txnip and the gapdh-siah1 signalling pathway in … · of self-discovery to appreciate both the...
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TXNIP AND THE GAPDH-SIAH1 SIGNALLING PATHWAY
IN DIABETIC NEPHROPATHY
by
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
© Copyright by Hui Ze (Lexy) Zhong (2019)
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TXNIP AND THE GAPDH-SIAH1 SIGNALLING PATHWAY IN DIABETIC NEPHROPATHY
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.
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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.
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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
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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
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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
ix
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
x
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
xi
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-/-
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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
xiii
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)
xiv
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
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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.
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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).
4
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
ENDOTHELIAL CELLS
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.
6
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
9
(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).
10
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,
12
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,
14
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,
15
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).
16
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).
18
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).
22
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).
23
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;
28
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).
29
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).
30
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.,
31
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
34
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).
36
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).
37
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
38
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.
42
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.
44
CHAPTER 2:
METHODS
45
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.
48
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.
49
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
described by Koop et al., 2003 using the formula 𝑤𝐹𝑃̅̅ ̅̅ ̅ =𝜋
4×
∑ 𝐺𝐵𝑀 𝑙𝑒𝑛𝑔𝑡ℎ
∑ 𝑠𝑙𝑖𝑡𝑠 , where ∑ 𝑠𝑙𝑖𝑡𝑠 is the
total number of slits counted, ∑ 𝐺𝐵𝑀 𝑙𝑒𝑛𝑔𝑡ℎ is the GBM length across which the slits were
counted, and 𝜋
4 corrects for random variations in the orientation in which the foot processes
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.
51
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.
53
CHAPTER 3:
RESULTS
54
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).
55
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
56
(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.
57
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 (mmol/L)
7.1±0.9 24.5±6.5a 6.8±1.0 29.4±4.8b
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 (LKW; g)
0.365±0.055 0.238±0.040a,b 0.370±0.034 0.242±0.041a,b
Right kidney weight (RKW; g)
0.365±0.051 0.245±0.039a,b 0.375±0.037 0.252±0.045a,b
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
59
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 (mmol/L)
10.6±1.8 31.2±5.2a,b 10.6±1.8 32.0±3.9a,b
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 (LKW; g)
0.337±0.047 0.272±0.065b,g 0.337±0.047 0.266±0.031b,g
Right kidney weight (RKW; g)
0.365±0.037 0.259±0.049b,c 0.416±0.051 0.274±0.025b,c
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±17.0100c,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
61
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.
62
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.
63
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.
64
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.
65
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.
66
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.
67
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.
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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).
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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
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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
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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.
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CHAPTER 4:
DISCUSSION, CONCLUSION, FUTURE DIRECTIONS
73
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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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(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
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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.
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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).
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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
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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,
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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-
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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
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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.
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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).
96
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.
98
CHAPTER 5:
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