Txnip and the Gapdh-Siah1 Signalling Pathway in Diabetic Nephropathy

TXNIP AND THE GAPDH-SIAH1 SIGNALLING PATHWAY IN DIABETIC NEPHROPATHY

Hui Ze (Lexy) Zhong

A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Medicine, Graduate Department of Institute of Medical Sciences University of Toronto

Hui Ze (Lexy) Zhong

Master of Science (2019)

Graduate Department of Institute of Medical Sciences

University of Toronto

ABSTRACT

Thioredoxin-interacting protein (TXNIP) is markedly upregulated by high glucose (HG) and contributes to

Diabetic Nephropathy (DN) development partly by inhibiting the endogenous antioxidant thioredoxin.

We postulate that this contributes to the nitrosylation and oxidation of GAPDH, which has been found in neuronal cells to promote GAPDH-Siah1 binding and nuclear translocation, leading to apoptosis. The goal of this study was to determine if TXNIP regulates GAPDH-Siah1 signalling and if DN can be prevented by blocking this pathway. In vitro results show that HG caused significant nuclear localization of both GAPDH and Siah1 and upregulation of apoptotic markers in cultured wildtype mesangial cells (MCs), but not

TXNIP-/- (KO) MCs. In vivo, deprenyl, an inhibitor of GAPDH-Siah1 binding, protected diabetic mice from developing various structural and functional markers of DN. These data suggest that the GAPDH-Siah1 pathway has a pathogenic role in DN and is downstream of TXNIP signalling.

ACKNOWLEDGEMENTS My successes during my graduate studies would not be possible without the help and support of my supervisor, colleagues, friends, and family. I am forever indebted to my supervisor, Dr I. George Fantus, who helped open my eyes to the holistic nature of the medical sciences and pushed me along this journey of self-discovery to appreciate both the small picture and big picture at large—from interactions at the molecular level to implications in other fields. Thank you for your patience, your support, and your guidance. You have inspired me to become a better scientist and an overall better thinker.

Furthermore, I would also like to thank my program advisory committee members, Dr James Dennis and Dr Adria Giacca, for their continual support and expertise. The insightful feedback they provided has helped me better focus my project and keep me on track.

I would also like to thank the previous and current members of the Fantus lab who have helped me throughout my journey and made this experience enjoyable. I would like to thank Dr Anu Shah for helping me get started on my MSc project and for performing some of the preliminary experiments that laid the foundations for this project. I am also deeply grateful for Dr Ling Xia for her technical support, guidance, and company in the lab.

In addition to the Fantus Lab, I would also like to acknowledge the other labs at the University Health Network (Toronto, ON), Mount Sinai Hospital (Toronto, ON), and McGill University Health Centre (Montreal, QC), for their technical support and experimental protocols.

Last but not least, I would like to thank my family and friends for their support and encouragement throughout my years of study. This accomplishment would not have been possible without them.

Thank you all.

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STATEMENT OF CONTRIBUTIONS The experiments in this thesis were conducted by Hui Ze (Lexy) Zhong with the help of colleagues. Dr Ling Xia assisted with mouse colony maintenance, animal harvests, and some immunohistochemistry staining and analyses. Dr I George Fantus helped with the design of studies and interpretation of results.

TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1. Normal Kidney Anatomy and Physiology ...... 2 1.1.1. General Structure and Function ...... 2 1.1.2. Glomerular Filtration Barrier ...... 3 1.1.3. Mesangial Cells ...... 7 1.2. Diabetic Nephropathy ...... 8 1.2.3. Histopathological Presentation in Diabetic Nephropathy ...... 12 1.3. Thioredoxin-interacting Protein in Diabetes Mellitus ...... 15 1.3.1. Background ...... 15 1.3.2. TXNIP is elevated in diabetes ...... 16 1.3.3. TXNIP is implicated in Diabetic Nephropathy ...... 17 1.4. Thioredoxin-interacting Protein in Diabetic Nephropathy ...... 18 1.4.1. Oxidative and Nitrosative Stress ...... 18 1.4.2. Fibrosis ...... 22 1.4.3. Inflammation ...... 23 1.4.4. Endoplasmic Reticulum Stress ...... 24 1.4.5. Apoptosis ...... 25 1.5. TXNIP and the GAPDH/Siah1 Pathway ...... 27 1.5.1. GAPDH Background ...... 27 1.5.2. GAPDH/SIAH1 Pathway ...... 29 1.5.3. Regulation by the Thioredoxin and Glutathione Systems ...... 33 1.5.4. Experimental Inhibition of the GAPDH/Siah1 Pathway ...... 36 1.5.5. Experimental techniques for the study of TXNIP function ...... 40 1.6. Project rationale, hypothesis, and specific aims ...... 42 1.6.1. Rationale ...... 42 1.6.2. Hypothesis ...... 42 1.6.3. Specific Aims...... 42

CHAPTER 2: METHODS 2.1. Glomeruli Isolation and Culturing of Primary Mesangial Cells ...... 45 2.2. Cell Culture ...... 46 2.3. Nuclear/Cytoplasmic Fractionation and Extraction ...... 47

2.4. Mice and Metabolic Studies ...... 47 2.5. Blood Profiling and Urinalysis ...... 49 2.6. Electron Microscopy...... 49 2.7. Tissue Histology and Immunohistochemistry ...... 50 2.8. Western Blotting ...... 51 2.9. Statistical Analyses ...... 51

CHAPTER 3: RESULTS 3.1. TXNIP and the GAPDH/Siah1 Pathway ...... 54 3.1.1. TXNIP, GAPDH, and Siah1 protein levels in total cell lysates ...... 54 3.1.2. GAPDH and Siah1 nuclear translocation ...... 55 3.1.3. Caspase-3 cleavage ...... 57 3.2. Effects of deprenyl on nephropathy in STZ-induced diabetic mice ...... 57 3.2.1. Metabolic profiles of the DBA/2J mice ...... 57 3.2.2. Histological Analyses ...... 62 3.2.3. Functional Analyses ...... 67 3.2.4. Oxidative Stress ...... 69

CHAPTER 4: DISCUSSION, CONCLUSION, FUTURE DIRECTIONS 4.1. Summary of results ...... 73 4.2. GAPDH-Siah1 pathway regulation ...... 77 4.3. Coordination of metabolic and cell death signals in DN ...... 78 4.3.1. GAPDH coordinates metabolic and cell death signals in DN...... 78 4.3.2. TXNIP may mediate both metabolic and stress signals of GAPDH ...... 80 4.4. GAPDH and Siah1: major effectors of TXNIP signaling in DN? ...... 81 4.5. Therapeutic potential of deprenyl ...... 82 4.5.1. Deprenyl protects against early structural and functional changes in DN ...... 82 4.5.2. Deprenyl treatment mimics the effects of partial TXNIP signalling blockade in TXNIP+/- mice ...... 84 4.5.3. Inflammation and oxidative stress may still be occurring ...... 85 4.5.4. Safety of long-term deprenyl use ...... 89 4.6. Conclusion ...... 90 4.7. Caveats and study limitations ...... 91

4.7.1. Mesangial cells in culture ...... 91 4.7.2. Human DN versus animal models of DN ...... 91 4.7.3. Deprenyl targets ...... 92 4.7.4. Urinalyses ...... 93 4.8. Future directions ...... 93 4.8.1. Further characterization of the GAPDH-Siah1 pathway in vitro ...... 93 4.8.2. Further characterization of deprenyl action in vivo ...... 95 4.8.3. Elucidating the direct role of TXNIP in GAPDH-Siah1 signalling ...... 96 4.8.4. Investigating GAPDH-Siah1 signalling in other diabetic complications ...... 97

CHAPTER 5: REFERENCES 5. REFERENCES ...... 99

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LIST OF ABBREVIATIONS 1,3BPGA D-glycerate 1,3-bisphosphate 8-OHdG 8-hydroxy-2'-deoxyguanosine AGE advanced glycation end product ALP alkaline phosphatase ALT alanine aminotransferase AMPK AMP-activated protein kinase AP amphetamine ASC apoptosis-associated speck-like protein containing a CARD ASK1 apoptosis signal regulating kinase-1 ATF5 activating transcription factor 5 ATG autophagy genes BSA bovine serum albumin BUN blood urea nitrogen CBP CREB-binding protein CGP 3466 (i.e. TCH346/Omigapil): Dibenzo[b,f]oxepin-10-ylmethyl-methyl-prop-2-ynyl-amine ChoRE carbohydrate response element ChREBP ChoRE-binding protein CREB cAMP-response element-binding protein CVD cardiovascular disease DA dopaminergic DAG diacylglycerol DM Diabetes Mellitus DMEM Dulbecco’s Modified Eagle’s Medium DN Diabetic Nephropathy DNA-PK DNA-activated protein kinase DNAzyme deoxyribozyme eIF2α eukaryotic initiation factor 2 on Ser51 of the alpha subunit EM electron microscopy EMT epithelial-to-mesenchymal transition eNOS endothelial NOS ER endoplasmic reticulum

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ESRD end-stage renal disease ET-1 endothelin-1 ETC electron transport chain EZH2 zeste homolog 2 FBS fetal bovine serum G6P glucose-6-phosphate GAP glyeraldehyde-3-phosphate GAPDH glyceraldehyde 3-phosphate dehydrogenase GBM glomerular basement membrane GEnC glomerular endothelial cell GFAT glutamine:fructose-6-phosphate aminotransferase GFB glomerular filtration barrier GFR Glomerular Filtration Rate GOSPEL GAPDH’s competitor of Siah protein enhances life GR glutathione reductase Grx glutaredoxin GSH glutathione GSNO S-nitrosoglutathione GSNOR protein–S-nitrosoglutathione reductase GSSG oxidized GSH

H2O2 hydrogen peroxide H3K27 histone 3 at lysine 27 HBP hexosamine biosynthesis pathway HBSS Hank’s Balanced Salt Solution HDAC2 histone deacetylase-2 HG high glucose HNO nitroxyl IHC immunohistochemistry IL interleukin iNOS inducible NOS IRE1α Inositol requiring enzyme 1 LDH lactate dehydrogenase MAO-B monoamine oxidase-B

MAP methamphetamine MC mesangial cells MLX Max-like protein X MMTS methylmethanethiosulfonate MPTP 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine MSR methionine sulfoxide reductase NAD+ nicotinamide adenine dinucleotide NADPH nicotinamide adenine dinucleotide phosphate NcoR nuclear corepressor NF-Y nuclear factor NG normal glucose NLRP3 nucleotide-binding domain and leucine-rich repeat-containing family, pyrin domain-containing-3 NMDA N-methyl-D-aspartate nNOS neuronal NOS NO nitric oxide NOS nitric oxide synthase Nox NADPH oxidase

− O2• superoxide anion OCT optimum cutting temperature O-GlcNAc O-Linked β-N-acetylglucosamine O-GlcNAcylation O-GlcNAc modification OH• hydroxyl radical ONOO− peroxynitrite anion PAI-1 plasminogen activator inhibitor-1 PARP-1 poly(ADP-ribose) polymerase-1 PAS Periodic Acid-Schiff PBS phosphate buffered saline PD Parkinson’s disease PDH pyruvate dehydrogenase PERK protein kinase R-like ER resident kinase PKC protein kinase C Prx peroxidase

PTEC proximal tubular epithelial cell PTM post-translational modification PUMA p53 upregulated modulator of apoptosis PVDF polyvinylidene difluoride R-(−)-deprenyl (i.e. Selegiline) (R)-N,α-dimethyl-N-2-propyn-1-yl-benzeneethanamine, monohydrochloride RAAS renin-angiotensin-aldosterone system RBC red blood cell count RGD Arg-Gly-Asp RISC RNA-interfering silencing complex RNS reactive nitrogen species ROS reactive oxygen species SD slit diaphragms SD standard deviation −SH thiol shRNA short hairpin RNA Siah1 seven in absentia homolog 1 E3 ubiquitin-protein ligase siRNA small interference RNA SIRT1 sirtuin-1 −SNO S-nitrosothiol

−SO2H sulfinic acid

−SO3H sulfonic acid −SOH sulfenic acid STZ streptozotocin T1DM Type 1 Diabetes Mellitus T2DM Type 2 Diabetes Mellitus TEM transmission electron microscopy TGF-β1 transforming growth factor-β1 TMT Tandem Mass Tag Trx thioredoxin TrxR thioredoxin reductase TXNIP HET TXNIP+/- TXNIP KO TXNIP-/-

TXNIP WT TXNIP+/+ TXNIP thioredoxin-interacting protein UAE urinary albumin excretion UDP uridine diphosphate UPR unfolded protein response VEGF-A vascular endothelial growth factor A WB western blotting WBC white blood cell count WT-1 Wilms’ tumour 1

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AMINO ACID ABBREVIATIONS Cys Cysteine Lys Lysine Ser Serine Thr Threonine

METHODOLOGICAL ABBREVIATIONS % percent °C degree celsius g gram h hour(s) l liters min minute(s) M/mol moles sec second(s) wk week

PREFIXES k kilo (x 103) c centi (x 10-2) m milli (x 10-3) μ micro (x 10-6) n nano (x 10-9) p pico (x 10-12)

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LIST OF TABLES CHAPTER 3: RESULTS Table 1.1. Metabolic profiles of DBA/2J mice in the 12-wk experiment ...... 60 Table 1.2. Metabolic profiles of DBA/2J mice in the 20-wk experiment ...... 61 Table 2.1. Blood profiles of DBA/2J mice in the 12-wk experiment ...... 62 Table 2.2. Blood profiles of DBA/2J mice in the 20-wk experiment ...... 64

LIST OF FIGURES CHAPTER 1: INTRODUCTION Figure 1.1. Structure of a renal corpuscle ...... 3 Figure 1.2. Schematic of the glomerular filtration barrier ...... 4 Figure 1.3. Schematic outline of the current paradigm of diabetic complications development ...... 29 Figure 1.4. Schematic of the denitrosylase functions of thioredoxins and glutathione ...... 35 Figure 1.5. Proposed role of TXNIP in GAPDH/Siah1-mediated apoptosis in DN ...... 41

CHAPTER 3: RESULTS Figure 3.1. High glucose-induced TXNIP upregulation in WT mouse MCs but not TXNIP KO MCs ...... 56 Figure 3.2. High glucose-induced GAPDH and Siah1 nuclear translocation in WT mouse MCs but not KO MCs ...... 57 Figure 3.3. High glucose-induced caspase-3 cleavage in WT mouse MCs but not TXNIP KO MCs ...... 58 Figure 3.4. Deprenyl treatment protected diabetic DBA/2J mice from mesangial matrix expansion ...... 65 Figure 3.5. Deprenyl treatment protected diabetic DBA/2J mice from increases in collagen IV production ...... 66 Figure 3.6. Deprenyl treatment protected diabetic DBA/2J mice from glomerulosclerosis ...... 67 Figure 3.7. Deprenyl treatment protected diabetic DBA/2J mice from glomerular basement membrane thickening and podocyte foot process effacement ...... 68 Figure 3.8. Deprenyl treatment protected diabetic DBA/2J mice from increases in proteinuria in the 12- wk experiment but not the 20-wk experiment ...... 69 Figure 3.9. Deprenyl treatment protected diabetic DBA/2J mice from increases in urinary albumin excretion (UAE) and urinary albumin-to-creatinine ratios in the 12-wk experiment but not the 20-wk experiment ...... 71 Figure 3.10. Deprenyl treatment protected diabetic DBA/2J mice from inreases in Nox4 expression ..... 72 Figure 3.11. Deprenyl treatment had no effect on urinary 8-OHdG levels ...... 73

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CHAPTER 1: INTRODUCTION

1.1. Normal Kidney Anatomy and Physiology 1.1.1. General Structure and Function The kidneys are a pair of highly vascularized bean-shaped organs found in the abdominal cavity of vertebrates. They participate in the regulation of many bodily functions but primarily serve to filter waste products (i.e. from protein, nucleic acid, and drug metabolism, as well as excess electrolytes and water) from the blood and eliminates them by the urine produced (Silverthorn, 2013). The functional units of the kidney are the nephrons, which consists of a renal corpuscle and a tubular system. Within each kidney, there are an estimated 1 million nephrons. The renal corpuscle is where filtration occurs. It is composed of a cluster of capillaries within a glomerulus, and a capsule enclosing the glomerulus called the Bowman’s capsule (Figure 1.1) (Tortora & Derrickson, 2014). There are three cell types in the glomerulus—the glomerular endothelial cells (GEnCs), mesangial cells (MCs), and podocytes. GEnCs form the monolayer lining of the glomerular capillaries and are characterized by their numerous fenestrations (Satchell & Braet, 2009). MCs are modified smooth muscle cells found in the mesangium in between the glomerular capillaries. Podocytes are specialized epithelial cells that function as part of the glomerular filtration barrier and are discussed in greater detail in section 1.1.2.3. In contrast, the Bowman’s capsule is composed of an outer parietal layer of simple squamous epithelium and a visceral layer composed of specialized epithelial cells called podocytes. It is the site at which filtration first occurs. Blood supplied to the glomerular capillaries by the afferent arteriole is filtered through the glomerular capsule, across the basement membrane of the Bowman’s capsule, and into the renal tubules. The remaining blood exits the glomerulus via the efferent arteriole.

Figure 1.1: Structure of a renal corpuscle. Figure reproduced with permission from the Clinical Journal of the American Society of Nephrology (Kitching & Hutton, 2016).

1.1.2. Glomerular Filtration Barrier Glomerular filtration is a highly selective process wherein only solutes of a certain charge, size, and shape can pass through the glomerular filtration barrier (GFB). Normally, only water and small molecules filter through, while macromolecules such as albumin are retained in the blood. This is because filtration occurs via gaps in between adjacent cells and is regulated by the GFB and the morphological and functional features of its constituents. The GFB is formed from the GenCs, glomerular basement membrane (GBM), and podocytes (Figure 1.2).

ENDOTHELIAL CELLS

Figure 1.2: Schematic of the glomerular filtration barrier. Figure reproduced with permission from the Journal of Clinical Investigation (Farquhar, 2006).

1.1.2.1. Glomerular Endothelial Cells (GEnCs) As previously mentioned, GEnCs are well fenestrated. More specifically, GEnCs contain large fenestrae approximately 60-80nm (Avasthi, Evan, & Hay, 1980; Lea, Silverman, Hegele, & Hollenberg, 1989; Rostgaard & Qvortrup, 1997), generally in the absence of a diaphragm (Bearer, Orci, & Sors, 1985; Reeves, Kanwar, & Farquhar, 1980). These fenestrations are mainly found clustered in the peripherally located endothelium in the glomerulus, on the opposite side of the podocyte foot processes relative to the glomerular basement membrane, and are thought to play an essential role in the filtration of low-molecular-weight waste products by restricting the passage of macromolecules across the glomerular capillary wall (Haraldsson, Nyström, & Deen, 2008). In addition to the fenestrations, the glomerular endothelial glycocalyx also plays an important part in regulating vascular permeability. The glycocalyx is the negatively charged mesh coating the luminal surface of the glomerular capillaries, composed of glycoproteins such as proteoglycans and sialoproteins, which give it both size and charge exclusion properties (A. Singh et al., 2007).

1.1.2.2. Glomerular Basement Membrane (GBM) The GBM is the basal lamina layer of the glomerulus comprised of a thick 3-layered extracellular matrix of approximately (300-370 nm in humans). It is the middle layer separating the GEnCs from the podocytes. The GBM is formed from collagen IV and XVIII, sialoglycoproteins, as well

5 as various non-collagenous glycoproteins (e.g. laminins, fibronectin, entactin/nidogen), and various proteoglycans and glycosaminoglycans (e.g. heparan sulfate). Heparan sulfate localized within the innermost and outermost layers helps select against negatively charged molecules (Farquhar, 2006; M. Ross & Pawlina, 2011). Collagen IV and laminin in the middle layer are organized in such a way that they form a size-selective filter (Farquhar, 2006). As a result, the GBM plays a role in the prevention of filtration of negatively charged molecules and macromolecules larger than 3.6 nm in radius (Farquhar, 2006). However, it is important to note that trace amounts of these proteins still sometimes filter through, but these are normally reabsorbed back into the bloodstream by endocytosis in the proximal convoluted tubules (M. Ross & Pawlina, 2011).

1.1.2.3. Podocytes Podocytes line the visceral epithelial layer of the Bowman’s capsule. They are a type of specialized epithelial cells possessing long foot processes with regularly spaced interdigitations. These foot processes wrap around glomerular capillaries to form approximately 40 nm-wide filtration slits, also known as slit diaphragms (SDs), between adjacent processes (Brinkkoetter, Ising, & Benzing, 2013; Quaggin & Kreidberg, 2008). The SDs, specifically, are zipper-like protein structures composed of nephrin and various other proteins that play an important role in glomerular development and filtration. The SDs also help filter molecules based on their charge, size, and shape (Quaggin & Kreidberg, 2008).

1.1.2.4. Glomerular Filtration Rate (GFR) The glomerular filtration rate (GFR) is often used in the clinic as an index of kidney function and health. It describes the rate of fluid flow through the kidney and represents the amount of blood that is filtered by the glomeruli per minute. Often, the GFR is determined by measuring the clearance (urinary concentration x urine flow rate/plasma concentration) of creatinine (Kaufman & Knohl, 2018). Creatinine is a breakdown product of creatinine phosphate from skeletal muscles that is released in a relatively constant amount in adults with normal metabolism. Changes in creatinine levels are therefore associated with changes in clearance (i.e. GFR) and not metabolic disturbances.

According to the National Kidney Foundation, the GFR for a healthy adult falls between 60-130 mL/min per 1.73 m2, with there being high variability due to age, ethnicity, body size, and gender (Delanaye et al., 2012). When the GFR falls below 60 mL/min per 1.73 m2, decreased renal function is indicated. It can be caused by a variety of reasons, including familial or inherited renal disorders, diabetic nephropathy, heart disease, high blood pressure, or a urinary tract obstruction/infection. In most of these conditions, the decline in GFR is owing to a decrease in the total number of functioning nephrons. However, the GFR can also be affected by changes to the glomerular filtration pressure, the surface area available for filtration, or the permeability of the filtration surface to solutes (Brenner, Deen, & Robertson, 1976).

However, a considerable number of glomerular diseases, including the early stages of diabetic nephropathy, can occur without alterations in the GFR and so measurements of GFR may not always provide an accurate measure of kidney function, especially in early disease states. Other measures, such as determinations of albuminuria, can be used instead. This is because macromolecules such as proteins normally cannot pass through the GFB, and so elevated levels of protein within urine are indicative of impaired filtration capacity resulting in solute loss and/or defective reabsorption of filtered albumin by tubular cells.

1.1.2.5. Damage to the Glomerular Filtration Barrier Damage to any one of the cells/components involved in the GFB can alter glomerular filtration and reduce renal function. Changes in the fractional area of GEnCs fenestrate, for example, have been found to greatly impact the GFR (Deen, Lazzara, & Myers, 2017). Damage to the GBM, such as GBM thickening—a phenomenon that occurs in diabetic nephropathy—can compromise its filtration capacity, resulting in abnormally high loss of albumin to the urine (i.e. albuminuria). These changes are normally associated with dysfunction in the outermost layer of the GBM— the lamina rara externa (M. Ross & Pawlina, 2011). Podocyte injury can have a plethora of effects. The SD plays an integral role in the GFB and so podocyte injury can significantly alter glomerular filtration and contribute to albuminuria (Shankland, 2006). Furthermore, podocytes are also a major producer of GBM components such as laminin β2 and collagen IV. They also play a role in the formation of GEnC by secreting angiopoietin-I and vascular endothelial growth

7 factor A (VEGF-A), which are pro-angiogenic factors important for the survival of fenestrated GEnC (Brinkkoetter et al., 2013). Moreover, increased expression of the transcription factor Wilms’ tumour 1 (WT-1) in podocytes appears to modulate podocyte crosstalk with other glomerular cells, promoting GEnC and mesangial cell differentiation and maturation (Quaggin & Kreidberg, 2008). Therefore, podocyte injury can further exacerbate GFB decline by affecting GBM and GEnC integrity and mesangial functioning.

1.1.3. Mesangial Cells Mesangial cells are another important specialized cell type of the kidney. MCs can be divided into two populations: 1) an intraglomerular MC population that accounts for approximately 30% of the total glomerular cell population, found in contact with the GBM and GEnCs in a space within the glomerulus called the mesangium, as well as 2) an extraglomerular MC population found near the vascular pole, as part of the juxtaglomerular apparatus (M. Ross & Pawlina, 2011). Under certain conditions, MCs can acquire functions resembling both smooth muscle cells (e.g. having contractile properties and expression of α-smooth muscle actin) and fibroblasts (e.g. production of interstitial collagen) (Johnson et al., 1992). As such, MCs are known to play several important roles in the kidney. Firstly, they produce and regulate the turnover of mesangial matrix components including the α1 and α2 chains of collagen IV, collagen V, collagen VI, laminin A, B1, and B2, fibronectin, heparan sulfate, and various proteoglycans (Schlondorff & Banas, 2009). Some of these components provide structural support to glomerular capillaries and podocytes (M. Ross & Pawlina, 2011), as well as mediate matrix-cell signalling. For example, the Arg-Gly-Asp (RGD)-containing type III repeats of fibronectin can activate integrin-linked cell signalling in response to mechanical stress (Bieritz et al., 2003). In addition, MCs can also produce various other bioactive agents essential for proper renal functioning, including various enzymes, (e.g. renin and proteinases), growth factors (e.g. platelet-derived growth factor and transforming growth factor β1), lipids (e.g. prostaglandins and platelet activating factors), cytokines (e.g. interleukins 1, 6, and 8, and tumour necrosis factor), vasoactive agents (e.g. nitric oxide and endothelin), and adhesion molecules (e.g. intercellular adhesion molecule 1 and vascular cell adhesion molecule-1) (Menè, 1996). The contractile properties of MCs also allow them to act as regulators of the local GFR by contracting or relaxing in response to vasoactive

8 agents such as vasopressin, angiotensin, and endothelin. This alters the local glomerular capillary flow and the surface area for glomerular ultrafiltration (Schlondorff & Banas, 2009). As such, MCs, especially those found in the juxtaglomerular apparatus, are important regulators of blood pressure via the renin-angiotensin-aldosterone system (RAAS) (M. Ross & Pawlina, 2011). Moreover, MCs also possess phagocytic properties that allow them to help clear trapped macromolecules that have entered into the subendothelial and mesangial space, to prevent the local accumulation of molecules that would hinder filtration (Schlondorff, 1987). Lastly, MCs also have some immunomodulatory functions as they can produce monocyte chemoattractant protein 1 and reactive oxygen species (ROS) to recruit immune cells to the mesangium and regulate immune cell effector function, respectively (Menè, 1996).

Evidently, MCs are critical regulators of glomerular function and their dysfunction is thought to be an important mediator of pathological kidney changes, including local kidney injury, cell proliferation, and basement membrane remodelling (Schlondorff, 1987). Furthermore, glomerular MCs also closely interact with GEnCs and podocytes, with changes in one cell type often accompanied by alterations in the others. Podocyte injury, for example, has been seen to result in MC proliferation (Morioka et al., 2001). MC injury, on the other hand, has been seen to be accompanied by podocyte foot process fusion and proteinuria. Cross-talk between these cells via cytokines has been proposed, but not fully elucidated, to mediate these changes. As a result, it is unsurprising that mesangial changes (either of the MCs or mesangium in which they reside) were determined to be one of the critical changes in DN, correlating with loss of renal function (Mason & Wahab, 2003; Qian, Feldman, Pennathur, Kretzler, & Brosius, 2008).

1.2. Diabetic Nephropathy 1.2.1. Overview of Diabetes Mellitus and Its Complications 1.2.1.1. The Global Burden of Diabetes Mellitus Diabetes mellitus (DM) is one of the most common chronic diseases of our century, affecting an estimated 451 million people worldwide in 2017 (Cho et al., 2018), and continues to be on the rise (Danaei et al., 2011). It is anticipated that as the global population grows and ages in the next 20 years, nearly every continent will observe an increase in the prevalence of diabetes

(Shaw, Sicree, & Zimmet, 2010). By the year 2045, diabetes is projected to affect 693 (Cho et al., 2018).

Diabetes mellitus is also associated with significant morbidity and mortality. In 2015, an estimated 5.0 million deaths were attributed to diabetes (Ogurtsova et al., 2017). With its high prevalence rates and numerous acute and chronic complications, diabetes undoubtedly places a heavy strain on the healthcare system. Diabetes mellitus is now a global concern requiring coordinated efforts to better understand the mechanisms underlying its pathogenesis as well as its progression to numerous metabolic and vascular complications.

1.2.1.2. Overview of Diabetes Mellitus Diabetes mellitus is a metabolic disorder involving the dysregulation of glucose homeostasis. It is characterized by chronic hyperglycemia (i.e. elevated blood glucose levels) resulting from insufficient insulin secretion, or a combination of dysfunctional insulin action with insufficient secretory compensation (Alberti & Zimmet, 1998; Mathis, Vence, & Benoist, 2001). There are four broad categories of diabetes mellitus: Type 1 (T1DM), Type 2 (T2DM), gestational, and other types (Alberti & Zimmet, 1998). T1DM is an idiopathic autoimmune disease involving the destruction of the insulin-producing β-cells of the pancreas. As a result, these patients suffer from insulin deficiency and hyperglycaemia follows because of the loss of the homeostatic control that insulin usually exerts on glucose (Alberti & Zimmet, 1998). T2DM, in contrast, is characterized by a disorder of insulin action due to insulin resistance combined with a disorder of insulin secretion (Guillausseau et al., 2008). In the presence of insulin resistance, the β-cells of these patients attempt to compensate via the hypersecretion of insulin (Prentki & Nolan, 2006). When this compensatory mechanism is insufficient, hyperglycaemia results (Prentki & Nolan, 2006). Gestational DM is a temporary form of DM, experienced by mothers during pregnancy. The other categories encompass a wide range of hyperglycemic conditions induced by genetics, drugs or chemicals, infections, other syndromes, endocrinopathies, or pancreatopathy (American Diabetes Association, 2014).

1.2.1.3. Diabetic Complications Regardless of the type, diabetes mellitus can present with a wide range of complications, both acute and long-term (Cooper, Gilbert, & Jerums, 1997). While acute complications such as ketoacidosis and diabetic comas are serious and potentially fatal, the long-term complications of diabetes place the greatest burden on the healthcare system due to their effect on multiple tissues and multiple organ systems (Cooper et al., 1997).

Long-term hyperglycemia and metabolic changes associated with diabetes can cause damage to the microvasculature of the eyes and kidneys, and to nerves, resulting in the development of diabetic retinopathy, nephropathy, and neuropathy, respectively. Diabetes can also affect the macrovasculature, including the coronary arteries, cerebrovascular and peripheral vascular circulation, by accelerating the process of atherosclerosis and causing the development of cardiovascular diseases (CVD), myocardial infarctions, strokes, and limb ischemia (Fowler, 2008).

1.2.2. Diabetic Nephropathy Overview Diabetic nephropathy (DN), also known as diabetic kidney disease (DKD), is a progressive disease that occurs in patients with chronic diabetes wherein individuals experience increasing levels of proteinuria, followed by a decline in kidney function, leading to kidney failure and death. It is a serious and common complication of hyperglycemia-induced tissue injury (Turner, 1998; UK Prospective Diabetes Study Group, 1998), which occurs in approximately 20-40% of individuals with T1DM or T2DM (American Diabetes Association, 2010; Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Research Group et al., 2009). In western societies, DN is the leading cause of end-stage renal disease (ESRD) requiring dialysis treatment or kidney transplantation (Gilbertson et al., 2005). In 2017, 78% of Canadians on the transplant waiting list had DN (The Kidney Foundation of Canada, 2019).

Generally, DN follows a defined clinical course, beginning with hemodynamic changes at the level of the nephron before clinical symptoms even present. Very early on, glomerular hyperfiltration, driven by both vascular and tubular abnormalities, contributes to increased capillary permeability to macromolecules and renal hypertrophy. One theory is that these hemodynamic changes result from increased glomerular plasma flow and elevated glomerular

11 capillary hydrostatic pressure caused by afferent arteriolar vasodilation, as commonly seen in patients with diabetes, and/or efferent arteriolar vasoconstriction due to activation of the renin- angiotensin-aldosterone system (Hostetter, 2003). In addition, glomerular hyperfiltration in DN has also been linked to abnormal tubuloglomerular feedback (TGF), wherein the decreased

+ concentration of Na in the tubular fluid that reaches the macula densa (due to increased upstream reabsorption of Na+ in, for example, the proximal tubule, possibly as a result of increased Na+/glucose cotransport) causes TGF signalling for a rise in single nephron GFR (Thomson, Vallon, & Blantz, 2004). The rise in single nephron in GFR, physiologically, functions to restore the electrolyte load to the distal tubule. Then, as DN progresses, ultrastructural changes detectable by histology develop, including glomerular basement membrane thickening, glomerular and tubular epithelial hypertrophy, mesangial expansion and matrix accumulation, and cell apoptosis, ultimately resulting in the development of glomerulosclerosis and tubulointerstitial fibrosis (Brezniceanu et al., 2010; Makino et al., 1996; Mauer et al., 1984). Clinically, in the early stages of DN, patients experience normo- to microalbuminuria (20-199 μg/min or 30-299 mg/ 24h urine) due to renal hyperfiltration. As the disease progresses, urinary albumin excretion increases and advances to macroalbuminuria (≥300 mg albumin in 24h urine), and patients are diagnosed as having overt nephropathy. In terms of kidney function, loss of function is relatively slow in the early stages of DN but rapidly declines in later stages. As such, significant renal dysfunction is not seen until late in the course of DN, making early intervention key.

At present, there is no specific or completely effective treatment available for DN. The current approach is to treat its known risk factors using a combination of antihypertensive, antihyperglycemic, and antidyslipidemic agents. Sodium-glucose co-transporter 2 (SGLT2) inhibitors, such as canagliflozin, dapagliflozin, and empagliflozin, are the newest line of oral antihyperglycemic agents that suppress glucose reabsorption in the proximal tubules and increase urinary glucose excretion. They have demonstrated very promising renoprotective effects in clinical trials, including reducing HG-induced tubular toxicity, glomerular hyperfiltration and intraglomerular pressure, and tubular hypertrophy (Andrianesis, Glykofridi, & Doupis, 2016; De Nicola et al., 2014; Kohan, Fioretto, Tang, & List, 2014; Lovshin & Gilbert,

2015; Panchapakesan et al., 2013; Thomas, 2014). Furthermore, SGLT2 inhibitors have also been shown to increase afferent arteriolar vasoconstriction and osmotic diuresis in patients, reduce albuminuria and tubulointerstitial hypoxia, and improve systolic blood pressure (Sano, Takei, Shiraishi, & Suzuki, 2016; Stenlöf et al., 2013). These SGLT-2 inhibitors can also be combined with more traditional antihyperglycemic agents such as the RAAS blockers angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs) to achieve greater renoprotection. However, the lack of research into the long-term efficacy and safety of SGLT2 inhibitors makes it difficult to determine if these inhibitors are able to fully block DN progression or simply slow it down, as well as which percentage of patients will respond. They are also not without their accompanying side-effects. Specifically, there is concern over their ability to increase the risk of developing diabetic ketoacidosis due to the induction of glycosuria, which artificially lowers plasma glucose levels and predisposes patients to increased ketogenesis (Rosenstock & Ferrannini, 2015; Taylor, Blau, & Rother, 2015). On a more minor note, SGLT2- inhibitors have also been suggested to increase the rate of urinary tract infections and genital infections due to glycosuria, but most patients respond to antimicrobial treatment (Johnsson et al., 2013; Rosenstock, Vico, Wei, Salsali, & List, 2012). As a result, there is still great interest in the field for the development of novel and more targeted therapies.

1.2.3. Histopathological Presentation in Diabetic Nephropathy Diabetes-induced renal injury manifests in all renal cell types, including podocytes, mesangial cells, GEnCs, vascular ECs, tubular epithelial cells, and interstitial fibroblasts (Wolf, 2004). Glomerular cell types are among the most affected in diabetes, with glomerular lesions being the most consistent and prominent alteration observable in renal biopsies from patients with DN (Najafian, Alpers, & Fogo, 2011). GBM thickening, mesangial matrix expansion, and podocyte loss are three key glomerular changes that occur in DN that impair renal functioning.

1.2.3.1. GBM Thickening The thickening of the GBM is one of the earliest asymptomatic renal changes that occur within a year or two after the onset of diabetes (Najafian et al., 2011). It has been characterized as GBM thicknesses >395 nm in females and >430 nm in males older than 9-years-of-age (Tervaert et al., 2010). This morphological change in the renal structure can be visualized via electron microscopy

13 at high magnifications. Although occurring early and becoming increasingly progressive as DN worsens, GBM thickening itself is not usually viewed as an initiating factor of advanced DN (Jefferson, Shankland, & Pichler, 2008; Najafian et al., 2011; Wolf, 2004). However, the loss of heparan sulfate proteoglycan from the GBM has been associated with increased proteinuria (Mason & Wahab, 2003). Often, GBM thickening is viewed as a marker of DN resulting from the increased accumulation of extracellular matrix protein due to increased synthesis and/or decreased degradation (Jefferson et al., 2008; Mason & Wahab, 2003). Collagen IV is one such matrix constituent that becomes significantly elevated as DN progresses and as GBM thickening worsens (Mason & Wahab, 2003; Zeisberg et al., 2002).

1.2.3.2. Mesangial matrix expansion Mesangial matrix expansion (i.e. accumulation) is a hallmark of DN and is detectable approximately 4-5 years after the onset of diabetes (Najafian et al., 2011). It results from either increased synthesis and deposition of extracellular matrix collagens (type IV, V, and VI), laminin, and fibronectin in the mesangium, or as a result of decreased protein degradation (Choudhury, Tuncel, & Levi, 2010). Glycosylation of matrix proteins has been identified as one factor contributing to decreased protein degradation in diabetes (Abrass, 1995). Furthermore, increased synthesis of proteinase inhibitors such as plasminogen activator inhibitor-1 (PAI-1) due to O-Linked β-N-acetylglucosamine (O-GlcNAc) modification (i.e. flux through the hexosamine biosynthesis pathway) promoting increased stabilization and DNA-binding activity of its transcription factor Sp1 may also be a contributing factor to the mesangial matrix expansion (Goldberg, Scholey, & Fantus, 2000; James, Fantus, Goldberg, Ly, & Scholey, 2000). PAI-1 is the major physiological inhibitor of tissue plasminogen activator and urokinase (Calles- Escandon, Mirza, Sobel, & Schneider, 1998; Kruithof, 1988; Sobel et al., 1998). Thus, increased activation of PAI-1 is predicted to lead to reductions in plasmin activation. Since plasmin has been implicated in the degradation of matrix constituents such as laminin, entactin, perlecan, and fibronectin (Eddy, 2002; Eddy & Fogo, 2006), increased PAI-1 activation in diabetes would ultimately lead to extracellular matrix accumulation via decreased protein degradation. Mesangial matrix expansion, combined with MC hypertrophy (though to a lesser extent), can compress glomerular capillaries, resulting in vascular occlusion and reduction in the GFR (Abrass,

1995; Mauer, 1994). This can also lead to other microvascular injuries such as mesangiolysis and EC detachment from the GBM (Ohe, 1993). Ultimately, mesangial matrix expansion can result in glomerulosclerosis, or the hardening of the glomeruli (Fogo, 1999). In addition, deposition of cellular debris, lipoproteins, and collagen I and II in the glomerular capillary loops can contribute to the development of Kimmelsteil-Wilson nodules (Jefferson et al., 2008; Mason & Wahab, 2003). These nodules are characteristic of advanced DN and accompanied by severe proteinuria and glomerulosclerosis (Najafian et al., 2011).

1.2.3.3. Podocyte loss In addition to mesangial matrix expansion, podocytopenia (i.e. podocyte loss) is also viewed as a contributing factor to glomerulosclerosis in DN due to the loss of mechanical support podocytes normally provide glomerular capillaries (Najafian et al., 2011). Podocytopenia can be due to high glucose-induced podocyte injury, detachment from the glomerular capillaries, or apoptosis. Additionally, longitudinal studies following patients with DN revealed a strong correlation between a decline in podocyte numbers with proteinuria progression (White et al., 2002). Two main mechanisms have been proposed. Firstly, loss of podocytes and the negatively charged podocalyxin proteins found in their foot processes results in perturbations to the charge barrier in the GFB (Jefferson et al., 2008). Furthermore, alterations to the normal architecture of the podocyte monolayer, to the SD, and to cell-cell connections can also compromise the size barrier in the GFB (Jefferson et al., 2008). As a consequence, there will be an increase in the passage of negatively charged and higher molecular weight molecules normally restricted by the GBM, in DN.

1.2.3.4. Tubulointerstitial fibrosis Tubulointerstitial fibrosis is the final common outcome of all kidney diseases leading to ESRD, including DN (Zeisberg & Neilson, 2010). Morphologically, it is characterized by tubular basement membrane thickening, tubular atrophy, interstitial fibrosis, and arteriosclerosis (R. E. Gilbert & Cooper, 1999). Its histopathological features include increased deposition of extracellular matrix in the interstitium, infiltration of inflammatory cells, tubular cell loss, fibroblast accumulation associated with increased epithelial-to-mesenchymal transition (EMT), and decreased density of the peritubular microvasculature (Bohle, Christ, Grund, & Mackensen,

1979; Declèves & Sharma, 2010; Zeisberg & Neilson, 2010). Extracellular matrix proteins, angiotensin II, and cytokines such as transforming growth factor-β1 (TGF-β1), which are all upregulated in high glucose conditions, have been implicated as direct mediators of tubulointerstitial fibrosis (R. E. Gilbert & Cooper, 1999). In addition, the renal tubule (the proximal segment especially), is exposed to glomerular effluent and all the harmful components contained within it (R. E. Gilbert & Cooper, 1999). In DN, the glomerular effluent has been found to contain high concentrations of advanced glycation end products (AGEs), glucose, and proteins that may induce TGF-β1 expression and fibrosis (R. E. Gilbert & Cooper, 1999). Unfortunately, the exact mechanisms underlying tubulointerstitial fibrosis remain incompletely understood. However, three potentially interdependent mechanisms have been proposed: 1) continuous production of pro-fibrotic cytokines in the glomerulus and tubulointerstitium; 2) increased protein load in the proximal tubule, resulting in peritubular inflammation and fibrosis; and 3) post-glomerular vasoconstriction accompanied by peritubular capillary rarefaction, tubular ischemia, and tubular atrophy (Kriz, Hosser, Hahnel, Gretz, & Provoost, 1998).

1.3. Thioredoxin-interacting Protein in Diabetes Mellitus 1.3.1. Background Thioredoxin-interacting protein (TXNIP), also known as vitamin D 3 upregulated protein-1, is one of six α-arrestin proteins, first isolated by Chen et al. in 1994 from the HL-60 human promyelocytic cell line stimulated with vitamin D (K.-S. Chen & DeLuca, 1994). It is a ubiquitously expressed protein that plays an important role in a wide range of physiological and pathological processes, including regulating the cellular redox state and cellular metabolism, inhibiting cellular proliferation and promoting apoptosis, and suppressing tumour growth in certain tissues. Furthermore, TXNIP gene transcription is known to be stimulated by numerous stress- related factors including high glucose, heat shock, ultraviolet rays, and mechanical stress (G. C. Cheng et al., 2004). The response of TXNIP to conditions of hyperglycemia is the most studied role of TXNIP due to its identification as one of the most highly upregulated genes when human islets, fibroblasts, mesangial cells, and proximal tubule cells are exposed to high glucose in vitro (D. W. Cheng et al., 2006; W. Qi et al., 2007; Shalev et al., 2002).

1.3.2. TXNIP is elevated in diabetes TXNIP levels are upregulated in numerous cell types under conditions of high-glucose as well as in many animal models of diabetes. In vitro studies demonstrate increased TXNIP expression in response to high glucose stimulation in retinal cells (L Perrone, Devi, Hosoya, Terasaki, & Singh, 2010), sensory neurons (Price et al., 2006), and various renal cells including mesangial cells, podocytes, and tubular cells (Gao et al., 2014; Huang et al., 2014; Kobayashi, Uehara, Ikeda, Itadani, & Kotani, 2003; Wei et al., 2013). In addition, increased TXNIP expression has also been demonstrated in various rodent models of diabetic nephropathy (Advani et al., 2009; Hamada & Fukagawa, 2007), and in renal tissue biopsied from patients with diabetes (Advani et al., 2009).

One possible mechanism of glucose-dependent upregulation of TXNIP is thought to involve activation of the Mondo (MondoA or ChoRE-binding protein (ChREBP)):Max-like protein X (MLX) transcription factor complex, a tetrameter composed of two heterodimers of Mondo:MLX, by glycolytic intermediates. Research indicates that MondoA/MLX is translocated into the nucleus in response to elevations in certain glycolytic intermediates, such as glucose-6-phosphate (G6P) (Stoltzman, 2008). In the nucleus, MondoA/MLX binds to two carbohydrate response elements (ChoREs) in the TXNIP gene promoter that are in proximity to one another to induce TXNIP gene expression (Minn, Hafele, & Shalev, 2005; Yu & Luo, 2009). On a minor note, these two ChoREs, alone, are insufficient to mediate induction of TXNIP expression by high glucose (Yu & Luo, 2009). Nuclear factor (NF-Y) occupancy of the CCAAT motifs in the TXNIP promoter also appears to be required and has been suggested to play a role in the recruitment of the MondoA/MLX complex to the TXNIP promoter by glucose. However, NF-Y occupancy of the TXNIP promoter has been suggested to occur constitutively even in glucose-free medium (Yu & Luo, 2009).

In addition, epigenetic changes including cytosine DNA methylation and posttranslational modification of histones have been implicated in driving TXNIP expression in HG. In humans, hypomethylation of the cg19693031 CpG site in the 3’ UTR of the TXNIP gene has been linked to T2DM in three different cohorts (Chambers et al., 2015; Florath et al., 2016; Kulkarni et al., 2015). Since DNA hypomethylation is usually associated with increased gene expression,(Portela & Esteller, 2010) hypomethylation at this site likely contributes to increased TXNIP gene

17 transcription in diabetes, possibly by increasing the binding affinity for transcription factors or by improving chromatin accessibility (Blattler & Farnham, 2013; Choy et al., 2010; Medvedeva et al., 2014; Zentner & Henikoff, 2014). Moreover, HG-induced histone modification can also increase TXNIP transcription. Siddiqi et al. found that depletion of histone methyltransferase enzyme enhancer of zeste homolog 2 (EZH2), an enzyme that catalyzes histone 3 lysine 27 trimethylation (H3K27me3) in the TXNIP gene, resulted in decreased H3K27me3 and increased binding of the Pax6 transcription factor to the TXNIP gene, leading to increased TXNIP expression, oxidative stress, and podocyte injury in diabetes (Siddiqi et al., 2016). They also demonstrated that upregulation of the EZH2 via the inhibition of its regulator microRNA-101 resulted in downregulation of TXNIP and attenuation of oxidative stress. Furthermore, De Marinis et al. reported increased stimulation of the activation marks H3K9ac, H3K4me1, and H3K4me3 and inhibition of the repression mark H3K27me3 in TXNIP in Sur1-E1506K+/+ mouse kidney and human mesangial cells with some species-specific differences (De Marinis et al., 2016). However, H3K9ac was consistently observed to be increased at the TXNIP promoter of diabetic mouse kidneys in vivo and mouse and human mesangial cells in vitro, suggesting that its induction by HG in preserved across species. In a rat retinal endothelial cell line, HG treatment was also found to increase H4K8 acetylation at the TXNIP promoter (L Perrone et al., 2010). In pancreatic beta cells, HG was observed to stimulate H4 acetylation (Cha-Molstad, Saxena, Chen, & Shalev, 2009). In all three studies, p300 recruitment/activity was found to mediate TXNIP histone acetylation and mRNA/protein upregulation. Treatment with the histone deacetylase (HDAC) inhibitor trichostatin A (TSA) and the competitive histone acetyltransferase (HAT) p300/CREB-binding protein (CBP) inhibitor C646), increased H4K8 acetylation and decreased H3K9ac acetylation, respectively, and induced and repressed TXNIP expression at both basal and HG levels, accordingly (De Marinis et al., 2016; L Perrone et al., 2010).

1.3.3. TXNIP is implicated in Diabetic Nephropathy Elevated levels of TXNIP in diabetes have been implicated in several pathologic processes in diabetes. It has been demonstrated to be involved in glucotoxicity of the pancreatic islets and kidneys as well as the development of diabetic vascular complications (J. Chen, Saxena, Mungrue, Lusis, & Shalev, 2008; Hamada & Fukagawa, 2007).

In more recent years, there has been increasing interest in investigating the role of TXNIP in DN as it is believed to be an important mediator of hyperglycemia-induced damage. We have previously shown in our lab that TXNIP-deficient mesangial cells are protected from glucose- induced production of reactive oxygen species, mitogen-activated protein kinase phosphorylation, and collagen expression, which have been shown in to contribute to DN pathology (Anu Shah et al., 2013). Furthermore, our lab has shown in a recent study that whole- body TXNIP knockout in a mouse model protected the kidneys from streptozotocin-induced diabetic renal injury and dysfunction (Anu Shah et al., 2015). Specifically, unlike the wildtype mice, TXNIP knockout mice did not experience increases in albuminuria, proteinuria, serum cystatin C, or serum creatinine levels, all measures of renal dysfunction (Anu Shah et al., 2015).

However, while attempts to lower TXNIP levels have been shown to improve diabetic kidney outcomes in vitro and in vivo models (Anu Shah et al., 2013, 2015), lowering TXNIP can also have deleterious effects elsewhere in the body where TXNIP serves a protective role. For example, TXNIP has been reported to have tumour suppressor functions and interventions to suppress TXNIP expression also increases the risk of cancer (Morrison et al., 2014). As a result, there has been increasing interest in studying downstream targets of TXNIP in order to develop effective therapies for diabetic nephropathy that do not interfere with the protective effects of TXNIP on cell homeostasis. Some of the major processes associated with DN pathogenesis and progression that have been linked to TXNIP are briefly reviewed.

1.4. Thioredoxin-interacting Protein in Diabetic Nephropathy 1.4.1. Oxidative and Nitrosative Stress 1.4.1.1. Oxidative and Nitrosative Stress in Diabetic Nephropathy Reactive oxygen species (ROS) are intracellular molecules containing highly reactive chemical species containing oxygen, that can oxidize lipids, proteins, and DNA (Birben, Sahiner, Sackesen, Erzurum, & Kalayci, 2012). They encompass a wide and diverse group of molecules, with

− commons examples being the superoxide anion (O2• ), hydrogen peroxide (H2O2), and hydroxyl radicals (OH•). They can be produced by numerous different systems, including xanthine oxidases, the cytochrome P450 system, uncoupled endothelial NO synthase, mitochondria, and nicotinamide dinucleotide phosphate (NADPH) oxidases (NOX), with the latter two being the

19 biggest contributors to oxidative stress in DN (J. M. Forbes, Coughlan, & Cooper, 2008a; Giacco & Brownlee, 2010; D. K. Singh, Winocour, & Farrington, 2011; Stanton, 2011). Mitochondrial ROS are produced as a natural by-product of oxidative phosphorylation due to the incomplete transfer of electrons to oxygen at the end of the electron transport chain (ETC). In contrast, the NOX enzymes are a family of transmembrane enzymes that function to transport electrons from NADPH across membranes and generates ROS as a result (Bedard & Krause, 2007; Sumimoto, 2008). ROS production by the mitochondria and NOX enzymes occurs in normal physiological conditions (Chance, Sies, & Boveris, 1979; Staniek & Nohl, 1999), but is increased in diabetes due to mitochondrial dysfunction (e.g. increased mitochondrial DNA mutations and impaired ETC) (Fosslien, 2001) as well as overexpression and overactivation of NOX enzymes (L. Li & Renier, 2006; S. Liu et al., 2007; Picchi et al., 2006).

At normal physiological levels, ROS-induced alterations in protein structure, function, and protein-protein interactions serve an important regulatory role in intracellular signalling, gene and protein regulation, cell growth and survival, and adaptive and innate immunity (Stanton, 2011). However, at pathophysiological levels, ROS can cause dysregulation of protein functioning and signalling. As such, the levels of ROS are carefully regulated by endogenous antioxidants. However, when the amount of ROS produced overwhelms the antioxidant systems available to detoxify them, a condition known as oxidative stress occurs (Betteridge, 2000). In addition, superoxide can react with nitric oxide (NO) to generate reactive nitrogen species (RNS) such as the peroxynitrite anion (ONOO−), which have similar functions to ROS (Stanton, 2011). Overproduction of RNS species, similarly, causes a condition called nitrosative stress. Since the overproduction of superoxide will lead to overproduction of RNS, oxidative stress and nitrosative stress often occur in tandem.

There is a plethora of evidence supporting the position of oxidative stress as a key contributor to the development and progression of DN (J. M. Forbes et al., 2008a). Free radicals such as superoxide have been shown to cause cellular and tissue injury by inducing activation of NF-κB and PKC and causing apoptosis (Ha, Hwang, Park, & Lee, 2008). Superoxides produced by the mitochondria were demonstrated by Munusamy et al. to play a critical role in mediating

20 mitochondrial damage and subsequent renal injury observed in DN (Munusamy & MacMillan- Crow, 2009). Thus, mitochondrial ROS are believed to play an important role in the early events leading to the development of diabetic complications (Brownlee et al., 2000). In fact, it has been demonstrated both in vitro and in vivo that reductions in the amount of mitochondrial ROS generated prevents renal damage in diabetes (Bock et al., 2013).

The simultaneously occurring nitrosative stress further exacerbates kidney pathology. The RNS, peroxynitrite anion (ONOO−), is cytotoxic and has been shown to initiate lipid peroxidation, oxidize sulfhydryl groups in proteins, and add nitrates to amino acids such as tyrosine, causing dysregulation of many signal transduction pathways (Beckman & Koppenol, 1996). Experimentally, the production of peroxynitrite is often indirectly inferred by measuring the levels of nitrotyrosine (Ischiropoulos, 1998). Numerous studies have observed increased nitrotyrosine formation in diabetic patients (A. Ceriello et al., 2001). Several have implicated high glucose as a direct cause as nitrotyrosine formation has been observed in the plasma of healthy subjects during a hyperglycemic clamp (Marfella et al., 2001) and in the artery wall of monkeys during hyperglycemia (Pennathur, Wagner, Leeuwenburgh, Litwak, & Heinecke, 2001). Nitrotyrosine formation is also increased in diabetic patients following postprandial hyperglycemia (Antonio Ceriello, Quagliaro, Catone, et al., 2002). As expected, high glucose- induced nitrotyrosine formation has been associated with overexpression of inducible nitric oxide synthase (iNOS) and dysregulation of NO and superoxide production (Antonio Ceriello, Quagliaro, D’Amico, et al., 2002). Since nitrotyrosine can directly harm endothelial cells (Mihm, Jing, & Bauer, 2000), functionally, increased nitrotyrosine formation can cause endothelial dysfunction even in healthy subjects (Pennathur et al., 2001). In diabetes, it has also been associated with increased apoptosis of myocytes, endothelial cells, and fibroblasts in heart biopsies from diabetic patients (Frustaci et al., 2000), in the hearts of streptozotocin (STZ)- induced diabetic rats (Kajstura et al., 2001), and in the working hearts of rats during hyperglycemia (Antonio Ceriello, Quagliaro, D’Amico, et al., 2002). Increased nitrotyrosine formation has also been observed in the renal tubules of diabetic patients (Thuraisingham, Nott, Dodd, & Yaqoob, 2000), and increased renal nitrotyrosine combined with superoxide and

21 hydrogen peroxide formation has been observed in rodent models of diabetes (Josephine M. Forbes et al., 2002; Ishii et al., 2001; Onozato, Tojo, Goto, Fujita, & Wilcox, 2002).

1.4.1.2. Thioredoxin, TXNIP and Oxidative Stress The thioredoxin (Trx)/thioredoxin reductase (TrxR) system is one of two major thiol-dependent antioxidant systems and is ubiquitous to nearly all known organisms from bacteria to plants to mammals. The Trx1 system is located in the cytosol whereas the Trx2 system is located in the mitochondria. A third, testis-specific Trx is also present. All three Trx systems are made up of Trx, TrxR, and NADPH. The primary function of Trx is to reduce oxidized protein cysteine residues and cleave disulfide bonds (H. Nakamura, Nakamura, & Yodoi, 2002). This reaction involves nucleophilic attack of target substrates by the Cys 32 residue of Trx, followed by reduction of the bond by the Cys 35 residue, resulting in a reduced target substrate and an oxidized Trx molecule with a disulfide bridge between its Cys 32 and Cys 35 residues. TrxR can then reduce Trx by consuming electrons from NADPH, to restart the cycle (Nagarajan, Oka, & Sadoshima, 2017; H. Nakamura et al., 2002). Another major antioxidant function of reduced Trx is to provide electrons to methionine sulfoxide reductases (MSRs) or thioredoxin-dependent peroxidases (Prx) to aid in their functioning. MSRs aid in protein repair by reducing and restoring function to methionine residues oxidized to methionine sulfoxide by ROS (Moskovitz, Berlett, Poston, &

Stadtman, 1998). Prx function to remove ROS such as hydrogen peroxide (H2O2), ROOH, and peroxynitrite (Wood, Schröder, Harris, & Poole, 2003).

Thioredoxin-interacting protein (TXNIP), as the name implies, mainly functions as an endogenous inhibitor of Trx (J. Chen et al., 2008; Nishiyama et al., 1999; Oslowski et al., 2012; Parikh et al., 2007). It does so by forming a disulfide bond between its Cys 247 residue and the Cys 32 residue in the active catalytic site of Trx, to inactivate the antioxidative properties of Trx (Nishiyama et al., 1999). TXNIP is thus recognized as a mediator of oxidative stress through its modulation of Trx (Junn et al., 2000; Nishiyama et al., 1999; Patwari, Higgins, Chutkow, Yoshioka, & Lee, 2006), especially under hyperglycemic conditions (Schulze & Mann, 2004).

Numerous in vitro studies support the role of TXNIP as a major contributor to hyperglycemia- induced oxidative stress in kidney cells. Advani et al. demonstrated that knockdown of TXNIP with small interference RNA (siRNA) in mesangial cells and proximal tubular cells limit high glucose-induced oxidative stress (Advani et al., 2009). Shi et al. also confirmed that knockdown of TXNIP with siRNA in mesangial cells inhibited ROS generation and that this was associated with increased Trx activity (Shi et al., 2011). Furthermore, our lab showed that mesangial cells from the HcB-19 mouse model with TXNIP deficiency are protected from high glucose-induced ROS (Anu Shah et al., 2013). We also showed that TXNIP knockdown with siRNA in human podocytes abolished high glucose-induced generation of mitochondrial oxidative species (Anu Shah et al., 2013). Two studies of in vivo models of DN have also shown that increased TXNIP expression coincides with increased markers of oxidative stress. Hamada et al. demonstrated this association in streptozotocin-induced rat models of early diabetic renal damage (Hamada & Fukagawa, 2007), and Advani et al. demonstrated this in diabetic m(Ren-2) 27 rats, a model of progressive DN (Advani et al., 2009).

1.4.2. Fibrosis 1.4.2.1. Fibrosis in Diabetic Nephropathy As previously mentioned, DN is characterized by the accumulation of extracellular matrix components in the glomerular mesangium and matrix expansion. This is due to a combination of increased accumulation of proteins normally present in these structures, deposition of proteins that are not normally associated with these structures, or decreased degradation of matrix production (e.g. due to glycosylation) (Ziyadeh, 1993). Over time, these processes contribute to the thickening of the glomerular and tubular basement membrane, resulting in glomerulosclerosis and tubulointerstitial fibrosis, respectively.

1.4.2.2. TXNIP and fibrosis Collagen is a main component of the glomerular basement membrane and a major contributor to renal fibrosis. It is well known that ROS promotes the activation of many vasoactive mediators involved in cellular proliferation and collagen production in various renal cell types, such as endothelin-1 (ET-1) and PAI-1 (mediated by TGF-β1) (Hughes, Stricklett, Padilla, & Kohan, 1996; E. A. Lee et al., 2005).

There have therefore been several studies into the role of TXNIP in the development of matrix accumulation and fibrosis in DN. Kobayashi et al. demonstrated both in vitro and in vivo that overexpression of TXNIP leads to increased tissue collagen accumulation (Kobayashi et al., 2003). Furthermore, Advani et al. have demonstrated that knockdown of TXNIP with siRNA in rat mesangial cells and proximal tubular cells attenuated glucose-induced 3H-proline incorporation, which is a marker of collagen production (Advani et al., 2009). In addition, Tan et al. demonstrated that suppression of TXNIP expression with a DNAzyme in a rodent model of DN prevented diabetes-induced oxidative stress within the tubulointerstitium and superoxide production in the renal cortex as well as reduced peritubular collagen IV accumulation (C. Y. R. Tan et al., 2015). Taken together, these data suggest that TXNIP is involved in extracellular matrix accumulation and fibrosis in DN in association with activation of the oxidative stress pathway.

1.4.3. Inflammation 1.4.3.1. Inflammation in Diabetic Nephropathy It is widely accepted that inflammation plays an important role in the development and progression of DN (Wada & Makino, 2013). Chronic hyperglycemia and hyperlipidemia observed in diabetes can alter renal hemodynamics and metabolism as well as stimulate the release of inflammatory factors.

1.4.3.2. TXNIP in inflammation In mammals, the immune system consists of two branches: innate immunity and adaptive immunity. Innate immunity acts as the first line of defence against invading microbes (Kawai & Akira, 2009). Recent findings have suggested that TXNIP may play a role in promoting inflammation in diabetes and in oxidative stress via the nucleotide-binding domain and leucine- rich repeat-containing family, pyrin domain-containing-3 (NLRP3) inflammasome (K. Schroder, Zhou, & Tschopp, 2010; A. Zhou et al., 2010). The NLRP3 inflammasome, also known as the NALP3 inflammasome, is composed of NLRP3, an apoptosis-associated speck-like protein containing a CARD (ASC), and procaspase-1. The NLRP3 inflammasome is a critical component of innate immunity; it senses disturbances in cellular homeostasis and activates caspase-1 in response to signals such as K+ efflux, Ca2+ signalling, elevations in ROS (e.g. induced by high

24 glucose), mitochondrial dysfunction and apoptosis, and lysosomal rupture (Y. He, Hara, & Núñez, 2016). Caspase-1 subsequently activates the cytokines interleukin (IL)-1β, IL-18, and IL-33 to cause an inflammatory response. IL-1β, in particular, is a key contributor to diabetes and has been correlated with renal injury in patients with DN in clinical trials (Youm et al., 2011). In addition, all NLRP3 agonists are known to trigger the production of ROS and so NLRP3 activation is also associated with oxidative stress. In addition, NLRP3 activation can also induce apoptosis by activating the apoptotic initiator caspase-8, leading to subsequent cleavage of effector caspases such as caspase-3 (Aachoui, Sagulenko, Miao, & Stacey, 2013; Sagulenko et al., 2013).

In addition, Feng et al. found that both high glucose and LPS, alone, are capable of inducing increases in mRNA levels and protein levels of TXNIP, NRLP3, procaspase-1, and IL-1β in mesangial cells, suggesting that both diabetes and LPS causes TXNIP/NRLP3/IL-1β signalling (Feng et al., 2016). They proposed that TXNIP binds to NLRP3 to induce NLRP3 inflammasome assembly with ASC and procaspase-1 and that subsequent activation of caspase-1 results in IL- 1β production, eventually promoting inflammation and oxidative stress in DN. More research is needed, however, to elucidate this pathway.

1.4.4. Endoplasmic Reticulum Stress 1.4.4.1. Endoplasmic-reticulum Stress in Diabetic Nephropathy Endoplasmic reticulum (ER) stress occurs when there is an excess of unfolded or misfolded proteins in the endoplasmic reticulum (Yoshida, 2007). It has been implicated in the pathogenesis of various diabetic vascular complications, including DN (Y. Chen et al., 2012; Khan, Pichna, Shi, Bowes, & Werstuck, 2009). ER stress can occur in response to a variety of cellular injuries that exhaust or disrupt normal protein folding in the ER and causes an accumulation of misfolded or unfolded proteins. ER stress in renal cells, for example, may be triggered by hyperglycemia, oxidative stress, albuminuria, and advanced glycation end products (AGEs) (Inagi et al., 2005; Lindenmayer & Likens, 2009).

1.4.4.2. TXNIP and Endoplasmic Reticulum Stress TXNIP has been recently identified as a possible link between ER stress, inflammation, and cell death in diabetes (Lerner et al., 2012; Oslowski et al., 2012). Firstly, ER stress has been shown to

25 activate the unfolded protein response (UPR) and subsequently induce TXNIP activation through the Inositol-requiring enzyme 1 (IRE1α) and PKR-like ER-resident kinase (PERK) pathways of the UPR. More specifically, IRE1α has been demonstrated to induce TXNIP expression post- transcriptionally by decreasing levels of its inhibitory microRNA miR-17. miRs destabilize target mRNAs and repress translation and thus IRE1α-mediated reductions in miR-17 would promote TXNIP mRNA stability (Lerner et al., 2012). PERK, on the other hand, promotes the transcriptional synthesis of TXNIP. PERK has been shown to mediate phosphorylation of eukaryotic initiation factor 2 on Ser51 of the alpha subunit (eIF2α) to cause translation of activating transcription factor 5 (ATF5) and ChREBP, which regulate TXNIP transcription (Oslowski et al., 2012). In addition to being upregulated under ER stress, TXNIP has been suggested to induce transcription of IL-1β mRNA through an NLRP3 inflammasome-dependent or independent mechanism (Koenen et al., 2011)1, which has been implicated in ER stress-mediated cell death (Oslowski et al., 2012).

1.4.5. Apoptosis 1.4.5.1. Apoptosis in Diabetic Nephropathy Apoptosis is a form of programmed cell death that involves the activation (via cleavage) of a group of cysteine proteases called caspases. It has been implicated as an underlying cause of renal cell loss in DN. High glucose conditions, for example, have been shown to induce apoptosis of various cell types in vitro and in vivo, including mesangial cells (Lin et al., 2006; Mishra, Emancipator, Kern, & Simonson, 2005), GEnCs (Kitamura et al., 1998) podocytes (Eid et al., 2009; Susztak, Raff, Schiffer, & Böttinger, 2006), and proximal tubular epithelial cells (PTECs) (Allen, Harwood, Varagunam, Raftery, & Yaqoob, 2003; Ortiz, Ziyadeh, & Neilson, 1997; Verzola et al., 2002). In addition, cellular apoptosis is known to be a contributing factor to DN pathogenesis and progression and has been correlated with mesangial matrix expansion, glomerulosclerosis, and worsening albuminuria (Sugiyama, Kashihara, Makino, Yamasaki, & Ota, 1996).

The Bcl-2 family of proteins that govern mitochondrial membrane permeability have been implicated in the regulation of cellular apoptosis in diabetes. Bcl-2 proteins have wide-ranging functions, with some providing pro-apoptotic signals and others providing anti-apoptotic signals. Bcl-2, Bcl-x, Bcl-XL, Bcl-XS, Bcl-w, BAG, are examples of some anti-apoptotic Bcl-2 family

26 members that have been identified, while Bcl-10, Bax, Bak, Bid, Bad, Bim, Bik, Blk, Puma and Noxa are examples of pro-apoptotic Bcl-2 family members (Elmore, 2007). The Bcl-2 proteins Bax and Bak, for example, function by forming pores in mitochondrial outer membranes to allow the release of pro-apoptotic proteins such as cytochrome c, SMAC/DIABLO, and others into the cytosol (Gross, Jockel, Wei, & Korsmeyer, 1998; H. Kim et al., 2009). Once in the cytosol, these factors can contribute to the formation of the apoptosome complex and activation of caspases to induce apoptosis (Youle & Strasser, 2008). Indeed, Bax overexpression has been associated with increased apoptosis in diabetes (McKenzie et al., 2010).

The tumour suppressor transcription factor p53 is another important regulator of apoptosis, known to regulate several members of the of Bcl-2 family, including Puma, Noxa, Bax, and Bcl-2 (Miyashita et al., 1994; Schuler & Green, 2001). p53 signalling in vitro has been found to cause overexpression of p53 upregulated modulator of apoptosis (PUMA), a Bid-like protein that is associated with increased Bax expression, conformational change (likely in the N and C terminals), and translocation to mitochondria to facilitate cytochrome c release (F. T. Liu, Newland, & Jia, 2003). p53 signalling has also been associated with increased mitochondrial localization of NADPH oxidase activator 1 (Noxa). There Noxa can interfere with the functioning of anti-apoptotic Bcl-2 family members to facilitate caspase-9 cleavage (Oda et al., 2000).

Moreover, apoptosis signal regulating kinase-1 (ASK1), a mitogen-activated protein kinase (MAPK) kinase kinase known to be involved in a variety of biological responses, is also implicated in apoptosis in diabetes (Thandavarayan et al., 2008). Oxidative stress in diabetes is thought to induce ASK1 activation by mediating dephosphorylation of ASK1 Ser 967 and phosphorylation of ASK1 Thr 845. This causes dissociation of its inhibitor, the 14-3-3ζ protein, from the C-terminal of ASK1 (Goldman, Chen, & Fu, 2004; X. Li et al., 2005). Trx, which is inhibited in high glucose by TXNIP, is reported to help inhibit ASK1 by binding to the N-terminal of ASK1 (X. Li et al., 2005).

1.4.5.2. TXNIP in apoptosis Overexpression of TXNIP has been implicated in promoting cellular apoptosis in DN and reductions of TXNIP levels are generally renoprotective. TXNIP knockdown with siRNA, for

27 example, was demonstrated by Shi et al. to protect against high glucose-induced mesangial cell apoptosis (Shi et al., 2011). Furthermore, TXNIP knockdown has also been observed to abolish high glucose-induced activation of the ASK1 as indicated by Thr 845 phosphorylation, and reduced expression of cleaved caspase-3 (Shi et al., 2011). While TXNIP has been noted to contribute to apoptotic pathways mediated by the NLRP3 inflammasome and ASK1, as outlined above, the mechanisms by which TXNIP promotes apoptosis remain incompletely understood. Our lab hypothesizes (see below) that an important and novel mechanism by which TXNIP promotes renal cell apoptosis and DN is via signalling through the glyceraldehyde 3-phosphate dehydrogenase (GAPDH)/E3 ubiquitin-protein ligase seven in absentia homolog 1 (Siah1) pathway.

1.5. TXNIP and the GAPDH/Siah1 Pathway 1.5.1. GAPDH Background GAPDH primarily exists as a tetramer composed of four identical 37 kDa subunits, each with a catalytic thiol group. In the cytoplasm, GAPDH mainly functions as a glycolytic enzyme that catalyzes the oxidation and phosphorylation of glyceraldehyde-3-phosphate (GAP) to D- glycerate 1,3-bisphosphate (1,3BPGA) in the presence of inorganic phosphate, while simultaneously reducing nicotinamide adenine dinucleotide (NAD+) to NADH. Although generally regarded as a housekeeping protein with an expression level that remains static, evidence suggests that under various conditions, its expression level, activity, and function may change.

As currently understood, diabetic complications research has focused on oxidative stress- mediated GAPDH deactivation in promoting disease pathology by causing flux of upstream glycolytic intermediates to alternative metabolic pathways including 1) the polyol pathway, 2) formation of advanced glycation end products, 3) activation of protein kinase C (PKC), and 4) the hexosamine biosynthesis pathway (Figure 1.3). As such, many have investigated the therapeutic potential of antioxidants (e.g. vitamins A, C, and E) to prevent diabetic complications, but the results remain inconclusive. Numerous studies in rodents, as well as several small and short- term studies in humans with T1DM and T2DM, have supported a possible role of antioxidants in improving glycemic control and lipid metabolism, and in protecting against diabetic retinopathy, nephropathy, and cardiovascular events (Bursell et al., 1999; Eriksson & Kohvakka, 1995;

Paolisso et al., 1993). In a small clinical study conducted by Bursell et al. involving 36 patients with T1DM, vitamin E treatment appeared to be protective against diabetic retinopathy and nephropathy by helping normalize retinal hemodynamic abnormalities and renal function, respectively (Bursell et al., 1999). However, the majority of large prospective randomized controlled clinical trials have failed to demonstrate a beneficial effect of antioxidants such as vitamin E in diabetic complications management, especially against cardiovascular outcomes (Marchioli, Schweiger, Levantesi, Tavazzi, & Valagussa, 2001; Vivekananthan, Penn, Sapp, Hsu, & Topol, 2003). This is possibly due to the limitation that general antioxidant therapies mainly function to scavenge already-formed oxidants without addressing the root of the problem—the sources of ROS overproduction or the key signalling pathways activated/altered by oxidative stress. In addition, each antioxidant has limited targets, and none directed at mitochondria have reached significant clinical investigation. As such, there has been growing interest in the identification of alternative and more targeted pathways and molecules in diabetic complications development.

Figure 1.3: Schematic outline of the current paradigm of diabetic complications development. ROS-mediated inhibition of GAPDH is theorized to cause the build-up of upstream glycolytic intermediates and flux through four alternative metabolic pathways: 1) the polyol pathway, 2) formation of AGEs, 3) activation of PKC and 4) the hexosamine biosynthesis pathway. Abbreviations: glutamine:fructose-6-phosphate aminotransferase (GFAT), uridine diphosphate (UDP), advanced glycation end products (AGEs), diacylglycerol (DAG), and protein kinase C (PKC).

In this study, we propose that, in addition to the traditional paradigm, GAPDH may play another and more direct role in diabetic complications development by mediating high glucose-induced cellular apoptosis via the GAPDH/Siah1 pathway.

1.5.2. GAPDH/SIAH1 Pathway Over the past two decades, studies into neurodegenerative disease such as Parkinson’s disease revealed a novel localization and function of GAPDH. When it localized to the nucleus, GAPDH was found to be in a complex with Siah1 and participate in pro-apoptotic signalling (Puthanveetil et al., 2012; Suarez, Mccollum, Jayagopal, & Penn, 2015). The GAPDH/Siah1 pathway has since been well elucidated in neuronal cell studies. Under normoglycemia, about 2% of total GAPDH and a small pool of Siah1 participate in this mechanism (M. R. Hara et al., 2005).

1.5.2.1. S-Nitrosylation of GAPDH at Cys 150 The GAPDH-Siah1 signalling pathway was determined to be initiated by S-nitrosylation of GAPDH, which is the attachment of nitric oxide (NO) to a reactive thiol of cysteine (-SH), forming S-nitrosothiols (-SNO). S-nitrosylation of proteins is a form of post-translational modification (PTM) used for regulation of protein functions, interactions, and redox states (Anand & Stamler, 2012). At physiological levels of oxidative and nitrosative stress, it also serves a protective function by protecting the thiols critical to protein functioning from further oxidation by ROS/reactive nitrogen species (RNS). This is because the d-orbitals of sulfur confers high reactivity and chemical flexibility upon the thiol group, allowing it to undertake multiple oxidation states from the reversible S-nitrosothiol (−SNO) to sulfenic acid (−SOH), to sulfinic acid

(−SO2H), and finally to irreversible sulfonic acid (−SO3H) (Gu et al., 2002; Hess, Matsumoto, Kim, Marshall, & Stamler, 2005). Irreversible thiol modification is usually an indicator of pathology as it leads to permanent loss of function and degradation of proteins whereas reversible thiol modifications are typically involved in redox signal transduction. However, during pathological levels of oxidative and nitrosative stress, the overproduction of NO causes S-nitrosylation dysregulation, contributing to disease pathogenesis. S-nitrosylation of GAPDH at the Cys 150 in its catalytic core, specifically, is associated with its complexing with Siah1, nuclear localization, and activation of the p300/CREB-binding protein (CBP) (M. R. Hara et al., 2005; Nilkantha Sen et al., 2008).

The NO driving S-nitrosylation of proteins is mainly generated from the metabolism of L-arginine by nitric oxide synthase (NOS). There are three isoforms of NOS, all of which are primarily non- nuclear; the constitutively expressed neuronal NOS (nNOS) and endothelial NOS (eNOS), and inducible NOS (iNOS) (Nathan & Xie, 1994). In diabetes, increased production of mitochondrial superoxide reduces eNOS activity but increases iNOS expression through NF-κB and protein kinase C (PKC), resulting in a net increase in NO generation (Baek, Thiel, Lucas, & Stuehr, 1993; Xue Liang Du et al., 2001; Spitaler & Graier, 2002). Furthermore, nitrite, a circulatory source of intravascular NO produced from the reaction of NO with oxygen, may also be used for protein S-nitrosylation (Bryan et al., 2004; Gladwin et al., 2000; Wang et al., 2004). Once in the cell, nitrite can be converted back to NO via nonenzymatic conversion (e.g. during conditions of low pH) or enzymatic conversion (mediated by xanthine oxidoreductase; XOR) (Berry & Hare, 2004; Kelm, 1999; Sun, Steenbergen, & Murphy, 2006; Weitzberg & Lundberg, 1998; Zweier,

- Samouilov, & Kuppusamy, 1999). NO-derived RNS such as peroxynitrite (ONOO ), NO2, and N2O3,

+ as well as metal-NO complexes (e.g. Fe2(NO )) can also cause S-nitrosylation of thiol groups (Foster, Hess, & Stamler, n.d.; Hess et al., 2005; Lipton et al., 1993). Increases in nitrosative stress, as frequently observed in diabetes (A. et al., 2002; A. Ceriello et al., 2001; Tannous et al., 1999), may thus promote S-nitrosylation of GAPDH and signalling of this pathway. Lastly, GAPDH S-nitrosylation is possible via transnitrosylation reactions with SNOs such as S-nitrosoglutathione (GSNO) or by other nitrosylated proteins (Benhar, Forrester, & Stamler, 2009; Hess et al., 2005; Hogg, 2002).

1.5.2.2. Binding of S-nitrosylated GAPDH to Siah1 Since Cys 150 is critical to the catalytic function of GAPDH, s-nitrosylation at this site inactivates GAPDH. It also confers upon GAPDH an ability to bind the E3-ubiquitin ligase Siah1, likely by inducing favourable conformational changes in GAPDH. Structural analyses of GAPDH and Siah1 performed by Jenkins and Tanner suggest that GAPDH binds Siah1 in a stoichiometry of 1:2, meaning a single GAPDH tetramer could interact with up to four Siah1 dimers (Jenkins & Tanner, 2006). However, this has yet to be tested experimentally. In addition, mutagenesis studies suggest that GAPDH amino acids 220-238 in mice (220-240 in humans) and Siah1 amino acids 270-282 in mice (272-284 in humans) mediates GAPDH-Siah1 interaction (M. R. Hara et al.,

2005). In addition, GAPDH Lys 225 in mice (227 in humans appears to be critical for this association since mutagenesis of this residue inhibits GAPDH-Siah1 complex formation (M. R. Hara et al., 2005). Lys 225/227 likely directly interacts with Siah1 (M. R. Hara et al., 2005). Furthermore, O-GlcNAc modification (O-GlcNAcylation) at Thr 227 (Thr 229 in humans) and acetylation at Lys 115, 225, and 249 in mice (Lys 117, 227 and 251 in humans) have also been suggested to promote GAPDH interaction with Siah1 (Ventura et al., 2010). But, whether these three different modifications must all occur or are separate and alternative pathways for GAPDH-Siah1 binding and nuclear transport still remains to be elucidated.

1.5.2.3. GAPDH/Siah1 Nuclear Translocation Since Siah1 has a nuclear translocation sequence (M. R. Hara et al., 2005), the GAPDH-Siah1 complex subsequently moves into the nucleus, where it can engage in three different signalling pathways: 1) acetylation of p300/CBP and activation of downstream targets ultimately inducing apoptosis, or 2) transnitrosylation or, 3) ubiquitination of nuclear proteins. The ubiquitinating activity of Siah1 does not appear to be involved in mediating nuclear translocation of this complex as Siah1 mutants lacking the RING finger domain responsible for its ubiquitinating activity can still mediate GAPDH nuclear translocation (M. R. Hara et al., 2005; Reed & Ely, 2002). Furthermore, GAPDH was not found to be ubiquitinated by Siah1 in this pathway (M. R. Hara et al., 2005).

1.5.2.4. GAPDH/Siah1 Nuclear Signalling Within the nucleus, SNO-GAPDH has been previously shown to transnitrosylate nuclear proteins such as deacetylating enzyme sirtuin-1 (SIRT1), histone deacetylase-2 (HDAC2) and DNA- activated protein kinase (DNA-PK) by transferring its NO group to the cysteine residue of the acceptor protein, and in doing so becomes denitrosylated itself (Kornberg et al., 2010). Since GAPDH nitrosylates Cys 387 and 390 in the catalytic core of SIRT1, this mechanism reduces the activity of SIRT1 (Kornberg et al., 2010). Nitrosylation of HDAC, in comparison, causes HDAC dissociation from chromatin, enhancing histone acetylation (Nott, Watson, Robinson, Crepaldi, & Riccio, 2008; N. Sen & Snyder, 2011). The effect of S-nitrosylation of DNA-PKs on DNA repair, however, remains to be determined, but NO has been suggested to

32 increase the transcription of, expression, and activity of DNA-PKs and reduce DNA damage (Xu, Liu, Smith, & Charles, 2000).

Moreover, nuclear Siah1 can cause ubiquitination and degradation of nuclear proteins such as nuclear corepressor (NcoR) (J. Zhang, Guenther, Carthew, & Lazar, 1998). Transnitrosylation and degradation of the aforementioned nuclear proteins mediated by nuclear SNO-GAPDH/Siah1 are thought to contribute to neurodegeneration in Parkinson’s Disease (T. Nakamura et al., 2013). In the context of diabetes, these pathways may contribute to metabolic dysfunction (Lagouge et al., 2006; Rodgers, Lerin, Gerhart-Hines, & Puigserver, 2008). But arguably, the most important action of the GAPDH/Siah1 complex in the nucleus may be its pro-apoptotic signalling through p300/CBP.

In the nucleus, the acetyltransferase p300/CBP can bind directly to and acetylate GAPDH at Lys 160 (Nilkantha Sen et al., 2008). Acetylated GAPDH will then stabilize and stimulate p300/CREB to undergo autoacetylation to enhance its catalytic activity in essentially a feed- forward activation mechanism. p300/CBP, a transcriptional co-activator, can subsequently acetylate and activate p53, a transcription factor. p53 will then upregulate the transcription of pro-apoptotic targets such as p53 upregulated modulator of apoptosis (PUMA) and Bax (Nilkantha Sen et al., 2008; L. Zhou & Zhu, 2009). This appears to be a key pathway of NO-induced apoptosis since mutagenesis of p53 resulting in p53 loss-of-function abrogates NO-induced apoptosis in human lymphoblastoid cells (C. Q. Li et al., 2004).

There is some supportive evidence for a role of this pathway in diabetes as studies of human embryonic kidney cells have shown that in diabetes, there is not only increased S-nitrosylation of GAPDH but also increased binding of Siah1 to GAPDH (Nilkantha Sen et al., 2008).

Note that although GAPDH has been shown to be capable of entering the nucleus via Siah1- independent pathways, nuclear GAPDH independent of Siah1 does not appear to signal through the p300/CBP and p53-mediated pathway for apoptosis. For example, under glucose starvation (but not amino acid starvation), AMP-activated protein kinase (AMPK) has been found to

33 phosphorylate GAPDH at Ser 122 to induce its translocation into the nucleus (without Siah1) to promote autophagy (Chang et al., 2015). In the nucleus, phosphorylated GAPDH directly interacts with the deacetylase SIRT1, displacing SIRT1’s repressor, thus activating SIRT1. This induces autophagy as SIRT1 has previously been shown to stimulate autophagy by deacetylating essential components of the autophagy machinery, such as autophagy genes (Atg)5, Atg7, and Atg8 (I. H. Lee et al., 2008).

1.5.3. Regulation by the Thioredoxin and Glutathione Systems 1.5.3.1. Denitrosylase Actions The two major disulfide reductase systems used by cells to maintain redox balance of thiol groups are the thioredoxin (Trx)/thioredoxin reductase (TrxR) system and the glutathione (GSH)/glutathione reductase (GR) system (Holmgren, 1989). These systems may, therefore, play a role as negative regulators of the GAPDH/Siah1 pathway by helping keep GAPDH in its reduced, non-nitrosylated, and active form, preventing downstream signalling.

The thioredoxin system, as previously mentioned, is comprised of thioredoxin (Trx), thioredoxin reductase (TrxR), and NADPH. Similarly, the glutathione system is comprised of glutathione (GSH), glutathione reductase (GR), and NADPH. While Trx and TrxR mainly exist as three different isoforms (cytosolic Trx1 and TrxR1, mitochondrial Trx2 and TrxR2, and testis-specific Trx3 and TrxR3 (Vlamis-Gardikas & Holmgren, 2002)), numerous isoforms of GR are available due to alternative start sites and splicing but mainly exists in the cytoplasm or mitochondria (Edwards, Rawsthorne, & Mullineaux, 1990; Outten & Culotta, 2004). Overall, the structure of mammalian TrxRs is similar to that of GRs, as most pyridine nucleotide disulfide reductases, including TrxR and GR, possess the same N-terminal CVNVGC motif in their active site (Q. Cheng, Sandalova, Lindqvist, & Arnér, 2009; Zhong, Arnér, & Holmgren, 2000). Both Trx and GSH can act as denitrosylases and directly remove NO moieties from proteins. Trx denitrosylates proteins by forming intermolecular disulfide between the cysteine in its active site with the cysteine of the target S-nitrosothiols, producing oxidized Trx and nitroxyl (HNO) or NO as by-products (Benhar et al., 2009). Oxidized Trx can subsequently be reduced by TrxR by consuming and oxidizing NADPH in the process. In contrast, GSH-mediated denitrosylation produces oxidized GSH (GSSG) as a by-product, which is reduced by GR back to GSH via the consumption and oxidation of

NAPDH (Benhar et al., 2009). There is growing evidence that the proteins denitrosylated by Trx and GSH are non-overlapping (Benhar, 2015; Benhar et al., 2009; Pader et al., 2014). Studies in Arabidopsis thaliana and C. reinhardtii have suggested that plant and algae GAPDH denitrosylation may be more regulated by GSH than Trx (Lebreton, Graciet, & Gontero, 2003; Zaffagnini et al., 2013), but it is unclear if the same holds true for mammalian cells. In a study by Yan et al., mammalian Trx and TrxR were found to restore the activity of inactive GAPDH in human lenses (Yan, Lou, Fernando, & Harding, 2006), suggesting that the thioredoxin system is involved in the regulation of GAPDH activity, but whether it is through the regulation of denitrosylation has yet to be determined.

Figure 1.4: Schematic of the denitrosylase functions of thioredoxins and glutathione. Figure reproduced with permission from Nitric Oxide (Altinoz & Elmaci, 2018).

1.5.3.2. ROS/RNS Scavenging Thioredoxin and glutaredoxin (Grx)/glutathione system activities may also favour the reduced form of GAPDH (and thus inhibition of GAPDH/Siah1 signalling) through their general activities as antioxidants, scavenging ROS and RNS, and diminishing the level of NO and NO derivatives available for S-nitrosylation reactions. These processes have been reviewed by Hanschmann, Godoy, Berndt, Hudemann, & Lillig, 2013 and were touched upon earlier in 1.4.1.2. Thioredoxin, TXNIP, and Oxidative Stress. Both Trx and Grx can reduce protein disulfides via the dithiol mechanism by attacking the substrate sulfide with their N-terminal active site thiol to form a mixed disulfide intermediate. Then, the C-terminal active site thiol is used to reduce the mixed disulfide, generating a reduced protein disulfide and an oxidized Trx or Grx. The disulfide in the

35 active site of Trx and Grx, similar to above, can be returned to its reduced form by TrxR or GR and GSH, respectively, with electrons supplied by NADPH. Furthermore, Grx can also reduce protein disulfides via a monothiol mechanism that only utilizes the N-terminal active site cysteinyl residue. This reaction generates a Grx-GSH mixed disulfide intermediate while reducing the protein disulfide. Lastly, the Grx-GSH mixed disulfide is reduced by a second GSH to regenerate reduced Grx. In addition, the glutathione system can also reduce GSNO, which is produced from S-nitrosylation of GSH and can, as previously mentioned, serve as a source of NO driving transnitrosylation of other proteins. Reduction of GSNO by protein–S-nitrosoglutathione reductases (GSNORs) in the presence of GSH produces GSSG (S. P. Singh, Wishnok, Keshive, Deen, & Tannenbaum, 1996), which can be further reduced back to GSH by GR (Benhar et al., 2009). Peroxiredoxins (Prx) are another class of endogenous antioxidants that can help scavenge

ROS and RNS such as hydrogen peroxide (H2O2), ROOH, and peroxynitrite (Wood et al., 2003). However, unlike Trx and Grx, Prxs reduce peroxides instead of protein disulfides. They have been reviewed by Lu & Holmgren, 2012. There are six Prxs distributed across various subcellular locations in humans. Like Trx and Grx, Prx also possesses two cysteines—an N-terminal

− peroxidatic CysP residue that exists as a thiolate (−S ) at neutral pH and a C-terminal resolving

CysR. Prxs attack hydrogen peroxide with the thiolate CysP residue to generate CysP sulfenic acid and water. Then, then C-terminal resolving CysR residue will react with CysP sulfenic acid to form an inter- or intramolecular disulfide bond, depending on the Prx type. Trx1 can then help these disulfide bonds in Prx1 and Prx2, and Trx2 and Grx2 can reduce the bonds in Prx3 to regenerate these Prxs.

1.5.3.3. ASK1-Mediated Functions Thioredoxin may also contribute to GAPDH/Siah1 pathway regulation by binding to the N- terminal of apoptosis signal-regulating kinase 1 (ASK1) to prevent its activation (Saitoh et al., 1998). ASK1 has been shown in HEK293 cells to bind directly to Siah1 and likely phosphorylates Siah1 at Thr 70/Thr 74 and Thr 235/Thr 239, triggering GAPDH-Siah1 signalling and downstream activation of p300/CBP in the nucleus (Tristan et al., 2015). It has also been shown that GAPDH augments ASK1-Siah1 direct binding (Tristan et al., 2015).

1.5.3.4. Thioredoxin and TXNIP in the GAPDH/Siah1 Pathway Overall, the current body of evidence supports a regulatory role of Trx in the GAPDH/Siah1 signalling pathway, regardless of whether Trx directly denitrosylates SNO-GAPDH or if it contributes as an antioxidant to decreasing NO pools through ROS and RNS scavenging and thereby keep GAPDH in its reduced, non-nitrosylated, and active form, or if Trx acts through inhibition of ASK1. It is therefore reasonable to infer that TXNIP, a physiological inhibitor of Trx (J. Chen et al., 2008; Nishiyama et al., 1999; Oslowski et al., 2012; Parikh et al., 2007), would regulate the GAPDH/Siah1h pathway by removing the inhibitory constraints Trx conferred upon GAPDH/Siah1 pathway activation. This is supported by data from MC cultures where TXNIP knockdown was associated with activation of ASK1 and high glucose-induced MC apoptosis (Shi et al., 2011).

1.5.4. Experimental Inhibition of the GAPDH/Siah1 Pathway 1.5.4.1. Physiological negative regulators Several approaches are available for inhibiting the GAPDH/Siah1 pathway to study its physiological relevance in disease or to employ it as a potential therapeutic intervention. The GAPDH’s competitor of Siah protein enhances life (GOSPEL) is an endogenous physiological negative regulator of the pathway (Nilkantha Sen et al., 2009). Viral delivery of GOSPEL in mice is associated with neuroprotection (Nilkantha Sen et al., 2009). Studies in neuronal cell cultures have demonstrated that GOSPEL, after S-nitrosylation at Cys 47, competes with Siah1 to bind SNO-GAPDH and form a GOSPEL-GAPDH dimer/oligomer complex. The GOSPEL-GAPDH complex can then serve as a seed for further GOSPEL and GAPDH aggregation via disulfide-crosslinking involving the Cys 156 or Cys 247 residues of GAPDH. By retaining it in the cytosol, GOSPEL thereby inhibits GAPDH nuclear translocation and activation of the apoptosis cascade (Nilkantha Sen et al., 2009). In addition, SNO-GAPDH can also transnitrosylate B23/nucleophosmin to negatively regulate its own signalling through the GAPDH/Siah1 pathway. SNO-B23 competes with SNO-GAPDH to bind Siah1, and in doing so not only decrease GAPDH-Siah1 binding but also inhibits the ubiquitin E3 ligase activity of Siah1 (S. B. Lee, Kim, Lee, & Ahn, 2012).

1.5.4.2. Pharmacological inhibitors Pharmacological inhibitors are also available, including (R)-N,α-dimethyl-N-2-propyn-1-yl- benzeneethanamine, monohydrochloride (commonly known as R-(−)-deprenyl or Selegiline and hereafter designated deprenyl) and Dibenzo[b,f]oxepin-10-ylmethyl-methyl-prop-2-ynyl-amine (commonly known as CGP 3466, or TCH346, or Omigapil).

Deprenyl was commonly used in the clinic as a monoamine oxidase-B (MAO-B) inhibitor to increase dopamine levels in the treatment of Parkinson’s disease (C. to T. P. S. Group, 1990; Kofman, 1993; Parkinson Study Group, 1989, 1993). At usual clinical oral doses of 5-20 mg daily (or 0.05-0.2 mg/kg), deprenyl selectively and irreversibly binds the MAO-B isoform via covalent- binding (Knoll, 1983). At higher dosages, deprenyl can also inhibit MAO-A (Knoll, 1983). However, data from several in vitro and in vivo neuronal models indicates that deprenyl can also block GAPDH S-nitrosylation, GAPDH/Siah1 binding, nuclear translocation of GAPDH, GAPDH- p300/CBP binding, p53 acetylation, and PUMA induction, elicited by dopamine neuronal toxin MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) (M. R. Hara et al., 2006; Nilkantha Sen et al., 2008). Inhibition of GAPDH-Siah1 binding has been achieved in in vitro models with as little as 10-11 M of deprenyl (M. R. Hara et al., 2006). Interestingly, these effects of deprenyl appear to be biphasic in nature. At low concentrations (10-9–10-13 M), deprenyl exerts anti- apoptotic effects without interacting with MAO-B in neuronal cell cultures (Magyar, 2011). But at concentrations higher than 10-7 M, deprenyl treatment becomes increasingly associated with pro-apoptotic signalling (Magyar, 2011). Deprenyl dosage in animal models, however, has been more variable. Inhibition of GAPDH S-nitrosylation and nuclear translocation has been observed at dosages as low as four intraperitoneal injections of 0.01 mg/kg delivered in 2 h intervals, and as high as a single intraperitoneal injection of 20 mg/kg (M. R. Hara et al., 2006; C. Li, Feng, Wu, & Zhang, 2012). Since intraperitoneal administration typically results in higher circulating concentrations of the drug than oral administration (due to first-pass metabolism) (Magyar, 2011), studies looking to utilize orally administered deprenyl to study the GAPDH-Siah1 pathway should consider dosages higher than 0.01 mg/kg. Furthermore, since the orally active dose in rats is typically ten times higher than that in humans (Knoll, 1983), investigations into the therapeutic potential of deprenyl in preventing

GAPDH/Siah1 signalling at clinically relevant levels in rodent models should consider dosages between 0.5-2 mg/kg, administered daily (i.e. ten times higher than the clinical recommendation for humans). In this study, cell cultures were treated with 10-8–10-7 M deprenyl and mice were orally administered deprenyl at 0.5 mg/kg daily (see Chapter 2).

CGP 3466 is a structurally-related analogue of deprenyl with approximately 100-fold more potent anti-apoptotic properties and without any MAO-A or MAO-B activity (Kragten et al., 1998). Although this compound is no longer under development due to disappointing initial human clinical trials in patients with Parkinson’s Disease and amyotrophic lateral sclerosis (ALS) (P. Waldmeier, Bozyczko-Coyne, Williams, & Vaught, 2006), it was through studying CGP 3466 that the anti-apoptotic actions of deprenyl were determined to be independent of its MAO-B- related function. Further studying of CGP 3466 has also provided some additional insight on likely binding interactions of deprenyl with GAPDH. For example, affinity precipitation and photoaffinity labelling studies using immobilized and photoaffinity-labelled CGP 3466, respectively, found that CGP 3466 binds specifically and tightly to GAPDH (as indicated by a slow dissociation constant) (Kragten et al., 1998). Deprenyl was thus presumed to likely also bind GAPDH directly. The binding site, however, has yet to be determined for either deprenyl or CGP 3466. It is made difficult by the fact that the tetrameric form of GAPDH contains multiple possible binding sites. But it is hypothesized that deprenyl likely binds GAPDH in or proximal to its Rossmann fold, a feature found within the NAD+ binding site of GAPDH that is shared by many NAD- and FAD-binding enzymes (Rossmann, Moras, & Olsen, 1974). This idea is drawn from observations that deprenyl inhibition of MAO-B, a FAD-binding enzyme that also has a Rossmann fold, is mediated by deprenyl forming a covalent bond with the FAD molecule (Kwan, Lewis, Zhou, & Abell, 1995). CGP 3466, however, may or may not bind in or near the NAD+ binding site. NAD+ has been found to inhibit GAPDH-CGP 3466 binding. But, because NAD+ binding to GAPDH involves negative cooperativity (wherein binding of one NAD+ molecule causes conformational changes within GAPDH that decrease its affinity for a second NAD+ in the second monomer (Conway & Koshland, 1968; de Vijlder & Slater, 1968; Douzhenkova, Asryants, & Nagradova, 1988)), it is difficult to ascertain if NAD+ inhibition of GAPDH-CGP 3466 binding is due to it competing with CGP 3466 for the same binding site in GAPDH or is due to it serving as an

39 allosteric negative modulator of GAPDH-CGP 3466 binding (Kragten et al., 1998). At physiologically relevant concentrations, CGP 3466 has not been observed to affect the dehydrogenase activity of GAPDH (Mochizuki, Mori, & Mizuno, 1997), suggesting that it does not bind within the catalytic core of GAPDH.

1.5.4.3. Other experimental techniques Gene-silencing techniques can also be used to knockdown Siah1 or other key downstream players (e.g. p300/CBP or p53) in different cell types and conditions to investigate the relevance of the GAPDH/Siah1 pathway in different disease states. Small interference RNA (siRNA) and short hairpin RNA (shRNA) are both popular and effective methods for reducing Siah1 expression (M. R. Hara et al., 2005; Yego & Mohr, 2010). siRNAs are chemically synthesized double-stranded RNA designed to react with Dicer enzymes to form an RNA-interfering silencing complex (RISC) that targets specific mRNA sequences to mediate their cleavage and destruction (Ichim et al., 2004; Ramaswamy & Slack, 2002). In contrast, shRNAs are synthesized within the nucleus of cells via a vector-based approach. After further processing, they are transported to the cytoplasm and also loaded into a RISC (Cullen, 2005). In cultures of retinal Müller cells, use of siRNA against Siah1 significantly decreased Siah1 mRNA expression, Siah1 protein levels, GAPDH nuclear translocation, as well as high glucose-induced increases in Bax expression and caspase-6 activity—two pro-apoptotic markers (Yego & Mohr, 2010). In HEK293 cells, the use of shRNA against Siah1 abolished N-methyl-D-aspartate (NMDA)-induced neuronal cell death (M. R. Hara et al., 2005).

Another possible technique is to generate mutants lacking specific amino acids or bearing amino acid substitutions in residues that undergo critical post-translational modifications. GAPDH C150S mutants, for example, have been transfected into HEK293 cells to effectively abolish GAPDH S-nitrosylation, GAPDH-Siah1 binding, and further downstream signalling in vitro (Kornberg et al., 2010). Similarly, mutation of residues known to mediate protein-protein interactions can also be used to prevent signal transduction within the GAPDH/Siah1 pathway. GAPDH K227 mutants, for example, have been used to transfect NIH373 cells and inhibit GAPDH- Siah1 binding in vitro (Ventura et al., 2010). Site-directed mutagenesis of plasmids can be

40 achieved via various experimental and commercial approaches, including with Quick Change- Site-Directed Mutagenesis, Stratagene Kit (Ventura et al., 2010).

Figure 1.5: Proposed role of TXNIP in GAPDH/Siah1-mediated apoptosis in DN.

1.5.5. Experimental techniques for the study of TXNIP function 1.5.5.1. In vitro techniques siRNAs are also a popular and common method of knocking down TXNIP expression (Advani et al., 2009; Lorena Perrone, Devi, Hosoya, Terasaki, & Singh, 2009). Advani et al. have shown that preincubation of mesangial cells with TXNIP siRNA can effectively ameliorate the increase in TXNIP mRNA induced by high glucose, whereas preincubation with scrambled siRNA (as control) has no effect (Advani et al., 2009). In contrast, adenoviruses are popular tools for overexpressing TXNIP to study the activation of its downstream targets (Jo, Kim, Park, Kim, & Ahn, 2013). Adenoviruses function by entering into the nucleus of host cells and hijacking their replication machinery to transcribe the engineered genes of interest (Russell, 2000).

1.5.5.2. In vivo techniques The use of deoxyribozyme (DNAzyme) is a popular approach to knocking down TXNIP expression in vivo. DNAzymes are single-stranded, synthetic DNA composed of two binding domains flanking a catalytic core of 15 deoxynucleotides (Santoro & Joyce, 1997). DNAzymes can be designed to bind to TXNIP mRNA near its translation start codon and catalyze its cleavage and degradation, resulting in decreased TXNIP translation (C. Y. R. Tan et al., 2015). DNAzymes can

41 be delivered in many different ways including via intraperitoneal boluses or infusions, intravenous boluses, subcutaneous injections, or direct injections into the target tissue or organ (Pun et al., 2004; Xiang et al., 2005). Fluorescently-labelled DNAzyme can be used to allow determination of localization after administration (Pun et al., 2004; C. Y. R. Tan et al., 2015). It is important to note that several studies have found that when administered systemically, TXNIP DNAzyme is mainly taken up by the renal tubulo-epithelial cells, with little to no uptake of DNAzyme in the glomerular and medullary cells of the kidney (Butler, Stecker, & Bennett, 1997; Rappaport et al., 1995; C. Y. R. Tan et al., 2015). This pattern of uptake may be due to the specific role of renal tubular cells in the reabsorption of glomerular filtrate or forms of endocytosis that permits DNAzyme to enter some cells and not into others. Regardless, this presents a limitation to the use of DNAzyme in studies of DN until the cell-type specific transmembrane passage of these oligonucleotides are better understood and more effective oligonucleotides are engineered.

1.5.5.3. Animal models Several animal models are available for studies of TXNIP function, including the HcB-19 strain, the TXNIP null model, and the TXNIP Cre-Lox strain. HcB-19 mice contain a nonsense mutation in the gene encoding TXNIP, resulting in TXNIP deficiency compared to the C3H matched wildtype controls (Hui et al., 2004). In comparison, in the TXNIP null model, both chromosomal copies of the TXNIP gene are deleted from the whole body. Lastly, the TXNIP Cre-lox strain uses the Cre/lox system to generate tissue-specific knockouts, allowing control over the location and timing of TXNIP gene expression. In a Cre/lox system, loxP flanks chromosomal DNA sequences of the gene of interest and the sequence is spliced with the enzyme Cre recombinase. LoxP- flanked TXNIP (TXNIPfl/fl) can thus be bred with transgenic mice overexpressing Cre recombinase under a tissue-restricted promoter to achieve tissue-specific TXNIP deletion.

In this study, pharmacological inhibitors and siRNAs were selected as the method for protein knockdown to study GAPDH/Siah1 signalling in vitro and the TXNIP null mouse model as the method for studying TXNIP regulation of the GAPDH/Siah1 pathway in vivo.

1.6. Project rationale, hypothesis, and specific aims 1.6.1. Rationale The purpose of this study is to provide a mechanistic view of a novel role of TXNIP in DN and as a result, open up new approaches for targeted drug development for prevention and treatment of diabetes complications. Furthermore, as drugs are already being used and developed for Parkinson’s Disease that specifically targets the GAPDH-Siah1 pathway in cell death by preventing GAPDH-Siah1 binding, our hypothesis, if true, would also support the repurposing of these drugs in the treatment of DN.

1.6.2. Hypothesis We hypothesize that the high glucose-induced upregulation of TXNIP leads to inhibition of Trx, which facilitates GAPDH S-nitrosylation and nuclear translocation, ultimately inducing renal cell apoptosis in DN.

1.6.3. Specific Aims Aim 1: To show that GAPDH-Siah1 pathway is necessary for HG-induced apoptosis. Aim 2: To show that the GAPDH-Siah1 pathway is regulated by the Trx-TXNIP system. Aim 3: To show that pharmacological inhibition of GAPDH-Siah1 binding using (R)-(-)-deprenyl (Selegiline) is protective in DN.

To concurrently address Aims 1 and 2, characteristic features of the GAPDH-Siah1 signalling pathway will be investigated in TXNIP+/+ and TXNIP-/- mesangial cells under normal and high glucose conditions in vitro. Recall that MCs have been well documented to undergo apoptosis in DN, despite overall glomerular mesangial expansion being a feature of DN progression(Lin et al., 2006; Mishra et al., 2005). Since the protocols for primary MC isolation and culturing are well established within our lab, MCs present a suitable model for early investigations of GAPDH-Siah1 signalling in the diabetic kidney. If the results are promising, similar experiments will be performed to investigate Aims 1 and 2 in cultured podocytes. To address Aim 3 and provide further support for Aim 1, the effects of deprenyl-treatment on DN outcomes will be investigated in streptozotocin (STZ)-induced diabetic mice in vivo. Later, CGP 3466 will be used

43 to confirm that the effects of deprenyl-treatment on DN outcomes are through GAPDH-Siah1 signalling blockade rather than MAO-B inhibition.

CHAPTER 2: METHODS

2.1. Glomeruli Isolation and Culturing of Primary Mesangial Cells Kidney glomeruli were isolated with M450 Tosylactivated Dynabeads (Invitrogen), which allows for greater purity than traditional sieving, using a modified version of a protocol previously described by Takemoto et al., 2002. All instruments and solutions used were autoclaved and/or cell-culture grade in order to maintain sterility. To prepare the Dynabeads, 200 μL of the Dynabeads solution, per mouse, was transferred to an Eppendorf tube the day before and attached to the DynaMag-2 magnetic particle concentrator (Invitrogen) to aggregate the Dynabeads and extract the supernatant. The Dynabeads were then washed twice with 1 mL 1X PBS (pH 7.4, without calcium or magnesium) containing 0.1% bovine serum albumin (BSA) by resuspending it in solution, rotating the Eppendorf tube on the magnetic particle concentrator, and removing the supernatant. The Dynabeads were then incubated overnight at 4°C in 0.2 M Tris buffer (pH 8) containing 0.1% BSA while being rotated in the magnetic particle concentrator. The next day, the Dynabeads were washed in 1X PBS containing 0.1% BSA as previously described, followed by a 1 h incubation in 70% ethanol while being rotated on the magnetic particle concentrator, and one last 1X PBS wash. The Dynabeads were finally resuspended in Hank’s Balanced Salt Solution (HBSS; without calcium or magnesium) and stored on ice.

Glomeruli isolation was performing under a laboratory hood. Mice were anaesthetized with isoflurane. Once the surgical plane of anaesthesia was reached, mice were perfused transcardially with 40-80 mL HBSS until the fluid exiting the right atrium ran clear. Then, 200 μL of the Dynabeads solution was dissolved in 40 mL of HBSS and used to further perfuse the mice. The kidneys were then harvested and stored in ice-cold HBSS while being transferred to a clean Class II biosafety cabinet. The kidneys were then dissected on a cell culture dish to obtain the kidney cortex and then further minced until the cortical samples were as small as possible. The kidney cortex homogenate was then aspirated twice through an 18-gauge needle and twice through a 21-gauge needle. The homogenate was then passed through a 100 μm metal sieve followed by a 70 μm metal sieve to filter out tubules. The glomeruli captured by the 70 μm sieve was collected into a new tissue culture dish by inverting the metal sieve and flushing the mesh with HBSS. The glomeruli-enriched mixture was then transferred into a falcon tube, into which 150 μL of collagenase A (1 mg/mL; Roche) was added per kidney. The Falcon tube was then

46 incubated at 37°C for 15 min and centrifuged at 1000 g for 10 min at 4°C. The supernatant was discarded and glomeruli were resuspended in Minimum Essential Medium (MEM) Eagle with L- glutamine and D-valine supplemented for L-valine (USBiological) containing 20 mM 4-(2- hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES; Sigma-Aldrich), 10% dialyzed fetal bovine serum (FBS, Gibco), 100U penicillin (Invitrogen Life Technologies) and 100 μg streptomycin (Invitrogen Life Technologies) and cultured in 25 cm2 cell culture-grade flasks (Corning). The cells were left undisturbed for 72 h and afterwards, the medium was changed every 2-3 days. These conditions preferentially select for mesangial cells since glomerular endothelial and epithelial cells have fastidious growth requirements and need a special culturing medium (Akis & Madaio, 2004; Brennan, Jevnikar, Takei, & Reubin-Kelley, 1990). Fibroblasts, however, have more permissive growing requirements and can pose a potential problem when culturing glomeruli for mesangial cell outgrowths. As such, D-valine is used in place of L-valine to select against fibroblasts. Since mesangial cells contain D amino acid oxidase, they can utilize D-valine and grow out from the isolated glomeruli. Fibroblasts, however, lack this enzyme and are thus prevented from growing in the D-valine modified medium (S. F. Gilbert & Migeon, 1975). Eventually, the glomeruli become fragmented, leaving only mesangial cells. Although dialyzed FBS (a specially formulated FBS containing fewer small molecules such as amino acids, hormones and cytokines) is often used for primary mesangial cell cultures (Foidart, Foidart, & Mahieu, 1980), it appeared to be insufficient to sustain our cells after several propagations. So, 7-10 days after the isolation, the cells were switched to a medium containing 10% non-dialyzed FBS, but otherwise identical to above, and cultured in 75 cm2 cell culture-grade flasks (Corning). Four weeks after the isolation, L-valine was reintroduced and the cells were switched to normal Dulbecco’s Modified Eagle’s Medium containing 5.6 mM D-glucose (DMEM; Sigma-Aldrich), containing 20 mM HEPES (Sigma-Aldrich), 10% non-dialyzed FBS (Thermo Scientific), 100U penicillin (Invitrogen Life Technologies) and 100 μg streptomycin (Invitrogen Life Technologies) for further propagations.

2.2. Cell Culture Mouse mesangial cells cultured from the isolated glomeruli of TXNIP WT and KO mice were used to investigate the effect of the TXNIP gene on the GAPDH/Siah1 pathway in vitro. All cells were

47 cultured as above in DMEM containing 5.6 mM D-glucose, 20 mM HEPES, 10% non-dialyzed FBS, 100U penicillin, and 100 μg streptomycin. Passages 8-15 were used in experiments. Cells for experiments were cultured on 60 x 15mm culture dishes for co-immunoprecipitation and nuclear/cytoplasmic fractionation experiments and 35x10mm culture flasks for all other experiments. At 50-70% confluency, cells were growth arrested in 0.5% FBS containing either 5.6 mM glucose (normal glucose; NG) or 25 mM glucose (high glucose; HG) for various lengths of time (e.g. 12 h, 24 h, 48 h). The total experimental time reflected the longest incubation period (e.g. NG for 48 h; NG for 36 h and HG for 12 h; NG for 24 h and HG for 24 h; or HG for 48 h). Some cells were also pre-incubated for 1 h with 50 nM or 100 nM deprenyl prior to HG treatment.

2.3. Nuclear/Cytoplasmic Fractionation and Extraction The nuclear and cytoplasmic fractions were extracted from cultured cells using the NE-PER Nuclear and Cytoplasmic Extraction Kit (Thermo Scientific), as per manufacturer’s instructions with slight modifications. Namely, after extraction of the cytoplasmic fraction, the pellet containing nuclei was washed once by suspending the pellet in cold 1X PBS to remove any remaining cytoplasmic components. The suspension was then centrifuged at 16,000 g for 5 min at 4°C to compact the pellet. The supernatant was discarded, leaving the pellet as dry as possible. The pellet was then suspended in ice-cold NER reagent and homogenized by passing it through a 26.5-gauge needle 10 times on ice. Following a 40 min incubation on ice, the suspension was centrifuged at 16,000 g for 5 min at 4°C and the supernatant (i.e. the nuclear fraction) was transferred to a new Eppendorf tube. The nuclear and cytoplasmic fractions were frozen at −70°C until further analysis.

2.4. Mice and Metabolic Studies TXNIP−/− mice were used to investigate the effect of TXNIP in GAPDH/Siah1 pathway. In addition, the streptozotocin (STZ)-induced diabetic DBA/2J mouse model, a widely used model of diabetes, was used to study the therapeutic potential of deprenyl to prevent DN.

TxNIP WT (TxNIP+/+) and TxNIP KO (TxNIP−/−) mice were kindly provided by Dr Richard T. Lee (Harvard University) (Yoshioka et al., 2007). These mice are chimaeras of the 129S4/SvJae and C57BL/6 strains, generated from the injection of J1 ES cells (embryonic stem cells from 129S4/SvJae embryos) containing plasmid vectors with loxP sites flanking exon 1 of the TXNIP gene into C57BL/6 blastocysts. Knockouts were achieved by cre-lox recombination, by breeding mice carrying a floxed exon 1 allele with Sox2-cre mice in order to excise exon 1 (Yoshioka et al., 2007). DBA/2J mice were obtained from the Jackson Laboratory. All mice were housed under standard conditions at the Animal Resource Center, University Health Network (UHN), Toronto, ON, Canada and provided a chow diet and water ad libitum. All experiments were approved by the Animal Care Committee of the UHN and were performed according to the guidelines of the Canadian Council of Animal Care.

Diabetes was induced in 8-12 male DBA/2J mice per group at 6–7 weeks of age using a modified version of the Low-Dose Streptozotocin Induction protocol from the Diabetic Complications Consortium (http://www.diacomp.org). Diabetic mice were given five daily intraperitoneal injections of streptozocin (STZ) in citrate buffer (40 mg/kg in fresh 0.1 M sodium citrate buffer, pH 4.5), following a 6 h food and water fast. Non-diabetic control mice received five daily injections of citrate buffer. Blood glucose levels were measured every 4 weeks via a glucometer (FreeStyle Lite, Alameda, CA). Diabetes was defined as persistent hyperglycemia >15 mmol/L four weeks post-injection, which is the waiting period recommended by the Diabetic Complications Consortium to allow the blood glucose levels to stabilize. Afterwards, oral deprenyl treatment was commenced. (R)-(-)-deprenyl (Tocris) was dissolved in lemon-flavored Jello (0.5 mg/kg at 0.34 g/mL). Untreated mice received 0.34 g/mL Jello vehicle as control. Twelve weeks after STZ injections, half of the DBA/2J mice were individually placed in Tecniplast metabolic chambers for 24 h to collect and determine the volume of urine excreted as well as the amount of food and water consumed. The collected urine was centrifuged at 1000 g for 1 min to remove any food debris and then frozen at −70°C for further analysis. The mice were then sacrificed, and renal tissues and blood collected as described below. Twenty weeks after STZ injections, the remaining DBA/2J mice were similarly placed in Tecniplast metabolic chambers, sacrificed, and harvested.

2.5. Blood Profiling and Urinalysis At the time of sacrifice, mice were anaesthetized with isoflurane according to UHN protocol. Samples of whole blood and plasma were collected via cardiac puncture and provided to the Animal Resources Centre of the UHN for comprehensive blood analyses. The VetScan HM5 (Abaxis) machine was used for determinations of red blood cell count (RBC) and white blood cell count (WBC), and the VetScan VS2 (Abaxis) machine was used for determinations of albumin, alkaline phosphatase, alanine aminotransferase, amylase, total bilirubin, blood urea nitrogen (BUN), calcium, phosphorus, creatinine, sodium, potassium, total protein, and globulin levels. Additional whole blood was collected for determinations of blood glucose via a glucometer (FreeStyle Lite, Alameda, CA).

Subsequently, all remaining blood was flushed from the system via transcardiac perfusion with cold 1X phosphate buffered saline (PBS). Finally, the mice were euthanized by cervical dislocation and tissues collected for further analyses. Both kidneys were coronally dissected to yield four samples in total. For histological analyses, one sample was fixed for 48 h in 10% formalin (Fisher Scientific) before being embedded in paraffin, and another sample was frozen in optimum cutting temperature (OCT) compound (Tissue-Tek). In preparation for electron microscopy (EM), the cortical regions of another kidney sample were dissected and fixed in EM fixative containing 1.5% glutaraldehyde and 1% paraformaldehyde. The remaining kidney samples were frozen in liquid nitrogen and transferred to −70°C for longer storage.

Urinary assays for albumin and creatinine (kits from Exocell) and 8-OHdG (StressMarq Biosciences) were performed according to the manufacturer’s instructions. Protein concentration was measured using the DC Protein Assay (Bio-Rad).

2.6. Electron Microscopy Transmission electron microscopy (TEM) was performed on renal cortical tissues from four mice per group (Nanoscale Biomedical Imaging Facility, Hospital for Sick Children). Representative images were taken at x25,000 and analyses performed on images taken x11,500, as the latter

50 allowed for visualization of a longer span of the GBM. Overall, 6-12 images for 3 random glomeruli were analyzed per mouse, using Image J software (National Institutes of Health). The glomerular basement membrane (GBM) thickness was determined in a similar fashion to that described by Taniguchi et al., 2013. An average of 254-278 length measurements was obtained from different GBM sites. Podocyte foot process effacement was quantified, as previously

휋 ∑ 퐺퐵푀 푙푒푛𝑔푡ℎ described by Koop et al., 2003 using the formula 푤̅̅̅̅̅ = × , where ∑ 푠푙𝑖푡푠 is the 퐹푃 4 ∑ 푠푙𝑖푡푠 total number of slits counted, ∑ 퐺퐵푀 푙푒푛𝑔푡ℎ is the GBM length across which the slits were 휋 counted, and corrects for random variations in the orientation in which the foot processes 4 were sectioned. An average GBM length of 185 μm was evaluated per glomerulus.

2.7. Tissue Histology and Immunohistochemistry Paraffin-embedded kidney samples were cut to obtain 3-μm sections. Periodic-acid Schiff (PAS) staining was performed by the Pathology department of the UHN. Masson’s trichrome staining (Sigma-Aldrich) was performed according to the manufacturer’s instructions. Immunohistochemistry staining using antibodies directed against collagen IV (1:2500; Rockland) was performed, as previously described (Taniguchi et al., 2013), using the Super Sensitive Polymer-HRP IHC Detection System/DAB kit (BioGenex). Slides were digitized using the Aperio AT Turbo bright field scanner (Leica). The percentage of positive staining over the total glomerular area was analyzed for 30-50 glomeruli per kidney section in Aperio ImageScope (Leica), using the Aperio Positive Pixel Count v9 algorithm (Leica), with the parameters as determined by Farris et al., 2011. The hue values used, which defines the color for positive staining (e.g. Red = 0.0, Green = 0.33, Blue = 0.66, Brown = 0.1), were 0.64 (trichrome), 0.854 (PAS), and 0.1 (all IHC). The hue widths used, which determines the range of hues acceptable for a positive detection (with 0 = narrowest hue width and 1 = the entire hue range), were 0.5 (trichrome), 0.035 (PAS), and 0.5 (IHC). In addition, to account for background PAS-positive staining, 10 extra-glomerular areas were assessed per kidney section and the average positive staining over area quantified was subtracted from the glomerular quantification in order to specifically analyze glomerular basement membrane PAS-positivity.

2.8. Western Blotting To obtain total cell lysates, mesangial cells were washed thrice in ice-cold PBS and then incubated on ice for at least 20 min in a lysis buffer containing 10 mM Tris, pH 7.4, 100 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton-X, 0.5% sodium deoxycholate, 0.1% SDS, 1 mM NaF, 2 mM

Na3VO4, 20 mM Na4P2O7, 5 mM β-glycerophosphate and a complete protease inhibitor cocktail (Roche). The cells were then scraped from the dishes. The cell suspensions were transferred into a clean Eppendorf tube and homogenized by passing it through a 26.5-gauge needle 10 times on ice. Samples were stored at −70°C until further analysis. In preparation for western blotting, the protein concentration for each sample was measured using the DC Protein Assay (Bio-Rad).

Samples were diluted with autoclaved ddH2O to the same concentration, mixed in a 3:1 ratio with 4X Laemmli sample buffer containing 0.006 % bromophenol blue and 10% 2- mercaptoethanol, boiled at 100oC for 5 min, centrifuged at 1000 g for 1 min, and finally vortexed. For immunoblotting, equal amounts of protein (ranging from 7-20 μg depending on the experiment) were separated by SDS-PAGE (ranging from 7.5%-10%) and transferred onto methanol-activated polyvinylidene difluoride (PVDF) membranes or nitrocellulose membranes. The membranes were blocked with 5% milk/Tris-buffered saline with 0.1% Tween 20 for non- phosphorylated proteins (TTBS), or 5% bovine serum albumin (BSA) in TTBS for phosphorylated proteins, as described (Anu Shah et al., 2013). The following primary antibodies were used, diluted in the appropriate blocking solution: TXNIP (1:1000; MBL), GAPDH (1:2000; Sigma- Aldrich), GAPDH (1:2000; Santa Cruz Biotechnology), Siah1 (1:1000; Proteintech), Siah1 (1:1000; Origene), LaminB1 (1:1000; Abcam), β-tubulin (1:1000; Santa Cruz Biotechnology), and β-actin (1:3000; Santa Cruz Biotechnology). The secondary antibodies used were anti-rabbit IgG HRP conjugate (1:4000; Bio-Rad) and Peroxidase-conjugated anti-mouse IgG (1:10,000; Jackson Immuno Research Lab). Immunocomplexes were visualized by the enhanced chemiluminescence detection kit (KPL Mandel Scientific) and densitometric analyses performed using Image J software (National Institutes of Health).

2.9. Statistical Analyses Results are presented as mean ± standard deviation (SD) unless otherwise stated. For comparisons of two groups, unpaired student t-tests were used for statistical analyses. For

52 comparisons of three or more groups, statistical significance was calculated by one-way ANOVA, followed by the Newman-Keuls post hoc method for multiple comparisons, using GraphPad Prism software, version 7.00 (GraphPad Prism, San Diego, CA). A P-value of 0.05 was used as the arbitrary threshold, with P-values <0.05 considered a statistically significant difference.

CHAPTER 3: RESULTS

3.1. TXNIP and the GAPDH/Siah1 Pathway 3.1.1. TXNIP, GAPDH, and Siah1 protein levels in total cell lysates High glucose (HG; 25 mM) induced TXNIP upregulation in WT mouse mesangial cells (MCs) but not TXNIP KO mouse MCs at all three time points (i.e. 12 h, 24 h, and 48 h incubation; Figure 3.1 A&B). GAPDH and Siah1 proteins level were unaffected by HG treatment in WT and KO MCs but interestingly, tended to be higher (p=n.s.) in KO MCs than WT MCs (Figure 3.1 C–F).

Figure 3.1: High glucose-induced TXNIP upregulation in WT mouse MCs but not TXNIP KO MCs. Total GAPDH and Siah1 protein levels were also elevated in KOs as compared to WT MCs. Representative western blots of total cell lysates of WT and KO MCs treated with normal glucose (NG; 5.6 mM), or high glucose (HG; 25 mM) for 12 h (H12), 24 h (H24), or 48 h (H48), blotted with primary antibodies against TXNIP (A), GAPDH (C), Siah1 (E), and the loading control β-actin. Corresponding quantitative analysis of total TXNIP (B), GAPDH (D), and Siah1 (F) protein levels. Results are expressed as means ± standard deviation (SD), n=1-2/condition. Currently working to increase the n number.

3.1.2. GAPDH and Siah1 nuclear translocation HG-induced TXNIP upregulation at 24 h and 48 h is associated with increased nuclear content of GAPDH and Siah1 in WT mouse MCs without changes in total cell levels of GAPDH and Siah1, consistent with increased nuclear translocation of the two proteins (Figure 3.2 A,D,E). This HG- dependent effect is absent in TXNIP KO MCs. It may be noted that there tended (p=n.s.) to be higher levels of nuclear GAPDH and Siah1 in KO MCs in the basal NG state (Figure 3.1 A,D,E), possibly owing to increases in overall protein expression in the KO MCs (Figure 3.2 C&D) due to adaptation. We could not detect significant changes in cytosolic GAPDH and Siah1 protein levels by HG, and these results were comparable between WT and KO MCs (Figure 3.2 A–C). This is consistent with a small pool of total GAPDH and Siah1 that are localized to the nucleus. Due to technical problems in finding a working cytosolic loading control, we cannot eliminate the possibility of contamination of cytoplasmic proteins within the nuclear fractions, owing to the observation of nuclear GAPDH and Siah1 in the basal state. However, the extraction kit used

(Thermo Scientific) generally allows for extraction with less than 10% contamination between compartments, which is sufficient purity for most experiments.

Figure 3.2: High glucose-induced GAPDH and Siah1 nuclear translocation in WT mouse MCs but not KO MCs. Representative western blots of cytoplasmic and nuclear fractions from WT and KO MCs treated with normal glucose (NG; 5.6 mM), or high glucose (HG; 25 mM) for 24 h (H24) or 48 h (H48), blotted with primary antibodies against GAPDH, Siah1, and the nuclear loading control Lamin B1 (A). Corresponding quantitative analysis of cytoplasmic GAPDH (B) and Siah1 (C), as well as nuclear GAPDH (D) and Siah1 (E) protein levels. Results are expressed as means ± SD, n=2/condition. Currently working to increase the n number. *P<0.05 48 h HG treatment versus NG treatment in WT mouse MCs; **P<0.005 48 h HG treatment versus NG treatment in

WT mouse MCs.

3.1.3. Caspase-3 cleavage Caspase-3 cleavage was significantly elevated in WT mouse MCs after 12 h of HG treatment as compared to the basal state (Figure 3.3). Caspase-3 cleavage also tended to be higher in WT MCs after both 24 h and 48 h of HG treatment than the basal state, but this did not reach statistical significance (p=n.s.). HG-induced GAPDH/Siah1 nuclear translocation in conjunction (Figure 3.2) with caspase-3 activation (Figure 3.3) is consistent with GAPDH-Siah1 pathway signalling. In TXNIP KO MCs, the loss of an HG-dependent effect on GAPDH and Siah1 nuclear translocation (Figure 3.2) was associated with a loss of HG-dependent caspase-3 activation (Figure 3.3), suggesting that HG-induced GAPDH/Siah1 signalling is mediated by TXNIP. However, both caspase-3 cleavage and nuclear GAPDH and Siah1 were observed to be higher in KO MCs in the basal state as compared to WT MCs, suggesting TXNIP-independent GAPDH-Siah1 signalling may partially be engaged in KO MCs at the basal state.

Figure 3.3: High glucose-induced caspase-3 cleavage in WT mouse MCs but not TXNIP KO MCs. Representative western blots of total cell lysates of WT and KO MCs treated with normal glucose (NG; 5.6 mM), or high glucose (HG; 25 mM) for 12 h (H12), 24 h (H24), or 48 h (H48), blotted with a primary antibody that detects both the uncleaved 35 kDA caspase-3 and cleaved 17 kDA caspase-3 (A). Corresponding quantitative analysis of cleaved caspase-3 normalized to uncleaved caspase-3 (B). Results are expressed as means ± SD, n=5/condition. *P<0.05 12 h HG treatment versus NG treatment in WT mouse MCs.

3.2. Effects of deprenyl on nephropathy in STZ-induced diabetic mice 3.2.1. Metabolic profiles of the DBA/2J mice Diabetes was induced by intraperitoneal administration of STZ to male DBA/2J mice as described in Methods. Nondiabetic (n=31) and diabetic (n=35) mice were divided equally into untreated

58 and orally-treated deprenyl (0.5 mg/kg) groups for the 12-week (n=8 untreated nondiabetics, n=8 treated nondiabetics, n=8 untreated diabetics, n=9 treated diabetics) and 20-week time point (n=7 untreated nondiabetics, n=8 treated nondiabetics, n=9 untreated diabetics, n=9 treated diabetics). STZ induced comparable levels of hyperglycemia, as assessed by measurements of blood glucose concentrations in untreated and treated mice 12- and 20-weeks post STZ-administration (Table 1.1 & 1.2). In addition, diabetic mice had significantly lower body weights than nondiabetic DBA/2J mice at both 12- and 20-week time point. The left and right kidneys of diabetic mice were also significantly smaller than that of nondiabetic mice at both 12 and 20 weeks. Mild or early diabetes may cause increases in kidney weights (J. Ross & Goldman, 1971), but the decrease observed in our mice is likely attributable to the significant overall weight loss experienced by these mice. Diabetic mice also exhibited significantly higher 24 h urine volumes, water intake, and food consumption than nondiabetic mice at both 12 and 20 weeks. Deprenyl treatment had no significant effects on any of the metabolic parameters.

Metabolic Profile DBA/2J (untreated) DBA/2J (Deprenyl-treated)

Nondiabetic Diabetic Nondiabetic Diabetic

Blood glucose 7.1±0.9 24.5±6.5a 6.8±1.0 29.4±4.8b (mmol/L)

Body weight (BW; g) 31.4±2.6 18.5±2.1a,b 29.4±2.5 17.7±1.5a,b

Left kidney weight 0.365±0.055 0.238±0.040a,b 0.370±0.034 0.242±0.041a,b (LKW; g)

Right kidney weight 0.365±0.051 0.245±0.039a,b 0.375±0.037 0.252±0.045a,b (RKW; g)

LKW/BW (%) 1.16±0.27 1.29±0.36 1.26±0.22 1.37±0.35

RKW/BW (%) 1.16±0.26 1.32±0.36 1.28±0.23 1.42±0.37

Urine volume (mL) 0.8220±0.7909 12.3800±4.9290c,d 0.6432±0.5126 10.2700±7.2620e,f

Water intake (mL) 4.5±1.5 20.0±8.5b,c 4.0±1.8 17.1±8.6e,f

Food consumed (g) 0.8±1.0 5.7±1.8a,b 0.8±0.9 5.0±2.3g,d

Table 1.1: Metabolic profiles of DBA/2J mice in the 12-wk experiment. Blood glucose levels, body weights, wet left kidney weight, and wet right kidney weights were obtained at harvest. 24 hr urine volumes, water intake, and food consumption were obtained by placing mice in metabolic cages a week prior to harvest. Results are expressed as means ± standard deviation (SD), n=8 mice/condition. aP<0.0001 versus untreated nondiabetic mice, bP<0.0001 versus deprenyl- treated nondiabetic mice, cP<0.001 versus untreated nondiabetic mice, dP<0.001 versus deprenyl-treated nondiabetic mice, eP<0.01 versus untreated nondiabetic mice, fP<0.01 versus deprenyl-treated nondiabetic mice, gP<0.05 versus untreated nondiabetic mice, hP<0.05 versus deprenyl-treated nondiabetic mice.

Metabolic Profile DBA/2J (untreated) DBA/2J (Deprenyl-treated)

Nondiabetic Diabetic Nondiabetic Diabetic

Blood glucose 10.6±1.8 31.2±5.2a,b 10.6±1.8 32.0±3.9a,b (mmol/L)

Body weight (BW; g) 30.5±1.0 17.4±1.9a,b 29.8±3.0 17.7±2.0a,b

Left kidney weight 0.337±0.047 0.272±0.065b,g 0.337±0.047 0.266±0.031b,g (LKW; g)

Right kidney weight 0.365±0.037 0.259±0.049b,c 0.416±0.051 0.274±0.025b,c (RKW; g)

LKW/BW (%) 1.10±0.19 1.56±0.54 1.13±0.27 1.50±0.34

RKW/BW (%) 1.20±0.16 1.49±0.44 1.40±0.31 1.55±0.32

Urine volume (mL) 0.4649±0.4426 27.0500±15.3700d,e 1.0000±0.4380 30.0500±17.010 0c,d

Water intake (mL) 2.5±1.9 34.4±20.9d,e 3.8±1.7 38.6±21.6b,c

Food consumed (g) 4.9±4.3 8.5±7.8d 0.6±0.9 9.3±6.1d Table 1.2: Metabolic profiles of DBA/2J mice in the 20-wk experiment. Results are expressed as means ± SD, n=7-11 mice/condition. aP<0.0001 versus untreated nondiabetic mice, bP<0.0001 versus deprenyl-treated nondiabetic mice, cP<0.001 versus untreated nondiabetic mice, dP<0.001 versus deprenyl-treated nondiabetic mice, eP<0.01 versus untreated nondiabetic mice, fP<0.01 versus deprenyl-treated nondiabetic mice, gP<0.05 versus untreated nondiabetic mice, hP<0.05 versus deprenyl-treated nondiabetic mice.

Comprehensive analyses of whole blood and plasma, 12 weeks after STZ-induction, revealed that diabetes was associated with significant increases in the levels of alkaline phosphatase (ALP), alanine aminotransferase (ALT), and blood urea nitrogen (BUN) (without apparent increases in creatinine levels1) in untreated and deprenyl-treated mice. Increases in total ALP

1 These measures of creatinine may not be accurate as they were not performed via HPLC. We are in the process of sending serum samples away for more accurate analyses.

60 activity is reported in patients with diabetes and has been strongly associated with the risk of adverse outcomes in patients with kidney failure (Blayney et al., 2008; Regidor et al., 2008). ALT is considered to be an indicator of hepatocellular damage. High levels of ALT is associated with fatty liver and/or hepatic inflammation (Rector, Thyfault, Wei, & Ibdah, 2008), and therefore suggest liver dysfunction in these mice. Elevated liver ALT has previously been reported in STZ- induced diabetic male albino rats (Zafar, Naeem-ul-Hassan Naqvi, Ahmed, & Kaimkhani, 2009). BUN, in contrast, is a marker of increased protein catabolism, volume contraction and/or kidney dysfunction, with elevated levels of BUN being correlated with increased mortality in patients with normal creatinine levels (Beier et al., 2011). Diabetes also caused a slight but significant decrease in sodium levels in untreated mice, which is consistent with the literature (de Châtel et al., 1977). Diabetes also appeared to induce slight elevations in phosphorus levels but did not reach statistical significance. Twelve weeks of deprenyl treatment appeared to protect against these modest elevations of phosphorus in diabetic mice.

Blood Profile DBA/2J (untreated) DBA/2J (Deprenyl-treated) Nondiabetic Diabetic Nondiabetic Diabetic RBC (109 cells/L) 11.62±0.43 12.20±1.03 11.44±0.93 12.58±0.62 Albumin (g/L) 31±3 25±4 31±2 29±2 Alkaline Phosphatase (ALP; U/L) 65±29 266±44b 80±10 294±71c Alanine aminotransferase (ALT; U/L) 21±12 112±43b 29±5 91±16a Amylase (U/L) 764±131 679±85 776±126 597±360 Total bilirubin (μmol/L) 6±0 6±1 6±0 6±1 Blood urea nitrogen (BUN; mmol/L) 2.6±0.1 8.2±3.3d 2.5±0.1 11.2±2.5c Calcium (mmol/L) 2.57±0.06 2.44±0.17 2.46±0.06 2.37±0.11 Phosphorus (mmol/L) 2.47±0.34 2.94±0.36 2.14±0.44 2.47±0.34e Creatinine (μmol/L) 18±0 18±0 18±0 18±0 Sodium (mmol/L) 152±2 146±3d 151±2. 151±2 Potassium (mmol/L) 6.6±1.4 6.2±0.8 6.1±0.8 6.1±0.3 Total protein (g/L) 54±5 44±7 52±4 48±6 Globulin (g/L) 24±5 20±3 210±3 20±4

Table 2.1: Blood profiles of DBA/2J mice in the 12-wk experiment. Comprehensive analyses of whole blood and plasma were performed by the Animal Resources Centre at the University Health Network. Blood creatinine levels were not measured via HPLC. Results are expressed as means ± SD, n=4 mice/condition. aP<0.05 versus deprenyl-treated nondiabetic mice, bP<0.001 versus untreated nondiabetic mice, cP<0.001 versus deprenyl-treated nondiabetic mice, dP<0.05 versus untreated nondiabetic mice, eP<0.05 versus untreated diabetic mice.

Twenty weeks after STZ-induction, RBC counts and WBC counts were comparable between nondiabetic and diabetic groups (Table 2.2). Diabetic mice with and without deprenyl treatment still demonstrated elevations in levels of ALP and ALT. However, the increase in ALT by diabetes in the untreated mice was no longer significant, possibly due to insufficient sample size. In addition, untreated and deprenyl-treated diabetic mice now had significantly lower levels of albumin and calcium than their respective nondiabetic controls. Total protein levels were also slightly decreased by diabetes in the untreated mice and significantly decreased in the deprenyl- treated mice. Low plasma albumin, calcium, and total protein levels may be attributed to uncontrolled diabetes, malnutrition, and weight loss, or renal dysfunction and may be suggestive of worsening nephropathy (Levey et al., 2005; Levin et al., 2007). Interestingly, both nondiabetic and diabetic deprenyl-treated mice were found to have significantly higher levels of phosphorus as compared to untreated treated nondiabetic mice. This finding suggests that deprenyl may promote increases in circulating levels of phosphorus, which has not been reported in the literature. A larger sample size and further investigations are required to ascertain if this effect is reproducible and significant.

Blood Profile DBA/2J (untreated) DBA/2J (Deprenyl-treated) Nondiabetic Diabetic Nondiabetic Diabetic RBC (109 cells/L) 9.26±0.45 8.44±1.76 9.17±0.23 8.38±0.61 Albumin (g/L) 30±4 20±5a 32±2.2 17±4b Alkaline Phosphatase (ALP; U/L) 80±12 252±58c 65±12 316±68d Alanine aminotransferase (ALT; U/L) 31±5 77±7 29±5 118±56e Amylase (U/L) 766±91 666±123 751±41 588±65 Total bilirubin (μmol/L) 6±0 5±1 5±1 4±1 Blood urea nitrogen (BUN; mmol/L) 8.3±1.3 16.4 8.7±2.1 13.0±2.3 Calcium (mmol/L) 2.39±0.13 2.06±0.22a 2.39±0.09 2.01±0.13h Phosphorus (mmol/L) 2.05±0.38 3.26±0.44 3.67±0.38a 3.74±1.04a Creatinine (μmol/L) 24±7 24±8 24±7 19±5 Sodium (mmol/L) 147±4 145±4 150±3 146±3 Potassium (mmol/L) 6.0±0.2 6.5±2.3 5.2±0.8 6.0±0.6 Total protein (g/L) 45±3 39±2 46±2 36±3b Globulin (g/L) 15±1 19±5 15±2 19±3 Table 2.2: Blood profiles of DBA/2J mice in the 20-wk experiment. Blood creatinine levels were not measured via HPLC. Results are expressed as means ± SD, n=4 mice/condition. aP<0.05 versus untreated nondiabetic mice, bP<0.001 versus deprenyl-treated nondiabetic mice, cP<0.01 versus untreated nondiabetic mice, dP<0.0001 versus deprenyl-treated nondiabetic mice, eP<0.01 versus deprenyl-treated nondiabetic mice.

3.2.2. Histological Analyses 3.2.2.1. Mesangial Matrix Expansion A Periodic Acid-Schiff (PAS) stain was performed to investigate mesangial matrix expansion. Glycoproteins are stained pink in a PAS stain. Diabetes was found to induce glomerular mesangial matrix expansion in DBA/2J mice at both the 12-week and 20-week time point (Figure 3.4). Deprenyl treatment had no effect on PAS staining on nondiabetic mice but protected diabetic DBA/2J mice from this increase and maintained mesangial matrix glycoproteins at levels comparable to nondiabetic controls at both time points.

Figure 3.4: Deprenyl treatment protected diabetic DBA/2J mice from mesangial matrix expansion. Representative images of mesangial matrix expansion assessed by Periodic-acid Schiff (PAS) staining of DBA/2J mice in the 12-wk experiment (A) and 20-wk experiment (B). Quantification of glomerular staining, expressed as fold change relative to untreated nondiabetic mice, was performed with Aperio ImageScope software (Leica), using the Aperio Positive Pixel Count v9 algorithm (Leica), for the 12-wk experiment (C) and 20-wk experiment (D). Results are mean ± SD, n=7-9 mice/condition, 30-50 glomeruli analyzed per mouse. *P<0.05 untreated diabetic mice versus untreated nondiabetic control; **P<0.005 untreated diabetic mice versus untreated nondiabetic control; ##P<0.01 deprenyl-treated diabetic mice versus untreated diabetic mice; ###P<0.005 deprenyl-treated diabetic mice versus untreated diabetic mice.

3.2.2.2. Glomerular Collagen IV Accumulation Immunohistochemistry (IHC) was performed with antibodies against collagen IV, to investigate collagen IV accumulation. Collagen IV is a mesangial matrix constituent synthesized by mesangial cells. Increased mesangial cell production of collagen IV is a contributing factor to mesangial matrix expansion in diabetes (Haneda et al., 1991). Collagen IV production, as expected, was found to be increased in untreated diabetic DBA/2J mice in the 20-wk experiment (Figure 3.5). Deprenyl-treatment protected diabetic DBA/2J mice from this increase and maintained mesangial matrix collagen IV at levels comparable to nondiabetic controls. (Staining is still in progress for the 12-wk time point.)

Figure 3.5: Deprenyl treatment protected diabetic DBA/2J mice from increases in collagen IV production. Representative images of collagen IV production assessed by immunohistochemistry (IHC) staining of DBA/2J mice in the 20-wk experiment (A). Quantification of glomerular staining, expressed as fold change relative to untreated nondiabetic mice, was performed with Aperio ImageScope software (Leica), using the Aperio Positive Pixel Count v9 algorithm (Leica), for the 20-wk experiment (B). Results are mean ± SD, n=6 mice/condition, 30-50 glomeruli analyzed per mouse. **P<0.005 untreated diabetic mice versus untreated nondiabetic control; #P<0.05 Deprenyl-treated diabetic mice versus untreated diabetic mice.

3.2.2.3. Glomerular Fibrosis A Masson’s trichrome stain was performed to investigate glomerular fibrosis (collagen detected by blue colour). Quantification of images is still in progress. However, a qualitative assessment of approximately 40 glomeruli per mouse found that diabetes appeared to induce glomerular fibrosis in DBA/2J mice at both the 12-week and 20-week time point (Figure 3.6). Deprenyl treatment also appears to have protected diabetic DBA/2J mice from this increase at both time points.

Figure 3.6: Deprenyl treatment protected diabetic DBA/2J mice from glomerulosclerosis. Representative images of glomerulosclerosis assessed by Masson’s trichrome staining (blue colour) of DBA/2J mice in the 12-wk experiment (A) and 20-wk experiment (B). Quantification in progress for n=7-9 mice/condition.

3.2.2.4. Glomerular Basement Membrane Thickening & Podocyte Effacement Glomerular structures were visualized via transmission electron microscopy (TEM). Diabetes was found to cause significant increases in the thickness of the glomerular basement membrane (GBM) and podocyte foot process effacement in DBA/2J mice at the 12-week and 20-week time point (Figure 3.7). Deprenyl treatment alleviated both diabetes-induced GBM thickening and podocyte foot process effacement in both experiments.

Figure 3.7: Deprenyl treatment protected diabetic DBA/2J mice from glomerular basement membrane thickening and podocyte foot process effacement. Representative electron microscopic images (magnification ×25,000) of glomeruli from control and diabetic DBA/2J mice with or without deprenyl treatment in the 12-wk experiment (A) and 20-wk experiment (B). Arrows indicate slit pores. Asterisks indicate podocyte foot processes (FP). GBM thickness and podocyte foot process effacement were quantified for the 12-wk experiment (C & E) and 20-wk experiment (D & F) from 9-12 images of 3 random glomeruli per mouse, at a magnification of x11,500 (n = 4 mice/group). GBM thickness was determined from 254-278 measurements obtained from different GBM sites, following the protocol of Taniguchi et al., 2013. Podocyte foot process effacement was quantified as previously described by Koop et al., 2003. Results are mean ± SD. **P<0.005 untreated diabetic mice versus untreated nondiabetic control; #P<0.05 deprenyl-treated diabetic mice versus untreated diabetic mice.

3.2.3. Functional Analyses 3.2.3.1. Proteinuria Diabetes caused significant increases in 24 h proteinuria in both the 12-week and 20-week experiments (Figure 3.8). Twenty-four-hour protein excretion was more severe in the untreated diabetic mice of the 12-wk experiment (406.4 ± 118.5 ng) than the 20-wk experiment (70.0 ± 8.85 ng), possibly due to increased hyperperfusion and hyperfiltration in the earlier stages of DN. Deprenyl treatment significantly attenuated diabetes-induced increases in proteinuria at the 12-week time point but proteinuria was not normalized to nondiabetic levels. Deprenyl treatment had no significant effect at the 20-week time point. It was noted that the decreased protein excretion at 20 weeks of diabetes was associated with increased water intake, urine volumes, and elevated BUN in the diabetic mice (Tables 1.1-1.2&2.1-2.2), indicating volume contraction. This could contribute to decreased glomerular filtration rate (GFR) and decreased proteinuria. This was somewhat more pronounced in the untreated diabetic mice (BUN = 16.4) versus the deprenyl-treated mice (BUN = 13.0) at 20 weeks. This might have contributed to a larger decrease in proteinuria in the untreated diabetic group. Alternatively, deprenyl may lose effectiveness against renal hemodynamic alterations over time.

Figure 3.8: Deprenyl treatment protected diabetic DBA/2J mice from increases in proteinuria in the 12-wk experiment but not the 20-wk experiment. Urinary protein levels of mice in the 12- wk experiment (A) and 20-wk experiment (B). 24hr proteinuria was determined using the DC

Protein Assay (Bio-Rad). Results are expressed as means ± SD, (n=5-6 mice/condition). *P<0.05 deprenyl-treated diabetic mice versus untreated nondiabetic mice; ***P<0.0005 untreated/deprenyl-treated diabetic mice versus untreated nondiabetic mice; #P<0.05 deprenyl- treated diabetic mice versus untreated diabetic mice.

3.2.3.2. Albuminuria Diabetes caused significant increases in 24 h urinary albumin excretion (UAE) and albumin-to- creatinine ratios in both the 12-week and 20-week experiments (Figure 3.9). Note that although high variation in UAE is observed in the diabetic groups, a Grubb’s statistical test for outliers indicates that there are no outliers in this group. Similar to the proteinuria experiments, 24 h UAE and urinary albumin-to-creatinine ratios were also more severe in the untreated diabetic mice of the 12-wk experiment (515.30 ± 614.50 µg; 168 ± 32 µg/mg) than the 20-wk experiment (61.22 ± 41.83 µg; 121 ± 43 µg/mg). Deprenyl treatment protected diabetic DBA/2J mice against the diabetes-induced increases in UAE and urinary albumin-to-creatine ratios in the 12-week experiment but, similar to the proteinuria results, had no effect in the 20-week experiment. The data suggest that deprenyl is only effective at attenuating diabetes-induced albuminuria during the earlier stages of DN. However, the confounding issue of volume contraction should be considered. An additional consideration is that long-term deprenyl treatment may confer some renal toxicity, since the 24 h UAE for deprenyl-treated nondiabetic mice, although not significant (p=n.s.), appears to nearly double at the 20-wk time point (13.38 ± 14.33 µg untreated versus 22.63 ± 18.75 µg treated), but not at the 12-wk time point (48.08 ± 60.78 µg untreated versus 45.56 ± 36.99 µg treated). Similar trends (p = n.s.) were also observed for urinary albumin-to- creatinine ratios at the 20-wk time point (121 ± 43 µg/mg untreated versus 215 ± 93 µg/mg treated fold change), which were absent at the 12-wk time point (168 ± 92 µg/mg untreated versus 184 ± 57 µg/mg treated). There are few data in the literature on the effect of long-term deprenyl use on kidney outcomes (see Discussion).

Figure 3.9: Deprenyl treatment protected diabetic DBA/2J mice from increases in urinary albumin excretion (UAE) and urinary albumin-to-creatinine ratios in the 12-wk experiment but not the 20-wk experiment. UAE and urinary albumin-to-creatine ratios of DBA/2J mice in the 12- wk experiment (A,C) and 20-wk experiment (B,D). Urinary albumin/creatinine ratios were determined by normalizing 24 h UAE to 24 h creatine excretion. Urinary albumin and creatine levels were measured via an ELISA kit (Exocell). Untreated and deprenyl-treated nondiabetic mice groups were similar (p=n.s.) and were thus pooled together for statistical analyses. Results are expressed as means ± SD, (n=5-9 mice/condition). *P<0.05 untreated/deprenyl-treated diabetic mice versus nondiabetic mice (untreated and deprenyl-treated).

3.2.4. Oxidative Stress 3.2.4.1. Glomerular Nox4 Upregulation IHC was performed with antibodies against Nox4, to investigate Nox4-dependent ROS generation. Nox4 is a member of the Nox family of NADPH oxidases but is unique in that, unlike other Nox enzymes, Nox4 is constitutively active and does not require activator or organizer

70 cytoplasmic subunits (Bedard & Krause, 2007). Research shows that Nox4 induction leads to spontaneous ROS release, without the need for an additional stimulus (Serrander et al., 2007). Due to the strong correlation between Nox4 mRNA and ROS generation (Serrander et al., 2007), Nox4 expression can often be used as a marker of Nox4-dependent ROS generation. Glomerular Nox4 expression, as expected, was found to be increased in untreated diabetic DBA/2J mice in the 12-wk experiment (Figure 3.10). Deprenyl-treatment protected diabetic DBA/2J mice from this increase and maintained Nox4 at levels comparable to nondiabetic controls. (Staining is still in progress for the 20-wk time point.)

Figure 3.10: Deprenyl treatment protected diabetic DBA/2J mice from inreases in Nox4 expression. Representative images of Nox4 expression assessed by IHC staining of DBA/2J mice in the 12-wk experiment (A). Quantification of glomerular staining, expressed as fold change relative to untreated nondiabetic mice, was performed with Aperio ImageScope software (Leica), using the Aperio Positive Pixel Count v9 algorithm (Leica), for the 12-wk experiment (B). Results are mean ± SD, n=4-6 mice/condition, 30-50 glomeruli analyzed per mouse. ****P<0.0001 untreated diabetic mice versus untreated nondiabetic control; ####P<0.0001 Deprenyl-treated diabetic mice versus untreated diabetic mice.

3.2.4.2. 8-hydroxy-2’-deoxyguanosine 8-hydroxy-2’-deoxyguanosine (8-OHdG) is produced when ROS and RNS cause oxidative damage to DNA. As a result, it is often used as a marker of oxidative stress. Urinary 8-OHdG has been found to represent oxidative stress in the kidney (Kakimoto et al., 2002; Loft, Fischer-Nielsen, Jeding, Vistisen, & Poulsen, 1993; Shigenaga, Gimeno, & Ames, 1989). Diabetes was found to be associated with significantly elevated levels

71 of oxidative stress in both the 12-wk and 20-wk time points (Figure 3.11). Urinary 8-OHdG was higher in the untreated diabetic mice of the 20-wk (20.07 ± 3.02 μg) than the 12-wk time point (12.95 ± 3.09 μg), suggesting oxidative stress increases with the progression of DN. Deprenyl treatment had no effect at either time point. This may be expected since ROS/RNS, sources of S- nitrosylation, may be largely upstream of the GAPDH-Siah1 signalling pathway and so inhibition of GAPDH-Siah1 binding via deprenyl may not have an effect on the total cell redox environment.

Figure 3.11: Deprenyl treatment had no effect on urinary 8-OHdG levels. Urinary 8-OHdG levels of mice in the 12-wk experiment (A) and 20-wk experiment (B). 24hr 8-OHdG was determined using a kit from StressMarq. Results are expressed as means ± SE, (n=5-7 mice/condition). **P<0.005 deprenyl-treated diabetic mice versus untreated nondiabetic mice; ***P<0.0005 untreated diabetic mice versus untreated nondiabetic mice; ****P<0.00005 untreated/deprenyl- treated diabetic mice versus untreated nondiabetic mice.

CHAPTER 4: DISCUSSION, CONCLUSION, FUTURE DIRECTIONS

4.1. Summary of results TXNIP upregulation by high glucose (HG) has been associated with pathological signalling in DN, especially in relation to oxidative stress. However, the mechanism of its action remains unknown. In Chapter 3, we investigate the role of TXNIP in HG-induced GAPDH-Siah1 signalling. We demonstrated in TXNIP WT mesangial cells (MCs) cultured in HG conditions and in STZ- induced DBA/2J mice that HG and diabetes activate the GAPDH-Siah1 pathway. In WT mouse MCs, HG induced TXNIP upregulation after 12 h, 24 h, and 48 h of exposure (Figure 3.1 A&B), increased localization of GAPDH and Siah1 in nuclear fractions after 24 h and 48 h of exposure (Figure 3.2 A–E; 12 h HG treatment not investigated), and caspase-3 cleavage after 12 h (p<0.05), 24 h (p=0.08), and 48 h (p=n.s.) of exposure (Figure 3.3). Both HG-induced TXNIP upregulation, increases in nuclear GAPDH and Siah1, and caspase-3 cleavage were absent in TXNIP KO MCs, suggesting that TXNIP is involved in the HG-induced GAPDH and Siah1 signalling (Figure 3.1 A&B, Figure 3.2 A–E, Figure 3.3). Interestingly, HG treatment did not have an effect on GAPDH and Siah1 total protein levels, but both tended to be higher (p=n.s.) in TXNIP KO MCs (Figure 3.1 C– F). This may be the result of increased Trx activity in TXNIP KO MCs due to the loss of TXNIP- dependent inhibition of Trx. Data from cancer studies have found that thioredoxin levels are strongly associated with GAPDH levels in cancer-prone Fanconi anemia cells (Kontou et al., 2004). Furthermore, incubation of fibroblasts with a solution of purified reduced Trx (i.e. active Trx) in vitro has been shown to stimulate increases in GAPDH mRNA synthesis (Kontou et al., 2004), suggesting that Trx may promote transcriptional upregulation. However, although HG treatment was unable to stimulate increases in GAPDH and Siah1 nuclear translocation and caspase-3 cleavage in TXNIP KO MCs, the levels of nuclear GAPDH and Siah1 and caspase-3 cleavage in the basal state all tended to be higher (p=n.s.) in KO MCs than WT MCs (Figure 3.1 C–F, Figure 3.2 & 3.3). There may be several explanations for this observation including 1) a global upregulation of GAPDH and Siah1, 2) stress-induced TXNIP-independent GAPDH-Siah1 signalling, and/or 3) activation of alternative signalling pathways. Further examination of downstream targets (e.g. p300/CBP acetylation and p53 activation) will be required to determine if GAPDH-Siah1 pathway signalling is increased in KO MCs in NG, or if GAPDH and Siah1 are being transported into the nucleus in these cells through an alternative pathway and have different functions. Of note, the increase in nuclear GAPDH in KO MCs in NG (approximately

14-fold change compared to WT MCs in NG) is much higher than that of nuclear Siah1 (approximately 2-fold change), suggesting that GAPDH may indeed be transported into the nucleus via a Siah1-independent manner. Overall, our data suggest that GAPDH-Siah1 signalling is upregulated in renal MCs in HG conditions, which may be associated pro-apoptotic signalling through the p300/CBP pathway (Carlile et al., 2000; Dastoor & Dreyer, 2001; Ishitani, Tanaka, Sunaga, Katsube, & Chuang, 1998; Kragten et al., 1998; Saunders, Chalecka-Franaszek, & Chuang, 1997; Sawa, Khan, Hester, & Snyder, 1997). However, additional experiments are required to determine if TXNIP-mediated HG-induction of GAPDH and Siah1 nuclear translocation in MCs is indeed associated with increased p300/CBP acetylation, p53 activation, and upregulation of pro-apoptotic genes and proteins. These experiments are covered in Future Directions and are already underway. Nevertheless, the current in vitro data are consistent with a recent study in human retinal pericytes, wherein HG treatment was also found to induce GAPDH nuclear translocation. Moreover, these cells also demonstrated increased GAPDH and Siah1 association and upregulation of apoptotic markers such as annexin V and caspase-3 enzymatic activity in HG (Suarez et al., 2015).

Together, the data support the hypothesis that increased signalling through the GAPDH-Siah1 pathway contributes to diabetic complications. In addition, our data also support the emerging role of TXNIP in regulating this pathway in diabetes/HG conditions.

To investigate the therapeutic potential of GAPDH-Siah1 pathway blockade in the prevention of DN, streptozotocin (STZ)-induced diabetic DBA/2J mice were treated orally with R-(-)-deprenyl, a known inhibitor of GAPDH-Siah1 binding and subsequent nuclear translocation. Deprenyl treatment was commenced four weeks after STZ-induction when the blood glucose levels of the mice were elevated and stable, and so this represents a preventative model. To study the effectiveness of deprenyl in blocking DN progression, two time points were assessed; half of the mice were treated for 8 wk and the other half for 16 wk, which equates to a 12-wk and 20-wk timepoint following STZ administration. Glycemic profiling and other blood analyses revealed no major differences between untreated and deprenyl-treated nondiabetic and diabetic mice, indicating that the differences in the renal parameters are due to kidney-specific actions of

75 deprenyl rather than systemic changes in metabolism (Table 1.1–2.2). Several outcomes, predictive of DN progression, were assessed. An early histopathological feature of DN is increased mesangial matrix expansion associated with increased synthesis and decreased degradation of mesangial matrix proteins such as collagen IV, fibronectin, laminin, and TGF-β1 (Kanwar et al., 2008; Qian et al., 2008). In addition, structural derangements of the glomerular filtration barrier can also occur, including mesangial matrix hypertrophy, increased collagen deposition, glomerular basement membrane thickening, podocyte foot process effacement and loss, arterial hyalinosis, and infiltration of inflammatory cells (Eid et al., 2009; Kanwar et al., 2008; Ly, Alexander, & Quaggin, 2004; Molitch et al., 2004; Rask-Madsen & King, 2013; Reidy, Kang, Hostetter, & Susztak, 2014). This structural damage will ultimately lead to functional abnormalities, including increased albuminuria and GFR. Although the GAPDH-Siah1 pathway has mainly been implicated in cellular apoptosis, we found that several pathological features of DN, including mesangial matrix expansion, increased glomerular collagen IV production, glomerulosclerosis, glomerular basement membrane thickening, and podocyte foot process effacement, were all markedly attenuated by deprenyl treatment at both time points (Figures 3.4–3.7). The observation that deprenyl has a wider range of renoprotective effects may be due to the highly interdependent nature of kidney cell types. That is to say, deprenyl-conferred protection of mesangial cells likely leads to improved structural and functional integrity of multiple cell types, including podocytes. Furthermore, indicators of renal dysfunction, including 24 h proteinuria, 24 h albuminuria and urinary albumin-to-creatinine ratios were also attenuated by deprenyl treatment for the 12-wk experiment (Figures 3.8 & 3.9). Deprenyl, however, appeared to not protect against these indicators of renal dysfunction at the 20-wk timepoint (Figure 3.8 & 3.9). This indicates that either A) deprenyl is unable to block DN progression and is less effective in more advanced stages, or that B) the degree of albuminuria/proteinuria were no longer an effective measure of renal dysfunction at this later timepoint. Blood analyses revealed that diabetic mice at the 20-wk timepoint had increased blood urea nitrogen (BUN) to blood creatinine levels than diabetic mice at the 12-wk ltimepoint (Table 2.1 & 2.2), indicating that they are more dehydrated (i.e. volume contracted). More specifically, untreated and deprenyl-treated diabetic mice had BUN/creatine ratios of approximately 0.456 and 0.622 at the 12-wk timepoint, respectively, and 0.683 and 0.684 at the 20-wk timepoint, respectively. As a

76 result of volume contraction, less blood is filtered by the kidneys to minimize volume loss via urine, leading to lower levels of proteins such as albumin being filtered into the urine. Indeed, while DN may be classically characterized by progressive proteinuria and albuminuria, studies have found that DN can sometimes present in patients with a decreased GFR with little to no albuminuria (Rosolowsky et al., 2008; Thomas, Weekes, Broadley, Cooper, & Mathew, 2006). Due to this, there has been growing interest in the identification of biomarkers and development of alternative tests for measuring renal dysfunction. Furthermore, the finding that 24 h proteinuria, 24 h albuminuria, and urinary albumin-to-creatinine ratios were more severe in the untreated diabetic mice of the 12-wk experiment (406.4 ± 118.5 ng proteinuria; 515.30 ± 614.50 µg albuminuria; 8.77 ± 9.09 albumin-to-creatinine ratio) than the 20-wk experiment (70.01 ± 8.85 ng; 61.22 ± 41.83 µg; 4.75 ± 3.57), which is associated with hyperperfusion and hyperfiltration occurring in the early stages of DN, also supports the hypothesis that less blood is being filtered in the older mice than in the younger mice. Thus, any functional protection conferred by deprenyl at the 20-wk time point could have been masked by the volume status of the mice. We are currently looking to obtain additional non-urine measures of renal function, including measurements of serum cystatin-C. Alternatively, if the urine protein results are to be taken at face value, the data would indicate that deprenyl has diminishing benefits as DN becomes more advanced. The inability of deprenyl to completely block DN development and progression is likely due to the occurrence of other pathological signalling pathways in DN. As reviewed in Chapter 1, in addition to apoptosis, TXNIP has been identified to play a role in promoting fibrosis, ER stress, and, more importantly, oxidative stress in the diabetic kidney. Since elevated ROS levels is a driving force of many pathological signalling events in DN, including the generation of RNS that we hypothesize serves as a source of NO for S-nitrosylation of GAPDH, the effects of deprenyl on oxidative stress was investigated in this study. 8-OHdG is a marker of DNA damage induced by ROS and urinary 8-OHdG is often used as an indirect measure of renal oxidative stress. In our study, deprenyl treatment had no effect on diabetes-induced increases in urinary 8-OHdG at both the 12-wk and 20-wk time point, suggesting that it is unable to protect against oxidative stress (Figure 3.11). However, deprenyl-treatment was observed to protect against glomerular Nox4 upregulation in diabetes at the 12-wk timepoint, with Nox4 being a major source of renal ROS production in DN (staining still in progress for 20-wk timepoint). The

77 inconsistency of this finding with the urinary 8-OHdG data may suggest that alternative sources of ROS generation (e.g. the mitochondria or other Nox isoforms) are still occurring. This raises the question of whether deprenyl treatment on its own is enough or if combination therapies are required to inhibit multiple pathological signalling pathways in order to effectively block DN development and progression. This will be explored in greater detail in the “Therapeutic potential of deprenyl” section below. Overall, the in vitro and in vivo data reveal a novel pathway through which TXNIP signals in the diabetic kidney and that inhibition of the GAPDH-Siah1 pathway is a promising target for drug development.

4.2. GAPDH-Siah1 pathway regulation The findings of this study have several implications. Firstly, they implicate TXNIP as a novel regulator of the GAPDH-Siah1 pathway, which may not only be relevant to diabetes research but also to neurodegenerative diseases. TXNIP, in fact, has previously been implicated in Parkinson’s disease (PD), but mainly in the context of dysregulated autophagy.

The accumulation of α‐synuclein‐containing Lewy bodies in dopaminergic (DA) neurons is a pathological hallmark of PD (Kalia & Kalia, 2015), resulting from disrupted protein degradation mechanisms, with autophagy being the main route for intracellular α‐synuclein degradation (Nixon, 2013; Winslow et al., 2010; Xilouri, Brekk, & Stefanis, 2016). In a recent study by Su et al., TXNIP was found to be overexpressed in A53T mice (a transgenic mouse line that overexpresses human α-synuclein with a PD-associated mutation A53T) and α‐synuclein‐ transfected HEK293 cells, which are both models of PD (Su et al., 2017). Overexpression of TXNIP in these models was associated with increased endogenous LC3 transformation into PE‐ conjugated LC3‐II, indicative of autophagosome formation. However, p62, a marker of autophagic degradation was also significantly elevated by TXNIP, suggesting that TXNIP blocked autophagic flux. Further analyses revealed that this blockade is likely due to TXNIP-mediated inhibition of ATP13A2, a lysosomal membrane protein critical to the maintenance of normal lysosome function. It was thus concluded that overexpression of TXNIP in these PD models contributed to α‐synuclein accumulation via inhibition of ATP13A2 and impairment of autophagic flux. Furthermore, stereotactic injection of lentiviruses containing TXNIP into mouse

78 substantia nigra has been found to result in the loss of dopaminergic neurons. Loss of dopaminergic neurons in the substantia nigra pars compacta is a characteristic feature of PD (Kalia & Lang, 2015). Interestingly, TXNIP overexpression in this study was also found to contribute to increased cellular apoptosis, but this was not investigated further. In considering that TXNIP, a pro-oxidant, is overexpressed in PD models (Su et al., 2017), oxidative stress is elevated in PD and a known contributor to DA degeneration (Jenner, 2003), and that GAPDH- Siah1 signalling is an established mechanism of neuronal cell degeneration (M. Hara, Cascio, & Sawa, 2006), it is thus very possible that TXNIP also mediates 78ignaling through the GAPDH- Siah1 pathway in PD.

4.3. Coordination of metabolic and cell death signals in DN 4.3.1. GAPDH coordinates metabolic and cell death signals in DN In complex multicellular organisms, biological and/or environmental stressors will oftentimes both change the energy demands of an organism and inflict damage to cells. In these contexts, organisms must alter their cellular energy supply to meet new energy demands as well as eliminate damaged cells in order to maintain the overall health and functioning of their organ systems (Fulda, Gorman, Hori, & Samali, 2010; Koga, Kaushik, & Cuervo, 2011; Takeda, Naguro, Nishitoh, Matsuzawa, & Ichijo, 2011). This makes mechanisms coordinating metabolic energy switching with cell death pathways essential for homeostatic control and the survival of these living organisms (Chiras, 2013; Langley & Johnson, 2010; Schulkin, 2003, 2004).

Due to the multiplicity of its functions, GAPDH is one such protein that appears to be positioned at the crux of this coordination. As a glycolytic enzyme, it plays a major role in the regulation of the cellular energy supply (Voet & Voet, 2011). In addition, its functions in pro-apoptotic nuclear signalling, when bound to Siah1, also positions GAPDH as a relay of the cellular stress signal (Ishitani et al., 1998; C.-I. Kim, Lee, Seong, Kim, & Lee, 2006; Kusner, Sarthy, & Mohr, 2004; Maruyama, Oya-Ito, Shamoto-Nagai, Osawa, & Naoi, 2002; Mazzola & Sirover, 2002, 2003; Saunders, Chen, & Chuang, 1999; Sawa et al., 1997; Tanaka et al., 2002; Tatton, 2000). Furthermore, GAPDH can also participate in various other stress responses involving DNA repair(Meyer-Siegler et al., 1991), membrane fusion, and transport (Tisdale, 2001), and tRNA

79 export (R. Singh & Green, 1993). Post-translational modifications and subcellular localization appear to be the main functional regulators determining the particular role of GAPDH (Sirover, 2012). For example, GAPDH mainly acts as a glycolytic enzyme in the cytosol and must translocate into the nucleus with Siah1 to engage in pro-apoptotic signalling.

Yet, the majority of diabetic complications research up until now has been centred around GAPDH’s glycolytic functions and the metabolic consequences of its dysregulation in diabetes. This is due to the popularization of Brownlee’s unifying hypothesis, first put forth in 2001. Brownlee proposed that in diabetes, oxidative stress, resulting from HG-induced mitochondrial ROS generation, inhibits GAPDH and causes the flux of upstream glycolytic intermediates through alternative metabolic pathways. To elaborate, high glucose entry may drive pyruvate oxidation and the citric acid cycle(Brownlee, 2001), resulting in the elevated production of electron-transport intermediates NADH and FADH2. It is theorized that due to the continuous flux of NADH and FADH2 through oxidative phosphorylation, the mitochondrial membrane potential will increase until it eventually reaches a threshold and blocks electron transfer to complex III (Trumpower, 1990). As these electrons escape the electron transport chain, they will reduce oxygen and form superoxide (X L Du et al., 2000). Increased superoxide levels are thought to then induce DNA strand breaks and activate poly(ADP-ribose) polymerase-1 (PARP-1) (Devalaraja-Narashimha & Padanilam, 2009; Giacco & Brownlee, 2010). PARP-1 is an enzyme that produces ADP-ribose by splitting NAD+ into nicotinic acid and ADP-ribose. The accumulation of ADP-ribose polymers on GAPDH, in a process known as ribosylation, has been shown to partially inactivate GAPDH (X. Du et al., 2003). Inactivation of GAPDH is thought to cause a halt in glycolysis, resulting in the accumulation of upstream glycolytic intermediates that are then shunted through alternative pathways, leading to AGE formation, PKC activation, and increase flux through the polyol and hexosamine biosynthesis pathway (Brownlee, 2005; Brownlee et al., 2000; Giacco & Brownlee, 2010). The products of these alternative pathways are hypothesized to mediate cellular and tissue toxicity observed in the development of many diabetic complications.

But, as we have shown here in renal mesangial cells, GAPDH-Siah1 stress signalling plays an important role in the development of glomerular pathologies in diabetic nephropathy. Similarly, others have shown in the recent decade that GAPDH-Siah1 signalling is also an important mediator of hyperglycemia-induced retinal Muller cell (Yego & Mohr, 2010) and pericyte (Suarez et al., 2015) cell loss, contributing to diabetic retinopathy pathologies. In light of this new evidence, a paradigm shift in the viewed role of GAPDH in diabetic complications is proposed. Instead of the traditional, singular focus on the metabolic consequences of GAPDH dysfunction, we must recognize that GAPDH also engages in stress signalling and is a potentially important mediator of energy switching and stress cascades in diabetes.

4.3.2. TXNIP may mediate both metabolic and stress signals of GAPDH Interestingly, TXNIP has the potential to not only signal through the GAPDH-Siah1 pathway but also to promote a pro-oxidative environment to initiate PARP-1-mediated GAPDH deactivation leading to metabolic dysfunction. Originally, TXNIP upregulation in diabetes was thought to contribute to ROS generation and oxidative stress, in part by inhibiting the endogenous antioxidant Trx. However, data from TXNIP deficient animal models revealed that TXNIP itself is a direct regulator of mitochondrial and Nox4-mediated ROS generation since in its absence, cells are unable to generate ROS above the basal threshold even if stimulated with high glucose (Anu Shah et al., 2013; Yoshioka et al., 2012). Evidence suggests that TXNIP is a critical mediator of mitochondria ROS generation via mitochondrial glucose oxidation (i.e. the citric acid cycle) in both normal glucose and high glucose conditions (e.g. in diabetes). Studies of TXNIP KO cardiomyocytes in the context of ischemia-reperfusion injury have demonstrated that TXNIP deficiency is associated with decreased mitochondrial glucose oxidation, accompanied by increased glycolysis (Yoshioka et al., 2012). Similar findings were observed in high glucose- treated mesangial cells derived from TXNIP-deficient HcB-19 mice (Anu Shah et al., 2013). This phenomenon of increased aerobic glycolysis and decreased oxygen consumption, despite high oxygen availability, is consistent with the Warburg effect often observed in cancer cells (Heiden, Cantley, & Thompson, 2009). However, the mechanism by which TXNIP promotes mitochondrial glucose oxidation is unclear. Two hypotheses have so far been proposed: 1) TXNIP regulates pyruvate dehydrogenase (PDH) and aids in citric acid cycle functioning, or 2) TXNIP represses

81 lactate dehydrogenase (LDH) expression/activity to minimize the consumption of pyruvate for lactate synthesis (DeBalsi et al., 2014; Anu Shah et al., 2013; Yoshioka et al., 2012). Whether these effects are mediated by the Trx binding site or the β-arrestin domains of TXNIP is also not yet known (Patwari et al., 2009). In addition to the mitochondria, NADPH oxidases (Nox) are also a significant source of ROS generation in diabetes (Gill & Wilcox, 2006; Gorin & Block, 2013; Kashihara, Haruna, Kondeti, & Kanwar, 2010). Of the three main isoforms known to exist in the kidney in rodents (i.e. Nox1, Nox2, Nox4), Nox4 has been posited as a major contributor to HG- induced ROS generation in DN in rodents (Block, Gorin, & Abboud, 2009; Gorin et al., 2005; Sedeek et al., 2010). TXNIP has been suggested to be involved in regulating Nox4 expression since diabetic WT mice demonstrate HG-induced upregulation of kidney cortical Nox4 mRNA and glomerular and tubular staining for Nox4 protein, but diabetic TXNIP KO mice do not (Anu Shah et al., 2015).

4.4. GAPDH and Siah1: major effectors of TXNIP 81signaling in DN? Over the years, a plethora of evidence has been gathered that point to TXNIP having an important role in diabetic complications pathogenesis and progression (Advani et al., 2009; D. W. Cheng et al., 2006; Masson et al., 2009; Parikh et al., 2007; S. M. Tan, Zhang, Cox, Kelly, & Qi, 2011). Firstly, TXNIP was identified as one of the highest upregulated genes in diabetes and high glucose conditions (Parikh et al., 2007; Shalev et al., 2002). This is paralleled with changes at the protein level, where TXNIP has been observed to be significantly increased in numerous cell types in diabetes, including renal mesangial cells, podocytes, and tubular cells (Gao et al., 2014; Huang et al., 2014; Kobayashi et al., 2003; Wei et al., 2013). Its upregulation in diabetes is not fully understood but is thought to be mediated by either 1) high glucose activation of the MondoA:Mlx complex, or 2) ROS, cellular stress, and shear stress (Cox, Winterbourn, & Hampton, 2010; Dunn, Buckle, Cooke, & Ng, 2010; Jones, 2008; Junn et al., 2000; S. Y. Kim, Suh, Chung, Yoon, & Choi, 2007; Maulik & Das, 2008; Saxena, Chen, & Shalev, 2010; Watanabe, Nakamura, Masutani, & Yodoi, 2010; Winterbourn & Hampton, 2008; Yamawaki, Pan, Lee, & Berk, 2005). Secondly, TXNIP has been shown to contribute to glomerular fibrosis in early DN. Kobayashi et al. and Chen et al. were among the first groups to demonstrate in mesangial cells that TXNIP mediated HG-induced increases in collagen accumulation and mesangial matrix

82 expansion (D. W. Cheng et al., 2006; Kobayashi et al., 2003; Anu Shah et al., 2013). In addition, renal protection of rodents from fibrosis with tranilast (S. M. Tan et al., 2011), an anti-fibrotic agent, and telmisartan (Wu et al., 2013), an angiotensin receptor blocker, has also been correlated with drug-induced decreases in renal TXNIP expression. Thirdly, investigations in TXNIP deficient animal models found that TXNIP knockdown confers renal MC protection from numerous standard DN equivalent outcomes. In vivo, our lab has previously observed that TXNIP KO mice display significantly reduced diabetes-induced albuminuria, mesangial matrix expansion, glomerular collagen IV production, interstitial fibrosis, GBM thickening, podocyte foot process effacement, and glomerular Nox4 expression as compared to their diabetic WT counterparts (Anu Shah et al., 2015). In the present study, similar experimental approaches were used and deprenyl-treated diabetic DBA/2J mice were also found to have significantly reduced albuminuria, mesangial matrix expansion, glomerular collagen IV production, glomerulosclerosis, GBM thickening, podocyte foot process effacement, and glomerular Nox4 expression compared to untreated diabetics (Figures 3.4–3.7 & 3.9–3.11). This mirroring of effect suggests that either TXNIP KO and deprenyl-treated mice are protected through independent pathways or, likely, that GAPDH-Siah1 signalling is downstream of TXNIP. Moreover, considering that TXNIP is a multifunctional protein with other roles in DN (e.g. regulating glucose oxidation), the observation that GAPDH-Siah1 pathway blockade closely mimicked the effects of TXNIP gene knockout in the diabetic kidney suggest that GAPDH/Siah1 may be major effectors of TXNIP signalling in DN. If so, deprenyl may serve as a viable treatment option for blocking pathological TXNIP signalling in the kidney without affecting the beneficial effects (e.g. the tumour suppressor functions) of TXNIP elsewhere in the body.

4.5. Therapeutic potential of deprenyl 4.5.1. Deprenyl protects against early structural and functional changes in DN Diabetic nephropathy is a serious complication of diabetes mellitus requiring medical intervention. As the leading cause of end-stage renal disease (ESRD), accounting for more than 50% of all cases in the Western world, it has become a major healthcare burden as the global “epidemic” of diabetes continues to rise (Dronavalli, Duka, & Bakris, 2008; T. D. C. and C. T. R. Group, 1993; Molitch et al., 2004). Currently, most medical interventions focus on glucose and

83 blood pressure control. However, attempts to normalize blood glucose levels and blood pressure with antihyperglycemic drugs and pharmacological blockade of the renin-angiotensin system have been unable to prevent DN development and progression to ESRD, requiring dialysis and/or kidney transplantation (Dronavalli et al., 2008; T. D. C. and C. T. R. Group, 1993; Molitch et al., 2004). As a result, there has been growing interest in the identification and development of novel drug targets and more effective and targeted therapies.

In this study, we have shown that R-(−)-Deprenyl (Selegiline), commonly used as an MAO-B inhibitor in the treatment of Parkinson’s disease (C. to T. P. S. Group, 1990; Kofman, 1993; Parkinson Study Group, 1989, 1993), also offers protection against DN. Here, oral supplementation with deprenyl for 8 weeks (corresponding to the 12-wk experiment) and 16 weeks (corresponding to the 20-wk experiment), beginning 4 weeks after STZ-induction, protected diabetic male mice against typical histological and morphological changes observed in DN. This includes diabetes-induced mesangial matrix expansion, upregulation of collagen IV production, and glomerulosclerosis, as visualized by PAS staining, IHC, and Masson’s trichrome. In addition, electron microscopy revealed that deprenyl treatment also conferred protection against diabetes-induced GBM thickening and podocyte foot process effacement. Functional improvements were also observed at the 12-wk timepoint as deprenyl-treated diabetic mice displayed significantly reduced levels of proteinuria and albuminuria than untreated diabetics. However, this protection was less dramatic as compared to the structural outcomes as deprenyl treatment was unable to completely protect against diabetes-induced increases in proteinuria and albuminuria. Furthermore, this protection was lost over time and by the 20-wk timepoint, deprenyl-treated diabetic mice had comparable levels of proteinuria and albuminuria to untreated diabetics. As aforementioned, these results may have been confounded by the volume status of the animals, as indicated by a higher BUN/creatinine ratio at the 20-wk timepoint as compared to the 12-wk timepoint. However, if real, these data suggest that deprenyl may offer therapeutic benefits against the functional changes occurring in early DN but cannot prevent progression or protect against more advanced functional changes occurring in later DN. There are two possible explanations for this observation: 1) structural changes do not correlate linearly with functional changes, or 2) potential errors or limitations of the urine

84 measurements may be confounding the data, rendering it an inaccurate snapshot of kidney function. The first is the more likely explanation. This is because kidney cell types are known to be highly interdependent, making it very possible that minute changes in a few cells (which most common tests are not sensitive enough to detect) may cause a cascade of disruptions resulting in a seemingly large functional change. Indeed, hemodynamic changes such as hyperfiltration and hyperperfusion are often one of the earliest changes observed in DN before structural changes such as GBM thickening and mesangial matrix expansion become detectable. This may be due to the highly interdependent nature of glomerular MCs, GEnCs, and podocytes, with changes in one cell type often contributing to disruptions in others. For example, podocyte injury has been observed to be accompanied by MC proliferation (Morioka et al., 2001). Likewise, MC injury has been seen to be accompanied by podocyte foot process fusion and proteinuria. Thus, it is possible that while deprenyl may be a potent inhibitor of early structural changes in DN it is unable to completely block DN progression and protect against renal functional decline. If so, deprenyl should probably be used in conjunction with antihypertensive drugs such as angiotensin receptor blockers in DN prevention/management in order to protect against both functional and structural pathological changes. Furthermore, alternative measures of kidney function should be used in future experiments to verify the observation that deprenyl is not effective against functional changes in more advanced DN, in order to ensure it is not affected by the limitations of the technique or errors in urine collection/analysis. The glomerular filtration rate, for example, can be used as a more direct measure of kidney function. The GFR can be measured via a variety of methods. In more recent years, tandem mass spectrometry has been used to quantify plasma creatinine levels as a measure of GFR in rodents (Takahashi, Boysen, Li, Li, & Swenberg, 2007). Please note that the blood creatinine values reported in Tables 2.1-2.2 of this thesis was not performed via HPLC and thus may be inaccurate. We are in the process of sending serum samples away for more accurate analyses.

4.5.2. Deprenyl treatment mimics the effects of partial TXNIP signalling blockade in TXNIP+/- mice Interestingly, the partial protection of deprenyl from functional changes despite the more dramatic protection from structural changes in DN is consistent with observations of the partial

85 blockade of TXNIP signalling. Previously, our lab has shown that although TXNIP+/- (HET) mice, which demonstrate HG-induced upregulation in TXNIP expression to a lesser extent than TXNIP+/+ (WT) mice, are protected against diabetes-induced increases in 24h urinary albumin excretion but less so than TXNIP-/- (KO) mice. In addition, TXNIP HET mice are not protected against diabetes-induced increases in urinary albumin/creatinine ratio or proteinuria (Anu Shah et al., 2015). Yet, TXNIP HET mice displayed protection against several morphological outcomes in DN, including diabetes-induced increases in glomerular collagen IV production, interstitial fibrosis, and podocyte foot process effacement. However, these mice were not protected against diabetes-induced increases in mesangial matrix expansion and GBM thickening. In contrast, TXNIP KO displayed more significant protection in all of these measures, with no or minimal increases from basal in diabetic mice. Overall, these data are consistent with the hypothesis that the GAPDH-Siah1 pathway is downstream of TXNIP signalling in the diabetic kidney. It also suggests that the inability of deprenyl to fully suppress diabetes-induced functional changes may be attributed to incomplete inhibition of TXNIP signalling in DN, as deprenyl is thought to block signalling downstream of TXNIP.

4.5.3. Inflammation and oxidative stress may still be occurring Since TXNIP is a multifunctional protein, it is possible that the other actions of TXNIP may also be playing a role in mediating functional changes in DN. As reviewed in Chapter 1, TXNIP may also mediate inflammatory responses and oxidative stress. The pro-inflammatory effects of TXNIP are thought to involve the induction of NLRP3 inflammasome assembly with ASC and procaspase-1, leading to caspase-1 activation and IL-1β production (Feng et al., 2016). But perhaps more important are the pro-oxidative effects of TXNIP as elevations in ROS levels are a well-documented contributor to diabetic complications development (J. M. Forbes, Coughlan, & Cooper, 2008b; Giacco & Brownlee, 2010; D. K. Singh et al., 2011). TXNIP can promote oxidative stress via several mechanisms. Firstly, TXNIP can directly bind to and inhibit thioredoxin (Trx). Since the thioredoxin system serves as one of the body’s main antioxidant systems, this would result in dramatic reductions in the body’s antioxidant defences. Furthermore, TXNIP can also promote mitochondrial and NADPH oxidase-dependent ROS generation (Anu Shah et al., 2015). TXNIP-mediated mitochondrial ROS production in diabetes is not well understood but has been

86 found to be associated with activation of the NLRP3/IL-1β axis (Han et al., 2018), likely through upregulation of ROS-promoting NLRP3 agonists (Kate Schroder, Zhou, & Tschopp, 2010). TXNIP promotion of NADPH oxidase ROS generation, in contrast, has been shown to involve glomerular upregulation of the Nox4 isoform. The exact mechanism of how Nox4 is upregulated in high glucose conditions is also not clear but has been proposed to involve transcriptional regulators NFκB, HIF-1α, AP-1, and Nrf-2 (Diebold, Petry, Hess, & Görlach, 2010; Manea, Manea, Gafencu, Raicu, & Simionescu, 2008; Manea, Tanase, Raicu, & Simionescu, 2010; Pendyala et al., 2011; L. Zhang, Sheppard, Shah, & Brewer, 2008), all of which are activated in HG (Brownlee et al., 2000; Giacco & Brownlee, 2010; X. He, Kan, Cai, & Ma, 2009). For example, it has been suggested that TXNIP binding to Trx releases the inhibitory control Trx normally exerts on ASK1 (C.-L. Chen et al., 2008; Saxena et al., 2010; Yamawaki et al., 2005). Activated ASK1 has been theorized to then stimulate p38 (Fujino et al., 2007; Hattori, Naguro, Runchel, & Ichijo, 2009; R. Zhang et al., 2004), and promote p38 activation of AP-1 (Lan et al., 2011; Lv et al., 2011). Furthermore, TXNIP may upregulate Nox4 expression by increasing NF-κB binding to the Nox4 promoter. Perrone et al. have demonstrated that TXNIP can induce NF-κB nuclear translocation and chromatin binding (Lorena Perrone et al., 2009). Moreover, all these transcription factors are also sensitive to ROS and oxidative stress (Giacco & Brownlee, 2010; X. He et al., 2009; Karin & Shaulian, 2001). TXNIP may thus increase their activity either by binding to these transcription factors or their cofactors directly or by increasing intracellular ROS. In all three scenarios, upregulation of TXNIP by HG will lead to increases in overall ROS levels, overwhelming the body’s antioxidant defences, resulting in oxidative stress. Oxidative stress, as previously reviewed in Chapter 1, can exert numerous effects on the kidney, contributing to DN development and progression. Tan et al., for example, demonstrated that diabetes-induced oxidative stress contributes to kidney fibrosis as measured by peritubular collagen IV accumulation, and that suppression of TXNIP via DNAzymes prevented both superoxide production and fibrosis in diabetic rats (Morrison et al., 2014). Furthermore, oxidative stress can promote GAPDH deactivation and the shunting of upstream glycolytic intermediates through alternative metabolic pathways, leading to increased AGE formation, PKC activation, polyol pathway flux and hexosamine biosynthesis pathway flux (Brownlee, 2005; Brownlee et al., 2000; Giacco & Brownlee, 2010). AGEs, formed from nonenzymatic reaction of glucose or other glycating compounds with proteins and lipids

(Candido et al., 2003; Wautier & Schmidt, 2004), can damage cells by 1) altering the function of proteins modified by AGEs, 2) interact with matrix components or matrix receptors on the surface of cells, causing aberrant signalling, or 3) bind to AGE receptors on macrophages, vascular endothelial cells, or vascular smooth muscle cells to activate transcription factor NFκB, causing pathological changes in gene expression (Goldin, Beckman, Schmidt, & Creager, 2006). Enhanced PKC activation by the glycolytic intermediate diacylglycerol (DAG) can result in extracellular matrix accumulation by inducing TGF- β1, fibronectin, and collagen IV expression in mesangial cells (Craven, Studer, Felder, Phillips, & DeRubertis, 1997; Pugliese et al., 1994). The polyol pathway utilizes aldo-keto reductases to reduce carbonyl compounds with NADPH to their respective sugar alcohols (polyols). Overactivation of this pathway can consequently lead to excessive consumption of NADPH. As NADPH is a cofactor required for the reduction of Trx and GSH by the thioredoxin and glutathione reductase systems, respectively, polyol pathway flux will deplete reduced Trx and GSH and further exacerbate oxidative stress (Chung, Ho, Lam, & Chung, 2003; Ii et al., 2004; A. Y. Lee & Chung, 1999; Z. Zhang, Apse, Pang, & Stanton, 2000). Lastly, increased flux of the glycolytic intermediate fructose 6-phosphate into the hexosamine biosynthesis pathway results in increased O-GlcNAcylation of both cytoplasmic and nuclear proteins (Y. Q. Chen et al., 1998; X L Du et al., 2000; Kolm-Litty, Sauer, Nerlich, Lehmann, & Schleicher, 1998; Sayeski & Kudlow, 1996). Increased O-GlcNAcylation of the transcription factor Sp1 is thought to contribute to diabetic complications by mediating the activation of the PAI-1 and TGF- β1 promoters to increase their gene transcription (Y. Q. Chen et al., 1998; X L Du et al., 2000).

Whether GAPDH-Siah1 pathway signalling is activated by or contributes to oxidative stress is inconclusive. In this study, GAPDH-Siah1 pathway blockade via deprenyl treatment abolished diabetes-induced increases in Nox4 glomerular expression at the 12-wk time point, suggesting that this pathway regulates Nox4-dependent ROS generation in diabetes. However, deprenyl was unable to protect against increases in urinary 8-OHdG, a marker of renal oxidative stress, at both the 12-wk and 20-wk time point, suggesting that it was unable to improve the overall redox state. One caveat is that the 8-OHdG results are also subject to the same potential technical errors as for the albuminuria and proteinuria results, as these experiments all utilized the same

88 urine samples. Furthermore, urinary 8-OHdG may theoretically also originate from ROS-induced DNA damage in other organ and vascular systems and so may be reflective of systemic oxidative stress in addition to renal oxidative stress. However, if real, the lack of protection against urinary 8-OHdG increases despite significant protection against Nox4-dependent ROS generation may be due to upregulation of other ROS-production pathways/enzymes. As mentioned, TXNIP can also promote mitochondrial-dependent ROS generation in diabetes. In addition, other Nox isoforms, such as Nox1 and Nox2, may also be upregulated in diabetes and unaffected by deprenyl treatment. Further investigations are needed to determine the effects of deprenyl, if any, on these alternative pathways. For example, mitochondrial ROS production can be measured in vitro via the MitoSOX Red assay and Nox1/Nox2 expression can be measured in vivo via IHC. However, an important note is that Nox4 is well regarded to be one of the major sources of renal ROS in DN. For example, Nox4 KO mice have been observed to be protected from albuminuria, mesangial matrix accumulation, and macrophage infiltration in diabetes (Jha et al., 2014). In contrast, Nox1 and Nox2 KOs only protected against macrophage infiltration and not albuminuria, mesangial matrix accumulation, or glomerular and tubulointerstitial fibrosis (Jha et al., 2014; You et al., 2013). Therefore, if Nox1 and Nox2 upregulation in DN is unaffected by deprenyl treatment, it would explain the still observable increase in urinary 8-OHdG but likely has a minor role in driving DN progression in deprenyl-treated diabetic mice.

It is important to note that complete attenuation of all TXNIP signalling pathways (e.g. with irreversible inhibitors targeting TXNIP directly) may not be the best goal of DN management or prevention of complications. This is because physiological levels of TXNIP are required for normal cell functioning, as TXNIP is known to play a role in mitochondrial glucose oxidation, hepatic gluconeogenesis, tumour suppression, and response to infection (Shalev, 2014; Spindel, World, & Berk, 2012). Lowering TXNIP concentrations to levels below basal would not only impair mitochondrial functioning but increase the risk for certain forms of cancers (J. Zhou & Chng, 2013). As such, the goal in diabetes management should be to either normalize TXNIP to physiological (nondiabetic) levels or to inhibit the major pathological signalling pathways it stimulates. With the latter being a more easily achievable goal, we believe that GAPDH-Siah1 pathway blockade is a promising target, deserving more attention in DN drug development.

Lastly, one caveat is that deprenyl appears to have diminishing gains with long-term use or more advanced DN, as several of the outcomes investigated demonstrated a trend towards slightly lesser protection at the 20-wk timepoint than at the 12-wk time point (Figure 3.8 & 3.9). This may suggest that deprenyl is more suitable as an early intervention. However, whether GAPDH- Siah1 pathway blockade inhibits progression from early to more advanced DN or simply significantly delays progression is still unclear. Further studies are needed to elucidate the physiological importance of the GAPDH-Siah1 pathway in DN pathogenesis and progression in order to determine how, when, and under which conditions deprenyl should be used.

4.5.4. Safety of long-term deprenyl use Studies into the safety of deprenyl are mainly centred on its use as an irreversible MAO-B inhibitor in the treatment of Parkinson’s disease. The findings of many clinical studies have been summarized below in order to give the reader some insight into the relative safety of deprenyl for the treatment of DN. The findings, however, should not be taken at face-value since 1) the dose of deprenyl needed to inhibit GAPDH-Siah1 signalling can be several folds lower than the dose needed to inhibit MAO-B (reviewed in Introduction), and 2) kidney outcomes were not thoroughly examined in these clinical trials.

Generally, deprenyl is well tolerated by patients with Parkinson’s disease. The most common adverse events seen when it is used as monotherapy have been nausea, dizziness, headache, benign cardiac arrhythmias, and musculoskeletal injuries (Heinonen & Myllyl, 1998; Volz & Gleiter, 1998). Deprenyl is also typically well tolerated when used in combination with other drugs. However, severe adverse effects have been reported when it is used with pethidine (i.e. meperidine) and so this combination is not recommended (Heinonen & Myllyl, 1998). In addition, deprenyl has also been found to potentiate the adverse effects of levodopa, the precursor to dopamine that is also commonly used to treat Parkinson’s disease. The common adverse effects of combinatory use include nausea, dizziness, dry mouth, fatigue, constipation, and insomnia. When used to treat more advanced Parkinson’s, these drugs can cause dyskinesia, orthostatic hypotension, and hallucinations (Heinonen & Myllyl, 1998; Volz & Gleiter, 1998).

Transient or continuing abnormalities in liver function have also been reported with long term use (Golbe, 1989). Specifically, serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), two markers of liver function, have been noted to be increased with deprenyl use (Yamada & Yasuhara, 2004). We also examined the levels of plasma ALT in our DBA/2J mice and it was found to be unchanged by deprenyl treatment, but elevated by diabetes. Additionally, blood urea nitrogen (BUN) and plasma creatinine, which are two markers of renal function, were also unchanged by deprenyl treatment in both nondiabetic and diabetic DBA/2J mice. In fact, all fifteen plasma markers investigated, with the exception of phosphorus, were unchanged by deprenyl treatment. Overall, the data suggest that deprenyl is a relatively safe drug with promising results in terms of offering protection from early structural and functional changes in DN. Though, further studies are still needed to fully elucidate the benefits and limitations of deprenyl use in DN management, as well as determine the safety of long-term deprenyl use on kidney and liver function.

4.6. Conclusion A growing body of evidence positions TXNIP as a key mediator of HG-induced structural and functional dysregulation in DN. However, the specific actions of TXNIP in DN pathogenesis and progression remains unclear. In recent years, the GAPDH-Siah1 cascade, originally identified in neuronal studies as a pro-apoptotic pathway, was suggested to play a role in diabetic complications development, specifically diabetic retinopathy. As data surrounding the denitrosylase and antioxidant functions of Trx implicates Trx as a potential inhibitor of this pathway, we hypothesized that TXNIP may contribute to GAPDH-Siah1 signalling in DN through inhibition of Trx. In this thesis, TXNIP was demonstrated to mediated HG-induced nuclear translocation of both GAPDH and Siah1 in MCs and HG-induced caspase-3 cleavage, which is strongly associated with the GAPDH-Siah1 pro-apoptotic signalling pathway. The therapeutic potential of GAPDH-Siah1 pathway blockade was also investigated in vivo using deprenyl, a known inhibitor of GAPDH-Siah1 binding and nuclear translocation. Deprenyl-treatment protected STZ-induced diabetic DBA/2J mice from diabetes-induced mesangial matrix accumulation, glomerulosclerosis, glomerular basement membrane thickening, and podocyte foot process effacement at both 12-wk and 20-wk time points. Deprenyl-treatment also

91 protected against diabetes-induced proteinuria, albuminuria, and glomerular Nox4 upregulation at the 12-wk timepoint, and diabetes-induced increases in glomerular collagen IV production at the 20-wk timepoint. Since diabetic TXNIP KO mice have also been observed to be resistant against developing similar DN outcomes (A. Shah et al., 2015), this suggests that not only is the GAPDH-Siah1 pathway possibly involved in DN pathogenesis but that GAPDH/Siah1 may serve as one of the downstream effectors of TXNIP signalling in DN. Additionally, these data suggest GAPDH-Siah1 pathway is a promising target for drug development.

4.7. Caveats and study limitations 4.7.1. Mesangial cells in culture Every in vitro experiment in this study used mesangial cells cultured on plasftic surfaces. This presents a major limitation in the generalizability of the results to mesangial cells in vivo. Kriz et al. previously reported that MCs cultured in fetal serum on plastic surfaces (e.g. flasks or culture dishes) expressed different phenotypic features than MCs under normal physiological conditions (Kriz, Elger, Mundel, & Lemley, 1995). Furthermore, MC cultures also represent a very artificial environment that is not reflective of the in vivo system wherein MCs form 3D networks with other glomerular cells and the GBM. As a result, we are unable to account for the role that cell to cell communication may play in MC pathology in DN. However, it is important to note that most published in vitro work on MCs were performed in similar non-physiological conditions and that this model is generally accepted as a good model for studying the effects of high glucose exposure on MCs. Moreover, in vivo studies were performed to verify the physiological relevance of the GAPDH-Siah1 pathway in DN and to confirm that the observations made were real and not a byproduct of the experimental model chosen.

4.7.2. Human DN versus animal models of DN The streptozotocin (STZ)-induced DBA/2J model used in our study is a widely used model of type 1 diabetes. Among the commonly used inbred mouse strains (e.g. C57BL/6J, DBA/2J, FVB/NJ, 129S6/SvEvTac, and KK/HlJ), the DBA/2J mouse has been reported to be one of the strains most susceptible to developing diabetic glomerulopathy (Brosius et al., 2009). In fact, STZ-induced DBA/2J mice have been found to readily develop glomerular mesangial matrix expansion,

92 glomerulosclerosis, GBM thickening, and enhanced albuminuria following STZ-induction, along with some podocyte foot process effacement (Z. Qi et al., 2005). This is consistent with the observations in our untreated diabetic DBA/2J mice. However, despite the plethora of diabetic mouse models available, all the models to date possess limitations in their ability to recapitulate all the features of human DN, from the early stages to the late stages. The tubulointerstitial and vascular lesions observed in human DN have been particularly hard to recreate in mouse models. Likewise, STZ-induced DBA/2J mice are reported to be resistant to developing interstitial fibrosis and tubular atrophy, even after 25 weeks of hyperglycemia (Z. Qi et al., 2005). Furthermore, STZ- induced DBA/2J mice were also found to lack diabetes-induced upregulation in JAK/STAT family members, as is commonly observed in humans with progressive DN (Berthier et al., 2009). This may be problematic since the overactivation of the JAK/STAT signalling pathway may contribute to DN progression not seen in the mouse model. In addition, several different types of diabetic complications often occur together in humans. However, DBA/2J mice are relatively resistant to developing atherosclerosis, a common macrovascular complication of diabetes, on a semisynthetic high-fat diet (Nishina et al., 1993), and are slightly resistant to diets containing high levels of fat and cholesterol (Kirk et al., 1995). This would prevent the study of the combined effects of multi-organ dysfunction in DN progression. In the future, as more and more human genetic variants associated with increased DN risk are identified, we may eventually be able to knock-in various DN risk variants to mouse strains such as the DBA/2J model, to generate a more holistic recreation of human DN. Until then, in order to overcome species-specific limitations, multiple animal models should be used to examine the effects of deprenyl treatment at various stages of DN as well as to determine if the effects of deprenyl are species-dependent.

4.7.3. Deprenyl targets Although data from studies of the deprenyl analogue CGP 3466 suggests that deprenyl binds GAPDH directly, the binding interactions are still not clear (Kragten et al., 1998). Moreover, all the binding partners of deprenyl have yet to be elucidated. Thus, we cannot exclude the possibility that deprenyl may be exerting some nonspecific or GAPDH/Siah1-independent effects in the DBA/2J mice. Additionally, the deprenyl metabolites, methamphetamine (MAP) and amphetamine (AP), have been reported to be biologically active and provide additional MAO-B-

93 independent therapeutic effects in Parkinson’s disease (Karoum et al., 1982). In order to determine if MAP, AP, or the p-hydroxylated form of these metabolites has any effect in diabetes, whether positive or negative, additional studies should be conducted using these metabolites in place of deprenyl treatment. Alternative methods of GAPDH-Siah1 pathway blockade including administration of CGP 3466 or siRNAs against Siah1, or overexpression of GOSPEL (the endogenous inhibitor of GAPDH-Siah1 binding) can also be used to consolidate the finding that the renal-protective effects of deprenyl are due to its inhibition of the pathway rather than other “off-target” effects.

4.7.4. Urinalyses As previously mentioned, thus far only urine-based tests (e.g. 24 h urinary albumin excretion, urinary albumin-to-creatinine ratios, and proteinuria) were used to assess renal function in the DBA/2J mice. The limitations in interpretation, technical issues, volume contraction and urine collection have been discussed above. In the future, additional non-urine-based tests for kidney functioning should be performed to exclude the effects of experimental error. For example, tandem mass spectrometry can be used to quantify plasma creatinine levels as a measure of glomerular filtration rate (Takahashi et al., 2007).

4.8. Future directions 4.8.1. Further characterization of the GAPDH-Siah1 pathway in vitro We are currently running several experiments to confirm activation of known downstream targets of GAPDH-Siah1 signalling in TXNIP WT and KO MCs. To look to GAPDH and Siah1 binding, we will be immunoprecipitating total cell lysates derived from TXNIP WT and KO MCs treated with NG and HG for Siah1 and subsequently immunoblotting the samples for GAPDH. To look at the activation of p300/CBP, we will be blotting total cell lysates for Acetyl-CBP (Lys 1535)/p300 (Lys 1499) and normalizing it to total p300/CBP levels. To look at the activation of the tumour suppressor p53, we will be blotting total cell lysates for acetyl-p53 (Lys 379) and normalizing it to total p53 levels. To confirm the induction of apoptosis, we will immunoblot total cell lysates for cleaved caspase 3 and normalizing it to the levels of un-cleaved caspase 3. We will also be culturing TXNIP WT and KO cells on coverslips in order to perform TUNEL staining to provide

94 another measure of apoptosis induction. The total cell lysates necessary for these experiments have already been collected. We are in the process of optimizing and running the western blots. Furthermore, S-nitrosylation of GAPDH can also be assessed to confirm the presence of this initial signal for GAPDH-Siah1 pathway activation. To test for S-nitrosylation of GAPDH, a method based on the Biotin Switch Assay can be used (Qin, Dey, & Daaka, 2013). Since Cys S-nitrosylation is labile and reversible upon exposure to reducing agents included in many buffers, a HEN buffer (250 mM Hepes-NaOH pH7.7, EDTA 1mM, neocuproine 0.1 mM) will be required. The first step of this assay requires the addition of 1 M methylmethanethiosulfonate (MMTS) to the cell lysis buffer with SDS to block free SH groups. S-nitrosylation is then reversed by exposure to 1 M sodium ascorbate and these free Cys-SH groups derivatized with iodo-Tandem Mass Tag (TMT) labelling reagent are then detected by immunoblotting (WB) with Anti-TMT antibody (Pierce). Once derivatized, total GAPDH is immunoprecipitated, then immunoblotted with Anti-TMT and anti-GAPDH to determine the extent of S-nitrosylation. Lastly, in order to confirm TXNIP activity, we will look at ASK1 activation by immunoblotting total cell lysates for phospho-ASK1 (Thr 845) and normalizing it to total ASK1 levels. We will also immunoblot for GOSPEL in TXNIP WT and KO MC lysates to determine if the levels of this natural endogenous inhibitor of GAPDH-Siah1 binding is altered by HG treatment and modulated by TXNIP activity.

In order to better correlate the effects observed in these cells to GAPDH-Siah1 pathway activation rather than alternative TXNIP actions, these experiments will be repeated in the future in TXNIP WT treated with deprenyl, CGP 3466, and Siah1 siRNA. Similarly, TAT-FLAG GAPDH or Siah1 directed peptides can also be used to block GAPDH-Siah1 binding to study the protection conferred by GAPDH-Siah1 pathway blockade. In addition, while these techniques reflect forms of pharmacological inhibition, overexpression of GOSPEL via lentiviruses or adenoviruses would allow for investigations of physiological inhibition. We hypothesize that treatment with these agents should produce a phenotype comparable to the TXNIP KO MCs. TXNIP adenovirus will also be used to reintroduce TXNIP. If GAPDH-Siah1 pathway signalling is enhanced by TXNIP adenovirus, this will provide further support that the GAPDH-Siah1 pathway is activated by TXNIP in HG conditions.

Lastly, the aforementioned experiments will all be eventually repeated in podocytes. Podocyte loss is an important and early event in DN, occurring even before albuminuria presents in patients with type 1 diabetes (Steffes, Schmidt, Mccrery, & Basgen, 2001). It is one of the strongest predictors of DN progression in Pima Indians with type II diabetes (Meyer, Bennett, & Nelson, 1999; Pagtalunan et al., 1997). Since podocyte apoptosis is thought to be the underlying cause of podocyte loss in DN (Susztak et al., 2006), podocytes present as one of the best candidates for GAPDH-Siah1 pathway toxicity.

4.8.2. Further characterization of deprenyl action in vivo In order to further characterize the effects of deprenyl treatment in vivo, we will be performing several immunohistochemistry stains using the protocol noted in Chapter 3. To look at podocyte loss, which is a marker of DN progression, we will be staining 3 µm and 9 µm paraffin-embedded sections with Wilm’s tumour antigen-1 (WT-1), a podocyte-specific transcription factor. The number of podocytes per glomeruli (i.e. glomerular cell volume) will be determined from analysis of both sections using a formula previously published (Sanden, Wiggins, Goyal, Riggs, & Wiggins, 2003). We expect diabetes to induce podocyte loss (i.e. decreases in glomerular podocyte numbers) and deprenyl treatment to attenuate this loss as apoptosis has been demonstrated to be a contributing factor to podocyte loss in DN (Anu Shah et al., 2015). Additionally, we will be looking at additional markers of nitrosative stress by performing IHC stains for glomerular nitrotyrosine expression. To further characterize the pathological status of DN, we will be performing IHC stains for F4/80, for macrophage infiltration, and TGF-β1. Several of these IHC stains have already been performed for the 12-wk DBA/2J mice and the slides are currently being digitalized in preparation for analysis.

Future experiments can also use CGP 3466, a structural analogue of deprenyl without any MAO- B activity, in place of deprenyl to confirm that the therapeutic benefits provided by deprenyl are through inhibition of the GAPDH-Siah1 pathway and independent of its MAO-B activities (P. C. Waldmeier, Spooren, & Hengerer, 2000).

Moreover, having only two time points (i.e. the 12-wk and 20-wk timepoint) is insufficient for the generation of a therapeutic window for deprenyl use in DN treatment. Periodic examination of renal function in deprenyl-treated STZ-induced diabetic DBA/2J mice should be employed in the future to allow investigators to track the contribution of the GAPDH-Siah1 pathway to DN pathogenesis and progression at each stage.

4.8.3. Elucidating the direct role of TXNIP in GAPDH-Siah1 signalling The mechanism by which TXNIP upregulates GAPDH-Siah1 signalling is still unclear. There appear to be four possible explanations: 1) Trx denitrosylates GAPDH to help keep it in its active, reduced, non-nitrosylated form, and TXNIP inhibition of Trx renders GAPDH susceptible to S- nitrosylation; 2) TXNIP inhibition of Trx decreases the overall antioxidant capacity of the cells, allowing for the accumulation of NO pools that drives S-nitrosylation of GAPDH; 3) ASK1, which is directly inhibited by Trx, stimulates GAPDH-Siah1 signalling, and TXNIP inhibition of Trx allows ASK1 to bind Siah1 and activation of the pathway; or 4) TXNIP mediates HG-induced ROS and RNS production (e.g. via mitochondrial superoxide production and/or Nox4 upregulation).

Since hypotheses 1-3 relies on TXNIP-mediated Trx inhibition, TXNIP-Trx binding should be prevented to test for the dependency of TXNIP on Trx to mediate activation of the GAPDH-Siah1 pathway. WT cells, for example, can be transferred with a C247S TXNIP mutant to see if the GAPDH-Siah1 pathway still occurs. As TXNIP-Trx binding involves the formation of a disulfide bond between the Cys 247 residue of TXNIP to the Cys 32 residue in the active catalytic site of Trx, a C247S TXNIP mutant would be unable to bind Trx. To test if Trx directly denitrosylates GAPDH (hypothesis #1), WT cells can be transfected with Trx or scrambled siRNA and the degree of S-nitrosylation of GAPDH under both conditions can be assessed with a modified Biotin Switch Assay, as described earlier. Total NO, nitrite, and nitrate levels should also be assessed to determine if Trx action is mainly through its denitrosylase function (hypothesis #1) or through decreasing NO pools (hypothesis #2). This can be done via a NO assay kit (Thermo Scientific). To determine if TXNIP-induced GAPDH-Siah1 pathway activation is ASK1-mediated (hypothesis #3), TXNIP WT and KO cells can be transfected with ASK1 siRNA and the degree of GAPDH/Siah-1 pathway activation measured after treating cells with high glucose and/or TXNIP adenovirus

97 under normal glucose conditions. Finally, if TXNIP upregulation of GAPDH-Siah1 signalling was determined to be Trx-independent (i.e. occurring even when a C247S TXNIP mutant is used), intracellular ROS levels, and Nox4 expression can be measured to determine TXNIP promotion of oxidative and nitrosative stress is its main mechanism of inducing GAPDH-Siah1 pathway activation (hypothesis #4).

4.8.4. Investigating GAPDH-Siah1 signalling in other diabetic complications Lastly, since TXNIP, GAPDH, and Siah1 are all ubiquitously expressed and diabetes can cause complications in other organ systems, GAPDH-Siah1 signalling in other tissues should also be investigated. The brains, hearts, and eyes are common targets of diabetic complications and have all been collected from untreated and deprenyl-treated DBA/2J mice. Examination of these tissues will not only allow for determination of the therapeutic potential of deprenyl in the treatment of these other diabetic complications but will also provide some insight into the systemic effects of deprenyl. That is to say; it will be interesting to see if deprenyl-mediated protection against DN is associated with a delay of other diabetic complications due to direct effects or, perhaps, decreased release of toxic products of renal injury to the bloodstream. Additionally, dysregulation of other organ systems or the general vasculature can also indirectly impact kidney health. For example, immune cells can mediate inflammation, which can contribute to pathological signalling in the kidney in DN.

CHAPTER 5: REFERENCES

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