Medicine, Faculty of
Pathology and Laboratory Medicine, Department of
DSpace
UBCV
Wang, Chris Kedong
Doctor of Philosophy - PhD
University of British Columbia
Chondroitin sulfate (CS) is a glycosaminoglycan that can be attached to membrane proteins on the cell surface to form chondroitin sulfate proteoglycans (CSPG). CSPGs are highly expressed in the placenta and associated with the ability of the placenta to maintain a high rate of proliferation, angiogenesis, as well as invasion of the uterine tissue. CSPG alterations are also described in cancers and have been associated with progression and poor patient outcomes. Our lab has discovered that placenta and cancer cells express a common CS modification named oncofeatal CS (ofCS) that can be detected by the malarial lectin VAR2CSA. Using recombinant VAR2 proteins (rVAR2), we developed a new technology to detect and target solid tumors. As a component of this technology, the effect of rVAR2 on cancer cells as well as its fate inside the cancer cells needs to be established. Therefore, I hypothesized that rVAR2 binding to cancer cells would affect intracellular signaling pathways and cellular behavior. I aspired to support the development of the rVAR2 technology through three research aims: 1) Investigate the effects of rVAR2 binding to cancer cells. I showed that rVAR2 binding induced NF-κB pathway activation and cytokine expression. 2) Characterize the cellular fate of rVAR2 after binding to cancer cells. I established that rVAR2 internalize though different pathways into the early endosomes without reaching lysosomes. 3) Establishing an rVAR2-based exosome-detection strategy. I evaluated rVAR2 as a tool to isolate cancer derived exosomes and showed its limitation. Overall, the data generated in my research have advanced our knowledge on ofCS in cancer biology and informed ongoing rVAR2 based therapeutic development.
Investigating the role of oncofetal chondroitin sulfate in cancer biology and optimizing rVAR2 technology for cancer by Chris Kedong Wang B.Sc., University of British Columbia, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY In THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Pathology and Laboratory Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2021 © Chris Kedong Wang, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Investigating the role of oncofetal chondroitin sulfate in cancer biology and optimizing rVAR2 technology for cancer submitted by Chris Kedong Wang in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Pathology and Laboratory Medicine Examining Committee: Mads Daugaard, Associate Professor, Urologic Sciences, UBC Supervisor Pauline Johnson, Professor, Microbiology & Immunology, UBC Supervisory Committee Member Vincent Duronio, Professor, Respiratory Medicine, UBC University Examiner Shyh-Dar Li, Professor, Pharmaceutical Sciences, UBC University Examiner Additional Supervisory Committee Members: Catherine Pallen, Professor Emeritus, Pathology and Laboratory Medicine, UBC Supervisory Committee Member Kevin Bennewith, Associate Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member Alexander Wyatt, Assistant Professor, Urologic Sciences, UBC Supervisory Committee Member iii Abstract Chondroitin sulfate (CS) is a glycosaminoglycan that can be attached to membrane proteins on the cell surface to form chondroitin sulfate proteoglycans (CSPG). CSPGs are highly expressed in the placenta and associated with the ability of the placenta to maintain a high rate of proliferation, angiogenesis, as well as invasion of the uterine tissue. CSPG alterations are also described in cancers and have been associated with progression and poor patient outcomes. Our lab has discovered that placenta and cancer cells express a common CS modification named oncofeatal CS (ofCS) that can be detected by the malarial lectin VAR2CSA. Using recombinant VAR2 proteins (rVAR2), we developed a new technology to detect and target solid tumors. As a component of this technology, the effect of rVAR2 on cancer cells as well as its fate inside the cancer cells needs to be established. Therefore, I hypothesized that rVAR2 binding to cancer cells would affect intracellular signaling pathways and cellular behavior. I aspired to support the development of the rVAR2 technology through three research aims: 1) Investigate the effects of rVAR2 binding to cancer cells. I showed that rVAR2 binding induced NF-κB pathway activation and cytokine expression. 2) Characterize the cellular fate of rVAR2 after binding to cancer cells. I established that rVAR2 internalize though different pathways into the early endosomes without reaching lysosomes. 3) Establishing an rVAR2-based exosome-detection strategy. I evaluated rVAR2 as a tool to isolate cancer derived exosomes and showed its limitation. Overall, the data generated in my research have advanced our knowledge on ofCS in cancer biology and informed ongoing rVAR2 based therapeutic development. iv Lay Summary The malaria parasite Plasmodium falciparum has a protein (VAR2CSA) that allows infected red blood cells to bind a sugar structure called CSA on the placenta. We have recently discovered that the same CSA structure is also present on human cancer cells and is associated with advanced cancer stage. In previous studies, engineered versions of malarial VAR2CSA (rVAR2) were used to take advantage of this interaction to deliver anti-cancer drugs directly to the tumor. This has led to further programs to use rVAR2 as a delivery tool against cancer. To be successful, how rVAR2 affect and what happens to rVAR2 after it attaches to cancer cells needs to be understood. My research aimed to help understand how rVAR2 can change the cells behavior and where rVAR2 is sent to after entering the cell. v Preface The work presented in this dissertation was conducted entirely at the Vancouver Prostate Centre under the supervision of Dr. Mads Daugaard. Regarding the work presented in Chapter 2. Dr. Thomas Mandel Clausen provided the initial microarray data and generated Figure 2-1A. I subsequently designed and performed experiments for the entirety of the study where I focused on characterizing the role of oncofetal chondroitin sulfate in inflammation. I conceived and carried out proof of concept experiments for the conditional lethal reporter screen as described. This screen has now spawned several subsequent projects that are currently being worked on by other students in the lab. A version of chapter 3 (including parts of chapter 4) is currently in preparation for publication: Chris Kedong Wang, Desmond Hui, Sarah Truong, Sarah Zhao, Fariba Ghaidi, Htoo Zarni Oo, Ali Salanti, Nader al Nakouzi, and Mads Daugaard. Internalization mechanism of a glycosaminoglycan binding malaria protein in cancer cells. I designed and performed 80% of the experiments. Desmond Hui, Sarah Truong, Sarah Zhao, and Fariba Ghaidi conducted 20% of the experiments. Dr. Ali Salanti provided rVAR2. Dr. Nader al-Nakouzi and Dr. Mads Daugaard were involved in methodology, data analysis, and preparing the manuscript. The work in chapter 4 was conceived by Dr. Mads Daugaard, Dr. Nader al Nakouzi, and myself. The aim was to investigate the feasibility of using rVAR2 technology to isolate cancer derived EVs with the aspiration of developing a diagnostic test. Sarah Truong, Sarah Zhao, and Fariba Ghaidi, and myself optimized and performed exosome extraction. vi I conducted experiments to test whether if rVAR2 is suitable for the proposed purpose. Mass spectrometry was performed at the VPC by Hans Adomat. vii Table of Contents Abstract ......................................................................................................................... iii Lay summary ................................................................................................................ iv Preface ........................................................................................................................... v Table of contents......................................................................................................... vii List of figures ............................................................................................................... xi List of abbreviations .................................................................................................. xiii Acknowledgements .................................................................................................. xviii Dedication ................................................................................................................... xix Chapter 1 â Introduction: Chondroitin sulfate in cancer ........................................... 1 1.1. Glycosaminoglycans ............................................................................................. 1 1.2. General chondroitin sulfate structure .................................................................... 2 1.3. Chondroitin sulfate biosynthesis ........................................................................... 3 1.4. Chondroitin sulfate proteoglycans ......................................................................... 5 1.4.1. Overview of chondroitin sulfate proteoglycans ............................................... 5 1.4.2. Proteins that interact with CS and CSPGs ...................................................... 6 1.4.3. Examples for the role of CSPGs in non-cancer diseases ............................... 7 1.4.3.1. CSPGs in the central nervous system and multiple sclerosis .................. 7 1.4.3.2. CSPGs in cartilage and osteoarthritis ...................................................... 8 1.5. Chondroitin sulfate in cancer ................................................................................. 9 1.5.1. Carbon-4 sulfation - CSA .............................................................................. 10 1.5.2. Carbon-6 sulfation - CSC ............................................................................. 11 1.5.3. Carbon-4,6 sulfation - CSE ........................................................................... 12 1.6. Oncofetal CSA .................................................................................................... 14 1.6.1. Placenta similarities to cancer ...................................................................... 14 1.6.2. Oncofetal chondroitin sulfate and the rVAR2 protein .................................... 15 1.6.3. Previous studies on oncofetal chondroitin sulfate ......................................... 16 1.6.3.1. Role of oncofetal chondroitin sulfate in cell motility ............................... 17 1.6.3.2. Novel therapeutic target in drug resistant bladder cancer...................... 17 1.6.3.3. Isolating circulating tumor cells .............................................................. 18 1.7. Central theme ..................................................................................................... 19 viii Chapter 2 â Investigate the effect of rVAR2 binding to cancer cells. ..................... 22 2.1. Background ......................................................................................................... 22 2.1.1. The NF-κB pathway ...................................................................................... 22 2.1.2. Role of NF-κB in select aspects of cancer biology ........................................ 24 2.1.2.1. Regulation of immunity by the NF-κB pathway ..................................... 24 2.1.2.2. The NF-κB pathway in cell proliferation and apoptosis ......................... 25 2.1.3. NF-κB inhibition as therapeutic target in cancer ........................................... 26 2.1.4. Chondroitin sulfate and the NF-κB pathway ................................................. 27 2.1.5. Rationale ...................................................................................................... 30 2.2. Materials and Methods ........................................................................................ 31 2.3. Results ................................................................................................................ 35 2.3.1. rVAR2 treatment in MG63 cells induce pro-inflammatory response ......... 35 2.3.2. rVAR2 alone is sufficient to induce pro-inflammatory gene expression .... 37 2.3.3. rVAR2 binds CSA to induce gene expression .......................................... 39 2.3.4. Endotoxins are not responsible for pro-inflammatory gene induction ....... 41 2.3.5. NF-κB pathway analysis ........................................................................... 43 2.3.6. Investigating the CD74/CD44 axis in rVAR2 induced response ............... 46 2.3.7. Activation of viral defense pathways ........................................................ 51 2.3.8. Conditional lethal reporter screen ............................................................. 56 2.3.8.1. Validating the conditional lethal reporter system with TNF-α ...... 58 2.3.8.2. Investigate the conditional lethal reporter response to rVAR2 .... 61 2.4. Discussion ........................................................................................................... 66 2.4.1. Role of oncofetal CSA in inflammation ..................................................... 66 2.4.2. Future possibilities with the conditional lethal reporter screen .................. 67 2.5. Conclusion .......................................................................................................... 69 Chapter 3 â Characterizing the cellular fate of rVAR2 in cancer cells .................... 70 3.1. Background ......................................................................................................... 70 3.1.1. Internalization pathways and their mechanisms ........................................... 70 ix 3.1.1.1. Endocytosis mechanisms ...................................................................... 70 3.1.1.2. Clathrin-mediated endocytosis .............................................................. 71 3.1.1.3. Caveolin-mediated endocytosis ............................................................. 72 3.1.1.4. Macropinocytosis ................................................................................... 72 3.1.2. Key cellular compartments involved in intracellular trafficking ...................... 73 3.1.2.1. Early endosome ..................................................................................... 73 3.1.2.2. Late endosome ...................................................................................... 74 3.1.2.3. Multivesicular body ................................................................................ 74 3.1.2.4. Lysosome .............................................................................................. 75 3.1.3. Exploiting intracellular trafficking in cancer ................................................... 76 3.1.3.1. Antibody-drug conjugates ...................................................................... 76 3.1.3.2. rVAR2-drug conjugates ......................................................................... 78 3.1.4. Rationale ...................................................................................................... 79 3.2. Materials and Methods ........................................................................................ 80 3.3. Results ................................................................................................................ 83 3.3.1. rVAR2 binds and internalizes into a variety of cancer cells ...................... 83 3.3.2. rVAR2 accumulates in cells over time ...................................................... 83 3.3.3. Internalization is a continuous process ..................................................... 86 3.3.4. Purified CSA compete for rVAR2 binding ................................................. 87 3.3.5. rVAR2 binding depends on sulfated ofCS ................................................ 89 3.3.6. Removing CS sulfation inhibits rVAR2 internalization .............................. 90 3.3.7. Clathrin and Caveolae-mediated endocytosis as a mechanism for rVAR2 internalization .................................................................................................... 91 3.3.8. Macropinocytosis as a mechanism for rVAR2 internalization ................... 94 3.3.9. Intracellular trafficking of rVAR2 ............................................................... 97 3.4. Discussion ......................................................................................................... 102 3.4.1. Designing future VDCs ........................................................................... 102 3.4.2. Abnormal rVAR2 accumulation in cells ................................................... 103 3.5. Conclusion ........................................................................................................ 105 x Chapter 4 â Establishing an rVAR2-based exosome detection strategy .............. 106 4.1. Background ....................................................................................................... 106 4.1.1. Extracellular vesicles .................................................................................. 106 4.1.2. Exosome biogenesis ................................................................................... 107 4.1.3. Exosomes in cancer biology ....................................................................... 108 4.1.3.1. Regulation of the immune microenvironment by exosomes ................ 108 4.1.3.2. Role of exosomes in metastases development ................................... 109 4.1.3.3 Exosomes in drug resistance ................................................................ 110 4.1.4. Exosomes as a biomarker for cancer development .................................... 111 4.1.5. Glycosaminoglycans and exosomes ........................................................... 112 4.1.6. Rationale .................................................................................................... 113 4.2. Materials and methods ...................................................................................... 114 4.3. Results .............................................................................................................. 118 4.3.1. rVAR2 is associated with exosomes ........................................................... 118 4.3.2. Cancer cell exosomes contain CSPGs ....................................................... 121 4.3.3. rVAR2 as detection tool for cancer exosomes ............................................ 122 4.3.4. ELISA based approach for exosome detection and capture ...................... 124 4.3.5. Utilizing rVAR2 for detection or capture of cancer derived exosomes ........ 126 4.3.6. Capture and detection of exosomes from cells pre-treated with rVAR2 ...... 128 4.4. Discussion ......................................................................................................... 132 4.5. Conclusion ........................................................................................................ 134 Chapter 5 â Concluding discussion ........................................................................ 135 Chapter 6 â Future Directions .................................................................................. 137 Bibliography .............................................................................................................. 142 Appendices ................................................................................................................ 170 xi List of figures Figure 1-1 Structure and composition of common GAGs ................................................ 1 Figure 1-2 Chondroitin sulfate subtypes. ......................................................................... 3 Figure 1-3 Chondroitin Sulfate biosynthesis .................................................................... 4 Figure 1-4 Recombinant VAR2 proteins ........................................................................ 16 Figure 2-i1 The canonical NF-κB pathway .................................................................... 23 Figure 2-1 rVAR2 induce global gene expression changes ........................................... 36 Figure 2-2 rVAR2 upregulated pro-inflammatory cytokine expression .......................... 37 Figure 2-3 rVAR2 alone is sufficient to induce gene expression ................................... 38 Figure 2-4 CSA competition inhibits rVAR2 induced gene expression .......................... 39 Figure 2-5 rVAR2 induced pro-inflammatory response requires sulfated CS ................ 41 Figure 2-6 Endotoxin free rVAR2 also induces pro-inflammatory response .................. 42 Figure 2-7 rVAR2 induces IκBα degradation ................................................................. 44 Figure 2-8 rVAR2 induces p65 nuclear translocation .................................................... 45 Figure 2-9 IκBα degradation correspond with p65 nuclear translocation ....................... 46 Figure 2-10 CD74 expression in cancer cell lines ......................................................... 47 Figure 2-11 CD44 expression in cancer cell lines ......................................................... 48 Figure 2-12 CD44 is modified with CS in MG63 cells .................................................... 48 Figure 2-13 rVAR2 and CD44 do not interact in MG63 cells ........................................ 50 Figure 2-14 rVAR2 activates viral defense pathways .................................................... 51 Figure 2-15 TBK1 is phosphorylated after rVAR2 treatment ......................................... 52 Figure 2-16 TBK1 phosphorylation timepoint ................................................................ 53 Figure 2-17 TBK1 phosphorylation timepoint repeats ................................................... 54 Figure 2-18 Conditional lethal reporter screen .............................................................. 57 Figure 2-19 dTK expression .......................................................................................... 59 Figure 2-20 dTK expression in response to TNF-α stimulation ..................................... 60 Figure 2-21 TNF-α induces GCV dependent cell death in dTK expressing cells ........... 61 Figure 2-22 IL8 and dTK expression in response to rVAR2 .......................................... 62 Figure 2-23 GCV response after rVAR2 stimulation ...................................................... 63 Figure 2-24 Cell proliferation progression in response to rVAR2 and GCV treatments . 65 Figure 3-i1 Major endocytic mechanisms ...................................................................... 70 Figure 3-i2 Intracellular trafficking ................................................................................. 76 xii Figure 3-1 rVAR2 internalization in cancer cell lines ..................................................... 83 Figure 3-2 Time lapse of rVAR2 internalization in COLO-205 cells ............................... 84 Figure 3-3 Intracellular rVAR2 accumulation ................................................................. 85 Figure 3-4 rVAR2 is continuously internalized ............................................................... 85 Figure 3-5 rVAR2 internalization depends on cell surface CS binding .......................... 88 Figure 3-6 Sodium chlorate inhibit rVAR2 binding ......................................................... 90 Figure 3-7 Sodium chlorate inhibits rVAR2 internalization ............................................ 91 Figure 3-8 Knockdown of clathrin or caveolae proteins alters rVAR2 internalization .... 93 Figure 3-9 Cytochalasin D alters rVAR2 binding and internalization ............................. 95 Figure 3-10 rVAR2 colocalizes with Dextran ................................................................. 96 Figure 3-11 rVAR2 is associated with micropinocytosis membrane structures ............. 96 Figure 3-12 rVAR2 colocalizes with early endosomes .................................................. 98 Figure 3-13 rVAR2 is inside the early endosome lumen ............................................... 99 Figure 3-14 rVAR2 does not colocalize with lysosomes .............................................. 100 Figure 3-15 Colocalization analysis ............................................................................. 101 Figure 4-i1 Extracellular vesicle distinction .................................................................. 106 Figure 4-i2 Exosome biogenesis ................................................................................. 108 Figure 4-i3 Flow chart for rVAR2 based purification of cancer derived exosomes ...... 113 Figure 4-1 rVAR2 colocalizes with exosome markers inside cells ............................... 119 Figure 4-2 rVAR2 is associated with purified exosomes ............................................. 120 Figure 4-3 Gene ontology analysis of purified exosomes ............................................ 120 Figure 4-4 PC3 and MG63 derived exosomes contain CSPGs ................................... 122 Figure 4-5 rVAR2 binds to PC3 exosomes .................................................................. 123 Figure 4-6 Schematic of sandwich ELISA for exosome detection ............................... 124 Figure 4-7 Evaluation of ELISA based exosome capture and detection...................... 126 Figure 4-8 rVAR2-ST did not detect cancer derived exosomes .................................. 127 Figure 4-9 rVAR2 cannot capture cancer derived exosomes ...................................... 128 Figure 4-10 Capture and detection of exosomes from cells pre-treated with rVAR2 ... 130 Figure 4-11 Using anti-V5 antibody to detect rVAR2 on captured exosomes .............. 131 xiii List of abbreviations ABCL Activated B-cell lymphoma ADC Antibody-drug conjugates AP2 Adaptor protein 2 B3GAT β3-glucuronyltransferase B4GALT7 β4-galactosyltransferase 7 B4GALT6 β3-galactosyltransferase 6 BCa Breast cancer BET Bromodomain and Extra-Terminal motif CavME Caveolin-mediated endocytosis CCP Clathrin coated pits CD44 Cluster of differentiation 44 CD63 Cluster of differentiation 63 CD74 Cluster of differentiation 74 CD81 Cluster of differentiation 81 CD9 Cluster of differentiation 9 ChaseABC Chondroitinase ABC CHC Clathrin heavy chain CHST Chondroitin sulfotransferase CLC Clathrin light chain CME Clathrin-mediated endocytosis CNS Central nervous system xiv COPD Chronic obstructive pulmonary disease CPZ Chlorpromazine CRISPR Clustered regularly interspaced short palindromic repeats CS Chondroitin sulfate CSA Chondroitin sulfate A CSC Chondroitin sulfate C CSE Chondroitin sulfate E CSGALNACT CS GalNAc transferase CSPG Chondroitin sulfate proteoglycan CTC Circulating tumor cell CytoD Cytochalasin D DAMP Damage-associated molecular patterns DNA Deoxyribonucleic acid DS Dermatan sulfate dTK Delta HSV1-thymidine kinase ECM Extracellular matrix EE Early eodosome ER Endoplasmic reticulum EV Extracellular vesicle FBS Fetal bovine serum FDA Food and drug administration FDR False discovery rate FGF Fibroblast growth factor xv FGFR Fibroblast growth factor receptor FOV Field of view GAG Glycosaminoglycan GAGBP Glycosaminoglycan binding protein Gal Galactose GalNAc N-acetylgalactosamine GCV Ganciclovir GlcA Glycuronic acid GPC1 Glypican-1 GSTP1 Glutathione S-transferase P1 HA Hyaluronic acid HS Heparan sulfate HSPG Heparan sulfate proteoglycan IKK IκB-kinase IL Interleukin IL-1B Interleukin 1B IL-6 Interleukin 6 IL-8 Interleukin 8 ILV Intraluminal vesicles iRBC Infected red blood cell IκB Inhibitor of kappaB IκBα Inhibitor of kappaB α LAMP1 Lysosome associated membrane protein 1 xvi LE Late endosome LPS Lipopolysaccharide MMP Metalloproteinase MMP2 Metalloproteinase 2 MMP9 Metalloproteinase 9 MP Macropinocytosis mRNA Messenger RNA MS Multiple sclerosis MVB Multivesicular body N-cad N-cadherin NEDD9 Neural-precursor cell expressed, developmentally down-regulated 9 NF-κB Nuclear factor-kappaB NTA Nanoparticle tracking analysis Nys Nystatin OA Osteoarthritis of-CSA Oncofetal chondroitin sulfate A PAMP Pathogen-associated molecular patterns PBS Phosphate buffer saline PG Proteoglycan P-gp P-glycoprotein pl-CSA Placental chondroitin sulfate A Poly:IC Polyinosinic:polycytidylic acid PRR Pattern recognition receptors xvii pTBK1 Phospho TANK binding kinase 1 qPCR Quantative polymerase chain reaction rContr Recombinant control protein RNA Ribonucleic acid ROI Region of interest rVAR2 Recombinant VAR2CSA SC Sodium chlorate SD Standard deviation SEM Standard error of mean ST Sulfo-Tag TBK1 TANK binding kinase 1 TEM Transmission electron microscopy TNFR Tumor necrosis factor receptor TNF-α Tumor necrosis factor α VDC rVAR2-drug conjugates Xyl Xylose XYLT1 Xylosyltransferase 1 XYLT2 Xylosyltransferase 2 xviii Acknowledgements The research conducted in this dissertation would not be possible without the guidance and mentorship of my supervisor Dr. Mads Daugaard. I am grateful for his belief and support in me throughout out my degree. I would like to extend my gratitude especially Dr. Nader al Nakouzi who has given me unparalleled support and friendship for which I am forever grateful. I would also like to thank my entire supervisory committee â Dr. Catherin Pallen, Dr. Kevin Benne With, Dr. Alexander Wyatt, and Dr. Pauline Johnson â for the excellent feedback and advice they provided over the past 5 years. In addition, I would like to thank the members of the Daugaard lab, past and present. they have been a source of strength, joy, and inspiration for me. If it were for all of you, this journey would have been quite boring. Lastly, and most importantly, I will always be grateful to my parents whoâs sacrifice and hard work has given me the opportunity to pursue my dreams. xix Dedication I dedicate my work to my parents âåäººæ ½æ ï¼åäººä¹åâ One generation plant the trees under whose shade future generations rests 1 Chapter 1 â Introduction: Chondroitin sulfate in cancer 1.1. Glycosaminoglycans Glycosaminoglycans (GAG) are large complex molecules consisting of long chains (20-200) of repeating disaccharide subunits. The main subtypes of GAGs are hyaluronic acid (HA), dermatan sulfate (DS), chondroitin sulfate (CS), heparin, and heparan sulfate (HS)[1]. GAGs can exist as free long chains or be attached to a protein core forming a proteoglycan (PG)[1]. The basic subunit of a linear GAG chain is a disaccharide consisting of an uronic acid and an amino sugar[1]. Variations in these subunits lead to the formation of the different types of GAG chains (Fig. 1-1). A complex panel of enzymes elongate and degrade the sugar chain while sulfotransferases sulfate the disaccharides at different positions leading to diverse GAGs chains with a variety of functions[2]. This dissertation will primarily focus on chondroitin sulfate. Fig. 1-1 Structure and composition of common GAGs. Schematic representing the carbohydrate components that constitute a basic linear GAG chain. 2 1.2. General chondroitin sulfate structure Chondroitin sulfate chains are long, unbranching, linear polysaccharides made up of repeating disaccharide subunits of N-acetylgalactosamine (GalNAc) and glucuronic acid (GlcA) residues[2]. A single CS chain can consist of up to several hundred covalently linked (GalNAC)-(GlcA) disaccharides. There are three main subtypes of CS based on the sulfation pattern. CSA is sulfated on carbon-4 of GalNAc (C4S), CSC is sulfated on carbon-6 of GalNAc (C6S), and CSE is sulfated on carbon-4 and carbon-6 of GalNAc (C4,6S) (Fig. 1-2)[3]. A fourth, lesser-known variant, CSD is sulfated on carbon-2 of GlcA and carbon-6 of GalNAc (C2,6S). Previously there was a class known as CSB, but it was reclassified as dermatan sulfate (DS) due to the action of epimerases that converted GlcA residue to a stereoisomer, iduronic acid (IdoA)[4]. These two residues are chemically identical but structurally different resulting in altered properties of the chain[4]. CS is usually found attached to proteins creating chondroitin sulfate proteoglycans (CSPGs) which are ubiquitously expressed on cell surfaces and in the stroma[1]. Figure 1-2 highlights the different sulfation patterns that are present in the three prominent types of CS. 3 Fig. 1-2 Chondroitin sulfate subtypes. Schematic representing the different CS subtypes. Position of the sulfate groups are shown on the disaccharide residues and along the entire chain. 1.3. Chondroitin sulfate biosynthesis CS biosynthesis is a complex process involving many enzymes that perform specific tasks such as linkage, initiation, chain polymerization, and chain modification of specific sites[2]. Synthesis begins with the attachment of CS molecules to proteins in the endoplasmic reticulum (ER). A tetrasaccharide linker of Xylose(Xyl)âGalactose(Gal)âGalactose(Gal)âGlcA is covalently bonded to specific serine residues on the core protein (Fig. 1-1). These serine residues are typically found on the peptide sequence Ser-Gly-X-Gly where X could be any amino acid[5]. Each monosaccharide in the linker is attached by a specific glycosyltransferase in the following order: Xylosyltransferase 1/2 4 (XYLT1/2)[6], β4-galactosyltransferase 7 (B4GALT7)[7, 8], β3-galactosyltransferase 6 (B3GALT6)[9], and finally β3-glucuronyltransferase 1/2/3 (B3GAT1/2/3)[9-11] (Fig. 1-3A). The same tetrasaccharide linker is used for all species of GAGs and the addition of the first monosaccharide immediately after the linker determines the type of GAG being produced[12]. For example, the addition of GlcNAc versus GalNAc following the linker results in the creation of HS and CS, respectively[12]. The addition of the first monosaccharide post linker is critical for the selective assembly of the GAG chains. In CS synthesis, the first GalNAc residue is attached to the linker by CS GalNAc transferase 1 (CSGALNACT1)[12-14]. This triggers the synthesis of the full CS chain by the actions of GlcA transferase II (GlcAT-II) and CS GalNAc transferase 2 (CSGALNACT2) which elongate the chain by adding GlcA and GalNAc[12, 13, 15], respectively (Fig. 1-3A). This creates the basic CS backbone. Fig. 1-3 Chondroitin Sulfate biosynthesis. A) Schematic representing the process of CS biosynthesis with the enzymes that are required for each step. B) Addition of sulfate groups on the GalNAc residue of CS disaccharide. Sulfation is controlled by the actions of several sulfotransferases (ST)[16]. Chondroitin sulfotransferase 11/12/13/14 (CHST11/12/13/14) sulfate carbon-4[17-20] and 5 CHST3/7/15 sulfate carbon-6[21-23] (Fig. 1-3B). Each site can be sulfated by multiple sulfotransferases however each sulfotransferase can only add sulfate to one site, creating a redundant system. All enzymes involved in CS biosynthesis show spatial and temporal regulation as well as cell type-dependent expression. This leads to heterogeneity in the CS pattern ranging from the length of the CS chains to the type and relative abundance of the different sulfations. 1.4. Chondroitin sulfate proteoglycans 1.4.1. Overview of chondroitin sulfate proteoglycans Like all proteoglycans, CSPGS are made up of a core protein attached to one or more linear unbranched CS chains. The chains are modified with the addition of sulfate groups at different positions on the CS chain. The type of CS chain is determined by the site of sulfation as detailed in section 1.2.1. Production of CSPGs starts with the production of the core protein in the ER followed by the attachment of the tetrasaccharide linker[2]. This complex is then transported to the Golgi apparatus where the CS chain is attached, elongated, and sulfated[2]. Once complete, the CSPG is transported to its designated destination which is either secreted out of the cell or embedded in the cell membrane. The prominent role of CSPGs in the extracellular matrix (ECM) is a key regulator of information between the cell and its surrounding environment[24-26]. Because of this, CSPGs have been documented to regulate a large set of biological functions, such as the development of the nervous system[24, 25] and controlling immune cell movement[26-6 28]. The following sections will be focused on the role of CSPGs in a select few biological functions. 1.4.2. Proteins that interact with CS and CSPGs All proteins that interact with CS belong to a larger family of proteins known as Glycosaminoglycan binding proteins (GAGBP). There are several hundred identified GAGBPs that interact with the different classes of GAGs[29]. Interactions between GAGBPs and GAGs are important to many physiological processes such as hemostasis, internalization, cell proliferation, cell migration, and immune modulation[29]. The structural diversity of CSPGs facilitates interaction with a broad range of molecules[24, 30-32]. The function of CSPGs is determined by a combination of factors, such as the core protein, type of CS chain, and the spatial-temporal localization of the entire CSPG complex[24, 30-32]. In addition, the sulfation patterns of CS chains have been shown to be critical in regulating specific molecular interactions. Studies on CS binding molecules have revealed that several CS subtypes bind with high affinity to different signaling molecules such as midkine[33, 34], fibroblast growth factors (FGF)[35-38], chemokines[28, 39-42], and pleiotrophin[33, 43]. 7 1.4.3. Examples for the role of CSPGs in non-cancer diseases 1.4.3.1. CSPGs in the central nervous system and multiple sclerosis CSPGs are especially prominent in the central nervous system (CNS). The largest family of CSPGs found in the CNS is known as the lectican group which includes aggrecan, four versican isoforms (V0, V1, V2, V3), neurocan, and brevican[44, 45]. Each member of the lectican family differs in their CS GAG chain attachment. Some have as few as five while others have up to several hundred chains. The function of individual CSPGs in the CNS is determined by the chain variation, length, and sulfation status. Depending on the pattern, CS chains can either be inhibitory or permissive for axonal growth[46]. For example, CSPGs that carry CSA chains are known to inhibit axonal growth[47] and this has been described as a major contributor to conditions such as multiple sclerosis (MS) which is characterized by chronic demyelination and axonal degradation of the CNS. The main drivers of these characteristics of MS are alterations in the immune environment leading to neurodegenerative change[48]. A prominent phenotype of MS is the migration of activated lymphocytes into the CNS parenchyma leading to myelin degradation and axonal destruction. Previous research shows how the ECM can regulate this process leading to direct implications in the development of MS. CSPGs are a major component of the ECM and have been found to be highly expressed in the expanding edge of MS lesions[49]. CSPGs have been recorded to have inhibitory effects on axonal regeneration and remyelination. In addition, they create a pro-inflammatory environment that can activate leukocytes and enhance their migration[26]. Target therapy based on small molecule inhibitors of CSPG production[50, 51] and 8 enzymatic removal of CS chains[52-54] have been developed to reverse the adverse effect of CSPGs on CNS regeneration in mice models of MS. 1.4.3.2. CSPGs in cartilage and osteoarthritis Cartilage is a type of connective tissue that is characterized by a complete lack of vasculature and the presence of specialized chondrocytes surrounded by specialized ECM[55]. Cartilage has two major functions: to reduce friction during movement and to lessen the physical impact on joints during motion[23]. The major components of cartilage ECM are type II collagen and CSPGs. The main CSPGs in cartilage are aggrecans which can contain up to 100 highly sulfated CS chains, consequently making them highly negatively charged[56]. They form large aggregates of PGs by attaching to the hyaluronan framework of cartilage. Water is attracted to the negatively charged aggregates giving the entire cartilage elastic properties to resist compression[56]. At the same time, water acts as a lubricant for bones and joints which helps the joint move around almost frictionless[56]. These properties of CSPGs highlights them as an essential component of cartilage and are necessary to maintain proper function It has been well established that oral ingestion of CS has a beneficial effect in patients with moderate to severe painful osteoarthritis (OA)[57]. The effect is theorized to be a result of the anti-inflammatory effects of CS in OA patients[58, 59]. This suggests that CSPGs could have immune-modulating effects. Several mechanisms have been proposed by which CS and CSPGs can interact and modulate the immune system. For example, intraperitoneal injections of CSA in mice with arthritis inhibited NF-κB activation and decreased inflammation[60]. Further experiments on THP-1 macrophage cells 9 showed that CSA prevented pathogen and damage associated molecules from binding to receptors on THP-1 cells[61]. This inhibited the ability of DAMPS to activate the NF-κB pathway and by extension produced an anti-inflammatory effect. Furthermore, CS chains have been reported to bind and regulate the movement of immune cells to tissues in order to control immune activity[28, 39-42]. 1.5. Chondroitin sulfate in cancer Given the biological importance of CSPGs in normal physiology, it is not surprising that CS and CSPGs have been implicated in cancer[62-65]. Various CSPGs can be found altered in almost all the different hallmarks of cancer. Previous studies have alluded to the importance of fibroblast growth factor to heparan sulfate (FGF-HSPG) interaction in biological functions[66-69]. Studies showed that HSPGs can contribute to cancer development by stabilizing FGF-FGFR interactions leading to increased proliferative signaling[68-71]. Similarly, studies in triple-negative breast cancers show that CS can bind P-selectins and contribute to metastatic dissemination[64, 72]. Furthermore, experiments performed on melanoma cells showed that CSA enhances matrix metalloproteinase 2 (MMP2) activation leading to increased metastasis[73]. Altogether, these data establish the importance of CS in cancer biology. There are currently three known types of CS that are classified based on the location of the sulfation site on the disaccharide subunit. The distribution of sulfation along the entire chain can be altered depending on context (Fig. 1-2). The biological relevance of each CS subtype in cancer is explored in the following sections. 10 1.5.1. Carbon-4 sulfation - CSA Carbon-4 sulfation (C4S) is a modification of CS where sulfate is added onto carbon 4 of the GalNAc sugar by CHST11/12/13/14 forming CSA. It is the most common type of sulfation modification for CS. CSA has a variety of biologically relevant functions. Evidence of how CSA contributes to cancer comes from studies of the CHST11 enzyme. CHST11 has been found to be highly expressed in breast cancer cell lines and correlate with high P-selectin binding of CSPG4[64]. CHST11 and CSPG4 expression was also observed in breast cancer tissue samples[64]. Further experiments in a 4T1 murine metastatic model showed that enzymatic removal of CSA reduced lung metastasis and promoted survival[64]. Other studies on CHST11 have shown the same pro-metastatic effect in hepatocellular carcinoma[74]. CHST11 has also been shown to regulate the epithelial-to-mesenchymal transition (EMT) in cancer stem cells[75]. More than 50% of melanoma cases express the BRAF-V600E mutation[76]. Initial treatment using a small molecule inhibitor, vemurafenib, is effective in reducing tumor burden[76-78]. However, resistance eventually develops, possibly due to the activation of receptor tyrosine kinase and the KRAS pathway[79]. A recent study showed that dietary CSA has tumorigenic effects on BRAF-V600E expressing melanoma cells where oral ingestion of CSA increased tumor burden in patient-derived xenografts (PDXs) and xenografts of melanoma mice models[80]. In addition, CSA also conferred resistance to BRAF-V600E inhibitors[80]. A proposed mechanism for this effect is that internalized CSA enhances casein kinase 2 (CK2) binding and the phosphorylation of PTEN, resulting in the inhibition of PTEN. This step is essential for sustained AKT activation in melanoma 11 cells that do not express mutated KRAS. This is interesting because dietary CSA is a widely available supplement used to reduce pain in moderate to severe OA. Because it is classified as a supplement, it does not require FDA approval. 1.5.2. Carbon-6 sulfation - CSC The role of CSC in cancer is rather poorly understood. There seems to be conflicting data on the effect of CSC in tumor progression[62]. ECM remodeling has shown that most tumors exhibit increased CS content[62], particularly CSC suggesting it plays an important role for tumor formation and growth[81-84]. While at the same time, CSC is shown to regulate inflammation and immune modulation in cancer. Studies of bone marrow macrophages showed that exposure to CSC induced overexpression of IL-10[85]. IL-10 is known for reducing inflammation in the tumor microenvironment[86]. It is produced by regulatory T-cells in the tumor and is the main driver for stunted CD8+ T-cell induced cell death[86]. Understanding the role of IL-10 in regulating an immune response is key to developing successful immunotherapies against cancer. Of all the different species of CS, CSC exhibits the lowest binding affinity for cytokines[87]. Not surprisingly, nuclear magnetic resonance spectroscopy showed that CSC had the lowest binding affinity to IL-10[88] and CSC had the least interference with the biological activities of IL-10 of all of the CS types[87]. The overabundance of CSC in tumors creates an environment where IL-10 activity is at its highest, resulting in reduced immune activity in the tumor. In addition, synthetic CSC hexasaccharides have been shown to bind well to IL-8 in comparison to other CS types[89]. Treatment with GAGs enhances the production of IL-8 induced reactive oxygen species (ROS) in neutrophils[90]. This suggests that the accumulation of IL-8 and CSC creates conditions that activate neutrophils. High 12 neutrophil infiltration in tumors has been associated with poor outcomes due to their ECM degrading effects[91]. This potentially results in an environment where the ECM of the tumor promotes tumor cell migration and dissemination. However, there are studies that also show that CSC may have the opposite effect on metastasis. Melanoma cell migration was significantly reduced by CSC when co-cultured with fibroblasts[92]. The proposed mechanism for this observation is that treatment with CSC creates acidic conditions in the environment which leads to stronger adhesion of the cells thereby preventing their migration[92]. Moreover, CSC treatment had no effect on the invasiveness of EMT6 breast cancer cells in 3D cultures[93]. Furthermore, migration and invasion are dependent on the proteolysis of ECM components at the leading edge of the migrating cells which are typically dependent on MMP activity. Activation of MMP2 is facilitated by CS and is dependent on the sulfation[73]. In this scenario, CSA is a better activator of MMP2 than CSC, therefore by depositing large amounts of CSC instead of CSA, MMP2 activity is reduced which in turn will reduce ECM degradation and cell migration[73]. Overall, CSC plays a complex role in cancer progression. It bears both pro- and anti-tumor effects. To utilize CSCs in the development of new therapies, more studies are needed on the biological role of CSCs in cancer. 1.5.3. Carbon-4,6 sulfation - CSE CSE is a subtype of CS that is sulfated at both carbon-4 and carbon-6 positions of the GalNAc monosaccharide. The enzymes involved in CSE synthesis are upregulated 13 in breast cancer cell lines suggesting that there is an increased production of CSE in those cells[94]. Basal-like breast cancer (BCa) cells, such as MDA-MB-231 and BT-549, exhibit an enhanced invasive capacity when treated with CSE[93-95]. One of the known binding partners for CSE is N-cadherins (N-cad), where CSE interaction regulates N-cad dependent signaling in osteoblastoma cells[96]. N-cads are also highly expressed in BCa cells and are believed to contribute to the invasive phenotypes of aggressive BCa cell lines. In BT-549 cells, CSE may serve as a ligand for signal transduction through the N-cadherin/β-catenin signaling pathway[95]. Matrix metalloproteinase 9 (MMP9) is produced as a result and degrades the surrounding ECM, allowing for the enhanced invasiveness of BT-549 cells[95]. In addition, there is emerging evidence that suggests the adhesion protein neural precursor cell expressed, developmentally down-regulated 9 (NEDD9) plays a role in breast cancer development and metastasis in a CSE dependent manner[94]. HCC38 breast cancer cells engineered to overexpress NEDD9 (HCC38-NEDD9) have highly upregulated levels of CD44 expression compared to the parental cell lines[94]. Antibodies against CSE confirmed the high expression of CSE in HCC38-NEDD9 cells and immunoprecipitation experiments confirmed that CSE is complexed with the CD44 core protein[94]. Enzymatic removal of all CS chains using chondroitinaseABC (chABC) and competitive blocking with anti-CSE antibodies inhibit cell colony formation of HCC38-NEDD9 cells[94]. Altogether, these data suggest that NEDD9 contributes to malignant phenotypes through the overexpression of CD44 and CSE. Studies in ovarian adenocarcinoma also showed increased expression of CSE biosynthesis enzymes in various cell lines[97]. Antibodies directed against CSE showed elevated levels of CSE in the ECM and on the cell surface of ovarian cancer cells compared to normal ovary. The 14 same antibody was able to compete with VEGF for CSE binding, implying that CSE plays a role in angiogenesis, although the exact nature of this is still being studied[97]. 1.6. Oncofetal chondroitin sulfate 1.6.1. Placenta similarities to cancer The placenta is a highly active organ in early-stage development that shares striking similarities to cancer development and progression[98]. Most notable are its high proliferation rate, its ability to invade and migrate into surrounding tissue (uterus), its ability to form new vascular networks, and its inhibitory effect on the local immune system of the mother[98]. Not surprisingly, cancer cells have taken advantage of these placental mechanisms for their own benefit[98]. These similarities have led researchers to investigate the shared mechanisms between cancer cells and placental cells to explore novel therapeutic opportunities. There are several molecular pathways of interest that are shared between the placenta and cancer. For example, they both utilize a highly active IGF pathway that stimulates MAPK via PI3K for cell growth and proliferation, and they both escape apoptosis via BCL2 expression[99]. Targeting a mechanism that is common between the placenta and cancer has great benefits. One of the major issues facing current targeted therapy is the adverse effects that a new drug may have because normal tissues expressing the same targets would also be harmed. A common target between the placenta and cancer would minimize this problem because the placenta is not normally present in the body. Therefore, it would be beneficial to find commonalities between the placenta and cancer so new therapies can be developed. 15 1.6.2. Oncofetal chondroitin sulfate and the rVAR2 protein The malaria parasite Plasmodium falciparum is an endemic problem in various parts of the world. It infects up to 200 million people and is responsible for >400,000 deaths worldwide[100]. Serious complications associated with malaria stem from its ability to disrupt the normal function of organs. When the malaria parasite replicates in an infected red blood cell (iRBC) it becomes susceptible to clearance via the spleen[100]. To avoid this problem, the parasite expresses several malarial adherence proteins on the surface of iRBCs[100]. These adhesion proteins bind various receptors around the host vasculature. This effectively removes iRBCs from circulation allowing the parasite to evade splenic clearance and replicate safely[100]. Sequestration of the iRBCs in the blood vessels blocks blood flow leading to organ damage resulting in severe forms of malaria such as cerebral malaria which often results in death[100]. In pregnant women, malaria is a leading cause of stillbirths and miscarriages in endemic areas. VAR2CSA is the malarial adhesion protein that mediates iRBC binding to placental syncytiotrophoblasts. The VAR2CSA protein recognizes a distinct type of CSA found only in the placenta[101-103]. Subsequent studies determined that placental CSA (pl-CSA) is over-sulfated on carbon-4 of the GalNAc sugar, making it unique compared to CSA found elsewhere in the body. Because of the similarities shared between the placenta and cancer, we explored the possibility that the same pl-CSA may also be present on cancer cells in a previous study[104]. A recombinant VAR2CSA (rVAR2) protein containing the minimal binding domain was created along with a control protein (rContr) that contains a non-CSA binding domain (Fig. 1-4)[104]. We showed that rVAR2 can bind tumor samples from >90% of cancer types and that the binding was 16 specific to rVAR2âCSA interaction since competition with purified CSA and enzymatic degradation of CSA was able to inhibit rVAR2 binding[104]. We subsequently showed that rVAR2 is an effective drug delivery tool in several in vivo models with minimal off-target toxicity[104]. This pilot study provided strong evidence that cancer cells indeed express unique oncofetal CS (ofCS) on the cell surface which can be targeted by the rVAR2 protein. Fig. 1-4. Recombinant VAR2 proteins. Schematic representing the full-length P. falciparum VAR2CSA protein as well as recombinant proteins with the minimal binding domain (rVAR2) and recombinant control protein from a non-binding region (rContr). V5 tags are present on recombinant proteins for detection. Adapted from Salanti and Clausen et al. Cancer Cell (2015) 28(4): 500-514.[104] 1.6.3. Previous studies on oncofetal chondroitin sulfate us rVAR2 Since rVAR2 binds ofCS presented on cancer cells, it has been utilized to understand the role of ofCS in biology. In addition, rVAR2 has also been used to develop new technologies to target ofCS for therapeutic and diagnostic purposes. A selection of studies is highlighted in the following sections to demonstrate the benefits of using rVAR2 to study cancer biology. 17 1.6.3.1. Role of oncofetal chondroitin sulfate in cell motility Previous sections (1.5) have described studies that provide evidence that different CS subtypes are implicated in cancer metastasis. The mechanism by which they influence metastasis is highly context dependent, however, the exact role that ofCS plays in metastasis is rather vague. In a study by Thomas Mandel-Clausen et al., they demonstrated that ofCS has an important role in cell adhesion and motility with implications in metastasis[105]. Several cancer cell lines were treated with rVAR2 and were shown to have reduced binding capacity to a plethora of ECM molecules such as fibronectin and collagen[105]. In MG63 cells, rVAR2 treatment was effective in reducing cell migration and invasion[105]. This effect was shown to be a result of decreased integrin signaling since rVAR2 treatment stunted Src and p-300cas phosphorylation[105]. 1.6.3.2. Novel therapeutic target in drug resistant bladder cancer Bladder cancer is an aggressive malignancy that has very few treatment options available[106]. Cisplatin based therapies are the current gold standard in treating muscle invasive bladder cancer[106]. However, there are very few long-term responses to cisplatin and most patients develop resistance rapidly[106]. In high grade cisplatin-resistant bladder cancer patients, ofCS is overexpressed[107]. In mice models, rVAR2 based drug delivery was able to significantly decrease tumor burden and improve overall survival[107]. This demonstrated that rVAR2 drug conjugates can be a promising second line therapy for cisplatin resistant bladder cancer. 18 1.6.3.3. Isolating circulating tumor cells Circulating tumor cells (CTCs) are cancer cells that have escaped the primary tumor and are found in the blood stream[108]. While not all CTCs are the same, some have the ability to generate new metastatic tumors at distant sites in the body[108]. The concentration of CTCs in the blood have been well correlated to disease stage in several cancer types[108]. In the study by Agerbæk et al., rVAR2 was used to develop a protocol to isolate CTCs from the blood through its interaction with ofCS[109]. It was shown that rVAR2 was able to capture a larger quantity of CTCs when compared to traditional EpCAM based isolation[109]. Furthermore, the concentration of isolated CTCs using rVAR2 was strongly correlated to disease stage in prostate cancer patient samples[109]. 19 1.7 Central theme We have previously demonstrated that rVAR2 can specifically target ofCS on cancer cells. This discovery has led to programs that aims to develop rVAR2 as a platform technology to detect and target solid tumors. Using xenograft mice models, we showed that rVAR2 can be used to shuttle cytotoxic drugs to tumors. To further improve the utility of the rVAR2 technology, the cellular consequences of rVAR2 binding as well as its intracellular fate needs to be established. My central hypothesis is that rVAR2 binding to cancer cells would lead to alterations in intracellular signaling pathways and cellular behavior. In my dissertation, I have investigated my hypothesis in three research chapters: Chapter 2: Investigate the effect of rVAR2 binding to cancer cells Current research provides evidence that the CS component on CSPGs is a key factor in inflammatory diseases such as MS and arthritis. Multiple studies have shown that the inflammatory phenotype of these diseases can be controlled by manipulating the CS content. Additionally, CS has been shown to regulate NF-κB activity, which is a key pathway that controls inflammation. However, the exact relationship between CS, especially cell surface CS, and the NF-κB pathway is not fully understood. We have shown that ofCS is expressed ubiquitously on cancer cells redundantly attached to a limited repertoire of CSPGs[101, 102]. Many of these CSPGs, like CD44, are known receptors that can activate signaling pathways[107-109]. Therefore, the interaction of rVAR2 with ofCS on the cancer cell plasma membranes might initiate specific pathway signaling. Using rVAR2 as a ligand, I investigated the role of ofCSA in the NF-κB pathway 20 in cancer cells. A deeper understanding of how rVAR2 can alter signaling pathways can potentially help guide or finetune the application of rVAR2-bases therapies. In addition, investigating the mechanisms of how rVAR2 may induce the NF-κB pathway could provide a link between ofCS and inflammation in cancer. Chapter 3: Characterize the cellular fate of rVAR2 in cancer cells The interaction between rVAR2 and ofCS on cancer cells provides an opportunity to develop rVAR2 drug conjugates (VDC) as new anti-cancer therapeutics. VDCs are made up of a cytotoxic compound attached to the rVAR2 protein via an enzyme-cleavable linker. Our lab has shown the effectiveness of VDCs in xenograft mice models in previous studies. The efficacy of VDCs can potentially be further improved by a more detailed understanding of the internalization process and intracellular trafficking of rVAR2. Internalized proteins are often trafficked through the endo-lysosomal pathway where each compartment has unique biochemical properties and catalytic enzymes. These characteristics can potentially alter the efficacy of VDCs. Deciphering the intracellular trafficking of rVAR2 could be useful in selecting appropriate linkers and payloads for further VDC development. This knowledge may help to design more potent VDCs to bolster its efficacy. Chapter 4: Establishing an rVAR2-based exosome detection strategy Exosomes are membrane bound extracellular vesicles that originates in the endo-lysosomal system of cells, meaning that exosomes carry molecular information of their cell of origin. Exosomes are secreted out of cells and circulate in the blood. This 21 characteristic has made exosomes a focus of extensive study to be used as biomarkers for cancer diagnosis and prognosis. Therefore, the ability to specifically isolate cancer-derived exosomes for diagnostic purposes is being investigated. Recent studies have provided evidence showing that exosomes from tumors contain proteoglycans, which may carry CS chains. Since these exosomes originate from cancer cells, they could be carrying ofCS and potentially be targeted using rVAR2. In this aim, I will investigate whether rVAR2 can be used to isolate cancer-derived exosomes for diagnostic purposes. 22 Chapter 2: Investigate the effect of rVAR2 binding to cancer cells 2.1. Background 2.1.1. The NF-κB pathway The Nuclear Factor-kappaB (NF-κB) proteins are a group of five transcription factors that bind DNA and regulate gene transcription[110]. Members of this family include p65, p100, p105, RELB, and c-REL transcription factors and they can form up to 15 different homo- or heterodimeric complexes[110]. The NF-κB transcription factors are known regulators of many cellular and biological functions, including the innate and adaptive immune responses and cell proliferation[110]. The NF-κB transcription factors are the termini of a pathway that is activated by a variety of extrinsic signals[110]. Once stimulated, this pathway activates downstream proteins that to alter gene transcription as a response to the stimuli[110]. Many pro-inflammatory signals, such as viral and bacterial infections, damage-associated proteins, and pro-inflammatory cytokines can activate the NF-κB pathway[111]. There are generally two types of NF-κB activation, the canonical and the non-canonical pathways[111]. The canonical pathway is the typical rapid response pathway that is stimulated by pro-inflammatory signals binding to their receptors. The non-canonical pathway responds slower since it depends on de novo synthesis of p100 and the activation of NF-κB inducing kinases (NIKs) [111]. In most cells, the NF-κB pathway is inactive by default because the transcription factors are sequestered in the cytoplasm by the IκB family of proteins[111]. These proteins bind and mask the nuclear localization signals on NF-κB transcription factors to prevent their translocation into the nucleus[111]. Therefore, activation of the NF-κB pathway largely depends on the 23 degradation of IκB proteins[111]. Although the non-canonical pathway has an important role in inflammation, this thesis will primarily focus on the role of the canonical pathway. The canonical pathway is activated by a host of pro-inflammatory ligands binding to their respective receptors[112]. Some of the most powerful canonical NF-κB activation ligands are TNF-α and lipopolysaccharide (LPS) which bind to TNFR and TLR4, respectively[112]. Ligand-receptor interaction leads to downstream signaling that culminates in the phosphorylation of IKKα and IKKβ within the IκB-kinase (IKK) complex. The IKK complex is composed of IKKα, IKKβ, and IKKγ. This phosphorylation activates the IKK complex where the IKKγ subunit then phosphorylates IκBα leading to its ubiquitination and subsequent proteasomal degradation. Under normal circumstances, IκBα binds to p50/p65 to sequester the complex into the cytoplasm and prevent it from entering the nucleus. When IκBα is degraded, p50/p65 is free to move into the nucleus and begin gene transcription[112]. This unique property of the canonical NF-κB pathway is what allows it to function as a rapid response pathway since the transcription factor is always present. Fig. 2-i1. The canonical NF-κB pathway. Schematic representing the molecular event for NF-κB activation. Adapted from Jost and Ruland. Blood (2007) 109 (7): 2700â2707. [112] 24 2.1.2. Role of NF-κB in select aspects of cancer biology 2.1.2.1. Regulation of immunity by the NF-κB pathway The pervasiveness of NF-κB in immune cells has led to it being dubbed as the orchestrator of inflammation and immunity[111]. In innate immune cells, activation of NF-κB is regulated by pattern recognition receptors (PRRs) that detect pathogen-associated patterns (PAMPs) [111]. This is an important line of defense against various microbial infections that the body may face. Some PRRs also recognize damage-associated molecular patterns (DAMPs) which are molecules that are the result of apoptotic or necrotic cell death[113]. In addition to these receptors, there are also other PRRs such as the NOD-like receptors, RIG-I, and cytosolic DNA receptors that can all recognize different components of microbial presence[114-117]. Although the different PRRs recognize different molecular patterns, they activate similar downstream signals that activates NF-κB. This leads to the production of pro-inflammatory cytokines such as IL-1B and TNF-α. The cytokines are released into the environment where they are free to bind their receptors and continuously activate NF-κB creating a positive loop that amplifies the inflammatory response[118-120]. In adaptive immune cells, NF-κB is also important for the formation of immune organoids and lymphocyte development during infections[121]. The primary role of NF-κB in lymphocyte development is to ensure cell survival and proliferation[121]. In both T- and B-cells, NF-κB signaling is required for the entire maturation process[121]. Activation of the T-cell and B-cell receptors on their respective cells initiates NF-κB signaling, contributing to their maturation[122]. 25 Aberrant NF-κB signaling is particularly prominent in lymphoid malignancies[123]. In many cases of lymphomas, mutations in the NF-κB pathway are detected and contribute directly to the malignant phenotype. In activated B-cell lymphomas (ABCL), constitutively active NF-κB signaling is a driver of the disease as it iss required for ABCL transformation and development[124]. This results in ABCL development being heavily dependent on NF-κB activity and in the absence of NF-κB signaling (eg. inhibition), ABCL cells die[125, 126]. This opens a great therapeutic opportunity for the development of targeted therapy. 2.1.2.2. The NF-κB pathway in cell proliferation and apoptosis The NF-κB pathway is active in cells as a pro-survival signal that controls proliferation and apoptosis[127, 128]. The pathway was first identified in activated B lymphocytes where it was active during B cell maturation and had major roles in their proliferation and development[121]. In tumor cells, constitutively active NF-κB stimulates the expression of genes that directly promote proliferation. Several cancer types (eg. ABCL, liver, lung, pancreatic etc.) display persistent NF-κB activity that directly promote disease progression[129]. Some activators of NF-κB, such as TNF-α, are also known to induce Bax dependent apoptosis in cells in addition to propagating a pro-inflammatory environment[130]. This presents a potential problem because NF-κB activation would therefore induce cell death as well. To prevent this, NF-κB activation also induces the expression of some anti-apoptotic members of the Bcl-2 family[130]. Upregulation of these proteins is known to promote survival in several types of lymphoma by inhibiting 26 Bax dependent apoptosis[130]. Other proteins such as Survivin were recently found to be expressed in bladder cancer cells under the control of NF-κB and promotes cell proliferation while inhibiting cell death[131]. 2.1.3. NF-κB inhibition as therapeutic target in cancer Due to the key role that NF-κB plays in cancer, its potential as a target for therapy has been explored extensively. Indeed, there are several drugs already available that exert their effect through NF-κB inhibition. However, the central role that NF-κB plays in biology also means that drugs that target this pathway would have many adverse effects[131]. The regulatory role that NF-κB has in immunity would suggest that global inhibition of this pathway would seriously reduce the normal function of an immune system[131]. New therapies based on NF-κB inhibition have been quite effective in some malignancies. For example, Bortezomib is used in treating multiple myeloma where many of the more aggressive characteristics are NF-κB driven[131]. Bortezomib inhibits NF-κB by blocking protease activity, which prevents IκBα degradation. Although relatively successful, therapies based on NF-κB inhibition have severe side effects that limit their therapeutic effectiveness[132, 133]. In addition, NF-κB inhibition may lead to an immune system shut down that could severely restrict the ability of T cells to kill tumor cells[110]. Given the importance of T-cells and the immune system in cancer, stunting its function by NF-κB inhibition could worsen outcomes in patients. NF-κB mediated inhibition of apoptosis, in some cells, also presents a challenge to anti-tumor drugs since many trigger apoptosis as a mechanism of cell death[134-136]. 27 This implies that cancers with increased NF-κB would be more resistant to drug induced apoptosis. However, combinations using NF-κB inhibitors along with other cytotoxic compounds provide a better therapeutic window for treatment. For example, a study in uveal melanoma showed that BET inhibitors induced the expression of NF-κB regulated genes and that combination with BET and NF-κB inhibitors provided better anti-tumor effects[137]. While another study has shown that combination therapy with bortezomib and docetaxel can decrease invasion of 8505C thyroid cells[137]. In addition, this study also screened clinically relevant compounds with NF-κB inhibition and found that a histone deacetylase offers synergistic effects in reducing BCPAP cell growth[137]. Due to the role that NF-κB has on apoptosis inhibition through Bcl-2, simultaneous inhibition of NF-κB and Bcl-2 has been shown to be effective in inducing cell death of cutaneous T-cell lymphoma cells[138]. NF-κB inhibition offers an attractive target for the development of new anti-tumor therapies. However, the broad role that NF-κB has in biology makes it a difficult direct target for developing new therapies. Further understanding of the regulatory factors involved might elucidate obscure mechanisms that appear upregulated in cancers which can potentially provide a more attractive target. 2.1.4. Chondroitin sulfate and NF-κB There has been some interest in studying the connection between CS and NF-κB. How they influence each other has been mostly studied in arthritis and multiple sclerosis (MS) since CS is a prominent actor in these two diseases. So far, the relationship between CS and NF-κB is not well defined in cancer. 28 Cartilage is a specialized tissue that is composed largely of CS[139]. Chronic inflammation, characterized by macrophage and lymphocyte infiltration of cartilage, leads to degradation of cartilage resulting in arthritis[140]. There are evidence in in vivo models of arthritis that shows upregulation of the NF-κB pathway is a driver in the development of arthritis because of its central role in inflammation[141-143]. Current research shows that soluble CS, especially CSA, has inhibitory effects on the NF-κB pathway[60, 61, 144]. CSA treatment slowed down the progression of arthritis by preventing inflammation and NF-κB activation in clinically relevant cell lines and mice models[57, 59, 60]. The specific mechanism of this effect is not entirely understood. Although one study showed that the anti-inflammatory effect of CSA is through its interaction with damage associated molecular patterns (DAMPs)[61]. DAMPs are molecules released by cartilage degradation and is a major contributor of inflammation[61]. This suggest that soluble CSA prevents inflammation by competing for DAMP binding and preventing its interaction to pro-inflammatory receptors resulting in NF-κB pathway inhibition. In MS, NF-κB activity is known to be upregulated during disease progression[145, 146]. MS is characterized as a chronic inflammatory condition in the CNS that leads to loss of neurons and widespread demyelination[145, 146]. Aberrant NF-κB activity in MS has been connected to disease progression by inducing chronic inflammation[145, 146]. Multiple cell types in the CNS associated with MS have increased NF-κB activity with different effects on MS pathology. In T cells, microglia, and astrocytes, NF-κB promotes MS progression by enhancing inflammation[147]. While NF-κB activation in oligodendrocytes and neurons protects the cells from inflammation induced MS progression[147]. 29 Emerging evidence shows that CSPG is a key driver of inflammation in MS[48, 50, 148-150]. High CSPG expression in MS has been observed in demyelinated patches in patient brain tissue samples. They are also known to prevent remyelination which is a prominent characteristic of MS progression. Strategies to decrease CSPG presence by drugs (eg. Fluorosamine[50]) and enzyme (Chondroitinase ABC[149, 150]) have shown promising results in slowing the progression of MS in mice models. In both disease models, the result is a reduction of CS rather than degradation of the core protein of the CSPG. This would suggest that the CS component of CSPGs is the main driver of inflammation in MS. However, the exact mechanism by which CS and/or CSPGs are linked to NF-κB is poorly understood. Less is known about the CS component on CSPGs, although CS have been shown to bind cytokines. Any effect beyond just an interaction is currently lacking. More research is needed to further understand the mechanism between CS and NF-κB. 30 2.1.5. Rationale CS has been implicated in inflammation and NF-κB pathway activation in diseases such as arthritis and MS. ofCS on cancer cells are attached to CSPGs that are known to regulate signaling pathways. Because of this, I hypothesize that rVAR2 binding to ofCS on cancer cells will lead to activation of signaling pathways. Specifically, I will determine whether rVAR2 binding to ofCS induces NF-κB pathway activation. The NF-κB pathway is a key regulator of immunity, which could be activated in response to foreign proteins like rVAR2. Since rVAR2 is being developed as a drug delivery vehicle for new therapeutics, the effect of rVAR2 on cell signaling needs to be studied. 31 2.2. Materials and methods Cell line and culturing condition MG63 cells were cultured in MEM media (ThermoFishcer 11095072) supplemented with 10% FBS (12483020) at 37°C and 5% CO2. MG63 cells were serum starved overnight prior to treatment with 100nM of rContr or rVAR2. For sodium chlorate treatments, the compound was added to cells while plating. 24hrs later, the cells were harvested for further analysis. Quantitative PCR RNA extraction was done using TRIzol® RNA Isolation Reagents (Invitrogen Life Technologies, Inc). 2μg of RNA was reversed transcribed using MMLV reverse transcriptase and random hexamers (Invitrogen) according to manufacture instructions. PCR primers and probes for the following genes were purchased from Life Technology and used according to the manufacturer's recommendations. qPCR was performed on a ABI ViiAâ¢ 7 RealâTime PCR detection system (Applied Biosystems) with a SyberGreen ROX master mix (Roche). The amount of sample RNA was normalized by the amplification of glyceraldehydeâ3âphosphate dehydrogenase (GAPDH) levels as internal control. The results are representative of at least three independent experiments. qPCR primer sequences for genes are listed in appendix 1. Western blot Wholeâcell lysates were obtained by lysing the cells in an appropriate volume of iceâcold RIPA buffer composed of 50 mmol/l TrisâHCl, pH 7.4, 150 mmol/l NaCl, 0.5% sodium deoxycholate, 1% Nonidet Pâ40, 0.1% SDS containing 1 mmol/l Na3VO4, 1 mmol/l NaF, 1 mmol/l phenylmethylsulfonylfluoride, and protease inhibitor cocktail tablets 32 (Roche Applied Science). Cellular extracts were clarified by centrifugation at 13,000 Ã g for 10 min and protein concentrations was determined by a BCA protein assay kit (ThermoFisher). Thirty micrograms of the extracts ware boiled for 5 minutes in SDS reducing sample buffer, separated by SDSâPAGE, and transferred onto a nitrocellulose membrane following standard methods. Membranes were probed with dilutions of primary antibodies IκBα (9242S), Phospho-TBK1 Ser172 (5483S), TBK1 (3504S), LaminB1 (13435S), CD74 (77274T), CD44 (2570T), and p65 (8242S) from Cell Signaling, V5 (cat. R96025) from ThermoFisher, and vinculin (cat. V4505) from Sigma, followed by incubation with either horseradish peroxidaseâconjugated secondary antibodies or fluorescent secondary antibodies. After washing, proteins were visualized by either a chemiluminescent detection system (GE Healthcare) or Odyssey Imaging System (LIâCOR). Immunoprecipitation Cells were lysed in Pierceâ¢ IP Lysis Buffer (ThermoFisher 87787) for 1hr on ice. Lysate were cleared by centrifugation and the incubated with rVAR2 or rContr for 1hr on ice then primary anti-V5 antibody at 1:50 dilution at 4â°C overnight. Immunoprecipitation was performed using the Dynabeads protein A immunoprecipitation kit (ThermoFisher 10006D) according to manufacturer's guideline. Eluded proteins were analyzed according to the western blot protocols. Confocal imaging Cells were grown to 60% confluency on glass coverslips, then incubated with rVAR2 at the indicated time points (5mins to 24hrs) prior to processing. Cells were fixed in 4% paraformaldehyde (PFA) for 15mins at RT, permeabilized with 0.05% triton-X100 33 for 5 mins and blocked in 3%BSA for 30 mins. Primary antibodies against p65 (Cell signaling 3288S), CD44 (2570T), and V5 (ThermoFisher R96025) were diluted at 1:100 in blocking buffer and incubated overnight at 4â°C in a dark humidifier chamber. The cells were washed thrice in PBS and incubated with secondary antibodies diluted at 1:250 in blocking buffer and 1hr at RT in the dark. The coverslip was washed thrice with PBS then mounted to cover slide with VECTASHIELD mounting media with DAPI (Vector Lab. H-1200-10). Images were taken on a Zeiss LSM 780 confocal microscope at 63x magnification. p65 nuclear localization quantification 100-150 cells were counted, and nucleus was define using DAPI staining. Negative cells were identified when p65 staining in the nuclear was absent. Cells were considered positive when the nuclear staining for p65 was present. A cell is considered positive if the nuclear staining was equal or greater than cytoplasm staining. Nuclear cytoplasmic fractionation Nuclear and cytoplasmic fractions were prepared using the NE-PER Nuclear and Cytoplasmic Extraction reagents (ThermoFisher 78833) according to manufacturerâs instruction. Protein concentration was quantitated using the BCA protein assay kit (ThermoFisher 23228). Equal amounts of protein were loaded in each lane and separated on an SDS-PAGE gel, then transferred onto a nitrocellulose membrane. Proteins were analyzed according to western blot protocols. 34 Proliferation Assay The WST-1 (Promega) assay was used to assess cell growth according to the manufacturer's protocol. 2000 cells were seeded in 96-well plates in complete media, and absorbance at 450 nm was measured after 96 hours. Chondrointinase ABC digestion Cell lysate was obtained using RIPA buffer as described above (western blot section). 50µg of protein was used for digestion with 1 unit of Chondroitinase ABC (ChaseABC) in a buffer containing 40mM TrisHCL, 40mM sodium acetate, 10mM EDTA. The solution was incubated in a 37ºC water bath for 4-8hrs then analyzed by western blot. Statistical analysis Statistical analyses were performed in GraphPad Prism V6. One-way ANOVA followed by Tukeyâs post hoc analysis were used for multiple comparison tests. At least three biological replicates were used to determine significance. Significance was set at p<0.05. 35 2.3. Results 2.3.1. rVAR2 treatment in MG63 cells induce pro-inflammatory response In a previous study, rVAR2 was shown to bind unique ofCS modification on the surface of cancer cells. This offers a lead to an opportunity to study how targeting ofCS using rVAR2 would alter cell signaling. To assess changes in signaling pathways, a whole transcriptome microarray experiment was done because it provides unbiased gene expression signatures for different pathways. MG63 pediatric osteosarcoma cells were chosen for this experiment because it showed one of the highest rVAR2 binding in the cells that were tested[104]. MG63 cells were grown to 60% confluence then switched to serum free media for overnight to reduce basal signaling in cells[151, 152]. After serum starvation, the cells were treated with 100nM rVAR2 or rContr (non-binding control protein) for 1hr followed by 10% fetal bovine serum (FBS) for another 1hr prior to collecting RNA for the assay. FBS was reintroduced so that cells would be under normal growth collections when RNA is collected to ensure maximum yield. Analysis of data showed 281 genes were upregulated at least 2-fold in rVAR2 treated cells as compared to rContr (Fig. 2-1A). In the top 25 upregulated genes, there is an enrichment for pro-inflammatory chemokines and cytokines such as IL-8 and IL-1B (Fig. 2-1B). This signature suggests a pro-inflammatory response in MG63 cells with the increased gene expression of multiple pro-inflammatory cytokines.. 36 Fig. 2-1 rVAR2 induce global gene expression changes. Whole transcriptome microarray of cells treated with rVAR2 vs rContr. A) Genes with at least 2-fold change in expression and B) list of top 25 upregulated genes. 37 The microarray data was validated by determining the expression of IL-8, IL6, and IL-1B through qPCR. These three genes were chosen due to their presence on the list of upregulated genes and are well established pro-inflammatory genes that are used as readouts for NF-κB activity[61, 153-158]. MG63 cells were prepared as described previously, RNA was collected at 2hr (1+1hr FBS) and 24hrs (23+1hr FBS) of rVAR2 treatment. qPCR analysis shows that rVAR2 treatment was able to induce the expression of all three genes at both time points (Fig. 2-2). This confirms the microarray data that shows increased expression of pro-inflammatory cytokines after rVAR2 treatment. Fig. 2-2 rVAR2 upregulated pro-inflammatory cytokine expression. qPCR analysis of IL-8, IL-6, and IL1B mRNA expression after rVAR2 treatment. Target gene expression was normalized to GAPDH level. Data represents mean ± SD of three technical replicates. 2.3.2. rVAR2 alone is sufficient to induce pro-inflammatory gene expression The previous experiments included a 1hr FBS treatment prior to extracting the RNA. FBS contains many growth factors and cytokines that can induce the expression of pro-inflammatory genes. To determine whether the effect was a result of rVAR2 alone, I serum starved the cells overnight then treated the cells with rVAR2 or rContr. In one set, 38 the cells were kept in serum starve conditions for full 2 hours of rVAR2 treatment (Fig. 2-3). In the other set, 10% FBS was reintroduced to the cells 1 hour after rVAR2 and RNA was collected after another hour (Fig. 2-3). qPCR analysis of IL-8, IL-6, and IL-1B showed increased expression of all three genes in both serum-free and FBS (Fig. 2-3). In fact, cells treated with rVAR2 in serum-free condition showed a higher relative upregulation of the three genes compared to cells that had 1hr 10% FBS. This experiment showed that rVAR2 alone is sufficient to induce the upregulation of pro-inflammatory genes. Further experiments from here on were performed in serum-free conditions unless specified otherwise. Fig. 2-3 rVAR2 alone is sufficient to induce gene expression. qPCR analysis of IL-8, IL-6, and IL1B mRNA expression after rVAR2 treatment with or without FBS. Target gene expression was normalized to GAPDH level. Data represents mean ± SD of three technical triplicates. 39 2.3.3. rVAR2 binds CSA to induce gene expression Next, I investigated whether the gene upregulation is due to rVAR2 binding to ofCS on the cell surface and not because of rVAR2âs presence in the environment. To test this, I used purified CSA in the media to compete for rVAR2 binding and test whether this can inhibit the expression of IL-8, IL-6, and IL-1B. Competition was done by introducing 400ug/mL of purified CSA in the media since this was previously shown to be sufficient for inhibiting rVAR2 binding[104]. qPCR showed significant induction of IL-8, IL-6, and IL-1B in rVAR2 treated cells compared to rContr and rVAR2+CSA (Fig. 2-4A). While the difference between rContr and rVAR2+CSA was not significant. In addition, an ELISA assay showed that the induction of gene expression was translated to the protein level with increase of IL-8 and IL-6 in the media 2hrs after rVAR2 treatment (Fig. 2-4B). Fig. 2-4 CSA competition inhibits rVAR2 induced gene expression. MG63 cells treated with rVAR2 in the presence or absence of CSA competition. A) qPCR analysis of IL-8, IL-6, and IL1B mRNA expression normalized to GAPDH level. Data represents mean ± SEM of three biological replicates. Significance was measured by ANOVA followed with Tukeyâs post-hoc analysis. B) ELISA of IL-8 and IL-6 concentration in media after treatment. Data represents mean ± SD of three technical replicates. 40 Furthermore, I sought to established whether rVAR2 is binding specifically to sulfated CSA on the cell surface to induce gene transcription. Here, I used sodium chlorate (SC) to inhibit the sulfation of GAGs by eliminating the pool of sulfate donors in the cells. This results in cells that produced GAG chains without any sulfate modifications. I treated the MG63 cells with SC while plating so that newly produced chondroitin chains would be without sulfates. I used 30mM of SC as treatment because it inhibited rVAR2 binding (Appendix 2A) while having minimal effects on cell proliferation or morphology at the time of rVAR2 incubation (Appendix 2B). For this experiment, MG63 cells seeded with or without 30mM SC were grown for 24 hours prior to serum starvation and rVAR2 treatment. With SC treatment, I observed that rVAR2 was not able to induce IL-8, IL-6, and IL-1B expression (Fig. 2-5). The statistical analysis of three independent biological replicates showed significant differences in gene expression between rVAR2 and rContr treated cells. In addition, there is a significant difference between rVAR2 and rVAR2 + SC treated cells. The three independent biological qPCR replicates along with the statistical analysis are shown in Appendix 3. This shows that SC pre-treatment was able to inhibit the rVAR2 induced expression of IL-8, IL-6, and IL-1B. 41 Fig. 2-5 rVAR2 induced pro-inflammatory response requires sulfated CS. MG63 cells were treated with rVAR2 after pre-treatment with SC. qPCR analysis of IL-8, IL-6, and IL1B mRNA expression was normalized to GAPDH. Data represents mean ± SEM of three biological replicates. Significance was measured by ANOVA followed by Tukeyâs post hoc analysis. Together, these data show that rVAR2 binds to sulfated CSA on the cell surface to induce upregulation of IL-8, IL-6, IL-1B in MG63 cells. 2.3.4. Endotoxins are not responsible for pro-inflammatory gene induction rVAR2 is a recombinant protein produced in bacteria. This was a major concern because there was the possibility that endotoxins, such as LPS, was present and is the responsible for the perceived rVAR2 induced pro-inflammatory response. Previous batches of rVAR2 have reported endotoxin levels ranging from 100-500EU/mL. These concentrations indicate a high amount of endotoxin contamination in the rVAR2 preparations which could induce a pro-inflammatory response. To address this issue, I used two separate batches of rVAR2 that had endotoxin removed by two different methods to see if endotoxin free rVAR2 is still capable of inducing the pro-inflammatory response. 42 In one batch, I used a commercially available endotoxin removal resin from ThermoFisher to purify a batch of rVAR2 (rVAR2-resin). In a separate rVAR2 batch, the Triton-X114 method was used to remove endotoxins (rVAR2-X114) during protein production. In both batches, I tested an endotoxin level to 0.1-0.6EU/mL which is similar to the clinically acceptable limit for medical use of <0.5EU/mL[159]. These two batches of rVAR2 were used to treat MG63 cells to test for gene expression changes. Both batches were able to induce upregulation of IL-8, IL-6, and IL-1B expression after treatment, and again CSA competition was able to inhibit this effect (Fig. 2-6). Moreover, the CSA used for competitive binding was not sterile or endotoxin cleaned but was still able to inhibit expression of the pro-inflammatory genes. With this data, I concluded that the observed pro-inflammatory effect was the direct result of rVAR2 binding rather than endotoxins. 43 Fig. 2-6 Endotoxin free rVAR2 also induces pro-inflammatory response. qPCR analysis of IL-8, IL-6, and IL-1B expression in MG63 cells treated with endotoxin free rVAR2 cleaned by A) thermofisher endotoxin removal resin and B) TritonX-114 method. Gene expression changes is normalized to GAPDH. Data represents mean ± SD of three technical replicates. 2.3.5. NF-κB Pathway analysis Although the upregulation of pro-inflammatory genes suggests NF-κB pathway activation. Analysis of the NF-κB pathway itself is necessary to confirm its activation. Specifically, I needed to determine whether rVAR2 causes IκBα degradation and p65 nuclear translocation because these are two key events that is required for NF-κB activation. IκBα is a protein that is normally bound to p65 to prevent it from entering the nucleus (Fig. 2-i1). Upon activation by upstream signals, IκBα is phosphorylated, then ubiquitinated and targeted for proteasome degradation. To determine this, I pretreated some cells with MG-132 to block proteasome function and verify its role in IκBα degradation after rVAR2 incubation. Western blot analysis shows IκBα degradation after rVAR2 treatment in MG63 cells and with CSA competition IκBα degradation is inhibited (Fig. 2-7). Furthermore, cells pretreated with 20uM of MG-132 did not result in IκBα 44 degradation in response to rVAR2 treatment (Fig. 2-7). This provides evidence that rVAR2 incubation leads to the proteasomal degradation of IκBα in MG63 cells. Fig. 2-7 rVAR2 induces IκBα degradation. Western blot for IκBα in MG63 cells treated with rVAR2 with or without MG-132. rContr and CSA competition were used as negative controls. Vinculin was used as loading control. The results shown here represents one of two experiments (n=2) IκBα degradation leaves p65 free to translocate to the nucleus for gene transcription meaning rVAR2 should induce p65 nuclear translocation in MG63 cells. First, I used immunofluorescence microscopy to directly visualize p65 localization following rVAR2 treatment. MG63 cells were plated and treated with rContr or rVAR2 from 5mins to 6hrs. At the 2hr timepoint, p65 is seen inside the nucleus of MG63 cells (Fig. 2-8A). Quantification of â¥100 cells for nuclear p65 shows that the translocation happens within 1hr of rVAR2 treatment and peaks at 2hrs with ~60% of cells being positive for nuclear p65 (Fig. 2-8B). 45 This observation was verified with a nuclear and cytoplasm fractionation in MG63 cell after rVAR2 or rContr incubation. Cytosolic and nuclear fractions were separated by centrifugation using a commercial kit from ThermoFisher according to manufacture instructions. Lysates were extracted at 0hr, 1hr, and 2 hrs following rVAR2 treatment. Western blot showed IκBα degradation only occurred in the cytosol of cells with 1hr and 2hr of rVAR2 incubation (Fig. 2-9). Corresponding time points in the nuclear fraction showed increased p65 level (Fig. 2-9). Fig.2-8 rVAR2 induces p65 nuclear translocation. Immunofluoresence of p65 in MG63 cells after rVAR2 treatment. A) representative images for cells after 5mins or 2hr of treatment. B) Quantification of â¥100 cells for percentage of cells with positive for nuclear p65. Scale bar represents 20µM. These data are representative of 3 experiments (n=3). 46 Fig. 2-9 IκBα degradation correspond with p65 nuclear translocation. Western blot analysis of nuclear cytoplasmic fractions in MG63 cells after rVAR2 treatment. LaminB1 was used a loading control for nuclear fraction and vinculin was used as loading control for the cytoplasmic fraction. The results shown here represents one of two experiments (n=2). Overall, these data strongly suggest NF-κB activation after rVAR2 treatment since both IκBα degradation and p65 nuclear translocation had occurred. 2.3.6. Investigating the CD74/CD44 axis in rVAR2 induced response Thus far, the data shows that rVAR2 treatment on MG63 cells can activate the NF-κB pathway which results in increased expression of IL-8, IL-6, and IL-1B. However, the specific receptor that is responsible for this effect is unknown. Because rVAR2 binds ofCS, which are found attached to CSPGs, I hypothesized that a CSPG must be responsible for initiating the signaling cascade. One of the most well-known CSPG is the transmembrane receptor CD44. It has been previously reported to carry many different GAG chains, including CSA[160-162]. We have previously shown that CD44 carries ofCS in C32 melanoma cells based on colocalization experiments with rVAR2[104]. In addition, CD44 is also known to regulate many key biological processes in cancer such as proliferation, migration, and survival[163, 164]. These processes are the result of CD44âs regulation of several signaling pathways, including NF-κB[165, 166]. In addition, CD44 47 can also form a complex with CD74 to regulate downstream NF-κB signaling as well[167, 168]. CD74 is also reported to carry a CSA chain. However, CD74 has only been reported to be expressed in monocytes or B-cells[169, 170]. Since cancer cells often express components of other cell lines, I tested whether MG63 cells express CD74 and CD44 and whether rVAR2 binds to CD74/CD44 on MG63 cells to activate NF-κB. First, I determined the expression of CD74 in a variety of cancer cell lines by western blot using Raji B-cell lymphoma cells as a positive control. Here, only Raji cells showed expression of CD74 (Fig. 2-10). Cell lines of osteosarcoma (MG63, MNNG, U2OS) and prostate cancer (PC3, LNCaP, and DU145) showed no expression of CD74 (Fig. 2-10). This indicates that CD74 cannot be responsible for the NF-κB pathway activation after rVAR2 treatment since it is not present on MG63 cells. Fig. 2-10 CD74 expression in cancer cell lines. Western blot for CD74 expression in various cancer cell lines. Vinculin was used as loading control. These data are representative of one experiment (n=1). Next, I probed the expression of CD44 in different cell lines and found that CD44 was present in MG63, U2OS, MNNG, PC3, and UC3 cells while absent in LNCaP and LNCaP-ABC cells (Fig. 2-11). Interestingly, CD44 in MG63 cells was accompanied with a large smear at higher molecular weights (Fig. 2-11). 48 Fig. 2-11 CD44 expression in cancer cell lines. Western blot for CD44 expression in various cancer cell lines. Actin was used as loading control. These data are representative of one experiment (n=1). I hypothesized that the smear could be the glycosylated form of CD44 that carry CS chains. To test this, I treated MG63 protein lysates with Chondroitinase ABC (ChaseABC) for 4h, 6h, and 8h to digest all CS chains. Western blot analysis shows that the CD44 smear disappeared after ChaseABC treatment which suggests that CD44 from MG63 were modified with CS (Fig. 2-12). This means that CD44 on MG63 cells could carry ofCS capable of binding rVAR2. Fig. 2-12 CD44 is modified with CS in MG63 cells. Western blot for CD44 in MG63 cell lysates treated with or without ChaseABC. These data are representative of two experiments (n=2). 49 Following this observation, I investigated whether rVAR2 interacts with CD44 in MG63 cells. First, I used immunofluorescence imaging to determine if rVAR2 colocalizes with CD44 in MG63 cells. Cells were treated with rVAR2 for 1hr then fixed and stained for CD44 and rVAR2 with anti-V5 antibody. Confocal images showed no localization of rVAR2 with CD44 after 1hr of continuous incubation (Fig. 2-13A). Next, I performed an immunoprecipitation to determine if CD44 is pulled down by rVAR2. MG63 proteins were extracted and incubated with either 100nM rVAR2 or rContr for 1hr on ice. rVAR2 and rContr were immunoprecipitated with anti-V5 antibodies at 4°C overnight. CSA competition was added as a control to inhibit rVAR2 binding. The samples were analyzed by western blot. Lane 1 contains input where I observed a band where CD44 (~89kDa) is expected to be and the same band appeared in all 4 lanes (Fig. 2-13B). However, this band was much more prominent in the rContr treated compared to rVAR2 treated samples (Fig. 2-13B lane 2 vs 3). This would mean that rContr was able to pulldown CD44 better than rVAR2. Moreover, CSA competition (Lane 4) should have been able to inhibit rVAR2 binding to any CSPG but the blot shows a similar band to rVAR2 (lane 3). The band observed in this blot is most likely a nonspecific band. The contradictory nature of this result indicates that this experimental setup may not be optimal for answering this question. With this data, I was not able to provide clear evidence on whether rVAR2 and CD44 are physically interacting in MG63 cells. CD44 is just one of many CSPGs that can carry an ofCS chain, attempting to use rVAR2 to pull down CSPGs one at a time is time consuming. Rather than continuing with the current trajectory, I decided that an unbiased approach is required to find the receptor that rVAR2 bind for NF-κB activation. 50 Fig. 2-13 rVAR2 and CD44 do not interact in MG63 cells. A) Maximum intensity projection of MG63 cells stained with CD44 (red) and rVAR2 (green) after 1hr rVAR2 incubation. B) Immunoprecipitation of CD44 with rVAR2 in MG63 cell lysates. Scale bars represent 5µM. These data are representative of two experiments (n=2). A possible explanation for the ChaseABC digest result is that CD44 in MG63 cells is perhaps carrying a different CS chain (eg. CSB/CSC) that rVAR2 does not bind. Since ChaseABC digests all CS, using it to treat protein lysates would digest all CS chains present. Moreover, the CS modified CD44 appears to make up only a small portion of the overall CD44. Non-glycosylated or non-CS modified versions constitute the majority of CD44 in MG63 cells since I observed a dense band at the expected molecular weight for CD44 in Figure 2-13. 51 2.3.7. Activation of viral defense pathways Targeting specific receptors one at a time is not an efficient method to determine the receptor involved in rVAR2 induced cytokine production. I decided to use an unbiased approach to elucidate the mechanism. The microarray data from Fig. 2-1 showed 281 genes with >2-fold increase after rVAR2 treatment. I used the DAVID bioinformatic tool for a gene ontology analysis to determine the most likely pathways that would give this gene signature[171]. The most significantly enriched pathways from the gene list were type 1 interferon signaling pathway, response to viral response, and negative regulation of viral genome replication (Fig. 2-14). This was surprising since rVAR2 is a recombinant malaria protein, to have viral defense pathways activated shows that the cells are sensing a virus infection. This suggests that rVAR2 incubation seems to have emulated a viral infection and induced subsequent responses within the cell. Fig. 2-14 rVAR2 activates viral defense pathways. Gene ontology analysis of all genes with expression increase of â¥2-fold after rVAR2 incubation. 52 Negative regulators of the viral genome replication was particularly interesting since no viral nucleic acid was present during the rVAR2 incubation. Because of this observation, I decided to investigate viral defense mechanisms more detail. Viral nucleic acid can be sensed by several different proteins like TLR3, RIG-I, and MDA-5[172, 173]. Upon detecting viral nucleic acid, these proteins initiate a signaling cascade that leads to phosphorylation of TBK1/IKKε which can induce IκBα degradation and NF-κB activation[174, 175]. To determine whether TBK1 is phosphorylated, I collected lysates from cells treated with rVAR2 for 2hrs. Western blot analysis showed cells treated with rVAR2 had elevated levels of p-TBK1 and decreased amount of IκBα (Fig. 2-15). Fig. 2-15 TBK1 is phosphorylated after rVAR2 treatment. Western blot of MG63 cells after 2hr rVAR2 treatment. Actin is used as loading control. These data represent one experiment (n=1). However, the increase of p-TBK1 after rVAR2 treatment when compared to rContr was not as high as I expected. This could be due to the timing of when samples were collected. Phosphorylation of TBK1 could be a rapid response event where cells immediately activate this pathway upon detection of viral DNA. Here, I collected the 53 samples 2hrs after rVAR2 or rContr treatment as consistent with my previous experiments with mRNA expression of cytokines. To test whether if TBK1 is phosphorylated earlier, I collected protein from cells treated with rVAR2 or rContr at different times ranging from 5mins (0h) to 4hrs. Western blot shows that at 5mins, both rVAR2 and rContr has a spike in pTBK1. However, in rContr treated cells, pTBK1 appears to decrease almost immediately while in rVAR2 treated cells the amount of pTBK1 appears to be sustained. Interestingly, there seem to be some internalized rContr proteins in the samples as evident by the presence of the V5 band in the rContr samples. This sample also does seem to have an increase in pTBK1 as well when compared to the NT sample. Despite this observation, there was no degradation of IκBα in the rContr treatments unlike in the rVAR2 treatments. Fig. 2-16 TBK1 phosphorylation timepoint. Western blot of MG63 cells treated with rVAR2 at the indicated timepoints. LPS was used as positive control for TBK1 phosphorylation. Actin was used as loading control. 54 Although promising, subsequent repeats with the same experimental setup could not corroborate this finding. In Repeat 1, I did not observe the sharp increase of pTBK1 at the 0hr timepoint in either rContr or rVAR2 treated cells as compared to previous figure (Fig. 2-17 top). I did observe an increase of pTBK1 over time in rVAR2 treatments when compared to rContr (Fig. 2-17 top). However, quantification of the western blots showed that this was only a slight increase in the ratio of phosphorylated to total TBK1. I then repeated the experiment again (repeat 2) and I did not observe any changes in pTBK1 level in rVAR2 vs rContr treated cells (Fig. 2-17 bottom). Quantification of the western blot images further supported this observation. Repeat 1: 55 Repeat 2: Fig. 2-17 TBK1 phosphorylation timepoint repeats. Western blot of biological replicates to TBK1 phosphorylation after rVAR2 timepoint treatment. Poly:IC was used as positive control for TBK1 phosphorylation as it is a viral nucleic acid mimic. Actin was used as loading control. Ratio of phosphor/total TBK1 was measured by densitometry analysis using imageJ. Because of these data, I could not decisively conclude that rVAR2 induced NF-κB activation is through TBK1. However, these experiments did corroborate earlier observations where rVAR2 incubation led to a decrease of IκBα since this was present in all three repeats. An interesting trend on these western blots is that the rContr protein also appeared in the lysates indicating that they were internalized into the cells. However, in repeat 2, purified CSA was able to compete for rVAR2 internalization while not with rContr meaning that rContr internalization was not dependent on CS binding. Regardless, the rContr treated cells did not induce IκBα degradation meaning that the NF-κB pathway was not activated. This further supports the hypothesis that NF-κB activation was due to rVAR2 binding to cell surface ofCS and not a result of non-specific internalization of foreign protein. 56 2.3.8. Conditional lethal reporter screen My efforts to identify receptors and downstream proteins involved in this pathway has not yielded tangible results. The NF-κB pathway has many receptors and proteins that can regulate it activity that results in p65 nuclear translocation. To test the upstream factors involved one at a time would be extremely time consuming and is not promised to lead to concrete results. Instead, I sought a nonbiased screening approach to find upstream factors responsible for my observations. The conditions for this screen must satisfy the following conditions: 1. Be able to identify specific proteins 2. Activate or remove proteins in a nonbiased manner 3. Minimize noise to reduce false discoveries To target specific proteins, I chose to utilize a whole genome CRISPR screen that can knock out one protein at a time. This technique has been used successfully to identify proteins that confer resistance to cytotoxic compounds in several cancer types[176, 177]. A whole genome CRISPR library is used to knock out one protein in each cell in a whole population. After CRISPR knockouts, the cells are treated with a cytotoxic compound at a therapeutic concentration. Over several days, the drug treatment kills any cells that is still sensitive to the compound. However, cells that survived the drug treatment would proliferate. This would indicate that knocked-out genes in these cells are responsible for conferring sensitivity to the drug. The identity of knockouts can be determined by next generation sequencing. In my screen, I would need to eliminate cells that increases IL-8 expression after rVAR2 treatment. To achieve this, I created a conditional lethal reporter where MG63 cells 57 contains a stable expression of a truncated HSV-1 thymidine kinase (dTK) gene under the control of the IL-8 promoter (MG63-IL8-TK). The IL-8 promoter was chosen since it showed consistently high increase of expression following rVAR2 treatment. dTK itself is not toxic to cells, however when the cells are treated with Ganciclovir (GCV), dTK converts it to a cytotoxic compound that will kill the cells. To perform this screen, I first apply the whole genome CRISPR knockout library to the MG63-IL8-TK cells, it will create cells that has random knock outs for specific proteins. I then treat these cells with rVAR2 to induce dTK expression followed by GCV treatment. This would kill all cells that has high expression of dTK after rVAR2 treatment, cells with knockouts that shut down the rVAR2 induced IL-8 expression will survive and proliferate. The surviving clones can be sequenced to see which genes has been knocked out. This would generate a list of the genes responsible for rVAR2 induced IL-8 expression. The system is summarized in the following figure: Fig. 2-18 Conditional lethal reporter screen. Schematic representing the conditional lethal reporter screen to determine proteins involved in rVAR2 induced IL-8 expression. 58 A limitation of with this screen would be that it can only detect knockouts for one gene in one cell. If simultaneous knockouts of multiple proteins are required for rVAR2 induced IL-8 expression, this screen would not be capable of satisfying this condition. Furthermore, if redundancies exist in the pathway, the cells might retain rVAR2-IL-8 expression with knockout for only one protein. This would lead to TK production and cell death after GCV treatment. 2.3.8.1. Validating the conditional lethal reporter system with TNF-α To my knowledge, a reporter system like this has not been used before so I must validate its effectiveness prior to initiating a rVAR2 screen. First, I validated the expression of dTK in the TK expressing cells. I designed three qPCR primers (dTK-1, -2. -3) based on the DNA sequence of dTK. qPCR on cells without treatments showed that dTK was only expressed in the MG63-IL8-TK cells and not wildtype MG63 cells (Fig. 2-19). 59 Fig. 2-19 dTK expression. qPCR analysis for dTK expression in wildtype and TK expressing MG63 cells. Gene expression was normalized to GAPDH levels. Data represents mean ± SD of three technical replicates. Next, I tested the functionality of the reporter by using TNF-α as a positive control since it is a very powerful inducer of IL-8 expression. First, I tested whether the expression of dTK changes in response to TNF-α treatment. MG63-IL8-TK cells were serum starved overnight and treated with 10ug/mL TNF-α for 2hrs prior to collecting mRNA. qPCR analysis shows that dTK expression was upregulated in TNF-α treated cells (Fig. 2-20). 60 Fig. 2-20 dTK expression in response to TNF-α stimulation. qPCR analysis of dTK expression in MG63-IL8-TK cells after TNF-a stimulation. Gene expression was normalized to GAPDH levels. Data represents mean ± SD of three technical replicates. Next, I tested whether treating the cells with TNF-α and GCV leads to cell death. This will provide evidence that the reporter is functional. I treated wildtype MG63 and MG63-IL8-TK with GCV in the presence and absence of TNF-α. Cell viability was determined by WST1, the stopping point was chosen when the control cells (0µM GCV) has reached 100% confluency. As expected, I observed minimal effect on cell proliferation in wildtype MG63 cells with only the highest concentration of GCV and TNF- α having a minimal effect on confluency (Fig. 2-21 blue and red lines). While MG63-IL8-TK cells showed a concentration dependent cell death in response to GCV alone (Fig. 2-21 green and purple lines). The concentration dependent response to GCV indicates that there is baseline expression of the reporter. This is not surprising since there is always a baseline expression of endogenous IL-8 in wildtype MG63 cells. However, when MG63-IL8-TK were treated with TNF-α and GCV together, there was a much stronger cell 61 death response. Even the lowest GCV concentration (~200nM) was able to completely kill the cells when TNF-α was present. This shows that TNF-α induced the expression of the reporter can be distinguished from baseline expression based on GCV sensitivity. Fig. 2-21 TNF-α induces GCV dependent cell death in dTK expressing cells. Wst1 analysis for live cells in response to TNF-α and GCV treatments. Data represents Mean ± SD of 6 wells. This set of data validated the reporter system by using TNF-α as a positive control to induce reporter expression. Cells stimulated with TNF-α all died with GCV as compared to no TNF-α treatment supporting the functionality of the reporter system. 2.3.8.2. Investigate the conditional lethal reporter response to rVAR2 With the success of validating the reporter system using TNF-α, I moved forward to validating the reporterâs response to rVAR2 treatment. I previously showed that MG63 62 cells upregulated IL-8 expression after 2 hrs of rVAR2 treatment (Fig. 2-4A). Here, I validated that MG63-IL8-TK cells retained this effect. qPCR showed that rVAR2 induced the upregulation of endogenous IL-8 after rVAR2 treatment and that CSA competition inhibited the effect (Fig. 2-22A). I also observed that rVAR2 treatment induced an upregulation of the dTK as well and CSA competition was also able to reduce rVAR2 induced dTK expression (Fig. 2-22B). Primer 1 and 3 hand a 1.5-fold expression increase while primer 2 had an almost 3-fold expression increase in response to rVAR2 (Fig. 2-22B). Although promising, the expression of dTK is not as prominent as endogenous IL8. Fig. 2-22 IL8 and dTK expression in response to rVAR2. qPCR analysis for A) IL-8 expression and B) dTK expression in MG63-IL8-TK cells after rVAR2 stimulation. Gene expression was normalized to GAPDH levels. Data represents mean ± SD of three technical replicates. I then investigated whether the reporter induces cell death after combined rVAR2 and GCV treatment. The modest upregulation of dTK after rVAR2 treatment could mean that the cells will not have a robust cell death to GCV as compared to TNF-α treatment. First, I determined the amount of cell death with GCV on MG63-IL8-TK in response to rVAR2 treatment. Cells were plated on to 96 well plated and after attachment were serum starved overnight. The cells were then with 100nM of rContr or rVAR2 for 6 hours to induce dTK expression and build up a pool of dTK protein in the cells. Afterwards, the 63 media was replaced with normal growth media with 10% FBS and varying concentrations of GCV from 0-60µM. When the control cells (0µM GCV) reached 100% confluency the amount of live cells was measured using WST1. Analysis shows that cells died in a concentration dependent manner in response to GCV in both rContr and rVAR2 treatments (Fig. 2-23). There were several GCV concentrations (0.08, 0.25, 0.74, 2.22µM) that showed less live cells with rVAR2 treatment (Fig. 2-23). However, the difference in the percentage of live cells between rVAR2 and rContr treated cells was less than I anticipated. Although it is possible that rContr is also inducing some expression of dTK which would cause the cells to be more sensitive to GCV. This is addressed in the following section where a no protein control is also used as a comparison for GCV sensitivity. Fig. 2-23 GCV response after rVAR2 stimulation. Wst1 analysis of MG63-IL8-TK cells treated with GCV after rVAR2 stimulation. Data represents Mean ± SD of 6 wells. I hypothesized that the small difference is perhaps due to the timing of the WST1 measurement. The measurement in Figure 2-23 was made 5 days after the addition of 64 GCV because the control (0uM GCV) had reached 100% confluency. The cells that were subjected to rVAR2 might have caught up in growth by day 5 and I could have missed the day when there was the greatest difference between rContr and rVAR2 treatments. Because of this, I sought to analyze the growth of cells over several days in response to GCV after rVAR2 or rContr. I prepared cells as described above with the addition of a no protein control (NT) that received no rContr or rVAR2 prior to GCV. The amount of live cells was measured with the IncuCyte every day for 6 days following GCV treatment. Analysis shows cells in NT, rContr, and rVAR2 cohort follow a normal exponential growth curve when no GCV is present (Fig. 2-24 top left). Cells that received any amount of GCV showed less growth overall in all three groups (Fig. 2-24). With 0.25µM GCV, NT and rContr cells followed identical growth curves while rVAR2 treatment showed less cells from day 3 onwards (Fig. 2-24 top right). A similar trend was observed in cells treated with 2.22µM and 6.67µM GCV except overall growth was lower in all three conditions and at day 5 confluency is seen decreasing indicating that cell have started to die (Fig. 2-24 bottom two panels). I also tested higher GCV concentrations (6.67-60µM) and observed that under these conditions, the cells were not able to grow at all. 65 Fig. 2-24 Cell proliferation progression in response to rVAR2 and GCV treatments. Incucyte analysis for cell proliferation after rVAR2 and GCV treatment. Data represents Mean ± SD of 6 wells. Based on these data, I concluded that rVAR2 is not able to induce dTK expression at a sufficient level in the cells to have a robust response to GCV dependent cell death. Although there are less cells alive in rVAR2 treated cells, there is still a significant amount of live cells as compared to rContr or NT. If I were to proceed to with the whole genome CRISPR screen, there would be too much background which would drown out actual hits. Therefore, as it currently stands the screen will not work with rVAR2. 66 2.4. Discussion 2.4.1. Role of oncofetal CS in inflammation My results clearly showed that rVAR2 binding to ofCS on the cells leads to the upregulation of three pro-inflammatory cytokines (IL-8, IL-6, and IL-1B). However, I was not able to identify the pathway that is activated to induce the response. There are several hypotheses that could explain these observations. First, the observed rVAR2 induction of NF-κB could just be a general pro-survival response that is not regulated by specific proteins. When rVAR2 binds the cell, it might be saturating all available binding sites on the surface. To remove rVAR2, the cells could be internalizing all the CSPGs that carry ofCS, since this is where rVAR2 is bound. Many CSPGs are involved in signaling events that are triggered by environmental factors. By suddenly removing them, it can lead to changes in the cell which can try to compensate by upregulating pro-survival pathways like NF-κB. In several experiments, when the cells were treated with rContr, I observed their presence inside the cells by western blot (Fig. 2-15, -16, -17). It is possible that rContr is binding to a molecule on the cell surface, and that by incubating the cells with rContr, they are also internalized and therefore activate some signaling events similar to rVAR2. Despite this, rContr does not appear to activate the NF-κB pathway since rContr treated cells did not trigger IκBα degradation which is a key indicator of activated NF-κB. Alternatively, this observation can also be a general anti-viral response. Viruses are known to bind GAGs as the first site of attachment prior to infecting cells[178]. rVAR2 binding to ofCS on the cells could mimic the first stages of a virus infection. NF-κB activation and subsequent pro-inflammatory cytokine production could be an 67 uncharacterized response to viral binding. Many antiviral responses require the cell to sense the presence of a viral infection using proteins that detect specific viral components[179, 180]. These sensors typically detect the virus after it has infected the cell. There could perhaps be an earlier response to viral infections that is yet to be characterized based on virus attachment to GAGs. To test this idea would require the production of recombinant viral proteins without the full virus and test if these proteins can trigger the same response as seen here. Another hypothesis for the observations in this study is that CSA has no active role in NF-κB signaling and that rVAR2 binding triggers a general response in the cells. This is not surprising because CSA or GAGs do not have any catalytic activity. GAGs in general are not capable of actively altering other molecules and therefore having an active role in signaling would be difficult to imagine. Their functions mainly rely on their physical properties where they act as scaffolds for the cell and some ligands. Most signaling pathways requires the actions of kinases, phosphatases, proteases, ubiquitinases, and many more other catalytic proteins. The effect I observed would just be a result of the cells responding to a sudden change in the environmental condition. Since rVAR2 is a foreign protein, an NF-κB and with pro-inflammatory cytokine response would logically be involved. 2.4.2. Future possibilities with the conditional lethal reporter screen Although the reporter system did not respond as anticipated with rVAR2, it did respond very well to TNF-α stimulation. TNF-α is a proinflammatory cytokine that regulates many cellular processes[181]. It has been extensively studied because of its 68 centralized position in these functions. However, more studies can be done to identify potentially unknown regulators of TNF-α signaling. Validation for the conditional lethal reporter showed a 100% cell death response when cells were treated with GCV after TNF-α, while cells treated with GCV alone were able to survive and proliferate. This presents a unique opportunity to study the TNF-α pathway since the screen can be easily adapted to search for factors in this pathway. This screen would potentially identify unknown regulators of TNF-α signaling. CRISPR knockouts for any genes that are positive regulators of the TNF-α pathway would be identified since only these cells would survive the selection process and would be enriched. The benefit of a TNF-α screen is that the pathway has already be studied extensively so knockouts for known positive regulators of this pathway are expected to be enriched. For example, I expect to see TNFR1, p65, and TRAF2 knockouts to be enriched since they are required for TNF-α induced signaling[181]. In addition, the dataset would also generate a list of negative regulators of TNF-α. Certain genes that inhibit the TNF-α induced pathway activation would never be enriched since their knockouts can potentially lead to higher dTK expression in response to TNF-α leading to increased sensitivity to GCV. For example, I would expect to never see IκBα knockout enrichment because it leads to constitutively active NF-κB signaling resulting in higher dTK expression. This screen can also easily be adapted to any other signaling pathways that results in IL-8 upregulation. For example, it can be used to study what is involved in PolyIC, LPS, and DAMP induced IL-8 expression. The potential success to a TNF-α screen would provide a powerful proof-of-concept to the conditional lethal reporter screen. 69 2.5. Conclusion In this chapter, I showed that rVAR2 treatment on MG63 cells increased expression of IL-8, IL-6, and IL-1B cytokines. I showed that this effect was the result of rVAR2 binding to ofCS on the surface of MG63 cells. In addition, I provided evidence of NF-κB pathway activation because rVAR2 treatment induced IκBα degradation and p65 nuclear translocation. Subsequent attempts to identify the protein components responsible for this observation were not successful. However, the evidence here does show that cell surface CS does have a role in regulating the NF-κB pathway. The exact mechanism by which this occurs requires further study to fully understand. 70 Chapter 3 â Characterizing the cellular fate of rVAR2 in cancer cells 3.1. Background 3.1.1. Internalization pathways and their mechanisms 3.1.1.1. Endocytosis mechanisms Endocytosis is the process by which cells internalize a variety of molecules from the environment. It is a tightly regulated process involving synchronization of many parts for proper function[182]. There are several endocytic pathways, the most well-known mechanisms are clathrin-mediated endocytosis (CME), caveolae-mediated endocytosis (CavME), and macropinocytosis (MP)[182]. Although they have different molecular mechanisms, the processes of all three involve reshaping the membrane to form endocytic vesicles that leads to the internalization of materials from outside the cell[182]. The three mechanisms can be summarized by Figure 3-i1, the molecular mechanism of these pathways will be discussed in more detail in the following sections. Fig. 3-i1 Major endocytic mechanisms. Diagram representing the three major endocytic mechanisms for internalization of biomolecules. Adapted from Chou et al. (2010) Chemical Society Reviews 40(1):233-45. [183] 71 3.1.1.2. Clathrin-mediated endocytosis Clathrin-mediated endocytosis (CME) is a major receptor-mediated endocytic pathway. It is the most widely studied and best characterized endocytic pathway. CME is responsible for transporting a wide range of cargoes from the plasma membrane into the cytoplasm[184]. CME is important for a variety of biological functions in cells including cellular homeostasis and cell signaling regulation. The cargo (eg. ligands bound to a receptor) is brought to the plasma membrane through interaction with a receptor. Ligand-receptor binding activates downstream adaptor proteins that binds the ligand-receptor complex. This leads to sequestration of the receptor at specific sites on the plasma membrane[184]. Here, clathrin coat proteins are recruited to the cargo-enriched areas where they interact with each other to form clathrin-coated pits (CCP) [184]. Formation of CCPs is dependent on clathrin coat proteins assembling into triskelia structures on the intracellular side of the plasma membrane. The triskelia structure is composed of three clathrin heavy chains (CHC) with tightly associated clathrin light chains (CLC) and the adaptor protein 2 (AP2). Assembly of the triskelia leads to the formation of CCPs which causes the plasma membrane to fold in on itself to form budding vesicles. These are released into the cytoplasm by the action of scission proteins that cut the budding vesicle from the plasma membrane. This results in the formation of clathrin-coated vesicles (CCV) whereupon the coat proteins rapidly disassemble, leaving a vesicle for transport to downstream compartments[184]. This is a simplified explanation of the complex machinery that is required for CME to occur. There are over 50 other known proteins that are involved in the process and the exact detail of CME is beyond the scope of this thesis. 72 3.1.1.3. Caveolin-mediated endocytosis Caveolin-mediated endocytosis (CavME) is very similar to CME in that it is also dependent on the action of coat proteins. CavME is more commonly associated with transcytotic trafficking activities where materials from one side of a polarized cell is moved to the other side[185]. For example, when materials are transported across the blood brain barrier (BBB). The sites where CavME occur are known as caveolae, they are flask shaped invaginations of the plasma membrane that is highly enriched for cholesterol and sphingolipids[185]. The main proteins involved in CavME are the adaptor proteins Caveolin-1, -2, -3, and coat proteins cavins. Caveolin proteins are integral membrane proteins that bind to cholesterol and act as scaffolds for downstream events[185]. Caveolae are static structures that are always present on the plasma membrane. CavME occurs only after sufficient receptor bound ligands have been concentrated in caveolae[185]. This triggers the budding of caveolae from the plasma membrane to form an endocytic vesicle[185]. However, the exact molecular mechanism and proteins involved in CavME is not as well understood as CME. 3.1.1.4. Macropinocytosis Macropinocytosis (MP) is the process by which the cell takes up materials in the surrounding environment in a less selective manner relative to CME or CavME[186]. It typically occurs at highly folded regions of the membrane where materials and fluids are brought into the cell through invagination of the membrane[186]. This process is controlled by the actions of actin formation where it leads to protrusions of the membrane that will fold in on itself forming a macropinosome[186]. The macropinosome is typically larger than other endocytic vesicles This process is highly specific to MP and leads to 73 unique membrane structures that can be easily identified by electron microscopy. Unlike the previously mentioned endocytic pathways, MP does not depend on the interaction between cargo and receptor. In immune cells such as macrophages and dendritic cells, there is a Ca2+ dependent constitutive macropinocytosis that is required for these cells to perform their immune functions[186]. However, this does not mean that receptor activation cannot trigger MP. Often, a sudden increase in growth factors and inflammatory signals have all been documented to trigger MP in various cells[187]. MP has been implicated in crucial physiological processes such as nutrient acquisition and immune surveillance. 3.1.2 Key cellular compartments involved in intracellular trafficking 3.1.2.1. Early endosome Early endosomes (EEs) are the first intracellular compartment that an internalized molecule is trafficked to. It is the site where the cargo is sorted to be transported to the intended destination[188]. The initial formation of the EE is not exactly understood, but it is directly derived from endocytic and golgi vesicles that fused together. The EE receives cargo from all endocytic pathways (CME, CavME, and MP) where cargo typically ends up in the EE within minutes of internalization. Most EEs are relatively small (~1um diameter) and exist near the periphery of the cytoplasm close to the plasma membrane[188]. However, there exists a heterogeneity in the morphology, composition, and function of EEs depending on cell type and environmental factors[188]. 74 Inside EEs, sorting decisions are made, and fates of internalized cargo and receptors are decided[188]. For example, cargo and receptors can be either rapidly recycled back to the plasma membrane through recycling endosomes, sent to the trans golgi network (TGN) for further sorting, or sent further along the endocytic pathway to the lysosome for degradation[188]. Regulation of this process is controlled through the spatial and temporal expression of endosomal identities which are controlled by special Rab GTPase proteins (eg. Rab5A) and tethering proteins like EEA1[188]. In addition, they also contain unique hydrolytic enzymes, such as furin, that are not found in lysosomes[189]. 3.1.2.2. Late endosome Late Endosomes (LEs) are a compartment formed by the maturation of EEs. They are typically more acidic (pH5-6)[190, 191]. Because of this, they exist as a secondary hub for sorting cargo after EEs. They receive cargo from upstream endocytic compartments, the transgolgi network, and other biosynthetic pathways[190]. They also perform functions that senses the nutrient level of the cell to determine the fate of cargo[190]. Like EEs the identity of LEs are dependent on enzymes that control their function. 3.1.2.3. Multivesicular body Multivesicular bodies (MVBs) are endosomal compartments that contain intraluminal vesicles (ILVs) formed by the inward budding of the endosomal membrane[191]. MVBs are often also referred to as LEs simply they are often found to be the same compartments[191]. They share many of the same markers such as Rab7 and Rab9[191]. However, the cargo of MVBs are rather unique where they are typically 75 packed with ILVs, which are positive for several tetraspanins such as CD9, CD81, and CD63[192]. MVBs are typically characterized by their distinct feature which are easily identified by electron microscopy images[193]. MVBs are either directed to the lysosome for degradation or towards the plasma membrane to release ILVs out of the cell as exosomes. 3.1.2.4. Lysosome The main function of the lysosome is to act as a waste disposal system where its many enzymes degrade complex molecules to their component forms for the cells to reuse[194]. They are very acidic (pH<5) and contain many hydrolytic enzymes (>60) that degrade all forms of organic molecules[194]. They are responsible for degradation of external particles from endocytosis and intracellular components from autophagy[194]. The end products are transported back into the cytoplasm by specific transport proteins. There are also many known membrane proteins associated with the lysosome that sense nutrient availability and control the fusion with other compartments for degradation[194]. Proteins like the lysosome associated membrane protein-1 (LAMP-1) are well-known markers for lysosome[194]. 76 Fig. 3-i2 Intracellular trafficking. Schematic representing intracellular trafficking with relevant organelles. Markers for specific organelles are labeled in green and pH is labeled in red. 3.1.3 Exploiting intracellular trafficking in cancer 3.1.3.1. Antibody-drug conjugates Antibody-drug conjugates (ADC) are a group of drug delivery vehicles that exploits the specific antigen binding capabilities of antibodies for drug delivery[195]. ADCs typically consist of a cytotoxic compound attached to a monoclonal antibody via a enzyme-cleavable linker[195]. This design combines the specificity of an antibody with the toxicity of a highly potent anti-tumor compound[195]. Conventional chemotherapy 77 relies on having a greater cytotoxic effect in rapidly dividing cells compared to normal cells. This results in a wide variety of adverse drug effects which can limit their antitumor effectiveness. By linking chemotherapy compounds to an antibody, their efficacy can be improved through two main mechanisms: 1) direct delivery of drugs to targeted cells and 2) concentrating the drugs to specific areas of the body where the antibodies bind. This would lead to a lower effective dose to treat tumors and reduce adverse effects. An example of a successful ADC is Kadcyla, which is a trastuzumab linked to the cytotoxic compound DM1[195, 196]. The FDA has since approved the use of Kadcyla to treat HER2+ breast cancers that have developed resistance to trastuzumab treatment. Clinical trials showed that it was able to improve overall survival compared to the standard treatment of trastuzumab-resistant breast cancer. The success of Kadcyla has opened the door for development of several ADCs against many different malignancies. Although ADCs have clear potential to be highly successful as a new class of treatment against cancer, some have failed due to less than expected effectiveness. The most obvious method to enhance the effectiveness of ADCs is to find targets that are highly expressed by cancer cells and not by normal cells. This would ensure that the ADCs are targeted mostly towards tumors and not normal tissue. However, an often-overlooked aspect in ADC design is their intracellular trafficking. This is important because ADCs are trafficked to intracellular compartments where each compartment has unique biochemical properties and enzymes[197]. These characteristics can directly influence drug release and efficacy. For example, the EE and lysosome contains different proteolytic enzymes (furin and cathepsins, respectively) which cleaves different peptide sequences. This has implications to how the linker should 78 be designed on ADCs. An ADC with a cathepsin cleaved linker that is not trafficked to the lysosome will have a reduced efficacy. Understanding where ADCs are trafficked after they enter the cells can help choose the correct linker to control which compartment the drug is released. The drug itself can also be designed in such a way to be activated only under special conditions such as found one of the compartments. By taking these steps into consideration, more effective or less toxic drug conjugates can be designed to increase their anti-tumor capabilities. 3.1.3.2. rVAR2-drug conjugates Like antibodies, rVAR2 binds a molecule that is found highly expressed on cancer cells. In a previous study, we designed a first generation rVAR2-drug conjugate (VDCs) that is comprised of a cytotoxic compound linked to the rVAR2 protein via an enzyme-cleavable linker. We showed that VDCs are effective in reducing tumor burden and improving overall survival in several in vivo models[104]. In a follow up study, we showed that a new VDC linked to hemiasterlin was an effective treatment against cisplatin-resistant muscle invasive bladder cancer in an in vivo model. This form of bladder cancer currently has very few effective treatment options, the results of this study provide validation that VDCs can be developed into novel therapies against drug resistant diseases[107]. However, future VDC design can be further improved upon to produce even more potent treatments by understanding the intracellular localization of rVAR2. 79 3.1.4 Rationale Our lab has reported the design of a first generation VDC and showed its pre-clinical efficacy in xenograft mouse models. VDCs can be further improved by a deeper understanding of the internalization process and intracellular trafficking of rVAR2. I hypothesize that after rVAR2 binds cancer cells, it will be internalized and trafficked to compartments of the endo-lysosomal pathway. Specifically, I will investigate the role of CME, CavME, and MP in rVAR2 internalization. Following this, I will investigate trafficking of rVAR2 to the compartments of the endo-lysosomal pathway. Deciphering this information can be useful for the selection of suitable linkers and payloads to develop VDCs with increased potency. 80 3.2. Materials and Methods Cell lines and growth conditions PC3, COLO-205, and MG63 cells were procured from ATCC (Manassas, VA, USA) and maintained in DMEM, RPMI, and MEM media, respectively. All media were supplemented with 10% FBS and grown in 5% CO2 at 37â°C. Flow cytometry binding assay PC3 and COLO-205 cells were grown to 80% confluency in appropriate growth media and harvested in an EDTA detachment solution (Cellstripper, Corning Cat# MT25056CI). Cells were incubated with rVAR2 (12.5-200nM) in PBS containing 2% fetal bovine serum (FBS) for 1hr on ice and binding was analyzed in a FACSCantoII (BD Biosciences) after a 1hr secondary incubation with an anti-V5-FITC antibody (Invitrogen Cat# R963-25). For CSA competition, cells were co-incubated with rVAR2 and 400ug/mL purified CSA (Sigma Cat# 27040). The data was analyzed using FlowJo V10.4.2. Live cell confocal COLO-205 cells were grown to 80% confluency in appropriate growth media on glass bottom dishes (MatTek Cat# P35G-1.5-14-C). The Nucleus was stained with 1:1000 dilution of Hoechst 33342 (ThermoFisher Cat# H3570) 15 mins prior to incubation with 20nM of rVAR2 directly labeled with an AlexaFluor-488 dye (rVAR2-488). Images were taken at 30 second intervals on a Zeiss Cell Observer spinning disc confocal microscope (Zeiss) using a 63x oil immersion lens. Still images were exported at the indicated time points. 81 High resolution confocal Cells were grown to 60% confluency on glass cover slips, then incubated with rVAR2 at the indicated time points and Hoechst 33342 15 mins prior to fixation. Cells were fixed in 4% paraformaldehyde (PFA) for 15mins at RT, permeabilized and blocked in 3% BSA/0.1% saponin for 30 mins. Primary antibodies against EEA1 (Cell signaling Cat# 3288S), LAMP1 (Cell Signaling Cat# 9091s), and V5 (ThermoFisher Cat# R96025) were diluted at 1:100 in blocking buffer and incubated overnight at 4â°C in a dark humidifier chamber. Secondary antibodies were diluted at 1:250 in the blocking buffer and incubated on cells for 1hr at RT in the dark. The cover slip was washed then mounted to cover slide with Prolong glass antifade mounting solution (ThermoFisher Cat# P36982) and left to cure for 24hrs. Z-stack Images were captured on an Olympus FLUOVIEW FV3000 confocal with high-sensitivity detectors followed by advanced constrained iterative deconvolution. Colocalization analysis Colocalization was measured using the Co-localization module in Zen 2013 (Black edition) software. An entire field of view is analyzed on a pixel-by-pixel basis. A modified Pearsonâs correlation coefficient (R) is generated to determine the colocalization of two channels. R = 1 represents perfect correlation where a pixel with 488 will always contain 594. R is reported for an entire image of 1024px by 1024px. Electron microscopy COLO-205 cells were grown on aclar disks treated with rVAR2 for 1hr prior to fixation with solution of 4% EM-grade PFA in 0.1M sodium cacodylate for 1 hour at RT. 82 Cells were permeabilized and blocked in 3% BSA/0.1% saponin for 30 mins. Primary antibody against V5 (ThermoFisher Cat# R96025) were diluted 1:50 in blocking buffer and incubated for 1hr RT in a dark humidifier chamber. Secondary antibody with gold nano particles (Nanoprobes Cat# 7001-0.5mL) were diluted in the blocking buffer at 1:100 and incubated on cells for 1hr at RT in a dark humidifier chamber. Cells were then processed with 1% osmium tetroxide for 1hr. Following this, cells were dehydrated using graded ethanol, and were then infiltrated, embedded, and polymerized in eponate resin. Ultrathin sections were cut and stained with 5% uranyl acetate and 2% lead citrate. Images were acquired on a Hitachi H7600. Statistical analysis Statistical analyses were performed in GraphPad Prism V6. One-way ANOVA followed by Tukeyâs post hoc analysis were used for multiple comparison tests. At least three biological replicates were used to determine significance. Significance was set at p<0.05. 83 3.3. Results 3.3.1. rVAR2 binds and internalizes into a variety of cancer cells Various cell lines representing different cancer indications were used to determine rVAR2 internalization. The cells were treated with 20nM of rVAR2 prior to fixing and permeabilization for confocal imaging. rVAR2 was detected by using an anti-V5 primary antibody followed by a secondary anti-rabbit-AF488. After 2 hours of treatment, rVAR2 are observed as clusters in cells originating from prostate cancer (PC3 and LNCaP), osteosarcoma (MG63 and U2OS), and colon cancer (COLO-205) (Fig. 3-1). This observation indicates that rVAR2 is concentrated in specific locations and not distributed uniformly within the cytoplasm. Fig. 3-1 rVAR2 internalization in cancer cell lines. Confocal images of internalized rVAR2 in cancer cell lines after 2 hours of treatment. Scale bars represent 5µM. These images are representative of at least three experiments for each cell line (nâ¥3). 3.3.2 rVAR2 accumulates in cells over time I investigated the kinetics of rVAR2 internalization for up to 2 hours of treatment using live cell confocal microscopy and western blotting. AlexaFluor-488 conjugated rVAR2 (rVAR2-488) was used to track internalization. Live cell imaging showed rVAR2 binding to COLO-205 cells within 10 minutes and internalization starting 30 minutes after treatment (Fig. 3-2 arrows). 84 Fig. 3-2 Time lapse of rVAR2 internalization in COLO-205 cells. Live cell confocal imaging of rVAR2 internalization. Still images were taken at the indicated times. Internalized rVAR2 (arrows) can be seen starting at 45 minutes. These images are representative of one experiment (n=1). The time lapse shows rVAR2 being actively internalized into the cells over time. This means that rVAR2 should accumulate inside cells over time leading to higher amount with longer incubation times. To test this, I decided to use western blot to determine the amount of internalized rVAR2 over time in COLO-205 and PC-3 cells. However, before I can proceed, it was necessary to be able to distinguish extracellular vs intracellular rVAR2. Trypsin is a protease that should be able to digest extracellular rVAR2 while leaving intracellular rVAR2 intact. By detaching the cells with trypsin, I should be able to remove most of the extracellular rVAR2. To test this idea, I treated COLO-205 and PC3 cells with 25nM rVAR2 for 1 hour, the cells were then detached by either directly scraping the plate with PBS or by trypsin. Detached cells were then centrifuged into pellets and lysed with RIPA buffer. Detection of rVAR2 was done 85 using a rabbit-anti-V5 antibody and secondary anti-rabbit-HRP. Western blot analysis shows that trypsinized cells had a noticeably weaker rVAR2 band in both COLO-205 and PC3 cells (Fig. 3-3A). This indicates that most of the rVAR2 detected were outside the cell, and that by using trypsin to detach the cells, most of the extracellular rVAR2 is removed. Therefore, the rVAR2 that I observed after trypsinization should be intracellular. I then investigated how rVAR2 accumulates within cells over time. To test this, COLO-205 and PC3 cells were treated with rVAR2 and lysates were collected over several time points ranging from 15 mins to 2 hours post treatment. Protein lysate was collected by trypsinizing cells to remove extracellular rVAR2. Here, I observed rVAR2 appearing in the cells after only 15 mins of treatment and accumulating over time (Fig. 3-3B). Interestingly, in both cell lines, there appears to be a decrease in rVAR2 level in the longer time points (Fig. 3-3B). Specifically, at the 2 hr time point for COLO-205 cells and starting at 1 hr time point for PC3 cells. Fig. 3-3 Intracellular rVAR2 accumulation. Western blot for rVAR2 accumulation in COLO-205 and PC3 cells. A) Lysates were collected by either scraping cells directly on the plate or trypsinizing the cells. B) Lysates were collected at the indicated timepoints by trypsin and blotted for rVAR2. Vinculin was used as loading control. These data are representative of 2 experiments (n=2). 86 3.3.3 Internalization is a continuous process Next, I tested whether rVAR2 is internalized continuously or it is internalized in waves after binding to cells. Because rVAR2 internalization depends on the presence of ofCS on the cell surface, a sudden saturation of rVAR2 on the cell surface could trigger increased internalization and prevent new rVAR2 from binding. This could explain the observed decrease in intracellular rVAR2 as seen in Figure 3-3 with the longer time points. I tested this hypothesis by using two fluorophores conjugated rVAR2 (-488 and -594). First, I pulse treated COLO-205 cells with rVAR2-488 for 15 mins then replaced the media with normal media for 1 hour to allow all bound rVAR2 to be internalized. I followed this immediately with a 15 mins rVAR2-594 pulse treatment. Using this treatment protocol, I observed that most of the rVAR2-488 are found in the cytoplasm with trace amount still bounded to the membrane while rVAR2-594 was largely bound only to the membrane (Fig. 3-4). Moreover, internalization of rVAR2-594 had already begun and can be seen co-localized with rVAR2-488 near the cell surface (Fig. 3-4). This experiment showed that rVAR2 is internalized continuously if it is bound to the cell surface. The observed decrease in Figure 3-3 likely shows that rVAR2 is being removed by either degradation or secretion outside the cell. Fig. 3-4 rVAR2 is continuously internalized. Immunofluorescence images for rVAR2-488 and rVAR2-594 in COLO-205 cells. Scalebar represents 5µM. 87 3.3.4. Purified CSA compete for rVAR2 binding My experiments thus far involve treating cells with rVAR2 in the media which might lead to internalization just by its presence in the environment. I must establish internalization is a result of rVAR2 binding to its target on cells. To test this, I used purified CSA in the media to compete for rVAR2 binding. Purified CSA contains a heterogeneous population of CSA, some of which are highly sulfated and similar to ofCS. In high enough concentration, there will be enough highly sulfated CSA present to out compete rVAR2 binding to cells. Cells were treated with rVAR2 with or without CSA competition and prepared for confocal imaging as described in Figure 3-1. The addition of 400ug/mL CSA inhibited rVAR2 binding and internalization in COLO-205 and PC3 cells indicating that the process requires interaction between rVAR2 and CSA (Fig. 3-5A). Degree of rVAR2 associated with cells was quantified by determining the intensity of the AF488 signal strength in single cells. This measurement was performed in ImageJ by drawing a region of interest (ROI) around each cell and recording the intensity of AF488 signal. Minimum of 30 cells was measured in each condition. AF488 intensity was significantly higher in rVAR2 treated cells as compared to rContr and rVAR2 + CSA treated cells (Fig. 3-5B). This measurement detects both the surface-bound and internalized rVAR2. The data would suggest that using CSA as a competition, the overall amount of surface bound and internalized rVAR2 is decreased. 88 Fig. 3-5 rVAR2 internalization depends on cell surface CS binding. A) Immunofluorescence of rVAR2 in COLO-205 and PC3 cells after 2 hours incubation. B) quantification for average intensity of AF488 signals in rContr, rVAR2, and rVAR2+CS treated cells. nâ¥30 cells, **** pâ¤0.0001 by ANOVA followed by Tukeyâs post hoc analysis. Scale bar represents 10µM. The images are representative of 3 experiments (n=3). *NOTE* There is a noticeable visual difference in the rVAR2 binding/internalization pattern between PC3 and COLO-205 cells. In COLO-205 cells, the membrane staining is very prominent as seen by the strong ring of green around the cell which is not seen in PC3 cells. This visual difference is likely due to the morphological differences between the two cell-lines. COLO-205 cells have a round shape and is much more cylindrical compared to PC3, which is visually more fibroblast like and flatter. Because of this, COLO-205 cells are âthickerâ than PC3 meaning that more of its membrane is on the side around the cell. Therefore, when COLO-205 cells are viewed from top-down, rVAR2 binding appears as a ring around the cell. PC3, being flatter, has most of its membrane on the top and bottom layer of the cell, so when viewed top-bottom, the side membrane is not 89 as prominently stained. Nevertheless, these images and the quantitative analysis show that purified CSA reduces the overall amount of membrane bound or internalized rVAR2. 3.3.5. rVAR2 binding depend on sulfated ofCS The previous experiment showed that competition with CSA can reduce rVAR2 binding to cells. However, it did not show whether rVAR2 requires binding to sulfated CS on the cell surface. To determine this, I would need to manipulate the ofCS so that it is not sulfated. Sodium chlorate (SC) is a drug that prevents sulfation of GAGs during synthesis by depleting the cellâs pool of sulfate donors. SC has been well established as a tool to eliminate sulfate groups on GAGs to study the effect of sulfation in cell biology with minimal effects on cell viability[198-200]. In this experiment, the cells are detached by trypsin then treated with SC as they are plated. This treatment removes all cell surface GAGs and prevents the cells from producing new sulfate GAGs as they are reattaching. 24 hours post treatment, the cells are detached with a non-enzymatic detachment agent to keep the cell surface molecules intact. To measure rVAR2 binding, I used a flow cytometry binding assay on non-permeabilized cells as described previously[104, 107]. The assay was performed on ice in the presence of sodium azide to prevent internalization of rVAR2 and the detection antibody. Briefly, detached cells were incubated with rVAR2 for 1hr followed by anti-V5-FITC detection antibody for 1hr. In between incubations, the cells were washed with ice cold flow buffer (PBS + 2% FBS + 0.01% sodium azide). Non-SC treated COLO-205 and PC3 cells showed concentration dependent binding of rVAR2 (Fig. 3-6). CSA competition can reduce rVAR2 binding even at the highest rVAR2 concentration (200nM). Treatment with SC at two concentrations 90 (20mM and 70mM) reduced rVAR2 binding in both cell lines (Fig. 3-6). This experiment showed that rVAR2 binding is dependent on sulfated CS, by removing sulfate groups I observed reduced rVAR2 binding in both COLO-205 and PC3 cells. Fig. 3-6 Sodium chlorate inhibit rVAR2 binding. Flow cytometry analysis for rVAR2 binding in COLO-205 and PC3 cells with and without SC treatment. 3.3.6. Removing CS sulfation inhibits rVAR2 internalization If rVAR2 binding to cells can be reduced with SC treatments, then the internalization of rVAR2 should also be reduced by SC as well. To test this hypothesis, COLO-205 and PC3 cells were treated with SC at various concentrations (0-70mM) as described in the previous section. 24 hours after SC treatment, the cells were incubated with rVAR2, trypsinized, and lysed as described in section 3.3.2. Western blot analysis of COLO-205 and PC3 cells treated with SC showed decrease of rVAR2 accumulation following SC treatment (Fig. 3-7A). Since the cells were trypsinized, the rVAR2 detected in these blots should be intracellular. In addition, I also prepared the cells for confocal imaging by using the same treatment protocol prior to fixation and processing for imaging. The images show nominal rVAR2 binding in non-treated cells (Fig. 3-7B). With increased 91 SC treatment, there was a noticeable reduction in rVAR2 intensity corroborating the western blot data (Fig. 3-7B). Fig. 3-7 Sodium chlorate inhibits rVAR2 internalization. A) Western blot analysis for internalized rVAR2 in COLO-205 and PC3 cells after SC treatment with the indicated concentrations. Vinculin was used as loading control. B) Immunofluorescence for rVAR2 after 1hour incubation in COLO-205 and PC3 cells after SC treatment. Scale bar represents 20µM. The results shown are representative of 2 experiments (n=2). Overall, these data show that rVAR2 internalization is mostly dependent on the binding of rVAR2 to cell surface CSA and not through nonspecific uptake. 3.3.7. Clathrin and Caveolae mediated endocytosis as a mechanism for rVAR2 internalization There are many endocytic mechanisms that cells can use to internalize external materials. Two major routes are CME and CavME. They are receptor dependent and 92 require coat proteins to form vesicles from the cell membrane. Because of this, clathrin and caveolae mediated endocytosis are typically triggered by specific events like ligand-receptor interactions. Various PGs, including CSPGs, are shown to be internalized through CME and CavME[201-203]. Because rVAR2 bind to ofCS attached to CSPGs, I hypothesized that both CME and CavME are involved in rVAR2 internalization. First, I investigated whether rVAR2 is internalized through these two pathways by using drugs to block their action. Specifically, 10ug/mL chlorpromazine (CPZ) was used to inhibit CME[186, 187] and 25ug/mL Nystatin (Nys) was used to inhibit CavME[188, 189]. The experiment was done at least twice to see if the drugs were able to alter rVAR2 accumulation in cells. Between two replicates, I obtained contradictory data on rVAR2 accumulation (Appendix 4). This was not surprising since using these drugs to study endocytic pathways are problematic due to non-specific effects in the cell. Because of this, the data generated with respect to CME and CavME in rVAR2 internalization was not conclusive. To inhibit CME and CavME in a more specific manner, I used siRNA to knockdown clathrin (CHC) and caveolin-1 (Cav-1) proteins. Although not perfect, knockdowns of key proteins in CME and CavME would provide a better method of inhibiting these pathways. I observed that knockdown of CHC and Cav-1 had different effects between PC3 and COLO-205 cells (Fig. 3-8). In PC3 cells, knockdown of CHC and Cav-1 seems to decrease rVAR2 accumulation in the cells. However, it was not able to completely inhibit rVAR2 internalization suggesting that rVAR2 is still taken up by the cell through other mechanisms. While in COLO-205 cells, knockdown of CHC seems to increase rVAR2 in the cells showing that CME is not an important mechanism for rVAR2 internalization (Fig. 93 3-8). Moreover, COLO-205 cells do not express Cav-1 and are still able to internalize rVAR2 indicating that rVAR2 internalization is not dependent on CavME in COLO-205. Fig. 3-8 Knockdown of clathrin or caveolae proteins alters rVAR2 internalization. Western blot for rVAR2, CHC, and Cav-1 in PC3 and COLO-205 cells after transfection with siSCR, siCHC, or siCav-1. Actin was used as loading control. The results shown are representative of 2 experiments (n=2). While it appears that inhibiting CME and CavME siRNA knockdown does alter rVAR2 internalization, no one pathway inhibition was able to completely stop rVAR2 from entering cells which shows that cells are using multiple mechanisms to take in rVAR2. In PC3 cells, inhibition of CME and CavME lead to alterations in rVAR2 accumulation inside the cells which indicated a role for these mechanism in rVAR2 endocytosis. While in COLO-205 cells the data suggested a different conclusion. Inhibition of CME did not alter rVAR2 accumulation to any noticeable degree. While at the same time, COLO-205 cells still internalize rVAR2 despite lacking Cav-1, a key protein in CavME, indicating that this mechanism is not important for COLO-205. It was not surprising that these data are not consistent between the two cell lines. ofCS can be on different CSPGs and each CSPG can be internalized through different mechanisms. PC3 and COLO-205 are two very different cell lines which most likely express different CSPGs. Therefore, inhibiting one pathway is not expected to completely stop rVAR2 internalization. 94 3.3.8. Macropinocytosis as a mechanism for rVAR2 internalization Since rVAR2 does not have a specific protein receptor and ofCS could be on many proteins, the internalization mechanism should be receptor independent. I investigated the possibility of macropinocytosis (MP) as the main mechanism of rVAR2 internalization. It can be triggered when there is a saturation of ligands bound to the cell surface. rVAR2 binding to the cell surface can act as the nonspecific saturation of ofCS on the plasma membrane which triggers the cells to use macropinocytosis to remove rVAR2 from the cell surface. The formation of vesicles through macropinocytosis is a well-defined process that forms unique structures on the cell membrane, which can be observed on electron microscopes[186]. To test for the role of MP in rVAR2 internalization, I used 5uM cytochalasin D (CytoD) to inhibit macropinocytosis[204, 205]. CytoD inhibits actin filament formation and by extension MP. Cells were treated in the same manner as described for CPZ and Nys treatment (1 hr drug pre-treatment followed by 2 hr of rVAR2). Western blot analysis shows no difference in intracellular rVAR2 in COLO-205 and increased rVAR2 in PC3 cells following CytoD treatment (Appendix 4). While, under confocal images CytoD had no noticeable effect on rVAR2 binding in PC3 cells (Fig. 3-9). However, the morphology of COLO-205 cells, and therefore rVAR2 staining, appear altered with CytoD treatment (Fig. 3-9). In COLO-205 cells, CytoD treatment induced the formation of protrusions from the membrane. This was not surprising since CytoD inhibits actin filament polymerization which is the mechanism that drives cell membrane protrusions. However, the same protrusions were not observed on PC3 cells. 95 Fig. 3-9 Cytochalasin D alters rVAR2 binding and internalization. Immunofluorescence of rVAR2 in cells treated with CytoD after 1hr rVAR2 incubation. Scale bar represents 5µM in COLO-205 images and 10µM in PC3 images. The images are representative of 3 experiments (n=3). Because of CytoDâs inhibitory effect on actin filament formation, it could lead to unintended effects like CPZ and Nys that may alter rVAR2 internalization independent of MP. I decided to use another method to analyze the role of MP in rVAR2 internalization. I tested whether rVAR2 co-localizes with dextran since it is a well-known marker for macropinocytosis[206, 207]. COLO-205 and PC3 cells were simultaneously incubated with AlexaFluor 594 labeled dextran (Dextran-594) and rVAR2-488. Cells fixed at different timepoints show rVAR2 and dextran colocalizing inside cells indicating they are in the same compartment (Fig. 3-10). Moreover, under transmission electron microscopy, I observed that rVAR2 is seen associated with unique macropinocytosis structures on the cell membrane (Fig. 3-11). 96 Fig. 3-10 rVAR2 colocalizes with Dextran. Immunofluorescence of cells incubated with rVAR2-488 and Dextran-594. Scale bar represents 5µM. These images are representative of two experiments (n=2). Fig. 3-11 rVAR2 is associated with micropinocytosis membrane structures. Electron microscopy of COLO-205 cells treated with rVAR2. Dotted lines represent the plasma membrane. Arrows points rVAR2 bound near membrane protrusions that corresponds to structures formed during macropinocytosis. Although these data provide evidence that macropinocytosis is involved in rVAR2 internalization, inhibition of macropinocytosis with CytoD in Appendix 4 did not prevent rVAR2 internalization indicating that rVAR2 can still enter the cell through other endocytic mechanisms. In fact, CytoD treatment in PC3 cells appears to have increased rVAR2 internalization which could be a way for the cells to compensate for inhibiting MP. This 97 may indicate that MP is not a prominent player for rVAR2 internalization in PC3 cells, and that other pathways may be more important. This is consistent with my siRNA experiment in figure 3-8 where knockdowns of CHC and Cav-1 reduced rVAR2 accumulation in PC3 cells showing that these two pathways play a bigger role. Overall, I showed that by inhibiting individual endocytic mechanisms, I can alter rVAR2 accumulation in the cells and thereby the cells capacity to internalize rVAR2. However, using drugs to inhibit endocytosis did not consistently alter rVAR2 internalization. In PC3 cells, rVAR2 is utilized all three mechanisms (CME, CavME, and MP). In COLO-205 cells, CavME is most likely not involved due to a lack of Cav-1. While CME and MP likely is involved with rVAR2 internalization in COLO-205 cells. The combined data shows that rVAR2 utilizes all the endocytic mechanisms to enter the cell. The degree to which endocytic mechanism is the most prominent is highly cell line dependent. This is not surprising since rVAR2 binds to ofCS which are attached to different CSPGs. Each CSPG would have its own preferred endocytic mechanism (CME, CavME, MP). When rVAR2 is bound to a CSPG, the internalization process would be dependent on how the specific CSPG is internalized into the cell. 3.3.9. Intracellular trafficking of rVAR2 While my experiments have shown that the different endocytic mechanism are all involved in rVAR2 internalization in different cells. The more important question is that after internalization, where is rVAR2 trafficked to because different intracellular compartments have various biochemical properties (eg. pH, enzyme, chemical content) that affects protein stability, drug release, and drug efficacy. Where rVAR2 is trafficked in 98 cells must be understood to enhance the effectiveness of VDCs. Therefore, it is necessary to determine which compartment rVAR2 is trafficked after internalization. To investigate this, COLO-205, PC3, and MG-63 cells were treated with rVAR2 for up to 24 then fixed and examined by confocal microscopy. rVAR2 presence in the early endosome (EE) and lysosome will be determined by co-localization with EEA1 and LAMP1, respectively. Confocal images show rVAR2 co-localizing with EEA1 in PC3 and MG-63 cells within 1 hour and up to 24 hours of continuous treatment (Fig. 3-12). COLO-205 cell shows no co-localization between rVAR2 and EEA1 due to having a frameshift deletion for EEA1 (COSMIC cell line mutation database). This indicates that rVAR2 enters the EE at least in PC3 and MG-63 cells. High resolution images clearly show rVAR2 present in the lumen of the EE (Fig. 3-13). Fig. 3-12 rVAR2 colocalizes with early endosomes. Immunofluorescence images of double stained cells with the early endosome marker EEA1 (red) and rVAR2 (green) after incubation with rVAR2 at the indicated time. Scale bars represents 5um. These data are representative of 2 experiments (n=2). 99 Fig. 3-13 rVAR2 is inside the early endosome lumen. Maximum intensity projection of Z stack with PC3 and MG63 cells stained for EEA1 (red) and rVAR2 (green) after 4hr rVAR2 incubation. Scale bar represents 5µm in large images and 500nm in small images. However, rVAR2 was not observed colocalizing with LAMP1 at any time point in all three cell lines indicating that most of the rVAR2 does not reach the lysosome (Fig. 3-14). 100 Fig. 3-14 rVAR2 does not colocalize with lysosomes. Immunofluorescence images of double stained cells with the lysosome marker LAMP1 (red) and rVAR2 (green) after incubation with rVAR2 at the indicated time. Scale bar represents 5um. These data are representative of 2 experiments (n=2). Colocalization was measured using a modified pearson correlation between rVAR2 and the marker signals. This calculation correlates intensity and location of two markers across an entire image of 1024px by 1024px. The measurement was done using at least 10 field of views (FOVs) with a minimum of 5 cells each. A control experiment on MG63 cells to determine correlation (R) between rVAR-488 and rVAR2-594 was used to determine a strong correlation. The cells were first treated with either rVAR2-488 or rVAR2-594 for 30 mins then treated with the other for another 30 mins prior to fixing. These cells should have high correlation between the two markers since both are rVAR2. For these samples, R value between rVAR2-488 and rVAR2-594 was measured at R=~0.6 for an entire FOV for both treatment conditions (Appendix 5A). An R value similar to this experiment is not expected between rVAR2 and EEA1/LAMP1. For rVAR2 and EEA1/LAMP1, the average of 10 FOVs for each condition is plotted in Figure 3-15. 101 Positive R indicates positive colocalization between rVAR2 and EEA1 in PC3 and MG-63 cells (Fig. 3-15). While the negative R values between rVAR2 and LAMP1 indicates negative correlation in all three cell lines (Fig. 3-15). Examples of the scatter plot between rVAR2 and the markers are shown in Appendix 5B. Although the R value seems small, it is the correlation of the rVAR2 and EEA1/LAMP1 signal for an entire image of 1024px by 1024px. Fig. 3-15 Colocalization analysis. Measurement of pearson correlation coefficient of red and green signals from images in Fig. 3-13 and 3-15. Values are average Â± standard error of at least 10 fields of view. Taken together, these observations show that rVAR2 is trafficked to EEs, but the majority does not reach the lysosome. 102 3.4 Discussion 3.4.1. Designing future VDCs A major ongoing project in the lab is to develop rVAR2 as a tool to deliver cytotoxic compounds directly to tumors. Much like ADCs, the goal is to exploit the expression of a specific target on cancer cells to deliver drugs with minimal off target effects. The study in 2015 showed that ofCS, which rVAR2 targets, is highly expressed in tumors. We designed a first generation VDC and showed that it was effective in reducing tumor burden in several in vivo models. However, the design of the VDC can be further improved to increase their efficiency. The aim of this study was to understand the endocytic mechanism and intracellular trafficking for rVAR2 in cancer cells. For the endocytic mechanism, I tested the role of CME, CavME, and MP in rVAR2 internalization in PC3 and COLO-205 cell lines. Using drug inhibitors of these mechanisms as well as siRNA knockdown of key proteins I showed that there is a cell line dependent preference of different mechanism for internalizing rVAR2. However, despite this difference the more important factor is which intracellular compartment rVAR2 is trafficked to after internalization. To my knowledge, the vesicles that originate from these endocytic mechanisms do not possess catalytic activities. Their primary function is to sort and transport cargo to the different intracellular compartments. This is important since each intracellular compartment has unique biochemical properties that can alter the efficacy of VDCs. For example, I showed that rVAR2 is trafficked to the EE so drugs conjugated to rVAR2 must be able to tolerate an environment with a pH of ~6.5. Should a conjugated drug be sensitive to this pH, it can be inactivated making it 103 useless even though rVAR2 delivered the compounds to the cells. Another point to consider in VDC design is to consider the linker used to conjugate the drug to rVAR2. In the lysosome, there are a variety of cathepsins that cleave peptides and many ADC designs incorporate cathepsin linkers in their design[208]. However, in this study I showed that the majority of the rVAR2 does not reach the lysosome. VDCs with cathepsin linkers would therefore have limited effectiveness. However, because rVAR2 is trafficked to the EE, linkers to enzymes found specifically in this compartment can greatly improve efficacy of future VDC designs. For example, Furin is a protease that is found in the early endosome. Some studies have shown that including a furin-cleavable linker in drug conjugates can enhance anti-tumor efficacy[189]. Incorporating this knowledge can lead to highly effective VDC designs that maximize their anti-tumor efficacy tailored to each malignancy. 3.4.3. Abnormal rVAR2 accumulation in cells In Figure 3-3 bottom panel western blot images, I noticed that the amount of rVAR2 inside PC3 and COLO-205 cells decreased with longer incubation times. The same phenomenon can be seen in MG63 cells from the previous chapter where I tested TBK1 phosphorylation over several time points. Western blots in Figure 2-16 and 2-17 show decreased rVAR2 after 2 hours incubation consistent with my observations in this study. I initially assumed this was due to degradation of rVAR2 in the lysosome. But, when I examined the intracellular trafficking of rVAR2, it was clear that the majority of 104 rVAR2 is not trafficked to the lysosome. Therefore, the decrease of rVAR2 cannot be the result of lysosomal degradation. One mechanism that cells can eliminate rVAR2 is through secretion via exosomes. They are small (30-150nm diameter) membrane bound vesicles created inside the endosomal lumen and may have a role in transferring antigen to T cells to generate an immune response[209, 210]. Since rVAR2 is a foreign protein, the cells may be creating and secreting exosomes containing rVAR2 which might be associated with exosomes outside the cell. This would mean that rVAR2 can be potentially used as a tool to isolate or capture cancer derived exosomes to study their content. This question is explored in the next chapter. 105 3.5 Conclusion In this study I examined the internalization kinetic, mechanism, and intracellular trafficking of rVAR2 in cancer cells. I found that rVAR2 internalization is dependent on its interaction with sulfated CSA on the cell surface. I also showed that cells internalize rVAR2 through three major endocytic mechanisms while the main mechanism is cell line dependent. Inside the cell, I discovered that rVAR2 is trafficked to the early endosome within 1 hour of internalization, but rVAR2 does not reach the lysosome. This information can be used to screen various VDC constructs for relative effectiveness in different cancer cell lines and in vivo models to maximize efficacy. 106 Chapter 4 â Establishing an rVAR2-based exosome detection strategy 4.1. Background 4.1.1. Extracellular vesicles Extracellular vesicles (EVs) are a class of small membrane bound vesicles that exist outside the cell[211]. There are several varieties of different EVs, they are typically classified by size[211]. Smaller EVs like exosomes are formed in endosomal compartment within the cells while larger microvesicles or apoptotic bodies are formed by direct budding from the plasma membrane or as a byproduct of apoptosis[211]. Figure 4-i1 shows the estimated size distribution for these EVs as well as their origin. Fig. 4-i1 Extracellular vesicle distinctions. Origin and size distribution of various forms of extracellular vesicles. Exosomes are most often defined as between 20-150nm in diameter, microvesicles as 100nm - 1µm in diameter, and apoptotic bodies with the largest range at 50nm - 5µm. Adapted from Gyorgy et al. (2011) Cell Mol Life Sci. [212] EVs carry cargo from cells that can contain protein, nucleic acids, lipids, and other organic molecules[211]. Due to their complex nature, they are involved in several 107 biological functions such as cargo delivery, cell-cell communication, antigen presentation, cellular reprogramming, etc[211]. As such, alterations in EV function and distribution are often seen in diseases such as cancer. 4.1.2. Exosome biogenesis The most well-known sub-population of EVs are exosomes which are defined as EVs that have a diameter of around 30-150nm[213]. They are positive for markers such as ALIX, ESCRT (-I and -II), TSG101, HSP70, and members of the tetraspanin family (CD9, CD63, CD81)[213]. Many of these markers are also involved in exosome formation by initiating or controlling the formation of Intraluminal Vesicles (ILVs) in endosomes[214]. Endosomal origin is a key characteristic of exosomes since they are formed by the inward budding of the endosomal membrane producing ILVs[214]. Endosomes with ILVs are known as Multivesicular Bodies (MVBs). MVBs are then trafficked to the cell surface where they fuse with the plasma membrane resulting ILVs released into the extracellular environment where they become exosomes[214]. Alternatively, MVBs can undergo fusion with lysosomes whereby their content is digested[214]. The steps in exosome formation and sorting are summarized in Figure 4-i2. 108 Fig. 4-i2 Exosome biogenesis. Schematic representing the endocytic pathway and exosome biogenesis. The endosomal biogenesis of exosomes means that the surface topology of exosomes is similar to the plasma membrane[215, 216]. Meaning the extracellular domain of proteins faces outwards while the lumen encapsulates cytosolic content. This naturally indicates that exosomes carry information from their cell of origin which can be utilized to study the current state of that cell. The value of exosomes will be discussed in more detail in the following sections. 4.1.3. Exosomes in cancer biology 4.1.3.1. Regulation of the immune microenvironment by exosomes Multiple studies have shown a vital role of exosomes in reprogramming immune cells in the tumor microenvironment[217-219]. Tumor derived exosomes are reported to regulate immune response towards tumor cells. Exosomes containing neo-antigens or 109 MHC-peptide complexes activate T cells by either direct presentation or antigen presentation through dendritic cells[220]. For example, exosomes from TS/A murine breast cancer cell lines contain several tumor heat shock proteins (HSP70-80). These exosomes are transferred to dendritic cells which drive a potent CTL dependent anti-tumor effect on mouse tumors[221]. In addition, exosomes from RAB27A over expressing lung cancer cells induced a potent anti-tumor immune response by upregulating MHC-II on dendritic cells which lead to increased CD4+ T cell proliferation[222]. However, tumor derived exosomes are also known to help tumor cells to evade immune surveillance. For example, exosomes from MDA-MB-231 breast cancer cell lines, with high amounts of PD-L1, can be transferred to other breast cancer cell lines that either have low or knockout for PD-L1[223]. This effectively provides low PD-L1 expressing cells with PD-L1 molecules to evade immune surveillance. Further studies have revealed that glioblastoma and melanoma tumors also secrete exosomes containing PD-L1 as a means to suppress local immune activity[224, 225]. In fact, the level of exosome PD-L1 can be correlated to patient response to anti-PD-L1 therapy [224, 225]. 4.1.3.2. Role of exosomes in metastases development Perhaps the most well studied aspect of exosomes in cancer is their role in metastasis. Exosomes have been observed to be involved in all processes that lead to metastasis such as cell migration, invasion, pre-metastatic niche formation, and regulating metastatic growth[226-229]. Many studies have shown that tumor derived exosomes in part are responsible for ECM remodeling and destruction[226-229]. A recent study for the role of exosomes in chronic obstructive pulmonary disease (COPD) revealed 110 that exosomes from activated leukocytes in COPD patients contribute to disease by degrading lung ECM via proteolytic proteins on their surface[230]. Exosomes from activated leukocytes were physically bound to ECM components via Mac-1 linker protein enabling MMP9 on the exosome surface to degrade collagen[230]. It is speculated that a similar mechanism could be employed by tumor cells to destroy their surrounding ECM thereby enabling them to disseminate from the primary tumor. Tumor derived exosomes also are involved in establishing pre-metastatic niches at distant sites to receive disseminated tumor cells. An example of this was shown in a study where melanoma derived exosomes traveled to lymph nodes resulting in ECM deposition and neovascular formation[231]. This led to enhanced migration of melanoma cells to lymph nodes with abundant melanoma derived exosomes[231]. In prostate cancer, tumor derived exosomes enhanced osteoblast activity in the bone which altered the ECM composition and allowed for enhanced bone metastasis of tumor cells[232, 233]. Subsequent studies in many cancer types have shown that exosomes prepare pre-metastatic niches to receive tumor cells through many different mechanisms[234]. 4.1.3.3. Exosomes in drug resistance Exosomes have also been shown to confer drug resistance in cancer through different mechanisms. Several cancer types package drugs in exosomes to expel them from the cell. This resulted in decreased sensitivity of tumor cells to cytotoxic compounds like cisplatin[235, 236]. Cancer cells sensitive to cytotoxic drugs take up exosomes from resistant cells to become drug resistant themselves. For example, drug resistant leukemia cells shed exosomes containing P-glycoprotein (P-gp) that were then taken up by 111 sensitive cells gaining functional P-gp[237]. This was hypothesized to be a mechanism for the emergence of multi-drug resistance after treatment. In breast cancer cells, similar effects were observed when Anthracycline sensitive cells took in Glutathione S-transferase P1 (GSTP1) loaded exosomes from resistant cells[238]. Other studies showed that some exosomes carry miRNAs which can confer resistance by regulating signaling pathways such as MAPK or mTOR[239]. 4.1.4. Exosomes as a biomarker for cancer development Since exosomes, and EVs in general, are released by cells in all stages of growth, they are an attractive tool to be exploited for the purposes of early diagnosis of cancer. Current screening techniques for cancer are invasive and do not capture the spatial and temporal heterogeneous nature of cancer. By their nature, exosomes carry information from the cell of origin which can be used to obtain information about the tumor where they originated. The key challenge is being able to isolate specific cancer derived exosomes. In the past decade, studies have revealed the presence of specific markers that are more commonly found on cancer derived exosomes[240]. This is an interesting new development that can be developed for diagnostic and prognostic purposes. For example, serum exosomes from prostate cancer patients have elevated levels of survivin even in low grade tumors (Gleason 6), which can be used for early detection or monitoring tumor progression[241]. While experiments in colon cancer derived exosomes showed higher expression of glypican-1 (GPC1) compared to health blood and can be used for prognostic purposes[242]. GPC1 is a well-known proteoglycan that carries a heparan sulfate side chain indicating that exosomes may carry GAG chain on their surface. 112 4.1.5. Glycosaminoglycans and exosomes There is currently limited information on the nature of GAGs and their role in exosomes[243]. A recent study in this area have shown that cell surface heparan sulfate (HS) act as receptors for exosomes to bind[244]. This is a mechanism by which cells attract and internalize exosomes from the environment. As mentioned above, GPC1 has been demonstrated to be associated with exosomes in prostate cancer. GPC1 is also present on exosomes originating from pancreatic cancer[245, 246]. In addition, CD44 was also found on the surface of exosomes originating from colorectal and B-cell lymphoma cell lines[247]. Exosomal CD44 was also shown to be elevated in B-cell chronic lymphocytic leukaemia patient plasma[247]. These studies hint at the possibility that exosomes express CS chains on their surface since both GPC1 and CD44 have been shown to be modified with CS in addition to HS. Presence of GPC1 and CD44 on tumor derived exosomes could also indicate that ofCS might be present on the surface of tumor derived exosomes. Because exosomes share surface topology with the plasma membrane, ofCS may be presented outwards where rVAR2 is able to bind. 113 4.1.6. Rationale There is currently no established method to specifically isolate cancer derived exosomes for further analysis. In this study, I hypothesize that cancer derived exosomes contain ofCS, and I will investigate if rVAR2 can use as a tool to isolate cancer derived exosomes for further analysis. Fig. 4-i3 Flow chart for rVAR2 based purification of cancer derived exosomes. Exosomes from patient samples (Blood/urine) to be extracted by size exclusion and further purified for cancer derived exosomes using rVAR2. Isolated exosomes will then be subjected to bioinformatic analysis for characterization. 114 4.2. Materials and Methods Cell culture and exosome isolation PC3 and MG63 cells were procured from ATCC (Manassas, VA, USA) and maintained in DMEM and MEM media, respectively, supplemented with 10% FBS and grown in 5% CO2 at 37â°C. At 50% confluency in 150âmm culture dishes, cells were washed thrice with PBS then supplemented with media containing 5% exosomes depleted FBS (System Biosciences Cat# EXO-FBS-250A-1). After 48âh, culture supernatant was collected and centrifuged twice at 1000âÃâg for 10âmin to remove cellular debris. 15mL of cleared media was then concentrated to ~1mL by centrifugation at 14,000 x g for 30 mins using Amicon® Ultra-15 centrifugal filter unit (Sigma Cat# UFC901008). Concentrated media was then was overlaid on 30nm qEV-original size exclusion columns (Izon Cat# SP5) followed by elution with PBS. 500uL fractions were collected and Protein concentration was determined by NanoDrop A280. Only fractions that contained low protein concentrations were used for subsequent experiments. Exosome characterization Purity, size, and concentration of all preparations of exosome fractions were analyzed using nano-tracking analysis (NTA) on a NanoSight LM10 (Malvern Panalytical). Particle counts and size was done with the NTA 3.1 software using a 50μL/min flow rate and read at 25°C on a 488nM laser/filter. All NTA readings were performed using PBS which had been previously depleted of nanoparticle background by filtration through a 0.02µM membrane. 115 rVAR2 and exosome marker colocalization PC3 and MG63 cells expressing CD9-mCherry or CD81-mCherry were plated on glass cover slips in a 24 well plate. 24 hours post seeding, cells were treated with 25nM rVAR2-488 for 24 hours, washed with PBS then fixed with 4% PFA for 20mins at RT. Coverslips were mounted to glass slides with ProLongGlass anti-fade reagent (ThermoFisher. Cat# P36980). Images were obtained on an Olympus FV3000RS confocal microscope at 60x magnification. Western blot analysis Exosome fractions were collected and concentrated using Amicon® Ultra-0.5 centrifugal filter unit (Sigma Cat# UFC501024) according to manufacturer instructions. Concentrated exosomes were lysed using reducing sample buffer (0.25M Tris HCl (pH 6.8), 40% glycerol, 8% SDS, 5% 2-mercaptoethanol and 0.04% bromophenol blue) and boiled for 10 minutes at 95â°C. Proteins were resolved as previously described. Membrane were probed with antibodies against CD9 (Abcam Cat# ab92726), CD81 (Novus Cat# NB100-65805), and V5 (ThermoFisher Cat# R96025). Mass spectrometry Exosome protein mass spectrometry was carried out as described by Pietrowska et al. (2017)[248]. Briefly, purified exosome was lysed using a lysis buffer (exoLB) consisting of 6% sodium dodecyl sulfate (SDS), 200mM dithiothreitol (DTT), and 200mM Tris-HCl pH7.6 at 95°C for 5 mins. The sample was then incubated with 10mM iodoacetamide for 20mins at room temperature in the dark. Protein digestion was done by adding 0.1ug/uL trypsin and incubating at 37°C for 18 hours. Digestion was terminated 116 by introducing trifluoroacetic acid to final concentration of 0.5%. Mass spectrometry was performed on a ThermoFisher Orbitrap LC-MS according to Pietrowska et al. (2017). Flow cytometry Streptavidin-conjugated magnetic beads were coupled with biotinylated anti-CD9 antibody according to the manufacturerâs protocol (System Biosciences Cat# EXOFLOW700A-1). 2x107 exosomes were incubated with the conjugated beads, and stained with rVAR2 at RT for 2 hours, followed by rabbit anti-V5 (Cell Signaling Cat# 13202S) and donkey anti-rabbit-BV421 (BioLegend Cat# 406410). Exosomes were then stained with pan-EV-FITC stain according to manufacturer instructions. Samples were washed and acquired on a FacsCanto II flow cytometer (BD Biosciences) and analyzed using FlowJo v10. Exosome ELISA Exosome detection kits (CD9, CD63, CD81) were acquired from MSD. Biotinylated capture antibodies were coated onto streptavidin plates according to manufacturer instructions. Exosomes were reconstituted in the assay diluent provided. Assay diluent (25 µl) was added to all wells and the plate sealed and incubated for 30s at RT on an orbital shaker and samples were added at 25 µl per well. The plate was sealed and incubated for 2h at RT on an orbital shaker. At the end of the incubation the wells were washed three times using 200 µl PBS + 0.05%Tween 20, soaking for 30s and then discarding. Detection antibodies or rVAR2-ST were added at 25 µl per well. Plates were sealed and incubated for 1 h at RT on an orbital shaker. At the end of the incubation the plate was washed three times as before. 150 µl of the MSD Read Buffer was added to 117 each well and the MSD plates were measured on the MSD Sector Imager 2400 plate reader. 118 4.3. Results 4.3.1. rVAR2 is associated with exosomes Earlier observations of intracellular rVAR2 indicate that with time, there is a decrease in rVAR2 level inside the cell (Figs.2-16, 2-17, and 3-3) despite its continuous presence in the environment. I initially hypothesized that this is a result of lysosome degradation. However, observations from Chapter 3 clearly shows that rVAR2 is not trafficked to the lysosome in three separate cell lines. Because of this, I altered my initial hypothesis and tested whether cells remove rVAR2 by secreting it into the external environment. A mechanism that the cell can utilize to expel foreign proteins is through the production and secretion of exosomes. Since exosomes are generated in the endosomes as ILVs, it is possible that rVAR2 is packaged with ILVs for secretion. To test this, I investigated whether rVAR2 is co-localized with exosome markers while still inside cells. I obtained PC3 and MG63 cells that had stable expression of CD9-mCherry and CD81-mCherry and treated them with rVAR2-488 for 24 hours prior to fixation and confocal imaging. Analysis of confocal images shows that rVAR2 and ILVs are in the same compartments based on its colocalization with the exosome markers (Fig. 4-1). 119 Fig. 4-1 rVAR2 colocalizes with exosome markers inside cells. Representative confocal images of PC3 or MG63 cells with stable expression of CD9-mCherry or CD81-mCherry after treatment with rVAR2-488 for 24 hrs. Scale bar represent 5um. These data are representative of two experiments (n=2). Next, I investigated whether rVAR2 is associated with exosomes outside the cell. To test this, I pulse treated PC3 and MG-63 cells with 100nM rVAR2 for 1 hour, then washed the cells thrice with PBS to clear all unbound rVAR2, finally added media supplemented with exosome-free FBS. 48 hours later I collected the media and harvested exosomes by size exclusion columns. Particle size (diameter) and concentration was determined by nanoparticle tracking analysis (NTA) where it showed that the fractions are purified in exosome size (30-150nm diameter) particles (Fig. 4-2). Western blot analysis of 20 billion particles showed that rVAR2 appears in fractions that contain purified exosomes as determined by the presence of CD81 (Fig. 4-2). Purified CSA was added 120 in the media to compete for rVAR2 binding as a negative control showing that the detected rVAR2 is obtained from the cells. Fig. 4-2 rVAR2 is associated with purified exosomes. Exosomes were purified from rVAR2 treated PC3 and MG63 cells, majority of the particles were ~110nm in diameter. 20 billion particles were used for western blot analysis, rVAR2 is detected by anti-V5 antibody. CD81 was used as loading control for exosomes. These data are representative of three experiments (n=3). To validate the exosome extraction protocol, I performed mass spectrometry analysis with purified exosomes from PC3 and MG63 cells. The mass spectrometry provided 697 and 501 high confidence protein hits from PC3 and MG63 cells, respectively. I used the DAVID bioinformatic tool to analyze this list of proteins for cellular component enrichment. In both PC3 and MG63 exosomes, the top three enriched cellular components were extracellular vesicles, extracellular organelles, and extracellular exosomes (Fig. 4-3). This provides further confidence that the extraction protocol purifies exosomes. 121 Fig. 4-3 Gene ontology analysis of purified exosomes. Mass spectrometry was used to generate a list of proteins present in PC3 and MG63 exosomes. High confidence hits were analyzed using the DAVID bioinformatics tool to determine the enriched cellular compartments. 4.3.2. Cancer cell exosomes contain CSPGs From previous data, I showed that rVAR2 binds and is internalized into cancer cells through binding with cell surface ofCS. Inside the cell, rVAR2 colocalizes with the early endosomal marker EEA1 and several exosomal markers (CD9/63/81). For rVAR2 to be physically associated with exosomes, there must be an ofCS component associated with exosomes. These ofCS would be attached to core proteins of CSPGs that may be present on exosomes. To test this, I searched the mass spectrometry data to identify known CSPGs. Figure 4-4 shows a list of high confidence with a false discovery rate (FDR) of â¤1%. The list is ranked in descending order according to the sum posterior error probability (PEP) score which is calculated based on the number of peptide spectrum matches (PSM). Present in this list are several known CSPGs such as CD44, Glypican-1, and CSPG4 (Fig. 4-4A yellow) as well as many exosome markers (Fig. 4-4A orange). 122 Fig. 4-4 PC3 and MG63 derived exosomes contain CSPGs. Mass spectrometry analysis for exosomes extracted from PC3 and MG63 cells. A) Known CSPGs are highlighted in yellow and exosome markers are highlighted in orange. Based on this observation, I hypothesized that cancer derived exosomes express CSPGs which could be modified with ofCS chains. 4.3.3. rVAR2 as detection tool for cancer exosomes The prospect of ofCS presence on cancer derived exosomes means it can potentially be isolated with rVAR2 for further study. To test this hypothesis, I need to show that rVAR2 can isolate or detect cancer derived exosomes. First, I utilized a flow-cytometry based approach to determine whether rVAR2 can bind PC3 exosomes. The exosomes were immobilized on anti-CD9 beads then incubated with rVAR2 followed by anti-V5 primary and a secondary BV421 antibody for detection (Fig. 4-5). The data shows the highest observed BV421 signal being exosomes treated 123 with rVAR2 suggesting that rVAR2 is bound to exosomes captured on the anti-CD9 beads (Fig. 4-5 #4). Competition with purified CSA was able to reduce the BV421 signal, indicating some degree of specificity in rVAR2 binding (Fig. 4-5 #5). However, when the beads were treated with rVAR2 without exosomes, there was high BV421 signals as well, which indicates non-specific binding of rVAR2 to the beads (Fig. 4-5 #7). Fig. 4-5 rVAR2 binds to PC3 exosomes. PC3 exosomes captured on CD9 antibody beads and treated with rVAR2 (50nM). rVAR2 was detected using FACS using anti-V5 and anti-mouse-BV421 antibodies. Free CSA was added as control to compete for rVAR2 binding and antibodies in absence of rVAR2 was used as background. This led me to conclude that although rVAR2 can potentially be used to detect exosomes on anti-CD9 beads, non-specific binding of rVAR2 to the beads hinders its utility for this purpose. The data also indicates that rVAR2 may not only be binding directly to the beads, but also forming aggregates since the second highest signal detected with rVAR2+antibodies without exosomes. Therefore, a different assay is needed to test whether is rVAR2 is able to isolate cancer derived exosomes. 124 4.3.4. ELISA based approach for exosome detection and capture Meso Scale Discovery (MSD) developed a highly sensitive ELISA-based assay for exosome capture and detection with CD9, CD63, and CD81 antibodies. In this system, exosomes are captured by biotinylated antibodies immobilized on streptavidin plates (Fig. 4-6). Their presence is detected using Sulfo-Tag (ST) conjugated antibodies that emit light after introducing an electric current to the plate (Fig. 4-6). The intensity of the emitted light is measured and directly correlates with the amount of exosomes present (Fig. 4-6). Fig. 4-6 Schematic of sandwich ELISA for exosome detection. Cancer derived exosomes are captured by CD9, CD63, or CD81 biotinylated antibodies onto streptavidin coated plates. Detection of captured exosomes were done using Sulfo-tagged CD9, CD63, or CD81 antibodies. Using this technology, I established a protocol to detect PC3 (extracted by me) and THP-1 (provided by MSD) exosomes. Exosomes from PC3 and THP-1 cells were incubated onto plates with the capture antibodies. Sulfo-Tagged (ST) antibodies against CD9, CD63, or CD81 were used to detect the presence of exosomes. All three exosome markers (CD9, CD63, and CD81) were detected on both PC3 (Fig. 4-7A) and THP1 (Fig. 4-7B) exosomes when capturing with one antibody (e.g. capture with CD9, detect with CD9, CD63, and CD81). Luminescence increased in a concentration dependent manner indicating the specificity of the detection antibodies. In addition, I also used a biotinylated 125 CD81 capture antibody from different sources (Biolegend). Again, I was able to detect all three exosome markers on PC3 (Fig. 4-7A) and THP1 (Fig. 4-7B) exosomes. Overall, these experiments highlight the specificity and sensitivity of this assay where exosomes were detected even at low concentrations (â¤106/mL). This technology will be utilized as the basis to determine the feasibility of using rVAR2 to capture or detect cancer derived exosomes. 126 Fig. 4-7 Evaluation of ELISA based exosome capture and detection. Different concentration of exosomes from A) PC3 and B) THP1 derived exosomes were captured with anti-CD9, CD63, CD81 from MSD or anti-CD81 from Biolegend. SulfoTagged CD9, CD63, or CD81 antibodies were used to detect exosome. 4.3.5. Utilizing rVAR2 for detection or capture of cancer derived exosomes Based on the previous data, I proceeded to investigate whether rVAR2 can capture or detect cancer derived exosomes. First, I tested whether rVAR2 can detect antibody captured exosomes. In this experiment, I used CD9/63/81 antibodies to immobilize exosomes into 96 well plates and used Sulfo-Tag conjugated rVAR2 (rVAR2-ST) to detect ofCS presence on exosomes (Fig. 4-8A). I observed no differences in rVAR2-ST signal as compared to mock control (absence of exosomes or capturing antibodies). This indicates that rVAR2-ST did not bind captured exosomes (Fig. 4-8B). 127 Fig. 4-8 rVAR2-ST did not detect cancer derived exosomes. Different concentration of exosomes from A) PC3 and B) THP1 derived exosomes were captured with biotinylated CD9, CD63, CD81 antibodies. rVAR2-ST was used to detect exosomes. Conditions with no exosomes or no capture antibodies were used as background for comparison. Next, I attempted to use rVAR2 to capture exosomes and probe for their presence using the marker antibodies. Biotinylated rVAR2 molecules were Immobilized onto streptavidin plates, then incubated with exosomes isolated from PC3 and THP-1 cells. Sulfo-tag conjugated CD9/63/81 antibodies and rVAR2-ST were used to detect exosomes (Fig. 4-9A). Using this setup, I did not observe a significant increase in antibody signals in the rVAR2 capture plates in either PC3 or THP-1 exosomes relative to background signals (Fig. 4-9A). This indicates that rVAR2 was not able to capture cancer exosomes. Furthermore, when using rVAR2-ST as the detection tool, there was a shift in luminescence (~4 orders of magnitude) independent of exosome presence (Fig. 4-9B). This may be a result of rVAR2 aggregation leading to an amplification of signal which would be consistent with observations from the flow cytometry data in Figure 4-5. 128 Fig. 4-9 rVAR2 cannot capture cancer derived exosomes. Biotinylated rVAR2 were coated onto streptavidin plates to capture exosomes. Presence of exosomes was detected by sulfo-tagged CD9/63/81 antibodies or rVAR2-ST. Conditions with no exosomes or no capture antibodies were used as background for comparison. 4.3.6. Capture and detection of cancer exosomes from cells pre-treated with rVAR2 Because I failed to directly detect or capture exosomes using rVAR2, I re-evaluated my initial hypothesis where ofCS would be ubiquitously present on exosome membranes. Instead, there might only be a small population of exosomes that carry the ofCS modification therefore would be difficult to detect. I then hypothesized that rVAR2 incubation and subsequent internalization would stimulate the production of ofCS positive exosomes. This could be a mechanism for the cells to eliminate intracellular rVAR2 by packaging it with exosomes. Furthermore, this would have the benefit of producing exosomes that already have surface bound rVAR2. 129 I can then use anti-V5 antibodies to indirectly capture or detect rVAR2. To prepare rVAR2 positive exosomes (rVAR2+EXs), I treated PC3 cells with rVAR2 and collected exosomes as described in Figure 4-2. To test this idea, I coated streptavidin plates with biotinylated anti-V5 antibodies to bind and capture rVAR2+EXs. Their presence was determined by detection with CD9/63/81 antibodies (Fig. 4-10). In this experimental setup, I did not observe a noticeable difference in luminescence between exosomes derived from rVAR2 treated, non-treated (NT), or rVAR2+CSA competition cells (Fig. 4-10). Furthermore, I did not observe a concentration dependent increase of luminescence in samples with high concentration (>1010 particles/mL) of exosomes (Fig. 4-10). I confirmed this observation by repeating the assay with a separate batch of rVAR2 treated exosomes (Appendix 6). This likely shows that I did not capture rVAR2+ EXs using the anti-V5 antibody. 130 Fig. 4-10 Capture and detection of exosomes from cells pre-treated with rVAR2. rVAR2+exosomes capture using a biotinylated anti-V5 antibody to bind rVAR2. Detection of captured exosomes was determined by CD9/63/81 antibodies. Exosomes from cells without rVAR2 treatment or CSA competition were used as negative controls. Previously I showed that rVAR2-ST failed to detect captured exosomes in Figure 4-9. This could be the result of the ST conjugation process where it altered the ability for rVAR2 to bind ofCS. To circumvent this issue, I used an anti-V5 antibody to detect rVAR2 on captured exosomes. rVAR2+EXs from treated PC3 cells were captured on plates with CD9/63/81 antibodies. Primary rabbit-anti-V5 and goat-anti-rabbit-ST antibodies were used to probe for the presence of rVAR2 on the captured exosomes (Fig. 4-11). Exosomes captured with CD9/63/81 antibodies were not detectable with this setup (Fig. 4-11). There were no concentration dependent changes in luminescence indicating that 131 the antibodies did not bind its target. Based on this, I concluded that I was not able to detect any exosome bound rVAR2. Fig. 4-11 Using anti-V5 antibody to detect rVAR2 on captured exosomes. rVAR2+exosomes capture using biotinylated CD9/63/81 antibodies. Detection of rVAR2 on captured exosomes was determined by anti-V5 and Sulfo-tagged secondary antibodies. Exosomes from cells without rVAR2 treatment or CSA competition were used as negative controls. 132 4.4. Discussion I currently have two working hypotheses on why I was not successful in using rVAR2 to isolate cancer derived exosomes. My initial assumption that rVAR2 and exosomes are bound together because they are in the same fraction from the media might not mean that rVAR2 is bound to the surface of exosomes. This was based on the assumption that ofCS would be present on the surface of the exosomes because they carry known CSPGs. However, there was no direct evidence to show this was the case. To answer this question there needs to be more experiments that directly investigate the presence of ofCS on exosomes as well as how it may interact with rVAR2. There are currently no published studies that conclusively show CS is present on the surface of exosomes. CSPGs are known to be present in exosomes and are known to have some functions in exosomes. For example, serglycin, a known CSPG, was detected in exosomes from human myeloma cell lines and patients[249]. Knockdown of serglycin in the same cell lines showed a lack of proteins that is required for cargo loading [249]. While other studies showed that certain CSPGs (GPC1 and CD44) were present on exosomes from patients and that their presence can be used for prognostic purposes. These studies together with my mass spectrometry data all point to CSPGs being associated with exosomes. Since CSPGs are normally membrane proteins that are found on the plasma membrane, I assumed that they would be on the surface of exosomes as well. Whether they carry a CS chain while on the exosome surface is still unknown. Further experiments are needed to investigate this hypothesis. Our lab recently optimized a mass spectrometry-based technique that allows us to determine the GAG composition 133 in biological samples. The technology has already been used to measure GAG composition on cells. This can be adapted to detect the GAG composition on exosomes. It will tell us whether exosomes carry CS, and if yes which type. There have been some studies that demonstrated HS is on the surface of exosomes, and they have a function in binding to cells or ECM. Future projects that study CS composition on exosomes can elucidate their function by altering the CS composition and see how this affects exosome function. Exosomes that carry different GAGs would be a mechanism for them to selectively bind different sites. Characterizing this can potentially shed light to exosome selection. Furthermore, some recent study on exosome surface proteins and receptors show that they can potentially have a reversed topology[216, 250]. If this were the case, then the CS carrying regions of the CSPGs would be inside the exosome and any bound rVAR2 would also be inside exosomes. This is another possible explanation as to why I was unable to detect or capture cancer derived exosomes using rVAR2 since the techniques I utilized were dependent on rVAR2 being on the surface of exosomes. Future research directions could also utilize transmission electron microscopy (TEM) to resolve the interaction between rVAR2 and exosomes. TEM offers an imaging resolution of 2nm which can determine not only the localization of proteins, but its localization relative to exosomes. It can also determine the orientation of proteins in relations to exosome membranes. I showed with confocal microscopy that rVAR2 and exosome markers appear in the same compartment inside the cells. I assumed that this meant rVAR2 and ILVs would be bound together. TEM allows more detailed investigation into their interaction and determine if rVAR2 is bound to ILVs or that they are simply in the same compartment without any interaction. In addition, TEM protocols for visualizing 134 exosomes are also available and can also be used to determine whether if rVAR2 and exosomes interact outside cells. 4.5. Conclusion While the data indicate that exosomes contain CSPGs, further experiments show that rVAR2 currently cannot be used to detect or isolate cancer exosomes. Although I detected rVAR2 in purified exosome fractions 48hrs after rVAR2 treatment, subsequent experiments to exploit a potential rVAR2-exosome interaction did not yield conclusive results. More studies are needed to understand the rVAR2- exosomes interaction to determine the feasibility of using rVAR2 for exosome isolation. 135 Chapter 5 â Concluding discussion For my dissertation, I studied the role of ofCS in the NF-κB pathway and to characterize the rVAR2 internalization process for future rVAR2 based applications. In chapter 2, I discovered that rVAR2 binding in MG63 cells induced activation of the NF-κB pathway and increased expression of pro-inflammatory cytokines. I designed a novel conditional lethal reporter screen that would, in combination with whole genome CRISPR knockout, reveal the key players of this effect. Despite the strong positive response to the pro-inflammatory cytokine TNF-α, the reporter did not activate to a sufficient degree in response to rVAR2 that would allow for the screen to move ahead as planned. The exact mechanism of how rVAR2 leads to a pro-inflammatory cytokine response remains a mystery. However, because of the positive response to TNF-α, a screen to discover new components to the TNFR-IL-8 pathway is currently underway. In chapter 3, I characterized the internalization process of rVAR2 in several cancer cell lines. I discovered that different cell lines internalize rVAR2 through different endocytic mechanisms. Once inside cells, rVAR2 is trafficked to the EE but not the lysosome. There are several key biochemical differences between the EE and lysosome. This information would be important to take this into consideration when deciding the linker and compound that would be used to construct future VDCs. This discovery could be vital for the future development of VDCs as a cancer therapeutic. The discovery in chapter 3 was a surprise since I originally expected rVAR2 to end up in the lysosome. Instead, I showed that rVAR2 is expelled out of the cell. This led to the research in chapter 4 where I discovered that rVAR2 is present outside cells along 136 with exosomes. Analysis of the protein content of exosomes from MG63 and PC3 cells showed presence of CSPGs which might carry ofCS. This led to the hypothesis that rVAR2 and exosomes might be physically interacting which could be exploited for diagnostic purposes. I tested this hypothesis by attempting to use rVAR2 to either detect or capture cancer derived exosomes. The results indicated that rVAR2 is currently not suitable for this purpose since it failed to detect or capture exosomes. Further research on the nature of rVAR2 and exosome interaction is necessary to definitively rule out using rVAR2 as a tool to isolate cancer derived exosomes. 137 Chapter 6 â Future directions Further analysis on the role of NF-κB in rVAR2 mediated cytokine response In chapter 2, I discovered that rVAR2 binding to cancer cells induced expression of IL-8, IL-6, and IL-1B cytokines. I also showed that rVAR2 binding activated the NF-κB pathway witch IκBα degradation and p65 nuclear translocation. However, rVAR2 induced upregulation of IL-8, IL-6, and IL-1B being mediated by NF-κB is not yet supported. It is well known that these genes are controlled by NF-κB activity. The next steps would be to confirm that rVAR2 induced expression of IL-8, IL-6, and IL-1B is mediated by the NF-κB pathway. A first step could be to use siRNA to knock down p65, this would eliminate NF-κB signaling specifically. With p65 knockdown, then use rVAR2 to see if it can still induce IL-8, IL-6, and IL-1B expression. A follow up with p65 overexpression to rescue the effect would further support the role of p65 in rVAR2 induced expression. To further show that p65 is necessary, a CHIP-PCR experiment can be performed to determine wither p65 binds to the IL-8, IL-6, and IL-1B promoters following rVAR2 treatment. These experiments would provide strong evidence that rVAR2 induced cytokine expression is mediated by the NF-κB pathway. Improving the conditional lethal reporter response to rVAR2 My experiments showed that rVAR2 was not a strong inducer of the conditional lethal reporter. Specifically, rVAR2 did not induce dTK expression at high enough level to induce cell death in the presence of GCV compared to non rVAR2 treated cells. This reporter is an artificial construct that has dTK under the control of an IL-8 promoter. The promoter is a 1.5kbp sequence immediately in front of the IL-8 start site. It may be lacking 138 certain regulatory elements that enhances IL-8 expression after stimulus. For example, in my experiments I observed ~8000-fold increase in endogenous IL-8 with TNF-α treatment (Appendix 7). In the same timeframe, dTK only increased by up to 7-folds following TNF-α (Fig. 2-20). A similar pattern was observed in my experiments there was a consistent â¥8-fold increase expression of endogenous IL-8 following rVAR2, while the reporter only had a maximum of 2-fold increase of dTK following rVAR2. To improve the efficiency of rVAR2 induced dTK expression another reporter needs to be designed. Recently, a group was able to use CRISPR technology to insert a dTK gene in a chromosomal breakpoint in prostate cancer and hepatocellular carcinoma cells[251]. The inserted dTK were under the control of endogenous promoters and were functionally active since it sensitized the cells to GCV. The same technique can be adapted to insert dTK into the endogenous IL-8 gene in MG63 cells. Specifically, dTK can be inserted right before the stop codon in IL-8. The dTK can be accompanied with a self-cleaving peptide sequence to ensure it does not alter IL-8 function. It also be linked with a drug resistance and fluorescence protein for selection. This dTK reporter would therefore be expressed in a 1:1 ratio as endogenous IL-8. Since rVAR2 can induce high endogenous IL-8 expression, it should also induce high expression of dTK. This design could potentially address the problem where rVAR2 did not induce high dTK expression. If the new design is successful and significantly sensitizes cells to GCV after rVAR2, then the CRISPR screen can move forward to identify the proteins involved in rVAR2 induced IL-8 expression. 139 Investigate viral defense pathways activation following rVAR2 binding The gene ontology analysis in chapter 2 revealed activation of various viral defense pathways following rVAR2 treatment. One of the top hits was a response to viral genome despite the cells being not exposed to genetic material of any kind. This was highly unusual and suggests that the cells interpreted rVAR2 binding as a viral infection. rVAR2 is neither a virus particle or a protein derived from a virus so the response was surprising. The only commonality between rVAR2 and viruses is that both bind GAGs[178]. It is well established that viruses can bind different GAGs as a first step for infection. A further project could test whether unspecific saturation of protein bound GAGs on the cell surface could lead antiviral signaling. GAGs are known to bind various human ligand as a precursor to it binding a receptor to initiate specific signaling events. For example, FGFs have been shown to bind HS and that this binding actually helps FGF to bind to FGFRs and initiate signaling[252, 253]. A virus that binds GAGs would not necessarily binding to a second receptor to initiate signaling. It is possible that any ligands binding to GAGs initiates a signaling cascade that eventually leads to anti-viral responses. However, if the ligand then binds and activate a receptor, the subsequent receptor initiated signaling cascade can inhibit the anti-viral signaling. To study this subject, recombinant viral proteins that bind GAGs can be produced like rVAR2, then used to study how binding to GAGs on the cell surface can alter signaling in cells. Also, virus like particles that has can infect but not replicate can also be used to study the effect of GAG binding. A lipid nanoparticle, with GAG binding envelope proteins can be created to mimic virus attachment and not subsequent viral replication stages. Future studies into this subject could illuminate the relationship between virus, GAGs, and viral defense signaling. 140 Designing and testing next generation of VDCs In chapter 3, I characterized the internalization and intracellular trafficking of the rVAR2 protein. This project was a part of a program for the development of rVAR2 into VDCs as cancer therapeutics. One of the key aspects of this study is to determine the exact intracellular localization of rVAR2 within the cell so that the appropriate drug and linker can be used to in the VDC design. Here I discovered that rVAR2 is trafficked to the early endosome (EE) but does not appear to reach the lysosome after entering the cell. The natural next step of this project is to develop several new versions of VDCs that would carry a linker more suited for the EE such as a furin cleavable linker[189, 197, 254]. The new VDCs can then be tested for efficacy in different cancer cell lines to determine how the cells respond. Based on the response in cell lines, the most effective VDC design can then be chosen for further testing in pre-clinical models. Determine the CS and overall GAG composition of cancer derived exosomes From my mass spectrometry data in chapter 4, I showed that cancer derived exosomes contains CSPGs. While it is known that exosomes can carry glycan modification on the surface. What is not known is whether these exosomes associated CSPGs actually carry a CS chain. Some of these CSPGs, such as CD44, carry CS chains and have been shown by us to have ofCS when expressed on the cell surface. Because of this, I reasoned that the same CSPGs on the exosomes would also carry ofCS meaning that rVAR2 can potentially be used to detect or isolate the cancer derived exosomes. Since I was not successful with this, it would suggest that cancer derived exosomes do not carry ofCS chains. A follow up study for this would be to investigate whether cancer derived exosome carry GAG chains and more specifically ofCS. Given that CSPGs are 141 present, it would not be surprising that GAGs are present as well. Our lab recently optimized a mass spectrometry protocol to study the CS content on the surface of prostate cancer cells. This technique can be adapted for exosomes and be expanded to include detection of other GAGs. Testing magnetic based approaches to isolate cancer derived exosomes using rVAR2 Another reason for why I was unable to isolate cancer derived exosomes could be that rVAR2 might be packaged inside exosomes. This would mean that the detection methods I employed did not work simply because the detection or capture antibodies was not able to access rVAR2. In section 4.3.6 I tested whether pre-treating cells with rVAR2 would induce the production of exosomes with rVAR2 on the surface. The experimental design was carried out with the assumption that rVAR2 would be on the surface of exosomes. However, if rVAR2 is inside the exosomes then using antibodies to pull down rVAR2 and these exosomes would not be possible. 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Knewtson, K.E., Perera, C., Hymel, D., Gao, Z., Lee, M.M., and Peterson, B.R., Antibody-Drug Conjugate that Exhibits Synergistic Cytotoxicity with an Endosome-Disruptive Peptide. ACS Omega, 2019. 4(7): p. 12955-12968. 170 Appendix 1 â List of primers Primer sequence 5' -> 3' IL-6 F AATTCGGTACATCCTCGACGG R TTGGAAGGTTCAGGTTGTTTTCT IL-8 F TTTTGCCAAGGAGTGCTAAAGA R AACCCTCTGCACCCAGTTTTC IL-1B F ATGATGGCTTATTACAGTGGCAA R GTCGGAGATTCGTAGCTGGA d-TK 1 F TTACGGGCTGCTTGCCAATA R CGAAAGCTGTCCCCAATCCT d-TK 2 F ATAGACGGTCCTCACGGGAT R TGTTCGCGATTGTCTCGGAA d-TK 3 F CGGAGGACAGACACATCGAC R GGGCCCGAAACAGGGTAAAT GAPDH F ACCCAGAAGACTGTGGATGG R CAGTGACTTCCCGTTCAG RPL32 F CCCCTTGTGAAGCCCAAGA R GACTGGTGCCGGATGAACTT 171 Appendix 2 â Sodium chlorate analysis on MG63 cells Sodium chlorate reduce rVAR2 binding. rVAR2 binding on MG63 cells following 30mM SC treatment. Concentration of rContr and rVAR2 used for binding is the same as used in experiments do induce cytokine expression. Effect of sodium chlorate on MG63 cell viability. Incucyte measurements of MG63 cell confluency up to 5 days after treatment with SC. The cells were treated with SC at the same time as they were plated (Day 0). For qPCR experiments, normal media was switched to serum free media with SC on day 1, then rVAR2 incubation was done on day 2. 172 Appendix 3 â qPCR following sodium chlorate treatment on MG63 cells qPCR results for Figure 2-5. Statistical analysis was done on the combined average value of the three experiments. 173 IL-8 analysis Tukey's multiple comparisons test Mean Diff. 95% CI of diff. Summary Adjusted P Value rVAR2 vs. rContr 15.74 0.7025 to 30.78 * 0.0394 rVAR2+csa vs. rContr 0.02266 -15.02 to 15.06 ns > 0.9999 rContr+SC vs. rContr -0.6352 -15.67 to 14.40 ns 0.9999 rVAR2+SC vs. rContr -0.7196 -15.76 to 14.32 ns 0.9998 rVAR2+csa vs. rVAR2 -15.72 -30.76 to -0.6799 * 0.0397 rContr+SC vs. rVAR2 -16.38 -31.41 to -1.338 * 0.0317 rVAR2+SC vs. rVAR2 -16.46 -31.50 to -1.422 * 0.0308 rContr+SC vs. rVAR2+csa -0.6578 -15.70 to 14.38 ns 0.9999 rVAR2+SC vs. rVAR2+csa -0.7422 -15.78 to 14.30 ns 0.9998 rVAR2+SC vs. rContr+SC -0.08442 -15.12 to 14.95 ns > 0.9999 IL-6 analysis Tukey's multiple comparisons test Mean Diff. 95% CI of diff. Summary Adjusted P Value rVAR2 vs. rContr 1.923 0.5380 to 3.309 ** 0.0071 rVAR2+csa vs. rContr -0.346 -1.731 to 1.039 ns 0.9178 rContr+SC vs. rContr -0.577 -1.962 to 0.8084 ns 0.6577 rVAR2+SC vs. rContr -0.6753 -2.061 to 0.7100 ns 0.5265 rVAR2+csa vs. rVAR2 -2.269 -3.655 to -0.8840 ** 0.0022 rContr+SC vs. rVAR2 -2.5 -3.886 to -1.115 ** 0.001 rVAR2+SC vs. rVAR2 -2.599 -3.984 to -1.213 *** 0.0008 rContr+SC vs. rVAR2+csa -0.231 -1.616 to 1.154 ns 0.9796 rVAR2+SC vs. rVAR2+csa -0.3293 -1.715 to 1.056 ns 0.9301 rVAR2+SC vs. rContr+SC -0.09833 -1.484 to 1.287 ns 0.9992 IL-6 analysis Tukey's multiple comparisons test Mean Diff. 95% CI of diff. Summary Adjusted P Value rVAR2 vs. rContr 5.158 1.572 to 8.745 ** 0.0056 rVAR2+csa vs. rContr 0.1995 -3.387 to 3.786 ns 0.9997 rContr+SC vs. rContr -0.3828 -3.969 to 3.203 ns 0.9962 rVAR2+SC vs. rContr -0.5317 -4.118 to 3.055 ns 0.9868 rVAR2+csa vs. rVAR2 -4.959 -8.545 to -1.373 ** 0.0073 rContr+SC vs. rVAR2 -5.541 -9.128 to -1.955 ** 0.0034 rVAR2+SC vs. rVAR2 -5.69 -9.276 to -2.104 ** 0.0028 rContr+SC vs. rVAR2+csa -0.5823 -4.169 to 3.004 ns 0.9815 rVAR2+SC vs. rVAR2+csa -0.7313 -4.318 to 2.855 ns 0.9584 rVAR2+SC vs. rContr+SC -0.1489 -3.735 to 3.437 ns > 0.9999 Statistical analysis of Figure 2-5. 174 Appendix 4 â Effect of endocytic inhibitors on rVAR2 internalization Inhibitors of endocytic mechanisms alter rVAR2 internalization. Replicates of western blot for internalized rVAR2 in COLO-205 and PC3 cells after treatment with CPZ, CytoD, or Nys. GapDH and Vinculin were used as loading controls. 175 Appendix 5 â Scatter plot for colocalization analysis Examples of colocalization between rVAR2-488 and rVAR2-594 in MG63 cells. Cells were treated with either rVAR2-488 or rVAR2-594 for 30min then with the other for 30mins prior to fixing. This experiment was done to generate a correlation plot between rVAR2 and itself to determine the correlation coefficient of two highly colocalized marker. Number on the top right of scatter plot is the calculated correlation coefficient (R) for an image of an entire field of view. 176 Examples of scatter plot for images that were used to calculate colocalization between rVAR2 and EEA1/LAMP1 in Figure 3-15. Timepoints of 0hr and 4hr rVAR2 incubation were chosen to demonstrate the amount of correlation. Top right number represents R for that specific plot. The average R of 10 images for each treatment condition (eg. rVAR2 + EEA1 at 4hr) were used in Figure 3-15. 177 Appendix 6 â Capturing rVAR2+exosomes Capture and detection of exosomes from cells pre-treated with rVAR2. rVAR2+exosomes capture using a biotinylated anti-V5 antibody to bind rVAR2. Detection of captured exosomes was determined by CD9/63/81 antibodies. Exosomes from cells with CSA competition were used as control. 178 Appendix 7 â TNF- α induction of IL-8 expression TNF-α induction of IL-8 mRNA expression. qPCR analysis of IL-8 expression in MG63 cells 2hrs after TNF-α (10ng/mL) treatment. Gene expression changes is normalized to GAPDH. Data represents mean ± SD of three technical replicates. Data represents mean ± SD three technical replicates.
Thesis/Dissertation
eng
Pathology and Laboratory Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Investigating the role of oncofetal chondroitin sulfate in cancer biology and optimizing rVAR2 technology for cancer
Text