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Modeling neurodegenerative diseases using induced pluripotent stem cells and identifying therapeutic targets

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MODELING NEURODEGENERATIVE DISEASES USING INDUCED PLURIPOTENT STEM
CELLS AND IDENTIFYING THERAPEUTIC TARGETS

by

Stephen Michael Scheeler

A Dissertation Presented To The
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY OF AGING)
August 2021

Copyright 2021 Stephen Michael Scheeler
ii

ACKNOWLEDGEMENTS
Success does not happen in a vacuum. The people that you surround yourself with and
that interact with you along the way play a large part in shaping your achievements. While I have
been pursuing my own goals, I have interacted with others pursuing their own goals in turn, and
in many cases, we have come together and helped each other out. This isn’t to say there haven’t
been hardships, or people that have actively tried to bring me down; I have encountered such
offenders many times. I have had the great fortune, however, of being surrounded by more than
enough good people to offset the negative individuals and outcomes that tend to crop up along
the way. In this section I hope to thank at least a small portion of those individuals and
organizations that have helped me.
To start off, I would like to thank my family for their help along the way, particularly my
parents. You have done so much more than merely raise me, and offering me guidance and
wisdom when I was young. You have been an endless source of encouragement and support,
and I don’t know what I would have done without you. Even when I’ve had trouble describing
my goals for myself, you have always been supportive of what I have tried to do, and I cannot
express my gratitude enough to you. I wish you all the best and will try to be there when you
need me, just as you have always been there for me when I needed you.
I would also like to thank the friends that I have made, both those that I have kept up with
and those that I have lost track of. Each of you has provided something new for me; a different
way to see or interact with the world around me. We have influenced one another, and I can
only see such growth as for the best. I thank my friends from the art department in high school,
iii

you all provided so many different perspectives and helped me grow to be more open minded.
To my friends in college, particularly Nick Swisher, I would like to thank you for always being a
good friend and for introducing me to the endless random fun of magic, and the creative joys of
tabletop games, and for always being up for a pointless argument. I hope we can once again get
together to play a few games and to watch Last of the Mohicans when I visit the east coast. To
my friends in graduate school, particularly Jesse Simons, I would like to thank you for the endless
laughs and for all the games played late into the night after experiments; these years would have
been much more difficult without you and the rest of the group.
Of the people I met in graduate school, I wish to give special mention to Megumi Mori.
As a senior member of the program, your insights and experiences were invaluable in progressing
through a new and ever-changing landscape, as requirements and obligations often changed on
a yearly basis. More than this, though, you have been an incredible partner and a wonderful
friend, who has been with me through the best and worst these last few years have had to offer.
You have provided endless encouragement and insight, and I am incredibly grateful to you. I look
forward to our continued time together, beyond the program and into the rest of what life has
in store for us, knowing that we will be facing it together.
In terms of my career, I would like to start by thanking the PI of the first lab I was ever in,
the lab of Patrick McNutt. Within your lab I learned more than I had in the entirety of my time
in undergrad, and I will always value the in-depth conversations the lab would have regarding
the research projects, as we tried to determine the story of the research and the future
directions. I think back fondly on my time with you and the other members in the lab: Ian Gut,
iv

Phil Beske, Kyle Hubbard, Katie Hoffman, and others, and I reminisce on misplaced fireworks and
Rumpelstiltskin. I would also like to thank my second PI from the NIA, Sige Zou, who saw some
promise in me over all other candidates for the post-baccalaureate position his lab, and taught
me so much about the aging research that fascinates me. I only regret that I joined at a time
when the lab was disbanding, and so was not able to get to know everyone as well as I could
have. Aside from that, Sige was the one that directed me to the newly formed Biology of Aging
PhD program between the Buck Institute and USC.
I am grateful to USC for taking in a student that was more passionate about the subject
of aging than about grades in undergrad. Joining this program I was able to learn a great deal
about the aging process, and meet wonderful people along the way. I would like to thank the
Graduate Student Society at the Buck Institute, which I joined shortly after deciding to stay at the
Buck, which has often assisted the students in achieving their goals and improving the program,
with or without support from the administration. The supplementary lectures really helped to
broaden my horizons in regards to the science in other institutes and job availabilities and
cultures outside of academia, and their social events really helped me to find new friends in this
new place far from my former home.
While at the Buck I’ve had the pleasure of meeting people that would become both
friends and colleagues in the years I have been here. I have already mentioned Jesse Simons, but
I would also like to thank Dr. Barbara Bailus for everything she did while we worked together.
You are a great friend and a trusted ally, and I don’t think I would have lasted without your help.
You taught me so much of the science within the lab, but also how to relax and pragmatically
v

deal with the incredible amount of issues that pop up when dealing with unexpected research
and unreasonable people. I may not have always learned the lessons in a timely manner, or at
all when it comes to ignoring unreasonable people, but I have always appreciated that you taught
me what you could and stood up for me at times when others would not. I would also like to
thank the other members of the lab, past and present, such as Alex, Melia, Maria, Kizito, Jennifer,
Ningzhe, Carlos, Kelly, and the many others that I have interacted with, who have provided their
own unique brands of conversation, scientific assistance, and camaraderie.
This brings me to the graduate committee, and especially Lisa Ellerby. I would like to
thank the committee for being a part of my graduate career since the time of my QE. You have
always been supportive of me and have often given me good advice, both scientifically and in
terms of the program. I am grateful to you and I am glad I chose you all as my committee
members, especially Gordon Lithgow, who has been a source of encouragement and
understanding since the very beginning of the program, when I was having trouble fitting in and
finding a lab. I would like to particularly thank Lisa Ellerby, my PI for my time at the Buck Institute.
You have introduced me to a large amount of research and ways of thinking of science that I had
not been familiar with before. You took me into your lab when so many others would not, and
allowed me to research the projects within, helping grow my skills as a scientist grow over the
last few years. I am very grateful to you, and cannot imagine how my life might have been in
another lab or program. Most importantly though, by being in your lab I was able to not only
grow through your influence, but through the influences of those in your lab and in the program
in general.
vi

In the following chapters, Chapter 2 is a manuscript currently being written with the
following authorship: Stephen Scheeler, Sicheng Song, Carlos Galicia Aguirre, Jesse Simons, Akos
Gerenscer, Kizito Tshilenge, Emily Parlan, Houda Benlhabib, Judith Campisi, Simon Melov, Sean
D. Mooney, Lisa M. Ellerby. I would like to thank Sicheng Song and Carlos Galicia Aguirre for their
work in the RNA-seq analysis. I would also like to thank Jesse Simons and Akos Gerenscer for
their work in analyzing the BioTek footage in order to calculate the density of the neuronal
network and the movement of the different APOE inhibitory GABANeurons.
Chapter 3 is a version of the journal article entitled “Modulating FKBP51 and Autophagy
Lowers Huntingtin Levels”, published in Autophagy (April; in press, 2021). This manuscript was
published with the following authors: Barbara J. Bailus, Stephen Scheeler, Jesse Simons, Maria A.
Sanchez, Kizito Tshilenge, Jordi Creus-Muncunill, Swati Naphade, Alejandro Lopez-Ramirez,
Ningzhe Zhang, Kuruwitage Lakshika Madushani, Stanislav Moroz, Ashley Loureiro, Katherine H.
Schreiber, Felix Hausch, Brian K. Kennedy, Michelle E. Ehrlich, and Lisa M. Ellerby. For all figures,
references to a given paper are provided per the standard APA citation; otherwise, individuals
are referenced. For almost all figures, input from all lab members was necessary, and data for
each figure may have been provided by multiple people. For all figures not open source, I
contributed in their data acquisition, data analysis, and/or the figure editing.
vii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ................................................................................................................................. ii
LIST OF FIGURES ........................................................................................................................................... xi
LIST OF TABLES ............................................................................................................................................ xii
ABBREVIATIONS ..........................................................................................................................................xiii
ABSTRACT .................................................................................................................................................... xvi
CHAPTER 1: INTRODUCTION ......................................................................................................................... 1
APOLIPOPROTEIN E ................................................................................................................................... 1
APOE General Overview ........................................................................................................................ 1
Impact of APOE Alleles in Neurological Diseases .................................................................................. 2
APOE Function ...................................................................................................................................... 2
APOE Expression ................................................................................................................................... 3
APOE Variants and Structure ................................................................................................................ 6
APOE Function in the Brain ................................................................................................................. 10
Neurodegenerative Disease and APOE ............................................................................................... 14
APOE2 and Exceptional Longevity ...................................................................................................... 15
HUNTINGTON’S DISEASE ......................................................................................................................... 18
Huntington’s Disease Overview .......................................................................................................... 18
Huntingtin Protein .............................................................................................................................. 19
Mechanisms of HD Pathogenesis ........................................................................................................ 21
Modeling HD with Induced Pluripotent Stem Cells ............................................................................ 23
Pathway Analysis of HD Models.......................................................................................................... 25
Therapeutic Targets and Treatments for HD ...................................................................................... 26
CHAPTER 2: OBSERVING APOE ALLELE-SPECIFIC EFFECTS IN GABANEURONS ........................................... 27
ABSTRACT ................................................................................................................................................ 27
BACKGROUND ......................................................................................................................................... 28
RESULTS .................................................................................................................................................. 31
GABANeuron Characterization ........................................................................................................... 31
RNA-seq of GABANeurons Reveals a Wide Array of Changes between Genotypes ........................... 33
viii

Single-Cell RNA-seq Shows Unique Populations of GABANeurons Relative to Genotype .................. 36
APOE4 GABANeurons Contain More DNA-Repair and Damage Metrics ............................................ 38
Repetitive Elements Affected by APOE Genotype .............................................................................. 40
APOE4 GABANeurons Exhibit A Wobbly Movement .......................................................................... 43
APOE4 GABANeurons Create Smaller Neural Networks ..................................................................... 46
DISCUSSION ............................................................................................................................................. 49
SUPPLEMENTS ......................................................................................................................................... 54
CHAPTER 3: MODULATING FKBP51 AND AUTOPHAGY LOWERS HUNTINGTIN .......................................... 61
ABSTRACT ................................................................................................................................................ 61
BACKGROUND ......................................................................................................................................... 62
RESULTS .................................................................................................................................................. 65
FKBP51 Is Decreased in the HD zQ175 Knock-in Mouse Model ......................................................... 65
FKBP Expression Is Altered in HD R6/2 Mice ...................................................................................... 67
Differential Expression of FKBPs in HD Striatum and Cortex .............................................................. 69
FKBP51 Levels Are Decreased in zQ175 Mice at 6 Months of Age ..................................................... 71
HTT and FKBP51 Co-IP in Knock-in Mouse Models of HD with Increasing PolyQ Repeat Length ...... 73
HD NSCs Have Altered Expression of FKBP51 ..................................................................................... 75
FKBP51 Expression Levels Are Decreased in HD Medium Spiny Neurons .......................................... 77
siRNA Knockdown of FKBP51 in HD NSCs Reduces mHTT Levels ....................................................... 79
SAFit2 Treatment of HD NSCs ............................................................................................................. 79
SAFit2 Treatment of HD NSCs Alters LC3-II and p62 Levels ................................................................ 83
Comparing Rapamycin and SAFit2 Molecular Mechanisms in HD NSCs ............................................. 86
SAFit2 Treatment in R6/2 and zQ175 Mice Lowers HTT Levels .......................................................... 89
DISCUSSION ............................................................................................................................................. 92
SUPPLEMENTS ......................................................................................................................................... 97
CHAPTER 4: MATERIALS AND METHODS .................................................................................................. 103
APOE METHODS .................................................................................................................................... 103
Culturing of APOE2 and APOE4 GABANeurons ................................................................................. 103
Immunocytochemistry of APOE2 and APOE4 GABANeurons ........................................................... 104
RNA Extraction of APOE2 and APOE4 GABANeurons ....................................................................... 105
Bulk RNA sequencing of APOE2 and APOE4 GABAergic Neurons ..................................................... 107
Single Cell RNA isolation of APOE2 and APOE4 GABAergic Neurons ................................................ 107
Neuronal Extension Methods ........................................................................................................... 107
ix

Cell Movement Analysis .................................................................................................................... 108
APOE2 and APOE4 GABAergic inhibitory neurons comet assay ....................................................... 109
RNA ANALYSIS AND RNA-SEQ LIBRARY CREATION ............................................................................... 111
RNA Sequencing Analysis .................................................................................................................. 111
Terminology Enrichment Analysis and Pathway Enrichment Analysis ............................................. 112
GeneMANIA Gene Regulatory Network Analysis ............................................................................. 112
Gene Regulatory Network Inference Through Data Curation .......................................................... 113
Gene Set Enrichment Analysis .......................................................................................................... 113
Repetitive Repeat Element Analysis ................................................................................................. 114
scRNA-seq Analysis ........................................................................................................................... 114
HD FKBP51 METHODS ........................................................................................................................... 115
NSC Culturing .................................................................................................................................... 115
Transcriptomic Analysis of HD NSC ................................................................................................... 116
siRNA Knockdown of FKBP51 in NSC................................................................................................. 116
Treatment with SAFit2 in NSC ........................................................................................................... 117
MSN Culture ...................................................................................................................................... 117
Maintenance and Breeding of zQ175 and R6/2 Mice ....................................................................... 118
SAFit2 Treatment of R6/2 and zQ175 Mice ...................................................................................... 118
Mouse Brain Dissection and Homogenization .................................................................................. 119
Western Blot Analysis for FKBP51 siRNA Knockdown in NSC ........................................................... 120
Western Blot Analysis for FKPB51 in NSC and MSN Treated with SAFit2 ......................................... 121
Western Blot Analysis of HTT and mHTT in HD NSC and MSN ......................................................... 121
LC3 Westerns .................................................................................................................................... 122
Western Blot Analysis Comparing SAFit2 and Rapamycin Treatment in NSC .................................. 122
Caspase Activity Assay Comparing SAFit2 to Rapamycin Treatment in NSC .................................... 124
Western Blotting for FKBPs in HD Transgenic Mouse Brain Lysates ................................................ 124
Western Blot Analysis in R6/2 and zQ175 Treated with SAFit2 ........................................................ 125
IHC Staining ....................................................................................................................................... 126
Co-Immunoprecipitation with HTT or FKBP51 Antibody .................................................................. 127
Immunocytochemistry of NSC .......................................................................................................... 128
Immunocytochemistry of Human MSN ............................................................................................ 128
Statistical Analysis ............................................................................................................................. 129
CHAPTER 5: CONCLUSIONS ....................................................................................................................... 130
x

REFERENCES .............................................................................................................................................. 135

xi

LIST OF FIGURES
Figure 1.1. Chylomicrons............................................................................................................................... 5
Figure 1.2. APOE Location and Structure ...................................................................................................... 8
Figure 1.3. APOE Structural Changes per Genotype ................................................................................... 14
Figure 1.4. Effect of APOE Genotype on Human Longevity ........................................................................ 16
Figure 1.5. Mutant Huntingtin Protein ....................................................................................................... 20
Figure 1.6. Mechanisms of HD Pathogenesis .............................................................................................. 22
Figure 2.1. GABANeurons exhibit expected GABAergic and neuronal markers ......................................... 32
Figure 2.2. Bulk RNA-seq of GABANeurons ................................................................................................ 34
Figure 2.3. GABANeuron scRNA-seq Analysis ............................................................................................. 37
Figure 2.4. APOE4 GABANeurons exhibit more DNA damage compared to APOE2 .................................. 39
Figure 2.5. GABANeuron Repetitive Elements Affected by APOE Genotype ............................................. 41
Figure 2.6. APOE4 GABANeurons exhibit a more wobbly movement ........................................................ 44
Figure 2.7. APOE4 GABANeurons extend fewer neurites over time .......................................................... 47
Supplemental Figure 2.1. Brightfield imaging of GABANeurons ................................................................ 54
Supplemental Figure 2.2. GABANeuron Network Analysis ......................................................................... 56
Supplemental Figure 2.3. Comet assay of NSCs with and without treatment ........................................... 58
Supplemental Figure 2.4. Lipidomics of NSCs ............................................................................................. 60
Figure 3.1. FKBP expression levels in HD zQ175 mouse model .................................................................. 66
Figure 3.2. FKBP expression levels in HD R6/2 mouse model ..................................................................... 68
Figure 3.3. Expression levels of FKBPs in cortex versus striatum in wild-type and zQ175 mice ................ 70
Figure 3.4. Temporal changes in FKBP51 levels in zQ175 mice .................................................................. 72
Figure 3.5. FKBP51 interaction and colocalization with HTT ...................................................................... 74
Figure 3.6. FKBP51 expression and localization in human neural stem cell model .................................... 76
Figure 3.7. FKBP51 levels in human medium spiny neurons derived from patient HD induced
pluripotent stem cells ................................................................................................................................. 78
Figure 3.8. Evaluation of HTT levels with genetic or pharmacological inhibition of FKBP51 in NSCs ........ 81
Figure 3.9. Changes in the expression of LC3B and p62 with SAFiT2 treatment in HD NSCs ..................... 84
Figure 3.10. Comparing SAFit2 and Rapamycin in HD NSC ......................................................................... 87
Figure 3.11. HTT levels in R6/2 and zQ175 mouse models treated with SAFit2 ......................................... 90
Figure 3.12. Model of the potential mechanism of clearance of HTT with FKBP51 modulation ............... 96
Supplemental Figure 3.1. HTT levels in zQ175 mice at 6 and 12 months of age ........................................ 97
Supplemental Figure 3.2. FKBP51 interacts with HTT in the midbrain ....................................................... 98
Supplemental Figure 3.3. FKBP51 and HTT colocalization .......................................................................... 99
Supplemental Figure 3.4. Dysregulation of FKBPs in human HD NSCs ..................................................... 100
Supplemental Figure 3.5. LC3-II flux analysis ............................................................................................ 101
Supplemental Figure 3.6. SAFit2 dose-response in R6/2 Mice ................................................................. 102
xii

LIST OF TABLES
Table 1.1. Human APOE Genotypes .............................................................................................................. 6
Supplemental Table 2.1. Repetitive elements ............................................................................................ 55
Supplemental Table 2.2. GABANeuron transcription factors ..................................................................... 57

xiii

ABBREVIATIONS
AD: Alzheimer’s disease
APOE: Apolipoprotein E
GWAS: Genome-Wide Association Studies
Aβ: Amyloid-β
APP: Amyloid Precursor Protein
HD: Huntington’s Disease
HTT: Huntingtin
PD: Parkinson’s Disease
ALS: Amyotrophic Lateral Sclerosis
LDL: Low Density Lipoprotein
VLDL: Very Low-Density Lipoprotein
HDL: High Density Lipoprotein
HSPG: Heparan Sulfate Proteoglycan
SNPs: Single Nucleotide Polymorphisms
CNS: Central Nervous System
BBB: Blood Brain Barrier
ER: Endoplasmic Reticulum
UPR: Unfolded Protein Response
PON1: Paraoxonase 1
MMP9: Matrix Metalloproteinase 9
TIMP1: Tissue Inhibitor of Metalloproteinase 1
xiv

HEAT: Huntingtin, Elongator factor3, PR65/A regulatory subunit of PP2A, and Tor1
iPSCs: induced Pluripotent Stem Cells
mHTT: mutant Huntingtin
GABANeuron: iPSC-Derived GABAergic Inhibitory Neuron
UMIs: Unique Molecular Identifiers
scRNA-seq: Single-Cell RNA Sequencing
ICC: Immunocytochemistry
VCAN: Versican
DSCAM: DS Cell Adhesion Molecule
NSCs: Neural Stem Cells
PolyQ: Polyglutamine
PCA: Principle Component Analysis
IHC: Immunohistochemistry
FKBP: FK506-Binding Protein
PTSD: Post-Traumatic Stress Disorder
Pink1: PTEN-Induced Putative Kinase
Co-IP: Co-Immunoprecipitation
LC3: Microtubule-Associated Protein Light Chain 3
BafA1: Bafilomycin A1
PPIase: Peptidyl-Prolyl cis/trans-Isomerases
WT: Wild-Type Control
KO: Knock Out
xv

ON: Overnight
BSA: Bovine serum albumin
DMSO: Dimethyl Sulfoxide
DTT: Dithiothreitol
MES: 2-Ethanesulfonic Acid
MOPS: 3-(N-Morphorlino)Propanesulfonic Acid
RT: Room Temperature
mTOR: Mammalian Target of Rapamycin
TBST: Tris-Buffered Saline, 0.1% Tween20
SDS-PAGE: Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis
xvi

ABSTRACT
My research in graduate school focused on studies of neurodegenerative diseases and
aging using induced pluripotent stem cells models, molecular and biochemical assays, and mouse
models. My two main projects focused on: one, understanding the role of FKBPs in HD; and two,
how the different APOE variants could differentially affect GABANeurons derived from human
iPSCs. In my first project, FKBPs were evaluated for how they influence HD pathology and
molecular mechanisms. We found that the family member FKBP51 had significantly altered levels
during disease progression in both human and mouse models of HD. We found FKBP51 affected
HD by altering the capacity of the cell to clear huntingtin aggregates and toxic fragments. This
research leads us to believe the FKBPs may be a valid target for therapeutic intervention in HD.
In my second project, different APOE alleles were studied in reference to GABANeurons in order
to understand how the APOE2 allele is involved in mediating exceptionally long-lived
individuates. Genomic and biochemical analysis of isogenic APOE GABANeurons (APOE2 vs
APOE4) revealed lower levels of endogenous DNA damage in the APOE2 GABANeurons. This
suggests a potential mechanism through which certain APOE alleles specifically promote healthy
aging, and thereby suggests avenues that may be targetable in future research.
1

CHAPTER 1: INTRODUCTION
APOLIPOPROTEIN E
APOE General Overview
Apolipoprotein E (APOE) is the gene encoding for the APOE protein, a fat-binding
glycoprotein that helps regulate fat metabolism in the body by transporting cholesterol and other
triglyceride-rich lipoproteins to cell surface lipid receptors to be metabolized (Mahley & Rall,
2000). The APOE genotype of an individual consists of any pairing of three allelic variants: APOE2,
APOE3, and APOE4 (Ghebranious, Ivacic, Mallum, & Dokken, 2005). Of the allelic variants, the
APOE3 allele is the most commonly found and accounts for 77% of the population while the
APOE2 and APOE4 alleles account for 8% and 15%, respectively. The differences between APOE
alleles lead to changes to the protein’s structure and function (Calero et al., 2016; Go et al., 2015;
S. Schmidt et al., 2002). These allelic differences garnered interest following several genome-
wide association studies (GWAS) (Broer et al., 2015), linking APOE with neurodegenerative
diseases (primarily Alzheimer’s) (Tudorache, Trusca, & Gafencu, 2017), cardiovascular disorders
(Meir & Leitersdorf, 2004), longevity (Jeck, Siebold, & Sharpless, 2012), oxidative stress, and
immunological processes (Dose, Huebbe, Nebel, & Rimbach, 2016; Jofre-Monseny, Minihane, &
Rimbach, 2008). These disease associations have been shown to be neutral, detrimental, or
possibly even protective depending on the APOE allele expressed by a given patient, particularly
in the context of neurodegenerative diseases (Broer et al., 2015; Dose et al., 2016).

2

Impact of APOE Alleles in Neurological Diseases
Neurodegenerative diseases are characterized by a progressive loss of function and
structure in healthy brains, predominantly by loss of neurons (Bredesen, Rao, & Mehlen, 2006;
Ising & Heneka, 2018). Diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD),
Amyotrophic Lateral Sclerosis (ALS), and Huntington’s disease (HD) are all examples of
neurodegenerative diseases (Forno, 1992). APOE has been shown to affect AD and PD in an allele
dependent manner (Giau et al., 2015). Whether there is a disease-modifying effect from the
APOE genotype is not known in the context of HD (Kalman et al., 2000; Panas, Avramopoulos,
Karadima, Petersen, & Vassilopoulos, 1999). HD and AD are highly studied neurodegenerative
diseases that demonstrate some common features. Both diseases are characterized by misfolded
proteins, which lead to an aggregation of proteins; short cleaved-toxic protein fragments,
resulting in endoplasmic reticulum (ER) and mitochondrial stress (Ehrnhoefer, Wong, & Hayden,
2011; Graves et al., 2017).

APOE Function
Discovered in the 1970’s, the APOE gene is located on chromosome 19 in the same
genomic cluster as apolipoprotein C1 and C2. Consisting of four exons and three introns, the
APOE gene encodes for the APOE protein. APOE is a 34 kDa glycoprotein that is involved in the
transport and metabolism of lipids. While up to 75% of all APOE is synthesized by liver
parenchymal cells (Mahley, 1988; Rall, Weisgraber, & Mahley, 1982), the remaining APOE is
synthesized in adipocytes, astrocytes, macrophages, and mesangial cells (G. Chen et al., 2001; D.
3

L. Williams, Dawson, Newman, & Rudel, 1985). The primary function of APOE is to facilitate the
delivery of complexes containing solubilized lipids and lipoproteins to cells throughout the body
(Ghebranious et al., 2005; Mahley & Rall, 2000). The background on APOE will be discussed in
reference to the most common allelic variant, APOE3, unless otherwise noted.

Lipoproteins are amphipathic molecules which bind to hydrophobic lipids which form
complexes of soluble lipids for delivery to the cell. Apolipoproteins bind to these complexes in
order to deliver these lipids to cells by binding to apolipoprotein specific receptors (Ang, Cruz,
Hendel, & Granville, 2008; Eichner et al., 2002). These lipid complexes are composed of fatty
acids, sterols, fat-soluble vitamins, and glycerides; and are used to store and produce energy, for
signaling, and act as components in cell structure (Fahy et al., 2009). Changes in the APOE gene
can lead to changes in APOE’s protein structure and resulting function. As one of the primary
methods of delivering lipids to cells throughout the body, changes in APOE function can have
detrimental effects in cells, particularly those that have high energy demands like neurons. One
of the ways in which APOE dysregulation has been shown to lead to disease, is by binding to the
wrong types of lipid complexes and delivering the wrong types or amount of lipids into the cells
(Tetali et al., 2010; Tudorache et al., 2017).

APOE Expression
4

APOE is primarily made in the liver (Mahley, 1988), but is fundamental for the delivery of
lipoproteins and lipids to tissues throughout the body. After translation and secretion, APOE can
be found in human blood and lymph. While in the blood, APOE facilitates the emulsification of
the insoluble lipids into a more soluble amalgamation of amphipathic proteins and fats (Eichner
et al., 2002). An example of this can be seen in digestion, where the intestine converts nutrients
into lipids and essential vitamins, which are then combined with regular lipoproteins to form
chylomicrons. These chylomicrons are then secreted into the bloodstream where they interact
with circulating APOE, which binds to receptors on liver and adipose tissue (Ang et al., 2008;
Beisiegel, Weber, & Bengtsson-Olivecrona, 1991). An example of a chylomicron and its
embedded apolipoproteins can be seen in Figure 1.1 (Gordon Betts et al., 2013). The liver will
also secrete APOE, along with phospholipids and cholesterol esters, in endogenously synthesized
very low-density lipoprotein (VLDL) particles. APOE is attached to these circulating particles
through a binding site with a high affinity for the low density lipoprotein (LDL) receptor family,
which allows the cells with the necessary receptors to take up the circulating lipid complexes
(Getz & Reardon, 2009; Weisgraber, 1994). APOE has also been discovered to have the metabolic
role of collecting lipoprotein remnants in the blood, and excess cellular cholesterol, which can
then be taken back up by the liver to be eliminated. In addition, by removing lipoproteins from
locations such as the arterial wall, APOE is able to act as a mechanism of cholesterol efflux in the
form of high-density lipoprotein (HDL) particles through HDL receptors, or through the heparan
sulfate proteoglycan (HSPG) binding domain. Changes to the binding affinities of APOE for
different lipoprotein particles have been shown to relate to the prevalence of neurodegenerative
disease (Rosenson et al., 2012; Rothblat & Phillips, 2010; Zanotti et al., 2011).
5

Figure 1.1. Chylomicrons

The degree to which APOE affects various diseases is related to its allele type (Table 1.1).
As stated earlier, APOE encodes for any paired combination for any three human allelic variants.
The allelic variants, found on exon 4 of APOE, are ε2, ε 3, and ε4, and give rise to the APOE2,
APOE3, and APOE4 isoforms (Greenow, Pearce, & Ramji, 2005). These alleles are single
nucleotide polymorphisms (SNPs), differing only in 1-2 base pairs located at amino acid positions
112 and 158. The APOE2 protein has two cysteines (Cys
112
and Cys
158
), APOE3 contains one
Figure 1.1. Chylomicrons.
“Chylomicrons contain triglycerides, cholesterol molecules, and other apolipoproteins (protein
molecules). They function to carry these water-insoluble molecules from the intestine, through the lymphatic
system, and into the bloodstream, which carries the lipids to adipose tissue for storage” (Betts et al., 2013)
Access for free at https://openstax.org/books/anatomy-and-physiology/pages/1-introduction.
6

cysteine and one arginine (Cys
112
and Arg
158
), and APOE4 contains two arginines (Arg
112
and
Arg
158
) (Dong & Weisgraber, 1996; Weisgraber, Rall, & Mahley, 1981). These allelic variants have
been shown to have inverse relationships in relation to disease and longevity, where APOE4 is
negatively correlated with health while APOE2 is positively correlated with health (Ang et al.,
2008; Weisgraber, 1994).

Table 1.1. Human APOE Genotypes
Genotype Amino Acids Prevalence AD Likelihood
APOE2 Cys
112
& Cys
158
8% 0.6-fold
APOE3 Cys
112
& Arg
158
77% 3.6-fold
APOE4 Arg
112
& Arg
158
15% 14.6-fold

APOE Variants and Structure
APOE is composed of three regions: the N-terminal domain, the C-terminal domain, and
a protease-sensitive hinge region which connects them (Raussens, Slupsky, Sykes, & Ryan, 2003).
The N-terminal domain is arranged in a four-helix bundle, which allows APOE to bind to low
Table 1.1 Human APOE Genotypes.
The three most common APOE genotype found in the human population are APOE2, APOE3, and
APOE4, occurring in the population with a prevalence of 8%, 77%, and 15%, respectively. At amino acids 112
and 158 APOE2 contains two cysteines, APOE3 contains a cysteine and an arginine (at either location), and
APOE4 contains two arginines. Depending on the APOE allele, one’s chance of developing AD changes; APOE4,
APOE3, or APOE2 can lead to altered risks of 14.6-fold, 3.6-fold, and 0.6-fold, respectively. Table made by
Stephen Scheeler.
7

density lipoprotein (LDL) receptors and contains the heparin sulfate proteoglycan (HSPG) binding
domain, which results in weaker lipid binding interactions (Wilson, Wardell, Weisgraber, Mahley,
& Agard, 1991). The C-terminal domain is arranged as multiple amphipathic α-helix repeats,
which mediate the binding affinity of APOE to other lipoproteins and allows for tetramers to form
through APOE self-association (J. Chen, Li, & Wang, 2011; Garai & Frieden, 2010; Wilson et al.,
1991). The functional regions of APOE have been shown to be evolutionarily conserved, with
variation arising more on the ends of the protein, away from the areas needed for high-affinity
lipid binding (Frieden, 2015).

Of the 299 amino acids that APOE consists of, the N-terminal domain constitutes amino
acids 1-191 while the C-terminal domain constitutes amino acids ~206-299. It is in the N-terminal
domain where the three allelic variants of APOE can be found. The N-terminal and C-terminal
domains consist of regions that interact with one another to form salt bridges; specifically at
residues Arg
61
and Glu
255
(Frieden & Garai, 2012, 2013). The different allelic APOE variants are
known to have their own affinities for this salt bridge formation, and as such can have different
structural preferences (Dong & Weisgraber, 1996). Figure 1.2 shows a general representation of
APOE and its N terminal and C terminal regions, as well as where they differ between genotypes
(Giau et al., 2015). The single nucleotide polymorphisms (SNPs) responsible for the differences
in allelic APOE variants are known to contribute to different protein structures. The allelic
variants of APOE have been shown to contribute directly to the structural differences found in
the N-terminal region of APOE; as well as indirectly to the C-terminal region, which then results
8

in changes in protein stability, binding affinity, and function (Chetty, Mayne, Lund-Katz,
Englander, & Phillips, 2017; H. Li et al., 2013; Mahley, 2016; Sakamoto et al., 2008).

Figure 1.2. APOE Location and Structure

9

In terms of protein stability, the APOE2 variant has been shown to be more stable when
compared to its arginine richer counterparts, particularly APOE4 (Acharya et al., 2002). The
stability of a salt-bridge formation is altered in APOE3 and APOE4, and may contribute to the
formation of a reactive globule state, or changes in tetramer-to-monomer protein abundance
(Mahley, 2016; Morrow et al., 2002). It has been suggested that APOE4 may even have unique
domain interactions, preferring to interact with VLDL, which is associated with negative
outcomes of Alzheimer’s disease (AD) and atherosclerosis (Gregg et al., 1986; Knouff et al., 1999;
Mahley, Weisgraber, & Huang, 2009). These structural changes are caused by the positively
charged Arg
112
residue, which interacts with the C-terminal domain to affect lipoprotein binding.
With an additional positive charge at Arg
158
, APOE4 is even more destabilized, due to the Arg
158
residue inhibiting normal domain interactions within the N-terminal region, and disrupts domain
interactions of the C-terminal region needed for lipoprotein binding (Frieden & Garai, 2012;
Nguyen et al., 2014; B. Williams, 2nd, Convertino, Das, & Dokholyan, 2015).

Changes in APOE structure lead to changes in binding affinity, and protein function. In
reference to the normal function of lipid transport, APOE3 and APOE4 have full receptor and
Figure 1.2. APOE Location and Structure
(A) Location of the APOE gene on chromosome 19, along with its 4 exons and 3 introns. (B) Of the
299 amino acids that APOE consists of, the N-terminal domain constitutes amino acids 1-191 while the C-
terminal domain constitutes amino acids ~206-299; connected with a protease sensitive hinge region. (C) At
amino acids 112 and 158 APOE2 contains two cysteines, APOE3 contains a cysteine and an arginine (at either
location), and APOE4 contains two arginines (Giau, Bagyinszky, An, & Kim, 2015).
10

lipoprotein binding capacity. However, APOE2 is defective in its capacity to bind to lipid
receptors, and has about 1% of the binding capacity of its counterparts (Weisgraber, Innerarity,
& Mahley, 1982). The highly reactive, yet unstable, nature of APOE4 makes the protein a
promising target of study in the context of many disorders, such as cardiovascular disease and
atherosclerosis (Mahley et al., 2009); through binding specificity with VLDL/HDL particles and
cholesterol reuptake (Cullen et al., 1998; Knouff et al., 1999). APOE variants are more strongly
attributed to neurodegenerative diseases like AD (Giau et al., 2015; Mahley, 2016), and as such
AD has been the primary focus upon which we evaluate APOE alleles.

APOE Function in the Brain
In order to fully provide lipid transport throughout the body, APOE must be present and
readily available for cell populations with high lipid needs (Saher et al., 2005). In order to ensure
this, the levels of APOE are kept constantly circulating in the blood and lymph. In addition, APOE
producing cells are usually found in proximity to energy dependent cells such as neurons
(Holtzman, Herz, & Bu, 2012; C. C. Liu, Liu, Kanekiyo, Xu, & Bu, 2013). The need for specialized
cells that create localized APOE is especially necessary in the brain, where APOE and lipids in the
blood cannot easily gain access due to the presence of the Blood Brain Barrier (BBB) (Poirier,
Hess, May, & Finch, 1991). In the central nervous system (CNS), APOE is produced primarily by
astrocytes, and it supplies neuronal populations through de novo synthesized lipids made in order
of decreasing amount: oligodendrocytes>astrocytes>neurons (Beffert et al., 1998; Bu, 2009).
Within the CNS, APOE has been shown to affect a wide array of biological functions in an allele
11

dependent manner, particularly in relation to neurodegenerative disease (Mahley, 2016; Mahley
et al., 2009; Manelli, Stine, Van Eldik, & LaDu, 2004).

APOE4 contains two arginines at amino acid residues Arg
112
and Arg
158
. These two
arginines result in the destabilized contraction of the APOE protein, and promote the formation
of a salt bridge between residues Arg
61
and Glu
255
on the N and C-terminal domains, respectively
(Dong & Weisgraber, 1996; Q. Xu, Brecht, Weisgraber, Mahley, & Huang, 2004). Formation of
the salt-bridge leads to a change in lipid particle binding preferences, away from the normal high
density lipoprotein (HDL), to very low-density lipoprotein (VLDL) (Nguyen et al., 2010). This
formation and loss of stability causes the endoplasmic reticulum to read the protein as misfolded,
resulting in endoplasmic reticulum (ER) stress (Ross, Olson, & Coppotelli, 2015; Zhong,
Ramaswamy, & Weisgraber, 2009). Prolonged ER and oxidative stress caused by APOE4 can then
lead to the activation the unfolded protein response (UPR) of the cell, which can result in the
activation of NF-kB and other pro-apoptotic factors, resulting in cell death following prolonged
activation of these pro-apoptotic factors (Bravo et al., 2012; Chaudhari, Talwar, Parimisetty,
Lefebvre d'Hellencourt, & Ravanan, 2014). APOE4 has also been shown to have an increased
susceptibility to proteolytic cleavage. The cleavage of APOE4 results in proteotoxic C-terminal
fragments (Harris et al., 2003; Y. Huang et al., 2001), which inhibit mitochondrial complexes III
and IV, decreasing glucose metabolism and ATP production (Chang et al., 2005; Nakamura,
Watanabe, Fujino, Hosono, & Michikawa, 2009). This inhibition of mitochondrial respiration has
been predominantly observed in neurons, suggesting tissue specific effects of these allelic
12

variants in relation to cellular energy needs, and neurodegenerative diseases (James et al., 2012;
P. T. Xu et al., 2006).

APOE4 has been demonstrated to have a lower binding and clearance affinity towards Aβ,
which results in accelerated accumulation of amyloid-β (Aβ) (Brecht et al., 2004; Namba,
Tomonaga, Kawasaki, Otomo, & Ikeda, 1991), and APOE4 fragments have been shown to increase
levels of hyper-phosphorylated tau (Harris, Brecht, Xu, Mahley, & Huang, 2004; Liraz, Boehm-
Cagan, & Michaelson, 2013; Namba et al., 1991). Levels of anti-oxidant and anti-inflammatory
metallothioneins, Nrf2, and paraoxonase 1 (PON1) have been shown to be lower with respect to
APOE4, compared to other allelic variants (Boesch-Saadatmandi et al., 2010; Graeser et al., 2011;
Graeser et al., 2012). Additional research has shown that APOE4 interacts with and regulates
levels of matrix metalloproteinase 9 (MMP9). This regulation of MMP9 affects downstream levels
of tissue inhibitor of metalloproteinase 1 (TIMP1), which has been shown to alter Aβ clearance
in the brain. APOE4 has also been shown to increase IL-1β and decreases IL-10 expression while
also decreasing levels of TNFα expression which then increases expression of TIMP1, leading to
an increase in inflammation (Dafnis, Tzinia, Tsilibary, Zannis, & Chroni, 2012; Yan et al., 2006).
The increase in inflammation has been observed in vitro and in vivo with co-cultured neurons and
glial cells showing increased markers of inflammation such as NF-kB (Petegnief, Saura, de
Gregorio-Rocasolano, & Paul, 2001), and in animal models (Teng et al., 2017). This increase in
inflammation is highly correlated with AD (Jofre-Monseny et al., 2008). Similarly, our lab has
shown that matrix metalloproteinases play a significant role in Huntington’s disease (HD)
13

pathology (Naphade, Embusch, Madushani, Ring, & Ellerby, 2017). It is expected that because
APOE regulates MMP9, the pathology of HD will be affected by APOE in an allele dependent
manner.

APOE2 contains two cysteines at amino acid residues Cys
112
and Cys
158
. These cysteines
confer added stability onto the protein and discourage salt-bridge formation between Arg
61
and
Glu
255
(Emi et al., 1988; Mahley et al., 2009; Weisgraber, 1994). Without salt-bridge formation,
APOE2 will preferentially bind to HDL particles (L. Wu & Zhao, 2016). An image of this salt bridge
and how it affects protein formation and binding can be seen in Figure 1.3. This allele, however,
is much less effective at binding to low density lipoprotein (LDL) receptors, comparable to 1% the
binding efficiency of the other allelic variants. This lower binding efficiency leads to increased
levels of circulating cholesterol, and can rarely lead to hyperlipoproteinemia (Weisgraber et al.,
1982). APOE2, compared to the other allelic variants, has been shown to confer neuroprotective
effects in neurodegenerative disease, particularly AD. It has been theorized that preferential
binding to high density lipoprotein (HDL) particles may confer some protection for neurons
against less healthy lipids, such as those in very low-density lipoprotein (VLDL) particles (L. Wu &
Zhao, 2016). Research into APOE isoforms has also shown isoform specific antioxidant properties
in order of APOE2>APOE3>APOE4 due to the number of free –SH groups on the respective allelic
variants and their propensity to be proteolytically cleaved (Miyata & Smith, 1996; Stephens, Bain,
& Humphries, 2008). APOE2 has been found to have an increased binding and clearance capacity
for aggregated Aβ, compared to APOE4 (Brecht et al., 2004; Namba et al., 1991; Verghese et al.,
14

2013). However, the exact mechanism of neuroprotection conferred by APOE2 is unknown,
possibly due to the rarity of the mutation within the population (~8% worldwide) (Dose et al.,
2016; L. Wu & Zhao, 2016).

Figure 1.3. APOE Structural Changes per Genotype

Neurodegenerative Disease and APOE
Alzheimer’s Disease (AD) was originally described by Alois Alzheimer in 1906 (Alzheimer,
Stelzmann, Schnitzlein, & Murtagh, 1995), and is a neurodegenerative disease that leads to
gradual cognitive decline, changes in behavior, and dementia (Alzheimer's, 2016; Mega,
Cummings, Fiorello, & Gornbein, 1996). The main features of AD are the formation of
extracellular amyloid precursor protein (APP) and amyloid-β (Aβ) plaques, and intracellular
hyper-phosphorylated tau tangles (Mahley, Weisgraber, & Huang, 2006). As of 2015 AD affected
Figure 1.3. APOE Structural Changes per Genotype
The N-terminal and C-terminal domains consist of regions which interact with one another to form
salt bridges; specifically at residues Arg
61
and Glu
255
. This formation occurs readily in the APOE4 allele, and
leads to protein instability (Fernandez, Hamby, McReynolds, & Ray, 2019).
15

about 29 million individuals worldwide, and this number is only expected to grow as the
percentage of the population that is 65 or older continues to rise (Disease, Injury, & Prevalence,
2016). Genome-wide association studies (GWAS) studies have shown that the likelihood of
developing AD is increased in an APOE allele dependent manner (Bertram & Tanzi, 2009; Kamboh
et al., 2012; Tosto & Reitz, 2013). Possessing homozygous alleles for APOE4, APOE3, or APOE2
can lead to increased or decreased risks of 14.6-fold, 3.6-fold, and 0.6-fold, respectively.
Compared to the common APOE3 variant, APOE2 appears to inhibit AD while APOE4 increases its
likelihood and age of onset (Farrer et al., 1997). In this same manner, the different APOE allelic
variants also exacerbate AD pathology, increasing a patient’s decline if they possess APOE4 in a
gene dose-dependent manner (Corder et al., 1993). The exact mechanisms behind how APOE
influences AD remain to be determined, but it has been hypothesized that the altered protein
structure and stability of APOE4 may lead to proteomic and metabolomic stress which may kill
or senesce the cells following an extended period of time (Dose et al., 2016; Tudorache et al.,
2017; Wei, Zhang, & Zhou, 1999).

APOE2 and Exceptional Longevity
APOE2 has been shown to contribute to exceptionally longevity in model organisms, and
in humans (Shinohara et al., 2020). The first indications of this trend came about in gene studies
between elderly and younger populations, showing a higher prevalence of the APOE2 allele in
both men and women that had survived into old age (Cauley, Eichner, Kamboh, Ferrell, & Kuller,
1993; Frisoni, Louhija, Geroldi, & Trabucchi, 2001; Schachter et al., 1994). This trend was
16

confirmed in several more cross-sectional studies that identified APOE2 in the exceptionally long-
lived, and APOE4 in those individuals with a greater degree of morbidity (Corder et al., 1996;
Hirose et al., 1997; Rosvall et al., 2009). APOE has also been shown to come up in more unbiased
genome-wide association studies (GWAS) studies that have explored/examined longevity, once
again finding that individuals with the APOE2 allele often survive longer than those with the
APOE3 and the APOE4 allelic variants of the gene (Deelen et al., 2011; Nebel et al., 2011;
Sebastiani et al., 2012; Zeng et al., 2016). From one such study, a Kaplan-Meier survival curve was
made to show the different survival rates for individuals with different APOE alleles (Figure 1.4).
However, despite these many comparisons, the mechanism behind which APOE2 may confer this
beneficial phenotype is rarely discussed, and the exact mechanisms are still not completely
understood (Z. Li, Shue, Zhao, Shinohara, & Bu, 2020). The facts are made more interesting when
presented with the knowledge that, while APOE2 may be protective and beneficial in relation to
AD, the benefits of APOE2 are also seen when comparing perfectly healthy and neurotypical
individuals with different alleles (Smith et al., 1994; Wolfson et al., 2001). This suggests that the
benefits of APOE2 may be completely independent of an individual’s disease state altogether,
benefiting perfectly healthy individuals (Di Battista, Heinsinger, & Rebeck, 2016; C. C. Liu et al.,
2013; Montagne et al., 2020; Shinohara et al., 2020).
Figure 1.4. Effect of APOE Genotype on Human Longevity

17

As mentioned earlier, APOE4 and APOE2 allelic variants have been found to affect the
clearance of senescent cells, the clearance of aggregated amyloid-β (Aβ) and
hyperphosphorylated tau, the oxidative capacity of the resulting APOE protein, endoplasmic
reticulum (ER) stress, the binding affinity to lipid particles like cholesterol and VLDL, and the
prevalence of a variety of proteins including: MMP9, IL-1β, IL-10, TNFα, NF-kB, Nrf2, and PON1
(Brecht et al., 2004; Chaudhari et al., 2014; Dafnis et al., 2012; Graeser et al., 2012; Mahley et al.,
2009; Ross et al., 2015; Shinohara et al., 2016; Stephens et al., 2008; Tudorache et al., 2017; L.
Wu & Zhao, 2016; Yan et al., 2006). These pathways and proteins work within and without AD
to impact health and longevity in individuals, and do not necessarily affect one another in a clear
Figure 1.4. Effect of APOE Genotype on Human Longevity
Compared to the most common APOE variant, APOE3; APOE2 has an increased correlation with the
likelihood of reaching exceptional longevity, while APOE4 decreases that same likelihood. An example of which
can be observed in the Kaplan-Meier survival curve (Shinohara et al., 2020).
18

manner. To be involved in so many processes in such a significant manner, it is possible that
APOE variants differentially affect the transcription of a variety of proteins within, at least, the
pathways mentioned earlier (Theendakara, Peters-Libeu, Bredesen, & Rao, 2018). Alternatively,
since APOE is primarily a transporter of lipids for energy, the allelic differences of APOE that affect
lipid binding could result in a change in activity levels in accordance with an individual’s APOE
allele variant (Shinohara et al., 2020). An increase in physical activity through APOE2, or a
decrease due to APOE4, based on the binding affinity of lipids for each allele, may affect the many
downstream pathways involved in exercise. Such an effect could change pathways in a way
similar to what we see above, as exercise itself has been shown to increase longevity, and alter
its many associated pathways (Basso & Suzuki, 2017; De Feo et al., 2003; Gremeaux et al., 2012;
Raichlen & Alexander, 2014).

HUNTINGTON’S DISEASE
Huntington’s Disease Overview
Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease
described by George Huntington in 1872 (Wexler, Wild, & Tabrizi, 2016). The disease is
characterized by a CAG expansion in in exon 1 of the Huntingtin gene (HTT), which codes for
glutamine, resulting in a polyglutamine (polyQ) expansion. HD affects 3-6 individuals per 100,000
worldwide, with a higher prevalence in people of European descent (Pringsheim et al., 2012). HD
symptoms can include chorea (a movement disorder), and like AD: cognitive decline, changes in
behavior, and eventual dementia (Alzheimer's, 2016; Mega et al., 1996; Pringsheim et al., 2012).
19

This CAG expansion results in a longer polyQ tract in the huntingtin protein (HTT) which is cleaved
into toxic fragments, or forms aggregates (Wang et al., 2008). Like amyloid precursor protein
(APP) in AD, HTT is ubiquitously in the body, but the primary disease phenotypes are seen in
neurons (Priller et al., 2006; van der Burg, Bjorkqvist, & Brundin, 2009). Also like AD, cholesterol
and lipid homeostasis are de-regulated, inhibiting energy dependent tissue functions such as:
axon guidance, cell migration, synaptic plasticity, and general cell survival (Arenas, Garcia-Ruiz,
& Fernandez-Checa, 2017; Handley et al., 2016; J. P. Liu et al., 2010; Trushina & Mielke, 2014).

Huntingtin Protein
Huntingtin protein (HTT) is a highly conserved pleiotropic, multifunctional protein of 350
kDa in size that is highly conserved, especially within vertebrates (Baxendale et al., 1995; Saudou
& Humbert, 2016; Tartari et al., 2008). HTT is expressed throughout the body but is more
abundant within the brain and testes; specifically in neurons and glial cells in within the brain
(Fusco et al., 1999; Landwehrmeyer et al., 1995; Sapp et al., 1997). The expression of HTT begins
during the embryonic stage, and the loss of the protein in knock-out mice is embryonic lethal,
suggesting a necessity of the protein during early development (Zeitlin, Liu, Chapman,
Papaioannou, & Efstratiadis, 1995). HTT has been shown to interact with a wide array of
subcellular compartments, particularly within different cell types, and may even change its
structural conformation depending on the cell type and subcellular localization (DiFiglia et al.,
1995; Gutekunst et al., 1995; Ko, Ou, & Patterson, 2001). As a base, though, HTT is believed to
20

have the conformation of flexible, elongated super-helical solenoid (W. Li, Serpell, Carter,
Rubinsztein, & Huntington, 2006).

This shape is similar to other conserved proteins that form protein-protein interactions,
which contain HEAT (Huntingtin, Elongator factor3, PR65/A regulatory subunit of PP2A, and Tor1)
repeats. HEAT repeats alter the protein conformation to allow for protein transport, microtubule
distribution, and chromosome segregation (Andrade & Bork, 1995; Neuwald & Hirano, 2000).
Like these other solenoid-shaped proteins, HTT variants between species also contain HEAT
regions, which seem conserved in their number, sequence, and distribution along the protein
(Tartari et al., 2008; S. Zhang, Feany, Saraswati, Littleton, & Perrimon, 2009). However, the
studies involving HTT are primarily in relation to HD, and the expansion of CAG in the HTT gene
that results in an expanded polyQ tract at the N-terminus of the HTT, thereby driving HD
pathogenesis (Hedreen, Peyser, Folstein, & Ross, 1991; Kiyama, Seto-Ohshima, & Emson, 1990).
Figure 1.5. Mutant Huntingtin Protein
21

Mechanisms of HD Pathogenesis
As mentioned previously, HD is an autosomal dominant neurodegenerative disease
characterized by a CAG expansion in exon 1 of the huntingtin gene (HTT). HD affects 3-6
individuals per 100,000 worldwide, with a higher prevalence in people of European descent
(Pringsheim et al., 2012). The CAG expansion of HTT results in a longer polyQ-mutated form of
the HTT which is cleaved into toxic fragments, or forms aggregates, where the rate of aggregate
formation is dependent on the length of the polyglutamine (polyQ) expansion (Scherzinger et al.,
1999; Wang et al., 2008). The expansion of the protein, and subsequent aggregation/cleavage
of the mutant form of the HTT protein, disrupts healthy biological processes, and can be viewed
Figure 1.5. Mutant Huntingtin Protein
Huntington’s Disease is characterized by a CAG expansion in exon 1 of the Huntingtin gene. This
expansion results in a mutant form of the protein that leads to the disease (Technology, 2011).
22

in the context of a loss-of-function coupled with a gain-of-toxicity (Imarisio et al., 2008; Schulte
& Littleton, 2011).

The mutant HTT’s loss-of-function is proportional to the length of the polyQ expansion,
resulting in a variety of negative effects (Scherzinger et al., 1999). As part of these negative
effects, the expanded mutant HTT is recognized as deleterious, and as such the cell tries to
remove it. However, this removal is incomplete and subsequently overloads the protein recycling
system of the ubiquitin-proteasomal degradation pathway, throwing off cellular homeostasis
(Bence, Sampat, & Kopito, 2001; Waelter et al., 2001). In addition, the mutant form of the protein
also binds to different proteins than it normally would, such as the CREB-binding protein, p53,
BDNF, and ULK1, thus lowering the viability of other proteins in the cell while simultaneously
increasing aggregation and increasing mitochondrial dysfunction (Nucifora et al., 2001; Steffan
et al., 2000; Wanker, Ast, Schindler, Trepte, & Schnoegl, 2019). To better study the pathogenic
mechanisms of mutant HTT, and well as its transcriptional effects, one significant tool of use has
been induced Pluripotent Stem Cells (iPSCs) (An et al., 2014; An et al., 2012; Naphade et al., 2017;
N. Zhang, Bailus, Ring, & Ellerby, 2016).

Figure 1.6. Mechanisms of HD Pathogenesis
23

Modeling HD with Induced Pluripotent Stem Cells
Neurological diseases can be difficult and expensive to study in the context of animal
models and humans. As an alternative, relevant cell types can be studied in relation to a given
disease, and in the cases of a genetic disease can be used as a model for that same disease. By
using human pluripotent stem cells, particularly iPSCs, one can have a cell line specific to a disease
Figure 1.6. Mechanisms of HD Pathogenesis
“Potential molecular pathogenesis of toxicity of mHtt aggregates. Mutant huntingtin may affect the
aberrant interaction with or sequester transcription factors leading to transcriptional dysregulation of many
genes. Moreover, mutant huntingtin causes defects in trafficking of vesicle and cellular organelle such as
mitochondria through long dendritic and axonal projections by affecting both molecular motors and
microtubules. Finally, mutant huntingtin directly influence to decrease the Ca
2+
threshold for MPT pore
opening by interaction with the outer mitochondrial membrane, leading to Cyt c release and apoptosis. Mutant
huntingtin (mHtt), cAMP response element binding protein (CREB) binding protein (CBP), TATA-binding protein
(TBP), specificity protein 1 (SP1) and TBP-associated factor, 135 kDa (TAFII-130), brain-derived neurotrophic
factor (BDNF), mitochondrial permeability transition (MPT), Cytochrome c (Cyt c)” (Kim & Kim, 2014).
24

that can be further differentiated into the cells of disease relevance, thereby easing the
constraints of studying said disease (Okita, Ichisaka, & Yamanaka, 2007). The use of iPSCs allows
for more efficient disease modeling, as well as the evaluation of cell specific therapies and
avenues towards regenerative medicine (Brennand et al., 2011; S. M. Wu & Hochedlinger, 2011).
Evaluating cell specific effects of a given disease can be further clarified through the use of
isogenic cells, in which a healthy cell line and a diseased cell line come from the same parent cell,
but have been genetically altered to differ only in relation to the disease (N. Zhang et al., 2016).

The Ellerby lab has performed extensive research in the field of neurodegeneration in HD
and in developing human isogenic iPSC lines. Specifically, the Ellerby lab has developed methods
of culturing HD iPSCs and their neuronal derivatives for use in studying HD pathogenesis and drug
development, as well as genetically corrected isogenic controls. These lines have been used for
a variety of comparative studies and have expressed a variety of differences between the two
lines, particularly in cells that have been either semi-differentiated or fully differentiated towards
a neuronal lineage (An et al., 2014; An et al., 2012; Naphade et al., 2017; N. Zhang et al., 2016).
Specifically, these alterations seemed to affect the maturation and homeostasis of fully
differentiated medium spiny neurons (MSNs), as well as a seeming loss of cell identity. The
changes identified through the study of these cell lines allows for the further identification of
relevantly affected pathways and, subsequently, therapeutic targets for HD (O'Brien et al., 2015;
Ring et al., 2015a).

25

Pathway Analysis of HD Models
Through the analysis of human brains, rodent models, and cell models, a variety of
changes between pathways and transcriptional alterations have been identified between healthy
and HD affected models (Cha, 2007; Crook & Housman, 2011; Ring et al., 2015a; Thomas et al.,
2011). These changes in transcription are most prominent in the cells most affected by HD, the
cells of the striatum (Tabrizi et al., 2013; Vonsattel et al., 1985). The transcriptional changes
observed may be due to the protein-binding changes mentioned earlier as a result of the mutant
huntingtin protein (mHTT), or they could be due to a direct interaction with the genomic DNA.
Both forms of the HTT protein have been shown to interact with genomic DNA, and could likely
alter transcription directly, or even alter the structure of the overall chromatin (Benn et al., 2008;
Seong et al., 2010; Thomas et al., 2008).

Several pathways that are significantly altered include gene groups that modulate the
downregulation of genes in the synaptic function pathways, pathways that are upregulation in
association with neurodegeneration, general mitochondrial dysfunction, abnormal alternative-
splicing, and a loss in cellular homeostasis (Labadorf et al., 2015; L. Lin et al., 2016; Ring et al.,
2015a; Seredenina & Luthi-Carter, 2012; van Hagen et al., 2017). Pathway and transcriptional
analyses of HD models have yielded a great deal of data, and subsequent research has delved
ever deeper into that same data in search of new findings and pathways. As a result, the field’s
understanding of how HD affects transcription, and subsequently pathology, is ever increasing,
26

and yields information on potentially therapeutic avenues (Ament et al., 2018; Bashir, 2019;
Pandey & Rajamma, 2018).

Therapeutic Targets and Treatments for HD
Transcriptional changes, in mouse models, are some of the earliest detectable markers
for HD pathogenesis (Bragg et al., 2017; Seredenina & Luthi-Carter, 2012). With greater
knowledge of the transcriptional changes between HD and healthy models, therapeutic targets
for treating HD can be gleaned. Of the pathways altered in HD, the targetable ones for potential
therapeutics have included: synaptic disruption, aberrant vesicle trafficking, impaired
proteostasis and aggregation, mitochondrial dysfunction, and excitotoxicity (Grimm & Eckert,
2017; Jones & Hughes, 2011). From these pathways, therapeutic avenues that have been
pursued involve: HTT lowering and modulation strategies, immunotherapy, stem cell
transplantation, and enhanced synaptic expression (Bashir, 2019). Even now, there are a number
of ongoing clinical trials searching for the best way to decrease HD pathogenesis (Rodrigues,
Quinn, & Wild, 2019). However, to date there are no candidates that have come forward that
successfully exhibit a therapeutic benefit into late-stage clinical trials (Travessa, Rodrigues,
Mestre, & Ferreira, 2017). To this end, new therapeutics and new strategies need to be
developed for diseases that still need successful avenues for treatment, including HD.

27

CHAPTER 2: OBSERVING APOE ALLELE-SPECIFIC EFFECTS IN
GABANEURONS
ABSTRACT
The exact cause(s) of Alzheimer’s disease (AD) are unknown and have thus far been
difficult to determine due to the complexity of the disease. However, we do know that
differences in one’s APOE alleles can significantly correlate with whether one develops AD, and
how severe that AD may be. Specifically, we know that individuals possessing the APOE2 allele
are relatively protected from AD, while individuals with the APOE4 allele are more prone to the
disease and are more likely to experience a more severe pathology. Like the cause of AD, we do
not know how precisely APOE affects the prevalence and pathology of AD. To determine the
exact affects APOE has on neurons, and how those affects may contribute to AD, we needed to
use a model that compared the alleles with as little background and confounding factors as
possible, as opposed to the current model of human studies. To that end, we utilized an iPSC
derived isogenic model of inhibitory GABAergic neurons (GABANeurons) to evaluate the genetic
and molecular changes that came about as a result of differences in this specific protein;
specifically, between APOE2 and APOE4. From this comparison of APOE2 and APOE4
GABANeurons, we were able to learn that different APOE alleles can differentially influence
endogenous DNA damage and repair. This suggested a potential method through which certain
APOE alleles are specifically detrimental to healthy aging, and thereby suggests avenues that may
be targetable in future research.
28

BACKGROUND
The APOE protein is a lipid-binding glycoprotein that helps control fat metabolism in the
body by transporting cholesterol and other triglyceride-rich lipoproteins to cell surface lipid
receptors to be metabolized. APOE facilitates the emulsification of the insoluble lipids into a
more soluble amalgamation of amphipathic proteins and fats (Eichner et al., 2002; Mahley & Rall,
2000). The APOE genotype of an individual consists of any pairing of three allelic variants: APOE2,
APOE3, and APOE4 (Ghebranious et al., 2005). The allelic variants of APOE have been shown to
correlate directly to the structural differences found in the N-terminal region of APOE; as well as
the C-terminal region indirectly, which then results in changes in protein stability, binding affinity,
and function (Chetty et al., 2017; H. Li et al., 2013; Mahley, 2016; Sakamoto et al., 2008). Changes
in the APOE gene lead to changes in APOE’s structure and resulting function. As one of the
primary methods of delivering lipids to cells throughout the body, changes in APOE function can
have detrimental effects in cells, particularly those that have high energy demands, such as
neurons in the central nervous system (CNS) (Tetali et al., 2010; Tudorache et al., 2017).
Accordingly, the individual APOE genotypes have been associated with unique metabolic profiles
and differential contributions to health and longevity (Mahley, 2016; Mahley et al., 2009; Manelli
et al., 2004).

The overarching effects of these unique allelic differences have garnered interest
following several GWAS (Broer et al., 2015), linking APOE to neurodegenerative diseases
(primarily AD) (Tudorache et al., 2017), cardiovascular disorders (Meir & Leitersdorf, 2004),
29

longevity (Jeck et al., 2012), oxidative stress, and immunological processes (Dose et al., 2016;
Jofre-Monseny et al., 2008). These disease associations have been shown to be neutral,
detrimental, or even protective depending on the APOE allele expressed by a given patient,
particularly in the case of neurodegenerative diseases. Specifically, APOE2 has been shown to be
protective in these metrics, while APOE4 has been shown to be detrimental, while the common
APOE3 variant is considered a baseline. However, the exact mechanism of neuroprotection
conferred by APOE2 is unknown, possibly due to the rarity of the mutation within the population,
thus making the study of APOE2 a relatively untapped field of interest (Broer et al., 2015; Dose
et al., 2016; L. Wu & Zhao, 2016).

Evaluating the effects of APOE2 compared to APOE4, however, can be difficult and
expensive to study in the context of animal models and humans. As an alternative, relevant cell
types can be studied in relation to a given disease, and in the cases of a genetic disease can be
used as a model for that same disease. By using human pluripotent stem cells, particularly
induced pluripotent stem cells (iPSCs), one can have a cell line specific to a disease that can be
further differentiated into the cells of disease relevance, thereby easing the constraints of
studying said disease (Okita et al., 2007). This ease of study facilitates disease modeling, as well
as the evaluation of cell specific therapies and avenues towards regenerative medicine
(Brennand et al., 2011; S. M. Wu & Hochedlinger, 2011). Evaluating cell specific effects of a given
disease can be further clarified through the use of isogenic cells, in which a healthy cell line and
30

a diseased cell line come from the same parent cell, but have been genetically altered to differ
only in relation to the disease (N. Zhang et al., 2016).

Isogenic lines can be further differentiated into cell lines of relevance for a given disease.
One of the most important cell types to evaluate regarding differential APOE alleles is GABAergic
inhibitory neurons. In AD, the most common disease associated with APOE, GABAergic inhibitory
neurons are molecularly dysregulated. Changes in GABA transporter expression and subunit
compositions have been shown to be altered in human hippocampal samples of AD, independent
of patient age (Fuhrer et al., 2017; Govindpani et al., 2017; Kwakowsky et al., 2018). Such
changes include network dysfunction, synaptic dysregulation, and general excitatory/inhibitory
balance, contributing to the symptoms of AD (Calvo-Flores Guzman et al., 2018; Marczynski,
1998). AD dysregulation is even dependent on the patient’s APOE allele, and therefore make a
fine model for the study of APOE in otherwise healthy cells (Najm, Jones, & Huang, 2019).

31

RESULTS
GABANeuron Characterization
GABAergic neurons are one of the most important cell types affected in AD. APOE has
the capacity to dysregulate GABA subunits and transporter expression, leading to a disruption of
the excitatory/inhibitory balance and downstream AD disease markers (Calvo-Flores Guzman et
al., 2018; Najm et al., 2019). In our research, we utilized human iPSC-derived GABAergic
inhibitory neurons (GABANeurons) homozygous for either APOE2, or APOE4. These two lines
were also isogenic to one another, meaning that they were derived from the same parent line
and differed only in their APOE alleles. To show that the derived lines were, in fact, GABAergic
neurons we performed immunocytochemistry (ICC) with a variety of markers, including: VGAT,
GABA, CALBINDIN, and NESTIN (Figure 2.1). Calbindin and NESTIN showed that these cells are
specifically neurons (Kojetin et al., 2006; Steinert et al., 1999). VGAT and GABA show that the
GABANeurons are positive for the neurotransmitter and its transporter (Chaudhry et al., 1998),
confirming that the cells are indeed GABAergic. Consistent with published works (Brecht et al.,
2004; Knoferle et al., 2014; Najm et al., 2019), we observed a loss of the GABAergic network in
our cells containing APOE4, compared to APOE2. Overall, GABANeurons of both genotypes stained
with similar intensity for Calbindin, NESTIN, and other markers (data not shown). The phase
contrast imaging using brightfield also provides further characterization of GABANeurons
(Supplement Figure 2.1).

32

Figure 2.1. GABANeurons exhibit expected GABAergic and neuronal markers

Figure 2.1. GABANeurons exhibit expected GABAergic and neuronal marker
GABANeurons were evaluated using ICC to look at GABAergic (VGAT and GABA) and neuronal
markers (NESTIN) as proof of their cell type. Both APOE2 and APOE4 were positive for each marker
evaluated. Cells were viewed at 63X using a Zeiss confocal microscope. Scale bar, 20 µm. Figure made by
Stephen Scheeler.
33

RNA-seq of GABANeurons Reveals a Wide Array of Changes between Genotypes
We evaluated our isogenic GABANeurons, with two distinct genotypes, using RNA-seq
analysis. This allowed us to discover the differences in transcription between the two different
APOE genotypes. For the analysis, bulk RNA-seq was performed on three wells of a 6-well plate
containing APOE2 and APOE4 GABANeurons. To determine the variance between the samples
and the genotypes, gene expression values were transformed into principle components, a set
of linearly uncorrelated features which represent the most relevant sources of variance in the
data, and subsequently visualized via scatterplot. This analysis showed that the APOE2 and the
APOE4 GABANeurons grouped separately, based on genotypes rather than sample (Figure 2.2A).
The RNA-seq data was further analyzed by volcano plot and heatmap, where the volcano plot
demonstrated the changes in gene expression that have been thresholded for the visualization
of the most differentially expressed genes. Genes further from the center are more enriched
between the two alleles, and each point in the plot represents an individual gene (Figure 2.2B).
The heatmap created shows that most genes cluster between samples of the same group and
displays: how many genes were altered in total, how many went up, and how many went down.
(Figure 2.2C). Both the heatmap and the volcano plot identify several unique genes that are
significantly enriched between the two APOE genotypes, with some interesting genes being
veriscan (VCAN) and DS cell adhesion molecule (DSCAM). The large list of genes that were altered
overall were categorized into bins and used to determine the overall biological, cellular, and
molecular pathways altered between the two genotypes (Figure 2.2D). Categories of particular
interest to us involve aspects of DNA damage and repair.
34

Figure 2.2. Bulk RNA-seq of GABANeurons

35

Figure 2.2. Bulk RNA-seq of GABANeurons
(A) Principle component analysis (PCA) of the three biological replicates for both APOE2 and APOE4,
with most of the variance coming from the genotype rather than the replicate. (B) Volcano plot of the RNA-
seq data, comparing APOE2 over APOE4, highlighting the most altered genes. (C) Heatmap of the RNA-seq
data relative rlog-transformed values of the count of the differentially expressed genes (adjust p-value<0.01)
across samples. Differentially expressed genes are named to the right of the figure. (D) Compiled list schematic
displaying the categorized gene ratios for the RNA-seq data obtained between the APOE2 and APOE4
GABANeurons. Figure made by Sicheng Song.
36

Single-Cell RNA-seq Shows Unique Populations of GABANeurons Relative to Genotype
To observe transcriptional differences at the resolution of a single cell, we performed 10X
single-cell RNA-seq (scRNA-seq) on APOE2 and APOE4 GABANeurons. 7,328 cells were obtained
after qualifying parameters, filtering for cells with at least 200 features and specifically more than
1000 unique molecular identifiers (UMIs), with fewer than 10% of the uniqueness coming from
mitochondrial transcripts. Seurat (Satija, Farrell, Gennert, Schier, & Regev, 2015) was used to
perform cluster analysis to identify differentially expressed genes. Cluster analysis of the scRNA-
seq revealed six distinct clusters, with both genotypes contributing cells to all clusters (Figure
2.3A). However, the cells were distributed differently among the clusters depending on their
genotype. Clusters 0, 1 and 3 had a higher number of APOE4 cells, while cluster 4 had a higher
proportion of APOE2 cells (Figure 2.3B). Genotypic markers for each cluster were then identified
by their gene abbreviations (Figure 2.3C). Markers that identified to cluster 4 (primarily APOE2)
included VGF, which is downregulated in AD (El Gaamouch et al., 2020). Markers that identified
to cluster 0 and 1 (primarily APOE4) included previously identified genetic risk factors for AD
(MEF2C, VCAN, FGF14, LUZP2), genes with altered expression in AD patients (GNAI1, VSNL1), and
genes that interact with amyloid beta (PLK2, SST) (Di Re, Wadsworth, & Laezza, 2017; Hokama et
al., 2014; Lee et al., 2019; J. Q. Li et al., 2018; C. W. Lin et al., 2015; Sao et al., 2018; Silver et al.,
2012; Solarski, Wang, Wille, & Schmitt-Ulms, 2018). In addition, we also identified the top
differentially expressed genes between APOE2 and APOE4 GABANeurons (Figure 2.3D). Many of
these differentially expressed genes have been found to be related to AD, such as GPM6B, RBP1,
BTF3, and SCG2, which are dysregulated in AD patients and can act as biomarkers of AD
37

progression (Khatib, Chisholm, Whiting, Platt, & McCaffery, 2020; Moya-Alvarado, Gershoni-
Emek, Perlson, & Bronfman, 2016; Patel et al., 2019).

Figure 2.3. GABANeuron scRNA-seq Analysis

Figure 2.3. GABANeuron scRNA-seq Analysis
(A) A UMAP plot of single cell RNA-seq of the APOE2 and APOE4 GABANeurons showing six distinct
clusters. (B) The percentage of cells in each cluster depending on APOE2 or APOE4 genotype. Clusters 0, 1 and
3 show a higher proportion of APOE4 cells while cluster 4 shows a higher proportion of APOE2 cells. (C)
Genotypic clusters for each of the clusters, identifying genes of interest. (D) A series of plots visualizing the
differentially expressed genes of interest, showing top markers for each cluster between both genotypes.
Figure made by Carlos Galicia.
38

APOE4 GABANeurons Contain More DNA-Repair and Damage Metrics
To see if the findings from our RNA-seq carried over to protein, we performed
immunocytochemistry (ICC) on the isogenic APOE2 and APOE4 GABANeurons looking specifically
at the DNA-damage repair associated protein γ-H2AX (Figure 2.4C). When looking at the
quantified H2AX staining per nucleus, between our two genotypes, the differences were
significant, showing greater values in APOE4 GABANeurons compared to APOE2 (**p=0.001059)
(Figure 2.4D). γ-H2AX foci have been shown to collect In the nucleus in a manner positively
correlating to double-stranded breaks, residual DNA damage, and apoptosis, making it a prime
target for evaluation using ICC (Ivashkevich, Redon, Nakamura, Martin, & Martin, 2012; Paull et
al., 2000; Sharma, Singh, & Almasan, 2012). To determine if the cells are undergoing more actual
damage though, and not just a change in repair pathways, we performed the comet assay , which
stains DNA directly and allows for the visualization of DNA breaks (Larson, 2016; Olive & Banath,
2006) (Figure 2.4A). We used the Cytation 5 BioTek imaging system (Larson, 2016) to further
analyze the tail DNA percent and the tail movement value of the comets. The Tail DNA Percent
parameter is derived from the percentage of the tail DNA over the DNA of the entire comet, head,
and tail together, to control for such cases where the head sizes may be significantly
different. The APOE4 GABANeurons showed a greater percentage of DNA in their comet tails
compared to those tails measured from APOE2 GABANeurons (***p=0.0004). The Tail
Movement Value parameter is derived from the total distance the tail has traveled, in addition
to the DNA percent (***p=0.0006) (Figure 2.4B). The APOE4 GABANeurons displayed larger
values, meaning longer tails, which means more DNA damage, matching with their greater
expression of DNA-damage repair markers.
39

Figure 2.4. APOE4 GABANeurons exhibit more DNA damage compared to APOE2

Figure 2.4. APOE4 GABANeurons exhibit more DNA damage compared to APOE2
(A) GABANeurons were assayed using the comet assay, demonstrating a difference in the level of DNA
damage present between APOE2 (left) and APOE4 (right). Zoomed-in representations (4X) of the damage can
be seen at the bottom-left portion of each representative image. SyberGold staining highlights the DNA of the
treated GABANeurons, allowing for visualization and quantification using the BioTek Gen5 system. Scale bars,
200 µm. (B) GraphPad Prism (9.0) analysis of the intensities of the stained DNA allowed for further
quantification of the Tail DNA Percent (P=0.0004) and the Tail Movement Value (P=0.0006) between our two
alleles. General trends observed suggest an increased amount of DNA damage in APOE4 compared to APOE2.
Scale bars, 200µm. Values represented are the combined values from three replicates, each replicate
containing over 1000 analyzed cells for each genotype. (C) GABANeurons were fixed and probed with
antibodies specific for DNA-damage repair protein H2AX, undergoing ICC, demonstrating a difference in the
number of repair puncta between APOE2 (top) and APOE4 (bottom). H2AX staining visualizes the repair puncta
of the GABANeurons, allowing for quantification between our two genotypes of interest using the BioTek Gen5
system. Scale bars, 20µm. GABANeurons aged 10-14 days. (D) Prism (9.0) analysis of the puncta obtained from
a BioTek masking analysis shows a significant (P=0.001059) difference in the number of puncta between APOE2
(left) and APOE4 (right). Observations suggest that there is more active DNA-damage repair in the APOE4
compared to isogenic APOE2 GABANeurons. Figure made by Stephen Scheeler.
40

Repetitive Elements Affected by APOE Genotype
Aberrant expression of repetitive elements (such as retrotransposons) is a hallmark of
aging and neurodegenerative diseases (De Cecco et al., 2019; Saleh, Macia, & Muotri, 2019).
These repetitive elements are often associated with hot spots of DNA damage (Argueso et al.,
2008). To determine if the APOE genotypes of GABANeurons altered the expression of repetitive
elements, we used RepEnrich2 to quantify the repetitive elements in our RNA-seq data. We also
used DESeq2 to identify differentially expressed repetitive elements (Supplemental Table 2.1)
(Love, Huber, & Anders, 2014). A principle component analysis (PCA) of the repetitive elements
showed that APOE2 samples cluster together regardless of whether they were cultured in
standard or in stress conditions, indicating a minimal change in the repetitive element
expression. In contrast, APOE4 GABANeurons formed two different clusters based on the
condition in which they were cultured, indicating changes in the repetitive elements they
expressed under stress conditions (Figure 2.5A). We analyzed the composition of the repetitive
element landscape based on class membership which includes rRNA, LINE, SINE, snRNA and LTR
elements (Figure 2.5B-E). APOE4 GABANeurons showed a relatively large increase in rRNA
expression under stress conditions and a reduction in SINE, LINE and LTR elements. APOE2
GABANeurons showed smaller changes in repetitive element expression. However, we did
observe an increase in LINE and non-coding DNA repetitive elements and a decrease in LTR, rRNA
and SINE elements (Figure 2.5F).

41

Figure 2.5. GABANeuron Repetitive Elements Affected by APOE Genotype

42

Figure 2.5. GABANeuron Repetitive Elements Affected by APOE Genotype
(A) PCA of repetitive elements obtained from the GABANeuron RNA-seq under normal and stressed
conditions. APOE2 samples grouped together regardless of stress suggesting a smaller change in repetitive
elements being expressed. APOE4 samples separated based on their condition, suggesting changes in repetitive
element expression under stress. Composition of repetitive element landscape in (B) APOE2 GABANeurons and
(C) APOE4 GABANeurons under normal conditions and (D) APOE2 and (E) APOE4 under stressed conditions. (F)
The change in composition of the repetitive element landscape under stress for each genotype. This shows
relevant changes in LTR, rRNA and SINE elements between genotypes. Figure made by Carlos Galicia.
43

APOE4 GABANeurons Exhibit A Wobbly Movement
As part of our RNA-seq analysis, we had determined that aspects of cell movement and
neurite extension had been altered between the analyzed APOE2 and APOE4 GABANeurons.
Specifically, we discovered differences in DSCAM and VCAN between our cells, with these two
proteins predominant among the most differentially expressed genes in APOE2 over APOE4
GABANeurons. Versican (VCAN) and DS Cell Adhesion Molecule (DSCAM) are involved in the
cellular processes of chemo-affinity regarding adhesion and repulsion for the use of cell mobility
(Hattori, Millard, Wojtowicz, & Zipursky, 2008; Wight, 2002; Y. J. Wu, La Pierre, Wu, Yee, & Yang,
2005). To evaluate these parameters ourselves contrasting the APOE2 and APOE4 GABANeurons,
we imaged the cells every 3 h for several days using the automated BioTek Gen5 Biospa imaging
system. These images were then compiled and evaluated to determine the movement for each
given cell in the field of view for each genotype (Figure 2.6A), and for each cell normalized to its
starting position, relative to origin (Figure 2.6B). Using the analyzed cell movement values from the
BioTek videos, we obtained and calculated the mean curvilinear velocity, and the average path velocity of
the GABANeurons. These values showed that on a curved path, the APOE4 cells tended to move faster
(**p=0.0059), while on the straight path the cells tended to move at their baseline speed
(****p<0.0001). These values were then used to calculate the mean wobble of the GABANeurons, where
we saw that the APOE4 GABANeurons “wobbled” to a larger degree, when compared to APOE2
counterparts (****p<0.0001) (Figure 2.6C).

44

Figure 2.6. APOE4 GABANeurons exhibit a more wobbly movement
45

Figure 2.6. APOE4 GABANeurons exhibit a more wobbly movement
(A) Time courses of label-free bright field micrographs of GABANeurons were recorded and analyzed
to quantify cell motility, for both APOE2 (left) and APOE4 (right) GABANeurons. Movement tracks in
representative fields from a 96-well microplate are viewed. Each colored track indicates the movement of a
single identified cell body during a 3 h (112 frames) recording, including only those cells that were tracked for
at least 10 consecutive frames. Filming is comprised of images taken at 3 h intervals, on the BioTek Gen5
System under brightfield conditions, at 4X. (B) Tracks were re-plotted by moving the starting point of each track
to origin. (C) Quantification of cell body motility for curvilinear velocity (left P=0.0059), average path velocity
(center P<0.0001) and wobble of the cells (right P<0.0001). For each well and time course an average value for
all tracks was calculated. Bars indicate mean ±SEM of N=3 technical replicates for these average values. Figure
made by Akos Gerenscer and Stephen Scheeler.
46

APOE4 GABANeurons Create Smaller Neural Networks
Looking deeper into our findings regarding DSCAM and VCAN, we found that the two
proteins are also utilized in cellular processes involving neurite outgrowth (Schmalfeldt,
Bandtlow, Dours-Zimmermann, Winterhalter, & Zimmermann, 2000; Zhu et al., 2013). We
reanalyzed our BioTek time-lapse of the GABANeurons, and used these videos and images for
further analysis. These same images that comprise the videos for the movement analyses were
also evaluated for the neurite outgrowth of each genotype over time. For the neurite outgrowth
assay, the neurite network for each genotype was evaluated over time, and between genotypes
(Figure 2.7A), showing that after the first read when the cells were normalized, the different
genotypes began to have different degrees of neurite outgrowth. Specifically, APOE4
GABANeurons showed a lower degree of neurite outgrowth when compared to APOE2
counterparts (Figure 2.7B), showing differences between genotypes (***p= 0.0004) and as a
function of time (*p=0.0141). These differences in movement and neurite outgrowth could
potentially be due to the differences seen in the RNA-seq data for both DSCAM and VCAN, as
both of these proteins have functions related to both cell mobility and neurite outgrowth,
thereby potentially corroborating further the findings of the RNA-seq.

47

Figure 2.7. APOE4 GABANeurons extend fewer neurites over time

Figure 2.7. APOE4 GABANeurons extend fewer neurites over time
(A) GABANeurons were filmed and analyzed in time dependant manner to determine how APOE
affects the neuronal network of GABAergic neurons. Representative perimeter networks can be seen here,
highlighted in yellow, for a related well from a 96-well plate, for both APOE2 (top) and APOE4 (bottom)
GABANeurons. Filming is comprised of images taken at 3 h intervals, on the BioTek Gen5 System under
brightfield conditions, at 4X. (B) Data points were chosen throughout the experimental duration to evaluate
neurite outgrown, showing that under the same conditions and normalized, APOE4 GABANeuron had less
neurite growth and extensions when compared to APOE2 counterparts over time (Genotype P=0.0004, Time
P=0.0141) when evaluated using PRISM (9.0). Individual data points along the time-course also showed
significance at their respective times (150 P=0.0185, 300 P=0.0763, 450 P=0.0124, 522 P=0.0075). Scale bar,
1000µm. Values represented were obtained from N=3 replicates. Figure made by Jesse Simons and Stephen
Scheeler.
48

49

DISCUSSION
Our research into APOE has shown its allele specific effects upon a variety of cellular
pathways and attributes, in GABANeurons. In some cases, these observations corroborate with
the known literature, while in other cases they expand upon the literature and even offer novel
insights into the inner workings of APOE in GABANeurons. In line with general literature findings
and expectations though, we have observed consistently negative phenotypes in our cells
containing APOE4, compared to our isogenic APOE2 counterparts.

In AD patients, APOE has been shown to affect the GABAergic extent of neurons and
influence their neuronal connections (Calvo-Flores Guzman et al., 2018; Najm et al., 2019). We
observed similar effects early on in our studies, when we began to characterize our
GABANeurons. While characterizing the GABANeurons, we noticed that the connections
between the cells were more often broken and exhibited a greater degree of stress-like puncta
long the axons in the cells expressing APOE4, rather than APOE2. The idea that this may have
been a result of the immunocytochemistry (ICC) process could not be ruled out, so we performed
time-lapse imaging experiments on the living cells in order to evaluate their processes. Our
previous observations were corroborated when we saw that there was a significant decrease in
the density and interconnectedness of the neuronal network in GABANeurons expressing APOE4,
compared to APOE2, in our time-lapse image analysis.

50

Looking at the time-lapse images of the cells, it was observed that the soma would move
over time, along the bottom of the chamber-slide. We created video footage of the culminated
time-lapse imaging, and analyzed the footage for differences in cell movement. Our studies
revealed that the APOE4 cells exhibited a more “wobbly” and erratic movement, compared to
the smoother paths of the APOE2 GABANeurons. In our studies, we had previously perform bulk
RNA-seq analysis on the GABANeurons, and so we looked into potential transcriptional changes
from our pre-existing data that could explain this observation.

The RNA-seq was performed using three different conditions: as bulk RNA-seq with the
cells under stressed conditions, as bulk RNA-seq with the cells under standard conditions, and as
single-cell RNA-sequencing (scRNA-seq) with the cells again under standard conditions.
Interestingly, the datasets had a fair amount of overlap, independent of the stress inducing
conditions. From these datasets we were able to discover new information relevant to the
differences brought about by a change in type of APOE expressed. Large numbers of genes had
been altered, and a variety of transcription factors had been found to be enriched between
APOE2 and APOE4 GABANeurons. Evaluating these datasets revealed that VCAN and DSCAM
were significantly altered between APOE4 and APOE2 GABANeurons, which could explain the
differences seen between the lines in regards to neuronal extensions and cell movement. DSCAM
and VCAN are utilized in cellular processes involving neurite outgrowth, and in chemo-affinity for
the use of cell mobility (Hattori et al., 2008; Wight, 2002; Y. J. Wu et al., 2005); (Schmalfeldt et
al., 2000; Zhu et al., 2013). We further observed that the DNA-damage and repair pathways had
51

been altered between the two genotypes, and in data showed that changes in the repetitive
elements may have caused such an alteration (Argueso et al., 2008).

Our studies indicated that DNA-damage and repair pathways may be altered as a result
of possessing different APOE alleles. To corroborate this observation, we evaluated the DNA-
repair potential of the GABANeurons by probing them with DNA-repair associated protein γ-
H2AX, using ICC. Probing for phosphorylated γ-H2AX would indicate, through its presence, an
activation of the DNA-repair pathway for double-stranded breaks, and would suggest with its
presence an increase in DNA damage (Ivashkevich et al., 2012; Paull et al., 2000; Sharma et al.,
2012). Experimentation showed a clear increase in the overall number of puncta in γ-H2AX
GABANeurons expressing APOE4, rather than APOE2. However, while γ-H2AX may indicate an
increase in DNA damage in APOE4 GABANeurons compared to APOE2, it does not guarantee such
a phenotype. We then turned our attention towards the comet assay to more directly evaluate
the DNA damage within the GABANeurons for each genotype. Corroborating our ICC, we
observed that the APOE4 genotype does indeed contain a greater degree of endogenous damage
compared to APOE2 GABANeurons. Initial studies with other cells have indicated that APOE may
also affect the capacity of the cell to response to exogenous damage, and such experiments are
planned for the future.

Aside from more comet assays utilizing exogenous damage, there are a variety of factors
we are still interested in looking into, that we have not yet evaluated, or have only just started
52

to look into. One aspect we have tested with other cells, but have not yet looked into with the
GABANeurons, is lipidomics. APOE is a lipid transporter, and it is well known that changes in
APOE alters its relationship with the lipids it binds to (Ghebranious et al., 2005; Mahley & Rall,
2000). Our preliminary studies have changes in lipids such as cholesterol, as well as a host of
others one might expect from modifying a lipid transport protein. However, we are interested in
what may not be known, and plan on evaluating the lipidomics of the GABANeurons in an effort
to evaluate these changes in and of themselves, and in relation to our other findings. We also
plan on checking the epigenetic status of the GABANeurons, specifically methylation, and to that
end we have already started performing preliminary assays measuring the methylation of the
GABANeurons. This epigenetic testing will be performed to evaluate APOE’s effects on
transcription, in an allele dependent manner, as had previously been observed in by
Theendakara, et al. (Theendakara et al., 2018). At this point, we need to focus on analyzing the
raw methylation data,. A final piece of analysis that is ongoing is interrogating the differences in
puncta between the two genotypes. Controlling for axonal density, we were interested in seeing
if the density of puncta along a given axon was different, per micron, between the two
GABANeuron genotypes. This analysis is also in progress, as we determine the best way to
calculate and analyze puncta across our many ICC images.

In summary, we have corroborated and uncovered a variety of differences between
APOE2 and APOE4 GABANeurons compared to what has previously been published in the
literature. We confirmed that our differentiated GABANeurons were indeed GABAergic
53

inhibitory neurons, and that cells expressing APOE4 had an impaired neuronal network compared
to cells expressing APOE2. Our studies revealed a possible source for this impaired network, and
a possible cause for the observed wobbly movement of APOE4 GABANeurons; due to differences
in the expression of VCAN and DSCAM. Independent of the literature, our research uncovered
an enrichment in DNA-repair pathways that correlated with an increase in overall DNA damage
in APOE4 GABANeurons. From our RNA-seq that showed this information, we determined a
possible cause; an increase in repetitive elements in APOE4 GABANeurons (Argueso et al., 2008).
Future studies will look further into the exact cause of the increased DNA damage in APOE4
GABANeurons, and will also investigate the lipidomics, synaptic puncta, and methylation status
between our two APOE genotypes. As we continue our investigations, we aim to find therapeutic
targets to make the cells more in line with APOE2, even if the cells contain endogenous APOE4.
54

SUPPLEMENTS
Supplemental Figure 2.1. Brightfield imaging of GABANeurons

Supplemental Figure 2.1. Brightfield imaging of GABANeurons
Representative brightfield image of living GABANeurons taken within 4-7 days after plating. Images
were on a Nikon Eclipse Ti-U microscope using the Plan Apo λ 20X/0.75 objective. From a visual standpoint,
the two genotypes do not look too dissimilar. Figure made by Stephen Scheeler.
55

Supplemental Table 2.1. Repetitive elements

Stressed Condition

APOE2_D APOE2_E APOE2_F APOE4_D APOE4_E APOE4_F
srpRNA 5923.031199 6521.000896 7667.000896 5074.000896 5108.544374 8403.500896
LTR 1226708.273 1306398.276 1279148.214 1103194.822 1052971.367 1063830.928
Satellite 25929.92194 26751.6763 26232.09378 22829.55837 21488.97746 22173.95484
rRNA 1329268.003 1990192.753 764668.5027 2449255.503 3243353.114 765994.5027
DNA 1079454.775 1139438.633 1097396.091 953815.4422 890992.519 907797.8302
snRNA 6454.677419 6945.177419 6589.010753 5477.010753 5094.010753 4947.510753
tRNA 14190.05556 15786.05556 13580.05556 10674.05556 11396.05556 12182.05556
RNA 2332.000896 2835.000896 1739.000896 3029.000896 3108.000896 1352.000896
Other 18731.59704 20267.81958 20435.24625 16169.69742 16159.88508 15946.42923
RC 1734.502688 1880.502688 1787.502688 1605.002688 1460.502688 1298.502688
LINE 3144446.556 3379421.888 3274715.158 2649892.694 2459106.497 2447799.851
SINE 4275599.52 4578427.626 4464176.211 3795453.639 3617271.653 3585552.083
scRNA 2434.085866 2801.590195 2809.912761 2233.573528 2162.873528 2326.850151

Standard Condition

APOE2_A APOE2_B APOE2_C APOE4_A APOE4_B APOE4_C
srpRNA 6963.612007 6201.334229 882.5008961 8721.750896 7265.000896 8565.265912
LTR 1372469.132 1285658.42 107606.0676 1530301.018 1414627.382 1512555.479
Satellite 26554.26912 25107.72185 4505.619839 23607.93425 24130.34754 28765.83839
rRNA 1598689.503 707713.5027 610996.5027 3833511.71 629995.5027 573163.5027
DNA 1060068.503 982892.0079 25830.26934 1099437.389 1078830.766 1200659.561
snRNA 5857.010753 5512.885753 154.4274194 6259.510753 5968.510753 6921.177419
tRNA 19966.05556 18221.05556 17.05555556 14443.05556 16368.05556 16595.05556
RNA 1876.500896 1462.000896 157.0008961 3184.500896 1683.667563 1917.000896
Other 27959.44182 25553.33832 14404.35823 30138.37971 27720.56933 28566.45117
RC 1706.502688 1657.502688 27.50268817 1951.502688 1783.550307 2025.002688
LINE 3022183.216 2838784.41 230426.9048 3262008.088 3103097.114 3533038.725
SINE 4643213.532 4346757.628 228482.8617 5018593.554 4737224.393 5026825.869
scRNA 2921.721995 2720.192429 581.9282898 3036.606316 2956.140988 2907.071174

Supplemental Table 2.1. Repetitive Elements
Tables of repetitive elements affected in GABANeurons dependent on APOE genotype. Table was
obtained using RepEnrich2 to quantify the repetitive elements in our RNA-seq data. DESeq2 was used to
identify differentially expressed repetitive elements. Tables show values for both stressed and non-stressed
plating conditions for multiple Ns. Tables made by Carlos Galicia and Stephen Scheeler.
56

Supplemental Figure 2.2. GABANeuron Network Analysis

Supplemental Figure 2.2. GABANeuron Network Analysis
A network analysis of key hub genes related to DNA damage and repair, obtained from the bulk RNA-
seq. BRCA1 is involved in the repair of double-stranded breaks (J. Zhang & Powell, 2005), CKD1 is an effecter
kinase that responds to DNA damage (Chow & Poon, 2013), PLK1 responds to DNA damage to maintain
chromosome structure (Addis Jones, Tiwari, Olukoga, Herbert, & Chan, 2019), and TOP2A helps regulate gene
expression in relation to DNA damage and double-stranded breaks (Haffner, De Marzo, Meeker, Nelson, &
Yegnasubramanian, 2011). Figure made by Lisa Ellerby.
57

Supplemental Table 2.2. GABANeuron transcription factors
Stressed Condition Standard Condition
Gene Frequency Genotype Gene Frequency Genotype
CENPA 3 E2 CENPA 3 E2
E2F1 3 E2 E2F1 3 E2
E2F7 3 E2 E2F7 3 E2
FOXM1 3 E2 FOXM1 3 E2
GLI2 3 E2 GLI2 3 E2
GLI3 3 E2 HOXA2 3 E2
MYBL2 3 E2 BNC2 2 E2
SALL4 3 E2 FOXF1 2 E2
ZNF367 3 E2 GLI3 2 E2
BNC2 2 E2 HMGA2 2 E2
DNMT1 2 E2 HOXA3 2 E2
GLIS2 2 E2 HOXB3 2 E2
HMGA2 2 E2 HOXD10 2 E2
NFATC4 2 E2 HOXD3 2 E2
NR2F2 2 E2 HOXD4 2 E2
PRRX1 2 E2 MEOX2 2 E2
PRRX2 2 E2 MKX 2 E2
TCF7L1 2 E2 MYBL2 2 E2
TEAD3 2 E2 NEUROG2 2 E2
TFDP1 2 E2 NR2F2 2 E2
ZNF695 2 E2 OSR1 2 E2
ZNF93 2 E2 PRRX1 2 E2
ATF7 2 E4 SOX2 2 E2
BPTF 2 E4 TCF7L1 2 E2
IKZF4 2 E4 TEAD2 2 E2
KMT2A 2 E4 TWIST2 2 E2
PCGF2 2 E4 ZNF367 2 E2
SPEN 2 E4 ZNF695 2 E2

DRGX 2 E4

EGR4 2 E4

EMX1 2 E4

KCNIP3 2 E4

NEUROD6 2 E4

NPAS4 2 E4

SCRT1 2 E4

SOHLH1 2 E4

ZBTB18 2 E4

Supplemental Table 2.2. GABANeuron transcription factors
A collection of transcription factors enriched in APOE2 over APOE4 GABANeurons, obtained from the
RNA-seq analysis. A wide degree of overlap was seen between the two replicates, and some transcription
factors were enriched in both subsets (lfc > 0, lfc >1, DETF), particularly: CENPA, E2F1, FOXM1, E2F7, GLI2.
Tables made by Sicheng Song and Stephen Scheeler
58

Supplemental Figure 2.3. Comet assay of NSCs with and without treatment

59

Drug Value Comparison Tail Movement Value (P) Tail DNA Percent (P)
Untreated: XCL-1 vs APOE KO ****p<0.0001 ****p<0.0001
100 nM: XCL-1 vs APOE KO ****p<0.0001 ****p<0.0001
500 nM: XCL-1 vs APOE KO **p=0.0042 **p=0.0022
1 µM: XCL-1 vs APOE KO **p=0.0031 p=0.3497
5 µM: XCL-1 vs APOE KO *p=0.0410 p=0.5047

Supplemental Figure 2.3. Comet assay of NSCs with and without treatment
Some assays were initially tested on in-house neural stem cells (NSCs) that were also isogenic for
APOE, before being tested on the GABANeurons. These two isogenic NSC lines include an APOE Knock out (KO)
and a line containing APOE3/APOE4. The base cells for these lines are referred to as XCL-1 NSCs. In this comet
assay, we tested the DNA-damaging drug camptothecin to evaluate if APOE had any effect on DNA damage in
NSCs, and as a trial run for a future camptothecin test in GABANeurons. Using a one-way ANOVA analysis, it
was determined that both the genotype (****p<0.0001) and the drug interactions (****p<0.0001) were
significant in influencing the tail movement value. In the same analysis, the genotype (****p<0.0001) and the
drug interaction (****p<0.0001) also significantly influenced the values of the Tail DNA percent. Significance
was also determined for each dose, between each genotype. Like the GABANeurons comet assay, these images
were taken on the BioTek, Scale Bar 200 µm. Figure and table made by Stephen Scheeler.
60

Supplemental Figure 2.4. Lipidomics of NSCs

0
100000
200000
300000
400000
500000
600000
700000
800000
Peak Area
Control_2 KO_2
Supplemental Figure 2.4. Lipidomics of NSCs
XCL-1 and APOE KO NSCs were also evaluated for lipidomics changes between the two lines.
Considering that APOE is a lipid transporter, it was decided that looking into differences in the lipids present
between the two lines would be beneficial. As such, we harvested, analyzed, and compared the lipids for three
biological replicates, and compared them to one another. The goal was to evaluate changes as a result of the
APOE allelic variant. This also acted as a first run before testing the process in GABANeurons. Provided here
is a representation of the differences observed between the two genotypes, showing only the lipids with the
highest fold-change. Figure made by Rishi Sharma.
61

CHAPTER 3: MODULATING FKBP51 AND AUTOPHAGY LOWERS
HUNTINGTIN
ABSTRACT
Current disease-modifying therapies for HD focus on lowering mutant huntingtin protein
(mHTT) levels, and the immunosuppressant drug rapamycin is an intriguing therapeutic for aging
and neurological disorders. Rapamycin interacts with FKBP12 and FKBP51, inhibiting the mTORC1
complex and increasing cellular clearance mechanisms. Whether the levels of FKBP (FK506-
binding protein) family members are altered in HD models and if these proteins are potential
therapeutic targets for HD have not been investigated. Here, we found levels of FKBP51 are
significantly reduced in HD R6/2 and zQ175 mouse models and human HD isogenic neural stem
cells (NSCs) and medium spiny neurons (MSNs) derived from iPSCs. Moreover, FKBP51 interacts
and co-localizes with HTT in the striatum and cortex of zQ175 mice and controls. Importantly,
when we decreased FKBP51 levels or activity by genetic or pharmacological approaches, we
observed reduced levels of mHTT in our isogenic human HD stem cell model. Decreasing FKBP51
levels by siRNA or pharmacological inhibition increased LC3-II levels and autophagic flux,
suggesting autophagic cellular clearance mechanisms are responsible for mHTT lowering. Unlike
rapamycin, the effect of pharmacological inhibition with SAFit2, an inhibitor of FKBP51, is mTOR
independent. Further, in vivo treatment for 2 weeks with SAFit2, results in reduced HTT levels in
both HD R6/2 and zQ175 mouse models. Our studies establish FKBP51 as a protein involved in
the pathogenesis of HD and identify FKBP51 as a potential therapeutic target for HD.

62

BACKGROUND
HD is a rare, age-associated, autosomal-dominant neurological disease caused by a triplet
repeat CAG expansion in exon 1 of the HTT gene (The Huntington's Disease Collaborative
Research Group (1993)). CAG codes for glutamine and, therefore, the expansion of CAG in the
HTT gene results in a polyglutamine (polyQ) expanded tract at the N-terminus of the HTT protein
which drives disease pathogenesis. HD patients suffer from significant progressive loss of
medium spiny and cortical neurons (Hedreen et al., 1991; Kiyama et al., 1990), which correlates
with a triad of disease symptoms: cognitive decline, chorea and delusions/personality changes
(Cardoso, 2014). Treatments for HD are limited, and therapeutic strategies to reduce the HTT
levels are being pursued in patients (Marxreiter, Stemick, & Kohl, 2020; Tabrizi et al., 2019).

FKBPs have been studied in relationship to their binding of immunosuppressant drugs
FK506 and rapamycin (Cioffi, Hubler, & Scammell, 2011; Hartmann et al., 2012; Siekierka, Hung,
Poe, Lin, & Sigal, 1989; Van Duyne, Standaert, Karplus, Schreiber, & Clardy, 1991, 1993) and are
highly expressed in the central nervous system (CNS). As a result of rapamycin binding, FKBPs can
regulate the mammalian target of rapamycin (mTOR) pathway, thus influencing transcriptional
and hormonal regulation, protein folding, and neuronal development (Blair et al., 2019; Gaali et
al., 2015; Harrar, Bellini, & Faure, 2001; Hausch, Kozany, Theodoropoulou, & Fabian, 2013; S.
Huang & Houghton, 2003; Riggs et al., 2007; Schreiber et al., 2015; Wochnik et al., 2005; B. Wu
et al., 2004; Yao, Liang, Huang, & Yang, 2011). The levels and ratios of FK506-binding protein
(FKBP) family members are important in determining the efficacy and side effects of rapamycin
(Schreiber et al., 2015), and the ratio of FKBP51 to FKBP52 regulates neurite outgrowth,
63

neuroendocrine feedback, and stress coping behavior in mice (Albu et al., 2014; Gaali et al., 2015;
Hartmann et al., 2012; O'Leary et al., 2011; Touma et al., 2011; Zgajnar et al., 2019).

FKBP51 and FKBP52 have been implicated in neurological disorders, including PD, AD,
post-traumatic stress disorder (PTSD) and schizophrenia (Blair et al., 2019; Criado-Marrero et al.,
2018; Daskalakis & Binder, 2015; Jinwal et al., 2010; U. Schmidt et al., 2015; Storer, Dickey,
Galigniana, Rein, & Cox, 2011; Taler-Vercic et al., 2017). FKBP52 binds to Tau, regulating its
function and degradation, and FKBP51 prevents Tau degradation and enhances neurotoxicity
(Blair et al., 2013; Jinwal et al., 2010). In Parkinson’s disease (PD), FKBP51 contributes to neuronal
cell death by acting as a substrate for PTEN-induced putative kinase (Pink1) (Boonying et al.,
2019). In PTSD, the glucocorticoid receptor and FKBP51 protein complex is elevated in PTSD
patients and disrupting this complex in mice reverses behavioral and molecular changes induced
by fear conditioning in mice (H. Li et al., 2020). FKBP51 is also linked to depression, bipolar
disorder, and schizophrenia through this pathway (Matosin, Halldorsdottir, & Binder, 2018). The
role of FKBP12, another family member, has been studied in detail in neurological diseases.
FKBP12 binds and affects the processing of amyloid precursor protein (APP), linking this enzyme
to AD pathology, and contributes to alpha-synuclein toxicity via a calcineurin-dependent process.
Currently, we do not know the relative contributions and mechanisms for these distinct FKBP
family members in neurological diseases.

Functionally, FKBPs are peptidyl-prolyl cis/trans-isomerases (PPIase), able to convert
prolines in proteins from a cis to a trans configuration. The isomerization activity of FKBP51
64

increases α-synuclein aggregation (Gerard et al., 2006; Gerard et al., 2010; Gold & Nutt, 2002).
HTT protein contains multiple proline-rich regions adjacent and near to the polyQ stretch that
could be targets for FKBP activity. The orientation of the proline-rich regions affects the
conformation of the HTT protein and subsequent formation of aggregates in vitro (Bhattacharyya
et al., 2006; Darnell, Orgel, Pahl, & Meredith, 2007; Dehay & Bertolotti, 2006; Wagner et al.,
2018; Wetzel, 2012). Given the potential of rapamycin as a therapeutic for HD and the
importance of the proline-rich region of HTT on protein conformation, we evaluated the role of
FKBPs in HD. In this study, we examined FKBP family members FKBP12, FKBP12.6, FKBP51 and
FKBP52, in both mice and an isogenic human cellular model of HD (Ring et al., 2015b). Of note,
small molecule inhibitors of FKBP family members have been developed, and one, called SAFit2,
is a promising pharmacological agent that inhibits FKBP51 proline isomerase activity and crosses
the blood brain barrier (BBB) (Balsevich et al., 2017; Gaali et al., 2015). This small molecule
selectively targets FKBP51, but not the closely related FKBP52. Our findings indicate that FKBP
levels are altered in HD, modulation of FKBP51 levels or inhibition of FKBP51 could be a
therapeutic target for HD by reducing HTT levels.

65

RESULTS
FKBP51 Is Decreased in the HD zQ175 Knock-in Mouse Model
zQ175 mice are a well-established knock-in mouse model of HD with 175 polyQ repeats
that exhibit numerous biochemical and neuropathological changes, reflecting HD phenotypes
starting at 6 months of age and are robust at 12 months of age (Heikkinen et al., 2012; L. B.
Menalled et al., 2012). The zQ175 model, which expresses full-length HTT protein, is ideal from
the genetic perspective because expression of mutant HTT occurs in the appropriate genetic and
protein level. We compared expression changes in FKBP family members in the zQ175 and
littermate controls (WT) at 12-months of age, using western blot analysis of proteins from the
striatum and cortex (Figure 3.1A,E). The striatum showed 3.4-fold less FKBP51 expression in
zQ175 mice than wild-type (WT) (Figure 3.1B, **p≤0.01). Expression of FKBP12 and FKBP12.6 was
slightly less in the zQ175 than WT in the striatum, but this did not reach statistical significance
(Figure 3.1C,D). The levels of FKBP51 were slightly lower in the cortex (Figure 3.1E,F). Levels
FKBP12 and FKBP12.6 in the cortex of zQ175 were essentially the same as in WT (Figure
3.1E,G,H). Analysis of the published transcriptomics of zQ175 mice compared to WT (RNA-seq)
did not show a significant decrease in FKBP51 mRNA expression levels (Langfelder et al., 2016).

66

Figure 3.1. FKBP expression levels in HD zQ175 mouse model

Figure 3.1. FKBP expression levels in HD zQ175 mouse model
(A) Representative western blots analysis of FKBP51, FKBP12.6 and FKBP12 in WT and zQ175
homozygote mouse striatum at 12 months of age. (B-D) Quantification of the expression levels of FKBP51 (B),
FKBP12.6 (C), and FKBP12 (D) in the striatum normalized to α-tubulin. Statistically significant difference in FKBP
expression is indicated (t-test, **p ≤ 0.01). (E) Representative western blots of FKBP51, FKBP12.6 and FKBP12
in WT, compared to zQ175 homozygote mouse cortex at 12 months of age. (F-H) Quantification of the
expression levels of FKBP51 (F), FKBP12.6 (G), and FKBP12 (H) in the cortex normalized to α-tubulin. No
statistically significant difference was observed between WT and zQ175 cortex FKBPs expression levels (t-test).
Figure made by Barbara Bailus.
67

FKBP Expression Is Altered in HD R6/2 Mice
Next, using western blot analysis, we evaluated the FK506-binding protein (FKBP)
expression levels in another mouse model of HD. The HD R6/2 mouse model expresses a protein
derived from the expanded exon 1 of HTT and has accelerated HD phenotypes and pathology, as
well as earlier onset of death at 21 weeks in a C57/B6 background (Cummings et al., 2012;
Mangiarini et al., 1996; L. Menalled et al., 2009; L. B. Menalled, Sison, Dragatsis, Zeitlin, &
Chesselet, 2003; Neto et al., 2017). We compared expression changes in FKBP family members in
the R6/2 and wild-type (WT) mice at 16 weeks of age in the striatum and cortex (Figure 3.2A,E).
At 16 weeks of age, the R6/2 have significant behavioral and neuropathological changes from the
expression of the toxic exon 1 derived HTT fragment. The expression of FKBP51 was 2.3-fold
lower in the striatum of the R6/2 mice than in WT mice at 16 weeks of age (Figure 3.2B, *p≤0.05).
There were also small but significant decreases in the levels of FKBP12.6 (Figure 3.2C,1.2-fold,
**p≤0.01) and FKBP12 (Figure 3.2D,1.3-fold, ***p≤0.001) in the R6/2 when compared to WT. In
the cortex, there was no statistical difference in any of the FKBP levels (Figure 3.2E-F). The altered
expression levels of FKBPs in the R6/2 were comparable to those observed in the zQ175 model.

68

Figure 3.2. FKBP expression levels in HD R6/2 mouse model

Figure 3.2. FKBP expression levels in HD R6/2 mouse model
(A) Representative western blot analysis of FKBP51, FKBP12.6 and FKBP12 in WT and R6/2 mouse
striatum at 4 months of age. (B-D) Quantification of the expression levels of FKBP51 (B), FKBP12.6 (C), and FKBP12
(D) in the striatum normalized to α-tubulin. Statistically significant difference in FKBP expression is indicated (t-
test, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001). (E) Representative western blots of FKBP51, FKBP12.6 and FKBP12 in
WT compared to R6/2 mouse cortex at 4 months of age. (F-H) Quantification of the expression levels of FKBP51
(F), FKBP12.6 (G), and FKBP12 (H) in the cortex normalized to α-tubulin. No statistically significant difference was
observed between WT and R6/2 cortex FKBPs expression levels (t-test). Figure made by Barbara Bailus
69

Differential Expression of FKBPs in HD Striatum and Cortex
In HD, both the cortex and the striatum are affected, and we compared the relative levels
of FKBP51 in the striatum to the cortex. FKBP proteins may affect these two regions of the brain
differently. We determined if the level of expression of the FKBP51 differs in the striatum and
cortex of the wild-type (WT) and zQ175 mice. Western blot analysis showed FKBP51 is expressed
1.7 times higher in the striatum than the cortex of WT mice (Figure 3.3A,B, *p ≤0.05) at 12 month
of age. Similarly, FKPB12.6 and FKBP12 had higher expression levels in the striatum than the
cortex (Figure 3.3C,D, **p ≤0.01). The difference in enrichment of FKBPs in the striatum was lost
in the zQ175 mice, consistent with the lower levels of FKBP51 observed in this HD model (Figure
3.3F-H).

70

Figure 3.3. Expression levels of FKBPs in cortex versus striatum in wild-type and zQ175 mice

Figure 3.3. Expression levels of FKBPs in cortex versus striatum in wild-type and zQ175 mice
(A) Representative western blot analysis of FKBP51, FKBP12.6 and FKBP12 in the striatum and cortex of
12-month-old WT mice. (B-D) Quantification of the expression levels of FKBP51 (B), FKBP12.6 (C), and FKBP12 (D)
in the striatum and cortex normalized to α-tubulin. Statistically significant difference in FKBP expression is
indicated (t-test, *p ≤ 0.05, **p ≤ 0.01). (E) Representative western blots of FKBP51, FKBP12.6 and FKBP12 in the
striatum and cortex of 12-month-old homozygote zQ175 mice. (F-H) Quantification of the expression levels of
FKBP51 (F), FKBP12.6 (G), and FKBP12 (H) in the striatum and cortex normalized to α-tubulin. No statistically
significant difference was observed in FKBPs expression levels between striatum and cortex of zQ175 mice (t-test).
71

FKBP51 Levels Are Decreased in zQ175 Mice at 6 Months of Age
To determine when FKBP51 levels change during disease progression, we measured the
levels in WT and zQ175 mice at 6 and 12 months of age. At 6 months of age, measurable changes
in mutant huntingtin protein (mHTT) aggregates, metabolites, brain atrophy and biochemical
changes were detected (Heikkinen et al., 2012; L. B. Menalled et al., 2012). WT mice showed no
change in FKBP51 striatal levels from 6 to 12 months (Figure 3.4A,B). There was a decrease in the
FKBP51 levels in the striatum of zQ175 mice at 6 (*p ≤0.05) and 12 months of age (**p ≤0.01),
compared to WT mice (Figure 3.4A,B, ***p ≤0.001). There was an age-dependent decrease in
FKBP51 levels in zQ175 mice at 6 and 12 months of age. The difference between the zQ175 and
WT FKBP51 levels in the striatum was statistically significant (***p ≤0.001) (Figure 3.4A,B). There
was a significant decrease in the levels of FKBP51 in the cortex of zQ175 mice at 6 and 12 months
of age (Figure 3.4C,D, *p ≤0.05). FKBP51 levels in the striatum and cortex decreased during HD
disease progression. The effect was more pronounced in the striatum. As expected, the levels of
soluble mHTT (defined as resolving on SDS page gel at HTT molecular mass) are decreased during
this time frame as measured by antibody to the polyQ stretch (1C2), consistent with previous
studies (Franich et al., 2018; Reindl et al., 2019). The changes in mHTT may directly correlate with
the lower FKBP51 levels (Supplemental Figure 3.1) and the physical interaction between the
proteins described below.

72

Figure 3.4. Temporal changes in FKBP51 levels in zQ175 mice

Figure 3.4. Temporal changes in FKBP51 levels in zQ175 mice
(A,C) Representative western blots analysis of striatum (A) and cortex (C) for FKBP51 expression in 6- and
12-month-old mice when compared to homozygote zQ175 to WT. (B,D) Graphs show quantification of the
expression levels of FKBP51 when normalized to α-tubulin. A significant decrease of FKBP51 expression is observed
for the striatum (B) and cortex (D) when comparing 12-month-old zQ175 to WT. Statistical analysis used ordinary
two-way ANOVA (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001). Figure made by Barbara Bailus.
73

HTT and FKBP51 Co-IP in Knock-in Mouse Models of HD with Increasing PolyQ Repeat Length
A possible mechanism for the lower levels of FKBP51 in the HD mouse models is a physical
interaction of FKBP51 with mHTT and the sequestration of FKBP51 into aggregates or insoluble
proteins. To determine if FKBP51 interacts with HTT, we used co-immunoprecipitation (CoIP) of
lysates from the striatum and cortex of HD knock-in mice with increasing CAG repeat lengths:
WT, Q50, Q92, and Q175 at 12 months of age (Langfelder et al., 2016). We found that FKBP51
and HTT co-immunoprecipitated with an α-HTT antibody in striatal and cortical lysates, but not
the IgG (negative control) (Figure 3.5A). The interaction of FKBP51 and HTT was observed in the
WT, Q50, Q92, and Q175 indicating that the expanded polyQ lengths did not affect the extent of
this interaction. We also examined CoIPs in the midbrain of a 12-month-old zQ175 homozygote
because the midbrain had equal levels of FKBP51 expression in WT and zQ175 mice. We observed
that mHTT co-immunoprecipitated with FKBP51 with the α-FKBP51 antibody (Supplemental
Figure 3.2) but with some background non-specific binding. This may be due to the FKBP51
antibody epitope (synthetic peptide corresponding to residues surrounding Arg222 of FKBP51)
that may obscure part of an interaction domain with HTT. The results overall indicate that there
is a direct or indirect interaction between FKBP51 and HTT. To determine if HTT and FKBP51
colocalized, we used immunohistochemistry (IHC) to examine zQ175 and WT mice at 12 months
of age. Expression of FKBP51 was detected in the cortex and striatum of HD and WT mice. By
confocal microscopy, HTT colocalized with FKBP51 (Figure 3.5B). HTT colocalized with FKBP51
(20-40%) in the cortex and striatum, and co-localization levels were lower in the zQ175 mice,
when compared to WT (Figure 3.5C).
74

Figure 3.5. FKBP51 interaction and colocalization with HTT

Figure 3.5. FKBP51 interaction and colocalization with HTT
(A) Cortex and striatal co-immunoprecipitation using α-HTT antibody (Millipore, MAB2166) and probing
for FKBP51 in heterozygous allelic HTT mice with increasing polyQ-repeat length. The arrow in the lower left panel
indicates the pull down of FKBP51, and the * is IgG protein from the IP and non-specific. We used heterozygous
mice as this was the tissue we had available. Additionally, the presence of both WT HTT and mHTT makes this a
reasonable comparison. (B) Confocal analysis of HTT (green) and FKBP51 (red) demonstrates the colocalization of
FKBP51 and HTT in the cortex in both WT and homozygous zQ175 mice. (C) Graphpad Prism quantification of the
percentage of HTT colocalizing with FKBP51 (right panel). Figure made by Barbara Bailus.
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HD NSCs Have Altered Expression of FKBP51
To determine if FKBP51 levels were changed in cellular models of HD we used our human
iPSC-derived isogenic model of HD to simulate if this model mirrored some of the expression
observed changes in HD mouse models. We performed western blot analysis on the HD and C116
neural stem cells (NSCs). We found that the HD NSC had lower levels of FKBP51 than C116 NSC
(Figure 3.6A,B, ****p ≤0.0001). FKBP12 and 12.6 levels were not altered (Figure 3.6A,C,D). Using
ICC, we detected co-localization of FKBP51 and HTT (Figure 3.6E, Supplemental Figure 3.3). We
observed a decrease in FKBP51 levels in the HD NSC and altered localization of HTT and FKBP51
in the HD NSC, with more FKBP51 localized in the nucleus of the HD NSC than in C116 NSC. We
had previously generated a transcriptomic profile of human isogenic HD vs. corrected (C116) NSC
lines (An et al., 2014; An et al., 2012; Ring et al., 2015b). The RNA-seq data set of the corrected
C116 and HD NSC showed that several FKBPs had a significant decrease in RNA expression levels
(Supplemental Figure 3.4). Accounting for false discovery rate, FKBP12.6 and FKBP51 were
expressed at lower levels in the HD NSC than C116 NSC (p=8.98E-18 and p=0.0002, respectively).
As described above, the HD mouse models do not have alterations in the FKBP51 levels at the
transcriptional level. Therefore, cell-specific effects not detected in mouse tissue will warrant
further analysis.

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Figure 3.6. FKBP51 expression and localization in human neural stem cell model

Figure 3.6. FKBP51 expression and localization in human neural stem cell model
(A) Representative western blots analysis of FKBP51, FKBP12.6 and FKBP12 in HD and corrected (C116)
NSC. (B-D) Quantification of the expression levels of FKBP51 (B), FKBP12.6 (C), and FKBP12 (D) in NSC normalized
to α-tubulin. A statistically significant difference in FKBP expression is indicated (t-test, ****p ≤ 0.0001). (E) ICC of
HD and C116 NSC stained with FKBP51 (red) and HTT (green) antibodies. Figure made by Barbara Bailus
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FKBP51 Expression Levels Are Decreased in HD Medium Spiny Neurons
One of the main cell types lost in HD is medium spiny neurons (MSNs). Therefore, we
differentiated our isogenic HD and C116 NSC into MSN to evaluate the impact of mHTT on FKBP51
levels in this highly relevant human cell type (Figure 3.7A). HD and C116 MSN express high levels
of DARPP-32, one of markers of MSN (Figure 3.7B). As expected, the levels of DARPP-32 are lower
in the HD MSN than the C116 MSN as this is a known signature of HD pathogenesis (Figure 3.7B).
We found the endogenous levels of FKBP51 in HD MSN were significantly lower than in WT MSN
(Figure 3.7C,D, **p ≤0.01).

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Figure 3.7. FKBP51 levels in human medium spiny neurons derived from patient HD induced
pluripotent stem cells

Figure 3.7. FKBP51 levels in human medium spiny neurons derived from patient HD induced pluripotent stem
cells
(A) Graphical illustration of MSN differentiation protocol. (B) Human corrected (C116) and HD MSN model
express DARPP-32 (green), MAP2 (red) and NESTIN (red). (C) Expression levels of FKBP51 in HD and C116 human
MSN as measured by western blot analysis. (D) Quantification of expression levels of FKBP51 normalized to
vinculin are shown (**p ≤ 0.01, t-test). Figure made by Kizito-Tshitoko Tshilenge and Barbara Bailus.

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siRNA Knockdown of FKBP51 in HD NSCs Reduces mHTT Levels
Transcriptomic and proteomic expression analyses in HD suggest that dysregulated
expression reflects compensatory, nonpathogenic, and disease-driving responses due to the
polyQ expansion in HD (Al-Ramahi et al., 2018). A critical target in the cellular response to HD is
the expression of the toxic mHTT protein. FKBP51 prevents degradation of Tau and contributes
to its aggregation and toxicity (Blair, Baker, Sabbagh, & Dickey, 2015; Blair et al., 2013). Therefore,
we decided to investigate the effect of lowering FKBP51 or blocking FKBP51 isomerase activity
on mHTT levels. We treated our isogenic HD and corrected C116 NSC lines with siRNA to FKBP51.
We observed a significant knockdown of FKBP51, approximately 20% in both HD and C116 NSC,
when compared to the non-targeting siRNA (**p<0.01, Figure 3.8A,B). The knockdown in HD NSC
produced a significant decrease in mHTT levels compared to the control siRNA when normalized
to β-actin (*p≤0.05, Figure 3.8C). This decrease in mHTT levels mediated by siRNA-enforced
reduction in FKBP51 suggests that modulation of this enzyme is a therapeutic strategy for HD.

SAFit2 Treatment of HD NSCs
FKBP51 and FKBP52 have been extensively studied for their roles in neurite retraction and
extension, as well as in stress responses in the CNS. SAFit2 is a promising pharmacological agent
that inhibits FKBP51 and modulates stress responses. (Balsevich et al., 2017; Gaali et al., 2015).
This small molecule selectively targets FKBP51, but not the closely related FKBP52. SAFit2 will
inhibit proline isomerize activity of FKBP51 and therefore its enzymatic activity may be required
for HTT clearance. To determine if pharmacological inhibition of FKBP51 lowers HTT levels, we
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tested SAFit2 in HD and C116 NSC. Treatment of NSC with SAFit2 displayed a significant reduction
in mHTT levels (*p=0.03) at 1 and 10 µM (Figure 3.8D,E). SAFit2 also lowered the WT HTT levels
but reached statistical significance only at a higher dose of 10 µM (Figure 3.8F).

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Figure 3.8. Evaluation of HTT levels with genetic or pharmacological inhibition of FKBP51 in
NSCs

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Figure 3.8. Evaluation of HTT levels with genetic or pharmacological inhibition of FKBP51 in neural stem cells
(A) A representative western blot analysis showing the decrease in FKBP51 and HTT levels when treated
with FKBP51 siRNA, compared to non-targeting siRNA (NT) and normalized to β-actin. PolyQ antibody 1C2
(Millipore, MAB1574) was used to quantify mHTT levels. (B) Quantification of levels of FKBP51 show a statistically
significant decrease in FKBP51 levels upon treatment with FKBP51 siRNA in both genotypes (HD and C116).
Statistical analysis used ordinary one-way ANOVA and t-test (*p ≤ 0.05, **p ≤ 0.01). (C) The levels of mHTT are
significantly (t-test, *p ≤ 0.05) lower (30%) in the siRNA-treated HD cells. (D) Representative western blot analysis
of treatment of HD NSC with SAFit2 at 1 and 10 µM probing with HTT and FKBP51 antibodies. HTT (Millipore,
MAB2166) was utilized to measure WT and mHTT levels separating the WT and mHTT on 3-8% Tris-acetate gels.
PolyQ antibody 1C2 (Millipore, MAB1574) was used to detect only mHTT levels.(E) Quantification of mHTT levels
at both 1 µM and 10 µM, when compared to control (*p ≤ 0.05, **p ≤ 0.01).(F) Quantification of normal HTT levels
at 1 µM and 10 µM SAFit2 when compared to control. Statistical analysis used ordinary one-way ANOVA and t-
test (*p ≤ 0.05). Figure made by Barbara Bailus.

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SAFit2 Treatment of HD NSCs Alters LC3-II and p62 Levels
Misfolded polyQ-expanded proteins are often cleared through autophagy or by the
proteasome. To investigate the mechanism of clearance mediated by SAFit2, we evaluated the
levels of microtubule-associated protein light chain 3 (LC3), a marker of autophagosomes. We
found a significant increase in LC3-I and LC3-II (LC3-PE) levels in C116 and HD NSC treated with
10 µM of SAFit2 (*p≤0.05; ****p≤0.0001, respectively) (Figure 3.9A,B, LC3-II shown in panel B).
We also evaluated whether pharmacological inhibition of FKBP51 altered the levels of p62,
another autophagy marker. Levels of p62 were increased upon treatment with SAFit2 (Figure
3.9C,D). p62, which is correlated with ubiquitinated proteins destined for autophagic
degradation, is decreased, or increased in expression depending upon the drug or cellular
stimulation that leads to autophagy (Jain et al., 2010; Panek, Kolar, Vohradsky, & Shivaya
Valasek, 2013; Pankiv et al., 2007). To test the impact of FKBP51 inhibition on autophagic flux,
we treated C116 and HD NSC with SAFit2 for 48 hours, followed by treatment with Bafilomycin
A1 (BafA1) for 4 hours. BafA1 is a V-ATPase inhibitor that blocks autophagic flux by preventing
the acidification of endosomes and autophagosome-lysosome fusion, leading to the
accumulation of LC3-II in normal cells under autophagic-inducing conditions (Figure 3.9). We
found that BafA1 treatment alone increased the levels as expected (Figure 3.9E). SAFit2 also led
to a significant increase in basal LC3-II. When BafA1 treatment was applied (Figure 3.9E), LC3-II
levels were increased with the greater BafA1-dependent increase in LC3-II, observed in SAFit2
treated cells, as expected (Supplemental Figure 3.5). This result confirms that SAFit2 increases
autophagy flux.
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Figure 3.9. Changes in the expression of LC3B and p62 with SAFiT2 treatment in HD NSCs

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Figure 3.9. Changes in the expression of LC3B and p62 with SAFiT2 treatment in HD neural stem cells
(A) A representative western blot analysis showing LC3B and vinculin expression after treatment of C116
and HD NSC with SAFit2 at 1 or 10 µM or vehicle (DMSO). (B) C116 NSC treated with 10 µM SAFit2 show increased
expression of LC3B (one-way ANOVA, *p = 0.013), compared to vehicle treated cells. In HD NSC, LC3B expression
is increased in cells treated with was 10 µM SAFiT2 (one-way ANOVA, ****p ≤ 0.0001), compared to vehicle
treated cells. LC3B expression was normalized to vinculin expression. 1 µM SAFiT2 compared to 10 µM SAFiT2 was
statistically significant (one-way ANOVA, ****p ≤ 0.0001). (C) A representative western blot analysis showing
increase expression of p62 and vinculin in C116 and HD after treatment with SAFit2 or DMSO. (D) C116 NSC treated
with 10 µM SAFiT2 show a significant increase in p62 (one-way ANOVA, **p ≤0.01) expression, compared to
DMSO-treated cells. A significant difference in p62 expression is also observed between C116 cells treated with 1
vs 10 µM of SAFit2 (one-way ANOVA, **p ≤ 0.01). In HD NSC, expression of p62 was increased in cells treated with
1 µM (one-way ANOVA, *p ≤0.05) and 10 µM SAFit2 (***p ≤0.001), compared to DMSO treated cells. A significant
difference in p62 expression is also observed between HD cells treated with 1 or 10 µM of SAFit2 (one-way ANOVA,
*p ≤0.05). p62 levels were normalized to vinculin expression. (E) C116 and HD NSC were treated with DMSO,
Bafilomycin A1 (BafA1), SAFit2, or SAFit2, followed by BafA1. Western blot analysis normalized to vinculin shows
increased levels of LC3-II after BafA1 (****p≤0.001), SAFit2 (****p≤0.001) and SAFit2 and BafA1 (****p≤0.001) in
both C116 and HD NSC. Figure made by Barbara Bailus and Maria Sanchez.

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Comparing Rapamycin and SAFit2 Molecular Mechanisms in HD NSCs
Rapamycin enhances health span and is protective in cellular and in vivo models of HD
(Menzies & Rubinsztein, 2010; Quarles et al., 2020). Rapamycin inhibits mammalian target of
rapamycin (mTOR), which triggers autophagy and is neuroprotective in HD (Sarkar et al., 2008;
N. Zhang et al., 2012). We compared the impact of rapamycin or SAFit2 on neurotoxicity by
treating isogenic HD NSC during serum withdrawal with these compounds (Figure 3.10A). Both
rapamycin and SAFit2 reduced the level of activated caspase enzymatic activity in HD NSC 48
hours after serum withdrawal (Figure 3.10A, left panel). Correspondingly, a dose response
suggested both SAFit2 and rapamycin (1, 1.5, 2.0 uM) lowered the levels of activated caspase-
3/7 overall (Figure 3.10A, right panel). To determine if the SAFit2 mechanism of autophagy
activation overlapped with rapamycin, we evaluated mTOR inhibition. We found that rapamycin,
but not SAFit2, resulted in the inhibition of the phosphorylation for S6, a substrate for mTOR
(Figure 3.10B). Further, rapamycin, but not SAFit2, resulted in a decrease in the phosphorylation
of mTOR (Figure 3.10C). Autophagy induction is controlled by the serine/threonine kinase, ULK1.
We found that SAFit2 increased the levels of phosphorylated ULK1 while rapamycin slightly
lowered the level of pULK1 (Figure 3.10D). Our results suggest that rapamycin and SAFit2 are
both neuroprotective, but the mechanisms of autophagy induction are distinct. SAFit2 induction
of autophagy is mTOR independent under normal growth conditions.

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Figure 3.10. Comparing SAFit2 and Rapamycin in HD NSC

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Figure 3.10. Comparing SAFit2 and Rapamycin in HD NSC
(A) Left panel. Caspase-3/7 activity normalized to protein levels in C116 and HD NSC treated for 24 hours in
starvation medium with 0.1% DMSO vehicle, 1 µM SAFit2 (two-way ANOVA, *p ≤ 0.05), or 1 µM rapamycin (two-way
ANOVA, **p ≤ 0.01). Right panel. Caspase-3/7 activity normalized to protein levels in C116 and HD NSC treated for 24
hours in starvation media with 0.1% DMSO vehicle, 1, 1.5 and 2 µM SAFit2 (two-way ANOVA, **p ≤ 0.001), or 1, 1.5, and
2.0 µM rapamycin (two-way ANOVA, ***p ≤ 0.001). The two first bars on the left are full media (NPM), media with 0.1%
DMSO vehicle, and starvation medium with 0.1% DMSO vehicle. (B,C,D) Quantification of the expression levels of pS6 (B),
pmTOR (C), and pULK (D) in C116 and HD NSC after 48 h of treatment with either 0.1% DMSO vehicle, 10 µM SAFit2, or
10 µM rapamycin treatment. Analysis of quantified levels indicates significant reduction in phosphorylated S6 with 10 µM
rapamycin (one-way ANOVA, ****p ≤ 0.0001, and one-way ANOVA, ***p ≤ 0.001) in C116 and HD NSC, respectively,
when compared to vehicle control. SAFit2 treatment did not significantly alter levels of phosphorylated S6 when
compared to control. Analysis of quantified levels indicates significant reduction in phosphorylated mTOR with 10 µM
rapamycin (one-way ANOVA, ***p ≤ 0.001, and one-way ANOVA, ****p ≤ 0.0001) in C116 and HD NSC, respectively,
when compared to vehicle control. Again, SAFit2 treatment did not significantly alter levels of phosphorylated mTOR
when compared to control. Analysis of quantified levels indicates significant reduction in phosphorylated ULK-1 with 10
µM rapamycin in C116 NSC (one-way ANOVA, *p ≤ 0.05). In HD NSC, treatment with 10 µM SAFit2 significantly increased
phosphorylated ULK-1 (one-way ANOVA, *p ≤ 0.05) when compared to control. Figure made by Barbara Bailus and Jesse
Simons.

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SAFit2 Treatment in R6/2 and zQ175 Mice Lowers HTT Levels
Current therapeutic treatments for HD focus on lowering HTT levels. Therefore, we
evaluated whether SAFit2, which crosses the blood brain barrier (BBB), could lower HTT levels in
mouse models of HD. We injected R6/2 mice with SAFit2 (7.5 mg/kg) intraperitoneally for 7 days
and analyzed the expression levels of the HTT exon 1 fragment in the striatum. We found that
SAFit2 treatment lowered the levels of the polyQ-expanded fragment in R6/2, compared to
vehicle-treated R6/2 mice with a possible shift in migration of the fragment with treatment
(Figure 3.11A,B). The insoluble HTT protein was not decreased at this dose of SAFit2 (Figure
3.11A,B). However, increasing the doses of SAFit2 to 10 and 15 mg/kg reduced the levels of both
the soluble and insoluble HTT protein (Supplemental Figure 3.6). We also evaluated the effect of
SAFit2 treatment in the zQ175 mouse model by treating them at 4 months for 2 weeks with
SAFit2 at 15 mg/kg a time point when HD pathogenesis is mild and the HTT protein relatively
soluble. We found SAFit2 treatment in the zQ175 also lowered the levels of HTT by ~20% as
measured using multiple to HTT antibodies (Figure 3.11C-E) in the striatum.

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Figure 3.11. HTT levels in R6/2 and zQ175 mouse models treated with SAFit2

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Figure 3.11. HTT levels in R6/2 and zQ175 mouse models treated with SAFit2
(A) A representative western blot showing HTT exon1 expression and aggregates in R6/2 mice treated
with SAFit2, 7.5 mg/kg or vehicle (PBS) for 7 days in the striatum. WT are control is followed by an empty lane.
The arrow indicates the polyQ-expanded HTT fragment. The line is the insoluble HTT aggregates that form in the
R6/2 mouse. HTT antibody (Sigma, 5492) was used. (B) Quantification showing a decrease in exon 1 fragments of
HTT in R6/2 mice treated with SAFit2 or PBS normalized to -Actin (t-test, **p≤ 0.01). No significant differences
were observed in HTT aggregates. (C) Representative western blot showing expression of HTT, and mHTT
normalized to vinculin in heterozygote zQ175 mice treated with SAFit2, 15 mg/kg or vehicle (PBS) for 14 days. The
thick arrow is the mHTT and the light arrow is WT HTT. (D), Quantification showing normal and mHTT decrease in
mice treated with SAFit2 compared to vehicle treated controls (t-test, *p ≤ 0.05). The heavy arrow indicates where
mHTT migrates and the lower light arrow is normal HTT. HTT antibody (1:500, Sigma, MAB5492) was used. (E),
Quantification of mHTT using the polyQ antibody 1C2 normalized to vinculin showing decreased levels in mice
treated with SAFit2 compared to vehicle treated controls (t-test, *p ≤ 0.05). Figure made by Barbara Bailus.
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DISCUSSION
Our studies identified FKBP51 as a novel therapeutic target for HD. FKBP51 levels are
reduced in multiple mouse models of HD (i.e., R6/2 and zQ175). The changes in expression in the
brain are regional and temporally specific, and the strongest effect on expression was in the
striatum. The decrease in FKBP51 can be detected at 6 months of age and is further decreased at
12 months of age in zQ175 mice. The change in levels of FKBP51 may correlate with the decrease
in soluble mHTT, which is observed in both our cellular and mouse models of HD. FKBP51
prevents degradation of Tau and contributes to its aggregation and toxicity (Blair et al., 2015;
Blair et al., 2013). Interestingly, we found further lowering the levels of FKBP51 by siRNA or
pharmacological inhibition decreases the levels of mHTT. We found that inhibiting FKBP51 in
human cellular models of HD and in multiple mouse models of HD resulted in the lowering of
mHTT levels. The levels of other FKBP family members (e.g., FKBP52, FKBP12 or FKBP12.6) were
unchanged or modest in mouse and human models of HD.

FKBP51 and HTT co-immunoprecipitated in extracts from multiple brain regions (striatal,
cortex and midbrain) in WT and zQ175 mice. Furthermore, studies with confocal microscopy
indicated that FKBP51 and mHTT co-localize in the striatum and cortex of WT and zQ175 mice.
The interaction of FKBP51 and mHTT may influence the amount of soluble FKBP51 or mHTT in
cells. The interaction could be through the proline-rich regions in HTT, and these interactions
could influence both the conformation and the aggregation of mHTT (Bhattacharyya et al., 2006;
Darnell et al., 2007; Wetzel, 2012). The reduction of aggregates in the R6/2 mice treated with a
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high dose of SAFit2 supports this hypothesis. Further analyses are needed to determine how and
if FKBP51 interacts differently with the normal vs. mHTT and if it contributes to protein
accumulation and aggregate formation in HD.

FKBP expression is also altered in AD and PD patients and models (Avramut & Achim,
2002; Giustiniani et al., 2012). Pharmacological and genetic inhibition of FKBPs reduces the levels
of α-synuclein aggregates and toxicity in Parkinson’s disease (PD) mouse models (Gerard et al.,
2006; Gerard et al., 2010; Gold & Nutt, 2002). Recently, studies showed that FKBP12 enhances
calcineurin phosphatase activity toward proteins affected by overexpression of alpha-synuclein;
deletion or pharmacological inhibition of FKBP12, restored phosphorylation of these proteins and
reduced cell toxicity (Caraveo et al., 2017). In AD, FKBPs and other peptidyl-prolyl cis/trans-
isomerases (PPIases) are thought to regulate pathology through their interaction with Tau
protein. Tau contains multiple phosphorylation sites that determine its function and disease
state. Tau is hyperphosphorylated in AD. It is worth noting that many of the phosphorylation sites
in Tau are located in proline-rich domains. These domains are targeted by PPIases, causing
structural changes that render the proteins more stable and thus hinder their clearance (Blair et
al., 2015). HTT also contains multiple proline domains adjacent to the polyQ tract. The proline
domains modulate aggregation and toxicity of mHTT (Caron, Desmond, Xia, & Truant, 2013;
Dehay & Bertolotti, 2006; Neveklovska, Clabough, Steffan, & Zeitlin, 2012). Furthermore, a recent
study in cellular and nematode models of HD found that increasing levels of FKBP12 changes the
structure of the aggregates and reduces their toxicity, suggesting that FKBPs regulate structural
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changes of mHTT (Sun et al., 2015). Our studies did not show dysregulation of FKBP12 across the
models tested, but our results support the notion that FKBP family members have an active role
in regulating the structure and stability of proteins associated with neurodegenerative diseases.

Reduced levels of mHTT correlates with a reduction in HD pathology (Guo, Liu, Cai, & Le,
2018; Martin, Ladha, Ehrnhoefer, & Hayden, 2015; Sarkar, Davies, Huang, Tunnacliffe, &
Rubinsztein, 2007; Sarkar, Ravikumar, Floto, & Rubinsztein, 2009). Several approaches have been
used to specifically suppress production of mHTT, while others have focused on the clearance of
mHTT (Leavitt, Kordasiewicz, & Schobel, 2020; Ravikumar et al., 2004). Linker molecules that
interact with both mHTT and microtubule-associated protein light chain 3 (LC3) can increase
clearance of mHTT and reduce pathology in cellular and animal models of HD (Z. Li et al., 2019).
The degradation of mHTT is impaired at several steps in autophagy, including cargo loading,
trafficking of autophagosomes and decreased fusion between autophagosomes and lysosomes
(Martin et al., 2015; Martinez-Vicente et al., 2010). Our results suggest that mHTT can be
processed by autophagy upon lowering of FKBP51 expression or activity. The mHTT conformation
that impairs these processes in autophagy is likely correlated with proline isomerization (See
model Figure 3.12). Alternatively normal HTT regulates autophagy, and lowering FKBP51
expression or activity restores the function of tmHTT (Steffan, 2010). We show that clearance of
HTT is likely due to increased autophagy, as indicated by increased LC3 levels, autophagic flux
and change in ULK1 phosphorylation in our HD cellular model. Our results suggest that rapamycin
and SAFit2 are both neuroprotective, but the mechanisms of autophagy induction are distinct.
SAFit2 induction of autophagy is mammalian target of rapamycin (mTOR) independent under
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normal growth conditions. Further studies are needed to elucidate the impact of the ULK1
pathway by SAFit2 inhibition. Rapamycin as expected inhibited mTOR and activated autophagy.

Previous studies have linked FKBP51 to autophagy pathways. Gassen et al. showed that
increasing FKBP51 levels primed autophagic pathways, and this is correlated with the ability of
an antidepressant to induce autophagic pathways (Gassen et al., 2014). They suggest that the
mechanism for this effect involves a physical interaction of FKBP51 with Beclin1 that facilitates
autophagy. In this study, changes in autophagic pathways were detected in the presence of
antidepressants. A comparison shows that the autophagic markers in WT and FKBP51 knock out
(KO) mice were not altered in rested/basal conditions, and therefore, the cellular signaling
pathways elicited by antidepressants may not overlap with the effects of FKPB51 on polyQ-
expanded HTT in our study.

In summary, we found that FKBP51 levels are altered in HD and that lowering the levels
of FKBP51 reduced mHTT in HD models both in vitro and in vivo, suggesting FKBP51 as a
therapeutic target for HD. Notably, FKBP51 knockout murine models exhibit few phenotypic
changes or behavioral alterations (Cheung-Flynn et al., 2005; O'Leary et al., 2011) supporting the
modulation of FKBP51 as a therapeutic target for neurological diseases. Our results suggest a
model in which lowering the levels or activity of FKBP51 leads to increased autophagy, a
structural change in the HTT protein likely due to a change in a proline site(s) in the protein and
subsequent clearance of the protein (Figure 3.12). Future studies will systematically evaluate if
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pharmacological or genetic modulation of this enzyme is beneficial in preclinical studies using HD
mouse models.

Figure 3.12. Model of the potential mechanism of clearance of HTT with FKBP51 modulation

Figure 3.12. Model of the potential mechanism of clearance of HTT with FKBP51 modulation
(A) The physical interaction and proline isomerization activity of FKBP51 with HTT leads to a conformation
of mHTT that is not cleared by autophagy. (B) Decreasing the physical interaction and/or proline isomerization
activity of FKBP51 with HTT leads to a conformation state of mHTT that can be cleared by autophagy. This can be
mediated by SAFit2 or knockdown of FKBP51. Figure made by artist commissioned by Lisa Ellerby.

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SUPPLEMENTS
Supplemental Figure 3.1. HTT levels in zQ175 mice at 6 and 12 months of age

Supplemental Figure 3.1. HTT levels in zQ175 mice at 6 and 12 months of age
Representative western blot analysis of cortex for HTT expression in 6- and 12-month-old mice
homozygote zQ175 and WT mice. MAB2166 HTT antibody shows the WT HTT levels do not change at 6 and 12
months of age. It should be noted that many HTT antibodies are more selective for the WT than the mHTT
protein. 1C2 (mHTT polyQ) antibody shows an age-dependent decrease in mHTT levels at 12-months when
compared to 6-month-old zQ175 mice. Antibodies that recognize WT HTT may not recognize the polyQ-
expanded form of HTT with the same affinity due to post-translational differences between the two proteins,
and therefore, quantification is not possible between WT and zQ175 mice. Figure made by Barbara Bailus.

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Supplemental Figure 3.2. FKBP51 interacts with HTT in the midbrain

Supplemental Figure 3.2. FKBP51 interacts with HTT in the midbrain
FKBP51 antibody was used in co-immunoprecipitation analysis of midbrain tissue of 12-month-old WT
and Q175 heterozygous mice. Detection with HTT antibody shows that FBKP51 interacts with HTT in WT and
Q175 mice. Figure made by Barbara Bailus.
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Supplemental Figure 3.3. FKBP51 and HTT colocalization

Supplemental Figure 3.3. FKBP51 and HTT colocalization
ICC of HD and C116 NSCs stained with FKBP51 (red) and HTT (green) antibodies without DAPI shown in
panels for FKBP51 and HTT. Figure made by Barbara Bailus and Stephen Scheeler.
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Supplemental Figure 3.4. Dysregulation of FKBPs in human HD NSCs

Supplemental Figure 3.4. Dysregulation of FKBPs in human HD NSCs
Transcriptomic analysis of RNA-seq data shows dysregulation of FKBPs in human HD NSCs, compared to
corrected (WT) isogenic cells. Fold-changes are expressed in logs as HD/corrected (WT) expression levels (*p ≤
0.05; **p ≤ 0.01; ****p ≤ 0.0001). Figure made by Swati Naphade and Barbara Bailus.
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Supplemental Figure 3.5. LC3-II flux analysis

Supplemental Figure 3.5. LC3-II flux analysis
The control ratio of the Bafilomycin A1 (BafA1) treatment in the presence and absence of SAFit2. Figure
made by Jesse Simons and Barbara Bailus.
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Supplemental Figure 3.6. SAFit2 dose-response in R6/2 Mice

Supplemental Figure 3.6. SAFit2 dose-response in R6/2 Mice
SAFit2 was administered to R6/2 mice at 7.5, 10 and 15 mg/kg for 7 days (N =2 per group). At 4 hours
after the last dose, tissue was collected and processed for protein analysis. Western blot analysis shows dose-
dependent reduction of Exon 1 fragments of the mHTT protein. Reduced aggregation was observed at 10 and 15
mg/kg. The blue arrow is the insoluble HTT protein and the salmon arrow is the soluble HTT fragment produced
from exon 1 of the HTT gene. Figure made by Barbara Bailus.
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CHAPTER 4: MATERIALS AND METHODS
APOE METHODS
Culturing of APOE2 and APOE4 GABANeurons
iCell reagents and cells were obtained from Fujifilm (Formerly Cellular Dynamics). APOE4
GABANeurons (Fujifilm, R1168) and APOE2 GABANeurons (Fujiflim, R1169) were sold as kits,
which included a proprietary iCell Neural Base Media 1 and its associated Supplement A (R1150),
which combined form the Complete Maintenance Media. The two isogenic cell lines had been
differentiated by Fujifilm using their proprietary methodologies into GABANeurons, and offered
in vials of 4 million cells apiece. Cell vials were stored in liquid nitrogen until just before plating.
Prior to plating, the iCell Neural Supplement A was mixed with the iCell Neural Base Media
1. Additionally, 8-well chamber-slides, 12-well plates, 6-well plates, and/or 96-well plates were
coated with one of two matrices and supplied with one of two medias, depending on whether
we intended to stress the cells or not. For stressful conditions, vessels were coated with 0.01%
Poly-L-Ornithine (Sigma Aldrich, P4957) and left overnight at 37°C. Vessel wells were rinsed 3X
each with Milli-Q water (Elga Superflex Model PF3), and then coated with a freshly made aliquot
of 3.3mg/mL Laminin (Sigma Aldrich, L2020) in Milli-Q for at least 1 h at 37°C. Vessel wells were
aspirated and fresh, room temperature Complete Maintenance Media is added to each well. For
non-stressed conditions, vessels were instead coated with 0.1mg/mL Poly-D-Lysine (Sigma
Aldrich, P6407) and left overnight at 37°C. Vessel wells were then rinsed 3X each with Milli-Q
water, and then coated with 0.5mg/mL Matrigel (Corning, CB-40234) overnight at 37°C. Vessel
wells were aspirated and refreshed with fresh SynaptoJuice B according to Kemp et al. (Kemp et
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al., 2016). With media and vessels prepared, the next step involved thawing the cells. Individual
vials of cells were taken from liquid nitrogen, and immediately immersed in a 37°C water bath
for exactly 3 min, with no swirling. Vials were ethanol (Koptec, V1001) sterilized and moved to a
biological safety cabinet where the vial was then emptied of its contents into a 50mL conical
using wide bore p1000 tips. The emptied vial was rinsed with 1mL of room temperature media,
and then added to the same 50mL conical dropwise while also swirling the 50mL conical. An
additional 8mL of media was added in the same manner, dropwise while gently swirling to
mix. An aliquot of the cell and media mixture was mixed with trypan blue, to be counted by an
automatic hemocytometer, and plated into the appropriately treated vessel. A full media change
was done the next day, and again partially every 3-5 days, by 50-75%. Cells were cultured for 6-
10 days.

Immunocytochemistry of APOE2 and APOE4 GABANeurons
GABANeurons were cultured for 6-10 days on 8-well chamber-slides (BD Falcon), washed
with dPBS once, aspirated, and then fixed with 4% paraformaldehyde in dPBS (Sigma, 158127)
for 15 minutes at room temperature in a chemical cabinet. Wells were washed in the same
cabinet 3X with dPBS. At this stage, chamber-slides were either taken to the next step, or stored
with a parafilm seal for a later time at 4°C. After rinsing with dPBS (Corning, 21-030-CVR), cells
were permeabilized for 15 minutes using 0.1% Triton X-100 (Fisher Scientific, BP151-100) in
dPBS. Cells were rinsed another two times with dPBS, and then blocked for 30-60 minutes with
1% bovine-serum albumin (BSA) (Sigma Aldrich, 03117332001) and 5% donkey serum (Sigma
105

Aldrich, D9663) in dPBS. An additional dPBS wash was performed, and the cells were probed
with primary antibodies overnight at 4°C. Cells were washed 3X with dPBS, and then probed with
antigen matched, fluorescent labeled secondary antibodies for at last 1.5 h in the dark. Cells
were washed 3X times with dPBS in the dark. Removing the last wash, the chambers of the
chamber-slide were then removed, and the cells were mounted with 1.5mm glass coverslips
using Prolong Gold with DAPI (ThermoFisher, P36931). Imaging was done using a Zeiss LSM 780
confocal on an Axio Observer Z1 inverted microscope with Plan-Apochromat 63x/1.40 NA Oil DIC
objective. Primary and secondary antibodies were applied in a solution of block mentioned
earlier, which had been mixed 1:1 with dPBS. Primary antibodies were all diluted 1:100 while
secondary antibodies were diluted 1:200-1:350. Primary antibodies used include: VGAT
(Synaptic Systems, 131011), GluR1 (Millipore, ABN241), VGluT (Millipore, MAB5502), PSD95
(Sigma, SAB5600103), GAD (Millipore, AB1511), phospho-H2AX (Millipore, 05-636), NESTIN
(Abcam, ab92391), GABA (Synaptic Systems, A2052), MAP2 (Abcam, AB5622), P16 (Abcam,
AB108349), Cleaved Caspase-3 (Cell Signaling, 9661), HMGB1 (AB18256), LaminB1 (Abcam,
ab16048). Secondary antibodies used include: donkey-anti mouse Alexa Fluor 488
(ThermoFisher, R37114) and donkey-anti rabbit Alexa Fluor 555 (ThermoFisher, A-31572).

RNA Extraction of APOE2 and APOE4 GABANeurons
Bulk RNA was extracted from the wells of 12-well plates using the Bioline Isolate II RNA
mini kit (Bioline, BIO-52073), using the provided protocol. Materials used were provided by the
kit unless otherwise noted to have been purchased from a separate supplier. Each well contained
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approximately 400,000 GABANeuron cells. Briefly, vessel wells were rinsed 1X with
dPBS. Afterwards, 350uL of Lysis Buffer RLY and 3.5uL β-mercaptoethanol were added to half of
the wells of interest. Wells with the lysis solution were then scraped using a cell lifter, and the
solution was moved to a second well of the same genotype and condition, which was scraped in
the same manner. In this way, each sample contained the total cells of 2 wells of a 12-well
plate. The mixture was pipetted into a 1.5mL microcentrifuge tube, and subsequently vortexed
vigorously. Vortexed lysate was added to a filter and spun down for 1 min at 11,000 x g into a
collection tube. Spun lysate was transferred to a fresh 1.5mL tube and 350uL of 70% ethanol in
water was added to the new tube. The ethanol and lysate were mixed by pipetting up and down
5 times. Mixed lysate was added into a new column and centrifuged for 30 s at 11,000 x g, into
a new collection tube. 350uL of Membrane Desalting Buffer as added to the same column, and
spun down in the same collection tube for 1 min at 11,000 x g. 95uL of DNase I reaction mixture
was added directly onto the center of the column’s membrane and incubated at room
temperature for 15 minutes. Afterwards, 200uL of Wash Buffer RW1 was added to the column,
and spun for 30 s at 11,000 x g. The column was moved to a new collection tube and 600uL of
Wash Buffer RW2 was added to the column, and spun for 30 s at 11,000 x g. The flow-through
was discarded and another 250uL of Wash Buffer RW2 was added to the column and spun for 2
min at 11,000 x g to completely dry. The column was placed into a 1.5mL collection tube and
60uL of RNAse-free water was added directly into the center membrane of the column. After 5
min, the column and collection tube were centrifuged for 1 min at 11,000 x g, and the resulting
elute was nanodropped for RNA concentration, and frozen at -20°C.

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Bulk RNA sequencing of APOE2 and APOE4 GABAergic Neurons
Isolated RNA was utilized for both bulk RNA sequencing and Single Cell RNA-seq, done by
the University of California – Davis, Genome Center. 3 samples of each genotype, for 6 total
samples, were submitted to be analyzed by the UC-Davis core. 3 separate samples were
submitted for the Single Cell run. For the Single Cell run, a Qubits library was provided. Using
the samples, the core ran a PE150 on HiSeq4000. The resulting data was then returned for
subsequent analysis.

Single Cell RNA isolation of APOE2 and APOE4 GABAergic Neurons
Wells from 12-well plates were harvested for use in Single Cell analysis. 3 wells per
genotype were harvested, for a total of 6 samples. We utilized the V3 Chromium Single Cell 3’
Reagent Kits User Guide and its associated Chromium i7 Multiplex Kit (10X, PN-120262). For
sample processing, we followed the kit user guide mentioned exactly as written.

Neuronal Extension Methods
APOE4 and APOE2 GABANeurons were cultured as previously described above. Cells
were visualized for neurite outgrowth on a BioTek Cytation 5 machine. BioTek Software Version
3.08 was used for visualization and analysis. Wells were visualized using brightfield 4X or 10X
magnification lenses (as needed), and images were taken every 3 hours for the duration of the
experiment. Images taken at 10X were stitched as needed, using a 10% overlap between images,
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and rendered at full size. Images were then pre-processed with a 5 nm rolling ball, on a light
background, at normal speed. Pre-processed images were then subject to a de-convolution step,
with a 3X cleaning step, to remove ‘blur’ effect. These images were used for both neurite
outgrowth calculations, as well as to form videos that were subsequently used to assay cell
movement (method below). Neurite outgrowth was calculated by applying a primary mask layer
to processed images, utilizing a 10 nm rolling ball, with a mask threshold of 750 units; pulling the
sum perimeter (SP) parameter from field of view (Sum Perimeter being the sum of all perimeter
values in view, as opposed to mean perimeter value, which is default value). Primary mask
analysis also included edge units, and the acceptable size range was objects between 5 nm, and
500000 nm. When the mask parameters were optimized, it was applied to all reads, of all wells.
A ratio was then created to calculate Sum Perimeter of all reads over the Sum Perimeter value of
read 1 for each well, respectively, this is called Sum Perimeter Normalized (SP Norm). The ratios
over time were graphed together using Graphpad PRISM 8.

Cell Movement Analysis
GABANeurons were cultured and imaged at 4X magnification as above described. 2×2
tiled images centered in each well of the microplate were stitched, registered, and exported using
H.264 (mp4) video compression using the BioTek software. Cell body motility was analyzed in
Image Analyst MKII (Image Analyst Software, Novato, CA). Positions of cell bodies in the bright
field image were obtained by highlighting cell bodies using band pass spatial filtering with
absolute value calculation, and segmentation of these processed images using a modification of
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the “Measure tracking parameters of cells in brightfield time lapses” pipeline. Using a simulated
annealing numerical optimization-based algorithm the frame-to-frame representations of
individual cell bodies (image segments) were determined. The use of low-frame-rate was
allowed based on proximity, size and shape information, with no requirement for spatial overlap
of segments in consecutive frames (“Track Objects” function). Tracks, that were shorter than 10
frames were discarded. Given the random motion of cells in the culture, it was assumed that the
sum of all cell’s dislocation vectors in a large view field is zero, and any residual is due to
insufficient registration of the frames of the multi-position time course. Therefore, this vectoral
sum was subtracted from the dislocations for each pair of frames. Furthermore, a cut off velocity
was used to remove erroneous tracking data points. The resultant tracks were directly graphed
or quantified by the (“Plot Tracking parameters” function). The “mean curvilinear velocity”
informs the mean absolute velocity for the entire track, calculated frame-by-frame and then
averaged. In order to quantify how straight and evenly cell bodies moved along trajectories,
“average path velocity” was calculated by a running average on the velocity vectors, before
calculating absolute velocities. This calculation results in smaller velocities for objects that often
change direction, but has no effect on objects that move straight. “Wobble” measures excursions
from (or wobbling around) the average path. Calculated as 1-"mean average path
velocity"/"curvilinear velocity". A value of 0 indicates smooth motion and greater values indicate
more wobbly motion. Calculations were applied for each track and then averaged.

APOE2 and APOE4 GABAergic inhibitory neurons comet assay
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APOE2 (Fujifilm, R1169) and APOE4 (Fujifilm, R1168) homozygous iCell GABANeurons
were cultured on Corning 6-well plates (Thermo-Fisher 140675) coated with 0.1mg/mL Poly-D-
Lysine (Sigma Aldrich, P6407) and left overnight at 37°C. Vessel wells were then rinsed 3X each
with Milli-Q water, and then coated with 0.5mg/mL Matrigel (Corning, CB-40234) overnight at
37°C. Vessel wells were aspirated and fresh SynaptoJuice B was added according to Kemp et al.
(Kemp et al., 2016). When ready, cells were transferred to low-light conditions for passaging and
cell count as mentioned previously (Ring et al., 2015a). The comet assay was performed using
the Trevigen comet assay kit (R&D Systems, 4250-050-03), and their recommended protocol was
primarily followed. Approximately 3000 cells/condition were added to 1.5mL microcentrifuge
tubes (Sorenson 11590) followed by 75 uL of warmed 1% Low-Melting temperature Agarose
(BioRad, #1613102). Agarose-cell mixture was then spread evenly onto proprietary Trevigen 2-
well slides obtained from their comet kit (R&D Systems, 4250-050-03). Slides were placed in a
ventilator box, covered from light, and allowed to cool and gel at 4°C for up to 30 min. Slides
were then submerged in the Trevigen Lysis Solution (R&D Systems, 4250-050-01), and placed in
a 4°C fridge, protected from light, and allowed to sit overnight. Another container was filled
with Unwinding solution comprised of 8 g NaOH (Millipore-Sigma, Emplura #1310-73-2) mixed
with 2 mL 500 nM EDTA (Invitrogen-Gibco, Cat# 15575-038) and 1 L of MilliQ water (Elga
Superflex Model PF3), resulting in no less than pH 13. Slides were then transferred to Unwinding
solution in the dark, and left for 1 hour at 4°C, while the Trevigen Electrophoresis container (R&D
Systems, 4250-050-ES) was prepared with more Unwinding solution. Slides were transferred in
the dark to Electrophoresis container. Electrophoresis was run in a 4°C cold room at 21 Volts for
10 minutes. After Electrophoresis, slides were washed twice with MilliQ water for 5 minutes,
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washed once with 70% ethanol for 5 minutes, then allowed to dry in a Carbon-Dioxide free 37°C
incubator for 10 minutes. Dried slides were dyed with 1X solution of SYBR Gold (ThermoFisher,
S-11494) dye in MilliQ water, for 30 minutes, at room temperature, protected from light. Dyed
slides were then rinsed with MilliQ water for 5 minutes, and then allowed to dry overnight at
Room Temperature, before visualizing. Slides were visualized with a BioTek Cytation 5 machine,
with software version 3.08, using the methods detailed by Brad Larson in his Biotek Application
notes paper (https://www.biotek.com/resources/application-notes/automated-comet-assay-
imaging-and-dual-mask-analysis-to-determine-dna-damage-on-an-individual-comet-basis/).

RNA ANALYSIS AND RNA-SEQ LIBRARY CREATION
RNA Sequencing Analysis
The low-quality base (quality score lower than 20), as well as the adapters of the raw
reads from the sequencing experiments, were removed using Trim Galore! 0.6.4
(https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). The external and internal
rRNA contamination were filtered through SortMeRNA 2.1b106. Then the filtered raw reads were
then mapped to the Genome Reference Consortium Mouse Build 38 striosome release
6(GRCm38.p6) assembly by GENCODE using STAR 2.7.2b107. The counts of reads mapped to
known genes were summarized by featureCounts, using GENECODE release M22 annotation
(GSE143276). The data discussed in this publication have been deposited in NCBI's Gene
Expression Omnibus GEO Series accession number, GSE143276. Next, R Bioconductor package
DESeq2108 was used to normalize raw read counts logarithmically and perform differential
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expression analysis. Differentially expressed genes were based on an arbitrary cutoff of adjusted
p-value less than 0.01.

Terminology Enrichment Analysis and Pathway Enrichment Analysis
Enrichment analysis was performed on gene clusters in specific databases to determine
whether a specific biological annotation could be considered as significantly represented under
the experiment result. Both terminology enrichment analysis and pathway enrichment analysis
were conducted by clusterProfiler109, a Bioconductor package. In our analysis, biological process
(BP), molecular function (MF), and cellular component (CC) terms in gene ontology (GO)57 as
well as pathway annotations derived from Kyoto Encyclopedia of Genes and Genomes (KEGG)
were chosen to identify predominant biological processes of the differentially expressed gene
clusters and differentially expressed transcription factor clusters involved in the development of
the striosome neurons. We conducted both analyses on the differentially expressed gene clusters
with the arbitrary cutoff of adjusted p-value less than 0.01 and the absolute Log2 fold change
greater than 0, 1, and 2 respectively, and we conducted both analyses on the differentially
expressed transcription factor clusters with the arbitrary cutoff of adjusted p-value less than 0.01
and the absolute Log2 fold change greater than 0 and 1 respectively.

GeneMANIA Gene Regulatory Network Analysis
GeneMANIA55 is an online tool using published and computational predicted functional
interaction data among proteins and genes to extend and annotate the submitted gene list by
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their interactive biological pathways and visualize its inferred interaction network accordingly.
We used GeneMANIA to conduct the interaction network inference analysis to transcription
factors enriched in either the striosome or matrix compartments with the arbitrary cutoff of
adjusted p-value less than 0.01 and the absolute log2 fold change greater than 1.

Gene Regulatory Network Inference Through Data Curation
A gene regulatory network links transcription factors to their target genes and represents
a map of transcriptional regulation. We used all the transcription factors and their target gene
data curated by ORegAnno67 to build the network. In order to simplify the network, we only
chose the compartmental differentially expressed transcription factors that are high on the
hierarchy. In other words, only the differentially expressed transcription factors that served as a
regulator of other differentially expressed transcription factors were chosen as the candidates of
our gene regulatory network.

Gene Set Enrichment Analysis
GO112 was performed using ranked list of differential gene expression with parameters
set to 2000 gene-set permutations and gene-set size between 15 and 200. The gene-sets included
for the GSEA analyses were obtained from Gene Ontology (GO) database (GOBP_AllPathways),
updated September 01, 2019 (http://download.baderlab.org/ EM_Genesets/). An enrichment
map (version 3.2.1 of Enrichment Map software113) was generated using Cytoscape 3.7.2 using
significantly enriched gene-sets with an FDR <0.05. Similarity between gene-sets was filtered by
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Jaccard plus overlap combined coefficient (0.375). The resulting enrichment map was further
annotated using the AutoAnnotate Cytoscape App.

Repetitive Repeat Element Analysis
We used RepEnrich2 (Criscione, Zhang, Thompson, Sedivy, & Neretti, 2014) to estimate
the number of repetitive elements being observed in our RNA-seq data. We used the repetitive
element annotation for Homo sapiens from Repeatmasker.org. Once repetitive elements were
quantified, DESeq2 (Love et al., 2014) was used to perform differential enrichment analysis.

scRNA-seq Analysis
Adaptors of raw single-cell RNA-sequencing (scRNA-seq) reads were trimmed using Trim
Galore!, using default settings. Trimmed reads were mapped to the human reference genome
build 37 using STAR34 (version: 020201) in two-pass alignment mode, using the default settings
proposed by the ENCODE consortium (STAR manual). Gene-level expression quantification was
performed using Salmon35 (version: 0.8.2), using the “--seqBias”, “--gcBias” and “VBOpt” options
using ENSEMBL transcripts (built 75). Transcript-level expression values were summarized at a
gene level (estimated counts per million (CPM)) and quality control of scRNA-seq data was
performed with the scater Bioconductor package in R37. Cells were retained for downstream
analyses if they had at least 50,000 counts from endogenous genes, at least 5000 genes with non-
zero expression, less than 90% of counts came from the 100 highest-expressed genes, less than
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15% of reads mapping to mitochondrial (MT) genes and they had a Salmon mapping rate of at
least 60%. Size factor normalization of counts was performed using the scran Bioconductor
package in R38. Expressed genes with an HGNC symbol were retained for analysis, where
expressed genes in each batch of samples were defined based on raw count >100 in at least one
cell prior to QC and average log2(CPM+1) >1 after QC. Normalized CPM data were log
transformed (log2(CPM+1)) for all downstream analyses.

HD FKBP51 METHODS
NSC Culturing
NSC were derived and cultured as described (Ring et al., 2015a). After harvesting from
rosettes, NSC were grown on Matrigel (Corning, CB40234)-coated dishes in Neural Proliferation
Medium (Life Technologies, neurobasal medium supplemented with 1XB27 (Life Technologies,
17504-044)), 2 mM L-glutamine (Gibco, 35050-061), 10 ng/mL leukemia inhibitory factor
(Peprotech, 300-05), 25 ng/mL bFGF (Peprotech, 100-18B), and 1% penicillin/streptomycin
(Corning, 15140-122). NSC were passaged with Accutase (A11105-01) every 7 d, passaging at a
1:3 ratio. Lysates were harvested by cell scrapping in MPER (Thermo Fisher Scientific, 78501) with
Complete Mini protease inhibitor cocktail (Roche, 11836170001), 1% phosphatase Inhibitor Set
II (EMD Millipore, 524625), 1% phosphatase Inhibitor Set III (EMD Millipore, 539134), 50 µM
tricostatin A (Sigma, T8552), 30 µM sodium butyrate (Sigma, 303410), and 30 mM nicotinamide
(Sigma, 72340). Lysate was sonicated once at 40 mA with 5 s pulses, 5X, with 5 s breaks between
each pulse. Sonicated lysate was then centrifuged at 12,000 rpm for 20 min at 4˚C. The
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supernatant was transfer to a new tube. Protein concentration of the supernatant was
determined using the BCA assay (Thermo Fisher Scientific, 23252).

Transcriptomic Analysis of HD NSC
We used our published RNA-seq data set to analyze the changes in mRNA of FKBP family
members (Ring et al., 2015a).

siRNA Knockdown of FKBP51 in NSC
HD and C116 NSC were cultured as described above. Cells were plated at a density of
100,000 per cm
2
on matrigel (Corning, CB40234)-treated six-well plates. Cells were transfected
at about 90% confluency. Before transfection, the medium was changed to antibiotic-free
medium. siRNAs (Dharmacon, FKBP51: J-004224009, non-targeting 001206-13-20) were diluted
into a stock solution of 10,000 nM, and 3 μL was used per well in a six-well plate (25 nM per well).
The siRNA was complexed with 6 μL of siLentFect Lipid reagent (Biorad, 170-3361). The siRNA
and siLentFect were incubated for 20 min at room temperature (RT) and then slowly added
dropwise to the well, and the plate was gently swirled. Fresh antibiotic-free medium was applied
48 h after transfection, and the cells were harvested 96 h after transfection on ice. Cells were
rinsed twice with DPBS and then collected by scrapping in MPER (Thermo Scientific, 78501) with
Complete Mini protease inhibitor cocktail (Roche, 11836170001), 1% Phosphatase Inhibitor Set
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II (EMD Millipore, 524625), and 1% Phosphatase Inhibitor III (EMD Millipore, 539134). Protein
concentrations were determined using the BCA assay (Thermo Fisher Scientific, 23252).

Treatment with SAFit2 in NSC
SAFit2 (Aobious, AOB6548) was dissolved in dimethyl sulfoxide (DMSO) to a final
concentration of 10 mM. Isogenic NSC, HD and C116 (N =4 per genotype), were treated with
DMSO, 1 μM SAFit2, and 10 μM SAFit2. SAFit2 was added to fresh culture medium and then
added to the six-well plates as part of the medium change. Cells were harvested 48 h after SAFit2
treatment on ice, rinsed 2X with DPBS and then scrapped with 200 μL of DPBS, and collected by
centrifugation at 12,000 rpm, and the pellets were frozen. Pellets were resuspended in MPER
(Thermo Fisher Scientific, 78501) and 1X Protease Inhibitor cocktail (Roche, 1183670001). Lysates
and protein determinations were carried out as described above.

MSN Culture
Activin A (25 ng/mL, Peprotech, AF-120-14E)-treated HD and C116 NSC were used to
prepare MSN. Nunc six-well plates were treated with poly-D-lysine hydrobromide (1 mL, 100
µg/mL, Sigma Aldrich, P6407) and incubated (37 C and 5% CO 2) overnight (ON). Plates were
washed once (1 mL, Corning cell culture grade water, 25-055-CVC) and left to air dry for 1 h inside
the culture hood. Next, the plates were treated with Matrigel (1 mL, 50 µg, Corning, CB-40234)
ON in an incubator. MSN were prepared according to Kemp et al. (Kemp et al., 2016). Isogenic
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HD and C116 MSN were cultured in 6-cm dishes (N=3 per genotype). Cells were harvested on ice
rinsed 1X with neurobasal A medium (Gibco, 10888-022) and scrapped in MPER lysis buffer
described above.

Maintenance and Breeding of zQ175 and R6/2 Mice
The Buck Institute animal facility is an AAALAC international-accredited institution (Unit
#001070). All procedures were approved by the Institutional Animal and Use Committee (A4213-
01). Mice were genotyped by PCR analysis with GoTaq Green (Promega, M7121). DNA extraction
from isolated tail snips of 3-week-old mice was performed, following the manufacturer’s protocol
(Qiagen, DNeasy Blood & Tissue kit, 69504). For zQ175 mice, 8 pmol of PCR control (F: 5′-
CATTCATTGCCTTGCTGCTAAG-3′, R: 5′-CTGAAACGACTTGAGCGACTC-3′) and sequence specific
primers (IDT) NeoF (F: 5′- GATCGGCCATTGAACAAGATG-3′ R: 5′-AGAGCAGCCGATTGTCTGTTG-3′),
were used to amplify genomic DNA (50 ng). Cycle conditions were: 96
o
C 10 min, 35 cycles of 96
o
C
30 s, 58
o
C 30 s, 72
o
C 30 s, followed by a 7-min incubation at 72
o
C. Samples were run on a 2%
agarose gel, positive mice yielded reaction products of the following lengths which distinguishes
WT, heterozygote, and homozygote zQ715 mice. R6/2 mice were genotyped, and CAG length was
carried out by Laragen.

SAFit2 Treatment of R6/2 and zQ175 Mice
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SAFit2 (Aobious, AOB6548) was reconstituted in 100% EtOH to a concentration of
0.1mg/µl and stored at -20°C. On the day of injection, the SAFit2 stock was diluted to the desired
concentration in a final solution with the following totals of 4% EtOH, 5% Tween80, 5% PEG400
in 0.9% saline. It is worth noting that the SAFit2 must be brought up to the final 4% EtOH before
the remaining components can be added. Mice were injected with 100 µL volume of either the
SAFit2 solution or the buffer solution (4% EtOH, 5% Tween80, 5% PEG400 in 0.9% saline). Animals
were daily intraperitoneal injected, and 4 h before harvest. Injection sites were altered between
the left or right side daily.

Mouse Brain Dissection and Homogenization
To collect whole mouse brains, mice were anesthetized with isoflurane (Butler Schein)
and cervically dislocated. An adult brain matrix (Bioanalytical Systems, RBM-2000c) was used to
section the brain into 1-mm coronal slices. Cortical and striatal regions were dissected from these
slices. All dissections and brain collections were performed on ice and flash frozen on dry ice.
Samples were store at 80
o
C overnight (ON) or until tissue homogenization. Lysis (200 µL striatum,
400 µL cortex) was performed with TPER (10 mL, Thermo Scientific,78510) containing protease
inhibitors (Complete Mini, Roche, 11836170001, 1 tablet/10 mL), DNAase (1 µL, Invitrogen,
EN0521), MgCl 2 (1.2 mM, Fluka), epoxomycin (1 µM, Sigma, E3652), Phosphatase Inhibitor
Cocktail II (100 µL, Calbiochem, PPI II, 524625), tricostatin A (50 µM, Sigma, T8552), nicotinamide
(30 mM, Sigma,72340) and sodium butyrate (30 µM, Sigma, 303410). A dounce homogenizer (2
mL) was used for tissue homogenization (2 X 60 pumps with a 30-s interval) on ice. After
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homogenization samples were stored at -80
o
C. Lysates were sonicated 3X at 40 mA with 5-s
pulses, 5X, with 5-s breaks between each pulse. Sonicated lysates were subjected to
centrifugation at 12,000 rpm for 20 min at 4˚C. Supernatants were saved in a new tube, and the
debris pellets were discarded. Protein concentrations of the supernatant were determined using
the BCA assay (Thermo Fisher Scientific, 23252).

Western Blot Analysis for FKBP51 siRNA Knockdown in NSC
Lysates (8-15μg) were prepared in 4X sample buffer (Invitrogen, NP0007) with 0.05 M
dithiothreitol (DTT), and boiled for 10 min at 95°C. Lysates were run on a 4–12% Bis-tris gel at
200V for 50 min in 2-ethanesulfonic acid (MES) running buffer (Invitrogen NP0002) with
antioxidant (Invitrogen NP0005). Transfer was performed overnight (ON), 20V for 840 min at 4°C,
onto a 0.4-μm nitrocellulose membrane. Blocking was done with 5% milk in tris-buffered saline,
0.1% Tween20 (TBST). After blocking membranes were probed with the following primaries:
FKBP51 (1:100, Cell Signaling, 12210), HTT (Millipore, MAB2166), polyQ 1C2 (1:500, Millipore,
MAB1574), vinculin (1:500, Sigma, V9131) or β-actin (1:1000-2000, Sigma, A5441). Membranes
were incubated with secondary anti-murine horseradish peroxidase (HRP)-coupled antibodies
(1:2500, GE Healthcare, NXA931) or anti-rabbit HRP-coupled antibodies (1:2500, GE Healthcare,
NA934 at RT for 2 h in 5% milk TBST solution. Protein bands were detected by chemiluminescence
(Pierce ECL Thermo Scientific, cat #32106). ImageQuant TL (v2005, Amersham Biosciences) was
used for densitometry analysis.

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Western Blot Analysis for FKPB51 in NSC and MSN Treated with SAFit2
Lysates (8-15 μg) were prepared in 4X sample buffer (Invitrogen, NP0007) with 0.05 M
dithiothreitol (DTT), and boiled for 10 min at 95°C. Lysates were run on a 4–12% Bis-tris gel at
200V for 50 min in 2-ethanesulfonic acid (MES) running buffer (Invitrogen NP0002) with
antioxidant (Invitrogen NP0005). Transfer was performed ON, 20V for 840 min at 4°C, onto a 0.4-
μm nitrocellulose membrane. Blocking was done with 5% milk in tris-buffered saline, 0.1%
Tween20 (TBST). After blocking membranes were probed with the following primaries: FKBP51
(1:100, Cell Signaling, 12210), vinculin (1:500, Sigma, V9131) or β-actin (1:1000-2000, Sigma,
A5441), or α-tubulin (1-1000 Sigma, T6199). Membranes were incubated with secondary anti-
murine HRP-coupled antibodies (1:2500, GE Healthcare, NXA931) or anti-rabbit HRP-coupled
antibodies (1:2500, GE Healthcare, NA934) at RT for 2 h in 5% milk TBST solution. Protein bands
were detected by chemiluminescence (Pierce ECL Thermo Scientific, cat #32106). ImageQuant TL
(v2005, Amersham Biosciences) was used for densitometry analysis.

Western Blot Analysis of HTT and mHTT in HD NSC and MSN
Lysates (20 μg) were prepared in 4X sample buffer (Invitrogen, NP0007) with 0.05 M
dithiothreitol (DTT), and boiled for 10 min at 95°C. mHTT and HTT bands were separated on a 3–
8% Tris-acetate gel, run for 90 min in Tris-acetate running buffer (Invitrogen, LA0041) and
antioxidant (Invitrogen, NP0005)) at 200V on ice. Transfer was performed at 4°C at a constant
20V for 840 min in transfer buffer (Invitrogen) onto a nitrocellulose membrane. Blocking was
done ON in 5% milk in TBST, primary antibody HTT (1:250, Millipore MAB2166), HTT (1:500,
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Millipore, 1574), vinculin (1:500, Sigma, V9131) was probed at 4°C ON in 5% milk TBST, secondary
HRP-coupled anti-mouse (GE Healthcare, NXA931, 1:2500) was probed ON at 4°C in 5% milk TBST.
Protein bands were detected by chemiluminescence (Pierce ECL Thermo Scientific, 32106).
ImageQuant TL (v2005, Amersham Biosciences) was used for densitometry analysis.

LC3 Westerns
Protein concentrations were determined by BCA protein assay (Thermo Scientific).
Lysates (5 μg) were prepared in 4X LDS buffer (Invitrogen, NP0007) and 0.05 M DTT. Samples
were boiled for 10 min at 95
o
C and separated by 1D-SDS-PAGE electrophoresis on a 12% Bis-Tris
gel at 200 V for 60 min in 3-(N-morphorlino)propanesulfonic acid (MOPS) running buffer and
antioxidant (Invitrogen, NP0005). Transfer was performed on ice in a cold room at 350 mA
constant for 1 h in transfer buffer (Invitrogen, NP00061) with 10% methanol onto a 0.2-μm PVDF
membrane (Millipore, LC20002). After blocking in 5% milk in TBST, primary antibody (20μg/ml,
Novous, 100-2220) was incubated ON in 5% milk in tris-buffered saline, 0.1% Tween20 (TBST) at
4
o
C. Membranes were incubated with secondary anti-rabbit (1:2500, GE Healthcare NA934) or
anti-murine (NXA931) HRP-coupled antibodies (1:2500, GE Healthcare) at RT for 2 h in 5% milk
TBST solution. Protein bands were detected by chemiluminescence (Pierce ECL Thermo Scientific,
32106). ImageQuant TL (v2005, Amersham Biosciences) was used for densitometry analysis.

Western Blot Analysis Comparing SAFit2 and Rapamycin Treatment in NSC
123

Lysates (15 μg) were prepared in 4X sample buffer (Invitrogen, NP0007) with 0.05 M DTT,
and boiled for 10 min at 95°C. Bands were separated on a 3–8% Tris-acetate gel, run for 90 min
in Tris-acetate running buffer (Invitrogen, LA0041) and antioxidant (Invitrogen, NP0005) at 200V
on ice. Transfer was performed at 4°C at a constant 20V for 840 min in transfer buffer (Invitrogen)
onto a nitrocellulose membrane. Blocking for all phosphorylated antibodies was done ON in 5%
bovine-serum albumin (BSA) (Sigma Aldrich, 03117332001) in TBST. Blocking for all non-
phosphorylated equivalents of antibodies was done ON in 5% milk in TBST. Primary antibody
phosphorylated mTOR (1:100, CST #2971), was normalized to total mTOR (1:100, CST, #2972).
Both antibodies were applied at 4°C ON in 5% milk TBST or 5% BSA TBST, secondary HRP-coupled
anti-rabbit (1:3000, GE Healthcare NA934). Further comparison was done by probing primary
antibodies phosphorylated ULK-1 (1:100, CST #6888) probed at 4°C ON in 5% BSA TBST,
normalized to total ULK-1 (1:100, MilliporeSigma 7481), probed at 4°C ON in 5% milk TBST. Both
antibodies utilized secondary HRP-coupled anti-rabbit (1:3000, GE Healthcare NA934) and were
probed ON at 4°C in 5% BSA or milk TBST, respectively. A 4-12% Bis-Tris gel, run for 50 min in
Tris-acetate running buffer (Invitrogen, LA0041) and antioxidant (Invitrogen, NP0005)) at 200V
on ice was used for S6. Primary antibodies utilized for probing were phosphorylated S6 (1:100,
CST #5364) probed at 4°C ON in 5% BSA TBST, normalized to total S6 (1:100, CST #2217), probed
at 4°C ON in 5% milk TBST. Both antibodies utilized secondary HRP-coupled anti-rabbit (1:3000,
GE Healthcare NA934) and were probed ON at 4°C in 5% BSA or milk TBST, respectively. Protein
bands were detected by chemiluminescence (Pierce ECL Thermo Scientific, 32106). ImageQuant
TL (v2005, Amersham Biosciences) was used for densitometry analysis.

124

Caspase Activity Assay Comparing SAFit2 to Rapamycin Treatment in NSC
A 96-well assay plate with black sides and clear flat bottom (Corning, Ref 3340) was coated
with Matrigel solution in DMEM F-12 Knockout medium (50 μL/well), and allowed to sit for 24 h
at 37
o
C. NSC were seeded at 20,000 cells/well, and allowed to culture in NPM until achieving 70%
confluency. Cells were then treated with 0.1% dimethyl sulfoxide (DMSO) in starvation medium
(NBM with 1% Penicillin/Streptomycin), SAFit2 in Starvation Media, or Rapamycin in Starvation
Media, for 24 h. After 24 h, Activated Caspase APO 3/7 HTS kit (Cell Technology, APO200-3),
including Cell lysis buffer #3005, and Caspase 3/7 Reagent (z-DEVD) 2 Rodamine 110 (Cell
Technology #4004). Caspase Activity read was done on PerkinElmer VictorX3 Multimode Plate
Reader. 100 reads at 488 nm emission were collected, and a best fit line was calculated through
the reads. The best fit line was then normalized to total protein value calculated by BCA assay
on a by well basis to calculate the Caspase Activity/Total Protein.

Western Blotting for FKBPs in HD Transgenic Mouse Brain Lysates
Cortical and striatal lysates (20–40 μg) were prepared in 4X LDS buffer (Invitrogen,
NP0007) and 0.05 M DTT. Samples were boiled for 10 min at 95
o
C and separated by 1D-SDS-PAGE
electrophoresis on a 12% Bis-Tris gel at 200 V for 50 min in 3-(N-morphorlino)propanesulfonic
acid (MOPS) running buffer and antioxidant (Invitrogen, NP0005). Transfer was performed on ice
in a cold room at 350 mA constant for 1 h in transfer buffer (Invitrogen, NP00061) with 10%
methanol onto a 0.2-μm PVDF membrane (Thermo Fisher, 88520). After blocking in 5% milk in
TBST, primary antibody FKBP12 (1:1000-4000, Abcam, ab2918), FKBP12.6 (FKBP1B) (1:100-200,
125

Abcam, ab82316); FKBP51 (FKBP5) (1:100-500, Cell Signaling, D5G2), FKBP52 (FKBP4) (1:100, Cell
Signaling, 11826), α-tubulin (1:500-4000, Sigma, T6199) were incubated ON in either 5% milk
TBST or 5% BSA TBST at 4
o
C. Membranes were incubated with secondary anti-rabbit (1:2500, GE
Healthcare NA934) or anti-murine HRP-coupled antibodies (1:2500, GE Healthcare, NXA931) at
room temperature (RT) for 2 h in 5% milk TBST solution. Protein bands were detected by
chemiluminescence (Pierce ECL Thermo Scientific, cat #32106). ImageQuant TL (v2005,
Amersham Biosciences) was used for densitometry analysis.

Western Blot Analysis in R6/2 and zQ175 Treated with SAFit2
Lysates (15-30 μg) were prepared in 4X sample buffer (Invitrogen, NP0007) with 0.05 M
DTT, and boiled for 10 min at 95°C. The R6/2 lysates were run on a 4–12% Bis-tris gel at 200V for
50 min in 2-ethanesulfonic acid (MES) running buffer (Invitrogen NP0002) with antioxidant
(Invitrogen NP0005). The zQ175 lysates were separated on 3-8% tris-acetate gel at 200V for 90
min in tris-acetate running buffer. Transfer was performed ON, 20V for 840 min at 4°C, onto a
0.4-μm nitrocellulose membrane. Blocking was done with 5% milk in TBST. After blocking
membranes were probed with the following primaries: HTT (1:500, Sigma, 5492), FKBP51 (1:100,
Cell Signaling, 12210), vinculin (1:500, Sigma, V9131) or β-actin (1:1000-2000, Sigma, A5441), or
α-tubulin (1-1000 Sigma,T6199). Membranes were incubated with secondary anti-murine HRP-
coupled antibodies (1:2500, GE Healthcare, NXA931) or anti-rabbit (1:2500, GE Healthcare
NA934) at room temperature (RT) for 2 h or ON at 4°C in 5% milk TBST solution. Protein bands
126

were detected by chemiluminescence (Pierce ECL Thermo Scientific, cat #32106). ImageQuant TL
(v2005, Amersham Biosciences) was used for densitometry analysis.

IHC Staining
Paraffin-embedded coronal mouse brain sections from zQ175 and WT mice (7 μm) were
deparaffinized and rehydrated with xylene and ethanol rinses, with a final rinse in deionized
water. The slides were then washed in TBS for 10 min. Antigen retrieval was performed using
citrate buffer at pH 6.0 for 5 min at 40% power in 1100 W microwave (Sanyo). The slides were
allowed to cool in the same buffer at room temperature (RT) for 20 min. Slides were then washed
in TBS. Sections were blocked for 60 min in blocking buffer containing 5% normal goat serum in
TBS and 2 μg/mL goat anti-mouse IgG (1 mg/ml, Aves Labs, Inc., IMU-1010). Primary antibodies
FKBP51 (1:25, Santa Cruz, sc-271547) and HTT (1:50, Sigma-Aldrich, H7540) were diluted in 1%
bovive-serum albumin (BSA) in TBS and left at 4
o
C ON in a humidified chamber. Slides were
washed three times in TBS. Secondary antibodies of goat-anti mouse Alexa Fluor 555 (1:500,
ThermoFisher, A21424) and goat-anti rabbit Alexa Fluor 488 (1:500, ThermoFisher, A11006) were
diluted in 1% BSA in TBS and incubated at RT for 2 h. Slides were washed for 10 min, three times
and then dried. Slides were mounted using Prolong Gold with DAPI (ThermoFisher P36931). All
images were collected using a Zeiss LSM 780 confocal on an Axio Observer Z1 inverted
microscope with Plan-Apochromat 63x/1.40 NA Oil DIC objective and zoom at 2.0. DAPI was
excited with a 405 nm Diode laser line at 36.8 % (Filter 410–498 nm). Alexa Flour 488 secondary
was excited with a 488 nm Argon laser line at 2.5% (Filter 490–544 nm). Alexa Fluor 555
127

secondary was excited with a 555 nm DPSS laser line at 10.0% (Filter 566–697 nm). Master gain
for all channels set to 650. Dimensions: x: 1584, y: 1584, z: 25, channels: 3, 12-bitImage size x:
71.03 µm, y: 71.03 µm, z: 7.74 µm. Z-series optical sections were collected with a step-size of
0.28 µm in 26 steps over 7 µm in the z dimension. Co-localization analysis was done using IMARIS
64 software (IMARIS x64 7.4.2 Feb 21 2012 Build 26649 for x64, Bitplane Scientific Software).

Co-Immunoprecipitation with HTT or FKBP51 Antibody
Total midbrain lysate was used from homozygote zQ175 and wildtype mice for co-
immunoprecipitation (Co-IP). Co-IP for striatal and cortical lysates used an allelic series of
heterozygote mice consisting of WT, 50Q, 90Q, and 175Q expansions. Total lysate (300 μg,
midbrain, striatal or cortical) was diluted into a final volume of 700 μL (T-PER, Thermo Fisher,
78510; Protease Inhibitor Complete tablet, Roche, 11697498001). The lysate was pre-cleared
with protein G beads (GE Healthcare, 17061801) for 1 h at 4°C. Pre-cleared brain lysates were
incubated ON at 4°C with HTT antibody (1:140, Millipore, MAB2166) or FKBP51 antibody (1:50,
Cell Signaling, 12210) or no antibody as a control. Lysate was incubated with protein G beads
(GE Healthcare, 17061801) for 5 h at 4°C. Beads were washed with TPER (5X) and eluted with 2X
LDS buffer (50 μL, Invitrogen, NP0007) and 0.1 M DTT, boiled for 10 min and then spun at 12,000
rpm for 10 min, and the supernatant was saved. 25 µL of the total 50 µL elute volume was boiled
for 10 min at 95°C and then separated by 1D-SDS PAGE electrophoresis on a 4–12% Bis-Tris gel
at 200V for 50 min in MES running buffer (Invitrogen NP0002) with antioxidant (Invitrogen
NP0005). Transfer was performed ON, 20V for 840 min at 4°C, onto a 0.4-μm nitrocellulose
128

membrane. Blocking was done with 5% milk in TBST. After blocking primary antibody of HTT
(1:100-300, Millipore, MAB2166) and FKBP51 (1:100-250, Cell Signaling,12210) and FKBP51
(1:100-300, Santa Cruz, 271547) was incubated ON at 4°C in 5% milk in TBST. Membranes were
incubated with secondary anti-rabbit (1:2500, GE Healthcare, NA934V) or anti-murine (1:2500,
GE Healthcare, NXA931) HRP-coupled antibodies at RT for 2 h or ON at 4°C in 5% milk TBST
solution. Protein bands were detected by chemiluminescence (Pierce ECL Thermo Scientific, cat
#32106). ImageQuant TL (v2005, Amersham Biosciences) was used for densitometry analysis.

Immunocytochemistry of NSC
NSC were cultured in eight-well Matrigel (Corning, CB40234)- coated-chamber slides
eight-well chamber slides (BD Falcon) and fixed with 4% PFA (Sigma, 158127) for 15 min at RT
and washed three times with PBS. Cells were permeabilized with 0.1% Triton X-100 (Fisher
Scientific, BP151-100) in PBS. Blocking was done with 1% BSA (Sigma Aldrich, 03117332001) and
5% donkey serum (Sigma Aldrich, D9663) in PBS. Primary antibodies (1:200, FKBP51, Santa Cruz
sc-271547) (1:200, HTT, Sigma-Aldrich H7540) were incubated for 24 h at 4ºC in a humid
chamber. Secondary antibodies (Alexa 488 and Alexa 546) were used at 1:250 at RT for 90 min in
the dark. Slides were mounted with 1.5-mm coverslips and ProLong Gold with DAPI antifade
mount. Imaging was done on a Nikon Eclipse Ti-U microscope using Plan Apo λ 20X/0.75
objective.

Immunocytochemistry of Human MSN
129

C116 and HD MSN were fixed using 4% paraformaldehyde in 0.1 M PBS, pH 7.4 (Corning,
21-040-CV) for 30 min. After three washes in PBS, cells were permeabilized and blocked for 1 h
at RT using 0.1% Triton X-100 (Thermo Fisher Scientific, 28313) and 4% donkey serum in PBS.
Primary antibodies were added in the presence of blocking buffer ON at 4°C. Secondary
antibodies (1:500) were added after three PBS washes in blocking buffer at RT for 1 h. The
following primary antibodies were used for the immunofluorescence studies: rabbit anti-DARPP-
32 (1:100, Santa Cruz, sc-11365), rabbit anti-MAP2 (1:100, Millipore, AB5622) and rabbit anti-
NESTIN (1:100, Abcam, ab92391). The secondary antibodies were donkey anti-rabbit IgG
conjugated with Alexa-488 (Invitrogen, A12379) or Alexa-647 (Invitrogen, A22287). Images were
acquired using a BioTek Cytation 5 microscope and were prepared using Fiji software (ImageJ).
Statistical Analysis
Student's paired t-test and ANOVA with Tukey’s multiple comparison test was used to
study differences in expression, and most other statistical analyses. All statistical analysis and
graph plotting were performed using PRISM 7-9 by GraphPad Software (La Jolla, CA, USA). p <
0.05 was considered as statistically significant result. Error bars are expressed using SEM.

130

CHAPTER 5: CONCLUSIONS

Through our research and experiments into FKBP and APOE, we have uncovered a great
deal of information pertaining to these proteins and their associated diseases. In particular, by
performing RNA-seq on isogenic cell lines of interest, we were able to tease out unique
differences that would normally be overshadowed by the usual variance between different
genetic origins. This ability to reduce the endogenous noise normally skewing such studies
allowed us to delve deeper and uncover unique aspects for each protein. Following said analysis,
we were able to confirm some of our findings. In some cases, we were able to use biochemical
assays, cell culture, and mouse work to determine the source of our observations, and elucidate
their possible mechanisms.

When studying FKBPs in relation to HD, we found that FKBP51 levels are altered in HD
and that lowering the levels of FKBP51 reduced mHTT in HD models both in vitro and in vivo. Our
experiments suggested that by lowering the levels or activity of FKBP51, autophagy could be
increased, thereby increasing the clearance of the protein from the cells, and preventing the
accumulation of aggregates and toxic fragments. This change in clearance is potentially due to a
structural change in the HTT protein, likely due to a change in a proline site(s) in the HTT as a
result of the expanded polyQ tract. This conformation change in mHTT may be therapeutically
targetable, and may be the mechanism through which SAFit2 treatment worked in HD cells. This
observation suggested that FKBP51 may be a suitable therapeutic target for HD, especially if it
131

can be targeted for decreased activity of proline isomerization in the HTT protein and its
associated aggregates.

When studying APOE in relation to AD, we found that DNA damage and repair pathways
are significantly altered between APOE2 and APOE4 GABANeurons in vitro. Our experiments
revealed significant changes in aspects of cell movement and neurite extension; findings which
were corroborated with the observation of significant differences in DSCAM and VCAN between
the APOE2 and APOE4 GABANeurons. Additional experiments showed significant transcriptional
changes in categories related to DNA damage and repair, and subsequent immunocytochemistry
and the comet assays confirmed this finding. Further analysis into the differences in DNA damage
between the genotypes yielded several theories, such as difference in R-loop factors and in
repetitive elements. The repetitive elements that are overrepresented in APOE4 GABANeurons
may be worth targeting therapeutically, and with their inhibition perhaps aspects of the cells may
become more akin to their beneficial APOE2 counterparts.

Both studies utilized the vast majority of the skills I had mastered in my tenure at the
Buck, and through the study of both I came to an even greater understanding of my own scientific
interests and capabilities. Both studies also yielded large amounts of data that we hope will
continue to be used for future research and publications. However, not all the experiments and
projects one pursues end up being completed within the timeframe of an individual’s
participation. Other projects of note that I have been involved with relate to: using Cas9 coupled
132

to a cell penetrating peptide to correct the CAG expansion in mutant HTT, and creating a series
of valid cell lines differing in both their APOE alleles and in their HTT genotype. Both incomplete
studies even relate to the completed studies, in part.

The cell penetrating peptide study was designed to be a therapeutic treatment for HD,
able to be injected and bypass the BBB in order to reach the neuronal cells of interest. For this
project I worked a great deal on purifying the Cas9 conjugated protein. Specifically, we utilized
Cas9 that was conjugated to the portion of a viral capsid responsible for bypassing the BBB. This
protein conjugation was tested in vitro for the treatment of HD, using the lab’s corrected and HD
isogenic lines, in order to test its efficacy and safety. Our initial results revealed a degree of
success in modifying the genome of the cells tested without significant toxicity. This was followed
by the mouse work, which involved injections and subsequent dissections; followed by co-
immunoprecipitations (Co-Ips), western blotting, and deep PCRs to determine the efficacy of the
treatment in vivo. This study taught me the value of properly performed mouse experiments,
and prepared me for the later mouse work performed for the FKBP study. This study is still
ongoing, and I hope to be able to learn the effectiveness of the cell penetrating peptide method
for in vivo Cas9 delivery when reading a future publication from this lab.

The project involving the creation of an isogenic series of HD lines differing in their APOE
alleles is still ongoing. There are some overlapping factors between AD and HD that the creation
of these lines would help elucidate. HD and AD are highly studied neurodegenerative diseases
133

and have some common features. Both diseases are characterized by misfolded proteins, which
lead to a collection of aggregated proteins, short cleaved-toxic protein fragments, and ER and
mitochondrial stress (Ehrnhoefer et al., 2011; Graves et al., 2017). Additional research has shown
that APOE4 interacts with and regulates levels of matrix metalloproteinase 9 (MMP9). This
regulation of MMP9 affects downstream levels of tissue inhibitor of metalloproteinase 1 (TIMP1),
which has been shown to alter Aβ clearance in the brain. Similarly, our lab has shown that matrix
metalloproteinases play a significant role in HD pathology (Naphade et al., 2017). It is expected
that because APOE regulates MMP9, the pathology of HD will be affected by APOE in an allele
dependent manner. To this end, myself and others in the lab have worked to create an isogenic
series of lines homozygous for each of the three APOE alleles in isogenic HD and corrected C116
iPSC lines . This work was also done in collaboration with outside interests, and some companies
have even made isogenic lines for us. The creation of these lines and their subsequent studies
are still ongoing, but I fully expect for them to reveal even more unique data, and I look forward
to reading future publications involving them.

Overall, our studies have pointed to the importance of limiting as many variables as
possible when observing differences in a given phenotype/genotype. By using isogenic lines as a
starting point, we have been able to uncover aspects of HD and AD that may well have been
overlooked had another cell line been used. As studies into the disease of AD and HD have
progressed, their interactions and relationships have also grown more complex, and have created
a variety of theories and mechanisms that do not always fit together. By limiting the number of
confounding variables in our studies, such as through the use of isogenic lines, we may be able
134

to consolidate the menagerie of theories and mechanisms into those most affects within the
narrower context. Science is complex, but hopefully by compartmentalizing the issues in better
defined spaces, we can begin to put these compartments next to one another and begin to see
the bigger picture. I, for one, am excited at the possibility of pursuing such a broad image, even
if it means narrowing my scope for a time.

135

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Abstract (if available)

Abstract My research in graduate school focused on studies of neurodegenerative diseases and aging using induced pluripotent stem cells models, molecular and biochemical assays, and mouse models. My two main projects focused on: one, understanding the role of FKBPs in HD; and two, how the different APOE variants could differentially affect GABANeurons derived from human iPSCs. In my first project, FKBPs were evaluated for how they influence HD pathology and molecular mechanisms. We found that the family member FKBP51 had significantly altered levels during disease progression in both human and mouse models of HD. We found FKBP51 affected HD by altering the capacity of the cell to clear huntingtin aggregates and toxic fragments. This research leads us to believe the FKBPs may be a valid target for therapeutic intervention in HD. In my second project, different APOE alleles were studied in reference to GABANeurons in order to understand how the APOE2 allele is involved in mediating exceptionally long-lived individuates. Genomic and biochemical analysis of isogenic APOE GABANeurons (APOE2 vs APOE4) revealed lower levels of endogenous DNA damage in the APOE2 GABANeurons. This suggests a potential mechanism through which certain APOE alleles specifically promote healthy aging, and thereby suggests avenues that may be targetable in future research.

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Asset Metadata

Creator Scheeler, Stephen Michael (author)

Core Title Modeling neurodegenerative diseases using induced pluripotent stem cells and identifying therapeutic targets

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Biology of Aging

Degree Conferral Date 2021-08

Publication Date 08/03/2023

Defense Date 05/21/2021

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

Tag Alzheimer's disease,APOE,apolipoprotein,autophagy,DNA damage,FKBP,GABAergic neurons,H2AX,HTT,huntingtin,Huntington's disease,induced pluripotent stem cells,iPSC,medium spiny neurons,Mice,neurodegeneration,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ellerby, Lisa (committee chair), Lee, David (committee member), Lithgow, Gordon (committee member), Melov, Simon (committee member), Tower, John (committee member)

Creator Email sscheele@usc.edu,sscheeler1612@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15676566

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Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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