PROTEOSTASIS OF GLIAL INTERMEDIATE FILAMENTS: DISEASE MODELS, TOOLS, AND MECHANISMS

Rachel Anne Battaglia

A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Cell Biology and Physiology in the School of Medicine.

Chapel Hill 2021

Approved by:

Natasha T. Snider

Carol Otey

Keith Burridge

Douglas Cyr

Mohanish Deshmukh

Damaris Lorenzo

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© 2021 Rachel Anne Battaglia ALL RIGHTS RESERVED

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ABSTRACT

Rachel Anne Battaglia: Proteostasis of Glial Intermediate Filaments: Disease Models, Tools, and Mechanisms (Under the direction of Natasha T. Snider)

Astrocytes are a major glial cell type that is crucial for the health and maintenance of the

Central Nervous System (CNS). They fulfill diverse functions, including synapse formation, neurogenesis, ion homeostasis, and blood brain barrier formation. Intermediate filaments (IFs) are components of the astrocyte cytoskeleton that support many of these functions in healthy individuals. However, upon cellular stress or genetic mutations, IF proteins are prone to accumulation and aggregation. These processes are thought to contribute to disease pathogenesis of different tissue-specific disorders, but therapeutic targeting of IFs is hindered by a lack of pharmacological tools to modulate their assembly and disassembly states. Moreover, the mechanisms that govern the formation and dissolution of IF aggregates are poorly defined. In this dissertation, I investigate IF aggregates called Rosenthal fibers (RFs), which form in astrocytes of patients with two pediatric neurodegenerative diseases, Alexander disease (AxD) and Giant Axonal Neuropathy (GAN). My aim was to gain a better understanding of the mechanisms of how astrocyte IF protein aggregates form and interrogate the role of post- translational modifications (PTMs) in this process. In Chapter 1, I introduce fundamental information about astrocyte biology, IF proteins and their functions, and specific roles of astrocyte IFs in neurodegenerative diseases. In Chapter 2, I present a method to simultaneously isolate mammalian IFs from several tissues in order to examine in vivo-relevant PTMs. In

Chapter 3, I demonstrate the utility of an image-based small molecule screen to identify IF-

iii selective compounds. In Chapter 4, I reveal mechanistic information in AxD and identify a specific PTM as a new marker of AxD severity. In Chapter 5, I describe IF proteostasis in neural progenitor cells and astrocytes from GAN patients. The work in Chapters 4 and 5 involves development of clinically relevant tools for these diseases using induced pluripotent stem cells

(iPSCs) and CRISPR/Cas9 gene editing technology. In Chapter 6, I summarize and contextualize the results of this work, acknowledge the limitations and remaining gaps, and highlight key future directions. In summary, these studies provide new methods, tools, disease models, and mechanisms to understand and target IF cytoskeleton abnormalities in human diseases.

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To my parents, who are my rock, my role models, and my eternal cheerleaders.

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank my advisor, Natasha Snider. Thank you for giving me the opportunity to be a part of your lab and for being part of my journey for the past five years. From day one, there has never ever been a dull moment in the Snider lab. I have enjoyed the excitement and fast-paced energy of the lab and the privilege to learn from my PI side-by-side at the bench. Thank you for giving me the freedom to follow my curiosity and the wisdom to not get spread too thin or pulled too far down the rabbit hole. Natasha, your dedication, passion, creativity, resilience, and heart is an inspiration to me. I am so thankful for your guidance, support, enthusiasm, and encouragement throughout this work and period of growth.

I also thank my research and career mentors outside of the lab. Many thanks to my dedicated thesis committee members: Carol Otey, Keith Burridge, Mohanish Deshmukh, Doug

Cyr, and Damaris Lorenzo for graciously donating their time, providing insights about my project, and supporting my career plans. Adriana Beltran, thank you for being my fearless partner in iPSC gene editing and for all of your encouragement and advice about science, career, and life. Thank you also to Felix Olivares-Quintero for generous technical assistance with gene editing. Diane Armao, I thank you for devoting your time and zealous energy to my education in the methods and interpretations of neuropathology and for our spirited conversations over the years. Virginia Godfrey, our collaborative animal projects did not make it into this dissertation, but I am grateful for your significant assistance and guidance with multiple animal experiments

vi and for our discussions on Alexander disease and beyond. I look forward to seeing our work come to fruition and to our future collaborations.

Ashalla Freeman and Jessica Harrell, thank you for welcoming me into the IMSD family and for your support through the first year in BBSP and advice in navigating rotations. I will never forget the kindness you showed me and invaluable service you provided for myself and my cohort. Adrienne Cox and Folami Ideraabdullah, thank you for your support in the later years of my PhD journey. Finally, Anna O’Connell, you have been an advisor, a confidant, a role model, and a dear friend to me through the years. You have challenged the way I think about communication and life, and I am so thankful for the time and advice you have given to me through the years. I really cannot put into words how thankful I am for your encouragement or how much I look up to you. I do not know where you find the time or the strength to do all of the wonderful things you do in service to your community and your friends, but I am forever thankful that you have always made time for me.

Next, I am very thankful to all of my labmates, both past and present. Thank you to the

McKim lab for welcoming me into the research world and carefully nurturing my passion for science. A special thank you to my previous PI, Kim McKim, for giving me the opportunity to work in his lab and to my dedicated bench mentor, Sarah Radford. I also thank my unofficial mentors and friends, Arunika Das, Victoria Wagner, Allysa Tuite, and Mercedes Enners for their support. Helen Willcockson, thank you for going above and beyond to help set the Snider lab up for success. I learned so much from you in my first few years in the lab: the importance of organization, managing a mouse colony, how to make friends and influence people across campus, and of course, the sweet tones of the Moody Blues. Parijat Kabiraj and Kathryn

Trogden, thank you for paving the way in the PTM and simvastatin projects. Thank you to our

vii fabulous undergraduates for their help to keep the lab running: Deekshita Ramanarayanan,

Megan Dew, Chloe Eaton, Samed Delic, Seyoung Jung, and Morgan Rouse. Jasmine Robinson, thank you for technical assistance and your diligent and careful attention to details, and of course, thank you for our varied and candid discussions. With your adaptability and determination, I am excited to see what you will achieve. Cassie Phillips, thank you for your careful review of the GAN chapter and for being such an enthusiastic mentee. I am happy that you have chosen to join the Snider lab family and look forward to hearing about your future successes. Dr. Maryam Faridounnia, thank you for being a part of team filament. I have enjoyed being your teammate, rock climbing companion, and friend. I am so proud of the effort you have poured into confidently carving out the brave new direction of RNA work and iPSC modeling in microfluidic devices. You are by far the fanciest member of the Snider lab, and I look forward to reading your published work. Last but never the least, Dr. Kaye Alcedo, you have been in the trenches with me almost my entire tenure in the Snider lab. Thank you for being my lab sister, providing the same support, care, communication, and encouragement as a true sister. I admire how dependable you are, how you are always up for a challenge, how you engage boldly with other scientists, and your absolute thirst for knowledge. I know that you will accomplish anything you set your energy towards, and I look forward to seeing you excel at Harvard and beyond.

I was also lucky to have a strong community outside of the lab. A virtual toast to the

Breakfast Club who brightened my Saturdays before lab and filled my weekends with concerts, game nights, and Cheezits. To The Lids (Neil, Lex, Phil, Emma, Julia, and Maggie), thank you for providing a musical release, unforgettable performance experiences, family dinners, and so many laughs. I’ll be your biggest fan in Boston! Garrett Sharpe, thank you for being my friend

viii and much-needed nature guide – both physically by bringing me along for hikes near and far and verbally by sharing your beautiful poems. Never let the iguana-ridden people in this world get you down. Drs. Adelaide Tovar and Yael Escobar, you are my best friends, and I am so grateful that you have been with me from day one at UNC. Thank you for always being there for me through the ups and downs to listen, to make me laugh, and to dole out the occasional hug when

I allow it. You are two of the most whimsical, talented, trustworthy, and genuine women I have ever known. I will always treasure our memories together and look forward to making more.

Last, I thank my family. Cassie and Katie, thank you for always cheering me on and being willing to hear about my research and life in the lab. Liza, you have been a pillar of strength for our family and for me. Thank you for being there for me always, no matter how small the problem or how large the disaster. I am so incredibly lucky to have you in my corner.

You have made NC feel like home, and I will sorely miss spending all my holidays and celebrating all the little things with you.

Mom and Dad, I thank you for everything. Not only would I not physically be here on this Earth without you, but I doubt that I would be completing a PhD program without the support and encouragement you have given me through the years. Thank you for shaping me into the person I am today. Thank you for teaching me to keep pushing even when it feels impossible.

Thank you for every card. Thank you for every sacrifice. Thank you for your unconditional love.

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TABLE OF CONTENTS

LIST OF TABLES ...... xv

LIST OF FIGURES ...... xvi

LIST OF ABBREVIATIONS ...... xix

CHAPTER 1: INTRODUCTION ...... 1

1.1 Astrocytes are crucial for the development and maintenance of the Central Nervous System ...... 1

1.2 Importance of the cytoskeleton in astrocyte structure and function ...... 2

1.3 Intermediate filaments are cell type-specific components of the cell cytoskeleton ...... 3

1.4 Introducing the astrocyte cytoplasmic IFs: vimentin, nestin, synemin, and GFAP ...... 7

1.5 The role of astrocytic IFs in development and physiology ...... 8

1.6 Astrocytic IFs in pathophysiology ...... 13

1.6.1 GFAP and vimentin contribute broadly to neurodegeneration through astrocyte reactivity ...... 13

1.6.2 Mutations in GFAP cause Alexander disease (AxD)...... 14

1.6.3 Giant Axonal Neuropathy (GAN) is caused by defective IF degradation ...... 21

1.7 Dissertation scope and objectives ...... 23

CHAPTER 2: ISOLATION OF INTERMEDIATE FILAMENT PROTEINS FROM MULTIPLE MOUSE TISSUES TO STUDY AGING-ASSOCIATED POST-TRANSLATIONAL MODIFICATIONS...... 26

2.1 Introduction ...... 26

2.2 Protocol ...... 28

x 2.3 Representative Results ...... 34

2.3.1 A new rapid method for high salt-based extraction of IF proteins from multiple mouse tissues using lysing matrix...... 34

2.3.2 Liver K8 and K18 are strongly upregulated and undergo increased phosphorylation and lysine acetylation in livers from old mice...... 35

2.3.3 GFAP is strongly upregulated and lysine acetylated in the brains from old mice...... 36

2.4 Discussion ...... 36

CHAPTER 3: AN IMAGE-BASED SMALL-MOLECULE SCREEN IDENTIFIES VIMENTIN AS A PHARMACOLOGICALLY RELEVANT TARGET OF SIMVASTATIN IN CANCER CELLS ...... 44

3.1 Introduction ...... 44

3.2 Materials and Methods...... 46

3.2.1 Abs, plasmids, and chemicals ...... 46

3.2.2 Cell cultures, immunofluorescence staining, imaging, and viability assay ...... 46

3.2.3 Small-molecule screen, imaging, and quantification of vimentin filament changes ...... 47

3.2.4 Preparation of cell lysates and biochemical analysis of vimentin ...... 48

3.2.5 Electron microscopy ...... 49

3.3 Results ...... 49

3.3.1 Image-based small-molecule screen identifies vimentin-targeting compounds ...... 49

3.3.2 Simvastatin and mevastatin, but not lovastatin and pravastatin, cause dose-dependent bundling of vimentin IFs ...... 50

3.3.3 Simvastatin induces time-dependent vimentin bundling independently of changes on actin filaments and microtubules ...... 51

3.3.4 Simvastatin causes time-dependent changes in vimentin solubility ...... 52

3.3.5 Simvastatin promotes vimentin bundling in vitro ...... 53

xi 3.3.6 Simvastatin, but not pravastatin, promotes vimentin-dependent cell death ...... 54

3.3.7 Ectopic vimentin overexpression sensitizes SW-vim- cells to simvastatin ...... 54

3.3.8 Simvastatin targets vimentin filaments and causes cell death in MDA-MB-231 breast cancer cells ...... 55

3.4 Discussion ...... 56

3.4.1 Intermediate filaments as novel drug targets ...... 56

3.4.2 Vimentin as a pharmacologically relevant target of statins ...... 56

3.4.3 Simvastatin targeting of vimentin ...... 57

3.4.4 Vimentin as a potential determinant of the sensitivity of cancer cells to simvastatin ...... 57

3.4.5 Vimentin as a potential player in statin-associated muscle symptoms ...... 58

3.4.6 The need for pharmacologic tools to probe the function of vimentin and other intermediate filaments ...... 59

CHAPTER 4: SITE-SPECIFIC PHOSPHORYLATION AND CASPASE CLEAVAGE OF GFAP ARE NEW MARKERS OF ALEXANDER DISEASE SEVERITY ...... 74

4.1 Introduction ...... 74

4.2 Results ...... 75

4.2.1 Phosphorylation of Ser13 on GFAP is a marker of the most aggressive form of AxD ...... 75

4.2.2 Phospho-mimic mutation at Ser13 promotes GFAP aggregation ...... 76

4.2.3 Generation of AxD induced pluripotent stem cells (iPSCs) and isogenic controls ..... 77

4.2.4 GFAP accumulation and perinuclear aggregation into RF-like structures in AxD iPSC-astrocytes ...... 78

4.2.5 pSer13-GFAP marks the core of perinuclear GFAP aggregates localized within deep nuclear invaginations ...... 79

xii 4.2.6 Phosphorylation at Ser13 promotes caspase-mediated cleavage of GFAP...... 80

4.2.7 Interference with GFAP cleavage by caspase-6 partially reduces aggregation of the phospho-mimic mutant S13D ...... 81

4.2.8 Caspase-6 expression and GFAP cleavage are upregulated in AxD patients ...... 81

4.3 Discussion ...... 82

4.4 Materials and Methods...... 87

4.4.1 Antibodies ...... 87

4.4.2 Cell lines ...... 88

4.4.3 Human brain tissues ...... 88

4.4.4 Mass spectrometry ...... 88

4.4.5 Site directed mutagenesis, in vitro assembly, transfections, and immunofluorescence ...... 89

4.4.6 Preparation of protein lysates and western blotting ...... 90

4.4.7 Cellular reprogramming, characterization and karyotyping of iPSCs ...... 91

4.4.8 CRISPR/Cas9 genome editing ...... 92

4.4.9 iPSC culture and astrocyte differentiation ...... 92

4.4.10 Transmission electron microscopy...... 93

CHAPTER 5: INTERMEDIATE FILAMENT PROTEOSTASIS FAILURE IN ASTROCYTES AND ASTROCYTE PROGENITORS WITH NATURALLY-OCCURRING KLHL16 (GAN) MUTATIONS ...... 118

5.1 Introduction ...... 118

5.2 Results ...... 121

5.2.1 Unique properties of the human KLHL16 gene necessitate development of a human disease model of Giant Axonal Neuropathy...... 121

5.2.2 Generation and characterization of mutant KLHL16 induced pluripotent stem cells (iPSCs) and isogenic controls ...... 122

xiii 5.2.3 KLHL16 mutant stem cells display aberrant expression of select cytoskeletal intermediate filaments (IFs) ...... 123

5.2.4 KLHL16 regulates the astrocyte IF cytoskeleton ...... 124

5.2.5 Vimentin interacts with GFAP to promote its aggregation...... 126

5.3 Discussion ...... 127

5.4 Materials and Methods...... 132

5.4.1 Cellular reprogramming, characterization, and gene editing of iPSCs ...... 132

5.4.2 iPSC culture and differentiation ...... 132

5.4.3 Antibodies ...... 133

5.4.4 Preparation of protein lysates and immunoblotting ...... 133

5.4.5 RNA isolation and quantitative real-time PCR (qRT-PCR) ...... 134

5.4.6 Immunofluorescence, imaging, and analysis ...... 134

CHAPTER 6: DISCUSSION ...... 148

6.1 IF phosphorylation in neurodegenerative disorders...... 150

6.2 Beyond phosphorylation: other PTMs contribute to IF-related disorders ...... 151

6.3 IFs as biomarkers and drug targets ...... 153

6.4 Rosenthal fibers (RFs): toxic or protective? ...... 154

6.5 Interactions between cytoplasmic IFs and the nucleus ...... 156

6.6 IFs and the spatial protein quality control system ...... 157

6.7 Limitations of iPSC-astrocyte models ...... 158

6.8 Future outstanding questions ...... 160

APPENDIX ...... 162

REFERENCES ...... 225

xiv LIST OF TABLES

Table 1.1 – Summary of Astrocytic IFs ...... 13

Table 3.1 – Positive hits and their corresponding primary targets ...... 73

Supplemental Table 4.1 – Donor information for AxD post-mortem human brain specimens...... 111

Supplemental Table 4.2 – Donor information for control (non-AxD) post-mortem human brain specimens...... 112

Supplemental Table 4.3 – Summary from off-target sequencing from CRISPR/Cas9 editing .. 113

Supplemental Table 4.4 – GFAP phosphorylation motifs and candidate kinases...... 114

Supplemental Table 4.5 – Key Reagents ...... 115

Supplemental Table 5.1 – GAN patient and control information ...... 145

Supplemental Table 5.2 – Summary of off-target Sanger sequencing from CRISPR/Cas9 editing of Patient 7 ...... 146

Supplemental Table 5.3 – qRT-PCR primers ...... 147

xv LIST OF FIGURES

Figure 1.1 – Summary of dissertation scope and objectives ...... 25

Figure 2.1 – Automated lysis and extraction of IF proteins from multiple mouse tissues ...... 41

Figure 2.2 – Molecular and biochemical differences in liver keratins from young and old mice 42

Figure 2.3 – Molecular and biochemical differences in GFAP from brain of young and old mice...... 43

Figure 3.1 – Image-based screen of 1120 biologically active small-molecule compounds for effects on vimentin filaments ...... 61

Figure 3.2 – Quantification of drug-induced changes in vimentin filaments ...... 62

Figure 3.3 – Dose-response effects of statins on vimentin filaments ...... 63

Figure 3.4 – Time-dependent effects of simvastatin on vimentin in SW13 cells ...... 64

Figure 3.5 – Comparison of simvastatin effects on actin filaments, microtubules, and vimentin filaments ...... 65

Figure 3.6 – Biochemical characterization of the simvastatin effect on vimentin in SW13 cells ...... 66

Figure 3.7 – Effects of simvastatin on in vitro-assembled purified vimentin ...... 67

Figure 3.8 – Simvastatin significantly inhibits the growth of SW13-vim+ cells, but not SW13-vim− cells ...... 68

Figure 3.9 – Simvastatin inhibits cell growth and induces dose-dependent apoptotic cell death in SW13-vim+ cells ...... 69

Figure 3.10 – Ectopic expression of vimentin sensitizes SW13-vim− cells to simvastatin treatment ...... 70

Figure 3.11 – Simvastatin induces vimentin bundling and cell death in the vimentin-positive breast cancer cell line, MDA-MB-231 ...... 71

Figure 3.12 – Proposed model for the mechanisms and consequences of vimentin targeting by simvastatin ...... 72

Figure 4.1 – GFAP is phosphorylated on head domain Ser13 in human AxD brain ...... 95

xvi Figure 4.2 – GFAP is phosphorylated on head domain Ser13 primarily in AxD brain from young patients ...... 96

Figure 4.3 – Effect of phospho-deficient and phospho-mimic S13 substitutions on GFAP filament assembly in cells and in vitro ...... 97

Figure 4.4 – Generation and characterization of AxD patient iPSC-astrocytes and isogenic controls...... 98

Figure 4.5 – Oligomerization and perinuclear aggregation of GFAP in AxD iPSC-astrocytes ... 99

Figure 4.6 – pSer13 marks perinuclear accumulation of GFAP within nuclear invaginations in AxD iPSC-astrocytes...... 100

Figure 4.7 – Phosphorylation of Ser13 on GFAP promotes caspase-6 cleavage of GFAP ...... 101

Figure 4.8 – Inhibition of GFAP cleavage by caspase-6 partially alleviates aggregation due to S13D phospho-mimic mutation ...... 102

Figure 4.9 – High expression of caspase-6 in young AxD patient brain tissue correlates with increased levels of cleaved GFAP ...... 103

Supplemental Figure 4.1 – Preparation of brain high salt extracts (HSE) for mass spectrometry analysis of GFAP...... 104

Supplemental Figure 4.2 – Optimization of transient expression for WT and AxD-associated GFAP mutant proteins in SW13vim- cells ...... 105

Supplemental Figure 4.3 – Characterization of pluripotency in AxD and isogenic control iPSCs ...... 106

Supplemental Figure 4.4 – Characterization of astrocyte differentiation ...... 107

Supplemental Figure 4.5 – Three types of staining pattern observed with the pSer13 GFAP antibody in AxD iPSC-astrocytes ...... 108

Supplemental Figure 4.6 – Analysis of major sites of phosphorylation on R79H GFAP expressed in SW13 vim- cells ...... 109

Supplemental figure 4.7 – Presence of cleaved GFAP in in post-mortem brain tissue of AxD children versus adults ...... 110

Figure 5.1 – Human KLHL16 mRNA contains unique properties ...... 136

xvii Figure 5.2 – Gene editing in GAN patient-derived iPSCs restores gigaxonin protein and selective IF proteostasis ...... 137

Figure 5.3 – Nestin is downregulated in GAN Neural Progenitor Cells (NPCs) ...... 138

Figure 5.4 – Vimentin filament organization is restored in GAN isogenic control iPSC-astrocytes ...... 139

Figure 5.5 – GFAP-expressing GAN iPSC-astrocytes exhibit deformed nuclei ...... 140

Figure 5.6 – iPSC-organoids develop GFAP+ astrocytes and display GFAP aggregation in GAN ...... 141

Figure 5.7 – Vimentin promotes aggregation of GFAP ...... 142

Supplemental Figure 5.1 – Generation of GAN iPSCs from patient fibroblasts ...... 143

Supplemental Figure 5.2 – Correction of KLHL16 mutations via CRISPR/Cas9 gene editing . 144

xviii LIST OF ABBREVIATIONS

2-ME 2-mercaptoethanol

DAPI 4’,6-diamidino-2-phenylindole

AD Alzheimer’s disease

ADP Adenosine diphosphate

ALDH1L1 Alcohol dehydrogenase 1 family member L1

ATP Adenosine triphosphate

AxD Alexander disease

BBB Blood brain barrier

BTB Bric-á-brac, tramtrack and broad-complex

CK2 Casein kinase 2 cAMP Cyclic adenosine monophosphate

CASP3 Caspase-3

CCND Cyclin D2

CHI3L1 Chitinase 3-like 1

CRISPR Clustered regularly interspaced short palindromic repeats

CNS Central Nervous System

Cul3 Cullin 3

DDX3X DEAD-box helicase 3 X-linked

DMSO Dimethyl sulfoxide

DTT Dithiothreitol

DRG Dorsal root ganglion

DMEM Dulbecco’s Modified Eagle Medium

xix EAAT2 Excitatory amino acid transporter 2

ECL Enhanced chemiluminescence

EB Embryoid body

EDTA Ethylenediaminetetraacetic acid eIF4A Eukaryotic initiation factor-4A

FDR False discovery rate

GAN Giant Axonal Neuropathy

GFAP Glial fibrillary acidic protein

GPCR G protein coupled receptor

GSK3 Glycogen synthase kinase-3

HD Huntington’s disease

HDAC Histone deacetylase hES Human embryonic stem (hES) cell

HGPS Hutchinson-Gilford progeria syndrome

HMG-CoA -Hydroxy -methylglutaryl-coenzyme A reductase

HMM High molecular mass

HSE High salt extract

HSP27 Heat shock protein 27

HSP70 Heat shock protein 70

IEF Isoelectric focusing

IF Intermediate filament

IPG Immobilized pH gradient iPSC Induced pluripotent stem cell

xx JNK Jun NH2-terminal kinase

JUNQ Juxtanuclear quality control

K Keratin

K8 Keratin 8

K18 Keratin 18

KLHL Kelch-like

KO Knockout

KRT8 Keratin 8

KRT18 Keratin 18

LAM Laminin

LC Liquid chromatography

LCD Low complexity domain

LINC Linker of Nucleoskeleton and Cytoskeleton

MAP1B Microtubule-associated protein 1B

MAP8 Microtubule-associated protein 8

MAPK Mitogen-activated protein kinase

MAPKAP2 MAPK activated protein kinase 2

MLK3 Mixed-lineage kinase-3

MS Mass spectrometry mTOR Mammalian target of rapamycin

MTT Methylthiazolyldiphenyl-tetrazolium bromide

NIM Neural induction medium

NPC Neural progenitor cell

xxi Nrf2 Nuclear factor erythroid 2-related factor 2

PAGE Polyacrylamide gel electrophoresis

PAP Peripheral astrocyte process

PARP Poly (ADP-ribose) polymerase

PBS Phosphate-buffered saline

PKA Protein kinase A

PKC Protein kinase C

PLO Poly-ornithine

PNS Peripheral Nervous System

PP1 Protein phosphatase 1

PP2A Protein phosphatase 2A

PQC Protein quality control

PTM Post-translational modification

PVDF Polyvinylidene difluoride qPCR Quantitative polymerase chain reaction

RACK1 Receptor for protein C kinase 1

RBP RNA binding protein

Rbx1 Ring box protein 1

RF Rosenthal fiber

SDS Sodium dodecyl sulphate

SGL Subgranular layer

SIMVA Simvastatin

SLC1A3 Solute carrier family 1 member 3

xxii STAT3 Signal transducer and activator of transcription 3

SVZ Subventricular zone

TBCB Tubulin Folding Cofactor B

TNFR2 Tumor Necrosis Factor Receptor 2

TX Triton X

ULF Unit length filament

UNT Untreated

UPS Ubiquitin proteasome system

V Vehicle

VIM Vimentin

WT Wild-type

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CHAPTER 1: INTRODUCTION

1.1 Astrocytes are crucial for the development and maintenance of the Central Nervous System

Astrocytes are one of the major glial cell-types of the mammalian Central Nervous

System (CNS). Originally called astrocytes because of their striking star-like appearance1, the current understanding of astrocyte morphology and identity has become even more complex.

Astrocytes are classified into many different subtypes based on morphology and brain region, but the two major classes are fibrous and protoplasmic astrocytes of the white and grey matter, respectively2. Improved imaging techniques have revealed that astrocytes contain many more intricate processes than initially suspected, making them poised to survey their environment3,4.

Astrocytes utilize their long and complex processes to communicate with nearly every cell type in the brain (neurons, microglia, oligodendrocytes, pericytes, endothelial cells) and often act as a bridge between cells, such as neurons and the vasculature5. Their myriad of cell-cell contacts underscores the crucial roles of astrocytes in most CNS functions, including formation and function of synapses6, synaptic plasticity7, neurogenesis8, ion and neurotransmitter homeostasis2, blood brain barrier formation9, and regulating cerebral blood flow5, among others. The structural and functional organization of astrocytes is closely tied to their cell-intrinsic functions as well as the ability to coordinate with other cells. Strikingly, a single human protoplasmic astrocyte can contact up to 2 million synapses while maintaining completely separate territories from its neighbors, so each astrocyte occupies largely its own unique microanatomical region3,4. Despite this apparent separation, astrocytes are connected in a syncytium to neighboring astrocytes via

1 gap junctions and are capable of coordination, such as propagation of calcium waves between cells10. This type of carefully controlled cell communication with diverse targets requires the guidance of a functional and specialized cytoskeleton.

1.2 Importance of the cytoskeleton in astrocyte structure and function

The cytoskeleton consists of three major filamentous systems – actin, intermediate filaments, and tubulin – as well as their associated proteins. Together, these cytoskeletal systems coordinate many fundamental cellular processes, including cell division, cell migration, differentiation, cell signaling, mechanotransduction, and maintenance of cell shape. Beyond these broad functions, the cytoskeleton is also involved in important astrocyte functions. For instance, astrocytes must maintain communication with other cells, such as neurons. The cytoskeleton structurally supports this by participating in regulation of cells shape11. Early evidence indicates that actin and tubulin are required for the astrocyte stellate shape12,13.

Furthermore, the actin cytoskeleton localizes subcellularly to the fine perisynaptic astrocyte processes (PAPs) and is involved in monitoring and adapting the shape of PAPs in response to neuronal signals because it is required for PAP mobility11,14. Similarly, microtubules have been observed in perivascular astrocyte processes15. Additionally, the cytoskeleton actively facilitates astrocyte glutamate metabolism. In response to extracellular glutamate, astrocyte cell-surface expression of glutamate transporters is increased by an actin polymerization dependent mechanism16. While actin and tubulin provide essential functions to all cells and also aid in astrocyte-specific functions, intermediate filaments offer abundant cell type-specific isoforms that allow for customized refinement of astrocyte-specific functions.

2 1.3 Intermediate filaments are cell type-specific components of the cell cytoskeleton

Intermediate filaments (IFs) are a major component of the cytoskeleton of metazoan cells. The IF gene family consists of over 70 genes that are expressed in a cell type- and differentiation-specific manner17. IFs are divided into six different types based on subcellular localization, sequence homology, net acidic charge, and assembly properties17,18. Nuclear IFs are classified as Type V and include Lamin A, Lamin C, Lamin B1, and Lamin B219. The bulk of the

IF family is encoded by cytoplasmic IFs, which are grouped into types I-IV and VI18. Types I and II include the acidic and basic keratins of epithelial cells20. Type III includes vimentin, desmin, glial fibrillary acidic protein (GFAP), and peripherin, which are expressed in mesenchymal cells, muscle cells, astrocytes, and peripheral neurons, respectively 21. Type IV contains neurofilaments, nestin, and -internexin, which are expressed in neurons and neural progenitor cells18. Lastly, type VI IFs consist of the beaded filaments of the lens18.

Regardless of their classification, IFs share unique structural and physical properties. All

IF proteins have a conserved domain structure consisting of a central -helical rod domain that is flanked by N-terminal and C-terminal low complexity domains, which are called the head and tail, respectively22,23. Cytoplasmic IF proteins assemble into mature filaments through a step- wise process whereby individual IF monomers either homo- or heterodimerize, and dimers associate in an anti-parallel orientation to form tetramers24. Eight tetramers combine laterally to form unit length filaments (ULFs). ULFs join together longitudinally to extend the filament, which undergoes radial compaction to achieve the classical 10 nm size that is “intermediate” between actin (5-7 nm) and tubulin (25 nm)24. This process is not dependent upon energy from nucleotides, such as ATP or GTP, and results in the formation of apolar filaments with unique visco-elastic properties compared to other cytoskeletal elements18,25. IF structures are

3 mechanically stable, yet amenable to reorganization in response to different stimuli. This property of IFs is strongly linked to their cell-protective properties, particularly under stress conditions26.

Biochemically, IFs are highly insoluble and resistant to detergent extraction27,28.

Additionally, IFs can have very long half-lives; for example, neurofilament that has a half-life of more than 2.5 months29. Remarkably, although IF structures provide cells with mechanical and structural integrity, they are dynamic and experience constant remodeling via both severing and annealing at the ends as wells as subunit exchange along the length of IF filaments30. IF dynamics are primarily achieved through post-translational modifications (PTMs). IFs are highly modified by phosphorylation, especially in the head and tail domains, which can affect solubility, assembly, subcellular localization, binding partners, and turnover31. These unique properties separate IFs from the other major filament networks of the cytoskeleton and contribute to their cellular functions.

The classic and most widely appreciated function of IFs is their role in providing cells and tissues with mechanical integrity and resistance to physical strain32. This is exemplified by the phenotype of the keratin-14 knockout mice, which exhibit severe skin blistering upon loss of this IF that is normally expressed in the basal cells of the stratified epithelium33. IFs support the mechanical integrity of cells and tissues by affecting the stiffness of cells and by regulating cell- cell and cell-environment interactions at desmosomes, hemidesmosomes, and focal adhesions18,32. However, as hinted by their dynamic properties, IFs are broadly involved in an array of non-mechanical cellular processes, including the cellular stress response, cell signaling, transcription, organization of the cellular space, cellular trafficking, and cell migration17,18.

4 IFs are critical players in the stress response, cellular signaling pathways, and transcription. Cells increase expression of IFs in response to multiple stressors, including heatshock, mechanical stress, aging, alcohol exposure, or infection26. Cellular stress can also affect post-translational modifications of IFs, particularly increasing phosphorylation31. It is hypothesized that IFs can act as a phosphate sponge to protect the cell from excess activity of stress kinases26,31. IF phosphorylation may also contribute directly to cellular signaling pathways by promoting binding to signaling components, such as the 14-3-3 family of adaptor proteins, which are involved in virtually all major cellular functions34. IFs regulate transcription indirectly through cellular signaling and also through direct mechanisms. For example, Keratin 8/Keratin

18 bind to the cytoplasmic domain of Tumor Necrosis Factor Receptor 2 (TNFR2) and suppress

35 TNF-induced Jun NH2-terminal kinase (JNK) signaling . Nuclear IFs regulate gene expression and cell differentiation by controlling the spatial organization of DNA and promoting heterochromatin epigenetic modifications36. More recently, it was shown that the cytoplasmic IF

Keratin 17 can enter the nucleus and regulate gene expression of inflammatory and proliferation related genes37,38. In fact, many other cytoplasmic IFs contain one or more nuclear localization signals, providing a new possible connection between the cytoplasm and the nucleus that could be exploited for diverse cell signaling mechanisms39.

IFs organize the intracellular space through their interactions with other cytoskeletal components and organelles18. For instance, cytoplasmic IFs associate with the nuclear membrane through Plectin, an IF linker protein, which binds to Nesprin-3, a member of the Linker of

Nucleoskeleton Cytoskeleton (LINC) complex40. Cytoplasmic IFs form a cage around the nucleus and mediate actin-driven nuclear positioning, a process that is important during cell migration and development41. Additionally, cytoplasmic IFs interact with many other organelles,

5 including mitochondria, the Golgi apparatus, and several components of the protein degradation pathway, including lysosomes, autophagosomes, and the proteasome18. The function of IF interactions with mitochondria may be to serve as an anchor for docking of mitochondria since loss of vimentin leads to increased mitochondrial motility and disrupted ATP production42.

Similarly, evidence from vimentin knockout cell lines indicates that cytoplasmic IFs also dock lysosomes and regulate their motility and positioning43. Cytoplasmic IFs are broadly involved in cellular trafficking through their interactions with the Golgi apparatus and various exocytic and endocytic pathway members44. These interactions contribute to protein trafficking to the membrane as well as endocytosis in a very cell type-specific manner44.

IFs also play an important but context-dependent role in cell migration, which is highly dependent upon the particular IF protein and the cellular context. Motile and invasive cells tend to upregulate the Type III IF, vimentin, which promotes cell migration in various ways, such as cell shape changes, loss of cell-cell contacts, and increased turnover of focal adhesions45-47. Loss of vimentin attenuates cell migration in fibroblasts46,48, leukocytes49, astrocytes50, and various cancer cells51-53. Vimentin participates in cell migration through interactions with microtubules, actin filaments, and focal adhesions. Vimentin filaments aid in maintenance of cell polarity during directed cell migration by providing a stable pattern for microtubules54. In support of this, vimentin expression positively correlated with persistence time in migration of cancer cells55.

Additionally, vimentin interacts with actin directly and indirectly through plectin to promote cell migration56,57. Transverse actin arcs drive retrograde movement of small vimentin particles to the perinuclear space in a plectin-dependent manner where it is suggested that vimentin reciprocally restricts retrograde actin flow58,59. Also in concert with actin and plectin, IFs promote focal adhesion localization and turnover during collective astrocyte migration, which restricts traction

6 forces to the leading edge of the leader cells60. This multitude of interactions of IFs that is involved with cell migration can vary depending on the cell type, as evidenced by the complex roles of epithelial keratins in cell migration61. As with cell migration, all of the general IF functions and interactions described here depend upon the particular IF protein and the cellular context.

1.4 Introducing the astrocyte cytoplasmic IFs: vimentin, nestin, synemin, and GFAP

One of the unique aspects of cytoplasmic IFs compared to other cytoskeletal filaments is the wide variety of IF genes that are expressed differentially depending on the cell type. Even within cell types, unique combinations of IFs are expressed in specific contexts, such as development, injury and aging. Astrocytes exemplify this context dependent IF expression.

Throughout development, astrocytes express four different IFs: vimentin, nestin, synemin, and glial fibrillary acidic protein (GFAP)62. Both astrocytes and neurons arise from radial glia progenitor cells, which reside in the ventricular zone and express vimentin, nestin and synemin63,64. As astrocytes mature, expression of these early IFs typically decreases and is replaced by GFAP65. However, while vimentin expression decreases to undetectable levels in most astrocytes66, it remains expressed in some astrocytes, such as the Bergmann glia of the cerebellum67. While GFAP is considered a classical marker of mature astrocytes, it is appropriate to mention that not all astrocytes express GFAP. Further, non-astrocytic cells have been reported to express GFAP, including peripheral glia (non-myelinating Schwann cells68 and enteric glia69), neurogenic stem cells in the subventricular zone (SVZ) of the lateral ventricles70 and of the subgranular layer (SGL) of the hippocampus71 and even speculatively in some cells outside of the nervous system (e.g. liver stellate cells72). Likewise, the other astrocytic IFs are not restricted

7 solely to astrocytes since vimentin is expressed in most mesenchymal cells, synemin is expressed in muscle cells, and nestin is expressed in neuroepithelial and other adult stem cells73,74.

When the CNS suffers injury, astrocytes undergo a process called astrocyte reactivity, which is generally characterized by hypertrophy of cell branches, increased proliferation, and gene expression changes, notably the upregulation of IF genes75,76. Reactive astrocytes express a mixture of immature and mature IF proteins, including nestin, vimentin, GFAP, and sometimes synemin, but the proportion and timing of gene expression is especially sensitive to the type of

CNS insult77-79. Interestingly, it was recently reported that aging astrocytes undergo transcriptional changes similar to neurotoxic reactive astrocytes80, and IF protein and gene expression increase in the aging brain, among other tissues81.

1.5 The role of astrocytic IFs in development and physiology

The cell type and developmental specificity of IFs suggests that individual family members may hold unique properties and functions that are required in different tissue contexts.

The function of the astrocytic IF cytoskeleton has been investigated using mutant mouse models, which harbor global deletions in vimentin, Gfap, or nestin82-88. Mice lacking vimentin, Gfap, or both genes are viable, develop and reproduce normally, and have no gross defects in CNS morphology82-86,89. Structurally, GFAP and vimentin can homo-polymerize or co-polymerize with each other. Since nestin and synemin cannot homo-polymerize with themselves or co- polymerize with each other, the vimentin/Gfap double KO animals lack all astrocytic cytoplasmic IF filaments90. Importantly, these mice do not upregulate other cytoplasmic IFs to compensate for the loss of one or two IF genes, making them a valuable model to tease out specific astrocytic IF functions. Although vimentin, Gfap, and vimentin/Gfap double knockout

(KO) mice do not display major CNS morphological or behavioral deficits, there are subtle

8 defects that reveal astrocytic IF functions in each of these animal models, which are summarized in Table 1. Importantly, while some functions are shared amongst all astrocytic IFs, others are unique to one particular IF. Of all the astrocytic IFs, only GFAP is uniquely restricted largely to astrocytes, making it an especially interesting candidate to execute astrocyte-specific functions65.

One of the major functions of IFs is to maintain the mechanical integrity of cells and tissues. Under basal conditions, the brain does not experience much mechanical strain since it is encased within the skull and cerebral spinal fluid. However, astrocytic IFs are still important for providing protection from mechanical stressors. In a model of traumatic brain injury, Gfap KO mice were hypersensitive to cervical spinal cord injury91. In the retina, vimentin/Gfap KO mice had decreased resistance to severe mechanical challenge92. These studies indicate that GFAP and vimentin are critical for protecting the CNS from mechanical forces. Nevertheless, astrocytic IFs play several non-mechanical roles in the CNS.

Astrocytic IFs promote cell migration. Primary astrocytes from vimentin KO, Gfap KO, and double KO mice displayed decreased individual cell migration speed50. In agreement with this, knockdown of nestin, vimentin, and Gfap in primary rat astrocytes revealed a decrease in collective cell migration speed60. Mechanistically, astrocytic IFs restrict traction forces of collectively migrating cells to the leading edge of leader cells by controlling localization and turnover of focal adhesions. Astrocytic IFs also participate in nuclear positioning during collective cell migration, which is important for polarization41. In both individual and collective cell migration, the difference in speed was additive, with the most significant difference occurring between wild-type and completely IF-deficient astrocytes.

Astrocytic IFs also regulate cell division. IFs are classically known to undergo phosphorylation during cell division to facilitate the dynamic reorganizations that take place

9 during this process31. Indeed, phosphorylation of vimentin and GFAP is required for cell division in cancer cells93,94. In primary astrocytes, it has been shown that vimentin phosphorylation is required for normal cell division95. However, recent studies indicate that the role of astrocytic IFs during cell division may extend beyond reorganization. For instance, vimentin maintains cellular proteostasis by coordinating asymmetric cell division of proteosomes and aggregated proteins in neural stem cells96. Evidence from primary mouse astrocytes indicates that vimentin may play a similar role in promoting asymmetric cell division in astrocytes where proteostasis is compromised97. Several studies in glioma cells and primary mouse astrocytes suggest that GFAP reduces proliferation98-101. These observations are in line with the fact that mature astrocytes typically do not divide. However, there is also evidence that GFAP can promote cell division under certain circumstances. For instance, Gfap deficient mice display reduced Schwann cell proliferation after damage to peripheral nerves, which causes delayed nerve regeneration102.

Additionally, GFAP-positive astrocytes can regain their proliferative ability during CNS injury, possibly achieving proliferation through reorganization of GFAP by PTMs, so GFAP related regulation of cell division is very context-dependent.

Astrocytes are very active secretory cells, and astrocyte IFs play a role in cell trafficking to support this function62. Chemical disruption of the astrocytic IF cytoskeleton in primary mouse astrocytes decreased the motility, track length, and displacement of exocytotic vesicles without affecting the structure of the microtubule and actin networks103. Recycling vesicles are also regulated by IFs in a stimulus-dependent manner. Upon stimulation with ATP, endosomes and lysosomes decreased mobility in wild-type astrocytes but did not change speed in vimentin/GFAP knockout astrocytes104. Recently, nestin deficient primary mouse astrocytes showed reduced exocytotic vesicle mobility and altered dynamics of exocytotic vesicles fusing to

10 the membrane105. Nestin also regulates recycling vesicle transport because endocytic vesicles were larger and had decreased mobility in nestin deficient astrocytes105. Thus, different astrocytic

IFs can promote baseline vesicle mobility, alter trafficking to respond to extracellular stimuli, and control vesicle-membrane dynamics.

GFAP plays a unique role in glutamate metabolism. In the Gfap KO mice, cell trafficking of glutamate receptors is specifically reduced, leading to a decreased capacity for glutamate uptake in Gfap deficient astrocytes106. Surprisingly, in the Gfap KO mice, there is also a decrease in neuronal expression of glutamate transporters, suggesting a potential non-cell autonomous effect of loss of astrocyte GFAP106. Moreover, GFAP has been shown to interact directly with the glutamate transporter GLAST and to preserve its localization to the plasma membrane during hypoxic stress107. Further, expression of GFAP has been correlated with inversely with glutamine synthetase expression and activity, indicating that changes in GFAP expression can disrupt glutamate metabolism108,109.

These cell biological roles of astrocyte IFs contribute to larger physiological processes, such as astrocyte reactivity110. The vimentin/Gfap double KO mice display attenuated astrocyte reactivity in multiple injury models, including spinal cord and cortex stab injuries and ischemia89,111. Specifically, the loss of both vimentin and Gfap prevents the increased astrocyte branch hypertrophy that is typically observed during astrocyte reactivity112,113. While synemin and nestin are also upregulated in reactive astrocytes, they cannot polymerize into filaments without either vimentin or GFAP, which explains why they cannot compensate structurally for the loss of vimentin and Gfap in the double KO mice90. The loss of Gfap alone contributed to reduced astrocyte reactivity in other injury models, including an Alzheimer’s disease model,

Experimental Autoimmune Encephalomyelitis inflammation model, and infectious disease

11 models114-116. This indicates that GFAP may play a unique role in certain contexts besides acute, mechanical injury where vimentin is able to compensate.

Astrocytes are also known to regulate neurogenesis2. The astrocytic IFs vimentin, GFAP, and nestin play an important role in this regulation. Vimentin/Gfap double KO mice display increased neurogenesis, and nestin KO mice phenocopy this increased neurogenesis117,118.

Surprisingly, although neural stem cells express nestin, astrocyte-derived nestin inhibits neurogenesis non-cell autonomously118. The loss of either Gfap and vimentin or nestin in astrocytes reduced endocytosis of Jagged-1, a ligand for the Notch receptor, which contributes to neuronal cell fate decisions117,118. Thus, multiple astrocytic IFs inhibit neurogenesis through

Notch signaling. Loss of astrocytic IFs affects learning and memory since nestin KO mice show impaired long-term memory and vimentin/Gfap KO mice show increased memory extinction118,119. The inhibitory effect on neurogenesis is further reflected in neural transplantation studies in the retina and spinal cord where enhanced transplantation efficiency was achieved in the vimentin/Gfap KO mice120,121.

During development and homeostasis, astrocytes are also key regulators of neuronal synapse plasticity and function122. Gfap KO mice displayed changes in synaptic plasticity in specific brain regions. In the cerebellum, Gfap KO mice are deficient in long-term depression, which coincided with a functional impairment in eyeblink conditioning86. In the hippocampus, a region that normally harbors many GFAP positive astrocytes, there are conflicting results with one group reporting enhanced long-term potentiation in Gfap KO mice and a separate group reporting no difference85,86. Potential explanations for this discrepancy could be the different genetic targeting strategies employed to knockout Gfap or heterogeneity of astrocytes in those regions. The role of GFAP in glutamate metabolism and vesicle trafficking likely positively

12 contributes to astrocyte support of neuronal function103,106. Conversely, GFAP can also negatively regulate neurite outgrowth in primary mouse astrocytes123.

Beyond neurons, astrocytes interact with many other cell types to support brain architecture. Astrocytes are important components of the blood brain barrier (BBB), where they ensheath blood vessel endothelial cells and pericytes9. Defects in the BBB structure and function were observed in aged Gfap KO mice124. In line with this finding, primary astrocytes from Gfap

KO mice were unable to promote BBB properties in co-cultures with endothelial cells125.

Astrocytes also interact with oligodendrocytes and contribute to myelin formation and maintenance126. Aged Gfap KO mice display disorganization of myelin and white matter loss, indicating that GFAP contributes to astrocyte long-term support of myelin maintenance124,127.

Table 1: Summary of Astrocytic IFs Name Classification Cell types Functions Nestin Type IV Astrocytes, neuroepithelial Cell migration, trafficking, stem cells, adult stem cells astrocyte reactivity, neurogenesis Synemin Type IV Astrocytes, muscle cells Astrocyte reactivity Vimentin Type III Astrocytes, mesenchymal Mechanical stress, cell cells, radial glia, adult neural migration, asymmetric cell stem cells division, docking organelles, astrocyte reactivity (injury), neurogenesis GFAP Type III Astrocytes, neural stem cells Mechanical stress, cell division, (SGL and SVZ), non- cell migration, glutamate myelinating Schwann cells, metabolism, trafficking, enteric glia, liver stellate cells astrocyte reactivity (injury and neurodegeneration), maintenance of blood brain barrier, myelin maintenance, neurogenesis

1.6 Astrocytic IFs in pathophysiology

1.6.1 GFAP and vimentin contribute broadly to neurodegeneration through astrocyte reactivity

Since the 19th century, it has been recognized that glia exhibit changes in pathological conditions128. It is now appreciated that reactive astrocytes are nearly ubiquitous in CNS

13 pathology, including neurodegeneration129. Depending on the injury, brain region, age, sex, and several other factors, reactive astrocytes can be incredibly heterogeneous and display both protective and toxic effects that are not necessarily mutually exclusive129. A characteristic feature of reactive astrocytes is the upregulation of astrocytic IFs, including vimentin, GFAP, nestin, and in some cases synemin77-79. The role of IFs in reactive astrocytes is complex and likely dependent upon the underlying CNS insult. Examination of vimentin/Gfap KO mice subject to stab injury of the cortex revealed that IFs are required for astrocyte reactive properties, including morphological and gene expression changes89,112. Another study reported an altered transcriptional response of vimentin/Gfap KO reactive astrocytes in an Alzheimer’s disease mouse model130. Ultimately, this attenuated astrocyte reactivity leads to functional consequences since knockout of vimentin and Gfap during stab injury leads to defective glial scar formation89.

In an Alzheimer’s disease background, loss of vimentin and Gfap decreases the interaction of astrocytes with extracellular amyloid  plaques130. Thus, increased astrocytic IF expression promotes morphological changes and migratory properties of reactive astrocytes, which can be beneficial during wound healing or harmful during regeneration. A better spatial and temporal understanding of the role of astrocytic IFs during CNS pathology could make them attractive therapeutic targets in a broad range of contexts.

1.6.2 Mutations in GFAP cause Alexander disease (AxD)

Since IFs are involved in multiple astrocyte functions, it is unsurprising that filament- altering mutations or stress-associated dysregulation of these proteins is linked to astrocyte dysfunction and disease. In fact, the first disease ever to be attributed primarily to astrocyte dysfunction is Alexander disease (AxD), which is caused by autosomal dominant mutations in

GFAP131. AxD is a rare and fatal neurodegenerative disease and leukodystrophy that is

14 characterized by abnormalities in the white matter132. While the natural history of AxD is still being defined, currently cases are divided into two different categories: Type I AxD and Type II

AxD133. These two forms of the disease impact different brain regions. Type I AxD largely affects the frontal lobe, whereas Type II AxD is localized to hindbrain regions, and this discrepancy leads to different manifestations of the disease. Type I AxD is characterized by an early age of onset usually within the first 4 years of life, macrocephaly, seizures, encephalopathy, developmental and motor delays, a general failure to thrive, and distinct MRI features133,134.

Type II AxD is characterized by a later age of onset ranging across the lifespan, autonomic dysfunction, bulbar symptoms, ocular movement abnormalities, and atypical MRI features133.

Over 90 percent of AxD patients contain mutations in the coding region of GFAP, and there is no clear genotype-phenotype correlation except for the hotspot mutations R79H and R239H, which account for about a third of AxD cases and tend to cause Type I AxD133. Still, some patients harbor the same exact point mutations at these or other GFAP residues but present with very different symptoms. Validated molecular markers that distinguish Type I and Type II AxD are not currently available.

Type I and Type II AxD share certain a number of molecular features. First, AxD astrocytes undergo a process called astrocyte reactivity. Reactive astrocytes in AxD have altered morphology and upregulate nestin, synemin, vimentin, and GFAP110,135. Second, GFAP mutations lead to increased expression and accumulation of GFAP within cytoplasmic aggregates called Rosenthal fibers (RFs). RFs are amorphous, electron dense inclusions that contain predominantly GFAP (both wild-type and mutant136), ubiquitin137, and small heat shock proteins136 and are surrounded by normal filaments when examined at the ultrastructural level138.

While RFs are present in other some contexts where reactive astrocytes are present, such as

15 astrocytic scars, multiple sclerosis plaques, and pilocytic astrocytoma, they are far more abundant and widespread in AxD and are considered the pathological hallmark of the disorder139,140. Recent mass spectrometry studies have revealed novel components of RFs, including receptor for activated protein C kinase 1 (RACK1), cyclin D2 (CCND2), and interestingly, several ribosomal proteins and stress granule proteins (40S ribosomal proteins, eiF4A, DDX3X)141. Finally, beyond RFs, total GFAP, including soluble subunits are also elevated, and it is suggested that the soluble mutant protein may be the most toxic142,143. As with many aggregation disorders, it is currently unclear whether RFs are protective, pathogenic, or inert.

To elucidate molecular mechanisms in AxD, multiple model systems and tools have been developed. Overexpression of mutant GFAP has been performed to study GFAP aggregation both in vitro (purified protein filament assembly reactions) and in different cell types, including adrenal carcinoma SW13 cells lacking vimentin (SW13vim-), glioma cell lines, and primary astrocytes136,144. These models have been especially helpful in deciphering the effects of GFAP mutations on GFAP protein and filament properties and interactions. For example, it has been demonstrated both in vitro and in cell culture that AxD-causing GFAP mutations decrease GFAP solubility136. Decreased solubility reduces the soluble GFAP pool available for subunit exchange along mature filaments, which could ultimately affect turnover and therefore the overall quality of the filament network. Further, GFAP filaments can tolerate a small amount of mutant protein or assembly-deficient isoforms145. Incorporation of mutant GFAP into the filament network may further compound filament-protein interactions, subunit exchange and turnover, and stability.

Mutant GFAP can also sequester proteins from their typical cellular localizations. One example of this is the binding of the chaperones heat shock protein 27 (HSP27) and B-crystallin to

16 GFAP aggregates in cells expressing AxD-causing mutations136. These chaperones normally interact with soluble and filamentous GFAP, but their sequestration within aggregates may compromise their other functions, such as involvement in the astrocyte stress response136,146.

Beyond in vitro and cellular models, animal models have helped to identify disease mechanisms and reveal crosstalk between astrocytes and other cell types in AxD. Transgenic

Drosophila models of AxD that overexpress mutant human GFAP in glial cells and are a powerful system to examine mechanisms of RF formation in vivo and to perform genetic and pharmacological screens147,148. More recently, zebrafish models of AxD have been developed that express human GFAP with disease-relevant point mutations from the zebrafish gfap promoter149. These zebrafish models also recapitulate cellular pathology and provide another system to examine disease mechanisms and perform screens149,150.

Likewise, transgenic mice have been used to model AxD. Overexpression of wild-type human GFAP (Tg) causes accumulation of RFs151. However, mice that overexpress only the wild-type protein do not accurately represent the human disease where both wild-type and mutant proteins are present. To address this, two knockin mouse models were engineered to express AxD-causing point mutations at the Gfap locus142. These mice harbor either the R76H or

R236H point mutations, which are equivalent to the human R79H and R239H hotspot mutations.

Even though the knockin mice have increased expression of total, soluble, and insoluble GFAP, accumulate RFs, and exhibit reactive astrocytes, their phenotype is fairly mild, displaying only a slight weight loss142. Crossing transgenic GFAP overexpressing mice (Tg) with the mutant knockin mice (KI) leads to lethality within approximately one month. However, this mouse model (Tg+KI) still fails to recapitulate key aspects of AxD – myelin abnormalities, macrocephaly, developmental delays, and reduced motor coordination. Most recently, a new rat

17 model of AxD, harboring the R237H point mutation (equivalent to human R239H) was generated and recapitulates not only the pathology evident in the mice but also more extensive reactive astrocytes, myelin deficits and behavioral abnormalities, including failure to thrive, reduced motor coordination, and partial lethality152.

Together, these models have revealed many of the downstream effects of AxD-related

GFAP mutations. A consistent finding across model systems is that the cellular proteostasis machinery is altered by mutant GFAP. It has been shown that mutant GFAP impairs proteasome activity153. Surprisingly, the soluble mutant GFAP displayed the strongest inhibitory effect on the proteasome143. Conversely, mutant GFAP increases autophagy, which promotes degradation of accumulated GFAP147,154. There is also evidence that protein folding pathways are impaired because chaperones co-localize with RFs and overexpression of chaperone proteins, including

B Crystallin, HSP27, and HSP70 reduces aggregation and is protective against cell death136,147,155. Many stress signaling pathways are activated by mutant GFAP and likely coordinate crosstalk between proteostasis networks. Mutant GFAP activates c-Jun N-terminal kinase (JNK) signaling, and this occurs through proteasome impairment153. Other members of the mitogen-activated protein kinase (MAPK) family are also activated downstream of mutant

GFAP, including mixed-lineage kinase 3 (MLK3) and p38, which negatively regulates mammalian target of rapamycin (mTOR) and promotes autophagy154. Additionally, signal transducer and activator of transcription 3 (STAT3) signaling has been implicated148. The Yes- associated protein (YAP) mechanosensitive signaling pathway is also upregulated in AxD

Drosophila, mouse and patient tissues, and this is accompanied by increased actin stress fibers and brain stiffness in model systems156. AxD mouse models also display increased oxidative stress, and suppression of this pathway through knocking out Nuclear factor erythroid 2-related

18 factor 2 (Nrf2) partially attenuates gliosis157. Similarly, overexpression of antioxidant genes in the Drosophila AxD model reduced cell death but not GFAP aggregation147. DNA damage markers, such as p53 are upregulated in GFAP positive cells in the Drosophila and mouse AxD models as well as AxD patient tissue148. Outside of astrocytes, there is also evidence that mutant

GFAP has non-cell autonomous effects. There is increased inflammation and upregulation of cytokines in AxD158,159. The AxD reactive astrocytes show decreased coupling to other astrocytes, thereby affecting astrocyte-to-astrocyte communication135. One inflammatory molecule, Chitinase 3-like 1 (CHI3L1), is secreted by AxD astrocytes and can impair oligodendrocyte development and function, which could contribute to the white matter abnormalities seen in AxD patients160. Decreased cell surface expression of glutamate transporters is observed in AxD astrocytes along with a decreased ability to buffer potassium levels that could contribute to an excitotoxic environment for neurons135,161. Mutant GFAP also promotes glial driven nitrous oxide signaling that mediate non-cell autonomous neuronal death148.

Although these AxD models have shed light on the molecular mechanisms of mutant

GFAP toxicity, the exact effect of IF aggregation on cell-specific functions in astrocytes and other cell types is still largely unknown, but with the advent of more sophisticated human cell models, there is a growing interest in this field. Several groups have described cell type-specific functional alterations in AxD, including compromised glutamate trafficking in astrocytes161, reduced intracellular trafficking in neurons and astrocytes162,163, and aberrant glial cell-cell interactions160. In order to fully reveal the effects of IF aggregation on these cell functions, a better understanding of the dynamics of aggregate formation and disassembly as well as better tools to manipulate these dynamics will be required.

19 The dynamics of how RFs form in real time and how stable they are is not known.

Fortunately, RFs are reversible, since strategies using either antisense oligonucleotides to reduce

Gfap mRNA or overexpression of the chaperone protein B-crystallin can both clear RFs in

AxD rodents152,155,164. Proteomic studies in AxD mice have also revealed that even though mutant GFAP accumulates in RFs, protein turnover is actually accelerated specifically for total mutant GFAP – although it’s not clear what form of GFAP (soluble subunit, filament, or aggregate) is being cleared165. Thus, AxD astrocytes maintain a remarkable capacity for GFAP proteostasis, and there is therapeutic potential in strategies that tip the scale of proteostasis by reducing production, increasing the degradation, or preventing aggregation of GFAP.

The formation of RFs has been examined at different developmental timepoints. In AxD mice, RFs vary considerably in their shapes, sizes, and numbers, but in general, RFs increase in number and size in older animals138. Small RFs are often observed surrounding larger RFs, and it is speculated that these smaller aggregates coalesce to form the larger aggregates, an idea that is supported by live imaging of overexpressed mutant GFAP97. Of particular interest is the presence of small 15-30nm granules, which can be labeled by GFAP and B crystallin by immunogold

EM labeling. These small, dense granules are present as deposits along otherwise normal-looking

IFs. In fact, RFs of all sizes are always observed among swirls of apparently normal looking intermediate filaments, which has led to the hypothesis that IF filaments may provide a scaffold upon which new aggregates are seeded138. One possible mechanism for this seeding hypothesis is the B-crystallin chaperone protein may act as a linker for mutant GFAP subunits or oligomers to tether onto GFAP filaments. Formation of RFs does not require mutant GFAP protein but seems to be rooted in an excess amount of GFAP protein, since RFs form upon overexpression of human GFAP151 and RFs can be cleared by decreasing Gfap mRNA expression152,164.

20 However, in AxD, mutant GFAP protein clearly plays a pivotal role since the actual mutant protein has been identified within RFs136 and mutant GFAP initiates the cascade of events that lead to RF accumulation in patients. The mechanism of how exactly GFAP mutations lead to impaired proteostasis and excess levels of GFAP is a major focus of this project. We hypothesize that mutations in GFAP disrupt post-translational modifications of GFAP and in turn, alter IF properties, such as protein-protein interactions, filament dynamics, and protein turnover, ultimately leading to compromised GFAP proteostasis and triggering cell stress pathways.

1.6.3 Giant Axonal Neuropathy (GAN) is caused by defective IF degradation

Beyond mutations in astrocytic IF genes, mutations in IF regulatory proteins can also cause IF accumulation and dysfunction, leading to disease. Giant Axonal Neuropathy (GAN) is a rare pediatric neurodegenerative disease associated with IFs, which was originally described in

1972166,167. It is a length-dependent distal axonopathy affecting both the PNS and CNS, along with several other tissues168. Evidence from GAN mouse models also implicates early impacts on the Autonomic Nervous System169. GAN patients display a characteristic physical appearance of a pale complexion, high forehead, skin abnormalities, and often, but not always, tightly curled hair, the lack of which is associated with a milder disease progression170. Individuals with GAN experience muscle weakness and ataxia, loss of ambulation, gastrointestinal issues (including constipation and regurgitation), and ultimately succumb to respiratory failure by the second or third decade of life168.

One remarkable pathological hallmark of GAN is the presence of IF accumulations in several tissues, including vimentin in fibroblasts, desmin in muscle cells, neurofilaments in neurons, GFAP in astrocytes, and more recently, GFAP in lens epithelial cells171-174. Other cells show IF accumulations of unknown origin, including melanocytes, endothelial cells, and

21 Schwann cells173. Even some keratins appear to be affected, as evidenced by the curly hair, which when examined experimentally, displays unusual longitudinal grooves and keratin structural abnormalities175,176. While IFs accumulate in multiple cell types and tissues in GAN patients171, the most severely affected cells by far are sensory and motor neurons, so these cells have attracted the most attention in research studies. In GAN patients, neurons accumulate disorganized bundles of neurofilaments in the cell body and axons, causing the characteristic axonal swelling from which the name Giant Axonal Neuropathy arises166,167. While many neurons express neurofilaments and accumulate IFs in the CNS and Peripheral Nervous System

(PNS), it is speculated that sensory and motor neurons in particular are so sensitive because of the length of their axons. In GAN, axon degeneration starts from the distal axonal compartment and progresses proximally towards the cell body166,177. Although these IF accumulations were the only early molecular clue as to the pathogenesis of GAN, the disorder is now known to be caused by homozygous loss-of-function mutations in the KLHL16 gene178.

KLHL16 encodes the protein gigaxonin, which is an E3 ubiquitin ligase substrate adaptor in the BTB (bric-á-brac, tramtrack and broad-complex)/KLHL (Kelch-like) gene family179.

Gigaxonin promotes degradation of proteins through the ubiquitin proteasome system, and it has been shown that gigaxonin can form a functioning E3 ligase complex with cullin 3 (Cul3) and ring box protein 1 (Rbx1) capable of ubiquitination180. Gigaxonin is ubiquitously expressed and has also been shown to interact with and promote degradation of many IF proteins, including vimentin, epidermal keratins, peripherin, -internexin, neurofilament, and GFAP162,181-184.

Beyond IFs, gigaxonin has also been reported to target microtubule-related proteins for degradation, including MAP1B, MAP8, and tubulin folding cofactor B (TBCB)168. However,

22 microtubules and actin appear normal in GAN primary patient fibroblasts, whereas vimentin forms large, perinuclear aggregates183,185.

As with AxD, the full effect of abnormally accumulated IFs in GAN are unknown.

Moreover, most examination of the impacts of IF aggregation on cell functions has been done in neurons. By ultrastructural examination of GAN patient neurons, it has been suggested that one of the main consequences of bundled neurofilaments is steric hindrance172,173. The aggregates are thought to disrupt neuronal trafficking and displace mitochondria and other organelles, which are often localized either within the bundles of IFs or towards the periphery of the axons171. This idea is further supported by evidence from KLHL16 knockout mouse primary neurons, which display decreased mitochondrial motility and altered metabolism162. Similar to AxD, GAN patients display abundant RFs within astrocytes173. However, the contribution of astrocytes to

GAN has been underappreciated, and the effects of GFAP aggregates on mitochondrial motility and other critical cell functions of astrocytes, which likely contribute to neuronal injury in GAN, are unknown.

1.7 Dissertation scope and objectives

The primary objective of this research project is to develop methods, tools, and models to investigate mechanisms of IF protein aggregation in astrocytes, which is graphically depicted in

Figure 1.1.

The chapters of this dissertation contain: 1) a novel method to simultaneously isolate IF proteins from mammalian tissues for identification of PTMs, including phosphorylation; 2) identification of IF-targeting compounds by an image-based small-molecule screen; 3) identification and characterization of phosphorylation at serine 13 of GFAP in AxD using overexpression, iPSC-astrocytes, and brain tissue from AxD patients; and 4) generation of a

23 cellular model for GAN utilizing 2- and 3-dimensional human iPSC-astrocytes. In Chapter 6, I summarize the results from Chapters 2-5, contextualize these findings into the broader fields of

IF-associated diseases and neurodegenerative diseases, and address the limitations of this work.

Finally, I provide a brief conclusion and highlight the most outstanding questions emerging from this research.

The aim of this work is to develop novel methods, tools, and disease models to examine

IF protein aggregation in the context of human astrocytes. Using a proteomics approach to identify candidate IF PTMs that are disrupted during aging and IF-associated disease, I intend to highlight the contribution of altered PTMs to defective proteostasis. I also plan to develop iPSC- based models of AxD and GAN using fibroblasts donated from patients that will allow for investigation of mechanisms in the appropriate genetic and species context, and differentiation of these cells to astrocytes will provide an invaluable cell type-specific context. I hope to lay a foundation of new tools and mechanisms to aid the long-term goals of understanding of the cell biological functions of IF proteins and developing IF-targeted therapies to improve the health and wellbeing of patients suffering from IF-associated disorders.

24

Figure 1.1 Summary of dissertation scope and objectives. Multiple strategies are implemented to understand the formation and dissolution of IF aggregates. Methods to identify PTMs are demonstrated (Chapter 2). An image-based small-molecule screen is designed and applied to generate tools to target IF proteins (Chapter 3). Clinically relevant disease models of AxD and GAN are developed to model GFAP aggregation caused by GFAP mutations (Chapter 4) and defective IF turnover (Chapter 5). Site-specific phosphorylation (P) is identified as a marker of severe AxD and mechanistic information of GFAP aggregation is revealed (Chapter 4). Proteostasis in neural progenitor cells and astrocytes from GAN patients is described (Chapter 5). Together, this work generates new methods, tools, disease models, and mechanisms to understand IF aggregation and therapeutically target IFs in human disease. This figure was created using Biorender.com.

25

CHAPTER 2: ISOLATION OF INTERMEDIATE FILAMENT PROTEINS FROM MULTIPLE MOUSE TISSUES TO STUDY AGING-ASSOCIATED POST- TRANSLATIONAL MODIFICATIONS1

2.1 Introduction

IFs are a family of proteins that in humans are encoded by 73 genes and categorized into six major types: types I-IV are cytoplasmic (e.g. epithelial and hair keratins (K), myocyte desmin, neurofilaments, glial fibrillary acidic protein (GFAP), and others); type V are the nuclear lamins; and type VI are IFs in the eye lens17. In terms of their molecular organization, IF proteins have three common domains: a highly conserved coiled-coil "rod" domain, and globular "head" and "tail" domains. IF protein tetramers assemble to form short filament precursors, which are ultimately incorporated into mature filaments that shape dynamic cytoskeletal and nucleoskeletal structures involved in mechanical protection186, stress sensing187,188, regulation of transcription37 and growth, and other critical cellular functions17,61,189.

The functional importance of the IF system is highlighted by the existence of many human diseases caused by missense mutations in IF genes, including neuropathies, myopathies, skin fragility disorders, metabolic dysfunctions, and premature aging syndromes73. Some IF gene mutations do not cause, but predispose their carriers to disease progression, such as the simple epithelial keratins in liver disease190. The latter is due to the critical stress-protective functions of

IFs in epithelia. IFs in general are among the most abundant cellular proteins under basal

1 This chapter previously appeared as an article in the Journal of Visualized Experiments. The original citation is as follows: Battaglia, R. A., Kabiraj, P., Willcockson, H. H., Lian, M. & Snider, N. T. Isolation of Intermediate Filament Proteins from Multiple Mouse Tissues to Study Aging-associated Post-translational Modifications. J Vis Exp, doi:10.3791/55655 (2017).

26 conditions, but are further strongly induced during various types of stress26. For example, recent studies evaluating proteome-wide changes in the nematode C. elegans demonstrated that multiple IFs are highly upregulated and prone to aggregation during organismal aging191,192.

Since maintenance of a proper IF structure is essential for cellular resistance to various forms of stress26, IF aggregation may also contribute to the functional decline during aging. However, organismal-level studies examining multiple mammalian IF proteins across different tissues undergoing stress are lacking.

IFs are highly dynamic structures that adapt to meet cellular demands. Keratins, for example, undergo a biosynthesis-independent cycling between soluble (non-filamentous) and insoluble (filamentous) protein pool193. Under normal physiologic conditions approximately 5% the total K8/K18 pool can be extracted in detergent-free buffer, in comparison to approximately

20% that can be solubilized in the non-ionic detergent Nonidet P-40, which is biochemically comparable to Triton-X100194,195. During mitosis there is a notable increase in the solubility of simple-type epithelial K8 and K18194, which is less apparent in epidermal keratins but more apparent in vimentin and other type III IF proteins195,196. Solubility properties of IF proteins are tightly regulated by phosphorylation, a key post-translational modification (PTM) for filament rearrangement and solubility31,197-199. Most IFs undergo extensive regulation by a number of

PTMs at conserved sites, resulting in functional changes31.

The purpose of this method is to introduce investigators who are new to the IF field to biochemical extraction and analytical methods for the study of IF proteins across multiple mouse tissues. Specifically, we focus on isolation of IF proteins using a high-salt extraction method and assessment of changes in PTMs via mass-spectrometry and by PTM-targeting antibodies. These methods build upon previously published procedures200 but include modifications for extracting

27 different IF protein types to uncover common mechanisms for regulation across the IF family.

For example, K8 acetylation at a specific lysine residue regulates filament organization, while hyperacetylation promotes K8 insolubility and aggregate formation201. Recent global proteomic profiling studies have additionally revealed that most tissue-specific IF proteins are also targets for acetylation and that most IF acetylation sites are confined to the highly conserved rod domain. This highlights the need for methods suitable for global profiling of the IF system. We also introduce a rapid method of isolating IF proteins from multiple tissues using automated homogenization in optimized lysing matrix. The resulting preparations are suitable for downstream PTM analysis via mass spectrometry and other methods.

2.2 Protocol

The protocol is approved and performed in accordance with the Institutional Animal Care and Use Committee (IACUC) at the University of North Carolina.

1. Preparations

1. Prepare Triton-X buffer (1% Triton X-100, 5 mM ethylenediaminetetraacetic acid

(EDTA), bring up volume in phosphate-buffered saline (PBS), pH 7.4). To make 500

mL: stir 5 mL each of Triton X-100 and 500 mM EDTA into 490 mL of PBS, pH 7.4.

Store Triton-X buffer solution at 4 °C.

2. Prepare High Salt Buffer (10 mM Tris-HCl, pH 7.6, 140 mM NaCl, 1.5 M KCl, 5 mM

EDTA, 0.5% Triton X-100, bring up volume in double distilled (dd) H2O). To make 500

mL: stir 10 mL of 0.5 M Tris-HCl (pH 7.6), 14 mL of 5 M NaCl, 55.9 g KCl, 5 mL of 0.5

M EDTA, and 2.5 mL of Triton X-100, adjust volume to 500 mL using double distilled

water). Store High Salt Buffer solution at 4 °C.

28 3. Just prior to use, supplement an appropriate amount of Triton-X and High Salt Buffer

(e.g. 1 mL for each 25 mg tissue sample) with a protease or protease/phosphatase

inhibitor cocktail and discard any unused buffer containing the inhibitors.

4. Prepare 5mM EDTA in 1x PBS, pH 7.4. To make 500 mL: stir 5 mL of 0.5 M EDTA into

495 mL of 1x PBS, pH7.4.

5. Isolate different mouse organs (brain, heart, lung, liver, pancreas, colon, intestine, kidney,

spleen) using approved protocols that comply with veterinary guidelines and institutional

standards202. The tissue collection procedure should take no more than 5 min to preserve

RNA and protein integrity of protease-rich tissues (e.g. pancreas should be processed

first).

6. Cut a small amount of tissue (~5-20 mg) and place in RNA storage solution, for

subsequent RNA extraction, cDNA synthesis, and quantitative real-time PCR analysis for

IF gene expression. Place RNA storage solution tubes with tissue at 4 °C overnight and

follow manufacturer protocol for further storage and isolation steps.

7. Cut the rest of the tissue into smaller fragments (e.g. 0.5 cm) and place in cryovial. Snap-

freeze and store vials at -80 °C or liquid nitrogen for longer term storage.

2. IF Gene Expression Analysis

1. Extract RNA from tissues preserved in the RNA storage solution reagent. Use any

suitable/preferred RNA extraction method according to manufacturer's protocol.

2. Quantify RNA concentration and use 2 μg of RNA to generate cDNA using a suitable

reverse transcription kit according to manufacturer's protocol.

29 3. Using the generated cDNA and mouse IF gene-specific primers set up qPCR reactions,

including three technical replicates of each sample as well as blank control according to

manufacturer's protocol.

4. Quantify IF gene expression as fold change comparing different conditions (e.g. young

versus old tissue).

3. Preparation of Total Tissue Lysates for Immunoblot

1. Homogenize 25 mg of tissue in 1 mL of 2x non-reducing SDS sample buffer. Omit

bromophenol blue dye from the sample buffer if a colorimetric protein assay is to be

performed to quantify protein concentration.

2. Add 5% (v/v) of 2-mercaptoethanol (2-ME) to make reduced samples. To 200 μL of the

non-reducing samples, add 10 μL of 2-ME.

3. Determine protein concentration using detergent- and reducing agent-compatible protein

assay. If dye is already included in the sample buffer, the protein amount can be

estimated after running a gel via a number of techniques, including Coomassie-stain203.

4. Vortex all samples and heat at 95 °C for 5 min.

5. Perform western blotting under both reducing and non-reducing conditions. Expose

membrane briefly (<1 min) to reveal monomeric species, and longer (>1 min) to reveal

high molecular mass complexes containing IF proteins. If monitoring aggregation,

examine entire gel/ membrane (including the bottoms of the gel wells).

6. Run a parallel gel and stain with a protein stain as a loading control203. In disease models

or injury experiments total protein stain should be used as a loading control, as opposed

to immunoblots for 'housekeeping' proteins (e.g. actin, GAPDH) because the latter

change under different stress conditions.

30 4. Preparation of Detergent-soluble and High-salt Extracts of Tissue-specific IFs

1. Add 1 mL of ice-cold Triton X-100 buffer into a glass tube homogenizer and place it on

ice.

2. Remove a small piece of tissue (~25 mg) from liquid nitrogen storage and place directly

into the glass homogenizer. Use a polytetrafluoroethylene pestle to homogenize (50

strokes) and avoid making bubbles. Keep the homogenizer and lysate cold at all times.

3. Transfer lysate to a 1.5 mL microcentrifuge tube on ice and centrifuge at 20,000 x g for

10 min in a pre-chilled centrifuge (4 °C).

4. Collect the supernatant fraction into a separate tube. This is the Triton X-soluble fraction,

which can be used for immunoprecipitation (i.p.) and analysis of the detergent-soluble

pool of IF proteins. Note that steps 4.1-4.4 may be repeated to achieve a cleaner IF

extract from brain tissue.

5. Add 1 mL of High Salt Buffer to the tissue pellet, transfer to a clean homogenizer and

dounce 100 strokes. Transfer the homogenate back to the microcentrifuge tube and place

the tube on a rotating shaker in the cold room for 1 h.

6. Centrifuge the homogenates at 20,000 x g for 20 min at 4 °C. Discard the supernatant.

7. Add 1 mL of ice-cold PBS/EDTA buffer to the pellet and homogenize the pellet (20

strokes) in a clean homogenizer as a final clean-up step*. Transfer to a new tube and

centrifuge at 20,000 x g for 10 min at 4 °C to obtain the IF protein-rich high salt extract

(HSE).

* Optionally, vortex instead of homogenization at this step.

31 8. Discard the supernatant and dissolve the pellets in 300 μL of non-reducing SDS sample

buffer that has been pre-heated. Break up the pellet initially by pipetting and vortexing,

and then heat the samples for 5 min at 95 °C.

9. Vortex and pipet as needed to ensure the pellet is dissolved. It may take several minutes

to fully dissolve the pellets.

10. Store all samples at -20 °C until analysis.

5. Automated Tissue Lysis for IF Protein Extraction in High-volume Experiments

1. For RNA extraction, place lysis buffer (600 μL buffer per 25 mg of tissue) in a tube

containing lysing matrix D (uses small ceramic spheres) and pulse twice for 25 s in the

tissue lyser. Separate lysate from the matrix by centrifugation at 20,000 x g and proceed

to the next step in isolation.

2. For protein extraction, place Triton X-100 (or SDS sample buffer if preparing total

lysate) in a lysing tube with lysing matrix SS (use a single stainless steel bead). After

testing multiple matrices, this was selected because it produces IF protein extracts that are

in similar quality as the traditional douncing method. Note that the automated method is

not optimal for pancreas and spleen, and the standard homogenization protocol should be

used for these tissues.

3. To proceed with preparation of High Salt Extract, remove the stainless steel bead from

the tube using a magnet, and centrifuge the tubes at 20,000 x g for 10 min at 4 °C.

4. Continue with step 4.4 (above) of the manual protocol.

5. Store all samples at -20 °C until analysis.

32 6. Immuno-enrichment of Post-translationally Modified IF Proteins

1. Prepare PBST buffer (0.02% Tween-20 in PBS). To make 50 mL, add 10 μL of Tween-

20 to 50 mL of PBS, pH 7.4.

2. Prepare PTM antibody solution (1-10 μg of antibody in 200 μL of PBST). In general, 3

μg of antibody/reaction is a good starting condition that can be further optimized if

needed.

3. For each reaction, aliquot 50 μL of magnetic beads into a microcentrifuge tube, place on

the magnet and aspirate the bead storage solution.

4. Conjugate the beads to the immunoprecipitation antibody by re-suspending in the

antibody solution and incubating on rotator (end-over-end, to ensure mixing of small

volumes) at room temperature for 20 min.

5. Place the tubes on magnet and aspirate antibody solution.

6. Rinse the antibody-conjugated beads once in 200 μL PBST and remove wash buffer.

7. Add 0.6-1 mL of the tissue lysate to the beads, mix by gentle pipetting and incubate for 3

h on rotator in a cold room.

8. Place tubes on magnet, remove lysate, and wash the beads five times with 200 μL of

PBST. After the last washing step, collect the beads in 100 μL of PBS (no Tween-20) and

transfer to a clean new tube. Place the tube on the magnet.

9. Aspirate the PBS and add 100 μL of non-reducing sample buffer. Remove 50 μL and add

2-ME (5%) to make reducing samples. Heat the samples to 95 °C for 5 min.

10. Separate the i.p. fraction from the beads on the magnet and collect it into a new tube.

11. Store samples at -20 °C until analysis.

33 7. Preparation of IF Protein Samples for Mass Spectrometry Analysis

1. Schedule a consultation with a proteomics expert prior to initiating a study since there is

significant time and cost involved with mass spectrometry analysis.

2. Take special precautions to avoid contamination. Handle all gels with clean gloves and

incubate in clean containers, washed only using ddH2O (avoid soap).

3. Run 20-50 μL of the HSE sample (from Sections 4 and 5) on an SDS-PAGE gel

according to standard conditions.

4. Stain with a protein stain for 1 h. Rinse multiple times and de-stain in ddH2O overnight.

The IF protein bands should be easily visible after de-staining.

5. Place the gel between plastic sheet protectors, scan and mark the bands that will be

excised and sent for analysis.

6. Excise the IF protein bands using a new clean razor.

7. Place the gel bands in clean microcentrifuge tubes and transfer to a mass spectrometry

facility.

2.3 Representative Results

2.3.1 A new rapid method for high salt-based extraction of IF proteins from multiple mouse tissues using lysing matrix.

The traditional method204,205 of isolating the bulk of the intermediate filament protein fraction from epithelial tissue was modified here to include 9 different organs and a more rapid procedure for tissue lysis. While 3 manual homogenization steps are required for the traditional method, the modified procedure only has 1 manual homogenization step, which shortens the procedure by several hours, especially when processing more than 6 samples. Figure 2.1A shows a typical result of HSEs from 9 mouse tissues, while the table of expected proteins in the tissue and the molecular weight of each protein is shown in Figure 2.1B for comparison. Of

34 note, the new automated method does not work well on pancreas and spleen; the traditional homogenization procedure works best for these tissues. This method should be useful for profiling IF proteins in a universal fashion across several tissues to assess organism-level responses to stress. As specific examples here, we show that IF proteins in liver (K8/K18) and brain (GFAP) from old mice are highly upregulated and undergo unique PTMs compared to the corresponding tissues in young mice.

2.3.2 Liver K8 and K18 are strongly upregulated and undergo increased phosphorylation and lysine acetylation in livers from old mice.

Figure 2.2 shows typical results from several of the analyses described in this protocol.

Panel A depicts expression analysis of the two major IF genes in the liver, keratin 8 (KRT8) and keratin 18 (KRT18), which encode the IF proteins K8 and K18, respectively. Epithelial keratins are strongly upregulated under various stress conditions. In the result shown, this occurs during aging, since KRT8 is significantly upregulated in the livers of 24 m old mice compared to 3 m old mice. The results at the protein level are more striking, as observed by the dramatic increase in K8 monomer as well as high molecular mass complexes in the old (24 m) livers. Coomassie- based protein stain here serves as a loading control to ensure equal loading of protein across samples. Note that with total tissue lysates it is easy to overload protein on a gel, as it is in this case. Loading a smaller volume or diluting the sample further in sodium dodecyl sulfate

(SDS) buffer will alleviate this problem (especially if it appears viscous and difficult to pipet).

Panel C depicts a typical result from a liver High Salt Extract obtained using the automated protocol, demonstrating the strong enrichment of keratins 8 and 18 on the gel. The red lines demarcate the area that was excised and submitted for mass spectrometry analysis. In panel D the results of the mass spec analysis of the samples in panel C shows that K8 and K18 in the old liver have multiple phosphorylation and acetylation sites that are not present in the young liver.

35 2.3.3 GFAP is strongly upregulated and lysine acetylated in the brains from old mice.

Figure 2.3 demonstrates that the methods used to extract IFs from epithelia can also be used on non-epithelial tissue. Furthermore, the results reveal a general pattern for aging- dependent upregulation of IF genes and proteins. The qPCR result in Figure 2.3A reveals a 5- fold induction of GFAP mRNA in the brains of 24 m old mice compared to 3 m old mice. Figure

2.3B reveals total proteins present in the Triton X-100 fraction and the IF-enriched HSE. Note the increase in the band intensity at 50 kDa marked by the arrow (corresponding to GFAP) in the old brain. Western blot analysis in Figure 2.3C further reveals the upregulation of GFAP and significant presence of GFAP monomer and potential high molecular mass complex in the Triton

X-100 fraction (both marked by arrows). Western blot analysis of the same samples with a pan- acetyl lysine antibody shows that the antibody recognizes a band at ~50 kDa in the HSE of the old mouse brain and at ~250 kDa in the Triton X-100 fraction (Figure 2.3D). Immuno- enrichment of acetylated proteins from the Triton X-100 fractions reveals increased presence of

GFAP protein in the lysate obtained from the old brain. Reducing and non-reducing conditions are shown to highlight GFAP monomer and high molecular mass complexes. Mass spectrometry analysis (similar to Figure 2.1) can be performed in this case to determine the site-specificity of the acetylated residues on GFAP in the old brain.

2.4 Discussion

Methods that enable biochemical characterization of IF proteins can be useful to understand numerous pathophysiological phenomena in mammalian systems, since IF proteins are both markers and modulators of cellular and tissue stress205. The principle behind the current method is based on the initial procedures developed in the 1970s and 1980s to isolate, separate and reconstitute IF proteins from cells and tissues, generally employing low and high salt

36 solutions and Triton X-100 detergent204,206-211. For historical insight into the studies that supported the biochemical isolation of IF proteins please see recent reviews212,213. The current method is based on more recent protocols developed for the study of simple epithelial keratins205.

The advantage of the method is that it can serve as an initial step to enable investigators who are new to the IF field to effectively isolate IF proteins from most mammalian tissues. It represents a visual guide of related procedures that are widely used by investigators in the field to study IF protein regulation200,214.

This technique can be used to study the regulation and function of IFs in stress communication mechanisms between mammalian tissues215,216. It is becoming well-appreciated that stress in one tissue can affect the functioning of other tissues, for example under conditions of nutritional stress217, protein misfolding218, metabolic stress219, and other changes. Of note, most of the work currently being done in this area is in Drosophila and C. elegans models, while studies on widespread stress responses in mammalian systems are lacking. As the major regulator of cellular stress190, the IF system can unlock clues to organismal-level stress responses, which can have important disease implications. As such, the current protocol can be used to study global pathophysiological mechanisms in various mouse models of stress, injury and disease.

One limitation of the current technique is that the high salt extracts are considered

"crude" IF preparations since they also contain other IF-associated proteins, such as plectin for example220. As shown previously, HSEs can be denatured in 8 M urea buffer to obtain highly pure IF proteins that are capable of in vitro re-assembly in phosphate buffer220. After two such cycles of disassembly and re-assembly, the stoichiometry of IF proteins (isolated from HeLa cells) remained the same, whereas the urea treatment removed plectin220. During desmin

37 purification, actin can be removed by solubilization of the HSE in acetic acid221. Whether additional purification steps should be included depends on the ultimate goal of the experiment.

For example, if the goal is to characterize the assembly state and generate in vitro reconstituted

IFs, the urea step is required to obtain highly pure IF proteins. On the other hand, if the goal is to identify context-dependent associations between IFs and other cellular proteins, then the "crude" preparations can be used. Interacting proteins can be identified by mass spectrometry using either in-gel digest for high abundance targets visible by gel stain, or in-solution digest for low abundance targets. Previous studies using immunoprecipitation of detergent-soluble IFs and

HSEs containing IF proteins identified functionally important specific interaction between keratins 8/18 and heat shock protein 70 (HSP70), which is potentiated after heat stress222, the adaptor protein 14-3-3 after phosphorylation223, and K8/K18 Raf-1 kinase under normal physiologic conditions224 among others. The current protocol can enable similar studies on other

IF proteins.

Use of the lysing matrix is a key modification from previous methods and should allow for rapid extraction of IFs from multiple tissues, with the exception of pancreas and spleen. The automation of the initial steps can be particularly useful for high-volume mouse experiments. For example, isolating IF proteins from 3 different tissues from 10 mice per condition (e.g. normal and some form of systemic stress or disease) will require processing of 60 individual tissues.

Using the traditional manual method this would need to be done in batches and can take several days. However, using the automated method with lysing matrix, the procedure can be done in just a few hours. Critical steps of the procedure include: ensuring the sample stays cold throughout the procedure; incubation in high salt buffer for 1 h (step 4.5); using hot buffer and extensive vortexing to ensure complete resuspension of the pellet before analysis (step 4.8); and

38 taking all precautions to avoid contamination of samples to be analyzed by mass spectrometry

(step 7.2).

The major drawback to mass spectrometry-based identification of PTM sites is that it works very well for certain PTMs – phosphorylation and lysine acetylation being prime examples – but others, such as sumoylation are much more challenging225. Nevertheless, proteomics is a powerful tool for studying protein regulation in normal and diseased tissues.

Agnetti et al. compiled a comprehensive up-to-date overview of cutting edge proteomic techniques currently in use to study various PTMs in the context of tissue-expressed proteins226.

The purpose of the current method is to generate samples that can be applied for different types of analyses. For example, in vitro sumoylation can be performed on immune-precipitated IF proteins, and overall sumoylation can be assessed on isolated IFs in HSE preparations using sumo-specific antibodies197,200. Therefore, the application of this method is not limited to generating samples only for mass spectrometry analysis but can be used to probe multiple IF properties using a variety of downstream techniques.

IF proteins are extensively regulated by numerous PTMs at conserved sites, resulting in altered solubility, filament assembly, and protein interactions31. Recent technological advances have unveiled the incredible magnitude, complexity, and disease significance of post- translational modifications (PTMs) on cellular proteins227-229. Improvement in detection methods for common PTMs, such as phosphorylation230 and acetylation227, have yielded vast catalogs of data, but understanding their biological meaning – cracking the “PTM code” – is a major remaining challenge231,232. One way to assess the functional roles and relative significance of each PTM is to determine how well it is conserved across multiple members of the IF protein family. For example, identification of a novel phospho-tyrosine site on K8 revealed that this site

39 is contained within a QYE motif found in essentially all cytoplasmic IF proteins and critical for regulating IF protein solubility and filament dynamics233. Importantly, mutation of the conserved tyrosine residue on GFAP to a negatively-charged “phosphomimic” aspartic acid (Y242D) causes Alexander Disease234. The representative data shown here demonstrate how two different tissue-specific IF protein types (K8 and GFAP) undergo similar pattern of upregulation and increased acetylation during aging, which may be an effect of altered metabolism201,227. This example illustrates the utility of the method in understanding the function of IF proteins on a global physiological scale.

40

Figure 2.1 Automated lysis and extraction of IF proteins from multiple mouse tissues. (A) Coomassie-based gel stain of HSEs from the designated mouse tissues. The tissues shown were obtained from the same adult (3 m old) male CBA mouse. Samples were processed as described in Section 5. Brain: note that NFL, NFM, and NFH are heavily phosphorylated27 and migrate slower than expected, at ~70, 145, and 200 kDa, respectively. Heart: In addition to the 53 kDa band corresponding to desmin and vimentin), heart samples also contain a ~42 kDa band, most likely representing actin, which is known to co-purify with desmin28. Large intestine: The identity of the prominent bands above 25 kDa that are co-extracted with K8/K18 are not known, but these bands are not present if the tissues are processed using the traditional dounce homogenizer method. Pancreas and Spleen: Note that the automated homogenization is not suitable for isolation of keratins from pancreas and vimentin from spleen, presumably because of the sensitivity of the lysate to the slight increase in temperature during the pulse in the tissue lyser. (B) Table showing the major IF protein types present in the different tissues and their predicted molecular weight as a reference for panel A.

41

Figure 2.2 Molecular and biochemical differences in liver keratins from young and old mice. (A) Analysis of KRT8 and KRT8 mRNA using standard procedure (Protocol 1) reveals significant induction in KRT8 transcript in the livers from old mice. n = 6 for each condition (3 male and 3 female CBA mice were used per group). **p <0.01; one-way ANOVA. (B) Total liver lysates were obtained from 3 young (3 months old) and 3 old (24 months old) male CBA mice using Protocol 2 and the samples were analyzed under non-reducing conditions. TS1 antibody was used to probe for K8 expression and Coomassie-based protein stain was used as a loading control. (C) High salt extracts from young and old mouse livers, corresponding to mice #1 and #4, respectively from panel B. The two arrows point to K8 and K18 on the gel and the red box indicates the part of the gel that was excised and submitted for mass spectrometry analysis. (D) PTM sites identified by mass spec analysis of the samples shown in C.

42

Figure 2.3 Molecular and biochemical differences in GFAP from brain of young and old mice. (A) Analysis of GFAP mRNA using standard procedure (Protocol 1) reveals significant induction in the brains from old mice. n = 6 for each condition (3 male and 3 female CBA mice were used per group). **p <0.01; one-way ANOVA. (B) Coomassie-based protein stain of Triton X-100 and HSE fractions from brain tissue of young (3 months old) and old (24 months old) mouse. Note the increase in ~50 kDa GFAP band in HSE from old brain (arrow). (C) GFAP immunoblot (mouse monoclonal; GA5 clone) of the same samples as panel B. (D) Acetyl-lysine immunoblot (rabbit polyclonal; Abcam ab80178) of the same samples as in panel B. (E) GFAP blot after immunoprecipitation with acetyl-lysine antibody. Samples were analyzed under non- reducing (NR) and reducing (R) conditions and arrows point to increase presence of GFAP in the immunoprecipitate from the old brain.

43

CHAPTER 3: AN IMAGE-BASED SMALL-MOLECULE SCREEN IDENTIFIES VIMENTIN AS A PHARMACOLOGICALLY RELEVANT TARGET OF SIMVASTATIN IN CANCER CELLS1

3.1 Introduction

The cytoskeleton, a critical structural element of all cells, is composed of intermediate filaments (IFs), actin filaments, and microtubules as its major components. Human IFs are encoded by 73 genes and grouped into 6 major types: types I–IV are cytoplasmic and include the epithelial and hair keratins, myocyte desmin, neurofilaments, and glial fibrillary acidic protein, among others; type V IFs are the nuclear lamins; and type VI are IFs expressed in the lens17.

Whereas pharmacologic agents that target actin filaments and microtubules are available and widely used in research, there are presently no known chemical probes that target IFs selectively.

The importance of developing IF-targeting chemical probes is clear in light of what actin- and tubulin-targeting drugs have done for our fundamental understanding of cell biology and for patients with cancer. There are more than 70,000 PubMed studies that refer to the use of these agents, and microtubule drugs are a major class of chemotherapeutic agents235,236.

The complexity of IF assembly mechanisms and the limited structural data on individual

IFs have hindered the development of pharmacologic tools for their targeting. All IF proteins share a common domain structure, a conserved coiled-coil rod domain that is flanked by globular

1 This chapter previously appeared as an article in Federation of American Societies for Experimental Biology Journal. The original citation is as follows: Trogden, K. P., Battaglia R. A., et al. An image-based small-molecule screen identifies vimentin as a pharmacologically relevant target of simvastatin in cancer cells. FASEB J 32, 2841-2854, doi:10.1096/fj.201700663R (2018).

44 head and tail domains24. Stable IFs are assembled from tetramers, the basic IF subunits, into 10- nm-thick mature filaments that are critical for mechanical protection, stress sensing, and the regulation of transcription and cell growth. Presently, it is not known if and how the IF cytoskeleton mediates desired or untoward effects of clinically used drugs, despite the well- known functions of IFs in cellular homeostasis and disease.

Vimentin, the major IF protein of mesenchymal cells, has been used as a prototype for elucidating IF structure, assembly, and dynamics237. As such, vimentin can serve as a model IF protein to study pharmacologically relevant interactions between IFs and small-molecule compounds. Withaferin A, a naturally occurring steroidal lactone, has been used as a vimentin inhibitor on the basis of findings that it promotes aggregation of vimentin238; however, withaferin

A affects numerous cellular targets, including other cytoskeletal components239,240, and, therefore, it is not selective for vimentin or IFs in general. On the other hand, examining functional associations between IF proteins and compounds with well-defined biochemical activities may illuminate signaling networks controlling IF dynamics and set the stage for future development of novel first-in-class IF-selective chemical probes.

In the present study, we hypothesized that a pharmacologic screen using a library of compounds with known biochemical activities can provide us with new tools with which to manipulate IF structures, uncover signaling pathways that regulate IF dynamics, and potentially implicate IFs as biologically significant targets of widely used research compounds and clinical agents. We used Tocriscreen, a set of 1120 research compounds and clinical drugs, to screen for effects on vimentin filaments in a cell-based assay. This led to the identification of several hits that included compounds that target GPCRs, protein-protein interactions, and various classes of enzymes. We subsequently characterized a functional direct interaction between one of the hits,

45 simvastatin, and vimentin. Simvastatin and other statins target the rate-limiting step in cholesterol synthesis and are among the most commonly used drugs in the world241. In addition to lowering cholesterol, clinical use of statins is associated with reduced cancer mortality242 and adverse muscle-related effects243 through largely unknown mechanisms. Our identification of vimentin as a direct target of simvastatin provides a novel potential mechanism of action that extends beyond lowering cholesterol.

3.2 Materials and Methods

3.2.1 Abs, plasmids, and chemicals

Abs used were rabbit anti-vimentin (total), pSer39, pSer56 and pSer83, total poly(ADP- ribose) polymerase (PARP) and cleaved PARP (Cell Signaling Technology, Danvers, MA,

USA), mouse anti-vimentin V9, and tubulin DM1α (Sigma-Aldrich, St. Louis, MO, USA).

Phalloidin (Molecular Probes, Eugene, OR, USA) was used to stain filamentous actin. Control, mEmerald-C1, and mEmerald-Vimentin-C-18 vectors were obtained from the Michael Davidson fluorescent protein collection (Addgene, Cambridge, MA). All chemicals used were purchased from Tocris (Bristol, United Kingdom), including the Tocriscreen screening set (a list of compounds is provided in the APPENDIX), in addition to individual lots of lovastatin, mevastatin, pravastatin, and simvastatin.

3.2.2 Cell cultures, immunofluorescence staining, imaging, and viability assay

Cell lines used in the study, SW13-vim+, SW13-vim−, MCF7, and MDA-MB-231, were cultured in DMEM with 10% fetal bovine serum, and 1% penicillin-streptomycin. For the small- molecule screen, cells were treated as described in the next section, then fixed with methanol and stained as previously described201. Cells were imaged on the EVOS-FL auto cell imaging system

(Thermo Fisher Scientific, Waltham, MA, USA) using a x20 (0.75 NA) objective. For triple

46 staining of actin, vimentin, and tubulin, cells were fixed in 4% paraformaldehyde for 10 min at room temperature, washed 3x in PBS, permeabilized in 0.1% Triton X-100 (Tx) for 5 min, washed 3x in PBS, and incubated in blocking solution (PBS/2.5% wt/vol bovine serum albumin).

Primary Abs for vimentin (rabbit) and tubulin (mouse) were added overnight in 4°C. The next day, slides were washed 3× in PBS and incubated with Alexa Fluor–conjugated secondary Abs and phalloidin for 1 h at room temperature. After overnight mounting in ProLong Diamond

(Thermo Fisher Scientific) that contained DAPI, cells were imaged on Zeiss 880 confocal laser scanning microscope using a x63 (1.4 NA) oil immersion objective (Zeiss, Jena, Germany).

Live/dead assays were performed by using the Ready Probes Cell Viability Imaging Kit

(Molecular Probes). One drop of NucBlue (stains all cell nuclei) and NucGreen (stains dead cell nuclei) (both from Thermo Fisher Scientific) were added to the cells in 500 μl of culture medium

15 min before imaging. Cells were imaged in 24-well plates by using the EVOS-FL auto system with a x10 (0.3 NA) objective. Quantification of nuclei from all cells (blue) and dead cells

(green) was performed using the EVOS-FL autorecognition counting software. MTT

(methylthiazolyldiphenyl-tetrazolium bromide) assay to determine cell viability on the basis of mitochondrial function was performed by using a commercial kit (Vybrant MTT; Thermo Fisher

Scientific) according to the manufacturer’s protocol.

3.2.3 Small-molecule screen, imaging, and quantification of vimentin filament changes

SW13-vim+ cells were plated on 96-well glass-bottom plates and treated the following day (14 plates, 80 compounds per plate; 10 μM final drug added in serum-free DMEM). Each plate in the screen included 8 untreated and 8 vehicle (0.1% DMSO)-treated wells in serum-free

DMEM as control. After 1 h of treatment, medium was aspirated and cells were fixed, stained, and imaged on the same day. Fixation and staining were performed as described in Snider et

47 al.201, and images were acquired on EVOS-FL auto using a x20 objective (0.75 NA).

Approximately 5% of the compounds caused cell liftoff, and they were not considered for additional analysis. The remaining wells were analyzed and scored manually for the appearance of the vimentin filament network (diffuse/nonfilamentous, peripheral redistribution, bundling, or aggregation). Compounds that produced one or more of these effects were considered positive hits, and all other compounds were considered negative hits in this screen. Quantification of vimentin-positive areas was performed by using ImageJ (National Institutes of Health, Bethesda,

MD, USA). Vimentin-positive areas were calculated by using the analyze objects program with a size exclusion of <10 pixels2. Raw images were converted to 8-bit images and the threshold was applied to remove background signal. Average vimentin area is defined as the total signal area of all objects divided by the number of objects in each image. The settings allowed the recognition of images >10 pixels2 and the removal of nonspecific signal and signal from the edges.

3.2.4 Preparation of cell lysates and biochemical analysis of vimentin

Total cell lysates, Tx-soluble, and Tx-insoluble pellets or high-salt extracts (HSEs) were prepared as previously described81. Two-dimensional gel electrophoresis samples were prepared in ReadyPrep buffer, which contained: 8M urea, 2% CHAPS, 50 mM DTT, 0.2% Bio-Lyte 3/10 ampholyte, 0.001% Bromophenol Blue (BioRad, Hercules, CA, USA) and subjected to isoelectric focusing: 250 V for 15 min, 8000 V for 2 h, 72,000 V hours, and 500 V hold. Cell lysates were resolved on 4–20% SDS-PAGE gels, then transferred to PVDF membranes.

Membranes were blocked by using 5% milk in 0.1% Tween 20/PBS and incubated with the designated Abs in milk, with the exception of phospho-specific Abs, which were incubated in

3% bovine serum albumin in 0.1% Tween 20/PBS.

48 3.2.5 Electron microscopy

Recombinant human vimentin was generated as previously described244, and vimentin filaments were assembled from tetramer buffer (5 mM Tris-HCL, pH 8.5, 1 mM EDTA, 1 mM

DTT) using an established protocol245 with an added 5-min preincubation step in the presence of vehicle or simvastatin. Vimentin filaments were negatively stained with 2% aqueous uranyl acetate (pH 4.5). A small droplet (2.5 µl) of protein suspension was applied to a glow-discharged formvar/carbon-coated 400 mesh copper grid and allowed to adsorb for 1–2 min. The grid was briefly floated on a droplet of deionized water to remove buffer salts and transferred to a droplet of 2% aqueous uranyl acetate for 30 s. Excess stain was removed by blotting with filter paper, and the grid was air dried. Grids were observed on a LEO EM 910 transmission electron microscope at 80 kV (Zeiss). Digital images were acquired by using a Gatan Orius SC1000 digital camera with Digital Micrograph software (v.2.3.1; Gatan, Pleasanton, CA, USA).

3.3 Results

3.3.1 Image-based small-molecule screen identifies vimentin-targeting compounds

We selected the Tocriscreen library because it consists of a collection of highly pure small molecules that are known to be biochemically active and that affect more than 300 pharmacologic targets, including GPCRs, kinases, ion channels, nuclear receptors, transporters, structural molecules, and protein complexes (a full compound list and known targets are included in the APPENDIX). We conducted the screen in SW13 adrenal carcinoma cells because these cells are commonly used in IF research and because of the availability of a vimentin-positive

(SW13-vim+) and a vimentin-negative (SW13-vim−) clone, the latter lacking all cytoplasmic

IFs246. The pharmacologic screen—conducted as outlined in Materials and Methods—identified

18 positive hits that produced significant changes in vimentin filament morphology within 1 h of

49 treatment (Figure 3.1 and Table 3.1). As a validation of the screening strategy, some of the positive hits we identified were expected on the basis of previous work, including kinase and phosphatase inhibitors and microtubule-targeting compounds. Among these, the PKC inhibitor, palmitoyl-carnitine chloride (Figure 3.1A), the PP1/PP2A inhibitor, cantharidin (Figure 3.1B), and the microtubule depolymerizer, nocodazole (Figure 3.1E), induced bundling and redistribution of vimentin filaments. We also identified several unexpected novel hits, such as diphenylene iodonium chloride (Figure 3.1H) and LY 2183240 (Figure 3.1I), which, among other activities, are known to target GPCR signaling related to cannabinoid actions and endocannabinoid signaling247. Some of the most prominent and diverse effects on vimentin were observed when cells were treated with compounds that target protein-protein interactions and multisubunit complexes (Figure 3.1L-P), which is relevant given the well-known scaffolding functions of vimentin and other IF proteins248. We also observed striking vimentin bundling in the presence of simvastatin (Figure 3.1Q)and mevastatin (Figure 3.1R), 2 fungal metabolites that inhibit HMG-CoA, the rate-limiting enzyme in the cholesterol biosynthesis pathway249.

Mevastatin is not used in the clinic, whereas simvastatin and 2 related fungal-derived compounds, lovastatin and pravastatin (both included in the screening library), are commonly used for lowering cholesterol in patients249. To validate and further explore mechanisms of the hits from the primary screen, we focused on statins because of their pharmacologic and clinical importance.

3.3.2 Simvastatin and mevastatin, but not lovastatin and pravastatin, cause dose-dependent bundling of vimentin IFs

The effects on vimentin filaments that were observed as part of the primary screen were confirmed by confocal imaging. Representative images in Figure 3.2A show that simvastatin treatment caused the reorganization and bundling of vimentin filaments to one side of the

50 nucleus in most cells (marked by arrowheads). Whereas some cells in the untreated group also exhibited a compact perinuclear ball-like vimentin structure, in the majority of untreated cells, vimentin filaments surrounded and extended away from the nucleus (Figure 3.2A, arrows). To quantify the kinetics of vimentin bundling, we used ImageJ software to calculate the ratio of the total vimentin-positive area to the total number of objects (i.e., cells), as described in Materials and Methods and shown in the representative images in Figure 3.2B. We used this analysis tool to quantify the effects of 4 different statins on vimentin filaments. Simvastatin, mevastatin, pravastatin, and lovastatin were tested at concentrations of 1 nM–10 μM and 1 h of treatment. As shown Figure 3.3A, total vimentin area decreased significantly and dose dependently in response to simvastatin and mevastatin, but not lovastatin or pravastatin. Furthermore, whereas simvastatin was active at concentrations that ranged from 10 nM to 10 µM, mevastatin was less potent and affected vimentin only at concentrations ≥1 µM (Figure 3.3B); therefore, we designed the next experiments to examine specifically the interaction between simvastatin and vimentin.

3.3.3 Simvastatin induces time-dependent vimentin bundling independently of changes on actin filaments and microtubules

We next assessed the vimentin bundling effects of simvastatin at several time points between 15 and 120 min. As shown in the bar graph in Figure 3.4A, the vimentin-positive area was reduced to 80, 67, and 50% of untreated control after 15, 30, and 60 min of simvastatin treatment, respectively. The maximal effect appeared after 60 min and was similar to the 120- min treatment. Representative confocal images in Figure 3.4B indicate that, after 15 min, vimentin filaments reorganize and shift to one side of the nucleus (arrowheads), followed by the appearance of compact ball-like perinuclear vimentin bundles at 30 and 60 min (Figure 3.4B, arrows). The rounded perinuclear bundles were also present in some cells after 120 min,

51 although, at this time point, many cells displayed diffuse nonfilamentous vimentin staining surrounding the nucleus (Figure 3.4B, asterisks). It has long been known that the inhibition of microtubule depolymerization causes vimentin to collapse to the perinuclear region of cells250, a phenomenon we observed in our screen with 2 microtubule depolymerizing compounds— nocodazole and D-64131 (Figure 3.1E, F). Microtubules act as tracks for the bidirectional transport of vimentin filaments to allow for a proper array251. This link between microtubules and vimentin prompted us to explore whether simvastatin affected another cytoskeletal network, thereby indirectly changing vimentin organization. To answer this question, we simultaneously stained for actin, tubulin, and vimentin in cells that were treated with DMSO vehicle or simvastatin for 60 min (Figure 3.5). At this time point, we did not observe significant changes to actin or tubulin, whereas perinuclear vimentin bundling (arrow) was as prominent as we had observed previously. These data indicate that simvastatin exhibits pharmacologic selectivity for vimentin over actin and tubulin.

3.3.4 Simvastatin causes time-dependent changes in vimentin solubility

We next examined how simvastatin affects the biochemical properties of vimentin. To study changes in solubility, we analyzed the presence of vimentin in the Tx-soluble fraction by immunoblot (Figure 3.6A), which revealed a 4-fold increase in Tx-soluble vimentin at 15–30 min after simvastatin treatment that returned to control levels at 60 min, as quantified in Figure

3.6B. A corresponding 10–20% decrease of Tx-insoluble vimentin was observed after 15 and 30 min of treatment, with no significant difference at 60 min (Figure 3.6B). The relative change in the Tx-insoluble pellet fraction was not as robust as the Tx-soluble fraction, as the bulk of vimentin is contained within the insoluble fraction27. Because phosphorylation regulates vimentin dynamics198, we examined whether simvastatin treatment altered vimentin

52 phosphorylation at 3 common phosphorylation sites in the vimentin head domain (Ser39/-56/-

83). As shown in Figure 3.6C, we did not detect significant changes in site-specific phosphorylation either in the Tx-soluble fraction or in the insoluble high-salt extract; however, 2- dimensional gel analysis revealed an increase in the abundance of a negatively charged isoform of vimentin after 60 min of simvastatin treatment (Figure 3.6D, arrow). This suggests that changes in vimentin phosphorylation at sites other than Ser39/-56/-83 or another post- translational modification accompany the morphologic changes in vimentin after 60 min of simvastatin treatment.

3.3.5 Simvastatin promotes vimentin bundling in vitro

We next examined the possibility that vimentin is a direct target of simvastatin. To test this possibility, we preincubated purified vimentin in tetramer buffer for 5 min in the presence of

DMSO vehicle or simvastatin, initiated vimentin filament assembly, and allowed the reactions to proceed for 15, 30, or 60 min. Upon termination of the reactions, samples were pelleted at

100,000 g, and pellet fractions were analyzed by vimentin immunoblot, which revealed high- molecular-mass vimentin complexes (>250 kDa) in the presence of simvastatin (Figure 3.7A).

Negative stain transmission electron microscopy analysis revealed bundling of vimentin filament precursors in the presence of simvastatin as early as 1 min after the initiation of assembly

(Figure 3.7B). The effect was more apparent on mature vimentin filaments (Figure 3.7C). After

30 min of assembly, we observed the typical ∼10-nm vimentin filaments, as well as ∼20-nm- thick filaments in the presence of simvastatin, whereas after 60 min, we observed thick vimentin filament bundles that may correspond to the high-molecular-mass vimentin species that was detected biochemically upon simvastatin treatment in Figure 3.7A. Of note, DMSO vehicle treatment itself promoted some bundling after 60 min, but this effect was relatively minor

53 compared with the effect of simvastatin. Our in vitro studies indicate that vimentin is a direct target of simvastatin and suggest that this mechanism most likely accounts for the rapid simvastatin-induced reorganization and bundling of vimentin filaments that we observed in

SW13-vim+ cells.

3.3.6 Simvastatin, but not pravastatin, promotes vimentin-dependent cell death

To probe the functional significance of the simvastatin-vimentin interaction, we assessed whether simvastatin exerts different effects in SW13-vim+ vs. SW13-vim− cells. After 24 h of simvastatin treatment, SW13-vim+ cells became rounded with a corresponding drop off in cell number to <30% of control (Figure 3.8A, B). In contrast, simvastatin treatment did not affect the morphology or number of SW13-vim− cells (Figure 3.8A, B). Pravastatin, which did not affect vimentin filaments (Figure 3.3), also did not alter cell morphology or cell numbers in SW13- vim+ cells (Figure 3.8C, D). To probe this effect further, we assessed the dose dependency of simvastatin-induced cytotoxicity in SW13-vim+ cells after 24 h of treatment. As shown in Figure

3.9A, simvastatin reduced the total number of SW13-vim+ cells at submicromolar concentrations, with a corresponding IC50 of 48 nM (Figure 3.9C). Furthermore, at low micromolar concentrations, simvastatin promoted significant induction of SW13-vim+ cell death

(Figure 3.9B, D). We observed the 89-kDa caspase-3/-7 cleavage product of PARP in the simvastatin-treated SW13-vim+ cells (Figure 3.9E, F), which indicated that the cells were dying by apoptosis252. Taken together, these results suggest that simvastatin targeting of vimentin may promote apoptotic cell death.

3.3.7 Ectopic vimentin overexpression sensitizes SW-vim- cells to simvastatin

To determine conclusively whether simvastatin sensitivity of SW13-vim+ cells is directly dependent on vimentin expression, we examined the response to simvastatin upon transient

54 transfection of mEmerald-vimentin or mEmerald vector in SW13-vim− cells. Vimentin overexpression sensitized cells to simvastatin treatment, which resulted in a 50% drop off in cell number when vimentin was transiently overexpressed compared with vector alone (Figure

3.10A). Similar effects were observed upon stable vimentin overexpression, in which case simvastatin treatment caused a significant increase in the number of cells that contained rounded perinuclear vimentin bundles or aggregates (Figure 3.10B, C). These effects mirror what we observed on endogenous vimentin in SW13-vim+ cells and demonstrate that vimentin is a functionally relevant direct target of simvastatin that is critical for simvastatin-induced death in

SW13 adrenal carcinoma cells.

3.3.8 Simvastatin targets vimentin filaments and causes cell death in MDA-MB-231 breast cancer cells

To determine whether our findings in SW13 cells extended to other cell types in which vimentin expression is known to regulate cellular properties, we tested 2 breast cancer cell lines:

MCF7, which are vimentin deficient, and MDA-MB-231, which are vimentin expressing (Figure

3.11B). It has previously been shown that vimentin regulates properties that are related to the epithelial-mesenchymal transition in these cell lines46. Similar to SW13 cells, vimentin filaments in MDA-MB-231 cells reorganized into perinuclear bundles in response to simvastatin (Figure

3.11A). Furthermore, we observed significant cell death when MDA-MB-231 cells were treated with simvastatin, but not pravastatin (Figure 3.11C). These data are also consistent with a published study that demonstrated that simvastatin induces cell death in MDA-MB-231 cells

253 (IC50 = 9 µM), whereas pravastatin does not . We also quantified cell viability on the basis of the mitochondrial MTT assay, which revealed a ∼40% decrease in viability in MDA-MB-231 cells (Figure 3.11D). In contrast, vimentin-deficient MCF7 cells were not sensitive to either drug

(Figure 3.11C, D). Taken together, these results demonstrate that the findings in SW13 adrenal

55 carcinoma cells translate to breast cancer cells in which vimentin is known to be functionally important.

3.4 Discussion

3.4.1 Intermediate filaments as novel drug targets

In the present study, we conducted a medium-throughput small-molecule screen that identified vimentin as a target for several known biochemically active small-molecule compounds. By using an image-based screen of the 1120-compound Tocriscreen library, we analyzed the effects on vimentin filaments after a relatively short exposure of 1 h to each test compound to minimize effects that may be secondary to other major cellular changes (e.g., apoptosis or transcriptional reprogramming). With a small compound library, such as that used in this study, qualitative scoring for major reorganization of vimentin filaments was possible; however, future studies that involve larger compound libraries will necessitate the development of computational image analysis tools to monitor and quantify small changes in the IF network architecture that may be functionally relevant. A recent study that examined the interactions between vimentin filaments and microtubules is one example54.

3.4.2 Vimentin as a pharmacologically relevant target of statins

Most of the novel hits we identified through the screen remain to be explored in detail, including the regulation of vimentin filaments by compounds that modulate GPCR signaling, protein-protein interactions, and enzymes that regulate post-translational modifications. An important future aspect of this work will be the subsequent validation studies that involve dose- response and time-course experiments to discern which of the hits are pharmacologically relevant. On the basis of the novelty and potential clinical relevance, we focused here on the interaction between statins and vimentin. We demonstrate that simvastatin promotes vimentin

56 filament bundling in the low nanomolar range, which is pharmacologically relevant as serum levels in patients who take statins are around 15 nM, with tissue exposure likely being higher254.

Our data suggest that the mechanism of action involves direct simvastatin-induced reorganization of vimentin filaments early on, followed by changes in vimentin post-translational modifications, and bundling at later time points, culminating in cell death (Figure 3.12).

3.4.3 Simvastatin targeting of vimentin

Statins are taken by millions of people worldwide, and new treatment guidelines may significantly increase the number of patients who take them241. The statins we tested are fungal metabolites that are related to the original statin (mevastatin), containing a hexahydronaphtalane ring249. Simvastatin differs from lovastatin and pravastatin at the dimethylbutyrate ester side chain, which suggests that this functional group may be important for the activity on vimentin.

Crystallographic data reveal that the presence of the additional methyl group on simvastatin affects the binding of simvastatin to Aspergillus terreus acyltransferase LovD255,256. Sequence alignment of LovD with human vimentin reveals 58% identify between the simvastatin-binding

LovD peptide sequence, 267FGGQGVFSGPGS278, and vimentin non–α-helical amino terminal

(head) domain peptide, 15FGGPGTASRPSS26. This suggests that the vimentin head domain may potentially mediate binding to simvastatin, although structural and mutagenesis studies will be required to elucidate the binding mechanism. Alternatively, the pharmacologic effects may be mediated via lipophilic interactions, as vimentin exhibits hydrophobic amino acid clusters and has previously been shown to have a high affinity for lipids257.

3.4.4 Vimentin as a potential determinant of the sensitivity of cancer cells to simvastatin

Numerous prior studies have reported antiproliferative and proapoptotic effects of statins in cancer cells258. Furthermore, previous observations across different cancer cell lines have

57 demonstrated that sensitivity to statin-induced cell death correlates with high levels of vimentin expression259. This bears significance as vimentin is a critical component of the epithelial- mesenchymal transition and regulates cell migration260. Our dose-response studies reveal that simvastatin inhibits SW13 cell proliferation with an IC50 of 48 nM in vimentin-expressing, but not vimentin-lacking, cells. This demonstrates that the effects we observed at the cellular level occur at pharmacologically relevant concentrations and warrant additional investigation using in vivo models. It would also be of interest to analyze the effects of synthetic statins, such as fluvastatin, atorvastatin, and cerivastatin, on vimentin filament reorganization and vimentin- dependent cancer cell death. Our findings show that pravastatin differs from simvastatin in terms of its lack of effect on vimentin filaments and cell death in the lines tested. In a recent large, population-based cohort study, patients who received simvastatin had a 20% reduction in cancer- specific mortality, whereas use of pravastatin offered no protective benefit261. The LUNGSTAR study, a multicenter phase III trial of pravastatin added to standard chemotherapy in small-cell lung cancer, concluded that there was no benefit of pravastatin262. Furthermore, simvastatin has the most favorable profile on breast cancer prognosis, whereas preclinical and clinical studies have not supported a similar protective role of pravastatin263.

3.4.5 Vimentin as a potential player in statin-associated muscle symptoms

Although desmin is the major IF in mature muscle fibers, vimentin is expressed during myogenesis and is increased during muscle injury264,265. Our results may also provide a potential mechanism for statin intolerance, a common clinical problem that limits the use of statins in a significant proportion of patients266,267. The molecular mechanisms of statin intolerance are poorly understood, and cytoskeletal and other structural or scaffolding proteins have not been implicated thus far268; however, a previous study found a significant decrease in skeletal muscle mitochondrial

58 DNA in patients who received simvastatin therapy269. IFs, including vimentin, are critical regulators of intracellular organelles, including mitochondria186,270. Vimentin filaments specifically are known to regulate structural and functional aspects of mitochondria271,272, and the binding of mitochondria to vimentin is regulated by the small GTPase Rac142. Because Rac1 is a known target of simvastatin in multiple cell types, including myoblasts, endothelial cells, and cancer cells273,274, it is plausible that vimentin may be an upstream mediator of the simvastatin effects on this pathway. The prevalence of muscle effects varies depending on the type of statin and correlates more strongly with simvastatin. In the Prediction of Muscular Risk in Observational conditions study, 18.2% of 1027 patients who received high-dose simvastatin therapy presented with muscular symptoms compared with 10.9% of 1901 patients who received high-dose pravastatin therapy275. Our finding that simvastatin, but not pravastatin, targets vimentin IFs raises the possibility that the simvastatin-vimentin interaction bears potential significance to simvastatin action in muscle. Given the high degree of similarity between vimentin and desmin, it would be of particular interest to examine the effects of different statins on desmin IFs in future studies.

3.4.6 The need for pharmacologic tools to probe the function of vimentin and other intermediate filaments

Whereas many aspects of basic IF protein function and regulation were elucidated over the past 40 years, much remains to be discovered. For example, mechanisms that underlie the crosstalk between IFs and other cytoskeletal systems, the identity and function of most IF-regulatory proteins, and the signaling pathways that mediate IF associations with the various cellular organelles are areas that lack in-depth understanding. Efforts to develop novel tool compounds to target IF proteins will accelerate our functional understanding of the IF cytoskeleton, which will open up new avenues for its pharmacologic manipulation in the clinic. To this end, the techniques

59 and approaches used in the present study may be generally applicable to future small-molecule screens that aim to identify novel IF-selective compounds.

60

Figure 3.1 Image-based screen of 1120 biologically active small-molecule compounds for effects on vimentin filaments. SW13-vim+ cells, plated on glass-bottom 96-well plates, were treated with 1 of 1120 different small-molecule compounds from the Tocriscreen library for 1 h, as described in Materials and Methods. Vimentin filaments were visualized in fixed cells by indirect immunofluorescence. Shown are untreated (UNT) cells and 18 positive hits. Positive hits are defined as those compounds that caused significant vimentin bundling, aggregation, and/or peripheral or cytoplasmic redistribution. Palmitoyl-carnitine chloride (A); cantharidin (B); CGP 52411 (C); m-3M3FBS (D); nocodazole (E); D-64131 (F); KF 38789 (G); diphenylene- iodonium chloride (H); LY 2183240 (I); carvedilol (J); SB 225002 (K); Ch 55 (L); INCA-6 (M); JK 184 (N); ivermectin (O); PNU 74654 (P); simvastatin (Q); and mevastatin (R). The compounds affect multiple targets and pathways, including: kinases, phosphatases, and lipases (A–D); microtubules and adhesion molecules (E–G); GPCRs and GPCR signaling (H–K); protein-protein interactions and multisubunit complexes (L–P); and cholesterol biosynthesis (Q, R). Note that some hits are expected (e.g., nocodazole), whereas most hits are novel and unexpected, including simvastatin and mevastatin. These data were contributed by Kathryn P. Trogden.

61

Figure 3.2 Quantification of drug-induced changes in vimentin filaments. (A) Representative images of vimentin filaments (red) in untreated (UNT) and simvastatin (SIMVA)-treated SW13- vim+ cells. Nuclei are stained with DAPI (blue). Vimentin filaments are marked by arrows and perinuclear vimentin bundles are marked by arrowheads. (B) Quantification of the vimentin- positive area in the absence (UNT) and presence of drug; simvastatin is shown as a representative example. Shown is a raw image of vimentin staining, threshold of the raw images with black representing signal, and outlines of all objects recognized in the threshold image by the analyze particles program in ImageJ. The settings allowed for the recognition of images >10 pixels2 to remove nonspecific signal (purple arrow), fill in holes (red arrow), and remove signal from the edges (green arrow). These data were contributed by Kathryn P. Trogden.

62

63

Figure 3.3 Dose-response effects of statins on vimentin filaments. (A) Simvastatin, mevastatin, pravastatin, and lovastatin were applied for 1 h at concentrations that ranged from 0.001 to 10 μM. Note the dose-dependent decrease in the vimentin-positive area in response to simvastatin and mevastatin, but not pravastatin and lovastatin. Shown are representative images of 3 concentrations (0.001, 0.1, and 10 μM) from at least 3 independent experiments. (B) Quantification of the total vimentin-positive area in cells that were treated with DMSO vehicle and the 4 statins (depicted structurally on the bottom) at different concentrations. Quantification was performed as described in Materials and Methods and Figure 3.2B. *P < 0.05, ****P < 0.0001 relative to vehicle control (1-way ANOVA). These data were contributed by Kathryn P. Trogden.

Figure 3.4 Time-dependent effects of simvastatin on vimentin in SW13 cells. (A) Bar graph depicting time-dependent effects of simvastatin (gray bars) on the vimentin area relative to vehicle control (black bars). Both conditions are expressed as the percent of untreated control (n = 3 for each condition). (B) Immunofluorescence images of vimentin (red) and DAPI (blue) at different time points after the addition of simvastatin (10 µM) or vehicle control. Vimentin filaments reorganize and shift to one side of the nucleus (arrowheads) after 15 min, followed by incorporation into ball-like perinuclear bundles at 30 and 60 min (arrows). At 120 min, many cells displayed diffuse nonfilamentous vimentin staining around the nuclei (asterisks). *P < 0.05, ***P < 0.001, ****P < 0.0001 (multiple Student’s t tests). These data were contributed by Kathryn P. Trogden.

64

Figure 3.5 Comparison of simvastatin effects on actin filaments, microtubules, and vimentin filaments. Quadruple staining for actin (green) tubulin (orange), vimentin (red), and DNA (blue) in fixed SW13-vim+ cells that were treated with vehicle (0.1% DMSO) or simvastatin (10 μM) for 60 min. Note the simvastatin-induced perinuclear vimentin bundling (arrow) and no changes in actin or tubulin. Shown are representative images of at least 3 independent experiments for each condition.

65

Figure 3.6 Biochemical characterization of the simvastatin effect on vimentin in SW13 cells. (A) Comparison of vimentin levels in the Tx-soluble and -insoluble (pellet) fractions relative to total vimentin levels in response to simvastatin treatment for 15, 30, and 60 min. Each condition was assayed in triplicate, and shown are representative blots of at least 3 independent experiments. (B) Quantification of the band intensity of the blots in panel A. ***P < 0.0001, *P < 0.05 compared with control (1-way ANOVA). (C) Immunoblot analysis of common vimentin phosphorylation sites in the presence and absence of simvastatin (10 μM; 60 min). (D) Two-dimensional gel analysis of vimentin from DMSO vehicle- or simvastatin (10 μM, 60 min)- treated SW13-vim+ cells. Note the increased abundance of negatively charged vimentin species (arrows) in the presence of simvastatin. HSE, high-salt extract. Panel D was contributed by Parijat Kabiraj.

66

Figure 3.7 Effects of simvastatin on in vitro–assembled purified vimentin. (A) Effect of 5- min, 10-μM simvastatin preincubation on purified vimentin after 15, 30, and 60 min in assembly reaction buffer. Note the time-dependent increase in high-molecular-mass (HMM) vimentin (>250 kDa) in the presence of simvastatin (S) compared with vehicle (V). (B) Negative stain transmission electron microscopy (TEM) images of vimentin filament precursors after 1 min of reaction time showing simvastatin-induced vimentin bundling. (C) TEM images of vimentin filaments after 30 and 60 min of assembly in the presence of DMSO or simvastatin. Note the presence of 20-nm-thick vimentin filaments at 30 min and large vimentin bundles after 60 min.

∼

67 68

Figure 3.8 Simvastatin significantly inhibits the growth of SW13-vim+ cells, but not SW13-vim− cells. (A) Phase-contrast images (top) and nuclear stain (bottom) of SW13-vim− cells (left 4 panels) and SW13-vim+ cells (right 4 panels). Note the decrease in the number of SW13-vim+ cells in response to 24 h of simvastatin treatment (10 μM), appearing smaller and more rounded compared with vehicle-treated cells. (B) Quantification of SW13 cell number after exposure to DMSO vehicle or 10 μM simvastatin for 24 h. Double immunoblot of actin and vimentin confirms vimentin presence and/or absence in cells. Data are representative of at least 3 independent experiments. (C) Phase-contrast images and nuclear stain of SW13 vim+ cells in response to 24 h of pravastatin treatment (10 μM), which does not affect cell morphology or viability. (D) Quantification of SW13 cell number after exposure to DMSO vehicle or 10 μM pravastatin for 24 h. **P < 0.01, ***P < 0.001 (2-way ANOVA).

Figure 3.9 Simvastatin inhibits cell growth and induces dose-dependent apoptotic cell death in SW13-vim+ cells. (A) Simvastatin induces dose-dependent reduction in the total number of SW13-vim+ cells at pharmacologically relevant concentrations (10 nM–10 µM). Blue stain marks the cell nuclei. Cell nuclei were circled (in green) and counted by using the EVOS-FL auto-count function. (B) Simvastatin induces dose-dependent cell death in SW13-vim+ cells. Green stain marks the nuclei of dead cells. All green nuclei were circled and counted by using the EVOS-FL auto-count function. (C) Log dose-response curve for the total number of cells per field as a function of simvastatin dose, demonstrating inhibitory effects at low nanomolar concentrations + (IC50 = 48.1 nM). (D) Quantification of SW13-vim cell death in the presence of different concentrations of simvastatin (micromolar). **P < 0.01, ****P < 0.0001 compared with vehicle control; 1-way ANOVA. (E) Western blot for full-length (∼116 kDa) and cleaved (∼89 kDa) PARP showing the presence of cleaved PARP after 24 h of simvastatin treatment. (F) Quantification of cleaved PARP band from multiple immunoblots (n = 5). **P < 0.001 (unpaired Student’s t test).

69

Figure 3.10 Ectopic expression of vimentin sensitizes SW13-vim− cells to simvastatin treatment. (A) Transient overexpression of vimentin (24 h), followed by simvastatin treatment (10 μM, 24 h), promotes simvastatin-dependent cell death in SW13-vim− cells. (B) Stable overexpression of mEmerald-vimentin sensitizes SW13-vim− cells to simvastatin treatment, which is accompanied by bundling and aggregation of mEmerald-vimentin. (C) Quantification of the number of cells that contained vimentin bundles in the presence of vehicle or simvastatin. **P < 0.01 (unpaired Student’s t test), ***P < 0.001 relative to all other groups (2-way ANOVA).

70

Figure 3.11 Simvastatin induces vimentin bundling and cell death in the vimentin-positive breast cancer cell line, MDA-MB-231. (A) Morphology of vimentin filaments (green) in MDA- MB-231 cells treated with DMSO vehicle (top) or 10 µM simvastatin for 30 min (middle) or 60 min (bottom). Note the time-dependent perinuclear bundling of vimentin. Nuclei are stained with DAPI. Scale bar, 50 µm. (B) Immunoblot of vimentin in MCF7 and MDA-MB-231 human breast cancer cells demonstrating the lack of vimentin in MCF7 cells. (C) Live/dead staining showing the nuclei of all cells (blue) and dead cells (green). Note the increase in dead cells in the presence of simvastatin, but not pravastatin (10 µM; 24 h), in vimentin-expressing MDA-MB- 231 cells. There is no increase in cell death in the vimentin-negative MCF7 in response to simvastatin or pravastatin. Scale bar, 200 µm. (D) MTT assay (absorbance measurement at 570 nm) of MCF7 and MDA-MB-231 cells that were treated for 48 h in the presence of DMSO vehicle or 10 µM simvastatin (n = 16). ****P < 0.0001 (1-way ANOVA).

71

Figure 3.12 Proposed model for the mechanisms and consequences of vimentin targeting by simvastatin. Simvastatin promotes the reorganization of vimentin filaments to one side of the nucleus within 15 min of drug addition in SW13-vim+ adrenal carcinoma cells. This timing is consistent with the known kinetics of simvastatin uptake in cells276. Combined with the in vitro results that demonstrated that simvastatin directly affects vimentin filament assembly, this leads us to hypothesize that the early reorganization of vimentin filaments is a direct effect of simvastatin binding. Direct binding of simvastatin changes the biochemical properties of vimentin, which leads to increased solubility in Tx detergent buffer. At later time points after simvastatin addition (30–60 min), vimentin is organized into compact perinuclear bundles. This effect is observed in both SW13-vim+ and MDA-MB-231 cells, and correlates with the sensitivity of these cells to simvastatin-induced apoptosis at later time points of 24–48 h. Cytotoxic effects are directly related to the combination of vimentin expression and simvastatin presence, as vimentin-lacking cells are not affected by simvastatin, and statins that do not affect vimentin (e.g., pravastatin) fail to induce cell death in vimentin-expressing cells.

72 Table 3.1 Positive hits and their corresponding primary targets Label Compound Primary target (compound Known functions of activity) primary target A Palmitoylcarinitine PKC (inhibitor); membrane Context-dependent cell chloride disruption signaling functions B Cantharidin PP1 and PP2A (inhibitor) Context-dependent cell signaling functions C CGP 52411 EGFR (inhibitor) Receptor tyrosine kinase regulating multiple signaling cascades D m-3M3FBS PLC (activator) Numerous cell signaling functions, including cytoskeletal dynamics E Nocodazole Microtubule depolymerizer Cell division, movement, (inhibitor) transport F D-64131 Microtubule depolymerizer Cell division, movement, (inhibitor) transport G KF 38789 P-selectin mediated adhesion Cell adhesion molecule of (inhibitor) endothelial cells and platelets H Diphenylene- GPR3 (agonist) Orphan GPCR involved in iodonium chloride aging, metabolism, and thermogenesis I LY 2183240 FAAH and anandamide uptake Endocannabinoid signaling (inhibitor) J Carvedilol -Adrenergic receptor GPCR targeted by (antagonist) catecholamines K SB 225002 CXCR2 chemokine receptor GPCR targeted by IL-8 (antagonist) and CXCL1 L Ch 55 RAR (agonist) Regulation of gene expression M INCA-6 Calcineurin-NFAT interaction Gene regulation in (inhibitor) response to environmental signals N JK 184 Hedgehog signaling (inhibitor) Cell survival, differentiation, and cancer O Ivermectin 7-Nicotinic receptor Ligand-gated calcium ion (activator) channel P PNU 74654 -Catenin-TCF4 interaction Transcriptional regulation (inhibitor) of cell adhesion and epithelial-mesenchymal transition Q Simvastatin HMG-CoA reductase (inhibitor) Cholesterol and nonsterol isoprenoid biosynthesis R Mevastatin HMG-CoA reductase (inhibitor) Cholesterol and nonsterol isoprenoid biosynthesis

73

CHAPTER 4: SITE-SPECIFIC PHOSPHORYLATION AND CASPASE CLEAVAGE OF GFAP ARE NEW MARKERS OF ALEXANDER DISEASE SEVERITY1

4.1 Introduction

Alexander disease (AxD) is a rare and invariably fatal neurological disorder that affects primarily infants and small children, but can also manifest later in life277-279. Autosomal dominant gain-of-function mutations in GFAP, which encodes glial fibrillary acidic protein

(GFAP), cause AxD131,279. GFAP is the major component of the intermediate filament (IF) cytoskeleton in astrocytes110. The accumulation and incorporation of mutant GFAP within cytoplasmic aggregates called Rosenthal fibers (RFs), causes reactive gliosis, leading to secondary injury to neurons and non-neuronal cells148,160,163,280. Silencing GFAP via antisense oligonucleotide intervention in vivo eliminates RFs, reverses the stress GFAP has been observed after various injuries of the central nervous system (CNS) including kainic acid-induced seizures, cold-injury, and hypoxic-ischemic models, where phosphorylated GFAP is expressed in reactive astrocytes281-283. These observations reveal that phosphorylation of GFAP is important for re- organization of the astrocyte IF cytoskeleton and plasticity in response to injury. However, it is not clear if, and how, abnormal GFAP phosphorylation compromises proteostasis and contributes to AxD pathogenesis.

1 This chapter previously appeared as an article in the eLife Journal. The original citation is as follows: Battaglia, R. A. et al. Site-specific phosphorylation and caspase cleavage of GFAP are new markers of Alexander disease severity. Elife 8, e47789 (2019).

74 Here, we identified a critical phosphorylation site in the GFAP head domain that is selectively and strongly upregulated in the brain tissues of AxD patients who died very young, independently of the position of the disease mutation that they carried. Further, we show that this site-specific phosphorylation promotes GFAP aggregation and is a marker of perinuclear GFAP aggregates associated with deep nuclear invaginations in AxD patient astrocytes, but not in isogenic control astrocytes. Finally, we demonstrate a correlation between site-specific GFAP phosphorylation and caspase cleavage in cells and in post-mortem brain tissue from AxD patients. Although our study does not establish a causal relationship between GFAP phosphorylation and caspase cleavage, we show that caspase-6 is a new marker for the most severe form of human AxD.

Collectively, our results reveal a new PTM signature that is associated with defective

GFAP proteostasis in the most severe form of AxD. Future interventional studies targeting these

PTMs will determine whether they contribute to, or are the consequence of, disease severity.

4.2 Results

4.2.1 Phosphorylation of Ser13 on GFAP is a marker of the most aggressive form of AxD

IFs undergo protein synthesis-independent turnover and re-organization to meet cellular demands284. PTMs are key in that process, as they regulate filament polymerization and depolymerization, protein-protein interactions, and oligomerization properties of IF proteins31.

Of all known PTMs that regulate IFs, phosphorylation is the most ubiquitous and can facilitate or antagonize other types of PTMs via complex cross-talk mechanisms285. We hypothesized that

AxD-associated GFAP missense mutations (Figure 4.1A) promote GFAP accumulation and aggregation by dysregulating site-specific phosphorylation. We extracted GFAP from post- mortem brain cortex tissue of 13 AxD patients, representing 10 different mutations

75 (Supplementary Table 4.1) and three non-AxD controls (Supplementary Table 4.2). GFAP from the insoluble high salt extracts (HSEs), prepared according to the procedure described in

Supplemental Figure 4.1, was used in phospho-proteomic analysis, revealing 12 unique phosphorylation sites on GFAP in AxD (Figure 4.1B–C). While the AxD-specific phospho- peptides localized to all three functional domains of GFAP (head, rod, tail), the most abundantly phosphorylated residue was a conserved serine (Ser13) in the head domain (Figure 4.1C–D).

Strikingly, we found that the pSer13-GFAP peptide was selectively elevated in the cortex tissue from AxD patients who died very young (median age at death = 1.7 years; range 0.5–14 years)

(Figure 4.2A). Overall, we did not observe significant phosphorylation of GFAP in the control subjects (Figure 4.2—source data 1), or in AxD patients who lived 27–50 years (median age at death = 38 years). Further, immunoblot analysis using a phospho-specific antibody (KT13)286 against pSer13-GFAP validated the mass spectrometry results in the AxD patients (Figure 4.2B–

C). Although there was one notable outlier in each age group (Figure 4.2B lanes 3 and 11), our results suggest that pSer13-GFAP is primarily associated with the more aggressive, infantile form of AxD. Furthermore, the differences in phosphorylation were not a result of age, since pSer13 GFAP was generally not present in the brain lysates from non-AxD control subjects, regardless of age (Figure 4.2D).

4.2.2 Phospho-mimic mutation at Ser13 promotes GFAP aggregation

To determine the functional significance of pSer13 on GFAP filament organization, we analyzed the filament properties of non-phosphorylatable (S13A) and phospho-mimic (S13D and

S13E) GFAP mutants. We optimized a transient over-expression system in the SW13 vimentin- negative adrenocarcinoma cells (SW13vim-) for this assay, which resulted in primarily filamentous WT GFAP and insoluble aggregated forms of common AxD mutants of GFAP

76 (Supplemental Figure 4.2). Compared to wild-type (WT) GFAP, the S13D and S13E mutants assembled primarily into large aggregates, similar to the most common AxD-associated mutant

R79H-GFAP (Figure 4.3A–B). S13A formed mostly filaments, although they appeared shorter compared to WT GFAP. To determine if the phospho-mimic mutation directly promotes aggregation, we compared the assembly properties of purified WT, S13A and S13D GFAP

(Figure 4.3C). Consistent with the phenotype observed in the transfected cells, the S13A mutant formed abnormally short filaments in vitro. In contrast, S13D was completely incapable of filament assembly, forming globular structures that were homogeneous in size and not aggregation-prone. Our results with the phospho-deficient and phospho-mimic mutants reveal that S13 is a key site that regulates the assembly properties of GFAP and that its phosphorylation status may modulate the dynamics between filaments and aggregates.

4.2.3 Generation of AxD induced pluripotent stem cells (iPSCs) and isogenic controls

In order to explore the function of this phosphorylation event in a disease-relevant system, we used an in vitro human astrocyte model of AxD. We generated iPSCs using fibroblasts from a young AxD patient and characterized their pluripotency by immunofluorescence staining (Figure 4.4A). Karyotype analysis showed that there were no chromosomal abnormalities due to the reprogramming process (Figure 4—figure supplement 1).

To generate isogenic control cells, we corrected the heterozygous point mutation in GFAP

(c.715C > T, p.R239C) using CRISPR/Cas9 mediated gene editing (Figure 4.4B).

Representative chromatograms are shown for the original patient cells and the isogenic controls

(Figure 4.4C). We also isolated ‘CRISPR control’ clones, which were edited on the wild-type

GFAP allele, thereby retaining the AxD-causing mutation and serving as an additional disease control for the gene editing procedure. Similar to the original patient cells, the edited cells were

77 karyotyped and characterized for pluripotency (Supplemental Figure 4.3). We confirmed that there were no off-target effects due to the editing procedure (Supplementary Table 4.3).

4.2.4 GFAP accumulation and perinuclear aggregation into RF-like structures in AxD iPSC- astrocytes

AxD, CRISPR control, and isogenic control iPSCs were differentiated to astrocytes

(iPSC-astrocytes) via neural progenitor cells (NPCs), as described in the Materials and methods and shown schematically in Figure 4D. After 54 days in culture, iPSC-astrocytes express classical astrocyte markers287, including alcohol dehydrogenase 1 family member L1

(ALDH1L1), solute carrier family 1 member 3 (SLC1A3), excitatory amino acid transporter 2

(EAAT2), Connexin 43 and GFAP (Supplemental Figure 4.4). To assess if our model recapitulated key features of AxD, we analyzed total GFAP expression in the iPSC-astrocytes by immunoblot, and found that GFAP levels were significantly higher in the cells that carried the heterozygous GFAP point mutation (AxD patient and CRISPR control lines) relative to the isogenic controls (Figure 4.4E–F). This is consistent with in vivo observations of GFAP levels in AxD patients288 and mouse models289. In addition, high-molecular-mass (hmm) GFAP oligomers were present in the AxD iPSC-astrocytes, similar to what we observed when we ectopically expressed the R239C-GFAP mutant (Figure 4.5A). Finally, we observed by immunofluorescence staining that the AxD mutant iPSC-astrocytes formed both GFAP filaments and perinuclear aggregates (Figure 4.5B), whereas the isogenic control iPSC-astrocytes formed only GFAP filaments (Figure 4.5C). In vivo, GFAP antibodies stain the periphery, while DAPI stains the core of RFs136,138. The in vitro-derived AxD iPSC-astrocytes displayed similar characteristics, with RF-like perinuclear aggregates staining positively for GFAP at their periphery and DAPI in the center (Figure 4.5B).

78 4.2.5 pSer13-GFAP marks the core of perinuclear GFAP aggregates localized within deep nuclear invaginations

Next, we determined if pSer13-GFAP was present in the AxD iPSC-astrocytes, similar to what we observed in the human brain tissues. As shown in Figure 4.6A, pSer13-GFAP signal was detected strongly within the core of the perinuclear GFAP aggregates of AxD iPSC- astrocytes. Somewhat surprisingly, we also observed pSer13-GFAP signal in the isogenic control cells, possibly triggered by the in vitro culture conditions. Nevertheless, unlike AxD astrocytes, in the isogenic control astrocytes pSer13-GFAP organization was filamentous and paralleled that of total GFAP. Therefore, the in vitro iPSC-astrocyte model revealed that, only in the presence of the AxD disease mutation, pSer13-GFAP is incorporated within the core of perinuclear inclusions. While in all AxD cells pSer13 signal was detected in the aggregates, we also observed cells with pSer13-positive diffuse cytoplasmic staining and filaments, likely reflecting different states of the GFAP network (Supplemental Figure 4.5). Furthermore, the pSer13- positive GFAP aggregates appeared adjacent to prominent nuclear invaginations (Figure 4.6A).

Nuclear deformations, similar to what we observed in the AxD iPSC-astrocytes, are also present in RF-bearing astrocytes in AxD human brain138. To determine whether the perinuclear aggregates compromised the nuclear envelope, we examined the AxD iPSC-astrocytes by electron microscopy. While we observed filamentous bundles on the cytoplasmic side of the nuclear invaginations, the nuclear envelope appeared intact (Figure 4.6B). Thus, pSer13-GFAP marks cytoplasmic GFAP aggregates adjacent to nuclear invaginations. It should be noted that the perinuclear aggregates containing disorganized GFAP filaments are not identical to the electron-dense RFs that are seen in post-mortem patient brain, but that they may reflect an intermediate state of GFAP accumulation.

79 4.2.6 Phosphorylation at Ser13 promotes caspase-mediated cleavage of GFAP

To understand the mechanism for how GFAP phosphorylation may promote GFAP aggregation, we conducted a biochemical analysis of the S13A, S13D and S13E GFAP mutants.

In line with our immunofluorescence result (Figure 4.3A), we observed an increase in high- molecular-mass ~100 kDa GFAP oligomer in the phospho-mimic mutant by immunoblot analysis (Figure 4.7A). However, more strikingly, we observed increased levels of a cleaved

GFAP fragment (24 kDa) in S13D and S13E, which was significantly lower in WT- and S13A-

GFAP (Figure 7A–B). Cleavage of GFAP by caspase-6 in vitro generates two fragments of 24 and 26 kDa size290. The 24 kDa C-terminal fragment is recognized by the monoclonal GA5 antibody290, which was used here. Therefore, we tested the effect of a peptide inhibitor of caspase-6 (Ac-VEID-CHO), and found that it significantly reduced the amount of cleaved S13D-

GFAP (Figure 4.7C–D). Furthermore, we observed augmented cleavage of S13D-GFAP when combined with an AxD-causing mutation (S13D/R79H double mutant), and this was also blocked by the caspase-6 inhibitor (Figure 4.7C–D). Further analysis of the AxD mutant R79H in the transfection system revealed phosphorylation not only at S13, but also at nearby Y14, S16, and S17 (Supplemental Figure 4.6 and Figure 4.7—source data 1). Of note, mutagenesis of

S16 and S17 to non-phosphorylatable alanines reduced both the cleavage and oligomerization of

R79H (Supplemental Figure 4.6). Phospho-motif analysis revealed that S13, S16 and S17 are part of a segment in the GFAP head domain that is a potential target for several kinases

(Supplementary Table 4.4). Candidate kinases include casein kinase 2 (CK2), protein kinase A

(PKA), PKC, MAP kinase activated protein kinase 2 (MAPKAP2), and glycogen synthase kinase 3 (GSK3). These data suggest that phosphorylation of Ser13 (and nearby S16/17) may promote caspase-6-mediated cleavage of GFAP in the context of AxD mutations. In line with

80 that, we observed increased levels of cleaved GFAP (upon normalization for total GFAP) in the

AxD iPSC-astrocytes compared to isogenic control astrocytes (Figure 4.7E), along with intense caspase-6 staining within perinuclear GFAP aggregates in AxD iPSC-astrocytes, but not isogenic control astrocytes (Figure 4.7F).

4.2.7 Interference with GFAP cleavage by caspase-6 partially reduces aggregation of the phospho-mimic mutant S13D

To determine how blocking GFAP cleavage affects aggregation, we performed site- directed mutagenesis to block cleavage of GFAP at Asp225. As shown in Figure 4.8A–B, the

D225E mutation reduced cleavage of S13D GFAP by >90%. This resulted in partial rescue of filament structure in S13D, although the D225E mutation on its own caused significant filament bundling and perinuclear structures that resembled large aggregates (Figure 4.8C–D). We also tested the effect of the caspase-6 inhibitor Ac-VEID-CHO, and found that it reduced both the size of the S13D aggregates (Figure 4.8E) and the presence of ~100 kDa hmm GFAP oligomers

(Figure 4.8F–G). However, similar to the mutagenesis experiment, filament bundles were observed in WT and S13D GFAP treated with Ac-VEID-CHO, suggesting that caspase-6 regulates both aggregation and normal GFAP filament reorganization.

4.2.8 Caspase-6 expression and GFAP cleavage are upregulated in AxD patients

Caspase-6 is not expressed highly in the normal human brain, especially after birth291.

Therefore, we wanted to examine its expression in the context of AxD. Using immunoblot analysis of total brain lysates, we found that caspase-6 is expressed in the brain tissue from all 8

AxD patients who died very young, but is essentially undetectable in the patients who survived longer (Figure 4.9A). To ensure caspase-6 expression is not simply more abundant in young individuals, we compared brain lysates from young and old AxD patients to non-AxD control

81 brains from age-matched individuals, and observed a significant increase in caspase-6 expression selectively in young AxD patients, but not in the other groups (Figure 4.9B–C).

Next we asked whether AxD patients, particularly young AxD patients that exhibit more pSer13-GFAP and caspase-6 expression, also displayed increased GFAP cleavage. To determine the extent of caspase-6-cleaved GFAP in AxD patient brains, we utilized an antibody that specifically recognizes N-terminally caspase-6-cleaved GFAP (D225)290. We detected cleaved

GFAP in extracts from AxD patient brains, and we observed a significant increase in the amount of D225 signal in young AxD patients, which paralleled the increased pSer13 signal in these samples (Figure 4.9D–E). In agreement with the biochemical evidence, brain tissues from young

AxD patients stained intensely for cleaved GFAP, while the signal was significantly weaker in

AxD patients who were older (Figure 4.9F; Supplemental Figure 4.7). The signal was particularly strong around perinuclear areas and surrounded circular structures that stained positive for DAPI (Figure 4.9F, bottom panels), similar to what we observed in the AxD iPSC- astrocytes. Thus, our results show that caspase-6 expression in AxD patient brain tissue parallels the presence of cleaved GFAP, and both are selectively and significantly elevated in patients who succumbed to the disease very early in life.

4.3 Discussion

Our study reveals that missense mutations, affecting discrete domains on the GFAP molecule, share a common PTM signature that is associated with compromised GFAP proteostasis in the severe form of AxD. Using patient brain tissue and human iPSC-derived AxD astrocytes, we show that head domain phosphorylation promotes defective filament assembly and perinuclear accumulation and incorporation of mutant GFAP within nuclear invaginations.

By taking an unbiased mass spectrometry proteomic approach, we were able to identify GFAP

82 phospho-peptides that were selectively elevated in human AxD brain tissue, and subsequently validated these results using a phospho-specific antibody against the most abundant epitope

(pSer13-GFAP). We demonstrate the importance of the Ser13 site for GFAP assembly in vitro and in cells. Phospho-mimetic mutation S13D completely abolished the ability of GFAP to form filaments in vitro, without leading to aggregation. In transfected SW13vim- cells, phospho mimic S13D-and S13E-GFAP mutants formed highly abnormal perinuclear aggregates that correlated with increased cleavage of GFAP by caspase-6. We detected a dramatic increase in caspase-6 expression, in association with Ser13 phosphorylation and cleavage of GFAP, in the brain tissue of AxD patients who succumbed to the disease very early in life. While the N- terminal caspase-6 fragment of GFAP promotes filament aggregation in vitro290, presently we do not have direct evidence of cause and effect between caspase-6 cleavage and GFAP aggregation in AxD patient cells. Nevertheless, our current findings provide a basis for exploring PTM-based diagnostic and potential therapeutic strategies in AxD.

Our study does not address whether Ser13 phosphorylation directly promotes caspase cleavage of GFAP, or if these two PTMs are independent markers of an increased cellular stress response in AxD. One possibility is that Ser13 phosphorylation destabilizes the filament structure, thereby promoting access of caspase-6 to the rod domain Asp225 residue, where the cleavage occurs. Another likely possibility is that the increased cleavage of GFAP is an indirect result of stress-dependent caspase-6 activation in the more severe form of AxD. This is supported by previous studies showing that AxD mutations promote activation and nuclear accumulation of p53148, which can directly induce caspase-6 expression292. Future studies in

AxD iPSC-astrocytes and animal models will be required to determine the timing of GFAP phosphorylation and caspase-6 activation in relationship to GFAP cleavage and aggregation.

83 Given our findings that pSer13-GFAP is enriched in the most aggressive form of AxD, monitoring the levels of this phospho-epitope (in addition to total GFAP) in AxD patient cerebrospinal fluid or blood may provide added sensitivity for disease activity288.

Phosphorylation of Ser13 by protein kinase C and cAMP-dependent protein kinase was initially described in vitro using purified recombinant GFAP293. In the presence of active kinases, Ser-13 phosphorylation occurred in conjunction with phosphorylation at three additional sites (Thr-7,

Ser-8, and Ser-34). Phosphorylation of monomeric GFAP at these sites prevented filament assembly, while phosphorylation of in vitro assembled GFAP filaments led to their disassembly293. Using the same antibody to pSer13-GFAP that we used in this paper (clone

KT13) it was later shown that Aurora-B and Rho-associated kinase phosphorylate GFAP in cultured astrocytoma cells during mitosis294. This may bear relevance to AxD, since human and mouse AxD astrocytes with RFs display mitotic abnormalities138. However, it was also shown using knock-in mice with the human GFAP head domain that, in vivo, the distribution of pSer13 localization was not limited to mitotic astrocytes, but that select astrocyte populations within multiple regions were pSer13 positive, such as those in the olfactory bulb, subpial regions, and subventricular zone159,282. Interestingly, the regional distribution of pSer13 largely overlaps with areas that are known to be most enriched in RFs in the AxD mouse model159. Therefore, this particular phosphorylation event on GFAP may occur during mitosis, or in phenotypically distinct astrocyte populations. This remains to be addressed in the future using the appropriate model systems, as over-expression studies in cancer cell lines (such as the SW13vim- cells we used here) may not be truly reflective of the signaling that occurs in astrocytes. In particular, it remains to be resolved whether phosphorylation of GFAP on Ser13 is part of a sequentially priming phosphorylation cascade involving nearby Ser16/17 (as predicted by the kinase motif

84 analysis) or if Ser16/17 phosphorylation is unique to the SW13 over-expression system.

Importantly, identifying the relevant in vivo kinase(s) that phosphorylate GFAP in human AxD may lead to potential novel interventions via kinase inhibition.

Caspase-mediated proteolysis of IF proteins is an important mechanism by which the filament networks re-organize during apoptosis. Although multiple effector caspases are capable of cleaving IF proteins, caspase-6 is frequently implicated in cleavage at a conserved motif within the linker L12 region of the rod domain, which results in the generation of two fragments of similar sizes. This was initially demonstrated to be the case for the type I keratins295, and later shown to also occur on vimentin296, desmin297, A-type lamins298, and GFAP110. Caspase-6 cleavage of GFAP at 222VELD225 in vitro generates an N-terminal 26 kDa fragment and a C- terminal 24 kDa fragment. The N-terminal fragment directly impairs assembly of full-length

GFAP and promotes aggregation in vitro290. Using a specific antibody recognizing the N- terminal GFAP fragment (D225), we show here that GFAP cleavage is significantly increased in

AxD tissues from patients presenting with an aggressive form of AxD, and that this parallels elevated expression of caspase-6. This could suggest that misregulation of caspase-6 may contribute to the severity of AxD. However, we were not able to demonstrate in cells that inhibition of caspase-6, or mutagenesis of the cleavage site on GFAP, can resolve aggregate formation. These results point to a more complex function for caspase-6, likely involving cytoskeletal remodeling in response to stress.

Indeed, caspase-6 upregulation has been reported in other neurodegenerative diseases involving protein aggregation, including Huntington’s Disease (HD) and Alzheimer’s Disease

(AD)299-301. Similar to GFAP, there is a caspase-6 cleavage site on the aggregation-prone proteins in both AD (amyloid precursor protein) and HD (huntingtin). Furthermore, in caspase-6

85 cleavage-resistant genetic mouse models of both HD and AD, neuronal dysfunction and degeneration are rescued302-304. Caspase-6 can promote neurodegeneration via induction of neuronal apoptosis or axon pruning305. However, the functions of caspase-6 in astrocytes are not clear. In the context of human AxD it still remains to be determined which astrocyte populations express caspase-6, and whether it promotes apoptosis or performs a non-apoptotic role, such as sculpting the cytoskeletal architecture in reactive astrocytes. Based on our demonstration that caspase-6 localizes within the perinuclear GFAP inclusions in the AxD iPSC-astrocytes, it is intriguing to speculate that, similar to keratin inclusions in epithelial cells306, RFs sequester active caspases away from other cellular substrates and may protect reactive astrocytes from apoptosis.

Recently, iPSC-derived patient astrocyte models have emerged as an important system for dissecting the cellular mechanisms in AxD. For example, these novel tools have revealed that

AxD astrocytes have defects in the secretory pathway, impaired ATP release, and attenuated calcium waves163; that they inhibit oligodendrocyte precursor cell proliferation160, providing a potential mechanistic explanation for the degeneration of white matter observed in patients; and that they have defects in mechanotransduction signaling pathways156. A novel aspect of the AxD astrocyte cell model that we generated in our study is the perinuclear accumulation of pSer13-

GFAP that was associated with prominent nuclear abnormalities. As such, these patient-derived cells replicate a key phenotypic characteristic of RF-bearing AxD astrocytes in vivo, since nuclear invaginations have been described in electron microscopy studies of AxD mouse models and AxD patient cortex138. Another important parallel is that the GFAP inclusions we observe in the AxD patient astrocytes in vitro stain positive for DAPI, and it was shown that DAPI is a reliable and sensitive marker of RFs in human and mouse brain138. Therefore patient-derived

86 iPSC-astrocytes provide a unique model system to investigate cytoplasmic-nuclear mechanics in

AxD.

Invaginations of the nucleus, such as those we observe here, have been described in physiological and pathological states307. Control of nuclear shape is critical for regulation of gene expression and response to mechanotransduction signals308. The effects of impaired nuclear morphology can be very severe, as evidenced by mutations in lamin A that lead to defective nuclear morphology in Hutchinson-Gilford Progeria Syndrome (HGPS), where patients experience accelerated aging309. An elegant study combining multiple 3D imaging strategies established a direct link between intermediate filaments, actin and the nuclear envelope within nuclear invaginations, and genetic evidence indicates that filamentous actin may play a role in generating these structures310,311. It is hypothesized that nuclear invaginations provide localized control of gene expression and nuclear-cytoplasmic transport deep within the nucleus, since they have been found to contain calcium receptors and nuclear pores307. Our study provides the first link between abnormal cytoplasmic PTM processing and perinuclear accumulation of mutant

GFAP with nuclear defects, setting the stage to address how nucleo-cytoskeletal coupling is adversely impacted by defective IF proteostasis in AxD and related human diseases.

4.4 Materials and Methods

4.4.1 Antibodies

The following antibodies were used: rabbit anti-GFAP (DAKO Agilent, Santa Clara, CA,

Z0334), rabbit anti-caspase-6 (Cell Signaling Technologies, Danvers, MA, 9762), rabbit anti-

Caspase-6 (abcam, Cambridge, UK, ab185645), rabbit anti-D225290, mouse anti-GFAP (Sigma,

GA5), mouse anti-pSer13-GFAP (KT13286), mouse anti-pan Actin, mouse anti-Tra-1– 60, mouse

87 anti-SSEA4, rabbit anti-Oct4, rabbit anti-Sox2, and Alexa 488- and Alexa 594-congujated goat anti mouse or rabbit antibodies (Thermo Fisher Scientific, Waltham, MA).

4.4.2 Cell lines

SW13vim- cells were provided by Dr. Bishr Omary and cultured in DMEM with 10% fetal bovine serum and 1% penicillin-streptomycin. Authentication of the cell line was done by short tandem repeat (STR) profiling by ATCC. Fibroblasts from a male 6 year old type I AxD patient were obtained from the Coriell institute (Camden, NJ). Sanger sequencing was performed to confirm the AxD mutation was present in the cells (c.715C > T; p.Arg239Cys). The cell lines used tested negative for mycoplasma contamination, as assayed using the Universal Mycoplasma

Detection Kit (ATCC 30–1012K).

4.4.3 Human brain tissues

De-identified post-mortem fresh-frozen and fixed AxD patient and control brain tissues were provided by the NIH NeuroBioBank and are described in Supplementary Table 4.1 and

4.2.

4.4.4 Mass spectrometry

Sample Preparation: HSEs from AxD patient post-mortem brain cortex tissue were prepared as described previously81,200 and in Figure 1—figure supplement 1, then subjected to

SDS-PAGE followed by Coomassie stain. Bands corresponding to GFAP were excised and the proteins were reduced, alkylated, and in-gel digested with trypsin overnight at 37 ̊C. Peptides were extracted, desalted with C18 spin columns (Pierce – Thermo Fisher Scientific) and dried via vacuum centrifugation. Peptide samples were stored at -80°C until further analysis. LC-

MS/MS Analysis: The peptide samples were analyzed by LC/MS/MS using an Easy nLC 1200 coupled to a QExactive HF mass spectrometer (Thermo Fisher Scientific). Samples were injected

88 onto an Easy Spray PepMap C18 column (75 m id x25 cm, 2 m particle size) (Thermo Fisher

Scientific) and separated over a 1 hr method. The gradient for separation consisted of 5–40% mobile phase B at a 250 nl/min flow rate, where mobile phase A was 0.1% formic acid in water and mobile phase B consisted of 0.1% formic acid in 80% ACN. The QExactive HF was operated in data-dependent mode where the 15 most intense precursors were selected for subsequent fragmentation. Resolution for the precursor scan (m/z 300–1600) was set to 120,000 with a target value of 3 x 106 ions. MS/MS scans resolution was set to 15,000 with a target value of 1 x 105 ions. The normalized collision energy was set to 27% for HCD. Dynamic exclusion was set to 30 s, peptide match was set to preferred, and precursors with unknown charge or a charge state of 1 and ≥7 were excluded. Data Analysis: Raw data files were processed using

Proteome Discoverer version 2.1 (Thermo Fisher Scientific). Peak lists were searched against a reviewed Uniprot human database, appended with a common contaminants database, using

Sequest. The following parameters were used to identify tryptic peptides for protein identification: 10 ppm precursor ion mass tolerance; 0.02 Da product ion mass tolerance; up to two missed trypsin cleavage sites; phosphorylation of Ser, Thr and Tyr were set as variable modifications. The ptmRS node was used to localize the sites of phosphorylation. Peptide false discovery rates (FDR) were calculated by the Percolator node using a decoy database search and data were filtered using a 5% FDR cutoff. The peak areas for the identified peptides were extracted and used for relative quantitation across samples.

4.4.5 Site directed mutagenesis, in vitro assembly, transfections, and immunofluorescence

Mutagenesis of GFAP (Origene, Rockville, MD, in vector CMV6-XL6) was performed using the QuikChange II mutagenesis kit (Agilent) to generate the designated point mutants.

Sanger sequencing of the entire coding sequence of GFAP was performed to confirm the wild-

89 type and mutant sequences. We used established procedures for the purification and in vitro assembly of GFAP312. For transfections, lipofectamine 2000 was used according to the supplier instructions (Invitrogen, Thermo Fisher Scientific, Carlsbad, CA), and experiments were performed 20–24 hr after transfection. For immunofluorescence, cells were fixed in methanol at -

20 ̊C for 10 min, washed three times in PBS and incubated in blocking solution (2.5% bovine serum albumin, 2% normal goat serum in PBS) for 1 hr at room temperature. Primary antibodies were diluted into blocking buffer and incubated overnight at 4°C. The next day, cells were washed 3 times in PBS and incubated with Alexa Fluor-conjugated secondary antibodies diluted into blocking buffer for 1 hr at room temperature. Cells were washed 3 times in PBS, incubated in DAPI for 5 min, washed 3 times and mounted in Fluoromount-G (SouthernBiotech,

Birmingham, AL) overnight. Cells were imaged on Zeiss 880 confocal laser scanning microscope using a 63x (1.4 NA) oil immersion objective (Zeiss, Jena, Germany).

4.4.6 Preparation of protein lysates and western blotting

High salt extracts (HSEs) and triton-X (TX) lysates were prepared as previously described81. Total lysates were prepared by homogenizing 25 mg tissue directly into hot 2X Tris-

Glycine SDS Sample Buffer (Thermo Fisher Scientific) and heating for 5 min at 95 ̊C.

Immunoblotting was performed as previously described313. Briefly, samples were resolved on 4–

20% gradient SDS-PAGE gels transferred onto activated polyvinylidene difluoride membranes at

40V overnight. The transferred gels were routinely stained with Coomassie blue and the membranes were blocked in 5% non-fat milk in 0.1% tween 20/PBS (PBST). Post-transfer

Coomassie-stained gels served as another loading control where the levels of housekeeping protein (actin) varied (Figure 4E). For immunoblotting, the membranes were incubated with the appropriate primary antibody diluted in 5% milk/PBST, with the exception of KT13, which was

90 incubated in 5% bovine serum albumin/PBST for blocking, primary antibodies and secondary antibodies. Antibodies were detected using ECL reagents (PerkinElmer Life Sciences,

Hopkinton, MA). For 2D gel analysis, HSEs were dissolved in 2-D starter kit rehydration/sample buffer (Biorad; 1632106) for separation by isoelectric focusing (IEF). Immobilized pH gradient

(IPG) strips (Biorad; 11 cm; pH 4–7; 1632015) were passively rehydrated in 2-D starter kit rehydration/sample buffer overnight. Cup loading method was employed to load the protein samples in cathode side (as isoelectric point of GFAP is 5.2) of the Protean IEF cell tray (Biorad;

1654020). The IEF separation was done using 72000 vh. After IEF separation the protein samples were further separated based on molecular weight using SDS-PAGE gel by applying constant 90 volts.

4.4.7 Cellular reprogramming, characterization and karyotyping of iPSCs

Skin fibroblasts were reprogrammed under feeder free conditions using Cytotune –iPS

2.0 Sendai Reprogramming kit and individual iPSC clones were picked for propagation in culture for 10 passages. To confirm stemness and differentiation capabilities of reprogrammed and edited iPSCs, we used the qPCR based TaqMan human Pluripotent Stem Cell Scorecard

Panel (Thermo Fisher Scientific). iPSCs were differentiated into all three germ layers using

STEMdiff Trilineage Differentiation Kit (StemCell Technologies, Vancouver, Canada), and a monolayer-based protocol was used to directly differentiate hES cells in parallel into the three germ layers (~1 week). Non-differentiated and differentiated cells were lysed and total RNA purified using the RNeasy kit (QIAGEN). RNA reverse transcription was performed following the Taqman Scorecard’s manufacture guidelines and the qRT-PCR was carried out using the

QuantStudio 7 Flex Real-Time PCR system. The TaqMan PCR assay combines DNA methylation mapping, gene expression profiling, and transcript counting of lineage marker

91 genes314. Reprogrammed and edited iPSCs were submitted to a standard G-band analysis consisting of 20 metaphase spreads. The analysis (carried out by Karyologic Inc) can identify gender, chromosome number, and detect aberrations that include trisomies, monosomies, deletions, insertions, translocations, duplications, breaks, polyploidy, among others. No abnormalities were found in our cell lines (Figure 4—figure supplement 2A).

4.4.8 CRISPR/Cas9 genome editing

We used the TrueCut Cas9 Protein V2, sgRNAs and the Neon Transfection system

(Thermo Fisher Scientific) to edit iPSCs. The recombinant TrueCut Cas9 V2 was diluted in resuspension buffer R provided in the kit and mixed with 900 ng of sgRNA and 2700 ng of single-stranded donor oligonucleotide, incubated 15 min at room temperature and then a total of

3 x 105 iPSCs were electroporated with the ribonucleoprotein mix. Seventy-two hours after electroporation, cells were dissociated into single cells, diluted, and seeded on Matrigel-coated

96-well plates. Single-cell colonies were selected after two weeks and tested for gene correction.

Genomic DNA of single clones was extracted and the gene of interest amplified by PCR using allele specific primers. Sanger sequencing of positive clones demonstrated single or double allele gene correction. Off-target sites within the exons of genes were predicted via selection of the top candidates using the MIT software (CRISPR.mit.edu). The analysis was performed via PCR of

400 bp fragments, which flanked the predicted off-target cut site followed by Sanger sequencing.

The chromatograms for edited clones were compared to sequences from the original AxD patient cells.

4.4.9 iPSC culture and astrocyte differentiation

iPSCs were maintained on Matrigel in StemFlex medium (Thermo Fisher Scientific) and passaged every 3–4 days with 0.5 mM EDTA dissociation solution. iPSCs were differentiated

92 into neural progenitor cells (NPC) using an embryoid body (EB) protocol. Briefly, iPSCs at 80% confluence were collected, resuspended in Neural Induction Medium (NIM, StemCell

Technologies) and seeded on one well of an Aggrewell 800 plate (StemCell Technologies) at 3 x

106 cells per well. At day five, EBs were seeded on poly-ornithine and laminin (PLO/LAM)- coated dishes in NIM. Rosette selection was performed after 12 days using Rosette Selection

Reagent (StemCell Technologies). NPCs were expanded for 7 days in Neural Progenitor

Medium (StemCell Technologies). NPCs were then differentiated into astrocyte precursors by seeding dissociated single cells at 1 x 105 cells/cm2 density on PLO/LAM dishes in STEMdiff astrocyte differentiation medium (StemCell Technologies). Astrocyte precursors were maintained for 20 days with medium changes every 48 hr and splitting every week with

Accutase (Millipore, Burlington, MA). Astrocytes were expanded for up to 120 days in

STEMdiff astrocyte maturation medium (StemCell Technologies).

4.4.10 Transmission electron microscopy

AxD iPSC-astrocytes grown on a polystyrene dish were fixed in 2.5% glutaraldehyde in

0.1M sodium cacodylate buffer, pH 7.4, for one hour at room temperature and stored at 4 ̊C. The cells were washed 3 times in 0.1M sodium cacodylate buffer followed by post-fixation in 1% buffered osmium tetroxide for 1 hr. After three washes in deionized water, the cells were dehydrated in ethanol, infiltrated and embedded in situ in PolyBed 812 epoxy resin

(Polysciences, Inc, Warrington, PA). The cell monolayer was sectioned en face to the substrate with a diamond knife and Leica UCT Ultramicrotome (Leica Microsystems, Inc, Buffalo Grove,

IL). Ultrathin sections (70 nm) were mounted on 200 mesh copper grids and stained with 4% uranyl acetate and lead citrate. The sections were observed and digital images were taken using a

JEOL JEM-1230 transmission electron microscope operating at 80kV (JEOL USA, Inc,

93 Peabody, MA) equipped with a Gatan Orius SC1000 CCD Digital Camera (Gatan, Inc,

Pleasanton, CA).

94

Figure 4.1 GFAP is phosphorylated on head domain Ser13 in human AxD brain. (A) Schematic displays the frequency and location of AxD patient GFAP mutations. (B) Method used to identify GFAP phospho-peptides. (C) Graph of AxD-specific GFAP phospho-peptides identified by mass spectrometry and type/position of patient mutations. PSM = peptide spectrum match. Green diamonds represent GFAP mutations in young patients (median age at death = 1.7 years; range 0.5–14 years) and pink diamonds represent older patients (median age at death = 38 years; range 27–50 years). (D) Amino acid conservation at the N-terminus of human, rat and mouse GFAP. The green box indicates the serine corresponding to human Ser13, which is conserved in rat and mouse.

95

Figure 4.2 GFAP is phosphorylated on head domain Ser13 primarily in AxD brain from young patients. (A) Quantification of pSer13-GFAP abundance by mass spectrometry in young (green) vs. old (pink) AxD patients (*=p < 0.05 unpaired t-test). (B) Validation of pSer13-GFAP by western blot of HSE from AxD patients, using a phospho-specific antibody to pSer13-GFAP. The order of samples, by AxD donor ID number, is: 1482, 1070, 885, 5488, 1161, 2768, 338, 613, 5377, 5517, M3596, 5109, and 4858 (listed in Supplementary Table 1). (C) Quantification of the relative intensity of pSer13-GFAP on western blot in young (green) and old (pink) AxD patients (*=p < 0.05 unpaired t-test). Signal intensity was normalized to total GFAP in each sample. (D) Western blot of pSer13-GFAP in non-AxD control brain lysates of different ages. The order of samples, by donor ID number, is: 1547, 5941, 103, 1791, 1670, 4898, 1706, 1711, 1011, 632, 4640, and 4915 (listed in Supplementary Table 2).

96

Figure 4.3 Effect of phospho-deficient and phospho-mimic S13 substitutions on GFAP filament assembly in cells and in vitro. (A) Representative images of immunofluorescence staining of DNA (blue) and GFAP (green) in SW13vim- cells transfected with wild-type GFAP (WT), R79H mutant GFAP (R79H), non-phosphorylatable GFAP (S13A), and phospho-mimic GFAP (S13D and S13E) as single or double mutations, as noted in the images. Scale bar = 5 µm. (B) Quantification of percentage of cells containing GFAP filaments, aggregates or both (n = 41– 103 cells per condition). RH = R79H; SA = S13A; SD = S13D; SE = S13E. (C) Electron micrographs showing the filament properties of in vitro assembled GFAP (WT, S13A and S13D). Bottom three panels represent magnified areas marked by the white boxes in the top panels. Scale bars = 500 nm.

97

Figure 4.4 Generation and characterization of AxD patient iPSC-astrocytes and isogenic controls. (A) Characterization of iPSC pluripotency. Bright field images of AxD patient fibroblasts (top left) and iPSCs (bottom left). Immunofluorescence staining for pluripotency markers in AxD iPSCs. (B) GFAP sequence for the AxD mutant allele and the corrected allele. Differences between the sequences are indicated by red text. The AxD-causing mutation is underlined, and all other changes are silent mutations. The area of gRNA recognition is indicated by the red line. (C) Chromatograms showing AxD heterozygous mutation in the original patient cells (top), correction of the mutant allele in the isogenic control (middle) and correction of the wild-type allele in the CRISPR control (bottom). Red arrows denote presence of the disease mutation and green check mark denote genetic correction and presence of silent mutations. (D) Schematic representation of astrocyte differentiation protocol. NIM, neural induction medium; NPM; neural progenitor medium; ADM, astrocyte differentiation medium; AMM; astrocyte maturation medium. (E) Immunoblot of GFAP in iPSC-astrocytes. Pan-actin blot and Coomassie stain serve as loading controls. (F) Quantification of band intensities for GFAP from panel E. ****p<0.0001 compared to isogenic control; one-way ANOVA.

98

Figure 4.5 Oligomerization and perinuclear aggregation of GFAP in AxD iPSC-astrocytes. (A) GFAP blot of AxD iPSC-astrocytes (left) and SW13vim- cells transfected with R239C mutant GFAP (right) reveals GFAP monomer and high-molecular-mass GFAP oligomers. Immunoblots on the bottom are of the same membranes at lower exposure. (B) Immunofluorescence staining for GFAP (magenta) and DAPI (white) in AxD iPSC-astrocytes reveals presence of perinuclear GFAP aggregates, marked by the yellow arrows. Scale bars = 10 µm. Boxed area in the merged image is shown by the enlarged image on the right. (C) Immunofluorescence staining for GFAP (magenta) and DAPI (white) in isogenic control iPSC-astrocytes. Scale bars = 10 µm.

99

Figure 4.6 pSer13 marks perinuclear accumulation of GFAP within nuclear invaginations in AxD iPSC-astrocytes. (A) Immunofluorescence staining of total GFAP (magenta), pSer13-GFAP (green) and DAPI (blue) in isogenic control (top panels) and AxD mutant (bottom panels) iPSC- astrocytes. Perinuclear GFAP aggregates are indicated by the yellow arrows. Scale bars = 10 µm. (B) Electron microscopy images of AxD patient iPSC-astrocytes revealing large, juxtanuclear fibrous bundles (boxed area on left), shown at higher magnification on the right. Scale bar = 5 µm (left) and 0.5 µm (right).

100

Figure 4.7 Phosphorylation of Ser13 on GFAP promotes caspase-6 cleavage of GFAP. (A) GFAP blot of SW13vim- cells transfected with vector, WT, S13A, S13D and S13E - GFAP. Full-length (fl) and cleaved fragment (cf) of GFAP are indicated by arrows. Immunoblot on the bottom shows GFAP monomer (fl) from the same membrane at a lower exposure. (B) Quantification of panel A by densitometry shows cleaved and full-length GFAP in phospho- mutants relative to WT GFAP (mean ± SD from three independent experiments; *p<0.05 two-way ANOVA). (C) GFAP blot in SW13vim- cells transfected with either S13D or S13D/R79H double mutant GFAP and treated for 48 hr with a caspase-6 inhibitor (Ac-VEID-CHO). (D) Quantification of GFAP bands in panel C by densitometry (mean ± SD from three biological replicates; **p<0.01; ****p<0.0001 two-way ANOVA). (E) Immunoblot for GFAP monomer (fl) and cleaved fragment (cf) in isogenic control and AxD iPSC-astrocytes. Different amounts of total protein were loaded to normalize GFAP monomer levels. (F) Immunofluorescence staining of caspase-6 (magenta), GFAP (green) and DAPI (blue) in human AxD and isogenic control iPSC-astrocytes showing caspase-6 co-localization within GFAP aggregates in the AxD cells, indicated by the arrowheads. Scale bars = 20 µm.

101

Figure 4.8 Inhibition of GFAP cleavage by caspase-6 partially alleviates aggregation due to S13D phospho-mimic mutation. (A) Western blot of GFAP total cell lysates from SW13vim- cells transfected with empty vector control, WT, S13D, D225E, and double S13D/D225E mutants. Shown are GFAP cleaved fragment (cf), full-length (fl) monomer and pan-actin (loading control). (B) Quantification of the abundance of cleaved GFAP in the three mutants shown in panel A relative to WT GFAP (mean ± SD from three biological replicates; ****p<0.0001 compared to S13D; one-way ANOVA). (C) Representative images of immunofluorescence staining of DNA (blue) and GFAP (green) in SW13vim- cells transfected with wild-type GFAP (WT), phospho- mimic GFAP (S13D), and non-cleavable GFAP (D225) as single or double mutations, as noted in the images. Scale bar = 10 µm. (D) Quantification of percentage of cells containing GFAP filaments, aggregates or both (n = 76–85 cells per condition). (E) Representative images of immunofluorescence staining of DNA (blue) and GFAP (green) in SW13vim- cells transfected with wild-type GFAP (WT) or phospho-mimic GFAP (S13D) and treated with vehicle (control) or the caspase-inhibitor Ac-VEID-CHO (10 µM, 48 hr). (F) Western blot analysis of SW13vim- total lysates transfected with S13D GFAP and treated with vehicle (control) or caspase-6 inhibitor Ac-VEID-CHO (10 µM, 24 hr), showing the 24 kDa caspase-cleaved fragment (cf), 50 kDa full- length (fl), and high-molecular-mass ~100 kDa GFAP. (G) Quantification of the relative abundance of hmm GFAP in control and Ac-VEID-CHO – treated cells. n = 3; **p<0.01; unpaired t-test.

102

Figure 4.9 High expression of caspase-6 in young AxD patient brain tissue correlates with increased levels of cleaved GFAP. (A) Immunoblot for caspase-6 in total lysates from human AxD post mortem brain tissue shows that caspase-6 is upregulated in young AxD patients. Pan- actin is used as a loading control. (B) Immunoblot for caspase-6 in total lysates from young and old non-AxD control and AxD patient post-mortem brain tissue. Pan-actin blot serves as a loading control. (C) Quantification of band intensities in panel B by densitometry of caspase-6 normalized to actin. **p<0.01; two-way ANOVA. (D) Western blotting for full-length GFAP or cleaved GFAP (D225 antibody) in HSEs from human AxD post-mortem brain tissue. (E) Quantification of band intensities from panel D by densitometry of D225, normalized to total GFAP (**p<0.01, unpaired t-test). (F) Immunofluorescence staining showing widespread presence of cleaved GFAP (D225; magenta) in cerebral cortex and underlying white matter of 347 day-old child with AxD and low expression of cleaved GFAP in a 42 year old AxD patient. Wider fields of view and sections from additional patients are shown in Figure 7 – figure supplement 1. DAPI nuclei are shown in white in bottom panels, and arrow highlights perinuclear aggregate containing cleaved GFAP and staining positively for DAPI in brain tissue from a child with AxD. Scale bar = 100 µm (top) and 10 µm (bottom).

103

Supplemental Figure 4.1 Preparation of brain high salt extracts (HSE) for mass spectrometry analysis of GFAP. Isolation of intermediate filament proteins using high salt extraction. Shown is an abbreviated version of the protocol referenced in Materials and methods. Adopted from Snider & Omary, Methods in Enzymology 2016. In the panel on the right, purified GFAP is resolved in parallel with a representative HSE from an AxD patient brain cortex tissue. Arrow points to the band that was excised for MS/MS phospho-proteomic analysis.

104

Supplemental Figure 4.2 Optimization of transient expression for WT and AxD-associated GFAP mutant proteins in SW13vim- cells. (A) Western blot of SW13vim- cells transfected for 24 hr with the designated GFAP constructs. NTC, non-transfected control. Top and bottom blots show GFAP and pan-actin, respectively, in the Triton X-100-soluble fraction (TX-100). Middle blot is a total cell lysate (TCL) blot of GFAP from the same transfections. (B) Corresponding immunofluorescence staining of GFAP in SW13vim- cells after 24 hr of transfection. Scale bars = 10 µm.

105

Supplemental Figure 4.3 Characterization of pluripotency in AxD and isogenic control iPSCs. (A) Karyotype analysis for original AxD patient iPSCs, isogenic control (MDCL11) and CRISPR control (MDCL14) iPSCs showing normal karyotypes for all three clones. (B) Immunofluorescence staining for iPSC pluripotency markers (red/green) and DAPI (blue). Scale bars = 400 µm. (C) TaqMan hPSC Scorecard Panel that compares the gene expression profile of the generated iPSCs against nine reference lines. Heat map of the genes that are up-regulated (red), have the same expression level (white) or are down -regulated (blue) in the iPSCs. Colors correlate to the fold change in expression of the indicated gene relative to the undifferentiated or Day seven embryoid body (EB) differentiated reference set. Shown at the bottom are differentiation index plots of changes in self-renewal genes (green) and differentiation genes (blue-ectoderm, orange- mesoderm, purple-endoderm) in the 1 week EB differentiated cells (left) and undifferentiated cells (right).

106

Supplemental Figure 4.4 Characterization of astrocyte differentiation. (A) Immunofluorescence staining for astrocyte markers ALDH1L1 and SLC1A3 (green) and DAPI (blue) in isogenic control, CRISPR mutant and AxD iPSC-astrocytes. Scale bars = 20 µm. (B) Immunofluorescence staining for astrocyte markers GFAP (magenta), Connexin-43 (green, top), EAAT2 (green, bottom) and DAPI (blue) in AxD iPSC-astrocytes. Scale bars = 10 µm.

107

Supplemental Figure 4.5 Three types of staining pattern observed with the pSer13 GFAP antibody in AxD iPSC-astrocytes. Three types of cells were observed with respect to pSer13 signal: Type I: primarily aggregates (arrows); Type II: aggregates and soluble cytoplasmic GFAP (asterisk) and Type III: aggregates and filamentous GFAP (arrowheads).

108

Supplemental Figure 4.6 Analysis of major sites of phosphorylation on R79H GFAP expressed in SW13 vim- cells. (A) Coomassie stain of a HSE extracts from WT and R79H GFAP analyzed by 2-dimensional (2D) gel electrophoresis. Red arrow points to a negatively charged species that was only present in R79H and analyzed by mass spectrometry. (B) Summary of phosphorylation state of the negatively charged GFAP species from panel A. (C) Effect of phospho-deficient mutants S16A and S17A on GFAP R79H oligomerization and cleavage.

109

Supplemental figure 4.7 Presence of cleaved GFAP in in post-mortem brain tissue of AxD children versus adults. Human brain sections were immunostained with the D225 antibody, which recognizes the N-terminal fragment of cleaved GFAP at Asp-225.

110 Supplemental Table 4.1 Donor information for AxD post-mortem human brain specimens. ID # age of age of sex GFAP PMI Race Cause of Death Other death death Mutation (hour refere (years) (days) s) nces 1482 0 244 F D395Y 2 Caucasian Complication of disorder 1070 0 347 M R239H 4 Caucasian Complication of Ann disorder Neurol , 2005, patient # 23 885 0 192 F E373K 18 Caucasian Complication of Ann disorder Neurol , 2005, patient # 37 5488 1 0 F R239H 7 Caucasian Complication of disorder 1161 2 175 M R239C 4 Caucasian Complication of disorder 2768 2 NA F N77S 17 Caucasian Complication of disorder 338 6 87 M R239C 12 Caucasian Complication of disorder 613 13 364 M R79C 7 Caucasian Complication of Nature disorder Geneti cs, 2001, patient # 1 5377 27 139 F K63E 22 Caucasian Complication of disorder 5517 28 245 F R79C 18 Caucasian Complication of disorder M35 33 273 F E210K 20 Caucasian Complication of Ann 96 disorder Neurol , 2005, patient # 13 5109 42 217 F D417A 4 Caucasian Complication of disorder 4858 50 139 F S247P 17 Caucasian Complication of disorder

111 Supplemental Table 4.2 Donor information for control (non-AxD) post-mortem human brain specimens. ID age of age of sex GFAP PMI Race Cause of number death death Mutation (hours) Death (years) (days) 1547* 1 259 Male N/A – 10 African Asthma Control American 5941 2 0 Male N/A – 9 Hispanic Drowning Control 103 2 75 Female N/A – 11 African Meningitis Control American 1791* 2 286 Female N/A – 12 African Drowning Control American 4670 4 237 Male N/A – 17 Caucasian Commotio Control Cordis 4898 7 272 Male N/A – 12 Caucasian Accident, Control Drowning 1706 8 214 Female N/A – 20 African Rejection of Control American Cardiac Allograft Transplantat ion 1670 13 99 Male N/A – 5 Caucasian Asphyxia Control By Hanging 1711* 27 340 Female N/A – 4 Caucasian Car Control accident, head and neck injuries 1011 29 305 Male N/A – 4 African Head Control American injuries 632 34 71 Male N/A – 6 Caucasian Accident, Control Multiple Injuries 4640 47 124 Female N/A – 5 Caucasian Pneumonia Control 4915 49 160 Male N/A – 5 Caucasian ASCVD Control

112 Supplemental Table 4.3 Summary from off-target sequencing from CRISPR/Cas9 editing. Gene name Gene ID AxD patient MDCL14 MDCL11 iPSCs (CRISPR (isogenic control) control) iPSCs iPSCs MRNIP/SQSTM1 51149/8878 1bp insertion of 1bp insertion of 1bp insertion of G in 3’UTR G in 3’UTR G in 3’UTR (chr5. (chr5. (chr5. 179840448) 179840448) 179840448) COMT 1312 Silent mutation Silent mutation Silent mutation CAC CAU CAC CAU CAC CAU (chr22.19962712) (chr22.19962712) (chr22.19962712 ) GNB1L/TBX1 54584/6899 No mutations No mutations No mutations

INPP4B 8821 No mutations No mutations No mutations

POLD1 5424 No mutations No mutations No mutations

RABEP2 79874 Mutation outside Mutation outside Mutation outside of exon A G of exon A G of exon A G (chr16.28906323) (chr16.28906323) (chr16.28906323 ) SGSM3 27352 No mutations No mutations No mutations

WNT5B 81029 Mutation outside Mutation outside Mutation outside of exon A C of exon A C of exon A C (chr12.1639668) (chr12.1639668) (chr12.1639668) DMPK 1760 No mutations No mutations No mutations

113 Supplemental Table 4.4 GFAP phosphorylation motifs and candidate kinases. Position Sequence Motif (red=phospho site) Features of motif described in the literature 11-13 RRS RXpS PKA kinase substrate motif 11-13 RRS (R/K)X(pS/pT) PKA kinase substrate motif 11-13 RRS (R/K)X(pS/pT) PKC kinase substrate motif 12-17 RSYVSS X(pS/pT)XXX(A/P/S/T) G protein-coupled receptor kinase 1 substrate motif 13-16 SYVS (pS/pT)XX(S/T) Casein Kinase I substrate motif 13-16 SYVS pSXX(E/pS*/pT*) Casein Kinase II substrate motif 13-16 SYVS (pS/pT)XX(E/D/pS*/pY*) Casein Kinase II substrate motif 13 - 17 SYVSS pSXXX(pS/pT) MAPKAPK2 kinase substrate motif 13 - 17 SYVSS pSXXXpS* GSK3 kinase substrate motif 16 - 19 SSGE pSXX(E/D) Casein kinase II substrate motif 16 - 19 SSGE (pS/pT)XX(E/D) Casein Kinase II substrate motif 16 - 19 SSGE (pS/pT)XX(E/D) Casein Kinase II substrate motif 17 - 19 SGE pSX(E/pS*/pT*) Casein Kinase II substrate motif

*indicates the residue that has to be phosphorylated already for the enzyme to recognize the motif PhosphoMotif Finder was used to generate the motif predictions315.

114 Supplemental Table 4.5 Key Reagents Reagent Designation Source or Identifiers Additional Information type reference (species) or resource Gene GFAP NA Gene ID: 2670 (human)

Cell line R239C- Coriell GM16825 Brenner et al. Nat Genet. (human) GFAP Institute 2001 fibroblasts from AxD patient Cell line R239C- Generated Generation of the AxD (human) GFAP in the study iPSCs is described in the induced Materials and Methods pluripotent section. Cells can be stem cells obtained by contacting the corresponding author. Cell line R239C- Generated Generation of the AxD (human) GFAP in the study iPSCs is described in the isogenic Materials and Methods control section. Cells can be induced obtained by contacting the pluripotent corresponding author. stem cells Cell line SW13 vim- Sarria et al. (human) J Cell Sci 1994 Biological Human brain NIH Listed in sample specimens Neurobioba Supplemental (human) nk Tables 1 and 2 Antibody Rabbit anti- Agilent/DA Clone Z0334 Dilution = 1:10,000 GFAP KO immunoblot, 1:500 immunofluorescence Antibody Rabbit anti- Cell Cat#9 762 Dilution = 1:1,000 caspase-6 Signaling immunoblot Technology Antibody Rabbit anti- abcam Cat# ab185645 Dilution = 1:100 caspase-6 immunofluorescence Antibody Rabbit anti- PMID: Gift from Dr. Ming Der D225 24102621 Perng, Dilution = 1:5,000 immunoblot (overnight), 1:150 immunofluorescence

115 Antibody Mouse anti- Sigma Clone GA5, Dilution = 1:3,000 GFAP Cat# G3893 immunoblot, 1:300 immunofluorescence Antibody Mouse anti- PMID: Gift from Dr. Masaki pSer13- 8647894 Inagaki, Dilution = 1:500 GFAP immunoblot (overnight), 1:20 immunofluorescence Antibody Mouse anti- NeoMarker Cat# MS-1295 Dilution = 1:3,000 pan Actin s immunoblot Antibody Mouse anti- ThermoFis Cat# 41-1000 Dilution = 1:300 Tra-1-60 her Antibody Mouse anti- ThermoFis Cat# 41-1100 Dilution = 1:300 Tra-1-81 her Antibody Mouse anti- ThermoFis Cat# 41-4000 Dilution = 1:300 SSEA4 her Antibody Rabbit anti- abcam Cat# ab19857 Dilution = 1:40 Oct4 Antibody Rabbit anti- ThermoFis Cat# 48-1400 Dilution = 1:125 Sox2 her Antibody Alexa 488- ThermoFis Cat# A32723 Dilution = 1:500 conjugated her goat anti- mouse Antibody Alexa 488- ThermoFis Cat# A32731 Dilution = 1:500 conjugated her goat anti- rabbit Antibody Alexa 594- ThermoFis Cat# A32742 Dilution = 1:500 conjugated her goat anti- mouse Antibody Alexa 594- ThermoFis Cat# A32740 Dilution = 1:500 conjugated her goat anti- rabbit Recombinan pCMV6- Origene Cat# t DNA XL6-GFAP SC118873 reagent Peptide, TrueCut ThermoFis Cat# A36499 recombinant Cas9 Protein her protein v2 Commercial Precision ThermoFis Cat# A29377 kit or assay gRNA her Synthesis kit

116 Commercial Agilent Agilent Cat# 200524 kit or assay Quikchange II Commercial Rneasy Kit Qiagen Cat# 74104 kit or assay Commercial Taqman ThermoFis Cat# A15870 kit or assay Scorecard her Chemical ECL Perkin NEL103E001E compound, Reagents Elmer A drug Chemical Ac-VEID- Millipore A6339 compound, CHO Sigma drug Software, CRISPR off- PMID: http://crispor.te algorithm target 27380939 for.net/

117

CHAPTER 5: INTERMEDIATE FILAMENT PROTEOSTASIS FAILURE IN ASTROCYTES AND ASTROCYTE PROGENITORS WITH NATURALLY- OCCURRING KLHL16 (GAN) MUTATIONS

5.1 Introduction

Giant Axonal Neuropathy (GAN) is a rare pediatric neurodegenerative disease and length-dependent distal axonopathy that affects the Central, Peripheral, and Autonomic Nervous

Systems166-169. Individuals with GAN experience a range of symptoms, including muscle weakness, gait abnormalities, and gastrointestinal issues, and usually succumb to respiratory failure within the second or third decade of life168. GAN is caused by recessive loss of function mutations in the KLHL16 gene (also known as GAN) that encodes gigaxonin, an E3 ubiquitin ligase adaptor protein178. Gigaxonin promotes degradation of many members of the intermediate filament (IF) gene family, which are cell type-specific cytoskeleton components181,183,184. Thus, without proper IF turnover, GAN patients accumulate IF aggregates in cell types where gigaxonin targets are expressed, including neurons, melanocytes, endothelial cells, lens epithelial cells, Schwann cells and astrocytes171-174. However, the defining pathological characteristic of

GAN, from which the name is derived, is the swollen, giant axons that are filled with bundles of intermediate filaments. For the past several decades, neurons have been the predominant focus of

GAN research. While sensory and motor neuron function is significantly impacted in GAN patients and can directly lead to loss of sensation and ambulation, the contribution of non- neuronal cells should also be examined, especially when considering therapeutic approaches, such as gene therapy.

118 Astrocytes, in particular, are important to consider in GAN because they likely contribute directly to neuronal dysfunction. When the Central Nervous System (CNS) suffers injury, astrocytes undergo a process called reactive astrogliosis, which is generally characterized by hypertrophy of cell branches, increased proliferation, and gene expression changes, notably the upregulation of IF genes75,76. Depending on the injury, reactive astrocytes can exert a wide spectrum of effects on surrounding cells ranging from toxic to beneficial316-318. Recently, reactive astrocytes have gained notoriety as an important factor in many neurodegenerative disorders since they contribute to disease progression through loss of neuroprotective functions and/or gain of neurotoxic functions76,319-324. Like most neurodegenerative diseases, GAN is characterized by the presence of reactive astrocytes172. In GAN, reactive astrocytes overwhelmingly display a characteristic feature referred to as Rosenthal fibers (RFs)173. RFs are aggregates containing glial fibrillary acidic protein (GFAP), the major IF of mature astrocytes, as well as ubiquitin, B- crystallin, and small heat shock protein 27136-138. RFs can also arise upon GFAP overexpression or in Alexander disease (AxD) where there are autosomal dominant GFAP mutations, and they coincide with astrocyte dysfunction and non-cell autonomous neuronal death131,151. While RFs are not completely unique to GAN and AxD, the widespread nature of RFs in these two pediatric neurodegenerative diseases is unparalleled. Therefore, it is important to determine the effect of altered IF proteostasis and reactivity on GAN astrocyte function.

Astrocytes are a major glial cell type in the CNS that regulate complex brain functions, including memory, learning, sleep, and injury response2,325. At the molecular level, astrocytes orchestrate this control through intricate cell processes that communicate with an array of different cell types: other astrocytes, neurons, oligodendrocytes, microglia, and perivascular cells325. While rodent and human astrocytes share many fundamental properties, human

119 astrocytes are larger and more complex than their rodent counterparts4. With a diameter measuring 2.6-fold larger, human astrocytes can initiate more cell-cell interactions than rodent astrocytes4. For example, it is estimated that a single human astrocyte can contact up to 2x106 neuronal synapses compared to only 1.2x105 for rodent astrocytes4. Beyond size, the morphology of human astrocytes is also more sophisticated. Based on their unique branching patterns and shapes, human astrocytes have been divided into several different classes, two of which have not been identified in rodents4. These morphological and size differences are accompanied by functional differences in astrocytes between species4. With these important caveats in mind, we sought to generate a human astrocyte model of GAN in order to identify novel disease mechanisms.

In this work, we use induced pluripotent stem cell (iPSC) technology and directed differentiation to develop a patient-derived human astrocyte model of GAN. We show that all

GAN iPSC lines lack gigaxonin protein, which can be restored upon genetic correction of the

KLHL16 mutation. This model reproduces astrocyte intermediate filament aggregation comparable to RFs that accumulate in GAN and AxD. The RF-like inclusions observed in GAN iPSC-astrocytes accompany deep nuclear invaginations, similar to those observed previously in

AxD iPSC-astrocytes, suggesting possible shared mechanisms326. Finally, we identify vimentin as a key player in promoting aggregation in astrocytes and astrocyte progenitor cells in GAN.

Together, our results highlight species differences in the KLHL16 gene and astrocyte physiology that may contribute to disease pathology of GAN. Future studies will apply this model to characterize the role of vimentin and post-transcriptional mechanisms in RF formation.

120 5.2 Results

5.2.1 Unique properties of the human KLHL16 gene necessitate development of a human disease model of Giant Axonal Neuropathy

Knowing that there are key differences between rodent and human astrocytes, we also wondered if there were also molecular differences in the KLHL16 gene itself that could contribute to GAN pathology. The KLHL16 gene is ubiquitously expressed in humans, and orthologous genes exist in many other vertebrates, including canines, rodents, and even the zebrafish model organism. The presence of KLHL16 orthologs in other species has allowed for the identification of a spontaneous case of GAN in canines and the generation of GAN models in rodents and zebrafish327-331. However, as depicted in Figure 5.1A, the human KLHL16 gene has unique properties compared to other species, most notably the unusually long 3’UTR.

Conspicuously, the canine KLHL16 gene also contains a lengthy 3’UTR whereas the rodent

KLHL16 genes have shorter 3’UTRs. This is especially interesting considering that canine GAN recapitulates many features of GAN while rodent GAN models fail to demonstrate the full spectrum of GAN symptoms327-330. When compared to other KLHL gene family members,

KLHL16 alone harbors an unusually long 3’UTR even though the resulting KLHL16 protein contains a similar number of amino acids to its related proteins (Figure 5.1B). This is noteworthy because the 3’UTR can regulate several aspects of mRNA biology, including degradation, translation, and localization, yet the human KLHL16 3’UTR is not represented in current animal models332. We found that Van Nostrand et al. previously reported significant associations between KLHL16 mRNA transcript and 18 RNA Binding Proteins (RBPs) through the high-throughput ENCODE eCLIP project (Figure 5.1C)333. Considering the growing appreciation for the role dysregulation of RBPs and RNA metabolism in neurodegenerative disease334, this new information motivated us to develop a human model for GAN in which we

121 could investigate the effect of KLHL16 mutations in a context with the human 3’UTR (Figure

5.1D, E).

5.2.2 Generation and characterization of mutant KLHL16 induced pluripotent stem cells (iPSCs) and isogenic controls

To examine the effect of KLHL16 mutations in a range of human cell types, we first obtained fibroblasts from seven different GAN patients (Supplemental Table 5.1). The GAN cell lines contained unique KLHL16 mutations that span all functional domains of gigaxonin, including null mutations (patients 3, 4) as well as mutations in the BTB (patient 2), Back (patient

3), or Kelch (patients 1, 5, 6, 7) domains (Figure 5.1D). Then we reprogrammed these fibroblasts to induced pluripotent stem cells (iPSCs). Pluripotency was evaluated by the

ThermoFisher Scorecard314, which compares gene expression of various self- renewal/pluripotency genes to that of genes distinct to the three germ layers (Supplemental

Figure 5.1A). The pluripotency was also assessed in each cell line via immunofluorescence staining for several markers of pluripotency (Supplemental Figure 5.1B). Finally, karyotyping was performed to ensure that no genomic abnormalities had arisen during reprogramming

(Supplemental Figure 5.1C).

Next, we used CRISPR/Cas9 gene editing in iPSCs to correct the point mutation and generate isogenic controls for patient 7 (G332R). This cell line was selected because it represents a strong levels of disease severity and harbors a point mutation which is especially amenable to gene editing using our methods in iPSCs326. We used an allele-specific PCR screen to select for edited clones, which were verified by Sanger sequencing (Supplemental Figure 5.2A).

Chromatograms from the target region are shown for the parental line as well as the corrected clones (Supplemental Figure 5.2B). We confirmed that there were no off-target mutations via

Sanger sequencing of the top 20 off-target regions within exons (Supplemental Table 5.2).

122 Since KLHL16 mutations deplete gigaxonin protein expression335, we performed immunoblotting for gigaxonin in our isogenic control iPSCs to determine whether genetic correction of KLHL16 mutation could rescue gigaxonin expression. As expected, GAN iPSCs showed little to no gigaxonin expression while isogenic control iPSCs displayed gigaxonin expression that was comparable to control stem cell lines (Figure 5.2A). Interestingly, although gigaxonin protein is reduced in GAN iPSCs, there is no difference in KLHL16 gene expression (Supplemental

Figure 5.2C).

5.2.3 KLHL16 mutant stem cells display aberrant expression of select cytoskeletal intermediate filaments (IFs)

KLHL16 is not an essential gene and mutations do not cause embryonic lethality, suggesting that there are not major deficiencies in stem cell function. As expected, GAN iPSCs divide and differentiate normally. However, knowing that iPSCs express various cytoskeletal and nuclear IFs, we asked whether proteostasis of IFs was disrupted in GAN stem cells. Starting with iPSCs, we examined nuclear IFs, and did not detect any visible differences in lamin B1 expression or morphology by biochemical and immunofluorescence analysis (Figure 5.2A, B).

We did not detect much lamin A/C expression by immunofluorescence in any of the iPSC lines

(data not shown), in agreement with previous findings that undifferentiated cells express low levels of lamin A/C 336,337.

Next, we examined cytoplasmic IFs that are known to be expressed in iPSCs by immunofluorescence: keratin 8 (K8) and vimentin. While we observed no difference in K8 expression or morphology, we noticed a striking upregulation of vimentin in iPSCs from GAN patient 7 compared to isogenic controls (Figure 5.2C). By western blot, we observed an increase in vimentin expression in iPSCs from GAN patients 2, 5, 6, and 7 (vim-high). In contrast, isogenic controls and GAN patient 1, 3, and 4 (vim-low) iPSCs did not display any detectable

123 vimentin expression (Figure 5.2D). Further examination of the morphology revealed that vimentin accumulated in abnormal bundles in the vim-high GAN iPSCs (Figure 5.2E, arrows).

Importantly, the observation of vimentin expression in GAN patient 7 but not the isogenic controls for patient 7 suggests that this phenotype is dependent upon the KLHL16 mutation and not variability between stem cell lines. However, gene expression of IFs can also become upregulated during cellular stress. To test whether the upregulation of vimentin protein was due to an increase in gene expression, we performed qRT-PCR in GAN and isogenic control iPSCs.

We observed a slight but nonsignificant increase in VIM expression in the vim-high iPSCs. Thus, the increase in vimentin protein likely involves a combination of transcriptional and posttranscriptional mechanisms. The specificity of abnormalities in vimentin expression and morphology suggests that dysregulation of IF proteostasis in GAN iPSCs is selective and unique to bona fide gigaxonin targets.

We next generated neural progenitor cells (NPCs), and since NPCs contain the type IV

IF, Nestin, we examined Nestin morphology and expression in these cells (Figure 5.3A, B).

Although Nestin IF morphology looked normal by immunofluorescence analysis, it was apparent that there were fewer Nestin positive cells in GAN NPCs (Figure 5.3A). This was supported by western blotting for Nestin in the NPCs, which revealed about two-fold less Nestin expression in

GAN NPCs compared to isogenic controls (Figure 5.3B, C). Future experiments will aim to understand whether vimentin expression in iPSCs persists in NPCs and impacts NPC production since vimentin has been shown to inhibit neurogenesis in the mouse hippocampus120.

5.2.4 KLHL16 regulates the astrocyte IF cytoskeleton

We further differentiated the GAN and isogenic control NPCs to astrocytes (iPSC- astrocytes) using our previously established protocol326. Throughout development, astrocytes

124 express a variety of IF proteins, first expressing vimentin, which is gradually replaced by GFAP.

To capture these different developmental time points, we examined the cells at 32 days and 64 days and classified them as early and late iPSC-astrocytes, respectively (Figure 5.4A, arrows).

As expected, immunofluorescence analysis showed that early iPSC-astrocytes express vimentin.

Early GAN iPSC-astrocytes display large, perinuclear accumulations of vimentin among vimentin filaments while isogenic control iPSC-astrocytes exhibit mostly normal vimentin filaments (Figure 5.4B, C). Vimentin aggregation often coincides with a collapse of the filament network and accumulation of vimentin in the perinuclear space, which can be measured by quantifying the vimentin area. Indeed, in early iPSC-astrocytes, GAN cells exhibit decreased vimentin area compared to isogenic control cells (Figure 5.4D). The decrease in vimentin area is even more striking in late iPSC-astrocytes (Figure 5.4E). Thus, we show that correction of the

KLHL16 mutation rescued vimentin morphology.

Late iPSC-astrocytes express either vimentin or both vimentin and GFAP (Figure 5.5A).

While late isogenic control iPSC-astrocytes show mostly normal GFAP and vimentin filaments, late GAN iPSC-astrocytes present with IF filaments that are accompanied by large GFAP aggregates that co-localize with vimentin (GFAP+/Vim+) (Figure 5.5A). Beyond cytoskeletal abnormalities, we noted irregular nuclear morphology in the late GAN iPSC-astrocytes expressing GFAP. Early or late GAN iPSC-astrocytes expressing only vimentin displayed mostly normal nuclei even in the presence of large vimentin aggregates. In contrast, late GAN iPSC- astrocytes expressing both vimentin and GFAP contained misshapen nuclei that occur alongside

GFAP+/Vim+ aggregates (Figure 5.5B). The GFAP aggregates in late GAN iPSC-astrocytes also co-localized with three different RF markers, including heat shock protein 27 (HSP27), B- crystallin (CRYAB), and ubiquitin (Figure 5.5C).

125 5.2.5 Vimentin interacts with GFAP to promote its aggregation

The iPSC-astrocyte system was useful for examining GFAP aggregation and detecting nuclear abnormalities. However, few late iPSC-astrocytes expressed GFAP, so we sought a system to generate a greater number of mature astrocytes. We differentiated iPSCs to brain organoids using an established protocol (Figure 5.6A)338. Based on this protocol, we expected that 20 percent of the cells will be GFAP+ astrocytes after 9 months in culture. Indeed, we saw robust GFAP expression in both the isogenic control and GAN brain organoids (Figure 5.6B).

Similar to the 2-dimensional system, the GAN brain organoids display large, perinuclear GFAP aggregates that are adjacent to nuclear deformations (Figure 5.6B). We observed a similar phenomenon of deformed nuclei bordered closely by perinuclear aggregates in our iPSC- astrocyte model of AxD. To investigate other similarities with AxD, we asked whether phosphorylation of GFAP played a role in GFAP aggregation in GAN where there is an absence of GFAP mutations. Previously, we showed that phosphorylation of GFAP at serine 13 (pSer13-

GFAP) promotes cleavage and aggregation of GFAP and is a marker of aggressive AxD. We found that pSer13-GFAP co-localized with total GFAP in GAN brain organoids, suggesting a possible shared mechanism of GFAP aggregation (Figure 5.7A).

To better understand the role of pSer13-GFAP in GFAP aggregation, we utilized validated phosphomimetic mutants of GFAP at serine 13 (S13D)326. We hypothesized that vimentin promotes aggregation of pSer13-GFAP, perhaps by acting as a scaffold, as proposed previously138. To test this, we overexpressed S13D in both SW13vim- and SW13vim+ cells

(Figure 5.7B-C). As expected, in SW13vim- cells, S13D forms aggregates or localizes diffusely to the cytoplasm. In SW13vim+ cells, however, S13D can be seen colocalizing with vimentin filaments, an observation that is in line with the finding that GFAP filaments can tolerate up to

126 10% of assembly-defective mutant IFs145. We also noted small GFAP foci of S13D in both the

SW13vim- and SW13vim+ cells. However, while the foci were dispersed throughout the cytoplasm in the SW13vim- cells, they often colocalized with vimentin in the SW13vim+ cells.

Finally, we observed large GFAP aggregates that colocalize with vimentin in the SW13vim+ cells (Figure 5.7B). Biochemically, we found that S13D formed significantly more GFAP oligomers in the presence of vimentin, supporting the idea that vimentin promotes GFAP aggregation (Figure 5.7C).

5.3 Discussion

In this study, we develop a model to examine intermediate filament proteostasis failure in human astrocytes. We find that the human KLHL16 gene contains unique properties compared to other species that uncover the possibility of novel posttranscriptional disease mechanisms.

Utilizing GAN patient-derived iPSCs and genetically engineered isogenic controls, we show that

KLHL16 mutations deplete gigaxonin protein and compromise vimentin and nestin IFs in stem cells and astrocyte progenitors. We also detect similarities between GAN and AxD iPSC- astrocytes, particularly the presence of pSer13-GFAP and deep, nuclear invaginations that accompany GFAP+/Vim+ aggregates. Finally, we show that vimentin promotes oligomerization of assembly deficient GFAP. Thus, our findings confirm that GAN iPSC-astrocytes and brain organoids are a useful model to examine uniquely human disease mechanisms in GAN and strongly implicate vimentin as an underappreciated contributor to RF formation.

Species differences in the clinical symptoms of GAN are apparent when comparing humans to canines, rodents, and zebrafish. Spontaneous canine GAN most closely mirrors the human clinical symptoms of GAN with some affected animals having curly hair and all affected animals showing symptoms early in life (~15 months)327,339,340. The affected GAN animals

127 display with ataxia, paresis, and muscle weakness resulting in death due to complications by approximately 2 years327,341. In contrast, while rodent models demonstrate similar pathology, they have normal lifespans and have only mild motor and sensory deficits that are late onset328-

330. The zebrafish model of GAN does have locomotion defects but also displays developmental defects in motor neurons that are not seen in other models, making the motor phenotype difficult to compare to other species331. Here, we highlight unique properties in the 3’UTR of the

KLHL16 gene in humans that may, at least partly, address some of these differences.

Importantly, the finding that canine KLHL16 also contains a long 3’UTR provides correlative evidence to support the idea that the 3’UTR may contribute to GAN pathogenesis. The discovery that numerous RBPs bind to human KLHL16 mRNA suggests several new avenues of investigation333. Importantly, these interactions must be examined in the context of GAN to understand whether KLHL16 mutations crosstalk with RBPs. It is possible that KLHL16 mutations promote enhanced RBP binding to the KLHL16 transcript. Such interactions could affect RBP subcellular localization, create a seed to promote aggregation or disrupt RNA metabolism. TARDP43 is an especially attractive target, as it has been implicated in different neurodegenerative diseases dubbed “TDP43 proteinopathies”, as well as AxD342,343. However, we acknowledge that other factors surely contribute to disparities in GAN severity among species, including lifespan, length differences in axons and differing complexity of cells (e.g. astrocyte complexity), and the involvement of the 3’UTR in GAN must be tested experimentally.

The recessive nature of GAN and the absence of detectable gigaxonin protein has led to the understanding that the disorder is caused primarily by loss of function of gigaxonin. In the case of null mutations, the cause of gigaxonin depletion is clear, but for missense mutations, it is not fully understood why gigaxonin protein is lost. Is has been proposed that the defect occurs

128 posttranslationally because missense mutations could destabilize the gigaxonin protein335.

However, this hypothesis is yet to be tested experimentally, and we suggest that a key intermediate step – translation – should be examined. Perhaps KLHL16 is transcribed and stalled at the mRNA transcript stage, an idea that is supported by the finding that KLHL16 mRNA can be stored in stress granules in human cells344. The possibility that posttranscriptional mechanisms may contribute to GAN opens up the question: Can GAN be caused by mutations outside the coding region? Although some mutations have been identified in splicing sites, sequencing is not routinely performed on non-coding regions of KLHL16 in suspected GAN patients, which is understandable considering the remarkable length of the KLHL16 gene345. This patient-derived iPSC model will provide a useful tool to address the mechanism of loss of gigaxonin in GAN.

Our model expands upon a previous iPSC model of GAN by focusing on the contributions of astrocytes346. The human iPSC-astrocyte and organoid models used in this work offer several advantages in the study of GAN and other neurodegenerative diseases. Broadly, iPSC-derived patient astrocytes have the potential to provide a platform for drug development and novel cell-based therapies. Here, we utilize this technology to model GAN and understand disease mechanisms in astrocytes. As discussed, human astrocytes differ from their rodent counterparts in size, shape, and function4. To fully examine astrocyte-related disease mechanisms, it is necessary to complement animal models with a model system that has these unique human properties. Advances in directed differentiation methods in combination with iPSC technology provide a minimally invasive option to generate human astrocytes338,347.

Further, organoid-derived astrocytes have demonstrated several astrocyte functions, and thus, our model can be utilized in the future to determine astrocyte functional deficits in GAN348. The use of patient-derived cells also allows for consideration of background genetics. This is especially

129 helpful in our studies of AxD and GAN where there is variability in disease severity likely due to genetic modifiers, but more broadly has also allowed for the study of other neurodegenerative disease where the underlying genetic cause is not known, such as spontaneous amyotrophic lateral sclerosis. Finally, the ability to generate isogenic controls is a powerful tool and is important for any transcriptomic and proteomic studies since iPSCs are known to show variability in gene expression and differentiation capacity349,350. It should be noted, however, that gene editing by homology-directed repair in iPSCs is especially difficult and requires costly reagents and a lengthy screening process for often low-frequency events351. Because of these limitations, we were only able to generate isogenic controls for one patient cell line (patient 7) and resorted to the use of iPSCs from a healthy donor as an alternative option for more controls.

Using our iPSC-astrocyte models, we reveal similarities between GAN and AxD that indicate these two disorders may share molecular mechanisms, opening the door to the possibility of drug repurposing. We previously showed that pSer13-GFAP is a marker of aggressive AxD326. Here, we observe pSer13-GFAP in GAN iPSC-brain organoids which lack

GFAP mutations, suggesting that this may be a common modification of GFAP under proteostasis failure. Our previous findings showed that phosphorylation at serine 13 promotes oligomerization and caspase cleavage of GFAP. Future work will focus on identifying the kinase(s) that promote phosphorylation of this residue to determine whether inhibiting phosphorylation could decrease aggregation and improve astrocyte function in AxD and GAN.

Another molecular similarity was the presence of deep, nuclear invaginations in the presence of

GFAP aggregates. Nuclear invaginations are not unique to AxD and GAN and are also observed in other neurodegenerative diseases352-354. It will be important to determine the effect of these

130 deformations on nuclear structure and functions, such as transcription, mRNA transport, and mRNA processing.

Finally, our results indicate that under IF proteostasis failure, vimentin and GFAP cooperate to promote aggregation. While IF aggregates tend to occur perinuclearly, the presence of deep nuclear deformations with GFAP+ aggregates suggests that GFAP could promote enhanced and possibly toxic interactions of IF aggregates with the nucleus. One possible cause for this could be related to plectin, which is an IF-associate protein that crosslinks IFs to other structures, including microtubules, actin, and the nuclear envelope40,355. While both vimentin and

GFAP are known to bind to plectin, it is possible that each IF develops distinct post translational modifications, which can alter plectin binding31,355-357. Another potential mechanism could be disrupted nuclear transport. GFAP and vimentin both contain a predicted nuclear localization signal in the C-terminal tail domain, but GFAP harbors an additional predicted domain in the N- terminal head domain39. However, it has yet to be shown that GFAP is transported into the nucleus, as has been demonstrated with keratin-17, an epithelial IF39. Using our overexpression system, we also show evidence that vimentin promotes oligomerization of assembly compromised GFAP. We suspect, as others have posited, that vimentin promotes aggregation by providing a scaffold on which aggregates are seeded138. Astrocytes are not the only cell that express more than one IF in development and disease. In fact, neurons can express at least 4 IFs: neurofilaments light, medium, and heavy, as well as peripherin or -internexin, which are assembled into filaments at very specific ratios358,359. It is likely that different IFs cooperate to promote aggregation in other IF-associated disorders where IF proteostasis and assembly are compromised. The abundance of IF aggregates in varying cell types in GAN makes our iPSC

131 model a powerful tool to broadly address the effects IF proteostasis failure in diverse human cellular contexts.

5.4 Materials and Methods

5.4.1 Cellular reprogramming, characterization, and gene editing of iPSCs

iPSCs were generate from GAN (OMIM#256850) patient fibroblasts and characterized using the methods described in Battaglia et al. 2019. Gene editing and off-target analysis was carried out as previously described in Battaglia et al. 2019.

5.4.2 iPSC culture and differentiation

iPSCs were maintained on Matrigel (Corning, Cat. No. 354480) in StemFlex Medium

(Thermo Fisher Scientific, Cat. No A3349401) and passaged every 3-4 days with 0.5 mM EDTA dissociation solution. Astrocytes were differentiated as described in Battaglia et al. 2019. Briefly,

Aggrewell 800 plates (StemCell Technologies) were used to generate EBs from iPSCs. To generate neural progenitor cells (NPCs), EBs were plated on poly-ornithine and laminin coated plates in Neural Induction Medium (StemCell Technologies) and rosettes were selected after 12 days and expanded in Neural Progenitor Medium (StemCell Technologies). NPCs were differentiated to astrocytes using the Stem Cell Technologies STEMdiff astrocyte differentiation and maturation kits during which they were split weekly with Accutase (Millipore).

Organoids were generated using the protocol described in Pasca et al. 2015 with the following modifications for feeder-free conditions338. The EBs were generated as described above on days 0-6. On day 7, the EBs were moved to neural medium (NM, Invitrogen) containing serum- free Neurobasal medium with B-27 without vitamin A (Invitrogen), GlutaMax (Gibco). NM was supplemented with FGF2 (20ng/mL, R&D Systems), and EGF (20ng/mL, R&D Systems). Media was changed daily for days 7-16 and every other day for days 17-25. After neural induction, the

132 media was replaced with NM supplemented with BDNF (20ng/mL, Peprotech), and NT3

(20ng/mL, Peprotech) every other day to promote differentiation. On day 43, NM without growth factors was used for medium changes every four days.

5.4.3 Antibodies

The following primary antibodies and concentrations were used in this study: rabbit anti-

GFAP (DAKO, Agilent, clone Z0334; IF 1:500, WB 1:10,000), mouse anti-GFAP (Sigma, clone

GA5; IF 1:300), mouse anti-pSer13-GFAP (gift from Dr. Inagaki, KT13, IF 1:20), mouse anti-

Gigaxonin (Santa Cruz Biotechnology, F3, WB 1:200), rabbit anti-Vimentin (Cell Signaling

Technology, D21H3, IF 1:100), mouse anti-Vimentin (Thermo Fisher Scientific, V9, WB 1:1000), mouse anti-Keratin 8 (Thermo Fisher Scientific, TS1, IF 1:100), rat anti-K8 (Developmental

Studies Hybridoma Bank, Troma I, WB 1:5000), rabbit anti-Lamin A/C (Santa Cruz

Biotechnology), rabbit anti-Lamin B1 (Abcam ab16048, IF 1:10,000, WB 1:10,000), mouse anti-

Nestin (Thermo Fisher Scientific, 10C2, IF 1:200), mouse anti-Tra-1-60 (Thermo Fisher Scientific,

41-1000, IF 1:300), mouse anti-Tra-1-81 (Thermo Fisher Scientific, 41-1100, IF 1:300), rabbit anti-Oct4 (Abcam, ab19857, IF 1:40), and rabbit anti-Sox2 (Thermo Fisher Scientific, 48-1400,

IF 1:125) The following secondary antibodies and concentrations were used: Alexa 488- and Alexa

594-conjugated goat anti mouse and rabbit antibodies (Thermo Fisher Scientific, IF 1:500), and peroxidase-conjugated goat anti-mouse and rabbit antibodies (Sigma, WB 1:5000).

5.4.4 Preparation of protein lysates and immunoblotting

Total lysates were prepared from 60-80% confluent iPSCs plated on 6-well plates by rinsing cells quickly with 1 mL of 1X PBS and adding 2X NovexTM Tris-Glycine SDS Sample

Buffer (ThermoFisher, Cat#LC2676) directly to the plate, collected into Eppendorf tubes, and heated at 95C for 5 min. Protein lysates were normalized by running Coomassie staining before

133 running western blots. Immunoblotting was performed as in (Trogden et al 2018). Briefly, samples were separated on a 4-20% gradient SDS-PAGE gel and transferred either for 1 hour at 110 V or overnight at 40 V onto an activated polyvinylidene difluoride membrane. Post-transfer Coomassie stained were performed on gels after every transfer to verify normalization for each experiment and are shown in the figures. Membranes were blocked in 5% non-fat milk dissolved into 0.1% tween 20/PBS (PBST) for 1 hour at room temperature and then incubated in primary and secondary antibodies diluted into blocking solution at the concentrations listed above. Antibodies were detected via ECL reagents (PerkinElmer).

5.4.5 RNA isolation and quantitative real-time PCR (qRT-PCR)

RNA was isolated from iPSCs using the ThermoFisher PureLink RNA mini kit (Thermo

Fisher Scientific, Cat#12183025) following all manufacturer guidelines and immediately converted to cDNA. The High-Capacity cDNA Reverse Transcription kit (Thermo Fisher

Scientific, Cat#4368814) was used to convert 2g of total RNA to cDNA. To measure gene expression, qRT-PCR was performed using PowerUp SYBR Green Master Mix (Thermo Fisher

Scientific, Cat#A25778) and the Applied Biosystems QuantStudio 6 Flex Real-Time PCR System.

The primers used to detect gene expression in this study are listed in Table 3.

5.4.6 Immunofluorescence, imaging, and analysis

Cells were fixed in methanol at -20C for 10 min, washed three times in PBS, and blocked in Buffer B (2.5% Bovine Serum Albumin (Sigma), 2% normal goat serum (Gibco)/PBS) for 1 hr at room temperature. Next, cells were incubated in primary antibodies overnight at 4C after which they were washed three times in PBS and incubated with Alexa Fluor-conjugated secondary antibodies 1 hr at room temperature. Cells were washed three times in PBS, incubated in DAPI

(Invitrogen), washed twice in PBS, and mounted in Fluoromount-G (SouthernBiotech) overnight.

134 Cells were imaged on Zeiss 880 confocal laser scanning microscope using a 40x oil immersion objective or for widefield on the EVOS-FL auto cell imaging system (Thermo Fisher Scientific) using a 20x (0.75 NA) objective. National Institute of Health ImageJ software was used to measure size and intensity of the vimentin cytoskeleton as shown in figure 3D by using the polygon tool to circle the vimentin signal and the measure tool, which provides a measure of area and average signal intensity.

135

Figure 5.1 Human KLHL16 mRNA contains unique properties. (A) Conservation of KLHL16 mRNA amongst species using the NCBI BLAST alignment tool. The human KLHL16 mRNA sequence is shown at the top in blue with the coding sequence represented by the black line. Conservation is indicated by colors, which represent the alignment scores where high scores indicate more conservation. Red bars indicate alignment scores greater than or equal to 200 (high conservation), magenta bars are 80-200, green bars are 50-80, blue bars are 40-50, and black lines are < 40 (low conservation). (B) Graphs displaying the length of KLHL family member proteins (left) and mRNA transcripts (right) where KLHL16 is represented by a pink circle and other KLHL family members are represented by blue circles. (C) Graph showing fold enrichment of KLHL16 mRNA bound to various RBPs determined experimentally by Van Nostrand et al. using eCLIP333. (D) Schematic of KLHL16 mutations mapped to the protein, gigaxonin. Numbers 1-7 refer to the patients listed in Supplemental Table 5.1. Asterisks indicate compound heterozygous mutations. (E) Schematic diagram of methods used to generate GAN iPSC-astrocytes and iPSC-brain organoids.

136

Figure 5.2 Gene editing in GAN patient-derived iPSCs restores gigaxonin protein and selective IF proteostasis. (A) Immunoblotting for gigaxonin and Lamin B1 in GAN patient and control iPSCs. Post-transfer Coomassie stained gel is shown below for a loading control. Patient and control samples are loaded in the same order as Supplemental Table 5.1 from left to right. (B) Confocal images of immunofluorescence of GAN patient 7 and isogenic control iPSCs stained for Lamin B1 (magenta) and DAPI (blue). The scale bar represents 10 m. (C) Confocal images of immunofluorescence of GAN patient 7 and isogenic control iPSCs stained for keratin-8 (green), vimentin (magenta), an DAPI (blue). The scale bar represents 20 m. (D) Immunoblotting for vimentin in GAN and control iPSCs. Post-transfer Coomassie stained gel is shown for a loading control. (E) Confocal images of immunofluorescence of GAN patient 7 and isogenic control iPSCs stained for keratin-8 (green), vimentin (magenta), an DAPI (blue). The white arrow indicates vimentin bundling. The scale bar represents 10 m.

137

138 Figure 5.3 Nestin is downregulated in GAN Neural Progenitor Cells (NPCs). (A) Confocal images of immunofluorescence of

NPCs stained for Nestin (magenta) and DAPI (blue). The scale bar represents 10 m. (B) Immunoblotting for gigaxonin and Nestin in NPCs. Post-transfer Coomassie stained gel is shown below for a loading control. (C) Quantification of western blot in panel B by densitometry.

Figure 5.4 Vimentin filament organization is restored in GAN isogenic control iPSC- astrocytes. (A) Schematic illustrating iPSC differentiation to astrocytes. (B, C) Representative images of immunofluorescence staining for vimentin (green) and DAPI (blue) in GAN and isogenic control 32-day old iPSC-astrocytes. (B) White arrows indicate vimentin aggregates. Scale bar represents 50 m. (C) Scale bar represents 200 m. (D, E) Quantification of vimentin area in iPSC-astrocytes at day 32 (D) and day 64 (E).

139

Figure 5.5 GFAP-expressing GAN iPSC-astrocytes exhibit deformed nuclei. All panels show confocal images of immunofluorescence staining in 66-day old iPSC-astrocytes. (A, B) GAN and isogenic control iPSC-astrocytes were stained in A for Vimentin (green), GFAP (magenta), and DAPI (blue) and in B for lamin B1 (green) and GFAP (magenta). The scale bars represent 10 m. (C) GAN iPSC-astrocytes were stained for GFAP (cyan), RF markers (magenta), and DAPI (blue).

140

Figure 5.6 iPSC-organoids develop GFAP+ astrocytes and display GFAP aggregation in GAN. (A) Schematic diagram of methods for generating brain organoids from iPSCs. The image displays a 9 month old brain organoid in a 24-well plate. (B) Confocal images of immunofluorescence staining for total GFAP (cyan), pSer13-GFAP (Magenta), and DAPI (blue) in GAN patient 7 and isogenic control iPSC-organoids. The scale bars in the top panels represent 20 m and the lower panels represent 10 m.

141

Figure 5.7 Vimentin promotes aggregation of GFAP. (A) Confocal images of immunofluorescence staining in GAN patient 7 iPSC-organoids for total GFAP (cyan), pSer13- GFAP (Magenta), and DAPI (blue). The scale bars represent 10 m. (B-D) SW13vim+ and SW13vim- cell lines were transfected with either wild-type GFAP or serine phosphomimetic mutant GFAP (S13D). (B) Confocal images of immunofluorescence staining for vimentin (green), GFAP (magenta), and DAPI (blue) in transfected cells. Scale bars represent 20 m. (C) Immunoblot for GFAP in total lysates of transfected SW13vim+ (left) and SW13vim- (right) cells. Immunoblot for pan-actin is shown as a loading control. (D) Quantification of GFAP oligomers relative to monomeric GFAP.

142

Supplemental Figure 5.1 Generation of GAN iPSCs from patient fibroblasts. (A-B) Pluripotency was assessed via ThermoFisher Taqman scorecard analysis and immunofluorescence staining for OCT4, SOX2, TRA1-60, and/or TRA1-81/SSEA4 pluripotency markers. (C) GAN iPSCs were characterized for genomic stability via karyotyping as shown by representative images from GAN patients 2, 4, and 7. The scale bars represent 400 m.

143

Supplemental Figure 5.2 Correction of KLHL16 mutations via CRISPR/Cas9 gene editing. (A) Gene editing strategy for GAN Patient 7 iPSCs. The G is the wild-type allele, the green shape represents the GAN mutation (G>A), and the yellow stars represent the silent mutations introduced by the repair construct. Black arrows depict universal primers, whereas the red arrow is an allele specific primer that exclusively binds to the corrected sequence. (B) Chromatograms display the original GAN mutant sequence (G332R, GGG>AGG; Y89S, TAC>TCC) and the sequence from a corrected clone where silent mutations are indicated by red arrows. (C) GAN gene expression in iPSCs by qRT-PCR using primers at the beginning (left) and end (right) of the gene.

144 Supplemental Table 5.1 GAN patient and control information Patient * NIH/in-house Mutation Sex Age MFM Score ID Control 0 ISCA N/A F NA Control 1 B3 Isogenic control for patient 7 M NA Control 2 G5 Isogenic control for patient 7 M NA Control 3 2D1 Isogenic control for patient 7 M NA Control 4 2D3 Isogenic control for patient 7 M NA 1 B15-56.1 R574C; deletion of G280 94.8 2 B13-69.1 Y89S (homozygous) M 72.9 3 B16-64.1 R138L: null (whole gene) 71.9 4 B16-78 IVS4+1G>A (null, homozygous) M 64.6 5 B16-74 Deletion of exons 10 and 11 61.5 6 B15-100.1 E486K; 3.2kb deletion (exons 7-9) 38.5 7 B16-02 G332R (homozygous) M 30.2 *Patient numbers were assigned by the authors in order of increasing disease severity, as measured by MFM for GAN and arbitrarily for controls.

145 Supplemental Table 5.2 Summary of off-target Sanger sequencing from CRISPR/Cas9 editing of Patient 7 Gene name Gene Parental Clone Clone Clone Clone ID B3 G5 2D1 2D3 ATXN2L 11273 Homozygous: T>A Same as Same as Same as Same as (chr.16, 28832748) parental parental parental parental BNIP3L 665 N.M. N.M. N.M. N.M. N.M. CLRN1- 116933 N.M. N.M. N.M. N.M. N.M. AS1 URI1 8725 N.M. N.M. N.M. N.M. N.M. SCN9A 6335 Heterozygous: A>G, Same as Same as Same as Same as G>A (chr.2, 166303519, parental parental parental parental 166303436) COL19A1 1310 N.M. N.M. N.M. N.M. N.M. RFLNB 359845 N.M. N.M. N.M. N.M. N.M. CSN1S2AP 286828 N.M. N.M. N.M. N.M. N.M. PDZD2 23037 N.M. N.M. N.M. N.M. N.M. PNLIPRP2 5408 Heterozygous: G>C (chr. Same as Same as Same as Same as 10, 116624046) parental parental parental parental DIP2A 23181 N.M. N.M. N.M. N.M. N.M. EGFEM1P 93556 N.M. N.M. N.M. N.M. N.M. GINS2 51659 N.M. N.M. N.M. N.M. N.M. SCN3A 6328 N.M. N.M. N.M. N.M. N.M. SGMS1 259230 N.M. N.M. N.M. N.M. N.M. RUBCNL 80183 N.M. N.M. N.M. N.M. N.M. c11orf58 10944 N.M. N.M. N.M. N.M. N.M. BTN1A1 696 N.M. N.M. N.M. N.M. N.M. USP50 373509 N.M. N.M. N.M. N.M. N.M. IRF4 3662 N.M. N.M. N.M. N.M. N.M. *N.M. = no mutations

146 Supplemental Table 5.3 qRT-PCR primers Primer Name Target Sequence (5’ 3’) GAN_exon2-3_F GAN TCGGTAATGGTTATGAGAGAGATCC GAN_exon2-3_R GAN TAAGGTCCGTCAGTAGCAGC GAN_exon10-11_F GAN CCGCCAGTTCCTCTTTTGTT GAN_exon10-11_R GAN GTCGGATGGAAGGAGTGGTTT LmnB1_F LMNB1 CTCGTCTTGCATTTTCCCGC LmnB1_R LMNB1 TGGCGTTTAGAGGAACGGAG Vim_F VIM AACGCCAGATGCGTGAAATG Vim_R VIM ATTCACGAAGGTGACGAGCC

147

CHAPTER 6: DISCUSSION

IFs are a major component of the cytoskeleton that provide both mechanical and non- mechanical cell type-specific functions to support tissue homeostasis17. Despite the importance of IFs in health and disease, our understanding of IF dynamics lag behind those of actin and microtubules, the other major cytoskeletal filament systems. IF drastically alter their dynamics during diverse cellular processes, such as cell division and cell migration. A better understanding of IF dynamics would improve our comprehension of IF function and perhaps our ability to modulate and harness those functions. Through the work presented in this thesis, I furthered our knowledge of IF dynamics using three strategies. First, I contributed a review article focused on the role of vimentin IFs in cell migration, which is of particular importance in astrocytes47.

Second, since PTMs are critical regulators of IF dynamics, I developed a method to simultaneously isolate and profile mammalian intermediate filaments from tissues in order to identify new PTMs81. I used this method to study changes in PTMs during aging and detected increased phosphorylation and acetylation of K8/K18 from aged mouse liver tissue. This study identifies new candidate PTMs that could regulate IF dynamics during development and aging.

Third, I helped design and apply an image-based small molecule screen to identify drugs that target IFs313. We used vimentin as a prototype IF and identified simvastatin as a novel regulator of vimentin dynamics that induces rapid reorganization followed by bundling of vimentin, resulting in cell death in certain cancer cell lines. This small molecule screen identifies vimentin as a probable direct target of simvastatin, offers implications for simvastatin-related muscle pain

148 and cancer mortality, and provides an approach for identifying IF-selective compounds that could be applied to other IFs.

The IF gene family is associated with many different human diseases, including skin fragility disorders, myopathies, liver disease, aging, cancer, and neurodegenerative diseases73.

Although united by their membership in the IF gene family, individual IF genes are associated with very different pathophysiological symptoms depending on the cell type in which the particular IF gene is expressed. The cell type, tissue, developmental, and species specificity of each IF is linked to unique characteristics and properties that can be tuned to specific contexts.

Many human cell models do not reflect the cellular context of disease because they 1) lack the unique patient genetic background and 2) often utilize cancer cell lines for expression of disease- relevant IFs. Similarly, animal models do not reflect human-specific qualities of the particular cell type, such as size and complexity.

To overcome these challenges, I examined the cell type-specific regulation of IFs in neurodegenerative disease in the context of human astrocytes, which are a major but underappreciated glial cell type in the CNS. I developed two different human iPSC-astrocyte models to investigate disease mechanisms in AxD and GAN. I uncovered a novel mechanism whereby phosphorylation at serine 13 of GFAP promotes caspase 6-cleavage and aggregation of

GFAP326. Additionally, I connect our in vitro findings to brain tissue from AxD patients and observe increased phosphorylation at serine 13 of GFAP and cleavage of GFAP in aggressive cases of AxD. Finally, I observe a crosstalk between vimentin and GFAP aggregation in GAN.

The presence of GFAP promotes interactions between vimentin aggregates and the nucleus, and conversely, the presence of vimentin promotes GFAP aggregation (Battaglia et al. In preparation). We also improve the GAN iPSC-astrocyte model by generating brain organoids in

149 3-dimensional co-culture of iPSC-astrocytes and neurons. These disease models provide tools for further exploration of IF-related disease mechanisms and development of IF-targeted therapeutics.

6.1 IF phosphorylation in neurodegenerative disorders

IFs are especially enriched in phosphorylation sites, which control their dynamics, binding partners, subcellular localization, and biochemical properties31,285. While phosphorylation is an important regulator of IFs under homeostasis, it is also associated with disease. IF aggregates are often hyperphosphorylated, both in IF-associated disorders and broadly in neurodegenerative disease360. For example, phosphorylated neurofilament aggregates are observed in many neurodegenerative diseases, including Alzheimer’s disease and

Amyotrophic Lateral Sclerosis360,361. GFAP phosphorylation is also altered broadly in neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, and frontotemporal lobar degeneration362. In addition to AxD, changes in phosphorylation at serine

13 of GFAP are observed in frontotemporal lobar degeneration and Parkinson’s disease363,364.

The significance of GFAP phosphorylation in common neurodegenerative diseases is not yet clear. In Parkinson’s disease, enteric glia exhibit decreased phosphorylation at serine 13 of

GFAP364. The authors hypothesize that decreased phosphorylation at serine 13 leads to an increase in filamentous GFAP and a decrease in soluble GFAP in reactive enteric glia. This is in direct contrast a different study where the authors observed increased phosphorylation at serines

8 and 13 of GFAP in reactive astrocytes induced by hypoxia283. It is possible that cell type and tissue differences between enteric glia (PNS) and astrocytes (CNS) contribute to these differences. In contrast to Parkinson’s disease, we observe increased phosphorylation at serine 13 in AxD and show that phosphomimetic mutation of this residue promotes aggregation and

150 caspase cleavage in vitro326. However, we observe that blocking phosphorylation by generating a nonphosphorylatable mutant at serine 13 in an AxD mutant background is unable to rescue aggregation. Therefore, it may be important for IF dynamics to maintain the ability to freely phosphorylate and dephosphorylate this key residue. In the future, it will be necessary to identify the kinase or kinases that phosphorylate GFAP at this residue in AxD. It is quite possible that separate kinases phosphorylate GFAP to promote healthy reorganization versus aggregation. If so, this would allow targeting of disease-associated kinases while allowing other kinases to phosphorylate GFAP for homeostatic maintenance. Alternatively, perhaps careful dose control could be utilized to achieve a low threshold of phosphorylation that is enough to maintain filament morphology but reduce the propensity for unhealthy aggregate formation. It will be important to understand which kinases regulate this phosphorylation in AxD astrocytes in order to pursue therapeutic strategies.

6.2 Beyond phosphorylation: other PTMs contribute to IF-related disorders

The abundance of tools to study and manipulate phosphorylation have made it an ideal

PTM to examine. However, other PTMs also regulate IF properties and dynamics. Caspase cleavage of IFs has been observed during apoptosis and also neurodegenerative disease31.

Caspase-3 is known to cleave GFAP and was recently identified as a candidate genetic modifier of disease severity in a whole exome-sequencing study of 31 AxD patients365. Additionally, we and others find that caspase-6 cleavage of GFAP promotes aggregation290,326. More broadly, caspase-6 cleavage of aggregation-prone proteins also occurs in Alzheimer’s disease (AD) and

Huntington’s disease (HD)302-304. However, whereas blocking caspase-6 cleavage of amyloid precursor protein or huntingtin rescued neuronal dysfunction and neurodegeneration in AD and

HD, blocking caspase-6 cleavage of GFAP disrupted filament morphology. This suggests that

151 some baseline level of caspase-6 cleavage of GFAP is important for filament organization, and like phosphorylation, completely blocking cleavage would be harmful to the cell. Future studies using genetic and pharmacological approaches to partially reduce caspase-6 expression or function should be performed to determine whether caspase-6 could be an effective therapeutic target.

Other PTMs are altered in IF-related disorders and likely contribute to IF aggregation, including sumoylation, transamidation, acetylation, and others previously reviewed31. Genetic mutations in KRT8 that predispose to chronic liver disease promote hypersumoylation of K8, which has been shown to decreases keratin solubility197. Chronic liver disease also involves transamidation of K8, which promotes the formation of K8/K18 aggregates called Mallory-Denk bodies through crosslinking lysine and glutamine residues366. Acetylation is another promising

PTM that should be pursued. Acetylation is directly implicated in AxD since histone deacetylases (HDACs) were identified as possible candidates in a whole exome-sequencing screen for genetic modifiers of AxD365. We also observe increased acetylation of mouse GFAP and K8 during aging81. However, this acetylation may be further enhanced in the context of neurodegeneration. GFAP was identified as the dominant differentially acetylated protein in ALS versus non-ALS control spinal cord tissue367. The functional consequences of acetylation at these residues are unknown. The development of additional tools to study IF protein PTMs besides phosphorylation, such as site-specific antibodies, PTM enzyme inhibitors, and mutant transgenic animal models will be needed to address the functions of other PTMs in IF functions as well as how conserved these PTMs are for different IF proteins.

The possibility of crosstalk between PTMs is also intriguing. PTMs could inhibit other

PTMs directly competing for the same amino acid or indirectly preventing binding of PTM

152 enzymes. For instance, lysine is an amino acid that can be modified by ubiquitination and acetylation, so hyperacetylation of IFs could negatively impact IF protein turnover. Likewise, serine can be modified by phosphorylation or glycosylation, so excess glycosylation of IFs could affect IF dynamics. Alternatively, PTMs could promote other PTMs (e.g. phosphorylation priming). Deciphering how these modifications regulate each other as well as how mutations affect PTMs will be important for understanding the effects of PTMs on IF properties and dynamics in health and disease.

6.3 IFs as biomarkers and drug targets

IFs are often utilized as biomarkers because they are highly abundant and can often serve as cell type specific markers of stress or physiological changes. Two ideal examples of this are the growing appreciation of IFs in cancer and CNS injury and disease. Vimentin is significantly upregulated during the epithelial to mesenchymal transition associated with cancer metastasis46.

Furthermore, vimentin has been recently suggested as a biomarker in hepatocellular carcinoma and pancreatic cancer368,369. GFAP is detected in blood serum or plasma and has been proposed or utilized as a biomarker for many CNS injuries, including multiple sclerosis, neuromyelitis optica spectrum disorder, traumatic brain injury, intracerebral hemorrhage, Alzheimer’s disease, and glioblastoma370-375. Identifying specific IF isoforms or PTMs could provide an even more nuanced interpretation of disease progression or severity.

In addition to providing a marker for cell stress and disease, IFs are emerging as active participants in disease pathogenesis, and thus potential targets. Vimentin is a promising target in treating cancer because of its role in metastasis. Several compounds have been identified that either reduce expression of vimentin, alter vimentin PTMs, or promote vimentin filament collapse376. This list includes many compounds that are being tested in cancer-related clinical

153 trials376. Interestingly, vimentin is also being considered as a potential target for treating COVID-

19 because of its role in viral attachment to cells and viral replication377,378. Both vimentin and

GFAP are upregulated in reactive astrocytes during neurodegeneration and CNS injury110.

Deletion of vimentin and Gfap has been shown to attenuate certain reactive astrocyte functions in animal models, and recent developments in antisense oligonucleotide methods to reduce Gfap expression make this a realistic treatment option for the near future112,152,379. A better understanding of the functions of GFAP in different subtypes of reactive astrocytes will lead to the strategic use of astrocytic GFAP-targeted therapies. Importantly, since IFs serve important homeostatic and stress-related functions, targeting specific PTMs may be a way to salvage healthy IFs while targeting toxic IFs. One successful example of an IF PTM-targeted therapy is the use of a farnesyltransferase inhibitor to pharmacologically target aberrant farnesylation of lamin A/C in Hutchinson-Gilford progeria syndrome380. Future identification of small molecules that selectively target IFs, specific IF isoforms, or enzymes that regulate IF PTMs will broaden the options for therapeutic strategies in IF-associated disorders.

6.4 Rosenthal fibers (RFs): toxic or protective?

Protein aggregates are commonly observed in neurodegenerative disease. Despite the correlation between protein aggregates and neurodegenerative disease, there is little consensus about whether aggregates cause, accelerate, slow, or simply mark disease progression381,382. IF proteins are especially prone to aggregation upon genetic mutations or cellular stress not only in neurons and glia but also in muscle cells, hepatocytes, and skin epithelial cells. Using the example of RFs, the evidence for protection, epiphenomenon, and toxicity is discussed.

There is some evidence supporting the idea that RFs could be protective. First, it is possible that RF formation is a protective mechanism to sequester toxic mutant GFAP away from

154 the cellular milieu. In support of this, one study found that the soluble mutant GFAP oligomers inhibit the proteasome143. Second, RFs could promote proteostasis by spatially bringing together mutant GFAP protein with protein chaperones and degradation machinery. Both chaperones and proteasome subunits have been shown to colocalize with RF137,143. Third, while animal models of

AxD and GAN faithfully display RF-like inclusions, mouse models fail to fully recapitulate disease symptoms142,328-330. While some assert this as evidence that aggregates are not toxic, others argue that the absence of a phenotype relates more to species differences, such as lifespan as well as size and complexity of cells. In addition to protective mechanisms, RFs may also represent a byproduct of injury in some cases. Outside of AxD and GAN, RFs can arise in cases of glial scarring, multiple sclerosis, and pilocytic astrocytoma139,140. Whether RFs contribute to disease pathology in these other contexts is unknown.

While soluble mutant GFAP oligomers are more toxic to the proteasome, large aggregates are not necessarily inert and harmless. RFs can also contribute to toxicity in GAN and

AxD by two potential mechanisms. First, RFs could sequester proteins within these inclusions, preventing them from performing a critical cell function. As discussed, RFs colocalize with chaperone proteins, including B-crystallin and HSP27137. While it may initially promote IF proteostasis, long-term sequestration of chaperone proteins within RFs could negatively affect proteostasis of a wide range of other proteins that interact with these chaperones. Mass spectrometry revealed that RFs also contain numerous RBPs, and the loss of RBPs could affect mRNA trafficking and metabolism141. Second, RFs could cause steric hindrance and disrupt subcellular organization of organelles. We observe striking changes in nuclear shape, which are discussed in the next section. Additionally, another AxD iPSC-astrocyte model shows structural alterations of the endoplasmic reticulum and lysosomes163. In GAN mouse DRG neurons,

155 mitochondria exhibit decreased motility and increased metabolic stress correlating with their colocalization with large neurofilament aggregates in axon162. Whether disrupting aggregate formation or promoting aggregate clearance improves cellular function has yet to be tested.

At present, there are no tools to target specific forms of IF proteins (subunits, oligomers, or aggregate) within cells. The best evidence involves either cellular fractionations or correlative colocalization studies. Until techniques are established to specifically target IF oligomers or disassemble IF aggregates, it will be difficult to parse out the protective versus toxic functions of different forms of IFs.

6.5 Interactions between cytoplasmic IFs and the nucleus

For decades, it has been noted that mature IFs accumulate in the perinuclear space and form a cage around the nucleus193. Cytoplasmic IFs interact with the nucleus during baseline conditions to control nuclear positioning and implement mechanotransduction18. However, it is not clearly understood why large IF aggregates tend to accumulate in the perinuclear space and what impact this may have on nuclear functions.

We hypothesize that mature IFs act as a scaffold upon which IF aggregates form. Thus, it is possible that IF aggregates simply accumulate where there is the highest concentration of assembled, mature IFs. In contrast to normal IFs, GFAP aggregates appear to have aberrant interactions with the nucleus. Instead of maintaining nuclear shape, GFAP aggregates deform the nucleus. Nuclear deformations are also observed in many other neurodegenerative diseases, including Huntington’s disease, Alzheimer’s disease, and frontotemporal dementia311,352,354.

While these invaginations did not rupture the nuclear membrane in AxD, it will be important to characterize how they affect nuclear functions, such as gene expression, mRNA processing, and mRNA trafficking. We did not carry out any cell migration assays, but it also would be

156 interesting to see how the nuclear positioning was affected by IF aggregates during cell migration.

Some RFs localize in regions besides the perinuclear space, such as astrocyte endfeet, which contact blood vessels. The distribution of mature IFs in a morphologically complex cell, such as an astrocyte or neuron, may be different than that of basic cellular models utilized for live imaging to study IF dynamics. In fact, it is well known that neurofilaments determine axon caliber and mature neurofilaments are present within axons383. Neurofilament aggregates often located multifocally along axon in GAN as opposed to the perinuclear space in the soma166. It would be interesting to examine whether IF aggregates form upon a filament scaffold or by an alternative mechanism in different subcellular regions.

6.6 IFs and the spatial protein quality control system

Beyond the IF scaffold hypothesis, the spatial protein quality control (PQC) system provides another potential explanation for the perinuclear localization of IF aggregates. The spatial PQC system includes different subcellular compartments that 1) protect the cell by spatially separate misfolded proteins from the remainder of the cytosol and 2) facilitate proteostasis by bringing together misfolded proteins with protein folding or degradation machinery384. Q-bodies are one spatial PQC compartment that forms very early and usually mediate protein degradation through the ubiquitin proteasome system (UPS)385. It is possible that the small GFAP foci are observed localizing along IFs are sequestered within q-bodies.

Intriguingly, one of the major PQC compartments localizes to the perinuclear space and is called the juxtanuclear quality control compartment (JUNQ)386. It has been shown that vimentin forms a cage around JUNQ and mediates asymmetric cell division of aggregated proteins387. Q-bodies that are unable to clear misfolded proteins coalesce to form JUNQ385. We have not performed

157 colocalization studies of JUNQ or other spatial PQC components with IF aggregates, but it will be important to examine in order to understand IF aggregate dynamics. Future experiments should be done to examine whether normal IFs interact with or scaffold other spatial PQC compartments in addition to JUNQ.

6.7 Limitations of iPSC-astrocyte models

Although iPSC-astrocytes provide a human cell type-specific context to examine disease mechanisms, it is important to indicate the limitations. The cell culture environment is clearly different from in vivo conditions in many ways: substrate stiffness, diversity of cell types, and serum in the media. Moreover, a major criticism of iPSC models is that even with careful differentiation, it is difficult to generate functionally mature cell types388. With this level of developmental immaturity, it can be challenging to identify functional deficits and to model neurodegenerative diseases that are progressive and do not show molecular signs or cause patient symptoms until later in life. Since we examined two pediatric neurodegenerative diseases for which patients demonstrate symptoms within the first few years of life, we do clearly visualize the cellular hallmark of RF-like inclusions. Notably, two other groups used 2-dimensional AxD iPSC-astrocytes to identify functional defects in organelle positioning, cell trafficking, and cell- cell communication160,163. Furthermore, in our GAN model, we utilize 3-dimensional iPSC brain organoid technology, which yields functionally mature astrocytes348.

Additionally, iPSC lines can be incredibly variable in terms of gene expression and differentiation capacity349,350. Because AxD is a rare disease we were unable to obtain more than the publicly available cell line described in the results section. We did not introduce patient mutations into ‘healthy’ controls, as it is often done, because AxD phenotypes are likely influenced by patient background genetic modifiers132. Therefore, one limitation is that we only

158 examine one iPSC line from a patient with Type I AxD. Future comparison of our findings in iPSC lines derived from patients diagnosed with Type II AxD would support our findings.

However, we were able to obtain post-mortem brain tissue from biobanks to bolster our conclusions from the AxD iPSC-astrocytes. In the future, we will obtain iPSC lines from other laboratories that have generated other iPSC lines from AxD patients160,163. Fortunately, because of the incredible efforts of patient family advocate groups in the GAN community, we had access to seven different iPSC lines, which strengthens our findings.

Lastly, iPSC-astrocytes lack the context of other cell types that exists in vivo. Astrocyte processes connect to many different cell types, including neurons, microglia, oligodendrocytes, endothelial cells, and pericytes325. Importantly, another group performed co-cultures with AxD iPSC-astrocytes and oligodendrocytes and discovered that AxD iPSC-astrocytes secrete cytokines that inhibit oligodendrocyte progenitor cell function160. While we begin to address the issue of co-cultures by generating 3-dimensional brain organoids that contain both neurons and astrocytes, there are still several cell types missing. The protocol we utilized to generate the iPSC-brain organoids specifically inhibits differentiation of mesoderm and thus, microglia338.

This is a particularly important limitation for studies involving neurodegenerative disorders that do not involve a primary astrocyte defect because microglia can promote astrocyte reactivity through cytokines389. Moreover, organoid technology has not advanced to include vascularization, so astrocyte interactions with the BBB cannot be examined in the current system. On the other hand, simpler systems involving fewer cell types can be very useful for identifying basic mechanisms before moving to a more complex in vivo environment. In the future, co-cultures and organoids incorporating microglia and vascular cells should be pursued to elucidate non-cell autonomous mechanisms in AxD and GAN.

159 6.8 Future outstanding questions

In summary, my thesis work introduced new tools, methods, and disease models and mechanisms to examine IF dynamics in health and disease. By developing methods to identify new IF PTMs, screen for IF-selective small molecules, and model IF-associated diseases in human astrocytes, I added to the growing arsenal of tools that can be used to elucidate the mechanisms of IF and astrocyte dysfunction in neurodegenerative disorders. In the future, these tools should be used to address the following outstanding questions in the field:

1. Deciphering the PTM code in IF-associated diseases.

a. Which kinase(s) regulate phosphorylation at serine 13 of GFAP, and is this site

conserved in other IF aggregates?

b. Do other PTMs contribute to GFAP aggregation?

c. Can targeting PTM enzymes that regulate IF proteins improve IF proteostasis in

AxD and GAN?

2. Understanding dynamics of IF aggregation.

a. Are IF aggregates protective, toxic, or both?

b. What is the mechanism of IF aggregation? What are the differences between

mutant GFAP (AxD) and wild-type GFAP (GAN) aggregation?

c. How are astrocytic IF aggregates spatially controlled?

d. Do GFAP and vimentin enter the nucleus?

3. Examining the impact of IF aggregation on cellular functions.

a. How do nuclear invaginations affect transcription, mRNA metabolism, and

nuclear export?

160 b. Why do GFAP aggregates contain RNA binding proteins (RBPs)? Are RBPs

involved in RF formation, or simply sequestered as a consequence?

c. How does astrocytic IF aggregation affect astrocyte functions, especially support

of other cells?

4. What genetic modifiers and environmental factors contribute to differences in disease

severity?

a. One recent study performs whole exon sequencing of 31 AxD patients (13 young,

18 adult) to detect genetic modifiers and identify several candidates, including

GAN, CASP3, and HDACs365

161 APPENDIX

Cat. Batch specific product Brief description Batch Concentration/ No. name Solvent 0114 NMDA Selective NMDA 31 10mM DMSO agonist solution 0162 S-Sulfo-L-cysteine Group I agonist 4 10mM DMSO sodium salt solution 0169 (RS)-AMPA Selective AMPA 25 10mM DMSO agonist solution 0180 ZAPA sulphate Agonist at ‘low 3 10mM DMSO affinity’ GABA solution receptor. More potent than GABA muscimol 0186 cis-ACPD Potent NMDA 3 10mM DMSO agonist. Also group II solution mGluR agonist 0188 L-Quisqualic acid AMPA/group I mGlu 31 10mM DMSO agonist solution 0189 DNQX Selective non-NMDA 12 10mM DMSO antagonist solution 0197 Homoquinolinic acid Selective, potent 7 10mM DMSO NMDA agonist solution 0216 L-Cysteinesulfinic acid NMDA and mGlu 18 10mM DMSO agonist solution 0222 Kainic acid Potent excitant and 60 10mM DMSO neurotoxin solution 0223 Kynurenic acid Broad spectrum EEA 55 10mM DMSO antagonist solution 0225 Quinolinic acid Endogenous NMDA 7 10mM DMSO agonist and solution transmitter candidate 0234 Guvacine hydrochloride Specific GABA 18 10mM DMSO uptake inhibitor solution 0235 Isoguvacine Selective GABAA 21 10mM DMSO hydrochloride agonist solution 0237 7-Chlorokynurenic acid Potent competitive 5 10mM DMSO inhibitor of L- solution glutamate uptake 0245 2-Hydroxysaclofen Selective GABAB 19 10mM DMSO antagonist, more solution potent than Cat. No 0246 0246 Saclofen Selective GABAB 12 10mM DMSO antagonist solution

162 0254 (S)-AMPA Selective AMPA 25 10mM DMSO agonist. Active isomer solution of (RS)-AMPA (Cat. No. 0169) 0281 (R)-(+)-HA-966 NMDA partial 11 10mM DMSO agonist/antagonist, solution acts at glycine site 0282 (S)-(-)-HA-966 NMDA 6 10mM DMSO antagonist/partial solution agonist 0286 5, 7-Dichlorokynurenic Potent NMDA 6 10mM DMSO acid antagonist, acts solution glycine site 0310 (RS)-4-Carboxy-3- Broad spectrum EAA 5 10mM DMSO hydroxyphenylglycine ligand solution 0312 (RS)-(Tetrazol-5- Highly potent NMDA 4 10mM DMSO yl)glycine receptor agonist solution 0320 (S)-4-Carboxy-3- Group I 14 10mM DMSO hydroxyphenylglycine antagonist/group II solution agonist 0328 (R)-3-Carboxy-4- Ionotropic glutamate 3 10mM DMSO hydroxyphenylglycine receptor antagonist solution 0329 (S)-3-Carboxy-4- Group I 4 10mM DMSO hydroxyphenylglycine antagonist/group II solution agonist 0342 (RS)-3,5-DHPG Selective group I 19 10mM DMSO mGLu agonist solution 0347 nor-Binaltorphimine Standard κ selective 9 10mM DMSO dihydrochloride antagonist solution 0348 2-BFI hydrochloride Potent, selective I2 3 10mM DMSO ligand. Putative solution agonist 0351 1-Acetyl-4- Nicotinic agonist 2 10mM DMSO methylpiperazine solution hydrochloride 0352 4-Acetyl-1,1- Nicotinic agonist 2 10mM DMSO dimethylpiperazinium solution iodide 0357 N-Acetyltryptamine Melatonin partial 1 10mM DMSO agonist (MT1/MT2). solution Also MT3 antagonist 0360 (±)-1-(1,2- NMDA antagonist, 1 10mM DMSO Diphenylethyl)piperidine acts ion channel site solution maleate 0373 NBXQ Potent AMPA 11 10mM DMSO antagonist. More solution

163 selective than CNQX (Cat. No. 0190) 0378 A-7 hydrochloride Calmodulin antagonist 1 10mM DMSO solution 0382 Arecaidine but-2-ynyl Muscarinic agonist 1 10mM DMSO ester tosylate solution 0383 Arecaidine propargyl Muscarinic agonist 2 10mM DMSO ester tosylate solution 4231 Nateglinide Kir6 (KATP) blocker; 1 10mM DMSO displays high affinity solution for SUR1/Kir6.2 channels 0386 3-Methyl-GABA Activator of GABA 1 10mM DMSO amino-transferase solution 0387 (RS)-Atenolol β1 antagonist 5 10mM DMSO solution 0391 Spaglumic acid Selective mGlu3 8 10mM DMSO agonist solution 0393 S-(-)-Atenolol β1-antagonist. Active 3 10mM DMSO isomer of atenolol solution (Cat. No. 0387) 0395 N-Acetylglycyl-D- Potent convulsant 1 10mM DMSO glutamic acid solution 0405 β-CCB Benzodiazepine 2 10mM DMSO inverse agonist, solution putative endogenous ligand 0411 MDL 73005EF Potent and selective 5- 1 10mM DMSO hydrochloride HT1A partial agonist solution 0412 MDL 72832 Potent 5-HT1A ligand 2 10mM DMSO hydrochloride solution 0415 Ro 20-1724 PDE4 inhibitor 3 10mM DMSO solution 0416 YS-035 hydrochloride Inhibits K+ 3 10mM DMSO outward/pacemaker solution current 0420 GBR 13069 Potent dopamine 3 10mM DMSO dihydrochloride uptake inhibitor solution 0421 GBR 12909 Selective DA uptake 4 10mM DMSO dihydrochloride inhibitor. Also σ solution ligand 0424 Benzoquinonium Nicotinic antagonist 1 10mM DMSO dibromide solution 0425 UK 14,304 α2 agonist 2 10mM DMSO solution

164 0426 L-693,403 maleate High affinity σ ligand 1 10mM DMSO solution 0427 Bromocriptine mesylate Selective D2-like 5 10mM DMSO agonist solution 0430 SC-10 Protein kinase C 2 10mM DMSO activator solution 0431 ML 9 hydrochloride Myosin light chain 2 10mM DMSO kinase inhibitor solution 0432 MY-5445 PDE5 inhibitor 3 10mM DMSO solution 0433 SC-9 Protein kinase C 2 10mM DMSO activator solution 0438 Etazolate hydrochloride PDE4 inhibitor 3 10mM DMSO solution 0439 DPCPX A1 selective 11 10mM DMSO antagonist solution 0440 m- Potent and specific 5- 4 10mM DMSO Chlorophenylbiguanide HT3 agonist solution hydrochloride 0442 4-Chlorophenylguanidine Urokinase inhibitor 1 10mM DMSO hydrochloride solution 0443 6-Chloromelatonin Melatonin agonist 2 10mM DMSO solution 0448 Calpeptin Calpain and cathepsin 3 10mM DMSO L inhibitor solution 0454 Carbetapentane citrate High affinity σ1 1 10mM DMSO ligand solution 0455 (S)-(-)-Carbidopa Aromatic L-amino 3 10mM DMSO acid decarboxylase solution inhibitor 0456 Chlormezanone Skeletal muscle 4 10mM DMSO relaxant solution 0457 Clomipramine 5-HT re-uptake 6 10mM DMSO hydrochloride inhibitor solution 0458 5- 5-HT1 agonist. Also 7 10mM DMSO Carboxamidotryptamine has high affinity for 5- solution maleate ht5a and 5-HT7 0460 Cinanserin hydrochloride Selective 5-HT2 2 10mM DMSO antagonist solution 0464 Capsazepine Vanilloid receptor 7 10mM DMSO antagonist. Also solution activator of ENaCδ 0469 Nitrocaramiphen Muscarinic antagonist, 1 10mM DMSO hydrochloride M1 > M2 solution 0474 Dihydroergocristine Partial α agonist. Non- 1 10mM DMSO mesylate selective solution

165 0475 Partial α agonist. Non- 1 10mM DMSO Dihydroergotamine selective solution mesylate 0478 Flurofamide Urease inhibitor 1 10mM DMSO solution 0481 Dilazep dihydrochloride Adenosine uptake 2 10mM DMSO inhibitor solution 0482 4-DAMP Muscarinic M3 1 10mM DMSO antagonist solution 0483 OR-486 Catechol-O-methyl 4 10mM DMSO transferase inhibitor solution 0485 Alrestatin Aldose reductase 1 10mM DMSO inhibitor solution 0486 1,3-Dipropyl-8- A1 selective 1 10mM DMSO phenylxanthine antagonist solution 0495 (±)-U-50488 Standard selective κ 2 10mM DMSO hydrochloride agonist solution 0496 (-)-U-50488 Standard selective κ 5 10mM DMSO hydrochloride agonist. More active solution enantiomer of (±)-U- 50488 (Cat. No. 0495) 0498 U-54494A hydrochloride κ agonist 1 10mM DMSO solution 0504 Diphenyleneiodonium Binds to flavoproteins 2 10mM DMSO chloride and inhibits NOS solution 0506 Dimaprit dihydrochloride Standard H2 selective 2 10mM DMSO agonist solution 0507 Dantrolene, sodium salt 4 10mM DMSO Ca2+ release inhibitor solution 0511 BD 1008 dihydrobromide Potent, selective σ 2 10mM DMSO ligand solution 0512 SKF 91488 Histamine N- 1 10mM DMSO dihydrochloride methyltransferase solution inhibitor 0513 GBR 12783 Potent, selective 3 10mM DMSO dihydrochloride dopamine uptake solution inhibitor 0514 GBR 12935 Selective dopamine 3 10mM DMSO dihydrochloride uptake inhibitor solution 0515 Dobutamine α1, β1 and β2 agonist 2 10mM DMSO hydrochloride solution 0518 EBPC Aldose reductase 2 10mM DMSO inhibitor solution 0522 Flunarizine Dual Na+/Ca2+ 5 10mM DMSO dihydrochloride channel (T-type) solution blocker

166 0529 Selective 5-HT1A 12 10mM DMSO agonist. Also has solution 8-Hydroxy-DPAT moderate affinity for hydrobromide 5-HT7 0535 HEAT hydrochloride Highly selective α1 1 10mM DMSO antagonist solution 0537 MR 16728 hydrochloride Stimulates ACh 1 10mM DMSO release solution 0538 trans-4-Hydroxycrotonic GHB receptor ligand 1 10mM DMSO acid solution 0541 Fasudil hydrochloride Inhibitor of cyclic 2 10mM DMSO nucleotide dependent- solution and Rho-kinases 0543 C-1 Protein kinase C 1 10mM DMSO inhibitor solution 0544 IPAG Potent σ antagonist 2 10mM DMSO solution 0545 Ifenprodil hemitartrate Non-competitive 8 10mM DMSO NMDA antagonist. solution Also σ ligand 0552 MMPX PDE1 inhibitor 1 10mM DMSO solution 0553 NAN-190 hydrobromide 5-HT1A antagonist 2 10mM DMSO solution 0554 FG 7142 Benzodiazepine 11 10mM DMSO inverse agonist solution 0556 BP 554 maleate Selective 5-HT1A 1 10mM DMSO agonist solution 0557 α-Methyl-5- 5-HT2B agonist 4 10mM DMSO hydroxytryptamine solution maleate 0558 2-Methyl-5- 5-HT3 agonist/potent 5 10mM DMSO hydroxytryptamine 5-HT6 ligand solution hydrochloride 0566 N-Methylquipazine 5-HT3 agonist 1 10mM DMSO dimaleate solution 0568 Metaphit Acylator of PCP and σ 1 10mM DMSO receptors solution 0569 (R)-(-)- α- Potent, standard H3 12 10mM DMSO Methylhistamine agonist solution dihydrobromide 0571 PCA 4248 PAF receptor 2 10mM DMSO antagonist solution 0572 (S)-(+)-a- H3 agonist, less active 3 10mM DMSO Methylhistamine enantiomer solution dihydrobromide

167 0573 N α-Methylhistamine Non-selective H3 1 10mM DMSO dihydrochloride agonist solution 0577 Methyl 2,5- EGFR-kinase 2 10mM DMSO dihydroxycinnamate inhibitor solution 0581 3-MPPI α1 ligand 1 10mM DMSO solution 0582 Methiothepin maleate Has moderate affinity 6 10mM DMSO for 5-ht5 and high solution affinity for 5-ht6 and 5-HT7. Also antagonist at 5-HT1 and 5-HT2 0583 Minoxidil K+ channel (KATP) 3 10mM DMSO opener solution 0584 L-(-)- α-Methyldopa Aromatic L-amino 1 10mM DMSO acid decarboxylase solution inhibitor 0588 5-Methylfurmethiodide Potent muscarinic 2 10mM DMSO agonist solution 0589 PRE-084 hydrochloride Highly selective σ1 2 10mM DMSO agonist solution 0590 Metergoline 5-HT2 antagonist. 1 10mM DMSO Also 5-HT1 solution antagonist and 5- HT1D ligand. Has moderate affinity for 5-HT6 and high affinity for 5-HT7 0593 NPPB Chloride channel 3 10mM DMSO blocker solution 0597 Naftopidil hydrochloride α1 antagonist 4 10mM DMSO solution 0599 Naloxone hydrochloride Broad spectrum 8 10mM DMSO opioid antagonist solution 0600 Nimodipine Ca2+ channel blocker 2 10mM DMSO (L-type) solution 0601 Nitrendipine Ca2+ channel blocker 1 10mM DMSO (L-type) solution 0602 7-Nitroindazole Non-selective NOS 5 10mM DMSO inhibitor solution 0604 Nicergoline α antagonist 1 10mM DMSO solution 2239 GW 583340 Potent dual 10mM DMSO dihydrochloride EGFR/ErbB2 solution inhibitor; orally active

168 0609 (±)-Palmitoylcarnitine Protein kinase C 1 10mM DMSO chloride inhibitor solution 0610 Parthenolide 5-HT release inhibitor 11 10mM DMSO solution 0620 4-PPBP maleate Specific σ ligand, 2 10mM DMSO NMDA antagonist solution 0623 Prazosin hydrochloride α1 and α2B 4 10mM DMSO antagonist. MT3 solution antagonist 0624 Propranolol β antagonist 4 10mM DMSO hydrochloride solution 0627 2-PMDQ Potent, selective α1 2 10mM DMSO antagonist solution 0629 Quipazine dimaleate 5-HT3 agonist 2 10mM DMSO solution 0630 Rosmarinic acid Anti-inflammatory, 3 10mM DMSO cytostatic and anti- solution viral 0631 Spiroxatrine Potent α2C antagonist 1 10mM DMSO solution 2559 TPCA-1 Potent, selective 10mM DMSO inhibitor of IKK-2 solution 0636 Salsolinol-1-carboxylic Endogenous amino 2 10mM DMSO acid acid solution 0640 MDL 72222 5-HT3 antagonist 3 10mM DMSO solution 0641 Tropanyl-3,5- 5-HT3 antagonist 2 10mM DMSO dimethylbenzoate solution 0644 Thioperamide H3 antagonist and H4 15 10mM DMSO inverse agonist solution 0646 HTMT dimaleate H1 / H2 agonist 2 10mM DMSO solution 0649 (S)-Timolol maleate β1 antagonist 2 10mM DMSO solution 0653 (±)-Vesamicol Inhibits ACh transport 3 10mM DMSO hydrochloride solution 0658 FGIN-1-27 Potent, specific ligand 2 10mM DMSO for mitochondrial DBI solution receptor 0659 FGIN-1-43 Potent, specific ligand 2 10mM DMSO for mitochondrial DBI solution receptor 0600 Mepyramine maleate Selective H1 inverse 2 10mM DMSO agonist solution 2828 NU 7026 Selective DNA-PK 10mM DMSO inhibitor solution

169 0662 trans-Triprolidine Standard H1 1 10mM DMSO hydrochloride antagonist, highly solution potent 0665 L-NAME hydrochloride Non-selective NOS 6 10mM DMSO inhibitor solution 0668 Amthamine Highly selective 2 10mM DMSO dihydrobromide standard H2 agonist solution 0670 PK 11195 Antagonist at 9 10mM DMSO peripheral solution benzodiazepine receptors 0671 AH 6809 EP1 and EP2 receptor 3 10mM DMSO antagonist solution 0675 NPC 15199 Increases intracellular 1 10mM DMSO Ca2+ levels solution 0678 (+)-AJ 76 hydrochloride Antagonist; 4 10mM DMSO preferential action at solution D2-like autoreceptors 0680 2-Phenylmelatonin Potent melatonin 3 10mM DMSO agonist solution 0681 L-690,330 Inositol 1 10mM DMSO monophosphatase solution inhibitor 0685 Diltiazem hydrochloride Ca2+ channel blocker 3 10mM DMSO (L-type) solution 0691 Dipyridamole Adenosine transport 3 10mM DMSO inhibitor solution 0692 Ipratropium bromide Muscarinic antagonist 4 10mM DMSO solution 0694 Pilocarpine Muscarinic agonist 1 10mM DMSO hydrochloride solution 0695 Retinoic acid Endogenous retinoic 2 10mM DMSO acid receptor agonist. solution 0699 BRL 52537 Potent and selective κ 3 10mM DMSO hydrochloride agonist solution 0701 3'-Fluorobenzylspiperone D2-like receptor 1 10mM DMSO maleate ligand solution 0702 BTCP maleate Potent dopamine 1 10mM DMSO uptake inhibitor solution 0703 Anpirtoline Highly potent 5-HT1B 5 10mM DMSO hydrochloride agonist. Also 5-HT3 solution antagonist 0705 L-701,252 NMDA antagonist, 4 10mM DMSO acts glycine site solution 0706 7-Hydroxy-DPAT Dopamine agonist (D3 3 10mM DMSO hydrobromide ≥ D2 < > D4) solution

170 0713 Cyclothiazide AMPA selective 6 10mM DMSO desensitization solution inhibitor 0719 7-Hydroxy-PIPAT D3 agonist (D3 > D2) 3 10mM DMSO maleate solution 0723 Tetrindole mesylate MAO-A inhibitor 1 10mM DMSO solution 0724 Pirlindole mesylate MAO-A inhibitor 1 10mM DMSO solution 0725 BU 224 hydrochloride Potent, selective I2 4 10mM DMSO ligand. Putative solution antagonist 0726 BU 239 hydrochloride Potent, highly 5 10mM DMSO selective I2 ligand solution 0727 Pyrrolidinedithiocarbama Inhibits NF-κB, 1 10mM DMSO te ammonium prevents increase in solution NOS mRNA 0728 RS 23597-190 5-HT4 antagonist 2 10mM DMSO hydrochloride solution 0729 Imetit dihydrobromide Standard H3 and H4 5 10mM DMSO agonist (H3 > H4) solution 0730 4-Phenyl-1,2,3,4- DA release inhibitor 2 10mM DMSO tetrahydroisoquinoline solution hydrochloride 0731 Sulfisoxazole Endothelin ETA 1 10mM DMSO antagonist solution 0735 3-Bromo-7-nitroindazole Selective nNOS 7 10mM DMSO inhibitor solution 0736 2-[1-(4- 5-HT4 agonist. Also 1 10mM DMSO Piperonyl)piperazinyl]be 5-HT3 antagonist solution nzothiazole 0737 2-Iodomelatonin High affinity 3 10mM DMSO melatonin agonist solution 0740 Naltrindole δ selective non- 12 10mM DMSO hydrochloride peptide antagonist solution 0741 GF 109203X Protein kinase C 5 10mM DMSO inhibitor solution 0742 L-689,560 Very potent NMDA 5 10mM DMSO antagonist solution 0743 DPPE fumarate Inhibitor of histamine 2 10mM DMSO binding at the solution intracellular binding site 0744 Ceramide Ser/Thr protein 4 10mM DMSO phosphatase activator solution

171 0748 4-IBP σ ligand, σ1 > σ2 1 10mM DMSO solution 0750 PG-9 maleate Presynaptic 1 10mM DMSO cholinergic modulator solution 0751 SM-21 maleate Presynaptic 3 10mM DMSO cholinergic modulator solution 0752 Clobenpropit Highly potent H3 3 10mM DMSO dihydrobromide antagonist and H4 solution partial agonist 0754 N-Benzylnaltrindole δ2 selective non- 3 10mM DMSO hydrochloride peptide antagonist solution 0757 Vinpocetine Na+ channel blocker 2 10mM DMSO solution 0760 AM580 Retinoic acid analog; 2 10mM DMSO RARα agonist solution 0761 TTNPB Retinoic acid analog; 3 10mM DMSO RAR agonist solution 0763 N-[2- σ1 selective ligand 1 10mM DMSO (Piperidinylamino)ethyl]- solution 4-iodobenzamide 0764 SNC 80 Highly selective non- 17 10mM DMSO peptide δ agonist solution 0767 Bifemelane MAO-A and MAO-B 2 10mM DMSO hydrochloride inhibitor solution 0768 Riluzole hydrochloride Glutamate release 9 10mM DMSO inhibitor/GABA solution uptake inhibitor 0773 Memantine NMDA antagonist; 9 10mM DMSO hydrochloride acts at ion channel site solution 0775 (+)-UH 232 maleate D2-like autoreceptor 3 10mM DMSO antagonist. D3 partial solution agonist. 0778 ICI-199,441 Potent κ agonist 2 10mM DMSO hydrochloride solution 0779 Iodophenpropit Potent, selective H3 2 10mM DMSO dihydrobromide antagonist solution 0780 NCS-382 Antagonist of γ- 3 10mM DMSO hydroxybutyric acid solution 0781 L-694,247 4 10mM DMSO 5-HT1D agonist solution 0782 2-CMDO D2-like antagonist. 1 10mM DMSO Displays some D4 solution selectivity 0783 N-MPPP Hydrochloride Selective κ agonist 1 10mM DMSO solution

172 0784 Ketotifen fumarate H1 antagonist 6 10mM DMSO solution 0785 SB 203186 hydrochloride 5-HT4 antagonist 3 10mM DMSO solution 0786 MCI-186 Anti-ischemic and 1 10mM DMSO antioxidant solution 0787 Aminoguanidine Irreversible iNOS 2 10mM DMSO hydrochloride inhibitor solution 0788 3-Aminobenzamide PARP inhibitor 2 10mM DMSO solution 0790 Rilmenidine α2 agonist. Also I1 4 10mM DMSO hemifumarate ligand solution 0791 Antazoline hydrochloride Imidazoline ligand 1 10mM DMSO solution 0792 Efaroxan hydrochloride α2 antagonist and I1, 2 10mM DMSO I3 ligand solution 0793 Idazoxan hydrochloride α2 antagonist. Also I2 5 10mM DMSO ligand solution 0800 7-NINA Non-selective NOS 4 10mM DMSO inhibitor. Sodium salt solution of 7-nitroindazole (Cat. No. 0602) 0805 (S)-3,5-DHPG Selective group I 31 10mM DMSO mGlu agonist. Active solution enantiomer of 3,5- DHPG (Cat. No. 0342) 0807 THIP hydrochloride GABAA agonist 12 10mM DMSO solution 0821 ICI-118,551 Very selective β2 1 10mM DMSO hydrochloride antagonist solution 0825 Clofibric acid PPAR agonist 2 10mM DMSO solution 0826 Tiotidine Potent, selective H2 2 10mM DMSO antagonist solution 0829 Pronethalol β antagonist 1 10mM DMSO hydrochloride solution 0830 Primidone Potentiates GABAA 1 10mM DMSO receptor function solution 0833 ICI-162,846 Potent H2 antagonist, 1 10mM DMSO active in vivo solution 0834 (S)-(-)-Propranolol β antagonist. More 1 10mM DMSO hydrochloride active enantiomer of solution propranolol (Cat. No. 0624)

173 0835 (R)-(+)-Propranolol β antagonist. Less 1 10mM DMSO hydrochloride active enantiomer of solution propranolol (Cat. No. 0624) 0836 ICI-185,282 Potent thromboxane 2 10mM DMSO receptor antagonist solution 0837 ICI 192,605 Potent thromboxane 2 10mM DMSO A2/TP receptor solution antagonist 0839 DHBP dibromide Ca2+ release inhibitor 3 10mM DMSO solution 0841 DTG High affinity ligand 3 10mM DMSO (σ1 = σ2) solution 0843 Oxotremorine Muscarinic agonist 4 10mM DMSO sesquifumarate solution 3918 Pyrimethamine Potent inhibitor of 1 10mM DMSO multi-drug and toxin solution extrusion (MATE) transporters; also inhibits DHFR 0855 MTPG Group II/group III 1 10mM DMSO mGlu antagonist. solution More selective for group II than group III 0864 GR 46611 5-HT1D agonist 3 10mM DMSO solution 0867 Aniracetam Desensitization 3 10mM DMSO inhibitor (AMPA > solution kainate) 0868 L-732,138 Potent, selective NK1 2 10mM DMSO antagonist solution 0869 Felbamate NMDA antagonist, 3 10mM DMSO acts glycine site solution 0870 MDL 11,939 5-HT2A antagonist 2 10mM DMSO solution 0871 AMT hydrochloride Potent, selective iNOS 3 10mM DMSO inhibitor solution 0876 AM 92016 hydrochloride KV channel blocker 3 10mM DMSO solution 0877 Luzindole Competitive 2 10mM DMSO melatonin MT1/MT2 solution antagonist 0878 Oleamide Potentiator at 5- 3 10mM DMSO HT2A/2C receptors. solution Also CB1 agonist

174 0879 Palmitoylethanolamide Selective GPR55 3 10mM DMSO agonist. FAAH and solution PAA substrate 0880 ODQ Selective inhibitor of 4 10mM DMSO NO-sensitive guanylyl solution cyclase 0881 Chlormethiazole Potentiates GABAA 1 10mM DMSO hydrochloride receptor function solution 0882 ZM 226600 KATP channel opener 1 10mM DMSO solution 0883 BD 1063 dihydrochloride Selective σ1 ligand, 3 10mM DMSO putative antagonist solution 0884 Dihydrexidine Selective D1-like 11 10mM DMSO hydrochloride agonist solution 0885 Guanabenz acetate α2 agonist. Also I2 2 10mM DMSO selective ligand solution 0888 Cirazoline hydrochloride Selective α1 agonist 10 10mM DMSO solution 0889 RS 45041-190 High affinity I2 12 10mM DMSO hydrochloride ligand. Highly solution selective 0890 Amiloride hydrochloride Na+ channel blocker. 3 10mM DMSO Also I2 imidazoline solution ligand 0891 Rauwolscine α2 antagonist 11 10mM DMSO hydrochloride solution 0892 Naltriben mesylate Standard δ2 selective 4 10mM DMSO antagonist solution 0894 (RS)-(±)-Sulpiride Standard selective D2- 2 10mM DMSO like antagonist solution 0895 (S)-(-)-Sulpiride Standard selective D2- 3 10mM DMSO like antagonist solution 0896 GR 135531 High affinity 6 10mM DMSO melatonin MT3 ligand solution 0897 S-Isopropylisothiourea iNOS inhibitor, acts 1 10mM DMSO hydrobromide arginine binding site solution 0899 BNTX maleate Standard δ1 selective 5 10mM DMSO antagonist solution 0905 Rolipram PDE4 inhibitor 17 10mM DMSO solution 0906 Betaxolol hydrochloride Selective β1 2 10mM DMSO antagonist solution 0907 L-701,324 NMDA antagonist, 3 10mM DMSO acts glycine site solution Tropicamide Selective M4 2 10mM DMSO 0909 muscarinic antagonist solution

175 0911 Glibenclamide K+ channel blocker 4 10mM DMSO (KATP) solution 0912 RU 24969 hemisuccinate 5-HT1B/1A agonist 4 10mM DMSO solution 0914 (±)-Bisoprolol β1 antagonist 2 10mM DMSO hemifumarate solution 0915 Cilostamide PDE3 inhibitor 2 10mM DMSO solution 0916 Remoxipride Selective D2-like 2 10mM DMSO hydrochloride antagonist solution 0917 3-CPMT Dopamine uptake 1 10mM DMSO inhibitor solution 3 α-Bis-(4-fluorophenyl) Potent dopamine 1 10mM DMSO 0918 methoxytropane uptake inhibitor solution hydrochloride 0919 TRIM nNOS/iNOS inhibitor 3 10mM DMSO solution 0922 SKF 38393 Selective D1-like 9 10mM DMSO hydrobromide agonist solution 0924 (+)-MK 801 maleate Non-competitive 9 10mM DMSO NMDA antagonist, solution acts at ion channel site 0925 SCH 23390 Standard selective D1- 14 10mM DMSO hydrochloride like antagonist. Also solution 5-HT2C agonist 0926 β-Funaltrexamine Irreversible μ- 7 10mM DMSO hydrochloride selective antagonist solution 0927 Fluoxetine hydrochloride 5-HT re-uptake 4 10mM DMSO inhibitor solution 0928 ARC 239 α2B antagonist 5 10mM DMSO dihydrochloride solution 0929 ICI 215,001 β3 agonist 2 10mM DMSO hydrochloride solution 0930 ZD 7114 hydrochloride β3 agonist 1 10mM DMSO solution 0932 Immepip dihydrobromide Standard H3 agonist. 4 10mM DMSO Also H4 agonist solution 0933 MM 77 dihydrochloride 5-HT1A 1 10mM DMSO (postsynaptic) solution antagonist 0934 Olvanil Potent vanilloid 3 10mM DMSO receptor agonist solution 0935 Maprotiline Noradrenalin re- 4 10mM DMSO hydrochloride uptake inhibitor solution 0937 Pimozide D2-like antagonist 7 10mM DMSO solution

176 0946 WB 4101 hydrochloride α1A antagonist 6 10mM DMSO solution 0947 Zaprinast PDE5/6/9/11 inhibitor 9 10mM DMSO solution 0948 BRL 37344, sodium salt β3 agonist 4 10mM DMSO solution 0950 Xamoterol hemifumarate β1 selective partial 3 10mM DMSO agonist solution 0952 Sotalol hydrochloride β antagonist 3 10mM DMSO solution 0955 (-)-MK 801 maleate NMDA antagonist, 1 10mM DMSO less active enantiomer solution 0956 BD 1047 dihydrobromide σ1 selective 3 10mM DMSO antagonist solution 0959 Spermidine Agonist at polyamine 3 10mM DMSO trihydrochloride site solution 0961 SYM 2206 Non-competitive 9 10mM DMSO AMPA antagonist solution 0963 9-AC Chloride transport 2 10mM DMSO inhibitor solution 0964 Diazoxide Blocks desensitization 4 10mM DMSO of AMPA receptors solution 0969 1-Phenylbiguanide 5-HT3 agonist 1 10mM DMSO hydrochloride solution 0985 RS 17053 hydrochloride α1A antagonist 1 10mM DMSO solution 0986 Imiloxan hydrochloride Highly selective α2B 4 10mM DMSO antagonist solution 0987 RS 79948 hydrochloride Potent, selective α2 2 10mM DMSO antagonist solution 0988 RS 56812 hydrochloride 5-HT3 partial agonist 2 10mM DMSO solution 0989 RS 67333 hydrochloride 5-HT4 partial agonist 6 10mM DMSO solution 0990 RS 67506 hydrochloride 5-HT4 partial agonist 2 10mM DMSO solution 0991 RS 39604 hydrochloride 5-HT4 antagonist 2 10mM DMSO solution 0992 Isamoltane hemifumarate 5-HT1B antagonist 1 10mM DMSO solution 0995 Spiperone hydrochloride 5-HT2A antagonist. 10 10mM DMSO Also D2-like solution antagonist 0997 Mianserin hydrochloride 5-HT2 antagonist. Has 4 10mM DMSO moderate affinity for solution 5-ht6

177 0998 AH 11110 hydrochloride Subtype-selective α1B 1 10mM DMSO ligand solution 0999 Tamoxifen citrate Estrogen receptor 3 10mM DMSO partial solution agonist/antagonist 1000 ZD 7288 Sino-atrial node 6 10mM DMSO function modulator (If solution inhibitor) 1002 L-745,870 Highly selective D4 5 10mM DMSO trihydrochloride antagonist solution 1004 Highly selective D4 1 10mM DMSO L-741,742 hydrochloride antagonist solution 1006 BMY 7378 Selective α1D 1 10mM DMSO dihydrochloride antagonist solution 1007 N-Desmethylclozapine 5-HT2C antagonist 2 10mM DMSO solution 1014 QX 314 bromide Na+ channel blocker 5 10mM DMSO solution 1015 RS 16566 5-HT3 ligand. Also 1 10mM DMSO dihydrochloride shows affinity for solution zacopride binding site 1018 LY 225910 Potent CCK2 2 10mM DMSO antagonist solution 1025 Nisoxetine hydrochloride Noradrenalin re- 5 10mM DMSO uptake inhibitor solution 1027 PHCCC Potent group I mGlu 5 10mM DMSO antagonist solution 1028 CPCCOEt Selective non- 11 10mM DMSO competitive mGlu1 solution receptor antagonist 1029 Methyllycaconitine α7 neuronal nicotinic 17 10mM DMSO citrate receptor antagonist solution 1031 Piribedil Dopamine agonist 2 10mM DMSO solution 1032 CP 93129 5-HT1B agonist 2 10mM DMSO dihydrochloride solution 1033 Fluvoxamine maleate 5-HT re-uptake 5 10mM DMSO inhibitor solution 1034 4-P-PDOT MT2 antagonist 4 10mM DMSO solution 1035 8-M-PDOT Melatonin agonist 1 10mM DMSO solution 1036 ZM 241385 Potent, highly 13 10mM DMSO selective A2A solution antagonist

178 1041 1-EBIO Activator of epithelial 6 10mM DMSO Kca channels solution 1042 N-Methyllidocaine Enhances biosynthesis 2 10mM DMSO iodide of solution phosphatidylinositol 1043 QX 222 Na+ channel blocker 1 10mM DMSO solution 1044 NBQX disodium salt Potent AMPA 23 10mM DMSO antagonist. More solution water soluble form of NBQX (Cat. No. 0373) 1046 Zardaverine PDE3/4 inhibitor 3 10mM DMSO solution 1047 ICI 182,780 Estrogen receptor 20 10mM DMSO antagonist solution 1048 1-BCP Modulates AMPA- 1 10mM DMSO mediated responses solution 1049 CHPG mGlu5 selective 17 10mM DMSO agonist solution 1050 RS 102221 hydrochloride Selective 5-HT2C 10 10mM DMSO antagonist solution 1052 A 61603 hydrobromide α1A agonist 2 10mM DMSO solution 1057 WIN 64338 Bradykinin B2 1 10mM DMSO hydrochloride antagonist solution 1059 BW 723C86 5-HT2B agonist 3 10mM DMSO hydrochloride solution 1060 (S)-(-)-Pindolol β3 partial agonist. 1 10mM DMSO More active solution enantiomer of pindolol (Cat. No. 0994) 1061 (-)-Quinpirole Selective D2-like 9 10mM DMSO hydrochloride agonist solution 1066 IB-MECA A3 selective agonist 3 10mM DMSO solution 1070 Zolantidine dimaleate Potent, centrally 2 10mM DMSO active H2 antagonist solution 1072 AGN 192403 I1 selective ligand 2 10mM DMSO hydrochloride solution 1074 (RS)-AMPA Selective AMPA 5 10mM DMSO hydrobromide agonist. More water solution soluble form of (RS)- AMPA (Cat. No. 0169)

179 1075 Nifedipine Ca2+ channel blocker 2 10mM DMSO (L-type) solution 1076 Ouabain Na+,K+-ATPase 6 10mM DMSO inhibitor solution 1079 (+)-SK&F 10047 σ1 selective agonist 5 10mM DMSO hydrochloride solution 1081 SKF 89976A Potent GABA uptake 1 10mM DMSO hydrochloride inhibitor. Penetrates solution blood brain barrier 1082 CFM-2 Non-competitive 4 10mM DMSO AMPA antagonist solution 1088 CGP 54626 Potent, selective 1 10mM DMSO hydrochloride GABAB antagonist solution 1089 8-Bromo-cGMP, sodium cGMP analog; 6 10mM DMSO salt activates PKG solution 1091 BU 226 hydrochloride Potent, highly 2 10mM DMSO selective I2 ligand solution 1093 Pirfenidone Antifibrotic agent; 2 10mM DMSO regulates cytokine solution levels in vivo 1097 Taxol Promotes assembly 5 10mM DMSO and inhibits solution disassembly of microtubules 1098 Tranilast Antiallergic, inhibits 1 10mM DMSO release from mast solution cells 1099 Forskolin Adenylyl cyclase 3 10mM DMSO activator solution 1101 Cyclosporin A Calcineurin inhibitor 4 10mM DMSO solution 1102 Procaterol hydrochloride Potent β2 agonist 2 10mM DMSO solution 1104 2-Cl-IB-MECA Highly selective A3 7 10mM DMSO agonist solution 1105 AF-DX 116 Selective M2 3 10mM DMSO antagonist solution 1107 ATPA Selective, potent 6 10mM DMSO GluR5 agonist solution 1109 GR 103691 Highly selective D3 1 10mM DMSO antagonist solution 1110 Genistein EGFR kinase 3 10mM DMSO inhibitor. Also solution estrogen and PPARγ ligand

180 1114 CGP 37157 Antagonist of 3 10mM DMSO mitochondrial solution Na+/Ca2+ exchange 1115 AM 281 Potent, selective CB1 7 10mM DMSO antagonist/inverse solution agonist 1116 AM 404 Anandamide transport 14 10mM DMSO inhibitor solution 1117 AM 251 Potent CB1 18 10mM DMSO antagonist. Also solution GPR55 agonist 1120 AM 630 CB2 selective 13 10mM DMSO antagonist/inverse solution agonist 1122 Telenzepine Potent, selective M1 1 10mM DMSO dihydrochloride antagonist solution 1123 (S)-(+)-Niguldipine α1 antagonist, L-type 1 10mM DMSO hydrochloride Ca2+ channel blocker solution 1124 (R)-(-)-Niguldipine α1 antagonist, L-type 1 10mM DMSO hydrochloride Ca2+ channel blocker. solution Less active enantiomer of Niguldipine hydrochloride (Cat. No. 1123) 1126 Dexamethasone Anti-inflammatory 2 10mM DMSO glucocorticoid solution 1129 BRL 54443 Potent 5-ht1E/F 2 10mM DMSO agonist solution 1130 LY 294002 Selective PI 3-kinase 3 10mM DMSO hydrochloride inhibitor solution 1131 KU14R Antagonist of 1 10mM DMSO pancreatic imidazoline solution receptor 1132 Harmane Putative endogenous 3 10mM DMSO imidazoline ligand. solution Also MAO inhibitor 1133 BRL 44408 maleate Selective α2A 9 10mM DMSO antagonist solution 1134 CGP 12177 β3 partial agonist. 4 10mM DMSO hydrochloride β1/β2 antagonist. solution 1139 L-NIL hydrochloride Selective iNOS 2 10mM DMSO inhibitor solution 1140 8-Bromo-cAMP, sodium Cell-permeable cAMP 4 10mM DMSO salt analog solution

181 1141 Dibutyryl-cAMP, sodium Cell-permeable cAMP 3 10mM DMSO salt analog solution 1145 L-733,060 hydrochloride Potent NK1 antagonist 3 10mM DMSO solution 1147 SKF 96365 STIM1-mediated 2 10mM DMSO hydrochloride Ca2+ influx inhibitor solution 1148 Siguazodan PDE3 inhibitor 1 10mM DMSO solution 1202 SB 203580 Selective inhibitor of 4 10mM DMSO p38 MAPK solution 1206 SC 19220 Selective EP1 receptor 3 10mM DMSO antagonist solution 1207 BRL 15572 Selective h5-HT1D 1 10mM DMSO hydrochloride antagonist solution 1210 FR 139317 Highly potent, 1 10mM DMSO selective ETA solution antagonist 1211 ZD 7155 hydrochloride Selective non-peptide 5 10mM DMSO AT1 antagonist solution 1212 MPEP hydrochloride mGlu5 antagonist and 10 10mM DMSO positive allosteric solution modulator at mGlu4 1213 PD 98059 Specific inhibitor of 5 10mM DMSO MEK solution 1214 SIB 1893 mGlu5 antagonist and 1 10mM DMSO positive allosteric solution modulator at mGlu4 1215 SIB 1757 Highly selective 2 10mM DMSO mGlu5 antagonist solution 1217 MRS 1220 Highly potent, 2 10mM DMSO selective hA3 solution antagonist 1218 DH 97 MT2 receptor 1 10mM DMSO antagonist solution 1219 IDRA 21 Inhibits AMPA 2 10mM DMSO receptor solution desensitization 1223 DL-TBOA Selective non- 13 10mM DMSO transportable inhibitor solution of EAATs 1224 2-APB IP3 receptor 2 10mM DMSO antagonist. Also TRP solution channel modulator 1226 Topoisomerase II 3 10mM DMSO Etoposide inhibitor solution

182 1227 Apigenin Protein kinase 1 10mM DMSO inhibitor solution 1228 Nocodazole Microtubule inhibitor 1 10mM DMSO solution 1231 Brefeldin A Disrupts protein 4 10mM DMSO translocation to Golgi solution 1236 BHQ Inhibitor of SERCA 1 10mM DMSO ATPase solution 1238 CCMQ Used to characterize 2 10mM DMSO NR2B-containing solution NMDA receptors 1242 SB 216641 hydrochloride Selective h5-HT1B 4 10mM DMSO antagonist solution 1243 (+)-PD 128907 D3 agonist (D3 ≥ D2 4 10mM DMSO hydrochloride > D4) solution 1244 KB-R7943 mesylate Na+/Ca2+ exchange 3 10mM DMSO inhibitor (reverse solution mode) 1248 CGP 55845 Potent, selective 3 10mM DMSO GABAB antagonist solution 1249 CY 208-243 Selective D1-like 1 10mM DMSO agonist solution 1250 SDZ 220-581 Competitive NMDA 10 10mM DMSO antagonist solution 1251 SDZ 220-040 Potent, competitive 1 10mM DMSO NMDA antagonist solution 1255 SDZ SER 082 fumarate Selective 5-HT2B/2C 1 10mM DMSO antagonist solution 1256 Vinblastine sulfate Disrupts microtubules 1 10mM DMSO solution 1259 1-Deoxymannojirimycin α-Mannosidase I 7 10mM DMSO hydrochloride inhibitor solution SR 95531 hydrobromide Selective, competitive 11 10mM DMSO 1262 GABAA receptor solution antagonist 1263 GR 144053 Glycoprotein IIb/IIIa 1 10mM DMSO trihydrochloride (αIIbβ3) receptor solution antagonist. Antithrombotic 1264 SB 202190 Potent, selective 2 10mM DMSO inhibitor of p38 solution MAPK 1267 Pifithrin- α p53 inhibitor. Also 2 10mM DMSO hydrobromide aryl hydrocarbon solution receptor agonist

183 1274 GR 159897 Non-peptide, potent 2 10mM DMSO NK2 antagonist solution 1282 GNTI dihydrochloride Potent, selective κ 4 10mM DMSO antagonist solution 1284 Olomoucine Cyclin-dependent 1 10mM DMSO kinase inhibitor solution 1295 Loreclezole Subtype-selective 2 10mM DMSO hydrochloride GABAA receptor solution modulator 1296 CI 966 hydrochloride Selective inhibitor of 1 10mM DMSO GAT-1 solution 1302 (S)-3,4-DCPG Potent, selective 10 10mM DMSO mGlu8a agonist solution 1304 BW-B 70C 5-Lipoxygenase 2 10mM DMSO inhibitor solution 1305 Monastrol Selective inhibitor of 3 10mM DMSO mitotic kinesin Eg5 solution 1307 Ciglitazone Selective PPARγ 1 10mM DMSO agonist solution 3857 Dexrazoxane Topoisomerase II 10mM DMSO hydrochloride inhibitor solution 1312 WY 14643 Selective PPARα 5 10mM DMSO agonist solution 1317 CP 94253 hydrochloride Potent, selective 5- 3 10mM DMSO HT1B agonist solution 1321 ZM 336372 Potent, selective c-Raf 2 10mM DMSO inhibitor solution 1322 GR 113808 Potent, selective 5- 3 10mM DMSO HT4 antagonist solution 1324 RX 821002 Potent, selective α2D 3 10mM DMSO hydrochloride antagonist solution 1325 RS 100329 hydrochloride Potent, subtype- 2 10mM DMSO selective α1A solution antagonist 1327 L-655,708 Selective for α5- 3 10mM DMSO containing GABAA solution receptors 1328 Flumazenil Benzodiazepine 3 10mM DMSO antagonist solution 3858 CH 223191 Potent aryl 10mM DMSO hydrocarbon receptor solution (AhR) antagonist 1348 UB 165 fumarate Subunit selective 1 10mM DMSO nAChR agonist solution 1349 (R)-(-)-Rolipram PDE4 inhibitor. More 12 10mM DMSO active enantiomer of solution

184 rolipram (Cat. No. 0905) 1350 (S)-(+)-Rolipram PDE4 inhibitor. Less 4 10mM DMSO active enantiomer of solution rolipram (Cat. No. 0905) 1356 MG 624 α7 neuronal nicotinic 4 10mM DMSO receptor antagonist solution 1357 U 99194 maleate Potent, selective D3 3 10mM DMSO antagonist solution 1361 Potent, selective non- 2 10mM DMSO PD 123319 peptide AT2 solution ditrifluoroacetate antagonist 1363 PD 81723 Allosteric potentiator 2 10mM DMSO of A1 receptors solution 1366 ZM 449829 Potent, selective 3 10mM DMSO JAK3 inhibitor solution 1367 ZM 39923 hydrochloride Potent, selective 1 10mM DMSO JAK3 inhibitor solution 1371 SB 200646 hydrochloride 5-HT2C/2B 1 10mM DMSO antagonist solution 1375 SB 228357 5-HT2C/2B 1 10mM DMSO antagonist/inverse solution agonist 1376 SB 218795 Potent, selective non- 1 10mM DMSO peptide NK3 solution antagonist 1379 SB 221284 Potent, selective 5- 1 10mM DMSO HT2C/2B antagonist solution 1380 PMPA (NAALADase GCP II inhibitor 2 10mM DMSO inhibitor) solution 1381 GW 5074 Potent, selective c- 3 10mM DMSO Raf1 kinase inhibitor solution 1382 L-152,804 Potent, selective non- 1 10mM DMSO peptide NPY Y5 solution antagonist 1383 BML-190 Potent, selective CB2 1 10mM DMSO ligand solution 1385 MRS 1334 Potent, highly 1 10mM DMSO selective hA3 solution antagonist 1393 SB 222200 Potent, selective non- 2 10mM DMSO peptide NK3 solution antagonist. Brain penetrant

185 1394 (RS)-3,4-DCPG Potent systemically 3 10mM DMSO active anticonvulsant. solution Racemate of (R)-3,4,- DCPG (Cat. No. 1395) 1395 (R)-3,4-DCPG AMPA 2 10mM DMSO antagonist/weak solution NMDA antagonist 1396 Fenretinide Synthetic retinoid. 7 10mM DMSO Potent anticancer solution agent 1397 PP 1 Potent, selective Src 1 10mM DMSO inhibitor solution 1398 Kenpaullone Potent cyclin- 2 10mM DMSO dependent kinase solution inhibitor. Also inhibits GSK-3 1399 CP 339818 hydrochloride Non-peptide, potent 1 10mM DMSO KV1.3 channel solution blocker 1400 SCH 202676 Inhibitor of ligand 1 10mM DMSO hydrobromide binding to G-protein- solution coupled receptors 1401 NU 1025 2 10mM DMSO Potent PARP inhibitor solution 1402 SB 203580 hydrochloride Selective inhibitor of 3 10mM DMSO p38 MAPK; water- solution soluble 1403 FPL 64176 Potent activator of 1 10mM DMSO Ca2+ channels (L- solution type) 1405 (-)-Terreic acid Selective inhibitor of 1 10mM DMSO BTK solution 1407 PP 2 Potent, selective Src 1 10mM DMSO inhibitor solution 1410 SDM25N hydrochloride Potent, selective non- 1 10mM DMSO peptide δ antagonist solution 1414 Scopolamine Non-selective 4 10mM DMSO hydrobromide muscarinic antagonist solution 1415 1400W dihydrochloride Potent, highly 5 10mM DMSO selective iNOS solution inhibitor 1417 Daidzein Arrests cell cycle in 2 10mM DMSO G1 phase solution 1418 Resveratrol Cyclooxygenase 4 10mM DMSO inhibitor solution

186 1419 Naloxone Full κ agonist, partial 1 10mM DMSO benzoylhydrazone μ and δ agonist and solution antagonizes agonist- induced activation of NOP 1422 DCEBIO Activates Cl- 3 10mM DMSO conductance and hIK1 solution K+ channels 1425 (S)-(+)-Dimethindene M2-selective 1 10mM DMSO maleate antagonist solution 1426 PPT Subtype-selective 7 10mM DMSO ERα agonist solution 1430 DuP 697 Cyclooxygenase-2 1 10mM DMSO (COX-2) inhibitor solution 1435 SQ 22536 Adenylyl cyclase 2 10mM DMSO inhibitor solution 1437 D609 Selective PC-PLC 1 10mM DMSO inhibitor solution 1440 BMY 14802 Sigma antagonist. 1 10mM DMSO hydrochloride Antipsychotic agent solution 1441 BMS 182874 Highly selective, 1 10mM DMSO hydrochloride orally active non- solution peptide ETA antagonist 1442 BMY 45778 Non-prostanoid 2 10mM DMSO prostacyclin IP solution receptor partial agonist 1447 SKF 81297 D1 agonist 1 10mM DMSO hydrobromide solution 1448 Potent and selective 1 10mM DMSO Formoterol hemifumarate β2 agonist solution 1453 Clemastine fumarate H1 antagonist 1 10mM DMSO solution 1454 GYKI 52466 Selective non- 7 10mM DMSO hydrochloride competitive AMPA solution antagonist 1459 SU 4312 Potent inhibitor of 1 10mM DMSO VEGFR tyrosine solution kinase 1460 PACOCF3 Phospholipase A2 1 10mM DMSO inhibitor solution 1461 Linomide Immunomodulator 1 10mM DMSO with antiangiogenic solution properties

187 1470 Flecainide acetate Cardiac Na+ channel 1 10mM DMSO blocker. solution Antiarrhythmic 1471 Etomidate GABA mimetic and 1 10mM DMSO GABA modulatory solution agent 1472 Suramin hexasodium salt Non-selective P2 5 10mM DMSO antagonist solution 1477 GR 127935 Potent, selective 5- 5 10mM DMSO hydrochloride HT1B/1D antagonist solution 1479 Mifepristone Progesterone and 1 10mM DMSO glucocorticoid solution antagonist 1480 FIT Irreversible δ agonist 1 10mM DMSO solution 1484 Oleylethanolamide GPR55 agonist. Also 3 10mM DMSO PPARα agonist solution 1493 CGP 78608 Potent, selective 2 10mM DMSO glycine-site NMDA solution antagonist 1496 SP 600125 Novel and selective 7 10mM DMSO JNK inhibitor solution 1497 Rimcazole σ2 antagonist. Also 1 10mM DMSO dihydrochloride DAT inhibitor solution 1505 Mycophenolic acid Inosine 1 10mM DMSO monophosphatase solution dehydrogenase inhibitor 1506 Terazosin hydrochloride α1 and α2B antagonist 1 10mM DMSO (α1 > α2B). Orally solution active 1507 FR 122047 hydrochloride Cyclooxygenase 3 10mM DMSO (COX-1) inhibitor solution 1508 GW 9662 Selective PPARγ 1 10mM DMSO antagonist solution 1509 TMS Cytochrome P450 2 10mM DMSO 1B1 inhibitor solution 1510 Ozagrel hydrochloride Selective 2 10mM DMSO thromboxane A2 solution synthetase inhibitor 1511 SR 59230A Potent and selective 4 10mM DMSO hydrochloride β3 antagonist solution 1512 SB 205384 GABAA receptor 1 10mM DMSO modulator; slows solution current decay

188 1513 CGP 7930 Positive modulator at 3 10mM DMSO GABAB receptors solution 1514 CGP 13501 Positive modulator at 1 10mM DMSO GABAB receptors solution 1516 SDZ 21009 β-adrenoceptor 1 10mM DMSO antagonist. Also 5- solution HT1A/1B antagonist 1518 5-Iodo-A-85380 High affinity α4β2 4 10mM DMSO dihydrochloride and α6β2 subtype- solution selective agonist 1524 LY 288513 Selective CCK2 1 10mM DMSO antagonist solution 1526 Mevastatin HMG-CoA reductase 1 10mM DMSO inhibitor solution 3862 Eletriptan hydrobromide Orally active, 10mM DMSO selective 5-HT1B/1D solution agonist 1530 Lovastatin HMG-CoA reductase 1 10mM DMSO inhibitor solution 1531 Icilin Activates a novel cold 4 10mM DMSO receptor. Cooling solution agent 1540 DCPIB Selective blocker of 1 10mM DMSO VSAC/ICl, swell. solution Inhibits glucose- stimulated insulin release 1542 Splitomicin Sir2p inhibitor 3 10mM DMSO solution 1544 (±)-Bay K 8644 Ca2+-channel 3 10mM DMSO activator (L-type) solution 1546 (S)-(-)-Bay K 8644 Ca2+-channel 4 10mM DMSO activator (L-type) solution 1547 NSC 95397 Selective Cdc25 dual 1 10mM DMSO specificity solution phosphatase inhibitor 1548 Cantharidin Protein phosphatase 1 1 10mM DMSO and 2A inhibitor solution 1549 CD 437 RARγ-selective 1 10mM DMSO agonist solution 1550 SC 560 Cyclooxygenase 1 10mM DMSO (COX-1) inhibitor solution 3865 Licarbazepine Active metabolite of 10mM DMSO oxcarbazepine (Cat. solution No. 3684)

189 1554 Piceatannol Inhibits TNF-induced 5 10mM DMSO NF-κB activation solution 1558 Tracazolate Subtype-selective 1 10mM DMSO hydrochloride GABAA allosteric solution modulator 1579 HEMADO High affinity selective 1 10mM DMSO A3 agonist solution 1580 Purvalanol A Cyclin-dependent 3 10mM DMSO kinase inhibitor solution 1581 Purvalanol B Cyclin-dependent 5 10mM DMSO kinase inhibitor solution 1586 SKF 83566 Potent, selective D1- 5 10mM DMSO hydrobromide like antagonist solution 1588 Indatraline hydrochloride Potent 5-HT uptake 2 10mM DMSO inhibitor. Also inhibits solution dopamine and noradrenalin uptake 1591 NBI 27914 hydrochloride Selective non-peptide 2 10mM DMSO CRF1 antagonist solution 1594 Ro 25-6981 maleate Subtype-selective 7 10mM DMSO NR2B antagonist solution 1612 SB 269970 hydrochloride Potent selective 5- 8 10mM DMSO HT7 antagonist. Brain solution penetrant 1614 SB 431542 Potent, selective 5 10mM DMSO inhibitor of TGF-βRI, solution ALK4 and ALK7 1615 SB 366791 Potent, selective, 1 10mM DMSO competitive TRPV1 solution (VR1) antagonist 1616 SB 216763 Potent, selective 2 10mM DMSO GSK-3 inhibitor solution 1617 SB 415286 Potent, selective 4 10mM DMSO GSK-3 inhibitor solution 1621 Streptozocin DNA alkylator; 2 10mM DMSO antitumor and induces solution diabetes 1622 Remacemide NMDA antagonist; 1 10mM DMSO hydrochloride blocks ion channel solution and allosteric modulatory site 1625 DFB Allosteric potentiator 1 10mM DMSO at mGlu5 solution 1636 IEM 1460 Open-channel blocker 3 10mM DMSO of AMPA currents; solution selective for non-

190 GluR2-containing receptors 1638 U 18666A Inhibitor of hedgehog 1 10mM DMSO (Hh) signaling. Also solution inhibits cholesterol synthesis 1639 AY 9944 Inhibitor of hedgehog 1 10mM DMSO (Hh) signaling. solution Inhibits Δ7- dehydrocholesterol reductase 1641 OLDA Potent, selective 2 10mM DMSO endogenous TRPV1 solution agonist 1643 D-64131 Inhibitor of tubulin 1 10mM DMSO polymerization. solution Antitumor in vivo 1646 Lonidamine Mitochondrial 3 10mM DMSO hexokinase inhibitor solution 1657 Ginkgolide B PAF receptor 1 10mM DMSO antagonist solution 1658 GR 125487 sulfamate Potent, selective 5- 1 10mM DMSO HT4 antagonist. solution Active in vivo 1659 Fenoldopam Selective D1-like 1 10mM DMSO hydrochloride partial agonist solution 4712 Salmeterol xinafoate Long-acting β2 1 10mM DMSO agonist; solution bronchodilator 1661 SB 206553 hydrochloride Potent, selective 5- 4 10mM DMSO HT2C/5-HT2B solution antagonist. Orally active 1662 SKF 77434 Selective D1-like 2 10mM DMSO hydrobromide partial agonist solution 1663 Potent, selective non- 1 10mM DMSO BW 373U86 peptide δ agonist solution 1664 Selective PPARγ 3 10mM DMSO GW 1929 agonist. Orally active solution 1671 Selective M4 2 10mM DMSO PD 102807 antagonist solution 1672 RU 28318, potassium salt Potent, selective 1 10mM DMSO mineralocorticoid solution receptor antagonist 1675 YM 90709 Interleukin-5 receptor 1 10mM DMSO antagonist solution

191 1676 T 0156 hydrochloride Highly potent, 1 10mM DMSO selective PDE5 solution inhibitor 1677 GW 7647 Highly selective, 2 10mM DMSO potent PPARα solution agonist. Orally active 1682 PIT P2Y ligand; displays 1 10mM DMSO mixed solution antagonism/potentiati on 1691 NECA Adenosine receptor 3 10mM DMSO agonist solution 1692 Cilostazol PDE3A inhibitor. 1 10mM DMSO Also adenosine uptake solution inhibitor 1694 Ibudilast PDE inhibitor (non- 1 10mM DMSO selective) solution 1695 Cisapride 5-HT4 agonist; 1 10mM DMSO stimulates intestinal solution ACh release 1697 Noscapine hydrochloride Tubulin inhibitor; 1 10mM DMSO induces apoptosis solution 1698 L 655240 Potent, selective 1 10mM DMSO thromboxane solution A2/prostaglandin endoperoxide antagonist 1699 CGS 15943 Potent adenosine 2 10mM DMSO receptor antagonist solution 1701 A 77636 hydrochloride Potent, selective D1- 2 10mM DMSO like agonist. Orally solution active 1702 N6- Potent, selective A1 4 10mM DMSO Cyclopentyladenosine agonist solution

1705 2-Chloro-N6- Potent, selective A1 4 10mM DMSO cyclopentyladenosine agonist solution

1706 Acetaminophen Cyclooxygenase 1 10mM DMSO inhibitor; may be solution selective for COX-3 1709 CL 218872 Benzodiazepine 1 10mM DMSO agonist solution 1710 CV 1808 Non-selective 3 10mM DMSO adenosine A2 receptor solution agonist

192 1742 R-96544 hydrochloride Potent, selective 5- 3 10mM DMSO HT2A antagonist solution 1746 Nemonapride Highly potent D2-like 1 10mM DMSO antagonist. Also 5- solution HT1A agonist 1747 Isoproterenol Standard selective β 2 10mM DMSO hydrochloride agonist solution 1757 ALX 5407 hydrochloride Selective non- 4 10mM DMSO transportable GlyT1 solution inhibitor 1758 PETCM Activator of caspase-3 1 10mM DMSO solution 1760 (±)-Blebbistatin Selective inhibitor of 6 10mM DMSO myosin II solution 1761 Baicalein 5- and 12- 1 10mM DMSO Lipoxygenase solution inhibitor 1762 Acifran Hypolipidemic agent; 3 10mM DMSO agonist for the solution GPR109A (HM74A) and GPR109B receptors 1767 Zimelidine Selective 5-HT uptake 1 10mM DMSO dihydrochloride inhibitor solution 1771 S 14506 hydrochloride Highly potent 5- 1 10mM DMSO HT1A agonist; solution displays unique binding mechanism 1772 Urapidil hydrochloride α1 antagonist. Also 5- 1 10mM DMSO HT1A receptor solution agonist 1777 Arctigenin Potent MKK1 1 10mM DMSO inhibitor. Also inhibits solution IκBα phosphorylation 1778 Ro 106-9920 Inhibitor of NF-κB 1 10mM DMSO activation solution NNC 711 Selective inhibitor of 1 10mM DMSO 1779 GAT-1 solution 1780 NNC 63-0532 Potent non-peptide 1 10mM DMSO NOP agonist; brain solution penetrant 1795 Zacopride hydrochloride Highly potent 5-HT3 2 10mM DMSO receptor antagonist. solution Also 5-HT4 agonist 1797 OMDM-2 Potent inhibitor of 1 10mM DMSO anandamide uptake solution

193 1798 Gabexate mesylate Serine protease 3 10mM DMSO inhibitor; inhibits solution thrombin, trypsin, kallikrein and plasmin 1801 WAY 161503 Potent, selective 5- 2 10mM DMSO hydrochloride HT2C agonist solution

1804 SR 2640 hydrochloride Potent, selective 1 10mM DMSO LTD4 /LTE4 receptor solution antagonist 1807 2-Methoxyestradiol Apoptotic and 1 10mM DMSO antiangiogenic agent solution 1808 E-4031 dihydrochloride hERG channel 2 10mM DMSO blocker; inhibits rapid solution delayed rectifier K+ current (IKr) 1809 Altanserin hydrochloride 5-HT2A receptor 1 10mM DMSO antagonist solution 1810 Raclopride Potent, selective 3 10mM DMSO D2/D3 antagonist solution 1813 Indirubin-3'-oxime GSK-3β inhibitor. 1 10mM DMSO Also inhibits other solution protein kinases 1816 ICI 63197 PDE4 inhibitor 2 10mM DMSO solution 1821 YM 976 PDE4 inhibitor 2 10mM DMSO solution 1838 IRL-2500 Potent ETB antagonist 1 10mM DMSO solution 1842 RHC 80267 Diacylglycerol lipase 1 10mM DMSO inhibitor solution 1847 Eticlopride hydrochloride Selective D2/D3 1 10mM DMSO antagonist solution 1849 EMD 66684 Potent, selective non- 2 10mM DMSO peptide AT1 solution antagonist 1850 Exo1 Inhibits Golgi-ER 1 10mM DMSO traffic; blocks solution exocytosis 1854 Ro 60-0175 fumarate Potent, selective 5- 2 10mM DMSO HT2C agonist solution 1856 L-165,041 Potent PPARδ agonist 1 10mM DMSO solution 1858 Impentamine Selective H3 1 10mM DMSO dihydrobromide antagonist solution

194 1862 PRIMA-1 Restores mutant p53 1 10mM DMSO activity; induces solution apoptosis 1866 MRS 1845 Potent SOC inhibitor; 2 10mM DMSO blocks capacitative solution Ca2+ entry 1867 NSC 663284 Potent, selective 1 10mM DMSO Cdc25 phosphatase solution inhibitor 1870 BTS Selective inhibitor of 1 10mM DMSO skeletal muscle solution myosin II ATPase activity 1937 NSC 693868 Cdk inhibitor. Also 1 10mM DMSO inhibits GSK-3 solution 1941 m-3M3FBS Phospholipase C 1 10mM DMSO activator solution 1942 o-3M3FBS Inactive analog of m- 1 10mM DMSO 3M3FBS (Cat. No. solution 1941) 1944 Loratidine Peripheral H1 1 10mM DMSO antagonist; solution antiallergic agent 1949 L 670596 Potent, selective 1 10mM DMSO thromboxane solution A2/prostaglandin endoperoxide antagonist 1952 DCB Allosteric modulator 1 10mM DMSO at mGlu5 solution 1953 DMeOB Negative allosteric 1 10mM DMSO modulator at mGlu5 solution 1955 Ritanserin Potent 5-HT2 2 10mM DMSO antagonist solution 1956 Bestatin Aminopeptidase 1 10mM DMSO inhibitor solution 1957 GR 79236 A1 agonist 1 10mM DMSO solution 1960 SB 334867 Selective non-peptide 5 10mM DMSO OX1 antagonist solution 1961 SB 258585 hydrochloride Potent, selective 5- 2 10mM DMSO HT6 antagonist solution 1962 SB 239063 Potent, selective p38 4 10mM DMSO MAPK inhibitor; solution orally active

195 1963 SB 408124 Selective non-peptide 2 10mM DMSO OX1 antagonist solution 1965 Simvastatin HMG-CoA reductase 3 10mM DMSO inhibitor solution 1967 Ranitidine hydrochloride Selective H2 1 10mM DMSO antagonist solution 1972 Tianeptine Selective facilitator of 1 10mM DMSO 5-HT uptake; solution antidepressant 1974 SANT-1 Inhibitor of hedgehog 1 10mM DMSO (Hh) signaling; solution antagonizes smoothened activity 1975 6-Iodo- Potent, competitive 1 10mM DMSO nordihydrocapsaicin vanilloid receptor solution antagonist 1982 Reboxetine mesylate Potent, selective 5 10mM DMSO noradrenalin uptake solution inhibitor; orally active 1994 ZK 93423 Potent benzodiazepine 1 10mM DMSO agonist solution 1995 Ro 19-4605 Benzodiazepine 1 10mM DMSO inverse agonist solution 1996 ZK 93426 hydrochloride Potent, competitive 1 10mM DMSO benzodiazepine solution antagonist 1997 Ro 15-4513 Benzodiazepine 3 10mM DMSO partial inverse agonist solution 1999 Linopirdine KCNQ channel 1 10mM DMSO dihydrochloride blocker solution 2000 XE 991 dihydrochloride Potent, selective 2 10mM DMSO KCNQ channel solution blocker; blocks M- current 2001 GS 39783 Positive modulator at 3 10mM DMSO GABAB receptors solution 2002 Ro 31-8220 mesylate Protein kinase 1 10mM DMSO inhibitor solution 2004 Isradipine Ca2+ channel blocker 1 10mM DMSO (L-type) solution 2005 Ro 04-5595 Selective NR2B 1 10mM DMSO antagonist solution 2007 Fluticasone propionate Selective high affinity 2 10mM DMSO glucocorticoid agonist solution

196 2008 SKF 86002 p38 MAPK inhibitor; 1 10mM DMSO dihydrochloride anti-inflammatory solution agent 2009 PSB 1115 Selective human A2B 2 10mM DMSO receptor antagonist; solution water-soluble 2011 Tomoxetine Potent, selective 2 10mM DMSO hydrochloride noradrenalin re-uptake solution inhibitor 2012 PSB 11 Potent, selective 1 10mM DMSO human A3 receptor solution antagonist/inverse agonist 2013 Guggulsterone Broad spectrum 1 10mM DMSO steroid receptor solution ligand. Antagonizes FXR and displays hypolipidaemic activity 2018 Mirtazepine Potent 5-HT2 2 10mM DMSO antagonist. Also 5- solution HT3, H1 and α2- antagonist. Antidepressant 2019 PSB 36 Potent and selective 1 10mM DMSO A1 antagonist solution 2020 Ch 55 1 10mM DMSO Potent RAR agonist solution 2021 LE 135 Selective RARβ 1 10mM DMSO antagonist solution 2022 SR 202 Selective PPARγ 1 10mM DMSO antagonist; solution antidiabetic and antiobesity agent 5160 Medetomidine Potent, highly 1 10mM DMSO hydrochloride selective α2 agonist solution 2025 Cinalukast Potent, selective 1 10mM DMSO CysLT1 (LTD4) solution antagonist; orally active 2037 SDZ 205-557 5-HT4/5-HT3 1 10mM DMSO hydrochloride receptor antagonist solution 2072 Aminopurvalanol A Cyclin-dependent 1 10mM DMSO kinase inhibitor solution 2078 UBP 296 Selective, potent 1 10mM DMSO kainate antagonist; solution

197 selective for GluR5- containing receptors 2079 UBP 302 Selective, potent 5 10mM DMSO kainate antagonist; solution active enantiomer of UBP 296 (Cat. No. 2078) 2086 GTP 14564 Class III receptor 1 10mM DMSO tyrosine kinase (RTK) solution inhibitor 2088 DMNB DNA-PK inhibitor 1 10mM DMSO solution 2089 RS 102895 hydrochloride CCR2b chemokine 2 10mM DMSO receptor antagonist solution 2095 PNU 37883 Vascular KATP 1 10mM DMSO hydrochloride channel blocker solution 2096 DAU 5884 hydrochloride M3 receptor 1 10mM DMSO antagonist solution 2097 SKI II Selective non-lipid 1 10mM DMSO inhibitor of solution sphingosine kinase; antitumor 2098 Apoptosis Activator 2 Promotes apoptosome 1 10mM DMSO formation and solution activates caspase- 9/caspase-3 pathway. Selectively induces tumor cell apoptosis 2132 Amisulpride Selective D2/D3 2 10mM DMSO receptor antagonist; solution atypical antipsychotic agent 2137 2,3-DCPE hydrochloride Selectively induces 1 10mM DMSO cancer cell apoptosis solution 2139 Lylamine hydrochloride CB1 agonist 1 10mM DMSO solution 2141 Paroxetine maleate Highly potent and 1 10mM DMSO selective 5-HT uptake solution inhibitor 2143 SCS Selective GABAA 1 10mM DMSO receptor antagonist; solution β1-subunit-selective 2147 Nicorandil KATP channel opener 1 10mM DMSO and NO donor solution 2148 (±)-McN 5652 Potent, orally active 5- 1 10mM DMSO HT uptake inhibitor. solution

198 Also inhibits noradrenalin and dopamine uptake in vitro 2150 nTZDpa Potent, selective 1 10mM DMSO PPARγ partial agonist solution 2151 API-2 Selective inhibitor of 3 10mM DMSO Akt/PKB signaling. solution Antitumor and antiviral 2152 NSC 625987 Cyclin-dependent 2 10mM DMSO kinase 4 (cdk4) solution inhibitor 2154 ZD 2079 hydrochloride β3-adrenoceptor 1 10mM DMSO agonist solution 2155 NTNCB hydrochloride Potent and selective 2 10mM DMSO non-peptidic NPY Y5 solution antagonist 2156 Embelin Inhibitor of X-linked 1 10mM DMSO inhibitor of apoptosis solution (XIAP); cell- permeable and antitumor 2160 Bax channel blocker Inhibits Bax-mediated 1 10mM DMSO mitochondrial solution cytochrome c release 2161 NSC 23766 Selective inhibitor of 3 10mM DMSO Rac1-GEF interaction; solution antioncogenic 2162 INCA-6 Inhibitor of 1 10mM DMSO calcineurin-substrate solution association 2172 AZ 10417808 Selective non-peptide 1 10mM DMSO caspase-3 inhibitor solution 2173 WAY 629 hydrochloride Selective 5-HT2C 1 10mM DMSO agonist solution 2175 Tetrabenazine Potent inhibitor of 2 10mM DMSO vesicular monoamine solution transport; depletes dopamine stores 2176 BVT 948 Non-competitive 3 10mM DMSO protein tyrosine solution phosphatase inhibitor; enhances insulin signaling

199 2183 ZK 164015 Potent estrogen 1 10mM DMSO receptor antagonist solution 2184 SN-6 Selective Na+/Ca2+ 2 10mM DMSO exchange inhibitor solution (reverse mode) 2186 CMPD-1 Non-ATP-competitive 2 10mM DMSO p38α inhibitor solution 2190 SR 27897 Potent and selective 1 10mM DMSO CCK1 antagonist solution 2191 S-Trityl-L-cysteine Potent, selective 1 10mM DMSO inhibitor of mitotic solution kinesin Eg5 2192 4-HQN PARP inhibitor 1 10mM DMSO solution 2193 Carmoxirole Selective, peripherally 1 10mM DMSO hydrochloride acting D2-like agonist solution 2194 R 59-022 Diacylglycerol kinase 1 10mM DMSO inhibitor; increases solution PKC activity 2195 Eliprodil Non-competitive 1 10mM DMSO NR2B-selective solution NMDA antagonist 2196 3-MATIDA Potent, selective 1 10mM DMSO mGlu1 antagonist solution 2197 L 755507 Very potent and 2 10mM DMSO selective β3 partial solution agonist 2198 Mibefradil Ca2+ channel blocker 3 10mM DMSO dihydrochloride (T-type) solution 2199 CGP 71683 Highly selective and 2 10mM DMSO hydrochloride potent non-peptide solution NPY Y5 receptor antagonist 2200 PD 160170 Selective non-peptide 1 10mM DMSO NPY Y1 antagonist solution 2201 PNU 22394 5-HT2C agonist and 1 10mM DMSO hydrochloride 5-HT2A/2B partial solution agonist 2202 Zatebradine Bradycardic agent; 1 10mM DMSO hydrochloride blocks If pacemaker solution current 3869 TCS 2002 Potent GSK-3β 10mM DMSO inhibitor solution 2208 LY 255283 Selective, competitive 1 10mM DMSO BLT2 receptor solution antagonist

200 2213 N20C hydrochloride Non-competitive 1 10mM DMSO NMDA receptor open- solution channel blocker 2214 ABT 724 Potent, selective D4 1 10mM DMSO trihydrochloride partial agonist; solution proerectile 2227 CI 976 Acyl-CoA:cholesterol 1 10mM DMSO acyltransferase solution (ACAT) inhibitor 2229 GW 0742 Highly selective, 2 10mM DMSO potent PPARδ agonist solution 2237 BRL 50481 Selective PDE7 1 10mM DMSO inhibitor solution 2238 GW 441756 Potent, selective TrkA 1 10mM DMSO inhibitor solution 2241 DMAB-anabaseine Partial agonist at α7- 1 10mM DMSO dihydrochloride containing receptors solution and antagonist at α4β2 neuronal nicotinic receptors 2242 Octopamine Invertebrate biogenic 1 10mM DMSO hydrochloride amine: activates β3 solution adrenoceptors 2252 Doxorubicin Antitumor antibiotic 3A/14 10mM DMSO hydrochloride agent. Inhibits DNA 4223 solution topoisomerase II 2254 ACDPP hydrochloride Selective mGlu5 1 10mM DMSO receptor antagonist solution 2255 CGS 9343B Calmodulin antagonist 2 10mM DMSO solution 2266 DY131 Agonist selective for 1 10mM DMSO estrogen-related solution receptors ERRβ and ERRγ 2268 NNC 55-0396 Highly selective Ca2+ 2 10mM DMSO dihydrochloride channel blocker (T- solution type) 2270 SCH 58261 Potent, highly 3 10mM DMSO selective A2A solution antagonist 4618 GW 6471 PPARα antagonist 1 10mM DMSO solution 2272 Ro 08-2750 Inhibits NGF binding 1 10mM DMSO to p75NTR and TrkA solution

201 2275 TBB Selective cell- 1 10mM DMSO permeable CK2 solution inhibitor 2276 FPL 55712 Leukotriene receptor 1 10mM DMSO antagonist solution 2280 Raloxifene hydrochloride Selective estrogen 1 10mM DMSO receptor modulator solution (SERM) 2281 2'-MeCCPA Very selective and 1 10mM DMSO potent A1 receptor solution agonist 2282 Moxonidine I1 receptor and α2- 1 10mM DMSO hydrochloride adrenoceptor agonist solution (I1 > α2) 2284 SEW 2871 Cell-permeable, 1 10mM DMSO selective S1P1 solution receptor agonist 2288 O-1918 Silent antagonist for 1 10mM DMSO putative abnormal- solution CBD (Cat. No. 1297) receptor 2291 1,2,3,4,5,6- Inhibits JAK2 1 10mM DMSO Hexabromocyclohexane autophosphorylation solution 2292 AQ-RA 741 Selective, high 1 10mM DMSO affinity M2 antagonist solution 2293 Zebularine DNA 1 10mM DMSO methyltransferase and solution cytidine deaminase inhibitor 2294 Cordycepin RNA synthesis 2 10mM DMSO inhibitor solution 2295 (±)-Cloprostenol sodium Water-soluble PGF2α 1 10mM DMSO salt analog and potent FP solution receptor agonist 2296 Prostaglandin E2 Major endogenous 2 10mM DMSO prostanoid solution 2301 T 0070907 Highly potent and 1 10mM DMSO selective PPARγ solution antagonist 2303 PNU 282987 Selective α7 nAChR 2 10mM DMSO agonist solution 2308 Tabimorelin Potent, orally active 1 10mM DMSO ghrelin receptor solution agonist 2310 SR 49059 Selective, orally 1 10mM DMSO active vasopressin solution

202 V1A receptor antagonist 2311 L-168,049 Potent, orally active 1 10mM DMSO human glucagon solution receptor antagonist 2312 DNQX disodium salt Selective non-NMDA 3 10mM DMSO antagonist. Water- solution soluble salt of DNQX (Cat. No. 0189) 2313 QX 314 chloride Na+ channel blocker 1 10mM DMSO solution 2315 Immethridine Potent H3 agonist, 1 10mM DMSO dihydrobromide highly selective over solution H4 2316 AMG 9810 Potent and selective, 2 10mM DMSO competitive antagonist solution of TRPV1 2318 Pravastatin sodium salt HMG-CoA reductase 1 10mM DMSO inhibitor solution 2322 BTS 54-505 Potent SNRI; active 1 10mM DMSO metabolite of solution sibutramine (Cat. No. 2290) 2324 Necrostatin-1 Novel inhibitor of 1 10mM DMSO non-apoptotic cell solution death (necroptosis) 2329 Ro 10-5824 Selective D4 receptor 1 10mM DMSO partial agonist solution 2330 DMP 543 K+ channel blocker 1 10mM DMSO and potent ACh solution release enhancer 2333 JNJ 16259685 Extremely potent, 2 10mM DMSO mGlu1-selective non- solution competitive antagonist 2337 Trequinsin hydrochloride Ultrapotent inhibitor 1 10mM DMSO of PDE3 solution 2339 WEB 2086 Potent PAF receptor 1 10mM DMSO antagonist solution 2342 4-Methylhistamine Selective, high 1 10mM DMSO dihydrochloride affinity H4 agonist solution 2344 CGH 2466 A1, A2B and A3 1 10mM DMSO dihydrochloride antagonist and solution inhibitor of p38 MAPK and PDE4

203 2345 ZK 200775 Competitive 1 10mM DMSO AMPA/kainate solution antagonist 2347 Salubrinal Selective inhibitor of 1 10mM DMSO eIF2α solution dephosphorylation 2348 Gavestinel Potent and selective 1 10mM DMSO glycine site solution antagonist. Orally available and active in vivo 2372 ABT 702 Adenosine kinase 1 10mM DMSO dihydrochloride inhibitor; orally active solution 2373 T 0901317 Potent liver X 1 10mM DMSO receptor (LXR) solution agonist 2374 GW 405833 Selective, high 2 10mM DMSO affinity CB2 receptor solution partial agonist 2382 EMD 386088 Potent 5-HT6 agonist 1 10mM DMSO hydrochloride solution 2385 The first selective 1 10mM DMSO AMN 082 mGlu7 agonist solution dihydrochloride 2386 Fenobam Potent and selective 1 10mM DMSO mGlu5 antagonist solution 2390 MPMQ hydrochloride Selective mGlu1 1 10mM DMSO antagonist solution 2392 JTE 013 S1P2 receptor 5 10mM DMSO antagonist solution 2394 SDZ NKT 343 Highly selective, 1 10mM DMSO human NK1 solution antagonist 2395 Sertraline hydrochloride 5-HT re-uptake 1 10mM DMSO inhibitor solution 2396 Glimepiride KATP channel 1 10mM DMSO blocker solution Eplerenone Selective 1 10mM DMSO 2397 mineralocorticoid solution receptor antagonist 2398 SNAP 5089 Subtype-selective α1A 1 10mM DMSO antagonist solution 2400 FK 888 High affinity NK1 1 10mM DMSO receptor antagonist solution 2404 Ambroxol hydrochloride Na+ channel blocker 1 10mM DMSO solution

204 2408 Ro 90-7501 Inhibitor of Aβ42 1 10mM DMSO fibril formation solution 2415 HA 1100 hydrochloride Cell-permeable, 1 10mM DMSO selective Rho-kinase solution inhibitor 2416 BIBX 1382 Highly selective 1 10mM DMSO dihydrochloride EGFR-kinase solution inhibitor 2417 BIBU 1361 Selective inhibitor of 1 10mM DMSO dihydrochloride EGFR-kinase solution 2418 LY 303511 Negative control of 1 10mM DMSO LY 294002 (Cat. No. solution 1130) 2421 Scriptaid Histone deacetylase 1 10mM DMSO inhibitor solution 2429 Fexofenadine H1 receptor 1 10mM DMSO hydrochloride antagonist; non- solution sedating antiallergic agent 2435 CCT 018159 Hsp90 inhibitor 3 10mM DMSO solution 2436 AC 55649 Selective RARβ2 1 10mM DMSO agonist solution 2438 TMPH hydrochloride Neuronal nicotinic 1 10mM DMSO receptor antagonist solution 2441 JNJ 10191584 maleate Selective H4 receptor 1 10mM DMSO antagonist; orally solution active 2442 CGP 53353 Selective inhibitor of 1 10mM DMSO PKCβII solution 2447 YM 298198, Desmethyl- Derivative of mGlu1 1 10mM DMSO antagonist YM solution 298198 (Cat. No. 2448) 2448 YM 298198 Highly potent, 1 10mM DMSO hydrochloride selective non- solution competitive mGlu1 antagonist 2451 LY 344864 Potent, selective 5- 1 10mM DMSO hydrochloride HT1F agonist solution 2452 LY 2183240 Novel, potent 1 10mM DMSO anandamide uptake solution inhibitor. Inhibits FAAH 2456 Co 101244 hydrochloride Highly selective 1 10mM DMSO NR2B antagonist solution

205 2457 Arcyriaflavin A Potent cdk4/cyclin D1 1 10mM DMSO and CaM Kinase II solution inhibitor. Antiviral agent (anti-HCMV) 2458 ZM 447439 Inhibits Aurora kinase 3 10mM DMSO B solution 2460 Lazabemide Selective MAO-B 1 10mM DMSO hydrochloride inhibitor solution 2465 SDZ WAG 994 Potent, selective A1 1 10mM DMSO agonist solution 2466 UK 14,304 tartrate α2 agonist. Water- 1 10mM DMSO soluble form of UK solution 14,304 (Cat. No. 0425) 2467 CGS 20625 Selective central 1 10mM DMSO benzodiazepine solution receptor partial agonist 2469 IBC 293 Selective agonist for 1 10mM DMSO the GPR109B (HM74) solution receptor. Antilipolytic 2471 ER 27319 maleate Selective Syk kinase 1 10mM DMSO inhibitor solution 2473 GW 4064 Selective farnesoid X 6 10mM DMSO receptor (FXR) solution agonist 2474 GW 3965 hydrochloride Orally active liver X 2 10mM DMSO receptor (LXR) solution agonist 2475 ZM 323881 Potent, selective 1 10mM DMSO hydrochloride inhibitor of VEGFR-2 solution

2476 SR 11302 Inhibitor of AP-1 1 10mM DMSO transcription factor; solution antitumor agent 2478 2-Pyridylethylamine H1 receptor agonist 2 10mM DMSO dihydrochloride solution 2479 JTE 907 Selective CB2 1 10mM DMSO receptor solution antagonist/inverse agonist 2481 JTC 801 Selective NOP 2 10mM DMSO antagonist solution 2483 Costunolide Inhibitor of human 2 10mM DMSO telomerase activity solution

206 2484 (±)-AC 7954 Non-peptide UT 1 10mM DMSO hydrochloride receptor agonist solution 2491 Xaliproden Orally active, high 2 10mM DMSO hydrochloride affinity 5-HT1A solution agonist 2492 MNITMT Immunosuppressant 1 10mM DMSO solution 2495 Melperone hydrochloride 5-HT2A/D2 receptor 1 10mM DMSO antagonist; solution neuroleptic 2496 DR 2313 Potent PARP inhibitor 1 10mM DMSO solution 2498 PNU 120596 Positive allosteric 2 10mM DMSO modulator of α7 solution nAChR; active in vivo 2499 ZM 306416 Inhibitor of VEGF 1 10mM DMSO hydrochloride receptor tyrosine solution kinase 2504 Irsogladine maleate PDE4 inhibitor; 1 10mM DMSO antiulcer agent solution 2508 Trap 101 Potent and selective 1 10mM DMSO NOP antagonist solution 2510 Talniflumate Chloride channel 1 10mM DMSO blocker solution (hCLCA1/mCLCA3 and Cl-/HCO3- exchange) 2513 Acyclovir Inhibits viral DNA 1 10mM DMSO polymerase; solution antiherpetic agent 2514 L-161,982 Selective EP4 receptor 1 10mM DMSO antagonist solution 2517 RS 504393 Highly selective 5 10mM DMSO CCR2 chemokine solution receptor antagonist 2520 PD 166793 Broad spectrum MMP 1 10mM DMSO inhibitor solution 2524 A 205804 Selective inhibitor of 1 10mM DMSO E-selectin and ICAM- solution 1 expression 2531 Ganaxolone Potent, positive 1 10mM DMSO allosteric modulator of solution GABAA receptors 2533 DPO-1 Blocker of KV1.5 1 10mM DMSO channel and IKur solution current

207 2536 Domperidone Peripheral D2-like 1 10mM DMSO antagonist solution 2539 IKK 16 Selective inhibitor of 1 10mM DMSO IKK solution 2542 Ki 8751 Potent, selective 1 10mM DMSO VEGFR-2 inhibitor solution 2545 Lofepramine 5-HT and 1 10mM DMSO noradrenalin re-uptake solution inhibitor (SNRI) 2554 CD 1530 Potent and selective 1 10mM DMSO RARγ agonist solution 2558 10-DEBC hydrochloride Selective Akt/PKB 1 10mM DMSO inhibitor solution 2560 SB 218078 Inhibitor of 1 10mM DMSO checkpoint kinase 1 solution (Chk1) 2563 Fexaramine Potent, selective 1 10mM DMSO farnesoid X receptor solution (FXR) agonist 2571 Amlodipine besylate Ca2+ channel blocker 1 10mM DMSO (L-type) solution 2574 PSB 06126 NTPDase 3 inhibitor 2 10mM DMSO solution 2577 Cetirizine Selective H1 1 10mM DMSO dihydrochloride antagonist solution 2578 Benazepril hydrochloride Angiotensin- 1 10mM DMSO converting enzyme solution (ACE) inhibitor 2579 Zamifenacin fumarate Selective M3 1 10mM DMSO antagonist solution 2580 Tenidap NSAID, 1 10mM DMSO cyclooxygenase solution (COX-1) inhibitor. Also opener of KIR2.3 2581 PAC 1 Activator of 1 10mM DMSO procaspase-3; pro- solution apoptotic 2582 Lansoprazole H+,K+-ATPase 1 10mM DMSO inhibitor solution 2583 Omeprazole H+,K+-ATPase 1 10mM DMSO inhibitor solution 2586 CFM 1571 hydrochloride Soluble guanylyl 1 10mM DMSO cyclase (sGC) solution activator

208 2591 TCS 359 Potent FLT3 inhibitor 1 10mM DMSO solution 2596 Mexiletine hydrochloride Na+ channel blocker, 1 10mM DMSO antiarrhythmic agent solution 2600 Clofarabine Deoxycytidine kinase 1 10mM DMSO (dCK) substrate solution 2605 PD 198306 Selective inhibitor of 1 10mM DMSO MEK1/2 solution 2609 Ryuvidine Cyclin-dependent 1 10mM DMSO kinase 4 (cdk4) solution inhibitor 2611 IMD 0354 1 10mM DMSO Inhibitor of IKK-2 solution 2615 PD 158780 Potent ErbB receptor 1 10mM DMSO family inhibitor solution 2623 Oxaliplatin DNA cross-linking 2 10mM DMSO antitumor agent solution 2624 Decitabine DNA 1 10mM DMSO methyltransferase solution inhibitor 2625 Zonisamide Blocks voltage- 1 10mM DMSO sensitive Na+ and solution Ca2+ channels (T- type) 2626 Carboplatin DNA cross-linking 1 10mM DMSO antitumor agent solution 2629 Cilnidipine Ca2+ channel blocker 1 10mM DMSO (dual L/N-type) solution 2633 WAY 170523 Potent and selective 1 10mM DMSO inhibitor of MMP-13 solution 2634 DAPT γ-secretase inhibitor 5 10mM DMSO solution 2635 NGB 2904 Potent and selective 1 10mM DMSO D3 antagonist solution 2638 ST 91 α2 agonist, putative 1 10mM DMSO α2C agonist solution 2639 CGK 733 Selective inhibitor of 2 10mM DMSO ATR and ATM solution kinases 2641 L-368,899 hydrochloride Potent, non-peptide 2 10mM DMSO oxytocin receptor solution antagonist 2642 Co 102862 State-dependent Na+ 1 10mM DMSO channel blocker; solution anticonvulsant

209 2645 Fananserin 5-HT2A antagonist. 1 10mM DMSO Also D4 antagonist solution 2647 NSC 632839 Inhibitor of ubiquitin 1 10mM DMSO hydrochloride isopeptidase activity solution 2649 GW 9508 Potent and selective 1 10mM DMSO FFA1 (GPR40) solution agonist 2651 AEG 3482 Inhibitor of JNK 1 10mM DMSO signaling solution 2652 WAY 213613 Potent, non-substrate 1 10mM DMSO EAAT2 inhibitor solution 2653 Pifithrin->m Inhibitor of p53- 1 10mM DMSO mitochondrial binding solution 2654 Compound W 1 10mM DMSO γ-secretase inhibitor solution 2655 Naproxen sodium Cyclooxygenase 1 10mM DMSO inhibitor solution 2657 JX 401 Potent, reversible 1 10mM DMSO p38α inhibitor solution 2661 UCL 2077 Slow 1 10mM DMSO afterhyperpolarization solution (sAHP) channel blocker 2662 PHTPP Selective ERβ 1 10mM DMSO antagonist solution 2665 BMS 191011 Potent Maxi-K 1 10mM DMSO channel opener solution (BKCa, KCa1.1) 2668 Artemisinin Antimalarial; inhibits 1 10mM DMSO P-type ATPase solution (PfATP6) of P. falciparum 2669 TC 1 High affinity σ1 1 10mM DMSO ligand solution 2671 Budesonide Synthetic 1 10mM DMSO glucocorticoid; anti- solution inflammatory and chemopreventive 2673 Acarbose Glucosidase α 1 10mM DMSO inhibitor (intestinal) solution 2676 Y 134 Selective estrogen 1 10mM DMSO receptor modulator solution (SERM), selective for ERα

210 2677 JLK 6 Inhibitor of γ- 1 10mM DMSO secretase-mediated solution βAPP processing 2678 GP 2a Selective CB2 agonist 1 10mM DMSO solution 2681 17-PA Antagonist of 1 10mM DMSO neurosteroid solution potentiation and direct gating of GABAA 2682 sodium 4-Phenylbutyrate Histone deacetylase 1 10mM DMSO inhibitor solution 2684 SN 38 DNA topoisomerase I 2 10mM DMSO inhibitor; antitumor solution 2685 Carvedilol β-adrenoceptor and 1 10mM DMSO α1-adrenoceptor solution antagonist 2688 CPT 11 DNA topoisomerase I 1 10mM DMSO inhibitor; antitumor solution 2691 Moexipril hydrochloride Angiotensin- 1 10mM DMSO converting enzyme solution (ACE) inhibitor 2693 PHA 665752 Potent and selective 3 10mM DMSO MET inhibitor solution 2694 PD 407824 Selective inhibitor of 1 10mM DMSO Chk1 and Wee1 solution 2700 Calcipotriol Vitamin D3 analog 2 10mM DMSO solution 2706 Temozolomide DNA-methylating 2 10mM DMSO antitumor agent solution 2709 Cilastatin sodium Dipeptidase inhibitor 1 10mM DMSO solution 2718 LY 364947 Selective inhibitor of 1 10mM DMSO TGF-βRI solution 2719 RWJ 21757 Toll-like receptor 7 1 10mM DMSO (TLR7) agonist solution 2725 SB 225002 Potent and selective 1 10mM DMSO CXCR2 antagonist solution 2731 CGP 57380 Selective inhibitor of 2 10mM DMSO Mnk1 solution 2737 TC 2559 difumarate Selective partial 2 10mM DMSO agonist at α4β2 solution receptors 2742 Reserpine Inhibitor of vesicular 2 10mM DMSO monoamine transport solution 2743 Caffeic acid phenethyl Specific inhibitor of 1 10mM DMSO ester NF-κB activation solution

211 2747 NNC 05-2090 GABA uptake 1 10mM DMSO hydrochloride inhibitor; moderately solution BGT-1 selective 2748 KF 38789 Selective inhibitor of 2 10mM DMSO P-selectin-mediated solution cell adhesion 2755 DCA Mitochondrial 1 10mM DMSO pyruvate solution dehydrogenase kinase (PDK) inhibitor 2764 GP 1a Highly selective CB2 1 10mM DMSO agonist solution 2768 PQ 401 IGF1R inhibitor 1 10mM DMSO solution 2777 Nefazodone 5-HT2A antagonist 1 10mM DMSO hydrochloride and 5-HT uptake solution inhibitor. Antidepressant 2814 PI 828 PI 3-kinase inhibitor, 2 10mM DMSO more potent than LY solution 294002 (Cat. No. 1130) 2902 D 4476 Selective CK1 1 10mM DMSO inhibitor. Also inhibits solution TGF-βRI 2908 EO 1428 Selective inhibitor of 1 10mM DMSO p38α and p38β2 solution 2926 FPA 124 Akt/PKB inhibitor 1 10mM DMSO solution 3528 SCIO 469 hydrochloride Selective p38 MAPK 1 10mM DMSO inhibitor solution 2977 GW 843682X Selective inhibitor of 2 10mM DMSO PLK1 and PLK3 solution 3000 Iressa Orally active, 1 10mM DMSO selective EGFR solution inhibitor 3037 SU 5416 VEGFR inhibitor. 3 10mM DMSO Also inhibits KIT, solution RET, MET and FLT3 2910 H 89 dihydrochloride Protein kinase A 10mM DMSO inhibitor solution 3063 1-Naphthyl PP1 Src family kinase 1 10mM DMSO inhibitor; also inhibits solution c-Abl 3194 BIO Potent, selective 1 10mM DMSO GSK-3 inhibitor solution

212 3269 SD 208 Potent ATP- 2 10mM DMSO competitive TGF-βRI solution inhibitor 3271 Compound 401 Selective DNA-PK 1 10mM DMSO and mTOR inhibitor solution 3314 BI 78D3 Selective, competitive 1 10mM DMSO JNK inhibitor solution 3318 SC 514 IKK-2 inhibitor; 1 10mM DMSO attenuates NF-κB- solution induced gene expression 3335 SU 6668 PDGFR, VEGFR and 1 10mM DMSO FGFR inhibitor solution 3336 A 769662 Potent AMPK 4 10mM DMSO activator solution 3341 JK 184 Downstream Hh 1 10mM DMSO signaling pathway solution inhibitor; inhibits alcohol dehydrogenase 7 3342 L-798,106 Potent and highly 2 10mM DMSO selective EP3 solution antagonist 3344 SLV 320 Potent and selective 1 10mM DMSO A1 antagonist solution 3347 SNAP 94847 Potent and selective 1 10mM DMSO hydrochloride MCH1 antagonist solution 3350 LY 393558 Dual 5-HT1B/1D 1 10mM DMSO antagonist and 5-HT solution re-uptake inhibitor 3353 PSN 375963 GPR119 receptor 1 10mM DMSO hydrochloride agonist solution 3360 CGP 52411 EGFR inhibitor. Also 1 10mM DMSO inhibits Aβ42 fibril solution formation 3361 JNJ 17203212 Reversible, 1 10mM DMSO competitive and solution potent TRPV1 antagonist 3362 MIRA-1 Restores mutant p53 1 10mM DMSO activity; proapoptotic solution 3366 WAY 200070 Selective ERβ agonist 1 10mM DMSO solution 3367 AT 101 Downregulates Bcl-2 1 10mM DMSO and Mcl-1; pro- solution apoptotic

213 3370 AC 264613 PAR2 receptor agonist 1 10mM DMSO solution Gedunin Hsp90 inhibitor; 1 10mM DMSO 3387 anticancer and solution antimalarial activity 3397 LY 2365109 Potent and selective 3 10mM DMSO hydrochloride GlyT1 inhibitor solution 3411 BMS 649 Pan RXR agonist 1 10mM DMSO solution 3427 Oncrasin 1 Induces abnormal 1 10mM DMSO nuclear aggregation of solution PKCι; proapoptotic 3429 4-IPP Inhibitor of 1 10mM DMSO macrophage migration solution inhibitor factor (MIF); suicide substrate 3430 CFTRinh 172 Voltage-independent, 1 10mM DMSO selective CFTR solution chloride channel blocker 3433 GSK 0660 GSK 0660 Selective PPARδ 1 10mM DMSO antagonist solution 3439 NH 125 CaM kinase III (eEF-2 1 10mM DMSO kinase) inhibitor solution 3440 SID 7969543 Selective 1 10mM DMSO steroidogenic factor-1 solution (SF-1, NR5A1) inhibitor 3489 Astemizole Orally active, potent 2 10mM DMSO H1 antagonist. Also solution hERG K+ channel blocker. 3495 Fludarabine Purine analog; inhibits 1 10mM DMSO DNA synthesis solution 3517 Nebivolol hydrochloride Highly selective β1- 1 10mM DMSO adrenoceptor solution antagonist 3521 Sirtinol Selective sirtuin 1 10mM DMSO family deacetylase solution inhibitor 3523 FERb 033 Potent and selective 1 10mM DMSO ERβ agonist solution 3532 endo-IWR 1 Axin stabilizer; 1 10mM DMSO promotes β-catenin solution phosphorylation

214 3534 PNU 74654 β-catenin binder; 1 10mM DMSO inhibits wnt signaling solution 3540 Orlistat Pancreatic, gastric and 1 10mM DMSO carboxylester lipase solution inhibitor; antiobesity and antihypercholesterole mic activity 3543 Suplatast tosylate Th2 cytokine 1 10mM DMSO synthesis inhibitor; solution antiasthmatic 3544 KU 55933 Potent and selective 4 10mM DMSO ATM kinase inhibitor solution 3547 Serotonin Endogenous 5-HT 1 10mM DMSO receptor agonist solution 3550 Melatonin Endogenous hormone; 1 10mM DMSO agonist at MT1 and solution MT2 3551 INH1 Hec1 inhibitor; causes 1 10mM DMSO arrest of mitosis solution 3568 Bretazenil Benzodiazepine 1 10mM DMSO partial agonist solution 3571 SB 657510 Selective urotensin-II 1 10mM DMSO (UT) receptor solution antagonist 3572 GSK 650394 Serum- and 1 10mM DMSO glucocorticoid- solution regulated kinase (SGK) inhibitor 3575 VU 0155069 Potent and selective 1 10mM DMSO PLD1 inhibitor solution 3576 (±)-5'-Chloro-5'-deoxy- Highly selective A1 1 10mM DMSO ENBA agonist solution 3577 G-1 Potent and selective 1 10mM DMSO GPR30 agonist solution 3579 5-BDBD Potent P2X4 receptor 1 10mM DMSO antagonist solution 3586 Sumatriptan succinate 5-HT1 receptor 1 10mM DMSO agonist solution 3589 PIM 1 Inhibitor 2 Pim-1 kinase inhibitor 1 10mM DMSO solution 3595 CHR 2797 Aminopeptidase 1 10mM DMSO inhibitor solution 3596 Voglibose Orally active α- 1 10mM DMSO glucosidase inhibitor solution

215 3597 Indiplon Subtype-selective 1 10mM DMSO GABAA receptor solution positive allosteric modulator 3599 TAK 165 Potent and selective 1 10mM DMSO ErbB2 inhibitor solution 3603 Kaempferol Mitochondrial Ca2+ 1 10mM DMSO uniporter (MCU) solution activator; proapoptotic 3605 (R)-CR8 Dual cdk1/cdk5 1 10mM DMSO inhibitor. Also inhibits solution CK1 3607 SU 3327 Selective JNK 1 10mM DMSO inhibitor solution 3609 Tizanidine hydrochloride α2 agonist 1 10mM DMSO solution 3610 (R)-DRF053 Dual CK1/cdk 1 10mM DMSO dihydrochloride inhibitor solution 3617 SANT-2 Inhibitor of hedgehog 1 10mM DMSO (Hh) signaling; solution antagonizes smoothened activity 3620 Topiramate GluR5 antagonist; 1 10mM DMSO anticonvulsant solution 3622 IPA 3 Group I p21-activated 2 10mM DMSO kinase (PAK) solution inhibitor 3634 VU 0238429 Selective positive 1 10mM DMSO allosteric modulator of solution M5 receptors 3639 SX 011 p38 MAPK inhibitor 1 10mM DMSO solution 3642 Src I1 Dual site Src kinase 1 10mM DMSO inhibitor solution 3650 SB 328437 Potent and selective 1 10mM DMSO CCR3 antagonist solution 3656 Neurodazine Induces neurogenesis 1 10mM DMSO in mature skeletal solution muscle cells 3657 Sal 003 Cell-permeable 1 10mM DMSO inhibitor of eIF2α solution dephosphorylation 3661 BAN ORL 24 Potent and selective 1 10mM DMSO NOP antagonist solution 3670 SKA 31 Activator of KCa3.1 1 10mM DMSO and KCa2 channels solution

216 3677 NPY 5RA972 Potent and selective 1 10mM DMSO NPY Y5 antagonist solution 3678 G-15 High affinity and 1 10mM DMSO selective GPR30 solution antagonist 3679 DS2 Positive allosteric 1 10mM DMSO modulator of δ- solution containing GABAA receptors 3683 CITCO 1 10mM DMSO Selective CAR agonist solution 3690 S26948 Selective PPARγ 1 10mM DMSO agonist; antidiabetic solution agent 3692 4,4- M2 proton channel 1 10mM DMSO Pentamethylenepiperidin blocker solution e hydrochloride 3701 A 740003 Potent and selective 1 10mM DMSO P2X7 antagonist solution 0190 CNQX Potent AMPA/kainate 32 10mM DMSO antagonist solution 0414 AG 490 EGFR-kinase 2 10mM DMSO inhibitor. Also JAK2, solution JAK3 inhibitor 0435 Cimaterol β agonist 2 10mM DMSO solution 0493 AG 18 EGFR/PDGFR-kinase 1 10mM DMSO inhibitor solution 0497 AG 99 EGFR-kinase 1 10mM DMSO inhibitor solution 0523 4-(4-Fluorobenzoyl)-1- Selective 5-HT2A 3 10mM DMSO (4-phenylbutyl)- antagonist solution piperidine oxalate 0579 Tyrphostin B44, (+) EGFR-kinase 1 10mM DMSO enantiomer inhibitor solution 0616 AG 556 EGFR-kinase 1 10mM DMSO inhibitor solution 0618 AG 555 Potent EGFR-kinase 1 10mM DMSO inhibitor solution 0619 AG 494 Potent EGFR-kinase 2 10mM DMSO inhibitor solution 0645 2-TEDC 5-, 12-, 15- 1 10mM DMSO Lipoxygenase solution inhibitor 0666 3-AQC 5-HT3 antagonist 1 10mM DMSO solution

217 0797 8-Hydroxy-PIPAT High affinity 5-HT1A 1 10mM DMSO oxalate agonist solution 0832 ICI-89406 β antagonist 1 10mM DMSO solution 0902 Cimetidine H2 antagonist, I1 3 10mM DMSO agonist solution 0993 Cyanopindolol 5-HT1A/1B 4 10mM DMSO hemifumarate antagonist. Also β- solution adrenergic antagonist 1045 CNQX disodium salt Potent AMPA/kainate 18 10mM DMSO antagonist. More solution water soluble form of CNQX (Cat. No. 0190) 1065 PD 168077 maleate High affinity, 2 10mM DMSO selective D4 agonist solution 1144 U0126 Potent, selective 4 10mM DMSO inhibitor of MEK1 solution and 2 1300 LFM-A13 Potent, selective BTK 1 10mM DMSO inhibitor solution 1323 Butabindide oxalate CCK-inactivating 4 10mM DMSO serine protease solution inhibitor 1347 Nafadotride Highly potent, 1 10mM DMSO preferential D3 solution antagonist 1377 Cromakalim KATP channel opener 1 10mM DMSO solution 1378 Levcromakalim KATP channel 1 10mM DMSO opener. Active solution enantiomer of cromakalim (Cat. No. 1377) 1412 Chromanol 293B IKs blocker. Also 1 10mM DMSO blocks ICFTR solution 1427 Citalopram Highly potent and 1 10mM DMSO hydrobromide selective 5-HT uptake solution inhibitor 1475 (-)-[3R,4S]-Chromanol IKs blocker. 2 10mM DMSO 293B Enantiomer of solution Chromanol 293B (Cat. No. 1412) 1494 DPN Highly potent ERβ 4 10mM DMSO agonist solution

218 1541 HA14-1 Bcl-2 inhibitor. 1 10mM DMSO Induces apoptosis solution 1555 AG 825 Selective ErbB2 1 10mM DMSO inhibitor solution 1634 Y 29794 oxalate Prolyl endopeptidase 1 10mM DMSO inhibitor solution 1690 SCH 28080 H+,K+-ATPase 2 10mM DMSO inhibitor solution 1743 Bay 11-7085 Irreversible inhibitor 2 10mM DMSO of TNF-α-induced solution IκBα phosphorylation 1744 Bay 11-7821 Irreversible inhibitor 1 10mM DMSO of TNF-α-induced solution IκBα phosphorylation 1806 SR 33805 oxalate Ca2+ channel blocker; 1 10mM DMSO binds allosterically to solution distinct site on L-type channels 1868 U0124 Inactive analog of 1 10mM DMSO U0126 (Cat. No. solution 1144) 1969 SL 327 Selective inhibitor of 1 10mM DMSO MEK1 and MEK2; solution brain penetrant 4154 TCN 201 Selective NR1/NR2A 1 10mM DMSO receptor antagonist solution 2076 Y-26763 KATP channel opener 1 10mM DMSO solution 2077 Y-27152 Prodrug of KATP 1 10mM DMSO channel opener Y- solution 26763 (Cat. No. 2076); orally active in vivo 2387 LY 320135 Selective CB1 1 10mM DMSO receptor solution antagonist/inverse agonist 2403 MRS 3777 hemioxalate High affinity, 1 10mM DMSO selective A3 solution antagonist 2477 Proxyfan Oxalate High affinity H3 3 10mM DMSO ligand solution 1254 Y-27632 dihydrochloride Selective p160ROCK 10mM DMSO inhibitor solution 3506 NVP DPP 728 Potent, orally active 1 10mM DMSO dihydrochloride DPP-IV inhibitor solution

219 3645 NXY 059 Free radical trapping 1 10mM DMSO agent; neuroprotectant solution 3665 Donitriptan 5-HT1B/1D agonist 1 10mM DMSO hydrochloride solution 3703 K 858 Selective ATP- 1 10mM DMSO uncompetitive mitotic solution kinesin Eg5 inhibitor 3706 FR 180204 Selective ERK 2 10mM DMSO inhibitor solution 3707 VU 0361737 Selective positive 1 10mM DMSO allosteric modulator at solution mGlu4 3718 AS 1949490 SH2 domain- 1 10mM DMSO containing inositol 5'- solution phosphatase 2 (SHIP2) inhibitor 3724 PD 161570 Selective FGFR 1 10mM DMSO inhibitor solution 3733 Efonidipine Ca2+ channel blocker 1 10mM DMSO hydrochloride (L- and T-type) solution monoethanolate 3734 BYK 204165 Selective PARP-1 1 10mM DMSO inhibitor solution 3736 UPF 1069 PARP-2 inhibitor 1 10mM DMSO solution 3737 Asenapine maleate Novel antipsychotic 1 10mM DMSO agent solution 3743 BF 2649 H3 receptor inverse 1 10mM DMSO agonist/antagonist solution 3745 RN 1747 Selective TRPV4 1 10mM DMSO agonist solution 3746 RN 1734 Selective TRPV4 1 10mM DMSO antagonist solution 3748 XAV 939 Tankyrase inhibitor; 1 10mM DMSO inhibits wnt signaling solution 3805 Repaglinide KATP channel 1 10mM DMSO blocker solution 3837 CCMI Positive allosteric 1 10mM DMSO modulator of α7 solution nAChR 3851 Cardiogenol C Induces 1 10mM DMSO hydrochloride cardiomyogenesis in solution ESCs 3852 Tranylcypromine Irreversible inhibitor 1 10mM DMSO hydrochloride of MAO-A, MAO-B solution and LSD1

220 0109 (-)-Bicuculline Water-soluble 16 10mM DMSO methobromide GABAA antagonist solution

0130 (+)-Bicuculline Potent GABAA 23 10mM DMSO antagonist solution 0131 (-)-Bicuculline Water-soluble 32 10mM DMSO methochloride GABAA antagonist solution 0285 Ibotenic acid Non-selective NMDA 28 10mM DMSO agonist solution 0289 Muscimol Potent GABAA 9 10mM DMSO agonist solution 0388 Ambenonium dichloride Cholinesterase 1 10mM DMSO inhibitor solution 0444 Clozapine Dopamine antagonist 5 10mM DMSO with some D4 solution selectivity. Also 5- HT2A/2C antagonist 0462 (E)-Capsaicin Prototypic vanilloid 5 10mM DMSO receptor agonist solution 0508 Doxepin hydrochloride Highly potent H1 4 10mM DMSO antagonist. Also binds solution to H4 receptor 0549 Methylergometrine Active metabolite of 1 10mM DMSO maleate methysergide (Cat. solution No. 1064) 4440 Rivastigmine tartrate Dual AChE and BChE 1 10mM DMSO inhibitor solution 0652 Thalidomide TNF-α synthesis 11 10mM DMSO inhibitor solution 0686 Galanthamine Cholinesterase 9 10mM DMSO hydrobromide inhibitor solution 0690 Clonidine hydrochloride α2 agonist. Also I1 4 10mM DMSO ligand solution 0759 Castanospermine Glucosidases α and β 2 10mM DMSO inhibitor solution 0840 Loperamide Peripherally acting μ 6 10mM DMSO hydrochloride agonist. Also Ca2+ solution channel blocker 0908 Ketanserin tartrate Selective 5-HT2A 4 10mM DMSO antagonist. Also solution antagonist at 5-HT1D 0931 Haloperidol Antagonist, partly D2 2 10mM DMSO hydrochloride selective solution 0940 4-Aminopyridine Non-selective KV 4 10mM DMSO channel blocker solution

221 0960 Piroxicam Cyclooxygenase-1 4 10mM DMSO (COX-1) inhibitor solution 0962 Buspirone hydrochloride 5-HT1A partial 2 10mM DMSO agonist solution 0965 Tacrine hydrochloride Cholinesterase 3 10mM DMSO inhibitor solution 0994 Pindolol β3 partial agonist 3 10mM DMSO solution 0996 Cyproheptadine 5-HT2 antagonist 1 10mM DMSO hydrochloride solution 1001 Chlorisondamine Nicotinic antagonist; 2 10mM DMSO diiodide slow offset solution 1030 Guanfacine α2A agonist 6 10mM DMSO hydrochloride solution 1064 Methysergide maleate 5-HT1/5-HT2 2 10mM DMSO antagonist solution 1067 Oxotremorine M 4 10mM DMSO Muscarinic agonist solution 1077 Lobeline hydrochloride 2 10mM DMSO Nicotinic agonist solution 1100 Camptothecin DNA topoisomerase 1 10mM DMSO inhibitor solution 1103 Ketoconazole Cytochrome P450c17 2 10mM DMSO inhibitor solution 1125 Quercetin Non-selective PI 3- 1 10mM DMSO kinase inhibitor solution 1127 Yohimbine Selective α2 5 10mM DMSO hydrochloride antagonist solution 1142 Oxymetazoline α1A agonist 1 10mM DMSO hydrochloride solution 1205 SR 57227 hydrochloride Potent, selective 5- 1 10mM DMSO HT3 agonist solution 1230 Methotrexate Cytotoxic agent 1 10mM DMSO solution 1235 Cyclopiazonic Acid Inhibitor of SERCA 6 10mM DMSO ATPase solution 1257 Vincristine sulfate 3 10mM DMSO Disrupts microtubules solution 1260 Ivermectin Allosteric modulator 1 10mM DMSO of α7 nicotinic solution receptors 1290 Anisomycin Protein synthesis 6 10mM DMSO inhibitor solution 1326 BADGE PPARγ antagonist 3 10mM DMSO solution

222 1364 Colchicine Inhibitor of tubulin 1 10mM DMSO solution (-)-Cytisine Potent, selective 1 10mM DMSO 1390 neuronal nicotinic solution agonist 1416 Homoharringtonine Inhibitor of protein 1 10mM DMSO synthesis. solution Antileukemic agent 1467 Daunorubicin RNA synthesis 1 10mM DMSO hydrochloride inhibitor solution 1620 Alprostadil Prostaglandin. 1 10mM DMSO Vasodilator and solution antiplatelet agent in vivo 1707 Sulindac Cyclooxygenase 1 10mM DMSO inhibitor (following solution metabolism to sulindac sulfide) 1708 Indomethacin Cyclooxygenase 1 10mM DMSO inhibitor (COX-1 > solution COX-2) 1759 Nilutamide Androgen receptor 1 10mM DMSO antagonist. Orally solution active 1769 Flurbiprofen Cyclooxygenase 1 10mM DMSO inhibitor solution 1869 Ipsapirone Selective 5-HT1A 1 10mM DMSO agonist solution 2073 (R)-(-)-Apomorphine Dopamine agonist; 3 10mM DMSO hydrochloride non-subtype-selective solution 2228 Leflunomide Dihydroorotate 1 10mM DMSO dehydrogenase solution inhibitor 2251 Cisplatin Potent pro-apoptotic 1 10mM DMSO anticancer agent; solution activates caspase-3 2459 Tropisetron Potent 5-HT3 receptor 1 10mM DMSO hydrochloride antagonist; orally solution active 2470 Nimesulide Cyclooxygenase-2 1 10mM DMSO (COX-2) inhibitor solution 2503 (-)-Bicuculline Water-soluble 6 10mM DMSO methiodide GABAA antagonist solution 2521 NSC 3852 Histone deacetylase 1 10mM DMSO inhibitor solution

223 2550 SUN-B 8155 Non-peptide 1 10mM DMSO calcitonin (CT) solution receptor agonist 2687 Pentylenetetrazole CNS stimulant 2 10mM DMSO solution 3545 Histamine Endogenous histamine 1 10mM DMSO dihydrochloride receptor agonist solution 3689 Cevimeline Selective M1 agonist 1 10mM DMSO hydrochloride solution 0873 EIT hydrobromide Selective iNOS 1 10mM DMSO inhibitor, acts arginine solution binding site

224 REFERENCES

1 Lenhossék, M. v. Der feinere Bau des Nervensystems im Lichte neuester Forschungen. (Fischer’s Medicinische Buchhandlung, 1895).

2 Parpura, V. et al. Glial cells in (patho)physiology. J Neurochem 121, 4-27 (2012).

3 Bushong, E. A., Martone, M. E., Jones, Y. Z. & Ellisman, M. H. Protoplasmic Astrocytes in CA1 Stratum Radiatum Occupy Separate Anatomical Domains. The Journal of Neuroscience 22, 183-192 (2002).

4 Oberheim, N. A. et al. Uniquely Hominid Features of Adult Human Astrocytes. J Neurosci 29, 3276-3287 (2009).

5 Takano, T. et al. Astrocyte-mediated control of cerebral blood flow. Nature Neuroscience 9, 260-267 (2006).

6 Ulian, E. M., Sapperstein, S. K., Christopherson, K. S. & Barres, B. A. Control of Synapse Number by Glia. Science 291, 657-661 (2001).

7 Araque, A. et al. Gliotransmitters Travel in Time and Space. Neuron 81, 728-739 (2014).

8 Song, H., Stevens, C. F. & Gage, F. H. Astroglia induce neurogenesis from adult neural stem cells. Nature 417, 39-44 (2002).

9 Abbott, N. J., Rönnbäck, L. & Hansson, E. Astrocyte-endothelial interactions at the blood-brain barrier. Nature Reviews Neuroscience 7, 41-53 (2006).

10 Cornell-Bell, A. H., Thomas, P. G. & Caffrey, J. M. Ca2+ and filopodial responses to glutamate in cultured astrocytes and neurons. Canadian Journal of Physiology and Pharmacology 70, S206-S218 (1992).

11 Schiweck, J., Eickholt, B. J. & Murk, K. Important Shapeshifter: Mechanisms Allowing Astrocytes to Respond to the Changing Nervous System During Development, Injury and Disease. Front Cell Neurosci 12, 261, doi:10.3389/fncel.2018.00261 (2018).

12 Goetschy, J. F., Ulrich, G., Aunis, D. & Ciesielski-Treska, J. The organization and solubility properties of intermediate filaments and microtubules of cortical astrocytes in culture. J Neurocytol 15, 375-387, doi:10.1007/bf01611439 (1986).

13 Goldman, J. E. & Abramson, B. Cyclic AMP-induced shape changes of astrocytes are accompanied by rapid depolymerization of actin. Brain Res 528, 189-196, doi:10.1016/0006-8993(90)91657-3 (1990).

14 Heller, J. P. & Rusakov, D. A. Morphological plasticity of astroglia: Understanding synaptic microenvironment. Glia 63, 2133-2151, doi:10.1002/glia.22821 (2015).

225

15 Eom, T. Y. et al. Direct visualization of microtubules using a genetic tool to analyse radial progenitor-astrocyte continuum in brain. Nat Commun 2, 446, doi:10.1038/ncomms1460 (2011).

16 Duan, S., Anderson, C. M., Stein, B. A. & Swanson, R. A. Glutamate induces rapid upregulation of astrocyte glutamate transport and cell-surface expression of GLAST. J Neurosci 19, 10193-10200, doi:10.1523/jneurosci.19-23-10193.1999 (1999).

17 Eriksson, J. E. et al. Introducing intermediate filaments: from discovery to disease. J Clin Invest 119, 1763-1771, doi:10.1172/JCI38339 (2009).

18 Etienne-Manneville, S. Cytoplasmic Intermediate Filaments in Cell Biology. Annu Rev Cell Dev Biol 34, 1-28 (2018).

19 Dechat, T., Adam, S. A., Taimen, P., Shimi, T. & Goldman, R. D. Nuclear Lamins. Cold Spring Harb Perspect Biol 2, a000547 (2010).

20 Jacob, J. T., Coulombe, P. A., Kwan, R. & Omary, M. B. Types I and II Keratin Intermediate Filaments. Cold Spring Harb Perspect Biol 10, a018275 (2018).

21 Hol, E. M. & Capetanaki, Y. Type III Intermediate Filaments Desmin, Glial Fibrillary Acidic Protein (GFAP), Vimentin, and Peripherin. Cold Spring Harb Perspect Biol 9, a021642 (2017).

22 Guzenko, D., Chernyatina, A. A. & Strelkov, S. V. Crystallographic Studies of Intermediate Filament Proteins. Subcell Biochem 82, 151-170 (2017).

23 Sysoev, V. O. et al. Dynamic structural order of a low-complexity domain facilitates assembly of intermediate filaments. Proc Natl Acad Sci U S A 117, 23510-23518 (2020).

24 Herrmann, H. & Aebi, U. Intermediate Filaments: Structure and Assembly. Cold Spring Harb Perspect Biol 8, a018242 (2016).

25 Block, J., Schroeder, V., Pawelzyk, P., Willenbacher, N. & Köster, S. Physical properties of cytoplasmic intermediate filaments. Biochim Biophys Acta 1853, 3053-3064 (2015).

26 Toivola, D. M., Strnad, P., Habetezion, A. & Omary, M. B. Intermediate filaments take the heat as stress proteins. Trends Cell Biol 20, 79-91 (2010).

27 Soellner, P., Quinlan, R. A. & Franke, W. W. Identification of a distinct soluble subunit of an intermediate filament protein: Tetrameric vimentin from living cells. Proc Natl Acad Sci U S A 82, 7929-7933 (1985).

226 28 Starger, J. M., Brown, W. E., Goldman, A. E. & Goldman, R. D. Biochemical and immuno- logical analysis of rapidly purified 10-nm filaments from baby hamster kidney (BHK-21) cells. J Cell Biol 78, 93-109 (1978).

29 Yuan, A. et al. Neurofilaments form a highly stable stationary cytoskeleton after reaching a critical level in axons. J Neurosci 29, 11316-11329 (2009).

30 Colakoglu, G. & Brown, A. Intermediate filaments exchange subunits along their length and elongate by end-to-end annealing. J Cell Biol 185, 769-777 (2009).

31 Snider, N. T. & Omary, M. B. Post-translational modifications of intermediate filament proteins: mechanisms and functions. Nat Rev Mol Cell Biol 15, 163-177, doi:10.1038/nrm3753 (2014).

32 Charrier, E. E. & Janmey, P. A. Mechanical Properties of Intermediate Filament Proteins. Methods Enzymol 568, 35-57 (2016).

33 Lloyd, C. et al. The basal keratin network of stratified squamous epithelia: defining K15 function in the absence of K14. J Cell Biol 129, 1329-1344 (1995).

34 Pennington, K. L., Chan, T. Y., Torres, M. P. & Andersen, J. L. The dynamic and stress- adaptive signaling hub of 14-3-3: emerging mechanisms of regulation and context- dependent protein–protein interactions. Oncogene 37, 5587-5604 (2018).

35 Caulin, C., Ware, C. F., Magin, T. M. & Oshima, R. G. Keratin-dependent, epithelial resistance to tumor necrosis factor-induced apoptosis. J Cell Biol 149, 17-22 (2000).

36 Gruenbaum, Y. & Foisner, R. Lamins: nuclear intermediate filament proteins with fundamental functions in nuclear mechanics and genome regulation. Annu Rev Biochem 84, 131-164 (2015).

37 Hobbs, R. P. et al. Keratin-dependent regulation of Aire and gene expression in skin tumor keratinocytes. Nat Genet 47, 933-938 (2015).

38 Escobar-Hoyos, L. F. et al. Keratin-17 Promotes p27KIP1 Nuclear Export and Degradation and Offers Potential Prognostic Utility. Cancer Res 75, 3650-3662 (2015).

39 Hobbs, R. P., Jacob, J. T. & Coulombe, P. A. Keratins Are Going Nuclear. Dev Cell 38, 227-233 (2016).

40 Ketema, M., Kreft, M., Secades, P., Janssen, H. & Sonnenberg, A. Nesprin-3 connects plectin and vimentin to the nuclear envelope of Sertoli cells but is not required for Sertoli cell function in spermatogenesis. Mol Biol Cell 24, 2454-2466 (2013).

41 Dupin, I., Sakamoto, Y. & Etienne-Manneville, S. Cytoplasmic intermediate filaments mediate actin-driven positioning of the nucleus. J Cell Sci 124, 865-872 (2011).

227

42 Matveeva, E. A., Venkova, L. S., Chernoivanenko, I. S. & Minin, A. A. Vimentin is involved in regulation of mitochondrial motility and membrane potential by Rac1. Biol Open 4, 1290-1297 (2015).

43 Styers, M. L. et al. The Endo-Lysosomal Sorting Machinery Interacts with the Intermediate Filament Cytoskeleton. Mol Biol Cell 15, 5369-5382 (2004).

44 Margiotta, A. & Bucci, C. Role of Intermediate Filaments in Vesicular Traffic. Cells 5, 20 (2016).

45 Hendrix, M. J., Seftor, E. A., Seftor, R. E. & Trevor, K. T. Experimental co-expression of vimentin and keratin intermediate filaments in human breast cancer cells results in phenotypic interconversion and increased invasive behavior. Am J Pathol 150, 483-495 (1997).

46 Mendez, M. G., Kojima, S.-I. & Goldman, R. D. Vimentin induces changes in cell shape, motility, and adhesion during the epithelial to mesenchymal transition. FASEB J 24, 1838-1852 (2010).

47 Battaglia, R. A., Delic, S., Herrmann, H. & Snider, N. T. Vimentin on the move: new developments in cell migration. F1000Res 7, F1000 Faculty Rev-1796 (2018).

48 Helfand, B. T. et al. Vimentin organization modulates the formation of lamellipodia. Mol Biol Cell 22, 1274-1289 (2011).

49 Nieminen, M. et al. Vimentin function in lymphocyte adhesion and transcellular migration. Nat Cell Biol 8, 156-162 (2006).

50 Lepekhin, E. A. et al. Intermediate filaments regulate astrocyte motility. Journal of Neurochemistry 79, 617-625 (2001).

51 Vuoriluoto, K. et al. Vimentin regulates EMT induction by Slug and oncogenic H-Ras and migration by governing Axl expression in breast cancer. Oncogene 30, 1436-1448 (2011).

52 Schoumacher, M., Goldman, R. D., Louvard, D. & Vignjevic, D. M. Actin, microtubules, and vimentin intermediate filaments cooperate for elongation of invadopodia. J Cell Biol 189, 541-556 (2010).

53 Paccione, R. J. et al. Keratin down-regulation in vimentin-positive cancer cells is reversible by vimentin RNA interference, which inhibits growth and motility. Mol Cancer Ther 7, 2894-2903 (2008).

54 Gan, Z. et al. Vimentin Intermediate Filaments Template Microtubule Networks to Enhance Persistence in Cell Polarity and Directed Migration. Cell Syst 3, 252-263 (2016).

228

55 Messica, Y. et al. The role of Vimentin in Regulating Cell Invasive Migration in Dense Cultures of Breast Carcinoma Cells. Nano Lett 17, 6941-6948 (2017).

56 Esue, O., Carson, A. A., Tseng, Y. & Wirtz, D. A direct interaction between actin and vimentin filaments mediated by the tail domain of vimentin. J Biol Chem 281, 30393- 30399 (2006).

57 Svitkina, T. M., Verkhovsky, A. B. & Borisy, G. G. Plectin sidearms mediate interaction of intermediate filaments with microtubules and other components of the cytoskeleton. J Biol Chem 135, 991-1007 (1996).

58 Jiu, Y. et al. Bidirectional Interplay between Vimentin Intermediate Filaments and Contractile Actin Stress Fibers. Cell Rep 11, 1511-1518 (2015).

59 Costigliola, N. et al. Vimentin fibers orient traction stress. Proc Natl Acad Sci U S A 114, 5195-5200 (2017).

60 Pascalis, C. D. et al. Intermediate filaments control collective migration by restricting traction forces and sustaining cell-cell contacts. J Cell Biol 217, 3031-3044 (2018).

61 Chung, B.-M., Rotty, J. D. & Coulombe, P. A. Networking galore: intermediate filaments and cell migration. Curr Opin Cell Biol 25, 600-612 (2013).

62 Potokar, M., Morita, M., Wiche, G. & Jorgačevski, J. The diversity of intermediate filaments in astrocytes. Cells 9, 1604 (2020).

63 Sultana, S., Surnett, S. W., Bellin, R. M., Robson, R. M. & Skalli, O. Intermediate Filament Protein Synemin Is Transiently Expressed in a Subset of Astrocytes During Development. Glia 30, 143-153 (2000).

64 Lendahl, U., Zimmerman, L. B. & McKay, R. D. G. CNS Stem Cells Express a New Class of Intermediate Filament. Cell 60, 585-595 (1990).

65 Middeldorp, J. & Hol, E. M. GFAP in health and disease. Prog Neurobiol 93, 421-443, doi:10.1016/j.pneurobio.2011.01.005 (2011).

66 Pixley, S. K. R. & Vellis, J. d. Transition Between Immature Radial Glia and Mature Astrocytes Studied With a Monoclonal Antibody to Vimentin. Developmental Brain Research 15, 201-209 (1984).

67 Bovolenta, P., Liem, R. K. H. & Mason, C. A. Development of Cerebellar Astroglia: Transitions in Form and Cytoskeletal Content. Developmental Biology 102, 248-259 (1984).

229 68 Bianchini, D. et al. GFAP expression of human Schwann cells in tissue culture. Brain Research 570, 209-217 (1992).

69 Kato, H. et al. Immunocytochemical characterization of supporting cells in the enteric nervous system in Hirschsprung's disease. Journal of Pediatric Surgery 25, 514-519 (1990).

70 Doetsch, F., Caillé, I., Lin, D. A., García-Verdugo, J. M. & Alvarez-Buylla, A. Subventricular Zone Astrocytes Are Neural Stem Cells in the Adult Mammalian Brain. Cell 97, 703-716 (1999).

71 Seri, B., García-Verdugo, J. M., McEwen, B. S. & Alvarez-Buylla, A. Astrocytes Give Rise to New Neurons in the Adult Mammalian Hippocampus. The Journal of Neuroscience 21, 7153-7160 (2001).

72 Carotti, S. et al. Glial fibrillary acidic protein as an early marker of hepatic stellate cell activation in chronic and posttransplant recurrent hepatitis C. Liver Transplantation 14, 806-814 (2008).

73 Omary, M. B. “IF-pathies”: a broad spectrum of intermediate filament–associated diseases. J Clin Invest 119, 1756-1762 (2009).

74 Wiese, C. et al. Nestin expression--a property of multi-lineage progenitor cells? Cell Mol Life Sci 61, 2510-2522 (2004).

75 Sofroniew, M. Astrogliosis. Cold Spring Harb Perspect Biol 7 (2014).

76 Liddelow, S. A. & Barres, B. A. Reactive Astrocytes: Production, Function, and Therapeutic Potential. Immunity 46 (2017).

77 Zamanian, J. L. et al. Genomic analysis of reactive astrogliosis. J Neurosci 32, 6391- 6410, doi:10.1523/JNEUROSCI.6221-11.2012 (2012).

78 Pekny, M. & Pekna, M. Astrocyte intermediate filaments in CNS pathologies and regeneration. Journal of Pathology 204, 428-437 (2004).

79 Pekny, T. et al. Synemin is expressed in reactive astrocytes and Rosenthal fibers in Alexander disease. Apmis 122, 76-80 (2014).

80 Clarke, L. E. et al. Normal aging induces A1-like astrocyte reactivity. PNAS 115, E1896- E1905 (2018).

81 Battaglia, R. A., Kabiraj, P., Willcockson, H. H., Lian, M. & Snider, N. T. Isolation of Intermediate Filament Proteins from Multiple Mouse Tissues to Study Aging-associated Post-translational Modifications. J Vis Exp, doi:10.3791/55655 (2017).

230 82 Colucci-Guyon, E. et al. Mice lacking vimentin develop and reproduce without an obvious phenotype. Cell 79, 679-694 (1994).

83 Gomi, H. et al. Mice devoid of the glial fibrillary acidic protein develop normally and are susceptible to scrapie prions. Neuron 14, 29-41 (1995).

84 Pekny, M. et al. Mice lacking glial fibrillary acidic protein display astrocytes devoid of intermediate filaments but develop and reproduce normally. The EMBO journal 14, 1590-1598 (1995).

85 McCall, M. et al. Targeted deletion in astrocyte intermediate filament (Gfap) alters neuronal physiology. Proceedings of the National Academy of Sciences 93, 6361-6366 (1996).

86 Shibuki, K. et al. Deficient Cerebellar Long-Term Depression, Impaired Eyeblink Conditioning, and Normal Motor Coordination in GFAP Mutant Mice. Neuron 6, 587- 599 (1996).

87 Mohseni, P. et al. Nestin is not essential for development of the CNS but required for dispersion of acetylcholine receptor clusters at the area of neuromuscular junctions. J Neurosci 31, 11547-11552 (2011).

88 Park, D. et al. Nestin is required for the proper self-renewal of neural stem cells. Stem Cells 28, 2162-2171 (2010).

89 Pekny, M. et al. Abnormal reaction to central nervous system injury in mice lacking glial fibrillary acidic protein and vimentin. The Journal of cell biology 145, 503-514 (1999).

90 Eliasson, C. et al. Intermediate filament protein partnership in astrocytes. Journal of Biological Chemistry 274, 23996-24006 (1999).

91 Nawashiro, H., Messing, A., Azzam, N. & Brenner, M. Mice lacking GFAP are hypersensitive to traumatic cerebrospinal injury. Neuroreport 9, 1691-1696 (1998).

92 Lundkvist, A. et al. Under stress, the absence of intermediate filaments from Müller cells in the retina has structural and functional consequences. J Cell Sci 117, 3481-3488 (2004).

93 Yasui, Y. et al. Protein kinases required for segregation of vimentin filaments in mitotic process. Oncogene 20, 2868-2876 (2001).

94 Yasui, Y. et al. Roles of Rho-associated kinase in cytokinesis; mutations in Rho- associated kinase phosphorylation sites impair cytokinetic segregation of glial filaments. The Journal of cell biology 143, 1249-1258 (1998).

231 95 Pablo, Y. d. et al. Vimentin phosphorylation is required for normal cell division of immature astrocytes. Cells 8, 1016 (2019).

96 Morrow, C. S. et al. Vimentin coordinates protein turnover at the Aggresome during neural stem cell quiescence exit. Cell stem cell 26, 558-568. e559 (2020).

97 Mignot, C. et al. Dynamics of mutated GFAP aggregates revealed by real-time imaging of an astrocyte model of Alexander disease. Experimental Cell Research 313, 2766-2779 (2007).

98 Rutka, J. T. et al. Effects of antisense glial fibrillary acidic protein complementary DNA on the growth, invasion, and adhesion of human astrocytoma cells. Cancer Research 54, 3267-3272 (1994).

99 Rutka, J. T. & Smith, S. L. Transfection of human astrocytoma cells with glial fibrillary acidic protein complementary DNA: analysis of expression, proliferation, and tumorigenicity. Cancer research 53, 3624-3631 (1993).

100 Toda, M. et al. Suppression of glial tumor growth by expression of glial fibrillary acidic protein. Neurochemical research 24, 339-343 (1999).

101 Pekny, M. et al. GFAP-Deficient Astrocytes Are Capable of Stellationin VitroWhen Cocultured with Neurons and Exhibit a Reduced Amount of Intermediate Filaments and an Increased Cell Saturation Density. Experimental cell research 239, 332-343 (1998).

102 Triolo, D. et al. Loss of glial fibrillary acidic protein (GFAP) impairs Schwann cell proliferation and delays nerve regeneration after damage. Journal of cell science 119, 3981-3993 (2006).

103 Potokar, M. et al. Cytoskeleton and vesicle mobility in astrocytes. Traffic 8, 12-20 (2007).

104 Potokar, M. et al. Intermediate filaments attenuate stimulation‐dependent mobility of endosomes/lysosomes in astrocytes. Glia 58, 1208-1219 (2010).

105 Lasič, E. et al. Nestin affects fusion pore dynamics in mouse astrocytes. Acta Physiologica 228, e13399 (2020).

106 Hughes, E. G., Maguire, J. L., McMinn, M. T., Scholz, R. E. & Sutherland, M. L. Loss of glial fibrillary acidic protein results in decreased glutamate transport and inhibition of PKA-induced EAAT2 cell surface trafficking. Molecular brain research 124, 114-123 (2004).

107 Sullivan, S. M. et al. Cytoskeletal anchoring of GLAST determines susceptibility to brain damage: an identified role for GFAP. Journal of Biological Chemistry 282, 29414-29423 (2007).

232

108 Pekny, M. et al. The impact of genetic removal of GFAP and/or vimentin on glutamine levels and transport of glucose and ascorbate in astrocytes. Neurochemical research 24, 1357-1362 (1999).

109 Lieth, E. et al. Glial reactivity and impaired glutamate metabolism in short-term experimental diabetic retinopathy. Penn State Retina Research Group. Diabetes 47, 815- 820 (1998).

110 Hol, E. M. & Pekny, M. Glial fibrillary acidic protein (GFAP) and the astrocyte intermediate filament system in diseases of the central nervous system. Current opinion in cell biology 32, 121-130 (2015).

111 Li, L. et al. Protective role of reactive astrocytes in brain ischemia. Journal of Cerebral Blood Flow & Metabolism 28, 468-481 (2008).

112 Wilhelmsson, U. et al. Absence of glial fibrillary acidic protein and vimentin prevents hypertrophy of astrocytic processes and improves post-traumatic regeneration. Journal of Neuroscience 24, 5016-5021 (2004).

113 Wilhelmsson, U. et al. Redefining the concept of reactive astrocytes as cells that remain within their unique domains upon reaction to injury. Proceedings of the National Academy of Sciences 103, 17513-17518 (2006).

114 Xu, K., Malouf, A. T., Messing, A. & Silver, J. Glial fibrillary acidic protein is necessary for mature astrocytes to react to β‐amyloid. Glia 25, 390-403 (1999).

115 Liedtke, W., Edelmann, W., Chiu, F.-C., Kucherlapati, R. & Raine, C. S. Experimental autoimmune encephalomyelitis in mice lacking glial fibrillary acidic protein is characterized by a more severe clinical course and an infiltrative central nervous system lesion. The American journal of pathology 152, 251 (1998).

116 Stenzel, W., Soltek, S., Schlüter, D. & Deckert, M. The intermediate filament GFAP is important for the control of experimental murine Staphylococcus aureus-induced brain abscess and Toxoplasma encephalitis. Journal of Neuropathology & Experimental Neurology 63, 631-640 (2004).

117 Wilhelmsson, U. et al. Astrocytes negatively regulate neurogenesis through the Jagged1‐ mediated Notch pathway. Stem Cells 30, 2320-2329 (2012).

118 Wilhelmsson, U. et al. Nestin regulates neurogenesis in mice through notch signaling from astrocytes to neural stem cells. Cerebral Cortex 29, 4050-4066 (2019).

119 Wilhelmsson, U. et al. The role of GFAP and vimentin in learning and memory. Biol Chem 400, 1147-1156 (2019).

233 120 Widestrand, A. et al. Increased Neurogenesis and Astrogenesis from Neural Progenitor Cells Grafted in the Hippocampus of GFAP-/-Vim-/- Mice. Stem Cells 25, 2619-2627 (2007).

121 Kinouchi, R. et al. Robust neural integration from retinal transplants in mice deficient in GFAP and vimentin. Nature neuroscience 6, 863-868 (2003).

122 Chung, W.-S., Allen, N. J. & Eroglu, C. Astrocytes control synapse formation, function, and elimination. Cold Spring Harbor perspectives in biology 7, a020370 (2015).

123 Menet, V. et al. Inactivation of the glial fibrillary acidic protein gene, but not that of vimentin, improves neuronal survival and neurite growth by modifying adhesion molecule expression. Journal of Neuroscience 21, 6147-6158 (2001).

124 Liedtke, W. et al. GFAP is Necessary for the Integrity of CNS White Matter Architecture and Long-Term Maintenance of Myelination. Neuron 17, 607-615 (1996).

125 Pekny, M., Stanness, K. A., Eliasson, C., Betsholtz, C. & Janigro, D. Impaired induction of blood‐brain barrier properties in aortic endothelial cells by astrocytes from GFAB‐ deficient mice. Glia 22, 390-400 (1998).

126 Domingues, H. S., Portugal, C. C., Socodato, R. & Relvas, J. B. Oligodendrocyte, astrocyte, and microglia crosstalk in myelin development, damage, and repair. Frontiers in cell and developmental biology 4, 71 (2016).

127 Giménez y Ribotta, M., Langa, F., Menet, V. & Privat, A. Comparative anatomy of the cerebellar cortex in mice lacking vimentin, GFAP, and both vimentin and GFAP. Glia 31, 69-83 (2000).

128 Andriezen, W. L. The Neuroglia Elements in the Human Brain. Br Med J 2, 227-230, doi:10.1136/bmj.2.1700.227 (1893).

129 Escartin, C. et al. Reactive astrocyte nomenclature, definitions, and future directions. Nat Neurosci 24, 312-325, doi:10.1038/s41593-020-00783-4 (2021).

130 Kamphuis, W. et al. GFAP and vimentin deficiency alters gene expression in astrocytes and microglia in wild-type mice and changes the transcriptional response of reactive glia in mouse model for Alzheimer's disease. Glia 63, 1036-1056, doi:10.1002/glia.22800 (2015).

131 Brenner, M. et al. Mutations in GFAP, encoding glial fibrillary acidic protein, are associated with Alexander disease. Nat Genet 27, 117-120 (2001).

132 Messing, A., Brenner, M., Feany, M. B., Nedergaard, M. & Goldman, J. E. Alexander disease. Journal of Neuroscience 32, 5017-5023 (2012).

234 133 Prust, M. et al. GFAP mutations, age at onset, and clinical subtypes in Alexander disease. Neurology 77, 1287-1294 (2011).

134 Knapp, M. S. v. d. et al. Alexander Disease: Diagnosis with MR Imaging. American Journal of Neuroradiology 22, 541-552 (2001).

135 Sosunov, A. A., Guilfoyle, E., Wu, X., McKhann, G. M., 2nd & Goldman, J. E. Phenotypic conversions of "protoplasmic" to "reactive" astrocytes in Alexander disease. J Neurosci 33, 7439-7450, doi:10.1523/JNEUROSCI.4506-12.2013 (2013).

136 Perng, M. D. et al. The Alexander Disease–Causing Glial Fibrillary Acidic Protein Mutant, R416W, Accumulates into Rosenthal Fibers by a Pathway That Involves Filament Aggregation and the Association of aB-Crystallin and HSP27. Am J Hum Genet 79, 197-213 (2006).

137 Tomokane, N., Iwaki, T., Tateishi, J., Iwaki, A. & Goldman, J. E. Rosenthal Fibers Share Epitopes with alphaB-Crystallin, Glial Fibrilary Acidic Protein, and Ubiquitin, But Not with Vimentin. Am J Pathol 138, 875-885 (1991).

138 Sosunov, A. A., McKhann, G. M., 2nd & Goldman, J. E. The origin of Rosenthal fibers and their contributions to astrocyte pathology in Alexander disease. Acta Neuropathol Commun 5, 27, doi:10.1186/s40478-017-0425-9 (2017).

139 Chin, S. S. M. & Goldman, J. E. Glial Inclusions in CNS Degenerative Disease. Journal of Neuropathology and Experimental Neurology 55, 499-508 (1996).

140 Wippold, F. J., Perry, A. & Lennerz, J. Neuropathology for the Neuroradiologist: Rosenthal Fibers. American Journal of Neuroradiology 27, 958-961 (2006).

141 Heaven, M. R. et al. Composition of Rosenthal Fibers, the Protein Aggregate Hallmark of Alexander Disease. J Proteome Res 15, 2265-2282, doi:10.1021/acs.jproteome.6b00316 (2016).

142 Hagemann, T. L., Connor, J. X. & Messing, A. Alexander disease-associated glial fibrillary acidic protein mutations in mice induce Rosenthal fiber formation and a white matter stress response. J Neurosci 26, 11162-11173, doi:10.1523/JNEUROSCI.3260- 06.2006 (2006).

143 Tang, G., Perng, M. D., Wilk, S., Quinlan, R. & Goldman, J. E. Oligomers of mutant glial fibrillary acidic protein (GFAP) Inhibit the proteasome system in alexander disease astrocytes, and the small heat shock protein alphaB-crystallin reverses the inhibition. J Biol Chem 285, 10527-10537, doi:10.1074/jbc.M109.067975 (2010).

144 Hsiao, V. C. et al. Alexander-disease mutation of GFAP causes filament disorganization and decreased solubility of GFAP. Journal of Cell Science 118, 2057-2065 (2005).

235 145 Perng, M.-D. et al. Glial Fibrillary Acidic Protein Filaments Can Tolerate the Incorporation of Assembly-compromised GFAP-delta, but with Consequences for Filament Organization and alpha B-Crystallin Association. Mol Biol Cell 19, 4521-4533 (2008).

146 Perng, M.-D. et al. Intermediate filament interactions can be altered by HSP27 and alphaB-crystallin. Journal of Cell Science 112, 2099-2112 (1999).

147 Wang, L., Colodner, K. J. & Feany, M. B. Protein Misfolding and Oxidative Stress Promote Glial-Mediated Neurodegeneration in an Alexander Disease Model. The Journal of Neuroscience 31, 2868-2877 (2011).

148 Wang, L. et al. Nitric oxide mediates glial-induced neurodegeneration in Alexander disease. Nat Commun 6, 8966, doi:10.1038/ncomms9966 (2015).

149 Candiani, S. et al. Alexander Disease Modeling in Zebrafish: An In Vivo System Suitable to Perform Drug Screening. GEnes (Basal) 11, 1490 (2020).

150 Lee, S.-H. et al. Aggregation-prone GFAP mutation in Alexander disease validated using a zebrafish model. BMC Neurology 17 (2017).

151 Messing, A. et al. Fatal Encephalopathy with Astrocyte Inclusions in GFAP Transgenic Mice. Am J Pathol 152, 391-398 (1998).

152 Hagemann, T. L. et al. Antisense therapy in a new rat model of Alexander disease reverses GFAP pathology, white matter deficits, and motor impairment. BioRxiv (2021).

153 Tang, G., Xu, Z. & Goldman, J. E. Synergistic effects of the SAPK/JNK and the proteasome pathway on glial fibrillary acidic protein (GFAP) accumulation in Alexander disease. J Biol Chem 281, 38634-38643, doi:10.1074/jbc.M604942200 (2006).

154 Tang, G. et al. Autophagy induced by Alexander disease-mutant GFAP accumulation is regulated by p38/MAPK and mTOR signaling pathways. Hum Mol Genet 17, 1540-1555, doi:10.1093/hmg/ddn042 (2008).

155 Hagemann, T. L., Boelens, W. C., Wawrousek, E. F. & Messing, A. Suppression of GFAP toxicity by alphaB-crystallin in mouse models of Alexander disease. Hum Mol Genet 18, 1190-1199, doi:10.1093/hmg/ddp013 (2009).

156 Wang, L. et al. Tissue and cellular rigidity and mechanosensitive signaling activation in Alexander disease. Nat Commun 9, 1899 (2018).

157 Hagemann, T. L., Jobe, E. M. & Messing, A. Genetic ablation of Nrf2/antioxidant response pathway in Alexander disease mice reduces hippocampal gliosis but does not impact survival. PLoS One 7, e37304, doi:10.1371/journal.pone.0037304 (2012).

236 158 Olabarria, M., Putilina, M., Reimer, E. C. & Goldman, J. E. Astrocyte pathology in Alexander disease causes a marked inflammatory environment. Acta Neuropathol Commun 130, 469-486 (2015).

159 Hagemann, T. L. et al. Gene expression analysis in mice with elevated glial fibrillary acidic protein and Rosenthal fibers reveals a stress response followed by glial activation and neuronal dysfunction. Hum Mol Genet 14, 2443-2458, doi:10.1093/hmg/ddi248 (2005).

160 Li, L. et al. GFAP Mutations in Astrocytes Impair Oligodendrocyte Progenitor Proliferation and Myelination in an hiPSC Model of Alexander Disease. Cell Stem Cell 23, 239-251 e236, doi:10.1016/j.stem.2018.07.009 (2018).

161 Tian, R. et al. Alexander Disease Mutant Glial Fibrillary Acidic Protein Compromises Glutamate Transport in Astrocytes. Journal of Neuropathology and Experimental Neurology 69, 335-345 (2010).

162 Israeli, E. et al. Intermediate filament aggregates cause mitochondrial dysmotility and increase energy demands in giant axonal neuropathy. Human Molecular Genetics 25, 2143-2157 (2016).

163 Jones, J. R. et al. Mutations in GFAP Disrupt the Distribution and Function of Organelles in Human Astrocytes. Cell Rep 25, 947-958 e944, doi:10.1016/j.celrep.2018.09.083 (2018).

164 Hagemann, T. L. et al. Antisense Suppression of Glial Fibrillary Acidic Protein as a Treatment for Alexander Disease. Annals of Neurology 83, 27-39 (2018).

165 Moody, L. R., Barrett-Wilt, G. A., Sussman, M. R. & Messing, A. Glial fibrillary acidic protein exhibits altered turnover kinetics in a mouse model of Alexander disease. The Journal of Biological Chemistry 292, 5814-5824 (2017).

166 Asbury, A. K., Gale, M. K., Cox, S. C., Baringer, J. R. & Berg, B. O. Giant Axonal Neuropathy -- A Unique Case with Segmental Neurofilamentous Masse. Acta Neuropathol 20, 237-247 (1972).

167 Berg, B. O., Rosenberg, S. H. & Asbury, A. K. Giant Axonal Neuropathy. Pediatrics 49, 894-899 (1972).

168 Johnson-Kerner, B. L., Roth, L., Greene, J. P., Wichterle, H. & Sproule, D. Giant Axonal Neuropathy: and Updated Perspective on its Pathology and Pathogenesis. Muscle Nerve 50, 467-476 (2014).

169 Armao, D., Bailey, R. M., Bouldin, T. W., Kim, Y. & Gray, S. J. Autonomic nervous system involvement in the giant axonal neuropathy (GAN) KO mouse: implications for human disease. Clin Auton Res 26, 307-313 (2016).

237

170 Roth, L. A., Johnson-Kerner, B. L., Marra, J. D., LaMarca, N. H. & Sproule, D. M. The absence of curly hair is associated with a milder phenotype in Giant Axonal Neuropathy. Neuromuscular disorders 24, 48-55 (2014).

171 Mohri, I. et al. A case of giant axonal neuropathy showing focal aggregation and hypophosphorylation of intermediate filaments. Brain Dev 20, 594-597 (1998).

172 Kretzschmar, H. A., Berg, B. O. & Davis, R. L. Giant axonal neuropathy. Acta Neuropathol 73, 138-144 (1987).

173 Peiffer, J., Schlote, W., Bischoff, A., Boltschauser, E. & Müller, G. Generalized Giant Axonal Neuropathy: A Filament-Forming Disease of Neuronal, Endothelial, Glial, and Schwann Cells in a Patient without Kinky Hair. Acta Neuropathol 40, 213-218 (1977).

174 Armao, D. et al. Advancing the pathologic phenotype of giant axonal neuropathy: early involvement of the ocular lens. Orphanet J Rare Dis 14, 27 (2019).

175 Treiber-Held, S. et al. Giant Axonal Neuropathy: A Generalized Disorder of Intermediate Filaments with Longitudinal Grooves in the Hair. Neuropediatrics 25, 89-93 (1994).

176 Soomro, A. et al. Giant axonal neuropathy alters the structure of keratin intermediate filaments in human hair. Journal of the Royal Society Interface 14, 20170123 (2017).

177 Thomas, C., Love, S., Powell, H. C., Schultz, P. & Lampert, P. W. Giant axonal neuropathy: correlation of clinical findings with postmortem neuropathology. Ann Neurol 22, 79-84, doi:10.1002/ana.410220118 (1987).

178 Bomont, P. et al. The gene encoding gigaxonin, a new member of the cytoskeletal BTB/kelch repeat family, is mutated in giant axonal neuropathy. Nat Genet 26, 370-374 (2000).

179 Dhanoa, B. S., Cogliati, T., Satish, A. G., Bruford, E. A. & Friedman, J. S. Update on the Kelch-like (KLHL) gene family. Human Genomics 7, 13 (2013).

180 Zhang, D. D. et al. Ubiquitination of Keap1, a BTB-Kelch Substrate Adaptor Protein for Cul3, Targets Keap1 for Degradation by a Proteasome-independent Pathway. The Journal of Biological Chemistry 280, 30091-30099 (2005).

181 Lin, N.-H. et al. The role of gigaxonin in the degradation of the glial-specific intermediate filament protein GFAP. Mol Biol Cell 27, 3980-3990 (2016).

182 Johnson-Kerner, B. L., Diaz, A. G., Ekins, S. & Wichterle, H. Kelch Domain of Gigaxonin Interacts with Intermediate Filament Proteins Affected in Giant Axonal Neuropathy. PLoS One 10, e0140157 (2015).

238 183 Mahammad, S. et al. Giant axonal neuropathy-associated gigaxonin mutations impair intermediate filament protein degradation. J Clin Invest 123, 1964-1975 (2013).

184 Büchau, F., Munz, C., Has, C., Lehmann, R. & Magin, T. M. KLHL16 Degrades Epidermal Keratins. J Invest Dermatol 138, 1871-1873 (2018).

185 Pena, S. D. J. Immunocytochemical studies of intermediate filament aggregates and their relationship to microtubules in cultured skin fibroblasts from patients with giant axonal neuropathy. European Journal of Cell Biology 31, 227-234 (1983).

186 Lowery, J., Kuczmarski, E. R., Herrmann, H. & Goldman, R. D. Intermediate Filaments Play a Pivotal Role in Regulating Cell Architecture and Function. J Biol Chem 290, 17145-17153, doi:10.1074/jbc.R115.640359 (2015).

187 Pérez-Sala, D. et al. Vimentin filament organization and stress sensing depend on its single cysteine residue and zinc binding. Nat Commun 6, 7287, doi:10.1038/ncomms8287 (2015).

188 Dialynas, G. et al. Myopathic lamin mutations cause reductive stress and activate the nrf2/keap-1 pathway. PLoS Genet 11, e1005231, doi:10.1371/journal.pgen.1005231 (2015).

189 Gruenbaum, Y. & Aebi, U. Intermediate filaments: a dynamic network that controls cell mechanics. F1000Prime Rep 6, 54, doi:10.12703/p6-54 (2014).

190 Omary, M. B., Ku, N. O., Strnad, P. & Hanada, S. Toward unraveling the complexity of simple epithelial keratins in human disease. J Clin Invest 119, 1794-1805, doi:10.1172/jci37762 (2009).

191 Walther, D. M. et al. Widespread Proteome Remodeling and Aggregation in Aging C. elegans. Cell 168, 944, doi:10.1016/j.cell.2016.12.041 (2017).

192 David, D. C. et al. Widespread protein aggregation as an inherent part of aging in C. elegans. PLoS Biol 8, e1000450, doi:10.1371/journal.pbio.1000450 (2010).

193 Windoffer, R., Beil, M., Magin, T. M. & Leube, R. E. Cytoskeleton in motion: the dynamics of keratin intermediate filaments in epithelia. J Cell Biol 194, 669-678, doi:10.1083/jcb.201008095 (2011).

194 Chou, C. F., Riopel, C. L., Rott, L. S. & Omary, M. B. A significant soluble keratin fraction in 'simple' epithelial cells. Lack of an apparent phosphorylation and glycosylation role in keratin solubility. J Cell Sci 105 ( Pt 2), 433-444 (1993).

195 Omary, M. B., Ku, N. O., Liao, J. & Price, D. Keratin modifications and solubility properties in epithelial cells and in vitro. Subcell Biochem 31, 105-140 (1998).

239 196 Lowthert, L. A., Ku, N. O., Liao, J., Coulombe, P. A. & Omary, M. B. Empigen BB: a useful detergent for solubilization and biochemical analysis of keratins. Biochem Biophys Res Commun 206, 370-379, doi:10.1006/bbrc.1995.1051 (1995).

197 Snider, N. T., Weerasinghe, S. V., Iñiguez-Lluhí, J. A., Herrmann, H. & Omary, M. B. Keratin hypersumoylation alters filament dynamics and is a marker for human liver disease and keratin mutation. J Biol Chem 286, 2273-2284, doi:10.1074/jbc.M110.171314 (2011).

198 Eriksson, J. E. et al. Specific in vivo phosphorylation sites determine the assembly dynamics of vimentin intermediate filaments. J Cell Sci 117, 919-932, doi:10.1242/jcs.00906 (2004).

199 Sahlgren, C. M. et al. Mitotic reorganization of the intermediate filament protein nestin involves phosphorylation by cdc2 kinase. J Biol Chem 276, 16456-16463, doi:10.1074/jbc.M009669200 (2001).

200 Snider, N. T. & Omary, M. B. Assays for Posttranslational Modifications of Intermediate Filament Proteins. Methods Enzymol 568, 113-138, doi:10.1016/bs.mie.2015.09.005 (2016).

201 Snider, N. T. et al. Glucose and SIRT2 reciprocally mediate the regulation of keratin 8 by lysine acetylation. J Cell Biol 200, 241-247, doi:10.1083/jcb.201209028 (2013).

202 Danneman, P., Suckow, M. A. & Brayton, C. The laboratory mouse. 2nd edn, (CRC Press, 2013).

203 Weiss, W., Weiland, F. & Görg, A. Protein detection and quantitation technologies for gel-based proteome analysis. Methods Mol Biol 564, 59-82, doi:10.1007/978-1-60761- 157-8_4 (2009).

204 Achtstaetter, T., Hatzfeld, M., Quinlan, R. A., Parmelee, D. C. & Franke, W. W. Separation of cytokeratin polypeptides by gel electrophoretic and chromatographic techniques and their identification by immunoblotting. Methods Enzymol 134, 355-371, doi:10.1016/0076-6879(86)34102-8 (1986).

205 Ku, N. O. et al. Studying simple epithelial keratins in cells and tissues. Methods Cell Biol 78, 489-517, doi:10.1016/s0091-679x(04)78017-6 (2004).

206 Huiatt, T. W., Robson, R. M., Arakawa, N. & Stromer, M. H. Desmin from avian smooth muscle. Purification and partial characterization. J Biol Chem 255, 6981-6989 (1980).

207 Zackroff, R. V. & Goldman, R. D. In vitro assembly of intermediate filaments from baby hamster kidney (BHK-21) cells. Proc Natl Acad Sci U S A 76, 6226-6230, doi:10.1073/pnas.76.12.6226 (1979).

240 208 Franke, W. W., Schmid, E., Osborn, M. & Weber, K. The intermediate-sized filaments in rat kangaroo PtK2 cells. II. Structure and composition of isolated filaments. Cytobiologie 17, 392-411 (1978).

209 Small, J. V. & Sobieszek, A. Studies on the function and composition of the 10-NM(100- A) filaments of vertebrate smooth muscle. J Cell Sci 23, 243-268 (1977).

210 Dahl, D. & Bignami, A. Glial fibrillary acidic protein from normal human brain. Purification and properties. Brain Res 57, 343-360, doi:10.1016/0006-8993(73)90141-8 (1973).

211 Steinert, P. M., Idler, W. W. & Zimmerman, S. B. Self-assembly of bovine epidermal keratin filaments in vitro. J Mol Biol 108, 547-567, doi:10.1016/s0022-2836(76)80136-2 (1976).

212 Goldman, R. D., Cleland, M. M., Murthy, S. N., Mahammad, S. & Kuczmarski, E. R. Inroads into the structure and function of intermediate filament networks. J Struct Biol 177, 14-23, doi:10.1016/j.jsb.2011.11.017 (2012).

213 Oshima, R. G. Intermediate filaments: a historical perspective. Exp Cell Res 313, 1981- 1994, doi:10.1016/j.yexcr.2007.04.007 (2007).

214 Mashukova, A., Forteza, R. & Salas, P. J. Functional Analysis of Keratin-Associated Proteins in Intestinal Epithelia: Heat-Shock Protein Chaperoning and Kinase Rescue. Methods Enzymol 569, 139-154, doi:10.1016/bs.mie.2015.08.019 (2016).

215 Schinzel, R. & Dillin, A. Endocrine aspects of organelle stress—cell non-autonomous signaling of mitochondria and the ER. Curr Opin Cell Biol 33, 102-110, doi:10.1016/j.ceb.2015.01.006 (2015).

216 Taylor, R. C., Berendzen, K. M. & Dillin, A. Systemic stress signalling: understanding the cell non-autonomous control of proteostasis. Nat Rev Mol Cell Biol 15, 211-217, doi:10.1038/nrm3752 (2014).

217 Lemieux, G. A. et al. Kynurenic acid is a nutritional cue that enables behavioral plasticity. Cell 160, 119-131, doi:10.1016/j.cell.2014.12.028 (2015).

218 van Oosten-Hawle, P., Porter, R. S. & Morimoto, R. I. Regulation of organismal proteostasis by transcellular chaperone signaling. Cell 153, 1366-1378, doi:10.1016/j.cell.2013.05.015 (2013).

219 Ulgherait, M., Rana, A., Rera, M., Graniel, J. & Walker, D. W. AMPK modulates tissue and organismal aging in a non-cell-autonomous manner. Cell Rep 8, 1767-1780, doi:10.1016/j.celrep.2014.08.006 (2014).

241 220 Aynardi, M. W., Steinert, P. M. & Goldman, R. D. Human epithelial cell intermediate filaments: isolation, purification, and characterization. J Cell Biol 98, 1407-1421, doi:10.1083/jcb.98.4.1407 (1984).

221 O'Shea, J. M. et al. Purification of desmin from adult mammalian skeletal muscle. Biochem J 195, 345-356, doi:10.1042/bj1950345 (1981).

222 Liao, J., Lowthert, L. A., Ghori, N. & Omary, M. B. The 70-kDa heat shock proteins associate with glandular intermediate filaments in an ATP-dependent manner. J Biol Chem 270, 915-922, doi:10.1074/jbc.270.2.915 (1995).

223 Liao, J. & Omary, M. B. 14-3-3 proteins associate with phosphorylated simple epithelial keratins during cell cycle progression and act as a solubility cofactor. J Cell Biol 133, 345-357, doi:10.1083/jcb.133.2.345 (1996).

224 Ku, N. O., Fu, H. & Omary, M. B. Raf-1 activation disrupts its binding to keratins during cell stress. J Cell Biol 166, 479-485, doi:10.1083/jcb.200402051 (2004).

225 Hendriks, I. A. & Vertegaal, A. C. A comprehensive compilation of SUMO proteomics. Nat Rev Mol Cell Biol 17, 581-595, doi:10.1038/nrm.2016.81 (2016).

226 Agnetti, G., Lindsey, M. L. & Foster, D. B. Manual of cardiovascular proteomics. (Springer, 2016).

227 Choudhary, C., Weinert, B. T., Nishida, Y., Verdin, E. & Mann, M. The growing landscape of lysine acetylation links metabolism and cell signalling. Nat Rev Mol Cell Biol 15, 536-550, doi:10.1038/nrm3841 (2014).

228 Herhaus, L. & Dikic, I. Expanding the ubiquitin code through post-translational modification. EMBO Rep 16, 1071-1083, doi:10.15252/embr.201540891 (2015).

229 Dephoure, N., Gould, K. L., Gygi, S. P. & Kellogg, D. R. Mapping and analysis of phosphorylation sites: a quick guide for cell biologists. Mol Biol Cell 24, 535-542, doi:10.1091/mbc.E12-09-0677 (2013).

230 Choudhary, C. & Mann, M. Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Biol 11, 427-439, doi:10.1038/nrm2900 (2010).

231 Creixell, P. & Linding, R. Cells, shared memory and breaking the PTM code. Mol Syst Biol 8, 598, doi:10.1038/msb.2012.33 (2012).

232 Minguez, P. et al. Deciphering a global network of functionally associated post- translational modifications. Mol Syst Biol 8, 599, doi:10.1038/msb.2012.31 (2012).

242 233 Snider, N. T., Park, H. & Omary, M. B. A conserved rod domain phosphotyrosine that is targeted by the phosphatase PTP1B promotes keratin 8 protein insolubility and filament organization. J Biol Chem 288, 31329-31337, doi:10.1074/jbc.M113.502724 (2013).

234 Gorospe, J. R. et al. Molecular findings in symptomatic and pre-symptomatic Alexander disease patients. Neurology 58, 1494-1500, doi:10.1212/wnl.58.10.1494 (2002).

235 Cooper, J. A. Effects of cytochalasin and phalloidin on actin. J Cell Biol 105, 1473-1478, doi:10.1083/jcb.105.4.1473 (1987).

236 Jordan, M. A. & Wilson, L. Microtubules as a target for anticancer drugs. Nat Rev Cancer 4, 253-265, doi:10.1038/nrc1317 (2004).

237 Chernyatina, A. A., Guzenko, D. & Strelkov, S. V. Intermediate filament structure: the bottom-up approach. Curr Opin Cell Biol 32, 65-72 (2015).

238 Bargagna-Mohan, P. et al. The tumor inhibitor and antiangiogenic agent withaferin A targets the intermediate filament protein vimentin. Chem Biol 14, 623-634, doi:10.1016/j.chembiol.2007.04.010 (2007).

239 Vanden Berghe, W., Sabbe, L., Kaileh, M., Haegeman, G. & Heyninck, K. Molecular insight in the multifunctional activities of Withaferin A. Biochem Pharmacol 84, 1282- 1291, doi:10.1016/j.bcp.2012.08.027 (2012).

240 Grin, B. et al. Withaferin a alters intermediate filament organization, cell shape and behavior. PLoS One 7, e39065, doi:10.1371/journal.pone.0039065 (2012).

241 Ioannidis, J. P. More than a billion people taking statins?: Potential implications of the new cardiovascular guidelines. Jama 311, 463-464, doi:10.1001/jama.2013.284657 (2014).

242 Nielsen, S. F., Nordestgaard, B. G. & Bojesen, S. E. Statin use and reduced cancer- related mortality. N Engl J Med 368, 576-577, doi:10.1056/NEJMc1214827 (2013).

243 Stroes, E. S. et al. Statin-associated muscle symptoms: impact on statin therapy-European Atherosclerosis Society Consensus Panel Statement on Assessment, Aetiology and Management. Eur Heart J 36, 1012-1022, doi:10.1093/eurheartj/ehv043 (2015).

244 Herrmann, H., Kreplak, L. & Aebi, U. Isolation, characterization, and in vitro assembly of intermediate filaments. Methods Cell Biol 78, 3-24, doi:10.1016/s0091- 679x(04)78001-2 (2004).

245 Kreplak, L., Richter, K., Aebi, U. & Herrmann, H. Electron microscopy of intermediate filaments: teaming up with atomic force and confocal laser scanning microscopy. Methods Cell Biol 88, 273-297, doi:10.1016/s0091-679x(08)00415-9 (2008).

243 246 Sarria, A. J., Lieber, J. G., Nordeen, S. K. & Evans, R. M. The presence or absence of a vimentin-type intermediate filament network affects the shape of the nucleus in human SW-13 cells. J Cell Sci 107 ( Pt 6), 1593-1607 (1994).

247 Alexander, J. P. & Cravatt, B. F. The putative endocannabinoid transport blocker LY2183240 is a potent inhibitor of FAAH and several other brain serine hydrolases. J Am Chem Soc 128, 9699-9704, doi:10.1021/ja062999h (2006).

248 Goldman, R. D., Grin, B., Mendez, M. G. & Kuczmarski, E. R. Intermediate filaments: versatile building blocks of cell structure. Curr Opin Cell Biol 20, 28-34, doi:10.1016/j.ceb.2007.11.003 (2008).

249 Goodman, L. S. Goodman and Gilman's the pharmacological basis of therapeutics. Vol. 1549 (McGraw-Hill New York, 1996).

250 Goldman, R. D. The role of three cytoplasmic fibers in BHK-21 cell motility. I. Microtubules and the effects of colchicine. J Cell Biol 51, 752-762, doi:10.1083/jcb.51.3.752 (1971).

251 Hookway, C. et al. Microtubule-dependent transport and dynamics of vimentin intermediate filaments. Mol Biol Cell 26, 1675-1686, doi:10.1091/mbc.E14-09-1398 (2015).

252 Chaitanya, G. V., Steven, A. J. & Babu, P. P. PARP-1 cleavage fragments: signatures of cell-death proteases in neurodegeneration. Cell Commun Signal 8, 31, doi:10.1186/1478- 811x-8-31 (2010).

253 Jiang, P. et al. In vitro and in vivo anticancer effects of mevalonate pathway modulation on human cancer cells. Br J Cancer 111, 1562-1571, doi:10.1038/bjc.2014.431 (2014).

254 Björkhem-Bergman, L., Lindh, J. D. & Bergman, P. What is a relevant statin concentration in cell experiments claiming pleiotropic effects? Br J Clin Pharmacol 72, 164-165, doi:10.1111/j.1365-2125.2011.03907.x (2011).

255 Jiménez-Osés, G. et al. The role of distant mutations and allosteric regulation on LovD active site dynamics. Nat Chem Biol 10, 431-436, doi:10.1038/nchembio.1503 (2014).

256 Gao, X. et al. Directed evolution and structural characterization of a simvastatin synthase. Chem Biol 16, 1064-1074, doi:10.1016/j.chembiol.2009.09.017 (2009).

257 Traub, P., Perides, G., Scherbarth, A. & Traub, U. Tenacious binding of lipids to vimentin during its isolation and purification from Ehrlich ascites tumor cells. FEBS Lett 193, 217-221, doi:10.1016/0014-5793(85)80155-1 (1985).

258 Gazzerro, P. et al. Pharmacological actions of statins: a critical appraisal in the management of cancer. Pharmacol Rev 64, 102-146, doi:10.1124/pr.111.004994 (2012).

244

259 Warita, K. et al. Statin-induced mevalonate pathway inhibition attenuates the growth of mesenchymal-like cancer cells that lack functional E-cadherin mediated cell cohesion. Sci Rep 4, 7593, doi:10.1038/srep07593 (2014).

260 Kidd, M. E., Shumaker, D. K. & Ridge, K. M. The role of vimentin intermediate filaments in the progression of lung cancer. Am J Respir Cell Mol Biol 50, 1-6, doi:10.1165/rcmb.2013-0314TR (2014).

261 Cardwell, C. R., Mc Menamin, Ú., Hughes, C. M. & Murray, L. J. Statin use and survival from lung cancer: a population-based cohort study. Cancer Epidemiol Biomarkers Prev 24, 833-841, doi:10.1158/1055-9965.Epi-15-0052 (2015).

262 Seckl, M. J. et al. Multicenter, Phase III, Randomized, Double-Blind, Placebo-Controlled Trial of Pravastatin Added to First-Line Standard Chemotherapy in Small-Cell Lung Cancer (LUNGSTAR). J Clin Oncol 35, 1506-1514, doi:10.1200/jco.2016.69.7391 (2017).

263 Ahern, T. P., Lash, T. L., Damkier, P., Christiansen, P. M. & Cronin-Fenton, D. P. Statins and breast cancer prognosis: evidence and opportunities. Lancet Oncol 15, e461-468, doi:10.1016/s1470-2045(14)70119-6 (2014).

264 Hamilton, S. M., Bayer, C. R., Stevens, D. L., Lieber, R. L. & Bryant, A. E. Muscle injury, vimentin expression, and nonsteroidal anti-inflammatory drugs predispose to cryptic group A streptococcal necrotizing infection. J Infect Dis 198, 1692-1698, doi:10.1086/593016 (2008).

265 Bryant, A. E., Bayer, C. R., Huntington, J. D. & Stevens, D. L. Group A streptococcal myonecrosis: increased vimentin expression after skeletal-muscle injury mediates the binding of Streptococcus pyogenes. J Infect Dis 193, 1685-1692, doi:10.1086/504261 (2006).

266 Fitchett, D. H., Hegele, R. A. & Verma, S. Cardiology patient page. Statin intolerance. Circulation 131, e389-391, doi:10.1161/circulationaha.114.013189 (2015).

267 Saxon, D. R. & Eckel, R. H. Statin Intolerance: A Literature Review and Management Strategies. Prog Cardiovasc Dis 59, 153-164, doi:10.1016/j.pcad.2016.07.009 (2016).

268 Gluba-Brzozka, A., Franczyk, B., Toth, P. P., Rysz, J. & Banach, M. Molecular mechanisms of statin intolerance. Arch Med Sci 12, 645-658, doi:10.5114/aoms.2016.59938 (2016).

269 Schick, B. A. et al. Decreased skeletal muscle mitochondrial DNA in patients treated with high-dose simvastatin. Clin Pharmacol Ther 81, 650-653, doi:10.1038/sj.clpt.6100124 (2007).

245 270 Schwartz, N. B. & Domowicz, M. S. Proteoglycans in brain development and pathogenesis. FEBS Lett 592, 3791-3805 (2018).

271 Chernoivanenko, I. S., Matveeva, E. A., Gelfand, V. I., Goldman, R. D. & Minin, A. A. Mitochondrial membrane potential is regulated by vimentin intermediate filaments. Faseb j 29, 820-827, doi:10.1096/fj.14-259903 (2015).

272 Tang, H. L. et al. Vimentin supports mitochondrial morphology and organization. Biochem J 410, 141-146, doi:10.1042/bj20071072 (2008).

273 Kou, R., Sartoretto, J. & Michel, T. Regulation of Rac1 by simvastatin in endothelial cells: differential roles of AMP-activated protein kinase and calmodulin-dependent kinase kinase-beta. J Biol Chem 284, 14734-14743, doi:10.1074/jbc.M808664200 (2009).

274 Zhu, Y., Casey, P. J., Kumar, A. P. & Pervaiz, S. Deciphering the signaling networks underlying simvastatin-induced apoptosis in human cancer cells: evidence for non- canonical activation of RhoA and Rac1 GTPases. Cell Death Dis 4, e568, doi:10.1038/cddis.2013.103 (2013).

275 Bruckert, E., Hayem, G., Dejager, S., Yau, C. & Bégaud, B. Mild to moderate muscular symptoms with high-dosage statin therapy in hyperlipidemic patients--the PRIMO study. Cardiovasc Drugs Ther 19, 403-414, doi:10.1007/s10557-005-5686-z (2005).

276 Atilano-Roque, A. & Joy, M. S. Characterization of simvastatin acid uptake by organic anion transporting polypeptide 3A1 (OATP3A1) and influence of drug-drug interaction. Toxicol In Vitro 45, 158-165, doi:10.1016/j.tiv.2017.09.002 (2017).

277 Alexander, W. S. Progressive Fibrinoid Degeneration of Fibrillary Astrocytes Associated with Mental Retardation in a Hydrocephalic Infant. Brain 72, 373-381 (1949).

278 Sosunov, A., Olabarria, M. & Goldman, J. E. Alexander disease: an astrocytopathy that produces a leukodystrophy. Brain Pathol 28, 388-398, doi:10.1111/bpa.12601 (2018).

279 Messing, A. Alexander disease. Handb Clin Neurol 148, 693-700, doi:10.1016/b978-0- 444-64076-5.00044-2 (2018).

280 Olabarria, M. & Goldman, J. E. Disorders of Astrocytes: Alexander Disease as a Model. Annu Rev Pathol 12, 131-152, doi:10.1146/annurev-pathol-052016-100218 (2017).

281 Valentim, L. M. et al. Effects of transient cerebral ischemia on glial fibrillary acidi protein phosphorylation and immunocontent in rat hippocampus. Neuroscience 91, 1291- 1297 (1999).

282 Takemura, M., Nishiyama, H. & Itohara, S. Distribution of phosphorylated glial fibrillary acidic protein in the mouse central nervous system. Genes to Cells 7, 295-307 (2002).

246 283 Sullivan, S. M. et al. Phosphorylation of GFAP is associated with injury in the neonatal pig hypoxic-ischemic brain. Neurochem Res 37, 2364-2378, doi:10.1007/s11064-012- 0774-5 (2012).

284 Robert, A., Hookway, C. & Gelfand, V. I. Intermediate filament dynamics: What we can see now and why it matters. Bioessays 38, 232-243, doi:10.1002/bies.201500142 (2016).

285 Omary, M. B., Ku, N. O., Tao, G. Z., Toivola, D. M. & Liao, J. "Heads and tails" of intermediate filament phosphorylation: multiple sites and functional insights. Trends Biochem Sci 31, 383-394, doi:10.1016/j.tibs.2006.05.008 (2006).

286 Sekimata, M. et al. Detection of protein kinase activity specifically activated at metaphase-anaphase transition. J Cell Biol 132, 635-641, doi:10.1083/jcb.132.4.635 (1996).

287 Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci 34, 11929-11947, doi:10.1523/jneurosci.1860-14.2014 (2014).

288 Jany, P. L. et al. CSF and Blood Levels of GFAP in Alexander Disease. eNeuro 2, doi:10.1523/eneuro.0080-15.2015 (2015).

289 Jany, P. L., Hagemann, T. L. & Messing, A. GFAP expression as an indicator of disease severity in mouse models of Alexander disease. ASN Neuro 5, e00109, doi:10.1042/an20130003 (2013).

290 Chen, M. H., Hagemann, T. L., Quinlan, R. A., Messing, A. & Perng, M. D. Caspase cleavage of GFAP produces an assembly-compromised proteolytic fragment that promotes filament aggregation. ASN Neuro 5, 293-308, doi:10.1042/AN20130032 (2013).

291 Godefroy, N., Foveau, B., Albrecht, S., Goodyer, C. G. & LeBlanc, A. C. Expression and activation of caspase-6 in human fetal and adult tissues. PLoS One 8, e79313, doi:10.1371/journal.pone.0079313 (2013).

292 MacLachlan, T. K. & El-Deiry, W. S. Apoptotic threshold is lowered by p53 transactivation of caspase-6. PNAS 99, 9492-9497 (2002).

293 Inagaki, M. et al. Phosphorylation Sites Linked to Glial Filament Disassembly in Vitro Locate in a Non-α-helical Head Domain. J Biol Chem 265, 4722-4729 (1990).

294 Inagaki, M. et al. Dynamic property of intermediate filaments: regulation by phosphorylation. BioEssays 18, 481-487 (1996).

247 295 Caulín, C., Salvesen, G. S. & Oshima, R. G. Caspase cleavage of keratin 18 and reorganization of intermediate filaments during epithelial cell apoptosis. J Cell Biol 138, 1379-1394, doi:10.1083/jcb.138.6.1379 (1997).

296 Byun, Y. et al. Caspase cleavage of vimentin disrupts intermediate filaments and promotes apoptosis. Cell Death Differ 8, 443-450, doi:10.1038/sj.cdd.4400840 (2001).

297 Chen, F., Chang, R., Trivedi, M., Capetanaki, Y. & Cryns, V. L. Caspase proteolysis of desmin produces a dominant-negative inhibitor of intermediate filaments and promotes apoptosis. J Biol Chem 278, 6848-6853, doi:10.1074/jbc.M212021200 (2003).

298 Ruchaud, S. et al. Caspase-6 gene disruption reveals a requirement for lamin A cleavage in apoptotic chromatin condensation. Embo j 21, 1967-1977, doi:10.1093/emboj/21.8.1967 (2002).

299 Albrecht, S. et al. Activation of caspase-6 in aging and mild cognitive impairment. Am J Pathol 170, 1200-1209, doi:10.2353/ajpath.2007.060974 (2007).

300 Graham, R. K. et al. Cleavage at the 586 amino acid caspase-6 site in mutant huntingtin influences caspase-6 activation in vivo. J Neurosci 30, 15019-15029, doi:10.1523/JNEUROSCI.2071-10.2010 (2010).

301 Guo, H. et al. Active Caspase-6 and Caspase-6-Cleaved Tau in Neuropil Threads, Neuritic Plaques, and Neurofibrillary Tangles of Alzheimer’s Disease. American Journal of Pathology 165, 523-531 (2004).

302 Graham, R. K. et al. Cleavage at the caspase-6 site is required for neuronal dysfunction and degeneration due to mutant huntingtin. Cell 125, 1179-1191, doi:10.1016/j.cell.2006.04.026 (2006).

303 Galvan, V. et al. Reversal of Alzheimer’s-like pathology and behavior in human APP transgenic mice by mutation of Asp664. PNAS 103, 7130-7135 (2006).

304 Saganich, M. et al. Deficits in Synaptic Transmission and Learning in Amyloid Precursor Protein (APP) Transgenic Mice Require C-Terminal Cleavage APP. The Journal of Neuroscience 26, 13428-13436 (2006).

305 Geden, M. J., Romero, S. E. & Deshmukh, M. Apoptosis versus axon pruning: Molecular intersection of two distinct pathways for axon degeneration. Neurosci Res 139, 3-8, doi:10.1016/j.neures.2018.11.007 (2019).

306 Dinsdale, D., Lee, J. C., Dewson, G., Cohen, G. M. & Peter, M. E. Intermediate filaments control the intracellular distribution of caspases during apoptosis. Am J Pathol 164, 395- 407, doi:10.1016/s0002-9440(10)63130-6 (2004).

248 307 Malhas, A., Goulbourne, C. & Vaux, D. J. The nucleoplasmic reticulum: form and function. Trends Cell Biol 21, 362-373, doi:10.1016/j.tcb.2011.03.008 (2011).

308 Uhler, C. & Shivashankar, G. V. Regulation of genome organization and gene expression by nuclear mechanotransduction. Nat Rev Mol Cell Biol 18, 717-727, doi:10.1038/nrm.2017.101 (2017).

309 Eriksson, M. et al. Recurrent de novo point mutations in lamin A cause Hutchinson- Gilford progeria syndrome. Nature 423, 293-298, doi:10.1038/nature01629 (2003).

310 Jorgens, D. M. et al. Deep nuclear invaginations are linked to cytoskeletal filaments - integrated bioimaging of epithelial cells in 3D culture. J Cell Sci 130, 177-189, doi:10.1242/jcs.190967 (2017).

311 Frost, B. Alzheimer's disease: An acquired neurodegenerative laminopathy. Nucleus 7, 275-283, doi:10.1080/19491034.2016.1183859 (2016).

312 Perng, M. D., Huang, Y. S. & Quinlan, R. A. Purification of Protein Chaperones and Their Functional Assays with Intermediate Filaments. Methods Enzymol 569, 155-175, doi:10.1016/bs.mie.2015.07.025 (2016).

313 Trogden, K. P. et al. An image-based small-molecule screen identifies vimentin as a pharmacologically relevant target of simvastatin in cancer cells. FASEB J 32, 2841-2854, doi:10.1096/fj.201700663R (2018).

314 Bock, C. et al. Reference Maps of human ES and iPS cell variation enable high- throughput characterization of pluripotent cell lines. Cell 144, 439-452, doi:10.1016/j.cell.2010.12.032 (2011).

315 Amanchy, R. et al. A curated compendium of phosphorylation motifs. Nat Biotechnol 25, 285-286, doi:10.1038/nbt0307-285 (2007).

316 Sofroniew, M. Astrocyte Reactivity: Subtypes, States, and Functions in CNS Innate Immunity. Trends Immunol 41, 758-770 (2020).

317 Anderson, M. A. et al. Astrocyte scar formation aids central nervous system axon regeneration. Nature 532, 195-200 (2016).

318 Hartmann, K. et al. Complement 3+-astrocytes are highly abundant in prion diseases, but their abolishment led to an accelerated disease course and early dysregulation of microglia. Acta Neuropathol 7, 83 (2019).

319 Robel, S. et al. Reactive Astrogliosis Causes the Development of Spontaneous Seizures. J Neurosci 35, 3330-3345 (2015).

249 320 Diaz-Castro, B., Gangwani, M. R., Yu, X., Coppola, G. & Khakh, B. S. Astrocyte molecular signatures in Huntington’s disease. Sci Transl Med 11, eaaw8546 (2019).

321 Yamanaka, K. et al. Astrocytes as determinants of disease progression in inherited amyotrophic lateral slcerosis. Nat Neurosci 11, 251-253 (2008).

322 Qian, K. et al. Sporadic ALS Astrocytes Induce Neuronal Degeneration In Vivo. Stem Cell Reports 8, 843-855 (2017).

323 Guttenplan, K. A. et al. Neurotoxic Reactive Astrocytes Drive Neuronal Death after Retinal Injury. Cell Rep 31, 107776 (2020).

324 Wheeler, M. A. et al. MAFG-driven astrocytes promote CNS inflammation. Nature 578, 593-599 (2020).

325 Verkhratsky, A. & Nedergaard, M. Physiology of Astroglia. Physiol Rev 98, 239-389 (2018).

326 Battaglia, R. A. et al. Site-specific phosphorylation and caspase cleavage of GFAP are new markers of Alexander disease severity. Elife 8, e47789 (2019).

327 Duncan, I. D. & Griffiths, I. R. Canine giant axonal neuropathy. Vet Rec 101, 438-441 (1977).

328 Ding, J. et al. Gene targeting of GAN in mouse causes toxic accumulation of microtubule-associated protein 8 and impairment of retrograde axonal transport. Hum Mol Genet 15, 1451-1463 (2006).

329 Dequen, F., Bomont, P., Gowing, G., Cleveland, D. W. & Julien, J.-P. Modest loss of peripheral axons, muscle atrophy and formation of brain inclusions in mice with targeted deletion of gigaxonin exon 1. J Neurochem 107, 253-264 (2008).

330 Ganay, T., Boizot, A., Burrer, R., Chauvin, J. P. & Bomont, P. Sensory-motor defecits and neurofilament disorganization in gigaxonin-null mice. Mol Neurodegener 6 (2011).

331 Arribat, Y. et al. Sonic Hedgehog repression underlies gigaxonin mutation-induced motor defecits in giant axonal neuropathy. J Clin Invest 129, 5312-5326 (2019).

332 Mayr, C. Regulation by 3’-Untranslated Regions. Annu Rev Genet 51, 171-194 (2017).

333 Nostrand, E. L. V. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat Methods 13, 508-514 (2016).

334 Conlon, E. G. & Manley, J. L. RNA-binding proteins in neurodegeneration: mechanisms in aggregate. Genes Dev 31, 1509-1528 (2017).

250 335 Boizot, A. et al. The instability of the BTB-KELCH protein Gigaxonin causes Giant Axonal Neuropathy and constitutes a new penetrant and specific diagnostic test. Acta Neuropathol Commun 2, 47 (2014).

336 Lehner, C. F., Stick, R., Eppenberger, H. M. & Nigg, E. A. Differential expression of nuclear lamin proteins during chicken development. J Cell Biol 105, 577-587 (1987).

337 Stewart, C. & Burke, B. Teratocarcinoma Stem Cells and Early Mouse Embryos Contain Only a Single Majot Lamin Polypeptide Closely Resembling Lamin B. Cell 51, 383-392 (1987).

338 Pasca, A. M. et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat Methods 12, 671-678 (2015).

339 Griffith, I. R., Duncan, I. D., McCulloch, M. & Carmichael, S. Further Studies of the Central Nervous System in Canine Giant Axonal Neuropathy. Neuropathol and App Neurobiol 6, 421-432 (1980).

340 King, R. H. M., Sarsilmaz, M., Thomas, P. K., Muddle, J. M. & Duncan, I. D. Axonal neurofilamentous accumulations: a comparison between human and canine giant axonal neuropathy and 2, 5-HD neuropathy. Neuropathol and App Neurobiol 19, 224-232 (1993).

341 Duncan, I. D., Griffiths, I. R., Carmichael, S. & Henderson, S. Inherited Canine Giant Axonal Neuropathy. Muscle Nerve 4, 223-227 (1981).

342 Chen-Plotkin, A. S., Lee, V. M.-Y. & Trojanowski, J. Q. TAR DNA-binding protein 43 in neurodegenerative disease. Nat Rev Neurol 6, 211-220 (2010).

343 Walker, A. K. et al. Astrocytic TDP-43 Pathology in Alexander Disease. J Neurosci 34, 6448-6456 (2014).

344 Khong, A. et al. The Stress Granule Transcriptome Reveals Principles of mRNA Accumulation in Stress Granules. Mol Cell 68, 808-820 (2017).

345 Lescouzères, L. & Bomont, P. E3 Ubiquitin Ligases in Neurological Diseases: Focus on Gigaxonin and Autophagy. Front Physiol 11, 1022 (2020).

346 Johnson-Kerner, B. L. et al. Intermediate filament protein accumulation in motor neurons derived from giant axonal neuropathy iPSCs rescued by restoration of gigaxonin. Hum Mol Genet 24, 1420-1431 (2015).

347 Chandrasekaran, A., Avci, H. X., Leist, M., Kobolak, J. & Dinnyes, A. Astrocyte Differentiation of Human Pluripotent Stem Cells: New Tools for Neurological Disorder Research. Front Cell Neurosci 10, 215, doi:10.3389/fncel.2016.00215 (2016).

251 348 Sloan, S. A. et al. Human Astrocyte Maturation Captured in 3D Cerebral Cortical Spheroids Derived from Pluripotent Stem Cells. Neuron 95, 779-790 (2017).

349 Kajiwara, M. et al. Donor-dependent variations in hepatic differentiation from human- induced pluripotent stem cells. PNAS 109, 12538 (2012).

350 Volpato, V. & Webber, C. Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility. Dis Model Mech 13, dmm042317 (2020).

351 Xu, X. et al. Efficient homology-directed gene editing by CRISPR/Cas9 in human stem and primary cells using tube electroporation. Sci Rep 8, 11649 (2018).

352 Cornelison, G. L., Levy, S. A., Jenson, T. & Frost, B. Tau-induced nuclear envelope invagination causes a toxic accumulation of mRNA in Drosophila. Aging Cell 18, e12847 (2018).

353 Fallini, C., Khalil, B., Smith, C. L. & Rossoll, W. Traffic jam at the nuclear pore: All roads lead to nucleocytoplasmic transport defects in ALS/FTD. Neurobiol Dis 140, 104835 (2020).

354 Fernández-Nogales, M. & Lucas, J. J. Altered Levels and Isoforms of Tau and Nuclear Membrane Invaginations in Huntington's Disease. Front Cell Neurosci 13, 574, doi:10.3389/fncel.2019.00574 (2019).

355 Castañón, M. J., Walko, G., Winter, L. & Wiche, G. Plectin-intermediate filament partnership in skin, skeletal muscle, and peripheral nerve. Histochem Cell Biol 140, 33-53 (2013).

356 Foisner, R. et al. Cytoskeleton-associated Plectin: In Situ Localization, In Vitro Reconstitution, and Binding to Immonibilzed Intermediate Filament Proteins. J Cell Biol 106, 723-733 (1988).

357 Favre, B. et al. Plectin interacts with the rod domain of type III intermediate filament proteins desmin and vimentin. Eur J Cell Biol 90, 390-400 (2011).

358 Yuan, A. et al. alpha-Internexin Is Structurally and Functionally Associated with the Neurofilament Triplet Proteins in the Mature CNS. J Neurosci 26, 10006-10019 (2006).

359 Yuan, A. et al. Peripherin Is a Subunit of Peripheral Nerve Neurofilaments: Implications for Differential Vulnerability of CNS and Peripheral System Axons. J Neurosci 32, 8501- 8508 (2012).

360 Didonna, A. & Opal, P. The role of neurofilament aggregation in neurodegeneration: lessons from rare inherited neurological disorders. Mol Neurodegener 14, 19, doi:10.1186/s13024-019-0318-4 (2019).

252 361 Sobue, G. et al. Phosphorylated high molecular weight neurofilament protein in lower motor neurons in amyotrophic lateral sclerosis and other neurodegenerative diseases involving ventral horn cells. Acta Neuropathol 79, 402-408, doi:10.1007/bf00308716 (1990).

362 Korolainen, M. A., Auriola, S., Nyman, T. A., Alafuzoff, I. & Pirttila, T. Proteomic analysis of glial fibrillary acidic protein in Alzheimer's disease and aging brain. Neurobiol Dis 20, 858-870, doi:10.1016/j.nbd.2005.05.021 (2005).

363 Herskowitz, J. H. et al. Phosphoproteomic analysis reveals site-specific changes in GFAP and NDRG2 phosphorylation in frontotemporal lobar degeneration. J Proteome Res 9, 6368-6379, doi:10.1021/pr100666c (2010).

364 Clairembault, T. et al. Enteric GFAP expression and phosphorylation in Parkinson's disease. J Neurochem 130, 805-815, doi:10.1111/jnc.12742 (2014).

365 Yasuda, R. et al. Towards genomic database of Alexander disease to identify variations modifying disease phenotype. Sci Rep 9, 14763, doi:10.1038/s41598-019-51390-8 (2019).

366 Strnad, P. et al. Transglutaminase 2 regulates mallory body inclusion formation and injury-associated liver enlargement. Gastroenterology 132, 1515-1526, doi:10.1053/j.gastro.2007.02.020 (2007).

367 Liu, D. et al. Proteomic analysis reveals differentially regulated protein acetylation in human amyotrophic lateral sclerosis spinal cord. PLoS One 8, e80779, doi:10.1371/journal.pone.0080779 (2013).

368 Sun, S. et al. Proteomics of hepatocellular carcinoma: serum vimentin as a surrogate marker for small tumors (

369 Wei, T. et al. Vimentin-positive circulating tumor cells as a biomarker for diagnosis and treatment monitoring in patients with pancreatic cancer. Cancer Lett 452, 237-243, doi:10.1016/j.canlet.2019.03.009 (2019).

370 Ayrignac, X. et al. Serum GFAP in multiple sclerosis: correlation with disease type and MRI markers of disease severity. Sci Rep 10, 10923, doi:10.1038/s41598-020-67934-2 (2020).

371 Aktas, O. et al. Serum Glial Fibrillary Acidic Protein: A Neuromyelitis Optica Spectrum Disorder Biomarker. Ann Neurol 89, 895-910, doi:10.1002/ana.26067 (2021).

372 Czeiter, E. et al. Blood biomarkers on admission in acute traumatic brain injury: Relations to severity, CT findings and care path in the CENTER-TBI study. EBioMedicine 56, 102785, doi:10.1016/j.ebiom.2020.102785 (2020).

253

373 Perry, L. A. et al. Glial fibrillary acidic protein for the early diagnosis of intracerebral hemorrhage: Systematic review and meta-analysis of diagnostic test accuracy. Int J Stroke 14, 390-399, doi:10.1177/1747493018806167 (2019).

374 Chatterjee, P. et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer's disease. Transl Psychiatry 11, 27, doi:10.1038/s41398- 020-01137-1 (2021).

375 Tichy, J. et al. Prospective evaluation of serum glial fibrillary acidic protein (GFAP) as a diagnostic marker for glioblastoma. J Neurooncol 126, 361-369, doi:10.1007/s11060- 015-1978-8 (2016).

376 Strouhalova, K. et al. Vimentin Intermediate Filaments as Potential Target for Cancer Treatment. Cancers (Basel) 12, doi:10.3390/cancers12010184 (2020).

377 Li, Z., Paulin, D., Lacolley, P., Coletti, D. & Agbulut, O. Vimentin as a target for the treatment of COVID-19. BMJ Open Respir Res 7, doi:10.1136/bmjresp-2020-000623 (2020).

378 Ramos, I., Stamatakis, K., Oeste, C. L. & Pérez-Sala, D. Vimentin as a Multifaceted Player and Potential Therapeutic Target in Viral Infections. Int J Mol Sci 21, doi:10.3390/ijms21134675 (2020).

379 Wood, M. J. A., Talbot, K. & Bowerman, M. Spinal muscular atrophy: antisense oligonucleotide therapy opens the door to an integrated therapeutic landscape. Hum Mol Genet 26, R151-r159, doi:10.1093/hmg/ddx215 (2017).

380 Gordon, L. B. et al. Clinical trial of a farnesyltransferase inhibitor in children with Hutchinson-Gilford progeria syndrome. Proc Natl Acad Sci U S A 109, 16666-16671, doi:10.1073/pnas.1202529109 (2012).

381 Espay, A. J. et al. Revisiting protein aggregation as pathogenic in sporadic Parkinson and Alzheimer diseases. Neurology 92, 329-337, doi:10.1212/wnl.0000000000006926 (2019).

382 Caughey, B. & Lansbury, P. T. Protofibrils, pores, fibrils, and neurodegeneration: separating the responsible protein aggregates from the innocent bystanders. Annu Rev Neurosci 26, 267-298, doi:10.1146/annurev.neuro.26.010302.081142 (2003).

383 Yuan, A., Rao, M. V., Veeranna & Nixon, R. A. Neurofilaments and Neurofilament Proteins in Health and Disease. Cold Spring Harb Perspect Biol 9, doi:10.1101/cshperspect.a018309 (2017).

254 384 Sontag, E. M., Samant, R. S. & Frydman, J. Mechanisms and Functions of Spatial Protein Quality Control. Annu Rev Biochem 86, 97-122, doi:10.1146/annurev-biochem-060815- 014616 (2017).

385 Escusa-Toret, S., Vonk, W. I. & Frydman, J. Spatial sequestration of misfolded proteins by a dynamic chaperone pathway enhances cellular fitness during stress. Nat Cell Biol 15, 1231-1243, doi:10.1038/ncb2838 (2013).

386 Kaganovich, D., Kopito, R. & Frydman, J. Misfolded proteins partition between two distinct quality control compartments. Nature 454, 1088-1095, doi:10.1038/nature07195 (2008).

387 Ogrodnik, M. et al. Dynamic JUNQ inclusion bodies are asymmetrically inherited in mammalian cell lines through the asymmetric partitioning of vimentin. Proc Natl Acad Sci U S A 111, 8049-8054, doi:10.1073/pnas.1324035111 (2014).

388 Shi, Y., Inoue, H., Wu, J. C. & Yamanaka, S. Induced pluripotent stem cell technology: a decade of progress. Nat Rev Drug Discov 16, 115-130, doi:10.1038/nrd.2016.245 (2017).

389 Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481-487 (2017).

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