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The role of inflammation in mediating effects of obesity on Alzheimer's disease
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The role of inflammation in mediating effects of obesity on Alzheimer's disease
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The role of inflammation in mediating effects of obesity on Alzheimer’s disease
V. Alexandra Moser
May 2018
A dissertation presented to the Faculty of the Graduate School of the University of Southern
California in partial fulfillment of the requirements for the degree of Doctor of Philosophy in
Neuroscience
2
Dedication
To my parents, who have always put my education first, and whose support is truly invaluable.
And to Dr. Judith Grisel, who believed that I would get a PhD in neuroscience long before I did.
3
Acknowledgements
Most importantly, I would like to thank my advisor, Dr. Christian Pike. Beginning with my
interview at USC, you went above and beyond in mentoring me. I required quite a bit of training,
given the fact that I started graduate school without knowing how to pipet. You were patient and
understanding, and devoted hours upon hours to helping me. In addition to training me in all
things science, you always took the time to talk to me and give me advice on life in general. I
am honored to have learned from you and will forever be grateful.
Thank you to current and former members of the Pike lab, including Dr. Anusha Jayaraman and
Dr. Joowon Lee, who trained me during my first year of graduate school, as well as Mariana
Uchoa and Jiahui Liu, who helped me with various aspects of the research presented here. I
would especially like to thank Dr. Amy Christensen. Not only did you teach me more things than
I could possibly list here, but you, perhaps more than anyone else, were there through both the
good and the bad of graduate school (especially given that for quite some time, you and I made
up the entirety of the Pike lab).
I would like to thank my parents, who have supported me every step of the way. You have
always pushed me to be better, beginning of course, with Dad teaching me to color inside the
lines as a child. Most importantly, knowing that I would have greater opportunities in the
American educational system, you sacrificed going back your friends and family in Germany.
For that, I am more grateful than I could ever express. And of course, thank you to Josh
Bigelow. Though I met you halfway through graduate school, your support during the last three
years helped me get through a lot of bad lab days.
4
I am especially grateful to my undergraduate mentor, Dr. Judith Grisel. I would never have
applied to graduate school if it weren’t for you. You, more than anyone, made me believe in
myself and taught me to voice my opinion. When I was about to settle for the familiar, you
pushed me to move across the country, which has turned out to be one of the best decisions of
my life. Thank you for everything you’ve done for me over the years.
I would also like to thank Dr. Tuck Finch as well as members of the Finch lab, Dr. Mafalda
Cacciottolo and Dr. Todd Morgan, for their input and assistance on various collaborative
projects.
Finally, I would like to thank the members of my dissertation committee, Dr. Alan Watts, Dr.
Scott Kanoski, and Dr. Pat Levitt, for their time and guidance throughout graduate school.
5
Table of Contents
List of Tables 6
List of Figures 7
Abstract 9
Chapter One: Introduction 10
1. Alzheimer’s disease pathology and risk factors 11
2. Obesity/metabolic syndrome as risk factors for AD 12
3. Possible mechanisms underlying obesity and Alzheimer’s disease interactions 17
4. Dissertation objectives and experimental paradigms 31
Chapter Two: Obesity accelerates Alzheimer-related outcomes in APOE4 but not APOE3
mice 34
Abstract 35
Introduction 36
Methods and materials 37
Results 43
Discussion 58
Chapter Three: Effects of high fat diet and testosterone across the lifespan of the male
brown Norway rat 63
Abstract 64
Introduction 65
Methods and materials 67
Results 75
Discussion 100
Chapter Four: The TLR4 antagonist TAK-242 blocks the adverse neural, but not
metabolic, effects of diet-induced obesity 106
Abstract 107
Introduction 108
Methods and materials 110
Results 118
Discussion 134
Chapter Five: Conclusions and future directions 139
Summary of findings 140
Dietary and obesity considerations 142
Mechanisms underlying obesity and increased Alzheimer’s disease risk 146
Future directions 158
References 163
6
List of Tables
1. TLR4 expression across species 27
2. Primer sequences 42
3. Relative gene expression in hippocampus 57
4. Primer sequences 73
5. Correlations among metabolic, inflammatory, and behavioral outcomes 98
6. Primer sequences 117
7
List of Figures
1. Interactions between various risk factors for AD 33
2. Metabolic outcomes associated with diet-induced obesity in E3FAD and E4FAD mice 46
3. Accumulation of amyloidogenic deposits assessed by thioflavin-S staining in 49
E3FAD and E4FAD mice across dietary treatments
4. Accumulation of b-amyloid deposits assessed by immunohistochemistry in 50
E3FAD and E4FAD mice across dietary treatments
5. Microglia number and morphological status assessed by IBA-1 54
immunohistochemistry in E3FAD and E4FAD mice across dietary treatments
6. Astrocyte number and morphological status assessed by GFAP 55
immunohistochemistry in E3FAD and E4FAD mice across dietary treatments
7. Body weight and adiposity outcomes associated with high fat diet in young, 77
middle-aged, and aged brown Norway rats
8. Glucose homeostasis as assessed by glucose tolerance testing in young, 80
middle-aged, and aged brown Norway rats on control or high fat diet
9. Peripheral effects of control or high fat diet in young, middle-aged, and aged 84
brown Norway rats
10. Microglia number and morphological status as assessed by IBA-1 87
immunohistochemistry in young, middle-aged, and aged brown Norway rats
across dietary and testosterone treatments
11. Astrocyte number and morphological status as assessed by GFAP 88
immunohistochemistry in young, middle-aged, and aged brown Norway rats across
diet and testosterone treatments
12. Inflammatory gene expression in young, middle-aged, and aged brown Norway rats 91
across diet and testosterone treatments
13. Effects of diet and testosterone on behavioral performance in the Barnes maze 95
and on neurogenesis in young, middle-aged, and aged brown Norway rats
14. Metabolic outcomes associated with diet-induced obesity in mice treated with 120
vehicle or the TLR4 antagonist, TAK-242
15. Adipose tissue inflammatory gene expression in mice fed control or high fat diets 122
and treated with vehicle or the TLR4 antagonist, TAK-242
16. Microglial number, morphological status, and soma size as assessed 124
by IBA-1 immunohistochemistry in control- and high fat diet-fed mice treated
with vehicle or the TLR4 antagonist, TAK-242
8
17. Hippocampal gene expression of inflammation-related factors and amyloid-b 127
factors in mice fed a control or high fat diet and treated with vehicle or the
TLR4 antagonist, TAK-242
18. Neurogenesis and cell proliferation as assessed by DCX and BrdU 129
immunohistochemistry in mice maintained on control or high fat diets and treated
with vehicle or the TLR4 antagonist, TAK-242
19. Exploration, anxiety-like, and depressive-like behaviors in control- and 131
high fat diet-fed mice treated with vehicle or the TLR4 antagonist, TAK-242
20. Working memory and cued and contextual memory performance in mice 133
maintained on control or high fat diet and given vehicle or the TLR4
antagonist, TAK-242
9
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, for which no treatments
are currently available, and the causes of which are only beginning to be understood. A number
of both genetic and environmental/lifestyle risk factors for AD have been identified, including the
E4 allele of the cholesterol transporter apolipoprotein E, obesity, and aging. Yet, how these
factors may be interacting with each other to drive disease is not well understood, and is rarely
addressed in experimental research. Thus, the first goal of my dissertation work was to examine
potential interactive effects between risk factors for AD. Chapter 1 provides a comprehensive
introduction to many of the topics that are relevant to my dissertation, including the role of
various risk factors in AD, and evidence that suggests possible interactions between them.
Chapter 2 describes a novel gene-environment interaction between apolipoprotein E4 and
obesity, wherein high fat diet increases AD-like pathology specifically in mice with human
apolipoprotein E4. In Chapter 3 I examined interactions between obesity and aging, by
evaluating the effects of high fat diet in young, middle-aged, and aged brown Norway rats.
Additionally, I examined the potential of testosterone in protecting against effects of obesity.
Results from both of these studies suggest that obesity may be driving adverse effects in brain
through inflammatory pathways. Thus, the second goal of my dissertation was to evaluate the
extent to which high fat diets exert their effect on neural health by signaling through the pro-
inflammatory toll-like receptor 4 pathway. This work is described in Chapter 4, and shows that
administering a toll-like receptor 4 antagonist protects against the adverse effects of obesity in
brain, without altering metabolic outcomes. Finally, Chapter 5 summarizes my research findings
and evaluates my work in the context of current knowledge in the fields of obesity, inflammation,
and Alzheimer’s disease.
10
Chapter One
Introduction
Adapted from: Moser VA, Pike CJ (2016). Obesity and sex interact in the regulation of
Alzheimer’s disease. Neuroscience & Biobehavioral Reviews, 67: 102-118.
11
1. Alzheimer’s disease pathology and risk factors
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is the leading
cause of dementia. The neuropathological hallmarks of AD include neuron loss, accumulation of
amyloid-β (Aβ) plaques and hyperphosphorylated tau in the form of neurofibrillary tangles and
neuropil threads, and gliosis (Glass et al., 2010; LaFerla, 2010; Cherry et al., 2014; Morris and
Tangney, 2014). There is compelling evidence that abnormal Ab accumulation (Mucke and
Selkoe, 2012; Tanzi, 2012) or hyperphosphorylated tau (Iqbal et al., 2010) or both (Zempel and
Mandelkow, 2014) are the primary driving force(s) in the pathogenesis as well as strong support
for key contributions by activated microglia and astrocytes (Glass et al., 2010; Cherry et al., 2014).
Regardless of the proximal cause(s) of the neural injury in the AD brain, successful therapeutic
intervention will require understanding of the factors that culminate in development of pathology.
The risk of AD is affected by numerous factors. Aging is the single greatest risk factor for
AD, with the prevalence doubling every five years after the age of 65 (Hebert et al., 2003).
However, the age-related physiological changes that contribute to this effect are uncertain. In
addition to aging, AD risk is regulated by genetic factors. A small percentage of AD cases result
from autosomal dominant mutations in the Aβ precursor protein, presenilin-1, and presenilin-2.
The key consequences of these mutations appear to be increased production of Aβ and/or a
change in the ratio of Aβ species, both of which foster Aβ accumulation (LaFerla, 2010; Tanzi,
2012). The most prevalent genetic risk factor for AD is the E4 allele (APOE4) of the cholesterol
transporter apolipoprotein E (Saunders et al., 1993; Strittmatter et al., 1993), which also appears
to regulate Aβ accumulation. In addition to APOE4, there are a number of single nucleotide
polymorphisms in genes that are associated with relatively subtle increases in AD risk. Among
these are several genes associated with innate immunity (Tanzi, 2012), pointing to a role of the
immune system, and microglia in particular, in AD pathogenesis. As with most diseases, AD risk
is also significantly affected by several environmental and lifestyle factors, including education
(Sharp and Gatz, 2011; Ferrari et al., 2014), head injury (Breunig et al., 2013), air pollution
12
(Calderón-Garcidueñas et al., 2012), and physical exercise (Brown et al., 2013; Tolppanen et al.,
2015). In recent years, an especially interesting risk factor has been obesity (Emmerzaal et al.,
2015), which may contribute to links between cardiovascular diseases and AD (Hayden et al.,
2006).
Here, we consider the individual and interactive effects of these AD risk factors as well as
possible mechanisms that may be underlying these relationships. We begin by examining obesity
as a risk factor for AD. We then discuss a number of possible mechanisms that may be underlying
the relationship between obesity and AD, focusing specifically on inflammation, APOE4, and their
interaction.
2. Obesity/metabolic syndrome as risk factors for AD
2.A. Epidemiological studies
Accumulating evidence over the past several years has identified obesity and related
conditions as significant risk factors for the development of AD. Body mass index (BMI) is a
commonly used measure of obesity, and though some studies show an association between BMI
and AD, with an up to 40% increased risk for obese individuals (Gustafson et al., 2003; Fitzpatrick
et al., 2009), others have found no association (Yoshitake et al., 1995; Qizilbash et al., 2015)
(reviewed in (Profenno et al., 2010). However, central adiposity may be a more important factor
and better predictor of AD risk than BMI (Whitmer et al., 2008; Gustafson et al., 2009; Luchsinger
et al., 2012), as visceral fat has been shown to be particularly harmful (Bloor and Symonds, 2014).
Central adiposity has been shown to be a risk factor for AD as well as for cognitive impairment
(Whitmer et al., 2008; Gustafson et al., 2009; Luchsinger et al., 2012; Feng et al., 2013), and
visceral fat deposits are associated with lower brain volumes at middle age (Debette et al., 2010).
Importantly, it appears that obesity at midlife is a particularly strong risk factor for onset of AD in
late life (Fitzpatrick et al., 2009; Profenno et al., 2010; Xu et al., 2011; Meng et al., 2014;
Emmerzaal et al., 2015). Intriguingly, the association between obesity and AD risk diminishes with
13
age. Weight loss and low BMI are actually associated with increased risk of AD in older adults,
whereas a higher BMI may be protective at advanced ages (Fitzpatrick et al., 2009; Hughes et
al., 2009; Profenno et al., 2010; Besser et al., 2014; Emmerzaal et al., 2015). In fact, one study
found that overweight and obese older adults were protected against AD, mild cognitive
impairment (MCI), and vascular dementia (Doruk et al., 2010). One interpretation of these findings
is that obesity at midlife may serve as a triggering factor for AD neuropathology, the effects of
which do not become apparent until onset of clinical dementia later in life.
Obesity is associated with increased risk for the development of metabolic syndrome and
type 2 diabetes (T2D), both of which are also independent risk factors for AD (Biessels et al.,
2006; Strachan et al., 2011; Samaras and Sachdev, 2012). In addition, both obesity and T2D are
risk factors for MCI (Samaras and Sachdev, 2012). Obesity is also linked with cognitive
impairments in the absence of dementia (Gustafson et al., 2003; Benito-León et al., 2013;
Mazzoccoli et al., 2014). In particular, central adiposity is a risk factor for cognitive decline, as
increased visceral adipose tissue is associated with decreased performance on verbal memory
and attention tasks, and with lower hippocampal volume (Isaac et al., 2011). Additionally, obesity
can impair cognition even in children and young adults (Yau et al., 2012; Schwartz et al., 2013;
Khan et al., 2014). Thus, there are likely to be two independent pathways; one by which obesity
impairs cognition and another pathway by which it promotes AD pathogenesis, that in turn impairs
cognition in late life. Interestingly, while the relationship between higher visceral adipose tissue
and lower cognitive performance is true in individuals under 70 years of age, this association does
not exist in those over age 70 (Yoon et al., 2012), again indicating a protective effect of increased
weight at older ages.
Another factor that may be involved in obesity and AD risk is age-related testosterone loss
in men, which by itself has been shown to increase risk for AD (Hogervorst et al., 2001; Moffat et
al., 2004; Paoletti et al., 2004). There appears to be a bi-directional relationship between
testosterone loss and obesity, such that low testosterone levels predispose to obesity, and obesity
14
causes decreases in testosterone levels (De Maddalena et al., 2012; Fui et al., 2014). In accord
with this idea, weight loss has been found to increase testosterone levels. For example, free
testosterone levels increase in men after bariatric surgery and are associated with improved
insulin and glucose sensitivity (Botella-Carretero et al., 2013), but men with low testosterone
levels are at an increased risk of regaining weight (Wang et al., 2013). Conversely, testosterone
supplementation can also lower risks of obesity and metabolic syndrome as long-term
testosterone replacement in older men is often associated with reduced body weight, waist
circumference, and BMI, and with a reduction in symptoms of metabolic syndrome (Yassin et al.,
2014).
Obesity and related metabolic syndromes increase the risk of vascular dementia to an
even greater extent than the risk of AD (Yoshitake et al., 1995; Hayden et al., 2006; Whitmer et
al., 2007; Xu et al., 2011). Many vascular components are associated with AD neuropathology,
including blood brain barrier disruption (Bell and Zlokovic, 2009; Bell, 2012) and cerebral amyloid
angiopathy, the accumulation of b-amyloid (Aβ) deposits in the cerebrovasculature (Hultman et
al., 2013). In addition, the presence of modifiable vascular risk factors at midlife increases risk of
all types of dementia, including AD, later in life (Whitmer et al., 2007; Exalto et al., 2014). As
obesity is a major risk factor for cardiovascular disease as well as AD, obesity and vascular factors
likely cooperatively contribute to AD pathogenesis.
2.B. Experimental studies
In agreement with epidemiological findings, experimental studies in animals have
demonstrated that obesity and T2D are associated with promotion of AD. First, various animal
models of obesity and diabetes exhibit brain changes consistent with early AD pathology
(Jayaraman et al., 2014). A commonly used approach is the use of high fat diet (HFD) in rodents,
which yields diet-induced obesity (DIO). Using this model, our lab and others have shown that
DIO in transgenic mouse models of AD increases levels of Ab (Ho et al., 2004; Julien et al., 2010;
15
Kohjima et al., 2010; Barron et al., 2013), a key protein in the initiation and progression of AD
(Selkoe, 2011). Tau pathology, the other neuropathological hallmark of AD, is also increased by
DIO in a number of strains of transgenic mice (Julien et al., 2010; Leboucher et al., 2013; Mehla
et al., 2014; Takalo et al., 2014).
As in the human literature, DIO is also associated with cognitive deficits in animal models
independent of underlying AD-related pathology. That is, rodents show impairments on a number
of cognitive tasks following HFD, without apparent changes in Aβ accumulation (Granholm et al.,
2008; Stranahan et al., 2008; Kanoski et al., 2010; Kanoski and Davidson, 2011; Davidson et al.,
2013; Hsu and Kanoski, 2014; Knight et al., 2014). In fact, even a short 9-day exposure to HFD
can cause cognitive impairment in rats (Murray et al., 2009).
In addition to DIO, genetic and pharmacological manipulations that model diabetes also
increase AD-related neuropathology. For example, the BBZDR/Wor rats, a strain genetically
prone to T2D have greater neuronal loss and a Aβ pathology than do BB/Wor rats, which are
genetically prone to Type 1 diabetes (Li et al., 2007). Further, treatment with streptozotocin (STZ),
which kills pancreatic β-cells, is commonly used to induce type I diabetes in animal models. STZ
has been found to increase Aβ in both mouse (Jolivalt et al., 2008; Wang et al., 2010; Currais et
al., 2012) and rat (Yang et al., 2013) models, as well as increase tau phosphorylation in brain
(Planel et al., 2007; Jolivalt et al., 2008; Kim et al., 2009a). Transgenic mouse models of obesity
include leptin deficient mice (ob/ob) and leptin receptor deficient mice (db/db). Even in the
absence of HFD, these transgenic mice show cognitive impairments, Aβ pathology, and tau
phosphorylation (Li et al., 2012; Ramos-Rodriguez et al., 2013). Moreover, endothelial cells
cultured from db/db mice show increased susceptibility to the toxic effects of Aβ (Carvalho et al.,
2014). Interestingly, the antidiabetic drug metformin has been shown to reduce AD-like pathology
in db/db mice, though it did not improve cognition (Li et al., 2012).
Notably, crossing the genetically obese and diabetic ob/ob and NSY mice with an AD
transgenic mouse increases cognitive impairment and diabetic outcomes (Takeda et al., 2010),
16
and is associated with severe cerebrovascular pathology (Niedowicz et al., 2014) without affecting
Aβ pathology (Takeda et al., 2010; Niedowicz et al., 2014). These studies suggest that adverse
metabolic outcomes can be exaggerated in the presence of AD, but genetically induced metabolic
outcomes do not necessarily increase AD pathology. Interestingly, complementary findings in an
AD transgenic mouse with DIO also suggest that metabolic disturbance may not be the driving
force in promotion of AD pathogenesis (Barron et al., 2013). As discussed above, the effect of
obesity on AD risk has been shown to vary with age in human populations. However, the majority
of experimental studies on DIO and AD use young adult animals, and do not address the
possibility of age being a mediating factor in this relationship. Given that the human literature
suggests a particular role of obesity at midlife in AD risk, this deserves further study. In summary,
a number of studies in animal models have demonstrated increased AD-like pathology in
presence of diet- and pharmacologically-induced as well as genetically induced obesity and
metabolic disturbances. Moreover, the literature suggests that dietary components may be
important in regulating AD pathology, even in the absence of obesity and metabolic syndromes.
2.C. Dietary components affect AD risk
Studies in both human and animal models suggest that particular dietary constituents may
be important in modulating AD risk. For example, different types of dietary fats appear to influence
risk, with trans and saturated fatty acids being associated with higher risk of AD and MCI (Barnard
et al., 2014; Morris and Tangney, 2014). In rodents, a diet high in saturated fatty acids was found
to be more detrimental than a high cholesterol diet (Takechi et al., 2013). Other rodent studies
have shown that trans and saturated fatty acids lead to a particularly robust increase in Aβ
(Oksman et al., 2006; Grimm et al., 2012). Conversely, diets with high omega 3 polyunsaturated
fatty acids are associated with decreased Aβ levels (Julien et al., 2010; Lebbadi et al., 2011;
Hjorth et al., 2013; Zerbi et al., 2014), and one study found that a diet low in fat and high in oleic
acid was able to reduce Aβ levels and pathology in transgenic mice (Amtul et al., 2011). In
17
addition, the high sucrose and fructose contents of Western diets are also associated with
cognitive impairment in humans (Francis and Stevenson, 2011) and increased Aβ (Lakhan and
Kirchgessner, 2013; Moreira, 2013; Orr et al., 2014), and tau pathology (Orr et al., 2014) in
rodents. Even in the absence of HFD, 10% sucrose water increased Aβ in a mouse model of AD
(Cao et al., 2007), and a high fructose diet impaired spatial memory in rats (Ross et al., 2009).
Thus, diets high in saturated fatty acids, sucrose, and fructose may contribute to AD
pathogenesis, whereas diets high in certain types of fatty acids may be protective.
3. Possible mechanisms underlying obesity and Alzheimer’s disease interactions
3.A. Apolipoprotein e4
3.A.1. Apolipoprotein e4 and Alzheimer’s disease
Apolipoprotein E is a cholesterol transporter that has three isoforms that vary by a single
amino acid: ApoE e2, e3, and e4, with the e4 allele (APOE4) being the strongest genetic risk factor
for AD (Corder et al., 1993; Saunders et al., 1993; Strittmatter et al., 1993). Only ~12% of the
general population are APOE4 carriers (de-Andrade et al., 2000), yet its frequency increases to
~50% in AD patients (Ward et al., 2012). Moreover, the onset of AD occurs earlier in APOE4
carriers (Corder et al., 1993), and ~40% of healthy middle-aged versus ~8% of non-carriers have
Aβ accumulation (Liu et al., 2013a; Lathe et al., 2014). Apart from increasing AD risk, APOE4 is
associated with greater cognitive decline over a 6 year period in healthy middle-aged adults (Blair
et al., 2005). APOE4 is also linked with greater AD-like pathology in mouse models, where it has
been shown to potentiate oligomerization of Aβ (Belinson and Michaelson, 2009), and accelerate
and worsen Aβ plaque formation (Youmans et al., 2012).
APOE4 is also associated with changes in vascular pathology and in blood brain barrier
function. Specifically, AD patients who are APOE4 carriers have greater arteriosclerosis and
cerebral amyloid angiopathy (Premkumar et al., 1996; Yip et al., 2005b), and though cerebral
18
amyloid angiopathy is rare in the absence of AD, it is found in otherwise healthy homozygous
APOE4 carriers (Walker et al., 2000). Mice expressing human APOE4 have reduced cerebral
vascularization at a young age and increased vascular atrophy at old age (Alata et al., 2015).
Moreover, APOE4 is associated with increased blood brain barrier permeability and breakdown
in both humans (Halliday et al., 2013) and in mouse models (Nishitsuji et al., 2011; Bell et al.,
2012). Thus, APOE4 may be acting through several different mechanisms, including regulation
of Aβ oligomerization and deposition, and disruption of the vasculature and the blood brain barrier.
Notably, carriers of one APOE4 allele have a ~30% lifetime risk of AD, meaning that a
significant proportion of APOE4 carriers never develop the disease (Genin et al., 2011). Thus,
APOE4 is neither necessary nor sufficient for AD. Consequently, APOE4 must interact with other
risk factors, perhaps including obesity and diabetes, to influence AD risk.
3.A.2. Apolipoprotein e4 and obesity
Abundant evidence indicates that APOE4 is a risk factor for several metabolic
disturbances and adverse cardiovascular outcomes, including hypertension (Niu et al., 2009),
increased systolic blood pressure and carotid artery thickness (Atabek et al., 2012), increased
triglyceride and low density lipoprotein cholesterol levels (de-Andrade et al., 2000; Kypreos et al.,
2009), decreased high density lipoprotein cholesterol levels (Zarkesh et al., 2012), and higher
pancreatic islet amyloidosis in diabetic patients (Guan et al., 2013). Female APOE4 carriers have
been found to have increased central adiposity (Oh et al., 2001) and frequency of APOE4 is
increased in metabolic syndrome patients of either sex (Sima et al., 2007). Additionally, among
obese men, those with APOE4 have higher insulin and glucose levels (Elosua et al., 2003;
Marques-Vidal et al., 2003). In a DIO mouse model, APOE4 was associated with greater
metabolic impairments and adipocyte hypertrophy (Arbones-Mainar et al., 2008). However, other
studies fail to find a significant relationship between APOE4 and metabolic disturbances including
insulin resistance (Meigs et al., 2000; Ragogna et al., 2012). The reason for these discordant
19
findings is unclear, but may reflect the growing appreciation of gene-environment interactions in
mediating the effects of APOE4 in aging and age-related diseases (Corella and Ordovás, 2014).
For example, deleterious effects of APOE4 on heart disease risk and outcomes are preferentially
observed in the context of high saturated fat diets (Yang et al., 2007; Corella et al., 2011).
A number of mechanisms that may underlie the association between obesity and APOE4
have been identified. For example, apoE isoforms have been shown to differentially interact with
hormones important in nutrient sensing and homeostasis, including adiponectin (Arbones-Mainar
et al., 2008) and leptin (Fewlass et al., 2004). Moreover, apoE acts in the hypothalamus to
suppress food intake. Obese rodents have decreased hypothalamic apoE levels (Shen et al.,
2008), but how this might differ by APOE genotype in not known. Importantly, APOE4 carriers
have lower levels of hippocampal insulin degrading enzyme, which is also involved in Aβ
clearance (Cook et al., 2003; Edland, 2004; Du et al., 2009). MCI patients with APOE4 have
higher levels of the more toxic, lipid depleted Aβ, while all APOE4 carriers have an increase in
lipid depleted apoE, which is less effective at clearing Aβ (Hanson et al., 2013). Interestingly,
levels of lipid depleted Aβ are increased in the presence of a diet high in saturated fats and
glycemic index, and decreased with a low saturated fat and glycemic index diet (Hanson et al.,
2013).
As with cardiovascular disease, the combination of obesity and APOE4 may also affect
AD outcomes. For example, mid-life obesity (Ghebranious et al., 2011) and high fat and calorie
intake (Luchsinger et al., 2002), are associated with greater risk for AD only in APOE4 subjects.
Men with both type 2 diabetes and the APOE4 allele have a 5.5 times greater risk of AD, as well
as greater AD neuropathology than men with neither risk factor (Peila et al., 2002). Further, the
relationship between obesity and diminished cognitive functions appears to be strongest in
APOE4 carriers (Zade et al., 2013). Thus, gene-environment interactions between APOE4 and
obesity appear to be important regulators of cognitive decline and AD risk.
20
3.B. Inflammation
3.B.1. Inflammation and Alzheimer’s disease
Multiple lines of evidence have established inflammation as a key component in the
initiation and/or progression of AD pathogenesis (Wyss-Coray and Rogers, 2012). Aging, the
most significant risk factor for late-onset AD, is associated with an increase in chronic
inflammation (Singh and Newman, 2011). Age-associated increases in pro-inflammatory
cytokines can promote APP processing resulting in increased Aβ levels, which in turn can activate
microglia and astrocytes to perpetuate a cycle of inflammation and Aβ production (Blasko et al.,
2004). Indeed, higher levels of circulating inflammatory cytokines are associated with increased
risk of developing AD (Tan et al., 2007). In the central nervous system, pro-inflammatory cytokines
including interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) are
upregulated in CSF even before detectable Aβ pathology (Eikelenboom et al., 2011; Avila-Muñoz
and Arias, 2014). Moreover, higher levels of inflammatory cytokines are associated with greater
cognitive decline (Rafnsson et al., 2007) and greater loss of entorhinal cortex volume (Avila-
Muñoz and Arias, 2014), as well as greater loss of total brain volume than would be expected for
a given age (Jefferson et al., 2007). Cerebral inflammation, independent of other AD pathology
markers, predicts early death caused by dementia (Nägga et al., 2014).
The roles of heritability and environmental factors in the association between inflammation
and AD have also been demonstrated. Recent genetic evidence links polymorphisms in several
components of the immune system as risk factors for AD, including CD33 (Bertram et al., 2008;
Hollingworth et al., 2011; Naj et al., 2011), TREM2 (Kleinberger et al., 2014), clusterin and CR1
(Harold et al., 2009; Lambert et al., 2009). Further, the production of IL-1β, TNF- α, and interferon
γ in response to the pro-inflammatory stimulant lipopolysaccharide (LPS) is greater in children
with a parental history of AD (van Exel et al., 2009). Environmental factors that increase
inflammation are also implicated in promoting AD. Traumatic brain injury, for example, is
associated with increased risk of AD, and chronic neuroinflammation has been suggested as a
21
mediator of this relationship (Breunig et al., 2013). Increased inflammation in response to air
pollution is well established, and even children living in highly polluted areas show increased pro-
inflammatory cytokines in brain that correlate with Aβ and tau pathology (Calderón-Garcidueñas
et al., 2012).
Activated microglia and astrocytes are found surrounding Aβ plaques in AD, and are
associated with increased production of pro-inflammatory factors (Glass et al., 2010).
Interestingly, response sites for NFκB, a major upstream regulator of inflammatory cytokine
production, have been found in the promoters of genes involved in production of Ab, and pro-
inflammatory cytokines increase the expression of these Ab-related genes in neurons (Sastre et
al., 2008; Glass et al., 2010). LPS increases production of pro-inflammatory cytokines and Aβ in
both wildtype mice (Brugg et al., 1995), and in AD-transgenic mice (Sheng, 2003). Conversely,
anti-inflammatory treatments reduce Aβ production and Aβ plaque deposition (Yan et al., 2003)
in rodent models and may have some efficacy in AD prevention (Zandi et al., 2002), although the
literature in this area is mixed. The specific role of different components of neuroinflammation in
AD pathogenesis is unclear, as some studies have found reduced Aβ pathology in response to
increasing microglia (Boissonneault et al., 2009), whereas others have found that attenuating pro-
inflammatory cascades decrease Aβ pathology (Heneka et al., 2013). Thus, some aspects of
neuroinflammation appear to be beneficial while others are harmful, indicating a need for more
research.
3.B.2. Inflammation and obesity
Strong links between obesity and chronic inflammation have been established over the
last several years (Weisberg et al., 2003; Zeyda and Stulnig, 2009). Not only does obesity appear
to drive inflammation, but chronic inflammation can also disrupt metabolic processes to further
drive obesity (Thaler and Schwartz, 2010). For example, healthy men with high levels of pro-
22
inflammatory cytokines in serum had an increased risk of weight gain over a 6 year period
(Engström et al., 2003). Conversely, obese subjects placed on a very low calorie diet for 28 days
had a decrease in pro- and an increase in anti-inflammatory cytokines (Clément et al., 2004),
pointing to the role of diet in the association between obesity and inflammation. In fact, saturated
fatty acids have been shown to induce secretion of pro-inflammatory factors in culture (Gupta et
al., 2012), whereas polyunsaturated fatty acids improve obesity-associated inflammation (Liu et
al., 2013b). Thus, in addition to the obese condition, various dietary constituents also affect
inflammatory responses. Inflammation also contributes to insulin resistance (McNelis and Olefsky,
2014), a central feature of the metabolic syndrome. For example, signaling through the
inflammatory NFκB pathway can result in insulin resistance peripherally (Arkan et al., 2005), as
well as leptin and insulin resistance in the hypothalamus (García-Cáceres et al., 2013). Thus,
inflammatory cascades associated with obesity are widely believed to contribute to the
relationship between obesity and development of metabolic syndrome and T2D.
Obesity is also associated with central nervous system inflammation. For example, a high
fat diet has been linked with a 30% increase in immune cell infiltration into the brain (Buckman et
al., 2014). Inflammation in the hypothalamus is a central feature of obesity and high fat diets (De
Souza et al., 2005; Milanski et al., 2009; Thaler et al., 2012). In fact, hypothalamic inflammation
precedes diet-induced obesity since it is present after even a few days of high fat diet consumption
(Thaler and Schwartz, 2010), and neonatally fed mice show preservation of an increased
inflammatory response in hypothalamus even in adulthood (Ziko et al., 2014). Furthermore, there
are also significant interactions between the hypothalamus and periphery in diet-induced obesity
and inflammation. Administering anti-inflammatory antibodies into brains of obese rats led to
decreased hypothalamic inflammation, increased leptin sensitivity in hypothalamus, and
increased insulin sensitivity in liver, along with restoring liver glucose production (Milanski et al.,
2012), demonstrating that manipulating central inflammation has direct effects on the periphery.
23
A relationship between obesity, inflammation, and cognition has also been established
(Freeman et al., 2014). An increase in inflammatory markers in subjects with metabolic syndrome
predicted subsequent cognitive decline (Yaffe et al., 2003). Subjects with both metabolic
syndrome and high levels inflammation had the worst cognitive performance (Dik et al., 2007).
Interestingly, one study found that cognitive performance improved and inflammation decreased
after bariatric surgery, though the two did not correlate (Hawkins et al., 2015). Similar findings
have been reported in rodent models. For example, levels of the pro-inflammatory cytokine IL-1β
correlated with adiposity and cognitive impairment in the db/db mouse model of obesity (Erion et
al., 2014). Moreover, treatment with an IL-1β antagonist prevented obesity-associated cognitive
impairments and synaptic dysfunction (Erion et al., 2014). Finally, associations between obesity
and inflammation have also been shown in the context of neuropsychiatric disorders such as
depression, in both humans (Soczynska et al., 2011; Viscogliosi et al., 2013; Castanon et al.,
2014), and in mice (André et al., 2014).
Moreover, the hypothalamus has been proposed to play a central role in the relationship
between obesity, inflammation, and cognition. That is, inflammatory cytokines and infiltrating
immune cells are thought to act in hypothalamus to activate local inflammation. This inflammation
then causes synaptic remodeling and degeneration in hypothalamus, thereby affecting any
regions to which the hypothalamus projects, including brain regions important in cognition (Miller
and Spencer, 2014). Additionally, greater hypothalamic damage as measured by diffusion tensor
imaging, was associated with higher levels of inflammatory cytokines and worse cognitive
performance in obese subjects (Puig et al., 2015).
Testosterone and inflammation have also been demonstrated to interact in the context of
obesity. Men with low testosterone are more likely to have metabolic syndrome and high levels of
CRP (Laaksonen et al., 2004). In men with metabolic syndrome, lower levels of testosterone were
associated with higher levels of IL-6 (Gautier et al., 2013). Similarly, men with T2D also have
lower testosterone and higher CRP levels (Bhatia et al., 2006). In animal models, inflammatory
24
effects of obesity can be attenuated by testosterone treatment (Vignozzi et al., 2012). Research
on the association of testosterone and inflammation in AD are limited, however, one study
demonstrated that men with AD have lower testosterone and luteinizing hormone levels, the latter
of which inversely correlate with levels of TNFα (Butchart et al., 2013). Our lab recently
demonstrated that testosterone depletion exacerbates metabolic, pro-inflammatory, and
peripheral nerve injury outcomes of DIO in male mice, effects that were reversed by testosterone
treatment (Jayaraman et al., 2014). Moreover, we have found that DIO-induced increases in Aβ
levels are associated with neuroinflammation, exacerbated by testosterone loss, and prevented
by testosterone treatment (Lee et al., unpublished). The anti-inflammatory effects of testosterone
are likely to be important in the relationship between obesity and AD.
Thus, there is strong evidence linking obesity with both peripheral and central
inflammation, and with downstream effects including insulin resistance and development of the
metabolic syndrome, and cognitive impairments. The short-term inflammatory effects of dietary
components are likely to have different effects from the long-term chronic inflammation associated
with obesity, though both aspects of the inflammatory cascade are likely important.
3.C. Interactions between inflammation and APOE in obesity and AD
In addition to being independent factors in the AD disease process, APOE4 and
inflammation also have important interactions with each other as well as in the context of obesity.
One established function of apoE is regulation of inflammation. In support of this role, glia from
apoE knock-out mice exhibit increased pro-inflammatory responses after exposure to Aβ (LaDu
et al., 2001) or LPS (Lynch et al., 2001). Importantly, APOE isoforms differ in their inflammatory
effects. That is, APOE4 is associated with greater levels of pro-inflammatory cytokines, both in
humans (Colton et al., 2004; Gale et al., 2014) and in mouse models (Lynch et al., 2003; Colton
et al., 2004; Ophir et al., 2005; Vitek et al., 2009). Interestingly, APOE4 carriers have lower
expression of apoE and, as young adults, increased levels of pro-inflammatory cytokines that
25
decrease with age, though this study may be limited by a small sample size (Ringman et al.,
2012). However, apoE can also take on a pro-inflammatory role when overproduced by activated
microglia, and this pro-inflammatory response is stronger in the presence of APOE4 than APOE3
(Guo et al., 2004). Additionally, LPS stimulation in the presence of APOE4 is associated with
increased endoplasmic reticulum stress and macrophage cell death (Cash et al., 2012), greater
neuron damage (Maezawa et al., 2006b), and failure to regenerate dendrites (Maezawa et al.,
2006c). Effects of APOE on inflammation may vary by cell type, as one study found that APOE3
astrocytes displayed increased astrogliosis after LPS, while APOE4 astrocytes had no response
(Ophir et al., 2003). Thus, APOE appears to be an important regulator of inflammatory processes,
with APOE4 generally having a more pro-inflammatory effect than APOE3.
The relationship between APOE and inflammation is also important in the context of AD
(Keene et al., 2011). Among AD patients, those with the APOE4 allele had greater baseline and
stimulated levels of IL-1β (Olgiati et al., 2010). ApoE co-localizes with microglia around Aβ
plaques (Liu et al., 2013a), and APOE4 mice have greater microgliosis and astrogliosis in
response to Aβ than do APOE3 mice (Belinson and Michaelson, 2009). Additionally, mice with
both human APOE4 and familial AD mutations have higher levels of pro-inflammatory cytokines
and increased microglial reactivity surrounding Aβ plaques than do their APOE3 counterparts
(Rodriguez et al., 2014). Further, APOE4 macrophages are less effective at clearing Aβ (Zhao et
al., 2009). APOE4, but not APOE2 or APOE3, activates a pro-inflammatory factor in pericytes
that causes blood brain barrier breakdown (Bell et al., 2012). Thus, APOE4’s pro-inflammatory
actions may contribute to the finding that APOE4 carriers have increased blood brain barrier
breakdown (Halliday et al., 2013). 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 possibility of significant interactions between APOE4 and inflammation in
AD.
26
The interplay between APOE and inflammation in the context of obesity has not been well
studied. Unfortunately, available evidence is inconsistent. Several studies indicate that APOE
knockout mice show reduced gains in fat mass and body weight in response to HFD (Arbones-
Mainar et al., 2008; Bartelt et al., 2010; Pereira et al., 2012; Wang et al., 2012a). Nonetheless,
how APOE status affects obesity-associated inflammation is unclear, as one study reported that
APOE knockout mice show a stronger pro-inflammatory response in adipose tissue than wildtype
mice (Pereira et al., 2012), whereas another study found that APOE knockout mice on HFD have
lower levels of inflammatory cytokines in adipose tissue and skeletal muscle (Wang et al., 2012a).
Interestingly, APOE4 mice fed a HFD are more susceptible to motor deficits after stroke, and this
is accompanied by increased inflammation (Dhungana et al., 2013). Overall, the literature
demonstrates an interaction between APOE and obesity, but more research is needed to clarify
how this relationship affects inflammation.
3.D. The role of toll-like receptor 4 signaling
In addition to overall increases in inflammatory pathways in general, increasing evidence
has implicated one specific inflammatory receptor; toll-like receptor 4 (TLR4). Part of a family of
pattern recognition receptors, TLR4 receptor responds most strongly to LPS, and its activation
leads to an intracellular cascade that results in activation of NFkB and cytokine production (Chow
et al., 1999). As seen in Table 1, TLR4 is widely expressed throughout the body, which has been
shown in humans as well as in a number of animal models, including rodents (Vaure and Liu,
2014). Interestingly, a role for TLR4 has been demonstrated both in the context of AD and obesity.
27
Organs Human Non-human
primate
Mouse Rat
Monocytes +++ +++ +++ ND
Macrophages +++ +++ +++ ND
Adipocytes ++ ND ND ND
Microglia ++ ND ++ ++
Astrocytes + ND - +/-
Neurons ND ND ND +/-
Oligodendrocytes + ND - -
Heart + ND +++ +
Kidney + ND ++ ND
Liver + ND ++ ND
Lung ++ ND +++ ND
Pancreas + ND + ND
Skeletal muscle + ND ND ND
Smooth muscle + ND ++ ND
Spleen +++ ND +++ ND
Stomach ND ND ND ND
Thymus + ND ND ND
Table 1: TLR4 expression across species. The expression of TLR4 in various tissues and cell
types compared across humans, non-human primates, mice, and rats (adapted from Vaure & Liu,
2014).
28
3.D.1. Toll-like receptor 4 in Alzheimer’s disease
One line of evidence for a role for TLR4 in AD comes from genetic linkage studies, which
have shown that single nucleotide polymorphisms in the TLR4 gene are associated with altered
risk for AD (Minoretti et al., 2006; Chen et al., 2012; Yu et al., 2012). In addition, TLR4 expression
is increased brains of AD patients and AD-transgenic mice (Walter et al., 2007), as well as in
neurons exposed to Ab (Tang et al., 2008). However, the role of TLR4 in AD is somewhat unclear,
as it has been shown to be beneficial to AD pathogenesis by some studies, and harmful by others.
For example, TLR4 signaling is necessary for the inflammatory response to Ab (Walter et al.,
2007; Jin et al., 2008; Udan et al., 2008; Reed-Geaghan et al., 2009), and mice with TLR4
mutations are protected against neuronal apoptosis (Lehnardt et al., 2003; Tang et al., 2008),
suggesting that TLR4 signaling may be detrimental. However, AD transgenic mice with TLR4
mutations show increases in plaques and soluble Ab (Tahara et al., 2006), as well as memory
impairments (Song et al., 2011), compared to AD transgenic mice with wildtype TLR4. Moreover,
repeated administration of a TLR4 agonist to APP transgenic mice was associated with improved
cognitive function and decreased Ab pathology (Michaud et al., 2013). The beneficial role of TLR4
in AD may be due to its effects on clearing Ab, as stimulating microglia with a TLR4 agonist
increased Ab uptake (Tahara et al., 2006). The role of TLR4 in clearance rather than in production
of Ab is further supported by the finding that TLR4 mutant AD transgenic mice do not have
increased pathology and cognitive impairment relative to AD transgenic mice with functional
TRL4, until later in life.
Though several lines of evidence have pointed to a role for TLR4 in AD, its exact function
is rather unclear. One possibility is that TLR4 signaling, as well as inflammation in general, may
be beneficial in the initial stages of AD pathology by aiding clearance of Ab. However, as the
disease progresses and inflammation cannot be resolved, TLR4 signaling may take on a harmful
role, eventually resulting in increased neuronal loss. More research on the role of TLR4 in AD is
29
clearly warranted, and should focus on harnessing the positive, while preventing the negative,
effects of TLR4 in Ab pathogenesis.
3.D.2. Toll-like receptor 4 and obesity
The link between obesity and TLR4 signaling has been rather well established across a
number of experimental paradigms. For example, TLR4 expression is increased in obesity across
several tissues, including peripheral blood mononuclear cells (Ahmad et al., 2012; Jialal et al.,
2012), adipose tissue (Vitseva et al., 2008; Ahmad et al., 2012), and muscle (Reyna et al., 2008).
Moreover, TLR4 expression is further increased in patients with Type 2 Diabetes or metabolic
syndrome and correlates with BMI, glucose, and HBA1c (Dasu et al., 2010; Ahmad et al., 2012),
as well as plasma free fatty acid and endotoxin levels (Jialal et al., 2012). Increased TLR4
expression with obesity has also been observed in animal models (Shi et al., 2006; Song et al.,
2006; Yan et al., 2010; Ladefoged et al., 2013).
Several of the adverse outcomes of obesity appear to depend, at least partially, on TLR4
signaling. This has been demonstrated by numerous studies in transgenic mice. That is, mice
lacking functional TLR4 receptors are protected against several effects of a HFD, including
changes in glucose (Poggi et al., 2007; Suganami et al., 2007; Liang et al., 2013; Jia et al., 2014;
Li et al., 2014), insulin resistance (Kim et al., 2007; Suganami et al., 2007; Radin et al., 2008; Jia
et al., 2014), and inflammation (Kim et al., 2007, 2015; Poggi et al., 2007; Suganami et al., 2007;
Saberi et al., 2009; Jia et al., 2014; Li et al., 2014), though some studies find that TLR4 mutations
do not protect against all of these changes (Ding et al., 2012; Kim et al., 2015). In addition to
genetic mutations, blocking TLR4 pharmacologically has also been shown to reduce HFD-
induced adipose tissue fibrosis (Vila et al., 2014). Furthermore, TLR4 appears to be required for
the effects of HFD on adverse outcomes in a number of other organs, including the vascular
system (Kim et al., 2007; Ding et al., 2012; Liang et al., 2013) the liver (Poggi et al., 2007; Rivera
et al., 2007; Jia et al., 2014; Ferreira et al., 2015), and the pancreas (Li et al., 2014). The beneficial
30
effects of reduced TLR4 activity have been demonstrated in humans as well, in that a single
nucleotide polymorphism in TLR4 is associated with decreased risk of coronary events (Kiechl et
al., 2002; Ameziane et al., 2003; Balistreri et al., 2004), and increased lifespan (Balistreri et al.,
2004). Interestingly, TLR4 mutations do not protect against changes in body weight associated
with HFD (Kim et al., 2007; Poggi et al., 2007; Suganami et al., 2007; Coenen et al., 2009; Saberi
et al., 2009; Ding et al., 2012; Jia et al., 2014), suggesting that TLR4 is involved in the metabolic
consequences of obesity, rather than in the actual development of the obese state.
A number of studies have demonstrated that TLR4 is directly involved in driving some of
the adverse metabolic changes of HFD, and may serve as a link between diet-induced
inflammation and insulin resistance. Importantly, TLR4 is one of the main pathways through which
HFDs increase inflammation, as a number of saturated fatty acids, and especially palmitic acid,
bind to TLR4, resulting in downstream activation of NFkB and cytokine release (Lee et al., 2001;
Shi et al., 2006; Reyna et al., 2008; Schaeffler et al., 2009; Wang et al., 2012b). In addition to
increasing inflammation, TLR4 also directly activates a number of targets that lead to increased
serine phosphorylation of insulin receptor substrate proteins and impaired insulin signaling (Shi
et al., 2006; Song et al., 2006). TLR4 signaling is necessary for these effects, as blocking the
receptor prevents the effects of saturated fatty acids on inflammation (Lee et al., 2001; Nguyen
et al., 2007; Reyna et al., 2008; Suganami et al., 2009; Wang et al., 2012b), as well as on insulin
signaling (Radin et al., 2008). The effects of diet on TLR4 activity appear to be rapidly mediated,
as one study found that subjects who ate a high fat/high carbohydrate meal had increased plasma
LPS and increased expression of TLR4 and SOCS-3, which inhibits insulin signaling, as well as
higher NFkB activity in blood mononuclear cells within 2 hours, compared to those who at a high
fiber and fruit meal (Ghanim et al., 2009).
While the role of TLR4 in the peripheral effects of HFD and obesity has been fairly well
established, significantly less is known about its role in the brain. Two studies have
31
demonstrated that TLR4 signaling is required for saturated fatty acids found in HFD to activate
hypothalamic inflammation (Milanski et al., 2009; Morari et al., 2014). Given that hypothalamic
inflammation has been proposed to drive obesity (Thaler and Schwartz, 2010), this again
highlights the role of TLR4 as a contributor to, rather than just a consequence of, obesity.
However, whether TLR4 plays a part in the cognitive and hippocampal effects of HFD and
obesity is largely unknown.
4. Dissertation objectives and experimental paradigms
Though the link between obesity and increased risk for AD has been well established in
the literature, some key questions remain regarding this relationship. One of these is whether and
how the main genetic risk factor for AD, APOE4, interacts with obesity. Increasing evidence from
human studies suggests that there may be a link between these factors, but this has not been
experimentally addressed. Thus, in Chapter 1, I used the EFAD mouse model, which combines
human AD-transgenes with either human APOE3 or APOE4. E3- and E4FAD mice were fed HFD
to examine whether the effects of obesity on AD-like pathology differed between the genotypes.
Another important factor that may modulate the relationship between obesity and AD is
aging. Several epidemiological studies have shown that obesity at midlife is especially harmful in
increasing AD risk later in life, but again, this has not been thoroughly experimentally evaluated.
Furthermore, age-related testosterone loss, which increases risk for AD, generally begins during
middle age. Thus, the combination of testosterone loss with obesity may be functioning to make
the brain more vulnerable to the accumulation of pathology that results in AD decades later. To
test this hypothesis, I examined the effects of HFD across young, middle aged, and aged Brown
Norway rats, as discussed in Chapter 3. Additionally, I administered testosterone to middle aged
and aged rats to examine the therapeutic potential of testosterone replacement in protecting
against aging and obesity.
32
Finally, though it is well established that both obesity and AD are characterized by
inflammation, the extent to which this may be a mechanism underlying their interaction is
unknown. TLR4 signaling in particular may be important in the disease development and
progression of both AD and obesity, though its role in mediating the neural effects of obesity is
largely unknown. In Chapter 4 I began to examine the role of TLR4 in this relationship by
administering a specific TLR4 antagonist, TAK-242, to wildtype mice fed HFD.
A schematic of the research questions posed in these aims is shown in Figure 1. Taken
together, results from these studies will demonstrate whether and how obesity interacts with other
risk factors like APOE4, aging, and testosterone loss, to drive AD pathogenesis. This will
potentially identify populations that may be at increased risk for AD. Moreover, these studies will
inform to what extent inflammation, and in particular TLR4, may be important in mediating the
relationship between obesity and neural health, and could identify a novel therapeutic target.
33
Figure 1: Interactions between various risk factors for AD. Obesity increases risk for AD and
may interact with other risk factors like APOE4 (Aim 1), and aging (Aim 2). All of these risk
factors are associated with increased inflammation, which has been proposed as a mechanism
underlying AD pathogenesis. Specifically, TLR4 signaling has been implicated in obesity and
AD, thus, its role in mediating this interaction is examined in Aim 3.
34
Chapter Two
Obesity accelerates Alzheimer-related pathology in APOE4 but not APOE3 mice
Moser VA, Pike CJ (2017) Obesity accelerates Alzheimer-related pathology in APOE4 but not
APOE4 mice. eNeuro 4(3).
35
Abstract
Alzheimer’s disease (AD) risk is modified by both genetic and environmental risk factors, which
are believed to interact to cooperatively modify pathogenesis. Although numerous genetic and
environmental risk factors for AD have been identified, relatively little is known about potential
gene-environment interactions in regulating disease risk. The strongest genetic risk factor for late-
onset AD is the e4 allele of apolipoprotein E (APOE4). An important modifiable risk factor for AD
is obesity, which has been shown to increase AD risk in humans and accelerate development of
AD-related pathology in rodent models. Potential interactions between APOE4 and obesity are
suggested by the literature but have not been thoroughly investigated. In the current study, we
evaluated this relationship by studying the effects of diet-induced obesity in the EFAD mouse
model, which combines familial AD transgenes with human APOE3 or APOE4. Male E3FAD and
E4FAD mice were maintained for 12 weeks on either a control diet or a western diet high in
saturated fat and sugars. We observed that metabolic outcomes of diet-induced obesity were
similar in E3FAD and E4FAD mice. Importantly, our data showed a significant interaction between
diet and APOE genotype on AD-related outcomes in which western diet was associated with
robust increases in amyloid deposits, b-amyloid burden and glial activation in E4FAD but not in
E3FAD mice. These findings demonstrate an important gene-environment interaction in an AD
mouse model that suggests that AD risk associated with obesity is strongly influenced by APOE
genotype.
36
1. Introduction
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, the underlying
causes of which are currently incompletely understood. Both genetic and environmental factors
are important in determining individual risk for AD. The strongest genetic risk factor for late-onset
AD is the e4 allele of apolipoprotein E (APOE4) (Strittmatter et al., 1993; Liu et al., 2013a). In the
US, roughly 12% of the population carries the E4 allele, but its frequency increases to ~60% in
AD patients (Rebeck et al., 1993). APOE4 not only increases risk, but also accelerates the age
of onset of AD (Corder et al., 1993; van der Flier et al., 2011). However, since homozygous
carriers of APOE4 have a ~50% lifetime risk of AD, a significant number of APOE4 carriers never
develop the disease (Genin et al., 2011). Thus, APOE4 likely interacts with other genetic and or
environmental factors to drive AD risk.
A significant modifiable risk factor for dementia is obesity. Obesity has numerous adverse
neural effects (Lee and Mattson, 2014) and increases the risk of dementia up to three-fold
(Whitmer et al., 2008). Body mass index, a commonly used measure of obesity, has been shown
to be associated with AD risk (Profenno et al., 2010) as well as with reduced brain volume in AD
patients (Ho et al., 2010). Several studies indicate that obesity may be particularly problematic at
midlife (Fitzpatrick et al., 2009; Profenno et al., 2010; Meng et al., 2014; Emmerzaal et al., 2015),
suggesting that obesity contributes to the development of AD. Similar relationships have been
observed in animal models. That is, diet-induced obesity (DIO) accelerates AD-related pathology
in mouse models of AD (Ho et al., 2004; Julien et al., 2010; Kohjima et al., 2010; Barron et al.,
2013; Orr et al., 2014). Further, genetic models of obesity and type 2 diabetes exhibit features of
AD-like neuropathology (Kim et al., 2009a; Jung et al., 2013; Ramos-Rodriguez et al., 2013).
The extent to which APOE4 and obesity interact to regulate AD risk is unclear.
Interestingly, APOE4 carriers can be more sensitive to metabolic consequences associated with
obesity (de-Andrade et al., 2000; Kypreos et al., 2009; Niu et al., 2009; Atabek et al., 2012;
Zarkesh et al., 2012; Guan et al., 2013). Although some studies do not report an APOE4 bias in
37
obesity-associated AD risk (Profenno and Faraone, 2008; Luchsinger et al., 2012), others have
found that AD risk is increased by obesity (Peila et al., 2002; Ghebranious et al., 2011) and diets
high in calories and fatty acids (Luchsinger et al., 2002) only in APOE4 carriers. Though the
human literature suggests a gene-environment interaction between APOE and obesity in
regulating development of AD, this question has not been addressed in experimental models. To
study these relationships, we utilized EFAD transgenic mice, which combine AD transgenes with
targeted replacement of mouse APOE with human APOE (Youmans et al., 2012). We compared
metabolic and AD-related effects of western diet in male APOE3 (E3FAD) and APOE4 (E4FAD)
mice. Here we report that diet-induced obesity increases amyloid pathology and gliosis almost
exclusively in E4FAD mice. Our data reveal a gene-environment interaction between APOE
genotype and obesity, suggesting that APOE4 carriers may be more susceptible to obesity
associated increases in AD risk.
2. Methods and materials
2.A. Animal procedures
A colony of EFAD mice, which are heterozygous for the 5xFAD transgenes and
homozygous for human APOE3 or APOE4 (Youmans et al., 2012), were maintained at vivarium
facilities at the University of Southern California from breeder mice generously provided by Dr.
Mary Jo LaDu (University of Illinois at Chicago). All animals were housed under a 12-hour
light/dark cycle with lights on at 6 AM and ad libitum access to food and water. At 3 months of
age, male E3FAD and E4FAD mice were randomized to dietary treatment groups (N = 7-
11/group): control diet (10% fat, 7% sucrose; #D12450J Research Diets, Inc., NJ, USA) or
western diet (45% fat, 17% sucrose; #D12451, Research Diets, Inc.). EFAD mice were
maintained on experimental diets for 12 weeks, an exposure period previously established to yield
obesity-induced metabolic impairments in APOE mice (Arbones-Mainar et al., 2010; Segev et al.,
2016). Body weight and food consumption were recorded weekly.
38
At the end of the treatment period, mice were anesthetized with inhalant isoflurane and
transcardially perfused with ice-cold 0.1 M PBS. The brains were rapidly removed and immersion
fixed for 48 h in 4% paraformaldehyde/0.1 M PBS, then stored at 4°C in 0.1 M PBS/0.3% NaN
3
until processed for immunohistochemistry. Gonadal and retroperitoneal fat pads were dissected
and weighed as a measure of adiposity, and snap frozen for RNA extraction. All animal
procedures were carried out under protocols approved by the University of Southern California
Institutional Animal Care and Use Committee and in accordance with National Institute of Health
standards.
2.B. Glucose, cholesterol and triglyceride measurements
Blood glucose readings were measured after overnight fasting (16 h) every four weeks
beginning at week 0 of the 12-week treatment period. 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, CA, USA).
Glucose tolerance testing was performed at week 11. Fasting, baseline glucose readings
were taken after which mice were administered a glucose bolus (2 g/kg body weight) via oral
gavage. Blood glucose levels were recorded 15, 30, 60, and 120 min after the glucose bolus was
given. Area under the curve was calculated.
Plasma cholesterol and triglyceride levels were enzymatically determined at the
conclusion of the experiment using commercially available kits (LabAssay Triglycerides #290-
63701, Wako Chemicals, VA, USA; Total Cholesterol Colorimetric Assay Kit, #K603, BioVision,
CA, USA). All samples were run in duplicate according to manufacturer’s instructions.
2.C. Thioflavin-S staining and quantification
Fixed hemi-brains were fully sectioned in the horizontal plane at 40 µm using a vibratome
(Leica Biosystems, IL, USA). Every eighth section was stained for thioflavin S (#230456, Sigma-
39
Aldrich, MO, USA) using standard methodology. Sections were mounted and allowed to dry
overnight, after which they were washed three times in 50% ethanol for 5 min each, then washed
in double-distilled H
2
O before being incubated for 10 min in 1% thioflavin-S dissolved in H
2
O.
Stained slides were then rinsed in 70% ethanol before being dehydrated and coverslipped in
aqueous anti-fade mounting medium (Vector Laboratories, CA, USA). Digital images were
captured at 20X magnification using an Olympus BX50 microscope equipped with a DP74 camera
and CellSens software (Olympus, Tokyo, Japan). The number of spherical thioflavin-positive
deposits were counted using NIH ImageJ 1.50i (US National Institutes of Health, MD, USA) with
the cell counter plugin to mark stained plaque-like structures. Thioflavin-positive deposits were
counted in entorhinal cortex (3 fields/section), subiculum (2 fields/ section), and hippocampal
subfields CA1 (3 fields/section) and CA2/3 (3 fields/section), across 4 sections per animal, for a
total of ~44 fields per brain.
2.D. Immunohistochemistry
Immunohistochemistry was performed using a standard avidin/biotin peroxidase approach
with ABC Vector Elite kits (Vector Laboratories). Ab immunohistochemistry was performed on
every eighth section using sections immediately adjacent to those processed for thioflavin S.
Briefly, sections were pre-treated with 95% formic acid for 5 min, then rinsed in TBS before being
treated with an endogenous peroxidase blocking solution for 10 min. After three 10 min washes
in 0.1% Triton-X/TBS, sections were incubated for 30 min in a blocking solution consisting of 2%
bovine serum albumin in TBS. Blocked sections were incubated overnight at 4°C in primary
antibody directed against Ab (#71-5800, 1:300 dilution, Invitrogen, CA, USA) that was diluted in
blocking solution. Next, sections were rinsed and incubated in biotinylated secondary antibody
diluted in blocking solution. Immunoreactivity was visualized using 3,3’-diaminobenzidine (Vector
Laboratories). Additional sections were similarly immunostained using IBA-1 (#019-19741,
40
1:2000 dilution, Wako), and GFAP (#ab7260, 1:1,000 dilution, abcam, MA, USA), but without
formic acid pretreatment.
To quantify the percent area occupied by Ab immunoreactivity (Ab load), images of non-
overlapping fields were taken at 20X magnification in entorhinal cortex (3 fields/section),
subiculum (3 fields/section), and hippocampal subfields CA1 (5 fields/section) and CA2/3 (3
fields/section) across 4 tissue sections, for a total of ~56 images per brain. Images were digitally
captured using an Olympus BX50 microscope and DP74 camera paired with a computer running
CellSens software (Olympus). The pictures were converted to grayscale images and thresholded
using NIH ImageJ 1.50i to yield binary images separating positive and negative immunostaining.
Ab load was calculated as the percentage of the total area that was positively immunolabeled.
Microglia and astrocyte activation was quantified using live imaging (Olympus BX50,
CASTGrid software, Olympus) at 40X magnification. Each cell was categorized as either resting
or reactive based on its morphology, as reported in previous studies (Ayoub and Salm, 2003;
Wilhelmsson et al., 2006). Specifically, microglia were scored as resting (type 1) if they had
spherical cell bodies, with numerous thin, highly ramified processes. Cells were scored as type 2
cells if they exhibited enlarged rod-shaped cell bodies with fewer processes that were shorter and
thicker, and scored type 3 cells if they had very few or no processes or several filopodial
processes. Both type 2 and type 3 morphologies were considered an activated microglia
phenotype. Astrocytes were visualized with GFAP immunostaining and categorized as exhibiting
either resting (normally sized cell bodies with a few rather short projections) or reactive (enlarged
cell bodies with increased number and length of projections) morphology phenotypes. Entorhinal
cortex (4 fields/section), subiculum (4 fields/section), and hippocampal subfields CA-1 (5
fields/section) and CA-2/3 (3 fields/section) were quantified for both microglia and astrocytes. The
number of cells across brain regions scored for each animal averaged ~700 microglia and ~600
astrocytes.
41
2.E. RNA isolation and real-time PCR
For RNA extractions, gonadal fat pads and hippocampi were homogenized using TRIzol
reagent (Invitrogen Corporation), following the manufacturer’s protocol. The RNA pellet was
treated with RNase-free DNase I (Epicentre, WI, USA) for 30 min at 37°C, and a
phenol/chloroform extraction was performed to isolate RNA. The iScript cDNA synthesis system
(Bio-Rad, CA, USA) was used to reverse transcribe cDNA from 1 µg of purified RNA. Real-time
quantitative PCR was performed on the resulting cDNA using SsoAdvanced Universal SYBR
Green Supermix (Bio-Rad) and a Bio-Rad CFX Connect Thermocycler. All measurements were
performed in duplicates. Quantification of PCR products was carried out by normalizing with a
combination of corresponding hypoxanthine-guanine phosphoribosyltransferase (HPRT) and
succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial (SDHA) expression
levels from the gonadal fat samples, and with b-actin expression levels from hippocampus, using
the DD-CT method to obtain relative mRNA levels. Gonadal fat was probed for levels of cluster of
differentiation factor 68 (CD68) and EGF-like module-containing mucin-like hormone receptor-like
1 (F4/80), while hippocampus was probed for b-secretase 1 (BACE1), neprilysin, insulin
degrading enzyme (IDE), CD68, glial fibrillary acidic protein (GFAP), and cluster of differentiation
factor 74 (CD74). Primer pair sequences are shown in Table 2.
42
Table 2. Primer sequences. Gene targets for the rtPCR analyses are listed with their
corresponding oligonucleotide sequences for the forward and reverse primers.
Target Gene Sequence
Cluster of differentiation factor 68 (CD68) Forward: 5’-TTCTGCTGTGGAAATGCAAG-3’
Reverse: 5’-AGAGGGGCTGGTAGGTTGAT-3’
EGF-like module-containing mucin-like
hormone receptor-like 1 (F4/80)
Forward: 5’-TGCATCTAGCAATGGACAGC-3’
Reverse: 5’-GCCTTCTGGATCCATTTGAA-3’
Hypoxanthine-guanine
phosphoribosyltransferase (HPRT)
Forward: 5’-AAGCTTGCTGGTGAAAAGGA-3’
Reverse: 5’-TTGCGCTCATCTTAGGCTTT-3’
Succinate dehydrogenase [ubiquinone]
flavoprotein subunit, mitochondrial (SDHA)
Forward: 5’-ACACAGACCTGGTGGAGACC-3’
Reverse: 5’-GGATGGGCTTGGAGTAATCA-3’
Neprilysin Forward: 5’-GAGAAAAGCCCACTTGCTTG-3’
Reverse: 5’-GAAAGACAAAATGGGGCAGA-3’
b-secretase 1 (BACE1) Forward: 5’-TCGCTGTCTCACAGTCATCC-3’
Reverse: 5’-AACAAACGGACCTTCCACTG-3’
Insulin degrading enzyme (IDE) Forward: 5’-TGTTTCCACACACAGGCAAT-3’
Reverse: 5’-ACCTGTGAAAAGCCGAGAGA-3’
Cluster of differentiation factor 74 (CD74) Forward: 5’-CAAGTACGGCAACATGACCC-3’
Reverse: 5’-GCACTTGGTCAGTACTTTAGGTG-3’
Glial fibrillary acidic protein (GFAP) Forward: 5’-AACGACTATCGCCGCCAACTG-3’
Reverse: 5’-CTCTTCCTGTTCGCGCATTTG-3’
b-actin Forward: 5’-AGCCATGTACGTAGCCATCC-3’
Reverse: 5’-CTCTCAGCTGTGGTGGTGAA-3’
43
2.F. Statistical analyses
For the analysis of body weight and glucose tolerance data, two-way repeated measures
ANOVAs were run using the Statistical Package for Social Sciences (SPSS; version 23, IBM, IL,
USA). All other data were analyzed by two-way ANOVA using Prism (Version 5, GraphPad
Software, Inc.; CA, USA). In the case of significant main effects, planned comparisons between
groups of interest were made using the Bonferroni correction. All data are presented as the mean
± the standard error of the mean (SEM). Significance was set at a threshold of p < 0.05.
3. Results
3.A. Obesity-related outcomes of western diet
To begin investigating whether there are gene by environment interactions between APOE
and western diet, we first compared measures of diet-induced obesity in E3FAD versus E4FAD
mice following the 12-week exposure to control and western diets. The control diet was associated
with < 1% gain in body weight in both E3FAD and E4FAD mice, whereas western diet yielded a
39 ± 7.7% increase in body weight in E3FAD and a 24 ± 7.21% increase in E4FAD mice (Fig.
2A), such that the effects of diet did not vary significantly across genotypes (p = 0.112; Fig. 2A).
A 2 x 2 repeated measures ANOVA revealed a significant main effect of diet on body weight (F
=
10.51, p = 0.003; Fig. 2A), such that western diet was associated with increased weight. APOE
genotype did not significantly affect body weight (p = 0.759; Fig. 2A). Between group comparisons
revealed that E3FADs fed a western diet weighed significantly more than E3FADs fed a control
diet at 4, 8, and 12 weeks (p < 0.05). However, there were no statistically significant differences
in body weights at any time point between control and western diet groups in E4FAD mice.
We next examined plasma levels of cholesterol and triglycerides as measures of adverse
metabolic effects of western diet. We found no significant effects of genotype (p = 0.103), or of
diet (p = 0.221), nor did we find an interaction effect (p = 0.119) on plasma cholesterol levels (Fig.
44
2B). Likewise, there were no effects of either genotype (p = 0.46), or of diet (p = 0.102), or an
interaction effect (p = 0.179) on plasma triglyceride levels (Fig. 2C).
Because metabolic impairments associated with obesity have been linked to adiposity,
we assessed fat deposition across groups. We observed a significant interaction effect (F
=
5.01, p = 0.033), such that on the control diets, E4FAD mice had more gonadal fat than E3FADs
(p = 0.027), but there was no difference between E3- and E4FAD mice on western diet (p =
0.230; Fig. 2D). Additionally, there was a significant main effect of diet (F
= 37.04, p < 0.001) on
weight of the gonadal fat pads, so that both E3- and E4FAD mice had increased fat pads with
western diet (Fig. 2D). Parallel findings were observed in the retroperitoneal fat pads (data not
shown). Because inflammation is an established hallmark of obesity, we examined gene
expression of the macrophage markers CD68 and F4/80 by rtPCR in the adipose tissue. We
found a significant main effect of diet on CD68 expression (F
= 11.54, p = 0.003), though this
effect reached statistical significance only in E3FADs but not in E4FADs (Fig. 2E). There was no
effect of genotype (p = 0.353), nor was there an interaction between diet and genotype (p =
0.366) on CD68 expression. Diet had a main effect on adipose F4/80 expression (F
= 7.02, p =
0.015), and again, this effect reached statistical significance only in E3FADs (Fig. 2F). There
was no effect of genotype (p = .768), and no interaction effect (p = 0.288) on F4/80 expression.
In addition to increasing body weight and adiposity, western diet can induce metabolic
impairments including dysregulation of glucose homeostasis. When examining glucose
clearance in the glucose tolerance test, we found a significant main effect of diet (F
= 5.03, p =
0.033), such that both E3- and E4FAD mice fed a western diet were impaired at clearing
glucose (Fig. 2G). There was no main effect of genotype (p = 0.886), or interaction effect
between diet and genotype (p = 0.750) on glucose clearance. We also calculated the area
under the curve (AUC) for GTT, and found that there was a significant main effect of diet (F
=
5.73, p = 0.023), but not of genotype (p = 0.817) on GTT AUC (Fig. 2H). However, the effect of
diet failed to reach statistical significance when examined separately in E3- and E4FAD mice.
45
There was no interaction between genotype and diet on GTT AUC (p = 0.737). Changes in
fasting glucose levels over the diet treatment period showed a trend towards a main effect of
diet (F
= 3.84, p = 0.059; Fig. 2I). There was no effect of genotype (p = 0.371) nor was there an
interaction between diet and genotype (p = 0.352) on changes in glucose levels.
46
Figure 2. Metabolic outcomes associated with diet-induced obesity in E3FAD and E4FAD mice.
A) Body weights in male E3FAD and E4FAD mice maintained on control and western diets taken
at baseline (week 0) and four-week intervals across the 12-week experimental period. B) Plasma
cholesterol levels and C) plasma triglyceride levels in E3FAD and E4FAD mice on control and
western diets at the end of the experimental period. D) Weight of the gonadal fat pads. Relative
mRNA expression of macrophage markers E) CD68 and F) F4/80 in gonadal fat, as determined
by rtPCR. Data show fold differences relative to the E3FAD + control diet group. G) Glucose
tolerance test showing blood glucose levels over time after a glucose bolus. H) Area under the
curve (AUC) for the glucose tolerance test. I) Percent change in fasting blood glucose levels
relative to baseline after 12-weeks of control or western diet. Data are presented as mean (±SEM)
values; n=7-11/group. E3FAD mice are shown as circles, E4FAD mice are shown as squares;
control diet are open symbols or bars, western diet are filled symbols or bars. * p < 0.05 relative
to genotype-matched mice in control diet condition. # p < 0.05 relative to E3FAD mice in same
diet condition.
47
3.B. Western diet increases b-amyloid deposition in E4FAD but not in E3FAD mice
The primary AD-related neuropathological change in EFAD mice at this age is
accumulation of b-amyloid protein, largely in the form of extracellular deposits, many of which
exhibit positive thioflavin-S (Thio-S) staining that is indicative of amyloid. Thus, to begin
assessing AD-related neuropathology, Thio-S positive plaques were counted in entorhinal
cortex and in subregions of the hippocampus. Visual inspection of stained sections qualitatively
showed not only the expected increase in amyloid deposits in E4FAD, but also the surprising
finding that western diet increased Thio-S positive plaques only in E4FAD mice (Fig. 3A).
Specifically, there were significant interaction effects between genotype and diet on Thio-S
positive plaques in subiculum (F
= 9.75, p = 0.004; Fig. 3C), CA1 (F
= 8.41, p = 0.007; Fig. 3D),
and CA2/3 (F
= 7.32, p = 0.011; Fig. 3E), and a non-significant trend towards an interaction in
entorhinal cortex (F
= 4.09, p = 0.053; Fig. 3B). Post-tests revealed that diet significantly
increased Thio-S positive plaque counts in E4FAD but not E3FAD males across all brain
regions sampled (p < 0.01). Additionally, there was a significant main effect of genotype even in
the absence of diet, such that E4FAD mice had a greater number of Thio-S positive plaques in
entorhinal cortex (F
= 50.30, p < 0.001; Fig. 3B), subiculum (F
= 59.40, p < 0.001; Fig. 3C), CA1
(F
= 80.58, p < 0.001; Fig. 3D), and CA2/3 (F
= 46.39, p < 0.001; Fig. 3E), than did E3FAD mice.
As a second measure of AD-like pathology, we assessed total b-amyloid burden by
immunohistochemistry. This provides a measure of complete b-amyloid, as the antibody
recognizes intra- and extracellular accumulations of Ab, even those that have not progressed to
Thio-S positive amyloid deposits. Results repeated the same general pattern observed with
Thio-S staining. That is, (i) E4FAD mice exhibit greater b-amyloid burden and, (ii) E4FAD but
not E3FAD mice show increased b-amyloid accumulation with western diet (Fig. 4A). We found
significant interaction effects between genotype and diet in entorhinal cortex (F
= 4.91, p =
0.035; Fig. 4B) and in CA2/3 (F
= 4.48, p = 0.043; Fig. 4E), but not in subiculum (F
= 0.11, p =
48
0.742; Fig. 4C) or in CA1 (F
= 2.71, p = 0.110; Fig. 4D). Bonferroni post hoc tests showed that
western diet significantly increased Ab load in E4FAD but not in E3FAD mice across all brain
regions surveyed (p < 0.05). There was a significant main effect of genotype with E4FADs
having greater Ab load than E3FADs in entorhinal cortex (F
= 21.38, p < 0.001; Fig. 4B),
subiculum (F
= 25.40, p < 0.001; Fig. 4C), CA1 (F
= 37.66, p < 0.001; Fig. 4D), and CA2/3 (F
=
47.27, p < 0.001; Fig. 4E).
49
Figure 3. Accumulation of amyloidogenic deposits assessed by thioflavin-S staining in E3FAD
and E4FAD mice across dietary treatments. A) Representative images of thioflavin-S staining in
the subiculum of E3FAD (APOE3) and E4FAD (APOE4) males fed control and western diets.
Scale bar = 50 µm. Numbers of thioflavin-S positive plaque numbers in E3FAD and E4FAD mice
maintained on control and western diets were quantified in B) entorhinal cortex, and hippocampal
subregions C) subiculum, D) CA1, and E) CA2/3. Data are presented as mean (±SEM) values;
n=7-11/group. E3FAD mice are shown as circles, E4FAD mice are shown as squares; control diet
are open symbols, western diet are filled symbols. * p < 0.05 relative to genotype-matched mice
in control diet condition. # p < 0.05 relative to E3FAD mice in same diet condition.
50
Figure 4. Accumulation of b-amyloid deposits assessed by immunohistochemistry in E3FAD and
E4FAD mice across dietary treatments. A) Representative images of b-amyloid immunoreactivity
in entorhinal cortex and hippocampus in E3FAD (APOE3) and E4FAD (APOE4) males maintained
on control and western diets. Scale bar = 100 µm. b-Amyloid burden was quantified as
immunoreactivity load in E3FAD and E4FAD mice on control and western diets in B) entorhinal
cortex, and hippocampal subregions C) subiculum, D) CA1, and E) CA2/3. Data are presented
as mean (±SEM) values; n=7-11/group. E3FAD mice are shown as circles, E4FAD mice are
shown as squares; control diet are open symbols, western diet are filled symbols. * p < 0.05
relative to genotype-matched mice in control diet condition. # p < 0.05 relative to E3FAD mice in
same diet condition.
51
3.C. Western diet increases gliosis more strongly in E4FAD than in E3FAD mice
Gliosis is an important neuropathological feature of AD that is also associated with both
obesity and APOE4. To assess gliosis, we compared both the relative cell numbers and
morphological activation state of microglia and astrocytes across groups. We found that, in
comparison to E3FAD mice, E4FAD mice consistently had a higher total number of glial cells as
well as a higher percentage of glial cells with reactive versus resting phenotypes. Moreover, the
effects of diet on glial number and reactivity were stronger in E4FAD than in E3FAD mice.
We first examined microglia number and morphology by IBA-1 staining. Figure 5A shows
a resting microglial cell with thin, ramified processes (Type 1), and activated cells with rod-shaped
cell bodies and fewer, thicker processes (Type 2), and amoeboid cells (Type 3). We found
significant interactions between genotype and diet when examining the total number of microglia
per mm
2
in subiculum (F
= 4.75, p = 0.038; Fig. 5C) and in CA1 (F
= 7.97, p = 0.009; Fig. 5D),
with Bonferroni post hoc tests showing that western diet increased microglia number in E4FAD
but not in E3FAD mice in these brain regions (p < 0.05). There were no interaction effects on
microglia number in entorhinal cortex (p = 0.316; Fig. 5B), or in CA2/3 (p = 0.180; Fig. 5E). There
was a significant effect of genotype on the total number of microglia per mm
2
in entorhinal cortex
(F
= 9.78, p = 0.004; Fig. 5B), subiculum (F
= 42.77, p < 0.001; Fig. 5C), CA1 (F
= 51.42, p <
0.001; Fig. 5D), and CA2/3 (F
= 21.64, p < 0.001; Fig 5E), such that E4FAD mice had a greater
total number of microglia across these brain regions than did E3FAD mice. However, in entorhinal
cortex, the effect of genotype was significant only in animals on a western diet.
Measures of microglial reactivity showed similar results as microglial number. Significant
interaction effects between genotype and diet were observed in entorhinal cortex (F
= 5.52, p =
0.027; Fig. 5F), CA1 (F
= 11.58, p = 0.002; Fig. 5H), and CA2/3 (F
= 32.66, p < 0.001; Fig. 5I), but
not in subiculum (p = 0.480; Fig. 5G). Bonferroni post hoc tests revealed that western diet
increased the percent of reactive microglia in entorhinal cortex, CA1, and CA2/3 of E4FAD, but
not E3FAD, male mice. There was a significant main effect of genotype even in the absence of
52
diet, such that E4FAD mice had a greater percent of reactive microglia than E3FAD mice in
entorhinal cortex (F
= 109.10, p < 0.001; Fig. 5F), subiculum (F
= 19.70, p < 0.001; Fig. 5G), CA1
(F
= 78.70, p < 0.001; Fig. 5H), and CA2/3 (F
= 165.70, p < 0.001; Fig. 5I).
We next examined astrocyte number and activation by GFAP staining. Figure 6A shows
a resting astrocyte with a normally sized cell body and a few short projections, versus a reactive
cell with an enlarged soma and an increased number of longer projections. For the measure of
astrocyte number, the effects of diet did not differ across genotype for any of the brain regions
sampled. We found significant main effects of genotype on the total number of astrocytes in
subiculum (F
= 9.95, p = 0.004; Fig. 6C), though this effect was only statistically significant in
animals on a western diet. There was a main effect of genotype on astrocyte number in CA1 (F
= 5.88, p = 0.022; Fig. 6D), but this did not reach statistical significance when examined
separately in control and western diet fed animals. There was a trend towards a significant
effect of genotype in entorhinal cortex (F
= 3.82, p = 0.060; Fig. 6B), but no effect in CA2/3 (p =
0.188; Fig. 6E). Diet had significant main effects on astrocyte number in subiculum (F
= 4.79, p
= 0.037; Fig. 6C), and CA2/3 (F
= 4.26, p = 0.048; Fig. 6E), with a trend towards a main effect in
CA1 (F
= 3.55, p = 0.069; Fig. 6D), though this effect did not reach statistical significance when
examined separately in E3- and E4FADs in any brain region. There was no effect of diet on
astrocyte number in entorhinal cortex (p = 0.593; Fig. 6B).
When examining astrocyte reactivity, we found similar trends as with microglial reactivity.
That is, there was a significant interaction effect between genotype and diet on astrocyte reactivity
in entorhinal cortex (F
= 4.82, p = 0.036; Fig. 6F), with western diet increasing reactivity only in
E4FAD mice. There were no significant interaction effects between genotype and diet in
subiculum (p = 0.989; Fig. 6G), CA1 (p = 0.160; Fig. 6H), or CA2/3 (p = 0.132; Fig. 6I). Moreover,
in the absence of diet, genotype had a significant effect on astrocyte reactivity, with E4FAD mice
having a greater percentage of reactive astrocytes in entorhinal cortex (F
= 46.97, p < 0.001; Fig.
6F), subiculum (F
= 27.72, p < 0.001; Fig. 6G), CA1 (F
= 87.49, p < 0.001; Fig. 6H), and CA2/3 (F
53
= 11.68, p = 0.002; Fig. 6I). In CA2/3 the effect of genotype was only significant in western diet
fed animals. Furthermore, western diet significantly increased astrocyte reactivity in CA1 (F
=
23.82, p < 0.001; Fig. 6H), and CA2/3 (F
= 7.83, p = 0.009; Fig. 6I) though this effect was only
significant in E4FADs in CA2/3. There was a nonsignificant trend towards an effect of diet in
subiculum (F
= 3.13, p = 0.088; Fig. 6G).
54
Figure 5. Microglia number and morphological status assessed by IBA-1 immunohistochemistry
in E3FAD (APOE3) and E4FAD (APOE4) mice across dietary treatments. A) Representative
images of microglial morphology. Scale bar = 40 µm. B-E) Densities of IBA-1 immunoreactive
microglia in E3FAD and E4FAD mice on control and western diets were quantified in B) entorhinal
cortex, and hippocampal subregions C) subiculum, D) CA1, and E) CA2/3. F-I) Percentages of
reactive microglia (types 2 and 3) were quantified in F) entorhinal cortex, and hippocampal
subregions G) subiculum, H) CA1, and I) CA2/3. Data are presented as mean (±SEM) values;
n=7-11/group. E3FAD mice are shown as circles, E4FAD mice are shown as squares; control diet
are open symbols, western diet are filled symbols. * p < 0.05 relative to genotype-matched mice
in control diet condition. # p < 0.05 relative to E3FAD mice in same diet condition.
55
Figure 6. Astrocyte number and morphological status assessed by GFAP immunohistochemistry
in E3FAD (APOE3) and E4FAD (APOE4) mice across dietary treatments. A) Representative
images of astrocyte morphology. Scale bar = 50 µm B-E) Densities of GFAP immunoreactive
astrocytes in E3FAD and E4FAD mice on control and western diets were quantified in B)
entorhinal cortex, and hippocampal subregions C) subiculum, D) CA1, and E) CA2/3. F-I)
Percentages of reactive astrocytes were quantified in F) entorhinal cortex, and hippocampal
subregions G) subiculum, H) CA1, and I) CA2/3. Data are presented as mean (±SEM) values;
n=7-11/group. E3FAD mice are shown as circles, E4FAD mice are shown as squares; control diet
are open symbols, western diet are filled symbols. * p < 0.05 relative to genotype-matched mice
in control diet condition. # p < 0.05 relative to E3FAD mice in same diet condition.
56
3.D. E4FAD mice have increased gene expression of inflammatory markers
In order to begin addressing possible mechanisms underlying the interactive effects of
APOE4 and western diet, we examined hippocampal gene expression of several markers
related to Ab production and clearance, as well as inflammation. Overall, our results indicate
that gene expression of factors involved in Ab clearance and production are not altered by
genotype or diet, and that inflammatory gene expression is increased in E4FAD mice, without
being altered by western diet (Table 3).
For levels of BACE1, the effects of diet did not vary across genotypes (p = 0.874), nor
was there an effect of genotype (p = 0.304), but there was a non-significant trend of diet
increasing BACE1 levels (p = 0.074). Levels of the Ab clearance factor, neprilysin were not
significantly affected by genotype (p = 0.902), diet (p = 0.126), nor was there an interaction
between genotype and diet (p = 0.802). Likewise, gene expression of IDE was not altered by
genotype (p = 0.785), diet (p = 0.955), or the interaction between genotype and diet (p = 0.489).
In assessing gene expression of inflammatory markers we found that E4FAD mice had
significantly greater levels of the microglial markers CD68 (F
= 10.75, p = 0.003), the astrocyte
marker GFAP (F
= 14.26, p < 0.001), and the innate immune marker CD74 (F
= 16.98, p < 0.001),
than did E3FAD mice. However, there were no significant effects of diet on levels of CD68 (p =
0.178), GFAP (p = 0.634), or CD74 (p = 0.184). Moreover, there were no significant interactions
between genotype and diet on levels of CD68 (p = 0.532), GFAP (p = 0.712), or CD74 (p = 0.335).
57
Table 3. Relative gene expression in hippocampus. Data are presented as mean fold differences
(±SEM) relative to the E3FAD mice on a control diet. The Kolmogorov-Smirnov test for normality
was performed, with p > 0.10 indicating a normal distribution. Genes related to b-amyloid
production (BACE-1), and clearance (neprilysin, IDE) showed no significant changes with either
diet or genotype, while genes related to glial activation (CD68, GFAP, and CD74) were increased
in E4FAD mice on both control and western diets.
Gene Mean ± SEM Kolmogorov-Smirnov
Test for Normality (p
value)
Statistical Significance
BACE1 E3FAD CTL = 1 ± N/A
E3FAD WD = 1.53 ± 0.31
E4FAD CTL = 1.32 ± 0.19
E4FAD WD = 1.76 ± 0.41
E3FAD CTL > 0.10
E3FAD WD > 0.10
E4FAD CTL > 0.10
E4FAD WD > 0.10
Genotype: F
1,28
= 1.10, p =
0.304
Diet: F
1,28
= 3.44, p = 0.074
Interaction: F
1,28
= 0.03, p =
0.874
Neprilysin E3FAD CTL = 1 ± N/A
E3FAD WD = 1.61 ± 0.79
E4FAD CTL = 0.94 ± 0.30
E4FAD WD = 1.79 ± 0.63
E3FAD CTL > 0.10
E3FAD WD > 0.10
E4FAD CTL > 0.10
E4FAD WD > 0.10
Genotype: F
1,28
= 0.02, p =
0.902
Diet: F
1,28
= 2.49, p = 0.126
Interaction: F
1,28
= 0.06, p =
0.802
IDE E3FAD CTL = 1 ± N/A
E3FAD WD = 1.27 ± 0.39
E4FAD CTL = 1.30 ± 0.39
E4FAD WD = 1.12 ± 0.35
E3FAD CTL > 0.10
E3FAD WD > 0.10
E4FAD CTL = 0.01
E4FAD WD > 0.10
Genotype: F
1,28
= 0.08, p =
0.785
Diet: F
1,28
= 0.00, p = 0.955
Interaction: F
1,28
= 0.49, p =
0.489
CD68 E3FAD CTL = 1 ± N/A
E3FAD WD = 1.21 ± 0.29
E4FAD CTL = 1.74 ± 0.30
E4FAD WD = 2.30 ± 0.29
E3FAD CTL > 0.10
E3FAD WD > 0.10
E4FAD CTL > 0.10
E4FAD WD > 0.10
Genotype: F
1,28
= 10.75, p =
0.003
Diet: F
1,28
= 1.91, p = 0.178
Interaction: F
1,28
= 0.40, p =
0.532
GFAP E3FAD CTL = 1 ± N/A
E3FAD WD = 1.02 ± 0.11
E4FAD CTL = 1.56 ± 0.21
E4FAD WD = 2.70 ± 0.04
E3FAD CTL > 0.10
E3FAD WD > 0.10
E4FAD CTL > 0.10
E4FAD WD > 0.10
Genotype: F
1,28
= 14.26, p <
0.001
Diet: F
1,28
= 0.23, p = 0.634
Interaction: F
1,28
= 0.14, p =
0.712
CD74 E3FAD CTL = 1 ± N/A
E3FAD WD = 1.28 ± 0.28
E4FAD CTL = 3.32 ± 0.62
E4FAD WD = 5.04 ± 1.30
E3FAD CTL > 0.10
E3FAD WD = 0.01
E4FAD CTL > 0.10
E4FAD WD > 0.10
Genotype: F
1,28
= 16.98, p <
0.001
Diet: F
1,28
= 1.86, p = 0.184
Interaction: F
1,28
= 0.96, p =
0.335
58
4. Discussion
The goal of this study is to examine whether APOE genotype and obesity interact to
promote AD pathogenesis. Comparing E3FAD and E4FAD mice maintained on standard versus
western diets, we demonstrate a significant gene-environment interaction whereby diet-induced
obesity drives AD-related pathology primarily in APOE4 mice. Our results are consistent with
previous findings in humans (Fitzpatrick et al., 2009; Profenno et al., 2010), and confirm studies
in rodent models (Ho et al., 2004; Julien et al., 2010; Kohjima et al., 2010; Barron et al., 2013)
that obesity increases risk for development of AD. Similarly, our findings replicate prior rodent
data (Fryer et al., 2005; Castellano et al., 2011; Youmans et al., 2012; Rodriguez et al., 2014;
Cacciottolo et al., 2016) that model the human observation that APOE4 increases the risk and or
accelerates the onset of AD pathology (Corder et al., 1993; Saunders et al., 1993; Strittmatter et
al., 1993; Morris et al., 2010; Jack et al., 2015). Importantly, our data indicate that the effects of
diet-induced obesity and APOE4 are not strictly additive. Although APOE4 status is associated
with greater AD-like pathology on both control and western diets, obesity increased AD-like
pathology in E4FAD but not E3FAD mice. Our finding that E3FAD mice did not show a diet-
induced increase in AD-related pathology is similar to null findings in some rodent models of
obesity (Zhang et al., 2013; Knight et al., 2014; Niedowicz et al., 2014). Thus, these data suggest
an important gene-environment interaction in which APOE4 carriers are more susceptible to the
AD-promoting effects of obesity.
How neural outcomes in human populations are impacted by the relationship between
APOE genotype and metabolic risk factors remains incompletely defined. Many studies simply
control for APOE genotype rather than considering its potential moderating role in the relationship
between obesity and AD risk (Vanhanen et al., 2006; Luchsinger et al., 2012). When APOE status
has been considered as a modulator of AD risk associated with metabolic factors, the results have
been mixed. In some studies, APOE4 carriers showed significantly more cognitive impairment in
association with adverse metabolic conditions including atherosclerosis, peripheral vascular
59
disease, type 2 diabetes (Haan et al., 1999), and high systolic blood pressure at midlife (Peila et
al., 2001). Further, levels of senile plaques and neurofibrillary tangles were highest in obese men
that were also APOE4 carriers (Peila et al., 2002). However, several other studies reported that
the AD risk associated with obesity and metabolic syndrome is stronger in APOE3 carriers (Dixit
et al., 2005; Leiva et al., 2005; Singh et al., 2006; Profenno and Faraone, 2008).
An important consideration in interpreting these seemingly discordant findings is the
potential role of sex differences. Although the impact of sex differences in the interactions among
obesity, APOE, and AD risk has not been thoroughly addressed, AD is characterized by numerous
sex differences (Li and Singh, 2014; Pike, 2017). Further, the AD-associated risk of APOE4
appears to disproportionately affect women (Payami, 1994; Farrer et al., 1997; Altmann et al.,
2014). Additionally, there are sex differences in various aspects of obesity (Lovejoy et al., 2009;
Mauvais-Jarvis, 2015; Moser and Pike, 2016), including observations that women exhibit relative
protection against obesity until menopause (Meyer et al., 2011; Sugiyama and Agellon, 2012;
Bloor and Symonds, 2014). Given that sex differences have been found in each of these factors,
future studies should address sex as a possible mediator in the relationship between APOE4 and
obesity. Ongoing projects in our lab have begun to address this issue using female E3FAD and
E4FAD mice.
How obesity and APOE interact to regulate AD pathogenesis remains to be determined.
One candidate mechanism linked to both factors is metabolic impairment. Obesity is strongly
associated with development of impaired glucose and insulin metabolism (Kahn et al., 2006;
Singla et al., 2010), which are also characteristic of AD patients and have been proposed as
possible mechanisms driving AD pathogenesis (Craft, 2005, 2009; Martins et al., 2006). Notably,
APOE genotype affects metabolic responses to diet (Snook et al., 1999; Barberger-Gateau et al.,
2011), and several studies show that APOE4 carriers are at increased risk for a number of
metabolic disturbances (de-Andrade et al., 2000; Oh et al., 2001; Elosua et al., 2003; Marques-
Vidal et al., 2003; Sima et al., 2007; Kypreos et al., 2009; Niu et al., 2009; Atabek et al., 2012;
60
Zarkesh et al., 2012; Guan et al., 2013), though some studies find no effect of APOE genotype
on metabolic outcomes (Meigs et al., 2000; Ragogna et al., 2012). Our findings suggest that
E3FAD mice may be more susceptible to some metabolic effects of western diet, though E4FAD
mice trend towards metabolic disturbances even in the absence of a western diet. Specifically,
relative to E4FAD mice, E3FAD mice showed greater diet-induced body weight gain, gonadal fat
inflammatory cytokine expression, and higher glucose levels on western diet. Conversely, E4FAD
mice had higher gonadal fat pad weight and a trend towards higher fasting glucose levels than
E3FAD mice under the control diet condition. These findings are consistent with several previous
reports showing that mice with human APOE3 gain more weight in response to a high fat diet
than mice with either human APOE4 (Arbones-Mainar et al., 2008; Segev et al., 2016) or mouse
APOE (Karagiannides et al., 2008). It is important to note that the western diet utilized in this study
has elevated levels of saturated fats, cholesterol, and sucrose, all of which have been
independently associated with increased AD-related pathology (Refolo et al., 2000; Oksman et
al., 2006; Cao et al., 2007; Takechi et al., 2010). Understanding how APOE genotype interacts
with various dietary components should be one target of future studies. Though metabolic factors
may have a role in AD pathogenesis, our findings that metabolic outcomes of diet-induced obesity
were greater in E3FAD than E4FAD mice argue against the possibility that metabolic impairment
significantly contributes to the observed APOE4 bias in diet-induced increases in AD-like
pathology.
There are several other mechanisms besides metabolic impairment that may contribute
to the observed interactions among obesity, APOE, and AD-like pathology. One established
consequence of obesogenic diets is pro-amyloidogenic alteration in the expression and/or activity
of factors that regulate generation and clearance of Ab including BACE1, neprilysin, and IDE
(Standeven et al., 2011; Maesako et al., 2012, 2015; Brandimarti et al., 2013; Wei et al., 2014).
Although we cannot exclude a significant role of such pathways in our observations, we did not
observe that mRNA levels of BACE1, neprilysin, and IDE were significantly altered by either the
61
simple or interactive effects of western diet and APOE. Another compelling candidate mechanism
is neuroinflammation, which is widely implicated as a significant regulator of AD risk and
development of AD pathology (Glass et al., 2010; Wyss-Coray and Rogers, 2012; Heneka et al.,
2015). Notably, both obesity and APOE4 are associated with increased inflammation in brain and
systemically. For example, obesity is linked with increased immune cell infiltration into brain
(Buckman et al., 2014), as well as increased glial activation (Koga et al., 2014; Dorfman and
Thaler, 2015; Douglass et al., 2017). In addition, obesity increases inflammation in peripheral
organs including adipose tissue (Weisberg et al., 2003; Zeyda and Stulnig, 2009) and liver (Park
et al., 2010a). APOE4 is also associated with greater levels of inflammation in the brain (Ophir et
al., 2005; Vitek et al., 2009) and throughout the body (Colton et al., 2004; Gale et al., 2014).
Moreover, stimulating innate inflammation in the presence of apoE4 increases cell death and
damage in macrophages (Cash et al., 2012), and in microglia and neurons (Maezawa et al.,
2006a, 2006c). In the context of AD pathology, APOE4 is associated with greater glial activation
in EFAD mice (Rodriguez et al., 2014). Similarly, we observed that E4FAD mice expressed
significantly higher mRNA levels of glial markers than E3FAD mice under both control and
western diets. Further, we found that both the total number and the relative level of morphological
activation of microglia and astrocytes were higher in E4FAD than E3FAD mice. Additionally, these
glial markers were significantly increased across several brain regions in response to diet-induced
obesity in E4FAD but not E3FAD mice. Perhaps in contrast to our results, middle-aged female
APOE4 mice showed higher levels of neuroinflammation in hippocampus under control diet but
decreased neuroinflammation with high-fat diet, relative to age- and sex-matched wild-type mice
(Janssen et al., 2016). Though the presence of familial AD transgenes and Ab pathology in the
EFAD model may account for these divergent findings, there may also be age and sex differences
in inflammatory responses to both diet and APOE4. Further, because reactive astrocytes and
microglia are associated with Ab plaques, the changes in gliosis we observe with APOE4 and
62
diet-induced obesity may be a consequence of, rather than a contributor to, Ab pathology. Thus,
additional research is needed to directly assess the potential mechanistic role of gliosis in the
interaction between APOE4 and obesity in AD.
To our knowledge, this is the first experimental investigation examining the interaction
between APOE4 and obesity in the context of AD. Interactions among genetic risk factors like
APOE4 and environmental and modifiable lifestyle risk factors in AD have thus far not been well
studied, though there are some epidemiological studies consistent with this possibility (Dufouil et
al., 2000; Hanson et al., 2013; Rajan et al., 2014; Wirth et al., 2014; Ishioka et al., 2016; Zheng
and Li, 2016). Our findings suggest that APOE genotype affects the relationship between obesity
and AD, such that APOE4 carriers may be more susceptible to obesity-associated risks than
APOE3 carriers. This illustrates an important gene-environment interaction and points to the need
for additional research exploring such relationships in the context of AD, as well as identifying
underlying mechanisms. Additionally, these findings identify a large population that may be at
increased risk of AD, but whose chance of developing the disease may be reduced by
preventative lifestyle changes.
63
Chapter Three
Effects of high fat diet and testosterone across the lifespan of the male brown Norway rat
64
Abstract
Alzheimer’s disease (AD) is a multifactorial disease for which both genetic and environmental
risk factors have been identified. Aging is by far the greatest risk factor for disease, and may
interact with obesity, a major lifestyle risk factor for AD. Findings in the human literature have
indicated that obesity at midlife may be especially harmful. Interestingly, this time period
coincides with male testosterone loss, which has also been shown to increase risk for AD. Our
lab has previously shown that obesity and loss of testosterone interact to exacerbate AD-related
outcomes in young rodents, and that testosterone is protective against both obesity and
cognitive decline. Whether the nature of this relationship changes across the lifespan is as of
yet unknown. The goal of the current study was to examine whether the effects of obesogenic
diets vary depending on age of exposure, and to examine the potential of testosterone to
mitigate this relationship. Male brown Norway rats were maintained on control (10% fat) or high
fat (60% fat) diets for 12 weeks beginning at 3 (young), 13 (middle-aged), or 23 months (aged)
of age. A separate cohort of middle-aged and aged animals was subcutaneously implanted with
silastic capsules filled with testosterone at the onset of diet manipulations. Endpoints included
metabolic indices, inflammation, learning and memory performance, and neural health
outcomes. Aging was associated with worse outcomes overall, and high fat diet exacerbated
these effects, especially in middle-aged animals. Testosterone treatment had minimal effects,
but reduced some measures of metabolic dysregulation and neuroinflammation. Understanding
the interactions between obesity, testosterone, and aging will provide significant insight into both
the development and prevention of age-related cognitive decline and AD.
65
1. Introduction
Obesity is a growing public health concern that increases risks for death and several
diseases, including type 2 diabetes, cardiovascular disease, and various cancers (Zheng et al.,
2017). Obesity is also associated with numerous adverse neural outcomes (Jayaraman and
Pike, 2014; Lee and Mattson, 2014). For example, obesity is linked with decreases in
hippocampal volume and white matter integrity (Jagust et al., 2005; Ho et al., 2010; Stanek et
al., 2011) as well as accelerated cognitive decline (Elias et al., 2005; Cournot et al., 2006).
Importantly, obesity also increases up to three-fold the risk of dementias (Whitmer et al., 2008),
including Alzheimer’s disease (AD) (Profenno et al., 2010) and vascular dementia (Hayden et
al., 2006).
Though advanced age is the single greatest risk factor for cognitive decline and dementia
(Niccoli and Partridge, 2012), whether and how aging affects neural outcomes of obesity is not
well understood. Weight gain increases during adulthood (Sheehan et al., 2003) leading to rates
of obesity that peak at late middle-age (Mizuno et al., 2004) and are associated with a wide range
of serious health outcomes (Zheng et al., 2017). Several studies indicate that obesity during
middle age is especially harmful, increasing rates of cognitive decline (Cournot et al., 2006; Dahl
et al., 2013; Bischof and Park, 2015) and risk of dementia (Fitzpatrick et al., 2009; Emmerzaal et
al., 2015) later in life. In animal studies, diet-induced obesity (DIO) is associated with reduced
neurogenesis (Lindqvist et al., 2006; Park et al., 2010b), cognitive impairment (Stranahan et al.,
2008; Kanoski et al., 2010; Knight et al., 2014), and increased AD-related pathology (Ho et al.,
2004; Julien et al., 2010; Kohjima et al., 2010; Barron et al., 2013). The role of aging in the neural
effects of DIO has only recently begun to be addressed (Larkin et al., 2001; Erdos et al., 2011;
Boitard et al., 2012; Spencer et al., 2017).
One normal physiological change that occurs in middle-aged men and is linked with the
development of both obesity (Zitzmann, 2009) and dementia (Pike et al., 2009) is a decrease in
testosterone levels. Importantly, there appears to be a bidirectional relationship between
66
testosterone loss and obesity, in that loss of testosterone can increase adiposity and risk for
obesity, and obesity, in turn, can decrease testosterone levels (De Maddalena et al., 2012; Fui
et al., 2014). Moreover, testosterone replacement therapy has been shown to have benefits in
reducing body weight, BMI, and metabolic syndrome symptoms (Traish et al., 2014; Yassin et
al., 2014), as well as decreasing visceral adiposity while increasing skeletal muscle (Allan et al.,
2008). Because testosterone exerts a number of neural benefits, including increased
neurogenesis, neuroprotection, improved performance in select cognitive tasks, and reduction
of AD-related neuropathology (Galea, 2008; Rosario and Pike, 2008), age-related testosterone
depletion may be expected to affect several neural functions. Though our lab previously found
an interaction between testosterone and obesity in a mouse model (Jayaraman et al., 2014),
how this relates to aging, and particularly to middle age when testosterone levels are declining,
has not been explored.
The available literature suggests the possibility of interactions between age, testosterone
loss, and obesity, that may combine to drive a number of adverse outcomes. To investigate
these interactions, we conducted parallel studies in Brown Norway rats in order to examine the
effects of DIO across the lifespan, as well as the role of testosterone in this relationship. To
accomplish this, we compared the effects of a high fat diet and testosterone supplementation
across age on metabolic, cognitive, inflammatory, and neural health outcomes. Therefore, in our
first experiment we placed young (3 mo.), middle-aged (13 mo.) and aged (23 mo.) rats on a
high fat diet to determine if the effects of DIO differed at various ages, and especially at middle
age, as suggested by findings in the human literature. The goal of our second experiment was
to examine whether testosterone supplementation could alter the effects of DIO in middle-aged
and aged animals, thus, we replicated diet manipulations from experiment 1 and simultaneously
treated animals with testosterone.
67
2. Methods and materials
2.A. Animal procedures
Experiment 1: Aging and diet. Male brown Norway rats at 3 (young), 13 (middle-aged),
and 23 (aged) months of age were provided by the Aged Rodent Colony at the National Institute
on Aging and subsequently maintained at vivarium facilities at University of Southern California.
Throughout the experimental period, animals were single housed under a 12-hour light/dark
cycle with lights on at 6 A.M. and had ad libitum access to food and water. After a 1-week gap to
allow animals to acclimate to the room in which they were housed, they were randomized to one
of two dietary treatment groups: control diet (10% fat; #D12450J, Research Diets, Inc., New
Brunswick, NJ, USA; CTL) or high fat diet (60% fat; #D12492, Research Diets, Inc.; HFD). The
animals were kept on experimental diets for 12 weeks during which body weight and food
consumption were recorded weekly.
At the end of the treatment period, rats 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/0.3% NaN
3
until processed for
immunohistochemistry. The remaining hemi-brain was snap frozen, then stored at -80°C prior to
processing for RNA extraction. Blood was collected in EDTA-coated tubes via cardiac puncture
and centrifuged to separate plasma, which was aliquoted and stored at -80°C. The liver was
dissected and snap frozen for RNA extraction. Gonadal and retroperitoneal fat were dissected
and weighed as a measure of adiposity.
Experiment 2: Testosterone and diet. In order to examine the effects of testosterone
supplementation on diet-associated outcomes during aging, we enrolled additional groups of 13
(middle-aged) and 23 (aged) month-old brown Norway rats that were treated with testosterone
and randomly assigned to either CTL or HFD. Rats were housed under the same conditions as
described above. Testosterone treatment was initiated simultaneously with dietary treatments.
68
Animals were anesthetized with inhalant isoflurane, and subcutaneously implanted between the
shoulder blades with a silastic capsule (1.57 mm ID x 3.18 mm OD; Dow Corning, Midland, MI,
USA) that was a total length of 2 cm with the inner 1 cm packed with testosterone (Steraloids,
Newport, RI, USA). Capsules were removed and replaced with fresh testosterone-containing
capsules at the 6-week time point. Dosing was based on previous studies showing that this
treatment results in physiological testosterone levels (Pinilla et al., 1999; Edinger and Frye,
2004; Frye et al., 2010). All animal procedures were carried out under a protocol approved by
the University of Southern California Institutional Animal Care and Use Committee and in
accordance with the National Institute of Health standards.
2.B. Glucose homeostasis
A glucose tolerance test (GTT) was performed at week 11. After a 16 h overnight fast,
baseline glucose levels were recorded and rats were administered a glucose bolus (2 g/kg body
weight) via intraperitoneal injection. Blood glucose levels were recorded 15, 30, 60, and 120 min
after administration of the glucose bolus. 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). The extent to which animals
returned to their baseline glucose reading after 120 min, and the total area under the curve
across 120 min were calculated.
2.C. Leptin and testosterone ELISAs
Plasma leptin levels were determined by ELISA using a commercially available kit (Rat
Leptin ELISA, #EZRL-83K, Millipore, Burlington, MA, USA). All samples were run in duplicate
according to manufacturer’s instructions. Plasma samples were also assayed for testosterone
using an adaptation of the ImmuChem Double Antibody testosterone RIA kit from MP
Biomedicals (#07-189102, Costa Mesa, CA, USA) with [
125
I] testosterone as the tracer and all
69
reagent volumes halved. The testosterone antibody (solid phase) cross-reacts slightly with 5a-
DHT (3.4%), 5 a-andros- tane-3b, 17b-diol (2.2%) and 11-oxotestosterone (2%) but does not
cross-react with progesterone, estradiol, or the glucocorticoids (all <0.01%). The minimum
detectable testosterone concentration was 0.1 ng/ml and the intra- assay coefficients of
variation was 6.0%.
2.D. Immunohistochemistry and quantification
Fixed hemi-brains were exhaustively sectioned in the horizontal plane at 40 µm using a
vibratome (Leica Biosystems, Buffalo Grove, IL, USA). Immunohistochemistry was performed
using a standard avidin/biotin peroxidase approach with ABC Vector Elite kits (Vector
Laboratories, Burlingame, CA, USA). Every thirteenth section was processed for glial fibrillary
acidic protein (GFAP), ionized calcium binding adaptor molecule 1 (IBA-1), and doublecortin
(DCX). Briefly, for GFAP staining, sections were rinsed in TBS before being treated with an
endogenous peroxidase blocking solution for 10 min. After three 10 min washes in 0.1% Triton-
X/TBS, sections were incubated for 30 min in a blocking solution consisting of 2% bovine serum
albumin in TBS. Blocked sections were incubated overnight at 4°C in primary antibody directed
against GFAP (#Z0334, 1:10,000 dilution, Dako, Carpinteria, CA, USA) that was diluted in
blocking solution. Sections were then rinsed and incubated in biotinylated secondary antibody
diluted in blocking solution. Immunoreactivity was visualized using 3,3’-diaminobenzidine
(Vector Laboratories).
Immediately adjacent tissue sections were similarly immunostained for IBA-1 with the
following slight modifications. First, a 0.2% Triton-X/TBS solution was used for rinses on Day 1,
and both the blocking solution and primary antibody solution contained 0.2% Triton-X. Second,
sections were blocked for 60 min before being incubated overnight in IBA-1 (#019-19741, 1:500
dilution, Wako Chemicals, Richmond, VA, USA).
70
In addition, sections were stained for doublecortin (DCX) as previously described
(Brummelte and Galea, 2010). Briefly, sections were treated with 0.3% hydrogen peroxidase for
30 min, and then incubated for 24 h in primary antibody (#sc-271390, 1:1000 dilution, Santa
Cruz Biotechnology, Santa Cruz, CA, USA), that was diluted in PBS with 0.04% Triton-X.
Sections were then incubated with secondary antibody for 24 h and with ABC complex for 4 h,
before being visualized using 3,3’-diaminobenzidine with 2.5% nickel. Tissue was rinsed in PBS
for 10 min between each step.
Microglia density and activation were quantified using live imaging (Olympus, BX50,
CASTGrid software, Olympus, Tokyo, Japan) and a 40x objective. IBA-1 immunoreactive cells
were scored as exhibiting either resting or reactive phenotypes based on morphological criteria,
as described in previous studies (Ayoub and Salm, 2003; Moser and Pike, 2017). In brief, IBA-1
labeled cells were scored as resting (type 1) microglia if their cell bodies were spherical with
numerous thin and highly ramified processes. Cells were scored as having an activated
phenotype if they had either a) enlarged, rod-shaped cell bodies with fewer and thicker
processes (type 2), or b) very few, short processes, or no processes with or without filopodia
(type 3). The hippocampus was quantified starting in CA1 where the pyramidal cell layer begins.
Five alternating fields were scored in each of 3 brain sections for a total of 15 fields and an
average of ~170 cells per brain.
Astrocytes were visualized by GFAP immunostaining and scored based upon
morphological phenotype. Digital images were captured using a 40x objective with an Olympus
BX50 microscope equipped with a DP74 camera and CellSens software. Images were taken
starting in CA1 of the hippocampus where the pyramidal cell layer begins, of four alternating
fields, across 3 brain sections for a total of 12 images per brain. This approach yielded an
average of ~450 cells scored per brain. GFAP-immunoreactive cells were scored using NIH
Image J 1.50i (National Institutes of Health, Rockville, MD, USA) with the cell counter plugin to
mark cells as having either a resting or a reactive morphological phenotype, as previously
71
described (Wilhelmsson et al., 2006; Moser and Pike, 2017). In brief, cells were scored as
having a resting phenotype if they displayed normal-sized somas with typical, generally short
projections. Conversely, GFAP-immunoreactive cells were scored as having a reactive
phenotype when they exhibited enlarged cell bodies and processes that appeared thicker.
DCX staining was quantified using a Nikon E600 light microscope (Nikon, Tokyo, Japan)
with a 100x oil immersion objective lens. Cells were counted throughout the granule cell layer
and area measurements were made of the granule cell layer using NIH Image J. A total of 10
sections was quantified. To obtain cell density, the total number of DCX-positive cells was
divided by the total area counted per brain.
2.E. RNA isolation and quantitative PCR
RNA extractions and PCR were performed as previously described (Jayaraman et al.,
2014; Moser & Pike, 2017). For RNA extractions, the liver, ventral hippocampus, and
hypothalamus were homogenized using TRIzol reagent (Invitrogen Corporation, Carlsbad, CA,
USA) following the manufacturer’s protocol. The RNA pellet was then treated with RNase-free
DNase I (Epicentre, Madison, WI, USA) for 30 min at 37°C to remove any remaining DNA
contamination, and a phenol-chloroform extraction was performed to isolate RNA. 1 µg of
purified RNA was used to reverse transcribe cDNA using the iScript cDNA synthesis system
(Bio-Rad, Hercules, CA, USA). Real-time quantitative PCR was then run on the resulting cDNA
using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) and a Bio-Rad CFX Connect
Thermocycler. All samples were run in duplicates. PCR products were quantified by normalizing
with corresponding b-actin expression levels using the DD-CT method to acquire relative mRNA
levels. Hippocampus and hypothalamus were probed for levels of interleukin 1 beta (IL1b),
cluster of differentiation factor 68 (CD68), and glial fibrillary acidic protein (GFAP). Liver was
72
probed for levels of CD68, sterol regulatory element binding protein-1 (SREBP1), and stearoyl-
CoA desaturase (SCD-1). Primer pair sequences are shown in Table 4.
73
Table 4. Primer sequences. Gene targets for the rtPCR analyses are listed with their
corresponding oligonucleotide sequences for the forward and reverse primers.
Target Gene Sequence
Interleukin 1b (IL1b) Forward: 5’-GACTTCACCATGGAACCCGT-3’
Reverse: 5’-GGAGACTGCCCATTCTCGAC-3’
Cluster of differentiation factor 68
(CD68)
Forward: 5’-AAGCAGCACAGTGGACATT-3’
Reverse: 5’-TTCCGCAACAGAAGCTTTG-3’
Glial fibrillary acidic protein (GFAP) Forward: 5’-TCAATGCCGGCTTCAAAGA-3’
Reverse: 5’-AGCGCCTTGTTTTGCTGTTC-3’
Sterol regulatory element binding
protein-1 (SREBP1)
Forward: 5’-GCTCACAAAAGCAAATCACT-3’
Reverse: 5’-GCGTTTCTACCACTTCAGG-3’
Stearoyl-CoA desaturase (SCD-1) Forward: 5’-GGGAAAGTGAAGCGAGCAA-3’
Reverse: 5’-GTGGTCGTGTAGGAACTGGAGA-3’
b-actin Forward: 5’-AGCCATGTACGTAGCCATCC-3’
Reverse: 5’-CTCTCAGCTGTGGTGGTGAA-3’
74
2.F. Behavioral testing: Barnes maze
Spatial memory was assessed during week 10 of the treatment period, using a modified
Barnes maze protocol (McLay et al., 1999). The maze was composed of a circular platform with
20 holes located around the border that was placed inside an area walled off with black curtains.
Visual cues were taped on each of the 4 sides inside the curtained area. An escape box was
located beneath one hole, and animals were trained to locate the correct hole, using the spatial
cues around the room, in order to escape the maze. The maze was cleaned with 70% ethanol
between each animal. Rats were habituated to the behavioral testing room and to the maze 24
h before the first training day. Specifically, after habituating to the room in their home cage for
30 min, they were placed on the maze in a vertical cylinder that they could not see out of for 3
min. The cylinder was then moved to guide the animal into the escape box for 1 min before they
were returned to their home cages. Animals were trained to locate the escape box over the
following 4 days, with 3 trials per day, and a 15 min inter-trial interval during which they were
returned to their home cage. During training trials, a bright light was directed above the maze,
and static was played from a speaker below the maze. On each training day, rats were again
habituated to the testing room for 30 min, before being placed in the cylinder on the maze for 10
s. The rats were then given 3 min to move about the maze freely and locate the escape box.
The animal was given 1 min in the box once it located it, before being returned to its home cage.
If animals did not locate the box within 3 min, they were gently guided into it and allowed to
remain inside for 1 min. 48 h after the last training trial, rats were tested on the probe trial, in
which the escape box was removed and rats were given 3 min to freely explore the maze.
The training and probe trials were recorded using Noldus Ethovision XT software
(Leesburg, VA, USA). For training trials, the latency to locate the escape box was recorded,
while for the probe trial, the number of correct hole investigations (defined as the hole where the
escape box was previously located, as well as 1 hole on either side), errors, and the distance
traveled to reach the correct hole for the first time were recorded.
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2.G. Statistical analyses
All data are reported as the mean ± the standard error of the mean (SEM). Raw data
were analyzed using Prism 7 (GraphPad Software, Inc., La Jolla, CA, USA). For the analyses of
body weight and glucose tolerance, which are measured across time, two-way repeated
measures analysis of variance (ANOVAs) were conducted. For all other measures, a 2x2
ANOVA was performed. In the case of significant main effects, planned comparisons between
groups of interest were made using the Bonferroni correction. Significance was set at a
threshold of p<0.05. Additionally, correlations between metabolic, inflammatory, and behavioral
outcomes were performed using the Spearman correlation coefficient.
3. Results
3.A. Body weight and adiposity
3.A.1. Body weight and adiposity: Diet and aging
In order to examine the independent and interactive effects of aging and HFD, we first
analyzed changes in body weight across the 12-week treatment period (Fig. 7A-C). There was a
significant effect of age (F=4.85, p=0.003), such that both middle-aged and aged animals
weighed more than young animals, and aged animals weighed more than middle-aged at every
time point (p<0.05). There was also a significant effect of diet in the young animals (F=10.04,
p=0.007; Fig. 7A). However, this effect reached statistical significance only at the 12-week time
point (p=0.017). There was no significant effect of diet on body weight at any time point in either
middle-aged (Fig. 7B) or aged animals (Fig. 7C). However, when comparing final body weights
at 12 weeks (Fig. 7F), we found a significant interaction effect between age and diet (F=6.84,
p=0.003), such that HFD only significantly increased body weight in middle-aged animals
(p<0.001). Moreover, there was a significant main effect of age (F=94.92, p<0.001), even in the
absence of diet. Post-tests revealed that middle-aged and aged animals weighed more than
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young rats on both CTL and HFD (p<0.01), but aged animals weighed more than middle-aged
animals only on a CTL diet (p<0.001).
In addition to increasing body weight, HFD is also associated with increased adiposity.
Thus, we analyzed weights of the gonadal and retroperitoneal (RP) fat pads. Aging was
associated with increases in weights of the gonadal (F=36.56, p<0.001; Fig. 7G) and RP
(F=51.98, p<0.001; Fig. 7H) fat pads, across both diets (p<0.01). HFD increased weight of the
gonadal fat pads (F=16.44, p<0.001), though this effect reached statistical significance only in
middle-aged animals (p<0.05). HFD also increased weight of the RP fat pads (F=61.29,
p<0.0001), across all age groups (p<0.05). There were no significant interactions between age
and diet on fat pad weights.
3.A.2. Body weight and adiposity: Diet and testosterone treatment
We also examined the effects of CTL versus HFD on metabolic outcomes in middle-
aged and aged rats that had received testosterone treatment throughout the experimental
period (Fig. 7D-E). Although rats maintained on HFD showed higher mean body weights, there
was no statistically significant main effect of diet on body weight across the 12 weeks in middle-
aged animals (Fig. 7D) or in aged animals (Fig. 7E). However, there was a main effect of diet on
final body weight (F=14.38, p<0.001; Fig. 7F), which was significant only in aged animals
(p=0.011). Additionally, there was a main effect of age on final body weight (F=7.98, p=0.009),
which failed to reach statistical significance when examined across diets.
For measures of adiposity, we found significant main effects of diet on weights of both
the gonadal (F=14.31, p<0.001; Fig. 7G) and RP (F=137.4, p<0.001; Fig. 7H) fat pads, with
post-tests revealing that HFD increased fat pad weights across both ages (p<0.05). Additionally,
there was a significant effect of age on RP fat pad weight (F=8.37, p=0.007), with aged animals
on HFD having greater fat pad weight than middle-aged animals (p=0.013).
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78
Figure 7. Body weight and adiposity outcomes associated with high fat diet in young, middle-
aged, and aged brown Norway rats. A-C) Body weights in A) young, B) middle-aged, and C)
aged male brown Norway rats maintained on control and high fat diet taken at baseline (week 0)
and 4-week intervals across the 12-week diet treatment. D-E) Body weights in testosterone-
treated D) middle-aged and E) aged rats fed control or high fat diet, across 12 weeks. F) Final
body weight in young, middle-aged, and aged male brown Norway rats after 12 weeks of high
fat diet-feeding. Adiposity is measured as weight of the G) gonadal fat pads and H)
retroperitoneal fat pads, relative to total body weight. Data are presented as mean (±SEM)
values; n=7-8/group. For figures A-E, young animals are shown as squares, middle-aged
animals as triangles, and aged animals as circles; control diet are open symbols, high fat diet
are closed symbols. For figures F-H, animals not treated with testosterone are shown in white,
and animals treated with testosterone in gray; control diets are solid white or gray bars, high fat
diets are striped bars. a p < 0.05 relative to young rats in same diet condition. b p < 0.05 relative
to middle-aged animal in same diet condition. c p < 0.05 relative to control diet-fed of the same
age. * p < 0.05 for main effect of age that does not reach statistical significance in posttests. # p
< 0.05 for main effect of diet that does not reach statistical significance in posttests.
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3.B. Glucose homeostasis
3.B.1. Glucose homeostasis: Diet and aging
One of the major systems disrupted by diet-induced obesity is glucose homeostasis. To
examine the effects of diet and aging on this system we performed glucose tolerance tests
(GTT). We found a significant main effect of diet on glucose clearance in young animals
(F=9.67, p=0.008; Fig. 8A), such that HFD-fed rats had higher glucose readings, though this
effect reached statistical significance only at 15 min (p<0.001) and 30 min (p<0.001) post
glucose bolus. Diet did not significantly affect rates of glucose clearance in middle-aged (Fig.
8B) and aged (Fig. 8C) animals. There were significant effects of both diet (F=8.27, p=0.006)
and age (F=4.02, p=0.026) on GTT as measured by area under the curve (AUC; Fig. 8F),
though both of these effects failed to reach statistical significance when examined across ages
and diets, respectively. Finally, we assessed the extent to which animals were able to restore
glucose homeostasis by calculating percent return to baseline glucose value (Fig. 8G). We
found a significant main effect of age (F=7.09, p=0.002), but not of diet, on GTT return to
baseline. Post tests revealed that aged animals on HFD were impaired at returning to baseline
glucose levels compared with young animals on HFD (p=0.011).
3.B.2. Glucose homeostasis: Diet and testosterone treatment
In testosterone-treated animals, we found a significant main effect of diet on GTT in
middle-aged animals (F=7.97, p=0.014; Fig. 8D), such that glucose levels were higher in HFD-
fed animals at 30 min (p=0.025). Diet did not significantly increase glucose levels in aged
animals at any time point (F=0.48, p=0.50; Fig. 8E). GTT AUC did not differ by age, though
there was a trend towards an increase in HFD groups that failed to reach statistical significance
(F=3.54, p=0.07; Fig. 8F). Finally, neither age nor diet had a significant effect on the extent to
which animals’ glucose returned to baseline levels (Fig. 8G).
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81
Figure 8. Glucose homeostasis as assessed by glucose tolerance testing in young, middle-
aged, and aged brown Norway rats on control or high fat diet. A-C) Blood glucose levels
measured in mg/dL at baseline (0 minutes) and 15, 30, 60, and 120 minutes after administration
of a glucose bolus in A) young, B) middle-aged, and C) aged rats on control or high fat diet. D-
E) Glucose levels in testosterone-treated D) middle-aged, and E) aged rats fed control or high
fat diet and administered a glucose bolus. F) Area under the curve (AUC) for the glucose
tolerance test. G) The extent to which animals returned to their baseline glucose levels after 120
min, calculated as a percent change from their baseline level. For figures A-E, young animals
are shown as squares, middle-aged animals as triangles, and aged animals as circles; control
diet are open symbols, high fat diet are closed symbols. For figures F-G, animals not treated
with testosterone are shown in white, and animals treated with testosterone in gray; control diets
are solid white or gray bars, high fat diets are striped bars. a p < 0.05 relative to young rats in
same diet condition. b p < 0.05 relative to middle-aged animal in same diet condition. c p < 0.05
relative to control diet-fed rats of the same age. * p < 0.05 for main effect of age that does not
reach statistical significance in posttests. # p < 0.05 for main effect of diet that does not reach
statistical significance in posttests.
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3.C. Peripheral effects of HFD
3.C.1. Peripheral effects of HFD: Diet and Aging
In addition to alterations in body weight and glucose homeostasis, HFD has numerous
other effects on a variety of peripheral organs and systems. One change is increased plasma
levels of the hormone leptin, which is released by adipose tissue. We observed a significant
interaction between age and diet on plasma levels (F=3.87, p=0.029; Fig. 9A), such that HFD
was associated with increased leptin levels only in middle-aged and aged rats (p<0.001), but
not in young. Additionally, there was a main effect of age (F=31.6, p<0.0001), such that middle-
aged animals on HFD and aged animals on either diet exhibited higher leptin than young adult
rats (p<0.05). There was a further significant increase in leptin levels in aged as compared to
middle-aged animals (p<0.05).
Another peripheral system that is vulnerable to the effects of HFD is the liver. We
examined the gene expression of CD68 as a measure of hepatic inflammation, and of SREBP-1
and SCD-1, as measures of liver fatty acid metabolism and lipogenesis. We found a significant
effect of age on CD68 mRNA levels (F=9.26, p=0.0005; Fig. 9B), in which aged animals had
higher levels than young animals across both diets (p<0.05). There was no significant effect of
diet, nor was there an interaction between age and diet on CD68 levels. Levels of the
transcription factor SREBP-1 were significantly affected by age (F=5.10, p=0.010; Fig. 9C) with
HFD-fed middle-aged and aged animals having decreased expression relative to young
animals. Additionally, there was a non-significant trend toward an effect of diet on SREBP-1
(F=3.99, p=0.053), but no significant interaction between age and diet. Finally, we found a
significant effect of diet on mRNA levels of SCD1 (F=23.54, p<0.0001; Fig. 9D), which was
significant only in middle-aged animals (p<0.0001). Though there was a trend towards an
interaction between age and diet on SCD1 levels, this failed to reach statistical significance
(F=2.80, p=0.073), and there was no significant effect of age.
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3.C.2. Peripheral effects of HFD: Diet and testosterone treatment
Examination of leptin levels in testosterone-treated animals showed a significant
interaction between age and diet (F=5.01, p=0.03; Fig. 9A). Post-tests revealed that the effect of
age was only significant in HFD-fed rats (p=0.002), whereas diet increased leptin levels in both
ages (p<0.01).
Assessment of gene expression in liver revealed a nonsignificant trend toward increased
CD68 levels with age (F=3.38, p=0.077; Fig. 9B) and no significant main effect of diet or
interaction between diet and age. However, there were significant effects of diet on levels of
both SREBP-1 (F=5.87, p=0.02; Fig. 9C), and SCD1 (F=15.57, p=0.0005; Fig. 9D). Posttests
revealed that for SREBP-1, HFD increased expression significantly in middle-aged animals only
(p=0.02), and for SCD1 HFD was associated with significantly decreased expression in both
middle aged and aged animals (p<0.05).
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Figure 9. Peripheral effects of control or high fat diet in young, middle-aged, and aged brown
Norway rats. A) Plasma leptin levels measured in ng/mL in young, middle-aged, and aged rats
on control or high fat diet, at the end of the experimental period. Relative mRNA expression in
the liver of B) CD68, a macrophage marker; C) SREBP1, a transcription factor regulating
lipogenesis and glycolysis; and D) SCD-1, a fatty acid metabolism enzyme, as determined by
rtPCR. Data show fold differences relative to the young rats on control diet. Animals not treated
with testosterone are shown in white, and animals treated with testosterone in gray; control diets
are solid white or gray bars, high fat diets are striped bars. a p < 0.05 relative to young rats in
same diet condition. b p < 0.05 relative to middle-aged animal in same diet condition. c p < 0.05
relative to control diet-fed rats of the same age. * p < 0.05 for main effect of age that does not
reach statistical significance in posttests. # p < 0.05 for main effect of diet that does not reach
statistical significance in posttests.
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3.D. Gliosis
3.D.1. Gliosis: Diet and aging
Both HFD and advanced age have been shown to increase activation states of microglia
and astrocytes, the two neural cell types most responsible for regulating neuroinflammation. We
examined the independent and interactive effects of diet and age on glial number and activation
state. Microglial reactivity was assessed by morphology. Figure 10A shows a resting microglial
cell, characterized by numerous thin, branching processes (type 1). Reactive cells typically have
enlarged rod-shaped cell bodies with fewer, thicker processes (type 2; Fig. 10B), or are
amoeboid (type 3; Fig. 10C). We first looked at microglial density in hippocampus, and observed
a significant main effect of age (F=22.95, p<0.001; Fig. 10D), in which middle-aged and aged
animals across both diets had greater numbers of cells than young animals (p<0.01). There was
no significant main effect of diet on the density of microglia. We then examined the proportion of
microglia with a reactive phenotype and found a significant interaction between age and diet
(F=4.25, p=0.021; Fig. 10E) such that HFD was associated with increased microglial reactivity in
young (p<0.001) and middle-aged (p<0.05), but not aged animals. Additionally, there was a
significant main effect of age (F=32.35, p<0.001), with middle-aged and aged animals having
higher proportions of reactive microglia than young animals across both diets (p<0.01).
We next performed parallel density and reactivity analyses with astrocytes. As shown in
Figure 11A-B, nonreactive astrocytes have normally sized somas with several long, thin
branches (Fig. 11A), whereas reactive astrocytes have enlarged somas and projections (Fig.
11B). We found no significant effect of either diet or age on astrocyte density (Fig. 11C).
However, there were significant main effects of both age (F=7.41, p<0.002) and diet (F=9.28,
p<0.004) on astrocyte reactivity (Fig. 11D). Between group analyses revealed that aged animals
had a higher percentage of reactive astrocytes than young animals (p=0.012), but the effect of
diet did not reach statistical significance when separated across ages.
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3.D.2. Gliosis: Diet and testosterone treatment
Next, we examined the relationship between aging and diet on inflammation outcomes in
middle-aged and aged animals that received testosterone supplementation. We found no
significant main effects or interactions on measures of microglial density (Fig. 10D) or the
proportion of reactive microglia (Fig. 10E). Similarly, there were no significant effects of diet or
age on astrocyte density (Fig. 11C). We found a significant main effect of age on the proportion
of reactive astrocytes (F=7.24, p=0.012; Fig. 11D), however, this effect was not significant when
examined separately across diets.
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Figure 10. Microglia number and morphological status as assessed by IBA-1
immunohistochemistry in young, middle-aged, and aged brown Norway rats across dietary and
testosterone treatments. A-C) Representative images of microglial morphology. Scale bar =
10µm. A) A type 1 or resting cell characterized by a small cell body with numerous, ramified
projections. Reactive cells are either B) type 2 cells with rod shaped cell bodies and fewer,
thicker processes, or C) amoeboid cells with no branches or filopodia. D) Densities of IBA-1
immunoreactive microglia in young, middle-aged, and aged rats on control or high fat diet were
quantified in hippocampus. E) Percentages of reactive microglia (types 2 and 3) were quantified
in hippocampus. Animals not treated with testosterone are shown in white, and animals treated
with testosterone in gray; control diets are solid white or gray bars, high fat diets are striped
bars. a p < 0.05 relative to young rats in same diet condition. b p < 0.05 relative to middle-aged
animal in same diet condition. c p < 0.05 relative to control diet-fed rats of the same age. * p <
0.05 for main effect of age that does not reach statistical significance in posttests. # p < 0.05 for
main effect of diet that does not reach statistical significance in posttests.
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Figure 11. Astrocyte number and morphological status as assessed by GFAP
immunohistochemistry in young, middle-aged, and aged brown Norway rats across diet and
testosterone treatments. A-B) Representative images of astrocyte morphology. Scale bar =
10µm. A) Non-reactive astrocytes have normally sized cell bodies and thin projections. B)
Reactive astrocytes have enlarged somas and thicker processes. C) Densities of GFAP
immunoreactive astrocytes and D) percentages or reactive astrocytes were quantified in
hippocampus of young, middle-aged, and aged rats on control or high fat diet and untreated or
treated with testosterone. Animals not treated with testosterone are shown in white, and animals
treated with testosterone in gray; control diets are solid white or gray bars, high fat diets are
striped bars. a p < 0.05 relative to young rats in same diet condition. b p < 0.05 relative to
middle-aged animal in same diet condition. c p < 0.05 relative to control diet-fed rats of the
same age. * p < 0.05 for main effect of age that does not reach statistical significance in
posttests. # p < 0.05 for main effect of diet that does not reach statistical significance in
posttests.
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3.E. Inflammatory gene expression
3.E.1. Inflammatory gene expression: Diet and aging
Another aspect of neuroinflammation that can be affected by HFD and aging is gene
expression of pro-inflammatory and glial factors. We used qPCR to examine gene expression of
several such genes in both hippocampus and hypothalamus. We found significant main effects
of age on hippocampal gene expression of IL1-b (F=5.41, p=0.002; Fig. 12A), CD68 (F=25.85,
p<0.001; Fig. 12B), and GFAP (F=8.19, p=0.001; Fig. 12C). Between group analyses revealed
that (1) on CTL diet, aged animals had greater levels of IL1-b than young animals (p=0.029); (2)
aged animals had higher expression of CD68 than both young and middle-aged animals on CTL
diet, and had higher levels than young animals on HFD (p<0.001), and (3) aged animals had
higher levels of GFAP than young animals on either diet (p<0.05). Neither diet nor the
interaction between age and diet significantly affected expression levels of these genes in
hippocampus.
Hypothalamic gene expression of IL1-b was not significantly affected by either age or
diet (Fig. 12D). There was a significant interaction between age and diet on levels of CD68
(F=3.68, p=0.034; Fig. 12E) and a trend towards an interaction effect on GFAP levels (F=2.98,
p=0.062; Fig. 12F), with diet increasing gene expression only in aged animals (p<0.01).
Additionally, there was a significant main effect of age on levels of CD68 (F=12.35, p<0.001)
and GFAP (F=7.69, p=0.002). Posttests revealed that on CTL diet middle-aged animals had
higher levels of CD68 than young animals (p<0.01), and on HFD aged animals had higher
levels of CD68 and GFAP, than both young and middle-aged animals (p<0.05).
3.E.2. Inflammatory gene expression: Diet and testosterone treatment
Among testosterone-treated rats, we found no significant effects of age, diet, or their
interaction on expression of IL1-b (Fig. 12A) and GFAP (Fig. 12C) in hippocampus. However,
90
there was an effect of age on levels of hippocampal CD68 (F=13.21, p=0.001; Fig. 12B), with
aged animals on CTL diet having higher CD68 than matched middle-aged animals (p=0.006).
Hypothalamic gene expression showed a significant interaction between age and diet on levels
of IL1-b (F=6.29, p=0.018; Fig. 12D), though this interaction failed to achieve statistical
significance in posttests. Likewise, we found a significant interaction between age and diet on
CD68 expression in hypothalamus (F=5.55, p=0.026; Fig. 12E), in which aged animals had
higher levels than middle-aged animals, but only on HFD (p=0.002). Finally, levels of
hypothalamic GFAP did not differ by age, diet, or their interaction (Fig. 12F).
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Figure 12. Inflammatory gene expression in young, middle-aged, and aged brown Norway rats
across diet and testosterone treatments. A-C) Relative mRNA levels in hippocampus were
determined by rtPCR for A) IL1b, a pro-inflammatory cytokine; B) CD68, a
microglia/macrophage marker; and C) GFAP, an astrocyte marker. D-F) Relative mRNA levels
for D) IL1b; E) CD68; and F) GFAP, were also determined in hypothalamus. Data show fold
differences relative to the young rats on control diet. Animals not treated with testosterone are
shown in white, and animals treated with testosterone in gray; control diets are solid white or
gray bars, high fat diets are striped bars. a p < 0.05 relative to young rats in same diet condition.
b p < 0.05 relative to middle-aged animal in same diet condition. c p < 0.05 relative to control
diet-fed rats of the same age. * p < 0.05 for main effect of age that does not reach statistical
significance in posttests. # p < 0.05 for main effect of diet that does not reach statistical
significance in posttests.
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3.F. Behavioral and neurogenesis outcomes
3.F.1. Behavioral and neurogenesis outcomes: Diet and aging
In order to determine whether age and diet modulate cognitive outcomes, we examined
behavior on the Barnes maze, a test of spatial reference learning and memory. We first looked
at learning behavior by examining the latency to reach the escape box on each trial during
training. Figures 13A-C show the latency averaged across the 3 trials per day. For all age and
diet groups the animals showed significant learning, as indicated by shorter latencies to reach
the escape box on days 2, 3, and 4, than on day 1 of training (p<0.05). However, rates of
learning differed across groups. There was a significant main effect of age across CTL diet
animals (F=7.02, p=0.004), with aged animals being slower at locating the escape platform than
both young (p=0.005), and middle-aged animals (p=0.045). However, this effect is likely due to
slower locomotion in aged animals, rather than to a learning disability, as all animals were able
to locate the escape box within 30 s by the last training day. There were no significant effects of
diet in any age group, suggesting that diet did not impair animals’ ability to learn the location of
the escape box.
When examining performance on the probe trial, we found a significant main effect of
age (F=6.79, p=0.003; Fig. 13F), such that on HFD, young animals approached the correct hole
more frequently than either middle aged (p=0.002) or aged animals (p=0.021). Likewise, when
looking at the percent of correct hole approaches (Fig. 13G), we found a trend towards a main
effect of age (F=2.54, p=0.089), though this failed to reach statistical significance. Neither diet,
nor the interaction between age and diet had significant effects on number or percent correct
hole approaches. There was a significant main effect of diet on errors (F=4.42, p=0.041; Fig.
13H), though this was not statistically significant when examined separately across the ages.
Finally, we found a significant main effect of diet on path length (F=4.54, p=0.039; Fig. 13I) that
did not reach statistical significance when examined across the age groups. However, our data
show a greater percent increase in path length associated with HFD in middle-aged (~132%)
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rats than in young (~70%) and aged (~80%) rats, suggesting that middle-aged animals were
most susceptible to the effects of HFD. Neither age, nor the interaction between age and diet,
had significant effects on errors or path length.
To examine neurogenesis, we analyzed DCX-labeled cell density in the dentate gyrus.
As shown in Figure 13J, there was a main effect of age (F=98.65, p<0.0001), such that, across
both diets, young animals had a higher number of DCX-labeled cells than middle-aged and
aged animals (p’s<0.0001). There was no significant effect of diet, or of an interaction between
diet and age, on DCX cell density.
3.F.2. Behavioral and neurogenesis outcomes: Diet and testosterone treatment
Behavior in the Barnes maze was also assessed in testosterone-treated animals. Both
middle-aged (Fig. 13D) and aged (Fig. 13E) rats located the escape box more quickly on day 2
and all subsequent days of training than on day one (p<0.05). There was a main effect of age
for both CTL diet (F=5.61, p=0.029) and HFD animals (F=14.6, p=0.001) with aged animals
taking longer to find the escape box. There was no significant effect of diet, nor was there an
interaction between age and diet on training latency.
Examination of probe trial performance yielded results similar to those observed in
animals not treated with testosterone. That is, we found a significant main effect of age (F=8.68,
p=0.005; Fig. 13F) with aged animals on CTL diet having less correct hole approaches than
middle-aged animals (p=0.026). Further, there again was a trend towards reduced correct hole
investigations with age (F=3.34, p=0.076; Fig. 13G). However, neither diet nor the interaction
between age and diet had significant effects on number or percent of correct hole investigations.
There were no significant main or interactive effects of aging and diet on the number of probe
trial errors (Fig. 13H). We found a significant main effect of age on path length (F=9.14,
p=0.005; Fig. 13I) such that in CTL diet animals, aged rats traveled further to locate the correct
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hole than did middle-aged (p=0.04). There was no main effect of diet, nor was there an
interaction between age and diet on path length.
When examining DCX-labeled cell density, we found a non-significant trend towards an
effect of age (F=3.73, p=0.064; Fig. 13J), but neither diet, nor the interaction between diet and
age, had a significant effect on DCX density.
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Figure 13. Effects of diet and testosterone on behavioral performance in the Barnes maze and
on neurogenesis in young, middle-aged, and aged brown Norway rats. A-C) Average latency to
located the escape box over the course of 4 days of training in A) young, B) middle-aged, and
C) aged male brown Norway rats maintained on control and high fat diet. D-E) Average latency
to locate the escape box in testosterone-treated D) middle-aged and E) aged rats fed control or
high fat diet. F) The number and G) percentage of correct hole visits during the Barnes maze
probe trial, 48 h after the last training trial. H) The number of errors made during the probe trial
and I) the distance traveled to locate the escape box for the first time during the probe trial. J)
Neurogenesis as measured by doublecortin-positive cell density in the dentate gyrus. Animals
not treated with testosterone are shown in white, and animals treated with testosterone in gray;
control diets are solid white or gray bars, high fat diets are striped bars. a p < 0.05 relative to
young rats in same diet condition. b p < 0.05 relative to middle-aged animal in same diet
condition. c p < 0.05 relative to control diet-fed rats of the same age. * p < 0.05 for main effect of
age that does not reach statistical significance in posttests. # p < 0.05 for main effect of diet that
does not reach statistical significance in posttests.
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3.G. Correlations
We examined correlations between various metabolic and inflammatory markers and
performance in the Barnes maze, specifically in animals from Experiment 1, results of which are
demonstrated in Table 5. We found that adiposity, as measured by RP fat pad weight,
significantly correlated with levels of leptin, GTT AUC, microglial and astrocyte activation, and
there was a trend towards a correlation between adiposity and correct hole entries, but not
errors or path length. Leptin again significantly correlated with GTT AUC and glial activation,
and trended towards correlating with correct hole entries, whereas GTT correlated significantly
only with microglial activation. Finally, microglial, but not astrocyte activation, correlated
significantly with correct hole entries, highlighting the role of microglia in this context.
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Measure Adiposity
(RP fat)
Leptin GTT
AUC
Microglia
activation
Astrocyte
activation
Correct
hole
entries
Errors Path
length
Adiposity
(RP fat)
_______ _______ _______ _______ _______ _______ _______ _______
Leptin .91**** _______ _______ _______ _______ _______ _______ _______
GTT AUC .40** .37* _______ _______ _______ _______ _______ _______
Microglia
Activation
.63**** .59**** .48*** _______ _______ _______ _______ _______
Astrocyte
Activation
.53**** .39** .12 .39** _______ _______ _______ _______
Correct
entries
-.28
T
-.28
T
-.24 -.38** -.21 _______ _______ _______
Errors .21 .22 .17 .23 .14 -.45** _______ _______
Path
length
.09 .17 .21 .33* .14 -.28
T
.66**** _______
Table 5. Correlations among metabolic, inflammatory, and behavioral outcomes. Data are
presented as Spearman r values. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.
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3.H. Effects of testosterone treatment
The main goals of this study were to examine (i) interactions between age and diet, and
(ii) the potential of testosterone to modulate age and diet outcomes. Because different sets of
animals were used in experiments 1 and 2, we cannot directly compare the effects of
testosterone treatment, but overall, testosterone appeared to have limited effects on the majority
of outcomes examined. When examining plasma testosterone levels, we found that the majority
of animals that had not been treated with testosterone had levels below the detection threshold.
Of those that did have detectable readings, young animals had an average of 0.256 ± 0.124
ng/mL; middle aged had 0.298 ± 0.129 ng/mL, and aged animals had 0.166 ± 0.004 ng/mL. All
testosterone-treated animals had detectable hormone levels, with middle aged animals having
an average of 0.382 ± 0.030 ng/mL, and aged animals having 0.294 ± 0.024 ng/mL. Thus,
testosterone treatment increased plasma testosterone levels in both middle-aged and aged rats.
We observed several subtle changes associated with testosterone treatment, that may
be of interest to examine in future studies. Though these effects cannot be statistically analyzed,
they are described briefly below. Specifically, testosterone treatment was associated with a
~21% reduction in gonadal fat mass (Fig. 7G) and a ~53% reduction in levels of plasma leptin
(Fig. 9A) in CTL-fed aged rats. Effects of testosterone in CTL-fed middle-aged animals were
more subtle, with only an ~8% decrease in gonadal fat mass and no change in leptin levels,
suggesting that testosterone may be protective against age-related metabolic dysregulation,
specifically at advanced ages.
Testosterone treatment did not appear to alter any inflammatory outcome in CTL-fed
rats. However, in untreated middle-aged animals HFD was associated with a ~25% increase in
microglial reactivity (Fig. 10E) as well as a ~25% increase in astrocyte reactivity (Fig. 11D).
Interestingly, in testosterone treated middle-aged animals HFD did not increase microglia or
astrocyte reactivity, suggesting that testosterone treatment blunted the effect of HFD in middle-
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aged animals. A similar effect of testosterone was observed in aged animals: in untreated aged
animals HFD was associated with a ~8% increase in microglial reactivity and a ~32% increase
in astrocyte reactivity, but HFD again did not increase microglial or astrocyte reactivity in
testosterone treated animals. Taken together, these findings suggest that testosterone may be
blunting or blocking the effects of HFD on gliosis.
Interestingly, in the Barnes maze, testosterone treatment in CTL-fed animals had
opposite effects at middle-age and old age. That is, testosterone was associated with a ~17%
decrease in path length (Fig. 13I) in middle-aged animals, but a ~56% increase in aged animals,
suggesting that it negatively affected cognitive performance in 26-month old rats. When
examining HFD-associated increases in path length in middle-aged animals, we found a ~132%
increase in untreated rats, that was reduced to a ~58% increase in testosterone treated rats,
thus muting the effect of HFD on behavioral performance at middle-age.
4. Discussion
The main goal of this study was to examine whether HFD differentially affected various
metabolic, inflammatory, and cognitive outcomes depending on the age of diet exposure. We
found that aging had the strongest adverse effects on a number of our outcome measures,
corroborating the idea that aging is the greatest driving force for negative health outcomes and
disease. For example, aging was associated with increased body weight, adiposity, and leptin
levels, suggesting a general shift towards worse metabolic outcomes with age. Indeed, previous
studies in rodent models also show increased adiposity with age (Wolden-Hanson et al., 1999;
Larkin et al., 2001), as well as greater weight gain in response to HFD in older animals (Erdos et
al., 2011). Moreover, both middle-aged and aged animals had increased microglial reactivity,
and aged animals showed higher expression of several pro-inflammatory factors. These findings
of increased inflammation with age are well documented in rodent models (Sierra et al., 2007;
Wu et al., 2007), as well as in humans (Freund et al., 2010).
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In addition to aging, HFD was associated with a number of adverse metabolic outcomes,
including body weight, adiposity, and leptin levels, as well as reduced glucose clearance in
young animals. Moreover, HFD was associated with increased microgliosis, without significantly
affecting astrocyte reactivity or cytokine expression. Though some studies show HFD-
associated increases in microglia and astrocyte reactivity as well as cytokines (Pistell et al.,
2010), others show changes in only a subset of neuroinflammatory markers (Setti et al., 2015),
or in some brain regions but not others (Guillemot-Legris et al., 2016). We also found that HFD
was overall associated with worsened spatial memory performance, which is consistent with
other reports (Molteni et al., 2002). Thus, age and diet were both independently associated with
a range of adverse metabolic, inflammatory, and behavioral changes.
In addition to independent effects of age and diet, the two factors exacerbated each
other’s effects in a number of instances. For example, we observed an effect of aging on
measures of ability to return to baseline in GTT, astrocyte reactivity, hypothalamic CD68 and
GFAP levels, and number of correct hole visits in the Barnes maze probe, specifically in aged
animals fed HFD. This finding of age and diet interactions was recently demonstrated in the
context of short-term HFD exposure, where only aged animals on HFD showed cognitive
impairments (Spencer et al., 2017). These data support the “two hit hypothesis” that has been
proposed for a number of diseases (Zhu et al., 2004; Hawkes et al., 2007), as well as in the
context of age-related inflammation (Franceschi et al., 2000). In this vein, aging may act as the
first hit increasing vulnerability of several systems to the adverse effects associated with a
second hit, like HFD. Such interactive effects between various risk factors are not often
experimentally addressed, though they may have important implications for prevention and/or
treatment of diseases.
Interestingly, we found that, in general, middle-aged animals were most vulnerable to
the adverse effects of HFD. That is, in response to HFD, middle-aged animals had the greatest
increases in body weight and adiposity, and showed diet effects in leptin, liver SCD1, and
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microglial activation, whereas young and aged animals had diet effects in some, but not all, of
these measures. Moreover, although not statistically significant, the effects of HFD on cognitive
performance in the Barnes maze was strongest in middle-aged animals. A number of studies in
the human literature have demonstrated increased risk for dementia (Fitzpatrick et al., 2009;
Emmerzaal et al., 2015) and accelerated brain aging (Ronan et al., 2016) in response to midlife
obesity. Effects of obesity at midlife have thus far not been thoroughly investigated in
experimental models. One study found that middle-aged rats showed memory impairments, loss
of dendrites, and microglial activation in response to a diet high in cholesterol and saturated
fatty acids (Granholm et al., 2008). However, no young or aged animals were included in this
study to examine whether middle age exacerbated these adverse outcomes. Thus, our study is
unique in its focus on comparing effects of HFD in young, middle-aged, and aged animals.
Though we did not examine the effect of midlife obesity on outcomes later in life in our animal
model, our data suggest that the exacerbated effect of HFD at midlife we observed may serve
as a catalyst that pushes the aging trajectory to poorer outcomes later in life.
In addition to aging and diet interactions, we also evaluated the effects of testosterone
treatment in a separate cohort of middle-aged and aged rats. Overall, we found very subtle
effects of testosterone treatment on our outcome measures. Specifically, testosterone reduced
adiposity and leptin levels in aged animals, prevented HFD-associated increases in microglial
activation in middle-aged, and astrocyte activation, and hypothalamic CD68 and GFAP levels, in
aged animals. The beneficial effects of testosterone supplementation in aging men have mainly
been demonstrated in the context of improving body composition and symptoms of metabolic
syndrome (Allan et al., 2008; Zitzmann, 2009; Traish et al., 2014), which is consistent with the
changes in adiposity we observed in our model. Additionally, our results on reduced HFD-
associated inflammation in testosterone-treated rats are supported by studies finding that
testosterone treatment in both hypogonadal men (Giltay et al., 2008; Kalinchenko et al., 2010),
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and in rodent models (Razmara et al., 2005; Jayaraman et al., 2014; Schwinge et al., 2015) is
associated with reduced inflammation.
Interestingly, testosterone treatment appeared to have opposite effects on cognition
depending on the age of the animals. That is, we found subtle, non-significant effects of
testosterone on cognitive performance in middle-aged rats, such that testosterone treatment
prevented the HFD-associated decline in performance seen in untreated animals. However,
testosterone treatment significantly reduced spatial memory in CTL-fed aged rats,
demonstrating an adverse effect of testosterone in these animals. Though some studies have
shown improved cognition with testosterone treatment in men (Janowsky et al., 1994; Cherrier
et al., 2005), as well as rodents (Frye and Seliga, 2001), many studies have failed to show an
association (Haren et al., 2005; Lu et al., 2006; Emmelot-Vonk et al., 2008; Puts et al., 2010).
Our finding that testosterone non-significantly protected against HFD-associated cognitive
impairment at middle age, but decreased performance in aged rats, suggests that age is an
important mediator of testosterone’s effects on cognition.
Though the exact pathways linking obesity and cognitive decline/AD are unknown, one
hypothesis is that metabolic impairments are driving the disease. It is well established that there
are changes in metabolic factors in the AD brain, and the disease has even been referred to as
Type 3 Diabetes (de la Monte and Wands, 2008). There is evidence that insulin deficiency and
resistance in the brain are associated with cognitive impairment and AD (Craft, 2005), and
reductions in insulin signaling are exacerbated as AD progresses (Rivera et al., 2005).
Moreover, high plasma insulin can increase levels of Ab42 in cerebrospinal fluid of healthy older
adults (Watson et al., 2003), and there is evidence that improving insulin sensitivity in AD
patients is associated with better cognitive outcomes (Watson et al., 2005; Reger et al., 2006).
However, we found that neither impairments in the glucose tolerance test, nor plasma leptin
levels or adiposity, significantly correlated with behavioral impairments. While these findings
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suggest that metabolic impairments did not predict behavioral outcomes, we did not specifically
examine glucose and insulin levels in brain and thus, cannot rule out their effects.
Another hallmark of aging and AD that has been proposed as a possible mechanism is
inflammation (Blasko et al., 2004; Glass et al., 2010). Levels of inflammatory cytokines are
associated not only with cognitive impairments in older adults (Schram et al., 2007; Trollor et al.,
2012), but also with greater risk for developing AD (Tan et al., 2007). Interestingly, it was
recently shown that inflammation at midlife is associated with greater loss of brain volume later
in life (Walker et al., 2017), which suggests that inflammation plays a role in the exacerbated
effects of obesity at middle age. Our findings of a significant correlation between microglial
reactivity and behavioral impairments supports the role of inflammation as a central factor in
mediating the effects of obesity on the brain. Interestingly, we found that astrocyte activation did
not significantly correlate with behavioral outcomes which may suggest that microglia have a
greater role in this relationship. This is supported by other studies showing a central role for
microglia in obesity (Tucsek et al., 2014; De Luca et al., 2016), including the finding that HFD
causes synaptic stripping by microglia (Hao et al., 2016).
Though we found that HFD increased adverse metabolic outcomes like adiposity and
leptin, and had subtle effects on glucose homeostasis, these changes did not correlate with
behavioral outcomes. Rather, increases in microglial activation significantly correlated with
behavior, suggesting that inflammation may be a more significant mediator of the effects of
obesity on the brain, than metabolic changes. This is supported by the finding that 8 weeks of
HFD-feeding resulted in cognitive deficits, decreased synaptic spines, and microglial activation,
but no changes in insulin or glucose levels (Bocarsly et al., 2015). In fact, inflammation may be
driving the metabolic changes seen with HFD-feeding and obesity (Maldonado-Ruiz et al.,
2017). For example, increased neuroinflammation can be observed after just 1 day of HFD-
feeding and before metabolic changes would occur (Thaler et al., 2012). Moreover, central
administration of pro-inflammatory cytokines has been shown to impair peripheral insulin
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signaling (Arruda et al., 2011), and exacerbate the effects of HFD-feeding (Oh-I et al., 2010).
Taken together, these findings suggest that inflammation is an early consequence of HFD and
may drive many of the adverse changes associated with obesity.
It is important to note that we observed relatively modest increases in body weight in
response to HFD, therefore, the changes we saw are likely due more to the effects of HFD than
obesity. It may be of interest in future studies to more specifically examine how the effects of
obesity rather than diet, differ across the lifespan. Additionally, we used only male rats in the
present study. Given that significant sex differences have been observed in the effects of
obesity (Hwang et al., 2010; Barron et al., 2013; Nadal-Casellas et al., 2013; Mauvais-Jarvis,
2015), future work should examine the extent to which aging and obesity interact differentially in
males and females.
Overall, our results show that the effects of HFD can vary at different ages, and that
middle age may present a particularly vulnerable time. Importantly, the rate of obesity peaks at
middle age, with ~43% of US adults between the ages of 40 and 59 classified as obese (Hales
et al., 2017). This represents a large population whose risk for a number of age-related
diseases, including AD, could be mitigated by lifestyle changes. Thus, determining the
underlying pathological processes that are driving adverse effects of obesity on the aging
trajectory will be critical.
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Chapter Four
The TLR4 antagonist TAK-242 blocks the adverse neural, but not metabolic, effects of diet-
induced obesity
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Abstract
Obesity is increasing at an alarming rate and is a major risk factor for a number of
diseases. Research has shown that obesity negatively impacts brain health, leading to
impairments in cognition and increasing the risks for Alzheimer’s disease (AD) and related
dementias. Though the mechanisms underlying these effects are largely unknown, one possible
pathway is inflammation. Obesity increases both neuro- and peripheral inflammation, and
chronic neuroinflammation causes neuronal cell death and tissue damage. One pathway that
has been shown to be particularly important in the relationship between obesity and
inflammation is toll-like receptor 4 (TLR4) signaling. TLR4 is a pattern recognition receptor that
can be activated by ligands including lipopolysaccharide and dietary saturated fatty acids, upon
which it drives expression of pro-inflammatory cytokines. Prior studies have shown that genetic
knockout or pharmacological blockade of TLR4 can protect peripheral tissues against metabolic
disturbances induced by a high fat diet in rodent models. However, the potential of TLR4
inhibition in reducing adverse neural outcomes of high fat diet has thus far not been
experimentally addressed. To investigate this issue, we used the approach of pharmacologically
blocking TLR4 signaling in a mouse model of diet-induced obesity. Male C57BL6/J mice were
fed either a control diet (10% fat), or a high fat diet (60% fat) beginning at 3 months of age.
Simultaneously, animals received daily intraperitoneal injections of either vehicle (saline) or the
specific TLR4 antagonist, TAK-242. Diet and drug treatments were administered for 12 weeks.
We examined outcomes on metabolic and behavioral measures, systemic and
neuroinflammation, and neural health markers. Our results demonstrate that TLR4 signaling
mediates the effect of obesity on inflammatory outcomes in peripheral tissues and in brain, as
well as on neurogenesis, without affecting metabolic outcomes. These findings demonstrate an
important role for TLR4 signaling in mediating the adverse effects of obesity on neural health.
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1. Introduction
The rising rate of obesity presents a major public health concern since obesity is linked
with increased risk for several diseases including type 2 diabetes, cardiovascular disease, and
cancer (Zheng et al., 2017). Research over the last decade has illustrated the adverse effects of
obesity on the brain and on neural health. Obesity is associated with decreases in hippocampal
volume and white matter integrity (Jagust et al., 2005; Ho et al., 2010; Stanek et al., 2011), as
well as with functional consequences that lead to accelerated cognitive decline (Elias et al.,
2005; Cournot et al., 2006), and increased risk of dementia (Whitmer et al., 2008). The harmful
effects of obesity on the brain have also been recapitulated in animal models of high fat diet-
feeding (Lindqvist et al., 2006; Julien et al., 2010; Park et al., 2010b; Pistell et al., 2010;
Jayaraman et al., 2014).
Though the exact ways in which obesity drives adverse neural health are still unknown,
one candidate mechanism is inflammation. Obesity is characterized by chronic, unresolved
inflammation in peripheral organs like adipose tissue (Weisberg et al., 2003; Zeyda and Stulnig,
2009) and liver (Park et al., 2010a), as well as in brain (Koga et al., 2014; Dorfman and Thaler,
2015). Additionally, obesity has been shown to increase recruitment of peripheral immune cells
into the brain (Buckman et al., 2014). Inflammation can be harmful to neurons in a number of
ways, including reduced neurogenesis (Ekdahl et al., 2003) and increased oxidative stress (Liu
et al., 2002; Qin et al., 2002).
One inflammatory signaling pathway that may be particularly important in driving obesity-
associated inflammation is the pattern recognition receptor toll-like receptor 4 (TLR4). TLR4 is
strongly activated by LPS and results in the downstream activation of NFkB and release of
inflammatory cytokines (Chow et al., 1999). Interestingly, TLR4 can also be bound by saturated
fatty acids, and may serve as the mechanism by which these dietary components increase
inflammation (Lee et al., 2001; Shi et al., 2006; Reyna et al., 2008; Schaeffler et al., 2009; Wang
et al., 2012b) and impair insulin signaling (Shi et al., 2006; Song et al., 2006). Indeed, TLR4
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expression has been shown to increase in various tissues in response to obesity (Reyna et al.,
2008; Vitseva et al., 2008; Ahmad et al., 2012; Jialal et al., 2012) and type 2 diabetes (Dasu et
al., 2010; Ahmad et al., 2012).
The role of TLR4 signaling in mediating effects of diet on peripheral tissues has been
well established. For example, mice with mutations rendering TLR4 nonfunctional are protected
against high fat diet (HFD)-induced glucose dysregulation (Poggi et al., 2007; Liang et al.,
2013), insulin resistance (Kim et al., 2007; Suganami et al., 2007), and inflammation (Jia et al.,
2014; Li et al., 2014; Kim et al., 2015). However, some studies find that these mice are not
protected against the entire range of metabolic and inflammatory effects of HFD (Ding et al.,
2012; Kim et al., 2015). Pharmacologically blocking TLR4 signaling also protects mice against
adipose inflammation and fibrosis associated with HFD (Vila et al., 2014). Notably, lack of TLR4
signaling does not protect against increases in body weight and adiposity that result from HFD-
feeding (Kim et al., 2007; Coenen et al., 2009; Saberi et al., 2009; Jia et al., 2014).
While a number of studies have shown a role for TLR4 signaling in mediating adverse
effects of HFD in peripheral systems, its role in brain has been explored far less. TLR4 signaling
has been shown to be involved in mediating diet-induced inflammation in the hypothalamus
(Milanski et al., 2009, 2012; Morari et al., 2014). However, whether and how it may be mediating
diet-induced effects in hippocampus is largely unknown. Given that microglia are thought to
drive many of the adverse effects of HFD in hippocampus (De Luca et al., 2016; Hao et al.,
2016), and TLR4 is highly expressed on microglia (Rehli, 2002; Vaure and Liu, 2014), it is
possible that TLR4 is also mediating diet-induced inflammation in this brain region. In order to
address this, we administered a TLR4 antagonist to mice fed HFD and evaluated outcomes on
metabolic indices, inflammation, neurogenesis and neuronal health, and behavior. Here we
report that blocking TLR4 signaling does not protect against weight gain and metabolic
dysregulation associated with HFD, but does protect against adipose inflammation and
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microgliosis, and restores neurogenesis. These findings support a role for TLR4 signaling in
HFD-induced neuroinflammation.
2. Methods and materials
2.A. Animal Procedures
Ten week old male C57BL6/J mice were purchased from Jackson Labs (Bar Harbor,
ME, USA) and allowed to acclimate to our facilities at the University of Southern California for 2
weeks. Animals were housed under a 12-hour light/dark cycle with lights on at 6 A.M. and ad
libitum access to food and water. At 12 weeks of age, mice were randomized to dietary and
drug treatments groups (N = 10-14/group) for a total of 4 experimental groups. Dietary
treatments were either control (CTL; 10% fat; #D12450J, Research Diets, New Brunswick, NJ,
USA) or high fat (HFD; 60% fat; #D12492, Research Diets). Drug treatments were either vehicle
(0.09% sterile saline) or the TLR4 antagonist TAK-242 (TAK; 3 mg/kg; #614316, EMD Millipore,
Billerica, MA, USA). Drugs were administered via intraperitoneal (IP) injection 6X/week. A
previous study reported that when administering TAK at 3 mg/kg via IP injection, brain levels of
the drug were sufficiently maintained for at least 24 h after administration (Hua et al., 2015).
Body weight was recorded daily while food consumption was recorded weekly.
At the end of the experimental treatment 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/0.3% NaN
3
until
processed for immunohistochemistry. Hippocampus was dissected from the remaining hemi-
brain and snap frozen to be used for RNA extraction. Blood was collected via cardiac puncture
in EDTA-coated tubes and centrifuged to separated plasma, which as was aliquoted and stored
at -80°C. Gonadal and retroperitoneal (RP) fat were dissected and weighed as a measure of
adiposity, and fat pads and liver were snap frozen for 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.
2.B. Body Composition
Body composition was determined one day prior to euthanizing the animals using the
Bruker LF90 Minispec (Bruker Optics, Billerica, MA, USA). Mice were placed in an acrylic
cylinder and loosely restrained inside using a plunger. The cylinder was then placed inside the
bore of the magnet and measurements of percent of fat, lean, and fluid mass were recorded.
Animals were returned to their home cages in less than 2 min.
2.C. Glucose, cholesterol, and triglyceride measurements
Beginning at week 0, and every 4 weeks thereafter, 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.
At the conclusion of the experiment, plasma cholesterol and triglyceride levels were
enzymatically measured. 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.
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2.D. 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 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).
2.D.1. Anxiety & Exploratory Activity: Open Field and Elevated Plus Maze (EPM)
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 were disinfected with 70% ethanol.
Open field testing was performed during week 8 of treatments. Briefly, animals were
placed into a 40 cm
2
Plexiglas 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; 3) crossings: the total number of times the animal
crossed a line entering a different square; 4) rearings: the number of times the animal stood on
its hind legs with its front paws and upper body in the air; 5) number of bouts of grooming, and
6) the number of times the animal defecated in the arena.
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)
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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.
2.D.2. Depression-like behavior: Forced swim test (FST)
The FST was conducted 1 week after fear conditioning, during week 11 and was the last
behavioral assessment. The protocol was adapted from (Can et al., 2012). Briefly, the animals
were placed into a 2 L cylindrical tank (20 cm height x 13 cm diameter) filled with 15 cm of
water. At this depth, the feet and tails of animals did not reach the floor of the cylinder. Water
was heated to 23-25°C and mice were placed into the cylinder for 5 min. Each session 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 and the total time spent immobile, as well as the duration of the longest bout of
immobility were recorded.
2.D.3. Learning & memory: Spontaneous alternation behavior (SAB) and fear conditioning
SAB was tested in the Y-maze during week 10 of treatments. 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 48 h after SAB, over 3 consecutive days. In this task,
mice are presented with a neutral stimulus (tone) in addition to an aversive stimulus (foot
shock). After repeated pairings, the animals learn to associate the stimuli and will display fear
behavior, in the form of freezing, to the tone. The fear conditioning chamber includes a box
(17cm x 17 cm x 25 cm) with an electrified grid floor, placed inside a sound attenuated chest.
White noise was used to block out external sounds.
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On day 1, animals were placed in the conditioning chamber, and after a 3 min
habituation 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. 24 h 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.
2.E. Immunohistochemistry and quantification
Fixed hemi-brains were exhaustively sectioned at 40 µm in the horizontal plane, using a
vibratome (Leica Biosystems, IL, USA). A standard avidin/biotin peroxidase approach using
ABC Vector Elite kits (Vector Laboratories, CA, USA) was used to perform
immunohistochemistry. Every eighth section was processed for ionized calcium binding adaptor
molecule 1 (IBA-1), doublecortin (DCX), and bromodeoxyuridine (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 10mM EDTA, pH 6.0 for 10 min, then
rinsed in water three times for 5 min each. For DCX staining, tissue was pretreated 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, then incubated in 2N HCl at 37°C for 30 min,
followed by 10 min in 0.1M boric acid and rinsing in TBS. Following the various antigen retrieval
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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 hour 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,
VA, USA); DCX (#sc-271390, 1:1000 dilution, Santa Cruz Biotechnology, TX, USA); or BrdU
(#MCA2483, 1:200 dilution, Bio-Rad, 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 at 40x
magnification (Olympus, BX50, CASTGrid software, Olympus, Tokyo, Japan). As described in
previous studies (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
defined as having spherical cell bodies with many thin and highly ramified 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 brain sections for a total of 64 fields and an average of ~450 cells per brain. Because
microglia show an increase in cell body size as they become activated, we also examined
microglial soma size, specifically in the CA1 subregion of the hippocampus. Images were
digitally captured using an Olympus BX50 microscope and DP74 camera paired with a
computer running CellSens software (Olympus). Microglial cell bodies were outlined and their
size was determined using NIH ImageJ 1.50i (US National Institutes of Health, MD, USA).
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DXC- and BrdU-positive cells were also quantified using live imaging and a 100x oil immersion
lens (Olympus). Cells were counted in the subgranular zone and granule cell layer of the
dentate gyrus, across 8 sections per animal.
2.F. RNA isolation and quantitative PCR
RNA was extracted from the gonadal fat pads and the hippocampus using TRIzol
reagent (Invitrogen Corporation, CA, USA), following the manufacturer’s protocol. To remove
any remaining DNA contamination, the RNA pellet was treated with RNase-free DNase I
(Epicentre, 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. All samples were run in duplicate, and PCR products were
normalized with corresponding b-actin expression levels in brain, and SHDA levels in adipose
tissue. The DD-CT method was used to determine relative mRNA levels. Both hippocampus and
adipose tissue were probed for levels of EGF-like module-containing mucin-like hormone
receptor-like 1 (F4/80), cluster of differentiation factor 68 (CD68), CD74, tumor necrosis factor a
(TNFa), interleukin 1b (IL1b), and interleukin 6 (IL6). Additionally, hippocampal tissue was
probed for major histocompatibility complex class II (MHC II), and the amyloid-b production and
clearance factors b-site APP leaving enzyme (BACE1) and neprilysin. Primer pair sequences
are shown in Table 6.
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Target Gene Sequence
EGF-like module-containing mucin-
like hormone receptor-like 1
(F4/80)
Forward: 5’-TGCATCTAGCAATGGACAGC-3’
Reverse: 5’-GCCTTCTGGATCCATTTGAA-3’
Cluster of differentiation factor 68
(CD68)
Forward: 5’-TTCTGCTGTGGAAATGCAAG-3’
Reverse: 5’-AGAGGGGCTGGTAGGTTGAT-3’
Cluster of differentiation factor 74
(CD74)
Forward: 5’-CAAGTACGGCAACATGACCC-3’
Reverse: 5’-GCACTTGGTCAGTACTTTAGGTG-3’
Tumor necrosis factor a (TNFa) Forward: 5’-CCCTCACACTCAGATCATCTTCT-3’
Reverse: 5’-GCTACGACGTGGGCTACAG-5’
Interleukin 1b (IL1b) Forward: 5’-GCAACTGTTCCTGAACTCAACT-3’
Reverse: 5’-ATCTTTTGGGGTCCGTCAACT-
3’
Interleukin 6 (IL6) Forward: 5’-CTCTGGGAAATCGTGGAAAT-3’
Reverse: 5’-CCAGTTTGGTAGCATCCATC-3’
Major histocompatibility complex
class II (MHC II)
Forward: 5’-CAGACGCCGAGTACTGGAAC-3’
Reverse: 5’-CAGCGCACTTTGATCTTGGC-3’
b-site APP leaving enzyme
(BACE1)
Forward: 5’-TCGCTGTCTCACAGTCATCC-3’
Reverse: 5’-AACAAACGGACCTTCCACTG-3’
Neprilysin Forward: 5’-GAGAAAAGCCCACTTGCTTG-3’
Reverse: 5’-GAAAGACAAAATGGGGCAGA-3’
Table 6. Primer sequences. Gene targets for the rtPCR analyses are listed with their
corresponding oligonucleotide sequences for the forward and reverse primers.
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2.G. Statistical Analyses
All data was analyzed using Prism software (version 7, GraphPad Software). Two-way
repeated measures ANOVAs were run for the analysis 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), and significance was set at a
threshold of p<0.05.
3. Results
3.A. Obesity-associated outcomes of high fat diet feeding
We first examined measures of diet-induced obesity in vehicle- and TAK- treated
animals in order to assess whether drug treatments had altered the effects of HFD (Fig.
14A&B). The control diet was associated with a ~5% weight gain in both vehicle- and TAK-
treated mice, whereas HFD was associated with a 39 ± 4.16% increase in body weight in
vehicle-treated mice and a 34 ± 3.01% increase in TAK-treated mice (Fig. 14A). A two-way
repeated measures ANOVA revealed that HFD significantly increased body weight (F=38.9,
p<0.0001). There was no 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), and this was true for both vehicle- and TAK-treated groups. When
examining final body weight, we found a main effect of diet (F=63.88, p<0.0001; Fig. 14B),
which was significant across both drug treatments (p<0.0001). There were no main effects of
drug or interactions between diet and drug on measures of body weight.
HFD is associated with increased adiposity; thus, we examined fat deposition by both
NMR using the Bruker LF90 Minispec, and by weighing gonadal and RP fat pads. We found that
HFD was associated with a significant increase in percent body fat (F=98.7, p<0.0001; Fig.
119
14C), in both vehicle- and TAK-treated mice (p<0.0001). There was no interaction effect
between diet and drug, nor was there a significant main effect of drug on percent body fat. The
same pattern was found when examining individual fat depots, such that HFD significantly
increased weight of both gonadal fat pads (F=108.4, p<0.0001) as well as RP fat pads
(F=117.1, p<0.0001), and there were no interaction effects or main effects of drug (data not
shown).
Another established outcome of HFD is dysregulation of glucose homeostasis. We
examined both changes in fasting glucose levels over the 12-week treatment period, as well as
glucose clearance efficiency in GTT. There was a main effect of diet on glucose clearance in
GTT (F=49.95, p<0.0001; Fig. 14D), that was significant in both vehicle- and TAK-treated mice
(p<0.05). We also calculated the AUC for GTT and again, found a significant effect of diet
(F=55.11, p<0.0001; Fig. 14E), such that HFD increased AUC regardless of drug treatment.
Additionally, there was a significant main effect of diet on percent change in glucose levels from
baseline to the end of the 12-week diet treatment (F=13.7, p<0.001; Fig. 14F). However, post-
tests revealed that the effect of HFD on increasing fasting glucose was only significant in
vehicle-treated (p<0.01), not in TAK-treated animals. There were no significant interaction
effects, nor main effects of drug treatment on any measure of glucose homeostasis.
Finally, we examined the effects of diet and drug treatments on levels of plasma
triglycerides and cholesterol. HFD significantly increased triglyceride levels (F=15.64, p<0.001;
Fig. 14G) in both drug treatment groups (p<0.05), and there was a non-significant trend towards
diet increasing cholesterol levels (F=3.41, p=0.07; Fig. 14H). There were no main effects of
drug, nor were there interactions between diet and drug on either triglycerides or cholesterol.
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Figure 14. Metabolic outcomes associated with diet-induced obesity in mice treated with vehicle
or the TLR4 antagonist, TAK-242. A) Body weights in male C57BL6/J mice maintained on control
and high fat diets and drug treatments taken at baseline (week 0) and four-week intervals across
the 12-week experimental period, and B) body weights at the end of the treatment period. C)
Adiposity as measured by percent body fat via NMR scan. D) Glucose tolerance test showing
blood glucose levels over time after administration of a glucose bolus, and E) area under the
curve (AUC) for the glucose tolerance test. F) Percent change in fasting blood glucose levels
relative to baseline after 12 weeks of control or high fat diet. G) Plasma triglyceride levels and H)
plasma cholesterol levels at the end of the experimental period. Data are presented as mean
(±SEM) values; n=10-14/group. 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. * p
< 0.05 relative to drug treatment-matched mice in control diet condition. # p < 0.05 relative to
vehicle-treated mice in same diet condition.
A B C
D E F
G H
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3.B. TAK-242 significantly reduced the effects of HFD on peripheral inflammation
HFD and obesity are known to increase inflammation in a number of organs, including
adipose tissue. Thus, we examined gene expression of markers of macrophage activation and
inflammatory cytokines in gonadal fat. We found a significant effect of diet on adipose tissue
levels of F4/80 (F=10.06, p<0.01; Fig. 15A), CD68 (F=17.14, p<0.001; Fig. 15B), CD74 (F=7.42,
p<0.05; Fig. 15C), IL6 (F=6.52, p<0.05; Fig. 15D), TNFa (F=10.85, p<0.01; Fig. 15E), and IL1b
(F=9.99, p<0.01; Fig. 15F). However, for the markers of F4/80, CD68, IL6, and TNFa, this effect
was only significant in vehicle-treated HFD-fed mice, and did not reach statistical significance in
TAK-treated HFD-fed mice. For CD74 and IL1b 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 any of genes probed for.
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Figure 15. Adipose tissue inflammatory gene expression in mice fed control or high fat diets and
treated with vehicle or the TLR4 antagonist, TAK-242. Relative mRNA levels were determined by
rtPCR for the macrophage markers, A) F4/80 and B) CD68, and the innate immune marker C)
CD74. Adipose tissue was also probed for expression of the inflammatory cytokines D) IL6, E)
TNFa, and F) IL1b. Data show fold differences relative to vehicle-treated mice fed a control diet.
Control diet-fed mice are shown as circles, high fat diet-fed mice are shown as squares; vehicle-
treated are open symbols or bars, TAK-242-treated are filled symbols or bars. * p < 0.05 relative
to drug treatment-matched mice in control diet condition. # p < 0.05 relative to vehicle-treated
mice in same diet condition. a p < 0.05 for main effect of diet that does not reach statistical
significance in posttests.
A B
D E
C
F
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3.C. TAK-242 prevented HFD-induced hippocampal gliosis
In addition to causing inflammation in peripheral tissues, HFD is associated with
increased neuroinflammation. Because microglia express high levels of TLR4 receptor and have
been shown to become reactive in response to HFD, we examined microgliosis in brain sections
stained for IBA-1. We analyzed both cell density and morphology in entorhinal cortex, and in the
subiculum, CA1, and CA2/3 of the hippocampus. Figures 16A-C show a resting (Type 1; Fig.
16A) microglia, with multiple, thin processes, and activated microglia with either a rod shaped
cell body and fewer, thicker processes (Type 2; Fig. 16B), or amoeboid cells (Type 3; Fig. 16C).
When examining microglial density, we found no significant effects of diet, drug treatment, nor
an interaction between these factors in entorhinal cortex (Fig. 16D), or in subiculum (Fig. 16F),
CA1 (Fig. 16H), or CA2/3 (Fig. 16J) of the hippocampus. However, we found significant
interactions between diet and drug treatment on microglial activation in entorhinal cortex
(F=36.27, p<0.0001; Fig. 16E), subiculum (F=38.93, p<0.0001; Fig. 16G), CA1 (F=47.16,
p<0.0001; Fig. 16I), and CA2/3 (F=31.7, p<0.0001; Fig. 16K). Post-tests revealed that, across
all brain regions, HFD increased microglial reactivity exclusively in vehicle-treated animals, and
TAK significantly reduced microglial reactivity exclusively in HFD-fed animals.
As microglia become activated, their cell bodies enlarge; thus, we measured microglial
soma size as another 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; Fig. 16L),
such that diet significantly increases soma size only in vehicle-treated animals (p<0.0001), and
TAK significantly decreases soma size only in HFD-fed animals (p<0.0001).
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A B C
D E
F G
H
I
J K
L
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Figure 16. Microglial number, morphological status, and soma size as assessed by IBA-1
immunohistochemistry in control- and high fat diet-fed mice treated with vehicle or the TLR4
antagonist, TAK-242. A-C) Representative images of microglial morphology. Scale bar = 10µm.
A) Resting (type 1) microglial cells are characterized by small cell bodies with numerous
branching processes. Reactive microglia are either B) type 2 cells with rod shaped cell bodies
and fewer, thicker projections, or C) amoeboid cells with no processes or with filopodia. Densities
of IBA-1 immunoreactive cells were quantified in D) entorhinal cortex, and in the F) subiculum, H)
CA1, and J) CA2/3 of the hippocampus. Percentages of reactive microglia (type 2 and 3 cells)
were quantified in E) entorhinal cortex, and in the hippocampal subregions G) subiculum, I) CA1,
and K) CA2/3. L) Microglial soma size was assessed specifically in CA1 of the hippocampus.
Data are presented as mean (±SEM) values; n=10/group. Control diet-fed mice are shown as
circles, high fat diet-fed mice are shown as squares; vehicle-treated are open symbols or bars,
TAK-242-treated are filled symbols or bars. * p < 0.05 relative to drug treatment-matched mice in
control diet condition. # p < 0.05 relative to vehicle-treated mice in same diet condition.
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3.D. Gene expression of inflammatory factors and amyloid-b enzymes
One possible outcome of microgliosis is the increased release of inflammatory cytokines.
Thus, we examined gene expression levels of a number of microglia/macrophage markers and
inflammatory cytokines in hippocampus. We found no significant effects of diet or drug
treatment, nor an interaction between these factors on hippocampal gene expression of F4/80
(Fig. 17A), CD68 (Fig. 17B), CD74 (Fig. 17C), or on expression of the pro-inflammatory
cytokines IL6 (Fig. 17D), TNFa (Fig. 17E), or IL1b (Fig. 17F). There was, however, a significant
main effect of drug reducing levels of MHC II (F=6.50, p<0.05; Fig. 17G), that did not reach
statistical significance across diets.
Additionally, we examined gene expression of the amyloid-b production factor, BACE1,
and the clearance factor, neprilysin. We found a significant interaction between diet and drug
treatment on levels of BACE1 (F=4.90, p<0.05; Fig. 17H). Post-tests revealed that diet
significantly increased BACE1 only in vehicle-treated animals (p<0.05), and there was a non-
significant trend towards TAK decreasing BACE1 in HFD-fed mice (p=0.07). Levels of neprilysin
were not significantly affected by either diet or drug treatment (Fig. 17I).
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Figure 17. Hippocampal gene expression of inflammation-related factors and amyloid-b factors
in mice fed a control or high fat diet and treated with vehicle or the TLR4 antagonist, TAK-242.
Relative mRNA levels were determined by rtPCR for the macrophage markers A) F4/80 and B)
CD68, and for the innate immune marker C) CD74. Expression of the pro-inflammatory cytokines
D) IL6, E) TNFa, and F) IL1b were also examined, as well as expression of the microglia-specific
antigen presentation complex, G) MHC II. Gene expression levels of the amyloid-b production
factor H) BACE1, and the clearance factor I) neprilysin in hippocampus. Data show fold
differences relative to vehicle-treated mice fed a control diet. Control diet-fed mice are shown as
circles, high fat diet-fed mice are shown as squares; vehicle-treated are open symbols or bars,
TAK-242-treated are filled symbols or bars. * p < 0.05 relative to drug treatment-matched mice in
control diet condition. # p < 0.05 relative to vehicle-treated mice in same diet condition. b p < 0.05
for main effect of drug treatment that does not reach statistical significance in posttests.
A B C
D E F
G H I
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3.E. TAK-242 increased survival of new neurons but not cell proliferation
Another effect of HFD previously observed in the brain is a reduction in neurogenesis.
Thus, we examined both DCX-labeled and BrdU-lableled cells in the dentate gyrus of the
hippocampus. Figure 18 shows representative images of DCX immunohistochemistry in control
diet-fed mice treated with vehicle (Fig. 18A), or TAK (Fig. 18B), and HFD-fed mice treated with
vehicle (Fig. 18C) or TAK (Fig. 18D). When quantifying the density of DCX-labeled cells, we
found a significant main effect of drug (F=5.01, p<0.05; Fig. 18E). Posttests revealed that this
effect of drug treatment was significant only in HFD-fed mice (p<0.05). In addition, there was a
non-significant trend towards an interaction between diet and drug (F=3.05, p=0.08; Fig. 18E).
There was no significant effect of diet on DCX-labeled cells. We also examined BrdU-labeled
cells but found neither significant main effects or diet or drug, nor an interaction between these
factors (Fig. 18F), suggesting that our drug treatment specifically increased survival of newborn
neurons, rather than cell proliferation in general.
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Figure 18. Neurogenesis and cell proliferation as assessed by DCX and BrdU
immunohistochemistry in mice maintained on control or high fat diets and treated with vehicle or
the TLR4 antagonist, TAK-242. A-D) Representative images of DCX immunohistochemistry in
mice treated with A) control diet and vehicle, B) control diet and TAK-242, C) high fat diet and
vehicle, and D) high fat diet and TAK-242. Scale bar = 50µm. E) Densities of DCX
immunoreactive and of F) BrdU-positive cells were quantified in the dentate gyrus. Data are
presented as mean (±SEM) values; n=10/group. Control diet-fed mice are shown as circles,
high fat diet-fed mice are shown as squares; vehicle-treated are open symbols or bars, TAK-
242-treated are filled symbols or bars. * p < 0.05 relative to drug treatment-matched mice in
control diet condition. # p < 0.05 relative to vehicle-treated mice in same diet condition.
B C D
E F
A
130
3.F. Exploration, anxiety-like, and depressive-like behaviors: Open field, elevated plus maze,
and forced swim test
To examine exploratory/locomotor activity and anxiety-like behavior we tested animals in
the open field. We found 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; Fig.19A), such that
HFD increased center crossings only in TAK-treated mice (p<0.05). Moreover, there was a
significant main effect of diet on time spent in the center field (F=4.23, p<0.05; Fig. 19B), such
that HFD again increased this measure specifically in TAK-treated animals (p<0.05). There
were no main or interaction effects on other open field measures including general locomotion
(Fig. 19C), rearing, grooming, or defecation (data not shown).
As another measure of anxiety-like behavior, we examined behavior in the elevated plus
maze (EPM), and found no main or interaction effects on the measures of time spent in the
open arm (Fig. 19D), open arm entries (Fig. 19E), or latency to enter the open arm (Fig. 19F).
Thus, although there were subtle changes in exploratory behavior specifically in TAK-treated,
HFD-fed mice in the open field, neither diet nor drug treatment significantly affected anxiety-
related behaviors in the EPM.
We tested mice in the forced swim test as a measure of depressive-like behavior. There
were no main effects of diet or drug, nor was there an interaction between these factors on the
amount of time (Fig. 19G) or the number of times (Fig. 19H) mice stopped swimming, nor were
there effects on the longest duration of immobility (Fig. 19I). Thus, neither the diet nor drug
treatment significantly affected depressive-like behavior.
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Figure 19. Exploration, anxiety-like, and depressive-like behaviors in control- and high fat diet-
fed mice treated with vehicle or the TLR4 antagonist, TAK-242. A-C) Explorative and anxiety-like
behaviors were examined in the open field. A) The number of times animals entered the center
square of the open field and B) the amount of time they spent in the center field. C) General
locomotor activity as assessed by the total number of square crossings. D-F) Anxiety-like behavior
was assessed in the elevated plus maze. D) The amount of time spent in the open arm of the
maze, and E) the number of times the animals crossed into the open arm. F) The latency to enter
the open arm for the first time. G-I) Depressive-like behaviors were examined in the forced swim
test. G) The total amount of time the animals spent immobile and H) the number of times the
animals were immobile. I) The length of the single longest time spent immobile. Data are
presented as mean (±SEM) values; n=10-14/group. Control diet-fed mice are shown as circles,
high fat diet-fed mice are shown as squares; vehicle-treated are open symbols or bars, TAK-242-
treated are filled symbols or bars. * p < 0.05 relative to drug treatment-matched mice in control
diet condition. # p < 0.05 relative to vehicle-treated mice in same diet condition.
A B C
D E
G H I
F
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3.G. Behavioral performance in cognitive tasks
Short-term working memory was assessed in the spontaneous alternation behavior
(SAB) task. Total arm entries did not vary by either diet or drug treatment (Fig. 20A). However,
there was a main effect of diet on alternation behavior (F=7.86, p<0.01; Fig. 20B), which was
significantly only in TAK-treated mice (p<0.05).
As a measure of hippocampal-dependent and -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 20C 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; Fig. 20D), 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
that had 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 (Fig. 20E). Thus, overall, we
found very subtle effects of diet on some measures of cognitive performance.
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Figure 20. Working memory and cued and contextual memory performance in mice maintained
on control or high fat diet and given vehicle or the TLR4 antagonist, TAK-242. A-B) Short-term
working memory was assessed by spontaneous alternation behavior. A) Total arm entries in the
Y maze. B) Alternation behavior in the Y maze. C-E) Cued and contextual memory was tested
in the fear conditioning paradigm. C) Learning was assessed by examining freezing in during
the trace period between the tone and shock, during the 5
th
trial on the training day. D) Cued
memory was tested 24 h later in a different context, by examining time spent freezing after the
first presentation of the tone. E) Contextual memory was assessed 24 h after the cued test, by
placing animals back into the same context as during training and recording the time spent
freezing. Data are presented as mean (±SEM) values; n=10-14/group. Control diet-fed mice are
shown as circles, high fat diet-fed mice are shown as squares; vehicle-treated are open symbols
or bars, TAK-242-treated are filled symbols or bars. * p < 0.05 relative to drug treatment-
matched mice in control diet condition. # p < 0.05 relative to vehicle-treated mice in same diet
condition. a p < 0.05 for main effect of diet that does not reach statistical significance in
posttests.
A B
C D E
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4. Discussion
The goal of this study is to examine the role of inflammatory TLR4 signaling in mediating
the effects of obesity on neural outcomes. Comparing animals fed control or HFD and treated
with or without the TLR4 antagonist, TAK-242, we demonstrate that blocking TLR4 signaling
reduced HFD-induced adipose tissue inflammation, microgliosis, and restored neurogenesis in
the dentate gyrus. However, TAK-242 treatment did not improve the metabolic dysregulation
induced by HFD-feeding. The finding that blocking TLR4 signaling did not protect against effects
of HFD on weight gain and adiposity is consistent with numerous other studies (Kim et al., 2007;
Poggi et al., 2007; Suganami et al., 2007; Coenen et al., 2009; Saberi et al., 2009; Ding et al.,
2012; Jia et al., 2014). 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 (Kim et al., 2007; Poggi et al., 2007; Suganami et al., 2007; Saberi et al., 2009; Liang et
al., 2013; Jia et al., 2014; Li et al., 2014). One possible reason for this discordant finding is that
many of these studies used mice with loss of function mutations in TLR4, or transgenic
knockouts of TLR4, whereas we used a pharmacological approach to blocking TLR4.
Constitutive absence of TLR4 signaling may result in metabolic changes even in the absence of
HFD, and are likely to have different outcomes than pharmacological administration of a TLR4
antagonist.
Our findings of reduced adipose tissue inflammation in HFD-fed mice treated with TAK-
242 agrees with previous findings (Poggi et al., 2007; Suganami et al., 2007; Saberi et al., 2009;
Jia et al., 2014; Kim et al., 2015). Importantly, we demonstrate here that blocking TLR4
signaling prevents HFD-induced microgliosis in hippocampus. To our knowledge, this is the first
study demonstrating that TLR4 signaling is necessary for the effects of HFD on hippocampal
microglial activation. Interestingly, we did not find significant effects of either diet or drug
treatment on levels of inflammatory cytokine in hippocampus. Though some studies find that
HFD increases both microglial activation and inflammatory cytokines (Pistell et al., 2010), others
135
show that only a subset of markers are changed with HFD (Setti et al., 2015), or only in some
brain regions but not others (Guillemot-Legris et al., 2016). Additionally, our drug treatment is
likely to have acted mainly on microglia in the brain, as expression of TLR4 is significantly
higher in microglia than in astrocytes (Vaure and Liu, 2014). This is in line with our finding of a
main effect of diet, that trended towards a statistically significant increase of MHC II only in
vehicle-treated animals. Importantly, MHC II is expressed solely by microglia (Bö et al., 1994),
and has been shown to be upregulated in AD brains (McGeer et al., 1988; Perlmutter et al.,
1992). Moreover, our 12-week dietary treatment may not have been sufficient to result in
increased hippocampal cytokine expression. This is consistent with recent findings in our lab
that show increased adipose, but not hippocampal, cytokine expression after 12 weeks of
western diet feeding (Moser and Pike, 2017).
We also examined hippocampal gene expression of two enzymes related to production
and clearance of the AD-related amyloid-b protein. HFD is known to alter expression and/or
activity of these factors (Standeven et al., 2011; Maesako et al., 2015). Additionally, it has been
shown that inflammation can increase levels of the amyloid-b production factor, BACE1 (Sastre
et al., 2008), and decrease the clearance factor, neprilysin (Wong et al., 2011). We found that
HFD increased levels of BACE1 in vehicle-treated, but not in TAK-treated mice, with not
changes in levels of neprilysin. This suggests that HFD caused a shift towards more pro-
amyloidogenic processing in the hippocampus, which was prevented by blocking TLR4
signaling.
When examining the effects of diet and drug treatments on neurogenesis, we found that
blocking TLR4 increased DCX-positive cells in dentate gyrus, specifically in HFD-fed mice.
Numbers of BrdU-positive cells were not affected by diet or drug treatments, suggesting that the
protective effect of TAK was specifically on survival of newborn neurons, rather than on cell
proliferation in general. The effects of HFD on neurogenesis and cell proliferation are somewhat
136
varied in the literature, with some studies finding decreases in both (Kim et al., 2009b; Yoo et
al., 2011), and some finding changes only in one marker (Tozuka et al., 2009; Park et al.,
2010b), or in neither marker (Rivera et al., 2011). Differences in both the composition of the diet,
as well as the duration of exposure may affect the extent to which cell proliferation and/or
survival of newborn neurons are affected by HFD. Our finding of increased neurogenesis with
TAK treatment in HFD-fed mice highlights a novel role for TLR4 signaling. These effects are
likely mediated by microglia, which have previously been shown to be harmful to neurogenesis
during states of activation, such as after LPS (Monje et al., 2003; Cacci et al., 2008) or seizure
(Ekdahl et al., 2003). Thus, the effect of TAK on increased neurogenesis is likely due to reduced
microgliosis in response to HFD.
We found very subtle effects of diet and drug treatments on overall behavioral outcomes.
Specifically, mice fed HFD and treated with TAK showed 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 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 demonstrate cognitive impairments after HFD exposure (Jurdak et al., 2008;
Hwang et al., 2010; Kosari et al., 2012; Kaczmarczyk et al., 2013; Arnold et al., 2014), others do
not (Mielke et al., 2006; Lavin et al., 2011; Tucker et al., 2012; Li et al., 2013a). The age at
which rodents are exposed to dietary treatments may be a factor in this. While one study found
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), another found behavioral impairments in response
to HFD in aged, but not young adult rats (Spencer et al., 2017). These studies suggest that
young adult rodents may be somewhat resistant to the effects of HFD on behavior.
Furthermore, the lack of effects of HFD on behavior may in part be due to the length of
exposure to the diet, as others have shown that 12 weeks of HFD do not impair behavior on
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SAB or novel object recognition (Lavin et al., 2011), or found impairments after 24 weeks of
HFD-feeding, but not after 8 weeks (Anderson et al., 2014). Our lab has previously
demonstrated behavioral impairments after 16 weeks of HFD-feeding (Lee et al., unpublished;
Barron et al., 2013; Christensen and Pike, 2017). Because we observe significant metabolic
dysregulation after 12 weeks of HFD, and because the paradigm in the present study required
daily injections, which can be stressful for animals, we elected to use a 12-week diet exposure.
While we did observe metabolic changes, adipose inflammation, and microgliosis, it may be the
case that this length of time is not sufficient to observe behavioral changes. In agreement with
this, we did not observe a significant effect of diet on DCX-labelled cells, but rather an effect of
TAK in HFD-fed mice. Perhaps longer exposure to HFD would have resulted in significantly
decreased neurogenesis in vehicle-treated mice, which would then have impaired behavior.
Though the exact mechanisms underlying the effects of HFD and obesity on the brain
are unknown, our findings suggest a stronger role for inflammation than for metabolic
dysregulation. That is, despite having similar weight gain and metabolic outcomes in response
to HFD, mice treated with a TLR4 antagonist showed significant reductions in glial activation
and increased neurogenesis when compared with vehicle-treated mice. This is in agreement
with findings in the human literature that the effects of obesity on cognitive impairment are
mediated by inflammatory, rather than metabolic, factors (Dik et al., 2007; Yaffe et al., 2007;
Spyridaki et al., 2014). The role of other mechanisms like vascular and microbiota changes in
the effects of obesity on the brain cannot be ruled out here, 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).
We examined outcomes specifically in the hippocampus in the present study, but other
brain regions are certainly affected by HFD. In fact, even short-term HFD-feeding increases
hypothalamic inflammation (Thaler et al., 2012), and hypothalamic TLR4 signaling has been
shown to mediate several effects of HFD (Milanski et al., 2009; Morari et al., 2014).
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Furthermore, we administered the TLR4 antagonist systemically, which prevents us from
determining whether the effects of the drug were due to its direct actions on microglia in brain,
or to reductions in peripheral inflammation that then signaled to brain and resulted in reduced
gliosis. However, ongoing studies in our lab are addressing the extent to which microglial TLR4
signaling is mediating the effects of obesity. Finally, as sex differences have been observed in
the effects of obesity on various peripheral tissues and neural health outcomes (Hwang et al.,
2010; Mueller et al., 2011; Medrikova et al., 2012; Barron et al., 2013; Mauvais-Jarvis, 2015;
Moser and Pike, 2016), it may be of interest to examine whether TLR4 signaling in obesity has
the same role in females as it does in males.
To our knowledge, this is the first study to examine the effects of TLR4 signaling and
obesity on neural health outcomes in the hippocampus. Though TLR4 is well established as
mediating the effects of saturated fatty acids on adverse outcomes in metabolic measures (Kim
et al., 2007; Poggi et al., 2007; Suganami et al., 2007; Saberi et al., 2009; Liang et al., 2013; Jia
et al., 2014; Li et al., 2014) and inflammation (Kim et al., 2007, 2015; Poggi et al., 2007;
Suganami et al., 2007; Saberi et al., 2009; Jia et al., 2014; Li et al., 2014; Vila et al., 2014), its
effects in brain have not been thoroughly investigated. Our finding that hippocampal gliosis is
reduced and neurogenesis is increased in HFD-fed mice treated with a TLR4 antagonist,
demonstrates the central role of inflammation, and specifically of microglia, in mediating the
effects of obesity. Additionally, these findings point to a novel therapeutic target in obesity, and
especially in addressing the neural effects of obesity.
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Chapter Five
Conclusions and future directions
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1. Summary of Findings
The goal of my dissertation was to examine the extent to which obesity interacts with
other risk factors for Alzheimer’s disease (AD) to drive risk of the disease. While a great number
of both genetic and environmental risk for AD have been identified, interactions between
multiple risk factors are not often investigated. Though studies in others areas of neuroscience
including Parkinson’s disease (Cannon and Greenamyre, 2013), autism (Chaste and Leboyer,
2012), and psychiatric disorders (Lesch, 2004; Caspi and Moffitt, 2006) have examined gene-
environment interactions, there is a paucity of this kind of work as it relates to AD. Thus, I
sought to examine to what extent various risk factors interact with each other to drive AD
pathogenesis.
I focused on how various risk factors interact with the environmental/lifestyle risk factor,
obesity, for several reasons. First, obesity is a well-established risk factor, not only for AD
(Gustafson et al., 2003; Profenno et al., 2010), but also for overall adverse brain health (Ho et
al., 2010; Raji et al., 2010) as well as dementia in general (Yoshitake et al., 1995; Whitmer et
al., 2008). Conditions related to obesity have also been shown to increase risk for dementia and
AD, including metabolic syndrome (Vanhanen et al., 2006; Yang et al., 2013), Type II diabetes
(Moreira, 2013; Jayaraman and Pike, 2014), and hypertension (Shah et al., 2012). In addition to
its well-established role in AD, obesity has also been modeled in rodents. Diet-induced obesity
(DIO) is known to have a range of negative neuronal effects including decreased neurogenesis
(Lindqvist et al., 2006; Park et al., 2010b), and long term potentiation (Hao et al., 2016), as well
as cognitive deficits (Elias et al., 2005; Reichelt et al., 2015). Prior work in our lab has also
demonstrated that DIO has negative outcomes on markers of neural health (Jayaraman et al.,
2014). Moreover, DIO increases amyloid pathology in rodent models of AD (Julien et al., 2010;
Barron et al., 2013), as well as in non-transgenic mice (Lee et al., unpublished). Thus, obesity is
a well-established risk factor for AD, that has been successfully modeled in rodents. Importantly,
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however, not all obese persons will develop AD, suggesting that obesity must be interacting with
other risk factors to drive disease.
One such factor that may be interacting with obesity is the E4 allele of apolipoprotein E
(APOE4). Though APOE4 represents the greatest risk factor for sporadic AD, a significant
proportion of carriers do not develop the disease (Genin et al., 2011), supporting the idea that
this gene interacts with other factors to ultimately determine who will develop AD. The possibility
of an interaction between APOE4 and obesity has been suggested by some findings in the
human literature that AD-related pathology was increased in men who were both obese and
APOE4 carriers (Peila et al., 2002). Thus, in Chapter 2, I examined this possible interaction
using a paradigm of DIO in a mouse model of AD that expressed either human APOE3 or
APOE4. Our results demonstrate a strong gene-environment interaction, such that AD
pathology is increased specifically in APOE4 mice, and is not changed in APOE3 mice.
By far the greatest risk factor for all diseases, including AD, is aging, and previous work
suggests that it may also be interacting with obesity. That is, several studies have shown that
obesity at midlife may be associated with particularly strong risk for dementia later in life
(Whitmer et al., 2007; Fitzpatrick et al., 2009; Xu et al., 2011; Exalto et al., 2014). Additionally,
levels of the male sex steroid hormone, testosterone, begin declining in middle age (Harman et
al., 2001; Feldman et al., 2002), and loss of testosterone is associated with increased risk of
obesity (Stellato et al., 2000; Laaksonen et al., 2004; Fui et al., 2014). This convergence of risk
factors at midlife may result in a worse aging trajectory that increases risk for AD. In Chapter 3, I
examined the interactions between DIO and aging by initiating high fat diet feeding during either
young adulthood, middle age, or old age in male brown Norway rats. Additionally, I assessed
the role of testosterone in this relationship by administering hormone replacement to a separate
cohort of middle-aged and aged animals. Findings from this study demonstrate that aging is the
strongest driver of adverse outcomes, and high fat diet generally has the worst effects in middle-
aged animals.
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Results in both, Chapters 2 and 3, as well as previous work in our lab (Lee et al.,
unpublished; Jayaraman et al., 2014), illustrated a role for inflammation in mediating the
adverse effects of DIO. That is, we have demonstrated that HFD-feeding is associated with
systemic as well as brain inflammation, both in mouse models of Alzheimer’s disease (Moser
and Pike, 2017, Chapter 2; Lee et al., unpublished) and in non-transgenic rodents (Chapter 3;
Lee et al., unpublished; Jayaraman et al., 2014). Thus, in beginning to investigate the potential
mechanisms underlying the association between obesity and neural health, I focused on
inflammation. Specifically, I examined the role of TLR4 signaling, which is known to be bound
and activated by saturated fatty acids found in diet (Lee et al., 2001; Milanski et al., 2009;
Reynolds et al., 2012). Findings from Chapter 4 demonstrate that TLR4 signaling mediates the
effect of HFD on peripheral inflammation, microgliosis, and neurogenesis.
The overall results of my dissertation indicate that there are important interactions
between obesity and various other risk factors for AD, including APOE4 and aging. Considering
these interactions in light of the relative paucity of research on gene-environment interactions in
AD highlights the need for such studies in the future. Moreover, my results demonstrate the
potential role of inflammation in mediating these risk factor interactions, and more specifically,
identify TLR4 as a mediator of the effects of HFD on the brain. In addition to these main
findings, there are a number of interesting observations resulting from these studies. Though
not specifically addressed experimentally in my work, they may be important to examine in
future research.
2. Dietary and obesity considerations
2.A. Role of HFD versus obesity
Studies in rodent models often do not distinguish between the effects of HFD-feeding
and diet-induced obesity, but rather assume that the former causes the latter. While this is
generally true for the commonly used male C57BL6/J mouse model, which shows significant
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weight gain in response to HFD, and develops many of the metabolic hallmarks of obesity, this
is certainly not always the case. It has not been clearly established at this time whether the
adverse effects associated with diet-induced obesity are due to dietary components found in
food or to the obesity resulting from prolonged ingestion of HFD, though both are likely
important. A number of studies have demonstrated that the effects of HFD-feeding are rapidly
apparent, with changes in behavior after 3 days of feeding (Spencer et al., 2017), and increased
expression of pro-inflammatory cytokines after only 1 day (Thaler et al., 2012). Considering the
fact that animals gain only a modest amount of weight and do not show metabolic disturbances
after short-term HFD exposure, these findings suggest that dietary components are critical in
mediating adverse effects.
However, our findings in Chapter 3 suggest that HFD-feeding alone is not sufficient to
induce the full range of negative effects. In this study, we used the brown Norway rat, which
showed only very modest weight gain in response to HFD, despite being on the diet for 12
weeks. Moreover, physiological responses to HFD were rather subtle in these animals, with no
significant change in glucose levels, and modest changes in liver function and cytokine levels.
In accord with this, we saw very limited changes in behavior and no effects on neurogenesis. If
dietary components alone were responsible for adverse changes, our animals should have been
impaired as they were exposed to HFD for 12 weeks. These findings of limited effects of HFD in
the absence of obesity point to the role of weight gain and metabolic responses.
Additionally, future research should examine the effects of short- versus long-term HFD
feeding, as studies like Thaler et al., (2012) suggest that there may be important adaptations
taking place. Specifically, their work demonstrates that there is a rapid increase in inflammation
after 1-3 days of HFD feeding, which is downregulated after 7 and 14 days, and increased again
at day 28. This suggests that homeostatic mechanisms may be attempting to regulate the initial
inflammatory response to HFD, but perhaps may be failing in the face of prolonged exposure.
This is likely to have important implications for the human condition. For example, while there is
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a large population of people with consistently unhealthy diets, there may be an even larger
population whose diet is overall healthy but who eat an occasional unhealthy meal. Findings of
increased inflammation after only 1 day of HFD suggest that even occasional lapses in healthy
eating may put people in increased risk for disease.
2.B. Dietary composition may be important in the effects of obesity on AD
The studies completed for my dissertation were largely focused on the effects of diet-
induced obesity on neural health and AD-like outcomes, rather than on specific dietary
components. Animals were fed a 60% fat diet in Chapters 3 and 4, and a western diet with 45%
fat and 17% sugar in Chapter 2. The fat content in these diets is largely composed of lard, and
high concentrations of saturated fatty acids have previously been associated with increased AD
risk in humans (Barnard et al., 2014; Morris and Tangney, 2014), and with increased Aβ in
rodents (Oksman et al., 2006; Grimm et al., 2012). In the case of Chapter 2, sucrose may also
have been an important contributor, as it has been shown to increase Aβ in APP/PS1 mice (Cao
et al., 2007), and cognitive impairment in rats (Ross et al., 2009; Hsu et al., 2015). Though I did
not address the role of specific dietary components in my work, these will be important to
consider in future work.
Moreover, it would be interesting to examine the effects of beneficial dietary components
in the context of my studies. For example, Chapters 2 and 3 demonstrated that APOE4 and
middle age were associated with increased vulnerability to the adverse effects of HFD. Whether
this effect is due to a specific interaction between HFD and these factors, or to an overall more
malleable state associated with APOE4 and middle age is unknown. Thus, it would be of
interest to examine whether a healthy diet confers more benefits to APOE4 mice and middle
aged rats, just as HFD increases adverse effects in these animals. In fact, a number of diets
have been shown to be beneficial in the context of AD. That is, diets high in omega 3
polyunsaturated fatty acids (Lebbadi et al., 2011; Hjorth et al., 2013; Zerbi et al., 2014) or oleic
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acid (Amtul et al., 2011) have been shown to decrease Aβ in rodents. In line with this,
adherence to the Mediterranean diet has been associated with decreased risk for AD
(Scarmeas et al., 2006), lower mortality in AD patients (Scarmeas et al., 2007), and protection
against cortical thinning in cognitively normal older adults (Mosconi et al., 2014). These studies
are conducted in older adults and simply control for APOE status, but it would be of interest to
examine whether the beneficial effects of a Mediterranean diet are exaggerated in at-risk
populations like middle-aged adults and APOE4 carriers.
2.C. Responses to diet may be mediated by a number of other factors
There are likely to be genetic factors that interact with dietary components to determine
how an individual responds to diet. For example, this has been demonstrated in the effects of
red wine on breast cancer risk, where red wine consumption was associated with decreased
cancer risk in BRCA1, but not in BRCA2, mutation carriers (Dennis et al., 2010). Interactions
between dietary components and genetics have also been demonstrated in mice, where
responses to western, ketogenic, and Mediterranean diets depended on the genetic background
of the mice (Wells et al., 2016). This suggests that the magnitude of adverse effects from a
particular diet or unhealthy meal may depend upon the genetic make-up of the individual. In
fact, our data in Chapter 2 support this idea in the context of APOE4 and AD risk, by showing
that AD pathology is accelerated specifically in APOE4-carrying mice. Though we used a diet-
induced obesity paradigm in our study, there is also some evidence to suggest that APOE4
carriers may have different responses to individual dietary components. That is, APOE4-
carrying women had increased cholesterol levels in response to a diet high in palmitic acid
(Snook et al., 1999). Such interactions between diet and genetics are only just beginning to
emerge and will be critical in determining the best dietary strategy for a given individual.
In addition to genetics, the age of the individual may also be important in determining
their response to a given diet. This is clearly demonstrated by the obesity paradox, wherein
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obesity at midlife is a risk factor for AD (Fitzpatrick et al., 2009; Profenno et al., 2010; Xu et al.,
2011; Meng et al., 2014; Emmerzaal et al., 2015), but obesity later in life can actually be
protective (Fitzpatrick et al., 2009; Hughes et al., 2009; Doruk et al., 2010; Profenno et al., 2010;
Besser et al., 2014; Emmerzaal et al., 2015). It is unclear what may be driving these differential
effects of obesity at middle versus late life. However, it is well established that amyloid
pathology begins to develop years before symptom onset (Jack et al., 2015). Thus, it seems
plausible that middle age may be a critical time period for amyloid aggregation, during which an
additional risk factor like obesity may push the system towards increased pathology, the effects
of which then become apparent years later. Conversely, old age is associated with a decline in
energy metabolism and glucose metabolism deficiency (Jové et al., 2014), and it may be the
case that a nutrient excess can be protective in this context. Future research should work to
identify changes that may be happening between mid-ife and old age, and that could underlie
the observed differential effects of obesity at these time points.
3. Mechanisms underlying obesity and increased Alzheimer’s disease risk
Though the exact mechanisms underlying the association between obesity and
increased risk of AD are still unknown, there are a number of changes that occur in both
disorders and have been proposed to play a causative role in disease progression. Among
these are metabolic disorders like glucose intolerance and insulin resistance, inflammation,
vascular changes including blood brain barrier breakdown, and alterations in gut microbiota.
Evidence for the involvement of these various systems will be discussed below. However,
because findings from all three of my studies point to a central role for inflammation in the
effects of obesity on the brain, particular attention will be paid to this mechanism.
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3.A. Metabolic dysregulation
There are a number of changes in metabolic factors that take place in AD, such that the
disease has been referred to as Type 3 Diabetes (de la Monte and Wands, 2008). For example,
in nursing home patients, with relatively similar diets and lifestyles, those with AD had increased
levels of total as well as low-density lipoprotein cholesterol levels (Lesser et al., 2009).
Decreases in cerebral glucose metabolism can be seen in the posterior parietal cortex of AD
patients (Minoshima et al., 1997), and these changes can predict conversion from mild cognitive
impairment to AD (Drzezga et al., 2003). Much research has focused on the role of insulin,
which can improve cognitive performance (Park et al., 2000). Importantly, levels of insulin and
its receptor decrease with normal aging (Frölich et al., 1998). Insulin signaling is impaired in
brains of AD patients (Craft et al., 1998; Frölich et al., 1998) and this is exacerbated as severity
of the disease increases (Rivera et al., 2005). Interestingly, administering insulin to AD patients
can improve measures on some cognitive outcomes (Watson et al., 2005; Reger et al., 2006),
but exogenously administered high levels of insulin also lead to increased levels of Ab in
cerebrospinal fluid of cognitively normal older adults (Watson et al., 2003), suggesting that there
is a fine line between the beneficial and harmful effects of insulin action in the brain.
The insulin degrading enzyme (IDE) may be particularly important in mediating the
relationship between metabolic factors and AD. This enzyme can bind to and clear both insulin
as well as Ab, though it preferentially binds insulin (Qiu et al., 1998), and pharmacological
inhibition of IDE results in impaired degradation of both insulin and Ab (Bennett et al., 2003).
Moreover, mutations that result in partial loss of function of IDE are associated with impairments
in Ab degradation (Farris et al., 2004). AD transgenic mice fed HFD show decreased levels of
IDE and increased Ab (Ho et al., 2004; Zhao et al., 2004). Because HFD would be expected to
both increase insulin levels and decrease IDE, it may be the case that the reduced amount of
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IDE is not sufficient to clear both insulin and Ab, thereby allowing build-up of amyloid in the
brain.
Genetic factors like APOE genotype may be important mediators of the effects of insulin
signaling and other metabolic factors. That is, exogenous insulin administration was shown to
be more beneficial for cognitive outcomes in APOE4 non-carriers (Reger et al., 2006), and sex
differences in response to insulin are also apparent only in non-carriers (Claxton et al., 2013). In
addition to insulin, APOE4 genotype is associated with increased levels of lipid-depleted apoE
proteins, which are less able to clear amyloid from the brain (Hanson et al., 2013). Finally,
APOE4 is associated with abnormalities in cholesterol transport (Leduc et al., 2010), as well as
decreased protein levels of apoE, and administering a statin to lower cholesterol levels in AD
patients led to increased levels of apoE and decreased levels of Ab in cerebrospinal fluid
(Poirier, 2005). Overall these studies point to the fact that the role of metabolic factors in driving
AD may depend in part on APOE status.
Though metabolic factors are likely to play a role in mediating the effects of obesity on
neural health and AD risk, results from all three of my studies suggest that they may not be the
main driving force. That is, in Chapter 2, despite the fact that western diet increased adverse
metabolic outcomes in both E3FAD and E4FAD mice, and in fact, this effect was sometimes
exaggerated in E3FAD mice, amyloid pathology increased specifically in E4FAD animals. This
suggests that metabolic changes do not account for increased pathology seen after western diet
feeding in these animals. Furthermore, results from Chapter 3 demonstrated that HFD-induced
adverse changes in glucose homeostasis, adiposity, and leptin did not correlate with behavioral
outcomes. Finally, though the anti-inflammatory treatment administered in Chapter 4 did not
ameliorate any of the adverse metabolic effects of HFD, it did significantly increase
neurogenesis. Though I found significant changes in metabolic outcomes in response to HFD-
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feeding in all three of my experiments, my results suggest that these are not the driving forces
behind adverse neural health changes.
3.B. Inflammation
3.B.1. Role of inflammation in Alzheimer’s disease
Another possible mechanistic pathway common to obesity and AD is inflammation.
Aging in general is associated with increased inflammation, which has been proposed as being
the major driving force in age-related impairments and diseases (Franceschi et al., 2000).
Indeed, higher levels of inflammation in healthy older adults are associated with increased risk
for visceral adiposity, lower sex steroid hormones, and depression (Singh and Newman, 2011).
Moreover, among non-demented older adults, higher expression of inflammatory factors is
associated with cognitive impairment (Yaffe et al., 2003; Trollor et al., 2012), as well as a
greater loss of brain volume than expected for a given age (Jefferson et al., 2007). Increased
aging-associated inflammation (Wu et al., 2007), and the adverse effects of inflammation on
brain health (Ekdahl et al., 2003), have also been demonstrated in rodent models.
Inflammation is a well-established hallmark of AD, with increased pro-inflammatory
cytokines and gliosis in brain, as well as elevated levels of cytokines and chemokines in blood
and plasma of AD patients (Wyss-Coray and Rogers, 2012; De Felice and Ferreira, 2014;
Heneka et al., 2015). In fact, elevated inflammation can be observed even before onset of
pathology (Avila-Muñoz and Arias, 2014) and higher production of inflammatory cytokines is
associated with increased risk of AD in otherwise healthy older adults (Tan et al., 2007).
Moreover, responses to a pro-inflammatory stimulus are exaggerated in individuals with a family
history of AD (van Exel et al., 2009; Eikelenboom et al., 2011). Inflammation can even alter the
trajectory of AD, with levels of cerebral inflammation correlating with earlier death in AD patients
(Nägga et al., 2014).
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A number of recently identified genetic polymorphisms that are associated with risk for
AD have a role in inflammation. This includes complement receptor 1, which can bind fibrillar Ab
(Afagh et al., 1996) and act on microglia, stimulating them to phagocytose amyloid (Fonseca et
al., 2004). Another one of these identified genes is triggering receptor expressed on myeloid
cells 2 (TREM2), which is expressed in microglia and regulates their phagocytic ability. It is
thought that TREM2 mediates the response of microglia to Ab, and mutations in TREM2 may
impair this (Ulrich et al., 2017). Finally, siglec-3 (CD33) is a receptor found on microglia, and its
expression is upregulated in AD. Mutations in CD33 are associated with decreased protein
expression and reduced levels of Ab in brain (Griciuc et al., 2013). Together, these findings
suggest that inflammation, and in particular microglia, are important regulators of AD pathology.
Interestingly, inflammation can alter the actions of several enzymes involved in the
amyloid pathway. For example, expression of BACE1, which is involved in Ab production, is
increased in response to pro-inflammatory cytokines in neuronal cultures (Sastre et al., 2003).
Conversely, non-steroidal anti-inflammatory drugs can increase a-secretase, which promotes
APP processing through the non-amyloidogenic pathway (Avramovich et al., 2002). Thus,
inflammation may be directly affecting amyloid pathogenesis by acting on various enzymes
involved in its production.
Additional evidence for the role of inflammation in AD comes from findings that apoE has
important roles in inflammation. In general, apoE has anti-inflammatory properties. This has
been shown in humans, where higher levels of apoE are associated with lower levels of the
inflammatory marker C reactive protein (Bach-Ngohou et al., 2001), as well as in rodents, where
knock-out of APOE leads to greater pro-inflammatory responses to Ab (LaDu et al., 2001) and
LPS (Lynch et al., 2001). However, APOE4 has a more pro-inflammatory phenotype than other
isoforms of the protein. That is, APOE4 is associated with greater expression of pro-
inflammatory cytokines both at baseline (Colton et al., 2004; Gale et al., 2014), and in response
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to LPS (Lynch et al., 2003; Ophir et al., 2005), and this is also true in AD patients (Olgiati et al.,
2010). These findings may be due in part to reduced overall levels of apoE protein in APOE4
carriers (Licastro et al., 2007), as well as a reduced binding affinity of apoE4 to its receptor, low
density lipoprotein receptor-related protein (LRP1) (Bell, 2012). This increased inflammatory
phenotype in APOE4 carriers has functional consequences, as seen by the fact that systemic
markers of inflammation better predict cognitive decline in APOE4 carriers than in non-carriers
(Schram et al., 2007). Remarkably, though the ability of nonsteroidal anti-inflammatory drugs to
protect against AD has been contested, it has been shown that these drugs reduce AD risk
specifically in APOE4 carriers (Yip et al., 2005a; Szekely et al., 2008), suggesting that
inflammation is a major pathway through which APOE4 increases risk for AD.
Microglia have been shown to play a central role in AD pathogenesis, and APOE4 has
important effects on this cell population. For example, APOE4-carrying AD patients have been
shown to have increases in both microglial number, as well as activation (Egensperger et al.,
1998). However, despite this increase in microglial number and activation, the ability of
microglia to phagocytose Ab is actually decreased in the presence of APOE4 (Zhao et al.,
2009). Finally, apoE has recently been shown to act as a transcription factor for a number of
genes, including inflammation-related genes (Theendakara et al., 2016). Specifically, binding of
apoE4 resulted in greater NFkB translocation to the nucleus than binding of either apoE2 or E3,
and also reduced levels of sirtuin 1, which has a role in cell survival and neuroprotection (Ng et
al., 2015). In summary, inflammation is not only a hallmark of AD, but may also be a major
pathway through which the main genetic risk factor for AD, APOE4, exerts its adverse effects.
3.B.2. Role of inflammation in obesity
Inflammation in obesity can be characterized by both, rapid increases in cytokines in
response to dietary components (Thaler et al., 2012), as well as chronic, low-grade
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inflammation in response to obesity (Hotamisligil, 2006). There appears to be a bi-directional
relationship between obesity and inflammation, such that, in addition to obesity increasing
inflammation, inflammation can lead to metabolic dysregulation that further drives obesity
(Thaler and Schwartz, 2010). This is illustrated by the finding that men at a healthy weight, but
with high levels of plasma cytokines, were at increased risk of weight gain over 6 years
(Engström et al., 2003). Moreover, obese people who ate a very low-calorie diet for 1 month
showed significantly decreased inflammation, despite only marginal weight loss (Clément et al.,
2004). Additionally, higher use of anti-inflammatory drugs like aspirin and statins are associated
with greater loss of body weight over time in type 2 diabetes patients (Boaz et al., 2009). These
studies highlight the fact that inflammation can drive obesity.
Obesity-associated inflammation is found throughout the body including adipose tissue
(Wellen and Hotamisligil, 2003), liver (Cai et al., 2005), and brain (Thaler et al., 2012). In the
brain, the hypothalamus shows robust neuroinflammation in response to obesity and HFD (De
Souza et al., 2005; Milanski et al., 2009; Thaler et al., 2012). Inflammation in the hypothalamus
has been shown to mediate several peripheral effects of obesity. For example, reducing
hypothalamic inflammation via administration of anti-inflammatory antibodies into the brain has
been shown to protect against HFD-associated metabolic dysregulation not only in
hypothalamus, but also in liver (Milanski et al., 2012). Importantly, our lab has previously shown
that diet-induced increases in neuroinflammation have a functional role. That is, primary
neurons co-cultured with microglia from HFD-fed mice show reduced cell survival and neurite
outgrowth (Jayaraman et al., 2014). Interestingly, the same effects are observed when primary
neurons are treated with conditioned media from these HFD-fed microglial cultures, suggesting
that these microglia are secreting factors that adversely affect neurons.
In line with findings in rodent models, obesity-associated inflammation has adverse
functional effects in humans as well, with negative effects on performance in a number of
cognitive tests (Dik et al., 2007; Yaffe et al., 2007; Spyridaki et al., 2014). In fact, these studies
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show that inflammatory measures, rather than metabolic indices, serve as the mediator between
obesity and cognitive decline. That is, though metabolic syndrome is associated with worse
cognitive performance in older adults, this is only true in the presence of high levels of
inflammatory cytokines so that people with low or normal cytokine levels did not show a
significant relationship between metabolic outcomes and cognition (Dik et al., 2007). This
finding was replicated by (Yaffe et al., 2007), where effects of metabolic syndrome on cognition
were exaggerated in people with high inflammation. Finally, BMI is associated with worse
cognitive performance, but this effect is mediated by increases in inflammation, rather than by
insulin resistance or adiposity (Spyridaki et al., 2014). Taken together, these studies
demonstrate that inflammation, rather than metabolic dysregulation, drives adverse effects of
obesity in brain.
Interestingly, evidence has emerged that suggests inflammation may be driving
disruptions in metabolic homeostasis. Specifically, NFkB, a major regulator of inflammatory
cytokine transcription, can be activated by TLR4 signaling which results in downstream
activation of IKKb. Knocking out IKKb in hepatocytes protects against HFD-induced insulin
resistance specifically in the liver, whereas IKKb knock-out in myeloid cells is protective against
insulin resistance in all tissues (Arkan et al., 2005). Moreover, activation of NFkB, even in the
absence of HFD, results in insulin resistance in the liver (Cai et al., 2005). The effects of NFkB
signaling on insulin resistance are thought to be due to the fact that pro-inflammatory cytokines
that are downstream of NFkB can phosphorylate serine resides in the insulin receptor substrate,
which then blocks insulin signaling (Zeyda and Stulnig, 2009). Additionally, increased
inflammatory cytokine production can also activate suppressor of cytokine signaling 3 (SOCS3),
which leads to ubiquitin-mediated degradation of the insulin receptor (Paragh et al., 2014), as
well as reduced leptin signaling (Howard and Flier, 2006). Conversely, it has been shown that
deleting macrophages, which are responsible for inflammatory signaling in response to obesity
154
in peripheral tissues, can restore glucose and insulin homeostasis (Patsouris et al., 2008). In
summary, these studies suggest that inflammation may in fact be driving adverse metabolic
outcomes associated with HFD and obesity.
Results from all three of my studies support the importance of inflammation. For
example, in Chapter 2, the effects of western diet on microglial and astrocyte activation, but not
metabolic changes, were similar to effects on amyloid pathology. That is, both E3 and E4FAD
mice showed metabolic dysregulation, but western diet increased gliosis and amyloid pathology
specifically in E4FAD mice. Moreover, results from Chapter 3 demonstrated that microglial
reactivity was correlated with memory impairments in brown Norway rats fed HFD, but
metabolic changes did not correlate with behavior. Finally, in Chapter 4, I found that blocking
the pro-inflammatory TLR4 pathway did not protect against metabolic dysregulation caused by
HFD, but did significantly reduced peripheral inflammation and microglial activation, and
restored neurogenesis. Thus, findings from all three of my studies highlight the extent to which
inflammation, rather than disruptions in metabolic homeostasis, drive the adverse effects of
obesity on neural health.
3.C. Vascular changes
A number of changes in the vascular system have been observed in AD, including
cerebral hypoperfusion (Johnson et al., 2005), blood brain barrier breakdown (Bowman et al.,
2007), and atherosclerosis (Hofman et al., 1997). Build-up of amyloid in blood vessels, called
cerebral amyloid angiopathy, is significantly increased with AD, as it was observed in ~70% of
AD brains, but only ~25% of brains from non-demented older adults (Attems et al., 2005).
Importantly, these vascular changes may be occurring before onset of AD and driving
pathogenesis. One study found correlations between hypertension and levels of plasma Ab at
midlife, which then predicted risk of AD 15 years later (Shah et al., 2012). Additionally,
155
atherosclerosis, especially of the carotid artery, was associated with increased risk of both AD
and vascular dementia (van Oijen et al., 2007).
A number of studies have demonstrated that APOE4 negatively affects vascular health.
For example, even in the absence of AD, APOE4 increased odds of cerebral infarction by 2.3-
fold, and this was independent of build-up of Ab in blood vessels (Schneider et al., 2005). The
effect of atherosclerosis in increasing risk of dementia is exacerbated in APOE4 carriers as well.
That is, while severe atherosclerosis is associated with a 1.4-fold increased risk of AD in non-
carriers, this increases to 3.9-fold in the presence of APOE4 (Hofman et al., 1997). The
interaction between atherosclerosis and APOE4 is even more pronounced for vascular
dementia: non-carriers have a 7.1-fold increased risk, whereas APOE4 carriers have a 19.8-fold
increased risk. Moreover, in AD patients, those with APOE4 had greater arteriosclerosis in small
vessels as well as increased perivascular macrophage infiltration (Yip et al., 2005b), suggesting
a role for inflammation in mediating the effects of APOE4 on vasculature. In fact, this interplay
between inflammation and the blood brain barrier has been demonstrated in a mouse model.
That is, mice with human APOE4 have an increase in the pro-inflammatory cyclophilin A
pathway in pericytes, which leads to blood brain barrier breakdown, and thus increased
neuronal uptake of neurotoxic proteins (Bell et al., 2012). Interestingly, this pathway is
suppressed in the presence of APOE3, but not APOE4. Accumulation of cyclophilin A in
pericytes and endothelial cells has been observed in AD brains as well, with a further increase
in the presence of APOE4 (Halliday et al., 2016). It would be of interest to determine the relative
role of inflammation in mediating these vascular changes.
Obesity is a well-established risk factor for cardiovascular diseases including myocardial
infarction, stroke, and congestive heart failure (Hubert et al., 1983; Van Gaal et al., 2006). In
addition, obesity has also been shown to have significant effects on neurovascular outcomes. In
humans, measures of midlife adiposity were positively correlated with disruption of the blood
brain barrier in later life (Gustafson et al., 2007). Numerous studies have demonstrated adverse
156
effects of HFD in rodents, including higher arterial pressure and decreased middle cerebral
artery diameter (Osmond et al., 2009); decreased cerebral blood volume (Hooijmans et al.,
2009); and decreased expression of tight junction proteins in the blood brain barrier that lead to
increased permeability (Kanoski et al., 2010; Takechi et al., 2013), and these changes are
associated with worse outcomes after stroke (Li et al., 2013b). Additionally, aging has been
shown to exacerbate the effects of HFD and obesity on blood brain barrier breakdown in mice
(Tucsek et al., 2014).
I did not examine vascular outcomes in my research, though these would certainly be of
interest in future studies. Based on the literature, I would expect that in Chapter 2, E4FAD mice
may have had increased blood brain barrier breakdown in response to western diet, while
middle-aged and aged rats fed HFD in Chapter 3 would also have had an increase in negative
cerebrovascular outcomes. It would be of particular interest to examine vascular and blood brain
barrier outcomes in the context of Chapter 4, as previous work has demonstrated that TLR4
knockout mice are protected against HFD-induced vascular inflammation (Kim et al., 2007).
Thus, If TLR4 knockout or inhibition were to also protect against blood brain barrier breakdown,
it would suggest that inflammation is driving adverse vascular changes.
3.D. Microbiota
The human gastrointestinal tract microbiome contains ~10
14
microbes that come from at
least 1000 different species (Zhao and Lukiw, 2015). These microbes are involved a variety of
processes including nutrient absorption, vitamin production, and regulation of immune function.
Interest in, and research on, the microbiome has increased dramatically in recent years and
there is some evidence to suggest that it may also have a role in neurodegenerative disorders
like AD. In cognitively impaired older adults, higher plasma amyloid levels correlated with
increased inflammatory cytokines, and these were positively correlated with a pro-inflammatory
strain of gut bacteria, but negatively correlated with an anti-inflammatory strain (Cattaneo et al.,
157
2017). Moreover, there is a decrease in microbial diversity in AD patients as well as differences
in abundance of certain strains, which correlate with levels of Ab and phosphorylated tau in
cerebrospinal fluid (Vogt et al., 2017). Interestingly, many species of gut bacteria also produce
large levels of both Ab and LPS (Zhao and Lukiw, 2015), and AD brains have increased LPS
levels by up to 26-fold in hippocampus (Zhao et al., 2017).
The influence of the microbiome on AD pathology has also been demonstrated in rodent
models. APP transgenic mice show shifts in microbiome composition compared to wildtype
animals, and raising these AD mice germ-free to deplete their microbiome reduces Ab
pathology (Harach et al., 2017). Interestingly, colonizing germ-free APP mice with microbiota
from normally raised APP mice increases Ab levels. Moreover, another study found that
decreasing microbial composition and diversity via antibiotic treatment resulted in reduced Ab
plaque deposition in an AD transgenic mouse (Minter et al., 2016). Additionally, these animals
also showed changes in plasma cytokines and decreases in plaque associated glial reactivity,
pointing to a role for inflammation.
Dietary composition and obesity have significant effects on microbiome composition.
Gut bacteria are essential in regulating the amount of energy extracted from foods, and it has
been proposed that there is a shift towards increased nutrient absorption in obesity (John and
Mullin, 2016). This has been shown in animal models, where microbiota transplantation from
HFD-fed to control diet, germ-free mice resulted in increases in adipose tissue (Turnbaugh et
al., 2008). One possible mechanism linking obesity, AD, and the microbiome is the finding that
obesity increases permeability of the intestinal barrier and reduces expression of tight junction
proteins (Cani et al., 2008). As mentioned previously, bacteria in the gut produce amyloid and
LPS (Zhao and Lukiw, 2015), which would be released systemically under conditions of a “leaky
gut.” Perhaps then, prolonged leaking of these factors in chronic obesity would lead not only to
158
amyloid accumulation in brain, but also increased inflammation. This is consistent with the
finding of increased LPS in brains of AD patients (Zhao et al., 2017).
It would be of interest to examine the involvement of the microbiome particularly in the
context of findings from Chapter 4 of my dissertation. Mice fed HFD would be expected to have
increased intestinal permeability, resulting in release of LPS. As TLR4 is the main receptor for
LPS, it seems likely that the mice treated with a TLR4 antagonist would have been protected
against this release of LPS. Additionally, a role for apoE in modulating immune responses to
microbiome changes was recently demonstrated (Saita et al., 2016). Thus, it would be
interesting to examine whether APOE3 and APOE4 differentially affect microbiome-mediated
inflammation.
4. Future Directions
The role of inflammation has been well established in the context of both, obesity and
AD. Moreover, as described above, many of the other hypothesized mechanisms like metabolic,
vascular, and microbiota changes show inflammatory components. Thus, it may be the case
that inflammation acts as a catalyst for dysfunction in multiple systems, and changes in these
could then further drive adverse changes in the brain that lead to increased AD pathogenesis.
Though the significance of inflammation is clear, more research is needed to determine
its exact role. For example, as of yet, it is unclear to what extent inflammation is beneficial
versus harmful in AD. This is particularly true in the case of microglia, where studies have
demonstrated both protective and toxic roles for these cells. The importance of microglia in
clearing pathogens and toxins from the brain is well established (Prinz and Priller, 2014), and
their role in phagocytosing Ab has been shown. That is, increasing microglial numbers and/or
activation through administration of either acute LPS (Morgan et al., 2005), or macrophage
colony stimulating factor (Boissonneault et al., 2009), is associated with reductions in amyloid
pathology. Conversely, mice lacking macrophage colony stimulating factor have a reduction in
159
both peripheral macrophages and brain microglia (Wegiel et al., 1998) and develop Ab deposits
in the brain (Kaku et al., 2003). Additionally, mice lacking C-C chemokine receptor type 2
(CCR2) have decreased microglial accumulation in brain, and increased Ab plaques and
mortality (El Khoury et al., 2007). Thus, a lack of microglia exacerbates AD pathology.
However, in addition to their role in phagocytosis, microglia also release inflammatory
chemokines and cytokines, and chronically high levels of factors like TNFa, IL1b, and IL6 are
directly neurotoxic (Jeohn et al., 1998). These cytokines can stimulate amyloidogenic
processing of APP, increasing synthesis of Ab, while decreasing non-amyloidogenic processing,
and thus decreasing levels of soluble APPa (Blasko et al., 1999). These harmful effects can be
blocked by the non-steroidal anti-inflammatory drug, ibuprofen, which has been shown to
decrease Ab synthesis in culture (Blasko et al., 2001) and Ab plaques in mice (Lim et al., 2000).
In addition to increasing Ab production, pro-inflammatory cytokines can decrease the phagocytic
potential of microglia, which can be restored by treatments with anti-inflammatory cytokines or
ibuprofen (Koenigsknecht-Talboo and Landreth, 2005). Importantly, astrocyte may also be
modifying this relationship, as they have been shown to have an even greater increase in pro-
inflammatory gene expression than microglia, in the context of AD (Orre et al., 2014).
A dual role for microglia in neurodegenerative disorders has been proposed (Hanisch
and Kettenmann, 2007), wherein microglia are initially beneficial and protective by
phagocytosing Ab, but their chronic activation and production of pro-inflammatory cytokines
eventually becomes harmful, increasing Ab production and neurotoxicity, while decreasing their
phagocytic ability. This idea is consistent with the finding that knockout of the chemokine
receptor Cx3cr1 in AD-transgenic mice resulted in reduced neuron loss, and decreased
microglial density around dying neurons, without affecting the phagocytic ability of microglia
(Fuhrmann et al., 2010). Moreover, peripheral macrophages from AD patients are less effective
at phagocytosing Ab, which may lead to a maladaptive compensatory upregulation of the innate
160
immune system, thus increasing cytokine expression, which would further suppress
phagocytosis (Fiala et al., 2005). Thus, a strategy to retain the beneficial, phagocytic effects of
microglia, while preventing their harmful, pro-inflammatory effects would appear to have
significant therapeutic potential.
It would be of interest to examine the role of TLR4 signaling in mediating the interaction
between obesity and AD pathogenesis. Microglial TLR4 receptors can sense both Ab (Tahara et
al., 2006), and saturated fatty acids (Lee et al., 2001), resulting in downstream activation of
NFkB (Landreth and Reed-Geaghan, 2009). NFkB activity is upregulated in both, AD (Ferrer et
al., 1998) and obesity (Carlsen et al., 2009), and blocking this pathway decreases Ab (Jiang et
al., 2014) and many adverse effects of HFD (Kim et al., 2007; Radin et al., 2008; Jia et al.,
2014). Yet, in the context of AD, TLR4 signaling may also be protective, as AD transgenic mice
with TLR4 mutations show increased Ab (Tahara et al., 2006), and a TLR4 agonist reduces Ab
(Michaud et al., 2013). However, whether TLR4 signaling is involved in mediating the
relationship between obesity and AD has not been examined. Additionally, it would be
interesting to examine obesity, TLR4, and AD interactions in the EFAD mice, as APOE4 is
associated with increased TLR4 signaling (Tai et al., 2015).
Though the results of all three of my studies point to a role for inflammation in mediating
effects of obesity on the brain, it is unclear what the relative roles of systemic versus
neuroinflammation are. In addition to signals from periphery being able to affect the brain,
macrophages from the periphery can cross the blood brain barrier and enter the brain in obesity
(Buckman et al., 2014). In fact, one study suggests that these infiltrating cells are critical in AD.
This was shown in an irradiation and bone marrow transplant model, where the majority of
microglia surrounding Ab plaques were newly recruited from the hematopoetic system (Simard
and Rivest, 2004). Findings in my studies could have been the result of systemic inflammation,
neuroinflammation, or of immune cell infiltration into the brain, and future work should clarify
161
this. Additionally, the pharmacological TLR4 antagonist administered in Chapter 4 was given
systemically, and while it did reduce HFD-induced microglial activation, it is unclear whether this
was due to the drug acting directly on microglia, or to reductions in peripheral inflammation that
then acted on brain to reduce gliosis. However, our lab will address this question in a newly
generated mouse model. This inducible knock-out of TLR4 specifically in microglia will allow us
to examine the extent to which microglial TLR4 signaling mediates the effects of HFD on neural
outcomes.
All three of the studies presented here were conducted in male rodents, in part because
female rodents are protected against the effects of HFD on metabolic dysregulation and
inflammation (Medrikova et al., 2012; Pettersson et al., 2012), as well as neural changes
(Hwang et al., 2010). This protection is likely due to effects of the female sex steroid hormones,
as it was demonstrated that premenopausal women are protected against obesity, but this effect
is lost at menopause (Meyer et al., 2011; Sugiyama and Agellon, 2012; Bloor and Symonds,
2014). However, significant sex differences exist in AD, such that women are at increased risk
(Li and Singh, 2014), and APOE4 has a greater effect on AD risk in women than in men
(Payami, 1994; Altmann et al., 2014). Rodent models confirm these findings with greater AD-like
pathology overall (Schäfer et al., 2007; Hirata-Fukae et al., 2008; Carroll et al., 2010), as well as
in the presence of APOE4 (Cacciottolo et al., 2016). Additionally, there may be sex differences
in the interaction of obesity and APOE genotype, as it was recently shown that female APOE3
mice had increased glial activation, while APOE4 females had decreased glial activation in
response to HFD (Janssen et al., 2016). Ongoing work in our lab is examining sex differences in
the effects of obesity on neural health and AD pathogenesis.
Perhaps the most significant outcome of my research is the finding that multiple risk
factors for AD can not only interact with each other, but can converge on the inflammatory
pathway to increase pathogenesis. Interactions between multiple risk factors, as well as gene-
environment interactions, have generally been understudied in the context of AD. However,
162
these studies may be vital in identifying populations that are at increased risk of the disease, but
who could benefit from preventative lifestyle changes. Taken together, my thesis work has
highlighted such interactions between risk factors, as well as the role of inflammation in
mediating the effects of obesity on neural health and AD pathogenesis.
163
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Abstract (if available)
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, for which no treatments are currently available, and the causes of which are only beginning to be understood. A number of both genetic and environmental/lifestyle risk factors for AD have been identified, including the E4 allele of the cholesterol transporter apolipoprotein E, obesity, and aging. Yet, how these factors may be interacting with each other to drive disease is not well understood, and is rarely addressed in experimental research. Thus, the first goal of my dissertation work was to examine potential interactive effects between risk factors for AD. Chapter 1 provides a comprehensive introduction to many of the topics that are relevant to my dissertation, including the role of various risk factors in AD, and evidence that suggests possible interactions between them. Chapter 2 describes a novel gene-environment interaction between apolipoprotein E4 and obesity, wherein high fat diet increases AD-like pathology specifically in mice with human apolipoprotein E4. In Chapter 3 I examined interactions between obesity and aging, by evaluating the effects of high fat diet in young, middle-aged, and aged brown Norway rats. Additionally, I examined the potential of testosterone in protecting against effects of obesity. Results from both of these studies suggest that obesity may be driving adverse effects in brain through inflammatory pathways. Thus, the second goal of my dissertation was to evaluate the extent to which high fat diets exert their effect on neural health by signaling through the pro-inflammatory toll-like receptor 4 pathway. This work is described in Chapter 4, and shows that administering a toll-like receptor 4 antagonist protects against the adverse effects of obesity in brain, without altering metabolic outcomes. Finally, Chapter 5 summarizes my research findings and evaluates my work in the context of current knowledge in the fields of obesity, inflammation, and Alzheimer’s disease.
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Asset Metadata
Creator
Moser, Vanessa Alexandra
(author)
Core Title
The role of inflammation in mediating effects of obesity on Alzheimer's disease
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Neuroscience
Publication Date
02/16/2019
Defense Date
12/14/2017
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
aging,Alzheimer's disease,apolipoprotein E4,dementia,high fat diet,Inflammation,microglia,OAI-PMH Harvest,obesity,rodent models,Toll like receptor 4
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Watts, Alan (
committee chair
), Kanoski, Scott (
committee member
), Levitt, Pat (
committee member
), Pike, Christian J. (
committee member
)
Creator Email
alexandra@vamoser.com,vmoser@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c40-476048
Unique identifier
UC11267221
Identifier
etd-MoserVanes-6049.pdf (filename),usctheses-c40-476048 (legacy record id)
Legacy Identifier
etd-MoserVanes-6049.pdf
Dmrecord
476048
Document Type
Dissertation
Rights
Moser, Vanessa Alexandra
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
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...
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Tags
Alzheimer's disease
apolipoprotein E4
dementia
high fat diet
microglia
obesity
rodent models
Toll like receptor 4