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2021

Characterizing Galectin and Lysosomal Rupture's Role in Characterizing Galectin and Lysosomal Rupture's Role in

Spreading Parkinson Disease Pathology Spreading Parkinson Disease Pathology

Kevin Burbidge

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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. Copyright © 2021 Kevin Burbidge

LOYOLA UNIVERSITY CHICAGO

CHARACTERIZING GALECTIN AND LYSOSOMAL RUPTURE’S ROLE IN SPREADING

PARKINSON DISEASE PATHOLOGY

A DISSERTATION SUBMITTED TO

THE FACULTY OF THE GRADUATE SCHOOL

IN CANDIDACY FOR THE DEGREE OF

DOCTORATE IN PHILOSOPHY

PROGRAM IN NEUROSCIENCE

BY

KEVIN BURBIDGE

CHICAGO, ILLINOIS

DECEMBER 2021

Copyright by Kevin Burbidge, 2021

All rights reserved.

iii

ACKNOWLEDGEMENTS

I would like to thank and acknowledge …

To my parents Ann and Steve Burbidge for believing me, raising me, and helping me get

to where I am now. I would not be here had it not been for your endless amounts of love, time,

and energy. You both have taught me so much on what it takes to be successful. You have

pushed me and provided me with the resources to allow me to do anything. To my Dad for

instilling me a scientific curiosity and the brains to understand what it means and to my Mom for

giving me the charisma to be a successful communicator.

To my brothers Andy and Jack for believing in me and supporting me. Thank you for

being the best siblings I could imagine.

To my grandparents, Jack and Nancy Burbidge and Jack and Ginger Overbye for all that

you have done to help me succeed in life. I cannot thank you enough for all that you have done

for me.

To my friends who helped me get here, taught me something new, took the time to make

me a better scientist, and gave me the support to be successful. I cannot count or list the number

of times you all have helped me.

To Ed Campbell for mentoring, polishing, and refining a rough stone to make it shine. I

appreciate to no end the skills that you have imparted to me and the relationship we have. You

have taught me much about how to be both a successful scientist and person.

To the members of the Campbell lab, past and present, who have taught me the scientific

methods and techniques to be successful. For supporting me, giving me a positive work

iv

environment, and generally being good people. To Alex Simon who cloned the DSP-α-syn

constructs and provided the initial foundation for my master’s degree project. To Bill Flavin for

imparting on me microscopy skills and the framework for my doctoral work. To Adarsh Dharan

for teaching me and assisting with endless amounts of technical help. A special thanks to Jessica

Mattick and Stephanie Zack for differentiating iPSCs into neurons for me.

To Dave Rademacher for helping me publish papers, differentiating iPSCs, making me a

better a writer, and showing me that scientific curiosity is never lost. Your assistance through

this last year is beyond what was expected, thank you.

To David, Melanie, and Steven for being a second family to me. You treat me like a

son/brother, and I am so blessed to have you all in my life. Thank you for all you have done to

help me get to where I am today. I look forward to the time we will spend together.

Finally, to my wife Kathleen for everything. For being there to celebrate in my victories

and assist me in my times of need. For being understanding in all things both school and life

related. Everyone listed here has played a role in helping me get to this point, but you have done

more. You have been a tireless foundation in supporting me, and I would not be here if not for

you. You inspire me to do good work and I am so lucky to have found someone like you. Thank

you so much for believing in me and loving me. I may not know what happens next, but I cannot

wait to spend it with you.

This work is dedicated in three parts. First, to my friends and family for all you have

done to get me here. Second, to my family members who have suffered with neurodegenerative

diseases. To my Grandma Nancy Burbidge who suffered with primary progressive aphasia and

my Grandpa Jack Burbidge for his commitment to her. To my great Grandma Virginia “Yoo-

Hoo” Mears who had Parkinson’s disease. Third, to the betterment of science and the hope of

finding a cure for Parkinson’s disease and all other neurodegenerative diseases; may this work

ultimately contribute to this process

vi

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ….......................................................................................................iii

LIST OF TABLES ..........................................................................................................................x

LIST OF FIGURES .......................................................................................................................xi

LIST OF ABBREVIATIONS .....................................................................................................xiv

ABSTRACT...............................................................................................................................xxvi

CHAPTER ONE: REVIEW OF LITERATURE

Introduction to Synucleinopathies................................................................................................1

Parkinson’s Disease......................................................................................................................3

Substantia Nigra, Dopaminergic Neurons, and Neuronal Degeneration......................................4

Lewy Pathology............................................................................................................................8

α-Syn’s Original Link to Parkinson’s Disease Pathology..........................................................10

α-Syn’s Structure........................................................................................................................10

Native α-Syn’s Structure and Function.......................................................................................11

Pathological Species of α-Syn....................................................................................................13

Conformation and Structure.....................................................................................................15

Cell-to-Cell Propagation of α-Syn..............................................................................................18

Braak Staging and Hypothesis.................................................................................................18

The Prion-Like Hypothesis......................................................................................................20

Cellular α-Syn Transmission Pathways......................................................................................21

The Unconventional Secretion of α-Syn.....................................................................................24

Extracellular Vesicle Associated Transmission of α-Syn........................................................26

Non-Extracellular Vesicle Associated α-Syn Transmission....................................................29

Autophagy...................................................................................................................................31

Microautophagy, Chaperone Mediated Autophagy, and Macroautophagy.............................32

Autophagy in Neurons.............................................................................................................35

Evidence for Autophagy’s Contribution to α-Syn Accumulation...........................................38

ALP Dysfunction, Secretory Autophagy, and α-Syn................................................................40

Lysosome Dysfunction is a Hallmark of PD..............................................................................42

Lysosomes................................................................................................................................44

Lysosomal Degradation Impairment in Parkinson’s Disease...................................................45

mTOR Signaling......................................................................................................................47

AMPK Signaling......................................................................................................................51

mTOR, AMPK and Parkinson’s Disease.................................................................................52

Transcriptional Regulation of Lysosomes and Autophagosomes.............................................54

TFEB and Neurodegenerative Diseases....................................................................................56

The Cellular Response to Lysosomal Stress.............................................................................59

Galectins’ Function in the Central Nervous System.....................................................................60

vii

Galectin Facilitated Clearance of Ruptured Lysosomes...............................................................62

Lysosomal Membrane Rupture and Galectins in Neurodegenerative Diseases.........................65

Galectins, Inflammation, and Neurodegenerative Diseases.......................................................66

Galectin-8..............................................................................................................................66

Galectin-1…..........................................................................................................................67

Galectin-3…..........................................................................................................................68

Galectins as Biomarkers for Neurodegenerative Diseases..........................................................71

Concluding Remarks...................................................................................................................74

CHAPTER TWO: MATERIALS AND METHODS

Tissue Culture.............................................................................................................................75

Culture of SH-SY5Y and HeLa Cell Lines................................................................................75

Culture of iCell DopaNeurons....................................................................................................75

Culture of Human IPSCs............................................................................................................76

Induction of Midbrain Floor Plate Progenitors...........................................................................77

Differentiation of MDA Neurons................................................................................................78

Generation of DSP-α-Syn Constructs, FLuc Gal3, mCherry Gal3, and Inducible YFP-LC3B

Plasmids...................................................................................................................................79

Generation of Stable α-Syn DSP A+B, FLuc Gal3, and mCherry Gal3 Cell Lines...................79

Generation of Gal3 CRISPR Plasmids and KO Cell Lines........................................................80

Stabile Expression of S15 mCherry Construct...........................................................................81

Purification of Extracellular Vesicles from S15 mCherry 293Ts...............................................81

Purification of Extracellular Vesicles from DSP-α-Syn SH-SY5Y or IPSC MDA Neurons.....82

Nanoparticle Tracking Analysis (NTA)......................................................................................83

Non-Reducing SDS-PAGE, Western Blots & Western Antibodies...........................................83

Transmission Electron Microscopy of Cells...............................................................................85

Transmission Electron Microscopy and Immunogold Labeling of EVs....................................86

Immunofluorescence Staining of EVs........................................................................................87

Indirect ELISA of EVs................................................................................................................88

Human Bodily Fluid Sample Collection, Preparation, and Processing......................................89

Saliva........................................................................................................................................89

Plasma......................................................................................................................................91

PKH Dye Labeling of EVs.........................................................................................................92

Cell treatment and EV-MAC Immunofluorescence Staining.....................................................93

Fixation and Immunofluorescence Staining of Cells..................................................................95

Imaging Analysis Paradigm........................................................................................................96

Wide-field Fluorescence Deconvolution Microscopy and Analysis..........................................96

Bitplane Imaris Spots and Surface Algorithm Generation.........................................................99

Sensitivity and Detection in Deconvolved and Undeconvolved Images..................................101

Serial Dilutions of S15Ch EVs.................................................................................................102

Empirical Measurement of the Point Spread Function and Binning........................................104

Flow Cytometry........................................................................................................................105

Characterization of α-Syn Fibrils..............................................................................................106

Cell Viability Assay..................................................................................................................108

α-Syn Fibril and Drug Concentrations and Treatment Paradigm.............................................109

siRNA Transfection..................................................................................................................109

viii

Renilla Luciferase Assay..........................................................................................................111

MDA Neuronal Culture Transduction and Firefly Luciferase Assay.......................................112

MDA Neuronal Culture Quantitative Real-Time Polymerase Chain Reaction Assay..............113

MDA Neuronal Culture YFP-LC3B Transduction, Induction, and Treatment..........................114

α-Syn and Galectin-3 Sandwich ELISA...................................................................................115

Lysosome Dysfunction Assay and Autophagic Flux................................................................117

Statistical Analysis....................................................................................................................118

CHAPTER THREE: GENERATION AND VALIDATION OF THE EXTRACELLULAR

VESICLE MULTIPLEX ANALYSIS OF CO-LOCALIZATION (EV-MAC) WORKFLOW

Rationale...................................................................................................................................119

Results.......................................................................................................................................122

Generation of S15-mCherry Positive EVs for the Characterization of

Immunofluorescence Analysis...........................................................................................122

Immunofluorescence Imaging to Characterize EV Populations by Multiplexed Analysis

of Co- localization (EV-MAC)...........................................................................................126

The Majority of EVs in Solution are Bound to Coverslips Following Spinoculation...........133

Serial Dilution Allows Relative EV Concentration to be Determined using EV-MAC........135

PKH Dye can be used in Conjunction with EV-MAC to Identify EVs.................................136

EV-MAC can be used to Interrogate Human Biological Samples.........................................136

Discussion.................................................................................................................................139

CHAPTER FOUR: LYSOSOMAL MEMBRANE DAMAGE, AND THE SUBSEQUENT

GAL3 MEDIATED RESPONSE, CONTRIBUTE TO THE SECRETION OF α-SYN BY

AN AUTOPHAGIC MECHANISM

Rationale...................................................................................................................................143

Results.......................................................................................................................................147

Exogenous α-Syn Fibrils are Re-secreted with Endogenous α-Syn and Gal3 by the ALP...147

Exogenous and Endogenous -Syn are Released in Gal3-Containing EVs..........................153

Characterization of Human IPSC Derived Midbrain Dopamine Neurons.............................160

Lysosomal Membrane Damage Inhibits mTOR and Upregulates Autophagy in Human

MDA Neurons....................................................................................................................162

The Reciprocal Secretion of α-Syn and Gal3 is Regulated by Trim16 and ATG16L1.........163

-Syn Fibrils Increase the Association of Trim16 and ATG16L1 with mCherry Gal3........168

Depletion of Gal3 and Trim16 in MDA Neurons Reduces the Recruitment of α-Syn

Fibrils to Autophagic Compartments..................................................................................171

Gal3 Depletion Impairs Lysosomal Degradative Function and Reduces Autophagic Flux

During α-Syn Fibril Treatment...........................................................................................172

Lysosomal Damage Increases α-Syn and Gal3 Secretion in Association with Increased

Autophagic Activity............................................................................................................176

Gal3 Affects Endo/Lysosomal Membrane Damage Induced Amphisome Formation..........182

Gal3 is Readily Released in α-Syn Associated EVs..............................................................185

Depletion of Gal3 or Trim16 Affects the Co-Localization of Secreted α-Syn......................188

Early Inhibition of Autophagy Reduces Autophagosome-Associated α-Syn Secretion........193

Discussion.................................................................................................................................195

Concluding Remarks ................................................................................................................203

ix

APPENDIX..................................................................................................................................208

REFERENCES ............................................................................................................................210

VITA ...........................................................................................................................................262

x

LIST OF TABLES

Table 1. Primer Table..................................................................................................................114

xi

LIST OF FIGURES

Figure 1: Structure and Domains of α-Syn. ..................................................................................13

Figure 2: Cellular Activation of Autophagy and Autophagosome Formation................................33

Figure 3: Lysosomal Signaling Pathways in Response to Various Stimuli.....................................50

Figure 4: Relationship between Lysosomal Function and TFEB Translocation.............................57

Figure 5: Detection of endogenous protein markers on EVs released from 293T cells.................123

Figure 6: EV-MAC Reproducibly Stains EV Populations Following Enrichment from Tissue

Culture Supernatant.......................................................................................................125

Figure 7: Masking Algorithms Selectively Removes S15Ch+ Aggregates and is Selective

for Cultured Media.......................................................................................................127

Figure 8: S15Ch EVs Relative Levels of LAMP1 and Tetraspanin Proteins Determined by

Indirect EV ELISA.......................................................................................................129

Figure 9: Deconvolution Does Not Change the Co-localization Distribution of S15Ch+ EVs

with Tetraspanins and LAMP1.....................................................................................130

Figure 10: Deconvolution Increases the Limit of Detection of Spots Masking Algorithm...........132

Figure 11: Analysis of EV Recovery and Quantification using EV-MAC....................................133

Figure 12: PKH Dyed WT 293T EVs Co-localize with CD81, LAMP1, and Readily Identify

S15Ch 293T EVs........................................................................................................136

Figure 13: EV-MAC of Concentrated EVs Isolated from Plasma and Saliva.............................138

Figure 14: Cartoon Schematics of the Dual Split Protein (DSP) α-Syn Construct........................147

Figure 15: Western blot Validation of α-Syn DSP -A and -B Construct Integrity in

Co-transduced Cells....................................................................................................148

Figure 16: DSP-α-Syn is Secreted in Response to Lysosomal Acidification Inhibition and

Exogenously Added α-Syn Fibrils..............................................................................149

xii

Figure 17: Secreted Gal3 and DSP-α-Syn are in EVs.................................................................154

Figure 18: Secreted Gal3 and DSP-α-Syn Co-localize with Exogenously Added α-Syn

Fibrils..........................................................................................................................156

Figure 19: DSP-α-syn Intensity is Greater in DSP-α-Syn EVs Positive for α-syn Fibrils.............158

Figure 20: Characterization of Human MDA Neuronal Cultures Derived from Human IPSCs....159

Figure 21: MDA Neurons Derived from Human iPSCs Form Symmetric (Functionally

Inhibitory or Modulatory) Synapses..........................................................................161

Figure 22: Lysosomal Damage Following α-Syn Fibril or LLOME Treatment Elicits an

Autophagic Response in MDA Neurons.....................................................................162

Figure 23: Neuronal Gal3 Influences α-Syn Secretion and is Reciprocally Secreted During

α-Syn Fibrils Treatment.............................................................................................164

Figure 24: Neuronal Depletion of Trim16 or ATG16L1 Influences α-Syn and Gal3 Secretion....166

Figure 25: TBK1 and Optineurin Knockdown Reduces DSP-α-Syn and Gal3 secretion in

DSP-α-syn SH-SY5Y Cells........................................................................................168

Figure 26: Stabile mCherry Gal3 Expression in SH-SY5Y Cells.................................................169

Figure 27: α-Syn Fibril Treatment Increases the Co-localization of Trim16 and ATG16L1

with mCherry Gal3 and α-Syn Fibrils........................................................................170

Figure 28: Gal3 and Trim16 Knockdown in MDA Neurons Reduces the Recruitment of

α-Syn Fibrils to Autophagosomes...............................................................................171

Figure 29: Gal3 Depletion or α-Syn Fibrils Treatment Impairs Lysosome Function....................173

Figure 30: Gal3 Depletion Reduces Autophagic Flux During α-Syn Fibrils Treatment...............175

Figure 31: Relative Gal3 and Gal8 Expression in DSP-α-Syn and MDA Neurons.......................176

Figure 32: Lysosomal Damage Increases α-Syn and Gal3 Secretion in Association with

Increased Autophagic Activity Relative Fold Difference in α-Syn............................178

Figure 33: Lysosomal Damage Affects Autophagosome Formation in Gal3 or Trim16

Depleted MDA Neurons.............................................................................................180

Figure 34: TFEB Translocation and Transcription is Maintained in Gal3 KO SH-SY5Y cells.....181

xiii

Figure 35: Lysosomal Rupture Increases the Formation of Amphisomes.....................................183

Figure 36: Gal3 KO Reduces Lysosome Rupture Induced Amphisome Formation......................184

Figure 37: Gal3 Inhibition Affects the Composition of Secreted Extracellular Vesicles from

MDA Neurons.............................................................................................................186

Figure 38: ATG7 Depletion Does Not Affect MDA Cell Viability..............................................189

Figure 39: Gal3 and Trim16 Depletion Reduce Gal3 and α-Syn Associated Secretion.................190

Figure 40: Impaired Autophagosomes Formation Decreases the Unconventional Secretion of

α-Syn and Gal3...........................................................................................................194

Figure 41: Summary Cartoon of Hypothesized Mechanism........................................................197

xiv

LIST OF ABBREVIATIONS

Microgram (μg)

Microliter (μl)

Micrometer (μm)

transmission electron microscopy (TEM)

central nervous system (CNS)

Alzheimer’s disease (AD)

Amyotrophic lateral sclerosis (ALS)

Parkinson’s disease (PD)

Huntington’s disease (HD)

multiple systems atrophy (MSA)

dopamine/dopaminergic (DA)

substantia nigra (SN)

SN pars compacta (SNpc)

Lewy Bodies (LBs

Acetylcholine/cholinergic (ACh)

norepinephrine/noradrenergic (NE)

1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)

Desmethylprodine (MPPP)

adenosine-triphosphate (ATP)

reactive oxygen species (ROS)

xv

L-dihydroxyphenylalanine (L-DOPA)

induced pluripotent stem cells (IPSCs)

midbrain DA (mDA)

glucocerebrosidase (GBA1)

Hematoxylin and eosin (H&E)

Lewy neurites (LNs)

multiple systems atrophy (MSA)

glial cytoplasmic inclusions (GCIs)

Alanine53Threonine (A53T)

amyloid-binding central domain (NAC)

both β- and γ-synuclein (β-syn and γ-syn)

terminal Soluble N-ethylmaleimide-sensitive factor Attachment Protein Receptor (SNARE)

complexes

picrotoxin (PTX),

gamma-aminobutyric acid (GABA)

tetrodotoxin (TTX)

kilodalton (kDa)

wild-type (WT)

Prion Protein (PrP)

amyloid beta (amyloid β/Aβ)

Alzheimer’s disease (AD)

TAR DNA-binding protein 43 (TDP43)

superoxide dismutase 1 (SOD1)

xvi

endoplasmic reticulum (ER)

interluken-1 (IL-1)

interluken-1beta (IL-1β)

ATP-binding cassette (ABC) transporters

extracellular vesicle (EVs)

intraluminal vesicles (ILVs)

multivesicular bodies (MVBs)

the endosomal sorting complex required for transport protein complex (ESCRT)

vacuolar protein sorting associated protein 4 (Vps4)

programmed cell death 6 interacting protein (ALIX)

tumor susceptibility gene 101 (TSG101)

cerebral spinal fluid (CSF)

dual-split protein (DSP)

4-hydroxynonenal (HNE)

human embryonic kidney (HEK)

lymphocyte activation gene 3 (LAG3)

α3-Na+/K+-ATPase (α3-NKA)

amyloid β precursor-like protein 1 (APLP1)

toll-like receptor-2 (TLR2)

autophagic-lysosomal pathway (ALP)

AuTophaGy-related (ATG)

chaperone mediated autophagy (CMA)

mammalian target of rapamycin (mTOR)

xvii

coat protein complex II (COPII)

Unc-51 Like Autophagy Activating Kinase 1 (ULK1/ATG1)

phosphatidylinositol 3-kinase (PI3K III)

phosphatidylinositol 3-phosphate (PI3P)

WD repeat domain phosphoinositide interacting protein 2 (WIPI2)

microtubule-associated protein 1 light chain 3 (LC3)

sequestosome 1 (p62/SQSTM-1)

nuclear dot protein 52 kDa/calcium-binding and coiled-coil domain containing protein 2

(NDP52/CALCOCO2)

neighbor of breast cancer 1 gene (NBR1)

optineurin (OPTN)

γ-aminobutyric acid receptor-associated protein (GABARAP)

short-hairpin RNA (shRNA)

ATG7 knockout (KO)

leucine-rich repeat kinase 2 (LRRK2)

3-methyladenine (3-MA)

high mobility group box 1 (HMGB1)

knockdown (KD)

bafilomycin-A1 (Baf-A1)

transcription factor EB (TFEB)

tubulin polymerization promoting protein (TPPP)

enhanced green fluorescence protein (eGFP)

parkin (PRKN)

xviii

PTEN inducible kinase 1 (PINK1)

genome wide associated studies (GWAS)

lysosome associated membrane protein -1 and -2 (LAMP1 and LAMP2)

glucocerebrosidase (GCase)

recombination activation gene (RAG)

guanosine triphosphatase (GTPase)

Na+/ glutamine and arginine amino acid transporter 2 (SLC38A2)

Coordinated Lysosomal Expression and Regulation (CLEAR)

MTOR Associated Protein, LST8 Homolog (mLST8)

DEP Domain Containing MTOR Interacting Protein (DEPTOR)

Tel2 interacting protein 1 (Tti1)

Telomere length regulation protein 2 (Tel2)

proline-rich AKT substrate of 40 kDa (PRAS40)

Rapamycin-insensitive companion of mammalian target of rapamycin (RICTOR)

Ras homolog enriched in brain (RHEB)

GTPase activating protein (GAP)

Adenosine diphosphate ribosylation factor-1 (ARF1)

Folliculin-Folliculin interacting protein 2 (FNIP2)

liver kinase B1 (LKB1)

AMP-activated protein kinase (AMPK)

adenosine monophosphate (AMP)

tuberous sclerosis complex -1 and -2 (TSC1-TSC2)

The microphthalmia transcription factor gene family (MIT/TFE)

xix

transcription factor binding to IGHM enhancer 3 (TFE3)

microphthalmia-associated transcription factor (MITF)

the Krüppel-associated box (KRAB)

c-Jun N-terminal kinase (JNK) activity

charged multivesicular body protein 4B (CHMP4B)

Calcium (Ca2+)

soybean ascorbate peroxidase (APE1)

carbohydrate recognition domain (CRD)

L1 neural cell adhesion molecule (L1CAM/CD171)

casein kinase 1 (CK1)

glycyl-l-phenylalanine 2-naphthylamide (GPN),

leucyl-L-leucine methyl ester (LLOME)

GFP-PolyQ74 (huntingtin protein)

lipopolysaccharide (LPS)

inducible nitric oxide synthase (iNOS)

cyclooxygenase-2 (COX-2)

tumor necrosis factor-α (TNF-α)

single nucleotide polymorphisms (SNPs)

interleukin-2 and -12 (IL-2 and IL-12)

human umbilical cord mesenchymal stem cells (hUC-MSCs)

Amyotrophic Lateral Sclerosis (ALS)

fused in sarcoma (FUS)

enzyme linked immunosorbent assay (ELISA)

xx

inflammatory bowel disease (IBD)

mini-mental state exam (MMSE)

fetal bovine serum (FBS)

American Type Culture Collection (ATCC)

reverse transcriptase-polymerase chain reaction (RT-PCR)

knockout serum replacement (KSR)

Small Mothers Against Decapetaplegic (SMAD)

human sonic hedgehog N-terminus (SHH)

human fibroblast growth factor 8 a isoform (FGF8a)

Minimum Essential Media Non-Essential Amino Acids solution (MEM-NEAA)

2-mercopatoethanol (2-ME)

human brain-derived neurotrophic factor protein (BDNF)

dibutyryl cyclic adenosine monophosphate (cAMP)

polyethylenimine (PEI)

Nanoparticle tracking analysis (NTA)

SDS-polyacrylamide gel electrophoresis (SDS-PAGE)

Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH)

2,4,6-Tris-(dimethylaminomethyl)phenol (DMP-30)

normal donkey serum (NDS)

streptavidin (SAV)

S15-mCherry (S15Ch)

wheat germ agglutinin (WGA)

piperazine-N-N′bis[2-ethanesulfonic acid] (PIPES)

xxi

fluorescein isothiocyanate (FITC)

Dulbecco’s phosphate buffered saline (DPBS)

4′,6-diamino-2-phenylindole (DAPI)

3-dimensional (3D)

maximum intensity projections (MIPs)

bicinchoninic acid (BCA)

yellow fluorescence protein (YFP)

point spread function (PSF)

full width half max (FWHM)

phosphate buffered saline (PBS)

phenylmethanesulfonyl fluoride (PMSF)

l-lactate dehydrogenase (LDH)

Methoxyphenazine methosulfate (MPMS)

nicotinamide adenine dinucleotide (NAD)

iodonitrotetrazolium chloride (INT)

dimethyl sulfoxide (DMSO)

Renilla luciferase (RLuc)

complementary DNA (cDNA)

Actin γ 1 (ACTG1)

NADH:ubiquinone oxidoreductase subunit B1 (NDUFB1)

3,3′, 5, 5-tetramethylbenzidine (TMB)

relative fluorescence units (RFU)

extracellular vesicle multiplex analysis of co-localization (EV-MAC)

xxii

single molecule localization microscopy (qSMLM)

autophagy-lysosomal pathway (ALP)

galectin-8 (Gal8)

galectin-9 (Gal9)

tripartite motif containing 16 (Trim16)

Dual Split Protein (DSP)

phospho-serine129 (pSer129) α-syn

positive (+)

Secondary antibody (2°Ab)

midbrain dopamine (mDA)

stage specific embryonic antigen 4 (SSEA-4)

tumor rejection antigen 1-81 (Tra-1-81)

octamer binding transcription factor 3/4 (OCT 3/4)

LIM homeobox transcription factor 1, alpha (LMX1A)

forkhead box protein A2 (FOXA2)

nuclear receptor related protein 1 (Nurr1)

tyrosine hydroxylase (TH)

transmission electron microscopy (TEM)

P70 ribosomal protein S6 kinase (P70 S6K1)

Firefly Luciferase (FLuc)

Small interfering ribonucleic acid (siRNA)

tank binding kinase 1 (TBK1)

optineurin (OPTN)

xxiii

Autophagy Related 16 Like 1 (ATG16L1)

yellow fluorescent protein-Microtubule-associated proteins 1A/1B light chain 3B (YFP-LC3B)

area under the curve (AUC)

relative light units (RLUs)

relative fluorescence units (RFUs)

Wortmannin (Wor)

phosphatidylinositol 3-kinase (PI3K)

xxiv

ABSTRACT

The misfolding and subsequent accumulation of alpha-synuclein (α-syn) is central to the

pathogenesis of Parkinson’s disease (PD) [258, 292, 304, 305, 407]. Several lines of evidence

suggest pathological α-syn spread cell-to-cell via a “prion-like” mechanism [258, 292, 304, 305,

407]. Furthermore, this pathological α-syn is capable of “seeding” further misfolding of non-

pathological α-syn, converting them to the pathological form. While a vast body of both genetic

and experimental evidence indicates that α-syn is critical to PD development, how α-syn induces

progressive neuronal dysfunction and cell death remains unclear.

Autophagy, conventional for macroautophagy, is the primary degradation pathway for α-

syn aggregates [211]. Autophagy also influences the unconventional secretion of both

pathological and non-pathological α-syn [63, 106, 182]. Evidence ranging from genetic,

experimental, and PD brain tissue strongly implicate impaired autophagy as both a symptom and

contributor to disease pathology [211, 386]. Additionally, autophagic dysfunction influences the

secretion of pathological α-syn via extracellular vesicles (EVs) [41, 132, 155, 184, 192, 225, 279,

387]. Notwithstanding, methods to identify and characterize subpopulations of EVs from the

total population are lacking. To address this, an imaging-based workflow utilizing

immunofluorescence staining and quantitative fluorescent microscopy was formulated to assess

the protein composition to characterize individual EVs via Multiplexed Analysis of Co-

localization (EV-MAC). Using this EV-MAC workflow secreted α-syn associated EVs were

analyzed in the context of PD pathological stimuli.

xxv

Our lab previously showed that treating cells with oligomerized, α-syn fibrils results in

their endocytosis into endo/lysosomal compartments, where they induced rupture, and are then

recruited to the autophagic-lysosomal pathway [147, 153]. PD brain tissue stained for the known

lysosomal rupture marker, galectin-3 (Gal3), revealed pathological α-syn aggregates were readily

Gal3 positive, suggesting a potential link between lysosomal rupture, Gal3, and α-syn

accumulation [147]. However, like α-syn, Gal3 is unconventionally secreted in association with

EVs and during autophagy impairment. Yet, whether Gal3 or lysosomal rupture affects α-syn

secretion, and the underlying mechanisms by which this process could occur are unknown.

Here, evidence for a cellular mechanism that explains the cell-to-cell transfer of

pathological forms of α-syn is provided. We demonstrate lysosomal rupture, Gal3 recruitment

and, in association with its autophagic adaptor protein, tripartite motif containing 16 (Trim16),

and autophagy related 16 like 1 (ATG16L1), stimulate α-syn secretion via an unconventional

autophagic pathway. Collectively, this work may contribute to improved diagnostic methods and

therapeutics for synucleinopathies.

1

CHAPTER 1

REVIEW OF THE LITERATURE

Introduction to Synucleinopathies.

The central nervous system (CNS), comprised of the brain and the spinal cord, is

responsible for communicating incredibly complex signals through-out the body in fractions of

seconds. This process occurs through the transfer of chemical messages that generate electrical

signals within the primary communicating cells of the CNS, known as neurons. Most of these

neurons are formed during gestation and are meant to last an entire lifetime. It is the deterioration

of neurons that results in neurodegenerative diseases. Initially, neuronal loss may result in mild

symptoms, such as an inability to remember a name or issues with coordination. As large

numbers of neurons are lost, symptoms worsen which can result in the inability to think clearly,

walk unassisted, or generally function in the world. Ultimately, most neurodegenerative diseases

are fatal.

It is unknown what causes neurodegenerative diseases. Familial cases from genetic

mutations within specific genes makeup a relatively small fraction of total cases. Thus, most

cases are of idiopathic (or sporadic) origin, with no known cause. Currently in the United States,

5.8 million with Alzheimer’s disease (AD), ~1 million with Parkinson’s disease (PD), 400,000

with multiple systems atrophy (MSA), 50,000 with Amyotrophic lateral sclerosis (ALS or Lou

Gehrig’s disease), and 41,000 with Huntington’s disease (HD) [301, 321, 351, 522, 549]. Age is

the greatest risk factor for the development of neurodegenerative diseases. Because

2

neurodegenerative diseases occur primarily later in life, the incidence rate is expected to rise

with an increasingly aging population. There is no cure for neurodegenerative diseases and few

treatment options exist. It is of increasing urgency to find treatment options and cures to combat

these diseases.

Overall, neurodegenerative diseases share several commonalities including the

accumulation of aggregated proteins within the brain and progressive cell death. The shared

commonalities between neurodegenerative disease make the discovery of therapeutic

interventions for one disease potentially useful for multiple neurodegenerative diseases.

Among the categories of neurodegenerative diseases are synucleinopathies. The defining

criterion of synucleinopathies is the accumulation of the protein alpha-synuclein (α-syn) within

the brain [8, 160]. During synucleinopathy disease progression, it is believed α-syn accumulation

stems from its misfolding. Misfolded α-syn can template and catalyze further misfolding of

native α-syn subsequently resulting in its aggregation. These aggregated forms of α-syn can

induce cellular dysfunction and result in the formation of dense, proteinaceous inclusions known

as Lewy pathology which accumulate in cells. In addition to accumulating within cells,

misfolded α-syn is thought to spread from cell-to-cell during disease progression. In this way,

misfolded α-syn can spread cellular dysfunction via transfer and subsequently templating further

misfolding of α-syn in the recipient cells in a continuous cycle. Thus, misfolded α-syn is

believed responsible for the spreading pathology and cellular dysfunction that is observed in PD

and other synucleinopathies. Understanding what influences α-syn misfolding, and how

misfolded α-syn is transferred and accumulates are integral to developing effective treatments.

In PD and the other synucleinopathies, the observed clinical symptoms are thought to

occur from self-propagating pathology that spreads throughout the brain [258, 292, 304, 305,

3

407]. The consequence of this spreading pathology results in physiological dysfunction,

inflammation, and cell death in the affected tissues [480]. The primary treatment for PD is

levodopa which is used as a replacement therapy during the early stages of the disease [92]. Yet,

the continuing progression of disease reduces the effectiveness of levodopa overtime. Deep brain

stimulation is another treatment for advance stage individuals with PD, but similarly does not

treat the underlying cause of the disease [388]. Therefore, understanding cellular mechanisms

associated with disease progression can inform possible treatment options.

In this chapter, a review of the following topics will be covered: (1) a general background

on synucleinopathies, (2) α-syn and cell-to-cell pathological spreading, (3) Autophagy and its

involvement in α-syn accumulation and transmission, (4) Autophagic-lysosomal pathway

dysfunction in connection with synucleinopathies, (5) The role of galectins in neurodegenerative

diseases.

Parkinson’s Disease

Among synucleionpathies, PD is the most infamous. PD is the second most prevalent

neurodegenerative disease and is estimated to affect 7 in 100,000 of individuals between the ages

of 40-50 [58]. This incidence rate increases with age, where it is estimated to affect 1,900 in

100,000 of people over 80 [58]. Historically, the first documented cases of PD were by James

Parkinson in his work “Essay on the Shaking Palsy” more than 200 years ago [366]. In his essay,

Parkinson characterized a disease associated with motor dysfunction from six individuals. Many

of the symptoms Parkinson’s initially described remain hallmark diagnostic criteria of the

disease including bradykinesia, tremors, rigid movement, and postural instability. On the whole,

these symptoms are referred to as “Parkinsonisms”. Yet, individuals with PD also suffer from

non-motor symptoms such as depression, constipation, and REM sleep disorder which may

4

precede motor symptoms in some individuals [388]. Additionally, even with efficacious

treatment of underlying motor symptoms ~80% of individuals will experience cognitive decline

and ultimately progress to Parkinson’s disease Dementia (PDD) [223]. Individuals who suffer

from either PDD or Dementia with Lewy bodies (DLB), another prominent synucleinopathy,

display similar levels of cognitive decline and have comparable deficits in executive function,

visual-spatial processing, and verbal learning [7]. Pathologically, distinguishing between PDD

and DLB is nearly impossible [7]. However, individuals with DLB first show signs of cognitive

deficits before progressing to motor dysfunction. Thus, PDD and DLB diagnostic criteria is

predicated on the temporal manifestation of symptoms rather than pathology [521]. Notably, in

addition to the formation of intracellular deposits α-syn, the majority of synucleinopathies have

eventual dopaminergic neuron degeneration. As a result, the majority of work surrounding PD

and other synucleinopathies focuses on the loss dopaminergic neurons (DA).

Substantia Nigra, Dopaminergic Neurons, and Neuronal Degeneration

The substantia nigra (SN), Latin for “black substance”, gets its name from its distinct

dark complexion. The striking difference in color makes it identifiable by the naked eye in

coronal, midbrain cross-sections relative to the other neighboring regions of the basal ganglia.

This unique characteristic is due to the large number of DA neurons, which produce a dark,

neuromelanin as a byproduct of dopamine synthesis and metabolism. Thus, DA neurons slowly

accumulate neuromelanin over one’s lifetime. The accumulation of this neuromelanin is most

prominent within the SN pars compacta (SNpc) and is the region that undergoes the most

extensive degeneration in PD.

In PD, disease diagnosis requires both the presence of Lewy Bodies (LBs) as well as

clinical motor symptoms resulting from selective loss of SN DA neurons. According to the

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United Parkinson disease Rating Scale, ~50% of the nigral DA neurons are lost by disease onset

and continuing symptoms correlate with increased loss. It has been shown other regions of the

brain are also affected. For example, the cholinergic (ACh) and noradrenergic (NE) neurons of

the pedunculopontine nucleus and locus coeruleus, respectively, both undergo degeneration

during PD [169, 205]. Increasing evidence suggests that PD pathology does not originate in the

SN, but rather progresses to it. The point remains, that the nigral DA neurons are the most

affected during synucleinopathy pathogenesis. Thus, the question is raised, “Why are the

dopaminergic neurons of substantia nigra particularly vulnerable?”

A multitude of mechanistic factors are known to contribute to nigral DA neuron death as

a part of PD and other synucleinopathy pathogenesis. The underlying cause can be from either

genetic or environmental means. The most extreme of these environmental factors being the

small group of drug addicts whom selectively induced DA neuron death and developed PD after

injecting the accidently synthesized 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)

during the production of the opioid, Desmethylprodine (MPPP) [272]. In addition, exposure to

pesticides which including the herbicide, paraquat, and the insecticide, rotenone, are associated

with increased PD [49, 163, 176, 201, 297]. Conversely, mutations in genes such as SNCA, the

gene that encodes for α-syn, are also causative in familial PD. Moreover, the misfolding,

accumulation, and transmission of α-syn is believed responsible for the development of

idiopathic PD and is supported by a vast body of literature. In this way, mechanisms that

contribute to the α-syn pathology such as impaired autophagy, lysosomal dysfunction, and

inflammation are ultimately responsible for developing PD and other synucleinopathies by

causing DA neuron death. Before reviewing these individual mechanisms, this section will focus

on the why nigral dopaminergic neurons are lost.

6

One compelling explanation articulated by Bolam and Pissadaki [39], but previously

proposed by others [45, 46, 227, 358], argues that the nigral DA neuron’s morphology and high

energy requirements put them at a unique disadvantage to stressors. One commonality among the

neurons within the regions that undergo degeneration (the ACh neurons of the pedunculopontine

nucleus, the NE neurons of the locus coeruleus, as well as the DA neurons of the SN) are their

relatively long, unmyelinated regions and extensive number of synapses which require

tremendous amounts of energy to maintain [39, 45, 46]. To further illustrate this point, if the

totality of arborizations from an average human SNpc DA neuron were arranged from end-to-

end, they would have an estimated length of 4.5 meters. In addition, it is estimated that these

neurons form between 1 and 2.4 million synapses on average [39]. In point of comparison, in rat

brains, the SNpc DA neurons form at least two orders of magnitude more synapses relative to

other basal ganglia neurons. DA neurons function as pacemaker neurons, constantly oscillating

between firing states, and subsequently require a massive pool of adenosine-triphosphate (ATP)

to reset their membrane potential. For frame of reference, while the brain is 2% of the total

human body by mass, it uses 20% of its energy at resting levels, of which the majority of that

20% is used to run the sodium (Na+)/potassium (K+) pump to reset neuronal membrane potential

[136]. Bolam and Pissadaki go on to argue that under normal conditions these dopaminergic

neurons are capable of maintain their function but have minimal room to compensate in response

to insult. Furthermore, they propose when these dopaminergic neurons misstep they can

snowball. Thus, slight impairments in DA neuronal function may ultimately escalate into total

misfunction.

Another commonality shared between pathogenically susceptible DA and NE neurons are

their neurotransmitter synthesis pathway, in that dopamine is a precursor to norepinephrine.

7

Fundamentally, the production of reactive oxygen species (ROS) is a toxic process and a

contributing factor in nearly all diseases including synucleinopathies. Mechanistically, ROS can

damage DNA, lipids, and proteins in cells resulting in cellular dysfunction and apoptosis. On an

intrinsic level, the generation of ATP by aerobic cellular respiration leads to the production of

ROS. Therefore, cells that utilize large amounts of energy, such as DA neurons, also have the

potential to generate large amounts of ROS. In the case of DA neurons, they also must cope with

being a terminally differentiated cell that are expected to last an entire lifetime, as well as the

produce and metabolize dopamine. The breakdown of DA generates ROS. Additionally,

dopamine and its precursor L-dihydroxyphenylalanine (L-DOPA) are highly prone to enzymatic

oxidation which also results in the production of ROS. To combat these issues, cells have

evolved so dopaminergic synaptic vesicles also contain antioxidant molecules, such as ascorbic

acid [477]. Interestingly, the generation of neuromelanin is thought to correlate with improper

cytosolic DA metabolism as artificially increasing cytosolic DA levels in a DA neuronal culture

model increased neuromelanin pigmentation [476]. Moreover, it is thought that increased

neuromelanin production is associated with increased cell death.

Further supporting DA neurons innate vulnerability and low stress tolerance, are studies

using induced pluripotent stem cells (IPSCs) initially derived from individuals with PD which

are then differentiated into midbrain DA (mDA) cultures. Multiple studies using cells derived

from individuals with PD, both familial and idiopathic, reproduced phenotypic changes when

differentiated into mDA neurons consistent with disease pathology despite being derived from

fibroblasts or hemopoietic cells [429, 440, 567]. MDA neurons generated from IPSCs derived

from familial PD patients, two different SNCA triplication lines and a N370S glucocerebrosidase

(GBA1) line, had reduced lysosomal degradative enzyme activity [330]. Multiple patient derived

8

IPSCs from individuals with idiopathic PD and the familial PD mutant, G2019S leucine-rich

repeat kinase 2 (LRRK2), had impaired autophagic capabilities and increased caspase-3

activation compared to control differentiated mDA neurons [429]. IPSC derived mDA neurons

from two individuals with early onset PD showed marked changes at the stage of neuronal stem

cells including impaired autophagy, cell division, and other phenotypic changes consistent with

accelerated aging [567]. Collectively, the low stress tolerance of DA neurons, because of their

unique structure and function, puts them at a marked disadvantage to cope with the consequences

of synucleinopathy pathogenesis. This results in their misfunction and cell death. Thus, DA

neurons may suffer from being the proverbial “canary in the coalmine” during PD disease

pathogenesis. However, unlike the canary which served to warn miners of imminent danger, by

the time motor dysfunction is observed, considerable DA neuron death has occurred [141, 414].

Therefore, the discovery of biomarkers for earlier synucleinopathy disease diagnosis in

combination with neuroprotectives to prevent vulnerable neuron cell death represent a possible

therapeutic avenue to slow disease progression.

Lewy Pathology

The histopathological diagnostic definition of synucleinopathies is the formation of

degradation resistant, proteinaceous inclusions primarily composed of misfolded, aggregated α-

syn in the brain. The identification of these inclusions was first described by Fritz Jacob Heinrich

Lewy in his document “Paralysis agitans” in 1912 [412]. In Fritz Lewy’s original work, he noted

that abnormal round, perinuclear deposits were present in PD brain tissue. Hematoxylin and

eosin (H&E) staining of this tissue revealed that these deposits were eosinophilic with a dense,

granular core. Six years later these structures would be coined “Lewy Bodies” (LBs) by

Konstantin Tretiakoff in his 1919 doctoral thesis where he first described an association between

9

the presence of LBs and significant degeneration of the SN among post-mortem PD brains [412].

In 1997, a paper demonstrated the presence of α-syn in Lewy bodies for the first time and in

1998, the same lab showed that filamentous α-syn surrounded the dense core of LBs by electron

microscopy [464, 465].

In addition to LBs, other types of α-syn deposits are present in synucleinopathies. Lewy

neurites (LNs) are another common histopathological feature in PD, DLB, as well as another rare

synucleinopathy known as multiple systems atrophy (MSA). Together LBs and LNs are referred

to as Lewy pathology and share similar features including filamentous α-syn, dense granular

structure. However, LNs differ from LBs in that they are more abundant and are found within

neuronal axons or dendrites rather than in close proximity to the nucleus [249]. By comparison,

pale bodies (PBs), are cytoplasmic inclusions also primarily composed of filamentous α-syn but

are structurally unorganized and not readily stained with H&E. PBs are often found in neurons

that also have LBs present and correlate in number. Yet, because PBs often outnumber LBs in

individuals with non-symptomatic or short duration synucleinopathies, PBs are thought to be

precursors to LBs [104]. Finally, glial cytoplasmic inclusions (GCIs) are commonly found in

MSA [512]. Relative to the previous described inclusions, GCIs are specifically found in the

glial oligodendrocytes rather than in neurons and are rarely found next to the nucleus [512].

While the identification of one or more of these inclusions in post-mortem brain tissue is

required for a definitive diagnosis of a synucleinopathy, their role in these diseases is unknown

and hotly debated. It was once believed the formation of these aggregates was responsible for

underlying disease pathogenesis. This line of thinking has since been contested, as current work

suggests the formation of these inclusions is a cellular defensive mechanism. Instead, the initial

10

misfolding and formation of higher order α-syn species is thought responsible for the underlying

cellular dysfunction and neurotoxicity observed in synucleinopathies.

α-Syn’s Original Link to Parkinson’s Disease Pathology

In 1988, Maroteaux et al. discovered alpha-synuclein (α-syn) after its extraction from

Torpedo Ray and named it based on its cellular localization at the synapse and nucleus [320]. It

was not until 1997 that mutations in SNCA, the gene that encodes for α-syn, were first linked to

PD [390]. To this end, In 1996 Polymeropoulos et. al, first determined mutations within the

chromosome 4q21-q23 segment were associated with familial PD from a large Italian kindred

with a strong pedigree of the disease [389]. In 1997, they identified the resulting mutation caused

a substitution in the 53rd amino acid of Alanine for Threonine (A53T) in α-syn [390].

Ultimately, it was approximated that among family members who carried the A53T mutation,

85% developed PD [390]. This was the first documented case of a single gene being sufficient

for the development of PD. Both the discovery of this α-syn mutation as an underlying cause of

familial PD as well as the inclusion of fibrillar α-syn in LBs [464, 465] would ultimately

contribute to α-syn becoming the primary candidate responsible for the development of

idiopathic PD.

α-Syn’s Structure

α-syn is a 140 amino acid protein made up of three domains, the lipid-binding α-helix

domain at the N-terminal, the amyloid-binding central domain (NAC), and the C-terminal acidic

domain [134]. The N-terminal domain represents the first 1-87 amino acids domains and

contains seven sequences of 11 amino acid repeats. Each of these 11 amino acids contains the

hexameric KTKEGV motif also found in the α-helical domain of apolipoproteins [514]. This

KTKEGV motif is responsible for α-syn’s ability to interact with lipid domains and form a

11

helical structure [48]. Markedly, this KTKEGV is also shared among both β- and γ-synuclein (β-

syn and γ-syn). In contrast to α-syn, both beta- and gamma-synuclein (β- and γ-syn) are not

linked to synucleionpathy pathogenesis [479].

The hydrophobic NAC domain overlaps with the N-terminal domain and is contained

within amino acids 61-95. It is this region that allows α-syn to undergo fibrillization and

aggregate by supporting the formation of a secondary β-structure [413, 460]. This is achieved by

the VTGVTAVAQKTV 12 amino acid stretch at 71-82 which also confers degradation

resistance [172]. Finally, the C-terminal domain represents the 96-140 amino acids and forms an

acidic tail which takes on a random coil structure. This negatively charged domain is thought to

interact with the amyloid-binding central domain to prevent α-syn aggregation. This interaction

is inhibited during phosphorylation of α-syn at Serine 129 (pS129) and is associated with

aggregation formation [139]. Interestingly, the C-terminal region shares homology with heat

shock proteins, and thus, suggested to give α-syn a role mediating cellular protein denaturing in

response to insult [248].

Native α-Syn’s Structure and Function

Despite the heavy focus on determining misfolded α-syn’s underlying involvement in PD

and other synucleinopathies pathogenesis, relatively little remains known about its native

structure and function. At present, there is no agreement on the native structure of α-syn. It has

been characterized as intrinsically unfolded [48, 494], helical [30, 525], or a mixture of the two

[55]. In part, this may be due to a lack of understanding of native α-syn’s function which could

provide insight into interacting partners. Notably, it has been shown that α-syn more readily take

on a helical structure in the presence of phospholipid membranes [159, 418].

12

The current consensus regarding α-syn’s function is that it promotes membrane

curvature, thus allowing it to contribute to vesicular trafficking and budding, particularly at

synaptic regions [65, 214]. In agreement with this idea, α-syn has been shown to associate with

presynaptic terminal Soluble N-ethylmaleimide-sensitive factor Attachment Protein Receptor

(SNARE) complexes [214] and promote assembly [54]. These interactions have led those to

hypothesize α-syn participates in regulating DA synaptic vesicle release. This has in turn led

others to investigate if α-syn transmission between cells occurs at synaptic terminals, indicating a

potential for α-syn to be intrinsically be transferred from cell-to-cell.

Indeed, a direct relationship between neuronal activity has been positively linked to α-syn

exocytosis [542]. While caveated that synaptic transmission may not be responsible for the

release, Yamada et al., 2018 demonstrated that administration of picrotoxin (PTX), a gamma-

aminobutyric acid (GABAA) receptor “A” antagonist, increased neuronal activity, and α-syn in

brain interstitial fluid collected from the hippocampal region of freely moving mice. Conversely,

the administration of the Na+ channel blocker tetrodotoxin (TTX), reduced neuronal activity, and

reduced α-syn levels in brain interstitial fluid. When α-syn was extracted from brain interstitial

fluid by size exclusion chromatography, it was primarily found to elute at 60kDa suggesting a

possible multimerization or association with other proteins.

Interestingly, while the current paradigm suggests that native α-syn is predominately

found as an unstructured monomer, there is some evidence to suggest that native α-syn forms

homotetrameric α-helix structures in the presence of lipids [30, 525] indicating that vesicular α-

syn may take on a helical structure.

13

Pathological Species of α-Syn

Since the originally identified A53T α-syn mutation as a source for familial PD [390],

five other autosomal dominant, single amino acid mutations have also been identified: A30P,

E46K, H50Q, G51D, and A53E [263, 289, 368, 392, 557]. In total, all six of these mutations are

found within the N-terminal domain of α-syn and thus disruption of this region is thought to be

pathogenic. To this end, both the A30P and the A53T have been shown to have an increased

propensity to undergo fibrilization compared to wild-type (WT) α-syn [291]. A recent series of

work investigating the conferred pathogenicity of α-syn’s N-terminal domain identified that a

portion of this region is necessary for its conversion into the β-cross secondary formation. It was

shown that antibodies against the 15-65 amino acid portion of α-syn reduced the formation of

higher weight molecular species [446]. Furthermore, it was more recently shown that amino

acids 36-42 are required for aggregation and during acidification this 36-42 amino acid

sequences interacts with amino acids 45-57 [124]. In this study, they went on to show that while

deletion of the 36-42 region prevents aggregation in vitro, it also changes α-syn’s ability to

mediate vesicle fusion by affecting its conformational properties when lipid bound [124].

In addition to SNCA mutations being linked to PD, increased gene copy number increases

the likelihood of developing PD and in these individuals a faster rate of disease occurs [69, 116,

142, 454]. Among those individuals’ duplications, triplications, and quadruplications are

associated with progressively earlier diagnosis and greater clinical severity with respect to

expression [69, 116, 142, 454]. These findings suggest that in addition α-syn mutations,

increased expression of WT α-syn is also associated with pathogenesis.

14

In further support of this paradigm, a paper identified β2-adrenoreceptor as a bidirectional

regulator of α-syn expression by modulating SNCA promoter and enhancer acetylation through

histone 3 lysine 27 [341]. To determine the relevance of this finding, they followed up on 4

million Norwegians over an 11-year period taking the β2-adrenoreceptor agonist salbutamol and

antagonist propranolol [341]. While taken to treat asthma, salbutamol as well as the β blocker,

propranolol, can readily cross the blood-brain barrier and thus were hypothesized to regulate α-

syn expression in the brain. The findings from this study showed the use of salbutamol was

associated with a reduced risk of developing PD and in contrast, propranolol was associated with

an increased risk [341].

Additionally, while knockout (KO) mouse models of SNCA have minor phenotypic

differences associated with alterations in DA transmission, no motor deficits or

neurodegeneration is observed [57]. However, it is also known that germ-line KO models can

develop compensatory expression to overcome loss of function. Thus, several groups have

explored the consequences of late-stage α-syn depletion.

Figure 1: Structure and Domains of α-Syn. N-terminal Domain amino acids (AA) 1-87, the amyloid-binding

central domain (NAC) Domain AA 60-95, C-terminal Domain 95-140. 11 amino acid repeat KTKEGV motifs

shown in Tan. Individual amino acids represent known mutations or post-translation location sites that affect α-

syn’s propensity to aggregate or function. familial PD autosomal dominant mutations A30P, E46K, H50Q, G51D,

A53T, and A53E. Identified artificial point mutations at E35K and E57K increase oligomerization. E57K, A53T,

E46K, and E35K mutants increases the rate of membrane interaction [501]. S87 and S129 phosphorylation

associated with pathological α-syn. However, S129 phosphorylation is associated with increased rate of

oligomerization while S87 phosphorylation is associated with reduced oligomerization. Y125 or S129 increases

the binding affinity of Cu(II), Pb(II), and Fe(II). Adapted from [303].

15

Marked differences are reported when α-syn is depleted in adult models, ranging from

substantial neuronal loss and degeneration to no differences [33, 174, 254, 564]. To explain these

differences, it has been proposed that the levels of α-syn depletion influence neuronal loss, where

complete depletion negatively impacts survival. This explanation is consistent with recent papers

where reducing α-syn expression does not negatively impact neuronal survival while complete

deletion does [174, 564]. These studies suggest α-syn has a role in normal DA neuronal function,

which can be compensated for in the cases of germ-like deletion. Particularly, they indicate

improper α-syn function, which may result from mutations that compromise native function,

increase its propensity to aggregate, or excessive affect expression levels lead to the development

of disease pathology.

Conformation and Structure.

While both mutation and relative expression of α-syn are directly linked to

synucleinopathies, the mechanism by which α-syn initially misfolds, accumulates, and

subsequently induces a diseased state remains unknown. Additionally, the primary toxic

aggregate species of α-syn is unknown; with compelling evidence to support α-syn fibrils as well

as prefibrillar species of α-syn such as oligomers. To further complicate this matter, work from

over the past couple decades demonstrates α-syn can form a multitude of oligomerized

conformations [405]. α-syn oligomers derived from recombinant α-syn can have different

molecular weights and secondary structures composed of varying degrees of α-helical, β-sheet,

and disorder regions based on the method of preparation. Interestingly, many of these conditions

can mimic those found in a cell. To this end, exposing α-syn to different physical and/or

chemical conditions such as lipids, metal ions, alcohol, or pH can influence oligomerization

propensity as well as structure [473]. These different conditions can also confer different

16

biological properties and ultimately influence cellular activity profiles to varying degrees [5,

100, 105, 369, 398, 513]. For example, in Danzer et al., 2007 α-syn oligomers were prepared

based on three separate protocols (A, B, and C) with or without the addition of iron (1 or 2) for a

total of six different preparations. Oligomers from each preparation were then characterized for

their physical structure, their ability to elicit changes in calcium (Ca2+) influx, cell toxicity,

caspase activation, and seeding potential. A1 and A2 oligomers were found to have a more

annular structure while B1 and B2 as well as C1 and C2 oligomers had a spheroid, globular

structure but differing degrees of particle size. When primary cortical neurons were treated with

each α-syn oligomer preparation, only A1 and A2 oligomers elicited a change in Ca2+ influx and

membrane potential or increased cell death and caspase-3 activation in the tested time frame. In

contrast, B1 and B2 as well as C1 and C2 α-syn oligomers were found to seed α-syn while A1

and A2 did not. Among the B and C oligomers, the oligomers purified in the presence of iron, B2

and C2, had markedly higher seeding potential compared to those purified in the absence of iron,

B1 and C1. This example demonstrates the wide range of qualities different α-syn oligomers can

possess.

While a vast number of conformations are possible, empirically α-syn oligomers

generally share a common quaternary structure. Both atomic force and electron microscopy

indicate α-syn oligomers predominately form a spheroid shape that is ~30 nm in diameter and 2

to 10 nm in height [74, 93, 94, 121]. Intrinsically, these oligomeric species can be further

subdivided into species that will either take on a fibril formation or exhibit stability that impedes

the formation of fibrils [100, 369]. Moreover, the formation of these different conformational

species is not mutually exclusive and can occur concurrently [100, 105, 369]. While great

advances have been made in these areas, α-syn’s ability to conform to a vast number of

17

oligomeric states with varying degrees of stability, seeding kinetics and experimental properties

makes it especially difficult to understand their specific contribution to underlying pathology.

Aside from oligomeric α-syn species, both protofibrils and fibrils are associated with

synucleinopathies. The fibril form of α-syn is predominately found associated with LBs [520].

Relative to oligomeric species of α-syn which have a size of ~400 kDa, α-syn fibrils take on

sizes a full order of magnitude higher, in the mDa [4]. These fibrillar species of α-syn are

believed to contribute to degeneration by seeding soluble α-syn into higher molecular weight

aggregates which in-turn disrupts cellular mechanisms. The administration of sonicated α-syn

fibril aggregates, has been shown to affect ion homeostasis [42], disrupt cellular proteostasis

[47], and compromise multiple organelle’s function including the endoplasmic reticulum, the

golgi apparatus, mitochondria, as well as lysosomes [147, 153]. α-syn fibril formations also

perpetuate an inflammatory state resulting in chronic inflammation another contributing factor to

neurodegeneration [181, 186, 207, 378]. In contrast to oligomers where lesions are not observed,

when extracted mDA sized α-syn aggregates are injected into the CNS of animal models ranging

from mice to non-human primates, they develop lesions akin to those observed in PD regardless

of whether the α-syn was originally derived from PD patient brain homogenates or Lewy

pathology-like deposits from other animal model brains [305, 306, 325, 346, 394, 404, 448, 527].

This occurs even in the absence of human α-syn over-expression [304, 325, 404, 448]. This

paradigm is also true from α-syn aggregates extracted from other types of synucleinopathies, α-

syn from individuals with MSA results in the development of brain lesions and a phenotype

consistent with MSA pathogenesis when injected into the CNS of animal models [304, 305, 325,

346, 394, 404, 448, 527]. Similarly, injecting de novo generated α-syn fibrils can also induce the

aggregation of endogenous α-syn in the brains of animal models akin to α-syn aggregates

18

isolated from PD or MSA brains [304, 305, 375, 406, 407, 519]. Thus, providing credence for

the use of de novo fibrils to study a synucleinopathy associated pathological state faithfully.

Finally, these works also support the primary accepted hypothesis for the underlying cause of

PD, largely postulated by the neuroanatomists couple Heiko and Eva Braak. known as the prion-

like hypothesis.

Cell-to-Cell Propagation of α-Syn

Braak Staging and Hypothesis.

In 2003, Braak et al. published a proposed staging system for the transmission of

pathological α-syn in relation to the progression of idiopathic PD. These works would mark the

foundation for the prion-like hypothesis of PD. In these works, Braak used neuroanatomical

evidence in which known areas involved in clinical PD were assessed for LNs and LBs within

neurons. These regions were assessed for pathology among a spectrum of cases and severities

using an assumption that non-symptomatic cases are indicative of mildest pathology and the

most symptomatic represent the end of disease spectrum. In his assumed premise, he postulated

that neuronal damage during disease is not random but follows a defined sequence based on

topographical characteristics. During this work, Braak identified origin sites of pathology based

on α-syn inclusions which moved upward through the brain in a total of six stages. In stage one,

α-syn associated legions initially found in the dorsal motor nucleus of the glossopharyngeal and

vagal cranial nerves as well as the anterior olfactory bulb. Stage two further includes the caudal

raphe nuclei, gigantocellular reticular nucleus, and coeruleus–subcoeruleus complex. Stage three

further includes the midbrain with a noted predominates of α-syn associated legions in the

substantia nigra pars compacta. Stage four further includes confined regions of the cortex

specifically the temporal mesocortex located in the transentorhinal region and CA2-plexus of the

19

allocortex. Stage five further includes progression into the higher order sensory areas and

prefrontal cortex. Finally, stage six further includes progression to first order sensory associated

areas of the cortex and premotor areas.

Braak published a second paper in 2003 which included the “Braak Hypothesis” based on

his staging system. In this second paper, Braak hypothesized idiopathic PD may initially

originate outside of the CNS potentially by a still unknown pathogen. Braak noted that both

regions of stage one, the dorsal motor nucleus and the anterior olfactory bulb, have neuronal

fibers that feed into regions outside of the CNS and could be potentially exposed to outside

environmental factors. He also noted α-syn inclusion could form and have been observed in the

submucosal Meissner plexus and Auerbach plexus of the gastrointestinal tract in PD patients. He

further rationalized if an unknown pathogen were responsible for PD, retrograde transport into

the CNS from these regions of the enteric nervous system could be achieved through the

visceromotor projections of the vagus nerve which synapses onto these regions.

In 2007, Christopher Hawkes and Heiko Braak published their “dual-hit hypothesis”,

providing further evidence for the origins of idiopathic PD as originating outside of the CNS

[193]. In their document, they highlighted the existence of a long phase of prodromal symptoms

(non-motor symptoms) that corresponded with early PD. Among these being loss of smell,

gastrointestinal disorders, cardiac distress, and sleep issues. Importantly, they postulated the

observed changes in the gastrointestinal tract, heart, and sleep during early PD all share neuronal

connectivity via the vagus nerve. They further rationalized an unknown infectious particle could

initially be exposed to the nasal passage and gain access to olfactory bulb through the cribifrom

plate. This infectious particle could then be swallowed, penetrate the intestinal lining, infect the

enteric nervous system via the Meissner’s plexus, and access the vagus nerve where it could

20

travel retrograde into the brain. Ultimately, they proposed that the source of this infection was an

unknown neurotropic factor of viral origin. They also suggested that a prion-like substance

would also fit their described criteria. While a viral pathogen has yet to be identified in

connection with PD, considerable overlap in qualities among prions and misfolded α-syn have

since been identified from this proposal.

The Prion-Like Hypothesis.

The word “prion” was originally coined in 1982 to refer to a proteinaceous, infectious

particle that caused transmissible spongiform encephalopathies in mammals [393]. The

originally discovered prion particles were primarily composed of the mammalian Prion Protein

(PrP). This discovery was unprecedented because it marked the discovery of an infectious,

disease spreading agent, that lacked nucleic acids yet was capable of disease transmission among

organisms. In 1994, Reed Wickner published a study highlighting the conformational

rearrangement of host proteins based on a misfolded variant of itself in yeast which he stated

functioned as a prion [535]. Wickner also noted these misfolded protein species catalyzed the

misfolding of the host protein and were transferred from cell-to-cell. The current defining criteria

of a prion is: Any unit capable of sustained propagation of protein or proteins through an altered

state and are propagated by infection within individual organisms (such as mammalian PrP) or

by inheritance into organisms’ progeny through cytoplasmic or nuclear material (such as

URE3/Sup35 in budding yeast [535]) [190]. In the recipient organism, propagation of the prions

is achieved by co-opting homologs, which may or may not be transgenic to the infecting protein

[190].

In 1997, Sunde et al. demonstrated six pathogen associated proteins, including PrP and

amyloid beta (amyloid β/Aβ), take on a near identical amyloid structure while in their misfolded

21

state by synchroton x-ray diffraction [478]. Amyloids are defined as aggregated proteins with a

fibrillar morphology, form a cross-β, β-sheet secondary structure, and is stained with congo red

dye [130]. It is now recognized that many neurodegenerative diseases are associated with the

formation of amyloid protein aggregates including AD, associated with the formation of amyloid

β and fibrillar tau; PD, associated with fibrillar α-syn; Huntington’s disease (HD), associated

with polyglutamate expanded fibrillar Huntingtin; ALS, associated with TAR DNA-binding

protein 43 (TDP43), superoxide dismutase 1 (SOD1), and fused in sarcoma (FUS) aggregates.

The commonalities shared among these neurodegenerative diseases and with transmissible

spongiform encephalopathies includes the formation of amyloid aggregates that are degradation

resistant, undergo transmission from cell-to-cell, propagate further protein misfolding by

catalyzed co-opting of the host proteins, and progressive cell death. Decidedly, because current

evidence suggests these listed neurodegenerative proteins do not undergo transmission from

person-to-person, they do not fit the criteria for a prion. Yet, due to shared commonalities among

these proteins and prions, they are often referred to as prion-like or propagons [137].

In the context of PD and other synucleinopathies, the prion-like hypothesis proposes the

abnormal, aggregation of protein of which is mainly α-syn, is the underlying source of disease.

To this end, abnormal α-syn undergoes misfolding into an amyloid structure, transmission to

neighboring brain regions, and causes further aggregation of the endogenous, native α-syn within

the recipient cells. This aggregation process is due to the misfolded α-syn’s ability to catalyze

further misfolding of native α-syn by acting as a template seed and thus propagating pathology.

The accumulation and transmission of this abnormal α-syn results in underlying cellular

dysfunction due to disrupted or compromised function, and eventually cell death resulting in the

observed disease phenotype.

22

Cellular α-Syn Transmission Pathways

Multiple levels of evidence indicate the transfer of misfolded α-syn from cell-to-cell

propagates the underlying cellular dysfunction that is observed in synucleinopathies. Among the

most prominent evidence for this pathological transmission comes from Kordower et al., 2008

and Li et al., 2008 where LB pathology was observed in the embryonic nigral grafts several years

after their transplant into PD patient brains. The observations in post-mortem brain tissue led

them to hypothesize that a host-to-graft transmission had occurred. Quickly after these

publications, Desplats et al., 2009 published work in which 15% of green fluorescent protein

(GFP) labeled mouse cortical neuronal stem cells were positive for human α-syn, four weeks

after their engraftment into the hippocampus of transgenic mice expressing human α-syn [115].

Since these initial publications, animal models ranging from rodents to non-human primates have

recapitulated these findings [106, 115, 153, 189, 258, 259, 282, 292, 305, 375, 407]. Indeed,

treatment with recombinantly generated α-syn fibrils substantially increases cell-to-cell

transmission in vivo alongside increases in the levels of insoluble forms of α-syn, akin to those

observed in PD patients [304-306, 375, 407]. These changes occur alongside progressive motor

dysfunction symptoms in treated animals [304-306, 375, 407]. Recasens et al., 2014

demonstrated injecting brain homogenates from PD patients spread PD pathology in WT mice

however, no pathology was observed in the α-syn KO mice. Moreover, they showed that

injecting PD brain homogenates into the striatum of rhesus macaques induced SN dopaminergic

neuronal loss and resulted in the formation of inclusions in multiple brain regions. Yet, despite

these observations, the cellular mechanisms responsible for cell-to-cell transmission remain

poorly understood.

23

While considerable work suggests the transmission of pathological α-syn species are

capable of propagating disease pathology, it remains unclear to what extent the transfer of native

(non-pathological) α-syn contributes to disease progression. Furthermore, it is unknown how α-

syn initially becomes misfolded. Genetic studies investigating families with SNCA duplication

and triplication indicate increased abundance of α-syn is sufficient for the development of the PD

and these individuals develop comparable brain histopathology to those with idiopathic PD [69,

116, 142, 454]. Animal models over-expressing human α-syn develop intracellular α-syn

inclusions, experience neuronal loss, and recapitulate a parkinsonian phenotype [34, 143, 150,

244, 323, 346, 448, 527]. IPSC mDA neurons over-expressing α-syn have comparable

phenotypes to those derived from familial PD mutations including increased α-syn secretion and

transfer as well as reduced lysosomal degradative capacity [144, 408, 440]. In contrast, evidence

from animal model suggests reducing the expression of α-syn can prevent disease manifestation

and/or reduce disease progression depending on the paradigm [174, 564]. These works suggest

increased α-syn results in the development of PD. Therefore, processes that dysregulate the

intracellular balance of native α-syn, such as abnormal transmission between cells, may play a

role in disease onset. However, considerably more research is still required to understand its role.

In Christopher Hawkes and Heiko Braak’s “dual-hit hypothesis”, they proposed PD may

start in the gut. While this proposal was considered controversial in 2007, increasing work

supports this hypothesis [296, 421, 500]. α-syn aggregates are observed in the intestinal tissue,

predominately in the enteric neurons in individuals with PD [167, 421]. Rats injected with

pathological α-syn into the vagal nerve at the location of the gut was sufficient to induce PD-like

pathology in the brain [209, 506]. In humans, the likelihood of developing PD is negatively

correlated with a truncal vagotomy, a once common procedure for peptic ulcers where the vagal

24

trunks are severed [270, 483]. Additionally, differences in gut microbiome are observed in

individuals with PD [145, 500]. These works support the premise, which is now generally agreed

upon, that synucleinopathies can start in the gut. However, it remains contested whether the gut

is the origin in all cases [328, 345]. Other work supports a second premise for the spread of α-

syn into the enteric nervous system [511]. Thus, despite the advancements in our understanding,

there is still a long way to go to determine the original insult that results in development of PD or

other synucleinopathies.

The Unconventional Secretion of α-Syn

Studies in the early 2000s first demonstrated that both monomeric and oligomeric species

of α-syn could be found outside cells and in human bodily fluids, including cerebral spinal fluid

(CSF) and plasma [40, 133]. However, by standard convention, α-syn lacks the prerequisite N-

terminal signal sequence to undergo classical secretion. Thus, how α-syn is transferred from cell-

to-cell to spread pathology remains an important unanswered question. Current work supports

that unconventional secretion by way of the endo-lysosomal pathway as well as by autophagy

can spread pathology by influencing α-syn’s exocytosis [9, 132, 135, 279, 340, 387]. The extent

these unconventional secretion mechanisms to contribute to spreading disease pathogenesis

remains unclear.

In 1999, Gunter Blobel won the Nobel Prize for discovering the N-terminal signal

peptide, a sequence that directs the ribosomes to the endoplasmic reticulum (ER) and transports

the protein undergoing synthesis across the ER membrane. A subset of proteins that finish their

synthesis in ER are translocated to the Golgi Apparatus and eventually trafficked to the plasma

membrane where they are secreted. This ER-Golgi secretion mechanism is known as classical

secretion. Yet, not all proteins that undergo secretion use this classical secretion mechanism.

25

Among the earliest evidence for non-classical secretion was the recognition that interluken-1 (IL-

1) did not contain a N-terminal signal sequence in 1984 by Auron et al. [14]. It was later shown

that IL-1β’s secretion in response to cellular stress was unaffected by pharmacological inhibition

of the classical secretion pathway by Rubartelli et al., 1990 suggesting an alternative secretory

pathway [420]. It has since been shown that a myriad of leaderless proteins lacking a signal

sequence undergo non-classical secretion by unconventional secretory mechanisms, including

the β-galactoside binding proteins, galectins [215, 307] as well as α-syn and other

neurodegenerative disease associated proteins [41].

Several secretory mechanisms have been identified to mediate the unconventional

secretion of proteins [399]. Collectively, these mechanisms are characterized as Types. Type I

refers to pore-mediated translocation across the plasma membrane; Type II refers to ATP-

binding cassette (ABC) transporters mediate secretion; and Type III refers to

endosome/autophagosome driven secretion. A Type IV secretion mechanism also exists known

as Golgi-bypass, where proteins with the ER N-terminal signal peptide are translated into the ER,

packaged, and directly trafficked to the plasma-membrane. An important consideration regarding

unconventional secretion is that some proteins, such as IL-1β, may use multiple secretion

mechanisms (Type I and III) [77, 224, 239, 561]. Considerable evidence supports that α-syn’s

secretion is mediated by a Type III mechanism, yet the exact process is unclear with evidence

supporting both unassociated and vesicular associated release processes [41, 132, 155, 184, 192,

225, 279, 387]. To this point are works demonstrating that α-syn is released in or in association

with exosomes, an endosome derived extracellular vesicle (EVs), as well as by autophagy

mediated secretion [9, 106, 127, 132, 208, 387, 507].

26

Extracellular Vesicle Associated Transmission of α-Syn.

Cells release populations of extracellular vesicles (EVs) of various sizes containing a

multitude of cargoes including RNA, proteins, and lipids [178, 457, 487]. While EVs were once

thought to be relegated strictly to the task of shedding unwanted cellular waste, it is now

appreciated that EVs play an integral role in cell-to-cell communication [308] and that all cell

types produce EVs under both healthy and pathological cellular states [545]. As a result,

understanding mechanisms associated with the transport and characterization of EVs has become

an increasingly popular focus. In particular, by identifying specific EV cargoes under

pathological conditions, EVs have the potential to be excellent biomarkers to assist in earlier

detection and diagnosis of a variety of diseases [456, 529].

The most cited subtypes of EVs are exosomes, microvesicles, and apoptotic bodies, all

derived from distinct cellular mechanisms and sub-cellular compartments. Due to the differences

by which these different EVs subtypes originate, they are normally distinguished based on their

size and protein composition [178, 260, 536, 537]. Experimentally, multiple methods are

normally required to validate these characteristics which in addition to evaluating protein

content, often require characterizing size and the existence of a membrane. Microvesicles have a

dynamic size range at 20-1000 nm and bud directly from the plasma-membrane, while apoptotic

bodies originate from dying cells and have a diameter over 1000 nm [178, 260, 536, 537].

Exosomes typically range between 30-150 nm and originate as intraluminal vesicles (ILVs)

formed from the inward budding of the endosome’s membrane and are enriched in tetraspanin

proteins [178, 260, 536, 537].

The specific type of endosomes that generates ILVs are known as multivesicular bodies

(MVBs) [178, 260, 536, 537]. There are two known mechanisms that form MVBs. The first is

27

the endosomal sorting complex required for transport protein (ESCRT) dependent pathway and

the second is the ESCRT independent pathway. The ESCRT dependent pathway is a stepwise

mechanism driven by the sequential interactions of the ESCRT -0, -I, -II, -III complexes [81, 85,

198, 280, 332, 462]. ESCRT complexes -0 and -I recognize ubiquitinated transmembrane cargo,

re-organize the endosomal microdomain surrounding the cargo, facilitate membrane curvature,

and recruit the ESCRT-III complex. Bound ESCRT-III complex subsequently interacts with the

ESCRT-II complex to promote the scission of the inward blebbing to form a nascent ILV in

combination with the ATPase, vacuolar protein sorting associated protein 4 (Vps4). Markedly,

the known ESCRT interacting protein programmed cell death 6 interacting protein (Alix) can

also mediate the interaction of ESCRT complexes to form ILVs independent of ubiquitination

through the intermediate Syndecan [21]. In the ESCRT independent pathway, ILVs are formed

based on the lipid raft architecture of the endosomal membrane. The presence of ceramides

formed during the conversion of sphingolipids by neutral sphingomyelinases and cholesterols

induces steric rigidity on the endosomal membrane that promotes its inward budding and the

formation of ILVs [457, 499, 530]. The formation of ILVs via these mechanisms results in their

enrichment in tetraspanins such as CD9, CD63, and CD81, as well as certain ESCRT complex

proteins such as tumor susceptibility gene 101 (Tsg 101) or Alix, the enrichment of these

proteins makes them commonly used identifiers to distinguish exosomes from the other EV

subtypes [61, 260, 487, 490].

Compelling evidence for exosomes’ ability to transfer pathological α-syn from cell-to-

cell exists. Emmanouilidou et al., 2010 was the first to publish the presence of α-syn in isolated

EVs, which were likely exosomes [135]. In the same study, they reported their model had

comparable α-syn concentrations in the conditioned culture media as what is reported in cerebral

28

spinal fluid (CSF). They further showed that concentrated EVs were capable of inducing toxicity

when added to WT cortical neurons [135]. Alvarez-Erviti et al., 2011 demonstrated α-syn

contained within EVs was taken up into recipient cells and that following EV membrane

disruption, this was prevented [9]. They also demonstrated lysosomal inhibition increased the

amount of α-syn observed within the exosomal fraction of cell cultured media [9].

Perhaps the most prolific early link for exosomes being a critical contributor for the

pathological transmission of α-syn being is Danzer et al., 2012 [106]. They demonstrated

detergent resistant, oligomeric species of α-syn were present in exosomes. Additionally, these α-

syn associated exosomes were preferentially taken-up and more toxic to recipient cell compared

to non-EV associated α-syn [106]. Furthermore, they demonstrated autophagy regulated the

release of α-syn and that α-syn was present both on the inside and the outside of EVs [106].

Therefore, α-syn is commonly referred to as being associated with EVs unless specifically

determined as residing inside of them.

Studies investigating the CSF from individuals with PD consistently report reduced levels

of total α-syn when compared to age matched controls. These reports have led others to

investigate whether this is also true within isolated EV found with these primary fluids. Stuendl

et al., 2016 reported lower levels of α-syn were found within both the total CSF and EV fraction

from those with PD and DLB. However, when exosomes isolated from equal volumes of CSF

were added to an α-syn oligomerization detecting, bimolecular fluorescence complementation

(BiFC) protein reporter cell-line, increased complementation was observed from the PD derived

exosomes while isolated exosomes from individuals with DLB did not. Yet, when the relative

concentrations of α-syn were normalized among DLB and control patient exosomes, on the

justification that lower levels may be due to increased cell death, an increase in complementation

29

was observed from the DLB derived exosomes. Consistent with these previous results, Ngolab et

al., 2017 found injecting exosomes derived from DLB patient CSF into the brains of non-

transgenic mice increased pS129 α-syn levels and human and mouse α-syn was found co-

localized when evaluated at four weeks post-injection.

Exosomes may also contribute to neurodegenerative disease pathogenesis through the

transfer of lipids. Grey et al., 2015 found that α-syn aggregates at a faster rate when exposed to

exosomes. Interestingly, these aggregation kinetics were not affected by whether the exosomes

were derived from control or α-syn over expressing cells [180]. This led them to postulate

phospholipid composition may be an important determinant to aggregation [180]. Consistent

with these findings, Zhang et al., 2018b found that α-syn over-expressing human embryonic

kidney (HEK) cells treated with the lipid peroxidation product 4-hydroxynonenal (HNE), which

forms during ROS exposure and was previously connected to autophagic impairment, increased

cellular α-syn aggregation [562]. When collected EVs were injected into the dorsal striatum of

wild type (WT) mice, the EVs derived from the HNE treated HEK cells resulted in α-syn

aggregate accumulations within the hippocampus, SN, and cerebral cortex while the EVs from

the untreated cells did not [562]. Zhang et al., 2018b proposed that because the formation HNE is

naturally elevated in aging brains, further elevated in association with α-syn pathology, and can

affect autophagy, it may be linked to pathogenesis through compromised mitochondrial turnover

and altered lipid content [562].

Non-Extracellular Vesicle Associated α-Syn Transmission.

While considerable evidence supports EVs capability to spread pathological α-syn

species among cells, whether they represent the primary form of transmission is unknown. A

considerable quantity of EVs are often used to demonstrate effects experimentally. This is in part

30

due to isolation strategies which regularly damage EVs in the process. A side effect of these high

levels raises the question of physiological relevance at times. EV associated α-syn represents a

small percentage of the total α-syn released from cells. Stuendl et al., 2016 reported that

approximately two percent of the total CSF α-syn was found in the exosomal fraction [474]. This

relatively low association of α-syn with EVs raises the question of whether other unconventional

secretion mechanisms might also contribute to the pathological spreading of α-syn. To this end,

both EV associated and non-EV associated α-syn can spread pathology.

Numerous studies have shown injecting α-syn fibrils into the CNS is sufficient for

inducing, spreading, and accumulating α-syn and pathology in a variety of animal models [304,

305, 325, 394, 404, 448, 527]. When α-syn fibrils are added to neurons, they are shown to

interact with the cellular plasma membrane where they are then endocytosed [204]. One

experiment found that α-syn fibrils were more readily taken up than α-syn oligomers [219]. This

has led researchers to look for the existence of a receptor that mediates this process. Heparan

sulfate proteoglycan [219], lymphocyte activation gene 3 (LAG3) [319], and α3-Na+/K+-ATPase

(α3-NKA) [449] have all been shown to preferentially bind to fibril conformation α-syn and

mediate its internalization into neurons compared to non-fibrillar conformations. Neurexin-1β as

well as amyloid β precursor-like protein 1 (APLP1) are also capable of internalizing α-syn into

neurons but are not conformationally specific [319]. In microglial cells, toll-like receptor-2

(TLR2) can mediate oligomeric α-syn’s endocytosis where it can be more readily cleared [245].

There is also some evidence for pore-forming conformation α-syn oligomers which could

theoretically mediate both α-syn’s entry and exit from cells [105, 107, 473]. Finally, while

apoptotic or necrotic neurons have the potential to secrete pathogenic α-syn, these processes

have repeatedly been discounted from being involved in spreading pathology [99, 182, 505].

31

In addition to pathological α-syn uptake, the mechanism by which pathological α-syn

could leave the cell is unclear. Large α-syn aggregates, such as fibrillar α-syn, are seemingly too

large for exosomes. However, because the autophagic-lysosomal pathway is thought to be the

primary method by which cells clear higher-order forms of α-syn and modulates the secretion of

both EV and non-EV associated α-syn [41, 132, 155, 184, 192, 225, 279, 387], it is considered a

primary candidate for α-syn transmission.

Autophagy

One of the primary cellular processes thought to play a fundamental role in the

propagation of α-syn is the autophagic-lysosomal pathway (ALP). Canonically, this pathway has

been characterized as being purely degradative, although an accumulating body of research from

over the last couple of decades now supports that autophagy plays a role in the unconventional

secretion of specific proteins, such as α-syn [31]. This unconventional secretion pathway, while

not technically a form of autophagy which is defined on the basis of protein degradation, is often

referred to as secretory autophagy. This terminology is due to both the degradative and secretory

autophagic pathways utilizing the same underlying framework and autophagic proteins[63]. The

criteria that distinguish whether an autophagosome, the key organelle formed in

macroautophagy, undergoes lysosomal mediated degradation or plasma-membrane fusion and

cargo secretion is unknown [63]. Thus, the shared overlap between the degradative and secretory

pathways is of particular interest in diseases where autophagic impairment is prevalent, such as

neurodegenerative diseases. In this regard, impaired autophagy may contribute to disease

progression by reducing the breakdown of pathological proteins as well as contribute to their

secretion and ultimately their transmission from cell-to-cell.

32

Microautophagy, Chaperone Mediated Autophagy, and Macroautophagy.

The term “Autophagy” comes from ancient Greek and means “self-eating” and was

originally coined by Christian de Duve in 1963 in his work “The Lysosome Concept” [110]. In

2016, Yoshinori Ohsumi was awarded the Nobel prize for his characterization of the autophagic

machinery proteins, specifically, the AuTophaGy-related (ATG) genes proteins [502].

Conceptually, autophagy is defined as a process by which cytoplasmic content is trafficked into

the lysosome for degradation [158]. While this characterization is broad, it is distinct in that

autophagy does not refer to when endocytosed, non-cytoplasmic content is routed to the

lysosome for degradation [158]. Although autophagy is commonly used to reference

macroautophagy, there are three major types of autophagy: microautophagy, chaperone mediated

autophagy, and macroautophagy. Microautophagy or endosomal microautophagy is the direct

degradation of internalized cytoplasmic content through the inward budding and formation of

ILVs in late endosomes or lysosomes. The distinction between microautophagy and endosomal

microautophagy is the use of ESCRT proteins, where inward budding of the membrane is

ESCRT independent in microautophagy and ESCRT dependent in endosomal microautophagy.

Regardless, these processes result in ILV degradation indirectly during late endosome-lysosome

fusion or directly when formed in the lysosome. In chaperone mediated autophagy (CMA),

cytosolic content is degraded through its direct insertion into the lysosome [240]. In contrast to

microautophagy, CMA does not use vesicles, and instead, single proteins are unfolded and

directly translocated across the lysosomal membrane through a pore-forming translocation

complex [240]. Proteins that undergo CMA are initially targeted and recruited to the lysosome

based on a conserved KFERQ sequence. Finally, in macroautophagy, the formation of an

enclosed double- or multi-layered isolation membrane isolation called an autophagophore occurs

33

that surrounds, engulfs cytosolic content, and upon closure, forms a vesicular compartment

known as an autophagosome [151, 533]. Afterwards, the engulfed content undergoes degradation

through autophagosome-lysosome fusion. While macroautophagy was initially identified during

starvation, where autophagophores were described to indiscriminate sequester cytoplasmic

content, it is now appreciated that selective clearance also occurs in macroautophagy [354, 502].

The precursor structure to the autophagosome known as an autophagophore begins as an

isolated membrane that grows in structure and shape at a location known as an omegasome

named for its resemblance to the Greek letter “omega” [264]. During the formation of the

autophagophore, the omegasome detaches from the ER to become the initial membrane

component. As the autophagophore grows, it receives membrane from multiple organelle sources

to varying degrees. The ER [16, 195], the endoplasmic reticulum-golgi intermediate complex

[166], mitochondria [188], plasma membrane [403], recycling endosomes [396], and coat protein

complex II (COPII) vesicles [447] have all been shown to donate membrane to the developing

autophagophore. However, it is unknown what determines an organelle’s specific contribution or

whether it is conserved among cell types.

In addition to receiving increasing membrane donations, the maturation of an omegasome

to an autophagophore and ultimately autophagosome depends on a large number of proteins.

This maturation process can be broken down into three primary steps: initiation, elongation, and

expansion [317]. In brief, the formation of an autophagosome begins with the inhibition of the

mammalian target of rapamycin (mTOR). which results in increased activity of Unc-51, like

Autophagy Activating Kinase 1 (ULK1/ATG1) [317]. This increase in ULK1 activity is crucial

for the formation of the multiprotein initiator complex. This complex is composed of several

proteins including: Beclin-1, as well as vacuolar protein sorting-associated protein 15 (VPS15)

34

and vacuolar protein sorting-associated protein (VPS34) which make up the phosphatidylinositol

3-kinase (PI3K III). This initiator complex facilitates phosphatidylinositol 3-phosphate (PI3P)

lipid domain enrichment at the omegasome, providing a site for future proteins to interact with,

and initializes its maturation to an autophagophore alongside ATG9 and WD repeat domain

phosphoinositide

interacting protein 2

(WIPI2) [41, 317]. The

autophagophore then

undergoes expansion

and elongation, which

are both driven and

regulated by a series of

transient protein

interactions akin to E3-

ubiquitin ligases.

Included among these

are the ATG5-ATG12-

autophagy related 16 like 1 (ATG16L1) system, facilitated by intermediate ATG proteins such as

ATG7, to ultimately conjugate and recruit essential autophagic proteins to the maturing

autophagophore such as cytosolic microtubule-associated protein 1 light chain 3 (LC3) [41, 317].

During this process cytosolic LC3, known as LC3-I, becomes conjugated to

phosphatidylethanolamine, and incorporated into the autophagophore, known as LC3-II, which is

essential for its closing to form the autophagosome [41, 317]. During selective autophagy,

Figure 2: Cellular Activation of Autophagy and Autophagosome Formation.

Cartoon diagram demonstrates protein cascade and that drive the formation of the

autophagophore and its maturating into the autophagosome. In brief, this is process

broken down into 3 major steps: Initiation – Where mTOR inhibition results in the

activation of Beclin-1 and ATG1(ULK1) to drive the initial formation of the

autophagophore with corresponding ATG proteins. The Elongation step is

characterized by the expansion of the autophagophore and conjugation of proteins

such as LC3 as well as the recruitment of cargo. Expansion – is the final phase in

which the autophagophore becomes closed and then fuses with the lysosome to

degrade its cargo.

35

autophagic adaptor proteins such as sequestosome 1 (p62/SQSTM-1), calcium-binding and

coiled-coil domain containing protein 2 (NDP52/CALCOCO2), neighbor of breast cancer 1 gene

(NBR1), and optineurin (OPTN) recognize ubiquitinated cargo and directly link them to the

nascently forming autophagophores by binding to LC3-II before closing. After the

autophagosome forms, it can then go on to fuse with other organelles including endosomes and

MVBs to form a hybrid organelle known as an amphisome. The autophagosome, or newly

formed amphisome, can then fuse with a lysosome to form an autolysosome and degrade its

sequestered cargo or alternatively fuse with the plasma-membrane and release its cargo into the

extracellular space.

Autophagy in Neurons.

Autophagy plays an important role in nearly all cell types including neurons. In

comparison to other cells, neurons have unique challenges with regards to autophagy.

Evolutionarily, the importance of proper brain development results in its prioritization for

nutrients [510]. CNS neurons are among the few cell types to express the glucose transporter 3

which has the highest glucose uptake rate among all twelve of the glucose transporters [484]. In

point of contrast to most cells, neurons do not store glucose, and thus depend on nutrients being

readily available. Astrocytes and erythrocytes facilitate neuronal energy requirements by

“feeding” them glucose and lactate in response to their activity [29, 53, 313, 417, 453]. This

process is tightly regulated and conducted in such a way that neurons receive the proper amounts

of glucose or lactate in direct relation to their active state. The tight regulation of glucose uptake

into brain results in relatively low fluctuation in their glucose levels even when blood glucose is

low [53, 417, 453]. Thus, the quintessential mTOR inhibition following starvation, and

subsequent upregulation in autophagy is unlikely to occur in neurons, as glucose starvation is

36

effectively non-existent in a health brain. [67, 187, 220, 229, 300, 337, 437, 438, 492, 539, 546].

Moreover, it is unclear if changes in nutrients influence neuronal autophagy; experiments

subjecting neurons to starvation suggest there is little to no effect on the neuronal autophagy

response [310, 342]. Amazingly, the same autophagic proteins used to generate autophagosomes

in other cells are also used in neurons [310, 311]. However, despite mTOR associated changes

being able to drive autophagy in neurons, starvation does not affect this process, and instead

neuronal autophagy is a constant process under basal conditions to compensate [310, 311].

Further challenges that neurons face regarding autophagy are their vast structure,

compartmentalization, trafficking across long distances, and constant remodeling through

activity dependent plasticity. Neurons combat these challenges by utilizing different forms of

autophagy in different subcellular compartments [468, 469, 558]. For example, lysosomes are

formed primarily in the neuronal cell-body while autophagosomes are formed primarily in distal

axons [310, 311]. Consequently, autophagosomes must be trafficked retrograde to the

perinuclear region of the cell-body to undergo lysosomal fusion to degrade cargo [310, 558].

While the generation of autophagosomes within dendrites and the cell-body is rare, it does occur.

When formed in these regions, autophagosomes can ultimately modulate neuronal excitation,

inhibition, and dendritic arborization depending on the neuronal subtype [79, 90, 311, 419]. The

γ-aminobutyric acid receptor-associated protein (GABARAP) family of proteins, which includes

LC3, were initially identified based on the interaction of GABARAP with the GABAA receptor

[524]. GABARAP plays an important role in trafficking the GABAA receptor and its lipidation

influences GABAA receptor’s localization at the cell surface [78, 286]. Collectively, the

GABARAPs family are shown to play a critical role in activating ULK1, phagophore

development during starvation, and are fundamental in the formation of the autophagosome [183,

37

266]. In ATG7 KO mice, which results in reduced autophagosomes formation, reduced cell

surface levels of the GABAA receptor occurs from its mislocalization in GABAergic

interneurons [78, 286]. In contrast, the upregulation of autophagy following rapamycin treatment

also reduced cell surface GABAA receptor localization but did not affect total cellular levels,

suggesting autophagy modulates GABAA receptors localization in GABAergic neurons [216]. In

excitatory neurons, dendritic autophagy influenced AMPA receptor degradation and its co-

localization with autophagic machinery in response to stimuli, indicating a role for autophagy in

synaptic remodeling [419]. Consistent with this premise, ATG7 depletion in forebrain excitatory

neurons in a mouse model impaired dendritic spine pruning in association with social behavior

defects [489]. Short-hairpin RNA (shRNA) mediated knockdown (KD) of ATG7 in hippocampal

neurons reduced dendritic spine elimination, suggesting impaired neuronal remodeling [489].

Tyrosine hydroxylase (TH) promoter driven conditional ATG7 KO resulted in swollen dendrites

in DA neurons, dendritic dystrophy in tyrosine hydroxylase positive neurons, and presynaptic

accumulation of both α-syn and the PD associated protein, leucine-rich repeat kinase 2 (LRRK2)

in a mouse model [154]. Hernandez et al., 2012 found autophagy in DA neurons affected their

function by modulating dopamine release and presynaptic recovery. The amplitude of dopamine

release was and the presynaptic recovery was increased in ATG7 KO mice. Whereas, rapamycin

induced mTOR inhibition increased presynaptic autophagosome number, decreased the number

of synaptic vesicles, and decreased axonal profile volumes in the WT neurons [199]. The totality

of these works indicate that autophagy influences neuronal function on multiple levels. In that

dendritic autophagosomes affect dendrite remodeling by either influencing synaptic spine

pruning, neurotransmitter receptor turnover, or the localization neurotransmitter receptors

depending on the neuron type. They also indicate that over- or under- active levels of autophagy

38

may be detrimental to neuronal function. This is outlined particularly in DA neurons, where

increased autophagy reduces neurotransmitter release through DA synaptic vesicle clearance and

neuronal profiling. Inversely, decreased autophagy affects α-syn accumulation but increases DA

release and DA neuronal firing recovery. Thus, the misbalance of neuronal autophagy has

dramatic consequences, and as such, requires that they walk a fine line. The consequences of

misbalancing autophagy are further exemplified in how it affects the clearance of aggregated

proteins.

Evidence for Autophagy’s Contribution to α-Syn Accumulation.

One challenge neurons face as post-mitotic cells, is the ability to successfully remove

stressors, such as defective organelles and aggregated proteins, so they may last an entire

lifespan. In this way, autophagy is critical to sustaining neuronal health. In studies investigating

autophagy impairment or altered levels α-syn and neuronal cell death are observed. Abnormal

expression of α-syn is associated with earlier synucleionopathy disease onset and disease

progression [116, 142, 454], SNCA familial mutations increase disease propensity and result in

age of onset, as covered earlier [263, 289, 368, 392, 557]. The underlying mechanism is

unknown, but work indicates that increased levels of WT α-syn or mutant α-syn may overwhelm,

resist standard cellular degradation mechanisms, and subsequently increase the accumulation of

α-syn in the brain. It is believed that the dysregulation of theses underlying mechanisms, such as

autophagy, may further impair the removal of other problematic cellular components which in-

turn contributes to increasing cellular dysfunction.

Following conditional ATG7 KO by way of DA neuron associated dopamine reuptake

transporter, engrailed homeobox 1, or TH promoters a substantial loss of neurons, reduced

striatal dopamine, increased α-syn accumulation, as well as increased p62 and ubiquitin

39

inclusions are reported [3, 436]. Similarly, while autophagic inhibition by Beclin-1 KD in a

neuroblastoma cell line increased the levels of α-syn oligomers [556], lentiviral induced over-

expression of Beclin-1 within the hippocampus and temporal cortex of α-syn transgenic mice

reduced α-syn accumulation [463]. The effects of Beclin-1 over-expression were further

increased by treating with rapamycin and partially prevented with the PI3K class III inhibitor, 3-

methyladenine (3-MA) [463]. Treatment of zebrafish with MPTP, reduced ATG5 levels in

association with impaired autophagic flux, DA neuronal loss, and motor deficits [213]. However,

following over-expression of ATG5, zebrafish were rescued from the effects of MPTP treatment

[213].

Over-expression of α-syn in mouse models and cell-lines was linked to autophagy

impairment by the mislocalization of ATG9 and subsequently decreased autophagophore

formation [538]. This mislocalization was due to RAB1A inhibition, which is involved in proper

trafficking of proteins, including ATG9, between the endoplasmic reticulum and the golgi

apparatus [538]. Over-expression of RAB1A reversed this mislocalization and restored this

autophagic impairment [538]. In PC12 cells, α-syn over-expression inhibited autophagy by

binding to high mobility group box 1 (HMGB1) [461]. α-syn bound HMGB1 in both the nucleus

and cytosol impaired the interaction between HMGB1 and Beclin-1, strengthening the

interaction between Beclin-1 and BCL2, and disrupting HMGB1 translocation from the cytosol

to the nucleus [461]. Over-expression of Beclin-1 restored autophagy and increased α-syn

clearance [461]. HMGB1 KD prevented the α-syn over-expression associated autophagic

impairment but reduced basal autophagy, while HMGB1 over-expression restored the α-syn

over-expression induced autophagic impairment [461]. In a study using differentiated IPSCs

from PD patients harboring the SNCA gene triplication, upregulated autophagy and greater

40

degree of autophagic flux was measured when comparing the change in LC3 puncta during

chloroquine induced lysosomal activity quenching [359]. The over-expression of WT or A53T α-

syn, but not A30P α-syn, in rat primary midbrain neurons increased the presence of LC3 when

lysosomal activity was quenched by bafilomycin-A1 (Baf-A1) treatment [253].

The differences reported among these studies may be the result of the models used, the

timeline in which cells were tested, or be a consequence of compensatory autophagy

upregulation. To this ladder point, autophagy may be critical for clearing monomeric α-syn and

subsequently become compromised when challenged with degradation resistant α-syn

aggregates. Consistent with this premise, an adeno-associated virus α-syn over-expression PD

mouse model, found increased Beclin-1 and LC3-II levels during early, pre-symptomatic stages

while at later stages, they were considerably reduced [111]. This progressive change in Beclin-1

and LC3-II were associated with increasing levels of α-syn and cytoplasmic retention of

transcription factor EB (TFEB) process, a master regulator of lysosomal protein transcription

[111].

ALP Dysfunction, Secretory Autophagy, and α-Syn.

In contrast to autophagy (degradative cargo breakdown through autophagic-lysosomal

fusion), during autophagy mediated secretion (or secretory autophagy) the autophagosome does

not fuse with the lysosome, but instead fuses with the plasma-membrane, and subsequently

releases its cargo into the extracellular space. During this process, the formation of an

amphisome during autophagosome-MVB fusion allow autophagy to also influence the release of

EVs [184].

Early studies investigating ALP dysfunction found that autophagy impairment increased

both EV and non-EV associated α-syn secretion [41, 132, 155, 184, 192, 225, 279, 387].

41

However, most of these early studies predated an understanding of secretory autophagy and did

not consider at what point autophagy was inhibited. Broadly speaking, commonly used

autophagic inhibitors either block the initiator complex activity through PI3K inhibition or

prevent lysosome acidification and lysosome-autophagosome fusion. Theoretically, these latter

“inhibitors” do not prevent secretory autophagy and may increase it by diverting

autophagosomes to the plasma-membrane for secretion rather than accumulate proteins. In

contrast, these former initiator complex inhibitors prevent the initial generation of

autophagosomes and thus would inhibit both secretory and degradative autophagy.

Collectively, the literature agrees that treatment with the lysosome acidification and/or

autophagosome fusion inhibitors chloroquine or Baf-A1, increase the secretion of both non-EV

and EV α-syn [9, 86, 106, 279, 340, 387]. In contrast, studies report marked differences when

autophagy is inhibited during the initiation step which prevents autophagosome formation. A

couple of studies found when cells over-expressing α-syn were treated with the PI3K inhibitor 3-

MA which blocks the initiator complex, an increase in α-syn secretion occurred [155, 279].

Within another study using immortalized dopaminergic precursor cells, 3-MA treatment did not

increase secretion within the non-vesicular fraction. Yet, they found that ATG5 depletion

concurrently increased the levels of α-syn and exosomal marker CD81 in the isolated EVs [155].

In another study, 3-MA did not increase secretion in either the total or EV fraction of cultured

media from a H4 cell model [387]. Moreover, PC12 cells over-expressing A30P α-syn as well as

tubulin polymerization promoting protein (TPPP), which they showed reduced autophagosome-

lysosome fusion, reduced α-syn secretion when depleted of ATG5 [132]. Furthermore, canonical

autophagic activation by rapamycin treatment reduces secretion [106] while trehalose, a non-

canonical autophagic activator that circumvents mTOR, increases secretion [132, 550].

42

Interestingly, in a recently published motor-degeneration model study, following trehalose

administration to motor neurons, small amounts of lysosomal damage were observed with

correspondingly increased levels of enhanced GFP (eGFP)-Gal3, increased TFEB nuclear

translocation, and upregulated autophagy [422] consistent with other works investigating

lysosomal membrane damage, galectins, and TFEB [231, 232, 234]. In total, these results

highlight the complicated relationship between α-syn and secretory autophagy, particularly with

regard to early autophagic inhibition, where mixed results are reported.

Lysosome Dysfunction is a Hallmark of PD

The largest risk factor for developing PD and other neurodegenerative disease is age. As

we age, cells deteriorate in association with changes that increase susceptibility to disease.

López-Otin et al., 2013 suggests that there are nine “hallmarks” of aging that can be categorized

into primary, antagonists, and integrative [302]. These hallmarks are widely used by those who

study aging. The primary hallmarks are genomic instability, telomere attrition, epigenetic

changes, and loss of cellular degradative mechanisms. The antagonistic hallmarks are

mitochondrial dysfunction, cellular senescence, and deregulated nutrient sensing which occur

alongside or in response to primary hallmarks and are believed to be compensatory but

ultimately deleterious. Finally, the integrative hallmarks are stem cell exhaustion and altered

intercellular communication which result from the primary and antagonist hallmarks, and

responsible for functional decline from aging. Regarding neurodegenerative diseases, reduced

protein degradation from a decline in cellular degradative mechanisms and deregulation of

nutrient sensing may explain the increased propensity for PD and age. There is considerable

genetic and experimental evidence to support that impaired lysosome function and autophagy is

a hallmark of disease pathogenesis.

43

PD is characterized as either being sporadic or familial depending on the cause. Sporadic

(or idiopathic) PD refers to disease development with no clear genetic link and accounts for 85-

90% of total cases [211]. Whereas, familial cases are characterized by a family history of PD,

thought to arise from mono- or multi- genic mutations, and account for 10-15% of total cases

[211]. In total, over 24 genes have been linked to familial PD [66]. While most PD cases are

sporadic, monogenic familial cases account for the majority of individuals who experience early

disease onset, ~10% of total cases [251]. Thus, familial cases are further characterized as being

either early- (before 60 years of age) or late-onset associated. While familial, early-onset PD is

rare [252], the identification of genes such as SNCA, parkin (PRKN), LRRK2, PTEN inducible

kinase 1 (PINK1) provide valuable insight into disease pathogenesis. Similarly, the use of

genome wide associated studies (GWAS) to determine common genetic mutations among

individuals with sporadic PD has helped determine genetic risk factors and underlying

dysfunctional cellular pathways. Among these are several genes that either regulate or contribute

to autophagy and lysosome function including: SNCA, LRRK2, PARK7, VPS35, GBA1, PINK1,

PRKN, F-Box only protein 7 (FBXO7), VPS13C [66, 251, 252]. Thus, it has been proposed that

idiopathic PD is not the result of a single monogenic mutation but arises from cellular

dysfunction that converges on lysosomal failure [251]. This failure could either be from a single

genetic mutation, multiple genetic mutations, a combination of genetic and environmental

factors, or purely environmental factors. However, despite the cause, this converging failure on

the lysosome and lysosome associated pathways could explain the observed accumulation of α-

syn that occurs in synucleinopathies.

44

Lysosomes.

Lysosomes are acidified organelles with a pH between 4-5 and are responsible for the

degradation of unwanted cellular content and the recycling of their components. The

acidification of the lysosome facilitates the degradation of cargo by both disrupting protein

interactions and activating low pH sensitive hydrolases including cathepsins. Lysosomal pH is

generated and maintained by the lysosomal transmembrane vacuolar-ATPase proton (V-ATPase

H+) pump in accompaniment with chloride channels and ion transporters. Most lysosomal

proteins, such as lysosome associated membrane protein -1 and -2 (LAMP1 and LAMP2), are

transmembrane and highly glycosylated on their intraluminal regions [138, 268, 290]. This

relative abundance of intraluminal glycans is thought to protect the lysosomal membrane from

self-degradation [22, 268]. Lysosome biogenesis begins with endocytic vesicles which progress

from early endosomes to late endosomes followed by lysosomes. During this maturation process,

these endocytic vesicles accumulate lysosomal proteins from the Golgi network and begin to

acidify. Proper trafficking of lysosomal hydrolases to endocytic vesicles results from their

mannose-6-phosphate glycosylation which serves as signaling sequence for the mannose-6-

phosphate receptors in the Golgi network [171, 384, 534]. Subsequently, these mannose-6-

phosphate receptors dissociate in low pH environments [171, 384, 534]. The mis-trafficking or

impairment of lysosomal hydrolase function can result in improper degradation of lysosomal

cargo and ultimately cellular dysfunction. For example, individuals homozygous for the N370S

GBA1 gene mutation, which encodes for the lysosomal hydrolase glucocerebrosidase (GCase),

develop the lysosomal storage disorder Gaucher’s disease. While heterozygous carriers of N370S

GBA1 do not develop Gaucher’s disease, they are five-to-six times more likely to develop PD

[451]. In individuals with Gaucher’s disease, significantly higher levels of oligomeric α-syn have

45

been observed in their plasma [355]. This has also been seen from individuals with other

lysosomal storage disorders such as Niemann-Pick type C disease and Krabbe’s disease [322,

373]. Increased α-syn accumulation has also been linked to the lysosomal storage disorders

Niemann-Pick type C disease, Tay-Sachs disease, β-galactosialidosis, Batten’s disease, and

Krabbe’s disease [131, 322, 481]. These finds suggest lysosomal dysfunction is central to PD

pathogenesis [251].

Lysosomal Degradation Impairment in Parkinson’s Disease.

Experimentally, mutations in PD associated genes support the concept that altered

lysosomal function dysregulates α-syn clearance and leads to disease pathogenesis. Mutant

VPS35 mouse models accumulate α-syn in their mDA neurons [488]. The familial PD associated

mutation, R524W VPS35 was shown to impair the trafficking of lysosomal hydrolases which

subsequently resulted in higher levels of α-syn aggregates relative to WT [149]. In a Sandhoff

disease mouse model, a lysosomal storage disorder associated with the loss of β-hexaminidase

activity, mice accumulated lipids due to impaired enzymatic degradation, along with increased α-

syn [481].

Further evidence for lysosomal dysfunction’s involvement in disease pathology includes

the lysosomal hydrolases, known as cathepsins. Of the 50 cathepsins contributing to protein

degradation, multiple have been shown to degrade both monomeric and aggregated α-syn in

lysosomes [87, 102, 335, 397, 444]. Specifically, cathepsin D can fully degrade monomeric and

aggregated forms of α-syn [102, 397, 444]. Cathepsin B and cathepsin L can degrade monomeric

and fibril α-syn in some capacity[87, 335]. However, in one model, these hydrolases resulted in

the production of highly aggregate prone C-terminal α-syn fragments [334].

46

Impaired GCase function also results in α-syn accumulation and aggregation in both cell

and animal models, although it does not directly degrade α-syn [329, 333, 482, 485, 568].

Mazzulli et al., 2011 found depletion of GCase impaired lysosomal protein degradative capacity

and led to α-syn accumulation in human IPSC cortical neurons [329]. In cells over expressing

either WT α-syn or A53T α-syn, GCase KD increased soluble and insoluble high molecular

weight α-syn aggregate species in accompaniment with increased neurotoxicity. Increased

soluble and insoluble high molecular weight α-syn was also observed in the brains of Gaucher’s

disease (4L/PS-NA) mouse models relative to WT. In Gaucher’s disease, reduced GCase activity

impairs the metabolism of glucosylceramide into ceramide resulting in glucosylceramide

accumulation [241]. Mazzulli et al., 2011 demonstrated that glucosylceramide stabilized soluble

oligomeric α-syn intermediates. Notably, the relative ratio of glucosylceramide to ceramide was

found to positively correlate with the ratio of α-syn dimers to monomers in blood samples from

individuals with Gaucher’s disease [344]. Thus, impaired GCase activity may contribute to

synucleinopathies by promoting degradation resistant forms of α-syn aggregates.

Another contributing aspect of GCase activity impairment and disease pathogenesis may

be from reduced ceramide levels in endo/lysosomal compartments [196, 333]. Ceramide binds to

and promotes the cleavage of cathepsin D to its active form [196]. Thus, lower ceramide and

subsequently reduced cathepsin D activity could result in increased α-syn accumulation. This

premise is consistent with lower levels of ceramide and altered ceramide acyl chain composition

in the anterior cingulate cortex from individuals with PD compared to those without PD [1, 82].

Experimentally, inhibition of acid ceramidase, the enzyme that metabolizes ceramide into

sphingosine and free fatty acids, increased ceramide levels in GCase-deficient cells and reduced

α-syn in IPSC DA neurons derived from an individual with the PD associated heterozygous

47

N370S GBA1 mutation [247]. Consistent with reduced ceramide being involved in PD, acid

sphingomyelinase, galactosylceramidase, and GCase catalyze ceramide production in lysosomes.

Like GCase, mutations in the genes SMPD1 and GALC which encode for acid sphingomyelinase

and galactosylceramidase, respectably, are known susceptibility factors for PD and other

synucleinopathies [6, 31, 89, 103, 161, 162, 322]. Reduced acid sphingomyelinase in the blood

was associated with earlier PD onset by 3.5 to 5.8 years [6]. Galactosylsphingosine which is

metabolized by galactosylceramidase into ceramide, was observed to be increased in the cerebral

cortex from individuals with PD compared to age matched controls [322]. Galactosylsphingosine

has also been shown to increase α-syn aggregation in a dose dependent manner in vitro [459].

The consequence of impaired α-syn degradation in lysosomal compartments could

facilitate its accumulation in endosomal or lysosomal compartments. This may in-turn accelerate

α-syn aggregation, as acidic pH has been shown to promotes α-syn’s oligomerization [48, 51,

508]. Treatment with the lysosomal acidification and lysosomal-autophagosome fusion inhibitor

[326, 327, 543], Baf-A1, increases α-syn accumulation as well as its exocytosis [9, 106, 132,

155, 387]. Haploid insufficiency of cathepsin D, GCase depletion, and heterozygous N370S

GBA1 mutants also increases the secretion of EV associated α-syn [19, 20, 247] and accumulate

α-syn in lysosomal compartments [185, 208, 235]. The increase in α-syn secretion may be

compensatory due to reduce α-syn aggregate accumulation and neurotoxicity [155]. The

secretion may also promote cell-to-cell transfer of pathological α-syn, compromise degradation

within the recipient cell, and perpetuate the cycle [471].

mTOR Signaling.

Recent studies demonstrate lysosomes are not simply degradative organelles but also

function as an important scaffolding for cellular signaling proteins, including one of the master

48

regulators of autophagy, mTOR[455]. mTOR, a singular serine/threonine kinase, is the primary

regulator of a myriad of essential functions within the body ranging from coordinating cell

growth, cell size, organ shape, and the degradation of proteins and lipids via the autophagic-

lysosomal pathway [17, 119, 423, 546]. in mammals, mTOR forms two distinct complexes

known as mTORC1 and mTORC2 [17, 546]. Both mTORC1 and mTORC2 share the common

interacting proteins MTOR Associated Protein, LST8 Homolog (mLST8), DEP Domain

Containing MTOR Interacting Protein (DEPTOR), and the Tel2 interacting protein 1

(Tti1)/Telomere length regulation protein 2 (Tel2) complex which function to stabilize mTOR

complex assembly. Only mTORC1 associates with the scaffolding protein Raptor which

influences mTORC1 assembly, stability, regulation, and substrate specificity [17, 432].

Additionally, the proline-rich AKT substrate of 40 kDa (PRAS40) exclusively blocks mTORC1

activity [17]. In contrast, mTORC2 associates with Rapamycin-insensitive companion of

mammalian target of rapamycin (RICTOR) and mSin1 [299, 432]. Rictor facilitates mTORC2

assembly, stability, substrate specificity, and subcellular localization [432]. mSin1 assists in

anchoring mTORC2 to the plasma-membrane to facilitate proper activation in response to stimuli

[299].

The different interacting partners of mTORC1 and mTORC2 ultimately result in distinct

regulation of their respective pathways by affecting their subcellular localization. While

mTORC2 is primarily plasma-membrane bound, mTORC1 is localized to the endosomal and

lysosomal membranes. Indeed, mTORC1 plays a critical role in regulating autophagy and is

considered the canonical master regulator. Moreover, only mTORC1 is sensitive to rapamycin

induced inhibition which has long been recognized to upregulate autophagy [432]. mTORC1

49

activity is also closely coupled to nutrient levels within the cell and becomes inactivated during

starvation which results in increased autophagy.

Generally, active mTORC1 promotes anabolism. These associated anabolic pathways

require cells to have the necessary building blocks to promote growth. Thus, the regulation of

mTORC1 activity is dependent on the cellular availability of these necessary building blocks,

where increased levels promote mTORC1 activation and reduced levels reduce mTORC1

activation. This paradigm is the basis of mTORC1 signaling and thus signaling factors associated

with growth, energy level, amino acid concentrations, oxygen availability, and DNA homeostasis

all influence mTORC1 activity.

The recombination activation gene (RAG) complex plays a prominent role in activating

mTORC1 by functioning as an amino acid sensor [246, 427, 428]. The RAG complex is a

heterodimer guanosine triphosphatase (GTPase) composed of different combinations of RAG

A/RAG B with RAG C/RAG D and is bound to the lysosomal membrane via Lamptor to form

the RAGULATOR complex [24, 427]. During amino acid stimulation, the RAG proteins bind

GTP and undergo a confirmational change that allows for mTORC1 to bind and interact with the

lysosomal bound Ras homolog enriched in brain (RHEB) [119, 220, 300, 427, 428, 492]. RHEB

interacts with the catalytic domain of mTORC1 and is require for its full activation [220, 300,

492].

Active mTORC1 resides on the outer membrane of lysosomes interacting with RHEB,

the RAG GTPase complex, and the lysosomal Na+/ glutamine and arginine amino acid

transporter 2 (SLC38A2). SLC38A2 forms a complex with the lysosomal transmembrane

protein, SLC38A9, which senses intraluminal arginine and glutamine levels and regulates the

RAGULATOR complex via Lamptor which subsequently control mTORC1 activity. This

50

SLC38A2-SLC38A9 complex also associate with the V-ATPase H+ pump responsible for

generating the proton gradient resulting in lysosomal acidification.

While active, mTORC1 keeps key regulators of autophagy and lysosomal biogenesis in

the inactive state. mTORC1 phosphorylates ULK1 [317] at S637 and S757 keeping it inhibited.

Similarly, transcription factor EB (TFEB), is also held in an inactive state when mTOR is active.

Subsequently, mTORC1 inhibition increases ULK1 activity and the translocation of TFEB to the

nucleus, binds to its Coordinated Lysosomal Expression and Regulation (CLEAR) motif,

increasing the expression of lysosomal and autophagosomal proteins [97, 222]. mTORC1 senses

Figure 3: Lysosomal Signaling Pathways in Response to Various Stimuli. Diagram demonstrates the

network of proteins that become active during nutrient rich, starvation, and cargo overload conditions. In

brief, the nutrient rich condition increases ZKSCAN3 and MYC translocation to the nucleus to reduce

lysosomal protein synthesis. Growth factors activate AKT to reduce FOXO and TSC function. Intraluminal

cholesterol and arginine in the lysosome are recognized by SLC38A9, act on RAGULATOR complex

proteins, affecting RHEB, keeping mTOR active, and subsequently resulting in increased TFEB/3

phosphorylation and its sequestration in the cytosol by the chaperone protein 14-3-3. In the starvation

condition or during ROS generation, increased TFEB/3 and FOXO translocation to the nucleus occurs

resulting in increased lysosomal protein transcription. LKB1 phosphorylates and activates AMPK. Low

intraluminal cholesterol and amino acids sensed by SLC38A9, results in mTOR inhibition and its

disassociation from the RAGULATOR complex. This process is mediated by other nutrient sensing cellular

mechanisms including: Castor1, FLCN/FNIP2, and GATOR1. Image originally published in: “How

Lysosomes Sense, Integrate, and Cope with Stress”[424]. Used with publisher’s permission.

51

cytosolic amino acids via its interaction with the GATOR1 and GATOR2 complexes, as well as

through adenosine diphosphate ribosylation factor-1 (ARF1) and folliculin-folliculin interacting

protein 2 (FNIP2) complex [68, 229, 275, 367, 376, 383, 503, 540]. GATOR1 is tethered to the

lysosomal membrane, is a GTPase activating protein (GAP) for RAG A/B, and thus inhibits

mTORC1 by hydrolyzing bound RAG-GTP [540]. Contrarily, GATOR2 positively regulates

mTORC1 by binding to lysosomal GATOR1 [68, 367]. GATOR2’s interaction with GATOR1

can be further regulated by Sestrin-2 and cytosolic arginine sensor for mTORC1 (CASTOR1) by

binding to GATOR2 and preventing the GATOR2-GATOR1 interaction [67, 437, 438, 539]. In

this way, Sestrin-2 and CASTOR1 function as amino acid sensors, as their interaction with

GATOR2 requires them to be unassociated with arginine and leucine, respectively [67, 437, 438,

539]. The ARF1 GTPase mediates the re-localization of mTORC1 in response to glutamine

levels [229]. The GDP-bound FNIP2 complex, which forms when not bound to arginine, binds to

mTORC1, functions as a GAP for RAG C/D, and thus positively regulates mTORC1 [275, 383,

503].

AMPK Signaling.

Like mTORC1, AMP-activated protein kinase (AMPK) regulates cell energy levels and

senses the cell’s energy status. In contrast to mTORC1, AMPK phosphorylates downstream

targets to increase catabolism to restore ATP levels [202]. Under basal conditions, a portion of

AMPK are constitutively localized to the outer membrane of the lysosome. During high cellular

glucose and abundant ATP conditions, AMPK remains inactive [560]. Conversely, AXIN and

the AMPK activating kinase, liver kinase B1 (LKB1), re-localize in response to low glucose to

form a complex with lysosomal AMPK via interactions with the RAGULATOR complex and the

V-ATPase [559, 560]. Additionally, increased adenosine monophosphate (AMP) levels

52

generated from high ATP consumptions results in AMP binding to the AMPK γ-subunit [194,

202]. Bound AMP-AMPK induces an allosteric conformational change which promotes the

AXIN-LKB1-AMPK interaction, resulting in the α-subunit’s phosphorylation, and subsequently

activate AMPK. Interestingly, while AMP binding to AMPK promotes its interaction with AXIN

and LKB1, it is not required for their interaction [559]. Thus, AMPK can be activated in

response to reduced glucose before abundant AMP becomes present. Upon AMPK activation, it

inhibits mTORC1 through both the phosphorylation of Raptor which promote complex

destabilization as well as by activating tuberous sclerosis complex -1 and -2 (TSC1-TSC2) which

in-turn phosphorylates and inhibits mTOR [119, 187, 221]. Additionally, AMPK also

phosphorylates ULK1 resulting in its activation. Collectively, these changes promote autophagy.

mTOR, AMPK and Parkinson’s Disease

Similar to impaired degradation at the level of hydrolases, altered degradative cell

signaling via mTOR is also associated with sporadic and familial PD. Crews et al., 2012

demonstrated that increased membrane bound mTOR was observed in the temporal cortex from

individuals with DLB when measured by immunoblot [101]. Immunohistochemical staining of

DLB brains revealed that neurons with increased α-syn accumulation also had more mTOR and

LC3 positive compartments [101]. Electron microscopy of these neurons revealed the presence

of abnormal autophagosomes, suggesting impaired autophagic clearance [101]. Consistent with

this premise, intra-cerebral rapamycin treatment reduced α-syn accumulation and ameliorated

associated neurodegenerative alterations in α-syn over-expression transgenic mice [101].

Similarly, α-syn over-expression was shown to increase mTORC1 and P70 S6K1 signaling in

SK-N-SH and transgenic mice with PD associated deficits and rapamycin reversed these effects

[164]. Indeed, rapamycin treatment is regularly reported to assist in the clearance of pathological

53

α-syn [111, 434, 435, 463, 528, 553]. Expression of the A53T α-syn mutation was also shown to

increase mTORC1 and P70 S6K1 signaling, impair autophagy, and subsequently promote A53T

α-syn aggregation and toxicity [236]. In contrast, depletion of mTOR resulted in the upregulation

of autophagy and A53T α-syn clearance [236].

Conflicting evidence for mTOR being neuroprotective in other PD models is also

reported. In the SNpc from individuals with PD, increased REDD1 expression is observed.

REDD1 negatively regulates mTORC1 activity and is upregulated in neurotoxin models of PD

[315, 316]. Most of these PD associated neurotoxin models including MTPT and rotenone, are

known to generate large levels of ROS within DA neurons [49, 364, 380, 409]. Thus, in these

cases, mTORC1 signaling has been suggested to prevent oxidative stress induced cell death

signaling. Consistent with this idea, Hydrogen peroxide or rotenone inhibit mTOR and lead to

neuronal cell death [73, 566]. Similarly, rapamycin treatment exacerbates neurotoxicity when

exposed to oxidative stress [84]. Thus, mTOR activation under different neurodegenerative

circumstances, such as increased ROS, may be protective.

Reduced AMPK activity is also associated with PD. Over-expression of AMPK in SH-

SY5Y cells reduced cell loss in response to MPTP [83]. Conversely, dominant negative AMPK

expression or pharmacological inhibition of AMPK increased MPTP induced cell death [83]. In

familial LRRK2 and Parkin mutant drosophila PD models, constitutively active AMPK rescued

PD associated deficits in motor dysfunction, DA neuron loss, and mitochondrial failure [350].

Dominant negative AMPK or siRNA mediated depletion of AMPK exacerbated the PD

associated deficits [350]. Metformin, the type two diabetes drug, increases AMPK activity and

was shown to increase pS129 α-syn clearance in SH-SY5Y and HeLa cells over-expressing α-

syn through a direct AMPK mechanism [379]. Metformin was also shown to reduce pS129 α-syn

54

in primary WT rat hippocampal neurons [379]. Overall, these findings further reinforce the

involvement of the autophagic-lysosomal pathway in PD.

Transcriptional Regulation of Lysosomes and Autophagosomes.

The microphthalmia transcription factor gene family (MIT/TFE) play a crucial role in

restoring cell homeostasis in response to various stress conditions. Included among the MIT/TFE

transcription factor family are TFEB, transcription factor binding to IGHM enhancer 3 (TFE3),

and microphthalmia-associated transcription factor (MITF). These MIT/TFE transcription factors

play an important role in regulating lysosomal and autophagosome protein transcription; where

they homo- or hetero- dimerize, translocate to the nucleus, and bind to the Coordinated

Lysosomal Expression and Regulation (CLEAR) motif (GTCACGTGAC) as well as the E-Box

motif (CACGTG) [324, 433]. Bioinformatic experiments have identified that the CLEAR motif

resides in the promotor region of a multitude of different functional lysosomal proteins including

ion channels, hydrolase, and transmembrane proteins [324, 433]. Thus, activation of TFEB as

well as TFE3 and MITF increases global transcription of lysosomal proteins and subsequently

enhances cellular lysosomal degradative capacity [324, 433].

In opposition to TFEB and TFE3, are transcription factors that recognize the the Krüppel-

associated box (KRAB) or SCAN domain, the most regularly reported being ZKSCAN3 [118].

These transcription factors bind to the promoter regions of many autophagy and lysosomal

proteins and repress their transcription [70]. Both ZKSCAN3 and TFEB are regulated by

starvation, in that ZKSCAN3 translocates from the nucleus while TFEB translocates to the

nucleus [70].

Under normal, nutrient rich conditions, both TFEB and TFE3 are recruited to the

lysosomal membrane by RAG C/D where they are phosphorylated by mTORC1 and kept

55

inactive. Chaperone protein 14-3-3 mediates their inactivity by recognizing and binding to

phosphorylated TFEB and TFE3, preventing their dimerization and occluding their nuclear

localization signal. Other proteins, such as GSK-3β, ERK2, can also phosphorylate TFEB and

TFE3 contributing to their interaction with 14-3-3[293, 443].

In response to cellular stress, such as nutrient deprivation, TFEB and TFE3 become

dephosphorylated due to mTORC1 inactivation. TFEB and TFE3 dephosphorylation releases

bound 14-3-3 resulting in their rapid translocation to the nucleus [97, 222]. TFEB and TFE3 can

also be directly dephosphorylated by specific phosphatases such as calcineurin or protein

phosphatase 2A to induce the translocation of these transcription factors. Thus, other stimuli can

also upregulate lysosomal and autophagosomal protein expression independent of mTOR by

acting on these phosphatases.

While TFEB, TFE3, and MITF all upregulate lysosomal and autophagic genes to increase

cellular degradative capacity, there are observable differences among the genes each

transcription factor upregulates [324]. In one study comparing the over-expression of TFEB,

TFE3, and MITF, MITF upregulated only 6 of 17 lysosomal genes tested. In contrast, both TFEB

and TFE3 upregulated 16 of the 17 lysosomal genes with a shared overlap in 15 of the genes.

Similarly, when a standard array of autophagy PCR primers was used to measure difference in

gene expression, 36 of 83 associated genes were upregulated by TFEB over-expression; 29 of 83

were upregulated by TFE3 over-expression; and 7 of 83 were upregulated by MITF over-

expression. Interestingly, both TFEB and TFE3 over-expression also increased SNCA

transcription, while MITF did not [324]. Others have also reported that increased TFEB

induction increases SNCA expression [11, 486].

56

Evaluation of autophagy in response to TFEB, TFE3, and MITF over-expression revealed

an increased relative ratio of LC3-II to LC3-I indicative of the increased autophagosomes among

transcription factor over-expressing cells compared to the control cells [324]. These finds are

consistent with TFEB and TFE3 both upregulating autophagy and lysosome production while

MITF had a lesser role in this process which had previously been suggested prior to this study

[206]. However, because concomitant over-expression of TFE3 and KD of TFEB elicits a similar

transcription profile to TFE3 over-expression alone and vice versa, it suggests both TFEB and

TFE3 are capable of upregulating lysosome and autophagy independent of each other [324, 371].

Ultimately, the differences among the genes each transcription factors upregulates may be

important for different physiological cellular responses. Consistent with this idea, the over-

expression of TFEB and TFE3 were shown to increase lysosomal exocytosis while MITF did not

in ARPE-19 retinal epithelial cells [324]. Both the over-expression of TFEB and TFE3 increased

the number of lysosomes near the plasma-membrane as well as promoted their fusion [324, 336].

The mechanism by which TFEB and TFE3 drove lysosomal exocytosis was the result of

upregulating and activating the lysosomal Ca2+ channel MCOLN1 which increases the

intracellular Ca2+ concentration [336]. Additionally, differences in TFEB, TFE3, and MITF are

reported from different tissue types which could correspond with differences in function in

association with differences in transcript expression [324].

TFEB and Neurodegenerative Diseases.

Changes in TFEB associated expression are thought to be linked to many

neurodegenerative diseases including PD and other synucleinopathies whereby reduced TFEB

driven CLEAR protein expression is associated with disease pathogenesis. In synucleionpathies,

this may be due to a direct mechanism, as α-syn may physically block TFEB translocation. α-syn

57

shares considerable homology with 14-3-3 and may block TFEB through a direct interaction

[360]. α-syn was shown to co-localize by immunofluorescence imaging in vivo in a PD-like

neurodegenerative rat model and co-immunoprecipitated with TFEB from brain extacts [112]. In

the PD Q311X Parkin mouse model, which results in α-syn accumulation and pathology, nuclear

translocation of TFEB in response to rapamycin treatment was reduced in the SN. This suggests

a potential involvement between α-syn and TFEB in disease pathology [450]. Reduced relative

nuclear to total TFEB was observed in the putamen and frontal cortex from individuals with

MSA compared to age matched healthy individuals [12]. Reduced nuclear TFEB translocation

within the SN has also been observed from individuals with PD [112]. Conversely, over-

expression of A30P α-syn was shown to inhibiting c-Jun N-terminal kinase (JNK) activity,

leading to increased nuclear ZKSCAN3, lower lysosomal and autophagic protein expression, and

ultimately inhibited autophagic flux in mDA neurons [284].

Notably, increased expression of CLEAR genes by TFEB has been linked to increased

clearance of pathological protein accumulations in several neurodegenerative diseases including

PD[97, 433, 498]. TFEB over-expression in mouse DA neurons increases cell survival in

response to MPTP treatment [309]. Neuron specific TFEB over-expression reduced PD

pathogenesis in an A53T α-syn rat model, including increased DA neuronal survival, better

motor performance, and reduced pathological α-syn inclusions [12]. Interestingly, in the same

study’s MSA mouse model, oligodendrocyte over-expression of TFEB was neuroprotective and

reduced pathological α-syn while neuron specific TFEB over-expression was not [12]. However,

these results are consistent with MSA pathogenesis in that α-syn accumulation is primarily

observed in oligodendrocytes during MSA [448, 509, 512, 527].

58

In addition to over-expression models, pharmacological induced TFEB activation

facilitates α-syn clearance [113, 243]. Treatment of mice with Ambraxol, an mucolytic

decongestant that induces the secretion of surfactant storing lysosomes via dysregulating Ca2+

stores [148], was also shown to increase GCase activity in WT, L444P GBA1 mutant, and α-syn

over-expressing mice as well as decrease α-syn found in the brains in the α-syn over-expressing

mice when compared to

untreated [339]. In another

study, Ambraxol treated mouse

cortical neurons were shown to

have increased TFEB

expression [312]. The same

study found that Ambraxol

also increased the secretion of

both α-syn and EVs but

appeared to impair autophagic

flux, as LC3-II levels were comparable when lysosomal acidification was quenched following

concomitant BAF-A1 treatment [312]. Interestingly, despite Ambraxol treatment increasing α-

syn expression and increasing total levels of intracellular α-syn, reduced pathologically

associated pS129 α-syn levels were found [312]. Reduced pS129 α-syn has also been observed in

other studies [158, 339]. The efficacy of Ambraxol has gone so far as to be considered as

preventative for individuals with GBA1, where it was shown to positively reduce PD related

symptoms [347]. These results indicate that upregulated TFEB facilitates clearance of

pathological α-syn by increasing autophagic and lysosomal proteins. Additionally, it suggests

Figure 4: Relationship Between Lysosomal Function and TFEB

Translocation. Cartoon depicts the relationship between TFEB

inhibition and TFEB translocation based on the active state of the

lysosome and mTOR.

59

TFEB upregulation may represent a promising avenue for therapeutics in treating

synucleinopathies and other neurodegenerative disorders.

The Cellular Response to Lysosomal Stress.

Lysosomal membrane stress can be induced by a variety of insults ranging from

intracellular pathogens, protein aggregates such as misfolded α-syn, and crystals such as silica

[80, 147, 153, 372, 496]. Continual membrane stress may eventually lead to lysosomal

membrane rupture, the release of intraluminal content including cathepsins and other hydrolases

into the cytosol, and subsequently cell death [523]. To combat this, cells have evolved a

mechanism to recognize lysosomal membrane rupture and either repair or remove damaged

lysosomes.

Small disruptions in the lysosomal membrane result in a repair response to reseal the

membrane. The resealing of the membrane is mediated by the ESCRT protein complexes

including Tsg101, Alix, and charged multivesicular body protein 4B (CHMP4B), which are

recruited through a Ca2+ dependent mechanism [458]. Lysosomes are among the cellular

organelles that store intracellular Ca2+ [276]. Thus, leaking Ca2+ following lysosomal membrane

damage may help facilitate this resealing response [276].

A recent publication indicates that Gal3 plays an important role in the recruitment of the

ESCRT proteins to facilitate disrupted lysosomal membrane repair [234]. Galectins are a family

of proteins containing a carbohydrate recognition domain (CRD) which allows them to bind to

the glycosylated proteins found within the intraluminal lysosomal membrane [237, 372, 496]. In

the recent publication, proximity based biotinylation was used by generating a recombinantly

linked APEX2-Gal3 construct. The APEX2 construct derived from the genetically modified

soybean ascorbate peroxidase (APE1) protein biotinylates proteins in the presence of hydrogen

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peroxide. Using this APEX2-Gal3 construct, a proteomic screen was performed to evaluate the

change in the Gal3 interactome following lysosomal membrane damage. They found that an

increased association between Gal3 with both lysosomal proteins and the ESCRT protein ALIX

occurred during early stages of lysosomal membrane damage [234]. They found a reduced

recruitment of ALIX to LAMP1 positive compartments in Gal3 KO cells and in the R186S Gal3

mutant which does not bind to damaged lysosomes [426].

Galectins’ Function in the Central Nervous System

Galectins are a family of proteins that share a CRD motif that interacts with different β-

galactoside sugars, which can present on proteins by either O- or N- glycosylation, and were

initially discovered based on their binding to glycosylated cell-surface proteins [237]. Fifteen

galectin family members have been identified in total, with broad degrees of expression among

members depending on the tissue or cell type [237]. Mechanistically, galectins are well

characterized for their ability to recognize ruptured endosomal or lysosomal membranes, to be

unconventional secreted, and moderate the activity and trafficking of glycoproteins within the

lumen of intracellular compartments or at the plasma-membrane. These characteristics have

implicated them in a broad range of cellular functions including immune responses, signaling,

inflammation, and autophagy [237].

Among the galectin family, roles for galectin-1 (Gal1), Gal3, and galectin-4 (Gal4) have

been identified in CNS development by influencing axonal guidance, growth, and branching as

well as proper myelination and re-myelination. Within the cells of the CNS, Gal1 is primarily

expressed in neurons where it plays an important role in their development. Gal1 KO mice are

shown to have aberrant olfactory axon topography, indicating that Gal1 facilitates neurite

arborization and contributes to axonal guidance, this has also been observed for somatosensory

61

neurons as well [165, 210, 395]. Expression of Gal3 and -4 occurs during neuronal development

and the onset of myelination but is then downregulated [123, 370, 467].

In myelinated neurons, L1 neural cell adhesion molecule (L1CAM/CD171) interacts with

oligodendrocyte contactin-1 to stimulate myelin basic protein synthesis and myelination at these

regions. Gal4 in cortical and olfactory neurons sorts and organizes the axonal glycoprotein,

L1CAM, by binding to terminal glycosylations to facilitate its distribution in microdomains

within the axonal plasma membrane [467, 515]. Similarly, Gal4 plays an important role in

organizing membrane microdomains in polarized epithelial cells [470]. Immobilized

extracellular pS6 Gal3 by casein kinase 1 (CK1), but not unphosphorylated Gal3, increased

branching in cultured hippocampal neurons through interactions with membrane bound L1CAM.

Conversely, microdomains of Gal4 are present in the axonal nodes of Ranvier where it interacts

with contactin-1, sequesters it, and precludes myelination at these regions [27, 120]. Secreted

neuronal Gal4 also regulates oligodendrocyte progenitor cells differentiation, influencing

temporal myelination onset. Gal4 produced and secreted by unmyelinated neurons has been

shown to bind to immature oligodendrocytes, modulating their differentiation and proliferation

[257, 515]. Oligodendrocytes also express Gal4 where it is thought to participate in the

trafficking of glycoproteins and lipids, similar to other polarized cells such as neurons and

epithelial cells [27, 314, 467, 470, 515]. In comparison to unmyelinated neurons, Gal4 does not

appear to be secrete from oligodendrocytes [27, 314, 515]. However, in oligodendrocytes Gal4

affects the trafficking of the crucial myelin proteolipid protein, the protein that makes up more

than 50% of CNS myelin’s total protein [28, 179, 191, 362]. Gal4 also affects the expression of

myelin basic protein by binding to transcription factor SP1 and activating p27 [531, 532].

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In comparison to WT mice, Gal3 KO mice had down-regulated myelin basic protein,

fewer myelinated axons, and less compact myelin [212, 370]. These Gal3 KO mice also had

increased oligodendrocyte progenitor cells and behavioral deficits consistent with

hypomyelination. Similar to Gal3 KO mice, Gal1 KO mice have significantly less myelinated

axons and less compact myelin [410]. These works suggest Gal3 is important for the

differentiation of oligodendrocyte progenitor cells as well as a role for Gal3 and Gal1 in proper

myelination of neuronal axons.

Galectin Facilitated Clearance of Ruptured Lysosomes

Although not shared among all the galectin family members, Gal1, Gal3, galectin-8

(Gal8), and galectin-9 (Gal9) all have CRD’s that interact and adhere to the glycans present on

the intraluminal membrane proteins of endosomes and lysosomes [237, 372, 496]. Thus, these

galectins re-localize and recognize exposed glycans on the inside of ruptured endosomes and

lysosomes that are otherwise normally hidden [237, 372, 496]. This re-localization event is best

characterized among Gal3 and Gal8 which subsequently recruit their respective, specific

autophagic adaptor proteins [237, 372, 496]. Gal8 recruits NDP52 while in contrast, Gal3

recruits Trim16 though its interaction with ULK1 and subsequently provides scaffolding for

autophagic proteins such as ATG16L1 to bind and form a complex to drive autophagy [71, 232,

234, 237, 265]. These subsequent interactions facilitate direct conjugation of the ruptured

lysosome to the autophagophore for clearance by selective autophagy [71, 232, 234, 237, 265].

In addition to facilitating the clearance of ruptured lysosomes, specific roles for Gal3,

Gal8, and Gal9 in coordinating autophagy and lysosome biogenesis at the mechanistic and/or

transcriptional level have been identified [71, 232, 234, 250, 265]. Gal8 was shown to bind to

SLC38A9 in response to lysosome membrane rupture induced by glycyl-l-phenylalanine 2-

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naphthylamide (GPN), where it ultimately regulated mTORC1’s translocation from the lysosome

to the cytosol [232]. Gal8 KO cells also had reduced nuclear translocation of TFEB when

following GPN treatment compared control KO cells [232]. Gal8 KO HeLa and bone marrow

derived macrophages had fewer autophagosomes, measured by LC3 puncta number, when

challenged with GPN. This suggests that Gal8 KO cells have maintained increase in mTORC1

signaling in response to lysosomal membrane rupture, indicating a role for Gal8 in regulating

mTORC1 signaling and cellular autophagy in this context.

In the same study investigating Gal8’s role in coordinating autophagy, Gal9 was found to

associate with transforming growth factor-β-activated kinase 1 (TAK1), an upstream regulator of

AMPK, which subsequently positively regulates ULK1, and negatively regulates mTORC1

following GPN treatment [232]. A follow-up paper from the same authors further clarified the

Gal9 associated signaling pathway during lysosomal membrane rupture [233]. Gal9 was found to

displace the deubiquitinase USP9X on TAK1, promote K63 ubiquitination, and lysosomal

AMPK activation [233]. In addition, another publication demonstrated Gal9 interacts with

LAMP2 to assist in maintaining lysosomal function under steady state conditions [475].

In contrast, Gal3 facilitates both lysosomal membrane repair during early membrane

stress that precedes rupture and coordinates degradation following membrane rupture. During

early stress, Gal3 recruited ESCRT proteins such as Alix to mediate ESCRT associated lysosome

membrane repair [234]. In response to rupture, the interaction between Gal3 and Alix became

decreased and an increased association with Trim16, ULK1, and ATG16L1 occurred [71, 75,

234]. While it was determined that WT and Gal3 KO cells had a similar response to mTORC1

associated signaling when challenged with GPN [232], Gal3 KO was shown to increase TFEB

translocation in response to the lysosomal membrane rupturing agent, leucyl-L-leucine methyl

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ester (LLOME) [234]. Similarly, Trim16 KO increased TFEB nuclear translocation at basal

conditions and in response to lysosomal rupturing agent [71, 226]. TFEB over-expression is

shown to promote autophagic flux, increase autophagosome number, and lysosome acidification

[565]. However, the number of autophagosomes, measured by the puncta number for LC3,

ATG13, and ATG16L1, were all reduced in both the Gal3 KO and the CRD inactivate R186S

Gal3 mutant cells relative to WT when treated with LLOME. Similarly, Trim16 KO significantly

reduced the number of autophagosomes, as measured by LC3 puncta, in response to LLOME

treatment relative to WT [71, 226]. Furthermore, both Trim16 KO and Gal3 KO cells had

reduced lysosomal acidification, lysosomal cathepsin activity, and degradative potential when

challenged with lysosomal membrane damaging stimuli including α-syn fibrils [43, 71, 226,

234]. Contrarily, Gal3 over-expression increased lysosomal cathepsin activity in the presence of

LLOME compared to WT [234]. These findings indicate that despite an increase in nuclear

translocation of TFEB, Gal3 and Trim16 appear to regulate autophagy on another unknown axis.

One possibility previously suggested is that Trim16 controls lysosome quality [265]. Gal3 also

maintains lysosome quality by facilitating lysosome repair. In further support of this premise,

Trim16 has been shown to regulate autophagic flux in motor neurons, and consequently, when

Trim16 KO cells were transfected with GFP-PolyQ74 (huntingtin protein) they accumulated

significantly increased levels of insoluble huntingtin protein compared to transfected WT [226].

KD of Gal3 or Trim16 reduced autophagic function while Gal3 or Trim16 over-expression

increased autophagic function in human bone marrow stem cells [75]. Ultimately, more work is

needed to determine the mechanism by which Gal3 and Trim16 regulate autophagy.

65

Lysosomal Membrane Rupture and Galectins in Neurodegenerative Diseases.

Previous work by our lab shows that pathological forms of α-syn, such as α-syn fibrils,

can induce lysosomal membrane rupture resulting in their recruitment to autophagic

compartments [147, 153, 237]. The ability to induce lysosomal membrane rupture was also

observed for pathological forms of huntingtin, Aβ, and aggregated tau assemblies [147]. When

brain tissue from individuals with PD were evaluated to determine the pathological relevance,

imaging revealed the presence of Gal3 around LBs, forming an outer corona. In total, 170 of the

305 LBs examined across five PD patients’ brain tissue had Gal3 coronas [147]. The presence of

Gal3 in endo/lysosomal and autophagic compartments as well as around LBs implies a history of

lysosomal membrane damage and a possible involvement of Gal3 in PD disease pathology.

Gal8 was shown to have a possible beneficial role in AD by promoting the clearance of

aggregated tau assemblies in cell models. Falcon et al., 2017 demonstrated exogenous

recombinant tau assemblies, escape from endosomal compartments, gain access to the cytosol,

and induce aggregation of soluble tau following clathrin-independent endocytosis in

immortalized cell-lines. These damaged endosomal compartments were shown to be recognized

by Gal8 and recruited to autophagic compartments following NDP52 binding to Gal8. Knocking-

down Gal8, NDP52, or LC3C increased the levels of insoluble tau in HeLa cells over-expressing

the P301S tau mutant and treated with pathologically associated P301S tau aggregates compared

to control siRNA cells. Similarly, SH-SY5Y cells over-expressing P301S tau as well as either the

NDP52 mutants lacking its Gal8 or LC3C binding domain had increased insoluble tau levels

following P301S tau aggregate treatment compared to those expressing WT NDP52.

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Galectins, Inflammation, and Neurodegenerative Diseases.

Microglia are the resident immune cells of the brain and play an important role in

homeostatic immune surveillance, phagocytic scavenging, and monitor neuronal activity [32,

157]. In response to inflammatory stimuli, such as injury or pathogens, microglia can become

activated resulting in a dramatic morphological change[32, 157]. Active microglia clear dead

neurons and other cellular debris as well as secrete cytokines and chemokines to assist in

resolving the inflammatory insults [32, 157, 283, 378]. In this way, acute inflammation may have

a positive role in protecting neurons.

A prominent feature of neurodegenerative disease pathogenesis is neuroinflammation

which arises from chronic inflammation. During neuroinflammation, which can stem from

misfolded proteins such as α-syn fibrils or Aβ plaques, activated microglia are thought to

contribute to neurodegeneration by generating increased levels of ROS and through the

continued secretion of inflammatory cytokines result in neuronal apoptosis [168, 283, 353, 378].

Galectin proteins are well documented mediators of inflammation and are thought to be involved

in a variety of diseases including PD and other neurodegenerative diseases [13, 25, 442, 491,

547, 548]. Secreted galectins can modulate neuroinflammation associated cascades in cells such

as microglia and astrocytes [237]. Recent work suggests Gal1, Gal8, Gal9 are anti-inflammatory

while Gal3 can be pro-inflammatory or anti-inflammatory, depending on the context.[466] These

qualities make them attractive candidates for potential neurodegenerative disease therapeutics.

Yet, the extent to which galectins contribute neurodegenerative diseases is unclear and has

recently begun to be studied.

Galectin-8. Pardo et al., 2019 found Gal8 pre-treatment followed by neurotoxic stimuli

including hydrogen peroxide-induced oxidative stress, glutamate-induced excitotoxicity,

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prolonged nutrient deprivation, or Aβ oligomers increased the survival of rat hippocampal

neuron primary cultures relative to non-pretreated neurons [365]. Concomitant pretreatment of

Gal8 and the Gal8 glycan domain inhibitor, thiodigalactoside, prevented the neuroprotective

effects relative to Gal8 only pretreatment and resulted in comparable decreases to non-pretreated

neurons. Evaluation of the mechanism by which Gal8 mediates neuroprotection revealed that

exogenously added Gal8 binds to β1-integrins, α3 and α5β1, and activates downstream ERK1/2

and AKT in hippocampal neurons. Blocking the activation of PI3K/AKT or ERK1/2 by treating

with the PI3K inhibitor, wortmannin, or ERK1/2 inhibitor, PD98059, increased hippocampal

neuron death in response to glutamate-induced excitotoxicity compared to the uninhibited

neurons. To determine the relevance of these findings, they demonstrated that Gal8 was

expressed in rat hippocampal neurons and in the hippocampus of mouse brains albeit relatively

low compared to other regions such as the thalamus. Additionally, Gal8 KO mice increased cell

death associated markers from hippocampal homogenates following stereotaxic injection of

hydrogen peroxide compared to WT. Similarly, primary culture rat hippocampal neurons had

reduced survival following culture with antibodies against human Gal8 isolated from individuals

with systemic lupus erythematosus. These findings indicate, Gal8 confers neuroprotection to

hippocampal neurons likely through binding to β1-integrins and mediating PI3K/AKT and

ERK1/2 signaling cascades.

Galectin-1. Gal1 was found to have a neuroprotective effect in a MPTP PD mouse model

in part by modulating neuroinflammation [294]. Li et al., 2020 observed treating BV2 microglial

cells with recombinant Gal1 reduced microglial activation in response to lipopolysaccharide

(LPS) and reduced SK-N-SH neuronal cell death following culturing with the BV2 microglial

cultured media. To determine if Gal1 influenced neuron survival in vivo, mice were co-

68

administered Gal1 and MPTP and compared to MPTP only. Co-administration of Gal1 and

MPTP in mice resulted in increased SN DA neuron survival, reduced microglia activation, and

increased motor associated behavior compared to MPTP only mice. Analysis of SN brain

homogenates revealed reduced inducible nitric oxide synthase (iNOS) and cyclooxygenase-2

(COX-2) protein levels, associated with microglial activation, and reduced inflammatory

cytokines, IL-1β and tumor necrosis factor-α (TNF-α), from the Gal1 MPTP co-treated mice

compared to the MPTP only mice. Mechanistic evaluation of the underlying pathway revealed

that Gal1 and LPS co-treatment reduced p38 and ERK1/2 phosphorylation and the associated

inflammatory IκB/NFκB signaling pathway compared to LPS only treated microglial cells. These

effects were prevented when Gal1 and LPS co-treatment was conducted with the Gal1 CRD

competitor, β-D-galactose. Collectively suggesting, Gal1 mediates microglial activation by

attenuating p38 and ERK1/2 driven IκB/NFκB signaling resulting in less inflammatory cytokine

production and increased neuronal survival.

Galectin-3. A recent GWAS primarily interested in identifying mitochondrial associated

genes linked to late-onset PD, identified Lgals3, the gene that encodes for Gal3 [37]. Another

study correlating disease phenotype and differentially expressed genes by analyzing individuals’

single nucleotide polymorphisms (SNPs) determined that the Lgals3 region was linked to PD

[122]. However, they dismissed its authenticity based on Bayesian test for co-localization

relative to previously published GWAS data [122]. A meta-analysis of Lgals3 SNPs from

previously published GWAS data identified a connection between Gal3 and AD [44]. While only

five of the sixty Lgals3 SNPs fit the criteria of three independent publications among GWAS

studies, all five were associated with increased AD frequency [44]. Interestingly, all five Lgals3

SNPs were associated with high Gal3 linkage disequilibrium, the region that allows Gal3 to self-

69

associate into pentamers. The importance of this unknown. Gal3 binding protein was also

identified in a random cross-species validation approach as a candidate for α-syn clearance

[416]. Depleting Gal3 binding protein in a drosophila model of PD reduced motor dysfunction

and increased α-syn clearance in a mammalian cell-line model [416].

In the context of PD and inflammation, Boze-Serrano et al., 2014 demonstrated that the

BV-2 microglial cell line became activated when treated with α-syn aggregate as determined by

increased iNOS levels and the inflammatory cytokines TNF-α, interleukin-2 and -12 (IL-2 and

IL-12) [43]. Concurrent treatment of α-syn aggregates and the Gal3 inhibitor, TD139 reduced the

levels of iNOS and inflammatory cytokines [43]. TD139 also reduced the phagocytic capacity of

microglia treated with α-syn aggregates while recombinant Gal3 treatment increased

phagocytosis [43]. Following the injection of oligomeric and fibril α-syn into the olfactory bulb

of mice, increased Gal3 expression was observed suggesting a potential role for Gal3 in the

phagocytosis of pathological α-syn [43].

Gal3 is also implicated in AD pathogenesis. Increased Gal3 is observed in the CSF and

plasma of individuals with AD [13, 526].Two studies published within months of each other by

Boza-Serrano et al., 2019 and Tao et al., 2020 both demonstrated elevated levels of Gal3 in AD

human brain slices and its co-localization with extracellular Aβ plaques [44, 491]. Heterozygous

deletion of Gal3 in amyloid precursor protein (APP)/presenilin 1 (PS1) mutant transgenic mice

and Gal3 KO in the 5 familial AD gene mutant (5xFAD) transgenic mice reduced Aβ

oligomerization, plaque formation, and correlated with better cognition compared to those

expressing Gal3 [44, 491]. Tao et al., 2020 demonstrated that Gal3 co-immunoprecipitated with

Aβ monomers from APP/PS1 AD mouse model hippocampal isolates and Aβ oligomerization

was increased in the presence of Gal3 in vitro in a concentration dependent manner [491]. Boza-

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Serrano et al., 2019, found concurrent Gal3 and monomeric Aβ treatment increased Aβ uptake

and 5xFAD Gal3 KO mice had increased Aβ42 oligomers in their CSF at six months [44].

Treatment with Aβ fibrils increased Gal3 secretion as well as inflammatory cytokine secretion

from microglial while Gal3 KO reduced inflammatory cytokine secretion [44]. Gal3 was also

shown to bind to triggering receptor expressed on myeloid cells 2 (TREM2), which has

previously been implicated in promoting a disease associated phenotype in microglial cells

[555]. These results suggest that Gal3 plays a detrimental role in AD by increasing Aβ plaque

formation by contributing to inflammatory microglial activation. Moreover, Gal3 may increase

Aβ uptake into microglial, where it fails to degrade, rather than allowing Aβ to drain from the

brain via the CSF.

Interestingly, Gal3 was shown to be important in preventing seeded tau hyper-

phosphorylation in human umbilical cord mesenchymal stem cells (hUC-MSCs) [295]. Control

hUC-MSCs injected into brains of 5x FAD mice reduced levels of hyper-phosphorylated tau.

Additionally, cultured media from control siRNA treated hUC-MSCs prevented the formation of

tau aggregates in vitro while Gal3 siRNA treated Gal3 hUC-MSCs was not [295]. Analysis of

Gal3 interactions revealed that it co-immunoprecipitates with pathological species of tau both in

cell culture and in the 5xFAD mouse model [295]. Injection of recombinant Gal3 into the brains

of 5X FAD mice reduced hyper-phosphorylated tau and was associated with reduced GSK-3β

activity as measured by reduced pY216 levels. GSK-3β is the primary kinase implicated in tau

hyper-phosphorylation during AD pathogenesis [274]. Therefore, Gal3 may prevent hyper-

phosphorylated tau aggregates but may promote Aβ oligomerization. Whether this is ultimately

positive or negative for AD in the context of human disease will require further research.

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ALS is another neurodegenerative disease characterized by motor dysfunction, loss of

motor neurons in the brain and spinal cord [331]. In individuals with sporadic ALS, increased

Gal3 and Gal9 levels were observed in spinal cords [288]. Immunofluorescence imaging

revealed the increased levels of Gal3 was primarily in microglial cells. Interestingly, in one study

using the G93A SOD1 ALS transgenic mouse model, concurrent Gal3 KO increased disease

severity. Longitudinal evaluation revealed Gal3 KO mice had earlier disease onset, reduced

motor performance on multiple tests, and reduced life expectancy compared to WT SOD1

transgenic mice [288]. Gal3 KO was also associated with increased spinal cord TNF- α and

aggregated protein levels at late-stage disease. Thus, Gal3 may be beneficial in the context of

ALS.

Galectins as Biomarkers for Neurodegenerative Diseases

The considerable advances in the last twenty years in identifying the underlying

mechanisms responsible for neurodegenerative diseases has led many to search for early

diagnostic biomarkers. Theoretically, the ability to diagnose neurodegenerative diseases before

they physically manifest could reverse or significantly reduce disease progression with proper

pharmacological intervention. At the forefront of biomarker candidates are secreted proteins.

Mechanistically, the release of proteins that undergo unconventional protein secretion in

response to underlying cellular pathology, such as dysfunctional protein degradation could offer

valuable insight into the underlying health of an individual. Unsurprising, evaluating secreted

pathological proteins such as misfolded α-syn is one such considered way for PD. However,

because red blood cells also express α-syn [26], discerning differences between α-syn from

plasma can be challenging and collecting CSF is painful, intrusive, and requires considerably

more training than plasma [493]. Thus, the ability to measure biomarkers in other fluids is

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desirable. To this end, changes in other proteins such as galectins have the potential to be early

diagnostic biomarkers.

In one study, mass spectrometry was used to characterize differences in proteins found in

the CSF from individuals with PD and healthy controls [294]. Based on the criteria that an

identified protein must be present in at least five of the ten PD CSF samples, a total of 482

proteins were identified, of which 32 proteins were significantly different compared to control

individuals. Following up on these differences, Gal1 was tested as a potential biomarker by

enzyme-linked immunosorbent assay (ELISA). When Gal1 CSF levels were compared among

individuals with PD, atypical parkinsonian disorders, and healthy controls, Gal1 was

significantly reduced in PD compared to control but was not significantly different compared to

atypical parkinsonian disorders. Those with atypical parkinsonian disorders trended toward

having lower Gal1 levels compared to controls but were not significant. As a predictive

diagnostic biomarker, reduced Gal1 was characterized as having a moderate-high accuracy by

the study at ~70%.

Similarly, pilot studies measuring difference in galectins concentrations in the serum

among individuals with PD and healthy controls have been conducted. Both Gal3 and Gal4 were

significantly elevated in the serum among individuals with idiopathic PD [64]. While the

variance for Gal3 among samples was relatively large, the concentrations of Gal4 were

considerably less varied among both control and PD individuals. These findings suggest it may

be a useful indicator of disease [64]. Interestingly, in a study using tear fluid as a biomarker

medium, mass spectroscopy profiling among individuals found that Gal3 and Gal3 binding

protein were reduced in individuals with PD compared to healthy controls [38].

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Serum Gal3 levels are increased in individuals with other diseases including

inflammatory bowel disease (IBD) which includes Crohn’s disease and ulcerative colitis [552].

Correlative work suggests individuals who develop IBD are also at an increased likelihood of

also developing PD [50]. Overlapping genetic propensities have also been identified between

Crohn’s disease and PD [50, 217, 415]. Constipation is regularly reported among individuals

with PD [374] and is a proposed prodromal symptom of PD by Braak [45, 46, 193].

Increased Gal3 concentrations in primary fluids have also been observed in several other

neurodegenerative diseases. Increased Gal3 in either the plasma or CSF in AD patients has been

reported in among studies [294, 526, 548]. In a Huntington’s disease mouse model, increased

Gal3 in the CSF and serum was observed, and correlated with disease severity [452]. In one

recent study, Gal3 levels in the serum and CSF were compared among individuals with AD,

ALS, and healthy controls [13]. They found Gal3 was significantly increased in both the CSF

and serum in those with AD compared to controls [13]. Gal3 levels in the CSF and serum were

increased in ALS patients relative to controls but were not significant [13]. No significant

difference in Gal3 levels were detected among AD and ALS individuals in either the serum or

CSF [13]. Another, earlier study found plasma Gal3 levels were significantly higher among

female ALS patients compared to female healthy controls but not significantly different among

males [544]. When Gal3 levels were compared among individuals with AD and ALS to their

performance on the mini-mental state exam (MMSE), a test used to evaluate dementia severity, a

significant positive correlation between Gal3 levels and dementia severity was observed [294].

Consistent with these findings, another study determined among those with AD, the stage of the

disease was significantly correlated with increased Gal3 levels, patient age, and disease duration

[548]. Collectively, these works suggest changes in secreted galectins can be observed in many

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neurodegenerative diseases. The ability to measure these changes may ultimately be useful in

disease diagnosis. The relative variance from individual-to-individual is a limitation that will

need to be addressed before using them as effective diagnostic biomarkers. To this end, the

simultaneous evaluation of several markers may ultimately be more effective for an accurate

diagnosis. Finally, determining if changes in galectins occurs before the onset of disease

symptoms will ultimately be necessary for early disease diagnosis but has enormous potential to

revolutionize the process.

Concluding Remarks

The understanding of neurodegenerative diseases has grown considerably in the past

decades. As of now, there is no cure for PD or any of the other synucleinopthies, although not

from a lack of trying. The tireless work and dedication performed by doctors and researchers to

unravel the underlying mechanisms of these diseases is palpable. The future is promising as

current therapies are underway to help with earlier disease diagnosis and treatment. The next

chapters focus on my contribution to the collective knowledge on PD and other

synucleinopathies.

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CHAPTER TWO

MATERIALS AND METHODS

Tissue Culture

The HEK293T (CRL-3216) and the THP-1 (TIB-202) immortalized cell-lines were

purchased from the American Type Culture Collection (ATCC). HEK293T were cultured at

37oC and 5% CO2 in Dulbecco's modified Eagle's Medium (DMEM) containing phenol red

(Invitrogen), supplemented with the 10% fetal bovine serum (FBS) (Hyclone), 10ug/ml

ciprofloxacin hydrochloride, 100IU/ml penicillin, and 100ug/ml streptomycin. To generate EV

depleted media, FBS was diluted 4x in DMEM was ultracentrifuged for 18 hours. Afterwards the

supernatant was collected and added to DMEM and supplemented with antibiotics.

Culture of SH-SY5Y and HeLa Cell Lines

The SH-SY5Y human neuroblastoma cell line and the HeLa immortalized cell line were

acquired from the American Type Culture Collection (ATCC). Cells were cultured in an

incubator at 37oC with 5% CO2 in Dulbecco's modified Eagle's Medium (DMEM) containing

phenol red (Invitrogen), supplemented with 10% fetal bovine serum (FBS) (Hyclone), 10 µg/ml

ciprofloxacin hydrochloride, 100 IU/ml penicillin, and 100 µg/ml streptomycin.

Culture of iCell DopaNeurons

The iCell DopaNeurons were purchased from Fujifilm Cellular Dynamics. iCell

DopaNeurons are a pre-differentiated culture of iPSC-derived human mDA neurons generated

using protocols licensed and adapted from the Lorenz Studer lab. Cells were thawed, plated, and

cultured using the protocol listed in the iCell DopaNeuron user guide. The cells were plated in

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24-well plates onto coverslips (Thermo Fisher Scientific) coated with 0.01% poly-L-ornithine

(Millipore Sigma) and laminin (Millipore Sigma) at 2 × 105 cells per well. The plated cells were

kept in culture for at least 7 days before any experiments were conducted to facilitate arborized

cellular morphology. Cells were cultured in an incubator at 37oC with 5% CO2 and incubated in

iCell Neural Base Medium containing iCell Nervous System Supplement 1 (Fujifilm Cellular

Dynamics), iCell Neural Supplement B (Fujifilm Cellular Dynamics), 100 IU/ml penicillin

(Thermo Fisher Scientific), and 100 µg/ml streptomycin (Thermo Fisher Scientific).

Culture of Human IPSCs

The iPSC cell line used in the experiments described herein was obtained from Joseph R.

Mazzulli laboratory (Northwestern University, Feinberg School of Medicine, Chicago, IL). This

iPSC cell line was produced from primary human fibroblasts by retroviral expression of the

reprogramming factors OCT4, SOX2, cMYC, and KLF4 (described in Seibler et al. 2011)[441].

This cell line has been characterized through the expression of pluripotency markers (i.e. Oct3/4,

Tra-1-60, SSEA-4, Nanog)[330], genomic integrity through G-banding karyotype analysis

(described in Mazzulli et al. 2011)[329], tetranoma analysis (described in Cooper et al. 2012)

[95], and reverse transcriptase-polymerase chain reaction (RT-PCR) analysis of pluripotency

markers (described in Cooper et al. 2012)[95]. iPSCs (passage 50-60) were cultured on 6-well

plates coated with either Matrigel (Stem Cell Technologies) or Vitronectin XF (Stem Cell

Technologies). Medium consisted of mTeSR1 basal medium containing 10% mTeSR1 5×

supplement (Stem Cell Technologies) and was changed every other day. iPSCs were groomed by

the manual removal of differentiated cells, passaged en bloc weekly by the manual dissection of

iPSC colonies into 2 mm2 chunks of cells and placement into a Matrigel (Stem Cell

Technologies) or Vitronectin (Stem Cell Technologies) coated 6-well plate. Prior to floor plate

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induction, iPSCs were grown to 80-90% confluence. iPSC cultures were monitored daily with an

in-hood EVOS Core XL cell imaging system (Thermo Fisher Scientific).

Induction of Midbrain Floor Plate Progenitors

iPSCs were incubated in knockout serum replacement (KSR) media containing the Small

Mothers Against Decapetaplegic (SMAD) inhibitors, LDN193189 (LDN, 100 nM; ReproCell)

and SB431542 (SB, 10 µM; Tocris) for 24 hours, KSR media containing LDN, SB, recombinant

human sonic hedgehog N-terminus (SHH, 100 pg/µl; R&D Systems), purmorphomine (2 µM;

ReproCell), and recombinant human fibroblast growth factor 8 a isoform (FGF8a, 100 pg/µl;

R&D Systems) for 48 hours, and KSR media containing LDN, SB, SHH, purmorphomine,

FGF8a, and CHIR99021 (3 µM, CHIR; ReproCell) for 48 hours. KSR media was comprised of

82.20% KnockOut Dulbecco’s Modified Eagle’s Medium (KO DMEM, Thermo Fisher

Scientific), 14.68% Knockout Serum Replacement (KO SR, Thermo Fisher Scientific), 0.98%

200 mM L-glutamine (Thermo Fisher Scientific), 0.98% Minimum Essential Media Non-

Essential Amino Acids solution (MEM-NEAA, Thermo Fisher Scientific), 0.18% 55 mM 2-

mercopatoethanol (2-ME), and 0.98% 10,000 U/ml penicillin-streptomycin solution (Thermo

Fisher Scientific). Next, iPSCs were incubated with a 3 to 1 ratio of KSR media to SM1 media

containing LDN, SB, SHH, purmorphomine, FGF8a, and CHIR for 48 hours, a 1 to 1 ratio of

KSR media to SM1 media containing LDN and CHIR for 48 hours, and 1 to 3 ratio of KSR

media to SM1 media containing LDN and CHIR for 48 hours. SM1 media was comprised of

96.16% Neurobasal Medium (Thermo Fisher Scientific), 1.92% NeuroCult SM1 Neuronal

Supplement (Stem Cell Technologies), 0.96% 200 mM L-glutamine solution (Thermo Fisher

Scientific), and 0.96% 10,000 U/ml penicillin-streptomycin solution (Thermo Fisher Scientific).

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The cultures were monitored daily with an in-hood EVOS Core XL cell imaging system (Thermo

Fisher Scientific).

Differentiation of MDA Neurons.

Floor plate induced iPSCs were differentiated to mature mDA neurons by incubating

them in SM1 media containing CHIR, DAPT (10 µM; ReproCell), recombinant human brain-

derived neurotrophic factor protein (BDNF, 20 pg/µl; R&D Systems), ascorbic acid (AA, 200

µM; Millipore Sigma), recombinant human glial-derived neurotrophic factor protein (GDNF, 5

pg/µl; R&D Systems), recombinant human transforming growth factor beta 3 protein (TGFβ3, 1

pg/µl; R&D Systems), and dibutyryl cyclic adenosine monophosphate (cAMP, 500 µM; Enzo

Life Sciences) for 48 hours and SM1 media containing DAPT, BDNF, AA, GDNF, TGFβ3, and

cAMP for 24-48 hours. The neuronal culture was allowed to expand by passaging them en bloc

by mechanical dissociation of the thickened cell layer into 2 mm2 squares, which were plated

onto poly-d-lysine (66 µg/ml, Millipore Sigma) and mouse laminin (1.25 µg/ml, Millipore

Sigma) coated 10 cm culture dishes at 2 × 105 cells/cm2. The neurons were incubated in SM1

media containing the neuralization factors DAPT, BDNF, AA, GDNF, TGFβ3, and cAMP for 10

days. The neurons were passaged by incubation with Accutase (Stem Cell Technologies) then

plated into poly-d-lysine (66 µg/ml, Millipore Sigma) and mouse laminin (1.25 µg/ml, Millipore

Sigma) coated 24-, 48-, or 96-well dishes. The neurons were incubated in SM1 media containing

DAPT, BDNF, AA, GDNF, TGFβ3, and cAMP for 15 days; this media was changed every 3

days. The neurons were incubated in SM1 media without the aforementioned neuralization

factors until analyses; this media was changed every 3 days. The neurons were considered

terminally differentiated to mDA neurons 50 days after floor plate induction had begun. Neurons

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were monitored daily with an in-hood EVOS Core XL cell imaging system (Thermo Fisher

Scientific).

Generation of DSP-α-Syn Constructs, FLuc Gal3, mCherry Gal3, and Inducible YFP-

LC3B Plasmids

Expression plasmids were all generated by PCR-based cloning and restriction enzyme

strategies. The initial generation α-syn DSP constructs A and B were made by first inserting the

generating PCR primers against DSP1-7 and DSP8-11, which have previously been

characterized in detail [255] and inserting them into the lentiviral pLVX plasmid after restriction

digest. Primers against α-syn were generated and the amplified sequence was then inserted on the

C-terminal of the DSP1-7 and DSP8-11 sequence. The pLVX DSP1-7 α-syn is the α-syn DSP A

construct and the pLVX DSP8-11 α-syn is the α-syn DSP B construct. Similarly, primers against

FLuc were used to first amplify by PCR and then insert into the pLVX lentiviral construct by

restriction digest. Primers against Gal3 were also generated to amplify the Gal3 sequence, which

was then subcloned by adding it to the C-terminal of the now pLVX FLuc construct by

restriction digest. pLVX mCherry Gal3 construct was made using primers to amplify Gal3 and

inserting it at the C-terminal of the pLVX-mCherry-C1 lentiviral construct. The generation of the

tetracycline inducible YFP-LC3B lentiviral plasmid was conducted by generating primers

against YFP-LC3B, amplifying the sequence by PCR, and inserting it into the EZ-TET-pLKO-

puromycin plasmid by restriction digest.

Generation of Stable α-Syn DSP A+B, FLuc Gal3, and mCherry Gal3 Cell Lines

To generate stable SH-SY5Y cell lines, lentiviral particles were generated by overnight

polyethylenimine (PEI) transfection of HEK 293T cells using equal concentrations of VSV-g,

ΔNRF or psPax2, and FLuc Gal3 or mCherry Gal3. The next morning, the cell medium was

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changed. The cultured medium was collected 48 hours later, and lentiviral particles were

collected and purified using a 0.45 µm (Millipore Sigma) syringe filter. The purified FLuc Gal3

or mCherry Gal3 lentiviral particles were added to SH-SY5Y cells and spinoculated at 13°C for

2 hours at 1200 × g. At 72 hours post-transduction, cells were treated with supplementing

DMEM with puromycin for selection (5 µg/ml, Hyclone). The generation of α-syn DSP A+B

SH-SY5Y and HeLa cell lines occurred by the simultaneous transduction of the α-syn DSP A

and α-syn DSP B constructs with purified lentiviral particles. The lentiviral particles were

generated by transfection of HEK 293T cells using equal concentrations of VSV-g, ΔNRF or

psPax2, and pLVX α-syn DSP A or pLVX α-syn DSP B and purified as stated previous. The

purified α-syn DSP A and α-syn DSP B lentiviral particles were added in equal concentration to

either SH-SY5Y or HeLa cells and spinoculated. At 72 hours post-transduction, cells were

treated with puromycin supplementing DMEM. Approximately 96 hours later, the puromycin-

selected cells were further selected by flow cytometry based on the GFP signal.

Generation of Gal3 CRISPR Plasmid and KO Cell Lines

The initial generation of CRISPR plasmids was conducted by identifying guide

sequences, which was accomplished through the use of the MIT CRISPR design tool and the

GeCKO library tool (http://guides.sanjanalab.org/#/). The oligonucleotide guide sequences

targeting Gal3 using the 5’-CACCGGGGAAGGGAAGAAAGACAGT-3’ sequence and the

control sequence 5’-CACCGGCACTACCAGAGCTAACTCA-3’. The guide sequence

oligonucleotides were annealed and inserted into the Lenti-CRISPRV2 plasmid (Addgene) by

BsmBI digestion and PCR reaction using the published protocol [431]. PEI transfection of HEK

293T to generate, collect, and purify lentiviral particles was then performed as described in the

previous section. Lentiviral particles were then added to generate Gal3 knockouts in the α-syn

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DSP A+B SH-SY5Y or WT SH-SY5Y cells. The transduced SH-SY5Y were then selected by

supplementing DMEM with hygromycin (5 µg/ml, Hyclone) 72 hours post-transduction.

Following a 1 weeklong selection period in hygromycin, knockout was validated with respect to

the control transduced cells by western blots. The heterogeneously selected cells were then used

for experiments. Western blots demonstrating successful KO are shown upon experiment

completion.

Stabile Expression of S15 mCherry Construct

HEK293T cells were transduced to stably express S15-mCherry using the lentiviral

vector (pLVX) backbone containing a CMV promoter to drive the expression of S15-mCherry.

Lentiviral particles were generated by polyethylenimine (PEI) transfection of HEK293T cells.

The transfection was performed overnight with equal DNA concentrations of VSV-g, ΔNRF or

psPax2, and pLVX-CMV-S15-mCherry plasmid. The next morning the cell medium was

changed. The cultured medium from the transfected HEK293T cells was collected 48 hours later

and filtered through a 0.45µm syringe filter (Millipore). The purified medium was directly added

to HEK 293T cells. 72 hours later the cells were selected for expression of our S15-mCherry

construct by supplementing DMEM or RPMI 1640 with 5ug/ml puromycin (Hyclone).

Purification of Extracellular Vesicles from S15 mCherry 293Ts

HEK293T Cells were plated in a 10cm or 15cm tissue culture plate at 50% confluency

with either 10ml or 25ml of media, respectively, for 48hrs before media was collected. EVs were

isolated in a 15ml or 50ml conical which was centrifuged in a tabletop centrifuge at 2000g for 20

minutes at 4°C. The supernatant was collected and added to either Beckman Coulter

polycarbonate centrifuge tubes (#349622) or (#344058) and spun at 10,000g with either SW41

TI or SW28 Beckman rotors, respectively, in an Optima L-90K ultracentrifuge at 4oC for 30

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minutes. Subsequently, the supernatant was collected and ultracentrifuged at 100,000g for 150

minutes at 4oC using new centrifuge tubes, as above. Afterwards, the supernatant was discarded,

and the pellet was resuspended in PBS. The resuspended pellet was subjected to another round of

100,000g centrifugation with same rotor and machine for 150 minutes at 4oC. The supernatant

was discarded, and the pellet was resuspended overnight in 100ul of PBS on an orbital shaker.

The resuspended pellets were stored at 4oC and used within two weeks. Resuspended EVs were

visually inspected before use to verify that a new pellet had not formed before use.

Purification of Extracellular Vesicles From DSP-α-Syn SH-SY5Y or IPSC MDA Neurons

Cultured media from DSP-α-syn SH-SY5Y or iPSC mDA neurons was spun in a 15ml or

50ml conical in a tabletop centrifuge at 300g for 10 minutes at 4°C. The supernatant was

collected and added to either Beckman Coulter polycarbonate centrifuge tubes (#349622) or

(#344058) and spun at 10,000g with either SW41 TI or SW28 Beckman rotors, respectively, in

an Optima L-90K ultracentrifuge at 4oC for 30 minutes. Subsequently, the supernatant was

collected and ultracentrifuged at 100,000g for 150 minutes at 4oC using new centrifuge tubes.

Afterwards, the supernatant was discarded, and the pellet was resuspended in PBS. The

resuspended pellet was subjected to another round of 100,000g centrifugation with same rotor

and machine for 150 minutes at 4oC. The supernatant was discarded, and the pellet was

resuspended in 50µl of PBS when used for TEM or EV-MAC. When used for non-reducing

SDS-PAGE, pellet was immediately lysed in lysis buffer plus protease inhibitor cocktail

(Roche). The resuspended pellets were stored at 4oC and used within two weeks. Concentrated

EVs from was collected from DSP-α-syn SH-SY5Y cells plated in 10cm plates at an initial

confluency of 60-70% percent. Culture media was collected from iPSC mDA neurons plated in a

24-well plate. Collected samples from multiple wells, n=3 or more, undergoing the same

83

conditions were used for vehicle, TD139, α-syn fibril, and α-syn plus TD139 conditions to

acquire sufficient sample size to assess EVs by non-reducing SDS-PAGE.

Nanoparticle Tracking Analysis (NTA)

NTA was performed using the NanoSight300 (NanoSight, Northwestern University, IL,

USA). 300 µl of the resuspended sample were injected into the NanoSight300 chamber with a

red, λ= 642 nm excitation laser. Five measurements (technical replicates) at 60 second intervals

were performed for each sample. The default “auto” adjustment provided by the software

developer were used for the “Blur” and “Minimal track lengths” settings. The camera level (13-

15) and detection threshold (4-5) were manually adjusted for each sample as per the

manufacturer’s recommendation. The NTA analysis software (NanoSight NTA version 3.4, build

3.4.4) was used to for data capture and analysis. In brief, each particle’s mean square

displacement was determined via the recorded video. The software then determined the diffusion

coefficient and sphere-equivalent hydrodynamic radius using the Stokes-Einstein equation.

Concentrated DSP-α-syn or mDA neuronal EVs were pelleted as previously describe and

samples were resuspended in 1ml of 1xPBS. DSP-α-syn resuspended samples were diluted 1:10-

70, with tested concentrations ranging among 4.66 x 108 - 9.16 x 108 particle per ml

(quintuplicate technical replicates from n = 3 samples). mDA resuspended samples were diluted

1:10, with tested concentrations ranging among 2.76 x 108 - 3.86 x 108 particle per ml

(quintuplicate technical replicates from n = 3 samples; samples from independent mDA

differentiations). EV sizes binned in 1 nm increments with a bin center of 0.5 nm.

Non-Reducing SDS-PAGE, Western Blots & Western Antibodies

Proteins from pelleted cells or concentrated media were isolated by the addition of lysis

buffer composed of 100 mM Tris pH 8.0, 1% NP- 40, and 150 mM NaCl and protease inhibitor

84

cocktail (Roche) on ice for 30 minutes. The lysates were centrifuged for 10 minutes at 10,000g

and afterwards the supernatant collected. The collected supernatants’ protein concentrations were

determined by Pierce BCA protein assay kit (Thermo Scientific). An equal fraction of non-

reducing SDS solution was added to the proteins and the contents were boiled on a dry-block for

5 minutes. Subsequently, the protein contents were equally loaded and ran on a 10%

polyacrylamide gel for SDS-polyacrylamide gel electrophoresis (SDS-PAGE). After separation,

the proteins were transferred to a nitrocellulose membrane (Bio-Rad), and probed overnight at

4°C with the respective primary antibodies diluted in powdered milk block solution at

2.5g/50mL of TBST. The primary antibodies were rabbit anti-ATG7 (1:350, Invitrogen), rabbit

anti-Lamp1 (1:2,000, Abcam), mouse anti-GAPDH (1:3,000, Santa Cruz), rabbit anti-pS757

ULK1 (1:500, Cell Signaling Technology), rabbit anti-ULK1 (1:1,000, Cell Signaling

Technology), anti-pT389 p70 S6K1 (1:1,000, Cell Signaling Technology), anti-p70 S6K1

(1:1,000, Cell Signaling Technology), mouse anti-α-synuclein (211, 1:2,000, Santa Cruz), rabbit

anti-α-synuclein (MJFR1, 1:2,000, Abcam), Rabbit anti-GFP (#A6455, 1:1000, Thermo

Scientific), mouse anti-GFP (B-2, 1:2000, Santa Cruz), rabbit anti-alpha-synuclein (phosphor-

S129) [MJF-R13 (8-8)] (1:2,000, Abcam), rabbit anti-alpha-synuclein aggregate antibody

[MJFR-14-6-4-2] (1:2,000, Abcam), rabbit anti-Trim16/EBBP (1:2,000, Bethyl Laboratory),

mouse anti-Trim16 (1:500, Santa Cruz), mouse anti-ATG16L1 (1:500, Santa Cruz), mouse anti-

Optineurin (1:500, Santa Cruz), mouse anti-TBK1 (1:500, Santa Cruz), mouse anti-galectin-3

(1:2,000, BD Pharmingen), rat anti-HA HRP conjugated (1:2000, Roche), mouse anti-ALIX

(1:500, Invitrogen), Mouse and rabbit anti-LC3B (1:2,000, Abcam). mouse anti-CD9 (1:1000,

BD Pharmingen #555370); mouse anti-CD63 (1:1000, BD Pharmingen #5556019); mouse anti-

CD81 (1:1000, BD Pharmingen #555675); rabbit anti-TSG101 (1:1000, Abcam ab125011);

85

rabbit anti-LAMP1 antibodies (1:2000, Abcam #24170); mouse anti-GAPDH (1:3000, Santa

Cruz SC-32233); or rabbit anti-mCherry (1:1000, Novus bio NBP2-25157). The nitrocellulose

was washed in TBST and probed with the respective HRP conjugated donkey anti-mouse or anti-

rabbit (ThermoScientific) diluted in milk block solution at 1:10,000 for 30 minutes. HRP was

detected with the addition of SuperSignal West Femto Chemiluminescent Substrate (Thermo

Fisher Scientific). Chemiluminescence levels were measured using the FlourchemE Imaging

System (Protein Simple) containing a CCD camera with 5 logs of bitrate.

Transmission Electron Microscopy of Cells

Terminally differentiated mDA neurons, grown in 24-well culture dishes with each well

containing one poly-d-lysine (66 µg/ml, Millipore Sigma) and mouse laminin (1.25 µg/ml,

Millipore Sigma) coated cover glass (Carolina Biological Supply Company, circles, 12 mm

diameter), were fixed by incubation in PBS containing 2% paraformaldehyde (Electron

Microscopy Sciences), 2% glutaraldehyde (Electron Microscopy Sciences), and 2% acrolein

(Polysciences, Inc) for 30 minutes at room temperature. Cells were fixed in phosphate buffer

(PB) containing 1% osmium tetroxide (Electron Microscopy Sciences) and 1.5% potassium

ferricyanide (Electron Microscopy Sciences) for 1 hour at room temperature in the dark. Next,

the cells were dehydrated by incubation in an ascending series of hexylene glycol (25, 50, 75, 95,

100%, Electron Microscopy Sciences) followed by incubation in a 1 to 1 ratio of hexylene glycol

to epoxy resin (comprised of a mixture of EMbed 812, nadic methyl anhydride, dodecenyl

succinic anhydride, and 2,4,6-Tris-(dimethylaminomethyl)phenol (DMP-30); Electron

Microscopy Sciences) for 12 hours at room temperature on a rotary mixer (Ted Pella, Inc). Next,

the cells were incubated with 100% epoxy resin for 12 hours at room temperature on a rotary

mixer (Ted Pella, Inc). The epoxy resin was changed, and the cells were incubated for 2 hours at

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room temperature on a rotary mixer (Ted Pella, Inc). The cover glass was removed from the

culture dish, inverted, placed onto an embedding capsule filled with epoxy resin, then baked at

70˚C for 12 hours. The cells, cover glass, and embedding capsule filled with epoxy resin were

immersed in liquid N2 to separate the cells from the cover glass. Ultrathin sections (70 nm) were

cut with an ultramicrotome (EM UC7, Leica Microsystems), mounted on formvar- and carbon-

coated 200 mesh copper grids then stained with filtered 2% uranyl acetate and Reynold’s lead

citrate prior to imaging. Samples were imaged with a Philips CM 120 transmission electron

microscope (TSS Microscopy) equipped with a BioSprint 16- megapixel digital camera

(Advanced Microscopy Techniques).

Transmission Electron Microscopy and Immunogold Labeling of EVs

Concentrated EV samples were fixed in 2% paraformaldehyde for 30 minutes at room

temperature. Formvar- and carbon-coated 200 mesh nickel grids (Electron Microscopy

Sciences), pretreated with 0.002% Alcian blue in 0.03% acetic acid to increase the hydrophilicity

of the grids, were floated on top of 50µl drops of fixed EV samples for 20 minutes at room

temperature. The grids were washed thrice in PBS then incubated with 50mM glycine to quench

free aldehyde groups. After the grids were thoroughly washed, they were incubated in PBS

containing 5% bovine serum albumin (BSA), 0.1% cold water fish skin gelatin, and 5% normal

goat serum (Electron Microscopy Sciences) for 30 minutes at room temperature. Grids were

thoroughly washed with PBS containing 0.2% acetylated bovine serum albumin (BSA-c)

(Electron Microscopy Sciences) then incubated with PBS-0.2% BSA-c containing 0.05%

saponin and either mouse anti-human CD63 (BD Pharmingen #5556019) (1:20) or mouse anti-

human CD81 (BD Pharmingen #555675) (1:20) for 2 hours at room temperature. The grids were

thoroughly washed in PBS-0.2% BSA-c then incubated with goat anti-mouse IgG conjugated to

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20 nm gold (Cytodiagnostics #AC-20-02) (1:20). The grids were incubated with 1%

glutaraldehyde for 15 minutes at room temperature then thoroughly washed with deionized

water. The sample was negatively stained by floating the grid on a 50µl drop of uranyl-oxalate

(pH 7) for 5 minutes at room temperature followed by floating the grid on an 50µl drop of a

methyl cellulose-uranyl acetate-phosphotungstic acid (MC-UA-PTA) solution (700µl 2% methyl

cellulose, 100µl 3% uranyl acetate (pH 3.5), 25µl 1% phosphortungstic acid (pH 7.2), and 75µl

deionized water) for 10 minutes on wet ice. After the grids were removed from the MC-UA-PTA

solution with nichrome loops (3.5mm internal diameter, Ted Pella, Inc), they were blotted

against a sheet of Whatman filter paper so that a thin layer of film was left on the EV side of the

grid. The sample was placed into a grid storage box and allowed to dry for 12 hours prior to

imaging with a Philips CM120 transmission electron microscope (TSS Microscopy) equipped

with a BioSprint 16-megapixel digital camera (Advanced Microscopy Techniques).

Immunofluorescence Staining of EVs

In order to adhere EVs to coverslips, either 80µl of resuspended concentrated EV was

added to 420µl of PBS totaling 500µl or, for unconcentrated EVs, 500µl of supernatant cultured

media was added into the well of 24-well plate containing a glass coverslip (Fischerbrand

microscope cover slides, 12-545-J, 22X60-1). Coverslips were initially held in a 50ml conical in

70% ethanol and were added to individual wells of a tissue culture, 24-well plate and

subsequently washed with PBS three times. The final round of PBS was left in the well and

aspirated immediately before continuing. The contents of the 24-well plate were spinoculated by

centrifugation at 13°C for 2 hours at 1200g onto the coverslips and subsequently fixed in a

solution of 0.1 M PIPES containing 3.7% formaldehyde (Polysciences) for 15 minutes and

washed 3 times with PBS. The coverslips were permeabilized with a 0.1% solution of saponin in

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block solution composed of 500mL of PBS supplemented with 10% normal donkey serum

(NDS), and 0.01% NaN3 for 5 minutes. After washing 3 times, the coverslips were incubated

with rabbit anti-LAMP1 antibodies (Abcam #24170) or rabbit anti-TSG101 (Abcam ab125011)

and either mouse anti-CD9 (BD Pharmingen #555370), mouse anti-CD63 (BD Pharmingen

#5556019), or mouse anti-CD81 (BD Pharmingen #555675), in the previously stated block

solution for 1 hour at room temperature. All primary antibodies were used at 1:1000. In

experiments using lectins, biotin conjugated lectins (Vector Laboratories) were used at a working

concentration of 5g/mL in place of primary antibodies for 1 hour at room temperature.

Afterwards the coverslips were washed with PBS and subsequently incubated with secondary

antibodies of conjugated donkey anti-mouse 488 (Jackson ImmunoResearch Laboratories, Inc.)

and donkey anti-rabbit 647 (Jackson ImmunoResearch Laboratories, Inc.) at a concentration of

1:400 for 30 minutes at room temperature diluted in PBS block solution and washed with PBS.

Additionally, FITC conjugated streptavidin (SAV) (Jackson ImmunoResearch Laboratories, Inc.

016-600-084) at 1:1000 was added for 1 hour at room temperature, diluted in PBS block, and

washed with PBS. Afterwards, coverslips were fixed and mounted (Electron Microscopy

Sciences, Fluoro-gel with Tris buffer, #17985-11) onto slides (Globe Scientific Inc., Diamond

White Glass 25x75x1mm, .5 gloss, #1380-30). In experiments using poly-L-lysine coated

coverslips, coverslips were coated by adding 500µl of 0.1% of poly-L-Lysine solution (Sigma

Aldrich) to the well and incubated at 37oC for 1 hour. Following incubation, the coverslips were

washed 3x with PBS before continuing with spinoculations.

Indirect ELISA of EVs

LAMP1 (Abcam #24170) antibody was diluted in pH 9.6 carbonate buffer to a final

concentration of 4µg/mL and used to coat the wells of a 96-well Maxisorp ELISA Plate (NUNC)

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at 4oC for 16 hours on orbital shaker. The wells were washed with PBS thrice before being

blocked in equal portions of 10% BSA supplemented DMEM and PBS for 2 hours at room

temperature and subsequently washed 5x with PBS. 25µl of concentrated EVs resuspended in

PBS were added to each well for 24 hours at 4oC. Wells were washed eight times with PBS.

Detection antibodies, either CD9, CD63, CD81 (BD Pharmingen) diluted to a final concentration

of 625ng/mL in 1x ELISA Diluent Solution (eBioscience), were added to sample captured wells

for 24 hours at 4oC on orbital shaker. Afterwards, wells were washed with PBS five times before

secondary anti-mouse HRP conjugated antibodies diluted in 1x ELISA Diluent Solution were

added for 30 minutes at room temperature on rocker. A final five times wash of the plate with

PBS was conducted before 100µl of 1xTMB (Invitrogen) was added for ~15 minutes before the

reaction was quenched with 2N H2SO4 solution. The absorbance was read at 450 nm on BioTek

PowerWave XS plate reader in conjunction with Gen5 software. 3 independent replicates were

conducted from independent preparations of S15-mCherry (S15Ch) isolated EVs that were

concentrated by ultracentrifugation as described previously. The concentrated EVs were

resuspended in 250µl of PBS. The same PBS was used as a control and added to wells in which

LAMP1 capture antibodies and either CD9, CD63, CD81 detection antibodies, followed by

secondary anti-mouse HRP conjugated antibody solutions were used.

Human Bodily Fluid Sample Collection, Preparation, and Processing

Saliva.

Whole saliva from a healthy male or female donor between the ages of 25 and 32 was

collected according to a previously published saliva collection protocol[109]. Samples were

either stored at 4°C for no more than 36 hours, or at -20°C until needed. If frozen, samples were

thawed at room temperature. The saliva was sequentially centrifuged. The initial centrifuged

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using in 2ml eppendorfs in a tabletop centrifuge at 2000g for 20 minutes at 4°C. The supernatant

was collected, pooled, and added to Beckman Coulter polycarbonate centrifuge tubes (#349622)

and spun at 10,000g with SW41 TI Beckman rotors in an Optima L-90K Ultracentrifuge at 4oC

for 30 minutes. Subsequently, the supernatant was collected and ultracentrifuged at 100,000g for

150 minutes at 4oC using new of the previously stated tubes, rotors, and ultracentrifuge.

Afterwards, the supernatant was discarded, and the pellet was resuspended in PBS. The

resuspended pellet was subjected to another round of 100,000g centrifugation with same rotor

and machine for 150 minutes at 4oC. The supernatant was discarded, and the pellet was

resuspended overnight in 500µl of PBS on an orbital shaker. The pelleted EVs were stored at -

20oC until needed and thawed at room temperature.

In these experiments, 2ml of saliva was subjected to differential ultracentrifugation, and

50µl of concentrated saliva EVs were mixed with 450µl of PBS so that a total volume of 500µl

was added to glass coverslips in a 24-well plate. The plate was spun at 13°C for 2 hours at 1200g

to spinoculate the samples onto the coverslips. The coverslips were fixed in a solution of 0.1 M

PIPES with 3.7% formaldehyde (Polysciences) for 15 minutes and washed 3 times with PBS,

followed by a 5 minute permeabilization step using a 0.1% saponin block solution composed of

500mL of PBS supplemented with 10% normal donkey serum (NDS), and 0.01% NaN3 and

washed another 3 times with PBS. The spinoculated saliva was incubated with biotin-conjugated

wheat germ agglutinin (WGA) (Vector Laboratories, B-1025) (at working concentration of

5ug/mL) at room temperature for 1 hour resuspended in the block solution like the

aforementioned one, but without the 0.1% saponin. After 1 hour of incubation, the WGA

solution was removed, and the samples washed 3 times with PBS. The samples were incubated

with FITC conjugated streptavidin (SAV) (Jackson ImmunoResearch Laboratories, Inc. 016-

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600-084) (1:2000) at room temperature for 1 hour, again in the saponin-free block solution. After

another 3 washes of PBS, the samples were incubated with rabbit anti-Lamp1 antibody (Abcam

#24170) (1:1000) and mouse anti-CD63 (BD Pharmingen #5556019) (1:1000) at room

temperature for 1 hour, again in the block solution without the saponin. Samples were washed 3

times with PBS and incubated in the no saponin block with secondary Fab antibodies conjugated

donkey anti-mouse 594 (Jackson ImmunoResearch Laboratories, Inc.) (1:400) and donkey anti-

rabbit 647 (Jackson ImmunoResearch Laboratories, Inc.) (1:400) at room temperature for 30

minutes. Coverslips were washed 3 times with PBS and mounted onto to glass slides. This

yielded on average 1245 WGA puncta per image.

Plasma.

Whole blood from 3 healthy male donors between the ages of 22 and 36 was drawn into

conicals containing 10%, by volume, of 3.8% sodium citrate, and subsequently homogenized in

new concials. Lymphocyte separation medium (Corning 25-072-CV) was carefully layered into

the bottom of the conical, and the whole blood was spun at 400g for 15 minutes. The plasma

layer was carefully pipetted out, aliquoted, and immediately stored at -80°C until needed. Frozen

samples were thawed at room temperature and was sequentially centrifuged. The initial

centrifugation was done using in 2ml eppendorfs in a tabletop centrifuge at 2000g for 20 minutes

at 4°C. The supernatant was collected, pooled, and added to Beckman Coulter polycarbonate

centrifuge tubes (#349622) and spun at 10,000g with a SW41 TI Beckman rotor in an Optima L-

90K Ultracentrifuge at 4oC for 30 minutes. Subsequently, the supernatant was collected and

ultracentrifuged at 100,000g for 150 minutes at 4oC using new of the previously stated tubes,

rotors, and ultracentrifuge. Afterwards, the supernatant was discarded, and the pellet was

resuspended in PBS. The resuspended pellet was subjected to another round of 100,000g

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centrifugation with same rotor and machine for 150 minutes at 4oC. The supernatant was

discarded, and the pellet was resuspended overnight in 500µl of PBS on an orbital shaker. The

pelleted EVs were stored at -20oC until needed and thawed at room temperature.

In these experiments, 2ml of plasma was subjected to differential ultracentrifugation, and

50µl of concentrated plasma EVs were mixed with 450µl of PBS so that a total volume of 500µl

was added to glass coverslips in a 24-well plate. Plasma samples were spinoculated, fixed, and

stained in the same manner as the saliva samples detailed above. This yielded on average 175

WGA puncta per image.

PKH Dye Labeling of EVs

EVs from saliva and plasma were isolated as previously described in the collection,

preparation and processing methodology section. Similarly, wildtype or S15Ch 293Ts pelleted

EVs were concentrated via previously described purification of extracellular vesicle

methodology using starting volumes that filled SW28, Beckman Coulter polycarbonate

centrifuge tubes (#344058). Afterwards, 293T concentrated EVs were directly resuspended in

100µl of PBS, while saliva and plasma EVs were resuspended in 500µl of PBS overnight on the

orbital shake 4oC as described previously. To prepare the EVs for staining, 50µl of either

concentrated WT or S15Ch 293T EVs were added to 950µl of Diluent C (Phanos Technologies)

while 200µl of concentrated saliva EVs or 500µl of plasma EVs were added to 800µl or 500µl of

Diluent C, respectively. Next, a master mix of 0.2µl or 0.4µl of PKH26 or PKH67 (Phanos

Technologies) was added to 1ml of Diluent C (Phanos Technologies) per sample to create a final

concentration of 200nM or 400nM, respectively. A 200nM PKH67 dye master mix was used for

both saliva and plasma EVs. 1ml of the PKH dye master mix was added to the 1ml EVs and

Diluent C mixture and was gently pipetted for 30 seconds, followed by being shaken at room

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temperate on an orbital shaker at 100rpm, for 5 minutes. Afterwards, the reaction was quenched

by adding 2ml of 10% BSA (Sigma-Aldrich, #05470) resuspended in PBS. 4.5ml of serum free

DMEM was added to the quenched reaction to a total volume of 8.5ml. Next, to minimize

turbulence, the 8.5ml mixture was very gently floated on-top of a 1.5ml .971M sucrose (Sigma-

Aldrich, #S9378) cushion in a Beckman Coulter polycarbonate centrifuge tube (#349622) and

topped off with serum free DMEM. This was followed by centrifugation at 190,000g for 2 hours

at 4oC via the SW41 TI rotor to pellet the EVs while removing excess dye. The supernatant and

the sucrose cushion were carefully aspirated, and the pellet was resuspended in 500µl of PBS by

gentle pipetting. The resuspended S15Ch or WT 293T pellet was used immediately or stored at -

20oC for later use. The resuspended saliva and plasma EVs were stored at -20oC until need and

thawed at room temperature. In preparation for imaging, 250µl of the WT or S15Ch

resuspension was added to 250µl of PBS and spinoculated onto uncoated coverslips per

condition. Conversely, 50µl of the thawed plasma or saliva EVs were added to 450µl of PBS and

spinoculated onto 0.1% Poly-L-Lysine (Sigma Aldrich) coated coverslips as described

previously. EVs were then subjected to immunofluorescence staining as described in

immunofluorescence staining methodology section.

Cell Treatment and EV-MAC Immunofluorescence Staining

The experiments involving α-syn DSP A+B HeLa cells were performed by treating cells

with either Baf-A1 and/or 200 nM α-syn fibrils (Proteos) labelled with Dylight 550 N-

hydroxysuccinimide (Thermo Fisher Scientific) or leaving them untreated for 24 hours. For the

experiments involving the iCell DopaNeurons, the cells were transfected with siRNA as

previously described and cultured media at 24-72 hours post-transfection. Collected cultured

media was initially centrifuged at 1,200 × g for 5 minutes at 4oC to remove cellular debris. Next,

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500 µl of the supernatant cultured media was added to a well of a 24-well plate containing a

glass coverslip (Thermo Fisher Sciences). Coverslips were initially held in a 50 ml conical in

70% ethanol and were added to individual wells of a 24-well tissue culture plate and

subsequently washed thrice with PBS. The final round of PBS was left in the well and aspirated

immediately before continuing. In order for the contents of the cultured media to adhere to the

coverslips, the cultured media was spinoculated by centrifugation at 13°C for 2 hours at 1,200 ×

g and subsequently fixed in a solution of 0.1M piperazine-N-N′bis[2-ethanesulfonic acid]

(PIPES) buffer containing 3.7% formaldehyde (Polysciences) for 15 minutes and washed thrice

with PBS. The coverslips were permeabilized with a 0.1% solution of saponin in block solution,

composed of 500 ml of PBS supplemented with 10% NDS and 0.01% sodium azide, for 5

minutes. After washing 3 times, the coverslips were incubated with either mouse anti-galectin-3

(1:1,000, BD Pharmingen), mouse anti-CD9 (1:1000, BD Bioscience), mouse anti-CD63

(1:1000, BD Bioscience), mouse anti-CD81 (1:1000, BD Bioscience), or rabbit anti-α-synuclein

(MJFR1, 1:1,500, Abcam) for 1 hour at room temperature and then washed three times with

PBS. Where applicable, biotin conjugated wheat germ agglutinin (WGA, Vector Laboratories)

was used at a working concentration of 5 g/ml for 1 hour at room temperature after incubation

with the solution containing primary antibody. Afterwards, primary antibodies were labelled

using fluorophore conjugated donkey anti-mouse or donkey anti-rabbit antibodies (1:400,

Jackson ImmunoResearch Laboratories) for 30 minutes at room temperature. Depending on the

set of experiments, the conjugated fluorophore color varied. Alexa 594 donkey anti-mouse was

used to label the Gal3 antibody from α-syn DSP A+B culture media. Alexa 647 donkey anti-

mouse and Alexa 594 donkey anti-rabbit were used to label the Gal3 and α-synuclein antibodies,

respectively, in iCell DopaNeuron culture media. Afterwards, the coverslips were washed with

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PBS. If the coverslips underwent lectin staining, biotin conjugated WGA was labelled using

PBS-diluted fluorescein isothiocyanate (FITC) conjugated streptavidin (SAV, 1:1,000, Jackson

ImmunoResearch Laboratories, Inc.) for 1 hour at room temperature. Afterwards, coverslips

were fixed and mounted with Fluoro-Gel (Electron Microscopy Sciences) onto slides (Globe

Scientific Inc.).

Fixation and Immunofluorescence Staining of Cells

In preparation for immunofluorescence antibody staining, cells were fixed in a solution of

4% formaldehyde in Dulbecco’s phosphate buffered saline (DPBS) for 15 minutes and gently

washed twice in DPBS. Cells were permeabilized, blocked, and probed with primary antibody in

a block solution composed of DPBS supplemented with 2% FBS, 2% NDS, and 0.2% Triton X-

100 sterile filtered through a 0.22 µm filter. The following immunofluorescence antibodies were

used at the specified concentrations: mouse anti-galectin-3 (1:1,000, BD Pharmingen), mouse

anti-OCT3/4 (1:500, Santa Cruz), rabbit anti-LMX1A (1:1,000, EMD Millipore), mouse anti-

nestin (1:500, Santa Cruz), rabbit anti-tyrosine hydroxylase (1:1,000, EMD Millipore), rabbit

anti-Trim16/EBBP (1:1,000, Bethyl Laboratory), mouse anti-Trim16 (1:500, Santa Cruz), rabbit

anti-LC3B (1:400, Abcam), mouse anti-ATG16L1 (1:500, Santa Cruz), mouse anti-CD63

(1:1000), and mouse anti-beta-III tubulin (1:500, Santa Cruz). Cells were incubated at room

temperature for 1 hour and washed thrice in DPBS. Non-overlapping conjugated donkey anti-

mouse and/or anti-rabbit were used at either 488, 594, or 647 (1:400, Jackson ImmunoResearch

Laboratories) in blocking buffer. When used, 4′,6-diamino-2-phenylindole (DAPI, Sigma-

Aldrich) and/or phalloidin (Sigma-Aldrich) was added to the secondary antibody solution. Cells

were incubated with secondary antibody solution for 30 minutes at room temperature and

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subsequently washed thrice with DPBS. Afterwards, coverslips were fixed and mounted onto

slides (Globe Scientific Inc.).

Imaging Analysis Paradigm

The collected z-stack images were used as reconstructed 3-dimensional (3D) maximum

intensity projections (MIPs) for analysis with Imaris software (version 7.6.4, Bitplane) in order

to use the 3D masking algorithm function. The same masking algorithm was applied to all

images of a single experiment regardless of condition to permit valid between groups

comparisons using the Batch Coordinator tool (Bitplane).

In cultured media experiments, non-representative images where the focal plane was not

correctly imaged were excluded. No more than 2 images per condition, per replicate were

excluded. The relative background levels of maximum intensity for each respective channel were

determined based on the secondary antibody controls for mouse, rabbit, and SAV (where

applicable) by determining the 95th percentile’s maximum fluorescence intensity from each

image and, subsequently, averaging them.

Wide-field Fluorescence Deconvolution Microscopy and Analysis

Imaging of cultured media or cells was performed on a DeltaVision wide-field

fluorescent microscope (Applied Precision, GE) outfitted with a digital camera (CoolSNAP

HQ2; Photometrics), while using the oil immersion Olympus Plan Apo 60× objective lens (1.42

numerical aperture) with ResolveTM immersion oil with a refraction index of 1.5150 (Richard

Allen Scientific, #M3004). A 250watt Xenon Arc lamp was used to direct excitation lighting

from the back of the microscope and focused from below onto the coverslip held on an Olympus

IX-71 stage. Dichroic filter set uses the Alexa setting: FITC Excitation: 475/28 Emission:

523/36; A594 Excitation: 575/25 Emission: 632/30; CY5 Excitation: 632/20 Emission: 676/34;

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DAPI Excitation: 390/18 Emission: 435/48. Exposure times range from experimental conditions

depending on staining conditions. Conditions for S15Ch experiments use FITC exposure time of

.05-.1 seconds; A594 exposure time of .08-.1 seconds; CY5 exposure time of .025-.1 seconds.

100% transmission was used for all exposure conditions. S15Ch EVs and WGA: FITC exposure

time .05 seconds, 50% transmission; A594 exposure time of .1 seconds, 100% transmission.

PKH26 Dye: FITC exposure time .01 seconds, 100% transmission; A594 exposure time of .05

seconds, 50% transmission; CY5 exposure time of .025 seconds, 100% transmission. PKH67

Dye: FITC exposure time .05 seconds, 50% transmission; A594 exposure time of .015 seconds,

100% transmission; CY5 exposure time of .025 seconds, 100% transmission. Exposure times

were not altered among staining conditions of a collected sample. Exposure times were kept

consistent among all collected samples and were only adjusted to prevent signal over-exposure.

Secondary only conditions were collected and compared to determine thresholds for each

collected sample.

The number of images taken per experiment is stated in the figure legend. Data was

collected by Z-stack imaging and was analyzed as maximum intensity projections. In each case,

the images taken were from different locations on the coverslip with the intent of creating a

representative sample of the total population. During some experiments, the “panels” function of

the SoftWoRx software (Applied Precision) was used to take non-biased images with the same

set of panels applied to each coverslip. In these cases, the z-stack distance was manually

recalibrated for each coverslip to ensure that the images remained in focus.

Data were collected by capturing a total of 30-40 z-stacks at a depth of .2µm between

each stack. These dimensions were constant among coverslips and all data shown uses the same

dimensional constants. Data was imaged so that Z-stacks started and ended on either side of the

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focal plane. The use of Z-stack imaging was used to facilitate the constrained Iterative

deconvolution performed by SoftWoRx (Applied Precision, Inc.) a type of image restorative

deconvolution to increase the signal-to-noise ratio (Lifting the Fog: Image Restoration by

Deconvolution). The OTF used in the deconvolution process was a prerecorded, empirically

generated OTF for the 60x 1.42NA Olympus Plan Apo objective created by Applied Precision

Inc. for deconvolution. The CoolSnap HQ2 camera; the Olympus 60x Plan Apo Objective; the

SoftWoRx deconvolution software and solid-state illumination source; are all standard for the

DeltaVision microscope system, which was initially purchased from Applied Precision, Inc.

(Now GE Biosciences)

Collected Z-stack images were ultimately used as reconstructed 3D maximum intensity

projections for analysis by with Bitplane: Imaris software version 7.6.4. These 3D

reconstructions facilitated the formation of a 3D masking algorithms which we built around our

signal of interest (i.e. S15Ch or a lectin) within the 3D deconvolved reconstructions. The

deconvolved reconstructions were used for all data sets if not specifically addressed.

All S15 mCherry acquired 3D reconstructions were subjected to the same spots masking

algorithm via the Batch Coordinator tool (Bitplane) to each respective signal. Non-representative

images indicating that the focal plane containing EVs was not imaged as intended were removed.

No more than 2 images per condition per replicate were excluded. Panels containing an example

of a Spots algorithm are shown as maximum intensity projections (MIPs). Panels containing no

spots algorithm are show as individual Z-Stacks within the 3D reconstruction.

Background levels of maximum intensity for each respective channel were determined based on

secondary antibody controls for mouse (CD9, CD63, and CD81) or rabbit (LAMP1 or TSG101),

and streptavidin (SAV)-only controls (WGA, LEL, etc) via a gating process analogous to

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flowcytometry. In cases where the threshold for above background signal was not visually

obvious, the 99th percentile was determined and used as the threshold when all images collected

from a single coverslip were pooled. When each image from a single coverslip was evaluated

individually, the 95th percentile from each image was used and subsequently averaged to

determine the above background signal value. The average number of THP-1 EVs among

experiments was in the range of 50-800 per image depending on the condition and sample

preparation.

To remove aggregates that were initially identified by our Spots masking algorithm, we

exclude Spots with more than 85 voxels. This was conducted by excluding Spot’s masks’ data

generated from the batching process which is provided under the “voxels” spreadsheet and

contains the voxel number of each spot identified. The visualization of these excluded spots was

conducted by going to the “Filters tab” of the Spots masking algorithm and changing the max

inclusion threshold to 85 voxels. Afterwards, a secondary Spots algorithm was created with the

applied voxel filter by “Replicating Spots”. A more in-depth explanation of how to exclude

initially included spots and the advantages to doing this rather than adding increased exclusion

criteria is provided in the legend of Fig. 7.

Bitplane Imaris Spots and Surface Algorithm Generation

The exact spots algorithm for our unconcentrated and concentrated Spots is as follows:

[Algorithm]

Enable Region Of Interest = false

Enable Region Growing = true

Enable Tracking = false

[Source Channel]

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Source Channel Index = 2

Estimated XY Diameter = 0.300 um

Estimated Z Diameter = 0.600 um

Background Subtraction = true

[Classify Spots]

"Quality" above 25.0

"Distance to Image Border XY" between 2.00 um and 64.0 um

[Spot Region Type]

Region Growing Type = Local Contrast

[Spot Regions]

Region Growing Automatic Threshold = false

Region Growing Manual Threshold = 40

Region Growing Diameter = Diameter From Volume

Create Region Channel = false

Small variations of this algorithm were used for other experiments. However, the core gating

variables remain relatively unchanged.

In experiments in which the surfaces mask feature in Imaris Bitplane was used, the

surfaces mask algorithm was created using the same gating strategy as the spots mask, with the

key parameter being “sphericity” instead of quality. A representative surfaces algorithm is shown

below:

[Algorithm]

Enable Region Of Interest = false

Enable Region Growing = false

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Enable Tracking = false

[Source Channel]

Source Channel Index = 1

Enable Smooth = true

Surface Grain Size = 0.100 um

Enable Eliminate Background = true

Diameter Of Largest Sphere = 0.400 um

[Threshold]

Enable Automatic Threshold = false

Manual Threshold Value = 234

Active Threshold = true

Enable Automatic Threshold B = false

Manual Threshold Value B = 5288.04

Active Threshold B = false

[Classify Surfaces]

"Sphericity" above 0.810

"Position X" between 2.00 um and 64.0 um

"Position Y" between 2.00 um and 64.0 um

Sensitivity and Detection in Deconvolved and Undeconvolved Images

15 fields from a dilute solution of tetraspecks, ranging from 1-6 beads, with increasing

exposure times in the FITC channel stopping at .1 seconds exposure time (Fig. 6). To determine

if the difference in spot number was a consequence of deconvolution artifacts, we manually

compared the images and the relative ability of our algorithm to identify individual puncta. Our

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algorithm did not detect puncta in deconvolved images that were not identifiable in

undeconvolved images at longer exposure conditions. between the undeconvolved and

deconvolved counterparts; representative examples shown in (Fig. 6A). the differences observed

appeared to be from our Spots masking algorithm’s inability to distinguish the signal at our

algorithm parameters. Collectively, the deconvolved images had a ~4.26-fold increase in signal

compared to their undeconvolved counterpart, suggesting the differences in spot number were

the result of increased signal-to-noise (Fig. 6B). To determine if these results were comparable

under conditions that better simulate what we normally observe for EVs, we used the same

experimental paradigm for two separate coverslips with more concentrated bead concentrations

the number of spots recognized at each exposure condition for the undeconvolved and

deconvolved counterparts, we observed that the spots algorithm quickly recognized the signal in

the deconvolved fields and remained at the same number while the undeconvolved fields reached

was less sensitive at lower exposure conditions (Fig. 6C).

Serial Dilutions of S15Ch EVs

200µl of concentrated S15Ch EVs, as previously described, were resuspended in 1ml of

PBS to generate the stock dilution of concentration X. 600µl of our stock dilution was serially

added to eppendorfs containing 600µl of PBS to generate subsequent S15Ch dilutions of .5X,

.25X, .125X, .0625X, and .03125X. From each dilution, 100µl was saved for bicinchoninic acid

(BCA) assay to determine initial protein concentration, while 500µl of each dilution was

spinoculated onto coverslips that were coated with 0.1% poly-L-lysine, as described previously.

After spinoculation, the PBS and EV mixture was collected and reserved for BCA to determine

the post-spinoculation protein concentration, and the coverslips were fixed and stained with

CD81 and LAMP1 as previously explained. Z-stack images of the coverslips were collected

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using the points function to generate 15 unbiased 3D reconstructions and number of S15Ch

puncta and CD81 puncta were measured using the following Spots algorithm:

[Algorithm]

Enable Region Of Interest = false

Enable Region Growing = true

Enable Tracking = false

[Source Channel]

Source Channel Index = 1 (CD81), 2 (S15Ch)

Estimated XY Diameter = 0.300 um

Estimated Z Diameter = 0.600 um

Background Subtraction = true

[Classify Spots]

"Quality" above 150

"Distance to Image Border XY" above 2.00 um

[Spot Region Type]

Region Growing Type = Local Contrast

[Spot Regions]

Region Growing Automatic Treshold = false

Region Growing Manual Threshold = 29

Region Growing Diameter = Diameter From Volume

Create Region Channel = false

To determine the initial and post-spinoculation protein concentration, a modified version

of the PierceTM BCA Protein Assay Kit (ThermoScientific, #23225) protocol was used in

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conjunction with its kit. The bovine serum albumin (BSA) standard was initially serial diluted to

create a standard curve to better assist in measuring protein concentrations within the expected

protein concentrations of our EVs. To generate a standard curve, the initial standard was

prepared by adding 8µl of 20% SDS, to 4µl of 2mg/ml of BSA, and 108µl of PBS. 60µl was

serial diluted by mixing with a mixture of 56µl of PBS and 4µl of 20% SDS. This was conducted

10 times. A negative control was also prepared by adding 4µl of 20% SDS to 56µl of PBS.

Subsequently, 30µl of each standard as well as the negative control were plated in duplicate in a

96-well plate (Pierce, #15041). Samples were prepared by adding 30ul of each sample in

duplicate with the addition of 2µl of 20% SDS. The plate was added to an orbital shaker for 30

seconds. Afterwards, a 170 µl of Reagent A and Reagent B mixed at a ratio of 20:1 was added to

each well by 12-well multichannel pipetter. A plate-sealer was added to the 96-well plate and the

plate was incubated at 37C for 4 hours before being read on BioTek PowerWave XS plate reader

in conjunction with Gen5 software at an absorbance wavelength of 562 nm.

Empirical Measurement of the Point Spread Function and Binning

To measure the point spread function (PSF), 100 nm Tetraspeck (Invitrogen, T7279)

Microspheres were originally diluted 1:5000 in purified water and applied to coverslips based on

the manufacture’s protocol. Ten fields containing 1-6 microspheres were collected as Z-stack

images at a depth of .2µm per stack. Each field was collected with increasing exposure times

ranging across 0.005, 0.010, 0.015, 0.025, 0.05, 0.08, and 0.1 seconds. The microspheres were

imaged in the FITC channel with the excitation laser set to 100% laser transmission and the

DeltaVision microscopes Alexa filter set with same size pinhole. All images were captured with

the CoolSNAP HQ2 (Photometrics) digital camera from the Olympus Plan Apo 60× objective

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lens (1.42 numerical aperture), while using ResolveTM immersion oil with a refraction index of

1.5150 (Richard Allen Scientific, #M3004).

The Point Spread Function (PSF) was calculated both before and after deconvolution

which includes using a bin size of 2x2 to enhance sensitivity as in EV-MAC experiments

(Appendix 1, Fig. 1A). Fields containing 1-6 fluorospheres, with increasing exposure conditions

were imaged to calculate the PSF by determining the full width half max (FWHM) from

individual fluorospheres with the single pinhole function of the ImageJ plugin, Adrian’s FWHM.

Adrian’s FWHM (Martin, A. (2008). (http://imagej.nih.gov/ij/plugins/ fwhm/index.html) uses a

Levenburg-Marquardt implementation to apply a Gaussian fit to determine the PSF. This was

conducted from 10 fields, and the same fluorosphere was measured for both undeconvolved and

deconvolved images at all tested exposure conditions. As per the plugin designer’s

recommendation, measured values with an error above 1e-6 were excluded. Undeconvolved

images had lower PSF functions at all exposure conditions calculated when compared to the

deconvolved (Appendix 1, Fig. 1A). The PSF of S15Ch EVs and FITC tetraspecks (100 nm)

revealed no significant differences, revealing that the majority of S15Ch EVs were below the

resolution limit of our microscope (Appendix 1, Fig. 1B). Similar resulted were observed using

1x1 binning (Appendix 1, Fig. 1C). No differences were observed when images were analyzed

using 1x1 or 2x2 binning (Appendix 1, Fig. 1D).

Flow Cytometry

The procedure to prepare mature mDA neurons for analysis by flow cytometry was based

on published protocol (Menon et al. 2014)[338] with minor modifications. A suspension of

single cells was created by incubating the cells with Accutase (Stem Cell Technologies). The

single cell suspension was centrifuged at 220 × g for 5 minutes at room temperature then

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resuspended in 2% FBS in phosphate buffered saline (PBS) (Stem Cell Technologies). The cells

were counted, distributed in aliquots, then fixed in 4% paraformaldehyde in PBS for 30 minutes

at room temperature on a rotary mixer (Ted Pella, Inc). When the antigen was intracellular, the

cells were permeabilized by incubation with a 0.5% saponin solution for 15 minutes at room

temperature on a rotary mixer (Ted Pella, Inc). This permeabilization step was omitted for

surface antigens. After a PBS wash, the cells were incubating with a primary antibody solution

containing primary antibody, 1% bovine serum albumin (BSA), 10% normal donkey serum

(NDS) in PBS for 45 minutes at room temperature on a rotary mixer. When the antigen was

intracellular, the primary antibody solution contained 0.5% saponin. When the antigen was

expressed on the cell surface, saponin was omitted from the primary antibody solution. The

primary antibodies used were TH (EMD Millipore), Nurr1 (Santa Cruz Biotechnology, Inc),

LMX1A (Abcam), and FOXA2 (Santa Cruz Biotechnology, Inc). After a PBS wash, the cells

were incubated in a secondary antibody solution containing secondary antibody, 1% bovine

BSA, 10% NDS in PBS for 45 minutes at room temperature on a rotary mixer. The secondary

antibody was either Alexa Fluor 488 or Alexa Fluor 647 (Vector Laboratories). Cells were

washed in PBS then resuspended in 2% FBS in PBS. Flow cytometry was performed on a FACS

Canto II (BD Biosciences) and analyzed using FACS DIVA software (BD Biosciences). Cells

were identified by light scatter for 10,000 gated events.

Characterization of α-Syn Fibrils

Human wild-type α-syn was expressed in E. coli BL21 DE3 CodonPlus cells (Stratagene,

San Diego, CA, USA) and purified as described previously [170]. Monomeric human wild-type

α-syn (100 µM) was assembled into fibrils by incubation in 50 mM Tris–HCl, pH 7.5, 150 mM

KCl at 37°C under continuous shaking in an Eppendorf Thermomixer set at 600 r.p.m for 5 days

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[42].The assembly reaction was monitored by withdrawing aliquots (20 µl) from the assembly

reaction at different time intervals, mixing them with Thioflavin T (10 µM final) and recording

the fluorescence increase on a Cary Eclipse Fluorescence Spectrophotometer (Varian Medical

Systems Inc., Palo Alto, CA, USA) using an excitation wavelength = 440 nm, an emission

wavelength = 480 nm and excitation and emission slits set at 5 and 10 nm, respectively. To label

α-syn fibrils with extrinsic fluorophores, the fibrils were centrifuged twice at 15,000 g for 10 min

and re-suspended twice in PBS at 1,446 g/L and two molar equivalents of ATTO-488 NHS-ester

(Atto-Tec GmbH, Siegen, Germany, #AD 488-35) fluorophore in DMSO were added. The mix

was incubated for 1h at room temperature. The labeling reactions were arrested by addition of

1mM Tris pH 7.5. The unreacted fluorophore was removed by a final cycle of two

centrifugations at 15,000 g for 10 min and resuspensions of the pellets in PBS. The fibrillar

nature of α-syn was assessed by Transmission Electron Microscopy (TEM) after adsorption of

the fibrils onto carbon-coated 200 mesh grids and negative staining with 1% uranyl acetate using

a Jeol 1400 transmission electron microscope. The images were recorded with a Gatan Orius

CCD camera (Gatan, Pleasanton, CA, USA). The resulting α-syn fibrils were fragmented by

sonication for 20 min in 2-ml Eppendorf tubes in a Vial Tweeter powered by an ultrasonic

processor UIS250v (250 W, 2.4 kHz; Hielscher Ultrasonic, Teltow, Germany) to generate

fibrillar particles with an average size 42-52 nm as assessed by TEM analysis. The fingerprint of

the resulting fibrils was obtained by subjecting them to limited proteolysis using proteinase K

[405]. The fibrillar polymorphs were diluted to 100 µM in PBS and incubated at 37°C with

proteinase K (3.8 µg/mL). At different time intervals, samples were withdrawn from the

degradation reaction and supplemented with protease inhibitor phenylmethanesulfonyl fluoride

(PMSF) (to a final concentration of 3.3 µM). Samples were then frozen in liquid nitrogen and

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dehydrated. Fibrils were then disassembled by treatment with 100% hexafluoro-2-propanol

(HFIP) for 1 hour. Samples were then air-dried, dissolved in 15 µl of Laemmli sample buffer and

denatured for 5 min at 80°C. Samples were then analyzed on Tris-Glycine-SDS-polyacrylamide

(15%) gel (SDS-PAGE), stained by Coomassie blue and imaged using a ChemiDocTM MP

(BioRad).

Cell Viability Assay

Cell viability was measured by l-lactate dehydrogenase (LDH) assay based on the

methodology described in Kaja et al., 2017 [238]. In brief, Assay buffer components A,

composed of 4mM iodonitrotetrazolium chloride (INT), in 0.2 M Tris-HCl, pH 8.2, and Assay

buffer B, composed of 6.4 mM nicotinamide adenine dinucleotide (NAD), 320 mM lithium

lactate, in 0.2 M Tris-HCl buffer, pH 8.2 were made, frozen at -20oC, and thawed at the time of

the experiment. 150 mM 1-Methoxyphenazine methosulfate (MPMS) in Tris buffer was also

made and frozen at -20oC. Activate Assay buffer was made by adding 5mL of Assay buffer A,

5mL of Assay buffer B, and 0.5μl of 150mM MPMS in Tris buffer. To measure released LDH,

50μl of a conditioned sample cultured media was plated in duplicate in a clear, flat-bottom 96-

well plate. 50μl of active Assay Buffer was added by multichannel pipetter, incubated in the dark

for one hour, at room temperature, and subsequently quenched with the addition of 50μl 1M

acetic acid. Signal was measured by reading absorbance at λ = 490 nm on a Synergy HTX Multi-

Mode plate reader (BioTek Instruments) in conjunction with Gen5 software. To determine

relative cell viability, secreted culture media LDH was compared to untreated lysed cells in lysis

buffer composed of 9% Triton X-100 in water. The volume of lysis solution used at a 1-to-1 ratio

with the experimental sample cell cultured media. Viability was calculated by taking the

background subtracted condition sample absorbance and dividing it by the background

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subtracted lysed cell absorbance. This value was subtracted by 1 and multiplied by 100 to

determine the percent viability. All reagents used were purchased from Sigma.

α-Syn Fibril and Drug Concentrations and Treatment Paradigm

The experiments involving α-syn fibril treatment were performed at a final concentration

of 200 nM. For the experiments involving drug treatments, the respective culture media was

supplemented with the stated drug at the listed final concentration: vehicle (0.1% dimethyl

sulfoxide (DMSO)), rapamycin (100 µM), Baf-A1 (100 nM), 3-MA (5 mM), Wor (100 nM)

(InvivoGen), TD139 (1 µM, Cayman Chemical), GW4869 (10 µM, Cayman Chemical), and

LLOME (2 µM). All experiments involving drug treatments, if not otherwise stated, were

performed for 24 hours except for those in which LLOME was used. LLOME treatments were

conducted for 4 hours. In the case of the mTOR activation experiments, vehicle, rapamycin, and

α-syn fibril treatment were conducted for 24 hours, and LLOME treatment was conducted for 4

hours so that all treatments finished at the same time. All drugs were purchased from Sigma

Aldrich unless otherwise stated.

siRNA Transfection

SMARTpool Accell siRNA for control, Gal3, Trim16, ATG16L1, and ATG7 were

purchased from Horizon Discovery to generate knockdowns in mDA neuronal cultures and iCell

DopaNeurons. siRNAs were initially resuspended in 1× siRNA Buffer at a stock concentration of

100 µM as described by Horizon Discovery’s siRNA Resuspension Protocol

(https://horizondiscovery.com/-/media/Files/Horizon/resources/Protocols/basic-sirna-

resuspension-protocol.pdf). Stock siRNAs were then aliquoted and frozen at -20oC to minimize

freeze-thaw. Before use, aliquots were thawed at room temperature and 5 µl was added to 500 µl

of room temperature Accell siRNA Delivery Media and gently mixed and added to the neuronal

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culture as stated in the Dharmacon Accell siRNA delivery protocol

(https://horizondiscovery.com/-/media/Files/Horizon/resources/Protocols/accell-delivery-

protocol.pdf). The transfection media was changed to SM1 Neurobasal media or iCell Neural

Base Medium 24 hours later. Experiments including transfections using iCell DopaNeurons were

conducted at initial cell concentrations of 2 × 105 cells per well at 72 hours post-transfection for

all targets. Experiments involving mDA neuronal cultures were performed at a 72 hours post-

transfection for all targets, except for the evaluation of α-syn and Gal3 levels in the cultured

medium from Trim16 KD cells, which was performed at 48 hours post-transfection. To evaluate

KD of each target, cells were removed from their adhered well with either the addition of

TrypLE express Enzyme 1×, no phenol red (Gibco) or trypsin and incubated at 37oC for 7

minutes and pipetted into 1 ml Eppendorf tubes. The wells were washed twice with an equal

volume of 10% FBS supplemented DMEM and added to the Eppendorf tube to quench trypsin.

The cells were then pelleted by sequential centrifugation at 4oC at 300 × g for 5 minutes

followed by 10,000 × g for 10 minutes, and the supernatant was carefully aspirated in order to

not disrupt the pellet.

DSP-α-syn SH-SY5Y cells transfections were performed with Lipofectamine 2000

(Thermofischer) using a modified version of the manufacturer protocol. 2000ng of 4x CLEAR-

luciferase reporter plasmid, 4µl of 10µM control, pooled Optineurin, or TBK1 siRNAs (Santa

Cruz), or 8 µl of Lipofectamine 2000 were added to 100 µl of GE Accell siRNA delivery

medium for 20 minutes at room temperature. Equal parts of siRNA or 4x CLEAR plasmid

containing media were briefly mixed with the Lipofectamine 2000 containing media, allowed to

rest for 30 minutes at room temperature, and 200 µl of combined transfection media was added

to 800 µl GE Accell siRNA delivery medium. 2 ml total mixture was added to a well of 70%

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confluency cells in a 6-well plate. The cells were incubated for 24 hours in transfection media

before fresh DMEM was added. For 4x CLEAR transfected cells experiments, cells were

trypsinized and moved to 4 wells of a 24-well plates at 24 hours post-transfection. At 48 hours

post-transfection, the cells were then treated with 200nM α-syn fibrils for 24 hours relative to

untreated or at 72 hours post-transfection to 1% DMSO vehicle or LLOME (2 µM). For siRNA

transfected cells, 48 hours post-transfection, cells were trypsinized and moved to 4 wells of a 24-

well plates. The conditioned 48-72 hours post-transfection media was collected from the siRNA

transfected cells to evaluate luciferase or Gal3 levels by luciferase or ELISA, respectively.

Additionally, cell lysates from the 72 hours post-transfection were collected for western blot to

evaluate knockdown efficiency. The 4XCLEAR-luciferase reporter was a gift from Albert La

Spada (Addgene plasmid # 66800 ; http://n2t.net/addgene:66800 ; RRID:Addgene_66800)

Renilla Luciferase Assay

Renilla luciferase (RLuc) experiments were performed with HeLa or SH-SY5Y α-syn

DSP A+B cells that were equally plated in a 24-well plate at a density of 0.65-2.1 × 105 cells per

well depending on the experiment. The cells were then treated with the stated drug at the

previously stated concentration. Vehicle or Baf-A1 were treated simultaneously with 200 nM α-

syn fibril for 24 hours. Afterwards, the cultured media was collected and centrifuged at 4oC for

5-10 minutes and at 1,200 × g. Next, the supernatant was collected and plated in triplicate at 50

µl per well, in a 96-well, for a luciferase assay. Depending on the experiment, 50-100 µl of

coelenterazine (NanoLight) solution was added via machine administration Veritas microplate

luminometer (Turner BioSystems, Inc) in combination with GloMax software (version 1.9.3).

Each experiment was performed in triplicate unless otherwise specified.

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MDA Neuronal Culture Transduction and Firefly Luciferase Assay

mDA neuronal culture experiments were performed by co-transducing mDA neuronal

cultures with lentiviral pLVX RLuc and either pLVX firefly Gal3 or an empty pLVX plasmid.

PEI transfection of HEK 293T cells to generate, collect, and purify lentiviral particles was

performed as described previously, using equal DNA concentrations of VSV-g and psPax2, as

well as either pLVX RLuc, empty pLVX, or pLVX firefly Gal3 plasmid. After purifying

lentiviral particles with a 0.45 µm syringe filter, 5 ml aliquots of the particle containing media

were concentrated by overnight centrifugation at 5,000 × g. The media was then aspirated and

the pellet was immediately resuspended in 1 ml of SM1 Neurobasal media by pipetting. The

resuspended pellet was then gently rocked on ice in a 4oC cold room for 1 hour to facilitate

redistribution of particles. The media was then allowed to come to room temperature and added

to 3 wells of a 24-well plate of mDA neurons. The media was changed 72 hours after

transduction and subsequently collected to evaluate α-syn levels by an α-syn ELISA. Afterwards,

firefly Gal3 cells were treated with 200 nM α-syn fibrils or left untreated for 48 hours and then

culture media was collected in preparation for a firefly luciferase assay. To evaluate the firefly

signal, cultured media was centrifuged at 4oC for 10 minutes and 10,000 × g to remove any cells.

Afterwards, the supernatant was collected and 150 µl was plated in triplicate in a 96-well plate.

The 96-well plate was then added to a Veritas microplate Luminometer and 150 µl of firefly

luciferin substrate was added by machine injection and immediately read in combination with

GloMax software (version 1.9.3). Background levels were determined by measuring the cultured

media from the empty vector cells.

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MDA Neuronal Culture Quantitative Real-Time Polymerase Chain Reaction Assay

RNA was isolated from mDA cell lysates following the manufacturer’s protocol for the

Nucleospin RNA Plus kit (Macherey-Nagel). The RNA was then reverse transcribed to the

complementary DNA (cDNA) sequence using an oligodeoxythymidylic acid primer following

the manufacturer’s protocol (Promega). Real-time PCR (RT-PCR) was performed on the cDNA

with iTaqTM Universal SYBR® Green Supermix (Bio-Rad). Briefly, the reaction mixture

contained 2 uL cDNA and 2.25 uM of each forward and reverse PCR primer for a 20 uL reaction

following the manufacturer’s protocol. For RT-PCR amplification of the target genes, the

reaction was performed at 50°C for 2 minutes, 95°C for 10 minutes, followed by 40 cycles of

95°C for 30 seconds then 50°C for 1 minute on a CFX96 real-time PCR detection system (Bio-

Rad). Assays were performed in triplicate to control for technical errors. To select appropriate

reference genes, the qBase+ software package was used to calculate gene expression stability

(GeNorm M) and to calculate the variation for using n reference genes to determine the lowest

number of genes required for accurate normalization (GeNorm V) as previously described

(BioGazelle, Zwijnaarde, Belgium) [197]. Actin γ 1 (ACTG1) and NADH:ubiquinone

oxidoreductase subunit B1 (NDUFB1) were used as the reference genes for normalization. The

mRNA levels of both LGALS3 and LGALS8 were normalized to ACTG1 and NDUFB1 mRNA

levels. Fold change in gene expression was then calculated.

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Table 1. Primer Table.

Gene

Name

Forward (5’-3’) Reverse (5’-3’)

ACTG1

AGAGGCTGGCAAGAACCAGTTGTT CAATGACGTGTTGCTGGGGCC

T

NDUFB

1

TTATTTAGACAGAAAGAGTGATGAAC

G

GGTAACTTCTTCACTGGGTTG

C

LGALS3 GCCAACGAGCGGAAAATGG TCCTTGAGGGTTTGGGTTTCC

LGALS8 ACACTCTGGGCATTTATGGC TTTAACGACGACAGTTCGTCC

MDA Neuronal Culture Transduction YFP-LC3B, Induction, and α-syn Fibrils Treatment

The PEI transfection of HEK 293T to generate, collect, and purify lentiviral particles was

performed as described previously, using equal DNA concentrations of either pLVX inducible

YFP-LC3B plasmid, VSV-g and psPax2. After purifying lentiviral particles with a 0.45 µm

syringe filter, the particles were concentrated by overnight centrifugation of 5 ml of lentiviral

particle-containing media at 5,000 × g. The media was then aspirated and the pellet was

resuspended in 1 ml of SM1 Neurobasal media (Gibco) by pipetting. The resuspended pellet was

then gently rocked on ice in a 4oC cold room for 1 hour to facilitate redistribution of particles.

The media was then allowed to come to room temperature and added to 3 wells of a 24-well

plate of mDA neuronal cultures. Experiments were conducted at least 72 hours post-transduction.

To perform experiments, cells were transfected with siRNAs as previously described. At

24 hours after transfection, the media was changed to SM1 Neurobasal media (Gibco)

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supplemented with doxycycline monohydrate (Sigma-Aldrich) at a working concentration of

1:10,000 for 24 hours to induce the expression of YFP-LC3B. Afterwards, the media was

changed, and the cells were treated with 200 nM ATTO 647 α-syn fibrils for 24 hours (48 hours

post-transfection). Cells were then fixed in 4% formaldehyde solution diluted in DPBS at 72

hours post-siRNA transfection. They were subjected to immunofluorescence staining as

previously described.

The number of images taken per experiment is stated in the figure legend. Data was

collected by z-stack imaging and was analyzed as maximum intensity projections (MIPs). Cells

were imaged using z-stacks with 0.5 µm between each stack and a total of 30 z-stacks. The

imaging of yellow fluorescence protein (YFP)-LC3B was conducted by visually identifying

YFP-LC3B positive cells. A subset of non-transduced YFP-LC3B cells were also imaged to help

evaluate background fluorescence levels in the siRNA-treated control cells. Similarly, DSP-α-

syn, Control KO, and Gal3 KO experiments were conducted with secondary only antibody

controls to determine background fluorescence. The same exposure conditions were used for all

coverslips and experiments.

α-Syn and Galectin-3 Sandwich ELISA

Evaluation of α-syn or Gal3 in the culture media was determined by an in-house

sandwich ELISA using commercially available antibodies. When serial dilutions were added to

uncultured media, a standard curve was generated for α-syn (Appendix 1, Fig. 2A) and Gal3

(Appendix 1, Fig. 2B). The characterization of antibodies used in an α-syn sandwich ELISA has

been previous reviewed in-depth including their specific binding epitopes [117, 273, 278, 439,

509]. Similarly, epitope mapping to specific regions of Gal3 with the following antibodies has

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been previously described and/or used in direct ELISA: anti-Gal3 antibodies B2C10 (Santa

Cruz), A3A12 (Santa Cruz), and M3/38 (Millipore Sigma) [13, 114, 173, 298, 385].

For the α-syn sandwich ELISA, mouse anti-α-synuclein (42/α-synuclein, BD

Biosciences) and Gal3 sandwich ELISA mouse anti-Gal3 (B2C10 or A3A12, Santa Cruz)

antibodies were diluted in pH 9.6 carbonate buffer to a final concentration of 1 µg/ml and added

to wells of a 96-well Maxisorp ELISA plate (Nunc) at 4oC for 16 hours on an orbital shaker to

coat the wells. The wells were then washed with PBS-T three times. Afterwards, the wells were

blocked in equal portions of 10% BSA supplemented DMEM and PBS for 2 hours at room

temperature and subsequently washed thrice with PBS-T. For the α-syn and Gal3 sandwich

ELISA, 50 µl or 100 µl of cultured media, respectively, from each sample as well as serial

dilutions of recombinant human α-syn monomer (Proteos) or Gal3 (Biolegend) were added in

triplicate for 24 hours at 4oC. Wells were then washed thrice with PBS-T. HRP conjugated

mouse anti-α-synuclein (211, Santa Cruz) was diluted to a final concentration of 1 µg/ml or

biotin conjugated rat anti-Gal3 (M3/38, Biolegend) was diluted to a final concentration of 500

ng/ml in 1× ELISA Diluent solution (eBioscience) and then added to wells for 24 hours at 4oC

on an orbital shaker. Afterwards, the wells were washed thrice with PBS-T. For the Gal3 ELISA,

HRP conjugated SAV was diluted to 1 µg/ml in 1× ELISA Diluent solution and added for 30

minutes at room temperature on a rocker. The plate was then washed 5 times and with PBS-T

and the HRP signal was detected with the addition of 1× 3,3′, 5, 5-tetramethylbenzidine (TMB,

Invitrogen) and the reaction was quenched with 2N sulfuric acid. The absorbance was read at

450 nm on a PowerWave XS plate reader (BioTek Instruments) in conjunction with Gen5

software.

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Lysosome Dysfunction Assay and Autophagic Flux

An equal concentration of mDA neuronal culture cells were plated in a clear bottom,

black 96-well plate (Costar) during the penultimate steps of the differentiation process to mature

until day 50 post-initial differentiation at ~100,000 cells per well. Cells were treated with control

or Gal3 siRNA as previously described. The media was changed 24 hours post-treatment. At 48

hours post-transfection, the media was changed and either supplemented with α-syn fibrils or left

untreated. At 72 hours post-transfection, the wells were washed thrice then treated with vehicle

(0.1% DMSO) or 200 nM Baf-A1 diluted in SM1 Neurobasal (Gibco) media for 2 hours then

subsequently loaded with reconstituted Magic Red cathepsin B (ImmunoChemistry

Technologies) for 30 minutes. Magic Red cathepsin B was reconstituted and loaded based on the

manufacturers’ protocol. Cells were then washed thrice with clear Neurobasal media (Gibco) at

room temperature, and clear Neurobasal media (Gibco) supplemented with vehicle or Baf-A1

was added to cells, based on previous treatments. The cells were then placed in a plate reader

warmed to 37oC and supplemented with 5% CO2. The Magic Red fluorescence signal was

measured using a 528 nm excitation wavelength and 628 nm emission wavelength at baseline

and every 15 minutes over a 4-hour period. At the end of the 4 hours, the cells were lysed and

saved for western blot analysis. A total of 6-8 wells were used for each of the vehicle treatments

(control/Gal3 siRNA and ± α-syn fibril) and a total of 2-4 wells for the Baf-A1 treatments

(control/Gal3 siRNA and ± α-syn fibril in each experiment). The graphed data was normalized to

the endpoint relative fluorescence units (RFU) of the control siRNA plus vehicle minus that of

the α-syn fibril and subsequently extrapolated to all time points and conditions within each row

of the 96-well plate, which contains one of every possible variable combination. Each row was

considered a biological replicate and was pooled with all other replicates from a total of 4

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independent experiments (n = 17-25 among conditions). Lysate from 2 wells from each treatment

condition was pooled to obtain enough protein, ran on an SDS-PAGE gel, then transferred for

western blot analysis. Wells in which insufficient KD was observed (< 40% Gal3 depletion)

relative to that of the comparable treatments’ control siRNA condition were excluded. Western

blot quantifications were used to determine autophagic flux: n = 8, control siRNA plus vehicle

conditions; n = 8, Gal3 siRNA plus vehicle; n = 6, control siRNA plus Baf-A1; n = 6, Gal3

siRNA plus Baf-A1; n = 6, control siRNA plus α-syn fibrils; n = 6, Gal3 siRNA plus α-syn

fibrils; n =6, control siRNA plus α-syn fibrils + Baf-A1; and n = 6, Gal3 siRNA plus α-syn

fibrils + Baf-A1.

Statistical Analysis

All statistical analyses were conducted with and graphs were made with Prism (version

6.0, GraphPad Software, Inc.). Data from a single cultured media preparation or coverslip are

given as M ± SD. Data from experiments in which multiple cultured media preparations and

independent coverslips were imaged are given as M ± SE. The statistical test and post hoc

analysis for each experiment are stated in the figure legend. Each graph shows the mean value,

and each data point indicates a biological replicate and is the mean value when a sample was

tested multiple times. For all statistical tests *, **, ***, ****, or #, ##, ###, ####, p < 0.05, 0.01,

0.001, and 0.0001, respectively.

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CHAPTER THREE

GENERATION AND VALIDATION OF THE EXTRACELLULAR VESICLE MULTIPLEX

ANALYSIS OF CO-LOCALIZATION (EV-MAC) WORKFLOW

Rationale

The ability to diagnose neurodegenerative diseases before they physically manifest could

reverse or significantly reduce disease progression with proper pharmacological intervention. At

the forefront of biomarker candidates are secreted proteins. Mechanistically, the release of

proteins that undergo unconventional protein secretion in response to underlying cellular

pathology such as dysfunctional protein degradation could offer valuable insight into the

underlying health of an individual. Unsurprising, evaluating secreted pathological proteins such

as misfolded α-syn is one such considered way. However, because red blood cells also express α-

syn, discerning differences between α-syn from plasma can be challenging and collecting CSF is

painful, intrusive, and requires considerably more training than plasma. Thus, the ability to

measure biomarkers in other fluids is desirable. To this end, changes in other proteins such as

those released in EVs have the potential to be early diagnostic biomarkers.

Cells release populations of EVs of various sizes which contain a multitude of cargoes

including RNA, proteins, and lipids [178, 457, 487]. While EVs were once thought to be

relegated strictly to the task of shedding unwanted cellular waste, it is now appreciated that EVs

play an integral role in cell-to-cell communication [308] and that all cell types produce EVs

under both healthy and pathological cellular states [545]. As a result, understanding mechanisms

associated with the transport and characterization of EVs has become an increasingly important.

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In particular, by identifying specific EV cargoes under pathological conditions, EVs have

the potential to be excellent biomarkers to assist in earlier detection and diagnosis of a variety of

diseases [456, 529].

The most often cited subtypes of EVs are exosomes, microvesicles, and apoptotic bodies,

which are derived from distinct mechanisms and sub-cellular compartments. As a result, the

different EV subtypes are characterized based on their size, protein composition and origin [178,

260, 536, 537]. Exosomes are typically appreciated to range between 30 and 150 nm in diameter

and originate as intraluminal vesicles (ILVs) formed within multivesicular bodies (MVBs) [178,

260, 536, 537]. The process of secretory autophagy leads to the release of an EV population

similar to exosomes, which is known to be upregulated by lysosomal dysfunction [203, 349, 517,

541]. In comparison, microvesicles have a more dynamic size range (i.e. 20-1000 nm in

diameter) and bud directly from the plasma-membrane, while apoptotic bodies are much larger

(>1000 nm) in diameter and originate from dying cells [178, 260, 536, 537]. In addition to size,

researchers often validate the isolation of exosomes or EVs by demonstrating the presence of

specific cellular proteins, including the tetraspanins CD9, CD63, and CD81 [61, 260, 487, 490,

495].

Experimentally, EV populations are typically isolated and separated from cell debris via a

series of centrifugation steps [256, 495]. Other commonly employed isolation methods include

filtration and purification based on highly enriched EV markers. These isolated EVs can then be

used for a variety of assays, such as western blots, ELISAs, or mass spectrometry to measure

protein composition. Utilizing these techniques and defining EVs by these previously stated

criterion has led to an impressive advancement in understanding the differences in EV

composition and their potential impact on neighboring cells [177]. However, there is also

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accumulating evidence indicating that in spite of existing criteria to classify EVs, heterogeneous

populations of EVs exist [177, 228, 260, 536, 537] and these subpopulations of EVs are

insufficiently resolved using currently used approaches. Other studies reveal that alterations of

cellular homeostasis in EV producing cells can alter the quantity and quality of the EVs released

from cells [340]. These observations suggest that bulk analysis of EV populations following

centrifugation or other methods that analyze the molecular composition of all EVs in a sample

fail to reveal heterogeneity in EV populations, especially in cases where potentially pathological

or therapeutic populations of EVs exist as a subpopulation within a larger population of EVs.

Thus, it is increasingly important that methods be created to distinguish EV subpopulations.

To address this issue, we formulated an imaging-based workflow that utilizes quantitative

fluorescent microscopy to characterize individual EVs via Multiplexed Analysis of Co-

localization (EV-MAC) of their protein and glycan determinants. This method extends on

previous studies imaging studies which have demonstrated that fluorescent fusion proteins are

incorporated into EVs [88, 96, 271] to provide a workflow to interrogate EV populations for the

presence of native, endogenous cellular proteins and glycan determinants. Following validation

of this methodology, we show that lectin staining, or fluorescent membrane dyes can allow for

the interrogation of EVs from biological samples, including saliva and plasma. These results

collectively provide a platform by which to interrogate the presence of proteins or glycans in

specific EV subpopulations, which could potentially provide insight into the biogenesis of EV

populations or aid in the discovery of biomarkers associated with pathological EVs.

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Results.

Generation of S15-mCherry Positive EVs for the Characterization of Immunofluorescence

Analysis.

In order to allow EVs secreted from cultured cells to be visualized, we transduced HEK

293T cells to express a S15-mCherry construct (S15Ch). The S15Ch construct contains the 15 N-

terminal amino acids of c-SRC appended to the N-terminal of the mCherry fluorescent protein. It

has previously been shown that these 15 amino acids are necessary and sufficient for the addition

of the myristoyl lipid anchor on the N-terminal glycine of c-SRC, leading to its plasma

membrane localization [60, 411]. In experiments in which this construct was used to label HIV-1

viral particles [60, 411], we also observed that this construct is incorporated into EVs which lack

HIV-1 p24 and is released from cells constitutively and independently of virus production [60],

similar to the manner in which a palmitoylated fluorescent protein is incorporated into EV

populations when expressed in cells [271]. To verify that this construct was incorporated into

EVs, we collected culture supernatant from 293T cells expressing S15Ch following transient

transfection and isolated EVs by differential ultracentrifugation. The S15Ch construct was

observed within the ultracentrifuged EV fraction, which also contained the canonical exosomal

proteins, CD9, CD63, and CD81 (Fig. 5A). To determine if the S15Ch protein was associated

with EV membranes, we also added 0.1% SDS to cultured medium prior to ultracentrifugation.

The addition of SDS, which causes disruption of lipid membranes, eliminated the presence of

S15Ch recovered following ultracentrifugation (Fig. 5A), demonstrating that S15Ch is

incorporated into EVs. Transmission electron microscopy of EVs isolated by ultracentrifugation

revealed lipid enclosed structures between 30 and 150 nm in diameter and were positive for

CD63 and CD81 (Fig. 5B), consistent with what is reported for EVs [178, 260, 536, 537].

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However, we were unable to identify any mCherry molecules following probing with mCherry

antibodies (Data not shown).

Figure 5: Detection of Endogenous Protein Markers on EVs Released From 293T Cells. Legend on the

next page

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We next sought to determine if endogenous proteins which are known to be incorporated

into EV populations could be detected on S15Ch+ EVs by immunofluorescence. To this end, we

spinoculated [356] EVs purified by ultracentrifugation or filtered tissue culture supernatant onto

glass coverslips. This low-speed centrifugation (2 hours at 1200x g) is sufficient to significantly

increase viral binding to cells and coverslips, allowing them to be interrogated by

immunofluorescence following fixation [60, 356]. Following fixation, EVs were stained with a

mouse antibody against the tetraspanin protein CD63 and a rabbit polyclonal antibody directed

against LAMP1, two proteins known to be present in EV populations [61, 260, 487, 490].

Following staining, coverslips were imaged using wide-field, fluorescent deconvolution

microscopy, which is ideally suited for quantification of such specimens due to its sensitivity and

field uniformity [348]. Following acquisition of 3D z-stack images and deconvolution, a subset

Figure 5: Detection of Endogenous Protein Markers on EVs Released From 293T Cells. A) Non-

reducing SDS-Page transferred to nitrocellulose of mCherry positive 293T cell lysate or S15Ch

concentrated extracellular vesicles (EVs) by ultracentrifugation with or without the addition of .1% SDS

before the concentration. Nitrocellulose was probed with antibodies against mCherry (band shown at

~30kDA), CD9 (band shown at ~25kDA), CD63 (center of band shown at ~50kDA), CD81 (band shown at

~22kDA), and GAPDH (band shown at ~37kDA). B) Representative transmission electron microscopy

images of concentrated S15Ch EVs showing primary antibodies against CD63 and CD81, respectively, and

secondary anti-mouse conjugated to 20nm gold particles. C) Representative z stack of EVs from S15Ch

293T cells spinoculated onto a coverslip. The individual mCherry, CD63, and LAMP1 channels are shown,

with a merge. D) Panel of representative 3D maximum intensity projection reconstruction from z stack

images demonstrating the S15Ch (S15Ch, red) channel alone (top) as well as the S15Ch signal with the

spots masking algorithm generated in Bitplane Imaris imaging software (bottom). E) Data shows the percent

of S15Ch spots that are also positive for mCherry antibody staining from cultured media that was first

spinoculated onto coverslips, fixed, and then subjected to .1% Saponin permeabilization or left

unpermeabilized. Secondary antibody controls for both conditions were used. The data shown is the mean

value from three independent media preparations. F) Spots masking algorithm in (D) was used to calculate

the percent of S15Ch EVs positive for the indicated proteins. XY co-localization plots of S15Ch spots and

the maximum intensity of the mouse anti-tetraspanin (CD9, CD63, CD81) or their secondary antibody as a

control on the X-axis and the maximum intensity of the rabbit ant-LAMP1 antibody or its secondary

antibody as a control. identical staining in the absence of primary antibody was performed. XY graphs show

co-localization from a single 3D reconstructed image. Green and Blue lines show the value determined to be

above background based on the secondary antibody only controls. G) Graphs show mean co-localization

percent of each of the antibody staining paradigms indicated in (E) from a single coverslip where 20 z-stack

images were taken. + (positive) and/or – (negative) reference the percent found from each quadrant of the

co-localization plot; for example, S15Ch (+), CD81 (+), LAMP1 (+) references the top, right quadrant of the

graph. Percentage of S15Ch spots described as positive or negative for each marker is indicated. Data are

expressed as M ± SD.

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of S15Ch+ puncta could be observed to co-localize with both markers to some degree (Fig. 5C),

although not all S15Ch+ puncta were positive for CD63 or LAMP1.

Figure 6: EV-MAC Reproducibly Stains EV Populations Following Enrichment from Tissue Culture

Supernatant A) Cultured media containing EVs were either left unconcentrated or concentrated via

differential ultracentrifugation. The EVs were then stained with either CD9, CD63, CD81, in addition to

LAMP1. Data shows the mean value from three independently collected S15Ch 293T media preparations,

where each of the media preparations were split among the four staining paradigms and spun onto a separate

coverslip. 20 images were taken per coverslip. Error bars depict standard error of the mean. No significant

differences were found among each respective staining paradigms among the unconcentrated and

concentrated EVS when subjected to a two-way ANOVA with Tukey’s multiple comparison post-hoc test. B)

Data compares the mean value among three independent coverslips and media preparations for S15 mCherry

co-localization as determined by either “each image measure” or “Pooled images measure. 20 images were

taken per coverslip. All data shown was subjected to two-way ANOVA with Tukey’s multiple comparison

post-hoc and found non-significant. Data are expressed as M ± SE, n = 3.

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To further validate that the S15Ch signal observed represented intact EVs, we next asked

if antibodies to mCherry could stain mCherry in the absence of detergent. We observed that

labeling S15Ch EVs with -mCherry antibody was detergent dependent, as most of the staining

was eliminated in the absence of detergent (Fig. 5E). These findings are agreement with our

inability to identify mCherry molecules when S15Ch EVs were subjected to immunogold

labeling and suggests the S15Ch label is on the intraluminal side of EVs.

Immunofluorescence Imaging to Characterize EV Populations by Multiplexed Analysis of

Co-localization (EV-MAC).

We next determined if this approach could allow for the reproducible multiplexed

analysis of EV populations based on the markers present on EVs detected by indirect

immunofluorescence. To this end, we stained individual coverslips with mouse antibodies to the

tetraspanins CD63, CD81 and CD9 and a rabbit polyclonal antibody directed against LAMP1.

Following acquisition of 3D z-stack images and deconvolution, software-based algorithms were

generated to identify individual S15Ch+ puncta using defined size and intensity criteria and

create individual 3D masks around these puncta (Fig. 5D). Following manual validation of the

algorithm in a subset of images to ensure that individual puncta were reliably identified, the

algorithm was applied to all images collected and the multiplexed intensity of tetraspanin and

LAMP1 staining present in each S15Ch+ puncta in these images was determined. Secondary

antibody controls were used as a negative control to define the background staining intensity in

each channel and establish threshold intensities above which individual EVs were considered

positive for individual tetraspanins or LAMP1 staining (Fig. 5F). For example, greater than 98%

of the S15Ch+ EVs analyzed exhibited CD81 staining above background, while approximately

50% were positive for CD9 (Fig. 5F). Regardless of the tetraspanin examined in parallel, the

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amount of LAMP1 signal on these EVs was observed to be consistent, with approximately 20%

of S15Ch+ EVs exhibiting LAMP1 staining above background (Fig. 5F). Multiplex analysis of

EV populations identified by masking events identified in the S15Ch channel revealed

differential staining of tetraspanin markers on these populations when data from individual

images was examined. When data from multiple images were compared as technical replicates,

the percentage of EVs positive for one or both markers was highly reproducible (Fig. 5G). For

comparative purposes, we refer to this type of analysis as “individual image measurement”.

We also observed that pooling data from the entire dataset yielded the same result

obtained when individual images were analyzed as independent technical replicates (Fig. 6A)

hereafter “pooled image measurement”. We found that the mean values obtained via both

methodologies were highly reproducible across multiple independent cultured media and

coverslip preparations, as nearly identical outcomes could be obtained regardless of analysis

approach (Fig. 6A).

We also observed that pooling data from the entire dataset yielded the same result

obtained when individual images were analyzed as independent technical replicates (Fig. 6A)

hereafter “pooled image measurement”. We found that the mean values obtained via both

methodologies were highly reproducible across multiple independent cultured media and

coverslip preparations, as nearly identical outcomes could be obtained regardless of analysis

approach (Fig. 6A).

We next asked if EV-MAC could detect differences EV populations obtained through

differential centrifugation and those spun directly onto glass from conditioned media. We did not

detect a significant difference in the relative staining of tetraspanins or LAMP1 when S15mCh+

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EVs were enriched by ultracentrifugation or spun directly onto coverslips from conditioned

media (Fig. 6B).

Figure 7: Masking Algorithms Selectively Removes S15Ch+ Aggregates and is Selective for Cultured

Media. A) Shows the amount of S15Ch puncta identified by the masking algorithm in uncultured media and

cultured media. The same masking algorithm was used for both samples. This was repeated three times, from

three independent media culture preparations. Error bars show standard error of the mean. B) The number of

S15Ch puncta from each image taken from a coverslip were averaged for the unconcentrated and concentrated

cultured media preparations. This was repeated for each coverslip where three independent media preparations

(replicates) were used for comparison. Cultured media was obtained from S15Ch 293T cells. All data shows

the mean value among replicates, error bars show standard error of the mean. C) Representative image of the

S15Ch+ EV spots masking algorithm and its ability to selectively remove large aggregated S15Ch+ puncta and

only include smaller punctum. To remove aggregates that were initially identified by our S15Ch spots masking

algorithm, we exclude spots over 85 voxels in size. This was conducted by one of two ways. Both techniques

result in the same end product. The first is done with the spot’s masks’ data generated from the batching

process which contains the voxels of each spot, among other characteristics. The second of these techniques,

which also allows us to visualize the actual exclusion events, is by creating a secondary spots sub-algorithm.

This is conducted by adding a filter to the initial spots mask and subsequently excluded spots with over 85

voxels. This filter is then applied by hitting the replicate spots.

The initial generation of a spots mask around puncta can exclude events based on “voxels”,

“diameter”, “area”, or “volume”, this exclusion criteria occurs before the spots’ mask “region growth”. We

find that by utilizing the “region growth”, which allows for the formation of different sized spots, we can more

successfully remove aggregates rather than adding extra exclusion criteria to the mask’s initial formation.

However, one may find that this is preferable in some instances especially if combined with a different set of

region growth parameters and initial spot size.

129

As a control, we

performed an identical

analysis on uncultured

media depleted of EVs. In

this situation, virtually all

of the algorithm identified

surfaces were eliminated,

demonstrating that S15Ch

EVs are effectively

identified with minimal

background. (Fig. 7A).

However, we did find that

centrifugation did enrich

the number of S15Ch

puncta present in each

image (Fig. 7B). We also

noted that differential

centrifugation tended to

lead to the appearance of

larger accumulations of

fluorescent signal that

likely represent EVs aggregated during centrifugation. However, these aggregations could be

excluded from the analysis by incorporation of size constraints into the mask algorithm (Fig.

Figure 8: S15Ch EVs Relative Levels of LAMP1 and Tetraspanin

Proteins Determined by Indirect EV ELISA. A) The absorbance of

concentrated S15Ch EVs that were subjected to indirect ELISA as

determined by quenched TMB. Plates were first coated with LAMP1

antibody before EV samples were added. The detection of LAMP1 positive

EVs for either CD9, CD63, or CD81 was determined by the addition of

antibody. The same concentration of antibody was used for each of the

tetraspanin antibodies. For the Control condition, the same PBS used to

resuspend concentrated S15Ch EVs was added to a well coated with

LAMP1 followed by a detection antibody for each of the tetraspanins tested,

respectively. B) Data from (A) was used to compare the relative distribution

of EVs captured by bound LAMP1 antibody and detected by CD9 antibody

to normalize and determine the corresponding abundance in distribution

relative to the captured LAMP1 and detection of either CD63 or CD81.

These relative levels were then compared to the percent of S15Ch+ EVs that

were also double positive for LAMP1 and either CD9, CD63, or CD81 from

concentrated S15Ch+ EVs. The data shown is the mean value from three

independent media preparations (replicates). Data was found non-significant

by one-way ANOVA. Data are expressed as M ± SE (n = 3).

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7C). The appearance of these aggregates could also be reduced by thorough resuspension of the

pelleted material following centrifugation (data not shown).

Moreover, to independently validate our findings regarding the distribution of EVs

double positive for LAMP1 and either CD9, CD63, or CD81, we utilized an indirect ELISA.

EVs from cultured media of S15Ch 293Ts were concentrated and subsequently captured on

plates coated with LAMP1 antibodies. An equal amount of CD9, CD63, or CD81 was used as a

detection antibody, followed by -mouse antibody conjugated to HRP to determine the relative

levels of each respective tetraspanin among the LAMP1 positive, captured EVs. We found that

the ELISA signal generated correlated to the relative tetraspanin staining observed in EV-MAC

analysis (Fig. 8A, B).

To exclude the possibility that deconvolution was influencing our results, we also

compared the undeconvolved images to the deconvolved counterparts. We found that the

distribution of co-localization among the S15Ch EVs was unaffected for the LAMP1 and the

respective tetraspanins between the deconvolved and undeconvolved counterparts (Fig. 9A).

However, we observed that deconvolved images did detect more spots than were observed in

undeconvolved images (Fig. 9B). We detected approximately 40% more puncta in deconvolved

images compared to undeconvolved images in experiments in which varying number of puncta

were observed in undeconvolved images (Fig. 5B). The correlation between undeconvolved and

deconvolved images at different EV concentrations was consistent with enhanced sensitivity of

detection in deconvolved images rather than the spontaneous introduction of artifacts in

deconvolved images. To ensure this deconvolution was not leading to the detection of artifactual

surfaces, we further characterized how deconvolution was influencing the limit of detection of

EV-MAC. To this end, a dilute solution of 100 nm fluorospheres, ranging from 1-6 per field, was

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imaged in the GFP

channel at

increasing the

exposure times

stopping at 0.1

seconds, which is

consistent with

our EV imaging

conditions. To

determine if the

difference in spot

number was a

consequence of

deconvolution

artifacts, we

manually

compared the

images and the

relative ability of

our algorithm to

identify individual

puncta at each

exposure

Figure 9: Deconvolution Does Not Change the Co-localization Distribution of S15Ch+

EVs with Tetraspanins and LAMP1. (A) differential ultracentrifugation concentrated

EVs from S15Ch 293T were spinoculated onto coverslips and subsequently stained with

either CD9, CD63, CD81, in combination with LAMP1 or left unstained. Each condition

was then subsequently stained with a mouse and rabbit secondary antibody. The secondary

only control condition was used to determine the background fluorescence levels and the

percent co-localization was determined for each image. Data shown is the mean value from

three separate coverslips from independently collected media preparations. Within each

preparation 15 images were taken per coverslip and the co-localization distribution was

determined for each image and subsequently averaged to determine that samples co-

localization. Data was found non-significant by one-way ANOVA when compared among

the undeconvolved and deconvolved percent positive distribution of LAMP1 and

tetraspanin, respectively. Error bars depict standard error of the mean. (B) Graphs show the

relative number and percent difference of S15Ch spots as determined by the same spots

masking algorithm from data collected in (A). Each replicate is an individual S15Ch EV

sample preparation where a total of 45 images was taken among four coverslips. All data

are expressed as M ± SD. Significant differences between conditions were determined via

two-tailed T-test. *** = p-value < .001.

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condition. Our algorithm did not detect puncta in deconvolved images that were not identifiable

in undeconvolved images at longer exposure conditions, as the number of puncta detected in

deconvolved images

plateaued and was

eventually matched in

undeconvolved images at

longer exposure times

(Fig. 10A). This is

consistent with the ability

of constrained iterative

deconvolution to restore

signal emanating from

out of focus planes to

their correct focal origin,

thus enhancing signal to

noise [348]. Collectively,

the deconvolved images

had a ~4.26-fold increase

in signal compared to

their undeconvolved counterpart, suggesting the differences in spot number were the result of

increased signal-to-noise (Fig. 10B). To determine if these results were comparable under

conditions that better simulate the number of EVs imaged in a representative field in our

experiments, we used the same experimental paradigm for two separate coverslips with more

Figure 10: Deconvolution Increases the Limit of Detection of Spots

Masking Algorithm: (A) Two Representative examples of undeconvolved

and deconvolved 3D reconstructed Max Intensity Projections (MIPs) of

100nm tetraspecks at increasing exposure times and our Imaris spot

algorithm’s ability to recognize their signal. Algorithm’s ability to detect

signal is based on center fluorescence intensity in contrast to local background

intensity. A total of 12 images were initially compared. (B) Comparative

center intensity of Tetraspeck spots from undeconvolved and deconvolved the

same 3D MIPs at .1 seconds exposure time. Data demonstrates that the spot’s

algorithm identified tetraspecks in the deconvolved images have a greater

center intensity than the undeconvolved. Data pooled from 15 images and

Error bars show standard deviation. (C) Spot’s algorithm’s ability to detect

larger concentrations of 100nm Tetraspecks signal from undeconvolved and

deconvolved images demonstrate similar limitations of sensitivity to dilute

levels. 15 images were captured from a single coverslip, data are expressed as

M ± SD (n = 3).

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concentrated fluorospheres. Using the same masking algorithm, we determined the number of

spots recognized for each image and compared that to the exposure time for the undeconvolved

and deconvolved images (Fig. 10C). These conditions recapitulated the results obtained at lower

bead concentration, where the number of puncta identified in deconvolved images plateaued at a

lower exposure time while the number of puncta identified in undeconvolved images approaches

the number of puncta identified in deconvolved images at longer exposure times. This suggests

that despite a decreased ability to identify signal, EV-MAC can be utilized on both

undeconvolved and deconvolved 3D reconstructed images, while deconvolution enhances the

sensitivity of detection of EVs at lower exposure times.

The Majority of EVs in Solution are Bound to Coverslips Following Spinoculation.

We next determined the fraction of EVs bound to coverslips by spinoculation. To

understand the fraction of EVs bound to coverslips by spinnoculation, we examined EV binding

using both untreated and poly-L-lysine (PLL) treated coverslips. PLL treatment significantly

increased the amount S15Ch observed (Fig. 11A). When supernatants were spinoculated onto

two coverslips in sequential spinoculations, we observed a reduction in the number of S15Ch+

puncta in the second spinoculation onto both uncoated and PLL coverslips (Fig. 11B). This was

mirrored by a reduction in the protein concentration in these samples measured before and after

spinoculation (Fig. 11C). The similar protein loss in each sample suggests that the increased

number of puncta observed in PLL treated coverslips is due to better retention of EVs on PLL

coverlips during the staining procedure rather than increased binding of EVs during

spinnoculation. No significant differences were observed when S15Ch+ EVs were stained for

CD81 and LAMP1 following spinnoculation onto untreated or PLL treated coverslips (Fig. 11D).

Furthermore, we observed no significant differences in marker co-localization in serial

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spinoculations, demonstrating that the population of EVs remaining in the media following the

first spinoculation is not substantially different than the EV population adhered to coverslips

(Fig. 11E). Collectively, these data indicate that spinoculation binds a significant fraction of EVs

to coverslips and this fraction of EVs which bind to the coverslip are representative of the EVs

which do not bind the coverslip.

Figure 11: Analysis of EV Recovery and Quantification using EV-MAC. Legend on

the next page

135

Serial Dilution Allows Relative EV Concentration to be Determined Using EV-MAC.

To determine the degree to which EV-MAC can measure relative concentrations of EVs

in a sample, we spinoculated serial dilutions of S15Ch EVs dilutions (X, 0.5X, 0.25X, 0.125X,

0.0625X, 0.03125X) onto PLL treated coverslips. We stained these EVs for CD81 and imaged

the number of S15Ch and CD81 puncta present per field in each coverslip (Fig. 11F, G). The

number of puncta observed did not exhibit linear changes in EV number corresponding to the EV

dilution being analyzed. However, there was a semilogarithmic relationship between the number

of puncta observed and EV concentration in the range of dilutions analyzed when either S15Ch

or CD81+ spots were calculated (Fig. 11F, G). Additionally, these differences remained constant

among dilutions, suggesting that this semilogarithmic relationship was not dependent on EV

concentration (Fig. 11H, I). These experiments demonstrate that EV-MAC analysis provides a

semi-quantitative measurement of EV concentrations in a sample and that analysis of numerous

sample dilutions can allow quantitative assessment of EV concentrations.

Figure 11: Analysis of EV Recovery and Quantification using EV-MAC. A) Data shows the number of

S15Ch puncta recovered as determined by EV-MAC workflow. The same algorithm was used for both poly L

lysine and uncoated coverslips. B) Sequential spinoculation of concentrated cultured media preparations where

samples were first (1o) spinoculated and then samples were then re-spinoculated (2o) onto another coverslip.

This was conducted for coverslips coated with poly L lysine. C) Protein concentrations as determined by BCA

for concentrated S15Ch EVs before and after spinoculation. The same initial (pre-Spinoculation) sample was

used for both uncoated and poly L lysine coated coverslips, one of the poly L lysine wells was excluded due to

pipetting error. D) The co-localization of CD81 and LAMP1 was identified for S15Ch by the same spots

masking algorithm for uncoated and poly L lysine coverslips to determine changes in EVs distribution,

respectively. E) The co-localization of CD81 and LAMP1 was identified for S15Ch by the same spots

masking algorithm as (D) to determine changes in EVs distribution between the 1o and 2o spinoculation. Mean

number of S15Ch spots detected in a 3D reconstructed image from serial dilutions of ultracentrifuge

concentrated media preparations of S15Ch 293T EVs (n = 3). (F) EVs were also stained for CD81 and mean

number per image was also plotted (G). The same S15Ch spots algorithm and CD81 spots algorithm was

applied for all dilutions and replicates (F, G) R2 shows the variance of a semilogarithmic line of best fit. H, I)

Graphs combine the replicates in (F, G) to show the mean number of S15Ch and CD81 spots per image from

the last 4 and three dilutions, respectively. Data demonstrates a consistent measurable difference among the

replicates. Replicates are defined as independent ultracentrifuge concentrated media preparations of S15Ch

EVs from 293Ts that were spinoculated onto poly L lysine coated coverslips. 15 images were taken per serial

dilution for each replicate. (A-E, H, I) All data shown is the mean percent of at three independent media

preparations and coverslips. Significant differences between the number of (A, B) S15Ch spots were

determined via two-tailed T-test. No significant differences in (D, E) co-localization among conditions were

found as determined by two-way ANOVA. (A-I) Data are expressed as M ± SE (n = 3).

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PKH Dye Can Be Used in Conjunction with EV-MAC to Identify EVs.

To move towards the ability to utilize EV-MAC to analyze EV populations lacking

fluorescently labelled proteins produced in tissue culture, we next sought to utilize PKH

fluorescent dyes to label EV populations. We collected and concentrated EVs from 293T cells

labelled EVs by incubation with PKH26 dye at concentrations of 200nM or 400nM. We

subsequently removed excess dye by ultracentrifugation through a sucrose cushion. Afterwards,

the resuspended EVs were subjected to our EV-MAC workflow and their co-localization with

CD81 and LAMP1 was determined (Fig. 12A, 12B, 12C). We found that both concentrations of

dye yielded similar co-localization with CD81 and LAMP1 markers (Fig. 12D, 12E). When

PKH67 was used to label S15Ch+ EVs produced from 293T cells, we observed that

approximately half of the PKH67+ puncta were positive for S15Ch (Fig. 12F), suggesting that

S15Ch labels a subset of EVs produced from 293T cells following transient transfection. To

estimate the effectiveness of EV labelling, we asked what percentage of S15Ch surfaces were

labeled with PKH67, finding that nearly 100% of S15Ch+ EVs were labelled with PKH67 (Fig.

12G). These data demonstrate that the fluorescent membrane dye PKH67 can be used to reliably

label EVs for EV-MAC analysis.

EV-MAC Can Be Used to Interrogate Human Biological Samples.

We next sought to apply EV-MAC to the analysis of biological samples. To this end, we

labelled EVs from saliva and plasma using either PKH67 or the lectin WGA, using signals from

these labels to create masks for subsequent analysis of CD63 and LAMP1 association (Fig. 13A-

D). After validating this method with EVs collected from cell culture supernatants, we asked if

our technique could be used to characterize EVs found in human bodily fluid samples. Samples

of saliva and plasma were paneled with combinations of the lectin Wheat Germ agglutinin

137

(WGA) and the LAMP1 and CD63 antibodies used in our cell culture studies (Fig. 13C, D). For

analysis, masks were created around WGA+ signal and the degree of association of the CD63

and LAMP1 was determined (Fig. 13C, D).

Figure 12: PKH Dyed WT 293T EVs Co-localize with CD81, LAMP1, and Readily Identify S15Ch+ 293T

EVs. Concentrated EVs from WT 293T cells or PBS were dyed with PKH26 (PKH Dye, green) at an initial dye

concentration of 200nM or 400nM. A) Representative z-stack from WT 293T EVs or PBS demonstrating co-

localization of PKH Dye(green), CD81(red), and LAMP1(Blue) or its respective secondary antibody control.

The dye labelled EVs were imaged, and 3D reconstructions were used to identify dye positive EVs using the

same spots masking algorithm for both dye concentrations to determine their co-localization with CD81 and

LAMP1. B, C) Representative co-localization from a 200nM PKH dye image demonstrate relative co-

localization. A total of three (D) and five (E) independent replicates were conducted for the 200nM and 400nM

conditions, respectively. 10-15 images were taken per coverslip for each condition for each replicate. The

number of EVs identified per image ranged from ~200-780. Concentrated S15Ch 293T EVs were either dyed

with PKH67 (Dye) with an initial dye concentration of 200nM or remained unstained. Both the undyed and dye

labelled S15Ch EVs were then imaged, and 3D reconstructions were used to generate a spots masking algorithm

around the dye positive (F) or S15Ch positive (G) EVs. F) The percent of Dye+ EVs, as identified by its spots,

demonstrate that the S15Ch could be reproducible found within the concentrated EVs. G) The percent of S15Ch

positive EVs, as identified by its spots algorithm, show their co-localization with the dyed EVs (left) are undyed

EVs (right). three independent replicates were conducted. 10-15 images were taken per replicate. The number of

identified Dye EVs per image ranged from ~1100-1500; number of S15Ch identified EVs per image ranged,

~350-600. Data are expressed as M ± SE (n = 3).

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In the EVs isolated from plasma, we observed approximately 28% of WGA+ puncta were

positive for CD63, approximately 3% were positive for LAMP1, and approximately 15% were

positive for both protein markers (Fig. 13F). In the EVs isolated from saliva, we observed

approximately 21% of WGA+ puncta were positive for CD63, approximately 10% were positive

for LAMP1, and approximately 11% were positive for both CD63 and LAMP1 (Fig. 13H).

Figure 13: EV-MAC of Concentrated EVs Isolated from Plasma and Saliva. Concentrated EVs from

plasma (A, C) and saliva (B, D) were bound to glass coverslips via spinoculation and stained with antibodies

against CD63 and LAMP1 as well as biotin conjugated wheat germ agglutinin (WGA) (C, D) or dyed with

PKH67 (A, B) at an initial dye concentration of 200nM onto poly L lysine coated coverslips. Representative

fields of view of plasma and saliva EVs (A). WGA in addition to SAV as well as SAV only spots were

evaluated for the presence of CD63 and LAMP1. (E, F, G, H) Co-localization data compiled from Dye (E, G)

or WGA (F, H) from individual coverslips. Co-localization was determined relative to CD63 and LAMP1

secondary antibody controls for each condition, respectively. Data are expressed as M ± SE (n = 3).

.

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Subsequently, we further validated EV-MAC within the context of plasma and saliva EVs by

dying them with 200 nM PKH67 dye (Fig. 13A, B). We found that the dye labelled EVs also co-

localized with CD63 and LAMP1 resulting in a co-localization distribution analogous to the

lectin labelled EVs for both the plasma and saliva samples (Fig. 13E-H). These data demonstrate

that EV-MAC can be used with biological samples and can reveal differences in the EVs

obtained from different biological fluids.

Discussion

In this chapter, we establish a method to determine the relative levels of exosomal

markers and cargoes present in EV populations using quantitative fluorescent microscopy. Prior

studies using fluorescent fusion proteins or dyes to label EVs have demonstrated that

microscopic methods can be used to effectively visualize and quantify fluorescent signals

associated with individual EVs [88, 96, 271]. An advantage of the EV-MAC approach, described

here, is the relative ease by which EVs can be spinnoculated onto coverslips and subsequently

analyzed using conventional immunofluorescent imaging techniques [60, 356]. Notably, other

studies have previously demonstrated that immunofluorescent interrogation of EV populations is

possible [35, 72, 230, 281, 287, 343, 352, 497, 516]. However, the workflow we describe allows

the heterogeneity of EV populations to be rapidly assessed by investigators without super-

resolution or single-molecule approaches described in prior studies. Moreover, super-resolution

techniques provide the opportunity to assess the size of individual EVs more accurately within a

population, compared to EV-MAC, which is limited by the resolution limit of conventional light

microscopy. Alternatively, single molecule techniques, including single molecule localization

microscopy (qSMLM) [287] have the advantage of allowing vesicle size to be more accurately

assessed and allowing the precise number of molecules on an EV to approximated. Such studies

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require access to super resolution instrumentation with single molecule sensitivity as well as

antibodies labeled in a way that ensures antibodies are conjugated to a single fluorophore. EV-

MAC may be an attractive alternative in cases where the relative differences in EV composition

without a need to appreciate the precise number of target proteins present on EVs need to be

assessed. The utility of EV-MAC in characterizing EV populations using conventional

microscopy, with or without deconvolution, using conventional combinations of primary and

secondary antibodies commonly used in immunofluorescence studies. In total, these may make it

more accessible and easily utilized to interrogate EV populations in many contexts.

In the workflow described in this manuscript, the generation of masks around individual

fluorescent signals, such as S15Ch, allows quantitative, multiplexed analysis of EV populations

according to their incorporation of canonical EV markers including tetraspanins, and LAMP1.

Additionally, the generation of this masking algorithm allowed us to analyze images in an

unbiased, semi-high throughput manner and evaluate thousands of EVs in a short period of time

with a small quantity of sample. This approach is highly reproducible across biological and

technical replicates (Fig. 6, 8, 12, 13). Furthermore, we demonstrate how it can be flexibly

adapted to either evaluate images individually in a process analogous to technical replicates

(Individual Image Measure) or by first pooling data from multiple images and then determining

co-localization (Pooled Image Measure). This latter method becomes useful when few events of

interest are observed in each image as determining co-localization from small sample sizes

becomes impractical due to increased variance. However, we demonstrate that this can be

overcome by pooling data from all the images when necessary, to increase sample size. These

experiments collectively reveal that EV populations are very heterogenous, consistent with

previous studies using other approaches [177, 228, 260, 536, 537] and EV-MAC can reliably

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and reproducibly monitor these differences. In this regard, it should be noted that while EV-

MAC can reproducibly characterize EVs following spinnoculation onto coverslips, it is possible

that spinnoculation may not capture all EVs present in a sample. It is possible that EVs of

extremely small size or those with electrostatic properties that make them less likely to bind

coverslips could be underrepresented in EV-MAC analysis. Altering the spinnoculation

conditions or coverslip treatment may be needed to overcome these issues if they are

encountered.

As is the case with flow cytometry, this method requires control staining lacking primary

antibodies specific for individual antigens, which we achieve by staining using secondary

antibody controls. Wide field, deconvolution microscopy was applied in this approach because of

its sensitivity and field uniformity [348], and because algorithm based analysis using Imaris

software requires 3D acquisition to establish reliable algorithm masks. Similar approaches using

different acquisition systems and masking approaches are likely possible, so long as the

fluorescent excitation field across an image is sufficiently uniform and the instrumentation is

capable of quantifying differences between secondary negative control samples and experimental

samples. It should be noted that the resolution limits of conventional fluorescent microscopy

preclude meaningful analysis of EV size, surface area or volume, as the majority of EVs are

below the 250 nm resolution limit of light microscopy. Moreover, as is the case with flow

cytometry, such an analysis is dependent on the specificity of the antibody(s) utilized. As such,

this method should not be used to determine the precise number of proteins of interest in a given

EV or used to infer that a given protein is completely absent from an EV or population of EVs.

This approach is best suited to studies using fluorescent protein fusions to proteins of interest and

imaging approaches capable of single molecule detection [96]. However, the ability to define EV

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populations of interest, according to their incorporation of endogenously expressed, native

proteins, provides a platform by which the composition of EVs containing specific cargoes of

interest can be interrogated.

We also exploited the broad staining ability of lectins and fluorescent dyes to serve as a

pan-EV marker in the analysis of EVs from biological samples, including saliva and serum.

These approaches yielded generally similar results (Fig. 9 and Fig. 10), providing a workflow to

use EV-MAC to characterize EV populations from patients. These approaches provided similar

results when saliva or plasma samples were analyzed. Using both labelling approaches in parallel

is particularly advantageous, as corroboration between methods can ensure that each label is

being applied in a reliable and reproducible manner. In this regard, careful titration of each label

using EVs at a defined concentration can ensure that these labelling methods are reliably

detecting EVs and artifacts are not being measured. In the case of lectin staining, we have

observed that the lectin staining must be performed prior to antibody labelling and carefully

titrated, as excess lectin can interact with glycans present on many antibodies if these aspects of

the workflow are not carefully controlled. Similarly, titration of PKH dye and removal of

unbound dye is critical to prevent the detection of dye micelles. However, the corroboration of

these methods (Fig. 13) demonstrate that these potential issues can be controlled to provide

reliable and reproducible multiplexed analysis of EV cargos. This approach is a highly attractive

way to characterize EV populations, as the relative composition of EVs containing any given

protein of interest can be compared to an internal control of all EVs labelled using these pan-EV

markers, thus allowing the identification of proteins which are enriched in specific EV

populations of interest. Cell-type specific markers may also be used in this way to identify EVs

released from specific cell types within a heterogenous population of EVs in biological samples.

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In the next chapter, the EV-MAC workflow will be leveraged to evaluate co-localization

among endogenously expressed α-syn with recombinantly added α-syn fibrils, α-syn with Gal3,

and from multiple cell types including induced pluripotent derived midbrain dopaminergic

neurons.

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CHAPTER FOUR

LYSOSOMAL MEMBRANE DAMAGE, AND THE SUBSEQUENT GAL3 MEDIATED

RESPONSE, CONTRIBUTE TO THE SECRETION OF α-SYN BY AN AUTOPHAGIC

MECHANISM

Rationale

Synucleinopathies are a category of neurodegenerative disorders associated with the

accumulation of α-syn aggregates and progressive cell death throughout the central nervous

system. Like many other neurodegenerative diseases, an integral aspect of PD and other

synucleinopathies is a self-propagating pathology that, ultimately, leads to physiological

dysfunction, inflammation, and cell death in the affected tissues. In PD and Lewy body dementia

(LBD), the accumulation of misfolded, insoluble α-syn in Lewy bodies (LBs) and neurites,

collectively called Lewy pathology, are the histopathological definition of these diseases [157],

and numerous converging lines of experimental and genetic evidence support the premise that α-

syn plays a central role in disease pathogenesis [200, 518].

The cell-to-cell spread of misfolded species of α-syn is thought to be paramount to

synucleinopathy disease progression. Multiple levels of evidence, ranging from tissue culture

[106, 153, 282], to rodent and non-human primate models [115, 189, 305, 375, 407] to putamen

grafts in PD patients [258, 259, 292], indicate that transfer of misfolded α-syn between cells is

associated with disease pathology. In addition to cell-to-cell transfer, another key aspect of

disease progression is misfolded α-syn’s ability to permissively template or “seed” the

conversion of endogenously expressed α-syn into misfolded forms. Indeed, the addition of

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exogenous recombinant, α-syn fibrils substantially increases detergent insoluble forms of α-syn

that are akin to those observed in pathology as well as cell-to-cell transmission in vivo [304-306,

375, 407]. However, a critical understanding of the cellular mechanisms responsible for this cell-

to-cell spread and propagation of further α-syn misfolding remains poorly understood.

The autophagy-lysosomal pathway (ALP) is one of the primary degradative pathways in

cells, responsible for the turnover of cytosolic content, such as protein aggregates, by lysosomal

degradation [151, 533]. In macroautophagy, hereafter referred to as autophagy, the formation of

an isolation double membrane, called an autophagophore, engulfs organelles and cytosolic

proteins, and subsequently closes to form a vesicular compartment known as an autophagosome

[151, 533]. Degradation then occurs upon autophagosome fusion with the lysosome, exposing

the engulfed contents to the hydrolytic enzymes within the lysosome [151, 533].

Canonically, the inhibition of the mammalian target of rapamycin (mTOR) drives the

autophagic cellular response [317]. The initial generation of an autophagophore begins with the

enrichment of a phosphatidylinositol 3-phosphate (PI3P) lipid domain, typically at the

endoplasmic reticulum. This process is driven by unc-51 like autophagy activating kinase 1

(ULK1) to form the multiprotein initiator complex, which includes the autophagy related (ATG)

proteins, beclin-1 and phosphatidylinositol 3 kinase (PI3K). The formation of PI3K is essential

for the recruitment of effector proteins to create the autophagophore [41]. The autophagophore

then undergoes expansion and elongation, increasing in size and protein recruitment; both are

driven and regulated by a series of transient protein interactions. Included among these are the

ATG5-ATG12-ATG16L1 complex that depends on intermediate ATG proteins, such as ATG7,

to ultimately conjugate and recruit proteins to the maturing autophagophore [41, 317]. Among

the proteins recruited, microtubule-associated protein 1 light chain 3 (LC3) is the most well-

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known and remains on the autophagosome throughout its lifecycle. During this process, cytosolic

LC3, known as LC3-I, is conjugated to phosphatidylethanolamine, known as LC3-II, by ATG

proteins and incorporated into the autophagophore and, ultimately, assists in its closing to form

the autophagosomes [41, 317]. The galectins are a family of proteins that share a CRD motif that

interacts with β-galactoside glycans [237]. Galectin proteins play a role in a broad range of

cellular functions including immune responses, signaling, inflammation, and autophagy [237].

Several galectins, including Gal1, Gal3, Gal8, and Gal9, have CRDs that interact and adhere to

the glycans present on the intraluminal membrane proteins of endosomes and lysosomes, such

that damage to these vesicles leads to the recruitment of galectin proteins to these damaged

vesicles [237, 372, 496]. Galectin recruitment to damaged vesicles is known to lead to the

recognition of these damaged vesicles by autophagic adapter proteins and, subsequently, to their

degradation [401, 402, 496]. We and others have previously demonstrated that fibrillar forms of

-syn, tau and other amyloids can induce vesicle damage following endocytosis, leading to the

recruitment of Gal3 and Gal8 and autophagic adaptors and effector proteins [56, 140, 147, 153].

When postmortem brain tissue from five PD patients was stained for Gal3 and pS129 α-syn to

identify LBs, consistent with LBs being a mix of aggregated α-syn and lipid organelles [445],

170 of 305 LBs examined displayed Gal3 coronas surrounding the pathological inclusions [147].

The presence of Gal3 in LBs suggests a history of membrane damage.

Accumulating evidence reveals that the biological functions of galectin proteins are

central to the ALP dysfunction that occurs in PD and other neurodegenerative diseases [13, 37,

122, 416, 422]. The Deretic group demonstrated that the re-localization of Gal3, Gal8, and Gal9

to damaged lysosomal compartments both coordinates and drives the cellular autophagic

response [71, 232, 234, 250, 265]. Specifically, in combination with tripartite motif containing

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16 (Trim16) and ULK1, Gal3 facilitated the removal of damaged lysosomal membranes in

response to rupture in part by recruiting ATG16L1 [234, 265]. Two recent genome wide

association studies reported that single nucleotide polymorphisms in LGALS3, the gene that

encodes for Gal3, were associated with PD [37, 122]. Additionally, increased Gal3 in the

cerebral spinal fluid of PD patients has been reported [13, 122, 526]. Mechanistically, recent

papers indicate that lysosomal damage triggers autophagy-dependent unconventional secretion of

cytokines utilizing Trim16 [250] and other groups found that these same cytokines require Gal3

for secretion in certain cell types by an unknown intracellular mechanism [452, 504].

Recent studies have also demonstrated that galectins and proteins normally associated

with ALP degradation also act to promote an unconventional secretory mechanism referred to as

secretory autophagy [126, 250, 391]. Although no studies have specifically examined how

secretory autophagy may drive the release of -syn, -syn is known to be released via an

unconventional secretory mechanism that is modulated by ALP perturbations [9, 86, 106, 279,

340, 387]. Specifically, studies have shown that dysfunctional alterations in the ALP influence

the secretion of both extracellular vesicle (EV) and non-EV associated α-syn [41, 132, 155, 184,

192, 225, 279, 387]. In the context of the cell-to-cell spread of -syn, the role of galectins in

mediating secretory autophagy and their association with vesicles damaged by fibrillar -syn and

other amyloid proteins raises the possibility that galectins, following vesicular damage, may

promote the release of -syn from cells, thereby contributing to the pathological spread of -syn

between cells. Thus, we sought to determine whether Gal3 contributes to the release of α-syn

from cells. We observe that both endogenously expressed -syn and exogenous -syn fibrils co-

localize in galectin positive intracellular vesicles and, ultimately, in EVs released from these

cells. We also observe that the depletion of Gal3 or the associated autophagic adaptor protein,

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Trim16, reduce the release of -syn from cells, including iPSC derived midbrain dopamine

neurons.

Results

Exogenous α-syn fibrils are re-secreted with endogenous α-syn and Gal3 by the ALP.

To determine if changes in autophagy affect the secretion of α-syn, we used a

complementation system similar to others that have been used to detect the release of

oligomerized -syn species [2, 59, 106, 108, 129, 189, 209, 242, 361]. We used complementing

halves of Dual Split Protein (DSP, DSP1-7 and DSP8-11) [255], which are composed of two

separate complementing fragments of Renilla luciferase (RLuc) and green fluorescent protein

(GFP) that require functional complementation with the other corresponding fragment (hereafter

referred to as DSP-A and DSP-B, Fig. 14). We generated constructs that linked α-syn on to the

N-terminal region of DSP-A and DSP-B. We then dually transduced HeLa and SH-SY5Y cells

with the α-syn DSP-A and -B constructs to generate stable DSP A+B cell-lines. The advantages

of this model include: (1) The ability to measure α-syn in a non-monomeric state, as the DSPs

require complementation to

gain activity; (2) The ability to

visualize α-syn via its GFP

signal and sensitively measure

secretion by measuring RLuc

activity in the culture

supernatant; and (3) The

ability to measure changes in

Figure 14: Cartoon Schematics of the dual split protein (DSP) α-syn

construct. A cartoon depiction of two separate complementing fragments

of Renilla luciferase (RLuc) and green fluorescent protein (GFP) linked

to α-syn.

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cell expressed -syn in response to exogenously added fibrillar -syn, which can otherwise be

hard to discern.

To validate whether either of the DSP-α-syn constructs were undergoing cleavage, lysate

from SH-SY5Y cells transduced with either α-syn DSP A, α-syn DSP B, or concurrently α-syn

DSP-A and -B was evaluated by western blot. An equal amount of transduced cell lysate was

loaded across multiple wells to evaluate construct authenticity using antibodies against α-syn,

RLuc, or GFP (Fig. 15A).

Figure 15: Western Blot Validation of α-Syn DSP -A and -B Construct Integrity in Co-transduced

Cells. (A) α-syn DSP-A and α-syn DSP-B transduced, and dual-transduced DSP-α-syn SH-SY5Y cell

lysates run in parallel, representative western blots. The same lysates probed against α-syn (211 epitope,

left), polyclonal RLuc, (left-center), polyclonal GFP (right-center), or GAPDH (right). Staining with an

antibody against RLuc resulted in bands at ~33 kDa in the α-syn DSP-B and α-syn DSP A+B lysates but not

in the α-syn DSP-A lysate. Varying degrees of faint bands at ~44-50 kDa, and at ~75 kDa were observed in

the α-syn DSP-A and α-syn DSP-B lysates. These bands are most likely due to non-specific binding rather

than high molecular weight α-syn, as one would expect to observe bands at different molecular weights

corresponding to the size of the α-syn DSP-A and DSP-B constructs, which differ. A GFP band was

observed at ~48 kDa in the α-syn DSP-A and α-syn DSP A+B cells but not the α-syn DSP-A lysate. (B, C)

Representative western blots of HeLa α-syn DSP A+B (B) and SH-SY5Y α-syn DSP A+B (C) lysates ran in

parallel and probed for GFP (B-2 epitope, left), α-syn (211 epitope, right), or GAPDH.

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Figure 16: DSP-α-syn is secreted in response to lysosomal acidification inhibitors and exogenously

added α-syn fibrils. Figure Legend next page.

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Notably, the α-syn DSP A+B lysate quantity was adjusted to better visualize differences

in construct expression relative to the α-syn DSP A and α-syn DSP B lysates. Both constructs

were observed at different molecular weights according to the predicted molecular weight of

these fusion proteins, which were visible using antibodies to -syn, RLuc, and GFP (Fig. 15A,).

Given the division of the GFP-RLuc DSP constructs, differences in the extent to which the RLuc

and GFP polyclonal antibodies recognized the α-syn DSP-A and -B halves were observed.

Both constructs were observed at different molecular weights according to the predicted

molecular weight of these fusion proteins ~33kDa and ~48kDA, which were visible using

antibodies to -syn, RLuc luciferase and GFP when lysates were run in parallel (Fig. 15A).

Staining for α-syn (Syn 211) resulted in the presence of band at ~17kDa in all lysates, consistent

with monomeric α-syn in SH-SY5Y cells [277]. Parallel lysates from WT HeLa and HeLa -syn

DSP A+B cells as well as WT SH-SY5Y and SH-SY5Y -syn DSP A+B cells further confirmed

the presence of endogenous -syn at ~17kDa (Fig. 15B, C) and that the monoclonal (B-2) GFP

antibody only recognizes the -syn DSP B construct.

Figure 16: DSP-α-Syn is Secreted in Response to Lysosomal Acidification Inhibitors and Exogenously

added α-Syn Fibrils. (A) Representative non-denaturing SDS-PAGE of SH-SY5Y DSP-α-syn cell lysates from

a single experiment ran in parallel (n = 3). The same representative lysates are used for A. SDS-PAGE probed

against (A) pSer129 α-syn (left), conformation specific α-syn (middle), GFP (right), GAPDH (bottom-left). (B)

Relative light units (RLUs) were measured from the Renilla Luciferase (RLuc) signal in the cultured media from

the stated cell lines 24 hours after treatment with vehicle (0.1% DMSO) or Bafilomycin-A1 (Baf-A1) indicating

the levels of complemented DSP-α-syn. (C) RLU signal detected in the cultured media from SH-SY5Y DSP-α-

syn cells 24 hours after control or α-syn fibril treatment. Statistical significance determined by paired t-test. Data

shows the mean value from at least three independent experiments where each data point indicates a biological

replicate. Data are expressed as M ± SE. For all statistical tests *, **, ***, ****, p < 0.05, 0.01, 0.001, and

0.0001, respectively. (D) A representative z-stack of dually transduced DSP-α-syn SH-SY5Y cells from

untreated and α-syn fibril treated cells probed for Gal3. Cell boundaries are shown as a white border. (E) and (F)

Z-stacks of untreated or fluorescently labelled α-syn fibril treated HeLa DSP-α-syn and mCherry Gal3

expressing cells. (E) Arrows indicates co-localization of DSP-α-syn+ and mCherry Gal3+ events in untreated

cells and co-localization of DSP-α-syn+, mCherry Gal3+, and α-syn fibril+ events in the α-syn fibril treated

cells. (F) Extracellular co-localization of DSP-α-syn+ and mCherry Gal3+ event observed in untreated cells

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We next evaluated how -syn fibril treatment influenced the formation of higher

molecular weight -syn in the DSP--syn SH-SY5Y cells. Previous work suggests that fibrillar

α-syn can recruit monomeric α-syn in cell culture resulting in increased high molecular weight α-

syn species [42]. Additionally, following α-syn fibril treatment, endogenous α-syn represents the

primary component of phospho-serine129 (pSer129) α-syn species found within inclusions

[306]. To better understand the species of -syn present in these cells before and after treatment

with exogenous fibrils, we used antibodies specific for pSer129 α-syn, aggregated conformation

-syn (MJFR-14-6-4-2) and GFP. DSP-α-syn SH-SY5Y cells lysates were assessed by non-

denaturing SDS-PAGE and the same quantity of lysates was evaluated multiple times (Fig. 16A).

In all cases, a band at ~48kDa was observed for both the control and the α-syn fibril treated cells

lysates regardless of the antibody used (Fig. 16A). However, higher molecular weight bands

were also observed in all blots. Moreover, when probed for pSer129 α-syn, α-syn fibril treatment

resulted in more intense high molecular weight (> 150 KDa) bands (Fig. 16A, left). An increased

band intensity at ~48 kDa was also observed in the α-syn fibril treated cell lysates when probed

for GFP, which should exclude the possibility of α-syn fibrils detection (Fig. 16A, right).

Interestingly, the GFP antibody appeared to recognize a considerably larger portion of the

smaller molecular DSP-α-syn when run under non-reducing conditions versus the portion of the

smaller molecular DSP-α-syn recognized when run under reducing conditions (Fig. 15A & Fig.

16A). Finally, increased band intensity was also observed in the α-syn fibril treated cell lysates

relative to the control lysates when probed with the α-syn aggregate specific antibody (Fig. 16A,

middle).

To determine how ALP impairment alters the secretion of DSP-α-syn, DSP--syn HeLa

and SH-SY5Y were treated with Bafilomycin A1 (Baf-A1), a lysosomal acidification inhibitor

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that also prevents lysosome-autophagosome fusion [326, 327, 543]. Baf-A1 treatment

significantly increased complemented DSP-α-syn secretion (Fig. 16B), consistent with other

studies examining native α-syn release and studies using similar complementation systems [9,

132, 144, 225, 279, 340, 387]. These data, therefore, demonstrate that the increase in -syn

secretion observed in other studies is recapitulated by DSP--syn, revealing it as a useful model

to monitor -syn release and characterize the pathway promoting secretion.

We next wanted to determine if the ALP dysfunction induced by exogenous -syn fibrils

can drive the release of endogenously expressed -syn, which would provide a putative

mechanism for the cell- to-cell transfer of -syn. To this end, we applied recombinant -syn

fibrils dyed with ATTO 647 [147, 304], to DSP--syn SH-SY5Y cells. Measuring the luciferase

activity specific to the DSP--syn fusion, we observed a modest but significant increase in the

release of DSP α-syn secretion after the SH-SY5Y cells were treated with α-syn fibrils (Fig. 16C,

left panel). These data suggest that late-stage impairment in autophagic degradation increases α-

syn secretion as does α-syn fibril treatment.

In previous work, we found that -syn fibril treatment induced lysosomal rupture

resulting in Gal3 re-localization in cells over-expressing mCherry Gal3 [147, 153]. Thus, we

performed experiments to determine if endogenous Gal3 was co-located with DSP--syn within

the DSP--syn SH-SY5Y cells in the presence and absence of -syn fibrils (Fig. 16D). In both

the untreated and -syn fibril treated DSP--syn cells, endogenous Gal3 was observed within

brighter DSP--syn regions. Additionally, in the -syn fibril treated cells, we observed instances

in which endogenous Gal3 was associated with dual labeled, DSP--syn positive (+) and -syn

fibril+ areas (Fig. 16D). These events were even more apparent in DSP--syn HeLa cells

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simultaneously expressing mCherry Gal3 (Fig. 16E). In the -syn fibril untreated cells abundant

instances of DSP--syn+ and mCherry Gal3+ were observed. Following -syn fibril treatment,

DSP--syn+, mCherry Gal3+, and -syn fibrils+ triple positive events were regularly observed

in abundance (Fig. 16E). , we also observed instances of what appeared to be examples of

extracellular accumulations of double positive DSP--syn+ and mCherry Gal3+ independent of

-syn fibrils treatment (Fig. 16F). Given these observations, we sought to determine whether this

was also true within the EVs released from these cells.

Exogenous and Endogenous -Syn are Released in Gal3-Containing EVs.

It is well documented that -syn is released in EVs and that these EVs have the potential

to mediate the spread of pathological -syn from cell-to-cell [106, 267, 340]. Furthermore,

treatment with either the lysosome acidification inhibitor Baf-A1 or chloroquine is known to

impact the composition of EVs, leading to a hybrid “autophagosome-EV signature”, with

increased levels of the autophagic protein LC3-II and α-syn [340]. These findings suggest that

ALP impairment affects α-syn associated EV secretion. In addition to -syn, Gal3 is also

secreted in EVs and has been shown to be increased during ALP impairment [23, 357]. We,

therefore, investigated if α-syn fibril treatment affected released DSP-α-syn EVs content. To test

this, the contents of EVs released from our DSP-α-syn SH-SY5Y cells were characterized in

response to α-syn fibrils alone or when co-treated with the commonly employed EV secretion

inhibitor GW4869 which acts to impair neutral sphingomyelinase 2, preventing the formation of

intraluminal vesicles in multivesicular bodies (MVBs), and, ultimately, blocking the release of

mature exosomes [62].

To this end, DSP-α-syn cells were pretreated with either vehicle or GW4869. After

pretreatment, continued vehicle or GW4869 treatment was performed with or without the

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addition of α-syn fibrils. The EVs from each condition were concentrated from equal volumes of

cultured media, and both the concentrate EVs and lysates were assessed by non-reducing SDS-

PAGE. GW4869 treatment reduced the levels of the canonical EV markers CD63 and Alix, as

well as Gal3 (Fig. 17A). Intense Gal3 bands were observed in the vehicle and vehicle plus α-syn

fibril treatment conditions, (Fig. 17A). A more intense pSer129 α-syn band in the vehicle plus α-

syn fibril EVs was observed compared to the other treatment conditions at molecular weights

consistent with α-syn DSP-A and -B (Fig. 17A).

Figure 17: Secreted Gal3 and DSP-α-Syn are in EVs. (A) Non-reducing SDS-PAGE of concentrated

extracellular vesicles (EVs) from vehicle (1% DMSO), GW4869, α-syn Fibril, or GW4869 + α-syn Fibril

treated SH-SY5Y DSP-α-syn cells. (B) Representative TEM images of CD63+ and Gal3+ (top) and CD63+ and

α-syn (bottom) immunogold labeled events from SH-SY5Y DSP-α-syn concentrated culture media (C) NTA

analysis of WT DSP-α-syn EVs from DSP-α-syn SH-SY5Y cells. Data binned in 1nm increments with a bin

center of 0.5nm. NTA of resuspended DSP-α-syn EVs were performed in technical quintuplicates, denoted as

different shades of green, blue, or red for 3 independent samples. (C) Cell viability of vehicle, GW4869, Baf-

A1, GW4869 + α-syn fibrils, or α-syn fibrils from DSP-α-syn SH-SY5Y treated cells measured by LDH assay

(M ± SE, n = 3). Statistical significance determined by one-way ANOVA.

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Previous works indicates that GW4869 also affects the formation of autophagosomes and the

secretion of LC3 [18, 285]. Thus, we also compared LC3-II levels across all conditions. In

agreement with these previous works GW4869 also reduced LC3-II levels compared to vehicle

as well as compared to GW4869 plus α-syn fibrils compared to vehicle plus α-syn fibrils

treatment (Fig. 17A). Immunogold labeling of CD63 (10 nm), α-syn (5 nm), and Gal3 (15 nm)

and TEM of concentrated, conditioned culture media revealed the presence of CD63+ and Gal3+

(Fig. 17B, top) as well as CD63+ and α-syn+ objects (Fig. 17B, bottom). Nanoparticle tracking

analysis (NTA) confirmed the relative size distribution of the vesicular structures had a 202.3 ± 4

nm mean diameter while the most common diameter was 144.5 nm from concentrated culture

media (Fig. 17C). No difference in cell viability among treatments thus far were observed when

measured by a l-lactate dehydrogenase (LDH) assay (Fig. 17D). Collectively, these data suggest

that released EVs from DSP-α-syn cells are primarily exosomes, contain Gal3, the pathologically

associated pSer129 α-syn, the autophagic marker LC3-II, and that their secretion is not stemming

from treatment induced cell death.

Based on these findings, we next imaged EVs released from HeLa DSP--syn cells

treated with ATTO 647 -syn fibrils to determine if DSP--syn could be detected in EVs in

association with Gal3. To this end, low speed centrifugation of DSP--syn cell culture

supernatant was performed to adhere EVs released from cells onto coverslips in preparation for

fluorescent microscopy imaging and analysis. Such low-speed centrifugation (i.e. 1,200 × g for 2

hours) is sufficient to bind viral particles and EVs to glass, but is insufficient to promote the

binding of soluble proteins [52, 356]. Following centrifugation, the bound EVs were fixed then

probed with an anti-Gal3 antibody to determine if -syn+ EVs were associated with cell

expressed Gal3. The EVs were then imaged by wide-field fluorescence microscopy where puncta

156

corresponding to exogenous -syn fibrils, Gal3 and DSP--syn were observed bound to the

coverslip. Notably, we observed instances of double co-localization among respective signals as

well as all three signals co-localizing in individual puncta (Fig. 18A).

Figure 18: Secreted Gal3 and DSP-α-Syn Co-localize with Exogenously Added α-Syn Fibrils. Figure

legend on the next page

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We next used EV multiplex analysis of co-localization (EV-MAC) [52], to characterize the EV

populations released from these cells. This approach allows for the interrogation of EVs at the

single EV level to assess the relative co-localization of individual EV cargos from within the

total EV population using a gating process similar to that used in flow cytometry. We applied

this approach to determine the relative percent positive puncta for DSP--syn, recombinant

exogenous -syn fibrils and Gal3 to discern the degree of dual labeled and triple labeled co-

located EVs. We first assessed the degree to which DSP-α-syn+ EVs co-localized with Gal3

from the cultured media of untreated cells as well as Gal3 and -syn fibrils from exogenous -

syn fibril treated cells. This was done by using image analysis software (Imaris, Bitplane) to

create a Spots mask around the DSP-α-syn signal and measuring the respective Gal3 and -syn

fibril maximum fluorescence intensity relative to the secondary antibody (2°Ab) only control.

The masking algorithm detected all of the DSP α-syn+ puncta (Fig. 18B). The degree of co-

Figure 18: Secreted Gal3 and DSP-α-Syn Co-localize with Exogenously Added α-Syn Fibrils. (A) A

representative z-stack from a constructed z-stack maximum intensity projection obtained from the cultured media

of untreated HeLa α-syn DSP-α-syn and those treated with α-syn fibrils. Representative maximum intensity

projections of z-stack images for cultured media obtained from untreated HeLa DSP-α-syn processed for

immunofluorescence without the primary antibody (2˚ Ab treatment) are also given. (B) Demonstration of the

Spots masking algorithm built around the DSP-α-syn channel from z-stack maximum intensity projection

showing specificity for the desired channel. (C) Representative co-localization plots of the maximum fluorescence

intensities of the Gal3 (Y-axis) and α-syn fibril (X-axis) signal found within each DSP-α-syn masking algorithm

Spot pooled from a total of 15 images from a single treatment conditions’ coverslip. Each data point represents a

single recognized DSP-α-syn+ spot within an image. Quadrant numbers indicate the percent co-localization of

DSP-α-syn+ where the relative background signal was determined based on the Gal3 and α-syn Fibril maximum

intensity signal found in the 2o Ab Control condition. Bottom left quadrants indicate single DSP-α-syn+ positive,

bottom right DSP-α-syn+ α-syn Fibril+ double positive, top left DSP-α-syn+ Gal3+ double positive, and top right

DSP-α-syn+ α-syn Fibril+ Gal3+ triple positive. (D) Summarized average co-localization of DSP-α-syn+ masking

algorithm Spots from four independent experiments. DSP-α-syn+ Spots that are single positive (green), DSP-α-

syn+ α-syn Fibril+ double positive (cyan), DSP-α-syn+ Gal3+ double positive (yellow), and DSP-α-syn+ α-syn

Fibril+ Gal3+ triple positive (white). (E) Representative co-localization plots of the maximum fluorescence

intensities of the DSP-α-syn (Y-axis) and α-syn Fibril (X-axis) signal found within each Gal3 masking algorithm

Spot from the same images set of images. Co-localization plots are from a single image. (F) Summarized average

co-localization of Gal3+ masking algorithm Spots from four independent experiments. Gal3+ Spots that are single

positive (red), Gal3+ α-syn Fibril+ double positive (magenta), Gal3 DSP-α-syn+ double positive (yellow), and

Gal3+ DSP-α-syn+ α-syn Fibril+ triple positive (white). Summarized average co-localization of (D) DSP-α-syn or

(F) Gal3 masking algorithm Spots from four independent experiments. Degree of co-localization DSP-α-syn or

Gal3 with respective other channels was determined based on the background signal found in the 2o Ab Control.

Data are expressed as M ± SE (n = 4 coverslips, where M was determined from 15 images). The scale bars are

equal to 2 µm.

158

localization of the DSP-α-syn Spots mask with Gal3 and -syn fibrils from each of 20 images

was calculated and, subsequently, averaged for each replicate. A representation of a single

image’s distribution shown in Fig. 18C. The totality of these experiments yielding a summarized

co-localization distribution (Fig. 18D). For the untreated cells, 70.8 ± 4.7% and 28.9 ± 4.6% of

the spots were single labeled, DSP--syn+ and dual labeled, DSP--syn+ and Gal3+,

respectively (Fig. 18D, left). Treatment with

exogenous α-syn fibrils decreased the percentage of

single labeled, DSP--syn+ EVs to 40.9 ± 12.3%

and dual labeled, DSP--syn+ and Gal3+ EVs to 9.6

± 3%. However, 21.4 ± 5.4% of DSP--syn+ EVs

were α-syn fibril+. Additionally, 28.1 ± 6.0% DSP-

-syn+ EVs were both Gal3+ and α-syn fibrils+

(Fig. 18D, middle).

The reciprocal analysis of the degree of

Gal3’s co-localization with DSP-α-syn from the

untreated cells and DSP-α-syn and α-syn fibrils from

the exogenously α-syn fibril treated cells was

conducted from the same set of images. This process was performed by using image analysis

software (Imaris, Bitplane) to create a Spots masking algorithm around the Gal3 signal and

measuring its co-localization from each image analogous to the previous DSP--syn analysis

(Fig. 18E). The subsequent analysis revealed that Gal3+ was single labeled 63.0 ± 3.0% of the

time and dual labeled, Gal3+ and DSP--syn+ 36.0 ± 3.0% of the time (Fig. 18F, left). Of the

EV from the exogenous α-syn fibrils treatment, 32.5 ± 7.8%, 10.2 ± 2.8%, and 32.7 ± 5.8% were

Figure 19: DSP-α-Syn Intensity is

Greater in DSP-α-Syn EVs Positive for α-

Syn Fibrils. Maximum fluorescence

intensities of DSP-α-syn+ masking algorithm

Spots that are either α-syn fibril- and α-syn

fibril+ from the cultured media of α-syn

fibril treated cells. α-syn fibril+ events were

determined based on 2oAb controls as shown

in Figure 18C. Data shown are representative

from one of four independent experiments

and are expressed as M ± SE (α-syn fibril-, n

= 860 spots; α-syn fibril+, n =238) from a

total of 15 images. Statistical significance

determined by unpaired t-tests. ***, p <

0.001.

159

single labeled Gal3+, dual labeled Gal3+ and α-syn fibrils+, and triple labeled Gal3+ and DSP-

-syn+ and α-syn fibrils+,

respectively (Fig. 18F, right).

Furthermore, we then

compared the average maximum

intensity of the DSP--syn signal

among the dual labeled, DSP--

syn+ and α-syn fibrils+ EVs to

the single labeled, DSP--syn+

EVs from within the α-syn fibril

treatment condition. We found

that the DSP--syn maximum

fluorescence intensity was significantly greater among the α-syn fibrils+ EVs relative to the α-

syn fibrils- EVs (Fig. 19). Notably, this was performed independent of whether EVs were also

Gal3+. Collectively, this indicates that: (1) EVs containing both Gal3 and α-syn are released

from cells (2) exogenous α-syn fibrils increased the relative abundance of DSP--syn in Gal3+

EVs, and (3) both endogenous and exogenous -syn can be released from cells in the same EVs,

suggesting they reside in similar intracellular compartments prior to release. Taken together with

prior studies by us and others showing that -syn fibrils can induce the rupture of lysosomes

following endocytosis [56, 147, 153, 430], this further suggests that lysosomal damage can

promote the release of endogenously expressed -syn as well as the original exogenous fibrillar

species.

Figure 20: Characterization of Human MDA Neuronal Cultures

Derived from Human IPSCs. (A) Representative images of human

iPSCs, neural progenitor cells, and mature mDA neurons based on

their expression of phenotypic markers using wide-field fluorescence

microscopy. The scale bars equal 20 µm.

160

Characterization of Human IPSC Derived Midbrain Dopamine Neurons.

To better understand the impact of -syn fibrils and lysosomal damage on -syn release

in a more biologically relevant model, we used dopaminergic (DA) neurons derived from human

induced pluripotent stem cells (iPSCs), including both commercially available DA neurons (iCell

Dopaneurons) and midbrain dopamine (mDA) neuronal cultures differentiated from an iPSC cell

line which we obtained from Joseph R. Mazzulli laboratory. This iPSC cell line was produced

from primary human fibroblasts by retroviral expression of the reprogramming factors OCT4,

SOX2, cMYC, and KLF4 (described in Seibler et al. 2011)[441] and was characterized by

measuring the expression of pluripotency markers (i.e. Oct 3/4, Tra-1-60, SSEA-4, Nanog)[330],

genomic integrity through G-banding karyotype analysis (described in Mazzulli et al.

2011)[329], teratoma analysis (described in Cooper et al. 2012)[95], and RT-PCR analysis of

pluripotency markers (described in Cooper et al. 2012) [95]. Flow cytometry and

immunofluorescence experiments show that, prior to the start of differentiation, greater than 85%

of the human iPSCs express the pluripotency marker octamer binding transcription factor 3/4

(OCT 3/4) (Fig. 20), tumor rejection antigen 1-81 (Tra-1-81), and stage specific embryonic

antigen 4 (SSEA-4). At day 14 post initial differentiation, we observed increased expression of

LIM homeobox transcription factor 1, α (LMX1A) and the neural progenitor marker, nestin (Fig.

20), consistent with what has previously been described [262, 330, 386]. Flow cytometry showed

that 87 ± 4% and 88 ± 5% (M ± SE, n = 3 independent experiments) of the terminally

differentiated cells were positive for the midbrain markers, forkhead box protein A2 (FOXA2)

and LMX1A, respectively. Flow cytometry showed that 63 ± 17% and 70 ± 10% (M ± SE, n = 3

independent experiments) of the terminally differentiated cells were positive for the mDA neuron

markers, nuclear receptor related protein 1 (Nurr1) and tyrosine hydroxylase (TH), respectively.

161

Immunofluorescence experiments showed that terminally differentiated cells expressed TH and

the neuronal marker, β-III tubulin (Fig. 21). Consistent with a previous report in which synapses

were observed in human mDA neurons derived from iPSCs [381], transmission electron

microscopy (TEM) analysis of terminally differentiated cells revealed evidence of the formation

of synapses (Fig. 21). As previously described [400, 563] and in accord with well recognized

criteria [382], a synapse was identified by the presence of presynaptic neurotransmitter vesicles

and a synaptic cleft with parallel, electron dense, thickened pre- and postsynaptic membranes

and the presence of electron dense content in the synaptic cleft.

Consistent with the report that greater than 90% of the dopaminergic synapses in the human

striatum are symmetric[269] and are functionally inhibitory or modulatory, all of the synapses

observed were symmetric synapses (Fig. 21).

Figure 21: MDA Neurons Derived from Human IPSCs form Symmetric (Functionally Inhibitory or

Modulatory) Synapses. (A) A symmetric synapse between a mDA axon terminal and a neurite. (B) A

symmetric synapse between a mDA neuron and a dendrite. In (A) and (B), note the presence of presynaptic

neurotransmitter vesicles, thickened, electron dense, and parallel pre- and postsynaptic membranes, a well-

defined synaptic cleft, and the presence of electron dense material in the synaptic cleft. The arrowheads point

to neurotransmitter vesicles. The scale bar in (B) equals 200 nm and is valid for both panels.

162

Lysosomal Membrane Damage Inhibits mTOR and Upregulates Autophagy in Human

MDA Neurons.

We have previously

demonstrated that treating

cells with α-syn fibrils results

in uptake into endo-lysosomal

compartments where they

elicit membrane damage [147,

153]. Recent studies have

shown that lysosomal damage

results in an upregulated

cellular autophagic response in

a variety of cell lines [71, 232,

234, 265]. Thus, experiments

were performed to determine

if similar results would be

obtained using human mDA

neurons in response to the

lysosomal damaging agent, L-

leucyl-L-leucine methyl ester

(LLOME), or α-syn fibril

treatment.

Figure 22: Lysosomal Damage Following α-Syn Fibril or LLOME

Treatment Elicits an Autophagic Response in MDA Neurons. (A) Cell

viability of mDA neurons from 24-hour control untreated, rapamycin, or α-

syn fibrils; or 4-hour vehicle (0.1% DMSO) or LLOME treated cells.(B, E)

Representative western blot of Lamp1, pS757 ULK1, total ULK1 (tULK1),

pT389 P70 S6K1, total P70 S6K1 (tP70 S6K1), Gal3, LC3-I, LC3-II, and

GAPDH for mDA neurons treated with vehicle (0.1% DMSO), rapamycin,

LLOME, or α-syn fibrils. (C, D) The quantification of pT389 S6K1, tP70

S6K1, pS757 ULK1, and tULK1. Quantification of Lamp1, Gal3, and LC3-

II for LLOME (E) and α-syn fibrils treatment (E). Cells were treated with α-

syn fibrils, rapamycin, or vehicle for 24 hours; cells were treated with

LLOME for 4 hours. (C) Data are expressed as M ± SE (n = 3). (D-F) Data

are expressed as M ± SE (n = 4). Statistical significance was determined

following natural log transformation and one-way ANOVA with Tukey’s

post hoc tests. For all statistical tests *, **, ***, ****, p < 0.05, 0.01, 0.001,

and 0.0001, respectively.

163

To assess the autophagic activation state in response to these treatments, phosphorylation of P70

ribosomal protein S6 kinase (P70 S6K1) at threonine 389 (T389) and the phosphorylation of

ULK1 at serine 757 (S757) was assessed. Notably, mTOR suppresses autophagy by

phosphorylating P70 S6K1 at T389 and ULK1 at S757. The ratio of each phosphorylated protein

was compared to its respective total levels to control for possible differences in protein

degradation or expression. Western blot analysis revealed that treatment with either LLOME, α-

syn fibrils, or the mTOR inhibitor rapamycin reduced the relative band intensity of both

phosphorylated ULK1 at S757 (pS757 ULK1) and phosphorylated P70 S6K1 at T389 (pT389

P70 S6K1) (Fig. 22B-D). No differences in cell viability were observed among treatment

conditions (Fig. 22A). These findings indicate that treatment of human mDA neurons with

LLOME, α-syn fibrils, or rapamycin resulted in an increased autophagic state.

In addition, we determined the effects of LLOME or α-syn fibril treatment on lysosome

and autophagosome levels by measuring the lysosomal marker, lysosome associated membrane

protein 1 (Lamp1) and the autophagosomal marker, LC3-II. Moreover, we also measured Gal3,

which we hypothesized would be elevated during increased lysosomal damage [472]. Both α-syn

fibril and LLOME treatment had no effect on Lamp1 LLOME (Fig. 22E-G). Additionally, both

LLOME and α-syn fibril treatment increased Gal3 and LC3-II (Fig. 22E-G). Collectively, these

data suggest that lysosomal damage results in an increased cellular autophagic response in

human mDA neurons.

The Reciprocal Secretion of α-Syn and Gal3 is Regulated by Trim16 and ATG16L1.

To determine if Gal3 directly affects α-syn secretion, two human mDA neuronal cell

lines were depleted of Gal3 by enzyme-linked immunosorbent assay (ELISA).

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Gal3 knockdown (KD) significantly decreased α-syn secretion in both mDA neuronal lines,

Figure 23: Neuronal Gal3 Influences α-Syn Secretion and is Reciprocally Secreted During α-Syn Fibrils

Treatment. (A, B) Mean fold difference in α-syn in human mDA neuron culture media 48-72 hours post

siRNA transfection and relative depletion of Gal3, and representative KD. (A) Different colors represent

matched replicates. (B) Values were determined by ELISA and normalized to mean control (C) Correlative plot

of the paired mean normalized α-syn secretion data combined from the data shown in (A, B) comparing relative

Gal3 depletion (X-axis) and the mean fold α-syn secretion (Y-axis). (D) Relative fold difference in α-syn from

mDA cultured media treated with TD139 (5 μM) or vehicle (0.1% DMSO) treated control after 24 hours

measured by ELISA. (E) RLUs of RLuc activity of cultured media from CRISPR-Cas9-mediated KO of Gal3

or control in SH-SY5Y DSP-α-syn cell-lines after 24 hours and demonstration of Gal3 KO. (F) RLUs of RLuc

activity from control or Gal3 CRISPR-Cas9 mediated KO SH-SY5Y DSP-α-syn cell lysates after 24 hours (n =

3). (G) Cell viability of control or Gal3 KO SH-SY5Y DSP-α-syn cells over 24 hours (n = 3). (H) Level of α-

syn in the cultured media of mDA neuronal cultures at 48 hours post-transfection measured by ELISA. (I) α-syn

concentration present in empty vector (EV) control or firefly Gal3 transduced mDA neurons cell lysates after 96

hours. (J) RLU levels detected from mDA neurons cultured transduced with FLuc Gal3 and either left untreated

or treated with α-syn fibrils at 48 hours post-transduction. (K) RLUs of RLuc activity from lysates of dually

transduced FLuc Gal3 and RLuc mDA neurons after 48 hours of untreated control or α-syn fibrils indicating

comparable transduction efficiency among conditions. All data are expressed as M ± SE (A, n = 8; B, n = 3; C,

n = 11; D, n = 6; E, F n = 6; G, n = 3; H, n = 12; I, n = 4; J, n = 5; K, n = 5). Statistical significance was

determined by a Pearson correlation with two tailed t-test (C), paired t-test (A, D, E, F, G, H, I, and J), or ratio

paired t-test (B) to better account for the distribution of values < 1. For all statistical tests *, **, ****, p < 0.05,

0.01. and 0.0001, respectively.

165

although the effect was modest in some experiments (Fig. 23A, B). However, we also observed

that the degree of Gal3 depletion by siRNA was variable between experiments, and that the

degree of Gal3 depletion correlated to the degree of reduction in -syn release observed (Fig.

23C). In follow-up, mDA neurons were treated with the Gal3 inhibitor, TD139. TD139 treatment

significantly reduced α-syn secretion compared to vehicle (Fig. 23D). Similarly, CRISPR-Cas9-

mediated Gal3 knockout (KO) in SH-SY5Y DSP-α-syn cells significantly decreased DSP--syn

secretion (Fig. 23E). Corresponding measurements of the lysates revealed that DSP--syn levels

were significantly greater in the SH-SY5Y DSP-α-syn Gal3 KO cells compared to that observed

in the lysates of the control KO cells (Fig. 23F), suggesting the change in α-syn secretion was not

driven by differences in relative α-syn levels. No differences in cell viability were observed in

these cells (Fig. 23G). In a reciprocal fashion, we also asked if Gal3 over-expression influences

endogenous α-syn secretion we generated a lentiviral HA-Firefly Gal3 (FLuc Gal3) construct.

Next, human mDA neurons were dually transduced with FLuc Gal3 and RLuc (to control for

transduction efficiency) and then endogenous α-syn release was evaluated. FLuc Gal3 over-

expression increased α-syn secretion (Fig. 23H). Conversely, FLuc Gal3 transduced mDA

lysates had significantly less α-syn when measured by α-syn ELISA (Fig. 23I). Moreover, when

the transduced human mDA neurons were treated with α-syn fibrils, increased FLuc Gal3 was

detected in the cultured media (Fig. 23J). Yet, the RLuc signal was comparable among the FLuc

Gal3 cells regardless of treatment, suggesting comparable transduction efficiency (Fig. 23K). An

analysis of the lysate revealed the FLuc construct was not undergoing cleavage (Fig. 24A), and

that cell viability was comparable among the empty vector control and the FLuc Gal3 cells (Fig.

24B). Finally, when wild type (WT) mDA neurons were treated with α-syn fibrils, significantly

increased Gal3 secretion was also observed in the cultured media when measured by ELISA

166

(Fig. 24C). These results indicate a reciprocal relationship between Gal3 and α-syn secretion.

To better understand the mechanism by which -syn release is regulated by Gal3, we

Figure 24: Neuronal Depletion of Trim16 or ATG16L1 Influences α-Syn and Gal3 Secretion. (A)

Representative western blot from FLuc Gal3 transduced mDA neurons probed against HA-tagged FLuc-Gal3

construct and endogenous Gal3 observed at 31 kDA. (B) Cell viability of EV control and FLuc Gal3 transduced

mDA neurons 96 hours post-transduction. (C) Fold difference in Gal3 present in the cultured media from the

untreated control or α-syn fibrils treated mDA neurons after 24 hours. (D) Relative fold difference in α-syn (left)

and Gal3 (middle) in the cultured media of mDA neuronal cultures at 24 to 48 hours post-transfection, relative

depletion of Trim16 (right), and representative KD (inset). Values were determined by ELISA and normalized to

mean control. The different colors represent matched replicates. (E) Correlative plot of data shown in (D)

comparing relative Trim16 depletion (X-axis) and mean fold α-syn secretion (E, left) or Gal3 secretion (E, right)

(Y-axis). (F) Relative fold difference in α-syn (left) and Gal3 (middle) in the cultured media of mDA neuronal

cultures at 24 to 72 hours post-transfection, relative depletion of ATG16L1 (right), and representative KD (inset).

(G) Correlative plot of data shown in (D) comparing relative Trim16 depletion (X-axis) and mean fold α-syn

secretion (F, left) or Gal3 secretion (F, right) (Y-axis). (H) Cell viability of control, Gal3, Trim16, or ATG16L1

siRNA transfected mDA neurons 48-72 hours post-transfection. Cell viability measured by LDH assay (n =3). All

data are expressed as M ± SE (B, n = 3; C, n = 6; D, E n = 6 (left), n = 5 (middle), n = 6 (right); F, G n = 8; H, n =

3). Statistical significance was determined by a Pearson correlation with two tailed t-test (E, G), a paired t-test (B),

ratio paired t-test (C, D, and F) to better account for the distribution of values <1, or one-way ANOVA (H). For all

statistical tests *, **, p < 0.05, and 0.01, respectively.

167

depleted human mDA neuron cultures of Trim16 and ATG16L1. Previous work suggests that

Gal3 recruits autophagic machinery through Trim16 via ULK1 when bound to damaged

endosomes and/or lysosomes [71, 226, 234]. This process was shown to be mediated by

connections with the autophagic protein ATG16L1 to clear damaged endosomes and/or

lysosomes [71, 226, 234]. Trim16 depletion significantly decreased the level of both α-syn and

Gal3 in the cultured media (Fig. 24D). When the relative levels of Trim16 depletion were

compared following Trim16 siRNA treatment, a consistent trend in Trim16 depletion relative to

α-syn and Gal3 ELISA data was observed (Fig. 24E). Similarly, following successful

knockdown ATG16L1, reduced secretion of both α-syn and Gal3 occurred in mDA neurons (Fig.

24F). The relative levels of ATG16L1 depletion also trended with the relative levels of α-syn and

Gal3 (Fig. 24G). No differences in cell viability were observed in response to Trim16,

ATG16L1, or Gal3 depletion in mDA neurons (Fig. 24H), suggesting that changes in secretion

were not the result of cell death. Collectively, these results indicate that the secretion of both -

syn and Gal3 are regulated by Trim16 and ATG16L1.

A previous study found that both tank binding kinase 1 (TBK1) and optineurin (OPTN)

were shown to be recruited to Gal3+ compartments in microglia following α-syn fibril treatment

[56]. Interestingly, when cells were treated with α-syn fibrils following specific inhibition of

TBK1, reduced levels of LC3 puncta formed but Gal3’s ability to recognized damaged

endosomal and/or lysosomal compartments remained intact. These findings suggest that TBK1

inhibition impairs the recruitment of endosomal and/or lysosomal compartments damaged by

treatment with α-syn fibrils. Given these findings, we also investigated if OPTN or TBK1

affected α-syn or Gal3 secretion in the DSP--syn SH-SY5Y cells.

168

Following the depletion of OPTN or TBK1 by siRNA treatment (Fig. 25A), reduced levels of

secreted DSP--syn and Gal3 occurred (Fig. 25B, C). No difference in cell viability was

observed among the control, OPTN, or TBK1 siRNA treated cells (Fig. 25D). These data further

suggest that impaired recruitment of autophagic proteins to damaged endosomal and/or

lysosomal compartments may affect the secretion of α-syn or Gal3 via an autophagic

mechanism.

α-Syn Fibrils Increase the Association of Trim16 and ATG16L1 with mCherry Gal3.

To determine if Trim16 and ATG16L1 were being recruited to membrane bound Gal3

following α-syn fibril induced endosomal and/or lysosomal damage, we measured the change in

Figure 25: TBK1 and Optineurin Knockdown Reduces DSP-α-Syn and Gal3 Secretion in DSP-α-Syn SH-

SY5Y Cells. (A) Representative depletion following TBK1 or Optineurin siRNA treatment in DSP-α-syn SH-

SY5Y cells 72 hours post-transfection. (B) RLUs of RLuc activity or (C) Level of Gal3 from SH-SY5Y DSP-α-

syn cell-line conditioned cultured media collected from cells 48 to 72 hours post-transfection measured by Renilla

luciferase assay or Gal3 sandwich ELISA (B, n = 4; C, n = 8). Different colors represent matched replicates. (D)

Cell viability of control, TBK1, or Optineurin siRNA transfected SH-SY5Y DSP-α-syn cells 72 hours post-

transfection (n = 4). Data are expressed as M ± SE. Statistical significance was determined by paired one-way

ANOVA with Dunnett’s post hoc tests (B, C) or Tukey’s post hoc tests (D). *, p < 0.05.

169

co-localization of Trim16 with Gal3, LC3, α-syn fibrils and CD63 in

response to α-syn fibril treatment. Similarly, the co-localization of

ATG16L1 with α-syn fibrils and Gal3 was measured following α-syn

fibril treatment. To test this, mCherry Gal3 transduced SH-SY5Y cells

were utilized. Following validation that our mCherry Gal3 construct

was not undergoing cleavage (Fig. 26), mCherry Gal3 SH-SY5Y cells

were treated with α-syn fibrils. Following treatment with fluorescently

labelled α-syn fibrils, immunocytochemistry, and microscopy of

labelled Trim16 and ATG16L1 was performed to determine the extent

to which Trim16 or ATG16L1 puncta co-localized with the various markers of interest (Fig.

27A-D). Following treatment, a significant degree of co-localization between α-syn fibrils and

Trim16 (Fig. 27E) and α-syn fibrils and ATG16L1 (Fig. 27I) was observed. Additionally, α-syn

fibril treatment also significantly increased mCherry Gal3 intensity within Trim16 (Fig. 27F) and

ATG16L1 (Fig. 27J) puncta, respectively. Furthermore, when mCherry Gal3 cells were treated

with unlabeled α-syn fibrils, co-stained for Trim16 and the autophagosome marker LC3B or

Trim16 and the multivesicular body marker CD63, an increased LC3B or CD63 fluorescence

signal was observed within Trim16 puncta (Fig. 27G, H). Collectively, these data support the

premise that following endosomal and /or lysosomal membrane damage induced by α-syn fibrils,

Gal3, Trim16, and ATG16L1 result in the recruitment of autophagic machinery and the

formation of autophagosomes.

Figure 26: Stabile

mCherry Gal3

Expression in SH-SY5Y

Cells. Representative

western blot from WT and

mCherry Gal3 SH-SY5Y

against mCherry.

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Figure 27: α-Syn Fibril Treatment Increases the Co-localization of Trim16 and ATG16L1 with

mCherry Gal3 and α-Syn Fibrils. Representative images from mCherry Gal3 SH-SY5Y cells treated with

α-syn fibrils and stained for Trim16 and LC3B (B), Trim16 and CD63 (C), or ATTO 647 labelled α-syn

Fibrils and stained for Trim16 (A) or ATG16L1 (D). The white arrows point to instances of triple co-

localization. (E-J) α-syn fibrils (A, D, E, I) mCherry Gal3 (A, B, D, F, J), LC3B (B, G) or CD63 (C, H)

within an Imaris masking algorithm built around Trim16 (A-C, E-H) or ATG16L1 puncta (D, J). Each data

point represents the averaged maximum intensity from the total masked puncta in an individual image. The

same algorithm was applied to all experiments and treatment conditions. Intensity data are expressed as M ±

SE (n = 3 independent experiments, 7-10 images per experiment). Statistical significance was determined by

unpaired t-tests. For all statistical tests *, **, ***, ****, p < 0.05, 0.01, 0.001, and 0.0001, respectively.

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Depletion of Gal3 and Trim16 in MDA Neurons Reduces the Recruitment of α-Syn Fibrils

to Autophagic Compartments.

Given our previous findings in the mCherry Gal3 SH-SY5Y cells, we next evaluated if

the depletion of Gal3 or Trim16 would affect α-syn fibrils recruitment to autophagic

compartments in mDA neurons. To test this, mDA neuronal cultures were transduced with

inducible yellow fluorescent protein-LC3B (YFP-LC3B), transfected with control, Gal3, or

Trim16 siRNA, then fluorescently labeled α-syn fibrils were added to the cells. Afterwards, the

cells were fixed, stained for Gal3, and imaged (Fig. 28A). Depletion of Gal3 or Trim16

significantly decreased the number of YFP-LC3B puncta per image.

Figure 28: Gal3 and Trim16 Knockdown in MDA Neurons Reduces the Recruitment of α-Syn Fibrils to

Autophagosomes. (A) Representative images of induced YFP-LC3 (green) transduced mDA neuronal cultures

treated with α-syn fibrils (blue) and stained for endogenous Gal3 (red) shown at the same intensities for each of

the channels for each condition. YFP-LC3 induction was initiated 24 hours post-transfection. α-Syn fibrils

treatment was done 48 hours post-transfection. Staining was conducted 72 hours post-transfection. (B)

Quantification of the number of YFP-LC3 puncta recognized by a masking algorithm among images for the

indicated siRNA pretreated condition. Quantification of the degree of masked YFP-LC3 puncta co-localized

with α-syn fibrils puncta (C) and Gal3 puncta (D). Each data point is the mean of 3 randomly selected

coverslips (n = 15-20 images per coverslip). Data are expressed as M ± SE. Statistical significance was

determined by one-way ANOVA with Tukey’s post hoc tests. For all statistical tests *, **, ***, ****, p < 0.05,

0.01, 0.001, and 0.0001, respectively.

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This finding suggests that Gal3 or Trim16 knockdown decreased the number of autophagosomes

(Fig. 28B). In addition, Gal3 or Trim16 knockdown decreased the degree of co-localization

between LC3B puncta and α-syn fibril puncta (Fig. 28C) and the degree of co-localization

between LC3B puncta and Gal3 puncta (Fig. 28D). Taken together, these results suggest that

initial depletion of Gal3 or Trim16 in mDA neurons reduces the recruitment of α-syn fibrils to

the autophagic pathway.

Gal3 Depletion Impairs Lysosomal Degradative Function and Reduces Autophagic Flux

During α-Syn Fibril Treatment.

Gal3 was recently shown to be recruited to lysosomes in response to membrane stress

that precedes full-out rupture, whereby it recruits ESCRT proteins to facilitate membrane repair

and maintain lysosomal acidification [234, 458]. However, during lysosomal rupture Gal3

switches to recruit Trim16 and other autophagic machinery [234, 458]. Based on these reports,

we investigated how changes in lysosomal acidification were affected by Gal3 depletion and

within the context of α-syn fibril treatment as a function of cathepsin B activity. To test this,

human mDA neuronal cultures were transfected with control or Gal3 siRNA followed by α-syn

fibril treatment. Afterwards, cathepsin B activity was measured to evaluate lysosomal

acidification based on the fluorescent Magic Red signal. To determine the relative lysosomal

contribution, the cells were treated with either vehicle, to assess total cellular levels of cathepsin

B activity, or with Baf-A1, to inhibit lysosome acidification, to assess the non-lysosomal fraction

of activity. Fluorescence intensity was then measured every 15 minutes for 4 hours (Fig. 29A,

B), the area under the curve (AUC) was integrated for each of the treatments, and the lysosomal

contribution was calculated by subtracting the AUC for the vehicle treated condition from the

AUC for the treatment condition (Fig. 29C). Gal3 depletion or treatment with α-syn fibrils

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decreased lysosomal degradative activity. There was no significant difference in the degree of

lysosomal degradative activity for mDA neurons that received a Gal3 KD and were treated with

α-syn fibrils compared to the mDA neurons that were Gal3 depleted or were treated with α-syn

fibrils alone (Fig. 29C).

Figure 29: Gal3 Depletion or α-Syn Fibrils Treatment Impairs Lysosome Function. (A, B) Lysosomal

dysfunction assay shows the normalized relative fluorescence units (RFU) from mDA neuronal cultures loaded

with Magic Red cathepsin B dye. Data from A and B are from the same experiments; the data is displayed in 2

graphs to increase clarity. RFU normalization was calculated from the endpoint of the control siRNA plus vehicle

(0.1% DMSO) condition. Validation of KD shown in Figure 30 and conducted as part of the same experiments. (C)

Quantification of the area under the curve for each stated condition. The vehicle minus Baf-A1 was used to

determine lysosomal activity. (D) Cell viability of Control and Gal3 siRNA transfected mDA neurons subsequently

treated with α-syn Fibrils or left untreated 48 hours post-transfection (n = 3). (E) Lysosomal dysfunction assay

shows the normalized relative fluorescence units (RFU) from SH-SY5Y DSP-α-syn cells loaded with Magic Red

cathepsin B dye. RFU normalization was calculated from the endpoint of the SH-SY5Y DSP-α-syn vehicle (0.1%

DMSO) treated condition. (F) Quantification of the AUC for each stated condition. The vehicle minus Baf-A1 was

used to determine lysosomal activity (n = 3). Data are expressed as M ± SE (n = 3). Statistical significance was

determined by paired two-tailed t-tests (F) or one-way ANOVA (C and D) with a Dunnett’s post-hoc test. For all

statistical tests *, **, ***, p < 0.05, 0.01, and 0.001, respectively.

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The degree of cell viability of the nondepleted and Gal3 depleted mDA neurons during α-syn

treatment was comparable to the degree of cell viability of untreated mDA neurons (Fig. 29D).

Similarly, when SH-SY5Y DSP-α-syn cells treated with the Gal3 inhibitor, TD139, and then

subjected to the same assay, significantly reduced lysosomal degradative activity relative to

those treated with vehicle was observed (Fig. 29E, F).

In follow up, the corresponding previously used mDA neurons were lysed to evaluate

autophagic flux by western blot. This was performed by comparing the relative levels of p62 and

LC3-II among the vehicle and Baf-A1 treated cells with respect to the other conditions (Fig.

30A). The levels of lysosome inhibited cells to the levels of lysosome uninhibited cells were

compared to determine if changes in autophagosomes in response to Gal3 depletion or α-syn

fibril treatment are the result of upregulated autophagy or reduced autophagic clearance.

Although Baf-A1 treatment significantly increased p62 levels in control siRNA treated cells, it

had no effect on the p62 levels in any of the other conditions. Treatment with either α-syn fibrils

or α-syn fibrils and Baf-A1 had no effect on p62 levels (Fig. 30B). Notably, following α-syn

fibril treatment in both Gal3 depleted and non-depleted cells, p62 levels were more variable, with

varying degrees of higher molecular weight bands observed. Both Baf-A1 and α-syn fibrils

increased LC3-II, while Gal3 KD decreased LC3-II (Fig. 30C). While it trended toward an

increase, no significant difference in LC3-II was observed in Baf-A1 or α-syn fibrils among Gal3

depleted mDA neurons. LC3-II levels were significantly greater for control siRNA treated than

Gal3 siRNA treated mDA neurons after Baf-A1 treatment (Fig. 30C). LC3-II levels were

significantly greater for mDA neurons co-treated with Baf-A1 and α-syn fibrils compared to

mDA neurons treated with α-syn fibrils only (Fig. 30C). In mDA neurons in which Gal3 was

depleted, there was no difference in LC3-II levels for those treated withα-syn fibrils and those

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co-treated with Baf-A1 and α-syn fibrils. No differences in LC3-II levels were observed between

Gal3 depleted mDA neurons and nondepleted neurons treated with α-syn fibrils and Gal3

depleted mDA neurons and nondepleted neurons co-treated with Baf-A1 and α-syn fibrils (Fig.

30C). Although treatment of mDA neurons with α-syn fibrils increased Gal3 levels, no other

between groups differences were observed (Fig. 30D). Finally, Gal3 levels were significantly

increased in the control siRNA α-syn fibrils treated cells compared to vehicle (Fig. 30D).

Collectively, these results suggest that Gal3 plays a role in governing lysosome function under

Figure 30: Gal3 Depletion Reduces Autophagic Flux During α-Syn Fibrils Treatment. Data shown

conducted in tandem with Data shown in Figure 38. (A) Representative western blot demonstrates p62, Gal3,

LC3-I, LC3-II, and GAPDH for the stated condition. (B-D) Quantification of western blot band intensity of

p62 (B), LC3-II (C), and Gal3 (D). The data given in A-C was from 4 independent experiments. (B-D)

Western blot quantification for each of the stated proteins and conditions whereby band intensity was

normalized to GAPDH. These values were then normalized to the mean control siRNA vehicle condition.

Data are expressed as M ± SE. Statistical significance determined by paired one-way ANOVA t-tests, with

Dunnett’s post hoc tests following natural log transformation to better account for the distribution of values

<1. For all statistical tests *, **, ***, p < 0.05, 0.01, and 0.001, respectively.

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basal conditions and that α-syn fibril

treatment disrupts lysosome function and

increases the cellular autophagy response

in mDA neurons. Additionally, based on

the changes in LC3-II levels during our

autophagic flux assay among the Gal3

depleted conditions, Gal3 depleted cells

may have impaired autophagic function

based on previously defined criteria [551].

Lysosomal Damage Increases α-Syn and

Gal3 Secretion in Association with

Increased Autophagic Activity.

Recent papers have reported that

the galectins are recruited to damaged

lysosomes, where they coordinate an autophagic response [71, 75, 232, 234, 250, 265].

Lysosomal damage has been reported to trigger the unconventional secretion of specific proteins

[250]. Furthermore, Gal3 depletion reduced the unconventional secretion of several

unconventionally secreted proteins, such as interleukin 1beta (IL-1β) and interleukin 6 [452,

504]. Additionally, Trim16 was found to be integral for IL-1β secretion in response to lysosomal

damage [250]. However, in the previous study [250], Gal8 but not Gal3 participated in the

induction of IL-1β release. It is known that Gal3 and Gal8 both bind to exposed glycans on the

intraluminal membrane of damaged lysosomes. However, differences in relative Gal3 and Gal8

expression were shown to influence the cellular response to Group A Streptococcus-induced

Figure 31: Relative Gal3 and Gal8 Expression in DSP-

α-Syn and MDA Neurons (A) Representative western

blot from WT and DSP-α-syn SH-SY5Y or (B) mDA

neuronal lysates against Gal8 and Gal3. (C) Quantitative

RT-PCR from mDA neurons under basal conditions.

ACTG1 and NDUFB1 were used as house-keeping genes

to normalize Gal3 and Gal8 expression (n = 3). Data are

expressed as M ± SE. Statistical significance was

determined by paired two-tailed t-tests (C). *, p < 0.05.

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lysosomal membrane damage [80]. Based on these differences, we evaluated the relative levels

of Gal3 and Gal8 in WT and DSP-α-syn SH-SY5Y cells as well as in mDA neurons lysates by

western blot. In the WT SH-SY5Y cell lysate, both the 40.3k Da (long) and 35.8 kDa (short)

isoforms of Gal8 [36] appeared to be more intense than the Gal3 band (Fig. 31A). However,

following the over-expression of the DSP-α-syn constructs, reduced levels of both Gal8 isoforms

and increased Gal3 levels were observed (Fig. 31A). Among the WT and DSP-α-syn SH-SY5Y

cells, the mDA neurons expression resembled the DSP-α-syn SH-SY5Y cells, with

corresponding lower levels of Gal8 and higher levels of Gal3 (Fig. 31B). In follow up, evaluation

of relative Gal3 and Gal8 expression in mDA neurons was determined by RT-PCR, which

revealed that a significantly greater amount of Gal3 mRNA was present compared to Gal8

mRNA (Fig. 31C). Taken together, this led us to hypothesize that lysosomal damage triggers

increased α-syn secretion and that Gal3 and Trim16 may be involved in coordinating this

response.

To determine if lysosomal damage influenced the secretion of α-syn and/or Gal3, human

mDA neurons were treated with LLOME then the cultured media was analyzed. LLOME

treatment of mDA neurons significantly increased extracellular levels of both α-syn and Gal3

(Fig. 32A, B). Similarly, LLOME treatment also increased the secretion of DSP-α-syn from SH-

SY5Y DSP-α-syn cells (Fig. 32C). To evaluate if Gal3 or Trim16 mediated this process, α-syn

secretion was measured in response to LLOME treatment in mDA neuronal cultures first

depleted of either Gal3 or Trim16. Western blot analysis of Gal3 or Trim16 levels demonstrated

successful Gal3 or Trim16 knockdown (Fig. 32F, H and Fig. 33A, C). Treatment of mDA

neurons with LLOME increased α-syn secretion in control siRNA, Gal3 siRNA, and Trim16

siRNA treated cells (Fig. 32D, E).

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Figure 32: Lysosomal Damage Increases α-Syn and Gal3 Secretion in Association with Increased Autophagic

Activity. Relative fold difference in α-syn (A) and Gal3 (B) in the cultured media of mDA cultures 4 hours after

vehicle (0.1% DMSO) or LLOME treatment measured by ELISA and normalized to mean vehicle (D) RLUs of RLuc

activity from vehicle (0.1% DMSO) or LLOME treated DSP-α-syn SH-SY5Y cells after 4 hours (n = 4). (D, E)

Relative fold difference in α-syn from cultured media from either control and Gal3 (D) or control and Trim16 (E)

siRNA transfected mDA neurons followed by 4 hours of vehicle or LLOME treatment. Vehicle or LLOME treatment

was performed 72 hours post control or Gal3 siRNA transfection, or 48 hours post control or Trim16 siRNA

transfection. (F, H) Representative mDA neuronal lysate western blots demonstrating phosphorylated (pS757) ULK1,

total(t) ULK1, phosphorylated (pT389) P70 S6K1, total (t)P70 S6K1, Gal3 (H) as well as successful knockdown of

Gal3 (F) or Trim16 (H) from the same experiments shown in (D and E). (G, I) Quantification of western blot relative

band intensities to control siRNA and normalized to GAPDH. (J) Cell viability of control, Gal3, or Trim16 siRNA

transfected mDA neurons 72- or 48- hours post-transfection, subsequently treated with vehicle or LLOME for 4 hours.

Data are expressed as M ± SE. Statistical significance was determined by paired two-tailed t-tests (A-C) or one-way

ANOVA with Dunnett’s post hoc tests (D-E, G, I, J) following natural log transformation to better account for the

distribution of values <1. For all statistical tests *, **, ***, p < 0.05, 0.01, and 0.001, respectively.

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However, the difference in the control siRNA treated cells was significantly greater than that

observed for the Gal3 KD or Trim16 KD mDA neurons treated with LLOME (Fig. 32F, H). No

difference in cell viability was observed among the vehicle and LLOME treated control, Gal3, or

Trim16 depleted cells (Fig. 32J). Thus, the lysosomal damage-induced increase in α-syn

secretion is dependent on Gal3 or Trim16 in human mDA neuronal cultures.

Next, we assessed the autophagic activation state in response to LLOME in Gal3 and

Trim16 depleted mDA neurons. Gal3 or Trim16 depletion did not influence baseline levels of

pS757 ULK1 to total ULK1 (tULK1) or pT389 P70 S6K1 to total p70 S6K1 (tP70 S6K1) (Fig.

32F-I). Additionally, the change in pS757 ULK1 or pT389 P70 S6K1 levels in response to

LLOME treatment was not different for the mDA neurons that had undergone prior Gal3 or

Trim16 siRNA treatment compared to those mDA neurons that had received control siRNA (Fig.

32F-I). No difference in cellular Gal3 levels were observed in control and Trim16 siRNA cells in

both the vehicle or LLOME treatment conditions (Fig. 32I), consistent with a previous report in

Trim16 depleted human bone marrow-derived mesenchymal stem cells [75]. No difference in

cell viability was observed among the vehicle and LLOME treated control, Gal3, or Trim16

depleted cells (Fig. 32J).

Similarly, evaluation of Lamp1 in the Gal3 or Trim16 depleted cells was not significantly

different for the LLOME-treated mDA neurons compared to Lamp1 observed in the mDA

neurons that had received the corresponding control siRNA treatment (Fig. 33A-D). Evaluation

of autophagosome levels revealed that Gal3 KD significantly decreased LC3-II levels in the

vehicle-treated cells (Fig. 33B); Trim16 KD had no effect on LC3-II levels (Fig. 33D). Whereas

LLOME treatment increased LC3-II levels, Gal3 KD slightly but significantly decreased LC3-II

levels while Trim16 KD did not affect the LLOME-induced increase in LC3-II levels (Fig. 33A,

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D). Notably, the changes in LC3-II levels among the Gal3 and Trim16 depleted mDA neurons in

response to either vehicle or LLOME suggest the occurrence of abnormalities in autophagosome

levels. Collectively, they indicate that in human mDA neurons: (1) Normal, steady state levels of

autophagosomes are dependent on the degree of Gal3 expression; (2) Normal, steady state levels

of autophagosomes are not dependent on the degree of Trim 16 expression; and (3) The

upregulation of autophagy and

autophagosomes that occurs in

response to lysosomal damage is

not dependent on either Gal3 or

Trim16, but they are likely

involved in autophagosome

turnover.

Recent work

demonstrates that in response to

lysosomal membrane damage,

mTOR inactivation subsequently

results in increased transcription

factor EB (TFEB) nuclear

translocation [231, 232, 234].

Following nuclear translocation,

TFEB binds to the coordinated lysosomal expression and regulation (CLEAR) gene network

comprised of lysosomal and lysosome-associated processes genes which include autophagy,

endocytosis, and exocytosis [363]. To further confirm our previous findings from Gal3 depleted

Figure 33: Lysosomal Damage Affects Autophagosome

Formation in Gal3 or Trim16 Depleted MDA Neurons. (A, C)

Representative mDA neuronal lysate western blots demonstrating

Lamp1 and LC3-I and LC3-II levels, as well as successful

knockdown of Gal3 (A) or Trim16 (C) from the same experiments

shown in Figure 32. (B, D) Quantification of western blot relative

band intensities to control siRNA and normalized to GAPDH. All

data are expressed as M ± SE (A-B, n = 3; C-D, n = 4). Statistical

significance was determined by a paired one-way ANOVA with

Dunnett’s post hoc tests following natural log transformation to

better account for the distribution of values <1. For all statistical

tests *, p < 0.05.

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mDA neurons, CRISPR-Cas9 control and Gal3 KO SH-SY5Y cells were assessed for changes in

the nuclear translocation of TFEB, following LLOME or α-syn fibrils treatment by

immunofluorescence microscopy (Fig. 34A). Validation of the CRISPR-Cas9 mediated knockout

was first confirmed by western blot (Fig. 34B). The mean fluorescence signal of endogenous

TFEB within the nucleus was significantly increased in response to LLOME or α-syn fibril

treatment in comparison to vehicle in both the control and Gal3 KO cell lines, respectively (Fig.

34C). Interestingly, the relative nuclear TFEB signal was significantly higher in the Gal3 KO

cells compared to the control KO cells under all treatment conditions (Fig. 34C).

Figure 34: TFEB Translocation and Transcription is Maintained in Gal3 KO SH-SY5Y Cells. (A)

Representative images from Control and Gal3 KO SH-SY5Y cells treated with vehicle or LLOME for 4

hours, or α-syn Fibrils for 24 hours. The same end point was used for conditions. (B) Representative western

blot from control or Gal3 CRISPR-Cas9 mediated KO SH-SY5Y cell lysates following heterogenous

selection. (C) Quantification of mean nuclear TFEB signal measured by Imaris masking algorithm built

around the DAPI signal. The same algorithm was applied to all experiments and treatment conditions (n = 3

independent experiments, 7-10 images per experiment). (D) Relative Firefly luciferase in cell lysates from 4x

CLEAR-Firefly luciferase reporter construct transfected Control and Gal3 KO SH-SY5Y cells. 48 hours post-

transfection, Control and Gal3 KO SH-SY5Y cells were treated with vehicle, rapamycin, or LLOME for 4

hours (left; vehicle and LLOME, n = 16; rapamycin control KO, n =8; rapamycin Gal3 KO, n =6), or control

untreated or α-syn Fibrils for 24 hours (right, n = 5). Data are from 3 independent experiments with multiple

biological replicates. All data are expressed as M ± SE. Statistical significance was determined by two-way

ANOVA with Sidak’s multiple comparisons post hoc tests.

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To determine whether the translocation of TFEB to the nucleus was increasing protein

expression of CLEAR genes, control and Gal3 KO cells were transfected with the previously

characterized CLEAR driven Firefly luciferase (FLuc) reporter construct [98]. Consistent with

the observed changes in nuclear translocation, increased FLuc expression was observed in

response to rapamycin and LLOME relative to vehicle treatments and in α-syn fibrils relative to

control treatments for both the control and Gal3 KO cells, respectively (Fig. 34D). Moreover,

significantly increased levels of FLuc expression in the Gal3 KO cells was observed among all

treatment conditions when compared to their control KO counterpart (Fig. 34D). These results

agree with previous works, where the depletion of Gal3 [234] or Trim16 [71, 226] increased

TFEB translocation in response to lysosomal membrane damage yet lysosomal quality was

lower.

Gal3 Affects Endo/Lysosomal Membrane Damage Induced Amphisome Formation.

Previous work suggests that autophagy may modulate the secretion of EVs and α-syn

through the formation of amphisomes. Amphisomes are a hybrid organelle that forms through

the fusion of autophagosomes and MVBs [91]. Thus, upon fusion with the plasma-membrane,

amphisomes can release EVs as well as engulfed autophagosome content into the extracellular

space. Indeed, previous work showed that Baf-A1 associated autophagic impairment increased

the release of α-syn associated EVs and the autophagy protein LC3 [340]. I kappa β kinase was

shown to mediate the secretion of EVs containing LC3 through the formation of amphisomes

[377]. Trim16 has previously been linked to autophagy mediated secretion of other

unconventionally secreted proteins [250, 561]. Based on these previous studies, the formation of

amphisomes can be determined based on the association of the MVB marker, CD63, with the

autophagosome marker, LC3B [76, 218, 425]. Notably, CD63’s high association with MVBs

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also makes it a readily used identifier for exosomes when found extracellularly [10, 218, 536].

To this end, we assessed whether LLOME or α-syn fibrils affected the co-localization of LC3B

with CD63 by immunofluorescence staining and microscopy (Fig. 35A). We also measured to

what degree LC3B co-localized with DSP-α-syn following LLOME or α-syn fibril treatment.

Treatment with LLOME or α-syn fibrils significantly increased levels of LC3B puncta relative to

vehicle (Fig. 35B). Additionally, both LLOME and α-syn fibrils significantly increased DSP-α-

syn intensity as well as CD63 intensity in LC3B puncta (Fig. 35C, D).

Collectively, this suggests that following endo-lysosomal membrane damage, increased

amphisomes form, and that these compartments have increased levels of DSP-α-syn. Based on

these findings, we next measured the co-localization of LC3B with CD63 in our previously used

CRISPR-Cas9 control and Gal3 KO SH-SY5Y cells (Fig. 31B). The formation of amphisomes

was evaluated in the control and Gal3 KO cells in response to vehicle, LLOME, and

Figure 35 Lysosomal Rupture Increases the Formation of Amphisomes. (A) Representative images from

DSP-α-syn SH-SY5Y cells treated vehicle or LLOME for 4 hours, or α-syn Fibrils for 24 hours, and

subsequently stained for LC3B and CD63. The same end point was used for all conditions. Arrows depict

instances of triple+ events among treatment conditions. (B) Quantification of the number of LC3B puncta

recognized by a masking algorithm among images. Quantification of DSP-α-syn (C) or CD63 (D) intensity in

masked LC3B puncta. Each data points represents the averaged max intensity from the total masked puncta in

an individual image. The same algorithm was applied to all experiments and treatment conditions. Intensity data

are expressed as M ± SE (Data pooled from n = 3 independent experiments, 7-10 images per experiment).

Statistical significance was determined by one-way ANOVA with Dunnett’s post hoc tests. For all statistical

tests *, **, ***, ****, p < 0.05, 0.01, 0.001, and 0.0001, respectively.

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fluorescently labelled α-syn fibril treatment, stained for LC3B and CD63, and analyzed by

microscopy (Fig. 36A). Significantly increased levels of LC3B puncta were observed in the

control KO in response to LLOME and α-syn fibril treatment compared to the respective

condition in the Gal3 KO cells (Fig. 36B).

No change in the number of LC3B puncta was observed in response to either treatment in the

Gal3 KO cells. Similarly, CD63 intensity within LC3B puncta was significantly greater in the

Figure 36: Gal3 KO Reduces Lysosome Rupture Induced Amphisome Formation. (A) Representative

images from Control and Gal3 KO SH-SY5Y cells treated with vehicle or LLOME for 4 hours, or Atto 550

labelled α-syn Fibrils for 24 hours, and subsequently stained for LC3B, CD63, and Gal3. The same end point

was used for conditions. Arrows depict instances of triple+ events among treatment conditions. (B)

Quantification of the number of LC3B puncta recognized by a masking algorithm among images.

Quantification of Gal3 (C), CD63 (D), or α-syn Fibrils (E) intensity in masked LC3B puncta. Each data points

represents the averaged max intensity from the total masked puncta in an individual image. The same algorithm

was applied to all experiments and treatment conditions. (F) Relative fluorescence units of Atto 550 labelled α-

syn Fibrils at 0 hours and after media change at 24 hours. Fluorescence signal of the total coverslip was read on

a plate reader set to (λ = EX: 554, EM: 575). Data are expressed as M ± SE (A-E, Data pooled from n = 5

independent experiments, 7-10 images per experiment), (F, n = 3). Statistical significance was determined by

unpaired t-test (C, E), paired two-tailed t-test (F) or one-way ANOVA with Dunnet’s post hoc tests (B, D). For

all statistical tests *, **, ***, ****, p < 0.05, 0.01, 0.001, and 0.0001, respectively.

185

control KO cells when compared to the Gal3 KO cells under vehicle, LLOME, and α-syn fibril

treatment conditions (Fig. 36D). No significant change in CD63 intensity within LC3B puncta

was observed in the Gal3 KO cells (Fig. 36D). Following LLOME treatment, increased Gal3

intensity in LC3B puncta was observed in the control KO cells (Fig. 36C). Furthermore,

significantly increased α-syn fibril intensity was observed in LC3B puncta in the control KO

cells relative to the Gal3 KO cells (Fig. 36E). In whole, these data suggest that Gal3 depletion

reduces the formation of autophagosomes, the recruitment of α-syn fibrils to autophagosomes,

and the formation of CD63-LC3B positive compartments consistent with amphisomes. To

validate that Gal3 KO did not affect these observed differences by affecting α-syn fibril uptake,

the fluorescence intensities of the α-syn fibrils was measured using a plate reader immediately

after treatment and 24 hours later, before the cells were fixed in preparation for imaging. No

significant difference was observed between the control and Gal3 KO cells at either time point

(Fig. 36F).

Gal3 is Readily Released in α-Syn Associated EVs.

Since the results of previous experiments suggested that Gal3 and Trim16 are associated

with specific recruitment of exogenous fibrillar α-syn to the secretory autophagic pathway, that

Gal3 and Trim16 depletion influence mDA neuronal secretion of endogenous α-syn, and that

Gal3, α-syn, and LC3B were secreted in association with EVs released from the DSP-α-syn SH-

SY5Y cell line, we rationalized that Gal3 may affect α-syn associated EVs released from mDA

neurons via this autophagic mechanism. To this end, EVs from mDA neurons were first

evaluated to determine if Gal3 associates with the EVs released from mDA neurons. EVs from

the cultured media of mDA neurons were concentrated by differential ultracentrifugation,

subsequently labelled for CD63 (10 nm), Gal3 (15 nm), and α-syn (5 nm) by immunogold, and

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TEM was performed (Fig. 37A).

Figure 37: Gal3 Inhibition Affects the Composition of Secreted Extracellular Vesicles from MDA

Neurons (A) TEM from concentrated WT mDA culture media following immunogold labeling.

Representative image shows α-syn+ (5 nm), CD63+ (10 nm), and Gal3+ (15 nm) triple positive EV. (B)

NTA analysis of EVs from WT mDA neurons following differential ultracentrifugation. EV sizes binned in

1nm increments with a bin center of 0.5nm. NTA of resuspended mDA EVs were performed in technical

quintuplicates, denoted as different shades of green, blue, or red for 3 independent samples. Each sample was

from distinct mDA differentiation. (C) Representative z-stack from a constructed z-stack maximum intensity

projection obtained from concentrated extracellular vesicles from mDA neuron cultured media. Concentrated

EVs were stained for Gal3, α-syn, and combined pool of mouse anti-tetraspanin antibodies against CD9,

CD63, and CD81 referred to as tetraspanin. Processed immunofluorescence with only the Gal3 primary

antibody and the secondary antibodies against all three channels (2o antibody control) was also performed to

determine relative background. (D) Summarized average co-localization of Gal3+ masking algorithm Spots

from three independent experiments. Gal3+ Spots that are single positive (green), Gal3+ α-syn+ double

positive (yellow), Gal3+ tetraspanin+ double positive (Cyan), and Gal3+ α-syn+ tetraspanin+ triple positive

(white). (E) Representative non-reducing SDS-PAGE of concentrated EVs and the corresponding lysates

from mDA neurons treated with vehicle, TD139, α-syn Fibrils, or TD139 + α-syn Fibrils. (F) Cell viability

of mDA neurons based on LDH present in the culture media. Data are expressed as M ± SE. Intensity data

(C-D) (n = 3 independent experiments, 15 images per experiment, pooled co-localization data from each

image among experiments). E, pooled cultured media from n = 3. F, n = 3 significance determined by one-

way ANOVA.

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Vesicles double- and triple-positive for the exosomal marker CD63, as well as Gal3 and α-syn

were observed (Fig. 37A). Further characterization by NTA revealed that the mean size of mDA

EVs was 149.4 ± 7.4 nm with the most common size being 106.5 nm (Fig. 37B). These results

are consistent with the triple positive vesicle’s size observed by TEM (Fig. 37A, right) and what

is commonly reported for exosomes in the literature [125].

Using the EV-MAC workflow, the extent Gal3 co-localized with α-syn and the collective

tetraspanin EV markers CD9, CD63, and CD81 was determined by using pooled antibodies

against the previously stated tetraspanin markers (hereafter referred to as a tetraspanin, Fig.

37C). The degree to which Gal3 co-localized with a tetraspanin independent of α-syn was 50.4 ±

2.4%, and double co-localized with a tetraspanin and α-syn was 21.0 ± 2.0% (Fig. 37D).

Conversely, the percentage of Gal3 co-localized with α-syn independent of the tetraspanins was

negligible. Based on observations of primary omit control staining, we interpret this finding to be

due to background staining. The degree to which Gal3 did not co-localize with either α-syn or a

tetraspanin marker was 27.8 ± 1.5% (Fig. 37D).

Given the results of our previous experiments, EVs from mDA neurons were evaluated

following an extended time course in which cells were subjected to vehicle or the Gal3 inhibitor,

TD139, treatment with or without an initial treatment of α-syn fibrils. The cultured media over

the course of a month was collected from each condition. Subsequently, the culture media was

pooled from three independent cultures from the same, respective treatment condition, and

concentrated to get a sufficient sample quantity.

Non-reducing SDS-PAGE of the concentrated EVs and lysate was performed (Fig. 37E).

The presence of the canonical EV marker CD81 was observed in all treatment conditions.

Similarly, CD63 was observed in all conditions. However, TD139 treatment appeared to reduce

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the CD63 band intensity. TD139 treatment also reduced the LC3-II band intensity relative to

vehicle. This trend was also observed relative to the TD139 plus α-syn fibril treatment and

vehicle plus α-syn fibril treatment conditions. Increased pSer129 α-syn band intensity was

observed in the vehicle plus α-syn fibril treatment condition compared to either vehicle or

TD139 only, as well as TD139 plus α-syn fibril treatment conditions. Additionally, vehicle and

α-syn fibril treatment resulted in a strong Gal3 band relative to all

other conditions, consistent with our previous FLuc and ELISA

data (Fig. 23H, J). No difference in cell viability between

treatment conditions was observed (Fig. 37F). Collectively, these

data support the notion that: (1) Gal3 is released in mDA EVs; (2)

Gal3 is associated with the release pathological α-syn; and (3)

During Gal3 inhibition, reduced autophagy associated EV release

may occur.

Depletion of Gal3 or Trim16 Affects the Co-localization of Secreted α-Syn.

Given the results of our previous experiments, the EV-MAC workflow was used to

determine the effects of Gal3 and Trim16 depletion, as well as ATG7 depletion which is thought

to be important for both general and secretory autophagy [175], on the degree of their co-

localization with secreted, endogenous α-syn, Gal3, and wheat germ agglutinin (WGA), a lectin

that has previously been used to isolate EVs from primary fluids [128]. To collect sufficient

quantities of cultured media for each condition and minimize exposure to Baf-A1, media was

collected from cultures between 48 and 72 hours after siRNA transfection. Consist with our other

siRNA treatments, ATG7 KD did not affect cell viability (Fig. 38).

Figure 38: ATG7 Depletion

Does Not Affect MDA Cell

Viability (A) Cell viability of

Control and ATG7 siRNA

transfected mDA neurons

evaluated from 48 to 72 hours

post-transfection (n = 3).

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Figure 39: Gal3 and Trim16 Depletion Reduce Gal3 and α-Syn Associated Secretion. Legend on the next

page

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Collected EVs from each condition were spinoculated onto coverslips, and α-syn and Gal3 were

identified by immunocytochemistry after light permeabilization of EVs. This process was also

applied within the context of lysosomal inhibition by Baf-A1 treatment. Evaluation of the

cultured media from mDA neurons by the EV-MAC workflow revealed that α-syn co-localized

with Gal3 (Fig. 39A), a finding consistent with what was observed in a HeLa DSP-α-syn cell line

EVs (Fig. 18). After validating proper KD of each respective target (Fig. 39B), the image

analysis software (Imaris, Bitplane) was used to build a Spots masking algorithm around the α-

syn fluorescence signal to determine its relative co-localization with Gal3 and WGA as

previously described. Using the secondary antibody control, the relative background signal for

Gal3 and WGA was determined (Fig. 39C). Based on these values, α-syn from the cultured

media of mDA neurons treated with either control, Gal3, Trim16, or ATG7 siRNA followed by

vehicle or Baf-A1 was compared. Moreover, we also evaluated the relative quantity of secreted

α-syn based on our generated masking algorithm. In the cultured media from mDA neurons

treated with siRNA vehicle, 54.1 ± 1.9% of the spots were single labeled, α-syn+, 17.3 ± 1.7% of

the spots were dual labeled, α-syn+ WGA+, 24.1 ± 2.7% of the spots were dual labeled, α-syn+

Gal3+, and 4.5 ± 0.7% of the spots were triple labeled, α-syn+ WGA+ Gal3+ (Fig. 39D). In

Figure 39: Gal3 and Trim16 Depletion Reduce Gal3 and α-Syn Associated Secretion. (A) A representative

z-stack from multiple z-stack MIPs of imaged mDA neurons’ cultured media. Cells were first transfected with

siRNAs followed by either vehicle (0.1% DMSO) or Baf-A1 treatment 48 hours post-transfection for 24 hours.

(B) A representative western blot that demonstrates successful depletion of Gal3, Trim16, or ATG7. (C)

Representative co-localization plot from a single image demonstrating relative co-localization of secreted

endogenously expressed α-syn as determined by antibodies against α-syn (clone MJFR-1) with Gal3 (y-axis) or

WGA (x-axis) based on their respective maximum fluorescence intensity from Imaris Spots masking algorithm

identified α-syn. The secondary antibody control (2o Ab control) was used to determine Gal3 and WGA

background levels. (D) Summarized percent co-localization of α-syn+ Spots that are single α-syn+ positive

(blue), α-syn+ WGA+ double positive (cyan), α-syn+ Gal3+ double positive (magenta), and α-syn+ WGA+

Gal3+ triple positive (white). The number of α-syn puncta (E) or WGA puncta (F) from each image based on the

same Imaris masking algorithm as a measure of relative differences between the conditions. Each data point is

the mean of 20 images for each independent experiments. There were 4 independent experiments. Statistical

significance was determined by a two-way ANOVA, with Dunnett’s post hoc tests. * indicates significance

relative to control plus vehicle, # indicates significance relative to control plus Baf-A1.

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control siRNA treated mDA neurons, Baf-A1 treatment decreased the percentage of single

labeled, α-syn+ spots yet had no effect on the percentage of dual labeled, α-syn+ Gal3+ spots,

the percentage of spots that were dual labeled, α-syn+ WGA+, and the percentage of spots that

were triple labeled, α-syn+ WGA+ Gal3+ (Fig. 39D). However, this change was observed in all

of the Baf-A1 treated mDA neurons except for those depleted of ATG7 (Fig. 39D). Gal3

depleted mDA neurons treated with vehicle had a decreased percentage of dual labeled, α-syn+

spots that were also Gal3+. Gal3 depletion had no effect on the percentage of spots were single

labeled, α-syn+, the percentage of spots that were dual labeled, α-syn+ WGA+, and the

percentage of spots that were triple labeled, α-syn+ WGA+ Gal3+. For mDA neurons that

received a Gal3 KD, Baf-A1 treatment decreased the percentage of single labeled, α-syn+ spots

and dual labeled, α-syn+ Gal3+ spots yet had no effect on the percentage of spots that were dual

labeled, α-syn+ WGA+ and the percentage of spots that were triple labeled, α-syn+ WGA+

Gal3+. Trim16 KD increased the percentage of spots were single labeled, α-syn+ and decreased

the percentage of spots that were dual labeled, α-syn+ Gal3+, yet had no effect on the percentage

of spots that were dual labeled, α-syn+ WGA+ and the percentage of spots that were triple

labeled, α-syn+ WGA+ Gal3+. For mDA neurons that received a Trim16 KD, Baf-A1 treatment

increased the percentage of single labeled, α-syn+ spots and decreased the percentage of dual

labeled, α-syn+ Gal3+ spots yet had no effect on the percentage of spots that were dual labeled,

α-syn+ WGA+, and the percentage of spots that were triple labeled, α-syn+ WGA+ Gal3+.

ATG7 knockdown followed by vehicle treatment did not significantly influence the percentage

of single labeled, α-syn+, dual labeled, α-syn+ Gal3+ or α-syn+ WGA+, as well as triple labeled,

α-syn+ WGA+ Gal3+ spots. However, ATG7 knockdown followed by Baf-A1 treatment reduced

the number of dual labeled, α-syn+ WGA+ spots yet had no effect on the percentage of spots that

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were single labeled, α-syn+, dual labeled, α-syn+ Gal3+, or triple labeled, α-syn+ WGA+ Gal3+

(Fig. 39D).

Whereas Gal3 or Trim16 KD significantly decreased, ATG7 knockdown significantly

increased the number of α-syn puncta per image in the context of vehicle treatment (Fig. 39E).

MDA neurons that received control siRNA and were treated with Baf-A1, increased the number

of α-syn puncta per image compared to vehicle (Fig. 39E). For mDA neurons that received either

a Gal3 or a Trim16 KD, Baf-A1 significantly increased the number of α-syn puncta per image.

For mDA neurons that received an ATG7 KD, Baf-A1 had no effect on the number of α-syn

puncta per image. Finally, compared to mDA neurons that received control siRNA and were

treated with vehicle, the number of α-syn puncta per image was decreased for mDA neurons that

received a Gal3, Trim16, or ATG7 KD and were treated with Baf-A1 (Fig. 39E). The same set of

images were assessed for the relative number of EVs based on their mean number of WGA

puncta (Fig. 39F). Trim16 or ATG7 but not Gal3 KD significantly increased the number of

WGA puncta per image. Baf-A1 treatment had no effect on the number of WGA puncta per

image for mDA neurons that received a Gal3 or Trim16 or ATG7 KD. Gal3 siRNA vehicle

treatment resulted in a reduced quantity of secreted α-syn puncta (Fig. 39E). Despite the decrease

in the amount of secreted α-syn in the Gal3 siRNA vehicle treatment, a portion of the secreted α-

syn still co-localized with Gal3 albeit at a significantly lower percentage compared to the control

siRNA treated cells (Fig. 39D). This observed co-localization may be the result of three

possibilities: (1) A reflection of dwindling levels of Gal3 still present from before 48 hours post

siRNA transfection; (2) A measure of co-localization percentage from reduced levels of secreted

α-syn events; or (3) The remaining Gal3 in the culture is mediating α-syn secretion and, thus,

intracellular levels of Gal3 may not be entirely reflective of extracellular levels of Gal3.

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Additionally, these data suggest that general reductions in autophagy, such as those experienced

during reduced levels of ATG7, do not change the mechanism associated with Gal3-based

secretion but rather dampen its effect. In contrast, the mechanism for Gal3- and/or Trim16-

mediated secretion is consistent with a change in a specific mechanism, such as autophagic

recruitment.

Early Inhibition of Autophagy Reduces Autophagosome-Associated α-Syn Secretion.

To determine how impaired autophagosome formation influences non-EV associated α-

syn secretion in a human cell culture model of PD, mDA neurons were treated with the early

autophagy inhibitors, Wortmannin (Wor) or 3-Methyladenine (3-MA). Wor and 3-MA prevent

autophagy by inhibiting PI3K, including the class III PI3K, which is required for initial

autophagophore formation. Treatment with Wor and 3-MA significantly reduced α-syn secretion

(Fig. 40A). In contrast, late-stage inhibition by Baf-A1 increased α-syn secretion (Fig. 40B).

Similar to the pharmacologically induced early autophagy inhibition, siRNA-mediated depletion

of ATG7 significant decreased α-syn secretion (Fig. 40C).

Treatment with Wor, 3-MA, or Baf-A1 had no effect on cell viability (Fig. 40D and E).

Collectively, these data suggest that the unconventional secretion of non-EV associated α-syn is

affected by autophagic impairment; specifically, an impairment of autophagy, at the stage of

autophagosome formation, reduced secretion while impairment, at the stage of autophagosome-

lysosome fusion, increased secretion.

Previous work suggests that Gal3 secretion is also regulated by non-classical mechanisms

including autophagy [357, 472]. To determine autophagy’s corresponding involvement,

transduced FLuc Gal3 SH-SY5Y cells were validated for maintained construct integrity (Fig.

40F), treated with 3-MA, Wor, or Baf-A1 and luciferase in the culture media was measured. Wor

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or 3-MA treatment significantly reduced the release of Fluc Gal3 (Fig. 40G). Conversely, Baf-

A1 treatment significantly increased FLuc Gal3 secretion (Fig. 40H). These corresponding

changes are in-line with what was observed for α-syn secretion (Fig. 40A and B).

Figure 40: Impaired Autophagosome Formation Decreases the Unconventional Secretion of α-Syn and

Gal3. mDA neurons were plated for 7 days then treated with early autophagy inhibitors Wor or 3-MA (A), the

late-autophagy inhibitor Baf-A1 (C) or vehicle (0.1% DMSO) for 24 hours. α-Syn levels were measured from

unlysed, cultured media by ELISA. (C) A representative western blot demonstrating KD of ATG7 from mDA

neurons and α-syn from unlysed, cultured media by ELISA. Cultured media was collected from 48-72 hours

post-transfection. (D) Cell viability of vehicle, 3-MA, and Wor (A), or vehicle and Baf-A1 (B) treated mDA

neurons. (E) Representative western blot of WT and FLuc Gal3 transduced SH-SY5Y cells probed against HA-

tagged FLuc-Gal3 construct and endogenous Gal3 observed at 31 kDA. Relative change in secretion of FLuc

Gal3 from FLuc Gal3 transduced SH-SY5Y cells in response to (G) vehicle (0.1% DMSO), Wor, and 3-MA or

(H) vehicle and Baf-A1 after 24 hours. Data are expressed as M ± SE. Statistical significance was determined by

(D) paired one-way ANOVA with Dunnett’s post hoc tests or (B) paired two-tailed t-tests.All data are expressed

as M ± SE. Statistical significance was determined by paired two-tailed t-tests.

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Discussion

In this study, we examined the secretory pathway of -syn to understand how cellular

perturbations of the ALP system may impact its release. To do this, we began by examining how

the release of a DSP--syn fusion protein was impacted by ALP stress, including ALP stress

induced by exogenous -syn fibrils. In these studies, we were able to utilize the differential

labelling of DSP--syn and exogenous -syn fibrils to observe colocalization of these forms of

-syn in cells and in EVs secreted from these cells. The DSP-α-syn model is unique in that it

allows for the evaluation of the interactions between endogenous α-syn and exogenously added

α-syn fibrils, which can otherwise be hard to evaluate. We also found that α-syn fibril treatment

influenced the amount of high molecular weight α-syn in our DSP-α-syn cells as well as both the

secretion and the fluorescence intensity of DSP-α-syn+ in EVs containing exogenous fibrils.

While it is possible that vesicular association of these two forms of -syn may potentially be

locations of templated misfolding of endogenous -syn to other, pathological species, the use of

DSP--syn fusions precludes such a conclusion, as such fusions may not behave the same as

endogenous, untagged forms of a-syn. However, they do reveal co-localization and secretion of

-syn in a context that is consistent with our previous studies of exogenous -syn fibrils [146,

153] and other studies examining the degradation or secretion of endogenous -syn [9, 132, 235,

387], as well as other similar fusions that increase the release of -syn [59, 106]. The

colocalization of these different forms of -syn minimally reflect the central role of ALP

pathway in the cellular response to exogenous -syn fibrils as well as the secretion of -syn.

We also observed that: (1) Gal3 and α-syn are secreted together by an autophagic

mechanism; (2) that Gal3 or Trim16 depletion reduces α-syn secretion; (3) This secretion occurs

in response to lysosomal rupture (Fig. 32). To this end, we found that lysosomal rupture triggers

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the secretion of α-syn and Gal3 and upregulates autophagy by inhibiting mTOR in mDA

neurons. Additionally, depletion of Gal3 or Trim16 reduced both basal and lysosomal rupture-

induced α-syn secretion; however, the changes in mTOR mediated phosphorylation of ULK1 or

p70 S6K1 were comparable to the control and Gal3 or Trim16 siRNA treated cells, respectively,

in response to rupture. Our investigation of the underlying mechanism revealed that reduced

recruitment of α-syn fibrils to autophagic compartments occurred when either Gal3 or Trim16

were depleted. Additionally, depletion of either Gal3 or Trim16 reduced the observed degree of

co-localization of secreted Gal3 and α-syn. Furthermore, Gal3 depletion was associated with

autophagic flux impairment and reduced lysosomal cathepsin activity. Collectively, this work

has led us to propose a mechanism whereby mDA cells respond to lysosomal membrane damage

which can be induced by pathological forms of α-syn by upregulating the cellular autophagy

response due to mTOR inhibition. Subsequently, Gal3 and Trim16 are recruited and facilitate the

ruptured lysosomes to autophagosomes, which, ultimately, increases α-syn secretion from mDA

neurons. This response may act as a defensive mechanism in response to compromised

lysosomal integrity. Additionally, we found that Gal3 also facilitates general lysosomal

degradative capacity under basal conditions. These mechanisms are illustrated in Fig. 41. To our

knowledge, we are the first to link Gal3 or lysosomal rupture to α-syn secretion.

In previous work by our lab, treating cells with α-syn fibrils resulted in their endocytosis

into endo-lysosomal compartments where they induced rupture followed by their recruitment to

the ALP [147, 153]. To evaluate the relevance of these findings, Gal3 immunoreactivity was

observed within and surrounding LBs in brain sections obtained postmortem from PD patients.

These findings suggest a link between α-syn aggregates, lysosomal rupture, and cellular

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pathology. In this work, we connected lysosomal rupture, which is induced by pathological α-

syn, to secretion, which could subsequently result in cell-to-cell transfer.

Figure 41: Summary cartoon of hypothesized mechanism Cartoon illustrates hypothetical mechanism where

endocytosed pathological α-syn enters the cell and induce lysosomal rupture. Lysosomal rupture results in

mTOR inactivation subsequently increasing ULK1 and p70 S6K1 activity. Concurrently, lysosomal rupture

exposures its inner-luminal glycans which are recognized by Gal3. Bound Gal3 recruit Trim16 and ULK1 to

mediate autophagic machinery assembly, increase cellular autophagy, and promote autophagosome

development. This response ultimately increases α-syn secretion. (Bottom) Gal3 recruitment to lysosomes

during early membrane stress facilitate membrane repair.

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Consequently, lysosomal rupture may explain how native and pathological α-syn interact to

subsequently seed further misfolding of native α-syn based on the observed co-localization of

secreted α-syn fibrils and DSP--syn. Moreover, this failure within the ALP could link spreading

and accumulation to a singular mechanism.

Recently, the Deretic group have extensively characterized the galectins’ involvement in

autophagy at both the mechanistic and cell signaling level [71, 232, 234, 250, 265]. They

originally demonstrated Gal3’s interaction with Trim16 through ULK1, which was also

demonstrated by another group in bone marrow derived mesenchymal stem cells [71, 75, 226].

Our work specifically links these protein interactions to PD pathology. We found that Gal3 was

involved in α-syn fibril’s recruitment to autophagic compartments and that depletion of either

Gal3 or Trim16 reduced its secretion via the ALP. Gal3 depletion also reduced autophagic flux

in response to α-syn fibril treatment, a finding that is consistent with what was recently described

in response to lysosomal rupture [234] and basally in bone marrow derived mesenchymal stem

cells [75]. Notably, Gal3 inhibited microglia were shown to have a reduced phagocytic capacity

and clear less α-syn fibrils [43], a finding consistent with the observed decrease in lysosomal

cathepsin B activity reported herein. Similar to Gal3, Trim16 KO cells had impaired autophagic

flux and a reduced capacity to clear protein aggregates and accumulated autophagosomes [226].

Yet, despite an impairment in autophagic clearance, both Trim16 KO and Gal3 KO cells were

reported to have increased TFEB translocation, which one would expect to increase degradative

capacity. Interestingly, when TFEB over-expressing cells underwent pharmacologically induced

impairment of autophagic flux, they lost their ability to clear protein aggregates [565]. Thus,

despite increases in TFEB translocation, they may be unable to clear α-syn due to impaired

autophagy at the level of recruitment or autophagosome formation. Collectively, these results

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may indicate that Gal3 is important for maintaining lysosome integrity and when impaired or

depleted, Gal3 slows turnover or secretion of damaged lysosomes by reducing their initiation

into the ALP. TBK1 and Optineurin were shown to be recruited to Gal3 positive compartments

in microglia following α-syn fibril treatment [56]. Treatment with the TBK1 specific inhibitor,

Amlexanox, was found to reduce LC3 puncta formation but did not prevent Gal3’s recognition

of damaged lysosomal compartments following α-syn fibril treatment. Gal3, ATG9A, ULK1,

OPTN, and Beclin-1 have all been shown to be recruited to salmonella or Coxiella burnetti

vacuoles to repair damaged endosomal membranes and have been suggested to use a conserved

pathway to facilitate this response [261, 318]. It is tempting to think these other proteins might

further be involved in recruitment of Gal3+ compartments to the autophagic pathway in mDA

neurons.

In this work, we demonstrate a link between Gal3, α-syn, and autophagy. However, we

cannot rule out the possibility that other galectins, such as Gal8 or Gal9, are also involved in PD

pathology, as it is now appreciated that Gal3, Gal8, and Gal9 all participate in coordinating an

upregulated cellular autophagic response at both the transcriptional and mechanistic levels [71,

232, 234, 250, 265]. Furthermore, we found that Trim16 and ATG16L1 were involved in α-syn

and Gal3 secretion via autophagy mediated secretion, which was previously shown to be

important for IL-1β secretion in response to lysosomal rupture and inflammasome activation

[250, 561]. However, in contrast to these previous findings, IL-1β secretion was dependent on

Gal8 and nuclear dot protein 52 kDa (NDP52) but not on Gal3 in THP-1 cells and mouse bone

marrow derived macrophages. However, intracellular Gal3 inhibition reduced the secretion of

inflammatory cytokines, such as IL-1β and IL-6, in microglial cells isolated from a Huntington’s

disease model [452]. Additionally, differences in relative Gal3 and Gal8 expression have

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previously been linked to altered cellular responses to lysosomal membrane rupture induced by

Group A Streptococcus [80]. It is well recognized that Gal3 participates in inflammation and

evokes an inflammatory response in immune cells [44, 452]. Thus, Gal3-mediated secretion may

be cell-type specific and to prevent self-stimulated inflammation. It is tempting to extrapolate

from this paradigm; given that because neurons have a lower phagocytic capacity relative to

macrophages or microglia [156, 157], they may secrete membrane bound Gal3 compartments for

microglia to clear rather than accumulate them. This paradigm is consistent with Boza-Serrano et

al., 2014 in which Gal3 inhibition reduced BV2 microglial activation, phagocytic capacity, and

clearance of α-syn fibrils [43]. They postulated a similar pathological ultimatum, in which

substantial reductions in phagocytosis and reduced microglia activation were observed during

Gal3 inhibition yet, consequently, increased α-syn phagocytosis by microglia could reduce

pathological α-syn load during early pathology. This may explain why increased Gal3 is

observed in the cerebrospinal fluid of those with PD as well as other neurodegenerative diseases

[13, 526]. Moreover, this paradigm is further supported by Danzer et al., 2011 where they

showed exosome associated α-syn released during degradative autophagy impairment was more

readily taken-up by microglial cells compared to neurons [106].

Canonically, autophagy is the process by which autophagosomes mediate the degradation

of engulfed content through their fusion with lysosomes. Therefore, an autophagic inhibitor is

defined based on its ability to impair this previously mentioned process. While Wortmannin, 3-

MA, and Bafilomycin-A1 are all autophagic inhibitors, the mechanism by which they prevent

autophagy differ. Wortmannin and 3-MA are best classified as early-stage inhibitors while Baf-

A1 is a late-stage inhibitor. Wortmannin and 3-MA are phosphatidylinositol 3-kinase (PI3K)

inhibitors. The class III PI3K, composed of Vps34 and Vps15, form a complex with Beclin-1,

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and play an essential role in the development of the autophagophore [554]. Thus, wortmannin

and 3-MA prevent autophagy by blocking the formation of autophagosomes to ultimately

prevent the degradation of content. In contrast, Baf-A1 inhibits the V-ATPase H+ pump on

lysosomes, preventing lysosome acidification, as well as preventing autophagosome-lysosome

fusion [326, 327]. Therefore, Baf-A1 prevents autophagy by affecting the lysosomes degradative

capacity. This impairs autophagy but does not affect the formation of autophagosomes.

Collectively, the literature agrees that late stage inhibition of autophagy increases both non-EV

and EV associated α-syn secretion [9, 86, 106, 279, 340, 387] and has been shown to also

increase the secretion of the autophagic markers LC3-II and p62 in primary neurons [340].

Similarly, Gal3 secretion can be modulated by autophagy [357, 472]. Our work reinforces these

previous findings, as both α-syn and Gal3 secretion were elevated when measured by multiple

assays and with several cell types.

In contrast, studies report marked differences during early autophagy inhibition [132,

155, 279, 387]. A couple of studies found that when cells over-expressing α-syn were treated

with 3-MA, an increase in α-syn secretion occurred [155, 279]. However, 3-MA treatment did

not increase secretion within the non-vesicular fraction in a study in which immortalized

dopaminergic precursor cells were used [155]. Yet, ATG5 depletion concurrently increased α-

syn secretion within the EV fraction as well as the exosomal marker CD81 [155]. Conversely, in

another study, 3-MA did not increase secretion in either the total or EV fraction of the cultured

media from H4 cells [387]. Another study found, in PC12 cells over-expressing A30P α-syn as

well as tubulin polymerization promoting protein, reduced autophagosome-lysosome fusion and

reduced α-syn secretion occurred when depleted of ATG5 [132]. Rab27b knockdown impaired

autophagy and reduced general α-syn secretion and clearance [507]. However, the α-syn that was

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secreted had a higher molecular weight. Here, we found that early autophagic inhibition in mDA

neurons by 3-MA, Wor, or ATG7 depletion decreased the secretion of non-vesicular α-syn but

increased the number of α-syn EVs measured by EV-MAC. Interestingly, Fussi et al., 2018

showed that inhibition of EV secretion blocked the increase in α-syn secretion when ATG5 was

knocked down [155]. They hypothesized that inhibition of autophagy was shunting α-syn into

the exosomal secretion pathway to compensate for failed degradation. Our work supports this

hypothesis. Furthermore, it may be that continued early autophagic inhibition pronounces this

compensatory mechanism alongside accumulation, which could explain the apparent

discrepancies between studies. However, more work is necessary before definitive conclusions

can be drawn.

Notably, a final category of autophagic modulators are activators. Canonical autophagic

activation by rapamycin treatment reduced α-syn secretion [106]. However, trehalose, a non-

canonical autophagic activator that circumvents mTOR, increased secretion [132, 550].

Interestingly, a recent paper found that trehalose increases autophagy in a motor-degeneration

model by inducing small amounts of lysosomal damage that upregulates TFEB and coincide with

increased levels of eGFP-Gal3 [422]. The mechanism by which lysosomal membrane damaging

agents drive α-syn secretion could be, in part, by compromised lysosomal function, which is

similar to our lysosome acidification inhibitor data, as well as through the specific recruitment of

damaged lysosomes to the autophagic pathway. Changes in TFEB associated gene regulation are

known to occur following lysosomal membrane damage. This process upregulates both

lysosomal and autophagy associated proteins [231, 232, 234, 265, 266] thus increasing the

number of lysosomes to restore the cellular pool following comprised function. Galectins

regulate this process and recognize damaged lysosomes. Therefore, galectins, such as Gal3, may

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act as sensors to deviate autophagosomes to the secretory pathway upon recognizing that

lysosome integrity is compromised following membrane damage. In neurons, this mechanism

may be especially important, as they are long-lived cells expected to last a lifetime.

Concluding Remarks

In this document, a mechanism by which mDA neurons secrete α-syn in response to

lysosomal membrane rupture following the recruitment of Gal3 and the subsequent autophagic

proteins including Trim16 and ATG16L1 is outlined. Additionally, it was shown that the cellular

response to lysosomal membrane rupture results in increased mTOR inhibition, increased TFEB

translocation, increased amphisome formation, and increased secretion of α-syn. This secreted α-

syn is observed in collected EVs in association with Gal3. Depletion of Gal3, Trim16, or

ATG16L1 reduces the secretion of α-syn. Similarly, depletion of Trim16 or ATG16L1 also

reduces the secretion of Gal3. Gal3 depletion also reduced α-syn secretion during lysosomal

rupture, the formation of amphisomes, and lysosomal degradative capacity. However, depletion

of Gal3 did not influence mTOR associated signaling. These processes support a premise that

pathological α-syn may result in a cellular defense mechanism resulting in its secretion in

association with Gal3 following lysosomal membrane damage.

It is ultimately unclear if this mechanism is beneficial or detrimental to the spreading of

disease. It is reasonable that this mechanism may facilitate the removal of pathological α-syn

from neuronal cells, which on a single cell level is beneficial. However, long-term this may be

ultimately detrimental if pathological α-syn is transferred to neighboring neurons or when the

surrounding microenvironment is over-burdened. In contrast, the secretion of α-syn via this

mechanism may facilitate total brain clearance, by degradative cells, such as microglia or by CSF

drainage. During the early stages of PD this process may be beneficial but ultimately problematic

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in later-stages when microglial become overwhelmed. Moreover, this secretion mechanism may

ultimately increase neuroinflammation by excessively activating microglia through increased

Gal3 secretion which is known to activate inflammatory cascades.

An important consideration for future work would be to look at how mDA neuron

associated secretion of Gal3 and α-syn affects the surround cells and whether microglia are able

to clear secreted pathological forms of α-syn. The result of this experiment would provide insight

into the previous unanswered questions. Additionally, this document primarily focuses on

autophagy in the context of secretion. The effects of Gal3 or Trim16 depletion on the

accumulation and degradation of α-syn are not readily explored in the context of DA neurons.

However, work by other supports suggests that Gal3 or Trim16 depletion would increase α-syn

accumulation [226, 295].

Here it was shown that Gal3 depletion reduced cathepsin activity. Whether this process is

the result of reduced lysosomal acidification, mistrafficking of cathepsins, or from impaired

lysosomal repair was not confirmed. However, an important consideration is that dopaminergic

secretory vesicles also depend on their acidification for antiport coupled uptake of dopamine. It

is feasible that Gal3 depletion also affects dopamine secretion by influencing dopaminergic

secretory vesicles V-ATPase in a process akin to the lysosomal V-ATPase, which could also

ultimately contribute to a PD phenotype by further reducing available dopamine following an

action potential.

Another important consideration is that the mechanism highlighted here also affects other

amyloid proteins. In other neurodegenerative diseases, particularly in the context of AD, Gal3

associated secretion of amyloid proteins may be further complicated by the presence of both

hyper-phosphorylated tau and amyloid-β. If the same mechanism identified here affects the

205

secretion of both hyper-phosphorylated tau and amyloid-β, then Gal3 may exacerbate early

amyloid-β pathology by increasing its secretion. In later stages of AD, secretion of hyper-

phosphorylated tau may further overwhelm disease pathology or contrastingly reduce cellular

burden at the single cell level. Further complicating things, increased Gal3 secretion is associated

with insulin resistance and diabetes which are linked to AD [152]. In agreement with this

proposed scenario, increased Gal3 expression was linked to increased amyloid-β associated

pathology but reduced intracellular hyperphosphorylated tau [44, 295, 491].

In other neurodegenerative diseases, several of the proteins identified here that affected

the secretion of α-syn have been identified in GWAS studies for other neurodegenerative

diseases. It is also known that other amyloid proteins also undergo unconventional secretion.

Specifically, mutations in the Gal3 gene, LGALS3, and the Optineurin gene, OPTN, have been

liked to other neurodegenerative diseases including Huntington’s disease and ALS [32]. These

transcriptional studies seem to indicate that inflammation pathways are also dysregulated by

these proteins. It is unknown if Gal3 or Optineurin also affect the secretion of inflammatory

cytokines in other cell-types or from neurons. Furthermore, it remains unclear the role neurons

specifically contribute to total CNS inflammation which may ultimately be useful to the

collective understanding of neurodegenerative disorders including synucleinopathies.

Another important point of clarification is Gal3’s role in facilitating autophagy also

remains unclear. The data here suggests that Gal3 depletion reduces the total activation of

autophagy, lowering autophagic flux and lysosomal associated degradative capacity. However,

TFEB associated signaling was not impaired in Gal3 depleted cells but instead heightened. Despite

this finding, Gal3 depletion or inhibition reduced lysosomal degradative capacity. This suggests

that Gal3 is involved in facilitating cellular degradative clearance at level beyond transcription.

206

Notably, upon scouring the literature, only GSK-3B and Trim16 were found to affect lysosomal

acidification and/or degradative potential when upregulated TFEB signaling was intact [15, 71,

75]. It was previously proposed that Trim16 depletion reduced lysosomal quality despite increased

TFEB translocation following lysosomal membrane damage [71]. This hypothesis is in line with

the data shown here. However, mechanistically this is puzzling, as in nearly all cases increased

TFEB translocation results in increased lysosomal degradative capacity [97, 266].

Here, I provide evidence that α-syn is released via an unconventional secretion

mechanism in mDA neurons using autophagic machinery and through the ALP. I highlighted

the involvement of Gal3 and its associating proteins, Trim16 and ATG16L1, to mediate selective

autophagy to drive secretion. Furthermore, I showed that secretion occurs in association with

Gal3 which, along with its autophagic adaptor, Trim16 and ATG16L1 mediate the autophagic

response to lysosomal damage induced by pathological species of α-syn. Additionally, I also

showed that Gal3 plays a role in maintaining lysosomal function and general autophagy in mDA

neuronal cultures. During the depletion of these proteins reduced α-syn occurs by failing to be

recruited to autophagic compartments. Based on my data, this process requires the formation of

autophagosomes and prevents the secretion of both Gal3 and α-syn. Additionally, this data

supports that specific recruitment α-syn to autophagic compartments by Gal3-Trim16-ATG16L1

is important for this process. Additionally, I found that autophagosome associated protein LC3-II

and Gal3 were secreted in EVs from both our DSP-α-syn and mDA neurons. Following the

inhibition of EV secretion within our DSP-α-syn cell-line or inhibition of Gal3 in mDA neurons,

lower levels of these proteins in EVs were observed while treatment with α-syn fibrils increased

these proteins. To this end, the mechanism is likely occurring through the formation of

amphisomes. The fusion of these amphisomes with the plasma membrane may explain how

207

autophagy modulates the release of EV associated α-syn via gal3. Thus, I propose that Gal3

assists in mediating α-syn release in mDA neurons. Theoretically, this response may initially be

beneficial, by increasing microglial uptake and clearance. However, as pathological α-syn load

increases, continual secretion may tip the scales in favor of a disease state due to excessive

microglial response, inflammation, and transmission between cells. This data may ultimately be

useful in the identification of biomarkers that are released by this mechanism. Furthermore,

drugs that modulate these pathways may ultimately be useful therapeutics for the treatment of

PD, synucleinopathies in general, and other neurodegenerative diseases.

208

APPENDIX

ADDITIONAL FIGURES FOR REFERENCE

209

Appendix Figure 1: Characterization of the Point Spread Function by Empirical Testing. A) Empirically

tested Full Width Half Max (FWHM) of 100nm Tetraspecks at increasing exposure times in the FITC channel to

determine point spread function. Images were collected with a Bin 2x2 to best compare the tetraspeck data to the

S15Ch data. B, C) FWHM of deconvolved 100nm FITC Tetraspecks and concentrated S15Ch EVs with similar

laser intensity and exposure conditions with a Bin size of 2x2 and 1x1, respectively. Data was subjected to a

two-tailed T-test, no significant differences were found. Error bars show SEM D) S15Ch co-localization

distribution for S15Ch EVs stained with CD81 and LAMP1 from Bin 1x1 and Bin 2x2 collected fields. 12

images were taken per coverslip. All data are expressed M ± SE (n = 3).

Appendix Figure 2: Validation of α-Syn and Gal3 ELISA An in-house α-syn (A) and Gal3 (B) sandwich

ELISA using commercially available antibodies. A representative example of α-syn (A) or Gal3 (B) serial

dilutions demonstrate the generation of a standard curve when added to uncultured media.

210

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VITA

The author, Kevin Burbidge, was born in Edina, MN on February 15, 1989 to Steve and

Ann Burbidge. He attended the University of Denver in Denver, Colorado where he earned a

Bachelor of Science in Biological and Psychological Sciences in June of 2011. In 2015, Kevin

matriculated into the Loyola University Chicago Stritch School Neuroscience Graduate Program

and joined the laboratory of Dr. Ed Campbell. Kevin completed his Master of Science in

Neuroscience in the summer of 2017, at Loyola University Chicago. In the fall of 2017, Kevin

subsequently matriculated into the Integrated Program in Biomedical Sciences to pursue his

Doctor of Philosophy continuing his work in the Laboratory of Dr. Ed Campbell. Upon

completion of his doctorate, Kevin Burbidge will be working as a post-doctoral fellow at the

Northwestern University in Chicago, Illinois in the laboratory of Dr. Joe Mazzulli.