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Insulin sensitivity in cognition, Alzheimer's disease and brain aging

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Insulin sensitivity in cognition, Alzheimer's disease and brain aging

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i
INSULIN SENSITIVITY IN COGNITION, ALZHEIMER’S DISEASE AND BRAIN
AGING

by

ISHAN Y. PATIL

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MOLECULAR PHARMACOLOGY AND TOXICOLOGY)

May 2018

Copyright 2018 Ishan Patil

ii
DEDICATION
I dedicate this dissertation to my grandfather, Madhav Suryawanshi, for inspiring me to
become a scientist. He motivates me to study and understand as much as I can about everything
in the universe, and has taught me the value of a holistic approach in solving scientific problems.
His advice for the doctoral program was to strive to develop the ability to recognize and solve
problems independently; advice which I have followed throughout the duration of the program,
and intend to adhere to for the rest of my life. Thank you for being you.

iii
ACKNOWLEDGEMENTS
I would first like to acknowledge my mentor, Dr. Enrique Cadenas. His constant support
and guidance have made this work possible. He inspires me to be the best version of myself
every day. I would also recognize my committee members, Dr. Wei-Chaing Shen and Dr. Curtis
Okamoto. Thank you for the mentorship and guidance at every stage of this doctoral program. I
also want to acknowledge Dr. Keiko Kanamori, Dr. John Walsh, and Garnik Akopian for their
technical guidance and help with crucial experiments.
The work done here would not be possible without the help of members of the Cadenas
lab; Harsh Sancheti, Fei Yin, Zhigang Liu, Tianyi Jiang, and Charles Caldwell- thank you for
helping me develop ideas that formed these projects and for helping me design my experiments.
Alick Tan, Maira Soto, Ania Papinska, Jordan Despanie, Sachin Jadhav, Barbara Paulson, and
Andrew Gould- thank you for being such amazing friends and for the constant support. Aditi, for
being a great girlfriend and making my life wonderful, I love you. This would not be possible
without your incredible support. To my parents, thank you for all your love and support, and for
instilling an appreciation for hard work and sincerity. To my grandfather, thank you again for all
the life lessons for inspiring me to be a better scientist every day.

iv
TABLE OF CONTENTS

Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
Table of Contents ........................................................................................................................... iv
List of Figures ................................................................................................................................. v
List of Tables ................................................................................................................................. vi
List of Abbreviations .................................................................................................................... vii
Abstract ........................................................................................................................................ viii
Chapter I: Introduction and Background ........................................................................................ 1
Chapter II: Brain Metabolic and Functional Alterations in a Liver-specific PTEN Knockout
Mouse Model ................................................................................................................................ 20
Introduction ............................................................................................................................... 20
Materials and Methods .............................................................................................................. 22
Results ....................................................................................................................................... 33
Discussion ................................................................................................................................. 44
Chapter III: The Metabolic↔Inflammatory Axis in Brain Aging ................................................ 48
Introduction ............................................................................................................................... 48
Methods ..................................................................................................................................... 53
Results ....................................................................................................................................... 55
Discussion ................................................................................................................................. 57
Chapter IV: Future Studies and General Conclusions .................................................................. 60
Future directions ........................................................................................................................ 60
General Conclusions ................................................................................................................. 65
References ..................................................................................................................................... 66

v
LIST OF FIGURES
Figure 1.1: Healthspan vs Lifespan 1
Figure 1.2: The PI3K-Akt arm of the insulin signaling pathway 5
Figure 1.3: Redox modulation of insulin signaling 13
Figure 1.4: The metabolic-inflammatory axis in brain aging 19
Figure 2.1: Representative NMR spectrum after [1-
13
C] glucose and [1. 2-
13
C]acetate
infusion 26
Figure 2.2: Typical labelling pattern after [1-
13
C] glucose and [1. 2-
13
C] acetate
infusion 28
Figure 2.3: Glucose clearance and ketone bodies levels in the liver-specific PTEN
KO mice 34
Figure 2.4: Brain glucose uptake and rate of glucose uptake 35
Figure 2.5: % Enrichment of
13
C labelled isotopomers of Glutamate, Glutamine,
Aspartate, and GABA 40
Figure 2.6: Metabolic ratios calculated after [1-
13
C] glucose + [1,2-
13
C]acetate
infusion 41
Figure 2.7: Changes in I/O in the brains of liver-specific PTEN KO mice 42
Figure 2.8: Western blot analysis of the levels of p-Akt (Ser473), Akt, and β-actin 44
Figure 3.1: Astrocyte-neuron metabolic cooperation 50
Figure 3.2: Self-promoting neuroinflammatory loop 51
Figure 3.3: Key parameters of mitochondrial respiration. 54
Figure 3.4: Mitochondrial bioenergetics after IL-1β treatment 56
Figure 3.5: Proposed link between inflammation and metabolic dysfunction 58
Figure 4.1: Brain glucose metabolism and the pentose-phosphate pathway 61
Figure 4.2: The ROS model of NLRP3 activation 64

vi
LIST OF TABLES
Table 2.1: Concentrations of different isotopomers of
13
C Glu, Gln, Asp, NAA,
GABA, and MI 37

vii
LIST OF ABBREVIATIONS

ACSF artificial cerebrospinal fluid
AD Alzheimer's disease
Akt Protein kinase B
ATP adenosine triphosphate
BBB blood-brain barrier
CNS central nervous system
DAMP danger-associated molecular pattern
DDPI-IV dipeptidyl-peptidase IV inhibitor
GABA gamma-aminobutyric acid
GLP glucagon-like peptide
GSK-3β glycogen synthase kinase-3 beta
GTT glucose tolerance test
IGF Insulin growth factor
IIS insulin signaling pathway
IL-1β Interleukin-1 beta
iNOS inducible nitric oxide synthase
IRS insulin receptor substrate
JNK c-Jun N-terminal kinase
LTP long term potentiation
MAPK mitogen-activated protein kinase
MAPKK mitogen-activated protein kinase kinase
MCI mild cognitive impairment
NF-kappaB nuclear factor- kappa B
NMDA N-methyl-D-aspartate
NMR nuclear magnetic resonance
NO nitric oxide
OCR oxygen consumption ratio
OXPHOS oxidative phosphorylation
PAMP pathogen-associated molecular pattern
PI3K Phosphoinositide 3-kinase
PPAR-γ peroxisome proliferator-activated receptor-gamma
PTEN Phosphatase and Tensin Homologue (PTEN)
ROS reactive oxygen species
SEM standard error of the mean
T2DM Type 2 Diabetes Mellitus
TNFalpha Tumor necrosis factor alpha
WHO world health organization

viii
ABSTRACT
Aging is the biggest risk factor for neurodegenerative diseases like Alzheimer’s disease
(AD) and related dementias, with growing evidence suggesting impairments in brain energy
metabolism and insulin responsiveness as a function of age to be major culprits. Insulin, a
hormone that drives brain glucose uptake. helps meet the energy demands of synaptic neurons
amongst other cell types, and regulates cognitive functions via the insulin signaling pathway. We
hypothesized that enhancing neuronal insulin responsiveness will enhance neuronal function.
This hypothesis was tested using a liver-specific Phosphatase and Tensin Homologue (PTEN)
knockout mouse model, a model of systemic insulin hypersensitivity. PTEN is a negative
regulator of the insulin signaling pathway (IIS), and so a liver-specific deletion of PTEN resulted
in increased flux of glucose into the liver due to robust insulin signaling, thus resulting in an
overall hypoglycemic and hypoinsulinemic state in the mice. To summarize the results, the
brains of the liver-specific PTEN KO mice model exhibited increased glucose uptake, enhanced
insulin signaling activity, improved rate of glycolysis and flux of metabolites in the TCA cycle,
and improved synaptic plasticity in the hippocampus. Studies in the liver-specific PTEN KO
mice strengthened the significance of insulin signaling in brain energetics and function.
Conversely, these findings also helped recognize deficits in diseases associated with insulin
resistance and how these impinge on brain function.
Aging is also associated with development of chronic-low grade inflammation that is
known to change the phenotype of microglia- the immune cells of the brain, as well as
astrocytes- the ultimate supporters of neuronal function. The shift in the behavior of microglia
and astrocytes from neuroprotective to neurotoxic could be major effectors in the decline of
cognitive function. Cognitive decline is also associated with metabolic dysfunction in the brain.

ix
However, it is still unclear whether decline of metabolic function is a consequence of the
changing inflammatory state of the brain, or is the reason for its existence in the first place.
Mechanistic studies were conducted in primary embryonic neurons to confirm whether
inflammatory cytokines could impair metabolism and mitochondrial function. The pro-
inflammatory cytokine IL-1β was found to impair insulin signaling and mitochondrial respiration
in embryonic neurons, which brings us one step closer to establishing the link between
metabolism and inflammation in the aging brain.

1
CHAPTER I: INTRODUCTION AND BACKGROUND
Healthspan versus Lifespan
As more advances are made in medicine and aging research, we find ourselves leading
longer lives than ever before. However, a longer lifespan also renders many of us, if not disabled,
then deteriorating towards to end- a fate that might not be worse than death, but is nonetheless
deprived. It has become crucial for aging research to evaluate whether it is improving the
healthspan of people and not merely their lifespan. Healthspan is duration of life that they are
‘healthy’, and not merely alive. Ideally, we would like to live in a world where our healthspan is
almost as long as our lifespan, but unfortunately, we are not there yet (Figure 1.1).

Figure 1.1: Healthspan vs Lifespan. Courtesy: Mark R Collins, Glenn Foundation for Medical
Research.

2
The CDC reported Alzheimer’s disease (AD) and Diabetes as the 5
th
and 6
th
leading
causes of death in the U.S in 2015. The World Health Organization reports that the world’s
population aged 60 years and older, is expected to nearly double, from 12% to 22% . Since age is
a major risk factor for both, AD and Diabetes, the health and economic costs to society are also
expected to rise monumentally in the coming decades. Health care costs for people with AD are
projected to increase from $259 billion in 2017 to more than $1.1 trillion in 2050 . Similarly, the
total healthcare costs of diagnosed diabetes in 2012 was $245 billion dollars ; and with the
number of Americans diagnosed with diabetes projected to increase from 11 million to 29
million in 2050, it is not a stretch to imagine that Diabetes will also cost the nation around the
trillion dollar mark by 2050.
As healthcare researchers around the world toil around the clock to find new ways of
treating or preventing AD and diabetes, this dissertation presents findings that might help
ameliorate the impending crises.

Insulin and the brain
Insulin is a large peptide hormone that facilitates uptake of glucose and satisfies the
energy demands of most cell types in the body, including neurons. Insulin cannot passively pass
the blood-brain barrier (BBB), but is still found in the cerebrospinal fluid (CSF) (Diehl et al.,
2017). There are three proposed sources of insulin in the brain (Akintola and van Heemst, 2015):
(1) Insulin that is transported in to the CNS by a saturable, insulin receptor mediated pathway;
(2) Insulin that flows directly into the CNS without crossing the BBB, through circumventricular
regions that lack typical BBB structures and porous capillaries that allow plasma to freely diffuse
into the CNS; and (3) Insulin produced in the brain itself, as discovered through experiments in

3
animal models. It is still controversial whether human brain cells can produce insulin (Gray et
al., 2014; Kullmann et al., 2016).
Insulin receptors are distributed widely within the brain, and the hippocampus,
hypothalamus, the cerebral cortex, olfactory bulbs, cerebellum, and amygdala being the regions
with the highest concentration of insulin receptors (Duarte et al., 2012b). Insulin is an important
modulator of growth and metabolic function in the central nervous system (CNS).

Insulin signaling and the brain
Insulin facilitates uptake of glucose that satisfies the energy demands of neurons, and
regulates cognitive functions via the insulin signaling pathway (IIS) (de la Monte, 2012a) (Figure
1.2). Insulin mediates its effects by signaling downstream through the insulin receptor substrate
(IRS) (Shpakov and Pertseva, 2000). Phosphorylation of tyrosine on specific motifs enable
transmission of signals that mediate growth, metabolic functions, and viability by interacting
with downstream molecules containing SH2 domains, including the growth-factor receptor
bound protein (Grb2), SHPTP-2 protein tyrosine phosphatase and the phosphatidylinositol-3-
kinase (PI3-kinase) (Giovannone et al., 2000; Shpakov and Pertseva, 2000). The binding of
phosphorylated IRS to Grb2 results in sequential activation of p21
ras
, mitogen-activated protein
kinase kinase (MAPKK), and ERK. ERK contributes to insulin-stimulated mitogenesis, neuritic
sprouting, and gene expression. Binding of phosphorylated IRS to PI3K stimulates glucose
transport and inhibits apoptosis by activating Akt/Protein kinase B and inhibiting glycogen
synthase kinase-3β (GSK-3β) (Pap and Cooper, 1998). Activation of the PI3K-Akt arm of the IIS
upregulates the membrane translocation of glucose transporters in neurons (GLUT-4 and GLUT-
8). Uptake of glucose and its subsequent metabolism provide neurons the energy required for

4
synaptic functions. Other insulin-related mechanisms, not directly related to modulation of
glucose uptake have been implicated in normal hippocampal function, which involves the
activation of NMDA receptor resulting in increased Ca
2+
influx. A high intracellular
concentration of Ca
2+
presumably activates α-calcium-calmodulin-dependent-kinase II (CaM
Kinase II) and other Ca
2+
dependent enzymes, which results in stronger synaptic associations
between neurons (Craft and Watson, 2004). CaM Kinase II is important in memory formation,
and deficits in this enzyme result in altered cognition and memory deficits. The localization of
insulin receptors in the hippocampus and medial temporal cortex in rats is consistent with
evidence that insulin influences memory (Singh et al., 1997). In rats, acute
intracerebroventricular insulin enhanced memory on a passive-avoidance task (Park et al., 2000).
In humans, acute intravenous insulin enhanced story recall (Craft et al., 1999). Conversely, high-
fat diet induced insulin resistance resulted in decreased synaptic plasticity in the rodent
hippocampal neurons (Liu et al., 2015).

5

Figure 1.2: The PI3K-Akt arm of the insulin signaling pathway upregulates membrane
translocation of glucose transporters in neurons (GLUT-4 and GLUT-8) through the activation of
IRS after insulin stimulation. Glucose is metabolized to pyruvate in the cytoplasm via glycolysis,
which is further metabolized to ultimately yield ATP by the tricarboxylic acid cycle and the
electron transport chain. The electron leak resulting from oxidative phosphorylation results in
increased H2O2 generation, which can diffuse back in to the cytoplasm to regulate insulin
signaling activity (Shpakov and Pertseva, 2000).

PDH
IR/IGF1R
IRS
PI3K
Akt
GSK3b
mitochondrial
function
GSK3b
JNK
PDK
O
2
. –
H
2
O
2
JNK Akt

glucose
glucose
pyruvate
pyruvate
GLUT
ketone
bodies
MCT
oxalo-
acetate
citrate
isocitrate malate
NADH
oxoglutarate
succinyl-CoA
fumarate
succinate
SCOT
acetyl-CoA
ketone
bodies
MPC
glycolysis
I III IV Q
c
V
glutamate
ADP
ATP
GABA
SYNAPTIC
PLASTICITY
GDH
GAD

Cytoplasm
Mitochondria

6
Insulin Signaling in brain aging, neurodegeneration and Alzheimer’s disease
Diabetes Mellitus or Type 2 Diabetes (T2DM) is a major disorder of insulin regulation.
Diabetes has been widely associated with slowly progressing degeneration of the brain, and is
known cause diabetic neuropathy and impairs cognitive function (Biessels and Gispen; Li and
Hölscher, 2007; Verdile et al., 2015). Although several molecular mechanisms have been
proposed, including AGE formation, aldose reductase activity, oxidative stress, activation of
protein kinase C, and hexosamine pathway flux as the link between diabetes mellitus and
impairments in cognitive function (Duarte et al., 2012a), impaired brain insulin signaling might
play a key role development of neurodegenerative disorders.
Aging is associated with an increase in insulin resistance. Studies in humans suggest that
low insulin levels, preserved insulin sensitivity, and a state of reduced flux through the insulin
signaling pathway (IIS) may represent key metabolic features of a human longevity phenotype
(Harper, 2014; van Heemst, 2010). In the brain, insulin and IGF-1 play critical roles in regulating
and maintaining cognitive function (de la Monte and Wands, 2005). A large multi-center clinical
study is currently underway to assess whether insulin, administered as a nasal spray, can improve
memory in adults with mild cognitive impairment or Alzheimer’s disease.0
Brain aging is associated with a reduced IIS activity entailing inactivation of the
PI3K/Akt signaling in rats (Jiang et al., 2013b; Yin et al., 2016). Several studies have
demonstrated the link between impaired IIS pathway and pathogenesis of AD. Previous studies
in our lab with a triple transgenic (3xTG) mouse model of AD showed that impairments in IIS
activity were associated with synaptic plasticity (Sancheti et al., 2013). Diet-induced insulin
resistance in a Tg2576 AD transgenic model promoted AD-type neuropathology (Ho et al.,

7
2004); intracerebroventricular streptozotocin injection to Tg2576 mice led to a brain insulin-
resistant state, reduced spatial cognition, increased AD pathology, and increased mortality
(Plaschke et al., 2010). Intake of sucrose-sweetened water in APP/PS1 mice induced insulin
resistance associated with memory impairment and Aβ accumulation in the brain (Cao et al.,
2007).
In a clinical study, brain insulin resistance, IGF-1 resistance and IRS1 dysfunction, was
shown to be early and common characteristics of AD (Talbot et al., 2012). Another clinical study
observed severe deficiency of PI3K-Akt signaling branch of IIS accompanied by
hyperphosphorylation of Tau protein in individuals with AD and type 2 diabetes (Liu et al.,
2011). A cross sectional population-based study found that features of insulin resistance were
associated with AD, independent of the apolipoprotein E status (Kuusisto et al., 1997). A
consequence from these clinical studies is the search for therapeutic options that alleviate insulin
resistance and, thus, potentially halt AD progression (Craft et al., 2013).
Several studies have demonstrated that cerebral metabolism declined prior to the
deterioration in cognitive function, suggesting that energy failure was the earliest reversible
hallmarks of AD. These observations led to the hypothesis that AD-associated abnormalities in
energy metabolism were caused by insulin resistance or reduced insulin action in the brain (Blass
et al., 2002; Blum-Degen et al., 1995; Hoyer, 2004a, b). Because the fundamental abnormalities
in AD represent effects of brain insulin resistance and deficiency, and overlap with those of Type
1 and Type 2 diabetes, the term Type 3 diabetes for AD has been proposed (de la Monte, 2014).

8
Insulin signaling in brain aging and neuroinflammation
The immune privilege of the CNS forbids circulating immune cells from gaining access
to it without the occurrence of inflammation or injury (Carson et al., 2006). While a prolonged
inflammatory state in the brain is detrimental to its function, the brain innate immune system can
also be beneficial, since it promotes cellular repair and clears debris (Yin et al., 2016). Aging is
also associated with formation of a chronic low-grade inflammatory state in the brain. It is
becoming increasingly appreciated that these inflammatory processes are associated with
alterations in cellular metabolism (De Felice and Ferreira, 2014; Sell et al., 2012; Spielman et al.,
2014). Chronic, low-grade inflammation positively correlates with age, and is associated with
most degenerative diseases of the elderly. Activation of the immune cells of the CNS, such as
microglia and astrocytes, is one of the universal components of neuroinflammation (Kapetanovic
et al., 2015). Initiation of inflammatory responses by the microglia are further amplified by the
astrocytes, generate neurotoxic factors leading to neurodegeneration (Glass et al., 2010).
Previous studies in our lab have revealed a shift from a neurotrophic to neurotoxic phenotype in
aging astrocytes, thus probably denying the neurons of essential energy substrates and neuro-
protective mechanisms (Jiang and Cadenas, 2014). Additionally, changes in the activation profile
of microglia and release of inflammatory cytokines have been hypothesized to induce
development of insulin resistance (De Felice and Ferreira, 2014; Spielman et al., 2014).
Characterizing behavior of aging neurons, in presence of microglia or astrocytes separately, can
improve our understanding of the link between inflammation and metabolism. We hypothesize
that the metabolic-inflammatory axis is critical for brain aging and is determined by the co-
ordination of the metabolic phenotype of aging neurons and the microglial inflammatory
responses.

9
Molecular components of the inflammatory response
An inflammatory response typically involves three stages: TLR-NFκB formation of pro-
cytokines, inflammasome assembly, and activation of caspase-1. The detection of pathological
triggers in an inflammatory response is mediated by pattern recognition receptor (PRRs) that
recognize danger-associated molecular patterns (DAMPs) or pathogen-associated molecular
patterns (PAMPs). While the pathological triggers are structurally diverse, there is considerable
overlap in the sensors, transducers, and effector mechanisms leading to the amplification of the
inflammatory responses. The initiators of inflammatory responses are the PRRs, including the
TLRs, and other co-receptors such as CD36, CD14 and CD47. The signaling pathways affected
regulate the activities of transcription factors NFκB and AP-1, which play a role in the
production of effector proteins, such as components of the inflammasome (e.g., NLRP3, NLRP1,
NLRC4), cytokines, oxidants and NO.
NFκB signaling – NFκB regulates immune responses through the transcriptional regulation of
cytokines and immune response genes (Janssen-Heininger et al., 2000). Under basal conditions,
NFκB is localized in the cytoplasm, bound to the inhibitor of NFκB (IκB). In response to stimuli-
induced phosphorylation of IκB, NFκB dissociates from the complex and translocates to the
nucleus to induce the transcription of its target genes (Kabe et al., 2005) (Fig. 5). NFκB is
sensitive to redox changes and inflammatory mediators and participates both protective and
damaging responses, based on the context of stimulation. H2O2 can positively or negatively
modulate NFκB activity. Mitochondria-derived H2O2 is key to the activation of NFκB (Csiszar et
al., 2008; Weinberg et al., 2015). Although excessive levels of H2O2 inactivate NFκB through
oxidation of its p50 subunit, moderate levels of H2O2 lead to IKK- or Syk-induced
phosphorylation, polyubiquitination, and degradation of IκB, and the activation of NFκB (Patten

10
et al., 2010; Takada et al., 2003) (Fig. 5).
NFκB can have a pro-survival role by inhibiting JNK, upregulating anti-apoptotic genes, and
decreasing the expression of manganese superoxide dismutase (MnSOD). Conversely, NFκB
activation can be damaging to cells by initiating and amplifying inflammatory gene expression.
Stimulation of NFκB by TNFα or IL1 leads to transcription of cytokines and components of the
inflammasomes (Bubici et al., 2006; Das et al., 1995; Pahl, 1999). NFκB and MAPK pathway
activation is apparent in oxidative stress and Aβ-induced neuronal cell death (Chen et al., 2009;
Chongthammakun et al., 2009; Song et al., 2004). The outcomes of NFκB activation can be age-
dependent: its activation by TNFα is neuroprotective against excitotoxicity and ischemic brain
injury in 10 month-old neurons, but the same stimulus in 24 month old neurons was shown to be
toxic (Patel and Brewer, 2008).
Inflammasomes – Cellular insults identified by PPRs activate cytosolic multiprotein
complexes called inflammasomes, which are responsible for the maturation of proinflammatory
cytokines, their release, and the activation of pyroptosis, an inflammatory form of cell death
(Strowig et al., 2012). The inflammasomes are either members of the NLR family or members of
the pyrin and HIN domain-containing (PYHIN) family (Walsh et al., 2014). A total of 23 genes
encode the NLRs, but only a few are capable of forming oligomeric complexes that activate
caspase-1 (Abais et al., 2015), including NLRP1, NLRP2, NLRP3, NLRP6, NLRP12 and
NLRC4 (Minkiewicz et al., 2013; Rathinam et al., 2012).
The activation of most inflammasomes requires a priming stimulus (signal 1) and an
activating stimulus (signal 2). These stimuli can be pathogens (PAMPs) or host-derived insults
(DAMPs). Inflammasome complexes are generally composed of three components: a cytosolic
PPR, caspase-1, and an adaptor protein, ASC (apoptosis-associated speck-like protein containing

11
a caspase activation and recruitment domain). A priming stimulus, through activation of NFκB
signaling, often leads to assembly of the inflammasome complex, which increases expression of
pro-interleukin-1β (pro-IL1β) and the inflammasome (e.g., NLRP3). The inflammasomes
oligomerize when activated by ligands or enzymes, to form a platform that can activate caspase-
1, often via complexation with ASC (Fig. 6). ASC facilitates interaction between the PYD
domain of the NLRP proteins and the CARD of procaspase-1. Caspase-1 regulates the
maturation and release of IL-1β and IL-18 and also triggers pyroptosis pathways (Walsh et al.,
2014).
NLRP3 inflammasome is the most widely implicated member of the NLR family and it will
be reviewed due to its unique property of being stimulated by both H2O2 and amyloid-β in the
brain. The NLRP3 inflammasome can be activated by a wide array of stimuli, including APMPs
such as bacterial, fungal and viral components, as well as DAMPs such as extracellular ATP. Its
ability to respond to a wide variety of stimuli suggests that it behaves as a general sensor of
cellular damage and stress. The activity of NLRP3 seems crucial in the pathogenesis of different
degenerative disorders such as AD, atherosclerosis, liver cirrhosis, and lung fibrosis (Walsh et
al., 2014).
The exact activation mechanism of the NLRP3 inflammasome is under debate, but all the
proposed models postulate that cytoplasmic K
+
concentration plays a crucial role. Three models
have been proposed. (a) The channel model proposes extracellular ATP to be the main activator,
which activates the P2X7 K
+
release channel, which ultimately gives rise to pannexin 1 pore
formation. The pores (which can be formed by bacterial toxins as well) allow cytoplasmic entry
of extracellular factors that directly activate NLRP3 and also allow K
+
efflux out of the cell
(Kahlenberg and Dubyak, 2004; Kanneganti et al., 2007). (b) The lysosome rupture model fits

12
when the activating stimuli are particulate activators (e.g., alum and silica). It proposes that these
particles are phagocytosed, causing lysosomal rupture release of Cathepsin B into the cytoplasm,
which activates the NLRP3 inflammasome (Halle et al., 2008; Hornung et al., 2008). (c) The
H2O2 model, being the most relevant model in the context of this review, proposes NLRP3 to be
a general sensor of cellular stress, where H2O2 serves as the secondary messenger that activates
the inflammasome (Petrilli et al., 2007; Zhou et al., 2010) (Fig. 6).
Redox regulation of insulin signaling
Mitochondrial H2O2 participates in the regulation of multiple cytosolic signaling pathways such
as the IIS and the MAPK signaling. In neurons and hepatocytes, mitochondrion-generated H2O2
is found to activate IIS (Storozhevykh et al., 2007). Low levels of H2O2 generated by
mitochondria are actually required for the initial activation of IIS and this process is termed as
“redox priming”. Collapse of the neuronal mitochondrial proton gradient by FCCP not only
eliminates mitochondrial O2 consumption and H2O2 production, but also suppresses the
phosphorylation (activation) of the insulin receptor even in the presence of insulin (Storozhevykh
et al., 2007). This is consistent with the observation that the spike signal of mitochondrion-
generated H2O2 precedes the autophosphorylation of the insulin receptor, and that
phosphorylation can be dose-dependently inhibited by N-acetylcysteine (Persiyantseva et al.,
2013). IIS is sensitive to H2O2 due to: (a) the oxidation of cysteine residues on the insulin
receptor and IGF-1 receptor facilitates autophosphorylation of both receptors and leads to the
activation of the downstream IRS (Loh et al., 2009) and (b) H2O2 oxidizes and inhibits two
negative regulators of IIS – the tyrosine phosphatases (e.g., PTP1B) and the lipid phosphatase
(PTEN) (Elchebly et al., 1999). Moreover, mitochondrial H2O2 is also involved in the activation
of Akt and its translocation to mitochondria and to the nucleus (Antico Arciuch et al., 2009).

13
Figure 1.3: Redox modulation of insulin signaling
Nevertheless, increasing evidence suggests that H2O2 modulates IIS in a concentration-
dependent manner (Fig. 4): while lower concentrations of H2O2 (~5 μM) activate IIS, higher
levels (~50 μM) inactivate IIS (Iwakami et al., 2011). IIS inactivation is likely due to the
stimulation by elevated H2O2 of inhibitory pathways of IIS such as JNK and IκB kinase (IKK)
(Csiszar et al., 2008; Yin et al., 2013), which constitutes the mechanism of stress and
inflammation-induced insulin resistance, respectively (Aguirre et al., 2000). The activation of
JNK by H2O2 has been thoroughly investigated (Nemoto et al., 2000; Pinzar et al., 2005; Yin et
al., 2012; Zhou et al., 2008) and it occurs at several levels: (a) H2O2 oxidizes thioredoxin and
releases the upstream kinase of JNK, ASK1 (Saitoh et al., 1998), (b) H2O2 oxidizes and inhibits
MAPK phosphatases (Foley et al., 2004), and (c) H2O2 disrupts the glutathione transferase-JNK
complex and releases the latter (Adler et al.,
1999). IIS activity (IRS1 and Akt
activation) declines in both the aged rat
brain and 3xTG-AD mouse brain, whereas
JNK phosphorylation and activity is
increased in both models with aging or with
AD genotype (Jiang et al., 2013a; Sancheti
et al., 2013).

IRS
PI3K
Akt
PTEN
PIP
2
PIP
3
PDK1
IR
H
2
O
2
O
2
. –
H
2
O
2
PTP1B
PTP2A
JNK

14
Redox regulation of NLRP3 inflammasome
All NLRP3 activators have been found to be able to trigger oxidant production, whereas
treatment with anti-oxidants blocks NLRP3 activation (Tschopp and Schroder, 2010). Although
the mechanism is unclear, K
+
efflux accompanies oxidant generation (Kowaltowski et al., 2009).
Association of NLRP3 with Trx-interacting protein (TXNIP), triggered by H2O2 production is
proposed to be the mechanism underlying the redox activation of NLRP3 (Zhou et al., 2010).
Under a resting mode, TXNIP is constitutively bound to Trx (Spindel and Berk, 2012) and
increased cellular H2O2 production dissociates this complex, thereby allowing TXNIP to bind to
NLRP3, thus and leading to its activation (Fig. 6). Knockdown of Trx potentiated inflammasome
activation and knockdown of TXNIP inhibited activation of caspase-1 and IL1β secretion upon
stimulation by NLRP3 agonists (Dostert et al., 2008; Zhou et al., 2010). However, caspase-1
activation is not completely blocked when TXNIP is knocked out, suggesting that there are
multiple pathways being capable of activating NLRP3. Redox changes are also important in
priming of the NLRP3 inflammasome. Priming of the inflammasome, which involves the
TLR/NFκB signaling, can occur through its deubiquitination at the hands of the TLR with
Myd88, in the presence of mitochondrial H2O2 (Abais et al., 2015). Several questions about the
oxidant-dependent model of NLRP3 activation remain unresolved and not all the stimuli that
lead to oxidant production were able to activate NLRP3 (e.g., TNFα). This implies that
activation of NLRP3 follows a specific mechanistic pathway, in which oxidants or specifically
H2O2 are essential but are not sufficient. In addition, in contrast to the H2O2 model of NLRP3
activation, O2
.–
was found to inhibit caspase-1 activation via redox signaling (Meissner et al.,
2008).
NADPH oxidases (NOX) were initially thought to be the primary activators of the NLRP3

15
inflammasome via the production of O2
.–
and H2O2. Some early inflammasome studies reported
the NOX enzymes to be important in activating NLRP3 in response to ATP and particulates
(Cruz et al., 2007; Dostert et al., 2008). However, macrophages deficient in certain NOX
subunits, like NOX1, NOX2, NOX3, and NOX4, responded normally to activating stimuli, and
in some cases, demonstrated slightly increased inflammasome activity, suggesting either
compensation by remaining members of the NOX family or occurrence of a other cellular
sources of oxidants for inflammasome activation (Zhou et al., 2011).
Recent studies suggest that mitochondria might be the organelles that integrate signaling for
inflammasome activation (Zhou et al., 2011). There is also evidence showing that NOX
activation (and the generation of O2
.–
and H2O2) requires initial priming by mitochondrial H2O2
(Zawada et al., 2011). In non-stimulated conditions, the NLRP3 protein is localized at the
endoplasmic reticulum (ER) but not mitochondria. This localization changed upon activation of
the inflammasome in response to several different stimuli: upon stimulation, NLRP3 translocated
to the perinuclear space, where it also co-localized with ER and mitochondria. A similar
ER/mitochondrial co-localization was also observed for ASC upon NLRP3 activation and
TXNIP was found to redistribute to the mitochondria upon inflammasome activation. Depletion
of mitochondrial DNA or inactivation of the voltage dependent ion channel (VDAC) was shown
to impair inflammasome activation. VDAC proteins, abundant in the outer mitochondrial
membrane, are channels responsible for ions and metabolite exchange between mitochondria and
the rest of the cell, particularly the ER. They are also involved in the regulation of mitochondrial
metabolism and mitochondrial O2
.-
release (Han et al., 2003). Moreover, pharmacologic
inhibition of complexes I and III, resulting strong release of O2
.-
and H2O2, led to NLRP3
activation. Additionally, inhibition of mitophagy/autophagy by 2-methyladenine resulted in the

16
accumulation of dysfunctional mitochondria with increased oxidant production, and as a
consequence, inflammasome was activated (Zhou et al., 2011). Other mitochondrion-generated
oxidants, such as ONOO
–
and O2
.–
, can activate the NLRP3 inflammasome (Abais et al., 2014;
Hewinson et al., 2008).

NLRP3 at the interface of inflammation and metabolism
The first link between inflammation and metabolism originated from early studies in models
of obesity, where the expression of pro-inflammatory cytokines such as TNF-α, IL-6 and IL-1β
was upregulated in adipose tissues of obese and diabetic subjects. The NLRP3 inflammasome,
which regulates secretion of IL-1β, is considered to be a sensor of altered metabolic homeostasis
and its activation is thought to induce insulin resistance. Increased activity of NLRP3 was also
implicated in the pathogenesis of metabolic diseases such as type 2 diabetes and metabolic
syndrome (Haneklaus and O'Neill, 2015), with insulin hypersensitivity being the primary
phenotype of the NLRP3- or caspase-1-deficient mice (Vandanmagsar et al., 2011; Wen et al.,
2011). Interestingly, deficiency of IL-1β protects rodents from insulin resistance induced by
high-fat diet (Stienstra et al., 2010; Wen et al., 2011), and the treatment with an IL-1 receptor
antagonist reduced hyperglycemia in diabetic rodents and improved glycemic control in type-2-
diabetes patients (Donath and Shoelson, 2011; Ehses et al., 2009). Studies using genetic mouse
models demonstrated that IL-1β inhibits IIS by upregulating TNFα, a known insulin resistance-
promoting cytokine (Hotamisligil et al., 1996; Jager et al., 2007). Although it is not clear whether
or not the NLRP3 inflammasome contributes to neuronal insulin resistance, it is likely that a
similar mechanism to that in adipose tissue might be prevalent in the CNS. Mitochondrion-
derived DAMPs, such as mtDNA, can directly induce inflammatory changes in microglial and

17
neuronal cells (Wilkins et al., 2015) but oxidation of mtDNA is still required (Shimada et al.,
2012), which supports the notion that redox control is the linking component transducing
metabolic signal to inflammatory response. In addition, extracellular ATP at various
concentrations can activate microglia and induce neuroprotective or neurotoxic effects by the
expression of pro- or anti-inflammatory cytokines (Davalos et al., 2005; Inoue, 2002).

Neuroinflammation in aging and Alzheimer’s disease
Several of the inflammatory factors discussed above can be general neurotoxic factors in
normal aging and neurodegenerative diseases. It is predicted that these inflammatory responses
are partly driven by positive feedback loops between microglia and astrocytes. Amplification of
inflammation by astrocytes worsens the neurotoxic environment and the damaged neurons can
further activate glial cells by releasing ATP and other DAMPs. This encompasses a self-
promoting cycle of inflammation and neuronal death, even after the withdrawal of initial stimuli.
This inflammatory mechanism is hypothesized to cause neurodegeneration and set the
foundation of neurological disorders such as AD (Glass et al., 2010).
Chronic, low-grade inflammation positively correlates with aging: with age, microglia
exhibit enhanced sensitivity (priming) to inflammatory stimuli (originating either from peripheral
tissues or brain), similar to that observed in brains with ongoing neurodegeneration (Norden and
Godbout, 2013). In both physiologically aged and senescence-accelerated mouse models,
profound microglia priming was characterized by increased basal production of pro-
inflammatory cytokines (IL-1β, IL-6, and TNFα), decreased production of anti-inflammatory
cytokines (IL-4 and IL-10), decreased activity of the TGFβ1-Smad3 signaling pathway, and
upregulated TLR expression. Aging is also associated with dysregulation of microglia, with

18
deficits in CD200 and fractalkine regulation. Similarly, astrocytes also exhibit proinflammatory
phenotypes during aging: an increased expression of pro-inflammatory cytokines including TNF-
α, IL-1β and IL-6 was observed in the rat cortex and striatum during aging (Campuzano et al.,
2009).
In the Alzheimer’s brain, Aβ is capable of activating microglia and astrocytes to induce
production of damaging molecules such as H2O2, ∙NO, pro-inflammatory cytokines, chemokines,
and prostaglandins (e.g., PGE2), which cause neuronal death (Kitazawa et al., 2004). Aβ plaques
can be detected through several sensors, including TLRs, NLRs, and RAGE (receptor for
advanced glycation end products) (Neeper et al., 1992; Reed-Geaghan et al., 2009; Richard et al.,
2008; Tschopp and Schroder, 2010). Aβ oligomers and fibril-induced lysosomal damage can
trigger activation of the NLRP3 inflammasome (Halle et al., 2008). In neurons, NALP1, a
member of the NLR family, can induce inflammatory response, similar to NLRP3 (Schroder and
Tschopp, 2010). These inflammatory signals, along with other risk factors converge to produce
an abnormal processing of the tau protein (Maccioni et al., 2009). Although neuroinflammation
can facilitate the formation of neurofibrillary tangles (NFTs) through tau kinases, it is not clear
that whether these NFTs affect inflammatory responses (Ballatore et al., 2007; Glass et al.,
2010).

The metabolic—inflammatory axis in brain aging
Compromised glucose metabolism and mitochondrial function are signatures of normal
brain aging and early stages of AD, while neuroinflammation is also observed in brain aging and
the late stage of AD animal models and postmortem AD brains. It is still debatable whether or
not neuroinflammation is the driven force of brain aging and AD or it is simply a consequence of

19
metabolic dysfunction occurring earlier in the progression of aging or AD. It is noteworthy that
although dysregulated neuroinflammation induces neurotoxicity and tissue damage, the primary
function of controlled immune responses is still to protect the brain from infectious agent and
injuries. Nevertheless, an increasing number of studies on cell lines, genetic rodent models, and
humans indicate that redox control might serve as a bidirectional link between energy
metabolism and inflammatory responses in the brain. The brain metabolic-inflammatory axis
entails interconnected cross-talks of energy metabolism, redox control, and neuroinflammation
(Fig. 7) and might serve as an integrated mechanism for brain aging and Alzheimer’s etiology.

Figure 1.4: The metabolic-inflammatory axis in brain aging
The following chapters (II and III) present findings that underline the importance of
preserving insulin signaling activity in brain aging, neurodegeneration and neuroinflammatory
states.

REDOX
CONTROL

INFLAMMATION

METABOLISM
THE METABOLIC–INFLAMMATORY AXIS

20
CHAPTER II: BRAIN METABOLIC AND FUNCTIONAL ALTERATIONS IN A LIVER-SPECIFIC PTEN
KNOCKOUT MOUSE MODEL
This chapter investigates the effect of the unique peripheral phenotype of the liver-
specific PTEN knockout mouse model on brain metabolism and neuronal function. The results
underline the significance of insulin signaling activity and enhanced bioenergetics on the
synaptic function and memory formation.
Introduction
The human brain consumes the most glucose: about 60% of the body’s resting state
glucose. Neuronal brain glucose uptake is mostly insulin-dependent and is dependent on the
insulin-sensitive glucose transporter GLUT4 (Bingham et al., 2002). Stimulation of insulin
receptors by insulin leads to activation of the insulin signaling pathway. The subsequent Akt-
dependent phosphorylation of many substrates, also results in translocation of GLUT4 from the
intracellular storage compartment to the plasma membrane (Rowland et al., 2011). Once inside
the cell, glucose is metabolized into energy substrates including neurotransmitters such as
glutamate and glutamine (See figure 1.2). These substrates generated from glucose metabolism
directly affect synaptic transmission; thus it can be surmised that synaptic plasticity is
susceptible to the bioenergetic state of the brain (Schubert, 2005).
The brain’s sensitivity towards insulin determines its ability to satisfy the bioenergetic
and functional demands of neurons. Insulin has been shown to influence synaptic transmission
by modulating the cell membrane expression of NMDA (N-methyl-D-aspartic acid) receptors,
affecting long-term potentiation (LTP) . Several clinical studies have shown decreased brain
glucose uptake to be a common condition in patients with neurodegenerative diseases such as

21
Alzheimer’s disease (AD) and mild cognitive impairment (MCI) (Mosconi, 2005; Mosconi et
al., 2009). Peripheral insulin resistance is also a typical feature of aging, with several studies
suggesting that high circulating insulin and insulin resistance to be important contributors to
progressive cognitive impairment and neurodegeneration. Hence, preserving insulin sensitivity is
being widely considered as a therapeutic approach for slowing the processes inherent in
neurodegeneration (Stefanelli et al., 2014).
Phosphatase and Tensin Homologue (PTEN) is a negative regulator of the
phosphatidylinositol 3-kinase (PI3K)/Akt pathway. Deletion of PTEN is known to enhance the
activity of the insulin signaling pathway and improve glucose uptake in several cell types. A
liver-specific deletion of PTEN in mice also resulted an enhanced liver insulin action, in addition
to increased fatty acid synthesis, accompanied by hepatomegaly and a fatty liver phenotype
(Stiles et al., 2004a). Interestingly, these mutant mice also show lower systemic insulin levels,
lower fasting glucose levels, and increased glucose uptake rates in comparison to wild-type mice.
We hypothesized the existence of a highly insulin-sensitive brain in these mutant mice because
of their low overall systemic insulin and circulating levels; and as a direct result, of a more
robust insulin signaling activity in the brain. The study aims at demonstrating the effect of
enhanced insulin sensitivity and insulin signaling activity on neuronal function and memory
formation.

22
Materials and Methods
Materials
[1-
13
C] glucose (99%) was purchased from Sigma-Aldrich (St Louis, MO, USA). [1, 2-
13
C] acetate (99%) and deuterium oxide (99.9%) were obtained from Cambridge Isotope
Laboratories (Andover, MA, USA). All other chemicals were the purest grade available from
Sigma-Aldrich. Rodent tail vein catheter and restraining apparatus were obtained from Braintree
Scientific, Inc (MO, USA). The constant infusion of [1-
13
C] glucose and [1, 2-
13
C] acetate was
carried out by using a pump from Bio-Rad Laboratories Inc (CA, USA).

Animals
PTEN
loxP/loxP
mice were bred with Alb-Cre mice to generate mice with a liver specific
deletion(Stiles et al., 2004b) and maintained at the University of Southern California (Los
Angeles, CA) following National Institutes of Health guidelines on use of laboratory animals and
an approved protocol by the University of Southern California Institutional Animal Care and Use
Committee. Mice were housed on 12-h light/dark cycles and provided ad libitum access to food
and water. Animals of 4 and a half months were used for the experiments. PTEN
loxP/loxP
; Alb-Cre
-

were used as wild type mice. C57BL/6J strain (Jackson Laboratories) of mice were used as the
background strain to breed the both groups of mice. PTEN
loxP/loxP
; Alb-Cre
+
mutant mice will be
referred to as ‘mutant mice’ or ‘MUT’ and the PTEN
loxP/loxP
; Alb-Cre
-
will be referred to as
either ‘wild type’ mice or ‘WT’ for the rest of this chapter.

23

Glucose Tolerance Test (GTT) and Ketone body levels
GTT was performed on the mice after a fasting period of 16 hours. For glucose
measurement, tail veins were punctured and a small amount of blood was released and applied
onto a OneTouch glucometer. For GTT, the mice were given a single dose (2 g/kg of body
weight) of D-Dextrose (Sigma) by i.p injection after a baseline glucose check. Circulating
glucose levels were then measured at 15, 30, 60 and 120 mins after glucose injection.
Ketone body (b-hydroxybutyrate) levels were assessed using a colorimetric assay kit
(Cayman Chem, 700190) using instructions provided by the manufacturer.

Intravenous Glucose and Acetate Infusion
Infusions were administered as previously described (Sancheti et al., 2014b). The mouse
was first restrained using a rotating tail vein restrainer. Anesthesia was not used during the entire
procedure, which allowed measuring metabolism in an awake non-anesthetized mouse. Awake
animals were used to avoid the effect of anesthesia on cerebral glucose utilization (Ori et al.,
1986). After restraining the mice, the basal glucose levels were tested as described in the GTT
procedure. The puncture made for testing the basal blood glucose levels was also used for
inserting a tail vein catheter in the mouse tail (Braintree Scientific, Inc, MA, USA). The catheter
was inserted using the manufacturer’s instructions and was tested for bulges by pushing some
saline solution through the catheter. Any bulge at the bottom of the tail, resistance, or back flow
of saline was considered as an improper insertion of catheter and the procedure was performed
again at a more proximal point in either the same vein or the next vein. The glucose and acetate

24
infusion protocol was carried out as described earlier (Fitzpatrick et al., 1990; Marin-Valencia et
al., 2012). Animals first received a bolus of [1-
13
C] glucose and [1,2-
13
C]acetate solution (0.6M)
to raise the blood glucose levels to normoglycemic range, followed by exponentially decreasing
amount of glucose for 8 min. Finally, infusion at a constant rate was performed for 150 mins to
achieve steady state concentration of labelled metabolites. Mice were kept in a quiet and warm
environment to avoid stress during the glucose and acetate infusion. The constant infusion was
carried out using a pump from Harvard Apparatus.

Tissue Collection and Extraction Procedure
At the end of the 150 min infusion, the catheter was removed and the final blood glucose
levels were measured as described earlier. The mouse brain was immediately frozen in liquid
nitrogen, and stored at -85
o
C. The entire procedure, from the end of the infusion to snap freezing
the brain, was ensured to be less than one minute for each mouse to avoid post-mortem
metabolic changes. After freezing of the brain, it was weighed and perchloric acid extraction was
performed as previously described (Farrow et al., 1990; Sancheti et al., 2014a). Tissue samples
were extracted in such a way as to maximize the concentration of metabolites for NMR analysis.
The brain was first grounded in a mortar to a fine powder using a pestle under liquid nitrogen
and then transferred to a weighed centrifuge tube. Ice-cold 20% (w/v) perchloric acid (2 ml/g of
tissue) was added to the powdered sample while ensuring that no thawing occurs. The mixture
was then homogenized and allowed to thaw on ice. The precipitate was removed by
centrifugation at 22000 g for 20 mins at 2
o
C. The supernatant was then neutralized to pH 7.4
using potassium hydroxide and the mixture was allowed to stand on ice for 20 mins. The
supernatant was then collected after centrifugation at 22000 g for 20 mins at 2
o
C and this final

25
brain extract supernatant was stored at -85
o
C until used for NMR analysis.

NMR Spectroscopy
The stored brain extracts were thawed and mixed in appropriate proportion with D2O,
sodium azide (preservative), and 1.5 µL of 1,4 Dioxane (chemical shift reference and internal
standard).
13
C NMR analysis was carried out on a Varian VNMRS 600MHz instrument at 150.86
MHz.
13
C spectra were acquired with proton-decoupling and nuclear Overhauser enhancement
with the following parameters: pulse angle of 45°, acquisition time of 1 s and a relaxation delay
of 5 s, 251 ppm spectral width with 32,768 spectral points. A total of 7312 scans were acquired
at 25 °C to obtain a good signal to noise ratio. The chemical shift reference peak of 1,4 dioxane
was set to exactly 67.4ppm, followed by peak identification using chemical shift values from
previous literature (Blüml et al., 2000; Preece and Cerdán, 1996).

The peak areas were
normalized by using the peak area of 1,4-dioxane as the internal standard. MestRenova software
from Mestrelab Research (CA, USA) was used to integrate relevant peaks after normalization of
peak areas. The quantification of each peak was carried out as follows:
13
C spectra of glutamate,
glutamine, aspartate, N-acetyl acetate, lactate and GABA at natural abundance of
13
C were
acquired in a single solution at different concentrations to construct a standard curve of peak area
vs.
13
C concentration for each carbon of the compound. This standard curve was used to convert
the observed peak area of each
13
C-metabolite (after normalization using the peak area of 1, 4-
dioxane as internal standard) to
13
C concentration. A representative NMR spectrum is depicted in
Figure 2.1.

26

Figure 2.1: Representative NMR spectrum after [1-
13
C] glucose and [1. 2-
13
C]acetate infusion
(Sancheti, 2014).

27

13
C Labeling Patterns and Interpretation
The Labelling pattern of brain metabolites from [1-
13
C] glucose and [1,2-
13
C] acetate has
been well described previously by Dr. Sonnewald(Nilsen et al., 2013). The
13
C labelling pattern
has been shown in Figure 2.2. Shortly after [1-
13
C] glucose is converted to [3-
13
C] pyruvate via
glycolysis, [3-
13
C] pyruvate can be either reduced to [3-
13
C] lactate, carboxylated to oxaloacetate
(OAA) (in astrocytes), transaminated to [3-
13
C] alanine, or decarboxylated to [2-
13
C] acetyl CoA
and enter the triacrboxylic acid (TCA) cycle. Once [1-
13
C] glucose enters the TCA cycle, it
undergoes several steps to form [4-
13
C] α-ketoglutarate that can be transaminated to form [4-
13
C]
glutamate (Glu). In the GABAergic neurons, [4-
13
C] glutamate is subsequently converted to [2-
13
C] GABA via the enzyme glutamic acid decarboxylase or to [4-
13
C] glutamine (Gln) in
astrocytes via the astrocyte-specific enzyme glutamine synthetase. [4-
13
C] glutamate released by
the glutamatergic neurons during neurotransmission is removed from the synaptic cleft via
transporters, followed by conversion to [4-
13
C] glutamine or [4-
13
C] α-ketoglutarate. [2-
13
C]-/[3-
13
C] OAA can be derived from [4-
13
C] α-ketoglutarate, which can be transaminated to [2-
13
C]-
/[3-
13
C] aspartate. If the
13
C label is not released in the 1
st
turn of TCA cycle, it would form [2-
13
C]-/[3-
13
C] glutamate and glutamine and [4-
13
C]-/[3-
13
C] GABA can be formed after several
steps if OAA labeled from the 1
st
turn of the cycle condenses with unlabeled acetyl CoA. In
astrocytes, [1,2-
13
C] acetate is converted to [1,2-
13
C]acetyl CoA. After entry into the TCA cycle,
[4,5-
13
C]α-ketoglutarate is formed after several steps, which is then converted to [4,5-
13
C]
glutamate and [4,5-
13
C] glutamine. After [4, 5-
13
C]glutamine is transferred to neurons, it is
converted to [4,5-
13
C]glutamate (by phosphate activated glutaminase) in the glutamatergic
neurons. It can also be converted to [1,2-
13
C] GABA in GABAergic neurons. If [4,5-
13
C]α-

28
ketoglutarate is metabolized in the TCA cycle, it might form [3-
13
C]/[1, 2-
13
C]glutamate and
glutamine and [3-
13
C]/[4-
13
C] GABA after the second turn of the cycle.

Figure 2.2: Typical labelling pattern after [1-
13
C] glucose and [1. 2-
13
C] acetate infusion (Nilsen
et al., 2013).

Metabolic Ratios
The cycling ratio provides information of how long the label stays in the TCA cycle
before getting converted to glutamate or glutamine. The cycling ratio for
13
C from [1-
13
C]
glucose was calculated as follows: ([3-
13
C] glutamate (glutamine) – [1,2-
13
C]glutamate
(glutamine))/[4-
13
C] glutamate. The cycling ratio for
13
C from [1,2-
13
C]acetate was calculated as

29
follows: [1,2-
13
C] glutamate (glutamine)/[4,5-
13
C] glutamate (glutamine). The acetate versus
glucose utilization ratio provides an estimate of the relative contribution from neurons and
astrocytes to glutamate, glutamine and GABA formation. The acetate versus glucose ratios are
expressed as [4,5-
13
C] glutamate (glutamine)/[4-
13
C] glutamate (glutamine) and [1,2-
13
C]
GABA/ [2-
13
C] GABA (Kondziella et al., 2006). Glycolytic activity was calculated by
calculating the % change in levels of [3-
13
C] alanine (Morken et al., 2013).

High Performance Liquid Chromatography (HPLC)
HPLC analysis to obtain total (
12
C+
13
C) Glu, Gln, GABA and Asp concentrations in the
brain was measured with the help of Keiko Kanamori (Huntington Medical Research Institute) as
previously described (Kanamori and Ross, 2001; Sancheti et al., 2014b). Total Glu, Gln, GABA
and Asp concentrations in the brain extract were measured, after precolumn derivatization
with o-phthaladehyde and 2-mercaptoethanol, followed by separation on a reverse-phase column
using fluorometric detection. Baseline separation of Asp, Glu, GABA and Gln was achieved
from adjacent peaks while minimizing the total elution time using the following chromatographic
program: elution with 25% methanol and 75% aqueous sodium phosphate buffer (50 mmol/L, pH
5.29) for 10 min followed by increase in the percentage of methanol to 49% in 15 minutes and to
100% in 8 min. The metabolites were quantified by comparison of the peak areas with those of
standards. These total metabolite concentrations were used to calculate percentage
13
C
enrichment for
13
C-labelled metabolite concentrations obtained NMR analysis.

30
Brain Glucose Uptake
Positron emission tomography utilizing the radiotracer fluoro-2-deoxy-2-[
18
F]-fluoro-D-
glucose (FDG-PET) was used in a clinical setting to measure brain glucose uptake by measuring
standard uptake value (SUV), 40 min post-injection of [
18
F]-FDG-PET as a tracer using a
MicroPET scanner. SUV represents the standardized uptake value after taking into consideration
the actual radioactivity concentration found in the brain at a specific time and the concentration
of radioactivity, assuming an even distribution of the injected radioactivity across the whole
body. Mice were fasted overnight and then sedated using 2% isoflurane by inhalation, followed
by i.v. administration of the radiotracer 2-deoxy-2-[
18
F]-fluoro-D-glucose. Mice were then
immobilized on the scanner bed with a warming bed (to maintain body temperature) and were
scanned using a Siemens MicroPET R4 PET scanner with a 19 cm (transaxial) by 7.6 cm (axial)
field of view and an absolute sensitivity of 4% with a spatial resolution of ~1.3 mm at the center
of view for a duration no longer than 90 min. Baseline blood glucose levels were measured
before administration of the tracer to ensure that abnormalities in glucose metabolism during
FDG-PET imaging are due to the phenotype of the mice and not due to differences in the
baseline blood levels. Immediately after the completion of the FDG-PET scan, the animals
underwent CT scanning with intravenous contrast material, providing information of the brain
structure and anatomical data.
PET data were reconstructed using the 2D-OSEM algorithm supplied by the MicroPET
manager (Siemens Medical Solutions USA, Inc., Knoxville, TN) into 128x128x63 images with
0.084 mm x 0.084 mm x 1.21 mm resolution. Two bed positions were used to obtain the CT
scans using the following settings: 80 kVp, 500 µA, 100 ms/180 steps covering 360 degrees and
finally reconstructed into 768x768x923 images with 0.105 mm isotropic resolution. PET and CT

31
images were co-registered using rigid transformations as both scans were performed using
warmed multi-modality imaging chambers. SUV were calculated by drawing the regions of
interest (Sancheti et al., 2013).
Insulin sensitivity assay
400 µm thick brain slices were obtained using a vibratome (Series 1000, St Louis, MO).
Slices with hippocampal regions were selected for the insulin sensitivity test. These slices were
exposed to 10 nM insulin in artificial cerebrospinal fluid ACSF for 10 min at room temperature,
followed by flash freezing in liquid nitrogen for protein extraction later.

Western blotting
Frozen brain tissues were pulverized using a mortar and pestle in liquid nitrogen to a fine
powder consistency. Approximately 40 mg of pulverized tissue was collected in 2.0 ml
Eppendorf tubes and 200 µl of T-PER (Thermo Fisher Scientific) was added to each sample
tube. T-PER also contained protease and phosphatase inhibitor cocktails (Sigma Aldrich) in a
1:100 ratio. Tubes were allowed to sit on ice for 10 min and were vortexed regularly, followed
by centrifugation at 10,000g for 10 min at 4
o
C. Supernatant from the centrifuged tubes was
collected in separate tubes, followed by a BCA protein assay (Thermo Scientific, IL) to obtain
protein concentration for each sample. Samples were diluted in SDS sample buffer to equalize
protein concentrations and were loaded into 10% Tris-Glycine Protein Gels (Thermo Fisher
Scientific) and were separated based on size of protein using gel electrophoresis, and were
transferred onto PVDF membranes using the wet transfer method. Appropriate primary
antibodies (1:1000) and secondary antibodies (1:2000) were used to visualize immune reactive

32
bands using chemiluminescence agents. Primary antibodies against β-actin (3700), phospho-
AKT (Ser473) (9271), AKT (9272) were obtained from Cell Signaling Technology (Danvers,
MA, USA). HRP-labeled secondary antibodies were obtained from Santa Cruz Biotechnology.

Long Term Potentiation (LTP)
LTP assay was performed as previously described (Liu et al., 2015). Preparation of
hippocampal slices: Each animal was decapitated under deep isoflurane anesthesia and the brain
was rapidly removed and immersed in sucrose-modified ACSF containing (in mM): 105 sucrose;
62 NaCl, 3 KCl, 4 MgCl2, 1.25 NaH2PO4, 26 NaHCO3, 10 glucose. The brain was allowed to
cool in the ice-cold ACSF solution for about 3-5 min before it was cut into 400 µm thick slices
using a vibratome (Series 1000, St Louis, MO). Sections containing hippocampal regions
surrounded by cortical tissue were selected and transferred to an incubation chamber containing
oxygenated ACSF that did not contain sucrose, that was maintained at 37
o
C till they were used
for recording. Electrophysiological recordings: Each hippocampal slice was incubated in the
incubation chamber for at least 1 hr before it was transferred to the recording chamber, in order
to achieve equilibrium. The platform allowed the hippocampal slice to be submerged in a thin
layer of flowing warm and humidified (95% O2-5% CO2) ACSF at a rate of 1.5–2 ml/min. Field
EPSPs (fEPSPs) were recorded from stratum radiatum of CA1 using a glass pipette filled with 2
M NaCl (yielding a resistance of 2–3 MΩ) in response to orthodromic stimulation (twisted
nichrome wires, 50 µm) of the Schaffer collateral-commissural projections in CA1 stratum
radiatum. Pulses of 0.1 ms duration were delivered to the stimulating electrode every 20 s. The
responses were amplified with Axoclamp 2A DC amplifier (Axon Instruments, Foster City, CA),
filtered at 6 kHz and digitized at 20 kHz. Clampex 9.0 software (Axon Instruments, Foster City,

33
CA) was used to acquire and record data. Input/output (I/O) curves were generated using
stimulus intensities from 100–350 µA in increments of 50 µA. Baseline fEPSPs were evoked at
30-50% of maximal fEPSP in 20 s intervals. LTP was induced at baseline intensity using Theta
Burst Stimulation (TBS) consisting of ten trains of five 100 Hz stimulation repeated at 5 Hz.
Post-stimulation recordings continued for at least 30 min following TBS. fEPSP slope magnitude
was calculated as the difference between two cursors, separated by 1 ms, and placed on the
middle portion of the ascending phase of the fEPSP. Three consecutive responses separated with
20 sec intervals were averaged and presented as a single point to reduce deviations. LTP was
expressed as a percentage of the average slope after TBS to that of baseline recordings.
Comparison of theta burst-induced plasticity was performed between groups using repeated
measures ANOVA (across all post-theta burst time points). The final 5 min of post-TBS
recordings were used to calculate the average change between groups for the average change in
fEPSP amplitudes. This analysis was followed by a post-hoc t-test for statistical significance.

Data analysis
GraphPad Prism version 7.0 was used to analyze these data. Student's two-tailed t-test
was used for statistical analysis of paired data. The level of statistical significance and the values
of n are indicated in the respective figure (*p ≤ 0.05, **p ≤ 0.01).

Results
Glucose Tolerance Test
The PTEN
loxP/loxP
mutant mice were faster at absorbing glucose from blood in comparison

34
to the wild-type mice (Figure 2.3). This result agrees with what has been previously reported by
Stiles et al (Stiles et al., 2004a). The mutant mice showed significantly faster rate of glucose
absorption at the 30 min and 60 min mark, with glucose clearance rates normalizing to those
seen in the wild type mice around the 120 min mark. These changes can be attributed to the
direct effect of a more robust insulin signaling activity in the livers of the mutant mice due to
PTEN deletion, leading to the liver absorbing glucose at a faster rate in the mutant mice in
comparison the wild-type mice. Plasma ketone body concentrations were found to be
significantly lower in the mutant mice than in the wild type mice.

Figure 2.3: Glucose clearance and ketone bodies levels in the liver-specific PTEN KO mice was
observed in comparison to the wild type mice. Glucose clearance rate was faster in the mutant
mice, and plasma ketone body levels were significantly lower than in the wild type mice as well.
Statistical significance calculated using student t-test (*P ≤ 0.05).

35
Brain Glucose Uptake
Dynamic [
18
F]-FDG-PET imaging was used to assess the rate glucose uptake in the
brains of the mutant and wild-type mice (n = 4 for each group). Mutant mice were observed to
have a higher rate of brain glucose uptake (Figure 2.4) and significantly greater total glucose
absorption, represented as standard uptake values (SUV). These results indicate the possibility of
a more glucose/insulin sensitive brain in the mutant mice.

WT MUT
180 seconds 3300 seconds
SUV
0
5

36
Figure 2.4: Brain glucose uptake and rate of glucose uptake: Standard uptake value (SUV) was
calculated after [
18
F]-FDG injection followed by dynamic PET and CT scanning as described in
the Materials and Methods section. Average rate of uptake of glucose with error bar indicating ±
SEM, total glucose uptake, and representative combined images from PET-CT scanning of wild
type and PTEN
loxP/loxP
;Alb-Cre
+
mice at 180 and 3300 s are shown above (n = 4 per group).

13
C Labelling of Brain Metabolites
Figure 2.1 shows a representative
13
C NMR spectrum after infusing mice with [1-
13
C]
glucose and [1,2-
13
C] acetate infusion. Both groups of mice were infused with a solution [1-
13
C]
glucose and [1,2-
13
C] acetate for 150 min, after which the brains were extracted to isolate
metabolites.
13
C labelled isotopomers of lactate, glutamate (Glu), glutamine (Gln), aspartate
(Asp), γ-aminobutyric acid (GABA), N-acetyl-aspartate (NAA), myoinositol (MI), and glucose
(C1α and β) were observed. Previous studies have examined changes in levels of major
13
C-
enriched metabolites after 8, 30, 60 and 150 min mark (Sancheti et al., 2014b). Although the flux
of
13
C-metabolites were shown to change only slightly after the 60 min mark, the 150 min time
point was chosen as the end point to allow for sufficient enrichment of all
13
C-metabolites and
therefore to allow us to examine all possible differences in the metabolic characteristics between
the mutant and wild type mice.
Table 2.1 shows the concentrations (expressed in mM ± SEM) of different
13
C-labelled
isotopomers of Glu, Gln, Asp, NAA, GABA and MI in the 4.5-month-old wild type mutant mice.
Greater overall labeling was observed in the mutant

mice samples than in the age-matched wild
type mice.

37

Table 2.1: Concentrations of different isotopomers of
13
C Glu, Gln, Asp, NAA, GABA, and
MI
Metabolite WT PTEN
loxP/loxP
;Alb-Cre
+

WT vs
PTEN
loxP/loxP
;Alb-Cre
+

P value
[4-
13
C]Glu 1.489 ± 0.17 1.840 ± 0.09 0.007 (*)
[3-
13
C]Glu 1.147 ± 0.07 1.381 ± 0.05 0.009 (*)
[2-
13
C]Glu 1.128 ± 0.09 1.384 ± 0.12 0.042 (*)
[1-
13
C]Glu 0.630 ± 0.04 0.717 ± 0.01 0.010 (*)
[4,5-
13
C]Glu 0.427 ± 0.04 0.517 ± 0.03 0.118
[2,3-
13
C]Glu 0.525 ± 0.08 0.839 ± 0.07 0.003 (*)
[3,4-
13
C]Glu 0.784 ± 0.13 1.073 ± 0.105 0.028 (*)
[1,2-
13
C]Glu 0.378 ± 0.06 0.507 ± 0.08 0.065

[4-
13
C]Gln 0.506 ± 0.07 0.550 ± 0.008 0.307
[3-
13
C]Gln 0.521 ± 0.05 0.594 ± 0.01 0.057
[2-
13
C]Gln 0.503 ± 0.02 0.597 ± 0.04 0.010 (*)
[1-
13
C]Gln 0.338 ± 0.04 0.384 ± 0.02 0.116
[4,5-
13
C]Gln 0.339 ± 0.06 0.380 ± 0.06 0.382
[2,3-
13
C]Gln 0.220 ± 0.04 0.278 ± 0.03 0.077
[3,4-
13
C]Gln 0.210 ± 0.25 0.445 ± 0.008 0.166
[1,2-
13
C]Gln 0.222 ± 0.05 0.258 ± 0.07 0.430

[4-
13
C]Asp 0.296 ± 0.07 0.298 ± 0.03 0.960
[3-
13
C]Asp 0.403 ± 0.03 0.433 ± 0.02 0.212
[2-
13
C]Asp 0.283 ± 0.02 0.315 ± 0.01 0.058
[1-
13
C]Asp 0.281 ± 0.09 0.236 ± 0.04 0.470
[3,4-
13
C]Asp 0.110 ± 0.03 0.113 ± 0.04 0.879
[2,3-
13
C]Asp 0.157 ± 0.03 0.204 ± 0.06 0.527
[1,2-
13
C]Asp 0.064 ± 0.02 0.111 ± 0.002 0.020 (*)

[3-
13
C]NAA 0.123 ± 0.02 0.132 ± 0.06 0.764
[2-
13
C]NAA 0.082 ± 0.02 0.089 ± 0.001 0.614
[6-
13
C]NAA 0.115 ± 0.02 0.111 ± 0.02 0.813

[4-
13
C]GABA 0.280 ± 0.03 0.342 ± 0.08 0.192
[3-
13
C]GABA 0.347 ± 0.02 0.370 ± 0.06 0.470
[2-
13
C]GABA 0.337 ± 0.04 0.380 ± 0.03 0.209
[1-
13
C]GABA 0.209 ± 0.03 0.342 ± 0.08 0.821
[3,4-
13
C]GABA 0.280 ± 0.001 0.040 ± 0.02 0.195
[2,3-
13
C]GABA 0.132 ± 0.008 0.118 ± 0.04 0.522

38
[1,2-
13
C]GABA 0.099 ± 0.04 0.095 ± 0.08 0.931

[4,6-
13
C]MI 0.097 ± 0.006 0.117 ± 0.02 0.098
[2-
13
C]MI 0.040 ± 0.01 0.035 ± 0.001 0.567
[1,3-
13
C]MI 0.105 ± 0.02 0.117 ± 0.02 0.493
[5-
13
C]MI 0.056 ± 0.02 0.070 ± 0.001 0.416
Table 2.1: ASP, aspartate; GABA, gamma-aminobutyric acid; Gln, glutamine; Glu, glutamate;
MI, myo-inositol; NAA, N-acetylaspartate. Concentrations of the different isotopomers of
13
C
Glu, Gln, Asp, NAA, GABA, and MI in 4.5 month old PTEN
loxP/loxP
;Alb-Cre
+
and wild type
mice after 150 min infusion. Results in column 2 and 3 are presented as average mM ± SD.
Results in column 4 are P values obtained from a two-tailed student t-test after comparing
between the two groups. *P ≤ 0.05, **P ≤ 0.01 (indicated in parenthesis); n = 4 per group.

Comparison of neuronal and glial metabolism after co-infusion of [1-
13
C]glucose+[1,2-
13
C]acetate
To assess the metabolic state of the brain, these studies were carried out by infusing [1-
13
C]glucose and [1,2-
13
C]acetate for a period of 150 min. The typical NMR trace (Figure 2.1)
and the typical labelling pattern for metabolites (Figure 2.2) after the co-infusion are shown. A
possible overall hypermetabolic state was observed in both neurons and astrocytes of the mutant
mice in comparison to the wild type mice brains, as seen by the significant increase in the levels
of the
13
C-labelled metabolite isotopomers at the end of the 150-min infusion.
All glutamate isotopomers labelled in the 1
st
, 2
nd
, and 3
rd
turns showed higher
concentrations of
13
C-labelling by an average of 25%, in comparison to those in the WT mice
(except for [4,5-
13
C] glutamate). Similar trends of increase in the labelled isotopomers for
alanine, lactate, glutamine, GABA, and aspartate were observed, although not statistically

39
significant. The levels of [4,5-
13
C] glutamine and [2,3-
13
C] glutamate reflect metabolites
originating from astrocytic metabolism and were found to be significantly higher in
concentration in the mutant mice. No significant difference was observed in the concentration of
[4,5-
13
C] glutamate. These results suggest a possible hypermetabolic state in the brain of the
mutant mice.
The enrichments of these metabolites (calculated as described in Materials and methods
section) reflect the relative content of different labelled isotopomers as a fraction of the total
concentration of metabolites. The total concentrations [
12
C +
13
C] of glutamate, glutamine,
GABA, and aspartate were analyzed by HPLC, and no significant differences were found in the
total metabolite concentrations between the two groups. The fractional enrichment (%) of the
various
13
C-labelled metabolite isotopomers of glutamate, glutamine, GABA, and aspartate
(Figure 2.5) were quantified based on the absolute concentrations. The fractional enrichment
values reveal the true differences in the metabolic states of neurons and astrocytes in these mice
brains. The mutant mice brains showed significantly higher levels of [4-
13
C]-Glu, [3-
13
C]-Glu,
[2-
13
C]-Glu, [1-
13
C]-Glu, [4,5-
13
C]-Glu, [2,3-
13
C]-Glu, [1,2-
13
C]-Glu, [4-
13
C]-Gln, [3-
13
C]-Gln,
[2-
13
C]-Gln, [1-
13
C]-Gln, [4,5-
13
C]-Gln, [3-
13
C]-Asp, [2-
13
C]-Asp, [2,3-
13
C]-Asp, [1,2-
13
C]-Asp,
[2-
13
C]-GABA, [4-
13
C]-GABA.

40

Figure 2.5: % Enrichment of
13
C labelled isotopomers of Glutamate, Glutamine, Aspartate, and
GABA after [1-
13
C] glucose + [1,2-
13
C] acetate infusion. Statistical significance calculated using
paired student t-test (*P ≤ 0.05).

Metabolic ratios
Metabolic ratios were calculated based on the concentration of different isotopomers after
infusion
13
C-labeled glucose and acetate as described in Materials and Methods section. The
brains of mutant mice showed higher glycolytic activity (calculated as the % change in the
concentration of [3-
13
C]alanine). A slight increase was observed in the
13
C cycling ratios for both
glucose and acetate metabolism and in the glucose versus acetate utilization index for Glu, Gln

41
and GABA but not statistically significant.

Figure 2.6: Metabolic ratios calculated after [1-
13
C] glucose + [1,2-
13
C]acetate infusion:
Metabolic ratios, calculated as described in the materials and methods section, after [1-
13
C]glucose + [1,2-
13
C]acetate infusion for 150 min are shown in the Graphs A-D. % Glycolytic
activity based on the levels of [3-
13
C]alanine, TCA cycle activity, and glucose versus acetate
utilization for formation of Glu, Gln and GABA are shown above.

Long Term Potentiation (LTP) and hippocampal synaptic plasticity
Insulin signaling plays an important role in modulating brain synaptic plasticity, which
can be assessed by changes in LTP and long term-depression (LTD). Synaptic plasticity can be
measured using electrophysiological experiments by examining Input/Output (I/O) responses at
baseline and after theta burst-stimulation (TBS) in the hippocampal CA1 region. The ability of
neurons to maintain a high output response after a high frequency electrical stimulation for a
prolonged length of time is indicative of strength of synaptic transmission and plasticity. The
mutant mice were able to produce a significantly higher I/O response and a steeper fEPSP slope
in comparison to the wild type mice (Figure 2.7). In addition, the mutant mice manifested a
substantially higher LTP value than the wild type mice represented as %fEPSP values (Figure

42
2.7). These data show that a liver-specific deletion of PTEN in the mutant mice, resulted in
hippocampal neurons developing better synaptic plasticity than wild type mice.

Figure 2.7: Changes in I/O in the brains of liver-specific PTEN KO mice. (electrophysiology
techniques as described in the Materials and Methods section). (A) Picture depicting plating of
electrodes in the CA1 region of the hippocampus to obtain I/O readings, (B) LTP induced at
baseline intensity using theta burst stimulation (TBS) consisting of ten trains of five 100 Hz
stimulation repeated at 5 Hz. Slope of EPSPs was measured and results normalized to the
average value measured during the 10 min baseline period, (C) Average of the last 5 min of
recordings post-TBS, which is considered as LTP, (D, E) I/O curves for both groups at
increasing stimulus intensities, (F) Average I/O curve slopes for both groups. Total n = 22
slices, n ≥ 10 slices/group and at n = 4 animals/group.

43
Insulin sensitivity test using Western blotting
Fresh tissue slices of 400µm thickness containing hippocampal regions were immersed in
aCSF containing 10 nM insulin (PeproTech) for 10 min at 37
o
C before they were processed to
initiate the western blotting assay. Total cell expression of AKT, phospho-AKT (Ser473) and β-
actin are shown in Figure 2.8. Insulin stimulated phosphorylation of AKT in both the wild type
and the mutant mice groups by activating the IRS-PI3K-AKT signaling cascade. A higher degree
of AKT phosphorylation was observed in the brains of the mutant mice, however, insulin
stimulation did not result in significantly greater phosphorylation of AKT as compared to insulin
treatment in the wild type mice. The ratio of p-AKT/AKT or p-AKT/β-actin with or without
insulin stimulation were similar in both groups. These results indicate that insulin signaling
activity in the mutant mice is more robust because of the relatively higher expression of Akt and
p-Akt in comparison to wild type mice, but not more sensitive to insulin, since the p-Akt/Akt
ratios remained similar to those in the wild type mice after insulin stimulation in both groups.

Figure 2.8: Western blot analysis of the levels of p-Akt (Ser473), Akt, and β-actin in whole brain
from wild type and liver-specific PTEN KO mutant mice with or without 10 nM insulin

44
treatment. Panels A and B correspond to average data from all samples (n = 4/group), and panel
C is a representative immunoblot with each lane corresponding to groups in the same sequence
as panels A and B. Bar graphs show average p-Akt and Akt, normalized to β-actin, with error
bars indicating ± SEM (*P ≤ 0.05).

Discussion
A liver specific deletion of PTEN resulted in improved liver insulin signaling activity
leading to higher glucose absorption in the liver, and increased glycogen synthesis, whereas the
levels triglyceride, leptin, insulin, and fasting glucose levels to be decreased in the plasma of the
PTEN
loxP/loxP
;Alb-Cre
+
mutant mice (Stiles et al., 2004a). This study aimed at establishing the
effect of this unique peripheral phenotype and its effect on brain metabolism and synaptic
plasticity. A previous study in our lab has reported that impairments in brain metabolism, due to
an induced insulin resistant state, can negatively affect synaptic plasticity (Liu et al., 2015). We
hypothesized that the presence of a relatively hypoglycemic and hypo-insulinemic state might
have forced the brain, to become more insulin- or glucose-sensitive, and we hypothesized that
this unique phenotype will directly improve synaptic plasticity and function in the mutant mice.
The first piece of evidence supporting our hypothesis of an insulin- or glucose-sensitive
brain comes from dynamic [
18
F]-FDG-PET imaging, where the mutant mice brains were
observed to be able to absorb glucose at a faster rate than the wild type mouse brains. The second
piece of evidence was the prominent increase in the enrichment of
13
C isotopomers of Glu and
Gln showing that more label was transferred to these metabolites in the mutant

mice as compared
to the wild-type mice, which in turn means that the mutant mice were more proficient in

45
absorbing and utilizing glucose and acetate for energetic purposes. Although not statistically
significant, neuronal glycolytic activity was also higher in the mutant mice. The insulin
sensitivity test, followed by immunoblotting for Akt and p-Akt protein expression levels
revealed the existence of a more robust insulin signaling activity in the mutant mice brains,
evidenced by higher expression of Akt; but not of enhanced responsiveness to insulin
stimulation, as seen by similar ratios of p-Akt/Akt expression with or without insulin stimulation.
The third and last piece of evidence supporting the original hypothesis came from
electrophysiological studies that revealed better I/O responses from the hippocampal CA1
neurons in the mutant mice and significantly higher LTP. These data emphasize the importance
of sensitive insulin signaling pathway and the prominent role it plays in modulating neuronal
function and synaptic plasticity.
It is important to acknowledge that these mutant mice develop a fatty liver phenotype
with age (Stiles et al., 2004a). However, the fatty liver phenotype observed in these mutant mice
is different (i.e. hypoinsulinemia, enhanced insulin signaling, and low plasma non-esterified fatty
acid levels or NEFAs) from that observed in the models of naturally occurring fatty liver states
(i.e., hyperinsulinemic and insulin-resistant). We know that steatophepatitis and hepatic insulin
resistance can be caused by various etiologies including Hepatitis C infection, alcohol abuse,
obesity, and nitrosamine exposures, which are associated with cognitive impairment and
neuropsychiatric disorders (Perry et al., 2008; Tong et al., 2010; Tong et al., 2009; Weiss and
Gorman, 2006). In fact, cognitive impairment and neuropsychiatric disorders correlate more with
steatohepatitis and insulin resistance rather than with T2DM or obesity (Kopelman et al., 2009;
Schmidt et al., 2005). Mechanistically, hepatic insulin resistance is known to dysregulate lipid
metabolism, leading to lipolysis (Kao et al., 1999), which then leads to increased production of

46
toxic lipids such as ceramides that have been reported to impair insulin signaling and
mitochondrial function (de la Monte, 2012b; Holland and Summers, 2008; Kraegen and Cooney,
2008; Langeveld and Aerts, 2009). On the contrary, when studying the effects on the brain,
which is primarily affected by changes in the peripheral phenotype, the PTEN
loxP/loxP
;Alb-Cre
+
mutant mouse model can be perceived as a model of dietary restriction (DR).
DR may be defined as the reduction in food intake without causing malnutrition (Hadem
et al., 2017). With levels of fasting plasma glucose, insulin, leptin, ketone bodies, and
triglycerides being lower than those in the wild type mice, the PTEN
loxP/loxP
;Alb-Cre
+
mutant
mice can fit the description of a DR model, at least when it comes to studying the brain. DR has
been studied extensively and is shown to slow down the onset of age-associated pathologies,
including cognitive decline, and increase the lifespan in many organisms (Hadem et al., 2017;
Speakman and Mitchell, 2011). Dietary restriction has been shown to improve gray matter
volume in the hippocampus and subcortical regions (Colman et al., 2009), improve levels of
neurotrophic factors such as BDNF (Kaptan et al., 2015), decrease oxidative stress, improve
mitochondrial function (Fontán-Lozano et al., 2008) - all of which are known to modulate
synaptic plasticity and neuronal function. Although this study is focused on the effect of altered
insulin signaling as the effector of changes in neuronal function, one cannot ignore the effects of
a calorie restricted state on brain function in these mutant mice.
With increasing incidence rates of neurodegenerative diseases like AD and insulin
resistance disease states such as type 2 diabetes, obesity, non-alcoholic fatty liver disease, and
metabolic syndrome, this study underlines the importance of preserving insulin sensitivity and
the integrity of the insulin signaling pathway (de la Monte and Tong, 2014). Clinical studies
show decreased brain glucose uptake to be a common condition in patients with Alzheimer’s

47
disease (AD) and mild cognitive impairment (MCI) (Mosconi, 2005; Mosconi et al., 2009). In
fact, multiple studies have indicated metabolic dysfunction and bioenergetic deficits as
antecedents to development of Alzheimer’s pathology and related dementias (Galindo et al.,
2010; Gibson et al., 1998; Hauptmann et al., 2009; Yao et al., 2009). Since insulin resistance can
affect cognition and brain function through disruption of brain glucose uptake and metabolism,
this study definitely fuels the concept of treating brain insulin resistance as a therapeutic
approach for slowing the process inherent in neurodegeneration (Stefanelli et al., 2014).

48
CHAPTER III: THE METABOLIC↔INFLAMMATORY AXIS IN BRAIN AGING

The following mechanistic study aims to find the link between metabolic dysfunction as a
direct result of inflammation in primary neurons. The results presented below are the first steps
to answering one key question: Is the metabolic phenotype of aging neurons the initiator of the
neuroinflammatory cycle, or merely victims of the changing microenvironment of the brain?

Introduction
Neurodegenerative diseases share a common predisposing factor, the aging of the brain,
which poses several questions. First, how can a human neuron survive for more than 100 years
and remain functionally competent? Second, do humans possess special mechanisms that protect
neurons from death? And, how and when do these protective mechanisms break down? The
answers to these questions will reveal new directions for treating neurodegenerative diseases.

Astrocytes and microglia help preserve neuronal function
Neurons are post-mitotic cells with limited regenerative potential (Kole et al., 2013).
Considering that neurons are likely to be exposed to a variety of stresses throughout their life,
one might expect neurons to have evolved the capacity to ensure long-term survival. However,
neurons depend on protective mechanisms provided by the surrounding astrocytes and microglia
to sustain functionality (Heneka et al., 2015). Astrocytes, which outnumber neurons three to one,
are the most significant supporters of neuronal survival and function. Astrocytes (a) provide

49
lactate to neurons via the lactate shuttle to meet the energy demands of neurons during synaptic
transmission (Belanger and Magistretti, 2009), (b) recycle neurotransmitter glutamate to avoid
excitotoxicity and feed it back to neurons in the form of glutamine (Bak et al., 2006), (c) regulate
vascular constriction and dilation during neuronal activity to ensure sufficient glucose and
oxygen supply (Carmignoto and Gomez-Gonzalo, 2010), (d) store glycogen in order to fulfill
neuron’s metabolic needs during hypoglycemic conditions (Magistretti and Allaman, 2007), (e)
release glutathione precursors for neurons to protect them from oxidative stress (Dringen, 2000),
and (f) modulate synaptic transmission by Ca
2+
-dependent release of neuroactive molecules such
as glutamate, D-serine, ATP, or adenosine (Volterra and Meldolesi, 2005) (Figure 4.1).
Microglia are important players in the maintenance and plasticity of neuronal circuits,
contributing to the protections and remodeling of synapses (Heneka et al., 2015). This is
mediated by the release of trophic factors, including brain-derived neurotrophic factor (BDNF),
which contributes to memory formation (Parkhurst et al., 2013). Microglia have been found to
protect neurons against ischemia through the synthesis of tumor necrosis factor (TNF). In
addition to BDNF, microglia also release IGF-1 which is a neuroprotective factor (Suh et al.,
2013).

50

Figure 3.1: Astrocyte-neuron metabolic cooperation

Aging changes the neuroprotective phenotype of astrocytes and microglia resulting in chronic
neuroinflammation
Aging is associated with activation of innate immune cells in the CNS (microglia and
astrocytes), which is one of the universal components of neuroinflammation (Glass et al., 2010).
With age, microglia exhibit enhanced sensitivity to inflammatory stimuli (originating either
from peripheral tissues or brain), similar to that noted in brains with ongoing neurodegeneration
(Norden and Godbout, 2013). This phenomenon is termed priming, which might be caused by

51
microglial senescence and might be associated with aging (Heneka et al., 2015). In
physiologically aged and senescence-accelerated mice, profound microglia priming was
characterized by increased production of cytokines and reactive oxygen species and enhanced
phagocytic capacity, all of which can contribute to neuronal death. Inflammatory mediators such
as TNF-α and IL-1β can act on astrocytes to induce secondary inflammatory responses (Glass et
al., 2010). Astrocytes are highly glycolytic, utilizing glucose for conversion to lactate which is
released in the extracellular space to
satisfy neuronal metabolic needs
(Belanger et al., 2011). However,
with age, an increase in the oxidative
metabolism was observed in
astrocytes in rats (Jiang and Cadenas,
2014). This might result in decreased
lactate release, required for proper
neuronal function. Aging was
associated with an increase in the expression of pro-inflammatory mediators such as TNFα, IL-
1β and IL-6 in the rat cortex. Constitutive levels of NFκB, the master regulator of inflammation,
were substantially increased with age along with the sensitivity to activation of NFκB-mediated
transcription (Jiang and Cadenas, 2014). Production of
.
NO by iNOS, regulated by NFκB at the
transcriptional level, increased substantially with age both constitutively and in the presence of
inflammatory cytokines.
.
NO derived from astrocytes was shown to result in glia-induced
neuronal death (Bal-Price and Brown, 2001). The damaged neurons are in-turn capable of further
activating glial cells through release of soluble cellular factors, such as damage-associated

Figure 3.2: Self-promoting neuroinflammatory loop
TNFa, IL
ROS, NO
ATP
NEURON
ASTROCYTE MICROGLIA
MODIFIERS AGING

52
molecular patterns (DAMPs). This process leads to a vicious cycle of self-promoting
inflammation and cell-death that is sustained even after removal of the initial stimuli (Spielman
et al., 2014). This perpetual inflammatory microenvironment is hypothesized to lead to
neurodegeneration (Figure 4.2), and also sets the groundwork for several neurological diseases
such as Alzheimer’s disease (AD) and Parkinson’s disease (Smith et al., 2012).

Inflammation is associated with insulin resistance and impairment of the insulin signaling
pathway
The first link between inflammation and metabolism came from early studies in models of
obesity, where an increase in the proinflammatory cytokines such as TNF-α, IL-6 and IL-1β was
observed in the adipose tissue of obese and diabetic subjects. Subsequent studies using genetic
mouse models demonstrated that pro-inflammatory cytokines inhibit insulin signaling by direct
serine phosphorylation of IRS-1 (Henao-Mejia et al., 2014). Inflammasomes, which are
multiprotein complexes that are activated in presence of various DAMPs and pathogen-activated
molecular patterns (PAMPs), are core to the recruitment and activation of proinflammatory
caspases, resulting in cleavage of the precursors of cytokines IL-1β and IL-18 into their bioactive
forms (Martinon et al., 2009). Particularly, the NLRP3 (Nod-like receptor, pyrin-domain-
containing 3) inflammasome is considered to be a sensor of altered metabolic homeostasis and its
activation is thought to induce insulin resistance. Increased activity of NLRP3 has also been
implicated in the pathogenesis of metabolic diseases such as obesity-induced inflammation, type
2 diabetes, and Alzheimer’s disease (Haneklaus and O'Neill, 2015). Interestingly, a key
phenotype of the NLRP3-deficient mouse is insulin hypersensitivity (Zhou et al., 2010).

53
However, the role of NLRP3 in the aging brain is yet to be explored.
We hypothesize that the metabolic-inflammatory axis is critical for brain aging and is
determined by the co-ordination of the metabolic phenotype of aging neurons and the microglial
inflammatory responses.

Methods
Metabolic flux analysis: XF-Extraflux Analyzer
Primary embryonic hippocampal and cortical neurons were cultured from day 18 (E18)
embryos of Sprague Dawley rats (Envigo) and were plated on Seahorse XF-24 plates at a density
of 100,000 cells/well (n = 2). Neurons were grown for 7 days before the experiment in
Neurobasal Medium + B27 supplement. 2 hr before the assay, Rat recombinant IL-1β (50ng/ml)
was added to the media in half of wells on the plate. 15 min prior to the end of the 2 hr IL-1β
incubation period, 100nM insulin was added to half the wells from each of the two groups
formed before. The XF-24 plate was then washed once with 750uL of warm Krebs-Henseleit
Buffer (KHB) (Sigma-Aldrich) supplemented with 25mM total glucose, 2mM sodium pyruvate,
2mM Glutamax, and pH 7.4 before adding 500uL of modified KHB for the final assay steps. The
plate was incubated at 37
o
C in a CO2-free incubator for 1 hour before starting the assay run.
Baseline measurements of oxygen consumption rate (OCR, measured by changes in O2
concentrations) and the extracellular acidification rate (ECAR, measured by changes in pH) were
recorded prior to injection of the mitochondrial inhibitors (in sequence): Oligomycin (2µM, ATP
synthase inhibitor), FCCP (1µM, mitochondrial respiration uncoupler), and Rotenone (1µM,
Complex I inhibitor). After completion of the assay, plates were saved and protein

54
concentrations in each well were measured to confirm equal cell number/well. Figure 4.3 shows
a representative Seahorse graph that depicts the different metabolic parameters that can be
calculated using the different mitochondrial inhibitors mentioned above.

Figure 3.3: Key parameters of mitochondrial respiration. Source: Agilent Seahorse.

Statistical Analysis
GraphPad Prism version 7.0 for Windows was used to analyze the data. One way
ANOVA, followed by Dunnett’s multiple comparison test were used to compare data. The level
of significance was set at 5%. Data are expressed as mean value ± standard error of mean (SEM).

55
Results
Effect of IL-1β and insulin on Cellular Bioenergetics
Addition of IL-1β to the E18 primary neurons resulted in a substantial decrease in the
oxygen consumption rates (OCR) (Figure 4.4): decreased basal respiration, OXPHOS-induced
respiration, maximal respiratory capacity, and reserve capacity. Insulin treatment resulted in an
overall increase in all OCRs in the E18 neuron wells that were not supplemented with IL-1β, but
had no effect on OCR in the cells that were treated with IL-1β prior to insulin addition.
Surprisingly, OCRs decreased even further in the insulin stimulated cells that were treated with
IL-1β than that of neurons only treated with IL-1β, although not statistically significant. Pro-
inflammatory cytokines have been shown to inhibit insulin signaling by direct serine
phosphorylation of IRS-1 in certain mouse models (Henao-Mejia et al., 2014). A decrease in
OCR after addition of IL-1β can explained by the inhibition of the insulin signaling pathway,
which is known to affect mitochondrial bioenergetics. Stimulation with insulin in IL-1β treated
neurons did not affect the already decreased OCRs most likely because the IRS-1 receptors were
rendered inactive by IL-1β.

56

Figure 3.4: Mitochondrial bioenergetics after IL-1β treatment. E18 primary hippocampal and
cortical neurons (CONT) were treated with IL-1β (50ng/ml) for 2 h, followed by insulin
(100nM) (INS) for 15 min before the end of the 2 h IL-1β treatment period. Oxygen

57
consumption rate (OCR) was determined using Seahorse XF-24 as described in the Methods
section. Mitochondrial respiration parameters such as reserve capacity, basal-, maximal-,
OXPHOS- induced, and non-mitochondrial- respiration were measured as described in the
Methods section. IL-1β treatment resulted in decreased OCR with or without insulin treatment.
Insulin treatment increased OCR in the CONT cell population, but had no effect on cells that
were treated with IL-1β. Statistical significance was calculated using one-way ANOVA
(*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001); plotted as mean with SEM.

Discussion
These data provide mechanistic evidence for determining the metabolic-inflammatory
axis in the aging brain (Figure 4.5). Proinflammatory cytokines originating from astroglia, are
thought to activate stress kinases (JNK, IKK, and PKR) and ER stress, which modulate IRS-1
activity and thus down-regulate IIS activity (Spielman et al., 2014). Chronic exposure to
relatively high levels of ROS have also been associated with impairment of insulin signaling
(Tiganis, 2011). Comparing effect of proinflammatory factors with the predominantly oxidative
factors derived from astrocytes will help determine which cell-type plays a major role in
determining development of neuronal insulin resistance in the old brain. Paradoxically, if insulin
resistance develops as a function of age and prior to development of chronic low-grade
neuroinflammation, metabolic changes in neurons leading to release of H2O2 and ATP might
prove to be the initiating factor which activates microglia.

58

Figure 3.5: Proposed link between inflammation and metabolic dysfunction.

The discovery of the NLRP3 (NLR family, pyrin domain containing 3) inflammasome provided
an important molecular mechanism in the induction of central pro-inflammatory cytokine pro-IL-
1β, via activation of caspase-1, which processes pro-IL-1β into its mature active form (Schroder
and Tschopp, 2010). A key phenotype of the NLRP3-deficient mouse is insulin sensitivity (Zhou
et al., 2010). In context of chronic inflammation, as in obesity triggered by a high fat diet, IL-1
signaling was shown to drive disease progression. IL-1β has been known to exert metabolic
effects, most notably being implicated in insulin resistance and obesity (Haneklaus and O'Neill,
2015). The link between inflammation and metabolism has been studied extensively in obesity-
associated models, indicating that members of the Toll-like receptor (TLR) family and
inflammasomes sense metabolic disturbances, which provide signals for inflammasome
activation. The elevated levels of NLRP3 inflammasome activation in adipose tissue
macrophages of obese mice results in at least two deleterious consequences in insulin-sensitive
tissues: (a) IL-1β secretion inhibits insulin signaling by direct serine phosphorylation of IRS1
and induces expression of TNFα, a well-characterized insulin resistance-promoting cytokine
(Hotamisligil et al., 1996; Jager et al., 2007), and (b) IL-1β and IL-18 induce an effector
glucose
glucose
GLUT
insulin
IRS
PI3K
IR/IGF1R
Akt
IGF1
IRS PI3K
Akt
PTEN
PIP
2
PIP
3
PDK1
IR
P–Ser
9
inactive
glucose
glucose
pyruvate
GLUT
Synaptic Plasticity
acetyl-CoA
! -ketoglutarate
TCA
Glutamate
GSK-3 ²
P–Ser
473
active
608
T yr –P
active
P–Ser
307
inactive
GLUT
IRS PI3K
Akt
PTEN
PIP
2
PIP
3
PDK1
IR
P–Ser
9
inactive
glucose
glucose
pyruvate
GLUT
Synaptic Plasticity
acetyl-CoA
! -ketoglutarate
TCA
Glutamate
GSK-3 ²
P–Ser
473
active
608
T y r –P
active
P–Ser
307
inactive
GLUT
GLUT
TNFa
TNFa R
IL-1R
IL-1b
IKK JNK
NF-kB

59
proinflammatory profile (Vandanmagsar et al., 2011), which may worsen metabolic outcomes.
Microglia, the resident macrophages in the brain, show enhanced sensitivity to inflammatory
stimuli with age (Lai et al., 2013). This enhanced sensitivity could activate inflammasomes,
triggered in presence of danger-associated molecular patterns (DAMPS), and lead to release of
proinflammatory factors that negatively regulate insulin signaling.

Future studies will assess the role of age-dependent changes in inflammatory, metabolic, and
redox signals originating from astrocytes and microglia, on neuronal metabolism and function.
Furthermore, the role of the NLRP3 inflammasome must be determined in the context of brain
aging.

60
CHAPTER IV: FUTURE STUDIES AND GENERAL CONCLUSIONS
Future directions
The aging brain reveals a plethora of correlated processes that contribute to its
senescence, yet to be fully understood on a molecular level. Aging is accompanied by cognitive
decline in a major segment of the population and is the primary risk factor for Alzheimer’s
disease and other prevalent neurodegenerative disorders (Imtiaz et al., 2014). Metabolic
dysfunction and neuroinflammation in the aging brain have major irreversible implication on
cognitive function, and therefore characterizing the behavior of neurons, astrocytes and
microglia as a function of age is crucial in understanding how and when the protective
mechanisms in the brain break down. The answers to these questions will reveal new directions
in slowing neurodegeneration.
Characterization of age-dependent changes in metabolic phenotype of adult primary neurons
Age-dependent changes in sensitivity to stressors and resulting inflammatory responses
of microglia have been well characterized. Previous studies in our lab have identified changes in
astrocytic metabolic and inflammatory phenotype as a function of age. However, the effect of
this changing metabolic and inflammatory landscape in the aging brain on the neuronal
population remains fairly unexplored. This is primarily due to the frailty of developed adult
neurons to survive in vitro. Recent advances in cell culture techniques (Brewer and Torricelli,
2007), specifically aimed at achieving viable neuronal cell cultures from the adult brain, can help
us isolate and culture adult neurons and neurospheres. These neuronal cultures, will allow us to
compare changes in hormonal, vascular and inflammatory influences as a function of age, that

61
otherwise complicate whole-brain studies. Cultures also offer the added advantage of identical
replicates for dose-response and time-course studies.
The development of neuronal insulin resistance is hypothesized to be a result of chronic
low-grade inflammation in the aging brain. Current evidence points towards proinflammatory
TNFα signaling initiated by microglia, leading to activation of stress kinases (JNK, IKK, and
PKR) and ER stress, which modulate IRS-1 activity and thus down-regulate IIS activity (de Luca
and Olefsky, 2008).
Neurons rely on oxidative metabolism
to meet their high energy needs, while
maintaining a low glycolytic rate (Belanger et
al., 2011). The activation of neuronal
glycolysis has been shown to lead to oxidative
stress and apoptosis (Herrero-Mendez et al.,
2009): the increase in glucose flux via
glycolysis occurs at the expense of
metabolism through the pentose phosphate
pathway (PPP)—which is essential for
production of NADPH and therefore maintenance of cellular antioxidant potential (Figure 6). It
is hypothesized that a fine balance between glycolysis and the PPP needs to be maintained in
neurons to meet their energy needs while maintaining their antioxidant potential—both aspects
being important for their survival (Belanger et al., 2011). Interestingly, neurons are known to
utilize lactate as an energy substrate, and even prefer lactate over glucose when both substrates

Fig 4.1: Brain glucose metabolism and the
pentose-phosphate pathway (Belanger et al.,
2011)

62
are present (Bouzier-Sore et al., 2006). Thus, use of lactate as an oxidative substrate may provide
a convenient means for neurons to produce high amounts of ATP while circumventing the
glycolytic pathway, thereby sparing glucose for PPP (Bolanos et al., 2010). Astrocytes support
metabolic needs of neurons by supplying lactate (Belanger and Magistretti, 2009). We
hypothesize that a decreased supply of lactate by astrocytes with age, further exacerbated by
increasing insulin resistance, might force neurons to rely on glycolysis, thus severely affecting
the antioxidant capacity of neurons.
Maintaining mitochondrial health, especially function of the respiratory enzyme complex,
is essential for efficient ATP turnover. The availability of energy from oxidative phosphorylation
is critical to neuronal function (Jones and Brewer, 2010). Hence, measuring activity of
respiratory complexes I-IV in aging neurons will help improve understanding of mitochondrial
dysfunction with age.
Assessing redox environment changes as a function of age
Even though the greater part of the brain’s oxidative metabolism occurs in neurons, this
cell type displays limited defense mechanisms against oxidative stress compared to astrocytes.
Several factors contribute to increased oxidative stress, including its high rate of oxidative
metabolism (a process that inevitably generates H2O2, a reactive oxygen species, ROS, as a
byproduct), and its relatively low intrinsic antioxidant capacity (Belanger and Magistretti, 2009;
Dringen, 2000). Increased ROS production/declining antioxidant potential could directly affect
neuronal survival. A major source of ROS is the nicotinamide adenine dinucleotide phosphatase
(NADPH) oxidases (NOX) family of enzymes, several of which are known to be expressed in

63
the CNS. At physiological circumstances, NOX enzymes are likely to generate low levels of
ROS in the CNS for cellular signaling. However, in pathological conditions, NOX-derived ROS
could prove to be neurotoxic. The differential expression of neuronal NOX enzymes with age
and their contribution to pathology of neurodegenerative diseases still remains relatively
unexplored (Nayernia et al., 2014).
Characterization of age-dependent changes in microglia and astrocytes
Although neurons account for most of the energy consumption during brain activation,
astrocytes outnumber neurons in the human brain and play a key role in neuroprotection,
including glutamate, ion and water homeostasis, defense against oxidative stress, energy storage
in the form of glycogen, tissue repair, and modulation of synaptic activity. Astrocytes are highly
glycolytic, releasing a large portion of the glucose entering the glycolytic pathway as lactate in
the extracellular space. As a result, neurons and astrocytes present different, but complimentary
metabolic profiles, paving the way for extensive metabolic cooperativity (Belanger et al., 2011).
Importantly, astrocytes play an important role in protecting neighboring neurons against
oxidative stress through the shuttling of glutathione (GSH) precursors from astrocytes to neurons
(Dringen, 2000). However, previous studies in our lab have revealed a shift from a neurotrophic
to neurotoxic phenotype in aging astrocytes accompanied by release of diffusible redox species
such as H2O2 and nitric oxide (NO), and probably denying the neurons of essential energy
substrates and neuro-protective mechanisms (Jiang and Cadenas, 2014).
As sentinels in the adult brain, microglia maintain homeostasis and possibly contribute to
the neural network by assisting in synaptic remodeling and plasticity, as well as removing excess
aberrant proteins and debris accumulating in the brain (Heneka et al., 2015). Positive feedback

64
loops between microglia, astrocytes and neurons leading to sustained inflammatory responses are
hypothesized to contribute to neurodegeneration (Glass et al., 2010). With aging, increased
expression of pro-inflammatory cytokines tumor necrosis factors (TNFα and TNFβ), Interleukin
(IL)-1α, IL-1β, and IL-6, as well as cytokine receptors (Lai et al., 2013), is thought to contribute
to the mild chronic inflammatory condition that develops. In particular, Toll-like receptors
(TLRs) and other pattern recognition receptors expressed on microglia are likely to play
significant roles in initiating inflammatory responses that are further amplified by astrocytes.
Signaling transduction pathways downstream of these receptors that regulate the activities of
transcription factors NF-κB and AP-1 appear to play general roles in mediating the production of
amplifiers and effector molecules. Crosstalk between microglia and astrocytes is predicted to
lead to amplification of inflammation and release of ATP by necrotic neurons, which is expected
to activate microglia further (Glass et al., 2010).
Elucidating the role of ROS in NLRP3 activation in the brain
Several reports provide
compelling evidence for a crucial
role of mitochondria and ROS in
NLRP3 activation (Heid et al.,
2013; Subramanian et al., 2013;
Zhou et al., 2011). Other studies
found that mitochondrial damage
and H2O2 are dispensable for
NLRP3 activation (Allam et al.,

Figure 4.2: The ROS model of NLRP3 activation
(Tschopp and Schroder, 2010)

65
2014; Bauernfeind et al., 2011). A recent report concluded that neither ROS and mitochondria
nor lysosomal damage are required for activation of the inflammasome. Instead, only potassium
efflux is necessary (Munoz-Planillo et al., 2013). However, all the NLRP3 agonists that have
been tested trigger the production of ROS. It is hypothesized that this results in NLRP3
activation through the release of the ROS-sensitive NLRP3 ligand thioredoxin-interacting protein
(TXNIP) from its inhibitor thioredoxin (TRX) (Tschopp and Schroder, 2010) (Figure 5.2). Age-
dependent increase in ROS production is observed in astrocytes, which outnumber neurons and
microglia in the brain. Therefore, in the brain, activation of NLRP3 could be dictated by ROS
generation in astrocytes. Activity of the TRX system (TRX reductase, TRX, and NADPH) needs
to be assessed as a function of age.

General Conclusions
Insulin is an important hormone that helps us utilize glucose: the major energy source for
most cell types of the body. Neurons rely on glucose and its metabolites for maintaining proper
cellular homeostasis and function. The loss of responsiveness to insulin is a major risk factor for
the development of age-related neurodegenerative diseases such as Alzheimer’s disease and
other dementias. Therefore, it is crucial to preserve the neuron’s ability to respond to insulin
action, including its sensitivity to insulin and insulin signaling activity. Although insulin-
sensitizing drugs might help achieve that goal, a more holistic understanding of the changes in
brain microenvironment with age is crucial in solving the neurodegeneration problem.

66
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Abstract (if available)

Abstract Aging is the biggest risk factor for neurodegenerative diseases like Alzheimer’s disease (AD) and related dementias, with growing evidence suggesting impairments in brain energy metabolism and insulin responsiveness as a function of age to be major culprits. Insulin, a hormone that drives brain glucose uptake. helps meet the energy demands of synaptic neurons amongst other cell types, and regulates cognitive functions via the insulin signaling pathway. We hypothesized that enhancing neuronal insulin responsiveness will enhance neuronal function. This hypothesis was tested using a liver-specific Phosphatase and Tensin Homologue (PTEN) knockout mouse model, a model of systemic insulin hypersensitivity. PTEN is a negative regulator of the insulin signaling pathway (IIS), and so a liver-specific deletion of PTEN resulted in increased flux of glucose into the liver due to robust insulin signaling, thus resulting in an overall hypoglycemic and hypoinsulinemic state in the mice. To summarize the results, the brains of the liver-specific PTEN KO mice model exhibited increased glucose uptake, enhanced insulin signaling activity, improved rate of glycolysis and flux of metabolites in the TCA cycle, and improved synaptic plasticity in the hippocampus. Studies in the liver-specific PTEN KO mice strengthened the significance of insulin signaling in brain energetics and function. Conversely, these findings also helped recognize deficits in diseases associated with insulin resistance and how these impinge on brain function. ❧ Aging is also associated with development of chronic-low grade inflammation that is known to change the phenotype of microglia—the immune cells of the brain, as well as astrocytes—the ultimate supporters of neuronal function. The shift in the behavior of microglia and astrocytes from neuroprotective to neurotoxic could be major effectors in the decline of cognitive function. Cognitive decline is also associated with metabolic dysfunction in the brain. However, it is still unclear whether decline of metabolic function is a consequence of the changing inflammatory state of the brain, or is the reason for its existence in the first place. Mechanistic studies were conducted in primary embryonic neurons to confirm whether inflammatory cytokines could impair metabolism and mitochondrial function. The pro-inflammatory cytokine IL-1β was found to impair insulin signaling and mitochondrial respiration in embryonic neurons, which brings us one step closer to establishing the link between metabolism and inflammation in the aging brain.

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Asset Metadata

Creator Patil, Ishan Yashwant (author)

Core Title Insulin sensitivity in cognition, Alzheimer's disease and brain aging

School School of Pharmacy

Degree Doctor of Philosophy

Degree Program Molecular Pharmacology and Toxicology

Publication Date 02/07/2018

Defense Date 01/12/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag aging,Alzheimer's disease,Inflammation,insulin,metabolism,OAI-PMH Harvest,synaptic plasticity

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Cadenas, Enrique (committee chair), Okamoto, Curtis (committee member), Shen, Wei-Chiang (committee member)

Creator Email ishanypatil@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-468263

Unique identifier UC11267299

Identifier etd-PatilIshan-6002.pdf (filename),usctheses-c40-468263 (legacy record id)

Legacy Identifier etd-PatilIshan-6002.pdf

Dmrecord 468263

Document Type Dissertation

Rights Patil, Ishan Yashwant

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...

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