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Novel gluconeogenesis regulators for anti-diabetic drug repurposing using transgenic zebrafish pck1 reporters
by
Ji Dong Bai
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Sciences
University of Toronto Supervisor: Dr. Xiao-Yan Wen
© Copyright by Ji Dong K. Bai 2015
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Novel gluconeogenesis regulators for anti-diabetic drug repurposing using transgenic zebrafish pck1 reporters
Ji Dong K. Bai
Master of Science Institute of Medical Sciences
University of Toronto 2015
Abstract
Phosphoenolpyruvate carboxykinase is an enzyme that catalyzes the rate-limiting step of
gluconeogenesis and is encoded by the pck1 gene. High levels of pck1 gene expression are
associated with type 2 diabetes. The main goal of the present study is to identify compounds
that can modulate pck1 expression. Furthermore, the knowledge gained from this study can
streamline the process of identifying regulators by high-throughput screening in the future. A
luminescent zebrafish reporter Tg(pck1:luc2) was used to screen the NIH Clinical Collections
library containing 727 small molecules. Four leads were identified and validated using the
fluorescence reporters Tg(pck1:Venus) and Tg(pck1:eGFP) as well as endogenous pck1
expression using quantitative PCR, where they down-regulated pck1 expression in larval
zebrafish. One of the validated compounds (levofloxacin) altered glucose metabolism in adult
zebrafish as determined by glucose tolerance tests. Methods were also established for efficient
screening of future chemical libraries to identify novel anti-diabetic therapeutics.
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Acknowledgements
I would like to express my thanks to my supervisor, Dr. Xiao-Yan Wen, for taking me
under his wing, for providing me with an opportunity to explore the wonderful research
applications of zebrafish, and for his continued guidance and mentorship during my master’s
program at University of Toronto. I am also thankful to my committee members, Dr. Gary Lewis
and Dr. Kim Connelly, for their critical input and lively discussions during our meetings
throughout my program.
The members of the Wen lab were also extremely supportive and helpful – Suzan, Tony,
Anju, Mei, Tina, Peter, Zhongduo, Koro, Shohreh, Jamie, Genna, Cherry, Xiaohua and Youdong. I
am grateful for their feedback, technical expertise, and willingness to share various equipment,
consumables, and food outside the wet bench lab. I would like to especially thank Xiaohua, who
diligently helped me maintain my zebrafish lines and tanks, and Youdong, for his technical
expertise and assistance during procedures such as fluorescence microscopy and zebrafish
galvaging/injection. I also would like to recognize a former member, Wing Hui, for generating
the transgenic line Tg(pck1:EGFP) that I used in my experiments.
The research community at St. Michael’s Hospital is an incredible one. I am thankful to
Richard (Wang Lab) for mentorship on RT-qPCR, and Xiaoming (Wang Lab) for guidance on
glucose tolerance test on adult zebrafish. Christine (Boyd Lab) was also extremely dependable
with advice on selection, maintenance, and usage of injection supplies required for glucose
tolerance test. Hospital staff Pam was extremely knowledgeable and provided me with the
necessary training for me to use the Agilent Bioanalyzer 2100 (for RIN determination and RNA
quantification). I also want to thank individuals who helped to review my thesis, Anju and
Gerald.
Most of the fish strains I used were generously provided by Dr. Philipp Gut,
Tg(pck1:luc2) and Tg(pck1:Venus) at University of California San Francisco. I am grateful I had
an opportunity to meet and talk to Dr. Gut at a conference event, where he provided me with
tips and tricks on using his fish for assays and subsequent analyses.
Lastly, I want to thank my family and friends for their support and patience during my
Master’s program, especially Shaalini, Shamini, Ahmed, David, Elizabeth, Emily, Jennifer, Asher.
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Contributions
I would like to recognize a former Wen Lab member, Wing Hui, for generating the
transgenic line Tg(pck1:EGFP) that I used in my experiments. The other zebrafish strains,
Tg(pck1:Luc2) and Tg(pck1:Venus), were generously provided by Dr. Philipp Gut, a post-doctoral
fellow (Stainier Lab) at University of California San Francisco. Youdong Wang (Wen Lab) assisted
with fluorescence microscopy and quantification. Xiaoming Li (Wang Lab) provided guidance on
glucose tolerance test on adult zebrafish.
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Table of Contents
Abstract............................................................................................................................................ii
Acknowledgements ........................................................................................................................ iii
Contributions .................................................................................................................................. iv
Table of Contents ............................................................................................................................ v
List of Abbreviations ...................................................................................................................... ix
List of Figures ................................................................................................................................. xii
List of Tables ................................................................................................................................. xiii
List of Equations ........................................................................................................................... xiii
Chapter 1: Introduction .............................................................................................................. 1
1.1 Diabetes and obesity .....................................................................................................................1
1.1.1 Metabolic disorders ...............................................................................................................1
1.1.2 Diabetes mellitus ...................................................................................................................2
1.1.3 Etiology and epidemiology of DMT2 .....................................................................................3
1.1.4 Physiology and Pathology ......................................................................................................5
1.1.5 Current anti-diabetic therapeutics and limitations ...............................................................8
1.2 The drug discovery and development process ........................................................................... 13
1.2.1 Overview ............................................................................................................................. 13
1.2.2 Challenges to traditional drug discovery ............................................................................ 16
1.2.3 In vivo drug screening ......................................................................................................... 17
1.2.4 Drug repurposing ................................................................................................................ 19
1.3 Zebrafish and high throughput drug screening .......................................................................... 20
1.3.1 Zebrafish as a model organism ........................................................................................... 20
1.3.2 Zebrafish and its suitability for HTS .................................................................................... 21
1.3.3 Zebrafish models of disease ............................................................................................... 23
1.3.4 Organ development in zebrafish: pancreas and liver ......................................................... 24
1.3.5 Glucose homeostasis in zebrafish ...................................................................................... 25
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1.3.6 Zebrafish models of glucose metabolism and diabetes ..................................................... 27
1.4 Target for gluconeogenesis: PEPCK ............................................................................................ 28
1.4.1 Overview ............................................................................................................................. 28
1.4.2 Modifying pck1 expression ..................................................................................................... 30
1.4.3 Regulation of pck1 .............................................................................................................. 31
1.4.4 Zebrafish gluconeogenesis model ...................................................................................... 33
Chapter 2: Rationale, hypothesis, and objectives ................................................................... 37
2.1 Rationale ..................................................................................................................................... 37
2.2 Hypothesis .................................................................................................................................. 40
2.3 Objectives ................................................................................................................................... 40
Chapter 3: Materials and Methods .......................................................................................... 41
3.1 Zebrafish maintenance ............................................................................................................... 41
3.1.1 Zebrafish husbandry ........................................................................................................... 41
3.1.2 Transgenic zebrafish strains ............................................................................................... 41
3.2 Compounds preparation ............................................................................................................ 42
3.2.1 NIH Clinical Collections library ............................................................................................ 42
3.2.2 Candidate drugs .................................................................................................................. 42
3.2.3 Compounds for gavaging (oral) and injection (i.p.) ............................................................ 43
3.3 Drug library screening ................................................................................................................ 44
3.3.1 Workflow and protocol ...................................................................................................... 44
3.3.2 Reagents and equipment ................................................................................................... 44
3.3.3 Screening results analysis ................................................................................................... 45
3.4 Fluorescence imaging ................................................................................................................. 48
3.4.1 Workflow and protocol ...................................................................................................... 48
3.4.2 Reagents and equipment ................................................................................................... 48
3.4.3 Fluorescence quantification analysis .................................................................................. 49
3.5 Gene expression quantification .................................................................................................. 51
3.5.1 RNA extraction .................................................................................................................... 51
3.5.2 RT-qPCR .............................................................................................................................. 51
3.6 Glucose tolerance test ................................................................................................................ 52
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3.6.1 Fish preparation and fasting ............................................................................................... 52
3.6.2 Adult fish gavaging ............................................................................................................. 53
3.6.3 Glucose injection ................................................................................................................ 54
3.6.4 Blood glucose measurements ............................................................................................ 54
3.7 Statistical analysis ....................................................................................................................... 55
3.8 Appendix: zebrafish-specific solution recipes ............................................................................ 56
Chapter 4: Results ..................................................................................................................... 57
4.1 High-throughput drug screening identified 120 compound “hits” ............................................ 57
4.1.1 About 120 compounds decrease luciferase activity by at least half in primary screen ..... 57
4.1.2 Twenty-three compounds showed toxic effects during primary screen ........................... 59
4.1.3 Identifying potential lead compounds ............................................................................... 60
4.2 Analysis and ranking of “hits” identified eleven compounds for further validation studies ..... 60
4.2.1 Compounds ranked using Z score and B score strategies showed similar results ............. 60
4.2.2 Thirty six compounds were selected for rescreening following analysis ........................... 62
4.2.3 Re-screening identified eleven compounds to be pursued for further validation ............. 64
4.3 Further validation studies identified four lead compounds down-regulate pck1 activity ......... 65
4.3.1 Dose-response studies confirmed down-regulation of pck1 activity ................................. 65
4.3.2 Four compounds decreased fluorescence intensity expressed under pck1 promoter ...... 67
4.3.3 Amlexanox, levofloxacin, naproxen, and dicloxacillin selected as lead compounds ......... 68
4.4 Four “leads” confirmed to down-regulate pck1 expression ...................................................... 70
4.4.1 Four “leads” reduces pck1 expression in larvae zebrafish ................................................. 70
4.4.2 Amlexanox, levofloxacin and naproxen reduces ISO-stimulated pck1 fluorescence ......... 73
4.4.3 Four “leads” reduces cAMP+DEX stimulated pck1 fluorescence intensity ........................ 75
4.4.4 Four “leads” reduces endogenous pck1 expression in WT zebrafish ................................. 77
4.4.5 Glucose tolerance test for amlexanox- or levofloxacin-treated adult zebrafish ................ 78
Chapter 5: Discussion ............................................................................................................... 80
5.1 High throughput drug screening ................................................................................................ 80
5.1.1 Initial screening revealed many “hit” compounds ............................................................. 80
5.1.2 Top “hits” were from several main categories of drugs ..................................................... 81
5.1.3 Four compounds decreased fluorescence intensity of Tg(Pck1:Venus) ............................. 82
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5.1.4 Strengths and limitations of the pck1 reporters ................................................................ 83
5.1.5 Strengths and limitations of the chemical screen .............................................................. 85
5.2 Lead compound: amlexanox ...................................................................................................... 87
5.2.1 Amlexanox reduces pck1 expression .................................................................................. 87
5.2.2 Amlexanox as an anti-inflammatory drug used to treat aphthous sores ........................... 88
5.2.3 Amlexanox regulates gluconeogenesis through activation of hepatic Stat3 ..................... 88
5.3 Lead compound: levofloxacin ..................................................................................................... 90
5.3.1 Levofloxacin reduces endogenous pck1 expression ........................................................... 90
5.3.2 Levofloxacin as an antibiotic drug used to treat various bacterial infections .................... 91
5.3.3 Fluoroquinolones shown to affect insulin release and gluconeogenesis ........................... 91
5.4 Lead compound: naproxen ......................................................................................................... 93
5.4.1 Naproxen reduces endogenous pck1 expression ............................................................... 93
5.4.2 Naproxen as an anti-inflammatory used to treat pain, fever, and swelling....................... 94
5.5 Lead compound: dicloxacillin ..................................................................................................... 95
5.5.1 Dicloxacillin reduces endogenous pck1 expression ............................................................ 95
5.5.2 Dicloxacillin as an anti-biotic used to treat infections from Gram positive bacteria ......... 95
Chapter 6: Conclusion .............................................................................................................. 97
Chapter 7: Future directions .................................................................................................. 100
References .................................................................................................................................. 105
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List of Abbreviations
ADME absorption, distribution, metabolism, extretion
AMPKα AMP-activated protein kinase α
ANOVA analysis of variance
ATF activating transcription factor
C/EBPβ CCAAT enhancer-binding protein β
cAMP cyclic adenosine monophosphate
cDNA complementary DNA
CREB cAMP response element binding protein
CREB3l3 cAMP response element binding protein 3-like 3
DEX dexamethasone
DMSO dimethyl sulfoxide
DMT2 diabetes mellitus type 2
dpf days post fertilization
hpf hours post fertilization
DPP-4 dipeptidyl peptidase 4
eGFP enhanced green fluorescent protein
FDA United States Food and Drug Administration
G6P glucose-6-phosphatase
GLP-1 glucagon-like peptide 1
hpf hours post fertilization
IKK IκB kinase
x
IRE-1α inositol requiring enzyme-1α
IRS-1 Insulin receptor substrates-1
ISO isoprenaline
LIP transcriptional inhibitory protein
luc luciferase
MET metformin
mTOR mammalian target of rapamycin
NCC NIH Clinical Collection
NFκB nuclear factor kappa light chain enhancer of activated b cells
NIH National Institutes of Health
PBS phosphate-buffered saline
PCK1 Phosphoenolpyruvate carboxykinase-1 gene
PEPCK phosphoenolpyruvate carboxykinase
PERK Protein kinase RNA-like endoplasmic reticulum kinase
PI-3 kinase phosphoinositide 3-kinase
PIP3 PI-3,4,5 triphosphate
PKB protein kinase B (or Akt)
PKC protein kinase C
PPARγ peroxisome proliferators activated receptor γ
ppm parts per million
SREBP sterol response binding protein 1 and 2
TBK1 TANK-binding kinase 1
xi
Tg transgenic
TNF-α tumor necrosis factor-α
TU Tuebingen
UPR unfolded protein response
WHO World Health Organization
WT wild type
xii
List of Figures Figure 1.1 Molecular pathways of insulin resistance caused by inflammation ............................................8
Figure 1.2 Various classes of type 2 diabetes drugs ................................................................................... 13
Figure 1.3 Process of drug discovery .......................................................................................................... 16
Figure 1.4 Typical workflow of a zebrafish-based screen .......................................................................... 23
Figure 1.5 Different cellular processes involving PEPCK ............................................................................ 29
Figure 1.7. Constructs of the transgenic zebrafish lines ............................................................................ 36
Figure 1.8. Merged brightfield and fluorescence images of zebrafish larvae ............................................ 36
Figure 3.1 Workflow of the drug library screening experiment. ................................................................ 47
Figure 3.2 Workflow of the fluorescence imaging experiment .................................................................. 50
Figure 3.3 Workflow of the glucose tolerance test .................................................................................... 53
Figure 3.4 Methods quantifying blood glucose levels in adult zebrafish.. ................................................. 55
Figure 4.1 Results from the initial drug screening experiment .................................................................. 58
Figure 4.2 Heatmap of a sample result from the plate reader .................................................................. 59
Figure 4.3 Z score and B score rankings .................................................................................................... 61
Figure 4.4 Rescreening results of the thirty six compounds ...................................................................... 64
Figure 4.5 Dose response curves for the 11 compounds pursued in validation studies ............................ 66
Figure 4.6 Effects of potential lead compounds on luciferase activity ...................................................... 67
Figure 4.7 Representative fluorescence images of 11 compounds pursued in validation studies ............ 69
Figure 4.8 Effect of amlexanox on luciferase activity of Tg(pck1:luc2) at various doses ........................... 71
Figure 4.9. Effect of levofloxacin on luciferase activity of Tg(pck1:luc2) at various doses ........................ 71
Figure 4.10. Effect of naproxen on luciferase activity of Tg(pck1:luc2) at various doses .......................... 72
Figure 4.11. Effect of dicloxacillin on luciferase activity of Tg(pck1:luc2) at various doses ....................... 72
Figure 4.12. Relative fluorescence intensity of Tg(Pck1:Venus) larvae treated with lead compounds ..... 74
Figure 4.13. Relative fluorescence intensity of Tg(Pck1:eGFP) larvae treated with lead compounds ...... 76
Figure 4.14. Relative pck1 expression of WT larvae treated with lead compounds .................................. 77
Figure 4.15. Glucose tolerance test of adult zebrafish treated with either amlexanox or levofloxacin .... 78
Figure 4.16. Summary of the workflow for the drug screening process. ................................................... 79
Figure 5.1 Chemical structure of amlexanox. ............................................................................................. 88
Figure 5.2 Chemical structure of fluoroquinolones, levofloxacin and gatifloxacin. ................................... 90
Figure 5.3 Chemical structure of naproxen. ............................................................................................... 94
Figure 5.4 Chemical structure of dicloxacillin. ........................................................................................... 95
xiii
List of Tables
Table 3.1 Embryo (E2) medium recipe.......................................................................................... 56
Table 3.2 Cortland’s Salt Solution recipe ...................................................................................... 56
Table 4.1 List of 36 compounds for rescreening........................................................................... 63
Table 4.2 List of eleven compounds for validation studies. ......................................................... 64
List of Equations
Equation 3.1 Equation used to calculate Z score for drug ranking. .............................................. 46
Equation 3.2 Equation used to calculate B score for drug ranking. ............................................. 46
1
Chapter 1: Introduction
1.1 Diabetes and obesity
1.1.1 Metabolic disorders
Living organisms have the ability to store organic compounds provided by nutrition and to
utilize them as necessary. In vertebrates, insulin and glucagon are primarily responsible for
metabolism and storage. Under normal conditions, insulin acts to increase glucose uptake into
skeletal tissue, convert carbohydrates to fats for storage, and decrease hepatic gluconeogenesis
(Sonksen and Sonksen 2000). Glucagon opposes these actions.
Throughout human evolution food and nutrients were often limited in availability, leading to
high consumption rates in times of plenty in order to replenish depleted body stores (Leonard
et al. 2010). However, recent advances in technology are interfering with this historical context.
Better agriculture techniques have produced an abundant supply of food in developed
countries. At the same time present day technology imposes less caloric demands on the
working force in these countries. Over-nutrition and high-calorie diets can result in insulin
resistance leading to an array of illnesses such as obesity, type 2 diabetes, nonalcoholic fatty
liver disease, atherosclerosis, and heart disease (Cani and Delzenne 2009, Samuel and Shulman
2012).
Impaired insulin action under pathological conditions causes decreased skeletal tissue uptake of
glucose, increased production of fatty acids, and increased hepatic gluconeogenesis (Gallagher
et al. 2010). These processes result in chronic hyperlipidemia and hyperglycemia. Specifically,
2
complications arising from chronic hyperglycemia and type 2 diabetes affect millions of adults
worldwide, and severely impacting the health care system. Current research thus aims at
identifying novel regulators of glucose homeostasis with the goal of developing potential
therapeutics for diabetic patients.
1.1.2 Diabetes mellitus
Diabetes mellitus is a constellation of metabolic disorders brought about by diminished cellular
capacity to mount an insulin response. In diabetes mellitus type 1 (DMT1) autoimmune events
destroy pancreatic β cells that produce insulin leading to decreased circulating insulin levels,
low glucose uptake, and hyperglycemia. As such, individuals almost always require the intake of
exogenous insulin to facilitate glucose absorption and to prevent ketoacidosis (Zimmet et al.
2001). By contrast, in diabetes mellitus type 2 (DMT2) there is appropriate insulin production in
the pancreas but glucose uptake is still impaired and hyperglycemia is present because cells
become insensitive to insulin stimulation. However, DMT2 is much more prevalent than its
childhood onset counterpart, accounting for over 90% of all cases (Zimmet et al. 2001).
Patients with DMT2 often experience symptoms such as polyurea (frequent urination),
polydipsia (excessive thirst), polyphagia (excessive hunger), blurred vision, fatigue, and weight
loss. Chronically high blood glucose levels in these patients can also their increase risk for other
pathophysiological complications over time, such as cardiovascular disease, stroke, renal
failure, neuropathy, retinopathy and increased rates of limb amputations. As a result, DMT2
patients are also likely at risk for increased rates of hospitalization. Indeed, the Canadian
3
Diabetes Association estimates that DMT2 and its associated complications are estimated to
cost the Canadian health care system $16.9 billion per year by 2020 (CDA, 2011).
1.1.3 Etiology and epidemiology of DMT2
DMT2 is a multi-factorial disease brought about by a combination of environmental and genetic
factors (Groop 1997). To date, over 120 variants involving more than 50 genes have been linked
to its development (McCarthy 2010, Prasad and Groop 2015). Of particular importance are
variants of the PPARG gene, which codes for a nuclear receptor responsible for fatty acid
metabolism, and the TCF7L2 gene which is a transcription factor which regulates insulin
production (Altshuler et al. 2000, Lyssenko et al. 2007, Jin and Liu 2008). Using genome-wide
association analysis, other genetic variants have been identified such as HHEX (a homeobox
domain transcription factor), WFS1 (the trans-membrane protein wolframin), HNF1A/1B (two
hepatocyte nuclear factors), MTNR1B (a melatonin receptor), and IRS1 (a insulin receptor
substrate gene coding for insulin signaling adapter protein) (Lyssenko et al. 2009, Pascoe et al.
2007, Rung et al. 2009, Sandhu et al. 2007, Winckler et al. 2007). Further demonstration for the
involvement of a genetic component is that fact that identical twins have higher chances of
developing DMT2 if one of the pair has developed the disease compared to fraternal twins
(Barnett et al. 1981, Newman et al. 1987, Poulsen et al. 2009).
Apart from genetic susceptibility, additional risk factors include demographic characteristics,
lifestyle choices, and existing health conditions. Sex, age, ethnicity, obesity, stress, and
sedentary behavior are identified contributors to DMT2 (Zimmet et al. 2001). Other
physiological contributors to DMT2 onset are insulin resistance, impaired glucose tolerance
4
(IGT), hypertension, and dyslipidemia (Grundy et al. 2005). In particular, those with IGT (defined
by WHO as high glucose levels after two hours of taking glucose), are not only at a high risk of
developing diabetes, but also have increased risk of developing other serious conditions, such
as cardiovascular disease (WHO 2006). Important age and socio-economic factors have also
been identified, such as global aging and a trend toward urbanization and westernization
(Zimmet et al. 2001).
DMT2 currently affects an estimated 285 million adults worldwide and at least 9% of Canada’s
adult population (Shaw et al. 2010). From an estimated total of 150 million adults affected
worldwide with DMT2 in 2000 the International Diabetes Federation has projected the number
of cases to increase to over 500 million by 2030 (Amos et al. 1997, IDF 2014). Significantly,
cases of DMT2 are also increasing at rapid rates in populations historically not afflicted with the
disease. In parts of Asia, India and the Pacific, studies have found the prevalence of DMT2 has
increased drastically (Chan et al. 2009, Ramachandran et al. 2012, Tan et al. 1999, Wild et al.
2004). One notable example is Nauru, a Pacific Island nation where DMT2 was rare several
decades ago, now has 23% of its adult population affected with the disease, including over 40%
of individuals aged above 55 (IDF 2013, Khambalia 2011).
Increases in adult DMT2 cases have been attributed to the medical successes of the 20th
century (Zimmet 2000). However, what is alarming is that DMT2 are also diagnosed at a
younger age than before, with scientists predicting that the incidence of type 2 diabetes can be
more prevalent than type 1 in children and adolescents among certain ethnicities (Zimmet et al.
2001). Cases have emerged from countries like Japan, US, UK, Australia (e.g., Dabelea et al.
5
2014, Drake et al. 2002, Ehtisham et al. 2004, Liu et al. 2009, Wiegand et al. 2004), where the
prevalence of type 2 diabetes are gradually increasing in younger populations. Studies have
shown that childhood obesity and type 2 diabetes are on the rise, with some ethnic populations
at higher risk than others (Chen et al. 2011, Fazeli Farsani et al. 2013, Ogden et al. 2007). In
Canada, children diagnosed with DMT2 were mostly of Aboriginal, Caucasian and Asian heritage
(Amed et al. 2010), where as in the United States, rates are highest among children of African
American, Hispanic, or Native Indian heritage (Imperatore et al. 2012). The increased rates of
DMT2 in adults and children has resulted DMT2 deemed to be a pandemic, and believed to be
one of the biggest threats to human health in the 21st century.
1.1.4 Physiology and Pathology
Under normal conditions, insulin is produced by the β cells of the pancreas. It stimulates
glucose uptake from the blood by skeletal muscles and fat tissue (Sonksen and Sonksen 2000).
At the same time insulin also inhibits liver gluconeogenesis and β oxidation of fatty acids.
Following feeding, insulin is able to reduce blood glucose levels to homeostatic levels. Type 2
diabetes occurs when the cells respond poorly to insulin signaling despite its being present at
physiological levels in the bloodstream.
There are three main mechanisms of insulin resistance recognized in the literature today: lipid
accumulation, unfolded protein response, and systemic inflammation (Samuel and Schulman
2012). In vivo studies have identified that accumulation of certain lipids in skeletal muscle
resulted in impaired insulin signaling and contributed to insulin resistance (Dresner et al. 1999,
Griffin et al. 1999). In particular, the accumulation of the free fatty acid diacylglycerol has been
6
found to impair insulin signaling by acting as a second signaling messenger through pathways
that ultimately activate novel protein kinase Cs (nPKCs) (Itani et al. 2002, Szendroedi et al.
2011). However, other lipids, such as triglycerides, ceramides and fatty acyl-coAs do not
contribute to insulin resistance (Krssak et al. 2000, Liu et al. 2007, Yu et al. 2002).
The protein kinase C family of enzymes are activated by diacylglycerol and has been implicated
in the pathogenesis of DMT2. So far, four isoforms of novel PKCs isoforms (nPKCs) have been
identified: δ, ε, η, and θ. In murine models, knocking out PKCε or PKCθ resulted in rodents
protected from insulin resistance caused by acute high-fat feeding or infusion (Kim et al. 2004,
Raddatz et al. 2011). Similarly, mice with decreases in PKCδ were also found to have improved
glucose tolerance (Bezy et al. 2011). These activated nPKCs subsequently inactivating insulin
receptor substrates (IRS-1) through phosphorylation, effectively decreasing the effectiveness of
insulin activation of IRS-1 and its ability to transmit its signals (Li et al. 2004, Yu et al. 2002).
The unfolded protein response (UPR) is a stress response mounted when improperly folded
proteins are found in the endoplasmic reticulum lumen during translation. The cell proceeds to
stop translation and break down the unfolded proteins (Okada et al. 2002). Increased UPR was
observed in leptin-knockout ob/ob mice (Ozcan et al. 2004). Decreased UPR was observed in
obese patients subsequent to weight loss (Gregor et al. 2009). Further studies inducing or
alleviating endoplasmic reticulum stress discovered that chemical inducers of UPR such as
tunicamycin and thapsigargin were found to impair insulin action in the mouse liver, while
chemical chaperones 4-phenyl butyric acid and ursodeoxycholic acid reduced ER stress and
restored insulin action (Ozcan et al. 2004, Ozcan et al. 2006).
7
Initiation of UPR requires the activation of three transducer proteins: protein kinase RNA-like
endoplasmic reticulum kinase (PERK), inositol requiring enzyme 1α (IRE-1α), and activating
transcription factor 6 (ATF-6) (Figure 1.1). PERK is responsible for stopping translation, through
phosphorylating and inactivating eukaryotic translation initiation factor 2α (Ron and Walter
2007). IRE-1α has endonuclease activity and splices the mRNA X-box binding protein 1 (XBP1),
while ATF-6 is a transcription factor that helps to upregulate genes associated with UPR (Calfon
et al. 2002, Zhang and Kaufman 2004). Studies inhibiting PERK in mice found lowered
triglyceride content in the liver and improved insulin sensitivity (Oyadomari et al. 2008).
Interestingly, mice with reduced IRE-1α or ATF-6 were phenotypically normal but developed
liver steatosis upon stimulation with chemical inducers of UPR (Yamamoto et al 2010, Zhang et
al 2011). The exact nature of how UPR induces insulin insensitivity still requires further studies.
Insulin resistance and obesity are often thought of as chronic inflammatory diseases; insulin
resistant mice were found to have elevated cytokine levels (tumor necrosis factorα, TNF- α) in
their bloodstreams (Hotamisligil et al. 1993). Obesity increases stimulation of the nuclear factor
kappa-light-chain-enhancer of activated B cells (NFκB) pathway (Arkan et al. 2005, Baker et al.
2010, Hotamisligil et al. 2006) (Figure 1.1). Activation of the NFκB pathway leads to subsequent
expression of non-canonical IκB kinases (IKK) such as IKKβ and IKKε, and TANK binding kinase 1
(TBK-1) in the liver, all of which contributes to obesity-induced insulin resistance (Saltiel 2012,
Wunderlich et al. 2008, Yuan et al. 2001). One group of researchers recently identified that IKKε
and TBK-1 phosphorylates and activates phosphodiesterase 3B in adipocytes, leading to
decrease in cAMP levels (Mowers et al. 2013). This causes a reduction in sensitivity of the
pancreatic β cell adrenergic receptor agonists, resulting in obesity.
8
Figure 1.1 Molecular pathways of insulin resistance caused by inflammation and stress. Increase ER stress is associated with obesity and an increase in UPR and its associated proteins PERK, IRE-1α, and ATF 6. Insulin and IRS inactivates JNK. Chronic inflammation and insulin insensitivity activates JNK, IKK, and NF-κB (Hotamisligil 2006). Reprinted with permission from Nature Publishing Group.
1.1.5 Current anti-diabetic therapeutics and limitations
Currently, there are four main types of anti-diabetic drugs: insulin secretogogues, insulin
sensitizers, α-glucosidase inhibitors, and analogues and inhibitors of the incretin hormone.
Insulin secretagogues include drugs of the sulfonylurea and meglitinides classes, which all binds
to receptors on β cells of the pancreas acts to enhance insulin secretion (Black et al. 2007,
Uwaifo and Ratner 2007). Examples of sulfonylurea class drugs include gliclazide, glyburide,
9
glimepiride, acetohexamide, chlorpropamide, tolazamide, and tolbutamide. While these drugs
are widely prescribed to diabetic patients, common side effects include weight gain and
hypoglycemia (Nathan et al. 2009). In addition, patients taking sulfonylureas have also been
associated with increased risk of cardiovascular disease (Thisted et al. 2006). Since these drugs
work by enhancing insulin secretion, β cells must be present to produce insulin. Sulfonylureas
are associated with progressive death of β cells and long-term therapy failure (Sena et al. 2010).
Meglitinides, on the other hand, is a new class of drugs with similar mechanisms of action as
sulfonylureas. The two drugs classified in this class are nateglinide and repaglinide. They are
attractive to patients with impaired renal function as they appear to be safe while maintaining
their efficacy (Hasslacher 2003). Patients taking meglitinides appear to have better preservation
of β cell function compared with sulfonylureas due to their short half-life and rapid acting
effects (Blicklé 2006), but the long-term clinical implications have yet to be determined.
Insulin sensitizers are comprised of the biguanides and thiazolidinediones classes of drugs.
Metformin, the well-known therapeutic for type 2 diabetes, is a biguanide derivative. Contrary
to insulin secretogogues, metformin works by enhancing cellular sensitivity to insulin rather
than stimulating insulin production (Kirpichnikov et al. 2002, Uwaifo and Ratner 2007). It acts
mostly in the liver through SLC22A1 (solute carrier family 22 member 1) to reduce hepatic
gluconeogenesis, as well as skeletal tissues to increase glucose uptake and reducing insulin
resistance (Nathan et al. 2009, Pernicova et al. 2014, Uwaifo and Ratner 2007). Its other effects
include reducing circulating free fatty acids and improving lipid profiles of patients with type 2
diabetes (Kirpichnikov et al. 2002, Krentz et al. 2008, Libby 2003). Side effects of metformin
10
include: fatal lactic acidosis, gastrointestinal (GI) symptoms, loss of β cell function and B12
deficiency (Krentz et al. 2008, Ting et al. 2006). Moreover, metformin is advised against
patients with impaired renal, hepatic, and cardiac function (Mayerson and Inzucchi 2002).
Thiazolidinediones are another class of insulin sensitizer, comprising of pioglitazone and
rosiglitazone drugs. This group of drugs works by increasing insulin sensitivity of the muscle,
adipose and liver tissues through activating peroxisome proliferators activated receptor γ
(PPARγ) (Ahmed et al. 2007, Nathan et al. 2009). They also reduce adipocyte lipolysis, thereby
decreasing circulating free fatty acids (Ye et al. 2004, Krentz et al. 2008). Evidence suggests that
thiazolidinediones work more synergically with other anti-hyperglycemic therapeutics like
metformin and insulin (Raskin et al. 2001, Stafford and Elasy 2007). Similar to metformin,
patients taking thiazolidinediones have an increased risk of cardiovascular diseases (Nissen et
al. 2007, Selvin et al. 2008). Those with impaired liver function are also advised against taking
these drugs (Marcy et al. 2004, Yki-Jarvinen 2004). Side effects of thiazolidinediones include
weight gain and fluid retention (Guan et al. 2005, Purnell and Weyer 2003). It important to note
that thiazolidinediones do not typically cause gastrointestinal (GI) symptoms, which is a main
side effect of metformin (Sena et al. 2010).
The α-glucosidase inhibitors acarbose, miglitol, and voglibose manage hyperglycemia by
competitively inhibiting α-glucosidase enzymes in the small intestines that are involved in
glucose absorption and decrease glucose transfer to the blood stream. As such, they do not
result in weight gain and are not as efficient at lowering blood glucose levels when compared
with other anti-diabetic therapeutics (Krentz and Bailey 2005, Nathan et al. 2009). They also
11
have unwanted GI side effects, such as flatulence (Krentz et al. 2008, Nathan et al. 2009, van de
Laar et al. 2005).
Therapeutics have also been developed for diabetic patients that take advantage of the incretin
hormone GLP-1 (glucagon-like peptide 1), secreted by cells of the small intestines. Shortly after
eating, incretin levels rise and stimulate insulin production while inhibiting glucagon release
(Baggio et al. 2007). Analogs and mimics that can increase GLP-1 levels by binding to cellular
receptors have been lately explored for anti-hyperglycemic properties (Drucker and Nauck
2006). Clinically, the GLP-1 receptor agonists (exenatide and liraglutide) have been shown to
reduce blood glucose levels as well as promoting weight loss, both as monotherapy or
combined with metformin or sulfonylureas (Heine et al. 2005, Kendall et al. 2005, Ratner et al.
2006). These drugs, however, require subcutaneous administration, which limits their
usefulness outside the clinic. Side effects of GLP-1 receptor agonists include GI symptoms
(though decreased with time) and pancreatitis (Heine et al. 2005).
The enzyme dipeptidyl peptidase 4 (DPP-4) degrades GLP-1 proteins; DPP-4 inhibitors are
another class of drugs that exploit the incretin system (Holst et al. 2009). DPP-4 inhibitors
sitagliptin and vildagliptin have anti-hyperglycemic effects while improving β cell function and
being weight neutral (Ahren et al. 2005, Aschner et al. 2006, Barnett et al. 2006, Raz et al.
2006). However, DPP-4 inhibitors are associated with a variety of side effects due to the
prevalence of DPP-4 protein in multiple tissue types. These side effects include upper
respiratory tract infections and nasopharyngitis (Labmeir et al. 2003, Sheffield et al. 2008).
12
SGLT2 (sodium-glucose co-transporter 2) inhibitors are a newly developed class of anti-diabetic
medications (Chao and Henry 2010). SGLT2 are expressed exclusively by cells in the proximal
convoluted tubule of the kidney and are responsible for the majority of the glucose
reabsorption from the urine via active transport (Brown 2000, Chao and Henry 2010, Yee and
Han 2007). Diabetic patients have been observed to have an increased rate of renal glucose
absorption, a factor that contributes to elevated blood glucose levels (Farber et al. 1951).
Inhibiting glucose reabsorption by blocking SGLT2 can thus be an effective therapeutic target.
Recently developed SGLT2 inhibitors include dapagliflozin and canagliflozin (Meng et al. 2008,
Nomura et al. 2010). Both are approved by the FDA and are currently in phase III clinical trials.
Clinical results indicate that dapagliflozin is more effective at reducing blood glucose levels,
both as monotherapy or in combination with metformin (Bailey et al. 2010, Char et al. 2012,
Ferrannini et al. 2010). Long-term adverse effects of SLGT2 inhibitors are currently not known.
13
Figure 1.2 Various classes of type 2 diabetes drugs, their mechanism of action and target site (Sena et al. 2010). Reprinted with permission from Springer.
1.2 The drug discovery and development process
1.2.1 Overview
Drug discovery is a process by which a chemical compound is identified with novel biological
effects while being safe and potent for clinical applications. Not surprisingly, it is a long, costly,
14
and often testing process, from the initial inception to the drug’s arrival on the market. The
drug discovery process may take as long as 15 years and cost more than $1 billion for each
developed drug that reaches the marketplace (DiMasi et al. 2003, Hughes et al. 2011). Many
drugs fail during development, due to being either not effective or too toxic to humans (Silber
2010).
The process starts with target selection. Appropriate and potential drug targets are selected.
These targets are typically key enzymes involved in a vital step of disease pathogenesis (Lindsay
2003). Next, screening assays of chemical libraries are conducted in order to select for
molecules also known as “hits”, which affect the drug target. These assays may be based on
fluorescence, absorbance, or chemiluminescence readings, and are frequently high-throughput
in nature (Silber 2010). The chemical library selected may be dependent on prior knowledge of
the likely classes of compounds that can interact with proposed drug targets (e.g., Boppana et
al. 2009).
Following the chemical screen, “hits” can subsequently be ranked according to efficacy and
consistency based on analysis of the screening results (Davies et al. 2006). These “hits” can also
be grouped according to chemical classes or structural similarities, and a whole class of
compounds can be selected as “leads” following validation studies with animal models
(Caldwell et al. 2001). Since the cost of drug development is high, the proper assessment and
selection from “hits” to “leads” can reduce attrition in the later and more expensive phases of
the process (Bleicher et al. 2009). There are also certain characteristics of a “hit” that resembles
a “lead”; for example, Lipinski and colleagues (1997) acutely observed that leads typically have
15
a molecular weight of less than 350 Daltons, and less than five total H-bond donors. They
coined these characteristics the Lipinsky Rule of Five (RO5). These rules can certainly assist with
the selection of “leads” from “hits” (Wunberg et al. 2006).
After “leads” are selected, they may be chemically engineered to have optimal efficacy and a
safe toxicological profile. Medicinal chemistry optimization of lead compounds can be very
laborious and time consuming. Pharmacokinematics and pharmacodynamics of these leads are
further studied, including the absorption, distribution, metabolism and excretion (ADME) of
these optimized “leads” (Bleicher et al. 2009). Pre-clinical validation studies, including tests
using animal models, are performed as well at this time (Silber 2010). Human clinical trials are
the deciding stage in drug development when a drug’s efficacy and toxicity in humans is
evaluated. It starts with phase I, where only a handful of volunteers (20-100) are recruited to
test the safety of the compound. Phase II consists of a larger group of volunteers (100-300) to
evaluate the efficacy of the compound and reaffirm its safety profile. A compound in phase III
clinical trial will be tried by more volunteers (1000-3000) to compare its effects with previously
established treatment options as well as the final stage of safety confirmation. Clinical trials end
with phase IV, where the drug’s long term effects on the general population are monitored
(Figure 1.3).
16
Figure 1.3 Process of drug discovery (Rudin and Weissleder 2003). Reprinted with permission from Nature Publishing Group.
1.2.2 Challenges to traditional drug discovery
Traditional drug discovery efforts have been fruitful for many diseases and illnesses. Conditions
that were once deadly and untreatable can now be managed or even cured with the proper
medications. For example, treatment options for patients suffering from hypertension,
osteoporosis, and depression are a lot better now than decades ago (Silber 2010). However,
there are still many challenges we need to overcome in order to find effective therapies for
those diseases that still do not have one yet.
Traditional drug discovery rely on the existence of a target (Lindsay 2003). Poor understanding
of disease pathology or lack of an identifiable target for small molecules to modulate
undoubtedly hinders drug discovery and development. In vitro methods remains an integral
part of the disease discovery process; however, in order to capture the totality of drug effects,
a variety of in vitro techniques and in vivo animal models must be used prior to the issuance of
an Investigational New Drug (IND) approval. Lastly, unpredicted in vivo outcomes due to ADME
17
and toxicity are common causes why “leads” fail during development. Drugs may be poorly
absorbed, metabolized extensively during their initial passage through the liver so that their
effect is minimal, or they may be metabolized too slow and impact other cellular or
physiological processes. Drugs may also not be able to reach their intended site of action, such
as cross the blood brain barrier to reach the cerebral cortex. Drugs could also be extremely
toxic. About 70% of the drugs that fail in pre-clinical testing are due to toxicity (Silber 2010).
Needless to say, overcoming one or more of any of these challenges would significantly
improve the process and substantially reduce the cost of drug discovery, research, and
development.
1.2.3 In vivo drug screening
In order to address the challenges faced by traditional high throughput screening process,
several modifications have been suggested. It is important to note that these slight changes to
the traditional methods are not intended to replace the drug discovery process. Rather, they
serve as a supplement to the customary ways to identify novel chemicals, mechanisms, or
targets that might be otherwise overlooked (Giacomotto and Ségalat 2010).
One of these suggested changes is that drug screening of large chemical libraries should
proceed in vivo in appropriate models. Using animal models early in the drug screening process
definitely has several advantages (Giacomotto and Ségalat 2010). First, depending on the
animal model, physiological and sometimes genetic similarities with the human body enable a
better modeling of diseases and their complex pathologies than conventional in vitro methods
that are usually based on few or single targets (e.g., West et al. 2000). Second, toxicity and
18
ADME can be observed simultaneously with the initial chemical screening. Drugs that are highly
toxic or poorly absorbed can be identified immediately. Yet, animal models such as mice, rats,
pigs, rabbits, or monkeys are too costly to be used for large-scale chemical screens. Smaller
models such as the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, or
zebrafish Danio rerio have been gaining popularity among researchers for use in high
throughput screening applications (Ségalat 2007).
All three of the smaller animal models share similar characteristics that make them attractive
for whole-organism high throughput screening. They require low cost and effort to maintain,
while maintaining important genetic and physiological similarities to humans. In these models,
drug selection is based on a phenotype rather than a single target. As a result, prior knowledge
of a drug’s target is not required (Giacomotto and Ségalat 2010). This phenotypic-based
screening method is advantageous in that it may serve to identify previously unsuspected
molecular pathways or chemical targets. The molecular mechanism of action for a particular
disease need not be previously understood (Anthony and Swinnay 2011). The number of novel
first-in-class drug discoveries using phenotypic screening is far more than those discovered
using the target-based screening approach (Anthony and Swinnay 2011).
C. elegans has been developed in the past few decades as a useful model organism with many
applications. The discovery of apoptotic genes, molecular mechanism of aging, and factors of
embryonic development are just some examples of the progress made using this organism
(Hanazawa et al. 2011, Peden 2008, Wolkow et al. 2000). Additionally, many strains have been
generated and are available, at the Caenorhabditis Genetics Center at the University of
19
Minnesota. RNAi technology has been used extensively in C. elegans to disrupt gene function,
and thanks to the development of advanced methods for cryopreservation, these strains are
available for future studies (Kamath et al. 2003, Stiernagle 2006). However, this invertebrate is
still evolutionarily distal to humans compared with other available organisms, and it is unable to
model some human diseases due to lack of homologous genes (Giacomotto and Ségalat 2010).
The fruit fly has traditionally been used by biologists for genetic studies. There have been
recent attempts in utilizing D. melanogaster for high throughput drug screens (e.g., Pandey and
Nichols, 2011). The fruit fly can also be used to model a variety of human diseases, such as
Alzheimer’s disease, Parkinson’s disease, sleep, and seizure disorders (Chee et al. 2005, Coulon
and Birman, 2004, Crowther et al. 2005, Koh et al. 2008, Kuebler and Tanouye 2000, Nishimura
et al. 2004). However, its disadvantages include lack of available cryo-preservation technology
and inability to be reared in liquid medium for chemical screening (Nagy et al. 2003).
Additionally, its physiology and anatomy is still poor in proximity with humans compared with
other vertebrate models such as zebrafish.
1.2.4 Drug repurposing
Drug repurposing is the idea of re-tasking previously approved drugs for new purposes (Sleigh
and Barton 2010). The idea has gained popularity in recent years because it is much cheaper
than the traditional drug development process, and is thought to increase the yields from
chemical drug screens (Ashburn and Thor 2004, Boguski et al. 2009). Repurposed drugs come
with known ADME, toxicity, and clinical effects profiles. There is also a wealth of information
available about these drugs’ mechanism of action further relieving some of the strains
20
associated with the development process and potentially bringing these medicines to market
faster. A principal drawback of this approach is the fact that drugs that have not previously
developed or explored will not be examined. Novel compounds therefore cannot be identified.
There are several examples already of repurposed drugs. The best known example is sildenafil,
sold under the brand Viagra. It was originally developed by Pfizer researchers to treat angina in
1991, but was later discovered as an effective therapeutic for erectile dysfunction (Ghofrani et
al. 2006). Thalidomide was made in 1954 by Ciba Pharmaceuticals for morning sickness, and
was withdrawn several years later due to its teratogenic properties (D’Amato et al. 1994, Mellin
and Katzenstein 1962). Later, FDA has recently approved thalidomide as a treatment for lesions
of erythema nodosum leprosum (Franks et al. 2004, Teo et al. 2002). Lastly, bupropion
(Wellbutrin) is a commonly used antidepressant, but has found uses in helping people quit
smoking, under the trade name Zyban (Moreira 2007, Wu et al. 2006).
1.3 Zebrafish and high throughput drug screening
1.3.1 Zebrafish as a model organism
Zebrafish has been used historically by biologists to study embryonic organ development
(Grunwald and Eisen 2002, Streisinger et al. 1981). About two decades ago, the first two large
scale genetic screens ever performed using a vertebrate model were completed using zebrafish
(Haffter et al. 1996, Driever et al. 1996). Since then, many of the zebrafish genes involved in key
biological processes that model human diseases were identified, with the results deposited in
the Zebrafish Information Network (ZFIN). The ZFIN database also provides gene, antibody,
transgenic, and anatomical data of zebrafish freely, adding to the growing popularity of
21
zebrafish as a model of human disease. Recently, the full zebrafish genome was sequenced, and
about 70% of all zebrafish genes were identified to have human analogues (Howe et al. 2013).
Zebrafish is an excellent stepping stone model organism between in vitro models and higher
vertebrate models like mice and rats.
A number of genetic tools have been adapted for zebrafish, including targeting induced local
lesions in genomes (TILLINGs), transcription activator-like effector nucleases (TALENs) zinc
finger nucleases (ZFNs), morpholino oligomer microinjections, and most recently, clustered
regularly interspaced short palindromic repeats (CRISPRs) (Chang et al. 2013, Doyon et al. 2008,
Huang et al. 2011, Nasevicius and Ekker 2000, Wienholds et al. 2003). Collectively, these
genetic tools have all contributed to the generation of numerous transgenic zebrafish lines, all
of which are stored in the Zebrafish International Resource Center (ZIRC) at University of
Oregon and are available upon request. Various zebrafish strains can also be preserved
indefinitely through cryo-preservation technology (Carmichael et al. 2009).
1.3.2 Zebrafish and its suitability for HTS
Zebrafish is a model organism aptly suited for in vivo drug screening. It is particularly appealing
due to its high fecundity, ex-uterine development of optically transparent embryo, and ease of
manipulation and maintenance. For example, a pair of zebrafish can produce up to 200
fertilized eggs with each mating (Adatto et al. 2011). Several pairs of fish and a few aquarium
tanks can thus provide a constant supply of embryos and larvae for research and screening
purposes (Peterson and MacRae 2012). Ex-uterine development combined with optical
transparency facilitates fluorescence imaging, bioluminescence assays, and observation of
22
organogenesis and morphological defects (Giacomotto and Ségalat 2010, Miscervic et al. 2012).
Additionally, zebrafish eggs are approximately 0.7 mm in diameter, and 7-day-old larvae are
about 5 mm in length (Kimmel et al. 1995). This makes zebrafish progeny easy to handle, simple
to rear, and convenient to manipulate with regular laboratory instrumentation.
The life cycle of the zebrafish is also quite rapid. Adults reach sexual maturity within four
months (Njiwa et al. 2004). Many of the internal organs and systems are formed in the embryo
within a few days (Westerfield 1995). For example, the pancreas is fully formed and
functionable by 3 days post fertilization (dpf) and the liver by 4 dpf (Tao and Peng 2009, Tehrani
and Lin, 2010). Zebrafish embryos and larvae are quite hardy; they can tolerate manipulation
and handling well. They also can survive for days in microtiter plates without changing the
liquid medium or providing them with food, as larvae are fed and sustained by their embryonic
yolk sac for several days after fertilization. As such, large scale in vivo chemical screens using
96-well plates are viable applications for zebrafish (e.g., Barros et al. 2008) (Figure 1.4). Most
importantly, zebrafish embryo and larvae are capable of absorbing chemical compounds from
their liquid surroundings readily, a characteristic that C. elegans and D. melanogaster does not
share (Johnston 2002, Jorgensen and Mango 2002, MacRae and Peterson 2003).
23
Figure 1.4 Typical workflow of a zebrafish-based high throughput screening project (Lieschke and Currie 2007). Reprinted with permission from Nature Publishing Group.
1.3.3 Zebrafish models of disease
There are many well-established human disease modeled in zebrafish. This has been made
possible by the fact that many genes are conserved between zebrafish and humans. In addition,
the two organisms share many biological and physiological similarities (Stoletov and Klemke
2008). Existing zebrafish disease models are primarily in the areas of inflammation, infection,
and cancer. For example, zebrafish has been successfully used to study human bacterial
infections, tuberculosis, hemorrhagic stroke, melanoma, liver carcinoma, and testicular cancer
(Carvalho et al. 2011, Dovey et al. 2009, Herbomel et al. 1999, Lam et al. 2006, Liu et al. 2007,
Neumann et al. 2009). Other human processes such as tissue regeneration, angiogenesis,
24
osteogenesis, and hematopoiesis have been studied in zebrafish (Clement et al. 2008, Martin et
al. 2011, Peal et al. 2010, Poss et al. 2002, Rawls and Johnson 2000). Behaviour modeling has
also been performed in adult and larvae zebrafish (Norton and Bally-Cuif 2010).
1.3.4 Organ development in zebrafish: pancreas and liver
Zebrafish share many anatomical and physiological similarities with higher order vertebrates
such as mice and humans. For example, zebrafish pancreas and liver are structurally and
functionally similar to the human organs and that of other mammals (Tehrani and Lin 2011).
Similar to what is seen with mammalian pancreatic development the zebrafish pancreas also
originates as two buds from the posterior foregut endoderm (Field et al. 2003). The dorsal bud
forms first, at 24 hours post fertilization (hpf), which contributes to the primary islet of the
pancreas as well as the α, β, γ, δ, and ε endocrine cells. The ventral bud is formed at 32 hpf and
contributes to endocrine cells (α, β, γ, δ, and ε) as well as exocrine cells. Both buds fuse at 50
hpf, with the primary islets comprising of endocrine cells surrounded by exocrine cells. The
mature pancreas is located on the right side of the zebrafish (Field et al. 2003). The main
difference between zebrafish and mammals is that both the dorsal and ventral buds in
mammals give rise to endocrine and exocrine cells (Pan and Wright 2011).
Each type of endocrine cells is responsible for production and secretion of a hormone. As in
mammals, the α cells of the pancreas in zebrafish are responsible for secreting glucagon, β cells
insulin, γ, cells pancreatic polypeptide, δ cells somatostatin, and ε cells ghrelin (Wilfinger et al.
2013). Interestingly, β cells originating from the ventral bud are found to secrete higher levels
of insulin than β cells from the dorsal bud in the mature pancreas (Hesselson et al. 2009). Due
25
to the similarity in pancreas development between zebrafish and mammals, many of the
signaling pathways are thought to be well conserved (Tehrani and Lin 2011).
The zebrafish liver arises from the ventral foregut endoderm, where hepatoblasts form the liver
bud. After proliferation and growth, these hepatoblasts differentiate into hepatocytes and
biliary duct cells. This process is complete and larval zebrafish have a fully functional liver at
around 4 dpf (Tehrani and Lin, 2010). A difference between fish and mammalian liver is that the
zebrafish liver is not well organized into lobes like the mammalian liver (Menke et al. 2011).
Additionally, the zebrafish liver does not contain Kupffer cells, macrophages part of the
mammals’ mononuclear phagocyte system (Menke et al. 2011).
The zebrafish liver is responsible for processing nutrients, detoxifying chemicals, and
synthesizing proteins like albumin, tasks that are very similar to mammalian livers. However,
during embryogenesis the zebrafish liver is not a major hematopoietic organ as it is in many
mammals (Reimold et al. 2000). In zebrafish, hematopoiesis is carried out instead by a
combination of organs: intermediate cell mass, posterior blood land, and kidneys (Jin et al.
2009, Thisse and Zon 2002). Even though this provides a point of divergence from the situation
in humans, it offers the advantage that liver function or morphology can be studied without
worrying about the consequences of experiments on blood formation and ensuing
development (Tao and Peng 2009).
1.3.5 Glucose homeostasis in zebrafish
The regulation of glucose homeostasis in zebrafish closely resembles that in mammals. During
embryonic development, absolute glucose levels in the larvae peaks at 24 hpf, and is followed
26
by a decline thought to be caused by the formation of the pancreas islets (Jurczyk et al. 2011).
Following organogenesis, blood glucose levels are dictated by fasting and feeding of the fish
(Eames et al. 2010). Many of the hormones involved in hunger and satiety have similar effects
in zebrafish as they do in humans. For example, neuropeptide Y, ghrelin, and Agouti-related
peptide (AgRP) have been identified in zebrafish to increase appetite; cocaine and
amphetamine regulated transcript (CART) peptide, and corticotrophin-releasing factor
decreased it (Kawauchi 2006, Zhang et al. 2012). Zebrafish, unlike lower organisms such as D.
melanogaster and C. elegans, are similar to humans in that it is capable of storing excess
nutrients in white adipocytes (Gesta et al. 2007).
Zebrafish blood glucose levels responds to feeding, fasting, and various metabolic hormones in
a similar manner to what has been reported in mammals, including humans. For example,
fasting increases AMPKα (AMP-activated protein kinase α) and CREB3l3 (cAMP response
element binding protein 3-like 3) gene expression in the liver while decreasing mTOR
(mammalian target of rapamycin) and SREBP1 and 2 (sterol response binding protein 1 and 2)
expression (Craig and Moon 2011). Furthermore, insulin production in zebrafish can be induced
by high glucose levels, and evidence suggests that insulin regulates glucose levels in like manner
to the situation seen in mammals (Jurcyzk et al. 2011). However, unlike humans, zebrafish
insulin is produced by two genes, INSa and INSb, instead of one (Papasani et al. 2004). Insa is
expressed in the pancreas and is considered to be the fish analogue of the human insulin gene,
while insb is mostly active during embryogenesis (Papasani et al. 2004).
27
1.3.6 Zebrafish models of glucose metabolism and diabetes
Zebrafish has been used as a model for various metabolic diseases, glucose metabolism and
diabetic complications. Feeding a high-calorie diet to healthy adult zebrafish for 8 weeks has
resulted in increased triglyceride levels and liver steatosis, capturing some of the characteristics
associated with obesity in humans (Oka et al. 2010). In transgenic zebrafish that overexpress
AgRP (which increases appetite), glucose intolerance and ectopic fat deposition have been
demonstrated (Song and Cone 2007). Morpholino knockdown of genes Mnx1 (motor neuron
and pancreas homeobox 1), Pdx1 (pancreatic and duodenal homeobox 1), and Irx3a (iroquois-
class homeodomain protein 3a) results in decreased number of β cells and/or reduced pancreas
size, and effectively model neonatal or monogenic diabetes (Ragvin et al. 2010, Wendik et al.
2004, Yee et al. 2001). A high-cholesterol diet fed to adult zebrafish is used as a model for
atherosclerosis, where these fish develop vascular lesions, lipoprotein oxidation, and lipid
uptake by macrophages (Stoletov et al. 2009). Lastly, incubation of adult and larva fish with
glucose solution induces hyperglycemia and models late-diabetic complications (Alvarez et al.
2010, Gleeson et al. 2007, Jorgens et al. 2012, Liang et al. 2010).
A zebrafish DMT1 model was developed by Pisharath et al. (2007), where β cells are
conditionally ablated using a combination of nitroreductase and metronidazole. Nitroreductase
is transgenically inserted under the INSa promoter, and is able to convert metronidazole into a
potent cytotoxic compound that destroys β cells. Additionally, Maddison and Chen (2012) used
a high-nutrition diet (glucose and lipids) to induce β cell neogenesis in healthy zebrafish larvae,
thus establishing a type 2 diabetes model in the zebrafish. Many of the models of obesity and
diabetes discussed above examine, study, and manipulate the insulin producing cells of the
28
pancreas. However, the liver and its activities of gluconeogenesis and fatty acid oxidation also
plays an important role in energy storage and metabolism as well as in glucose homeostasis.
Insulin insensitivity allows liver gluconeogenesis to proceed unimpeded adding to the pathology
of DMT2 (Stumvoll et al. 2005). Thus, the gluconeogenesis process in the liver is another
potential therapeutic target for DMT2. This possibility was explored at length by the
experiments described in this thesis.
1.4 Target for gluconeogenesis: PEPCK
1.4.1 Overview
The pck1 gene on chromosome 20 encodes for the phosphoenolpyruvate carboxykinase
(PEPCK) enzyme in zebrafish. It is a 622-amino acid protein that is responsible for converting
oxaloacetate into phosphoenolpyruvate and carbon dioxide, a rate-limiting step of
gluconeogenesis in the liver (Beale et al. 2007). This enzyme is also involved in
glyceroneogenesis and cataplerosis processes (Figure 1.5). PEPCK-C is the cytosolic form and
has been widely studied, and most abundantly present in murine models. PEPCK-M is the
mitochondrial form of the protein and is encoded by the pck2 gene located on chromosome 14,
and contains 640 amino acids. Both forms are found in the liver, kidney, and adipose tissue. The
PEPCK-C protein is well conserved across the animal kingdom, particularity the vertebrates. In
zebrafish, amino acid sequence alignment of PEPCK-C protein showed 73% similarity with
humans. The pck1 gene can be found on chromosome 6 encoding 630 amino acids, while pck2
is found on chromosome 24 encoding 636 amino acids in humans.
29
Figure 1.5 Different cellular processes involving PEPCK. It converts oxaloacetate into phosphoenolpyruvate and carbon dioxide in gluconeogenesis, glyceroneogenesis, and cataplerosis (Beale et al. 2007). Reprinted with permission from Springer.
One of the results of insulin resistance is the inability to regulate gluconeogenesis in the liver,
thereby contributing to hyperglycemia. During fasting, hepatic gluconeogenesis is activated by
enzymes such as glucose-6-phosphatase (G6Pase), fructose 1,6-bisphophatase (Fbpase), and
PEPCK-C (Oh et al. 2013). The end product of gluconeogenesis is glucose; increasing expression
of these activating enzymes lead to increases in blood glucose levels. PEPCK catalyzes one of
the two rate-limiting steps in the gluconeogenesis pathway. Indeed, mice with over-expression
of PEPCK in the liver were hyperglycemic (Valera et al. 1994). These features have position
these enzymes as targets for diabetic therapeutics (e.g., Beale et al. 2007).
If over-expression of PEPCK-C leads to hyperglycemia, then inhibition or knockout of PEPCK
should reverse this effect. Early studies have documented individuals with PEPCK deficiencies
who were hypoglycemic, thereby suggesting a role for PEPCK in glucose homeostatis (Hommes
30
et al. 1976, Vidnes and Sovik 1976). Clinical studies later emerged suggesting an association
between pck1 and diabetes (Hani et al. 1996). Recently, pck1 polymorphisms have been linked
with the development of diabetes. A single nucleotide polymorphism (SNP) was identified in
the pck1 promoter at position -232 relative to the gene start site, which corresponded to a cis-
acting element involved in transcriptional regulation (Cao et al. 2004). This study was
conducted among Canadian Aboriginals and Caucasians, and subsequent studies found similar
results in UK-South Asian populations (Rees et al. 2009).
1.4.2 Modifying pck1 expression
Transgenic mice with pck1 knockouts died shortly after birth (She et al. 2000). These mice were
generated using Cre/Lox technology. It was believed their deaths were related more to lack of
cataplerosis than lack of gluconeogenesis (Beale et al. 2007). Surprisingly, when Cre
recombinase was expressed under the control of a liver specific promoter, mice exhibited
normal glucose levels (She et al. 2000). They were found to have slower citric acid cycles
leading to fat accumulation in the liver during fasting (Burgess et al. 2004). Furthermore, when
liver pck1 expression was reduced using adenovirus induced RNAi technology in db/db mice,
improved insulin sensitivity was observed (Gómez-Valadés et al. 2008). Lastly, lowering pck1
levels in adipose tissue resulted in reduced glyceroneogenesis, whereas increased insulin
secretion (Olswang et al. 2002, Millward et al. 2010).
Increased pck1 expression led to more expected results. Over-expression of liver pck1
expression in mice led to hyperglycemia, hyperinsulinemia, and altered hepatic glycogen
contents (Valera et al. 1994). This is likely due to increased gluconeogenesis. Similarly, over-
31
expression of pck1 expression in adipose tissue resulted in increased glyceroneogenesis; mouse
became obese without insensitivity to insulin due to increased fatty acid reesterification
(Franckhauser et al. 2002).
1.4.3 Regulation of pck1
Early studies of pck1 documented that an increase in expression was observed in livers of
diabetic rats, and an injection of insulin substantially decreased its levels (Shrago et al. 1963).
Similar studies like these indicated pck1 had a close connection with glucose homeostasis, and
subsequent years explored the molecular mechanisms by which insulin regulates the
expression of pck1. The widely accepted model is regulation of pck1 by insulin occurs via the PI-
3 kinase pathway.
The insulin receptor (IR) is a transmembrane tyrosine kinase receptor. Upon activation by
insulin, IR phosphorylates insulin receptor substrates (IRS). This action recruits
phosphoinositide 3-kinase (PI-3 kinase), which then phosphorylates PI-4,5 bisphosphate into PI-
3,4,5 triphosphate (PIP3) on the plasma membrane. One of the downstream targets of PIP3 is its
activation of protein kinase B (PKB, also known as Akt). PKB is a serine threonine protein kinase
that regulates many factors related to pck1 expression (Chakravarty et al. 2005). In vivo studies
showed that over-expression of hepatic PKB resulted in decreased levels of PEPCK (Schmoll et
al. 2000). Not surprisingly, in vivo models of PKB knockout increases hepatic glucose output,
resulting in hyperglycemic mice (Cho et al. 2001).
Foxo-1 (Forkhead box protein O1) is a transcription factor that is inactivated by PKB
phosphorylation (Mounier and Posner 2006). In its dephosphorylated state, it binds to insulin
32
response elements (AF2) on the pck1 promoter, facilitating in the transcription of pck1
(Chakravarty et al. 2005). Upon phosphorylation by PKB, however, Foxo-1 is retained in the
cytoplasm, incapable of inducing pck1 transcription (Brunet et al. 1999, Puigserver et al. 2003).
This also prevents the binding of a transcriptional co-activator, PGC-1α (peroxiome proliferative
activated receptor γ co-activator 1) (Yoon et al. 2001, Puigserver et al. 2003). Similar results
were also found in vivo (Matsumoto et al. 2007). Recent studies found that the disassociation
of FOXO-1 from the pck1 promoter can take as little as 3 minutes in cell culture (Hall et al.
2006).
CREB (cAMP response element binding protein) is a transcription factor that is activated
through phosphorylation by protein kinase A (PKA), which is in turn activated by increased
cytosolic cAMP levels. A variety of cellular processes produce this outcome, including glucagon
stimulation (Oh et al. 2013). During fasting, CREB binds to the cAMP response element (CRE)
site of the pck1 promoter, as well as the promoter of other genes responsible for hepatic
glucose production (Hanson and Reshef 1997). Its translational co-activators include CBP (CREB
binding protein) and p300, which are histone acetyltransferases, as well as TORC (transducers
of regulated CREB activity) (Altarejos and Montminy 2011, Kwok et al. 1994, Orgyzko et al.
1996). Under normal conditions, insulin contributes to the phosphorylation of CBP through
protein kinase C (PKC) and results in the disassociation of CREB and its co-activators complex
(He et al. 2009, Zhou et al. 2004). This effectively decreases pck1 transcription in the liver
(Figure 1.6).
33
The accessory factor C/EBPβ (CCAAT enhancer-binding protein β) associated with CREB/CBP
complex can also affect pck1 gene expression (Lechner et al. 2001). C/EBPβ has been shown to
recruit CBP to enhance transcription (Duong et al. 2002). When insulin is present, it increases
transcriptional inhibitory protein (LIP) through PI-3 kinase. LIP inhibits C/EBPβ, and
disassociates the transcriptional promoting complex (Duong et al. 2002).
Figure 1.6. Regulation of pck1. FOXO1 and CREB-CBP/p300 are transcriptional regulators of gluconeogenesis, binding to AF2 and CRE region of the pck1 promoter, respectively.
1.4.4 Zebrafish gluconeogenesis model
In zebrafish, PEPCK expression was found to be reduced in larvae exposed to glucose treatment
and activated by larvae treated with cAMP and dexamethasone (DEX) (Elo et al. 2007). These
34
results were consistent with previous findings that feeding reduces PEPCK expression while
glucocorticoids like DEX increases PEPCK expression (Stafford et al. 2010). Because direct
glucose measurement in zebrafish larvae is not possible with routine instrumentation, PEPCK
can be used as a marker of blood glucose levels for the purpose of enabling chemical screens
(Elo et al. 2007). Further attesting to the validity of the model is the fact that PEPCK expression
is reduced when known anti-diabetic drugs such as metformin, rosiglitazone
(thiazolidinedione), and glipizide (sulfonylurea), were added to cAMP/DEX stimulated larval
zebrafish (Elo et al. 2007).
Transgenic zebrafish were generated with a fluorescent and luminescent reporter under the
control of a 2.8kb pck1 promoter (Gut et al. 2013) (Figures 1.7, 1.8). Expression of the reporter
gene increases as nutrients in the larval yolk sac are depleted within the first week, indicative of
pck1 promoter activation. Furthermore, isoprenaline (ISO) increases fluorescence or
luminescence intensity while treatment with metformin decreased expression of the reporter
of ISO stimulated fish (Gut et al. 2013). ISO is a beta-adrenergic agonist that functions similar to
adrenaline. It increases production of cAMP which activates the pck1 promoter. To facilitate
elimination of fish that do not express the constructs, the fluorescent (Tg(pck1:Venus)) and
luminescent (Tg(pck1:luc2)) lines also contain a cryaa:mCherry transgene, where the lens of
zebrafish is tagged with a red fluorophore. This allows for a quick check of the mCherry protein
expression in the lens in order to decide if the constructs are present in the fish (Figure 1.8).
Gut and colleagues (2013) applied the luminescent reporter line in a large scale chemical screen
to determine novel modulators of gluconeogenesis. This demonstrates the applicability of this
transgenic line and its application for high throughput screening (HTS). In our study, we used
35
Tg(pck1:luc2) to perform the initial screening on a different compound library - the NIH Clinical
Collections that contains 727 FDA-approved drugs. The identified hit compounds were then
validated on flurescent reporter lines including Tg(pck1:eGFP) that was generated in our own
lab.
Tg(pck1:eGFP) was generated by Wing Hui, a previous graduate student in the Wen Lab,
Toronto, Ontario (unpublished data, Figures 1.7, 1.8). The main difference relative to the
luciferase reporter is that the GFP has a longer half-life. The GFP protein is under the control of
a 3.6kb pck1 promoter, which is much longer than that in Tg(pck1:Venus) and Tg(pck1:luc2)
lines. One of the advantages of having a longer promoter sequence is the potential to identify
more regulatory elements that interact with small molecule drugs. These reporters were
employed to perform an in vivo HTS screen of a chemical library. As a result, several compounds
were identified that may be further explored in drug repurposing for the treatment of DMT2.
The experimental protocols described here have been optimized to facilitate future larger
screening efforts and have potential to be modified to automated robotic screens.
36
Figure 1.7. Constructs of the transgenic zebrafish lines used for this project.
Figure 1.8. Merged brightfield and fluorescence images of the fluorescent transgenic zebrafish larvae used in this project. PEPCK is predominantly expressed in the liver (green) starting at 4 dpf. Cryaa is expressed in the eye lens, observable at 48 hpf. A) Tg(pck1:eGFP) and B) Tg(pck:Venus, cryaa:mCherry).Both fish are at 6 dpf reared under nomal conditions (untreated).
37
Chapter 2: Rationale, hypothesis, and objectives
2.1 Rationale
Diabetes is a metabolic disorder where high blood glucose levels increases the risk of
cardiovascular diseases and other pathophysiological conditions. Current medical therapies for
diabetic patients include sulphonylureas, alpha-glucosidase inhibitors, biguanidines, glitazones,
and glucagon like peptide-1 receptor agonists. Each class of anti-diabetics is associated with
various side effects. Furthermore, pre-existing heart, liver, or kidney disease can limit access to
these medications. Thus, novel medical therapies that are effective at managing glucose
homeostasis while having minimal side effects are needed.
Hyperglycemia has been associated with an inability to decrease hepatic gluconeogenesis. pck1
is one of the target genes on this pathway and has been identified as a potential therapeutic
target for regulators of liver glucose production (Beale et al. 2007). PEPCK is a soluble
phosphoenolpyruvate carboxykinase enzyme that catalyzes the rate-limiting step of
gluconeogenesis, converting oxaloacetate into phospoenolpyruvate. High levels of pck1
expression in the liver have been observed in diabetic mice (Valera et al. 1994). Reduced pck1
levels improved insulin sensitivity in obese db/db mice (Gómez-Valadés et al. 2008). Genome-
wide association studies identified a specific single nucleotide polymorphism (SNP) in the pck1
promoter that is associated with type 2 diabetes (Cao et al. 2004). Therefore, the principal
objective of the present work was to identify novel pck1 inhibitors.
In vivo based drug screening has several advantages over the traditional in vitro methods of
drug discovery. By considering the drug in the context of a live organism, in vivo models are
38
often better at modeling the complex physiologies associated with diseases. Furthermore, such
screens can assess toxicity and absorption, distribution, metabolism and excretion (ADME)
characteristics of each compound, thereby eliminating toxic, ineffective, or poorly absorbed
drugs early in the process. Indeed, compounds identified from preclinical drug discovery
strategies utilizing in vitro models often fail, as the desired in vitro effects often cannot be
repeated in vivo (Giacomotto and Ségalat 2010). Zebrafish has recently emerged as a key model
organism aptly suited for high throughput in vivo drug screening for the following reasons: a)
high fecundity rates, b) compatibility with high throughput technology, and c) ex-uterine
development of transparent larvae facilitates fluorescence imaging and bioluminescence assays
(Miscevic et al. 2012). Critically, the regulation of glucose metabolism and the physiological
response upon anti-diabetic drug treatment in zebrafish is similar to that seen in humans and
other mammals (Elo et al. 2007). Furthermore, transcriptional regulation of pck1 by glucagon,
insulin and known pck1 modulators in zebrafish resembles that of humans (Elo et al. 2007). This
suggests that pck1 inhibitors identified in zebrafish may be useful metabolic regulators in
humans as well.
Traditional drug screening uses novel compounds as the basis for drug research and discovery.
However, a novel compound has many uncertainties, such as its safety profile and long-term
effects. Drug repurposing, or re-tasking previously approved drugs for new purposes, can
bypass years of pre-clinical animal trials as well as some of the uncertainties associated with
safety and toxicity.
39
In order to assess pck1 expression, a pck1 luminescent reporter line was generated (Gut et al.
2013). The luciferase activity is shown to increase with pck1 stimulating compound isoprenaline
(ISO), while decrease with pck1 inhibiting and anti-diabetic drug metformin (MET), and can be
determined through a simple luciferase assay on a microtiter plate. Additionally, the techniques
and methods from this study can also be applied in large-scale automated chemical screens in
the future. By combining the high throughput capability of zebrafish, and pck1 as a target for
glucose homeostasis, compounds identified that regulate pck1 expression could potentially be
developed as therapeutics for diabetes.
40
2.2 Hypothesis
pck1 is a valuable therapeutic target for diabetes drug discovery; the working hypothesis is that
larval zebrafish pck1 reporter system Tg(Pck1:Luc2), Tg(Pck1:Venus) and Tg(Pck1:eGFP) are
useful screening tools for identification of novel gluconeogenesis regulators in a high
throughput screening platform.
Additionally, I hypothesize that a portion of the “hit” compounds can regulating pck1
expression and blood glucose levels in adult zebrafish.
2.3 Objectives
The objectives of this project include:
1) To perform a high throughput screening of a FDA-approved chemical compound library
to determine “hits” that may regulate gluconeogenesis using the transgenic zebrafish line
Tg(Pck1:Luc2). A “hit” is defined as any compound that can reduce the luciferase activity level
by at least half when compared to that of the control (DMSO only).
2) To perform follow up studies on these “hits” to identify 3 to 4 “lead” compounds by
performing follow-up experiments using transgenic zebrafish lines Tg(Pck1:Venus) and
Tg(Pck1:eGFP). “Leads” as defined in this study is any compound with verified to down-regulate
pck1 levels as determined from gene expression levels and fluorescence imaging assays.
3) To evaluate the efficacy of the “lead” compounds in regulating gluconeogenesis through
validation studies (glucose tolerance test).
41
Chapter 3: Materials and Methods
3.1 Zebrafish maintenance
3.1.1 Zebrafish husbandry
Zebrafish were reared in a ZebTEC aquarium housing system (Tecniplast Inc, USA), with water
temperature maintained at 28.0°C, pH 6.8 - 7.4, and a 14:10 light:dark cycle. The fish were fed a
mixed diet consisting of TetraMin Tropical Flakes (Big Al’s Canada, Toronto, Ontario) and brine
shrimp twice daily. Embryo and larval zebrafish were kept in 5-cm petri dishes in embryo water
(E2, see Table 3.1 in appendix) in incubators maintained at 28.0°C and normal oxygen
concentrations (20%). The fish housing and facilities are all located at Li Ka Shing Knowledge
Institute, St Michael’s Hospital, Toronto, Ontario.
3.1.2 Transgenic zebrafish strains
The zebrafish strains used in my experiments are as follows: wild type Tuebingen (TU) provided
by the Zebrafish International Resource Center, Tg(pck1:luc2) and Tg(pck1:Venus), provided by
Dr. Philipp Gut (Stainier Lab, University of California, San Francisco). The Tg(pck1:eGFP) strain
was previously generated in the Wen Lab, at St. Michael’s Hospital, University of Toronto. The
wild types striped long fin and spotted Leo, both of which have a mixed genetic background. To
note, the promoter size of Pck-1 used to generate the Tg(pck1:eGFP) zebrafish is approximately
3.4 kb, whereas it is 2.8 kb for Tg(pck1:luc2) and Tg(pck1:Venus).
42
3.2 Compounds preparation
3.2.1 NIH Clinical Collections library
The original NIH Clinical Collections (NCC) library consisted of 727 small molecules distributed
across ten 96-well plates with the first and last columns (#1 and #12) of each plate left empty
for control purposes. Every compound in this repository has been FDA approved for human
therapeutic use, and may also possess undiscovered therapeutic benefits. Each well consists of
a compound dissolved in 100% DMSO at 10 mM. Dilutions of the library were made subsequent
to its arrival in the lab, and I obtained an aliquoted library at 50 µM in 1% DMSO. I also further
aliquoted and diluted the library to 10 µM in 1% DMSO. These were the stock solutions that I
used for my drug screening experiment. All incubations were performed with drugs at the final
concentrations of 5 µM and 1 µM in 0.5% DMSO, for high and low dosage treatments,
respectively.
3.2.2 Candidate drugs
The following candidate drugs identified from the initial drug screen were purchased from
Sigma-Aldrich (Oakville, Ontario, Canada) for further testing purposes: amlexanox, levofloxacin,
naproxen sodium, and dicloxacillin sodium salt monohydrate. Drugs were dissolved in 100%
DMSO at 100 mM, and stored at -20°C. Working solutions of 1 mM at 1% DMSO were
subsequently made for experiments. These solutions were kept to less than ten freeze-thaw
cycles or less than one month as working solutions at -20°C. Any unused (undissolved) drugs
were maintained at 4°C for future use.
43
Additionally, isoprenaline hydrochloride (Sigma-Aldrich), metformin (Sigma-Aldrich), cAMP
(Abcam), and dexamethasone (Sigma-Aldrich) were also purchased for follow-up validation
experiments. cAMP was dissolved in 0.5% DMSO to a 50X working solution of 5 mM each time
it was to be used. All other compounds were prepared in a similar manner to the candidate
drugs mentioned above. For larval anesthesia, 100 parts per million (ppm) of clove oil (eugenol)
was prepared in embryo medium. Methylcellulose (Sigma-Aldrich), a gel-like substance used for
mounting and imaging, was prepared as a 3% solution in chilled double distilled water (ddH20)
and left at 4°C for at least 24 hours to facilitate dissolution. The water was first heated to
boiling temperatures to remove any gasses.
3.2.3 Compounds for gavaging (oral) and injection (i.p.)
Control fish were fed a drug-free saline solution. Cortland’s salt solution (Table 3.2) was used
because the recipe was optimized for the physiological conditions of fresh water teleosts, as
was suggested by former researchers (Eames et al. 2010, Perry et al. 1995). Amlexanox and
levofloxacin were first dissolved under basic conditions (NaOH), to a final working
concentration of 10 mg/mL. Then, solutions were adjusted to pH 7.4 for amlexanox and pH 7.6-
7.8 for levofloxacin using Tris-HCl and hydrochloric acid (HCl), respectively. Final drug solutions
were prepared in polyvinylpyrrolidone (PVP) to a final concentration of around 3-4%. For
example, to create a solution of amlexanox at 10 mg/mL, I dissolved 10 mg of amlexanox in 174
µL of 250 mM NaOH. I vortexed the solution vigorously to ensure all of the solids dissolve. Next,
I added 253 µL of 0.5M Tris-HCl pH 7.4, 73 µL of ddH20, and 500 µL of 8% PVP. Levofloxacin was
similarily dissolved using 10 mg of drug powder, 200 µL of 250 mM NaOH, 8 µL of 6.0 M HCl,
44
292 µL ddH20, and 500 µL of 8% PVP. D-glucose (Sigma-Aldrich) was dissolved in ddH2O at 0.5
g/mL, and sterilized with 0.2 micrometer filter prior to use.
3.3 Drug library screening
3.3.1 Workflow and protocol
Homozygous Tg(pck1:luc2) were mated with wild type (WT) Tuebingen (TU) zebrafish, to avoid
any potential effects caused by insertional mutation of the transgene. Embryos were collecting
the following morning, and embryo medium was changed daily. Unfertilized and dying embryos
were promptly removed. At 3 days post fertilization (dpf), larvae were checked for
cryaa:mCherry expression by examining the presence of mCherry fluorophore in the eye lens.
Larvae absent of such expression were removed from the experiment since they do not carry
the luc2 protein. At 4 dpf, larvae without fully inflated swimming bladders were discarded.
Larvae were then distributed to 96-well plates. Each well consisted of 5 healthy larvae in a total
volume of 200 µL 0.5% DMSO. DMSO was used as a solvent to dissolve the compounds. Larvae
zebrafish can tolerate up to 1% DMSO without adverse effects. Compounds from the screening
library were applied into each well directly to the water. For each plate, wells A1-A8 and H1-H8
were left as controls (no drugs added). Plates were incubated at 28°C. The luciferase assay was
conducted at 6 dpf. In total, the larvae were exposed to small molecules transdermally for at
least 48 hours. Figure 3.1 depicts a visual representation of the experimental protocol.
3.3.2 Reagents and equipment
The luciferase assay was conducted using Steadylite Plus™ (Perkin Elmer) reagents following the
manufacturer’s instructions. Reagent mix was added to larvae to make lysate. A ratio of 25 µL
45
of reagent mix per 100 µL of solution was used, applied directly to each well on the plate,
followed by an one-hour incubation at 28°C in darkness. This luciferase assay protocol was
adapted from Gut et al. (2013).
The 96-well Microlite™ White Microtiter™ plates (Thermo Scientific) were used to maximize
luminescent reading results, due to their highly reflective well coating. The plate reader
SpectraMax M5e (Molecular Devices) and its accompanying software SoftMax Pro were used
for luciferase activity quantification. Default settings for luminescence assays were selected on
the SoftMax Pro program. Each well was read twice by the plate reader at room temperature
and the average of the two reads was used for following analysis.
3.3.3 Screening results analysis
The library was screened three times, twice at high concentration (5 µM) and once at low
concentration (1 µM). The data was normalized by the luciferase activity of the controls each
time the experiment was conducted (control = 1.0). Treatments were ranked according to the
Z-score and B score methods for high-throughput screening data analysis described by Malo et
al. (2006), with the equation for calculating each score reproduced below. The top 10% of
compounds (80 compounds) that reduced luciferase activity were examined as candidates for
subsequent experiments. These compounds reduced luciferase activity compared to control by
approximately more than two fold.
46
Equation 3.1 Equation used to calculate Z score for drug ranking. x1 represents the measurement value for each drug, xavg represents the average measurement for all of the drugs in the screen, and sx represent the standard deviation of the measurements from all of the drugs.
Equation 3.2 Equation used to calculate B score for drug ranking. r represents the residual, or the difference between the observed and expected value. The expected value is calculated by determining the average of the plate. Median absolute deviation (MAD) is determines the spread of residuals r.
Out of the 80 compounds, 18 were consistent across both high and low concentrations. The top
29 compounds that were effective at high concentrations were also selected. From this list of
47, several drugs were eliminated because they were 1) previously known anti-diabetic (1
drug), 2) anti-psychotics/sedatives/muscle relaxants (8 drugs), and 3) compounds that were
surrounded with controversial health benefits (resveratrol, a stilbenoid, and its glucoside
derivative piceid). Anti-psychotics, sedatives and muscle relaxants were not further explored
because they may possess unwanted side-effects, such as drowsiness or behavior change. The
remaining 36 compounds were retested using whatever was remaining from the library aliquot.
The top eleven compounds were identified and preceded onto secondary screening.
Consistency and efficacy were both considered in the selection process. Luciferase activity data
was transformed to a logarithmic scale (base 2) for ease of interpretation and visualization of
graphs. Using the results from secondary screening, the best four compounds were selected on
the basis of efficacy and consistency.
47
Figure 3.1 A visual protocol depicting the drug library screening experiment. Embryos were collected following mating. Any non-transgenic luciferase larvae were removed at 3 dpf by detection of mCherry fluorophore in the lens. At 4 dpf, larvae were sorted into 96-well plates with 5 larvae per well in a total volume of 200 µL. Drug concentrations in these wells were either 1 µM (low) or 5 µM (high). After 48 hours of incubation, luciferase activity was measured to assess compounds’ effect on pck1 promoter activity.
48
3.4 Fluorescence imaging
3.4.1 Workflow and protocol
Validation studies of compounds regulating pck1 promoter activity was conducted using
fluorescence reporter lines. Heterozygous Tg(pck1:Venus) or Tg(pck1:eGFP) were mated with
wild type (WT) Tuebingen (TU) zebrafish. Embryos were collecting the following morning, and
embryo medium was exchanged once daily. Unfertilized and dying embryos were promptly
removed. At 4 dpf, healthy larvae were sorted into 24-well plates with 10 larvae per well. Small
molecules were applied directly to the water, with a final volume of 1 mL 0.5% DMSO per well.
The final concentration of the drugs ranged from 1 µM to 10 µM, depending on the drug’s
efficacy concentration.
To stimulate glucose metabolism in order to observe drug effects in cases where the
Tg(pck1:Venus) construct was used, a final concentration of 10 µM of isoprenaline (ISO) was
applied to each well (Gut et al. 2013). Control wells contained no drugs. In experiments where
the Tg(pck1:eGFP) construct was used 100 µM of cAMP and 1 µM of DEX were applied to each
well in addition to the drugs to stimulate pck1 activity. Plates were incubated at 28°C during the
experiment. Fluorescence imaging was conducted at 6 dpf. In total, the larvae were exposed to
small molecules transdermally for at least 48 hours. Figure 3.2 depicts a visual representation of
the experimental protocol.
3.4.2 Reagents and equipment
Following anesthesia, larval zebrafish were mounted using 3% methylcellulose with its right side
against the plate (left side up). Images were taken at 40X magnification, with exposure time
49
held constant and adjusted each experiment according to the fluorescence level of the positive
controls. The 24-well plates used were tissue and cell-culture plates purchased from Sarstedt.
3.4.3 Fluorescence quantification analysis
Fluorescence quantification was completed using the ImageJ software (version 1.48, NIH). The
Pck-1 expression values were normalized to the control. The experiment was repeated
independently at least three times.
50
Figure 3.2 Visual protocol for fluorescence imaging validation experiments. Embryos were collected following breeding. At 4 dpf, larvae were sorted into 24-well plates with 10 larvae per well in a total volume of 1 mL. Larvae were stimulated with either ISO (10 µM) or DEX/cAMP (1 µM/100µM). Drug concentrations in these wells ranged from 1 µM to 10 µM, depending on their efficacy determined from the initial drug screen. After 48 hours of incubation, liver fluorescence was quantified to assess compounds’ effect on pck1 promoter activity.
51
3.5 Gene expression quantification
3.5.1 RNA extraction
WT Long Fin larvae were used for RNA extraction and Pck-1 gene expression quantification by
quantative real time RT-qPCR analysis. The larvae were treated following a similar procedure as
mentioned above in the previous drug screening and fluorescence validation experiments.
Small molecules were applied to the water at 4 dpf, at a final concentration of 10 µM for
amlexanox, naproxen sodium, and dicloxacillin sodium salt monohydrate, and a final
concentration of 5 µM for levofloxacin. Each well consisted of 10 healthy larvae in 1 mL of 0.5%
DMSO in 24-well plates incubated for at least 48 hours.
At 6 dpf, the 10 larvae from each well was transferred to a 1.5 mL centrifuge tube (Eppendorf),
and spun at 13,200 rpm for 3 minutes. Excess water was removed and the embryos were
suspended in Trizol®(Invitrogen) at -80°C until needed. RNA was extracted using a modified
Trizol method (manufacturer’s protocol) with RNeasy® Mini Kit (Qiagen), as described by
Untergasser (2008). RNA quantification and RNA Integrity Number (RIN) was determined using
the 2100 Bioanalyzer (Agilent Technologies Inc.) following the manufacturer’s protocol. The
RNA used for subsequent RT-qPCR applications had a RIN of at least 8.0.
3.5.2 RT-qPCR
Reverse transcription was completed using the QuantiTect Reverse Transcription Kit (Qiagen)
and qPCR was conducted using Power SYBR® Green PCR Master Mix (Life Technologies), both
following the manufacturer’s instructions. Reaction volume totaled to 10 µL per well in 384-well
52
plates. Gene expression was analyzed using the ΔΔCt method with expression normalized to
reference gene GAPDH. Each biological replicate had at least three technical replicates.
The following primer sequences were used: PEPCK Fwd 5’-GAGTGGGACAAAGCCATGAA-3’,
PEPCK Rev 5’-AGCTCCACCCCTATCTTGGA-3’, GAPDH Fwd 5’-GATTGCCGTTCATCCATCTT-3’, and
GAPDH Rev 5’-GGTCACATACACGGTTGCTG-3’. A standard curve for each set of primers was
conducted, using 1:10 serial dilutions. R2=0.999, with a slope of -3.13 corresponding to 108.6%
efficiency for GAPDH, and R2=0.995, slope = -3.236 and efficiency = 103.7% for PEPCK. The PCR
protocol is as follows: 95.0 °C for 10 minutes, 40 cycles of 15 seconds at 95.0°C, 30 seconds at
55.0°C, and 30 seconds at 72.0°C, followed by 15 seconds at 95.0°C and 10 minutes at 60.0°C.
3.6 Glucose tolerance test
3.6.1 Fish preparation and fasting
Adult wild type spotted Leo zebrafish were used for this experiment. These fish were
approximately 4 to 5 cm in length and 0.8 to 1.3 grams in weight, aged 6 to 12 months, and
purchased from Big Al’s (Toronto, Canada). The sex ratio of the group was approximately one
third male and two thirds female. The fish were starved for four days prior to measuring their
blood glucose, as suggested by Eames et al. (2010). On the first day, the fish were gavaged a
control (saline) solution or a drug (either amlexanox or levofloxacin) solution. The fish were
then placed in translucent containers containing 4 litres of aquarium water from the reservoir
tank, with 4 to 6 fish per box to reduce stress (Eames et al. 2010, Groff and Zinkl 1999). Each
container was gender specific, to prevent mating and ensuing ingestion of eggs. The containers
were kept in incubators maintained at 28.0°C.
53
The fish were gavaged daily for four days (a total of five gavages per fish per treatment). For
each drug treatment, amlexanox or levofloxacin, the fish were gavaged with a dosage of 50
mg/kg. Each day, about 10-15% of the container water was exchanged, and any waste matter
was siphoned using a turkey baser. Figure 3.3 depicts a visual representation of the
experimental protocol.
Figure 3.3 Visual protocol for the glucose tolerance test. Normal adult zebrafish were fasted for 96 hours. Small molecules (amlexanox or levofloxacin) were administered by gavage in a final volume of less than 10 µL per fish at a daily dose of 50 mg/kg. After 96 hours, adult fish were injected with 1 mg/g glucose, and blood glucose levels were determined at various time points following glucose injection to determine the fish’s ability to metabolize and clear glucose from the blood.
3.6.2 Adult fish gavaging
Fish were dried using a bath towel and weighted on a scale. Next, they were anesthetized using
ice bath maintained at 10°C using reservoir water until their body movements slowed. A sponge
with a deep slit was cut to hold the fish in place during the gavaging process. The sponge was
held in a 5-cm petri dish for sturdiness, and saturated with ice water. Next, a 50.0 µL glass
syringe (Hamilton Company) was used for gavaging. The total volume gavaged of either control
54
or drug treatment never exceeded 10 µL. It is important that the fish were not kept at 10°C for
more than a few minutes. The fish were placed back in the container for subsequent recovery.
3.6.3 Glucose injection
For glucose injection, the fish were weighed, anesthetized, and held in place in a similar
restraining device as that used for the gavaging procedure. A 5.0 µL glass microliter syringe
(Hamilton Company, Model 75 RN) and a 33 gauge needle (Hamilton Company, 0.5 inch, point
4) was used to inject glucose, at a dose of 1 mg/g, following a procedure previously performed
by Eames et al. (2010). The needled was inserted into the abdomen on the ventral side,
between the pelvic fins, at about a 45° angle with the needle point directed cranially, to a depth
of at least 0.4 inch.
3.6.4 Blood glucose measurements
To determine their blood glucose level, fish were anesthetized and the skin dried by absorbant
towels. This step is important because water trapped within the scales or on the skin can dilute
blood glucose concentrations when measured by the glucose meter. The fish was cut posterior
to the anal fin, in a transverse manner such that the dorsal aorta and posterior cardinal vein,
the major artery and vein running across the anteroposterior axis of the fish and ventral to the
vertebral column, are exposed (Figure 3.4). The glucose meter OneTouch Ultra (LifeScan) and its
compatible test strip was applied to determine glucose concentrations. Each fish had its blood
glucose measured twice. The average of the two technical replicates was used for analysis.
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Figure 3.4 Determining blood glucose levels of adult zebrafish. A) After anesthesia, fish were dried using paper towels. B) Adult fish were cut in a transverse manner posterior of the anal fin. Yellow line represents the location of incision C) Dorsal aorta and posterior cardinal vein are exposed. D) Blood glucose concentrations were measured using One Touch Ultra glucose meter.
3.7 Statistical analysis
All statistical analysis was completed using GraphPad Prism software, version 5.03. Two-tailed
student’s t-tests or ANOVA with post-host test Tukey’s HSD was conducted for each experiment
as appropriate. Heat maps were generated using statistical program R v.3.2.0 with the Graphics
Package. Non-linear regression analysis was used for the EC50 dose response curves. A statistical
significant threshold of p<0.05 was used. Error bars represent +/- 1 SEM.
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3.8 Appendix: zebrafish-specific solution recipes
Table 3.1 Embryo (E2) medium recipe
13.7 mM NaCl 5.4 mM KCl 0.25 mM Na2HPO4 0.44 mM KH2PO4 1.3 mM CaCl2 1.0 mM MgSO4 4.2 mM NaHCO3 To pH 7.2 NaOH (Nusslein-Volhard and Dahm 2002)
Table 3.2 Cortland’s Salt Solution recipe
124.1 mM NaCl 5.1 mM KCl 2.9 mM Na2HPO4 1.9 mM MgSO4∙7H2O 1.4 mM CaCl2∙2H2O 11.9 mM NaHCO3 4% Polyvinylpyrrolidone (PVP) 10,000 USP/L Heparin (Perry et al. 1984)
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Chapter 4: Results
4.1 High-throughput drug screening identified 120 compound “hits”
4.1.1 About 120 compounds decrease luciferase activity by at least half in primary screen
The NIH Clinical Collections library consisting of 727 small compounds was screened. The screen
was conducted three times: twice at a high concentration (5 µM) and once at a low
concentration (1 µM). Luciferase activity of larval Tg(pck1:luc2) zebrafish was assessed as a
measurement of pck1 promoter activity. Luminescence data were normalized to that of the
control. The results of the initial high-throughput drug screen are illustrated in Figure 4.1a. To
facilitate interpretation and visualization of results, the raw luminescence data has been log-
transformed to with a base of 2 (Figure 4.1b). For example, a value of 1 indicates the compound
increased luciferase activity (relative to control) by 2 times (21=2), whereas a value of -1
represents a reduction in luciferase activity by half (2-1=0.5). An example of the plate reader
results is shown in Figure 4.2.
The results from the initial screen indicated that many drugs did not affect luficerase activity, as
the luminescence expression neither increased nor decreased. This was expected, as most
compounds are not expected to regulate gluconeogenesis or pck1 expression. Approximately
6.8% (50 compounds) increased luciferase activity by at least 2-fold compared with control.
More importantly, about 120 compounds (16.5%) decreased luciferase activity by at least half.
58
A
B
Figure 4.1 A) Raw results from the initial drug screening experiment. Luminescence values are normalized to that of control. A threshold of y=0.5 was set. B) Log-transformed luminescence data from the initial drug screening experiment. A threshold of y= -1 was set, representing compounds that reduced luciferase activity by at least half. Blue dots highlight the 36 potential lead compounds that were subsequently re-screened.
59
Figure 4.2 An example of the results obtained from the plate reader using one 96-well plate. This result is from plate from the drug library, with each well containing a unique small molecule, at 5 µM. The SoftMax Pro software outputs raw values (shown in numerical values for each well). This heat map was generated using statistical program R v3.2.0. The colours represent the luciferase activity of the drug treated well compared with control as a ratio. Columns 1 and 12 are control (not shown). For example, a well coloured lime-green means the compound has similar luciferase activity as control (ratio value of 1). A well coloured orange means the compound has reduced luciferase activity by half (ratio value of 0.5), compared with control. The “-1” depict dead larvae with near zero luminescence values.
4.1.2 Twenty-three compounds showed toxic effects during primary screen
The NIH Clinical Collections library consists of 727 compounds. Each compound was screened
three times, once at low concentration (1 µM) and twice at high concentration (5 µM). Ten
compounds showed consistent toxicity effects at high concentrations, while thirteen
compounds seemed to have toxic effects at both the high and low concentrations. Larvae death
was determined by both visual confirmation and an almost non-existent luminescent reading.
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The compounds that were toxic to the larvae were not further pursued at a lower dosage, and
thus were also eliminated for any following experiments.
4.1.3 Identifying potential lead compounds
The remaining wells were then quantified for luciferase activity. In particular, compounds that
were able to decrease luminescence following 48 hours of incubation were of particular
interest. A threshold value of half the average intensity for the control wells on each plate was
set as a benchmark to identify potential candidate drugs. Two hundred and seventy compounds
fulfilled this requirement, and were thus deemed to be “hits”. Due to the sheer number of
these drugs however, a systemic method needs to be employed to rank these “hit” compounds,
from highest to lowest potential for further examination. Compounds were ranked in order of
potency to prioritize them for futher consideration.
4.2 Analysis and ranking of “hits” identified eleven compounds for
further validation studies
4.2.1 Compounds ranked using Z score and B score strategies showed similar
results
Screened compounds were ranked according to their efficacy and consistency, based on
methods proposed by Malo et al. (2006). The Z score and B score methods were used to rank
the initial drug screening results. Because drug efficacy was compared across multiple plates,
the Z score method was used to normalize individual compound readings relative to the
average value and the standard deviation for all drugs in the assay.
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The B score, by comparison, utilizes residual values calculated from the median of the data. It is
similar to the Z score method in principle, but is more robust and better handles outliers and
measurement errors. Nonetheless, a comparison of the Z score and B score ranking methods of
the initial drug screening experiment revealed that although the absolute values of Z or B
scores varied, the rankings of the compounds were identical regardless of the ranking system
used (Figure 4.3).
For example, those that consistently increased luciferase activity were ranked the highest (Z
score=3 or B score=6), while drugs that decreased luciferase activity had the lowest scores (Z
score=-3 or B score=-6). Since the screen was to identify small molecules that would potentially
down-regulate Pck-1 gene expression, the 10% drugs with the lowest scores (approximately 80
compounds) for each of the high and low concentration treatment were further examined.
A B
Figure 4.3 Z score (blue) and B score (green) ranking methods for drugs from the initial drug screening experiment at A) low (1µM) and B) high (5µM) concentrations. Although the absolute values of Z and B scores differed among compounds, the ranking of each compound were the same despite the methods used.
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4.2.2 Thirty six compounds were selected for rescreening following analysis
The final list of drugs considered for further evaluation consisted of 18 drugs that reduced
luciferase activity at both concentrations and 29 additional drugs that reduced luciferase
activity only at the high concentration. From this list of 47 drugs, the known anti-diabetic
compound acarbose was removed. Furthermore, sedatives, anti-psychotics, and muscle
relaxants (totaling 8 compounds) such as chlordiazepoxide, valproic acid, aripiprazole and
pancuronium were also removed. This is because these drugs have a variety of side effects in
humans, such as muscle weakness, diarrhea, and depression. Lastly, compounds with
controversial health benefits, resveratrol and its derivative, piceid, were decided to not be
further pursued. The remaining 36 drugs were re-screened (denoted as blue dots in Figure
4.1b). The full list of these drugs with their current uses and disease applications is shown in
Table 4.1, in no particular order.
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Table 4.1 List of 36 compounds that were re-screened following the initial drug screening experiment.
Drug name Uses Disease applications
Fluorocytosine Anti-fungal Candida or Cryptococcus infections Moxifloxacin Anti-bacterial (fluoroquinolone) Various bacterial infections Naproxen Anti-inflammatory (NSAID) Pain, fever, inflammation 6-azauridine Anti-viral N/A Chloroxine Anti-bacterial Diarrhea, seborrheic dermatitis Cefazolin Anti-bacterial (β-Lactam) Various bacterial infections Proxymetacaine Anesthetic Ophthalmic solutions Carbinoxamine Anti-histamine, anti-cholinergic Hay fever, urticaria, allergic conjunctivitis Mitoxantrone Anti-neoplastic Breast cancer, myeloid leukemia, non-
Hodgkin’s lymphoma Cefuroxime Anti-bacterial (β-Lactam) Various bacterial infections (bronchitis, Lyme
disease) Dicloxacillin Anti-bacterial (β-Lactam) Various bacterial infections (skin infections,
osteomyelitis, septicaemia) Daunorubicin Anthracycline Leukemia Diclofenac Anti-inflammatory (NSAID) Pain, inflammation, dysmenorrheal Amlexanox Anti-inflammatory Aphthous ulcers Riluzole N/A amyotropic lateral sclerosis (ALS) Tolterodine Anti-muscarinic Urinary incontinence Milnacipran Serotonin-norepinephrine reuptake
inhibitor Fibromyalgia
Amlodipine Calcium channel blocker High blood pressure Imatinib Tyrosine-kinase inhibitor Chronic myelogenous leukemia Stanozolol Synthetic steroid N/A Propylthiouracil N/A Hyperthyroidism Hydrocortisone Steroid Minor skin irritations 19-Norethindrone Progestogen Premenstrual syndrome, irregular periods Acyclovir Anti-viral Herpes, chickenpox, shingles Estradiol Steroid N/A Diphenhydramine Anti-histamine Allergies Levofloxacin Anti-bacterial (fluoroquinolone) Respiratory, urinary, gastrointestinal infections Cefatrizine Anti-bacterial (β-Lactam) N/A Zolpidem Non-benzodiazepine hypnotic Insomnia, brain disorders Epirubicin Anthracycline Breast cancer Esomeprazole Proton pump inhibitor Gastroesophageal reflux disease, peptic ulcer
disease Saquinavir Anti-retroviral HIV Diphenoxylate Opioid agonist Diarrhea Tinidazole Anti-parasitic Protozoan infections Vardenafil PDE5 inhibitor Erectile dysfunction Dolasetron Serotonin receptor agonist Nausea and vomiting after chemotherapy
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4.2.3 Re-screening identified eleven compounds to be pursued for further
validation
The remaining 36 compounds were re-screened (Figure 4.4). Eleven compounds were chosen
for further validation studies due to their efficacy and consistency. These compounds are listed
(in no particular order) in Table 4.2.
Figure 4.4. Rescreening of the thirty six compounds following the initial drug screening experiment showing log-transformed luminescence data. Green line represents y= -1. Luminescence values are normalized to that of the controls. Eleven compounds that were selected for future validation studies are in blue.
Table 4.2 List of eleven compounds selected for validation studies.
Drugs
Amlexanox
Fluorocytosine
Levofloxacin
Tolterodine
Zolpidem
Epirubicin
Esomeprazole
Naproxen
Proxymetacaine
Dicloxacillin
Daunorubicin
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4.3 Further validation studies identified four lead compounds down-
regulate pck1 activity
4.3.1 Dose-response studies confirmed down-regulation of pck1 activity
The eleven compounds to be used for validation studies were tested at various drug doses for
effects on luciferase activity. All eleven compounds showed a dose-dependent inhibition of
luciferase activity driven from pck1 promoter (Figure 4.5). For comparison, these activities are
shown in Figure 4.6. Amlexanox, fluorocytosine, levofloxacin, and tolterodine show strong
inhibition of activity at both 1 and 5 µM while the remaining compounds were more effective at
regulating luciferase activity at only the high concentrations (5 µM) (Figure 4.6).
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Figure 4.5 Dose response curves for the 11 compounds pursued in further validation studies. Each point represents results from 2-3 wells (10-15 larvae). These were preliminary results indicating the trend of the dose response for each drug. Detailed dose response curves were later performed for the lead compounds. A) amlexanox, B) fluorocytosine, C) levofloxacin, D) tolterodine, E) zolpidem, F) epirubicin, G) esomeprazole, H) naproxen, I) proxymetacaine, J) dicloxacillin, and K) daunorubicin.
A B C
D E F
G H I
J K
amlexanox fluorocytosine levofloxacin
tolterodine zolpidem epirubicin
esomeprazole naproxen proxymetacaine
dicloxacillin daunorubicin
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Figure 4.6 Comparison of effects of the potential lead compounds on luciferase activity at 1 µM and 5 µM dose levels.
4.3.2 Four compounds decreased fluorescence intensity expressed under pck1
promoter
In parallel with luciferase assays, the potential effects of lead compounds regulating pck1
promoter activity were investigated using the fluorescence reporter Tg(Pck1:Venus). This line of
fish were created with the Venus fluorophore under the control of the same pck1 promoter as
in the luminescence reporter line Tg(Pck1:Luc2). A fluorescence line was used in parallel as an
68
independent validation tool to measure the efficacy of gluconeogenesis regulators identified
using the luciferase line from the initial drug screen.
Larval Tg(Pck1:Venus) expression was stimulated with 10 µM isoprenaline (ISO) at 4 dpf. After
48 hours of incubation with each of the eleven compounds, four drugs showed a decrease in
total fluorescence of ISO stimulated pck1:Venus larvae. These compounds are: amelxanox,
levofloxacin, naproxen, and dicloxacillin (Figure 4.7). Others showed no apparent reduction in
fluorescence activity. Since each compound was only tested with a few larvae (n=5-10), and
individual variation between larvae were not small, no official statistics were conducted.
4.3.3 Amlexanox, levofloxacin, naproxen, and dicloxacillin selected as lead
compounds
Using luciferase activity in zebrafish larvae driven from the pck1:Luc2 promoter the original
727-drug library was narrowed down to 36 potential lead compounds. Of these, 11 compounds
demonstrated to have dose-dependent effects on the promoter activity. Using a secondary
assay in which the same promoter drives fluorescence activity in the presence of isoprenaline,
four drugs demonstrated consistent effects and were selected for further testing: amelxanox,
levofloxacin, naproxen, and dicloxacillin. They appeared to be most effective and consistent in
their efficacy as determined by validation experiments. Compounds that had minute effects of
fluorescence expression were eliminated due to lack of effectiveness. A compound is deemed
effective if it reduced stimulated fluorescence levels similar to metformin, a known anti-
diabetic that also regulates pck1, among other genes.
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Control
ISO
ISO+MET
A
B
C
D
E
F
G
H
I
J
K
Figure 4.7 Representative fluorescence images of the larvae zebrafish liver for the 11 compounds pursued in validation studies. Control received no drug treatment. All other groups were stimulated with ISO at 10 µM. MET consisted of metformin 100µM with ISO, to ensure efficacy of the reporter. A) amlexanox, B) fluorocytosine, C) levofloxacin, D) tolterodine, E) zolpidem, F) epirubicin, G) esomeprazole, H) naproxen, I) proxymetacaine, J) dicloxacillin, and K) daunorubicin. Compounds A, B, C, and D had a final concentration of 1 µM, while all others had a final concentration of 5 µM.
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4.4 Four “leads” confirmed to down-regulate pck1 expression
4.4.1 Four “leads” reduces pck1 expression in larvae zebrafish
Amlexanox, levofloxacin, naproxen, and dicloxacillin all decrease luciferase activity in zebrafish
larvae at 6 days post fertilization (Figure 4.8-4.11). Luciferase activity was measured on a
logarithmic scale with base of 2. A value of 0 means little or no difference with respect to
control. A value of 2 implies a 22=4 fold increase in luciferase activity, where a value of -2 means
a 2-2=0.25 or a four-fold decrease in luciferase activity with respect to the control. A
concentration of 10 µM amlexanox decreases the larval luciferase activity by eight times (2-
3=1/8) (Figure 4.8a), a concentration of 5 µM levofloxacin decreases it by 2 fold (Figure 4.9a), 10
µM of naproxen by more than 2 fold (Figure 4.10a), and 10 µM of dicloxacillin by less than 2
fold (Figure 4.11a). Amlexanox, levofloxacillin, and dicloxacillin decrease the larval luciferase
activity in a dose-dependent manner, from 0.001 µM to 1.0 mM tested (Figure 4.8, 4.9, 4.11).
Doses higher than 1 mM were not tested as drug solubility would become an issue. These doses
would also be pharmacologically irrevelant in humans. For naproxen, dose-dependent
responses in luciferase activity were observed from 0.001 µM to 40 µM (Figure 4.10), as a
dosage higher than 100 µM resulted in toxicity. Prior to luciferase assay treatment, the larvae
were healthy in appearance and behavior when examined.
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A
B
Figure 4.8 A) Amlexanox reduces luciferase activity in Tg(Pck1:Luc2) larvae at 10 µM and B) in a dose dependent manner. Luminescence values are normalized to control values, and are reported as a log2 ratio. For example, a value of -1 represents a decrease in luciferase activity by 0.5 (2
-1=0.5). *** represents p<0.001, and numbers
depict n (number of wells measured). For B), n = 10-16 for each point. Error bars represent +/- 1 SEM.
A
B
Figure 4.9. A) Levofloxacin reduces luciferase activity of 6 dpf in Tg(Pck1:Luc2) larvae at 5 µM and B) in a dose dependent manner. Luminescence values are normalized to control values, and are reported as a log2 ratio. *** represents p<0.001, and numbers depict n (number of wells measured). For B), n = 10-16 for each point. Error bars represent +/- 1 SEM.
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A
B
Figure 4.10. A) Naproxen reduces luciferase activity of 6 dpf in Tg(Pck1:Luc2) larvae at 10 µM and B) in a dose dependent manner. Luminescence values are normalized to control values, and are reported as a log2 ratio. For example, a value of -1 represents a decrease in luciferase activity by 0.5 (2
-1=0.5). ** represents p<0.01, and
numbers depict n (number of wells measured). For B), n = 10-16 for each point. Error bars represent +/- 1 SEM.
A
B
Figure 4.11. A) Dicloxacillin reduces luciferase activity of 6 dpf in Tg(Pck1:Luc2) larvae at 10 µM and B) in a dose dependent manner. Luminescence values are normalized to control values, and are reported as a log2 ratio. For example, a value of -1 represents a decrease in luciferase activity by 0.5 (2
-1=0.5). ** represents p<0.01, and
numbers depict n (number of wells measured). For B), n = 10-16 for each point. Error bars represent +/- 1 SEM.
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4.4.2 Amlexanox, levofloxacin and naproxen reduces ISO-stimulated pck1
fluorescence
To examine the effect of “lead” compounds on pck1 controlled fluorescence intensity, larvae
were stimulated with 10 µM ISO. ISO is an adrenoreceptor agonist that increases blood glucose
and has also been documented to induce pck1 activity (Gut et al. 2013). Metformin (100 µM)
was used as a positive control. It significantly decreased pck1 promoter activity to 67% of of
that observed with ISO-stimulated larvae. Fluorescence expression of the Venus protein in the
presence of amlexanox was quantified and normalized to that of ISO.
Amlexanox-treated larvae demonstrated on average 75%-78% of the control’s fluorescence
intensity both at high and low concentration of the drug. Levofloxacin-treated larvae have on
average 60%-80% of the control’s fluorescence intensity, and naproxen-treated larvae have
71%-79%. Dicloxacillin does not reduce pck1 promoter activity in ISO-treated larval zebrafish
(Figure 4.12). Non-treated wells (0.5% DMSO only) had approximately 45% of the control’s
fluorescence intensity. By comparison, MET (100 µM) have 67% of the control’s fluorescence
intensity (Figure 4.12). The data represent results from at least three independent experiments.
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A
Figure 4.12. A) Relative fluorescence intensity of 6 dpf Tg(Pck1:Venus) larvae treated with MET (100 µM), or varying doses of lead compounds. All larvae are also treated with ISO (10 µM). Numbers depict n, or number of fish quantified. *** represents p<0.001, and error bars represent +/- 1 SEM. B-K) Representative fluorescence images used for quantification in A). B) Control, ISO (10 µM). C) ISO + MET (100 µM). D) ISO + Amlexanox (5 µM). E) ISO + Amlexanox (10 µM). F) ISO + Levofloxacin (1 µM). G) ISO + Levofloxacin (5 µM). H) ISO + Naproxen (5 µM). I) ISO + Naproxen (10 µM). J) ISO + Dicloxacillin (5 µM). K) ISO + Dicloxacillin (10 µM).
B C
J K
F G
D E
H I
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4.4.3 Four “leads” reduces cAMP+DEX stimulated pck1 fluorescence intensity
To further test the effect of amlexanox on the pck1 promoter, its activity was induced with
dexamethasone and cAMP that were previously shown to stimulate pck1 promoter activity (Elo
et al. 2007). As a positive control, the compounds of DEX (1 µM) and cAMP (100 µM) were
applied to the water containing larval zebrafish and fluorescence expression of the protein
eGFP was quantified. Similar to the observations associated with ISO stimulation, metformin-
treated larvae exhibited 65% of the control’s fluorescence intensity. Amlexanox-treated larvae
also significantly reduced pck1 promoter activity, on average 68% and 64% of the control’s
fluorescence intensity at 5 µM and 10 µM, respectively. Levofloxacin-treated larvae significantly
reduced pck1 promoter activity, having on average 78% and 61% of the control’s fluorescence
intensity at 1 µM and 5 µM, respectively. Naproxen-treated larvae significantly reduced pck1
promoter activity to 68% of the control’s fluorescence intensity at 10 µM. Lastly, dicloxacillin-
treated larvae have on average 55% and 62% of the control’s fluorescence intensity at 5 µM
and 10 µM, respectively (Figure 4.15). Non-treated wells (0.5% DMSO only) had approximately
33% of the control’s fluorescence intensity. The data represent results from three independent
experiments.
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A
Figure 4.13. A) Relative fluorescence intensity of 6 dpf Tg(Pck1:eGFP) larvae treated with MET (100 µM), or varying doses of lead compounds. All larvae are also treated with cAMP (100 µM) + DEX (1 µM). Numbers depict n, or number of fish quantified. *** represents p<0.001, and error bars represent +/- 1 SEM. B-K) Representative fluorescence images used for quantification in A). B) Control, cAMP + DEX. C) cAMP + DEX + MET (100 µM). D) cAMP + DEX + Amlexanox (5 µM). E) cAMP + DEX + Amlexanox (10 µM). F) cAMP + DEX + Levofloxacin (1 µM). G) cAMP + DEX + Levofloxacin (5 µM). H) cAMP + DEX + Naproxen (5 µM). I) cAMP + DEX + Naproxen (10 µM). J) cAMP + DEX + Dicloxacillin (5 µM). K) cAMP + DEX + Dicloxacillin (10 µM).
B C
F G
J K
D E
H I
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4.4.4 Four “leads” reduces endogenous pck1 expression in WT zebrafish
To examine the effect of these “lead” compounds in the organism, qRT-PCR analysis of the
mRNA levels of endogenous PEPCK was performed. Larvae were treated with 10 µM
amlexanox, 5 µM levofloxacin, 10 µM naproxen, 10 µM dicloxacillin, or 0.5% DMSO only
(control). Ten healthy larvae were pooled for each sample of RNA extraction, RT and qPCR
quantification of PEPCK mRNA transcript. Gene expression results are normalized to
housekeeping gene GAPDH. For larvae treated with amlexanox, levofloxacin, and dicloxacillin, a
significant reduction in its pck1 level of less than half of that observed in the control was noted
(Figure 4.14). Naproxen-treated larvae resulted in a reduction in pck1 level of more than half
compared with that of the control (Figure 4.14).
Figure 4.14. Relative pck1 expression of WT larvae treated with one of the lead drugs: amlexanox (10 µM), levofloxacin (5 µM), naproxen (10 µM), or dicloxacillin (10 µM) compared with control. All values are normalized with regulatory gene GAPDH. Numbers depict n, sample number; each n represents RNA extracted from 10 zebrafish larvae at 6 dpf. ** represents p<0.01, and error bars represent +/- 1 SEM.
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4.4.5 Glucose tolerance test for amlexanox- or levofloxacin-treated adult
zebrafish
Next, we compared glucose tolerance in adult zebrafish fed with amlexanox or levofloxacin for
four days relative to drug-free controls. Upon injection of glucose, blood glucose levels spikes
within 30 minutes in both control and drug-treated fish, similar to previous studies (Eames et
al. 2010). In amlexanox-fed zebrafish, there is no difference in glucose clearance compared to
that of control, where as this process is slowed in levofloxacin-fed zebrafish (Figure 4.15).
Fasting blood glucose levels at 0 minutes (no injection) between either treatment was not
significantly different from that of the control. A workflow summarizing the entire screening
process can be found in Figure 4.16.
Figure 4.15. Glucose tolerance test of normal zebrafish and zebrafish fed with either amlexanox (in green, 50 mg/kg) or levofloxacin (in yellow, 50 mg/kg). Adult zebrafish were fed either drug or saline for 4 days, and were injected with glucose (1 mg/g). n=5-8 for each time point. ** represents p<0.01. Error bars represent +/- 1 SEM.
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Figure 4.16. Summary of the workflow for the drug screening process. The initial screen was performed on Tg(pck1:luc) using 727 drugs. Approximately 80 compounds were selected for analysis, and the top 36 compounds were rescreened. The best 11 compounds were selected for validation experiments using fluorescence lines. Four leads were indentified, and two compounds were tested using adult zebrafish models.
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Chapter 5: Discussion
5.1 High throughput drug screening
5.1.1 Initial screening revealed many “hit” compounds
This study aims at detecting molecules that can lower pck1 expression. Since PEPCK is involved
in other vital processes of the organism, completing eliminating pck1 expression or generating a
pck1 knockout may result in serious side effects.The initial chemical screen using the
luminescent reporter Tg(Pck1:Luc2) identified 120 compounds that decreased luciferase activity
by at least half, compared with that of the control. Given the threshold, this screen resulted in a
hit rate of 16.5%. This value is slightly higher compared to other drug screens, with hit rates
typically less than 10% (e.g., Engel et al. 2010, Rodemns et al. 2004). For comparison, Engel and
colleagues’ chemical screen resulted in a hit rate of 6.9%, while Rodemns and colleagues’
resulted in 1.4%. Both of the studies are cell-based in vitro assays, however.
A higher than expected hit rate can be attributed to two reasons. One, the threshold value was
slightly higher. A lower threshold value was not selected because this initial assay measured the
luciferase activity of the enzyme under the endogenous pck1 promoter without stimulation.
These larvae are otherwise healthy, and drugs were not co-applied with a pck1 stimulator such
as isoprenaline (ISO) or cAMP/dexamethasone (DEX). Previous studies determined that some
anti-diabetics significantly lower pck1 gene expression in WT zebrafish larvae that have been
stimulated with cAMP/DEX (Elo et al. 2007). Additionally, pilot experiments treating
Tg(Pck1:Luc2) larve with metformin (MET) showed only moderate decreases in luciferase
81
activity (data not shown). Thus, a more generous threshold was set to reduce of any potential
“misses”.
Another reason that can result in a higher than expected hit rate is the intrinsic genetic
variation among the zebrafish embryos. For cell culture based screening, the genetic makeup of
each cell is identical to the next, thereby reducing variation in the results due to genetic
differences. The zebrafish, on the other hand, is a model organism introduced into the
laboratory setting only in recent years. Unlike mice, which many strains are homozygous at over
98% of the loci due to 90 years of inbreeding, the zebrafish remains genetically variable (Beck et
al. 2000). The fish used in the initial chemical screens have a mix genetic background of TL, AB,
and TU. As such, the growth and development of the larvae are slightly variable, despite
constant environmental conditions such as temperature and density. The luciferase activity can
depend on pck1 transcription, but also on the development and size of the liver. For example, a
“hit” could be the result of one or more larvae in the well that were slower in development or
have a smaller liver, which caused a decrease in the total luciferase activity of that well.
5.1.2 Top “hits” were from several main categories of drugs
Ranking of the results from the chemical screening using both the Z score and B score methods
lead to similar rankings. The only difference being the absolute Z and B scores. This was not
unexpected, as Z and B scores use similar methods of “normalizing” the screening data. The Z
score is more biased towards outliers, as it compares the average values, whereas the B score
compares the median and thus is more robust to outlier values (Malo et al. 2006). Once the
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dead wells were eliminated from the analysis, however, there were few outliners in the data.
Similar ranking results are therefore expected.
The NIH Clinical Collections compound library used in the screen contain an assortment of
drugs; the chemicals vary in their drug class, target, uses, and mechanisms of action. As such,
the top ranking “hits” can be classified into several classes according to its clinical use. These
include anti-bacterial, anti-viral, anti-inflammatory, various receptor agonists or antagonists,
sedatives, and anti-psychotics. A surprisingly large number of compounds (8) are sedatives,
anti-psychotics or muscle relaxants. It is worthy to note that known anti-diabetics were also
identified as “hits”, such as acarbose and tolbutamide, demonstrating the efficacy of the
chemical screen.
5.1.3 Four compounds decreased fluorescence intensity of Tg(Pck1:Venus)
Eleven compounds reduced luciferase activity in Tg(Pck1:Pck2). After performing preliminary
fluorescence quanitification and analysis, I selected four compounds that decreased the ISO-
stimulated fluorescence intensity in Tg(Pck1:Venus). ISO is a beta adrenergic agonist that
increases PEPCK expression and has shown to increase fluorescence intensity of Tg(Pck1:Venus)
(Gut et al. 2013). Two of the eleven compounds were unable to reduce fluorescence intensity,
which may be attributed in part due to differences between the luminescence and fluorescence
assays. These compounds are epirubicin and daunorubicin, both anthracycline drugs used to
treat various types of cancer.
As suggested by their names, both are red-coloured in solution form. The luciferase assay
measures the emission of light of all wavelengths in each well following the addition of the
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reagents, whereas the fluorescence quantification measures the relative intensity of the eGFP
or Venus proteins. Drugs that formed dark coloured solutions will result in a lower value when
read by the plate reader in the luciferase assay. Thus, for compounds like epirubicin and
daunorubicin, it is highly likely that they do not modulate pck1 expression but were designated
as “hits” due to their chromatic properties. This may explain why epirubicin and daunorubicin
did not appear to lower fluorescence intensity of Tg(Pck1:Venus).
5.1.4 Strengths and limitations of the pck1 reporters
The three transgenic zebrafish reporters used in the drug screening and discovery progress
have its strengths and limitations. For Tg(Pck1:Luc2), it is well suited for high throughput
screening purposes (Gut et al. 2013). This line requires minimal preparation during the
screening process. The luciferase assays are also simple and fast; a microplate photometer is
used to quantify the luminescent values. Having homozygous zebrafish parents is even more
convenient: mating with wild type ensures luciferase expression in all larvae without having to
separate transgenics and wild types. This model is limited in assessing whether or not luciferase
activity has been modulated by chemicals with a coloured solution.
The two fluorescent reporter lines Tg(Pck1:Venus) and Tg(Pck1:eGFP) served as good models to
validate “hits” from the initial drug screen. From a pragmatic perspective, Tg(Pck1:Venus) is
more preferred because transgenic larvae can be detected. Zebrafish liver develops around 4
dpf, and only at 5 dpf can visualizing the presence of fluorescent protein PEPCK in the liver be
done with certainty. As per the experimental workflow, drug treatment occurs at 4 dpf. If the
parental zebrafish are heterozygotes, not separating the transgenic and the wild type larvae
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would imply half of the treated embryos cannot be used to for fluorescence intensity
assessment. Conveniently, Tg(Pck1:Venus) zebrafish are tagged with a red fluorophore in the
lens, visible as early as 48 hpf. Thus, it is easy to identify and subsequently select transgenic
larvae for drug treatment.
As a validation tool, Tg(Pck1:eGFP) is different from Tg(Pck1:Venus) in that the fluorescent GFP
protein has a longer half life and is under a longer pck1 promoter (3.8kb). The stability of the
long half life protein facilitates fluorescence imaging and quantification, while limiting
sensitivity. The fact that the protein is under the control of a longer promoter sequence
indicates that the fluorescence intensity likely to correspond well with endogenous pck1
promoter activity. A longer promoter sequence is likely to contain more transcriptional
regulatory elements (Haruyama et al. 2010).
There are also limitations of using these transgenic pck1 reporters for drug screening
applications. For example, this study has quantified pck1 promoter activity and gene
expression. It did not examine the protein expression of PEPCK. There remains a small
possibility that compounds might regulate pck1 promoter activity but remain relatively
ineffective at modulating protein expression. Future studies quantifying PEPCK protein
expression should be conducted to confirm the effects of the “lead” compounds. It is also
important to note that compounds that regulate PEPCK protein without affecting its promoter
activity (i.e., through post-translational modifications) may not be identified from this drug
screen.
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Hepatic glucose production and pck1 is one of many targets for anti-diabetic therapeutics. A
main limitation of using pck1 reporters for a drug screen with the ultimate goal of diabetic drug
discovery/repurposing is the fact that not all anti-diabetic compounds downregulate pck1
expression. As mentioned previously, sulphonylureas or α glucosidase inhibitors target other
pathologies involved in DMT2. Drugs that can improve insulin sensitivity without regulating
pck1 are not likely to be identified from the screen in this study. Chemical screening that
directly measures fasting blood glucose levels may be helpful in screening for anti-diabetic
compounds, regardless of their targets or sites of action. In zebrafish, larval glucose
measurements have been carried out, although these methods have not been adapted for high
throughput applications (Jurczyk et al. 2011).
5.1.5 Strengths and limitations of the chemical screen
The strengths of the chemical screen include 1) in vivo, phenotype-based high throughput
screening, 2) applicability of gene target to human disease, and 3) use of a general FDA
approved chemical compound library. In vivo screens are advantageous over traditional in vitro
target-based approaches in that it can assess the compound’s ADME characteristics along with
its effect on the zebrafish phenotype (Zon and Peterson 2005). In this screen, compounds that
were toxic resulted in larvae death, and a near zero luminescent value. Drugs are that poorly
absorbed would also have minimal effects of luciferase activity in relation to controls.
The regulation of the pck1 gene has been identified as a therapeutic target for glucose
homeostasis and anti-diabetic therapeutics (Beale et al. 2007). This gene is well conserved in
zebrafish, and the luciferase activity and fluorescence intensity of the transgenic reporter lines
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is indicative of pck1 promoter activity (Gut et al. 2013). Lastly, the use of a general chemical
compound library will facilitate in the identification of novel classes of compounds previously
unknown to affect gluconeogenesis. This approach may also uncover novel mechanisms of
gluconeogenesis regulation and insulin insensitivity. Since these drugs were also previously
approved by the FDA for other applications, marketing these drugs for anti-diabetic uses would
be significantly easier and the process considerably faster.
There are also several limitations to this chemical screen. Firstly, the initial screening using the
luminescent reporter line resulted in two types of false positive “hits”. As explained previously,
these false “hits” include drugs that are coloured in solution as well as sedatives and anti-
psychotics that may affect glucose metabolism tangentially. Secondly, the chemical library
contains compounds previously identified for various purposes. As such, newly engineered
compounds and other previously untested chemicals were not assessed. Third, the drug
screening effort is still likely to have missed “hit” compounds due to deaths (by toxicity or
handling errors), or ineffective concentrations. For example, compounds were screened at 1
µM and 5 µM concentrations. It may be the case that some chemicals were toxic to the larvae
at 1 µM but are able to regulate pck1 at lower concentrations. Similarily, some chemicals may
only be able to modulate pck1 promoter activity at concentrations greater than 5 µM. It is
important to note that the strengths of large scale chemical screens rely on the efficiency and
number of compounds that can be screened over a short period of time. While potential
“misses” are inevitable, the more chemicals that are screened, the more “leads” that may be
developed. Lastly, as with any chemical library screening, this study identified the singular
effects of compounds on pck1 promoter. Drug syngerism, or the phenomenon where two or
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more compounds may cause an effect stronger than each individual component, has not been
explored in the screening part of this study (Tallarida 2001). Future studies may include
examining the effects of lead compounds on pck1 expression together to known anti-diabetics
such as metformin.
5.2 Lead compound: amlexanox
5.2.1 Amlexanox reduces pck1 expression
Amlexanox was identified from the chemical screen as a compound that reduced both
luciferase activity and fluorescence intensity in the three models of gluconeogenesis by larval
zebrafish (Figure 4.8, 4.12, 4.13). As expected, validation studies revealed amlexanox decreases
endogenous pck1 expression levels (Figure 4.14).
In healthy adult zebrafish, those fed with amlexanox had similar responses when challenged
with a dose of glucose compared with those fed with a drug-free saline solution (Figure 4.15).
This may be due to the fact that amelxanox, as a regulator of hepatic gluconeogenesis, may not
necessarily affect insulin sensitivity, since insulin production is accomplished by the β cells of
the pancreas. Additionally, fasting blood glucose levels in these two groups of adult zebrafish
were also not significantly different (Figure 4.15). These adult zebrafish were healthy, and had
regular blood glucose levels; it is possible that amlexanox can act to reduce fasting blood
glucose levels in hyperglycemic conditions but is unable to cause hypoglycemia in normal
zebrafish.
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Figure 5.1 Chemical structure of amlexanox.
5.2.2 Amlexanox as an anti-inflammatory drug used to treat aphthous sores
Amlexanox was first developed in the 1990s as an oral paste for the treatment of aphthous
stomatitis, more commonly known as canker sores (Binnie et al. 1997, Khandwala et al. 1997).
Since aphthous stomatitis is thought to have an immune basis, amlexanox is believed to have
anti-inflammatory characteristics (Bell 2005, Ship 1996). For example, it was found that
amlexanox increases cAMP content of mast cells thereby inhibiting histamine release in rats
(Makino et al. 1987). Amlexanox was also shown to be effective against other inflammatory
conditions, such as oral lichen planus, asthma, and allergic rhinitis (Fu et al. 2012, Makino et al.
1987). As a previously discovered drug, amlexanox is safe when used in oral applications (Liu et
al. 2006, Meng et al. 2009).
5.2.3 Amlexanox regulates gluconeogenesis through activation of hepatic Stat3
It appears from the results that amlexanox decreases hepatic gluconeogenesis through
regulation of PEPCK. Since amlexanox is thought to have anti-inflammatory characteristics, it
may help to increase insulin sensitivity and decrease blood glucose levels through decreasing
the body’s inflammatory response. Obesity leads to an activation of the NF-κB pathway, which
leads to an increase in IKKε and TBK-1 activity in the liver (Reilly et al. 2013). A previous study
identified amlexanox as an inhibitor of IKKε and TBK-1 in a target-based chemical screen of
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150,000 compounds (Reilly et al. 2013). Subsequent studies determined that amlexanox
increases cAMP levels in adipocytes of obese mice and promotes the release of cytokine IL-6
(interleukin 6) (Reilly et al. 2015). IL-6 then proceeds to induce Stat3 phosphorylation (signal
transducer and activator of transcription 3), which activates the transcription factor and
translocates it into the nucleus (Reilly et al. 2015). In the liver, phosphorylated Stat3 acts to
decrease gluconeogenesis by preventing the translation of G6PC (glucose-6-phosphatase), an
enzyme that converts glucose-6-phosphate into glucose in the last step of gluconeogenesis
(Reilly et al. 2015). The results from this study further supports recent findings that amlexanox
decreases hepatic glucose production. Since G6PC and PEPCK are both enzymes of the
gluconeogenesis pathway, it is not surprising that they both share transcription factors such as
Foxo-1 (Schmoll et al. 2000, Yeagley et al. 2001). Thus, it is possible that amlexanox suppresses
pck1 expression via phosphorylation of hepatic Stat3. Further studies quantifying liver Stat3 and
phosphorylated Stat3 levels may confirm this hypothesis.
Taken together, these results suggest that 1) the chemical screening performed in this study is
effective and capable of identifying novel regulators of gluconeogenesis, and 2) both target-
based and phenotypic-based approaches, when designed properly, may arrive at the same
results. These results additionally demonstrate the efficacy of phenotype-based chemical
screening over target-based screening: amlexanox was identified in among about 700 chemicals
in this study while Reilly et al. (2013) identified it among 150,000 chemicals through target-
based assay. It is likely that other chemicals in the 150,000-compound library may modulate
hepatic gluconeogenesis, but in a different pathway than inhibiting IKKε and TBK-1 proteins.
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5.3 Lead compound: levofloxacin
5.3.1 Levofloxacin reduces endogenous pck1 expression
Levofloxacin was identified from the chemical screen as a compound that reduced both
luciferase activity and fluorescence intensity in the three models of gluconeogenesis by larval
zebrafish (Figure 4.9, 4.12., 4.13). Validation studies revealed levofloxacin decreases
endogenous pck1 expression levels (Figure 4.14).
Interestingly, levofloxacin-fed adult zebrafish did not respond as well as the control in the
glucose tolerance test (Figure 4.15). This may suggest that levofloxacin may alter the ability to
uptake glucose from the blood in healthy zebrafish. Similar to amlexanox, fasting blood glucose
levels were not significantly different between fish fed with levofloxacin and those fed with
saline. Again, this may be attributed to the fact that these fish are healthy, adult zebrafish with
normal blood glucose levels rather than diabetic/obese organisms with hyperglycemia. Future
studies may involve inducing hyperglycemia in adult zebrafish to assess the potential anti-
diabetic properties of the lead compounds. This may be accomplished through incubation of
glucose solution for transdermal absorption (Gleeson et al. 2007).
A
B
C
Figure 5.2 A) General chemical structure of fluoroquinolones. R denotes different functional groups. B) Chemical structure of levofloxacin. C) Chemical structure of gatifloxacin.
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5.3.2 Levofloxacin as an antibiotic drug used to treat various bacterial
infections
Levofloxacin was developed as an antibiotic of the fluoroquinolone class, for the treatment of
various bacterial infections such as bacterial pneumonia, urinary track infections, and bronchitis
(Preston et al. 1998). It is a broad-spectrum antibiotic, and is effective against bacteria such as
myoplasma, Chlamydia, legionella and mycobacteria (Fish and Chow 1997). Levofloxacin can be
administered orally, in tablets of 500mg, and is well absorbed by the body, with a half life of 6-8
in humans with healthy kidney function (Preston et al. 1998). Compared with other antibiotics
of the same class, levofloxacin is relative safe. Its major side effects include nausea, vomiting,
and diarrhea. Levofloxacin and other fluoroquinolones target DNA gyrase and DNA
topoisomerase IV in bacteria. Both of these enzymes play an import role in DNA replication
during mitosis. Inhibition of these enzymes by levofloxacin thus prevents cell division and leads
to death of the bacteria (Drlica and Zhao 1997).
5.3.3 Fluoroquinolones shown to affect insulin release and gluconeogenesis
Research involving fluoroquinolones and their effects of blood glucose homeostasis has mainly
conducted on another drug of the same class, gatifloxacin (Figure 5.2). Medical therapy using
fluoroquinolones have resulted in hypoglycemic and hyperglycemic conditions in humans as
well as rat models (Ishiwata et al. 2006, Park-Wyllie et al. 2006). For example, Ishiwata and
colleagues (2006) found that gatifloxacin caused hypoglygemia in normal rats while increased
blood glucose levels in diabetic rats. It is thought that fluoroquinolones directly cause
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hypoglycemia primarily through increasing in insulin release by β cells of the pancreas (Bhasin
et al. 2005).
In vitro studies show that fluoroquinolones block the KATP channels of the pancreatic β-cells,
leading to the depolarization of the cell membrane and opening of the voltage-dependent
calcium channels (Saraya et al. 2004, Willenborg et al. 2011, Zunkler and Wos 2003). This then
allows the release of insulin into the blood stream. It is important to note that fluoroquinolones
do not directly stimulate, but enhances, insulin release (Ghaly et al. 2009). There is also
literature to suggest that fluoroquinolones affect other process in addition to insulin release,
and collectively contribute to hypoglycemia. For example gatifloxacin has been found to reduce
GLUT1 (glucose transporter 1) expression, resulting in decrease glucose absorption in vitro and
subsequent dysglycemia (Ge et al. 2007). It also decreases renal and hepatic gluconeogenesis
by impairing the mitochondrial pyruvate uptake (Drozak et al. 2008).
The experiments with levofloxacin conducted in this study further support the notion that
fluoroquinolones causes abnormalities in glucose homeostasis. Although levofloxacin may be
an effective modulator of pck1 expression, it is apparent from previous studies that it does not
exclusively affect hepatic gluconeogenesis. Further studies are needed in order to understand
how levofloxacin and fluoroquinolones cause dysglycemia. This also demonstrates a limitation
in targeting pck1 as an anti-diabetic therapeutic: compounds that can downregulate pck1 and
hepatic gluconeogenesis may not always have glucose-lowering effects. These compounds may
have secondary effects and interact with other tissues or sites.
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Performing the glucose tolerance test on adult zebrafish under the amlexanox or levofloxacin
treatment has been challenging. For each blood glucose measurement taken, one fish needs to
be sacrified. Adult zebrafish are small in size and have enough blood available for one or two
tests using the glucose meter test strips. Approximately 40 to 50 fish are required for one
treatment of the glucose tolerance test, which all requires anestizing and gavaging for four days
prior to the experiment. A further disadvantage is that the blood glucose values across the
various timepoints come from various individuals of zebrafish, rather than from one individual
like in mice models. Since the strength of the zebrafish model lies in its high throughput
adaptibility, it seems more reasonable to examine the effects of the “lead” compounds on
blood glucose levels in mammalian models such as diabetic mice. This is why the effects
naproxen and dicloxacillin on blood glucose levels of adult zebrafish were not examined.
5.4 Lead compound: naproxen
5.4.1 Naproxen reduces endogenous pck1 expression
Naproxen was identified from the chemical screen as a compound that reduced both luciferase
activity and fluorescence intensity in the three models of gluconeogenesis by larval zebrafish
(Figure 4.10, 4.12, 4.13). Validation studies revealed naproxen decreases endogenous pck1
expression levels (Figure 4.14).
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Figure 5.3 Chemical structure of naproxen.
5.4.2 Naproxen as an anti-inflammatory used to treat pain, fever, and swelling
Naproxen is of the propionic acid drug class, and considered to be a non-steroidal anti-
inflammatory drug (NSAID). It is used to treat a variety of inflammatory conditions, such as
fever, migraines, rheumatoid arthritis, menstrual cramps, buritis, ankylosing spondylitis, and
gout. Adverse effects include gastrointestinal symptoms and cardiovascular events, although it
remains the safest out of all NSAIDs in terms of cardiac safety (Trelle et al. 2011).
As an anti-inflammatory, naproxen inhibits COX-1 (cyclooxygenase) and COX-2 enzymes
(Duggan et al. 2010). These COX enzymes are responsible for the production of prostaglandins,
prostacyclin, and thromboxane. Currently, there are no records in the literature about the
effect of naproxen or NSAID on glucose homeostasis or hepatic gluconeogenesis. The exact
mechanism of action between naproxen and hepatic gluconeogenesis thus remains to be
studied. Since insulin resistance may be brought about by chronic inflammation, one likely
route of action may be inhibition of one of the enzymes associated with the NF-κB pathway
involved in inflammation (Hotamisligil et al. 2006).
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5.5 Lead compound: dicloxacillin
5.5.1 Dicloxacillin reduces endogenous pck1 expression
Dicloxacillin was identified from the chemical screen as a compound that reduced both
luciferase activity and fluorescence intensity in two of the three models of gluconeogenesis by
larval zebrafish (Figure 4.11, 4.12, 4.13). Validation studies revealed dicloxacillin decreases
endogenous pck1 expression levels (Figure 4.14).
Figure 5.4 Chemical structure of dicloxacillin.
5.5.2 Dicloxacillin as an anti-biotic used to treat infections from Gram positive
bacteria
Dicloxacillin is of the penicillin drug class, and is a β-lactam antibiotic. It is frequently used to
treat infections caused by gram positive bacteria, such as Staphylococcus spp. (Sherertz et al.
1989, Sutherland et al. 1970). Common side effects include diarrhea, nausea and allergic
symptoms. The bactericidal property is due to its ability to prevent peptidoglycan aggregation
in the cell wall of the bacteria. Compared with other drugs in the penicillin class, dicloxacillin is
more potent and reaches higher blood glucose levels upon oral administration (Gravenkemper
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et al. 1965). Similar to naproxen, there are currently no studies that have examined the effect
of dicloxacillin on blood glucose levels or hepatic gluconeogenesis. Out of the four lead
compounds selected from this experiment, the efficacy of dicloxacillin is the least consistent.
However, the exact mechanisms by which dicloxacillin affect gluconeogenesis and pck1 needs
to be examined further.
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Chapter 6: Conclusion
The inability to suppress hepatic glucose production is a key hallmark of the pathologies of type
2 diabetes. One of the key enzymes involved in the hepatic gluconeogenesis pathway is PEPCK,
catalyzing the rate limiting step converting oxaloacetate into phosphoenolpyruvate and carbon
dioxide. The gene pck1 is hypothesized to contribute to obesity and diabetes when
dysregulated (Beale et al. 2007). Increases in pck1 gene expression have been associated with
insulin resistance in obese mice and type 2 diabetic patients (Cao et al. 2004, Valera et al.
1994). Recently, researchers have began to use zebrafish as a model of metabolic diseases in
addition to conventional mammalian models like mice and rats (Seth et al. 2013). pck1 has been
proposed to be a target for potential anti-diabetic compounds as well as an indicator for blood
glucose levels in the larval zebrafish (Elo et al. 2007). Small molecules that can effectively down-
regulate pck1 expression can thus be potential anti-diabetic drugs.
The transgenic zebrafish Tg(Pck1:Luc2), Tg(Pck1:Venus), and Tg(Pck1:eGFP) are useful tools that
can be used to identify regulators of pck1 and gluconeogenesis. Due to the transparent nature
of the zebrafish larvae, quantification of luminescence or fluorescence in these reporter lines
are not difficult to complete. Results from this study, along with previous work, suggest that
these quantifications are a reliable measurement of pck1 promoter activity (Gut et al. 2013).
Known modulators of pck1 such as metformin, isoprenaline, and cAMP/dexamethasone all alter
pck1 expression in the larval zebrafish in expected ways. Additionally, these reporter lines serve
as validation tools; the effects of compounds on pck1 determined from one transgenic line can
be confirmed using one or both of the remaining reporter lines. The current study has validated
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the three reporter strains for use in chemical genetic screening. This will provide the foundation
for future high throughput screening applications using the pck1 reporters as well as adapting
the process for automation.
A caveat of using the transgenic reporters is that the fluorescence and luminescence intensity is
only indicative of pck1 promoter activity. Compounds that alter promoter activity may not
necessarily affect protein quantity or activity. Conversely, compounds that interact with the
PEPCK protein will not be identified from the current chemical screen using these in vivo tools.
Another caveat is that the strains used in this study are healthy larvae zebrafish; they do not
possess any of the pathologies associated with obesity and/or diabetes. The effects of small
molecules regulating pck1 expression in healthy organisms may alter in a pathological context.
Additionally, being an aquatic organism, zebrafish has a unique drug delivery system.
Compounds are applied directly to the water and transdermally to the larvae. It is possible that
differences in drug delivery methods compared with humans or other mammalian models may
result in differences in the effects observed.
Measuring blood glucose levels in adult zebrafish is feasible, a technique reaffirmed in this
project. While this procedure has been successfully performed by a few other researchers, it
requires a large amount of effort to assess changes in blood glucose levels through various time
points since each fish must be sacrificed prior to measurement (Eames et al. 2010). Repeated
blood collection from zebrafish can reduce the variation between individuals, but such task is
accomplished only recently and requires high technical skills using a unique glucose meter
(Zang et al. 2013). The work from this study suggests that it may be useful to measure blood
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glucose levels in adult zebrafish; however, for anti-diabetic drug screening/development
purposes, efforts are better used towards testing the effects of lead compounds on blood
glucose levels in higher vertebrates such as rats and mice.
The high throughput screening of the chemical compound library identified four “lead”
compounds that consistently reduced endogenous pck1 expression: amlexanox, levofloxacin,
naproxen, and dicloxacillin. Amlexanox has been recently demonstrated by another group of
researchers to decrease hepatic gluconeogenesis and lower blood glucose levels in obese mice
(Reilly et al. 2015). While the literature on the effect of levofloxacin on blood glucose levels is
scarce, its same-class (fluoroquinolone) compound gatifloxacin has been reported to cause
hyperglycemia and hypoglycemia in humans (Lodise et al. 2007). The exact mechanism of how
fluoroquinolones affect glucose homeostasis still eludes us. Nonetheless, this information
supports the efficacy of the chemical screen performed in this study and its ability to detect
regulators of gluconeogenesis.
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Chapter 7: Future directions
The current study used several zebrafish reporters in a high throughput chemical drug screen to
identify novel regulators of pck1 and gluconeogenesis. The protocol optimized from this study
can be applied in the future to other screening projects. A variety of chemical libraries are
commercially available and are used by many scientists used for drug discovery research,
including high throughput chemical screening in in vivo models like zebrafish (Hong et al. 2006,
Kokel et al. 2010, Wong et al. 2004). These libraries include bioactive compounds from
LOPAC1280 (Library of Pharmacologically Active Compounds, 1280 compounds), Spectrum
Collection (2400 compounds) and Prestwick Chemical Library® (1280 compounds), as well as
larger libraries like Maybridge HitFinder™ (14,400 compounds) and Chembridge DIVERSet™
(50,000 compounds), where compounds are selected because they were “drug-like”. Custom
made libraries and those with a particular type or group of chemicals can also be generated,
depending on the characteristics of the targets or phenotype observed (e.g., Das et al. 2010,
Milan et al. 2003). Future studies may involve screening these libraries to identify regulators of
gluconeogenesis with the ultimate goal of developing anti-diabetic therapeutics.
With so many compounds and library available for screening, automating the screening
procedures can accelerate the process of drug discovery. Increases in the number of
compounds that can be screened within a period of time can increase the number of “lead”
compounds identified for additional studies. Various technologies are required to achieve
automation of the drug screening process. For example, high content breeding of zebrafish
embryos was recently developed to increase embryo yield to maximize the number of
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compounds screened (Adatto et al. 2011). A commercially available product iSpawn
(Techniplast, Italy) has been made based on the design described by Adatto and colleagues.
Compared with conventional breeding, a movable mesh platform is used. Raising the platform
decreases the water level, which signals the zebrafish to breed. Embryos are collected through
a valve at the bottom of the breeding tank. The strength of the design is that this structure can
house about 30 zebrafish, generating up to 8600 embryos at a time. The embryos can be at the
same developmental stage, or at various stages determined by the researcher by means of
raising or lowering the mesh platform (Miscevic et al. 2012).
Harvested embryos can proceed to be sorted, through the use of COPAS (complex object
parametric analyzers and sorters). Union Biometrica currently manufactures a series of COPAS
machinery (Holliston, Massachusetts, USA). The COPAS platform is capable of sorting zebrafish
embryos into welled plates according to fluorescence and other parameters such as axial
length. It can also be used to sort cells, seeds, and other small organisms such as
Caenorhabditis elegans and Drosophila melanogaster. Several drug screening studies have used
the COPAS machinery to sort small organisms like nematode worms or zebrafish embryos into
individual wells with great success (Carvalho et al. 2011, Gosai et al. 2010).
Dispensing drugs, reagents, and/or other liquids into welled plates is also an important step in
the screening procedure. Automation of such tasks can be accomplished with the help of liquid
handling robots. Liquid handling technology is already present in many chemistry and
biochemistry laboratories and their efficiencies have been established. It is available from many
companies, such as Tecan, Hudson Robotics, Gilson, Thermo Scientific and PerkinElmer.
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Complex procedures such as in situ hybridization of zebrafish embryos can also be
accomplished with the proper integration of washers and other add-ons (Miscevic et al. 2012).
These liquid handling robots eliminates human errors and maximizes accuracy.
It is important to note that most drug screening facilities already employ automation in some of
the steps in the drug screening procedure (Miscevic et al. 2012). However, achieving full
automation of the workflow of the drug screening procedure would be ideal for drug discovery
research, and requires auxiliary equipment focused on integrating the various components
necessary for the screen (embryo sorter, liquid handling, and plate reader platforms). For
example, robotic arms are needed to facilitate the physical integration of various devices by
moving the drug screening plate from one component to the next, while software development
kits facilitate the electronic integration by communicating between these equipments. With
robots performing the entire procedure, drug screening can be performed around the clock
without concern for human error or fatigue.
Full automation of the drug screening workflow has yet to be accomplished. However, the
equipment and technology for a fully automated zebrafish drug screening platform is available
in our lab at the Zebrafish Centre for Advanced Drug Discovery (St. Michael’s Hospital) (Miscevic
et al. 2012). The platform contains automated equipment such as COPAS, laser scanning
confocal microscopy (LSCM), liquid handling machinery, and embryo sorter. By combining the
methods developed from this study with advanced and automated technologies, increases in
the number of “lead” compounds discovered by screening massive number of chemicals in a
short period of time is highly probable.
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The chemical library screen performed in this study identified four compounds that consistently
decreased pck1 promoter activity. The observed efficacy of the four “lead” compounds can be
further pursued in mammalian models of obesity or diabetes. For example, a common type 2
diabetes mouse model is developed by feeding a high fat diet to young C57BL/6 mice for
several weeks. Comparing blood glucose levels as well as the glucose tolerance in fasting, drug-
administered diabetic mice can attest to the efficacy or inefficacy of these “lead” compounds in
mammalian systems.
Drugs are often not administered alone; combination therapies are used in many diseases such
as tuberculosis, cancer, and HIV/AIDS to provide an aggressive treatment for any potential
drug-resistant pathogen or tumor. For diabetes, combination therapies of thiazolidinediones
with metformin or DPP-4 inhibitors with metformin have improved patient glycemic control
and insulin sensitivity (Fonseca et al. 2000, Goldstein et al. 2007). Combination therapies are
used for diabetes because an array of anti-diabetic agents can target several underlying
pathologies of diabetes and hyperglycemia. Thus, it would be prudent to assess the anti-
diabetic effects of the “lead” compounds from this study in combination with known diabetic
therapeutics such as sulphonylureas or DPP-4 inhibitors for future work.
The current study did not investigate the mechanism of action of the four “lead” compounds
nor how they may have affected the pck1 promoter in zebrafish. Promoter deletion studies can
be performed to determine regions the compounds interact with to regulate pck1 gene
transcription. Various regions of the promoter can be mutated or deleted while measuring the
level of transcription (e.g., Better et al. 1985). Compounds that can still cause a in reduction in
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pck1 activity means the region of interaction has yet to be deleted. Once a promoter region has
been identified, further studies can investigate how the compound may interact with
transcription factors associated with the particular promoter region.
The effect of the “lead” compounds on protein expression is also an aspect for future studies.
While these compounds reduce pck1 expression, a reduction of PEPCK protein expression has
not been confirmed. Anti-PEPCK antibodies for zebrafish currently do not exist, and mice
antibodies predicted to work in zebrafish has failed in several attempts to assess protein
expression during this study. Future work quantifying the effect of the “lead” compounds on
protein expression should be completed using mammalian models like rats or mice, where
antibodies for PEPCK are efficient and available. Additionally, protein expression of
transcription factors thought to be affected by these compounds should also be quantified. For
example, there is evidence that amlexanox decreases hepatic gluconeogenesis by increasing
phosphorylated hepatic Stat3 protein (Reilly et al. 2015).
Once a promising candidate compound has been identified, optimizing the potency of the
chemical may be necessary through molecular modifications of functional groups. This process
usually involves generating a collection of compounds with different functional groups but
possess similar biological and chemical properties as the lead compound, known as bioisosteres
(Thornber 1979). The collection of compounds needs to be screened in order to assess their
efficacy and toxicity on animal models. The idea of isosterism is used to increase drug potency,
decrease toxicity, and alter bioavailability of the chemical compound for maximum therapeutic
benefits (Langmuir 1919).
105
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