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Innate immune response to amyloid-beta: relevance to Alzheirmer’s disease and neuroinflammation
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Innate immune response to amyloid-beta: relevance to Alzheirmer’s disease and neuroinflammation
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Content
INNATE IMMUNE RESPONSE TO AMYLOID-BETA: RELEVANCE TO ALZHEIRMER’S
DISEASE AND NEUROINFLAMMATION
by
Mariana Figueiredo Uchoa
______________________________________________________________________________
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirement for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)
August 2020
ii
Acknowledgements
First, I want to thank Dr. Terrence Town for welcoming me into his lab, allowing me to grow as a
scientist. I would also like to extend a whole-hearted thank you to Dr. Jeannie Chen and Dr Michael
Jakowec for never failing to make themselves available to discuss experimental plans and talk
about academics and career. I would like to specially thank the Neuroscience Graduate Program
for continuously taking a chance on me, which allowed me to successfully complete my studies.
Finally, I’d like to thank Dr. Hyungjin Eoh for opening his lab to me and to a wonderful
collaboration, Dr. Karen Chang for taking time to listen to and commenting my thesis defense, and
Dr. Christian Pike for providing me with guidance and with a space to develop interesting projects.
I’d also like to thank the entire Town lab. I feel so grateful to have found such an incredible family
that supported me through the ups and downs of the PhD. Chris, thank you for being with me
throughout my entire PhD and being my go-to person to discuss science. But most of all, thank
you for always believing in me, even when I didn’t. Alex Vesling, without you I could not have
pursued the exciting scientific questions we faced during this journey. Thank you for being an
amazing partner and for the countless moments you saw me struggling and offered your help.
Rachel, thank you for showing me what teamwork really means. I am so proud of the science we
did together and working with you was an immense honor to me. Riyaz, thank you for the hard
work you put into the projects I offered you. I am very proud of you and of how much you have
grown scientifically over the years. Brian, thank you for showing me the way when I joined the
new lab, for the innumerous late nights of experiments and for always staying with me until the
end of them. Anakha, thank you so much for your insightful inquiries and your precious hugs.
Alicia, thank you for being careful and thoughtful and for checking on me regularly. Nima, thank
you for your intense hard work and help during my final years of PhD. Balint, thank you for your
trust and for your insights and help with my projects. I would also like to thank Alex Moser for
guiding me during my first years of the PhD, for her patience and love in collaborating in projects
with me and helping me adapt to Los Angeles. I am glad to say I have found friends for life during
this experience!
iii
More than anyone, I need to thank my beloved husband, Jeremy Cairl. Thanking you can never be
enough to describe how truly grateful I am for all your support and help over the last four years.
Thank you for listening and participating on every event related to my PhD, whether they were lab
parties or hearing about my experiments and complaints, celebrating my victories and offering
your shoulders to comfort me during anxious moments. Thank you for teaching me to how to better
express my ideas and with becoming a good writer. All my accomplishments would have been
meaningless without you.
Lastly, I want to thank my entire family for all their encouragement and understanding over the
last six years. Being away from home for the pursue of knowledge is not easy, but many times
your love has given me strength to keep going on. Mom, thank you for being with me (virtually)
every day and for your emotional support during this program. Thank you for all the hours you
listened to me vent about my experiments and for your enthusiasm. I also thank Jeremy’s family
for always accommodating my schedule when we made plans, and for all the love and support.
iv
Table of Contents
Acknowledgements ...………………………………………………………….… ii
List of Figures …………...…………………………………………………….. viii
Abbreviations ………………………………………………………………….... xi
CHAPTER 1. INTRODUCTION
1.1 Alzheimer’s disease …………………………………………………………………. 1
1.1.1 AD Pathology ………………………..…………………………….………. 3
1.2 Inflammation ……………………………………………………………………….... 7
1.2.1 Cellular mediators of inflammation ……………………………………….. 8
1.2.2 Soluble mediators of inflammation ……………………………………..... 12
1.3 Clearance of toxic aggregates …………………………………………………...…. 14
1.4 Conclusion …………………………………………………………………………. 16
1.5 References ………………………………………………………………………….. 18
CHAPTER 2. STAT3 DELETION INHIBITS CEREBRAL INNATE IMMUNITY AAND
EXACERBATES ALZHEIMER-LIKE PATHOLOGY IN FEMALE APP/PS1 MICE
2.1 Summary ………………………………………………………………………..….. 39
2.2 Introduction ……………………………………………………………….……..…. 39
2.3 Material and Methods …………………………………………………….………... 41
2.4 Results
2.4.1 Innate immunity is targeted in Csf1r-Cre
+
mice ………………….……. 46
2.4.2 Stat3 recombination decreases innate immune activation ……….…...... 50
2.4.3 Stat3 deletion decreases the brain proinflammatory cytokine profile in a
sex-specific manner ………............……………………………….….... 50
2.4.4 Stat3 recombination decreases Aβ uptake in a sex-specific manner ….. 53
2.4.5 Innate immune Stat3 deletion results in a sex-specific increase in cerebral
Aβ ……………............………………..…………………………...…... 55
2.4.6 Innate immune Stat3 deletion impairs female behavior ..…...…………. 57
2.5 Discussion ………………………………………………………………………….. 59
v
2.6 References ………………………………………………………………………….. 62
CHAPTER 3. INTERACTIONS BETWEEN INFLAMMATION, SEX STEROIDS, AND
ALZHEIMER’S DISEASE RISK FACTORS
3.1 Summary ………………………………………….……………………..……….… 66
3.2 Female bias in Alzheimer’s disease ……………………………………………...… 66
3.3 Sex Steroid hormones and Alzheimer’s disease ………………………………….... 68
3.2.1 Estrogen and AD ……………………………………………………….… 68
3.2.2 Testosterone and AD ………………………………..………………….… 69
3.4 Sex Steroid hormones and inflammation ……………………………..……………. 70
3.4.1 Sex steroid hormones modulate glia …………………..…………….....… 71
3.4.1 Glial cells can produce neurosteroids ……..…………..…………….....… 73
3.5 Modifiers of Alzheimer’s disease risk and their interaction with inflammation and sex
steroid hormones ……………………………………………………………………..… 75
3.5.1 Apolipoprotein E …………………………………………………………. 76
3.5.2 Obesity …………………………………………………….……………... 80
3.5.3 Air pollution ……………………………………………………...………. 86
3.6 Conclusion ……………………………………………...………………………….. 89
3.6 References ………………………………………………………………………….. 91
CHAPTER 4. TLR4/NFk-B IMPACT ON NEUROINFLAMMATION
4.1 Summary ……………………………………………...…………………………... 112
4.2 Introduction ……………………………………………………………………….. 113
4.3 Material and Methods …………………………………………………………..… 115
4.4 Results
4.4.1 Effects of high-fat diet and TAK-242 on body weight and adiposity …... 122
4.4.2 Effects of high-fat diet and TAK-242 on metabolic outcomes …….....… 125
4.4.3 Effects of high-fat diet and TAK-242 on peripheral inflammation ….…. 127
4.4.4 Effects of high-fat diet and TAK-242 on hippocampal microgliosis …... 129
4.4.5 Effects of HFD and TAK-242 on hippocampal gene expression …….… 132
4.4.6 Effects of HFD and TAK-242 on neurogenesis …………………..…….. 135
vi
4.4.7 Effects of high-fat diet and TAK-242 on amyloidogenic pathways …..... 137
4.4.8 Effects of high-fat diet and TAK-242 on behavioral performance ……... 138
4.5 Discussion ……………………………………………………………………….... 142
4.6 Conclusion ………………………………………………………………………... 146
4.7 References …………………………………………………………………..…….. 146
CHAPTER 5. INTERLEUKIN-10 REDUCES METABOLIC FITNESS DRIVING Ab
IMMUNE TOLERANCE IN MONONUCLEAR PHAGOCYTES
5.1 Summary ………………………………………………………………………….. 159
5.2 Introduction ……………………………………………………………………….. 159
5.3 Material and Methods ……………………………………………………..……… 162
5.4 Results
5.4.1 IL-10 is elevated around Ab plaques and affects mononuclear phagocyte’s
phenotype …………………………………………………………………...… 172
5.4.2 IL-10 suppresses Ab-treated mononuclear phagocytes function ……….. 174
5.4.3 IL-10 reduces glycerophospholipid content of mononuclear phagocytes . 178
5.4.4 IL-10 induces accumulation of lipid droplets in mononuclear phagocytes . 180
5.4.5 Repurposing lipid droplet rescues phagocytic deficit in mononuclear
phagocytes ………………………………………………...…………………... 183
5.4.6 Lipid droplet accumulation is prominent in APP/PS1 and human AD brains
…………………………………………………………………………………. 188
5.5 Discussion …………………………………………………………………...……. 191
5.6 References ……………………………………………………………………….... 195
CHAPTER 6. INNATE IMMUNE-TARGETED THERAPY FOR ALZHEIMER’S
DISESE
6.1 Immunotherapy modalities to target the innate immune compartment in AD ….... 202
6.2 Conclusion ………………………………………………………………………... 206
6.7 References ……………………………………………………………………...…. 207
vii
Appendix A. INNATE IMMUNE IL-10 RECEPTOR DELETION IN APP/PS1 MICE . 210
Appendix B. TARGETING IRAK-M TO PROMOTE BENEFICIAL INNATE
IMMUNOMODULATION IN ALZHEIMER’S DISEASE …………….………..………. 216
Appendix C. IL-10-INDUCED MITOCHONDRIAL ALTERATIONS .……….……….. 227
viii
List of Figures
CHAPTER 1.
Figure 1: Rebalancing innate immunity in Alzheimer’s disease …………………………. 17
CHAPTER 2.
Figure 2.1: Cre recombination efficiency in Csf1r-Cre innate immunity …………......…… 48
Figure 2.2: Stat3 recombination decreases innate immunity markers ……………………..... 51
Figure 2.3: Stat3 recombination alters cytokine profile in the brains of APP/PS1 mice ….… 52
Figure 2.4: Stat3 deficiency in Aβ-phagocytosis by Iba1 cells …………………………….... 54
Figure 2.5: Innate immune Stat3 deletion increases amyloid levels in female mice ………... 55
Figure 2.6: Stat3 recombination affect female mice behavior ………………………………. 58
CHAPTER 3.
Figure 3: Inflammation as a common denominator between risk factors of AD and sex
steroid hormones ………………………………………………..…………….... 90
CHAPTER 4.
Figure 4.1: Metabolic outcomes associated with diet-induced obesity in mice treated with
vehicle or the TLR4 inhibitor TAK-242 …………………………..…………… 124
Figure 4.2: Peripheral effects of diet-induced obesity in mice treated with vehicle or the TLR4
inhibitor, TAK-242 …………………………………...……………………….. 126
Figure 4.3: Expression of mRNA levels of genes associated with macrophage activation and
inflammation in adipose tissue from vehicle (Veh) and TAK-242 (TAK)-treated
mice fed with a control (CTL) or high-fat (HFD) diet ……….………………… 128
Figure 4.4: Microglial number, morphological status, and soma size as assessed by Iba-1
immunohistochemistry in vehicle (Veh) and TAK-242 (TAK)-treated mice fed
with a control (CTL) or high-fat (HFD) diet ………………………………….. 131
Figure 4.5: Hippocampal mRNA expression of genes associated with activated microglial
phenotypes and neuroinflammation in mice treated with vehicle (Veh) and TAK-
242 (TAK)-treated and fed with a control (CTL) or high-fat (HFD) diet …...…. 134
ix
Figure 4.6: Neurogenesis and cell proliferation as assessed by DCX and BrDU
immunohistochemistry in mice maintained on control or high-fat (HFD) diet and
treated with vehicle and TAK-242 ……………………………………….……. 136
Figure 4.7: Expression of Ab production and degrading factors and soluble Ab42 in mice fed
control or high-fat diet and treated with vehicle and TAK-242 ………………. 138
Figure 4.8: Behavioral performance of mice fed control or high-fat diet and treated with vehicle
and TAK-242 ………………………………………………………………….. 140
CHAPTER 5.
Figure 5.1: IL-10 expression is elevated in the brains of APP/PS1 mice ………………...… 173
Figure 5.2: IL-10 treatment restrains mononuclear phagocyte activation …………………. 175
Figure 5.3: IL-10 treatment decreases glycerophospholipid content in mononuclear phagocytes
………………………………………………………………….……………… 179
Figure 5.4: IL-10 treatment induces lipid droplet accumulation in mononuclear phagocytes
……………………………………………………………...………………….. 182
Figure 5.5: Ab and IL-10 induced suppression is rescued by targeting lipid droplets with
GW1929 treatment ………………………………………………………….… 185
Figure 5.6: Cortical periplaque mononuclear phagocytes displays accumulation of lipid
droplets …………………………………………………...…………………… 189
Figure 5.7: Working model of IL-10-induced tolerance in periplaque mononuclear phagocytes
………………………………………………………………...……………….. 193
Appendix A.
Figure A.1: Innate immune Il10r deletion impacts mouse behavior …………….……….... 211
Figure A.2: Il10r deletion alters Aβ immunoproteostasis ………………………………..... 212
Figure A.3: Il10r deletion decreases markers of innate immune activation ………..……... 214
Appendix B.
Figure B.1: Elevated IRAK-M/NF-κB signaling in AD patient brains ……….…………... 218
Figure B.2: Alternative NF-κB signaling in Alzheimer’s disease ……………………….... 219
Figure B.3: Irak-m deletion in APP/PS1 mice improves Ab clearance …………………... 220
x
Figure B.4: Irak-m deficiency modifies macrophage NF-kB signaling ………………...… 222
Appendix C.
Figure C.1: Ab and IL-10 effects on oxidative phosphorylation …………….…………...…... 229
Figure C.2: Ab and IL-10 effects on mitochondrial health …………………………………... 230
Figure C.3: Low Ab concentration still affects mitochondrial membrane potential …..……... 231
Figure C.4: GW1929 effects on mitochondria ………………………………………………... 233
xi
Abbreviations
AD Alzheimer’s disease
ADRC Alzheimer’s Disease Research Center
AKT Protein kinase B
AMPK AMP-activated protein kinase
ANOVA Analysis of variance
AP-1 Activator protein 1
ApoE Apolipoprotein E
APP Amyloid precursor protein
APP/PS1 APPswe PSEN1dE9
AR Androgen receptor
ATG Atglistatin
ATP Adenosine triphosphate
AUC Area under the curve
Ab
Amyloid-beta
BACE-1 Beta-secretase 1
BNDF Brain-derived neurotrophic factor
BrdU bromo-deoxyuridine
CC Cingulate cortex
CD Cluster of differentiation
CNS Central nervous system
COX2 Cyclooxygenase 2
CR1 Complement receptor 1
CSF Cerebrospinal fluid
CSF1R Colony- stimulating factor 1 receptor
CTL Control
CTL-4 Cytotoxic T lymphocyte-associated antigen 4
Cx3cr1 CX3C chemokine receptor 1
CYP11A1 Cholesterol side-chain cleavage enzyme P450
DCX Doublecortin
DIO Diet-induced obesity
DS Down Syndrome
E2 17β-estradiol
EC Entorhinal cortex
EGF Endothelial growth factor
ELISA Enzyme-linked immunosorbent assay
EOAD Early onset Alzheimer’s disease
EPM Elevated plus maze
eQTL Expression quantitative trait locus
ER Estrogen receptor
F4/80 EGF-like module-containing mucin-like hormone receptor-like 1
FBS Fetal bovine serum
FCCP Carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone
FcR Fc receptor
xii
FST Forced swim test
GFAP Glial fibrillary acidic protein
GTT Glucose tolerance testing
GWAS Genome-wide association studies
HC Hippocampus
HFD High-fat diet
HIF1a
Hypoxia-induced factor 1 alpha
HSP72 Heat-shock protein 72
i.p. Intraperitoneal
Iba1 Ionized calcium binding adaptor molecule 1
IDE Insulin-degrading enzyme
IF Interferon
IL Interleukin
Il10R IL-10 receptor
IRAK-M Interleukin-1 receptor-associated kinase-3 (monocytes)
JAK Janus kinase
JNK Jun N-terminal Kinase
KO Knockout
Lamp1 Lysosomal-associated membrane protein 1
LOAD Late onset Alzheimer’s disease
LPL Lipoprotein lipase
LPS Lipopolysaccharide
LRP1 Low density lipoprotein receptor-related protein 1
MAP3K3 Mitogen-activated protein kinase kinase kinase 3
MCI Mild cognitive impairment
MHC Major histocompatibility complex
mTOR Mammalian target of rapamycin
MyD88 Myeloid differentiation primary response 88
NAFLD Non-alcoholic fatty liver disease
NF-kB
Nuclear Factor kappa-light-chain-enhancer of activated B cells
NFT Neurofibrillary tangles
NLRP3 NOD-, LRR- and pyrin domain-containing protein 3
NMR Nuclear magnetic ressonance
NSAID Anti-inflammatory drug
OCR Oxygen consumption rate
P2RY4 Purinergic 2Y
4
receptors
PBS Phosphate-buffered saline
PCR Polymerase chain reaction
PD-1 Programmed cell death 1
PFA Paraformaldehyde
Pgk1 Phosphoglycerate kinase 1
PM Particulate matter
PPAR Peroxisome proliferator-activated receptor
PPR Pattern-recognition receptors
xiii
PSEN Presenilin
ROS Reactive oxygen species
RP Retroperitoneal
s.c. Subcutaneous
SAB Spontaneous alternation behavior
SDHA Succinate dehydrogenase complex, subunit A, flavoprotein
SEM Standard error of the mean
SERM Selective estrogen receptor modulators
Sirt 1 Sirtuin
SREBP1 Sterol-regulatory element-binding protein
StAR Steroidogenic acute regulatory protein
Stat3 Signal transducer and activator of transcription 3
Stat3 cKO Stat3 conditional knockout
TAK
Transforming growth factor b-activated kinase 1
TBS Tris-buffered saline
TCA Tricarboxylic acid
TGF Transforming growth factor
TGF-βR1 Transforming growth factor receptor 1
TLR Toll-like family of receptor
TNF Tumor necrosis factor
TR Targeted-replacement mice
TRAF Tumor necrosis factor receptor (TNFR)-associated factor
TREM2 Triggering receptor expressed on myeloid cells 2
TSPO Translocator protein 18kDa
Veh Vehicle
WT Wild-type
1
CHAPTER 1. INTRODUCTION
Acknowledgements: Thanks to Alexis Gorin and Chris Im for helping with proofreading this
chapter and for precious advice in constructing this thesis. Special thanks for the Town lab and the
Pike lab for essential support.
1.1 Alzheimer’s disease
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that currently affects
5.8 million people in the United States alone (Hebert et al., 2013). This neuropathy is considered
the sixth leading cause of death in America (Hebert et al., 2013). Of those affected, 5.5 million are
over the age of 65 and nearly two-thirds of them are women. The risk for AD increases with age,
and the population over the age of 65 in the United States is expected to grow from 53 million to
88 million by 2050 (Reitz and Mayeux, 2014). As a result, the prevalence of the disease is expected
to nearly triple from 5.7 million to 13.2 million by 2050 (Hebert et al., 2013; Rocca et al., 2011).
By 2030, it is estimated that the global cost of dementia could grow to US$2 trillion, which could
overwhelm the health and social care systems (Kirson et al., 2016; Wimo et al., 2017).
Currently, three stages of AD have been recognized: preclinical Alzheimer’s disease, mild
cognitive impairment (MCI), and dementia due to Alzheimer’s disease (Albert et al., 2011;
McKhann et al., 2011; Sperling et al., 2011). In the latter phase, AD manifests as cognitive
impairments, such as memory loss that disrupts daily life, challenges in planning or solving
problems, difficulty completing a familiar task, confusion with time or place, trouble
understanding visual images and spatial relationships, new problems with words in speaking or
writing, poor judgement, social withdrawal, and changes in mood and personality (Albert et al.,
2011; McKhann et al., 2011; Reisberg et al., 1985; Sperling et al., 2011). When the early changes
2
related to AD occur, the brain initially compensates for them, enabling individuals to continue to
function normally (Howieson, 2016). As the damage continues, the brain can no longer
compensate for the changes, and individuals show subtle decline in cognitive function (Zsoldos et
al., 2018). Hence, it is common that patients get diagnosed when they are already manifesting signs
of dementia. Biomarkers for AD currently comprise of cerebrospinal fluid (CSF) collection and
PET scans, which are not routinely performed (Blennow and Zetterberg, 2018). One of the main
challenges in the AD field is to discover early biomarkers that are reliable, cost-effective and
accessible to identify people in pre-symptomatic phases and who are at high risk to transition to
dementia (O’Bryant et al., 2017).
Of those who develop AD, a minority of less than 5% of all patients comprises of familial
cases, also known as early-onset AD (EOAD) (Reitz and Mayeux, 2014). EOAD is determined by
genetic factors and is an autosomal-dominant inheritance that leads to early manifestations of
dementia before the age of 65. Genetic mutations in three proteins have been identified as
deterministic in causing the disease: amyloid precursor protein (APP), presenilin 1 (PSEN1), and
presenilin 2 (PSEN2). The mutations that cause EOAD occur either in the substrate (APP) or the
protease (PSEN1 and 2) of the enzymatic reaction that generates a thirty-nine to forty-two amino
acid amyloid-b (Ab) peptide
(Selkoe, 2001). In the non-amyloidogenic pathway, APP is cleaved
by a-secretase and forms APPa, a soluble amyloid fragment that is associated with neuronal
development and protection (reviewed in Habib et al., 2017). In the amyloidogenic pathway, APP
is cleaved by b- and g-secretases to form Ab peptide that leads to synaptic damage and
neurodegeneration (reviewed in Pereira et al., 2004). In AD, the predominant Ab species are forty
or forty-two amino acids in length. Ab
40
is prone for vasculature deposition, while the
hydrophobicity of the final two amino acids in Ab
42 makes it highly susceptible to aggregate and
form senile plaques (Barage and Sonawane, 2015; Masters and Selkoe, 2012).
Late-onset AD (LOAD), which represents over 95% of the cases (Reitz and Mayeux,
2014), is a multifactorial disease with a number of well-established genetic and environmental risk
factors. The single greatest risk factor is aging, with prevalence of AD approximately doubling
every 5 years after the age of 65 (Hebert et al., 2013; LaFerla, 2010). In terms of genetic risk, the
most significant genetic risk factor for LOAD is the ε4 allele of the cholesterol transporter
3
apolipoprotein E (APOE4) (Altmann et al., 2014; van der Flier et al., 2011; Jun et al., 2010; Tang
et al., 1998). Among other effects, APOE4 increases risk in part by facilitating Aβ accumulation
and synaptic damage (Jun et al., 2010; Lane-Donovan and Herz, 2017; Pimenova et al., 2017).
Additionally, a number of single nucleotide polymorphisms in genes have been uncovered by
genome-wide association studies (GWAS), demonstrating an important link between innate
immunity and increased risk for AD (Lambert et al., 2013; Tanzi, 2012).
As there is currently no successful therapeutic intervention to stop or slow the progression
of AD, research has focused on identifying risk factors for, as well as mechanisms underlying, the
disease. For example, the following factors have been shown to have a positive correlation with
AD risk: lower education (Ferrari et al., 2014; Sharp and Gatz, 2011), head injury (Breunig et al.,
2013), obesity (Emmerzaal et al., 2015), and air pollution (Calderón-Garcidueñas et al., 2011). On
the other hand, higher education (Sharp and Gatz, 2011) and greater physical exercise (Brown et
al., 2013; Tolppanen et al., 2015) are negatively correlated with AD risk. Interestingly, many of
these environmental factors also affect inflammation, possibly providing a shared mechanism
through which they modulate AD risk.
1.1.1 AD Pathology
EOAD and LOAD brains display similar neuropathology characterized by the
accumulation of Aβ as senile plaque, the intraneuronal hyperphosphorylation of tau forming
neurofibrillary tangles (NFT), gliosis, and synaptic and neuronal loss (LaFerla, 2010; Perl, 2010;
Tanzi, 2012). Several hypothesis have been proposed to explain the origin and progression of AD.
Amongst the most prevalent is the amyloid cascade hypothesis, which posits that Ab accumulation
is the central event of AD (Hardy and Higgins, 1992; Selkoe and Hardy, 2016). However, the
recent failure of several amyloid-targeted therapeutics in humans has brought the amyloid cascade
hypothesis into question, making room for other theories that explain the etiology and progression
of AD pathologies (Ricciarelli and Fedele, 2017; Tolar et al., 2019). Here I will review the
pathological events of AD under the light of the amyloid cascade hypothesis and other competing
theories.
4
The “amyloid cascade hypothesis”, first proposed by John Hardy and Gerald Higgins in
1992, states that the accumulation of Ab is the primary cause of AD pathogenesis and acts as a
trigger for neuroinflammation, neuronal injury, the formation of neurofibrillary tangles (NFTs),
and neuronal death (Hardy and Higgins, 1992). It explains that Ab peptides are toxic in its varied
forms and that their excess will lead to the development of the other hallmarks of the disease,
culminating in the manifestation of the cardinal clinical cognitive symptoms (Hardy and Higgins,
1992; Selkoe and Hardy, 2016). The amyloid cascade hypothesis was developed after studying
EOAD and Down Syndrome (DS). The latter is characterized by patients having a third copy of
chromosome 21, where is the APP gene is located. The overexpression of APP leads to a high
prevalence of AD among DS patients, with approximately 60-75% of them over the age of fifty
diagnosed (Bakkar et al., 2010; Head et al., 2012; Holland et al., 1998). The excess APP in DS
patients- and the subsequent discovery of senile plaque pathology in their brains- further supports
the hypothesis of Ab as a driving factor in AD disease pathogenesis.
In 2016, the amyloid cascade hypothesis was revised to account for amyloid accumulation
in LOAD (Selkoe and Hardy, 2016). Evidence shows that to explain the wide presentation of the
disease, rather than considering only the EOAD inherited mutations driving Ab overproduction,
one should consider the imbalance between production and clearance, leading to excess Ab
accumulation (Guillot-Sestier et al., 2015a; Nalivaeva and Turner, 2019; Selkoe and Hardy, 2016;
Tarasoff-Conway et al., 2015). Decreased Ab clearance is present in EOAD and LOAD, although
more significantly prominent in LOAD (Fiala et al., 2005; Mawuenyega et al., 2010).
The innate immune system is the body’s first line of defense to respond to Ab, resulting in
the early activation of glial cells and secretion of pro-inflammatory cytokines (Heneka et al., 2015;
Wyss-Coray and Rogers, 2012). Failure to clear Ab and the persistent inflammation surrounding
plaques creates a toxic milieu for the immediate brain tissue and synaptic environment. Multiple
studies have shown that soluble Ab oligomers decrease the density of dendritic spines, long-term
potentiation and synaptic plasticity (Chapman et al., 1999; Jacobsen et al., 2006; Walsh et al.,
2002; Wei et al., 2010). Additionally, inflammation can independently have similar effects (Detrait
et al., 2014; Di Filippo et al., 2013; Mastrangelo et al., 2009; Song et al., 2013). Hence, Ab-
5
induced synaptic damage and inflammation explain many of the clinical hallmarks of AD,
including declined cognition, weakened associative learning, and increased memory impairment.
In addition to synaptic injury, Ab further damages neurons through its direct effects on tau
protein (Zheng et al., 2002). Tau is a cytoplasmic protein that binds to tubulin, stabilizing
microtubules for transport within an axon (Šimić et al., 2016). Experiments in vitro and in
transgenic mouse models have shown that Ab fibrils can induce tau hyperphosphorylation and
reduce their capacity to bind to microtubules (Gong and Iqbal, 2008; Šimić et al., 2016). When tau
is hyperphosphorylated it causes the tau protein to dislodge from the microtubules and increases
the likelihood of tau monomers to aggregate, generating intermediate tau oligomers, that will
ultimately form NFTs. NFTs accumulate within neurons, preventing synaptic communication and
inducing neurodegeneration (Kopeikina et al., 2012; Šimić et al., 2016).
The last pathological hallmark of AD to manifest is neuronal death. The accumulation of
Ab promotes cytotoxic effects that can either directly or indirectly cause neurodegeneration, such
as oxidative stress, dysregulation of calcium homeostasis, and apoptosis (Morishima et al., 2001;
Mukhin et al., 2017; Niikura et al., 2006). Additionally, Ab and tau synergize to interfere with
neuronal maturation and potentiate neuronal loss (Pascoal et al., 2017; Rajmohan and Reddy,
2017). Lastly, the inflammatory mediators also promote neuronal degeneration in AD (Streit et al.,
2009). The overall result in AD patients is marked brain atrophy, specifically in regions associated
with learning and memory such as the hippocampus and the entorhinal cortex (Mukhin et al.,
2017).
While there is considerable evidence supporting the amyloid cascade hypothesis, there are
observations that seem to be inconsistent (Ricciarelli and Fedele, 2017). For instance, many
individuals have significant amyloid plaque burden, assessed with PET scan, without showing
symptoms of memory impairment (Aizenstein et al., 2008; Delaère et al., 1990; Katzman et al.,
1988), and while Aβ oligomers are known to induce neuronal death in vitro (Kim et al., 2002;
Lambert et al., 1998; Niikura et al., 2006), neuronal cell death is virtually absent in APP or
APP/PS1 transgenic mice modelling human AD (Jankowsky et al., 2003). Hence, other theories
aim to use the vast amount of complex evidence available to build a narrative to explain the
etiology and pathogenesis of AD.
6
The biggest competitor of the amyloid-cascade hypothesis is the tau hypothesis. It posits
that hyperphosphorylation of the microtubule-associated protein tau is the causal factor in AD and
claims that tau should be targeted for AD therapeutics (Kametani and Hasegawa, 2018). One of
the strongest evidences in its favor is that the prion-like spreading of tau pathology is strongly
correlated with the extent of cognitive and clinical symptoms and neurodegeneration, as measured
by Braak score (Bejanin et al., 2017; Okamura and Yanai, 2017). Tau pathology appears first in
the entorhinal region (Braak stages I and II), then spreads to the limbic region (stages III and IV)
and neocortical areas (stages V and IV) (Braak and Braak, 1991). Additionally, tau has been shown
to induce the other hallmarks of the disease: Ab production (Bright et al., 2015; Nisbet et al.,
2015), neuroinflammation (Morales et al., 2013; Perea et al., 2018; Vogels et al., 2019), and
neuronal cell death (Kopeikina et al., 2012; Šimić et al., 2016). Interestingly, the tau hypothesis
suggests that EOAD development is related to APP metabolism impairments, which leads to the
accumulation of intraneuronal APP C-terminal fragments, rather than Ab overproduction. This
accumulation impairs the vesicle transport machinery, affecting tau (Klevanski et al., 2015; Roy
et al., 2005; Tamayev et al., 2012).
The mitochondrial cascade hypothesis focuses on LOAD and postulates that mitochondrial
dysfunction triggers the development of AD (Swerdlow and Khan, 2004). This hypothesis
incorporates the influence of environmental risk factors and aging, which have been shown to
cause somatic mitochondrial DNA mutations over time (Ross et al., 2013; Trifunovic et al., 2004).
Moreover, mitochondrial dysfunction affects APP expression and metabolism, Aβ accumulation,
tau phosphorylation and inflammation (Baik et al., 2019a; Chen et al., 2012; Eckert et al., 2011;
Gasparini et al., 1997; Pereira et al., 2004; Tamagno et al., 2002). This hypothesis has an
interesting argument: maternal risk has been reported to contribute more to AD inheritance, which
can be explained by with how mitochondria is predominantly inherited from the mother side
(Swerdlow et al., 2014).
Recently, alternative hypothesis such as the “antimicrobial protection hypothesis” and the
“metabolic hypothesis” have been gaining strength (Du et al., 2018). Findings demonstrating Aβ
is an antimicrobial peptide acting against bacteria, fungi, and viruses and that the brains of AD
patients contain pathogens trapped in the core of senile plaques gives credibility to the hypothesis
of an infectious agent as the etiology of AD (Balin et al., 2008; Miklossy, 2011; Moir et al., 2018).
7
Further, genetic, biochemical, pharmacological evidence strongly support a role for cholesterol
and lipid metabolism in Aβ generation, deposition, and clearance, implicating lipid metabolism
impairment as causative in AD (Fassbender et al., 2001; Hartmann, 2006; Hone et al., 2019; Jick
et al., 2000; Liu and Zhang, 2014; Vassar et al., 1999; Wellington, 2004).
Independently from the etiological factor in AD, several studies have now shown that
modulating inflammation and promoting immunity-dependent clearing of toxic aggregates
improve cognition in animal models of AD (Baik et al., 2019b; Detrait et al., 2014; Guillot-Sestier
et al., 2015b; Town et al., 2008). Increasing evidence shows that Ab-induced inflammation plays
a significant role in disease pathogenesis (Guillot-Sestier and Town, 2013; Heneka et al., 2015;
Wyss-Coray and Rogers, 2012). At homeostatic levels, increased inflammation is beneficial and
necessary to overcome an insult to the body. However, in AD, the chronic nature of the disease
shifts the immune system towards a pathological state. The prodromal nature of Ab accumulation
emphasizes the critical need to understand the events prior to onset of dementia, including the role
of inflammation in disease pathogenesis. Understanding how to rebalancing immunity is the next
frontier for future therapies that aim at halting the progression of AD.
1.2. Inflammation
Inflammation is a key pathological component in AD that has been proposed as a major
mechanism both in the initiation and progression of the disease (Wyss-Coray and Rogers, 2012).
Normal aging is associated with an increase in chronic inflammation (Singh and Newman, 2011),
suggesting that inflammation is one of several age-related changes that may cooperatively increase
AD risk. Several pathways through which inflammation can drive AD pathogenesis have been
identified. For example, increased levels of pro-inflammatory cytokines can stimulate APP
processing to generate more Aβ, which not only directly impairs neural health, but also acts on
microglia and astrocytes to further increase inflammation (Blasko et al., 2004). In this way,
inflammation has been proposed to be both a driving force and a consequence of AD pathology
(Heneka et al., 2014, 2015). Interestingly, levels of cytokines are elevated in serum even before
there is detectable Aβ pathology (Avila-Muñoz and Arias, 2014; Eikelenboom et al., 2011),
8
pointing to a role for inflammation in the initiation of disease. Indeed, several conditions associated
with neural and systemic inflammation increase AD risk (Fleminger et al., 2003; Kamer et al.,
2008; O’Brien and Markus, 2014; Ownby et al., 2006; Xu et al., 2011), pinpointing the immune
system as a major player in AD.
The involvement of the immune system in AD pathogenesis is supported by GWAS, which
identified several alleles related to immunity as AD risk factors (Lambert et al., 2013). These
immune-related genes include myeloid-cell receptors, as well as immunometabolism-related genes
(Hollingworth et al., 2011). Further, integrative genomic studies to find key regulatory molecules
related to AD pointed to cytokines and immune intracellular mediators as major players in
regulating innate immunity and response to AD pathology (Li et al., 2015; Morabito et al., 2019;
Zhang et al., 2013a). Interestingly, analysis of expression quantitative trait locus (eQTL)
demonstrated an overrepresentation of monocyte-specific eQTLs among AD disease variants (Raj
et al., 2014), implicating the innate immune compartment specifcially. Collectively, these findings
strongly support the position that AD pathogenesis is promoted by neuroinflammation, a process
that involves both innate immune cells and the cytokines they produce.
1.2.1 Cellular mediators of inflammation
Microglia and astrocytes are glial cell types that are essential for brain homeostasis. Both
cell types are activated in AD, a response characterized by morphological changes and increased
production and secretion of cytokines, chemokines, complement proteins, and acute-phase
proteins (Heneka et al., 2015). Chronic activation of microglia and astrocytes has been observed
in the pathophysiology of AD in humans and in rodent models (Perl, 2010).
Microglia are tissue-resident macrophages in the brain. They are immune cells that are
responsible for tissue surveillance and represent the first line of defense in the central nervous
system (CNS). Microglia are able to phagocytose foreign particles and are important participants
in the elimination of pathogens from the brain (Prinz and Priller, 2014). Although they are myeloid
cells, they differ to some extent from other macrophages that reside in other tissues: microglia
originate from hematopoietic stem cells of the yolk during development and not from the bone-
marrow, and they are long-lived cells that are able to self-renew (Ginhoux et al., 2010; Yona et al.,
9
2013). In addition to screening the brain parenchyma for abnormalities (Sierra et al., 2014), under
normal conditions, microglia participate in many functions that promote neural health including
synaptic pruning and remodeling (Paolicelli et al., 2011; Schafer et al., 2012) and synaptic
plasticity (Parkhurst et al., 2013). Upon encountering pathogens or injuries, microglia adopt (an)
activated phenotype(s). Activated states differ from the resting state by alterations in morphology
(the cytoplasmatic projections retract and cell bodies become more amoeboid), as well as changes
in surface protein expression, phagocytic ability, mobility, and proliferative capacity. Macrophage
responses to pathogens are typically categorized into M1 or M2 polarization. The M1 state is pro-
inflammatory, cytotoxic and phagocytic, whereas the M2 state supports tissue remodeling,
promotes fibrosis and is anti- inflammatory (Durafourt et al., 2012). This classification system has
been extended to microglia as well, but it cannot account for the entire range of phenotypes that
can be found in the brain, especially under chronic inflammatory conditions (David and Kroner,
2011; Town et al., 2005). A range of activated microglial phenotypes can be generated depending
on the insults and modulators they experience from the microenvironment (Hanisch and
Kettenmann, 2007). Because AD is a multifactorial disease in which both lifestyle factors and
genetic variants impact the outcome of the disease, microglial phenotypes will also vary
significantly based on these factors.
A number of studies have examined the effects of inhibiting microglial activity on AD
outcomes. Inhibiting microglial proliferation via pharmacological blockade of the colony-
stimulating factor 1 receptor (CSF1R) improved memory and prevented synaptic degeneration in
a mouse model of AD, without affecting Aβ plaques (Olmos-Alonso et al., 2016). Likewise,
eliminating microglia prevented neuronal loss and neuroinflammation, and improved memory,
without altering levels of Aβ (Spangenberg et al., 2016). Furthermore, administration of
minocycline, a tetracyclic antibiotic that inhibits microglial activation, ameliorated AD-like
pathology in transgenic mice and downregulated inflammatory markers, partially through
inhibition of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) and beta-
secretase 1 (BACE-1) (Ferretti et al., 2012). Deficiency of the NLRP3 (NOD-, LRR- and pyrin
domain-containing protein 3) inflammasome skewed activated microglia towards a pro-phagocytic
state in AD transgenic mice, resulting in increased Aβ clearance and enhanced tissue remodeling
10
(Heneka et al., 2013), suggesting that shifting microglial activation state to a more anti-
inflammatory phenotype could be an effective strategy.
While inflammation has classically been labeled as deleterious in AD progression (Wyss-
Coray and Rogers, 2012), studies aimed at reducing inflammation as a therapeutic approach,
including treatment with non-steroidal anti-inflammatory drugs (NSAIDs), have failed to
clinically improve patients (Meyer et al., 2019). In fact, treating patients with MCI with NSAIDs
has sped up the progression from MCI to AD, highlighting a beneficial role of inflammation in
AD (Meyer et al., 2019; Thal et al., 2005). To that point, several studies promoting activation of
myeloid cells, rather than a generalized suppression, via targeting inflammatory pathways have
been proven beneficial to ameliorate AD-like pathology in animals (Baik et al., 2019a; Guillot-
Sestier et al., 2015b; Town et al., 2008). Not only inflammatory pathways modulate innate
immunity, but so does cell-cell interactions. Astrocytes and neurons, for instance, notoriously
suppress microglial functions, such as phagocytosis (Biber et al., 2007; DeWitt et al., 1998; Liu et
al., 1994).
Astrocytes also play a relevant role in AD (Ceyzériat et al., 2018; González-Reyes et al.,
2017; Liddelow et al., 2017; Perez-Nievas and Serrano-Pozo, 2018; Sadick and Liddelow, 2019).
Activated astrocytes are characterized by increased expression of glial fibrillary acidic protein
(GFAP) and functional impairment. Like microglia, astrocytes release cytokines, nitric oxide, and
other cytotoxic molecules after exposure to Aβ, thus exacerbating neuroinflammation (Johnstone
et al., 1999; Liddelow et al., 2017). Astrocyte activation may occur even before Aβ deposition and
thus contribute to both early and late phases of AD pathogenesis (Carter et al., 2012; Pike et al.,
1995). Furthermore, ApoE is needed for astrocyte-mediated Aβ clearance (Koistinaho et al., 2004;
Wyss-Coray et al., 2003) and astrocyte-dependent lipidation of ApoE increases the ability of
microglia to clear Aβ (Terwel et al., 2011). However, in addition to their beneficial effects on Aβ
clearance, astrocytes also contribute to a feedback process that exacerbates Aβ pathology. For
example, Aβ decreases glutamate uptake by astrocytes, which can increase excitotoxicity and
decrease neuron viability (Antuono et al., 2001; Matos et al., 2008; Verkhratsky et al., 2010).
Moreover, astrocytes increase APP expression upon neuronal injury, which may contribute to
increased Aβ accumulation after injury (Siman et al., 1989). Human astrocytes synthesize Aβ
40
and Aβ
42
when stimulated by the pro-inflammatory cytokines, events that can occur early in AD
11
development (Blasko et al., 2000; Monson et al., 2014). Thus, astrocytes can have both beneficial
and harmful roles in the context of AD.
It is now known that the level of steady-state immune privilege varies dramatically between
brain compartments, raising the possibility that other immune cells may contribute to CNS
homeostasis and pathologies (Mrdjen et al., 2018). In homeostatic conditions, the brain
parenchyma is populated by microglia, whereas outside of the parenchyma immune surveillance
is mediated by bone-marrow-derived dendritic cells (Greter et al., 2005; Kivisäkk et al., 2009) and
CNS border-associated macrophages, which are localized to the meninges, choroid plexus, and
perivascular spaces (Bechmann et al., 2001). During neuroinflammation, the immune landscape
gets dramatically altered, in which the parenchyma can be infiltrated by peripheral macrophages
and, eventually, T cells (Greter et al., 2005; Kivisäkk et al., 2009; Schreiner et al., 2009). In AD
though, the infiltration of peripheral immune cells have been controversial (Mildner et al., 2007;
Reed-Geaghan et al., 2020). For instance, it has been technically challenging to find a reliable
marker to differentiate microglia from peripheral macrophages (Greter et al., 2015; Mrdjen et al.,
2018), which can adopt a microglial-like phenotype after entering the CNS (Lund et al., 2018).
Furthermore, tracing experiments using engrafted GFP-labelled bone-marrow cannot rule out the
effect of radiation-induced damage to the blood-brain barrier (Lampron et al., 2012). With
peripheral macrophages being better phagocytes than microglia (Durafourt et al., 2012; Fiala et
al., 2005), their recruitment to the CNS has been exploited as potential therapy to propel Ab
clearance (Town et al., 2008).
Other cells of the immune system may play important roles in AD as well, although their
contributions to pathology are still poorly understood. Recently, increased attention has been given
to the adaptive immune system, which is able to coordinate and control the innate immune system
(Abbas et al., 2019). B cells and T cells have been suggested to modulate AD pathogenesis in that
they can regulate microglial function by stimulating phagocytic ability with antibodies and
controlling release of inflammatory cytokines (Marsh et al., 2016). Evidence showing changes in
the adaptive immunity with disease progression in mice and humans (Ciccocioppo et al., 2019;
Gate et al., 2020; Oberstein et al., 2018; St-Amour et al., 2019) point to a possible therapeutic route
by targeting the adaptive immune compartment. For instance, data from our lab and others
demonstrate that T regulatory T cells transient inhibition and T helper 17 T cells neutralization
12
ameliorate AD-like pathology and cognitive performance in mice (Baruch et al., 2015; Cristiano
et al., 2019; Zhang et al., 2013b). Additionally, Aβ
42
immunization, which aims to modulate CNS
immune cells by increasing the amount of antibodies directed against Aβ in the serum, prevents
deposition, enhances clearance of amyloid plaques, and decreases gliosis in animal models of AD
(Schenk et al., 1999). In humans, Aβ immunotherapy enhances plaque clearance, and reduces
microglia and astrocyte activation (Nicoll et al., 2003; Zotova et al., 2013). Nevertheless, the exact
mechanism underlying of successful pre-clinical and clinical immunotherapies remain to be fully
clarified.
1.2.2 Soluble mediators of inflammation
Cytokines are key mediators of neuroinflammation. Cytokines are a soluble,
multifunctional, heterogeneous group of proteins that can act locally, in a paracrine or autocrine
way, although they can travel through the bloodstream to mediate effects on numerous tissues
(Abbas et al., 2019). Interleukins (IL), tumor necrosis factors (TNF), interferons (IF), transforming
growth factors (TGF), and chemokines comprise the major cytokines that can activate cells, cause
apoptosis, and attract cells to a site of injury (Abbas et al., 2019). Although cytokines can typically
be classified as pro-inflammatory and anti-inflammatory, recent findings have demonstrated that
several cytokines can act on a polar opposite of the spectrum depending upon context (Masli and
Turpie, 2009; Mühl, 2013; Scheller et al., 2011). Still, the balance between pro- and anti-
inflammatory mediators enables an immediate and tightly controlled response that is balanced
against tissue destruction (Abbas et al., 2019). In AD, evidence points to an impairment in the
resolution of the inflammatory process, tipping the balance between pro-inflammatory and anti-
inflammatory cytokines (Heneka et al., 2015).
In the cerebrospinal fluid (CSF) of AD patients, both pro- and anti-inflammatory cytokines
are elevated (Brosseron et al., 2014), suggesting a disruption of immune system homeostasis rather
than a biased upregulation of only pro-inflammatory components. Levels of some cytokines like
IL-1β, correlate with cognitive deterioration (Cacabelos et al., 1991). Cytokine polymorphisms
have been found to interact with other AD risk factors (Arosio et al., 2004; Di Bona et al., 2008;
Lee et al., 2015), including APOE4 (Chapuis et al., 2009; Liu et al., 2014; Wang and Jia, 2010;
13
Yu et al., 2009). Thus, cytokines are intrinsically involved in the inflammatory processes
associated with AD.
Several studies have successfully reduced AD-like pathology in transgenic mouse models
using anti-inflammatory strategies. For example, targeting TNF-α synthesis (Gabbita et al., 2015;
Tweedie et al., 2012) or the TNF receptor (Detrait et al., 2014) reduced Aβ and tau pathology and
restored memory deficits in AD transgenic mice. However, some studies have found the opposite,
instead showing attenuation of Aβ deposition in the hippocampus of transgenic AD mice that
overexpress TNF-α (Chakrabarty et al., 2011). One important aspect to account for divergent
findings may be the timing of the intervention. That is, cytokine overexpression prior to significant
pathology may be beneficial, as has been observed with TNF-α (Chakrabarty et al., 2011), IL-6
(Chakrabarty et al., 2010a), and IFN-g (Chakrabarty et al., 2010b). However, chronic TNF-α
overexpression leads to an increase in inflammation and ultimately to neuronal cell death in 3xTg-
AD mice (Janelsins et al., 2008). Thus, a heightened inflammatory response may be beneficial at
early stages of AD pathogenesis but detrimental once pathology has progressed.
In addition to their role in inflammation, cytokines also play important roles in other
aspects of AD, including memory, cell death, tau hyperphosphorylation and amyloidogenesis. For
instance, IL-1β production promotes APP processing and tau pathology, contributing to impaired
synaptic plasticity and memory formation (Pickering and O’Connor, 2007; Sheng et al., 2000),
and neutralizing antibodies against IL-1β improve cognitive deficits in an AD mouse model
(Kitazawa et al., 2011). IL-6 also contributes to APP processing and neurofibrillary tangle
formation (Spooren et al., 2011), and its levels are correlated with cognitive decline in humans
(Weaver et al., 2002). Hence, soluble inflammatory factors can influence AD pathology through
multiple fronts.
Anti-inflammatory responses have been largely overlooked as culprits in AD. The anti-
inflammatory cytokine, IL-10, has a functional polymorphism that has been associated with risk
for LOAD in some, but not all populations (Arosio et al., 2004; Depboylu et al., 2003; Lio et al.,
2003; Ma et al., 2005). Further, all elements of the IL-10 signaling pathway are abnormally
elevated in AD patients’ sera and brains and in rodent models of the disease (Angelopoulos et al.,
2008; Asselineau et al., 2015; Gezen-Ak et al., 2013; Guillot-Sestier et al., 2015b). Likewise, the
14
levels of the anti-inflammatory cytokine TGF-β1 are increased in AD patients’ CSF and serum
(Chao et al., 1994a, 1994b). TGF-βR1 immunoreactivity is associated with amyloid-β plaques
(van der Wal et al., 1993), and is elevated in AD patient reactive microglia (Lippa et al., 1998).
Our lab has reported that Il10 genetic ablation in a mouse model of cerebral amyloidosis activated
microglia to clear amyloid, without coming at the cost of bystander neuronal injury (Guillot-Sestier
et al., 2015b). In agreement to our findings, cerebral Il10 overexpression in TgCRND8 and Tg2576
mice exacerbated Aβ plaque number and size and decreased synaptic protein abundance;
worsening cognitive impairment (Chakrabarty et al., 2015). In TgCRND8 transgenic mouse model
of AD, brain TGF-β1 is elevated, where it amplifies Aβ-induced neurodegeneration (Salins et al.,
2008). These data suggest that the AD brain may over-compensate for Aβ insult by inappropriately
producing high levels of anti-inflammatory cytokines such as TGF-β1 (Wyss-Coray and Mucke,
2002; Wyss-Coray et al., 1997), thereby inducing inefficient neuroinflammation that impairs Aβ
clearance. Collectively, these findings indicate that inhibition of AD pathogenesis will likely
require modulation rather than broad inhibition or activation of innate immune activities.
1.3 Clearance of toxic aggregates
One important role for innate immunity in AD is their participation in clearance (Prinz and
Priller, 2014). Notably, the ability of innate immune cells to effectively clear Aβ is impaired in
AD (Fiala et al., 2005; Mawuenyega et al., 2010). Interestingly, two interactive regulators of
phagocytosis, cluster of differentiation (CD33) and triggering receptor expressed on myeloid cells
2 (TREM2), have polymorphisms linked to increased risk for AD (Ma et al., 2014; Walker et al.,
2015). TREM2 is enriched in white matter and in microglia surrounding Aβ plaques and has
functions associated with promoting phagocytosis while suppressing cytokine signaling.
Heterozygous loss-of-function mutation in TREM2 predisposes to AD (Guerreiro et al., 2013).
CD33 is found in monocytes and contains an immunoreceptor that is typically an inhibitor of
cellular activity (Bradshaw et al., 2013). CD33 expression is increased in the microglia of AD
brains and it inhibits uptake and clearance of Aβ42 (Griciuc et al., 2013). Although the
relationships between Aβ clearance, CD33, and TREM2 remain to be fully resolved, their
association suggests an imbalance in this pathway (Malik et al., 2013).
15
Other receptors related to clearance have been implicated in AD. Scavenger receptors, such
as CD36, alpha6beta1 integrin, and CD47, have been implicated in AD as a key element for
fibrillar Aβ-induced innate immune activation and uptake (Koenigsknecht and Landreth, 2004).
Distinctive Aβ conformations can trigger different immune responses, such that oligomers can
induce an inflammatory response that disturb phagocytosis and clearance of Aβ fibrils, thereby
contributing to immune tolerance characteristic of AD (Pan et al., 2011). The complement receptor
1 (CR1) in immune cells interacts with activated complement components and triggers the
clearance of bound factors, like fibrillar Aβ (Afagh et al., 1996) as well as to neuronal synapses
(Hong et al., 2016). These actions can stimulate innate immune cells to phagocytose both Aβ and
synapses, which could be either beneficial or detrimental in the context of AD (Fonseca et al.,
2004).
Lastly, immune cells can also sense Aβ via cell-surface receptors including the toll-like
family of receptors (TLR) (Fassbender et al., 2004; Salminen et al., 2009; Tahara et al., 2006).
TLR activation leads to a signaling cascade that culminates in activation of immunomodulatory
transcription factors (Israel, 2010). Polymorphisms in one member of this family, the TLR4
receptor, have been associated with increased AD risk (Balistreri et al., 2008). Importantly, TLR4
interacts with other modulators of AD, including apoE4 (Tai et al., 2015), saturated fatty acids
(Lee et al., 2001), and pollution particulate matter (Bauer et al., 2012). TLR4 can bind to Aβ,
leading to activation of the transcription factor NF-kB and increased expression of inflammation-
related genes (Reed-Geaghan et al., 2009; Stewart et al., 2010). NF-kB is a key transcription factor
involved in inflammation, cell division, and apoptosis (O’Neill and Kaltschmidt, 1997).
Interestingly NF-kB upregulation is observed in the brain of AD patients (Ferrer et al., 1998), and
blocking NF-kB decreases Aβ in cell culture and animal models of AD (Collister and Albensi,
2005; Solberg et al., 2014), although there is still uncertainty regarding the role of microglial
specific NF-kB signaling in AD (Weitz et al., 2014). Additionally, innate immunity is able to
uptake soluble amyloid beta peptide Aβ
42
through pinocytosis via a process involving activation
of purinergic 2Y4 receptors (P2RY4) (Li et al., 2013). Importantly, innate immune cells may
internalize Aβ through a receptor-independent mechanism, as soluble Aβ has been shown to be
consumed through a nonsaturable macropinocytic mechanism that is distinct from phagocytosis
and receptor-mediated endocytosis (Mandrekar et al., 2009).
16
Tau aggregates has been shown to be cleared by microglia in vitro and in vivo (Asai et al.,
2015; Bolós et al., 2015; Das et al., 2020; Luo et al., 2015). Although not much is known regarding
which receptors are implicated in this process, it seems to be partially dependent on Fc receptor
(FcR) and Cx3cr1 receptors (Bolós et al., 2017; Funk et al., 2015). One specific challenge with
promoting tau uptake is doing so without causing efferocytosis of healthy neurons, as tau can
expose the eat-me signal phosphatidyl serine and induce neuronal phagocytosis (Funk 2015,
Brelstaff, 2018). Therefore, tau-based immunotherapies look for candidates that promote clearance
of hyperphosphorylated tau, leading to improvement in cognitive functions (Funk et al., 2015; Luo
et al., 2015; Vogels et al., 2019).
Innate immune cells are recruited to Aβ plaques and NFTs during the progression of AD,
but studies have suggested that they are not able to efficiently degrade aggregates (Fiala et al.,
2005; Paresce et al., 1997). This failure in clearance is connected to an exacerbated inflammatory
response (Sokolowski and Mandell, 2011). Both decreased phagocytosis and increased cytokine
production are associated with cognitive decline in AD (Mawuenyega et al., 2010; Orre et al.,
2014). Interestingly, manipulation of key components of the innate immune system has been
shown to successfully break innate immune tolerance and induce clearance (Baik et al., 2019b;
Guillot-Sestier et al., 2015b; Town et al., 2008). Thus, in order to clear toxic proteins, innate
immunity must be able to sense and recognize its presence, trigger an inflammatory response to
phagocytose and degrade Aβ and tau. Disruption in any of these important functions can contribute
to the accumulation of toxic proteins associated with AD pathology.
1.4 Conclusion
While preventative and therapeutic strategies are being pursued, an effective AD treatment
does not exist. In past decades, cerebral Aβ accumulation–widely considered to drive AD
pathogenesis–has been a major therapeutic target. In one embodiment, drugs have been designed
to inhibit the secretases responsible for Aβ production from APP. Yet, this has been problematic
(Volloch and Rits, 2018). An alternative is targeting the other side of the equation: Ab clearance.
This concept is rooted in the notion that failed Ab clearance, rather than overproduction, is the
17
likely etiologic culprit in sporadic AD . Moreover, this concept can be extended to other toxic
aggregates, such as tau. Recent efforts focusing on tau or Ab based immunotherapy have been
encouraging (Funk et al., 2015; Luo et al., 2015; Nicoll et al., 2003, 2019; Sengupta et al., 2016;
Vogels et al., 2019). Nevertheless, new targets that focus on directly modulating the immune
system immunity consist of a promising next therapeutic frontier (Cummings et al., 2019;
Marciani, 2019).
This thesis work is largely based on the amyloid-cascade hypothesis, presupposing that
clearing Ab will lead to amelioration of AD pathology and symptoms. The overarching hypothesis
of this work is that ‘rebalancing’ the innate immune system, rather than generally silencing it, will
be beneficial against AD (Figure 1). Here I investigate different innate immune pathways that
regulate cells’ response to Ab. Specifically, the STAT3, TLR4/NF-kB, and IL-10 pathways are
examined for their involvement in both AD development and in controlling innate immune
clearance. Further, I manipulate these pathways to test their significance to Ab clearance as a
therapeutic route.
Figure 1. Rebalancing innate immunity in Alzheimer’s disease. In AD, there is an imbalance
between production and clearance of Ab. Innate immune cells are able to clear Ab via
phagocytosis, but they fail to perform in AD, leading to amyloidosis. Immunity is at the center-
stage of AD pathogenesis as a driver of the disease. Hence, investigation of key
18
immunomodulators such as IL-10, STAT3 and TLR4/NF-kB pathways, will further our
understanding on how to rebalance innate immunity to fight AD pathologies.
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38
CHAPTER 2: STAT3 DELETION INHIBITS CEREBRAL INNATE IMMUNITY AND
EXACERBATES ALZHEIMER-LIKE PATHOLOGY IN FEMALE APP/PS1 MICE
Acknowledgements: I can’t express how grateful I am for working alongside to Rachel Oseas,
Alexander Vesling and Riyaz Razi for the completion of this project. I would like to thank Dr.
Marie-Victoire Guillot-Sestier, Dr. Kevin Doty and Dr. Terrence Town for conceptualizing this
project. The Town lab members were of essential support, specifically, Anakha Ajayan, Alicia
Quihuis and Dr. Balint Der. Thanks Chris W Im for helpful discussion. This work utilized the
Meso Sector S 600 in the Translational Research Laboratory and the LSRII in the USC Stem Cell
Flow Cytometry Facility, made possible through the University of Southern California School of
Pharmacy and Keck School of Medicine. Special thanks to Dr. Junji Watanabe, Dr. Bernadette
Masin and Dr. Jeffrey Boyd for technical support. This work was supported by the National
Institute of Health (R01 AG053982-01A1, to T.T.).
Contributions: Conceptualization, M.V.G.S., K.R.D., M.F.U. and T.T.; Methodology, M.V.G.S.,
K.R.D., M.F.U, R.O. and T.T; Investigation, M.F.U., R.O., A.W.V., M.V.G.S., K.R.D., A.A.,
N.S., A.M.Q., C.J.M., B.D.; Validation, A.W.V., R.R., B.P.L and C.J.M; Formal analysis, M.F.U.,
R.O. and A.W.V.; Funding acquisition, M.V.G.S, K.R.D and T.T.; Resources, T.T.; Project
administration, M.F.U, K.R.D and T.T.
*This chapter has been based on the manuscript in submission: Mariana F Uchoa, Rachel Oseas,
Alexander W Vesling, Kevin R Doty,
Nima Shajarian, Cole J Miller, Riyaz Razi, Anakha Ajayan,
Alicia M Quihius, Balint Der, Brain P Leung,
Marie-Victoire Guillot-Sestier, Terrence Town.
Conditional Stat3 deletion inhibits cerebral innate immunity and exacerbates Alzheimer-like
pathology in female APP/PS1 mice. In submission to Cell Reports, 2020.
39
2.1 Summary
Failure of cerebral innate immune cells to clear toxic misfolded proteins is now recognized as an
early event in Alzheimer’s disease (AD) evolution. Signal transducer and activator of transcription
3 (Stat3) impacts multiple innate immune processes and is a key neuroinflammatory mediator in
AD. However, the specific role of Stat3 in the innate immune response to amyloid-beta (Ab) has
remained elusive. To address this, we conditionally deleted Stat3 in Csf1r
+
innate immune cells
(Stat3 cKO) in the adult APP/PS1 mouse model of AD and analyzed effects on brain immune
inflammatory responses and AD-like pathology. Surprisingly, female APP/PS1 Stat3 cKO
mice
displayed selectively reduced activation of cerebral innate immunity. Additionally, females (but
not males) had increased abundance of cerebral Ab together with reduced amyloid uptake by
cortical innate immune cells. This work demonstrates that loss of innate immune Stat3 signaling
impacts AD-like pathology in a sex-specific manner.
2.2 Introduction
At the cellular level, microglia, the main innate immune component of the brain, become
activated in response to amyloid accumulation. After chronic exposure to amyloid-b (Ab),
microglia display reduced phagocytic capacity and upregulate their release of cytokines, creating
an inflammatory and cytotoxic milieu (Hickman et al., 2008). Hence, controlling inflammation
while preserving innate immune clearing functions become a desirable outcome for Alzheimer’s
disease (AD) treatment. Yet, interventions that have aimed to attenuate inflammation or promote
inflammation have both succeeded and failed depending upon the context of the study
(Chakrabarty et al., 2010; Mastrangelo et al., 2009; Meyer et al., 2019; O’Connor and Coogan,
1999).
Signal transducer and activator of transcription 3 (Stat3) has been identified by integrative
genomic studies as being upregulated in late onset AD (Li et al., 2015; Zhang et al., 2013), which
our laboratory has biochemically confirmed in AD brain samples (Guillot-Sestier et al., 2015a).
Stat3 is a transcription factor that exists in an inactive form in the cytoplasm until it is
phosphorylated by Janus kinase (JAK), resulting in dimerization and translocation to the nucleus.
Once in the nucleus, it binds DNA and controls downstream gene transcription of a variety of
40
immunological functions (Hutchins et al., 2012), including the regulation of macrophage
polarization, migration, proliferation, survival and phagocytosis (Hillmer et al., 2016).
Stat3 has been shown to be activated in response to diverse classes of ligands, including
interleukin 6 and 10 (IL-6 and IL-10), interferons (IFN), colony-stimulating factor (CSF),
endothelial growth factor (EGF), leptin, and others, emphasizing Stat3’s biological complexity
(Hillmer et al., 2016). In immunity, Stat3 effects are highly context dependent, in which Stat3 can
act as a pro-inflammatory or anti-inflammatory mediator (Braun et al., 2013). Studies that disrupt
Stat3 signaling either genetically, pharmacologically, or even more importantly, in a tissue or cell-
specific manner, have shed light to the context specific nature of Stat3 signaling. For example,
conditional knockout of Stat3 in macrophages and neutrophils revealed that the absence of Stat3
leads to activation of these myeloid cells, potentiating their response to bacterial
lipopolysaccharide (Takeda et al., 1999). Whereas in this context the lack of Stat3 primarily
impacted the IL-10/Stat3 mediated resolution of inflammation, another group reported that a gain-
of-function mutation of Stat3 exacerbates immune activation via IL-6 signaling and increases the
risk for autoimmunity (Vogel et al., 2015). Furthermore, one study demonstrated Stat3 signaling
is necessary for phagocytosis of apoptotic cells (Campana et al., 2018), yet, in a different study,
Stat3 prevented Ab phagocytosis, with Stat3 silencing rescuing microglial phagocytic potential in
vitro (Guillot-Sestier et al., 2015a). With Stat3 uniquely situated at the intersection of pro and anti-
inflammatory signaling, it has become central to studies aimed at ‘re-balancing’ innate immunity
rather than independently targeting one aspect over the other. Pharmacological Stat3 inhibition has
been extensively researched as an immunotherapeutic drug to increase macrophage activity against
tumors (Munoz et al., 2014) and has recently been tested systemically in an AD model
(Reichenbach et al., 2019). Yet, the importance of microglial Stat3 to their response to Ab in vivo
remains unclear. To assess Stat3’s role in innate immunity within the context of AD, we generated
an inducible and conditional knockout mouse to delete Stat3 in brain innate immune cells (Stat3
cKO) and analyzed cerebral amyloidosis and immune phenotypes.
41
2.3 Material and Methods
Key Resources table
REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
Rat anti-mouse CD45- Pacific Blue
(Clone 30-F11), 1:200
BioLegend BioLegend Cat# 103134, RRID:
AB_2562559
Rat anti-mouse CD11b- BV550 (Clone
M1/70), 1:200
BioLegend BioLegend Cat# 101239, RRID:
AB_11125575
Rabbit anti-Iba1, 1:200 Wako Wako Cat# 019-19741,
RRID:AB_839504
Goat anti-Aif1, 1:200 LifeSpan LifeSpan Cat# LS-B2645-50,
RRID:AB_1664318
Rat polyclonal anti-CD68 clone FA-11,
1:100
Abcam Abcam Cat# ab53444,
RRID:AB_869007
Rabbit polyclonal anti- Ki67, 1:200 Cell Signaling Technology CST Cat# 9129, RRID:AB_2687446
Mouse monoclonal amyloid 1-16
(6E10), 1:600
Covance Covance Cat# SIG-39320,
RRID:AB_662798
Mouse monoclonal amyloid 1-16
(4G8), 1:600
BioLegend Covance Cat# SIG-39300-200,
RRID:AB_662803
APP C-terminal fragments clone
D54D2, 1:1000
Cell Signaling Technology CST Cat# 8243, RRID:AB_2797642
mouse monoclonal PS1, 1:1000 BioLegend Covance Cat# SIG-39194-1000,
RRID:AB_10718503
Synaptophysin, 1:500 Abcam Abcam Cat # ab7837,
RRID:AB_306124
PSD-95, 1:500 Merck Millipore Millipore Cat# AB9708,
RRID:AB_2092543
mouse monoclonal β-actin, 1:1000 ProteinTech Proteintech Cat# 66009-1-Ig,
RRID:AB_2687938
β-tubulin Sigma- Aldrich Sigma-Aldrich Cat# T0198,
RRID:AB_477556
Reagents
42
Hoechst 33342 Thermo Fischer Cat # 62249
Tamoxifen Sigma- Aldrich Cat # T5648
Critical Commercial Assays
V-PLEX Plus Aβ Peptide Panel 1
(6E10) Kit
Meso Scale Discovery Cat #K1500G
Customized V-PLEX mouse pro-
inflammatory panel 1 Kit
Meso Scale Discovery Cat# K15048D
Aβ bound to 82E1 antibody in
sandwich ELISA
IBL International Cat# JP27725
Experimental Models:
Organisms/Strains
B6.129S1-Stat3tm1Xyfu/J, Jackson Lab stock #016923
FVB-Tg(Csf1rcre/Esr1*)1Jwp/J, Jackson Lab stock #019098
B6.Cg
Tg(APPswe,PSEN1dE9)85Dbo/Mmja
x MMRRC
Jackson Lab stock #034832
B6.Gt(ROSA)26Sor
tm14(CAG-tdTomato)Hze
/J Jackson Lab stock #007914
Software and Algorithms
FlowJo v.10 BD Life Sciences https://www.flowjo.com
Imaris Bitplane v.7.6.5 Oxford Instruments.
Oxfordshire, UK.
https://www.imaris.oxinst.com
GraphPad Prism 8.0 Software GraphPad Software. La
Jolla, CA.
https://www.graphpad.com
Bio-Rad Image Lab Software Bio-Rad. Hercules, CA. https://www.bio-rad.com/Imaging
Fiji Image J Open-source https://www.imajej.net
Experimental Model and Subject Details
Ethics Statement
All animal experiments were approved by the University of Southern California
Institutional Animal Care and Use Committee and performed in strict accordance with National
Institutes of Health guidelines and recommendations from the Association for Assessment and
Accreditation of Laboratory Animal Care International.
43
Mice
To conditionally and inducibly delete Stat3 in macrophages, we crossed mice
carrying loxP-site–flanked (floxed) alleles of the Stat3 gene (referred to as Stat3
f/f
) to Csf1r-
cre
ESR
and APP/PS1 transgenic mice. Filial Csf1r-cre
ESR+/-
Stat3
f/f
were crossed with APP/PS1
+/-
Stat3
f/f
to generate experimental animals: 1) APP/PS1 Csf1r-cre
ESR+/-
Stat3
f/f
here termed solely
APP/PS1,
and 2) APP/PS1 Csf1r-cre
ESR+/-
Stat3
f/f
here termed Stat3 cKO. To assess cre
recombination efficiency, mice carrying floxed alleles of the STOP codon adjacent to the Td-
Tomato gene (referred to as Rosa mice) were crossed to Csf1r-cre
ESR
to generate experimental
animals. Both male and female mice were used to account for sex as a biological variable. All
mice were housed under standard conditions with free access to food and water.
Methods Details
Mice Tamoxifen-induced cre recombination
For all experiments, we induced cre activation in both experimental groups via
subcutaneous (s.c.) injections of tamoxifen (daily for 5 days; 100µl of 20mg/mL tamoxifen or PBS
vehicle) at 6 and 7½ months of age. At 12 months of age, animals were euthanized for analysis of
AD-like pathology. Csf1r-Cre Rosa TdTomato
f/f
mice received s.c. or intraperitoneal (i.p.)
tamoxifen injection at 2 months and were euthanized at different timepoints (2, 4, 6, 8 and 16
weeks after tamoxifen injections).
Tissue Isolation and Preparation
Animals were euthanized in a CO
2
chamber, perfused with ice-cold PBS, and the anterior quarters
of the brain were snap-frozen and randomly assigned to protein or mRNA analyses while the
posterior quarters were fixed in 4% paraformaldehyde (PFA) overnight and randomly assigned to
paraffin or agarose embedding. The spleens of Csf1r-Cre Rosa TdTomato
f/f
mice were isolated and
processed for TdTomato expression and immune cell analysis by flow cytometry.
Flow Cytometry
Splenocytes were filtered, fixed and permeabilized (BD Cytofix/Cytoperm
TM
Plus, BD
Biosciences) for 20 min on ice. Cells were incubated with primary antibodies against CD45 and
CD11b for 30 min on ice (detailed information regarding antibodies can be found in supplementary
material). Samples were analyzed using BD LSR II Flow Cytometer (BD Biosciences).
44
Immunohistochemistry
Ten μm sections were deparaffinized, boiled (95°C, 30 min) in a modified citrate buffer
(pH 6.1, Dako) to retrieve antigens, and blocked in serum-free protein block (Dako). Prior
to immunoperoxidase labeling, sections were treated with 3% H
2
O
2
in PBS for 30 min to remove
endogenous peroxidase activity. Thereafter, sections were incubated in Iba1 (1:200, Wako)
overnight at 4°C. Immunoreactivity was visualized in single labeling experiments with
biotinylated goat anti-rabbit IgG (1:200, Vector Laboratory) followed by
diaminobenzidine/diaminobenzidine (Vector Laboratory) resulting in a brown reaction precipitate.
Other staining were performed on 50 μm brain sections embedded in 2% agarose. Brain
sections were blocked for 1 h in tris-buffered saline (TBS) with 0.5% Tween-20 supplemented
with donkey serum (1:1000) before staining with antibodies directed against CD68 (rat polyclonal;
1:100, Abcam), Ki67 (rabbit polyclonal; 1:200, Cell Signaling Technology), 6E10 (mouse
monoclonal; 1:400, BioLegend) and Iba1 (1:200, LSBio or Wako). Alexa
488/594/647
-coupled
secondary antibodies were used for immunofluorescence experiments (Life Technologies).
Sections were then mounted with Prolong anti-fade reagent with DAPI (Molecular Probes).
Enzyme-linked immunosorbent assay
Evaluation of IL-1β, IFN-g, IL-6, and TNF-a levels was performed using a Multi V-PLEX
assay (mouse pro-inflammatory panel 1 and mouse cytokine panel 1, Meso Scale Discovery).
Brain homogenates were run according to the manufacturer's instructions.
The triton-soluble and guanidine-HCl-soluble fractions of brain homogenates were used as
input for ELISA detection of Aβ
1-40
and Aβ
1-42
peptides (V-PLEX Plus Aβ Peptide Panel 1 (6E10)
Kit, Meso Scale Discovery) according to the manufacturer’s recommendations. Ab oligomers were
quantified from the triton-soluble fraction using enzyme immunoassay for determination of Aβ
bound to 82E1 antibody in sandwich ELISA (IBL International). Samples were run according to
the manufacturer's instructions. BCA protein assay (BioRad) was used to determine total protein
concentrations in each fraction and values were used for normalization.
mRNA isolation and quantitative Real Time Polymerase Chain Reaction
RNA was extracted with TRIzol (Life Technologies), from snap-frozen frontal quarter
brains. Expression levels were determined using primers for APP, PS1, Ifng, Il1b, Il6, Tnf and
Actb (detailed information regarding primer sequence can be found in supplementary table S1).
45
Western Blot
Mouse brain tissue was homogenized using a mechanical cell disperser with 5 volumes of
ice-cold 1X tissue lysis buffer (Cell Signaling Technology) supplemented with protease inhibitor
cocktail (Sigma-Aldrich) and phosphatase inhibitor cocktails 2 and 3 (Sigma-Aldrich). After 15
min of incubation on ice, homogenates were centrifuged at 14,000 g for 15 min at 4°C. Protein
concentration in the supernatants was determined using the BCA protein assay (Biorad). Samples
were prepared according to manufacture’s instruction (Bolt, Thermo). Gels were then transferred
to methanol-activated PVDF membrane (0.45μm pore, Merck Millipore) using the Trans-Blot(R)
TurboTM transfer system (Bio-Rad). Primary antibodies were directed against: APP C-terminal
fragments (clone D54D2), PS1, β-actin, Synaptophysin, PSD-95 and β-tulubin. Densitometric
analyses were performed using Bio-Rad Image Lab Software, and band densities were normalized
to b-actin or β-tulubin.
Behavioral analysis
Prior to behavioral testing, mice were subjected to neurological screening to assess
auditory, visual, and olfactory acuity and response to a tactile stimulus. Additionally, coordination,
balance, and grip strength were tested (Guillot-Sestier and Town, 2013). Mice that did not perform
well for each of the neurological screening tests were excluded in subsequent behavioral assays.
We assessed nesting behavior by placing single animals with one pressed nestlet (NES3600,
Ancare Corp) overnight and qualitatively grading their nest on a scale from 1(nestlet not noticeably
touched)-5 (>90% of nestlet torn to create a crater) (Deacon, 2006). Locomotor activity was
assessed by placing mice in an open field for 30 minutes and recording their distance moved (cm),
and working memory was assessed by placing mice in a Y-maze for 5 minutes, as previously
described (Guillot-Sestier et al., 2015a).
Quantification and Statistical Analysis
Microscopy
Staining coverage
Four brain sections were stained for ThioS, 6E10, Iba1, CD45, and GFAP per animal, with
1 image taken per section of the entorhinal cortex (EC) and hippocampus (HC) at 10x. Fiji Image
J was used to determine immunostaining coverage. Plaque size was determined assigning plaques
46
to three mutually exclusive size categories based on maximum diameter: small <25μm, medium
25-50μm, and large >50μm.
Aβ phagocytosis and proliferation
Brain sections were stained for Iba1, 6E10, and CD68 (phagocytosis) or Iba1 and Ki67
(proliferation). Confocal image stacks of the EC and HC were taken at 60x magnification. Four
sections were stained per animal, with 3 images taken per region. The images were converted to
3D images using the surface-rendering feature of Imaris Bitplane software (version 7.6.1) and the
volume of Ab inside the Iba1
+
CD68
+
was quantified as previously described (Guillot-Sestier et al.,
2016). To measure proliferation, the percent of Iba1
+
cells colocalized with Ki67 was quantified.
Stereology
Stereological quantification of Iba1
+
brain macrophages was performed using a brightfield
microscope fitted with a motorized stage was used along with computer-assisted stereological
toolbox version 11.10.2 64-bit (Stereo Investigator, MF Biosciences). Four slices of the
hippocampus and entorhinal cortex were analyzed per brain, in which 25% of the total area of
these regions was counted using a .04mm
2
frame size via stereological random sampling.
Statistics
Two-way analysis of variance (ANOVA) followed by Sidak’s post hoc test was used when
sex was considered a relevant variable. Else, Student’s t-test was performed. In all cases, p ≤ 0.05
was considered to be statistically significant and p ≤ 0.10 was considered trending. All data are
presented as mean ± SEM.
2.4 Results
2.4.1 Innate immunity is targeted in Csf1r-Cre
+
mice
To assess the recombination efficiency of the Stat3
cKO model, we first generated
Rosa
+
Csf1r-Cre
+
mice and analyzed TdTomato expression in macrophages as a result of Cre
recombination. We assessed the brain compartment via histological quantification of
colocalization between TdTomato and brain innate immune cells (Iba1) staining. We found ~50%
recombination in Iba1
+
cells in the cortex two weeks post-injection and ~80% four weeks post-
injection, maintaining significant levels up to sixteen weeks after treatment, due to microglia’s
ability of self-renewal (Ajami et al., 2007). We observed comparable recombination efficiency in
47
hippocampal and cortical Iba1
+
cells (Figure 2.1. A-B). Although we noticed the Csf1r promoter
was also active in a subpopulation of neurons, in particular in the dentate gyrus region of the
hippocampus, we confirmed no significant Cre-dependent changes in markers of synaptic health
(Figure 2.1. C). Further, splenocyte analysis revealed that approximately 23% of the peripheral
CD45
+
cells in the Rosa
+
Csf1r-Cre
+
expressed TdTomato two weeks after tamoxifen i.p.
injections. TdTomato
+
macrophages became virtually absent four weeks after tamoxifen treatment
and was nonexistent in the Rosa
+
Csf1r-Cre
-
mice (Figure 2.1. D-E). We confirmed similar
recombination efficiency in this model with both subcutaneous and intraperitoneal tamoxifen
administration (Figure 2.1. F) and additional analysis showed ~36% recombination in
CD45
+
CD11b
+
macrophages upon tamoxifen s.c. injections (Figure 2.1. G).
In order to assess Stat3’s role in innate immunity within the context of AD, we induced
Stat3 deletion in macrophages at 6 and 7
1/2
months, a time point that represents early pathology in
APP/PS1 mice, displaying small Ab plaque deposits and gliosis (Jankowsky et al., 2001).
Previously, we have shown that total and active pStat3 levels are increased in the brains of AD
patients (Guillot-Sestier et al., 2015a). Here, we confirmed that 12 months-old APP/PS1 mice
display increased Stat3 immunofluorescence signal compared to wild-type mice (Figure 2.1. H)
and that, strikingly, most of their Iba1
+
cells display phosphorylated Stat3 (Figure 2.1. I). Thus, we
analyzed the brains of Stat3 cKO mice at 12 months and observed approximately 80% reduction
in Iba1
+
cells expressing Stat3 (Figure 2.1. J) and a trend towards a decrease in brain Stat3 mRNA
levels (Figure 2.1. K). Further, we detected no difference in recombination efficiency between
male and female mice (data not shown). Taken together, this data confirm that long-term Stat3
deletion occurred successfully in the brain innate immune compartment in our model.
48
49
Figure 2.1. Cre recombination efficiency in Csf1r-Cre innate immunity.
(A) Representative cortical confocal images from Rosa
+
Csf1r-Cre
+
and Cre
-
mice two and four
weeks after tamoxifen treatment showing TdTomato expression (red) in cells that were
immunostained with Iba1 (green) and marked with DAPI (blue).
(B) Quantitation of Iba1
+
cells expressing TdTomato at different time-points after tamoxifen
treatment in the hippocampus and cortex (n=3-6, n.d= not detected).
(C) Representative images and quantitation of western blot of PSD95 and synaptophysisn relative
levels in frontal cortex homogenates of mice with the indicated genotypes (n=6-7, n.s.= not
significant).
(D) Representative flow cytometry dot-plots from splenocytes showing TdTomato expression two
and four weeks after tamoxifen treatment of Rosa
+
Csf1r-Cre
+
and Cre
-
mice.
(E) Bar graph denotes quantitation of CD45
+
cells expressing TdTomato at different timepoints
following tamoxifen treatment (n=3-5).
(F) Quantitation of TdTomato expressing cells in the spleen and cerebral cortex 2 weeks after
intraperitoneal or subcutaneous injections (n=4, n.s.= not significant). Bars represent mean + SEM.
(G) Quantitation of TdTomato expression in CD45
+
CD11b
+
splenocytes 2 weeks after tamoxifen
s.c. injection (n=4).
(H) Quantitation of Stat3 immunolabeling in the cortex of WT and APP/PS1 mice (n=3-4). Data
is presented as mean + SEM. Student t-test was performed; **** p<0.0001.
(I) Representative confocal image of phosphorylated Stat3 expression (green) in microglia (Iba1
+
,
red) proximal to Ab plaques (4G8, white). White arrow heads denote Iba1
+
pStat3
+
cells. Bar graph
denotes quantitation of pSTAT3 expressing Iba1
+
cells in WT and APP/PS1 cortices (n= 6-13,
n.d= not detected).
(J) Representative confocal image of Stat3 (green) in microglia (Iba-1
+
, red) proximal to Ab
plaques (4G8 immunostained, white) from APP/PS1 and APP/PS1 Stat3 cKO cortices. Bar graph
denotes quantitation of cortical Stat3 expressing Iba-1
+
cells of APP/PS1 Stat3 cKO mice in
comparison to APP/PS1 Stat3 sufficient mice (n= 4). All data is presented as mean + SEM. Student
t-test was performed; ** p<0.01.
(K) qPCR analysis of Stat3 mRNA levels in the frontal brain homogenate from mice with the
indicated genotypes. The mRNA levels were normalized to b-actin (n= 9-10). Data is presented as
mean + SEM. Student t-test was performed; † p<0.1.
50
2.4.2 Stat3 recombination decreases innate immune activation
We first examined the innate immune cells in the brains of APP/PS1 mice to observe the
effect of Stat3 deletion on their profile in the brains of APP/PS1 mice. Analysis of Iba1 coverage
indicated a surprising sex-specific decrease in immunostaining in the EC of APP/PS1
Stat3
cKO
females (Figure 2.2. A-B). Likewise, Iba1 mRNA levels were significantly decreased only in
females (Figure 2.2. C). Iba1 is constitutively expressed by brain innate immune cells and
upregulated upon activation, hence the decreased Iba1 could reflect a decrease in activation or in
numbers of Iba1
+
cells. Interestingly, there was no significant genotype-specific differences in a
marker of proliferation (Ki67) nor overall changes in Iba1
+
cell numbers, indicating that the
decrease in Iba1 load for females is not a result of decreased proliferation (Figure 2.2 D-E). On
the other hand, CD68 immunostaining also trended toward a reduction in the EC of female mice
(Figure 2.2. F), denoting overall reduced microglial activation. Intriguingly, these changes were
restricted to the EC as we did not observe changes in these markers in the hippocampus.
2.4.3 Stat3 deletion decreases the brain proinflammatory cytokine profile in a sex-specific
manner
Pro-inflammatory cytokines (IFN-g, IL-1β, IL-6, TNF-α) are elevated in the brains,
cerebrospinal fluid, and plasma of AD patients, reflecting alterations in cytokine levels and
disturbance of the immune system in these patients (Brosseron et al., 2014). In particular, IFN- g
and IL-1β have been shown to directly induce toxicity to neurons by both impairing synaptic
function and inducing cell death (O’Connor and Coogan, 1999; Zheng et al., 2016). Investigating
major pro-inflammatory cytokines in AD revealed a significant female sex bias, in which we
observed decreased protein levels of IL-1β and IFN- g only in female APP/PS1 Stat3
cKO mice
(Figure 2.3. A-B). No substantial changes in TNF-α and IL-6 protein (Figure 2.3. C-D) or mRNA
levels were found regardless of sex (Figure 2.3. E-H). Altogether, this data further suggests that
inducible innate immune Stat3 deletion in APP/PS1
mice decreases immune activation in female
mice.
51
Figure 2.2. Stat3 recombination decreases innate immunity markers.
(A) Representative images of Iba1 immunostaining in the entorhinal cortex of male and female
APP/PS1 and APP/PS1 Stat3 cKO.
(B) Quantitation of Iba1 coverage in the entorhinal cortex of mice with the indicated genotypes
(n=7-12). Bars represent mean + SEM.
(C) Quantitation Iba1 mRNA expression in frontal cortex homogenate of mice with the
indicated genotypes normalized to b-actin (n=4-6).
(D) Quantitation of Ki67 expressing cortical Iba1 cells.
(E) Stereological quantification of Iba1
+
cells in the entorhinal cortex of male and female
APP/PS1 Stat3 cKO mice (n=4-5).
(F) CD68 immunostaining coverage in the cortex from mice of each specified genotype (n=
5-8). All Bars represent mean + SEM. 2-way ANOVA statistical test was performed; †
p<0.1, * p< 0.05.
52
53
Figure 2.3. Stat3 recombination alters cytokine profile in the brains of APP/PS1 mice.
(A) Brain frontal cortex homogenates from male and female mice of the specified genotypes
were assessed for interferon-g (IF-g) via electrochemical ELISA (n= 8-13).
(B) Brain frontal cortex homogenates from male and female mice of the specified genotypes
were assessed for interleukin-1β (IL-1β) via electrochemical ELISA (n= 8-13).
(C) Brain homogenates from male and female mice of the specified genotypes were assessed
for tumor necrosis factor-a via electrochemical ELISA (n= 8-13).
(D) Brain homogenates from mice of the specified genotypes were assessed for interleukin-6
via electrochemical ELISA (n= 8-13).
(E) Brain homogenates from mice of the specified genotypes were assessed for IF-g mRNA
levels via qPCR and normalized to β-Actin (n= 4-9).
(F) Brain homogenates from of the specified genotypes were assessed for IL-1b mRNA levels
via qPCR and normalized to β-Actin (n= 4-9).
(G) Brain homogenates from mice of the specified genotypes were assessed for TNF-a mRNA
levels via qPCR and normalized to β-Actin (n= 4-9).
(H) Brain homogenates from mice of the specified genotypes were assessed for IL-6 mRNA
levels via qPCR and normalized to b-Actin (n= 4-9). For all graphs, bars represent mean +
SEM. 2-way ANOVA statistical test was performed.
2.4.4 Stat3 recombination decreases Aβ uptake in a sex-specific manner
While the etiological factor of early onset AD is the overproduction of Aβ due to familial
mutations in the APP and PSEN1 genes, recent studies have demonstrated the development of late
onset AD to arise, rather, from a failure in clearance mechanisms (Mawuenyega et al., 2010;
Nalivaeva and Turner, 2019). Innate immunity is particularly equipped to clear Aβ via
phagocytosis, however they fail to do so in AD over time (Hickman et al., 2008). Thus, we
analyzed the functional consequence of Stat3 deletion in microglia by assessing Aβ phagocytosis.
To accomplish this, we examined volume of Aβ uptake within phagolysosomes, using our novel
methodology for 3D reconstruction of confocal images, q3Dism (Guillot-Sestier et al., 2016). Both
male and female APP/PS1
Stat3
cKO mice displayed Iba1
+
cells with reduced phagolysosome
volume (Figure 2.4. A-C). Strikingly, in the entorhinal cortex, only female APP/PS1
Stat3
cKO
54
mice showed a significant decrease in Aβ uptake within phagolysosomes of Iba1
+
cells when
compared to their male counterparts (Figure 2.4. A-B and D), suggesting that Stat3 is important
for an effective phagocytic response in vivo.
Figure 2.4. Stat3 deficiency decreases Aβ-phagocytosis by Iba1 cells.
(A) Representative micrographs of cortical amyloid deposits of APP/PS1 versus APP/PS1
Stat3 cKO female mice.
(B) 3D reconstruction of confocal images of amyloid deposits are labeled with 6E10 (red),
microglia with Iba1 (white) and phagolysosomes with CD68 (green). Lower panel shows
a 90
o
turn on the Z-axis.
(C) Quantitation of the amount of CD68
+
phagolysosomes inside Iba1
+
cell in the entorhinal
cortex of mice with the indicated genotypes
(D) Quantitation of the amount of Aβ inside CD68
+
phagolysosomes of Iba1
+
cell in the
entorhinal cortex of mice with the indicated genotypes. Bars represent mean + SEM. 2-
way ANOVA statistical test was performed; * p<0.05, ** p<0.01.
55
2.4.5 Innate immune Stat3 deletion results in a sex-specific increase in cerebral Aβ
Biochemical analysis of human Aβ species in brain homogenate revealed a significant
increase in Aβ
1-40
(Figure 2.5. A-B) and Aβ
1-42 (Figure 2.5. C-D) levels in the triton-soluble and
insoluble only in APP/PS1
Stat3
cKO female mice, following the same trend as the data presented
previously herein. We further analyzed the triton-soluble oligomeric Aβ fraction and found no
genotype-specific effect (Figure 2.5. E). We also performed a quantitative histological analysis of
Ab plaques using the commonly employed staining method via 6E10 antibody to examine the
effect of the intervention on amyloid plaque area coverage. We found no significant differences
due to the absence of Stat3 in the EC of the mice (Figure 2.5. F-G). Furthermore, there were no
differences in plaque size as a consequence of Stat3 recombination (Figure 2.5. H), suggesting that
the differences present in the biochemical analysis might reflect compaction of plaques.
Additionally, we analyzed APP production and processing in our model by assessing APP and PS1
mRNA levels, PS1 cleavage (26kDa C-terminal product) and APP h-processing (a neurotoxic
25kDa product) (García-Ayllón et al., 2017). We found no evidence of altered transgene
expression by all measures (Figure 2.5. I-J).
56
Figure 2.5. Innate immune Stat3 deletion increases amyloid levels in female mice.
(A) Biochemical analysis of two-step-extracted brain homogenates from mice with the
determined genotypes were assayed for triton-soluble Aβ
1-40
(n= 7-11).
57
(B) Biochemical analysis of guanidine HCl-soluble Aβ
1-40
species from mice with the
determined genotypes (n= 7-11).
(C) Biochemical analysis of two-step-extracted brain homogenates from mice with the
determined genotypes were assayed for triton-soluble Aβ
1-42
(n= 7-11).
(D) Biochemical analysis of guanidine HCl-soluble Aβ
1-42
species from mice with the
determined genotypes (n= 7-11). Bars represent mean + SEM. 2-way ANOVA statistical
test was performed; * p< 0.05, ** p< 0.01.
(E) Detection of oligomeric Ab from triton-soluble frontal brain homogenates, using 82E1
antibody ELISA (n=4-10).
(F) Representative images of the entorhinal cortex of male and female APP/PS1 and APP/PS1
Stat3 cKO mice immunostained for Ab using 6E10 antibody.
(G) Quantitation of plaque coverage in the entorhinal cortex using 6E10 staining.
(H) Quantitation of plaque size from 6E10
+
plaques in the entorhinal cortex. Plaques were
categorized into three mutually exclusive size categories based on maximum diameter:
small <25μm, medium 25-50μm, and large >50μm. Bars represent mean + SEMs. One way
ANOVA was performed.
(I) Representative images showing no difference between groups on APP and PS1 processing,
assessed via western blot in frontal brain homogenates and normalized to b-actin or b-
tubulin (n=4-6).
(J) APP and PS1 mRNA levels were determined via RT qPCR and normalized to b-actin
(n=5). For all experiments, bars represent mean + SEMs. 2-way ANOVA statistical test
was performed, except for graph H, to which one-way ANOVA was conducted; ; * p<
0.05, ** p< 0.01.
2.4.6 Innate immune Stat3 deletion impairs female behavior
To evaluate cognitive consequences of increased Aβ burden in APP/PS1 Stat3
cKO female
mice, all four groups of littermates were evaluated. We first assessed overall health and welfare
via nesting behavior. Interestingly, APP/PS1
Stat3
cKO female mice showed decreased ability to
build a nest, compared to APP/PS1 female mice (Figure 2. 6. A). Locomotion and spontaneous
activity were assessed in an open field. While male mice displayed no distinct differences,
58
APP/PS1
Stat3
cKO
female mice exhibited overall higher locomotion (Figure 2.6. B). Spatial
working memory was evaluated by spontaneous alternation in the Y-maze, which did not reveal
differences between APP/PS1
and APP/PS1
Stat3
cKO mice (Figure 2.6. C-D).
Figure 2.6. Stat3 recombination affect female mice behavior.
(A) Graded ability of mice to form a nest using one cotton nestlet (nesting behavior; n= 6-10).
(B) Spontaneous activity was tested in the open field over a 30 minute period (n=5-8).
(C) Determination of number of arms entered in the Y-maze (n= 10).
(D) Quantitation of spontaneous correct alternation in the Y-maze (n= 10). For all experiments,
bars represent mean + SEMs. 2-way ANOVA statistical test were performed, except for
graph A, to which Kruskal-Wallis test was conducted; * p< 0.05.
59
2.5 Discussion
Here we investigated the role of Stat3 in AD innate immunity by
crossing APPswe/PS1dE9 mice with Csf1r-cre
ESR
Stat3
f/f
mice. In this study, we have shown that
these inducible conditional knockouts display transient peripheral deletion due to the turnover of
peripheral macrophages (Yona et al., 2013), which is in agreement with the observations in
inducible Cx3cr1-Cre
ERT2
mice (Goldmann et al., 2013; McCubbrey et al., 2017), a widely used
model to target microglia. Further, we demonstrated stable brain knockout of Stat3 upon tamoxifen
injections, possibly due to microglia’s ability of self-renewal (Ajami et al., 2007). The peak in
cerebral innate immune recombination efficiency was achieved four weeks after tamoxifen
injections, which can be explained by the prolonged action of tamoxifen in the system long after
the injection time-point (Reinert et al., 2012) and the fact that consecutive tamoxifen injections
can lead to delayed clearance of the drug and its metabolites in the brain (Jahn et al., 2018).
Importantly, we report that innate immune Stat3 deletion diminishes their ability to react to Aβ,
measured by Iba1 immunostaining, brain proinflammatory cytokine levels, and Aβ phagocytic
capacity. While inflammation has classically been labeled as deleterious in AD progression (Wyss-
Coray and Rogers, 2012), studies aimed at reducing inflammation as a therapeutic approach,
including treatment with non-steroidal anti-inflammatory drugs (NSAIDs), have failed to
clinically improve patients (Meyer et al., 2019). In fact, treating patients with mild cognitive
impairment (MCI) with NSAIDs has sped up the progression from MCI to AD, highlighting the
beneficial role of inflammation in AD (Meyer et al., 2019; Thal et al., 2005). This present work
supports this hypothesis by demonstrating that Stat3 is necessary for innate immune activation and
appropriate response to amyloid accumulation.
Strikingly, we have also observed a strong sex bias of the effects of Stat3 conditional
knockout in brain innate immunity, including increased amyloid levels and decreased immune
activation in female mice. This result is consistent with previous reports that show that Stat3
differentially affect microglial response and inflammation of females in health and in different
disease models (Dziennis et al., 2007; Rahimian et al., 2019; Villa et al., 2015). Specifically, the
sex steroid hormone estradiol is able to modulate Stat3 activation in microglia and regulate
inflammation via crosstalk between Stat3 and estrogen receptors (Dziennis et al., 2007; Rahimian
et al., 2019; Villa et al., 2015; Wang et al., 2001), corroborating the notion that females may be
60
more susceptible to Stat3 signaling deficiency than male counterparts. Hence, the interaction
between Stat3 and estradiol in the context of AD deserves additional exploration, as it may
contribute to the disproportionate number of females affected by the disease (Uchoa et al., 2016).
Furthermore, studies have previously reported the impact of tamoxifen use and other selective
estrogen receptor modulators (SERMs) on AD development in men and women (Gillies and
McArthur, 2010), as well as in mice (Dluzen and Mickley, 2005; Herrera et al., 2011; Pandey et
al., 2016). Our paradigm was not designed to compare the differential tamoxifen effects in male
and female, which could have played a role in the findings described herein.
For this study, experimental mice were administered tamoxifen at 6 and 7
1/2
months of age,
time points which represent the initiation phase of AD-like pathology in APP/PS1 mice
(Jankowsky et al., 2001). Recent studies have shown that promoting innate immune activation
once amyloid deposits occur improves disease outcomes (Baik et al., 2019; Chakrabarty et al.,
2010). Initially, brain immune become activated by Aβ, but upon chronic exposure, they reach a
“tolerant” state characterized by reduced phagocytosis and low-grade cytokine release (Baik et al.,
2019). At the timepoint we ablated Stat3 expression, it is possible that Stat3 was mainly activated
by the proinflammatory axis, which, when inhibited, prevented effective microglial activation to
respond to Aβ deposition. Evidences suggest that Aβ-induced Stat3 signaling changes over time
(Eufemi et al., 2015), thus deleting Stat3 later in the disease progression, when brain immune
tolerance is predominant, may be more advantageous (Guillot-Sestier et al., 2015b). In order to
test the consequences of ablating the anti-inflammatory axis of Stat3 early in disease, we applied
the same paradigm to delete interleukin-10 receptor alpha from brain innate immunity of APP/PS1
mice. We found similar worsening of amyloidosis and decreased innate immune activation,
without clear sex-differences (see results in Appendix A), evidencing that sex-differences in Stat3-
mediated innate immunity deserves to be further understood. Importantly, specific astrocyte Stat3
conditional knockout in the context of AD was proven beneficial in improving cognitive and glial
functions and reducing plaque load (Reichenbach et al., 2019). However, when the same group
systemically targeted Stat3 in 8-10 month old mice, despite enhanced cognitive functions, they
found only modest pathological improvements, which could account for the differential response
between microglial and astrocytic Stat3 deletion (Reichenbach et al., 2019). Once again, this data
61
underlines the important nature of context in determining the overall organismal response to Stat3
modulation.
We then assessed the cognitive consequences of our intervention. We saw no differences
between groups regarding spatial working memory, measured by Y maze spontaneous alternation,
a behavior that depends on the crosstalk between hippocampus and pre-frontal cortex (Kraeuter et
al., 2019). Intriguingly, we reported a APP/PS1 female-specific increase in locomotion as a result
of innate immune Stat3 deletion, whereas a different study has shown that microglial Stat3
conditional KO male wildtype mice display reduced depressive-like behavior and decreased
locomotion in the open field test (Kwon et al., 2017). Likewise, only female APP/PS1 Stat3 cKO
mice displayed reduced ability to build nests, a behavior that is a highly integrated phenotypic trait
that relies in part on limbic circuitry and is a sensitive measure of dysfunction (Deacon, 2006;
Jirkof, 2014; Lin et al., 2007), further demonstrating that our innate immune Stat3 deletion
disproportionately affected outcomes in the female cohort.
In AD brains, there are a wide variety of innate immune phenotypes, some of which are
beneficial and others detrimental (Guillot-Sestier and Town, 2013). Based on these results, we
conclude that inhibiting Stat3 signaling blocks the ability of innate immunity to respond to Ab and
significantly worsening AD-like pathology. Stat3 is downstream of both pro- and anti-
inflammatory cytokine pathways, as well as of homeostatic signaling molecules such as CSF1,
which is necessary for microglial differentiation and survival (Davies et al., 2013), albeit we have
not observed Stat3 dependent effects on microglial proliferation and total numbers. Therefore,
rebalancing inflammation by blocking specific immunosuppressive factors may be more effective
at remediating AD-like pathology. We have previously demonstrated the importance of
appropriate immune responses to fight AD by deleting the immunosuppressive factors IRAK-M,
TGF-b and IL-10 (Guillot-Sestier et al., 2015a; Town et al., 2008) and observing increased Ab
clearance.
Altogether, this work is the first of its kind to temporally and conditionally delete Stat3 in
brain innate immunity during amyloidosis. Importantly, this work demonstrated that Stat3 deletion
reduces microglial activation in response to Ab and increases amyloidosis in female APP/PS1
mice. As Stat3 lies at the intersection of several innate immune pathways, future work needs to be
62
done to establish which specific Stat3 pathways positively affect clearance of Ab and how this
transcription factor can be manipulated to help rebalance the innate immune response in AD.
Furthermore, our sex-specific findings highlight the need to better assess why female mice are
more susceptible to amyloidosis in the face of innate immune Stat3 deletion. After positive
findings of Stat3 deletion in other cell types, therapies that aim to pharmacologically target Stat3
should be cognizant of the negative effects it may have on microglia, especially in female
populations.
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CHAPTER 3. INTERACTIONA BETWEEN INFLAMMATION, SEX STEROIDS, AND
ALZHEIMER’S DISEASE RISK FACTORS
Acknowledgements: Thank you Dr. Alexandra Moser for guidance and unconditional help, not
only in writing the review on which this chapter is based, but for numerous exciting scientific
discussions.
*This chapter has been partially based on the publication Uchoa, M.F., Moser, V.A., Pike, C.J.
Interactions between inflammation, sex steroids, and Alzheimer’s disease risk factors. Frontiers
in Neuroendocrinology, 2016.
3.1 Summary
Alzheimer’s disease (AD) is an age-related neurodegenerative disorder for which there are no
effective strategies to prevent or slow its progression. Because AD is multifactorial, recent research
has focused on understanding interactions among the numerous risk factors and mechanisms
underlying the disease. One mechanism through which numerous risk factors may be acting is
inflammation. Several genetic and environmental risk factors for AD increase inflammation,
including apolipoprotein E4, obesity, and air pollution. Additionally, sex steroid hormones appear
to contribute to AD risk, with age-related losses of estrogens in women and androgens in men
associated with increased risk. Importantly, sex steroid hormones have anti- inflammatory actions
and can interact with several other AD risk factors. This chapter examines the individual and
interactive roles of inflammation and sex steroid hormones in AD, as well as their relationships
with the AD risk factors apolipoprotein E4, obesity, and air pollution.
3.2 Female bias in Alzheimer’s disease
Sex differences impact risk of Alzheimer’s disease (AD), with women accounting for
approximately two-thirds of AD patients (Hebert et al., 2013). Moreover, the progression of the
disease differs between sexes, with men showing a more rapid progression (Lapane et al., 2001;
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Stern et al., 1997), but women showing greater severity for clinical dementia (Barnes et al., 2005;
Corder et al., 2004; Irvine et al., 2012). These sex differences are likely to be due to differences in
neurophysiological substrates between men and women as well as differential actions of sex
steroid hormones. Both estrogens and androgens have neuroprotective effects and age- related loss
of these sex steroid hormones increases risk for AD in both sexes.
No single factor genetic or environmental entirely drives AD risk. Rather, there are
multiple risk factors that interact to determine AD risk. Importantly, genetic and environmental
risk factors have been shown to differentially affect men and women, and to interact with sex
steroid hormones. Though there are multiple pathways through which these factors may interact
to drive AD pathogenesis, inflammation has been increasingly regarded as an essential component
of AD pathogenesis, of which manifestation display sexual dimorphism. Thus, understanding the
complex interaction between inflammation and AD risk factors and how it manifest differently in
men and women, can breach to new therapeutic modalities that focus on sex-dependent
susceptibility pathways.
Significant sex differences exist in AD, with women being at heightened risk, even after
controlling for the fact that women live longer than men (Li and Singh, 2014). Sex differences in
genetic and environmental risk factors for AD have not been well studied, though there is evidence
women are disproportionally affected by some factors. For example, APOE4 is regarded as the
single greatest genetic risk factor for AD, however, this risk is modified by sex, as a single copy
of APOE4 increases risk approximately four-fold in women, but has a comparatively modest AD
risk in men (Farrer et al., 1997; Payami et al., 1994). A more recent study found that presence of
APOE4 increases rates of conversion from cognitively normal to mild cognitive impairment (MCI)
and from MCI to AD significantly more strongly in women than in men (Altmann et al., 2014).
Interestingly, there is often a female sex bias in rodent models of AD. Our lab and others
have demonstrated that female AD transgenic mice have significantly greater AD-like
neuropathology than males (Carroll et al., 2010; Hirata-Fukae et al., 2008; Schafer et al., 2007).
Intriguingly, even the sex bias associated with APOE4 is replicated in transgenic mice, as we have
recently shown that presence of human APOE4, compared to human APOE3, increases AD-like
pathology more strongly in female than in male AD-transgenic mice (Cacciottolo et al., 2016).
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Though these sex differences may involve inherent neural differences between men and women,
there is a wealth of data demonstrating the importance of sex steroid hormones in modulating AD
risk.
3.3 Sex steroid hormones and Alzheimer’s disease
3.2.1 Estrogen and AD
The primary female sex steroid hormone, 17β-estradiol, is protective against AD, and its
age-associated decline increases risk of developing the disease (Manly et al., 2000; Pike et al.,
2009). Low circulating levels of 17β-estradiol (E2) are associated with AD (Rosario et al., 2011;
Yue et al., 2005), and women with AD have lower brain levels of estrogens than age-matched
cognitively normal controls (Rosario et al., 2011; Yue et al., 2005). Moreover, surgically induced
menopause performed prior to natural menopause, results in prematurely low E2 levels and
increased risk of AD (Phung et al., 2010; Rocca et al., 2007).
Experimental findings in rodent models support the idea that E2 is protective and loss of
this sex steroid hormone can accelerate AD-like pathology. For example, depleting sex steroid
hormones in female AD-transgenic mice via ovariectomy increases Aβ and worsens behavior
(Carroll et al., 2007; Levin-Allerhand et al., 2002; Xu et al., 1998; Zheng et al., 2002).
Additionally, in these same studies, treatment with E2 in ovariectomized female AD- transgenic
mice reverses the adverse effects of ovariectomy, suggesting protective roles of E2 in AD.
Though studies in both humans and rodents have demonstrated the adverse effects of E2
loss, the benefits of estrogen-based hormone therapy are not yet clear. A number of studies found
decreased rates of dementia in women using hormone therapy (Kawas et al., 1997; Paganini-Hill
and Henderson, 1994; Tang et al., 1996; Zandi et al., 2002). However, a large double-blinded,
placebo-controlled clinical trial, the Women’s Health Initiative, found that hormone therapy
actually increased rates of cognitive decline and risk of dementia (Shumaker et al., 2004; 2003).
Nevertheless, there is evidence that initiation of hormone therapy near the onset of menopause
may be necessary to realize protection from AD (Shao et al., 2012; Whitmer et al., 2011). Recent
clinical trials that included early initiation of hormone treatment found that it was associated with
reduced Aβ accumulation (Kantarci et al., 2016), but without cognitive benefits (Gleason et al.,
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2015; Henderson et al., 2016). Thus, though the loss of E2 is clearly a risk factor for AD and E2
does have several neuroprotective roles, its therapeutic applicability is not straightforward and
requires further research.
3.2.2 Testosterone and AD
As appears to be the case for estrogens in women, testosterone may protect against AD in
men. Indeed, most (Hogervorst et al., 2001; Moffat et al., 2004; Paoletti et al., 2004) but not all
(Pennanen et al., 2004) studies report that age-related loss of testosterone in men is associated with
increased risk of AD. The relationship between testosterone and AD is apparent at least ten years
prior to clinical diagnosis (Moffat et al., 2004), suggesting that low testosterone contributes to,
rather than results from, the disease process. Consistent with this possibility, low brain levels of
testosterone are linked with AD diagnosis and are inversely correlated with Aβ levels in men with
evidence of early AD pathology (Rosario et al., 2011; 2004). Parallel to surgical menopause in
women, prostate cancer patients treated with androgen-deprivation therapy have increased plasma
Aβ levels (Gandy et al., 2001), and an increased risk of developing AD (Nead et al., 2016).
Research on the effects of testosterone in male rodents is consistent with findings in
humans. For example, age-related loss of testosterone in male rats correlates with increased brain
levels of soluble Aβ (Rosario et al., 2009). Moreover, gonadectomizing male mice, which depletes
~95% of endogenous testosterone, increases Aβ levels while treating with non- aromatazible
androgens blocks the effects of gonadectomy, both in non-transgenic mice (Ramsden et al., 2003)
and AD transgenic mice (Rosario et al., 2010; 2006). Further, genetic modifications that yield
increased testosterone are associated with decreased neuropathology in AD transgenic mice
(McAllister et al., 2010).
Research on androgen replacement therapy is very limited. However, one study found
improvements in spatial and verbal memory in cognitively normal older men given weekly
injections of testosterone (Cherrier et al., 2005a). Moreover, weekly testosterone treatments
improved spatial and verbal memory in men with mild cognitive impairment or AD (Cherrier et
al., 2005b), and improved reported quality of life in AD patients (Lu et al., 2006). However, long-
term effects of androgen-replacement therapy on AD outcomes have thus far not been studied.
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In summary, age-related losses in sex steroid hormones are associated with increased levels
of Aβ and increased risk of AD in both men and women. Importantly, these relationships are
observed in rodent models as well. The sex-specific associations of AD with estrogens in women,
and with androgens in men, may contribute to observed sex differences in AD, although early
developmental effects of sex steroid hormones may also be relevant (Pike et al., 2016). Hence, a
number of pathways through which sex steroid hormones represses inflammation may exert their
protective effects against AD (Pike et al., 2009; Singh and Su, 2013).
3.4 Sex steroid hormones and inflammation
One important factor regulating inflammation is sex, as there are innate sex differences in
susceptibility to inflammation. Several lines of evidence point to the role of sex steroid hormones
in contributing to sex differences in inflammation (Angele et al., 2006; Kalaitzidis and Gilmore,
2005; Pike et al., 2009). One of the most compelling pieces of evidence is the finding that females
are protected against several inflammation-related diseases during adulthood, but become
susceptible to them during aging after sex steroid hormones levels decline (Greendale et al., 2011;
Manly et al., 2000; Zandi et al., 2002). In adulthood, prior to the middle age onset of menopause
in women, men tend to exhibit a higher inflammatory predisposition than women (Albertsmeier et
al., 2014). Interestingly, mirroring the effects of age-dependent hormonal decline, girls of age 10
hospitalized with respiratory/inflammatory conditions showed an increased response in all
inflammatory parameters analyzed when compared to a matched boy, suggesting that females may
be more susceptible to inflammation in the absence of sex steroid hormones (Casimir et al., 2010).
In fact, decreased levels of sex steroid hormones in women as well as in men are associated with
increased inflammation (Straub, 2007; Tang et al., 2014). This phenomenon is seen in hypogonadal
men (Kalinchenko et al., 2010), in aged men (Nakhai-Pour et al., 2007) and in post-menopausal
women (Pfeilschifter et al., 2002).
Physiological levels of E2 are generally protective, therefore the decrease in E2 levels
during menopause and perimenopause offers an explanation to the pro-inflammatory profile seen
in aged female brains (Rocca et al., 2011; M. X. Tang et al., 1996). Moreover, decreased sex-
hormone levels are associated with onset of some neurological disorders (Vegeto et al., 2008). In
women, diminished E2 production and the consequent decrease in estrogen receptor (ER)-
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mediated anti-inflammatory activity may represent a trigger for postmenopausal associated brain
dysfunction (Benedusi et al., 2012). In fact, E2 availability and regulation of inflammation appear
to interact in regulating AD risk in women. Specifically, polymorphisms in aromatase, the rate-
limiting enzyme in E2 synthesis, increase risk and/or decrease age of onset of AD (Corbo et al.,
2009), an effect that appears strongest in women (Chace et al., 2012; Medway et al., 2014).
Interestingly, the AD risk associated with aromatase polymorphisms interacts with a
polymorphism in the anti-inflammatory cytokine IL-10 (Medway et al., 2014). Given that E2 is
able to increase IL-10 expression (de Medeiros and Maitelli, 2011; Dimayuga et al., 2005; Velders
et al., 2012; Yates et al., 2010), the age-dependent decrease in E2 coupled with alterations in E2
production associated with aromatase polymorphisms may contribute to the inflammatory
pathways implicated in AD pathogenesis.
Male sex steroid hormones also have anti-inflammatory effects. Blood levels of
testosterone begin to drop around age 30 in males, which leads to functional changes in androgen
receptor (AR)-regulated tissues, altering metabolic processes and inflammatory responses
(Harman et al., 2001; Maggio et al., 2005). Estradiol can exert its protective effects through its
antioxidant capacity (Wang et al., 2006), by binding to ERs and altering gene expression or kinase
pathways (Pike et al., 2009). Testosterone inhibits expression and release of cytokines and
chemokines by acting through AR as well as through non-classical surface receptors (Maggio et
al., 2005; Malkin et al., 2004; Rettew et al., 2008). Furthermore, glial cells express receptors for
sex steroid hormones (Jung-Testas and Baulieu, 1994) and regulate glial functions, suggesting that
sex steroid hormones can modulate neurodegenerative disease progression in part by regulating
neuroinflammation (Vegeto et al., 2008).
3.4.1 Sex steroid hormones modulate glia
Microglia and astrocytes express ERα, ERβ (Azcoitia et al., 2001; Vegeto et al., 2001) and
AR in the nervous system (Puy et al., 1995). These receptors are upregulated during injury and
neurodegeneration (García-Ovejero et al., 2002; Savaskan et al., 2001). Sex steroids have effects
on various cell processes involved in injury and cell death, including effects on myelination (Curry
and Heim, 1966), vasculature (Mendelsohn, 2002), apoptosis (Garcia- Segura et al., 1998), cell
survival (Doncarlos et al., 2009) and inflammation (Straub, 2007). Estradiol has been shown to
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reduce both acute and chronic inflammation. For example, pretreatment with E2 reduces acute
inflammation after lipopolysaccharide injection in both male and female mice (Tapia-Gonzalez et
al., 2008). Under conditions of chronic inflammation associated with AD, E2 attenuates microglial
activation and decreases the number of microglia surrounding plaques in animal models of AD
(Vegeto et al., 2006). Moreover, E2 increases Aβ uptake by microglia derived from human cortex
(Li et al., 2000). Sex steroid hormones have significant effects on several functions of microglia
(Nalbandian and Kovats, 2005). For example, E2 can modulate microglia’s antigen-presenting
function by changing expression of the major histocompatibility complex (MHC) and co-
stimulatory molecules, which alters the way microglia and dendritic cells interact with
lymphocytes (Tzortzakaki et al., 2003). Moreover, E2 modulates pathogen-sensing by altering how
microglia perceive the environment (Hirata et al., 2007). There is still no consensus on whether
ERα or ERβ is more important in mediating the effects of E2 on microglial responsiveness to
insults (Baker et al., 2004; Saijo et al., 2011; Sierra et al., 2008; Vegeto et al., 2006; 2003; Wu et
al., 2013), although the activation of both receptors by ER ligands appear to induce anti-
inflammatory responses (Chadwick et al., 2005; Ghisletti et al., 2005).
Androgens also suppress inflammation as a consequence of activating ARs and/or non-
classical surface receptors (Liu et al., 2005), which are associated with decreasing both humoral
and cell-mediated immune responses (Koçar et al., 2000). AR expression is upregulated on
microglia and astrocytes in response to injury. In a model of brain injury, either pre- or post-
treatment with testosterone and its metabolites, E2 and dihydrotestosterone, decreased reactive
gliosis (Barreto et al., 2007). Testosterone binding to AR after injury also activates genes related
to repair (Garcia-Segura et al., 1999; García- Ovejero et al., 2002). Furthermore, testosterone
modulates the innate immune system by downregulating TLR4 expression through non-classical
surface receptors (Rettew et al., 2008).
In addition to their effects on microglia, sex steroid hormones also modulate astrocytes.
For example, E2 can regulate morphology (Luquin et al., 1993), transcriptome machinery
(Mydlarski et al., 1995; Tomás-Camardiel et al., 2005), and the secretome (Garcia-Segura et al.,
1996; Stone et al., 1997) of astrocytes. Moreover, E2 acts on mitochondrial respiratory complexes
(Araújo et al., 2008) and upregulates synthesis of other steroids, like progesterone (Sinchak et al.,
2003). Sex steroid hormones are able to modulate astrocyte communication with other astrocytes,
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endothelial cells, neurons, and microglia. Therefore, regulation by sex steroid hormones influences
several processes including synaptic plasticity (McCarthy et al., 2002), blood flow (García-
Ovejero et al., 2005) and inflammation (Cerciat et al., 2010). For example, in hypothalamic
astrocytes, synaptic connectivity is regulated by E2 (Garcia-Segura et al., 1994). Astrocytes exhibit
decreased secretion of the cytokines and chemokines IL-6, IL-1β, TNFα, IFN-γ-inducible protein
10, and MPP9 following treatment with E2 (Cerciat et al., 2010; Lewis et al., 2008). Consistent
with an anti-inflammatory role, ovariectomy-induced E2 depletion results in increased IL-1β levels
in the hippocampus via NLRP3 inflammasome, which interacts with the TLR4/NFκB pathway to
sustain and further increase inflammation (Xu et al., 2016). On the other hand, E2 administration
to astrocytes decreases inflammasome activation as well as NF-κB activation, likely by impairing
its ability to translocate to the nucleus (Cerciat et al., 2010; Xu et al., 2016). Furthermore, E2
decreases cell body enlargement of astrocytes, often called astrocytosis, that is associated with
age-related increases in inflammation (Lei et al., 2003).
Estradiol does not always decrease activation of astrocytes. In models of excitotoxicity in
the olfactory bulb and in spinal cord injury, E2 increases expression of GFAP, a marker of
astrocyte activation (Lewis et al., 2008; Ritz and Hausmann, 2008). Likewise, testosterone
injection in the hippocampus can promote astrocytosis and memory impairment in male rats
(Emamian et al., 2010). The regional differences in astrocytic responsiveness to hormones can
partially be explained by the existence of subpopulations of astrocytes with different properties
within each region, as well as by the interaction with other cells that can modulate astrocytic
function (Ma et al., 1994; Torres-Aleman et al., 1992). Additional research is needed in order to
establish under what conditions sex steroid hormones either reduce or exacerbate astrocyte
activation.
3.4.2 Glial cells can produce neurosteroids
One important function of glial cells is synthesis of neurosteroids from cholesterol in the
brain (Papadopoulos et al., 1992). Neurosteroids can modulate neuronal excitability, as well as
glial function (Papadopoulos et al., 2006). In order to form active neurosteroids, cholesterol
molecules bind to steroidogenic acute regulatory protein (StAR) and to the translocator protein
18kDa (TSPO) on the mitochondrial surface, and are then translocated to the inner mitochondrial
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membrane and cleaved by CYP11A1 to form pregnenolone (Papapopulos and Walter, 2012; Rone
et al., 2009; Selvaraj and Stocco, 2015), which is a precursor for testosterone and E2 (Reddy,
2010).
Though controversial, several lines of evidence indicate that TSPO may be a key regulator
of steroidogenesis and inflammation. When TSPO is knocked down, steroidogenesis is impaired
(Kelly-Hershkovitz et al., 1998; Hauet et al., 2005) and levels of pro-inflammatory cytokines are
increased (Bae et al., 2014). However, a new TSPO knockout mouse model has challenged
previous findings (Papadopoulos et al., 1997), as it was demonstrated that steroid levels and
fertility were not affected by the absence of this protein (Morohaku et al., 2014; Tu et al., 2014).
Regardless of its role in neurosteroidogenesis, TSPO has important roles in glial function and
inflammation.
TSPO expression is upregulated by glial cells under conditions of neuronal injury and
inflammation (Papadopoulos, 1993; Vowinckel et al., 1997). In line with this evidence, TSPO is
upregulated in many neurological disorders such as glioma (Cornu et al., 1992), multiple sclerosis
(Vowinckel et al., 1997), Parkinson’s disease (Gerhard et al., 2006), Huntington’s disease
(Schoemaker et al., 1982), epilepsy (Nadler, 1981), schizophrenia (van Kammer et al., 1993) and
AD (McGeer et al., 1988). Interestingly, treatment with TSPO ligands in animal models decreases
inflammation, suggesting therapeutic potential of TSPO ligands. Indeed, microglia exhibit reduced
activation when exposed to TSPO ligands (Barron et al., 2013; Karlstetter et al., 2014), and have
decreased expression of cytokines, chemokines, and reactive oxygen species (Bae et al., 2014;
Karlstetter et al., 2014; Lin et al., 2015; Wang et al., 2014). TSPO ligands also improve the
proliferative capacity and increase the phagocytic ability of microglia, thereby increasing their
ability to clear debris after injury or neurodegeneration (Choi et al., 2011; Karlstetter et al., 2014).
Research suggests that these outcomes may be partially mediated by TSPO reducing expression
of NF-κB and/or AP-1 transcription factors (Bae et al., 2014; Zhao et al., 2012). Moreover, TSPO
overexpression decreases inflammation whereas knocking down TSPO increases inflammation
(Bae et al., 2014). However, it is not clear whether the protective effects of TSPO are solely
dependent upon its role in neurosteroidogenesis. The effects of TSPO ligands on glial modulation
could be independent of the steroidogenic machinery, and instead be mediated by other cellular
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processes including calcium influx, mitochondrial function, and apoptosis (Casellas et al., 2002;
Hong et al., 2006; Lin et al., 2015; Yiangou et al., 2006).
Therapeutic usage of TSPO ligands to treat AD has been previously proposed
(Papadopoulos et al., 2006; Veenman and Gavish, 2000). This possibility is supported by the
abilities of TSPO to modulate microglial phenotype and decrease inflammation, while in turn
could promote plaque clearance. In fact, previous work in our lab demonstrated that treatment of
male 3xTg-AD mice with TSPO ligands significantly attenuated glial activation, reduced Aβ
accumulation, and improved behavioral performance (Barron et al., 2013). The potential role of
sex steroid hormones in these actions has yet to be determined. It is worth noting that TSPO ligands
increase levels of several neurosteroids, including allopregnanolone which may have a therapeutic
role in AD (Irwin et al., 2014).
As described above, TSPO appears to play an important role in the synthesis of sex steroid
hormones in brain, and treatment with sex steroid hormones has been shown to decrease
inflammation. Since glial cells are particularly sensitive to the effects of sex steroid hormones and
also participate in their metabolism, another approach to modulate inflammation is through
manipulation of the steroidogenic pathway. TSPO is a unique target in this regard, as its function
in modulating inflammation has been shown to be via both steroid-dependent and independent
pathways. Because inflammation is an essential component of AD, increasing levels of sex steroid
hormone in the brain may present a viable therapeutic approach.
3.5 Modifiers of Alzheimer’s disease risk and their interaction with inflammation and steroid
hormones
The degree of heritability and development of AD varies greatly in the human population
(Coon et al., 2007; Gatz et al., 2006). This implies that several genetic and environmental factors
modify risk for AD (Rosenthal et al., 2012; Ryman and Lamb, 2006). Identifying and determining
the relative contribution of the many environmental and genetic risk factors for AD is presumed
to increase understanding of the mechanisms driving AD pathogenesis. Moreover, identification
of modifiable risk factors may also reveal potential therapeutic targets. In this chapter, we focus
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on apolipoprotein E ε4 allele (APOE4), obesity, and air pollution, AD risk factors that both involve
inflammatory pathways and are modulated by sex steroid hormones.
3.4.1 Apolipoprotein E
The APOE4 allele is the greatest genetic risk factor for late onset AD (Corder et al., 1993).
Three isoforms of APOE exist in humans: ε2 (APOE2), ε3 (APOE3), ε4 (APOE4). APOE3 is the
most common allele (77% frequency) and APOE2 is the least common (8%) (Mahley, 1988). The
presence of one APOE4 allele can confer up to a 3 – 4 fold increased risk of developing AD
(Corder et al., 1993). However, APOE4 is neither necessary nor sufficient to cause AD, suggesting
that APOE4 likely interacts with other risk factors to modulate vulnerability to AD. Importantly,
APOE4 increases risk of AD significantly more strongly in women than it does in men (Altmann
et al., 2014; Farrer et al., 1997; Payami et al., 1994), but how APOE4 and sex interact is still
unclear.
In animal models, APOE4 is also linked to greater AD-like pathology, where it has been
shown to potentiate oligomerization of Aβ (Belinson and Michaelson, 2009) and accelerate Aβ
plaque formation (Youmans et al., 2012). ApoE is mainly synthesized by astrocytes, to a lesser
extent by microglia, and very little is made by neurons. ApoE has several important biological
roles in brain, the efficacy of which is significantly affected by APOE genotype. For example, a
key function of ApoE in brain is to transport cholesterol from astrocytes to neurons (Bu, 2009),
and apoE4 is less efficient in doing so than ApoE3 (Gong et al., 2002; Rapp et al., 2006). Moreover,
the lipidation state of ApoE determines its half-life in brain, its ability to inhibit
neuroinflammation, and its ability to bind and clear Aβ through receptors in the blood-brain barrier
(Castellano et al., 2011; Hirsch-Reinshagen et al., 2004; Holtzman et al., 2000; Tai et al., 2015).
Lipidation of apoE by ABCA1, which is produced by microglia and astrocytes, is also isoform-
dependent with the following rank order of efficacy: ApoE2 > ApoE3 > ApoE4 (Boehm-Cagan
and Michaelson, 2014; Tai et al., 2013; Wahrle et al., 2004).
The result is that ApoE4 carriers have lower brain levels of ApoE, enhanced
neuroinflammation, and greater Aβ accumulation (Licastro et al., 2007; Tai et al., 2015). These
differences between ApoE3 and ApoE4 have important effects on biological functions including
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synaptogenesis, mitochondrial function, brain volume, and Aβ clearance (Cedazo- Mínguez, 2007;
Huang, 2010; Kim et al., 2009), as well as on risk of cardiovascular disease and atherosclerosis
(Hixson, 1991; Stengard et al., 1998). Importantly, the three APOE isoforms are known to have
significantly different effects on inflammation, which may be one mechanism underlying their
divergent effects on AD risk.
ApoE modulates inflammation- ApoE4 has been shown to increase susceptibility to
inflammation (LaDu et al., 2000), in both animal models and in humans. For example, following
a systemic lipopolysaccharide (LPS) injection, targeted-replacement (TR) mice expressing human
APOE4 have a greater increase in pro-inflammatory cytokines, both in brain and peripherally, than
do APOE3-TR mice (Lynch et al., 2003). Microarray analysis has shown that the greatest
differences between apoE3 and apoE4 in response to LPS are in genes involved in the NF-κB
signaling pathway (Ophir et al., 2005).
As in animal models, apoE4 is associated with greater baseline as well as LPS-stimulated
levels of inflammatory cytokines among non-AD (Gale et al., 2014) and AD patients (Olgiati et
al., 2010). Interestingly, non-steroidal anti-inflammatory drugs have been found to reduce risk for
AD only in apoE4 carriers (Barger and Harmon, 1997; Schram et al. 2007), reinforcing the idea
that there are important interactions between apoE4 and inflammation in AD.
The role of apoE in inflammation appears to be partly mediated via its modulation of
macrophages, microglia, and astrocytes (Vitek et al., 2009). For instance, ApoE binds to the LRP1
receptor on glial cells, suppressing JNK activation, and thereby reducing inflammation
(Pocivavsek et al., 2009). JNK belongs to the mitogen-activated protein kinase family and
coordinates responses to harmful stimuli (Arthur and Ley, 2013). Interestingly, ApoE4 has less
affinity for LPR1 than do apoE2 and apoE3 (Bell et al., 2012). Thus, APOE4 carriers have lower
overall circulating apoE levels, due to decreased lipidation of ApoE4, as well as reduced binding
of apoE to its receptor, contributing to higher neuroinflammation in this population (Licastro et
al., 2007). A similar outcome is observed in mice, in which there is a faster turnover and lower
steady state concentration of ApoE, as well as greater inflammation, in APOE4-TR mice (Riddell
et al., 2008).
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The effects of ApoE4 on AD risk appear to be closely tied to its role in regulating microglial
function. For example, among AD patients, APOE4 carriers have an increase in the number of
microglia, as well as in microglial activation (Egensperger et al., 1998). Two important functions
of microglia are the release of cytokines and chemokines, and the clearing of debris and pathogens
via phagocytosis, and these processes are usually tightly correlated (Fiala et al., 2007; Zhu et al.,
2011). Under normal conditions, when microglia encounter an insult, they switch from a resting
surveillance state to an active state, and both pro- inflammatory genes and phagocytosis-related
genes are upregulated (Fu et al., 2014). However, in mice with AD-like pathology, microglial
motility and Aβ phagocytosis are impaired even though cytokine production is increased (Krabbe
et al., 2013). Additionally, macrophages and microglia expressing apoE4 show deficits in Aβ
phagocytosis compared to ApoE2-expressing cells (Guillot-Sestier et al., 2015; Zhao et al., 2009).
Thus, normal microglial functions are impaired both in the presence of AD pathology and apoE4,
and these may interact to exacerbate AD risk. The reasons why microglia exhibit impaired ability
to clear debris in chronic diseases is uncertain. In a state of chronic disease, even when microglia
are able to perform phagocytosis of Aβ, not all of it is successfully degraded by the lysosomes
(Guillot-Sestier and Town, 2013). Intracellular Aβ degradation can be promoted via cholesterol
efflux by accelerating trafficking of Aβ to the endocytic system (Lee et al., 2012). Cholesterol
efflux activity is ApoE isoform-dependent and APOE4 carriers have poorer efficiency of
cholesterol efflux, which possibly contributes to the higher risk of AD in APOE4 carriers (Hara,
2002; Jiang et al., 2008a; 2008b; Michikawa et al., 2000). In line with this, co-localization of Aβ
and late endosomes/lysosomes is significantly reduced when microglia are pretreated with apoE4
compared to apoE2 (Mahley and Rall, 2000). Thus, counteracting apoE4 effects by increasing
ApoE levels or lipidation status has been shown to ameliorate AD pathology in several mouse
models of AD (Cramer et al., 2012; Jiang et al., 2008a; Wahrle et al., 2008).
Recent findings suggest that ApoE4 also may increase inflammation by acting as a
transcription factor for numerous genes, including several associated with immunoregulation. In
an in vitro model, ApoE was found to bind DNA and alter gene expression. Interestingly, ApoE4
binding both decreased Sirt 1 levels and induced NF-κB translocation to the nucleus to a greater
extent than either ApoE2 or ApoE3 (Theendakara et al., 2016). Sirt 1 is a histone deacetylase
involved in neuroprotection, cell survival, and metabolism (Zschoernig and Mahlknecht, 2008).
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Thus, the ability of apoE4 to suppress Sirt 1 and stimulate NF-κB signaling negatively affects
neuronal health while simultaneously increasing inflammation.
To summarize, ApoE is an important regulator of a number of inflammatory processes and
modulates the functions of microglia and macrophages in brain. Importantly, the strength of ApoE
actions are often isoform-dependent, with ApoE4 generally increasing inflammation while
impairing the ability of immune cells to clear debris.
ApoE interacts with sex steroid hormones—In addition to its role in immunity, APOE status
also interacts with sex steroid hormones. For example, the effects of hormone replacement therapy
in menopausal women appear to vary between APOE3 and APOE4 carriers. More specifically,
estrogen-based hormone therapy is associated with memory improvement and slower cognitive
decline in non-APOE4 carriers, but not in APOE4 carriers (Burkhardt et al., 2004). Similar effects
have been reported in mice, where E2 treatment in EFAD mice (contain both human APOE
genotypes and AD transgenes) reduces Aβ pathology in ovariectomized APOE2 and APOE3 mice,
but increases pathology in ovariectomized APOE4 EFAD mice (Kunzler et al., 2014). The
association between APOE4 and E2 remains to be fully resolved as other reports show that
estrogen-based hormone therapy exerts cognitive benefits (Ryan et al., 2009), reduces risk of AD
(Rippon et al., 2006) and slows cellular aging (Jacobs et al., 2013), even in female APOE4 carriers.
Interactive effects between sex and APOE are especially prevalent in the innate immune
system. Adult macrophages from APOE4-TR male mice produce significantly higher levels of
nitric oxide (NO) than those from APOE3-TR male mice, but female macrophages show no
difference between APOE3 and APOE4 (Brown et al., 2002). The protective effect of sex-steroid
hormones also varies with APOE status. Microglia cultures from APOE3-TR have suppressed
LPS/IF-γ mediated NO production upon E2 treatment, whereas microglia cultures from APOE4-
TR show only a very modest reduction in NO (Brown et al., 2008).
Interactions between APOE status and testosterone have also been demonstrated. For
example, male APOE4-TR mice have greater baseline levels of nitrite and inflammatory cytokines
than do APOE3-TR males (Colton et al., 2005). However, removal of circulating testosterone via
castration results in a significant increase in levels of nitrite and cytokines in APOE3-TR but not
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APOE4-TR males (Colton et al., 2005). Interestingly, APOE4-TR male mice have greater
cognitive impairments after castration, than do APOE3-TR males (Pfankuch et al., 2005; Raber et
al., 2002). One suggested mechanism by which apoE interacts with testosterone is that apoE4
decreases tissue sensitivity to the hormone. Androgen receptor levels are downregulated (Raber,
2008), and androgens have reduced binding to AR in the presence of ApoE4 (Raber et al., 2002).
The ApoE – testosterone interaction also is seen in hippocampal size, with volume being smallest
in APOE4 men who have low testosterone (Panizzon et al., 2010). Additionally, cognitively
normal older men with APOE4 exhibit significantly lower levels of testosterone than non-carriers,
suggesting that APOE status may affect testosterone levels (Hogervorst et al., 2002). The ApoE –
testosterone interaction may also extrapolate to females, as suggested by the finding that spatial
learning and memory were improved with testosterone treatment only in APOE4-TR but not
APOE3-TR female mice (Raber et al., 2002).
In summary, APOE4 is associated with exaggerated pro-inflammatory immune responses.
Though both E2 and testosterone exert largely anti-inflammatory actions, their effects differ
depending upon APOE isoform. Additional research is needed to further elucidate APOE and sex
interactions, and the mechanisms underlying them.
3.4.2 Obesity
Accumulating evidence points to a positive correlation between AD and obesity
(Fitzpatrick et al., 2009; Gustafson et al., 2009; Jayaraman and Pike, 2014; Moser and Pike, 2016),
although this is not always the case (Qizilbash et al., 2015). Parallel relationships have been
observed in animal models. AD transgenic mice maintained on high-fat diet (HFD) and other
obesogenic diets exhibit increased levels of Aβ accumulation and/or tau phosphorylation (Barron
et al., 2013; Ho et al., 2004; Julien et al., 2010; Kohjima et al., 2010). In non-transgenic models,
rodents show cognitive impairment and changes in behavior after HFD without presenting AD-
like pathology, which may indicate a role of obesity in exacerbating rather than initiating AD (Hsu
et al., 2014; Kanoski et al., 2010).
Interestingly, there may be a window during which obesity increases risk of AD, which
could explain some of the discordant results in the human literature (Whitmer et al., 2005). That
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is, obesity in midlife seems to be an especially strong risk factor for AD (Emmerzaal et al., 2015;
Fitzpatrick et al., 2009). During this period, adiposity is correlated with obesity-related vascular
diseases, increased inflammation, and changes in blood-brain barrier integrity and brain
morphology (Emmerzaal et al., 2015; Gustafson et al., 2007; Pannacciulli et al., 2006; Yaffe et al.,
2004). Additionally, the deleterious effects of obesity may be further potentiated by a decrease in
sex steroid hormones at midlife, which could be prevented by hormone therapy (Whitmer et al.,
2011).
Obesity interactions with sex steroid hormones—Rates of obesity are similar between sexes,
however the consequences of increased adiposity exhibit significant sex differences (Ogden et al.,
2014). For example, middle-aged women are more susceptible to obesity-associated inflammation
(Ahonen et al., 2012), whereas men have higher rates of metabolic syndrome (Pradhan, 2013).
Animal studies corroborate these links, showing that male mice maintained on HFD have higher
relative increases in weight and adiposity than females, and these are associated with greater
impairments in glucose tolerance and insulin sensitivity (Estrany et al., 2013; Garg et al., 2011).
In contrast, when exposed to HFD, female mice have less fat deposition and infiltrating
macrophages, stronger insulin sensitivity and lipid production, and better synaptic plasticity than
male mice (Hwang et al., 2010; Medrikova et al., 2012; Petterson et al., 2012). Interestingly, some
of the protection against obesity observed in women is lost at menopause, suggesting a role for sex
steroid hormones (Bloor and Symonds, 2014; Meyer et al., 2011).
Estrogens are generally protective against weight gain and adiposity. In response to HFD,
E2 upregregulates the heat shock protein HSP72, which decreases inflammation, thereby
protecting against the development of insulin resistance (Chung et al., 2008). Interestingly, female
rats fed HFD show a downregulation of ERα, decreasing their sensitivity to E2, and making them
more susceptible to glucose intolerance (Gorres et al., 2011). Likewise, male and female ERα
knockout mice have increased adiposity, as well as insulin resistance and impaired glucose
tolerance (Heine et al., 2000; Ribas et al., 2010).
As is the case with E2, testosterone is also largely protective against excess adiposity.
There are reciprocal relationships between testosterone, adiposity, and its health consequences in
aging men (Zitzmann, 2009). Increasing adiposity is associated with decreased levels of
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testosterone (Tang Fui et al., 2014). This is a bidirectional relationship as low testosterone is a risk
factor for obesity (De Maddalena et al., 2012; Tang Fui et al., 2014). Testosterone replacement
therapy may be a viable option, as it has been shown to reduce body weight and lower the risks of
obesity and metabolic syndrome (Yassin et al., 2014). A recent meta- analysis of observational
studies confirms the potential benefits of testosterone therapy in aging men (Corona et al., 2016).
However, the effects of sex steroid hormones may be sex- dependent, as androgens have been
reported to decrease insulin sensitivity in women’s adipose tissue (Corbould, 2007). The animal
literature is also consistent with beneficial effects of testosterone on obesity. In the obese Zucker
rat, testosterone supplementation reduced body weight and significantly improved metabolic
outcomes, including plasma insulin levels and glucose tolerance (Davis et al., 2012). Conversely,
depletion of endogenous testosterone by gonadectomy worsens the effects of HFD in male mice.
Our lab previously reported that gonadectomized male mice on HFD have a greater increase in
blood glucose levels, insulin insensitivity, and pro-inflammatory cytokine expression than do
gonadally intact males maintained on HFD (Jayaraman et al., 2014). Moreover, the effects appear
to extend to brain as conditioned media collected from cultured glial cells generated from obese
mice reduced neuron survival and neurite outgrowth in primary neurons (Jayaraman et al., 2014).
Obesity interacts with inflammation—Obesity is characterized by a chronic state of low-grade
inflammation (Hotamisligil, 2006; Kratz et al., 2014). Macrophages residing in metabolically
active tissues modulate cytokine production and lipid metabolism, actions that are modulated by
adoption of an activated state in response to circulating saturated fatty acids (Kratz et al., 2014).
Adipose tissue, liver, and gut have been reported to contribute to overall systemic inflammation,
although their relative and temporal influences are still incompletely defined. It appears that
adipose inflammation occurs prior to liver inflammation in a C57BL/6J mice model of diet-
induced obesity (van der Heijden et al., 2015). Additionally, in the same model, it was shown that
an imbalance in the gut microbiome triggers systemic inflammation and brain inflammation prior
to weight gain (Bruce-Keller et al., 2015). Much work has been done on the effects of obesity on
inflammation in various tissues, which are briefly addressed below.
The adipose tissue: Adipose tissue plays an important role regulating healthy metabolism. Indeed,
mice lacking white adipose tissue exhibit insulin resistance, hyperglycemia, hyperlipidemia, and
liver steatosis, all of which can be reversed via adipose tissue transplants (Gavrilova et al., 2000).
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In obesity, adipose tissue is characterized by hypertrophic adipocytes and infiltration of
macrophages, which are an important source of inflammation (Wellen and Hotamisligil, 2003).
Additionally, adipocytes also can secrete pro-inflammatory cytokines and adipokines, further
increasing inflammation and attracting macrophages (Greenberg and Obin, 2006). It is thought
that pro-inflammatory cytokines contribute to the disruption in glucose homeostasis and insulin
resistance often linked with obesity (Xu et al., 2003). In support of this position, deletion of
macrophages can restore insulin and glucose homeostasis associated with obesity (Patsouris et al.,
2008).
Central or visceral adipose tissue appears to be especially problematic because it
preferentially accumulates triglycerides and is less sensitive to insulin than other fat depots (Märin
et al., 1992; Wajchenberg, 2000). In the case of AD, central adiposity may be the best metabolic
predictor of disease risk (Luchsinger et al., 2012; Whitmer et al., 2008). The distribution of fat
differs between sexes, with abdominal visceral fat being more prevalent in men than in women
(Bouchard et al., 1993; Enzi et al., 1986). Interestingly, visceral fat increases with aging,
particularly in obese women, which may be attributed to depletion of estrogens at menopause
(Matsuzawa et al., 1995). The gene profile in fat tissue also changes in a sex-specific manner. In
response to obesity, males have a greater increase in expression of genes involved in inflammatory
pathways, whereas females have a greater increase in expression of genes involved in insulin
signaling and lipid metabolism. This may contribute to the observation that females have less
central adiposity than men and are relatively protected against glucose and insulin resistance.
These effects cannot be entirely explained by the presence of sex steroid hormones, since
prepubertal ovariectomy only partially shifts the genetic profile to a more “male-like” expression
(Grove et al., 2010).
The liver: The liver is also significantly affected by obesity-induced inflammation. Both diet- and
genetically-induced obesity in animal models results in non-alcoholic fatty liver disease (NAFLD),
which is characterized by the presence of steatosis, insulin resistance, systemic inflammation, and
increased NF-κB activity (Cai et al., 2005; Fabbrini et al., 2009). NFκB activation alone can cause
insulin resistance without steatosis, which suggests that inflammation interferes with insulin
signaling (Cai et al., 2005). Moreover, neutralizing antibodies against the pro-inflammatory
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cytokines IL-6 and TNFα are sufficient to partly reverse liver pathology associated with obesity
(Fabbrini et al., 2009; Li et al., 2003).
The toll-like receptors, which recognize pathogen-associated molecular patterns, appear to
be especially important in obesity-induced liver inflammation. Activation of TLRs culminates in
NF-κB signaling cascade activation, which controls the expression of inflammatory genes
including IL-6, pro-IL-1β, TNFα and COX2 (Kawai and Akira, 2007). TLR4 is of special interest
because of the ability of saturated fatty acids to activate this receptor (Lee et al., 2001; Shi et al.,
2006), although this effect is still controversial (Erridge and Samani, 2009). Most findings suggest
that TLR4 is involved in a number of inflammatory pathways associated with various
neuropathologies as well as obesity, metabolic syndrome, and insulin resistance (Ahmad et al.,
2012; Crack and Bray, 2007; Jia et al., 2014; Jialal et al., 2012; Pascual et al., 2011; Reyna et al.,
2008; Wang et al., 2013). TLR4 is particularly important in the liver, where it was demonstrated
that hepatocyte- specific TLR4 knockout mice maintained on HFD exhibited improved metabolic
and inflammatory parameters, including ameliorated steatosis, glucose tolerance, insulin
sensitivity, and reduced expression of pro-inflammatory cytokines in plasma, fat and liver, in
comparison to wild-type mice fed HFD (Jia et al., 2014).
The association between obesity and systemic inflammation has raised the question of
whether this relationship could influence AD outcomes. Indeed, an acute model of NAFLD
increases inflammation in the brain of non-transgenic and AD transgenic mice. Chronic NAFLD
accelerates cerebral amyloid angiopathy, tauopathy and neuron loss, suggesting that aging and
NAFLD are sufficient to trigger AD-like pathology (Kim et al., 2016).
The microbiome: Recently, the role of the gut and microbiome in obesity and inflammation has
received increased attention. The human gut microbiome is the largest reservoir of microbes in the
body, containing about 10
14
microorganisms (Bhattacharjee and Lukiw, 2013). It is becoming
evident that the intestinal microbiome influences the host’s function well beyond the gut. Indeed,
the microbiome has been implicated in a variety of diseases, including obesity, diabetes, non-
alcoholic fatty liver disease, autism, multiple sclerosis, and cardiovascular disease (Caracciolo et
al., 2014). Further, recent reviews have suggested a link between AD and the microbiome
(Bhattacharjee and Lukiw, 2013; Hill et al., 2014a; 2014b; Shoemark and Allen, 2015), an idea
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supported by evidence of relationships between the microbiome, systemic inflammation, brain
inflammation, and cognitive impairment (Bruce-Keller et al., 2015; Daulatzai, 2014).
One of the main factors affecting microbiome composition is diet (Caracciolo et al., 2014).
Microbiome imbalance and disruptions in gut homeostasis have been observed in diet- induced as
well as in genetic models of obesity (Ley et al., 2005; Turnbaugh et al., 2008). Microbiome
imbalance leads to increased intestinal permeability, translocation of bacteria to the bloodstream,
and systemic inflammation (Cani et al., 2008). In turn, systemic inflammation and consumption of
high-energy diet can disrupt the blood brain barrier and cause cognitive impairments (Kanoski et
al., 2010; Zlokovic, 2008). It is thought that these processes may facilitate the entrance of activated
immune cells and bacterial components into the brain, and contribute to cognitive impairment
(Pistell et al., 2010). An interesting development in support of this hypothesis is the recent finding
that Aβ can act as a pore- forming antimicrobial peptide, suggesting that Aβ accumulation could
occur in response to infection (Kumar et al., 2016). Perhaps consistent with this idea is the finding
that the endotoxin LPS can potentiate Aβ fibrillogenesis, which suggests that elevated endotoxin
levels during infections and gut leakage may drive pathogenesis AD not only by increasing
inflammation, but also by increasing Aβ deposition (Asti and Gioglio, 2014). In general, infections
are associated with increased risk of AD (Alonso et al., 2014; Miklossy, 2011; Nee and Lippa,
1999; Perry et al., 2003) and some viral infections may actively contribute to AD pathogenesis,
since pathogens that evade elimination by the immune system lead to chronic inflammation,
neuronal damage and Aβ deposition (Hill et al., 2009; Miklossy, 2011; Zhao and Lukiw, 2015).
Interestingly, there are sex differences in the microbiome. In microbiome transplantation
experiments, the sex of the microbiome donor determined the metabolic outcomes in the recipient.
Specifically, female mice that received a male microbiome transplant showed increased levels of
serum testosterone and lowered serum concentrations of glycerophospholipid and sphingolipid
long-chain fatty acids, which are characteristically male (Markle et al., 2013). Moreover, blocking
AR signaling attenuated all of the male microbiome–specific changes in female host metabolites,
suggesting that the increased testosterone from the male microbiome transfer was critical for the
generation of host metabolomic phenotypes (Markle et al., 2013). Other studies have also
demonstrated sex differences in microbiome manipulations, including altered BNDF and serotonin
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levels in germ-free male animals but not in females, which suggests that the sexes differ in their
sensitivity to microbiome changes (Clarke et al., 2012).
Obesity and AD are both characterized by sex differences and regulated by sex steroid
hormones. Moreover, inflammation represents a point of interaction between obesity and AD,
which can also be modulated by sex steroid hormones. Adipose tissue, liver, and gut contribute to
the systemic inflammation and impaired glucose homeostasis associated with obesity, as well as
to cognitive deficits. Thus, obesity may accelerate the onset of AD and exacerbate its progression
at least in part through inflammatory pathways, which can be modulated by sex steroid hormones,
offering an opportunity for therapeutic interventions.
3. 5.3 Air pollution
An environmental risk factor for AD that may contribute to the relationships between AD,
inflammation, and sex is air pollution. Polluted air is a mixture of gases and particulate matter
(PM) of heterogeneous size and composition. Epidemiological data indicates that air pollution is
responsible for 5.5 million deaths worldwide and 141.5 million disabilities (Forouzanfar et al.,
2015). One well established effect of air pollution is cognitive decline. For example, women
chronically exposed to coarse (2.5 μm – 10 μm diameter) and fine (< 2.5 μm diameter) particles
show faster cognitive decline with aging (Weuve et al., 2012). Likewise, middle-aged and old men
and women living in areas with high concentrations of fine particles show worse cognitive
performance (Ailshire and Clarke, 2015). Similar results have been reported other components of
air pollution, including ozone (Chen and Schwartz, 2009). Findings in human populations have
been reproduced in rodent models, with varying degrees of memory impairment associated with
both acute and chronic exposure to air pollution paradigms (Avila-Costa et al., 1999; Cheng et al.,
2016b; Fonken et al., 2011; Rivas-Arancibia et al., 2010; Zanchi et al., 2010).
Strikingly, neurodegeneration has been found in people who live in areas with extreme
levels of PM, regardless of age. Some of these findings indicate that exposure to air pollution
environments can increase Aβ production and deposition as well as elevate neuroinflammation in
children and young adults (Calderon-Garciduenas et al., 2003; 2008). Similar links between air
pollution and Aβ have been reported in mice and dogs (Bhatt et al., 2015; Calderón-Garcidueñas
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et al., 2003), which collectively point to an interaction between air pollution and AD pathogenesis
(Block and Calderón-Garcidueñas, 2009). Given the role of inflammation in the pathogenesis of
AD, it seems reasonable that air pollution may be acting through pro-inflammatory pathways to
increase AD risk. Perhaps consistent with this idea is evidence that persons with factors that
increase inflammation, like APOE4, are at even greater risk of developing AD-like neuropathology
when exposed to air pollution (Calderon-Garciduenas et al., 2008; 2015).
Air pollution induces systemic inflammation and brain inflammation—An important
deleterious effect of air pollution exposure is induction of focal as well as systemic inflammation.
Ultrafine particles (< 100nm) are able to translocate into the lung epithelia (Semmler-Behnke et
al., 2007), can be taken up by resident macrophages (Donaldson et al., 1998), and can form
ultrafine particulate-protein complexes (Kreyling et al., 2006). These complexes allow air
pollution particles to circulate throughout the body and deposit in organs including the heart and
liver (Kreyling et al., 2002; Semmler et al., 2004) and perhaps brain. Therefore, air pollution
exposure represents a risk for other major inflammatory diseases like pulmonary disease,
cardiovascular disease, and stroke (Brook et al., 2010; Chauhan and Johnston, 2003; Liu et al.,
2016). The presence of PM in tissue leads to macrophage recruitment and leukocyte infiltration,
as well as systemic microvascular dysfunction (Nurkiewicz et al., 2005), and production of
cytokines and acute-phase proteins, which can be detected in the bloodstream (Goldsmith et al.,
1998; Tan et al., 2000; van Eeden et al., 2001). Air pollution not only induces chronic
inflammation, but also has synergistic effects with other inflammatory conditions including aging,
diabetes or hypertension (Dubowsky et al., 2006; Genc et al., 2012). Behavioral changes triggered
by air pollution include memory impairment and depressive-like behaviors (Avila-Costa et al.,
1999; Cheng et al., 2016a; Davis et al., 2013; Fonken et al., 2011; Morgan et al., 2011).
In the brain, PM exposure can directly induce degeneration and neuroinflammation. PM
can also indirectly contribute to the effects of air pollution through systemic inflammation.
Inhalation of PMs can induce oxidative stress (Zhang et al., 2012), inflammation, and even
neuronal cell death, and particles can be taken up by the olfactory neurons into the brain (Cheng
et al., 2016b; Oberdoster et al., 2004). The effects of cytokines and PM exposure on the
microvasculature can also lead to blood brain barrier breakdown (Calderon-Garciduenas et al.,
2008). Additionally, PM can directly affect glutamatergic neuronal health through downregulation
88
of GluA1 and increased susceptibility to excitotoxicity (Morgan et al., 2011). PM also activate
glial cells and induce cytokine release (Cheng et al., 2016a; Fonken et al., 2011; Levesque et al.,
2011; Morgan et al., 2011), which is associated with fewer dendritic spines in the hippocampus
and impaired memory (Fonken et al., 2011). Thus, air pollution induces a range of systemic and
neural effects that may increase vulnerability to AD.
Exposure to air pollution affects males and females differently—Deleterious effects of air
pollution show sex differences. In terms of mortality, exposure to coarse and fine particles and to
ozone were strongly correlated with lung cancer deaths, systemic inflammation, and all natural
cause mortality only in males (Abbey et al., 1999; Hoffmann et al., 2009), although other studies
identified no sex differences in air pollution-related all- cause mortality rates (Naess et al., 2007;
Pope et al., 1995). The interaction of age and sex may be more important than sex alone. Age-
dependent reduction in sex hormone levels, which is more drastic in women, may affect
susceptibility to air pollution. Women > 60 years of age are at a five-fold greater risk for coarse
particle-associated heart mortality than women < 60. In comparison, men aged > 60 years are only
two-fold more likely to have coarse particle-associated heart death than young men (Zeka et al.,
2006). In line with this, female patients are more sensitive to the effects of air pollution before the
age of 15 and after age 65, indicating that the reduced levels of sex steroid hormones during these
ages may play a role in susceptibility to air pollution in women (Wang and Chau, 2013). In terms
of neural effects, a comparison of cognitive impairment in children exposed to air pollution found
the greatest deficits in girls that were both obese and APOE4 carriers (Calderón- Garcidueñas et
al., 2016).
Sex steroid hormones have been shown to interact with air pollution. Estrogen treatment
protects against neurodegeneration and oxidative stress induced by ozone inhalation in
ovariectomized rats (Angoa-Pérez et al., 2006). Air pollution can also affect sex steroid production.
One study demonstrated that traffic policemen exposed to urban pollutants have lower free
testosterone values than control administrative staff policemen (Sancini et al., 2010). It is likely
that different components of air pollution and different exposure times will have distinct effects
on the interaction with sex steroid hormones, but few studies have examined this relationship. One
study shows that male rats exposed to oil paint vapor for 10 weeks have increased serum
testosterone levels if exposed for 1 hour/day, but significantly lower levels if exposed for 8
89
hours/day (Ahmadi et al., 2015). Several compounds in air pollution are described as endocrine
disruptors and can act on multiple organs by affecting metabolism, which links air pollution to
obesity, diabetes, cardiovascular problems and AD (Maqbool et al., 2016; Newbold et al., 2008;
Rudel et al., 2003). Despite the compelling advances in this area, this relationship between air
pollution and sex remains to be fully elucidated.
Air-pollution associated neurodegeneration may be the result of chronic systemic
inflammation, as well as of oxidative stress, neuroinflammation, and Aβ production. Exposure to
air pollution increases risk of cognitive impairment and AD, which may be exacerbated by other
factors that increase inflammation, such as APOE4, aging, and decrease in sex steroid hormone
levels.
3.6 Conclusion
AD is a multifactorial disease for which sex differences are observed in both the
vulnerability to its development and the manifestation of its pathology. How sex affects AD has
only been partially determined. Risk for AD in both men and women appears to be increased by
the normal, age-related decrease in their primary sex steroid hormones, testosterone and estradiol,
respectively. As has been reviewed previously (Brinton, 2008; Li and Singh, 2014; Pike et al.,
2009), the increased risk for AD associated with hormone depletion is generally thought to result
from the loss of numerous neuroprotective actions of estradiol, testosterone, and other
neurosteroids. With the increasing appreciation of the contributions of both systemic and neural
inflammation in AD pathogenesis, the established role of sex steroid hormones as regulators of
glial function and inhibitors of inflammatory signaling has acquired new significance.
Nonetheless, the translation of the potential therapeutic benefits of sex steroids to the prevention
and perhaps treatment of AD has yet to be realized.
In addition to age-related reductions in sex steroid levels, AD risk is also increased by
numerous genetic and environmental factors. Among these, perhaps the most important is APOE4.
The interactions among sex, sex steroid hormones, and APOE4 have been noted for many years,
yet they remain poorly defined. Notably, the AD risk associated with APOE4 disproportionately
affects females both in humans (Altmann et al., 2014; Farrer et al., 1997; Payami et al., 1994) and
90
rodents (Cacciottolo et al., 2016). Interestingly, whereas sex steroid hormones generally exert anti-
inflammatory effects, APOE4 is linked with exaggerated pro-inflammatory responses. The
associations between sex, sexual differentiation, and microglia (Lenz et al., 2013; Schwarz et al.,
2012) suggest interesting possible links with APOE4 that may be relevant to AD.
AD risk is also increased by a range of lifestyle and environmental factors, including
obesity and air pollution. The gene – environment interactions among APOE4, obesity and air
pollution as they apply to AD are largely unknown. As suggested by initial evidence, AD risk is
predictably worsened by the combination of risk factors. However, the nature of such interactions
is unclear, including how they are affected by sex, whether they share common mechanisms such
as inflammation, and whether they are mitigated by sex steroids (Figure 3). For example, obesity
appears to have more deleterious effects on men than women, whereas women are more impacted
by APOE4. In an interactive context, how do the combination of obesity and APOE4 status affect
AD risk in men versus women? Similar arguments can be made for air pollution and a host of other
risk factors. Moving forward, it appears untenable to focus on individual components of
pathogenesis in solving the AD crisis. Like cancers and other complex age-related diseases, AD is
multifactorial and differs according to sex and, by extension, by sex steroid hormones. Progress in
identifying at-risk populations and both developing and applying therapeutics will require attention
to individual gene profiles, lifestyle and environmental exposures, sex, and how these variables
interact.
91
Figure 3. Inflammation as a common denominator between risk factors of AD and sex steroid
hormones. Inflammation is widely theorized to act as a contributor to AD. Neuroinflammation is
associated with activation of microglia and astrocytes, which increase expression of pro-
inflammatory cytokines that can promote accumulation of the pathological proteins Ab and
hyperphosphorylated tau. Genetic (ApoE4), and environmental (obesity and air pollution) factors
that increase AD risk are associated with elevated inflammation. Sex steroid hormones may affect
AD risk in part by inhibiting inflammation, modulating glial cells, and regulating interactions
among risk factors.
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CHAPTER 4. TL4/NF-kB IMPACT ON NEUROINFLAMMATION
Acknowledgements: I want to thank Dr. Moser for this incredible collaboration and partnership
that taught me so much. Thanks as well to the Pike lab for essential support.
*This chapter has been partially based on the publication Moser, V.A., Uchoa, M.F., Pike, C.J.
TLR4 inhibitor TAK-242 attenuates the adverse neural effects of diet-induced obesity. J
Neuroinflammation, 10.1186/s12974-018-1340-0 (2018)
4.1 Summary
Obesity exerts negative effects on brain health, including decreased neurogenesis, impaired
learning and memory, and increased risk for Alzheimer’s disease and related dementias. Because
obesity promotes glial activation, chronic neuroinflammation, and neural injury, microglia are
implicated in the deleterious effects of obesity. One pathway that is particularly important in
mediating the effects of obesity in peripheral tissues is toll- like receptor 4 (TLR4) signaling. The
potential contribution of TLR4 pathways in mediating adverse neural outcomes of obesity has not
been well addressed. To investigate this possibility, we examined how systemic pharmacological
inhibition of TLR4 affects the peripheral and neural outcomes of diet-induced obesity. Male
C57BL6/J mice were maintained on either a control or high-fat diet for 12 weeks in the presence
or absence of the specific TLR4 signaling inhibitor TAK-242. Peripherally, TAK-242 treatment
was associated with partial inhibition of inflammation in the adipose tissue but exerted no
significant effects on body weight, adiposity, and a range of metabolic measures. In the brain,
obese mice treated with TAK-242 exhibited a significant reduction in microglial activation,
improved levels of neurogenesis, and inhibition of Alzheimer-related amyloidogenic pathways.
These results demonstrate a significant protective effect of TLR4 inhibition on neural
consequences of obesity, findings that further define the role of microglia in obesity-mediated
outcomes and identify a strategy for improving brain health in obese individuals.
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4.2 Introduction
The high prevalence of obesity presents a major public health concern since obesity is
strongly linked with increased risk for several diseases including type 2 diabetes, cardiovascular
disease, and cancer (Zheng et al., 2017). Importantly, obesity is also associated with adverse effects
on the brain and neural function. In humans, obesity is linked with decreases in hippocampal
volume and white matter integrity (Bischof and Park, 2015; Ho et al., 2010; Medic et al., 2019) as
well as with functional consequences that lead to accelerated cognitive decline (Cournot et al.,
2006; Elias et al., 2005) and increased risk of dementia (Whitmer et al., 2008). In rodent models,
diet-induced obesity (DIO) has been demonstrated to impair neurogenesis (Lindqvist et al., 2006;
Park et al., 2010b), synaptic plasticity (Hao et al., 2016; Stranahan et al., 2008), and neural function
(Jayaraman et al., 2014), as well as promote Alzheimer’s disease (AD)-related pathology (Barron
et al., 2013; Julien et al., 2010; Moser and Pike, 2017).
Although the mechanisms by which obesity impairs neural health have yet to be fully
elucidated, pathways associated with microglial activation are compelling candidates. Obesity is
characterized by chronic activation of macrophages in peripheral tissues and both microglia and
astrocytes in the brain (Buckman et al., 2014; Cancello et al., 2006; García-Cáceres et al., 2013;
Maldonado-Ruiz et al., 2017; Weisberg et al., 2003). Activated macrophages yield unresolved
inflammation in peripheral organs including the adipose tissue (Weisberg et al., 2003) and liver
(Park et al., 2010a), whereas activated microglia can drive neuroinflammation in the brain (Koga
et al., 2014; Maldonado-Ruiz et al., 2017). Neuroinflammation is associated with numerous
deleterious effects including reductions in neurogenesis (Ekdahl et al., 2003) and synaptic
plasticity (Di Filippo et al., 2013) and acceleration of AD (Wyss-Coray and Rogers, 2012). In
addition to promoting pro-inflammatory pathways, activated microglia exhibit diverse phenotypes
that are characterized by a range of morphological and gene expression signatures and presumed
to underlie both beneficial and adverse effects (Guillot-Sestier and Town, 2013; Guillot-Sestier et
al., 2015; Wyss-Coray and Rogers, 2012). The pathways that may contribute to the neural effects
of obesity remain to be fully defined.
The pattern recognition receptor Toll-like receptor 4 (TLR4) activates signaling pathways
that may be particularly important in mediating obesity-associated microglial activation and its
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consequences. TLR4 stimulation results in downstream activation of at least two key transcription
factors: NF-kB, which increases expression of pro-inflammatory cytokines (Chow et al., 1999),
and interferon regulatory factor 3, which promotes activated microglial phenotypes that are
predominantly anti-inflammatory (Mathur et al., 2017; Tarassishin et al., 2011). Thus, TLR4
activation may be expected to yield a range of activated microglial phenotypes. Interestingly,
TLR4 binds to and is activated by saturated fatty acids, which are abundant in obesogenic diets
and may contribute to obesity-induced increases in inflammation (Lee et al., 2001; Reyna et al.,
2008; Schaeffler et al., 2009; Shi et al., 2006; Wang et al., 2012) and impaired insulin signaling
(Shi et al., 2006; Song et al., 2006). Prior work has implicated TLR4 signaling as an important
regulator of DIO effects on peripheral tissues. For example, mice with either nonfunctional or
deleted TLR4 exhibit significant protection against high-fat diet (HFD)-induced glucose
dysregulation (Chao-Fan et al., 2013; Poggi et al., 2007), insulin resistance (Francis et al., 2007;
Suganami et al., 2007), and peripheral inflammation (Jia et al., 2014; Kim et al., 2015; Li et al.,
2014), though other studies indicate these mice are not protected against the entire range of
metabolic and inflammatory effects of HFD (Kim et al., 2015; Yilei et al., 2012). Pharmacological
inhibition of TLR4 also protects mice against HFD-associated adipose inflammation and fibrosis
(Vila et al., 2014) and insulin resistance (Zhang et al., 2015). Disruption of TLR4 signaling appears
to have only modest effects on increases in body weight and adiposity that result from HFD
(Coenen et al., 2009; Francis et al., 2007; Jia et al., 2014; Saberi et al., 2009).
The potential role of TLR4 signaling in mediating obesity-induced microglial activation
and associated neural impairment is unclear. Prior work has implicated TLR4 in pro-inflammatory
effects of saturated fatty acids and HFD in hypothalamus, which in turn may regulate diet-induced
changes in metabolic function (Milanski et al., 2009, 2012; Morari et al., 2014). Given that TLR4
is highly expressed in microglia (Vaure and Liu, 2014), TLR4 signaling pathways are implicated
in activated microglial phenotypes, and activated microglia are thought to drive many of the
adverse effects of obesity and HFD in hippocampus and other brain regions (Hao et al., 2016; De
Luca et al., 2016), TLR4 may mediate HFD-induced microglial activation and dysfunction in
hippocampus. To address this possibility, we evaluated HFD-induced effects on metabolic,
inflammatory, microglial, and neural outcomes in the presence and absence of a pharmacological
inhibitor of TLR4 signaling. We report that treatment with a specific TLR4 inhibitor reduced
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peripheral inflammation and largely prevented both microglia activation and impaired
neurogenesis in hippocampus independently of the effects on weight gain and metabolic
dysregulation associated with HFD.
4.3 Material and Methods
Animal procedures
Ten-week-old male C57BL6/J mice were purchased from Jackson Labs (Bar Harbor, ME,
USA) and allowed to acclimate to our vivarium facility at the University of Southern California
for 2 weeks. Animals were housed under a 12-h light/dark cycle with lights on at 6 AM and ad
libitum access to food and water. At 12 weeks of age, mice were randomized to a total of four
dietary and drug treatments groups (N = 10–14/group). Dietary treatments were either control
(CTL; 10% fat; #D12450J, Research Diets, New Brunswick, NJ, USA) or high-fat diet (HFD; 60%
fat; #D12492, Research Diets). Drug treatments were either vehicle (0.09% sterile saline) or the
TLR4 inhibitor TAK-242 (3 mg/kg in saline; #614316, EMD Millipore, Billerica, MA, USA).
Drugs were administered via intraperitoneal (IP) injection 6 days/week. Dosage was based upon a
previous study in which TAK-242 delivered at 3 mg/kg via IP injection yielded significant brain
levels of the drug that were sufficiently maintained for at least 24 h after administration (Hua et
al., 2015). Treatments were maintained over a 12-week experimental period, during which body
weights were recorded daily and food consumption was measured weekly.
At the conclusion of the experimental period, mice were euthanized with inhalant carbon
dioxide and the brains were rapidly removed. One hemi-brain was immersion fixed for 48 h in 4%
paraformaldehyde/0.1 M PBS, then stored at 4 °C in 0.1 M PBS/0.03% NaN
3
until processed for
immunohistochemistry. Hippocampus was dissected and snap frozen for subsequent use in RNA
extraction, while the remainder of the hemi-brain was snap frozen for subsequent use in protein
extraction to examine soluble β-amyloid (Aβ) levels. Blood was collected via cardiac puncture
into EDTA-coated tubes and centrifuged to separate plasma, which was stored in aliquots at − 80
°C. Gonadal and retroperitoneal (RP) fat pads were dissected and weighed as measures of
adiposity. Both fat pads were snap frozen for subsequent RNA extraction. All animal procedures
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were conducted under protocols approved by the University of Southern California Institutional
Animal Care and Use Committee and in accordance with National Institute of Health standards.
Body composition
Body composition was determined 1 day prior to euthanization using the Bruker LF90
Minispec (Bruker Optics, Billerica, MA, USA). Mice were placed and loosely restrained inside an
acrylic cylinder. The cylinder was placed inside the bore of the magnet, and measurements of fat,
lean, and fluid mass percentages were recorded. Animals were returned to their home cages in less
than 2 min.
Glucose, cholesterol, and triglyceride measurements
At weeks 0, 4, 8, and 11, blood glucose readings were measured after overnight fasting (16
h). Blood was collected from the lateral tail vein and immediately assessed for glucose levels using
the Precision Xtra Blood Glucose and Ketone Monitoring System (Abbott Diabetes Care,
Alameda, CA, USA).
At week 11, glucose tolerance testing (GTT) was performed. First, baseline fasting glucose
levels were taken. Mice were then administered a glucose bolus (2 g/kg body weight) via IP
injection. Blood glucose levels were recorded from lateral tail vein 15, 30, 60, and 120 min after
the glucose bolus. Area under the curve (AUC) was calculated.
Plasma cholesterol and triglyceride levels were measured enzymatically at the conclusion
of the experimental period. Commercially available kits for both cholesterol (Total Cholesterol
Colorimetric Assay kit, #K603, BioVision, Milpitas, CA, USA) and triglycerides (LabAssay
Triglycerides, #290-63701, Wako Chemicals, Richmond, VA, USA) were used following the
manufacturers’ protocols.
Behavioral analyses
All behavioral testing was conducted between the hours of 6 AM and 1 PM. For all
behavioral assays, mice were brought into the behavior room and allowed to acclimate for 30 min
prior to testing. After each trial, animals were returned to their home cages and the testing arenas
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were disinfected with 70% ethanol. Open field and forced swim testing were video recorded and
analyzed by a rater blind to experimental treatment groups. Elevated plus maze and spontaneous
alternation behavior were scored live. Fear conditioning was recorded using Noldus Ethovision
XT software (Leesburg, VA, USA) and the Ugo Basille Fear Conditioning System NG (Gemonia,
Varese, Italy).
Anxiety and exploratory activity: open field and elevated plus maze (EPM)
Open field test was performed during week 8 of treatments. Briefly, animals were placed into a
40-cm
2
plexi-glass arena and allowed to move freely for 5 min. The arena floor was lightly marked
off into 9 squares, with 3 squares along each wall and 1 center square. The following behaviors
were recorded: (1) center crossings: the number of times the animal crossed into the center square
with both front paws; (2) center time: the amount of time the animal spent with both front paws in
the center square; and (3) crossings: the total number of times the animal crossed a line entering a
different square. EPM testing was performed on the day immediately following the open field
assay. After being habituated to the room, mice were placed in the center of the EPM, facing a
closed arm, and allowed to move freely on the maze for 5 min. The following behaviors were
recorded: (1) open arm entries: the number of times the mouse placed both front paws into the
open arm; (2) open arm time: the amount of time the animal spent with both front paws in the open
arm; and (3) latency to enter the open arm for the first time.
Learning and memory: spontaneous alternation behavior (SAB) and fear conditioning
At week 10, SAB was tested in the Y-maze as previously described (Carroll et al., 2010;
Christensen and Pike, 2017). Briefly, animals were placed into the long arm and allowed to explore
the maze for 5 min. Arm choices were recorded, and behavior was scored as the number of
alternations divided by the total number of arm entries. Fear conditioning was performed over 3
consecutive days beginning 48 h after SAB. On day 1, animals were placed in the conditioning
chamber, a box (17 cm× 17 cm × 25 cm) with an electrified grid floor, placed in- side a sound
attenuated chest (Ugo Basile). White noise was used to block out external sounds. After a 3 min
habituation, mice were exposed to 5 tone-and-foot shock pairings that were each placed 3 min
apart (20 s tone at 85 dB and 2 kHz, followed by a 20 s trace period, and a 1 sec 1 mA foot shock).
Animals were returned to their home cages 1 min after the final tone-shock pairing. Twenty-four
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hours after training, cued fear conditioning was tested by placing animals back into the chamber
but changing the context by altering the pattern of the walls, placing a floor board over the grid
floor, and adding a cotton ball with vanilla extract to change the scent of the chamber. After a 3-
min baseline period, the tone was played 3 times, but was not followed by the foot shock. Freezing
behavior (defined as the absence of all movement except breathing) to the tone and during the 20
s after the tone was recorded. On day 3, 24 h after cued testing, contextual fear conditioning was
assessed by placing animals back into the chamber that had the same appearance and odor as it did
during training on day 1. Freezing behavior was measured over 8 min. Behavior in the fear
conditioning chamber was recorded using Noldus Ethovision XT software.
Depression-like behavior: forced swim test (FST) was conducted 1 week after fear
conditioning, during week 11, and was the last behavioral assessment. As previously described
(Carroll et al., 2010), the animals were placed into a 2-L cylindrical tank (20 cm height × 13 cm
diameter) filled with 15 cm of water heated to 23–25 °C. At this depth, neither the feet nor tails of
animals reached the floor of the cylinder. Mice remained in the cylinder for 5 min, during which
behavior was videotaped from the side of the cylinder. Animals were scored as being immobile if
they were making only the movements necessary to keep their head above water. The number of
immobile bouts, the total time spent immobile, and the duration of the longest bout of immobility
were recorded.
Immunohistochemistry and quantification
Fixed hemi-brains were completely sectioned at 40 μm in the horizontal plane, using a vibratome
(Leica Biosystems, Buffalo Grove, IL, USA). A standard avidin/biotin peroxidase approach using
ABC Vector Elite kits (Vector Laboratories, Burlingame, CA, USA) was used to perform
immunohistochemistry, as previously described (Moser and Pike, 2017). Every eighth section was
processed for ionized calcium binding adaptor molecule 1 (IBA-1), doublecortin (DCX), and
bromo-deoxyuridine (BrdU). A different initial antigen retrieval step was performed for each
antibody, after which the same protocol was followed. For IBA-1 staining, sections were boiled in
10 mM EDTA, pH 6.0 for 10 min, then rinsed in water three times for 5 min each. For DCX
staining, tissue was pre- treated with 95% formic acid for 5 min, followed by rinsing in TBS.
Finally, for BrdU staining, sections were placed in 1% NP40 detergent for 20 min, rinsed in TBS,
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then incubated in 2 N HCl at 37 °C for 30 min, followed by 10 min in 0.1 M boric acid and rinsing
in TBS. Following the various antigen retrieval steps, sections were treated with an endogenous
blocking solution for 10 min, then rinsed with 0.2% Triton-X in TBS, 3 times for 10 min each.
Tissue was then incubated for 1 h in a blocking solution consisting of 2% bovine serum albumin
and 0.2% Triton-X in TBS for IBA-1, plus 2% normal goat serum for BrdU. For DCX, the blocking
solution was made up of 3% normal horse serum and 0.2% Triton-X in TBS. Blocked sections
were incubated overnight at 4 °C in primary antibody directed against IBA-1 (#019-19741, 1:500
dilution, Wako Chemicals); DCX (#sc-271390, 1:1000 dilution, Santa Cruz Biotechnology,
Dallas, TX, USA); or BrdU (#MCA2483, 1:200 dilution, Bio-Rad, Hercules, CA, USA). All
primary antibodies were diluted in the respective blocking solution used. On the following day,
sections were rinsed and incubated in biotinylated secondary antibody diluted in blocking solution.
Finally, immunoreactivity was visualized using 3,3′-diaminobenzidine (Vector Laboratories).
Density and activation states of microglia were determined using live imaging under
bright-field microscopy with a × 40 objective (Olympus, BX50, CASTGrid software, Olympus,
Tokyo, Japan). As previously described (Ayoub and Salm, 2003; Moser and Pike, 2017), each cell
was scored as having either a resting or reactive phenotype. Specifically, resting or type 1
microglia were de- fined as having spherical cell bodies with numerous thin, branched processes.
Both type 2 and 3 microglia were considered reactive: type 2 cells had enlarged, rod-shaped cell
bodies with fewer and thicker processes, while type 3 cells were enlarged and had either very few
or no processes, or several filopodia. Microglia were quantified in the entorhinal cortex (4
fields/section), subiculum (4 fields/section), CA1 (5 fields/section), and CA2/3 (3 fields/section)
across 4 tissue sections for a total of 64 fields and an average of ~ 450 cells per brain. Because
increased soma size is a robust indicator of microglial activation (Kozlowski and Weimer, 2012),
we also examined microglial soma size. Images of IBA-1 immunostaining in the CA1 subregion
of the hippocampus were digitally captured using an Olympus BX50 microscope and DP74 cam-
era paired with a computer running CellSens software (Olympus). Microglial cell bodies were
outlined, and their area was determined using NIH ImageJ 1.50i (US National Institutes of Health,
Bethesda, MD, USA).
DCX- and BrdU-immunoreactive cells were also quantified using live imaging under
bright-field microscopy with a × 100 oil immersion lens (Olympus). Cells were counted in non-
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overlapping fields of the subgranular zone and granule cell layer of the dentate gyrus, across 8
sections per animal. Additionally, to examine the relative maturity of DCX-expressing cells, the
morphology of their dendritic processes was assessed as previously described (Breunig et al., 2007;
Hamson et al., 2013; Plümpe et al., 2006). Briefly, immature or type 1 cells were defined as having
very short or no processes, intermediate or type 2 cells as having processes that extended only
within the granule cell layer and do not extend into the molecular layer, and post-mitotic or type 3
cells as having dendrites that extend and branch into the molecular layer or having multiple
branches within the granule cell layer. Morphology of DCX-positive cells was assayed across 4
sections per animal, and the relative proportions of type 1, 2, and 3 cells were calculated.
RNA isolation and quantitative PCR
RNA was extracted from the gonadal fat pads and the hippocampus using TRIzol reagent
(Invitrogen Corporation, Carlsbad, CA, USA), following the manufacturer’s protocol. To remove
any remaining DNA contamination, the RNA pellet was treated with RNase-free DNase I
(Epicentre, Madison, WI, USA) for 30 min at 37 °C after which a phenol-chloroform extraction
was performed to isolate RNA. cDNA was reverse transcribed from 1 μg of purified RNA using
the iScript cDNA synthesis system (Bio-Rad). The resulting cDNA was used to run real-time
quantitative PCR using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) and a Bio-Rad
CFX Connect Thermocycler, as previously described (Moser and Pike, 2017). Both hippocampus
and adipose tissue were analyzed for expression levels of cluster of differentiation 68 (CD68),
EGF-like module-containing mucin-like hormone receptor-like 1 (F4/80), major
histocompatibility complex class II (MHC II), cluster of differentiation 74 (CD74) transcript
variant 1, interleukin-6 (IL-6), and interleukin-1β (IL-1β). Additionally, hippocampal tissue was
assessed for lipoprotein lipase (LPL) and CD36, as well as for the Aβ clearance and production
factors neprilysin, insulin-degrading enzyme (IDE), and β-site APP cleaving enzyme (BACE1).
Finally, levels of the cytokine tumor necrosis factor α (TNFα) transcript variant 1 were examined
in adipose tissue. Primer pair sequences for target genes are shown in Table 4.1. All samples were
run in duplicate, and PCR products were normalized with corresponding expression levels of β-
actin and/or phosphoglycerate kinase 1 (Pgk1) in the brain and succinate dehydrogenase complex,
subunit A, flavoprotein (SDHA) in the adipose tissue. The ΔΔ-CT method was used to determine
relative mRNA levels. For hippocampal samples, the Ct value of each reference gene (β-actin
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and/or Pgk1) was subtracted separately from the target genes and the resulting values were
averaged and used to calculate fold changes relative to the control-diet, vehicle-treated group.
CD68, F4/80, MHCII, CD74, IL6, IL1β, LPL, and CD36 were run with both reference genes and
neprilysin, IDE, and BACE1 only with β-actin.
Table 4.1 Target genes for the PCR analyses are listed with their corresponding GeneID number and
oligonucleotide sequences for the forward and reverse primers
Target gene Primer sequence
β-actin
Gene ID: 11461
β-site APP cleaving enzyme (BACE1)
GeneID: 23821
Cluster of differentiation factor 36 (CD36)
GeneID: 12491
Cluster of differentiation factor 68 (CD68)
GeneID: 12514
Cluster of differentiation factor 74 (CD74),
transcript variant 1
GeneID: 16149
EGF-like module-containing mucin-like
hormone receptor-like 1 (F4/80)
GeneID: 13733
Insulin-degrading enzyme (IDE)
GeneID: 15925
Interleukin-1β (IL1β)
GeneID: 16176
Interleukin-6 (IL6)
GeneID:16193
Lipoprotein lipase (LPL)
GeneID: 16956
Major histocompatibility complex class II (MHC
II) GeneID: 14961
Neprilysin
GeneID: 17380
Forward: 5′-AGCCATGTACGTAGCCATCC-3′
Reverse: 5′-CTCTCAGCTGTGGTGGTGAA-3′
Forward: 5′-TCGCTGTCTCACAGTCATCC-3′
Reverse: 5′-AACAAACGGACCTTCCACTG-3′
Forward: 5′-TATTGGTGCAGTCCTGGCTG-3′
Reverse: 5′-CTGCTGTTCTTTGCCACGTC-3′
Forward: 5′-TTCTGCTGTGGAAATGCAAG-3′
Reverse: 5′-AGAGGGGCTGGTAGGTTGAT-3′
Forward: 5′-CAAGTACGGCAACATGACCC-3′
Reverse: 5′-GCACTTGGTCAGTACTTTAGGTG-3′
Forward: 5′-TGCATCTAGCAATGGACAGC-3′
Reverse: 5′-GCCTTCTGGATCCATTTGAA-3′
Forward: 5′-TGTTTCCACACACAGGCAAT-3′
Reverse: 5′-ACCTGTGAAAAGCCGAGAGA-3′
Forward: 5′-GCAACTGTTCCTGAACTCAACT-3′
Reverse: 5′-ATCTTTTGGGGTCCGTCAACT-3′
Forward: 5′-CTCTGGGAAATCGTGGAAAT-3′
Reverse: 5′-CCAGTTTGGTAGCATCCATC-3′
Forward: 5′-GGGCCCAGCAACATTATCCA-3′
Reverse: 5′-GGGGGCTTCTGCATACTCAA-3′
Forward: 5′-CAGACGCCGAGTACTGGAAC-3′
Reverse: 5′-CAGCGCACTTTGATCTTGGC-3′
Forward: 5′-GAGAAAAGCCCACTTGCTTG-3′
Reverse: 5′-GAAAGACAAAATGGGGCAGA-3′
Forward: 5′-GCCTGTTGACTTTGTCACTGC-3′
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Phosphoglycerate kinase 1 (Pgk1)
GeneID: 18655
Succinate dehydrogenase complex, subunit A,
flavoprotein (SDHA) Gene ID: 66945
Tumor necrosis factor α (TNFα), transcript
variant 1
GeneID: 21926
Reverse: 5′-GAGTGACTTGGTTCCCCTGG-3′
Forward: 5′-ACACAGACCTGGTGGAGACC-3′
Reverse: 5′-GGATGGGCTTGGAGTAATCA-3′
Forward: 5′-CCCTCACACTCAGATCATCTTCT-3′
Reverse: 5′-GCTACGACGTGGGCTACAG-5′
β-Amyloid enzyme-linked immunosorbent assay
Levels of soluble Aβ42 peptides were determined by enzyme-linked immunosorbent assay
(ELISA). Briefly, the remaining hemi-brain portions were homogenized in buffer (0.2%
diethylamine, 50 mM NaCl, 1 mL/200 mg tissue) using a polytron on ice. Resulting homogenates
were centrifuged at 4 °C for 1 h at 15,000 g. Supernatants were collected and neutralized with
1/10th volume of 0.5 M Tris-HCl, pH 6.8. Samples were then analyzed using a commercially
available Aβ42 ELISA (Human/Rat β Amyloid 42 ELISA Kit High Sensitive; 292-64501; Wako
Chemicals) according to manufacturer’s directions.
Statistical analyses
All data were analyzed using Prism software (version 7, GraphPad Software, La Jolla, CA,
USA). Two-way re- peated measures ANOVAs were performed for the ana- lyses of body weight
and glucose tolerance. All other data were analyzed by two-way ANOVAs. In the case of
significant main effects, planned comparisons between groups were made using the Bonferroni
correction. All data are represented as the mean ± the standard error of the mean (SEM).
Significance was set at a threshold of p < 0.05.
4.4 Results
4.4.1 Effects of HFD and TAK-242 on body weight and adiposity
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We first examined measures of DIO in vehicle-treated and TAK-242-treated animals to
assess whether drug treatment altered the obesogenic effects of HFD. The control diet was
associated with a ~ 5% weight gain in both vehicle-treated and TAK-242-treated mice, whereas
HFD was associated with a 39.6 ± 4.2% increase in body weight in vehicle-treated mice and a 34.4
± 3.0% increase in TAK-242-treated mice (Figure 4.1A). Two-way repeated measures ANOVA
showed that HFD significantly increased body weight (F = 38.9, p < 0.0001). There was no
significant effect of drug treatment on body weight. Between-group comparisons revealed that
mice fed HFD weighed more than those fed CTL diets at the 4-, 8-, and 12-week time points (p <
0.05); (p < 0.05); this was true for both vehicle-treated and TAK-242-treated groups. When
examining final body weight, we found a main effect of diet (F = 63.88, p < 0.0001; Figure 4.1B),
which was significant across both drug treatments (p < 0.0001). There were no significant
interactions between diet and drug on measures of body weight.
Next, we examined adiposity by both body composition analysis and weights of gonadal
and RP fat pads. We found that HFD was associated with a significant decrease in percent lean
body mass (F = 103.0, p < 0.0001; Figure 4.1C) and a corresponding significant in- crease in
percent body fat (F = 98.7, p < 0.0001; Figure 4.1D) in both vehicle-treated and TAK-242-treated
mice (p <0.0001). There was no interaction effect between diet and drug, nor were there significant
main effects of drug on lean mass or body fat. The same pattern was found with fat depot weight,
such that HFD significantly increased weights of both RP (F = 117.1, p < 0.0001; Figure 4.1E)
and gonadal (F = 108.4, p < 0.0001; Figure 4.1F) fat pads. There were neither significant main
effects of drug nor interaction effects between diet and drug.
We also examined food intake and found that HFD feeding was associated with a
significant increase in the average daily kilocalorie consumption (F = 52.5, p < 0.0001; Figure
4.1G), as would be expected given the higher caloric density of HFD relative to control diet.
Importantly, drug treatment did not significantly affect caloric intake, and there were no significant
interactions between diet and drug.
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125
Figure 4.1. Metabolic outcomes associated with diet-induced obesity in mice treated with
vehicle or the TLR4 inhibitor TAK-242.
(A) Body weights in male C57/BL6/J mice maintained on control (CTL) and high-fat (HFD)
diets and vehicle (Veh) and TAK-242 (TAK) treatments taken at baseline (week 0) and 4-
week intervals across the 12-week experimental period.
(B) Body weight at the end of the treatment of the treatment period.
(C) Lean mass at week 11, measured via NMR scan.
(D) Percent body fat at week 11, measured via NMR scan.
(E) Adiposity as measured by retroperitoneal (RP) fat pad weight at week 12.
(F) Adiposity as measured by gonadal fat pad weight at week 12.
(G) Average daily caloric intake across the experimental period.
All data are presented as mean + SEM; n=10-12/group. For data presented across time, control
diet-fed mice are shown as circles, high-fat diet-fed mice are shown as squares; vehicle-treated
are open symbols, TAK-242-treated are filled symbols; for all other panels, vehicle-treated
animals are shown in white bars and TAK-242-treated are shown in black bars. Statistical
significance is based on ANOVA followed by Bonferroni correction. *p<0.05 relative to drug-
treatment-matched mice in control condition.
4.4.2 Effects of HFD and TAK-242 on metabolic outcomes
Another established outcome of DIO is dysregulation of glucose homeostasis (Soltis et al.,
2017; van den Top et al., 2017). We first examined changes in fasting glucose levels over the
treatment period. We found that HFD was associated with a significant increase in glucose levels
(F = 23.78, p < 0.0001; Figure 4.2.A) at the 4-, 8-, and 11-week time points. Additionally, there
was a significant main effect of diet on percent change in glucose levels from baseline to the end
of the treatment period (F = 13.7, p < 0.001; Figure 4.2B). However, between-group comparisons
revealed that the effect of HFD on increasing fasting glucose was only significant in vehicle-
treated, not in TAK-242-treated animals. There were neither significant interaction effects nor
main effects of drug treatment on these measures of glucose homeostasis.
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In addition to fasting glucose levels, we examined responses to a glucose bolus. There was
a main effect of diet on glucose clearance in GTT (F = 49.95, p < 0.0001; Figure 4.2C) that was
significant in both vehicle-treated and TAK-242-treated mice. We also calculated the AUC for
GTT and again found a significant effect of diet (F = 55.11, p < 0.0001; Figure 4.2D) such that
HFD increased AUC regardless of drug treatment. There were no significant interactions or main
effects of drug treatment on GTT measures.
Finally, we examined the effects of diet and drug treatments on levels of plasma
triglycerides and cholesterol. HFD was associated with significantly increased triglyceride levels
(F = 15.64, p < 0.001; Figure 4.2E) in both drug treatment groups. There was a non-significant
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Figure 4.2. Peripheral effects of diet-induced obesity in mice treated with vehicle or the
TLR4 inhibitor, TAK-242.
(A) Baseline fasting glucose levels in male C57/BL6/J mice maintained on control (CTL) and
high-fat (HFD) diets and vehicle (Veh) and TAK-242 (TAK) treatments taken at baseline
(week 0) and weeks 4, 8 and 11.
(B) Percent chance in fasting blood glucose levels relative to baseline after 12 weeks of control
or high-fat diet.
(C) Glucose tolerance test showing blood glucose levels over time after administration of a
glucose bolus at week 11.
(D) Area under the curve (AUC) for the glucose tolerance test.
(E) Plasma triglyceride levels at week 12.
(F) Plasma cholesterol levels at the end of the experimental period.
All data are presented as mean + SEM; n=10-14/group. For data presented across time, control
diet-fed mice are shown as circles, high-fat diet-fed mice are shown as squares; vehicle-treated
are open symbols, TAK-242-treated are filled symbols; for all other panels, vehicle-treated
animals are shown in white bars and TAK-242-treated are shown in black bars. Statistical
significance is based on ANOVA followed by Bonferroni correction. *p<0.05 relative to drug-
treatment-matched mice in control diet condition.
trend towards increased cholesterol levels by HFD (F = 3.41, p = 0.07; Figure 4.2F). There were
no main effects of drug nor were there interactions between diet and drug on levels of either
triglycerides or cholesterol.
4.4.3 Effects of HFD and TAK-242 on peripheral inflammation
DIO is known to increase inflammation in a number of organs, including adipose tissue
(Lee and Lee, 2014). To assess effects of HFD on peripheral tissue inflammation, we examined
gene expression of markers of macrophage activation and inflammatory cytokines in gonadal fat
(Figure 4.3). We found a significant effect of diet on adipose tissue levels of CD68 (F = 17.14, p
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< 0.001; Figure 4.3A), F4/80 (F = 10.06, p<0.01; Figure 4.3B), MHCII (F=16.42, p<0.001; Figure
4.3C), CD74 (F = 7.42, p < 0.05; Figure 4.3D), IL-6 (F = 6.52, p < 0.05; Figure 4.3E), IL-1β (F =
9.99, p < 0.01; Figure 4.3F), and TNFα (F = 10.85, p < 0.01; Figure 4.3G), demonstrating that
HFD induces phenotypical changes in macrophages toward an activated state. However, TAK-242
treatment did not prevent innate immune activation in the adipose tissue.
Figure 4.3. Expression of mRNA levels of genes associated with macrophage activation and
inflammation in adipose tissue from vehicle (Veh) and TAK-242 (TAK)-treated mice fed
with a control (CTL) or high-fat (HFD) diet.
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(A) mRNA from gonadal fat pad was probed for levels of the macrophage marker CD68.
(B) mRNA from gonadal fat pad was probed for levels of the macrophage marker F4/80.
(C) mRNA from gonadal fat pad was probed for levels of the macrophage marker and antigen
presenting molecule MHCII.
(D) mRNA from gonadal fat pad was probed for levels of the antigen presenting molecule
CD74.
(E) mRNA from gonadal fat pad was probed for levels of the pro-inflammatory cytokine IL-
6.
(F) mRNA from gonadal fat pad was probed for levels of the pro-inflammatory cytokine IL-
1b.
(G) mRNA from gonadal fat pad was probed for levels of the pro-inflammatory cytokine TNF-
a.
All data are presented as fold difference + SEM relative to vehicle-treated mice fed a control
diet; n=10/group. Vehicle-treated animals are shown in white bars and TAK-242-treated are
shown in black bars. Statistical significance is based on ANOVA followed by Bonferroni
correction. *p<0.05 relative to drug-treatment-matched mice in control diet condition,
a
p<0.05
for main effect of diet that does not reach statistical significance in between-group
comparisons.
For the markers of CD68, F4/80, MHC II, IL6, and TNFα, this effect was only statistically
significant in vehicle-treated HFD-fed mice and did not reach statistical significance in TAK-242-
treated HFD-fed mice. For CD74 and IL-1β the main effect of diet failed to reach statistical
significance in either of the HFD-fed groups. There was neither a main effect of drug, nor an
interaction effect between diet and drug on expression of any probed genes.
4.4.4 Effects of HFD and TAK-242 on hippocampal microgliosis
In addition to causing macrophage activation and inflammation in peripheral tissues, HFD
is associated with increased glial activation and inflammation in the brain (Carlsen et al., 2009;
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Valdearcos et al., 2017). Because microglia express high levels of TLR4 (Vaure and Liu, 2014)
and have been shown to adopt activated phenotypes in response to HFD (Bocarsly et al., 2015;
Gzielo et al., 2017; Wang et al., 2012), we examined microgliosis in brain sections. We analyzed
both cell density and morphology of labeled cells following immunostaining for Iba-1 in entorhinal
cortex and in the subiculum, CA1, and CA2/3 regions of the hippocampus. Figure 4.4A-C
illustrates morphological phenotype characteristic of resting (type 1; Figure 4.4A) microglia, with
multiple, thin processes, and activated microglia with fewer, thicker processes (type 2; Figure
4.4B), or amoeboid appearance (type 3; Figure 4.4C). When examining microglial density, we
found neither significant effects of diet or drug treatment, nor an interaction between these factors
in entorhinal cortex (Figure 4D), subiculum (Figure 4F), CA1 (Figure 4H), or CA2/3 (Figure 4J).
However, we found significant interactions between diet and drug treatment on microglial
activation in entorhinal cortex (F = 36.27, p < 0.0001; Figure 4E), subiculum (F = 38.93, p <
0.0001; Figure 4G), CA1 (F = 47.16, p < 0.0001; Figure 4I), and CA2/3 (F = 31.7, p < 0.0001;
Figure 4K). Between-group comparisons revealed that across all brain regions, HFD increased
microglial reactivity exclusively in vehicle-treated animals, and TAK-242 was associated with
significantly reduced microglial reactivity specifically in HFD-fed animals.
Activation phenotypes of microglia are also associated with increased soma size
(Kozlowski and Weimer, 2012). We measured microglial soma size as a complementary measure
of microgliosis, specifically in the CA1 region of the hippocampus. Our data show similar results
to findings on microglial morphology. That is, there is a significant interaction effect between diet
and drug treatment (F=8.62, p< 0.01; Figure 4L), such that diet significantly increased soma size
only in vehicle-treated animals, and TAK-242 treatment was associated with significantly de-
creased soma size compared to the vehicle group only in HFD-fed animals.
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Figure 4.4. Microglial number, morphological status, and soma size as assessed by Iba-1
immunohistochemistry in vehicle (Veh) and TAK-242 (TAK)-treated mice fed with a
control (CTL) or high-fat (HFD) diet.
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(A) Representative image of microglial morphology type 1 (resting), scale bar = 10µm.
(B) Representative image of microglial morphology type 2 (rod-shaped), scale bar = 10µm.
(C) Representative image of microglial morphology type 3 (amoeboid), scale bar = 10µm.
(D) Quantification of the densities of Iba-1 immunoreactive cells in the entorhinal cortex.
(E) Microglial reactivity (%)- type 2 and 3 cells- in the entorhinal cortex.
(F) Quantification of the densities of Iba-1 immunoreactive cells in the subiculum.
(G) Microglial reactivity (%)- type 2 and 3 cells- in the subiculum.
(H) Quantification of the densities of Iba-1 immunoreactive cells in the hippocampal CA1.
(I) Microglial reactivity (%)- type 2 and 3 cells- in the CA1.
(J) Quantification of the densities of Iba-1 immunoreactive cells in the hippocampal CA2/3.
(K) Microglial reactivity (%)- type 2 and 3 cells- in the CA2/3.
(L) Quantitation of microglial soma size in the CA1 region of the hippocampus.
All data are presented as mean+ SEM relative to vehicle-treated mice fed a control diet;
n=10/group. Vehicle-treated animals are shown in white bars and TAK-242-treated are shown
in black bars. Statistical significance is based on ANOVA followed by Bonferroni correction.
*p<0.05 relative to drug-treatment-matched mice in control diet condition, #p<0.05 relative to
vehicle-treated mice in the same diet condition.
4.4.5 Effects of HFD and TAK-242 on hippocampal gene expression
One frequent consequence of microgliosis is the increased expression of various
microglia/macrophage markers and pro-inflammatory cytokines (Chhor et al., 2013; Hanisch,
2002). We examined gene expression levels of several such factors in hippocampus (Figure 4.5),
including CD68 and F4/80 as general microglia/macrophage markers (Perego et al., 2011), with
CD68 being characteristic of a more activated cell phenotype (Bodea et al., 2014; Perego et al.,
2011). We found a significant interaction between diet and drug (F = 7.06, p < 0.05; Figure 4.5A)
on expression levels of CD68. Between-group comparisons revealed that HFD increased CD68
expression only in vehicle-treated but not in TAK-242-treated animals, and TAK-242 significantly
decreased CD68 only in HFD-fed mice. There were neither significant effects of diet or drug
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treatment, nor an interaction between these factors on hippocampal gene expression of F4/80
(Figure 4.5B).
We next examined levels of MHC II, which is increased in microglia by HFD (Hao et al.,
2016), and of CD74, which is also expressed specifically by microglia and macrophages (Zeiner
et al., 2015), and is involved in the formation and transport of MHC II (Cresswell, 1994).
Additionally, CD74 expression has been shown to correlate with obesity-induced increases in body
weight and metabolic changes in adipose tissue (Chan et al., 2018), and is increased in
hippocampus of HFD-fed mice (Setti et al., 2015). There was a statistically non-significant trend
of increased MHC II in the HFD-vehicle group (Figure 4.5C), and no significant diet or drug
effects on CD74 expression (Figure 4.5D).
Increased production of pro-inflammatory cytokines is a one potential outcome of
increased microglial activation (Hanisch, 2002) as well as of obesity (Maldonado-Ruiz et al.,
2017). Thus, we probed for two pro-inflammatory cytokines: IL-6 (Figure 4.5E) and IL-1β (Figure
4.5F). Though diet did not significantly affect gene expression of either cytokine, we found a
significant main effect of drug on levels of IL-6 (F = 4.73, p <0.05); however, this did not reach
statistical significance in either CTL-fed or HFD-fed mice.
Finally, we examined mRNA levels of two factors involved in fatty acid transport and
uptake as well as regulation of activated microglial phenotypes: LPL (Bruce et al., 2018; Gao et
al., 2017) and CD36 (Bonen et al., 2006; Goudriaan et al., 2005). Results demonstrated a
statistically non-significant trend of a main effect of diet on LPL levels (F = 3.03, p = 0.08; Figure
4.5G), as well as a statistically significant effect of diet on CD36 expression (F = 10.99; p < 0.01;
Figure 4.5H). Between-group comparisons showed that HFD significantly increased CD36 only
in vehicle-treated but not in TAK-242-treated mice.
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Figure 4.5. Hippocampal mRNA expression of genes associated with activated microglial
phenotypes and neuroinflammation in mice treated with vehicle (Veh) and TAK-242 (TAK)-
treated and fed with a control (CTL) or high-fat (HFD) diet.
(A) mRNA expression of hippocampal microglia/macrophage marker CD68.
(B) mRNA expression of hippocampal macrophage marker F4/80.
(C) mRNA expression of hippocampal microglia/macrophage marker and antigen presenting
molecule MHCII.
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(D) mRNA expression of hippocampal microglia/macrophage marker CD74.
(E) mRNA expression of hippocampal pro-inflammatory cytokine IL-6.
(F) mRNA expression of hippocampal pro-inflammatory cytokine IL-1b.
(G) mRNA expression of hippocampal lipid catabolism LPL.
(H) mRNA expression of hippocampal lipid uptake CD36.
All data are presented as fold difference + SEM relative to vehicle-treated mice fed a control
diet; n=10/group. Vehicle-treated animals are shown in white bars and TAK-242-treated are
shown in black bars. Statistical significance is based on ANOVA followed by Bonferroni
correction. *p<0.05 relative to drug-treatment-matched mice in control diet condition, #p<0.05
relative to vehicle-treated mice in the same diet condition,
b
p<0.05 for main effect of drug
treatment that does not reach statistical significance in between-group comparison.
4.4.6 Effects of HFD and TAK-242 on neurogenesis
An established negative consequence of DIO is impaired neurogenesis (Lindqvist et al.,
2006; Park et al., 2010b). We examined neurogenesis across groups using techniques to quantify
both neural stem cells committed to a neuronal phenotype (DCX-labeling) and proliferation of
neural stem cells (BrdU labeling) in the dentate gyrus region of the hippocampus. Figure 4.6 shows
representative images of DCX immunohistochemistry, which qualitatively show a decrease in
labeled cells with HFD that is prevented by TAK-242 treatment (Figure 4.6A-D). Quantification
of DCX-labeled cell density showed a significant main effect of drug (F=5.01, p< 0.05; Figure
4.6E). Between-group comparisons revealed that this effect of drug treatment was significant only
in HFD-fed mice. In addition, there was a non-significant trend towards an interaction between
diet and drug (F = 3.05, p = 0.08; Figure 4.6E). There was no significant effect of diet on DCX-
labeled cells. We also determined the relative maturity of DCX-positive cells by examining their
dendritic morphology and arborization (Hamson et al., 2013; Plümpe et al., 2006). Diet and drug
treatments did not significantly affect maturation states of new neurons, as the proportion of
subtypes was roughly equivalent between treatment groups (Figure 4.6F). Parallel assessment of
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BrdU-labeled cells revealed neither significant main effects of diet or drug, nor an interaction
between these factors (Figure 4.6G).
Figure 4.6. Neurogenesis and cell proliferation as assessed by DCX and BrDU
immunohistochemistry in mice maintained on control or high-fat (HFD) diet and treated
with vehicle and TAK-242.
(A) Representative image of DCX in mice treated with a control diet (CTL) and vehicle (Veh).
(B) Representative image of DCX in mice treated with a control diet (CTL) and TAK-242
(TAK).
(C) Representative image of DCX in mice treated with a high-fat diet (HFD) and vehicle
(Veh).
(D) Representative image of DCX in mice treated with a high-fat diet (HFD) and TAK-242
(TAK).
(E) Quantification of densities of DCX immunoreactive cells in the dentate gyrus.
(F) The maturation state of DCX-positive cells was assessed, and cells were categorized as
type 1, type 2, or type 3 based on their dendritic morphology.
(G) BrdU-positive cells were quantified in the dentate gyrus.
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All data are presented as mean + SEM; n=10/group. Vehicle-treated animals are shown in
white bars and TAK-242-treated are shown in black bars. Statistical significance is based on
ANOVA followed by Bonferroni correction. #p<0.05 relative to vehicle-treated mice in the
same diet condition.
4.4.7 Effects of HFD and TAK-242 on amyloidogenic pathways
DIO has been shown to promote Alzheimer-related amyloidogenic pathways in rodent
models, in part by regulating neural expression of factors involved in Aβ production and clearance
(Brandimarti et al., 2013; Maesako et al., 2012; Wei et al., 2014). We measured hippocampal
expression levels of three key genes to investigate if HFD and TAK-242 treatments affect Aβ
homeostasis pathways. We found no significant effects of diet or drug treatment, nor an interaction
between these factors, on expression levels of the Aβ-degrading enzymes neprilysin (Figure 4.7A)
and IDE (Figure 4.7B). However, we found a significant interaction between diet and drug
treatment (F = 4.90, p < 0.05; Figure 4.7C) on levels of the pro-amyloidogenic Aβ enzyme BACE1.
Between-group comparisons revealed that HFD significantly increased BACE1 in vehicle-treated
animals but not in TAK-242-treated mice. Finally, we determined levels of soluble Aβ42 peptides
by ELISA. There were no statistically significant effects of diet or drug treatment and no
interaction between these factors, though there were non-significant trends consistent with the
BACE1 data (Figure 4.7D).
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Figure 4.7. Expression of Ab production and degrading factors and soluble Ab42 in mice fed
control or high-fat diet and treated with vehicle and TAK-242.
(A) Hippocampal gene expression of the Ab clearance factor neprilysin.
(B) Hippocampal gene expression of the Ab clearance factor insulin-degrading enzyme.
(C) Hippocampal gene expression of the Ab production factor BACE1.
(D) Protein levels of soluble Ab42 as measured by ELISA.
Data in A-C are presented as fold difference + SEM; data in D is presented as mean +SEM;
n=10/group. Vehicle-treated animals are shown in white bars and TAK-242-treated are shown
in black bars. Statistical significance is based on ANOVA followed by Bonferroni correction.
*p<0.05 relative to drug-treatment-matched mice in control diet condition.
4.4.8 Effects of HFD and TAK-242 on behavioral performance
Finally, we examined cognitive performance in two behavioral assays. The spontaneous
alternation behavior (SAB) task assays short-term working memory and visual attention. Total arm
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entries did not vary by either diet or drug treatment (Figure 4.8A). However, there was a main
effect of diet on alternation behavior (F = 7.86, p < 0.01; Figure 4.8B), which was significantly
only in TAK-242-treated mice, though performance of the TAK-242-treated HFD mice (49%) is
not significantly different from that observed in vehicle-treated HFD mice (50%).
As a measure of hippocampal-dependent and hippocampal-independent memory, we tested
animals using the fear-conditioning paradigm. When examining freezing during the day 1 training
trials, we found no significant differences between groups in initial freezing before the tone/shock
pairing or in freezing during the tone/shock pairings and inter-trial periods. Figure 4.8C shows
time spent freezing during the trace period between the tone and shock during the final presentation
of the tone/shock pairing on day 1. Cued memory was assessed on day 2 by changing the
appearance and odor of the chamber and examining freezing to the tone, without presenting the
shock. There were no group differences in freezing during the baseline period before presentation
of the tone (data not shown). There was a main effect of diet on freezing during the trace period
immediately after the first presentation of the tone (F = 4.76, p < 0.05; Figure 4.8D), but this did
not reach statistical significance across the different drug treatments. There were no significant
group differences on freezing during the following 2 tone presentations (data not shown).
Contextual memory was examined on day 3 by placing animals back into the chamber with the
same appearance and odor as during day 1 and examining freezing to this context. There were no
significant effects of either diet or drug treatment, nor was there an interaction between these
factors, on freezing in response to the context (Figure 4.8E).
To determine whether the drug treatment affected general behavioral performance, we
compared animals on measures of activity, anxiety, and depression, using the behavioral assays of
open field, elevated plus maze, and forced swim test, respectively. We found no statistically
significant main effects of drug treatment on any of these three behavioral tasks (Figure 4.8 F-N).
There were only two statistically significant effects on any of the outcome measures in these
assays, and both of these were in exploratory activity in the open field. First, there was a significant
interaction between diet and drug treatment on the number of times the animals crossed into the
center field (F = 4.91, p < 0.05; Figure 4.8F), such that HFD increased center crossings only in
TAK-242-treated mice. Second, there was a significant main effect of diet on time spent in the
center field (F = 4.23, p < 0.05; Figure 4.8G), such that HFD again increased this measure
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specifically in TAK-242-treated animals. There were no statistically significant effects on any
measures of anxiety-like or depressive-like behaviors.
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Figure 4.8. Behavioral performance of mice fed control or high-fat diet and treated with
vehicle and TAK-242.
(A) Total arm entries in the Y-maze as a measure of anxiety
(B) Short-term working memory assessed by spontaneous alternation behavior (SAB) in the
Y-maze.
(C) Learning was assessed by examining freezing behavior during the trace period between
the tone and shock on the 5
th
trial of the training day of fear conditioning test.
(D) Cued memory (sound) was tested by examining freezing behavior 24h after last day of
training, in a different context/environment.
(E) Contextual memory was assessed by examining freezing behavior 24h after the cued test.
(F) The number of times animals entered the center square of the open field arena
(G) Amount of time animals spent in the center field of the arena.
(H) General locomotor activity as assessed by the total number of square crossing.
(I) Amount of time spent in the open arm of the elevated plus maze as a measure of anxiety-
like behavior.
(J) Number of times animals crossed into the open arm of the elevated plus maze.
(K) The latency to enter the open arm of the elevated plus maze.
(L) Total amount of immobile time during the forced swim test to investigate depressive-like
behavior.
(M) The number of times animals spent immobile
(N) The length of the single longest time spent immobile.
Data are presented as mean + SEM; n=10/group. Vehicle-treated animals are shown in white
bars and TAK-242-treated are shown in black bars. Statistical significance is based on
ANOVA followed by Bonferroni correction. *p<0.05 relative to drug-treatment-matched mice
in control diet condition.
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4.5 Discussion
The goal of this study is to examine the role of TLR4 signaling in mediating the effects of
obesity on micro- glial activation and adverse neural outcomes. Comparing animals fed control
versus HFD in the presence or absence of the TLR4 inhibitor TAK-242, we demonstrate that TAK-
242 treatment was associated with attenuation of HFD-induced adipose tissue inflammation,
microgliosis, and reduction in neurogenesis in the hippocampus. However, TAK-242 treatment
did not improve the metabolic dysregulation induced by HFD feeding. The finding that TLR4
inhibition did not protect against effects of HFD on weight gain and adiposity is consistent with
numerous other studies (Coenen et al., 2009; Francis et al., 2007; Jia et al., 2014; Poggi et al.,
2007; Saberi et al., 2009; Suganami et al., 2007; Yilei et al., 2012). In contrast to our findings,
however, many of these studies show that obesity-associated dysregulation of insulin and glucose
signaling was improved in the absence of TLR4 signaling (Chao-Fan et al., 2013; Francis et al.,
2007; Jia et al., 2014; Li et al., 2014; Poggi et al., 2007; Suganami et al., 2007). One possible
reason for this discordance is that several studies used mice with either knockout or dysfunctional
TLR4, whereas we used a pharmacological approach to inhibit TLR4. Constitutive absence of
TLR4 signaling may result in metabolic changes even in the absence of HFD and is likely to result
in more complete inhibition of TLR4 and different outcomes than pharmacological approaches.
Our findings support the conclusion that TLR4 contributes to obesity-induced activation
of peripheral macrophages and brain microglia. First, our observations in adipose tissue of partial
reductions in both markers of macrophage activation (CD68, F4/80, MHCII) and pro-
inflammatory cytokines (IL-6, TNFα) in HFD-fed mice treated with TAK-242 is consistent with
previous findings (Coenen et al., 2009; Jia et al., 2014; Kim et al., 2015; Poggi et al., 2007;
Suganami et al., 2007). Second, we demonstrate that the TLR4 inhibitor attenuates HFD-induced
microgliosis in hippocampus, as evidenced by changes in microglial morphology and soma size
and mRNA levels of the activated microglia markers CD68 and, to a lesser extent, CD36 or fatty
acid translocase. CD36 is a pattern recognition receptor that exhibits increased expression in
obesity (Bonen et al., 2006) as well as in AD (Martin et al., 2017; Ricciarelli et al., 2004), where
it mediates recruitment of microglia to Aβ deposits (El Khoury et al., 2003; Moore et al., 2002).
Interestingly, CD36 has been found to form a complex with TLR4 and TLR6 through which both
Aβ and lipids can induce inflammation (Stewart et al., 2010). Collectively, these findings support
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and extend prior work by Milanski and colleagues that implicated TLR4 in obesity-induced glial
activation in hypothalamus, which may contribute to systemic metabolic disturbances (Milanski
et al., 2012).
Although we observed an increase in activated microglia in response to HFD, it is
noteworthy that hippocampal expression of the pro-inflammatory cytokines IL-6 and IL1-β was
not increased by obesogenic diet. Though HFD is often associated with both microglial activation
and increased cytokine expression (Pistell et al., 2010), others show changes only in some brain
regions (Guillemot-Legris et al., 2016), or no changes in pro-inflammatory cytokines (Baumgarner
et al., 2014). Here, we find DIO-associated changes in specific markers of activated microglia but
not in cytokines, which is consistent with previous work by Setti and colleagues (Setti et al., 2015).
Although it is reasonable to predict that a more chronic exposure to HFD may be required for
increased neural cytokine expression, cytokine expression in hypothalamus is significantly
increased by high-fat diet exposures as brief as 1 day (Thaler et al., 2012). We posit that the
observed microglial activation in the absence of significantly increased expression of pro-
inflammatory cytokines is consistent with the known heterogeneity in activated microglial
phenotypes (Dubbelaar et al., 2018; Wyss-Coray and Rogers, 2012). Indeed, accumulating
evidence indicates that deleterious effects of microglia are mediated by numerous factors rather
than simply increased levels of pro-inflammatory cytokines (Ekdahl et al., 2003; Singhal and
Baune, 2017). The extent to which various activated microglial phenotypes differentially affect
neural outcomes is an important topic that remains to be fully elucidated.
One deleterious neural consequence common to both diet-induced obesity and activated
microglia is promotion of amyloidogenesis. HFD is known to increase gene expression and/or
enzyme activity of the pro-amyloidogenic BACE1 (Maesako et al., 2012) and decrease levels of
the Aβ-degrading enzymes neprilysin (Standeven et al., 2011) and insulin-degrading enzyme
(Brandimarti et al., 2013). These effects on Aβ homeostasis likely contribute to observations that
experimental obesity drives Aβ accumulation in transgenic mouse models of AD (Barron et al.,
2013; Julien et al., 2010). Additionally, it has been shown that inflammation can increase levels of
BACE1 (Sastre et al., 2008) and decrease levels of neprilysin (Wong et al., 2011). Thus, microglial
activation and associated neuroinflammation are likely significant mediators of the obesity-
induced increase in Aβ. We found that HFD significantly increased levels of BACE1 in vehicle-
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treated, but not in TAK-242-treated mice. This suggests that HFD caused a shift towards more
pro-amyloidogenic processing via neuronal or astrocytic TLR4 signaling, since BACE1 is mainly
expressed by these cells (Almeida et al., 2005; Roßner et al., 2005). Further, although not
statistically significant, our analyses of soluble brain Aβ42 showed trends towards increased levels
in HFD mice in the absence but not the presence of TLR4 inhibitor. In AD, other circulating
pathogen-mimetic associated molecular pattern causes TLR4 activation, such as Ab itself (Tang
et al., 2008; Udan et al., 2008). We also investigated the inhibition of the TLR/NF-kB pathway in
innate immunity via deletion of negative regulator of NF-kB, IRAK-M. This manipulation
demonstrated that inhibition of NF-kB in microglia/macrophages ameliorates AD-like pathology
mostly by promoting Ab clearance (see results in Appendix B), suggesting that TLR4 inhibition in
different cells of the brain is beneficial to overcome neuroinflammation through distinct pathways.
Another negative effect of obesity is attenuation of neurogenesis. We found that treatment
with TAK-242 significantly increased the number of new neurons in dentate gyrus specifically in
HFD-fed mice, indicating a protective effect of TLR4 inhibition on obesity-related impairment in
neurogenesis. Because BrdU labeling, a marker of cell proliferation, was not affected by diet or
drug treatments, the protective effect of TAK-242 appears to involve the survival and or
differentiation of newborn neurons rather than stem cell proliferation. The possibility that TLR4
inhibition yielded a generalized increase in new neuron survival is consistent with our finding that
the relative proportion of subtypes of newly formed neurons was not significantly altered by either
diet or TLR4 inhibition. The reported effects of HFD on neurogenesis and cell proliferation are
somewhat mixed in the literature, with some studies finding decreases in both (Kim et al., 2009;
Yoo et al., 2011) and others finding changes in only one (Park et al., 2010b; Tozuka et al., 2009)
or neither (Rivera et al., 2011) marker of neurogenesis. Differences in experimental parameters
including the composition of the diet may affect the extent to which cell proliferation and/or
survival of newborn neurons are affected by HFD. Our observed effects were likely mediated by
microglia, which have previously been shown to attenuate neurogenesis during states of activation
such as after LPS (Cacci et al., 2008; Monje et al., 2003) or seizure (Ekdahl et al., 2003). Further,
our finding of increased neurogenesis with TAK-242 treatment in HFD-fed mice is consistent with
prior data showing that TLR4 signaling regulates neurogenesis in response to neural injury and
microgliosis (Moraga et al., 2014; Mouihate, 2014). Importantly, adult neurogenesis is regulated
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both positively and negatively by a range of activated microglial phenotypes (Kohman and
Rhodes, 2013), reinforcing the emerging complexity of the associations between microglial
functions and their activation states.
One limitation of this study is that we were not able to fully determine the effects of TLR4
inhibition on HFD-induced behavioral changes. Although behavioral impairment is often
associated with obesity, we found very subtle effects of diet and drug treatments on overall
behavioral outcomes. Specifically, mice fed HFD and treated with TAK-242 showed small but
significantly increased exploratory behavior/decreased anxiety-like behavior in the open field test,
and worse spontaneous alternation in the Y-maze, and HFD was associated with decreased cued
memory in fear conditioning. There were no significant effects of our diet or drug manipulations
on anxiety-like behavior in EPM, depressive-like behavior in forced swim, or on contextual fear
conditioning. Though a number of studies demonstrated cognitive impairments after HFD
exposure (Arnold et al., 2014; Hwang et al., 2010; Jurdak et al., 2008; Kaczmarczyk et al., 2013;
Kosari et al., 2012), others did not (Lavin et al., 2011; Li et al., 2013; Mielke et al., 2006; Tucker
et al., 2012). The age at which rodents are exposed to diet-induced obesity may be a factor. For
example, one study found significant effects of HFD on behavior in mice started on diet at 5 weeks
of age, but not in animals started at 8 weeks (Valladolid-Acebes et al., 2013), whereas another
found behavioral impairments in response to HFD in aged but not young adult rats (Spencer et al.,
2017). These studies suggest that the age at which exposure to HFD occurs may be important in
determining whether behavioral deficits are observed. As with the induction of neuroinflammation,
it is unlikely that the length of HFD exposure is the key variable in whether or not behavioral
impairment occurs. Previous studies of HFD outcomes in rodents have showed changes in both
neuroinflammation and behavioral outcomes within 3 days of HFD feeding (Kanoski and
Davidson, 2010; Spencer et al., 2017; Thaler et al., 2012). Moreover, deficits in cognitive
performance have also been observed after 9 days (Murray et al., 2009), 1 month (Hsu et al., 2015),
and 3 months (Kanoski and Davidson, 2010) of diet exposure.
Though complete elucidation of the mechanisms underlying the effects of obesity on the
brain remains to be established, our findings suggest a stronger role for microglial activation than
for metabolic dysregulation. That is, despite having similar weight gain and metabolic outcomes
in response to HFD, mice treated with a TLR4 inhibitor showed significant reductions in microglial
146
activation and increased neurogenesis in comparison to vehicle-treated mice. This position is
consistent with findings in the human literature that the effects of obesity on cognitive impairment
are mediated largely by glial/inflammatory rather than metabolic factors (Dik et al., 2007;
Spyridaki et al., 2014; Yaffe et al., 2007). Because TAK-242 has systemic effects and we observed
partial attenuation of inflammation in adipose tissue, peripheral effects of TLR4 inhibition may
have contributed to the observed neural benefits. The role of other mechanisms like vascular and
microbiota changes in the effects of obesity on the brain cannot be ruled out and should be
addressed in future studies, especially given that inflammation may be important in these systems
as well (Bell et al., 2012; Zhao and Lukiw, 2015).
4.6 Conclusion
To our knowledge, this study provides the first evidence that TLR4 signaling significantly
contributes to the adverse effects of obesity on the hippocampus. Though TLR4 is well established
as mediating the effects of saturated fatty acids on adverse outcomes in metabolic measures (Chao-
Fan et al., 2013; Francis et al., 2007; Jia et al., 2014; Li et al., 2014; Poggi et al., 2007; Saberi et
al., 2009; Suganami et al., 2007) and inflammation (Francis et al., 2007; Jia et al., 2014; Li et al.,
2014; Poggi et al., 2007; Saberi et al., 2009; Suganami et al., 2007; Vila et al., 2014), its regulation
of obesity-related changes in brain have not been thoroughly investigated. Our data demonstrate
that treatment with an inhibitor of TLR4 signaling in the context of obesogenic diet attenuates
microgliosis, increases neurogenesis, and trends towards reductions in pro-amyloidogenic
pathways. These findings implicate a significant role for microglial function as a key mediator of
the neural effects of obesity. Additionally, these findings point to TLR4 as a therapeutic target for
obesity, which has important health implications for a range of systemic and neuro-immune
disorders including type 2 diabetes, cardiovascular disease, and dementia.
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CHAPTER 5. INTERLEUKIN-10 REDUCES METABOLIC FITNESS DRIVING Ab
IMMUNE TOLERANCE IN MONONUCLEAR PHAGOCYTES
Acknowledgements: I extremely grateful for worked alongside to Alexander Vesling for the
completion of this project. Special thanks to Jae-Jin Lee and Hyungjin Eoh for the willingness to
collaborate and for helpful discussion. The Town lab members were of essential support,
specifically, Chris W Im, Riyaz Razi, Nima Shajarian, Dr. Balint Der, Dr. Leung, Anakha Ajayan
and Alicia Quihuis. This work utilized the Meso Sector S 600, Agilent Seahorse XFe96 Analyzer
and Biotek Cytation 5 Cell Imager in the Translational Research Laboratory and the LSRII in the
USC Stem Cell Flow Cytometry Facility, made possible through the University of Southern
California School of Pharmacy and Keck School of Medicine. Special thanks to Dr. Junji
Watanabe, Dr. Bernadette Masinsin and Dr. Jeffrey Boyd for technical support. This work was
supported by the National Institute of Health (R01 AG053982-01A1, to T.T.) and the American
Federation of Aging Research (Diana Jacobs Kalman/AFAR fellowship 2019, to M.F.U.).
Contributions: Conceptualization, M.F.U. and T.T.; Methodology, M.F.U, A.W.V., J.L., H.E.;
Investigation, M.F.U., A.W.V., J.L., A.A., K.W.I., N.S., C.J.M., B.D., B.P.L.; Validation, A.W.V.,
R.R. and C.J.M; Formal analysis, M.F.U., J.L., A.W.V., and H.E; Funding acquisition, M.F.U,
M.V.G.S, K.R.D and T.T.; Resources, T.T.; Project administration, M.F.U, and T.T.; Writing-
original draft, M.F.U, A.W.V., J.L. and H.E; Writing-review & editing, T.T and H.E.
*This chapter has been based on the manuscript in submission: Mariana F Uchoa, Alexander W
Vesling, Jae-Jin Lee, Balint Der, Chris Im, Brian Leung, Nima Shajarian, Anakha Ajayan, Riyaz
Razi, Cole J Miller,
Marie-Victoire Guillot-Sestier, Kevin Doty, Hyungjin Eoh, Terrence Town.
Interleukin-10 reduces metabolic fitness and renders cerebral innate immune cells tolerant to
amyloid-beta. In submission to Neuron, 2020.
5.1 Summary
Innate immune cells are able to clear amyloid-b (Ab) via phagocytosis, however, these cells can
enter a tolerant state that endorses Ab accumulation and Alzheimer’s disease (AD) progression.
Previously, we demonstrated that interleukin-10 (IL-10) suppresses innate immune Ab clearance,
and others have shown that IL-10 mediates metabolic reprograming of myeloid cells. Furthermore,
dampening the immune response via tolerance has been linked to metabolic breakdown. We
hypothesize that IL-10 reduces the metabolic fitness of innate immune cells, thus driving Ab
immune tolerance. In twelve months old APP
swe
/PS1
ΔE9
mice, we report that periplaque
mononuclear phagocytes experience a microenvironment with elevated IL-10, inhibiting
activation of immunometabolic markers. Moreover, IL-10 tolerized mononuclear phagocytes
undergo metabolic reprograming, earmarked by significant dysregulation in lipid metabolism.
Metabolomics analysis showed that Ab and IL-10 co-treatment reduced cellular phospholipid
content. Furthermore, they displayed intracellular accumulation of lipid droplets and altered
expression of lipid metabolism-associated enzymes, suggesting a shift in the glycerolipid
metabolism of tolerant-mononuclear phagocytes. Collectively, this study provides the evidence
that Ab phagocytosis can be enhanced by regulating innate immune phospholipid metabolism and
thereby proposes pharmacological application of lipid metabolism of mononuclear phagocytes.
5.2 Introduction
Alzheimer’s disease (AD) is characterized by intracellular neurofibrillary tangles,
extracellular amyloid deposits and neuroinflammation (Serrano-Pozo et al., 2011). The most
prevalent form of the disease, late-onset AD, has a complex etiology likely due to the combination
of environmental and genetic factors (Lambert et al., 2013; Uchoa et al., 2016; Wainaina et al.,
2014). Yet, in late-onset AD, disease is largely driven by failed clearance, rather than over
production of amyloid-beta (Ab) (Mawuenyega et al., 2010). Among other mechanisms, clearance
is achieved by activated mononuclear phagocytes in the brain (Guillot-Sestier and Town, 2013).
Paradoxically, positron-emission tomography scan imaging of AD brains has revealed widespread
innate immunity activation, and clinicopathological studies show a strong correlation between
microglial abundance and disease severity (Cagnin et al., 2006; Okello et al., 2009; Vehmas et al.,
2003). While these cells’ phenotypes resemble a state of activation, a number of studies have
demonstrated that, in fact, microglia from AD patients lose the ability to eliminate Aβ deposits via
phagocytosis (El Hajj et al., 2019; Hickman et al., 2008; Wegiel et al., 2003), supporting the view
that plaque accumulation in the brain is, at least in part, due to the inability of brain mononuclear
phagocytes to effectively clear aggregated Aβ, as they enter a tolerant state (Guillot-Sestier and
Town, 2013; Guillot-Sestier et al., 2015a; Hickman et al., 2008). This phenomenon has been
reported not only in the central innate immune compartment but also in peripheral macrophages
from AD patients (Fiala et al., 2005; Town et al., 2008). Innate immune tolerance is known to be
associated with reduction in tissue damage caused by excessive inflammation, however, it also
induces cellular dysfunction and worsens prognosis for diseases such as cancer and sepsis (Cheng
et al., 2016; Sugimura et al., 2015).
Recent studies have linked immune tolerance to cellular metabolism, demonstrating that
the cell’s metabolic signature affects not only the way cells utilize energy but also their expression
of immune-related genes and long-term functions (O’Neill et al., 2016). Upon classic
inflammatory stimulation, innate immune cells alter their metabolism towards aerobic glycolysis,
redirecting tricarboxylic acid (TCA) cycle intermediates towards lipid biosynthesis, and
committing mitochondria to reactive oxygen species production (Borregaard and Herlin, 1982;
Cheng et al., 2014; O’Neill et al., 2016). Ultimately, these changes precede the induction of pro-
inflammatory mediators and support immune behaviors, such as cytokine secretion and
phagocytosis (Cheng et al., 2014). Accordingly, innate immune cells can also adopt a trained
immunity program, which enhances immune responses after repetitive stimulation by switching
the cell’s metabolism towards aerobic glycolysis (Cheng et al., 2014; Netea et al., 2016). During
sepsis-induced immune tolerance, however, macrophages display an impaired metabolic program,
marked by suppression of all major metabolic pathways and inhibition of the upstream regulator
AKT-mTOR-HIF1a pathway (Cheng et al., 2016). Of relevance to AD, studies suggest that
microglial activation in response to Ab induces metabolic reprogramming from oxidative
phosphorylation to glycolysis, which becomes generally defective over time (Baik et al., 2019;
Rubio-Araiz et al., 2018; Ulland et al., 2017). Mounting evidence suggests that in a chronic setting,
macrophage metabolic fitness plays an essential part in adaptation to disease. However, little is
known about how signals from the microenvironment interact to influence innate immunity
metabolic signature, and hence, the behavior of cells.
The cytokine interleukin-10 (IL-10) and all elements of the IL-10 signaling pathway are
abnormally elevated in AD patient’s sera and brains (Gezen-Ak et al., 2013; Guillot-Sestier et al.,
2015b), affecting circulating and tissue-resident mononuclear phagocytes. While little is known
about the role of IL-10 in human AD, functional polymorphisms are linked to increased AD risk
in some (Arosio et al., 2004; Lio et al., 2003; Ma et al., 2005), but not all populations (Depboylu
et al., 2003). Further, IL-10 has been reported to metabolically counteract the effects of LPS and
to induce wound healing by controlling essential immunometabolic pathways, including mTOR
signaling, and shifting metabolism toward oxidative phosphorylation and fatty acid oxidation (Ip
et al., 2017a; Zhang et al., 2019). Additionally, data from our laboratory showed that complete
Il10 deletion in the APP/PS1 model of cerebral amyloidosis modulates neuroinflammation and
licenses Ab phagocytosis by activated mononuclear phagocytes (Guillot-Sestier et al., 2015b). Yet,
the molecular basis of IL-10 effects in the context of AD remains to be clarified. Understanding
the role of IL-10 in metabolic processes is pivotal to deciphering pathways capable of breaking
Ab immune tolerance in mononuclear phagocytes.
In this study, we investigated how IL-10 exposure promotes Ab immune tolerance in
mononuclear phagocytes by affecting their metabolic fitness. We report high levels of IL-10
around Ab plaques in twelve month-old APP
swe
/PS1
ΔE9
mice, and changes of immunometabolic
markers in plaque-associated mononuclear phagocytes. Furthermore, our data demonstrates that
IL-10 tolerized macrophages undergo metabolic reprograming with significant accumulation of
lipid droplets and reduced glycerophospholipid content. Pharmacological repurposing of lipid
droplets to generate phospholipids restores Ab phagocytosis, highlighting mononuclear phagocyte
lipid metabolism as a potential AD therapeutic target.
5.3 Material and Methods
Key Resources table
REAGENT or RESOUCE SOURCE IDENTIFIER
Antibodies
Rat anti-mouse CD45- Pacific Blue
(Clone 30-F11), 1:200
BioLegend BioLegend Cat# 103134,
RRID: AB_2562559
Rat anti-mouse CD11b- BV550 (Clone
M1/70), 1:200
BioLegend BioLegend Cat# 101239,
RRID: AB_11125575
Rabbit anti-Iba1, 1:200 Wako Wako Cat# 019-19741,
RRID:AB_839504
Rabbit anti-ADFP/Plin2 (EPR3713),
1:50
Abcam Abcam Cat# ab108323,
RRID:AB_10863476
Rabbit anti-HIF1a (D2U3T), 1:1000
(WB), 1:200 (IHC)
Cell Signaling Technology Cell Signaling Technology
Cat# 14179,
RRID:AB_2622225
Rabbit anti-phospho-AKT (Ser473)
(D9E) XP, 1:1000
Cell Signaling Technology Cell Signaling Technology
Cat# 4060,
RRID:AB_2315049
Rabbit ani-AKT (pan) (11E7), 1:1000 Cell Signaling Technology Cell Signaling Technology
Cat# 4685,
RRID:AB_2225340
Rabbit anti-phospho-Stat3 (Tyr705)
(D3A7) XP, 1:1000
Cell Signaling Technology Cell Signaling Technology
Cat# 9145,
RRID:AB_2491009
Rabbit anti-Stat3 (D3Z2G), 1:1000 Cell Signaling Technology Cell Signaling Technology
Cat# 14047,
RRID:AB_2728821
Rabbit anti-phospho-AMPKa
(Thr172) (40H9)
Cell Signaling Technology Cell Signaling Technology
Cat# 2535,
RRID:AB_331250
Rat anti-mouse IL10R (CD210)
neutralizing antibody
Bio X Cell Bio X Cell Cat# BE0050,
RRID:AB_1107611
Rat anti-murine IL-10 antibody, 1:50 PeproTech PeproTech Cat# 500-M128-
500,
RRID:AB_1268867
Rabbit anti-murine IL-10 antibody,
1:50
PeproTech PeproTech Cat# 500-P60-100,
RRID:AB_14797
Rabbit anti-CD11b (EPR1344), 1:200 Abcam Abcam Cat# ab133357,
RRID:AB_2650514
Rabbit anti phospho-NF-kB p65
(Ser536) (93H1), 1:1000
Cell Signaling Technology Cell Signaling Technology
Cat# 3033, RRID:AB_331284
Rabbit anti-NF-kB (D14E12) XP Cell Signaling Technology Cell Signaling Technology
Cat# 8242,
RRID:AB_10859369
Rabbit anti-TNFAIP3, 1:1000 Cell Signaling Technology Cell Signaling Technology
Cat# 4625,
RRID:AB_2204524
Goat anti-Aif1, 1:200 LifeSpan LifeSpan Cat# LS-B2645-50,
RRID:AB_1664318
Rat polyclonal anti-CD68 clone FA-11,
1:100
Abcam Abcam Cat# ab53444,
RRID:AB_869007
Mouse monoclonal amyloid 1-16
(6E10), 1:600
Covance Covance Cat# SIG-39320,
RRID:AB_662798
mouse monoclonal β-actin, 1:1000 ProteinTech Proteintech Cat# 66009-1-Ig,
RRID:AB_2687938
Reagents
Recombinant mouse IL-10 R&D System Cat# 417-ML-005
Hoechst 33342 Thermo Fischer Cat # 62249
HCS LipidTox
TM
green Thermo Fischer Cat # H34350
Human Ab1-42 Anaspec Cat# AS-24224
Human Ab1-42 Hylite
TM
Fluor 488 Anaspec Cat# AS-60479-01
Human Ab1-42 Hylite
TM
Fluor 555 Anaspec Cat# AS-60480-01
Propidium Iodide 1mg/ml solution in
water
Thermo Fischer Cat# P3566
GW1929 MedChem Express Cat # HY-15655
Atglistatin MedChem Express Cat # HY-15859
(+)-Carnitine chloride MedChem Express Cat # HY-B1453
Etomoxir MedChem Express Cat # HY-50202
Reconstitution buffer 2 (BSA/PBS) R&D System Cat# RB02
Oil-O-red Sigma-Aldrich Cat# O0625-25G
Thioflavin S Sigma-Aldrich Cat# T1892-25G
Nile Red Sigma-Aldrich Cat# 19123
CST 10X lysis buffer Cell Signaling Technnology Cat# 9803S
Fluid Sterile Thioglycollate Medium Millipore Sigma Cat# STBMFTM12
Critical Commercial Assays
Customized V-PLEX mouse pro-
inflammatory panel 1 Kit
Meso Scale Discovery Cat# K15048D
Seahorse XF Cell Mito Stress Test Kit Agilent Cat# 103015-100
Mouse TNF-a Quantikine ELISA Kit R&D System Cat# MTA00B
Experimental Models: Organisms/Strains
C57BL/6J Jackson Lab stock #000664
B6.Cg
Tg(APPswe,PSEN1dE9)85Dbo/Mmja
x MMRRC
Jackson Lab stock #034832
B6.129P2-Il10
tm1Cgn
/J Jackson Lab stock #002251
Software and Algorithms
FlowJo v.10 BD Life Sciences https://www.flowjo.com
Imaris Bitplane v.7.6.5 Oxford Instruments. Oxfordshire, UK. https://www.imaris.oxinst.com
GraphPad Prism 8.0 Software GraphPad Software. La Jolla, CA. https://www.graphpad.com
Bio-Rad Image Lab Software Bio-Rad. Hercules, CA. https://www.bio-
rad.com/Imaging
Fiji Image J Open-source https://www.imajej.net
Partek Genomic Suite Partek Inc. https://www.partek.com
Ingenuity Pathway Analysis QIAGEN https://www.qiagen.com
QInsight Quertle https://www.quertle.com
Agilent Qualitative Analysis B.07.00 Agilent https://www.agilent.com
Profinder B.08.00 Agilent https://www.agilent.com
MetaboAnalyst v.4.0 Open-source https://www.metaboanalyst.ca
Seahorse Wave Agilent https://www.agilent.com
Experimental Model and Subject Details
Ethics Statement
All animal experiments were approved by the University of Southern California
Institutional Animal Care and Use Committee and performed in strict accordance with National
Institutes of Health guidelines and recommendations from the Association for Assessment and
Accreditation of Laboratory Animal Care International.
Human patients
Frozen human brain frontal cortex used for Western blotting was obtained from the
Alzheimer’s Disease Research Center (ADRC, NIA AG05142) Neuropathology Core (AD=12,
age: 82.3 + 3.94, post-mortem interval: 6h + 0.68 ; and non-demented control=10, age: 90.9 +
1.35, post-mortem interval: 6.8h + 0.67. Table 1).
Table 1. Human patients information from USC/ADRC.
ID Diagnosis Sex Age Braak Score Co-morbidities Post-mortem
interval (h)
670
Non-demented
control
F 91 2 n/a 5.25
675 F 95 2 n/a 10
679 F 91 0 AD pathology present, but
insufficient for diagnoses
4.5
688 F 90 0 Basilar vessels, artherosclerosis 5.75
715 F 99 0 Cerebrum infarct, arteriosclerosis 9
804 M 82 0 n/a 9
824 F 87 0 Amyloid angiopathy 7
827 F 91 0 n/a 8.75
845 F 90 0 n/a 5
877 M 93 0 Amyloid Angiopathy 3.75
902
Alzheimer’s
disease
patients
F 91 5.5 Amyloid angiopathy 3.25
903 F 80 5.5 Amyloid angiopathy 3.75
904 M 51 5.5 Amyloid angiopathy 6
905 M 81 3 Amyloid angiopathy 8
908 M 85 5 Microinfarct, basal ganglia, remote 8.75
899 M 83 5.5 Amyloid angiopathy 10
910 F 100 4 Amyloid angiopathy 4.75
920 F 83 5 Amyloid angiopathy 6.25
923 M 86 4 Amyloid angiopathy 4.5
947 F 91 5 Basilar vessel, atherosclerosis;
arteriosclerosis
4
949 M 65 5 Lewy body disease 5
950 M 92 6 n/a 8.5
Mice
Wild-type (WT, C57BL/6J) and APP
Swe
PS1
dE9
(referred to as APP/PS1; B6.Cg
Tg(APPswe,PSEN1dE9)85Dbo/Mmjax MMRRC) were used for analysis. Tg(APP
swe
,PSEN1
ΔE9
)
transgenic mice (Jankowsky et al., 2003) were bred with Il10 knockout mice (Kühn; et al., 1993)
(B6.129P2-Il10
tm1Cgn
/J) to generate the APP/PS1 Il10KO mice. Mice of both sexes, 6 and/or 12
months old, were used in experiments for immunohistochemistry and RNA analysis. Wild type
mice of both sexes, approximately 4 months old, were used to extract peritoneal macrophages for
in vitro experiments. All mice were housed under standard conditions with free access to food and
water.
Methods Details
Tissue Isolation and Preparation
Animals were euthanized in a CO
2
chamber, perfused with ice-cold PBS, and the anterior
quarters of the brain were snap-frozen and randomly assigned to protein or mRNA analyses while
the posterior quarters were fixed in 4% paraformaldehyde (PFA) overnight and randomly assigned
to paraffin or agarose embedding.
Primary cell culture treatments
C57BL/6J (male and female of equal numbers; 4 months-old + 2months) were injected i.p.
with 1ml of 0.5% thioglycolate solution for two days, 96h before collection as previously described
(Zhang et al., 2008). Peritoneal macrophages were plated and cultured under complete media
(DMEM, 10% heat inactivated fetal bovine serum, 1% Glutamax
TM
, 1% Pyruvate, 1%
penicillin/streptomycin). Media was changed daily for 2 days and cells were checked for healthy
morphology under the microscope before beginning of experiments.
In all experiments, cells were starved with reduced media Opti-MEM
TM
(Gibco) for 8h
before beginning of treatments. Cells were treated with Opti-MEM
TM
(vehicle), mouse
recombinant IL-10 (10ng/ml), aggregated Ab
42
(1µg/ml) or Ab+ IL-10 for 24, followed by a 30
minutes wash in Opti-MEM
TM
. Then, cells were treated with Opti-MEM
TM
(vehicle), IL-10
(10ng/ml), aggregated Ab
42
, aggregated Ab
42
-555 (1µg/ml) or Ab+ IL-10 for 30min, 1h, 3h, 24h
or 48h, depending on the analysis (the length of 2
nd
treatment is described in figure legends for
each experiment). For experiments in figure 5 and 6, cells were treated with GW1929 10µM and
or atglistatin 10µM with the second treatment, in these cases vehicle consisted of equivalent
amounts of DMSO. Phospholipid488 incorporation was performed by incubating cells with
LipidTox
TM
1X with 2
nd
treatment for 48h. Cell viability experiments were carried on by adding
500 nM propidium iodide on the last 30 min of the second treatment.
Recombinant mouse IL-10 was prepared by reconstituting the lyophilized product with
sterile PBS BSA 0.1% (R&D systems) to form a 100µg/ml stock solution. Ab was reconstituted
by adding 40µl of 1% NH
4
OH to lyophilized power and diluting it in PBS to form a 1mg/ml stock
solution. Ab aliquots were stored at -20
o
C and incubated at 37
o
C for 24h to form microaggregates
before experiment, as previously described (Stine et al., 2011). GW-1929 and atglistatin were
diluted in DMSO to form a 10mM stock solution.
Flow Cytometry
Cells were harvested, fixed and permeabilized (BD Cytofix/Cytoperm
TM
Plus, BD
Biosciences) for 20 min on ice. Generally, cells were incubated with primary antibodies against
CD45 and CD11b (1:100) for 30 min on ice. For lipid droplet content, cells were incubated with
nile red (0.5µg/ml) for 10 min. Samples were analyzed using BD LSR II Flow Cytometer (BD
Biosciences) or Guava
®
easyCyte
TM
(Luminex Corp). Phospholipid-green incorporation and
propidium iodide content were analyzed according to manufacture’s protocol.
Immunohistochemistry/cytochemistry
50 μm mouse brain sections embedded in 2% agarose, or cells fixed onto glass coverslips,
were blocked in for 1 h in tris-buffered saline (TBS) with 0.2% Tween-20 supplemented with
donkey serum (1:1000). Prior to immunoperoxidase labeling, sections were treated with 3%
H
2
O
2
in PBS for 30 min to remove endogenous peroxidase activity. Thereafter, sections were
incubated in anti-IL-10 primary antibody (1:50) overnight at 4°C. Immunoreactivity was
visualized in single labeling experiments with biotinylated goat anti-rabbit or anti-rat IgG (1:200,
Vector Laboratory) followed by diaminobenzidine/diaminobenzidine (Vector Laboratory)
resulting in a brown reaction precipitate. Else, staining with antibodies directed against pAMPK
(1:100), HIF1-a, (1:100) 6E10 (1:400), CD11b (1:200), Iba1 (1:200), or CD68 (1:400).
Alexa
488/594/647
-coupled secondary antibodies (1:200) were used for immunofluorescence
experiments (Life Technologies). Nile red was incubated for 10 min followed by 10 min incubation
with Hoescht. Sections or coverslips were then mounted with Prolong anti-fade reagent (Molecular
Probes).
Microscopy and quantification
Brain sections were immunolabeled for Iba1, 6E10 and HIF1a or pAMPK or stained with
nile red, and confocal image stacks of the cortex were taken at 60x magnification. Four sections
were stained per animal, with 3 images taken per section. The images were converted to 3D
reconstructions using the surface-rendering feature of Imaris Bitplane software (version 7.6.1) and
the relative volume of HIF1a or pAMPK or nile red signal inside Iba1
+
cells was quantified as
previously described (Guillot-Sestier et al., 2016). Signal derived from voxels that colocalized
with 6E10 were excluded from the analysis.
For in vitro analysis of phagocytosis, cells treated with aggregated Ab
555
were
immunolabeled for CD68, and confocal image stacks were taken at 60x magnification. Two
coverslip were stained per biological replicate, with 4 images taken per coverslip (~100 cells). The
images were converted to 3D reconstructions using the surface-rendering feature of Imaris
Bitplane software (version 7.6.1) and the relative volume of Ab
555
within CD68
+
phagolysosomes
was quantified as previously described (Guillot-Sestier et al., 2016).
Enzyme-linked immunosorbent assay
Evaluation of IL-10 levels was performed using a Multi V-PLEX assay (mouse pro-
inflammatory panel 1 and mouse cytokine panel 1, Meso Scale Discovery). Brain homogenates
were run according to the manufacturer's instructions. BCA protein assay (BioRad) was used to
determine total protein concentrations in each fraction and values were used for normalization.
TNFa release in the media was quantified using R&D Systems Quantikine kit. Briefly, 96-well
plates (0.5 × 10
5
cells/well) were treated and the supernatant was collected 4h after the last
stimulation. Assay was run according to the manufacturer's instructions.
mRNA isolation and quantitative Real Time Polymerase Chain Reaction
RNA was extracted with TRIzol (Life Technologies), from ~ 2-3 million cells per
biological replicate. Expression levels were determined using primers for targeted genes: Tnf,
Slc27a1, Srebp1, Acsl1, Lpl, Atgl, Lpin1, Dgat1, Itgav, Itgb5, and Actb (Table 2).
Table 2. Primers designed to target mus musculus genes.
Gene Gene ID Primer sequence
Tnf NM_013693.3 F:ATGGCCTCCCTCTCATCAGT
R: TTTGCTACGACGTGGGCTAC
Slc27a1(Fatp1) NM_011977.4 F: GTTTCTGGGACTTCCGTGGAC
R: GAGGCCAAAGAGGTCTCGC
Srebp1 NM_001159555.1 F: CTGGTGAGTGGAGGGACCAT
R: GACCGGTAGCGCTTCTCAAT
Acsl1 NM_001302163.1 F: TGGGGTGGAAATCATCAGCC
R: CATTGCTCCTTTGGGGTTGC
Lpl NM_008509.2 F: GGAGAAGCCATCCGTGTGAT
R: CTCAGGCAGAGCCCTTTCTC
Atgl NM_001163689.1 F: CCATGGTCCTCCGAGAGATG
R: AGGCTGCAATTGATCCTCCT
Lpin1 NM_001130412.1 F: ATGAATTACGTGGGGCAGC
R: CCACTTTCTCTCGGGAGCG
Dgat1 NM_010046.3 F: TAGAAGAGGACGAGGTGCGA
R: GTCTTTGTCCCGGGTATGGG
Pdk4 NM_002612 F:AAAGATGCTCTGCGACCAGT
R:GGGTCAAGGAAGGACGGTTT
Cpt1a XM_006531658.3 F: GCTCTACATCACCCCAACCC
R: GCAGAGCAGAGGGGAATTGT
Itgav NM_008402 F: GGATTCGCCGTGGACTTCTT
R: CAAACTCAATGGGCTGGCAC
Itgb5 NM_001145884.1 F: TCTTCTTCACTGCCACCTGC
R: AGCCCACAGGTGTATGTTCC
Cd36 NM_001159555.1 F: TGGAGGCATTCTCATGCCAG
R: TTGCTGCTGTTCTTTGCCAC
Actb NM_007393 F:AGAGGGAAATCGTGCGTGAC
R:CAATAGTGATGACCTGGCCGT
RNA sequencing
Data available from Guillot-Sestier et al 2015 was used for RNA sequencing analysis.
Briefly, strand-specific libraries were generated with 1 μg of input RNA using the TruSeq Stranded
mRNA Sample Prep Kit (Illumina) on an Illumnia HiSeq 2000. Reads were aligned to the mouse
mm19 reference, clustering dendograms, PCA analysis, gene enrichment score, and differential
expression data was generated using Partek genomic suite. False discovery rate was controlled for
by the Benjamini and Hochberg algorithm (Benjamini and Hochberg, 1995). Data was further
analyzed using Ingenuity pathway analysis from differentially regulated gene list. List of genes
associated with lipid droplet metabolism was generated using Qinsight to derive top 100 genes
reported to be associated to lipid droplets. From these, 36 (Table 3) were used for unsupervised
cluster analysis using Partek genomic suite.
Table 3. List of genes related to lipid droplet metabolism.
Anxa3 Csf1 Mfn2 Rab18
Apoe Ddit3 Mt3 Rab32
Arfrp1 Dgat1 Nr1h3 Rab40c
Atg5 Fabp3 Plin2 Rab8a
Aup1 Fads2 Plin3 Rora
C2 Lpcat2 Pnpla2 Sh3glb1
Cav1 Lpl Ppara Srebf1
Cideb Mapk3 Ppard Tlr4
Cpt1a Mapk8 Rab10 Vim
Western Blot
Snap-frozen human tissue or murine cells were harvested and lysed in ice-cold 1X tissue
lysis buffer (Cell Signaling Technology) supplemented with protease inhibitor cocktail (Sigma-
Aldrich) and phosphatase inhibitor cocktails 2 and 3 (Sigma-Aldrich). After 15 min of incubation
on ice, homogenates were centrifuged at 14,000 g for 15 min at 4°C. Protein concentration in the
supernatants was determined using the BCA protein assay (Biorad). Reduced samples were
prepared according to manufacture’s instruction (Bolt, Thermo). Gels were then transferred to
methanol-activated PVDF membrane (0.45μm pore, Merck Millipore) using the Trans-Blot®
TurboTM transfer system (Bio-Rad). Primary antibodies were directed against: HIF1a, pAKT,
AKT, pAMPK, pP65, P65, TNFAIP3, pSTAT3, STAT3, PLIN2 and β-actin (1:1000).
Densitometric analyses were performed using Bio-Rad Image Lab Software, and band densities
were normalized to b-actin.
Bioenergetics analysis
The Seahorse Extracellular Flux (Xfe96) Analyser (SeaHorse Bioscience, USA) was used
to carry out bioenergetic analysis of cells. Real-time oxygen consumption rate (OCR) was
measured according to the manufacturer’s instructions, with minor modifications. Briefly, 70,000
cells/well were plated on the Xfe96 cell culture microplate coated with poly-L-lysisne (5µg/ml).
Cells rested for 24h before being treated for the first time with the combinations of vehicle, Ab
and IL-10 in substrate-limited medium (DMEM, 0.5mM glucose, 1mM glutaMAX, 0.5mM
carnitine, 1% FBS). Following incubation, cells were washed with the FAO assay medium
according to the manufacturer’s instructions. Cells were treated for the second time using FAO
assay media and the plate was incubated in a CO
2
-free incubator (37 °C, 45min). Fifteen minues
before plate was run, wells were treated with etomoxir 10X (400 µM). Oligomycin (15 μM),
carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (5 μM; FCCP) and Rotenone+Antimycin
A (5 μM) were loaded into the appropriate ports (10X) for sequential delivery (Agillent).
Following calibration of Seahorse Analyzer, oxygen consumption rate (OCR) were measured
every 6-7 min for 73 min and the appropriate compounds were injected sequentially at 18 min
intervals. OCR was automatically calculated using the Seahorse Xfe96 software and 8 replicates
were assessed for each separate sample and normalized to cell number per well. The oxygen
generation attributable to fatty acid oxidation was derived from the difference between the
condition with and without etomoxir. The cartridge plate was hydrated with XF calibrant buffer
and incubated overnight (37
o
C, CO
2
-free); the FAO assay medium (5mM HEPES, 2.5mM glucose,
0.5mM carnitine, 111mM NaCl, 4.7mM KCl, 1.25mM CaCl
2
, 2mM MgSO
4
, 1.2mM NaHPO
4
, pH
7.4) was prepared immediately before assay.
Metabolite extraction and LC-MS metabolomics profiling
Approximately 2*10
^6
cells were washed with ice-cold PBS three times and quenched by
200µl of acetonitrile:methanol:H
2
O (40:40:20) precooled to –40 °C. Metabolites were extracted
by mechanical lysis with 0.1-mm zirconia beads in Precellys tissue homogenizer for 4 min
(6000 rpm) twice under continuous cooling at or below 2 °C. Lysates were clarified by
centrifugation and then filtered across a 0.22-µm spin-X column. The residual protein content of
metabolite extracts was determined (BCA protein assay kit, Thermo Scientific) to normalize the
samples to cell biomass. Extracted metabolites were separated on a Cogent Diamond Hydride Type
C column (gradient 3) (Microsolve Technologies) and the mobile phase consisted of solution A
(ddH
2
O with 0.2% formic acid) and solution B (acetonitrile with 0.2% formic acid). The mass
spectrometer used was an Agilent Accurate Mass 6230 time of flight (TOF) coupled with an
Agilent 1290 liquid chromatography (LC) system. Detected ions were deemed metabolites on the
basis of unique accurate mass-retention time identifiers for masses exhibiting the expected
distribution of accompanying isotopologs. The abundance of extracted metabolites was extracted
using Agilent Qualitative Analysis B.07.00 and Profinder B.08.00 software (Agilent
Technologies) with a mass tolerance of <0.005 Da. The clustered heatmap and hierarchical
clustering trees were generated using Cluster 3.0 and Java Tree View 1.0. Principal component
analysis was conducted using MetaboAnalyst (v 4.0).
Statistics
One-way analysis of variance (ANOVA) followed by Bonferroni post hoc test was used.
Two-way ANOVA followed by Sidak’s multiple comparison test was used when two independent
variables were considered. Else, Student’s t-test was performed or it is specified under figure
legend. In all cases, p ≤ 0.05 was considered to be statistically significant and p ≤ 0.10 was
considered trending. All data are presented as mean ± SEM.
5.4 Results
5.4.1 IL-10 is elevated around Ab plaques and affects innate immune phenotype
To assess the local relevance of IL-10 to periplaque mononuclear phagocytes, we first
analyzed IL-10 levels in the frontal cortex of 6 and 12 months-old APP/PS1 mice. These ages
represent time-points in which animals display initial plaque deposition and gliosis, and developed
Ab plaques and behavioral deficits, respectively (Jankowsky et al., 2003). IL-10 is elevated in an
age and disease-dependent fashion in the prefrontal cortices of APP/PS1 mice (Figure 5.1 A). To
delineate difference in microenvironment, twelve months-old APP/PS1 brain sections were used
to assess IL-10 content localized to Ab-plaques, via immunohistochemistry. Using two different
primary antibodies we demonstrated that IL-10 was highly localized at and around thioflavin S
(ThioS
+
) plaques in the cortices of 12 months-old APP/PS1 mice (Figure 5.1 B).
IL-10 has been shown to inhibit key immunometabolic pathways in mononuclear
phagocytes (Baik et al., 2019; Ip et al., 2017a). The mammalian target of rapamycin (mTOR)
complex is a central regulator triggered in activated immune cells and is associated to downstream
metabolic changes that sustain the immunological response (Cheng et al., 2014; Lee et al., 2018).
Thus, we investigated whether the spatial distribution of mononuclear phagocytes in regards to Aβ
plaques affects the expression of markers of the AKT-mTOR-HIF1a axis. Strikingly, Iba1
+
mononuclear phagocytes proximal to Aβ plaques showed reduced accumulation of HIF1a (Figure
5.1 C) in 12 months-old brains. Moreover, they displayed increased buildup of activated AMPK
(pAMPK), an inhibitor of mTOR (Figure 5.1 D), suggesting an overall suppression of the AKT-
mTOR-HIF1a axis near Aβ plaques.
Figure 5.1 IL-10 expression is elevated in the brains of APP/PS1 mice.
(A) IL-10 levels from mouse prefrontal brain homogenates measured via ELISA (n=6, 6 months
animals; n=10-18 for 12 months animals).
(B) Microscope images of IL-10 immunostaining (DAB staining, anti-rat antibody on the left and
immunofluorescence staining using anti-rabbit antibody on the right) and ThioS
+
Ab plaques
(bottom row, yellow arrow head) in the entorhinal cortex of 12 months-old APP/PS1 mice.
(C) Quantitation of HIF1a signal in Iba1
+
cells of 12 months-old wild-type (WT) and APP/PS1
mice (n=5-9). Iba1
+
cells were further parsed into proximal and distal from Ab plaques.
(D) Quantitation of pAMPKa signal in Iba1
+
cells of 12 months-old wild-type (WT) and APP/PS1
mice (n=5-6), further parsed into proximal and distal from Ab plaques.
Graphs denote mean + SEM. Student’s t-test was performed. * p< 0.05, ** p< 0.01.
5.4.2 IL-10 suppresses Ab-treated mononuclear phagocytes function
In order to address the effects of IL-10 on mononuclear phagocytes response to Ab, we
established an in vitro experimental paradigm in which thioglycolate-elicited peritoneal
macrophages from adult C57BL/6 mice were isolated, cultured and treated with vehicle, and
aggregated Aβ
1-42
, IL-10 or Aβ + IL-10 for 24h. Cells were washed with serum-reduced medium
for 30 min and repeated the treatment for various lengths of time (Figure 5.2 A). This paradigm
allowed us to isolate the functional relevance of IL-10 to the microenvironment around Ab
plaques. Mononuclear phagocytes were exposed for 4h to fluorescently labeled and aggregated
Aβ
1-42
in the presence or absence of IL-10 during the second treatment to assess Aβ phagocytosis
via confocal assisted 3D reconstruction and measurement of Aβ volume within CD68
+
phagolysosomes. Interestingly, two consecutive exposures to Aβ promoted amyloid uptake within
phagolysosomes, whereas the presence of IL-10 suppressed Aβ phagocytosis (Figure 5.2 B and
5.2 C). Importantly, we confirmed that the volume of Aβ found inside phagolysosomes during the
second exposure was not influenced by the amount ingested during the first exposure (Figure 5.2
D). Accordingly, the presence of IL-10 prevented the expression and release of the
proinflammatory cytokine TNFa in response to Aβ (Figure 5.2 E and 5.2 F).
We next investigated immunometabolic pathways of importance to innate immunity
activation. Similarly to what we found in vivo, the inhibitory molecule pAMPK was upregulated
by IL-10 (Figure 5.2 G), whereas activation of AKT via phosphorylation of Ser473 was induced
by Ab and prevented by IL-10 (Figure 5.2 H), suggesting an overall repression of the AKT-mTOR-
HIF1a axis in a similar fashion to that seen in mononuclear phagocytes proximal to plaques.
Furthermore, the NF-kb pathway was suppressed by IL-10, as indicated by reduced
phosphorylation of P65 (Figure 5.3 I) and induction of TNFa inhibitory protein 3 (TFNAIP3)
(Figure 5.2 J), a cytoplasmatic factor suppressor of NF-kb. Treatment with neutralizing antibody
against IL-10’s cognate receptor, IL10Ra, successfully blocked the effects of IL-10 treatment
(Figure 5.2 K-M). Noteworthy, Ab alone did not induce IL-10 release in our condition; only
cultures that received recombinant murine IL-10 showed significant levels of IL-10 (Figure 5.2
N). Taken together, these data suggest that IL-10 alters the microenvironment toward the
suppression of mononuclear phagocyte’s function by inhibiting immunometabolic pathways.
Figure. 5.2 IL-10 treatment restrains mononuclear phagocyte activation.
(A) Diagram of the treatment paradigm: peritoneal macrophages were treated for 24h with vehicle,
Ab (1µg/ml), IL-10 (10ng/ml) or Ab + IL-10, followed by 30min wash in Opti-MEM and a
second treatment of varied length depending upon the assay.
(B) Confocal images of mononuclear phagocytes treated for 4h with aggregated fluorescently
labeled Ab
42
(red) and marked for phagolysosomes (CD68, white) and nuclei (DAPI, blue).
Right column shows 3D reconstruction of Ab inside phagolysosomes; scale bars denotes 5mm.
(C) Quantitation of volume of Ab within CD68
+
phagolysosomes of mononuclear phagocytes
exposed to different treatments (n=7).
(D) Mononuclear phagocytes were treated with aggregated fluorescently labeled Ab
42
(green) for
24h, washed and treated aggregated fluorescently labeled Ab
42
(red) for 4h. Volume of Ab red
and green within CD68
+
phagolysosomes were quantified per cell based and correlation
analysis was performed (n=4).
(E) Tfn expression measured by RT qPCR under different treatment conditions (n=4).
(F) TNFa secretion in the supernatant measured by ELISA under different treatment conditions
(n=4).
(G) Quantitation of AMPK phosphorylation, an inhibitor of mTOR, immunoblots of cell
homogenates treated with specified conditions (n=4).
(H) Quantitation of AKT phosphorylation immunoblots of cell homogenates treated with specified
conditions (n=4).
(I) Quantitation of P65 phosphorylation, part of the NFkb pathway, immunoblots of cell
homogenates treated with specified conditions (n=3).
(J) Quantitation of TNFAIP3 levels, an inhibitor of the NFkb pathway, immunoblots of cell
homogenates treated with specified conditions (n=4).
(K) Representative images of phosphorylated and native Stat3 immunoblots, as well as b-Actin,
of cells treatment with IL-10 (10 ng/ml) and increasing concentrations of neutralizing antibody
against IL10Ra (0, 0.1 and 1 µg/ml).
(L) Quantitation of AKT phosphorylation immunoblots of cell homogenates treated with specified
conditions (n=3).
(M) Quantitation of AMPK phosphorylation, an inhibitor of mTOR, immunoblots of cell
homogenates treated with specified conditions (n=3).
(N) Assessment of IL-10 levels via ELISA in cell homogenates treated with specified conditions
(n=4).
Graphs denote mean + SEM, except for graph D. Two-way ANOVA was performed. † p< 0.1,
* p< 0.05, ** p< 0.01.
5.4.3 IL-10 reduces glycerophospholipid content of mononuclear phagocytes
Given the alterations we described on the AKT-mTOR-HIF1a axis in mononuclear
phagocytes treated with Ab and IL-10 and the profound effect of mTOR signaling on metabolism,
we further investigated the metabolic profile of these cells. Importantly, both IL-10 and Ab have
independently been shown to provoke deep metabolic changes in macrophages and microglia
(Baik et al., 2019; Ip et al., 2017a). However, their interaction still remains elusive. Thus, we
performed targeted metabolomics on mononuclear phagocytes exposed to vehicle, Ab or Ab + IL-
10 for 24h, followed by a second 24h exposure, as designed in in vitro paradigm (Fig. 5.2 A),
corresponding to the conditions exhibiting significant functional difference.
Our targeted metabolomics allowed us to detect ~ 500 annotated metabolites, part of a
diverse set of metabolic pathways such as, glycolysis, pentose-phosphate pathway, TCA cycle,
amino acid metabolism, antioxidant system and lipid metabolism. Metabolic remodeling
associated with Ab mediated immunometabolism and IL-10 mediated tolerance was determined
by comparing the metabolites being differentially regulated (Figure 5.3 A). To elucidate the impact
of IL-10 on Ab accumulation mediated metabolic remodeling, a pathway analysis was conducted
using the metabolite subset that was differentially downregulated by the dual Ab + IL-10 treatment
as compared to that of Ab condition. The pathway mapping result revealed glycerophospholipid
metabolism as a top ranked pathway (Figure 5.3B). Strikingly, ~39% of the upregulated subset of
metabolites in the condition that received two Ab exposures when compared to vehicle were
metabolites involved in glycerophospholipid metabolism pathway such as
phosphatidylethanolamine (PE), phosphatidyl choline (PC), or phosphatidic acid (PA), having
varying chain length (Figure 5.3 A). Intriguingly, IL-10 supplementation mostly prevented
mononuclear phagocytes from accumulating glycerophospholipid metabolism pathway
intermediates in response to Ab exposure, corroborating the functional interaction of IL-10 on Ab
mediated cellular immunometabolic remodeling (Figure 5.3C). Targeted metabolomics and
pathway mapping confirmed the linkage between the immunometabolic and functional regulation
of mononuclear phagocytes and negative interaction of IL-10 and Ab machineries.
Figure. 5.3 IL-10 treatment decreases glycerophospholipid content in mononuclear
phagocytes.
(A) Volcano plot showing significantly differentially regulated metabolites among 500 metabolites
detected.
(B) Pathway analysis demonstrating top ranked pathways altered in mononuclear phagocytes
exposed to Ab + IL-10 during the first and second treatments, compared to cultures that
received only Ab.
(C) Top most changed metabolites between conditions (n=3). PE= phosphatidylethanolamine, PC=
phosphatidyl choline, PA= phosphatidic acid.
Graphs denote mean + SEM. One-way ANOVA was performed in graphs from panel C. † p< 0.1,
* p< 0.05, ** p< 0.01, *** p< 0.001, **** p< 0.0001.
5.4.4 IL-10 induces accumulation of lipid droplets in mononuclear phagocytes
Glycerolipid metabolism has been reported to shift from phospholipids to triacylglycerol
synthesis during injury/stress (Jarc and Petan, 2019; Yang et al., 2019a). Further, phospholipid
and triacylglycerol share diacylglycerol as a common substrate (Markgraf et al., 2014; Viktorova
et al., 2018; Yang et al., 2019a), and because we reported reduction in glycerophospholipid content
in the Ab + IL-10 condition, we investigated the effects of IL-10 on triacylglycerol accumulation.
Lipid droplets are highly dynamic organelles that comprise a single membrane enclosing a
core containing mostly triacylglycerol and cholesterol esters (den Brok et al., 2018; Jarc and Petan,
2019). Hence, we analyzed lipid droplets levels by detecting the mean fluorescence intensity of
the neutral lipid dye, nile red, in CD45
+
CD11b
+
mononuclear phagocytes via flow cytometry and
by quantifying the number of nile red
+
cells via microscopy under the different treatment
conditions. Consistent with our metabolomics profiles, we observed that only mononuclear
phagocytes exposed Ab and IL-10 displayed significantly increase lipid droplets levels (Figure 5.4
A), and a significant increase in number of cells displaying lipid droplets (Figure 5.4 B).
The accumulation of lipid droplet in the Ab + IL-10 condition could serve as a dynamic
source of energy for the cell. Anti-inflammatory cytokines, including IL-10, have been reported to
promote lipid b-oxidation in specific contexts, such as efferocytosis and tissue remodeling
(Divakaruni et al., 2018; Zhang et al., 2019). Thus, in order to assess whether the dynamic use of
endogenous fatty acid for energy generation was affected by our treatment paradigms, we
performed Seahorse analysis under glucose-restricted conditions (more details in methods).
During this assay, the oxygen consumption rate (OCR) bioenergetics profile due to endogenous
lipid utilization is generated based on the difference between the measures of the conditions with
and without treatment with a lipid transporter inhibitor, etomoxir, which prevents lipid b-
oxidation. Importantly, this assay assesses only mitochondrial-derived energy generation, and does
not account for peroxisomal b-oxidation. Strikingly, mononuclear phagocytes treated with Ab +
IL-10 displayed reduced maximal respiration due to endogenous fatty acid utilization in
comparison to cells treated with Ab (Figure 5.4 C).
A recent study in macrophages demonstrated that phagocytosis relies on the availability of
free fatty acids derived from lipid droplets degradation, specifically pointing to the role of the
lipase ATGL in freeing fatty acids and supporting phagocytosis (Chandak et al., 2010). Analysis
of key mRNA levels related to fatty acid anabolism, but not catabolism, were upregulated in
mononuclear phagocytes treated with Ab + IL-10 (Figure 5.4D), suggesting that lipid droplets are
being actively formed and not degraded via lipases. Collectively, these data suggest that Ab and
IL-10 treatment induce lipid droplet accumulation, thereby reducing the amount of free fatty acid
available for energy generation or phagolysosome functional support.
Figure. 5.4 IL-10 treatment induces lipid droplet accumulation in mononuclear phagocytes.
(A) Flow cytometric dot plots showing neutral lipid content (nile red) in CD45
+
CD11b
+
cells
(n=4).
(B) Representative images of lipid droplet positive (nile red, red) cells (CD11b, white) and
quantitation of number of lipid droplet
+
macrophages after treatments (n=4).
(C) The oxygen consumption rate (OCR) bioenergetics profile consisting of three baseline
measures of OCR followed by sequential measures following exposure to oligomycin,
carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP) and rotenone+antimycin A
is shown. Cells received lipid transporter inhibitor, etomoxir, to establish the baseline OCR
upon restriction of lipid b-oxidation. Upper graph shows bioenergetic profile of 24h of Ab
1µg/ml, followed by a second Ab treatment for 1h; bottom graph exhibits the bioenergetic
profile in the presence of IL-10 (10 ng/ml). Black arrow shows the OCR attributable to
endogenous lipid utilization. Right bar graph shows the maximal respiration due to endogenous
lipid (n=4).
(D) Real-time quantitative PCR analysis of genes involved in fatty acid (FA) metabolism (n=3-5).
Graphs denote mean + SEM. One-way ANOVA for multiple comparisons and Student t-test
were performed. † p< 0.1, * p< 0.05, *** p< 0.001.
5.4.5 Repurposing lipid droplet rescues phagocytic deficit in mononuclear phagocytes
Fatty acid synthesis, as driving force for enhanced phospholipid synthesis, is essential for
development of macrophage’s filopodia and cellular membranous organelles. Additionally,
functional studies have shown that suppression of fatty acid synthesis or phospholipid biosynthesis
decreases phagocytosis (Ecker et al., 2010; Lee et al., 2018; Rubio et al., 2018; Werb and Cohn,
1972). Of note, lipid droplet degradation can offer substrate for phospholipid biosynthesis
(Viktorova et al., 2018).
PPARg agonists have been reported to induce transcription of the lipases, ATGL and LPL
(Laplante et al., 2003; Philippe et al., 2000), which are able to free fatty acids from lipid droplets.
Furthermore, PPARg agonists have been previously shown to promote macrophage phagocytosis
in some (Kielian et al., 2008; Serghides and Kain, 2001; Serghides et al., 2009; Yamanaka et al.,
2012) but not all contexts (Majai et al., 2007; Zizzo and Cohen, 2015). Hence, we used 10 µM of
the potent non-thiazolidinedione PPARg agonists, GW1929, in conjunction with the second
treatment and analyzed for mononuclear phagocyte’s ability to perform Ab phagocytosis under
the influence of IL-10. The used concentration of GW1929 largely activates PPARg (Brown et al.,
1999) (Figure 5.5 A). Strikingly, GW1929 significantly improved the ability of mononuclear
phagocytes to uptake aggregated Ab1-42 only in the double treatment with Ab + IL-10 (Figure 5.5
B and 5.5 C). Interestingly, cells that received Ab + IL-10 + GW1929 displayed decreased lipid
droplet content, suggesting that intracellular neutral lipid accumulation did not occur or that lipid
droplets were degraded (Figure 5.5 D). Thus, we sought to inhibit lipid droplet degradation and
investigate whether it could prevent GW1929 effects on phagocytosis. We then cotreated the cells
with 40µM of atglistatin (ATG) during the second exposure to inhibit the lipase ATGL, therefore
inhibiting, at least partially, the lipid droplet degradation process. Mononuclear phagocytes treated
with GW1929 in the presence of Ab+ IL10 showed increased phagocytosis, which was blocked
by ATG cotreatment (Figure 5.5 E). None of the treatment conditions reduced cellular viability
(Figure 5.5 F). Collectively, these data suggest that lipid droplet repurposing endorses Ab
phagocytosis in tolerant mononuclear phagocytes.
Since lipid droplets can be repurposed toward phospholipid synthesis or other metabolites,
we used metabolomics analysis to assess the metabolic restoration caused by GW1929. Principal
component analysis revealed that cells treated with Ab + IL-10 and GW1929 restored overall
metabolome pattern of cells treated with Ab and IL-10 toward a pattern similar to that of Ab alone
(Figure 5.5 G). Indeed, heatmap clustering analysis confirmed that some, but not all, metabolites
altered by the addition of IL-10 were restored in an opposite direction by GW1929 treatment
(Figure 5.5 H). Notably, several glycerophospholipid metabolites of varied chain length that were
downregulated by IL-10 were rescued with GW1929 treatment (Figure 5.5. I). To further confirm
that GW1929 treatment mediated restored various glycerophospholipid metabolites were
originated from lipid droplet, we inhibited the lipase ATGL to prevent lipid droplet degradation.
Indeed, the ATGL inhibitor, ATG, partially prevented GW1929 effects on increasing
fluorescently-labeled phospholipid incorporation into cells and these findings suggested that the
phospholipid anabolism enhanced by GW1929 treatment is in part dependent on accumulated lipid
droplets as substrates (Figure 5.5K). Taken together, these data suggest that lipid droplets can be
repurposed toward phospholipid synthesis by GW1929 to break innate immune tolerance.
Figure 5.5 Ab and IL-10 induced suppression is rescued by targeting lipid droplets with
GW1929 treatment.
(A) Real-time quantitative PCR analysis of genes downstream of PPARg, PPARa and
PPARd upon treated with vehicle or GW1929 10µm (n=3-4).
(B) Representative confocal images of mononuclear phagocytes treated for 24h with Ab + IL-10,
followed by 30 min wash in Opti-MEM and a second 4h treatment with Ab-555 + IL-10, in
the presence or absence of GW1929 (10 µM). 3D reconstruction of Ab inside phagolysosomes
(CD68, white); scale bars denotes 5µm.
(C) Quantitation of volume of Ab within CD68
+
phagolysosomes of mononuclear phagocytes
exposed to different treatments in the presence and absence of GW1929 (n=3).
(D) Lipid droplet content in CD45
+
CD11b
+
cells measured via flow cytometry is decreased with
GW1929.
(E) Quantitation of volume of Ab within CD68
+
phagolysosomes of mononuclear phagocytes
treated with Ab + IL-10 in the presence and absence of GW1929 and ATGL inhibitor,
atglistatin (ATG; n=3).
(F) Cell viability measured via incorporation of propidium iodide (P.I.) into DNA of non-viable
cells (n=4) and quantified by flow cytometry.
(G) Principal component analysis showing similarities between metabolomics data of described
treatment groups.
(H) Clustered heatmap depicting levels of ~220 metabolites. Columns show results for different
treatment groups; red rectangles mark relevant metabolite clusters showing phenotypic
similarity to Ab condition, when macrophages are treated with GW1929.
(I) Top most upregulated metabolites in macrophages with respective treatments (n=3).
(J) Cells were treated with fluorescently labeled phospholipid (PL) and analyzed for incorporation
48h hours after second treatment via flow cytometry (n=4).
Graphs denote mean + SEM. Two-way ANOVA was performed in A, C and D; One-way
ANOVA was performed in E, F, I and J. † p< 0.1, * p< 0.05, ** p< 0.01, *** p< 0.001, ****
p< 0.0001.
5.4.6 Lipid droplet accumulation is prominent in APP/PS1 and human AD brains
Finally, we addressed the relevance of lipid droplet accumulation in mononuclear
phagocytes to animal models and, more importantly, Alzheimer’s disease patients. First, we
applied immunofluorescence techniques and used of confocal 3D reconstruction to analyze the
amount of nile red
+
lipid droplets inside Iba1
+
cells to assess innate immune intracellular lipid
droplets volume. Mononuclear phagocytes of twelve-months old APP/PS1 mice displayed
significantly higher levels of lipid droplets than the age-matched wild-type. Furthermore, in
parsing mononuclear phagocytes proximal versus distal to Ab plaques, periplaque mononuclear
phagocytes exhibited ~3 times more lipid droplet accumulation than cells far from Ab plaques
(Figure 5.6 A and 5.6 B).
Because APP/PS1 Il10
KO
mononuclear phagocytes have been shown to display improved
phagocytic capacity (Guillot-Sestier et al., 2015b), we analyzed brain transcriptional changes
related to metabolism. Gene-ontology pathway analysis from RNA sequencing data of APP/PS1
Il10
KO
frontal cortex homogenate, in comparison to APP/PS1 brains, revealed alteration of
membrane-bound organelles and metabolic processes (Figure 5.6 C). Moreover, APP/PS1 Il10
KO
brains showed an upregulation of the mTOR pathway when compared to APP/PS1 (Figure 5.6 D).
PCA analysis demonstrates that the RNAseq data correctly segregates genotypes (Figure 5.6 E).
Interestingly, unsupervised clustering analysis using only 37 lipid droplet metabolism-related
genes (Table 2) was sufficient to correctly cluster the subjects into APP/PS1 and APP/PS1 Il10
KO
,
suggesting that lipid-droplet metabolism is an important feature that distinguishes these genotypes
(Figure 5.6 F).
Western blot analysis of cingulate cortex homogenate revealed that the abundace of the
lipid-droplet-coating protein PLIN2 positively correlated to AD patients disease severity, as
measured by Braak score (Figure 5.6 G). These findings are in agreement with cortical RNA
sequencing from AD patients that shows increased PLIN2 mRNA in AD compared to controls and
significant upregulation in the myeloid component of the brain (Figure 5.6 H). Taken together,
these data indicate that lipid droplet accumulation in mononuclear phagocytes of AD patients is
associated with disease progression.
Figure 5.6 Cortical periplaque mononuclear phagocytes displays accumulation of lipid
droplets.
(A) Confocal image showing lipid droplet (nile red, red) in microglia (Iba1, white) and Ab plaques
(6E10, green). Bottom images show 3D reconstruction denoting cells distal (>30um) or
proximal to Ab plaques.
(B) Quantitation of lipid droplet volume in Iba1
+
cells of wild-type (WT) and APP/PS1 cortices,
further parsed out into proximal and distal from Ab plaques (n=5-9).
(C) Top ranked pathways from gene ontogeny analysis of differentially regulated genes between
APP/PS1 and APP/PS1 Il10
KO
brain homogenates (n=5). Red denotes upregulated genes and
green denotes downregulated genes in the and APP/PS1 Il10
KO
.
(D) Ingenuity pathway analysis of differentially regulated genes of the mTOR pathway.
(E) Volcano-plot of differentially regulate genes between APP/PS1 and APP/PS1 Il10
KO
brains
(left). PCA analysis of differentially regulate genes between APP/PS1 (blue) and APP/PS1
Il10
KO
(red) brains (n=5). Description of sex and genotype of samples included in the analysis.
(F) Unsupervised clustering analysis of samples using 37 lipid droplet metabolism-related genes
(n=5).
(G) Brain PLIN2 expression assessed via immunoblot correlation to AD severity, measured by
Braak score (n=18).
(H) Brain PLIN2 RNA levels in the cortices of Alzheimer’s disease patients (AD) and non-
demented controls (NDC) (left panel), and of different purified cells from human brains (right
panel) (The Myeloid Landscape, n=11).
Graphs denote mean + SEM (B, C and I). Student’s t-test was performed in B and I; one-way
ANOVA was performed in C; and simple linear regression (H). * p< 0.05, ** p< 0.01, ****
p< 0.0001.
5.5 Discussion
Microglial ‘paralysis’’ in AD has been hypothesized to be a result of constant exposure to
increasing Ab burden with disease progression (Bennett and Liddelow, 2019; Guillot-Sestier and
Town, 2013; Guillot-Sestier et al., 2015a), however, as many changes accumulate with time, it
becomes hard to distinguish the contributing factors to innate immune tolerance in AD. Here we
demonstrate that Ab deposits contain high levels of surrounding IL-10 in later phases of disease,
consistent with previous findings in Tg2576 mice (Apelt and Schliebs, 2001). We and others have
shown that IL-10 exposure leads to tolerogenic features in mononuclear phagocytes, including
decreased phagocytic capacity, feedforwarding amyloidosis (Chakrabarty et al., 2015; Guillot-
Sestier et al., 2015b). The present study revels that IL-10 reduced Ab uptake, decreased cytokine
expression, and prevented activation NF-kb in mononuclear phagocytes. IL-10 tolerized
macrophages also showed decreased Akt activation and increased phosphorylation of AMPK, an
mTOR complex suppressor. Additionally, by analyzing spatial location of Iba1 cells, and thus the
degree of exposure to Ab and IL-10, we demonstrated decreased HIF1a and increased pAMPK
accumulation in periplaque mononuclear phagocytes.
Many reports have established that Akt-mTOR-HIF-1α pathway represents the metabolic
basis of trained immunity, which is characterized by hyperactivated immune responses toward
repetitive stimulus (Cheng et al., 2014; Netea et al., 2016, 2019). Our work suggests that cells
exposed to aggregated Ab and IL-10 display inhibition of this pathway, as well as in vivo
periplaque mononuclear phagocytes. Remarkably, diseases characterized by immune tolerance,
such as sepsis immunoparalysis, also exhibit inhibition of the Akt-mTOR-HIF1a axis (Cheng et
al., 2016). In fact, treatment of monocytes of septic patients with rapamycin, to inhibit mTOR, or
metformin, to activate AMPK, worsens innate immune dysfunction, whereas treatment with IFNg
shifts the metabolic landscape toward trained immunity and improves prognosis (Cheng et al.,
2014). Likewise, recent findings indicate mTOR-dependent metabolic reprogramming toward
glycolysis is central for microglial activation and Ab clearance (Baik et al., 2019; Son et al., 2016).
Exposure to oligomeric Ab over several days induces a tolerant phenotype in microglia,
characterized by suppression of phagocytosis and the Akt-mTOR-HIF1a pathway (Baik et al.,
2019). Consistent with our results, IL-10 has been shown to prevent LPS-induced activation of
mTOR (Ip et al., 2017b). Thus, our work supports the view that AD is marked by immune tolerance
via suppression of the Akt-mTOR-HIF1a pathway, specifically in the microenvironment around
plaques, which may be facilitated by IL-10.
Recently, attention has been given to the intracellular metabolic landscape associated to
changes in the macrophage’s environment that can lead to functional alterations (Viola et al.,
2019). For instance, 5XFAD Trem2-deficient mice, that display increased amyloid deposits,
improved microglial clustering around Ab plaques and mitigated periplaque dystrophic neurites
after restoration of energy through cyclocreatine supplementation by enhancing Akt-mTOR-
HIF1a activation (Ulland et al., 2017). This is consistent with finding that tolerant mononuclear
phagocytes are characterized by downregulation of all major metabolic pathways, which impact
energetically expensive immunological behaviors, such as phagocytosis (Cheng et al., 2016).
Similarly, treatment of tolerant AD microglia with IFNg induced the Akt-mTOR-HIF1a
activation, promoting fast ATP-generating aerobic glycolysis, increasing phagocytosis and
microglial recruitment to sites of injury (Baik et al., 2019). Nevertheless, others have shown that
promoting oxidative phosphorylation can also restore Ab phagocytic capacity in myeloid cells
(Rubio-Araiz et al., 2018), suggesting that, in fact, innate immune tolerance presupposes a
maladapted metabolic intermediate phenotype and manipulation of different pathways can render
similar phenotypical effects. For instance, in an model of LPS-induced trained immunity and
tolerance, APP23 mice displayed aggravation of amyloidosis upon induction of trained immunity
before the onset of Ab deposition, and alleviation under tolerance, potentially due to increased Ab
phagocytosis (Wendeln et al., 2018).
Importantly, this work uncovered an underappreciated pathway involving lipid
metabolism. Our model reveals that exposure to IL-10 shifts the glycerolipid metabolism of
mononuclear phagocytes from phospholipids to neutral lipids. Further, repurposing of lipid
droplets toward glycerophospholipid biosynthesis improves Ab phagocytosis. This is consistent
with findings showing that fatty acid synthesis is important for macrophage activation (Ecker et
al., 2010; Lee et al., 2018; Rubio et al., 2018; Werb and Cohn, 1972) and that supplementation
with glycerophospholipid increases phagocytic capacity of macrophages in vitro (Rubio et al.,
2018).
The glycerol phosphate pathway is an important mechanism for controlling glycerolipid
levels in cells and is modulated by stress (Jarc and Petan, 2019; Liu et al., 2015; Marschallinger et
al., 2020; Yang et al., 2019b). A recent report shows an inverse association between neutral lipids
and phospholipids levels in neurons: upon injury, cells upregulate neutral lipid synthesis and
downregulate phospholipids. By rewiring lipid metabolism via degradation of lipid-droplets, the
authors showed increased phospholipid content and axon regeneration (Yang et al., 2019b).
Strikingly, we also demonstrated that GW1929, a PPARg activator, induces lipid droplet
degradation in tolerant rendered mononuclear phagocytes resulting in increased phospholipid
content and rescuing phagocytic functions. These findings contributes to the growing literature
showing that preventing lipid droplets formation or promoting lipid droplets degradation
ameliorate innate immune performance (Liu et al., 2015; Marschallinger et al., 2020).
Notably, we show AD severity correlates with the content of lipid-droplet coating protein,
PLIN2, and that AD patients microglia show increased levels of lipid-droplets, consistent with
Alois Alzheimer early observations of glia with “adipose saccules” (Alzheimer, 1907; translated
by Stelzmann et al., 1995). We also demonstrate that Ab and IL-10 exposure is sufficient to induce
lipid droplet accumulation in mononuclear phagocytes, a feature also observed in sepsis-induced
immunoparalysis (Bozza et al., 2009; Rosas-Ballina et al., 2015) and some tumor-associated
macrophages (Wu et al., 2019). Likewise, lipid-droplet accumulation has been observed in aging
microglia, which are inflammatory in nature and display defective phagocytosis (Marschallinger
et al., 2020). Aging lipid-droplet-accumulating microglia (LAM) are dysfunctional
(Marschallinger et al., 2020); with aging being the main risk factor for AD and many AD risk
genes being involved in lipid processing (El Gaamouch et al., 2016; Karch and Goate, 2015), LAM
could be related to AD pathogenesis. Collectively, our findings suggest that repurposing lipid-
droplets may restore immunological function necessary to overcome neuroinflammatory states
(Figure 5.7).
Inflammatory reactions ought to be controlled to protect the host against excessive
inflammation via modulation of certain cellular metabolic processes (Baik et al., 2019; Cheng et
al., 2016; Ip et al., 2017a; Johnson et al., 2016; Min et al., 2016). However, in AD, dampening
innate immune activation, instead, decreases clearance of toxic aggregates (Chakrabarty et al.,
2012, 2015; Meyer et al., 2019; Thal et al., 2005). Thus, studies that aim to promote innate immune
activation has shown to mitigate AD pathology (Baik et al., 2019; Chakrabarty et al., 2010b, 2010a,
2011; Guillot-Sestier et al., 2015b; Town et al., 2008). Our study support the view that the brain
immune system should not be silenced, but rebalanced.
IL-10 is a pleiotropic cytokine involved in tissue restoration and homeostasis (Couper et
al., 2008). IL-10’s effects on myeloid cells are protective in models of IBD/LPS (Ip et al., 2017a)
and amyotrophic lateral sclerosis (Ayers et al., 2015), whereas harmful in models of Parkinson’s
disease (Chakrabarty et al., 2020) and AD (Chakrabarty et al., 2015; Guillot-Sestier et al., 2015b).
Hence, IL-10 global suppression could elicit too many side-effects and leave older patients
vulnerable (Couper et al., 2008). Instead, targeting metabolic features induced by IL-10 may reveal
a more realistic therapeutic route to break immune tolerance. Importantly, we show that lipid
immunometabolism in mononuclear phagocytes critically determine clearance functions and
provide an impetus for discovering a new class of AD immunotherapeutics.
Figure 5.7 Working model of IL-10-induced
tolerance in periplaque mononuclear
phagocytes. Innate immune cells around
plaques are exposed to an environment with
high levels of interleukin-10 (IL-10) and
amyloid-beta (Ab, in brown). IL-10 inhibits
the AKT-mTOR-HIF1a and NF-kB pathways
via activation of AMPK. Downstream changes
include increased expression of genes related
to lipid anabolism (SREBP1 and ACSL1) and
accumulation of lipid droplet in detriment of
phospholipid biosynthesis (blue arrows).
Repurposing lipid droplets toward
phospholipid synthesis improves Ab
phagocytosis of mononuclear phagocytes
exposed to Ab and IL-10 (red arrows).
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CHAPTER 6. INNATE IMMUNE-TARGETED THERAPY FOR ALZHEIMER’S
DISEASE
Immunotherapy is an exciting treatment option that promises to harness the body’s own
power to fight diseases. In recent decades, the knowledge of the immune system has yielded
personalized medicine and improved patient outcomes for many conditions (Luke et al., 2017).
Innate immune-targeted therapies are presently restricted to presenting a cocktail of antigens and
tolerogenic proteins to dendritic cells in autoimmune diseases (Flórez-Grau et al., 2018) or
presenting tumor-specific antigens to activate dendritic cells in cancer (Wculek et al., 2020).
Although Alzheimer’s disease (AD) possesses a strong immunological component that can
amplify pathology (Heneka et al., 2015; Wyss-Coray and Mucke, 2002), innate immunity can also
be modulated to prevent progression of the disease by reducing the release of cytotoxic molecules
and increasing clearance of toxic proteins (Guillot-Sestier and Town, 2013, 2018; Guillot-Sestier
et al., 2015a). Many studies have now shown that rebalancing innate immunity can ameliorate
pathology, synaptic health and behavior in pre-clinical models of AD (Baik et al., 2019a;
Chakrabarty et al., 2010b, 2010a; Guillot-Sestier et al., 2015b; Town et al., 2008), and they put the
immune system at the forefront battle against AD.
6.1 Immunotherapy modalities to target the innate immune compartment in AD
In 2019, there were 132 pharmacological agents in clinical trials for the treatment of AD,
of which approximately 41% were biologicals or small molecules that targeted amyloid or tau
directly (Cummings et al., 2019). Currently, antibody-based immunotherapies targeting amyloid-
beta (Ab) dominates attempts to modify the development of AD, without any successfully
finishing phase 3 yet (Cummings et al., 2019). Many trials demonstrate efficacious reduction in
amyloid levels, phosphorylated tau burden and changes in microglial states, conferring a general
downregulation of inflammation, but increased phagocytic microglia around Ab plaques (Boche
et al., 2010; Holmes et al., 2008; Nicoll et al., 2003; Zotova et al., 2013), suggesting that these
approaches are at least partially mediated by immunity (Weitz and Town, 2016). Nevertheless,
some agents presented equally harmful side-effects, such as meningoencephalitis (Fox et al., 2005)
and mild edema (Sperling et al., 2011).
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Alternatively, targeting inflammation in AD directly to mitigate harmful effects or to
promote clearance of toxic proteins is also an attractive therapeutic option. However inflammation-
targeted immunotherapy are still a minority, composing approximately 11% of the total agents
submitted to clinical trials in 2019 (Cummings et al., 2019). Two of these focused on the triggering
receptor expressed on myeloid cells 2 (TREM2) and its coreceptor CD33, which were first
implicated in AD through genome-wide studies (Lambert et al., 2013). TREM2 has been
demonstrated to be an important Ab sensor and to promote phagocytosis. Deletion of TREM2 in
animal models prevented clustering of microglia around plaques and Ab clearance (Jay et al., 2017;
Ulrich et al., 2014; Wang et al., 2015), resulting in an accumulation of dystrophic neurons (Wang
et al., 2016). Overexpression of TREM2 mitigated AD-like pathology and preserved synapses
(Jiang et al., 2014), corroborating the success of developing therapies aiming to activate TREM2.
Signal transducer and activator of transcription 3 (STAT3) is yet another molecule of
relevance to AD (Li et al., 2015). The work described in chapter 2 demonstrated that loss of brain
innate immune Stat3 signaling impacts AD-like pathology in an animal model of cerebral
amyloidosis, APP/PS1. Surprisingly, innate immune Stat3 deletion worsened amyloidosis in the
brains of females, but presented no effects on males. This is the first study, to our knowledge, to
demonstrate the importance of Stat3 to innate immunity activation and response to Ab in a sex-
specific way. Nevertheless, Stat3 deletion in the context of AD in other cell types have conveyed
different outcomes. For instance, Stat3 deletion in the CD4 T cell compartment (Im et al., 2020)
or in astrocytes (Reichenbach et al., 2019) mitigates AD-like pathology, whereas inhibition of
STAT3 in hippocampal neurons has been associated to memory impairments (Chiba et al., 2009).
We add to the field by elucidating the deleterious effects of inhibiting STAT3 in brain innate
immune cells in AD, specifically in the at risk population of women. As drug inhibitors of STAT3
are already viable because of its highly regarded interest in immune-oncology (Wong et al., 2017),
target specificity is key to prevent unwanted effects that could overshadow the total net-value of
the treatment.
Sex differences in immune responses are well known, and women generally mount a
stronger immune response than men (Klein and Flanagan, 2016). Women are also more likely to
develop AD (Gillies and McArthur, 2010; Irvine et al., 2012; Mielke, 2018; Pike, 2017). In
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chapter 3, we discuss neuroinflammation as a framework to understand sex differences in AD.
For instance, several genetic and environmental risk factors for AD increase inflammation,
including apolipoprotein E4, obesity, and air pollution, and may interact with sex steroid hormones
(Uchoa et al., 2016). The combination of the gender-associated risk, genetic and environmental
factors, as well as reduced protection from sex steroid hormones with aging, places women at a
particular vulnerable spot when facing AD. Similar sex bias has already been shown to impact the
efficacy of cancer immunotherapy (Wang et al., 2019b). Thus, during drug and clinical trials
designs, sex/gender bias deserves a special attention when delineating paradigms to develop or
validate AD immunotherapies.
Although many drugs seem promising, other variables can affect their efficiency. For
instance, the direct administration of biologics or small molecules often result in suboptimal
pharmacokinetics, vulnerability to biodegradation, and compromised targeting (Fang and Zhang,
2016). Therefore, encapsulation of immunotherapeutic agents into biocompatible nanoparticulate
carriers has become an emerging strategy for improving delivery. Such approaches can address
many challenges, including additional protection to payload and potentially elevating the target
specificity of the drugs (Fang and Zhang, 2016). The application of nanoparticle-based therapies
are vast, and brain therapies may benefit even further, since the blood-brain-barrier significantly
decreases access of drugs to the brain compartment (Dong, 2018). One of such example is the use
of inhibitors of the NF-kB pathway, that have been nanoformulated to increase penetration to the
blood-brain-barrier, which showed to ameliorate pathology in an animal model of AD (Zhou et
al., 2014). In chapter 4, we have demonstrated that pharmacologically inhibiting toll-like receptor
4 (TLR-4), which signals through the NF-kB, successfully decreased neuroinflammation in a
model of diet-induced obesity (DIO). Obesity is a well-known risk factor for AD (Whitmer et al.,
2008) and we specifically showed that overt activation of NF-kB during DIO-induced
neuroinflammation is detrimental to hippocampal neurogenesis and can be restored by
administration of a TLR-4 inhibitor. On the other hand, innate immunity benefits from NF-kB
activation to mount an effective response to Ab. We showed in appendix B that deletion of IRAK-
M, a myeloid inhibitor of the NF-kB pathway, rescues innate immune Ab phagocytosis and
ameliorates AD-like pathology in mice, conceptually suggesting that manipulation of this pathway
may be beneficial, although differently depending upon context of AD or obesity.
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Neutralizing antibody against circulating cytokines are yet another modality that have
shown promising results in many neuroinflammatory conditions (Azodi and Jacobson, 2016).
Several anti-tumor necrosis factor alpha (TNF-α) antibodies, often developed for rheumatoid
arthritis or cancer treatment, have been tested on AD rodent models. For instance, intracerebral
delivery of the anti-TNF-α antibody in APP/PS1 mice reduced Aβ load and tau phosphorylation,
and induced activation of brain immune cells (Shi et al., 2011). Additionally, peripheral
administration of anti-TNF-α resulted in improved cognitive outcomes (Detrait et al., 2014).
Likewise, targeting anti-inflammatory cytokines has recently received attention. Our laboratory
and others have shown that interleukin-10 (IL-10) type of signaling exacerbates AD-like pathology
by decreasing protein degradation and phagocytosis by brain immune cells (Chakrabarty et al.,
2015; Guillot-Sestier et al., 2015b). Although targeting the IL-10 pathway with neutralizing
antibody therapy may be a good strategy, the central role of IL-10 to tissue homeostasis still poses
a risk to safety (Couper et al., 2008).
In chapter 5, we demonstrated that IL-10 tolerizes mononuclear phagocytes by inhibiting
the AKT-mTOR-HIF1a axis near Ab plaques. The AKT-mTOR-HIF1a is central for the control
of immunometabolic settings and has been implicated in innate immune memory. Immunological
memory program of tolerance is a mechanism to limit the extension of hyperreaction and tissue
damage in the case of repeated or chronic infection, whereas the memory-dependent enhancement,
or trained immunity, is that of improving tissue surveillance and protection in situations of
weakness or frailty (Netea et al., 2016). Nevertheless, both immune tolerance and trained immunity
can be involved in the pathological states of inflammation, such as seen in sepsis, autoimmunity
and AD (Baik et al., 2019a; Gourbal et al., 2018; Netea et al., 2016, 2019; Wendeln et al., 2018).
The molecular mechanism of immune memory has been identified as metabolic
reprogramming that leads to epigenetic changes (Netea et al., 2016). In chapter 5, we have also
demonstrated that IL-10 tolerized mononuclear phagocytes undergo profound metabolic
reprograming, earmarked by significant changes in glycerolipid metabolism, with increased lipid-
droplet accumulation and reduction of phospholipid content. By targeting downstream events
rather than IL-10 itself, we successfully improved innate immune clearance. Pharmacological
induction of lipid droplets use toward generation of phospholipids generation significantly
improved Ab phagocytosis. Importantly, lipid-droplet accumulation has also been reported in
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aging (Marschallinger et al., 2020) and genetic diseases with mitochondrial defects (Liu et al.,
2015), and decrease in lipid droplets by progranulin inhibition or lipase overexpression have been
shown to be beneficial in these settings.
Immunological memory is necessary to fight persistent insults, and allocation of resources
play an important role in determining the adopted strategy (Netea et al., 2016; Wang et al., 2019a).
Many pre-clinical studies that aimed at targeting intracellular metabolism were also successful in
reprograming the phenotype of innate immune cells to clear aggregates in AD. For instance,
treatment with recombinant interferon gamma (IFN-g) improved tolerized microglial functions via
restoration of the glycolytic machinery, enhanced Ab clearance and mitigated AD-like pathology
in mice (Baik et al., 2019b), while blocking TLR2 has been shown to trigger oxidative
phosphorylation and to improve Ab phagocytosis (Rubio-Araiz et al., 2018). Additionally, dietary
supplementation with cyclocreatine, a creatine analog that can supply ATP, was sufficient to
alleviate autophagy impairments, improve microglial clustering around plaques, and decrease
periplaque neuronal dystrophy in mice in APP/PS1 mice with defective Trem2 signaling (Ulland
et al., 2017), indicating that microglia may not have enough energy available to both sustain
housekeeping and immunological functions (Wang et al., 2019a). On the other hand, we and others
have shown a deficit in metabolic substrate can be equally impactful. Phospholipids are important
for membrane reorganization during immune activation for release of cytokines and phagocytic
engulfment. We showed that rescuing phospholipid biosynthesis is important to overcome immune
tolerance in AD (refer to chapter 5). Additionally, others have shown that AD microglia display
excessive arginine catabolism, and that pharmacological intervention in this pathway reversed
memory loss and decreased amyloid deposition (Kan et al., 2015). Hence, immunometabolism-
focused approaches provide a framework for developing agents to regulate myeloid cell’s long-
term immune responses and for exploring their potential to treat a range of immune-related
diseases, including AD.
6.2 Concluding remarks
In summary, innate immune-targeted therapy comprises of a very promising category of
treatment for chronic neuroinflammatory diseases, such as AD. Although much remain to be
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uncovered regarding the molecular control of these very dynamic cells, the use of immunotherapy
drugs to modify disease progression already moves a market of approximately $ 110 billion dollars
and is predicted to grow considerably (Markets and Markets, 2017). In this thesis we described the
modulation of specific systems of the brain innate immunity, namely STAT3, TLR4/NF-kB and
IL-10/ glycerolipid metabolism, with the purpose of studying their effects on innate immunity
response to Ab and neuroinflammation, hallmarks of AD pathology. It is just a matter of time
before cumulative knowledge generated over the past 20 years regarding innate immunity is
translated into a viable drug to treat AD. Among the candidates, antibodies and small-molecules
that target cytokines, myeloid receptors, intracellular immune-mediators and metabolic enzymes
fuels hope for a cure to AD before the turn of the next decade.
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APPENDIX A. INNATE IMMUNE IL-10 RECEPTOR DELETION IN APP/PS1 MICE
Acknowledgements: Thank you Riyaz Razi for your dedication to this project. Thank you Rachel
Oseas and Alexander Vesling for helping me with this project as well. This work utilized the Meso
Sector S 600 in the Translational Research Laboratory and the LSRII in the USC Stem Cell Flow
Cytometry Facility, made possible through the University of Southern California School of
Pharmacy and Keck School of Medicine. Special thanks to Dr. Junji Watanabe, Dr. Bernadette
Masin and Dr. Jeffrey Boyd for technical support. This work was supported by the National
Institute of Health (R01 AG053982-01A1, to T.T.).
Contributions: Conceptualization, M.V.G.S., K.R.D., M.F.U. and T.T.; Methodology, M.V.G.S.,
K.R.D., M.F.U, R.O., A.W.V. and T.T; Investigation, M.F.U., R.R., R.O., A.W.V., K.R.D.;
Formal analysis, M.F.U., R.R. and A.W.V.; Funding acquisition, M.V.G.S, K.R.D and T.T.;
Resources, T.T.
Introduction
Failure in Aβ clearance−rather than overproduction−is becoming a widely-recognized
etiologic culprit for sporadic AD (Mawuenyega et al., 2010). The prototypical anti-inflammatory
cytokine gene, interleukin-10 (IL-10), is linked to increased risk of late-onset AD by recent
genome-wide association and integrative genomics studies (Li et al., 2015; Zhang et al., 2013),
and data from our laboratory confirm hyperactivated IL-10 signaling in AD brains (Guillot-Sestier
et al., 2015). Furthermore, elevated brain IL-10 signaling is pathological in the APP/PS1 mouse
model, and our laboratory has demonstrated that Il10 global deficiency activates mononuclear
phagocytes to restrict Ab deposits, preserving synaptic health and cognitive function (Guillot-
Sestier et al., 2015). Key limitations of this approach, however, include not being able to
temporally control Il10 deletion, nor pinpoint the major cellular responder’s to the elevated IL-10
signaling in AD. Thus, we aimed to conditionally and inducibly delete IL-10 receptor (Il10R) in
211
APP/PS1 innate immune cells early in disease development and evaluate the consequences of that
manipulation on behavioral performance and AD-like pathology.
Results and brief discussion
As detailed in methods (Chapter 2.3), we induced innate immune Il10Ra recombination by
injecting tamoxifen (daily for 5 days; 20 mg/mL tamoxifen) subcutaneously at 6−7.5 months of
age. These time points were chosen to represent the initiation phase of AD-like pathology in
APP/PS1 mice, when they first present with small Ab plaques and gliosis. Sex-balanced mouse
groups were behaviorally tested at 12 months of age in both hippocampus-dependent (Y maze,
novel object recognition and Barnes maze) and -independent (open-field and nesting) tasks. There
were no significant alterations in hippocampus-dependent behavioral outcomes from Cre-induced
Il10Ra recombination and deletion, even when stratified by sex (data not shown). Nevertheless,
we observed increased motility and decreased anxiety-like behavior in the Cre
+
mice (Figure A.1).
Figure A.1: Innate immune Il10r deletion impacts mouse behavior.
(A) Open field assessment of distanced traveled by twelve-month-old Csf1r-Cre Il10ra
f/f
mice.
(B) Open field assessment of number of crossings to center, an indication of anti- anxiety-like
behavior, by twelve-month-old Csf1r-Cre Il10ra
f/f
mice.
(C) Y maze assessment of number of entries, an indication of anxiety-like behavior, by twelve-
month-old Csf1r-Cre Il10ra
f/f
mice. All graphs denote mean + SEM; n = 8-17 mice/group; * p
< 0.05, ** p < 0.01, *** p < 0.001.
212
Having established that innate immune Il10Ra Cre-dependent deletion had effects on
behavior, we moved on to investigate cerebral amyloid pathology in APP/PS1 Csf1r-Cre Il10ra
f/f
mice. APP/PS1 Csf1r-Cre
+
Il10ra
f/f
mice displayed increased dense cored Ab plaques as measured
by ThioS staining in the entorhinal cortex and hippocampus (Figure A.2.A). Intriguingly though,
using a biochemical method to detect Ab species abundance in the brain, APP/PS1 Csf1r-Cre
+
Il10ra
f/f
mice had a trend toward increased Ab40 in the guanidine HCl-soluble fraction, but trended
toward reduced Ab42 species in the triton-soluble fraction (Figure A.2.B). We found no evidence
of altered transgene expression, since APP and PS1 abundance did not change significantly
between groups (Figure A. 2.C).
213
Figure A. 2: Il10r deletion alters Aβ immunoproteostasis.
(A) Representative image of Ab deposits (ThioS) in the entorhinal cortex of twelve-month-old
Csf1r-Cre
+
Il10ra
f/f
mice and quantitation of Ab deposits coverage in the(ThioS) in the
cingulate cortex (CC), entorhinal cortex (EC) and hippocampus (HC).
(B) Quantitation of guanidine-soluble and triton-soluble Ab levels detected via electrochemical
ELISA.
(C) Quantitation of amyloid-precursor protein (APP) and presenilin 1 (PS1) transgene expression
by Western blot. For all experiments, n = 6-12 mice/group; Student’s t-test was used to assess
p-values;†p < 0.1; *p < 0.05.
We moved on to measure brain innate immunity changes. Interestingly, Il10ra deficient
mice showed reduced Iba1 load (a marker of brain innate immune activation; Figure A.3.A),
corroborating the idea that immune activation is necessary to mount a response to Ab and promote
clearance. Furthermore, we quantified Ab uptake into Iba1 CD68
+
phagolysosomes as a
measurement of phagocytosis in vivo. We saw reduced Ab volume within Iba1 cells
phagolysosomes in the entorhinal cortex (Figure A.3.B). Taken together, these results indicate that
temporal Il10r deletion in innate immune cells has differing effects from global Il10 deletion
(Guillot-Sestier et al., 2015).
Our present data reveal a surprising brain innate immunity dependence on IL-10/Stat 3
signaling in order to clear brain amyloid plaques. Despite publications reporting a negative
relationship between IL-10 and innate immune Ab phagocytosis (Guillot-Sestier et al., 2015), both
innate immune Stat3 and IL-10R deletion in early stages of disease (6 months of age) were not
beneficial to overall Ab clearance. This effect suggests that one or more innate immune processes
may dependent on IL-10 at that time-point, perhaps those related to homeostatic programs; and
that IL-10/Stat 3 signaling dysfunction impacts Ab clearance—at least when targeted early on in
the disease process.
214
Figure A.3: Il10r deletion decreases markers of innate immune activation.
(A) Iba1
+
immunostaining coverage in the entorhinal cortex of twelve-month-old Csf1r-Cre
+
Il10ra
f/f
.
(B) Quantitation of Ab volume inside CD68
+
phagolysosomes of Iba1
+
cells in the entorhinal
cortex. For all experiments, n = 6-12 mice/group; Student’s t-test was used to assess p-
values;†p < 0.1; *p < 0.05.
Methods
Refer to methods in chapter 2.3
215
References
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Deficiency Rebalances Innate Immunity to Mitigate Alzheimer-Like Pathology. Neuron 85, 534–548.
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and upstream regulators in late onset Alzheimer’s disease. Sci. Rep. 5, 12393.
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216
APPENDIX B. TARGETING IRAK-M TO PROMOTE BENEFICIAL INNATE
IMMUNOMODULATION IN ALZHEIMER’S DISEASE
Acknowledgements: Thank you Alexander Vesling, Nima Shajarian and Dr. Gate for the
opportunity to collaborate in this awesome project.
*This chapter has been partially based on the publication Gate, D., Doty, K., Vesling, A., Leung,
B., Uchoa, M.F., Shajarian, N., Town, T. 2019. IRAK-M rebalances immunity in Alzheimer’s
disease. Under Review at Nature Communications. (2020)
Introduction
Neuroinflammation has recently taken center stage in Alzheimer’s disease (AD) research,
as genetic studies have strongly implicated innate immune genes as top risk factors for late-onset
AD (Lambert et al., 2013; Wyss-Coray and Rogers, 2012). A deeper understanding of not only the
signals that drive inflammation, but also the immune suppressive processes that become overly
active in AD will be key to developing future therapies.
One mechanism by which microglia interact with Aβ is through toll-like receptors (TLRs)
(Lotz et al., 2005; Reed-Geaghan et al., 2009; Song et al., 2006; Udan et al., 2008). Ligands binding
to TLRs results in dimerization of the adapter protein MyD88, and subsequent recruitment of
IRAK-4 (and later IRAK-1 and/or IRAK-2) to from myddosomes (Lin et al., 2010; Muzio et al.,
1997). Phosphorylation of the various IRAK species results in the formation of IRAK-1/TRAF6
complexes that dissociate from the receptor and activate TAK1. This leads to IκBα
phosphorylation and degradation, culminating in ‘classical NF-κB’ activation and transcription of
pro-inflammatory genes (Takeuchi and Akira, 2010). While most IRAK family members transduce
TLR signaling, the notable exception is IRAK-M, which is inhibitory (Kobayashi et al., 2002).
Specifically, IRAK-M suppresses classical NF-κB activation by binding to the MyD88/IRAK-
4/IRAK-1 myddosome and blocking downstream IRAK-1/TRAF6 signaling (Kobayashi et al.,
2002). Just recently, a novel, active role was discovered for IRAK-M whereby it redirects IRAK-
217
4 signaling toward TAK-1-independent, MAP3K3-dependent ‘alternative NF-κB’ signaling.
Activation of this pathway leads to IRAK-M mediated transcription of negative regulators of TLR
signaling (Zhou et al., 2013).
By acting as an important negative regulator of inflammation, IRAK-M can also produce
immunoparalysis (Deng et al., 2006; Kobayashi et al., 2002). This has been extensively studied in
endotoxin tolerance and sepsis, where, like in AD, there exists a misbalance between
proinflammatory and compensatory anti-inflammatory mechanisms (van ‘t Veer et al., 2007; Deng
et al., 2006; Escoll et al., 2003; Kobayashi et al., 2002). IRAK-M levels in septic patients correlate
with mortality,
and IRAK-M deficient and septic mice are more capable of clearing secondary
infections (Escoll et al., 2003). This raises the possibility that a similar phenomenon may exist in
AD, whereby IRAK-M is induced to suppress inflammation, yet this induction cripples brain
macrophages’ capacity to continually clear plaques.
Results and brief discussion
To clinically verify IRAK-M relevance in AD, we examined IRAK-M/NF-κB signaling in
post-mortem samples from AD and age-matched, non-demented control brains. Strikingly,
western blot analyses of hippocampal protein homogenates revealed significantly increased levels
of total IRAK-M in AD brains vs. controls (Figure B.1A and B.1B). IRAK-M has previously been
shown to be cleaved by caspase-6, which results in monocytic activation by neutralizing IRAK-
M’s inhibitory role (Kobayashi et al., 2011). Surprisingly, cleaved IRAK-M was also increased in
AD (Figure B.1A and B.1C) and total caspase-6 and active caspase-6 were both elevated in AD
brains vs. controls (Figure B.1D, B.1E and B.1F). Interestingly, despite increased levels of cleaved
IRAK-M in the AD brain, cleaved IRAK-M levels showed a strong, inverse correlation (r
2
= 0.87,
p<0.001) with BRAAK pathology score, a measure of disease severity (Figure B.1G).
218
Figure B.1. Elevated IRAK-M/NF-κB signaling in AD patient brains.
(A) Representative images of western blot of IRAK-M in AD vs. control hippocampal
homogenates.
(B) Quantification of total protein levels of IRAK-M relative to β-tubulin.
(C) Quantification of cleaved IRAK-M to total IRAK-M in hippocampal homogenates.
(D) Representative images of western blot of Caspase-6 in AD vs. control hippocampal
homogenates.
(E) Quantification of total Caspase-6 relative to β-actin.
(F) Quantification of cleaved Caspase-6 to total Caspase-6 in AD vs. control hippocampal
homogenates.
(G) Correlation between cleaved IRAK-M levels in AD hippocampal homogenates and BRAAK
score; p<0.001.
219
AD n = 9, control n = 4. Data in B,C,E, and F are plotted as individual points and violin-plot
showing the median. Student’s T-test was applied, p values are annotated on the panels.
Further, we investigated the modulation of specific mediators of alternative NF-κB
signaling in AD, as this pathway is induced by IRAK-M. MAP3K3 protein and mRNA levels were
significantly increased in AD vs. control brains (Figure B.2A and B.2B). Moreover, quantitative
PCR results revealed a trending increase in A20 (also known as TNF-α inhibitory protein 3) mRNA
levels in AD brains vs. controls, a gene transactivated during alternative NF-κB signaling (Figure
B.2C). Interestingly, there was no between-group difference in TAK1 mRNA levels, a marker of
classic NF-κB signaling (data not shown). Taken together, these data suggest that active IRAK-M
and downstream activation of alternative NF-κB pathway are relevant to AD.
Figure B.2. Alternative NF-κB signaling in Alzheimer’s disease.
(A) Representative images of western blot of MAP3K3 in AD vs. control hippocampal
homogenates.
(B) Quantification of total MAP3K3 relative to β-actin. AD n = 9, control n = 4. Data is shown as
individual points and violin-plot displaying the median.
(C) mRNA levels of A20 from hippocampal homonegates relative to β-actin levels. AD n = 9,
control n = 4. Data is shown as fold difference + SEM. † p<0.1
While evidence has shown Aβ signals through TLRs on activated microglia to orchestrate
the neuroinflammatory response to cerebral amyloidosis (Heneka et al., 2014; Reed-Geaghan et
al., 2009), conflicting reports utilizing genetic ablation of TLRs have clouded its role in microglial
amyloid clearance (Liu et al., 2012; Richard et al., 2008; Song et al., 2011; Tahara et al., 2006).
220
Thus, we genetically deleted Irak-m in APP/PS1 mice, a model of cerebral amyloidosis, and tested
test whether ablation of Irak-m impacted cerebral Aβ burden and plaque-associated microgliosis.
Amyloid burden was quantified in combined cortical and hippocampal regions on entire hemibrain
sections, which revealed a significant decrease in 4G8 immunoreactivity in APP/PS1-Irak-m
–/–
vs.
APP/PS1-Irak-m
+/+
animals (Figure B.3A and B.3B). To determine whether decreased plaque load
was associated with microglial Aβ clearance, we performed immunofluorescence with antibodies
for reactive microglia (Iba1), Aβ (4G8), and Lamp1. Confocal microscopy of Iba1
+
microglia in
plaque-enriched regions revealed co-localization of Lamp1 with 4G8 in APP/PS1-Irak-m
–/–
but
rarely in APP/PS1-Irak-m
+/+
mice (Figure B.3C and B.3D), indicating an increase in Aβ clearance
in IRAK-M knockout mice.
Figure B.3. Irak-m deletion in APP/PS1 mice improves Ab clearance.
221
(A) Representative images of brains of Irak-m deleted and sufficient mice immunolabeled against
the microglial marker Iba1 and amyloid using 4G8 antibody.
(B) Amyloid burden quantified via 4G8 immunoreactivity in combined cortical and hippocampal
regions on entire hemibrain sections in APP/PS1-Irak-m
–/–
(n=7) vs. APP/PS1-Irak-m
+/+
(n=5)
animals.
(C) 3D reconstruction of confocal images showing 4G8
+
Aβ encapsulated within Lamp1
+
vesicles
in APP/PS1-Irak-m
–/–
and in APP/PS1-Irak-m
+/+
microglia. Insets are rotated at 45° to
demonstrate three-dimensionality of lysosomes containing Aβ. Scale bar indicates 10 μm.
(D) Quantitation of Aβ-containing Iba1
+
cells per plaque of APP/PS1-Irak-m
–/–
vs. APP/PS1-Irak-
m
+/+
brains (n=50 3D-reconstructed plaques per group).
Graphs B and D are plotted as individual points and violin-plot showing the median. Student’s T-
test was applied, p values are annotated on the panels.
To evaluate the beneficial phenotype observed in Irak-m deficient mononuclear
phagocytes, we performed RNA sequencing on CD11b
+
cells isolated from the brains of wildtype,
APP/PS1 and APP/PS1/Irak-m
-/-
brains. Interestingly, upstream regulator analysis, which
identifies transcriptional regulators that can explain the observed gene changes, revealed many
major inflammatory regulators that were significantly elevated in the APP/PS1 brain. I.e. TNF,
NF-kB complex, LPS signaling, RELA, TLR3, P38 MAPK, etc. returned toward homeostatic
levels in APP/PS1 Irak-m deficient brain CD11b cells (Figure B.4A). A second analysis of gene
enrichment associated to canonical signaling pathways revealed an almost identical pattern of
neuroinflammatory pathways elevation in APP/PS1 CD11b
+
cells, and a return toward homeostatic
levels with the removal of Irak-m (Figure B.4B). Finally, pathway analysis revealed significant
increases in alternative NF-κB pathway in APP/PS1 mice (Figure B.4C) that are absent in Irak-m
deficient APP/PS1 mice (Figure B.4D). Alternative NF-κB signaling was first defined by Zhou et
al., in 2013 as being critically dependent on and the IRAK-4/IRAK-M interaction, leading to
subsequent activation of MEKK3 and ultimately the transcription of four inhibitors of classical
NF-κB signaling (IκBα, A20, SOCS1 and SHIP1). Here we provide the first in vivo genetic
evidence that deletion of Irak-m reduces alternative NF-κB signaling in CD11b
+
brain cells (Figure
B.4E).
222
In conclusion, these data highlight the relevance of IRAK-M and IRAK-M-mediated
alternative NF-κB pathway in restraining clearance by AD macrophages. Furthermore, we show
that IRAK-M deficiency remediates pathology associated to cerebral amyloidosis. Taken together,
these data point to targeting cleavage of IRAK-M, or its knockdown, as a therapeutic option in the
treatment of AD.
Figure B.4. Irak-m deficiency modifies macrophage NF-kB signaling.
(A) Comparison analysis between differentially regulated genes in CD11b
+
brain cell from
APP/PS1 (A/P) vs wildtype (WT) and APP/PS1 Irak-m
-/-
(A/P/M-KO) vs APP/PS1 brains
after applying upstream regulators analysis. The table displays the rank of significant upstream
regulators by z-score, in which orange signifies increase in expression and blue signifies a
decrease in expression.
(B) Comparison analysis of the denoted groups after analyzing for enriched gene-sets in canonical
pathway.
(C) Diagrammatic representation of increased alternative NF-kB signaling in APP/PS1 vs. WT
CD11b
+
cells. Relative expression of annotated genes of APP/PS1 vs wildtype is overlaid in
the diagram, in which red denotes activation and green denotes suppression.
(D) Diagrammatic representation of suppression of alternative NF-kB signaling in APP/PS1 Irak-
m
-/-
(A/P/M-KO) vs APP/PS1 CD11b
+
cells.
223
(E) Chart showing relative expression of the four IRAK-M-induced alternative NF-kB genes,
normalized to wild-type mice. Black asterisk indicate statistical comparison to wild-type
levels, while red asterisk indicate statistical comparison to APP/PS1 levels. * p < 0.05, ** p <
0.01, *** p < 0.001, **** p < 0.0001.
Methods
Human Brain Samples
Frozen human hippocampal brain tissue used for western blotting was obtained from the
University of Southern California Alzheimer’s Disease Research Center (ADRC, NIA AG05142)
Neuropathology Core (ten AD patient samples, 51–100 years old; average post-mortem
delay=6.38 hours, and six control hippocampal samples, 74–93 years old; average post-mortem
delay=6.33 hours).
Animals
APP/PS1 transgenic mice (B6.Cg-Tg(APPswe,PSEN1dE9) (Jankowsky et al., 2003) were bred
with Irak-m knockout mice (Kobayashi et al., 2002). Both mouse strains are on the C57BL/6
background and were obtained from the Jackson Laboratory. All mice were housed under standard
conditions with free access to food and water. All animal experiments were approved by the
University of Southern California Institutional Animal Care and Use Committee and performed in
strict accordance with National Institutes of Health guidelines and recommendations from the
Association for Assessment and Accreditation of Laboratory Animal Care International.
Tissue Handling
Mice were perfused with ice-cold PBS and brains were extracted and quartered. The anterior two
quarters were snap-frozen and posterior quarters were fixed in 4% paraformaldehyde overnight for
subsequent agarose or paraffin embedding.
Immunohistochemistry, imaging and antibodies
For immunohistochemistry, brain sections were incubated in protein block [PBS containing 10%
fetal bovine serum (FBS) and 0.3% Triton X-100] for 1 h at room temperature. Antibodies to the
224
following proteins were variously used: goat anti-Iba1 (1:500; Wako), mouse anti-human 4G8
(1:500; BioLegend), rabbit anti-Lamp1 (Cell Signaling). Antibodies were diluted in protein block
(10% normal donkey serum with 0.01% Triton-X) and incubated overnight at 4 °C. After three
rinses for 5 min each in PBS, samples were incubated for 1 h at 25 °C with appropriate Alexa
Fluor-conjugated secondary antibodies. After an additional three rinses for 5 min each with PBS
at 25 °C, samples were air-dried in the dark and mounted with Prolong Gold media containing
DAPI (Invitrogen-Molecular Probes). Brain regions were imaged at 10×, 40× and 63× in separate
channels with a Nikon A1R confocal microscope mounted onto a Ti-E inverted system with
immersion Apochromat objectives. Three to six images per brain section were quantified for each
mouse for each brain region.
Western blotting
The samples were prepared and then heated to 95-100°C for 5 min to be loaded on an SDS-PAGE
gel. IRAK-M (Cell Signaling, 1:1000), Caspase-6 (Cell Signaling; 1:1000), MAP3K3 (Cell
Signaling; 1:1000), Tubulin (Abcam, 1:1000), Actin (Abcam; 1:1000) and rabbit or mouse anti-
IgG (Invitrogen; 1:3000) were then analyzed by Western blotting using 5% dried milk for primary
incubation and appropriate secondaries conjugated to horseradish peroxidase.
RNA isolation and sequencing
RNA was extracted with Trizol (Life Technologies), from cell pellets sorted using CD11b MACS
microbeads (Miltenyi Biotec) in combination with an OctoMACS Separator according to the
manufacturer’s instructions. The extracted mRNA was dissolved in water and cleaned using the
RNAeasy Mini Kit (Qiagen), then reverse-transcribed using the SuperScript III first strand
synthesis system (Life Technologies). RNA was sequenced by the UCLA genomic core. Reads
were aligned to the mouse mm10 reference genome and analyzed using the Partek Genomic Suite
and Ingenuity Pathway analysis.
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APPENDIX C. IL-10 INDUCED MITOCHONDRIAL ALTERATIONS
Acknowledgements: Special thanks to Anakha Ajayan, Chris Im, Dr. Balint Der, Alexander
Vesling, and Dr. Brian Leung for supporting me during my investigations.
Introduction
Accumulation of dysfunctional mitochondria is a hallmark of various neurodegenerative
diseases including Alzheimer's disease (AD) (Eckert et al., 2011; Gasparini et al., 1997; Swerdlow
and Khan, 2004). For instance, the toxic AD peptide amyloid-b (Ab) causes mitochondrial fission,
which is implicated in the disruption of the electron-transport chain (Baik et al., 2019a; Katoh et
al., 2017; Mills et al., 2017), and Ab ramps up production of reactive oxygen species (ROS) by
the mitochondria, which induces neuroinflammation and decreases cell viability (Parajuli et al.,
2013; Schilling and Eder, 2011). Moreover, treatment of microglia with monomeric, oligomeric
or fibrillar Ab all decrease oxidative phosphorylation (Baik et al., 2019a). The cell is equipped to
recycle dysfunctional mitochondria via mitophagy, and promotion of mitophagy has been shown
to mitigate neuroinflammation in the context of AD (Lautrup et al., 2019). However, the
autophagic machinery is often broken in AD, further potentiating mitochondrial defects (Uddin et
al., 2018).
Interleukin-10 (IL-10) has been associated to innate immune dysfunction in AD
(Chakrabarty et al., 2015; Guillot-Sestier et al., 2015). In macrophages, IL-10 prevents the
metabolic program switch induced by inflammatory stimuli. Specifically, IL-10 inhibits
lipopolysaccharide-induced glucose uptake and glycolysis and promotes oxidative
phosphorylation (Ip et al., 2017). Furthermore, IL-10 promotes mitophagy of LPS-induced
dysfunctional mitochondria (Ip et al., 2017). With new discoveries that the metabolic fate of
macrophages dictates their immunological phenotype (O’Neill et al., 2016), we investigated
228
whether IL-10 affects macrophage’s bioenergetics state, ultimately influencing their behavior in
response to Ab.
Results and brief discussion
In order to determine IL-10’s bioenergetic effects on Ab phagocytosis by immune tolerant
macrophages, we assessed the bioenergetic shift induced by IL-10 in macrophages exposed to Ab
in vitro via SeaHorse
TM
analysis. This technique makes use of sensors to measure the oxygen
consumption rate of cells and inhibitors of specific points of the mitochondrial electron transport
chain to interrogate changes in mitochondrial respiration. Analysis of oxygen consumption rate
(OCR) indicated that IL-10 opposed Ab treatment by reducing basal respiration and
mitochondrial-derived ATP production (Figure C.1). Importantly, this decrease did not seem to be
a result of mitochondrial malfunctioning, since proton leak was not significantly increased (Figure
229
Figure C.1. Ab and IL-10 effects on oxidative phosphorylation.
(A) Representative bioenergetic graph of macrophages treated with vehicle, Ab+Ab or Ab/IL-10
+ Ab/IL-10 and specific mitochondrial inhibitors (see methods in chapter 5).
(B) Quantification of average basal respiration (first 20 min), ATP production (difference between
OCR from min 20-40 to basal), and proton leak (20-40min) compared to vehicle-treated
condition (n=6). Data are plotted mean + SEM. Student’s T-test was applied, * p<0.05, † p<0.1.
C.1D). Surprisingly, these data do not recapitulate the metabolic shift toward glycolysis that others
have previously reported (Baik et al., 2019b; Rubio-Araiz et al., 2018). Points to consider may be
that our treatment used of a lower concentration of Ab (1µg/ml) than Baik and colleagues (~4µg/ml
Ab) or Rubio-Araiz and colleagues (100ng/ml LPS +10µg/ml Ab), and that others have shown
that some Ab properties have can display antagonistic responses dependent on concentration
(Brothers et al., 2018).
Recent evidence, suggest that glycolysis is metabolically inefficient for long-term
processes, producing only 2 molecules of ATP per glucose, compared with oxidative metabolism
which yields ~ 30 molecules of ATP. Therefore, a persistent failure in engaging in oxidative
phosphorylation could have a detrimental effect on metabolically expensive processes like
phagocytosis (Rubio-Araiz et al., 2018). This hypothesis explains the reduced Ab phagocytosis
experienced by macrophages exposed to Ab+IL-10 (see chapter 5, figure 5.2)(Guillot-Sestier et
al., 2015). Nevertheless, others have shown that promoting aerobic glycolysis can also restore
phagocytic function of myeloid cells (Baik et al., 2019a), suggesting that, in fact, innate immune
tolerance presupposes a maladapted intermediate phenotype (Cheng et al., 2016).
The electric potential on the mitochondrial membrane (Dy
m
) is a critical element in the
mechanism of disposal of dysfunctional mitochondria (Zorova et al., 2018). The hydrogen content
in the intermembrane space generally determines the mitochondrial polarization, thereby being
significant factor in the energy transformation during oxidative phosphorylation (Zorova et al.,
2018). However, the Dy
m
gradient performs other functions not related to energy production, such
as regulating heat generation (Liesa and Shirihai, 2013), mitochondrial dynamics (Westermann,
2012), production of ROS (Suski et al., 2018), and aminoacid import (Huang et al., 2002). Thus,
230
we examined the Dy
m
of macrophages under Ab, IL-10 or Ab+IL-10 conditions, 1h after the 2
nd
treatment (see treatment paradigm in chapter 5). We found that treatment with Ab or Ab +IL-10
induces mitochondria depolarization (Figure C.2A and C.2B). Interestingly, this effect seem to be
reversible, as washing off the treatment resulted in restoration of the Dy
m
to basal levels (Figure
C.2B). Additionally, we interrogated whether the depolarization could be associated with an
increased production in ROS by indirectly measuring the dye CellROX
TM
green, which
significantly increases its fluorescent intensity upon oxidation. Interestingly, the group of
macrophages treated with Ab + IL-10 showed trending increase in ROS production (Figure C.2C).
Figure C.2. Ab and IL-10 effects on mitochondrial health.
(A) Representative images peripheral macrophages under different treatment conditions (annotated
on the left) and marked with Mitotracker Red CMXRo for the mitochondrial membrane
potential (Dy
m
), lectin-488 for macrophage membrane, and DAPI for nuclei.
(B) Quantification the mean fluorescent intensity (MFI) of mitotracker red in CD45
+
CD11b
+
cells
via flow cytometry (n=4).
(C) Quantitation of the MFI of the CellROX
TM
green dye for ROS analysis (n=4). One-way
ANOVA was used for statistical analysis. † p<0.1, * p<0.05, ** p<0.01.
231
Because both ROS production and mitochondrial membrane potential can reflect loss of
cell viability, and Ab is known to induce cell toxicity (Lambert et al., 1998), we repeated the
experiment using 10 times less Ab. Strikingly, 0.1µg/ml of Ab during the 2
nd
stimulus equivalently
depolarized the mitochondria of macrophages (Figure C.3A). Importantly, this treatment paradigm
also resulted in decreased pro-inflammatory cytokine IL-6 expression (Figure C.3B), reduced AKT
phosphorylation (Figure C.3C) and increased AMPK phosphorylation (Figure C.3D) upon IL-10
treatment (similarly to results from figure 5.2), consistent with a tolerant phenotype.
Figure C.3. Low Ab concentration still affects mitochondrial membrane potential.
(A) Representative images peripheral macrophages under different treatment conditions and
marked with Mitotracker Red for the Dy
m
, CD11b for macrophage membrane, and DAPI for
nuclei. Quantification the mean fluorescent intensity (MFI) of mitotracker red in
CD45
+
CD11b
+
cells via flow cytometry (n=6).
(B) Quantification of mRNA levels of IL-6 relative to β-actin in macrophages 3h after the last
treatment (n=6)
232
(C) Quantitation of protein levels of phosphorylated AKT relative to total cellular AKT under
different conditions. Cells were collected 30 minutes after second stimulus (n=4).
(D) Quantitation of protein levels of phosphorylated AMPK relative to total cellular AMPK under
different conditions. Cells were collected 30 minutes after second stimulus (n=4). Data are
plotted as mean + SEM. One-way ANOVA was applied, † p<0.1, * p<0.05, ** p<0.01.
Finally, we have shown in chapter 5 that the PPARg activator GW1929 significantly
improved the ability of mononuclear phagocytes to uptake fluorescently-labeled aggregated Ab
only in mononuclear phagocytes exposed to the double treatment of Ab+ IL-10. Since changes in
oxidative phosphorylation status can influence phagocytosis as well, we used SeaHorse
TM
analysis
(Figure C.4) and demonstrated that GW1929 increases basal respiration by inducing proton leak
(Figure C.4B), suggesting that this drug does not act by improving oxidative phosphorylation.
Altogether, these data suggest that low concentrations of Ab and IL-10 cause modest and
reversible effects to the mitochondria. The fact that Ab and IL-10 can still exert effects on the
metabolism of membranous cellular components of mononuclear phagocytes without considerably
impacting the mitochondria deserves to be further investigated, as little attention has been brought
to alternative pathways of metabolic control of the cellular machinery that does not significantly
involve the mitochondria.
233
Figure C4. GW1929 effects on mitochondria.
(A) Representative bioenergetic graph of macrophages treated with vehicle, vehicle or Ab/IL-10
+/- GW1929, and specific mitochondrial inhibitors (see details in methods, chapter 5).
(B) Quantitation of average basal respiration, ATP production, proton leak and maximal
respiration compared to vehicle condition (n=6). Data are plotted mean + SEM. Two-way
ANOVA was applied. † p<0.1, * p<0.05, ** p<0.01.
234
Methods
General methods are described in chapter 5
Seahorse
TM
analysis
The Seahorse Extracellular Flux (XFe96) Analyser (SeaHorse Bioscience, USA) was used to carry
out bioenergetic analysis of cells. Real-time oxygen consumption rate (OCR) was measured
according to the manufacturer’s instructions, with minor modifications. Briefly, 70,000 cells/well
were plated on the XFe96 cell culture microplate coated with poly-L-lysisne (5µg/ml). Cells rested
for 24h before being treated for the first time in Opti-MEM media (Gibco). Following incubation,
cells were washed with the assay medium for the mitochondrial stress test according to the
manufacturer’s instructions, and the plate was incubated in a CO
2
-free incubator (37 °C, 1 h). Cells
were treated for the second time using Seahorse assay media and plates were run 1h later.
Oligomycin (15 μM), carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (5 μM; FCCP) and
Rotenone+Antimycin A (5 μM) were loaded into the appropriate ports for sequential delivery
(Agillent). Following calibration of Seahorse Analyzer, oxygen consumption rate (OCR) were
measured every 6-7 min for 73 min and the appropriate compounds were injected sequentially at
18 min intervals. OCR was automatically calculated using the Seahorse XFe96 software and 8
replicates were assessed for each separate sample. The cartridge plate was hydrated with XF
calibrant buffer and incubated overnight (37
o
C, CO
2
-free); the assay medium (XF base medium
containing 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose) was prepared immediately
before assay.
Mitochondrial-membrane potential and reactive oxygen species analysis
Cells were seeded into 12-well, 24 or 96 well-plates and treated for 24h in Opti-MEM. Then, cells
were washed with PBS for 30 min and treated for a second time for 1h. During the last 30 min,
cells received Mitotracker Red CXMRos (final well concentration= 250nM). Mononuclear
phagocytes were fixed and stained for CD45- Pacific Blue and CD11b-BV650 for quantitation
using Guava Flow Cytometer, or cells were immunolabeled for CD11b and Alexa-488 or Lectin-
488 for microscopy analysis. For microscopy analysis, MFI was determined using Fiji Image J of
20X pictures.
235
Similarly, for ROS analysis, cells were treated with CellROX
TM
green during the last 30 min of
the second treatment (final well concentration= 5µM). Mononuclear phagocytes were fixed and
stained for CD45- Pacific Blue and CD11b-BV650 for quantitation using Guava Flow Cytometer.
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Abstract (if available)
Abstract
Alzheimer's disease (AD) is an age-related neurodegenerative disorder for which there are no effective strategies to prevent or slow its progression. The most prominent hallmark is the chronic accumulation of cerebral amyloid-beta, which is hypothesized to be the etiological factor that cascade the other pathophysiological features of AD. Failure to clear toxic amyloid-beta is now recognized as an early event in AD evolution. Innate immune cells are able to clear amyloid-beta via phagocytosis, however, these cells can enter a tolerant state that endorses amyloid-beta accumulation and AD progression. After chronic exposure to amyloid-beta, microglia display reduced phagocytic capacity and upregulate their release of cytokines, creating an inflammatory and cytotoxic milieu. Thus, controlling inflammation while preserving innate immune clearing functions is a desirable outcome for AD treatment. This body of work investigates the innate immune response to amyloid-beta by manipulating AD-relevant pathways (STAT3, IL-10 and TLR4/IRAK-M) and discussing their significance to amyloid clearance, neuroinflammation and AD.
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Asset Metadata
Creator
Figueiredo Uchoa, Mariana (author)
Core Title
Innate immune response to amyloid-beta: relevance to Alzheirmer’s disease and neuroinflammation
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Neuroscience
Publication Date
06/24/2020
Defense Date
08/11/2020
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
IL-10,immunometabolism,IRAK-M,lipid,microglia,OAI-PMH Harvest,obesity,sex differences,Stat3,TLR4
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Jakowec, Michael (
committee chair
), Chang, Karen (
committee member
), Chen, Jeannie (
committee member
), Eoh, Hyungjin (
committee member
)
Creator Email
mfigueir@usc.edu,uchoa.marianaf@gmail.com
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c89-322132
Unique identifier
UC11663789
Identifier
etd-Figueiredo-8616.pdf (filename),usctheses-c89-322132 (legacy record id)
Legacy Identifier
etd-Figueiredo-8616.pdf
Dmrecord
322132
Document Type
Dissertation
Rights
Figueiredo Uchoa, Mariana
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
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The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...
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Repository Location
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Tags
IL-10
immunometabolism
IRAK-M
lipid
microglia
obesity
sex differences
Stat3
TLR4