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Neuroinflammation and ApoE4 genotype in at-risk female aging: implications for Alzheimer's disease
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Neuroinflammation and ApoE4 genotype in at-risk female aging: implications for Alzheimer's disease
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Content
Neuroinflammation and ApoE4 Genotype in At-risk Female Aging:
Implications for Alzheimer’s disease
by
Aarti Mishra
A Dissertation Presented to the
FACULTY OF USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfilment of the
Requirement for the Degree
DOCTOR OF PHILOSOPHY
(Clinical and Experimental Therapeutics)
August 2019
Copyright 2019 Aarti Mishra
ii
Dedication
To my parents, Dinesh and Ratna Mishra,
for their unrelenting love and support.
Thank you for instilling me with your light,
your sense of positivity and happiness.
You have taught me to chase my
dreams and to never quit.
To my brother, Akshat, for being
my best critique and supporter.
To my best friend and companion, Dhruv,
for being patient, kind, supportive and loving.
iii
Acknowledgements
To my advisor, Dr. Roberta Diaz Brinton, I am deeply grateful for your guidance and
mentorship. Thank you for your vision, your scientific tutelage, the philosophical training
and most importantly, helping me realize the dream of a wonderous research journey.
I am thankful for the participation and guidance of my committee members: Drs. Enrique
Cadenas, Kathleen Rodgers and Christian Pike. I would like to acknowledge Drs.
Kathleen Rodgers and Maira Soto as incredible collaborators and mentors from whom I
have learnt a lot. I would also like to thank Dr. Theodore P. Trouard, University of
Arizona, for helping me advance my research in making it more translationally relevant
with his support and knowledge of non-invasive rodent imaging.
I am thankful for the support of my labmates, current and past. I would especially like to
thank Dr. Shuhua Chen and Zisu Mao for their support and their willingness to always
help. I would also like to thank my peers Drs. Yiwei Wang and Eliza Bacon, for providing
insightful scientific conversations, help in troubleshooting experiments and the
occasional yet vital humorous reprieve when things got harder.
And, thank you to all my friends and family for the unconditional love and support
through this journey!
iv
Table of Contents
Dedication ii
Acknowledgements iii
Table of Contents iv
List of Abbreviations vii
List of Figures viii
List of Tables ix
Abstract 1
Chapter 1: Neuroinflammation and ApoE4 Genotype in Conferral of Risk for Alzheimer’s
in Female Aging 3
Alzheimer’s disease 3
Female Aging and Increased Risk for Alzheimer’s disease 4
Neuroinflammation as modifiable risk factor in Alzheimer’s 6
Chronic systemic and neuro-inflammation and risk for Alzheimer’s disease 6
Anti-inflammatory Drugs: Epidemiological and Clinical Prevention Trials for Alzheimer’s disease
9
Age-related neuroinflammation 10
Immunometabolic sensor of aging: targeting aging as a disease 15
Menopause and inflammation 17
Molecular neuro-inflammatory mechanisms in menopause 19
Hormone therapy 22
Microglia in Neurodegeneration and Alzheimer’s disease 24
ApoE4 and Alzheimer’s disease 26
Sources of neuroinflammation in ApoE4 26
ApoE4 and metabolism 32
Study hypothesis: Neuroinflammation and ApoE4-related metabolic profile is impacted
by female endocrine aging. 34
Chapter 2: The Immunometabolic Crisis in the Aging Female Brain: Implications for
Alzheimer’s disease 37
Abstract 37
v
Introduction 38
Materials & Methods 41
Animals 41
Ovariectomy, estradiol treatment and prevention 42
Brain Dissection 43
RNA extraction 43
RNA Sequencing (RNA-Seq) 44
Ingenuity pathway analysis (IPA) 45
Heatmap analysis 45
Single tube quantitative Real Time-PCR 45
Epigenetic analyses 46
Tissue sectioning & Immunohistochemistry 46
Adult brain dissociation 47
Microglia and astrocyte isolation 48
Metabolic flux assays 49
Measurement of oxidative stress and microglial reactivity 50
Phagocytic capacity assay 50
Gene Expression Omnibus (GEO) dataset analyses 51
Results 53
Transcriptomic profiling of the hippocampus in the aging female brain 53
Epigenetic modulation of neuroinflammatory genes during female aging 56
Microglial reactivity during endocrine aging has spatial selectivity towards white matter. 57
Microglial oxidative stress and phagocytic capacity are impacted by female endocrine aging 59
Astrocytic and microglial mitochondrial function are differentially impacted due to female aging
61
Estradiol regulate neuroinflammation in the aging female brain 62
Clinical validation of the neuro-inflammatory gene expression profile and sex differences in
transcriptional profiling of the hippocampus 64
Discussion 66
References 75
Chapter 3: Sex differences in metabolic aging of the brain in humanized ApoE4 Knock-in
rat model 80
Abstract 80
Introduction 81
Materials and Methods 83
Animals 83
Plasma collection 84
Plasma triglyceride, ketone body and insulin measurement 84
Glucose tolerance test 84
Brain Glucose uptake measurement 85
Brain dissection 86
RNA isolation 86
RNA Sequencing (RNA-Seq) 87
Protein extraction 88
Amyloid-b measurement 88
Brain perfusion 89
Magnetic Resonance Imaging (MRI) 89
Image analysis 90
vi
Metabolomic analyses 91
Heatmap analyses 91
Statistical analyses 91
Results 92
ApoE4 humanized knock-in impacted physiological weight gain due to aging 92
ApoE4 affects the aging metabolic profile differently in males and females. 93
Glucose Tolerance Test 96
ApoE4 and sex affects brain glucose uptake 97
Cortical Amyloid-b 42 is higher in females at 15-16 months 99
Structural neuroimaging for regional volume-based analysis 101
Diffusion magnetic resonance imaging for analysis of white matter integrity. 102
ApoE4 and sex has a broad systems-level impact on the transcriptome 104
Glucose metabolism and b-oxidation are affected in female ApoE4 animals 106
Discussion 109
References 117
Chapter 4: Inflammatory biomarkers in predicting therapeutic response of
Allopregnanolone in patients with early Alzheimer’s disease 120
Abstract 120
Introduction 121
Materials and Methods 123
Study design 123
Magnetic resonance imaging 125
Inflammatory biomarker assessment 126
Statistical analysis 127
Results 127
ApoE4 carriers are differentially distributed between males and females 127
Inflammatory biomarkers can serve as pharmacodynamic marker for Allo treatment 128
Inflammatory biomarkers as predictive biomarkers for structural volume changes in
hippocampus on Allo treatment. 131
Inflammatory biomarkers as predictive biomarkers for changes in microstructural assessment on
Allo treatment 135
Discussion 138
References 145
Chapter 5: Discussion and Concluding remarks 149
References 160
vii
List of Abbreviations
Ab Amyloid-b
AD Alzheimer’s disease
Allo Allopregnanolone
ApoE Apolipoprotein E
CD Cluster of differentiation
CRP C-Reactive Protein
CT Computed Tomography
dMRI Diffusion magnetic resonance imaging
DTI Diffusion tensor imaging
EOAD Early-onset Alzheimer’s disease
FA Fractional anisotropy
GEO Gene expression omnibus
GTT Glucose tolerance test
HLA Human leukocyte antigen
IFN Interferon
IL Interleukin
LOAD Late-onset Alzheimer’s disease
MHC-I Major histocompatibility complex class I
MHC-II Major histocompatibility complex class II
MMSE Mini-mental state exam
MRI Magnetic resonance imaging
PET Positron emission tomography
ROS Reactive oxygen species
TBI Traumatic brain injury
TGF-b Transforming growth factor-b
TREM2 Triggering receptor expressed on myeloid cells 2
TYROBP TYRO protein kinase binding protein
viii
List of Figures
Figure 1.1 Effect of aging on the microglial immunophenotype. .................................... 11
Figure 1.2 Dynamic interactions between neurons, astrocytes and microglia during
aging ............................................................................................................................... 15
Figure 1.3 Shift microglial phenotype caused by ovariectomy. ...................................... 20
Figure 1.4 Effects of ApoE4 genotype on immune function. .......................................... 28
Figure 2.1 Transcriptomic profiling of the hippocampus of the aging female. ................ 54
Figure 2.2 Validation of RNA-Seq results using RT-PCR .............................................. 55
Figure 2.3 Spatial mapping of microglial reactivity in the female aging brain. ................ 58
Figure 2.4 Flow cytometry analysis of microglial oxidative stress and function. ............ 60
Figure 2.5 Assessment of effect of age mitochondrial respiratory capacity in glial cells 62
Figure 2.6 Estradiol regulates neuroinflammation in the aging female brain: ................ 63
Figure 2.7 Clinical Validation of Neuro-inflammatory gene expression profile ............... 65
Figure 3.1 Comparison of weight change in WT and ApoE4 animals after 9 months .... 92
Figure 3.2 Plasma triglyceride levels measured across the longitudinal follow-up. ....... 93
Figure 3.3 Plasma ketone body (beta-hydroxy butyrate) levels measured across the
longitudinal follow-up ...................................................................................................... 94
Figure 3.4 Plasma insulin levels measured across the longitudinal follow-up ................ 95
Figure 3.5 Glucose tolerance test (GTT): ....................................................................... 96
Figure 3.6 Brain glucose uptake ..................................................................................... 98
Figure 3.7 Cortical amyloid-b measurement and correlation with FDG-PET ................ 100
Figure 3.8 Structural neuroimaging for regional brain volume measurement .............. 102
Figure 3.9 Diffusion magnetic resonance imaging for measurement of fractional
anisotropy ..................................................................................................................... 103
Figure 3.10 Sex differences in transcriptomic profiling of the hippocampus at 15-16
months .......................................................................................................................... 105
Figure 3.11 Sex differences in cortical metabolic profiling at 15-16 months ................ 108
Figure 4.1 Clinical trial design for Allopregnanolone Phase IB/IIA clinical trial ............ 124
Figure 4.2 Percentage of total participants by ApoE3/3 and ApoE3/4 genotype stratified
by sex ........................................................................................................................... 128
Figure 4.3 Cohort based analysis changes in cytokines and chemokines during the
duration of treatment. ................................................................................................... 130
Figure 4.4 Correlation of change in Eotaxin-3 levels and hippocampal volume from
baseline during clinical trial duration ............................................................................ 133
Figure 4.5 Correlation of change in IP-10 levels and hippocampal volume from baseline
during clinical trial duration ........................................................................................... 134
Figure 4.6 Correlation of change in IL-8 levels and fractional anisotropy from baseline
after 12 weeks .............................................................................................................. 138
Figure 5.1 Inflammation integrates Alzheimer’s disease risk factors of female sex,
chronological age, endocrine aging, and APOEε4 genotype ....................................... 157
Figure 5.2 Immune drivers involved in aging, menopause, and APOEε4 genotype
related inflammation. .................................................................................................... 158
ix
List of Tables
Table 2.1 Age and hippocampal sample information used for female aging analysis from
the GSE11882 dataset ................................................................................................... 51
Table 2.2 Age and hippocampal sample information used for male aging analysis from
the GSE11882 dataset ................................................................................................... 52
Table 2.3 Epigenetic modulation of neuro-inflammatory genes in the hippocampus ..... 56
Table 4.1 Demographic information for participants for Phase Ib/IIa clinical trial for
Allopregnanolone ......................................................................................................... 125
Table 4.2 Spearman’s correlation coefficient for changes in detectable cytokines and
chemokines with left hippocampal volume in participant subgroups stratified by ApoE
genotype and sex ......................................................................................................... 132
Table 4.3 Spearman’s correlation coefficient for changes in detectable cytokines and
chemokines with changes in fractional anisotropy of the medial core of the fornix in
participant subgroups stratified by ApoE genotype and sex ........................................ 136
1
Abstract
Alzheimer’s disease is a progressive neurodegenerative disease that is characterized by
prodromal state that starts 20 years prior to the symptomatic cognitive decline and the
etiology of the disease remains largely unknown. The prevalence of Alzheimer’s is higher
in women than men. The female endocrine transition: perimenopause, is a unique time-
locked transition that occurs before the onset of the prodromal state and is typified by
amyloid-b deposition, bioenergetic deficit and myelin catabolism- factors that contribute
to Alzheimer’s disease pathology. Neuroinflammation and ApoE4 genotype are factors
that affect disease progression. Yet, the interplay of female aging, especially endocrine
aging, with genetic risk factor ApoE4 and neuroinflammation has not yet been elucidated.
To characterize the interplay between female aging and neuroinflammation we used an
animal model that mimics the human perimenopausal transition, we characterized the
neuroinflammatory profile of the hippocampus during perimenopausal transition. We also
evaluated the brain regional susceptibility to inflammation, along with characterizing an
endocrine state specific glial functional phenotype. Using mechanistic animal models and
clinical microarray datasets we evaluated the translational validity of the findings.
To characterize the effect of ApoE4 genotype on female aging we conducted a sex
difference study that comprised of longitudinal metabolic analyses, followed up cross-
sectional analyses. Longitudinal assessment of brain glucose uptake to evaluate the
2
effect of female reproductive aging. Metabolomic and transcriptomic analyses were done
to validate the longitudinal assessments.
Based on the preclinical findings, we hypothesized that stratification of patients by ApoE
genotype and sex would affect the inflammatory biomarker response. To test this we
conducted inflammatory biomarker analysis to identify biomarkers that can be predictive
therapeutic response in patients with Alzheimer’s. Stratification based on ApoE genotype
and sex was done to identify responders.
3
Chapter 1: Neuroinflammation and ApoE4 Genotype in Conferral of Risk for
Alzheimer’s in Female Aging
[This work has been partly published in Mishra A and Brinton RD (2018) Inflammation:
Bridging Age, Menopause and APOEe4 genotype to Alzheimer’s disease. Front. Aging
Neurosci. 10:312. doi: 10.3389/fnagi.2018.00312]
Alzheimer’s disease
Alzheimer’s disease (AD) is a progressive neurodegenerative disease and is the most
common cause of dementia (2018). Pathologically the AD brain is characterized by the
deposition of amyloid-b plaques, neurofibrillary tau tangles, neuronal loss and gliosis(Perl,
2010). While mutations in certain genes that participate in amyloid-b metabolism and
processing, confer a genetic risk of early-onset AD (EOAD) in some individuals, the
occurrence of these mutations is relatively infrequent in comparison to sporadic AD, also
referred as late-onset AD (LOAD)(Koedam et al., 2010;Zhu et al., 2015;Lanoiselee et al.,
2017). Although there are no genetic mutations linked to LOAD, there are genetic and
modifiable risk factors such as, the APOEe4 (ApoE4) allele and traumatic brain injury
respectively, which increase the predisposition to develop LOAD(2018).
Recent failures in treatments for AD by use of monoclonal antibodies targeted towards
amyloid-b or by inhibiting beta-secretase (BACE) have highlighted the flaw in the amyloid
cascade hypothesis, which attributes the occurrence of symptomatic dementia as a
consequence to the cascading effect of presence of amyloid-b(Abbott and Dolgin,
4
2016;Mullard, 2018;Panza et al., 2019).
AD is characterized by an extended prodromal phase of, typically, 10-20 years duration
prior to clinical manifestation of cognitive decline (Amieva et al., 2008). The prodromal
phase of AD consists of both- pre-stage symptoms and mild cognitive impairment (MCI)
(Wilson et al., 2011). Clinical studies have shown that the prodromal phase is
characterized by metabolic dysfunction, b-amyloid (Ab) deposition in the brain, mild to
moderate cognitive dysfunction, and chronic low-grade inflammation (Habeck et al.,
2012;Olsson et al., 2013;Brinkmalm et al., 2014;Wirz et al., 2014;Rajan et al.,
2015;Mosconi et al., 2017a;Mosconi et al., 2017b). Yet, therapies that can mitigate
Alzheimer’s risk or prevent the onset of AD based on the prodromal endophenotype have
not yet been developed.
Female Aging and Increased Risk for Alzheimer’s disease
Endocrine transition states may predispose women to develop neurodegenerative
diseases. The peri-to-menopausal transition is characterized by hot flashes, sleep
deprivation, mood swings and cognitive dysfunction and is therefore considered a
neurological tipping point(Brinton et al., 2015a).
The perimenopausal transition is typified by decline in brain glucose metabolism and
mitochondrial respiration (Yao et al., 2010;Ding et al., 2013a;Brinton et al., 2015a;Yin et
al., 2015b;Mosconi et al., 2017a;Mosconi et al., 2017b) , myelin catabolism (Klosinski et
al., 2015) and loss of white matter volume (Mosconi et al., 2017a;Mosconi et al., 2017b),
5
amyloid-b deposition in brain (Mosconi et al., 2017a;Mosconi et al., 2017b) and changes
in neurological function (Brinton et al., 2015a). All factors that characterize the prodromal
phase of Alzheimer’s.
Later age at natural and surgical menopause is associated with better verbal memory
(Kuh et al., 2018). Surgically induced menopause prior to natural menopause is
associated with rapid cognitive decline and earlier onset of AD (Rocca et al., 2008;Bove
et al., 2014). Further, studies have shown that post-menopausal women with higher
estradiol levels have a reduced risk of developing AD (Manly et al., 2000).
Risk for Alzheimer’s conferred by ApoE genotype is worse in women than men(Altmann
et al., 2014;Ungar et al., 2014;Neu et al., 2017). Prospective cohort studies have
suggested that female ApoE4 carriers are at greater risk of converting from MCI to AD
than males (Altmann et al., 2014) and they have a higher rate of cognitive decline than
APOEe3 (ApoE3) carriers (Holland et al., 2013). Leukocyte telomere length is greatly
reduced in female ApoE4 carriers relative to age-matched controls, reflecting premature
aging of female ApoE4 carriers (Jacobs et al., 2013). ApoE4 genotype in combination
with a poor metabolic profile in post-menopausal women is associated with reduced
cognition(Karim et al., 2019).
The endocrine transition of the perimenopause to the post-menopause, while associated
with loss of reproductive function(Brinton et al., 2015a), is also associated with increase
in chronic low-grade inflammation (Yin et al., 2015b). Chronic low-grade inflammation is
6
also considered to be fundamental to AD progression. Sex differences in inflammatory
mechanisms that participate in AD have been scarcely studied. Despite the recent and
widespread recognition of neuroinflammation in the pathogenesis of AD, there has been
sparse expansion of inflammation-based biomarkers and preventive strategies,
especially with respect to female aging
Herein, we review the dynamic interplay between modifiable and genetic risk factors for
AD: inflammation and ApoE4 respectively, with respect to female aging and how they
contribute to the etiology of Alzheimer’s(Mishra and Brinton, 2018).
Neuroinflammation as modifiable risk factor in Alzheimer’s
Chronic systemic and neuro-inflammation and risk for Alzheimer’s disease
Substantial evidence documents reactive microgliosis around plaque deposition and is
now a hallmark of AD pathology (McGeer et al., 1988;Mattiace et al., 1990a;Xiang et al.,
2006). Reactive microgliosis and neuroinflammation in AD patients is considered a
consequence of Ab plaque deposition (McGeer et al., 1987). Microgliosis in AD is
evidenced both microscopically and biochemically with increased levels of the
proinflammatory cytokines including tumor necrosis factor-α (TNFα), IL-6, and IL-1β
(Itagaki et al., 1989;Dickson et al., 1993;T. Ferretti and C. Cuello, 2011;Eikelenboom et
al., 2012;Latta et al., 2014). While the inflammatory response to amyloid-b (Ab) plaque
deposition is irrefutable, it is a late stage response in the inflammatory cascade. Indicators
7
of earlier inflammatory responses are apparent in multiple conditions that are risk factors
for later development of AD.
Associations between the occurrence of systemic infections and chronic inflammatory
conditions with Alzheimer’s disease, suggests an active participation of inflammation in
early stages of disease development.
Patients with higher erythrocyte sedimentation rate (ESR), which is a clinical indicator of
non-specific inflammation, are at greater risk of developing AD (LI et al., 2012). This is
further corroborated by epidemiological studies that show that patients who suffer from
chronic periodontal infection (Van Den Heuvel et al., 2007) and HIV have a higher risk of
developing AD (Stanley et al., 1994;Alisky, 2007;Xu and Ikezu, 2009;Chakradhar, 2018).
Recapitulating the clinical effect, Kristic and colleagues established an animal model that
displayed AD like neuropathology by inducing chronic inflammation prenatally using a
viral antigen, thereby showing that chronic inflammation potentiates the development of
AD (Krstic et al., 2012).
Traumatic brain injury (TBI) also increases the risk of developing AD, and is considered
a modifiable risk factor for AD. Lesions that develop during TBI lead to an acute
inflammatory response that includes microglial activation to facilitate debris removal and
neuroprotection (Van Den Heuvel et al., 2007;Breunig et al., 2013;Habib et al., 2014).
Incomplete resolution of the acute inflammatory response in TBI, however, is often
followed by hypoxia and oxidative stress, which leads to the chronic activation of microglia
8
and the release of neurotoxic proinflammatory cytokines (Van Den Heuvel et al.,
2007;Breunig et al., 2013;Habib et al., 2014). More recently, Herpesviridae (HSV) has
been associated with Alzheimer’s. HSV caused an increase in amyloid-b plaques, which
was found to anti-infective and anti-microbial(Itzhaki et al., 1997;Wozniak et al.,
2009;Readhead et al., 2018).
Chronic inflammatory conditions such as autoimmune disorders alter the risk of
development of dementia. A recent study found patients admitted to the hospital for an
autoimmune disorder have greater risk for subsequent hospitalization due to dementia
(Wotton and Goldacre, 2017). This association was particularly significant for multiple
sclerosis and systemic lupus erythematosus for AD. While patients with rheumatoid
arthritis (RA) had a reduced risk of developing Alzheimer’s disease, they had an
increased risk of vascular dementia(Wotton and Goldacre, 2017). Multiple studies
indicate that AD incidence is lower in persons with RA (Policicchio et al., 2017). Some
attribute this reduction in incidence to the regular use of nonsteroidal anti-inflammatory
drugs (NSAIDs) (McGeer et al., 1996;Etminan et al., 2003).
An alternative mechanism involves upregulation of granulocyte macrophage-colony
stimulating factor (GM-CSF) with a probable gain of function in myeloid cells, thus
enabling effective debris clearance is also hypothesized to reduce the incidence of AD in
RA patients (McGeer et al., 1996;Boyd et al., 2010). In mice, increased levels of GM-CSF
(both intrahippocampal and subcutaneous administration) significantly reduced
amyloidosis and reversed cognitive impairment (McGeer et al., 1996;Boyd et al., 2010).
9
More recent findings indicate that RA and risk of AD can be stratified based on treatment.
Case-controlled study conducted on electronic medical records from 8.5 million
commercially insured adults, indicate that RA patients treated with an anti-TNFα therapy,
etanercept, had a lower risk of AD whereas those on other anti-inflammatory agents had
increased risk of AD (Chou et al., 2016).
Anti-inflammatory Drugs: Epidemiological and Clinical Prevention Trials for
Alzheimer’s disease
Disparate results from epidemiological studies and randomized clinical trials highlight the
complexity of response to anti-inflammatory agents (Thal et al., 2005). Epidemiological
analyses indicated that long-term NSAIDs users have a lower risk of developing AD
(McGeer et al., 1996;Vlad et al., 2008). Based on epidemiological findings, a clinical
study- ADAPT (Alzheimer’s Disease Anti-Inflammatory Prevention Trial) was conducted
in cognitively intact elderly individuals with a family history of AD. In this trial, the selective
cyclooxygenase-2 (COX-2) inhibitor Celecoxib and non-selective COX inhibitor
Naproxen, were used as preventive therapies. The trial was discontinued 15 months after
randomization, due to the increased cardiovascular risk of these therapies. On extended
follow-up after 7 years, treatment with celecoxib or naproxen for 1-3 years did not prevent
cognitive decline (Group, 2007;Group*, 2008;Breitner et al., 2011;2013). More recently,
a study conducted in pre-symptomatic persons with family history of AD, and average
age of 63 years, were treated with low dose of Naproxen for 2 years. Treatment with
naproxen worsened Alzheimer’s progression and also resulted in increased adverse
events(Meyer et al., 2019).
10
Collectively, these findings indicate that targeting inflammation therapeutically is complex.
Prolonged treatment with anti-inflammatories like NSAIDs can inhibit the beneficial
functions of immune cells, which is to support tissue remodeling, repair and provide
neurotrophic factors in the brain. Pan anti-inflammatory drugs shut down the regulatory
functions of inflammation, causing aberrant side effects. Establishment of timing of the
inflammatory modulatory therapy is also critical(McGeer et al., 1996;Vlad et al., 2008).
Discrepancies between the epidemiological and clinical trial findings indicate the need for
greater refinement in considering patient populations and anti-inflammatory therapies.
Elucidating the inflammatory phenotype which emerges during the progression of AD
requires consideration of the triggers that initiate chronic inflammation. In the sections
below, we address how age, endocrine status and APOE genotype impact inflammatory
processes across AD progression from risk to late stage disease.
Age-related neuroinflammation
Aging has a broad systems-level effect on human biology, which is evidenced by
alterations in physiological function, metabolism, cognition and inflammation (Smith et al.,
2005). The effect of aging on immune responses are extensive and complex. In some
individuals, adaptive immune responses will decline with age, whereas, others will
11
experience aberrant immune responses leading to autoimmune disorders(Goronzy and
Weyand, 2012;Vadasz et al., 2013;Fougère et al., 2017). Aging is associated with
accumulation of oxidative stress and DNA damage and chronic low-grade
inflammation(Cui et al., 2012). Though the effect of aging on cognitive function is variable,
age remains the greatest risk factor for Alzheimer’s in which inflammation is an early and
persistent hallmark of the disease(von Bernhardi et al., 2015).
Figure 1.1 Effect of aging on the microglial immunophenotype.
Aging causes the priming of microglia which is marked by upregulation of CD68 and Major
Histocompatibility complex-II (MHC-II). Chronic activation of microglia can lead to
immunosenescence.
12
CNS microglia play a prominent role in innate immunity. Microglia constantly conduct
surveillance of brain parenchyma to detect foreign pathogens and clear debris (Streit et
al., 2004;von Bernhardi et al., 2015). Microglia detect and respond to a broad range of
triggers including traumatic brain injury, infections and damage associated molecular
patterns (DAMPs). Reactive oxygen species (ROS), extracellular DNA and ATP all act as
DAMPs (von Bernhardi et al., 2015;Gulke et al., 2018).
Innate immune responses by microglia are phenotypically typified by enlargement of the
cell body, and molecularly by the upregulation of CD68, major histocompatibility complex-
II (MHC-II) along with costimulatory molecules and secretion of pro and anti-inflammatory
cytokines (Kim and Joh, 2006). The onset of innate immune responses leads to activation
of the adaptive immune response. The innate activation of the adaptive immune
response results in infiltration of peripheral immune cells, particularly T cell invasion of
the brain (Kim and Joh, 2006). Together the innate and adaptive immune responses
create the chronic low-grade inflammation typical of aging (Kim and Joh, 2006) (Figure
1.1 and 1.2).
Microglial phenotype is dynamic. Aging affects the microglial phenotype, transcriptome
and function. The telomerase deficient accelerated aging mouse model exhibits reduced
microglial numbers and deficient morphological and cellular processes (Khan et al.,
2015). Further, microglial response to activation is stage of development dependent.
Production of cytokines and trophic factors by microglia increases linearly with age (Lai
et al., 2013). Microglia isolated from younger mice (2-4 months) exhibit a lower expression
13
of proinflammatory cytokines: TNFα, IL-6, and IL-1β than older mice (Helenius et al.,
1996;Crain et al., 2013;Latta et al., 2015). On activation by ATP, microglia isolated from
neonatal rats and 13-15 month old adult rats have a more robust inflammatory response
exhibited by upregulation of nitric oxide, TNFα and brain derived neurotrophic factor
(BDNF) in comparison to microglia derived from younger animals (2-11 month) (Lai et al.,
2013).
Aging distinctly affects migratory function of microglia; younger microglia on encountering
activating signals exhibit an increase in motility and rapid extension of ramifications,
whereas older microglia are less dynamic. Transcriptomic studies corroborate a
reduction in migratory ability of microglia with age as age affects actin cytoskeleton
reogranization which is vital in both phagocytosis and migration (Damani et al., 2011;Orre
et al., 2014).
Comparison of the transcriptomic profiles of young and aged microglia revealed that
microglial receptors (Trem2c, P2yr12, P2yr13, and Adora) involved in recognizing
DAMPs such as, oxidised low-density lipoprotein, mitochondrial DNA, extracellular ATP
decreased with age (Orre et al., 2014). In contrast, the expression of receptors that
recognize pathogens and microbes (Tlr2, CD74, Ltf, Clec7a, Cxcl16, and Ifitm6)
increases with age (Orre et al., 2014).
Age-related changes in microglial transcriptome are not ubiquitous as the expression of
phagocytic receptors (Cd14, Cd68, Cd11b, and ICAM) remained unaltered in aging
14
(Hickman et al., 2013;Smith and Dragunow, 2014). However, activation of microglial
phagocytosis is diminished in aging. Studies characterizing microglial phagocytic
capacity, report a reduction in phagocytosis with age which is especially evident following
activation (Li et al., 2015a;Ritzel et al., 2015). These findings indicate that despite stable
expression of phagocytic receptors, the functional capacity of microglia decreases with
age (Li et al., 2015a;Ritzel et al., 2015). For example, the ability of microglia to
phagocytose Ab is affected by age, with microglia isolated from postnatal animals
effectively phagocytosing Ab fibrils, whereas adult microglia lose their capacity to do so
(Floden and Combs, 2011). Other contributing factors to microglial senescence are age-
related myelin degeneration and lysosomal storage in microglia, which in turn burden
microglial clearance function (Holtman et al., 2015;Safaiyan et al., 2016).
Systemic inflammation and aging cause microglial priming. Primed microglia have a
lower threshold for activation, are hypersensitive, develop an exaggerated immune
response on activation, and have a distinct molecular signature from the M1-M2
phenotype (Perry and Teeling, 2013;Holtman et al., 2015;Ojo et al., 2015). The molecular
signatures of primed microglia include the overexpression of antigen presentation, redox
pathways, oxidative phosphorylation, and lysosomes (Perry and Teeling, 2013;Holtman
et al., 2015;Ojo et al., 2015). During aging primed microglia generate a pro-inflammatory
cascade due to activation by DAMPs, which causes a lower threshold of activation,
enhanced reactivity, and limited functional capacity on encountering secondary triggers.
The chronic activation of microglia coupled with age-related microglial priming hasten the
process of senescence to cause loss of function over time (Franceschi et al., 2000;Streit
15
and Xue, 2014) (Figure 1.1).
Immunometabolic sensor of aging: targeting aging as a disease
The inflammasome complex is a sensor of DAMPs. DAMPs act as an inflammatory
challenge to the host defense mechanism and lead to the activation of the NLRP3 (Nod-
like receptor pyrin domain 3) inflammasome complex (Youm et al., 2013;Zhang et al.,
2013). Within the family of innate inflammasome sensors, the NLRP3 inflammasome has
the unique ability to detect sterile inflammatory triggers. It can detect a wide range of
metabolic and aging-related DAMPS, such as ROS production, glucose tolerance and
insulin resistance (Vandanmagsar et al., 2011;Salminen et al., 2012;Walsh et al., 2014),
lipotoxic fatty acids, ceramides, free cholesterol, uric acid, and ATP, and it releases IL-1β
and IL-18 (Youm et al., 2012;Youm et al., 2013;Zhang et al., 2013) (Figure 1.2).
Figure 1.2 Dynamic interactions between neurons, astrocytes and microglia during
aging
The NLRP3 inflammasome complex activation is a two-step process. Molecular
pathogens like lipopolysaccharide (LPS) have been shown to prime cells, leading to the
16
activation of pattern recognition receptors (PRRs), the release of IL-1β, and increased
expression of NLRP3. When followed by a secondary trigger such as ATP, this process
causes the inflammasome to assemble and causes further activation. NLRP3 is also
activated by the accumulation of damaged mitochondria due to the inhibition of
autophagy, resulting in excessive production of ROS. Oxidized mitochondrial DNA is also
implicated in the activation of NLRP3 (Dixit, 2013). The activation of NLRP3 by ROS is
mediated by thioredoxin interacting protein (TXNIIP) (Youm et al., 2012;Youm et al.,
2013;Zhang et al., 2013).
The activation patterns of NLRP3 are similar in both macrophages and microglia. NLRP3
activation leads to the priming of microglia and reducing the threshold for activation (Halle
et al., 2008;Heneka et al., 2013). Increase in caspase-1 activity in postmortem MCI and
AD brains indicates the possible participation of the NLRP3 inflammasome in AD
pathogenesis (Heneka et al., 2013). Targeting NLRP3 and NF-kB (nuclear factor kappa-
light-chain-enhancer of activated B cells) has been associated with a reduction in
pathology of AD (Tang et al., 2015). In animal models carrying AD pathology, NLRP3
knockout and caspase-1 knockout caused spatial memory to remain intact. Moreover, the
microglial phenotype in the NLRP3 knockout model shifted to the M2 anti-inflammatory
phenotype with greater neuroprotection improved clearance of the plaque burden (Halle
et al., 2008;Heneka et al., 2013).
With several studies linking NLRP3 inflammasome activation to chronic low-grade
inflammation observed in aging, therapeutics targeting this sensor have also emerged. In
17
a recent study, the ketone body- β–hydroxybutyrate (BHB) was found to suppress NLRP3
activation caused by urate crystals, lipotoxic fatty acids, and ATP. The levels of BHB
increase with starvation, caloric restriction, and high-intensity exercise. Aging also marks
a shift in the fuel usage and dependence on different fuel mechanisms. The inhibition of
NLRP3 by BHB resulted in a decrease of IL-1β and IL-18 production by monocytes. BHB
also reduced caspase-1 activation and IL-1β secretion in mouse models of NLRP3-
mediated chronic inflammatory diseases like Muckle-Wells syndrome (Youm et al., 2015).
Another molecule, MCC950, blocked canonical and non-canonical activation of NLRP3
and attenuated experimental autoimmune encephalomyelitis (EAE). Both, MCC950 and
BHB, were used in a mouse model of Muckle-Wells syndrome, which is characterized by
chronic inflammation mediated by NLRP3. Thus, targeting NLRP3 in aging and aging-
related disorders could be an important therapeutic strategy (Coll et al., 2015).
Menopause and inflammation
Post-menopausal women are at higher risk for developing autoimmune disorders and
obesity (Doran et al., 2002;Bove, 2013). The incidence of RA is higher in peri- and post-
menopausal women (Doran et al., 2002). The pathology of multiple sclerosis worsens
after menopause (Tutuncu et al., 2013). Post-menopausal women are more prone to
robust immune responses. The lack of ovarian steroidal hormones potentiates the
inflammatory process predisposing menopausal women to immune disorders (Benedusi
et al., 2012;Kireev et al., 2014;Sharma et al., 2018).
18
Menopause and the associated lack of steroidal hormones further potentiate
inflammation, which is reflected in levels of circulating cytokine levels and inflammatory
responses. IL-6 and sIL-6 levels are higher in postmenopausal women (Giuliani et al.,
2001). IL-4 and IL-2 levels also increase with menopause, which can be reversed by
hormone therapy (Yasui et al., 2007). Serum IFN-g levels increase during early
menopause but decrease in later menopause (Goetzl et al., 2010b). Peripheral blood
mononuclear cells (PBMCs) isolated from postmenopausal women produced higher IL-
6, IL-1β and TNF-α upon induction by lipopolysaccharide (LPS) than PBMCs isolated
from premenopausal women (Brooks-Asplund et al., 2002).
In addition to an altered cytokine profile, changes in T cell biology occur in women during
this endocrine transition. Pre-menopausal women have higher CD4 counts than men and
thus a more robust response to vaccination. Menopause causes a reduction in CD4 T-
cell numbers. This eventually causes an inversion of the CD4/CD8 T-cell ratio, which is
indicative of aging and can be correlated with increased oxidative stress (Larbi et al.,
2008;Gameiro et al., 2010;Muller et al., 2015). The number of B2 cells (involved with
antibody production) also decreases with menopause, especially during late menopause
in comparison to perimenopause (Kamada et al., 2001).
In mice, ovariectomy causes a reduction in the LPS-induced proliferation of leukocytes
and subsequent chemotaxis, which is indicative of premature immune senescence
(Baeza et al., 2011). Ovariectomized animals generally have a reduced and delayed
adaptive response to vaccination, leading to decreased IgG titers in comparison to
19
animals with intact ovaries (Haberthur et al., 2010).These changes are indicative of
immune senescence occurring during menopausal transition. In the context of AD, the
systemic effect of menopause on inflammation combined with effects on neurological
function indicates cruciality of the menopausal transition in AD pathogensis.
Molecular neuro-inflammatory mechanisms in menopause
Menopause is composed of three transitions; the perimenopause that precedes
menopause, the cessation of reproductive capacity, menopause, and the years following
menopause, post-menopause. Concomitant with this endocrine aging is chronological
aging as the endocrine transition states span multiple years. Each of these endocrine
stages is characterized by complex hormonal fluctuations(Brinton et al., 2015b).
Decline in estradiol level during perimenopause and menopause coincides with a
bioenergetic deficit in brain (Ding et al., 2013a). Estradiol is a master regulator of
metabolic function in the female (Rettberg et al., 2014b). Clinical evidence of decline in
glucose metabolism in brain and the coincident bioenergetic deficit is evidenced by
reduced uptake of 18-fludeoxyglucose detected by positron emission tomography (PET)
in perimenopausal and menopausal women (Mosconi et al., 2017a;Mosconi et al.,
2017b). The bioenergetic deficit precedes a shift to utilization of ketone bodies as a
compensatory response to decline in brain glucose as bioenergetic fuel to generate ATP
in brain. This shift to utilizing an auxiliary fuel during female endocrine aging activates
catabolism of white matter as an endogenous lipid source of ketone bodies in brain
(Klosinski et al., 2015) and a concomitant increase in microglial and astrocytic reactivity
20
(Xie et al., 2013;Suenaga et al., 2015).
Figure 1.3 Shift microglial phenotype caused by ovariectomy.
Analyses of microarray data obtained from the brains of postmenopausal women made
available by NCBI revealed an inflammatory gene expression profile in the post central
and superior frontal gyrus (Sárvári et al., 2012b). In comparison to pre-menopausal
women, post-menopausal women showed an upregulation in microglial markers CD14,
CD18, and CD45, as well as TLR4 and MHC-II markers CD74 and C3 (Sárvári et al.,
2012b). These findings in the human female brain were consistent with the pattern of
gene expression in the frontal cortex of ovariectomized middle-aged rats (13 months old)
(Sárvári et al., 2012b). Ovariectomy caused an upregulation of microglial reactivity
21
markers CD11b, C18, CD45, and CD86, complement pathway C3, and phagocytic
markers Msr2 and CD32. Together, these data are indicative of a shift in the microglial
phenotype to an activated state (Figure 1.3).
Hippocampal inflammatory gene expression in middle-aged rats drastically changed upon
ovariectomy. A similar upregulation of microglial markers (CD45, IBA1, CD68, CD11b,
CD18, Fcgr1a, and Fcgr2b) to that witnessed in the cortex was observed in the
hippocampus. The gene expression results imply a possible activation of microglia. This
effect was mitigated by treatment with estradiol and selective estrogen receptor-α (ERα)
and estrogen receptor-β (ERβ) agonists (Sarvari et al., 2015). Human post-menopausal
gene expression in the hippocampus corroborated the inflammatory gene expression
pattern in ovariectomized rats, with an upregulation of microglial reactivity markers
CD11b, CD18, IBA1, CD14, and complement C3 (Sarvari et al., 2015).
In parallel, aging is associated with a marked upregulation in genes encoding the major
histocompatibility complex class I and class II (MHC-I & MHC-II) (VanGuilder et al., 2011),
alterations in Toll like receptors (TLR)(Shaw et al., 2011), and the complement
pathway(Reichwald et al., 2009). This effect is more pronounced in women and
represents the sexual dimorphism of the immune system (Blalock et al., 2003;Berchtold
et al., 2008a). The dynamics involved between age and menopause-related increase in
myelin degeneration and microglial priming can be a tipping point in the neuro-
inflammatory system. The increased myelin antigen load and upregulation of antigen
presentation by microglia can set forth a cascade that leads to dysregulated glial
22
metabolism and hypertrophy, eventually causing altered extracellular matrix (Blalock et
al., 2003). Each event is pivotal in the development of AD.
Hormone therapy
Hormone therapy (HT) promotes neuronal survival and has been shown to improve
cognitive function and episodic memory in perimenopausal and postmenopausal women
(Morrison et al., 2006;Brinton, 2008). Epidemiological studies have shown that HT delays
the onset of AD as well as reduces the risk of developing AD (Tang et al.;Persad et al.,
2009;Dye et al., 2012). Women transitioning through their menopause benefit most from
HT as compared to women who have already transitioned(Girard et al., 2017). Results
from several clinical trials have emphasized on the timing of treatment with HT and the
drawbacks of missing the window of treatment (Hogervorst et al., 2000;Zandi et al.,
2002;Shumaker et al., 2003). This effect of estradiol in HT has been explained by two
theories: the healthy cell bias of estrogen action (Brinton, 2008) and critical window
hypothesis (Maki, 2013). Healthy cell bias highlights that neuronal viability and health at
baseline are important for estradiol to exert its therapeutic efficacy, whereas the critical
window hypothesis focuses on the perimenopausal transition, when cells are still healthy,
being a key phase for using HT. The use of estradiol in HT provides a therapeutic
opportunity to target inflammatory pathways that simultaneously modulate metabolic
functions, thereby providing a supportive milieu for neuronal survival and growth (Vegeto
et al., 2008;Zhao et al., 2014). HT restores the hormonal levels in post-menopausal
women to those of premenopausal women. Post-menopausal women using HT have
higher lymphocyte numbers and higher circulating monocytes in comparison to post-
23
menopausal women who are not on HT (Kamada et al., 2000). Likewise, levels of B2 cells
involved in antibody production are significantly higher in HT users in comparison to non-
users (Kamada et al., 2001).
ERα and ERβ are abundantly expressed in astrocytes, microglia, and neurons, and both
ERα and ERβ are involved in regulating the immunomodulatory responses exerted by
astrocytes and microglia (Liu et al., 2003;Khan and Ansar Ahmed, 2015). In
ovariectomized middle-aged rats, estradiol induces downregulation of the complement
pathway and macrophage-associated genes in the frontal cortex. This effect was
mediated through ERα and ERβ (Sárvári et al., 2011). Estradiol treatment in microglial
cells induces a dose-dependent attenuation in superoxide release, phagocytic activity and
a concomitant increase in iNOS activity, without altering NF-kB expression (Bruce-Keller
et al., 2000;Drew and Chavis, 2000). Some studies have shown that sex steroids reduce
neuroinflammation via inhibiting the inflammasome complex, a possible downstream
effect mediated by ERα and ERβ (Slowik and Beyer, 2015).
Astrocytes also participate in mediating the neuroprotective anti-inflammatory effect of
estradiol via ERα (Spence et al., 2011). In contrast to microglial cells, estradiol inhibits
NF-kB expression in astrocytes (Giraud et al., 2010;Acaz-Fonseca et al., 2014). Estradiol
inhibits secretion of proinflammatory cytokines IL-6, TNF-α, IL-1β, expression of matrix
metalloproteinases 9 (MMP-9), and interferon gamma-inducible protein 10 (IP-10) in
astrocytes. Estradiol also reduces proinflammatory cytokines secreted by astrocytes
when exposed to Aß (Giraud et al., 2010;Acaz-Fonseca et al., 2014).
24
Much like estradiol, selective estrogen receptor modulators (SERMs) exert a
neuroprotective effect by reducing neuroinflammation. Tamoxifen and raloxifen both
reduce microgliosis, astrogliosis, and the production of proinflammatory cytokines IL-6
and IP-10 induced by LPS administration (Arevalo et al., 2012). They have also been
demonstrated to protect neurons against neurotoxicity caused by neuroinflammation
through an ER mediated pathway(Ishihara et al., 2015). SERMs reduce the
proinflammatory response produced by astrocytes and are helpful in potentiating their
neurotrophic function (Tapia-Gonzalez et al., 2008;Cerciat et al., 2010;Arevalo et al.,
2012;Ishihara et al., 2015).
Microglia in Neurodegeneration and Alzheimer’s disease
With the advancement in the RNA-Seq technology to the use of single-cell RNA-Seq
analysis, several studies recently have characterized distinct molecular signatures that
describe the microglial phenotype in disease.
Characterization of a disease associated microglial phenotype, showed that microglial
phenotype evolved dynamically, and was not binary as was once thought. Homeostatic
microglia were defined by the expression of microglial markers CX3CR1 and P2RY12.
The disease associated microglia showed a 2-step activation, the 1
st
step was
independent of TREM2 and showed a downregulation of microglial markers CX3CR1,
P2RY12 and TMEM119 an upregulation of TYROBP, APOE and B2M. The subsequent
stage of activation was dependent on TREM2 and is characterized by upregulation of
25
ITGAX, CLEC7A, LPL and CD9(Keren-Shaul et al., 2017a;Deczkowska et al., 2018).
Another study, conducted on an accelerated neurodegeneration model Cpk-25, showed
that as part of the late stage neurodegenerative response microglia show an upregulation
of type I and type II interferon response genes and overexpression of MHC-II
genes(Mathys et al., 2017b). Upregulation of MHC-II genes and interferon response is
also critical to myelin integrity(Raj et al., 2017). Dysregulation in the interferon signaling
by knocking out ubiquitin specific protease 18 (USP18) causes microgliosis in the white
matter and the onset of interferonopathy(Goldmann et al., 2015). Interferon signaling is
also dysregulated in multiple sclerosis. Low-dose of interferon-gamma (IFNg) is
neurotrophic and protective, but high doses of IFN-g becomes cytotoxic and causes the
upregulation of MHC-II, Interferon inducible protein-10 (IP-10), promotes demyelination
and presentation of myelin as antigens(Ottum et al., 2015).
How does female aging affect neuroinflammation, remains to be understood. White
matter loss, disruption of myelin integrity is evident in menopausal transition.
Neuroinflammatory mechanisms that par-take in this disruption of myelin integrity are yet
unknown. Elucidation of a neuroinflammatory profile that is endocrine state specific is
crucial for development of therapeutics and mitigation of risk.
26
ApoE4 and Alzheimer’s disease
ApoE4 is the primary genetic risk factor for the late onset form of AD (Scacchi et al.,
1995;Liu et al., 2014a;Manning et al., 2014). The human form of the APOE gene
possesses three polymorphic alleles: APOEe2 (ApoE2), APOEe3 (ApoE3), and APOEe4
(ApoE4). The ApoE3 allele occurs more frequently (77%) than ApoE2 (8%) and ApoE4
(14%) (Eisenberg et al., 2010). The ApoE4 allele occurs in 40% of AD patients (Farrer et
al., 1997). However, 91% of homozygous ApoE4 carriers and 47% heterozygous carriers
go on to develop AD(Corder et al., 1993).
Apolipoprotein E (ApoE) is a key regulator of lipid homeostasis, mostly expressed by the
liver and the brain. In the brain, it functions to shuttle lipid molecules from astrocytes and
microglia to neurons, via lipoprotein complexes (Liu et al., 2013). In the periphery, ApoE
is expressed in macrophages and liver. In the central nervous system, ApoE is mainly
produced by astrocytes and microglia (Liu et al., 2013).
Sources of neuroinflammation in ApoE4
ApoE is known to exert an immunosuppressive effect by inhibiting lymphocyte
proliferation, Ig synthesis, and neutrophil activation. ApoE also exerts this
immunosuppressive property on microglial activation (Guo et al., 2004;Baitsch et al.,
2011;Christensen et al., 2011). Relative to ApoE2 and ApoE3 carriers, ApoE4 carriers
generate less ApoE protein in periphery and brain (Larson et al., 2000). Given the reduced
amounts of ApoE protein in ApoE4 carriers, this population could be predisposed to a
27
heightened inflammatory response when encountering a sterile inducer or infection
(Ukkola et al., 2009;Gale et al., 2014a;Tai et al., 2015).
Due to the differences in cysteine and arginine residues translated at positions 112 and
158, each of the three different isoforms of APOE exhibit different conformations. The
conformational change in the protein affects its stability, folding characteristics, and the
propensity to bind lipoprotein particles (Jofre-Monseny et al., 2008). ApoE4 has a globule-
like structure and preferably binds to very low-density lipoprotein (vLDL) and low-density
lipoprotein (LDL) particles. However, ApoE2 and ApoE3 tend to bind high-density
lipoprotein (HDL) particles (Jofre-Monseny et al., 2008). This difference in protein
structure affects the lipid shuttling ability of ApoE, which leads to hypercholesterolemia in
ApoE4 carriers and increases the predisposition for generation of plaques (Jofre-
Monseny et al., 2008). ApoE also exerts an inhibitory effect on the oxidation of LDL in an
isoform-specific manner (E2>E3>E4) (Miyata and Smith, 1996). Among smokers, ApoE4
carriers have significantly higher amounts of ox-LDL (Jofre-Monseny et al., 2008).
Cholesterol, ox-LDL, and Ab are sterile inducers of inflammation called DAMPs (Miller et
al., 2011;Clark and Vissel, 2015). DAMPs are recognized by PRRs expressed on
macrophages, dendritic cells, monocytes, microglia, and neutrophils, which trigger the
activation of an inflammatory process (Silverstein and Febbraio, 2009). One such PRR is
CD36. In the context of recognizing DAMPs and plaque formation, it was recently
demonstrated that CD36 (expressed on monocytes, macrophages and microglia)
recognizes soluble ligands such as oxidized LDL and soluble Ab and converts them to
28
crystals and fibrils, respectively. This leads to the assembly and activation of the NLRP3
inflammasome and the consequent release of the proinflammatory cytokine IL-1b
(Sheedy et al., 2013;Oury, 2014)
Figure 1.4 Effects of ApoE4 genotype on immune function.
Due to the increased probability of ApoE4 carriers to develop plaques, cholesterol
crystals, and amyloid depositions, cellular immune function and reactivity are affected. In
ApoE4 carriers there is a reduced clearance and efflux of cholesterol from macrophages
29
(Cash et al., 2012). Moreover, increased nitric oxide production in ApoE4 causes
increased platelet aggregation and secretion of adhesion molecules, further enabling the
plaque formation in the periphery. In the brain, microglial and astrocytic clearance of
debris is also diminished (Guo et al., 2006).
ApoE also affects Ab uptake and oligomerization and thus can be a key factor in Ab
turnover. AD patients possessing the ApoE4 allele were found to have higher levels
oligomeric Aβ in their brains as compared to ApoE3 carriers, implicating an association
between ApoE with Ab (Hashimoto et al., 2012). In vitro experiments correspond to
clinical findings and have shown that ApoE4 has the greatest effect on promoting Ab
oligomerization in comparison to other isoforms (Hashimoto et al., 2012). Blocking Ab and
ApoE interaction by ApoE Ab antagonist in hippocampal neuronal and astrocytic co-
culture systems led to decreased accumulation and oligomerization of Ab. Treatment with
ApoE Ab antagonists also inhibited the loss of synaptic proteins induced by Ab
accumulation (Kuszczyk et al., 2013;Liu et al., 2014b).
Coupled with the Ab oligomerization, ApoE also affects Ab uptake. Astrocytes secrete
ApoE as a lipoparticle into the interstitial fluid, where it binds with Ab. Neurons endocytose
and internalize these lipoparticles, thus promoting Ab uptake. ApoE4 isoform has maximal
binding affinity to Ab and thus causes a greater uptake of Ab by neurons in comparison
to other isoforms (Mulder et al., 2014). ApoE4 prevents the uptake of oligomeric Ab by
astrocytes and the uptake of both oligomeric and fibrillar Ab by microglia thereby inhibiting
its clearance (Mulder et al., 2014).
30
ApoE also modulates Ab clearance by microglia by regulating Aβ clearing enzymes such
as neprilysin intracellularly and insulin degrading enzyme extracellularly. Effective
degradation of Aβ depends on Liver X Receptor (LXR) activation, the isoform of APOE
expressed, and the lipidation status of ApoE particles (Hashimoto et al., 2012;Kuszczyk
et al., 2013;Liu et al., 2014b). Activation of LXR/RXR (Retinoid X receptor) potentiates Ab
clearance as it compensates for the loss of ApoE4 function and induces the expression
of ATP- binding cassette transporter subfamily A member 1 (ABCA1) and ApoE, thus
inducing clearance by microglia and astrocytes (Lefterov et al., 2007;Terwel et al.,
2011;Lee et al., 2012;Mandrekar-Colucci et al., 2012;Tai et al., 2014). Therefore, the
presence of ApoE4 promotes the production of Ab and uptake by neurons while
preventing clearance and enabling the production of DAMPs and chronic low-grade
inflammation.
Given the dysregulated lipid metabolism and impairment of cellular function with age, the
ApoE4 allele is associated with increased systemic inflammation. It is expected that this
would be reflected in cytokine levels, which are inflammatory markers such as C-Reactive
Protein (CRP) measured in plasma/serum. Surprisingly, however, this is not the case, at
least with CRP. Studies have consistently shown that CRP levels are lower in ApoE4
carriers (Ukkola et al., 2009;Lima et al., 2014;Metti et al., 2014;Yun et al., 2015). Marz et
al. proposed that the metabolism of CRP might be associated with the
mevalonate/cholesterol synthetic pathway, which might be downregulated in ApoE4
carriers (Marz et al., 2004). Interestingly, a recent study found that ApoE4 carriers with
31
higher CRP levels had an increased risk of developing AD(Tao et al., 2018). On the other
hand, studies have shown that levels of IL-1β and vascular inflammatory marker: Vascular
cell adhesion protein-1 (VCAM-1) are higher in E4 carriers (Olgiati et al., 2010) (Figure
1.4).
The ApoE4 allele also accelerates aging, which is reflected in the shorter telomeres in
ApoE4 women in comparison to ApoE3 women (Jacobs et al., 2013). The accelerated
aging phenotype is also evident in the reduction of T cell numbers. Age-related reduction
in T cells for women is more dramatic during menopause, which is even more pronounced
if the woman is an ApoE4 carrier (Begum et al., 2014). In comparison to ApoE3 derived
microglia, estradiol has a reduced anti-inflammatory effect on microglia derived from
ApoE4 (Brown et al., 2008). The ApoE4 allele is also a risk factor for metabolic syndrome
and has been associated with RA, thus increasing the risk of comorbidities that can, in
turn, affect systemic inflammation (Sima et al., 2007;Gungor et al., 2012;Toms et al.,
2012;Johnson et al., 2013). ApoE4 also affects immune function by causing blood brain
barrier dysfunction (Nelson et al., 2016).
Inflammatory responses triggered by innate immune agonists are highest in ApoE4
carriers (Vitek et al., 2009;Gale et al., 2014a). This finding holds true in cells isolated from
both humans and rodents and in both the periphery and the brain (Gale et al., 2014b;Li
et al., 2015b). Therefore, inflammatory triggers such as traumatic brain injury, infection,
and DAMPs produced from metabolic syndrome significantly increase the inflammatory
response in ApoE4 carriers. This may potentially lead to the incomplete resolution of
32
inflammation to initiate chronic inflammatory processes that result in neurotoxicity. This
trend is evident in HIV-associated dementia, for which E4 carriers have increased risk
(Chang et al., 2011).
A heightened inflammatory response occurred in microglia derived from humanized
ApoE4 KI mice upon on treatment with the TLR3 and TLR4 activator LPS compared to
ApoE3 mice (Vitek et al., 2009;Heneka et al., 2015). The inflammatory response was
characterized by altered cell morphology, increased nitric oxide production, COX-2
expression, prostaglandin E-2 (PGE-2) expression, and cytokine production (IL-6, TNF-
a, and IL-12p40). In contrast, TREM2 expression was decreased (Vitek et al.,
2009;Heneka et al., 2015). A comparable inflammatory response was observed in
peripheral macrophages isolated from ApoE4 mice. The E4 allele increases the reactivity
of glial and peripheral immune cells, thus aggravating the neurotoxic proinflammatory
response (Vitek et al., 2009;Heneka et al., 2015).
ApoE4 and metabolism
The ApoE4 genotype renders the ApoE protein as an ineffective lipid transporter. This
causes ApoE4 carriers to have high cholesterol and triglyceride levels(Dallongeville et al.,
1992;Bennet et al., 2007). Plasma ApoE levels are impacted by the isoform of ApoE
expressed. ApoE4 tends to have lowest levels of ApoE, whereas ApoE2 the highest. High
ApoE levels stimulates the liver to produce very-low density lipoprotein (VLDL), whereas
deficiency in ApoE level causes the stimulation of lipolysis and prevents the clearance of
triglyceride rich lipoproteins from the plasma (Rasmussen, 2016). Due to their inability to
33
take up and store lipids, ApoE4 carriers often have lower body mass index (BMI), and
experience weight loss and have higher risk for cardiovascular problems (Vanhanen et
al., 2001;Jimenez et al., 2017;Jones and Rebeck, 2018).
ApoE4 also interferes with glucose metabolism, as ApoE4 carriers tend to have higher
insulin levels and poor brain glucose uptake(Mosconi et al., 2004;Reiman et al., 2004).
More recently, it was found that ApoE4 inhibits insulin signaling in neurons by trapping
the insulin receptor(Zhao et al., 2017). Recent studies have shown that, ApoE4 carriers
are more dependent on lipids as a fuel source (Arbones-Mainar et al., 2016). Conversely,
a drug based on inducing ketogenesis worked better in non-carriers, indicating that
ApoE4 carriers may be dependent on endogenous ketone bodies as their fuel
source(Henderson et al., 2009).
Studies characterizing the effect of ApoE4 gene on the menopausal transition and the
resulting cognitive profile have been scarce. A study recently has shown that women with
poor metabolic profile, who are also ApoE4 carriers, perform poorly cognitively 10 years
after menopause(Karim et al., 2019). Another study showed that Caucasian women, who
are ApoE4 carriers, between the ages of 60-70 at increased risk of developing cognitive
decline than males(Neu et al., 2017).
Substantial gaps remain in understanding the interaction of menopause and ApoE4, and
the consequential metabolic profile has not been elucidated. Despite of a sex difference
34
in disease severity in ApoE4 women, sex differences in the metabolic aging profile of the
brain, in ApoE4 carriers has also not been studied.
Study hypothesis: Neuroinflammation and ApoE4-related metabolic profile is
impacted by female endocrine aging.
The prevalence of Alzheimer’s is higher in women than men. Prodromal phase of
Alzheimer’s precedes the onset of symptoms by 20 years. The female endocrine
transition: perimenopause, is a unique time-locked transition that occurs before the onset
of the prodromal state and exhibits several properties of it. The onset of perimenopause
causes a shift in fuel utilization, causing an increased dependence on ketone bodies
derived from myelin.
Neuroinflammation is integral to the pathology of Alzheimer’s disease (AD) and presents
as a modifiable risk factor for AD. Chronological aging and disease both affect microglial
transcriptome, function and phenotype. Menopause causes an increase in inflammation
peripherally and in the brain, yet, neuroinflammatory profile for distinct reproductive aging
windows has not yet been characterized. It is hypothesized that the perimenopausal
transition will have distinct inflammatory profile that is linked with myelin antigen
presentation. Aging and endocrine senescence may affect microglial reactivity and
function. Age related myelin debris accumulation may potentiate the upregulation of
antigen presentation molecules thereby causing a dysregulation in glial metabolism.
35
ApoE4 is a genetic risk factor for AD, and women ApoE4 carriers are at an increased risk
of developing AD. Women ApoE4 carriers experience worse disease severity and
progression than men. ApoE4 affects glucose metabolism and overall metabolic profile.
Yet, sex differences in metabolic aging and specifically the effect of perimenopause on
glucose metabolism remains unknown. It is hypothesized that the combination of female
sex and endocrine aging may negatively impact energy metabolism and overall metabolic
profile in ApoE4 carriers
As ApoE4 genotype, neuroinflammation and chromosomal sex alter the course of
Alzheimer’s disease progression Combinative use of inflammatory markers, ApoE4
genotype and chromosomal sex, may help in developing biomarkers that are predictive
of therapeutic response of a disease modifying drug and identify responders.
In Chapter 2, a comprehensive profile of the dynamics of neuroinflammation in the female
aging brain, which encompasses each stage of the perimenopausal transition, is
constructed. The goal of the study is to evaluate the changes in glial function and
phenotype that occur during perimenopausal transition and if it can be therapeutically
targeted.
In Chapter 3, we evaluate the sex differences in the metabolic aging of the brain in a
rodent model with ApoE4 knock-in. The goal of the study is to evaluate systematically
aging windows that show differences in energy metabolism and metabolic profile, to
identify time-windows in which therapeutic interventions can be made.
36
In Chapter 4, we evaluate the neuroinflammatory biomarkers as predictive and
pharmacodynamic biomarkers for therapeutic response for neurological outcomes. The
goal of the study is to identify unique biomarkers that are predictive of therapeutic
response and can identify responders based on ApoE genotype and sex, for future clinical
trials.
37
Chapter 2: The Immunometabolic Crisis in the Aging Female Brain: Implications
for Alzheimer’s disease
Abstract
The prevalence of Alzheimer’s disease is higher in women. Molecular mechanisms that
cause disparity in the prevalence of Alzheimer’s disease are largely unknown.
Neuroinflammation is fundamental to the etiology and development of Alzheimer’s
disease. Aging and endocrine transition states, such as the perimenopause have
significant impact on inflammation across the body. Yet, an endocrine state specific effect
of female aging on neuroinflammation has not yet been characterized. In this study, we
are trying to uncover the neuroinflammatory mechanisms at play during the
perimenopause and its relevance to neurodegeneration, using the perimenopausal
animal model. Hippocampal transcriptomic profiling of endocrinological and chronological
aging groups revealed that the reproductively irregular phase was typified by an
upregulation of type I and type II interferon response. Spatial mapping of microglial
reactivity marker MHC-II revealed that, white matter tracts: cingulum, corpus callosum
and fimbria showed an overexpression of MHC-II in reproductively irregular group.
Reproductive irregularity also affected phagocytic response and redox status of microglial
cells. Aging also impacted the mitochondrial function in astrocytes and microglia.
Estradiol regulation of the upregulation of interferon response genes was validated by
ovariectomy and estradiol prevention. Clinical microarray data from the hippocampus was
also analyzed to accomplish translational validity of the findings and, establish if the
upregulation of MHC-II was preferentially observed in females. Findings from the study
suggest that neuroinflammation is significantly impacted in females during the
38
menopause and could be used to target therapeutically to mitigate Alzheimer’s risk in
aging women.
Introduction
Neuroinflammatory processes are at the core of the dysregulated pathophysiology
observed in Alzheimer’s disease (AD), evidenced by microgliosis around amyloid-beta
plaques, increased production of pro-inflammatory cytokines and expression of microglial
reactivity markers(McGeer et al., 1987;Itagaki et al., 1989;McGeer et al., 1989;Mattiace
et al., 1990b;Eikelenboom et al., 1994). Yet, the process in which inflammation aids the
development of pathophysiology in the prodromal phase of late-onset AD is unknown.
Inflammation increases with age, in the brain and periphery. While the increase in
inflammation is evident in both males and females, the trajectories undertaken are
different. Sex differences in immunity on aging have been well-documented in peripheral
immune cells(Klein and Flanagan, 2016). Females experience an increase in
inflammation during the perimenopausal transition which is marked by an increase in
CD4/CD8 T cell ratio, number of CD4 T cells, B cells and cytokine levels – Interferon
(IFN)-g, IL-6, IL-8(Klein and Flanagan, 2016). Much like in the periphery, menopause is
related to an increase in inflammation in the brain. Ovariectomy causes an increased
expression of microglial reactivity markers CD45, CD68, CD11b, FCR1A, FCGR2B in the
hippocampus and CD11b, CD14, CD74 and co-stimulatory molecule CD86 in the frontal
cortex(Sárvári et al., 2012a;Sárvári et al., 2014).
39
AD has a higher prevalence in women than men (Niu et al., 2017;2018;Nebel et al., 2018).
The inflammatory processes that contribute to the disparity in prevalence have not been
well-elucidated. It can be hypothesized that there is a sex-difference in inflammatory
trajectories undertaken during aging, and it is affected by sex-specific endocrine
transitions(Zhao et al., 2016;Mosconi et al., 2017b;Mosconi et al., 2018). One such
transition is the perimenopause, which is a tipping point in neurological aging(Brinton et
al., 2015b). The perimenopausal transition in females is marked by bioenergetic deficit
that causes reduced glucose metabolism marked by downregulation of GLUT3, PDH1
and oxidative phosphorylation(Yao and Brinton, 2012;Yao et al., 2012;Ding et al.,
2013a;Ding et al., 2013b;Yin et al., 2015a). To offset the metabolic decline, there is an
increased dependence on ketone bodies, and to supplement the ketone body demand,
fatty acids are broken down from myelin(Klosinski et al., 2015). Metabolic decline in the
brain and utilization of ketone bodies to supplement neuronal metabolic demand is also
an early sign in the prodromal phase of AD(Yao et al., 2011a;Yao et al., 2011b). Efforts
to characterize the neuro-inflammatory phenotype during this unique endocrine transition
that causes significant neurological and metabolic effects have been scarce. Studies thus
far have characterized the neuro-inflammatory changes in aging female brain using
ovariectomized animals (Sárvári et al., 2012a;Sárvári et al., 2014). While ovariectomy is
an important method used to understand the effect of steroidal hormones on physiology,
the sudden loss of sex steroids, as evidenced in ovariectomy, can have confounding
effects on inflammation, cognition and metabolism(Rocca et al., 2007;Parker et al., 2009).
To model the human perimenopausal transition in rodents, Yin et. al., developed the
perimenopausal animal model – a rodent model that mimics the perimenopausal
40
transition in its emergence of reproductive irregularity followed by acyclicity(Yin et al.,
2015a). Utilizing the perimenopausal animal model, which distinguishes endocrine aging
from chronological aging, in this study, we have tried to elucidate the neuro-inflammatory
phenotype of the perimenopause.
Recent studies conducted to characterize a disease associated microglial phenotype that
contributes to AD and neurodegenerative diseases using familial AD model, reveal that
microglial phenotype is not binary as it was previously thought to be(Song and Colonna,
2018). IFN response, MHC, TGF-beta signaling, APOE, TREM2, TYROBP have been
implicated in a neurodegenerative microglial phenotype (Keren-Shaul et al.,
2017b;Mathys et al., 2017a).
Using the perimenopausal animal model and bulk RNA-Seq of the hippocampus, we
investigated the inflammatory mechanisms that are up/downregulated during specific
endocrine and chronological aging windows, and if a microglial phenotype associated
with disease emerges during the perimenopause. We show that the neuro-inflammatory
phenotype dynamically changes during female aging highlighting the spectrum of glial
phenotype involved. We also investigated the regional vulnerability to female aging by
spatially mapping microglial reactivity marker. The effect of female aging on microglial
phagocytic function, redox status and expression of reactivity markers is also studied.
Ovariectomized models were used to validate the transcriptomic changes evident during
the perimenopause. Clinical data analysis was also conducted to establish translational
41
validity of the rodent findings and investigate sex-specific changes on aging in immune
markers.
Materials & Methods
Animals
All animal studies and procedures were conducted using the National Institutes of Health
guidelines for procedures on laboratory animals. The procedures were approved by the
University of Southern California and University of Arizona Institutional Care and Use
Committee. Rats were housed in a facility with 12h light/dark cycle and food and water
was supplied ad libitum.
Wild-type Sprague Dawley female rats of the ages of 5 months and 8 months were
procured from Envigo laboratories (New Jersey, NJ, US). The animals were characterized
for their reproductive cyclicity for a month using vaginal lavages conducted daily between
9 am and 11 am one week after they arrives, as previously described (Yin et al., 2015a)
. The vaginal lavages were fixed using 95% alcohol and stained using Giemsa stain for
characterization of the cell types. A typical reproductive cycle of the female rat is
comprised of: Estrus (E) phase signified by large cornified cells, Metestrus (M) phase,
which is marked by leukocytes, cornified cells and epithelial cells, Diestrus (D) phase
comprised of leukocytes and Proestrus (P) phase which can be identified by nucleated
epithelial cells. During reproductively competent phases rats’ cycle through the four
phases (E, M, P and D) in 4-5 days, which is referred to as Regular cycling. Rats were
enrolled into Regular 6-month (Reg 6m) or Regular 9-10-month (Reg 9-10m) group, if
42
they had at least two consecutive regular cycles by the time of dissection. During the ages
of 9-10 months, when the rats begin to transition to a reproductively incompetent phase
the lengths of their cycles increase to 6-9 days, which is referred to as the reproductively
irregular phase. Rats were enrolled into the Irregular 9-10-month (Irreg 9-10) group if
they had at least two consecutive irregular cycles. The onset of a reproductively
senescent phase is established by 10-12-day long cycle usually composed of constant
estrus, which is referred to as acyclic. Animals were enrolled into the Acyclic 9-10 month
(Acyclic 9-10m) if they had been on constant estrus for 10 days or more. Animals that did
not meet these criteria were aged further to 15 months. They were reproductively
monitored at 15 months for two weeks, and only animals that were constant estrus were
used for the reproductively senescent group: Acyclic 15 months (Acyclic 15m). Animals
were euthanized on the day of estrus to eliminate the confounding effects of the estrus
cycle. The Reg 6m and Acyclic 15m groups were used for studying the chronological
aging phase preceding and succeeding the endocrinological transition phase. For
understanding the effects of endocrinological aging without the confounding effects of
aging the Reg 9-10m, Irreg 9-10m and Acyc 9-10m groups were used. For RNA-Seq,
epigenetic and histochemical analyses N=4-7/group was used.
Ovariectomy, estradiol treatment and prevention
A total of 40 rats were used for this experiment. Ovariectomy (OVX) or sham (SHAM)
surgery was conducted on 6-month-old Sprague Dawley rats. A subset of ovariectomized
rats were enrolled in the estradiol treatment (E2 Treatment) group. The treatment
paradigm started 2 weeks after the surgery and included 3 weeks of estradiol treatment.
43
Another subset of rats was enrolled in the estradiol prevention (E2 Prevention) group.
The prevention paradigm started the day after the surgery and included 5 weeks of
estradiol treatment. Ovariectomized and sham animals were aged up to 5 weeks after the
surgery.
Brain Dissection
Animals were anesthetized using intraperitoneal injection of ketamine (80 mg/kg) and
xylazine (10 mg/kg). After a midline incision and lateral separation of the cranium, the
whole brain was harvested from the skull. The brain was rapidly dissected on ice using a
procedure previously described (Yin et al., 2015a). Briefly, the meninges were peeled off
following which the hypothalamus, cerebellum and brain stem were removed sequentially.
The two hemispheres of the brain were separated. The cortex was peeled laterally,
revealing the hippocampus which was rolled out. All the harvested brain regions were
frozen on dry ice and stored at -80°C for further processing.
RNA extraction
The left hippocampus was cryopulverized and aliquoted for further processing. The tissue
was homogenized in TRIzol™reagent (Invitrogen™, cat# 15596026) using 0.5 mL of
regent per 20-30 mg of tissue. The tissue was homogenized using Bullet Blender™ and
RNAase-free silicon beads for 3-5 mins at speed 6. The homogenized tissue was
incubated with TRIzol™reagent for 5-7 minutes at RT. Chloroform was added to extract
RNA, using a 1:5 ratio of chloroform: TRIzol™ reagent, and vigorously mixed. The mixture
was centrifuged at 12,000 g, for 15 minutes at 4°C. The upper chloroform phase was
44
separated and further purified using the PureLinkâ
RNA mini kit (Invitrogen™, cat#
12185010) using the manufacturer’s protocol. RNA was eluted using UltraPure™ water
(Invitrogen™, cat# 10977015). RNA concentration and ratios to estimate RNA integrity,
was measured on NanoDrop™ One (Thermo Scientific™,cat# ND-ONE-W)
RNA Sequencing (RNA-Seq)
For conducting unbiased discovery-based assessment of differentially expressed genes
during female aging, RNA-Seq was conducted at Vanderbilt Technologies for Advanced
Genomics (VANTAGE), Vanderbilt University. Quality control was performed on the RNA
samples, and samples with RNA Integrity Index (RIN) > 8 were used for further
processing. Enrichment of poly A tailed RNA (m-RNA and some long non coding RNA)
and cDNA library preparation was conducted using stranded mRNA (poly A selected)
sample prep kit. Sequencing was conducted on NovaSeq600 at 100 bp paired-end.
Demultiplexed FASTQ files were developed containing reads on average at 30 million
reads/sample. The FASTQ files were mapped to cDNA library of the rat genome
(Ensemble release 95) to retrieve the count information using Salmon (Patro et al., 2016).
To generate counts table from the Salmon output TIxmportV.16.0 was used (Soneson et
al., 2015) and DeSEQ2 (Love et al., 2014) was utilized to generate differentially
expressed gene list comprised of normalized read counts for each gene/transcript (DEG).
Using the normalized counts, inter-sample variability was evaluated by conducting
Pearson’s correlation in R to identify outliers. Fold changes were established by
computing the ratio of the experimental group’s average normalized read count versus
the control group’s average normalized read count (Reg 6m).
45
Ingenuity pathway analysis (IPA)
IPA was used to conduct discovery-based assessment of the transcriptomic changes
between female aging groups. DEG (gene id, p-values, false discovery rate and log
expression fold change) files developed from RNA-Seq were uploaded into IPA and only
genes that had a p-value lesser than 0.05 were considered for assessment using core
analysis. Top canonical pathways and predicted activation and inhibition of upstream
regulators generated on the basis of the log expression fold change of the selected genes
were used to guide further analysis. Gene lists based on the canonical pathways most
pertinent to the biology of the central nervous system were curated. Comparison analysis
was conducted to identify systems most affected by female aging.
Heatmap analysis
Based on the gene lists curated from IPA and from the thorough literature survey, fold
changes were computed relative to Regular 6-month group and analyzed using heatmap
function using Morpheus (https://software.broadinstitute.org/morpheus).
Single tube quantitative Real Time-PCR
To further corroborate findings from the RNA-seq and pathway analysis, single-tube PCR
was conducted on select targets. Single-tube PCR was conducted using TaqMan®
probes (Thermo Fisher Scientific) for specific targets: MHC-II (RT1-Db: Rn01429350_m1,
RT1-Da: Rn01427980_m1, RT1-Ba: Rn01428452_m1, RT1-Bb: Rn01429090_g1),
Matrix metallaoprotease-9 (MMP- 9: Rn00579162_m1), costimulatory molecule – (CD74:
Rn00565062_m1), CD70: Rn01527380_m1. A total of 25 ng of m-RNA was used to
46
convert to c-DNA. Further amplification was done on Applied Biosystems
QuantStudio™12K Flex. Ct values generated were converted to ∆Ct, on normalization
with b- actin Ct values. ∆∆Ct values for each sample were generated by normalization
with mean values from Reg 6m group. Fold changes with respect to Reg 6m group were
generated using -2∆∆Ct.
Epigenetic analyses
The CpG sites for DNA methylation were mapped by conducting modified protocol of
reduced representative bisulphite sequencing (RRBS) on hippocampal genomic DNA
(200-500NG), sequence reads were conducted on Illumina Base calling software, and
alignment was done using Zymo Research pipeline ®, as previously described (Bacon
et al., 2019). Top 2000 hyper- and hypomethylated genes (introns, exons and promoter
regions) were analyzed for pertinent neuroinflammatory genes curated for transcriptomic
analysis.
Tissue sectioning & Immunohistochemistry
Animals were anesthetized after an intraperitoneal injection of ketamine (80 mg/kg) and
xylazine (10 mg/kg). Animals were transcardially perfused with phosphobuffered saline
(PBS) for 5 minutes and then perfused-fixed with 4% paraformaldehyde (FujiFilm Wako
Pure Chemical corporation, cat# 163-20145) for 15-20 minutes, until the peripheral limbs
stiffened. A midline incision was made on the cranium and whole brain was harvested
from the skull. Meninges was peeled and the brain was immersion fixed in 4%
paraformaldehyde overnight, then washed and stored in PBS overnight at 4°C. To
47
cryopreserve the brains were transferred to a 20% sucrose solution (in PBS) for 2 days
at 4°C to remove the excess water. The brains were then stored in PBS at 4°C until
sectioned. The brains were embedded in gelatin using the MultiBrainâ
Technology
(Neuroscience Associates, Knoxville, TN) and were coronally sectioned into 40µ thick
sections. The sections were immunostained with Microglial marker Iba-I (FujiFilm Wako
Pure Chemical corporation, cat# 019-19741, 1:500, 4°C, overnight) and reactivity marker
MHC-II (Abcam, cat# ab23990, 1:500, 4°C, overnight) followed by secondary antibodies
anti-rabbit Alexa Fluor 555 (Thermo fisher cat# A-21428,1:500, RT, 1 hr) and anti-mouse
Alexa Fluor 488 (Thermo fisher cat# A-11001,1:500, RT, 1 hr). 3D stacked fluorescent
images were taken on Axiovert 200M Marianas Digital Microscopy Workstation, using
Intelligent Imaging Innovation, SlideBook6 digital microscopy software (Denver, CO). The
images were deconvoluted to the nearest neighbors and a projection was created. Extent
of colocalization of the Cy3 and FITC channel was computed using the Pearson’s
correlation by defining a background region. For the corpus callosum including the
cingulum, 8-10 10x images were obtained for each animal. For the Fimbria and
Hippocampus CA2 region 3-4 10x images were taken per animal. For each group,
average of correlation of the images was computed, and the standard error of mean was
computed. P-values were calculated using one-way ANOVA.
Adult brain dissociation
Animals of the age 6 month, 9-10 months and12 months were used for primary cell culture
and assays. As mentioned earlier, animals from the age groups 6 months and 9-10
months were monitored for reproductive cyclicity for 1 month by vaginal lavages and
48
enrolled into Reg 6m, Reg 9-10m, Irreg 9-10m or Acyc 9-10m groups by the definitions
stated above. The 12-month group was not monitored for reproductive cyclicity, as the
group was considered reproductively senescent due to age. Euthanasia of the animals
was not subjective to the day of cycling in the estrus cycle, to minimize inter-day variability
in the functional assays performed. Animals were sedated and the brain was harvested
from the skull and kept in ice-cold D-PBS (Gibco™, cat# 14287072). The meninges were
peeled off and cortical regions, hippocampi and the white matter were separated on ice.
The brain was dissociated using the Miltenyi Biotec adult brain dissociation kit (cat# 130-
107-677), using the manufacturer’s protocol.
Microglia and astrocyte isolation
Using the MACS Miltenyi Biotec CD11b/c magnetic microbeads (cat# 130-105-634) for
rat microglial cells, microglia from the single neural cell suspension generated from the
adult brain dissociation kit were magnetically tagged. They were isolated using the
manufacturer’s protocol. Briefly, separation column was placed in a magnetic field and
the magnetically tagged cell suspension was applied to the column. The column was
washed and the flow through was collected. The column was removed from the magnetic
field, and the microglia were eluted out. The cells collected from the flow through were
resuspended in media containing DMEM/F-12 (Gibco™, cat# 11039021) and 10% fetal
bovine serum (ATCC, cat# 30-2020) and seeded in T25 flask (Thermo Scientific™, cat#
156367) coated with poly-d-lysine (37°C, 2-3 hours) to selectively culture astrocytes.
Astrocytes were cultured until 80-90% confluency and were trypsinized and seeded for
49
the metabolic flux assays. Isolated microglia were also resuspended in 1mL of DMEM/F-
12 and 10% fetal bovine serum and were seeded for metabolic flux assays.
Metabolic flux assays
Using the XFe24 flux analyzer, mitochondrial function was assessed by the measurement
of oxygen consumption rate (OCR). Primary microglia and astrocytes cultured from 6-
month and 12-month-old animals were seeded in XFe24 cell plates at the densities of
50,000 cells/well and 75,000 cells/well respectively. Assay design and method was
developed from studies published in the literature (Irwin et al., 2011a;Irwin et al.,
2011b;Orihuela et al., 2016;Sarkar et al., 2017). Microglia were cultured in the plate for
3 days after isolation and, astrocytes for 24 hrs. Day before the assay, the calibration
plate was hydrated overnight in a non-CO2 incubator at 37°C. On the day of the assay,
the media was substituted to DMEM (Sigma Aldrich, cat# D5030), which was
supplemented with 25mM glucose, 1mM sodium pyruvate and 2mM Glutamine (Gibco™,
cat# 25030081). The pH of the medium was adjusted to 7.4 ± 0.05. Following the
substitution in medium, the plates were incubated at 37°C in a non-CO2 incubator for 1
hour. The assay includes baseline measurement of OCR, and serial injections of
Oligomycin (1 µM for microglia, 4 µM for astrocytes) (MP Biomedicals, cat# 02151786),
FCCP (1 µM for microglia, 2 µM for astrocytes) (Tocris Bioscience, cat# 0453) and
rotenone/antimycin (0.5 µM for microglia, 1 µM for astrocytes) (Sigma Aldrich, cat# A-
8674). Each injection was followed by the 3 measurements of OCR. This method is used
to compute the baseline respiration, maximal respiration, spare respiratory capacity, ATP
50
production and proton leak. Each assay plate was normalized to protein content to reduce
the effect of variances due to cell seeding, cell death or proliferation.
Measurement of oxidative stress and microglial reactivity
For measurement of microglial oxidative stress and microglial reactivity. MitoSOX™
(Invitrogen ™, cat# M36008) and MHC-II (Miltenyi Biotec, cat# 130-108-776) staining was
conducted by incubating the single cell suspension with 13 µM MitoSOX™ for 10 mins
(37°C, non-CO2 incubator), washed with PBS + 0.5% BSA. The cells were Fc Blocked
using Anti-CD32 antibody (BD Pharmingen, cat# 550271) then immunostained with an
antibody cocktail: CD11b (Miltenyi Biotec, cat# 130-105-273), CD45 (Miltenyi Biotec, cat#
130-111-774), MHC-II (Miltenyi Biotec, cat# 130-108-776) and CD68 (Miltenyi Biotec,
cat# 130-103-364) for 30 mins on ice. CD68 signal did not vary much between aging
female groups and hence was not reported. Flow cytometry was conducted on
MACSQuant Analyzer 10. Data was analyzed using Flowlogic™V7 (Miltenyi Biotec, cat#
150-000-381).
Phagocytic capacity assay
For measurement of phagocytic capacity, single cell suspension was incubated with
pHrodo™ Red S. aureus Bioparticles™ conjugate (Invitrogen™, cat# A10010) at 37°C in
a non-CO2 incubator for 2 hours. Following which, the cell suspension was washed with
PBS +0.5% BSA. The cells were immunostained with CD11b (Miltenyi Biotec, cat# 130-
105-273), CD45 (Miltenyi Biotec, cat# 130-111-774). The cells were analyzed using
51
MACSQuant Analyzer 10. Data was analyzed using Flowlogic™V7 (Miltenyi Biotec, cat#
150-000-381).
Gene Expression Omnibus (GEO) dataset analyses
To further validate the gene expression data from the perimenopausal animal model, a
clinical gene expression GEO dataset GSE11882 was used (Berchtold et al., 2008b).
Using GEO2R tool, the top 250 gene differentially expressed in the hippocampus were
analyzed between 20-34 years (early aging), 35-59 years (mid-aging) and 60 -75 years
(late-aging) in females and males (Table 1 & 2). Using the early aging group as a sex-
matched control, fold changes during mid-aging and late-aging were computed using
GEO2R tool, using default settings, for males and females. Parameters used for analysis
are false discovery rate, p-value and log fold change.
Sex
Age
group
Accession
number
Age
(years)
Female
20-34
years
GSM300219 34
GSM300272 26
GSM300298 30
35-59
years
GSM300187 45
GSM300231 37
GSM300290 44
GSM300294 48
GSM300321 47
60-75
years
GSM300190 74
GSM300197 74
GSM300223 74
GSM300239 70
GSM300243 64
Table 2.1 Age and hippocampal sample information used for female aging analysis
from the GSE11882 dataset
52
Sex
Age
group
Accession
number
Age
Male
20-34
years
GSM300276 20
GSM300280 20
GSM300301 20
GSM300305 33
GSM300309 22
35-59
years
GSM300262 52
GSM300313 42
GSM300317 45
GSM300174 45
60-75
years
GSM300255 69
GSM300286 69
GSM300325 69
GSM300333 75
Table 2.2 Age and hippocampal sample information used for male aging analysis
from the GSE11882 dataset
Statistical analyses
Statistical analysis was conducted using GraphPad Prism version 8.1. One-way ANOVA
was conducted for statistical analysis. Correction for multiple test was conducted by
Tukey’s.
53
Results
Transcriptomic profiling of the hippocampus in the aging female brain
Transcriptomic profiling, conducted by bulk RNA-Seq, of the hippocampus during
chronological and endocrinological aging revealed that genes involved in
neuroinflammation were significantly impacted and each stage of the aging window had
a unique profile. Upregulation of APOE, TREM2, TYROBP, a gene expression signature
which has recently been implicated for a disease associated microglia (DAM) phenotype
(Keren-Shaul et al., 2017b) was evident in the chronological aging phases preceding the
onset of perimenopause (Reg 9m vs Reg 6m) (Figure 2.1). Co-incident with the
expression of the DAM genes, was an upregulation of complement genes, and microglial
reactivity markers such as CD68, AIF1, FSCN1, TGFA and MHC-I and II (RT1-A1, RT1-
DMb). A neuronal marker, CD200, which communicates with microglia through CD200R
was significantly downregulated at outset of chronological aging.
The perimenopause (Irreg 9m vs Reg 6m) was uniquely marked by the downregulation
of genes involved in TGF-b signaling (MAPK3, NOG, TGFB3), DAM and complement
signaling. Interestingly, the perimenopause was characterized with the upregulation in
genes involved in myelin metabolism (ABCA1, VEGFA, NOS3, IDE, PLA2G10) and type
I and type II IFN response (B2M, IRF4, ITGB7, IFNAR2, TXNIP, USP18). The changes
in the gene expression profile of the perimenopause was partially capitulated in the
chronological aging preceding it by an upregulation of B2M, IRF1, IRF2, IRF7.
54
Figure 2.1 Transcriptomic profiling of the hippocampus of the aging female. Bulk
RNA-Seq of the hippocampus revealed that genes involved in microglial reactivity, myelin
metabolism, complement, TGF-b signaling, MHC-I, MHC-II and type I & type II interferon
were affected.
Type I & Type II Interferon Response Genes
Microglial reactivity
Complement
Lipid metabolism
TGF-β signaling
MHC-I
MHC-II
55
Development of acyclicity and reproductive senescence (Acyc 9m) was marked with a
downregulation of type I and type II IFN responses and myelin metabolism from the
reproductively irregular phase. The Acyc 9m group is also characterized by an
upregulation of CD68, ABCA7 and phospholipase PLA2G4B. Much like the Acyc 9m
group, aging post-menopause was marked with an upregulation in CD68, ABCA7 and
PLA2G4B. Distinctively, this phase is marked by an upregulation of the MHC-II genes
(RT1-Ba, RT1-Bb, RT1-Da, RT1-Db1 and RT1-DB2). This data suggests that microglial
and astrocytic reactivity, function and metabolism may be significantly impacted during
female aging.
The RNA-Seq results were validated by conducting real
time quantitative PCR (RT-PCR) on some targets that
were shown to change (RT1-Ba, RT1-Da, RT1-Db).
Other immune markers MMP9, CD70 and CD74 were
also assessed (Figure 2.2). RT-PCR assessment of
gene expression validated the RNA-Seq gene
expression pattern as an upregulation of MHC-II
molecules is seen in aged reproductively senescent
animals.
Figure 2.2 Validation of RNA-
Seq results using RT-PCR
56
Epigenetic modulation of neuroinflammatory genes during female aging
We hypothesized that shift in gene expression observed in the transcriptomic profiling
would be due to modulation in DNA methylation across aging windows. To investigate
that DNA methylation was estimated by the use of reduced-representation of bisulphite
sequencing (RRBS). Methylation (hypo or hypermethylation) of CpG sites of top 2000
genes in the hippocampus was studied to identify genes that contribute to
neuroinflammation and those that were identified by transcriptomic profiling. Table 2.3
lists the genes that were hypo or hypermethylated in their promoter, intron or exon. The
genes included are MHC-II genes (RT1-DB2, RT1-Doa), complement factors (C4a, C4b),
costimulatory molecule CD86, microglial marker TMEM119. Interestingly, the RNA-seq
revealed similar genes changing in expression during the perimenopause.
Reg 6m vs Reg 9m Reg 9m vs Irreg 9m Irreg 9m vs CE 9m Reg 6m vs CE 9m
Hyper
methylated
Hypo
methylated
Hyper
methylated
Hypo
methylated
Hyper
methylated
Hypo
methylated
Hyper
methylated
Hypo
methylated
CD4 RT1-DB2 C2 CD9 CD3g
CD3g Ikbkb
Tnfrsf1b Tnfrsf1a C4a CD86 Nkbil1
CD9 Stat5a
CD3e
C4b Nfkil1
TMEM119 CD40
RT1-m5
RT1-CE15 Mbp
RT1-Doa
Ikbkap Tlr9
Tgbr3
Table 2.3 Epigenetic modulation of neuro-inflammatory genes in the hippocampus.
In each, aging window neuroinflammatory genes are hyper/hypomethylated in their
exons, introns or promoter, thereby affecting the gene expression profile.
57
Microglial reactivity during endocrine aging has spatial selectivity towards white
matter.
On the basis of transcriptomic evidence of impact of female aging on neuroinflammation,
we investigated microglial reactivity across different brain regions. We used a microglial
marker, Ionized calcium binding adaptor molecule (IBA-I, red) and Major
Histocompatibility Complex-II (MHC-II, green) to detect microglial reactivity across
different brain regions (Figure 2.3A, B and C).
On spatial mapping the microglial reactivity across the brain we found that white matter
areas: corpus callosum, cingulum and fimbria, showed a preferential increment in
microglial reactivity as observed by an overexpression of MHC-II by microglia during
reproductively irregular phase. The increment in microglial reactivity in Irreg 9-month
group was not significant in the hippocampus but had a similar trend as the white matter
regions (Figure 2.3 B & C). The upregulation of microglial reactivity preferentially in white
matter tracts suggests myelin uptake and clearance may be impacted during female aging
affecting microglial reactivity.
IBA-I MHC-II
Regular 9-10 m Irregular 9-10 m
IBA-I MHC-II
IBA-I MHC-II
IBA-I MHC-II
IBA-I MHC-II
IBA-I MHC-II
A)
58
Figure 2.3 Spatial mapping of microglial reactivity in the female aging brain. Several
regions in the brain were mapped for microglial reactivity by staining for microglial marker
IBA-I (red) and reactivity marker, MHC-II (green) and DAPI (blue) A) Representative
images demonstrating the colocalization of MHC-II with IBA-I and the spatial distribution
and differences in extent of MHC-II upregulation in the corpus callosum between Reg 9m
and Irreg9m groups. B) Quantification of the colocalization signals between IBA-I and
MHC-II using pearson’s correlation in the in corpus callosum, fimbria and in CA2 of the
hippocampus, C) Representative images demonstrating the spatial mapping of IBA-I and
MHC-II in the corpus callosum, fimbria and in CA2 of the hippocampus. *p≤0.05, **p≤0.01;
calculated using one-way ANOVA. Plotted as mean and error bars represent SEM.
Reg 6m Reg 9m
Irreg 9m
Acyc 9m Acyc 15m
Corpus callosum HippocampusCA2 Fimbria
Reg 6 month
Reg 9 month
Irreg 9 month
Acyclic 9 month
Acyclic 15 month
0.0
0.1
0.2
0.3
0.4
R squared (pearsons correlation)
**
*
Corpus callosum
Reg 6 month
Reg 9 month
Irreg 9 month
Acyclic 9 month
Acyclic 15 month
0.0
0.1
0.2
0.3
R squared (pearsons correlation)
*
Fimbria
Reg 6 month
Reg 9 month
Irreg 9 month
Acyclic 9 month
Acyclic 15 month
0.0
0.1
0.2
0.3
0.4
R squared (pearsons correlation)
Hippocampus CA2
IBA-I, MHC-II,
DAPI
B)
C)
59
Microglial oxidative stress and phagocytic capacity are impacted by female
endocrine aging
During the reproductively irregular phase, we observed an upregulation of Type I&II
interferon response and increased microglial reactivity in white matter. Based on this
evidence, we hypothesized that the transition to reproductive senescence and aging may
impact phagocytic function and production of free radicals. To study this, single cell
suspension from dissociated cortices, hippocampi, fimbria and corpus callosum were
pooled together. These tissues were harvested from Reg 6m, Reg 9m, Irreg 9m, Acyc 9m
and Acyc 12m animal groups to conduct flow cytometry. Microglial cells were gated for
by first isolating the total cells (Figure 2.4A), and then singlets (Figure 2.4B) and then
gating for CD11b (FITC) high and CD45 (VioBlue) Intermediate (Figure 2.4C).
Microglial mitochondrial generation of reactive oxygen species (ROS) was assessed by
measurement of uptake of MitoSOX™ by gating for cells that were positive (Figure 2.4D).
The uptake of MitoSOX™ was highest in the Irreg 9m group, and was significantly
increased in comparison to Reg 6m, Reg 9m and Acyc 9m groups (Figure 2.4G). A similar
gating strategy was applied for determining phagocytic capacity of microglia (Figure
2.4E). Coincident with the increased mitochondrial ROS production was a reduction in
phagocytic capacity in the Irreg 9m group, evidenced by the reduction in percentage of
cells phagocytosing pHrodo™ S. aureus Bioparticle conjugates (Figure 2.4H).
Measurement of microglial reactivity was made by quantifying the expression of MHC-II
expressed by microglia (Figure 2.4F). The Acyc 12m group had the highest expression
of MHC-II and phagocytic capacity (Figure 2.4I). The data suggests that microglial
60
phagocytic capacity and mitochondrial oxidative stress are impacted by female endocrine
aging.
Figure 2.4 Flow cytometry analysis of microglial oxidative stress and function.
Figures A-F are representative figures demonstrating the gating strategy. Microglia were
selected for from the single cell suspension by first gating for A) Total cells from the
suspension, then, B) singlets and C) then gating for microglia using CD11b (FITC) high
and CD45 (Vio-Blue) intermediate as a gating strategy. Microglial cells expressing CD11b
(FITC) were used for gating for D) Mitochondrial oxidative stress (PE) E) output of
phagocytic capacity (PE) and F) MHC-II expression (PE-Vio770). G) Quantification of
Mitochondrial oxidative stress H) Quantification of phagocytic capacity I) Quantification of
MHC-II expression. Plotted as mean and error bars represent SEM. *p≤0.05, **p≤0.01,
***p≤ 0.001, calculated using one-way ANOVA.
Reg 6m
Reg 9m
Irreg 9m
Acyc 9m
Acyc 12m
50
60
70
80
90
100
% of Microglia cells
*
***
*
Reg 6m
Reg 9m
Irreg 9m
Acyc 9m
Acyc 12m
0
2
4
6
% of Microglia cells
**
*
*
Reg 6m
Reg 9m
Irreg 9m
Acyc 9m
Acyc 12m
0
2
4
6
8
% of Microglia cells
*
A)
B) C)
D) E) F)
G)
H) I)
Mitochondrial Superoxide
production
Microglial Phagocytic
Capacity
Microglial MHC-II
expression
61
Astrocytic and microglial mitochondrial function are differentially impacted due to
female aging
To study the impact of female aging on glial mitochondrial respiratory capacity, primary
cultures of astrocytes and microglia from 6-month-old and 12-month-old animals were
used.
The cumulative effects of aging and the transition to reproductive senescence significantly
increased astrocytic maximal respiration and spare respiratory capacity (Figure 2.5A).
Basal respiration, proton leak and ATP production were not impacted. In the case of
microglia, non-mitochondrial oxygen consumption was significantly increased on age.
Basal respiration, maximal respiration and ATP production though not significantly
affected, trended to be higher in younger 6-month-old animals (Figure 2.5B).
This data suggests that astrocytes and microglia reacted differently to aging. The
mechanisms to compensate for increased energy demand due to increase in
inflammation had a cell-specific difference, addressing their broader role in initiating,
amplifying and regulating inflammatory signals.
62
Figure 2.5 Assessment of effect of age mitochondrial respiratory capacity in glial
cells. Seahorse XFe24 analyzer was used to conduct metabolic flux assays on astrocytes
and microglia. A) Astrocytes: Left panel: oxygen consumption rate normalized to protein
in astrocytes, Right panel: derived mitochondrial function parameters. B) Microglia: Left
panel: oxygen consumption rate normalized to protein in microglia, Right panel: derived
mitochondrial function parameters. Plotted as mean, and error bars show SEM. p≤0.05,
****p≤ 0.0001; calculated by using unpaired student t-test.
Estradiol regulate neuroinflammation in the aging female brain
To investigate if the effect on neuroinflammation in the hippocampal transcriptome
observed during endocrinological and chronological aging, was due to depletion in
steroidal hormones, we ovariectomized (OVX) 6-month-old female rats. To see if estradiol
mitigates neuroinflammation and rescues the phenotype, we used an estradiol treatment
(initiated 2 weeks after OVX, and duration of treatment: 3 weeks), and prevention
paradigm (initiated a day after OVX, and duration of treatment: 5 weeks).
0 20 40 60 80 100
0
200
400
600
800
1000
Time (mins)
OCR (pmole/min/protein)
6 months
12 months
Non Mitochondrial Oxygen Consumption
Basal Respiration
Maximal Respiration
Proton Leak
ATP Production
Spare Respiratory Capacity
0
20
40
60
80
OCR (pmole/min/protein)
6 months
12 months
*
0 20 40 60 80 100
0
20
40
60
80
100
Time (mins)
OCR (pmole/min/protein)
6 months
12 months
A)
B)
Non Mitochondrial Oxygen Consumption
Basal Respiration
Maximal Respiration
Proton Leak
ATP Production
Spare Respiratory Capacity
0
200
400
600
800
OCR (pmole/min/protein)
6 months
12 months
****
****
63
Bulk RNA-Seq on the hippocampus, revealed that
ovariectomizing the animals causes an upregulation of
genes involved in type I and type II IFN response (B2M,
IRF7, IFGNGR1, FCER1G) a gene expression pattern
previously observed in the Irreg 9m group (Figure
2.6A). Ovariectomy also causes an upregulation of
genes involved in the complement system and TREM2,
which was previously observed in the chronological
aging phases preceding the perimenopause (Figure
2.6B). The estradiol treatment paradigm somewhat
mitigated the effect of ovariectomy. Some genes
involved in the IFN response (IFIT3, IFNGR1, MHC-I:
RT1-A2), C1S and TREM2 remained unaffected by
estradiol treatment. Whereas, estradiol prevention
caused a more robust recovery, by down regulating
genes involved in the IFN response, complement
system (Figure 2.6A &B).
These data suggest that the lack of steroidal hormones
causes an upregulation of type I and type II IFN
response. Estradiol treatment to some extent and
Figure 2.6 Estradiol regulates
neuroinflammation in the aging
female brain: A) Heatmap
analysis of interferon response
genes in the hippocampus,
showing ovariectomy causes an
upregulation. B) Heatmap analysis
of genes contributing to
inflammation, myelin metabolism
and neuronal markers indicating
ovariectomy impacts the systems.
All genes shown in heatmap are
p<0.05 for OVX vs SHAM.
A)
B)
64
estradiol prevention to a greater extent can mitigate the upregulation of type I & type II
IFN response genes observed on ovariectomy.
Clinical validation of the neuro-inflammatory gene expression profile and sex
differences in transcriptional profiling of the hippocampus
To establish the clinical validation of the inflammatory gene expression profile from the
perimenopausal animal model, we conducted a hippocampal gene expression analysis
on geodata set GSE11882(Su and Freeman, 2009) using the GEO2R tool on three
groups of females of different ages. Three age groups, 20-34 years, 35-59 years, 60-75
years, designed to capture pre-menopausal/early aging, peri-to-menopausal/mid-aging
and post-menopausal/late aging respectively were investigated for differentially
expressed top 250 genes. Of the top 250 genes, several were related in function to the
immune system. Consistent with the preclinical findings, expression of MHC- I and II,
increased on aging. MHC II expression especially, HLA-DMA, HLA-DMB, HLA-DQA1,
HLA-DRB1, HLA-DRB6, was increased during mid-aging (35-59 years). Late aging phase
in females also caused an increased expression of MHC-II molecules: HLA-DMA, HLA-
DQB1, HLA-DRA, HLA-DRB6 by several folds (Figure 2.7A).
In males, a similar pattern with MHC-II expression was seen but, the increased
expression was not significant and was to a lesser extent during mid-aging phases.
Significant changes in the males in MHC-II was evident during late aging between 60-75
years in HLA-DRB1(Figure 2.7B). Consistent to what was reported by the authors,
analysis of GSE11882 by GEO2R tool and pathway mapping revealed that oxidative
65
phosphorylation was more significantly impacted in males during the later aging phase
(Su and Freeman, 2009)(data not shown).
Figure 2.7 Clinical Validation of Neuro-inflammatory gene expression profile.
Hippocampal gene expression analysis was conducted on GSE11882 on stratified age
groups in females and males. Fold changes computed with the help of GEO2R tool are
shown. MHC-II expression stratified by age in A) females and B) males. Markers for
microglial and neuron communication stratified by age in C) females and D) males.
Plotted as fold change with respect to 20-34 years. *p≤0.05, **p≤0.01, ***p≤ 0.001,
calculated using one-way ANOVA followed by unpaired student t-test.
Microglial markers and neuronal communication markers that were detected to have
changed in the rodent aging analysis, also were affected during aging in males and
females. Microglial phagocytic receptor TREM2 and TYROBP increased with aging in
both males and females, but it was to a greater extent in females (Figure 2.7C &D).
HLA-A
HLA-B
HLA-C
HLA-DMA
HLA-DMB
HLA-DOA
HLA-DOB
HLA-DQA1
HLA-DQB1
HLA-DRA
HLA-DRB1
HLA-DRB6
0
1
2
3
4
8
10
Fold change (with respect to 20-34 years)
20-34 years
35-59 years
60-75 years
*
*
**
** *
**
*
**
**
*
**
**
Females
HLA-A
HLA-B
HLA-C
HLA-DMA
HLA-DMB
HLA-DOA
HLA-DOB
HLA-DQA1
HLA-DQB1
HLA-DRA
HLA-DRB1
HLA-DRB6
0
1
2
3
4
8
10
Fold change (with respect to 20-34 years)
Males
20-34 years
35-59 years
60-75 years
*
CD200 CX3CL1 CX3CR1 TREM2 TYROBP NLRP3
0
1
2
3
4
5
Fold change (with respect to 20-34 years)
20-34 years
35-59 years
60-75 years
*
**
**
CD200 CX3CL1 CX3CR1 TREM2 TYROBP NLRP3
0
1
2
3
4
5
Fold change (with respect to 20-34 years)
20-34 years
35-59 years
60-75 years
*
*
*
A)
B)
C)
D)
66
Interestingly in females, microglial marker CX3CR1 was significantly upregulated during
mid-aging phase 35-59 years (Figure 2.7C). The ligand for CX3CR1 expressed on
neurons, CX3CL1 showed a similar pattern in expression as CX3CR1 in both males and
females (Figure 2.7C &D). CD200 neuronal communication marker expression was not
significantly affected but followed a similar pattern of expression in male and female
aging, where it increased during mid-aging and decreased during late-aging phases
(Figure 2.7C &D).
These data suggest that the rodent model mimics clinical aging. It also suggests that
aging with respect to inflammation in the hippocampus differs in males and females.
Extent of MHC-II upregulation is much higher in females than males. And, the top 250
differentially expressed genes includes several that affect immune function, amongst
which several are MHC-II molecules, indicating that the IFN and MHC signaling have a
greater predisposition to estrogen decline in the female brain.
Discussion
This study is unique as it is the first to characterize the effect of the female aging and the
transition to reproductive senescence on the inflammatory phenotype in areas of the brain
that are most impacted in AD. By using a model that more closely resembles the human
perimenopausal transition, we have tried to discern the individual effects of chronological
and endocrinological female aging on glial gene expression, function and metabolism.
67
Hippocampal transcriptomic profiling shows that inflammation is not a linear continuum in
female aging. Each aging window is typified by a distinctive inflammatory program. Aging
preceding the perimenopause is marked by a distinct upregulation in a subset of genes
implicated in disease associated microglial phenotype (APOE, TREM2, TYROBP, B2M)
and microglial reactivity markers AIF1, C3, CD68. This chronological aging window
causes a sustained downregulation in CD200, which is a neuronal marker that
participates in checkpoint inhibition of microglial phagocytic responses. The
reproductively irregular phase is typified by the upregulation type I and II IFN response
genes (B2M, IRF1, IRF4, IFNAR2, adhesion molecule VCAM-1) and downregulation of
TGF-b signaling, APOE, TREM2, TYROBP and microglial reactivity markers compared
to the Reg 9m group. The onset of menopause causes a downregulation of type I & II
IFN response genes and MHC-II genes compared to the irregular cycling 9m group. Aging
post-menopause is specifically marked by upregulation of MHC-II genes. There also
seems to be an epigenetic control of inflammatory gene expression, as several genes
partaking in the immune response were hypo-or hypermethylated during the course of
endocrine aging, suggesting another control of the immune function.
Estrogen in the brain and circulation reduce during female aging(Judd and Fournet,
1994;Su and Freeman, 2009;Hoyt and Falconi, 2015). Estrogen is a master regulator
controlling neuronal and glial bioenergetics, neurogenesis, microglial inflammation and
glucose metabolism(Rettberg et al., 2014a). The inflammatory gene expression pattern
emerging during female aging may be a result of the cascading effect of declining
estradiol levels in female aging, which causes a bioenergetic deficit, that results in an
68
inability to meet neuronal metabolic demands, thereby resulting in a shift to ketogenic fuel
source and utilization of white matter to generate ketone bodies(Klosinski et al., 2015).
Evidence of regulation of the inflammatory phenotype by estradiol is imminent in the
hippocampal transcriptomic profiling of ovariectomized animals. Ovariectomizing animals
at 6 months of age caused the manifestation of gene expression pattern combinative of
aging preceding perimenopause and perimenopause. Ovariectomy caused the
upregulation of type I and II IFN response, MHC-I genes, C4b, TREM2. The upregulation
of IFN response mimics what was previously observed in Irregular 9-10-month animals.
While the upregulation of TREM2, C4b, CLEC7a matches aging preceding the
perimenopause.
While this mechanistically validates that steroidal hormones regulate the neuro-
inflammatory phenotype it also hints that perturbing natural aging by ovariectomy causes
an overt inflammatory reaction that combines inflammatory programs of chronological and
endocrinological aging. Interestingly, ovariectomy did not cause significant changes in
MHC-II expression indicating that upregulation of MHC-II expression in aged reproductive
senescent animals was in part caused by aging.
The estradiol prevention paradigm rescued the inflammatory phenotype developed after
ovariectomy, while estradiol treatment program did so to a lesser extent. This indicates
that the declining levels of estradiol affected the neuro-inflammatory phenotype caused
due to ovariectomy and early intervention can mitigate the development of inflammation.
The emergence of IFN response as a consistent gene expression pattern in the
69
perimenopausal animal model and ovariectomized animals highlights its importance in
estrogen dysregulation in the brain. Microglial activation of T cells mediated by type I and
type II IFN is also at the core of pathophysiology of multiple sclerosis, an auto-immune
disorder more prevalent in women than men(Schetters et al., 2018). Interestingly, IFN-g
levels in women, increase during perimenopause, but decline later at onset of
menopause, which correlates with the gene expression pattern observed in the
hippocampus(Teitelbaum, 2004;Straub, 2007;Goetzl et al., 2010a). Mitigation of
inflammation and especially IFN response genes by estradiol prevention paradigm can
be helpful in disease state specific targeting of inflammation in prodromal states of AD.
The differences observed on estradiol treatment and prevention can also be addressed
by the caveat in the experimental design. To maintain consistency in age between the
two groups, the estradiol treatment spanned for 3 weeks whereas prevention paradigm
spanned for 5 weeks. As the estradiol treatment was not continued as long, the
differences in gene expression can possibly be attributed to that.
Recent characterization of models of familial Alzheimer’s disease and neurodegeneration
(5xFAD, CK-p25) have revealed a distinct phenotype of microglia, which is disease state
specific and involves the participation of: type I and type II IFN response genes, MHC,
APOE, TREM2, TYROBP and TGF-b signaling(Keren-Shaul et al., 2017b;Mathys et al.,
2017a). Female aging and specifically, the perimenopausal transition, has been not yet
associated with a molecular signature of the microglia that is associated with
neurodegeneration. Of note, the upregulation of type I and type II IFN response genes
70
during the reproductively irregular phase followed by the subsequent upregulation of
MHC-II molecules in the reproductively senescent aged animals matches the “late
response microglia” in the CK-p25 model. Consistency in the expression of “late stage
microglia” between neurodegeneration and the menopausal transition signifies that
neurodegenerative programs are exhibited as a part of natural aging, possibly indicating
the onset of a prodromal state. Given that each stage during the chronological and
endocrinological aging is typified by a unique inflammatory program, therapeutic
intervention targeting a specific aging window for prevention of AD might be considered.
Interestingly, in the perimenopausal animal model, white matter tracts showed a distinct
upregulation of microglial reactivity. During the reproductively irregular phase we see an
increased expression of MHC-II in the corpus callosum, cingulum and fimbria. It is during
the same aging window we also see an upregulation of the type I and type II IFN in the
hippocampus. Given the co-occurrence of the two events and that MHC-II expression is
regulated by IFN-g, causal and temporal relationships between the two need to be
examined. Microglia are known to present myelin antigens to T-cells after phagocytosis
by upregulating MHC-II expression(Cash et al., 1993). Aging, specifically female aging,
has been associated increased dependency on ketone bodies as a fuel source(Ding et
al., 2013a;Ding et al., 2013b). Previous studies have shown that to generate ketone
bodies in the brain, astrocytes depend upon the fatty acids from myelin(Klosinski et al.,
2015). Myelin integrity in the female brain deteriorates due to a shift in metabolic fuel.
Reduction in microglial phagocytosis is associated with age-related increase in myelin
debris(Safaiyan et al., 2016). Thus, it can be speculated that the increase in MHC-II
71
expression in white matter tracts can be due to an increase in myelin debris, which in-turn
could be causing an upregulation of the interferon responses in the hippocampus.
Glial mitochondrial function is also affected by female aging. Astrocytic maximal
respiration and spare respiratory capacity, and microglial non-mitochondrial oxygen
consumption increased with age. The disparity between the cellular mitochondrial
response to aging can be addressed by their broader function in the brain. Microglia are
the initiator of an inflammatory response and astrocytes amplify those inflammatory
signals and work to meet neuronal metabolic demands. Increase in maximal respiration
and spare respiratory capacity in astrocytes with age is indicative of increase of age-
related inflammation as an energy expending exercise (Jiang and Cadenas, 2014). The
functional duality posed by astrocytes, which also includes increased beta oxidation to
generate ketone bodies, is addressed by increasing respiratory capacity. Contrarily,
microglia respond to aging by increasing the non-mitochondrial oxygen consumption,
which possibly indicates increase in glycolysis and free radical production. Consistent
with peripheral immune cells, microglia also react to inflammatory challenges by
increasing glycolysis and reducing mitochondrial respiration.
Incidentally, microglial isolation from grey and white matter areas and subsequent
functional profiling showed that mitochondrial free radical production increased, and
phagocytosis decreased in reproductively irregular animals. While MHC-II expression
was the highest in the reproductively senescent aged 12-month animals. The variances
between spatial mapping and flow cytometry analysis in MHC-II expression can be
72
explained by the technical caveat that microglia isolated for flow cytometry were from
cortices, hippocampi, corpus callosum and fimbria unlike the immunohistochemical
analysis where most analysis indicated the highest expression of MHC-II in the
reproductively irregular animals in the white matter tracts. And, the reproductively
senescent aged age groups were different in both analyses.
Clinical gene expression analysis of differentially expressed genes in females during
aging (20-34 years – reproductively regular, 35-59 years- reproductively irregular and 60-
75 years – aged and reproductively senescent) shows that rodent analysis mimics human
aging. As endocrine aging status of the clinical samples were unavailable, stratification of
groups was done on average age of perimenopausal to menopausal transition. We are
therefore unable to study the endocrine state-wise associated transitions in the
inflammatory phenotype as done in the rodents. Incremental upregulation of MHC-II and
IFN (data not shown) with age in females is key in responding to evolving physiological
conditions in the brain after menopause. Interestingly, the list of the top 250 genes in
males stratified similarly, did not include MHC and IFN response genes (data not shown).
The sex difference in the gene expression profile in the hippocampus on aging indicates
that, even though inflammation is a significant part of aging, it contributes differently to
the aging process in men and women.
Sex differences in immunity on aging is well-documented. Increase in age-related in
inflammation, while evident in both sexes, is higher in females (Klein and Flanagan,
2016). Shift in CD4/CD8 ratios and increase CD4 T cells is evident during mid-late aging
73
phases of aging in females. During this time, robust antibody response on vaccination is
also seen in females. IFN-g levels are higher in females than males (Klein and Flanagan,
2016). But, evidence around sex differences on aging in microglia is sparse. Sex
difference studies in microglia have centered on embryonic and early adulthood stages.
MHC-II expression in early adulthood in mice is higher in males than females. Contrarily,
clinical analysis conducted in this study to evaluate the effects of aging use sex-matched
young controls, reveals that extent of MHC-II upregulation was higher in females than
males during mid and late aging phases. Moreover, the females also had a higher
expression of TREM2 and TYROBP, genes implicated in disease associated microglia.
Consistent with the observation of the authors, we see that while oxidative
phosphorylation reduces in males and females on aging, but it is greater in males
(Berchtold et al., 2008b). The sex differences in the extent of upregulation of microglial
reactivity markers and downregulation of oxidative phosphorylation points to differences
in aging paradigms in the male and female brain.
This study is unique to characterize the effects of female aging on inflammation, but also
leaves several unanswered questions. Causal relationships between the upregulation of
interferon response with metabolic dysfunction needs to be explored experimentally. And,
the role microglial and astrocytic cross-talk in female aging needs to be further
characterized. Interventional studies using drugs targeting MHC-II and IFN signaling and
resulting effects on cognition, white matter integrity and inflammation need to be
conducted to validate the effect of interferon signaling in the aging female brain. The IFN
mediated signaling and antigen presenting pathways are dysregulated in multiple
74
sclerosis, the etiology of which worsens after menopause, aging trajectories that lead to
the development of autoimmune disorders or neurodegenerative disorders such as AD
need to be understood using translational animal models
Higher prevalence of AD in females needs to be understood from the perspective of aging
in females, especially transition states that are unique to females. In this study, we show
that dynamic shifts happen in the neuroinflammatory phenotype across various stages of
endocrine and chronological female aging in the brain. This study shows that co-incident
with this metabolic dysregulation perimenopausal transition in female brain is the
upregulation of IFN response and MHC-II expression in the hippocampus and white
matter tracts. Glial redox status, phagocytic capacity and respiratory capacity are also
affected due to female aging process. The transitions in inflammatory phenotype and
upregulation of IFN response genes is validated using ovariectomized and clinical
models. These data highlight that the perimenopausal transition causes a significant shift
in neuroinflammation, affecting glial function and metabolism, and also signifies that this
phase can be targeted therapeutically for early interventions.
75
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Chapter 3: Sex differences in metabolic aging of the brain in humanized ApoE4
Knock-in rat model
Abstract
Women APOEe4 (ApoE4) carriers are susceptible to accelerated aging and undergo
faster rates of cognitive decline. Metabolic dysfunction precedes the symptomatic
cognitive decline in Alzheimer’s disease (AD). We hypothesized that the sexual
dimorphism in ApoE4 carriers would be evident in metabolic aging and in women, it would
be susceptible to endocrine aging. To study this we used humanized ApoE4gene knock-
in rat model and conducted a longitudinal study. ApoE4 and wildtype (WT), male and
female rats, were assessed at four aging windows: 7-8 months (m), 9-10 m, 12-13 m and
15-16 m. Reproductive cyclicity in female rats was assessed by vaginal lavages. During
the longitudinal follow-up, we conducted
18
FDG-microPET/CT(18-fludeoxyglucose micro
Positron Emission Tomography/Computational Tomography) to measure brain glucose
uptake. Hippocampal RNA-Seq and cortical metabolomic profiles were generated at end-
of-study. Magnetic resonance imaging was conducted to assess regional brain volumes
and myelin integrity. ApoE4 females had a significantly lower brain
18
FDG uptake than
ApoE4 males. The decline in glucose uptake in ApoE4 females worsened with the onset
reproductive senescence. Quantitative brain volume assessment showed that white
matter areas in the ApoE4female brain trended to be bigger whereas grey matter areas
trended to be smaller than ApoE4 males. White matter microstructural assessment
showed that ApoE4 females had significantly lower fractional anisotropy than males,
possibly indicating lower myelin integrity. Transcriptomic and metabolomic analyses
support imaging findings, as gene expression for oxidative phosphorylation and
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mitochondrial genes was upregulated, and TCA metabolites were significantly higher in
the males in comparison to females. This longitudinal study demonstrates that ApoE4in
combination with endocrine transition worsens the metabolic trajectory in females
predisposing them to cognitive decline. [This abstract was previously published in Mishra
A, Mao Z, Do L, Bernstein AS, Yin F, Trouard TP, Brinton RD, Sex Differences in
Metabolic Aging of the Brain in Humanized ApoE4 Knock-in Rats: Implications for
Alzheimer’s disease [abstract]. Organization for the Study of Sex Differences
(www.ossdweb.org), Washington D.C., 2019](2019)
Introduction
Women APOEe4 (ApoE4) carriers are at increased risk of developing Alzheimer’s.
Progression of disease severity is worse in women ApoE4 carriers than men(Sohn et al.,
2018). Yet few studies have been conducted to investigate the underlying mechanism
that render the sex difference.
Apolipoprotein E (ApoE) functions cholesterol shuttling molecule that is largely expressed
by astrocytes and microglia in the brain, and by macrophages in the periphery. The ApoE4
isoform causes conformational changes in ApoE that affects its primary function(Liu et
al., 2013). The ApoE4 genotype is also associated with lower levels of ApoE, which in
turn affects the clearance of triglycerides from plasma(Riedel et al., 2016).
Metabolic dysfunction and reduction in glucose metabolism precede the onset of
Alzheimer’s disease. ApoE4 carriers have been shown to have reduced brain glucose
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uptake since young age(Reiman et al., 2004). More recently, it has been reported that
ApoE in neurons derived from mice with a targeted replacement of humanized ApoE4
gene showed inhibited insulin signaling(Zhao et al., 2017). Moreover, studies have shown
that ApoE4 carriers depend on lipids as a fuel source(Arbones-Mainar et al., 2016).
Female ApoE4 mice have been shown to have a greater propensity to use ketone bodies
as a fuel source(Wu et al., 2018).
Given that there is a disparity in risk conferred by ApoE4 based on chromosomal sex and
that metabolic dysfunction is one of the earliest indicators of AD, we hypothesized that
the interaction of ApoE4 with chromosomal sex specific aging and endocrine transition
will render a unique metabolic profile of aging.
To address this hypothesis, we conducted a longitudinal follow-up study in rats with
humanized ApoE4 knock-in and used wild type animals as controls. During the course of
this study we characterized the peripheral metabolic profile and brain glucose metabolism
with respect to endocrine aging windows in the females. Through this longitudinal study
we were able to ascertain sex differences in the metabolic profile. We also conducted a
cross sectional assessment at the end-of-study to elucidate molecular markers and
metabolites that are indicative of sex specific aging in ApoE4 animals. By the use
magnetic resonance imaging techniques, we investigated the interaction of ApoE4 and
sex with myelin integrity.
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Materials and Methods
Animals
All animal studies and procedures were conducted using the National Institutes of Health
guidelines for procedures on laboratory animals. The procedures were approved by the
University of Southern California Institutional Care and Use Committee.
Female and male Wild Type (WT) and humanized APOEe4 (ApoE4) Knock-in Sprague
Dawley rats were procured from Horizon discovery (Cambridge, United Kingdom). A total
of 80 animals were procured, 20 per group. Longitudinal assessment of metabolic
parameters was started at 6 months age and was conducted for 9 months until 15-16
months. Longitudinal assessment was conducted on the following aging windows 7-8
months, 9-10 months, 12-13 months and 15-16 months. To ascertain the metabolic
phenotype elucidated by the ApoE4 genotype the longitudinal assessment comprised of
monthly weight measurements for all animals, vaginal cytology on all except the 15-16
months age group, tail vein blood draws metabolic characterization and measurement of
brain glucose uptake. The 15-16 month age marked the end-of-study, cross-sectional
analysis was conducted at that time point.
Female rats were assessed for reproductive cyclicity at 6-7 months, 9-10 months and 12-
13 months by conducting daily vaginal lavages between 9.00 -11.00 am. The lavages
were conducted for 2 weeks to estimate cycling status of the animal as previously
described. Animals were classified as regular cycling if they had two consecutive cycles
that spanned 4-5 days from estrus to estrus, irregular if they had 8-10 day cycles between
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estrus to estrus and acyclic if they were on estrus for 10-12 days. The lavages were fixed
using 95% alcohol and stained with Geimsa for characterization of the cells observed in
the lavage.
Plasma collection
Blood collected through tail vein blood collection, was collected in EDTA coated
polypropylene microcentrifuge tubes. The blood was allowed to sit at RT for at least 15-
20 mins, after it which it was centrifuged at 1500 rpm for 20 mins at RT. Plasma was
pipetted off and stored at -80°C until further processing.
Plasma triglyceride, ketone body and insulin measurement
Triglycerides in plasma were measured using Triglyceride Colorimetric Assay kit
(Cayman chemicals, cat# 10010303), ketone bodies were measured using b-hydroxy
butyrate Colorimetric Assay kit (Cayman chemicals, cat# 700190) and insulin was
measured using Ultra Sensitive Rat Insulin ELISA kit (Crystal chem, cat# 90060) following
the manufacturer’s protocol. All samples were run in duplicates.
Glucose tolerance test
Animals were faster over-night. Micro-volume blood samples by the means of tail-vein
puncture were collected before the injection of d-glucose (2mg/kg) to establish fasting
blood glucose. Following which, micro-volume blood samples were taken at 5, 15, 30,
60 and 90 minutes of injection to measure glucose levels using glucometer. For each
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group, average concentration of glucose at the timepoints was plotted. Area under the
curve was calculated and plotted. N =14-20 animals/group.
Brain Glucose uptake measurement
Animals were fasted overnight. Brain glucose uptake measurement was conducted by
using micro positron emission tomography (PET) and computed tomography (CT) using
2-Deoxy-2-[18F] fluoroglucose (FDG) as a radiotracer, using a protocol previously
described. PET and CT scans were conducted using Siemens Inveon™ (Siemens
Medical Solutions USA, Knoxville, TN) which integrates PET and CT imaging. Fasting
blood glucose was measured by collecting micro volume blood samples from tail vein
punctures before the injection to ensure that there was no significant difference in glucose
levels at baseline and therefore not confounding the findings of the study. Animals were
anesthetized and injected with a predetermined and measured dose of FDG. Residual
radioactivity was measured and documented. After 1 hour from the injection time, the
animals were placed in the imaging chamber of the PET/CT scanner to immobilize them
and where they were continued to be anesthetized with 2% isoflurane in oxygen. The
imaging chamber was equipped with a heating pad to maintain body temperature. PET
imaging of the brain (10.8 cm transaxial and 8 cm axial field of view) was conducted and
CT imaging was conducted sequentially.
Co-registration of the PET with CT images were conducted using AMIDE software.
Estimation of brain glucose uptake was done by measurement of standard uptake value
(SUV) in the region of interest (brain without the cerebellum) which is elected for in
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AMIDE. SUV is a measure for the concentration of the radiotracer found in the brain at
the specific time, assuming that the distribution of the radiotracer throughout the body has
been equal. It is value that has been adjusted for weight of the animal and the decay of
the radiotracer.
Brain dissection
Animals at 15-16 months, were anesthetized using intraperitoneal injection of ketamine
(80 mg/kg) and xylazine (10 mg/kg). After a midline incision and lateral separation of the
cranium, the whole brain was harvested from the skull. The brain was rapidly dissected
on ice using a procedure previously described (Yin et al., 2015a). Briefly, the meninges
were peeled off from the brain, following which the hypothalamus, cerebellum and brain
stem were removed sequentially. The two hemispheres of the brain were separated. In
the cohort of rats used for longitudinal assessment for brain glucose uptake
measurement, the right half of the brain was stored in mitochondrial isolation buffer (MIB)
for mitochondrial isolation, the results for which have not been shown in this study due to
extensive inter-day variability. The cortex was peeled laterally, revealing the hippocampus
which was rolled out. All the harvested brain regions were frozen on dry ice and stored at
-80°C for further processing.
RNA isolation
The left hippocampus was cryopulverized and aliquoted for further processing. The tissue
was homogenized in TRIzol™reagent (Invitrogen™, cat# 15596026) using 0.5 mL of
regent per 20-30 mg of tissue. The tissue was homogenized using Bullet Blender™ and
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RNAase-free silicon beads for 3-5 mins at speed 6. The homogenized tissue was
incubated with TRIzol™reagent for 5-7 minutes at RT. Chloroform was added to extract
RNA, using a 1:5 ratio of chloroform: TRIzol™ reagent, and vigorously mixed. The mixture
was centrifuged at 12,000 g, for 15 minutes at 4°C. The upper chloroform phase was
separated and further purified using the PureLinkâ
RNA mini kit (Invitrogen™, cat#
12185010) using the manufacturer’s protocol. RNA was eluted using UltraPure™ water
(Invitrogen™, cat# 10977015). RNA concentration and ratios to estimate RNA integrity,
was measured on NanoDrop™ One (Thermo Scientific™,cat# ND-ONE-W)
RNA Sequencing (RNA-Seq)
For conducting unbiased discovery-based assessment of differentially expressed genes
during female aging, RNA-Seq was conducted at Vanderbilt Technologies for Advanced
Genomics (VANTAGE), Vanderbilt University. Quality control was performed on the RNA
samples, and samples with RNA Integrity Index (RIN) > 8 were used for further
processing. Enrichment of poly A tailed RNA (m-RNA and some long non coding RNA)
and cDNA library preparation was conducted using stranded mRNA (poly A selected)
sample prep kit. Sequencing was conducted on NovaSeq600 at 100 bp paired-end.
Demultiplexed FASTQ files were developed containing reads on average at 30 million
reads/sample. The FASTQ files were mapped to cDNA library of the rat genome
(Ensemble release 95) to retrieve the count information using Salmon (Patro et al., 2016).
To generate counts table from the Salmon output TixmportV.16.0 was used (Soneson et
al., 2015) and DeSEQ2 (Love et al., 2014) was utilized to generate differentially
expressed gene list comprised of normalized read counts for each gene/transcript (DEG).
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Using the normalized counts, inter-sample variability was evaluated by conducting
Pearson’s correlation in R to identify outliers. Fold changes were established by
computing the ratio of the experimental group’s average normalized read count versus
the control group’s average normalized read count (WT male).
Protein extraction
Cortical tissue was cryopulverized and aliquoted for further processing. A mixture of
tissue protein extraction reagent T-PER™ (Thermo Scientific™, cat# 78510) and
protease and phosphatase inhibitors, was added to the pulverized tissue, (100µL per 200-
300 mg of tissue). The tissue was homogenized using The Bullet Blender™ and silicon
beads, at speed 6 for 5 minutes. Following which, the tissue was centrifuged at 12,000 g,
for 20 mins at 4°C. The supernatant was collected and diluted with 1 part of T-PER™ and
protease inhibitor. Protein concentration was estimated using Bio-Rad protein assay (Bio-
Rad, cat# 5000006). The protein lysate was stored at -80°C until further processing.
Amyloid-b measurement
Amyloid-b measurements was conducted on cortical tissue lysate using Meso Scale
Discovery V-PLEX Plus Aβ Peptide Panel 1 (4G8) Kit (Meso Scale discovery, cat#
K15199G-1), following the manufacturer’s protocol. A total of 100µg of protein was loaded
in each well for detection of rat amyloid-b 38, amyloid-b 40 and amyloid-b 42. All samples
were run in duplicates. Plates were analyzed by Meso QuickPlex SQ120. Standard
curves and estimation of concentration of each were calculated using the DISCOVERY
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WORKBENCH version 4.0. The positive control supplied with kit was used to estimate
the % recovery of the analyte and for quality control of the standard curve.
Brain perfusion
Animals were sedated with ketamine and xylazine. The animals were transcardially
perfused with 4% paraformaldehyde for 5 minutes. Following which, the skull of the
animal was detached and stored in Trump’s fixative (1% glutaraldehyde and 4%
formaldehyde) at RT for 24 hours and then moved to 4°C until further processing. A total
of 8 animals, 2 animals/group were used.
Magnetic Resonance Imaging (MRI)
Animals that were perfused with intact skulls were used MRI post-fixation. They were
imaged on 7T Bruker BioSpec® preclinical MRI scanner. Anatomical 3-dimesional T2-
weighted RARE (Rapid Acquisition and Refocused Echoes) images were collected with
TR (Repetition time)/TE (Echo time) =1500/10ms, RARE factor of 8, and 100µm isotropic
resolution. In addition, three sets of Diffusion Magnetic Resonance Imaging (dMRI) were
collected using 8-shot echo planar imaging with 32 directions and a diffusion weighting of
b=1000s/mm
2
, and 4 b=0 images. In plane resolution was 200µm and slice thickness
was 600µm. Three contiguous datasets shifted by 200µm in the slice direction were
collected such that super resolution reconstruction produced dMRI datasets with 200µm
isotropic resolution.
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Image analysis
High-resolution anatomical MRI images were semi-automatically brain extracted using
MRIcron (www.nitrc.org) and Mango Image processing software
(www.ric.uthscsa.edu/mango/). Bias field-corrected using N4 implemented in Advanced
Normalization Tools (ANTs) (www.nitrc.org). The data was further analyzed by registering
a T2-weighted reference image and atlas with 115 regions of interest (ROIs) to each
animal using the SyN algorithm in ANTs(Papp et al., 2014). Volumes of specific regions
of the brain, inclusive of white matter and grey matter areas, were compared across the
4 groups (male and female; WT and ApoE4) using two-way ANOVA.
Raw, low-resolution, dMRI images were motion and eddy-current corrected using FSL’s
eddy-correct(Jenkinson et al., 2012) and denoised using a diffusion-matched principal
component analysis technique(Chen et al., 2018). Subsequently, the three low resolution
datasets were reconstructed using in-house super-resolution reconstruction software,
written in Julia(Smith et al., 2015), to generate 200µm isotropic dMRI data. The brain was
then semi-automatically extracted from non-brain tissue and bias field corrected and two
SyN registrations were performed in ANTs to create a labeled atlas in individual diffusion
space. The high-resolution dMRI data were then fit to the diffusion tensor imaging (DTI)
model using weighted linear least squares(Basser et al., 1994). From the DTI fit, fractional
anisotropy (FA), and mean diffusivity (MD) were calculated on a voxel-by-voxel basis
using MRTrix (www.mrtrix.org) and directionally encoded color maps were generated.
Parameter maps were analyzed by registering the labeled rat atlas to each individual fixed
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rat brain dMRI data, and then comparing the mean value of the top quartile of FA in white
matter ROIs.
Metabolomic analyses
Metabolomic analyses were conducted by METABOLON®, using their global
metabolomics platform which can identify and measure 1000 metabolites using liquid
chromatography/mass spectroscopy. Metabolite classes identified include amino acids,
carbohydrates, fatty acids and lipids. Cortical tissue ~250mg was utilized for the analyses.
Heatmap analyses
Heatmaps were developed to analyze metabolic and gene expression changes. WT
males were used as controls and analyzed using heatmap function using Morpheus
(https://software.broadinstitute.org/morpheus).
Statistical analyses
Statistical analysis was conducted using GraphPad Prism version 8.1. One-way ANOVA
or unpaired t test was conducted as a statistical analysis. Correction for multiple test was
conducted by Tukey’s.
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Results
Figure 3.1 Comparison of weight change in WT and ApoE4 animals after 9 months.
A) The difference in weight at 15 months from 6 months is plotted as bar graphs, and the
error bars are indicative of the standard error of mean. B) The difference in weight at 15
months from 6months is plotted as a scatter plot, with the mean is indicated as a black
bar. Statistical analysis conducted using ANOVA, *p≤ 0.05, **p≤ 0.01, ***p≤ 0.001,
****p≤ 0.0001.
ApoE4 humanized knock-in impacted physiological weight gain due to aging
As a part of the longitudinal follow-up of all the animals in the cohort monthly weight
measurements were made. We found that WT animals, both males and females gained
between 75-100 g on average. However, in comparison to their WT counterparts, ApoE4
males and females did not gain a significant amount of weight over the period of 9 months
(Figure 3.1). In ApoE4 females there was almost no change in weight. This indicates that
the humanized APOEe4 knock-in interferes with the metabolism of the animals affecting
their age-related weight gain.
WT Females
ApoE4 Females
WT Males
ApoeE4 Males
0
50
100
150
grams Difference in weight gained from 6m to 15m
**
****
*
WT Females
ApoE4 Females
WT Males
ApoeE4 Males
-100
0
100
200
grams
Difference in weight gained from 6m to 15m
**
*
****
A)
B)
93
ApoE4 affects the aging metabolic profile differently in males and females.
To establish the impact of the ApoE4 on metabolism, as part of the longitudinal follow-up
we measured triglyceride levels, ketone body levels and insulin levels in the plasma at 7-
8, 9-10, 12-13 and 15-16 months.
Figure 3.2 Plasma triglyceride levels measured across the longitudinal follow-up.
Triglyceride levels were measured during the following aging windows: A) 7-8 months, B)
9-10 months, C) 12-13 months and D) 15-16 months. The bar graphs indicate mean, and
error bars are SEM. Statistical analysis conducted using ANOVA, *p≤ 0.05, **p≤ 0.01,
***p≤ 0.001, ****p≤ 0.0001.
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1000
2000
3000
mg/dl)
****
**** ****
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1000
2000
3000
mg/dl
****
*
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1000
2000
3000
mg/dl
*
**
****
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1000
2000
3000
mg/dl
****
*
**
7-8 months
9-10 months
12-13 months
15-16 months
Plasma Triglyceride levels
A)
B)
C)
D)
94
Triglyceride levels were significantly higher in ApoE4 males and females than their WT
controls across all time points (Figure 3.2). Aging caused the triglyceride levels to
increase in ApoE4 animals. Triglyceride levels in WT animals also seemed to increase
with age, but to a lesser extent (Figure 3.2). Male ApoE4 animals had significantly higher
triglyceride levels than females across all aging windows (Figure 3.2).
Figure 3.3 Plasma ketone body (beta-hydroxy butyrate) levels measured across the
longitudinal follow-up. Ketone body levels were measured during the following aging
windows: A) 7-8 months, B) 9-10 months, C) 12-13 months and D) 15-16 months. The
bar graphs indicate mean, and error bars are SEM. Statistical analysis conducted using
one-way ANOVA, *p≤ 0.05, **p≤ 0.01, ***p≤ 0.001, ****p≤ 0.0001.
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
mM
*
****
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
mM
**
****
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
mM
****
***
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
mM
*
**
****
7-8 months 9-10 months
12-13 months 15-16 months
Plasma Ketone body levels
A) B)
C)
D)
95
ApoE4 genotype and sex impacted the plasma ketone body (b-hydroxy butyrate) levels
as the ketone body levels were significantly higher in male ApoE4 animals than WT males
(Figure 3.3). The ketone body levels were significantly higher in ApoE4 females than WT
females only during the 7-8 months and 15-16 months aging windows. Sex difference in
ketone body levels in ApoE4 animals emerges at 9-10 months and sustains through aging
(Figure 3.3).
Figure 3.4 Plasma insulin levels measured across the longitudinal follow-up. Insulin
levels were measured during the following aging windows: A) 7-8 months, B) 9-10
months, C) 12-13 months and D) 15-16 months. The bar graphs indicate mean, and error
bars are SEM. Statistical analysis conducted using one-way ANOVA, *p≤ 0.05, **p≤ 0.01,
***p≤ 0.001, ****p≤ 0.0001.
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
ng/ml
**
*
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
ng/ml
**
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
ng/ml
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
ng/ml
7-8 months
9-10 months
12-13 months
15-16 months
Plasma Insulin levels
A)
B)
C)
D)
96
Male ApoE4 animals had significantly higher insulin levels than ApoE4 females at 7-8 and
9-10 months aging windows (Figure 3.4). Male ApoE4 animals tended to have higher
insulin levels throughout the longitudinal study. Higher insulin levels in male ApoE4
animals can be attributed to both genotype and sex. No sex differences were observed
in WT animals.
These data suggest that ApoE4 genotype impacted the metabolic profile in the animals
causing higher triglyceride levels and higher ketone body levels. Sex differences are also
imparted to the metabolic profile due to ApoE4 genotype through aging. Lack of significant
difference in ketone body levels at 9-10 and 12-
13 months between ApoE4 and WT females,
could be indicative of an endocrine transition
specific increase in ketone body levels in WT
females.
Glucose Tolerance Test
Glucose tolerance test (GTT) was conducted at
7-8 months to ascertain the effect of sex and
ApoE4 genotype on systemic glucose tolerance.
ApoE4 males had a significantly lower area
under the curve for the GTT (Figure 3.5)
compared to WT males, females and ApoE4
females. Both WT and ApoE4 females had high
WT females
ApoE4 Females
WT males
ApoE4 Males
0
10000
20000
30000
40000
AUC
Glucose tolerance test
*
***
***
Figure 3.5 Glucose tolerance
test (GTT): Area under the curve
(AUC) for GTT is plotted. The bar
graphs indicate mean, and error
bars are SEM. Statistical analysis
conducted using one-way
ANOVA, *p≤ 0.05, **p≤ 0.01,
***p≤ 0.001, ****p≤ 0.0001.
97
AUC. This data suggests that there is a sex difference in the impact ApoE4 has on
glucose tolerance.
ApoE4 and sex affects brain glucose uptake
To investigate the effects of age and ApoE4 on sex differences in brain glucose uptake
we conducted longitudinal imaging using PET/CT and FDG as a radiotracer (except the
cerebellum and brainstem). The animals were imaged at 9-10, 12-13 and 15-16 months.
Sex differences in brain glucose uptake are evident at 9-10 months in WT and ApoE4
animals (Figure 3.6A). Female WT and ApoE4 animals had significantly lower brain
glucose uptake in comparison to male WT and ApoE4 animals respectively. Vaginal
cytology conducted during this age group confirmed that some female animals, both WT
and ApoE4, were experiencing reproductive irregularity during this phase marking it as
an endocrine transition state. Number of regular, irregular and acyclic animals in both
groups were not significantly different. Of note, fasting blood glucose measured prior to
the injection of FDG had no significant differences.
At 12-13 months, when all female animals had turned reproductively senescent, we
observed that female ApoE4 animals had significantly lower brain glucose uptake in
comparison to WT females and ApoE4 males (Figure 3.6B). Sex difference in brain
glucose uptake sustains through 15-16 months (Figure 3.6C). The effect of age on brain
glucose uptake is only seen in ApoE4 female group, in which SUV for FDG reduces
significantly on aging (data not shown). Male ApoE4 animals were not affected in glucose
uptake by aging.
98
Figure 3.6 Brain glucose uptake. Top panel: Representative images of the standard
uptake value (SUV) of 2-Deoxy-2-[18F] fluoroglucose (FDG) in the brain of WT and
ApoE4 males and females, at 9-10, 12-13 and15-16 months captured using positron
emission tomography. Bottom panel: Standard uptake value (SUV) of FDG in the brain
(without cerebellum and brainstem) at A) 9-10, B) 12-13 and C) 15-16 months. Plots
represent means and error bars represent standard error of mean (SEM). Unpaired t-test
used for statistical analysis, n=6-8. *p≤ 0.05, **p≤ 0.01.
9-10 months 12-13 months
15-16 months
WT Female ApoE4 Female
WT Male ApoE4Male
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
5
9-10 months
SUV
*
**
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
5
12-13 months
SUV
** **
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
5
15-16 months
SUV
**
A) B) C)
99
These data suggest that a combination of sex and ApoE4 genotype affects brain glucose
uptake. While WT females seem to rebound after the endocrine transition, onset of
reproductive senescence, seems to worsen the recovery of ApoE4 females.
Cortical Amyloid-b 42 is higher in females at 15-16 months
To investigate if ApoE4 affects amyloid-b synthesis and generation in the rat brain and if
any sex has a higher predisposition for it. We used the soluble fraction of the tissue to
measure amyloid-b in the cortex from animals at 15-16 months. Three species of amyloid-
b were measured: 38, 40 and 42. Amyloid-b 38 was undetectable due to the buffer used
for protein extraction, as the positive control spiked with the protein extraction buffer also
had a reduced recovery. Amyloid-b 40 and 42 were detectable, but the concentration was
low. Amyloid-b 42 was significantly higher in WT and ApoE4 females in comparison to
WT males. While there was no significant sex differences in amyloid-b 40 and 42 levels,
amyloid-b 42 tended to be higher in ApoE4 females than males. Within females, there
was no genotype effect on amyloid-b 42 levels. Amyloid-b 40 seemed to be more sensitive
to the effect of genotype, as ApoE4 females and males tended to have higher amyloid-b
40. As amyloid b 42/40 ratios are used as a marker to assess Alzheimer’s pathology, we
calculated these ratios to discern if any group had significantly higher amyloid-b 42/40
ratios. No group had significantly higher ratios, though females both, WT and ApoE4,
tended to have higher cortical amyloid-b 42/40 ratios.
To investigate if the levels of amyloid-b detected had any relationship with brain glucose
uptake discussed in the earlier section. We found that brain glucose uptake values had
100
an inverse relationship (spearman correlation r= -0.56, p-value= ) with cortical amyloid-b
42/40 ratios.
Figure 3.7 Cortical amyloid-b measurement and correlation with FDG-PET. A) Levels
of amyloid-b 40 in cortical protein lysates. B) Levels of amyloid-b 42 in cortical protein
lysates C) Cortical amyloid-b 42/40 ratios. D) Correlation (spearman correlation
coefficient r) of amyloid-b 42/40 ratios with standard uptake value (SUV) of 2-Deoxy-2-
[18F] fluoroglucose (FDG) in the brain of WT and ApoE4 animals. Groups in the scatter
plot are represented by pink-WT females; red-ApoE4 females; light blue - WT males ;,
dark blue - ApoE4 males. Labels next to the animal indicate animal ID in the study. Plots
represent means and error bars represent standard error of mean (SEM). Unpaired t-test
used for statistical analysis. *p≤ 0.05.
0.0 0.5 1.0 1.5 2.0
0
2
4
6
Amyloid-β 42/40 ratio
FDG- SUV
551
552
555
557
558
560
572
576
581
583
584
590
593
594
597
599
602
610
612
615
616
619
622
r = -0.56
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
4
5
Aβ-40
pg/ml
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1
2
3
Aβ42
*
*
pg/ml
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0.0
0.2
0.4
0.6
0.8
1.0
Cortical Aβ 42/40
A) B) C)
D)
End-of-study analysis: 15-16 months
101
These data suggest that although rats are not predisposed to generate amyloid-b 40
and 42, there is a sex difference in their levels. Inverse correlation of amyloid-b 42/40
ratios with brain glucose uptake measurement suggests that there might be a causal
relationship to it.
Structural neuroimaging for regional volume-based analysis
We conducted T2-weighted MRI to evaluate if there are sex differences in ApoE4 animals
in volumes of areas of the brain that are most affected by Alzheimer’s. Total brain volumes
were sexually dimorphic as both, WT and ApoE4 males, had higher brain volumes than
WT and ApoE4 females respectively (Figure 3.8A).
For further assessment of regional brain volumes, in place of comparing average absolute
volumes of each group, we used percentage of total brain volume to prevent the sexual
dimorphism in total brain volumes from being a confounding factor in the analysis.
The hippocampal formation in ApoE4 females and males tended to be a smaller fraction
of the total brain in comparison to the WT females and males (Figure 3.8B). The
neocortex, comprised of the cortical regions, also showed a sexual dimorphism like the
total brain volumes (Figure 3.8C). White matter of the hippocampus, which is composed
of the alveus, ventral hippocampal commissure and fimbria was of a significantly bigger
fraction in ApoE4 females than ApoE4 males (Figure 3.8D). The corpus callosum did not
show any significant differences (Figure 3.8E). These data suggest that ApoE4 and sex
may be impacting regional brain volumes.
102
Figure 3.8 Structural neuroimaging for regional brain volume measurement A) Total
brain volume, B) Volume of hippocampal formation, C) Volume of neocortex, D) Volume
of white matter of the hippocampus (alveus, fimbria, ventral hippocampal commissure) E)
volume of the corpus callosum. Plots represent means and error bars represent standard
error of mean (SEM). One-way ANOVA and unpaired t-test used for statistical analysis.
*p≤ 0.05, ** p≤ 0.01.
Diffusion magnetic resonance imaging for analysis of white matter integrity.
To further evaluate the effect of sex and genotype on myelin integrity we conducted
diffusion magnetic resonance imaging of perfused fixed animals at 15-16 months.
Microstructural parameter, fractional anisotropy (FA) of several white matter tracts in the
brain, was assessed to study the differences in myelin integrity.
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
10
25.0
27.5
30.0
% total brain volume
Neocortex
*
**
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
4
6
7
8
% total brain volume
Hippocampal formation
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0.0
0.3
0.4
0.5
0.6
0.7
% total brain volume
White matter of the hippocampus
*
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
2
3.0
3.5
4.0
% total brain volume
Corpus callosum
WT Females
ApoE4 Females
WT Males
ApoE4 Males
0
1800
1800
2000
2200
2400
Total brain volume (mm
3
)
Total brain volumes
*
*
End-of-study analysis: 15-16 months
A) B)
C)
D) E)
103
White matter tracts: corpus callosum, fornix, media lemniscus, anterior commissure
(anterior), anterior commissure (posterior), anterior commissure, alveus of the
hippocampus, ventral hippocampal commissure, fimbria, optic tract and optic chiasm,
supraoptic decussation were assessed for fractional anisotropy. Within these regions, the
average of the top quartile values of FA was computed for plotting to amplify the signal
from each group.
Figure 3.9 Diffusion magnetic resonance imaging for measurement of fractional
anisotropy Average of top quartile values were assessed. Plots represent means and
error bars represent standard error of mean (SEM). Two-way ANOVA used for statistical
analysis.
While there were no significant differences, FA values tended to be higher in WT males
and ApoE4 males across all the white matter tracts assessed (Figure 3.9). Both, WT and
ApoE4 females tended to have smaller FA, but ApoE4 females seemed to have the
smallest FA values (Figure 3.9). These data suggest that sex and ApoE4 genotype may
Corpus callosum
Fornix
medial lemniscus
anterior commissure, anterior part
anterior commissure, posterior part
anterior commissure
alveus of the hippocampus
ventral hippocampal commissure
fimbria of the hippocampus
optic tract and optic chiasm
supraoptic decussation
0.0
0.2
0.4
0.6
0.8
Fractional anisotropy
Fractional anisotropy
Average of top quartile
WT Females
ApoE4 Females
WT Males
ApoE4 Males
End-of-study analysis: 15-16 months
104
be impacting the microstructural parameter: FA, and probably the myelin integrity, with
the females, ApoE4 females, being most impacted.
ApoE4 and sex has a broad systems-level impact on the transcriptome
To investigate molecular mechanisms that are involved in the phenotypic metabolic
differences imparted by sex and genotype we conducted bulk-RNA seq of the
hippocampus. Curated gene lists from literature were used for conducting the heatmap
analysis (Figure 3.10) and WT males were used as controls.
Transcriptional profiling revealed that inflammation was upregulated in ApoE4 females,
as evident by upregulation of major histocompatibility complexes class I and II (RT1-A2,
RT1-CE12, RT1-Db2), along with other microglial reactivity markers such as CD74,
CD40, ITGAX. Genes that participate in interferon response were also upregulated
(IFITM1, IFITM2, IFNG, IRF7) (Figure 3.10).
Oxidative phosphorylation was upregulated in ApoE4 males in comparison to all groups,
which was evident with the upregulation of genes that participate in b-oxidation (HADHA,
HADHB,) and the citric acid cycle (TCA) (PDHX). Ketogenesis (BDH1, HMGCL,
HMGCLL1) seemed to be upregulated in males, both WT and ApoE4. Whereas
phospholipases seemed to be upregulated in ApoE4 animals. These data indicate that
ApoE4 genotype and sex affect the transcriptome at a systems level (Figure 3.10).
105
Figure 3.10 Sex differences in transcriptomic profiling of the hippocampus at 15-
16 months. Genes involved in inflammation, oxidative phosphorylation and ketogenesis
and, phospholipases were mapped
Inflammatory markers
Oxidative phosphorylation Ketogenesis
Phospholipases
106
Glucose metabolism and b-oxidation are affected in female ApoE4 animals
To validate the findings from brain glucose uptake measurements and transcriptomic
profiling, we conducted metabolomic analyses to study differences in the metabolites of
the TCA cycle, b-oxidation and evaluate if the ApoE4 genotype affects fatty acid
metabolism.
To that end, we found that malate and fumarate, metabolites of the TCA cycle ((Figure
3.11E), were significantly downregulated in ApoE4 females in comparison to males. Other
metabolites that partake in glycolysis were also downregulated in the females. ApoE4
males showed high levels of lactate and glucose. WT females also showed a similar
metabolic profile as the ApoE4 females with respect to glucose metabolism. A distinction
they had was that WT females had higher levels of pyruvate (Figure 3.11A).
Acylcarnitines of varying lengths are formed when fatty acids are transported across the
mitochondrial membrane for b-oxidation (Figure 3.11F). Comparison of acylcarnitine
levels can shed light on the mechanics of b-oxidation as function of APOE genotype and
sex. Through metabolomics we wanted to investigate if any one group had a greater
susceptibility for b-oxidation. Heatmap analysis revealed that ApoE4 females had one of
the highest levels of acylcarnitines within the groups compared (Figure 3.11B).
Saturated, mono- and poly-unsaturated long chain fatty acids were the lowest in ApoE4
males, followed by ApoE4 females, indicating that the ApoE4 genotype affects free fatty
107
acid levels (Figure 3.11 C, D and G). These data indicate that ApoE genotype and sex
interact with the metabolome, and affect glucose metabolism, b-oxidation and fatty acid
synthesis.
A)
B)
Glucose metabolism
Beta-oxidation
E)
F)
108
Figure 3.11 Sex differences in cortical metabolic profiling at 15-16 months.
Metabolites participating in A) glucose metabolism, B) beta-oxidation and, long chain fatty
acids: C) saturated and D) monounsaturated and polyunsaturated was analyzed using
heatmaps, with WT males as a control. To guide pathway interpretation E) The citric acid
cycle (TCA), F) acylcarnitine’s in b-oxidation and G) Fatty acid synthesis and break down
have been demonstrated.
Long chain fatty acids : mono and poly unsaturated
Long chain fatty
acids: saturated
C)
D)
G)
109
Discussion
Sex differences in Alzheimer’s disease prevalence, rate of progression and risk are
imminent in ApoE4 carriers. We wanted to investigate the underlying sex differences in
metabolic aging, of the brain and systemically, in ApoE4 carriers. To do so, we conducted
a longitudinal study in humanized ApoE4 knock-in rats (males and females) and used WT
males and females as controls, to evaluate the impact of the ApoE4 knock-in. The
longitudinal study spanned for 9 months and was comprised of monthly weight
measurements, developing a plasma metabolic profile, glucose tolerance test and brain
glucose uptake measurement at 7-8, 9-10, 12-13 and 15-16 months. The rationale for
choosing these aging windows was that each aging window is a reproductively distinct
aging window for the female rats; 7-8 months - reproductively competent phase, 9-10
months – reproductively irregular phase, 12-13 months – reproductively senescent and
15-16 months reproductively aged and senescent.
The longitudinal assessment sheds light on the differing metabolic profiles of males and
female ApoE4 rats. Body weight measurements at the start and end of the study,
indicated that ApoE4 knock-in interfered with physiological weight-gain observed during
aging, more so in females. This has also been observed clinically, as ApoE4 carriers
have shown to have lower body mass index (BMI) in comparison to ApoE2 and ApoE3
carriers(Vanhanen et al., 2001;Jones and Rebeck, 2018). The peripheral metabolic
profile, which comprised of measurements of triglycerides, ketone bodies and insulin
highlighted the distinction in the aging process of ApoE4 males and females. While both
ApoE4 males and females had higher triglyceride levels, males had significantly higher
110
triglyceride levels than females across all timepoints. Higher triglyceride levels in ApoE4
animals corresponds to what has been observed in ApoE4 carriers clinically(Dallongeville
et al., 1992;Bennet et al., 2007). Inability to gain weight in ApoE4 animals can be
explained by the high triglyceride levels. The ApoE4 genotype renders the apolipoprotein
E (ApoE) as an ineffective cholesterol transporter thus, causing an accumulation of
triglycerides in the plasma. The age-related increase in triglyceride levels should be
subverted by storing fatty acids in lipid stores but the inability of ApoE as a lipid transporter
causes the triglycerides to end up in circulation affecting the overall body weight in ApoE4
animals.
Plasma ketone body levels also increased in ApoE4 animals with age. The difference was
significant between WT and ApoE4 males across all timepoints. While the difference in
females was only significant during reproductively competent phase at 7-8 months and
reproductively senescent phase at 15-16 months. The increased levels of ketone bodies
in WT females during these phases can be explained by the fact that during the 9-10
months window, the rats are undergoing an endocrine transition, after which they become
reproductively senescent. It has been shown in both preclinical and clinical aging that,
this endocrine transition leads to increased production and utilization of ketone bodies as
an alternative energy substrate for the brain. Ketone bodies from the periphery
supplement this utilization to some extent. Sex differences in ketone body levels in ApoE4
animals emerge at 9-10 months and sustains through aging. In the periphery, the source
of ketone bodies is the liver. Higher ketone body levels in the ApoE4 animals, especially
in males, could be due to the increased triglycerides in circulation. A physiological
111
explanation would entail desertification of the triglycerides to generate fatty acids and
glycerol, and as ApoE functions as a poor lipid transporter, these fatty acids end up in
circulation. To reduce the circulating fatty acids in circulation they are converted to ketone
bodies by the liver.
The ApoE4 knock-in caused an increased propensity for higher insulin levels in males.
Higher insulin levels are observed in ApoE4 carriers clinically, but the sexual disparity as
observed in this rodent study has not been documented yet. Higher insulin levels could
be indicative of development of insulin resistance(Jones and Rebeck, 2018).
The longitudinal assessment also revealed that the ApoE4 genotype and sex impacted
brain glucose uptake. Female ApoE4 animals had significantly lower brain glucose uptake
than males across all aging timepoints. Interestingly, sex differences in brain glucose
uptake are evident at 9-10 months, in both WT and ApoE4 animals. But WT females
seemed to restore their ability to take up glucose after becoming reproductively senescent
at 12-13 months. Interestingly, at 12-13 months, when the female rats become
reproductively senescent, a genotype effect is evident in brain glucose uptake. This
indicates that female ApoE4 start their mid-aging phase and enter into the endocrine
transition with poor glucose uptake. Reproductively senescence and aging worsens this
phenotype.
Preclinical and clinical evidence focused on ApoE4 and glucose metabolism have shown
that the ApoE4 genotype has an increased propensity to depend on lipids as a fuel
112
source(Arbones-Mainar et al., 2016). A recent study observed concluded that
neuroblastoma cells expressing ApoE4 gene had the highest maximal respiration in
response to ketone bodies, in comparison to ApoE2 and ApoE3(Wu et al., 2018).
Impairment in glucose metabolism due to insulin signaling in neurons derived from mice
with a targeted replacement of ApoE4 gene has also been shown(Zhao et al., 2017). Poor
glucose uptake and metabolism in ApoE4 carriers has been well-documented across
various aging groups. Yet, the question about the sex differences in the glucose
metabolism in ApoE4 carriers have not yet been answered. This study is the first to report
a longitudinal reproductive aging based imaging analysis of the sex difference in glucose
metabolism in ApoE4 carriers. Importantly, the worsening in glucose uptake after the
onset of reproductive senescence, which needs to be translated in humans, can be used
to therapeutically intervene. This finding may potentially implicate a mechanism by which
ApoE4 predisposes to faster cognitive decline on aging.
The longitudinal study was completed at 15-16 months of age. A cross-sectional study
investigating molecular pathways, and metabolomic signatures, regional brain volumes
and microstructural parameters, and amyloid-b load was conducted on the 4 groups.
As the rats do not have a human transgene that overexpresses amyloid-b, we wanted to
investigate the effects of ApoE4 gene on endogenous rat species of amyloid-b. In
addition, we wanted to investigate if there is an imaging marker of amyloid-b levels in the
brain. On evaluating cortical lysates, we found that cortical amyloid-b 42 was significantly
higher in females, WT and ApoE4, than WT males. Cortical amyloid-b42/20 ratio also had
113
a significant inverse correlation with brain glucose uptake measurement. Several clinical
studies have reported similar findings, with reduced glucose uptake and increased
presence of amyloid-b(Mosconi et al., 2018). The earliest decline in brain glucose uptake
was apparent at 9-10 months, in females, which only worsens with age. The decline in
brain glucose uptake can also be indicative of presence of amyloid-b in the brain.
Temporal correlations between amyloid-b and brain glucose uptake need to be examined
in younger age groups to establish their causal relationship.
Imaging studies conducted on ApoE4 carrier and non-carrier infants have shown lesser
myelin water fraction in ApoE4 carriers in posterior cingulate, splenium of the corpus
callosum, optic and corticospinal tracts than non-carriers(Dean et al., 2014). This
indicates that ApoE4 gene affects white matter metabolism early in development. We
hypothesized that age-related changes in myelin integrity would be further exacerbated
in ApoE4 animals, more so in the females. We used non-invasive rodent imaging to
analyze structural volume and microstructural properties in the ApoE4 and WT animals.
Regional brain volume was normalized to the total brain volume of the animal, to eliminate
the confounding factor of sex difference in total brain volumes. ApoE4 females had
significantly higher proportion of white matter associated with the hippocampus (alveus,
ventral hippocampal commissure and fimbria). Estimation of microstructural parameter:
fractional anisotropy indicated that females, especially ApoE4 females, had the lowest
fractional anisotropy (FA) amongst several white matter tracts compared. FA is a measure
of movement of water and lower values of it are associated with increased water
diffusivity. Water diffusivity is increased when integrity of myelin has been
114
reduced(Kantarci et al., 2017). Lower FA values in ApoE4 females can be indicative of
reduced myelin integrity in the white matter tracts. Interestingly, male ApoE4 animals do
not show a similar pattern as seen with the females and showed no difference from the
WT male in this measure. While the magnetic resonance imaging study was helpful in
giving a preliminary idea of changes in regional brain volumes and fractional anisotropy,
it had several caveats. The number of animals per group was low (2/group) and hence
significant differences between the groups was mostly undetected. The animals were
imaged post-fixation, which could have impacted the movement of water molecule
thereby distorting the FA reads. Further experimentation by longitudinal assessment
needs to be conducted with sex and genotype controls to discern age related effects on
each group and to use a normalized measure, such as atrophy rate to compare between
separate groups.
Transcriptomic profiling corroborated the findings of the brain glucose uptake
measurement by PET, as oxidative phosphorylation was downregulated in ApoE4
females. Coincidentally, ApoE4 females showed a drastic upregulation of inflammatory
markers that participate in type I and type II interferon response, MHC-I and MHC-II
upregulation and increased expression of microglial reactivity markers CD40, CD74 and
ITGAX. Given the low FA values in several white matter tracts, which may indicate
disruption of myelin integrity, the upregulation of these inflammatory markers may be due
to the participation of microglia in progressing the white matter pathology. Another
evidence of that would be the upregulation of phospholipases in the ApoE4 animals. The
115
ketogenesis gene expression pattern did not support this evidence as the ketogenic
pathway seemed to be more activated in males, both WT and ApoE4.
ApoE4 females had significantly lower malate and fumarate levels than ApoE4 males.
Malate and fumarate are metabolites of the TCA cycle. Reduced levels of these
metabolite corroborate transcriptomic and imaging findings that indicate glucose
metabolism in ApoE4 females is dysregulated. While transcriptomic evidence hinted at
decreased ketogenesis in the females, metabolomic evaluations suggest that a key
metabolite of b-oxidation was increased in ApoE4 females. Discrepancy between the
transcriptomic and metabolomic profile could be attributed to the difference in brain region
analyzed. Interestingly, long chain fatty acids – saturate, mono- and poly unsaturated fatty
acids were the lowest in ApoE4 males. Both WT females and males had higher levels of
these fatty acids suggesting this phenotype is driven by the ApoE4 genotype. Low levels
of polyunsaturated fatty acids, which are key to the brain for cell membranes, can worsen
the metabolic profile. The significant reduction in long chain fatty acids in ApoE4 animals
could be because of the dysfunctional ApoE expressed by the genotype. The sex
difference in the levels of long chain fatty acids could be due to the increased energy
metabolism seen in the males by the transcriptomic (increased oxidative phosphorylation)
and metabolomic profiling (higher TCA products).
While this study provided insights of how ApoE4 affects the metabolic aging differently in
males and females, there were several technical limitations to the study. Inability to have
a humanized APOEe3 (ApoE3) knock-in models as a control to unambiguously
116
investigate the effect of ApoE4 genotype was the biggest caveat. MRI based assessment
were lacking in number and could hint towards a trend between animals. Further analysis
to corroborate these findings is required. Several experiments in the study point toward a
dysregulated metabolism in ApoE4 animals, yet the question remains- what is their
source of energy and how does it cause the sex difference in the metabolic profile needs
to be investigated.
In summary, we show that a combination of ApoE4 and sex renders a unique metabolic
profile. The metabolic profile in ApoE4 animals indicate can early disruption of the
metabolic trajectory, with elevated triglyceride and ketone body levels. In females, this
leads to reduced glucose metabolism and uptake. Lower brain glucose uptake is evident
as early as 9 months, when the animals are reproductively irregular. Transcriptomic and
metabolomic analyses corroborate the dysregulated glucose metabolism observed in
ApoE4 females.
117
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Chapter 4: Inflammatory biomarkers in predicting therapeutic response of
Allopregnanolone in patients with early Alzheimer’s disease
Abstract
Allopregnanolone (Allo) is a first in class regenerative therapeutic for delaying progression
and treating Alzheimer’s disease (AD), currently undergoing the regulatory approval
process. Allo through its action on the gamma-aminobutyric acid subclass A (GABAA)
receptor modulates neuronal physiology and ameliorates glial induced inflammation. In
this study, we used Allo’s modulation of the inflammatory response to develop therapeutic
blood-based biomarkers that can help in identifying responders for future clinical trials. A
phase Ib/IIa clinical trial (Clinicaltrials.gov identifier: NCT02221622) randomized double-
blind, placebo-controlled, multiple ascending dose, was conducted in patients with mild
cognitive impairment (MCI) due to AD or early AD. Magnetic resonance imaging was
conducted to assess structural brain volume changes alongside microstructural changes
to determine white matter integrity. Plasma biomarker assessment was conducted to
assess changes in cytokines, chemokines, clinical inflammatory markers between
baseline and end-of-study. In comparison to the placebo group, the levels of pro-
inflammatory cytokines and chemokine showed a reduction in response to Allo treatment.
There was an inverse correlation between reduction of pro-inflammatory mediators and
increase in hippocampal volume. Interestingly, immune regulators that attenuate
inflammatory response and participate in tissue remodeling and healing remained stable
on Allo treatment in comparison to the placebo group in whom the levels decreased over
the treatment period. The relationship of white matter microstructural assessment and
121
circulating cytokines levels was also investigated. These data indicate that a targeted
profile of immune mediators could serve as a biomarker of therapeutic response to Allo
which could be predictive of regional brain volume changes and microstructural changes.
In addition, we show that targeted analysis to identify responders can be conducted to
stratify and recruit patients for future trials.
Introduction
Alzheimer’s disease (AD) is pathologically characterized by the deposition of amyloid-b
plaques, neurofibrillary tangles and microgliosis in the brain (Perl, 2010). Repeated efforts
to develop drugs that target the disease’s pathology, by targeting amyloid-b have all been
unsuccessful(Abbott and Dolgin, 2016;Mehta et al., 2017;Mullard, 2018;Panza et al.,
2019). Newer approaches that target the disease through underlying mechanisms that
lead to the development of disease pathology need to be examined.
Allopregnanolone (Allo), a derivative of progesterone and a neurosteroid(Faroni and
Magnaghi, 2011;Liang and Rasmusson, 2018), has been shown to improve executive
functioning, episodic memory and learning in persons with Fragile X syndrome (Wang et
al., 2017). Patients with AD have been shown to have reduced levels of Allo in the
temporal cortex(Naylor et al., 2010)g. Allo is an agonist of gamma-aminobutyric acid
subclass A (GABAA) receptor and through its effect on this receptor in preclinical models
of AD and other neurodegenerative diseases, Allo has shown to neurogenesis, neural
stem cell differentiation, reduction of amyloid-b and white matter generation(Liao et al.,
2009;Chen et al., 2011;Singh et al., 2012;Brinton, 2013;Irwin et al., 2014;Irwin et al.,
122
2015). Allo has also shown to have anti-inflammatory activity(Noorbakhsh et al., 2014).
Through its action on the GABAA receptor on immune cells, Allo has been shown to
reduce the secretion of proinflammatory cytokines and inhibit signaling through toll-like
receptor-4 (TLR-4)(Balan et al., 2019). Allo has shown to have beneficial anti-
inflammatory effects in early treatment of traumatic brain injury(VanLandingham et al.,
2007).
Allo is a first in class regenerative therapeutic, currently undergoing the regulatory
approval process for delaying progression and treating AD. To establish its safety profile
of Allo in patients with mild cognitive impairment (MCI) due to AD or early AD, a double
blind, placebo-controlled, multiple-dose ascending phase Ib/IIa trial was conducted
(Clinicaltrials.gov identifier: NCT02221622). Secondary outcomes of the trial was to
develop imaging methods for quantifying therapeutic response of Allo by assessing
regional brain volume changes and microstructural parameters and to evaluate
biomarkers that are predictive of any therapeutic response that can be used in future
clinical trials.
We conducted this study to address the secondary outcomes of this clinical trial, which
was to develop blood-based biomarkers that are predictive of Allo’s therapeutic response
and can serve as pharmacodynamic response biomarkers, based on FDA’s Biomarkers,
EndpointS and other Tools (BEST) resource (Group, 2016). The biomarker analysis was
designed to exploit Allo’s multimodal mechanism of action. We hypothesized that
treatment with Allo will cause neuro-regeneration and simultaneously modulate
123
inflammation and immune markers. To that end, to quantify regenerative effects of Allo,
regional brain volume changes and microstructural assessment was conducted. And,
circulatory immune markers were measured to establish Allo’s effect on inflammation.
The goal of this study was to assess changes in regional brain volume, microstructural
parameters and immune markers during the duration of treatment and conduct
correlational analysis to evaluate biomarkers that are 1) predictive of the therapeutic
response, 2) sensitive to Allo’s dose and 3) are predictive of therapeutic response based
on isoform of Apolipoprotein-E (ApoE) and sex.
Materials and Methods
Study design
This study utilizes plasma samples and imaging data procured from a phase Ib/IIa clinical
trial of Allopregnanolone as a regenerative therapeutic in mild cognitive impairment (MCI)
due to AD or early AD. The study was approved by the Institutional Review Board at the
University of Southern California. Participants of the trial had provided a written informed
consent. Detailed information of the clinical trial design has been published in an earlier
report (Solinsky, 2017). Briefly, a randomized double-blind, placebo-controlled phase
Ib/IIa clinical trial was conducted on patients with mild cognitive impairment (MCI) due to
Alzheimer’s disease or early Alzheimer’s disease (Clinicaltrials.gov identifier:
NCT02221622). Participants enrolled in the trial were >55 years and had mini mental
state exam (MMSE) scores ≥ 20. Twenty-four participants were enrolled in the trial, and
were randomly assigned to 2, 4 or 6 mg/kg of Allo or placebo. The participants were
treated once weekly, intravenously, for 12 weeks. The trial was segregated by dose, into
3 cohorts and 8 participants were enrolled into each cohort. The primary outcome of the
124
trial was to conduct a safety assessment of Allo and to establish the maximally tolerated
dose of Allo in patients with MCI and early AD. The secondary outcome was to conduct:
1) T2-weighted and diffusion-weighted magnetic resonance imaging (MRI) and establish
changes to structural volumes and myelin integrity, and 2) to conduct plasma biomarker
analysis which can predict the therapeutic response of Allo in responders. ApoE
genotyping of the participants was also conducted, on total DNA extracted from the blood.
Figure 4.1 Clinical trial design for Allopregnanolone Phase IB/IIA clinical trial
(Clinicaltrials.gov identifier: NCT02221622)
Randomized Double-Blind Phase I clinical trial with
multiple ascending dose
Cohort 1
Cohort 2 Cohort 3
Dose: 2 mg/kg
Treated once weekly
intravenously, for 3
months
N=8
(2 placebos, 6 treated)
Dose: 4 mg/kg
Treated once weekly,
intravenously for 3 months
N=8
(2 placebos, 6 treated)
Dose: 6 mg/kg
Treated once weekly
intravenously, for 3 months
N=8
(2 placebos, 6 treated)
Primary outcome:
- Safety assessment of Allo in patients
with MCI and Early AD.
- Establishment of maximally tolerated
dose of Allo in patients with MCI and
Early AD
Secondary outcome:
- T2-weighted MRI to establish structural
volume changes.
- Diffusion weighted tensor imaging to
assess myelin integrity on Allo treatment
- Development blood-based biomarkers for
identifying responders for designing
Phase II clinical trial
125
Table 4.1 Demographic information for participants for Phase Ib/IIa clinical trial for
Allopregnanolone
Magnetic resonance imaging
T2-weighted and diffusion-weighted magnetic resonance imaging was conducted at
baseline and end-of-study and the analysis was led and conducted by Dr. Yonggang Shi,
Stevens Neuroimaging and Informatics Institute, University of Southern California. The
T2-weighted structural imaging was conducted to evaluate regional brain volume changes
on Allo treatment in the treated group or progression of disease severity in the placebo
group. Segmentation of regions of interest and estimation of regional brain volumes was
conducted on FreeSurfer 6.0. Diffusion tensor imaging was done to assess
microstructural properties (fractional anisotropy, medial diffusivity and axial diffusivity) of
white matter tracts. Changes in hippocampal volume and fractional anisotropy of the
medial core fornix, from baseline are used for analysis.
126
Inflammatory biomarker assessment
Plasma biomarker analysis for inflammatory markers was done on the MESO QuickPlex
SQ 120 (Meso Scale Discovery, Rockville). The V-PLEX Plus Human Biomarker 40-plex
kit (Meso Scale Discovery, cat# K15209G-1) was used. Following manufacturer’s
protocols, the following analytes were measured :C-reactive protein (CRP), Eotaxin,
Eotaxin-3, Granulocyte Macrophage Colony Stimulating Factor (GM-CSF), Intercellular
Adhesion Molecule-1 (ICAM-1), Interferon- gamma (IFNg), Interleukin (IL)- 1a (IL-1a), IL-
1b, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-8 (HA), IL-10, IL-12/IL-23p40, IL-12p70, IL-13, IL-
15, IL-16, IL-17A, Interferon-inducible protein-10 (IP-10), Monocyte Chemoattractant
Protein-1 (MCP-1), Monocyte Chemoattractant Protein-4 (MCP-4), Macrophage derived
chemokine (MDC), Macrophage Inflammatory protein-1a (MIP-1a), Macrophage
Inflammatory protein-1b (MIP-1b), Serum Amyloid-A (SAA), Thymus and Activation
Regulated Chemokine (TARC), Tumor Necrosis Factor-a (TNFa) , Tumor Necrosis
Factor-b (TNFb), Vascular Cell Adhesion Molecule-1 (VCAM-1) and Vascular Endothelial
Growth Factor (VEGF). Analytes were measured in baseline and end-of-study plasma
samples collected before infusion was conducted. All samples were run in duplicates.
Estimation of concentration of each analyte was done using the DISOCVERY
WORKBECH software, version 4.0. Positive controls supplied with the kit were used to
127
estimate % recovery of each analyte which was used to validate the standard curve and
for minimizing inter-plate variability. To minimize variability due to biological
heterogeneity, difference in analyte levels from baseline was used for correlational and
estimation of pharmacologic response. Analytes included in the biomarker analysis were
in the detectable ranges, were sensitive to Allo treatment and passed the quality control
criteria.
Statistical analysis
Statistical analysis was conducted using GraphPad Prism version 8.1. To establish
pharmacologic response of inflammatory biomarkers to Allo treatment, average
difference from baseline based on cohort was plotted. Statistical significance was
calculated by one-way ANOVA. Correlational analysis of the changes in neurological
imaging parameters and inflammatory biomarkers were conducted by using Spearman’s
correlation, as it was not assumed that the changes in the parameters will follow a
gaussian distribution. Predictive biomarker analysis was conducted using correlational
analysis.
Results
ApoE4 carriers are differentially distributed between males and females
A total of 24 participants were recruited for the trial. An equal number of males and
females were recruited. ApoE genotype affects Alzheimer’s disease rick and progression
and so, the participants were tested for their ApoE genotype for neurological and blood-
128
based biomarker analysis. The distribution of ApoE3/3, ApoE3/4 and ApoE4/4 carriers
between the two sexes was unequal. Of the total participants recruited 29% were male
ApoE3/3 carriers, 21% were male ApoE4 carriers, 8% were female ApoE 3/3 and 42%
were female ApoE4 carriers. Female ApoE4 carriers made up the biggest subgroup of
the trial based on stratification done on ApoE genotype and sex.
Figure 4.2 Percentage of total participants by ApoE3/3 and ApoE3/4 genotype
stratified by sex. Percentage of participants that are ApoE 3/3 and ApoE 3/4 or 4/4
carriers based on their sex.
Inflammatory biomarkers can serve as pharmacodynamic marker for Allo treatment
Allo has been documented to have anti-inflammatory effects, and it was hypothesized
that Allo will have a pharmacologic effect on circulating cytokines and chemokines. The
anti-inflammatory effect could be dose dependent. To address this hypothesis, we plotted
the difference from baseline of cytokine and chemokines to see if Allo had a dose
response curve. We found that IL-2, IL-8, IFNg and IP-10 levels decreased from baseline
and cohort 2, dose=4 mg/kg seemed to have the biggest effect (Figure 4.3). For
Males Females
0
10
20
30
40
50
% of Participants
APOE 3/4 OR 4/4
APOE 3/3
129
chemokines, MCP-4 and Eotaxin-3, levels reduced drastically in the placebo groups but
remained unchanged in the Allo treated group, irrespective of dose. The cytokine IL-15
increased in Placebo and cohort 1 but did not change from baseline in cohort 2 and 3.
The treatment effect in any cohort was not significant, as the variability due to genetics,
sex and lifestyle was high and the n was low. These data suggest that pro-inflammatory
cytokines – IL-2, IL-8, IFNg, IP-10 show are susceptible to the dose of Allo and they could
be, after further assessment, used as potential pharmacodynamic biomarkers (Figure
4.3).
A)
B)
C)
D)
130
Figure 4.3 Cohort based analysis changes in cytokines and chemokines during the
duration of treatment. Difference from baseline in A) Interleukin-2 (IL-2), B) Interleukin-
8 (IL-8), C) Interferon-gamma (IFNg), D) Interferon gamma inducible protein 10 (IP-10) E)
Monocyte chemoattractant protein- 4 (MCP-4) F) Eotxain-3 and G) Interleukin-15 (IL-15).
Data presented in box plots, the line represents the median value and error bars are
representative of the range from minimum to maximum.
E)
F)
G)
131
Inflammatory biomarkers as predictive biomarkers for structural volume changes
in hippocampus on Allo treatment.
Allo has a neuro-regenerative and anti-inflammatory effect in the brain and periphery. We
hypothesized that changes in the inflammatory biomarkers from baseline could be
predictive of neurological changes such as regional brain volume measurements. As
previous study had observed left hippocampal volume changes to be an indicator of
therapeutic response to Allo (Solinsky, 2017), we chose this neurological parameter to
analyze predictive effects of inflammatory biomarkers.
Spearman correlational coefficient for differences from baseline for Eotaxin-3 (Figure 4.4),
IP-10 (Figure 4.5), MCP-4, IL-2, IL-6, IL-10, IFNg, VEGF, IL-12, IL-15 and IL-16 and
differences in hippocampal volume were calculated to see if the changes in chemokines
and cytokines can predict hippocampal volume change. Spearman correlations were
conducted on all participants, ApoE3/4 and ApoE4/4 carriers, ApoE3/3 carriers, males
and females (Table 4.2). As the n for subgroup analysis inclusive of sex and ApoE
genotype stratification would have been very low, further stratification of the participants
was not done. Subgroup analysis based on cohort was also not done as the n for
correlational analysis was low. Participants randomized to the placebo and the Allo active
arm were both included to see if the correlational analysis can discern the effects between
the two groups.
132
Table 4.2 Spearman’s correlation coefficient for changes in detectable cytokines
and chemokines with left hippocampal volume in participant subgroups stratified
by ApoE genotype and sex. Spearman’s correlation coefficient (r) for differences from
baseline in: Eotaxin-3, Interferon Inducible Protein-10 (IP-10), Monocyte Chemoattractant
Protein-4 (MCP-4), IL-2, IL-6, IL-10, IFNgamma (IFNg), IL-8, VEGF (Vascular Endothelial
Growth Factor), IL-12, IL-15 and IL-16, and changes in left hippocampal volume are
mentioned in the table. Analysis of the correlation coefficient was done on stratification of
subgroups based on ApoE genotype and sex. Orange: Correlation coefficient >0.5 and
green: correlation coeeficient <0.5. Significance of the correlation for each subgroup is
also indicated.
Correlational analysis conducted on all participants to identify biomarkers predictive of
neurological changes revealed that Eotaxin-3 had a positive correlation (r=0.59, p=0.01)
with the change in hippocampal volume (Table 4.2). Pro-inflammatory cytokines such as
IP-10, IL-2, IL-6, IL-10, IFNg, IL-12, IL-15 and IL-16 had a negative correlation with the
increase in hippocampal volume.
Eotaxin-
3
IP-10 MCP-4 IL-2 IL-6 IL-10
IFN
g
IL-8 VEGF IL-12 IL-15 IL-16
Participant subgroup: All participants
r
0.59 -0.36 0.14 -0.43 -0.32 -0.15 -0.30 0.14 -0.28 -0.30 -0.16 -0.33
P (two-tailed)
0.01 0.13 0.58 0.07 0.18 0.53 0.23 0.57 0.27 0.23 0.53 0.18
P value
summary
** ns ns ns ns ns ns ns ns ns ns ns
Participant subgroup: ApoE3/4 and ApoE4/4
r
0.75 -0.45 -0.08 -0.62 -0.38 -0.48 -0.13 -0.12 -0.48 -0.49 -0.13 -0.35
P (two-tailed)
0.02 0.19 0.84 0.06 0.28 0.17 0.74 0.76 0.17 0.15 0.73 0.33
P value
summary
* ns ns ns ns ns ns ns ns ns ns ns
Participant subgroup: ApoE3/3
r
0.58 -0.47 0.42 -0.13 -0.25 0.33 -0.30 0.63 0.00 -0.19 -0.05 -0.14
P (two-tailed)
0.11 0.21 0.27 0.74 0.52 0.39 0.44 0.08 >0.99 0.66 0.93 0.75
P value
summary
ns ns ns ns ns ns ns ns ns ns ns ns
Participant subgroup: Males
r
0.53 -0.15 0.44 -0.13 0.04 0.32 -0.43 0.19 -0.13 -0.02 -0.25 -0.28
P (two-tailed)
0.12 0.68 0.20 0.73 0.92 0.37 0.25 0.61 0.74 0.98 0.52 0.46
P value
summary
ns ns ns ns ns ns ns ns ns ns ns ns
Participant subgroup: Females
r
0.83 -0.57 -0.13 -0.62 -0.70 -0.62 -0.13 0.03 -0.48 -0.68 -0.15 -0.38
P (two-tailed)
0.01 0.12 0.74 0.09 0.04 0.09 0.74 0.95 0.19 0.05 0.71 0.31
P value
summary
** ns ns ns * ns ns ns ns ns ns ns
133
Change in Eotaxin-3 across the treatment duration was a robust biomarker predictive of
hippocampal volume change. The correlation was not much impacted by stratification by
ApoE genotype and sex. Though the correlation between increase in Eotaxin-3 and
hippocampal volume was highest in females and ApoE4 carriers in comparison to males
and ApoE3/3 carriers respectively (Figure 4.4).
Figure 4.4 Correlation of change in Eotaxin-3 levels and hippocampal volume from
baseline during clinical trial duration. Spearman correlation coefficient r = 0.59, the
red dots are participants enrolled in the placebo arm and blue squares indicate
participants randomized to the Allo treatment arm. Labels indicate participant number.
Analysis of biomarkers predictive neurological - structural volume changes, by ApoE
genotype stratification revealed that ApoE genotype impacted the inflammatory milieu
differently at least in some biomarkers. Most inflammatory markers analyzed: IP-10, IL-2,
-400 -200 -100 100 200
-600
-400
-200
200
400
600
103
104
105
109
111
201
203
204
217
219
221
301
303
305
307
310
311
312
316
R= 0.5877
Difference in hippocampal volume from baseline
(mm3)
Differences in Eotaxin-3 levels baseline (pg/ml)
Placebo
Allo Treated
r = 0.59
134
IL-6, IL-10, IFNg, IL-8, VEGF, IL-12, IL-15 and IL-16, had a negative correlation with
increase in hippocampal volume in ApoE4 carriers. The difference in IL-2 levels from
baseline had almost significant negative correlation with increase in hippocampal volume.
Interestingly, the negative correlation with VEGF in ApoE4 carriers was absent in
ApoE3/3 carriers. ApoE3/3 carriers had a positive correlation between change in MCP-4,
IL-10 and IL-8 levels and change in hippocampal volume.
Figure 4.5 Correlation of change in IP-10 levels and hippocampal volume from
baseline during clinical trial duration. Spearman correlation coefficient r = -0.36, the
red dots are participants randomized to the placebo arm and blue squares indicate
participants randomized to the Allo treatment arm. Labels indicate participant number.
Biomarker analysis was also impacted by sex. Changes in proinflammatory biomarker
levels in females across treatment duration had a negative correlation with changes in
hippocampal volume, indicating that an increase in these cytokines led to a reduction in
-1000
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Differences in IP-10 levels baseline (pg/ml)
Difference in Hippocampal volume from baseline
(mm3)
R= -0.36
Placebo
Allo Treated
r = - 0.36
135
hippocampal volume in females. IL-6 had a significant negative correlation (r= -0.70, p
=0.04). IP-10, IL-2, IL-10 and IL-12, followed a similar trend. In males, though a similar
pattern of negative correlation was observed, the spearman correlation coefficient R was
not substantial enough. Much like ApoE3 carriers, males also showed a positive
correlation with MCP-4.
These data suggest that inflammatory biomarkers are impacted by sex and ApoE
genotype in predicting structural volume changes on Allo treatment. But the variances
caused due to ApoE genotype and sex in the correlational association between biomarker
expression and structural volume changes was not as robust in Eotaxin-3.
Inflammatory biomarkers as predictive biomarkers for changes in microstructural
assessment on Allo treatment
We wanted to investigate if we can develop biomarkers that can predict microstructural
changes in white matter tracts on Allo treatment. For studying this, we chose to conduct
microstructural assessment of the medial core of the fornix. Microstructural assessment
included analysis of fractional anisotropy, axial diffusivity, radial diffusivity and mean
diffusivity. For the purposes of this analysis we chose to focus on changes in fractional
anisotropy, as it is a ratio that is indicative of direction of movement of water and can
indirectly hint at myelin integrity.
136
Eotaxin-
3
IP-10 MCP-4 IL-2 IL-6 IL-10 IFN
g
IL-8 VEGF IL-12 IL-15 IL-16
Participant subgroup: All participants
r -0.17 -0.27 -0.33 -0.05 -0.01 -0.29 -0.02 -0.51 0.06 -0.20 -0.23 -0.07
P (two-tailed) 0.50 0.27 0.18 0.83 0.97 0.25 0.94 0.03 0.81 0.44 0.37 0.79
P value summary ns ns ns ns ns ns ns * ns ns ns ns
Participant subgroup: ApoE3/4 and ApoE4/4
r -0.04 -0.33 -0.27 0.24 0.19 -0.01 -0.12 -0.54 0.22 -0.33 -0.70 -0.14
P (two-tailed) 0.92 0.35 0.45 0.51 0.61 >0.9999 0.78 0.11 0.54 0.35 0.03 0.71
P value summary ns ns ns ns ns ns ns ns ns ns * ns
Participant subgroup: ApoE3/3
r 0.00 -0.10 -0.05 -0.40 -0.17 -0.29 -0.24 -0.38 -0.46 0.25 0.43 -0.21
P (two-tailed) >0.9999 0.84 0.93 0.33 0.70 0.50 0.58 0.36 0.30 0.59 0.35 0.66
P value summary ns ns ns ns ns ns ns ns ns ns ns ns
Participant subgroup: Males
r -0.05 0.10 -0.15 -0.15 0.18 -0.38 0.00 -0.73 -0.55 0.14 -0.19 -0.21
P (two-tailed) 0.91 0.81 0.71 0.71 0.64 0.31 >0.9999 0.03 0.17 0.75 0.66 0.62
P value summary ns ns ns ns ns ns ns * ns ns ns ns
Participant subgroup: Females
r -0.45 -0.37 -0.53 -0.15 -0.03 -0.17 0.08 -0.32 0.37 -0.08 -0.05 -0.10
P (two-tailed) 0.23 0.34 0.15 0.71 0.95 0.68 0.84 0.41 0.34 0.84 0.91 0.81
P value summary ns ns ns ns ns ns ns ns ns ns ns ns
Table 4.3 Spearman’s correlation coefficient for changes in detectable cytokines
and chemokines with changes in fractional anisotropy of the medial core of the
fornix in participant subgroups stratified by ApoE genotype and sex. Spearman’s
correlation coefficient (r) for differences from baseline in: Eotaxin-3, Interferon Inducible
Protein-10 (IP-10), Monocyte Chemoattractant Protein-4 (MCP-4), IL-2, IL-6, IL-10,
IFNgamma (IFNg), IL-8, VEGF (Vascular Endothelial Growth Factor), IL-12, IL-15 and IL-
16, and changes in fractional anisotropy are mentioned in the table. Analysis of the
correlation coefficient was done on stratification of subgroups based on ApoE genotype
and sex. Green: correlation coefficient <0.5. Significance of the correlation for each
subgroup is also indicated.
We conducted correlational analysis and calculated the Spearman’s correlation
coefficient for differences from baseline for Eotaxin-3, IP-10, MCP-4, IL-2, IL-6, IL-10,
IFNg, VEGF, IL-12, IL-15 and IL-16 and changes in fractional anisotropy from baseline
after the duration of the treatment period. Spearman correlations were conducted on all
137
participants, ApoE3/4 and ApoE4/4 carriers, ApoE3/3 carriers, males and females (Table
4.3). Sub-group considerations for conducting the spearman’s correlations were similar
to that mentioned for the biomarker analysis for therapeutic response in hippocampal
volume.
The correlational analysis on all participants revealed that IL-8 had a significant negative
correlation (r= -0.51, p-value=0.03) with changes in fractional anisotropy in the medial
core of the fornix, indicating that an increase in IL-8 levels led to a reduction in fractional
anisotropy (Figure 4.6). No other chemokine or cytokine showed similar sensitivity to
changes to fractional anisotropy, on assessing all the participants from the trial. But, all
of them had a negative correlation with changes in fractional anisotropy.
Stratification by ApoE genotype did not impact the biomarker profile much, except IL-15
had a significant negative correlation in ApoE4 carriers (r= -0.71, p-value=0.03) and IL-
8 had a stronger correlation in ApoE4 carriers than ApoE3/3, but the trend remained the
same.
Stratification for sex also impacted correlation analysis of IL-8. Males showed a significant
negative correlation (r= -0.73, p-value= 0.03) with changes in IL-8, and females the
correlation was weaker. Females had a negative correlation with MCP-4, which was not
observed in any other sub-groups.
138
Figure 4.6 Correlation of change in IL-8 levels and fractional anisotropy from
baseline after 12 weeks. Spearman correlation coefficient r = -0.51, the red dots are
participants randomized to the placebo arm and blue squares indicate participants
randomized to the Allo treatment arm. Labels indicate participant identification number
for the trial.
Discussion
The goal of this study was to develop blood-based biomarkers that are predictive of
neurological changes in response to Allopregnanolone (Allo), a regenerative therapeutic.
Using the biomarker guidance released by the FDA as a reference(Group, 2016), we
-8 -6 -4 -2 2
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Placebo
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R = -0.51
Difference in Fractional anisotropy from baseline
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r = -0.51
139
investigated blood-based biomarkers that are predictive of changes in hippocampal
volume and fractional anisotropy in a Phase Ib/IIa clinical trial.
This clinical trial was designed to primarily evaluate Allo’s safety as a regenerative
therapeutic for treatment of Alzheimer’s disease, and secondarily to determine
biomarkers predictive of any therapeutic response. To address the development of
biomarkers for assessing therapeutic response we used the rationale that Allo also has
anti-inflammatory effects as seen in multiple sclerosis(Noorbakhsh et al., 2014). We
hypothesized that Allo’s anti-inflammatory effect may be susceptible to its dose and
hence, could serve as pharmacodynamic biomarker.
For detecting blood-based biomarkers that can serve as pharmacodynamic biomarkers,
we used a 40-biomarker panel composed of inflammatory and vascular markers that can
be detected in an ELISA based format. Proinflammatory cytokines and chemokines: IL-
2, IL-8, IFNg and IP-10 showed a reduction on Allo treatment in comparison to Placebo
group. While not significant, participants in cohort 2 experienced most reduction in these
cytokines, in which Allo was administered at 4 mg/kg dose. Peripherally, IL-2, IFNg and
IP-10 are produced by activated T cells, macrophages, dendritic cells, and NK
cells(Turner et al., 2014). And, IL-8 is secreted by macrophages and endothelial cells and
is known to be a neutrophil chemoattractant(Turner et al., 2014). The levels of Eotaxin-3
and monocyte chemoattractant protein-4 (MCP-4) reduced in the placebo group,
remained almost unchanged in the Allo treated group. In the periphery, Eotaxin-3 is
expressed by vascular endothelial cells and fibroblasts after stimulus from IL-4 and IL-
140
13(Huber et al., 2018). Eotaxin-3 and MCP-4 both induce chemotaxis of peripheral
immune cells, such as eosinophils, monocytes. In the context of neuroinflammation,
Eotaxin-3 and MCP-4 are associated with a phenotype of microglia that participates in
tissue repair and remodeling(Ogilvie et al., 2003;Kohan et al., 2010;Nakayama et al.,
2010;Walker and Lue, 2015). Changes in the levels of these cytokines and chemokines
is indicative of Allo’s modulation of the inflammatory system. Allo could be mediating its
effect through the GABAA receptors, which are expressed by macrophages, T cells and
microglia. In the context of inflammation, Allo has shown to reduce the levels of TNFa in
macrophages on treatment with lipopolysaccharide (LPS)(Ghezzi et al., 2000) and may
modulate myelin mediated antigenic restimulation of T cells, thereby affecting their
proliferation and differentiation(Noorbakhsh et al., 2014). In microglia, while the action of
Allo might be anti-inflammatory, it may cause the activation of a GABA-A dependent
NADPH-oxidase (NOX) enzyme, which may be neuroprotective in nature(Mead et al.,
2012;Silvia et al., 2012;Noorbakhsh et al., 2014).
Studies characterizing mechanisms by which Allo modulates inflammation have been
scarce. This clinical biomarker evaluation of Allo and circulating cytokines and
chemokines is the first in its kind and hints at Allo’s modulation of inflammation broadly.
As the power for conducting a pharmacodynamic biomarker evaluation in a phase Ia/IIb
trial is really low, no significant differences were observed between placebo and treatment
groups. Further proof of concept experiments, by means of in-vitro assessments on
peripheral blood mononuclear cells (PBMCs), which are largely comprised of monocytes
141
and lymphocytes can be used to elucidate the mechanism of Allo’s inflammatory
modulation.
Apart from the production of amyloid beta plaques, disease progression in Alzheimer’s
involves hippocampal atrophy and increase in inflammation, which is evident through
increased secretion of proinflammatory cytokines(Henneman et al., 2009;Wang et al.,
2015). Given that Allo can modulate inflammation and serve as a regenerative
therapeutic, we hypothesized that inflammatory blood-based biomarkers will be helpful in
predicting the therapeutic activity of Allo on regional brain volume changes thus, can
serve as predictive biomarkers. To evaluate this hypothesis, we conducted correlational
analysis between changes in hippocampal volume and changes in inflammatory markers
after 12 weeks of treatment. Amongst the markers tested, Eotaxin-3 had the strongest
correlation with left hippocampal volume. As ApoE4 genotype and female sex are the two
major risk factors for Alzheimer’s disease(2018), we wanted to evaluate if the biomarker
profile is affected by subgroup stratification based on ApoE genotype and sex. Eotaxin-3
showed strong correlations despite of breakdown by subgroups. The effect was stronger
in females and ApoE4 carriers, than males and ApoE 3/3 carriers respectively. As 63%
of the participants in the trial were ApoE4 carriers, of which two/thirds were females,
correlational effect seen in ApoE4 carriers could be driven by the females and vice-a-
versa.
Eotaxin-3, also known as chemokine (C-C motif) ligand 26 (CCL26) is generally produced
in response to IL-4 and IL-13 secretion from Th2 cells(Stubbs et al., 2010;Huber et al.,
142
2018). Some studies have reported higher Eotaxin-3 levels in Alzheimer’s patients in
comparison to healthy controls and MCI patients in serum and cerebrospinal fluid
(CSF)(Soares et al., 2012). Plasma eotaxin-3 levels have also been reported to increase
in multiple sclerosis(Huber et al., 2018). It has also been noted that eotaxin-3 cannot
predict rate of disease progression in Alzheimer’s(Huber et al., 2018). Yet, evaluation of
eotaxin-3 as predictive biomarker for therapeutic response in neurodegenerative
diseases has not been reported. The eotaxin class of molecules function to induce
chemotaxis of a class of white blood cells- eosinophils. These molecules are also known
to have permeability through the blood brain barrier(Huber et al., 2018).
In the context of neuroinflammation, eotaxin-3 has been shown to engage with microglial
receptor CX3CR1(Nakayama et al., 2010;Ransohoff and El Khoury, 2015). Given they
are released in response to IL-4 and IL-13, it is believed that eotaxin-3 has an immune-
modulatory role(Petkovic et al., 2004). Of note, is the pharmacodynamic response of Allo
on eotaxin-3 seen in the cohort analysis. Eotaxin-3 levels decreased in the placebo group
but remained almost unchanged in the treated groups (Figure 4.3F). As Allo functions to
induce neurogenesis and, has an overall anti-inflammatory effect – also evidenced by the
reduction in proinflammatory cytokine levels in this study, it can be hypothesized that the
modulation of inflammation that Allo invokes, requires a phenotype of microglia that
participates in neuroprotection with the contribution of eotaxin-3. Further investigation to
elucidate the mechanism of action needs to be conducted.
143
Allo affects white matter metabolism. It has been shown that progesterone, which Allo is
a derivative of, induces myelin formation(Liao et al., 2009). Allo itself induces the gene
expression of myelin basic protein (MBP) by oligodendrocytes(Baulieu and Schumacher,
1997). Fractional anisotropy (FA) is a ratio that signifies direction of water movement,
which is measured using MRI. Higher values of FA are associated with less diffusivity of
water. One of the conditions that affects water diffusivity is myelin integrity. If myelin
integrity is disrupted it would cause a more diffuse movement of water, which would lower
fractional anisotropy(Kantarci et al., 2017).
We wanted to develop biomarkers that can help us detect changes in myelin integrity of
white matter tracts on Allo treatment. Differences from baseline in IL-8 levels had a
negative correlation with changes in fractional anisotropy over the treatment duration. IL-
8 is a neutrophil chemoattractant, secreted by endothelial cells(Ramesh et al., 2013). A
recent study found IL-8 to be a marker of white matter hyperintensities in Alzheimer’s
patients. Serum IL-8 was elevated in patients with Alzheimer’s in comparison to healthy
controls and was specifically associated with white matter hyperintensities(Zhu et al.,
2017). A similar negative association with increased IL-8 levels and reduced myelin
integrity was observed in a recent study in bipolar disorder(Benedetti et al., 2016).
Although, temporal tracking of IL-8 levels with respect to Alzheimer’s disease progression
and myelin integrity has not yet been conducted, the evidence thus far indicates a causal
relationship between them. Further analysis of Allo’s action on myelin formation with
respect to IL-8 needs to be investigated. Interestingly, the negative correlation of IL-8 was
especially pronounced in ApoE4 carriers in comparison to ApoE3/3, possibly indicating
144
that the dysfunctional apolipoprotein in ApoE4 carriers may be affecting this
pathogenesis.
A caveat of this study was that the number of participants were too small to conduct any
confirmatory analysis. This study was only designed to conduct an exploratory analysis
of biomarkers that could be predictive of neurological changes on treatment with Allo and
would have a pharmacodynamic response to Allo treatment based on dose. With this
study we were able to isolate candidate molecules that could be used for future clinical
trials. Identification of these candidates and further mechanistic studies can help our
understanding of Allo’s modulation inflammation. As several molecules were not detected
while conducting the assay, due to the multiple freeze-thaw cycles and 2-3 years in
storage, future validation analysis can seek to maintain consistency in sample handling
to expand the biomarker list. Usage of random forest analysis and multiple regression
can be done to finesse the outcomes of the study.
In summary, we used Allo’s multimodal mechanism of action (regenerative and anti-
inflammatory activity) to identify biomarkers that can predict neurological changes and
that can be sensitive to Allo’s dosing. We were able to characterize blood-based
biomarkers that can predict the therapeutic response to Allo in patients with MCI due to
AD or Early AD.
145
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Chapter 5: Discussion and Concluding remarks
[Part of this section has been published in Mishra A and Brinton RD (2018) Inflammation:
Bridging Age, Menopause and APOEe4 genotype to Alzheimer’s disease. Front. Aging
Neurosci. 10:312. doi: 10.3389/fnagi.2018.00312]
The focus of this study was to elucidate the mechanisms by which neuroinflammation and
ApoE4 confer risk of Alzheimer’s to the aging female brain. In the female aging profile,
we focused specifically on the perimenopausal transition and the chronological aging
preceding and following it. Studies reviewed herein, show that the perimenopause causes
the emergence of a distinct inflammatory signature in the brain and ApoE4 genotype
impairs energy metabolism in females. Use of inflammatory markers, sex and ApoE
genotype was made to develop biomarkers that are predictive of therapeutic response,
to conduct responder analysis.
In chapter 2, to evaluate the effect of female endocrine aging on neuroinflammation, we
used the perimenopausal animal model, in which each reproductive aging window can be
studied individually. Transcriptomic profiling of the hippocampus indicated that type I and
type II interferon response genes were upregulated during the perimenopause. Aged
senescent animals had an increased MHC-II expression. This distinct gene expression
pattern has been observed in models of neurodegeneration and Alzheimer’s(Keren-Shaul
et al., 2017a;Mathys et al., 2017b). The gene expression pattern during the
perimenopausal transition indicates a step-wise activation of distinct inflammatory
150
programs during different reproductive aging windows, indicating the presence of unique
inflammatory signals during this transition.
Spatial mapping of microglial reactivity marker indicated that white matter tracts – corpus
callosum and fimbria, during reproductively irregular phases were especially susceptible
to inflammation. Increased microglial reactivity in white matter is also seen during
dysregulated interferon signaling – in interferonopathies, when USP18, a regulator of
interferon signaling is knocked out (Goldmann et al., 2015). Distinct upregulation of the
interferon response and expression of microglial reactivity marker MHC-II in the white
matter contributed to pathology in multiple sclerosis (Ottum et al., 2015). Convergence of
these data in the reproductively irregular phase is remarkable, given that the inflammatory
profile observed is a byproduct of endocrine aging. Upregulation of MHC-II in white matter
tracts also bears significance with respect to the metabolic transitions observed in during
the perimenopause. Microglia may be aiding the evolving metabolic demands of the brain,
by presenting myelin antigens. Microglial reactivity throughout the aging phases was
much higher in white matter than grey matter areas indicating that the white matter may
be the source of dysregulation of inflammation.
Unexpectedly, in both the transcriptomic profiling and spatial mapping, reproductively
senescent animals of the same age as reproductively irregular animals, show a starkly
different inflammatory profile from the reproductively irregular animals. They show the
upregulation of myelin clearance and phagocytic receptors, could be compensatory
mechanism. Previous studies have shown that astrocytes utilize fatty acids from myelin
151
to generate ketone bodies that further used to supplement neuronal metabolic demands
(Klosinski et al., 2015). The increased expression of phagocytic receptors and myelin
clearance genes may be participating in the cross-talk with astrocytes.
Microglial phagocytic capacity and mitochondrial superoxide production was also affected
during this endocrine transition. This is important in relation to, increased expression of
microglial reactivity marker in white matter tracts. The increased reactivity could be the
source of superoxide production, which in turn may be impairing microglial phagocytic
capacity. Mitochondrial function in astrocytes and microglia was also affected due to
aging. The effect observed in astrocytes was not only due to female aging, but it has also
been observed in males (Jiang and Cadenas, 2014). Ovariectomy caused the
development of a phenotype that was combinative of chronological and endocrinological
aging, indicating it induces a distinct phenotype. Clinical datasets validated gene
expression patterns observed in rodents and highlighted the sex difference in MHC-II
expression. Though MHC-II expression increases with age, females had much higher
expression values than males. These findings indicate that each reproductive aging
window affects inflammation uniquely.
In chapter 3, we studied the sex differences in metabolic aging using ApoE4 knock-in rats
by conducting a 9 month long longitudinal study followed by a cross-sectional study. This
was to evaluate the effects of ApoE4 genotype and chromosomal sex on metabolic aging
of the brain. Sex difference studies that have evaluated the effect of ApoE4 genotype on
metabolism are scarce. In this study, we showed that the ApoE4 knock-in rat model can
152
be used for modeling the effects of humanized ApoE4 gene. The ApoE4 knock-in rat
model, corresponding to the clinical findings, did not gain weight, had dysregulated
triglyceride levels and high insulin levels. Longitudinal brain glucose uptake
measurements highlighted the importance of endocrine transition in regulating energy
metabolism. Sex differences in brain glucose uptake in both WT and ApoE4 animals was
evident at 9-10 months. While WT females seemed to recover from this, ApoE4 females
did not. Their brain glucose uptake worsened with age. Given the evidence of increased
dependence on ketone bodies as an alternative energy substrate in ApoE4 females, the
reduced glucose metabolism with age may indicate an increasing dependence on ketone
bodies. But it’s not clear when the increased dependence on ketone bodies starts.
Cortical Ab42/40 ratio had an inverse correlation with brain glucose uptake values. In
some populations, Ab42 is the first to emerge as a biomarker, followed by
hypometabolism. Given the inverse correlation we observed between the two, it can be
hypothesized that the reduced glucose uptake in the ApoE4 females can be predicted by
measurement of Ab42. Transcriptomic and metabolomic studies confirmed what was
seen by PET. This study shows that males and females have very different metabolic
profiles through aging.
In chapter 4, to illustrate the point that inflammatory markers are affected by ApoE
genotype and sex, we use the combination of these factors to develop blood markers that
can be predictive of therapeutic response.
153
Future directions
Female endocrine aging has been proven to be critical in development of the prodromal
phase of AD, but not all women who go through menopause are affected by cognitive
dysfunction. A subset of women come out of menopause, being affected with
hypertension and/or autoimmune disorders such as rheumatoid arthritis. Menopause also
causes a worsening of disease severity of multiple sclerosis. Some these diseases, as
mentioned in the previous section, have overlapping mechanisms. Yet, what causes
females to undertake a certain disease trajectory is unknown. Evaluating this disease
trajectory with respect to neuroinflammation in critical for evaluating therapeutics and
prevention paradigms.
Comorbidities like presence of chronic infection and systemic inflammation during the
perimenopause also need to be investigated to develop disease trajectories. Combination
of modifiable risk factors with comorbidities can be investigated to better understand the
resulting outcome.
Given that the female sex is more affected by autoimmune disorders, especially after
menopause(Fairweather et al., 2008). Steroidal hormone regulation of neuroinflammation
needs to more thoroughly be examined. Usage of anti-inflammatory drugs that suppress
immune cell function through aging can further worsen Alzheimer’s disease trajectory.
Targeting specific inflammatory pathways which may be modulated by steroidal
hormones directly or indirectly, may be a more suitable approach.
154
Energy metabolism in ApoE4 carriers needs to be more carefully evaluated with respect
to the fuel source ApoE4 require and at what stage of development. The onset of their
dependence on lipids and ketone bodies as a fuel source needs to be investigated. If this
is occurring early in development, then can interventional therapy be given then to meet
the metabolic demand.
Evidence that ketogenic drugs do not work in ApoE4 carriers, indicates that ketone bodies
from exogenous sources are not preferred and is more complicated than expected.
Trajectory capturing changes in metabolism, dependence on alternate fuel source and
what is the source of the alternate energy substrate all need to be examined.
Sex differences in the metabolic trajectory with respect to age also needs to be examined.
And if especially, female endocrine aging shifts the balance of the energy substrate. The
combination of endocrine transition and ApoE4 genotype affects the female uniquely.
Correlation of the metabolic trajectory and its effect on myelin integrity also needs to be
examined carefully.
Cross-sectional studies characterizing the perimenopause in ApoE4 animals need to be
conducted to develop a endocrine state specific metabolic and inflammatory profile of
ApoE4.
155
Considerations for neuro-inflammation as a therapeutic target
The etiology of the prodromal phase of AD presents as a complex interplay between
several risk factors, which is relevant to therapeutic interventions and preventive
strategies (figure 5.1 and 5.2). This implies that therapeutic strategies should employ
stratification of patient populations regarding parameters of age, sex, and ApoE genotype.
It also calls for the use of combination therapies that modulate inflammation, lipid-based
metabolism in ApoE4 carriers, and loss of estrogenic control in menopausal women. For
example, preventive strategies to reduce age-related inflammation could include a
combination of NSAIDs and statins in middle-aged adults (45-55 years). In women, this
therapy could be modified to include HT during their perimenopausal transition. Patient’s
medical histories and electronic health records are a source of indicators for chronic
inflammation. These strategies should be tailored to the patient’s metabolic profile,
genetic history, and endocrine-related transition states (Figure 5.1 and 5.2)(Mishra and
Brinton, 2018).
This understanding of the disease progression also calls for change in design of clinical
trials that target the amyloidogenic pathway and treat later stages of the disease
pathogenesis. Trial design should incorporate the identification of persons with an
increased risk of developing AD and utilize a risk-factor-based responder analysis.
Inflammation-mediated therapeutic and preventive strategies will largely depend upon
this stratification of patient populations.
156
The inflammatory response is influenced by age, chromosomal sex, endocrine transition
-menopause and APOE genotype. Inflammation is characteristic of each of these
modifying factors and can be a driving force for development of AD. Thus, inflammation
has been a therapeutic target in multiple clinical trials for AD. However, each of these
trials have failed to meet primary endpoints. Going forward, in both discovery and clinical
science, it will be important to delineate the etiology of the inflammatory response, the
stage of the inflammatory cascade, and the activated network of inflammatory signaling.
Inflammation is a moving target and thus requires a precision approach to identifying
etiology, stage and appropriate therapeutic target.
157
Figure 5.1 Inflammation integrates Alzheimer’s disease risk factors of female sex,
chronological age, endocrine aging, and APOEε4 genotype. The three-hit model of
Alzheimer’s risk: aging, menopause, and APOEε4 genotype collectively induce a
compromised bioenergetic system in brain that is impacted by the chronic low-grade
innate inflammation of aging coupled with APOEε4 dysregulated cholesterol homeostasis
lead to activation of the adaptive immune response. The inflammatory immune response
is the factor that bridges across each of the risk factors for AD. Immune system regulators
that are specific to stage of disease and inflammatory phenotype would provide a
therapeutic strategy to disconnect the bridge that drives disease.
158
Figure 5.2 Immune drivers involved in aging, menopause, and APOEε4 genotype
related inflammation. Key immune drivers contributing to inflammation due to aging,
menopause and APOEε4 genotype related inflammation are detailed to give a global
picture of immune dynamics (upward and downward arrows indicate increased and
decreased expression, respectively).
159
Conclusion
The etiology of Alzheimer’s is largely unknown. Neuroinflammation and ApoE4 are
modifiable and genetic risk factors for AD. There is a disparity in prevalence of the disease
in men and women and it can be hypothesized that sex modifies the disease trajectory.
We investigated the interaction of female aging with neuroinflammation and ApoE4
genotype. Herein, we show that aging in females, especially the perimenopausal
transition, causes dynamic shifts in neuroinflammatory profile and renders a metabolic
aging trajectory different from males in ApoE4 animals. Based on this study, approaches
to target the perimenopausal transition to mitigate Alzheimer’s risk should be explored.
160
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Abstract (if available)
Abstract
Alzheimer’s disease is a progressive neurodegenerative disease that is characterized by prodromal state that starts 20 years prior to the symptomatic cognitive decline and the etiology of the disease remains largely unknown. The prevalence of Alzheimer’s is higher in women than men. The female endocrine transition: perimenopause, is a unique time-locked transition that occurs before the onset of the prodromal state and is typified by amyloid-β deposition, bioenergetic deficit and myelin catabolism—factors that contribute to Alzheimer’s disease pathology. Neuroinflammation and ApoE4 genotype are factors that affect disease progression. Yet, the interplay of female aging, especially endocrine aging, with genetic risk factor ApoE4 and neuroinflammation has not yet been elucidated. ❧ To characterize the interplay between female aging and neuroinflammation we used an animal model that mimics the human perimenopausal transition, we characterized the neuroinflammatory profile of the hippocampus during perimenopausal transition. We also evaluated the brain regional susceptibility to inflammation, along with characterizing an endocrine state specific glial functional phenotype. Using mechanistic animal models and clinical microarray datasets we evaluated the translational validity of the findings. ❧ To characterize the effect of ApoE4 genotype on female aging we conducted a sex difference study that comprised of longitudinal metabolic analyses, followed up cross-sectional analyses. Longitudinal assessment of brain glucose uptake to evaluate the effect of female reproductive aging. Metabolomic and transcriptomic analyses were done to validate the longitudinal assessments. ❧ Based on the preclinical findings, we hypothesized that stratification of patients by ApoE genotype and sex would affect the inflammatory biomarker response. To test this we conducted inflammatory biomarker analysis to identify biomarkers that can be predictive therapeutic response in patients with Alzheimer’s. Stratification based on ApoE genotype and sex was done to identify responders.
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Mishra, Aarti
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Neuroinflammation and ApoE4 genotype in at-risk female aging: implications for Alzheimer's disease
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Clinical and Experimental Therapeutics
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