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Identification of the biochemical pathways affected by the anticancer agents Motexafin Gadolinium and Sapphyrin through gene expression profiling

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Identification of the biochemical pathways affected by the anticancer agents Motexafin Gadolinium and Sapphyrin through gene expression profiling

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Content IDENTIFICATION OF THE BIOCHEMICAL PATHWAYS AFFECTED BY THE
ANTICANCER AGENTS MOTEXAFIN GADOLINIUM AND SAPPHYRIN
THROUGH GENE EXPRESSION PROFILING
by
Samantha Mahadevappa Yeligar
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOCHEMISTRY AND MOLECULAR BIOLOGY)
May 2006
Copyright 2006 Samantha Mahadevappa Yeligar
UMI Number: 1437583
1437583
2006
Copyright 2006 by
Yeligar, Samantha Mahadevappa
UMI Microform
Copyright
All rights reserved. This microform edition is protected against
unauthorized copying under Title 17, United States Code.
ProQuest Information and Learning Company
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by ProQuest Information and Learning Company.
ii
Acknowledgements
I would like to thank Dr. Joseph Hacia, my mentor and Committee Chair,
whose leadership and guidance have made my time at the University of Southern
California a valuable learning experience. My special thanks to Dr. Zoltan Tokes,
Vice Chair of the Master Program, for his continued interest and encouragement
throughout my time here. I also extend my appreciation to Drs. Robert Stellwagen
and Baruch Frenkel for their participation on my thesis committee.
Further, I am grateful to the members of the Hacia laboratory at the Institute
for Genetic Medicine, including Brian Pike, Winnie Yik, Dr. Mazen Karaman, and
Dr. Krishna Ramaswamy, for the support I have received and the good times I have
enjoyed.
iii
Table of Contents
Acknowledgements ii
List of Tables v
List of Figures vi
Abstract viii
Introduction 1
I. Motexafin Gadolinium 3
A. Expanded Porphyrins 3
B. Texaphyrins 4
C. Gadolinium Texaphyrins 5
1. General Information 5
2. Cellular Extrinsic & Intrinsic Apoptotic Pathways 7
3. Transcription Factors Relevant to Cellular Response to Oxidative Stress 11
a. Hypoxia-Inducible Factor-1 (HIF-1) 12
b. Metal Transcription Factor-1 (MTF-1) 17
c. NF-E2 Related Factor 2 (Nrf2) 22
II. Sapphyrin 26
A. General Information 26
B. Anion Binding Agents & Utility in Fluorescent Phosphate Anion Sensing 27
C. Interactions of Sapphyrin & Phosphorylated Species of Biological Interest 30
D. Sap phyrin Induction of Apoptosis in Hematopoietic Tumor -Derived Cells 33
III. Experimental 37
A. Microarrays 37
B. WebGestalt 42
IV. Results and Discussion 49
A. Motexafin Gadolinium 49
1. Motexafin Gadolinium Affects Cellular Zinc Metabolism 49
2. Motexafin Gadolinium & Zinc Affect Cellular Oxidative Stress 52
B. Sapphyrin 59
1. Genes Up-Regulated in Response to Sapphyrin Treatment 59
2. Up-Regulated Genes Involved in Transcriptional Repression 66
iv
V. Conclusion 72
Bibliography 83
v
List of Tables
Table 1: Target genes of MTF-1. 21
Table 2: HIF-1, MTF-1, and NRF2 activated pathways. 24
Table 3: AMP elution times of different anionic mobile phases from Sapphyrin-
functionalized silica gel. 28
Table 4:
31
P NMR and UV-visible spectroscopic data for Sapphyrin in the
presence of mononucleotides. 31
Table 5: Differential gene regulation in MGd-treated A549 cultures. . 51
Table 6: Differentially expressed genes in response to treatment with MGd. 54
Table 7: Transcriptional responses of stress-related genes in Ramos cells co-
treated with MGd and zinc. 56
Table 8: Transcriptional responses of selected genes related to apoptosis and cell
cycle control in Ramos co-treated with MGd and zinc. 57
Table 9: Up-regulated Sapphyrin-treated and down-regulated Actinomycin-D-
treated genes. 61
Table 10: KEGG analysis of differentially expressed genes up-regulated in
response to 2.50 µM Sapphyrin treatment. 64
Table 11: KEGG analysis of differentially expressed genes up-regulated with 2.50
µM Sapphyrin treatment and down-regulated with Actinomycin D. 64
Table 12: BioCarta analysis of differentially expressed genes up-regulated in
response to 2.50 µM Sapphyrin treatment. 65
Table 13: BioCarta analysis of differentially expressed genes up-regulated with
2.50 µM Sapphyrin treatment and down-regulated with Actinomycin D. 65
Table 14: WebGestalt Gene Ontology (GO) of transcriptional repression. 66
Table 15: WebGestalt GO identified genes involved in transcriptional repression
and up-regulated in Sapphyrin-treated cells but down-regulated in
Actinomycin D-treated cells. 69
vi
List of Figures
Figure 1: Structures of a general porphyin and an example of an expanded
prophyrin molecule. 3
Figure 2: Structures of water-soluble texaphyrins. 5
Figure 3: Redox cycling by MGd. 7
Figure 4: Outline of cellular apoptotic pathways. 8
Figure 5: HIF-1 protein synthesis regulation. 13
Figure 6: Hydroxylation of HIF-1 regulated by cellular oxygen sensing. 14
Figure 7: Representative target genes of HIF-1. 16
Figure 8: The transactivation domains of the human MTF-1 protein. 17
Figure 9: Core structures of corrole, porphyrin, and Sapphyrin. 27
Figure 10: Structures of Sapphyrins and their variants. 27
Figure 11: Fluorescence intensity of Sapphyrin-phosphate interactions. 30
Figure 12: Absorbance spectra of Sapphyrin. 31
Figure 13: UV-visible spectra of 3µM Sapphyrin with increasing phosphate. 32
Figure 14: Structures of water-soluble Sapphyrins PCI-2000 and PCI - 2010. 33
Figure 15: Caspase activation in Ramos cells treated with PCI-2000. 34
Figure 16: BCL-2 overexpression blocks apoptosis but not growth by PCI-2000
treatment. 36
Figure 17: Fundamental types of arrays used in monitoring gene expression. 39
Figure 18: Schematic overview of WebGestalt and component modules. 43
Figure 19: Structure of motexafin gadolinium. 49
Figure 20: Schematic diagram of MT gene regulation under oxidative stress. 50
vii
Figure 21.1: Gene Ontology (GO) Tree of genes up-regulated in response to
2.50µM Sapphyrin treatment associated with biological processes. 75
Figure 21.2: Gene Ontology (GO) Tree of genes up-regulated in response to
2.50µM Sapphyrin treatment associated with molecular function. 76
Figure 21.3: Gene Ontology (GO) Tree of genes up-regulated in response to
2.50µM Sapphyrin treatment associated with cellular components. 77
Figure 22.1: Gene Ontology (GO) Tree of genes up-regulated in response to
Actinomycin D treatment associated with biological processes. 78
Figure 22.2: Gene Ontology (GO) Tree of genes up-regulated in response to
Actinomycin D treatment associated with molecular function and cellular
components. 79
Figure 23.1: Gene Ontology (GO) Tree of differentially expressed genes
up-regulated in response to 2.50µM Sapphyrin treatment and
down-regulated in response to Actinomycin D treatment associated with
biological processes. 80
Figure 23.2: Gene Ontology (GO) Tree of differentially expressed genes up-
regulated in response to 2.50µM Sapphyrin treatment and down-regulated
in response to Actinomycin D treatment associated with molecular
function and cellular components. 81
viii
Abstract
This thesis explores the biochemical pathways affected by the anticancer
agents Motexafin Gadolinium (MGd) and Sapphyrin. Utilizing oligonucleotide
microarrays and Gene Ontology approaches, we analyzed the functional
relationships of differentially expressed genes in cultured cancer cells treated with
these agents. MGd, a metal-containing porphyrin-like molecule, is a zinc mimetic
which activates oxidative stress responses in treated cells. We propose that MGd
induces apoptosis by increasing oxidative stress and disrupting metal ion
homeostasis in cancer cells. In addition, MGd activates possible cellular survival
responses regulated by the transcription factors HIF-1, MTF-1, and NRF2.
Sapphyrin, another porphyrin-like molecule, decreases global mRNA levels and
induces MAPK stress response pathways. This mimics cellular responses to the
established anticancer agent actinomycin D. Overall, pathway-based analyses of
gene expression profiles in drug-treated cell lines provides a valuable means of
characterizing cellular responses to drugs and the mechanisms of drug action.
1
Introduction
Expanded porphyrins, specifically those of the lanthanide texaphyrin series,
serve as effective radiosensitizers for the treatment of cancer patients [121]. They
exhibit many properties that make them especially attractive for clinical use. For
example, they can be administered easily to patients via intravenous methods,
localize to neoplastic tissues specifically, and target hypoxic cancer cells showing
less sensitivity to irradiative therapy [121]. Two such expanded porphyrins, water-
soluble lutetium and gadolinium texaphyrins (discussed later) have potential utility
in X-ray radiation therapy (XRT), since they show selective uptake by tumors and
have anti-cancer activity in both in vitro and in vivo model systems as well as in the
clinic [182]. Although the detailed mechanism of action is not completely
elucidated, the combination of XRT and texaphryins produces reactive oxygen
species (ROS) [153] in tumors which leads to programmed cell death [182].
Sapphyrins represent another promising class of expanded porphyrins that
exhibit similar tissue diffusion and favorable tumor localization characteristics, but
they can function in the absence of XRT. As effective phosphate anions binders,
they have potential utility as fluorescent phosphate anion sensors in various
biological contexts including the regulation of osmotic pressure, cell signaling and
transduction of energy, and control of genetic information [182]. Such regulatory
abilities implicate sapphyrins as a possible chemotherapeutic drugs for a variety of
cancers.
2
In this paper, we seek to more fully define the anticancer properties of
expanded porphyrins, like texaphyrins and sapphyrins, by analyses of gene
expression profiles of cancer cell lines. Particular emphasis is placed on the role that
signal transduction and biochemical pathways play in the activity of these
compounds.
3
I. Motexafin Gadolinium
A. Expanded Porphyrins
Expanded porphyrins are modified porphyrins with similar chemical
properties to their parent compounds but possess more stable structures. Shown in
Figure 1, expanded porphyrins are formed by the incorporation of additional meso-
like bridging carbon atoms and/or by the insertion of extra heterocyclic rings into the
original porphyrin macrocyclic frame [182], they are chromophores with intense
coloration and aromaticity [153]. Furthermore, they have unique tumor localization
properties [39]. In both the presence and absence of XRT, they produce significant
amounts of singlet oxygen and other reactive species to confer tumor cell
destruction. The unique magnetic, electrochemical, and photochemical
characteristics of expanded porphyrins are the result of their core modifications
which change the electron structure of the parent porphyrin molecule [153].
Figure 1: Structures of a general porphyin (left) [213] and an example of an expanded prophyrin
molecule (right) [113].
4
In light of the afore mentioned distinctive properties of expanded porphyrins,
it is of no surprise that they and their derivatives play important roles in x-ray
radiation cancer therapy (XRT) and anion recognition [182].
B. Texaphyrins
Texaphyrins are a subgroup of expanded porphyrins that were identified
during a Schiff base condensation reaction involving diformyltripyrrane and an
aromatic 1,2-diamine which were developed by Sessler and coworkers at the
University of Texas [153]. Shown in Figure 2, texaphyrins are water-soluble, fully
aromatic monoanionic ligands that have five atoms of nitrogen within their central
cores, which are about 20% larger than porphyrin cores [153, 181]. This feature
allows them to form st able 1:1 complexes and bind to large metal cations, Zn(II),
Co(II), Fe(II)[182], and those of the lanthanide series [39]. Easily reduced,
texaphyrins have their strongest energy absorption in the red-shifted region of the
visible spectrum [183, 218], where tissues and blood are the most transparent [181].
These distinctive properties of lanthanide-complexed texaphyrins have made them
desirable candidates for XRT and PDT (photodynamic therapy) as anticancer agents
as well as for photoangioplasty (PA) and age-related macular degeneration (ARMD)
light-based treatments [182].
The two main water-soluble lanthanide texaphyrin derivatives that have been
recently studied in Phase II and Phase III clinical trials are motexafin lutetium (Lu-
Tex) and motexafin gadolinium (Gd-Tex) [181], each having a lutetium or
5
gadolinium in its central core respectively. Lu-Tex has been utilized as a successful
oncology photosensitizer and its derivatives have been used in the treatment of
various diseases [182]. Gd-Tex has been shown to be an effective enhancing agent
in XRT and contrasting agent in magnetic resonance imaging (MRI) [153].
Figure 2: Structures of water-soluble texaphyrins. (1) General texaphyrin structure, (2) Structure of
motexafin gadolinium, and (3) Structure of motexafin lutetium [182].
C. Gadolinium Texaphyrins
1. General Information
In order to improve the clinical benefit of radiation therapy (XRT),
radiosensitivity enhancing agents are used to target tumor cells specifically and
reduce toxicity of adjacent normal cells. The limited efficacy of radiation therapy
alone can be attributed to the presence of hypoxic cancer cells in tumors which
exhibit 2.5 to 3 times less sensitivity to XRT than normal oxygenated cells [10, 15,
27, 62, 211]. Improved radiosensitizers should demonstrate tumor localization
6
properties, low inherent toxicity, and activity under both aerobic and anaerobic
conditions [120].
The advantage of using Gd-Tex as a radiation therapy enhancer over Lu-Tex
is based on its unique properties of electron affinity and excitability. While both of
the electron affinities for Lu-Tex and Gd-Tex were found to be high in comparison
to other lanthanides, there are differences in the excitability of each of these
compounds as seen in the electron configurations of the 4f shell. Lanthanum (La)
has a highly unstable 4f shell, whereas the 4f shell of Gd is stabilized by half filling
of its electrons. Lu is the most stable of the lanthanides due to its full 4f outer
electron shell and is therefore not very electrophilic. Gd provides a stabilized
electrophilic molecule that has been given greater attention recently for its potential
role in anti-cancer therapy [20].
XCYTRIN, a Gd-Tex drug, has been studied as a potential enhancer of XRT
in both human studies and animal models. In the former case, patients receiving
radiation therapy to the whole brain for their metastatic brain cancers [181]. In three
murine tumor models (EMT6, SMT-F, and MCa) [118, 217], XCYTRIN showed
drug dose-response effect for single-fraction and multi-fraction radiation treatment
schedules, delaying tumor growth and lowering morbidity as a result of tumor
progression complications [21]. As an easily reduced macrocycle that absorbs the
electrons released when x-rays interact with water, the combination of x-rays and
water in a system devoid of oxygen augments the accumulation of hydroxyl radicals.
7
When oxygen is present, the same electron absorption event generates a metastable
Gd-Tex
+
product that reacts with oxygen to produce superoxide [140, 220].
Figure 3: Redox cycling by MGd. MGd catalyzes the oxidation of reducing metabolites like NADPH
to generate reactive oxygen species (ROS) [60].
The combinatorial effect of Gd-Tex and x-rays to produce reactive oxygen
species (ROS) (Figure 3) has significant biological consequences. Since Gd-Tex is
thought to localize to mitochondria, the production of superoxide anions in this
organelle will both trigger intrinsic pathways of apoptosis (i.e. through the release of
cytochrome c) and extrinsic apoptotic pathways (e.g. by the release of messenger
factors that provoke apoptotic events in nearby cells through cascade-based
processes [140, 220]). These pathways are discussed in greater detail below.
2. Cellular Extrinsic & Intrinsic Apoptotic Pathways
Apoptosis is an important aspect of biological development, immune
response, pathophysiology, and morphogenesis [152]. Programmed cell death is
regulated by a variety of mechanisms including caspases, death receptors,
mitochondria, BCL-2, and tumor suppressor genes. The activation of specific
8
apoptotic pathways largely depends on the cell type and the initiating factor [46]. In
metal-induced cell death, mitochondria are most relevant in mediating apoptosis via
metal-induced reactive oxygen species production [23]. In these cells, apoptotic
pathways can be either extrinsic or intrinsic in nature.
Figure 4: Outline of cellular apoptotic pathways. The arrows () denote activation and the circles
() denote inhibition. Convergence of the apoptotic pathways involving death receptors and those
mediated by the loss of survival factors at the mitochondria is noteworthy [152].
In extrinsic apoptosis, external ligands or signals interact with receptors
present on the plasma membrane to initiate a cascade of events that lead to death of
the cell [152]. Caspases are aspartate-specific cysteine proteases that are involved in
initiating and effecting apoptosis [136]. Initiator caspases (caspases-8, -9, -10)
trigger and amplify a death signal, and effector caspases (caspases-2, -3, -6, -7)
degrade various cellular components [31]. Activation of initiator caspases can lead
9
to a caspase cascade that results in apoptosis, a process that is also regulated by
various proteins that are both cofactors (APAF-1) and inhibitors (FLIP and IAP) [69,
101].
The cellular death receptor family includes tumor necrosis factor receptor
(TNFR), Fas, death receptors, and decoy receptors. Members of this family contain
a cysteine-rich ligand binding domain extracellularly and a death domain
intracellularly [152]. When a ligand binds to the receptor, an interaction transpires
with death domain adaptor proteins, which include FADD, RIP, DAX, and TRADD
[128, 189]. These adaptor proteins can activate caspases and signaling pathways
(MAPK and NF-B), and while remaining attached to their receptors, they can
directly activate initiator caspases [128, 189].
Stimulated death receptors can often result in activation of other signaling
pathways that have important roles in programmed cell death [152]. TNFR
activation leads to initiation of the JNK/MAPK pathway, resulting in the
phosphorylation of pro- and anti-apoptotic proteins involved in the regulation of
gene expression [112]. The JNK pathway inhibits BCL-2, an anti-apoptotic factor,
and phosphorylates the essential transcription factor c-Jun [112]. Further, p38
MAPK has demonstrated pro-apoptotic effects while Erk has shown anti-apoptotic
effects [132, 216], which can be attributed to NF-B up-regulating anti-apoptotic
protein expression [112].
Intrinsic apoptosis is mediated by mechanisms that disrupt cellular
homeostasis, specifically in the mitochondria, and are initiated intracellularly [147].
10
The loss of mitochondrial membrane potential causes the release of cytochrome c
which binds to APAF-1 and activates caspase-9, which in turn activates caspase-3 to
instigate apoptosis. In a caspase-independent pathway, an increase in mitochondrial
membrane potential releases apoptosis-inducing factor (AIF) and activates
endonuclease G, a mitochondria DNase that is critical for apoptosis [100, 202].
Mitochondrial membrane permeability is regulated by a proto-oncogene
family [152]. Members of the BCL-2 family are anti-apoptotic (BCL-2), to inhibit
Bax, or pro-apoptotic (BAD, BAX) [200], to increase permeability and membrane
potential [43, 58]. Excess reactive oxygen species in the mitochondrial membranes
increase membrane permeability and damages the respiratory chain, leading to
increased production of reactive oxygen species [23]. Damage in the mitochondrial
membrane initiates the release of cytochrome c, via the MPTP (mitochondrial
permeability transition pore) leading to apoptosis that is often associated with metal
exposure [152].
There are also a variety of tumor-suppressor genes that can induce apoptosis
[152]. Activation of p53 suppresses BCL-2 and transactivates genes contributing to
apoptosis, such as Bax or Fas [66, 170]. In caspase-independent pathways, p53
activation of Bax triggers the release of cytochrome c from mitochondria [133],
activating caspase-9 and initiating the activation of caspases-3, -6, and -7 [186].
Additionally, p53 up-regulates the expression of genes implicated in reactive oxygen
species production and metabolism [150].
11
The interruption of cellular metal cation homeostasis causes oxidative stress
within the cell that can play an important role in cancer development and progression
[25, 64, 72, 95, 97]. Intracellular zinc levels regulate biological pathways implicated
in carcinogenesis [49, 83, 85, 177, 201] and moderate the efficiency of the immune
system [30]. In regulating carcinogenesis, zinc can modulate mitogen-activated
protein kinase (MAP kinase), protein kinase C, extracellular signal-regulated kinase,
and NF-B signaling pathways [25, 64, 72, 95, 97]. In moderating the immune
system, zinc levels can affect the expression of genes related to amplification of the
Th1 immune response [30], and the observation that genes involved in intracellular
zinc-trafficking with members of the MT gene family are often over- or under-
expressed in a variety of tumors [64, 72, 85] indicate that these may be involved in
regulation of tumor growth [65].
3. Transcription Factors Relevant to Cellular Response to Oxidative Stress
Based on microarray analyses, our laboratory has found that cancer cell lines
exposed to metals and oxidative stress up-regulate genes that lie downstream of three
specific transcription factors: hypoxia-inducible transcription factor-1 (HIF-1),
metal transcription factor-1 (MTF-1), and NF-E2 related factor 2 (NRF2) [93, 109].
In general, many genes up-regulated by these transcription factors mediate survival
responses by the cell towards these environmental insults. The following is a review
the activities and properties of these transcription factors.
12
a. Hypoxia-Inducible Factor-1 (HIF-1)
Tumor microenvironments are characterized by hypoxia, low pH, and low
growth factor and nutrient availabilities, all of which are factors in malignant
phenotypes [141, 193]. Tumor cells survive by taking advantage of hypoxic stress to
evolve into more malignant variants that are proangiogenic, highly invasive, exhibit
less dependence on aerobic production of ATP, and are less sensitive to apoptosis.
These consequences implicate hypoxia as a direct and positive regulator in tumor
progression [68]. Hypoxia develops in solid tumors at distances beyond the oxygen
diffusion capacity from blood vessels and in tumor areas with abnormal vasculature
formation and/or high interstitial pressure [86]. Malignant phenotypes and hypoxia
can result in the following: genomic instability [4, 29, 154, 159], gene amplification
[155], avoidance of apoptosis via p53 activity elimination [56], proto-oncogene
expression induction [8, 122], an increase in angiogenic signaling [173], greater
resistance to radiotherapy and chemotherapy [28, 139, 164, 185, 204], and
augmented invasion and metastasis [16, 88, 144].
Acting as an intracellular oxygen sensor, HIF-1 was first identified in its role
in binding the erythropoietin (Epo) gene which maps to a hypoxia-responsive
element (HRE) in its promoter [176, 208, 209]. The HRE element can bind directly
to vascular endothelial growth factor (VEGF) and many glycolytic genes [172].
HIF-1 is a heterodimer comprised of two subunits, HIF-1 which is regulated on the
posttranslational level in response to cellular oxygen levels, and HIF-1, which is
constitutively expressed [174]. Human cancers exhibit overexpression of HIF-1 as
13
a result of hypoxia in addition to genetic alterations by gain-of-function in oncogenes
as in ERBB2 or loss-of-function in tumor suppressors as in von Hippel-Lindau (VHL)
and PTEN [175].
Figure 5: HIF-1 protein synthesis regulation. When a growth factor binds to a receptor tyrosine
kinase, the phophatidylinositol 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK)
pathways become activated. In the PI3K pathway, PI3K activates downstream serine/threonine
kinases (AKT) like protein kinase B (PKB) and mammalian target of rapamycin (mTOR). In the
MAPK pathway, upstream MAP/ERK kinase (MEK) activates extracellular-signal-regulated kinase
(ERK), which in turn activates MNK. ERK and mTOR phosphorylates p70 S6 kinase (S6K) to
phosphorylate the ribosomal S6 protein and the eukaryotic translation initiation factor 4E 9eIF-4E)
binding protein (4E-BP1). This binding interaction inactivates 4E-BP1 to inhibit cap-dependent
mRNA translation. The phosphorylation of 4E-BP1 prevents binding to eIF-4E, and the
phosphorylation of eIF-4E by MNK directly stimulates its activity. The result of growth factor
binding and subsequent signaling is an increase in the translation of a certain subset of mRNAs that
include HIF-1 mRNA [175].
In normal oxygen conditions, HIF-1 is constitutively expressed with rapid
turnover. As the concentration of oxygen falls, the HIF-1 steady state increases
exponentially, in part to regulate HIF-1 activity posttranscriptionally involving prolyl
14
hydroxylase-domain proteins 1-3 (PHDs 1-3) [123]. These PHDs alter two proline
residues in the oxygen-dependent degradation domain (ODD) of HIF-1 under normal
oxygen conditions [123]. The proline-hydroxylated ODD is recognized by the VHL
tumor suppressor protein which is a component of the E3 ubiquitin protein ligase,
targeting HIF-1 for proteasomal degradation [175]. Since PHD and asparagine
hydroxylases require oxygen, iron, and 2-oxoglutarate, their activities are inhibited
by hypoxia, leading to HIF-1 accumulation and consequent translational activation
of target genes [123].
Figure 6: Hydroxylation of HIF-1 regulated by cellular oxygen sensing. At the N-terminal end of
HIF-1, there are basic helix-loop-helix (bHLH) and Per-ARNT-Sim homology (PAS) domains. At
the C-terminal end of HIF-1, there are the transactivation domains (TAD-N and TAD-C). HIF-1
prolyl hydroxylases (HPH)/prolyl hydroxylase domain proteins (PHD) 1-3 hydroxylate Pro-402 and
Pro-564, and factor inhibiting HIF-1 (FIH-1) hydroxylates Asn-803. In order for HIF-1 to interact
with von Hippel-Lindau tumor-suppressor protein (VHL), proline hydroxylation is required. This is
important in the recognition component of an E3 ubiquitin-protein ligase for proteasomal degradation
of HIF-1. The hydroxylation of asparagine prevents the association of HIF-1 with coactivators
CBP and p300. These coactivator enzymes contain Fe(II) at their active sites, which can be
inactivated by desferrioxamine (DFX) and other iron chelators. Generally, oxygen seems to be the
rate-limiting component of this system under physiological conditions [174].
15
Increased expression of HIF-1 can lead to activation of genes involved in
glycolysis, apoptosis resistance, angiogenesis, and invasion. Glycolytic genes are
up-regulated due to oxidative phosphorylation being bypassed under hypoxic
conditions. In glycolysis, enolase, glucose transporters 1 and 3, glyceraldehydes-3-
P-dehydrogenase, hexokinase, lactate dehydrogenase, phosphoglycerate kinase,
pyruvate kinase, and triosephosphate isomerase are all up-regulated to promote
tumor growth [123]. In resistance to apoptosis, endothelin-1, Epo, insulin growth
factor-1, nitric oxide synthanse 2, and transforming growth factor- are up-regulated
to promote tumor growth [123]. Additionally, in angiogenesis, endoglin, leptin,
VEGF, and vascular endothelial growth factor receptor 2 are up-regulated to promote
tumor growth as well [123]. An increase in tumor invasion leading to metastasis is
the result of up-regulation in autocrine motility factor, cathepsin D, Met receptor,
and urokinase plasminogen activator receptor [123].
16
Figure 7: Representative target genes of HIF-1. HIF-1 functions in the transcriptional activation of a
variety of genes encoding signaling proteins that are secreted. Some of these include: angiogenic
growth factors, cytokines, and survival factors (ADM, adrenomedullin; ANGPT, angiopoietin; EDN,
endothelin; EPO, erythropoietin; IGF, insulin-like growth factor; LEP, leptin; PDGF-B, platelet-
derived growth factor beta polypeptide; PGF, placental growth factor; PROK, prokineticin; STC,
stanniocalcin; TF, transferrin; TGFA and TGFB, transforming growth factor- and –; VEGF,
vascular endothelial growth factor); cell surface receptors (ADRA1B,
1B
-adrenergic receptor; CXCR,
chemokine receptor; TFRC, transferring receptor; VEGFR, VEGF receptor); extracellular matrix
proteins and enzymes (COL5A1, collagen V 1-subunit; CTSD, cathepsin D; FN, fibronectin; MMP,
matrix metalloproteinase; P4HA1, prolyl-4-hydroxylase 1-subunit; PLAUR, urokinase-type
plasminogen activator receptor); transcription factors (DEC, differentiated embryo chondrocyte
expressed; ETS, erythroblastosis virus transforming sequence; CITED, CREB binding protein
(CBP)/p300-interacting transactivator); cytoskeletal proteins (KRT, keratin; VIM, vimentin);
proapoptotic proteins (RTP801; NIP, BCL2/adenovirus E1B 19-kDa-interacting protein; NIX, NIP3-
like); and glucose transporters and glycolytic enzymes (GLUT, glucose transporter; ALD, aldolase;
ENO, enolase; GPI, glucose phosphate isomerase; HK, hexokinase; LDHA, lactate dehydrogenase A;
PFKL, phosphofructokinase L; PFKFB3, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3;
TPI, triose phosphate isomerase; GAPDH, gyceraldehyde-3-phosphate dehydrogenase; PGK,
phosphoglycerate kinase). This image was taken from Semenza, 2003 [174].
As discussed above, HIF-1 expression/transactivation is increased in tumor
cells under hypoxic conditions, upon activation of some oncogenes (Ki-ras and v-
Src), upon inactivation of certain tumor suppressor genes (p53, VHL, and PTEN,
abnormal signaling in the PI3-K and MAPK pathways, and growth factor stimulation
(IGF-2 and TGF-) [123]. The HIF-1 subunit is constitutively expressed in cells
[174], thereby serving a possible role in the maintenance of cellular processes like
survial. These aspects of HIF-1 up-regulation implicates this transcrip tion factor in
17
cellular survival responses, making HIF-1 and its effector genes important drug
targets in anticancer treatment.
b. Metal Transcription Factor-1 (MTF-1)
MTF-1 is a ubiquitous protein that acts as a redox sensitive transcription
factor in that it controls metal-induced and constitutive transcriptional expression of
the metallothionein (MT) family of proteins [7, 17, 41, 221] and zinc transporter-1
via direct binding to metal response elements (MREs) [89]. The MT family is
multifunctional and cysteine-rich, and they are involved in cell cycle, cellular energy
producing, and apoptotic pathways [77, 129, 215]. They can grant protection against
ischemic or reperfusion injury, reactive nitrogen or oxygen intermediates,
electrophilic anti-cancer agents, various mutagenic agents, and ionizing radiation
[19, 75, 76, 92, 203, 210]. MTs can also influence the activity of zinc-containing
proteins such as the p53 tumor suppressor [116].
Figure 8: The transactivation domains of the human MTF-1 protein. Shown here are the positions of
the six zinc fingers (F1-F6), nuclear localization (NLS) and nuclear export (NES) signals, and the
regions of the activation domains that are acidic, proline-rich, and serine/threonine-rich [123].
MTF-1 has shown hypoxia-responsiveness and has demonstrated increased
transcriptional activity [12, 33] in response to oxidative stress [57, 124]. It responds
18
to both an excess in cellular concentrations of metal and redox fluctuations,
including those caused by hypoxia and oxidants [123]. MTF-1 is involved in the
hypoxically induced expression of two isoforms of metallothioneins, MT-I and MT-
IIA [57, 124, 125].
Although the specific mechanism of action is unknown, MTF - 1 exhibits
regulatory activity in the production and/or hypoxia stabilization of HIF-1 [123].
On the other hand, HIF-1 may indirectly contribute to the up-regulation of PlGF
through Epo-dependent activation of MTF-1 activity [2, 9, 145] . Additionally, MTF-
1 can couple with c-fos and upstream stimulatory factor-1 (USF-1) to activate MTs
by surplus zinc [6, 34]. MTF-1 can coordinate gene regulation of those involved in
zinc homeostasis and those that confer protection against metal toxicity, like MTs
and zinc transporter-1 [103]. With the exception of zinc, transcriptional activators of
MTF-1 exercise their strength by releasing zinc from intracellular stores. The
activation of MT gene promoters is achieved not only by zinc, but also by cadmium,
copper, and oxidative stress[222]. Hypoxia-induced production of reactive oxygen
species oxidizes zinc metallothioneins, releasing zinc to stimulate MTF-1[123].
Studies by Saydam et al. 2002 [165] have demonstrated that the MTF-1/MRE
interaction responsible for the regulation of MT transcription is controlled by several
signal transduction cascades, affecting the phosphorylation of MTF-1. Since the
level of MTF-1 phosphorylation had been previously reported to be modified after
exposure to cadmium or zinc in vivo [90], it was postulated that the phosphorylation
of MTF-1 may be influenced by signal transduction in one or more metal- or stress-
19
responsive pathways. In studying different species, Saydam and coworkers
identified the presence of three evoluntionarily conserved, potential sites of
phosphorylation, which included 11 protein kinase C sites, 13 casein kinase II sites,
and one tyrosine kinase site [165]. The exposure of cells to signal transduction
cascade activators caused an increase in steady-state MT mRNA levels [51, 81, 91,
131], and signal transduction cascade inhibitors similarly decreased levels of metal-
inducible MT mRNA expression [219]. Further, many of the effectors involved in
activation of MT transcription like metals (zinc, cadmium, chromium, arsenic,
mercury) and environmental stressors (radiation, oxidative stress) regulate
intracellular signal transduction cascades [11, 79, 191, 212].
Based on the results of the previous study, it was concluded that MTF-1
phosphorylation played an important role in its activation by zinc and cadmium.
Also, inhibitor studies indicated that many kinase and signal transduction cascades,
such as those responsible for the modulation of protein kinase C, casein kinase II,
and tyrosine kinase, were essential to transcriptional activation by zinc and cadmium.
The inhibition of transcriptional activation was found to not impair MTF - 1 binding
to DNA, suggesting that phosphorylation of MTF-1 does not regulate DNA binding.
However, the phosphorylation of MTF-1 was elevated when protein kinase C was
inhibited, suggesting that the specific dephosphorylation of MTF-1 plays a role in its
activation [165].
MTF-1 contributes to various cellular mechanisms that are responsible for
tumorigenesis. Apoptotic resistance involving optimal expressions of MT-I and MT-
20
II and PI3-K/Akt/mTOR/elF4E (PI3-K/Akt-regulated pathway involved in cell
survival via the downstream mammalian target of rapamycin and elongation factor
E4) depends on maintenance of MTF-1 expression [123]. Escape from host fibrotic
response is dependent upon inhibition of transforming growth factor-1 and tissue
transglutaminase expression which is caused by MTF-1 expression. MTF-1
expression leads to invasion based on its stimulation of MT1-matrix
metalloproteinase (MT1-MMP) and MMP-2 activity, but also inhibition of tissue
inhibitor of metalloproteinases and cadherins. Drug and radiation resistance
involving MTF-1 expression depends on its subsequent activation of MT-I and MT-II
and PI3-K/Akt/mTOR. Angiogenesis occurs by MTF - 1 activating PlGF and PI3-
K/Akt/mTOR/(HIF-1/VEGF), and HIF-1 synthesis and stabilization transpires
through MTF-1 expression resulting in PI3-K/Akt/mTOR/(heat shock proteins 70 and
90) activation [123].
21
Table 1: Target genes of MTF-1. (Based on the screenings of Lichtlen et.al. 2001).
Gene Source of evidence
MRE in regulatory region
a
MT-II Detailed in vivo and in vitro
evidence (4,16; E13.0 microarray)
6 MREs in promoter (M)
AFP SABRE, E13.0/E12.5 microarray 5 MREs in promoter/enhancers (R)
Glucose 6-phosphate dehydrogenase Database search 5 MREs in promoter (H)
Transforming growth factor ß-1 Database search 3 MREs in promoter (M)
Cytosolic zinc-binding protein Database search 1 MRE in promoter (R)
Tear lipocalin Cell culture data and in vitro 5 MREs in promoter (H)
AMD-1 S -adenosylmethionine decarboxylase Database search 6 MREs in promoter (M)
BiP (hsp 70 family) SABRE
No
c
Vacuolar adenosine triphosphatase subunit b E12.5 microarray Missing sequence information
Fetuin E13.0 microarray 3 MREs in promoter (M)
Corticoid-binding globulin E13.0 microarray 1 MRE in promoter (M)
Plasma retinol-binding protein E13.0 microarray
No
c
HNF-3 Database search 3 MREs in promoter (M)
HNF-3 Database search 5 MREs in promoter (R)
Hfh-1L Database search 8 MREs in promoter (M)
C/EBP Database search 5 MREs in 5'-upstream region (R)
ALK-6 (TGFß receptor type I) E13 microarray Missing sequence information
Inhibin/activin bc subunit Database search 1 MRE in promoter (M)
FGF receptor E13 microarray Missing sequence information
Gas3/PMP22 E13 microarray 2 MREs in promoter (M)
Notch-1 Database search 3 MREs in promoter (M)
SV2 SABRE Missing sequence information
Neuronal nicotinic acetylcholine receptor Database search 4 MREs in promoter (M)
Dopamine receptor D1 Database search 1 MRE (R)
Testes-specific kinase 1 Database search 6 MREs in promoter (M)
Synaptonemal complex protein I Database search 3 MREs in promoter (M)
XIST SABRE 3 MREs in promoter (M)
ESTs
ms20h07 E13.0 microarray Missing sequence information
mg75d07 E13.0 microarray Missing sequence information
mq10d02 E13.0 microarray Missing sequence information
va1e02 E13.0 microarray Missing sequence information
vk53c08 E13.0 microarray Missing sequence information
vn65b01 E13.0 microarray Missing sequence information
vl04h06 E13.0 microarray Missing sequence information
C76222 E13.0 microarray Missing sequence information
Cell division control protein 3 E13.0 microarray Missing sequence information
NF-YB transcription factor E13.0 microarray
No
c
Activin receptor II(B) E13.0 microarray 7 MREs in upstream region (–1.8
kb) (H)
a
(M), mouse; (H), human; (R), rat. Human or rat were considered when mouse sequence was unavailable.
b
MREs were tested only from the indicated subset of genes.
c
A remote cis -regulatory region with MREs cannot be excluded. Alternatively, indirect transcriptional regulation via MTF-1 is possible.
Tissue-specific proteins
Other transcripts
Hepatopoietin receptor (ERV1) E12.5 microarray Missing sequence information
Signal pathway factors
Stress-responsive genes
Secretory liver proteins
Liver-enriched transcription factors
22
c. NF-E2 Related Factor 2 (NRF2)
Although the complete mechanism of the activity of NRF2 action remains to
be elucidated, it was first identified as a substrate in the ubiquitin proteasome
pathway [171] and has been recognized for its important role in the expression of
phase II detoxification proteins (glutathione S-transferases (GSTs), uridine
diphosphate-glucuronosyltransferases, and NAD(P)H:quinone reductase) [82] and
some antioxidant genes (HO1, NQO1, GCLR, Ferritin, thioredoxin reductase (TR),
glutathione reductase (GR), copper/zinc superoxide dismutase (SOD1), and multiple
heat shock proteins). Tert-butylhyroquinone (tBHQ), a quinine compound, confers
transcriptional activation of phase II detoxification proteins and/or some antioxidant
genes via its cis-acting antioxidant responsive element (ARE) that modulates both or
either constitutive or inducible gene expression [98]. NRF2 functions as a
transactivation factor responsible for the up-regulation of ARE-motivated gene
expression [135, 205]. In a study conducted in 2004 by Jiang Li and coworkers, the
stabilization of NRF2 was found to be a potential mechanism in the prevention of
oxidative stress. They demonstrated that tBHQ treatment stabilized the NRF2
protein granting protection against H
2
O
2
-induced cytotoxicity in human neural stem
cells (hNSCs) and that this stabilization effect was also demonstrated in IMR-32
neuroblastoma cells. It was also found that NRF2 overexpression through
adenovirus-mediated infection protects hNSCs from cytotoxicity induced by
oxidative stress [99].
23
Polypeptide sequences that are enriched with proline (P), glutamic acid (E),
serine (S), and threonine (T) (PEST regions) function in targeting proteins for fast
destruction by serving as proteolytic signals [160]. Murine Nrf2 was discovered as
having several weak PEST candidates and one probable PEST sequence at position
350-380aa, where the serine and threonine residues may undergo phosphorylation
that is required for degradation [190]. The variable turnover exhibited by Nrf2 is
achieved via at least two degrons, Neh2 (17-32aa) and Neh6 (329-379aa) which can
potentially regulate either homeostatic Keap1-dependent Nrf2 degradation or Keap1-
independent Nrf2 degradation under conditions of oxidative stress [70, 114]. Nrf2
stabilization induced by tBHQ can possibly be controlled through both degradation
pathways. While it was found that tBHQ did not prevent the ubiquitination of Nrf2,
it did demonstrate specificity in stabilization of ubiquitinated Nrf2 [98]. Further,
Keap1 is also involved in Nrf2 stabilization in that Keap1-dependent Nrf2
degradation plays a role in the negative regulation of ARE-motivated gene
expression [114]. These findings show that tBHQ may control Nrf2 stabilization by
manipulating the binding partners of Nrf2 upstream of the ubiquitin-proteasome
pathway [98].
Cells treated with tBHQ are protected from oxidative stress [40, 126] that
may lead to cell injury or apoptosis through tBHQ activation of ARE. Clusters of
detoxification proteins and antioxidant genes, known as programmed cell life genes,
showed up-regulation and responsibility for the protective effect of tBHQ. Most of
these genes exhibited Nrf2 dependence to create a strong antioxidant network. These
24
results contribute to a therapeutic strategy entailing the development of high
antioxidant capacity precursor cells that can overcome both endogenous and
exogenous oxidative stressors [98], implicating NRF2 in cellular survival responses
against environmental stressors.
Table 2: HIF-1, MTF-1, and NRF2 activated pathways. Also included are subsequent affected genes
that lead to tumor invasion and metastasis.
Gene
activated in
oncogenic
cells
Activated
Pathway
Target Genes Affected
ENO (enolase)
Glut 1 (glucose transporter 1)
Glut 3 (glucose transporter 3)
GADPH (glyceraldehydes-3-P-dehydrogenase)
HK (hexokinase)
LDH (lactate dehydrogenase)
PGK (phosphoglycerate kinase)
PK (pyruvate kinase)
Glycolysis
TPI (triosephosphate isomerase)
ET-1 (endothelin-1)
EPO (erythropoietin)
IGF-2 (insulin growth factor-1)
NOS2 (nitric oxide synthanse 2)
Apoptosis
Resistance
TGF- (transforming growth factor-)
ENG (endoglin)
Leptin
VEGF (vascular endothelial growth factor)
Angiogenesis
VEGFR2 (vascular endothelial growth factor receptor 2)
AMF (autocrine motility factor)
CathD (cathepsin D)
MetR (Met receptor)
Hypoxia-Inducible Transcription Factor-1 (HIF-1)
Invasion
UPAR (urokinase plasminogen activator receptor)
25
Table 2: HIF-1, MTF-1, and NRF2 activated pathways. (continued)
MT-I (metallothionein I)
MT-II (metallothionein II) Apoptosis
Resistance PI3-K/Akt/mTOR/elF4E (phosphoinositide-3-kinase/Akt
kinase/mammalian target of rapamycin/elongation factor E4)
TGF-1 (transforming growth factor-1) Escape From Host
Fibrotic Response tTG (tissue transglutaminase)
MT1-MMP (MT1-matrix metalloproteinase)
MMP-2 (matrix metalloproteinase 2)
TIMPs (tissue inhibitor of metalloproteinases)
Invasion
Cadherins
MT-I (metallothionein I)
MT-II (metallothionein II)
Drug and
Radiation
Resistance
PI3-K/Akt/mTOR (phosphoinositide-3-kinase/Akt kinase/mammalian
target of rapamycin)
PlGF (placenta growth factor)
PI3-K/Akt/mTOR (phosphoinositide-3-kinase/Akt kinase/mammalian
target of rapamycin)
HIF-1 (hypoxia-inducible transcription factor-1)
Angiogenesis
VEGF (vascular endothelial growth factor)
PI3-K/Akt/mTOR (phosphoinositide-3-kinase/Akt kinase/mammalian
target of rapamycin)
Metal Transcription Factor-1 (MTF-1)
HIF-1 Synthesis
and Stabilization
Hsp70 and 90 (heat shock proteins 70 and 90)
ARE (antioxidant response element)
HO1 (heme oxygenase 1)
NQO1 (NAD(P)H dehydrogenase, quinone 1)
GCLR (-glutamylcysteine ligase regulatory subunit)
Ferritin
TR (thioredoxin reductase)
GR (glutathione reductase)
SOD1 (copper/zinc superoxide dismutase)
NF-E2 Related Factor2
Antioxidant
Response
Multiple heat shock proteins
26
II. Sapphyrin
A. General Information
Although MGd treatment of neoplastic tissues is promising as an inducer of
apoptosis via various genetically regulated mechanisms, this drug is limited in its
anti-cancer therapy utility due to its requiring activation by irradiative therapy.
Sapphyrins and their derivatives, on the other hand, provide viable alternatives to
MGd treatment by exhibiting anti-tumor activity in the absence of light-based factors
of activation [130]. In 1966, R.B. Woodward and his coworkers isolated the first
expanded porphyrin, sapphyrin, while synthesizing the corrin core of Vitamin B
12
,
but production of the blue-green material was stopped when it became evident that
synthesis was so tedious that resulting product availability was restricted. In the
1990s, synthesis of sapphyrins was improved and increased availability of products
led to investigations into the biological uses of sapphyrins [182]. Sapphyrins are
pentapyrrolic macrocycles, which contain 22 -electron aromatic peripheries that can
bind several anion classes under various solution phase conditions and in the solid
state [179]. They are heteroannulenes that are an aromatic “size” larger than
porphyrins and corroles where an extra pyrrole has been inserted between a meso-
carbon and an -pyrrolic position. Compared to porphyrins and corroles, the Soret-
like absorbance of sapphyrins are red-shifted and usually more intense [178].
27
Figure 9: Core structures of corrole, porphyrin, and Sapphyrin. The shortest pathway of each
macrocycle’s
-electron conjugation is shown in bold [178].
Figure 10: Structures of Sapphyrins and their variants. General structures are shown in sapphyrins 4-
6, and water-soluble sapphyrins are shown in 7-9 [182].
B. Anion Binding Agents & Utility in Fluorescent Phosphate Anion Sensing
Initially, sapphyrins were difficult to characterize structurally. Attempts at
crystallizing the bis-HPF
6
salt of sapphyrin-derivative 4 (above), produced a mixed
HF-HPF
6
salt containing a fluoride anion in its central core. The ability of
sapphyrins and other expanded porphyrins to uniquely bind anions has helped
classify these molecules as effective anion binding receptors. Detailed studies of
sapphyrins by U-tube transport and silica gel-based HPLC separation experiments
28
have shown that various phosphorylated anions, including those found in
mononucleotides and DNA, are bound with high specificity in steady states and
solution, leading to the possibility that sapphyrins might be useful as biological
phosphate anion sensors [182].
A study conducted in 1995 by Krl and Sessler illustrated that the two
reputed recognition motifs of sapphyrin-phosphate binding and Watson-Crick
complementary base pairing work together to selective and effective transport of
sapphyrins. Sapphyrin selectivity was found in both nucleotides and between
different isomers of the same nucleotide monophosphates [178]. Results of silica
gel-based HPLC separation experiments showed that the rates of elution of AMP in
the presence of various anions paralleled the relative protonated sapphyrin-anion
binding affinities seen in solution (arsenate > phosphate > chloride > sulfate > nitrate
= bromide > iodide > acetate) [180].
Table 3: AMP elution times of different anionic mobile phases from Sapphyrin-functionalized silica
gel[180].
29
In aqueous solutions, the binding of water-soluble sapphyrins to organic
phosphate-type anions has been found to correlate with changes in sapphyrin
aggregation state, emphasizing the ability of protonated sapphyrins to bind anions
such as phosphates [179]. Phosphorylated entities play significant parts in biology
and medicine via their roles in regulation of osmotic pressure, cell signaling, cellular
energy transduction, and control of genetic information. Even inorganic phosphates
play essential roles in metabolism regulation and defining cell status. Water-soluble
sapphyrins offer promising potential as in vitro phosphate anion sensors because they
are generally highly aggregated, non-fluorescent at neutral pH, relatively non-toxic,
and exhibit tumor localization properties [182]. Studies involving fluorescence
emission spectroscopy of sapphyrins revealed that the monomeric forms of
sapphyrin were highly fluorescent compared to dimer and higher order aggregates.
The same studies performed in the presence of phosphate anions showed a
significant increase in fluorescence emission, correlating with the increase in
phosphate concentration of solution [179]. The emission enhancement is thought to
result from the release of small amounts of the fluorescent aggregate monomers from
sapphyrin-phosphate binding interactions [178]. Since water-based sapphyrins have
demonstrated tumor localization properties, they have potential utility as phosphate
anion sensors in these neoplastic tissues [179].
30
Figure 11: Fluorescence intensity of Sapphyrin-phosphate interactions. When 2µM sapphyrin 9
(shown in Figure 11) is titrated with increasing quantities of inorganic phosphate in water at neutral
pH and
excit.
= 450 nm, fluorescence intensity increases. The inset graph depicts emission intensity
versus inorganic phosphate concentration [182].
C. Interactions of Sapphyrin & Phosphorylated Species of Biological Interest
When localized in neoplastic tissues, sapphyrins can absorb light in the red
region of the visible spectrum to generate reactive oxygen species in good quantum
yield [73, 142, 161, 194]. This feature, combined with evidence illustrating the
interaction of sapphyrin with phosphorylated species of biological interest as in the
case of solid tumors, it is worthwhile to understand the mechanism of this interaction
since it could have potential benefit to the development of anti-cancer drugs.
Investigations by Brent Iverson and Jonathan Sessler in 1996 illustrated three distinct
modes of sapphyrin interaction with phosphorylated entities: phosphate chelation,
hydrophobic interaction, and highly ordered aggregation [71].
Phosphate chelation describes the mechanism by which protonated
sapphyrins bind to the anionic phosphodiester backbone of DNA via interactions
with the nucleic acids and nucleotides. Influenced by anion binding in aqueous
31
solution, the aggregation state of sapphyrin can be influenced to yield three
monoprotonated forms, monomers, dimers, or non-covalent and aggregated polymers
[71].
Figure 12: Absorbance spectra of Sapphyrin. The peaks indicate the presence of three different
prominent monoprotonated states, monomer, dimer, and aggregate [179].
The various forms of sapphyrin can interact with nucleic acids and
nucleotides via phosphate chelation, exemplified by the solid state structure complex
monobasic cAMP forms with sapphyrin. In this case, specific chelation of the cAMP
oxyanion occurs with the protonated sapphyrin core through Coulombic interactions
that include hydrogen bonds [71].
Table 4:
31
P NMR and UV-visible spectroscopic data for Sapphyrin in the presence of
mononucleotides [71].
32
Spectropically, it was found that complexes formed between the dimeric form
of sapphyrin and phosphorylated nucleotides, and hydrophobic interactions were
responsible for nucleotide-dependent associations between sapphyrin and monomeric
and single-stranded polymeric nucleotides. In double-stranded DNA, nucleotide-
dimeric sapphyrin complex binding occurs preferentially with more flexible
copolymers, such as poly - (dA-dT)
2
over poly-(dG-dC)
2
. At low phosphate ester to
sapphyrin ratios, the highly ordered aggregation of sapphyrin on the surface of
specific helical, double-stranded nucleic acids is influenced by the highly ordered
structure of the associating nucleic acid polymers [71].
Figure 13: UV-visible spectra of 3µM Sapphyrin with increasing phosphate. The absorbance is
chronicled in the presence of increasing ssDNA-phosphate and dsDNA-phosphate concentrations,
respectively [71].
33
D. Sapphyrin Induction of Apoptosis in Hematopoietic Tumor-Derived Cells
In contrast to texaphyrins that become activated photosensitizers in response
to radiation therapy, some water-soluble sapphyrins can exhibit anti-cancer
characteristics in the absence of light, ionizing radiation, or other factors of
activation. In a study conducted by Louie Naumovski and Dong Gyu Cho in 2005, a
dihydroxylated water-soluble sapphyrin derivative (PCI-2000) and PCI-2010, a
tetrahydroxy bis-carbamate derivative of PCI-2000, demonstrated the ability to
induce apoptosis in lymphoma (Ramos, DHL-4, and HF-1), leukemia (Jurkat and
HL-60), and myeloma (8226/S, 1-310, C2E3, and 1-414) tumor cell lines via
mitochondrial apoptotic pathways [130].
Figure 14: Structures of water-soluble Sapphyrins PCI-2000 and PCI-2010 [130].
Results of fluorescent microscopy and apoptosis assays demonstrated the
abilities of PCI-2000 and PCI-2010 to localize in the cytoplasm of Ramos lymphoma
34
cells and induce apoptosis in lymphoma, leukemia, and myeloma-derived tumor cell
lines. In all experiments, similar results were seen in PCI-2010 treated tumor-
derived cell lines when compared to those treated with PCI-2000 [130]. After 24
hours of treatment, PCI-2000 triggered the release of cytochrome c from
mitochondria to encourage caspase-9 complex formation with apoptotic protease
activating factor 1 [35]. Subsequent cleavage of caspase-9 causes the cleavage and
activation of caspase-3 [35, 168]. This cascade of events then leads to the cleavage
and activation of poly(ADP-ribose) polymerase to cleave and activate caspase-8 via
a feedback loop of caspase-9 and -3 cleavage and recruits Annexin V for binding
[130].
Figure 15: Caspase activation in Ramos cells treated with PCI-2000 (0.5 to 5µmol/L for 8 hours or
5µmol/L for varying times to induce apoptosis). Western blotting shows the cleavage of caspases-9, -
3, and -8 and substrate poly(ADP-ribose) polymerase (PARP). Here, the heat shock cognate, Hsc 70,
was a loading control [130].
Further, cells treated with sapphyrin showed an increase in phosphorylated
p38 MAP kinase levels. Commonly phosphorylated in response to stress-inducing
35
stimuli [163, 206], tumor-derived cell lines co-treated with PCI-2000 and SB
203580, an inhibitor of p38 phosphorylation which blocks sapphyrin-induced
phosphorylation of p38, resulted in the stimulation of cytotoxicity due to a
synergistic interaction between PCI-2000 and the inhibitor [130].
Upon overexpression of the anti-apoptotic protein BCL-2 or treatment with a
cell membrane-permeable, irreversible, caspase inhibitor (z-VAD-fmk) apoptosis
was partially blocked due to the inhibition of caspase activity [179] (Figure 17).
BCL-2 is an oncogene that blocks BCL-2 homology domain 3-only members from
releasing cytochrome c from mitochondria [24]. Sapphyrin normally acts upstream
of BCL-2 to induce the apoptotic pathway [179]. Additionally, both overexpression
of BCL-2 and z-VAD-fmk treatment significantly reduced the number of Annexin
V-positive cells that were present in each of the tumor-derived cell lines of interest
[130].
36
Figure 16: BCL-2 overexpression blocks apoptosis but not growth by PCI-2000 treatment. HL-60neo,
control vector, and HL-60BCL-2 overexpressing cells were not treated with PCI-2000. (A) Decreased
Annexin V binding in PCI-2000 treated BCL-2 overexpressing cells compared to control cells shows
that BCL-2 overexpression inhibits apoptosis. (B) Decreased caspase-3 activation in BCL-2
overexpressing cells compared to controls also indicates inhibition of apoptosis by BCL-2
overexpression. (C) Growth inhibition by BCL-2 overexpression is the same as that seen by treatment
with PCI-2000 and control cells, even though they do not undergo apoptosis. (D) Caspase inhibition
leads to attenuation of PCI-2000-induced apoptosis [130].
37
III. Experimental
While the mechanism of sapphyrin localization to neoplastic tissues still
remains to be fully understood, the present study was undertaken to elucidate
potential biochemical pathways through which sapphyrins stimulate apoptosis in
tumor-derived cell lines. Functional genomics is essential to the biological
understanding of such mechanisms of action.
A. Microarrays
The purpose of functional genomics is to understand biology in the context of
DNA sequences that can dictate gene function. This includes how cells
work/develop, how disease affects cells, aging at the cellular level, or drug
development [104]. Nucleic acid arrays have been developed to investigate cellular
gene expression patterns that can be applied to the study of human disease, gene
function, and regulation of gene transcription.
Nucleic acid arrays used in this study entailed the hybridization of
fluorescently labeled DNA in solution to fluorescently labeled DNA molecules
attached to specific locations on a glass slide. The hybridization of a sample of
interest to the microarray is the result of a highly parallel search where each
molecule seeks a matching partner on an affinity matrix [54, 74, 96, 134, 188, 225].
The microarray consists of DNA fragments that have been synthesized based on
specific sequence information, and they are spotted on a glass slide that functions as
a substrate. The fragments of DNA that have been arrayed often come from cDNA,
38
genomic DNA or plasmid libraries [36, 47, 48, 143, 166, 184]. Figure 18 below
outlines the fundamental types of microarrays and their hybridization processes that
are used in gene expression monitoring [104].
39
Figure 17: Fundamental types of arrays used in monitoring gene expression. Nucleic acid arrays are
usually produced either by the robotic deposition of nucleic acids onto a glass slide or in situ
oligonucleotide synthesis. This figure shows pseudocolor images of (a) an oligonucleotide array and
(b) a cDNA array after the hybridization of the labeled samples and detection of fluorescence has
occurred. In both (a) and (b), the transcripts have been colored to show, in this case, the relative
number of yeast transcripts that are present in two different growth conditions. Red (dark gray)
indicates a high number in condition 1 and low in condition 2, green (light) gray indicates a high
number in condition 2 and low in condition 1, yellow (white) indicates a high number in both
conditions, and black indicates a low number in both conditions. For oligonucleotide microarrays,
multiple probes per gene are usually spotted on the microarray, whereas in the case of robotic
deposition in cDNA arrays, a single and longer double-stranded DNA probe is spotted on the micro
for each gene or EST. The probes in both cases are designed from sequence information from the 3’
end of the gene of interest. After the hybridization of labeled samples occurs, the microarrays are
scanned and a quantitative fluorescence image generated is assessed for the presence or absence of a
particular molecule at each spot. The signal intensity given by each spot in the microarray gives the
number of molecules that are bound to that spot and the identity of these bound molecules, which can
be determined from the known oligonucleotide or cDNA sequence at each location. Quantitative
estimates of the number of transcripts per cell can be calculated by taking the average signal from
multiple probes. The relative gene transcript concentration information obtained from cDNA arrays is
given as a ratio which can be derived from competitive, two-color hybridizations. The figure in (c)
shows different methods of labeling material for gene expression measurements. RNA can be labeled
directly, labeled nucleotides can be integrated into cDNA while or after the reverse transcription of
polyadenylated RNA, or cDNA can carry a T7 promoter at its 5’ end to serve as a transcription
template for the integration of labeled nucleotides into cRNA via reverse transcription. The
commonly used labels in this process are fluorophores fluorescin (Cy3 or Cy5), or nonfluorescent
biotin subsequently labeled through staining with a fluorescent streptavidin conjugate. The figure in
(d) shows the two-color hybridization strategy used with cDNA arrays. The cDNA from two different
conditions are labeled with two different dyes, and they are co-hybridized to an array. After washing
and scanning the microarray at two different wavelengths, the relative transcript abundance for each
condition can be detected. The cDNA array image was obtained from J. DeRisi and P.O. Brown
(http://cmgm.standford.edu/pbrown/yeastchip.html) [104].
40
The function of nucleic acid microarays is to monitor gene expression
(mRNA abundance) in the study of transcriptomes, collections of expressed or
transcribed genes from genomic DNA, provides valuable insight into potential
cellular phenotype and function. In understanding gene functions, knowing the
details of when, where and how much a gene is expressed is key to elucidating the
activity and biological function of its encoded protein. Further, changes in the
expression profiles of various genes can aid in the discovery and understanding of
their regulatory mechanisms and widespread cellular functions in biochemical
pathways. Such investigations can help resolve the causes and consequences of
disease, how disease treatments work in cells and organisms, and determine potential
gene products that may have therapeutic uses themselves or may act as suitable
targets for therapeutic intervention. The advantage of using microarrays to clarify
these gene expression-based issues compared to other methods of gene expression
analysis is that microarrays can contain probes for tens of thousands of genes that
may yield surprising and novel gene expression patterns, regardless of whether the
functions for these genes are known [104].
In studying human disease, genomics and gene expression experiments are
helpful in discovering new genes involved in a pathway, possible targets for
treatment, or expression markers that can be used as predictors or in the diagnosis of
disease [104]. Golub et al. [55] illustrated the use of monitoring numerous genes to
identify robust expression markers that are accurately predicative of clinical
outcome. It is also possible to use these methods to comprehend what goes amiss in
41
cells that are cancerous and transformed and to distinguish the genes responsible for
the disease [104]. The effects leading to disease and possible therapeutic targets can
be identified by establishing which genes are enriched in various tumor types [3,
146, 162, 167], and certain candidate genes can be overexpressed or treated with
growth factors to identify downstream gene targets in signaling pathways [44, 63,
94]. Tumorigenesis is usually accompanied by changes in chromosomal DNA, like
genetic amplifications, rearrangements, or losses of chromosomal loci, and
developmental abnormalities arising from aberrations in DNA copy number.
Comparisons of the copy number of these genomic regions or the genotypes of
genetic markers can be used to detect the chromosomal regions and the genes that
may be amplified or deleted in pre-cancerous or cancerous cells [115, 148, 149].
This information can be used to classify cancer types and identify regions of the
genome that may contain tumor-suppressor genes [104].
In an effort to relate the gene expression pattern for a gene to the function of
its protein product, a common approach is to use the gene expression data collected
over multiple experiments to cluster the genes into groups [26]. The basic
assumption in this approach is that genes exhibiting similar gene expression patterns
are likely to be functionally related, which, as it turns out, is true. Gene expression
patterns can be used to identify new cis-regulatory elements, which are over-
represented genomic sequence motifs in an area of similarly behaving genes in
genomic DNA, and sets of co-regulated genes. These cis-regulatory elements and
co-regulated genes represent the basic units of underlying cellular circuitry, as
42
evidenced by a strong correlation between gene expression patterns and the presence
of specific sequence motifs in gene promoter regions. Additionally, new cis-
regulatory elements could be discovered by investigating classes of co-regulated
genes [104].
Gene expression profiles can serve as transcriptional fingerprints that can be
used to determine drug targets [111]. By comparing the gene expression profile of a
cell that has been treated with a drug to the cell profiles where single genes have
been inactivated individually, specific mutants can be matched to particular drugs,
and therefore act as targets for drugs. Such expression profiles can be used to
classify drugs and their modes of action [104].
Nucleic acid arrays provide valuable information on the gene expression
patterns of large gene sets. This information can be applied to the study of human
disease and the understanding of gene function and regulation. Based on collected
data, genes can be grouped according to their functions and mechanisms of
regulation, and they can then provide some insight into the relationships between
specific genes and biochemically interesting pathways [104].
B. WebGestalt
In order to fully explore large gene sets obtained from DNA microarrays,
high throughput technologies, such as the WEB-based GEne SeT AnaLysis Toolkit
(WebGestalt) (http://genereg.ornl.gov/webgestalt/), have been developed to aid
biologists in understanding the role of these gene sets in a variety of biological
43
contexts. The gene and gene product information for this system is mainly taken
from Affymetrix, BioCarta, CGAP, Ensembl, Gene Ontology (GO) Consortium,
HomoloGene, KEGG (Kyoto Encyclopedia of Genes and Genomes,
http://www.genome.ad.jp/kegg), NCBI LocusLink or Entrez Gene, Swiss-Prot,
UCSC and Unigene. Composed of four modules, WebGestalt serves in gene set
management, information retrieval, organization/visualization, and statistics [223].
Figure 18: Schematic overview of WebGestalt and component modules: gene set management,
information retrieval, organization/visualization, and statistics. This figure has been adapted from
Zhang et al. 2005 [223].
44
In the module of managing gene sets, human and mouse sets are currently
accepted as files, GO categories, or location ranges on chromosomes. Input files are
submitted by users in a plain text file which includes the appropriate gene identifiers,
like Affymetrix probe set IDs, Entrez Gene IDs, Ensembl IDs, gene symbols and
Swiss-Prot IDs, and microarray ratios or other values may be included as well.
These gene sets can then be saved, retrieved, or deleted in the future. The
organization/visualization module can be used to generate subsets of genes from an
existing set of genes. The gene set management module also conducts Boolean
operations to elucidate the union, intersection, and difference between two or more
existing sets of genes, the latter through recursive application of the Boolean
operations [223].
The information retrieval module gives the user swift access to existing
information regarding all of the genes in the gene set. The gene characteristics that
can be retrieved include identifiers to various databases, gene nomenclature, and
chromosomal map and functional information. The reference databases connected to
this system are Entrez Gene ID, Refseq_NM, Refseq_NP, Unigene ID, Ensembl ID,
and Swiss-Prot ID. The gene nomenclature information retrieved entail the gene
symbol, symbol alias, gene name, and name alias. Map information obtained can be
either cytogenetic or physical, and the functional gene information attained is the
domain name, OMIM ID, PubMed ID, GRIF record, GO term, KEGG, BioCarta, and
phenotype [223].
45
The organization/visualization module allows users to study large gene sets
in a variety of biological contexts using the following features: GO Tree, KEGG and
BioCarta tables and maps, Protein Domain Table (not discussed here), Tissue
Expression Bar Chart, Chromosome Distribution Chart, and PubMed and GRIF
tables. GO Tree is used to organize large gene sets based on GO Directed Acyclic
Graph (DAG) and visualize these genes in an expandable tree, a bar chart at
particular annotation specificity, and an enriched DAG. The gene ontology of the set
can be explored interactively with the expandable tree, and the tree can be used to
generate bar charts at specific annotation levels for further use in presentations and
publications. The statistics module can be used to identify the enriched gene
numbers found by visualizing the GO categories of the enriched DAG [223].
In order to identify the biological pathways containing the gene set being
studied, KEGG and BioCarta tables and maps are used to organize these genes into
either KEGG or BioCarta tables based on these biochemical pathways. The table
includes information on the gene set associated pathways, the number of genes in
each pathway, the Entrez Gene IDs for the genes in the set, and P-values to indicate
significant enrichment for each pathway. All of the pathways are hyperlinked to the
KEGG map or BioCarta map respectively [223].
Tissue Expression Bar Charts are utilized to organize gene sets based on gene
expression data from various tissue and organ types of the CGAP-expressed
sequence tag (EST) project [192]. In the chart, tissues are represented by bars and
the height of the bars denotes the number of active genes in a set and the number of
46
genes also expressed in the tissue. WebGestalt can evaluate the over- or under-
representation of individual genes in each tissue type using the statistics module.
The Chromosome Distribution Chart can be used to visualize the chromosome
distribution of genes in a set based on information provided by the UCSC genome
databases (ftp://hgdownload.cse.ucsc.edu/goldenPath/currentGenomes/). Genes are
denoted by “red cross” symbols on the chromosome, allowing for the efficient
visualization of clustered genes in the chart [223].
PubMed and GRIF tables can organize genes based on their co-occurrence in
publications, according to gene-publication correlation information retrieved from
the LocusLink database from both indices [151]. Tables are then generated showing
PubMed or GRIF IDs for associated gene publications, the number of genes in each
publication, and the Entrez Gene IDs for the genes in the set. Each of the PubMed
IDs are hyperlinked to its corresponding PubMed record. GRIF tables follow a
similar format but include a column for GRIF comments as well [223] .
Biologists require statistical analysis to discover the statistically significant
functional categories associated with a gene set. To identify these categories with
significantly enriched numbers of genes, the statistics module is used to compare the
gene set of interest to a reference set for the ratio of genes in the category. Gene
enrichment in categories can be discerned based on the following situation. Suppose
that there are n genes in the gene set of interest (A) and there are m genes in the
reference gene set (B), and that there are k genes in (A) and j genes in (B) that belong
to category (C). Based on (B), the expected value of k would be given as k
e
=
47
(n/m )* j. The genes in category C are enriched if k>k
e
, where the enrichment ratio (r)
is r = k/k
e
. The gene enrichment significance can be calculated by the following
equations:
Equation A shows the hypergeometric test used by WebGestalt to assess the
significance of gene set (A) enrichment in category (C) when (B) represents the
group from which genes in (A) are selected. Equation B shows the Fisher’s exact
test used by WebGestalt to assess the significance of gene set (A) enrichment when
(A) and (B) are two independent gene sets [223].
Over- or under-representation of genes in tissues can be discerned based on
the following situation. Suppose that there are d EST sequences for a specific gene
in all tissues and b EST sequences for all of the genes in all tissues, and that there are
c EST sequences for the specific gene in a certain tissue and a EST sequences for all
of the genes in the tissue. A gene is considered over-represented in a tissue if c >
(d/b)*a and under-represented if c < (d/b)*a. The significance of over- or under-
representation of genes can be calculated by the following equations:
48
Equation A shows the significance of over-representation of a specific gene
in a certain tissue, and Equation B shows the significance of under-representation of
a specific gene in a certain tissue [223].
The information that can be obtained using WebGestalt to investigate large
gene sets in various biological contexts and the ability to organize these sets using
the capabilities offered by the system allows for further mutational analyses of the
data sets of interest.
49
IV. Results and Discussion
A. Motexafin Gadolinium
1. Motexafin Gadolinium Affects Cellular Zinc Metabolism
Motexafin gadolinium (MGd), as a metal cation-containing chemotherapeutic
drug, disrupts zinc metabolism and modifies cellular zinc availability. As described
previously, MGd is an expanded porphyrin with a lanthanide cation gadolinium in its
central core [109]. This electron-affinic complex mediates electron transfer from
intracellular reducing species, like ascorbate, NADPH, and thiols, to oxygen to form
hydrogen peroxide and superoxide [14, 108, 110, 158]. Most simple redox reactions
occur with vicinal thiols, a motif usually associated with zinc binding [14].
Figure 19: Structure of motexafin gadolinium [109].
In order to understand the mechanism of action of MGd, gene expression
profiling analyses were conducted on plateau phase A549 lung cancer cells [78].
Studies on all MGd-treated cells were done in the Hacia laboratory (University of
Southern California) in collaboration with Pharmacyclics (Sunnyvale, California).
N
N
N
N
N
OH
OH
Gd
O
OOO O
OO O
OAc AcO
50
Experiments showed that after A549 cells were incubated with the drug in the
presence of exogenous zinc acetate, the cells displayed synergistic increases in
intracellular free zinc levels, metallothionein transcripts, inhibition of thioredoxin
reductase activity, and cell death. Similar results were seen in PC3 prostate cancer
and Ramos B-cell lymphoma cell lines. These observations show that, in the
absence of exogeneous zinc, drug treatment increased intracellular free zinc levels,
indicating that MGd can mobilize bound intracellular zinc [109].
MGd induces the transcriptional expression of genes that play major roles in
controlling free levels of zinc, increasing intracellular free zinc levels, moderating
cellular zinc toxicity, and inhibiting human cancer cell line bioreductive activity by
elevating transcript levels of metallothionein isoform and ZnT1, as described above.
Based on gene expression profiling analyses, a mechanism of MGd activity was
proposed [109].
Figure 20: Schematic diagram of MT gene regulation under oxidative stress [109].
This MGd mechanistic model proposes that the disregulation of zinc homeostasis
presents a potentially significant approach to anticancer therapy [109].
MTF1
(inactive)
MTF1
(active)
MT
Bound Zn
(e.g. MT •Zn)
Free Zn(II)
metal regulated
protein kinase
signal transduction
Oxidative
stress
increased MT transcription
51
Table 5: Differential gene regulation in MGd-treated A549 cultures. These genes showed statistically
significant 2-fold differential expression in response to treatment with MGd [109].
Table 5 (above) shows the genes that were found to exhibit differential gene
expression upon treatment with MGd. After treatment with 50µM MGd for 4, 12,
and 24 hours in triplicate and analyzed using oligonucleotide microarrays, the
plateau-phase A549 cultures showed eleven genes that had been statistically
significantly up-regulated in response to drug treatment. The most notable result of
MGd treatment of these A549 cultures was the up-regulation of a variety of
metallothionein isoform transcripts across time intervals, which represents ten of the
eleven up-regulated transcript. The other transcript, hbc647, that also showed up-
regulation at all three time points [109] is located downstream of the zinc transporter
1 (ZnT1) gene and may be a component of ZnT1 [196]. All of the transcripts found
to be up-regulated in response to MGd treatment are under the control of MTF-1 [5,
GenBank ID* 4hr
†
12hr
†
24hr
†
Avg.
‡
Gene Name
M10943 37.1 49.2 36.7 41.0 metallothionein 1F
AF078844
§
39.7 47.3 30.3 39.1 RNA helicase-related protein
NM_005951 29.8 32.0 9.5 23.8 metallothionein 1H
AF333388 21.7 23.2 9.4 18.1 metallothionein 1H-like
NM_002450 23.2 20.0 10.0 17.8 metallothionein 1L
NM_005952 16.5 16.2 13.5 15.4 metallothionein 1X
NM_005953 9.8 8.6 7.3 8.6 metallothionein 2A
NM_005950 8.0 6.0 8.7 7.6 metallothionein 1G
AI972416 6.8 4.7 3.5 5.0 zinc transporter 1
AL031602 4.4 3.7 4.8 4.3 similar to metallothionein 1E
BF217861 2.2 2.5 1.6 2.1 metallothionein 1E
*http://www.ncbi.nlm.nih.gov/Entrez/index.html
†
ratio of gene expression scores for cultures treated and not treated with 50 M motexafin
gadolinium for the indicated time frame.
‡
ratio of gene expression scores treated and not treated with 50 M motexafin gadolinium for
all time frames. All genes that achieved significance using statistical analysis of microarray
(SAM) criteria described in Materials and Methods are shown.
§
protein has a C-terminal domain similar to human metallothionein 1F.
52
6, 53, 102], a metal- and stress-responsive transcription factor [165] described
previously. The up-regulation of these transcripts in drug-treated cells suggests that
the cells are activating survival response pathways to rescue them from oxidative
stress.
2. Motexafin Gadolinium & Zinc Affect Cellular Oxidative Stress
The results of the previous experiment and their possible effects on future
anticancer therapy led to the investigation of MGd combined with exogenous zinc to
treat exponential phase human Ramos B-cell lymphoma and other hematologic cell
lines. The joint treatment affected intracellular free zinc levels, oxidative stress,
proliferation, and cell death. Increased intracellular oxidative stress and free zinc
lead to and are associated with cell cycle arrest and apoptosis via disruptions in
redox balance. Cotreatment of Ramos cells with MGd and zinc acetate demonstrated
increases in intracellular levels of free zinc and associated increases in oxidative
stress. These drug-induced effects suggest that cotreatment with MGd and zinc
acetate induces adaptive cellular survival responses, but cell death is an eventual
response due to the disruption of intracellular redox balance [93].
Zinc exhibits pro- or anti-apoptotic properties depending upon its
intracellular levels [50, 109]. Elevated free intracellular zinc levels moderate and
inhibit a variety of genes involved in immune response and biological pathways [93].
They moderate the activity of protein kinase C [32], NF-B [18], p53 activities [61]
and other signaling pathways relevant to carcinogenesis [59, 138]. The genes
53
inhibited in various biological pathways include those affecting glycolysis via
glyceraldehyde phosphate dehydrogenase, the citric acid cycle via -ketoglutarate
dehydrogenase complex, mitochondrial respiration, and the activity of thioredoxin
reductase [93].
Ramos cells treated with MGd and zinc showed intracellular free zinc
elevation and an increase in oxidative stress contributing to increases in the
expressions of genes under the control of transcription factors MTF-1, HIF-1, and
NRF2, including those involved in transition metal cation homeostasis and oxidative
stress. The genes affected by transition metal cation homeostasis are those engaged
in metal sequestration and metal transporters (Table 6). The genes affected by
oxidative stress are antioxidants, chaperone related, glutathione related, transporters,
metabolism, receptors and ligands, apoptosis related, cell cycle control, signaling,
and those relevant to the function of transcription factors [93] (Tables 7 and 8).
The elevated levels of oxidative stress and intracellular free zinc correlated
with arrest in the cell cycle and apoptosis in Ramos cells. Treatment with MGd and
zinc halted the activity of S-phase cells to synthesize DNA and inhibited cell
initiation into and progression through G1 and G2/M phases [93]. In other
lymphoma cell lines, MGd modulation of zinc activity showed that proliferation of
B-cell lines were more sensitive than T-cell and myeloid cell lines and, in all cell
lines, sensitivity to zinc was increased [93].
54
Table 6: Differentially expressed genes in response to treatment with MGd. These represent 25
transcripts with annotated function that showed statistically significant differential gene expression in
response to drug treatment (1.5-fold, p 0.005) [93].
The Ramos cell cultures that were treated with MGd or zinc acetate
stimulated transcriptional responses that were characterized by HIF-1 and MTF-1
regulated genes. These cultures further demonstrated increases in levels of HIF-1
and MTF-1 controlled transcripts and additional transcripts controlled by NRF2. As
previously described, the HIF-1, MTF-1 and NRF2 transcription factors and their
related target genes function in cellular survival responses to oxidative stress.
In this study, gene expression profiling analyses of MGd-treated Ramos cells
demonstrated differential expression in response to drug treatment in 29 transcripts
Gene
Gene ID
a
Symbol Gene description FC
e
p -value FC
e
p -value FC
e
p -value
7779 SLC30A1 Solute carrier family 30 (zinc transporter), member 1 7.6 0.000 7.3 0.005 20.0 0.001
4502 MT2A Metallothionein 2A 5.5 0.000 8.2 0.004 42.4 0.003
54541 DDIT4 DNA-damage-inducible transcript 4 5.2 0.000 3.7 0.006 30.1 0.001
4501 MT1X Metallothionein 1X 4.9 0.001 7.3 0.005 34.3 0.002
AF333388
f
--- Metallothionein 1H-like protein 3.7 0.002 7.5 0.002 26.4 0.013
4494 MT1F Metallothionein 1F (functional) 3.5 0.000 4.7 0.001 18.7 0.003
AF078844
f
--- Hqp0376 protein 2.6 0.004 3.4 0.001 10.6 0.005
3303 HSPA1A Heat shock 70kDa protein 1A 2.3 0.002 2.2 0.002 26.7 0.001
57181 SLC39A10 Solute carrier family 39 (zinc transporter), member 10 2.1 0.002 1.9 0.006 2.8 0.003
3304 HSPA1B Heat shock 70kDa protein 1B 1.9 0.004 1.7 0.010 10.7 0.000
54583 EGLN1 Egl nine homolog 1 (C. elegans) 1.8 0.000 2.0 0.001 3.3 0.000
5567 PRKACB Protein kinase, cAMP-dependent, catalytic, beta 1.8 0.003 1.4 0.007 1.8 0.001
113791 MGC17330 HGFL gene 1.6 0.004 2.3 0.024 4.8 0.002
5209 PFKFB3 6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 1.6 0.000 1.5 0.014 2.0 0.004
2976 GTF3C2 General transcription factor IIIC, polypeptide 2, beta 110kDa 1.6 0.000 1.9 0.042 1.9 0.013
55818 JMJD1A Jumonji domain containing 1A 1.6 0.001 1.4 0.001 1.8 0.001
115330 GPR146 G protein-coupled receptor 146 1.6 0.003 1.0 0.585 1.6 0.002
54407 SLC38A2 Solute carrier family 38, member 2 1.6 0.002 2.2 0.002 9.2 0.001
154743 FLJ31818 Hypothetical protein FLJ31818 1.5 0.000 1.4 0.000 3.1 0.006
55900 ZNF302 Zinc finger protein 302 1.5 0.001 1.5 0.014 1.8 0.006
AI339606
f
--- --- -1.5 0.005 -1.4 0.013 -1.8 0.002
7752 ZNF200 Zinc finger protein 200 -1.7 0.005 -1.9 0.009 -2.1 0.000
55114 ARHGAP17 Rho GTPase activating protein 17 -1.8 0.001 -2.2 0.000 -4.7 0.000
1839 DTR Diphtheria toxin receptor -1.8 0.003 -1.7 0.013 -1.6 0.013
1102 CHC1L Chromosome condensation 1-like -1.8 0.003 -1.5 0.031 -2.6 0.001
57181 SLC39A10 Solute carrier family 39 (zinc transporter), member 10 -2.4 0.005 -2.4 0.005 -2.4 0.005
a
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene.
b
Cells treated with 10µM MGd compared to control cells treated with mannitol.
c
Cells treated with 50µM zinc compared to control cells treated with mannitol.
d
Cells treated with 10µM MGd and 50µM zinc compared to
control cells treated with mannitol.
e
Fold Change.
f
Accession number.
MGd
b
Zinc
c
MGd + Zinc
d
55
(25 with annotated functions in Table 6), up-regulation of MTF-1 regulated genes
including MT and zinc family transporters as seen previously in the plateau phase
A549 lung cancer cultures, and down-regulation of six transcripts, including
SLC39A10, which encodes a transporter involved in zinc uptake. A splice variant of
this transporter was increased to regulate intracellular levels of free zinc. HIF-1
related transcripts, including DDIT4, EGLN1, and PFKFB3 (Tables 6-8), also
exhibited significant changes with treatment where total cellular amounts of HIF-1
increased 1.5 to 3-fold upon treatment with MGd, zinc, or a combination of both
[93]. Since zinc has the ability to displace iron from the active sites of HIF-1 related
hydroxylases thereby inhibiting their activity [67, 169], it can be postulated that
MGd can induce hypoxia-mimetic transcriptional response in this process due to
HIF-1 stabilization dependence on intracellular free zinc and/or production of
reactive oxygen species. Although HIF-1 is usually considered to be necessary for
tumor growth, HIF-1 activation under specific conditions can negatively effect tumor
growth through the induction of target genes that are linked to apoptosis, like BNIP3,
E2IG5, PMAIP1, and DDIT4, or via the metabolic alteration of cells in tumor
environments that are low in nutrients [93].
Further, the levels of both MTs and heme oxygenase-1 (HO-1), which are
expressed due to induction by MTF-1 and NRF2 respectively, were found to be
augmented following co-treatment with MGd and zinc. The expression of NRF2-
related transcripts in this group are involved in the induction of antioxidants (GCLM,
HMOX1, and NQO3A2) [42, 137] and transporters responsible for the cellular uptake
56
of amino acids required for glutathione synthesis (TXNRD1, CTH, GSR, and
SLC7A11) [93]. Additionally, the induction of thioredoxin reductase, TXNRD1, and
elevated glutathione levels function in the restoration of Keap-2 binding of NRF2 in
a feedback loop under oxidative stress conditions [37].
Table 7: Transcriptional responses of stress-related genes in Ramos cells co-treated with MGd and
zinc. These are transcripts that show differential gene expression at larger magnitudes relative to
individual treatments [93].
FC
b
p -value FC
b
p -value FC
b
p -value FC
b
p -value
4493 MT1E Metallothionein 1E (functional) 3.2 0.050 1.9 0.025 1.1 0.128 1.3 0.112
4494 MT1F Metallothionein 1F (functional) 18.7 0.003 8.5 0.001 3.5 0.000 4.7 0.001
4495 MT1G Metallothionein 1G 9.1 0.005 5.1 0.022 1.6 0.176 2.0 0.124
4496 MT1H Metallothionein 1H 27.4 0.002 13.6 0.001 2.5 0.017 5.0 0.008
--- --- Metallothionein 1H-like 26.4 0.013 24.6 0.001 3.7 0.002 7.5 0.002
4501 MT1X Metallothionein 1X 24.6 0.002 14.3 0.001 3.4 0.008 5.2 0.004
--- MT2A Metallothionein 2A 42.4 0.003 20.4 0.004 5.5 0.000 8.2 0.004
--- --- Similar to human metallothionein-IF 10.6 0.005 7.0 0.001 2.6 0.004 3.4 0.001
7779 SLC30A1 Solute carrier family 30 (zinc transporter), member 1 20.0 0.001 12.0 0.001 7.6 0.000 7.3 0.005
7779 SLC30A1 Solute carrier family 30 (zinc transporter), member 1 6.7 0.007 2.7 0.002 1.3 0.108 2.1 0.020
57181 SLC39A10 Solute carrier family 39 (zinc transporter), member 10 2.8 0.003 2.7 0.000 2.1 0.002 1.9 0.006
57181 SLC39A10 Solute carrier family 39 (zinc transporter), member 10 -2.4 0.005 -2.4 0.005 -2.4 0.005 -2.4 0.005
23516 SLC39A14 Solute carrier family 39 (zinc transporter), member 14 2.6 0.010 -1.0 0.423 1.0 0.482 2.3 0.007
55334 SLC39A9 Solute carrier family 39 (zinc transporter), member 9 3.7 0.015 1.2 0.205 1.2 0.430 2.4 0.072
7037 TFRC Transferrin receptor (p90, CD71) 5.3 0.001 1.3 0.357 1.6 0.111 5.4 0.002
3162 HMOX1 Heme oxygenase (decycling) 1 4.4 0.010 1.0 1.000 1.0 1.000 1.0 1.000
5034 P4HB Procollagen-proline, 2-oxoglutarate 4-dioxygenase 2.7 0.001 1.4 0.039 1.4 0.062 2.4 0.002
6415 SEPW1 Selenoprotein W, 1 2.8 0.003 1.9 0.005 1.4 0.110 1.4 0.095
83667 SESN2 Sestrin 2 4.4 0.000 2.2 0.007 1.2 0.315 1.3 0.103
7296 TXNRD1 Thioredoxin reductase 1 2.0 0.004 2.1 0.000 1.2 0.120 1.3 0.006
22824 APG-1 Heat shock protein (hsp110 family) 2.6 0.000 1.6 0.003 1.0 0.910 1.4 0.102
9531 BAG3 BCL2-associated athanogene 3 8.0 0.005 3.1 0.000 1.4 0.031 1.2 0.251
821 CANX DnaJ (Hsp40) homolog, subfamily B, member 1 2.5 0.002 1.2 0.190 1.2 0.263 2.0 0.004
3337 DNAJB1 DnaJ (Hsp40) homolog, subfamily B, member 4 4.7 0.000 2.3 0.000 1.3 0.013 -1.5 0.017
11080 DNAJB4 DnaJ (Hsp40) homolog, subfamily B, member 6 3.0 0.002 1.5 0.003 1.1 0.354 1.3 0.030
10049 DNAJB6 DnaJ (Hsp40) homolog, subfamily C, member 3 3.2 0.000 1.9 0.006 1.3 0.047 1.4 0.021
5611 DNAJC3 Heat shock 105kDa/110kDa protein 1 3.3 0.001 1.2 0.270 1.1 0.418 3.1 0.007
3303 HSPA1A Heat shock 27kDa protein 1 26.7 0.001 6.8 0.000 2.3 0.002 2.2 0.002
3304 HSPA1B Heat shock 60kDa protein 1 (chaperonin) 10.7 0.000 4.7 0.000 1.9 0.004 1.7 0.010
3308 HSPA4 Heat shock 70kDa protein 1A /1B 3.6 0.001 1.7 0.061 1.5 0.116 2.0 0.016
3315 HSPB1 Heat shock 70kDa protein 1B 4.2 0.020 1.9 0.009 1.1 0.090 1.2 0.113
3326 HSPCB Heat shock 70kDa protein 4 2.2 0.003 1.4 0.064 1.3 0.154 1.7 0.025
3329 HSPD1 Heat shock 90kDa protein 1, beta 4.6 0.002 1.2 0.344 1.3 0.226 1.6 0.007
10808 HSPH1 Heat shock protein (hsp110 family) 6.6 0.000 2.6 0.008 1.4 0.148 2.3 0.009
1491 CTH Cystathionase (cystathionine gamma-lyase) 3.9 0.000 2.2 0.005 1.3 0.120 1.7 0.007
2730 GCLM Glutamate-cysteine ligase, modifier subunit 2.4 0.005 1.6 0.000 1.0 0.623 1.2 0.223
84706 GPT2 Glutamic pyruvate transaminase 2 1.8 0.001 1.2 0.081 1.1 0.471 1.3 0.046
2744 GLS Glutaminase -2.0 0.001 -1.7 0.001 -1.2 0.027 -1.2 0.066
2936 GSR Glutathione reductase 2.2 0.008 1.2 0.123 1.1 0.347 1.9 0.001
6509 SLC1A4 Solute carrier family 1, member 4 3.3 0.000 1.5 0.011 1.1 0.377 2.0 0.009
6520 SLC3A2 Solute carrier family 16, member 1 2.1 0.006 1.4 0.027 1.1 0.244 1.3 0.108
6566 SLC16A1 Solute carrier family 16, member 6 3.4 0.006 1.3 0.273 1.6 0.060 3.7 0.002
9120 SLC16A6 Solute carrier family 3, member 2 2.8 0.000 1.6 0.016 1.1 0.552 1.5 0.013
54407 SLC38A2 Solute carrier family 38, member 2 10.1 0.000 4.0 0.000 1.6 0.012 2.2 0.002
8501 SLC43A1 Solute carrier family 43, member 1 2.1 0.001 1.6 0.001 1.2 0.009 1.3 0.030
6541 SLC7A1 Solute carrier family 7, member 1 2.4 0.010 1.3 0.263 1.0 0.986 1.8 0.057
23657 SLC7A11 Solute carrier family 7, member 11 10.2 0.003 4.8 0.003 1.8 0.018 2.2 0.107
10 mM MGd 50 mM Zn
10 mM MGd
+ 50 mM
10 mM MGd
+ 25 mM
Metal Sequestration Metal Transporters
Category
Transition Metal Cation Homeostasis
Gene Description GeneID
a
Symbol
Antioxidant Chaperone-Related
Oxidative Stress
Glutathione
Related
Transporters
a
Entries with identical NCBI Gene ID designations represent results from different probe tilings interrogating the same gene.
b
Fold Change.
57
Table 7: Transcriptional responses of stress-related genes in Ramos cells co-treated with MGd and
zinc. (continued)
FC
b
p -value FC
b
p -value FC
b
p -value FC
b
p -value
178 AGL Amylo-1, 6-glucosidase, 4-alpha-glucanotransferase -1.6 0.009 -1.3 0.029 -1.1 0.321 -1.1 0.362
7915 ALDH5A1 Aldehyde dehydrogenase 5 family, member A1 3.5 0.000 1.6 0.003 1.3 0.026 3.5 0.019
4329 ALDH6A1 Aldehyde dehydrogenase 6 family, member A1 1.9 0.001 1.1 0.222 -1.1 0.377 1.3 0.077
1717 DHCR7 7-Dehydrocholesterol reductase -1.6 0.004 -1.3 0.078 -1.2 0.045 -1.3 0.024
2023 ENO1 Enolase 1, (alpha) 1.7 0.004 1.2 0.001 1.2 0.005 1.5 0.005
2194 FASN Fatty acid synthase -2.9 0.000 -1.4 0.011 -1.2 0.023 -1.7 0.005
65220 FLJ13052 NAD kinase 2.2 0.007 1.4 0.113 1.3 0.176 1.7 0.010
51706 NQO3A2 NAD(P)H:quinone oxidoreductase type 3, polypeptide A2 2.3 0.000 1.9 0.001 1.3 0.145 1.7 0.030
5209 PFKFB3 6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 2.0 0.004 1.7 0.016 1.6 0.000 1.5 0.014
389753 --- Similar to phosphoglucomutase 5 4.0 0.000 2.1 0.005 1.8 0.018 3.3 0.001
10 mM MGd
+ 25 mM
10 mM MGd 50 mM Zn
10 mM MGd
+ 50 mM Gene Description
M etabolism
Category GeneID
a
Symbol
a
Entries with identical NCBI Gene ID designations represent results from different probe tilings interrogating the same gene.
b
Fold Change.
Table 8: Transcriptional responses of selected genes related to apoptosis and cell cycle control in
Ramos co-treated with MGd and zinc. These are transcripts that show differential gene expression at
larger magnitudes relative to individual treatments [93].
Gene Description
FC
b
p -value FC
b
p -value FC
b
p -value FC
b
p -value
8772 FADD Fas (TNFRSF6)-associated via death domain -1.6 0.001 -1.1 0.278 -1.2 0.053 -1.4 0.023
55179 FAIM Fas apoptotic inhibitory molecule -1.7 0.001 -1.3 0.010 -1.1 0.102 -1.4 0.006
25816 TNFAIP8 TNF, alpha-induced protein 8 -1.6 0.000 -1.4 0.001 -1.2 0.007 -1.1 0.060
8795 TNFRSF10B TNF receptor superfamily, member 10b 1.6 0.003 1.0 1.000 1.0 1.000 1.0 0.423
608 TNFRSF17 TNF receptor superfamily, member 17 -2.2 0.003 -1.5 0.014 -1.3 0.018 -1.4 0.005
7132 TNFRSF1A TNF receptor superfamily, member 1A 2.3 0.004 1.6 0.003 1.2 0.120 1.0 0.934
944 TNFSF8 TNF (ligand) superfamily, member 8 1.9 0.001 1.2 0.042 1.2 0.042 1.6 0.001
332 BIRC5 Baculoviral IAP repeat-containing 5 (survivin) 1.8 0.000 1.3 0.058 1.2 0.326 1.6 0.044
51651 BIT1 Bcl-2 inhibitor of transcription -2.6 0.000 -1.4 0.044 -1.2 0.004 -1.2 0.010
664 BNIP3 BCL2/adenovirus E1B interacting protein 3 2.1 0.004 1.9 0.003 1.4 0.002 1.6 0.015
665 BNIP3L BCL2/adenovirus E1B interacting protein 3-like 1.7 0.000 1.7 0.003 1.4 0.022 1.5 0.001
836 CASP3 Caspase 3, apoptosis-related cysteine protease -1.6 0.000 -1.4 0.072 -1.2 0.150 -1.2 0.131
1613 DAPK3 Death-associated protein kinase 3 1.9 0.000 -1.1 0.059 -1.1 0.072 1.9 0.001
162989 DEDD2 Death effector domain containing 2 4.2 0.003 2.1 0.004 1.3 0.007 1.2 0.036
1677 DFFB DNA fragmentation factor, beta polypeptide -1.6 0.002 -1.1 0.507 -1.1 0.279 -1.2 0.013
26355 E2IG5 Growth and transformation-dependent protein 2.0 0.001 2.1 0.000 1.5 0.000 1.4 0.001
29923 HIG2 Hypoxia-inducible protein 2 1.6 0.005 1.6 0.009 1.2 0.164 1.1 0.244
8739 HRK Harakiri, BCL2 interacting protein 3.1 0.002 1.6 0.099 1.3 0.217 1.4 0.142
4615 MYD88 Myeloid differentiation primary response gene -1.8 0.000 -1.3 0.007 -1.1 0.011 -1.3 0.002
11188 NISCH Nischarin -1.5 0.003 -1.1 0.115 -1.2 0.096 -1.3 0.016
22984 PDCD11 Programmed cell death 11 -1.8 0.002 -1.7 0.013 -1.5 0.011 -1.5 0.010
27250 PDCD4 Programmed cell death 4 2.4 0.000 1.5 0.022 1.2 0.137 1.8 0.005
9141 PDCD5 Programmed cell death 5 -1.5 0.001 -1.3 0.111 -1.1 0.669 -1.2 0.232
5366 PMAIP1 Phorbol-12-myristate-13-acetate-induced protein 2.7 0.000 1.8 0.004 1.3 0.068 1.6 0.009
79155 TNIP2 TNFAIP3 interacting protein 2 -1.6 0.002 -1.5 0.013 -1.3 0.003 -1.5 0.000
50 mM Zn
10 mM MGd
+ 50 mM Zn
10 mM MGd
+ 25 mM Zn
10 mM MGd
Category
Gene
ID
a
Receptors and Ligands Apoptosis Related
Symbol
a
Entries with identical NCBI Gene ID designations represent results from different probe tilings interrogating the same gene.
b
Fold Change.
58
Table 8: Transcriptional responses of selected genes related to apoptosis and cell cycle control in
Ramos co-treated with MGd and zinc. (continued)
Gene Description
FC
b
p -value FC
b
p -value FC
b
p -value FC
b
p -value
472 ATM Ataxia telangiectasia mutated 1.7 0.000 1.0 0.450 1.1 0.046 1.6 0.103
545 ATR ataxia telangiectasia and Rad3 related -1.5 0.005 -1.3 0.025 -1.1 0.011 -1.2 0.015
54619 CCNJ Cyclin J -2.0 0.003 -1.5 0.002 -1.2 0.225 -1.3 0.124
8881 CDC16 CDC16 cell division cycle 16 homolog -1.8 0.001 -1.3 0.006 -1.2 0.017 -1.4 0.004
995 CDC25C Cell division cycle 25C 2.0 0.000 1.7 0.032 1.2 0.018 1.4 0.083
996 CDC27 Cell division cycle 27 1.7 0.002 1.1 0.548 1.2 0.053 1.7 0.003
988 CDC5L CDC5 cell division cycle 5-like (S. pombe) -1.7 0.001 -1.3 0.008 -1.1 0.083 -1.3 0.013
157313 CDCA2 Cell division cycle associated 2 -1.6 0.005 -1.2 0.029 -1.1 0.066 -1.3 0.009
23097 CDK11 Cyclin-dependent kinase (CDC2-like) 11 2.0 0.003 1.7 0.012 1.3 0.090 1.8 0.014
1017 CDK2 Cyclin-dependent kinase 2 3.3 0.002 1.2 0.306 1.4 0.122 2.9 0.000
1027 CDKN1B Cyclin-dependent kinase inhibitor 1B (p27, Kip1) 2.4 0.001 1.9 0.000 1.3 0.008 1.3 0.035
1869 E2F1 E2F transcription factor 1 1.6 0.001 1.5 0.003 1.1 0.125 1.2 0.139
1871 E2F3 E2F transcription factor 3 1.6 0.003 1.3 0.027 1.1 0.325 1.2 0.054
4193 MDM2 Mdm2, transformed 3T3 cell double minute 2 2.6 0.003 1.3 0.131 1.1 0.423 2.2 0.002
5591 PRKDC Protein kinase, DNA-activated, catalytic 1.5 0.002 1.1 0.480 1.1 0.117 1.5 0.002
5925 RB1 Retinoblastoma 1 (including osteosarcoma) -1.7 0.002 -1.8 0.000 -1.2 0.008 -1.4 0.005
1649 DDIT3 DNA-damage-inducible transcript 3 6.6 0.000 1.7 0.014 1.2 0.337 1.1 0.337
54541 DDIT4 DNA-damage-inducible transcript 4 30.1 0.001 13.6 0.001 5.2 0.000 3.7 0.006
54583 EGLN1 Egl nine homolog 1 3.3 0.000 2.9 0.004 1.8 0.000 2.0 0.001
8518 IKBKAP IKB, kinase-associated protein -1.6 0.005 -1.3 0.005 -1.2 0.022 -1.3 0.026
9641 IKBKE IKB, kinase epsilon 1.8 0.001 1.6 0.031 1.3 0.029 1.6 0.030
359948 IRF2BP2 Interferon regulatory factor 2 binding protein 2 2.7 0.001 1.6 0.023 1.2 0.073 1.7 0.004
55233 MOBKL1B MOB1, Mps One Binder kinase activator-like 1B 3.4 0.001 1.1 0.403 1.3 0.104 3.6 0.012
4763 NF1 Neurofibromin 1 1.7 0.000 1.4 0.001 1.2 0.154 1.4 0.001
9975 NR1D2 Nuclear receptor subfamily 1, group D, member 23.7 0.001 1.2 0.163 1.2 0.157 2.8 0.000
8554 PIAS1 Protein inhibitor of activated STAT, 1 2.9 0.000 1.8 0.005 1.3 0.124 3.2 0.013
5567 PRKACB Protein kinase, cAMP-dependent, catalytic, beta 3.0 0.003 1.7 0.001 1.4 0.020 2.0 0.015
5580 PRKCD Protein kinase C, delta -3.0 0.002 -1.3 0.064 -1.0 0.647 -1.7 0.015
25865 PRKD2 Protein kinase D2 -1.8 0.005 -1.5 0.006 -1.4 0.010 -1.7 0.002
117289 TAGAP T-cell activation GTPase activating protein 2.9 0.001 1.8 0.066 1.3 0.159 1.3 0.198
57761 TRB3 Tribbles homolog 3 3.9 0.003 1.2 0.020 1.1 0.445 1.5 0.204
7422 VEGF Vascular endothelial growth factor 2.2 0.000 -1.0 0.838 1.3 0.035 1.6 0.003
467 ATF3 Activating transcription factor 3 3.9 0.005 1.0 1.000 1.0 1.000 1.1 0.423
571 BACH1 BTB and CNC homology 1 3.3 0.004 1.8 0.036 1.0 0.567 1.4 0.032
865 CBFB Core-binding factor, beta subunit 2.2 0.003 1.4 0.120 1.2 0.286 2.1 0.006
8452 CUL3 Cullin 3 -1.7 0.000 -1.2 0.001 -1.0 0.088 -1.1 0.007
2309 FOXO3A Forkhead box O3A 2.0 0.001 1.4 0.007 1.2 0.072 1.5 0.004
26959 HBP1 HMG-box transcription factor 1 3.0 0.001 2.3 0.002 1.5 0.031 1.4 0.029
204851 HIPK1 Homeodomain interacting protein kinase 1 3.1 0.000 1.2 0.205 1.1 0.437 2.8 0.000
3398 ID2 Inhibitor of DNA binding 2 2.8 0.000 1.6 0.002 1.1 0.119 1.2 0.047
3727 JUND Jun D proto-oncogene 2.0 0.004 1.3 0.039 1.1 0.042 1.0 0.553
57157 PHTF2 Putative homeodomain transcription factor 2 3.0 0.003 1.2 0.410 1.3 0.117 3.2 0.004
5971 RELB v-Rel reticuloendotheliosis viral oncogene B 1.6 0.002 1.3 0.061 1.2 0.096 1.2 0.148
6596 SMARCA3 SWI/SNF related, regulator of chromatin, a3 -1.5 0.000 -1.1 0.016 -1.0 0.471 -1.2 0.003
6597 SMARCA4 SWI/SNF related, regulator of chromatin, a4 -1.8 0.000 -1.1 0.126 -1.1 0.070 -1.4 0.012
8467 SMARCA5 SWI/SNF related, regulator of chromatin, a5 -1.6 0.000 -1.3 0.001 -1.1 0.129 -1.2 0.002
7428 VHL von Hippel-Lindau tumor suppressor 2.0 0.003 1.1 0.246 1.1 0.337 1.8 0.002
50 mM Zn
Category
Gene
ID
a
Symbol
10 mM MGd 10 mM MGd 10 mM MGd
Cell Cycle Control Signaling
Relevant to the Function of Transcription
Factors
a
Entries with identical NCBI Gene ID designations represent results from different probe tilings interrogating the same gene.
b
Fold Change.
The data obtained in this experiment shows that co-treatment of MGd and
zinc in Ramos cell cultures induces transcriptional cascades under the control of
MTF-1, HIF-1, and NRF2. Under conditions of oxidative stress, these transcription
59
factors and their targets exhibit increased sensitivity to MGd treatment as a result of
already increased levels of intracellular zinc, exhausted stores of reducing
equivalents like NADPH [93], and dependence on important antioxidant enzymes
like thioredoxin reductase [13]. This increased sensitivity would allow for the
selectivity of targeting MGd as a treatment for diseases involving oxidative stress
[93].
Although treatment of neoplastic tissues with MGd is promising in its metal-
induction of cellular oxidative stress and apoptosis through disruption of redox
cycling, a limit to this drug as an anti-cancer therapeutic agent is that it requires
activation by irradiative therapy. Sapphyrins, on the other hand, provide viable
alternatives to MGd treatment by exhibiting anti-cancer therapy characteristics in the
absence of light-based factors of activation.
B. Sapphyrin
1. Genes Up-Regulated in Response to Sapphyrin Treatment
Experiments conducted to study the effects of sapphyrin on gene expression
were compared to the effects seen on the gene expression profiles of those treated
with a known transcription inhibitor, Actinomycin D. Investigations by Wadkins et
al. in 1998 demonstrated that the binding of Actinomycin D to single-stranded DNA
aided in the stabilization of DNA secondary structure hairpins. This stabilization
may be an important aspect of the ability of Actinomycin D to inhibit transcription
[207]. Studies on all sapphyrin-treated cells explored here were done in the Hacia
60
laboratory (University of Southern California) in conjunction with Pharmacyclics
(Sunnyvale, California).
While the gene expression profiles of cells treated with sapphyrin and
Actinomycin D both demonstrate global decreases in levels of mRNA, their
mechanisms of action seem to differ. In order to gain perspective on the underlying
mechanisms of sapphyrin action, oligonucleotide microarray analyses were
conducted on A549 lung cancer cultures that had been treated with each drug to
identify any genes that might be significantly up-regulated in 2.5µM sapphyrin
treated cells and down-regulated in cells treated with Actinomycin D (p<0.05).
Table 9 (below) shows the genes that met this criterion.
Table 9: Up-regulated Sapphyrin-treated and down-regulated Actinomycin D-treated genes.
Identification Gene
Probe ID: 231292_at
Gene ID: 493861
LOC493861, similar to mouse 1700027M21Rik gene E1A-like
inhibitor of differentiation 3
Probe ID: 233223_at
Gene ID: 4739
NEDD9, Neural precursor cell expressed, developmentally down-
regulated 9
Alias: HEF1 protein
Probe ID: 227195_at
Gene ID: 84858
ZNF503, zinc finger protein 503
Probe ID: 203378_at
Gene ID: 51585
PCF11, pre-mRNA cleavage complex II protein Pcf11
Probe ID: 208686_s_at
Gene ID: 6046
BRD2, bromodomain containing 2
Probe ID: 218880_at
Gene ID: 2355
FOSL2, FOS-like antigen 2
Probe ID: 242671_at
Gene ID: 4281
MID1, Midline 1
Alias: Opitz/BBB syndrome
Probe ID: 244503_at
Gene ID: 627
BDNF , Brain-derived neurotrophic factor
Probe IDs: 1555789_s_at,
223081_at
Gene ID: 79142
MGC2941, hypothetical protein MGC2941
Alias: PHD finger protein 23
Probe ID: 227866_at
Gene ID: 22828
RBM16, RNA binding motif protein 16
Probe ID: 51228_at
Gene ID: 389677
LOC389677, similar to RIKEN cDNA 3000004N20
Alias: RNA binding motif protein 12B
61
Table 9: Up-regulated Sapphyrin-treated and down-regulated Actinomycin-D-treated genes.
(continued)
Probe ID: 223295_s_at
Gene ID: 55692
LUC7L, LUC7-like (S. cerevisiae)
Probe ID: 209158_s_at
Gene ID: 9266
PSCD2, pleckstrin homology, Sec7 and coiled-coil domains 2
Alias: cytohesin-2
Probe ID: 226146_at
Gene ID: 255458
LOC255458, Hypothetical protein LOC255458
Probe ID: 203752_s_at
Gene ID: 3727
JUND, jun D proto-oncogene
Probe ID: 226924_at
Gene ID: 400657
LOC400657, hypothetical gene supported by BC036588
Probe ID: 225933_at
Gene ID: 339229
LOC339229, Hypothetical protein LOC339229
Probe ID: 225183_at
Gene ID: 29035
PRO0149, PRO0149 protein
Probe ID: 204908_s_at
Gene ID: 602
BCL3, B-cell CLL/lymphoma 3
Probe ID: 228065_at
Gene ID: 283149
BCL9L, B-cell CLL/lymphoma 9-like
Probe ID: 231205_at Development and differentiation enhancing factor 1
Probe ID: 1553103_at
Gene ID: 4799
NFX1, nuclear transcription factor, X-box binding 1
The genes that exhibited up-regulation upon treatment with sapphyrin and
down-regulation upon treatment with Actinomycin D were found to play important
roles in gene regulation (Table 9). The genes involved in regulation can be classified
in the context of four categories: cell cycle, differentiation, immune response, and
transcription. These gene expression profile data in response to drug treatment
suggests that sapphyrin has the ability to activate cellular responses to decrease
transcription globally. Additionally, the expressed genes in the regulated categories
may be implicated in the mechanism of sapphyrin action in which cytotoxicity is
induced to activate various cellular apoptotic pathways[130].
In cell cycle regulation, BRD2 is up-regulated during mitosis and meiosis
[199], and FOSL2 is down-regulated during cell development [52]. During
62
differentiation, a number of gene expressions are affected in response to sapphyrin
treatment. LOC493861 is down-regulated during differentiation and cell cycle
exiting [119, 127], but it shows up-regulation during terminal differentiation [107].
NEDD9 is up-regulated in developing embryos [117], and ZNF503 is down-
regulated in neural development [22]. Additionally, MID1 was up-regulated in
chondroinduced human dermal fibroblasts [214], and developmental and
differentiation enhancing factor 1 showed down-regulation of gonadotropins in
primary human granulose cells [157]. In immune response, BCL3 and NFX1 both
demonstrated up-regulation. The up-regulation of BCL3 showed repression of TNF-
induced NF-B transactivation [156], while the up-regulation exhibited by NFX1
repressed HLA class II genes [87]. The genes involved in the regulation of cell cycle
and differentiation in response to sapphyrin treatment show that this drug may affect
various stress-response pathways during cell growth and development.
Transcriptional regulation by the differentially expressed genes can be
distinguished into four groups: chromatin-mediated, gene expression/silencing,
regulation via the kinase pathway, and termination/mRNA degradation. All
transcriptional regulation influenced by the expression of these genes can play
important roles in reducing global mRNA levels.
MGC2941 [1] and BCL3 [187] both demonstrated participation in chromatin-
mediated transcriptional regulation. In the transcriptional regulation of gene
expression, up-regulation was seen in BDNF [38, 197], PSCD2 [198], and JUND via
a combined effect with c-fos [105] and the cAMP-dependent intracellular signaling
63
pathway [84]. In silencing, LUC7L showed up-regulation in antisense RNA to
induce transcriptional silencing in normal and diseased cells [106] .
During regulation via kinase pathways, BDNF shows up-regulation by
calcium signals which link to the calcium/calmodulin kinase pathway [195]. JUND
is up-regulated by the MAPK pathway which effects the heterodimerization of
transcription factors that bind to DNA, interact with basal transcriptional machinery,
and recognition by modification enzymes [80] to effect growth arrest. PSCD2 was
found to be down-regulated by the activation of the MAP kinase pathway [45].
These kinase pathways are closely associated with extracellular and intracellular
apoptotic signaling pathways mediated by mitochondria [152] in response to
environmental stress.
During transcriptional termination, PCF11 was found to be up-regulated due
to its interactions with RNA polymerase II elongation complex [224] . mRNA
degradation plays an important role in apoptotic induction through the ubiquitin-
proteasome pathway. In the process of mRNA degradation, transforming growth
factor-1 has been shown to transcriptionally regulate the expression of NEDD9,
also known as HEF1, via increasing HEF1 mRNA levels by down-regulating HEF1
mRNA degradation [226].
64
Table 10: KEGG analysis of differentially expressed genes up-regulated in response to 2.50 µM
Sapphyrin treatment.
Pathway p-value GeneID Gene Symbol Gene Name
0.019 112724 RDH13 retinol dehydrogenase 13 (all-trans and 9-cis)
2762 GMDS GDP-mannose 4,6-dehydratase
5210 PFKFB4 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4
0.011 31 ACACA acetyl-Coenzyme A carboxylase alpha
32 ACACB acetyl-Coenzyme A carboxylase beta
3948 LDHC lactate dehydrogenase C
0.008 31 ACACA acetyl-Coenzyme A carboxylase alpha
32 ACACB acetyl-Coenzyme A carboxylase beta
3948 LDHC lactate dehydrogenase C
0.001 31 ACACA acetyl-Coenzyme A carboxylase alpha
32 ACACB acetyl-Coenzyme A carboxylase beta
0.010 112724 RDH13 retinol dehydrogenase 13 (all-trans and 9-cis)
9249 DHRS3 dehydrogenase/reductase (SDR family) member 3
0.025 51547 SIRT7 sirtuin (silent mating type information regulation 2 homolog) 7 (S. cerevisiae)
80025 PANK2 pantothenate kinase 2 (Hallervorden-Spatz syndrome)
Bisphenol A
degradation
Pantothenate and
CoA biosynthesis
Fructose and
mannose
metabolism
Pyruvate
metabolism
Propanoate
metabolism
Fatty acid
biosynthesis
Table 11: KEGG analysis of differentially expressed genes up-regulated with 2.50 µM Sapphyrin
treatment and down-regulated with Actinomycin D.
Pathway p-value GeneID Gene Symbol Gene Name
Biosynthesis of
steroids
Pantothenate and
CoA biosynthesis
Terpenoid
biosynthesis
Caprolactam
degradation
0.031 91734 IDI2 isopentenyl-diphosphate delta isomerase 2
0.040
0.0111972
0.0295938
51547
91734
51547
SIRT7
IDI2
SIRT7
sirtuin (silent mating type information regulation 2 homolog) 7 (S. cerevisiae)
isopentenyl-diphosphate delta isomerase 2
sirtuin (silent mating type information regulation 2 homolog) 7 (S. cerevisiae)
After KEGG analysis (Tables 10 and 11), pathways enriched in carbon
catabolite metabolism showed that cells treated with sapphyrin favored glycolysis
over oxidative phosphorylation, an observation consistent with studies conducted on
MGd-treated cells that induce oxidative stress in cancer cell lines. Fatty acid
biosynthesis was also enriched by sapphyrin treatment, suggesting a specific cellular
response to drug activity or general cellular survival response (unpublished data).
65
Table 12: BioCarta analysis of differentially expressed genes up-regulated in response to 2.50 µM
Sapphyrin treatment.
Pathway p-value GeneID Gene Symbol Gene Name
0.039 8289 ARID1A AT rich interactive domain 1A (SWI- like)
6597 SMARCA4
SWI/SNF related, matrix associated, actin dependent
regulator of chromatin, subfamily a, member 4
0.016 8289 ARID1A AT rich interactive domain 1A (SWI- like)
6597 SMARCA4
SWI/SNF related, matrix associated, actin dependent
regulator of chromatin, subfamily a, member 4
Control of Gene Expression
by Vitamin D Receptor
Chromatin Remodeling by
hSWI/SNF ATP-dependent
Complexes
Table 13: BioCarta analysis of differentially expressed genes up-regulated with 2.50 µM Sapphyrin
treatment and down-regulated with Actinomycin D.
Pathway p-v alue GeneID Gene Symbol Gene Name
Phosphorylation of MEK1 by
cdk5/p35 down regulates the
MAP kinase pathway
0.018 1958 EGR1 early growth response 1
FOSB gene expression and drug
abuse
0.008 3727 JUND jun D proto-oncogene
Bone Remodelling 0.021 2355 FOSL2 FOS-like antigen 2
Ghrelin: Regulation of Food
Intake and Energy Homeostasis
0.021 3487 IGFBP4 insulin-like growth factor binding protein 4
B Cell Survival Pathway 0.021 3727 JUND jun D proto-oncogene
After analysis of gene expression profiles with BioCarta (Tables 12 and 13),
the biochemical pathways enriched in cellular metabolism further substantiates
sapphyrin treatment in cells to effect general cellular survival response, which may
be an indication of specific cell response to drug activity. Additional enrichment in
pathways associated with gene expression via chromatin remodeling complexes
implicates sapphyrin action in transcriptional regulation, a mechanism similar to that
seen in MGd-treated cells where transcription factors relevant to cellular response to
oxidative stress were affected by drug-treatment.
66
2. Up-Regulated Genes Involved in Transcriptional Repression
In order to obtain current information on the specific genes that have
exhibited transcriptional repression, WebGestalt Gene Ontology (GO) was used to
compile a list of genes that fit this criterion. Table 14 (below) shows these genes
which are all involved in biological processes in the context of negative general
transcriptional regulation during mitosis, RNA polymerase II promoter
transcriptional regulation during mitosis, transcriptional regulation by carbon
catabolites, and transcriptional regulation by glucose. No genes were identified by
WebGestalt GO in the negative regulation of transcription mediated by RNA
polymerase I and RNA polymerase III promoters during mitosis or during meiosis.
Table 14: WebGestalt Gene Ontology (GO) of transcriptional repression.
GO Information Gene & Related Information
GO:0007068
Negative regulation of
transcription, mitotic:
Any process that stops,
prevents or reduces the rate,
frequency or extent of
transcription during
mitosis.
UME6 sequence - C6 zinc finger URS1-binding protein, gene from
Saccharomyces cerevisiae
CCL1 sequence - TFIIK subunit, a subcomplex of transcription factor TFIIH,
cyclin, gene from Saccharomyces cerevisiae
CG32721 sequence - CG32721, gene from Drosophila melanogaster
CG5874 sequence - CG5874, gene from Drosophila melanogaster
crk1 - cyclin-dependent kinase activating kinase Crk1, gene from
Schizosaccharomyces pombe
Nelf-E sequence - Negative elongation factor E, gene from Drosophila
melanogaster
NELFE_DROVI - protein from Drosophila virilis
RAD3 sequence - gene from Saccharomyces cerevisiae
SSL1 sequence - RNA polymerase transcription factor TFIIH component, gene
from Saccharomyces cerevisiae
SSL2 sequence - DNA helicase, human XPBC, ERCC3 homolog, gene from
Saccharomyces cerevisiae
TFB1 sequence - transcription initiation factor IIb, 75 kDa subunit component,
gene from Saccharomyces cerevisiae
TFB2 sequence - TFIIH subunit, gene from Saccharomyces cerevisiae
TFB3 sequence - TFIIH subunit, gene from Saccharomyces cerevisiae
TFB4 sequence - transcription initiation factor TFIIH subunit, gene from
Saccharomyces cerevisiae
TFIIH3 sequence - general transcription factor IIH, polypeptide 3, TFIIH
subunit, gene from Dictyostelium discoideum
TH1 sequence - TH1, gene from Drosophila melanogaster
67
Table 14: WebGestalt Gene Ontology (GO) of transcriptional repression. (continued)
GO:0007070
Negative regulation of
transcription from RNA
polymerase II promoter,
mitotic:
Any process that stops,
prevents or reduces the rate,
frequency or extent of
transcription from an RNA
polymerase II promoter
during mitosis.
CCL1 sequence - TFIIK subunit, a subcomplex of transcription factor TFIIH,
cyclin, gene from Saccharomyces cerevisiae
CG32721 sequence - CG32721, gene from Drosophila melanogaster
CG5874sequence - CG5874, gene from Drosophila melanogaster
crk1 - cyclin-dependent kinase activating kinase Crk1, gene from
Schizosaccharomyces pombe
Nelf-E sequence - Negative elongation factor E, gene from Drosophila
melanogaster
NELFE_DROVI - protein from Drosophila virilis
RAD3 sequence - gene from Saccharomyces cerevisiae
SSL1 sequence - RNA polymerase transcription factor TFIIH component, gene
from Saccharomyces cerevisiae
SSL2 sequence - DNA helicase, human XPBC, ERCC3 homolog, gene from
Saccharomyces cerevisiae
TFB1 sequence - transcription initiation factor IIb, 75 kDa subunit component,
gene from Saccharomyces cerevisiae
TFB2 sequence - TFIIH subunit, gene from Saccharomyces cerevisiae
TFB3 sequence - TFIIH subunit, gene from Saccharomyces cerevisiae
TFB4 sequence - transcription initiation factor TFIIH subunit, gene from
Sacscharomyces cerevisiae
TFIIH3 sequence - general transcription factor IIH, polypeptide 3, TFIIH
subunit, gene from Dictyostelium discoideum
TH1 sequence - TH1, gene from Dros ophila melanogaster
GO:0045013
Negative regulation of
transcription by carbon
catabolites:
Any process involving
carbon catabolites that
stops, prevents or reduces
the rate of transcription.
Carbon catabolite
repression is a mechanism
of genetic regulation in
bacteria in which the
accumulation of catabolites
of one substance in the cell
represses the formation of
enzymes that contribute to
the catabolism of other
substances.
CBU0743 sequence - phosphocarrier protein HPr, protein from Coxiella burnetii
RSA 493
CBU0744 sequence - Hpr(Ser) kinase/phosphatase, protein from Coxiella
burnetii RSA 493
CREC_EMENI - protein from Emericella nidulans
CHM1A_HUMAN sequence - Splice Isoform 1 of Charged multivesicular body
protein 1a, protein from Homo sapiens
cyr1 - adenylate cyclase, gene from Schizosaccharomyces pombe
dot2 - EAP30 family protein Dot2, gene from Schizosaccharomyces pombe
git11 - heterotrimeric G protein gamma subunit Git11, gene from
Schizosaccharomyces pombe
git3 - G-protein coupled receptor Git3, gene from Schizosaccharomyces pombe
git5 - heterotrimeric G protein beta subunit Git5, gene from
Schizosaccharomyces pombe
git7 - SGT1-like protein Git7, gene from Schizosaccharomyces pombe
gpa2 - heterotrimeric G protein alpha-2 subunit Gpa2, gene from
Schizosaccharomyces pombe
GRH1 - GRR1-LIKE PROTEIN 1, gene from Arabidopsis thaliana
pka1 - cAMP-dependent protein kinase catalytic subunit Pka1, gene from
Schizosaccharomyces pombe
pyp1 - tyrosine phosphatase Pyp1, gene from Schizosaccharomyces pombe
SNF8 - gene from Saccharomyces cerevisiae
tup1 - transcriptional co-repressor Tup1, gene from Schizosaccharomyces
pombe
tup11 - transcriptional co-repressor Tup11, gene from Schizosaccharomyces
pombe
VPS25 sequence - gene from Saccharomyces cerevisiae
VPS36 sequence - gene from Saccharomyces cerevisiae
68
Table 14: WebGestalt Gene Ontology (GO) of transcriptional repression. (continued)
GO:0045014
Negative regulation of
transcription by glucose:
Any process involving
glucose that stops, prevents
or reduces the rate of
transcription. The presence
of glucose in the growth
medium inhibits the
synthesis of certain
enzymes in bacteria
growing on the medium.
For example, transcription
of some catabolic operons is
under negative control by
specific repressors and
glucose is an anti-inducer of
xylose utilization and
glycerol kinase.
CHM1A_HUMAN sequence - Splice Isoform 1 of Charged multivesicular body
protein 1a, protein from Homo sapiens
cyr1 - adenylate cyclase, gene from Schizosaccharomyces pombe
dot2 - EAP30 family protein Dot2, gene from Schizosaccharomyces pombe
git11 - heterotrimeric G protein gamma subunit Git11, gene from
Schizosaccharomyces pombe
git3 - G-protein coupled receptor Git3, gene from Schizosaccharomyces pombe
git5 - heterotrimeric G protein beta subunit Git5, gene from
Schizosaccharomyces pombe
git7 - SGT1-like protein Git7, gene from Schizosaccharomyces pombe
gpa2 - heterotrimeric G protein alpha-2 subunit Gpa2, gene from
Schizosaccharomyces pombe
GRH1 - GRR1-LIKE PROTEIN 1, gene from Arabidopsis thaliana
pka1 - cAMP-dependent protein kinase catalytic subunit Pka1, gene from
Schizosaccharomyces pombe
pyp1 - tyrosine phosphatase Pyp1, gene from Schizosaccharomyces pombe
SNF8 sequence - gene from Saccharomyces cerevisiae
tup1 - transcriptional co-repressor Tup1, gene from Schizosaccharomyces
pombe
tup11 - transcriptional co-repressor Tup11, gene from Schizosaccharomyces
pombe
VPS25 sequence - gene from Saccharomyces cerevisiae
VPS36 sequence - gene from Saccharomyces cerevisiae
The genes identified as being up-regulated in sapphyrin-treated cells and
down-regulated in Actinomycin D-treated cells (Tables 9-10, 12) were then cross-
referenced with those identified by WebGestalt Gene Ontology to play a role in
transcriptional repression (Table 14) for functional homology. Table 15 (below)
shows the genes that exhibited transcriptional repression activity in those that
showed any up-regulation by sapphyrin and down-regulation by Actinomycin D.
69
Table 15: WebGestalt GO identified genes involved in transcriptional repression and up-regulated in
Sapphyrin-treated cells but down-regulated in Actinomycin D-treated cells.
Negative regulation of transcription, mitotic
Identification Gene
Probe ID: 207533_at
Gene ID: 6346
CCL1, chemokine (C-C motif) ligand 1
Probe IDs: 202224_at, 202225_at,
202226_s_at
Gene ID: 1398
CRK, v-crk sarcoma virus CT10 oncogene homolog (avian)
Probe ID: 209219_at
Gene ID: 7936
RDBP, RD RNA binding protein
Probe IDs: 209903_s_at, 209902_at,
233288_at
Gene ID: 545
ATR, ataxia telangiectasia and Rad3 related
Probe ID: 221540_x_at, 223758_s_at
Gene ID: 2966
GTF2H2, general transcription factor IIH, polypeptide 2, 44kDa
Probe ID: 225076_s_at
Gene ID: 57169
KIAA1404, KIAA1404 protein
Probe IDs: 202453_s_at,
202451_at
Gene ID: 2965
GTF2H1, general transcription factor IIH, polypeptide 1, 62kDa
Probe IDs: 219169_s_at, 228075_x_at
Gene ID: 51106
TFB1M, transcription factor B1, mitochondrial
Probe ID: 203577_at
Gene ID: 2968
GTF2H4, general transcription factor IIH, polypeptide 4, 52kDa
Probe ID: 222104_x_at
Gene ID: 2967
GTF2H3, general transcription factor IIH, polypeptide 3, 34kDa
Probe IDs: 225261_x_at, 225865_x_at,
225006_x_at, 220607_x_at
Gene ID: 51497
TH1L, TH1-like (Drosophila)
Negative regulation of transcription from RNA polymerase III promoter, mitotic
Identification Gene
Probe ID: 207533_at
Gene ID: 6346
CCL1, chemokine (C-C motif) ligand 1
Probe IDs: 202224_at, 202225_at,
202226_s_at
Gene ID: 1398
CRK, v-crk sarcoma virus CT10 oncogene homolog (avian)
Probe ID: 209219_at
Gene ID: 7936
RDBP, RD RNA binding protein
Probe IDs: 209903_s_at, 209902_at,
233288_at
Gene ID: 545
ATR, ataxia telangiectasia and Rad3 related
Probe IDs: 221540_x_at, 223758_s_at
Gene ID: 2966
GTF2H2, general transcription factor IIH, polypeptide 2, 44kDa
Probe ID: 225076_s_at
Gene ID: 57169
KIAA1404, KIAA1404 protein
Probe IDs: 202453_s_at, 202451_at
Gene ID: 2965
GTF2H1, general transcription factor IIH, polypeptide 1, 62kDa
Probe IDs: 219169_s_at, 228075_x_at
Gene ID: 51106
TFB1M, transcription factor B1, mitochondrial
Probe ID: 203577_at
Gene ID: 2968
GTF2H4, general transcription factor IIH, polypeptide 4, 52kDa
Probe ID: 222104_x_at
Gene ID: 2967
GTF2H3, general transcription factor IIH, polypeptide 3, 34kDa
Probe IDs: 225261_x_at, 225865_x_at,
225006_x_at, 220607_x_at
Gene ID: 51497
TH1L, TH1-like (Drosophila)
70
Table 15: WebGestalt GO identified genes involved in transcriptional repression and up-regulated in
Sapphyrin-treated cells but down-regulated in Actinomycin D-treated cells. (continued)
Negative regulation of transcription by carbon catabolites
Identification Gene
Probe ID: 237230_at
Gene ID: 170589
GPHA2, glycoprotein hormone alpha 2
Probe IDs: 1560577_at, 218391_at,
242790_at
Gene ID: 11267
EAP30, EAP30 subunit of ELL complex
Probe IDs: 217427_s_at, 1569560_at,
240451_at
Gene ID: 7290
HIRA, HIR histone cell cycle regulation defective homolog A (S.
cerevisiae)
Probe ID: 240086_at, 222478_at
Gene ID: 51028
C13orf9, Chromosome 13 open reading frame 9
Negative regulation of transcription by glucose
Identification Gene
Probe ID: 237230_at
Gene ID: 170589
GPHA2, glycoprotein hormone alpha 2
Probe IDs: 1560577_at, 218391_at,
242790_at
Gene ID: 11267
EAP30, EAP30 subunit of ELL complex
Probe IDs: 217427_s_at, 1569560_at,
240451_at
Gene ID: 7290
HIRA, HIR histone cell cycle regulation defective homolog A (S.
cerevisiae)
Probe IDs: 240086_at, 222478_at
Gene ID: 51028
C13orf9, Chromosome 13 open reading frame 9
In assessing the ability of sapphyrin to effect negative transcriptional
regulation on the genes that exhibit differential expression in response to treatment,
the drug does rather well overall, identifying 15 transcripts that exhibited up-
regulation in response to sapphyrin treatment. The identified genes associated with
negative general transcriptional regulation during mitosis are expected to be
consistent with those involved in the negative regulation of transcription mediated by
the RNA polymerase III promoter, since this protein plays a significant role in
transcription. Since glucose is a carbon catabolite, a similar expectation is made for
the negative regulation of transcription by carbon catabolites and glucose. Based on
these results, it can be postulated that the mechanism of sapphyrin action in
71
transcriptional repression occurs via the mitotic RNA polymerase III promoter and
glucose.
In comparing the transcripts that were up-regulated and showing involvement
in transcriptional repression in response to sapphyrin treatment with those
demonstrating differential gene expressions in MGd and zinc co-treated cells, two
genes were found to be common in both data sets. JUND (Table 8) was categorized
as being relevant to the function of transcription factors in the study of MGd-treated
cancer cells, which indicates that sapphyrin may have a similar mechanism of action
as MGd on this gene in MAPK stress response pathways. The second transcript,
ATR (Table 8) was classified according to its involvement in cell cycle control in the
MGd study, implicating this gene in activating cell survival responses in sapphyrin-
treated cancer cells in a similar manner to that seen in cancer cells treated with MGd.
It is important to recognize the activated signal transduction pathways
associated with neoplastic environments to understand the cellular processes affected
by tumor invasion and progress. This information gives valuable insight into
developing potential anti-cancer treatments that target genes implicated in the cancer
cell’s natural instinct towards immortality. MGd and sapphyrin are prime anti-
cancer therapeutic agents because they combat tumor induction and metastasis by
affecting genes responsible for regulating various cellular processes that are involved
in oxidative stress responses and activation of apoptosis.
72
V. Conclusion
During the early stages of cancer therapy, light-based methods of intervention
were the forerunners of the new chemotherapeutic age. As effective radiosensitizers
for therapies such as PDT and XRT, expanded porphyrins, like texaphyrins, could be
intravenously administered to patients to localize in neoplastic tissues, without
affecting surrounding normal cells. Moreover, they could efficiently target cancer
cells under hypoxic conditions that were previously showing resistance to irradiative
therapy. The lanthanide series of texaphyrins, such as lutetium texaphyrin and
gadolinium texaphyrin, emerged as easily reduced chemotherapeutic agents that
showed deep penetration into tumor tissues. The electron affinity and unique
excitability characteristics of gadolinium texaphyrin compared to its lutetium
counterpart made this drug a more promising PDT therapeutic agent.
In MGd trials of plateau phase A549 lung cancer cells, drug-treatment had a
significant effect on genes responsible for intracellular zinc transport and metabolism
via metallothioneins. Intracellular free zinc levels serve in regulating the biological
pathways associated with carcinogenesis through kinase- and signal transduction-
mediated pathways and modulating immune response. MGd functioned to induce
cytotoxicity in tumor cells through redox reactions involving intracellular zinc levels
and production of reactive oxygen species via NADPH oxidation involving
thioredoxin reductase.
When Ramos B-cell lymphoma cells were co-treated with MGd and zinc, the
joint drug treatment showed the expression of genes implicated in regulating
73
intracellular free zinc levels, oxidative stress, proliferation, and cell death. To effect
anti-tumor tr eatment in cancer cells, co -treatment of MGd and zinc induced various
extrinsic and intrinsic signaling-mediated survival response pathways by affecting
transcription factors HIF-1, MTF-1, and NRF2 and their respective target genes
relevant to cellular response to oxidative stress. HIF-1 activation under specific
conditions can induce apoptosis through regulation of its target genes or alter the
metabolism of cells in low nutrient tumor environments. MTF-1 regulated genes,
including metallothioneins and the zinc family transporters, regulate intracellular
levels of free zinc. NRF2-related transcripts are responsible for antioxidant activity,
thioredoxin reductase induction, and glutathione synthesis that is associated with an
NRF2 feedback loop under cellular oxidative stress conditions.
Although the application of MGd as an anti-cancer therapeutic agent has been
shown to be effective in A549 lung cancer cells and Ramos B-cell lymphoma cell
lines, the limit to using this drug is its requisite for light-based activation.
Sapphyrins, on the other hand, serve potential uses in neoplastic intervention in the
absence of light. Also expanded porphyrins, sapphyrins exhibit similar neoplastic
tissue penetration abilities and tumor localization properties to texaphyrins, but they
can bind several classes of anions under a variety of solution phase conditions and in
the solid state as well. Their ability to effectively bind anions, specifically phosphate
anions via phosphate chelation, give them potential utility as fluorescent phosphate
anion sensors in various biological contexts of interest, such as regulation of osmotic
74
pressure, cell signaling and transduction of energy, and control of genetic
information.
Used as a treatment for various cancers, sapphyrin has proven to be highly
effective. In hematopoietic tumor-derived cell lines, sapphyrin treatment showed
anti-tumor activity in these cells via induction of apoptosis. By triggering the release
of cytochrome c from mitochondria to activate a caspase cascade, increased
phosphorylated p38 MAPK levels and expression of BCL-2 stimulated various
apoptotic pathways [130].
When studied in A549 lung cancer cells, treatment with sapphyrin induced
differential expression of genes responsible for the regulation of the cell cycle,
differentiation of precursor cells, cellular immune response, and transcription. The
genes implicated in transcriptional regulation were involved in chromatin-mediated
transcription, gene expression/silencing, kinase-mediated pathways, and
transcriptional termination/mRNA degradation (Figure 21.1-21.3). In understanding
the affect of sapphyrin treatment on transcriptional repression, global reduction in
mRNA levels were seen in transcripts associated with the mitotic RNA polymerase
III promoter and glucose, a similar effect to that seen in actinomycin D-treated cells
(Figure 22.1-22.2 and Figure 23.1-23.2).
75
Figure 21.1: Gene Ontology (GO) Tree of genes up-regulated in response to 2.50µM Sapphyrin treatment associated with
biological processes.
76
Figure 21.2: Gene Ontology (GO) Tree of genes up-regulated in response to 2.50µM Sapphyrin treatment associated
with molecular function.
77
Figure 21.3: Gene Ontology (GO) Tree of genes up-regulated in response to 2.50µM Sapphyrin treatment associated with cellular components.
78
Figure 22.1: Gene Ontology (GO) Tree of genes up-regulated in response to Actinomycin D treatment associated with biological
processes.
79
Figure 22.2: Gene Ontology (GO) Tree of genes up-regulated in response to Actinomycin D treatment associated with molecular
function and cellular components.
80
Figure 23.1: Gene Ontology (GO) Tree of differentially expressed genes up-regulated in response to 2.50µM Sapphyrin treatment
and down-regulated in response to Actinomycin D treatment associated with biological processes.
81
Figure 23.2: Gene Ontology (GO) Tree of differentially expressed genes up-regulated in response to 2.50µM Sapphyrin treatment
and down-regulated in response to Actinomycin D treatment associated with molecular function and cellular components.
82
Additionally, the differential gene expression seen in JUND and ATR in both MGd-
and sapphyrin-treated cells shows that these drugs may have similar mechanisms of
action in cellular response to oxidative stress and cell cycle control.
Further studies involving MGd and sapphyrin may help to identify new
differentially expressed genes in other various biological pathways of interest to
understand more fully the mechanism of action of these anti-cancer treatments.
Some of the classes of genes identified in the studies presented here are classic
targets of chemotherapeutic agents, while others can become novel targets for
potential drug development in the future.
83
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Creator Yeligar, Samantha Mahadevappa (author)

Core Title Identification of the biochemical pathways affected by the anticancer agents Motexafin Gadolinium and Sapphyrin through gene expression profiling

School Graduate School

Degree Master of Science

Degree Program Biochemistry and Molecular Biology

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag biology, genetics,biology, molecular,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Advisor Hacia, Joseph (committee chair), Frenkel, Baruch (committee member), Stellwagen, Robert H. (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-51515

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Identifier 1437583.pdf (filename),usctheses-c16-51515 (legacy record id)

Legacy Identifier 1437583.pdf

Dmrecord 51515

Document Type Thesis

Rights Yeligar, Samantha Mahadevappa

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...

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