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Summer 8-15-2018

Smooth as SILK: Translating Discoveries to NewTherapies in Amyotrophic Lateral SclerosisWade SelfWashington University in St. Louis

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Recommended CitationSelf, Wade, "Smooth as SILK: Translating Discoveries to New Therapies in Amyotrophic Lateral Sclerosis" (2018). Arts & SciencesElectronic Theses and Dissertations. 1653.https://openscholarship.wustl.edu/art_sci_etds/1653

WASHINGTON UNIVERSITY IN ST. LOUIS

Division of Biology and Biomedical Sciences

Neurosciences

Dissertation Examination Committee:

Timothy M. Miller, Chair

Randall J. Bateman

Johnathan R. Cirrito

Anne M. Fagan

Conrad C. Weihl

Smooth as SILK: Translating Discoveries to New Therapies in Amyotrophic Lateral Sclerosis

by

Wade Kerner Self

A dissertation presented to

The Graduate School

of Washington University in

partial fulfillment of the

requirements for the degree

of Doctor of Philosophy

August 2018

St. Louis, Missouri

© 2018, Wade Self

ii

Table of Contents List of Figures ................................................................................................................................ iv

List of Tables ................................................................................................................................. vi

Acknowledgments......................................................................................................................... vii

Abstract of the Dissertation ......................................................................................................... xiii

Chapter 1: Towards a New Treatment for ALS ............................................................................ 1

Amyotrophic Lateral Sclerosis: A relentless degenerative disorder ........................................... 4

The Role of SOD1 in Amyotrophic Lateral Sclerosis ................................................................ 7

SOD1 in ALS Pathobiology ........................................................................................................ 8

Controversial Role of SOD1 in Sporadic ALS ......................................................................... 14

Toxicity by Misfolded SOD1: Similarity to other ALS subtypes ............................................. 16

Learning from Past Failures for ALSSOD1

Clinical Trial Design .............................................. 16

Therapeutic Strategies for ALSSOD1

.......................................................................................... 19

Antisense Oligonucleotides for the Treatment of ALSSOD1

...................................................... 23

Considerations for ASO Clinical Trial Design in ALSSOD1

...................................................... 25

Summary of Disse rtation .......................................................................................................... 26

Chapter 2: Defining SOD1 ALS Natural History to Guide Therapeutic Clinical Trial Design . 29

ABSTRACT .............................................................................................................................. 30

INTRODUCTION ..................................................................................................................... 31

METHODS................................................................................................................................ 33

RESULTS.................................................................................................................................. 36

DISCUSSION ........................................................................................................................... 46

ACKNOWLEDGEMENTS ...................................................................................................... 50

Chapter 3: Characterizing SOD1 Protein Kinetics in the Cerebrospinal Fluid of Individuals with

ALS ............................................................................................................................................... 51

INTRODUCTION ..................................................................................................................... 52

METHODS................................................................................................................................ 54

RESULTS.................................................................................................................................. 54

DISCUSSION ........................................................................................................................... 67

ACKNOWLEDGEMENTS ...................................................................................................... 71

iii

Chapter 4: Protein Production is an Early Biomarker for RNA-Targeting Therapies ................ 78

ABSTRACT .............................................................................................................................. 79

INTRODUCTION ..................................................................................................................... 80

METHODS................................................................................................................................ 82

RESULTS.................................................................................................................................. 88

DISCUSSION ........................................................................................................................... 98

ACKNOWLEDGEMENTS .................................................................................................... 101

Chapter 5: Summary and Future Directions.............................................................................. 111

ALSSOD1

clinical outcomes remain unchanged over time ....................................................... 112

Understanding drivers of ALSSOD1

heterogeneity ................................................................... 114

SOD1 protein is long-lived protein in ALS human CSF ........................................................ 115

Analyzing CSF SOD1 protein behavior to elucidate contributions to ALS pathobiology ..... 116

New Protein Production is a viable biomarker for RNA-lowering therapies ......................... 123

Protein Turnover as a measure of pharmacodynamics for SOD1-targeting therapies ............ 124

Understanding the mechanisms of CNS protein deposition into the CSF .............................. 125

Concluding Remarks ............................................................................................................... 126

References ................................................................................................................................... 128

Curriculum Vitae ........................................................................................................................ 148

iv

List of Figures CHAPTER 2

Figure 2.1 Mean age at onset and disease duration for ALSSOD1

.................................................. 42

Figure 2.2 Decreased survival probability from time of disease onset for SOD1A4V

compared to

SOD1Non-A4V

familial ALS ............................................................................................................ 43

Figure 2.3 SOD1A4V

ALS patients exhibit greater decline in ALS-FRS and FVC rates compared

to SOD1Non-A4V

patients ................................................................................................................ 45

CHAPTER 3

Figure 3.1 Representative Kinetic Curve of a participant labeled with 10 g 13

C6-Leucine via 16

hour intravenous infusion. ............................................................................................................ 61

Figure 3.2 Theoretical modeling of CSF SOD1 protein levels after ASO Treatment. ................. 70

Supplementary Figure 3.1. Compartmental model for kinetic data using plasma free 13

C6-Leucine

(input), CSF total protein, plasma total protein, and CSF SOD1 species for each subject to

caclulate protein FTR. ................................................................................................................... 72

CHAPTER 4

Figure 4.1 Lowered hTau protein synthesis precedes protein lowering after ASO treatment in

hTau transgenic mice .................................................................................................................... 89

Figure 4.2 Lowered 13

C6-Leucine-labeled hSOD1 observed in cerebrospinal fluid after ASO

treatment in hSOD1 transgenic rats .............................................................................................. 93

Figure 4.3 A therapeutic window exists to observe lowered protein synthesis pharmacodynamics

in the central nervous system ........................................................................................................ 96

Figure 4.4 A model of the interplay between new protein production and protein concentration

pharmacodynamics in RNA-lowering treatment strategies .......................................................... 99

Supplementary Figure 4.1 No differences in 13

C6-Leucine oral labeling between treatment groups

in all experiments performed. ..................................................................................................... 106

Supplementary Figure 4.2 Correlation between all hTau peptides measured ........................... 107

Supplementary Figure 4.3 Correlation between all hSOD1 peptides measured. ....................... 108

Supplementary Figure 4.4 No pharmacodynamics observed in the periphery after 1000 ug

hSOD1 ASO intracerebroventricular injection. .......................................................................... 109

v

Supplementary Figure 4.5 No protein concentration pharmacodynamics observed in CSF after

1000 ug hSOD1 ASO intracerebroventricular injection at 50 days post-treatment.................... 110

vi

List of Tables CHAPTER 2

Table 2.1 Clinical Characteristics of SOD1A4V

versus SOD1Non-A4V

mutation populations in

familial ALS.................................................................................................................................. 37

Table 2.2 Disease characteristics by mutation type in SOD1 familial ALS ................................. 40

CHAPTER 3

Table 3.1 Participant Demographics ............................................................................................. 60

Table 3.2 Protein Behavior in participants grouped by mutation and disease status .................... 63

Table 3.3 CSF SOD1 protein in participants with SOD1 mutations ............................................ 66

Supplementary Table 3.1 Transition ions used for SOD1 tandem liquid chromatography-mass

spectrometry analysis using GluC digestion protocol. ................................................................. 74

Supplementary Table 3.2 Transition ions used for SOD1 tandem liquid chromatography-mass

spectrometry analysis using LysC/Trypsin digestion protocol for mutant peptide detection ....... 77

CHAPTER 4

Table 4.1. Antisense Oligonucleotides ...................................................................................... 103

Supplementary Table 4.1. Primers and probes used for genotyping and qPCR analysis of hTau

(MAPT), hSOD1 (SOD1), and mouse GAPDH (GAPDH) DNA .............................................. 103

Supplementary Table 4.2. Transition ions used for hSOD1 tandem LC-MS/MS. GLHGFHVHE

peptide measurements were used as representative data in Figures. .......................................... 104

Supplementary Table 4.3 Transition ions used for hTau tandem LC-MS/MS. TPSLPTPPTR

peptide measurements were used as representative data in Figures. .......................................... 105

vii

Acknowledgments

I received financial support from the Washington University Institute of Clinical and

Translational Sciences grant UL1TR002345, sub-award TL1TR002344 from the National Center

for Advancing Translational Sciences (NCATS) to conduct the research described here.

Additionally, I thank the National Institute of Health for funding provided from the following

sources: R01NS09871601, R01NS078398, R25NS065743, U01NS084970, R01NS095773, as

well as support from the Seattle Veterans Affairs Medical Center, the Amyotrophic Lateral

Sclerosis Association, the Harvard NeuroDiscovery Center, the Dr. Anne B. Young

Neuroscience Translational Medicine Fellowship, the Muscular Dystrophy Association, and the

MetLife Foundation Award to support the work performed in the laboratory of Dr. Timothy

Miller and our collaborators.

The arduous journey to finish this body of work would not be realized without the help of

the extraordinary group of individuals I met along the way. Words will fail to articulate the

magnitude of my feelings towards you all. Nonetheless, I will try to express gratitude for those

that have been instrumental throughout my scientific development. More importantly, you all

have made lasting impacts on my life that I will cherish forever.

First, I thank my thesis mentor, Dr. Timothy Miller. Although he granted me the

autonomy to pursue multiple projects in my early years of training, Tim had a knack for gently

guiding my scattered mind back to the path when I strayed too far away from productivity. “We

won’t know until we see the data” was an oft-mentioned phrase in our weekly meetings, and I

appreciate his patience in allowing me to openly express my thoughts. Further, Tim’s optimism

and excitement for new ideas, even in the face of failure, provided the spark to reignite my

viii

motivation for research repeatedly. It also helped that he provided me a healthy work

environment with brilliant lab mates and collaborators who were indispensable for performing

experiments and developing my scientific skill set. Finally, I appreciate Tim’s willingness to

support my career development. I involved myself in many activities outside of laboratory work,

and Tim never gave anything less than his full support. I cannot thank him enough for these

selfless gestures, and I can only hope people will view me half as fondly as I do for Tim as a

mentor, scientist, and human being.

Tim instilled in me the importance of working in teams, as he surrounded me with

additional mentors that were instrumental in cultivating a doctoral-level body of work. I must

first thank Dr. Randy Bateman, as his willingness to allow me as a quasi-member of his research

group was necessary and sufficient for my technical growth as a researcher. Despite being one of

the busiest people at Washington University, I deeply admire Randy’s focus and attention to

detail with every piece of data he comes across, while simultaneously asking questions that push

you towards a higher level of learning. I also thank the rest of my thesis committee, Drs. John

Cirrito, Chris Weihl, and Anne Fagan, for providing valued discussions about my work and

patience when my enthusiasm resulted in convoluted answers to their poignant questions.

I must also thank my mentors as a member of the TL1 Predoctoral Clinical Research

Training Program, Drs. Jay Piccirillo, Jeff Peipert, Susy Stark, Ana Maria Arbelaez, and David

Brody. By exposing me to concepts in clinical research, the TL1 program was instrumental in

helping me understand the need to bridge laboratory research and clinical practice to become a

well-rounded translational scientist. I must also thank my first research mentor, Dr. Jeff

Capadona of Case Western Reserve University, who was the fundamental inspiration and role

model for my career in science.

ix

I thank my lab mates and collaborators for being far more superior scientists than I could

ever dream. Drs. Matt Crisp and Sarah DeVos were senior graduate students when I joined Tim’s

group, and their mentorship in the infancy of my graduate life strongly influenced my outlook on

science. As they continue to excel at every step along their career, I am proud to not only call

them mentors and role models, but my friends. I also thank my laboratory mentors in the

Bateman Lab: Drs. Kwasi Mawuenyega and Jim Bollinger. I thank Kwasi for his patience with

me as he guided a naïve graduate student through the fundamentals of mass spectrometry. After

the retirement of the TSQ-Vantage, I thank Jim Bollinger for grabbing the reins as my mentor in

the SOD1 Xevo and Lumos adventures. Jim’s endless enthusiasm for science and teaching were

the breath of fresh air I needed through the many days of struggle that came with research. I am

deeply indebted to him for all of the time he sacrificed on his primary projects to help me. I also

thank my lab mom, Amy Wegener, whose tough love helped my organization and collaboration

skills blossom from appalling to mediocre. I am also in hours of debt to Tao Shen, who

tremendously increased my quality of life as a graduate student with her incredible work ethic

and gratitude. I also extend a huge acknowledgement to the Miller lab clinical research team who

allowed me to realize the possibility of working with human participants: Jennifer Jockel-

Balsarotti, Debbie King, Jesse Markway, Amber Malcolm, Dr. Bob Bucelli, and Dr. Arun

Varadarchary.

In the 4+ years I spent in the Miller lab trenches, I may have logged the most hours with

Alex Cammack, Dr. Kathleen Schoch, and Dr. Mariah Hoye than anyone else in the city of St.

Louis. From being his “buddy” during candidate interviews, creating a new transgenic mouse

that no one ever used, sharing a love for sports, and being the most reliable friend to grab a beer,

I am proud to call Alex a colleague and friend. Kathleen’s scientific mentorship has been crucial

x

in improving my technical laboratory skills, as well as her willingness to be a collaborator with

me in designing experiments. As we simultaneously came into new roles as a postdoctoral

researcher and graduate student, I have thoroughly enjoyed having Kathleen by my side to grow

as scientists. We also grew together personally, as we felt the terrors and uncertainty of moving

in with a significant other to launching our own passion projects outside of lab. The origin of my

decision to join the Miller Lab starts with an hour long conversation with Mariah Hoye late one

night after class, and I am so glad her persuasion prevailed. I hope an infinitesimal bit of her

work ethic, passion, and undying curiosity for science rubbed off from her bay to mine during

our time together. I express my sincerest gratitude for her patience, willingness to engage in

passionate “discussions” about science, having such a hunk for a husband, and sharing an

impeccable taste for fantasy literature with me. I could not ask for a better coworker and friend to

travel with along our parallel dissertation paths together, and I look forward to seeing the next

steps in life and awards that lay ahead for her.

To friends old and new, thank you for keeping me sane throughout the last 5 years of

this emotional rollercoaster. Our time in The BALSA Group and Washington University

Intramural sports has led to the creation of lasting friendships, no matter how far we reach across

the globe. Although we came from different scientific backgrounds, the shared experience of

pursuing a PhD forms a lasting bond uniting us all, and I appreciate how we all served as

fantastic distractions from science when we are together. I extend a special thanks to my dear

friend Alexandra Russo. You have helped me in more ways than you know, and I am deeply

saddened by the thought of us living apart. Despite its bittersweet nature, I am comforted

knowing we will always be there for each other, no matter what obstacles life throws our way.

xi

To my family, I thank you for your everlasting love and support at every stage of my life.

I cannot say for certain if other families share more moments of laughter and tears, but I would

not have it any other way. To my mother, Pam Self, I thank you for being the foundation of my

life. You can read my mind before I open my mouth, and your love, guidance, support, and

threats to cut me off are essential components which shape the person that I am today. I will

never be able to repay you for everything you have done, but I will certainly try my best to make

you feel as proud as I am of you.

And finally, a million times over, I thank Zoe Gross. Your partnership gives my life

meaning in ways I never thought possible. I love you.

Wade K. Self

Washington University in St. Louis

August 2018

xii

To Ralph, Peggy, David, and all participants in human disease research.

In the face of an intractable disease, your sincerity, positivity, curiosity, and willingness to

sacrifice for the good of mankind is an inspiration to us all.

xiii

“Let us keep looking, in spite of everything. Let us keep

searching. It is indeed the best method of finding, and

perhaps, thanks to our efforts, the verdict we will give

such a patient tomorrow will not be the same we must

give this man today.”

Charcot – 1889

“Isn’t it nice to know a lot? And a little bit, not.”

Little Red Riding Hood – 1986

xiv

Abstract of the Dissertation

Smooth as SILK: Translating Discoveries to New Therapies in Amyotrophic Lateral Sclerosis

by

Wade Kerner Self

Doctor of Philosophy in Biology and Biomedical Sciences

Neurosciences

Washington University in St. Louis, 2018

Professor Timothy Miller, Chair

No effective therapies exist for the treatment of patients suffering from amyotrophic

lateral sclerosis, a devastating neurodegenerative disease characterized by motor neuron loss,

paralysis, and death on average 3 – 5 years after diagnosis. Although multiple promising

candidates have emerged from preclinical experiments in disease models, failure rates of

randomized control trials assessing efficacy in humans are over 95%, demonstrating a need to

improve the translation of preclinical findings to human biology. In this dissertation, I present

original work investigating amyotrophic lateral sclerosis caused by mutations in SOD1

(ALSSOD1

) for the successful translation of SOD1-targeting strategies into meaningful treatment

options for patients. Through a retrospective study of ALSSOD1

natural history, my research

showed that the most common mutation causing ALSSOD1

in North America, Alanine>Valine

(A4V), is rapidly progressing, while different mutations show a wide range of variability in

clinical outcomes. I then describe initial characterization of SOD1 protein half-life in the

cerebrospinal fluid (CSF) of ALSSOD1

patients. Similar to healthy control subjects, I found SOD1

xv

is a long-lived protein in ALS patient CSF, with a half-life on the order of weeks. Moreover,

A4V SOD1 mutant protein showed differential behavior compared to wild type SOD1 in CSF in

one ALS participant, preliminarily suggesting that CSF SOD1 measures may implicate the

pathogenic state of SOD1 protein. Finally, I show that measures of CSF SOD1 protein

production serve as a pharmacodynamics biomarker for Antisense Oligonucleotides (ASOs)

treatment targeting SOD1 mRNA transcripts in the central nervous system (CNS). Lowering of

new SOD1 protein production after ASO treatment preceded lowering of total SOD1 protein

concentration, suggesting measures of new protein production are more sensitive for mRNA-

targeting therapies and are translatable to human trials to assay drug target engagement. Taken

together, these data provide insights that contribute to a further understanding of ALS biology, in

addition to the study design and interpretation of randomized control trials in ALS.

1

Chapter 1: Towards a New Treatment for ALS

2

The average life expectancy in the United States has increased from 68 years in 1950 to

79 years in 2013, and one in four people in the United States are projected to be 65 years or older

by 2060 (Mather et al 2015). These trends present new challenges to human healthcare, including

a higher number of people afflicted with diseases associated with aging. Chief among these are

progressive neurodegenerative diseases, a family of disorders characterized by the loss of

structure and function of neurons in the brain and spinal cord. Neurodegenerative diseases

include Alzheimer’s Disease (AD) and other forms of dementia (Alzheimer 1906), Huntington’s

Disease (HD) (Huntington 1872), Parkinson’s Disease (PD) (Parkinson 1969), and Amyotrophic

Lateral Sclerosis (ALS) (Charcot 1874). Despite the original descriptions of these diseases over

two centuries ago, no current cures exist. If the present therapeutic landscape remains

unchanged, projected costs of neurodegenerative disease patient care will reach over $1 trillion

by 2050 (WHO 2012). Therefore, a dire societal need exists to develop novel therapies to treat

these debilitating diseases.

Many neurodegenerative diseases converge on a similar pathology in the central nervous

system (CNS): the accumulation of insoluble protein aggregates within neuronal populations that

degenerate throughout disease time course. Research into the genetics and pathology that

underlie these diseases revealed multiple proteins that are prone to misfold and aggregate within

neurons (Forman et al 2004). These observations have led to a generally accepted hypothesis that

misfolded proteins impart toxic effects on specific neuronal populations, resulting in cell death.

Therefore, an attractive therapeutic strategy to treat neurodegenerative diseases is to prevent the

accumulation of misfolded proteins within the nervous system to maintain neuronal viability

throughout aging.

3

Despite decades of research that identified potential compounds which ameliorate protein

aggregation pathologies in disease models, clinical trials for neurodegenerative disorders show a

failure rate of over 95% (Mitsumoto et al 2014; Petrov et al 2017), and as high as 99% in

Alzheimer’s Disease (Banik et al 2015; Soejitno et al 2015). Multiple causes may contribute to

these alarming failure rates, including: a lack of understanding of the mechanisms that govern

neurodegeneration, the absence of effective tools to measure biological effects of treatments in

humans, and effective clinical trial design. Moving forward, it is imperative for translational

researchers to consider all facets of preclinical and clinical research in progressive

neurodegenerative diseases to bring more effective or curative therapies to patients. Furthermore,

because neurodegenerative diseases share similar misfolded protein pathologies that converge on

common mechanisms of neuronal death, the development of an effective treatment in one disease

condition may provide valuable insight into the development of therapeutic strategies for

multiple disorders.

Thus, in an effort to better define the biological and clinical manifestations of

neurodegenerative disease, the focus of this dissertation will be ALS, colloquially known as Lou

Gehrig’s Disease, caused by mutations in Superoxide Dismutase 1 (SOD1) (Kiernan et al 2011).

This dissertation will begin with an introduction to the genetics and clinical characteristics of

ALS, the role of mutant SOD1 misfolding in ALS pathobiology and clinical phenotypes, the

development of therapeutic strategies that target SOD1, and previous challenges in translating

scientific discoveries to effective treatments for ALS caused by SOD1 mutations (ALSSOD1

). It

will then discuss the novel research I performed in this dissertation, including a retrospective

cohort study on the most recent understanding of ALS natural history caused by SOD1 mutations

in North America, efforts to understand SOD1 involvement in human ALS through analyzing

4

SOD1 protein turnover within the cerebrospinal fluid (CSF) of ALS patients, and the

development of a new pharmacodynamics biomarker for SOD1-targeting therapies. The

dissertation will conclude with a discussion of the implications of these studies in translating

biomedical research into effective therapies for ALS, as well as future research directions for

both clinical trial design and understanding of human ALS pathobiology.

Amyotrophic Lateral Sclerosis: A relentless degenerative disorder

In 1874, Jean-Marie Charcot first described ALS as a disease of upper and lower motor

neuron degeneration within the brainstem and spinal cord (Charcot 1874). ALS affects ~3

individuals per 100,000 in Europe (Worms 2001) and ~5 individuals per 100,0000 in the United

States (Mehta et al 2016). ALS is the third most common neurodegenerative disorder in the

United States, trailing only Parkinson’s Disease and Alzheimer’s Disease (Kiernan et al 2011).

ALS onset typically begins within the limbs (limb-onset) or facial region (bulbar-onset).

Symptoms will therefore manifest depending on onset location - with brisk reflexes and local

muscle wasting in limb-onset ALS or trouble with speech and swallowing in bulbar-onset

disease (Rowland 2001). Symptoms quickly spread to other regions as additional motor units are

lost, resulting in spasticity, deep tendon reflexes, fasciculation, wasting, and eventual paralysis

(Kiernan et al 2011). ALS is rapidly progressive, where 50% of patients with the disease die

within 30 months from presentation of symptoms and 20% survive between 5 and 10 years after

symptom onset (Talbot 2009). Currently, ALS is diagnosed by an exclusionary process of

eliminating all other possible causes of patient symptoms. The lack of an established biomarker

to accurately diagnose ALS may contribute to poor patient outcomes, as symptoms manifest after

irreversible degeneration of motor neurons has already occurred in the CNS. Upon diagnosis,

only two FDA-approved therapies are available for treatment of ALS: Riluzole (Bensimon et al

5

1994; Lacomblez et al 1996; Miller et al 2012), an inhibitor of glutamate release, and Edaravone

(Abe et al 2017; Kalin et al 2017; Yoshino & Kimura 2006), a free-radical scavenger. These

treatments are not effective for long-term survival of ALS patients, where the median disease

progression in randomized control trials slowed by only ~6 months, and controversy remains

over the reproducibility of results and cost effectiveness of these treatments (Fang et al 2018;

Messori et al 1999). Therefore, a clear need exists to better understand ALS disease pathogenesis

for developing treatments that effectively manage or cure this devastating disease.

Recent advances in genetics have helped to identify potential mechanisms that underlie

disease pathogenesis in familial forms of ALS, which account for ~10% of total cases

(Robberecht & Philips 2013). The first gene implicated in ALS, SOD1 (Rosen et al 1993), was

identified in 1993. For almost 20 years, mutations in SOD1 were the only known cause of

familial ALS. In the last ten years, researchers have seen an explosion in the discovery of new

ALS genes that describe ~60% of familial ALS and 11% of sporadic ALS cases (Renton et al

2014). Of the newly identified genetic causes, the most prevalent are hexanucleotide repeat

expansions found in the first intron of the C9ORF72 gene, accounting for ~40% of familial ALS

in the United States and Europe (Majounie et al 2012; Renton et al 2011), and mutations in

TARDBP and FUS, each accounting for ~5% of familial ALS (Sreedharan et al 2008; Vance et al

2009). These newly discovered genes exhibit a variety of proposed functions, including protein

clearance (UBQLN2 (Deng et al 2011), SQSTM1 (Rubino et al 2012), VCP (Johnson et al 2010),

TBK1 (Cirulli et al 2015)), vesicle trafficking and cytoskeleton dynamics (PFN1 (Wu et al 2012),

OPTN (Maruyama et al 2010), TUBA4A (Smith et al 2014), C9ORF72 (Shi et al 2018),

KIF5A(Nicolas et al 2018)), RNA processing (TDP43 (Ou et al 1995), FUS (Yang et al 1995),

6

MATR3 (Johnson et al 2014), TIA1 (Mackenzie et al 2017)) and reactive oxygen species

metabolism (SOD1 (McCord & Fridovich 1969b)).

ALS is classically described as a motor disorder, but reports suggest that up to 48% of

ALS patients exhibit cognitive abnormalities associated with frontotemporal lobar degeneration,

a progressive dementing condition characterized by neuronal loss in the frontal and anterior

temporal lobes of the brain (Lomen-Hoerth et al 2002; Portet et al 2001). These observations

suggest that ALS and frontotemporal dementia (FTD) fall on a “spectrum” of neurodegenerative

disease states. This claim is supported by genetic evidence, where mutations in multiple genes

cause familial cases of ALS, FTD, and ALS-FTD (Robberecht & Philips 2013). Moreover, the

majority of sporadic and familial ALS and FTD cases share similar pathological features, such as

the presence of protein inclusions containing ubiquitin and p62 in neuronal populations affected

by disease (Neumann et al 2006; Steinacker et al 2008). Although the ALS-FTD spectrum adds

additional complexity to understanding the individual diseases, the similarity of familial genetics

and pathologies suggest a potential unifying mechanism of degeneration underlying these

conditions.

Although ~80% of ALS cases are sporadic with no known genetic cause, 98% of all ALS

cases converge on a shared pathology: cytoplasmic protein inclusions containing TDP-43 in

neurons and glia in the brain and spinal cord (Lomen-Hoerth et al 2002). These observations led

researchers to hypothesize that modeling familial forms of ALS may reveal common

mechanisms that mediate ALS pathogenesis to target for therapeutic intervention. Conversely,

the genetic heterogeneity of ALS may suggest that different ALS subtypes demand unique

treatment strategies. For example, mutations in SOD1 account for ~2% of all ALS cases and,

with the exception of one reported patient (Nakamura et al 2015), present without cognitive

7

deficits or TDP-43-related pathology. Instead, the primary protein pathology observed in motor

neurons is the accumulation of insoluble, ubiquitin-positive aggregates containing mutant SOD1

protein. Despite these discrepancies in pathology between ALSSOD1

and the majority of the ALS

population, almost 25 years of research in understanding the role of SOD1 in ALS has garnered

important insights into mechanisms that govern neurotoxicity associated with protein misfolding,

concurrently leading to the development of novel therapeutic strategies for the treatment of ALS

and other progressive neurodegenerative diseases.

The Role of SOD1 in Amyotrophic Lateral Sclerosis

The SOD1 gene encodes for SOD1, a 153 amino acid, homodimeric, Copper-Zinc

metalloenzyme that catalyzes the conversion of superoxide anion (O2-) into hydrogen peroxide to

regulate the concentration of reactive oxygen species within cells (McCord & Fridovich 1969a).

The SOD1 protein contains eight beta-barrel sheets, connecting loops, two intrachain disulfide

bonds, and two metal-binding domains, with the enzymatic activity of the protein dependent

upon homodimerization (Parge et al 1992). Rosen and colleagues first identified 11 single

amino-acid changes in SOD1 on the basis of genomic DNA sequences in members of 13

different familial ALS families in the United States (Rosen et al 1993). Concurrently, a research

team in Japan identified a Histidine>Arginine amino acid change at residue 46 (H46R) in two

familial ALS families in Japan (Aoki et al 1993). Since these initial discoveries, investigators

have identified over 180 mutations in SOD1 implicated in familial forms of ALS with varying

degrees of penetrance. These mutations are predominantly missense, but also include nonsense

and truncation mutations (for an updated list of all SOD1 mutations, visit the ALS Online

Database). The majority of SOD1 mutations are found in familial ALS, but SOD1 mutations

have also been reported in sporadic ALS (Chio et al 2009b; Chiò et al 2008).

8

Different SOD1 mutations display a broad spectrum of clinical phenotypes that influence

disease onset and disease progression after symptom presentation. Interestingly, in contrast to

other neurodegenerative diseases such as Alzheimer’s Disease where earlier disease onset is

characterized by more aggressive neurodegeneration and rapid disease progression (Lanoiselee et

al 2017), early disease onset in ALSSOD1

is associated with slower disease progression

(Cudkowicz et al 1997; Juneja et al 1997; Rosen et al 1994). However, the relationship between

SOD1 mutation and the heterogeneity of clinical disease characteristics remains poorly

understood. This complexity is seen in family pedigree analyses, where different SOD1

mutations display varying degrees of disease penetrance (Felbecker et al 2010). For example,

description of a large family with ALSSOD1

caused by an I113T mutation shows 50% penetrance

at 60 years, with age of onset ranging from 39 – 67 years (Lopate et al 2010). Given the

heterogeneity in disease penetrance between mutants, one may hypothesize that mutations which

cause fast-progressing forms of disease may exhibit higher penetrance. However, an aggressive

form of ALSSOD1

caused by an I112M mutation in four Mediterranean families had several

obligate carriers in the family pedigree despite an aggressive disease duration of less than 2 years

(Gamez et al 2011). The high variability observed between different mutations and within

families carrying the same SOD1 mutation raises concerns over the validity of these variants as

disease-causing, making the prognosis of ALSSOD1

challenging within the clinic. Therefore, a

better understanding of mutant SOD1 protein behavior in living patients may provide valuable

insight into the prognosis of patients carrying these SOD1 variants.

SOD1 in ALS Pathobiology

SOD1 mutations that cause ALS are found throughout the entire 153 amino acid protein

sequence and, with the exception of the D90A mutation in the Scandanavian population

9

(Andersen et al 1997b), display autosomal dominant inheritance. In line with SOD1 genetics, in

vitro studies have shown that many mutant SOD1 proteins retain enzymatic activity and the

ability to dimerize with both mutant and WT SOD1 (Borchelt et al 1994; Wiedau-Pazos et al

1996). Further, knockout of SOD1 in transgenic mice does not result in global motor neuron

degeneration (Reaume et al 1996; Shefner et al 1999). These data ultimately suggest that SOD1

loss-of-function is not the primary driver of ALSSOD1

.

The common pathology observed in all ALSSOD1

patients is the presence of misfolded

SOD1 protein in ubiquitin-positive, insoluble aggregates within motor neurons (Shibata et al

1996). Accumulating evidence suggests that mutant, misfolded SOD1 acquires a toxic gain-of-

function which leads to motor neuron degeneration. Indeed, the overexpression of a transgene

containing the human SOD1 gene with an ALS-causing G93A point mutation in mice was the

first in vivo model of ALS to show a disease phenotype (Gurney et al 1994). In these transgenic

mice, mutant SOD1 overexpression causes a rapidly progressing degeneration of motor neurons

in the brain and spinal cord, muscle atrophy, and hind-limb paralysis by ~6 months of age. These

animals show the same mutant SOD1 pathology in the brain and spinal cord as human ALS,

where insoluble protein aggregates are observed in the cell body and processes of motor neurons

and glial cell types (Johnston 2000; Shibata et al 1998). These events in transgenic animal

models appear specific to misfolded, mutant SOD1, as over-expression of WT SOD1 protein

does not result in an ALS-like phenotype of motor neuron degeneration (Avraham et al 1988;

Chan et al 1998). Additionally, WT SOD1 overexpression in the presence of mutant SOD1 does

not accelerate the rate of disease in vivo (Bruijn 1998).

Many mechanisms by which misfolded SOD1 causes motor neuron toxicity have been

suggested including impaired protein clearance due to proteasome (Cheroni et al 2009;

10

Matsumoto et al 2005; Urushitani et al 2002) and autophagy (Bandyopadhyay et al 2014;

Rudnick et al 2017; Xie et al 2015) inhibition, mitochondrial dysfunction (Ferri et al 2006;

Israelson et al 2010; Jaarsma et al 2001; Li et al 2010; Liu et al 2004; Pasinelli et al 2004; Vande

Velde et al 2008), endoplasmic reticulum stress (Filezac de L'Etang et al 2015; Lee et al 2016b;

Nagano et al 2015; Saxena et al 2009), and impaired axonal transport (Williamson & Cleveland

1999). Because motor neurons are selectively vulnerable to degeneration in ALS, early efforts

focused on understanding motor neuron-specific cellular mechanisms that contribute to disease.

However, conditional overexpression of mutant SOD1 in neurons yielded conflicting results.

Driving expression of mutant SOD1 with a neuron-specific promoter was not sufficient to

recapitulate the motor neuron degeneration observed with ubiquitous expression of mutant SOD1

in vivo (Pramatarova et al 2001). Other reports showed a mild motor neuron degeneration

phenotype in mice conditionally overexpressing mutant SOD1 in neurons using the Thy1

promoter (Jaarsma et al 2008). However, the Thy1 promoter is also expressed in glial cells,

which confounds the interpretation of degeneration driven by neuron-specific expression of

SOD1 (Kemshead et al 1982). Also, SOD1 pathology is not restricted to motor neurons, as early

characterization of transgenic G85R SOD1 mouse models and human autopsies reported

inclusions of misfolded SOD1 and downregulation of glial glutamate transporters in astrocytes

(Bruijn et al 1997; Kato et al 2000; Lin et al 1998). These observations suggest that cell-

autonomous effects of mutant SOD1 in motor neurons are not solely responsible for ALS

pathology.

Seminal experiments in vivo and in cell culture further revealed the importance of non-

neuronal cell types as contributors to neurotoxicity caused by expression of mutant SOD1.

Chimeric mice expressing WT human SOD1 in glial cells and mutant G37R SOD1 in motor

11

neurons exhibited extended survival compared to mice that ubiquitously express mutant SOD1

(Clement et al 2003). These results were corroborated by genetic experiments where conditional

knockout of mutant SOD1 was performed using a Cre-Lox recombinase system. In this

paradigm, removal of G37R SOD1 in motor neurons resulted in delayed onset of disease without

altering disease progression. However, conditional knockout of G37R SOD1 in either astrocytes

or microglia drastically delayed disease progression (Boillee 2006). Similar removal of mutant

SOD1 from oligodendrocyte progenitors delayed disease onset and progression in transgenic

animal models (Kang et al 2013). Glial cells may also directly contribute to motor neuron

toxicity. Co-culture of astrocytes and oligodendrocytes expressing mutant SOD1 is sufficient to

induce motor neuron cell death. This toxicity is independent on cellular contact, as conditioned

media from glial cell cultures is also sufficient to induce motor neuron toxicity in vitro (Di

Giorgio et al 2008; Di Giorgio et al 2007; Ferraiuolo et al 2016). It is unknown if this toxicity

caused by glia is due to the release of a secreted neurotoxic factor or the result of critical nutrient

depletion for motor neurons by mutant-expressing glial cells. In sum, these data highlight the

critical role of mutant SOD1 expression in glia in mediating motor neuron degeneration in ALS

by non-cell autonomous mechanisms independent of motor neuron function.

One of the proposed mechanisms that drive mechanisms of toxicity in ALS is

inflammation, which involves multiple cell types in both the CNS and periphery. Compromised

blood brain barrier and pericyte dysfunction are early pathological hallmarks of mutant SOD1

transgenic animal models (Zhong et al 2008), indicative of inflammation within the brain and

spinal cord. Outside of the CNS, ALSSOD1

patients present with compromised T-cell function,

resulting in a pro-inflammatory profile that correlates with disease severity and progression

(Beers et al 2017). Within the CNS, mutant SOD1 secreted from neurons can be phagocytosed

12

by surrounding microglia, causing a pro-inflammatory activation of microglia that is ultimately

neurotoxic (Meissner et al 2010; Urushitani et al 2002; Zhao et al 2010). This role of

inflammation in ALS disease progression further demonstrates that non-cell autonomous

mechanisms are not restricted to intracellular activity of mutant SOD1.

The discovery of many non-cell autonomous mechanisms of mutant SOD1 toxicity in

neurons, glia, and peripheral immune cells adds to the complexity of ALS disease pathogenesis

and helps explain the challenges in developing current treatments for patients. However, the

diversity of pathways also provides an opportunity for the development of different therapeutic

strategies to effectively target and ameliorate SOD1-mediated pathology. Furthermore, the wide

array of pathways implicated in ALS also suggest that targeting of SOD1 in all cell types

throughout the entire CNS is important for developing an effective treatment strategy.

An attractive hypothesis relating SOD1 behavior to these toxic pathways, and ultimately

to clinical outcomes, is that SOD1 mutations which cause greater protein destabilization result in

more rapidly progressing forms of disease by catalyzing the conversion of mutant SOD1 into

toxic, misfolded protein species. This hypothesis is supported by experiments using recombinant

SOD1 protein in vitro. Shi and colleagues demonstrated that the failure of dimerization between

mutant and wild type SOD1 protein correlates with a more rapid disease progression (Shi et al

2016). Additional analyses of point mutations at the G93 residue in recombinant SOD1 protein

showed a positive correlation between the propensity for protein aggregation and disease

progression (Pratt et al 2014). In patients, Sato and colleagues tested the hypothesis that unstable

SOD1 mutant protein species would be rapidly turned over and undetectable in anuclear red

blood cells. Indeed, they reported that patients who carried SOD1 mutant protein that was not

detectable in red blood cells exhibited a more rapid disease progression compared to detectable

13

mutant SOD1 protein carriers (Sato et al 2005). Despite the promise of these suggestive

correlations, no current data characterizes the biochemical behavior of mutant and wild type

SOD1 in the CNS of ALS patients, limiting understanding of the biological relevance of these

findings to disease.

Despite extensive descriptions of SOD1 misfolding and aggregation in post mortem brain

and spinal cord tissues of ALS patients, the ability to effectively analyze pathological SOD1

behavior in the CNS of living ALS patients remains elusive. For other neurodegenerative

diseases, researchers developed imaging agents that bind to protein aggregates such as amyloid-β

and microtubule associated protein tau in Alzheimer’s disease to visualize protein misfolding by

positron emission tomography (Klunk et al 2004; Villemagne et al 2018). These imaging agents

are able to bind to β–sheet rich, amyloid aggregate structures and have been instrumental in

characterizing the pathological events that underlie neurodegeneration in AD. By contrast,

misfolded SOD1 forms an amorphous, non-amyloid aggregate, thereby limiting the development

of such agents to identify SOD1 misfolding in ALS (Kerman et al 2010; Mulligan et al 2012).

The primary methods used to tackle this unmet need in ALS are assays that measure

SOD1 protein levels in CSF of patients via enzyme-linked immunosorbent assay (ELISA) or

SOD1 activity assays. However, all studies fail to measure significant differences in CSF SOD1

between ALSSOD1

from controls. Jacobsson and colleagues showed no difference in CSF SOD1

protein activity between ALSSOD1

, sporadic ALS, and control patients (Jacobsson et al 2001).

Further, Zetterstrom and colleagues developed a misfolded SOD1-specific ELISA and tested for

differences in levels of misfolded SOD1 in CSF of 38 neurological controls and 96 ALS patients.

Misfolded SOD1 was present in all samples, but further analysis could not distinguish between

ALS and controls, or ALSSOD1

and sporadic ALS (Zetterstrom et al 2011). In a separate cohort,

14

Winer and colleagues reported in an increase in CSF SOD1 levels in ALSSOD1

compared to

healthy controls. However, CSF SOD1 was also elevated in neurological disease controls,

questioning the specificity of this result to ALS (Winer et al 2013). A key limitation in the

development of SOD1 assays is the challenge of discriminating mutant and WT SOD1 in any

measure. Most single amino acid changes caused by mutations do not drastically change any

bulk biochemical properties of the SOD1 protein such as molecular weight that can be resolved

by methods such as gel electrophoresis. Moreover, single amino acid changes make the

development of novel antibodies that specifically recognize mutant SOD1 protein challenging.

Given these conflicting data, a clear need exists to further investigate SOD1 in human ALS to

better understand its role in both familial and sporadic forms of the disease.

Controversial Role of SOD1 in Sporadic ALS

Because protein misfolding is a hallmark of ALS, and mutant SOD1 protein is prone to

misfolding and aggregation, researchers have investigated the potential for wild-type SOD1

protein to misfold and aggregate. Such data may implicate misfolded SOD1 as a common

disease mechanism in both familial and sporadic forms of ALS in which SOD1 is not mutated. In

vitro perturbations to WT SOD1 such as demetallation and oxidation cause protein misfolding

and denaturation (Rakhit et al 2002; Rodriguez 2002), and these misfolded WT SOD1 species

share similar conformations with mutant SOD1 variants (Durazo et al 2009). Further, co-

expression of mutant SOD1 protein with WT SOD1 protein in cell culture induces WT SOD1

misfolding using a disease-specific epitope antibody (Grad et al 2011). In vivo, crossing mutant

SOD1 transgenic mice with WT SOD1 transgenic mice results in the identification of WT SOD1

in detergent-insoluble spinal cord fractions (Prudencio et al 2010). Additionally, overexpression

of A4V SOD1 in transgenic mice does not show an ALS phenotype unless crossed with human

15

WT SOD1-expressing transgenic mice to co-express both mutant and WT SOD1 protein (Deng

et al 2006). A key limitation of these studies is the use of overexpression paradigms and high

concentrations in vitro, which calls into question the validity of WT SOD1 misfolding in human

ALS pathophysiology.

Despite these observations from mouse models, evidence for misfolded WT SOD1 in

either human ALSSOD1

or sporadic ALS without SOD1 mutations is conflicting. In post-mortem

studies, multiple groups have reported misfolded SOD1 in sporadic ALS spinal cord autopsies

(Bosco et al 2010; Forsberg et al 2010). However, these findings appear to be antibody-specific,

as results could not be recapitulated using different antibodies that recognize misfolded SOD1

protein with overlapping epitopes (Da Cruz et al 2017). Moreover, each of these post-mortem

studies have a relatively small sample sizes to test for misfolded SOD1 positivity in sporadic

ALS samples, limiting the generalizability and interpretation of results.

The limited number of assays that exist to determine the state of misfolded SOD1 protein

in living patients also presents challenges in implicating misfolded SOD1 in sporadic ALS. The

identification of misfolding of wild type SOD1 in sporadic ALS is a key outstanding question for

identifying the relevance of SOD1-targeting therapeutics for ALS outside SOD1 mutant carriers.

Interestingly, lowering of SOD1 in sporadic ALS astrocytes reduces motor neuron toxicity in cell

culture (Haidet-Phillips et al 2011), suggesting that therapeutic targeting of SOD1 may be

beneficial in sporadic ALS. Further data clarifying the role of SOD1 in sporadic ALS may

broaden the utility of SOD1-targeting therapies beyond the ~2% of ALS patients that have

disease caused by SOD1 mutations.

16

Toxicity by Misfolded SOD1: Similarity to other ALS subtypes

Although the presence of misfolded SOD1 pathology outside of ALSSOD1

remains

controversial, the gain-of-toxicity function associated with misfolded proteins may be a common

mechanism that unites many disease subtypes, as other proteins implicated in ALS pathobiology

are prone to misfolding and aggregation. In patients who harbor expansions in the first intron of

C9ORF72 gene, the GGGGCC expansion is unconventionally translated by a non-ATG-related

mechanism to produce dipeptide-repeat proteins that are prone to misfolding and form insoluble

protein aggregates within cells (Ash et al 2013; Mori et al 2013a; Mori et al 2013b; Zu et al

2013). Similar to misfolded SOD1, these dipeptide-repeat proteins are toxic to neurons in vitro

(Donnelly et al 2013; Kwon et al 2014; Mizielinska et al 2014) and in vivo (Chew et al 2015; Liu

et al 2016). Additionally, full-length and C-terminal fragments of TDP-43 found in familial and

sporadic ALS have the ability to induce TDP-43 misfolding and aggregation that is toxic to

neurons (Fang et al 2014; Nonaka et al 2013). Mechanisms of toxicity that have been proposed

for these misfolded protein species are similar to misfolded SOD1(Sareen et al 2013). Therefore,

an understanding of toxicity caused by misfolded SOD1 protein may provide the foundations to

develop therapeutic strategies in other forms of ALS that target different pathological proteins to

test in randomized control clinical trials.

Learning from Past Failures for ALSSOD1

Clinical Trial Design

The average success rate of drug development programs reaching market approval for use

in patients is ~14% (Wong et al 2018). This low probability of success highlights the difficulties

in developing novel therapies for any disease indication. The situation is even more dire in ALS,

where over 50+ randomized control trials have led to two, marginally effective FDA-approved

treatments (Mitsumoto et al 2014). Despite failure rates of over 95% for ALS randomized

17

control trials, previous efforts gleaned valuable lessons to identify key factors that influence

successful clinical trial design. Among these factors include a growing appreciation for the

heterogeneity of clinical disease characteristics in ALS and the need for adequate tools inform on

the biological efficacy of a drug.

Developments in DNA sequencing techniques have provided tremendous insight into the

genetic complexity of disease where, in cases like cancer tumors, subpopulations of cells may

exhibit unique genomic alterations (Fisher et al 2013). Indeed, the growing number of mutations

implicated in ALS suggests that different subtypes of ALS may exist which still result in the

same motor neuron degeneration phenotype. Further, natural history data from all ALS patients

shows median survival time from onset to death ranges from 20 to 48 months, but 10-20% of

ALS patients have survival longer than 12 years (Chio et al 2009a). If we accept the hypothesis

that different rates of disease progression are the result of multiple ALS phenotypes, therapeutic

efficacy of a new treatment may vary between different ALS populations. With the absence of

established prognostic biomarkers for ALS in neurology clinics, the most valuable data available

to help understand clinical outcomes are previous epidemiology studies of natural history in the

ALS population. Therefore, a key component for future trial design is to understand the natural

history of the disease in different genetic, geographic, and topographic variables in ALS

populations.

One proposed solution to account for these differing clinical phenotypes in randomized

control trial design is to stratify participants into fast- and slow-progressing ALS subgroups for

analysis. Indeed, most researchers design ALS randomized control trials to monitor clinical

outcomes after a 12-18 month period. This relatively short time frame demands recruitment of

patients with fast progressing phenotypes to accurately capture changes in survival during the

18

course of a trial. This challenge of trial design is further magnified by the rarity of ALS (~30,000

patients in the United States) (Kiernan et al 2011), limiting the number of patients available for

recruitment into a randomized control trial.

Another proposed solution to address the challenges of disease heterogeneity is the use of

historical controls as an alternative to randomized, placebo-treated participants. For example, a

multicenter, randomized control trial testing the efficacy of lithium carbonate in improving

survival enrolled 107 unique ALS patients and matched these participants with 249 historical

controls pooled from previous trials (Miller et al 2011). For subpopulation stratification or the

use of historical controls to be effective in a prospective trial design, it is imperative to

understand if clinical disease characteristics change over time, as improvements in standard of

care may confound the ability to accurately match or stratify ALS subpopulations for effective

interpretation of treatment efficacy.

An additional glaring failure of previous ALS clinical trials is the lack of tools to

properly determine drug target engagement, ie.) pharmacodynamics biomarkers, in the CNS.

From 2004 – 2014, researchers performed 23 large, multicenter, randomized clinical trials for

disease-modifying ALS drugs with multiple therapeutic targets (Aggarwal et al 2010; Beghi et al

2013; Berry et al 2013; Chiò et al 2010; Cudkowicz et al 2013; Cudkowicz Merit et al 2006;

Dupuis et al 2012; Gordon et al 2007; Graf et al 2005; Kaufmann et al 2009; Lauria et al 2009;

Meininger et al 2006; Meininger et al 2004; Meininger et al 2009; Milane et al 2009; Miller et al

2007; Mosley et al 2007; Pascuzzi et al 2010; Rosenfeld et al 2008; Shefner et al 2004; Sorenson

et al 2008; Verstraete et al 2012). All trials failed to show a difference in the primary clinical

outcomes of survival or change in the ALS Functional Rating Scale (ALSFRS) compared to

placebo. Further, 22 of the 23 trials lacked a pharmacodynamics biomarker to determine if the

19

experimental drug effectively engaged with its intended pathological target. Equally troubling,

recent phase III randomized control trials whose data supported the approval of Edaravone failed

to analyze the drug’s mechanism of action to scavenge free radicals in the CNS (2017; Abe et al

2017). Failure to identify drug target engagement limits the interpretation of all results in these

trials, as investigators confine interpretation of results to assessment of clinical effects. These

analyses leave an understanding of the underlying human biology with the experimental

treatment unknown. Especially in a disease such as ALS with unknown pathogenesis, it is

essential to understand if new treatments have any biological effects on desired targets to modify

hypotheses in the field and focus new drug development efforts. Despite the progress of

technologies to assay biofluid-based markers of neurodegeneration such as neurofilament light-

chain in plasma and CSF (Bacioglu et al 2016; Gaiani et al 2017; Lu et al 2015) or extracellular

domain P75 in urine (Shepheard et al 2017), no methods currently exist to test objective

biomarkers in ALS patients. Therefore, investigators must strive to include pharmacodynamics

biomarkers in trial design to provide more insight into the relationship between improved clinical

outcomes to effective changes in ALS pathobiology.

Therapeutic Strategies for ALSSOD1

Given the accumulating evidence of mutant SOD1 toxicity to motor neurons, an

attractive therapeutic strategy is to eliminate the presence of misfolded SOD1 protein species

within the CNS. The predominant strategies under investigation for targeting misfolded SOD1

protein in neurons include: 1) promoting clearance of misfolded SOD1 protein through the

ubiquitin-proteasome system and autophagy degradation pathways, 2) enhancing the activity of

SOD1 molecular chaperones to prevent protein misfolding, and 3) lowering protein synthesis of

SOD1 within cells. Despite studies demonstrating improved survival in preclinical disease

20

models using these therapeutic strategies, key limitations inhibited translation of these

approaches to successful clinical trials in human ALS patients.

Cells clear mutant SOD1 through the ubiquitin-proteasome system (UPS) and autophagy

pathways (Kabuta et al 2006). These pathways provide investigators with multiple targets to test

the hypothesis that enhanced mutant SOD1 protein clearance will lead to improved disease

outcomes. Indeed, researchers identified multiple E3 ligases that specifically interact with mutant

SOD1 to promote protein clearance within motor neurons. Overexpression of the E3 ligases

Dorfin and NEDL1 led to reduced motor neuron toxicity by mutant SOD1 in vitro and increased

overall survival in transgenic animal models (Miyazaki et al 2004; Niwa et al 2002; Sone et al

2010). Additionally, treatment with small molecules that increase proteasome activity (Lee et al

2010) and mTOR-dependent autophagy (Castillo et al 2013; Gomes et al 2010) enhance the

clearance of misfolded proteins and reduce neuronal toxicity in cell culture.

However, conflicting studies cast doubt on the feasibility of targeting the UPS and

autophagy pathways in vivo. In the case of autophagy, treatment with rapamycin led to an

exacerbation of motor neuron degeneration in SOD1G93A

transgenic mice (Zhang et al 2011).

Similarly, treatment with another small molecule, trehalose, to enhance autophagy in SOD1G85R

transgenic mice saw improvements in overall survival but did not alter disease onset. These

results suggest authophagy enhancement is not sufficient to ameliorate all motor neuron toxicity

caused by mutant SOD1. Moreover, the importance of ubiquitin in synaptic maintenance (Wilson

et al 2002) suggests that enhancing proteasome activity may result in detrimental effects to

neurons. Therefore, despite an understanding of the role that these pathways play in the clearance

of mutant protein from cells, the complexities of these molecular pathways in vivo have hindered

the development of an effective therapeutic strategy targeting protein clearance mechanisms.

21

Similar challenges are seen in the development of strategies to prevent protein misfolding

by enhancing the unfolded protein response (UPR) and molecular chaperones associated with the

heat shock proteins (Lindquist 1986). Researchers hypothesized that these target pathways would

enhance the stability of mutant SOD1 and prevent toxic protein misfolding and aggregation for

efficient protein clearance. Researchers observed initial promise when SOD1G93A

transgenic

mice treated with arimoclomol, a co-inducer of heat shock protein expression through the

activation of heat shock factor-1, resulted in increased survival (Kieran et al 2004), leading to a

randomized control trial to test arimoclomol in ALS patients (Benatar et al 2018). Also, Nagy

and colleagues demonstrated that transgenic overexpression of the chaperone HSP110 improves

survival in SOD1G85R

transgenic mice without altering SOD1 mRNA or protein levels (Nagy et

al 2016). However, activation of other heat shock proteins do not exhibit a similar response, as

overexpression of HSP70 in SOD1G93A

transgenic mice is not sufficient to reduce mutant SOD1

protein toxicity (Liu et al 2005). Moreover, inhibition of HSP90 by a small molecule showed

opposite effects in lowering misfolded protein toxicity in vitro and in vivo (Cha et al 2014),

providing insight into the tight regulation of the HSP protein family in vivo. Therefore, these

conflicting data highlight that although HSP chaperones and regulators of cellular stress response

pathways are an attractive target for therapeutic intervention, complications of specificity,

sustained activation, and compensatory mechanisms of different chaperones result in the absence

of effective drug candidates for treatment of ALS.

The complications associated with targeting the maintenance of SOD1 led researchers to

test a therapeutic intervention further upstream of proteostasis: the production of new SOD1

protein. Because most SOD1 mutations are caused by a single missense mutation, current

technologies are limited in their ability to specifically target the mutant allele. However, the

22

observation that genetic deletion of the SOD1 gene is generally well-tolerated in vivo (Reaume et

al 1996; Shefner et al 1999) suggests that therapeutics lowering global SOD1 synthesis is

feasible. However, identification of small molecule therapeutics that lower SOD1 transcription

remains elusive, as Wright and colleagues failed to identify any small molecules that alter SOD1

promoter activity from a screen of 1,040 FDA-approved compounds (Wright et al 2010).

Further downstream from transcriptional control, another attractive strategy includes

RNAi-based SOD1 gene silencing. Two parallel studies initially demonstrated the therapeutic

efficacy of RNAi-based approaches in vivo by transducing SOD1G93A

transgenic mice with

lentiviral vectors expressing short-hairpin RNA (shRNA) targeting SOD1 mRNA transcripts.

Ralph and colleagues demonstrated that intramuscular lentiviral injection of SOD1 shRNA

delayed disease onset by 100% and increased survival by more than 80% (Ralph et al 2005b).

Concurrently, Raoul and colleagues showed increases in overall survival of 20% compared to

untreated animals after intraspinal lentivirus injection (Raoul et al 2005). In both instances,

improvements in survival were the result of delaying motor neuron degeneration in the CNS, as

Miller and colleagues showed that restricted SOD1 mRNA silencing in muscle is not sufficient

to improve animal survival (Miller et al 2006). Further advancements in the delivery of SOD1

shRNA by adenoviral vector AAV9 showed improvements in delayed disease onset and overall

survival through peripheral administration (Foust et al 2008) and injection into the motor cortex

(Thomsen et al 2014) of SOD1 transgenic models.

Despite the promise of RNAi-based approaches in preclinical models of ALSSOD1

,

significant traction fails to exist for translation to clinical trials. Lack of progress for a DNA-

based therapeutic strategy is primarily driven by continued safety concerns with viral packaging

in which there is no ability to “turn off” shRNA expression. Additionally, the need for shRNA to

23

be processed by endogenous transcriptional machinery such as Dicer and Argonaute proteins

presents risks of toxicity through competition with endogenous mRNAs for proper RNA

homeostasis. Indeed, Grimm and colleagues showed that high levels of shRNA within liver cells

result in oversaturation of micro-RNA processing machinery, leading to animal fatality (Grimm

et al 2006). Due to these concerns, an opportunity exists for an alternative approach that utilizes

RNAi independent of viral-based approaches that uses alternative cellular machinery. One such

treatment that has gained significant traction is the use of antisense oligonucleotides.

Antisense Oligonucleotides for the Treatment of ALSSOD1

Antisense oligonucleotides (ASOs) are short, single-stranded sequences of DNA

composed of a phosphate backbone and ribose sugar rings (Schoch & Miller 2017). ASOs bind

by standard Watson-Crick base pairing (Watson & Crick 1953) to specific mRNA in the nucleus

and cytoplasm of cells to exert gene modulating effects. The sequence design and chemical

modifications to the phosphate backbone and sugar groups result in multiple potential

mechanisms of action for ASOs, including mRNA transcript degradation by recruitment of

RNase enzymes (DeVos & Miller 2013a), splicing modifications (Schoch et al 2016), stalling of

translation (Bennett & Swayze 2010), or targeting microRNAs (Janssen et al 2013; Koval et al

2013). ASOs present multiple advantages over shRNA technology for RNAi-based therapies.

First, ASOs are readily taken up by tissues, most likely through interaction with high- and low-

binding plasma proteins, internalized into lysosomal or endosomal compartments, and trafficked

within cells (Geary et al 2015). Further, chemical modifications increase resistance of ASOs to

nuclease degradation and eliminate toll-like receptor responses (Henry et al 2000), resulting in

superior pharmacokinetic properties of ASOs without the need for viral packaging. Although

these highly negatively charged DNA-like molecules do not cross the blood brain barrier,

24

empirical data in non-human primates demonstrates that intrathecal delivery of ASOs results in

broad distribution within the CNS (DeVos et al 2017; Kordasiewicz et al 2012; Smith et al

2006). Researchers performed the first clinical trials using ASOs in acute myelogenous

leukemia (Bayever et al 1993) over 25 years ago, and the first market approval for an antisense

therapeutic for the treatment of cytomegalovirus retinitis in patients with immunodeficiency

occurred in 1998 (Marwick). These previous successes highlight the feasibility of using ASOs

for the treatment of human disease. Indeed, the recent approval of ASO treatments for two

neuromuscular diseases: Duchenne muscular dystrophy (Mendell et al 2016) and spinal muscular

atrophy (Finkel et al 2017) further supports the prospects of translating ASO treatments for

neurodegenerative diseases into medical practice.

As with previous studies using shRNA, ALSSOD1

is an ideal candidate on which to test an

ASO therapy. Using ASOs that contain a phosphorothioate DNA backbone designed to recruit

RNase H effectively degrade SOD1 mRNA transcripts (Wu et al 2004). Smith and colleagues

first demonstrated the broad distribution of these SOD1-lowering ASOs in the CNS after

infusion into cerebrospinal fluid (CSF) in rats and rhesus monkeys. ASOs targeting human

SOD1 mRNA resulted in global lowering of SOD1 mRNA in the brain and spinal cord and

improved survival of SOD1G93A

transgenic rats relative to saline-treated animals by ~10% (Smith

et al 2006). Further optimization of SOD1 ASOs for sequence specificity and tolerability led to

increases in overall survival of ~40% with a single, intrathecal bolus injection of SOD1 ASO

compared to artificial CSF-treated animals (McCampbell et al 2018). Excitingly, SOD1 ASOs

administered via intrathecal injection were shown to be safe in a human Phase I clinical trial

(Miller et al 2013). This pioneering work in the use of ASOs for ALSSOD1

has led to the

development of ASOs for the treatment of other neurodegenerative diseases such as Alzheimer’s

25

Disease (DeVos et al 2017), Huntington’s Disease (Kordasiewicz et al 2012), and Parkinson’s

Disease (Zhao et al 2017b) that are being pursued in human randomized control clinical trials.

Despite these promising data, key challenges in clinical trial execution remain for successful

translation of SOD1 ASOs as effective therapeutics for SOD1ALS

.

Considerations for ASO Clinical Trial Design in ALSSOD1

Because of the unresolved role of SOD1 outside of ALSSOD1

, current ASO clinical trials

limit patient population recruitment to those patients with known SOD1 mutations. This

population only accounts for ~2% of all ALS cases, drastically lowering the number of subjects

available. As previously discussed, the ALSSOD1

population displays heterogeneity in clinical

phenotypes dependent upon each mutation. The mean disease duration of ALSSOD1

is 3.9 ± 5.5

years (Cudkowicz et al 1997; Juneja et al 1997), ranging from rapidly progressing disease with a

mean disease duration of ~1 year such as A4V (Rosen et al 1994), to slowly progressing disease

where patients live 20+ years after diagnosis such as I104F (Abe 1997) and I151T (Kostrzewa et

al 1996). A4V is a mutation of particular interest, as it accounts for ~50% of North American

ALSSOD1

with little variability in disease progression (Juneja et al 1997). With a high percentage

of rapidly progressing patients and a rare disease population, an SOD1 ASO clinical trial may

benefit from stratification and the use of historical controls. However, multicenter ALSSOD1

natural history studies past the year 2000 are largely absent, as most current research focuses on

characterization of newly identified SOD1 mutations. Thus, recent changes to ALSSOD1

prognosis

are not well understood. It is possible that advancements in ALS standard of care have improved

ALSSOD1

patient outcomes. Supporting this claim, a retrospective analysis of ALS patients cared

for from 1984 – 1999 and 1999 – 2004 suggests improvement to contemporary patient standard

of care in the 21st century (Czaplinski et al 2006b). Therefore, it is important to understand if

26

revised disease management results in changes to ALSSOD1

epidemiology since initial

characterization of SOD1 mutations in the late 1990s for the optimization of current SOD1 ASO

clinical trial design.

In contrast with previous trials, targeting SOD1 by ASO treatment provides an exciting

opportunity to incorporate pharmacodynamics biomarkers into outcomes measures. Lowering of

SOD1 mRNA and protein in brain and spinal cord leads to subsequent lowering of CSF SOD1

protein levels after ASO treatment in ALS rodent and non-human primate models (McCampbell

et al 2018; Winer et al 2013). As mRNA is difficult to isolate from CSF, it is encouraging to

demonstrate the feasibility of more easily available, protein-based ASO pharmacodynamics

biomarkers. Moreover, these data provided the rationale to evaluate the effects of ASO treatment

on SOD1 protein in CSF as a secondary objective in an ongoing SOD1 ASO Phase I trial

(NCT02623699). However, in a rapidly progressing disease such as ALS, it is imperative to

assess treatment efficacy as quickly as possible to maximize patient outcomes. To that end,

SOD1 protein measurement in CSF may not be the fastest assessment of ASO

pharmacodynamics. Protein concentration is dependent on both the production of new protein,

but also on the clearance of existing protein as determined by protein half-life. SOD1 is a long-

lived protein in the human CNS, with a previously calculated half-life of ~25 days in CSF (Crisp

et al 2015) . Therefore, further potential exists to develop pharmacodynamics biomarkers that

more directly and quickly reflect the mechanism of ASO action to lower target mRNA levels and

inhibit the synthesis of new protein.

Summary of Dissertation

The work in subsequent chapters represents new insights into the translation of

preclinical drug candidates toward new therapies for ALSSOD1

. Chapter 2 describes the largest

27

effort in the last 20 years to study the natural history of ALSSOD1

in North America. In this

retrospective cohort study analyzing 175 ALSSOD1

patients cared for past the year 2000, I found

that mean age of onset and median survival of ALS caused by SOD1 mutations is consistent

across previous natural history studies. Additionally, I demonstrated that the A4V point mutation

remains an aggressive form of disease with a significantly shorter median survival after symptom

onset with lower variability compared to non-A4V mutation carriers. Given that the A4V

mutation is the most common variant in North America, my power analysis suggests that the

design of a randomized control trial with SOD1 A4V carriers to test therapeutic interventions in

ALSSOD1

is possible.

Chapter 3 describes my efforts to characterize SOD1 protein turnover in the CSF of ALS

participants using stable isotope labeling kinetics (SILK) analysis (Bateman et al 2006). We

describe preliminary results to characterize SOD1 protein behavior in the CSF of both ALSSOD1

and sporadic ALS patients. Similar to normal controls, SOD1 is a long-lived protein in the CSF

of ALS participants, with a protein half-life of ~20-25 days. Excitingly, I also show that mutant

SOD1 protein exhibits different behavior than WT SOD1 in the CSF of SOD1 mutant carriers, as

a participant harboring an A4V SOD1 mutation exhibits changes in CSF protein behavior

between mutant and wild-type protein. Interestingly, two participants with different SOD1

mutations (E100K, A89V) show similar CSF SOD1 protein behavior compared to WT SOD1.

These data suggest that, similar to the differences in ALSSOD1

clinical phenotypes by mutation,

different SOD1 mutant proteins may show differential biochemical properties in human CSF.

Chapter 4 highlights the development of a protein-based pharmacodynamics biomarker

for ASO treatment in the CNS. Using a stable isotope labeling method to measure incorporation

of 13

C6-leucine into newly produced proteins after ASO treatment, I show significant lowering of

28

target protein synthesis in CNS tissues and CSF after ASO treatment in transgenic animal

models. Excitingly, we observed lowering of target protein synthesis at an earlier time point than

protein concentration-based measures in tissue and CSF, suggesting that new protein production

may be a more sensitive pharmacodynamics biomarker than protein concentration in clinical

trials. The final chapter will then discuss these results and consider future directions for research

into developing effective treatments for ALS.

29

Chapter 2: Defining SOD1 ALS Natural History to Guide

Therapeutic Clinical Trial Design

30

ABSTRACT

Understanding the natural history of familial amyotrophic lateral sclerosis (ALS) caused

by SOD1 mutations (ALSSOD1

) will provide key information for optimizing clinical trials in this

patient population. In this study, we sought to establish an updated natural history of ALSSOD1

in

a retrospective cohort study from 15 medical centers in North America. We evaluated records

from one hundred seventy-five ALS patients with genetically confirmed SOD1 mutations, cared

for after the year 2000. Age of onset, survival, ALS function rating scale (ALS-FRS) scores, and

respiratory function were analyzed as primary measures. Patients with the A4V (Ala-Val) SOD1

mutation (SOD1A4V

), the largest mutation population in North America with an aggressive

disease progression, were distinguished from other SOD1 mutation patients (SOD1Non-A4V

) for

analysis. Mean age of disease onset was 49.7 ± 12.3 years (mean ± standard deviation) for all

SOD1 patients, with no statistical significance between SOD1A4V

and SOD1Non-A4V

(p=0.72,

Kruskal-Wallis). Total SOD1 patient median survival was 2.7 years. Mean disease duration for

all SOD1 was 4.6 ± 6.0 years and 1.4 ± 0.7 years for SOD1A4V

. SOD1A4V

survival probability

(median survival 1.2 years) was significantly decreased compared to SOD1Non-A4V

(median

survival 6.8 years) (p<0.0001, log-rank). A statistically significant increase in ALS-FRS decline

in SOD1A4V

compared to SOD1Non-A4V

subjects (p=0.02) was observed, as well as a statistically

significant increase in ALS-FVC decline in SOD1A4V

compared to SOD1Non-A4V

(p=0.02).

Similar to previous investigations, SOD1A4V

remains an aggressive, but relatively homogeneous

form of ALS. These SOD1-specific ALS natural history data will be important for the design

and implementation of clinical trials in the ALSSOD1

patient population.

31

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease affecting

both upper and lower motor neurons, resulting in severe weakness and fatal respiratory failure

within 2-5 years. The annual incidence of ALS is 1.7 in 100,000 people (Sorenson et al 2002),

yet the only FDA-approved treatment, riluzole, has minimal effects on disease course(Miller et al

2009).

Inherited or familial ALS (FALS) accounts for roughly 10% of all ALS cases(Byrne et al

2011). The second most common cause of FALS is the gene encoding copper zinc superoxide

dismutase 1 (SOD1), which accounts for 10-20% of FALS and mostly follow an autosomal

dominant inheritance pattern(Rosen et al 1994), although an aspartic acid to alanine (D90A)

point mutation found in the Scandanavian population displays recessive inheritance(Andersen

2001). Multiple efforts (ClinicalTrials.gov NCT00706147)(Gros-Louis et al 2010; Lange et al

2013; Miller et al 2005; Miller et al 2013; Patel et al 2014; Ralph et al 2005a; Raoul et al 2005;

Ray et al 2005; Riboldi et al 2014; Smith et al 2006; Winer et al 2013) have focused on targeted

therapeutic approaches for SOD1-related ALS (ALSSOD1

).

To test the efficacy of SOD1-focused therapeutics, an ALSSOD1

natural history dataset is

required to determine the patient sample size needed to observe the desired effect. Considering

the rare nature of SOD1 mutations, these historical control data may also be used in future

clinical trials provided that updated data show no change in disease measures. Given the

heterogeneity among different SOD1 mutations(Cudkowicz et al 1997), it is important to

clinically stratify mutations in SOD1 according to disease duration and rate of progression. The

A4V (SOD1A4V

) is known for an exceptionally aggressive disease course and relatively low

32

inter-patient clinical variability, and represents about 50% of ALSSOD1

in the United

States(Cudkowicz et al 1997; Rosen et al 1993).

We report the findings from a multicenter retrospective chart review of genetically-

confirmed ALSSOD1

cases cared for after the year 2000 and discuss implications for future

ALSSOD1

clinical trials.

33

METHODS

Data Acquisition

We corresponded with 37 sites within the Northeast ALS Consortium Network (NEALS),

an international, independent, non-profit group of research sites who collaboratively conduct

ALS clinical research. Sites performed a retrospective chart review of their respective ALSSOD1

patients cared for from the year 2000 to present. Data were sent to the coordinating center at the

Washington University in St. Louis and entered into a central database. Information for all

patients was de-identified, and data collection and processing were approved by the local IRB

committees. The following 17 centers sent charts as part of this study: Washington University

School of Medicine, Northwestern University Feinberg School of Medicine, University of

Washington Medical Center, Neurology Clinical Research Institute at Massachusetts General

Hospital, Mayo Clinic Florida, Emory University School of Medicine, Johns Hopkins

University, Wake Forest School of Medicine, Perelman School of Medicine at the University of

Pennsylvania, Methodist Neurological Institute at The Methodist Hospital, Neurosciences

Institute at Albany Medical Center, University of Utah, Sunnybrook Health Sciences Centre,

University of California Irvine, University of Michigan, University of Kansas Medical Center,

and Cedars-Sinai Medical Center.

Data collection and analysis

All sites received a standardized form with the following items requested: gender, genetic

mutation, month/year of birth, month/year of symptom onset, month/year of death, month/year of

permanent ventilation (if applicable), ALS-Functional Rating Scale (ALS-FRS) at time points

where available, and forced vital capacity (FVC) at time points where available. Symptom onset

was defined as the first reported loss of function, such as limb weakness or dysarthria.

34

Genetic testing was performed by individual institutions at the time of diagnosis, and

sites were asked to only report the specific SOD1 mutation found. Data were collected and

analyzed from individual patients or families of index cases with confirmed SOD1 mutations

only; mutations recorded as “self-reported” were not included in the analysis. All mutations are

reported in this study using the traditional numbering nomenclature, i.e. not counting the first

(ATG) codon. When time of onset could not be narrowed down to more than a range of several

months, e.g. “fall 2011”, the onset was determined to be the midpoint of that range, e.g. “October

2011.” Patients still alive at the time of data collection were reported as such, and their last

recorded FVC or ALS-FRS, whichever came later, was used for survival analyses. ALS-FRS is

understood to be the revised scale (ALSFRS-R), a 12-item questionnaire with the highest,

normal score being 48(Cedarbaum et al 1999). When both sitting and lying FVC readings were

available, sitting values were used. Additional clinical features, such as site of onset and

presence of non-motor symptoms and signs, were not analyzed in this study.

Statistical Analysis

SAS Version 9.3 (Cary, NC) was used to perform all statistical analyses. Descriptive

statistics were provided for gender, age, and disease duration by mutation group (A4V and Non-

A4V) and total patients, respectively. Additionally, mean, standard deviation of age, and disease

duration were calculated for each interested mutation. The primary study endpoints included

ALS-FRS and FVC while secondary endpoint was overall survival (OS). OS was defined as the

date of symptom onset to death from any cause or last follow-up. Kaplan-Meier (KM) curves for

OS were generated that provide unadjusted survival estimates for total sample and between

mutation groups (Non-A4V vs. A4V), respectively. Difference between mutations groups were

determined by log-rank testing.

35

The study sample includes ALS-FRS and FVC measurements for multiple visits. To

account for correlations among repeated measures from the same patient, the longitudinal data

was analyzed using a generalized estimating equation (GEE) model with log link function to

examine the change in ALS-FRS and FVC measurements over the first three time points

recorded for a patient. The autoregressive of first order as working correlation structure was used

and the patients with missing values at any assessment number were excluded from GEE

analysis. The GEE model includes the group indicator, time points, the interaction term between

group and time points. The p-values of the interaction term from type 3 analysis in the GEE

model were estimated to assess whether the measurements across all time points between A4V

and Non-A4V groups were significantly different. The least square mean and standard error of

the mean were provided.

36

RESULTS

Descriptive Features

Applicable data were collected from 15 medical centers. A total of 175 subjects with

confirmed SOD1 mutations were analyzed. Of subjects collected, 63 had SOD1A4V

mutations

(36.4%), the most common mutation observed in this cohort. 112 subjects had SOD1Non-A4V

mutations (63.6%). There was a slight male dominance in this cohort with a male-female ratio of

1.30:1 in all SOD1 subjects, but there was no significant difference in the male-female ratio

between SOD1A4V

(1.33:1) and SOD1Non-A4V

(1.29:1) subjects (p = 0.9089, Chi-Squared testing)

(Table 2.1).

A total of 36 distinct, missense SOD1 mutations were found in this cohort. The most

common SOD1Non-A4V

mutation was I113T (Ile-Thr) in 32 subjects (18.3%), followed by G41D

(Gly-Asp) (11 subjects; 6.4%), and E40K (Glu-Lys) (10 subjects; 5.7%). The frequencies and

clinical characteristics of all subjects included in this study are shown in Table 2.2.

37

SD = Standard Deviation

a. χ

2 test for categorical variable; Kruskal-Wallis test for continuous variable; Log-rank test for overall

survival.

Total SOD1 SOD1A4V

SOD1Non-A4V

p valuea

No. of subjects (%) 175 (100) 63 (36.4) 112 (63.6)

Male-Female Ratio 1.30 : 1 1.33 : 1 1.29 : 1 0.91

Mean age at onset

(yr) ± SD (n)

49.7 ± 12.3 (162) 50.0 ± 12.4 (57) 49.5 ± 12.3 (105) 0.72

Mean disease duration

(yr) ± SD (n)

4.6 ± 6.0

(134)

1.4 ± 0.7

(51)

6.6 ± 7.0

(83)

<0.0001

Median survival (yr) 2.7 1.2 6.8 <0.0001

Table 2.1 Clinical Characteristics of SOD1A4V

versus SOD1Non-A4V

mutation populations in

familial ALS

38

Mutationa

Exon Codon Substitution No. of

Patients (%)

Mean Age at Onset

(yr) ± SD (n)

Mean Durationb

(yr) ± SD (n)

Median

Duration

(yr)

1 4 Ala-Val 63 (36.6) 49.9 ± 12.3 (57) 1.4 ± 0.7 (51) 1.2

4 113 Ile-Thr 32 (18.3) 54.2 ± 10.7 (29) 5.3 ± 4.8 (22) 3.3

2 41 Gly-Asp 11 (6.4) 34.2 ± 7.8 (8) 23.5 ± 14 (4) 21

4 100 Glu-Lys 10 (5.7) 39.6 ± 5.9 (10) 8.9 ± 5.2 (8) 9.1

4 100 Glu-Gly 5 (2.9) 42.6 ± 9.1 (5) 9 ± 6.2 (4) 7.2

4 89 Ala-Val 4 (2.3) 56 ± 10.4 (4) 8 + 1.6 (2) 8

1 4 Ala-Thr 3 (1.7) 50 ± 10.1 (3) 0.8 ± 0.05 (2) 0.8

4 90 Asp-Ala 3 (1.7) 57.7 ± 9.6 (3) 9.7 ± 5.4 (2) 9.7

4 93 Gly-Ala 3 (1.7) 49 ± 12.5 (3) 2.2 ± 0.6 (2) 2.2

5 137 Thr-Ala 3 (1.7) 38.3 ± 9.2 (3) 9.6 ± 7 (3) 6.4

1 14 Val-Gly 3 (1.7) 50.3 ± 21.0 (3) 2.6 ± 1 (2) 2.6

1 6 Cys-Ser 2 (1.2) 54 ± 16.9 (2) 4.1 ± 0.5 (2) 4.1

5 133c Glu-Ala 2 (1.2) 62 ± 2.8 (2) 0.9 ± 0.6 (2) 0.9

2 49 Glu-Lys 2 (1.2) 59 ± 19.8 (2) 6.8 ± 1.9 (2) 6.8

5 141 Gly- --- 2 (1.2) 42.5 ± 2.1 (2) 1.1 ± 0.7 (2) 1.1

39

Mutationa

Exon Codon Substitution No. of

Patients (%)

Mean Age at Onset

(yr) ± SD (n)

Mean Durationb

(yr) ± SD (n)

Median

Duration

(yr)

2 37 Gly-Arg 2 (1.2) 26.5 ± 6.4 (2) 7.8

2 41 Gly-Ala 2 (1.2) 44.5 ± 10.6 (2) 17.3 ± 0 (2) 17.3

4 85 Gly-Arg 2 (1.2) 56.5 ± 9.2 (2) 2.2 ± 0.3 (2) 2.2

5 139 Asn-Lys 2 (1.2) 51 ± 2.8 (2) 1.5 ± 0.7 (2) 18.5

5 148 Val-Gly 2 (1.2) 49.5 ± 0.7 (2) 0.7 ± 0.4 (2) 0.7

1 4c Ala-His 1 (0.6) 50

5 125c Asp-Ala 1 (0.6) 63 1.1

3 76 Asp-Tyr 1 (0.6) 75 3.7

4 108c Gly-Arg 1 (0.6) 67 1.7

1 10c Gly-Ala 1 (0.6) 59 4.8

5 141 Gly-Ala 1 (0.6) 43

2 37 Gly-Val 1 (0.6) 36 4.6

2 41 Gly-Ser 1 (0.6) 67 0.4

4 93 Gly-Ser 1 (0.6) 45

2 43 His-Arg 1 (0.6) 56 0.3

2 46 His-Arg 1 (0.6) 47 23.4

40

Mutationa

Exon Codon Substitution No. of

Patients (%)

Mean Age at Onset

(yr) ± SD (n)

Mean Durationb

(yr) ± SD (n)

Median

Duration

(yr)

5 144 Leu-Phe 1 (0.6) 66 6.2

5 144 Leu-Ser 1 (0.6) 56 7.8

1 14 Val-Met 1 (0.6) 54 4.1

4 87 Val-Met 1 (0.6) 39 8.8

5 134 Thr-Cys 1(0.6) 55

N/A 1 (0.6)

Total 175 d (100%) 162

d 134

d

aThe mutations are ordered by decreasing number of affected patients. Nomenclature corresponds to

amino acid sequence excluding ATG start codon.

bCensored data are included in determination of mean or median duration

cMutations that are not listed in the ALSoD Database as of 5/5/15

dTotal number of subjects with available data

Table 2.2 Disease characteristics by mutation type in SOD1 familial ALS

41

Age of Disease Onset

Age of onset data was provided for 162 (92.5%) total subjects, with data for 57 SOD1A4V

and 105 SOD1Non-A4V

patients available for analysis. The mean age of onset for all subjects was

49.7 ± 12.3 years (mean ± standard deviation). The mean age of onset varied across different

mutations, with G37R (Gly-Arg) and D133A (Asp-Ala) mutations associated with the youngest

and oldest mean ages at onset, respectively (Figure 2.1A). There was no significant difference in

mean age of onset between SOD1A4V

(50.0 ± 12.4 years) and SOD1Non-A4V

(49.5 ± 12.3 years)

subjects (p = 0.72, Kruskal-Wallis) (Table 2.1).

Disease Duration

Disease duration information was available for 134 (76.6%) of total subjects, including

51 SOD1A4V

and 83 SOD1Non-A4V

patients. The mean disease duration was 4.6 ± 6.0 years for all

SOD1 subjects, 1.4 ± 0.7 years for SOD1A4V

subjects, and 6.6 ± 7.0 years for SOD1 Non-A4V

(Table 2.1). Disease duration varied between mutation type, ranging from rapidly progressing

mutations such as A4T (Ala-Thr) (0.8 ± 0.1 years) to slow progressing mutants such as G41D

(Gly-Asp) (23.5 ± 14.0 years) (Figure 2.1B).

Kaplan-Meier survival analysis was performed comparing the survival probabilities from

time of disease onset to death between SOD1A4V

and SOD1Non-A4V

(Figure 2.2). Of 120 subjects

analyzed, 35 were SOD1A4V

and 85 were SOD1Non-A4V

. The median survival for all subjects was

2.7 years, 1.2 years for SOD1A4V

, and 6.8 for SOD1non-A4V

. SOD1A4V

median survival was

significantly shorter than SOD1Non-A4V

by log-rank test (p <0.0001). No SOD1A4V

subjects

survived to four years (Figure 2.2B).

42

(A.) The mean age at onset and (B.) mean disease duration were reported for each mutation

population with a minimum n = 2. There was no statistical significance between all SOD1

patients (n =162), SOD1Non-A4V

patients (n = 105), SOD1A4V

patients (n = 57) (p = 0.7217,

Kruskal-Wallis). (A.) Average age at onset varied amongst mutations, with the SOD1G37R

and

SOD1D133A

mutations associated, respectively, with the youngest and oldest mean ages at onset

among mutations with more than 1 patient. (B.) Average disease duration varied with different

mutations, with SOD1A4T

and SOD1G41D

mutations associated, respectively, with the shortest and

longest disease durations. SOD1A4V

disease duration (n =51) is significantly shorter than

SOD1Non-A4V

disease duration (n = 83) (p < 0.0001, Kruskall-Wallis). Error bars represent

standard deviation.

Figure 2.1 Mean age at onset and disease duration for ALSSOD1

43

Figure 2.2 Decreased survival probability from time of disease onset for SOD1A4V

compared to SOD1Non-A4V

familial ALS

(A.) Plot for survival probability of all SOD1 mutation patients. 112 subjects were analyzed. The

median survival for all SOD1 patients is 2.7 years. The survival probabilities for SOD1 patients

and the corresponding 95% confidence intervals were 0.82 (0.73, 0.88), 0.51 (0.41, 0.60), and

0.43 (0.33, 0.53) at years 1, 3, and 5, respectively, after disease onset. (B.) Plot for survival

probability for SOD1A4V

vs. SOD1Non-A4V

mutation patients. Analysis consisted of 33 SOD1A4V

and 79 SOD1Non-A4V

. The presence of SOD1A4V

mutations was associated with shorter survival

(p < 0.0001, log-rank). The survival probability for SOD1A4V

patients and the corresponding

95% confidence intervals were 0.60 (0.41, 0.75), 0.04 (0.00, 0.15), and 0.00 (n/a, n/a) at years 1,

3 and 5, respectively, after disease onset. No SOD1A4V

patients survived to year 4. The survival

probability of SOD1Non-A4V

patients was 0.91 (0.82, 0.96), 0.70 (0.59, 0.79), and 0.60 (0.48, 0.71)

at 1, 3 and 5 years after disease onset.

44

FRS and FVC

Only three data points were considered for FRS and FVC. 30 SOD1A4V

and 75 SOD1Non-

A4V subjects were used for ALS-FRS and FVC analysis. The least mean and standard error of

ALS-FRS for SOD1A4V

were 37.8 ± 1.4, 30.0 ± 2.0, and 22.3 ± 3.7; while those of SOD1Non-A4V

were 35.9 ± 0.9, 32.9 ± 1.1, and 29.9 ± 1.3 at time points 1, 2, and 3, respectively. A statistically

significant difference was seen in ALS-FRS across three time points in SOD1A4V

compared to

SOD1Non-A4V

subjects (p = 0.02, Figure 2.3A). Additionally, 37 SOD1A4V

and 81 SOD1Non-A4V

subjects were used for FVC analysis. Dates of permanent ventilation were rarely available (less

than 5% of total subjects), most likely due to death without receiving tracheal ventilation, and so

were not included in final analysis. Similarly, a statistically significant increase in ALS-FVC

decline was observed in SOD1A4V

compared to SOD1Non-A4V

(p = 0.02, Figure 2.3B). The least

mean and standard error of FVC for SOD1A4V

were 86.2 ± 4.1, 58.8 ± 5.1, and 50.7 ± 9.8; while

those of SOD1Non-A4V

were 73.2 ± 2.8, 62.2 ± 3.2, and 54.7 ± 4.0 at time points 1, 2, and 3,

respectively.

45

(A.) Least squares mean of ALS-FRS over multiple assessment periods. SOD1A4V

patients

decline from mean ALS-FRS 37.6 (35.1, 40.6) (95% CI) at assessment point 1, 29.9 (26.2, 34.3)

at assessment point 2, to 22.3 (16.1, 31.0) at assessment point 3. SOD1Non-A4V

patients decline

from mean ALS-FRS 35.9 (34.1, 37.8) at assessment point 1, 35.9 (34.1, 37.8) at assessment

point 2, to 29.9 (27.4, 32.2) at assessment point 3.The SOD1A4V

FRS decline is significantly

increased compared to SOD1Non-A4V

(p = 0.0168). (B.) Least squares mean of FVC over multiple

assessment periods. SOD1A4V

patients decline from mean ALS-FRS 86.2 (78.6, 94.5) (95% CI)

at assessment point 1, 58.8 (49.6, 69.8) at assessment point 2, to 50.8 (34.8, 74.0) at assessment

point 3. SOD1Non-A4V

patients decline from mean FVC 74.7 (69.5, 80.4) at assessment point 1,

63.4 (57.5, 69.8) at assessment point 2, to 55.6 (48.4, 64.0) at assessment point 3.The SOD1A4V

FVC decline is significantly increased compared to SOD1Non-A4V

(p = 0.0168). Error bars

represent standard error of the mean (SEM).

Figure 2.3 SOD1A4V

ALS patients exhibit greater decline in ALS-FRS and FVC rates compared

to SOD1Non-A4V

patients

46

DISCUSSION

We collected data from 15 North American centers, studying 175 subjects with

confirmed SOD1 mutations, cared for from the year 2000 to present. The number of patients

available for this study represent the largest effort to study ALSSOD1

natural history in almost 20

years. Combining data from multiple studies(Andersen et al 1997a; Andersen et al 2003;

Cudkowicz et al 1998; Cudkowicz et al 1997; Gellera et al 2001; Juneja et al 1997; Rosen et al

1994; Sato et al 2005; Stewart et al 2006) suggests mean disease duration for all SOD1 is 3.9 ±

5.5 years and SOD1A4V

is 1.4 ± 0.9 years. Three of these studies (Cudkowicz et al 1997; Juneja

et al 1997; Rosen et al 1994) represent 92% of the 199 reported patients and use data collected

more than 20 years ago and published in the 1990’s. Our calculated disease duration for all

SOD1 of 4.6 ± 6.0 and SOD1A4V

of 1.4 ± 0.7 years indicates no significant change in the natural

history of the disease since these historical data were collected. Our results provide updated

survival data that could serve in the design and interpretation of future clinical trials specific for

ALSSOD1

patients.

A total of 36 missense SOD1 mutations were captured, with the four most common

(SOD1A4V

, SOD1I113T

, SOD1G41D

, and SOD1E100K

) accounting for two-thirds of all subjects. Our

data show a smaller percentage (36.4%) of SOD1A4V

among all ALSSOD1

subjects than

previously reported (50%). This may reflect the recent expansion in genetic testing and

improvement of ALS care, leading to increased detection of SOD1 mutations with less

aggressive or typical presentations. Interestingly, male predominance was present, although

mild, in ALSSOD1

subjects as a whole (M:F of 1.3). These findings may reflect potential bias in

the dataset, as they are in contrast to a recent literature review (McCombe & Henderson 2010).

However, ALSSOD1

cohorts published earlier report similar male predominance (Cudkowicz et al

47

1997). Age of onset for all SOD1 patients (49.7 ± 12.3 years) was not statistically different from

SOD1A4V

age of onset (50.0 ± 12.4 years), which might be unexpected due to the aggressive

nature of SOD1A4V

. However, these findings are consistent with previous reports (Cudkowicz et

al 1997) in which onset is similar between SOD1A4V

and all SOD1 patients, while progression is

more rapid in the SOD1A4V

population.

Our results are limited by the design of this study as retrospective. As with all

retrospective studies, inherent biases exist based upon the availability of data collected. Of the

original 37 NEALS medical centers which we corresponded, 17 participated in this study. No

obvious variables such as geographic location or size of center were linked to the 20 medical

centers that did not respond. Correspondingly, data included in the study have come from clinical

centers spanning wide geographical regions and likely capture a significant portion of the

ALSSOD1

population in North America. However, assumptions must be made when compiling

survival data from multiple centers, including consistency of standard of care. Also, given that

our data were drawn from large ALS referral centers, survival and progression rate numbers may

be different than outcomes in the community.

Natural history data is optimally collected prospectively, and in fact a portion of our data

from 2013-2014 was collected as such. Efforts to collect prospective natural history data on

ALSSOD1

, and FALS in general, should be considered in the future and will be better able to

capture real-time clinical information, such as site of onset and presence of extra-motor findings,

in addition to more closely documenting paraclinical endpoints such as ALS-FRS, FVC and

time-to-permanent-ventilation. Despite these limitations, the rarity of ALSSOD1

coupled with the

urgent need for enabling new clinical trials helps justify the use of a retrospective study design.

48

In clinical trials with a rare disease population, such as ALSSOD1

, historical controls may

be implemented to assess outcomes. As recruitment of patients to a trial in a rare disease

population is challenging, the use of historical controls would be valuable to design an

adequately-powered trail. However, it is important to validate if historical data has changed over

time. In contrast to our results focusing on ALSSOD1

, previous natural history studies (Czaplinski

et al 2006b; Qureshi et al 2009) of the ALS population suggest an increase in survival and

conclude this may reflect improvement in respiratory, nutritional and supportive clinical care.

These findings may suggest that, although overall ALS survival has improved over time,

ALSSOD1

may represent a unique ALS subpopulation. Although our data suggests the ALSSOD1

has not changed over time, the use of historical controls in clinical trials is challenging, and key

limitations exist in our data for direct implementation as historical controls. To properly match

patients in trials with historical controls, additional data such as standard of care (riluzole use,

non-invasive ventilation), cognitive status, and site of disease onset must be collected, leading to

enhanced comparisons with a future cohort. Future prospective studies will focus on these

missing data in future prospective natural history studies in ALSSOD1

or other ALS disease

populations. Nevertheless, the data reported regarding updated survival statistics for this

population are one important consideration in the use of historical controls.

The first reported cause of FALS was a mutation in SOD1, and it is now widely accepted

that the pathogenesis of mutations in SOD1 is related to a gain of toxic function of the SOD1

protein. Therefore, global reduction of toxic SOD1 in ALS has been recognized as a possible

therapeutic intervention. Our work with SOD1 antisense oligonucleotides (Crisp et al 2015;

Miller et al 2005; Miller et al 2013; Smith et al 2006; Winer et al 2013) and others

(ClinicalTrials.gov NCT00706147, (Gros-Louis et al 2010; Lange et al 2013; Patel et al 2014;

49

Ralph et al 2005a; Raoul et al 2005; Ray et al 2005; Riboldi et al 2014) have focused on targeted

SOD1-lowering therapeutics approaches for ALSSOD1

. Given the heterogeneity of disease

progression in different mutation carriers, one may envision the design of a trial that stratifies

results from patients with rapidly progressing disease as defined by unchanged natural history

data. For example, our analysis of the SOD1A4V

patient population in this study suggests that

SOD1A4V

disease characteristics are homogeneous, and natural history has remained unchanged

over time with a median survival of 1.2 years. As SOD1A4V

is the most common mutation found

in North America, recruitment for a SOD1A4V

-focused trial for therapeutic efficacy may be

feasible. Assuming an accrual interval of 1 year and additional follow-up interval of 2 years, 52

SOD1A4V

subjects per group (new treatment and placebo) are needed to achieve at least 80%

power at a 0.05 significance level to detect the statistical difference when the median survival in

the treatment group is 2.4 years using two-sided log-rank test, where hazard ratio of new

treatment to control is 0.5. In contrast, our findings suggest that the median survival in all SOD1

subjects is 2.7 years, and thus 88 SOD1 subjects per group (new treatment and placebo) are

needed when the median survival probability in the treatment group is 5.4 years, where hazard

ratio of new treatment to control is 0.5. Although the effect sizes discussed here are large, we

believe such a result is feasible, as SOD1 antisense oligonucleotides directly inhibit the cause of

motor neuron toxicity and death in ALSSOD1

. The prior Phase I trial using SOD1 antisense

oligonucleotides (Miller et al 2013) in this same population recruited 21 unique patients,

suggesting that such a trial is feasible.

Overall, the data presented here will help guide clinical trial design and may serve as a

historical control database for ALSSOD1

interventional trials, with the hope of demonstrating that

one of the upcoming therapies extends survival.

50

ACKNOWLEDGEMENTS

Funding provided by Seattle VA Medical Center, Department of Veterans Affairs

research funds (T.D.B.) for collection of SOD1 data at University of Washington; Amyotrophic

Lateral Sclerosis Association (E.R.) for collection of SOD1 clinical information at MGH;

Harvard NeuroDiscovery Center (E.R.) for collection of SOD1 clinical information at

Massachusetts General Hospital; the Dr. Anne B. Young Neuroscience Translational Medicine

Fellowship (Massachusetts General Hospital Neurology and Biogen Idec) (E.R.) for collection of

SOD1 clinical information at Massachusetts General Hospital; Muscular Dystrophy Association

(T.M.M.) for design and conduct of the study, collection of clinical information, data

management and analysis, interpretation of the data, and preparation and review of the

manuscript; NIH/NINDS R25NS065743 (E.R.) for collection of SOD1 clinical information at

Massachusetts General Hospital, R01NS078398 (T.M.M.) for salary support to T.M.M. during

the conduct of the study, U01NS084970 (T.M.M.) for salary support to T.M.M. during the

conduct of the study.

51

Chapter 3: Characterizing SOD1 Protein Kinetics in the

Cerebrospinal Fluid of Individuals with ALS

52

INTRODUCTION

Autosomal dominant mutations in SOD1 were the first reported genetic cause of ALS

(Rosen et al 1993). Accumulating evidence suggests that a gain of toxicity function associated

with the pathological misfolding of mutant SOD1 protein results in the death of motor neurons

(Ilieva et al 2009). In vitro characterization of mutant, recombinant SOD1 supports this

hypothesis, as data suggest that the altered stability of mutant SOD1 proteins correlates with

faster disease progression (Pratt et al 2014; Shi et al 2016). However, the ability to effectively

analyze pathological SOD1 behavior in the central nervous system (CNS) of living, human

patients remains elusive, limiting the ability to confirm the biological relevance of in vitro

observations to ALS prognosis.

A key biochemical characteristic of mutant SOD1 protein cell culture (Borchelt et al

1994) and in vivo (Farr et al 2011) is accelerated turnover caused by protein instability compared

to wild-type (WT) SOD1. Our group also demonstrated that mutant G93A SOD1 protein exhibits

faster protein turnover, indicated by a shortened protein half-life, compared with WT SOD1

protein in the CNS of transgenic rat models. Moreover, misfolded G93A SOD1 protein displayed

a shorter half-life than the total G93A SOD1 protein pool. These observed effects in the brain

and spinal cord were mirrored in the CSF of these animals, suggesting that measures of protein

turnover within CSF are a sufficient proxy for CNS protein biology (Crisp et al 2015). Therefore,

differences in mutant and WT SOD1 half-life in CSF may provide insight into the identification

of misfolded SOD1 within the CNS of ALS patients for the first time.

53

Outside of ALSSOD1

, the role of SOD1 in sporadic ALS is controversial. In post-mortem

studies, multiple groups reported misfolded SOD1 in sporadic ALS spinal cord autopsies (Bosco

et al 2010; Forsberg et al 2010). However, others are unable to reproduce these results (Da Cruz

et al 2017). These findings have direct therapeutic implications, as SOD1-targeting therapies are

currently under development for the treatment of ALSSOD1

(Benatar et al 2018; Miller et al 2013),

but only ~2% of the ALS population carry SOD1 mutations. Further data that supports a

biological role for pathological, misfolded SOD1 in sporadic ALS would be an important finding

to broaden the utility of targeting SOD1 for therapeutic intervention in the 90% of ALS patients

with no genetic causes (Kiernan et al 2011).

We previously calculated the half-life of CSF SOD1 in normal, health human subjects as

~25 days using stable isotope labeling kinetics (SILK) analysis via tandem liquid

chromatography-mass spectrometry (LC-MS/MS) (Crisp et al 2015). In these studies, we

administered 13

C6-Leucine via the drinking water of participants fed a low leucine diet. However,

feedback from healthy control participants suggested dissatisfaction with this method of labeling.

Considering ALS patients already exhibit a compromised quality of life, it is imperative to

design clinical studies that ease the burden on the participant population, while still capturing

accurate measures protein turnover.

Here, we performed a preliminary analysis of an ongoing, multicenter study

(NCT03449212) to determine SOD1 protein half-life in ALS patients. In this study, we wish to

test the hypothesis that CSF SOD1 kinetics is altered in ALS by comparing: 1. Total SOD1

protein turnover between ALS participants and healthy controls, and 2. Mutant versus WT SOD1

protein turnover within ALS participants with SOD1 mutations. To improve participant

satisfaction, we employed a modified protocol to deliver 13

C6-Leucine via intravenous infusion

54

to patients over a 16 hour labeling period, and compared results to our previous 10 day oral

labeling paradigm data. In addition to understanding ALS pathobiology, calculating CSF SOD1

half-life will also be a key parameter to model pharmacokinetic-pharmacodynamics (PK-PD)

relationships of experimental SOD1-targeting therapies currently in clinical trials for the

treatment of ALSSOD1

.

METHODS

Human Subjects

This study involving human subjects was approved by the Washington University Human

Studies Committee and the Clinical Research Unit (CRU) Advisory Committee (an Institute of

Clinical and Translational Sciences (ICTS) Resource Unit) and the Institutional Review Board of

the Partners Human Research Committee at Massachusetts General Hospital. Written informed

consent was obtained from all participants prior to inclusion in the study.

The study population consists of subjects with: known SOD1 positive ALS status, SOD1

positive carriers without ALS, known C9ORF72 positive ALS status, C9ORF72 positive carriers

without ALS, sporadic ALS, and non-ALS (normal controls). Inclusion criteria includes (with

exceptions for each subgroup): males or females aged 18 years or older, positive for SOD1

mutation, positive for C9ORF72 repeat expansion, diagnosis of definite, probable, or possible

ALS in accordance with El Escorial Criteria (ALS only) (Westeneng et al 2018), able to hold

position and breathe comfortably for duration of lumbar puncture procedure. Exclusion criteria

for all groups include: invasive ventilator dependence, medical inability to undergo lumbar

puncture, any active dermatologic disease, any connective tissue disease, any known or

suspected abnormal CSF pressure or intracranial/intraspinal tumors, use of anticoagulant

medication that cannot be safely withheld until coagulation parameters have normalized prior to

55

lumbar puncture and for up to a week following the lumbar puncture, blood dyscrasia, abnormal

bleeding diathesis, the use of dialysis for renal failure, safety lab values greater than 2X the

upper limit of normal, allergy to lidocaine, pregnancy, or any contradiction for lumbar puncture.

13C6-Leucine Administration and Sample Collection

Participants were admitted to the Clinical Research Unit (Center for Applied Research

Sciences, Washington University), received a low-leucine diet (Washington University Research

Kitchen), and administered an intravenous infusion of 10 g 13

C6-leucine (Cambridge Isotope

Laboratories CLM-2262, Cambridge, MA) at a rate between 4 – 10 mg/kg/hr dependent on the

participant’s weight for 16 hours. Venous blood draws were collected at designated time points

during the infusion period. CSF, venous blood, and urine were collected over five follow up

lumbar punctures (LPs) from day 8 after infusion through day 120.

Participants from Massachusetts General Hospital and select participants from

Washington University received 10 g of 13

C6-Leucine via the oral labeling method described

previously(Crisp et al 2015). Briefly, participants consumed 13

C6-leucine after dissolving 330 mg

13C6-leucine in 120 mL of tap water + Kool-Aid® flavored powder three times per day (total

daily dose = 990 mg) for 10 days. Overnight fasting blood was collected at day 1, 4, 8, and 10 of

the 13

C6-leucine labeling period. CSF via lumbar puncture and venous samples were collected at

four designated time points between 14-85 days after 13

C6-leucine labeling was initiated.

Blood was centrifuged at 1000xg for 10 minutes at room temperature, and 500 uL plasma

was aliquoted into low-binding 1.7 mL tubes (Axygen, MCT-175-L-C) and frozen on dry ice.

CSF (20-25 mL) was centrifuged at 1000xg for 10 minutes at 4°C, and 1 mL aliquots were

56

frozen on dry ice. Urine (10 mL) was divided into 1 mL aliquots and frozen on dry ice. All

samples were stored at -80°C before use.

Isolation and mass spectrometric analysis of CSF SOD1, free 13

C6-Leucine in CSF and Plasma,

and 13

C6-Leucine labeled total protein

SOD1 was immunoprecipitated from 1 mL of CSF using 50 uL of M-270 Epoxy

Dynabeads (Invitrogen, Carlsbad, CA) coupled to anti-SOD1 (mouse monoclonal; Millipore

Sigma S2147, Burlington, MA) supplemented with 0.1% Tween-20 and protease inhibitors for 2

hours at room temperature. Before immunoprecipitation, 100 ng of N15-labeled recombinant

SOD1 (ProSpec-Tany Technogene, Rehovot, Israel) was added to each sample. In SOD1 mutant

carriers, an additional 50 ng of mutant recombinant SOD1 was added to each sample. Protein

samples were washed with 25 mM ammonium bicarbonate, reduced with DL-Dithiothreitol in

100 mM triethylammonium bicarbonate for 30 minutes at 37°C, alkylated with iodoacetamide

for 30 minutes at RT in the dark, and washed two times with 25 mM ammonium bicarbonate.

SOD1 was eluted from magnetic beads using 50 uL of formic acid. The formic acid eluent was

transferred to a new tube and lyophilized via speed vacuum (Labconco, CentriVap), washed with

100 uL methanol, and lyophilized again via speed vacuum. Dry samples were resuspended in 20

uL of 50 mM triethylammounium bicarbonate, pH 8.8.

Proteolytic digestion was performed on resuspended samples with either 600 ng of

Endoproteinase GluC (Millipore Sigma 11047817001) at 25°C for 16 hours, or a combination

digest with 250 ng LysC (Millipore Sigma 11047825001) at 37°C for 6 hours followed by 250

ng Trypsin (Millipore Sigma 11418475001) at 37°C for 12 hours. Samples were desalted and

washed with TopTipC18 10-200 uL Tip-Columns (Glygen TT2C18.96, Columbia, MD),

57

lyophilized via speed vacuum, and resuspended in 25 uL of 2% acetonitrile/0.1% formic acid

prior to liquid-chromatography-triple quadrupole-mass spectrometry analysis (Xevo, Waters).

The 13

C6-Leucine/12

C6-Leucine tracer-to-tracee ratio (TTR) of SOD1 peptides was

quantified by comparing the area under the curves for each peptide in the presence or absence of

13C6-Leucine. SOD1 TTR measurements were calibrated using a

13C6-leucine labeled SOD1

standard curve derived from HEK cell lysates, as described previously (Crisp et al 2015). SOD1

protein concentration was quantified by comparing the area under the curve of CSF SOD1

peptide to the recombinant, N15-labeled peptide (N14-SOD1:N15-SOD1), and calibrated to a

recombinant SOD1 protein standard curve. See Supplementary Tables 1 and 2 for a summary of

peptides analyzed.

Plasma free, plasma protein-bound, and CSF protein-bound 13

C6-leucine was determined

by precipitation of proteins with 10% tricholoroacetic acid overnight at 4°C, followed by

capillary chromatography-negative chemical ionization-quadrupole-mass spectrometry (Agilent

6890N Gas Chromatograph and Agilent 5973N Mass Selective Detector) as described previously

(Potter et al 2013).

Compartmental Modeling

Modeling was conducted using SAAM II (The Epsilon Group, Charlottesville, NC).

Kinetic data for plasma free 13

C6-leucine, CSF total protein, plasma total protein, and CSF SOD1

species for each subject were incorporated into a compartmental model that uses the shape of the

plasma leucine enrichment time course as a “front end” to describe the enrichment time course

shapes for the product proteins (Supplemental Figure 1). Each protein (CSF SOD1, CSF total

protein, and plasma total protein) is assumed to be at steady state over the ~120 day time course.

58

The total rate of production of each protein was thus assumed to be constant over time, but the

rate at which isotopically labeled protein is synthesized was dependent on the mole fraction of

isotopically labeled plasma leucine monitored over the ~120 day period. The model describes the

turnover of each protein as unlabeled protein is initially replaced with labeled protein, and then

labeled protein is cleared and replaced with unlabeled protein. It was necessary to assume that

each protein was kinetically heterogeneous, consisting of a mixture of faster and slower turning

over components, in order to optimally fit the shapes of each protein enrichment time course. In

addition, the slower component of SOD1 was optimally described as a minimal 2-compartment

delay chain, i.e. two compartments in series. All CSF SOD1 peptides analyzed were used as

individual replicates at each time point for modeling. All TTR values were converted to mole-

fraction label (MFL) for modeling using the following equation:

Modeling results are reported as fractional turnover rates (FTR, pools/d; the fraction of the pool

turning over per day) and an estimate of the error (reported as a coefficient of variation) for each.

Protein half-life was then calculated by the following equation:

Statistical Analysis

All quantitative results are expressed as mean ± standard deviation. Due to low sample

sizes within each subgroup examined (n = 2-3), no formal statistical tests were performed.

59

RESULTS

Participant Demographics

To date, 12 individual participants initiated the protocol (Table 3.1). Of these, 1

participant withdrew from the study due to complications associated with the lumbar puncture

procedure, resulting in a current study completion rate of over 90% (11/12). The mean

participant age was 54.5 ± 9.5 years, and the majority of participants were male (75%, 9/12) and

Caucasian (92%, 11/12). Of the 11 participants analyzed, 1 received the 10 day oral labeling

protocol of 13

C6-Leucine, while the remaining received a 16 hour IV infusion of 13

C6-Leucine.

Protein Kinetics in Participants via 16 hour, 13

C6-Leucine intravenous infusion

We employed a labeling protocol where we administered 10g of 13

C6-Leucine via IV

infusion over a 16-h time period, with subsequent blood draws up to 24 hours from label

administration. An example of the tracer kinetics observed for participants with this protocol is

seen in Figure 3.1. Over the five time points between 7 – 120 days when we collected CSF,

maximum 13

C6-Leucine enrichment in CSF total protein and SOD1 approaches ~1% and decays

over time (Figure 3.1B). Calculations of CSF SOD1 half-life in normal, healthy controls by

compartmental modeling confirmed CSF SOD1 is a long-lived protein, with half-life on the

order of weeks. This is similar to our previous calculations with the 10 day oral labeling method

(Figure 3.1B, Table 3.2). We also calculated the half-life of CSF total protein on order of

weeks, which is similar to previous measures of the predominant protein found in human CSF-

human serum albumin (half-life of 15-20 days) (Iwao et al 2006). Taken together, these data

validate the utility of our 16 h- IV infusion protocol to assay CSF protein kinetics.

60

Participant Disease Type Disease Stage Labeling

Method Age Race Gender

Control 1

16 hour IV 60 Caucasian Female

Control 2

16 hour IV 51 Caucasian Male

Control 3

16 hour IV 43 African

American Female

ALS 1 SOD1 (A89V) Symptomatic 10 day oral 48 Caucasian Male

ALS 2 SOD1 (E100K) Symptomatic 16 hour IV 49 Caucasian Male

ALS 3 SOD1 (A4V) Asymptomatic 16 hour IV 52 Caucasian Male

ALS 4* SOD1 (A4V) Asymptomatic 10 day oral 37 Caucasian Male

ALS 5 Sporadic Symptomatic 16 hour IV 64 Caucasian Male

ALS 6 Sporadic Symptomatic 16 hour IV 62 Caucasian Male

ALS 7 Sporadic Symptomatic 16 hour IV 56 Caucasian Male

ALS 8 C9ORF72 Symptomatic 16 hour IV 69 Caucasian Female

ALS 9 C9ORF72 Asymptomatic 16 hour IV 63 Caucasian Male

* Participant withdrew from study after first lumbar puncture

Table 3.1 Participant Demographics

61

0 .0 0 .2 0 .4 0 .6 0 .8 1 .0

0 .0

T im e (d a y s )

Mo

le F

ra

cti

on

La

be

l (M

FL

)

0 .10

0 .20

0 .30

0 .40

0 .50

P la s m a F re e L e u c in e

P la s m a T o ta l P ro te in

1 2 0

T im e (d a y s )

Mo

le F

ra

cti

on

La

be

l (M

FL

)

7 14 31 63

0 .005

0 .010

0 .015

0 .030

0 .045 C S F S O D 1 : t1 /2 = 1 7 .1 d a y s

C S F T o ta l P ro te in : t1 /2 = 1 6 .4 d a y s

P la s m a F re e L e u c in e

P la s m a T o ta l P ro te in : t1 /2 = 4 .1 d a y s

1

A .

B .

Figure 3.1. Representative Kinetic Curve of a participant labeled with 10 g 13

C6-Leucine

via 16 hour intravenous infusion.

(A.) 13

C6-Leucine plasma enrichment during the first 24 hours of procedure. Peak 13

C6-Leucine

enrichment in plasma free leucine is observed at end of 16 hour labeling period near 40% of total

leucine pool, with rapid clearance of 13

C6-leucine maximum incorporation into plasma protein

observed within the first 8 hours of the chase period. (B.) 13

C6-Leucine enrichment in CSF and

plasma proteins over time. Representative protein half-life values (t1/2) calculated from

compartmental modeling corresponds to participant Control 1.

62

Total CSF SOD1 Behavior in ALS

To analyze total CSF SOD1 protein behavior in ALS, we categorized participants into

four groups: SOD1 mutation carriers, sporadic ALS, C9ORF72 repeat expansion carriers, and

healthy controls (Table 3.2). In addition to ALSSOD1

and sporadic ALS groups, analyzing CSF

SOD1 protein in the C9ORF72 group serves as a negative control to test for specificity of altered

SOD1 behavior, as SOD1 pathology is not implicated in the C9ORF72 disease population.

Finally, the healthy control group serves as a control to identify differences in SOD1 protein

behavior due to disease pathology. At this time, the sample sizes of these individual subgroup are

not sufficient to determine any statistically significant differences between the groups (n = 2 – 3

per group).

Normal control subjects had an average CSF SOD1 protein concentration of 164.7 11.2

ng/mL, with an average CSF SOD1 protein half-life of 20.6 8.5 days. The ALS groups (SOD1,

sporadic, and C9ORF72) appear to show elevated CSF SOD1 concentration compared with

normal controls, ranging from 191.0 6.5 ng/mL (C9ORF72) to 270.5 62.5 ng/mL (SOD1).

Moreover, average CSF SOD1 half-life in ALS groups ranged between 25.0 7.1 (sporadic) to –

29.3 20.2 days (SOD1). Due to the variable nature and small sample size in these groups, no

we cannot draw definitive conclusions that suggest there are clear changes in in total CSF SOD1

protein turnover in ALS versus controls.

.

63

CSF SOD1 CSF Protein Plasma Protein

Group Concentration

(ng/mL ± SD)

Half-Life

(Days ± SD)

Half-Life

(Days ± SD)

Half-Life

(Days ± SD)

SOD1*

(n=3) 270.5 ± 62.5 29.3 ± 20.2 30.5 ± 9.2 3.4 ± 0.5

Sporadic+

(n =3) 247.9 ± 98.7 25.0 ± 7.1 29.3 ± 0.7 4.1 ± 0.7

C9ORF72& (n= 2)

191.0 ± 6.5 26.6 ± 14.4 37.2 ± 12.2 3.8 ± 0.7

Control#

(n = 3) 164.7 ± 11.2 20.6 ± 8.5 22.7 ± 6.1 4.8 ± 1.0

* Participants with confirmed SOD1 mutations (ALS 1, ALS 2, ALS 3)

+ Symptomatic ALS participants without identified mutations (ALS 5, 6, 7)

& Participants with confirmed hexanucleotide repeat expansion in the C9ORF72 gene (ALS 8, ALS9)

# Healthy participants without neurological disease (Control 1, Control 2, Control 3)

Table 3.2 Protein Behavior in participants grouped by mutation and disease status

64

Mutant versus Wild-Type CSF SOD1 in SOD1 Mutation Carriers

Previous efforts to characterize CSF SOD1 protein concentration and turnover analyzed

the entire SOD1 protein pool. However, three participants had genetically confirmed point

mutations in the SOD1 gene with a familial history of disease (Table 3.1). These individuals

carry one copy of the mutant SOD1 gene, and one copy of WT SOD1, and an advantage of our

mass spectrometry-based SILK analysis is the ability to distinguish these two proteins within

CSF. Thus, we are able to test hypotheses related to differential behavior in both forms of the

protein with regards to SOD1 protein concentration and half-life. Two participants were

previously diagnosed with ALS and were similar in age (48 and 49 years old). Interestingly,

natural history data suggests the median disease duration in these two mutant SOD1 carriers is

similar (A89V median survival: 8 years, E100K median survival: 9.1 years, (Bali et al 2017)).

An additional participant carried an A4V mutation, the most common mutation in the North

American population, with rapidly aggressive disease progression (median survival: 1.2 years

(Bali et al 2017)).

Average WT CSF SOD1 protein concentration ranged from 96.4 ng/mL to 214.4 ng/mL

in participants (Table 3.3). In symptomatic participants carrying A89V and E100K mutations,

the concentration of mutant CSF SOD1 protein appeared elevated relative to WT CSF SOD1,

while the half-life of mutant and WT CSF SOD1 were similar within each individual patient.

Contrary to these findings, the asymptomatic participant carrying an A4V mutation showed a ~9-

fold decrease in mutant CSF SOD1 concentration. Due to limitations in instrument sensitivity,

SOD1 A4V half-life could not be calculated. These discrepancies appear to be independent from

global changes in protein metabolism, as the plasma total protein half-life is similar between all

65

patients (Table 3.3). Thus, these data suggest that different SOD1 mutations may exhibit

differential protein behavior within human CSF.

66

CSF WT SOD1 CSF Mutant SOD1

Participant Mutation

Concentration

(ng/mL)

Half-Life

(Days)

Concentration

(ng/mL)

Half-Life

(Days)

ALS 1 A89V 137.7 26.3 205 25.7

ALS 2 E100K 96.4 31.6 135.4 28.8

ALS 3 A4V 214 25.3 23.6 -

Table 3.3 CSF SOD1 protein in participants with SOD1 mutations

67

DISCUSSION

These data provide the first insight into SOD1 protein kinetics in the central nervous system

of patients with ALS. Although limited in overall interpretation due to small sample size, this

preliminary analysis provides a foundation for future data interpretation when the remaining 15

currently enrolled and future participants complete this study. To our knowledge, this study is

also the first examination of mutant versus WT protein kinetics in an autosomal dominant

neurological disorder. These findings are valuable to understand disease pathobiology in human

ALS. Further, these data also provide crucial values for parameters in randomized control

clinical trial design to test new for this devastating disease.

Our previous efforts to calculate SOD1 protein half-life in human CSF employed an oral

labeling method with 13

C6-Leucine (Crisp et al 2015). However, the 16-hour infusion protocol

described here provides multiple advantages over an oral labeling paradigm for tracer-based

kinetic analysis. From an analytical perspective, our IV protocol allows for a higher sampling of

plasma leucine time points during the 13

C6-Leucine labeling period, as previous measurements of

13C6-Leucine input into plasma were sampled with 2-3 blood draws over a 10-day period. This

higher sampling frequency limits the variance in tracer input modeling, improving the quality of

fit of the model to data. Our kinetic analysis calculated CSF total protein half-life on the order of

20-30 days, which is relatively similar to the previously half-life of human serum albumin, the

predominant protein found in Human CSF, between 15-20 days (Iwao et al 2006). Importantly,

the 16 hour IV protocol is less burdensome for patients compared to adhering to a strict diet and

self-administered labeling paradigm over a 10 day labeling period. Therefore, controlled

administration of 13

C6-Leucine with IV infusion may limits variability in study compliance and

68

improves the overall participant experience in an already invasive clinical study associated with

multiple lumbar punctures.

Calculations of SOD1 protein half-life are directly applicable to current clinical trials in ALS,

as multiple experimental therapeutics target SOD1 by inhibiting protein synthesis (Lange et al ;

Miller et al 2013) or promoting protein degradation (Benatar et al 2018). For example, Antisense

Oligonucleotides that target SOD1 mRNA for degradation rely on measures of CSF SOD1

protein concentration as a biomarker to assay target engagement of ASO with SOD1 mRNA in

the central nervous system of patients (Winer et al 2013). However, decreases in CSF SOD1

protein concentration also rely on clearance of SOD1 protein that is unaffected by ASO

treatment, and this rate of protein clearance is determined by protein half-life. Therefore, our

calculations of CSF SOD1 half-life as ~25 days here and in previous work (Crisp et al 2015)

establish a critical parameter to understand the relationship between SOD1 ASO treatment and

maximum lowering of CSF SOD1 protein concentration (Figure 3.2).

Our preliminary results show similar values of total SOD1 protein half-life between

participants with SOD1 mutations and controls. However, comparison of mutant versus WT

SOD1 protein in an A4V SOD1 participant suggests that A4V SOD1 protein exhibits altered

protein behavior compared to WT SOD1. An attractive hypothesis to explain this phenomenon is

that SOD1 half-life correlates to the overall stability of the protein, suggesting that alterations in

protein half-life indicate protein misfolding. Indeed, these results have been observed with

different SOD1 mutants in cell culture (Borchelt et al 1994), and measures of mutant SOD1

protein stability in patient red blood cells appear to correlate with rate of disease progression

(Sato et al 2005). Interestingly, the half-life of E100K and A89V SOD1 in two participants

showed similar measures of protein half-life to WT SOD1, suggesting mutant-specific

69

differences in SOD1 kinetics between ALS participants. However, the small sample size of this

experiment, combined with an inability to confirm the misfolded state of CSF SOD1 protein

leaves any interpretation of these results at this stage to be speculative. Our mass spectrometry-

based approach has multiple advantage for future studies, as we are able to interrogate multiple

forms of SOD1 protein within a sample. In addition to distinguishing mutant versus WT SOD1

protein, future investigation should also search for posttranslational modifications of CSF SOD1

that have been implicated in forms of pathological SOD1, such as deamidation (Schmitt & Agar

2017), SUMOylation (Niikura et al 2014), and disulfide reduction (Bosco et al 2010; Nagano et

al 2015; Tiwari & Hayward 2003).

Similar efforts to analyzed CSF SOD1 half-life can be employed to investigate the potential

role of SOD1 in sporadic ALS. Such data will contribute to resolving the controversial

identification of misfolded, WT SOD1 in post-mortem tissue sections (Bosco et al 2010; Da

Cruz et al 2017; Kerman et al 2010) or by CSF ELISA (Zetterstrom et al 2011). An important

consideration in developing experiments to identify changes in CSF SOD1 protein behavior is to

ensure that alterations in CSF SOD1 protein concentration or half-life are specific to sporadic

ALS. For example, Zetterstrom and colleagues failed to distinguish differences in misfolded

SOD1 between ALS patients and normal controls, questioning the validity of their misfolded

SOD1 ELISA. To help control for changes observed in CSF SOD1 protein in sporadic ALS,

future studies will also measure CSF SOD1 concentration and half-life in other neurological

disease states, such as Alzheimer’s Disease and Parkinson’s Disease. These participants will help

to control for any effects observed due to overall neurodegeneration. An additional consideration

is the overall variance in CNS protein metabolism individual subjects. Thus, incorporation of

other CSF protein half-life measures such as microtubule-associated protein tau (Sato et al 2018),

70

0 5 0 1 0 0 1 5 0 2 0 0

0

5 0

1 0 0

T im e (d a y s )

SO

D1

Pro

tein

(%

of

init

ial)

S O D 1 P ro te in : t1 /2 = 2 5 d a y s

S O D 1 A S O C o n c e n tra tio n

T im e to m a x im u m in h ib it io n

1 2 3 d a y s

Figure 3.2 Theoretical modeling of CSF SOD1 protein levels after ASO treatment.

Indirect response model used to simulate SOD1 protein levels after 4 doses of SOD1 antisense

oligonucleotides using a 1 compartment drug model with inhibition of input response model:

Where Kin = 1, Kout = SOD1 half-life (25 days), IC50 = 10, and Cp is the SOD1 ASO

concentration. SOD1 protein concentrations were normalized to % of initial value. Drug (SOD1

Antisense Oligonucleotide) tissue concentrations simulated after 4 bolus doses administered on

Days 0, 28, 56, and 84. Assumed drug t1/2 = 30 days.

71

apolipoprotein E (Wildsmith et al 2012), and alpha synuclein (Fanara et al 2012) will provide

valuable information to distinguish the specificity of SOD1 changes in protein turnover for ALS

participants. These data will provide valuable information for the field to determine the utility of

SOD1-targeting therapeutics in the remaining 98% of ALS patients without SOD1 mutations.

In sum, our work characterizing CSF SOD1 half-life will lead to a better understanding of

ALS biology, while also helping to optimize clinical trial design advance SOD1-targeted

therapies through the therapeutic pipeline for ALS.

ACKNOWLEDGEMENTS

We thank Jennifer Jockel-Balsarotti, Debbie King, Jesse Markway, Amber Malcolm,

Robert Bucelli, Arun Varadhachary, and members of the CRU at Washington University School

of Medicine for their efforts in participant recruitment, lumbar puncture and blood draw

procedures, and clinical management of this study. We also thank Drs. James Bollinger and

Kwasi Mawuenyega of Dr. Randall Bateman’s lab, Drs. Cheryl Frankfater, John Turk, and Bruce

Patterson for continued collaboration in our LC-MS/MS, GC-MS, and kinetic modeling analyses,

respectively. Research reported in this publication was supported by the Washington University

Institute of Clinical and Translational Sciences grant UL1TR002345, sub-award TL1TR002344

from the National Center for Advancing Translational Sciences (NCATS), and the National

Institute for Neurological Disorders and Stroke grant R-01-NS098716-01 (TM Miller, PI) from

the National Institutes of Health.

72

Supplementary Figure 3.1 Compartmental model for kinetic data using plasma free 13

C6-

Leucine (input), CSF total protein, plasma total protein, and CSF SOD1 species for each

subject to caclulate protein FTR.

73

Peptide Sequence Charge Label Precursors

Ions Product Ion

Cone

Voltage

(V)

Collision

Energy (V)

24SNGPVKVWGSIKGLTE

39 +++ WT 557.9737 547.3086 35 19

707.4087 35 14

735.9194 35 14

804.4462 35 14

13C6-Leu 559.9804 553.3287 35 19

710.4187 35 14

738.9294 35 14

810.4663 35 14

N15 564.6206 553.2908 35 19

715.3849 35 14

744.3942 35 14

813.4195 35 14

40GLHGFHVHE

48 ++ WT 516.7541 308.1717 35 13

649.3205 35 18

725.3365 35 18

748.3889 35 18

13C6-Leu 519.7642 314.1918 35 13

655.3406 35 18

725.3365 35 18

754.409 35 18

N15 524.2319 313.1569 35 13

659.2909 35 18

735.3069 35 18

759.3563 35 18

79RHVGDLGNVTADKDGVADVSIE

100 +++ WT 756.3803 425.2199 35 26

474.7541 35 26

565.2841 35 31

910.9479 35 21

13C6-Leu 758.387 428.23 35 26

477.7642 35 26

565.2841 35 31

913.958 35 21

N15 766.0183 432.1992 35 26

482.2319 35 26

575.2545 35 31

923.4109 35 21

101DSVISLSGDHC[+57.0]IIGRTLVVHE

121 +++ WT 769.7302 302.1347 35 26

74

Peptide Sequence Charge Label Precursors

Ions Product Ion

Cone

Voltage

(V)

Collision

Energy (V)

846.9279 35 21

946.986 35 21

1003.528 35 21

13C6-Leu 771.7369 846.9279 35 21

849.938 35 21

949.9961 35 21

1006.5381 35 21

N15 779.0359 305.1258 35 26

857.8953 35 21

958.9504 35 21

1015.991 35 21

122KADDLGKGGNEE

133 ++ WT 616.7913 315.1663 35 22

430.1932 35 22

690.3053 35 22

803.3894 35 22

13C6-Leu 619.8014 315.1663 35 22

430.1932 35 22

690.3053 35 22

809.4095 35 22

N15 624.2691 319.1544 35 22

624.2691 435.1784 35 22

624.2691 699.2786 35 22

624.2691 813.3597 35 22

134STKTGNAGSRLAC[+57.0]GVIGIAQ

153 +++ WT 654.3459 681.3331 35 22

730.8674 35 22

815.9201 35 17

872.4622 35 17

13C6-Leu 656.3527 684.3432 35 22

733.8774 35 22

818.9302 35 17

875.4722 35 17

N15 662.9869 690.805 35 22

740.8377 35 22

826.8875 35 17

883.9281 35 17

Supplementary Table 3.1 Transition ions used for SOD1 tandem liquid chromatography-

mass spectrometry analysis using GluC digestion protocol.

75

Peptide Sequence Charge Label Precursors

Ions

Product

Ion

Cone Voltage

(V)

Collision Energy

(V)

4AVC[+57.0]VLK

9 (WT) ++ WT 345.2044 260.1969 35 16

359.2653 35 16

519.2959 35 10

13C6-Leu 348.2144 266.217 35 16

365.2854 35 16

525.3161 35 10

N15 348.694 263.188 35 16

363.2534 35 16

524.2811 35 10

79

HVGDLGNVTADK91

++ WT 613.3122 333.1769 35 22

434.2245 35 22

533.293 35 22

647.3359 35 22

817.4414 35 22

989.4948 35 22

13C6-Leu 616.3223 333.1769 35 22

434.2245 35 22

533.293 35 22

647.3359 35 22

823.4615 35 22

995.5099 35 22

N15 621.2885 337.165 35 22

439.2097 35 22

539.2752 35 22

655.3122 35 22

827.4188 35 22

1001.452 35 22

116TLVVHEK

122 ++ WT 413.2541 276.1554 35 14

306.1792 35 14

413.2143 35 14

512.2827 35 14

611.3511 35 14

13C6-Leu 416.2551 276.1554 35 14

306.1792 35 14

413.2143 35 14

512.2827 35 14

611.3511 35 14

N15 418.2303 279.1465 35 14

76

Peptide Sequence Charge Label Precursors

Ions

Product

Ion

Cone Voltage

(V)

Collision Energy

(V)

310.1673 35 14

419.1965 35 14

519.262 35 14

619.3274 35 14

4VVC[+57.0]VLK

9 ++ A4V 359.22 260.1969 35 18

519.2959 35 14

618.3643 35 14

13C6-Leu 362.2301 266.217 35 18

525.3161 35 14

624.3845 35 14

N15 362.7096 263.188 35 18

524.2811 35 14

624.3466 35 14

79HVGDLGNVTVDK

91 ++ A89V 627.3279 361.2082 32 22

462.2558 32 22

561.3243 32 22

675.3672 32 22

845.4727 32 22

1017.5211 32 22

13C6-Leu 630.3379 361.2082 32 22

462.2558 32 22

561.3243 32 22

675.3672 32 22

851.4928 32 22

1023.5412 32 22

N15 635.3041 365.1963 32 22

467.241 32 22

567.3065 32 22

683.3435 32 22

855.4431 32 22

1029.4855 32 22

101DSVISLSGDHC[+57.0]IIGR

115 +++ E100K 543.6069 507.7429 28 14

607.801 28 14

664.343 28 16

713.8772 28 14

13C6-Leu 545.6136 507.7429 28 14

610.811 28 14

667.3531 28 16

716.8873 28 14

N15 550.2538 514.7222 28 14

77

Peptide Sequence Charge Label Precursors

Ions

Product

Ion

Cone Voltage

(V)

Collision Energy

(V)

615.7772 28 16

672.8178 28 14

722.8505 28 14

Supplementary Table 3.2 Transition ions used for SOD1 tandem liquid chromatography-

mass spectrometry analysis using LysC/Trypsin digestion protocol for mutant peptide

detection

78

Chapter 4: Protein Production is an Early Biomarker for RNA-

Targeting Therapies

79

ABSTRACT

Clinical trials for progressive neurodegenerative disorders such as Alzheimer’s Disease

and Amyotrophic Lateral Sclerosis have been hindered due to the absence of effective

pharmacodynamics markers to assay target engagement. We tested whether measurements of

new protein production would be a viable pharmacodynamics tool for RNA-targeted therapies.

To test this hypothesis, transgenic animal models expressing human proteins implicated in

neurodegenerative disorders -- microtubule-associated protein tau (hTau) or superoxide

dismutase-1 (hSOD1) -- were treated with antisense oligonucleotides (ASOs) delivered to the

central nervous system to target these human mRNA transcripts. Simultaneously, animals were

administered 13

C6-leucine via drinking water to measure new protein synthesis after ASO

treatment. Measures of new protein synthesis and protein concentration were assayed at

designated time points after ASO treatment using targeted proteomics. ASO treatment lowered

hTau mRNA and protein production (measured by 13

C6-leucine-labeled hTau protein) earlier

than total hTau protein concentration in transgenic mouse cortex. In the CSF of hSOD1

transgenic rats, ASO treatment lowered newly generated hSOD1 protein driven by decreases in

newly synthesized hSOD1 protein, not overall protein concentration 30 days after treatment. At

later time points, decreases in newly generated protein driven by overall lowering of protein

concentration, not the rate of newly synthesized protein. Taken together, measures of newly

generated protein show earlier pharmacodynamics changes for RNA-lowering therapeutics

compared with total protein concentration. However, a therapeutic window exists to measure

new protein production before pharmacodynamics effects reach steady state. Measuring new

protein production in CSF may be a promising early pharmacodynamics marker for RNA-

targeted therapeutics.

80

INTRODUCTION

The number of persons aged 65 and older globally is projected to triple by the year 2050

(WHO 2012), increasing the number of people living with adult-onset, progressive

neurodegenerative diseases in the population. There is a dire need to develop novel therapeutics

for these conditions, as current FDA-approved drugs do little more than manage symptoms, and

no treatments exist to reverse disease pathology. Despite promise of new therapies in preclinical

models over the past 30 years, these compounds have failed in Amyotrophic Lateral Sclerosis

(ALS) and Alzheimer’s Disease (AD) clinical trials at rates greater than 95 and 99%,

respectively (Cummings et al 2014; Petrov et al 2017). One important way to improve the

current treatment landscape is to focus on the development of novel methods to determine

whether a therapeutic has engaged the intended target in human clinical trials.

A central hypothesis in neurodegenerative disease is that the accumulation of misfolded

proteins over time imparts selective toxicity to specific neuronal populations, causing

progressive neurodegeneration and manifestation of symptoms. This pathological misfolding of

proteins in AD (Braak & Braak 1995), ALS (Neumann et al 2006; Shibata et al 1996),

Parkinson’s Disease (PD) (Bethlem & Den Hartog Jager 1960), and Huntington’s Disease (HD)

(DiFiglia et al 1997) suggests that an approach to inhibit the production of toxic proteins within

the cell may be an effective therapy. One such approach includes lowering levels of RNA to

decrease protein translation using molecules such as antisense oligonucleotides (ASOs) or small

interfering RNA (siRNA). ASOs are short, DNA-like molecules that can be designed to

selectively bind mRNA transcripts and cause the catalytic destruction of particular mRNAs

(DeVos & Miller 2013a). ASOs that lower mRNA levels and thus decrease translation of

proteins prone to misfolding show great promise in preclinical models of tauopathy (DeVos et al

81

2017), ALS (Becker et al 2017; Donnelly et al 2013; Smith et al 2006), and HD (McBride et al

2011), and are relatively safe in humans (Miller et al 2013). These promising data have led to the

launch of multiple clinical trials for ASOs in neurodegenerative diseases (NCT03225846,

NCT02623699, NCT02519036, NCT03186989).

For RNA-targeted therapeutics, it is crucial to establish measurements that validate

compound engagement with its desired biological target, mRNA (Mitsumoto et al 2014).

However, it is challenging to isolate mRNA from cerebrospinal fluid (CSF), the primary biofluid

available to assay target engagement in the human central nervous system (CNS). Because

mRNA translation leads to the production of new protein, protein-based biomarkers in CSF

represent an attractive avenue to pursue target engagement for RNA-targeted therapeutics. In this

study, we used nonradioactive, stable isotope labeling with an essential amino acid (Bateman et

al 2007; Bateman et al 2006) to identify inhibition of new protein production after ASO

treatment. Using two preclinical models of ASO targets in clinical trials for neurodegenerative

disease, we tested the hypotheses that stable isotope labeling with 13

C6-Leucine will: 1) identify

pharmacodynamics at an earlier time point than protein concentration-based measures and 2)

show measurable effects in CSF to support translation of this assay approach from animal

models to human CNS. These results would support the use of new protein synthesis

measurements in clinical trials for RNA-lowering therapies in neurodegenerative diseases.

82

METHODS

Transgenic Animals

Transgenic mice expressing human tau (hTau) (Andorfer et al 2003) (Jackson

Laboratories, Bar Harbor, ME) were maintained through in-house breeding on a C57/Bl6

background. Transgenic rats expressing human superoxide dismutase-1 (hSOD1WT

) (Chan et al

1998) were provided by Pak Chan (Stanford University) and maintained through in-house

breeding on a Sprague-Dawley background. Genotyping was performed by PCR amplification of

tail DNA (Supplementary Table 1). All breeding and experimental protocols were approved by

the Institutional Animal Care and Use Committee at Washington University and conducted

according to the NIH Guide for Care and Use of Laboratory Animals.

Antisense Oligonucleotides (ASOs)

ASOs that recruit RNase-H to degrade target mRNA and an inactive, control ASO with

no sequence specificity were provided by Ionis Pharmaceuticals (DeVos et al 2017; McCampbell

et al 2018) (Table 4.1). All ASOs were diluted in artificial CSF before use.

Surgical Procedures

All animals were anesthetized by 2-5% inhalant isoflurane, received constant isoflurane

flow during the procedure, and were given an injection of bupivacaine prior to incision.

Following the surgical procedure, animals received carprofen and were placed on a 37°C heating

pad for recovery.

For intracerebroventricular injections, 60-day-old hTau transgenic mice received 300 µg

of ASO at -1.0mm M/L, -0.3mm A/P, -3.0mm D/V from Bregma (DeVos & Miller 2013b).

83

ASO Sequence/Chemistry

hTau ASO CCoGoToTTTCTTACCAoCoCT

hSOD1 ASO TToAoATGTTTATCAoGoGAT

Inactive ASO CCoToAoTAGGACTATCCAoGoGoAA

Table 4.1. Antisense Oligonucleotides

Oligonucleotides containing phosphothioate backbone modifications: unmodified phosphodiester

linkage (red o), 2’-O-methoxyethylribose, (orange) and constrained ethyl (blue) groups. All

cytosine residues are 5’methylcytosine.

84

Similarly, 120-day-old hSOD1 transgenic rats received 1000 µg of ASO injected at -1.4mm

M/L, -0.4mm A/P, -3.5mm D/V from Bregma. For intrathecal ASO injection, 120-day-old

hSOD1 transgenic rats received 1000 µg of ASO between the L3 and L5 vertebrae (Mazur et al

2017). Both male and female transgenic animals were used in these studies, and researchers were

blinded to the identity of treatment throughout the duration of the experiment.

13C6-Leucine Oral Labeling and Tissue Collection

Methods for oral labeling in rodents with 13

C6-Leucine were described previously using

unlabeled L-leucine or labeled, 13

C6-L-Leucine (Cambridge Isotope Laboratories, Cambridge,

MA) dissolved in water at 5 mg/mL (Crisp et al 2015). Labeling was consistent in all

experiments between groups (Supplementary Figure 1).

At indicated time points after surgery, animals were anesthetized with isoflurane and

perfused with cold phosphate buffered saline (PBS) containing 0.03% heparin. Before perfusion,

CSF was extracted from rat cisterna magna via a 23x¾” winged infusion butterfly needle

(Terumo, Somerset, NJ). Blood was collected from mechanically ruptured vena cavae at

beginning of perfusion, and the brain and spinal cord were harvested after perfusion. All samples

were flash frozen in liquid nitrogen and stored at -80 °C before use.

mRNA Isolation and Analysis

mRNA isolation from flash-frozen tissues was performed using the RNeasy® Mini Kit

(Qiagen, Venlo, Netherlands). The EXPRESS One-Step Superscript qRT-PCR Universal Kit

(Invitrogen, Carlsbad, CA) was used for reverse transcription and amplification of SOD1 and

MAPT mRNA transcripts. Comparative analysis by the ΔΔCt method, with GAPDH mRNA

transcripts as an internal control, was performed with the ABI PRISM 7500 Fast Real-Time

System (Applied Biosystems, Waltham, MA) (Supplementary Table 1).

85

N15 Recombinant Protein Standards

N15-labeled, recombinant hTau protein (isoform 2N4R 411) was a generous gift from

Drs. Isabelle Huvent and Guy Lippens (Université des Sciences et Technologie de Lille 1,

France).

Recombinant hSOD1 was produced in Rosetta 2 E Coli (Novagen) using an hSOD1

cDNA construct with an N-terminal GST tag subcloned into a pGEX4T-1 backbone. Bacterial

cultures were inoculated overnight in 15N-Celtone Complete Medium (Cambridge Isotope

Laboratories, Inc) with shaking at 37°C and induced with 1mM IPTG for hSOD1 production.

Protein was isolated by incubation with 600 µL of glutathione sepharose 4B beads (GE

Healthcare) for 30 minutes, 1 unit of thrombin (Thrombin Cleavage Capture Kit, Millipore) for

20 hours, and 30 µL of streptavidin agarose for 30 minutes, performed at room temperature.

Samples were spun at 1,000xg for 5 minutes, and supernatant containing N15-labeled, hSOD1

recombinant protein was collected.

Tissue Homogenization, Protein Isolation, and Mass Spectrometric Analysis

hTau transgenic mouse brain tissue was homogenized in PBS plus protease inhibitors

(P8340; Sigma-Aldrich, St. Louis, MO). Protein concentration of brain lysate was determined by

Pierce BCA assay (ThermoFisher Scientific, Waltham, MA). Total hTau was

immunoprecipitated from 100 µg of tissue lysate using 30 µL of CNBr-activated Sepharose

beads (GE Healthcare, Chicago, IL) crosslinked to anti-hTau antibody (Tau 1 monoclonal,

produced by Nicholas M. Kanaan, Michigan State University) at 3 mg antibody/g of beads in 1

mL PBS plus 1% NP-40, 5 mM guanidine, and protease inhibitors for 1.5 hours at room

temperature. Before immunoprecipitation, 5 ng of N15-lableled hTau was added to each sample.

hTau was digested using 400 ng of sequencing-grade endoproteinase Trypsin (Promega,

86

Madison, WI) for 16 hours at 37°C. Samples were lyophilized and reconstituted in 25 uL of 2%

acetonitrile/0.1% formic acid solution prior to liquid chromatography- quadrupole-orbitrap mass

spectrometry analysis (OrbiTrap Fusion, ThermoFisher).

hSOD1 was isolated from transgenic rat tissue or CSF as described previously (Crisp et al

2015). Before immunoprecipitation, 200 ng of N15-labeled hSOD1 was added to each brain or

spinal cord sample, and 50 ng of N15 SOD1 was added to CSF samples. Isolated hSOD1 was

digested with 600 ng of sequencing-grade endoproteinase GluC in 25 µL of 50 mM NaHCO3

buffer (pH 8.8) for 17 hours at room temperature. After solid phase extraction, samples were

lyophilized and resuspended in 25 µL 2% acetonitrile/0.1% formic acid solution prior to liquid

chromatography-triple quadrupole mass spectrometry analysis (Xevo TQ-S, Waters).

Newly synthesized proteins were quantified by analyzing the incorporation of 13

C6-

Leucine into target proteins, indicated by the tracer-to-tracee ratio (TTR), by comparing the area

under the chromatogram curve for SOD1 or tau peptides containing 13

C6-Leucine or 12

C6-

Leucine. Relative protein concentration was quantified by measuring the ratio between the area

under the curve of protein immunoprecipitated from animal tissue and N15-labeled recombinant

protein (Supplementary Tables 4.2 and 4.3). To account for non-steady state conditions due to

mRNA lowering in this experiments, newly generated protein was also analyzed by multiplying

the relative protein concentration by protein TTR at each time point. For figure presentation, data

for hTau is represented by the TPSLPTPPTR peptide and data for hSOD1 is represented by the

GLHGFHVHE peptide.

Plasma-free 13

C6-Leucine abundance was analyzed from TCA precipitation of 100 µL rat

or mouse plasma via capillary gas chromatography-negative chemical ionization-quadrupole

mass spectrometry (Agilent) as described previously (Crisp et al 2015).

87

Statistical Analysis

All statistical analyses were performed using Graphpad Prism 7.0 (GraphPad Software).

All hypothesis tests performed were two-sided assuming a normal distribution of data. P < 0.05

was determined significant. Based on previous results (DeVos et al 2017; McCampbell et al

2018), a cutoff of 50% target mRNA reduction relative to the mean mRNA levels in inactive

ASO-treated animals was used as inclusion criteria for subsequent analysis. In CSF analysis,

samples were excluded if blood contamination was visibly present or if the volume of CSF

collected was less than 75 uL. For relative quantification, samples were normalized to a single

sample in each experiment from the inactive ASO treatment group. Quantitative results are

expressed as mean difference and 95% confidence interval.

88

RESULTS

Lowering New hTau Production Precedes Protein Concentration Changes after ASO Treatment

We performed ICV injection of an ASO targeting human tau mRNA (DeVos et al 2017)

in transgenic mice expressing the human tau transgene (hTau mice) to understand lowering of

tau mRNA and total protein over time. To analyze newly generated hTau protein, we

administered 13

C6-Leucine in the drinking water at designated time points (Figure 4.1A). We

organized the timing of ASO treatment, 13

C6-Leucine administration, and euthanasia to observe

consistent, maximum label incorporation of proteins within the CNS at each time point assayed .

hTau ASO treatment lowered hTau mRNA in the cerebral cortex over time (Figure

4.1B). As early as two days post-injection, hTau ASO treatment lowered hTau mRNA levels by

~50% relative to inactive ASO, with further change of hTau mRNA by ~72% by 23 days post-

injection. hTau ASO treatment also decreased hTau protein levels over time (Figure 4.1C), with

significant effects of ~32% observed at 23 days. The discrepancy between overall mRNA

lowering and protein lowering is consistent with tau as a long-lived protein in the CNS (Sato et

al 2018).

Using percent of 13

C6-Leucine incorporation (TTR) as a measure of newly synthesized

protein, hTau ASO treatment lowered of newly synthesized hTau protein over time (Figure

4.1D). We observed lowering of new hTau protein production at 13 days after ASO treatment by

~71%. Combined, these data suggest that newly generated hTau protein is a reliable measure of

hTau ASO pharmacodynamics in central nervous system tissue, with newly generated hTau

showing an earlier (13 vs. 23 days) and greater magnitude of effect (71% vs. 32%) compared to

hTau protein concentrations (Figure 4.1E).

89

(A.) Experimental Design. hTau transgenic mice received 300 μg of either hTau ASO or inactive

ASO via intracerebroventricular injection and were labeled with13

C6-Leucine (13

C6-Leu) at

designated time points. (B.) hTau mRNA levels in temporal cortex by qPCR. Compared to

inactive ASO control, hTau ASO treatment lowered hTau mRNA over time (2-Way ANOVA F3,24

= 10.04, p = 0.0145). hTau mRNA levels are significantly lowered as early as two days after

ASO treatment by 50% (95% CI: 34.8%, 66.9%), and remain lowered as late as 23 days post

treatment by 72% (55.5%, 87.2%). (C.) hTau protein levels relative to N15-labeled recombinant

standard in temporal cortex by LC-MS. hTau ASO treatment lowers hSOD1 protein over time

Figure 4.1 Lowered hTau protein synthesis precedes protein lowering after ASO

treatment in hTau transgenic mice

90

(2-Way ANOVA F3,24 = 3.374, p = 0.0349). This effect is observed at 23 days post ASO treatment

by 32% (0.05%, 64.0%). (D.) Newly synthesized 13

C6-Leu-labeled hTau in temporal cortex by

LC-MS. Values are measured as the tracer to trace ratio (TTR) of 13

C6-Leu-labeled:12

C6-Leu-

unlabeled hTau. Lowering of 13

C6-Leu-labeled hTau is observed over time (2-Way ANOVA,

F3,24 = 5.748, p = 0.0041), and as early as 13 days post ASO treatment by 71% (95% CI: 29.4%,

123%). (E.) Newly generated hTau protein measured by multiplying hTau protein concentration

and TTR to account for non-steady state conditions. **** p < 0.0001, *** p < 0.001, ** p < 0.01.

n = 4 per treatment group per time point. Error bars in figures represent standard error of the

mean (SEM).

91

Lowering of 13

C6-Leucine-labeled hSOD1 Occurs in CSF after ASO Treatment

hSOD1WT

rats were treated with an ICV injection of 1000 µg hSOD1 ASO in the lateral

ventricle and administered 13

C6-Leucine in drinking water (Figure 4.2A). Based upon previous

measurements of hSOD1 half-life in these rats (Crisp et al 2013), and that lowering of CSF

SOD1 after ASO treatment in the G93A SOD1 transgenic rat model was observed at 42 days

post ASO treatment (Winer et al 2013), we euthanized animals 30 days after treatment to assay

hSOD1 total protein levels and new hSOD1 protein production to look for early ASO

pharmacodynamics. hSOD1 ASO treatment lowered hSOD1 mRNA globally within the CNS

(Figure 4.2B), leading to overall hSOD1 protein lowering by ~62% in cortex and ~40% in

lumbar spinal cord (Figure 4.2C). Similarly, we observed a decrease in newly synthesized

hSOD1 protein by ~34% in cortex and ~50% in lumbar spinal cord (Figure 4.2D). At this time

point, lowering of both hSOD1 protein concentration and newly synthesized hSOD1 protein

drive overall decreases in newly generated hSOD1 protein (Figure 4.2E).

In CSF, hSOD1 treatment failed to lower hSOD1 protein concentration (Figure 4.2F).

However, SOD1 ASO treatment decreased newly synthesized 13

C6-Leucine-labeled hSOD1 in

CSF by ~30% relative to the inactive ASO treatment group (Figure 2G). This effect of lowered

13C6-Leucine-labeled hSOD1 is specific to CSF, as we did not measure any differences in

peripheral hSOD1 protein in plasma (Supplemental Figure 4.1). To test SOD1 protein

concentration at a later time point, we treated a separate cohort of animals without 13

C6-Leucine

labeling using the same ASO treatment paradigm and analyzed CSF hSOD1 concentration at 50

days post-surgery (Supplemental Figure 4.5). Although we observed lowering in hSOD1

mRNA throughout the CNS 50 days post-ASO treatment (Supplemental Figure 4.5A), we did

not observe decreases in CSF hSOD1 protein concentration (Supplemental Figure 4.5B),

92

suggesting an even longer incubation time may be required to observe protein lowering in CSF.

Taken together, these data suggest changes in newly synthesized, 13

C6-Leucine-labeled target

proteins drives early pharmacodynamics biomarker effects in the CSF after ASO treatment

(Figure 4.2H).

93

Figure 4.2 Lowered 13

C6-Leucine-labeled hSOD1 observed in cerebrospinal fluid after ASO

treatment in hSOD1 transgenic rats

94

(A.) Experimental Design. hSOD1 transgenic rats received 1000 μg of either hSOD1 ASO or

inactive ASO via intracerebroventricular injection at designated time points. (B.) hSOD1 mRNA

levels in the CNS by qPCR. hSOD1 mRNA levels are lowered globally in the CNS, with mRNA

lowering of ~70% in cortex and lumbar spinal cord and ~55% in cerebellum. (C.) hSOD1 protein

levels relative to N15-labeled recombinant standard in CNS cortex by LC-MS. Lowering of

hSOD1 protein is observed in both cortex at ~61% (95% CI: 41.9%, 81.3%) and lumbar spinal

cord at ~40% (24.6%, 54.3%). (D.) 13

C6-Leucine (13

C6-Leu) labeled hSOD1 in CNS by LC-MS.

Values are measured as the tracer to trace ratio (TTR) of 13

C6-Leu-labeled:12

C6-Leu-unlabeled

hSOD1. Significant lowering of 13

C6-Leu-labeled hSOD1 is observed in both cortex at ~34%

(15.5%, 51.9%) and lumbar spinal cord at ~50% (33.3%, 66.1%). (E.) Newly generated hSOD1

protein measured by multiplying hSOD1 protein concentration and TTR in tissues. (F.) hSOD1

protein levels relative to N15-labeled recombinant standard in CSF by LC-MS. No changes in

CSF hSOD1 protein levels were observed (Student’s T-Test t = 0.448, p = 0.67). (G.) 13

C6-Leu

labeled hSOD1 in CSF by LC-MS. Significant lowering of 13

C6-Leu-labeled hSOD1 by ~30%

(95% CI: 12.0%, 48.4%) observed relative to inactive ASO control (Student’s T Test t = 3.923, p

= 0.0057). (H.) Newly generated hSOD1 protein measured by multiplying hSOD1 protein

concentration and TTR in CSF. One sample in inactive ASO group was excluded in CSF

analysis due to blood contamination during CSF collection. **** p < 0.0001, *** p < 0.001, ** p

< 0.01. Error bars in figures represent SEM.

95

A Therapeutic Window Exists for Newly Synthesized Protein Pharmacodynamics

To enhance the translational relevance of our findings, we performed similar experiments

in hSOD1 rats using intrathecal ASO injection, the method of drug delivery used in current

clinical trials. We analyzed CNS tissue 10, 30, and 60 days after ASO treatment compared to

inactive ASO control to better understand longitudinal changes in hSOD1 protein (Figure 4.3A).

Contrary to hSOD1 ASO delivery by ICV injection, we observed differential effects of hSOD1

mRNA lowering in CNS tissues after intrathecal ASO injection. We observed a significant

hSOD1 mRNA decrease in the lumbar spinal cord over time, with an average hSOD1 mRNA

lowering at 60 days by ~66%. However, hSOD1 treatment did not significantly lower hSOD1

mRNA in the cortex or cerebellum over time (Figure 4.3B). Because it is likely that SOD1

mRNA will need to be lowered globally in the CNS to affect SOD1 CSF levels, we focused on

protein measurements in the spinal cord, where we confidently documented mRNA lowering.

To further understand the relationship between protein lowering and lowering of newly

synthesized hSOD1 after ASO treatment, we analyzed hSOD1 protein in lumbar spinal cord.

hSOD1 ASO treatment decreased hSOD1 protein levels across all time points measured, and

treatment effect was greatest at day 60, with 50% lowering of hSOD1 protein (Figure 4.3C).

hSOD1 ASO treatment significantly lowered newly synthesized hSOD1 compared to inactive

ASO-treated animals (Figure 4.3D). Interestingly, ASO treatment decreased newly synthesized

hSOD1 at 10 and 30 days by 40 and 45%, but there was no difference in 13

C6-Leucine-labeled

hSOD1 between groups at day 60 due to lowering of total hSOD1 protein concentration.

Therefore, at later time points, decreases in newly generated hSOD1 protein by ASO treatment

are driven by overall lowering of protein concentration (Figure 4.3E).

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Figure 4.3 A therapeutic window exists to observe lowered protein synthesis

pharmacodynamics in the central nervous system

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(A.) Experimental Design. hSOD1 transgenic rats received 1000 ug of either hSOD1 ASO or

inactive ASO at designated time points via intrathecal injection. (B.) hSOD1 mRNA levels in the

CNS by qPCR. hSOD1 mRNA levels are significantly lowered in spinal cord after hSOD1 ASO

treatment (2 Way ANOVA F2,39 = 10.91, p = 0.002), with a maximum lowering of ~66% (95%

CI: 54.6%, 78.2%) 60 days post-treatment. No significant hSOD1 mRNA lowering was

observed in frontal cortex (2 Way ANOVA F2,40 = 1.48, p = 0.24) or cerebellum. (C.) hSOD1

protein levels relative to N15-labeled recombinant standard in lumbar spinal cord by LC-MS.

Lowering of hSOD1 protein is observed at over time (2 Way ANOVA F2,36 = 10.04, p = 0.0004),

with a maximum lowering by ~50% (95% CI: 42.5%, 79.8%) at Day 60. (D.) 13

C6-Leu labeled

hSOD1 in CNS by LC-MS. Values are measured as the tracer to trace ratio (TTR) of 13

C6-Leu-

labeled:12

C6-Leu-unlabeled hSOD1. Significant lowering of newly synthesized hSOD1 observed

after hSOD1 ASO Treatment (2 Way ANOVA F2,36 = 6.269, p = 0.0046). Lowering of 13

C6-

Leu-labeled hSOD1 is observed 10 and 30 days after ASO treatment by ~40-45% (30%, 88%).

60 days post ASO treatment, there was no difference in 13

C6-Leu-labeled hSOD1 between groups

(adjusted p value = 0.9976). (E.) Newly generated hSOD1 protein measured by multiplying

hSOD1 protein concentration and TTR in tissues. **** p < 0.0001, *** p < 0.001, * p < 0.05.

hSOD1 ASO: n = 5 - 10 per time point, Inactive ASO: n = 5 – 7 per time point. Error bars in

figures represent SEM.

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DISCUSSION

Our data show that, following treatment with a mRNA lowering therapeutic, the lowering

of newly synthesized protein occurs before lowering of total protein concentration in tissue.

Further, our findings indicate that labeled, newly generated protein measures direct effects of

protein translation in the source tissues, which are not seen in CSF protein concentrations at early

time points. Given the direct measurement of protein translation, labeling protein production has

higher sensitivity than protein concentration-based measures for mRNA-lowering therapies.

These studies suggest that identifying newly generated protein after ASO treatment is an

effective protein-based biomarker for assaying ASO target engagement (Figure 4.4).

Previous work supports the use of CSF protein concentration as a promising

pharmacodynamics biomarker for ASO therapy (Gendron et al 2017; Winer et al 2013), and CSF

SOD1 concentration is a secondary outcome measurement in an ongoing Phase I clinical trial

(NCT0262399). However, a key limitation in the use of CSF protein concentration as a measure

of ASO efficacy is the indirect mechanism informed by this assay. ASOs inhibit the production

of new protein, and measures of protein concentration also rely on transport to CSF and the

clearance of existing protein which may be unaffected by ASO treatment. Especially in a rapidly

progressing neurodegenerative disease such as ALS (Bali et al 2017), it is important to develop

pharmacodynamics tools that identify direct mechanisms of action at early time points that

reflect the actual drug effect in the tissue of interest.

Our study design is translatable to humans, as stable isotope labeling is safe and effective

in measuring the kinetics of proteins implicated in neurodegenerative disease within human CSF

(Bateman et al 2007; Bateman et al 2006; Bertram & Tanzi ; Crisp et al 2015; Sato et al 2018).

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(A.) Normal protein turnover at steady state synthesis includes production (top arrow) and

clearance (bottom arrow) rates in the CNS. (B.) Early after treatment, lowering in new protein

synthesis is observed due to lowering of target mRNA. (C.) In addition to lowering of new

protein synthesis, proteins present before 13

C6-labeling are cleared at the rate of turnover or half-

life. (D.) mRNA lowering reaches steady state, resulting in a consistent lowered protein synthesis

rate. Protein clearance remains constant and faster than production, leading to a new lowered

steady state protein concentration detected at a later time and less magnitude.

Figure 4.4 A model of the interplay between new protein production and protein

concentration pharmacodynamics in RNA-lowering treatment strategies

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However, these experiments were performed over a 36-hour time period, as the half-life of

amyloid beta peptide is ~9 hours in CSF (Bateman et al 2006). To our knowledge, the results

presented here are the first observations measuring new protein production of long-lived protein

targets in the CNS after therapeutic intervention. Our approach can be directly applied to

multiple RNA-directed strategies, such as siRNAs (Pfister et al 2009) and ASOs (Huynh et al

2017; Zhao et al 2017a), that target long-lived proteins in neurodegeneration for clinical trials.

Besides its lowered protein production readout, stable isotope labeling could be a powerful tool

to examine increased protein production for therapeutic strategies such as gene therapy

(Tuszynski et al 2015) and splicing ASO treatments (Finkel et al 2017; Schoch et al 2016).

Moreover, an understanding of protein kinetics using stable isotope labeling can also inform on

assays for therapies that target protein turnover, such as those approaches that activate protein

clearance mechanisms (Lee et al 2010) or promote proper protein folding (Das et al 2015).

Another advantage of our mass spectrometry-based assay is the ability to specifically

isolate mutant from wild type protein in patient populations. Many familial forms of

neurodegenerative diseases are caused by autosomal dominant mutations [31] and, thus, exhibit

both mutant and normal protein forms. However, it is currently unknown if mutant proteins

display altered protein kinetics compared to their wild type counterparts in humans. Future

studies in symptomatic, mutation-carrying patients are needed to characterize the half-life of

these target proteins in human CSF. Protein half-life measures will be crucial to determine the

timing required to observe pharmacodynamics effects in humans when designing randomized-

control clinical trials.

One limitation in this study is the inability to identify target protein lowering in CSF after

SOD1 ASO treatment. Without this effect, the relationship between lowered protein production

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and total protein concentration cannot be determined in the biological fluid important for human

clinical trials. Previous data suggests this limitation may be due to the drug delivery paradigm in

these experiments. The current study used a single bolus injection of ASO in rodent models

while previous studies that observed total target protein lowering in CSF after ASO treatment in

non-human primate models were performed with multiple intrathecal ASO injections (DeVos et

al 2017; McCampbell et al 2018). Additionally, both transgenic animal models used here

express wild type forms of the target protein, where no pathological misfolding of either hSOD1

or hTau is observed (Andorfer et al 2003; Chan et al 1998). Therefore, future studies should

examine how new protein production changes in the presence of misfolded proteins within the

CNS.

In sum, the approach described here provides a framework for the development of assays

that isolate the direct mechanism of action of target engagement in neurodegenerative disease

therapies, with the hope that such data will lead to a higher success in clinical trial outcomes and

approval of more therapies for patients.

ACKNOWLEDGEMENTS

We thank Drs. Cheryl Frankfater and John Turk of the Biomedical Mass Spectrometry

Core at Washington University in St. Louis for assistance with gas chromatography-mass

spectrometry analysis. Research reported in this publication was supported by the Washington

University Institute of Clinical and Translational Sciences grant UL1TR002345, sub-award

TL1TR002344 from the National Center for Advancing Translational Sciences (NCATS), the

National Institute for Neurological Disorders and Stroke grant U01NS084870, grant R-01-

NS098716-01 (TM Miller, PI) and R-01-NS095773 (RJ Bateman, PI) from the National

Institutes of Health, as well as the MetLife Foundation Award (RJ Bateman, PI) (NIH). The

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content is solely the responsibility of the authors and does not necessarily represent the official

view of NIH.

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Primer Sequence

MAPT Genotyping Forward

5’-ACTTTGAACCAG GATGGCTGAGCCC-3’

MAPT Genotyping Reverse 5’-CTGTGCATGGCTGTCCACTAACCTT -3’

SOD1 Genotyping Forward 5’-TTGTTCAGAAAACTCTCTCCAACTTTGCACT-3’

SOD1 Genotyping Reverse 5’- GGGTTTTAACGTTTAGGGGCTACTCTACTG -3’

MAPT qPCR Forward 5’-AGAAGCAGGCATTGGAGAC-3’

MAPT qPCR Reverse 5’-TCTTCGTTTTACCATCAGCC-3’

MAPT qPCR Probe 5’-/56-FAM/ACGGGACTGGAAGCGATGACAAAA/3IABkFQ/-3’

SOD1 qPCR Forward 5’-TGCATCATTGGCCGCA-3’

SOD1 qPCR Reverse 5’-TTTCTTCATTTCCACCTTTGCC-3’

SOD1 qPCR Probe 5'- /56FAM/ACTGGTGGTCCATGAAAAAGCAGATGACTT/36-TAMTph/-3'

GAPDH qPCR Forward 5’-TGCCCCCATGTTGTGATG -3’

GAPDH qPCR Reverse 5’-TGTGGTCATGAGCCC TTCC -3’

GAPDH qPCR Probe 5’- /56-FAM/AATGCATCCTGCACCACCAACTGCTT/3IABkFQ/-3’

Supplementary Table 4.1 Primers and probes used for genotyping and qPCR analysis of

hTau (MAPT), hSOD1 (SOD1), and mouse GAPDH (GAPDH) DNA

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Sequence Label Precursors Product Collision Energy (V)

24SNGPVKVWGSIKGLTE

39 557.9737 547.3086 19

557.9737 707.4087 14

557.9737 735.9194 14

557.9737 804.4462 14

13

C6-Leu 559.9804 553.3287 19

559.9804 710.4187 14

559.9804 738.9294 14

559.9804 810.4663 14

N15 564.6206 553.2908 19

564.6206 715.3849 14

564.6206 744.3942 14

564.6206 813.4195 14 40

GLHGFHVHE48

516.754 308.1717 13

516.754 649.32 18

516.754 725.336 18

516.754 748.388 18

13

C6-Leu 519.764 314.1918 13

519.764 655.3406 18

519.764 725.3365 18

519.764 754.4090 18

N15 524.2319 313.1569 13

524.2319 659.2909 18

524.2319 735.3069 18

524.2319 759.3563 18

78RHVGDLGNVTADKDGVADVSIE

99 756.3083 425.2199 26

756.3083 474.7541 26

756.3083 565.2841 31

756.3083 910.9479 21

13

C6-Leu 758.3870 428.2300 26

758.3870 477.7642 26

758.3870 565.2841 31

758.3870 913.9850 21

N15 766.0183 432.1992 26

766.0183 482.2319 26

766.0183 575.2545 31

766.0183 923.4109 21

Supplementary Table 4.2 Transition ions used for hSOD1 tandem LC-MS/MS.

GLHGFHVHE peptide measurements were used as representative data in Figures.

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Sequence Label Precursors Product Collision Energy (V)

212TPSLPTPPTR

221 533.7982 668.3726 20

13

C6-Leu 536.8083 668.3726 20

N15 540.2789 677.3459 20

243LQTAPVPMPDLK

254 655.3629 658.3729 15

13

C6-Leu 658.3729 896.4910 15

260IGSTENLK

267 431.2374 691.3621 15

13

C6-Leu 434.2475 697.3822 15

Supplementary Table 4.3 Transition ions used for hTau tandem LC-MS/MS. TPSLPTPPTR

peptide measurements were used as representative data in Figures.

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Plasma 13

C6-Leucine values were analyzed in all samples analyzed and compared between

groups. (A.) hTau ICV ASO plasma 13

C6-Leucine at Day 23 post surgery, as seen in Figure 1. No

differences were observed (Student’s T-test, p =0.4496). (B.) hSOD1 ICV ASO plasma 13

C6-

Leucine at Day 30. No differences were observed (Student’s-T test) (C.) hSOD1 IT ASO plasma 13

C6-Leucine at all time points assessed. No differences were observed (2-Way ANOVA). Error

bars in figures represent SEM.

Supplementary Figure 4.1 No differences in 13

C6-Leucine oral labeling between

treatment groups in all experiments performed.

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Linear regression analysis comparing peptides shows strong correlations exist between all three

peptide measurements. All p values < 0.0001 for each analysis.

Supplementary Figure 4 Correlation between all hTau peptides measured

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Linear regression analysis comparing peptides shows strong correlations exist between all three

peptide measurements. All p values <0.0001 for each analysis.

Supplementary Figure 4.3 Correlation between all hSOD1 peptides measured.

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(A.) hSOD1 protein levels relative to N15-labeled recombinant standard by LC-MS in plasma.

No changes in plasma hSOD1 protein levels were observed (Student’s T-test). (B.) 13

C6-Leu

labeled hSOD1 in plasma by LC-MS. No changes in plasma 13

C6-Leu labeled hSOD1 was

observed (Student’s T-Test). Error bars in figures represent SEM.

Supplementary Figure 4.4 No pharmacodynamics observed in the periphery after 1000 ug

hSOD1 ASO intracerebroventricular injection.

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Supplementary Figure 5 No protein concentration pharmacodynamics observed in CSF

after 1000 ug hSOD1 ASO intracerebroventricular injection at 50 days post-treatment.

(A.) hSOD1 mRNA levels in the CNS by qPCR. hSOD1 mRNA levels are significantly lowered

in spinal cord and frontal cortex by ~70% and 50%, respectively, after ASO treatment relative to

inactive ASO controls (Student’s T-test). (B.) hSOD1 protein levels relative to N15-labeled

recombinant standard by LC-MS. No changes in CSF hSOD1 protein levels were observed

(Student’s T-test). n = 5 for each treatment group, with one CSF sample excluded from each

group due to blood contamination. **** p < 0.0001. Error bars in figures represent SEM.

111

Chapter 5: Summary and Future Directions

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Advances in elucidating the genetic causes of ALS and generating models of disease

have rapidly enhanced our understanding of ALS pathophysiology in recent years, leading to the

development of multiple drug candidates for this devastating condition. However, multiple

challenges still exist for successful translation of preclinical discoveries to effective therapies in

the clinic. In this dissertation, I presented novel findings that further the understanding of human

ALS epidemiology and CSF SOD1 biology. Further, these findings generate crucial parameters

that can be directly implemented into randomized control clinical trial design for ALS patients.

ALSSOD1

clinical outcomes remain unchanged over time

In Chapter 2, I detailed findings from a retrospective cohort study to examine ALSSOD1

across 15 medical centers in the United States and Canada. Multiple studies in the late 1990’s

and early 2000’s characterized the natural history of the ALSSOD1

population, but our efforts to

analyze 175 subjects represent the largest patient population in almost 20 years. With the advent

of multidisciplinary care for the ALS patient population, previous studies reported an

improvement in ALS natural history, as defined by slower disease progression, from the end of

the 19th

century to the beginning of the 20th

century (Czaplinski et al 2006b; Qureshi et al 2009).

Therefore, it is important to determine if similar trends exist specifically in the ALSSOD1

population, as such trends will improve our understanding of clinical outcomes and influence

randomized control trial design for new potential treatments in this disease population.

Our calculated mean disease duration for all ALSSOD1

is similar to previous studies

(Table 2.1). Of particular interest in ALSSOD1

epidemiology is the A4V point mutation that

accounts for ~50% of the disease population in North America. Indeed, the highest percentage of

patients in our analysis carried the A4V mutation, and our calculated mean disease duration of

1.4 ± 0.7 years is unchanged compared to previous studies (Table 2.2). These data have

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important implications, as the contrast to the trend of slower disease progression in the entire

ALS population over time suggests ALSSOD1

may be a unique subpopulation of disease.

As multiple therapeutic candidates are emerging to target SOD1 in ALS (Benatar et al

2018; Miller et al 2013), our natural history data have important implications for randomized

control trial design. Previous ALS trial enrollment is low and highly variable between study

sites, presenting a need to design more efficient clinical trials (Bedlack et al 2008). From our

natural history data, multiple opportunities exist for such considerations in the ALSSOD1

population. The increased prevalence of A4V mutations in the SOD1 population suggests

stratification of trial data to this specific mutation population. Such a strategy may be feasible

due to the limited variability in survival of SOD1A4V

patients, as our survival analysis showed no

SOD1A4V

patients living past 3.5 years (Figure 2.2). Indeed, our power calculations showed that

only 52 SOD1A4V

subjects per treatment group are needed to show hazard ratio of 0.5 the

experimental and control treatment groups. In contrast, 88 subjects per treatment group from the

entire SOD1 population are needed to detect the same statistical difference in median survival.

Moreover, unchanged natural history of ALSSOD1

may provide support for the use of

historical controls in early phase (I/II) trials (Donofrio & Bedlack), as seen in previous studies

(Miller et al 2011). The use of historical controls addresses many of the challenges associated

with previous clinical trial design, including reduced cost, improved efficacy, and appeal to

patients (Gordon 2009). Czaplinski and colleagues argued that historical controls are particularly

useful for experimental compounds that have received FDA approval for other indications, as

data for safety in humans already exists (Czaplinski et al 2006a). However, multiple limitations

exist in the use of historical controls such as matching bias, and the lack of key clinical

information such as comorbidities, current medication use, site of disease onset, and others.

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Therefore, in future ALS natural history studies, it is important to collect data in a prospective

manner, capturing all clinically relevant data in the ALSSOD1

population. These data will be

crucial for not only improving clinical trial design in ALS, but further understanding the factors

that contribute to ALS pathobiology to better understand patient prognosis.

Understanding drivers of ALSSOD1

heterogeneity

My results also show that the remaining, non-A4V ALSSOD1

population exhibits highly

variable measures in mean age of disease onset (49.5 ± 12.3 years) and mean disease duration

(6.6 ± 7.0 years) (Table 2.1). We identified 36 missense variants in our cohort (Figure 2.1), and

150+ variants have been identified as pathogenic in ALS, yet these variants exhibit a varying

degree of disease penetrance (Felbecker et al 2010; Lopate et al 2010). Taken together, these

observations question the validity of all SOD1 variants as pathogenic. Further efforts must be

made in understanding this variability in the global ALSSOD1

population, as the majority of data

have focused on the North American disease population. Interestingly, Rechtman and colleagues

reported gender and ethnic differences in ALS incidence and age at diagnosis in the United

States (Rechtman et al 2015). Particular geographic areas of interest to update ALSSOD1

natural

history include Europe (Gamez et al 2006), Canada (Eisen et al 2008), and Asia (Chen et al

2015). Therefore, a combination of natural history data and more rigorous genome sequencing

data with advanced techniques (MacArthur et al 2014) may provide valuable insight into

understanding the pathogenicity of each SOD1 variant as a contributor to disease.

A missing component in many natural history studies, including our data, is an analysis

considering comorbidities and environmental factors as contributors to disease outcomes.

Environmental and lifestyle factors such as chemical toxins (Su et al 2016), smoking (Alonso et

115

al 2010; Armon 2009), military service (Weisskopf et al 2015; Weisskopf et al 2005), and

physical activity (Beghi et al 2010; Pupillo et al 2014) have all been implicated as risk factors of

ALS. These environmental factors may contribute to the heterogeneity of outcomes observed

between and within different SOD1 ALS variants, highlighting one of the key limitations in

retrospective cohort analyses. A further understanding of the interplay between genetics and

environmental contributions to ALS disease pathogenesis will be invaluable for the application

of prognostic models to individual patient management (Westeneng et al 2018). Further,

understanding of environmental factor contribution may help in formulating inclusion criteria for

randomized control trials, limiting the variability in primary and secondary outcome measures to

improve the statistical power of Phase II/III efficacy studies.

CSF SOD1 is a long-lived protein in ALS

In Chapter 3, I showed the first efforts to determine CSF SOD1 protein concentration and

kinetics in ALS patients using stable isotope labeling of essential amino acids combined with

tandem liquid chromatography-mass spectrometry. In these participants, 13

C6-Leucine-labeled

SOD1 persists as long as 120 days after the end of the 16 hour labeling period, further validating

previous observations (Crisp et al 2015) that SOD1 is a long-lived protein within the CNS of

humans (Figure 3.1). Additionally, CSF SOD1 protein concentration appeared to be increased in

ALS participants compared to controls (Table 3.2), similar to previous results measured by CSF

SOD1 ELISA (Winer et al 2013). Variability and limited sample size inhibited conclusive

differences in CSF SOD1 half-life between groups, with a calculated average of ~20 – 25 days

(Table 3.2). CSF SOD1 half-life is a crucial parameter for randomized control trial design for

experimental therapies in ALSSOD1

, as CSF SOD1 protein concentration is a secondary outcome

measure in current trials that inhibit the synthesis of new SOD1 protein (NCT01083667,

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NCT02623699). Effects observed by these experimental treatments are influenced by the

intrinsic synthesis and degradation rates of SOD1 in the CNS at steady state, as defined by the

protein’s half-life. This relationship is clearly illustrated through PK-PD modeling, as we

demonstrated how a calculated CSF SOD1 half-life of 25 days will result in an incubation time

of ~120 days to observe changes in CSF SOD1 protein concentration after SOD1 ASO treatment

(Figure 3.2). These kinetic parameters are also valuable for improving participant experience in

clinical trials, as an understanding of the PK-PD relationship of experimental treatments may

reduce the number of invasive procedures such as lumbar punctures that must be performed in

order to establish therapeutic efficacy of drug target engagement.

Analyzing CSF SOD1 protein behavior to elucidate contributions to ALS pathobiology

Besides considerations for clinical trial design, an understanding of SOD1 kinetics may

provide valuable insight into the pathogenic status of SOD1 in ALS. Previous efforts to identify

misfolded SOD1 in biofluids of living ALS patients, even those with SOD1 mutations, have

remained elusive. Additionally, the role of misfolded SOD1 in sporadic ALS remains

controversial. Similar to observations of alterations in protein turnover in human

neurodegenerative disease, determination of CSF SOD1 protein turnover may provide an

opportunity to interrogate the stability of SOD1 protein within the CNS of ALS patients.

Moreover, if the degree of misfolded, aggregated protein in the brain and spinal cord of ALS

patients relates to protein stability, an attractive hypothesis is that potential changes in SOD1

protein kinetics may better inform on the prognosis of ALS patients. Currently, we are

underpowered to test this hypothesis, as to-date, we only have three participants (each harboring

different SOD1 mutations). However, my analysis in these SOD1 mutant carriers is the first to

dissociate wild type from mutant protein kinetics in the cerebrospinal fluid of patients with

117

mutations that cause an autosomal dominant form of a neurodegenerative disease. With the

recruitment of more participants, we hope to further investigate questions related to the

potentially misfolded state of mutant SOD1 protein including: the ability to identify misfolded

SOD1 in CSF, the role of SOD1 dimerization affecting protein stability in vivo, and how SOD1

stability may differentiate between pathogenic and benign variants as contributors to ALS

pathobiology.

Accumulating evidence in vitro and in vivo suggests that we may see altered CSF SOD1

protein kinetics in ALS, indicative of SOD1 protein misfolding in the CNS. In cell culture, the

half-life of soluble, mutant SOD1 is accelerated compared to WT protein (Borchelt et al 1994).

Similar behavior of mutant SOD1 is observed in vivo, where the half-life of both G93A SOD1

(Crisp et al 2015) and G85R SOD1 (Farr et al 2011) are accelerated compared to WT hSOD1 in

transgenic animal models. Moreover, isolation of misfolded G93A SOD1 by

immunoprecipitation with an antibody that specifically recognizes misfolded hSOD1 revealed

that misfolded SOD1 shows faster protein kinetics than the entire G93A SOD1 pool (Crisp et al

2015). Faster protein turnover is also observed in other models of neurological disorders, as

mutations in glial fibrillary acidic protein which cause similarly decrease the half-life of mutant

protein compared to wild type in animal models of Alexander’s Disease (Moody et al 2017).

Further, Sato and colleagues used the presence of mutant SOD1 protein in red blood cells as a

marker for altered protein turnover, where fast SOD1 turnover by erythrocytes suggests very

misfolded proteins will not be present in these cells (Sato et al 2005). Because SOD1 is a

ubiquitously-expressed protein, isolation from blood limits the ability to specifically understand

SOD1 protein behavior in the CNS. However, we previously observed that CSF SOD1 half-life

is similar to SOD1 from the brain and spinal cord (Crisp et al 2015), suggesting that our stable

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isotope labeling method to measure CSF protein half-life will be informative of SOD1

biochemistry within the human CNS.

Identification of misfolded SOD1 protein in humans by protein turnover will be of great

value to developing ALS biomarkers, as the amorphous structure of SOD1 aggregates

(Matsumoto et al 2006; Matsumoto et al 2005) impedes the development of imaging tracers to

visualize protein misfolding events in human CNS tissues. For example, efforts to develop

positron emission tomography radiotracers that bind amyloid-β (Klunk et al 2004) and tau

(Johnson et al 2016) protein aggregates in Alzheimer’s Disease have proven successful due to

the β-sheet rich, amyloid structures formed by these proteins. Therefore, to confirm that

alterations in SOD1 protein kinetics describes misfolded SOD1 protein, future work must

continue to develop other biochemical assays that specifically identify misfolded, pathogenic

SOD1. A newly emerging biochemical property of misfolded proteins that cause

neurodegenerative diseases is the amplification of a protein aggregate by templated

conformational changes, ie.) “seeding”, similar to prion diseases (Kaufman & Diamond 2013;

Prusiner 1982). Multiple assays have been developed to identify pathogenic protein seeds for

proteins such as tau (Holmes et al 2013), alpha synuclein (Groveman et al 2018), and huntingtin

(Tan et al 2015) in both CNS tissue and CSF of AD, PD, and HD patients. Despite the non-

amyloid structure of SOD1 aggregates, multiple research groups demonstrated the ability of

recombinant and cellular/tissue derived mutant SOD1 to seed intracellular SOD1 and propagate

extracellularly (Ayers et al 2014; Chia et al 2010; Munch et al 2011). Therefore, the

development of an SOD1 seeding assay may overcome previous limitations of antibody capture-

based methods to identify misfolded SOD1 species, while helping to establish the relationship

between CSF SOD1 turnover and protein misfolding.

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Future studies must also aim to elucidate the biochemical composition of SOD1 in human

CSF to understand the relationship of a CSF biomarker to protein misfolding in the CNS.

Dimerization is a unique feature of SOD1 structure compared to other proteins that contribute to

neurodegeneration, and has been studied extensively to understand mutant SOD1 toxicity.

Thermodynamic analyses of recombinant SOD1 protein (Khare & Dokholyan 2006; Lindberg et

al) and imaging of SOD1 aggregation in live cells (Kim et al 2014) suggest that dimer

destabilization is the first step in the production of SOD1 monomers that are capable of

misfolding and forming protein aggregates. These observations are further supported by our

natural history data, as A4 SOD1 mutants (A4V, A4T), and C-terminal missense and truncation

mutants (V148G, G141X) are found at the SOD1 dimerization interface (Keerthana &

Kolandaivel 2015) and show fast mean disease durations of less than 2 years. Moreover, the

strongest correlation of SOD1 biochemical parameters in vitro and ALS clinical phenotypes is

rate of disease progression and aggregation propensity of mutant protein (Pratt et al 2014;

Prudencio et al 2009; Wang et al 2008).

An added layer of complexity presents itself when considering ALSSOD1

, as three potential

SOD1 dimer pairs are possible: WT-WT, WT-Mutant, and Mutant-Mutant. Both mutant

homodimers and WT-mutant heterodimers are implicated in motor neuron toxicity. In a subset of

transgenic models, multiple reports suggest that crossing mutant SOD1-expressing transgenic

mice with WT hSOD1-expressing mice accelerates disease phenotypes (Deng et al 2006; Wang

et al 2009). Similarly, Witan and colleagues created artificial heterodimer and homodimer

constructs of mutant and WT SOD1 and showed WT-mutant heterodimers induce a more toxic

phenotype than mutant-mutant homodimers in C. Elegans (Witan et al 2008). The process of

heterodimerization also appears to correlate with SOD1ALS

disease progression, as Shi and

120

colleagues demonstrated faster disease progression in SOD1 mutants that have a more favorable

Gibbs free energy association of heterodimerization between mutant and WT recombinant SOD1

protein (Shi et al 2016).

Despite these extensive characterizations in vitro or overexpression paradigms in vivo, there

are relatively few analyses examining SOD1 monomer and dimer state in humans that validate

the physiological relevance of these laboratory findings to human ALS. The mass spectrometry-

based methods I developed to quantify protein concentration and turnover of mutant and WT

SOD1 protein in human CSF provide the foundation to investigate these questions in living ALS

participants. Further technical developments of “Top-Down” analyses to examine intact protein

(Padula et al 2017) should thus aim to establish a relationship between CSF SOD1 biochemical

characteristics and ALS clinical phenotypes. Future experiments using induced pluripotent stem

cell-derived motor neuron should also examine how these observations in human CSF relate to

motor neuron toxicity. By using iPSC’s derived from SOD1 mutant carriers, one can also create

isogenic WT SOD1 or mutant SOD1-expressing lines and compare the three cell lines to test

hypotheses describing the role of mutant SOD1 protein stability, dimer stability, and the

presence of WT SOD1 modulating motor neuron toxicity (Imamura et al 2017; Wainger et al

2014).

Multiple studies have suggested a relationship between mutant SOD1 stability and rate of

disease progression after symptom onset, but no work has reported a correlation with age of

disease onset. One possible hypothesis that explains these results is that changes in proteostasis

associated with aging drive the rate of disease onset, as protein degradation rates decrease over

time within cells (McShane et al 2016). Previous work supports this hypothesis in other

neurodegenerative diseases, as Patterson and colleagues demonstrated a highly significant

121

correlation between age and slow amyloid-beta turnover rates, suggesting that slowing of

amyloid-beta turnover increases the likelihood of protein misfolding that leads to deposition

(Patterson et al 2015). Similarly, single cell analysis of motor neurons in culture suggests that

slower turnover of proteins implicated in ALS correlates to a cell’s susceptibility to degeneration

(Barmada et al 2014), and aging is one of the strongest risk factors for ALS (Niccoli et al 2017).

SOD1 is degraded by the ubiquitin proteasome system and macroautophagy within cells (Kabuta

et al 2006), two pathways that slow over time (Dhondt et al 2017), suggesting that age-dependent

effects of SOD1 turnover in the CNS and CSF may be observed in ALSSOD1

. Families that carry

the A4V SOD1 mutation are an ideal population to investigate the association between aging,

SOD1 turnover, and clinical phenotypes, as the mutation exhibits a high degree of disease

penetrance, with a rapid disease progression after symptom onset with little variability between

patients. To determine if these changes in SOD1 turnover relate to motor neuron degeneration

during disease time course, additional analyses should examine changes in CSF SOD1 kinetics

with prognostic biomarkers associated with neuronal death, such as neurofilament light chain in

CSF or plasma (Bacioglu et al 2016; Gaiani et al 2017; Lu et al 2015) or p75 extracellular

domain fragments found in patient urine (Shepheard et al 2017).

Future investigation of the ALSSOD1

population will be critical to establish any

relationship between SOD1 half-life measures and disease pathobiology, and will determine the

feasibility of identifying misfolded WT SOD1 by kinetics-based measures in ALS patients that

do not have SOD1 mutations. Without a basic understanding of mutant SOD1 protein behavior,

any investigation into kinetics-based approaches to test the hypothesis of misfolded SOD1 in

sporadic ALS without SOD1 mutations will be difficult to interpret. Although the identification

of SOD1 involvement in sporadic ALS would have a significant impact on treatment strategies

122

for the entire ALS population, further investigation in ALSSOD1

may confirm that pathogenic

mechanisms associated with SOD1 mutations are specific to this subpopulation. If the

interpretation of changes in CSF SOD1 kinetics is ultimately specific to ALSSOD1

, future efforts

should aim to use similar approaches on other proteins implicated in disease to expand the scope

of the relationship between protein turnover and ALS pathogenesis.

Of particular interest is TDP-43, the protein found in insoluble aggregates of motor

neurons in the remaining 98% of ALS patients that do not carry SOD1 mutations (Neumann et al

2006). Stable isotope labeling with 13

C6-Leucine to measure TDP-43 protein kinetics in CSF of

ALS patients is theoretically feasible, and the recent identification of TDP-43 in exosomes

derived from CSF suggests that a CNS-specific pool of TDP-43 can be analyzed to measure

protein turnover (Feneberg et al 2018; Feneberg et al 2014). Similar experiments described

previously for CSF SOD1 can be directly translated to studies of TDP-43 in ALS, as the two

proteins share similar characteristics of misfolding such as the involvement of post-translational

modifications (Fujishiro et al 2009; Igaz et al 2008), and prion-like propagation (Nonaka et al

2013). Aggregates of TDP-43 and C-terminal fragments of TDP-43 also appear to form amyloid

structures (Bigio et al ; Garnier et al 2017), opening the possibility for future development of an

imaging agent to observe TDP-43 aggregates in living ALS patients. Combining our SILK

analysis with imaging would be invaluable to understand the relationship between CSF protein

biomarkers and disease progression in the brain and spinal cord of ALS patients, as CSF protein

biomarkers may provide a great cost effectiveness to implement in in the clinic compared to high

costs associated with PET imaging scans (Mitka). In sum, future investigations of CSF protein

kinetics should aim to identify a mechanistic link between protein homeostasis and

neurodegeneration to better understand disease biology. Such findings would be crucial to help

123

understand the variability in prognosis and clinical phenotypes in ALS, with the hopes of leading

to improved patient outcomes. Further, as new therapeutic candidates emerge that correct TDP-

43 pathology (Becker et al 2017), an understanding of TDP-43 turnover in the CNS will be

needed.

New Protein Production is a viable biomarker for RNA-lowering therapies

Understanding of protein turnover has direct application for experimental therapies under

investigation for the treatment of ALS. In Chapter 4, I described how measurements of new

protein production using 13

C6-Leucine labeling serves as a pharmacodynamics assay to

determine target engagement after therapeutic treatment. The primary focus of

pharmacodynamics measures for ASOs in neurodegenerative diseases is target protein

concentration in cerebrospinal fluid, confirmed by results in preclinical disease models that

suggest changes in CNS protein levels are reflected in CSF (DeVos et al 2017; McCampbell et al

2018). Excitingly, we observed changes in newly produced protein at earlier time points than

protein concentration in both the CNS and CSF after ASO treatment (Figures 4.1, 4.2),

suggesting that measures of newly produced protein are more sensitive measures of target

engagement for treatments that inhibit protein synthesis. As stable isotope labeling is safe in

humans (Crisp et al 2015; Ovod et al 2017; Sato et al 2018), measures of new protein production

to determine drug target engagement are directly translatable to randomized control trials with

drugs that inhibit SOD1 synthesis (Thomsen et al 2014) or promote protein clearance (Kieran et

al 2004). As SOD1 ASO treatment is currently in clinical trials and using CSF SOD1 protein

concentration as a secondary outcome (NCT02623699), our measures of new protein production

can also be implemented into future studies. As most previous multicenter randomized control

trials in ALS have failed to analyze any pharmacodynamics biomarkers (Mitsumoto et al 2014),

124

our results open a new opportunity to use protein kinetics to better understand the effects of

experimental drugs on human ALS biology.

Protein Turnover as a measure of pharmacodynamics for SOD1-targeting therapies

My research specifically focused on new protein production using stable isotope labeling

with 13

C6-Leucine, but measures of protein turnover and half-life also have potential utility as

pharmacodynamics biomarkers. For example, an emerging class of drugs targets the proper

folding and maturation of SOD1 to prevent mutant SOD1 toxicity. In a chaperone-mediated

strategy, Das and colleagues developed a small molecule that inhibits protein phosphatase 1 to

increase the availability of chaperones to bind to misfolded SOD1. Treatment with this molecule

lowered misfolded SOD1 protein levels without changing total SOD1 protein concentration in

G93A SOD1 transgenic mice, preventing motor neuron loss and rescuing motor deficits (Das et

al 2015). Nagy and colleagues showed similar results through genetic overexpression of HSP110

in mutant SOD1 transgenic animals (Nagy et al 2016). In an alternative approach, Capper and

colleagues recently described a cysteine-reactive small molecule that promotes dimerization of

SOD1 protein, restoring the monomer-dimer equilibrium of A4V SOD1 to wild-type in cell

culture (Capper et al 2018). These data suggest that measures of protein concentration will not

serve as a successful pharmacodynamics biomarker. However, stabilizing mutant SOD1 protein

may lead to alterations in protein turnover, suggesting that half-life measurements of mutant

SOD1 protein after treatment may be a viable biomarker. These hypotheses can be directly tested

in vivo using our current labeling methods in preclinical models and humans, but also have the

utility to be used as an assay to screen compounds that target proteostasis in neuronal cell culture

(Sato et al 2018; Zhang et al 2014). To ultimately serve as a pharmacodynamics biomarker, it is

imperative to determine the effect that such treatments will have on CSF SOD1. Such findings

125

have utility to both identify better measures of drug mechanism of action, but also our

understanding of the biology underlying SOD1 release from cells into CSF.

Understanding the mechanisms of CNS protein deposition into the CSF

Despite significant progress in our understanding of the correlations between CNS and

CSF protein behavior, the mechanisms that govern SOD1 protein release into the extracellular

space remain poorly understood. Traditionally thought to be a passive process associated with

motor neuron degeneration, emerging evidence suggests that human cells secrete misfolded

proteins through an active process (Fontaine et al 2016; Lee et al 2016a). Multiple groups

reported unconventional secretion of mutant SOD1 in lower-order organisms such as yeast

(Cruz-Garcia et al 2017) and human neurons through interaction with neurosecretory vesicles

(Urushitani et al 2006). An additional factor implicated in extracellular release of proteins is

neuronal activity, as increased neuronal activity leads to elevated levels of both amyloid beta

(Cirrito et al 2003) and tau (Yamada et al 2014). Evidence suggests mutant SOD1-expressing

neurons exhibit a hyperexcitability phenotype, so neuronal activity in the brain and spinal cord

may influence extracellular release of SOD1 (Wainger et al 2014). Although previous data has

primarily focused on neuronal release of misfolded proteins, it is also unknown how each cell

type of the brain contributes to the pool of SOD1 found in CSF. Experiments in both transgenic

animal models and motor neuron cell cultures can contribute to characterizing changes in SOD1

turnover and extracellular release over time and through disease time course. Excitingly, new

drug candidates that target proper SOD1 folding and maturation provide an opportunity to

provide a more complete picture on the role of SOD1 protein turnover affecting the behavior of

CSF SOD1 protein by measures of protein concentration and half-life in vivo.

126

Similarly, no studies have examined the clearance of either mutant or WT SOD1 protein

from CSF. The identification of paravascular and dural lymphatic vascular systems that drive

interstitial fluid and CSF flow (Aspelund et al 2015; Iliff et al 2012) present exciting

opportunities to determine how the clearance of SOD1 protein influences measures of CSF

SOD1 protein half-life. Within both vascular systems, the clearance of small fluorescent tracers

and soluble amyloid beta peptide from CSF required specific receptors such as Aquaporin 4 and

Vascular Endothelial Growth Factor Receptor. The molecular weight of SOD1 dimers (32 kDA)

is similar to the size of fluorescent tracers used in these studies, suggesting experiments

examining tagged SOD1 clearance from CSF are feasible to test hypotheses related to similar

ligand-receptor relationships for the clearance of SOD1 protein in animal model systems.

Therefore, future endeavors into understanding the mechanisms that drive SOD1 entrance and

clearance from CSF will further advance our understanding of CSF protein biology and its utility

as a biomarker in disease states.

Concluding Remarks

The original report of ASOs targeting SOD1 in ALS was released over 10 years ago

(Smith et al 2006), pioneering the use of RNA-lowering therapies for diseases of the CNS. The

recent approval of ASOs that modulate splicing in juvenile-onset spinal muscular atrophy (Finkel

et al 2017) pave the way for potential of RNA-lowering therapies for progressive

neurodegenerative diseases to be realized in the near future. However, multiple challenges will

persist as these promising therapeutic candidates advance through the clinic, and the SOD1 ASO

randomized control trials will define the ability of the neurology field to learn from past failures

in clinical trial design. In this dissertation, I provided new insights into ALSSOD1

epidemiology,

CSF SOD1 protein behavior, and pharmacodynamics biomarker development to hopefully lead

127

to successful approval of ASO treatment in ALSSOD1

. These challenges, among others, will

continue to present themselves in the development of RNA-targeted therapies for other types of

ALS, Alzheimer’s Disease, Parkinson’s Disease, Huntington’s Disease, Charcot-Marie Tooth

Disease (Zhao et al 2018), as well as future disorders with yet to be defined targets. In sum, I

hope the work described in this dissertation provides the foundation for future investigators in

disease research, where experiments in the laboratory and clinic combine to reveal breakthroughs

that ultimately lead to improvements in our understanding of human biology to combat and one

day cure these devastating neurological diseases.

128

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Curriculum Vitae Wade K. Self

Washington University in St. Louis

Email: wade.self@wustl.edu

Education

2013- Current Washington University in St. Louis, St. Louis, MO

Doctoral Candidate (Expected August 2018)

Division of Biology and Biomedical Sciences

Graduate Program: Neuroscience

2009-2013 Case Western Reserve University, Cleveland, OH

Bachelor of Science in Engineering (May 2013)

GPA: 3.83

Major: Biomedical Engineering, Tissue Engineering Track

Research Experience

2013 - Current Doctoral Candidate

Washington University in St. Louis School of Medicine

Department of Neurology

Principal Investigator: Timothy Miller, M.D, Ph.D.

Dissertation: Understanding superoxide dismutase 1 turnover to improve

patient outcomes in amyotrophic lateral sclerosis

2012- 2013 Research Assistant

Case Western Reserve University, Department of Pathology

Principal Investigator: Xiongwei Zhu, Ph.D.

Project Description: Determining the aberrant localization of SREBP2 in

neurons of post mortem Alzheimer’s Disease hippocampal brain sections

2012 Summer Undergraduate Research Fellow

Mayo Clinic, Department of Neuroscience

Principal Investigator: Guojun Bu, Ph.D.

Project Description: Screening small molecule modulators of APOE

expression in astrocytes for the treatment of Alzheimer’s Disease

2010 - 2012 Undergraduate Research Assistant

Case Western Reserve University, Department of Biomedical Engineering,

Supervisor: Jeffrey Capadona, Ph.D.

Project Description: Preventing neuronal loss after intracortical

microelectrode implantation for electrical functional stimulation

149

Selected Grants and Academic Awards

2018 Burroughs Wellcome Travel Fund, Association of Clinical and Translational

Science National Meeting, Washington D.C.

2017 3rd

Place Outstanding Poster Award, Association of Clinical and Translational

Science National Meeting, Washington D.C.

2017 Outstanding Citizenship Award, Clinical Research Training Center, Washington

University School of Medicine

2016 Outstanding Continuing Mentor, Young Scientist Program, Washington

University in St. Louis

2016 - 2018 TL1 Predoctoral Clinical Research Fellow, Washington University in St. Louis

Institute of Clinical and Translational Sciences

2014 Graduate Research Fellowship Program Honorable Mention, National Science

Foundation

2013 B.S.E. Biomedical Engineering magna cum laude, Case Western Reserve

University

2013 Biomedical Engineering Scholarship Award, Department of Biomedical

Engineering, Case Western Reserve University

2013 Philip K. “Nip” Heim Award, Department of Athletics, Case Western Reserve

University

2012 Alpha Eta Mu Beta Biomedical Engineering Honors Society

2012 Tau Beta Pi Engineering Honors Society

2012 Louis Spencer Dupre Scholarship, Mayo Clinic

2012 Summer Undergraduate Research Fellowship, Mayo Clinic

2012 Cristina A. Camardo Memorial Award, Department of Biomedical Engineering,

Case Western Reserve University

2011 Case Alumni Association Undergraduate Research Grant Award,

Case Western Reserve University

2009-2013 Trustee’s Scholarship, Case Western Reserve University

Teaching Experience

2017 Teaching Fellow

Course: When I’m 64: Transforming your Future

Washington University in St. Louis

Professors: Brain Carpenter, Ph.D., Nancy Marie-Howell, Ph.D., Susan Stark,

Ph.D.

2015 Tutor

Course: Cellular Neurobiology

Washington University School of Medicine

150

Professor: Paul Taghert, Ph.D.

2015 Teaching Assistant

Course: Neural Sciences

Washington University School of Medicine

Professor: David Van Essen, Ph.D.

2012 Teaching Assistant

Course: EBME 370- Introduction to Biomedical Engineering

Case Western Reserve University

Professor: Steven Eppell, Ph.D.

Students Mentored

2017 – 2018 Ajit Perla, High School Student, St. Louis County, MO

2015 - 2018 Jacob Alex, Undergraduate Student, Washington University in St. Louis

2013 - 2016 Mykeal Franklin, High School Student, St. Louis MO

Peer- Reviewed Publications

1. Self W.K., Schoch K.M., Alex J., Barthelemy N., Bollinger J.G., Sato C., Cole T.,

Kordasiewicz H.B., Bateman R.J., Miller T.M. (2018) Protein production is an early

biomarker for RNA-targeting therapies. Under Review

2. Bali T.*, Self W.K.*, Siddique T., Wang LH., Ratti E., Boylan KB., Glass JD., Maragakis

N.J., Caress J.B., Scherer S.S., Appel S.H., Wymer J.P., Gibson S., Zinman L., Mozaffar

T., Jockel-Balsarotti J., Allred M., Liu J., Fisher E.R., Lopate G., Pestronk A., Cudkowicz

M.E., Miller T.M. (2016) Defining SOD1 ALS natural history to guide therapeutic clinical

trial design. Journal of Neurology, Neurosurgery, and Psychiatry. 10.1136/jnnp-2016-

313521.

3. Crisp M.J., Mawuenyega K.G., Patterson B.W., Reddy N.C., Chott R., Self W.K., Weihl

C.C., Jockel-Balsarotti J., Varadachary A., Bucceli R., Yarasheski K.E., Bateman R.J.,

Miller T.M. (2015). Slow CNS SOD1 turnover rate in rats and humans quantified using a

novel in vivo stable isotope protein labeling approach. Journal of Clinical Investigation.

125(7):2772-80

4. Potter K.A., Buck A.C, Self W.K., Callanan M.E., Sunil S., Capadona J.R. (2013). The

effect of reseveratrol on neurodegeneration and blood brain barrier stability surrounding

intracortical microelectrodes. Biomaterials. 34(29), 7001-15.

5. Potter K.A., Buck A.C, Self W.K., Capadona J.R. (2012). Stab injury and device

implantation within the brain results in inversely multiphasic neuroinflammatory and

neurodegenerative responses. Journal of Neural Engineering. 9(4):046020.

151

* Both authors contributed equally to the publication

Book Chapters

1. Self W.K., Miller T.M. (2016) Antisense Oligonucleotides for Amyotrophic Lateral

Sclerosis. Translational Neuroscience – Fundamental Approaches for Neurological

Disorders. IBSN: 978-1-4899-7654-3

Selected Abstracts

1. Self, WK, Schoch KM, Bollinger JG, Mauwenyega KG, Cole T, Kordasiewicz H, Bennett

CF., Bateman RJ, Miller TM. Protein production as an early pharmacodynamics

biomarker for RNA-targeting therapy in neurodegenerative diseases. AAT-AD/PD Joint

Meeting, March 2018. (Podium)

2. Self, WK., Bollinger JG., Mawuenyega KG., Patterson B., Baker-Nigh A., Jockel-

Balsarotti J., Turk J., Bateman RJ., Miller, TM. Determining SOD1 half-life for clinical

trial design. Association for Clinical and Translational Science, Washington D.C., April

2017. (Poster)

3. Self, WK., Mauwenyega KG., Wegener AJ., Kirmess, K., Cole T., Kordiewicz H.,

Yarasheski KE., Bennett CF., Bateman RJ., Miller TM. Stable Isotope Labeling of Amino

Acids Identifies Pharmacodynamics of next generation antisense oligonucleotides

targeting human SOD1 transcripts in an ALS animal. CHSL Neurodegeneration, Cold

Spring Harbor, NY, November 2016. (Poster)

4. Bali T.*, Self W.K.*, Siddique T., Wang LH., Ratti E., Boylan KB., Glass JD., Maragakis

N.J., Caress J.B., Scherer S.S., Appel S.H., Wymer J.P., Gibson S., Zinman L., Mozaffar

T., Jockel-Balsarotti J., Allred M., Liu J., Fisher E.R., Lopate G., Pestronk A., Cudkowicz

M.E., Miller T.M. Survival and disease progression in SOD1 Familial ALS in North

America. American Academy of Neurology, Vancouver, BC, April 2016. (Poster)

5. Self, WK., Mauwenyega KG., Wegener AJ., Kirmess, K., Cole T., Kordiewicz H.,

Yarasheski KE., Bateman RJ., Miller TM. A novel pharmacodynamics biomarker for

antisense oligonucleotide-mediated protein reduction in ALS. Translational Science 2016,

Association for Clinical and Translational Science, Washington D.C, April 2016. (Poster)

6.

Self W.K., Wren M.C., Bu G. Identification of Compounds that Modulate Expression of

Apolipoprotein E in Astrocytes.Mayo Clinic SURF Seminar, Jacksonville, FL, July 2012.

(Podium)

7. Ravikumar M., Self. W.K., Jain S., Selkirk S.M., Capadona J.R. Understanding

Inflammation-mediated Neurodegeneration. Department of Veterans Affairs Research

152

Week, Cleveland, OH, May 2011. (Poster)

Memberships

2016 – Present American Academy of Neurology

2016 – Present Association for Clinical and Translational Science

2015 – Present Society for Neuroscience

Professional Experience

2017 – Present Analyst, Life Sciences Fund, Cultivation Capital Venture Firm, St. Louis, MO

2016 – Present President and Co-Founder, Pocket PhDs, Inc. St. Louis, MO

2015 – 2016 Idea Supporter, The BALSA Foundation. St. Louis, MO

2015 – 2017 Director of Operations, Project Manager, The BALSA Group, St. Louis MO