represent n=3 samples per group.
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Table 5-2: List of significantly enriched gene ontology categories in WTSO amyloid-β 1-40-infused compared to non-surgery mice.
Based on n=3 mice per group. Benjamini Yekutieli false discovery rate (BY), Gene ontology (GO), wildtype mice fed safflower oil
(WTSO
Significantly Enriched GO Categories in WTSO Non-surgery vs Amyloid-β-Infused Mice
GO Term
BY
Corrected
p-value
Number of
Genes
Driving
Enrichment GO Term
BY
Corrected
p-value
Number of
Genes
Driving
Enrichment
Antigen processing and
presentation of exogenous
peptide antigen 4.05E-07 5
Regulation of adaptive immune
response based on somatic
recombination of immune
receptors built from
immunoglobulin superfamily
domains 0.011 4
Antigen processing and
presentation of exogenous
antigen 8.05E-07 5 Arp2/3 protein complex 0.012 2
Antigen processing and
presentation of exogenous
peptide antigen via MHC
class II 3.34E-06 4
Regulation of acute
inflammatory response 0.013 3
Antigen processing and
presentation of peptide
antigen via MHC class II 4.55E-06 4
Regulation of adaptive immune
response 0.013 4
Antigen processing and
presentation of peptide or
polysaccharide antigen via
MHC class II 7.74E-06 4 Extracellular organelle 0.013 9
Antigen processing and
presentation of peptide
antigen 2.46E-05 5
Positive regulation of immune
response 0.013 5
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Immune system process 6.20E-05 12
Extracellular membrane-
bounded organelle 0.013 9
Immune effector process 9.72E-05 7 Extracellular exosome 0.013 9
Immune response 9.76E-05 9
Defense response to other
organism 0.013 5
Lymphocyte mediated
immunity 1.42E-04 5 Extracellular vesicle 0.013 9
Antigen processing and
presentation 1.53E-04 5
Positive regulation of immune
system process 0.018 6
Adaptive immune response
based on somatic
recombination of immune
receptors built from
immunoglobulin superfamily
domains 2.18E-04 5
Regulation of type I
hypersensitivity 0.019 2
Defense response 3.39E-04 9
Regulation of production of
molecular mediator of immune
response 0.025 3
Leukocyte mediated immunity 3.42E-04 5 Side of membrane 0.025 5
Adaptive immune response 6.58E-04 5 Phagocytosis 0.027 3
Response to external biotic
stimulus 6.58E-04 8 Leukocyte activation 0.028 5
Response to other organism 6.58E-04 8 Protein binding 0.029 22
Response to biotic stimulus 7.93E-04 8 Immunoglobulin binding 0.029 2
Immunoglobulin mediated
immune response 7.93E-04 4
Production of molecular
mediator involved in
inflammatory response 0.032 2
B cell mediated immunity 9.17E-04 4 Regulation of hypersensitivity 0.035 2
Regulation of immune system
process 0.003 8 Membrane-bounded vesicle 0.036 10
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Antigen binding 0.004 4
Regulation of acute
inflammatory response to
antigenic stimulus 0.045 2
Regulation of immune
response 0.004 6 Extracellular region 0.045 12
Cell surface 0.005 7 Vesicle 0.046 10
IgG binding 0.01 2 Mast cell activation 0.046 2
Response to bacterium 0.01 6 Cell activation 0.047 5
Antigen processing and
presentation of exogenous
peptide antigen via MHC
class I 0.01 2
Regulation of immune effector
process 0.049 4
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5.4.2 Lipid mediator-associated genes
Although not significantly different among any of the genotype/diet or surgery groups after false
discovery rate correction, it was of interest to conduct exploratory analysis of the expression of
genes associated with the production of eicosanoids and docosanoids because of their relevance
to our hypotheses. Uncorrected 2-way ANOVA identified no significant differences between the
diet/genotype or surgery groups for cPLA2 (Figure 5-4A, p=0.0531, Pla2g4a) iPLA2 (Figure 5-
4B, Pla2g6), COX-2 (Figure 5-4C, Ptgs1), PGE synthase (Figure 5-4D, Ptges), 5-LO (Figure 5-
4E, Aloxa5), 15-LO (Figure 5-4G, Alox15) or cytochrome P450 (Figure 5-4H, Por). A
significant main effect of surgery was identified for 12-LO (Figure 5-4F, Aloxa12) with no
differences remaining significant in the post-hoc test.
5.4.3 Microarray Validation
To validate the gene ontology results, expression of MHC II, MHC I and Fcgr2b were measured
by RT-qPCR in an independent cohort of non-surgery animals and animals that underwent icv
infusion of amyloid-β 1-40 or control peptide. When normalized to control peptide via the
ΔΔCt, WTSO exhibited increased expression of MHC I and MHC II upon icv infusion of
amyloid-β compared to WTFO mice (Figure 5-5 A and B), confirming the microarray results.
Contrary to the microarray results however, fat-1 mice were not significantly different from the
WTSO mice, and had significantly higher expression of MHC I than WTFO mice in response to
amyloid-β. None of the groups differed significantly in expression of FcGr2b, though a similar
pattern of expression was observed (Figure 5-5C). Similar results were obtained when the data
were analyzed as ΔΔCts normalized to non-surgery mice instead of control peptide-injected
animals (data not shown). The samples used in the microarray were re-analyzed using qPCR,
and the expression of all three genes demonstrated a similar pattern to what was observed with
the microarray, although with greater variability (data not shown). This suggests that the
different results between the microarray and qPCR experiments for the fat-1 mice are the result
of true variation in the samples, rather than experimental error.
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Figure 5-3: Genes driving enrichment of neuroinflammation-associated gene expression
categories in wildtype safflower oil-fed mice.
193
A) MHC II or H2-Ab1: Histocompatibility 2, Class II antigen A beta1, Major
Histocompatibility Complex II, B) MHC I or H2-K1: Histocompatibility 2, K1m K region, C)
Fcgr2b: Fc Receptor IgG low-affinity 2b, D) Fcer1g: Fc Receptor IgE High-Affinity Gamma
Polypeptide, F) UNC93b1: Unc-93 homolog B1, F) Apobec3: Apolipoprotein B mRNA editing
enzyme, G) C1qb: Complement component 1q sub-compartment beta polypeptide H) Igkj1:
Immunoglobulin kappa joining 1, I) Ctsc: Cathepsin 1, J) Irf8: Interferon regulatory factor 8, K)
Msn: Moesin, J) Alox5ap: Arachidonate 5-lipoxygenase activating protein. Gene names are
provided with common name and abbreviated gene name in brackets. Bars represent means and
standard error of n=3 mice per group. * indicates that the WTSO group was significantly higher
than all other groups followingpost-hoc tests, main effects are listed below each graph. Major
histocompatibility complex (MHC), Wildtype safflower oil-fed mice (WTSO), wildtype fish oil-
fed mice (WTFO).
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Figure 5-4: Genes involved in the synthesis of eicosanoids and docosanoids.
A) cPLA2: cytosolic phospholipase A2 B) iPLA2: calcium independent phospholipase A2 C) COX-2 or Ptgs1: Prostaglandin
endoperoxide synthase 1, D) Ptges: Prostaglandin E synthase, E) 5-LO or Alox5: Arachidonate 5-lipoxygenase, F) 12-LO or Alox12:
Arachidonate 12-lipoxygenase, G) 15-LO or Alox15: Arachidonate 15- lipoxygenase, H) Cytochrome P450 or Por: P450 Cytochrome
Oxidoreductase. Gene names are provided with common name and abbreviated gene name in brackets. Bars represent means +/-
standard error of the mean for n=3 mice per group. Cyclooxygenase (COX), Wildtype safflower oil-fed mice (WTSO), wildtype fish
oil-fed mice (WTFO)
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5.4.4 Eicosanoids and Docosanoids
Most of the EPA derived eicosanoids and DHA derived docosanoids included in the assay were
not detected in any groups, including: resolvin D1, resolvin D2, resolvin E1, maresin 1 and PD1.
A full list of the measured fatty acid derivatives that were not detected is presented in Table 5-3.
There was a significant main effect of genotype/diet for 4-hydroxy DHA (HDHA), with fat-1
mice exhibiting significantly higher levels than the WTSO mice (Figure 5-6A). There were no
significant main effects or interactions for any other docosanoids. For EPA derived eicosanoids
(Figure 5-6B), there was a significant main effect of genotype/diet for 17(18)-epoxy
eicosatetraenoic acid, with the WTFO mice having significantly higher levels than the WTSO
mice, and for 17(18)-diHETE, with both fat-1 and WTFO mice having higher levels than WTSO
mice. There were no other significant main effects or interactions.
For ARA derived eicosanoids, there was a significant genotype/diet x surgery interaction for 15-
HETE. WTSO control peptide injected mice had significantly higher levels of 15-HETE than
amyloid-β injected fat-1 mice, and non-surgery and control peptide-injected WTFO mice.
WTSO non-surgery mice had significantly higher levels than control peptide-injected WTFO
mice. There were no other significant main effects or interactions for any of the other ARA
derived eicosanoids measured (Figure 5-7).
5.5 Discussion
DHA, the most abundant n-3 PUFA in the brain, may exert anti-inflammatory effects through a
variety of mechanisms, including conversion to docosanoids. Though diets high in n-3 PUFA are
known to modify levels of docosanoids and other bioactive lipid mediators in the periphery 386,
there is limited research on the effect of these diets on brain docosanoid and eicosanoid
composition. In addition, while it is known that n-3 PUFA have anti-neuroinflammatory
properties, it is yet unknown whether changing brain concentrations of these fatty acids alter the
response to a neuroinflammatory insult via changes in the production of bioactive lipid
mediators.
We saw that higher levels of brain DHA and EPA, attained either through a diet containing fish
oil or via endogenous synthesis in the fat-1 transgenic mouse, were associated with a
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Table 5-3: List of measured fatty acid derivatives that were not detected. Molecules were
considered detected if they were measured in at least half the samples from at least one of the
genotype/diet or surgery groups. epDPE: epoxy docosapentaenoic acid, epETE: epoxy
eicosapentaenoic acid, ETE: eicosatetraenoic acid, DHET: dihydroxyeicosatrienoic acid, HEPE:
hydroxyeicosapentaenoic acid, HDHA: hydroxy docosahexaenoic acid, HETE:
hydroxyeicosapentaenoic acid, LX: lipoxin, LT: leukotriene, PG: prostaglandin, Tx:
thromboxane
DHA Docosanoids EPA Eicosanoids ARA Eicosanoids
17R resolvin Resolvin E1,
13, 14-dihydro-19(R)-hydroxy
PGE1
6,15-diketo-13,14-
dihydro PGF1α
Resolvin D1 LXA5, 2,3-dinor PGE1 PGD2
Resolvin D2 LTB5 19(R)-hydroxy PGA2 Δ12-PGJ2
Protectin D1 9-iso-PGF3α 19(R) hydroxy PGE2 2,3-dinor-11β-PGF2α
Maresin 1 PGF3α 19(R)-hydroxy PGF1α PGK1
7-HDHA PGE3 15(R),19(R)-hydroxy PGE2 PGK2
10-HDHA PGD3 19(R)-hydroxy PGF2α PGH2
16(17)-EpDPE
D17-6keto-
PGF1α 15(R),19(R)-hydroxy PGF2α 20-hydroxy PGE2
TXB3 15(R), 19(R)-hydroxy PGE1
PGE2-p
benzamidophenylester
11-dehydro TXB3 PGF2α
5(S),15(S) DiHETE,
8(S)
197
5-HEPE 20-hydroxy PGF2α 15(S) DiHETE
8-HEPE 8-iso PGF2α 9-HETE
9-HEPE
8-iso-13,14-dihydro-15-keto
PGF2α LTB4
11-HEPE 8-iso-15keto PGF2α 20-carboxy LTB4
12S-HEPE 2,3-dinor-8-iso-PGF2α
20-hydroxy
leukotriene
15S-HEPE 5-iPF2α-VI 12-epiLTB4
8(9)-EpETE 8-iso-15-keto PGE2 6-trans LTB4
6-keto PGF1α
2,3-dinor
thromboxane
2,3-dinor-6-keto PGF1α TXB2
carboxylic TXA2 LXA4
5(S),6(S)-DiHETE 5(S),6(R)-LXA4
TRXA3 TRXB3
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substantially blunted enrichment in gene expression categories associated with inflammation in
response to icv infusion of amyloid-β peptide relative to wildtype mice fed a diet deprived of n-3
PUFA. These differences in the inflammatory response were not associated with changes in
expression of any genes associated with the production of bioactive lipid mediators, or with
hippocampal concentrations of any of the best-known lipid mediators. A validation study of the
microarray in a separate cohort of animals measuring several genes responsible for driving the
categorical enrichment seems to confirm these results for the wildtype groups fed the n-3 PUFA-
deprived safflower oil or n-3 PUFA-containing fish oil diets, however in this study the fat-1 mice
did not differ from the WTSO mice.
The microarray study was hypothesis generating, seeking genes and gene expression categories
that could be differentially affected by the genotype/diet groups in response to icv infusion of
amyloid-β, and thus had a relatively low sample size (n=3). In contrast, the validation study was
hypothesis testing, and was better powered to identify differences between the groups (n=7-9).
Thus, we conclude that WTFO mice, but not fat-1 mice, have attenuated neuroinflammatory
gene expression response to amyloid-β relative to WTSO mice.
The discrepancy between the microarray and validation studies is difficult to explain. Genotypes
were checked at least twice for all animals in both studies. The same diets were used in both
studies, and the fatty acid compositions of the diets have been checked periodically in our lab
with no major between batch differences observed. Different batches of reagents, including the
amyloid-β 1-40, were used between the studies which could account for some variability,
however, it is not clear why only the fat-1 mice would be affected if this were an important
source of variation. The animals used in each study, while from the same colony, were born
nearly a year apart, and came from a different stock of breeders. Thus, it is possible that some
genetic variation has occurred in the animals, though this did not seem to translate into any
changes in the brain fatty acid phenotype of the fat-1 mice, which closely resembles that of
WTFO mice. There was considerable variability in the expression of all genes used in the
validation study within genotype/diet and surgery groups. Because similar patterns of gene
expression were obtained for the microarray samples when they were analyzed by qPCR, it
seems likely that the n=3 fat-1 mice selected for the microarray happened to have lower than
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Figure 5-5: Validation of a subset of genes driving the enrichment of inflammation-
associated gene expression categories in the microarray.
A) MHC II or H2-Ab1: Histocompatibility 2, Class II antigen A beta1, Major Histocompatibility
Complex II, B) MHC I or H2-K1: Histocompatibility 2, K1m K region, C) Fcgr2b: Fc Receptor
IgG low-affinity 2b. Graphs represent mean +/- standard error of the mean of n=10-11 mice per
group. Different letters denote significant differences by Tukey’s post-hoc test. Reverse axis
shown, lower delta delta Cts indicate a lower doubling time, and thus a higher mRNA
concentration. Major histocompatibility complex (MHC), Wildtype safflower oil-fed mice
(WTSO), wildtype fish oil-fed mice.
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average inflammatory gene expression, whereas the n=11 fat-1 mice used in the validation study
better reflect the true mean response.
There are several possible reasons why the neuroinflammatory responses of the fat-1 and WTFO
mice differed. Though these mice have the same levels of hippocampal eicosanoids, docosanoids
and total and free DHA, they differ in the duration of exposure to n-3 PUFA. The fat-1 mouse,
because it endogenously synthesizes n-3 PUFA, is exposed in utero and throughout
development, while the WTFO mice begin exposure when switched to the fish oil-containing
diet at 3 weeks of age. Fetal programming effects, or variation in the development of the central
nervous system could therefore explain the differences in inflammatory response between these
animals. In addition, dietary fish oil diet contains many other components, such as iodine,
vitamin E and furan fatty acids that may influence the brain neuroinflammatory response 363, 364,
366, as well as tert-butylhydroquinone, an anti-oxidant added to the menhaden oil by the
manufacturer to prevent oxidation that has been shown to have anti-inflammatory and anti-
oxidant properties in a cellular model of neurodegeneration 387. Fat-1 mice also consumed 10%
more linoleic acid via the safflower oil diet than the WTFO mice. It has previously been shown
that rats fed a 10% fat diet containing 27.6% of fatty acids as linoleic acid had a greater increase
in COX-2 gene expression in response to icv lipopolysaccharide than rats consuming a diet
containing 2.3% of linoleic acid as a percent of fatty acids 388. Our 10% safflower oil diet
contained over 70% linoleic acid as a percent of fatty acids, so it is possible that this contributed
to the greater neuroinflammatory gene expression in the WTSO and fat-1 groups.
No differences in the expression of any genes involved in the production of lipid mediators were
observed either between the amyloid-β-infused and non-surgery groups, or between the
diet/genotype groups in this study. Expression of COX-2, prostaglandin E synthase and 5, 12 and
15-LO are increased in post-mortem human brain samples from patients with AD relative to
controls 134, 389-391, so a difference in expression with surgery had been expected. The lack of
effect in this study may be attributed to the acute nature of the icv amyloid-β model, or the
possibility that expression of these enzymes is induced by other pathological features of AD,
such as neuronal death. Several other animal studies have reported a lack of effect of n-3 PUFA
feeding on the expression of enzymes involved in the production of lipid mediators, such as no
change in striatum cPLA2 or COX-2 gene expression with dietary EPA supplementation in two
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studies in Parkinson’s disease models 392, 393, and no change in cPLA2 or COX-2 protein in a
model of NMDA-induced cytotoxicity 394. In addition, in our lab, we identified no effect of
supplementation with DHA or ALA on the expression of 15-LO, cPLA2, iPLA2, PGES, or COX-
2 in the cortex, hippocampus, striatum, brainstem or rest of brain relative to a low n-3 PUFA
control in rats 395. In contrast, a previous study from our lab that examined inflammatory gene
expression using fat-1 and wildtype mice fed the same diets as those described here 24 hours
after an infusion of intracerebroventricular lipopolysaccharide reported attenuated expression of
hippocampal COX-2 mRNA in mice fed the fish oil diet, and of COX-2, cPLA2 and PGES in
fat-1 mice relative to wildtype mice fed the safflower oil diet 103. The type of inflammatory
insult, or the timing of the measurement of neuroinflammation may explain the discrepancies
between these studies.
None of the pro-resolving lipid mediators were detected in any of the genotype/treatment groups
under any of the surgery conditions, including resolvins, protectin and maresin. Limited other
docosanoids and EPA derived eicosanoids were detected. 4-HDHA was higher in the
hippocampi of fat-1 than WTSO mice, however this does not seem to be related to
neuroinflammatory gene expression because it is an auto-oxidative product of DHA that is not
known to have anti-inflammatory activity, and its levels do not change with amyloid-β or control
peptide infusion. Among EPA products, 17(18)-EpETE was elevated in WTFO mice, while its
metabolite 17(18)-diHETE was elevated in both fat-1 and WTFO mice relative to WTSO mice.
Elevations in both of these molecules were reported with aging in the plasma of transgenic
APP/tau mice, while 17(18)-diHETE was increased in the brains of these mice relative to
wildtype mice during the pre-symptomatic stage of the disease 396. 17(18)-EpETE is a
vasodilator 397, and may be anti-inflammatory via PPARγ 398, so it is possible that elevations in
these molecules could be protective in AD. However, their levels were not influenced by icv
infusion of amyloid-β or control peptide in our study, and were not different between the fat-1
and WTFO mice, which suggests that they were not responsible for mediating the different
neuroinflammatory responses in these animals. Numerous ARA derived eicosanoids were
detected, but with the exception of 15-HETE, none differed between any of the groups. There
was a significant genotype/diet x surgery interaction for 15-HETE, however there was no
difference in the post-hoc tests between concentrations in the amyloid-β injected groups of
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animals. It is therefore unlikely that levels of 15-HETE explain the higher inflammatory gene
expression with amyloid-β infusion, or the greater expression in the WTSO mice.
These results indicate that brain lipid mediator concentrations do not change concomitant with
attenuations in brain inflammatory gene expression our model. One previous study in our lab
detected various lipid mediators, including maresin 1 and NPD1, in the mouse brain under basal
conditions 103, however two of our more recent studies (359 and one recently accepted) also failed
to detect pro-resolving lipid mediators either under basal conditions or upon stimulation via
intraperitoneal lipopolysaccharide. Two studies in animal models of stroke detected increases in
brain NPD1 or resolvin D1 with n-3 PUFA interventions, concordant with a lower
neuroinflammatory response 353, 368. These mediators are known to be produced in response to
the ischemia that occurs with CO2, asphyxiation 359, so it is possible that the ischemic conditions
in the stroke models explain the differences between these studies and ours. It is also possible
that mediators were present in our samples, but at concentrations less than our lower detection
limit of 0.005 ng, however our detection limit is below the levels reported by these other
researchers. In addition, it is possible that mediators were produced at other timepoints between
baseline and 10 days post-surgery that were not captured by our measurements.
It is possible that increases in non-esterified DHA or EPA in the fat-1 and WTFO mice were
instead responsible for the differences in the neuroinflammatory response. A previous study in
our lab showed that brain non-esterified DHA decreases the neuroinflammatory response to LPS,
which did not seem to be directly proportional to its conversion to NPD1 103. Although many
studies have reported lower neuroinflammation in various disease models with higher brain n-3
PUFA, very few measured docosanoids and eicosanoids. Thus, it is possible that these effects
were mediated by DHA or EPA directly, rather than via the production of bioactive lipid
mediators.
A limitation of this study is that the icv amyloid-β mouse model recapitulates less of the
symptoms and pathology of human AD than transgenic mouse models that endogenously
produce amyloid-β and hyperphosphorylated tau. The icv model was used because it induces
self-limiting inflammation with little neuronal death 143. In addition, DHA and NPD1 down-
regulate the production of amyloid-β in mouse models and promote neuronal survival 132, 140,
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which could indirectly affect the neuroinflammatory response by decreasing the magnitude of the
insult. The icv model, despite its limitations in pathophysiological relevance, allowed us to focus
specifically on neuroinflammation-modulating effects of n-3 PUFA in this study, without this
potential confounding.
Another limitation of this work is that it only allows us to determine whether anti-
neuroinflammatory effects of increasing brain DHA are associated with changes in docosanoids
and eicosanoids in an AD model, rather than providing evidence of causation. Demonstrating
this association will be a useful first step in this field as no one has yet measured brain
concentrations of eicosanoids or docosanoids following an n-3 PUFA intervention in an AD
model, however future experiments that directly infuse mediators in an AD model, or that block
the activity of enzymes involved in mediator synthesis are needed.
Finally, our study cannot conclude that lipid mediators are unnecessary to mediate the changes in
neuroinflammatory gene expression noted in this study, but only that they do not seem to be
required at concentrations above our lower detection limit at the time points we examined. It is
possible that changes in mediators did occur at pico or attomolar concentrations that were not
detected by our analysis, or that these mediators were present at other time points that were not
we did not examine. Future research using other detection methods, establishing minimal
concentrations of these mediators required for inflammation-modulating effects in the brain, or
examining the production of these mediators at other time points would be useful for clarifying
this point.
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Figure 5-6: Hippocampal docosanoid and EPA eicosanoid concentrations: A) Detectable
docosanoids B) EPA eicosanoids. No other docosanoids or EPA derived eicosanoids tested were
detected in any group, including resolvins, protectin, maresin, or other HDHA or HEPEs. Bars
represent mean +/- standard error of the mean, n=7-9 per group. Different letters denote
significant differences by Tukey’s post-hoc test following identification of a significant main
effect of genotype/diet. There were no main effects of surgery, and no genotype/diet x surgery
interactions. Ab: amyloid-β, Ctrl: control, DHA: docosahexaenoic acid, epDPE: epoxy
docosapentaenoic acid, epETE: epoxy eicosapentaenoic acid, EPA: eicosapentaenoic acid,
HEPE: hydroxyeicosapentaenoic acid, HDHA: hydroxy docosahexaenoic acid, HDPA: hydroxy
docosapentaenoic acid, NS: non-surgery, WTSO: wildtype mice fed the safflower oil diet,
WTFO: wildtype mice fed the fish oil diet.
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Figure 5-7: Hippocampal ARA eicosanoid concentrations: A) Detectable ARA eicosanoids.
No other ARA derived eicosanoids tested were detected in any group. Bars represent mean +/-
standard error of the mean, n=7-9 per group. Different letters denote significant differences by
Tukey’s post-hoc test following identification of a significant genotype/diet x surgery
interaction. There were no other significant main effects or interactions. Ab: amyloid-β, ARA:
arachidonic acid, Ctrl: control, ETE: eicosatetraenoic acid, DHET: dihydroxyeicosatrienoic acid,
HETE: hydroxyeicosapentaenoic acid, NS: non-surgery, PG: prostaglandin, Tx: thromboxane,
WTSO: wildtype mice fed the safflower oil diet, WTFO: wildtype mice fed the fish oil diet.
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5.6 Conclusion
In conclusion, a diet containing fish oil attenuates neuroinflammatory gene expression in
response to amyloid-β 1-40 relative to a safflower diet deprived of n-3 PUFA. The fat-1 mouse
does not exhibit the same attenuation in neuroinflammatory gene expression despite similar
tissue concentrations of DHA, EPA and ARA. The attenuation in inflammatory gene expression
in the fish oil fed mice occurs independent of detectable changes in the concentrations of most
bioactive lipid mediators, most of which were not detected at baseline or following a
neuroinflammatory insult, or expression of the genes involved in their synthesis. Changes in
brain lipid mediator concentrations >0.005 ng do not seem to be necessary for the inflammation-
modulating effects of the fish oil diet.
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Chapter 6: General Discussion
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6.1 Review of Findings and General Discussion
This thesis aimed to explore the modulation of neuroinflammation by brain n-3 PUFA in an AD
model as a way of better understanding the mechanism by which n-3 PUFA may be protective in
AD. We had hypothesized that neuroinflammation would be an important pathological feature of
AD, and that increasing brain levels of n-3 PUFA would decrease the neuroinflammatory
response to amyloid-β.
While elevations in microglia are often cited as rationale for studies investigating the
neuroinflammatory hypothesis of AD, no systematic review has ever been conducted on this
topic. In our systematic review in Chapter 2, we show that microglia may appear to be elevated
in AD or not depending on the markers used to measure them. Markers associated with
microglial activation were consistently up-regulated, while general microglial markers were less
consistently increased. This suggests that microglial activation, but not necessarily an absolute
increase in cell number, is a more consistent neuropathological feature of the AD brain.
In Chapter 3, we showed that that iba1-labeled microglia cell counts increase in response to icv
amyloid-β and resolve by about 28 days post-surgery in both our chow-fed C57BL/6 model and
our fat-1 and wildtype mouse model fed safflower or fish oil. Wildtype safflower-fed mice,
which had lower brain DHA concentrations, exhibited a greater increase in these microglial cell
counts at 10 days post-icv. They also had greater numbers of degenerating neurons and a more
activated microglial morphology. The analysis of astrocyte morphology in Chapter 4 showed
that these mice also have a blunted astrocyte activation response to amyloid-β relative to fish oil-
fed animals, though in this case they were not different from fat-1 mice. Wildtype mice fed the
n-3 PUFA deplete safflower oil diet had greater increases in inflammatory gene expression
relative to the fish oil-fed mice in response to icv amyloid-β in Chapter 5, though it is unclear
whether they differed from the fat-1 mice. Interestingly, these changes in inflammatory gene
expression were not associated with changes in the concentrations of bioactive lipid mediators or
any genes involved in their synthesis.
This work is novel in several respects. Our systematic review in Chapter 2 is the first such
review of an inflammatory marker in AD. For Chapters 3-5, while others have examined the
effect of increasing brain n-3 PUFA on neuroinflammatory markers (summarized in Table 1),
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our work is the most thorough to date, measuring inflammation via cell counts, gene expression,
and cell morphology. We are also the first to measure inflammatory markers as a time course to
examine the concept of resolution in an AD-related model. Importantly, we are the first to relate
neuroinflammation to brain levels of most eicosanoids and docosanoids in an AD-related model,
and the first to determine whether the levels of these molecules can be modulated by changing
brain fatty acid composition. This is a key first test of the hypothesis that n-3 PUFA are
protective in AD via changing brain levels of bioactive lipid mediators.
Our systematic review showed that iba1 is not consistently elevated in the brains of patients with
AD, going up in only about half the studies while staying the same or decreasing relative to
control in the other half. Despite this, we used iba1 to measure microglia in our experimental
chapters, and contrary to our review, report increases in iba1-positive cells in response to
amyloid-β. The experiments described in Chapters 3-5 were begun years before the systematic
review was completed, and thus we could not benefit from the opportunity to select our marker
based on the totality of the human post-mortem evidence. Iba1 is a very common marker of
microglia used in the literature, and increases in iba1 have been reported in many different AD
models using a variety of measurement techniques399-403, including cell counts as we used
here404-407. It is possible then, that iba1-positive microglia increase in animal models of AD but
not in human subjects. Another possible explanation for this discrepancy is that iba1 may be
more sensitive to confounding by factors such as medication use, plaque burden or psychiatric
history that were poorly controlled for in the post-mortem human studies included in the
systematic review, which could mask a true increase that was detected in our animal study.
The fat-1 mouse did not exhibit a consistent neuroinflammatory response to amyloid-β between
the three studies. In Chapter 3, fat-1 mice had the lowest increase in microglia and were similar
to fish oil-fed animals for neuronal death, and morphology measures. In Chapters 4 and 5
however, the responses of the fat-1 mice were more similar to the safflower oil-fed animals, with
respect to GFAP expression, area per astrocyte, and inflammatory gene expression. It is possible,
as discussed in Chapter 4, that these differences in response can be explained by other variables
beyond brain fatty acids that affect aspects of the neuroinflammatory response differently in the
fat-1 mice than in the wildtype mice fed fish oil. This could include other dietary components
such as linoleic acid, iodine, furan fatty acids or anti-oxidants, or differences in the timing of
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exposure to n-3 PUFA. Another possibility is that biological or methodological variation is
responsible for these different effects. The three studies were conducted over a period of
approximately 4 years. The breeding mice that produced the fat-1 and wildtype mice in these
studies were replaced numerous times over those years, as were most reagents. Variations in
technique likely also occurred with practice. The differences in the neuroinflammatory response
of the fat-1 mice between the studies appears to be a true effect, rather than the product of
methodological variation, because the relative responses of the safflower and fish oil-fed animals
were similar between the studies, and methodological variation would be unlikely to affect just
one of the three genotype/diet groups.
Overall, this thesis has shown that microglial markers associated with activation are consistently
elevated in the brains of patients with AD. Increasing brain n-3 PUFA decreases microglial
activation in response to amyloid-β, and also influences astrocyte morphology. Fish oil feeding
produces more pronounced effects than the fat-1 transgene despite similar brain fatty acid
compositions, including greater decreases in neurodegeneration, greater increases in astrocyte
activation, and attenuation of inflammatory gene expression relative to wildtype safflower oil-fed
mice. Detectable levels of lipid mediators, such as resolvins, protectin, or maresin do not seem to
be required for these effects, which suggests that different mechanisms may be involved in
controlling the resolution of inflammation in the brain than in the periphery.
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6.2 Strengths
A key strength of this work is the use of both the fat-1 mouse and fish oil feeding to raise brain
n-3 PUFA. A challenge of nutrition experiments is that increasing the levels of any one dietary
component requires a proportional decrease in another component if energy intakes are to remain
unchanged. For example, the fish oil dietary intervention used in this thesis is both an
intervention of increased long chain n-3 PUFA, and of decreased linoleic acid, because 2% of the
safflower oil was replaced with fish oil. In addition, unless purified fatty acids are used, which
are less relevant to understanding human diets, increasing dietary long chain n-3 PUFA usually
involves adding fish oil, which includes other components, such as iodine, vitamin D, anti-
oxidants, and furan fatty acids, that may have independent effects on neuroinflammation. These
potential confounders limit the ability of a fish oil feeding study to draw conclusions regarding
n-3 PUFA specifically. The fat-1 mouse removes these potential confounders because the mice
can consume the same diet as the wildtype safflower fed mice, yet attain the same brain levels of
n-3 PUFA as the fish oil fed animals. Meanwhile, the fish oil-fed mice are also important to the
studies because they demonstrate that the tissue levels of n-3 PUFA attained by the fat-1 mice
are physiologically possible, and because the dietary intervention is more relevant to exploring
mechanisms underlying human diets than the transgenic model. Different responses to amyloid-β
were identified for fat-1 and fish oil-fed mice for several neuroinflammatory markers examined
in these studies, which highlights the importance of including both groups.
Another strength of these studies is the use of the control peptide to account for the
neuroinflammatory effects of the icv surgery alone. While the 33-gauge needles used in the icv
studies is extremely small (0.21mm diameter), inserting one into the ventricle almost certainly
causes damage that would elicit an immune response. In addition, the incision on the scalp and
drilling of the skull also likely caused peripheral inflammation, which is known to influence the
brain immune response. For these reasons, the control peptide was vital for ensuring the reported
neuroinflammatory effects are in response to amyloid-β, and are therefore relevant to AD.
The use of multiple experimental methods to measure neuroinflammation and the inclusion of a
time course is another important strength of this work. Inflammation is a complex process, which
involves the production of many cytokines, chemokines and lipid mediators, and the activation
of various cell types throughout the initiation, peak and resolution of the immune response.
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Measuring microglia and astrocyte proliferation over time allowed us to see how the
neuroinflammatory response was influenced by brain n-3 PUFA composition, which could have
included a lower peak of activation, or a shift in the timing of the immune response that could
not have been captured by a single static measurement. Measuring neuroinflammation using cell
counts, cell morphology, inflammatory gene expression and lipid mediators also allowed us to
see if changing brain n-3 PUFA composition affected specific aspects of the neuroimmune
response. Because we included all these measures, we were able to see that increasing brain n-3
PUFA alone via the fat-1 transgene affects microglia, and to a lesser extent astrocyte, activation,
but not does not affect the expression of pro-inflammatory genes or lipid mediators, which
provides a much more nuanced mechanistic understanding than we could have obtained by
measuring just one or two inflammatory markers as was done in previous studies.
6.3 Limitations and Future Directions
One limitation in our systematic review is that markers for microglia, and no other inflammation-
associated factors such as astrocytes, cytokines, chemokines or lipid mediators, were included.
This was because the large body of post-mortem literature in this field (nearly 200 papers
identified for astrocytes alone) made including all inflammatory markers unfeasible. Microglia
are considered the initiators of inflammation in the brain, so the increases in activation-
associated markers in AD reported in our systematic review likely mean that other inflammatory
markers are elevated in AD as well, however this cannot be determined from the current work. In
the future, systematic reviews of these other neuroinflammatory markers in the brains of patients
with AD should be completed to determine whether or not this is the case.
While the experiments described in Chapter 3 showed that higher brain n-3 PUFA, particularly
from dietary fish oil, is associated with decreases in neuroinflammatory markers and
modulations in glial cell morphology in response to amyloid-β infusion, they did not establish
whether these changes would be neutral, harmful or beneficial in AD because no measures of
cognition or memory were included. We did, however, find lower levels of neuronal death with
higher brain n-3 PUFA, and other studies have shown that interventions that increase brain DHA
improve cognitive measures in AD models113. It seems likely, therefore, that the higher brain n-3
PUFA levels in the fat-1 and fish oil-fed animals would have had protective or neutral effects on
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cognition in in our studies, however future experiments that include a measure of cognition are
necessary to prove this point.
A limitation of Chapters 3 and 4 are that they compared amyloid-β-injected mice to non-
surgery mice, rather than to control peptide-injected animals. The time-course presented
Chapter 3 included over 100 animals between the three groups. Adding a control peptide-
injected arm would have required double this number, which was practically and ethically
challenging. The brain samples used in Chapter 4 were obtained from the same study, so these
also did not include a control peptide-injected group. Control peptide was used in the astrocyte
and microglia counts in Figure 3-1 that were used to establish the icv amyloid-β model, and in
the gene expression validation study in Chapter 5. A good signal to background ratio was found
between amyloid-β and control peptide-injected animals in both experiments, which justified our
not including the control arm in the other studies. It is still possible, however, that surgery
influenced some of the neuroinflammatory outcomes in this thesis independent of amyloid-β
injections. Future experiments using a control peptide-injected group would be useful,
particularly for the astrocyte and microglial morphology measurements, as these were never
measured in control peptide-injected animals.
Another limitation to this work is that the lipid mediator measurements in Chapter 5 only show
modulation of neuroinflammation by PUFA is not associated with changes in the concentrations
of lipid mediators at 10 days post-icv, rather than demonstrating causation. This could be
addressed in the future by repeating the icv amyloid-β infusions in the same groups of animals,
and administering inhibitors of COX-2 or 12/15-LO following surgery until brain collection to
determine whether the differences in inflammation between the groups would persist. This
experiment could also be done in transgenic AD mice fed DHA or n-3 PUFA deplete diets with
or without COX-2 or 12/15-LO inhibitors to see whether this would prevent the attenuations in
neuronal death, plaque deposition and cognitive decline that have been described in other
studies113.
Another important future experiment would be to directly examine the effects of lipid mediators
derived from n-3 PUFA on neuroinflammation in AD. This could be accomplished by
implanting an osmotic pump following icv infusion of amyloid-β, and administering resolving
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D1, maresin or NPD1 over days following the surgery and seeing if and how neuroinflammation
and activation of neuroimmune cells is affected, as our lab has done previously in an LPS
model103. Fluorescent beads could be administered to determine the phagocytic capacity of the
microglia in vivo. Experiments such as these are important for determining whether pro-
resolving lipid mediators are protective in AD.
The time-course experiments in Chapter 3 were intended to examine the effect of brain n-3
PUFA composition on resolution in an AD model. No differences in the time for astrocyte or
microglia counts to return to baseline were detected in any of the groups. While it is entirely
possible that there truly are no differences in resolution between the groups, the time points
selected for the study did not sufficiently capture the gradual increases and decreases in cell
counts that likely occurred in vivo, which prevented our being able to fit curves to the data and
calculate resolution indices as are commonly used for studies in the periphery408. One of the
challenges of conducting these types of experiments in the brain is that different mice have to be
used for each time point, whereas models in the periphery, such as peritonitis, allow the serial
measurement of fluid to build smoother curves. Future experiments using PET imaging, which
would allow microglia activation to be measured over time in vivo, would provide much more
conclusive results about the effect of brain n-3 PUFA composition on resolution in the brain or in
an icv amyloid-β model.
Another important limitation to the interpretation of this thesis is that the analysis for the lipid
mediators in Chapter 5 is only completed for half of the samples collected. Doubling the
sample size could reveal differences between the groups for some eicosanoids and docosanoids
that did not reach significance in this preliminary analysis. The main finding of this chapter,
however, is that lipid mediators, including resolvins, NPD1 and maresin were not detected in the
brain of any of the mice. This is unlikely to change with the addition of more samples.
6.4 Significance
This thesis will contribute to the understanding of neuroinflammation in AD, and how it is
modified by brain n-3 PUFA. An estimated 50 million people suffer with dementia world-wide,
of which the majority have AD409. The estimated cost of dementia was 818 billion dollars in
2015, which amounts to 1.09% of the global GDP. This enormous cost is dwarfed by comparison
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with the social costs to sufferers and their loved ones. If n-3 PUFAs may be protective in AD,
perhaps by delaying onset or progression, they could have a substantial impact on the global
disease burden87. Establishing a biologically plausible mechanism is essential for justifying
large-scale clinical trials, and for determining whether a cause and effect relationship exists
between an exposure and a disease outcome, particularly in fields that rely heavily on
epidemiology410, 411. This is the case with nutrition and AD. Because AD primarily occurs in
adults over the age of 65, and because protective effects of diet may be the result of long
exposures throughout the lifecycle (or even during critical periods such as childhood, infancy or
in utero!), clinical trials of diet and prevention of AD are extremely challenging and costly.
While nothing can replace the importance of well-conducted randomized controlled trials,
dietary recommendations must rely on the combination of observational data, animal data and
biological plausibility in their absence. To this end, the research described in Chapters 3, 4 and
5 contribute to the body of evidence that could be used for planning future human interventional
studies, or to the development of dietary guidelines for the prevention of dementia. Chapter 5 in
particular suggests that the resolution of inflammation in the brain may be mediated by different
mechanisms than in the periphery, as no specialized pro-resolving lipid mediators were detected
in any group despite differences in brain fatty acid concentration and neuroinflammation. This
will be important for future studies on n-3 PUFA and/or inflammation in the brain in AD.
Understanding mechanism is also vitally important for selecting the most appropriate
intervention in randomized controlled trials to maximize safety and efficacy. For example, the
ADAPT trial used celecoxib as one of its intervention drugs, which is a selective COX-2
inhibitor81. Selective inhibition of COX-2 prevents the production of prostaglandin D2, which is
necessary for programming the resolution of inflammation412. Celecoxib treatment may therefore
have resulted in a low-grade, un-resolving inflammation in the brain rather than its desired
inflammation-lowering effect. Better understanding of the mechanisms by which fatty acids and
their mediators influence inflammation and its resolution in an AD model could help to develop
or select anti-inflammatory interventions that are not, as they have been termed, “resolution
toxic” 412, 413. Aspirin, which blocks PGE2 synthesis but stimulates the production of a class of
aspirin-triggered resolvins and lipoxins, may be a better choice for future inflammation-
modulating interventions in AD.
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To our knowledge, Chapter 2 is the first systematic review ever conducted of a
neuroinflammatory marker in AD. The neuroinflammatory hypothesis is influential in the AD
field, and has even led to large-scale clinical trials. Systematic reviews are critical for ensuring
that hypotheses are supported by the totality of the evidence, and are not influenced by cherry-
picking or other biases in the literature. We anticipate that our findings will be important for
directing future work on microglia in AD towards activation, and for helping other researchers
choose appropriate microglial markers in their experiments. We also identify several important
potential confounders that could contribute to variability between studies on inflammatory
markers in post-mortem human brain samples, such as the use of NSAID use prior to death and
degree of AD neuropathology, which may also help in designing future experiments.
6.5 Conclusions
Following from the three hypotheses outlined in Section 1.52, this thesis has shown that:
1) Elevations in microglial markers, particularly those associated with activation, are a
neuropathological feature of AD
2) Raising brain n-3 PUFA, through either a dietary or a transgenic approach, reduces the
neuroinflammatory response to amyloid-β. This includes attenuating the increase in iba1-positive
microglia, lowering neuronal death, and for fish oil-fed mice, lowering the expression of
inflammation-associated genes relative to animals with low brain n-3 PUFA. Microglia in mice
with high brain n-3 PUFA had a less activated morphology, while in fish oil-fed animals,
astrocytes had a more activated morphology.
3) Contrary to our Hypothesis 3, these neuroinflammatory modifications were not associated
with increases in pro-resolving lipid mediators or decreases in pro-inflammatory lipid mediators
Together, these experiments show that brain fatty acid composition can modulate the
neuroinflammatory response to amyloid-β. This supports the notion that the protective
relationship between DHA consumption and AD that has been observed in epidemiology and
animal models could be explained at least in part by modulation of neuroinflammation in AD.
217
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7 Appendices
6.6 Appendix 1: Summary of microglial marker functions and
expression
Marker Molecule Function M1 or M2 Phenotype Expression on other cell types
HLA-DR (MHC
II)147, 193
٠ Antigen recognition and
display, activation of the adaptive
immune response M1 and M2b
All antigen presenting cells
(macrophages, B cells, dendritic
cells), activated T cells
Iba1190-193
٠Binds actin
٠Participates in membrane
reorganization and phagocytosis
٠Labels all microglia
regardless of phenotype
(resting or activated)
٠Some increase in expression
with activation Monocyte lineage cells
CD68147, 193
Lysosome marker, upregulated
with phagocytosis M2
Monocyte lineage cells,
including perivascular or
infiltrating macrophages
CD11b147, 148, 193, 223
٠Forms part of complement
receptor 3
٠Monocyte activation, adhesion,
and migration
٠Involved in recognition and
phagocytosis of amyloid-β
٠Labels all microglia
regardless of phenotype
(resting or activated)
٠Some increase in expression
with activation
Neutrophils, natural killer cells,
macrophages
CD45225, 414
٠Transmembrane protein,
receptor-linked protein tyrosine
phosphatase ٠
Involved in signal transduction
and leukocyte activation
٠Labels all microglia
regardless of phenotype
(resting or activated)
٠Some increase in expression
with activation
Most nucleated hematopoetic
lineage cells, including T cell
and macrophages
Ferritin + cells229,
230, 415
٠Generation of free radicals to
perpetuate the immune response M1
Astrocytes, macrophages, all
cells. L-ferritin upregulated
with activation in microglia
CD33199, 203
٠Cell adhesion, migration
٠May be involved with amyloid-β
phagocytosis in the brain M2
Neurons and myeloid lineage
cells, including macrophages,
neutrophils
TREM2194, 238
٠Mediates TLR4 signalling
٠May contribute to phagocytosis,
particularly of degenerating
neurons Thought to be M2
Myeloid lineage cells,
including macropahges,
neutrophils
CD11c416
٠Transmembrane cell surface
integrin, acts as a receptor to
various ligands
٠ Unclear, CD11+ cells
express both M1 and M2
markers
Monocytes, macrophages,
dendritic cells, neutrophils,
259
٠Involved in innate immune
activation
some B cells and activated T
cells
IL-1α expressing
microglia417
٠ Stimulates immune cell
activation, proliferation
٠Helps stimulate the production
of other pro-inflammatory
cytokines, such as TNF-α and
IFN-γ M1
This analysis is intended to be
restricted to IL-1α+ microglia,
however infiltrating or
perivascular macrophages can
be difficult to distinguish from
parenchymal microglia
RCA-1247, 418 ٠Cell surface lectin
٠ Unclear, appears to bind
both ramified and amoeboid
microglia
RCA-1 stains blood vessels,
which suggests it is expressed
by a variety of cell types.
Within the CNS, microglia
appeasr to be the only resident
RCA-1+ cells
TSPO419, 420
٠Involved in substrate transport
to the mitochondria
Expressed by activated
microglia, possibly more in
M1, though not known to
differentiate between M1 and
M2 in vivo
Expressed on mitochondria of
many cell types, highly
expressed by mitochondria and
macrophages
CD163252
٠Involved in phagocytosis,
hemoglobin scavenging M2
Monocyte lineage cells,
including perivascular or
infiltrating macrophages
Appendix 1: Cluster of differentiation (CD), Human leukocyte antigen (HLA), Interleukin (IL),
Interferon (IFN), Ionized calcium-binding adaptor molecule (Iba), Major histocompatibility
complex (MHC), Ricinus communis agglutinin, Tumor necrosis factor (TNF), Triggering
receptor expressed on myeloid cells (TREM)
260
6.7 Appendix 2: Chapter 2: Full search for Embase – other database
searches used similar terms
Results of Search October 14th 2015 EMBASE full (kw + exp) – 6699 references in restricted, 14471 in full
search
Embase with restrictions October 14th – removes conference abstracts, editorials, conference proceedings and
reviews in the ovid search
1. Alzheimer disease/
2. ((((Alzheimer* or ase or mild) adj2 cognitive adj2 impairment) or cognitive) adj2 decline).ti,ab,kw.
3. 1 or 2
4. (Brain* or hippocamp* or encephalon or Blood Brain Barrier or hemato-encephalic barriers or barriers brain-
blood or hemato encephalic barrier or barriers hemato-encephalic or barrier hemato-encephalic or hemato-
encephalic barrier or truncus cerebrus or truncus cerebri or cerebri truncus or brainstems or cerebrus truncus or
Mesencephalon or mesencephalon or mesencephalons or midbrains or midbrain or Cerebral Peduncle or Cerebral
Crus or Substantia Nigra or nigras substantia or nigra substantia or substantia nigras or Pars Compacta or Pars
Reticulata or Tegmentum Mesencephali or midbrain trigeminal nucleus or nucleus peripeduncular or annulari
nucleus or nervi trochlearis nucleus or midbrain tegmentum or mesencephalus tegmentum or tegmental nucleus
ventral or mesencephalic tegmentums or midbrain tegmentums or trigeminal nucleus mesencephalic or tegmentums
midbrain or trochlearis nucleus nervi or nucleus annularis or trigeminal nucleus midbrain or nucleus annular or
mesencephali tegmentum or darkshevichs nucleus or tegmentums mesencephalic or ventral tegmental nucleus or
mesencephalic trigeminal nucleus or nervi trochleari nucleus or nucleus darkshevich's or darkschewitsch nucleus or
tegmentum of midbrain or nucleus annulari or cajal interstitial nucleus or mesencephalic tegmentum or nuclei
accessory oculomotor or trochlear nucleus or annularis nucleus or nucleus mesencephalic trigeminal or nucleus of
darkschewitschor peripeduncular nucleus or oculomotor nuclei accessory or tegmentum midbrain or tegmentum
mesencephali or nucleus nervi trochlearis or darkshevich nucleus or nucleus tractus mesencephalici nervi trigemini
or interstitial nucleus of cajal or Cerebral Aqueduct or ducts mesencephalic or mesencephalic ducts or aqueduct
mesencephalic or sylvian aqueducts or duct mesencephalic or sylvius aqueduct or cerebrus aqueductus or
aqueductus cerebrus or cerebral aqueduct or aqueduct sylvian or aqueduct of sylvius or mesencephalic duct or
cerebral aqueducts or aqueducts sylvian or aqueduct cerebral or sylvian aqueduct or aqueductus cerebri or aqueducts
mesencephalic or cerebri aqueductus or mesencephalic aqueduct or Midbrain Reticular Formation or
Pedunculopontine Tegmental Nucleus or nucleus tegmentalis pedunculopontinus or nucleus pedunculopontine
tegmental or tegmental nucleus pedunculopontine or pedunculopontine tegmental nucleus or Oculomotor Nuclear
Complex or Edinger-Westphal Nucleus or Periaqueductal Grey or greys central periaqueductal or griseum centrales
or central grey substance of midbrain or periaqueductal greys central or grey matter periaqueductal or grey central
periaqueductal or substantia grisea centralis or periaqueductal grey matter or central periaqueductal grey or grisea
centralis substantia or periaqueductal grey or centrale mesencephali griseumor centrale mesencephalus griseum or
centrale griseum or grey matters periaqueductalor centrales griseum or periaqueductal grey central or substantia
grisea centralis mesencephali or mesencephalus griseum central or midbrain central grey or central grey
mesencephalic or central periaqueductal greys or central grey midbrain or griseum centrale mesencephali or Raphe
Nuclei or nucleus incertus or nucleus superior central ornuclei raphe ornucleus interfascicular or superior central
nucleus or raphe nuclei or interfascicular nucleus or raphe nucleus or incertus nucleus or central nucleus superior or
rostral linear nucleus of the raphe or caudal linear nucleus of the raphe or rostral linear nucleus of raphe or nucleus
261
rapheor Dorsal Raphe Nucleus or Interpeduncular Nucleus or Midbrain Raphe Nuclei or Red Nucleus or nucleus
ruber or red nucleus or nucleus red or Ventral Tegmental Area or tegmentalis ventralis area or tegmentalis ventrali
area or area tegmentalis ventralis or ventral tegmental area of tsai or ventral tegmental area or tegmental area ventral
or area tegmentalis ventrali or Locus Coeruleus or coeruleus complex locus or complices locus coeruleus or locus
caeruleus or complex locus ceruleus or complices locus ceruleus or coeruleus complices locus or ceruleus complex
locus or locus ceruleus complex or complex locus coeruleus or locus ceruleus complices or locus ceruleus or nucleus
pigmentosus pontis or locus coeruleus complices or pontis nucleus pigmentosus or ceruleus complices locus or locus
coeruleus or locus coeruleus complex or Tectum Mesencephali or corpora quadrigemina or inferior colliculus
commissures or colliculus commissures superior or colliculus commissures inferior or quadrigeminal plates or
superior colliculus commissure or plate quadrigeminal or commissure of superior colliculus or quadrigemina
corpora or commissure of inferior colliculus or lamina quadrigemina or inferior colliculus commissure or colliculus
commissure inferior or quadrigeminal plate or tectum mesencephalus or mesencephalus tectum or plates
quadrigeminal or quadrigemina lamina or colliculus commissure superior or Inferior Colliculi or colliculi inferior or
inferior colliculi or inferiors colliculus or posterior colliculus or brachial nucleus of the inferior colliculus or caudal
colliculus or colliculus inferiors or colliculus caudal or inferior colliculus or colliculus posterior or colliculus inferior
or Subcommissural Organ or subcommissural organs or subcommissural organ or organs subcommissural or organ
subcommissural or Superior Colliculi or mammalian optic lobesor optic lobe mammalian or optic tectums or
superior colliculi or optic tectum or anterior colliculus or colliculus superior or human optic lobes or superior
colliculus or optic lobes human or optic lobes mammalian or optic lobe human or colliculi superior or tectum optic
or tectums optic or mammalian optic lobe or human optic lobe or colliculus anterior or Reticular Formation or
formations reticular or reticular formation or reticular formations or formation reticular or edunculopontine
Tegmental Nucleus or nucleus tegmentalis pedunculopontinus or nucleus pedunculopontine tegmental or tegmental
nucleus pedunculopontine or pedunculopontine tegmental nucleus or Respiratory Center or centers respiratory or
respiratory centers or center respiratory or respiratory center or hombencephalon or hind brains or brains hind or
rhombencephalons or hindbrain or hindbrains or brain hind or rhombencephalon or hind brain or Medulla Oblongata
or medulla oblongata or nucleus ambiguous or arcuate nucleus-1 or accessory cuneate nucleus or nucleus external
cuneate or cuneate nucleus accessory or nucleus ambiguous or medulla oblongatas or arcuate nucleus of the medulla
or cuneate nucleus lateralor nucleus lateral cuneate or ambiguous nucleus or cuneate nucleus external or arcuate
nucleus 1 or external cuneate nucleus or ambiguus nucleus or arcuate nucleus-1s or lateral cuneate nucleus or Area
Postrema or area postremas or trigger zone chemoreceptor or chemoreceptor trigger zone or chemoreceptor trigger
zones or trigger zones chemoreceptor or zone chemoreceptor trigger or postrema area or zones chemoreceptor
trigger or area postrema or Olivary Nucleus ornucleus basalis olivary or nucleus olivary or basalis olivary nucleus or
nucleus olivary basal or olivary basal nucleus or basal nucleus olivary or olivary nucleus or Raphe nuclei or nucleus
incertus or nucleus superior central or nuclei raphe or nucleus interfascicular or superior central nucleus or raphe
nuclei or interfascicular nucleus or raphe nucleus or incertus nucleus or central nucleus superior or rostral linear
nucleus of the raphe or caudal linear nucleus of the raphe or rostral linear nucleus of raphe or nucleus raphe or
Nucleus Raphe Obscurus or Nucleus Raphe Pallidus or Solitary Nucleusor solitary nuclear complices or nucleus of
tractus solitaries or complex solitary nuclear or tractus solitarii nuclei or nucleus solitaries or solitarius nucleus
tractus or tractus solitarius nucleus or solitarius nuclei tractus or solitary tract nucleus or nucleus solitary tract or
solitary nuclear complex or tractus solitarius nuclei or nuclear complices solitary or nuclei tractus solitarii or solitary
nucleus ornucleus solitaryor nucleus of the solitary tract or nuclear complex solitary or complices solitary nuclear or
nucleus of solitary tract or nucleus tractus solitaries or Trigeminal Nucleus, Spinal or trigeminal nucleus spinal or
nucleus spinal trigeminal or spinal trigeminal nucleus or Trigeminal Caudal Nucleus or caudal nucleus trigeminal or
nucleus trigeminal caudal or trigeminal caudal nucleus or Metencephalon or Cerebellumor corpus cerebellus or
parencephalons or cerebellus corpus or cerebellum or cerebellums or corpus cerebelli or parencephalon or cerebelli
corpus or Cerebellar Cortex or cerebelli cortex or cortex cerebellus or cerebellar cortex or cortex cerebelli
orcerebellus cortex or cortex cerebellar or Cerebellar Vermis or Purkinje Cells or purkinje cells or cells purkinje or
Cerebellar Nuclei or nucleus dentatus or Cerebellopontine Angle or central nucleus or central nucleus or interposed
262
nucleus anterior or nucleus globosus or medial cerebellar nucleus or emboliformis nucleus or nuclei cerebellar or
intracerebellar nuclei or nucleus fastigii or nucleus fastigial or fastigii nucleus or central nuclei or nuclei central or
deep cerebellar nucleus or intracerebellar nucleus or nucleus fastigial cerebellar or nucleus anterior interposed or
nucleus intracerebellar oranterior interposed nucleus or nucleus anterior interpositus or nucleus medial cerebellar or
nuclei intracerebellar or nucleus dentate or dentate nucleus or interpositus nucleus anterior or globosus nucleus
orcerebellar nucleus deep or nucleus central or nucleus cerebellar or cerebellar nuclei deep or nucleus dentate
cerebellar or anterior interpositus nucleus or cerebellar nucleus medial or cerebellar nuclei or fastigial cerebellar
nucleus or Pons or pons or varolii ponsor pontes or pons varolius or varolius pons or pons varolii or ponte or
Barrington's Nucleusor Cochlear Nucleus or cochlear nucleus or nuclei cochlear or cochlear nuclei or nucleus
cochlear or Kolliker-Fuse Nucleus or Middle Cerebellar Peduncle or Pontine Tegmentum or Abducens Nucleus or
Facial Nucleus or Parabrachial Nucleus or Nucleus Raphe Magnus or Superior Olivary Complex or Trapezoid Body
or Trigeminal Motor Nucleus or Vestibular Nuclei or schwalbes nucleus or nucleus schwalbe or vestibular nuclei or
vestibular nucleus medial or nuclei vestibular or schwalbe's nucleus or nucleus schwalbe's or medial vestibular
nucleus or schwalbe nucleus or nucleus medial vestibular or Vestibular Nucleus, Lateral or deiters nucleus or
deiter's nucleus or nucleus of deiters or lateral vestibular nucleus or nucleus lateral vestibular or vestibularis laterali
nucleus or nucleus vestibularis laterali or vestibular nucleus lateral or vestibularis magnocellulari nucleus or
vestibularis magnocellularis nucleus or deiter nucleus or nucleus vestibularis magnocellularis or nucleus vestibularis
magnocellulari or nucleus deiter or nucleus vestibularis lateralis or vestibularis lateralis nucleus or nucleus deiter's
or Tectospinal Fibers or Trigeminal Nuclei or trigeminal nucleus or trigeminal nuclear complices or trigeminal
nuclear complex or nuclei trigeminal or trigeminal nuclei or nucleus trigeminal or nuclear complices trigeminal or
nuclear complex trigeminal or Grey Matter or grey matter or grey matters cerebellar or grey matter cerebellar or
matters grey or matter cerebellar grey or grey matter cerebellar or cerebellar grey matters or grey matters or matter
cerebellar grey or cerebellar grey matter or matters grey or cerebellar grey matters or matters cerebellar grey or grey
matters cerebellar or matters cerebellar grey or grey matter or cerebellar grey matter or matter grey or matter grey or
White Matter or white matter cerebellar or matter cerebellar white or matter white or matters cerebellar white or
white matters cerebellar or cerebellar white matters or cerebellar white matter or matters white or white matter
orwhite matters or Cerebral Ventricles or cerebral ventricle or cerebral ventriclesor monro foramen or ventricles
cerebral or foramen of monro or cerebral ventricular system or ventricle cerebral or Choroid Plexus or choroideus
plexus or plexus choroideusor choroid plexus or chorioid plexus or plexus chorioid or plexus choroid or Ependyma
or ependymal or ependymas or Fourth Ventricle or ventricolo quarto or ventricles fourth or ventricle fourth or 4th
ventricle or quarto ventricolos or ventricle 4th or ventricles 4th or fourth ventricle or ventricolos quarto or fourth
ventricles or 4th ventricles or quarto ventricolo or Lateral Ventricles or lateral ventricle orsubventricular zones or
lateral ventricles or ventricle lateral or zone subventricular or ventricles lateral or subventricular zone or zones
subventricular or Septum Pellucidum or septum supracommissural or pelusidum septum or septum pellucidum or
lucidums septum or supracommissural septum or pellucidum septum or septum pelusidums or septum pelusidum or
pelusidums septum or septum lucidums or supracommissural septums or septums supracommissural or lucidum
septum or septum lucidum or Third Ventricle or 3rd ventricle or ventricles third or ventricles 3rd or third ventricle or
ventricle 3rd or 3rd ventricles or third ventricles or ventricle third or Limbic System or limbic system or system
limbic or systems limbic or limbic systems or Amygdala or amygdaloid bodies or corpus amygdaloideums or
nucleus intercalated amygdaloid or corpus amygdaloideum or amygdaloid body or complex amygdaloid nuclear or
amygdaloid nuclear complices or amygdaloid nucleusor intercalata massa or amygdaloideums corpus or intercalatas
massa or amygdaloid nucleus intercalated or nuclear complices amygdaloid or archistriatums or amygdala or massa
intercalates or nucleus amygdaloid or amygdaloideum corpus or amygdalae nucleus or nuclear complex amygdaloid
or archistriatum or nucleus amygdalae or amygdaloid nuclear complex or Basolateral Nuclear Complex or Central
Amygdaloid Nucleus or Corticomedial Nuclear Complex or Periamygdaloid Cortex or epithalamus or Habenula or
commissure habenular or habenula complex or habenulas or complices habenula or nucleus habenularis or habenular
commissures or complex habenula or habenula complices or nucleus habenular or nucleus habenulari or
commissures habenular or habenula or habenularums commissura or commissura habenularum or habenularis
263
nucleus or habenular nuclei or commissura habenularums or nuclei habenular or habenulari nucleus or habenular
nucleus or Pineal Gland or pineales corpus or body pineal or glands pineal or pineal glands or pineal body or cerebri
epiphysis or corpus pineales or gland pineal or pineale corpus or bodies pineal or corpus pineale or pineal gland or
pineal bodies or epiphysis cerebri or Hippocampus or hippocampal formation or propers hippocampus or
hippocampus propers or formations hippocampal or horn ammon's or schaffer collateral or ammon horn or
hippocampus or horn ammon or cornu ammonis or hippocampus proper or proper hippocampus or collaterals
schaffer or formation hippocampal or hippocampal formations or subiculum or subiculums or ammon's horn or CA1
Region, Hippocampal or regio superior of hippocampus or field hippocampus ca1 or ca1 stratum radiatum or
stratum radiatum ca1 or hippocampal sector ca1 or hippocampus ca1 field or hippocampus regio superior or ca1
stratum radiatums or sector ca1 hippocampal or ca1 field hippocampus or radiatums ca1 stratum or stratum
radiatums ca1 or ca1 hippocampal sector or ca1 pyramidal cell area or ca1 region hippocampal or ca1 pyramidal cell
layer or ca1 stratum pyramidale or stratum pyramidale ca1 or cornu ammonis 1 area or radiatum ca1 stratum or CA2
Region, Hippocampal or ca2 stratum pyramidale or radiatums ca2 stratum or cornu ammonis 2 area or ca2 field
hippocampus or stratum pyramidale ca2 or stratum radiatum ca2 or ca2 stratum radiatums or radiatum ca2 stratum
or sector ca2 hippocampal or region hippocampal ca2 or ca2 field of hippocampus or stratum radiatums ca2 or ca2
region hippocampal or hippocampal sector ca2 or hippocampal ca2 region or hippocampus ca2 field or ca2
pyramidal cell layer or field hippocampus ca2 or ca2 pyramidal cell area or CA3 Region, Hippocampal or stratum
lucidum ca3 or ca3 stratum lucidum or stratum lucidums ca3 or lucidum ca3 stratum or ca3 region hippocampal or
ca3 pyramidal cell area or hippocampus ca3 field or ca3 hippocampal sector or sector ca3 hippocampal or ca3
stratum radiatum or ca3 stratum lucidums or hippocampal ca3 regions or cornu ammonis 3 area or ca3 field of
hippocampus or radiatum ca3 stratum or field hippocampus ca3 or stratum radiatums ca3 or ca3 pyramidal cell layer
or lucidums ca3 stratum or region hippocampal ca3 or radiatums ca3 stratum or ca3 stratum pyramidale or ca3 field
hippocampus or Dentate Gyrus or ca4 region hippocampal or dentate fascia or cornu ammonis 4 area or hilus gyri
dentate or ca4 field of hippocampal formation or ca4 hippocampal sector or gyrus dentate or sector ca4 hippocampal
or hippocampal ca4 region or area dentata or region hippocampal ca4 or dentata area or field hippocampal ca4 or
gyrus dentatus or hilus of the fascia dentata or hilus of dentate gyrus or dentate gyrus or area dentatas or dentata
fascia or hippocampal sector ca4 or hippocampal ca4 field or ca4 of lorente de no or Mossy Fibers, Hippocampal or
hippocampal mossy fiber or mossy fibers hippocampal).ab,kw,ti.
5. (hippocampal mossy fibers or mossy fiber hippocampal or Fornix, Brain or hippocampal commissure or
hippocampal commissures or commissures dorsal hippocampal or fornix commissures or fornices or brain fimbrias
or fornical commissures or fornical commissure or fornix or hippocampal commissures dorsal or commissures
hippocampal or fornix-fimbria or hippocampal commissure dorsal or fimbria or fornix fimbria or fimbria of
hippocampus or brain fornices or dorsal hippocampal commissure or commissure fornical or commissure dorsal
hippocampal or commissure of fornix or commissures fornical or commissure hippocampal or fornix commissure or
fimbria-fornix or fimbria fornix or fimbria brain or hippocampus fimbrias or hippocampus fimbria or brain fimbria
or Hypothalamus or preoptico-hypothalamic areas or preoptico hypothalamic area or lamina terminalis or
hypothalamus or areas preoptico-hypothalamic or area preoptico-hypothalamic or preoptico-hypothalamic area or
Hypothalamic Area, Lateral or area hypothalamica laterali or hypothalamica laterali area or hypothalami area
lateralis or lateralis area hypothalamica or hypothalamus area lateralis or laterali area hypothalamica or areas lateral
hypothalamic or lateralis hypothalami area or lateral hypothalamic areas or accessory nucleus of the ventral horn or
lateral tuberal nuclei or tuberal nucleus lateral or lateral hypothalamus or area hypothalamica lateralis or
hypothalamus lateral or tuberomammillary nucleus or hypothalamic area lateral or nucleus tuberomammillary or
nuclei lateral tuberal or nucleus lateral hypothalamic or lateralis hypothalamus area or area lateral hypothalamic or
hypothalamic nucleus lateral or area lateralis hypothalamus or nucleus lateral tuberal or Hypothalamus, Anterior or
commissures anterior hypothalamic or anterior hypothalamic decussation of ganser or hypothalamic commissures
anterior or anterior hypothalamic commissures or commissure anterior hypothalamic or periventricular nucleus
anteroventral or nucleus anteroventral periventricular or anterior hypothalamic commissure or hypothalamic
264
commissure anterior or hypothalamus anterior or hypothalamus supraoptic or anteroventral periventricular nucleus
or anterior hypothalamus or supraoptic hypothalamus or Anterior Hypothalamic Nucleus or areas anterior
hypothalamic or hypothalamic area anterior or nucleus anterior hypothalamic or anterior hypothalamic nucleus or
hypothalami nucleus anterior or hypothalamic areas anterior or anterior hypothalami nucleus or anterior
hypothalamic area or area anterior hypothalamic or nucleus anterior hypothalamus or hypothalamus nucleus anterior
or anterior hypothalamic areas or anterior hypothalamus nucleus or nucleus anterior hypothalami or hypothalamic
nucleus anterior or Organum Vasculosum or Paraventricular Hypothalamic Nucleus or hypothalamic paraventricular
nucleus or paraventricular hypothalamic nucleus or nucleus paraventricular hypothalamic or nucleus hypothalamic
paraventricular or nucleus paraventricular or paraventricular nucleus or hypothalamic nucleus paraventricular or
paraventricular nucleus hypothalamic or Preoptic Area or area medial preoptic or preoptic area medial or preoptic
nucleus or nuclei preoptic or lateral preoptic area or preoptic areas lateral or area preoptic or areas medial preoptic or
area lateral preoptic or preoptic areas medial or lateral preoptic areas or preoptica area or nucleus preoptic or medial
preoptic areas or areas lateral preoptic or area preoptica or areas preoptic or preoptic nuclei or medial preoptic area
or preoptic area or preoptic areas or Suprachiasmatic Nucleus or nucleus suprachiasmatic or suprachiasmatic
nucleus or Supraoptic Nucleus or hypothalamus supraoptic nucleus or supraoptic group accessory or accessory
supraoptic groups or supraoptic nucleus of hypothalamus or supraopticus nucleus or groups accessory supraoptic or
nucleus supraoptic or group accessory supraoptic or accessory supraoptic group or nucleus supraopticus or
supraoptic groups accessory or supraoptic nucleus or Hypothalamus, Middle or regions intermediate hypothalamic
or hypothalamic region intermediate or region intermediate hypothalamic or middle hypothalamus or hypothalamus
medial or hypothalamic regions intermediate or intermediate hypothalamic regions or intermediate hypothalamic
region or hypothalamus middle or medial hypothalamus or Arcuate Nucleus of Hypothalamus or nucleus arcuate or
arcuate nucleus or hypothalamus arcuate nucleus or nucleus infundibular or infundibular nucleus or arcuate nucleus
of hypothalamus or Dorsomedial Hypothalamic Nucleus or nucleus arcuate or arcuate nucleus or hypothalamus
arcuate nucleus or nucleus infundibular or infundibular nucleus or arcuate nucleus of hypothalamus or
Hypothalamo-Hypophyseal System or hypothalamic pituitary unit or hypothalamo hypophyseal system or
hypothalamo-hypophyseal system or hypothalamic-pituitary unit or Median Eminence or eminentia medianas or
median eminence or eminences medial or eminence medial or medial eminences or medianas eminentia or eminentia
mediana or mediana eminentia or eminence median or medial eminence or Pituitary Gland or hypophyseal
infundibulum or infundibular hypothalamus or pituitary glands or infundibulum or stalk infundibular or
hypothalamus infundibular or infundibulums or pituitary stalks or pituitary gland or hypophysis or pituitary stalk or
infundibular stem or stalks infundibular or glands pituitary or hypophysis cerebri or hypophyseal stalks or cerebri
hypophysis orstalk hypophyseal or infundibular stalk or infundibular stalks or hypophysis cerebrus or hypophyseal
stalk or Pituitary Gland, Anterior or lobus anteriors or anterior lobe of pituitary or anterior lobus or pituitary pars
distalis or anterior pituitary glands or anteriors lobus or lobus anterior or pituitary gland anterior or
adenohypophyses or pituitary glands anterior or adenohypophysis or pituitary anterior lobe or anterior pituitary
gland or pars distalis of pituitary or Corticotrophs or Gonadotrophs or lh producing cells or lh-secreting cells or fsh
cells or gonadotrophs or lh cell or fsh-secreting cellsor fsh secreting cells or fsh-producing cells or fsh-producing
cell or fsh cell or lh-producing cells or fsh producing cells or lh secreting cells or fsh-secreting cell or gonadotroph
or lh-producing cell or lh-secreting cell or lh cell or Lactotrophs or pituitary prolactin-secreting cells or lactotrophs
or pituitary prolactin cell or prolactin-secreting cell pituitary or prolactin-secreting cells pituitary or lactotroph or
prolactin cell pituitary or prolactin cells pituitary or pituitary prolactin cells or pituitary prolactin-secreting cell or
pituitary prolactin secreting cells or Somatotrophs or gh cell pituitary or somatotrophs or gh cells pituitary or
pituitary growth hormone-secreting cells or pituitary gh cell or pituitary growth hormone secreting cells or pituitary
gh cells or somatotroph or Thyrotrophs or Pituitary Gland, Intermediate or Melanotrophs or Pituitary Gland,
Posterior or lobes neural or posterior pituitary glands or neural lobe or pituitary pars nervosa or infundibular
processes or infundibular process or process infundibular or neurohypophysis or lobe neural or gland posterior
pituitary or pituitary posterior lobe or pars nervosa of pituitary or posterior lobe of pituitary or neural lobes or
nervosus lobus or lobus nervosus or pituitary gland posterior or processes infundibular or Tuber Cinereum or
265
cinereums tuber or cinereum tuber or tuber cinereum or tuber cinereums or Ventromedial Hypothalamic Nucleus or
nucleus ventromedial hypothalamic or hypothalamic nucleus ventromedial or ventromedial hypothalamic nucleus or
Hypothalamus, Posterior or posteriors area hypothalamica or area hypothalamica posterior or mammillary regions or
region mammillary or nucleus posterior periventricular or hypothalamic regions posterior or hypothalamus
posteriors or mammillary region or posterior area hypothalamica or posterior hypothalamic regions or
supramammillary commissures or region posterior hypothalamic or supramammillary commissure or regions
posterior hypothalamic or posterior hypothalamus or commissures supramammillary or premammillary nucleus or
hypothalamic region posterior or posterior hypothalamic region or commissure supramammillary or hypothalamus
posterior or hypothalamica posteriors area or periventricular nucleus posterior or nucleus premammillary or
Mammillary Bodies or mammillary bodies ormammillary body or body mammillary or mamillary bodies or body
mamillary or bodies mamillary or bodies mammillary or mamillary body or Limbic Lobe or Gyrus Cinguli or gyrus
cingular or anterior cingulate gyrus or cingulate gyri posterior or cortex anterior cingulate or posterior cingulate
gyrus or cinguli anteriors gyrus or mesial region superior or gyrus cingulate or cingulate cortex anterior or cingulate
cortex or superior mesial regions or regions cingulate or cortex posterior cingulate or anterior cingulate cortices or
posterior cingulates or cingulate bodies or cingulates anterior or cortices anterior cingulate or posterior cingulate
cortices or mesial regions superior or posterior cingulate cortex or regions posterior cingulate or cingulate posterior
or posterior cingulate region or region posterior cingulate or body cingulate or cortex cingulate or posterior cingulate
regions or cingulate gyrus anterior or cingulate gyrus or cingulate gyrus posterior or cingular gyrus or bodies
cingulate or cingulate area or anterior cingulates or area cingulate or cingulate regions or regions superior mesial or
ingulates posterior or areas cingulate or cingulate cortices anterior or anterior gyrus cinguli or gyri posterior
cingulate or gyrus anterior cingulate or gyrus cinguli anteriors or cinguli anterior gyrus or superior mesial region or
anterior cingulate or gyrus cinguli anterior or cingulate anterior or region cingulate or cingulate areas or
Parahippocampal Gyrus or gyrus parahippocampal or gyri parahippocampal or parahippocampal gyri posterior or
hippocampal gyrus or gyri posterior parahippocampal or posterior parahippocampal gyrus or gyrus
parahippocampalis or parahippocampal gyrus uncus or presubiculums or posterior parahippocampal gyri or gyrus
posterior parahippocampal or parahippocampal gyrus posterior or uncus of parahippocampal gyrus or gyri
hippocampal or parahippocampal gyrus or presubiculum or gyrus hippocampi or uncus parahippocampal gyrus or
gyrus uncus parahippocampal or gyrus hippocampal or parahippocampal gyri or Entorhinal Cortex or area
entorhinali or areas entorhinal or entorhinalis area or entorhinal area or area entorhinal or entorhinal cortices or area
entorhinalis or cortices entorhinal or entorhinali area or olfactory cortices secondary or secondary olfactory cortex or
cortex secondary olfactory or cortices secondary olfactory or entorhinal cortex or olfactory cortex secondary or
secondary olfactory cortices or cortex entorhinal or entorhinal areas or Olfactory Pathways or olfactory pathways or
pathways olfactory or olfactory pathway or pathway olfactory or Perforant Pathway or pathway perforant or
pathways perforant or perforant paths or perforant pathways or perforant pathway or fasciculus perforating or paths
perforant or perforant path or perforating fasciculus or path perforant or Septum of Brain or paraterminal body or
brain septums or brain septum or septum of brain or paraterminal bodies or area septal or bodies paraterminal or
body paraterminal or septal area or region septal or septal region or Septal Nuclei or nucleus of the stria terminalis
or septi lateralis nucleus or septal nuclear complices or nucleus of anterior commissure or terminali nucleus striae or
laterali nucleus septalis or nucleus lateralis septi or nucleus lateralis septus or nuclear complices septal or
septofimbrial nucleus or diagonal band nucleus or nucleus septofimbrial or nucleus septi lateralis or laterali nucleus
septi or nucleus triangular septal or medial septal nucleus or nucleus of diagonal band or nucleus septalis lateralis or
nucleus striae terminali or nuclear complex septal or septum nucleus lateral or lateral septal nucleus or lateralis
nucleus septalis or septal nucleus lateral or septalis laterali nucleus or nuclei septal or anterior commissure nucleus
or septus nucleus lateralis or septi laterali nucleus or nucleus medial septum or nucleus septalis laterali or
triangularis septus nucleus or lateralis nucleus septi or complex septal nuclear or nucleus striae terminalis or nucleus
of stria terminalis or septalis lateralis nucleus or dorsal septal nucleus or nucleus triangularis septus or nucleus
lateral septumor nucleus triangularis septi or nucleus lateral septal or septal nucleus triangular or terminalis nucleus
striae or septi nucleus lateralis or septi nucleus triangularis or triangular septal nucleus or septus nucleus triangularis
266
or nucleus medial septal or complices septal nuclear or lateralis septus nucleus or medial septum nucleus or lateralis
septi nucleus or Substantia Innominata or innominata substantia or substantia innominata or Prosencephalon or
prosencephalon or forebrains or forebrain or Diencephalon or diencephalon or interbrain or interbrains or Optic
Chiasm or chiasmas optic or optic chiasms or decussation optic or chiasma optic or optic chiasm or optic chiasma or
optic decussation or opticums chiasma or optic decussations or chiasma opticum or decussations optic or opticum
chiasma or optic chiasmas or chiasma opticums or chiasms optic or chiasm optic or Optic Tract or Subthalamus or
subthalamus or fasciculus thalamic or field h nucleus or campi forelus nucleus or fasciculus lenticular or field h1
forel's or campi foreli nucleus or enticular fasciculus or forels field h2 or forel field h2 or thalamicus fasciculus or
fasciculus thalamicus or thalamic fasciculus or forelus nucleus campi or nucleus of ansa lenticularis or foreli nucleus
campi or nucleus campi forelus or nucleus of field h or forels field h1 or forel's field h2 or field h1 of forel or forel
field h1 or Entopeduncular Nucleus or Subthalamic Nucleus or nucleus of luys or luys subthalamic nucleus or
corpus luysi or luys body or subthalamic nucleus of luys or subthalamicus nucleus or luys nucleus or nucleus
subthalamic or luysi corpus or body of luys or nucleus subthalamicus or subthalamic nucleus or Zona Incerta or
Thalamus or thalamencephalon or thalamencephalons or thalamus or Thalamic Nuclei or nuclei thalamic or thalamic
nuclei or Anterior Thalamic Nuclei or nucleus anterodorsal thalamic or anterior nuclear group or nucleus
anteromedial thalamic or nucleus anteroventral thalamic or thalamus anterior nucleus or anterior thalamic nucleus or
nucleus anteroventral or anteroventral nucleus or thalamic nucleus anterodorsal or nuclei anterior thalamic or
thalamic nuclei anterior or anteromedial nucleus or anteromedial thalamic nucleusor thalamus anterior or nucleus
anteromedial or anterodorsal nucleus or anterior thalamus or anterior thalamic nuclei or anterodorsal thalamic
nucleus or nucleus anterodorsal or thalamic nucleus anteroventral or Geniculate Bodies or nucleus geniculate or
medial geniculate nucleus or geniculate complex medial or geniculatum mediales corpus or bodies geniculate or
nucleus lateral geniculate or mediales corpus geniculatum or geniculate bodies medial or mediale corpus
geniculatum or geniculate body or geniculatum mediale corpus or geniculate nucleus lateral or geniculate bodies or
geniculate bodies lateral or metathalamus or corpus geniculatum mediale or geniculate body lateral or complex
medial geniculate or nucleus geniculatus lateralis pars dorsalis or geniculate body medial or geniculate complices
medial or geniculate nucleus or complices medial geniculate or medial geniculate body or medial geniculate bodies
or geniculate nucleus medial or Intralaminar Thalamic Nuclei or nucleus paracentrali or centrum medianum or
paracentrali nucleus or centromedian thalamic nucleus or central lateral nucleus or thalamic nucleus parafascicular
or central lateral thalamic nucleus or parafascicular thalamic nucleus or thalamic nucleus intralaminar or nucleus
central dorsal or parafascicular nucleus of the thalamus or centromedian nucleus or intralaminar nuclei rostral or
intralaminar nuclear group or thalamic nucleus centromedian or parafascicularis nucleus or nucleus central lateral or
thalamic nuclei intralaminar or central dorsal thalamic nucleus or interlaminar nuclei of thalamus or rostral
intralaminar nuclei or thalamus nucleus parafascicularis or centrum medianums nucleus or medianum centrum or
thalamic nucleus paracentral or thalamus reticulate nucleus or nucleus paracentral or nucleus central medial or
paracentral thalamic nucleus or median nucleus centre or nuclei intralaminar thalamic or nuclei rostral intralaminar
or central medial nucleus or nucleus centrum medianums or nucleus centre median or medianum nucleus centrum or
nucleus paracentral thalamic or nucleus centromedian thalamic or nucleus parafascicularis thalamus or nucleus
intralaminar thalamic or nucleus centrum medianum or nucleus parafascicularis thalami or parafascicularis thalami
nucleus or parafascicularis thalamus nucleus or reticulate nuclei of thalamus or nucleus parafasciculari or centrum
medianums or centrum medianum nucleus or paracentralis nucleus or lateral nucleus central or parafascicular
nucleus or central medial thalamic nucleus or nucleus centromedian or Lateral Thalamic Nuclei or medial pulvinar
nucleus or Pulvinar or anterior pulvinar nucleus).ab,kw,ti.
6. (anterior pulvinar nucleus or pulvinar nucleus or nucleus anterior pulvinar or lateral pulvinar nucleus or pulvinar
nucleus oral or oral pulvinar nucleus or pulvinar nucleus inferior or pulvinars or pulvinari nucleus or nucleus oral
pulvinar or nucleus pulvinar or pulvinar thalami or nucleus pulvinari or nucleus lateral pulvinar or thalami pulvinar
or nucleus inferior pulvinar or pulvinaris nucleus or nucleus pulvinaris or pulvinar thalamus or inferior pulvinar
nucleus or pulvinar nucleus lateral or Mediodorsal Thalamic Nucleus or medialis dorsali nucleus or medial dorsal
267
thalamic nucleus or mediodorsal nucleus or dorsomedialis thalamus nucleus or nucleus dorsomedial thalamic or
nuclei medial thalamic or dorsali nucleus medialis or nucleus mediodorsal or nucleus dorsomedialis thalamus or
nucleus medialis dorsali or nucleus mediodorsal thalamic or thalami nucleus dorsomedialis or thalamus nucleus
dorsomedialis or thalamic nucleus medial or dorsomedialis thalami nucleus or thalamic nuclei medial or nucleus
medial thalamic or mediodorsal thalamic nucleus or nucleus dorsomedial or nucleus medialis dorsalis or thalamic
nucleus mediodorsal or dorsal medial nucleus or medialis dorsalis nucleus or nucleus dorsomedialis thalami or
medial thalamic nucleus or Midline Thalamic Nuclei or parataenial nucleus or nucleus reunien or rhomboid
nucleusor nucleus subfascular or nuclear group midline or paratenial nucleus or rhomboidal nucleus or rhomboid
thalamic nucleus or nucleus rhomboid thalamic or reuniens nucleus or subfascular nucleus or thalamus nucleus
reuniens or thalami nucleus reuniens or nucleus paraventricular thalamic or reunien nucleus or reuniens thalami
nucleusor paraventricular nucleus of thalamus or paraventricular thalamic nucleus or midline thalamic nucleus or
thalamic nuclei midline or paratenial thalamic nucleus or thalamic nucleus rhomboid or periventricular nuclei of
thalamus or thalamic nucleus reuniens or reuniens thalamus nucleus or thalamus midline nucleus or nucleus
paratenial or thalamus paraventricular nucleus or midline thalamic nuclei or thalamic nucleus subfascular or nucleus
reuniens thalamus or thalamic nucleus paratenial or nucleus reuniens or nucleus rhomboid or thalamic nucleus
paraventricular or midline nuclear group or Posterior Thalamic Nuclei or supergeniculate nucleus or posterior
nuclear complicesor nucleus supergeniculateor posterior thalamic nuclei or suprageniculate thalamic nucleus or
submedial nucleus or limitans nucleus or thalamic nuclei posterior or nucleus limitan or thalamic nucleus
suprageniculate or nucleus submedial or nuclear complices posterior or complices posterior nuclear or posterior
nucleus of thalamus or nucleus limitans or nucleus suprageniculate thalamic or posterior thalamic nucleus or
posterior thalamic nuclear group or posterior nuclear complex or nuclear complex posterior or thalamus posterior
nucleus or Ventral Thalamic Nuclei or posterior nucleus ventral or ventrolateral thalamic nucleus or intermedius
nucleus ventralis or ventral posterior nucleus or ventralis intermedius nucleus or ventrobasal complex or ventralis
posteromediali nucleus or nuclear mass ventral or group ventral nuclear or ventralis posterolateralis nucleus or
nucleus ventral anterior or ventral nuclear groups or laterali nucleus ventralis or nucleus ventral posterolateral or
ventralis posterior nucleus or masses ventral nuclear or nucleus ventralis posterolaterali or ventral lateral nucleus or
nucleus ventralis intermedius or ventral anterior thalamic nucleus or thalamic nucleus ventral or posterolaterali
nucleus ventralis or ventral posteromedial thalamic nucleus or nucleus ventrolateralis thalamus or ventrobasal
complices or nucleus ventralis posteromedialis or nuclei ventral thalamic or nucleus ventrolateralis thalami or mass
ventral nuclearor ventrolateralis thalami nucleus or ventrolateralis thalamus nucleus or posterolateral nucleus ventral
or nuclear group ventral or arcuate nucleus 3 or nucleus ventralis posteriors or ventral posterior thalamic nucleus or
ventral posterior medial nucleus or ventral posteroinferior nucleus or posteroinferior nucleus ventral or posteriors
nucleus ventralis or arcuate nucleus-3 or thalamus nucleus ventrolateralis or nucleus ventralis or posteromediali or
complex ventrobasal or ventral lateral thalamic nucleus or ventral thalamic nuclei or ventral lateral thalamic nuclei
or ventral posteromedial nucleus or posteromedialis nucleus ventralis or ventral anterior nucleus or ventral
posterolateral nucleus or nuclear masses ventral or ventral posterior inferior thalamic nucleus or thalamic nucleus
ventrolateral or ventral nuclear group or thalamus ventrolateral or thalami nucleus ventrolateralis or posteromediali
nucleus ventralis or posterolateralis nucleus ventralis or posterior nucleus ventralis or nucleus ventral posteromedial
or nucleus ventralis laterali or ventral posterolateral thalamic nucleus or nucleus ventral thalamic or ventralis
lateralis nucleus or ventral nuclear mass or ventralis posteriors nucleus or ventralis laterali nucleus or nucleus
ventral posterior or ventral thalamic nucleus or ventrolateral thalamus or nucleus ventralis lateralis or Telencephalon
or telencephalon or endbrain or endbrains or Cerebrum or cerebrum or cerebral hemisphere left or cerebral
hemisphere right or cerebral hemispheres or right cerebral hemisphere or cerebral hemisphere or left cerebral
hemisphere or Basal Ganglia or ganglia basal or nuclei basal or basal ganglia or ganglion basal or basal nuclei or
claustrum or Corpus Striatum or lenticular nucleus or nucleus lentiform or lentiformis nucleus or lentiform nucleus
or corpus striatum or nucleus lenticular or nucleus lentiformis or lentiform nuclei or striatum corpus or nuclei
lentiform or Globus Pallidus or pallidum or paleostriatum or globus pallidus or pallidums or Neostriatum or Caudate
Nucleus or nucleus caudatus or caudate nucleus or caudatus nucleusor nucleus caudate or caudatus or High Vocal
268
Center or Putamen or putamens or nucleus putamens or putamens nucleus or putamen nucleus or nucleus putamen
or putamen or Ventral Striatum or Nucleus Accumbens or nucleus accumbens or accumbens septus nucleus or
accumbens septi nucleusor nucleus accumbens septi or septi nucleus accumbens or accumbens nucleus or septus
nucleus accumbens or nucleus accumbens septus or Olfactory Tubercleor Islands of Calleja or Basal Nucleus of
Meynert or nucleus basalis of meynert or meynert basal nucleus or nucleus basalis magnocellularis or basal nucleus
of meynert or meynert nucleus basalisor Cerebral Cortex or plates cortical or insular cortex or cerebral cortices or
archipalliums or paleocortex or allocortices or periallocortices or plate cortical or cerebri cortex or cortices cerebral
or paleocortices or cortices insular or insular cortices or cortex insular or periallocortex or archipallium or cortical
plates or cortex cerebral or cortex cerebri or reil insula or cortex cerebrus or cortical plate or Frontal Lobe or gyrus
anterior centralor central gyrus anterior or lobe frontalor frontal lobeor cortex frontal or gyrus precentralis or frontal
eye fieldor supplementary eye field or gyrus precentrali or frontali lobusor precentrali gyrus or frontal lobes or
frontal cortex or field supplementary eye or lobes frontal or eye field supplementary or lobus frontali or
supplementary eye fields or frontalis lobus or gyrus precentral or eye fields supplementary or eye fields frontal or
anterior central gyrus or fields frontal eye or lobus frontalis or Motor Cortex or motor area or primary motor cortex
or motor area precentral or strip motor or somatomotor areas or strips motor or motor cortices primary or premotor
areas or motor area secondary or cortex precentral motor or motor area somatic or supplementary motor areas or
area primary motoror area premotor or secondary motor area or motor cortices secondary or area motor or secondary
motor areas or area somatomotor or motor areas or motor cortex secondary or precentral motor areas or cortices
secondary motor or area supplementary motor or motor areas supplementary or area precentral motor or cortices
primary motor or precentral motor cortices or areas somatic motor or area somatic motor or areas motor or motor
cortex precentral or motor areas precentral or motor strips or cortex primary motor or somatomotor area or premotor
area or precentral motor cortex or primary motor area or somatic motor area or motor areas somatic or areas
premotor or areas somatomotor or areas precentral motor or areas supplementary motor or motor cortex primary or
cortex secondary or motor primary motor cortices or motor cortex or motor cortices precentral or motor area
supplementary or cortices precentral motor or somatic motor areas or cortex motor or areas secondary motor or
Prefrontal Cortex or orbital gyrus).ab,kw,ti.
7. (gyrus orbital or sulcus olfactoryor convolutions superior frontal or orbitofrontal cortices lateral or gyrus frontalis
superior or rectal gyrusor cortices ventromedial prefrontal or orbital cortices or cortex orbital or prefrontal cortices
ventromedial or inferiors gyrus frontalis or orbital gyri or orbital area or convolution superior frontal or frontalis
superiors gyrus or inferior frontal gyrus or gyri orbitofrontal or orbitofrontal regions or frontalis inferiors gyrus or
frontal sulcus or prefrontal cortex ventromedial or straight gyrus or cortex lateral orbitofrontal or gyrus frontalis
inferior or sulci olfactory or orbital areas or orbitofrontal gyri or area orbital or orbitofrontal region or cortices
lateral orbitofrontal or lateral orbitofrontal cortex or superior frontal convolution or cortex orbitofrontal or medial
frontal gyrus or gyrus orbitofrontal or gyrus straight or superior frontal gyrus or frontal gyrus medial or
ventromedial prefrontal cortex or gyrus rectal or subcallosal area or olfactory sulcus or prefrontal cortex or superior
frontal convolutions or sulcus frontal or olfactory sulci or region orbitofrontal or superiors gyrus frontalis or superior
gyrus frontalis or gyrus superior frontal ororbitofrontal cortex or frontal gyrus inferior or gyrus frontalis inferiors or
cortex ventromedial prefrontal or marginal gyrus or rectus gyrus or orbital cortex or gyrus medial frontal or orbitali
gyrus or orbitofrontal gyrus or inferior gyrus frontalis or frontal gyrus superior or gyri orbital or areas orbital or
cortex prefrontal or cortices orbital or gyrus rectus or frontalis superior gyrus or lateral orbitofrontal cortices or
orbitofrontal cortices or gyrus frontalis superiors or orbitofrontal cortex lateral or gyrus marginal or Broca Area or
Neocortex or neocortical molecular layer or neocortices cerebral or isocortex or cerebral neocortices or
neopalliumsor corticalis substantiaor multiform layer neocortical or neocortical multiform layer or layer neocortical
molecular or cortices neopallial or neopallial cortex or neocortical internal pyramidal layer or molecular layer
neocortical or neopallial corticesor cortex neopallial or layers neocortical multiform or neocortex cerebralor
molecular layers neocortical or neocortical internal granular layer or neocortical multiform layers or cerebral
neocortex or neocortical external pyramidal layer or neocortical molecular layers or isocortices or external granular
269
layer or substantia corticali or corticali substantia or layer neocortical multiformor multiform layers neocortical or
Occipital Lobe or occipital cortex or cuneus or gyrus annectant or sulcus lunate or gyrus lingual or gyrus occipitalor
calcarine fissures or regions occipital or occipitotemporal gyrus medial or sulcus calcarine or lunate sulcus or
occipital lobe or gyrus medial occipitotemporal or cuneate lobule or region occipital or fissures calcarine or
calcarinus sulcus or sulcus calcarinus or lobe occipital or lobes occipital or occipital region or gyrus lingualis or
occipital gyrus or annectant gyrus or occipital regions or lobules cuneate or occipital sulcus or cortices occipital or
calcarine sulcus or fissure calcarine or lingual gyrus or lobule cuneate or cortex cuneus or linguali gyrus or Visual
Cortex or primary visual cortices or visual cortex primaries or primaries visual cortex or cortices extrastriate or
visual cortices primary or extrastriate cortices or cortex primaries visual or cortex primary visual or striate cortex or
cortices primary visual or visual cortex primary or visual cortex or cortex striate or cortex extrastriate or cortex
visual or extrastriate cortex or primary visual cortex or Olfactory Cortex or Basal Forebrain or Piriform Cortex or
sulcus intraparietal or regions parietal or lobes parietal or paracentral lobules posterior or gyrus supramarginal or
precuneus cortices or parietal cortex or gyrus angulari or praecuneus or gyrus angularis or gyrus supramarginali or
parietal regions or gyrus prelunate or lobules parietal or lobe parietal or parietal cortices posterior or gyrus
supramarginalis or angulari gyrus or supramarginali gyrus or marginal sulcus or posterior parietal cortex or
prelunate gyrus or posterior parietal cortices or intraparietal sulcus or angularis gyrus or region parietal or parietal
lobules or precuneus or cortex parietal or gyrus angular or precuneus cortex or lobule parietal or parietal lobule or
cortices precuneus or posterior paracentral lobule or lobules posterior paracentral or sulcus marginal or posterior
paracentral lobules or Parietal Lobe or sulcus intraparietal or regions parietal or lobes parietal or paracentral lobules
posterior or gyrus supramarginal or precuneus cortices or parietal cortex or gyrus angulari or praecuneus or gyrus
angularis or gyrus supramarginali or parietal regions or gyrus prelunate or lobules parietal or lobe parietal or parietal
cortices posterior or gyrus supramarginalis or angulari gyrus or supramarginali gyrus or marginal sulcus or posterior
parietal cortex or prelunate gyrus or posterior parietal cortices or intraparietal sulcus or angularis gyrus or region
parietal or parietal lobules or precuneus or cortex parietal or gyrus angular or precuneus cortex or lobule parietal or
parietal lobule or cortices precuneus or posterior paracentral lobule or lobules posterior paracentral or sulcus
marginal or posterior paracentral lobules or omatosensory Cortex or postcentral gyrus or somatosensory cortices
primary or somatosensory cortex primary or cortex anterior parietal or cortices anterior parietal or cortices primary
somatosensory or anterior parietal cortices or cortex secondary sensory or areas primary somatosensory or primary
somatosensory cortices or cortex si or gyrus post central or secondary somatosensory areas or post central gyrus or
parietal cortices anterior or gyrus postcentrali or secondary somatosensory cortex or somatosensory cortex or areas
secondary somatosensory or somatosensory cortex secondary or area primary somatosensory or postcentralis gyrus
or cortices secondary sensory or secondary sensory cortex or primary somatosensory cortex or somatosensory areas
secondary or si cortex or primary somatosensory areas or secondary somatosensory cortices or gyrus postcentralis or
area secondary somatosensory or primary somatosensory area or cortex primary somatosensory or secondary
somatosensory area or secondary sensory cortices or cortices secondary somatosensory or primary somatic sensory
area or postcentrali gyrus or gyrus postcentral or Wernicke Area or Sensorimotor Cortex or Auditory Cortex or
gyrus transverse temporal or auditory areas temporal or temporal auditory areas or areas auditory or cortex primary
auditory or transverse temporal gyrus or cortex auditory or auditory areas or transverse temporal gyri or
convolutions heschl's or auditory cortex or auditory area or auditory cortex primary or auditory cortices primary or
areas temporal auditory or area auditory or gyri transverse temporal or primary auditory cortices or heschl gyri or
heschl convolutions or temporal gyri transverse or auditory area temporal or temporal auditory area or heschls gyri
or primary auditory cortex or Temporal Lobe or temporal operculums or superior temporal gyrus or occipito-
temporal gyrus lateral or gyrus fusiform or gyrus lateral occipito-temporal or gyrus temporalis superior or
occipitotemporal gyrus or horns temporal or inferior horn of lateral ventricle or temporal sulcus or regions temporal
or operculums temporal or cortex temporal or lobes temporal or planum polares or temporalis superior gyrus or
gyrus superior temporal or horn temporal or lobe temporal or temporal horn or temporal cortices or gyrus lateral
occipitotemporal or temporalis superiors gyrus or region temporal or fusiformi gyrus or temporal region or temporal
horns or lateral occipito-temporal gyrus or temporal cortex or gyrus temporal or temporal operculum or temporal
270
regions or operculum temporal or cortices temporal or temporal horn of the lateral ventricle or polare planum or
fusiformis gyrus or sulcus temporal or polares planum or planum polare or temporal lobe or Diagonal Band of Broca
or diagonal band of broca or broca diagonal band or External Capsule or olfactory tracts or olfactory tract lateral or
main olfactory bulbs or bulbs main olfactory or bulb olfactory or glomerulus olfactory or lateral olfactory tracts or
olfactory tract or bulb main olfactory or olfactory bulbs or olfactory bulb main or accessory olfactory bulb or
accessory olfactory bulbs or tracts olfactory or bulbs accessory olfactory or olfactory glomerulus or bulbs olfactory
or olfactory bulb accessory or tract olfactory or bulb accessory olfactory or tract lateral olfactory or olfactorius
bulbus or olfactory bulb or lateral olfactory tract or Olfactory Bulb or olfactory tracts or olfactory tract lateral or
main olfactory bulbs or bulbs main olfactory or bulb olfactory or glomerulus olfactory or lateral olfactory tracts or
olfactory tract or bulb main olfactory or olfactory bulbs or olfactory bulb main or accessory olfactory bulb or
accessory olfactory bulbs or tracts olfactory or bulbs accessory olfactory or olfactory glomerulus or bulbs olfactory
or olfactory bulb accessory or tract olfactory or bulb accessory olfactory or tract lateral olfactory or olfactorius
bulbus or olfactory bulb or lateral olfactory tract or Telencephalic Commissures or Anterior Cerebellar Commissure
or Corpus Callosum or corpus callosums or commissures neocortical or neocortical commissures or corpus callosum
or callosums corpus or interhemispheric commissure or interhemispheric commissures or neocortical commissure or
callosum corpus or commissures interhemispheric or commissure interhemispheric or commissure neocortical or
Internal Capsule or internal capsules or interna capsula or capsules internal or capsule internal or capsula internas or
internal capsule or capsula interna or internas capsula or Myelencephalon).ab,kw,ti.
8. exp brain/
9. 4 or 5 or 6 or 7 or 8
10. exp nervous system inflammation/
11. exp glia/
12. exp astrocyte/
13. exp leukocyte/
14. exp antigen presenting cell/
15. exp leukocyte antigen/
16. (Inflammat* or Eicosanoid* or Leukotriene* or lta4 or lta 4 or ltb4 or ltb 4 or 512-hete or 512 dihete or SRS-A
or ltc4 or ltd4 or lte4 or Prostaglandin* or prostanoid* or pgg or pgh2 or pga or pgb or pgd or pgd2 or pge or pge2 or
pgf or pgf2 or Alprostadil or Dinoprostone or pgf* or Dinoprost or 6-Ketoprostaglandin F1 alpha or Epoprostenol or
prostacyclin* or Thromboxane* or Histamine* or eplene or Kinin* or Bradykinin* or Kallidin or Kininogen* or
Tachykinin* or prekinins or thiostatin or prokinins or Eledoisin or Kassinin or Neurokinin* or Physalaemin or
Substance P or Urotensin* or thrombocyte aggregating activity or paf acether or factor platelet activating or 1-alkyl-
2-acetyl-sn-glycerophosphocholine or platelet aggregation enhancing factor or 1 alkyl 2 acetyl sn
glycerophosphocholine or platelet activating factor or acetyl glyceryl ether phosphorylcholine or platelet activating
substance or agepc or paf-acether or platelet-activating substance or platelet aggregating factor or phosphorylcholine
acetyl glyceryl ether or aggregating factor platelet or Platelet Activating Factor or Chemokine* or cytokine* or
intercrine* or beta-Thromboglobulin or CCL1 or CCL3 or CCL4 or CCL5 or CCL11 or CCL17 or CCL19 or
CCL20 or CCL21 or CCL22 or CCL24 or CCL27 or Monocyte Chemoattractant Protein* or CCL2 or CCL7 or
CCL8 or CXCL1 or CXCL2 or CXCL5 or CXCL6 or CXCL9 or CXCL10 or CXCL11 or CXCL12 or CXCL13 or
Interleukin* or Platelet Factor 4 or CX3C or CX3CL1 or Macrophage Inflammatory Proteins or CCL3 or CCL19 or
271
CCL20 or CXCL2 or mip 2 or mip2alpha or chemokine mip-2 or mip 2alpha or inflammatory protein-2alpha
macrophage or ldncf-2 or mip-3-alpha or interferon* or ifn or lymphokine* or lymphocyte mediators or monokine*
or tumor necrosis factor* or tnf or Oncostatin M or Leukemia Inhibitory Factor or Transforming Growth Factor or
tgf or tgfbeta or Astrocyte* or astroglia* or glia* or microglia or epithelioid cell* or Macrophage* or monocyte* or
histiocyte* or neutrophil* or cells le or le cell or leukocyte polymorphonuclear or Antibody* producing cell* or
antibody* secreting cell* or immunoglobulin* producing cell* or immunoglobulin* secreting cell* or bursa-
dependent or b-cell or bcell or granulocyte or Antigen presenting cell* or dendritic cell* or interdigitating cell* or
veiled cell* or Lymphocyte* or leukocyte* or lymphoid cell* or t-cell* or t-lymphocyte* or t cell* or t lymphocyte*
or th1 cell* or th2 cell* or th17 cell* or treg or tregs or th3 cell* or tr1 cell* or t8 cell* or tc2 cell* or tc1 cell* or
lympholytic cell* or nkt cell* or inkt cell* or t helper or t cytotoxic or t regulatory or epidermal cell derived
thymocyte activating factor or il-1 or il1 or il-2 or il2 or ru49637 or ro-23-6019 or ru 49637 or ro236019 or ru-49637
or ro 236019 or ro-236019 or thymocyte stimulating factor or tcgf or ro 23 6019 or eosinophil-mast cell growth-
factor or colony-stimulating factor multipotential or erythrocyte burst-promoting factor or burst-promoting factor
erythrocyte or hematopoietin 2 or colony stimulating factor mast cell or eosinophil mast cell growth factor or p cell
stimulating factor or colony-stimulating factor 2 alpha or p-cell stimulating factor or mast-cell colony-stimulating
factor or multipotential colony stimulating factor or erythrocyte burst promoting factor or il-3 or il3 or colony-
stimulating factor mast-cell or multipotential colony-stimulating factor or burst promoting factor erythrocyte or
colony stimulating factor multipotential or mast cell growth factor 2 or binetrakin or mcgf-2 or bcgf-1 or il-4 or il4
or b-cell growth factor-1 or bsf-1 or il 5 or il5 or t-cell replacing factor or differentiation factor eosinophil or
eosinophil differentiation factor or bcgf-ii or growth factor hybridoma or il6 or il 6 or hybridoma growth factor or
growth factor plasmacytoma or plasmacytoma growth factor or bsf-2 or hepatocyte-stimulating factor or mgi-2 or
myeloid differentiation inducing protein or differentiation-inducing protein myeloid or ifn-beta 2 or myeloid
differentiation-inducing protein or lymphopoietin-1 or lymphopoietin 1 or il7 or il-7 or il8 or il 8 or cxcl8 or amcf-I
or il9 or il 9 or il10 or il 10 or csif-10 or il 11 or il11 or inhibitory factor adipogenesis or adipogenesis inhibitory
factor or factor adipogenesis inhibitory or il12 or il 12 or edodekin alfa or il13 or il 13 or il 15 or il15 or il16 or il 16
or lcf factor or il 17 or il17 or il 17e or il 17f or il 17c or il 17a or il 17b or il18 or il-18 or il23 or il 23 or il27 or il 27
or il-17d or CD 11 or CD11 or CD 11b or CD11b or CD68 or CD40 or CD45 or Ox-42 or OX42 or ed-1 or ed1 or
cd200 or cd 200 or Iba 1 or Iba1 or ly6g or cd3 or mpo or mcp1 or mcp-1 or ccr2 or arg1 or arg 1 or mhc or major
histocompatibility complex or aldh1 or aldh 1 or hla dr or cd20 or nf kb or nfkb or calpronectin or enkaphalin or
COX or COX2 or COX1 or cPLA2 or iPLA2 or sPLA2 or txa2 or tx a2 or txb2 or tx b2 or lta4 or lt a4 or ltb4 or lt
b4 or resolving or protectin or maresin or 6 ketoPGF or 6ketoPGF or lipoxygenase or LOX or 12 lox or 12lox or 15
lox or 15lox or 5 lox or 5lox or COX or COX2 or COX1 or cPLA2 or iPLA2 or sPLA2 or txa2 or tx a2 or txb2 or tx
b2 or lta4 or lt a4 or ltb4 or lt b4 or resolving or protectin or maresin or 6-ketoPGF or 6ketoPGF or lipoxygenase or
LOX or 12-lox or 12lox or 15-lox or 15lox or 5-lox or 5lox or iNOS or RELB or mPGES or tnf-a or tnfa or il1a or
il1 a or il1b or il1 b or s100* or kynuren*).ab,kw,ti.
17. (animal not human).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer,
drug manufacturer, device trade name, keyword]
18. exp neurogenic inflammation/
19. exp cytokine/
20. exp chemokine/
21. exp autacoid/
22. exp lymphocyte/
272
23. exp lymphocyte/
24. exp mast cell/
25. exp complement/
26. exp complement/
27. exp complement system/
28. exp prostaglandin synthase/
29. exp prostanoid/ or "prostaglandins,thromboxanes and leukotrienes"/
30. exp icosanoid/
31. exp cytosolic phospholipase A2/
32. exp calcium independent phospholipase A2/
33. exp histamine/
34. exp arachidonate 15 lipoxygenase/ or exp arachidonate 12 lipoxygenase/ or exp lipoxygenase/ or exp
arachidonate 5 lipoxygenase/
35. immunoglobulin/ or exp "antibodies,antisera and immunoglobulins"/
36. (nissl or gliosis).ti,ab,kw.
37. 10 or 11 or 12 or 13 or 14 or 15 or 16 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or
30 or 31 or 32 or 33 or 34 or 35 or 36
38. 3 and 9 and 37
39. 38 not 17
40. limit 39 to (conference abstract or conference paper or conference proceeding or "conference review" or
editorial)
41. limit 39 to "review"
42. 40 or 41
43. 39 not 42
273
6.8 Appendix 3: Chapter 5: Genes altered by Amyloid-β Infusion
Shared Between Genotype/diet Groups
Genes altered by Amyloid-β Infusion Shared Between Genotype/diet Groups
Gene Description EntrezGene ID
Shared between WTFO
and WTSO
Gm12249 Predicted gene 12249
Aatk Apoptosis-associated tyrosine kinase 11302
Ptcd2 pentatrico peptide repeat domain2 68927
Mir1957a microRNA1957a 100316707
Ldlrap1
low density lipoprotein receptor
adaptor protein1 100017
Shared between WTFO
and Fat-1
Mroh2a
maestroheat-like repeat family
member 2A 100040766
Shared between Fat-1
and WTSO
Nudt13
nudix(nucleoside diphosphate linked
moietyX)-typemotif13 67725
Gm17250 predictedgene,17250 228025
LOC102634517|Gm13110
uncharacterized
LOC102634517|predictedgene13110 102634517
Gtf2ird1
general transcription factor III repeat
domain-containing1 57080
274
6.9 Appendix 4: Chapter 5: Genes altered by Amyloid-β Infusion
Unique to Each Genotype/diet Groups
Genes significant in one way ANOVA surgery vs non-surgery UNIQUE to genotype diet group
WTSO WTFO Fat-1
Stat1 Atp6v1h Lipt1|Gm26805
D230017M19Rik|B930094L07Rik Zfand2b Sox17
Inpp5d Scly 4933415F23Rik
Agap1 Gm26418 Gm4847
Slc45a3 2010300C02Rik Fgd6
Gm26706 Olfr1414 Nab2
Selp Slco4c1 Mip
Nos1ap Gm10827 Olfr225
Fcgr4 Avil Mfsd6l
Kcnj10 Amd2 Timm22
Fcgr2b Usp15 Trim25
Fcer1g Zfp830 Prop1
Dusp23 D630032N06Rik Gm16040
Pld5 1700106J16Rik 2810032G03Rik
Gm26752|LOC102631579 Scrn2 9030617O03Rik
Aig1 Itgb4 Mir3070b|mmu-mir-3070b
Mcm9 Inpp5j
Mir136|mmu-mir-
136|mmu-mir-3071
Srgn Ccng1 D630036H23Rik
275
Mypn Gm11186 Slc34a1
Gm23041 Gm11651 Gm7969
Asb3 Gm17758 Snora31
A630014C17Rik Stxbp6 Gm23816
Ccl11 Adarb2 Grhl2
Wfdc17 Rbm24 Olfr288
Mrpl27 G630093K05Rik Btnl1
Rpl27 Zfp85os BC051142
Asb16 Kif2a Gm4719
Grn Cacna2d3 9630014M24Rik|Arhgap26
Syngr2 Fzd3 Cd226
Rnf213 Cdh10 Prob1
Rasd1 Them6 Vps37c
Cxcl16 Higd1c|Mettl7a2Higd1c Ms4a4a
Scimp Ydjc Mir669j
Lgals9 Cox17 Rpl7a
Gm11493 Gm6712 Tor1b
Abi3 Zfp598 S100a5
Gm11523|LOC102641351 Zfp523 Gm26107
Krt26 1600002H07Rik Fndc3b
Krt10 Heatr5b Gm20515
Alyref E430002N23Rik Gm15688
Twistnb Syt4 Hs2st1
Pnpla8 Ablim3 Atpaf1|Gm14117
Crip1 Syt7 Tmem51os1
Ighv1-34 Klf9 Tpm2
Tcrg-V4 Ccnj Gm24666
Foxf2 Zfpl1 Gfi1
Gm2762 4430402I18Rik Cald1
Erap1 Loxl4 Try4
Nedd9 Lrrc4c LOC102638461|Gm26793
Gm904|Gm8694 Wdr76 Gm17484
Nr2f1 Kcnk15 Osgin1
Gm26120 Npepl1 9430091E24Rik
Plp2|Gm13669 Gm14295 Myo5c
276
Olfr743 Gm22005 Nrg4
Setdb2|Phf11c Gm13941 Fam47c
1700092C10Rik Pak7 Gemin8
Kcns2 Gm25152 Gm23557
Gm24098|Gm25381|Mir692-3|Mir692-
2b|LOC100862446|Gm20746|Ftl1|Mir692-
2a|Gm22752|Gm22774 Gm20031 LOC100505143
Myc Fnip2
Gtpbp1 Dnajb4
Apobec3 Gm436
Fam118a Tardbp
Gml Ski
Csf2rb2 Fam20c
Cyp2d40 LOC102641980|Gm15860
Sp7 Asprv1
Top3b Gm26911|LOC102637100
Hes1 Ogg1
Gm25617 Pex26
Btla Gm26075
Ppl Gm15704|LOC102641081
Tnp2 Siglec5
Pi4ka Fuz
Gmnc Tmem86a
Stfa2 Gm16938
Gm17103 Fcgrt|Mup1
1700010I14Rik B230209E15Rik
Spsb3 Dgat2
H2-Ab1 Btbd10
Vars Nup35
Nfkbie Pard3
D17Ertd648e Gm22509
Zfp213 Rnf166
Snhg9 Zmynd10
Gm16196 Smarcc1
277
H2-K1 Cul5
Prrc2a Fam63b
Dennd1c Tmem108
Gm22562 Igsf1
Gm4951 Mcf2
Gm23283 Sms|Gm14680
Unc93b1 Gm21464
Ptar1 Gm9753
Gm5519 Gm19344
Cabp4
Pcnxl3
Ms4a6d
Vim
Prrx2
Ptges2
LOC102636022|Gm13572
Slc43a3
Nusap1
B2m
Dut
Sdcbp2
Traf1
Ggta1
Rbms1
Frzb
Mdk
Cd44
B4galt5
LOC100502777|Zfp64
Gpr160
Plrg1
Lrif1
Lpar3
Gm7977
Fcgr1
Cd53
Gm22713
Rab2a
278
Ube2r2
Echdc2
2610528J11Rik
Gnl2
Laptm5
Snhg12
Gm17029|LOC102635786
Gm26840|Vwa1
Fhl5
Zdhhc21
C1qb
C1qc
Plekhm2
Gm22755
Fgl2
Ube2d2b
Gm13824|LOC102638537
Oas1b
Gm26205
Rasl11a
Alox5ap
Gbp11
Fbrsl1
Selplg
Gusb
Pom121
Gm13856
Zfp862-ps
Igkj1|Igkv9-124|Igkv4-70|Igkv19-93|Igk-
V28|LOC637260|LOC672450|LOC434035
Usp18
Foxj2
Tmem176b
Gm24096
1700030F04Rik
Iqsec3
279
Apobec1
Tead4
Clec7a
Caprin2
Zfp667
Zfp108
Zfp93
Klk1b21
2410002F23Rik
Ctsc
Ucp2
Vwa3a
Il4ra
Itgam
Zim1
Psg21
Kcnk6
Fxyd5
Emp3
Gm23862
Gm22131
LOC102640399|Gm26365
Iqgap1
Gm19950
D830044I16Rik
Ifitm2
Champ1
Gm3336
Clgn
1700067K01Rik
Man2b1
Irf8
4933430N04Rik
Primpol
Slc25a42
Nfix
Dnaja2
Esam
280
Amica1
Apoa5
Gsta4
Ctsh
Camkv
Shisa5
Neo1
Gm17324|Mto1
Prickle3
Slc6a14
Il13ra1
Rhox2e
Gria3
Bgn
Msn
Plp2
Pnma5
Irak1
Flna
Il2rg|Gm20489
2610002M06Rik|Chmp1b
Tlr7
Gm20831|Ssty1|LOC10105617
Gm20831|Ssty1|LOC10105617
Arpc1b|Gm5637
281
6.10 Appendix 5: Chapter 5: DAVID Version 6.7 Gene Ontology of
Genes Changed in Fat-1 amyloid-β-infused vs Fat-1 non-surgery
DAVID Version 6.7 Gene Ontology of Genes
Changed in Fat-1 amyloid-β-infused vs Fat-1 non-
surgery
Term
Number of
Genes
P-Value Benjamini
actin cytoskeleton 3 5.80E-02 9.80E-01
endoplasmic reticulum
part
3 7.20E-02 9.10E-01
actin binding 3 8.80E-02 1.00E+00
Butyrophylin-like 2 9.30E-02 1.00E+00
282
6.11 Appendix 6: Chapter 5: DAVID Version 6.7 Gene Ontology of
Genes Changed in WTFO amyloid-β-infused vs WTFO non-
surgery
Term
Number
of
Genes
P-
Value
Benjamini
ion binding 21 1.00E-
02 3.60E-01
cation binding 21 8.90E-
03 4.40E-01
zinc ion binding 13 2.60E-
02 5.00E-01
protein/synaptotagmin 2 4.50E-
02 5.00E-01
transition metal ion binding 15 2.50E-
02 5.60E-01
synaptotagmin 2 3.20E-
02 6.20E-01
metal ion binding 21 8.00E-
03 6.40E-01
metal-binding 18 9.90E-
03 7.30E-01
VHP 2 3.20E-
02 7.70E-01
283
zinc 13 3.10E-
02 8.70E-01
Axon guidance 3 5.60E-
02 8.70E-01
autocatalytic cleavage 2 7.20E-
02 9.10E-01
basal part of cell 2 8.70E-
02 9.30E-01
Cyclin, N-terminal 2 8.90E-
02 9.50E-01
lyase 3 6.90E-
02 9.60E-01
basal plasma membrane 2 8.50E-
02 9.60E-01
basolateral plasma
membrane
3 6.80E-
02 9.70E-01
Zinc finger, C2H2-type 6 8.40E-
02 9.70E-01
coated vesicle 3 5.40E-
02 9.80E-01
284
Villin headpiece 2 2.70E-
02 9.90E-01
Zinc finger, C2H2-like 6 8.20E-
02 9.90E-01
Synaptotagmin 2 5.70E-
02 9.90E-01
clathrin-coated vesicle 3 4.00E-
02 1.00E+00
metal ion-binding
site:Copper 2
5.60E-
02 1.00E+00
domain:HP 2 3.00E-
02 1.00E+00